U.S. patent application number 17/026836 was filed with the patent office on 2021-01-07 for sample mixing control.
This patent application is currently assigned to AZURE VAULT LTD.. The applicant listed for this patent is AZURE VAULT LTD.. Invention is credited to Ze'ev RUSSAK.
Application Number | 20210003602 17/026836 |
Document ID | / |
Family ID | |
Filed Date | 2021-01-07 |
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United States Patent
Application |
20210003602 |
Kind Code |
A1 |
RUSSAK; Ze'ev |
January 7, 2021 |
SAMPLE MIXING CONTROL
Abstract
A method for controlling the mixing of a plurality of samples
subject to a chemical process, the method comprising computer
executed steps, the steps comprising: for each one of the samples,
receiving respective data on a result obtained for the sample using
the chemical process, and for each one of at least two of the
samples, further receiving respective data on classification of the
sample into one of at least two classes, for each one of the
samples, calculating a respective rank based on the result obtained
for the sample using the chemical process, finding among the
samples, at least one pair of samples classified into different
ones of the classes, such that for each respective one of the found
pairs, none of the samples having a calculated rank in between the
ranks calculated for the two samples of the found pair are
classified into one of the classes.
Inventors: |
RUSSAK; Ze'ev; (NETANYA,
IL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
AZURE VAULT LTD. |
RAMAT-GAN |
|
IL |
|
|
Assignee: |
AZURE VAULT LTD.
RAMAT-GAN
IL
|
Appl. No.: |
17/026836 |
Filed: |
September 21, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16086303 |
Sep 18, 2018 |
10782310 |
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PCT/IB2016/053423 |
Jun 10, 2016 |
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17026836 |
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62310844 |
Mar 21, 2016 |
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Current U.S.
Class: |
1/1 |
International
Class: |
G01N 35/00 20060101
G01N035/00; B01D 3/42 20060101 B01D003/42; B01D 3/00 20060101
B01D003/00; C12Q 1/68 20060101 C12Q001/68; B01J 4/00 20060101
B01J004/00; G05B 15/00 20060101 G05B015/00; B01L 7/00 20060101
B01L007/00; C12M 1/36 20060101 C12M001/36; C12Q 3/00 20060101
C12Q003/00; G16B 40/00 20060101 G16B040/00 |
Claims
1. A method for controlling the mixing of a plurality of samples
subject to a chemical process, the method comprising computer
executed steps, the steps comprising: a) for each one of the
samples, receiving respective data on a result obtained for the
sample using the chemical process, and for each one of at least two
of the samples, further receiving respective data on classification
of the sample into one of at least two classes; b) for each one of
the samples, calculating a respective rank based on the result
obtained for the sample using the chemical process; c) finding
among the samples, at least one pair of samples classified into
different ones of the classes, such that for each respective one of
the found pairs, none of the samples having a calculated rank lower
than the calculated rank of a first one of the samples of the found
pair and higher than the calculated rank of a second one of the
samples of the found pair, are classified into one of the classes;
d) identifying a pair consisting of samples that are least close to
each other in their calculated ranks among the found pairs; and e)
generating instructions for mixing at least one pair of samples
qualitatively identical to the identified pair, to yield a
respective new sample, said generating being conditioned upon none
of the samples having a calculated rank lower than the calculated
rank of a first one of the samples of the identified pair and
higher than the calculated rank of a second one of the samples of
the pair identified through steps a to d.
2. The method of claim 1, wherein the generated instructions are
usable in controlling a machine for mixing the at least one pair of
samples qualitatively identical to the identified pair.
3. The method of claim 1, further comprising generating
instructions for mixing at least two pairs of samples qualitatively
identical to the identified pair, each pair of samples being mixed
in a different ratio.
4. The method of claim 1, further comprising an additional step
preceded by step d, the additional step comprising obtaining data
on classification of a sample of a calculated rank closest to an
average of the ranks calculated for the samples in the identified
pair, and following said obtaining, performing again said steps b
to d.
5. The method of claim 1, further comprising an additional step
preceded by step d, the additional step comprising obtaining data
on classification of a sample of a calculated rank closest to an
average of the ranks calculated for the samples in the identified
pair from a user, and following said obtaining, performing again
said steps b to d.
6. The method of claim 1, further comprising an additional step
preceded by step d, the additional step comprising retrieving data
on classification of a sample of a calculated rank closest to an
average of the ranks calculated for the samples in the identified
pair from a database, and following said retrieving, performing
again said steps b to d.
7. The method of claim 1, further comprising an additional step
preceded by step d, the additional step comprising classifying a
sample having a calculated rank closest to an average of the ranks
calculated for the samples in the identified pair according to
predefined heuristics, and following said classifying, performing
again said steps b to d.
8. The method of claim 1, wherein said generating of the
instructions for the mixing is further conditioned upon compliance
with a predefined criterion.
9. The method of claim 1, wherein said generating of the
instructions for the mixing is further conditioned upon compliance
with a criterion predefined for the chemical process.
10. The method of claim 1, wherein said generating of the
instructions for the mixing is further conditioned upon compliance
with a criterion predefined for a machine to be used for the mixing
of the samples.
11. The method of claim 1, further comprising a step of running a
PCR (Polymerase Chain Reaction) process on the sample for obtaining
the result.
12. The method of claim 1, further comprising a step of deriving
the results from measurements carried out during the chemical
process.
13. The method of claim 1, wherein said calculating of the rank is
further based on a Fiedler Vector.
14. The method of claim 1, further comprises identifying a most
significant parameter based on the received results, wherein said
calculating of the ranks is further based on the identified most
significant parameter.
15. Apparatus for controlling the mixing of a plurality of samples
subject to a chemical process, comprising: a computer processor; a
data receiver, implemented on the computer processor, configured to
receive for each one of the samples, respective data on a result
obtained for the sample using the chemical process, and for each
one of at least two of the samples, to further receive respective
data on classification of the sample into one of at least two
classes; a rank calculator, in communication with said data
receiver, configured to calculate for each one of the samples, a
respective rank based at least on the result obtained for the
sample using the chemical process; a pair finder, in communication
with said rank calculator, configured to find among the samples, at
least one pair of samples classified into different ones of the
classes, such that for each respective one of the found pairs, none
of the samples having a calculated rank lower than the calculated
rank of a first one of the samples of the found pair and higher
than the calculated rank of a second one of the samples of the
found pair, are classified into one of the classes; a least close
pair identifier, in communication with said pair finder, configured
to identify a pair consisting of samples that are least close to
each other in their calculated ranks among the found pairs; and an
instruction generator, in communication with said least close pair
identifier, configured to generate instructions for mixing at least
one pair of samples qualitatively identical to the identified pair,
to yield a respective new sample, said generating being conditioned
upon none of the samples having a calculated rank lower than the
calculated rank of a first one of the samples of the identified
pair and higher than the calculated rank of a second one of the
samples of the identified pair.
16. The apparatus of claim 15, wherein the generated instructions
are usable in controlling a machine for mixing the at least one
pair of samples qualitatively identical to the identified pair.
17. The apparatus of claim 15, further comprising a reaction
apparatus, in communication with said data receiver, configured to
run the chemical process on the sample for obtaining the result,
wherein said data receiver is adapted for receiving the data on the
result obtained for the sample using the chemical process, from
said reaction apparatus.
18. The apparatus of claim 15, further comprising a PCR machine, in
communication with said data receiver, configured to run a PCR
(Polymerase Chain Reaction) process on the sample for obtaining the
result, wherein said data receiver is adapted for receiving the
data on the result obtained for the sample using the chemical
process, from said PCR machine.
19. A non-transitory computer readable medium storing computer
processor executable instructions for performing steps of
controlling the mixing of a plurality of samples subject to a
chemical process, the steps comprising: a) for each one of the
samples, receiving respective data on a result obtained for the
sample using the chemical process, and for each one of at least two
of the samples, further receiving respective data on classification
of the sample into one of at least two classes; b) for each one of
the samples, calculating a respective rank based on the result
obtained for the sample using the chemical process; c) finding
among the samples, at least one pair of samples classified into
different ones of the classes, such that for each respective one of
the found pairs, none of the samples having a calculated rank lower
than the calculated rank of a first one of the samples of the found
pair and higher than the calculated rank of a second one of the
samples of the found pair, are classified into one of the classes;
d) identifying a pair consisting of samples that are least close to
each other in their calculated ranks among the found pairs; and e)
generating instructions for mixing at least one pair of samples
qualitatively identical to the identified pair, to yield a
respective new sample, said generating being conditioned upon none
of the samples having a calculated rank lower than the calculated
rank of a first one of the samples of the identified pair and
higher than the calculated rank of a second one of the samples of
the pair identified through steps a)-d), the at least one pair of
samples being mixed being.
20. The computer readable medium of claim 19, wherein the generated
instructions are usable in controlling a machine for mixing the at
least one pair of samples qualitatively identical to the identified
pair.
Description
FIELD AND BACKGROUND OF THE INVENTION
[0001] The present invention relates to automation of laboratories.
and more particularly, but not exclusively to a method and an
apparatus for controlling the mixing of samples subject to a
chemical process such as PCR (Polymerase Chain Reaction), an HPLC
(High Performance Liquid Chromatography) process, etc.
[0002] In recent years, advanced laboratory automation has often
been the result of new challenges that laboratories--especially
laboratories engaged in testing large numbers of samples for the
presence of viruses such as HIV (Human Immunodeficiency Virus) or
the Hepatitis-C Virus--are often faced with.
[0003] Indeed, laboratory automation and the growing emergence of
laboratory machines (say robotics) have transformed the typical
workday for many scientists and laboratory technicians in those
laboratories.
[0004] For example, liquid handling robots, say robots that
dispense selected quantities of physical samples (say blood
samples, saliva samples, etc.) to a designated container, or
similar machines are very often used in automation of
laboratories.
[0005] In one example, a simple laboratory robot may simply
dispense an allotted volume of a liquid (say a blood sample) from a
motorized pipette or syringe.
[0006] More sophisticated robots may also manipulate the position
of the dispensers and containers and/or integrate additional
laboratory devices, such as centrifuges, micro plate readers, heat
sealers, heater, shakers, bar code readers, photometric devices,
storage devices, incubators, etc.
[0007] Some robots may also perform multiple operations such as
sample transport, sample mixing, manipulation and incubation,
transporting vessels between workstations, etc.
[0008] Subsequently to one or more of the above mentioned
operations, the samples may undergo a chemical process--say in a
PCR machine, an HPLC instrument, etc., as known in the art. Then,
in a separate and final step, a classification model developed
beforehand is used to classify each of the samples, say for
diagnosing the person from whom one of the samples is originally
obtained, as HIV Positive, HIV Negative, etc.
SUMMARY OF THE INVENTION
[0009] According to one aspect of the present invention, there is
provided a method for controlling the mixing of a plurality of
samples subject to a chemical process, the method comprising
computer executed steps, the steps comprising: a) for each one of
the samples, receiving respective data on a result obtained for the
sample using the chemical process, and for each one of at least two
of the samples, further receiving respective data on classification
of the sample into one of at least two classes, b) for each one of
the samples, calculating a respective rank based on the result
obtained for the sample using the chemical process, c) finding
among the samples, at least one pair of samples classified into
different ones of the classes, such that for each respective one of
the found pairs, none of the samples having a calculated rank in
between the ranks calculated for the two samples of the found pair
are classified into one of the classes, d) identifying a pair
consisting of samples that are least close to each other in their
calculated ranks among the found pairs, and e) provided none of the
samples have a calculated rank in between the ranks calculated for
the samples of the identified pair, generating instructions for
mixing at least one pair of samples qualitatively identical to the
identified pair, to yield a respective new sample.
[0010] According to a second aspect of the present invention, there
is provided an apparatus for controlling the mixing of a plurality
of samples subject to a chemical process, comprising: a computer
processor, a data receiver, implemented on the computer processor,
configured to receive for each one of the samples, respective data
on a result obtained for the sample using the chemical process, and
for each one of at least two of the samples, to further receive
respective data on classification of the sample into one of at
least two classes, a rank calculator, in communication with the
data receiver, configured to calculate for each one of the samples,
a respective rank based at least on the result obtained for the
sample using the chemical process, a pair finder, in communication
with the rank calculator, configured to find among the samples, at
least one pair of samples classified into different ones of the
classes, such that for each respective one of the found pairs, none
of the samples having a calculated rank in between the ranks
calculated for the two samples of the found pair are classified
into one of the classes, a least close pair identifier, in
communication with the pair finder, configured to identify a pair
consisting of samples that are least close to each other in their
calculated ranks among the found pairs, and an instruction
generator, in communication with the least close pair identifier,
configured to generate instructions for mixing at least one pair of
samples qualitatively identical to the identified pair, to yield a
respective new sample, provided none of the samples have a
calculated rank in between the ranks calculated for the identified
pair.
