A device may receive a master data set for a first spectroscopic model; receive a target data set for a target population associated with the first spectroscopic model to update the first spectroscopic model; generate a training data set that includes the master data set and first data from the target data set; generate a validation data set that includes second data from the target data set and not the master data set; generate, using cross-validation and using the training data set and the validation data set, a second spectroscopic model that is an update of the first spectroscopic model; and provide the second spectroscopic model.

A camera system includes one or more spectral illuminators, a tunable optical filter, and a sensor array. Active spectral light emitted from the one or more spectral illuminators towards a scene is dynamically tuned to an illumination sub-band selected from a plurality of different illumination sub-bands. Sequentially for each of a plurality of fluorescing light sub-bands different than the selected illumination sub-band, the tunable optical filter is adjusted to block light from being transmitted from the scene to the sensor array in all but a tested fluorescing light sub-band from the plurality of different fluorescing light sub-bands, and the sensor array is addressed to acquire one or more image of the scene in the tested fluorescing light sub-band.

A sensor (e.g., an optical sensor) that may be implanted within a living animal (e.g., a human) and may be used to measure an analyte (e.g., glucose or oxygen) in a medium (e.g., interstitial fluid, blood, or intraperitoneal fluid) within the animal. The sensor may include a sensor substrate, electrode or housing, an analyte indicator covering at least a portion of the sensor, and one or more optical signal stabilizing additives in an environment of the sensor.

The present invention is related to the field of bio/chemical sensing, assays and applications. Particularly, the present invention is related to collecting a small amount of a vapor condensate sample (e.g. the exhaled breath condensate (EBC) from a subject of a volume as small as 10 fL (femto-Liter) in a single drop), preventing or significantly reducing an evaporation of the collected vapor condensate sample, analyzing the sample, analyzing the sample by mobile-phone, and performing such collection and analysis by a person without any professionals.

Methods and systems are disclosed for remotely and/or automatically controlling a probe to measure signals.

A device may receive a master data set for a first spectroscopic model; receive a target data set for a target population associated with the first spectroscopic model to update the first spectroscopic model; generate a training data set that includes the master data set and first data from the target data set; generate a validation data set that includes second data from the target data set and not the master data set; generate, using cross-validation and using the training data set and the validation data set, a second spectroscopic model that is an update of the first spectroscopic model; and provide the second spectroscopic model.

In some embodiments, data from a vehicle-borne gas leak detection survey are used to generate an aggregate leak indication search area (LISA) indicator for a plurality of leak indications (measurement peaks) characterizing a single leak or localized set of leaks. A clustering algorithm (e.g. Markov, DBScan) may be used to group a set of indications into a cluster characterizing the leak. Leak indications may be pre-filtered for quality control before assignment to a cluster according to a number of parameters including background gas level, inter-peak distance, peak shape, wind speed, wind direction and/or variability, vehicle speed and/or acceleration, and/or a lower detection threshold for leak flow rate.

Disclosed herein is method and system for determining quality of semen sample. Trajectories of objects, identified in each of plurality of image frames of semen sample, are generated by tracking movement of the objects across image frames, and compensating a drift velocity of the semen sample. Further, generated trajectories are classified into sperm and non-sperm trajectories. Finally, total concentration estimate and total motility estimate of the semen sample are computed to generate a semen quality index, which indicates quality of the semen sample. In an embodiment, the method of present disclosure uses a multi-level Convolutional Neural Network (CNN) analysis technique for effectively classifying the object trajectories into sperm and non-sperm objects. Also, since the present method includes estimating and compensating drift velocity in the semen sample, it enhances overall accuracy of motility estimation and semen quality analysis.

A preparation device (1) for preparing a sample for measurement of a target biomolecule in a sample of a bodily fluid, such as anti-Mullerian hormone in blood, comprises a substrate (12) with a capillary network configured to transport the sample of the bodily fluid along the substrate, wherein the substrate (12) is provided with a fluorescent, reflective or self-luminescent marker adapted to bind with the target biomolecule, said fluorescent, reflective or self-luminescent marker being adapted, when bound to the target biomolecule and excited by an excitation source of a diagnostic apparatus, together with the target biomolecule to emit reaction radiation at an intensity proportional to a quantity of the target biomolecule in the probe of the bodily fluid. The preparation device may preferably comprise a near-field communication (NFC) chip (114) storing a unique identifier of the preparation device.

The present disclosure relates to methods and systems for obtaining image information of an organism including a set of optical data; calculating a growth index based on the set of optical data; and calculating an anticipated harvest time based on the growth index, where the image information includes at least one of: (a) visible image data obtained from an image sensor and non-visible image data obtained from the image sensor, and (b) a set of image data from at least two image capture devices, where the at least two image capture devices capture the set of image data from at least two positions.