Soil salinity is a major constraint to wheat production because it disrupts plant water relations, photosynthesis, oxidative balance and yield formation. Although numerous physiological traits have been investigated individually, integrative approaches for evaluating coordinated physiological responses to salinity remain limited. This study investigated whether multitrait physiological integration could contribute to the characterization of salinity tolerance in the evaluated wheat genotypes using conventional physiological analyses together with cost–benefit slope analysis and the integrated physiological efficiency index (IPEridge). Three contrasting bread wheat genotypes [BAW 1147 (tolerant), BARI Gom 25 (moderately tolerant) and BARI Gom 28 (susceptible)] were evaluated under control, 5 and 10 dS m-1 electrical conductivity (EC) of the irrigation solution in a split-plot experiment arranged in a randomized complete block design. Water relations, gas exchange, photosynthetic pigments, compatible solute accumulation, oxidative stress, antioxidant defence and yield-related traits were assessed and physiological coordination was further evaluated using cost–benefit slope analysis and the integrated physiological efficiency index (IPEridge). Salinity (EC) of the irrigation solution reduced plant water status, photosynthetic performance, pigment stability and yield while increasing compatible solute accumulation, oxidative stress indicators and antioxidant enzyme activities. Among the evaluated genotypes, BAW 1147 generally maintained higher relative water content, photosynthetic rate, water use efficiency, chlorophyll content and grain yield, together with lower oxidative damage, than BARI Gom 25 and BARI Gom 28. Correlation analysis suggested stronger coordination among physiological and yield-related traits under saline irrigation conditions than under control conditions. Cost–benefit slope analysis revealed genotype- and irrigation solution EC-dependent differences in the relative contributions of the physiological response modules, whereas IPEridge values were generally higher in the tolerant genotype than in the moderately tolerant and susceptible genotypes. The findings suggest that salinity tolerance in the evaluated wheat genotypes was associated with coordinated physiological responses rather than the performance of individual traits alone. The proposed cost–benefit slope analysis and IPEridge index may provide complementary quantitative approaches for characterizing multitrait physiological responses within the present dataset. However, further validation across larger and more diverse wheat populations and environmental conditions is required before their broader applicability can be established.

Tin-lead (Sn-Pb) perovskite (TLP) serve as potential narrow-bandgap absorbers for photovoltaics; unfortunately, issues such as uncontrolled crystallization, Sn2+ oxidation, and interfacial defects continue to limit device efficiency and stability. In this study, we present a chelating coordination strategy at buried and top perovskite interface utilizing ethylenediamine diacetate (EDDA) to synergistically regulate perovskite crystallization and defect passivation in TLP. Post-treatment of PEDOT:PSS using EDDA mitigates surface acidity and reduces insulating PSS-rich domains through ionic exchange, thereby shifting the buried interface potential. The carboxylate (─COO-) groups of EDDA coordinate with undercoordinated Pb2+/Sn2+ cations, while the ─NH3 + groups form hydrogen bonds in TLP. These interactions at buried interface reduce nucleation concentration and promote homogeneous and orderly (100)-facet crystal growth across TLP films. The chemical polishing of the TLP top surface inhibits the Sn2+ oxidation, reduces defects and facilitates more efficient charge extraction. As a result, the Target devices demonstrate a power conversion efficiency (PCE) of 23.30% (0.09 cm2), with an enhanced open-circuit voltage of 0.877 V and a fill factor of 81.81%. Importantly, the Target device shows high stability for 300 s under continuous sunlight at maximum power point tracking and maintains 90% efficiency after 1800 h of storage in N2.

In recent years, significant progress in inverted (p-i-n) perovskite solar cells (PSCs) has been observed, primarily due to the development of innovative self-assembled monolayer (SAM) materials for the hole transport layer (HTL). SAM materials have gained more popularity over the conventional polymer HTL material poly[bis(4-phenyl)(2,4,6-trimethylphenyl)amine] (PTAA) because of their efficient hole transport and ability to passivate interfaces. Conjugated SAMs are considered as a good choice for PSCs because of their electrical and photostability. In this research, methoxy-functionalized conjugated SAMs (C21 and C22) were synthesized and studied for inverted Pb-based PSCs. Good coverage of the perovskite layer was observed on the C21 and C22 SAMs. The SAM molecules on fluorine-doped tin oxide (FTO) tuned the work function (W-F) of the FTO with the number of methoxy groups in the SAM molecule. The methoxy group positioned at ortho- and para-positions was found favorable for the growth of good perovskite layers on the SAM surface. As a result, the C22 SAM with the Pb-based triple cation perovskite in the inverted PSC exhibited a champion power conversion efficiency (PCE) of 21.58%. These findings suggest that conjugated SAM functionalized with methoxy groups at the ortho- and para-positions are promising for the further development of inverted PSCs.