[0011] According to a third aspect of the present invention, there
is provided a non-transitory computer readable medium storing
computer processor executable instructions for performing steps of
controlling the mixing of a plurality of samples subject to a
chemical process, the steps comprising: a) for each one of the
samples, receiving respective data on a result obtained for the
sample using the chemical process, and for each one of at least two
of the samples, further receiving respective data on classification
of the sample into one of at least two classes, b) for each one of
the samples, calculating a respective rank based on the result
obtained for the sample using the chemical process, c) finding
among the samples, at least one pair of samples classified into
different ones of the classes, such that for each respective one of
the found pairs, none of the samples having a calculated rank in
between the ranks calculated for the two samples of the found pair
are classified into one of the classes, d) identifying a pair
consisting of samples that are least close to each other in their
calculated ranks among the found pairs, and e) provided none of the
samples have a calculated rank in between the ranks calculated for
the samples of the identified pair, generating instructions for
mixing at least one pair of samples qualitatively identical to the
identified pair, to yield a respective new sample.
[0012] Unless otherwise defined, all technical and scientific terms
used herein have the, same meaning as commonly understood by one of
ordinary skill in the art to which this invention belongs. The
materials, methods, and examples provided herein are illustrative
only and not intended to be limiting.
[0013] Implementation of the method and system of the present
invention involves performing or completing certain selected tasks
or steps manually, automatically, or a combination thereof.
Moreover, according to actual instrumentation and equipment of
preferred embodiments of the method and system of the. present
invention, several selected steps could be implemented by hardware
or by software on any operating system of any firmware or a
combination thereof. For example, as hardware, selected steps of
the invention could be implemented as a chip or a circuit. As
software, selected steps of the invention could be implemented as a
plurality of software instructions being executed by a computer
using any suitable operating system. In any case, selected steps of
the method and system of the invention could be described as being
performed by a data processor, such as a computing platform for
executing a plurality of instructions.
BRIEF DESCRIPTION OF THE DRAWINGS
[0014] The invention is herein described, by way of example only,
with reference to the accompanying drawings. With specific
reference now to the drawings in detail, it is stressed that the
particulars shown are by way of example and for purposes of
illustrative discussion of the preferred embodiments of the present
invention only, and are presented in order to provide what is
believed to be the most useful and readily understood description
of the principles and conceptual aspects of the invention. The
description taken with the drawings making apparent to those
skilled in the art how the several forms of the invention may be
embodied in practice.
[0015] In the drawings:
[0016] FIG. 1 is a block diagram schematically illustrating a first
exemplary apparatus for controlling the mixing of a plurality of
samples subject to a chemical process, according to an exemplary
embodiment of the present invention.
[0017] FIG. 2 is a block diagram schematically illustrating a
second exemplary apparatus for controlling the mixing of a
plurality of samples subject to a chemical process, according to an
exemplary embodiment of the present invention.
[0018] FIG. 3 is a block diagram schematically illustrating a third
exemplary apparatus for controlling the mixing of a plurality of
samples subject to a chemical process, according to an exemplary
embodiment of the present invention.
[0019] FIG. 4 is a flowchart schematically illustrating an
exemplary method for controlling the mixing of a plurality of
samples subject to a chemical process, according to an exemplary
embodiment of the present invention.
[0020] FIG. 5A is a simplified diagram schematically illustrating a
first example of uncertainty regions, according to an exemplary
embodiment of the present invention.
[0021] FIG. 5B is a simplified diagram schematically illustrating a
second example of uncertainty regions, according to an exemplary
embodiment of the present invention.
[0022] FIG. 5C is a simplified diagram schematically illustrating a
third example of uncertainty regions, according to an exemplary
embodiment of the present invention.
[0023] FIG. 5D is a simplified diagram schematically illustrating a
fourth example of uncertainty regions, according to an exemplary
embodiment of the present invention.
[0024] FIG. 5E is a simplified diagram schematically illustrating a
fifth example of uncertainty regions, according to an exemplary
embodiment of the present invention.
[0025] FIG. 6 is a block diagram schematically illustrating a
non-transitory computer readable medium storing computer executable
instructions for performing steps of controlling the mixing of a
plurality of samples subject to a chemical process, according to an
exemplary embodiment of the present invention.
[0026] FIG. 7 is a first flowchart schematically illustrating an
exemplary scenario of controlling the mixing of a plurality of
samples subject to a chemical process, according to an exemplary
embodiment of the present invention.
[0027] FIG. 8 is a second flowchart schematically illustrating the
exemplary scenario of controlling the mixing of a plurality of
samples subject to a chemical process, according to an exemplary
embodiment of the present invention.
[0028] FIG. 9 is a third flowchart schematically illustrating the
exemplary scenario of controlling the mixing of a plurality of
samples subject to a chemical process, according to an exemplary
embodiment of the present invention.
DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0029] The present embodiments comprise an apparatus and a method
of controlling the mixing of a plurality of samples subject to a
chemical process.
[0030] Laboratory automation and the growing emergence of
laboratory robotics have transformed the typical workday for many
scientists and laboratory technicians in nowadays laboratories.
[0031] For example, machines such as liquid handling robots are
very often used in automation of laboratories--say robots that
dispense selected quantities of samples to a container or perform
multiple operations such as sample mixing, and manipulation.
[0032] Usually, subsequently to one or more of the above mentioned
operations, the samples handled by such laboratories undergo a
chemical process--say in a PCR or an HPLC machine.
[0033] Then, in a separate and final step, a classification model
developed beforehand is used to classify each of the samples, say
for diagnosing each person from whom a respective one of the
samples is originally obtained, as HIV Positive, HIV Negative,
etc.
[0034] Classification models of the sort used in diagnosis need to
be developed based on many (say hundreds and even thousands) test
samples of known in advance classification (say a classification
based on clinical examination of the patients from whom the many
samples arc originally obtained).
[0035] The development of a classification model is thus a process
that may take a long period of time and require extensive resources
(say medical examinations of the patients by physicians,
examinations of reaction curves obtained from the samples by
experts, etc).
[0036] Further, the classification models may need to be verified
and validated according to strict criteria defined by government
agencies or intergovernmental agencies such as the FDA (Food and
Drug Administration), WHO (World health organization), etc.
[0037] However, in some cases, such a classification model may be
unavailable yet (say because no classification model has been
developed yet or because validation and verification processes
required for FDA Approval of the model have not been finalized
yet).
[0038] For example, in an early stage of a rapid outbreak of a new
disease or of a new strand of a life threatening virus such as the
Zika Virus outbreak of 2015 such a classification model may be
unavailable yet.
[0039] In the early stage, a laboratory team would typically
receive many hundreds and even thousands of samples (say blood
samples) obtained from people who live in an area in which the
outbreak occurs. However, in the early stage, only a small number
of the samples belong to people already diagnosed with the new
disease or virus strand.
[0040] Under such circumstances, the laboratory team may need to
develop a classification model themselves.
[0041] However, with hitherto used methods, the laboratory team
would not be able to develop such a classification model before a
large enough number of the samples are already classified.
[0042] In one example, the already classified samples are samples
taken from people that already show clear symptoms of the disease
and can thus be diagnosed with the disease.
[0043] In a second example, the samples are samples classified by
experts say according to reaction curves that represent the
progress of a chemical reaction such as PCR (Polymerase Chain
Reaction), as described in further detail hereinbelow.
[0044] Thus, with the hitherto used methods, the development of the
classification model is likely to take many weeks or even months.
Meanwhile, the disease may spread into wider areas and claim the
lives of more and more victims.
[0045] Potentially, according to an exemplary embodiment of the
present invention, machines (say robots) in use for mixing samples
in a laboratory may be controlled according to instructions that
are generated, so as to arrive at a classification model faster and
based on a classification of a smaller number of samples.
[0046] According to an exemplary embodiment of the present
invention, in a method for controlling the mixing of a plurality of
samples, each one of the samples is subject to a chemical process
(PCR, HPLC, etc.).
[0047] Subsequently, in the exemplary method, for each one of the
samples, there is received respective data on a result obtained for
the sample using the chemical process.
[0048] In one example, the data on the result includes physical
parameter values such as fluorescence values, etc., as measured
over a chemical apparatus in which the sample is subject to a PCR
Process, during the PCR Process, and a respective time of
measurement of each specific one of the values.
[0049] Further, for each one of at least two of the samples, there
is further received data on classification of the respective sample
into one of at least two classes (say as Negative or as Positive),
as described in further detail hereinbelow.
[0050] In one example, a few of the samples are received with data
on a classification of the few samples according to clinical
symptoms.
[0051] In the example, each one of the few classified samples is
received with data on classification of the sample as positive or
rather as negative, based on clinical symptoms expressed by a
specific person (i.e. patient) from whom the sample is
obtained.
[0052] In a second example, a few of the samples are received with
data on a classification of the few samples according to
examination of sample results by Experts.
[0053] In the example, each one of the few classified samples is
received with data on classification of the sample as positive or
rather as negative based on examination of the data on the results
by Experts, say by PCR Experts who examine reaction curves that
depict the results, as known in the art.
[0054] Optionally, one of the classes is simply a class of samples
that could not be classified into any one of the other classes,
which class may also be referred to as a class of Ambiguous
Samples.
[0055] Thus, in one example, a class of Ambiguous Samples includes
samples Obtained from persons who appear sick but have clinical
symptoms characteristic of different diseases, and therefore cannot
be categorically classified as Positive or Negative with respect to
a specific disease based on their clinical symptoms alone.
[0056] In a second example, a class of Ambiguous Samples includes
samples that the results received for are too marginal for the
Experts to determine whether the samples are positive or
negative.
[0057] The remaining samples are not classified yet say because the
remaining samples belong to people who live in the geographical
area in which the new disease erupts, but do not express clinical
symptoms yet or because no time is left for the Experts to classify
the remaining samples.
[0058] In the exemplary method, for each one of the samples, there
is calculated a respective rank based at least on the result
obtained for the sample using the chemical process, as described in
further detail hereinbelow.
[0059] Then, there is found among the samples one or more pairs of
samples that are classified into different ones of the classes,
such that for each respective one of the found pairs, none of the
samples that have a calculated rank in between the ranks calculated
for the two samples of the found pair are classified into any of
the classes.
[0060] That is to say that each of the found pairs defines a gap
among the ranks calculated for the samples--i.e. a region of
uncertainty as to a location of a borderline that separates between
classes in terms of rank values, and thus a gap in a classification
model evolving based on the results obtained for the samples,
through the steps of the method.
[0061] Next, there is identified a pair that consists of samples
that are least close to each other in their calculated ranks among
the found pairs, thus identifying the widest gap in the evolving
model, as described in further detail hereinbelow.
[0062] Then, provided none of the samples for which the data is
received have a calculated rank in between the ranks calculated for
the identified pair, there are generated instructions for mixing at
least one pair of samples qualitatively identical to the identified
pair, to yield a respective new sample.
[0063] Thus, upon identifying the widest gap--namely, the one
defined by that identified pair that consists of samples that are
least close to each other in their calculated ranks among the found
pairs--there are generated instructions for mixing one or more
pairs of samples qualitatively identical to the identified
pair.
[0064] The mixing is believed to yield a respective new sample that
is likely to be calculated a rank in between the ranks of the two
samples of the identified pair. The gap defined by the identified
pair is thus likely to be divided into two smaller gaps.
[0065] Thus, with the present invention, a machine (say a
laboratory robot) may be instructed to mix samples in a way which
iteratively narrows down gaps in the evolving classification
model.
[0066] The narrower are the gaps among the calculated ranks, the
more accurate is a partition of the ranks' range of values into a
plurality of ranges, each range corresponding to a respective
class, and the more accurate the classification model becomes.
[0067] Thus, with embodiments of the present invention, rather than
mixing samples according to instructions prescribed arbitrarily in
advance to yield a final and planned in advance set of samples, a
machine (say robot) may be instructed dynamically to mix samples
according to instructions that are generated dynamically.
[0068] Potentially, the dynamic generation of the instructions
allows a classification model to evolve more quickly, while relying
on an in-advance classification of only a small number of the
received samples (say according to the clinical symptoms expressed
by fewer persons or according to examination of fewer results by
the Experts).
[0069] With exemplary embodiments of the present invention, a
mixing of samples (say by laboratory robots) is thus driven and
controlled using instructions derived from an evolving
classification method.
[0070] Consequently, the classification model is likely to be
developed within a relatively short time period, using a
significantly lower number of samples classified before having the
fully developed classification model, as described in further
detail hereinbelow.
[0071] Thus, potentially, with the exemplary embodiments of the
present invention, a laboratory team may be able to cope more
efficiently with a rapid outbreak of a new disease or life
threatening virus strand, by developing a classification model
using a much smaller number of clinical tests.
[0072] The principles and operation of an apparatus and s method
according to the present invention may be better understood with
reference to the drawings and accompanying description.
[0073] Before explaining at least one embodiment of the invention
in detail, it is to be understood that the invention is not limited
in its application to the details of construction and the
arrangement of the components set forth in the following
description or illustrated in the drawings.
[0074] The invention is capable of other embodiments or of being
practiced or carried out in various ways. Also, it is to be
understood that the phraseology and terminology employed herein is
for the purpose of description and should not be regarded as
limiting.
[0075] Reference is now made to FIG. 1, which is a block diagram
schematically illustrating a first exemplary apparatus for
controlling the mixing of a plurality of samples subject to a
chemical process, according to an exemplary embodiment of the
present invention.
