О некоторых мерах государственной поддержки субъектов сельскохозяйственной деятельности в рамках интеграционных процессов

Материалы IX Междунар. науч.-практ. конф., Гомель, 26-27 ноября 2015

Exploring the functional composition of the human microbiome using a hand-curated microbial trait database

Even when microbial communities vary wildly in their taxonomic composition, their functional composition is often surprisingly stable. This suggests that a functional perspective could provide much deeper insight into the principles governing microbiome assembly. Much work to date analyzing the functional composition of microbial communities, however, relies heavily on inference from genomic features. Unfortunately, output from these methods can be hard to interpret and often suffers from relatively high error rates. We built and analyzed a domain-specific microbial trait database from known microbe-trait pairs recorded in the literature to better understand the functional composition of the human microbiome. Using a combination of phylogentically conscious machine learning tools and a network science approach, we were able to link particular traits to areas of the human body, discover traits that determine the range of body areas a microbe can inhabit, and uncover drivers of metabolic breadth. Domain-specific trait databases are an effective compromise between noisy methods to infer complex traits from genomic data and exhaustive, expensive attempts at database curation from the literature that do not focus on any one subset of taxa. They provide an accurate account of microbial traits and, by limiting the number of taxa considered, are feasible to build within a reasonable time-frame. We present a database specific for the human microbiome, in the hopes that this will prove useful for research into the functional composition of human-associated microbial communities.https://doi.org/10.1186/s12859-021-04216-

The Role of the Medial Prefrontal Cortex in Regulating Social Familiarity-Induced Anxiolysis

Overcoming specific fears and subsequent anxiety can be greatly enhanced by the presence of familiar social partners, but the neural circuitry that controls this phenomenon remains unclear. To overcome this, the social interaction (SI) habituation test was developed in this lab to systematically investigate the effects of social familiarity on anxiety-like behavior in rats. Here, we show that social familiarity selectively reduced anxiety-like behaviors induced by an ethological anxiogenic stimulus. The anxiolytic effect of social familiarity could be elicited over multiple training sessions and was specific to both the presence of the anxiogenic stimulus and the familiar social partner. In addition, socially familiar conspecifics served as a safety signal, as anxiety-like responses returned in the absence of the familiar partner. The expression of the social familiarity-induced anxiolysis (SFiA) appears dependent on the prefrontal cortex (PFC), an area associated with cortical regulation of fear and anxiety behaviors. Inhibition of the PFC, with bilateral injections of the GABAA agonist muscimol, selectively blocked the expression of SFiA while having no effect on SI with a novel partner. Finally, the effect of D-cycloserine, a cognitive enhancer that clinically enhances behavioral treatments for anxiety, was investigated with SFiA. D-cycloserine, when paired with familiarity training sessions, selectively enhanced the rate at which SFiA was acquired. Collectively, these outcomes suggest that the PFC has a pivotal role in SFiA, a complex behavior involving the integration of social cues of familiarity with contextual and emotional information to regulate anxiety-like behavior

Broad-Band Visually Evoked Potentials: Re(con)volution in Brain-Computer Interfacing.

Brain-Computer Interfaces (BCIs) allow users to control devices and communicate by using brain activity only. BCIs based on broad-band visual stimulation can outperform BCIs using other stimulation paradigms. Visual stimulation with pseudo-random bit-sequences evokes specific Broad-Band Visually Evoked Potentials (BBVEPs) that can be reliably used in BCI for high-speed communication in speller applications. In this study, we report a novel paradigm for a BBVEP-based BCI that utilizes a generative framework to predict responses to broad-band stimulation sequences. In this study we designed a BBVEP-based BCI using modulated Gold codes to mark cells in a visual speller BCI. We defined a linear generative model that decomposes full responses into overlapping single-flash responses. These single-flash responses are used to predict responses to novel stimulation sequences, which in turn serve as templates for classification. The linear generative model explains on average 50% and up to 66% of the variance of responses to both seen and unseen sequences. In an online experiment, 12 participants tested a 6 × 6 matrix speller BCI. On average, an online accuracy of 86% was reached with trial lengths of 3.21 seconds. This corresponds to an Information Transfer Rate of 48 bits per minute (approximately 9 symbols per minute). This study indicates the potential to model and predict responses to broad-band stimulation. These predicted responses are proven to be well-suited as templates for a BBVEP-based BCI, thereby enabling communication and control by brain activity only

Layout optimization.

<p>To prevent cross-talk between neighbouring cells the allocation of bit-sequences to cells is optimized. An initial (left) and optimized (right) layout are shown. Numbers indicate bit-sequences. The shade indicates the correlation between responses to codes from neighbouring cells. The correlations are depicted between horizontal, vertical, and diagonal neighbours. For diagonal neighbours, the maximum correlation of the two diagonals is shown. In this perspective darker colours represent less correlation and better neighbours, hence an increased potential to distinguish.</p

Grand average responses.

<p>Grand averages of both spatially filtered ERPs (solid lines) and predicted responses (dashed lines) are shown. The quality of fit by generating the response to the same bit-sequence as reconvolution was trained on is shown at the top (<i>r</i>² = 0.343). The quality of fit by predicting the response to a bit-sequence that was not used during training is shown at the bottom (<i>r</i>² = 0.476).</p

Colour feedback.

<p>While a trial progresses, colour feedback is given regarding the classifier’s certainty. All cells start gray (A). A cell is coloured more green if the cell is more likely to be selected, whereas a cell is coloured more red when it is likely to not be selected. The colours are scaled to the specific margin and the maximum and minimum correlation between the single-trial and templates. Here, the target was ‘T’.</p

The online pipeline.

<p>Three stages exist: training, calibration and testing. During training, responses <i>X</i> to stimuli from <i>V</i> are recorded. During calibration, <i>X</i> is deconvolved to pulse responses <i>r</i> using <i>V</i>. Template responses <i>T</i>_<i>V</i> and <i>T</i>_<i>U</i> are generated by convolving these <i>r</i> with the bit-sequences <i>V</i> and <i>U</i>, respectively. Templates are multiplied (circles) with filters (<i>W</i>_<i>X</i>, <i>W</i>_<i>T</i>) designed by CCA. The subset and layout are optimized giving <i>U</i>′ and </p><p></p><p></p><p></p><p><mi>T</mi><mi>U</mi><mo>′</mo></p><p></p><p></p><p></p>, and stopping margins <i>m</i> are learned. In the testing phase, a new single-trial <i>x</i> is assigned the class-label <i>y</i> that maximizes the correlation between the spatially filtered single-trial <i>x</i> and templates <p></p><p></p><p></p><p><mi>T</mi><mi>U</mi><mo>′</mo></p><p></p><p></p><p></p>. The classifier emits the class-label if the maximum correlation exceeds the threshold margin. In the case wherein the margin is not reached, more data is collected.<p></p

Platinum subsets.

<p>The steps for finding a subset of <i>p</i> = 3 codes from a set of <i>n</i> = 10 codes is shown. First the full set is clustered grouping similar points (A₁ till A₃). Then, iteratively (B till D) each cluster is collapsed into a single point by selecting one candidate. This candidate is chosen by maximizing the distance to all other living points outside the cluster (B₂, C₂, D₂). The remaining points form the Platinum subset (D₃).</p