Lead (Pb)-Tin (Sn) mixed perovskite solar cells (LTPSCs) attract considerable attention due to their potential application as rear subcells in tandem solar cells. However, the LTPSCs suffer from low fill factor (FF) and lower open-circuit voltage (V OC) due to a large energy mismatch between the perovskite and charge-transport materials, as well as inadequate crystallization. Here, based on the self-adaptive anhydrous passivation strategy, a multifunctional anhydrous molecule, Trimellitic Anhydride (TMAH), is introduced into the perovskite precursor. Owing to the self-adaptive property of TMAH, it generates its in situ hydrolysis product, Trimellitic Acid (TMA). The -C & boxH;O and -COO- functional groups in TMAH, together with TMA, form stable chelate complexes with metal cations (Pb2+/Sn2+), thereby regulating the nucleation and crystallization. In addition, TMA facilitates secondary defect passivation and improves resistance to moisture and oxygen infiltration, thereby protecting the perovskite from harsh environmental conditions. As a result, TMAH together with TMA promotes (100)-oriented crystal formation, lowers the perovskite work function, and provides better energy-level alignment with the carrier transport layer, thereby reducing the V OC and FF deficiencies. Ultimately, a power conversion efficiency of 24.05% is achieved for LTPSCs with the highest V OC value of 0.905 V and exhibited robust operational stability.

Salinity severely constrains wheat productivity by disrupting biomass allocation and ionic homeostasis. Although many studies have described individual physiological responses to salinity, fewer have integrated organ-specific ion regulation with whole-plant biomass dynamics to explain why some genotypes tolerate salt better than others do. To address this gap, the present study aimed to identify the physiological mechanisms that underpin salinity tolerance by combining biomass partitioning, organ-resolved ion profiling and multivariate causal modelling. Three wheat genotypes—BAW 1147 (tolerant), BARI Gom 25 (moderately tolerant), and BARI Gom 28 (susceptible)—were evaluated under control, 5 dS m⁻¹ and 10 dS m⁻¹ salinity conditions. The plants were subsequently divided into nine organs to quantify dry weight (DW) and the organ-specific ion status of sodium (Na⁺), potassium (K⁺), calcium (Ca²⁺) and magnesium (Mg²⁺). Salinity reduced the total DW in all the genotypes, but BAW 1147 consistently retained more organ-specific biomass (roots, stems, flag leaf blades) and preserved reproductive allocation. Ion profiles in BAW 1147 revealed increased sequestration of Na⁺ in roots and structural tissues. In contrast, Na⁺ concentrations remained lower in flag leaves and grain. Moreover, K⁺, Ca²⁺ and Mg²⁺ levels were maintained across organs, resulting in superior K⁺:Na⁺ ratios in photosynthetic and reproductive tissues. Correlation analysis indicated stress-dependent strengthening of positive links among biomass traits and beneficial cations, whereas principal component analysis resolved PC₁ as a tolerance axis characterized by increased biomass; higher K⁺, Ca²⁺ and Mg²⁺ concentrations; increased K⁺:Na⁺ ratios; and decreased Na⁺ levels. Structural equation modelling (SEM) revealed K+:Na+ as the strongest positive causal determinant of shoot biomass and total biomass, with Na⁺ exerting negative effects on photosynthetic and yield tissues. Overall, this integrative approach demonstrates that wheat salinity tolerance arises from coordinated biomass buffering and strategic ion partitioning across organs. These findings provide practical physiological indicators, such as high K+:Na+, stable Ca²⁺-Mg²⁺ homeostasis and balanced root investment, that can be used to select and develop salt-resilient wheat genotypes for coastal and irrigated saline environments.

