Graph-based Features for Automatic Online Abuse Detection

While online communities have become increasingly important over the years, the moderation of user-generated content is still performed mostly manually. Automating this task is an important step in reducing the financial cost associated with moderation, but the majority of automated approaches strictly based on message content are highly vulnerable to intentional obfuscation. In this paper, we discuss methods for extracting conversational networks based on raw multi-participant chat logs, and we study the contribution of graph features to a classification system that aims to determine if a given message is abusive. The conversational graph-based system yields unexpectedly high performance , with results comparable to those previously obtained with a content-based approach

Evidence for clustered mannose as a new ligand for hyaluronan-binding protein (HABP1) from human fibroblasts

We have earlier reported that overexpression of the gene encoding human hyaluronan-binding protein (HABP1) is functionally active, as it binds specifically with hyaluronan (HA). In this communication, we confirm the collapse of the filamentous and branched structure of HA by interaction with increasing concentrations of recombinant-HABP1 (rHABP1). HA is the reported ligand of rHABP1. Here, we show the affinity of rHABP1 towards D-mannosylated albumin (DMA) by overlay assay and purification using a DMA affinity column. Our data suggests that DMA is another ligand for HABP1. Furthermore, we have observed that DMA inhibits the binding of HA in a concentration-dependent manner, suggesting its multiligand affinity amongst carbohydrates. rHABP1 shows differential affinity towards HA and DMA which depends on pH and ionic strength. These data suggest that affinity of rHABP1 towards different ligands is regulated by the microenvironment

Toward impactful collaborations on computing and mental health

We describe an initiative to bring mental health researchers, computer scientists, human-computer interaction researchers, and other communities together to address the challenges of the global mental ill health epidemic. Two face-to-face events and one special issue of the Journal of Medical Internet Research were organized. The works presented in these events and publication reflect key state-of-the-art research in this interdisciplinary collaboration. We summarize the special issue articles and contextualize them to present a picture of the most recent research. In addition, we describe a series of collaborative activities held during the second symposium and where the community identified 5 challenges and their possible solutions

A Comparison of Classical Versus Deep Learning Techniques for Abusive Content Detection on Social Media Sites

The automated detection of abusive content on social media websites faces a variety of challenges including imbalanced training sets, the identification of an appropriate feature representation and the selection of optimal classifiers. Classifiers such as support vector machines (SVM), combined with bag of words or ngram feature representation, have traditionally dominated in text classification for decades. With the recent emergence of deep learning and word embeddings, an increasing number of researchers have started to focus on deep neural networks. In this paper, our aim is to explore cutting-edge techniques in automated abusive content detection. We use two deep learning approaches: convolutional neural networks (CNNs) and recurrent neural networks (RNNs). We apply these to 9 public datasets derived from various social media websites. Firstly, we show that word embeddings pre-trained on the same data source as the subsequent classification task improves the prediction accuracy of deep learning models. Secondly, we investigate the impact of different levels of training set imbalances on classifier types. In comparison to the traditional SVM classifier, we identify that although deep learning models can outperform the classification results of the traditional SVM classifier when the associated training dataset is seriously imbalanced, the performance of the SVM classifier can be dramatically improved through the use of oversampling, surpassing the deep learning models. Our work can inform researchers in selecting appropriate text classification strategies in the detection of abusive content, including scenarios where the training datasets suffer from class imbalance

Correlation of spirometry and six minute walk test in patients with chronic obstructive pulmonary disease from Sundargarh, Odisha, India

