Abstract: A method for detecting false positive variant calls in a next generation sequencing analysis pipeline involves obtaining a plurality of read pileup windows associated with a first sample genome. The method also involves obtaining, for each reference nucleotide position represented in the plurality of read pileup windows, a label indicating that the reference nucleotide position is either (i) a known variant or (ii) a non-variant. The method further involves training a neural network based on data indicative of the plurality of read pileup windows and the labels. Additionally, the method involves receiving a read pileup window associated with a second sample genome. Further, the method involves determining, using the trained neural network, a likelihood that the read pileup window associated with the second sample genome represents a variant.

Abstract: Provided herein are, inter alia, methods for identifying subjects suffering from depression that will respond to treatment with an antidepressant.

Abstract: Provided herein are improved methods for detecting aneuploidy in a sample. The methods in certain embodiments are used for the analysis of circulating DNA in serum samples, such as circulating fetal DNA or circulating tumor DNA. In certain embodiments, chromosome or chromosome segments of interest are used to set a bias model and/or a control value for a z-score determination, in illustrative examples without the use of a control chromosome.

Abstract: A signal processing system used for GMR-based detection of a target analyte in a sample under test, comprising: a measurement circuit configuration unit configured to build a GMR sensor measurement circuit by routing in at least one GMR sensor, and to build a reference resistor measurement circuit by routing in at least one reference resistor; a magnetic field excitation unit configured to apply an AC magnetic field of frequency ?2 to the at least one GMR sensor; a carrier signal applying unit configured to apply a carrier signal of frequency ?1 to the GMR sensor measurement circuit, and apply carrier signals of frequency ?1, ?1+?2, and ?1??2 to the reference resistor measurement circuit; a measurement signal pick-up unit coupled to the measurement circuits, configured to collect reference resistor measurement signals from the reference resistor measurement circuit and GMR sensor measurement signals from the GMR sensor measurement circuit; and a phase sensitive solution unit coupled to the measurement signal

Abstract: An arrangement for determining external damage to hair is given. A device comprising: a reading unit adapted to obtain optical characteristics of hair; and a processing unit adapted to receive the optical characteristics from the reading unit and to determine surface properties of the hair from the optical characteristics, wherein the processing unit implements a pattern recognition algorithm and is adapted to assign, by employing the pattern recognition algorithm, the surface properties to a predetermined damage including at least one damage type and an associated damage level and to output the damage.

Abstract: Systems, media, compositions, methods, and kits disclosed herein relate to a panel of protein biomarkers for the early detection of colon cell proliferative disorders, including colorectal cancer. The presence or levels of the proteins in a biological sample for the protein panels described herein may be used for classifier generation, and as inputs in machine learning models useful to classify subjects in a population for the detection of colon cell proliferative disorders.

Abstract: Disclosed herein are systems and uses of systems operating between fully quantum coherent and fully classical states. Examples include a hybrid quantum-classical computing system comprising a plurality of quantum processors connected via classical means.

Abstract: The present invention provides processes for determining accurate risk probabilities for fetal aneuploidies. Specifically, the invention provides non-invasive evaluation of genomic variations through chromosome-selective sequencing and non-host fraction data analysis of maternal samples.

Abstract: A mechanism is provided in a data processing system comprising at least one processor and a memory comprising instructions which, when executed by the at least one processor, causes the at least one processor to implement a real-time prediction engine for real-time predication of chemical properties through combining calculated, structured, and unstructured data at large scale. Offline components executing within the real-time prediction engine store a computational representation for each of a plurality of chemical structures in a unified storage. Each computational representation maps a respective chemical structure to a vector of calculated chemical structure features and properties, unstructured chemical features and properties, and structured chemical features and properties. The offline components train a computational real-time predictive model based on the computational representations.

Abstract: Systems and methods include a method for training machine learning-based proxy models as surrogates for process-based reactive transport modeling (RTM). Training sample data is generated. Training sample cases are executed using the training sample data. A set of parameter-specific proxy models represented by a neural network is trained. Each parameter-specific proxy model corresponds to a specific RTM parameter from a set of RTM parameters. Blind tests are performed using the set of parameter-specific proxy models. Each blind test tests a specific one of the parameter-specific proxy models. Predictions are generated using the set of parameter-specific proxy models.

Abstract: This disclosure provides methods, systems, compositions, and kits for the multiplexed detection of a plurality of analytes in a sample. In some examples, this disclosure provides methods, systems, compositions, and kits wherein multiple analytes may be detected in a single sample volume by acquiring a cumulative measurement or measurements of at least one quantifiable component of a signal. In some cases, additional components of a signal, or additional signals (or components thereof) are also quantified. Each signal or component of a signal may be used to construct a coding scheme which can then be used to determine the presence or absence of any analyte.

