Maize is a crop of global economic importance and is widely cultivated throughout the Brazilian territory. The use of biostimulants can increase yield and improve crop yield. Unmanned aerial vehicles can be employed in arable areas, allowing their use in an economically way. This study to evaluate the use of biostimulant and the best application timing using photogrammetric indexes in maize, and indicate the most suitable plant index for yield increase through a Pearson\'s correlation. The DJI Drone coupled with RGB camera was used, and the images were processed through the AgisoftPhotoscan® software to generate the orthomosaic, and the QGIS® software version 3.4.15 with GRASS was used to generate thematic maps with the classification of the indexes of vegetation (NGRDI, EXG, SAVI, TGI, GLI, RI). A matrix of Pearson correlation coefficients between the variables was also created, and the results were analyzed with the R software. In general, the products Pyroligneous Extract (PE) and the hormonal product (HP) were the best for the two seasons studied. However, the HP was the best product to mitigate plant water stress in the dry period. Application at phenological stage V3 showed the lowest growth in the rainy season and in application to the seeds in the dry season. Dose 4 of the pyroligneous extract increased productivity in the rainy season and level 3.4 for the hormone product. Among the indexes evaluated, only the SAVI index showed significant differences between the others and showed significance for productivity in the two periods.

Under two detection schemes, this study analyzes one of the most destructive weather systems - the explosive cyclones - in the South Atlantic, from 2010 to 2020. Then, two methods are presented to study these systems: the Observational Method (OBSM) and the Automated Method (AUTM). The first uses visual analysis of the mean sea level pressure (mslp) fields and functions to identify the local minimums using the Grid Analysis and Display (GrADS) software. The second utilizes a function from OpenGrADS called mfhilo. It shows the local minimum in the grid using laplacian, magnitude, and percentile. Two shell algorithms for data manipulation are used for the AUTM: one to trace the cyclones\' trajectories according to a previously defined fixed area and the other to separate them into explosives. The OBSM methodology showed 271 cases averaging 25 yearly and revealed important characteristics regarding the intensities. According to AUTM\'s methodology, from the 2705 ordinary cyclone cases identified, 299 are explosives. There is a clear seasonality pattern in the systems\' distribution along South America, similar to OBSM, but more highlighted. In summer, they concentrate at high latitudes, while in winter and spring, they are assembled near southern Brazilian and Uruguayan coasts.

The relationship between the environment and animal life began to be seen as an important tool to help control zoonoses. Climate variations lead to changes in the environment, which can influence the spatial distribution of species and, consequently, the spread of diseases to humans. Considered the main non-human definitive host species of Schistosoma mansoni in Brazil, the wild rodent Nectomys squamipes plays an important role as a reservoir in maintaining the schistosomiasis cycle in the absence of humans. This study demonstrates the results of ecological niche modeling of intermediate and definitive wild hosts of S. mansoni in the Regional Health Superintendence of Barbacena (Minas Gerais State), which has registered 31 municipalities, 80% of which are classified as endemic for parasitosis. Environmental variables associated with the distribution of each species were used based on information from the scientific collections of Global Biodiversity Information Facility (GBIF) and Species Link to project the ecological niche model in the geographic space. Abiotic variables such as the mean annual temperature, isothermality, and precipitation seasonality were obtained from World Clim. Ecological niche modeling of the wild host, N. squamipes, revealed the occurrence of the species in geographic overlap with the Biomphalaria species. Knowing the influence of bioclimatic variables and identifying favorable conditions for the establishment, occurrence, and distribution of species are important information for developing strategic actions for the surveillance and control of this endemic species. The presence of the definitive wild host needs to be considered by control programs of schistosomiasis.

BACKGROUND: The number of living kidney donors in the United States has declined since 2005, with variations based on the donor-recipient relationship. The reasons for this decline are unclear, and strategies to mitigate declined donations remain elusive. We examined the change in donor number monthly (within-year) versus annually (between-years) to inform potentially modifiable factors for future interventions.
METHODS: In this registry-based cohort analysis of 141 759 living kidney donors between 1995 and 2019, we used linear mixed-effects models for donor number per month and year to analyze between-year and within-year variation in donation. We used Poisson regression to quantify the change in the number of donors per season before and after 2005, stratified by donor-recipient relationship and zip-code household income tertile.
RESULTS: We observed a consistent summer surge in donations during June, July, and August. This surge was statistically significant for related donors (incidence rate ratio [IRR] range: 1.12-1.33) and unrelated donors (IRR range: 1.06-1.16) across donor income tertiles.
CONCLUSIONS: Our findings indicate lower rates of living kidney donation in non-summer months across income tertiles. Interventions are needed to address barriers to donation in non-summer seasons and facilitate donations throughout the year. Since the Organ Donor Leave Law provides a solid foundation for supporting year-round donation, extending the law\'s provisions beyond federal employees may mitigate identified seasonal barriers.