Bangla language consists of fifty distinct characters and many compound characters to be named. Several notable studies have been performed to recognize the Bangla characters both as handwritten and optical characters. Our approach is to use transfer learning to classify the basic distinct as well as compound Bangla Handwritten characters avoiding the vanishing gradient problem. Deep Neural Network techniques such as 3D Convolutional Neural Network (3DCNN), Residual Neural Network (ResNet), and MobileNet have been applied to generate an end-to-end classification of all the possible standard formation of the handwritten characters in Bangla language. Bangla Lekha Isolated dataset is used to apply this classification model, which has a total of 1,66,105 Bangla Character images sample data categorized in 84 distinct classes. This classification model achieved 99.82% accuracy on training data and 99.46% accuracy on test data. Comparison has been made among the various state-of-the-art benchmarks of Bangla Handwritten Characters classification, which shows that this proposed model got better accuracy in classifying the data.

Flash floods in the Haor regions of Bangladesh frequently destroy Boro rice crops at maturity or near maturity, leading to farmers' reluctance to invest adequately in cultivation, resulting in poor crop management and reduced productivity. However, adopting improved agricultural practices, such as selecting suitable rice varieties and optimizing planting schedules, can significantly enhance Boro rice yields and mitigate the impact of flash floods. An on-farm experiment was conducted from December 2021 to May 2022 in the flood-prone region of Sunamganj, Bangladesh, using a three-factor split-split plot design with three replications. The study evaluated the effects of sowing dates (15 December and 30 December), rice varieties (BRRI dhan28, a short-duration variety; and BRRI dhan92, a long-duration variety), and management practices (poor, moderate, and good) on Boro rice production. Results indicated that good management practices led to significant yield increases of 22.2% and 55%, along with gross margin improvements of 23.8% and 64.5%, compared to moderate and poor management. The long-duration variety (BRRI dhan92) performed best with early planting, while the short-duration variety (BRRI dhan28) excelled with later planting. Despite the high yield potential of BRRI dhan92, early planting posed a higher risk of flash flood damage at maturity. These findings highlight the importance of strategic cultivar selection, optimized planting times, and improved management practices to maximize Boro rice productivity in flood-prone regions and reduce the risk of crop loss due to flash floods.

Wide-bandgap perovskite materials are gaining enormous attention recently, particularly in multijunction photovoltaics. Despite the encouraging development, light-induced phase segregation still impedes their operational stability, primarily due to the high content of bromide constituents. Here, we report a bilateral interface design to mitigate the phase instability of 2.1 eV bandgap all-inorganic CsPbIBr2 perovskite solar cells (PSCs)─(1) buried interface: strong chemical interactions occur between nickel oxide (NiOx) and self-assembled monolayer (SAM) via phosphonic acid anchoring groups, establishing an interfacial bridge that promotes efficient hole extraction. (2) Top surface: a solution-processed BCP (s-BCP) layer is introduced to passivate the perovskite film and suppress trap-assisted recombination, resulting in reduced phase segregation. The synergistic effect of dual interfaces reduces defect formation, moisture penetration, and phase transition, contributing to enhanced phase stability. Optimal energetic alignment and defect passivation lead to improved photovoltaic (PV) performance. As a result, the dual interface modification delivers a power conversion efficiency (PCE) of 10.2% with a fill factor of 82.3%. Additionally, the modified device retains >87% of its initial efficiency after 110 h of continuous operation and exhibits merely 5% degradation after 300 days of storage, which is one of the most stable performances reported for all-inorganic CsPbIBr2 PSCs. This work reveals a key strategy to address inherent phase instability in wide-bandgap perovskites through interface engineering.

Business sentiment analysis (BSA) is one of the significant and popular topics of natural language processing. It is one kind of sentiment analysis techniques for business purpose. Different categories of sentiment analysis techniques like lexicon-based techniques and different types of machine learning algorithms are applied for sentiment analysis on different languages like English, Hindi, Spanish, etc. In this paper, long short-term memory (LSTM) is applied for business sentiment analysis, where recurrent neural network is used. LSTM model is used in a modified approach to prevent the vanishing gradient problem rather than applying the conventional recurrent neural network (RNN). To apply the modified RNN model, product review dataset is used. In this experiment, 70% of the data is trained for the LSTM and the rest 30% of the data is used for testing. The result of this modified RNN model is compared with other conventional RNN models and a comparison is made among the results. It is noted that the proposed model performs better than the other conventional RNN models. Here, the proposed model, i.e., modified RNN model approach has achieved around 91.33% of accuracy. By applying this model, any business company or e-commerce business site can identify the feedback from their customers about different types of product that customers like or dislike. Based on the customer reviews, a business company or e-commerce platform can evaluate its marketing strategy.

