This paper proposes a tropical cyclone formation prediction network based on pyramid attention feature extraction and multi-scale feature fusion, aiming to enhance the prediction of whether tropical cloud cluster (TCC) precursors intensify to tropical storm (TS) strength by integrating multi-source reanalysis data. First, reanalysis data from ERA5 and NCEP/NCAR are represented as images at different scales and labeled according to tropical cyclone (TC) formation events derived from the TCC and IBTrACS datasets. The labels include information on whether TC formation occurred and its location. Then, a feature extraction module based on a Pyramid Attention Mechanism (PAM) is designed to extract features related to TC formation. Next, the features at different scales are input into a PAM-based feature fusion module that dynamically generates weights for different features to perform weighted fusion and unify the feature scales. Finally, a lightweight Convolutional Neural Network (CNN) is designed as the prediction module to predict TC formation occurrence and location. Experimental results over five independent data splits show that at a lead time of 24 hours, the proposed method achieves a Probability of Detection (POD) of [Formula: see text], a False Alarm Ratio (FAR) of [Formula: see text], and a location prediction Root Mean Square Error (RMSE) of [Formula: see text] grid units (≈437 km), demonstrating competitive performance in balancing POD and FAR. Ablation studies confirm the contribution of each proposed module to overall performance.
Fipronil, a phenylpyrazole insecticide, is widely used for the management of rice pests; however, its environmental fate and potential risks in tropical paddy ecosystems to food safety under varying soil conditions remain insufficiently understood. A field study was conducted in a tropical paddy ecosystem with sandy loam soil to evaluate the uptake, translocation, and dissipation dynamics of fipronil under different soil moisture regimes (flooded, saturated, and field capacity) with and without organic matter amendment. Fipronil and its metabolites in soil and plant samples were quantified using LC-MS/MS following the QuEChERS extraction method validated using standard method validation parameters. Fipronil applied at 75 g a.i. ha⁻¹ (0.3G formulation) at 20 days after transplanting showed a progressive decline in soil residues from 0.70, 0.79, and 1.37 µg g⁻¹ (day 1) to 0.01, 0.02, and 0.03 µg g⁻¹ (30 DAA) under flooded, saturated, and field capacity conditions, respectively, indicating faster dissipation under flooded conditions. The dissipation of fipronil was 1.10% and 0.76% higher under flooded than saturated and field capacity conditions respectively. Organic matter-amended Fipronil soils showed higher persistence, with half-lives of 11.87 days (flooded without organic matter) to 13.75 days (field capacity with organic matter) in cropped soils and 20.82 days in non-cropped soil, indicating prolonged residue retention and potential environmental risks. Plant uptake was rapid, peaking at 2 days after application, followed by a gradual decline. Residue levels in plants were lower under organic amendment, indicating reduced bioavailability. Fipronil predominantly accumulated in roots, followed by stem and leaves, facilitating translocation to aerial parts. Among metabolites, Fipronil sulfone dominated, reaching 0.82 µg g⁻¹ in field capacity, 0.63 µg g⁻¹ in saturated, and 0.60 µg g⁻¹ in flooded soils at 15 DAA, indicating oxidative persistence and higher toxicity. Sulfide (0.10 µg g⁻¹ at 15 DAA), reflecting anaerobic degradation, while desulfinyl remained below 0.01 µg g⁻¹. Harvest residues remained below MRL (< 0.01 µg g⁻¹), indicating low health risk. These findings provide important scientific evidence for developing irrigation- and organic amendment-based pesticide management strategies to support safer fipronil use, residue regulation, and sustainable rice production in tropical paddy ecosystems.
Lameness is a major welfare and economic challenge in dairy herds and is associated with reduced milk yield and compromised milk quality; however, evidence under tropical production systems such as Bangladesh remains limited. This observational study was conducted at a commercial dairy farm in Mymensingh, Bangladesh (July 2024-June 2025) to evaluate the effects of lameness on milk yield and inflammatory milk quality indicators. Sixteen Holstein-Friesian lactating cows were enrolled, including 12 clinically lame cows and 4 healthy controls. Lame cows were observed at three predefined stages: 7 days before clinical diagnosis, on the diagnostic day, and 7 days after diagnosis; healthy cows were sampled once as baseline references. Lameness was assessed using a standardized 1-5 locomotion scoring system. Milk yield was obtained from farm records, and milk quality traits, including pH, electrical conductivity, somatic cell count (SCC), fat, protein, lactose, solid-not-fat, and total solids, were analyzed using Ekomilk Horizon Unlimited. Multivariable linear mixed-effects models revealed a significant reduction in milk yield during lameness (15.0 +/- 0.6 L/day) compared with pre-diagnosis values (19.0 +/- 0.5 L/day; p < 0.001), representing a 21% decline. SCC increased more than two-fold (812.8 +/- 45.6 × 10^3 cells/mL; p < 0.001), accompanied by significant increases in electrical conductivity, pH, milk fat, lactose, and total solids. Diagnostic-day lameness showed strong positive correlations with SCC (r = 0.84) and electrical conductivity (r = 0.89), and a strong negative correlation with milk yield (r = -0.76). These findings indicate that lameness is associated with measurable changes in milk quality and reduced milk production under tropical Bangladeshi field conditions.
Modern savanna fire management is based on climatic season and fire frequency. However, the different biogeographic origins of savannas across the world influence their ecosystem functioning, making them floristically highly dissimilar. We examine the effect of fire frequency on the composition of vegetation of different biogeographic origins, to understand how frequently an ecosystem can be subject to fire but still retain its evolutionary diversity. We surveyed savanna subject to a long-term fire-frequency experiment in Darwin, Australia, and analysed vegetation changes according to species' biogeographic origin as either Sunda (Southeast Asia) or Sahul (ancient Australia and New Guinea). We found dramatic structural and biogeographic change in less than 20 years of frequent burning. Vegetation transformed from dry tropical woodlands into savannas, with the lens of biogeographic origin revealing deeper trends. Plots subject to fire every 1-3 years caused a shift from mostly Sahul-origin, multi-strata vegetation into simple tree-grass systems dominated by Sunda-origin Andropogoneae annual grasses, with ramifications for all Sahul-origin taxa. Despite the common physiognomy of the world's savannas, no single fire-frequency regime suits all-local ecosystem composition and dynamics need to underpin all prescribed burning regimes. In northern Australia, fire management that ultimately promotes the shift to Sunda-origin grasses threatens the continued existence of ancient Sahul-origin plants and animals which had not evolved with grass-fire cycles. We call for inclusion of species origin in analyses of ecosystems wherever modern and ancient elements cohabit. In a policy and management sense, the promotion of annual grasses by frequent fire also affects the management of savannas.
Despite the implementation of ongoing mass drug administration (MDA) campaigns, South Sudan remains endemic for six Preventive Chemotherapy Neglected Tropical Diseases (PC-NTDs); this persistent endemicity is attributed to complex sociocultural barriers and healthcare delivery challenges. Integrating gender equity and social inclusion (GESI) is essential to identify barriers to equitable MDA delivery and develop targeted, inclusive solutions. This study includes a assessment of barriers among adults who did not receive MDA during the most recent campaign, which employed WI-HER's iDARE methodological framework, integrating root cause analysis and qualitative interviews with a total of 256 participants in Awerial County. The study explored factors such as gender roles, cultural norms, and access disparities that affect MDA participation among marginalized populations including nomadic pastoralists, women, internally displaced persons, and persons with disabilities. Findings revealed persistent barriers including geographical challenges, limited awareness, mobility constraints, and sociocultural norms that differentially impact population groups. Gender-specific risk factors were identified, with men exposed through cattle farming and hunting activities, while women faced risks through domestic responsibilities like water collection. Although most respondents (70.4%) perceived equal healthcare access, notable disparities were found in MDA participation, particularly among mobile populations. The study underscores the importance of integrating GESI principles into NTD programming to enhance equitable participation of everyone at risk of the disease and address inequities in access to interventions. Recommendations include gender-responsive strategies, community-driven engagement, and targeted training for MDA implementers. Embedding GESI into NTD programming is essential for equitable MDA coverage and sustainable elimination in post-conflict settings.
Anomaly detection in tropical heritage structures is often constrained by geometry-only point-cloud descriptors that inadequately capture moisture-related radiometric variation, while prior evaluations frequently confound descriptor contributions with detector-specific behaviour and provide limited statistical attribution. This study isolates the contribution of a hybrid geometric-radiometric descriptor through controlled multi-detector validation. We introduce 3D-FPFH-Int, extending Fast Point Feature Histogram with local three-dimensional intensity histograms. An explicit ablation study (geometric-only, radiometric-only, hybrid) is conducted across three detectors-PatchCore, Isolation Forest, and kNN-using 500 synthetic instances (including adversarial weak-contrast subsets) and three non-overlapping field scan segments (two bridge spans and one tunnel segment) comprising 28.9 million points acquired via terrestrial LiDAR. Training and evaluation employ spatially disjoint partitions to prevent data leakage. The statistical protocol includes two-way ANOVA (Descriptor × Detector), Bonferroni-adjusted post-hoc comparisons, bias-corrected and accelerated 95% bootstrap confidence intervals, Cohen's d with confidence bounds, and post-hoc power analysis (1 - β = 0.82 for moderate interaction effects, η2 ≥ 0.05). The hybrid achieves a weighted mean F1 = 0.559 [0.538-0.580], representing a 114% relative improvement over FPFH-only (Cohen's d = 1.38 [1.27-1.49]). The Descriptor × Detector interaction was not statistically significant (p = 0.15, η2 = 0.02), indicating that the relative ranking of descriptors remains broadly consistent across the evaluated detectors within the tested conditions. Under 40% intensity contrast reduction, ΔF1 remains + 0.294 relative to FPFH. Early crack detection (0.3-0.5 mm) yields F1 = 0.158 with localization error < 16 mm. Moisture-related anomaly detection achieves recall = 0.85 [0.80-0.90] with 68% fewer condensation-induced false positives than intensity-only baselines. Performance degradation remains < 12% under ± 20% point-density perturbation and controlled intensity noise. Validation is restricted to masonry and concrete structures in tropical humid environments using terrestrial LiDAR; generalization to other materials, climates, or sensing modalities requires independent verification. Code and synthetic data are publicly available to support reproducibility.
While deep learning-based methods are the potential technological solutions for the diagnosis of skin Neglected Tropical Diseases (skin NTDs), limited efforts were seen toward the use of such tools in Ethiopia. Data scarcity, methods, and models selection issues created further challenges in an attempt to close the previous gap. This study attempts to design a benchmark image-based diagnostic model for skin NTDs through a synergistic combination of feature extraction pretrained models, a custom-designed convolutional neural network (CNN) model trained on the extracted features, and an integrated data augmentation method applied dynamically. For this study, a new skin images dataset is created using skin photographs collected by a team of researchers from the NTDs research center of Arba Minch University Medical College. The new dataset contains 1495 images in 3 classes having severe class imbalance. Extensive experiments were conducted to find the optimal deep learning approach by designing a new CNN model, applying transfer learning, and designing the 2-stage approach that uses pretrained models for feature extraction and trains the new CNN model using the extracted features from the pretrained models and applying data augmentation based on the integrated 2-stage approach. For model selection, the study proposed a novel approach, the funnel framework with cascaded selection of methods and models. After hyperparameter tuning, the model trained using DenseNet121 feature extractor scored the highest accuracy of 96.6%, F1-score of 95%, and sensitivity of 95%, while the MNv2-based model scored comparable results of 95.6% accuracy, 90% F1-score, and 90% sensitivity. This study finally selected the DenseNet121 and MNv2 models for feature extraction to build the final model for skin NTDs classification. The 2-stage approach significantly boosted the models' performance compared with other methods, while the data augmentation method further enhanced the performance of the selected models. Finally, this study suggests further studies using advanced class-balancing methods with more data and a possible integration of other clinical data types.
