BioSense (United States)

The number of farmers who use smart phones is increasing rapidly and furthermore RGB and thermal cameras are becoming more and more available either as smart phone gadgets or as integrated parts of the smart phone. Using them, farmers could have early information about the wheat yield. Currently, counting ears on part of a field and extrapolating the values for the whole field requires ears to be counted manually, which is prone to subjective evaluation, takes a lot of time and requires large human resources. In the case of larger fields, samples must be taken from more than one location, which additionally slows down the process. The aim of the research was to develop a system for wheat ear recognition and counting, which is necessary step for further estimation of wheat ear coverage density in the field, and yield prediction. Images of winter wheat were taken at 4 dates during the growing season and segmented manually to acquire the ground truth. Image segmentation was done using deep learning. Namely, convolutional neural networks were applied to RGB and thermal images and the results were compared to the ground truth to assess the system accuracy. Development of a comprehensive system for ear counting and yield prediction has a huge practical value for crop monitoring and optimal decision-making in wheat production.

We have previously described a new rapid approach that relies on monitoring intentionally stressed bacteria in contaminated platelet concentrates (PCs). This earlier work included human cell lysis with Triton X-100 and filtration as steps in the sample preparation. This study was undertaken to develop an improved and time-saving protocol that enables direct bacterial detection in PCs without lysis and filtration. Apheresis- or whole blood-derived PCs were spiked with 17 model bacteria and tested at final concentrations from 10(3) to 10(6) colony-forming units (CFUs)/mL. The contaminated PCs were treated with a chemical compound that induces a stress response in bacteria and monitored using differential impedance sensing to detect and record subtle changes in the dielectric permittivities of the contaminated platelet (PLT) samples. No measurable responses from sterile PLT samples were observed during exposure to the compounds used as stressors. In contrast, distinct response profiles were obtained without exception for all 17 bacterial species for all bacterial concentrations tested. Bacterial presence was established within 5 to 10 minutes for high inocula (10(6) and 10(5) CFUs/mL) while low inocula (10(4) and 10(3) CFUs/mL) were usually detectable within 20 minutes. The entire testing process routinely took less than 30 minutes from the point of sampling to the time that the final results are available. The results described here demonstrate that monitoring the development of stress in bacteria is a fast and simple way to detect 10(3) CFUs/mL or more bacteria in complex cellular blood products such as PCs.

Currently approved culture-based methods for the bacterial testing of platelet concentrates (PCs) require an extended period of time to obtain results. A new approach based on the monitoring of the bacterial response to physiologic stress is presented. Because the stress response is independent of the growth rate, decisive results can be obtained in near real time. PCs were spiked with Gram-positive Staphylococcus epidermidis and Propionibacterium acnes and Gram-negative Escherichia coli organisms commonly implicated in posttransfusion septic reactions. All suspensions were treated with Triton X-100 at concentrations that caused human cell lysis while acting as a stressor for bacterial cells. Normalized impedance response (NIR) profiles of these suspensions were recorded using the BioSense Technologies differential impedance sensing platform specifically designed to monitor subtle changes in the dielectric properties of biologic suspensions. Further development of the approach included measurement of NIRs for spiked PCs treated with nonlysing chemical stressors. NIR profiles for bacteria conventionally growing under optimal conditions were characterized by an overall increase in the NIR values. In contrast, the corresponding responses from chemically stressed bacteria revealed immediate and continuous decreases in value-enabling bacterial detection in less than 30 minutes. These pilot experiments demonstrated that monitoring of the bacterial stress response is a fast and effective way to detect bacteria in PCs. In addition, special culture conditions are not required for detecting anaerobes and fastidious species.