The paradise threadfin, Polynemus paradiseus, is an anadromous fish in Bangladesh that enters the freshwater river system from saline water (the Bay of Bengal) to spawn. The gut bacteria of paradise threadfin were characterized both physiologically and molecularly, and their antibiogram profile was formulated after collection from the Tetulia River, Bhola, Bangladesh. A total of 24 isolates were identified under the genera Bacillus, Salmonella, Brucella, Enterobacter, Citrobacter, Pseudomonas, and Kluyvera. Pseudomonas was one of the most dominant genera based on bacterial abundance in the gut of P. paradiseus. The majority of bacterial isolates showed maximum growth between 30 and 40 °C and at pH 8.5. All of the strains were multidrug resistant and showed up to 100% resistance to cefixime, penicillin, ampicillin, and neomycin. Strains of Enterobacter, Citrobacter, Kluyvera, and Pseudomonas showed 100% resistance to seven antibiotics. Bangla. J. Microbiol. 2025, Vol. 41, P: 1-6

Tin–lead (Sn–Pb) mixed perovskite solar cells (PSCs) are promising as bottom subcells in all‐perovskite tandem solar cells, but the oxidation of Sn 2+ remains challenging for long‐term stability. This study reveals a compositional gradient in Sn–Pb perovskite films, where excess Sn ions accumulate at the surface, intensifying oxidation and recombination losses. To address this issue, we introduce a PbBr 2 ‐TOAB wet surface treatment strategy during the fabrication of Sn–Pb perovskite films. X‐ray photoelectron spectroscopy analysis confirms that this treatment achieves more balanced Sn:Pb stoichiometry from ~4.36:1 to ~2.74:1, ensuring improved film quality and resistance to Sn 2+ oxidation. The treatment strategy boosts the power conversion efficiency (PCE) to 21.61% (0.09 cm 2 ), with an independently certified (from the National Institute of Advanced Industrial Science and Technology) PCE of 19.12%, compared to 21.13% for control PSCs. Moreover, target larger PSCs (1 cm 2 ) achieved an impressive PCE of 20.83%. The target PSCs show enhanced stability, retaining 80% of their initial PCE after 50 h of continuous light soaking. More importantly, encapsulated target PSCs maintain 95% of their initial PCE for 300 s under continuous illumination at maximum power point tracking conditions. Time‐dependent photoluminescence examination confirms that PbBr 2 ‐TOAB treatment significantly reduces ion migration, improving stability under light illumination.

Deep learning is used to tackle a wide range of real-world problems. Face anti-spoofing is one of them which refers to the process of stopping fraudulent facial verification by substituting a mask, image, video, or other image to authorize individuals. Face anti-spoofing is important to prevent print and replay attacks that pose a significant danger to facial recognition systems. Numerous algorithms have been suggested to prevent such fraud. However, they showed poor accuracy. Therefore, we developed a detection method to detect facial movement and texture signals. The optical flows of a continuous video clip were extracted and analyzed for the movement's amplitude and direction. Next, the video frames were concatenated with the recovered optical flows as the network's input. To distribute the classification weights in an adaptable manner, region, and channel attention techniques were concurrently introduced. Finally, the combined motion and texture cues were sent into a convolutional network to extract features and determine whether the input video sequence represented a real face or not. Experimental results showed that the method showed high accuracy in detecting fraud.

