Study on Air Bacteria at Different Altitudinal Locations in Tezpur to Tawang Axis

Microflora plays an important role in modulating environmental quality. Among microflora, bacteria are omnipresent in the environment. Pathogenic bacteria, present in air, are known to affect significantly the health and well-being of human, animal or plant populations. Air bacteria monitoring is thus essential for surveillance of pathogenic microorganisms from public health perspective besides its significant implications in detection and mitigation of biothreat related issues. Despite the geo-politically strategic importance of northeast India, there is scarcity of data on human health and disease surveillance. Considering these facts, we, for the first time studied the bacterial diversity of air at six important sites adjacent to the international border in the northeast region of India, having an altitude range of 73 m (Tezpur) to 4170 m (Sela Pass) above sea level. Standard microbiological techniques, such as Tryptone Soya Agar, Mannitol salt and McConkey agar strips and plates were used for air bacterial load assessment and culture for subsequent analysis using biochemical and molecular techniques. Following RFLP study, twenty six different bacterial colonies were isolated. Subsequently, bacteria identification was carried out by examining the substrate utilisation patterns, sequencing 16S rRNA gene and phylogenetic analysis. Results of the study reveal that the isolates mostly belong to two genera Bacillus and Staphylococcus (eleven in each genus), along with Micrococcus, Pseduomonas and Acinetobacter. Based on significant match of our sequences with that of medically important bacterial 16S rRNA sequences available at 16SpathDB 2.0 and review of available literature, we found that a number of these bacterial species have the pathogenic potential. In this manuscript we report our results and discuss the importance of air bacterial surveillance from the perspective of human health, hygiene and biothreat mitigation

Potential Application of Bacteriophage in Decontaminating Biothreat Agents

Multidrug resistant bacterial infections have become a potent risk, globally and there is an urgent need to phage and phage-derived enzymes as a therapeutic agent. The risk is more prominent in underdeveloped nations, where high population density, poor drinking water, inadequate sanitary and health care facilities ease the spread of infection. Bacteriophages (or ‘phages’) are abundant in nature and highly specific in their infection and pathogenicity, allowing their isolation, enrichment and use against specific bacteria. Employing bacteriophages as a tool for neutralizing potential biological threat agents can thus be an effective approach towards preparedness for biothreat mitigation. Unlike chemical antibiotics, phages are self-propagating, i.e., starting with a small number they can sustain their population, do not affect non-target/ beneficial bacterial populations. The tremendous potential of bacteriophages has recently been shown in treating multidrug resistant bacterial infections in terminally ill human subjects with unprecedented success. The natural anti-bacterial properties can be harnessed for decontamination of food, water, crops and for many other purposes including pathogen reduction in wastewater etc. Additionally, with the advancement in genetic engineering, deliberate use of such engineered multidrug resistant bacteria by state/non-state players has also become a reality. Owing to their resistance to several of the available antibiotics, control and mitigation of emerging pathogens is going to be great challenge. In this context, bacteriophages could be of potential use, since these viruses specifically infect bacterial hosts, often leading to their destruction

Cervical fibroid: an uncommon presentation

Fibroids arising from cervix are rare tumours accounting for 2% of all fibroids. A cervical leiomyoma is commonly single and is either interstitial or subserous, rarely it becomes submucous and polypoidal. Anterior cervical fibroid may press on urinary bladder and urethra and displace the urethro-vesical junction giving rise to urinary frequency and retention. Management of symptomatic cervical fibroid is hysterectomy or myomectomy and need an expert hand. Here we report a case of huge anterior cervical fibroid of 15x15x7cm with an unusual presentation of menorrhagia of only 2 days and no urinary symptoms. Inspite of the fibroid being huge and impacted, hysterectomy was done successfully without any injury to bladder and ureters.

Financial Numeric Extreme Labelling: A Dataset and Benchmarking for XBRL Tagging

The U.S. Securities and Exchange Commission (SEC) mandates all public companies to file periodic financial statements that should contain numerals annotated with a particular label from a taxonomy. In this paper, we formulate the task of automating the assignment of a label to a particular numeral span in a sentence from an extremely large label set. Towards this task, we release a dataset, Financial Numeric Extreme Labelling (FNXL), annotated with 2,794 labels. We benchmark the performance of the FNXL dataset by formulating the task as (a) a sequence labelling problem and (b) a pipeline with span extraction followed by Extreme Classification. Although the two approaches perform comparably, the pipeline solution provides a slight edge for the least frequent labels.Comment: Accepted to ACL'23 Findings Pape

IMPACT LOADING ANALYSIS OF PARTICULATE POLYMER COMPOSITES WITH AN EFFICIENT HYBRID MACHINE LEARNING APPROACH

The fracture behaviour of particle composites made of polymers under impact loading is predicted in this research using a hybrid machine learning approach dubbed Hybrid Artificial Neural Networks and Random Forest (HANN-RF), with a focus on mode-I fracture. The goal of the study is to create a model for prediction that accurately links input variables to histories of crack initiation, fracture toughness, and the intensity of the stress factor (SIF). A full dataset is created, with inputs for the composites' compositional properties and impact loading scenarios. The HANN-RF model combines a Random Forest (RF) method and an ANN (Artificial Neural Network) in order to improve robustness and accuracy in forecasting. Metrics like MAE, MAPE for short, and accuracy are used in model evaluation. The outcomes show that the HANN-RF technique successfully predicts and forecasts mode-I fracture behaviour, offering insightful information for evaluating the effect on resilience and longevity of particle polymer composites in a variety of applications

The Effect of Modifications of Activated Carbon Materials on the Capacitive Performance: Surface, Microstructure, and Wettability

none7siopenKouao Dujearic-Stephane; Meenal Gupta; Ashwani Kumar; Vijay Sharma; Soumya Pandit; Patrizia Bocchetta; Yogesh KumarDujearic-Stephane, Kouao; Gupta, Meenal; Kumar, Ashwani; Sharma, Vijay; Pandit, Soumya; Bocchetta, Patrizia; Kumar, Yoges

FinRED: A Dataset for Relation Extraction in Financial Domain

Relation extraction models trained on a source domain cannot be applied on a different target domain due to the mismatch between relation sets. In the current literature, there is no extensive open-source relation extraction dataset specific to the finance domain. In this paper, we release FinRED, a relation extraction dataset curated from financial news and earning call transcripts containing relations from the finance domain. FinRED has been created by mapping Wikidata triplets using distance supervision method. We manually annotate the test data to ensure proper evaluation. We also experiment with various state-of-the-art relation extraction models on this dataset to create the benchmark. We see a significant drop in their performance on FinRED compared to the general relation extraction datasets which tells that we need better models for financial relation extraction.Comment: Accepted at FinWeb at WWW'2