Changes in the abundance of the house fly, Musca domestica, was studied for a period of one year in two poultry farms in Penang, Malaysia: one in Balik Pulau, located in Penang island, and the other in Juru, located on mainland Penang. The sampling of house flies were carried out from March 2007 to April 2008 using the Scudder grill, and the correlation with meteorological conditions particularly rainfall, relative humidity and temperature were observed. In Balik Pulau, the fly abundance showed an inverse relationship to relative humidity and total rainfall. However, no significant correlations were found between the abundance of flies and the above mentioned climatic factors. In contrast, the occurrence of flies in Juru showed strong correlation indices with relative humidity (r=0.803, p<0.05) and total rainfall (r=0.731, p<0.05). Temperature had no significant effect on the abundance of flies in both poultry farms due to imperceptible changes in monthly temperature.
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JMIR Public Health Surveill
January 2025
School of Public Health, National Defense Medical Center, Taipei City, Taiwan.
Background: Japanese encephalitis (JE) is a zoonotic parasitic disease caused by the Japanese encephalitis virus (JEV), and may cause fever, nausea, headache, or meningitis. It is currently unclear whether the epidemiological characteristics of the JEV have been affected by the extreme climatic conditions that have been observed in recent years.
Objective: This study aimed to examine the epidemiological characteristics, trends, and potential risk factors of JE in Taiwan from 2008 to 2020.
BMC Med
January 2025
Department of Epidemiology, National Vaccine Innovation Platform, School of Public Health, Nanjing Medical University, Nanjing, China.
Background: While previous reports characterised global and regional variations in RSV seasonality, less is known about local variations in RSV seasonal characteristics. This study aimed to understand the local-level variations in RSV seasonality and to explore the role of geographical, meteorological, and socio-demographic factors in explaining these variations.
Methods: We conducted a systematic literature review to identify published studies reporting data on local-level RSV season onset, offset, or duration for at least two local sites.
Nat Commun
January 2025
Centre for Marine Magnetism (CM2, Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China.
Under sustained global warming, Arctic climate is projected to become more responsive to changes in North Pacific meridional heat transport as a result of teleconnections between low and high latitudes, but the underlying mechanisms remain poorly understood. Here, we reconstruct subarctic humidity changes over the past 400 kyr to investigate the role of low-to-high latitude interactions in regulating Arctic hydroclimate. Our reconstruction is based on precipitation-driven sediment input variations in the Subarctic North Pacific (SANP), which reveal a strong precessional cycle in subarctic humidity under the relatively low eccentricity variations that dominated the past four glacial-interglacial cycles.
View Article and Find Full Text PDFJ Food Prot
January 2025
Department of Food Science, University of Arkansas System Division of Agriculture, Fayetteville, AR, USA. Electronic address:
Pathogen contamination and harborage in low-moisture food (LMF) processing environments have resulted in outbreaks and recalls, but researchers are limited in their abilities to investigate solutions. Methods used in most laboratory studies do not accurately reflect the route of contamination or harborage of pathogens in LMF environments, which complicates studying of sanitation methods. Inoculation methods were compared to establish low-moisture food persistent bacterial populations (LMF PBPs) that realistically reflect populations found in LMF environments.
View Article and Find Full Text PDFJ Environ Manage
January 2025
Hubei Subsurface Multi-scale Imaging Key Laboratory, School of Geophysics and Geomatics, China University of Geosciences, Wuhan, China.
Groundwater plays a key role in the water cycle and is used to meet industrial, agricultural, and domestic water demands. High-resolution modeling of groundwater storage is often challenging due to the limitations of observation techniques and mathematical methods. In this study, two machine learning (ML) algorithms, namely random forest (RF) and artificial neural networks (ANNs), were employed to estimate groundwater level anomaly (GWLA) and groundwater storage anomaly (GWSA) with a 0.
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