Presenting a Labelled Dataset for Real-Time Detection of Abusive User Posts

Social media sites facilitate users in posting their own personal comments online. Most support free format user posting, with close to real-time publishing speeds. However, online posts generated by a public user audience carry the risk of containing inappropriate, potentially abusive content. To detect such content, the straightforward approach is to filter against blacklists of profane terms. However, this lexicon filtering approach is prone to problems around word variations and lack of context. Although recent methods inspired by machine learning have boosted detection accuracies, the lack of gold standard labelled datasets limits the development of this approach. In this work, we present a dataset of user comments, using crowdsourcing for labelling. Since abusive content can be ambiguous and subjective to the individual reader, we propose an aggregated mechanism for assessing different opinions from different labellers. In addition, instead of the typical binary categories of abusive or not, we introduce a third class of ‘undecided’ to capture the real life scenario of instances that are neither blatantly abusive nor clearly harmless. We have performed preliminary experiments on this dataset using best practice techniques in text classification. Finally, we have evaluated the detection performance of various feature groups, namely syntactic, semantic and context-based features. Results show these features can increase our classifier performance by 18% in detection of abusive content

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

Improved cyberbullying detection using gender information

As a result of the invention of social networks, friendships, relationships and social communication are all undergoing changes and new definitions seem to be applicable. One may have hundreds of ‘friends’ without even seeing their faces. Meanwhile, alongside this transition there is increasing evidence that online social applications are used by children and adolescents for bullying. State-of-the-art studies in cyberbullying detection have mainly focused on the content of the conversations while largely ignoring the characteristics of the actors involved in cyberbullying. Social studies on cyberbullying reveal that the written language used by a harasser varies with the author’s features including gender. In this study we used a support vector machine model to train a gender-specific text classifier. We demonstrated that taking gender-specific language features into account improves the discrimination capacity of a classifier to detect cyberbullying

Some aspect of reproductive biology of Sillago sihama in Persian Gulf

This study was carried out to evaluate some biological reproductive aspects of Sillago sihama, a valuable gonochoristic species living in coastal areas of the Persian Gulf, from August 1997 to September 1998. Forty fish were caught each month, and their total length, standard length and total weight were measured. Fish were then diseeted and liver, stomach and gonads were weighted. A piece of gonad samples 01 different stages of maturity were taken and placed in bouins fixative. Minimum and maximum fish length were and 23.5 centimeters respectively. Length-weight relationship showed isometric growth in this species. Gonadosomatic index (GSI), gastrosomatic index (GI) and hepatosomatie index (HSI) were also measured. GSI was averaged 4.5 in March for females and 1.50 in April for males. Histological evidence showed that this fish has an ovary with free riped ova at the same time and spawn once a year in spring (synchronos). Sex ratio was measured (1 M:1.2 F (α=0.05,df=1, X^2=5), Size of maturity were 11.4 and 12.6cm for males and females respectively

A 21-year-old Man with Delayed Puberty

Abstract Delayed puberty is defined clinically by the absence or incomplete development of secondary sexual characteristics bounded by an age at which 95 percent of children of that sex and culture have initiated sexual maturation. The upper 95th percentile in the United States for age for boys is 14 (an increase in testicular size being the first sign) and for girls is 12 (breast development being the first sign). Delayed puberty pathophysiologically is classified according to the circulating levels of the gonadotropins luteinizing hormone (LH) and follicle-stimulating hormone (FSH) in to two groups of high serum LH/FSH and low or normal serum LH/FSH concentrations which are related to primary hypogonadism and hypothalamic dysfunction respectively. Patient Presentation. A 21 year old boy presented with severe respiratory distress syndrome due to pneumonia and generalized edema. Laboratory studies showed pancytopenia which made clinicians work up for hematologic disorders, leading to bone marrow aspiration and biopsy which was consistent with megaloblastic anemia resulting from vit B12 deficiency. Another manifestation of this patient was delayed puberty which had been ignored over these years. Evaluation of delayed puberty revealed a low serum LH/FSH concentration. Accompaniment of delayed puberty resulting from hypothalamic origin with edema and hypoalbuminemia made clinicians work up for a malabsorption syndrome. Therefore upper endoscopy and colonoscopy were done and duodenal biopsies were consistent with celiac sprue. The unusual symptom of this patient was vit B12 deficiency which is rare in celiac disease. Conclusion. Neglected celiac sprue can be accompanied by vit B12 deficiency probably because of involvement of more distal parts of small intestine over the time