In regions where Loa loa is coendemic with lymphatic filariasis and onchocerciasis, mass drug administration of ivermectin has posed a risk for serious adverse reactions in individuals with high microfilarial loads. To overcome this problem, easy-to-use point-of-care diagnostic tools are crucial for identifying individuals at risk in population-based control programs. The NTDscope is a device that uses smartphone components for video microscopy and may support decentralized L. loa microfilariae screening under field conditions. This study evaluated the usability and operational feasibility of the NTDscope during population-based screening in a rural setting. A total of 200 adults were screened and 187 adults were enrolled locally by six trained health care workers with no experience using the device. From blood collection to automated analysis, the process was assessed to evaluate usability and identify potential challenges in field deployment. The device was well accepted by health care workers and participants because of its intuitive interface, portability, and rapid result output. Operational challenges included environmental factors and minor technical limitations. Nonetheless, the device performed reliably in the field without reliance on internet connectivity or electricity and required minimal training. Feedback highlighted the need for improved heat management, optimized error recognition, and enhanced data security. The NTDscope proved highly usable in low-resource settings where infection with L. loa is most endemic and offers significant potential for supporting large-scale treatment programs in L. loa-coendemic regions. Although this study focused on field usability, future research should evaluate the implementation in the frame of large-scale treatment.
Culex quinquefasciatus is an abundant domestic mosquito in tropical and subtropical areas, and an important disease vector. Despite its major importance as a vector of Wuchereria bancrofti and the significant biting nuisance caused by nocturnal biting, it has never been the target of any large-scale control programs in Brazil. In contrast, Aedes aegypti, the main vector of dengue, Zika, and other viruses to humans, is the target of multiple control programs worldwide. We tested the efficacy of an insecticidal paint, CARBAPAINT 10 (propoxur 1.0%), against Cx. quinquefasciatus using a delivery method where only the room skirting, close to the floor, is treated with black insecticidal paint, a method designed primarily to target Ae. aegypti. Our results showed that the Cx. quinquefasciatus population in Recife was highly susceptible to the insecticide, with 100% mortality in WHO cone tests. In free-flight tests in an experimental room, painted wall skirtings were also effective, especially if black paint was used as mortality rates were significantly higher than with white insecticidal paint (70% and 30%), p < 0,0001). These results indicate that Cx. quinquefasciatus will also be impacted if this approach is eventually used for vector control of Ae. aegypti. While not essential, the ability to impact all mosquitoes that bite humans is an important prerequisite for determining the likelihood of high acceptance of a control approach by target communities.
Snakebites represent a significant public health concern in tropical and subtropical countries. In 2025, approximately 21,000 incidents of snakebites were reported in Brazil, with 1.7% attributed to coral snakes. Although the incidence of coral snake envenomation in Brazil is lower compared to accidents caused by pit vipers, its venom is highly toxic and always considered a severe medical emergency requiring immediate intervention, a need that highlights the importance of maintaining Micrurus for antivenom production. Maintaining and feeding M. corallinus ex situ presents a significant challenge due to the lack of regular availability of their natural prey, snakes and amphisbaenians. This study developed an artisanal sausage for this species kept ex situ. The artisanal sausage was tested as a supplemental diet alongside traditional prey, focusing on acceptance rates, weight gain, and nutritional adequacy. For the sausage filling we used beef protein mixed with chicken liver, supplemented with essential and non-essential amino acids and calcium carbonate. Our findings revealed no significant difference in acceptance between sausages and snake prey, with 77% acceptance rate for sausages. Weight gain was comparable across diet types, suggesting that the sausages may provide nutritional support to maintain growth under the conditions evaluated. However, because the bromatological analysis was limited to lipids, protein, calcium, and phosphorus, the nutritional adequacy of the sausage diet could not be fully assessed, as other important dietary components were not evaluated. Despite this, the alternative diet should be considered a promising supplemental feeding strategy for Micrurus in captivity, rather than a fully validated substitute for natural prey. Further research is recommended to investigate the long-term health effects of this diet and to refine its formulation for optimal nutritional balance.
