Attitudes, Perceptions and Intimate Partner Violence: A Study of the Nigerian Context

Intimate partner violence (IPV) is a major public health issue affecting many women around the world. It is a topic that has attracted a great deal of research over the years, but the dynamics of the issue in some parts of the world, especially in Sub-Saharan Africa, is still very vague, necessitating more research in the region. This study uses a cross-sectional population-based survey to explore attitudes of women towards gender roles in a Sub-Saharan African country – Nigeria, as this is one of the factors that is likely to influence IPV occurrence. The results show that attitudes towards gender roles in Nigeria are more supportive of male dominance and women being subservient to their husband/partner, and also suggest that addressing such attitudes may be an important strand of action in tackling IPV issues in the country

A GIS–Integrated Wireless Sensors Network Tool for Water Risk Monitoring – Case of Khanh Hoa Water Supply and Sewerage Company, Vietnam

This paper investigates the feasibility of deploying a wireless sensor network (WSN) to monitor raw water quality at 2 major water treatment plants (WTPs) sites, operated by the The Khanh Hoa Water Supply and Sewerage Company (KHAWASSCO) in Vietnam: Canh Vo and Xuan Canh on the Cai River. The main aim is to propose a WSN for both WTPs which includes 2 clusters of sensors with 4 nodes each in order to monitor various parameters of water quality. Data management is integrated with a geographical information system (GIS) tool in order to provide a comprehensive spatio-temporal database in real time. This will assist decision makers in improving the management of the raw water quality at Cai River

Neural Analyses Validate and Emphasise the Role of Progesterone Receptor in Breast Cancer Progression and Prognosis

Oestrogen receptor (ER) expression is routinely measured in breast cancer management, but the clinical merits of measuring progesterone receptor (PR) expression have remained controversial. Hence the major objective here was to assess the potential of PR as a predictor of response to endocrine therapy. We report analyses of the relative importance of ER and PR for predicting prognosis using robust multilayer perceptron artificial neural networks. Receptor determinations use immunohistochemical (IHC) methods or radioactive ligand binding assays (LBA). In view of the heterogeneity of intratumoral receptor distribution, we examined the relative merits of the IHC and LBA methods. Our analyses reveal a more significant correlation of IHC-determined PR than ER with both nodal status and 5-year disease-free survival (DFS). In LBA, PR displayed higher correlation with survival and ER with nodal status. There was concordance of correlation of PR with DFS by both IHC and LBA. This study suggests a clear distinction between PR and ER, with PR displaying greater correlation than ER with disease progression and prognosis, and emphasises the marked superiority of the IHC method over LBA. These findings may be valuable in the management of patients with breast cancer

Real-Time Evaluation of Breast Self-Examination Using Computer Vision

Breast cancer is the most common cancer among women worldwide and breast self-examination (BSE) is considered as the most cost-effective approach for early breast cancer detection. The general objective of this paper is to design and develop a computer vision algorithm to evaluate the BSE performance in real-time. The first stage of the algorithm presents a method for detecting and tracking the nipples in frames while a woman performs BSE; the second stage presents amethod for localizing the breast region and blocks of pixels related to palpation of the breast, and the third stage focuses on detecting the palpated blocks in the breast region. The palpated blocks are highlighted at the time of BSE performance. In a correct BSE performance, all blocks must be palpated, checked, and highlighted, respectively. If any abnormality, such as masses, is detected, then this must be reported to a doctor to confirm the presence of this abnormality and proceed to perform other confirmatory tests. The experimental results have shown that the BSE evaluation algorithm presented in this paper provides robust performance

Real-Time Evaluation of Breast Self-Examination Using Computer Vision

Breast cancer is the most common cancer among women worldwide and breast self-examination (BSE) is considered as the most cost-effective approach for early breast cancer detection. The general objective of this paper is to design and develop a computer vision algorithm to evaluate the BSE performance in real-time. The first stage of the algorithm presents a method for detecting and tracking the nipples in frames while a woman performs BSE; the second stage presents amethod for localizing the breast region and blocks of pixels related to palpation of the breast, and the third stage focuses on detecting the palpated blocks in the breast region. The palpated blocks are highlighted at the time of BSE performance. In a correct BSE performance, all blocks must be palpated, checked, and highlighted, respectively. If any abnormality, such as masses, is detected, then this must be reported to a doctor to confirm the presence of this abnormality and proceed to perform other confirmatory tests. The experimental results have shown that the BSE evaluation algorithm presented in this paper provides robust performance

