Seasonal Diversity of Spiders (Arachnida: Araneae) and Collection Methods in Barpeta District, Assam, India

Barpeta district, Assam, India covers an area of 2754 square kilometers. The Spider specimens were collected by visual search method and the collection methods adopted were Aerial hand collection, Ground hand collection and Beat sheet method. 70 spider species from the different habitats of Barpeta District were documented. The study shows the difference in quantity and quality of spider fauna concerning the seasons and collection method. The spider species were more active in the post-monsoon season and some were inactive during winter. Almost 75% of spider species were collected by the beat sheet method which shows that it is more significant than all other trapping techniques. The spider species richness and spider abundance were significantly affected by seasons, studied by Simpson’s diversity index and Shannon – Weiner’s diversity index. However, further study needs to be carried out for the effective conservation of these species

Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives

Deep learning has demonstrated remarkable performance across various tasks in medical imaging. However, these approaches primarily focus on supervised learning, assuming that the training and testing data are drawn from the same distribution. Unfortunately, this assumption may not always hold true in practice. To address these issues, unsupervised domain adaptation (UDA) techniques have been developed to transfer knowledge from a labeled domain to a related but unlabeled domain. In recent years, significant advancements have been made in UDA, resulting in a wide range of methodologies, including feature alignment, image translation, self-supervision, and disentangled representation methods, among others. In this paper, we provide a comprehensive literature review of recent deep UDA approaches in medical imaging from a technical perspective. Specifically, we categorize current UDA research in medical imaging into six groups and further divide them into finer subcategories based on the different tasks they perform. We also discuss the respective datasets used in the studies to assess the divergence between the different domains. Finally, we discuss emerging areas and provide insights and discussions on future research directions to conclude this survey.Comment: Under Revie

Data efficient deep learning for medical image analysis: A survey

The rapid evolution of deep learning has significantly advanced the field of medical image analysis. However, despite these achievements, the further enhancement of deep learning models for medical image analysis faces a significant challenge due to the scarcity of large, well-annotated datasets. To address this issue, recent years have witnessed a growing emphasis on the development of data-efficient deep learning methods. This paper conducts a thorough review of data-efficient deep learning methods for medical image analysis. To this end, we categorize these methods based on the level of supervision they rely on, encompassing categories such as no supervision, inexact supervision, incomplete supervision, inaccurate supervision, and only limited supervision. We further divide these categories into finer subcategories. For example, we categorize inexact supervision into multiple instance learning and learning with weak annotations. Similarly, we categorize incomplete supervision into semi-supervised learning, active learning, and domain-adaptive learning and so on. Furthermore, we systematically summarize commonly used datasets for data efficient deep learning in medical image analysis and investigate future research directions to conclude this survey.Comment: Under Revie

Transnationalism, social capital and gender – young Pakistani Muslim women in Bradford, UK

This work was funded by the Leverhulme Trust.This article considers the relationship between transnationalism and social capital amongst young Pakistani Muslim women in Bradford, West Yorkshire. The central aim of the article is to explore how second generation Pakistani Muslim women accrue faith based social capital to negotiate and resist transnational gendered expectations, norms and practices. In particular, they use faith-based social capital that is transnationally informed: to challenge the patriarchal expectations and norms of their families; to gain access to higher/further education and thereby improve their life opportunities; and to resist growing anti-Muslim sentiment. This paper draws on qualitative research (in-depth interviews) conducted in BradfordPostprintPeer reviewe

Increased resistance to Nalidixic acid and Ciprofloxacin in Salmonella isolates from the Sub Himalayan region

Background: During the last two decades, increased resistance to nalidixic acid and ciprofloxacin has become a cause of global concern. The present study was undertaken to ascertain nalidixic acid and ciprofloxacin resistance in Salmonella isolates from our region. To know the true pattern of ciprofloxacin resistance by determining the minimum inhibitory concentration (MIC) through E-test.Methods: All the Salmonella isolates recovered from blood cultures were screened for nalidixic acid resistance using 30µg disc by the Kirby Bauer disc diffusion method. Ciprofloxacin susceptibility was done both by disc diffusion and MIC using CLSI breakpoints.Results: We analysed a total of 80 Salmonella isolates during the last three years. Salmonella enterica serovar Typhi was the predominant serovar in 51 (64.8%) isolates, followed by Salmonella enterica serovar Paratyphi A comprising 28 (36.2%) isolates. Amongst the total isolates 78 (97.5%) were nalidixic acid resistant. Of these 54 (67.5%) showed intermediate susceptibility and 9 (11.2%) were ciprofloxacin resistant by the disc diffusion technique. On the contrary 29 (36.2%) had decreased susceptibility to ciprofloxacin; while a larger number 38 (47.5%) were detected resistant to ciprofloxacin on determination of MIC by the E-test.Conclusions: Screening for nalidixic acid acts as a surrogate marker to detect ciprofloxacin resistance. However, the true pattern of ciprofloxacin resistance can be determined by calculating the MIC by the E-test