Tropical rainforests, particularly the Amazon, function as the Earth's lungs yet absorb mercury (Hg) emitted worldwide. By introducing climate-driven variations in foliar functional traits into a global model of forest Hg uptake, we uncovered an inter-continental spatial decoupling between Hg sources and sinks. Unexpectedly, the minimally industrialized rainforests of South America and Africa exhibit the world's highest atmospheric Hg accumulation rates and greatest biomass, thus disproportionately sequestering Hg released from industrialized regions. This imbalance arises from climate-specific leaf traits that enhance Hg fixation towards lower latitudes. The model constrains global forest Hg uptake to 1155 ± 422 Mg yr-1, sharply reducing prior uncertainties (320-3138 Mg yr-1) and nearly equilibrating with global litterfall deposition (1180 ± 710 Mg yr-1). These findings urge a re-assessment of the Minamata Convention's effectiveness and highlight the vulnerability of tropical forests to anthropogenic Hg inputs and to climate-induced shifts in vegetation and terrestrial Hg reservoirs.
Conventional Water Quality Indices (WQIs) are commonly used to assess anthropogenic impacts on aquatic systems, but they often oversimplify complex parameter interactions, rely on subjective weighting, and inadequately capture spatial or temporal variations. This study hypothesizes that a parameter-based index can enhance diagnostic precision and provide clearer insights into pollutant behavior. The objective was to develop and apply a Parametric WQI that disaggregates water quality into parameter-specific sub-indices for high-resolution monitoring without subjective weighting. Water samples were collected during the peak rainy season from Wupa Sewage Treatment Plant (WSTP), Abuja, Nigeria-covering influent, effluent, the point of discharge (POD), and sites 1-2 km downstream. Thirteen chemical parameters, including nutrients, organic load indicators, and heavy metals, were analyzed. Pollutant levels peaked at the POD but generally declined downstream due to dilution, sedimentation, and microbial degradation. Dissolved Oxygen (DO) increased downstream in June and August but dropped in July (-4.30% at 2 km). TDS rose at the POD in July (+ 16.67%), while TSS fell sharply near the POD (-86.64% in June). Nutrients exhibited strong variability: NH₄⁺ increased (+ 60%), PO₄3⁻ rose sharply (+ 185%), and NO₃⁻ declined (-38.46%), suggesting eutrophication risk. Cu2⁺ spiked (+ 134.17% at POD), whereas Pb2⁺ and Fe2⁺ declined downstream. PWQI classified water quality as poor to very poor at influent, moderate at effluent, and good to excellent downstream. The approach enhances interpretative accuracy, identifies residual contamination (notably NH₄⁺, TSS, and alkalinity), and offers a robust, transferable framework for sustainable wastewater and river system management in tropical environments.
The Mubuku River catchment in western Uganda is highly sensitive to hydroclimatic variability, yet quantitative evidence linking rainfall anomalies to water quality degradation remains limited. This study presents a decadal (2014-2024) integrated assessment combining the Standardized Precipitation Index (SPI), rainfall deviation analysis, GIS-based spatial interpolation, and laboratory evaluation of physicochemical and microbial parameters. Results reveal significant temporal and spatial deterioration in water quality, with Total Dissolved Solids increasing by ~35%, hardness rising from 300 to 400 mg/L, and nitrate concentrations exceeding 50 mg/L at multiple sites. Microbial contamination also intensified, with fecal coliform counts increasing from 50 to 75 CFU/100 mL, surpassing WHO permissible limits. SPI analysis identified recurrent drought years (2014, 2016, 2023, 2024) alongside high-runoff periods, demonstrating a dual vulnerability framework where both precipitation deficits and excesses exacerbate contamination. Spatial analysis highlighted critical hotspots (WS4, WS6, WS8), where pollutant concentrations were 40-60% higher than the catchment average. The findings further indicate that drought conditions enhance mineral dissolution and baseflow contamination, while wet periods accelerate runoff-driven pollutant transport from agricultural and urban sources. This study contributes a novel climate-water quality linkage framework for tropical catchments by integrating hydroclimatic indices with spatial water quality assessment. The results provide actionable insights for targeted interventions, including hotspot-based management, seasonal regulation strategies, and improved wastewater control, to enhance water security and climate resilience.
