Miracle-dz/UniFAR-FTUs huggingface dataset:
A Gated Hierarchical LSTMs for Target-based Sentiment Analysis

The deep neural model combining attention mechanism has achieved remarkable success in the task of targetbased sentiment analysis. In current research, the attention mechanism is more broadly combined with LSTM(Long ShortTerm Memory) networks, however, such neural network-based architectures generally rely on complex computation and only focus on the single target. We propose a gated hierarchical LSTMs(GH-LSTM) model of combining regional LSTM and sentence-level LSTM via a gated operation for the task of targetbased sentiment analysis. This approach can distinguish different polarities of sentiment of different targets in the same sentence through a regional LSTM, and is able to concentrate on the long dependency of target in the whole sentence via a sentence-level LSTM. The experimental results on multi-domain datasets of two languages from SemEval2016 indicate that, our approach yields better performance than SVM(Support Vector Machine) and several typical neural network models.
Recurrent epileptiform discharges in the medial entorhinal cortex of kainate‐treated rats are differentially sensitive to antiseizure drugs

Approximately 30% of patients with epilepsy are refractory to existing antiseizure drugs (ASDs). Given that the properties of the central nervous systems of these patients are likely to be altered due to their epilepsy, tissues from rodents that have undergone epileptogenesis might provide a therapeutically relevant disease substrate for identifying compounds capable of attenuating pharmacoresistant seizures. To facilitate the development of such a model, this study describes the effects of classical glutamate receptor antagonists and 20 ASDs on recurrent epileptiform discharges (REDs) in brain slices derived from the kainate‐induced status epilepticus model of temporal lobe epilepsy (KA‐rats).
Impact of electromagnetic fields on in vitro toxicity of silver and graphene nanoparticles

ABSTRACT The correlation between shape and concentration of silver nanoparticles (AgNPs), their cytotoxicity and formation of reactive oxygen species (ROS) in the presence of electromagnetic fields (EMFs) has been investigated. In addition, the bio-effects caused by the combination of EMFs and graphene nanoparticles (GrNPs) have been also assessed. The AgNPs of three shapes (triangular, spherical and colloidal) and GrNPs were added in high concentrations to the culture of human fibroblasts and exposed to EMF of three different frequencies: 900, 2400 and 7500 MHz. The results demonstrated the dependence of the EMF-induced cytotoxicity on the shape and concentration of AgNPs. The maximal cell killing effect was observed at 900 MHz frequency for NPs of all shapes and concentrations. The highest temperature elevation was observed for GrNPs solution irradiated by EMF of 900 MHz frequency. The exposure to EMF led to significant increase of ROS formation in triangular and colloidal AgNPs solutions. However, no impact of EMF on ROS production was detected for spherical AgNPs. GrNPs demonstrated ROS-protective activity that was dependent on their concentration. Our findings indicate the feasibility to control cytotoxicity of AgNPs by means of EMFs. The effect EMF on the biological activity of AgNPs and GrNPs is reported here for the first time.
Graph-based people segmentation using a genetically optimized combination of classifiers

Abstract. Many approaches for background subtraction and people detection have been developed so far. However, the best state-of-the-art methods do not yet give satisfactory results in real transportation environments. Indeed, these latter configurations imply several difficulties such as fast brightness changes, noise, shadows, scrolling background, etc., and a single approach cannot deal with all these. We propose an approach for people segmentation and tracking in videos that is suited for real-world conditions. Our strategy combines several state-of-the-art methods for people detection, silhouette appearance modeling and tracking. Each process also uses its own frame preprocessing pipeline. The optimal combination of the people classifiers used, as well as the optimal parameters of each of the combined methods, being too difficult to be determined altogether, a genetic algorithm is used to determine the optimal classifier parameters and their combination weights. The output of the latter is used as an initialization for a multiframe graph-cut operating on superpixel graphs. Our proposed approach is evaluated on the BOSS European project database that was acquired in moving trains and contains typical scientific locks encountered in real transportation systems.
A new BCR-ABL1 Drosophila model as a powerful tool to elucidate the pathogenesis and progression of chronic myeloid leukemia