UNASSIGNED: Community-level changes in population mobility can dramatically change the trajectory of any directly-transmitted infectious disease, by modifying where and between whom contact occurs. This was highlighted throughout the COVID-19 pandemic, where community response and nonpharmaceutical interventions changed the trajectory of SARS-CoV-2 spread, sometimes in unpredictable ways. Population-level changes in mobility also occur seasonally and during other significant events, such as hurricanes or earthquakes. To effectively predict the spread of future emerging directly-transmitted diseases, we should better understand how the spatial spread of infectious disease changes seasonally, and when communities are actively responding to local disease outbreaks and travel restrictions.
UNASSIGNED: Here, we use population mobility data from Virginia spanning Aug 2019-March 2023 to simulate the spread of a hypothetical directly-transmitted disease under the population mobility patterns from various months. By comparing the spread of disease based on where the outbreak begins and the mobility patterns used, we determine the highest-risk areas and periods, and elucidate how seasonal and pandemic-era mobility patterns could change the trajectory of disease transmission.
UNASSIGNED: Through this analysis, we determine that while urban areas were at highest risk pre-pandemic, the heterogeneous nature of community response induced by SARS-CoV-2 cases meant that when outbreaks were occurring across Virginia, rural areas became relatively higher risk. Further, the months of September and January led to counties with large student populations to become particularly at risk, as population flows in and out of these counties were greatly increased with students returning to school.

East Asian herbivorous waterfowl intensively use farmland in spring, next to their natural habitat. Accordingly, they might have expanded their migration strategy from merely tracking the green wave of newly emerging vegetation to also incorporating the availability of post-harvest agricultural seeds (here dubbed the seed wave). However, if and how waterfowl use multiple food resources to time their seasonal migration is still unknown. We test this migration strategy using 167 spring migration tracks of five East Asian herbivorous waterfowl species and mixed-effect resource selection function models. We found that all study species arrived at their core stopover sites in the Northeast China Plain after agricultural seeds became available, extended their stay after spring vegetation emerged and arrived at their breeding sites around the emergence of vegetation. At the core stopover sites, all study species used snowmelt as a cue to track seed availability, although smaller-bodied species tended to arrive later. At the breeding sites, swans tracked the onset of vegetation emergence and geese tracked the mid- or end phases of snowmelt. Our findings suggest that waterfowl track multiple resource waves to fine-tune their migration, highlighting new opportunities for conservation.

BACKGROUND: Influenza B/Yamagata viruses exhibited weak antigenic selection in recent years, reducing their prevalence over time and requiring no update of the vaccine component since 2015. To date, no B/Yamagata viruses have been isolated or sequenced since March 2020.
METHODS: The antibody prevalence against the current B/Yamagata vaccine strain in Italy was investigated: For each influenza season from 2012/2013 to 2021/2022, 100 human serum samples were tested by haemagglutination inhibition (HAI) assay against the vaccine strain B/Phuket/3073/2013. In addition, the sequences of 156 B/Yamagata strains isolated during the influenza surveillance activities were selected for analysis of the haemagglutinin genome segment.
RESULTS: About 61.9% of the human samples showed HAI antibodies, and 21.7% had protective antibody levels. The prevalence of antibodies at protective levels in the seasons between the isolation of the strain and its inclusion in the vaccine was between 11% and 25%, with no significant changes observed in subsequent years. A significant increase was observed in the 2020/2021 season, in line with the increase in influenza vaccine uptake during the pandemic. Sequence analysis showed that from 2014/2015 season onward, all B/Yamagata strains circulating in Italy were closely related to the B/Phuket/2013 vaccine strain, showing only limited amino acid variation.
CONCLUSIONS: A consistent prevalence of antibodies to the current B/Yamagata vaccine strain in the general population was observed. The prolonged use of a well-matched influenza vaccine and a low antigenic diversity of B/Yamagata viruses may have facilitated a strong reduction in B/Yamagata circulation, potentially contributing to the disappearance of this lineage.

BACKGROUND: Ongoing conflict between multiple armed groups, including pastoralist herders in the Central African Republic (CAR) causes frequent population displacements, food insecurity and scarcity of healthcare services. The inaccessibility and insecurity of many areas of CAR limit data collection and assessments from national nutritional surveys. Community health workers (CHWs) trained by an international non-governmental organisation, The MENTOR Initiative, deliver basic healthcare to children under 5 years old living in hard-to-reach and conflict-affected areas in eight subprefectures of north-west CAR. Their nutritional status and its associations with geography, malaria, season and conflict are unknown.
METHODS: CHW monthly records (October 2015-August 2021), Armed Conflict Location and Event Data project conflict data and The World Bank Group meteorological data for eight subprefectures of north-west CAR were analysed. Associations between counts of global acute malnutrition (GAM) assessed by mid-upper arm circumference and malaria, season and conflict were investigated using negative binomial regression.
RESULTS: Of the 457,325 consultations with children aged 6-59 months, 6.2% and 0.4% were classified as moderately or severely malnourished, respectively. The negative binomial model demonstrated differences in counts of GAM by subprefecture. Counts of GAM were positively associated with the case rate of severe malaria (IRR = 1.045; 95% CI: 1.04-1.06) and the rainy season (July-September) (IRR = 1.10; 95% CI: 1.03-1.17). Conflict events coded as Battles in ACLED were associated with lower counts of GAM (IRR = 0.78; 95% CI: 0.62-0.97).
CONCLUSIONS: This analysis shows geographical differences in levels of malnutrition in north-west CAR and demonstrates clear associations between malnutrition, season and malaria. It provides evidence that levels of GAM may be underestimated in north-west CAR in areas experiencing conflict. These findings highlight the need for targeted nutritional support to reach children most at risk of malnutrition. CHWs are a proven effective means of delivering essential primary healthcare services in hard-to-reach, conflict-affected areas.