Background: Six‑Minute Walk Test (6MWT) is a simple, objective, reproducible test which correlated well with different spirometric indices, and thus able to predict severity of Chronic Obstructive Pulmonary Disease (COPD) and can replace spirometry in resource poor set‑up. Here, author evaluated the correlation of 6 minute walk distance (6MWD) with spirometric indices in COPD patients and the potential of 6MWT as an alternative to the assessment of severity of COPD.Methods: This cross-sectional observational study included a total of 80 COPD patients, diagnosed by GOLD criteria (Post bronchodilator FEV1/ FVC ratio <0.7). Modified Medical Research Council (mMRC) grading was used (age, weight, height, body mass index- BMI and breathlessness) and all the patients underwent spirometric measurement of FEV1, FVC and FEV1/ FVC ratio and tests were repeated after bronchodilation using 200-400 μg of salbutamol. 6MWT was performed following American Thoracic Society (ATS) protocol of 6MWT and distance was measured in meters.Results: Author found significant negative correlation of 6MWT with age (r=-0.384, p=0.00) and mMRC grading of dyspnea (r=-0.559, p=0.00) and significant positive correlation with height (r=0.267, p=0.019) and weight (r=0.293, p=0.008). Significant positive correlation of 6MWD was noted with post bronchodilator FEV1(r=0.608, p=0.00), FEV1% (r=0.429, p=0.00), FVC (r=0.514 p=0.00), FVC% (r=0.313 p=0.005), FEV1/FVC % (r=0.336, p=0.001). Positive correlation was also observed between 6MWT and BMI but statistically insignificant (r=0.177, p=0.116). There was significant negative correlation between 6MWT and GOLD staging (r=-0.536, p=0.00).Conclusions: This finding concludes that 6MWT can be used for the assessment of severity of disease in COPD patients in places where spirometry is not available

Detecting hate speech on twitter using a convolution-GRU based deep neural network

In recent years, the increasing propagation of hate speech on social media and the urgent need for effective counter-measures have drawn significant investment from governments, companies, as well as empirical research. Despite a large number of emerging scientific studies to address the problem, existing methods are limited in several ways, such as the lack of comparative evaluations which makes it difficult to assess the contribution of individual works. This paper introduces a new method based on a deep neural network combining convolutional and long short term memory networks, and conducts an extensive evaluation of the method against several baselines and state of the art on the largest collection of publicly available datasets to date. We show that our proposed method outperforms state of the art on 6 out of 7 datasets by between 0.2 and 13.8 points in F1. We also carry out further analysis using automatic feature selection to understand the impact of the conventional manual feature engineering process that distinguishes most methods in this field. Our findings challenge the existing perception of the importance of feature engineering, as we show that: the automatic feature selection algorithm drastically reduces the original feature space by over 90% and selects predominantly generic features from datasets; nevertheless, machine learning algorithms perform better using automatically selected features than the original features

Identification and characterization of EhCaBP2: a second member of the calcium-binding protein family of the protozoan parasite entamoeba histolytica

Entamoeba histolytica, an early branching eukaryote, is the etiologic agent of amebiasis. Calcium plays a pivotal role in the pathogenesis of amebiasis by modulating the cytopathic properties of the parasite. However, the mechanistic role of Ca2+ and calcium-binding proteins in the pathogenesis of E. histolytica remains poorly understood. We had previously characterized a novel calcium-binding protein (EhCaBP1) from E. histolytica. Here, we report the identification and partial characterization of an isoform of this protein, EhCaBP2. Both EhCaBPs have four canonical EF-hand Ca2+ binding domains. The two isoforms are encoded by genes of the same size (402 bp). Comparison between the two genes showed an overall identity of 79% at the nucleotide sequence level. This identity dropped to 40% in the 75-nucleotide central linker region between the second and third Ca2+ binding domains. Both of these genes are single copy, as revealed by Southern hybridization. Analysis of the available E. histolytica genome sequence data suggested that the two genes are non-allelic. Homology-based structural modeling showed that the major differences between the two EhCaBPs lie in the central linker region, normally involved in binding target molecules. A number of studies indicated that EhCaBP1 and EhCaBP2 are functionally different. They bind different sets of E. histolytica proteins in a Ca2+-dependent manner. Activation of endogenous kinase was also found to be unique for the two proteins and the Ca2+ concentration required for their optimal functionality was also different. In addition, a 12-mer peptide was identified from a random peptide library that could differentially bind the two proteins. Our data suggest that EhCaBP2 is a new member of a class of E. histolytica calcium-binding proteins involved in a novel calcium signal transduction pathway

