International Business Machines Corporation

Philip Morris Products S.a.

Pablo Meyer Rojas

Huan Hu

Stacey M Gifford

Guillermo A Cecchi

Gustavo Alejandro Stolovitzky

Joshua T Smith

Amit Dhurandhar

John Jeremy Rice

Raquel Norel

Elkin Dario Gutierrez

James R Kozloski

Alejandro Gabriel Schrott

Levente Klein

Ajay K Royyuru

Julia Hoeng

Wendy Chong

Manuel Claude Peitsch

Elif Eyigoz

Stephanie M Corthesy

Joerg J Sprengel

Thomas Bonk

Katrin Stolle

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.

Artificial intelligence based classification for taste and smell from natural language descriptions

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.

Creation of detailed perceptual description ratings from general perception ratings

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.

Positional delivery and encoding by oligonucleotides of biological cells for single cell sequencing (POS SEQ)

The method comprises contacting a silicon substrate with a silver salt and an acid for a time effective to produce spikes having a first end disposed on the silicon substrate and a second end extending away from the silicon substrate. The spikes have a second end diameter of about 10 nm to about 200 nm, a height of about 100 nm to 10 micrometers, and a density of about 10 to 100 per square microns. The nanostructures provide antimicrobial properties and can be transferred to the surface of various materials such as polymers.

Nanostructures fabricated by metal asisted chemical etching for antibactertial applications

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.

Prediction of olfactory and taste perception through semantic encoding

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.

Predicting human discriminability of odor mixtures

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.

Correlating olfactory perception with molecular structure

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.

Identification of enzyme activity through determination of its localization

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.

Method system and medium for personalized expert cosmetics recommendation using hyperspectral imaging

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. The antimicrobial article may include a component of an electronic device, a biomedical article, a household product, a food grade article, a transportation component, or a public building component.

Lateral silicon nanospikes fabricated using metal-assisted chemical etching

Nanostructures fabricated by metal assisted chemical etching for antibacterial applications

A method of implementing verification of a complex workflow includes partitioning the workflow into modules, wherein the modules have inputs, processing steps and outputs; selecting, from the workflow, one of the partitioned modules for independent verification by challenge thereof; running, with a computing device, a challenge of the selected module, the challenge comprising comparing reference outputs to outputs of the selected module, wherein reference inputs are received by the selected module and the reference outputs are generated using the reference inputs and one of an ideal performing module or a well-established module; determining whether outputs of the selected module meet verification criteria with respect to the reference outputs, and based on the determining, implementing one of: declaring the selected module verified; subdividing the selected module into smaller modules and repeating the challenge on the smaller modules; or declaring the selected module not verified.

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Brooklyn, NY, United States of America

Pablo Meyer Rojas