Abstract: Taste and smell classification from multilanguage descriptions can be performed by extracting, by one or more processors using natural language processing, a text including one or more words associated with taste and smell perceptions from an input received from a plurality of users. The input includes multilanguage information regarding at least one of changes in smell and changes in taste perceived by each of the plurality of users. Feature vectors are generated for the text extracted from the input using global vectors, and a distance between the feature vectors and a plurality of reference descriptors associated with taste and smell is calculated for determining a similarity between the text and the reference descriptors and creating a training dataset based on which a classification model is generated for categorizing the plurality of users according to the at least one of changes in smell and changes in taste.

Abstract: Embodiments of the invention include computer-implemented methods, computer systems, and computer program products for predicting sensory perception. A non-limiting example of the computer-implemented method includes receiving at a processor a library including a plurality of indexed sensory descriptors. A sensory target descriptor is also received at the processor. The processor is configured to calculate a coefficient matrix based in part on the semantic distance between an indexed sensory descriptor and a sensory target descriptor. The processor is further configured to generate a perceptual descriptor prediction for the sensory target.

Abstract: Techniques for positional delivery and position encoding by oligonucleotides of biological cells for single cell RNA sequencing are provided. In one aspect, a method of positional delivery and encoding of cells in a biological sample includes: encoding the cells in the biological sample for single cell sequencing by delivering molecular probes inside the cells that encode a position of the cells in the biological sample. A system for positional delivery and encoding of cells in a biological sample is also provided.

Abstract: Techniques for positional delivery and position encoding by oligonucleotides of biological cells for single cell RNA sequencing are provided. In one aspect, a method of positional delivery and encoding of cells in a biological sample includes: encoding the cells in the biological sample for single cell sequencing by delivering molecular probes inside the cells that encode a position of the cells in the biological sample. A system for positional delivery and encoding of cells in a biological sample is also provided.

Abstract: Taste and smell classification from multilanguage descriptions can be performed by extracting, by one or more processors using natural language processing, a text including one or more words associated with taste and smell perceptions from an input received from a plurality of users. The input includes multilanguage information regarding at least one of changes in smell and changes in taste perceived by each of the plurality of users. Feature vectors are generated for the text extracted from the input using global vectors, and a distance between the feature vectors and a plurality of reference descriptors associated with taste and smell is calculated for determining a similarity between the text and the reference descriptors and creating a training dataset based on which a classification model is generated for categorizing the plurality of users according to the at least one of changes in smell and changes in taste.

Abstract: Techniques for positional delivery and position encoding by oligonucleotides of biological cells for single cell RNA sequencing are provided. In one aspect, a method of positional delivery and encoding of cells in a biological sample includes: encoding the cells in the biological sample for single cell sequencing by delivering molecular probes inside the cells that encode a position of the cells in the biological sample. A system for positional delivery and encoding of cells in a biological sample is also provided.

Abstract: Embodiments of the invention include methods, systems, and computer program products for predicting olfactory perception. A non-limiting example of the method includes receiving a library including a plurality of indexed olfactory descriptors. The method also includes receiving an olfactory target descriptor. The method also includes calculating a coefficient matrix and a perceptual distance between an indexed olfactory descriptor and an olfactory target descriptor. The method also includes generating a perceptual descriptor prediction for the olfactory target.

Abstract: A system for compressing data during neural network training, comprising of memory that stores computer executable components; a processor that executes computer executable components stored in the memory, wherein the computer executable components comprise of a compilation component that compiles respective molecular descriptors regarding a first set of molecules; a perception component that learns human perception information related to olfactory perceptions of the first set of molecules, and generates predictions of human olfactory perceptions of a second set of molecules; a fitting component that fits distance predictions from the perception component regarding the second set of molecules against measured correct classifications regarding the second set of molecules; and a vector component that generates a perceptual vector distance between two olfactory targets.

Abstract: Embodiments of the invention include computer-implemented methods, computer systems, and computer program products for predicting sensory perception. A non-limiting example of the computer-implemented method includes receiving at a processor a library including a plurality of indexed sensory descriptors. A sensory target descriptor is also received at the processor. The processor is configured to calculate a coefficient matrix based in part on the semantic distance between an indexed sensory descriptor and a sensory target descriptor. The processor is further configured to generate a perceptual descriptor prediction for the sensory target.

Abstract: Predicting human olfactory perception based on molecular structure is described. Molecular descriptor data indicative of molecular descriptors associated with a group of molecular samples can be obtained. Olfactory perception indicator (OPI) data for a set of OPIs can also be obtained with respect to the molecular samples. A training model can be executed on the molecular descriptor data and the OPI data to yield an output model that correlates molecular attributes with OPIs for a single individual or across an aggregate of individuals. The output model can be used to predict olfactory perception for a particular compound or mixture based on which OPIs are correlated with molecular descriptors of the compound or mixture in the output model. The output model can also be inverted and used to identify molecular descriptors that are correlated with a desired set of OPIs. A molecular construct having the molecular descriptors can then be generated.

Abstract: A method of determining enzyme activity and identifying and classifying cellular targets, enzymatic pathways, and enzymatic agents involved in regulating metabolism in order to treat pathophysiological disorders. Monitoring enzyme activity is performed via a label-free bio cellular assay or fluorescence imaging. The identified and classified agents are used, together with a therapeutic agent, in the treatment of various metabolism-related diseases.

Abstract: A system for compressing data during neural network training, comprising of memory that stores computer executable components; a processor that executes computer executable components stored in the memory, wherein the computer executable components comprise of a compilation component that compiles respective molecular descriptors regarding a first set of molecules; a perception component that learns human perception information related to olfactory perceptions of the first set of molecules, and generates predictions of human olfactory perceptions of a second set of molecules; a fitting component that fits distance predictions from the perception component regarding the second set of molecules against measured correct classifications regarding the second set of molecules; and a vector component that generates a perceptual vector distance between two olfactory targets.

Abstract: Various embodiments provide a customized cosmetics recommendation for a specific user. In one embodiment a method comprises capturing an image that includes the face of the specific user, producing a set of hyperspectral images from the image, analyzing the hyperspectral images to determine a set of spectral components of the face, and providing a recommendation for one or more cosmetics customized for the specific user based on the set of spectral components and cosmetician expert judgement. The image may be captured using a hyperspectral imaging camera. The set of spectral components is compared to a plurality of previous sets of spectral components to find a match and one or more cosmetics mapped to the match are provided as the recommendation. Additionally, a set of conditional options may be received and one or more cosmetics mapped to the set of conditional options and the set of spectral components are provided as the recommendation.

Abstract: The present disclosure relates to methods for forming an antimicrobial nanostructure and antimicrobial articles. The methods may include: providing a master template of a layout of the antimicrobial nanostructure on a silicon substrate, depositing a silicon nitride layer on a top surface of the silicon substrate, forming a patterned lithographic resist mask layer on a top surface of the silicon nitride layer, generating certain silicon pillars according to the patterned lithographic resist mask using a resist and reactive ion etching, forming certain lateral silicon nanospikes on the silicon pillars by performing metal assisted chemical etching (MacEtch), and removing the silicon nitride layer and bonding a top cover glass on the silicon pillars to form the antimicrobial nanostructure having lateral silicon nanospikes.

Abstract: Embodiments of the invention include methods, systems, and computer program products for predicting olfactory perception. A non-limiting example of the method includes receiving a library including a plurality of indexed olfactory descriptors. The method also includes receiving an olfactory target descriptor. The method also includes calculating a coefficient matrix and a perceptual distance between an indexed olfactory descriptor and an olfactory target descriptor. The method also includes generating a perceptual descriptor prediction for the olfactory target.

Abstract: The present disclosure relates to methods for forming an antimicrobial nanostructure and antimicrobial articles. The methods may include: providing a master template of a layout of the antimicrobial nanostructure on a silicon substrate, depositing a silicon nitride layer on a top surface of the silicon substrate, forming a patterned lithographic resist mask layer on a top surface of the silicon nitride layer, generating certain silicon pillars according to the patterned lithographic resist mask using a resist and reactive ion etching, forming certain lateral silicon nanospikes on the silicon pillars by performing metal assisted chemical etching (MacEtch), and removing the silicon nitride layer and bonding a top cover glass on the silicon pillars to form the antimicrobial nanostructure having lateral silicon nanospikes.

Abstract: The present disclosure relates to methods for forming an antimicrobial nanostructure and antimicrobial articles. The methods may include: providing a master template of a layout of the antimicrobial nanostructure on a silicon substrate, depositing a silicon nitride layer on a top surface of the silicon substrate, forming a patterned lithographic resist mask layer on a top surface of the silicon nitride layer, generating certain silicon pillars according to the patterned lithographic resist mask using a resist and reactive ion etching, forming certain lateral silicon nanospikes on the silicon pillars by performing metal assisted chemical etching (MacEtch), and removing the silicon nitride layer and bonding a top cover glass on the silicon pillars to form the antimicrobial nanostructure having lateral silicon nanospikes.

Abstract: A method of determining enzyme activity and identifying and classifying cellular targets, enzymatic pathways, and enzymatic agents involved in regulating metabolism in order to treat pathophysiological disorders. Monitoring enzyme activity is performed via a label-free bio cellular assay or fluorescence imaging. The identified and classified agents are used, together with a therapeutic agent, in the treatment of various metabolism-related diseases.

Abstract: Predicting human olfactory perception based on molecular structure is described. Molecular descriptor data indicative of molecular descriptors associated with a group of molecular samples can be obtained. Olfactory perception indicator (OPI) data for a set of OPIs can also be obtained with respect to the molecular samples. A training model can be executed on the molecular descriptor data and the OPI data to yield an output model that correlates molecular attributes with OPIs for a single individual or across an aggregate of individuals. The output model can be used to predict olfactory perception for a particular compound or mixture based on which OPIs are correlated with molecular descriptors of the compound or mixture in the output model. The output model can also be inverted and used to identify molecular descriptors that are correlated with a desired set of OPIs. A molecular construct having the molecular descriptors can then be generated.

Abstract: Systems, methods, and computer-readable media are described for predicting consumer response to a stimulus based on olfactory characteristics of the stimulus. An intrinsic factor score associated with a product can be determined based on an intrinsic attribute of the stimulus, and optionally, further based on data indicative of historical consumer response to olfactory characteristics of the stimulus. A social factor score associated with a user can also be determined using available olfactory preference data associated with the user and/or data representative of one or more social signals indicative of a predicted response of the user to olfactory characteristics of the stimulus. A collaborative filtering technique can be employed to determine a recommendation score for the stimulus using the intrinsic factor score and the social factor score. The recommendation score can be compared to a threshold value to determine whether to recommend the stimulus to the user.