Fabrication of Germanium-on-insulator in a Ge wafer with a crystalline Ge top layer and buried GeO2 layer by Oxygen ion implantation

The paper reports fabrication of Germanium-on-Insulator (GeOI) wafer by Oxygen ion implantation of an undoped single crystalline Ge wafer of orientation (100). Oxygen ions of energy 200 keV were implanted. The implanted wafer was subjected to Rapid Thermal Annealing to 650 C. The resulting wafer has a top crystalline Ge layer of 220 nm thickness and Buried Oxide layer (BOX) layer of good quality crystalline Germanium oxide with thickness around 0.62 micron. The crystalline BOX layer has hexagonal crystal structure with lattice constants close to the standard values. Raman Spectroscopy, cross-sectional HRTEM with SAED and EDS established that the top Ge layer was recrystallized during annealing with faceted crystallites. The top layer has a small tensile strain of around +0.4\% and has estimated dislocation density of 2.7 x 10^{7}cm^{-2}. The thickness, crystallinity and electrical characteristics of the top layer and the quality of the BOX layer of GeO_{2} are such that it can be utilized for device fabrication

Evaluation of the effects of the anti-retroviral drug regimen (zidovudine + lamivudine + nevirapine) on CD4 count, body weight, and Hb% of the HIV patients-a retrospective study

Abstract The main objective of study is to evaluate the effectiveness of triple drug therapy Zidovudine + lamivudine + nevirapine on cd 4 counts, weight and Hb% for the duration of 6 months. In this retrospective study, data was collected from the anti-retroviral therapy (ART) centre where 315 subjects infected with HIV received ZIDOVUDINE + LAMIVUDINE + NEVIRAPINE. Baseline and after 6 months of therapy; CD4 count, weight and Hb% were recorded and compared. Records with incomplete data were excluded. Statistical analysis was done using paired &apos;t&apos; test for body weight , Hb% and CD4 count.It was found that after 6 months of treatment, both CD4 count and body weight improved significantly(p value: 0.001). Whereas in case of haemoglobin %, even after the treatment period, no significant changes were observed in Hb % (p value: 0.227).It was concluded that ZLN regimen for treatment in HIV patient is efficacious in improving both CD4 count and body weight and not Hb%

Potential pathway for recycling of the paper mill sludge compost for brick making

This study's focus was to develop a potential pathway for recycling of the paper mill sludge compost (PMSC) in brick making. Composting reduces the paper mill sludge (PMS) moisture content considerably and shredding becomes easier. The addition of PMSC leads to an increase of porosities in bricks and makes them lighter, besides delivering energy to the firing process from burning organics. Lighter construction materials help minimize construction outlay by reducing labour and transportation costs and lesser expense on foundation construction. The variability in the experimental data and the brick properties were investigated for two types of soils, typical in the brick industry of India (alluvial and laterite soil), blended with PMSC in five mix ratios (0%, 5%, 10%, 15% and 20%). The samples of oven-dried bricks were fired at two different temperatures (850 and 900 ˚C) in an electrically operated muffle furnace representing typical conditions of a brick kiln. Various properties of bricks were analyzed which included linear shrinkage, bulk density, water absorption and compressive strength. Conclusions were drawn based on these properties. It was found that the addition of PMSC to the alluvial and laterite soil by up to 10% weight yield mechanical properties of fired bricks compliant with the relevant Indian and ASTM codes. Toxicity characteristic leaching procedure (TCLP) tests showed that PMSC incorporated fired bricks are safe to use in regular applications as non-load-bearing and infill walls. This study is timely in light of the European Green Deal putting focus on circular economy. Besides, it fulfils the objective of UN sustainable development goals (SDG)