Physiologically informed organismal climatologies reveal unexpected spatiotemporal trends in temperature.

Body temperature is universally recognized as a dominant driver of biological performance. Although the critical distinction between the temperature of an organism and its surrounding habitat has long been recognized, it remains common practice to assume that trends in air temperature-collected via remote sensing or weather stations-are diagnostic of trends in animal temperature and thus of spatiotemporal patterns of physiological stress and mortality risk. Here, by analysing long-term trends recorded by biomimetic temperature sensors designed to emulate intertidal mussel temperature across the US Pacific Coast, we show that trends in maximal organismal temperature ('organismal climatologies') during aerial exposure can differ substantially from those exhibited by co-located environmental data products. Specifically, using linear regression to compare maximal organismal and environmental (air temperature) climatologies, we show that not only are the magnitudes of body and air temperature markedly different, as expected, but so are their temporal trends at both local and biogeographic scales, with some sites showing significant decadal-scale increases in organismal temperature despite reductions in air temperature, or vice versa. The idiosyncratic relationship between the spatiotemporal patterns of organismal and air temperatures suggests that environmental climatology cannot be statistically corrected to serve as an accurate proxy for organismal climatology. Finally, using quantile regression, we show that spatiotemporal trends vary across the distribution of organismal temperature, with extremes shifting in different directions and at different rates than average metrics. Overall, our results highlight the importance of quantifying changes in the entire distribution of temperature to better predict biological performance and dispel the notion that raw or 'corrected' environmental (and specially air temperature) climatologies can be used to predict organismal temperature trends. Hence, despite their widespread coverage and availability, the severe limitations of environmental climatologies suggest that their role in conservation and management policy should be carefully considered.

Frequency of SARS-CoV-2 variants identified by real-time PCR in the AUNA healthcare network, Peru.

Introduction: In Peru, on 11 February 2023, the Ministry of Health registered 4 million patients infected with COVID-19 and around 219,260 deaths. In 2020, the SARS-CoV-2 virus was acquiring mutations that impacted the properties of transmissibility, infectivity, and immune evasion, leading to new lineages. In the present study, the frequency of COVID-19 variants was determined during 2021 and 2022 in patients treated in the AUNA healthcare network. Methods: The methodology used to detect mutations and identify variants was the Allplex™ SARS-CoV-2 Variants Assay I, II, and VII kit RT-PCR. The frequency of variants was presented by epidemiological weeks. Results: In total, 544 positive samples were evaluated, where the Delta, Omicron, and Gamma variants were identified. The Delta variant was found in 242 (44.5%) patients between epidemiological weeks 39 and 52 in 2021. In the case of Gamma, it was observed in 8 (1.5%) patients at weeks 39, 41, 43, 45, and 46 of 2021. The Omicron variant was the most frequent with 289 (53.1%) patients during weeks 49 to 52 of 2021 and 1 to 22 of 2022. During weeks 1 through 22 of 2022, it was possible to discriminate between BA. 1 (n = 32) and BA.2 (n = 82). Conclusion: The rapid identification of COVID-19 variants through the RT-PCR methodology contributes to timely epidemiological surveillance, as well as appropriate patient management.

Pronounced increases in nitrogen emissions and deposition due to the historic 2020 wildfires in the western U.S.

Wildfire outbreaks can lead to extreme biomass burning (BB) emissions of both oxidized (e.g., nitrogen oxides; NOx = NO+NO2) and reduced form (e.g., ammonia; NH3) nitrogen (N) compounds. High N emissions are major concerns for air quality, atmospheric deposition, and consequential human and ecosystem health impacts. In this study, we use both satellite-based observations and modeling results to quantify the contribution of BB to the total emissions, and approximate the impact on total N deposition in the western U.S. Our results show that during the 2020 wildfire season of August-October, BB contributes significantly to the total emissions, with a satellite-derived fraction of NH3 to the total reactive N emissions (median ~ 40%) in the range of aircraft observations. During the peak of the western August Complex Fires in September, BB contributed to ~55% (for the contiguous U.S.) and ~ 83% (for the western U.S.) of the monthly total NOx and NH3 emissions. Overall, there is good model performance of the George Mason University-Wildfire Forecasting System (GMU-WFS) used in this work. The extreme BB emissions lead to significant contributions to the total N deposition for different ecosystems in California, with an average August - October 2020 relative increase of ~78% (from 7.1 to 12.6 kg ha-1 year-1) in deposition rate to major vegetation types (mixed forests + grasslands/shrublands/savanna) compared to the GMU-WFS simulations without BB emissions. For mixed forest types only, the average N deposition rate increases (from 6.2 to 16.9 kg ha-1 year-1) are even larger at ~173%. Such large N deposition due to extreme BB emissions are much (~6-12 times) larger than low-end critical load thresholds for major vegetation types (e.g., forests at 1.5-3 kg ha-1 year-1), and thus may result in adverse N deposition effects across larger areas of lichen communities found in California's mixed conifer forests.

