Methods, apparatus, and storage medium for determining a condition of a biostructure by a neural network based on quantitative imaging data (QID) corresponding to an image of the biostructure. The method includes obtaining specific quantitative imaging data (QID) corresponding to an image of a biostructure; determining a context spectrum selection from context spectrum including a range of selectable values by: applying the specific QID to an input layer of a context-spectrum neural network, wherein the context-spectrum neural network is trained, according to a combination of focal loss and dice loss, based on previous QID and constructed context spectrum data associated with the previous QID; mapping the context spectrum selection to the image to generate a context spectrum mask for the image; and determining a condition of the biostructure based on the context spectrum mask.

Methods, apparatus, and storage medium for determining a condition of a biostructure by a neural network based on quantitative imaging data (QID) corresponding to an image of the biostructure. The method includes obtaining specific quantitative imaging data (QID) corresponding to an image of a biostructure; determining a context spectrum selection from context spectrum including a range of selectable values by: applying the specific QID to an input layer of a context-spectrum neural network, wherein the context-spectrum neural network is trained, according to a combination of focal loss and dice loss, based on previous QID and constructed context spectrum data associated with the previous QID; mapping the context spectrum selection to the image to generate a context spectrum mask for the image; and determining a condition of the biostructure based on the context spectrum mask.

A vibration damping structure (60), a detection system and a sequencing system. The vibration damping structure (60) is used in the detection system. The vibration damping structure (60) comprises a main body (62) and a support body (64), the main body (62) is connected to the detection system by means of the support body (64), the main body (62) comprises an imaging module (10), an upper layer structure (66), a lower layer structure (68) and an intermediate structure (70), the imaging module (10) is mounted on the upper layer structure (66), the lower layer structure (68) bears the upper layer structure (66) by means of the intermediate structure (70), and the natural frequency of the main body (62) is greater than or equal to √{square root over (2)} times the internal excitation frequency.

The present disclosure relates to materials and methods for spatial detection of nucleic acid in a tissue sample or a portion thereof. In particular, provided herein are materials and methods for detecting RNA so as to obtain spatial information about the localization, distribution or expression of genes in a tissue sample. In some embodiments, the materials and methods provided herein enable detection of gene expression in a single cell.

The present disclosure provides methods and compositions for the determining the abundance and/or concentration of protein biomarkers in a biological sample.

The present disclosure provides methods and compositions for the determining the abundance and/or concentration of protein biomarkers in a biological sample.

The technology disclosed corrects inter-cluster intensity profile variation for improved base calling on a cluster-by-cluster basis. The technology disclosed accesses current intensity data and historic intensity data of a target cluster, where the current intensity data is for a current sequencing cycle and the historic intensity data is for one or more preceding sequencing cycles. A first accumulated intensity correction parameter is determined by accumulating distribution intensities measured for the target cluster at the current and preceding sequencing cycles. A second accumulated intensity correction parameter is determined by accumulating intensity errors measured for the target cluster at the current and preceding sequencing cycles. Based on the first and second accumulated intensity correction parameters, next intensity data for a next sequencing cycle is corrected to generate corrected next intensity data, which is used to base call the target cluster at the next sequencing cycle.

Described herein are improved methods for the detection of endometriosis. Generally, the methods include, but are not limited to, applying machine learning algorithm to miRNA levels in order to detect, predict, diagnose, or monitor the presence or absence of endometriosis.

Described herein are improved methods for the detection of endometriosis. Generally, the methods include, but are not limited to, applying machine learning algorithm to miRNA levels in order to detect, predict, diagnose, or monitor the presence or absence of endometriosis.

We propose a neural network-implemented method for base calling analytes. The method includes accessing a sequence of per-cycle image patches for a series of sequencing cycles, where pixels in the image patches contain intensity data for associated analytes, and applying three-dimensional (3D) convolutions on the image patches on a sliding convolution window basis such that, in a convolution window, a 3D convolution filter convolves over a plurality of the image patches and produces at least one output feature. The method further includes beginning with output features produced by the 3D convolutions as starting input, applying further convolutions and producing final output features and processing the final output features through an output layer and producing base calls for one or more of the associated analytes to be base called at each of the sequencing cycles.