Land-use and land-cover change (LULCC) is a major source of anthropogenic CO₂ emissions, yet projections remain scarce. Here, we use the reduced-complexity Earth system model OSCAR to generate national LULCC carbon emission trajectories through 2100, across 150 socioeconomic and policy-relevant scenarios. Deforestation and forest regrowth dominate variability in LULCC carbon emission, with policy timing and ambition exerting strong control. Ending gross deforestation by 2030 yields large, persistent removals (about -30 Pg C by 2100), whereas net forest area balance still emits 4-9 Pg C. The strongest sinks are projected to emerge in China and Indonesia, while Brazil and the Democratic Republic of the Congo dominate global sources. The accompanying open dataset enables country-level scenario assembly and policy evaluation. Our findings underscore that early and ambitious land governance, particularly in tropical regions, is essential for transforming the land sector into a durable carbon sink aligned with global temperature goals.
Consumptive interactions across trophic levels underpin ecosystem stability. Although theoretical and experimental studies have examined the diversity-stability relationship, empirical evidence linking trophic interactions to ecosystem stability remains limited. Here we evaluate how trophic interactions relate to stability across 430 plant-herbivorous insect networks from temperate and tropical ecosystems. We quantified interaction indicators alongside ecosystem resistance and resilience under drought and wet events. Higher plant diversity was associated with drought resistance but with reduced resilience, and these relationships weakened after accounting for sampling and environmental covariates. In contrast, drought resistance increased with network modularity, whereas resilience decreased with robustness, although these patterns were absent in temperate forests. Diversity and interaction indicators did not predict stability under wet events. Although both directions of trophic interaction-stability relationships were significant, the pathway from trophic interactions to stability (top-down) was stronger than the reverse. Our findings provide large-scale empirical evidence that top-down effects probably dominate the relationship between plant-herbivorous insect interactions and ecosystem stability under climate change.
The occurrence of elevated concentrations of metallic trace elements (MTEs) in chocolate and cocoa-derived products is a growing global concern. Although agroforestry systems such as cocoa-cabruca (AS-CC) are widely regarded as sustainable, their soils have rarely been evaluated regarding metal-related risks and geogenic or anthropogenic origins. This study was conducted in the largest cocoa-producing region of Brazil, a high-productivity area where monitoring MTEs is essential due to their persistence and potential toxicity in soils. The objectives were to quantify MTE concentrations and vertical distribution in AS-CC soils, assess contamination and ecological risks using geochemical and ecological indices (Igeo, CF, EF, ERᵢ, and PERI), and evaluate potential anthropogenic influences. Cd, Co, Cr, Cu, Mo, Ni, Pb, V, Zn, and Fe were analyzed across AS-CC farms distributed among ten municipalities. A total of 50 composite soil were collected at depths of 0-5, 5-10, 10-20, 20-40, and 40-60 cm. Soil fertility and texture were determined, MTEs were extracted following EPA Method 3051A, and concentrations were measured by ICP-OES. Mean concentrations of Co, Cu, Mo, Pb, and Zn slightly exceeded local soil quality reference values, whereas Cd, Cr, Ni, V, and Fe remained below established thresholds. The Igeo classified 88% of surface samples (0-5 cm) as unpolluted to moderately polluted, and EF values indicated negligible anthropogenic enrichment. Moderate ecological risk was observed only for Cd, while PERI values reflected low overall ecological risk, although elevated Mo and Cd warrant targeted monitoring. Comparable or higher concentrations of Cd, Cr, V, Zn, and Fe at depth reflect the influence of parent materials such as granite, diorite, and basalt. Lithological diversity thus provides a key geochemical framework explaining vertical and spatial variability, reinforcing the importance of integrated, multi-index approaches to guide sustainable soil management in tropical cocoa agroforestry systems.