Satellite Flood Inundation Assessment and Forecast Using SMAP and Landsat.

The capability and synergistic use of multisource satellite observations for flood monitoring and forecasts is crucial for improving disaster preparedness and mitigation. Here, surface fractional water cover (FW) retrievals derived from Soil Moisture Active Passive (SMAP) L-band (1.4 GHz) brightness temperatures were used for flood assessment over southeast Africa during the Cyclone Idai event. We then focused on five subcatchments of the Pungwe basin and developed a machine learning based approach with the support of Google Earth Engine for daily (24-h) forecasting of FW and 30-m inundation downscaling and mapping. The Classification and Regression Trees model was selected and trained using retrievals derived from SMAP and Landsat coupled with rainfall forecasts from the NOAA Global Forecast System. Independent validation showed that FW predictions over randomly selected dates are highly correlated (R = 0.87) with the Landsat observations. The forecast results captured the flood temporal dynamics from the Idai event; and the associated 30-m downscaling results showed inundation spatial patterns consistent with independent satellite synthetic aperture radar observations. The data-driven approach provides new capacity for flood monitoring and forecasts leveraging synergistic satellite observations and big data analysis, which is particularly valuable for data sparse regions.

[Construction of a forecast system for prediction of schistosomiasis risk in China based on the flood information].

OBJECTIVE: To create a model based on meteorological data to predict the regions at risk of schistosomiasis during the flood season, so as to provide insights into the surveillance and forecast of schistosomiasis. METHODS: An interactive schistosomiasis forecast system was created using the open-access R software. The schistosomiasis risk index was used as a basic parameter, and the species distribution model of Oncomelania hupensis snails was generated according to the cumulative rainfall and temperature to predict the probability of O. hupensis snail distribution, so as to identify the regions at risk of schistosomiasis transmission during the flood season. RESULTS: The framework of the web page was built using the Shiny package in the R program, and an interactive and visualization system was successfully created to predict the distribution of O. hupensis snails, containing O. hupensis snail surveillance site database, meteorological and environmental data. In this system, the snail distribution area may be displayed and the regions at risk of schistosomiasis transmission may be predicted using the species distribution model. This predictive system may rapidly generate the schistosomiasis transmission risk map, which is simple and easy to perform. In addition, the regions at risk of schistosomiasis transmission were predicted to be concentrated in the middle and lower reaches of the Yangtze River during the flood period. CONCLUSIONS: A schistosomiasis forecast system is successfully created, which is accurate and rapid to utilize meteorological data to predict the regions at risk of schistosomiasis transmission during the flood period.

Construction of a forecast system for prediction of schistosomiasis risk in China based on the flood information / 中国血吸虫病防治杂志

Objective To create a model based on meteorological data to predict the regions at risk of schistosomiasis during the flood season, so as to provide insights into the surveillance and forecast of schistosomiasis. Methods An interactive schistosomiasis forecast system was created using the open-access R software. The schistosomiasis risk index was used as a basic parameter, and the species distribution model of Oncomelania hupensis snails was generated according to the cumulative rainfall and temperature to predict the probability of O. hupensis snail distribution, so as to identify the regions at risk of schistosomiasis transmission during the flood season. Results The framework of the web page was built using the Shiny package in the R program, and an interactive and visualization system was successfully created to predict the distribution of O. hupensis snails, containing O. hupensis snail surveillance site database, meteorological and environmental data. In this system, the snail distribution area may be displayed and the regions at risk of schistosomiasis transmission may be predicted using the species distribution model. This predictive system may rapidly generate the schistosomiasis transmission risk map, which is simple and easy to perform. In addition, the regions at risk of schistosomiasis transmission were predicted to be concentrated in the middle and lower reaches of the Yangtze River during the flood period. Conclusions A schistosomiasis forecast system is successfully created, which is accurate and rapid to utilize meteorological data to predict the regions at risk of schistosomiasis transmission during the flood period.

Surveillance and risk assessment system of schistosomiasis in Jiangsu Prov-ince I Distribution of surveillance site and effectiveness of the system / 中国血吸虫病防治杂志

