Genetic sampling for estimating density of common species.

Understanding population dynamics requires reliable estimates of population density, yet this basic information is often surprisingly difficult to obtain. With rare or difficult-to-capture species, genetic surveys from noninvasive collection of hair or scat has proved cost-efficient for estimating densities. Here, we explored whether noninvasive genetic sampling (NGS) also offers promise for sampling a relatively common species, the snowshoe hare (Lepus americanus Erxleben, 1777), in comparison with traditional live trapping. We optimized a protocol for single-session NGS sampling of hares. We compared spatial capture-recapture population estimates from live trapping to estimates derived from NGS, and assessed NGS costs. NGS provided population estimates similar to those derived from live trapping, but a higher density of sampling plots was required for NGS. The optimal NGS protocol for our study entailed deploying 160 sampling plots for 4 days and genotyping one pellet per plot. NGS laboratory costs ranged from approximately $670 to$3000 USD per field site. While live trapping does not incur laboratory costs, its field costs can be considerably higher than for NGS, especially when study sites are difficult to access. We conclude that NGS can work for common species, but that it will require field and laboratory pilot testing to develop cost-effective sampling protocols

Deep learning based Image Compression for Microscopy Images: An Empirical Study

With the fast development of modern microscopes and bioimaging techniques, an unprecedentedly large amount of imaging data are being generated, stored, analyzed, and even shared through networks. The size of the data poses great challenges for current data infrastructure. One common way to reduce the data size is by image compression. This present study analyzes classic and deep learning based image compression methods, and their impact on deep learning based image processing models. Deep learning based label-free prediction models (i.e., predicting fluorescent images from bright field images) are used as an example application for comparison and analysis. Effective image compression methods could help reduce the data size significantly without losing necessary information, and therefore reduce the burden on data management infrastructure and permit fast transmission through the network for data sharing or cloud computing. To compress images in such a wanted way, multiple classical lossy image compression techniques are compared to several AI-based compression models provided by and trained with the CompressAI toolbox using python. These different compression techniques are compared in compression ratio, multiple image similarity measures and, most importantly, the prediction accuracy from label-free models on compressed images. We found that AI-based compression techniques largely outperform the classic ones and will minimally affect the downstream label-free task in 2D cases. In the end, we hope the present study could shed light on the potential of deep learning based image compression and the impact of image compression on downstream deep learning based image analysis models.Comment: - Update github link; - correct the author name; - update the table (correct some errors during calculation); - update the implementation detail section and the discussion sectio

An Assessment of Forecasting Methods: Cooperative Logistics Supply Support Arrangement (CLSSA) Investment Items

The United States (US) promotes collective security in the Free World via the Foreign Military Sales (FMS) program. FMS customers prefer to acquire weapon system logistic support through FMS rather than by direct commercial vendor support. Ninety-seven percent of the follow-on logistics requirements are submitted via a special program called Cooperative Logistics Supply Support Arrangement CLSSA. CLSSA, while sound in theory, has been a poor performer. The USAF must modify the CLSSA program or risk losing future FMS to competing nations. Modifying CLSSA to utilize an automated forecasting process will greatly improve customer service. Efficient and timely logistic support is a key decision factor as friendly nations evaluate the source of their next major weapon system acquisition. The US as a whole will gain from the USAFs new approach to CLSSA through the political, military and economic benefits that remit from increase FMS demand for US weapon systems. This study measured the relative accuracy of four time series forecasting methods in predicting future demands for CLSSA Investment Items. The double exponential smoothing, adaptive response, and classical decomposition were compared to the AFSAC retention model to determine the impact of changing to an automated method. The results favored the implementation of the AFSAC retention method with some minor modifications in the weighting scheme, rounding rules, and demand smoothing