U.S. patent application number 16/244139 was filed with the patent office on 2019-06-06 for method and system for determining olfactory perception signature.
This patent application is currently assigned to Yeda Research and Development Co. Ltd.. The applicant listed for this patent is Yeda Research and Development Co. Ltd.. Invention is credited to Dana Shoshana BAR ZVI MILDWORF, Idan FRUMIN, Liron PINCHOVER, Lavi SECUNDO, Sagit SHUSHAN, Kobi SNITZ, Noam SOBEL, Kineret WEISSLER.
Application Number | 20190171673 16/244139 |
Document ID | / |
Family ID | 60660291 |
Filed Date | 2019-06-06 |
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United States Patent
Application |
20190171673 |
Kind Code |
A1 |
SOBEL; Noam ; et
al. |
June 6, 2019 |
METHOD AND SYSTEM FOR DETERMINING OLFACTORY PERCEPTION
SIGNATURE
Abstract
A method of determining olfactory perception signature of a
subject is disclosed. The method comprises: providing the subject
with a plurality of physical odorant samples for sniffing; for each
sniffed odorant sample, presenting to the subject, by a user
interface, a set of odorant descriptors and a respective set of
rating controls, and receiving ratings entered by the subject using
the rating controls. Each rating is indicative of a descriptiveness
of a respective odorant descriptor for the odorant sample, thereby
obtaining a set of descriptiveness levels for the odorant sample.
The method also comprises calculating, by a computer, relations
between pairs of sets of descriptiveness levels corresponding to
pairs of odorant samples, to provide a vector of relations, wherein
the vector represents the olfactory perception signature of the
subject.
Inventors: |
SOBEL; Noam; (Jaffa, IL)
; SECUNDO; Lavi; (Rehovot, IL) ; SNITZ; Kobi;
(Rehovot, IL) ; WEISSLER; Kineret; (Rehovot,
IL) ; PINCHOVER; Liron; (Rehovot, IL) ;
FRUMIN; Idan; (Rehovot, IL) ; BAR ZVI MILDWORF; Dana
Shoshana; (Rehovot, IL) ; SHUSHAN; Sagit;
(Rehovot, IL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Yeda Research and Development Co. Ltd. |
Rehovot |
|
IL |
|
|
Assignee: |
Yeda Research and Development Co.
Ltd.
Rehovot
IL
|
Family ID: |
60660291 |
Appl. No.: |
16/244139 |
Filed: |
January 10, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
15188104 |
Jun 21, 2016 |
10204176 |
|
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16244139 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 5/4011 20130101;
G16H 10/20 20180101; G16H 50/30 20180101; G06F 16/90335 20190101;
G16H 40/63 20180101; A61B 2503/12 20130101 |
International
Class: |
G06F 16/903 20060101
G06F016/903; G16H 10/20 20060101 G16H010/20; A61B 5/00 20060101
A61B005/00 |
Claims
1. A client system for communicating in a matching service for
matching members of an online community, the client system
comprising: a transceiver arranged to receive and transmit
information on a communication network; and a processor arranged to
communicate with the transceiver, and perform code instructions,
comprising: code instructions for displaying a set of rating
controls on a user interface; code instructions for receiving
sniffing ratings entered by a member using said rating controls;
code instructions for calculating an olfactory perception signature
of the member based on said ratings; code instructions for
transmitting said olfactory perception signature to a server
computer; and code instructions for receiving from said server
computer an indication whether or not a matching member has been
found in a database, based on said transmitted olfactory perception
signature.
2. The system of claim 1, wherein said processor is arranged to
display on said user interface a set of odorant descriptors,
respectively corresponding to said set of rating controls, wherein
said sniffing ratings are descriptiveness levels corresponding to
said odorant descriptors.
3. The system of claim 2, wherein said processor is arranged to
display said set of odorant descriptors and said a set of rating
controls a plurality of times, and to receive said sniffing ratings
a respective plurality of times, thereby to obtain a plurality of
sets of descriptiveness levels, wherein said calculating said
olfactory perception signature comprises calculating relations
between pairs of sets of descriptiveness levels.
4. The system of claim 3, wherein said calculation of said
relations comprises, for each pair of sets, averaging squared
differences between descriptiveness levels of a first set pair, and
respective descriptiveness levels of a second set of said pair.
5. The system of claim 4, wherein said code instructions comprise
code instructions for determining and displaying likelihood for
successful relationship between the member and the matching
member.
6. A server system for communicating in a matching service for
matching members of an online community, the server system
comprising: a transceiver arranged to receive and transmit
information on a communication network; and a processor arranged to
communicate with the transceiver, and perform code instructions,
comprising: code instructions for receiving from a client computer
an olfactory perception signature of a member; code instructions
for accessing a computer readable database having a plurality of
database olfactory perception signatures of other members of the
community; code instructions for searching said database for a
database olfactory perception signature that is similar to said
olfactory perception signature of the member; and code instructions
for transmitting to said client computer an indication that a
similar database olfactory perception signature has been found.
7. The system of claim 6, wherein said code instructions for
searching said database employ comparison by a metric selected from
the group consisting of statistical correlation, Euclidian
distance, Log-Euclidean distance, Angular distance, significance
test distance, Chebyshev distance, Manhattan distance, and
Minkowski distance.
8. A method of determining personality trait of a subject, the
method comprising: providing the subject with a plurality of
physical odorant samples for sniffing; for each sniffed odorant
sample, presenting to the subject, by a user interface, a set of
odorant descriptors and a respective set of rating controls, and
receiving ratings entered by the subject using said rating
controls, each rating being indicative of a descriptiveness of a
respective odorant descriptor for said odorant sample, thereby
obtaining a set of descriptiveness levels for said odorant sample;
and calculating, by a computer, relations between pairs of sets of
descriptiveness levels corresponding to pairs of odorant samples,
to provide a vector of relations, said vector representing the
olfactory perception signature of the subject; accessing a computer
readable database, each entry of said database having a database
olfactory perception signature and annotation information
pertaining to a personality trait; searching said database for a
database olfactory perception signature that is similar to said
olfactory perception signature of the subject; extracting from the
database annotation information associated with said similar
database olfactory perception signature; and generating an output
indicative of a respective personality trait.
9. The method of claim 8, comprising determining a psychological
condition of the subject based on said extracted annotation
information.
10. The method of claim 8, wherein each of at least some annotation
information of said database is openness to experience.
11. The method of claim 8, wherein each of at least some annotation
information of said database is conscientiousness.
12. The method of claim 8, wherein each of at least some annotation
information of said database is extraversion.
13. The method of claim 8, wherein each of at least some annotation
information of said database is agreeableness.
14. The method of claim 8, wherein each of at least some annotation
information of said database is neuroticism.
15. The method of claim 8, further comprising predicting an outcome
of a psychological test for the subject, based on said extracted
annotation information.
Description
RELATED APPLICATIONS
[0001] This application is a division of U.S. patent application
Ser. No. 15/188,104 filed on Jun. 21, 2016.
[0002] The contents of the above application are all incorporated
by reference as if fully set forth herein in their entirety.
FIELD AND BACKGROUND OF THE INVENTION
[0003] The present invention, in some embodiments thereof, relates
to olfactory perception and, more particularly, but not
exclusively, to a method and a system for determining olfactory
perception signature.
[0004] Odors are complex mixtures of chemical species, and so
contain many constituent molecules. The biological olfactory system
is a remarkable sensor having many olfactory cells or odorant
receptors, but not very many different types of olfactory cells.
The characterization of a scent or odor is typically through the
combined response of many of the receptors.
[0005] Because any two individuals differ by .about.30% of their
olfactory receptor subtype genome, this renders a potentially
unique nose for each person. If one could capture this uniqueness
with a perceptual test, a sort of perceptual olfactory fingerprint,
this should then be informative on the underlying individual
olfactory receptor subtype genome. The notion of a psychophysical
test informing on underlying genes is of course well known from
vision where color blindness charts inform about genes coding for
different opsins in the retina.
[0006] U.S. Pat. No. 6,558,322 teaches methods and kits for
determining olfactory perception. A test person's olfactory
perception is evaluated and then determined by first providing the
test subject with a palette of varying odors and fragrances, and
then having that person describe, in full detail, each scent
sample.
[0007] Background art includes Milinski M & Wedekind C (2001)
Behav Ecol 12(2):140-149.
SUMMARY OF THE INVENTION
[0008] According to an aspect of some embodiments of the present
invention there is provided a method of determining olfactory
perception signature of a subject. The method comprises: providing
the subject with a plurality of physical odorant samples for
sniffing; for each sniffed odorant sample, presenting to the
subject, by a user interface, a set of odorant descriptors and a
respective set of rating controls, and receiving ratings entered by
the subject using the rating controls. Each rating is indicative of
a descriptiveness of a respective odorant descriptor for the
odorant sample, thereby obtaining a set of descriptiveness levels
for the odorant sample. The method also comprises calculating, by a
computer, relations between pairs of sets of descriptiveness levels
corresponding to pairs of odorant samples, to provide a vector of
relations, wherein the vector represents the olfactory perception
signature of the subject.
[0009] According to some embodiments of the invention the method
comprises generating a graphical output describing the vector of
relations.
[0010] According to some embodiments of the invention the method
comprises obtaining an olfactory perception signature of another
subject and comparing the olfactory perception signature of the
subject with the olfactory perception signature of the other
subject.
[0011] According to some embodiments of the invention the olfactory
perception signature of another subject is obtained by accessing a
computer readable database and selecting the olfactory perception
signature of the other subject from the database.
[0012] According to some embodiments of the invention the method
comprises, based on the comparison, determining likelihood for
successful relationship between the subject and the other
subject.
[0013] According to some embodiments of the invention the method
comprises, based on the comparison, determining likelihood for
Human leukocyte antigen (HLA) matching between the subject and the
other subject.
[0014] According to some embodiments of the invention the
comparison is by a metric selected from the group consisting of
statistical correlation, Euclidian distance, Log-Euclidean
distance, Angular distance, significance test distance, Chebyshev
distance, Manhattan distance, and Minkowski distance.
[0015] According to some embodiments of the invention the method
comprises: accessing a computer readable database, each entry of
the database having a database olfactory perception signature and
annotation information; searching the database for a database
olfactory perception signature that is similar to the olfactory
perception signature of the subject; and extracting from the
database annotation information associated with the similar
database olfactory perception signature.
[0016] According to some embodiments of the invention each
annotation information of the database is a personality trait, and
the method comprises determining a psychological condition of the
subject based on the extracted annotation information.
[0017] According to some embodiments of the invention each of at
least some annotation information of the database is selected from
the group consisting of: openness to experience, conscientiousness,
extraversion, agreeableness, and neuroticism.
[0018] According to some embodiments of the invention the method
further comprises predicting an outcome of a psychological test for
the subject, based on the extracted annotation information.