Characteristics and Challenges of Big Data

In today’s digital-era, we are bowed down by the massive data that is generated at exponential rates. Technically, this massive data is referred to as Big Data. Simultaneously, the need to manage Big Data arises. Big Data, due to its high volume, velocity, veracity, value, variety, leads to various issues. In this paper, we talk about the various challenges faced because of the exorbitant amount of data. We not only face challenges in processing, but also in designing, analysing, storage, management, privacy and security issues

Study on pattern of consumption of fruits and vegetables and associated factors among medical students of Delhi

Background: Fruits and vegetables are a rich source of essential micronutrients i.e. vitamins/minerals and dietary fibers required for the normal daily functionality of the body. Young adults such as medical students are a particularly vulnerable population in terms of health issues and adequate diet. Objective of the study was to find the pattern of fruits and vegetables consumption in undergraduate medical students of Delhi.Methods: A cross-sectional study was planned among 300 undergraduate students from medical college in New Delhi. The questionnaire consisted of questions about identification data, pattern of fruit and vegetable consumption. Data was analyzed by SPSS software version 21.0 and for qualitative data analysis chi-square test was used.Results: Mean age of study subjects was 20.82±2.1 years and females (52.7%) were more as compared to (47.3%) males. Out of 300 participants, only one third (33.3%) of study participants consumed more than five servings of fruits and vegetables. More than half of study participants felt that unsafe use of pesticides, difficult to eat five servings in a day, poor handling and poor quality of fruits and vegetables were the most common barriers in consumption of FVs. Age and semester of study participants and education status of mothers were found significant predictors of consumption of recommended number of serving of FVs in day.  Conclusions: This study concludes that only one third of study participants consumed more than five servings of fruits and vegetables which is recommended number of serving in a day. So, there is a need to increase awareness about importance of fruits and vegetables consumption among study population

Rust Disease Classification Using Deep Learning Based Algorithm: The Case of Wheat

Rusts are plant diseases caused by obligate fungi parasites. They are usually host-specific and cause greater losses of yields in crops, trees, and ornamental plants. Wheat is a staple food crop bearing losses specifically due to three species of rust fungi namely leaf rust (Puccinia triticina), stem rust (Puccinia graminis), and yellow rust (Puccinia striiformis). These diseases are usually inspected manually by a human being but at a large scale, this process is labor-intensive, time-consuming, and prone to human errors. Therefore, there is a need for an effective and efficient system that helps in the identification and classification of these diseases at early stages. In the present study, a deep learning-based CNN (i.e., VGG16) transfer learning model has been utilized for wheat disease classification on the CGIAR image dataset, containing two classes of wheat rust disease (leaf rust and stem rust), and one class of healthy wheat images. The deep learning models produced the best results by tuning the various hyper-parameters such as batch size, number of epochs, and learning rate. The proposed model has reported the best classification accuracy rate of 99.54% on 80 epochs using an initial learning rate from 0.01 and decayed to 0.0001

Hormonal profile and haematological parameters of male wistar albino rats treated with methanloic extract of Parthenium hysterophorus L.

Changes in hormonal and haematological level were assessed in male wistar albino rats treated with methanolic extract of Parthenium hysterophorus L. The result showed that methanolic extract treatment caused a significant (p < 0.01) reduction of 20 % and 40% in total RBC count (6.25 ± 0.025 to 5 ± 0.5 x 106/µL) and haemoglobin (17.1 ± 0.1892 to 10.2 ± 0.79 g / dL) respectively in treated rats over control. Unlike haematological parameters, hormonal profile showed a significant increase of 40% (p < 0.05), 200% (p < 0.01), 100% (p < 0.01) and 45.08% (p < 0.001) in follicle stimulating hormone, leutinizing hormone, prolactin and testosterone respectively. The reduction of blood parameters is due to less haemopoiesis or induction of anemia. The increase in hormone level may be a cause of prostate cancer in wistar albino rats