[0076] Apparatus 1000 for controlling the mixing of a plurality of
samples subject to a chemical process may be implemented using
electric circuits, computer software, computer hardware, etc.
[0077] The apparatus 1000 may be implemented on a computer, say on
a computer on a chip connectable to one or more machine(s) in use
in a laboratory (say to a reaction apparatus such as a PCR
Apparatus, to a laboratory robot used for mixing, etc.), which chip
may be installed on the machine or be in communication therewith,
etc.
[0078] Thus, in one example, the apparatus 1000 is implemented on a
computer chip that is a part of a computerized controller (say a
computerized controller used in a chemical laboratory)--say a
controller which controls a robot used for mixing physical samples
(say blood samples, saliva samples, etc.) in a laboratory of a
central institution for monitoring the spread of infectious
diseases.
[0079] The apparatus 1000 may thus include one or more computer
processors.
[0080] The apparatus 1000 further includes one or more additional
parts described in further detail hereinbelow, such as the
classification obtainer or the parts denoted 110-150 in FIG. 1, as
described in further detail hereinbelow.
[0081] The additional parts may be implemented as software--say by
programming the one or more computer processors to execute the
method described in further detail and illustrated using FIG. 4
hereinbelow, as hardware--say as an electric circuit that
implements at least a part of the method, etc., or any combination
thereof.
[0082] The apparatus 1000 includes a data receiver 110.
[0083] The data receiver 110 receives data on a result obtained for
each respective one of two or more physical samples (say blood
samples, saliva samples, urine samples, etc.) using a chemical
reaction that the sample is subjected to, as described in further
detail hereinbelow.
[0084] The result may be obtained, for example, using a chemical
process such as a PCR (Polymerase Chain Reaction) Process or a HPLC
(High Performance Liquid Chromatography) process, as described in
further detail hereinbelow.
[0085] Thus, in a first example, the data received by the data
receiver 110 is data on a result that includes physical parameter
values say fluorescence intensity values, measured over a reaction
apparatus in which the sample is subject to a PCR Process, during
the PCR Process.
[0086] In the first example, the data receiver 110 further receives
in the data, a respective time of measurement of each one of the
fluorescence intensity values, or a temperature measured in the
reaction apparatus in which the PCR process takes place when each
respective one of the fluorescence intensity values is
measured.
[0087] In a second example, the chemical process is a HPLC (High
Performance Liquid Chromatography) process. In the example, a
chemical process apparatus in use is a HPLC instrument which
includes a sampler, a pump, one or more detectors, and a
microprocessor. The detectors may include, but are not limited to a
UV-Vis Absorbance Detector, a Chromatography Detector, etc., as
known in the art.
[0088] The sampler brings the sample into a mobile phase stream
which carries the sample into an analytical column, as known in the
art, and the pump delivers the desired flow and composition of the
mobile phase through the analytical column.
[0089] In the example, each detector generates a signal
proportional to the amount of a specific component or a specific
component type present in the sample when the sample emerges from
the analytical column, as known in the art.
[0090] In the example, the signals generated by the detectors are
processed by the HPLC instrument's microprocessor, to yield the
data on the result for the sample.
[0091] Then, the data on the result for the sample is communicated
from the HPLC Instrument (say over a Local Area Network or a Wide
Area network--as known in the art), and is received by the data
receiver 110, as described in further detail hereinbelow.
[0092] For each one of at least two of the samples, the data
receiver 110 further receives data on classification of the sample
into one of at least two classes (say as Negative or as Positive),
as described in further detail hereinbelow.
[0093] Thus, in a first example, a few of the samples are
classified according to clinical symptoms. In the example, for each
one of the few classified sample, the data receiver 110 receives
data on classification of the sample as positive or rather as
negative, based on clinical symptoms expressed by a specific person
from whom the sample is obtained.
[0094] In a second example, the few samples are rather samples
classified beforehand (i.e. prior to receipt of the data by the
data receiver 110) by an expert through manual examination of the
results obtained for each specific one of the few sample, as
described in further detail hereinbelow.
[0095] For example, a PCR Expert may classify the sample based on a
curve that depicts the course of a PCR process that the sample is
subject to, as measured fluorescence values per time or cycle of
the PCR process, as known in the art. The values per time or cycle
thus form the result for the sample.
[0096] Similarly, a HPLC Expert may classify the sample based on
the result obtained for the sample based on a HPLC process carried
out using a HPLC Instrument, as described in further detail
hereinbelow.
[0097] Optionally, one. of the classes is simply a class of samples
found to be non-classifiable into any one of the remaining classes,
which class may also be referred to as a class of Ambiguous
Samples.
[0098] Thus, in one example, a class of Ambiguous Samples includes
samples obtained from persons who appear sick but have clinical
symptoms characteristic of different diseases. Consequently, the
samples cannot be categorically classified beforehand (i.e. prior
to receipt of the data) as Positive or Negative with respect to a
specific disease based on their clinical symptoms alone.
[0099] In a second example, the class of Ambiguous Samples includes
samples that a PCR or a HPLC Expert hired to classify some of the
samples beforehand, tries to classify but finds to be
ambiguous.
[0100] The remaining samples are samples that are not classified
yet, say because the remaining samples belong to people who do not
express clinical symptoms yet or because the number of samples is
too high for a PCR or HPLC Export to classify within a time
available or budgeted for.
[0101] Indeed, for example, during an outbreak of a life
threatening disease such as Ebola, many hundreds or even thousands
of samples (especially positive ones which are by definition, less
common) may need to be taken from people who live in a wide
geographical area over which the disease appears to spread
rapidly.
[0102] During such an outbreak, the hundreds or thousands of
samples may need to be used to develop a classification mode
quickly, while most of the people from whom the samples are taken
do not express any clinical symptom typical of the disease yet.
However, the samples taken from the people who do not express any
clinical symptom typical of the disease cannot be classified
yet.
[0103] The apparatus 1000 further includes a rank calculator 120,
in communication with the data receiver 110.
[0104] For each one of the samples for which the data receiver 110
receives the respective data on the result obtained for the sample,
the rank calculator 120 calculates a respective rank.
[0105] The rank calculator 120 calculates the rank based at least
on the result obtained for the sample using the chemical process,
according to a predefined formula, rule, parameter, etc., as
described in further detail hereinbelow. The formula, rule,
parameter may be defined in advance, say by an operator,
administrator, or programmer of apparatus 1000.
[0106] In one example, the rank calculator 120 identifies in the
received results, a most significant parameter or a combination of
most significant parameters, and calculates the rank based on the
identified most significant parameter or combination, as described
in further detail hereinbelow.
[0107] The most significant parameter may be, for example, a
parameter that shows a maximal variance, or a parameter that is
very often relied on for classifying samples using a chemical
reaction of the type used to obtain the results--say a Threshold
Cycle (Ct) Value for PCR, etc., as known in the art.
[0108] Thus, in a first example, the rank calculator 120 calculates
the rank based on a Fiedler Vector that the rank calculator 120
derives based on an analysis of the results received by the data
receiver 110, say as the value of the Fiedler Vector as calculated
for the sample, as described in further detail hereinbelow.
[0109] In a second example, the rank calculator 120 calculates the
rank based on a discriminating function that the rank calculator
120 calculates through an SVM (Support Vector Machine) based
analysis of the results received by the data receiver 10, as
described in further detail hereinbelow.
[0110] In a third example, the respective data received by the data
receiver 110 on each sample's result is made of fluorescence values
measured during a PCR Process that the sample is subject to. The
received data further includes a respective time, of measurement of
each respective one of the values, or a respective number of the
cycle at which the value, is measured, as described in further
detail hereinbelow.
[0111] In the third example, for calculating the rank, the rank
calculator 120 identifies a PCR Threshold Cycle (Ct) Value, one or
more other elbow points, or both the Ct Value and the other one or
more elbow points, in a curve that depicts the progress of a PCR
process that the samples is subject to, say using a Monotonicity
Test, or another method, as known in the art. The Ct Value, one or
more of the elbow points, or a value derived therefrom, may thus
serve as the rank for the sample.
[0112] In the third example, the curve is calculated for the
sample, by the rank calculator 120, based on the fluorescence
values measured during the PCR Reaction that the sample is subject
to, and the respective time of measurement or cycle number of each
one of the measured values, as described in further detail
hereinbelow.
[0113] The apparatus 1000 further includes a pair finder 130, in
communication with the rank calculator 120.
[0114] The pair finder 130 finds among the samples, at least one
pair of samples.
[0115] Each one of the pairs found by the sample finder 130
includes samples classified into different ones of the classes, and
none of the samples (if any) having a calculated rank in between
the ranks calculated for the two samples of the found pair are
classified into one of the classes.
[0116] That is to say that each of the found pairs defines a gap
among the ranks calculated for the samples--i.e. a region of
uncertainty as to the borderline between classes in terms of rank
values, and thus a gap in a classification model evolving based on
the results obtained for the samples, through the steps of the
method.
[0117] The apparatus 1000 further includes a least close pair
identifier 140, in communication with the pair finder 130.
[0118] The least close pair identifier 140 identifies a pair that
includes two samples that are least close to each other in their
calculated ranks among the found pairs, thus identifying the widest
gap in the evolving model, as described in further detail
hereinbelow.
[0119] Optionally, the apparatus 1000 further includes a
classification obtainer (not shown) in communication with the least
close pair identifier 140.
[0120] The classification obtainer obtains data on classification
of a sample of a calculated rank closest to an average of the ranks
calculated for the samples in the identified pair, as described in
further detail hereinbelow.
[0121] Optionally, the data on the classification of the sample of
the calculated rank closest to the average of the ranks calculated
for the samples of the identified pair is obtained from a user of
apparatus 1000--say from a user who is a PCR Expert, an HPLC
Expert, etc., as described in further detail hereinbelow.
[0122] Optionally, the data on the classification of the sample of
the rank closest to the average is obtained by retrieving the data
from a database.
[0123] In one example, the database is shared and updated by
medical teams in a geographical area in which an Ebola outbreak
erupts. In the example, the database is updated with data on
classification of a sample whenever a person from whom the sample
originates is diagnosed with Ebola or is rather clinically found to
be clearly free of Ebola (say in a clinical examination).
[0124] That is to say that in the. example, the data on the result
of the sample that originates from the person is received by the
data receiver 110 when the person neither shows any symptom nor is
clinically found to be clearly free of Ebola. Only later on, is the
data on the classification of the person's sample say as positive,
as negative, or as ambiguous present in the database, and can thus
be retrieved from the database by the classification obtainer.
[0125] Optionally, the classification obtainer obtains the data on
the classification of the sample of the rank closest to the average
calculated for the samples in the identified pair according to
heuristics. The heuristics may be predefined, say by a programmer,
administrator, or user of apparatus 1000.
[0126] Thus, based on the heuristics, the classification obtainer
may classify the sample of the rank closest to the average into one
of the classes, as described in further detail hereinbelow.
[0127] The gap defined by the identified pair is thus divided into
two smaller gaps.
[0128] Following that obtaining of the data on the classification
of the sample of the rank closest to the calculated average, all
the samples for which data is received by the data receiver 110 are
subjected again to the ranking by the rank calculator 120, finding
by the pair finder 130, identifying by the least close pair
identifier 140 and possibly, also the obtaining by the
classification obtainer.
[0129] That is to say that optionally, following that obtaining,
the sequence of the steps of ranking, finding, identifying, and
possibly, obtaining too, may be iterated over until there is no
sample with a calculated rank in between a last identified pair of
samples that are least close among pairs found by the pair finder
130, as described in further detail hereinbelow.
[0130] The apparatus 1000 may further include an instruction
generator 150, in communication with the least close pair
identifier 140.
[0131] Optionally, when none of the samples for which the data is
received by the data receiver 110 have a calculated rank in between
the ranks calculated for the identified pair, the instruction
generator 150 generates instructions for mixing between one or more
pairs of samples qualitatively identical to the identified pair.
Using the mixing, there is yielded a respective new sample per each
pair of samples thus mixed.
[0132] The samples qualitatively identical to the identified pair
are samples each of which originates with one of the samples in the
identified pair--i.e. a sample divided or subdivided from one of
the samples of the identified pair.
[0133] A sample qualitatively identical to one of the identified
pair's samples may thus be, for example, a sample divided from the
identified pair's sample, a sample divided from a sample divided
from the identified pair's sample, a sample divided from the later
sample, etc., as described in further detail hereinbelow.
[0134] Thus, with the instruction generator 150, upon automatically
identifying a gap in the evolving classification model by
identifying that pair that consists of samples that are least close
to each other in their calculated ranks among the found pairs, the
apparatus 150 generates instructions for mixing at least one pair
of samples qualitatively identical to the identified pair.
[0135] The mixing is believed to yield a respective new sample that
is likely to be calculated a rank in between the ranks of the two
samples of the identified pair, as described in further detail
hereinbelow. The gap is thus likely to be narrowed by the new
sample.
[0136] Optionally, the generated instructions are for preparing a
series of new samples, by mixing at least two pairs of samples
qualitatively identical to the identified pair, each pair of
samples being mixed with each other in a different ratio. The
ratios may be defined in advance, say by a programmer, user or
operator of apparatus 1000, as described in further detail
hereinbelow.