[0019] According to some embodiments of the invention the computer
is remote from the user interface, and the method comprises
transmitting the set of descriptiveness levels over a communication
network to the computer.
[0020] According to an aspect of some embodiments of the present
invention there is provided a method for matching members of an
online community. The method comprises: providing to a member of
the community a plurality of physical odorant samples for sniffing.
At a client computer: receiving sniffing ratings entered by the
member using rating controls of a user interface of the client
computer, calculating an olfactory perception signature of the
member based on the ratings, and transmitting the olfactory
perception signature to a server computer. At the server computer:
accessing a computer readable database having a plurality of
database olfactory perception signatures of other members of the
community searching the database for a database olfactory
perception signature that is similar to the olfactory perception
signature of the member, and transmitting to the client computer an
indication that a similar database olfactory perception signature
has been found.
[0021] According to some embodiments of the invention the method
comprises displaying on the user interface a set of odorant
descriptors for each odorant sample, wherein the sniffing ratings
are indicative of descriptiveness of each odorant descriptor of the
set.
[0022] According to some embodiments of the invention the
calculation of the olfactory perception signature comprises
calculating relations between pairs of sets of descriptiveness
levels corresponding to pairs of odorant samples.
[0023] According to some embodiments of the invention the
calculation of the relations comprises, for each pair of odorant
samples, averaging squared differences between descriptiveness
levels of a first odorant sample of the pair, and respective
descriptiveness levels of a second odorant sample of the pair.
[0024] According to an aspect of some embodiments of the present
invention there is provided a server system for communicating in a
matching service for matching members of an online community. The
server system comprises: a transceiver arranged to receive and
transmit information on a communication network; and a processor
arranged to communicate with the transceiver, and perform code
instructions, comprises: code instructions for receiving from a
client computer an olfactory perception signature of a member; code
instructions for accessing a computer readable database having a
plurality of database olfactory perception signatures of other
members of the community; code instructions for searching the
database for a database olfactory perception signature that is
similar to the olfactory perception signature of the member; and
code instructions for transmitting to the client computer an
indication that a similar database olfactory perception signature
has been found.
[0025] According to an aspect of some embodiments of the present
invention there is provided a client system for communicating in a
matching service for matching members of an online community. The
client system comprises: a transceiver arranged to receive and
transmit information on a communication network; and a processor
arranged to communicate with the transceiver, and perform code
instructions, comprises: code instructions for displaying a set of
rating controls on a user interface; code instructions for
receiving sniffing ratings entered by a member using the rating
controls; code instructions for calculating an olfactory perception
signature of the member based on the ratings; code instructions for
transmitting the olfactory perception signature to a server
computer; and code instructions for receiving from the server
computer an indication whether or not a matching member has been
found in a database, based on the transmitted olfactory perception
signature.
[0026] According to some embodiments of the invention the processor
is arranged to display on the user interface a set of odorant
descriptors, respectively corresponding to the set of rating
controls, wherein the sniffing ratings are descriptiveness levels
corresponding to the odorant descriptors.
[0027] According to some embodiments of the invention the processor
is arranged to display the set of odorant descriptors and the a set
of rating controls a plurality of times, each times, and to receive
the sniffing ratings a respective plurality of times, thereby to
obtain a plurality of sets of descriptiveness levels, wherein the
calculation of the olfactory perception signature comprises
calculating relations between pairs of sets of descriptiveness
levels.
[0028] According to some embodiments of the invention the
calculation of the relations comprises, for each pair of sets,
averaging squared differences between descriptiveness levels of a
first set pair, and respective descriptiveness levels of a second
set of the pair.
[0029] Unless otherwise defined, all technical and/or scientific
terms used herein have the same meaning as commonly understood by
one of ordinary skill in the art to which the invention pertains.
Although methods and materials similar or equivalent to those
described herein can be used in the practice or testing of
embodiments of the invention, exemplary methods and/or materials
are described below. In case of conflict, the patent specification,
including definitions, will control. In addition, the materials,
methods, and examples are illustrative only and are not intended to
be necessarily limiting.
[0030] Implementation of the method and/or system of embodiments of
the invention can involve performing or completing selected tasks
manually, automatically, or a combination thereof. Moreover,
according to actual instrumentation and equipment of embodiments of
the method and/or system of the invention, several selected tasks
could be implemented by hardware, by software or by firmware or by
a combination thereof using an operating system.
[0031] For example, hardware for performing selected tasks
according to embodiments of the invention could be implemented as a
chip or a circuit. As software, selected tasks according to
embodiments of the invention could be implemented as a plurality of
software instructions being executed by a computer using any
suitable operating system. In an exemplary embodiment of the
invention, one or more tasks according to exemplary embodiments of
method and/or system as described herein are performed by a data
processor, such as a computing platform for executing a plurality
of instructions. Optionally, the data processor includes a volatile
memory for storing instructions and/or data and/or a non-volatile
storage, for example, a magnetic hard-disk and/or removable media,
for storing instructions and/or data. Optionally, a network
connection is provided as well. A display and/or a user input
device such as a keyboard or mouse are optionally provided as
well.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS
[0032] The patent or application file contains at least one drawing
executed in color. Copies of this patent or patent application
publication with color drawing(s) will be provided by the Office
upon request and payment of the necessary fee.
[0033] Some embodiments of the invention are 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 embodiments of the
invention. In this regard, the description taken with the drawings
makes apparent to those skilled in the art how embodiments of the
invention may be practiced.
[0034] In the drawings:
[0035] FIGS. 1A and 1B are schematic illustrations describing a
technique for obtaining olfactory fingerprints, according to some
embodiments of the present invention. Odorant ratings along
visual-analogue scales (VAS) are converted into numbers reflecting
location on the VAS line. Pairwise odorant relation is calculated
as the distance across all descriptors used, and pairwise person
similarity is calculated as the correlation across odors. All this
assures that fingerprints are odorant-specific but
descriptor-independent. For example, imagine John who was raised on
an island smelling real coconuts, and Jane who knows coconut only
from Bounty chocolate bars. "Coconut" is very different for these
two individuals. John may rate Odor A as 47% like coconut, Odor B
as 49% like coconut, and Odor C as 4% like coconut. Thus, odors A
and B are highly similar, and both are dissimilar from odor C. Jane
may rate same Odor A as 21% like coconut, Odor B as 19% like
coconut, and Odor C as 100% like coconut. Once again, odors A and B
are highly similar, and both are dissimilar from odor C. Thus, John
and Jane will have very similar olfactory fingerprints derived from
these three odorants and one descriptor, even though they are in
total disagreement as to what coconut smell is like.
[0036] FIGS. 2A-2G show olfactory fingerprints and their
characterizations, according to some embodiments of the present
invention. The fingerprints were consistent within individuals and
different across individuals. To visualize fingerprints, 378
pairwise similarities were interpolated. A) An example olfactory
fingerprint of one individual. B) The olfactory fingerprint of the
same individual from A, but here derived using a different set of
non-overlapping descriptors. C) The olfactory fingerprint of the
same individual from A and B, but here obtained separately 16 days
later. D) The olfactory fingerprint of a different individual.
Correlations: A B, r=0.89; A C, r=0.61; A D, r=0.25. E) The
best-correlated descriptors across all odors. F) Heat-map matrix of
distances between all subject pairs. G) Histogram of correlation
coefficients of all non self-self pairs.
[0037] FIGS. 3A-3B illustrate that olfactory fingerprints are
independent of descriptor identity. A) Heat-map matrix of distances
between fingerprints A and B for 89 subjects, where A and B were
derived using the same odorants but different descriptors. The
diagonal represents the correlation of a subject to him/herself. B)
Violin plots comparing correlation coefficients of all self-self
pairs (using different descriptors) to all self other pairs, the
distribution of correlation coefficients of self-self and
self-others are shown in orange, the mean and median of the
distribution are depicted in black and red respectively.
[0038] FIGS. 4A-4D illustrate that fingerprints depend on the
number of odors and descriptors and the passing of time. A)
Heat-map of fingerprint ability to distinguish self-self from
self-other pairs (represented in Z-Score values) as a function of
number of odors and descriptors used. Dashed line represents
Z-Score value of 1.65 (p=0.05). B) 3D plot of Z-Score values as a
function of number of odors and descriptors used to generate a
fingerprint. C) First test-retest. Violin plot comparing
correlation coefficients of 23 subjects refingerprinted across
time. Left side represents correlation coefficients distribution of
a subject to him/herself over time. Right side represents
correlation coefficients distribution of a subject to other
subjects over time. The mean and median of the distribution are
depicted in black and red respectively. D) Second test-retest with
five repetitions. Right Y axis is correlation across retests (r)
shown in yellow. Left Y axis is the ability of the fingerprint to
discriminate self from others in Z-score values when comparing the
first to ensuing retests (black bars) or each two consecutive
retests (red line).
[0039] FIGS. 5A-5B illustrate that similar olfactory fingerprints
imply high Human leukocyte antigen (HLA) matching. A) 16770
pairwise comparisons of olfactory fingerprint distance vs HLA
match. The dotted red line reflects the cutoff for saved tests. B)
ROC curves of HLA comparisons saved vs. HLA matches missed. The
diagonal identity line reflects no gain or loss. ROCs: Red=using
all 11 odors, Gray=200 testing curves using 4 odorants, Black and
Blue=median and mean of the 4 best odorants respectively.
[0040] FIGS. 6A-6B are exemplary raw data showing the use of 54
descriptors in experiment 1A across all subjects and all odors.
Some descriptors were rated zero for some odors; however, all of
the descriptors were rated above 80 for some odors and subjects.
(A) Dot plot. (B) Violin plot.
[0041] FIGS. 7A-7B are exemplary raw data showing the use of 54
descriptors in experiment 2 across all subjects and all odors. Some
descriptors were rated zero for some odors; however, all of the
descriptors were rated above 80 for some odors and subjects. (A)
Dot plot. (B) Violin plot.
[0042] FIGS. 8A-8E. (A) An example of calculating the Z value for
one subject. A Gaussian is fitted (magenta line) to the
distribution of correlation coefficients (CC) between subject's
fingerprint A of and all other subjects (blue bars). Z value of
subject's CCs between fingerprint A and B (red bar) is calculated
using the mean and SD obtained from the fitted Gaussian. (B)
Distribution of olfactory fingerprint CC. (Upper) Distribution of
CC between a subject and all other subjects. (Lower) Distribution
of CC between a subject and him/herself. (C) PERMANOVA test to
compare intrasubject distance to intersubject distance within the
same session. Distribution of bootstrapped (flipped labels)
PERMANOVA pseudo F values (blue bars) compared with the real pseudo
F values (red arrow). (D) PERMANOVA test to compare intrasubject
distance to intersubject distance between sessions. Distribution of
bootstrapped (flipped labels) PERMANOVA pseudo F values (blue bars)
compared with the real pseudo F values (red arrow). (E) Olfactory
fingerprint distance vs. HLA match; 16,770 pairwise comparisons of
olfactory fingerprint distance (calculated using CC) vs. HLA match
value. Blue circles represent subject pairs with low (0-4) HLA
match, green circles represent subject pairs with high (5, 6) HLA
match.