The oncoprotein BCR-ABL1 triggers chronic myeloid leukemia. It is clear that the disease relies on constitutive BCR-ABL1 kinase activity, but not all the interactors and regulators of the oncoprotein are known. We describe and validate a Drosophila leukemia model based on inducible human BCR-ABL1 expression controlled by tissue-specific promoters. The model was conceived to be a versatile tool for performing genetic screens. BCR-ABL1 expression in the developing eye interferes with ommatidia differentiation and expression in the hematopoietic precursors increases the number of circulating blood cells. We show that BCR-ABL1 interferes with the pathway of endogenous dAbl with which it shares the target protein Ena. Loss of function of ena or Dab, an upstream regulator of dAbl, respectively suppresses or enhances both the BCR-ABL1-dependent phenotypes. Importantly, in patients with leukemia decreased human Dab1 and Dab2 expression correlates with more severe disease and Dab1 expression reduces the proliferation of leukemia cells. Globally, these observations validate our Drosophila model, which promises to be an excellent system for performing unbiased genetic screens aimed at identifying new BCR-ABL1 interactors and regulators in order to better elucidate the mechanism of leukemia onset and progression.
Early and Late Protective Effect of Bone Marrow Mononuclear Cell Transplantation on Radiation-Induced Vascular Dysfunction and Skin Lesions

Skin lesions caused by accidental exposure to radiation or by radiotherapy are a major clinical challenge. We evaluated the effect of bone marrow mononuclear cells (BMMNC) on collagen remodeling and vascular function in radiation-induced skin lesions in the acute and late phases in mice. We studied the effect of BMMNC transplantation in a mouse model of cutaneous radiation injury combining local skin gamma-irradiation and biopsy punch wound. Mice were first irradiated, punched and then BMMNC were intramuscularly administered. Seven days after injury, BMMNC promoted wound healing by (i) increasing re-epithelialization, tissue collagen density and mRNA levels of collagens 1A1, 1A2, and 3A1, and (ii) inhibiting the radiation-induced vascular activation and limiting interactions between leukocytes and the vascular endothelium compared with control. Importantly, BMMNC did not amplify the inflammatory response despite the infiltration of neutrophils and macrophages associated with the expression of IL-6 and MCP-1 mRNAs in the tissue. Remarkably, the beneficial effects of BMMNC therapy on matrix remodeling were maintained for 2 months. Furthermore, BMMNC injection restored vascular function in skin tissue by increasing vascular density and vascular permeability. This therapeutic strategy based on BMMNC injection protects against radiation-induced skin lesions by preventing vascular dysfunction and unfavorable remodeling in the acute and late phases.
A Novel Two-Stage Model Predictive Control for Modular Multilevel Converter With Reduced Computation

Modular multilevel converter (MMC) has become a promising topology applied in medium/high-voltage applications due to its advantages. However, there are several challenges in the MMC, such as the appearance of harmonic circulating current. In this paper, a novel two-stage model predictive control (TSMPC) is proposed to suppress the harmonic circulating current. First, the mathematical model of the MMC is discretized to describe steady-state objectives including output currents, circulating currents, summation, and imbalance of capacitor voltages. Then, an optimal control options set consisting of inserted numbers is built by the front stage of TSMPC, while the inserted number applied to the MMC ultimately is selected from the above-mentioned set by the back stage of TSMPC. The proposed TSMPC can not only improve the control performance of harmonic circulating current but also ensure the overall control performance of the MMC system. In addition, a simplified rolling optimization method is proposed to reduce computation from the aspect of simplifying each control option prediction. An optimized search space method is also proposed to design the control options set to be evaluated from the aspect of ensuring the output current tracking and harmonic circulating current suppression. Finally, the effectiveness of the proposed method is validated by experiment results.
Analyzing OpenStreetMap Road Data and Characterizing the Behavior of Contributors in Ankara, Turkey

The usage of OpenStreetMap (OSM), one of the resources offered by Volunteered Geographic Information (VGI), has rapidly increased since it was first established in 2004. In line with this increased usage, a number of studies have been conducted to analyze the accuracy and quality of OSM data, but many of them have constraints on evaluating the profiles of contributors. In this paper, OSM road data have been analyzed with the aim of characterizing the behavior of OSM contributors. The study area, Ankara, the capital city of Turkey, was evaluated with several network analysis methods, such as completeness, degree of centrality, betweenness, closeness, PageRank, and a proposed method measuring the activation of contributors in a bounded area from 2007–2017. An evaluation of the results was also discussed in this paper by taking into account the following indicators for each year: number of nodes, ways, contributors, mean lengths, and sinuosity values of roads. The results show that the experience levels of the contributors determine the contribution type. Essentially, more experience makes for more detailed contributions.
An automatic graph generation method for scholarly papers based on table structure analysis

For papers reporting on experiments, the types of experiment performed are important, as are the results they generate. Tables are frequently used to show experimental results and are suited to understanding accurate numerical values. However, they are not suited to visually comparing or observing changes in many values at once. In this paper, we propose an automatic graph-generation method for data presented in tabular form. We first convert PDF files of scholarly papers into XML files to analyze the structure of their tables. We then generate graphs based on this table-structure analysis. We also examine the types of graphs that are easy to understand visually.
Alternative polyadenylation analysis in animals and plants: newly developed strategies for profiling, processing and validation

Alternative polyadenylation is an essential RNA processing event that contributes significantly to regulation of transcriptome diversity and functional dynamics in both animals and plants. Here we review newly developed next generation sequencing methods for genome-wide profiling of alternative polyadenylation (APA) sites, bioinformatics pipelines for data processing and both wet and dry laboratory approaches for APA validation. The library construction methods LITE-Seq (Low-Input 3'-Terminal sequencing) and PAC-seq (PolyA Click sequencing) tag polyA+ cDNA, while BAT-seq (BArcoded, three-prime specific sequencing) and PAPERCLIP (Poly(A) binding Protein-mediated mRNA 3′End Retrieval by CrossLinking ImmunoPrecipitation) enrich polyA+ RNA. Interestingly, only WTTS-seq (Whole Transcriptome Termini Site sequencing) targets both polyA+ RNA and polyA+ cDNA. Varieties of bioinformatics pipelines are well established to pursue read quality control, mapping, clustering, characterization and pathway analysis. The RHAPA (RNase H alternative polyadenylation assay) and 3'RACE-seq (3' rapid amplification of cDNA end sequencing) methods directly validate APA sites, while WTSS-seq (whole transcriptome start site sequencing), RNA-seq (RNA sequencing) and public APA databases can serve as indirect validation methods. We hope that these tools, pipelines and resources trigger huge waves of interest in the research community to investigate APA events underlying physiological, pathological and psychological changes and thus understand the information transfer events from genome to phenome relevant to economically important traits in both animals and plants.
STEM: A Simulation-Based Testbed for Electromagnetic Big Data Management

—With the development of networked radars and wireless communication technologies, electromagnetic data is becoming a new type of big data. Compared with other kinds of big data, such as Internet big data, financial big data, and healthcare big data, electromagnetic big data has some special properties. For example, they usually contain rich and varying labels that describe the features of electromagnetic space. However, existing big-data benchmark tools cannot support the generation and test of electromagnetic big data. In this paper, we aim at providing a simulation-based testbed for electromagnetic big data. The testbed, named STEM, can simulate real-world electromagnetic big data. It supports generating real-time electromagnetic data streams with varying labels. In addition, it is reusable, reconfigurable, and flexible for users to generate workloads for different scenarios. After a brief introduction on the architecture of STEM, we present the implemental details of STEM. Then, we present a case study as well as performance evaluation to demonstrate the usability and flexibility of STEM.
Can abnormal dispersion of ventricular repolarization be a predictor of mortality in arrhythmogenic right ventricular cardiomyopathy: The importance of Tp‐e interval

