Miracle-dz/UniFAR-FTUs huggingface dataset:
BanglaBait: Semi-Supervised Adversarial Approach for Clickbait Detection on Bangla Clickbait Dataset

Intentionally luring readers to click on a particular content by exploiting their curiosity defines a title as clickbait. Although several studies focused on detecting clickbait titles in English articles, low-resource language like Bangla has not been given adequate attention. To tackle clickbait titles in Bangla, we have constructed the first Bangla clickbait detection dataset containing 15,056 labeled news articles and 65,406 unlabelled news articles extracted from clickbait-dense news sites. Each article has been labeled by three expert linguists and includes an article’s title, body, and other metadata. By incorporating labeled and unlabelled data, we finetune a pre-trained Bangla transformer model in an adversarial fashion using Semi-Supervised Generative Adversarial Networks (SS-GANs). The proposed model acts as a good baseline for this dataset, outperforming traditional neural network models (LSTM, GRU, CNN) and linguistic feature-based models. We expect that this dataset and the detailed analysis and comparison of these clickbait detection models will provide a fundamental basis for future research into detecting clickbait titles in Bengali articles.
IJS@LT-EDI : Ensemble Approaches to Detect Signs of Depression from Social Media Text

This paper presents our ensembling solutions for detecting signs of depression in social media text, as part of the Shared Task at LT-EDI@RANLP 2023. By leveraging social media posts in English, the task involves the development of a system to accurately classify them as presenting signs of depression of one of three levels: “severe”, “moderate”, and “not depressed”. We verify the hypothesis that combining contextual information from a language model with local domain-specific features can improve the classifier’s performance. We do so by evaluating: (1) two global classifiers (support vector machine and logistic regression); (2) contextual information from language models; and (3) the ensembling results.
Deep Learning Approaches to Detecting Safeguarding Concerns in Schoolchildren’s Online Conversations

For school teachers and Designated Safeguarding Leads (DSLs), computers and other school-owned communication devices are both indispensable and deeply worrisome. For their education, children require access to the Internet, as well as a standard institutional ICT infrastructure, including e-mail and other forms of online communication technology. Given the sheer volume of data being generated and shared on a daily basis within schools, most teachers and DSLs can no longer monitor the safety and wellbeing of their students without the use of specialist safeguarding software. In this paper, we experiment with the use of state-of-the-art neural network models on the modelling of a dataset of almost 9,000 anonymised child-generated chat messages on the Microsoft Teams platform. The data was manually classified into eight fine-grained classes of safeguarding concerns (or false alarms) that a monitoring program would be interested in, and these were further split into two binary classes: true positives (real safeguarding concerns) and false positives (false alarms). For the fine grained classification, our models achieved a macro F1 score of 73.56, while for the binary classification, we achieved a macro F1 score of 87.32. This first experiment into the use of Deep Learning for detecting safeguarding concerns represents an important step towards achieving high-accuracy and reliable monitoring information for busy teachers and safeguarding leads.
Supernova@DravidianLangTech 2023@Abusive Comment Detection in Tamil and Telugu - (Tamil, Tamil-English, Telugu-English)

This paper focuses on using Support Vector Machines (SVM) classifiers with TF-IDF feature extraction to classify whether a comment is abusive or not.The paper tries to identify abusive content in regional languages.The dataset analysis presents the distribution of target variables in the Tamil-English, Telugu-English, and Tamil datasets.The methodology section describes the preprocessing steps, including consistency, removal of special characters and emojis, removal of stop words, and stemming of data. Overall, the study contributes to the field of abusive comment detection in Tamil and Telugu languages.
BB25HLegalSum: Leveraging BM25 and BERT-Based Clustering for the Summarization of Legal Documents

