A method for identifying a microorganism to be identified, which includes the following steps: obtaining an Excitation-Emission EEM of the microorganism to be identified, analysing the main components of the EEM matrix using at least one reference EEMr matrix, projecting the result of the analysis onto a plane defined by two main components, and identifying the microorganism to be identified.

The present technology relates to systems and/or methods for enabling a respiratory device to be configured when a clinician or healthcare professional is remote from the respiratory device. One form provides a method of configuring a respiratory device, the respiratory device comprising a processor configured to control operation of the respiratory device in accordance with a plurality of operating parameters. The method comprises determining a combination of settings for the device from an identifier sent to the device, the identifier corresponding to the combination of settings, and configuring the respiratory device accordingly. Another form provides a method of verifying the configuration of the respiratory device by outputting an identifier corresponding to the combination of settings for the device, and determining the settings from the identifier.

A computer that analyzes data is described. During operation, the computer may access the data in the memory. Then, the computer may transform the data into a spatial representation. For example, for biological data, the transformation may be based at least in part on a predefined relationship between the biological data and corresponding spatial locations in a genome. Moreover, the computer may selectively normalize the transformed data to obtain normalized transformed data. Notably, the selective normalization may use different normalization ranges based at least in part on expression levels in a type of biological sequencing. Next, the processor may convert the normalized transformed data into an output image. Furthermore, the processor may analyze the image using an image-analysis technique (such as a pretrained neural network) to determine a classification. Additionally, the processor may perform: storing the classification; displaying the classification; and/or providing the classification to an electronic device.

A computer assisted system is disclosed that includes an optical tracking system and one or more computing devices. The optical tracking system includes an RGB sensor and is configured to capture color images of an environment in the visible light spectrum and tracking images of fiducials in the environment in a near-infrared spectrum. The computer assisted system is configured to generate a color image of the environment using the color images, identify fiducial locations using the tracking images, generate depth maps from the color images, reconstruct three-dimensional surfaces of structures based on the depth maps, and output a display comprising the reconstructed three-dimensional surface and one or more surgical objects that are associated with the tracked fiducials. The computer assisted system can further include a monitor or a head-mounted display (HMD) configured to present augmented reality (AR) images during a procedure.

A method for visualizing microbiome data is described. Respective microbes and/or genes in microbiome data stored In in a database are identified. A network comprising nodes interconnected by edges is generated in a memory of a computer, each node representing one or more identified microbes or one or more microbial metabolites, and each edge of the network representing an association between a respective pair of the one or more identified microbes or a reaction mediated between two metabolites by an enzyme encoded in the one or more identified genes, with at least some nodes and edges of the network being each associated with a condition attribute identifying a groups and/or a timestamp associated with a sample in the database. The displayed network is dynamically updated in accordance with a filtering of the microbiome data based on the condition attributed and/or the timestamp attributed. Corresponding systems and computer-readable storages are also described.

A respiratory pressure therapy system for providing continuous positive air pressure to a patient via a patient interface configured to engage with at least one airway of the patient. The system includes: a flow generator configured to generate supply of breathable gas for delivery to the patient via the patient interface; at least one sensor; a display; and a computing device. The computing device is configured to: receive sensor data that is based on measured physical property of the supply of breathable gas; control, based on the received sensor data, the flow generator to adjust a property of the supply of breathable gas; receive, an input indicating assistance is needed with using the patient interface; receive one or more images of the patient with the patient interface; analyse the received one or more images; and based on the analysis, display instructions for positioning the patient interface.

The present application provides a method and system for dynamically predicting a deoxynivalenol content of wheat at harvest, including: on the basis of historical data, screening out by particle swarm optimization algorithm combined factors suitable for establishing a prediction model, and establishing the prediction model by using the combined factors; on the basis of data of a current year, predicting a second flowering date and a second harvest date of wheat in the current year by an agricultural model; then obtaining a weather forecast on the basis of the second flowering date and the second harvest date, and combining the weather forecast and geographic data into correlated factors; and finally predicting the deoxynivalenol content of wheat at harvest by means of the prediction model and the correlated factors. Compared with the prior art, statistical items in the prediction model are more comprehensive, and growth period data of the current year can be dynamically predicted on the basis of growth period indexes model, thus continuously adjusting and establishing the prediction model. In addition, an overhead time for screening multi-dimensional large-batch data by the particle swarm optimization algorithm has more advantages, and the prediction model established by a multiple linear regression algorithm has higher precision.

A computer-implemented system and method for predicting female sex bovine offspring to result from a bovine embryo by processing video image data of the embryo. The method includes receiving image data derived from video of a target embryo taken at substantially real-time frame speed during an embryo observation period of time. The video contains recorded morphokinetic movement of the target embryo occurring during the embryo observation period of time. The movement is represented in the received image data and the received image data is processed using a model generated utilizing machine learning and correlated embryo outcome data.

Provided herein is a method for analyzing polynucleotides such as genomic DNA. In certain embodiments, the method comprises: (a) treating chromatin isolated from a population of cells with an insertional enzyme complex to produce tagged fragments of genomic DNA; (b) sequencing a portion of the tagged fragments to produce a plurality of sequence reads; and (c) making an epigenetic map of a region of the genome of the cells by mapping information obtained from the sequence reads to the region. A kit for performing the method is also provided.

The present invention relates to a device, system and method for less obtrusively determining a tissue characteristic of a subject, the device comprises a first control unit (11) configured to control an electromechanical transducer (31) by a first control signal (21) to transfer mechanical waves varying in a frequency range or with varying frequency content to an exposed tissue area of the subject; a second control unit (12) configured to control an electromagnetic radiation emitter (32) by a second control signal (22) to emit electromagnetic radiation towards the exposed tissue area of the subject; a radiation signal input (13) configured to obtain a radiation signal (23) indicative of electromagnetic radiation reflected from the exposed tissue area of the subject; and a processor (14) configured to determine a tissue characteristic signal (24) indicative of a tissue characteristic of the exposed tissue area of the subject derived from a frequency response or a frequency transfer function obtained from the obtained radiation signal in said frequency range or for said varying frequency content.