Air Pollution in Bangalore, India: A Six-Year Trend and Health Implication Analysis

Air pollution is increasingly becoming a global concern and is believed to be amongst the leading causes of death in the world today. Developing countries, with rapidly growing economies, are struggling between the focus on economic development and curbing air pollution emissions. Bangalore is one of India’s fastest growing metropolises and, although benefiting economically due to its rapid development, has a rapidly deteriorating environment. This paper provides a critical analysis of the air pollution trend in the city over the period 2005-2011 at 6 specific locations where measurements have been consistently recorded. It also discusses the potential health implications pertaining to exceeding levels of pollutants where these are applicable

Spatial Interpolation of Air Pollutants in Bangalore: 2010-2013

Air pollutants and their ill effects on the environment and health of populations are well known. However for informed decisions on the protection of the health of populations from elevated levels of air pollution, an understanding of spatial-temporal variance of air pollutant patterns is necessary. Bangalore and other similar developing cities do not have an adequate number of fixed monitoring stations that could provide a complete coverage of the air pollution levels for the entire city. This can be overcome by using geospatial interpolation techniques that provide a complete coverage of the levels of pollutants. The aim of this study is to locate sample points, characterise distribution patterns, map air pollutant distributions using interpolation techniques, highlight areas exceeding standard levels and in doing so determine spatial and temporal patterns of the levels of air pollutants. An air pollution map indicating levels of the variability of the pollutants will aid in the analysis of effects on health in populations due to elevated levels of pollutants

Effects of Pulsed Electromagnetic Fields on Breast Cancer Cell Line MCF 7 Using Absorption Spectroscopy

We present an analysis of the effects of pulsed electromagnetic fields (PEMF) with 3.3 MHz carrier frequency and modulated by audio resonant frequencies on MCF-7 breast cancer cell line in vitro using absorption spectroscopy. This involves a fluorescence dye called PrestoBlue™ Cell Viability Reagent and a spectrophotometry to test the viability of MCF-7 breast cancer cells under different PEMF treatment conditions in terms of the cell’s absorption values. The DNA molecule of the MCF-7 breast cancer cells has an electric dipole property that renders it sensitive and reactive to applied electromagnetic fields. Resonant frequencies derived from four genes mutated in MCF-7 breast cancer cells [rapamycin-insensitive companion of mammalian target of rapamycin (RICTOR), peroxisome proliferator-activated receptor (PPARG), Nijmegen breakage syndrome 1 (NBN) and checkpoint kinase 2 (CHEK2)] were applied in generating square pulsed electromagnetic waves. Effects were monitored through measurement of absorption of the samples with PrestoBlue™, and the significance of the treatment was determined using the t-test. There was a significant effect on MCF-7 cells after treatment with PEMF at the resonant frequencies of the following genes for specific durations of exposure: RICTOR for 10 minutes, PPARG for 10 minutes, NBN for 15 minutes, and CHEK2 for 5 minutes

Spatio-Temporal Analysis of the Effects of Air Pollution Hazards on Cardiovascular Health Outcomes in Bangalore, India

Recent research has established a link between exposure to certain pollutants and exacerbation or onset of cardiac diseases. Diseases have a spatial context and the evolution of computer applications, such as Geographical Information Systems (GIS), has favoured the studies of environment and their effects on health and populations. To aid in understanding the extent of air pollution and cardiac diseases in the city of Bangalore (India), this research explores the data requirements and GIS analysis tools that could be used to undertake a spatio-temporal analysis by developing a web based GIS application. The ultimate goal is to identify hotspots of air pollution, explore the relationships between environmental pollution hazards and cardiovascular diseases, integrate the available data to enable sharing among decision makers and disseminate information

Impact of COVID-19 on Air Quality in Hanoi and Ho Chi Minh City, Vietnam

Vietnam has had one of the fastest growing economies in Asia over the years. However, the COVID-19 pandemic has proven to be a major hindrance to this growth as the country’s GDP plummeted significantly. Air pollution can further amplify the impact of the pandemic since residents exposed to high levels of pollution are likely to increasingly suffer from respiratory illnesses, such as asthma. This paper investigates the impact of COVID-19 on air quality and how air quality can influence the spread of the virus. Finally, the paper proposes suitable machine learning practices for predicting air quality, based on historical trends, using spatial and temporal data