Objective To establish the surveillance and risk assessment system of schistosomiasis in Jiangsu Province,so as to provide technical support for timely understanding of the risk of schistosomiasis transmission and implementation of target-ed control measures. Methods The surveillance sites of schistosomiasis were assigned according to the epidemic status and en-demic type of schistosomiasis as well as the characteristics of the water system,and the prevalence of Schistosoma japonicum in humans and domestic animals,and snail status were investigated. In addition,the quality control of serum detection of S. japon-icum infections was performed. The prevalence of human and animal S. japonicum infections,snail status and missing diagnosis of serum detection were analyzed and compared among regions. Results A total of 27 surveillance sites of schistosomiasis were set up in 26 counties of 10 cities,Jiangsu Province,including 14 sites in transmission-interrupted villages and 13 sites in trans-mission-controlled villages,and 15 sites in marshland and lake regions,9 sites in plain regions with water network and 3 sites in mountainous region. In the 27 surveillance sites,a total of 16 617 residents were screened for S. japonicum infection by using dipstick dye immunoassay(DDIA),and 326 were sero-positive,with a sero-prevalence of 1.96%(2.17%for men and 1.8%for women). Of the 326 individuals undergoing parasitological examination,2 positive cases were detected in the marshland and lake region,with a S. japonicum human prevalence of 0.01%. Of the 762 floating population detected,10 were positive for blood test,with a sero-prevalence of 1.31%,and no egg-positive individuals were detected. No infection was found in the 476 do-mestic animals. Of the 746 settings surveyed,a total of 240.7 hm2 snail area was detected,with a mean snail density of 0.06 snails/0.1 m2,and no infected snails were found. There were 780 quality-control sera detected in 26 surveillance sites of schisto-somiasis,and the gross coincidence rate was 95.13%,with misdiagnosis rate of 1.28%and missing diagnosis rate of 19.23%. Conclusion The surveillance sites of schistosomiasis show reasonable distribution in Jiangsu Province,and the endemic situa-tion of schistosomiasis appears a low level in the whole province.

Controle da requeima em batata cv. 'Asterix' como base para modelos de previsão da doença / Controlling potato cv. 'Asterix' late blight base on forecast systems

O controle da requeima da batata requer aplicação freqüente de fungicidas, o que encarece a produção, impactando de modo desnecessário o ambiente. A utilização de modelos de previsão dessa doença permitiria reduzir as aplicações sem afetar a produção. Neste trabalho, objetivou-se avaliar os modelos "Blitecast e Prophy" como referência para o controle da requeima por fungicidas. Os experimentos foram conduzidos na primavera de 2004 e no outono de 2005, em Santa Maria, RS. Os dados meteorológicos foram medidos no centro da área experimental, a 0,10 e a 1,50 m acima da superfície do solo. Utilizaram-se diferentes valores de severidade (VS) acumulada, calculada pelos modelos "Blitecast" (VS= 18, 24, 30, 36 e 42) e "Prophy" (VS= 15, 20, 25, 30 e 35) que se constituíram os tratamentos, adicionando-se o tratamento com aplicação semanal e a testemunha, sem aplicação. O delineamento foi inteiramente casualizado com quatro repetições, sendo cada parcela composta de quatro fileiras de plantas com 5 m de comprimento. Avaliou-se a severidade da requeima por parcela a cada três a cinco dias. Verificou-se que o uso do modelo "Blitecast" com 18 valores de severidade acumulados, incrementou, em pelo menos, 42,6 por cento a produtividade de tubérculos comerciais em relação às áreas sem aplicação de fungicida e reduziu o número de aplicações em 25 por cento nos períodos úmidos e, em 70 por cento nos períodos secos, em relação às aplicações semanais. A eficiência de controle da requeima foi similar à obtida com aplicações semanais de fungicida nos tratamentos Bli18 e Pro15. O uso do modelo "Prophy" requer maior número de aplicações do que o "Blitecast" e não resultou em melhor controle.

The control potato late blight needs a great number of fungicide sprayings. These increase the costs of cropping and cause undesirable environmental impacts. The use of forecast systems to predict disease has the potential of reducing fungicide applications without reducing yield. The objective of this study was to evaluate the performance of Blitecast and Prophy systems as a reference model for predicting late blight potato and support decision of spray fungicides. Experiments were carried out during Spring 2004 and Autumn 2005, in Santa Maria, RS, Brazil. Meteorological data were measured in the center of the experimental area at 0.10 and 1.50 m above soil surface. Different accumulated severity values (VS) were calculated with 'Blitecast' (VS = 18, 24, 30, 36 and 42) and 'Prophy' (VS = 15, 20, 25, 30 and 35) forecast systems. These values were used to perform. Two additional treatments were the weekly sprays and without fungicides. The experimental design was a completely randomized, with four replications. Each plot had four rows plants with 5 m length. Late blight severity was evaluated in each three to five days. The fungicide spraying schedule based on Blitecast system with 18 accumulated severity values increased tuber yield at least 42.6 percent compared to the control without fungicides treatment. It also reduced the number of sprayings by 25 percent during wet periods and 70 percent during dry periods compared to weekly sprayings. The efficiency of controlling late blight was similar to the weekly sprayings treatment in the treatments Bli18 e Pro15. The Prophy model predicted higher number of fungicide sprayings than the Blitecast system and did not improve disease control.