BACKGROUND: Antananarivo, the capital city of Madagascar, is experiencing a steady increase in population growth. Due to the abundance of mosquito vectors in this locality, the population exposed to mosquito-borne diseases is therefore also increasing, as is the risk of epidemic episodes. The aim of the present study was to assess, in a resource-limited setting, the information on mosquito population dynamics and disease transmission risk that can be provided through a longitudinal entomological study carried out in a multi-host single site.
METHODS: Mosquitoes were collected every 15 days over 16 months (from January 2017 to April 2018) using six CDC-light traps in a peri-urban area of Antananarivo. Multivariable generalised linear models were developed using indoor and outdoor densities of the predominant mosquito species as response variables and moon illumination, environmental data and climatic data as the explanatory variables.
RESULTS: Overall, 46,737 mosquitoes belonging to at least 20 species were collected, of which Culex antennatus (68.9%), Culex quinquefasciatus (19.8%), Culex poicilipes (3.7%) and Anopheles gambiae sensu lato (2.3%) were the most abundant species. Mosquito densities were observed to be driven by moon illumination and climatic factors interacting at different lag periods. The outdoor models demonstrated biweekly and seasonal patterns of mosquito densities, while the indoor models demonstrated only a seasonal pattern.
CONCLUSIONS: An important diversity of mosquitoes exists in the peri-urban area of Antananarivo. Some well-known vector species, such as Cx. antennatus, a major vector of West Nile virus (WNV) and Rift-Valley fever virus (RVFV), Cx. quinquefasciatus, a major vector of WNV, Cx. poicilipes, a candidate vector of RVFV and An. gambiae sensu lato, a major vector of Plasmodium spp., are abundant. Importantly, these four mosquito species are present all year round, even though their abundance declines during the cold dry season, with the exception of Cx. quinquefasciatus. The main drivers of their abundance were found to be temperature, relative humidity and precipitation, as well as-for outdoor abundance only-moon illumination. Identifying these drivers is a first step towards the development of pathogen transmission models (R0 models), which are key to inform public health stakeholders on the periods of most risk for vector-borne diseases.

BACKGROUND: Malaria remains an important public health problem, particularly in sub-Saharan Africa. In Rwanda, where malaria ranks among the leading causes of mortality and morbidity, disease transmission is influenced by climatic factors. However, there is a paucity of studies investigating the link between climate change and malaria dynamics, which hinders the development of effective national malaria response strategies. Addressing this critical gap, this study analyses how climatic factors influence malaria transmission across Rwanda, thereby informing tailored interventions and enhancing disease management frameworks.
METHODS: The study analysed the potential impact of temperature and cumulative rainfall on malaria incidence in Rwanda from 2012 to 2021 using meteorological data from the Rwanda Meteorological Agency and malaria case records from the Rwanda Health Management and Information System. The analysis was performed in two stages. First, district-specific generalized linear models with a quasi-Poisson distribution were applied, which were enhanced by distributed lag non-linear models to explore non-linear and lagged effects. Second, random effects multivariate meta-analysis was employed to pool the estimates and to refine them through best linear unbiased predictions.
RESULTS: A 1-month lag with specific temperature and rainfall thresholds influenced malaria incidence across Rwanda. Average temperature of 18.5 °C was associated with higher malaria risk, while temperature above 23.9 °C reduced the risk. Rainfall demonstrated a dual effect on malaria risk: conditions of low (below 73 mm per month) and high (above 223 mm per month) precipitation correlated with lower risk, while moderate rainfall (87 to 223 mm per month) correlated with higher risk. Seasonal patterns showed increased malaria risk during the major rainy season, while the short dry season presented lower risk.
CONCLUSIONS: The study underscores the influence of temperature and rainfall on malaria transmission in Rwanda and calls for tailored interventions that are specific to location and season. The findings are crucial for informing policy that enhance preparedness and contribute to malaria elimination efforts. Future research should explore additional ecological and socioeconomic factors and their differential contribution to malaria transmission.