A system for measuring chlorophyll concentration in a leaf sample includes a leaf-holding illuminator device with a main body containing a power source, a plurality of switchable light sources emitting light at different spectra (e.g., red and white light from a broadband light source), and a cap detachably secured to the main body using one or more fastening means. The leaf sample is interposed between the main body and the cap and held in place during imaging. The system includes a mobile electronic device having a camera configured to capture an image of the leaf illuminated by the plurality of switchable light sources, the mobile electronic device having wireless connectivity to a network and an application contained therein configured to transfer the images to a remote sever or computer via the network for data processing. A final chlorophyll index value is calculated based on the transferred images.

A nucleic acid sequence measuring apparatus (1) measures a target (TG) having a specific nucleic acid sequence included in a sample. The nucleic acid sequence measuring apparatus (1) includes a detector (12) configured to detect fluorescence emitted from a nucleic acid sequence measuring device (DV) which emits fluorescence due to an addition of the target (TG), and a calculator (25) configured to measure the target based on a difference between a first amount of light indicating an amount of fluorescent light emitted from a predefined measurement region of the nucleic acid sequence measuring device (DV) at a first time point before or immediately after an addition of the sample to the nucleic acid sequence measuring device (DV) and a second amount of light indicating an amount of fluorescent light emitted from the measurement region at a second time point after a predefined time has elapsed from the addition of the sample to the nucleic acid sequence measuring device (DV), based on a detection result of the detector (12).

Machine learning analysis for classifying agglutination of fluid samples. A method includes scanning a unique scannable code printed on a test card, wherein the test card comprises a negative control fluid sample, a positive control fluid sample, and a test fluid sample. The method includes capturing an image of the test card and providing the image of the test card to a machine learning algorithm configured to assess agglutination of the test fluid sample based on the image. The method includes receiving from the machine learning algorithm one or more of a qualitative analysis or a quantitative analysis of the agglutination of the test fluid sample.

Systems, devices and methods for calibrating or increasing the accuracy of light sensing devices. The methods can include calibrating a light sensing device with a calibration source that is adapted to mimic at least one representative spectrum.

A method of ranking embryos to indicate their development potential. The method comprises: obtaining values for a plurality of characteristics relating to the morphological development of the embryos during an observation period; determining for respective ones of the embryos whether or not the embryo has undergone a direct cleavage event, and ranking the embryos determined to have undergone a direct cleavage event with a ranking that indicates a lower development potential than for the embryos not determined to have undergone a direct cleavage event; and for the embryos not determined to have undergone a direct cleavage event, determining whether or not a duration of a predefined developmental stage for the embryo exceeds a predefined threshold duration, and ranking embryos for which the duration of the predefined developmental stage is determined to exceed the predefined threshold duration with a ranking that indicates a lower development potential than for the embryos for which the duration of the predefined developmental stage is not determined to exceed the predefined threshold duration; and for the embryos for which the duration of the predefined developmental stage is not determined to exceed the predefined threshold duration, determining whether or not the relative durations of two predefined developmental stages for the embryo is outside a predefined range, and ranking embryos for which the relative durations of two predefined developmental stages for the embryo is outside a predefined range with a ranking that indicates a lower development potential than for the embryos for which the relative durations of two predefined developmental stages for the embryo is not outside the predefined range.

A system usable to adapt an optical device to calculate a condition value. The system utilizes data from an optical device about a field of vision to calculate a condition value such as temperature for a target within the field of vision. The system makes use of an adapter connected to the optical device for transmitting adapter output data and a converter that accesses the adapter output data to calculate the condition value. The adapter components can weigh less than 3 ounces, and encompass a volume of less than 4 cubic inches, making it suitable for deployment on a drone, or remotely operated vehicle.

An example system includes a light intensity measuring apparatus couplable to a food processing apparatus and a computing system. The light intensity measuring apparatus includes a chamber configured to receive a water sample from the food processing apparatus, a light source, a detector configured to detect light that has passed through the water sample and measure multiple times intensities of wavelengths of the light to obtain multiple sets of measured intensities of wavelengths, and a communication module configured to provide the multiple sets of measured intensities of wavelengths. The computing system may receive the multiple sets of measured intensities, process the multiple sets to obtain a set of values, apply a first set of decision trees to the set of values to obtain a first result indicating a positive or negative foodborne pathogen detection, generate a notification indicating either the positive of negative detection, and provide the notification.

Protecting information by partitioning data across multiple storage resources. A method includes receiving an image of a test card and an identity of a patient associated with the test card. The method includes associating the image of the test card with the identity of the patient as a data pair on a first storage resource. The method includes providing the image of the test card to a second storage resource by way of a network, wherein the first storage resource is independent of the second storage resource. The method includes receiving a result for the test card and associating the result for the test card with the identity of the patient based on the data pair stored on the first storage resource.

Test cards for agglutination assays, wherein the test cards are configured for computer-implemented image analysis. A test card includes a negative control test region for receiving a negative control fluid sample, a positive control test region for receiving a positive control fluid sample, and a test sample region for receiving a test fluid sample. The test card includes one or more unique scannable codes comprising data for instructing a processor to capture an image of the test card that is suitable for computer-implemented image analysis.

Machine learning image analysis for quantitative and qualitative analysis of agglutination samples. A method includes receiving an image of an agglutination assay comprising a negative control sample, a positive control sample, and a test sample. The method includes providing the image to a machine learning algorithm trained to classify agglutination of the test sample on a quantitative scale. The machine learning algorithm calibrates the quantitative scale based at least in part on the negative control sample and the positive control sample.