There is limited evidence regarding the association between weather and Plasmodium vivax (Pv), particulary in Latin America where Pv is the predominant malaria species and key challenge for countries to achieve malaria elimination. We analyzed the association between weather and Pv malaria incidence from 2017 to 2024 in 136 communities in the Peruvian Amazon. Monthly community-level incidence was calculated using Pv case data from Notiweb, the national epidemiological surveillance system, and population census data. Predictors included weekly minimum and maximum temperature and total weekly precipitation and were calculated using hourly weather from the climate dataset ERA5. Non-linear distributed lag models were fit using a lookback period of 2-16 weeks. Temperature models were adjusted for total precipitation; precipitation models were adjusted for maximum temperature. Sub-group analyses were conducted by community type (adjacent to river versus highway) and El Niño Southern Oscillation (ENSO) period. Minimum temperature at the 90th percentile (23.7°C) was associated with 10% (95% CI 5-14%) higher malaria incidence compared to the 5th percentile (20.5°C) at a 7-week lag. Maximum temperature at the 90th percentile (33.7°C) was associated with 10% (95% CI 8-13%) higher malaria incidence compared to the 5th percentile (29.6°C) at a 9-week lag. Total weekly precipitation at the 90th percentile (1000 mm) was associated with 29% (95% CI 24-33%) higher malaria incidence compared to weeks with the 5th percentile (57 mm) at an 11-week lag. Incidence was higher and associations were stronger in communities adjacent to rivers versus highways. Malaria incidence was lower during El Niño periods, and there was evidence of interaction on the multiplicative scale for the association between incidence, all weather predictors, and ENSO period. Pv malaria incidence was positively associated with higher temperatures and precipitation in an elimination setting in Peru, particularly in riverine communities during non-El Niño years, with longer lag periods than previously reported for such associations. These findings can inform malaria elimination interventions to combat the long-lasting effects of weather on Pv transmission.
Schistosomiasis is a neglected tropical disease caused by human-infective schistosomes (Trematoda: Schistosoma). Intestinal schistosomiasis in sub-Saharan Africa and the Neotropics is caused primarily by Schistosoma mansoni and is transmitted by several Biomphalaria planorbid snail species. Adult male and female parasites in the definitive mammalian host pair and reside in the mesenteric vasculature; females lay eggs that traverse the intestinal wall to be excreted, but a significant proportion become trapped in host tissues, especially the liver, eliciting granulomatous immune responses that underlie most disease pathology. Schistosoma mansoni is the primary lab model for research, and, due to the abundance and ease of harvesting, liver-derived eggs are almost exclusively used to maintain the life cycle and to study miracidia and subsequent larval stages. However, recent evidence shows that eggs from the liver or intestine have key morphometric, transcriptomic, and antigenic differences, which can profoundly affect experimental outcomes. To determine whether these differences extend to the miracidia stage, we compared miracidia hatched from mouse liver and intestine-derived eggs, sequencing their transcriptomes and assessing their unstimulated behaviors over time in an arena allowing for high-resolution tracking of miracidia behavior at a large spatiotemporal scale. We found that while transcriptomic profiles of miracidia are distinguishable based on egg tissue origin, only a small subset of genes is differentially expressed. Further, the basic, unstimulated behavior of miracidia that developed in different niches of the definitive host was significantly different. These different behavioral programs may reflect intrinsic developmental programming or differential viability and hardiness related to tissue origin. These findings underscore the importance of egg source in experimental design and interpretation, with significant implications for the maintenance of laboratory life cycles and the use of miracidia in schistosomiasis research.
