Generative AI Text Classification using Ensemble LLM Approaches

Large Language Models (LLMs) have shown impressive performance across a variety of Artificial Intelligence (AI) and natural language processing tasks, such as content creation, report generation, etc. However, unregulated malign application of these models can create undesirable consequences such as generation of fake news, plagiarism, etc. As a result, accurate detection of AI-generated language can be crucial in responsible usage of LLMs. In this work, we explore 1) whether a certain body of text is AI generated or written by human, and 2) attribution of a specific language model in generating a body of text. Texts in both English and Spanish are considered. The datasets used in this study are provided as part of the Automated Text Identification (AuTexTification) shared task. For each of the research objectives stated above, we propose an ensemble neural model that generates probabilities from different pre-trained LLMs which are used as features to a Traditional Machine Learning (TML) classifier following it. For the first task of distinguishing between AI and human generated text, our model ranked in fifth and thirteenth place (with macro $F1$ scores of 0.733 and 0.649) for English and Spanish texts, respectively. For the second task on model attribution, our model ranked in first place with macro $F1$ scores of 0.625 and 0.653 for English and Spanish texts, respectively

The Drug Changing Sensitivity and Resistance Pattern of Different Antibiotics and their Minimum Inhibitory Concentration against Salmonella

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Determination of Antibody Titration between Clinical and Community-Based Patients for O, H, AH and BH Antigens in Salmonella Samples

The aim of the study is to determine the baseline antibody titer of Salmonella bacteria in different positive samples with a view to establishing the significant titer for Widal agglutination test in Kashmir. The Widal test was performed on each serum. The slide agglutination test was first done and then positive samples were further subjected to tube agglutination for quantitative titration. The most commonly utilized diagnostic test for enteric fever is a Widal test, which detects agglutinating antibodies against the O, H, AH and BH antigens of S. typhi. The results of the Widal test showed that out of 413 samples 219 were positive for Antigen O, 165 were positive for Antigen H, 17 were positive for Antigen AH and 12 were positive for Antigen BH. The highest percentage cases were with Antigen-O whereas the lowest percentage was found in Antigen-BH. The difference between clinical and community-based patients have been studied. Keywords: Aetiology, Antigen, Enteric fever, Morbidity, Salmonella typhi, Widal test

In-Vivo Studies on Anti-Diabetic Potential of Leucas Aspera in Streptozotocin Induced Diabetic Wistar Albino Rats

Nanotechnology is being a utilized in medicine for diagnosis, therapeutic drug delivery and for the development of treatment for many ailments and disorders specifically in the areas of drug delivery, as medical diagnostic tools, and as diseases cure agents. During the past decades, the biosynthesis of metal nanoparticles has received considerable attention due to the growing need to develop environmentally sociable technologies in material synthesis. In this study, we investigated the anti-diabetic potential of Leucas aspera leaf extract in streptozotocin-induced diabetic Wistar albino rats and, serum creatinine, blood urea, protein content, enzymatic antioxidant, and non-enzymatic antioxidant was estimated.  This study evidenced the efficacy of the anti-diabetic potential of Leucas aspera leaf extract in the in-vivo model. Keywords: Diabetes mellitus, Iron oxide nanoparticles, Leucas aspera, Streptozotocin (STZ)

Formulation of Effective Microbial Consortium and Its Application for Industrial Wastewater Treatment

The present study was conducted for auto mobile industry, food industry and pharmaceutical industries waste water treatment using effective microbial consortium. The effective microorganisms like Acinetobacter pittii, Escherichia coli, Fictibacillus nanhaiensis, Lysinibacillus xylanilyticus and Planococcus maritimus were isolated from respective sources. The microbial consortium was formulated using molasses as medium at pH 3.8 and incubated at 37°C for 3 days. The results showed that the formulated consortium was efficient for industrial waste water treatment and thereby it reduced the environmental impact. Keywords: Bio-remediation, Microbial consortium, Industrial waste water, Heavy metal

Solanum tuberosum extract mediated synthesis and characterization of iron oxide nanoparticles for their antibacterial and antioxidant activity

In the present study, the potential of aqueous extract of Solanum tuberosum for synthesis of Iron Oxide nanoparticles (Fe3O4) was evaluated. An eco-friendly synthesis of iron oxide nanoparticles and characteristics of the obtained Fe3O4 nanoparticles were studied using Ultraviolet-visible spectroscopy (UV-Vis), Fourier Transform Infra-Red Spectroscopy (FTIR), Scanning Electron Microscope (SEM), Energy-dispersive X-ray spectroscopy (EDX), X-Ray Diffraction (XRD) and High Performance Liquid Chromatography (HPLC). The synthesized Iron oxide nanoparticles were effectively utilized for the antibacterial activity and antioxidant studies. The rapid biological synthesis of iron oxide nanoparticles using the extract of S. tuberosum provides an environment friendly, simple and efficient route. From the results, it is suggested that synthesized Iron Oxide could be used effective in future biomedical engineering. Keywords: Antibacterial, Antioxidant, Iron oxide (Fe3O4) nanoparticles, Solanum tuberosum

Predicting functional associations from metabolism using bi-partite network algorithms

<p>Abstract</p> <p>Background</p> <p>Metabolic reconstructions contain detailed information about metabolic enzymes and their reactants and products. These networks can be used to infer functional associations between metabolic enzymes. Many methods are based on the number of metabolites shared by two enzymes, or the shortest path between two enzymes. Metabolite sharing can miss associations between non-consecutive enzymes in a serial pathway, and shortest-path algorithms are sensitive to high-degree metabolites such as water and ATP that create connections between enzymes with little functional similarity.</p> <p>Results</p> <p>We present new, fast methods to infer functional associations in metabolic networks. A local method, the degree-corrected Poisson score, is based only on the metabolites shared by two enzymes, but uses the known metabolite degree distribution. A global method, based on graph diffusion kernels, predicts associations between enzymes that do not share metabolites. Both methods are robust to high-degree metabolites. They out-perform previous methods in predicting shared Gene Ontology (GO) annotations and in predicting experimentally observed synthetic lethal genetic interactions. Including cellular compartment information improves GO annotation predictions but degrades synthetic lethal interaction prediction. These new methods perform nearly as well as computationally demanding methods based on flux balance analysis.</p> <p>Conclusions</p> <p>We present fast, accurate methods to predict functional associations from metabolic networks. Biological significance is demonstrated by identifying enzymes whose strong metabolic correlations are missed by conventional annotations in GO, most often enzymes involved in transport vs. synthesis of the same metabolite or other enzyme pairs that share a metabolite but are separated by conventional pathway boundaries. More generally, the methods described here may be valuable for analyzing other types of networks with long-tailed degree distributions and high-degree hubs.</p