Skeletal muscle PGC-1α1 reroutes kynurenine metabolism to increase energy efficiency and fatigue-resistance

The coactivator PGC-1α1 is activated by exercise training in skeletal muscle and promotes fatigue-resistance. In exercised muscle, PGC-1α1 enhances the expression of kynurenine aminotransferases (Kats), which convert kynurenine into kynurenic acid. This reduces kynurenine-associated neurotoxicity and generates glutamate as a byproduct. Here, we show that PGC-1α1 elevates aspartate and glutamate levels and increases the expression of glycolysis and malate-aspartate shuttle (MAS) genes. These interconnected processes improve energy utilization and transfer fuel-derived electrons to mitochondrial respiration. This PGC-1α1-dependent mechanism allows trained muscle to use kynurenine metabolism to increase the bioenergetic efficiency of glucose oxidation. Kat inhibition with carbidopa impairs aspartate biosynthesis, mitochondrial respiration, and reduces exercise performance and muscle force in mice. Our findings show that PGC-1α1 activates the MAS in skeletal muscle, supported by kynurenine catabolism, as part of the adaptations to endurance exercise. This crosstalk between kynurenine metabolism and the MAS may have important physiological and clinical implications

Ligand Binding Study of Human PEBP1/RKIP: Interaction with Nucleotides and Raf-1 Peptides Evidenced by NMR and Mass Spectrometry

Background Human Phosphatidylethanolamine binding protein 1 (hPEBP1) also known as Raf kinase inhibitory protein (RKIP), affects various cellular processes, and is implicated in metastasis formation and Alzheimer's disease. Human PEBP1 has also been shown to inhibit the Raf/MEK/ERK pathway. Numerous reports concern various mammalian PEBP1 binding ligands. However, since PEBP1 proteins from many different species were investigated, drawing general conclusions regarding human PEBP1 binding properties is rather difficult. Moreover, the binding site of Raf-1 on hPEBP1 is still unknown. Methods/Findings In the present study, we investigated human PEBP1 by NMR to determine the binding site of four different ligands: GTP, FMN, and one Raf-1 peptide in tri-phosphorylated and non-phosphorylated forms. The study was carried out by NMR in near physiological conditions, allowing for the identification of the binding site and the determination of the affinity constants KD for different ligands. Native mass spectrometry was used as an alternative method for measuring KD values. Conclusions/Significance Our study demonstrates and/or confirms the binding of hPEBP1 to the four studied ligands. All of them bind to the same region centered on the conserved ligand-binding pocket of hPEBP1. Although the affinities for GTP and FMN decrease as pH, salt concentration and temperature increase from pH 6.5/NaCl 0 mM/20°C to pH 7.5/NaCl 100 mM/30°C, both ligands clearly do bind under conditions similar to what is found in cells regarding pH, salt concentration and temperature. In addition, our work confirms that residues in the vicinity of the pocket rather than those within the pocket seem to be required for interaction with Raf-1.METASU

Studying protein–protein affinity and immobilized ligand–protein affinity interactions using MS-based methods

This review discusses the most important current methods employing mass spectrometry (MS) analysis for the study of protein affinity interactions. The methods are discussed in depth with particular reference to MS-based approaches for analyzing protein–protein and protein–immobilized ligand interactions, analyzed either directly or indirectly. First, we introduce MS methods for the study of intact protein complexes in the gas phase. Next, pull-down methods for affinity-based analysis of protein–protein and protein–immobilized ligand interactions are discussed. Presently, this field of research is often called interactomics or interaction proteomics. A slightly different approach that will be discussed, chemical proteomics, allows one to analyze selectivity profiles of ligands for multiple drug targets and off-targets. Additionally, of particular interest is the use of surface plasmon resonance technologies coupled with MS for the study of protein interactions. The review addresses the principle of each of the methods with a focus on recent developments and the applicability to lead compound generation in drug discovery as well as the elucidation of protein interactions involved in cellular processes. The review focuses on the analysis of bioaffinity interactions of proteins with other proteins and with ligands, where the proteins are considered as the bioactives analyzed by MS