Regional disparities and seasonal differences in climate risk to rice labour
Ontology highlight
ABSTRACT:
SUBMITTER: Simpson C
PROVIDER: S-EPMC8592055 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Climate change will affect the abundance and seasonality of West Nile virus (WNV) vectors, altering the risk of virus transmission to humans. Using downscaled general circulation model output, we calculate a WNV vector's response to climate change across the southern United States using process-based modeling. In the eastern United States, Culex quinquefasciatus response to projected climate change displays a latitudinal and elevational gradient. Projected summer population depressions as a result of increased immature mortality and habitat drying are most severe in the south and almost absent further north; extended spring and fall survival is ubiquitous. Much of California also exhibits a bimodal pattern. Projected onset of mosquito season is delayed in the southwestern United States because of extremely dry and hot spring and summers; however, increased temperature and late summer and fall rains extend the mosquito season. These results are unique in being a broad-scale calculation of the projected impacts of climate change on a WNV vector. The results show that, despite projected widespread future warming, the future seasonal response of C. quinquefasciatus populations across the southern United States will not be homogeneous, and will depend on specific combinations of local and regional conditions.
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Project description:BackgroundThe objective of the study is to analyse the regions, age and sex differences in the incidence of knee osteoarthritis (KOA).MethodsData were extracted from the global burden of diseases (GBD) 2019 study, including incidence, years lived with disability (YLD), disability-adjusted life-years (DALYs) and risk factors. Estimated annual percentage changes (EAPCs) were calculated to quantify the temporal trends in age standardized rate (ASR) of KOA. Paired t-test, paired Wilcoxon signed-rank test and spearman correlation were performed to analyze the association of sex disparity in KOA and socio-demographic index (SDI).ResultsThere were significant regional differences in the incidence of knee osteoarthritis. In 2019, South Korea had the highest incidence of knee osteoarthritis (474.85,95%UI:413.34-539.64) and Thailand had the highest increase in incidence of knee osteoarthritis (EAPC = 0.56, 95%CI = 0.54-0.58). Notably, higher incidence, YLD and DALYs of knee osteoarthritis were associated with areas with a high socio-demographic index (r = 0.336, p < 0.001; r = 0.324, p < 0.001; r = 0.324, p < 0.001). In terms of age differences, the greatest increase in the incidence of knee osteoarthritis was between the 35-39 and 40-44 age groups. (EAPC = 0.52, 95%CI = 0.40-0.63; 0.47, 95%CI = 0.36-0.58). In addition, there were significant sex differences in the disease burden of knee osteoarthritis (P < 0.001).ConclusionsThe incidence of knee osteoarthritis is significantly different with regions, age and sex.
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Project description:Global warming is predicted to increase in the future, with detrimental consequences for rainfed crops that are dependent on natural rainfall (i.e. non-irrigated). Given that many crops grown under rainfed conditions support the livelihoods of low-income farmers, it is important to highlight the vulnerability of rainfed areas to climate change in order to anticipate potential risks to food security. In this paper, we focus on India, where ~ 50% of rice is grown under rainfed conditions, and we employ statistical models (climate envelope models (CEMs) and boosted regression trees (BRTs)) to map changes in climate suitability for rainfed rice cultivation at a regional level (~ 18 × 18 km cell resolution) under projected future (2050) climate change (IPCC RCPs 2.6 and 8.5, using three GCMs: BCC-CSM1.1, MIROC-ESM-CHEM, and HadGEM2-ES). We quantify the occurrence of rice (whether or not rainfed rice is commonly grown, using CEMs) and rice extent (area under cultivation, using BRTs) during the summer monsoon in relation to four climate variables that affect rice growth and yield namely ratio of precipitation to evapotranspiration (PER), maximum and minimum temperatures (Tmax and Tmin ), and total rainfall during harvesting. Our models described the occurrence and extent of rice very well (CEMs for occurrence, ensemble AUC = 0.92; BRTs for extent, Pearson's r = 0.87). PER was the most important predictor of rainfed rice occurrence, and it was positively related to rainfed rice area, but all four climate variables were important for determining the extent of rice cultivation. Our models project that 15%-40% of current rainfed rice growing areas will be at risk (i.e. decline in climate suitability or become completely unsuitable). However, our models project considerable variation across India in the impact of future climate change: eastern and northern India are the locations most at risk, but parts of central and western India may benefit from increased precipitation. Hence our CEM and BRT models agree on the locations most at risk, but there is less consensus about the degree of risk at these locations. Our results help to identify locations where livelihoods of low-income farmers and regional food security may be threatened in the next few decades by climate changes. The use of more drought-resilient rice varieties and better irrigation infrastructure in these regions may help to reduce these impacts and reduce the vulnerability of farmers dependent on rainfed cropping.
