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

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

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

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

The bioenergetic roles of PGC-1α1, kynurenines, and GPR35 in exercise and obesity [Elektronisk resurs]

Obesity is a major cause of medical comorbidity with detrimental effects on health span. Fundamentally, obesity is a condition of disrupted energy metabolism, where energy intake chronically exceeds expenditure. Over time, this disruption causes a systemic low-grade inflammation and impairs energy homeostasis. Physical activity can counteract these effects by increasing energy expenditure and orchestrating a myriad of healthy molecular and cellular adaptations in skeletal muscle and other metabolic organs. Many adaptations to exericse are mediated by the transcriptional co-activator PGC-1a1. The work presented in this thesis identifies novel roles of PGC-1a1 as a regulator of the kynurenine pathway of tryptophan degradation with important bioenergetic implications in obesity and exercise. In skeletal muscle, PGC-1a1 regulates the kynurenine pathway by increasing the levels of kynurenine aminotransferases. Kynurenine aminotransferases are enzymes that serve as an important gateway of the pathway, driving kynurenine metabolism towards the production of kynurenic acid while generating glutamate in the process. Here, we show that peripheral kynurenic acid signals through GPR35 in adipose tissue, and induces a transcriptional signature consistent with beige adipocytes and anti-inflammatory immune cells. This signaling axis sensitizes adipocytes to b-adrenergic signaling, increases systemic energy expenditure, and protects against high fat diet induced metabolic disruptions and weight gain. Conversely, we find that both whole-body and hematopoietic-specific genetic deletion of Gpr35 impairs energy homeostasis. Locally in skeletal muscle, we identify that kynurenine metabolism is an integral part of the malate aspartate shuttle through Kynurenine aminotransferase 4. Exercise, via PGC-1a1, allows trained skeletal muscle to use kynurenine as a substrate to increase bioenergetic efficiency by supporting glucose oxidation through restoring cytosolic NAD+ and anaplerotically feeding the TCA cycle via glutamate. Importantly, we show that inhibition of the malate-aspartate shuttle and kynurenine aminotransferases with Carbidopa, a drug used in the treatment of Parkinson’s disease, mitigates exercise performance and adaptations. Collectively, the work presented in this thesis has culminated in the discovery of new bioenergetic roles of the kynurenine pathway of tryptophan degradation in adipose tissue and skeletal muscle with implications in obestiy and exercise adaptation

Roadway Safety Data Integrator (RSDI) Tool & Integrated Highway Safety Information System (HSIS) Datasets

The Highway Safety Information System (HSIS) is a database that maintains crash data, roadway inventory, and traffic volume data for several US states. It is an excellent source of data to highway safety research and can be used to investigate many research questions. However, to prepare an analysis-ready roadway safety dataset based on the HSIS or any databases that store the data in multiple different subsets and follow linear referencing, the researchers should integrate multiple datasets, merge or unmerge and remove certain inconsistent records, and finally clean the dataset. The HSIS staff is usually accommodating and eager to help, but sometimes the nature of data needs is complicated and laborious. A tool named Roadway Safety Data Integrator (RSDI) was developed for combining, segmenting, and selecting homogeneous HSIS roadway segments and also crash assignment by desired crash fields (e.g., crash severity or type). The RSDI tool can be helpful for integrating different safety-related datasets such as roadway inventory (including grade, curve, and other subsets), traffic volume, and crash data; also, it can do required segmentation and identify the homogeneous roadway segments over the desired years of study that are the basis for development and calibration of the HSM predictive models. The RSDI tool can be used for similar purposes and not only limited to the HSIS data. It can be used for segmentation and finding homogeneous segments of any datasets that follow linear referencing.The data consists of the following items:- The RSDI tool and its guide- Integrated and raw HSIS data from states of Illinois (R2U: 2005-10) and Washington (R2U: 2010-15)R2U: Rural two-lane, two-way roadsTHIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV