Miracle-dz/UniFAR-FTUs huggingface dataset:
Suppression of Hyperpolarization-Activated Cyclic Nucleotide-Gated Channel Function in Thalamocortical Neurons Prevents Genetically Determined and Pharmacologically Induced Absence Seizures

Hyperpolarization-activated cyclic nucleotide-gated (HCN) channels and the Ih current they generate contribute to the pathophysiological mechanisms of absence seizures (ASs), but their precise role in neocortical and thalamic neuronal populations, the main components of the network underlying AS generation, remains controversial. In diverse genetic AS models, Ih amplitude is smaller in neocortical neurons and either larger or unchanged in thalamocortical (TC) neurons compared with nonepileptic strains. A lower expression of neocortical HCN subtype 1 channels is present in genetic AS-prone rats, and HCN subtype 2 knock-out mice exhibit ASs. Furthermore, whereas many studies have characterized Ih contribution to “absence-like” paroxysmal activity in vitro, no data are available on the specific role of cortical and thalamic HCN channels in behavioral seizures. Here, we show that the pharmacological block of HCN channels with the antagonist ZD7288 applied via reverse microdialysis in the ventrobasal thalamus (VB) of freely moving male Genetic Absence Epilepsy Rats from Strasbourg decreases TC neuron firing and abolishes spontaneous ASs. A similar effect is observed on γ-hydroxybutyric acid-elicited ASs in normal male Wistar rats. Moreover, thalamic knockdown of HCN channels via virally delivered shRNA into the VB of male Stargazer mice, another genetic AS model, decreases spontaneous ASs and Ih-dependent electrophysiological properties of VB TC neurons. These findings provide the first evidence that block of TC neuron HCN channels prevents ASs and suggest that any potential anti-absence therapy that targets HCN channels should carefully consider the opposite role for cortical and thalamic Ih in the modulation of absence seizures. SIGNIFICANCE STATEMENT Hyperpolarization-activated cyclic nucleotide-gated (HCN) channels play critical roles in the fine-tuning of cellular and network excitability and have been suggested to be a key element of the pathophysiological mechanism underlying absence seizures. However, the precise contribution of HCN channels in neocortical and thalamic neuronal populations to these nonconvulsive seizures is still controversial. In the present study, pharmacological block and genetic suppression of HCN channels in thalamocortical neurons in the ventrobasal thalamic nucleus leads to a marked reduction in absence seizures in one pharmacological and two genetic rodent models of absence seizures. These results provide the first evidence that block of TC neuron HCN channels prevents absence seizures.
Denser Trajectories of Anchor Points for Action Recognition

Human action recognition is beneficial for numerous practical applications, e.g., gaming, video surveillance, and video search. In this paper, we address the problem of designing good low-level features for human action recognition. We introduce the Denser Trajectories of Anchor Points scheme that improves state-of-the-art dense trajectories by paying attention to the selection of feature points for trajectory tracking. Firstly, the dense interest points are extracted from video frames. Different from the previous dense trajectory feature, a new interest point will be tracked regardless of whether another actively tracked trajectory also commencing at the same location. Then, the interest points of large ambiguity are pruned based on the boundary/edge cue. Finally several descriptors are extracted from the tracked points along of trajectories and encoded with any off-the-shelf feature coding methods, which are further fed into any classifier model for action classification. Experimental results on four standard action datasets (i.e., KTH, UCF Sports, HMDB51, and UCF101) demonstrate the superiority of the proposed feature over state-of-the-art low-level features.
Systematic Monetary Policy and the Macroeconomic Effects of Shifts in Residential Loan‐To‐Value Ratios

What are the macroeconomic consequences of changing aggregate lending standards in residential mortgage markets, as measured by loan‐to‐value (LTV) ratios? Using a structural vector autoregression (VAR), we find that GDP and business investment increase following an expansionary LTV shock. Residential investment, by contrast, falls after a small initial uptick, a result that depends on the systematic reaction of monetary policy. We show that, historically, the Fed tended to respond to expansionary LTV shocks by raising the monetary policy instrument, and, as a result, mortgage rates increased and residential investment declined. House prices are affected in a similar manner.
A Virtual Machine Placement Policy via Biogeography-based Optimization in the Cloud

With the rapid development and popularization of cloud computing, cloud datacenters urgently need some highly efficient policies to improve their resource utilizations (e.g., power and physical machine resources). Although there are numerous virtual machine placement (VMP) policies which are designed to identify some most suitable physical machines for virtual machine requests, they are still not enough to improve their resource utilizations. Therefore, in this paper, we first exploit original biogeography-based optimization (BBO) algorithm to solve the VMP problem. In our BBO-based VMP policy, we first map the VMP problem to an ecosystem model, and then we redefine operators of the original BBO algorithm according to the ecosystem model. Finally, we perform a thorough performance evaluation of our BBO-based VMP policy in terms of the robustness, performance, and scalability. Experimental results show that our proposed policy outperforms other related policies.
Extraction of Aspects from Drug Reviews Using Probabilistic Aspect Mining Model