Elevated atmospheric heat is considered as one of the bottlenecks for global wheat production. Screening potential wheat genotypes against heat stress and selecting some suitable indicators to assist in understanding thermotolerance could be crucial for sustaining wheat cultivation. Accordingly, 80 diverse bread wheat genotypes were evaluated in controlled lab condition by imposing a week-long heat stress (35/25 °C D/N) at the seedling stage. The response of heat stress was evaluated using multivariate analysis techniques on 20 morpho-physiological traits. Results showed significant variations in the studied traits due to the imposition of heat stress. Eleven seedling traits that contributed significantly to the genotypic variability were identified using principal component analysis (PCA). A substantial correlation between most of the selected seedling attributes was observed. Hierarchical cluster analysis identified three distinct clusters among the tested wheat genotypes. Cluster 1, consisting of 33 genotypes, exhibited the highest tolerance to heat stress, followed by Cluster 2 (18 genotypes) with moderate tolerance and Cluster 3 (29 genotypes) showing susceptibility. Linear discriminant analysis (LDA) approved that nearly 93 % of the wheat genotypes were appropriately ascribed to each cluster. The squared distance analysis confirmed the distinct nature of the clusters. Using multi-trait genotype-ideotype distance index (MGIDI), all 12 identified tolerant genotypes (BG-30, BD-468, BG-24, BD-9908, BG-32, BD-476, BD-594, BD-553, BD-488, BG-33, BD-495, and AS-10627) originated from Cluster 1. Selection gain in MGIDI analysis, broad-sense heritability, and multiple linear regression analysis together identified shoot and root dry and fresh weights, chlorophyll contents (a and total), shoot tissue water content, root-shoot dry weight ratio, and efficiency of photosystem II (PS II) as the most vital discriminatory factors explaining heat stress tolerance of 80 wheat genotypes. The identified genotypes with superior thermotolerance would offer resourceful genetic tools for breeders to improve wheat yield in warmer regions. The traits found to have greater contribution in explaining heat stress tolerance will be equally important in prioritizing future research endeavors.

The implementation of salt stress mitigation strategies aided by microorganisms has the potential to improve crop growth and yield. The endophytic fungus Metarhizium anisopliae shows the ability to enhance plant growth and mitigate diverse forms of abiotic stress. We examined the functions of M. anisopliae isolate MetA1 (MA) in promoting salinity resistance by investigating several morphological, physiological, biochemical, and yield features in rice plants. In vitro evaluation demonstrated that rice seeds primed with MA enhanced the growth features of rice plants exposed to 4, 8, and 12 dS/m of salinity for 15 days in an agar medium. A pot experiment was carried out to evaluate the growth and development of MA-primed rice seeds after exposing them to similar levels of salinity. Results indicated MA priming in rice improved shoot and root biomass, photosynthetic pigment contents, leaf succulence, and leaf relative water content. It also significantly decreased Na+/K+ ratios in both shoots and roots and the levels of electrolyte leakage, malondialdehyde, and hydrogen peroxide, while significantly increasing proline content in the leaves. The antioxidant enzymes catalase, glutathione S-transferase, ascorbate peroxidase, and peroxidase, as well as the non-enzymatic antioxidants phenol and flavonoids, were significantly enhanced in MA-colonized plants when compared with MA-unprimed plants under salt stress. The MA-mediated restriction of salt accumulation and improvement in physiological and biochemical mechanisms ultimately contributed to the yield improvement in salt-exposed rice plants. Our findings suggest the potential use of the MA seed priming strategy to improve salt tolerance in rice and perhaps in other crop plants.

Water stress is a major constraint for crop productivity and culturing right cultivars may produce a considerable yield under such stressful condition. An experiment was conducted inside a vinyl house to evaluate the effect of water stress on dry matter distribution, yield, and seed quality of eight soybean genotypes, viz. G00006, BD2336, AGS383, PK472, BCS-1, NCS-1, BU Soybean-1 and BARI Soybean-6. They were grown in pots and subjected to water stress (20% of field capacity, FC) and control (80% of FC). The water stress reduced plant height, leaf number, leaf, stem, and root dry matter by 23, 45, 46, 45 and 19%, respectively, across the genotypes. Under water stress, the soybean genotypes G00006, BCS-1, NCS-1 and BARI Soybean-6 beard only 6 to 30% pod and 5 to 34% seed compared to the control condition. The results further indicated that yield of BD2336 and AGS383 were less affected by the stress than those of other genotypes. Interestingly, water stress exerted positive effect on seed germination, viability, speed of germination and vigor index in BD2336 and AGS383, respectively. Nitrogen and seed protein content were found the highest in BCS-1 under control (9.82 and 58.42% respectively) followed by AGS383. Phosphorus content in seed also reduced by the stress in the tested genotypes, except BD2336 (0.29% in control and 0.96% in water stress) and BARI Soybean-6. Potassium content in seed was reduced by the stress in the tested genotypes, except G00006 and BARI Soybean-6. Based on the findings related to water stress effects on yield and seed quality, particularly seed protein of the tested eight soybean genotypes, it was concluded that genotypes AGS383 and BD2336 might be considered for field trial under water deficit condition.