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is an inherited cardiomyopathy characterized by ventricular arrhythmias and specific ventricular pathology. Repolarization abnormalities, the significant contributor to life‐threatening arrhythmias and mortality, are frequently observed ECG changes in patients with ARVC.
PyBox: An automated box-model generator for atmospheric chemistry and aerosol simulations

Air pollution and climate change are two of the biggest multidisciplinary challenges in society today. The need to understand the chemical and physical processes in the atmosphere that dictate the impacts of both has created a wide range of research platforms. These include numerical models. Volatile organic compounds (VOCs), emitted from both natural and anthropogenic sources, are oxidised in the atmosphere to form lower-volatility species that form organic particulate matter in the atmosphere through gas-to-particle partitioning. Chemical mechanisms, such as the Master Chemical Mechanism (MCM) (Jenkin, Saunders, Wagner, & Pilling, 2003), have been built to hold information of all relevant species and reactions in the atmosphere.
Diffusional Kurtosis along the Corticospinal Tract in Adult Normal Pressure Hydrocephalus

BACKGROUND AND PURPOSE: Normal Pressure Hydrocephalus is a reversible form of dementia characterized by enlarged ventricles, which can deform and cause disruptions to adjacent white matter fibers. The purpose of this work was to examine how diffusion and kurtosis parameters vary along the corticospinal tract and determine where along this path microstructure is compromised in patients diagnosed with normal pressure hydrocephalus. We hypothesized that disruption of the corticospinal tract from ventricular enlargement can be measured using diffusion MR imaging and this will be quantified in periventricular regions. MATERIALS AND METHODS: We developed a method to analyze diffusion parameters at discrete points along neural tracts. We then used diffusion MR imaging data from patients with Alzheimer disease and healthy controls to compare whether diffusion along the corticospinal tract differs from that of patients with normal pressure hydrocephalus. RESULTS: We found that diffusion parameters can differentiate patients with normal pressure hydrocephalus from those with Alzheimer disease and healthy controls: Axial diffusion, axial kurtosis, and the axonal water fraction were found to differ significantly across groups (P < .05) in an area located close to the superior internal capsule and corona radiata but below the cortex. CONCLUSIONS: A lower axonal water fraction indicates a lower axonal density in the corticospinal tract, which may indicate permanent damage. Lower axial kurtosis may imply that axons are being more aligned due to compression.
Analysis of imputation bias for feature selection with missing data

We study risk/beneﬁt tradeoﬀ of missing value imputation in the context of feature selection. We caution against using imputation methods that may yield false positives: features not associated to the target becoming dependent as a result of imputation. We also investigate situations in which imputing missing values may be beneﬁcial to reduce false negatives. We use causal graphs to characterize when structural bias arises and introduce a de-biased version of the t-test.
Pollen grain recognition using convolutional neural network

This paper addresses two problems: the automated pollen species recognition and counting them on an image obtained with a lighting microscope. Automation of pollen recognition is required in several domains, including allergy and asthma prevention in medicine and honey quality control in the nutrition industry. We propose a deep learning so-lution based on a convolutional neural network for classiﬁcation, feature extraction and image segmentation. Our approach achieves state-of-the-art results in terms of accuracy. For 5 species, the approach provides 99.8% of accuracy, for 11 species — 95.9%.
Variation consistency of attributes-based postverification method for copy image retrieval

Abstract. The state-of-the-art approaches of copy image retrieval are based on the bag-of-visual-words model, which represents an image with a set of visual words obtained by quantizing local features. However, the quantization process reduces local features’ discriminative power and thus causes many false matches of local features between images. As a consequence, this brings down the effectiveness of copy image retrieval in large-scale image dataset. In order to handle this problem, postverification methods have been proposed to reject false matches. Previous works of the postverification method focused mainly on geometric relationship consistency among matches of local feature between query image and its candidate for rejecting false candidates. The variation consistency of local feature’s attributes is proposed to verify if two pairs of matches are consistent. The matching reliability of local features can be measured by a voting-based method, which is based on the number of consistent matches between two images. This method can easily integrate more attributes of local feature, such as dominant orientation, position, and scale, rather than position of local feature. Experiments on the large-scale datasets demonstrate the effectiveness and efficiency of the proposed approach and show it outperforms the state-of-the-art postverification approaches.
Leveraging Writing Systems Change for Deep Learning Based Chinese Emotion Analysis