[0043] FIGS. 9A-9E. (A) An example of calculating the Z value for
one subject. A Gaussian is fitted (magenta line) to the
distribution of Euclidian distances (ED) between subject's
fingerprint A of and all other subjects (blue bars). Z value of
subject's ED between fingerprint A and B (red bar) is calculated
using the mean and SD obtained from the fitted Gaussian. (B)
Distribution of olfactory fingerprint ED. (Upper) Distribution of
ED between a subject and all other subjects. (Lower) Distribution
of ED between a subject and him/herself. (C) PERMANOVA test to
compare intrasubject distance to intersubject distance within the
same session. Distribution of bootstrapped (flipped labels)
PERMANOVA pseudo F values (blue bars) compared with the real pseudo
F values (red arrow). (D) PERMANOVA test to compare intrasubject
distance to intersubject distance between sessions. Distribution of
bootstrapped (flipped labels) PERMANOVA pseudo F values (blue bars)
compared with the real pseudo F values (red arrow). (E) Olfactory
fingerprint distance vs. HLA match; 16,770 pairwise comparisons of
olfactory fingerprint distance (calculated using ED) vs. HLA match
value. Blue circles represent subject pairs with low (0-4) HLA
match, green circles represent subject pairs with high (5, 6) HLA
match.
[0044] FIGS. 10A-10E. (A) An example of calculating the Z value for
one subject. A Gaussian is fitted (magenta line) to the
distribution of log of Euclidian distances (LoED) between subject's
fingerprint A and all other subjects (blue bars). Z value of
subject's LoED between fingerprint A and B (red bar) is calculated
using the mean and SD obtained from the fitted Gaussian. (B)
Distribution of olfactory fingerprint LoED. (Upper) Distribution of
LoED between a subject and all other subjects. (Lower) Distribution
of LoED between a subject and him/herself. (C) PERMANOVA test to
compare intrasubject distance to intersubject distance within the
same session. Distribution of bootstrapped (flipped labels)
PERMANOVA pseudo F values (blue bars) compared with the real pseudo
F values (red arrow). (D) PERMANOVA test to compare intrasubject
distance to intersubject distance between sessions. Distribution of
bootstrapped (flipped labels) PERMANOVA pseudo F values (blue bars)
compared with the real pseudo F values (red arrow). (E) Olfactory
fingerprint distance vs. HLA match; 16,770 pairwise comparisons of
olfactory fingerprint distance (calculated using LoED) vs. HLA
match value. Blue circles represent subject pairs with low (0-4)
HLA match, green circles represent subject pairs with high (5, 6)
HLA match.
[0045] FIG. 11 is a flowchart diagram of a method suitable for
determining olfactory perception signature of a subject, according
to some embodiments of the present invention.
[0046] FIG. 12 is a schematic illustration of a collection of
odorant samples, which can be used for determining olfactory
perception signature according to some embodiments of the present
invention.
[0047] FIG. 13 is a schematic illustration of a client-server
configuration which can be used for determining olfactory
perception signature according to some embodiments of the present
invention.
DESCRIPTION OF SPECIFIC EMBODIMENTS OF THE INVENTION
[0048] The present invention, in some embodiments thereof, relates
to olfactory perception and, more particularly, but not
exclusively, to a method and a system for determining olfactory
perception signature.
[0049] Before explaining at least one embodiment of the invention
in detail, it is to be understood that the invention is not
necessarily limited in its application to the details of
construction and the arrangement of the components and/or methods
set forth in the following description and/or illustrated in the
drawings and/or the Examples. The invention is capable of other
embodiments or of being practiced or carried out in various
ways.
[0050] FIG. 11 is a flowchart diagram of a method suitable for
determining olfactory perception signature of a subject, according
to various exemplary embodiments of the present invention. It is to
be understood that, unless otherwise defined, the operations
described hereinbelow can be executed either contemporaneously or
sequentially in many combinations or orders of execution.
Specifically, the ordering of the flowchart diagrams is not to be
considered as limiting. For example, two or more operations,
appearing in the following description or in the flowchart diagrams
in a particular order, can be executed in a different order (e.g.,
a reverse order) or substantially contemporaneously. Additionally,
several operations described below are optional and may not be
executed.
[0051] At least part of the operations described herein can be can
be implemented by a data processing system, e.g., a dedicated
circuitry or a general purpose computer, configured for receiving
data and executing the operations described below. At least part of
the operations can be implemented by a cloud-computing facility at
a remote location. The data processing system or cloud-computing
facility can serve, at least for part of the operations as an image
processing system, wherein the data received by the data processing
system or cloud-computing facility include image data.
[0052] Computer programs implementing the method of the present
embodiments can commonly be distributed to users by a communication
network or on a distribution medium such as, but not limited to, a
floppy disk, a CD-ROM, a flash memory device and a portable hard
drive. From the communication network or distribution medium, the
computer programs can be copied to a hard disk or a similar
intermediate storage medium. The computer programs can be run by
loading the code instructions either from their distribution medium
or their intermediate storage medium into the execution memory of
the computer, configuring the computer to act in accordance with
the method of this invention. All these operations are well-known
to those skilled in the art of computer systems.
[0053] The method of the present embodiments can be embodied in
many forms. For example, it can be embodied in on a tangible medium
such as a computer for performing the method operations. It can be
embodied on a computer readable medium, comprising computer
readable instructions for carrying out the method operations. In
can also be embodied in electronic device having digital computer
capabilities arranged to run the computer program on the tangible
medium or execute the instruction on a computer readable
medium.
[0054] Referring to FIG. 11 the method begins at 10 and optionally
and preferably continues to 11 at which the subject is provided
with a plurality of physical odorant samples for sniffing.
[0055] Each odorant sample can contain an odorant component or an
odorant mixture containing a plurality of odorant components.
[0056] As used herein, an "odorant component" is a monomolecular
substance which can be sensed by the olfactory receptors and is
perceived as having a smell in humans.
[0057] Optionally and preferably, one or more of the odorant
samples is in solid state, but odorant samples in liquid or gaseous
states are also contemplated. In various exemplary embodiments of
the invention at least 5 or at least 10 or at least 15 or at least
20 odorant samples are provided. Preferably the number of odorant
samples is less than 50. The number of odorant samples is denoted
below by M.
[0058] The M odorant samples can be provided in any form. For
example, they can be provided as a scratch-and-sniff stickers or
cards, liftoff-and-sniff stickers or cards, sniff-jars, sealed
absorbing pads, and the like. A collection of odorant samples
provided as scratch-and-sniff cards is illustrated in FIG. 12,
showing an array 120 of eight scratch-and-sniff cards 122 each card
containing an odorant component or an odorant mixture. It is
appreciated that array 120 can include any number of cards 122, and
that the subject can be provided with one, or more than one array
120.
[0059] Any odorant component or odorant mixture can be used for the
odorant sample. A non-exhaustive list of possible odorant
components or odorant mixtures includes, without limitation, root
beer, cola, vanilla, chocolate, mint, peanut butter, apple, orange,
grapefruit, peach, cinnamon, leather, ocean, burning rubber, cut
grass, carrot, hard-boiled egg, butterscotch, strawberry, banana,
blueberry, bubblegum, lavender, rose, pepper, clove, coffee, tea,
tomato sauce, oregano, mustard, magic marker, pumpkin pie,
raspberry, lemon, vinegar, dill, pineapple, sour apple, almond
extract, licorice, cotton candy, popcorn, cherry, pine, chicken
noodle soup, macaroni and cheese, hot dog, ginkgo, olive, apple
pie, BBQ, birthday cake, candy corn, caramel, cheddar cheese,
cherry pie, chili, fish, fresh bread, gingerbread, hamburger, pecan
pie, hot dog, jelly bean, licorice, marshmallow, Mexican food,
popcorn, pumpkin pie, roast beef, lemon lime, spaghetti, waffle,
honey, root beer, spiced cider, apple, banana, blueberry, cherry,
coconut, grape, green apple, lemon, lemonade, chocolate, chocolate
mint, cola, cotton candy, peanut butter, pie crust, pina colada,
almond, cucumber, dill pickle, carnation, daffodil, gardenia,
general floral, geranium, hay, hibiscus, honey suckle, lawn, lilac,
lily, magnolia, mulberry, orchid, pine, spruce pine, rose, wheat,
tulip, sunflower, violet, hyacinth, maple, blue spruce, basil,
butterscotch, black pepper, cinnamon, clove, garlic, hazelnut,
mesquite, airy fresh, band-aid, balsam, baby powder, bergamot,
bubble gum, cigar, frankincense, perfume, soothing, leather,
menthol, money, new car, soap, sea breeze, suntan oil, tobacco,
tooth paste, campfire, invigorating, uplifting, ash tray, compost,
manure, jasmine, cedar, pine, juniper, ginger, myrrh, truffle,
chocolate chip cookies, pizza, anchovy, anise, and eucalyptus.
[0060] In experiments performed by the present inventors the
following odorant samples were used: moth ball, eucalyptus,
strawberries, burnt rubber, sweat, natural gas, dill pickle, fish,
cigar, manure, musk, ashtray, root beer, compost, green apple,
cheese, mango, garlic, maple, anise, rose, blue spruce, clove,
banana, banana (isoamyl acetate), eucalyptus (1,8-cineole), wet
grass (cis-3-hexen-1-ol), and isovaleric acid.
[0061] Referring again to FIG. 11, at 12 the subject is presented
with a set of odorant descriptors for each sniffed odorant sample.
The odorant descriptors are optionally and preferably presented by
a user interface such as, but not limited to, a graphical user
interface displayed on a computer screen, a smart TV screen, or a
screen of a mobile device, e.g., a smartphone device, a tablet
device or a smartwatch device. In some embodiments, a set of rating
controls is also displayed, preferably on the same screen.
[0062] The odorant descriptors are human-language descriptors and
are presented in a human-readable form to allow the subject to read
and decipher them. Typically, each of the descriptors is associated
with a known odor, not necessarily odor that is emitted by one of
the odorant samples, or a subjective perception of odor. For
example, a descriptor can be a textual phrase, such as, but not
limited to, "smells like coconut" or "smells like rubber" or "does
not smell like gasoline" or "has a pleasant smell" or "has an
unpleasant smell" or the like.
[0063] The number of odorant descriptors is not necessarily the
same as the number of odorant samples. It was found by the present
inventors that a relatively small number of odorant descriptors is
sufficient for determining the olfactory perception signature of
the subject. The number of odorant descriptors can therefore be
smaller than the number of odorant samples. For example, the number
of odorant descriptors can be from about 5 to about 15. The number
of odorant descriptors is denoted below by N.
[0064] While embodiments in which N<M are preferred, it is to be
understood that embodiments in which N=M or N>M are also
contemplated.
[0065] The rating controls that are displayed can be of any type
generally known in the field of graphical user interface design.
Representative examples include, without limitation, a slider, a
dropdown menu, a combo box, a text box and the like. A
representative set of human-language descriptors with a respective
set of rating controls is illustrated in FIG. 1A.
[0066] Odorant descriptors sets that are presented at 12 for
different odorant samples need not to be disjoint sets. In
preferred embodiments, an intersection set of at least two sets of
odorant descriptors (one set for each odorant sample) is a
non-empty set, so that there is at least one or at least two or
more odorant descriptors (which are elements of the intersection
between the sets) that is/are presented at 12 for two different
odorant samples. In some embodiments, the same set of odorant
descriptors is repeatedly presented for all odorant samples.
[0067] At 13, sniffing ratings are received from the subject. In
embodiments in which rating controls are displayed, the user enters
the ratings in the rating controls, and the ratings are received
from the rating controls. Each received rating is indicative of a
descriptiveness of the respective odorant descriptor for the
respective odorant sample, as perceived by the subject upon
sniffing that odorant sample. For example, when the odorant
descriptor is "has a pleasant smell," the sniffing rating indicates
to what extent the subject perceives the pleasantness of the odor
of the respective odorant.
[0068] Since the subject is presented with a set of N odorant
descriptors for each odorant sample, the method preferably obtains
at 13 a set s of perceived descriptiveness levels p(1,k), p(2,k), .
. . , p(N,k) for each odorant sample k. The descriptiveness levels
are preferably numerical according to a predetermined scale, for
example, 0 to 100. The ratings, on the other hand, are not
necessarily numerical. For example, the ratings can be positions on
a slider or textual phrases from a dropdown menu. In embodiments in
which the ratings are not numerical, the method optionally and
preferably parses the ratings and maps them to numerical
descriptiveness levels according to a predetermined mapping
protocol. It is appreciated, however, that some subjects may not
provide a rating for each and every odorant descriptor that is
displayed, since, for example, some subjects may find a particular
odorant descriptor irrelevant for a particular odorant sample. In
such a scenario, the method can exclude the particular
descriptiveness level from the set of descriptiveness level that
corresponds to the respective odorant sample, so that the size of
the set s is less than N for the respective odorant sample.
Alternatively, the method can substitute a value for that
particular descriptiveness level, according to a predetermined
procedure, so that the size of the set s remains N. The substituted
value is preferably a statistical measure, such as, but not limited
to, the mean or median of all descriptiveness levels that were
obtained from the subject for the same odorant descriptor after
sniffing other odorant samples.
[0069] Once all the ratings are received for all the odorant
samples, a collection C, including M sets s.sub.1, s.sub.2, . . . ,
s.sub.M of descriptiveness levels, is obtained.
[0070] At 14, an olfactory perception signature of the subject is
calculated based on the ratings. This is optionally and preferably
executed by calculating relations between pairs of sets of the
collection C, which pairs of sets correspond to pairs of odorant
samples. This provides a vector v of relations, which vector
represents the olfactory perception signature of the subject. The
dimension of the vector v is therefore equal to or less than
M(M-1)/2 which is the numbers of pairs in the collection C. Thus,
denoting the relation between set s.sub.i of collection C and set
s.sub.j of collection C by r.sub.ij, where i,j.ltoreq.M(M-1)/2, the
components of the vector v are r.sub.12, r.sub.13, r.sub.23, etc.
When the vector v has its maximal dimension M(M-1)/2, namely when
all possible pairwise relations are calculated, the vector v can be
written as v=(r.sub.12, r.sub.13, . . . , r.sub.1M, r.sub.23, . . .
, r.sub.M-1,M).
[0071] The relations can be calculated in more than one way. In one
embodiment, squared differences (p(k,i)-p(k,j)).sup.2 between
descriptiveness levels p(k,i) of a first odorant sample i of the
pair, and respective descriptiveness levels p(k,j) of a second
odorant sample j of the pair are calculated. The squared
differences can be averaged for each pair (i,j) of odorant samples,
for example, by summing over the odorant descriptor index k, and
dividing by the size N of the sets s. Thereafter, a square root of
this average is optionally and preferably obtained to provide the
relation value r.sub.ij between odorant sample i and odorant sample
j (or, equivalently between set s.sub.i and set s.sub.j). These
embodiments are schematically illustrated in FIG. 1A.
[0072] It is appreciated that such a calculation of the relation
r.sub.ij is equivalent to a normalized Euclidian distance between
two vectors u.sub.i=(p(1,i), p(2,i), . . . , p(N,i)) and
u.sub.j=(p(1j), p(2,j), . . . , p(N j)) each vector being formed of
one set of descriptiveness levels and therefore represent one
odorant sample, wherein the normalization factor is the square root
of the dimension of the vectors u.sub.i and u.sub.j (the number of
descriptiveness levels in each set).
[0073] In another embodiment, the relation r.sub.ij is calculated
using a non-Euclidian distance between the two vectors u.sub.i and
u.sub.j. Optionally, the non-Euclidian distance can be normalized
by the square root of the dimension of the vectors. Representative
examples of non-Euclidian distance include, without limitation, a
Chebyshev distance, a Manhattan distance, and a Minkowski distance.
Other relations between pairs of sets, such as a statistical
correlation (e.g., Pearson correlation, Spearman correlation,
Kendall correlation) or a t-test distance between the vectors
u.sub.i and u.sub.j, are also contemplated.
[0074] In cases in which a relation r.sub.ij is calculated for
vectors of different size the calculation optionally and preferably
includes only descriptiveness levels that correspond to odorant
descriptors to which the subject provided ratings for both odorant
samples i and j.
[0075] The relations r.sub.ij provide indication regarding the
similarity between the respective sets. It is appreciated that
whether the calculated relation r.sub.ij increases or decreases
with the level of similarity between the sets depends on the
procedure employed for calculating the relations r.sub.ij. For
example, when the calculation is based on distances (Euclidian
distance, non-Euclidian distance, t-test distance), high value of
the calculated relations r.sub.ij indicates low similarity level,
and when the calculation is based on correlation, high value of the
calculated relations r.sub.ij indicates high similarity level.
[0076] The method can optionally and preferably proceed to 15 at
which a graphical output describing the vector of similarities is
generated. The graphical output can be a color coded output. A
representative example of such an output is shown in FIGS. 2A-2E,
described in greater detail in the Examples section that
follows.
[0077] The method can optionally and preferably proceed to 16 at
which an olfactory perception signature of another subject is
obtained and to 17 at which the olfactory perception signatures are
compared. The comparison can be based, for example, on a metric
selected from the group consisting of statistical correlation
(e.g., Pearson correlation, Spearman correlation, Kendall
correlation), Euclidian distance, Log-Euclidean distance, Angular
distance, significance test (e.g., t-test) distance, Chebyshev
distance, Manhattan distance, Minkowski distance and the like.
Thus, for example, a distance or statistical correlation between
the olfactory perception signatures can be calculated and the
calculated value of the distance or statistical correlation can be
used as a similarity measure describing the level of similarity
between the two signatures. For example, when a statistical
correlation is calculated, higher correlation value indicates
higher similarity between the signatures, and when an Euclidian
distance, a Log-Euclidean distance, a non-Euclidean distance, an
Angular distance or a significance test distance is calculated,
lower distance value indicates higher similarity between the
signatures.
[0078] The comparison can be utilized in more than one way. In some
embodiments, the comparison is utilized for matching between
members of a community, e.g., an online community. For example,
based on the comparison, a likelihood for successful relationship
between the subject and the other subject can be determined,
wherein when the signatures are more similar the likelihood for
successful relationship is higher and when the signatures are less
similar the likelihood for successful relationship is lower.
[0079] In some embodiments, the comparison is utilized for
determining likelihood for HLA matching between the subject and
other subject, wherein when the signatures are more similar the
likelihood for HLA matching is higher and when the signatures are
less similar the likelihood for HLA matching is lower. As
demonstrated in the Examples section that follows, it was found by
the present Inventors that similarity between the olfactory
fingerprints of the present embodiments is significantly higher for
highly HLA-matched individuals than for poorly HLA-matched
individuals.
[0080] The olfactory perception signature of the other subject can
be calculated as described above. Alternatively, the olfactory
perception signature to which the subject's signature is compared
can be obtained from an entry in a database of olfactory perception
signatures.
[0081] Each entry in such a database can include olfactory
perception signature and annotation information. The annotation
information can be stored separately from the olfactory perception
signature (e.g., in a separate file on a computer readable medium).
The annotation information corresponds to the individual or
individuals for which the database olfactory perception signature
pertains. For example, the annotation information can include
details of the community member that is characterized by the
database olfactory perception signature. The annotation information
can alternatively or additionally include HLA data of the
individual that is characterized by the database olfactory
perception signature.
[0082] Also contemplated are embodiments in which the annotation
information relates to psychological traits, for example, each
olfactory perception signature can be associated with a
psychological trait (e.g., openness to experience,
conscientiousness, extraversion, agreeableness, neuroticism), and a
high similarity between the signature of the subject and the
database signature can be indicative that the subject can be
described by the respective psychological trait. Thus, a search
within a database of psychologically annotated olfactory perception
signatures allows determining the psychological condition of the
subject, and/or predicting an outcome of a psychological test for
the subject.
[0083] Representative examples of psychological tests for which the
results can be predicted by the present embodiments include,
without limitation, NEO-PI, 16PF, Occupational Personality
Questionnaire, Beck Depression Inventory, Glover Numbing Scale,
Eysenck Personality Questionnaire, Life Experiences Survey,
Perceived Stress Scale, State-Trait Anxiety Inventory (STAI) Form
Y-2, STAI Form Y-1, Pittsburgh Sleep Quality Index, Kohn Reactivity
Scale, Pennebaker Inventory for Limbic Languidness, Short Form 12
Health Survey v2, SF-36, Pain Catastrophizing Scale, In vivo Coping
Questionnaire, Coping Strategies Questionnaire-Rev, Lifetime
Stressor List& Post-Traumatic Stress Disorder (PTSTD) Checklist
for Civilians, Multidimensional Pain Inventory v3, Comprehensive
Pain & Symptom Questionnaire, Symptom Checklist-90-R (SCL-90R),
Brief Symptom Inventory (BSI), Beck Depression Inventory (BDI)1
Profile of Mood States Bi-polar, Pain Intensity Measures, and Pain
Unpleasantness Measures.
[0084] The method ends at 18.
[0085] The determination of olfactory perception signature and the
optional comparison to another olfactory perception signature can
be executed according to some embodiments of the present invention
by a server-client configuration, as will now be explained with
reference to FIG. 13.