: Reviews of medication from patients are numerous on the internet. This review provides a brief overview of approaches to aspect mining as they relate to drug discovery. Many adverse drug reactions on chronic diseases are not discovered during limited pre-marketing clinical trials, they are only observed after long term post-marketing investigation of drug usage. The detection of adverse drug reactions, as early as possible, is an important topic of research for the pharmaceutical industry. Mining significant topics from short and noisy reviews is big challenge. In light of this, such problem is addressed by proposing the probabilistic aspect mining model (PAMM) for identifying the aspects/topics relating to class labels. Because of unique feature of PAMM it focuses on finding aspects relating to one class only rather than finding aspects for all classes simultaneously in each execution. Besides the aspects found also have the property that they are class distinguishing, that means they can be used to distinguish a class from other classes. It helps to reduce the chance of having aspects formed from mixing concepts of different classes; hence the identified aspects are easier to be interpreted by people.
Does suppressive antiviral therapy for herpes simplex virus prevent transmission in an HIV-positive population? A systematic review.

Background Among individuals with genital herpes simplex virus (HSV), co-infection with human immunodeficiency virus (HIV) has been shown to increase the frequency and severity of HSV symptoms, HSV shedding, and risk of HSV transmission. Objective To assess whether suppressive antivirual therapy for genital HSV in an HIV-positive populatation prevents HSV transmission to a susceptible partner. Methods A systematic search of the literature was conducted using MEDLINE and EMBASE databases to identify randomized controlled trials published between January 2005 and June 2015. Inclusion criteria were trials written in English or French utilizing suppressive antiviral therapies for HSV. Studies had to report on outcomes related to HSV transmission from HIV-positive populations. Surrogate markers of HSV transmission risk, such as HSV detection and viral load, were also included. Articles underwent a risk of bias assessment, and those with low risk of bias underwent data extraction to complete a narrative synthesis. Results This review identified thirteen papers. Only one study directly measured transmission of HSV. The overall transmission rate was <10%, and suppressive antiviral therapy had no significant protective effect (9% transmission rate in the acyclovir group vs. 6% in the placebo group; hazard ratio [HR]: 1.35, 95% CI: 0.83-2.20). The remaining 12 papers addressed surrogate markers of transmission risk: HSV detection and viral load. Suppressive acyclovir appears to be effective in reducing HSV detection among HIV-positive populations, but it does not appear to reduce viral load. Suppressive valacyclovir may be effective in reducing HSV detection and viral load among HIV-positive patients who are antiretroviral therapy (ART)-naïve, but its effect appears to be nullified among those concurrently on ART. Conclusion Based on current evidence, suppressive antiviral therapy may reduce HSV detection and viral load, but its impact on HSV transmission is unclear. Clinicians should caution HIV-positive patients with HSV that suppressive therapy may not reduce risk of HSV transmission to susceptible partners.
Data-Driven Chance Constrained Optimization under Wasserstein Ambiguity Sets

We present a data-driven approach for distri-butionally robust chance constrained optimization problems (DRCCPs). We consider the case where the decision maker has access to a finite number of samples or realizations of the uncertainty. The chance constraint is then required to hold for all distributions that are close to the empirical distribution constructed from the samples (where the distance between two distributions is defined via the Wasserstein metric). We first reformulate DRCCPs under data-driven Wasserstein ambiguity sets and a general class of constraint functions. When the feasibility set of the chance constraint program is replaced by its convex inner approximation, we present a convex reformulation of the program and show its tractability when the constraint function is affine in both the decision variable and the uncertainty. For constraint functions concave in the uncertainty, we show that a cutting-surface algorithm converges to an approximate solution of the convex inner approximation of DRCCPs. Finally, for constraint functions convex in the uncertainty, we compare the feasibility set with other sample-based approaches for chance constrained programs.
Salient region detection based on Adaboost and integration of multi-features space

Based on the existing methods, this paper tries to propose a salient region detection based on integrated theory of features and Adaboost algorithm. The integration theory of features indicates that the salient region corresponds to multi-features space, such as color, direction, shape, texture and so on. And the visual system deals with independent features in parallel. The visual system make individual characteristics become a salient area. Adaboost algorithm is able to integrate multiple independent weakly classifiers into a high-performance and powerful classifier. In order to obtain better salient region result, this paper introduces the Adaboost theory to integrate multi-features space.
Recovery of unburned carbonaceous matter (UCM) from sugar mill bottom ash