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Project description:Mammalian hibernation presents a unique opportunity to study naturally occurring neuroprotection. Hibernating ground squirrels undergo rapid and extreme physiological changes in body temperature, oxygen consumption, and heart rate without suffering neurological damage from ischemia and reperfusion injury. Different brain regions show markedly different activity during the torpor/arousal cycle: the cerebral cortex shows activity only during the periodic returns to normothermia, while the hypothalamus is active over the entire temperature range. Therefore, region-specific neuroprotective strategies must exist to permit this compartmentalized spectrum of activity. In this study, we use the Illumina HiSeq platform to compare the transcriptomes of these two brain regions at four collection points across the hibernation season: April Active, October Active, Torpor, and IBA. In the cerebral cortex, 1,085 genes were found to be differentially expressed across collection points, while 1,063 genes were differentially expressed in the hypothalamus. Comparison of these transcripts indicates that the cerebral cortex and hypothalamus implement very different strategies during hibernation, showing less than 20% of these differentially expressed genes in common. The cerebral cortex transcriptome shows evidence of remodeling and plasticity during hibernation, including transcripts for the presynaptic cytomatrix proteins bassoon and piccolo, and extracellular matrix components, including laminins and collagens. Conversely, the hypothalamic transcriptome displays upregulation of transcripts involved in damage response signaling and protein turnover during hibernation, including the DNA damage repair gene RAD50 and ubiquitin E3 ligases UBR1 and UBR5. Additionally, the hypothalamus transcriptome also provides evidence of potential mechanisms underlying the hibernation phenotype, including feeding and satiety signaling, seasonal timing mechanisms, and fuel utilization. This study provides insight into potential neuroprotective strategies and hibernation control mechanisms, and also specifically shows that the hibernator brain exhibits both seasonal and regional differences in mRNA expression.
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Project description:We integrated recent research on cardinal temperatures for phenology and early leaf growth, spikelet formation, early morning flowering, transpirational cooling, and heat- and cold-induced sterility into an existing to crop growth model ORYZA2000. We compared for an arid environment observed potential yields with yields simulated with default ORYZA2000, with modified subversions of ORYZA2000 and with ORYZA_S, a model developed for the region of interest in the 1990s. Rice variety 'IR64' was sown monthly 15-times in a row in two locations in Senegal. The Senegal River Valley is located in the Sahel, near the Sahara desert with extreme temperatures during day and night. The existing subroutines underestimated cold stress and overestimated heat stress. Forcing the model to use observed spikelet number and phenology and replacing the existing heat and cold subroutines improved accuracy of yield simulation from EF = -0.32 to EF =0.70 (EF is modelling efficiency). The main causes of improved accuracy were that the new model subversions take into account transpirational cooling (which is high in arid environments) and early morning flowering for heat sterility, and minimum rather than average temperature for cold sterility. Simulations were less accurate when also spikelet number and phenology were simulated. Model efficiency was 0.14 with new heat and cold routines and improved to 0.48 when using new cardinal temperatures for phenology and early leaf growth. The new adapted subversion of ORYZA2000 offers a powerful analytic tool for climate change impact assessment and cropping calendar optimisation in arid regions.
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Project description:BackgroundRegional labour markets and their properties are named as potential reasons for regional variations in levels of SARS-CoV-2 infections rates, but empirical evidence is missing.MethodsUsing nationwide data on notified laboratory-confirmed SARS-CoV-2 infections, we calculated weekly age-standardised incidence rates (ASIRs) for working-age populations at the regional level of Germany's 400 districts. Data covered nearly 2 years (March 2020 till December 2021), including four main waves of the pandemic. For each of the pandemic waves, we investigated regional differences in weekly ASIRs according to three regional labour market indicators: (1) employment rate, (2) employment by sector, and (3) capacity to work from home. We use spatial panel regression analysis, which incorporates geospatial information and accounts for regional clustering of infections.ResultsFor all four pandemic waves under study, we found that regions with higher proportions of people in employment had higher ASIRs and a steeper increase of infections during the waves. Further, the composition of the workforce mattered: rates were higher in regions with larger secondary sectors or if opportunities of working from home were comparatively low. Associations remained consistent after adjusting for potential confounders, including a proxy measure of regional vaccination progress.ConclusionsIf further validated by studies using individual-level data, our study calls for increased intervention efforts to improve protective measures at the workplace, particularly among workers of the secondary sector with no opportunities to work from home. It also points to the necessity of strengthening work and employment as essential components of pandemic preparedness plans.
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Project description:Accurate estimates of seasonal infection risk can be used by animal health officials to predict future disease risk and improve understanding of the mechanisms driving disease dynamics. It can be difficult to estimate seasonal infection risk in wildlife disease systems because surveillance assays typically target antibodies (serosurveillance), which are not necessarily indicative of current infection, and serosurveillance sampling is often opportunistic. Recently developed methods estimate past time of infection from serosurveillance data using quantitative serological assays that indicate the amount of antibodies in a serology sample. However, current methods do not account for common opportunistic and uneven sampling associated with serosurveillance data. We extended the framework of survival analysis to improve estimates of seasonal infection risk from serosurveillance data across population and regional scales. We found that accounting for the right-censored nature of quantitative serology samples greatly improved estimates of seasonal infection risk, even when sampling was uneven in time. Survival analysis can also be used to account for common challenges when estimating infection risk from serology data, such as biases induced by host demography and continually elevated antibodies following infection. The framework developed herein is widely applicable for estimating seasonal infection risk from serosurveillance data in humans, wildlife, and livestock.
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