Habitat preference in the critically endangered yellow‐tailed woolly monkey (Lagothrix flavicauda) at La Esperanza,Peru

Habitat loss is one of the main threats to wildlife. Therefore, knowledge of habitat use and preference is essential for the design of conservation strategies and identification of priority sites for the protection of endangered species. The yellow‐tailed woolly monkey (Lagothrix flavicauda Humboldt, 1812), categorized as Critically Endangered on the IUCN Red List, is endemic to montane forests in northern Peru where its habitat is greatly threatened. We assessed how habitat use and preference in L. flavicauda are linked to forest structure and composition. The study took place near La Esperanza, in the Amazonas region, Peru. Our objective was to identify characteristics of habitat most utilized by L. flavicauda to provide information that will be useful for the selection of priority sites for conservation measures. Using presence records collected from May 2013 to February 2014 for one group of L. flavicauda, we classified the study site into three different use zones: low‐use, medium‐use, and high‐use. We assessed forest structure and composition for all use zones using 0.1 ha Gentry vegetation transects. Results show high levels of variation in plant species composition across the three use zones. Plants used as food resources had considerably greater density, dominance, and ecological importance in high‐use zones. High‐use zones presented similar structure to medium‐ and low‐use zones; thus it remains difficult to assess the influence of forest structure on habitat preference. We recommend focusing conservation efforts on areas with a similar floristic composition to the high‐use zones recorded in this study and suggest utilizing key alimentation species for reforestation efforts.     

The cucumber mosaic virus 1a protein regulates interactions between the 2b protein and ARGONAUTE 1 while maintaining the silencing suppressor activity of the 2b protein

The cucumber mosaic virus (CMV) 2b viral suppressor of RNA silencing (VSR) is a potent counter-defense and pathogenicity factor that inhibits antiviral silencing by titration of short double-stranded RNAs. It also disrupts microRNA-mediated regulation of host gene expression by binding ARGONAUTE 1 (AGO1). But in Arabidopsis thaliana complete inhibition of AGO1 is counterproductive to CMV since this triggers another layer of antiviral silencing mediated by AGO2, de-represses strong resistance against aphids (the insect vectors of CMV), and exacerbates symptoms. Using confocal laser scanning microscopy, bimolecular fluorescence complementation, and co-immunoprecipitation assays we found that the CMV 1a protein, a component of the viral replicase complex, regulates the 2b-AGO1 interaction. By binding 2b protein molecules and sequestering them in P-bodies, the 1a protein limits the proportion of 2b protein molecules available to bind AGO1, which ameliorates 2b-induced disease symptoms, and moderates induction of resistance to CMV and to its aphid vector. However, the 1a protein-2b protein interaction does not inhibit the ability of the 2b protein to inhibit silencing of reporter gene expression in agroinfiltration assays. The interaction between the CMV 1a and 2b proteins represents a novel regulatory system in which specific functions of a VSR are selectively modulated by another viral protein. The finding also provides a mechanism that explains how CMV, and possibly other viruses, modulates symptom induction and manipulates host-vector interactions.     

A Mathematical Framework for Combining Decisions of Multiple Experts toward Accurate and Remote Diagnosis of Malaria Using Tele-Microscopy

We propose a methodology for digitally fusing diagnostic decisions made by multiple medical experts in order to improve accuracy of diagnosis. Toward this goal, we report an experimental study involving nine experts, where each one was given more than 8,000 digital microscopic images of individual human red blood cells and asked to identify malaria infected cells. The results of this experiment reveal that even highly trained medical experts are not always self-consistent in their diagnostic decisions and that there exists a fair level of disagreement among experts, even for binary decisions (i.e., infected vs. uninfected). To tackle this general medical diagnosis problem, we propose a probabilistic algorithm to fuse the decisions made by trained medical experts to robustly achieve higher levels of accuracy when compared to individual experts making such decisions. By modelling the decisions of experts as a three component mixture model and solving for the underlying parameters using the Expectation Maximisation algorithm, we demonstrate the efficacy of our approach which significantly improves the overall diagnostic accuracy of malaria infected cells. Additionally, we present a mathematical framework for performing ‘slide-level’ diagnosis by using individual ‘cell-level’ diagnosis data, shedding more light on the statistical rules that should govern the routine practice in examination of e.g., thin blood smear samples. This framework could be generalized for various other tele-pathology needs, and can be used by trained experts within an efficient tele-medicine platform.     

Bayesian Nonparametric Modeling for Comparison of Single-Neuron Firing Intensities

Summary .  We propose a fully inferential model-based approach to the problem of comparing the firing patterns of a neuron recorded under two distinct experimental conditions. The methodology is based on nonhomogeneous Poisson process models for the firing times of each condition with flexible nonparametric mixture prior models for the corresponding intensity functions. We demonstrate posterior inferences from a global analysis, which may be used to compare the two conditions over the entire experimental time window, as well as from a pointwise analysis at selected time points to detect local deviations of firing patterns from one condition to another. We apply our method on two neurons recorded from the primary motor cortex area of a monkey's brain while performing a sequence of reaching tasks.     

A Technique for Predicting the Solvent-Producing Ability of Clostridium acetobutylicum

Changes in colony morphology were associated with the degeneration of solvent-producing strains of Clostridium acetobutylicum. The most efficient solvent-producing strains gave rise exclusively to colonies with dense centers containing large numbers of spores. Many outgrowths of various morphologies developed from the perimeter of such colonies after several days of incubation. The most degenerate cultures did not produce solvents and gave rise to large diffuse colonies that did not contain spores. These diffuse colonies did not produce outgrowths. Intermediate colony types were also observed. These could be derived from liquid cultures that were relatively poor solvent producers or from the outgrowths of colonies of efficient solvent-producing strains. Some of these intermediate types produced spores but did so less frequently than the high-solvent-producing strains. The spores of the intermediate types could not be distinguished from those of the most efficient solvent producers on the basis of heat sensitivity. The relationship observed between colony morphology and solvent production provides a method for predicting the solvent-producing potential of C. acetobutylicum cultures.