ABSTRACT Millions of tons of coal fly and bottom ash are produced each year in many countries including Turkey. Coal combustion which is the main source of energy in sugar mill and many other industries produce massive quantities of waste. The present study aims to separate the unburned carbonaceous matter (UCM) from the bottom ash of Bor, Nigde-Turkey sugar plant has been studied using the mechanical flotation technique. For this purpose, the performances of three classical oils were investigated and then chemical parameters such as sodium silicate (Na2SiO3), collector (fuel-oil) and frother (pine-oil) were systematically examined by using Yates’ statistical technique. A product with 41.58% combustible grade at 87.44% recovery was obtained at 0 g/t sodium silicate, 7464 g/t fuel-oil, 888 g/t pine-oil, 15% pulp density, 1200 rpm impeller speed and natural pH. The results obtained from the experiments indicated that it was possible to enrich the bottom ash by using conventional flotation.
On the Weibull-X family of distributions

In this paper, the Weibull-X family is proposed and some of its properties are discussed. A member of the Weibull-X family, the Weibull-logistic distribution, is defined and studied. Various properties of the Weibulllogistic distribution are obtained. The distribution is found to be unimodal and the shape can be symmetric, right skewed or left skewed. The structural analysis of the distribution in this paper includes limiting behavior, mode, quantiles, moments, skewness, kurtosis, Shannon’s entropy and order statistics. The method of maximum likelihood estimation is proposed for estimating the model parameters. A real data set is used to illustrate the application of the Weibull-logistic distribution.
Transport Infrastructure and Macroeconomic Performance in European Countries

The share of investment in transport infrastructure of the gross domestic product (GDP) has risen substantially in central and eastern European countries, and the effectiveness of public investment in transport infrastructure to improve economic performance is receiving increasing attention. This paper explores the dynamic impact of transport infrastructure on employment, GDP, exports, and industry production in European countries. With the use of data from 1995 to 2010, the paper analyzes the causality among variables in a dynamic panel framework. The results reveal that transport infrastructure and macroeconomic indicators are cointegrated and a significant unidirectional causal relationship exists from public investment in transport infrastructure to economic performance. The findings of this paper support the traditional notion that an increase in government spending on transport infrastructure contributes to economic growth. More specifically, the paper finds that transport infrastructure is a forcing variable of economic output and exports of European countries. Examination of the economic impacts of transport infrastructure between developed and developing economies shows that economic performance of central and eastern European countries is more sensitive to a change in investment in transport infrastructure than that of Western European countries. A major policy implication of these findings is that expanding transportation infrastructure could be an effective fiscal policy tool to boost economic performance of developing European economies.
Reactive frequency band-based real-time motor imagery classification

The requirement of an effective online processing algorithm becomes very vital to fulfilling the demand of the low-cost brain–computer interface (BCI) system. The authors proposed a very first and robust unsupervised machine learning algorithm, for the real-time classification of movement imagination. The reactive frequency band (RFB) of the individual subject has been identified through the dominant frequency detection algorithm over the training dataset. Based on the identified RFB, the feature extraction process has been applied to the testing dataset. The estimated 'feature' further classified as per probabilistic Bayesian classifier. The effectiveness of the proposed RFB detection method of electroencephalogram (EEG) signal is validated by self-generated artificial sine wave signal, single subject and nine subject movement imagery (MI) BCI competition dataset. The proposed method of EEG signal processing outperformed the conventional wavelet-based BCI competition II results and the wavelet-based algorithm applied over the BCI competition IV dataset.
Flexible and accurate decoding of neural populations through stochastic comodulation

Sensory-guided behavior requires reliable encoding of information (from stimuli to neural responses) and flexible decoding (from neural responses to behavior). In typical decision tasks, a small subset of cells within a large population encode task-relevant stimulus information and need to be identified by later processing stages for relevant information to be transmitted. A statistically optimal decoder (e.g., maximum likelihood) can utilize task-relevant cells for any given task configuration, but relies on complete knowledge of the relationship between the task and the stimulus-response and noise properties of the encoding population. The brain could learn an optimal decoder for a task through supervised learning (i.e., regression), but this typically requires many training trials, and thus lacks the flexibility of humans or animals, that can rapidly adjust to changes in task parameters or structure. Here, we propose a novel decoding solution based on functionally targeted stochastic modulation. Population recordings during different discrimination tasks have revealed that a substantial portion of trial-to-trial variability in cell responses can be explained by stochastic modulatory signals that are shared, and that seem to preferentially target task-informative neurons (Rabinowitz et al., 2015). The variability introduced by these modulators corrupts the encoded stimulus signal, but we propose that it also serves as a label for the informative neurons, allowing the decoder to solve the identification problem. We show in simulations of a modulated Poisson spiking model that a linear decoder with readout weights proportional to the estimated neuron-specific strength of modulation achieves performance close to an optimal decoder.
Mirror Neurons Modeled Through Spike-Timing-Dependent Plasticity are Affected by Channelopathies Associated with Autism Spectrum Disorder

