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Effective population size reflects the history of population growth, contraction, and structuring. When the effect of structuring is negligible, the inferred trajectory of the effective population size can be informative about the key events in the history of a population. We used the IBDNe and DoRIS approaches, which exploit the data on IBD sharing between genomes, to reconstruct the recent effective population size in two population datasets of Russians from Eastern European plain: (1) ethnic Russians sampled from the westernmost part of Russia; (2) ethnic Russians, Bashkirs, and Tatars sampled from the Volga-Ural region. In this way, we examined changes in effective population size among ethnic Russians that reside in their historical area at the West of the plain, and that expanded eastward to come into contact with the indigenous peoples at the East of the plain. We compared the inferred demographic trajectories of each ethnic group to written historical data related to demographic events such as migration, war, colonization, famine, establishment, and collapse of empires. According to IBDNe estimations, 200 generations (~6000 years) ago, the effective size of the ancestral populations of Russians, Bashkirs, and Tatars hovered around 3,000, 30,000, and 8,000 respectively. Then, the ethnic Russians exponentially grew with increasing rates for the last 115 generations and become the largest ethnic group of the plain. Russians do not show any drop in effective population size after the key historical conflicts, including the Mongol invasion. The only exception is a moderate drop in the 17th century, which is well known in Russian history as The Smuta. Our analyses suggest a more eventful recent population history for the two small ethnic groups that came into contact with ethnic Russians in the Volga-Ural region. We found that the effective population size of Bashkirs and Tatars started to decrease during the time of the Mongol invasion. Interestingly, there is an even stronger drop in the effective population size that coincides with the expansion of Russians to the East. Thus, 15–20 generations ago, i.e. in the 16–18th centuries in the trajectories of Bashkirs and Tatars, we observe the bottlenecks of four and twenty thousand, respectively. Our results on the recent effective population size correlate with the key events in the history of populations of the Eastern European plain and have importance for designing biomedical studies in the region.

Image-based automatic classification of vector mosquitoes has been investigated for decades for its practical applications such as early detection of potential mosquitoes-borne diseases. However, the classification accuracy of previous approaches has never been close to human experts’ and often images of mosquitoes with certain postures and body parts, such as flatbed wings, are required to achieve good classification performance. Deep convolutional neural networks (DCNNs) are state-of-the-art approach to extracting visual features and classifying objects, and, hence, there exists great interest in applying DCNNs for the classification of vector mosquitoes from easy-to-acquire images. In this study, we investigated the capability of state-of-the-art deep learning models in classifying mosquito species having high inter-species similarity and intra-species variations. Since no off-the-shelf dataset was available capturing the variability of typical field-captured mosquitoes, we constructed a dataset with about 3,600 images of 8 mosquito species with various postures and deformation conditions. To further address data scarcity problems, we investigated the feasibility of transferring general features learned from generic dataset to the mosquito classification. Our result demonstrated that more than 97% classification accuracy can be achieved by fine-tuning general features if proper data augmentation techniques are applied together. Further, we analyzed how this high classification accuracy can be achieved by visualizing discriminative regions used by deep learning models. Our results showed that deep learning models exploit morphological features similar to those used by human experts.

Haplotype-based methods are a cost-effective alternative to characterize unobserved rare variants and map disease-associated alleles. Moreover, they can be used to reconstruct recent population history, which shaped distribution of rare variants and thus can be used to guide gene mapping studies. In this study, we analysed Illumina 650 k genotyped dataset on three underrepresented populations from Eastern Europe, where ancestors of Russians came into contact with two indigenous ethnic groups, Bashkirs and Tatars. Using the IBD mapping approach, we identified two rare IBD haplotypes strongly enriched in asthma patients of distinct ethnic background. We reconstructed recent population history using haplotype-based methods to reconcile this contradictory finding. Our ChromoPainter analysis showed that these haplotypes each descend from a single ancestor coming from one of the ethnic groups studied. Next, we used DoRIS approach and showed that source populations for patients exchanged recent (

The human β-amyloid (Aβ) cleaving enzyme (BACE-1) is a target for Alzheimer’s disease (AD) treatments. This study was conducted to determine if acacetin extracted from the whole Agastache rugosa plant had anti-BACE-1 and behavioral activities in Drosophila melanogaster AD models and to determine acacetin’s mechanism of action. Acacetin (100, 300 and 500 μM) rescued amyloid precursor protein (APP)/BACE1-expressing flies and kept them from developing both eye morphology (dark deposits, ommatidial collapse and fusion and the absence of ommatidial bristles) and behavioral (motor abnormalities) defects. The reverse transcription polymerase chain reaction analysis revealed that acacetin reduced both the human APP and BACE-1 mRNA levels in the transgenic flies, suggesting that it plays an important role in the transcriptional regulation of human BACE-1 and APP. Western blot analysis revealed that acacetin reduced Aβ production by interfering with BACE-1 activity and APP synthesis, resulting in a decrease in the levels of the APP carboxy-terminal fragments and the APP intracellular domain. Therefore, the protective effect of acacetin on Aβ production is mediated by transcriptional regulation of BACE-1 and APP, resulting in decreased APP protein expression and BACE-1 activity. Acacetin also inhibited APP synthesis, resulting in a decrease in the number of amyloid plaques.

In mosquitoes, precise and efficient finding of a host animal is crucial for survival. One of the poorly understood aspects of mosquito blood-feeding behavior is how these insects target an optimal site in order to penetrate the skin and blood vessels without alerting the host animal. Here we provide new findings that a piercing structure of the mouthpart of the mosquitoes, the stylet, is an essential apparatus for the stage in blood feeding. Indeed, the stylet possesses a number of sensory hairs located at the tip of the stylet. These hairs house olfactory receptor neurons that express two conventional olfactory receptors of Aedes aegypti (AaOrs), AaOr8 and AaOr49, together with the odorant co-receptor (AaOrco). In vivo calcium imaging using transfected cell lines demonstrated that AaOr8 and AaOr49 were activated by volatile compounds present in blood. Inhibition of gene expression of these AaOrs delayed blood feeding behaviors of the mosquito. Taken together, we identified olfactory receptor neurons in the stylet involved in mosquito blood feeding behaviors, which in turn indicates that olfactory perception in the stylet is necessary and sufficient for mosquitoes to find host blood in order to rapidly acquire blood meals from a host animal.