Legal document summarization aims to provide a clear understanding of the main points and arguments in a legal document, contributing to the efficiency of the judicial system. In this paper, we propose BB25HLegalSum, a method that combines BERT clusters with the BM25 algorithm to summarize legal documents and present them to users with highlighted important information. The process involves selecting unique, relevant sentences from the original document, clustering them to find sentences about a similar subject, combining them to generate a summary according to three strategies, and highlighting them to the user in the original document. We outperformed baseline techniques using the BillSum dataset, a widely used benchmark in legal document summarization. Legal workers positively assessed the highlighted presentation.
Unveiling the Hidden Viromes Across the Animal Tree of Life: Insights from a Taxonomic Classification Pipeline Applied to Invertebrates of 31 Metazoan Phyla

Invertebrates constitute the majority of animal species on Earth, including most disease-causing agents or vectors, with more diverse viromes when compared to vertebrates. Recent advancements in high-throughput sequencing have significantly expanded our understanding of invertebrate viruses, yet this knowledge remains biased toward a few well-studied animal lineages. In this study, we analyze invertebrate DNA and RNA viromes for 31 phyla using 417 publicly available RNA-Seq datasets from diverse environments in the marine-terrestrial and marine-freshwater gradients. This study aims to (i) estimate virome compositions at the family level for the first time across the Animal Tree of Life, including the first exploration of the virome in several phyla, (ii) quantify the diversity of invertebrate viromes and characterize the structure of invertebrate-virus interaction networks, and (iii) investigate host phylum and habitat influence on virome differences. Results showed that a set of few viral families of eukaryotes, comprising Retroviridae, Flaviviridae and several families of giant DNA viruses, were ubiquitous and highly abundant. Nevertheless, some differences emerged between phyla, revealing for instance a less diverse virome in Ctenophora compared to the other animal phyla. Compositional analysis of the viromes showed that the host phylum explained over five times more variance in composition than its habitat. Moreover, significant similarities were observed between the viromes of some phylogenetically related phyla, which could highlight the influence of co-evolution in shaping invertebrate viromes. Importance This study significantly enhances our understanding of the global animal virome by characterizing the viromes of previously unexamined invertebrate lineages from a large number of animal phyla. It showcases the great diversity of viromes within each phylum and investigates the role of habitat shaping animal viral communities. Furthermore, our research identifies dominant virus families in invertebrates and distinguishes phyla with analogous viromes. This study sets the road towards a deeper understanding of the virome across the Animal Tree of Life.
Dengue virus exploits the host tRNA epitranscriptome to promote viral replication

The 40-50 RNA modifications of the epitranscriptome regulate posttranscriptional gene expression. Here we show that flaviviruses hijack the host tRNA epitranscriptome to promote expression of pro-viral proteins, with tRNA-modifying ALKBH1 acting as a host restriction factor in dengue virus infection. Early in the infection of human Huh-7 cells, ALKBH1 and its tRNA products 5-formylcytidine (f5C) and 2’-O-methyl-5-formylcytidine (f5Cm) were reduced. ALKBH1 knockdown mimicked viral infection, but caused increased viral NS3 protein levels during infection, while ALKBH1 overexpression reduced NS3 levels and viral replication, and increased f5C and f5Cm. Viral NS5, but not host FTSJ1, increased f5Cm levels late in infection. Consistent with reports of impaired decoding of leucine UUA codon by f5Cm-modified tRNALeu(CAA), ALKBH1 knockdown induced translation of UUA-deficient transcripts, most having pro-viral functions. Our findings support a dynamic ALKBH1/f5Cm axis during dengue infection, with virally-induced remodeling of the proteome by tRNA reprogramming and codon-biased translation.
Optimal Liquid-Based DNA Preservation for DNA Barcoding of Field-Collected Fungal Specimens

