A method for determining an acceptable spinal surgical plan for a subject using pathology prediction, comprising generating a potential spinal surgical plan, obtaining clinically relevant data of the subject, obtaining pre-operative three-dimensional images of a spinal region of the subject, determining relationships between pairs of vertebrae in the images, predicting relationships between pairs of vertebrae that are expected from the surgical plan, accessing a multiple patient database, obtaining sets of data from the database for patients with similar characteristics to the subject, determining risks of pathology types for the subject, using artificial intelligence to combine the determined risks to calculate an overall risk for pathology types for the subject, and if the overall risks are unacceptable, selecting an alternative spinal surgical plan, and if the said overall risks are acceptable, determining that said surgical plan is acceptable.

Method(s) and a system for predicting the refining characteristics of an oil sample are described. The method of predicting the refining characteristics, such as distillate yield profile, processability, product quality or refinery processing cost, may include development of a prediction model based on regression analysis. The method may further include determining the physical properties of the oil sample and predicting the refining characteristics based on the developed prediction model. The determination of the physical properties of the oil sample includes determining at least one of Conradson Carbon Residue (CCR) content, Ramsbottom Carbon Residue (RCR) and Micro Carbon Residue (MCR).

A system, method and apparatus for executing a bioinformatics analysis on genetic sequence data includes an integrated circuit formed of a set of hardwired digital logic circuits that are interconnected by physical electrical interconnects. One of the physical electrical interconnects forms an input to the integrated circuit that may be connected with an electronic data source for receiving reads of genomic data. The hardwired digital logic circuits may be arranged as a set of processing engines, each processing engine being formed of a subset of the hardwired digital logic circuits to perform one or more steps in the bioinformatics analysis on the reads of genomic data. Each subset of the hardwired digital logic circuits may be formed in a wired configuration to perform the one or more steps in the bioinformatics analysis.

A computer simulation method for a macromolecular material and filler is disclosed, wherein a polymer model of a macromolecular chain of the macromolecular material and a filler model of the filler are defined; and a molecular dynamics calculation is performed using the filler model and the polymer models disposed in a space in order to compute the thickness of an interface layer between the filler and the macromolecular material. To compute the thickness, the space is partitioned into domains bounded by boundary surfaces; relaxation moduli of the domains are computed; and based on a variation of the relaxation moduli of the domains, the thickness of the interface layer is computed.

A system and method that predicts whether a given protein-ligand pair is active or inactive and outputs a pose score classifying the propriety of the pose. A 3D bioactivity platform comprising a 3D bioactivity module and data platform scrapes empirical lab-based data that a docking simulator uses to generate a dataset from which a 3D-CNN model is trained. The model then may receive new protein-ligand pairs and determine a classification for the bioactivity and pose propriety of that protein-ligand pair. Furthermore, gradients relating to the binding affinity in the 3D model of the molecule may be used to generate profiles from which new protein targets may be determined.

A system and method for biomarker-outcome prediction and medical literature exploration which utilizes a data platform to analyze, optimize, and explore the knowledge contained in or derived from clinical trials. The system utilizes a knowledge graph and data analysis engine capabilities of the data platform. The knowledge graph may be used to link biomarkers with molecules, proteins, and genetic data to provide insight into the relationship between biomarkers, outcomes, and adverse events. The system uses natural language processing techniques on a large corpus of medical literature to perform advanced text mining to identify biomarkers associated with adverse events and to curate a comprehensive profile of biomarker-outcome associations. These associations may then be ranked to identify the most-common biomarker-outcome association pairs. Having a comprehensive profile of ranked biomarker-outcome data allows the system to predict biomarkers associated with a given disease and serious adverse events linked to biomarker data.

For any multiplicative congruential generator (d, z) with an odd modulus d and a multiplier z coprime to d, a computationally innovative method is presented as specialized forms of 2nd degree spectral tests of (d, z^i) with 2≦i≦6, at the least. Providing with sharp and powerful sieving tools, the method enables the excavation of the integer set (d, z) as a generator of uniform and independent random numbers of excellent statistics with sufficiently long periods for simulations, and furnishes the selected generator with ways of clear, unambiguous and quantitative specifications of its performance.

Systems and methods are disclosed for using geometry sensitivity information for guiding workflows in order to produce reliable models and quantities of interest. One method includes determining a geometric model associated with a target object; determining one or more quantities of interest; determining sensitivity information associated with one or more subdivisions of the geometric model and the one or more quantities of interest; and generating, using a processor, a workflow based on the sensitivity information.

Methods for prevention and treatment of kidney transplant rejection are described that involve determination, analysis and computation of a 3-gene molecular signature of levels of specific RNAs (IP-10 mRNA, CD3ε mRNA, and 18S rRNA) in urinary sample cells. The methods and devices described herein are diagnostic and prognostic of acute cellular rejection in kidney allografts.

A machine learning system receives a witness function that is determined based on an initial sample of a dataset comprising multiple pairs of stimuli and responses. Each stimulus includes multiple features. The system receives a holdout sample of the dataset comprising one or more pairs of stimuli and responses that are not used to determine the witness function. The system generates a simulated sample based on the holdout sample. Values of a particular feature of the stimuli of the simulated sample are predicted based on values of features other than the particular feature of the stimuli of the simulated sample. The system applies the holdout sample to the witness function to obtain a first result. The system applies the simulated sample to the witness function to obtain a second result. The system determines whether to select the particular feature based on a comparison between the first result and the second result.