Deep learning holds great significance in machine learning since it effectively addresses a wide range of problems. The ability to identify violence from various video surveillance systems is one such crucial real-world application. Many crimes are occurring in numerous public spaces as a result of inadequate security. Many methods have been proposed to solve this specific problem, however they have drawbacks. Additionally, they are ineffective since they are dependent on certain conditions. Therefore, we presented an efficient automatic violence detection method for video datasets. We created a video dataset consisting of 1000 videos, half of which featured violent content and the other half did non-violent content. We used a Deep Neural Network technique called MobilNet for detecting violence from videos. Additionally, we employed a variety of deep learning and machine learning strategies to increase the precision. With the training data, the classification model showed an accuracy of 97.50% while with the test data, the accuracy was 95.60%. Performance evaluation results demonstrated that the suggested method identified violent content in videos successfully. The method for video violence detection performed better than many other methods already in use.

Developing high-performance and stable Sn-based perovskite solar cells (PSCs) is difficult due to the inherent tendency of Sn2+ oxidation and, the huge energy mismatch between perovskite and Phenyl-C61-butyric acid methyl ester (PCBM), a frequently employed electron transport layer (ETL). This study demonstrates that perovskite surface defects can be passivated and PCBM's electrical properties improved by doping n-type polymer N2200 into PCBM. The doping of PCBM with N2200 results in enhanced band alignment and improved electrical properties of PCBM. The presence of electron-donating atoms such as S, and O in N2200, effectively coordinates with free Sn2+ to prevent further oxidation. The doping of PCBM with N2200 offers a reduced conduction band offset (from 0.38 to 0.21 eV) at the interface between the ETL and perovskite. As a result, the N2200 doped PCBM-based PSCs show an enhanced open circuit voltage of 0.79 V with impressive power conversion efficiency (PCE) of 12.98% (certified PCE 11.95%). Significantly, the N2200 doped PCBM-based PSCs exhibited exceptional stability and retained above 90% of their initial PCE when subjected to continuous illumination at maximum power point tracking for 1000 h under one sun. The primary challenges related to Sn-based perovskite solar cells (PSCs) are the inherent susceptibility of Sn2+ oxidation and the significant energy mismatch between the perovskite and electron transport layer (ETL). Doping of n-type polymer (N2200) into PCBM retards the Sn2+ oxidation and reduces the conduction band offset energy at the perovskite/ETL interface. This makes Sn-PSCs highly stable under operational conditions. image

BACKGROUND:A number of studies have detected relationships between weather and diarrhea. Few have investigated associations with specific enteric pathogens. Understanding pathogen-specific relationships with weather is crucial to inform public health in low-resource settings that are especially vulnerable to climate change. OBJECTIVES:Our objectives were to identify weather and environmental risk factors associated with diarrhea and enteropathogen prevalence in young children in rural Bangladesh, a population with high diarrheal disease burden and vulnerability to weather shifts under climate change. METHODS:We matched temperature, precipitation, surface water, and humidity data to observational longitudinal data from a cluster-randomized trial that measured diarrhea and enteropathogen prevalence in children 6 months-5.5 years from 2012-2016. We fit generalized additive mixed models with cubic regression splines and restricted maximum likelihood estimation for smoothing parameters. RESULTS:Comparing weeks with 30°C versus 15°C average temperature, prevalence was 3.5% higher for diarrhea, 7.3% higher for Shiga toxin-producing Escherichia coli (STEC), 17.3% higher for enterotoxigenic E. coli (ETEC), and 8.0% higher for Cryptosporidium. Above-median weekly precipitation (median: 13mm; range: 0-396mm) was associated with 29% higher diarrhea (adjusted prevalence ratio 1.29, 95% CI 1.07, 1.55); higher Cryptosporidium, ETEC, STEC, Shigella, Campylobacter, Aeromonas, and adenovirus 40/41; and lower Giardia, sapovirus, and norovirus prevalence. Other associations were weak or null. DISCUSSION:Higher temperatures and precipitation were associated with higher prevalence of diarrhea and multiple enteropathogens; higher precipitation was associated with lower prevalence of some enteric viruses. Our findings emphasize the heterogeneity of the relationships between hydrometeorological variables and specific enteropathogens, which can be masked when looking at composite measures like all-cause diarrhea. Our results suggest that preventive interventions targeted to reduce enteropathogens just before and during the rainy season may more effectively reduce child diarrhea and enteric pathogen carriage in rural Bangladesh and in settings with similar meteorological characteristics, infrastructure, and enteropathogen transmission.