Preserving fungal tissue DNA in the field is essential for molecular ecological research, enabling the study of fungal biodiversity and community dynamics. This study systematically compares two liquid-based preservation solutions, RNAlater and DESS, for their effectiveness in maintaining macrofungi DNA integrity during field collection and storage. The research encompasses both controlled experiments and real-world field collections. In the controlled experiments, two fungal species were preserved in RNAlater and DESS at different temperatures and durations. DNA extraction success rates were high, but DNA quality and quantity metrics exhibited variations across samples. However, both preservation solutions demonstrated their viability for preserving fungal DNA, with no significant differences between them. In the field-collected macrofungi experiment, 200 fungal specimens were collected and preserved in RNAlater and DESS. The DNA extraction success rate was 98%, with a few exceptions. The statistical analysis, including paired and independent t-tests, showed no statistically significant differences in DNA quality and quantity between the two preservation methods for the field-collected fungal samples. Overall, this study provides valuable insights into the effectiveness of RNAlater and DESS for preserving macrofungi DNA in field conditions. Researchers can confidently choose between these methods based on their specific needs, without compromising the integrity of the DNA. This research contributes to the advancement of fungal molecular ecology and has broader implications for DNA preservation strategies in ecological and environmental studies.
Conservation of molecular responses upon viral infection in the non-vascular plant Marchantia polymorpha

After their transition from water to land around 450 million years ago, plants colonized new habitats facing unprecedented pathogenic microbes. That expansion was mostly supported by a growing anatomical complexity based on the acquisition of developmental innovations, such as roots, stomata and vascular tissue. Despite several of those innovations became central for the interaction between plants and their associated microbes, little is known about their impact on plant immune programs and on the diversification of infection strategies of their pathogens. A paradigmatic case is the close relationship between plant vasculature and viruses. Vascular tissues provide a unique cellular environment for viral replication and existence, besides constituting a fast track for viral systemic spread throughout the plant. Since most of our knowledge about plant-virus interactions come from studies in vascular plants, we here present a comparative study to contribute to the understanding of the evolution of plant-virus interactions by molecularly characterizing the interplay between the bryophyte Marchantia polymorpha and viruses. Virome analysis of Marchantia plants shows that they are primarily associated with RNA viruses in natural settings. Additional molecular characterization of the interaction between Marchantia and tobacco mosaic virus (TMV) show conserved basic processes with vascular plants and divergent features. Viral infection triggers an extensive transcriptional reprogramming in Marchantia encompassing broad range defence responses, inhibition of cell cycle and photosynthesis and a sustained wound response that prevents further viral movement. Additionally, infected plants show premature aging and organ maturation. Notably, we found that some core responses that occur in infected areas in Marchantia, were described to be restricted to vascular tissues in Nicotiana, suggesting that evolutionary appearance of developmental innovations that became central in plants-virus interactions resulted in re-routing of defence responses. Finally, we uncover the conserved role of a transcription factor interacting with the TMV silencing suppressor p126 in specifically abrogating TMV infection.
Polymer Implantable Electrode Foundry: A shared resource for manufacturing polymer-based microelectrodes for neural interfaces

Large scale monitoring of neural activity at the single unit level can be achieved via electrophysiological recording using implanted microelectrodes. While neuroscience researchers have widely employed chronically implanted electrode-based interfaces for this purpose, a commonly encountered limitation is loss of highly resolved signals arising from immunological response over time. Next generation electrode-based interfaces improve longitudinal signal quality using the strategy of stabilizing the device-tissue interface with microelectrode arrays constructed from soft and flexible polymer materials. The limited availability of such polymer microelectrode arrays has restricted access to a small number of researchers able to build their own custom devices or who have developed specific collaborations with engineering researchers who can produce them. Here, a new technology resource model is introduced that seeks to widely increase access to polymer microelectrode arrays by the neuroscience research community. The Polymer Implantable Electrode (PIE) Foundry provides custom and standardized polymer microelectrode arrays as well as training and guidance on best-practices for implantation and chronic experiments.
PROLONG: Penalized Regression for Outcome guided Longitudinal Omics analysis with Network and Group constraints