Social media text written in Chinese communities contains mixed scripts including major text written in Chinese, an ideograph-based writing system, and some minor text using Latin letters, an alphabet-based writing system. This phenomenon is called writing systems changes (WSCs). Past studies have shown that WSCs can be used to express emotions, particularly where the social and political environment is more conservative. However, because WSCs can break the syntax of the major text, it poses more challenges in Natural Language Processing (NLP) tasks like emotion classification. In this work, we present a novel deep learning based method to include WSCs as an effective feature for emotion analysis. The method first identifies all WSCs points. Then representation of the major text is learned through an LSTM model whereas the minor text is learned by a separate CNN model. Emotions in the minor text are further highlighted through an attention mechanism before emotion classification. Performance evaluation shows that incorporating WSCs features using deep learning models can improve performance measured by F1-scores compared to the state-of-the-art model.
Fast MCMC Sampling Algorithms on Polytopes

We propose and analyze two new MCMC sampling algorithms, the Vaidya walk and the John walk, for generating samples from the uniform distribution over a polytope. Both random walks are sampling algorithms derived from interior point methods. The former is based on volumetric-logarithmic barrier introduced by Vaidya whereas the latter uses John's ellipsoids. We show that the Vaidya walk mixes in significantly fewer steps than the logarithmic-barrier based Dikin walk studied in past work. For a polytope in $\mathbb{R}^d$ defined by $n >d$ linear constraints, we show that the mixing time from a warm start is bounded as $\mathcal{O}(n^{0.5}d^{1.5})$, compared to the $\mathcal{O}(nd)$ mixing time bound for the Dikin walk. The cost of each step of the Vaidya walk is of the same order as the Dikin walk, and at most twice as large in terms of constant pre-factors. For the John walk, we prove an $\mathcal{O}(d^{2.5}\cdot\log^4(n/d))$ bound on its mixing time and conjecture that an improved variant of it could achieve a mixing time of $\mathcal{O}(d^2\cdot\text{polylog}(n/d))$. Additionally, we propose variants of the Vaidya and John walks that mix in polynomial time from a deterministic starting point. The speed-up of the Vaidya walk over the Dikin walk are illustrated in numerical examples.
Gonorrhoea gone wild: rising incidence of gonorrhoea and associated risk factors among gay and bisexual men attending Australian sexual health clinics.

Background: Gonorrhoea notifications continue to rise among gay and bisexual men in Australia and around the world. More information is needed on infection trends, accounting for testing and complimented by demographics and risk practices. Methods: A retrospective cohort analysis was undertaken using repeat gonorrhoea testing data among gay and bisexual men from 2010 to 2017, which was extracted from a network of 47 sexual health clinics across Australia. Poisson and Cox regression analyses were used to determine temporal trends in gonorrhoea incidence rates, as well as associated demographic and behavioural factors. Results: The present analysis included 46904 gay and bisexual men. Gonorrhoea incidence at any anatomical site increased from 14.1/100 person years (PY) in 2010 to 24.6/100 PY in 2017 (P<0.001), with the greatest increase in infections of the pharynx (5.6-15.9/100 PY, P<0.001) and rectum (6.6-14.8/100 PY, P<0.001). After adjusting for symptomatic and contact-driven presentations, the strongest predictors of infection were having more than 20 sexual partners in a year (hazard ratio (HR)=1.9, 95% confidence interval (CI): 1.7-2.2), using injecting drugs (HR=1.7, 95%CI: 1.4-2.0), being HIV positive (HR=1.4, 95%CI: 1.2-1.6) and being aged less than 30 years old (HR=1.4, 95%CI: 1.2-1.6). Conclusions: Gonorrhoea has increased dramatically among gay and bisexual men in Australia. Enhanced prevention efforts, as well as more detailed, network-driven research are required to combat gonorrhoea among young men, those with HIV and those who use injecting drugs.