The aged hematopoietic system promotes hippocampal‐dependent cognitive decline

The aged systemic milieu promotes cellular and cognitive impairments in the hippocampus. Here, we report that aging of the hematopoietic system directly contributes to the pro‐aging effects of old blood on cognition. Using a heterochronic hematopoietic stem cell (HSC) transplantation model (in which the blood of young mice is reconstituted with old HSCs), we find that exposure to an old hematopoietic system inhibits hippocampal neurogenesis, decreases synaptic marker expression, and impairs cognition. We identify a number of factors elevated in the blood of young mice reconstituted with old HSCs, of which cyclophilin A (CyPA) acts as a pro‐aging factor. Increased systemic levels of CyPA impair cognition in young mice, while inhibition of CyPA in aged mice improves cognition. Together, these data identify age‐related changes in the hematopoietic system as drivers of hippocampal aging.     

Three critical factors affecting automated image species recognition performance for camera traps

Ecological camera traps are increasingly used by wildlife biologists to unobtrusively monitor an ecosystems animal population. However, manual inspection of the images produced is expensive, laborious, and time‐consuming. The success of deep learning systems using camera trap images has been previously explored in preliminary stages. These studies, however, are lacking in their practicality. They are primarily focused on extremely large datasets, often millions of images, and there is little to no focus on performance when tasked with species identification in new locations not seen during training. Our goal was to test the capabilities of deep learning systems trained on camera trap images using modestly sized training data, compare performance when considering unseen background locations, and quantify the gradient of lower bound performance to provide a guideline of data requirements in correspondence to performance expectations. We use a dataset provided by Parks Canada containing 47,279 images collected from 36 unique geographic locations across multiple environments. Images represent 55 animal species and human activity with high‐class imbalance. We trained, tested, and compared the capabilities of six deep learning computer vision networks using transfer learning and image augmentation: DenseNet201, Inception‐ResNet‐V3, InceptionV3, NASNetMobile, MobileNetV2, and Xception. We compare overall performance on “trained” locations where DenseNet201 performed best with 95.6% top‐1 accuracy showing promise for deep learning methods for smaller scale research efforts. Using trained locations, classifications with <500 images had low and highly variable recall of 0.750 ± 0.329, while classifications with over 1,000 images had a high and stable recall of 0.971 ± 0.0137. Models tasked with classifying species from untrained locations were less accurate, with DenseNet201 performing best with 68.7% top‐1 accuracy. Finally, we provide an open repository where ecologists can insert their image data to train and test custom species detection models for their desired ecological domain.     

Grassland area determines beetle assemblage dissimilarity from surrounding floodplain forest

Patch size is known to affect biodiversity in fragmented landscapes, but is usually examined in systems where the surrounding matrix habitat is unfavourable. We examined beetle diversity in a floodplain ecosystem that is characterised by naturally occurring grassland patches within a dominant matrix of contrasting yet habitable forest. We asked whether differences in the beetle assemblage between grassland and forest vegetation depended on the area of the grassland patch, which is a function of its flooding frequency and duration: smaller grasslands tend to be higher on the floodplain and are flooded less often and for shorter periods than larger grasslands. We found a negative relationship between grassland area and beetle abundance and species richness, and a positive relationship between grassland area and compositional dissimilarity from the surrounding forest. As expected, we found an overall difference in composition between forest and grassland assemblages, with five beetle species more common in the grasslands. Our study indicates that floodplain grasslands not only support beetle assemblages that are distinct from the surrounding forest, but that assemblages from the larger grasslands are compositionally more distinct than those from smaller grasslands. A likely cause of this pattern is the reduced edge effects and greater environmental contrast between forest and large grasslands that may be exposed to greater variation in local climate. Ongoing changes to flood regimes and potential encroachment of forest plants may decrease grassland area in the future, which may reduce spatial heterogeneity in the insect community in this unique floodplain ecosystem.     