[0086] FIG. 13 illustrates a client computer 30 having a hardware
processor 32, which typically comprises an input/output (I/O)
circuit 34, a hardware central processing unit (CPU) 36 (e.g., a
hardware microprocessor), and a hardware memory 38 which typically
includes both volatile memory and non-volatile memory. CPU 36 is in
communication with I/O circuit 34 and memory 38. Client computer 30
preferably comprises a graphical user interface (GUI) 42 in
communication with processor 32. I/O circuit 34 preferably
communicates information in appropriately structured form to and
from GUI 42. Also shown is a server computer 50 which can similarly
include a hardware processor 52, an I/O circuit 54, a hardware CPU
56, a hardware memory 58. I/O circuits 34 and 54 of client 30 and
server 50 computers preferable operate as transceivers that
communicate information with each other via a wired or wireless
communication. For example, client 30 and server 50 computers can
communicate via a network 40, such as a local area network (LAN), a
wide area network (WAN) or the Internet. Server computer 50 can be
in some embodiments be a part of a cloud computing resource of a
cloud computing facility in communication with client computer 30
over the network 40.
[0087] GUI 42 and processor 32 can be integrated together within
the same housing or they can be separate units communicating with
each other. GUI 42 can optionally and preferably be part of a
system including a dedicated CPU and I/O circuits (not shown) to
allow GUI 42 to communicate with processor 32. Processor 32 issues
to GUI 42 graphical and textual output generated by CPU 36.
Processor 32 also receives from GUI 42 signals pertaining to
control commands generated by GUI 42 in response to user input. GUI
42 can be of any type known in the art, such as, but not limited
to, a keyboard and a display, a touch screen, and the like. In
preferred embodiments, GUI 42 is a GUI of a mobile device such as a
smartphone, a tablet, a smartwatch and the like. When GUI 42 is a
GUI of a mobile device, processor 32, the CPU circuit of the mobile
device can serve as processor 32 and can execute the code
instructions described herein.
[0088] Client 30 and server 50 computers can further comprise one
or more computer-readable storage media 44, 64, respectively. Media
44 and 64 are preferably non-transitory storage media storing
computer code instructions as further detailed herein, and
processors 32 and 52 execute these code instructions. The code
instructions can be run by loading the respective code instructions
into the respective execution memories 38 and 58 of the respective
processors 32 and 52. Storage media 64 preferably also store one or
more databases including a database of psychologically annotated
olfactory perception signatures as further detailed
hereinabove.
[0089] In operation, processor 32 of client computer 30 displays on
GUI 42 a set of rating controls, such as, but not limited to, a
slider, a dropdown menu, a combo box, a text box and the like.
Preferably, processor 32 also displays on GUI 42 a set of odorant
descriptors, respectively corresponding to the set of rating
controls, as further detailed hereinabove and exemplified in the
upper left pane of FIG. 1A. A subject, which can be a member of an
online community, and which is provided with physical odorant
samples (e.g., samples 122) for sniffing, enters the sniffing
ratings using the rating controls displayed on GUI 42.
[0090] Processor 32 receives the subject's ratings from GUI 42 and
can calculate an olfactory perception signature of the subject
based on these ratings. For example, similarities between pairs of
sets of descriptiveness levels can be calculated to provide a
vector of similarities as further detailed hereinabove. Processor
32 can then transmit the olfactory perception signature to server
computer 50.
[0091] Alternatively, processor 32 can receive the subject's
ratings from GUI 42 and transmit these ratings to server computer
50. In these embodiments, the calculation of the olfactory
perception signature of the subject, based on the transmitted
ratings, is executed by server computer 50, e.g., by calculating
the similarities between pairs of sets to provide a vector of
similarities as further detailed hereinabove.
[0092] Server computer 50 can access a database of olfactory
perception signatures stored on media 64, search searches the
database for a database olfactory perception signature that is
similar to the olfactory perception signature of the subject, and,
when such similar database signature is found, transmit to client
computer 30 an indication that a similar database olfactory
perception signature has been found. Client computer 30 can receive
the indication from server computer 50 and display it on GUI
42.
[0093] Server computer 50 can also transmit to client computer 30
the annotation information associated with the similar database
signature, and client computer 30 can display this information on
GUI 42. For example, when the comparison between signatures is for
the purpose of social matching, the server computer 50 can pull
from the database member details pertaining to the member
associated with the found database signature, and transmit these
details to client computer 30 for displaying on GUI 42.
[0094] As used herein the term "about" refers to .+-.10%.
[0095] The word "exemplary" is used herein to mean "serving as an
example, instance or illustration". Any embodiment described as
"exemplary" is not necessarily to be construed as preferred or
advantageous over other embodiments and/or to exclude the
incorporation of features from other embodiments.
[0096] The word "optionally" is used herein to mean "is provided in
some embodiments and not provided in other embodiments". Any
particular embodiment of the invention may include a plurality of
"optional" features unless such features conflict.
[0097] The terms "comprises", "comprising", "includes",
"including", "having" and their conjugates mean "including but not
limited to".
[0098] The term "consisting of" means "including and limited
to".
[0099] The term "consisting essentially of" means that the
composition, method or structure may include additional
ingredients, steps and/or parts, but only if the additional
ingredients, steps and/or parts do not materially alter the basic
and novel characteristics of the claimed composition, method or
structure.
[0100] As used herein, the singular form "a", "an" and "the"
include plural references unless the context clearly dictates
otherwise. For example, the term "a compound" or "at least one
compound" may include a plurality of compounds, including mixtures
thereof.
[0101] Throughout this application, various embodiments of this
invention may be presented in a range format. It should be
understood that the description in range format is merely for
convenience and brevity and should not be construed as an
inflexible limitation on the scope of the invention. Accordingly,
the description of a range should be considered to have
specifically disclosed all the possible subranges as well as
individual numerical values within that range. For example,
description of a range such as from 1 to 6 should be considered to
have specifically disclosed subranges such as from 1 to 3, from 1
to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as
well as individual numbers within that range, for example, 1, 2, 3,
4, 5, and 6. This applies regardless of the breadth of the
range.
[0102] Whenever a numerical range is indicated herein, it is meant
to include any cited numeral (fractional or integral) within the
indicated range. The phrases "ranging/ranges between" a first
indicate number and a second indicate number and "ranging/ranges
from" a first indicate number "to" a second indicate number are
used herein interchangeably and are meant to include the first and
second indicated numbers and all the fractional and integral
numerals therebetween.
[0103] 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
or as suitable in any other described embodiment of the invention.
Certain features described in the context of various embodiments
are not to be considered essential features of those embodiments,
unless the embodiment is inoperative without those elements.
[0104] Various embodiments and aspects of the present invention as
delineated hereinabove and as claimed in the claims section below
find experimental support in the following examples.
EXAMPLES
[0105] Reference is now made to the following examples, which
together with the above descriptions illustrate some embodiments of
the invention in a non limiting fashion.
[0106] Generally, the nomenclature used herein and the laboratory
procedures utilized in the present invention include molecular,
biochemical, microbiological and recombinant DNA techniques. Such
techniques are thoroughly explained in the literature. See, for
example, "Molecular Cloning: A laboratory Manual" Sambrook et al.,
(1989); "Current Protocols in Molecular Biology" Volumes I-III
Ausubel, R. M., ed. (1994); Ausubel et al., "Current Protocols in
Molecular Biology", John Wiley and Sons, Baltimore, Md. (1989);
Perbal, "A Practical Guide to Molecular Cloning", John Wiley &
Sons, New York (1988); Watson et al., "Recombinant DNA", Scientific
American Books, New York; Birren et al. (eds) "Genome Analysis: A
Laboratory Manual Series", Vols. 1-4, Cold Spring Harbor Laboratory
Press, New York (1998); methodologies as set forth in U.S. Pat.
Nos. 4,666,828; 4,683,202; 4,801,531; 5,192,659 and 5,272,057;
"Cell Biology: A Laboratory Handbook", Volumes I-III Cellis, J. E.,
ed. (1994); "Culture of Animal Cells--A Manual of Basic Technique"
by Freshney, Wiley-Liss, N. Y. (1994), Third Edition; "Current
Protocols in Immunology" Volumes I-III Coligan J. E., ed. (1994);
Stites et al. (eds), "Basic and Clinical Immunology" (8th Edition),
Appleton & Lange, Norwalk, Conn. (1994); Mishell and Shiigi
(eds), "Selected Methods in Cellular Immunology", W. H. Freeman and
Co., New York (1980); available immunoassays are extensively
described in the patent and scientific literature, see, for
example, U.S. Pat. Nos. 3,791,932; 3,839,153; 3,850,752; 3,850,578;
3,853,987; 3,867,517; 3,879,262; 3,901,654; 3,935,074; 3,984,533;
3,996,345; 4,034,074; 4,098,876; 4,879,219; 5,011,771 and
5,281,521; "Oligonucleotide Synthesis" Gait, M. J., ed. (1984);
"Nucleic Acid Hybridization" Hames, B. D., and Higgins S. J., eds.
(1985); "Transcription and Translation" Hames, B. D., and Higgins
S. J., eds. (1984); "Animal Cell Culture" Freshney, R. I., ed.
(1986); "Immobilized Cells and Enzymes" IRL Press, (1986); "A
Practical Guide to Molecular Cloning" Perbal, B., (1984) and
"Methods in Enzymology" Vol. 1-317, Academic Press; "PCR Protocols:
A Guide To Methods And Applications", Academic Press, San Diego,
Calif. (1990); Marshak et al., "Strategies for Protein Purification
and Characterization--A Laboratory Course Manual" CSHL Press
(1996); all of which are incorporated by reference as if fully set
forth herein. Other general references are provided throughout this
document. The procedures therein are believed to be well known in
the art and are provided for the convenience of the reader. All the
information contained therein is incorporated herein by
reference.
General Methods
Subjects
[0107] A total of 238 generally healthy subjects participated in
three experiments. Experiment 1A: 89 subjects, 40 women, mean
age=25.7.+-.3.1 years; Experiment 1B: 18 subjects, 11 women, mean
age 26.8.+-.3.4; Experiment 2: 130 subjects, 65 women, mean
age=29.93.+-.8.44 years).
Odorants
[0108] Two forms of odorant presentation were used in this study.
Experiment 1A contained 24 odorants in scratch-and-sniff form
provided by The PrintBox Inc (NY, USA) and 4 odorants presented in
sniff-jars. Experiment 1B contained 22 odorants presented in
sniff-jars. Experiment 2 contained 11 odorants all in jars; the 4
jar odorants from Experiment 1 (isoamyl-acetate, 1,8-cineole,
cis-3-hexen-1-ol, isovaleric acid) and 7 additional odorants.
Because the initial test-retest experiment used mostly
scratch-and-sniff odorants and the second test-retest experiment
used jars, any difference between these methods of presentation
could be assessed. No significant difference in test-retest was
found when comparing scratch-and-sniff (r=0.59.+-.0.14) to jars (r
first-second=0.58, .+-.0.21, t(39)=0.24, p=0.81).
