Rapid antimicrobial susceptibility testing (AST) can be an integral tool to mitigate the unnecessary use of powerful and broad spectrum antibiotics that leads to the proliferation of multi-drug resistant bacteria. Methods and systems for a sensor platform composed of surface enhanced Raman scattering (SERS) sensors with surfaces having molecular control of nano architecture and surface chemistry and machine learning processes for analyzing SERS data, are described to detect metabolic profiles from susceptible antibiotic resistant bacteria strains for rapid AST.

Methods and systems for improving computer efficiency by intelligently selecting subsets of possible short tandem repeat (STR) allele combinations for further deconvolution analysis are disclosed. In one embodiment, at each locus, for a currently analyzed contribution ratio scenario of a plurality of contribution ratio scenarios, a processor computes an adjusted evidence profile. For a first, or next, unidentified contributor having a pre-determined highest remaining contribution ratio in the currently analyzed contribution ratio scenario for the plurality of contributors, a processor computes a first range of expected peak heights using at least the pre-determined highest remaining contribution ratio, a selected degradation value, and a peak height ratio distribution. Also disclosed are methods and systems for intelligently estimating the number of contributors to a biological sample.

The technology disclosed generates a reference array of variant data for locations that are shared between read results which are to be compared, and generates hashes over a selected pattern length of positions in the reference array to independently produce non-unique window hashes for base patterns in the read results. It then selects for comparison window hashes that occur less than a ceiling number of times and compares the selected window hashes to identify common window hashes between the read results. It then determines a similarity measure for the read results based on the common window hashes.

The technology disclosed generates a reference array of variant data for locations that are shared between read results which are to be compared, and generates hashes over a selected pattern length of positions in the reference array to independently produce non-unique window hashes for base patterns in the read results. It then selects for comparison window hashes that occur less than a ceiling number of times and compares the selected window hashes to identify common window hashes between the read results. It then determines a similarity measure for the read results based on the common window hashes.

The present disclosure provides methods of determining one or more tissues and/or cell-types contributing to cell-free DNA (“cfDNA”) in a biological sample of a subject. In some embodiments, the present disclosure provides a method of identifying a disease or disorder in a subject as a function of one or more determined more tissues and/or cell-types contributing to cfDNA in a biological sample from the subject.

The present disclosure provides methods of determining one or more tissues and/or cell-types contributing to cell-free DNA (“cfDNA”) in a biological sample of a subject. In some embodiments, the present disclosure provides a method of identifying a disease or disorder in a subject as a function of one or more determined more tissues and/or cell-types contributing to cfDNA in a biological sample from the subject.

Disclosed is a method for evaluating in vivo protein nutrition based on an LC-MS-MS technique, including the following steps: (1) collecting contents from different intestinal segments, and extracting and isolating protein ingredients; (2) determining the concentration of proteins; (3) treating before carrying out mass spectrometry: including digestion and desalting of a whole protein solution; (4) LC-MS-MS analysis; (5) database searching; and (6) data processing. Proteomic technology is used to identify proteins in the contents of different intestinal segments and digestive products thereof, and the source of the proteins in the contents of different intestinal segments and the contents thereof can be determined therefrom. Through bioinformatic analysis, the function of differential proteins in the body can be further understood, where the gene expression of enzymes related to protein digestion and metabolism may be different, thereby providing a scientific basis for further scientific evaluation of protein digestion and utilization.

In accordance with some embodiments, systems, methods, and media for determining relative quality of oligonucleotide preparations. In some embodiments, a system comprises a processor programmed to: (a) receive genetic sequencing results for multiple libraries with target concentrations of oligonucleotides; (b) calculate at least one prediction band; (c) repeat (a) and (b) for multiple preparations; (d) determine boundaries for a final prediction band based on the prediction bands calculated at (b) for each of the preparations; and (e) present a report indicative of quality of the libraries, including metrics indicative of the final prediction band.

The technology disclosed generates a reference array of variant data for locations that are shared between read results which are to be compared, and generates hashes over a selected pattern length of positions in the reference array to independently produce non-unique window hashes for base patterns in the read results. It then selects for comparison window hashes that occur less than a ceiling number of times and compares the selected window hashes to identify common window hashes between the read results. It then determines a similarity measure for the read results based on the common window hashes.

The technology disclosed generates a reference array of variant data for locations that are shared between read results which are to be compared, and generates hashes over a selected pattern length of positions in the reference array to independently produce non-unique window hashes for base patterns in the read results. It then selects for comparison window hashes that occur less than a ceiling number of times and compares the selected window hashes to identify common window hashes between the read results. It then determines a similarity measure for the read results based on the common window hashes.