Satellite tracking a wide-ranging endangered vulture species to target conservation actions in the Middle East and East Africa

Vultures comprise the most endangered avian foraging guild (obligate scavengers) and their loss from ecosystems can trigger trophic cascades, mesopredator release, and human rabies epidemics, indicating their keystone species status. Vultures’ extremely large home ranges, which often cross international borders of countries that have differing laws and capacity for wildlife conservation, makes conserving them challenging. However, satellite-tracking data can be used to identify habitat preferences and critical sites to target conservation actions. We tracked 16 Egyptian Vultures, Neophron percnopterus, in the Middle East and East Africa. We used dynamic Brownian bridge movement models to calculate home ranges and core-use areas, and we analyzed habitat use in a resource selection framework. Combined summer and winter ranges (99% utilization distributions) of all birds covered 209,800 and 274,300 km², respectively. However, the core-use areas (50% utilization distributions) in the summer and winter ranges, accounted for only 0.4–1.1% of this area (900 and 3100 km², respectively). These core-use areas are where the home ranges of multiple individuals overlapped and/or where individuals spent a lot of time, such as feeding and roosting sites, and are places where conservation actions could focus. Resource selection models predicted Egyptian Vulture occurrence throughout little-studied parts of the species’ range in the Middle East and East Africa, and revealed strong selection for proximity to highways, power distribution lines, and towns. While providing roosts (e.g. power pylons) and food (e.g. garbage dumps), anthropogenic features may also function as ecological traps by increasing exposure to electrocution and dietary toxins.     

Population Level Inference for Multivariate MEG Analysis

Multivariate analysis is a very general and powerful technique for analysing Magnetoencephalography (MEG) data. An outstanding problem however is how to make inferences that are consistent over a group of subjects as to whether there are condition-specific differences in data features, and what are those features that maximise these differences. Here we propose a solution based on Canonical Variates Analysis (CVA) model scoring at the subject level and random effects Bayesian model selection at the group level. We apply this approach to beamformer reconstructed MEG data in source space. CVA estimates those multivariate patterns of activation that correlate most highly with the experimental design; the order of a CVA model is then determined by the number of significant canonical vectors. Random effects Bayesian model comparison then provides machinery for inferring the optimal order over the group of subjects. Absence of a multivariate dependence is indicated by the null model being the most likely. This approach can also be applied to CVA models with a fixed number of canonical vectors but supplied with different feature sets. We illustrate the method by identifying feature sets based on variable-dimension MEG power spectra in the primary visual cortex and fusiform gyrus that are maximally discriminative of data epochs before versus after visual stimulation.     

Single-stage photofermentative biohydrogen production from sugar beet molasses by different purple non-sulfur bacteria

Bioprocess and Biosystems Engineering - Biohydrogen production via fermentative routes offers considerable advantages in waste recycling and sustainable energy production. This can be realized by...     

The hippocampus and dopaminergic midbrain: old couple, new insights

Humans have a natural ability to gain new insights by generalizing from previous experience. In this issue of Neuron, Shohamy and Wagner reveal how generalizations naturally emerge during associative learning through a partnership between putatively dopaminergic circuitry in the midbrain and the hippocampus.     

Increasing power of genome-wide association studies by collecting additional single-nucleotide polymorphisms

Genome-wide association studies (GWASs) have been effectively identifying the genomic regions associated with a disease trait. In a typical GWAS, an informative subset of the single-nucleotide polymorphisms (SNPs), called tag SNPs, is genotyped in case/control individuals. Once the tag SNP statistics are computed, the genomic regions that are in linkage disequilibrium (LD) with the most significantly associated tag SNPs are believed to contain the causal polymorphisms. However, such LD regions are often large and contain many additional polymorphisms. Following up all the SNPs included in these regions is costly and infeasible for biological validation. In this article we address how to characterize these regions cost effectively with the goal of providing investigators a clear direction for biological validation. We introduce a follow-up study approach for identifying all untyped associated SNPs by selecting additional SNPs, called follow-up SNPs, from the associated regions and genotyping them in the original case/control individuals. We introduce a novel SNP selection method with the goal of maximizing the number of associated SNPs among the chosen follow-up SNPs. We show how the observed statistics of the original tag SNPs and human genetic variation reference data such as the HapMap Project can be utilized to identify the follow-up SNPs. We use simulated and real association studies based on the HapMap data and the Wellcome Trust Case Control Consortium to demonstrate that our method shows superior performance to the correlation- and distance-based traditional follow-up SNP selection approaches. Our method is publicly available at http://genetics.cs.ucla.edu/followupSNPs.     

Recent chemo‐/biosensor and bioimaging studies based on indole‐decorated BODIPYs

BODIPY is an important fluorophores due to its enhanced photophysical and chemical properties including outstanding thermal/photochemical stability, intense absorption/emission profiles, high photoluminescence quantum yield, and small Stokes' shifts. In addition to BODIPY, indole and its derivatives have recently gained attention because of their structural properties and particularly biological importance, therefore these molecules have been widely used in sensing and biosensing applications. Here, we focus on recent studies that reported the incorporation of indole‐based BODIPY molecules as reporter molecules in sensing systems. We highlight the rationale for developing such systems and evaluate detection limits of the developed sensing platforms. Furthermore, we also review the application of indole‐based BODIPY molecules in bioimaging studies. This article includes the evaluation of indole‐based BODIPYs from synthesis to characterization and a comparison of the advantages and disadvantages of developed reporter systems, making it instructive for researchers in various disciplines for the design and development of similar systems.     

Evaluation of Acetic Acid-Induced Chronic Gastric Ulcer Healing by Propionyl-L-Carnitine Administration

The aim of our study was to investigate the healing effect of propionyl-L-carnitine (PLC) on chronic gastric ulcers and its underlying mechanisms. This study included rats with gastric ulcers induced by applying serosal glacial acetic acid. These rats were then given either saline (vehicle) or PLC at doses of 60 and 120 mg/kg, administered orally 3 days after ulcer induction for 14 consecutive days. Our study found that treatment with PLC resulted in a reduction of the gastric ulcer area, a faster rate of ulcer healing, and stimulated mucosal restoration. Additionally, the treatment with PLC reduced the number of Iba-1+ M1 macrophages while increasing the number of galectin-3+ M2 macrophages, as well as desmin+ microvessels, and α-SMA+ myofibroblasts in the gastric ulcer bed. The mRNA expression of COX-2, eNOS, TGF-β1, VEGFA, and EGF in the ulcerated gastric mucosa was greater in the PLC-treated groups compared with the vehicle-treated rats. In conclusion, these findings suggest that PLC treatment may accelerate gastric ulcer healing by stimulating mucosal reconstruction, macrophage polarization, angiogenesis, and fibroblast proliferation, as well as fibroblast-myofibroblast transition. This process is associated with the upregulation of TGF-β1, VEGFA, and EGF, as well as modulation of the cyclooxygenase/nitric oxide synthase systems.