Background & Objectives: Weather and climate are directly linked to human health including the distribution and occurrence of vector-borne diseases which are of significant concern for public health.
Methods: In this review, studies on spatiotemporal distribution of dengue, Barmah Forest Virus (BFV) and Ross River Virus (RRV) in Australia and malaria in Papua New Guinea (PNG) under the influence of climate change and/ or human society conducted in the past two decades were analysed and summarised. Environmental factors such as temperature, rainfall, relative humidity and tides were the main contributors from climate.
Results: The Socio-Economic Indexes for Areas (SEIFA) index (a product from the Australian Bureau of Statistics that ranks areas in Australia according to relative socio-economic advantage and disadvantage) was important in evaluating contribution from human society.
Interpretation & Conclusion: For future studies, more emphasis on evaluation of impact of the El Niño-Southern Oscillation (ENSO) and human society on spatio-temporal distribution of vector borne diseases is recommended to highlight importance of the environmental factors in spreading mosquito-borne diseases in Australia and PNG.
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http://dx.doi.org/10.4103/0972-9062.337510 | DOI Listing |
Environ Geochem Health
January 2025
School of Civil Engineering, Vellore Institute of Technology, Tamil Nadu, Vellore, 632014, India.
Urban environments are heavily influenced by various activities, leading to contamination of water sources by emerging contaminants (ECs). Among these, caffeine (CAF) and N, N-diethyl-meta-toluamide (DEET) are notable ECs frequently found in domestic sewage due to human activities. Despite extensive research on emerging contaminants, limited studies have focused on the seasonal variations, human health and ecological risks of CAF and DEET in urban groundwater, particularly in Indian cities.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Finance, Zhejiang University of Finance and Economics, Hangzhou, China.
This study explores the intricate dynamics of volatility within high-frequency financial markets, focusing on 225 of Chinese listed companies from 2016 to 2023. Utilizing 5-minute high-frequency data, we analyze the realized volatility of individual stocks across six distinct time scales: 5-minute, 10-minute, 30-minute, 1-hour, 2-hour, and 4-hour intervals. Our investigation reveals a consistent power law decay in the auto-correlation function of realized volatility across all time scales.
View Article and Find Full Text PDFPLoS One
January 2025
Institute of Physical Education, Xinjiang Normal University, Urumqi, Xinjiang, China.
This paper aims to investigate the trend, spatio-temporal distribution, and socioeconomic inequality of the low birthweight rate (LBWR) in China from 1992 to 2021 and to project the LBWR to 2030. We performed a secondary analysis of data from the China Health Statistics Yearbook. LBWR refers to the ratio of the number of infants born with a birth weight less than 2,500 grams to the number of live births in a given year.
View Article and Find Full Text PDFSci Rep
January 2025
Morphogenesis of Macro Algae, UMR8227, CNRS - Sorbonne University, Station Biologique de Roscoff, Place Georges Teissier, Roscoff, 29680, France.
The initiation of embryogenesis in the kelp Saccharina latissima is accompanied by significant anisotropy in cell shape. Using monoclonal antibodies, we show that this anisotropy coincides with a spatio-temporal pattern of accumulation of alginates in the cell wall of the zygote and embryo. Alginates rich in guluronates as well as sulphated fucans show a homogeneous distribution in the embryo throughout Phase I of embryogenesis, but mannuronate alginates accumulate mainly on the sides of the zygote and embryo, disappearing as the embryo enlarges at the start of Phase II.
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January 2025
State Key Laboratory of Desert and Oasis Ecology, Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, 818 South Beijing Road, Urumqi, 830011, China.
Litterfall load is crucial in maintaining ecosystem health, controlling wildfires, and estimating carbon stock in arid regions. However, there is a lack of spatiotemporal analysis of litterfall in arid riparian forests. This study aims to estimate Litterfall load using a BP neural network based on vegetation indices from Landsat 5 and 8 satellite images, litterfall inventory data, slope, and distance to major river tributaries.
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