WRKY transcription factors are major regulators of plant stress responses and development, yet their evolutionary dynamics across major cereals and dicot needs further characterization. Previous studies cataloged WRKY genes individually in single species, but no comprehensive comparative analysis integrating phylogenomic, syntenic, and compositional analyses across the monocot-dicots divide has been conducted. This knowledge gap limits our ability to identify conserved functional constraints versus lineage-specific evolutionary innovations in WRKY regulatory networks. A high-resolution comparative genomic analysis of 547 WRKY genes was performed across seven plant genomes: Arabidopsis thaliana, Oryza sativa subspecies japonica (126 genes), indica (109 genes), and the previously uncharacterized O. glaberrima (51 genes), Brachypodium distachyon (56 genes), Zea mays (105 genes), and Triticum aestivum (52 genes). Phylogenetic analysis revealed distinct group proportions, with Group III representing 70% of total WRKY genes in cereals compared to only 20.8% Group I and 12.8% Group II, representing a monocot-specific expansion. Group III genes constitute the dominant WRKY classification across all cereal species examined, with pronounced enrichment in cereals (mean 64.9%; range 59.6-69.8%) representing a 2.6-fold difference relative to Arabidopsis (25.0%). This cereal-specific expansion is mechanistically driven by tandem duplication events significantly enriched for Group III genes (Fisher's exact test, p = 0.007), maintained under strong purifying selection (mean Ka/Ks = 0.141). Synteny analysis identified 218 collinear gene pairs between rice and Brachypodium, 186 with Zea mays, 164 with Triticum aestivum, and 142 with Arabidopsis, indicating lineage-specific conservation patterns. Evolutionary rate analysis revealed highly conserved WRKY domains (Ka/Ks = 0.08-0.12) juxtaposed against rapidly evolving flanking regions (Ka/Ks = 0.42-0.78), suggesting strong purifying selection on DNA-binding function. t-SNE analysis identified 22 bridge genes with intermediate compositional profiles spanning the monocot-dicot divide, distributed across four cereal lineages and exhibiting structural properties consistent with a directional evolutionary trajectory from ancestral Group I to derived Group III configurations. Notably, O. glaberrima showed reduced Group I representation (9.8%) and elevated Group III proportion (80.4%), indicating lineage-specific retention patterns during independent domestication. This comprehensive analysis establishes a quantitative framework for dissecting WRKY gene family evolution in cereals, identifies stress-responsive orthologs prioritized for crop improvement, and demonstrates that ancient polyploidy, recent segmental duplication, and differential selection pressure collectively shape cereal regulatory architecture. The study provides a foundation for targeted breeding strategies to enhance climate resilience in major cereal and dicot crops.
Dengue is a rapidly expanding mosquito-borne viral disease and a growing global public health challenge. Simultaneously, the prevalence of diabetes mellitus (DM) continues to rise worldwide. DM, often referred to as a silent pandemic, is characterized by chronic inflammation, endothelial dysfunction, and impaired immune responses, mechanisms that may worsen dengue outcomes. Given this context, our study aimed to systematically synthesize evidence on the association between DM and severe dengue outcomes, especially severe dengue and dengue-related mortality. PubMed, Scopus, and Embase were searched, alongside manual screening of reference lists, for English-language observational studies published between 1987 and 2025 reporting dengue outcomes in patients with and without DM. Severe dengue definitions based on both WHO 1997 and WHO 2009 classifications were considered. Screening, data extraction, and risk-of-bias assessment were performed independently by two reviewers, with disagreements resolved by consensus. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) were estimated using random-effects models. A total of 27 eligible studies involving 734,123 participants met the inclusion criteria. Compared with non-diabetic patients, those with DM had significantly higher odds of severe dengue (OR: 2.66, 95% CI: 1.90-3.73) and of dengue-related mortality (OR: 3.95, 95% CI: 2.98-5.23). DM is independently associated with severe dengue and dengue-related mortality. These findings underscore the need for early identification and close monitoring of diabetic patients in dengue-endemic settings, and for integrated management strategies addressing both infectious and non-communicable diseases. Further research is needed to elucidate the underlying mechanisms and evaluate targeted interventions in this vulnerable population.