Abstract: An apparatus for monitoring comprehensive growth condition of potted lettuce consists of a polarized hyperspectral imaging system and a 3D laser scanning system. The polarized hyperspectral imaging system includes a control system, a dual-coordinate sample holder, an image acquisition system, and a light source system, and the polarized hyperspectral imaging system detects water and fertilizer stress in lettuce by polarized hyperspectral multi-dimensional reflection imaging of characteristics on a canopy/leaf scale. The 3D laser scanning system detects morphological characteristics such as the biomass, stem diameter, plant height, and leaf area of lettuce.

Abstract: Provided herein is a system and method for review of forensic report computer files. The method can involve notifying a plurality of service providers of a job to be performed, accepting a bid from a service provider to perform the job, providing the computer file to the service provider, receiving from the service provider a forensic result such as a reviewed file.

Abstract: An optimized Gamma-prime (??) strengthened austenitic transformation induced plasticity (TRIP) steel comprises a composition designed and processed such that the optimized ?? strengthened austenitic TRIP steel meets property objectives comprising a yield strength of 896 MPa (130 ksi), and an austenite stability designed to have Ms?(sh)=?40° C., wherein Ms?(sh) is a temperature for shear, and wherein the property objectives are design specifications of the optimized ?? strengthened austenitic TRIP steel. The optimized ?? strengthened austenitic TRIP steel is Blastalloy TRIP 130.

Abstract: This invention provides a device for screening a candidate substance for an active ingredient to prevent or treat declined kidney function etc. A screening device 1 for screening a candidate substance for an active ingredient to prevent or treat declined kidney function etc. comprises a first measurement value obtaining unit 11 for obtaining a measurement value of a kidney function prediction marker protein and/or a measurement value of mRNA of the protein in a test-substance-treated specimen, and a candidate substance determination unit 14 for determining that the test substance is a candidate substance for the active ingredient based on the measurement value(s) obtained by the first measurement value obtaining unit 11.

Abstract: A method for producing a streak-suppressed magnetic resonance (MR) image of a subject includes generating an interference covariance matrix {circumflex over (?)}R front N coil images Ij (x,y), j={1, 2, . . . , N}, each of the N coil images Ij (x,y) corresponding to MR signals detected by a respective one of a phased array of N coils of an MRI scanner. The MR signals originate in voxels of the subject corresponding to an artifact-corrupted region of a coil image. Coordinates (x,y) correspond to a location within a cross-sectional plane of the subject. The method also includes, for subject-regions of cross-sectional plane centered at a respective location (x,y), determining a coil weight vector W (x,y) from {circumflex over (?)}R. The method also includes generating the streak-suppressed MR image as a weighted sum of the coil images Ij (x,y), each weight of the weighted sum being Wj* (x,y), a jth element of a complex conjugate of coil weight vector W (x,y).

Abstract: Methods and systems are provided for determining a variant of interest by analyzing sizes and sequences of cfDNA fragments obtained from a test sample. The methods and systems provided herein implement processes that synergistically combine size and sequence information, thereby improving specificity and sensitivity of assays over conventional methods.

Abstract: A system configured to characterize a number of contributors to a DNA mixture within a sample, the system comprising: a sample preparation module configured to generate initial data about the DNA mixture within the sample; a processor comprising a number of contributors determination module comprising a machine-learning algorithm configured to: (i) receive the generated initial data; (ii) analyze the generated initial data to determine the number of contributors to the DNA mixture within the sample; and an output device configured to receive the determined number of contributors from the processor, and further configured output information about the received determined number of contributors.

Abstract: A device for medical imaging by X-ray is provided. More specifically, it relates to the simulation of the deployment of an endoprosthesis in order to assist the surgeon in an endovascular surgical procedure. The invention makes use of a single 2D image in order to determine certain characteristics of a simplified model of the endoprosthesis: 2D positions, and deployment value of the stents; to determine the inherent rotation of at least one stent; then to determine the deployment of a model representing the structure of the stents, initialized on the basis of the preceding steps, in a 3D model of a vascular structure.

Abstract: The technology disclosed presents a deep learning-based framework, which identifies sequence patterns that cause sequence-specific errors (SSEs). Systems and methods train a variant filter on large-scale variant data to learn causal dependencies between sequence patterns and false variant calls. The variant filter has a hierarchical structure built on deep neural networks such as convolutional neural networks and fully-connected neural networks. Systems and methods implement a simulation that uses the variant filter to test known sequence patterns for their effect on variant filtering. The premise of the simulation is as follows: when a pair of a repeat pattern under test and a called variant is fed to the variant filter as part of a simulated input sequence and the variant filter classifies the called variant as a false variant call, then the repeat pattern is considered to have caused the false variant call and identified as SSE-causing.