List of Odorants Used for Experiment 1A
[0109] 1. Moth ball; 2 Eucalyptus; 3 Strawberries; 4 Burnt rubber;
5 Sweat; 6 Natural gas; 7 Dill pickle; 8 Fish; 9 Cigar; 10 Manure;
11 Musk; 12 Ashtray; 13 Root beer; 14 Compost; 15 Green apple; 16
Cheese; 17 Mango; 18 Garlic; 19 Maple; 20 Anise; 21 Rose; 22 Blue
spruce; 23 Clove; 24 Banana; 25 Banana (isoamyl acetate); 26
Eucalyptus (1,8-cineole); 27 Wet grass (cis-3-hexen-1-ol); 28
Isovaleric acid.
[0110] Odorants 1-24 were presented as scratch-and-sniff cards
(obtained from The Print Box, Inc.). Odorants 25-28 were single
molecules presented in small jars.
List of Descriptors Used for Experiment 1A
[0111] 1. Fishy; 2 Sour milk; 3 Hot cool; 4 Coconut; 5 Aromatic; 6
Fresh eggs; 7 Crushed grass; 8 Anise; 9 Burnt candle; 10 Household
gas; 11 Almond; 12 Fruity (not citrus); 13 Citrus; 14 Creosote; 15
Cologne; 16 Floral; 17 Chocolate; 18 Oily/fatty; 19 Medicinal; 20
Woody/resinous; 21 Pleasant; 22 Nutty; 23 Nail polish remover; 24
Strawberry; 25 Poisonous edible; 26 Mild/intense; 27
Annoying/soothing; 28 Pleasant; 29 Familiar; 30 Weak/strong; 31
Sweet; 32 Clean/dirty; 33 Causes physical tension/causes physical
relaxation; 34 Feminine; 35 Fresh/stale; 36 Dull/sharp; 37
Volatile; 38 Repulsive/attractive; 39 Nose stuffing/nose opening;
40 Artificial/natural; 41 Burnt; 42 Erotic; 43 Green; 44 Medicinal;
45 Salty; 46 Hot/cold; 47 Bitter; 48 Sour; 49 Heavy/light; 50
Smoked; 51 Poisonous/edible; 52 Masculine; 53 Disgusting.
List of Odorants Used for Experiment 1B
[0112] 1. Ambrarome absolu; 2 Anisic aldehyde/aubepine; 3 Castoreum
artess resin 246/2 IFR; 4 Carvone laevo; 5 Cistus labdanium oil
Spain RB; 6 Civet artessence absolute; 7 Eucaliptus globulus oil
China; 8 Fennel oil sweet; 9 Fir balsam oil Canada; 10 Galbanum oil
concentrated; 11 Ginger ol; 12 Grapefruit oil California; 13 Guava
duplcation CS; 14 Hexanol 3-CIS; 15 Hydrocarboresin SB; 16 Jasmin
absolute communelle; 17 Nutmeg oil Indonesia; 18 Pepper black oil;
19 Peppermint oil; 20 Peru balsam oil; 21 Vitiver oil Haiti; 22
Mugest C5 RIFM.
List of Descriptors Used for Experiment 1B
[0113] 1. Body odor; 2 Pleasant; 3 Fresh/rotten; 4 Sweet; 5.
Poisonous/edible; 6 smooth/textured; 7 Flowery; 8 Feminine; 9
Light/heavy; 10 Erotic; 11 Cold/hot; 12 Weak/strong; 13 Burnt; 14
Sour; 15 Masculine; 16 Complex; 17 Clean/dirty; 18
Artificial/natural; 19 Calming/disturbing; 20 Dull/sharp; 21
Bitter; 22 Dry/wet; 23 Aromatic.
Ratings
[0114] Each subject rated 28 odorants along 54 verbal descriptors
in Experiment 1A, 22 odorants along 23 verbal descriptors in
Experiment 1B, and 11 odorants along 57 verbal descriptors in
Experiment 2, using visual analogue scales (VAS). For example, the
question "please rate the odorant" was displayed together with a 14
cm line ranging from "not at all smells like coconut" at one end,
to "very much smells like coconut" at the other end. After sniffing
the odorant presented in scratch-and-sniff or jar, participants
crossed the line at a point reflecting their perception, and the
line was later parsed to 100 for analysis. Odor order was random
across participants, and inter-odor-interval was >40 seconds. To
account for individual differences in use of scales, each subject's
data was normalized by first subtracting the minimal value applied
by the subject, then dividing by the maximal remaining value, and
multiplying by 100. This generated a normalized range between 0 and
100.
HLA Typing
[0115] 5-10 mL of blood were drawn from each volunteer and kept at
4.degree. C. until DNA was extracted. Genomic DNA Extraction was
carried out from 400 .mu.L of whole blood using the MagNA Pure
Compact Nucleic Acid Isolation Kit I (Roche Diagnostics GmbH,
Mannheim, Germany). DNA samples were stored at -20.degree. C. HLA
typing was performed utilizing LUMINEX.TM. technology and Immucor
Transplant Diagnostic (Stamford, Conn.) kits to obtain HLA A*,
B*and DRB1* loci typings at low/intermediate resolution.
HLA Matching
[0116] HLA match was calculated using methods previously reported
in reference (16). There are three general groups of HLA: HLA-A*,
HLA-B*and HLADRB1*, and within each group there are different
specific HLA proteins (there are 59 different HLA-A* proteins, 118
different HLA-B*and 124 different HLA-DRB1*). Each of these HLA
groups (A*, B*and DRB1*) is notated by a 2-digit numerical
designation (e.g. HLA-A* 01:07 HLA-B*15:15, HLA-DRB1*15:33). A
match is calculated by counting the number of HLA proteins (in each
group separately) present in one subject that are also present in
another subject. Since there are 2 digits for each HLA group, the
count can be 0, 1 or 2. Once the count for each HLA group is
obtained, a match is calculated by summing the values of all the
groups. In other words, for each donor/recipient pair, the number
of antigens present in the donor that matched an antigen in the
recipient were counted. Homozygous antigens in the recipient that
matched a donor antigen were counted as two matches. Since three
HLA loci were counted, there was a potential for a maximum of 7
matches. For example if donor A has the following HLA genotype
A*24,68 B*14,35 DRB1*01, 11 and recipient B has the following HLA
genotype A*03,23 B*41, 47 DRB1*10,11 this pair (A->B) will have
an HLA match score of 0+0+1=1. However if donor C has the following
HLA genotype A*02, 30 B*13, 50 DRB1*07, 07 and recipient D has the
following HLA genotype A*02, 02 B*27, 41 DRB1*07, 11 then the pair
(C->D) will have an HLA match score of 2+0+1=3 and the pair
(D->C) will have an HLA match score of 1+0+2=3 (note that even
though the total HLA match score is the same it is not symmetric
between C<->D).
Distance Metrics
[0117] The present embodiments contemplate several types of metrics
to calculate the distance between olfactory fingerprints. In the
present Example, three different distance metrics were used to
define the distance between olfactory fingerprints of subject i and
subject j (i.e. d.sub.i,j). These include, (A) Correlation, (B)
Euclidean distance, and (C) Log-Euclidean distance. These metrics
were compared, and their impact on discriminability and stability
were evaluated. In all metrics, the olfactory fingerprint FP was
calculated in the same manner (see EQs. 1 and 2 below). The
notation FP.sub.i.sup.A denotes fingerprint of subject i, generated
using set A of descriptors or during session A and FP.sub.j.sup.B
denotes fingerprint of subject j, generated using set B of
descriptors or during session B. To evaluate the effectiveness of
each method several indices were defined.
[0118] a) Z-value index: Comparison of within-subject
(intrasubject) distance to between-subjects (intersubject) distance
by using different descriptors during the same session. This index
can be determined for each subject by calculating how many SDs
his/hers intersubject's score lies from the mean of the
distribution of intrasubject scores (the distribution of
intrasubject scores is calculated only between one subject and all
others).
[0119] To calculate the Z-Value, a Gaussian was fitted to the
distribution of all intrasubject distances and calculated the mean
and standard deviation of the fitted Gaussian. Then the
quantity
Z j = ( x j - mean ( x i ) sd ( x i ) ) i .noteq. j
##EQU00001##
was calculated for determining how far from the mean an individual
typically falls. Thus, for each row of the distances matrix, the
value in the diagonal of the matrix was compared to a Gaussian
fitted to the distribution of all non-diagonal values. The
individual Z-scores were averaged and to obtain an average Z-value
index.
[0120] b) PERMANOVA Pseudo-F value: Permutational Multivariate
ANOVA (PERMANOVA) was used to compare intrasubject distance to
intersubject distance. PERMANOVA implements a flexible
non-parametric distance-based analogue of analysis of variance for
multivariate data that provides a distribution-free means of
testing differences between treatments in their multivariate
profile (Anderson, 2001).
[0121] c) Same as (a) but using same descriptors during the
different sessions.
[0122] d) Same as (b) but using same descriptors during the
different sessions.
[0123] e) P-value index: Using Wilcoxon rank sum test to compare
olfactory fingerprint distance of low and high HLA match.
[0124] 1. Distance Metric A: Correlation
[0125] Pearson correlation was used as a metric for distances
(d.sub.i,j) between olfactory fingerprints,
d.sub.i,j=corrolation(FP.sub.i.sup.A,FP.sub.j.sup.B), more
specifically,
[0126]
d.sub.i,j=COV(FP.sub.i.sup.A,FP.sub.j.sup.B)/(.sigma.(FP.sub.i.sup.-
A).sigma.(FP.sub.j.sup.B)), where COV denotes a covariance as and
.sigma. denotes a standard deviation of the respective olfactory
fingerprint.
[0127] a. Different descriptors comparison, see (a) above: Mean
difference between individual's two fingerprints d=0.76.+-.0.02,
mean difference between two different individual's fingerprints
d=0.25.+-.0.007. Average Z-value Z=4.91. FIGS. 8A-B.
[0128] b. PERMANOVA test comparing intrasubject distance to
intersubject distance within the same session but using different
descriptors: pseudo F=8.16, p<10.sup.-6. FIG. 8C.
[0129] c. Different sessions comparison, see (b) above: Mean
difference between an individual's two fingerprints:
d=0.58.+-.0.15, mean difference between two different individual's
fingerprints d=0.31.+-.0.076, Average Z-value Z=2.67.
[0130] d. PERMANOVA test comparing intrasubject distance to
intersubject distance between two sessions: pseudo F=4.23,
p<10.sup.-6. FIG. 8D.
[0131] e. Wilcoxon rank sum test comparing olfactory fingerprint
distance of low and high HLA match: Z=2.14, p<0.03. FIG. 8E.
[0132] 2. Distance Metric B: Euclidean Distance
[0133] We used Euclidean distance as a metric for distances
(d.sub.i,j) between olfactory fingerprints:
d.sub.i,j= {square root over
(.SIGMA..sub.i=1.sup.n(FP.sub.i.sup.A-FP.sub.j.sup.B).sup.2)}
Formula:
[0134] Note: when using Euclidean distance as a metric for
distances, the distribution of olfactory fingerprints distances (d)
is not Gaussian; hence a Z-score value might not be a good
candidate for comparison intrasubject to intersubject distance.