[0137] Optionally, after the mixing, the new samples created by the
mixing are subject the chemical process, and data on a result
obtained for each respective one of the samples using the chemical
reaction, is sent to the data receiver 110.
[0138] Optionally, the data receiver 110 receives the data on the
results obtained for the new samples, and the ranking of the
samples, finding of the pairs, identifying of the pair of least
close samples among found, and possibly, the obtaining of the data
on the classification or the generation of instructions, are
performed again, as described in further detail hereinbelow.
[0139] Thus, with the present invention, a machine (say a pipetting
robot or another laboratory robot) may be instructed to mix samples
in a way which iteratively narrows down gaps in the evolving
classification model, as described in further detail
hereinbelow.
[0140] The narrower are the gaps, the more accurate the
classification model becomes and the more likely the classification
model is to comply with standards defined by the FDA or by one of
the other governmental and intergovernmental agencies, as described
in further detail hereinbelow.
[0141] Optionally, the instruction generator 150 conditions the
generation of the instructions for mixing at least one pair of
samples identical to the identified pair to yield a respective new
sample according to a predefined criterion (say a threshold such as
minimal resolution predefined by a programmer, administrator, or
operator of apparatus 1000).
[0142] In a first example, the criterion is a minimal resolution
that is predefined for the chemical process, say by a user of
apparatus 1000.
[0143] In the first example, the user defines a maximal allowed
difference between ranks calculated for samples that undergo a PCR
process.
[0144] Accordingly, in the first example, when the data received by
the data receiver 110 is on results of a PCR process, that
user-defined maximal allowed difference is applied on the ranks
calculated for the samples of the identified pair, thus serving as
the predefined minimal resolution and criterion of the first
example.
[0145] Per that conditioning by the instruction generator 150, in
the example, the instructions are generated only if the difference
between the ranks calculated for the samples of the identified pair
is higher than the user-defined maximal allowed difference, as
predefined for samples that undergo a PCR process.
[0146] In a second example, the criterion (say the minimal
resolution) is predefined for a machine to be used for the mixing
of the samples, say to a specific robot in use for mixing the
samples.
[0147] In the second example, a user or administrator of apparatus
1000 defines a maximal allowed difference between the calculated
ranks of the samples of the identified pair, for the robot that is
to be controlled using the instructions.
[0148] In the second example, the instruction generator 150
generates the instructions for the machine (say robot), for which
machine the minimal resolution is defined, only if the difference
between the ranks calculated for the samples of the identified pair
is higher than the user-defined maximal allowed difference. The
user-defined maximal allowed difference thus serves as the
predefined minimal resolution and criterion of the second
example.
[0149] Optionally, the apparatus 1000 further includes a reaction
apparatus in which the results for the samples for which data is
received by the data receiver 110 are obtained using the chemical
reaction, as described in further detail, for example using FIG. 3,
hereinbelow.
[0150] Reference is now made to FIG. 2, which is a block diagram
schematically illustrating a second exemplary apparatus for
controlling the mixing of a plurality of samples subject to a
chemical process, according to an exemplary embodiment of the
present invention.
[0151] A second exemplary apparatus for controlling the mixing of a
plurality of samples subject to a chemical process may be
implemented using electric circuits, computer software, computer
hardware, one or more machines (say robots), etc., as described in
further detail hereinbelow.
[0152] The second apparatus includes Apparatus 1000, as described
in further detail hereinabove.
[0153] As a part of the second apparatus, apparatus 1000 may be
implemented on a computer (say computer chip, or an industrial
controller) in communication with one or more machines 290--say one
or more robots in use in a laboratory.
[0154] Thus, in one example, the apparatus 1000 is implemented on a
computer chip that is a part of a computerized controller (such as
a computerized controller used in a chemical laboratory)--say a
controller which controls one or more robots 290 that are used for
mixing samples in a laboratory.
[0155] The apparatus 1000 may thus include at least one computer
processor.
[0156] The apparatus 1000 further includes the one or more
additional parts described in further detail hereinabove, such as
the parts denoted 110-150 and the classification obtainer, as
described in further detail hereinabove.
[0157] The second apparatus further includes one or more machines
290 controlled by apparatus 1000 using the instructions generated
by the instruction generator 150, as described in further detail
hereinabove.
[0158] Optionally, the one or more machines 290 include one of more
robots.
[0159] For example, the machines 290 may include liquid handling
robots or similar machines--say robots that dispense selected
quantities of samples to a common container, mix and stir the
content of the container, etc.
[0160] With the second apparatus, when none of the samples for
which the data is received by the data receiver 110 have a
calculated rank in between the ranks calculated for the identified
pair, the instruction generator 150 generates instructions for
mixing between one or more pairs of samples qualitatively identical
to the identified pair, as described in further detail
hereinabove.
[0161] The instructions generated by the instruction generator 150
are used to control the machine (say robot 290), say using one or
more electric circuits, as known in the art of industrial
controlling, so as to control a mixing of the samples qualitatively
identical to the identified pair by the robot 290.
[0162] The samples qualitatively identical to the identified pair
are samples each of which originates with one of the samples in the
identified pair--i.e. a sample divided or subdivided from one of
the samples of the identified pair.
[0163] A sample qualitatively identical to one of the identified
pair's samples may thus be, for example, a sample divided from the
identified pair's sample, a sample divided from a sample divided
from the identified pair's sample, a sample divided from the later
sample, etc., as described in further detail hereinabove.
[0164] Thus, upon automatically identifying a gap in the evolving
model by identifying that pair of samples that are least close to
each other in their calculated ranks among the found pairs, the
apparatus 1000 control the machine 290, for mixing at least one
pair of samples qualitatively identical to the identified pair.
[0165] The mixing is believed to yield a respective new sample that
is likely to be calculated a rank in between the ranks of the two
samples of the identified pair. The gap is thus likely to be
narrowed by the new sample, as described in further detail
hereinabove.
[0166] Optionally, based on the generated instructions, the machine
(say robot) 290 mixes at least two pairs of samples qualitatively
identical to the identified pair, each pair of samples being mixed
with each other in a different ratio.
[0167] The ratios may be defined in advance, say by a programmer,
user or operator of apparatus 1000, as described in further detail
hereinbelow.
[0168] Thus, with the present invention, one or machines (say a
laboratory robot) 290 may be instructed to mix samples in a way
which iteratively narrows down gaps in the evolving classification
model, as described in further detail hereinabove.
[0169] Optionally, the instruction generator 150 conditions the
generation of the instructions for mixing the samples identical to
the identified pair, upon a predefined criterion (say the minimal
resolution), as described in further detail hereinabove.
[0170] Reference is now made to FIG. 3, which is a block diagram
schematically illustrating a third exemplary apparatus for
controlling the mixing of a plurality of samples subject to a
chemical process, according to an exemplary embodiment of the
present invention.
[0171] A third exemplary apparatus for controlling the mixing of a
plurality of samples subject to a chemical process may be
implemented using electric circuits, computer software, computer
hardware, one or more machines (say robots), a chemical apparatus,
etc., as described in further detail hereinbelow.
[0172] The third apparatus includes apparatus 1000, as wall as one
or more machines (say robots) 290 that are controlled for mixing
samples, using the instructions generated by the instructor
generator 150, as described in further detail hereinabove.
[0173] The third apparatus further includes a reaction apparatus
310.
[0174] Optionally, the reaction apparatus 310 includes a reaction
chamber in which each of the samples for which the data is later
received by the data receiver 110, is subject to a chemical process
such as PCR or HPLC, as described in further detail
hereinabove.
[0175] Optionally, the reaction apparatus 310 further includes one
or more sensors that measure values of a physical property such an
intensity of fluorescence or another quality during or after the
chemical process that the sample is subject to, as described in
further detail hereinabove.
[0176] In a first example, the reaction apparatus 310 is a PCR
(Polymerase Chain Reaction) machine (also known as a PCR Cycler) or
another instrument used for running a PCR Reaction, as known in the
art.
[0177] In the first example, the chemical process is thus a PCR
process, and the sensors are photometric sensors installed in
proximity of the reaction chamber. The photometric sensors measure
intensity of light emitted from the reaction chamber, as the PCR
process progresses. The photometric sensors may measure the
emission of light (i.e. fluorescence values) from the reaction
chamber using standard fluorescence methods, as known in the
art.
[0178] In the example, the reaction apparatus 310 further includes
a cycle counter that is connected to the sensors.
[0179] The cycle counter instructs the sensors to take measurement
of the fluorescence intensity, say once in an interval of time.
Optionally, the interval of time and hence the length of each
cycle, is predefined by a user, as known in the art.
[0180] In the example, the reaction apparatus 310 further includes
an Analog-to-Digital (A2D) converter that is connected to the
sensors. The Analog-to-Digital (A2D) converter converts the
measured fluorescence intensity values into a digital format.
[0181] In the example, the reaction apparatus 310 further includes
a data accumulator in communication with the A2D converter.
[0182] The data accumulator receives the measured values from the
A2D converter and stores the measured values. The data accumulator
may include, but is not limited to a CD-ROM, a Flash Memory, a RAM
(Random Access Memory), etc., as known in the art.
[0183] The reaction apparatus 310 further includes a communications
module say a one which includes a communications card that is
connected to the data accumulator, and a processor which implements
a GUI (Graphical User Interface) on a screen (say a small LCD
screen, as known in the art) that is in communication with the
processor.
[0184] In the example, using the GUI, an operator of the reaction
apparatus 310 instructs the communications card to communicate the
data accumulated by the data accumulator (i.e. the data on the
result of the chemical process that the specific sample is subject
to) to the data receiver 110.
[0185] Using data received by the data receiver 110 on the result
obtained that way for each respective one of two or more samples
subject to the PCR process, the instruction generator 150 generates
the instructions for the mixing by the machines (say the
robot).
[0186] Optionally, the instruction generator 150 further forwards
the generated instructions to the machines (say robots) 290 used
for mixing the samples qualitatively identical to the samples
identified by the least close pair identifier 140, as described in
further detail hereinabove.
[0187] Optionally, the new sample created by the mixing of the
samples qualitatively identical to the samples identified by the
least close pair identifier 140, is then forwarded to the reaction
apparatus 310.
[0188] In one example, the sample is forwarded to the reaction
apparatus 310 by a robot that is instructed by the instruction
generator 150 to take the new sample and pour the new sample into a
reaction chamber of the reaction apparatus 310.
[0189] In the example, after the new sample is poured into the
reaction chamber, the instruction generator 150 further controls
the reaction apparatus 310, so as to initiate a PCR process that
the new sample is thus subject to.
[0190] Further in the example, when the chemical process appears to
end, the instruction generator 150 further communicates with the
communications module of the reaction apparatus 310, for receiving
data on a result of the chemical process.
[0191] Consequently, the data on the result (say the fluorescence
values accumulated during the PCR process and a respective time of
measurement of each of the values) obtained for the new sample, is
received by the data receiver 110.
[0192] The data received for the new sample thus adds to the data
received earlier on results obtained for samples subjected earlier
to the chemical process.
[0193] Then, both the data received on the result of the new assay
and the data previously received on the other samples, are subject
to a ranking, finding of pairs, identifying of a pair of least
close samples, and possibly, a mixing of samples qualitatively
identical to the identified pair, or an obtaining of classification
data.
[0194] The secondary apparatus may thus implement the steps of an
exemplary method as described in further detail and illustrated
using FIG. 4 hereinbelow.
[0195] Reference is now made to FIG. 4, which is a flowchart
schematically illustrating an exemplary method for controlling the
mixing of a plurality of samples subject to a chemical process,
according to an exemplary embodiment of the present invention.
[0196] An exemplary method for controlling the mixing of a
plurality of samples subject to a chemical process may be
implemented using electric circuits, computer instructions,
etc.
[0197] The method may be implemented for example, on a remote
server computer, or on a computer chip connected to a machine,
installed on the machine, or in remote communication with the
machine, etc., as described in further detail hereinabove.
[0198] The machine may be for example, a laboratory device such as
a robot used to mix samples in a laboratory, a chemical reaction
apparatus, etc., as described in further detail hereinabove.
[0199] For example, the method may be implemented on a computerized
controller in communication with a machine (say a robot), with a
reaction apparatus (say a PCR Cycler or an HPLC Instrument), or
with both the machine and the reaction apparatus, as described in
further detail hereinabove.
[0200] In the exemplary method, there is received 410 respective
data on a result obtained for each one of two or more samples. For
example, the data may be received 410 by the data receiver 110 of
apparatus 1000, as described in further detail hereinabove.
[0201] The result may be obtained, for example, using a chemical
process--say a PCR (Polymerase Chain Reaction) Process or a HPLC
(High Performance Liquid Chromatography) process, as described in
further detail hereinabove.
[0202] Thus, in a first example, the data received 410 on the
result includes physical parameter values such as fluorescence
intensity values measured over a reaction chamber of a reaction
apparatus (say a PCR Cycler) in which the sample is subject to a
PCR process, during the PCR process, as described in further detail
hereinabove.
[0203] In the first example, there is further received 410 a
respective time of measurement of each respective one of the
fluorescence intensity values, or a temperature measured in the
chamber in which the PCR process takes place when each respective
one of the fluorescence intensity values is measured.