Mirror neurons fire action potentials both when the agent performs a certain behavior and watches someone performing a similar action. Here, we present an original mirror neuron model based on the spike-timing-dependent plasticity (STDP) between two morpho-electrical models of neocortical pyramidal neurons. Both neurons fired spontaneously with basal firing rate that follows a Poisson distribution, and the STDP between them was modeled by the triplet algorithm. Our simulation results demonstrated that STDP is sufficient for the rise of mirror neuron function between the pairs of neocortical neurons. This is a proof of concept that pairs of neocortical neurons associating sensory inputs to motor outputs could operate like mirror neurons. In addition, we used the mirror neuron model to investigate whether channelopathies associated with autism spectrum disorder could impair the modeled mirror function. Our simulation results showed that impaired hyperpolarization-activated cationic currents (Ih) affected the mirror function between the pairs of neocortical neurons coupled by STDP.
Novel Object-Based Filter for Improving Land-Cover Classification of Aerial Imagery with Very High Spatial Resolution

Land cover classification using very high spatial resolution (VHSR) imaging plays a very important role in remote sensing applications. However, image noise usually reduces the classification accuracy of VHSR images. Image spatial filters have been recently adopted to improve VHSR image land cover classification. In this study, a new object-based image filter using topology and feature constraints is proposed, where an object is considered as a central object and has irregular shapes and various numbers of neighbors depending on the nature of the surroundings. First, multi-scale segmentation is used to generate a homogeneous image object and extract the corresponding vectors. Then, topology and feature constraints are proposed to select the adjacent objects, which present similar materials to the central object. Third, the feature of the central object is smoothed by the average of the selected objects’ feature. This proposed approach is validated on three VHSR images, ranging from a fixed-wing aerial image to UAV images. The performance of the proposed approach is compared to a standard object-based approach (OO), object correlative index (OCI) spatial feature based method, a recursive filter (RF), and a rolling guided filter (RGF), and has shown a 6%–18% improvement in overall accuracy.
A Novel Posture for Better Differentiation Between Parkinson's Tremor and Essential Tremor

Due to a lack of reliable non-invasive bio-markers, misdiagnosis between Parkinson's disease and essential tremor is common. Although some assistive engineering approaches have been proposed, little acceptance has been obtained for these methods lack well-studied mechanisms and involve operator-dependent procedures. Aiming at a better differentiation between the two tremor causes, we present a novel posture, termed arm-rested posture, to ameliorate the quality of recorded tremor sequences. To investigate its efficacy, the posture was compared with another common posture, called arm-stretching posture, in fundamental aspects of tremor intensity and dominant frequency. A tremor-affected cohort comprising 50 subjects (PD = 26, ET = 24) with inhomogeneous tremor manifestation were recruited. From each subject, acceleration data of 5 min in terms of each posture were recorded. In the overall process, no operator-dependent procedures, such as data screening, was employed. The differentiation performance of the two postures were assessed by the index of discrimination coefficient and a receiver operating characteristic analysis based on binary logistic regression. The results of the differentiation assessment consistently demonstrate a better performance with the arm-rested posture than with the arm-stretching posture. As a by-product, factors of disease stage (incipient, progressed stage), spectrum estimate (PSD, bispectrum) and recording length (5–300s) were investigated. The significant effect of disease stage was only found in PD in terms of tremor intensity [F(1, 516) = 7.781, P < 0.05]. The bispectrum estimate was found to have better performance than the PSD estimate in extracting dominant frequency in terms of the discrimination coefficient. By extending the recording length, we noticed an increase in the performance of dominant frequency. The best result of the arm-rested posture was obtained with the maximum recording length of 300 s (area under the curve: 0.944, sensitivity: 92%, 1-specificity: 0%, accuracy: 96%), which is better than that of the arm-stretching posture in the same condition (area under the curve: 0.734, sensitivity: 54%, 1-specificity: 12%, accuracy: 72%). Thus, we conclude that the arm-rested posture can assist in improving tremor differentiation between Parkinson's disease and essential tremor and may act as a universal tool to analyze tremor for both clinical and research purpose.