Motivation There is a growing interest in longitudinal omics data, but there are gaps in existing methodology in the high-dimensional setting. This paper focuses on selecting metabolites that co-vary with Tuberculosis mycobacterial load. The proposed method is applied to general continuous longitudinal outcomes with continuous longitudinal omics predictors. Simple longitudinal models examining a single omic predictor at a time do not leverage the correlation across predictors, thus losing power. We propose a penalized regression approach on the first differences of the data that extends the lasso + Laplacian method (Li and Li 2008) to a longitudinal group lasso + Laplacian approach. Our method, PROLONG, leverages the first differences of the data to address the piecewise linear structure and the observed time dependence. The Laplacian network constraint incorporates the dependence structure of the predictors, and the group lasso constraint induces sparsity while grouping metabolites across their first differenced observations. Results With an automated selection of model hyper-parameters, PROLONG correctly selects target metabolites with high specificity and sensitivity across simulation scenarios and sizes. PROLONG selects a set of metabolites from the real data that includes interesting targets identified during EDA. Availability R package ‘prolong’ is in development. Conclusions PROLONG is a powerful method for selecting interesting features in high dimensional longitudinal omics data that co-vary with some continuous clinical outcome. Contact sb2643@cornell.edu
MIOSTONE: Modeling microbiome-trait associations with taxonomy-adaptive neural networks

Motivation The human microbiome, a complex ecosystem of microorganisms inhabiting the body, plays a critical role in human health. Investigating its association with host traits is essential for understanding its impact on various diseases. Although shotgun metagenomic sequencing technologies have produced vast amounts of microbiome data, analyzing such data is highly challenging due to its sparsity, noisiness, and high feature dimensionality. Results Here we propose MIOSTONE, a novel machine learning method that leverages the intercorrelation of microbiome features due to their phylogeny-based taxonomic relationships. MIOSTONEemploys a novel taxonomy-encoded deep neural network (DNN) architecture that harnesses the capabilities of DNNs with mitigated concerns of overfitting. In addition, MIOSTONE has the ability to determine whether taxa within the corresponding taxonomic group provide a better explanation in a data-driven manner. We empirically assessed MIOSTONE’s accuracy and interpretability on seven real microbiome datasets, demonstrating its superior performance and biological interpretability. Availability TheApache licensed source code is available at (https://github.com/batmen-lab/MIOSTONE).
A WIDESPREAD PICORNAVIRUS AFFECTS THE HAEMOCYTES OF THE NOBLE PEN SHELL (PINNA NOBILIS) LEADING TO IMMUNOSUPPRESSION

The widespread mass mortality of the noble pen shell (Pinna nobilis) has occurred in several Mediterranean countries in the past seven years. Single-stranded RNA virus affecting immune cells and leading to immune disfunction have been widely reported in human and animal species. Here we present data linking P. nobilis mass mortality events (MMEs) to haemocyte picornavirus (PV) infection. This study was performed on 30 specimens, from wild and captive populations. We sampled P. nobilis from two regions of Spain, Catalonia [24 animals] and Murcia [two animals]), and one region in Italy (Venice [four animals]). The low number of analyzed specimens was due to the scarcity of remaining individuals in the Mediterranean Sea. In 100% of our samples, ultrastructure revealed the presence of a virus (20nm diameter), capable of replicating within granulocytes and hyalinocytes, leading to the accumulation of complex vesicles of different dimensions within the cytoplasm. As the PV infection progressed, dead haemocytes, infectious exosomes, and budding of extracellular vesicles were visible, along with endocytic vesicles entering other cells. The THC (total haemocyte count) values observed in both captive (eight animals) (3.5 x 104 - 1.60 x105 ml-1 cells) and wild animals (14 samples) (1.90 - 2.42 x105 ml-1 cells) were lower than those reported before MMEs. Sequencing of P. nobilis (six animals) haemocyte cDNA libraries revealed the presence of two main sequences of Picornavirales, family Marnaviridae. The highest number of reads belonged to animals that exhibited active replication phases and abundant viral particles from Trasmission Electron Microscopy (TEM) observations. These sequences correspond to the genus Sogarnavirus - a picornavirus identified in the marine diatom Chaetoceros tenuissimus (named C. tenuissimus RNA virus type II). Real time PCR performed on the two most abundant RNA viruses previously identified by in silico analysis revealed positive results only for the sequences similar to C. tenuissimus RNA virus. These results may not be considered conclusive of picornavirus identification in noble pen shell haemocytes, and require further studies. Our findings suggest that picornavirus infection likely causes immunosuppression, making individuals prone to opportunistic infections which is a potential cause for the MMEs observed in the Mediterranean.
Spatial conservation planning of forest genetic resources in a Mediterranean multi-refugial area