[0204] In a second example, the chemical process is a HPLC (High
Performance Liquid Chromatography) process. In the second example,
a chemical process apparatus in use is a HPLC instrument which
includes a sampler, a pump, an analytical column, one or more
detectors, and a microprocessor. The detectors may include, but are
not limited to a UV-Vis Absorbance Detector, a Chromatography
Detector, etc., as known in the art.
[0205] The sampler brings the sample into a mobile phase stream
which carries the sample into the analytical column, and the pump
delivers the desired flow and composition of the mobile phase
through the analytical column.
[0206] In the example, each detector generates a signal
proportional to the amount of a specific component or a specific
component type present in the sample when the sample emerges from
the analytical column, as known in the art.
[0207] Further in the example, the signals generated by the
detectors are processed by the HPLC instrument's microprocessor, to
yield the data on the result for the sample, hence providing for
quantitative analysis of the sample.
[0208] For example, the HPLC instrument's microprocessor may encode
the result in the yielded data as a set of numerical values that
indicate the presence and amount of each one of the specific
components or component types present in the sample when the sample
emerges from the analytical column.
[0209] Then, the data on the result for the sample is communicated
from the HPLC Instrument (say over a Local Area Network or a Wide
Area network) and is received 410 say by the data receiver 110 of
apparatus 1000, as described in further detail hereinabove,
[0210] Further in the exemplary method, for each one of at least
two of the samples, there is further received 410 respective data
on classification of the sample into one of at least two classes
(say as Negative or rather as Positive) beforehand (i.e. before the
data on the results is received 410), as described in further
detail hereinbelow.
[0211] Thus, in a first example, a few of the samples are
classified according to clinical symptoms. in the example, for each
one of the few classified sample, there is received 410 data on
classification of the sample as positive or negative, based on
clinical symptoms expressed by a specific person from whom the
sample is obtained.
[0212] In a second example, the few samples are rather classified
by an expert through manual examination of the result obtained for
each respective one of the few samples.
[0213] For example, a PCR Expert may classify each respective one
of the few samples based on a curve that depicts the course of a
PCR Reaction that the sample is subject to, say as fluorescence
values per time of the reaction, so as to obtain the result for the
sample, as known in the art.
[0214] Similarly, a HPLC Expert may classify each respective one of
the few samples based on a result obtained for the sample using a
HPLC process carried out using an HPLC Instrument, as described in
further detail hereinabove.
[0215] Optionally, one of the classes is simply a class of samples
found to be non-classifiable into any one of the remaining classes,
which class may also be referred to as a class of Ambiguous
Samples.
[0216] Thus, in a first example, the class of Ambiguous Samples
includes samples obtained from persons who appear sick but have
clinical symptoms characteristic of several different diseases, and
therefore cannot be categorically classified as Positive or
Negative with respect to a specific disease based on their clinical
symptoms alone.
[0217] In a second example, the class of Ambiguous Samples includes
samples that a PCR or a HPLC Expert hired to classify some of the
samples beforehand, tries to classify but finds to be confusing
(say due to contradictory findings in the results, as known in the
art) and thus ambiguous.
[0218] The remaining samples are samples that are not classified
yet (i.e. samples that are not even found to be unclassifiable),
say because the remaining samples belong to people who do not
express clinical symptoms yet, or because the number of samples is
too high for a PCR or HPLC Export to classify in time.
[0219] Indeed, during an outbreak of a new life threatening
disease, many hundreds or even thousands of samples may need to be
taken from people who live in a wide geographical area over which
the disease appears to spread rapidly.
[0220] During such a outbreak, the hundreds or thousands of samples
may need to be used to develop a classification mode quickly, while
most of the people from whom the samples arc taken do not express
any clinical symptom typical of the disease yet and Experts
available on site do not have enough time to classify more than a
few dozens of the samples using the results.
[0221] In one example, the exemplary method further includes a step
in which the data received 410 on the result is used to represent
each sample as a point in a mathematical embedded space, say by the
rank calculator 120. Consequently, each point in the embedded space
is a parameterized representation of the result obtained for a
specific one of the samples using the chemical reaction.
[0222] In a second example, the received 410 data already includes
the position of a point representative of the result obtained for
the respective sample in a mathematical embedded space, and is thus
a parameterized representation of the sample subject to the
chemical reaction used to obtain the result for that sample.
[0223] Optionally, the exemplary method further includes a step in
which the embedded space is subject to a process of dimensionality
reduction, say using diffusion mapping, as known in the art.
[0224] In the exemplary method, for each one of the samples, there
is calculated 420 a respective rank based at least on the result
obtained for the sample using the chemical process, according to a
predefined formula, rule, parameter, etc., say by the rank
calculator 120 of apparatus 1000, as described in further detail
hereinabove.
[0225] For example, the method may include as step of identifying
in the received 410 data on the results, a most significant
parameter or a combination of two or more most significant
parameters, and a calculation 420 of the rank based on the
identified most significant parameter or combination of most
significant parameters.
[0226] The most significant parameter may be, a dimension of the
embedded space, along which dimension the points' positions have a
maximal variance, a parameter usually relied on for classifying
samples using the chemical reaction used to obtain the results--say
a PCR Ct (Threshold Cycle), etc., as known in the art.
[0227] In one example, the most significant parameter is identified
during the dimensionality reduction of the embedded space.
[0228] For example, in some dimensionality reduction techniques,
the most significant parameter may be a most significant dimension
arrived at during the dimensionality reduction, say a Fiedler
Vector. The Fiedler Vector is an eigenvector associated with
algebraic connectivity and is thus an indicator which may show
which points are likely to belong to a same class, as known in the
art of Algebraic Connectivity.
[0229] In a second example, only for some of the samples, is there
received 420 data on classification into one of two or more
classes, as described in further detail hereinabove. In the
example, a Support Vector Machine (SVM) or another affinity
measuring algorithm (such as the Logistics Classifier) may be used
to find the most significant parameter that may be, for example,
the discriminating function calculated during SVM, as known in the
art.
[0230] Then, there is found 430 among the samples, one or more
pairs of samples, say by the pair finder 130 of apparatus 1000, as
described in further detail hereinabove.
[0231] Each one of the found 430 pairs includes samples classified
into different ones of the classes. However if there are, among the
remaining samples, any samples that have a calculated 420 rank in
between the ranks calculated 420 for the samples included in the
found 420 pair, none of those samples are classified into any one
of the classes (i.e. not even to a class of Ambiguous Samples).
[0232] Thus, each one of the found 430 pairs defines an uncertainty
region of samples--i.e. a series of samples that may include only
unclassified samples with calculated 420 ranks that are in between
the ranks calculated 420 for the two samples of the found 430 pair.
The two samples of the found 430 pair thus define that uncertainty
region, as illustrated using FIG. 5A-5E hereinbelow.
[0233] That is to say that each of the found 430 pairs defines a
gap among the ranks calculated 420 for the samples--i.e. a region
of uncertainty as to the borderline between classes in terms of
rank values, and thus a gap in a classification model evolving
based on the results obtained for the samples, through the steps of
the method.
[0234] Reference is thus diverted to FIG. 5A-5E, which are
simplified diagrams each of which schematically illustrates
exemplary uncertainty regions, according to an exemplary
embodiment. of the present invention.
[0235] In FIG. 5A-5E, for the purpose of schematic illustration,
the samples are represented by a plurality of dots arranged along a
line which represents a range of rank values. Each point is
positioned on the line, in a position which reflects the rank
calculated 420 for the sample represented by the point, and thus,
the closer the point is to the right end of the line, the higher is
the rank calculated 420 for the sample represented by the
point.
[0236] In a first example, as illustrated in FIG. 5A, only two of
the samples are classified.
[0237] More specifically, the rightmost point represents a sample
classified as Positive, whereas the leftmost point represents a
sample classified as Negative, and none of the remaining samples
are classified yet.
[0238] In the first example, the pair of classified points defines
an uncertainty region of samples. The defined uncertainty region
spans all samples with ranks that are in between the ranks
calculated 420 for the only sample classified as Positive and the
only sample classified as Negative. The uncertainty region may also
be referred to as a Positive-Ambiguous Uncertainty Region or as a
Negative-Ambiguous Uncertainty Region.
[0239] In a second example, as illustrated in FIG. 5B, three of the
samples are classified. More specifically, two of the points
represent samples classified as Positive, only one of the points
represents a sample classified as Negative, and none of the
remaining samples are classified yet.
[0240] In the example, the leftmost point representing a sample
classified as Positive and the only point representing a sample
classified as Negative define an uncertainty region of samples. The
uncertainty region defined by those two points spans all samples
with ranks in between the ranks calculated 420 for those two
points. In this example too, the uncertainty region may also be
referred to as a Positive-Ambiguous Uncertainty Region or as a
Negative-Ambiguous Uncertainty Region.
[0241] In a third example, as illustrated in FIG. 5C, four of the
samples are classified. More specifically, two of the points
represent samples classified as Positive, and one of the points
represents a sample classified as Negative. Further in the example,
one of the points represents a sample classified as Ambiguous (say
due to confusing clinical symptoms or results, as described in
further detail hereinabove), and none of the remaining samples are
classified yet.
[0242] In the example, the leftmost point representing a sample
classified as Positive and the only point that represents a sample
classified as Ambiguous define a first uncertainty region of
samples. The first uncertainty region spans all samples with
calculated 420 ranks in between the ranks calculated 420 for those
two points. Further, the only point that represents a sample
classified as Ambiguous and the only point that represents a sample
classified as Negative define a second uncertainty region of
samples. The second uncertainty region spans all samples with ranks
in between the ranks calculated 420 for those two points. The two
regions may also be referred to as a Positive-Ambiguous Uncertainty
Region and a Negative-Ambiguous Uncertainty Region,
respectively.
[0243] In a fourth example, as illustrated in FIG. 5D, five of the
samples are classified. More specifically, two of the points
represent samples classified as Positive and two of the points
represent samples classified as Negative. Further in the example,
one point positioned between the two points that represent Negative
samples, represents a sample classified as Ambiguous (say due to
confusing clinical symptoms or results), and none of the remaining
samples are classified yet.
[0244] In the example, the leftmost point representing a sample
classified as Positive and the rightmost point that represents a
sample classified as Negative define a first uncertainty region of
samples. Further, the only point that represents a sample
classified as Ambiguous and the remaining point that represents a
sample classified as Negative (i.e. the leftmost point) define a
second uncertainty region. The regions may also be referred to as a
Positive-Ambiguous Uncertainty Region and a Negative-Ambiguous
Uncertainty Region, respectively.
[0245] In a fifth example, as illustrated in FIG. 5E, six of the
samples are classified. More specifically, two of the points
represent samples classified as Positive and three of the points
represent samples classified as Negative. Further in the example,
one point positioned between two points that represent Negative
samples, represents a sample classified as Ambiguous (say due to
confusing clinical symptoms or results), and none of the remaining
samples are classified yet.
[0246] In the example, the leftmost point representing a sample
classified as Positive and the rightmost point that represents a
sample classified as Negative define a first uncertainty region of
samples. Further, the only point that represents a sample
classified as Ambiguous and the second point from the left--a point
that represents a sample classified as Negative--define a second
uncertainty region. The regions may also be referred to as a
Positive-Ambiguous Uncertainty Region and a Negative-Ambiguous
Uncertainty Region, respectively.
[0247] Reference is now returned to FIG. 4.
[0248] Next, in the exemplary method, there is identified 440 a
pair which includes two samples that are least close to each other
in their calculated 420 ranks among the found 430 pairs, say using
the, least close pair identifier 140 of apparatus 1000, as
described in further detail hereinabove.
[0249] The identified pair of samples defines the widest gap in the
evolving classification model, as described in further detail
hereinabove.
[0250] Optionally, the exemplary method further comprises an
additional step in which there is obtained data on classification
of a sample of a calculated 420 rank closest to an average of the
ranks calculated 420 for the samples in the identified 440
pair.
[0251] In one example, the data on the classification of the sample
of the calculated 420 rank closest to the average of the ranks
calculated 420 for the samples in the identified 440 pair is
obtained from a user of apparatus 1000--say a user who is a PCR
Expert, a HPLC Expert, etc. For example, the PCR Expert may
calculate or be presented a reaction curve calculated from the data
received 410 on the sample's result (say by the rank calculator
140), and asked to classify the sample as Positive, Negative, or
Ambiguous, based on the presented curve.
[0252] In a second example, the data on the classification of the
sample of the rank closest to the average is obtained by retrieving
the data from a database.
[0253] In the second example, the database may be a database shared
and updated by medical teams in a geographical area in which an
Ebola outbreak erupts. The database is updated whenever a person
from whom one of the received 410 samples originates, is diagnosed
with Ebola or is rather clinically found to be clearly free of
Ebola (say in a clinical examination).
[0254] That is to say that in the example, the data on the result
obtained for the sample that originates from the person is received
410 by apparatus 1000 before the person shows any symptom or is
clinically found to be clearly free of Ebola. Only later on, when
the additional step is carried out, is the data on the
classification of that sample--say as positive, negative, or
ambiguous--present in the database.
[0255] In a third example, the data on the classification of the
sample having the calculated 420 rank closest to the average
calculated for the samples in the identified 440 pair is obtained
according to predefined heuristics. In the example, the heuristics
is defined in advance of the receipt 410 of the data, say by a
programmer, administrator, or user of apparatus 1000. Based on the
predefined heuristics, the sample of the calculated 420 rank
closest to the average is classified into one of the classes.
[0256] In one example, the heuristics is based on Signal-to-Noise
Ratio (SNR). In the example, there is calculated an SNR value per
each sample for which the data is received 410.
[0257] In the example, there is subtracted a baseline calculated
for a curve that depicts the progress of the PCR process that
involves the sample from the maximal fluorescence intensity value
measured during that PCR process.
[0258] The baseline may be calculated, for example, by applying a
monotonicity test on the curve, to find an early part of curve
which precedes the point in which the chemical process begins, and
applying linear regression on that early part of the curve, as
known in the art.
[0259] Then, the result of the subtraction is divided by a standard
deviation of the fluorescence intensity values measured during that
PCR process, to yield the SNR value. When the SNR value is higher
than a predefined range of values, the sample is classified as
positive, and when the SNR value is lower than a predefined range
of values, the sample is classified as positive. Otherwise (i.e.
when the SNR value is within the predefined range), the sample is
classified as Ambiguous.
[0260] In a second example, the result of the subtraction of
baseline from the maximal fluorescence intensity value itself is
compared to a predefined RFU (Relative Fluorescence Unit)
threshold. When the result of the subtraction is higher than the
RFU threshold, the sample is classified as Positive, whereas when
the result of the subtraction is lower than the RFU threshold, the
sample is classified as negative.
[0261] In a third example, the heuristics is based on a predefined
Ct (Threshold Cycle) value range. When the PCR process that
involves the sample has a Ct value within that predefined Ct range,
the sample is classified as Positive, whereas when the Ct value is
not within that predefined Ct range, the sample is classified as
Negative.
[0262] The gap defined by the identified 440 pair is thus divided
into two smaller gaps.
[0263] Following the obtaining of the data on the classification of
the sample of the calculated 420 rank closest to the calculated
average, there are performed again the steps of ranking 420,
finding 430, identifying 440, and possibly, the additional step
too.
[0264] Optionally, following that obtaining, steps 420-440 and
possibly, the step of obtaining the classification data, are
iterated over time and again, until there is no sample with a
calculated 420 rank in between the last identified 440 pair of
samples that are least close among found 430 pairs, as described in
further detail hereinbelow.
[0265] Optionally, when none of the samples for which the data is
received 410 have a calculated 420 rank in between the ranks
calculated 420 for the identified 440 pair, there are generated 450
instructions for mixing between one or more pairs of samples
qualitatively identical to the identified 440 pair, to yield a
respective new sample.
[0266] Optionally, the instructions are generated 450 by the
instruction generator 150 of apparatus 1000, as described in
further detail hereinabove.
[0267] The samples qualitatively identical to the identified 440
pair are samples each of which is taken from one of the samples in
the identified 440 pair--i.e. a sample divided or subdivided from
one of the samples of the identified 440 pair.
[0268] A sample qualitatively identical to one of the identified
440 pair's samples may be, for example, a sample divided from the
identified 440 pair's sample, a sample divided from a sample
divided from the identified 440 pair's sample, a sample divided
from the later sample and thus subdivided from one of the
identified 440 pair's samples, etc.
[0269] Thus, upon automatically identifying a gap in the evolving
classification model by identifying 440 the pair of samples that
are least close to each other in their calculated 420 ranks among
the found 430 pairs, there may be generated 450 the instructions
for the mixing.
[0270] The mixing is believed to yield a respective new sample that
is likely to be calculated 420 a rank in between the ranks of the
two samples of the identified 440 pair, as described in further
detail hereinbelow. The gap is thus likely to be divided into two
smaller gaps, thus narrowing down the gaps in the evolving
classification model, as described in further detail
hereinabove.
[0271] Optionally, the generated 450 instructions are for preparing
a series of new samples, by mixing at least two pairs of samples
qualitatively identical to the identified pair, each pair of
samples being mixed with each other in a different ratio. The
ratios may be defined in advance, say by a programmer, user or
operator of apparatus 1000, as described in further detail
hereinbelow.
[0272] Thus, with the present invention, one or more machines (say
a laboratory robot) may be instructed to mix samples in a way which
iteratively narrows down gaps among in the evolving classification
model, as described in further detail hereinbelow.
[0273] The narrower are the gaps in the evolving classification
model, the more accurate the classification model becomes and the
more likely the classification model is to comply with standards
defined by the FDA or by one of the other governmental and
intergovernmental agencies, as described in further detail
hereinbelow.
[0274] Optionally, the generating 450 of the instructions for
mixing at least one pair of samples identical to the identified 440
pair to yield a respective new sample is further conditioned
according to a predefined criterion--say a minimal resolution
predefined by a programmer, administrator, or operator of apparatus
1000.
[0275] In a first example, the criterion (say the minimal
resolution) is predefined for the chemical process, say by a user
of apparatus 1000.
[0276] In the first example, the user defines a criterion--say a
maximal allowed difference between ranks calculated 420 for samples
that undergo a PCR process.
[0277] Accordingly, in the first example, when the received 410
results are of a PCR process, that user-defined maximal allowed
difference is applied on the ranks calculated 420 for the samples
of the identified 440 pair, thus serving as the predefined minimal
resolution and the predefined criterion of the first example.
[0278] In the example, the instructions are generated 450 only if
the difference between the ranks calculated 420 for the samples of
the identified 440 pair is higher than the user-defined maximal
allowed difference, as predefined for samples that undergo a PCR
process.
[0279] In a second example, the criterion is predefined for a
machine to be used for the mixing of the samples, say to a specific
robot in use for mixing the samples.
[0280] In the second example, a user or administrator of apparatus
1000 defines a maximal allowed difference between the calculated
420 ranks of the samples of the identified 440 pair, for the
machine (say the robot) that is to be controlled using the
generated 450 instructions.
[0281] In the second example, the instructions for the machine, for
which machine the minimal resolution is defined, are generated 450
only if the difference between the ranks calculated 420 for the
samples of the identified 440 pair is higher than that user-defined
maximal allowed difference, as predefined for the machine.
[0282] Optionally, a part of the mixing or a forwarding of the
generated 450 instructions to a machine is carried out by a
laboratory technician, a user or operator of apparatus 1000,
etc.
[0283] Optionally, the exemplary method further includes a
preliminary step of running the chemical process on each sample for
which the respective data is later received 410, say using a
reaction apparatus, as described in further detail hereinabove.
[0284] Optionally, the exemplary method further includes a
preliminary step of running a PCR process on each sample for which
the respective data is later received 410, for obtaining the result
(say the fluorescence values and a corresponding time or
temperature for each fluorescence value), as described in further
detail hereinabove.
[0285] Optionally, the exemplary method further includes a step of
deriving the results from measurements made during the chemical
process--say from the fluorescence values and the corresponding
time or temperature for each fluorescence value, as described in
further detail hereinabove.
[0286] In one example, the chemical process is a PCR process and
the exemplary method includes a step of identifying a respective
threshold value (Ct) point in each specific one of several curves.
Earlier in the step, each one of the curves is generated based on
the fluorescence values and corresponding time or temperature and
depicts the progress of the PCR Process for a respective one of the
samples--i.e. the fluorescence value per time or temperature.
[0287] The result may thus be a Ct point identified using any one
of the methods known in the art, say using one of the monotonicity
methods, as known in the art.
[0288] Similarly, in a second example, the result may include two
or more elbow points, and the method may include a step in which
the elbow points (i.e. the result) are identified in the curves
that depict the fluorescence value per time or temperature, using
one of the methods known in the art.
[0289] Reference is now made to FIG. 6, which is a block diagram
schematically illustrating a non-transitory computer readable
medium storing computer executable instructions for performing
steps of controlling the mixing of a plurality of samples subject
to a chemical process, according to an exemplary embodiment of the
present invention.
[0290] According to an exemplary embodiment of the present
invention, there is provided a non-transitory computer readable
medium 6000, such as a Micro SD (Secure Digital) Card, a CD-ROM, a
USB-Memory, a Hard Disk Drive (HDD), a Solid State Drive (SSD), a
computer's ROM chip, etc.
[0291] The computer readable medium 6000 stores computer executable
instructions, for performing steps of controlling the mixing of a
plurality of samples subject to a chemical process, say according
to steps of the exemplary method described in further detail
hereinabove, and illustrated using FIG. 4.
[0292] The instructions may be executed on one or more computer
processors.
[0293] The instructions may be executed for example, on a remote
server computer, on a computer chip connected to a machine say to a
laboratory device such as a robot used to mix samples in a
laboratory, to a reaction apparatus, etc. The computer chip may be
installed on the machine, be in remote communication therewith,
etc., as described in further detail hereinabove.
[0294] Thus, in one example, the instructions may be executed on a
computerized controller in communication with the machine (say
robot), with a reaction apparatus (say a PCR Reaction Chamber or an
HPLC Apparatus), or with both the machine and the reaction
apparatus, as described in further detail hereinabove.
[0295] The computer executable instructions include a step of
receiving 610 data on a result obtained for each respective one of
two or more samples, as described in further detail
hereinabove.
[0296] The result may be obtained, for example, using a chemical
process--say a PCR (Polymerase Chain Reaction) Process or a HPLC
(High Performance Liquid Chromatography) process, as described in
further detail hereinabove.
[0297] Thus, in a first example, the data received 610 on the
result includes physical parameter values such as fluorescence
intensity values measured over a chemical apparatus in which the
sample is subject to a PCR Process, during the PCR Process.
[0298] In the first example, there is further received 610 a time
of measurement of each respective one of the fluorescence intensity
values, or a temperature measured in a chamber in which the process
takes place when each respective one of the fluorescence intensity
values is measured over that chamber.
[0299] In a second example, the chemical process is a HPLC (High
Performance Liquid Chromatography) process. In the second example,
a chemical process apparatus in use is a HPLC instrument which
includes a sampler, a pump, one or more detectors, and a
microprocessor. The detectors may include, but are not limited to a
UV-Vis Absorbance Detector, a Chromatography Detector, etc., as
known in the art.
[0300] The sampler brings the sample into a mobile phase stream
which carries the sample into an analytical column, and the pump
delivers the desired flow and composition of the mobile phase
through the analytical column, as known in the art.
[0301] In the example, each detector generates a signal
proportional to the amount of a specific component or of a specific
component type present in the sample when the sample emerges from
the analytical column.
[0302] The signals generated by the detectors are processed by the
HPLC instrument's microprocessor, to yield the data on the result
obtained for the sample, thus allowing for quantitative analysis of
the sample. Then, the data on the result Obtained for die sample is
communicated from the HPLC Instrument (say over a Local Area
Network or a Wide Area network--as known in the art), and is
received 610, as described in further detail hereinabove.
[0303] Using the computer executable instructions, for each one of
at least two of the samples, there is further received 610 data on
a classification of the respective sample into one of at least two
classes (say as Negative or as Positive), as described in further
detail hereinabove.
[0304] In a first example, a few of the samples for which the data
on the results is received 610 are classified according to clinical
symptoms. In the example, for each one of the few classified
samples, there is received 610 respective data on a classification
of the sample as positive or rather as negative. In the example,
the classification of the sample is based on clinical symptoms
expressed by a person from whom the sample is obtained, as
described in further detail hereinabove.
[0305] In a second example, for each one of the few classified
samples, there is received 610 data on the classification, as
determined by an expert through manual examination of the results
obtained for each specific one of the few sample beforehand, as
described in further detail hereinabove.
[0306] For example, a PCR Expert may classify the sample based on a
curve that depicts a course of a PCR Reaction that the sample is
subject to, as fluorescence values per time of the reaction, as
known in the art.
[0307] Similarly, a HPLC Expert may classify the sample based on
the result obtained for the sample based on a HPLC process carried
out using a HPLC Instrument, as described in further detail
hereinabove.
[0308] Optionally, one of the classes is simply a class of samples
found to be non-classifiable into any one of the remaining classes,
which class may also be referred to as a class of Ambiguous
Samples.
[0309] Thus, in one example, a class of Ambiguous Samples includes
samples obtained from persons who appear sick but have confusing
clinical symptoms, as described in further detail hereinabove.
[0310] In the example, the samples in the class of Ambiguous
Samples belong to persons that have confusing clinical symptoms
(say symptoms that are characteristic of several different
diseases), and therefore cannot be used to categorically classify
the samples as Positive or Negative with respect to a specific
disease.
[0311] In a second example, the class of Ambiguous Samples includes
samples that a PCR or HPLC Expert hired to classify some of the
samples beforehand, tries to classify but finds to be ambiguous, as
described in further detail hereinabove.
[0312] The remaining samples are not classified yet, say because
the remaining samples belong to people who do not express clinical
symptoms yet, because the number of samples is too high for a PCR
or HPLC Export to classify in time (say during a rapid outbreak of
a disease), etc., as described in further detail hereinabove.
[0313] The computer executable instructions further include a step
in which, for each one of the samples, there is calculated 620 a
respective rank based at least on the result obtained for the
sample using the chemical process, say using a predefined formula,
as described in further detail hereinabove.
