Abstract: A device for on-site detection of soil organic matter, including a pre-processing module, a centrifugal system, a microfluidic chip, and a photoelectric detection module. The pre-processing module is configured to process a soil sample into a soil solution. The centrifugal system is configured to generate a centrifugal force. The microfluidic chip is configured to allow mixing of the soil solution and an extraction solvent for extraction under the centrifugal force to obtain an extract. The photoelectric detection module is configured to detect the extract to determine organic matter content in the soil solution. An on-site detection method and a microfluidic chip are also provided. The microfluidic chip includes a channel layer, a cover layer arranged above the channel layer, and a base plate layer arranged below the channel layer.

Abstract: A device for detecting soil nutrients on site, including an extracting grid, an on-site real-time detection assembly and a transfer assembly for transferring a soil extract from the extracting grid to the on-site real-time detection assembly. A soil nutrient detection method using the device and a microfluidic chip are also provided. The microfluidic chip includes a cover plate and a base plate. The base plate includes a soil extract feeding groove, a quantitative feeding groove, a reagent storage groove, and a serpentine groove.

Abstract: A processing system uses a Bayesian inference based model for targeted sequencing or variant calling. In an embodiment, the processing system generates candidate variants of a cell free nucleic acid sample. The processing system determines likelihoods of true alternate frequencies for each of the candidate variants in the cell free nucleic acid sample and in a corresponding genomic nucleic acid sample. The processing system filters or scores the candidate variants by the model using at least the likelihoods of true alternate frequencies. The processing system outputs the filtered candidate variants, which may be used to generate features for a predictive cancer or disease model.

Abstract: Systems and methods for predicting survival outcomes in patients diagnosed with Myelodysplastic Syndrome (MDS) are disclosed. One method may include: receiving DNA sequencing data derived from a methylation assay performed on a biological sample associated with the at least one patient; computing methylation beta-values for one or more CpG-sites identified in the sequencing data; identifying one or more differentially methylated regions (DMRs) based on statistical analysis of the methylation beta-values for the one or more CpG-sites; selecting, via a feature selection process, a subset of the one or more DMRs to utilize as training data; and training, using the training data, the classifier to predict the survival outcome of the at least one patient. Other aspects are described and claimed.

Abstract: Methods and systems for segmenting sequencing regions obtained from a sample interval are disclosed. sample contamination detection are disclosed. In particular, an analytics system accesses test sequences from a sample. The test sequences each include a sequencing region which, in aggregate, form an aggregate sequencing region. The analytics system segments sequencing regions from the aggregate sequencing region into sequencing subregions. Several methods of segmenting sequencing regions into sequencing subregions are disclosed: (1) maximizing cancer vs. non-cancer methylation beta differences, (2) minimizing cancer vs. non-cancer methylation beta differences, (3) segmentation based on CpG density in regions, (4) dynamic generation of sequencing subregions based on mutual information scores and cancer classification propensity. The analytics system applies selects sequencing subregions and applies a cancer classifier to those subregions to identify cancer presence in the sample.

Abstract: A system and method are disclosed for training a cancer classifier. The method includes, for each training sample comprising a plurality of methylation sequence reads: for each methylation sequence read, applying a probabilistic noise model, corresponding to a genomic region of a plurality of genomics regions that the methylation sequence read overlaps with, to the methylation sequence read to determine an anomaly score indicating a likelihood of observing the methylation pattern in healthy samples. Each probabilistic noise model is trained with methylation sequence reads from healthy samples. The method includes determining a feature vector comprising a feature for each genomic region based on a count of methylation sequence reads overlapping the genomic region with an anomaly score below a threshold anomaly score. The method includes training the cancer classifier with the feature vectors of the training samples to determine a cancer prediction based on an input feature vector.

Abstract: The present description provides a cancer assay panel for targeted detection of cancer-specific methylation patterns. Further provided herein includes methods of designing, making, and using the cancer assay panel to detect cancer and particular types of cancer.

Abstract: The present description provides a cancer assay panel for targeted detection of cancer-specific methylation patterns. Further provided herein includes methods of designing, making, and using the cancer assay panel for detection of cancer tissue of origin (e.g., types of cancer).

Abstract: The present description provides a cancer assay panel for targeted detection of cancer-specific methylation patterns. Further provided herein includes methods of designing, making, and using the cancer assay panel to detect cancer and particular types of cancer.