The Kunming-Montreal Global Biodiversity Framework recognised the urgency of taking action to conserve intraspecific genetic diversity (IGD) as an insurance against habitat degradation and environmental change. Recent work suggests that 90-99% of IGD should be conserved to safeguard viability of future generations. Here, we addressed such a conservation issue in three forest tree species in Italy: silver fir (Abies alba Mill.), Heldreich’s pine (Pinus heldreichii H. Christ), and pedunculate oak (Quercus robur L.). We used microsatellite markers to measure IGD of 36 (A. alba), 15 (P. heldreichii) and 25 (Q. robur) natural sites, including several putative glacial refugia. We developed a spatial conservation planning (SCP) analysis to quantify the genetic irreplaceability of each site and identify the minimum set coverage ensuring IGD protection. Finally, we compared SCP results with the contributions to allelic diversity within and between sites, total allelic diversity and private allelic richness. We found that between 44% and 73% of sites were required to conserve 90-99% of the alleles, and that this conservation effort held even when targeting lower percentages of alleles to protect (50-75%). Glacial refugia were often included in the minimum set coverage, confirming theoretical and biogeographical expectations. Finally, sites with high genetic irreplaceability were found to have higher private allelic richness on average. These results are discussed in the light of the biogeographic history of the species studied and the current policies for the conservation of forest genetic resources.
Bioenergetic mapping of ‘healthy microbiomes’ via compound processing potential imprinted in gut and soil metagenomes

Despite mounting evidence of their importance in human health and ecosystem functioning, the definition and measurement of ‘healthy microbiomes’ remain unclear. More advanced knowledge exists on health associations for compounds used or produced by microbes. Environmental microbiome exposures (especially via soils) also help shape, and may supplement, the functional capacity of human microbiomes. Given the synchronous interaction between microbes, their feedstocks, and micro-environments, with functional genes facilitating chemical transformations, there exists an intriguing opportunity to examine microbiomes in terms of their capacity to process compounds relevant to human health. Here we integrate functional genomics and biochemistry frameworks to derive new quantitative measures of the potential for human gut and environmental soil metagenomes to process major compound classes (e.g., lipids, carbohydrates) and selected biomolecules (e.g., vitamins, short-chain fatty acids) linked to human health. Metagenome functional potential profile data were translated into a universal compound mapping ‘landscape’ based on bioenergetic van Krevelen mapping of function-level meta-compounds and corresponding functional relative abundances, reflecting imprinted genetic capacity of microbiomes to metabolize an array of different compounds. We show that measures of ‘compound processing potential’ associated with human health and disease (examining atherosclerotic cardiovascular disease, colorectal cancer, type 2 diabetes and anxious-depressive behavior case studies), and displayed seemingly predictable shifts along gradients of ecological disturbance in plant-soil ecosystems (three case studies). Ecosystem quality explained 60–92% of variation in soil metagenome compound processing potential measures in a post-mining restoration case study dataset. With growing knowledge of the varying proficiency of environmental microbiota to process human health associated compounds, we might design environmental interventions or nature prescriptions to modulate our exposures, thereby advancing microbiota-oriented approaches to human health. Compound processing potential offers a simplified, integrative approach for applying metagenomics in ongoing efforts to understand and quantify the role and linkages of microbiota in environmental- and human-health.