[0314] For example, the computer executable instructions may
further include a step of identifying, in the received results, a
most significant parameter or a combination of two or more most
significant parameters. Then, the calculation 620 of the ranks may
be based on the identified most significant parameter or
combination, as described in further detail hereinabove.
[0315] In one example, the most significant parameter is a
dimension along which the positions of the points as per the data
received 610 on the results of the respective samples have a
maximal variance.
[0316] In another example, the most significant parameter is a
parameter usually relied on for classifying samples using the
chemical reaction used to obtain the results--say a Ct (Threshold
Cycle) for PCR, etc., as known in the art.
[0317] In one example, the most significant parameter is identified
during the dimensionality reduction of the embedded space. For
example, the most significant parameter is a most significant
dimension arrived at during the dimensionality reduction, say a
Fiedler Vector, as known on the art.
[0318] In a second example, only for some of the samples, is there
received 610 data on the classification to one of two or more
classes, as described in further detail hereinabove. In the
example, a Support Vector Machine (SVM) or another affinity
measuring algorithm (such as Logistics Classifier) may be used to
find the most significant parameter that may be, for example, the
discriminating function calculated during SVM, as known in the
art.
[0319] The computer executable instructions further include a step
of finding 630 among the samples at least one pair of samples.
[0320] Each one of the found 630 pairs consists of samples
classified into different ones of the classes, and none of the
samples (if any) having a calculated rank in between the ranks
calculated for the samples included in the found 630 pair are
classified into one of the classes.
[0321] The computer executable instructions further include a step
of identifying 640 a pair that consists of samples that are least
close to each other in their calculated 620 ranks among the found
630 pairs of closest samples, as described in further detail
hereinabove.
[0322] Optionally, the computer executable instructions further
include an additional step. In the additional step, there is
obtained data on classification of a sample of a rank closest to an
average of the ranks calculated 620 for the samples in the
identified 640 pair.
[0323] In one example, the data on the classification of the sample
of the rank closest to the average is obtained from a user say a
PCR Expert, a HPLC Expert, etc., as described in further detail
hereinabove.
[0324] In a second example, the data on the classification of the
sample of the calculated 620 rank closest to the average is
obtained by retrieving the data on the classification of a sample
of a rank closest to the average from a database.
[0325] In the second example, the database may be a database shared
and updated by medical teams in a geographical area in which an
Ebola outbreak erupts. The database is updated whenever a person
from whom one of the samples originates, and on whose sample, data
on the result is received 410 earlier, is diagnosed with Ebola.
[0326] That is to say that in the example, the data on the result
obtained for the sample that originates from the person is received
610 before the person shows any symptom typical of Ebola, and only
when the additional step is carried out, is the data on the
classification of that sample as positive present in the
database.
[0327] In a third example, the data on the classification of the
sample of the calculated 620 rank closest to the average is
obtained according to heuristics predefined, say by a programmer,
administrator, or user, as described in further detail hereinabove.
Thus, based on the heuristics, the sample of the calculated 620
rank closest to the average is classified into one of the
classes.
[0328] The computer executable instructions include a step in
which, following that obtaining of the data on the classification
of the sample of the calculated 620 rank closest to the average,
there are performed again the steps of ranking 620, finding 630,
identifying 640, and optionally, the additional step too.
[0329] Optionally, the sequence of steps 620-640 and optionally,
the additional step too, is iterated over until there is no sample
with a calculated 620 rank in between the last identified 640 pair
of samples, as described in further detail hereinabove.
[0330] The computer executable instructions may further include a
step in which, when none of the samples for which the data is
received 610 have a rank in between the ranks calculated 620 for
the identified 640 pair, there are generated 650 instructions, The
generated 650 instructions are for mixing one or more pairs of
samples qualitatively identical to the identified pair, to yield a
respective new sample per each pair of samples being mixed with
each other, as described in further detail hereinabove.
[0331] The samples qualitatively identical to the identified 640
pair are samples each of which is taken from one of the samples in
the identified 640 pair--i.e. a sample divided or subdivided from
one of the samples of the identified 640 pair.
[0332] For example, a sample qualitatively identical to one of the
identified 640 pair's samples may be a sample divided from the
identified 640 pair's sample, a sample divided from a sample
divided from the identified 640 pair's sample, a sample divided
from the later sample, and. thus subdivided from one of the
identified 640 pair's samples, etc.
[0333] Thus, upon automatically identifying a gap in the evolving
model by identifying 640 the pair of samples that are least close
to each other in their calculated 620 ranks among the found 630
pairs, there are generated 650 instructions for mixing at least one
pair of samples qualitatively identical to the identified 640
pair.
[0334] The mixing is believed to yield a respective new sample that
is likely to be calculated 620 a rank in between the ranks of the
two samples of the 640 identified pair, as described in further
detail hereinabove. The gap is thus likely to be divided into two
smaller gaps, by the new sample, as described in further detail
hereinabove.
[0335] Optionally, the generated 650 instructions are for preparing
a series of new samples, by mixing at least two pairs of samples
qualitatively identical to the identified pair, each pair of
samples being mixed with each other in a different ratio. The
ratios may be defined in advance, say by a programmer, user or
operator, described in further detail hereinbelow.
[0336] Thus, with the present invention, a machine (say a
laboratory robot) may be instructed to mix samples in a way which
iteratively narrows down gaps in the evolving classification model,
as described in further detail hereinabove.
[0337] Optionally, the computer executable instructions further
condition the generating 650 of the instructions upon a predefined
criterion, as described in further detail hereinabove.
[0338] In a first example, the predefined criterion is a minimal
resolution predefined for the chemical process say by a user, as
described in further detail hereinabove.
[0339] In the first example, the user defines a maximal allowed
difference between ranks calculated 620 for samples that undergo a
PCR process. Accordingly, in the first example, when the received
610 results are of a PCR process, that user-defined maximal allowed
difference is applied on the samples of the identified 640 pair,
thus serving as the predefined criterion and the predefined minimal
resolution.
[0340] In that first example, the instructions are generated 650
only if the difference between the ranks calculated 620 for the
samples of the identified 640 pair is higher than the user-defined
maximal allowed difference, as predefined for samples that undergo
a PCR process.
[0341] In a second example, the predefined criterion is a minimal
resolution predefined for a machine (say robot) to be used for the
mixing of the samples, say to a robot in use for mixing the
samples.
[0342] In the second example, a user or administrator defines a
maximal allowed difference between the calculated 620 ranks of the
samples of the identified 640 pair, for the machine that is to be
controlled using the generated 650 instructions, as described in
further detail hereinabove.
[0343] In the second example, the instructions for the machine, for
which machine the minimal resolution is defined, are generated 650
only if the difference between the ranks calculated 620 for the
samples of the identified 640 pair is higher than that user-defined
maximal allowed difference, as described in further detail
hereinabove.
[0344] Optionally, the computer executable instructions further
include a preliminary step of running the chemical process on each
sample for which the data is later received 610, say by controlling
a reaction apparatus, as described in further detail
hereinabove.
[0345] Optionally, the computer executable instructions further
include a preliminary step of running a PCR (Polymerase Chain
Reaction) process on each respective sample for which the data is
later received 610, and for obtaining the result for the respective
sample. The result may include for example, the measured physical
parameter values (say fluorescence values) and the corresponding
time or temperature for each one of the measured values, as
described in further detail hereinabove.
[0346] Optionally, the computer executable instructions further
include a step of deriving the results from measurements made
during the chemical process--say from the fluorescence values and
the corresponding time or temperature for each fluorescence value,
as described in further detail hereinabove.
[0347] In one example, the chemical process is a PCR process and
the computer executable instructions include a step in which a
respective Threshold Cycle (Ct) point is identified in each one of
several curves, and is thus derived from the measurements. Each one
of the curves depicts the progress of the PCR Process for a
respective one of the samples--i.e. the fluorescence value (or
other measured physical parameter value) per time or temperature.
The result my thus be a a point identified using any one of the
methods known in the art, say using one of monotonicity methods, as
known in the art.
[0348] Similarly, in a second example, the result may include two
or more elbow points, and the computer executable instructions may
include a step in which the elbow points are identified in the
curves that depict the physical parameter value, say fluorescence
value, per time or temperature, using one of the methods known in
the art.
[0349] General Further Discussion
[0350] In an exemplary scenario, a new strand of the Zika Virus is
identified in Brazil during an outbreak of the disease in a faraway
rural community. A new real-time PCR test is developed to identify
and classified the new strand of Zika virus.
[0351] In the scenario, the test is based on a real-time PCR.
Indeed, there exist several known in the art algorithms for
extracting numerical features from real-time PCR amplification
curves.
[0352] Further in the scenario, there exists a method for
developing a model for classifying the real-time PCR amplification
curves, provided the number of samples already labeled (i.e.
classified into one of at least two classes) is sufficiently
diverse for calibrating the model (i.e. for accurately defining the
borders among different classes using the model).
[0353] However, in an early stage of the Zika outbreak, only a
small number of the samples are classified in advance (say because
only a small number of the people who live in the community show
clinical symptoms specific to Zika because of a shortage in Experts
available for classifying the samples). Consequently, the number of
samples that are already of labeled (i.e. classified into one of
the at least two classes) is not sufficiently diverse for
calibrating the model, as described in further detail
hereinabove.
[0354] Reference is now made to FIG. 7, which is a first flowchart
schematically illustrating an exemplary scenario of controlling the
mixing of a plurality of samples subject to a chemical process,
according to an exemplary embodiment of the present invention.
[0355] In order to overcome the problem of the insufficient
diversity of labeled samples in the early stage of the first
exemplary scenario, there is received 710 data on the results
obtained using the real-time PCR process for all the samples, but
for only a few of the samples is there received labeling data.
[0356] In the first scenario, each one of the received 710 results
includes numerical features extracted from a real-time PCR
amplification curve derived from fluorescence values measured
during the real-time PCR process that a respective one of the
samples is subject to, as described in further detail
hereinabove.
[0357] Further in the scenario, at least one of the samples is
known to be Positive and at least one of the samples is known to be
Negative, thus at least two of the samples arc received with
classification data, as described in further detail
hereinbelow.
[0358] Based on the received 710 results, there is performed a
calibration 720, in which the classification model is enhanced, say
through the methods described in further detail hereinabove and
illustrated, for example, in FIG. 4.
[0359] Once calibrated 720, the classification model may be used
730 as a Classifier for any sample obtained later from the people
in the faraway rural community, and potentially, for future Zika
outbreaks that involve the new strand of the virus.
[0360] Reference is now made to FIG. 8, which is a second flowchart
schematically illustrating the exemplary scenario of controlling
the mixing of a plurality of samples subject to a chemical process,
according to an exemplary embodiment of the present invention.
[0361] Thus, in the first scenario, there is received 810 data on
the results obtained using the real-time PCR process for a few
samples, at least one of which received 810 samples is known to be
Positive and at least one of which received 810 samples is known to
be Negative, as described in further detail hereinabove.
[0362] Using the known positive sample and known negative sample,
there is prepared 820 a dilution series. In the preparation of the
series, the negative sample is used as blank. Thus, in the first
scenario, to yield the dilution series, the positive sample is
diluted using the negative sample in predefined ratios (say in
ratios of 1:10, 1:10.sup.2, 1:10.sup.3, 1:10.sup.4 and 1:10.sup.5),
as described in further detail hereinabove.
[0363] Each one the positive sample, negative sample, and the
samples created when of the dilution series is prepared, is run
through a real-time PCR process, and based on a result obtained
using the process, a rank is calculated 830 for each respective one
of the samples. The rank is calculated 830 by depicting the
progress of the sample's PCR process in a real-time PCR
amplification curve, and projecting the curve to a Fiedler Vector,
as known in the art.
[0364] Then, the boundaries between different classes for which
samples may be classified are found, in an attempt to calibrate a
classification model, say through steps of the exemplary method
illustrated using FIG. 4 hereinabove.
[0365] Then, the classification model is verified 850 against
predefined criterion, say by verifying that none of the uncertainty
regions that may be found among the samples when arranged according
to their calculated 830 ranks exceed a user-defined maximal allowed
difference, as described in further detail hereinabove.
[0366] If the classification model still fails to comply with the
predefined criteria, there is synthesized 860 additional data.
[0367] The additional data may be synthesized 860 by obtaining
classification data on a selected sample, or by mixing a pair of
samples to yield one or more new samples likely be calculated a
rank in between the ranks calculated for the samples in the pair,
as described in further detail and illustrated using FIG. 4
hereinabove.
[0368] Then, after a number of iterations, the classification model
complies with the predefined criteria, and the calibration of the
classification ends 870 successfully, thus achieving a defined
classifier. The defined classifier is thus a classification model
in which the borders among different classes are judged to be
accurately defined say based on verifying that all gaps (i.e.
uncertainty regions) that remain in the model are narrower than the
user-defined maximal allowed difference, as described in further
detail hereinabove.
[0369] Reference is now made to FIG. 9, which is a third flowchart
schematically illustrating the exemplary scenario of controlling
the mixing of a plurality of samples subject to a chemical process,
according to an exemplary embodiment of the present invention.