[0135] a. Different descriptors comparison, see (a) above: Mean
difference between an individual's two fingerprints d=140.+-.29,
mean difference between two different individual's fingerprints
d=280.+-.34. Average Z-value Z=2.82. FIGS. 9A-9B.
[0136] b. PERMANOVA test to compare intrasubject distance to
intersubject distance within the same session but using different
descriptors: pseudo F=7.3, p<10.sup.-6. FIG. 9C.
[0137] c. Different sessions comparison, see (b) above: Mean
difference between an individual's two fingerprints: d=200.+-.42,
mean difference between two different individual's fingerprints
d=320.+-.41, Average Z-value Z=1.75.
[0138] d. PERMANOVA test to compare intrasubject distance to
intersubject distance between two sessions: pseudo F=4.23,
p<10.sup.-6. FIG. 9D.
[0139] e. Wilcoxon rank sum test comparing olfactory fingerprint
distance of low and high HLA match: Z=3.21, p<0.0015. FIG.
9E.
[0140] 3. Distance Metric C: Log-Euclidean Distance
[0141] To reshape the distribution of Euclidean distances (see 2
above) be more Gaussian log of Euclidean distance was used as a
metric for distances (d.sub.i,j) between olfactory
fingerprints:
d.sub.i,j=log( {square root over
(.SIGMA..sub.i=1.sup.n(FP.sub.i.sup.A-FP.sub.1.sup.B).sup.2)})
Formula:
[0142] a. Different descriptors comparison, see (a) above: Mean
difference between an individual's two fingerprints d=5.+-.0.22,
mean difference between two different individual's fingerprints
d=5.6.+-.0.1. Average Z-value Z=3.47. FIGS. 10A-10B.
[0143] b. PERMANOVA test to compare intrasubject distance to
intersubject distance within the same session but using different
descriptors: pseudo F=6.5, p<10.sup.-6. FIG. 10C.
[0144] c. Different sessions comparison, see (b) above: Mean
difference between an individual's two fingerprints: d=5.3.+-.0.2,
mean difference between two different individual's fingerprints
d=5.7.+-.0.13, Average Z-value Z=2.23.
[0145] d. PERMANOVA test to compare intrasubject distance to
intersubject distance between two sessions: pseudo F=4.23,
p<10.sup.-6. FIG. 10D.
[0146] e. Wilcoxon rank sum test comparing olfactory fingerprint
distance of low and high HLA match: Z=3.21, p<0.0015. FIG.
10E.
Derivation of Olfactory Fingerprints
[0147] Fingerprints were derived using a matrix of perceived odor
similarities (21). A palette of 28 odors (listed above) that
provided for 378 pairwise similarities (28.times.27/2=378). Such a
378-dimensional olfactory fingerprint allows for characterization
of many individuals.
[0148] Rather than directly obtaining pairwise relation estimates,
the present inventors derived pairwise relation from 54 different
descriptors applied to each odorant alone (listed above). A derived
relation rating, as opposed to a direct relation rating, was
selected because whereas the two are highly correlated, derived
relation is much easier and faster to obtain. For example, direct
relation ratings of the 378 possible odorant pairs in this study
would entail 756 individual odorant presentations (A vs.
B.times.378) each followed by one question: "rate similarity". Such
a large number of odorant presentations (756) may be difficult to
process by a human subject. On the other hand, 378 derived relation
ratings entail 28 odorant presentations each followed by several
questions (e.g., "rate how coconut", "rate how lemony", etc), and
derivation of relation from the answers. This remains a feasible
experiment, and moreover, the number of questions can later be
reduced based on the current analysis.
[0149] Use of odorant descriptors likely entails personal (23) and
cultural differences (24), yet the technique optionally and
preferably does not assume or rely on any agreement across
individuals in the application of a given descriptor (FIGS. 1A and
1B contain a schematic of fingerprint acquisition that explains
this issue). Thus, an individual olfactory fingerprint was
calculated by computing all the pairwise distances between all
odorants rated. For a measure of distance between odorant k and
odorant m, the following equation was used:
distance k , m = i = 1 n ( P i k - P i m ) 2 n ( EQ . 1 )
##EQU00002##
[0150] Where P.sub.i.sup.k is the perceptual rating of odorant k
using descriptor i. and P.sub.i.sup.m is the perceptual rating of
odorant m using descriptor i. In words: the distance between
odorant k and odorant m is the square root of the mean of the
squared difference between all perceptual ratings for those two
odorants. Once all the pairwise distances are computed, an
olfactory fingerprint may be notated in the following form:
FP.sub.k+m-1=distance (EQ. 2)
[0151] Where k=1 . . . N and m=k+1 . . . N. In words; each element
of the olfactory fingerprint is the distance between pairs of
odorants, where each pairwise distance is calculate only once
(since distance.sub.k,m=distance.sub.m,k) and the distance between
an odorant to itself is omitted (since distance.sub.k,k=0).
Consequently, if N odors are uses to construct an olfactory
fingerprint the resulting olfactory fingerprint will have
N.times.(N-1)/2 elements. To negate the impact of variance in the
number of descriptors used by subjects (e.g., one subject may have
used only 50 of the 54 descriptors, and another only 45, leaving
all other descriptors unrated, see FIGS. 6A-6B and 7A-7B for
descriptor usage), each distance between odor-pairs was normalized
by the number of mutual descriptors used for this pair. For
example, if odors A and B were rated along 50 descriptors by
subject 1 and 45 descriptors by subject 2, the distance between
odor pairs was dividing by 50 for subject 1 and by 45 for subject
2, thereby allowing direct comparison of the perceived distances
between odors A and B for subject 1 and 2.
[0152] The derivations of relation according to some embodiments of
the present invention do not assume that people agree with each
other along any given descriptor (e.g., "how coconut"), they only
assume that a person agrees with him/herself (FIGS. 1A and 1B). In
other words, such relation matrices are odor dependent, but
descriptor-independent. This was verified in experiments where
fingerprints for the same individual obtained with the same
odorants but different descriptors remained highly correlated.
[0153] For fingerprint visualization purposes, a rectangular image
was generated by interpolating the 378 fingerprint values to 500
values. Then, these values were projected onto a 25.times.20
matrix, and a two-dimensional interpolation was conducted,
providing a 2K.times.2K matrix, which were then projected onto a
semi-circle (FIGS. 2A-2D). This visualization technique was applied
for 89 subjects (40 women, mean age=25.7.+-.3.1 years).
Olfactory Fingerprints were Individually Specific
[0154] To test whether olfactory perception is similar across
individuals, the present inventors calculated the mean rating along
each descriptor for each odorant, and then calculated the
correlation of each individual with the mean. Note, here
fingerprints are not being compared, but rather ratings along
specific descriptors. This revealed that individuals are indeed
similar to each other in their gross perception. For example, in
all top 10 correlated descriptors the mean perception was a very
good predictor of individual perception (all r >0.68) (FIG. 2E).
Moreover, the primary perceptual dimension of odor pleasantness (2,
26) was particularly highly correlated across individuals and
odors, at r=0.73.+-.0.1 (FIG. 2E). In other words, the average
description of an odorant using common descriptors is a pretty good
estimation of what any given individual will say about that
odor.
[0155] The present inventors next set out to determine whether
despite this gross agreement on odor perception, the sensitive
perceptual test of the present embodiments can uncover a unique
olfactory perceptual fingerprint. Consistent with this hypothesis,
it was found that across the 89 subjects that were tested, no two
subjects had the same fingerprint (e.g., FIG. 2A vs. FIG. 2D, FIG.
2F and FIG. 2G). The present inventors calculated all pairwise
distances between olfactory fingerprints (using Pearson's
correlation) and found that the average correlation across
individuals (omitting self-self) was r=0.3.+-.0.13 (FIG. 2G). In
other words, whereas gross perception was similar across
individuals, a fine measure of perception revealed individual
perception for each of the 89 subjects that were tested.
Olfactory Fingerprints were Independent of Descriptor Identity
[0156] The emergence of 89 individual fingerprints alone does not
necessarily imply that individual olfactory perception was captured
because one can obtain such a result (89 different fingerprints)
with random odor relation ratings. To verify that olfactory
fingerprints captured individual olfactory perception, for each of
the 89 Participants, the present inventors now generated two
alternative fingerprints, A and B, each utilizing a random
independent half of the descriptors used to derive relation. They
computed a matrix of all the pairwise distances between olfactory
fingerprints A and B (89.times.89) and tested whether the distance
of a subject from him/herself (using different descriptors) was
smaller than the distance of a subject to anyone else. In other
words whether, despite the use of different descriptors each time,
a subject remained more similar to him/herself than to anyone else
(distance between fingerprints was estimated by correlation, see
General methods, herein above). This was repeated 1000 times, each
time selecting a different set of nonoverlapping descriptors for
fingerprints A and B, and the distances between fingerprints was
assessed.
[0157] The present inventors again plotted the heat-map correlation
matrix of each individual with all other individuals, this time
however each pairwise distance is computed as the correlation
between olfactory fingerprints A and B (FIG. 3A). It was found that
the distance between an individual's two fingerprints based on the
same odors but different descriptors (the diagonal in FIG. 3A, and
FIG. 2A vs. FIG. 2B) was overwhelmingly smaller than the average
distance between two different individuals (non-diagonal values in
FIG. 3A and FIG. 2A vs. FIG. 2D) (mean difference between an
individual's two fingerprints r=0.75.+-.0.025, mean difference
between two different individual's fingerprints r=0.25.+-.0.008,
paired t-test, t (999)=885.7, p<10.sup.-10) (FIG. 3B). It was
also found that the maximum of the heat-map correlation matrix lies
on the diagonal (i.e. self-self correlation). In other words,
olfactory fingerprints A and B of the same individual were always
more similar than olfactory fingerprints of different individuals.