The spatial scaling of beta diversity

Beta diversity is an important concept used to describe turnover in species composition across a wide range of spatial and temporal scales, and it underpins much of conservation theory and practice. Although substantial progress has been made in the mathematical and terminological treatment of different measures of beta diversity, there has been little conceptual synthesis of potential scale dependence of beta diversity with increasing spatial grain and geographic extent of sampling. Here, we evaluate different conceptual approaches to the spatial scaling of beta diversity, interpreted from ‘fixed’ and ‘varying’ perspectives of spatial grain and extent. We argue that a ‘sliding window’ perspective, in which spatial grain and extent covary, is an informative way to conceptualize community differentiation across scales. This concept more realistically reflects the varying empirical approaches that researchers adopt in field sampling and the varying scales of landscape perception by different organisms. Scale dependence in beta diversity has broad implications for emerging fields in ecology and biogeography, such as the integration of fine‐resolution ecogenomic data with large‐scale macroecological studies, as well as for guiding appropriate management responses to threats to biodiversity operating at different spatial scales.     

Effects of Temperature on Lipid Unsaturation

Abstract

The ability to genetically modify pigs has enabled scientists to create pigs that are beneficial to humans in ways that were previously unimaginable. Improvements in the methods to make genetic modifications have opened up the possibilities of introducing transgenes, knock-outs and knock-ins with precision. The benefits to medicine include the production of pharmaceuticals, the provision of organs for xenotransplantation into humans, and the development of models of human diseases. The benefits to agriculture include resistance to disease, altering the carcass composition such that it is healthier to consume, improving the pig’s resistance to heat stress, and protecting the environment. Additional types of genetic modifications will likely provide animals with characteristics that will benefit humans in currently unimagined ways.     

Applications of Biocatalysis to Biotechnology

β-lactam antibiotics (e.g. penicillins, cephalosporins) are of major clinical importance and contribute to over 40% of the total antibiotic market. These compounds are produced as secondary metabolites by certain actinomycetes and filamentous fungi (e.g. Penicillium, Aspergillus and Acremonium species). The industrial producer of penicillin is the fungus Penicillium chrysogenum. The enzymes of the penicillin biosynthetic pathway are well characterized and most of them are encoded by genes that are organized in a cluster in the genome. Remarkably, the penicillin biosynthetic pathway is compartmentalized: the initial steps of penicillin biosynthesis are catalyzed by cytosolic enzymes, whereas the two final steps involve peroxisomal enzymes. Here, we describe the biochemical properties of the enzymes of β-lactam biosynthesis in P. chrysogenum and the role of peroxisomes in this process. An overview is given     

The Glassy Wall Boundary for Simulations of Inhomogeneous Systems

Abstract

For simulations on systems that invlove a physical inhomogeneity at infinite dilution, the usual periodic boundary conditions are inappropriate. In previous studies on such systems, surfaces have been re-introduced in order to contain the particles of the sample. When smooth surfaces are used to contain liquids, undesirable structural artifacts in the liquid are promoted by these surfaces. In this study a rigid but structurally liquid-like containing surface, that we call a glassy wall boundary, is introduced, in an attempt to solve this problem. It serves as a containing surface, but “looks” nearly liquid-like to molecules in the mobile liquid near it. We illustrate the properties of this boundary for a system that consists of an isolated polyion surrounded by SPC water at 300K and ～ 1 gcm^?3. We show that this boundary reduces or eliminates some of the problems caused by a smooth surface.     

Simulations on the Primitive Electrolyte Environment of a High Charge-density Polyelectrolyte. A Sampling Problem and its Solution

Abstract

We show that the classical Metropolis Monte Carlo (MMC) algorithm converges very slowly when applied to the primitive electrolyte environment for a high charge-density polyelectrolyte. This slowness of convergence, which is due to the large density inhomogeneity around the polyelectrolyte, produces noticeable errors in the ion distribution functions for MMC runs of 1.3 × 10⁶ trial steps started from nonequilibrium distributions. We report that an algorithm which we call DSMC (for density-scaled Monte Carlo) overcomes this problem and provides relatively rapid convergence in this application. We suggest that DSMC should be well-suited for other Monte Carlo simulations on physical systems where large density inhomogeneities occur.