Abstract: The present description provides a hematological disorder (HD) assay panel for targeted detection of methylation patterns or variants specific to various hematological disorders, such as clonal hematopoiesis of indeterminate potential (CHIP) and blood cancers, such as leukemia, lymphoid neoplasms (e.g. lymphoma), multiple myeloma, and myeloid neoplasm. Further provided herein includes methods of designing, making, and using the HD assay panel for detection of various hematological disorders.

Abstract: Methods for determining a disease condition of a subject of a species are provided that comprises obtaining a dataset of fragment methylation patterns determined by methylation sequencing of nucleic acid from a biological sample of the subject. A fragment methylation pattern comprises the methylation state of each CpG site in the fragment. A patch including a channel comprising parameters for the methylation status of respective CpG sites in a set of CpG sites in a reference genome represented by the patch is constructed by populating, for each respective fragment in the plurality of fragments that aligns to the set of CpG sites, an instance of all or a portion of the plurality of parameters based on the methylation pattern of the respective fragment. Application of the patch to a patch convolutional neural network determines the disease condition of the subject.

Abstract: Methods and systems for detecting cancer and/or determining a cancer tissue of origin are disclosed. In some embodiments, a multiclass cancer classifier is disclosed that is trained with a plurality of biological samples containing cfDNA fragments. The analytics system derives a feature vector for each sample, and the multiclass classifier predicts a probability likelihood for each of a plurality of tissue of origin (TOO) classes. In some embodiments, the plurality of TOO classes include hematological subtypes, including both hematological malignancies and precursor conditions. In one embodiment, non-cancer samples having high tissue signal are pruned from the training sample set. In another embodiment, the analytics system stratifies samples according to tissue signal and applies binary threshold cutoffs determined for each stratum.

Abstract: In various embodiments, an analytics system uses models to determine features and classification of disease states. A disease state can indicate presence or absence of cancer, a cancer type, or a cancer tissue of origin. The models can include a binary classifier and a tissue of origin classifier. The analytics system can process sequence reads from test biological samples to generate data for training the classifiers. The analytics system can also use combinations of machine learning techniques to train the models, which can include a multilayer perceptron. In some embodiments, the analytics system uses methylation information to train the models to determine predictions regarding disease state.

Abstract: A predictive cancer model generates a prediction of cancer tissue source of origin for a subject of interest by analyzing values of one or more types of features that are derived from cfDNA obtained from the individual. Specifically, cfDNA from the individual is sequenced to generate sequence reads using one or more physical assays, examples of which include a small variant sequencing assay. The sequence reads of the physical assays are processed through corresponding computational analyses to generate small variant features and other features. The values of features can be provided to a prediction model that generates a prediction of cancer tissue source of origin and/or cancer presence.

Abstract: A predictive cancer model generates a cancer prediction for an individual of interest by analyzing values of one or more types of features that are derived from cfDNA obtained from the individual. Specifically, cfDNA from the individual is sequenced to generate sequence reads using one or more physical assays, examples of which include a small variant sequencing assay, whole genome sequencing assay, and methylation sequencing assay. The sequence reads of the physical assays are processed through corresponding computational analyses to generate each of small variant features, whole genome features, and methylation features. The values of features can be provided to a predictive cancer model that generates a cancer prediction. In some embodiments, the values of different types of features can be separately provided into different predictive models. Each separate predictive model can output a score that can serve as input into an overall model that outputs the cancer prediction.

Abstract: A processing system uses a Bayesian inference based model for targeted sequencing or variant calling. In an embodiment, the processing system generates candidate variants of a cell free nucleic acid sample. The processing system determines likelihoods of true alternate frequencies for each of the candidate variants in the cell free nucleic acid sample and in a corresponding genomic nucleic acid sample. The processing system filters or scores the candidate variants by the model using at least the likelihoods of true alternate frequencies. The processing system outputs the filtered candidate variants, which may be used to generate features for a predictive cancer or disease model.

Abstract: A system and a method are described for applying a noise model for predicting the occurrence and a level of noise that is present in cfDNA read information. The significance model is trained for a plurality of stratifications of called variants using training data in the stratification. Stratifications may include a partition and a mutation type. The significance model predicts the likelihood of observing a read frequency for a called variant in view of two distributions of the significance model. The first distribution predicts a likelihood of noise occurrence in the sample. The second distribution predicts a likelihood of observing a magnitude of the read frequency for the called variant. The two distributions may further depend on a baseline noise level of blank samples. With these two distributions, the significance model, for a particular stratification, more accurately predicts the likelihood of a false positive for a called variant.