[0370] In the first exemplary scenario, the verifying 850 of the
classification model against the predefined criteria includes
identifying 910 a Negative-Ambiguous uncertainty region and a
Positive-Ambiguous uncertainty region, as described in further
detail hereinabove, and as illustrated for example, in FIG. 5C.
[0371] Then, there is found 920 the largest among the two
uncertainty regions of the instant scenario, as described in
further detail hereinabove.
[0372] In the scenario, there is checked 930 if the largest
uncertainly region contains samples of a calculated rank in between
the ranks calculated for the samples that define the region (say
the ones at the edges of the region, as illustrated for example in
FIG. 5C), for that verifying 850, as described in further detail
hereinabove.
[0373] If the largest uncertainly region does contain samples of a
rank in between the ranks calculated for the samples that define
that region, there is found 940 among those samples, a sample with
a rank closest to the average of the ranks calculated for the
samples that define that region, as described in further detail
hereinabove.
[0374] Then, there is obtained 950 a classification for the found
940 sample, as described in further detail hereinabove.
[0375] Following the obtaining 950 of the classification data for
the found 940 sample, steps 910-930 are repeated over for that
sample and the remaining samples.
[0376] Steps 910-940 may be iterated over until the largest
uncertainty region remaining among the samples does not contain any
sample 930, thus ending 960 a round of data obtaining 950 for
samples within uncertainty regions.
[0377] However, the classification model may still fail to comply
with the predefined criterion, say because there can still be found
an uncertainly region defined by samples for which the difference
between calculated ranks is greater than the user-defined maximal
allowed difference (say the minimal resolution).
[0378] In one example, if the classification model fails to comply
with the criterion, there are generated instructions for mixing one
or more pairs of samples qualitatively identical to the pair of
samples that define the uncertainty region, to yield one or more
new samples, as described in further detail hereinabove.
[0379] In the example, subsequently to the mixing, each one of the
new samples undergoes the chemical process, and data on a result
obtained for the respective sample subject to the chemical process
is received, say by the data receiver 110, as described in further
detail hereinabove.
[0380] Then, new ranks are calculated for all samples for which
data on the results is received, and steps 910-950 are iterated
over again, until 960 no remaining uncertainty region is larger
than the user-defined maximal allowed difference, as described in
further detail hereinabove.
[0381] In the first exemplary scenario, there is thus learned the
range of ranks which correspond to a responsive amplification
curves, the range of ranks which correspond to non-responsive
amplification curves, and the range of rank which corresponds to
ambiguous amplification curves. Accordingly, in the scenario, the
ranges correspond to a class of positive samples, a class of
negative samples, and a class of ambiguous samples,
respectively.
[0382] More specifically, the steps of the scenario may be carried
out, for example, according to the method described in further
detail hereinabove and illustrated using FIG. 4, say using the
"Data Scanning procedure", as described in further detail
hereinbelow.
[0383] In the scenario, in an early stage of the Zika outbreak,
only a small number of the samples for which the data on the
results is received are classified in advance.
[0384] At a first stage, only the ranks of the amplification curve,
generated for the negative sample and the rank of the amplification
curve generated for the positive sample are generated. However, no
information about the partition of the range of ranks into classes
is known.
[0385] Optionally, in the scenario, uncertainty regions are found
using the "Searching uncertainty regions procedure", as described
in further detail hereinbelow.
[0386] In the scenario, in order to narrow down the uncertainty
ranges (i.e. the gaps in the evolving classification model of the
scenario), a user (say the PCR Expert) is asked to classify a
sample having a rank that is closet to the mean of the ranks
calculated for the pair of classified samples that defines that
uncertainty region. The sample on which the user is asked may be
selected for example, using the "Acquire observation from region
procedure", as described in further detail hereinbelow.
[0387] After the user responds by providing classification data on
the sample that the point nearest to the average represents, the
partition of the range of rank values to classes is
re-calculated.
[0388] In one example, if the user classified the sample as
Positive, any sample with a calculated rank higher than that of the
sample that the user classifies as Positive, is deemed responsive
and is accordingly classified as Positive. However, it is still
unclear how samples with a calculated rank lower than the sample
that the user classifies as Positive are to be classified.
[0389] However, as the user is iteratively asked to classify more
samples (say using their PCR amplification curves), the uncertainty
regions are recalculated, and become narrower and narrower, as
described in further detail hereinabove.
[0390] In one example, after three iterations one of the remaining
uncertainty regions does not contain any sample, but is still
larger than the predefined threshold (say the predefined minimal
resolution), as described in further detail hereinabove.
[0391] In the example, in order to narrow further the uncertainty
ranges, instructions for mixing are generated and forwarded to a
machine (say a pipetting robot), as described in further detail
hereinabove. The instructions may be generated for example using
the "synthesize new observation procedure", as described in further
detail hereinbelow.
[0392] In the example, each of the new samples created by the robot
is subject to a real-time PCR, and to classification by the user,
based on an amplification curve calculated based on the PCR process
carried out on the sample.
[0393] Then, iteratively, new samples are created, respective
amplification curves are generated, the user is asked to classify
the samples using the curves, and uncertainty regions are
recalculated.
[0394] After a number of iterations, the stop criteria procedure
indicates that the largest uncertainty region left is narrower than
a threshold (say the user-defined maximal allowed difference), the
model is deemed calibrated. At this point, the classification model
may be used to rank and classify future samples based on results
carried obtained for future samples using a real-time PCR
process.
[0395] Thus, in the exemplary scenario, there is defined a
measurable ordering of the samples, based on the respective PCR
reaction curves. Specifically, the samples are ordered along a
range of rank values that spans a Negative (say non-responsive)
part, an Ambiguous part, and a Positive (say responsive) part.
[0396] More specifically, in the exemplary scenario, for each one
of the samples for which there is received respective data on the
result obtained for the sample, there is calculated a respective
rank based on the Fiedler Vector value calculated for the sample,
as described in further detail hereinabove.
[0397] In the scenario, there is found a part of the range of
Fiedler Vector values that corresponds to responsive and hence
positive curves, a part of the range of Fiedler Vector values that
corresponds to non-responsive and hence negative curves, and a part
of the range of Fiedler Vector values that corresponds to ambiguous
curves and is situated between the other two parts.
[0398] In the instant scenario, the method illustrated hereinabove
using FIG. 4 may be implemented using the specific procedures
detailed hereinbelow.
Synthesize New Observation Procedure
[0399] Input: A group of partially labeled points `T`, uncertainty
region R. [0400] 1. Sort the points in T by the Fiedler Vector
value. [0401] 2. Select the highest point in T which is lower than
R, and call it lower point. [0402] 3. Select the lowest point in T
which is higher than R, and call it higher point. [0403] 4. Order
the machine (say robot) to mix the sample associated with the lower
point with the sample associated with the higher point. [0404] 5.
Obtain and return results of the PCR process and classification for
the samples created by the mixing. Acquire Observation from Region
Procedure
[0405] Input: A group of partially labeled points `T`, uncertainty
region R. [0406] 1. Create a subset S of T which contains only the
points inside of R. [0407] 2. If S is empty, call the synthesize
new observation procedure with the parameters T and R, return the
point returned from the procedure, and finish this procedure.
[0408] 3. Select the observation in S that is closest to the middle
of R. [0409] 4. Return the selected observation.
Stop Criteria Procedure
[0409] [0410] Input: Uncertainty region and a group of partially
labeled points `T`. [0411] 1. Inspect the two points at the ends of
the uncertainty region. [0412] 2. Get a dilution ratio for the two
points. [0413] 3. If the dilution ratio is smaller than the
machine's resolution or the PCR reaction's (say qPCR's)
quantification resolution return `true`, else return `false`.
Searching Uncertainty Regions Procedure
[0414] Input: A group of partially labeled points, with labels
`positive`, `negative`, `ambiguous` [0415] 1. Create a subset S
which contains only the labeled points. [0416] 2. Sort S by the
value of the Fiedler Vector value. [0417] 3. Iterate on S from the
lowest calculated Fiedler Vector value to the highest calculated
Fiedler Vector value. [0418] 4. If the first point is not labeled
as negative, raise exception [0419] 5. While the selected point is
labeled as negative, continue the iteration. [0420] 6. Choose the
last point which is labeled as negative to be the start of the
negative-ambiguous uncertainty region. [0421] 7. Choose the point
after the start of the negative-ambiguous uncertainty region to be
the end of the negative-ambiguous uncertainty region. [0422] 8.
Iterate on S from the highest calculated Fiedler Vector value to
the lowest calculated Fiedler Vector value. [0423] 9. If the first
point is not labeled as positive, raise exception [0424] 10. While
the selected point is labeled as positive, continue the iteration.
[0425] 11. Choose the last point which is labeled as positive to be
the end of the positive-ambiguous uncertainty region. [0426] 12.
Choose the point after the end of the positive-ambiguous
uncertainty region to be the start of the positive-ambiguous
uncertainty region. [0427] 13. Return the negative-ambiguous
uncertainty region and positive-ambiguous uncertainty region with
the points that define the uncertainty regions.
Add Label to Observation Procedure
[0428] Variant A--Human Expert
[0429] Input: An observation (i.e. a result obtained for one of the
sample) denoted `O` [0430] 1. A human expert is asked to examine a
curve that depicts the progress of the PCR process for the sample,
and give a label, say using a GUTI (Graphical User Interface), thus
classifying the sample as `positive`, `negative` or `ambiguous`
[0431] 2. Return the label (i.e. classification of the sample)
given by the human expert.
[0432] Variant B--Heuristics
[0433] Input: An observation (i.e. a result obtained for one of the
sample) denoted `O`. [0434] 1. one or more type of heuristics is
used to classify to the observation, such as: [0435] a. an SNR--a
measure of the ratio between the signal power and the signal
standard deviation. For example, a chromatographic peak SNR can be
calculated by taking the maximum intensity of the signal minus the
baseline, divided by the standard deviation of the signal. A high
SNR value is attributed to positive observation. [0436] b. Ct.
Range--Given a range of possible PCR efficiency and initial
concentration, a positive observation will have Ct value in a
certain range. [0437] c. RFU Threshold measured by taking the
maximum signal and subtracting the baseline. RFU above a certain
threshold is attributed to positive observation. [0438] 2. Return
the label
[0439] Variant C--Using LOD with Ambiguous
[0440] Input: An observation `O`, the dilution ratio of the
observation `R`, the initial specimen quantity `Q`, Limit Of
Detection `LOD` [0441] 1. Using R and Q, the quantity of the
observation is determined. [0442] 2. The label is decided by the
observation quantity and the LOD: [0443] a. If the observation
quantity is above the LOD then the label is positive [0444] b. If
the observation quantity is below the LOD and above zero
concentration then the label is ambiguous [0445] c. if the
observation quantity is equal to zero concentration then the label
is negative [0446] 3. Return the label
[0447] Variant D--Using LOD Without Ambiguous
[0448] Input: An observation `O`, the dilution ratio of the
observation `R`, the initial specimen quantity `Q`, Limit Of
Detection `LOD` [0449] 4. Using R and Q, the quantity of the
observation is determined. [0450] 5. The label is decided by the
observation quantity and the LOD [0451] a. If the observation
quantity is above the LOD then the label is positive [0452] b. If
the observation quantity is below the LOD then the label is
negative [0453] 6. Return the label
Data Scanning Procedure
[0454] Input: A group of partially labeled points `T`--each point
representing a result of a respective one of the samples, with
labels `positive`, `negative`, `ambiguous` [0455] 1. Calculate
negative-ambiguous uncertainty region and positive-ambiguous
uncertainty region using Searching uncertainty regions procedure.
[0456] 2. Select the largest uncertainty region. [0457] 3. Call
stop criteria procedure with the largest uncertainty region and T.
[0458] 4. If the stop criteria procedure returned true, finish this
procedure. [0459] 5. Acquire new observation using the acquire
observation from region procedure, with the parameters T and the
selected uncertainty region. [0460] 6. Label to the acquired
observation using the "Add label to observation procedure". [0461]
7. Add the acquired now labeled observation to T. [0462] 8. Goto
line 1
[0463] It is expected that during the life of this patent many
relevant devices and systems will be developed and the scope of the
terms herein, particularly of the terms "Computer", "Processor"
"Chip", "Robot", "PCR", and "HPLC" is intended to include all such
new technologies a priori.
[0464] It is appreciated that certain features of the invention,
which are, for clarity, described in the context of separate
embodiments, may also be provided in combination in a single
embodiment. Conversely, various features of the invention, which
are, for brevity, described in the context of a single embodiment,
may also be provided separately or in any suitable
subcombination.
[0465] Although the invention has been described in conjunction
with specific embodiments thereof, it is evident that many
alternatives, modifications and variations will be apparent to
those skilled in the art. Accordingly, it is intended to embrace
all such alternatives, modifications and variations that fall
within the spirit and broad scope of the appended claims.
[0466] All publications, patents and patent applications mentioned
in this specification are herein incorporated in their entirety by
reference into the specification, to the same extent as if each
individual publication, patent or patent application was
specifically and individually indicated to be incorporated herein
by reference. In addition, citation or identification of any
reference in this application shall not be construed as an
admission that such reference is available as prior art to the
present invention.
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