The analysis of this data was repeated using a Permutational
Multivariate ANOVA (PERMANOVA) to compare the distance between an
individual's two fingerprints based on the same odors but different
descriptors (e.g., FIG. 2A vs. FIG. 2B) to the distance between two
different individuals (e.g., FIG. 2A vs. FIG. 2D). PERMANOVA
implements a flexible non-parametric distance-based analogue of
analysis of variance for multivariate data that provides a
distribution-free means of testing differences between treatments
in their multivariate profile (27). Again, the mean difference
between an individual's two fingerprints was r=0.75.+-.0.025, while
the mean difference between two different individual's fingerprint
was r=0.25.+-.0.008 (PERMNOV test, pseudo F=8.16, p<10.sup.-6)
(FIG. 8C). Thus, the fingerprint genuinely captured personal
identity, and a subject's odorant-specific olfactory fingerprint
remains unique even when different descriptors are used to
construct it. Once the present inventors established the main
effect using PERMANOVA, they set out to extrapolate the ability of
the olfactory fingerprint to identify an individual beyond their
sample. For this one needs to calculate whether a subject's
correlation to him/herself (calculated between fingerprints A and
B) is within the distribution of correlations of a subject to all
other subjects (between fingerprint A of a subject to fingerprint A
of all other subjects). In other words: in order to conclude that a
subject has a unique fingerprint the intersubject correlation
should not belong (low probability) to the distribution of
intrasubject correlations. They fitted a Gaussian to the
intrasubject distances distribution, then calculated how many SDs a
intersubject's score lies from the mean of the distribution of
intrasubject scores (i.e Z-Value). From this they determined the
probability of a subject's correlations to him/herself to be within
the distribution of correlations of a subject to all other subjects
(i.e. p-value). All the individual Z-Value scores were averaged,
and the overall p-value was calculated. The distribution of
correlations of a subject to all other subjects is not Gaussian,
and the subject's correlation to him/herself is limited by 1 (or
-1) hence other metrics for distance between subjects may yield
modestly different results (see General Materials and methods).
They repeated this procedure 1000 times, each time randomly halving
the descriptors used to derive relation (with one half used to
generate fingerprint A and the other half used to generate
fingerprint B), and averaged across all iterations, and all
subjects. They obtained an average Z-Value of 4.9 that corresponds
to an ability to use the 28-odor olfactory fingerprint to identify
one person out of about two million individuals.
Fingerprint Specificity
[0158] In initial experiments, as many as 54 descriptors and 28
odors were used because the present inventors wanted to explore the
impact of these parameters. To estimate the dependence of
fingerprint discriminability on the number of descriptors and
odorants used, the present inventors again generated two
alternative fingerprints for each subject, A and B, each utilizing
a random independent half of the descriptors used to derive
relation. Here, however, the number of odorants and descriptors
used was successively reduced. Each analysis was repeated 1000
times, each time shuffling the particular odorants and descriptors
omitted. The averaged fingerprint specificity (averaging the
Z-Score across subjects and iterations) was plotted as a function
of the number of descriptors and odorants used to generate it
(FIGS. 4A, 4B). A monotonic decrease in the specificity of the
fingerprint was observed, yet even with only 7 odors and 11
descriptors the correlation between an individual's two
fingerprints based on the same odors but different descriptors was
significantly above the correlation between two different
individuals z=1.65, p<0.05. Thus, meaningful olfactory
fingerprints can be obtained in under 10 minutes. In turn, the
present inventors extrapolated to estimate how many odors and
descriptors were necessary in order to obtain an individual
fingerprint for each of the .about.7 billion people on earth, and
reached at 34 odors and 35 descriptors. Obtaining such a detailed
fingerprint would take approximately 5 hours.
Olfactory Fingerprints Remained Specific Despite Fluctuation Over
Time
[0159] Olfactory perception is not only variable across
individuals; it is also highly variable within individuals over
time (28). This variability may reflect in part that odor
perception is the combination of a given receptor activation
pattern with the fluctuating homeostatic state in which it is
perceived (hunger/satiety, mood, arousal, etc.) (2). To test the
persistence of the olfactory fingerprint at retest, 23 participants
were refingerprinted at a time ranging between 10 and 30 days
following their initial fingerprinting (e.g., FIG. 2A vs. FIG. 2C).
It was found that the average distance of a person from him/herself
remained significantly lower than the average distance between
different individuals (mean difference between an individual's two
fingerprints over time r=0.58.+-.0.15, mean difference between two
different individual's fingerprints r=0.31.+-.0.076, paired t test,
t (44)=7.69, p<10-8) (FIG. 4C). In other words, despite the
passage of time, a person remained significantly more correlated
with him/her self than with others.
[0160] Despite the above result, the slight reduction in self-self
correlation over retests raises the concern that given additional
retests the self-self correlation advantage may disappear
altogether. To address this concern the present inventors
re-fingerprinted an additional group of 18 subjects across five
fingerprinting sessions that spanned 14 to 30 days (list of
odorants and descriptors for experiment 1B provided in the
materials and method section). A repeated measures ANOVA revealed
that at each repetition (II, III, IV, V) the average distance of a
person from his/her first fingerprint remained unchanged
(F(17,3)=2.24, p=0.09, mean difference between an individual's two
fingerprints across retests r first-second=0.58.+-.0.21, r
first-third=0.54.+-.0.18, r first-fourth=0.54.+-.0.2, r
first-fifth=0.49.+-.0.19) (FIG. 4D yellow). Moreover, the
fingerprint stability in fact improved after the first retest
(F(17,3)=6.08, p<0.001) such that the second to third
(r=0.66.+-.0.19), third to fourth (r=0.68.+-.0.20), and fourth to
fifth (r=0.69.+-.0.16) repetitions were all significantly better
than the first to second (r=0.58.+-.0.21, all t(17)>2.67, all
p<0.02) (FIG. 4D). Taken together it may be concluded that
despite the passage of time and repeated testing, a person remained
significantly more correlated with him/her self than with others.
The present inventors recalculated the ability of the olfactory
fingerprint to identify an individual beyond the sample, this time
comparing the initial and the later (few weeks later) fingerprints
with the all the other subjects, and observed a decreased yet
significant discriminability (z1-2=2.67, p<0.01, z1-3=2.67,
p<0.01, z1-4=2.67, p<0.01, z1-5=2.67, p<0.01) (FIG. 4D
black and red). This amounts to an ability to use the current
olfactory fingerprint to identify one subject out of about three
hundred individuals. Moreover, given this variability over time, to
effectively obtain long-lasting olfactory fingerprints for the
entire world population it was found by extrapolation that rather
than 34 odors with 35 descriptors 160 odors are needed with 35
descriptors. Note that this reduced discriminability is not only
because of the extent of shift in fingerprint over time; self
correlation over time decreased from r=0.75 to r=0.58, which
remains significantly higher than the correlation across
individuals. However, because on average all subjects shifted in
this way, the ability to identify one person out of a crowd is
significantly reduced.
Similar Olfactory Fingerprints Imply High HLA Matching
[0161] The hypothesis underlying the present effort was that
fingerprints would provide a unique perceptual counterpart of an
individual's unique olfactory receptor subtype genome. Consistent
with this notion, 28-odor based fingerprints were special to the
tune of 1-in-two million. The present inventors set out to test
whether olfactory fingerprints can nevertheless remain informative
of genetic traits linked to olfaction, in this case HLA. To test
this the present inventors studied an additional 130 subjects (65
women, mean age=29.93.+-.8.44 years) who provided blood samples for
HLA typing (see methods), and olfactory fingerprints using the
following 11 odors:
[0162] 1. Isoamyl acetate
[0163] 2 Vanillin
[0164] 3 Isovaleric acid
[0165] 4 cis-3-hexen-1-ol, wet grass
[0166] 5 Androstadienone
[0167] 6 Dibutyl amine
[0168] 7 Ethyl pyrazine: 2-ethyl pyrazine
[0169] 8 Eucalyptol (1,8-cineole)
[0170] 9 Hexanol: 1-hexanol
[0171] 10 Methyl anthranilate
[0172] 11 Tolualdehyde: Ortho-tolualdehyde
[0173] Combinatorically, 130 subjects provide for 16770 possible
donor-recipient pairs. This is because HLA match is not symmetric,
i.e., in a given pair, a subject can have a high HLA match as a
donor but poor HLA match as a recipient (note that the terms donor
and recipient are used to describe the directionality of HLA
matching). Therefore, 130 subjects resulted in 16770 possible pairs
(130*129) and not in 8385 (130*129/2). For each pair, an olfactory
fingerprint match was calculated using Euclidean distance (see
materials and methods) and an HLA match along a seven point scale
(0-6, 0=no match) previously described (16). Only 65 out of 16770
possible pairs of individuals had a high HLA match of 5 or 6 (FIG.
5A).
[0174] It was found that the olfactory fingerprint match of these
individuals was significantly better than the olfactory fingerprint
match for poorly HLA-matched individuals (HLA 5-6: mean olfactory
fingerprint match in arbitrary units (AU) of Euclidean
distance=12.7.+-.4.1 [A.U.], HLA 1-4: mean olfactory fingerprint
match=14.6.+-.5.3 [A.U.], Wilcoxon rank sum test: Z=3.2,
p<0.0015). In other words, the olfactory perceptual fingerprint
similarity was significantly informative on HLA matching, implying
that it captured meaningful genetic information.
[0175] To further assess the strength of the link between olfactory
fingerprint match and HLA match, the present inventors asked what
would happen if one used olfactory fingerprints to screen for
potentially high HLA matches in the population. To this end, the
percentage of high HLA matches one would potentially miss (incurred
cost) versus the percentage of matches one could identify (gain)
was calculated and presented using a receiver operating
characteristic (ROC) curve (red line--FIG. 5B).
[0176] It was found that all points in the ROC curve fall above the
identity-line; hence the olfactory fingerprints of the present
embodiments can identify pairs of individuals likely to have a high
HLA match. Given the extended time needed to test 11 odorants as
carried out in data collection, the present inventors next asked if
they could optimize this test. To this end, the data were halved
into training (8387 subject pairs) and testing sets (8385 subject
pairs), each maintaining the original fractions of each level of
HLA match. In the training set, the olfactory fingerprints was
calculated using all possible combinations of 3 to 11 odorants, and
the 4 best-performing odorants were selected and then tested in the
testing set. This was repeated 200 times (gray lines--FIG. 5B).
Taking the median score (black line--FIG. 5B), it was found that a
selection of 4 odors (Isoamyl acetate, Isovaleric acid, 2-Ethyl
Pyrazine, and 1-Hexanol) decreased the average olfactory
fingerprint distances of high HLA matched individuals to
10.8.+-.3.7 [A.U.] compared to an olfactory fingerprint distance
for poorly HLA matched individuals of 13.8.+-.5.8 [A.U.] (Z=4.35,
p<0.000015).
[0177] The actual savings implicated were calculated as follows.
The 65 high HLA matches in the data comprised 45 individuals (some
individuals were matched with more than one). One individual was
iteratively selected from these 45 as "recipient", and "donors"
were randomly drawn until a high HLA match was encountered. This
was repeated 10000 times. Consistent with the expectation from
chance, an average of 65.35.+-.37.5 donors had to be tested in
order to identify a match. These procedures were then repeated, but
rather than randomly drawing donors, they were drawn in rank order
in accordance with their rapidly obtainable optimized olfactory
fingerprint distance, starting with the closest. It was found that
the average number of individuals that had to be tested in order to
identify a match was 44.+-.29, implying a 32% savings (t (64)=5.5,
p<10.sup.-6). In other words, using this brief perceptual test
one could rank-order the population in order to save more than 30%
of HLA tests.
[0178] 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.
[0179] 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. To the extent that section headings are used,
they should not be construed as necessarily limiting.
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