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Scrub typhus, a vector-borne bacterial infection, is an important but neglected disease globally. Accurately characterizing the burden is challenging because of nonspecific symptoms and limited diagnostics. Prior seroepidemiology studies have struggled to find consensus cutoffs that permit comparisons of estimates across contexts and time. In this study, we present a novel approach that does not require a cutoff and instead uses information about antibody kinetics after infection to estimate seroincidence. We use data from three cohorts of scrub typhus patients in Chiang Rai, Thailand, and Vellore, India, to characterize antibody kinetics after infection and two population serosurveys in the Kathmandu Valley, Nepal, and Tamil Nadu, India, to estimate seroincidence. The samples were tested for IgM and IgG responses to Orientia tsutsugamushi-derived recombinant 56-kDa antigen using commercial enzyme-linked immunosorbent assay kits. We used Bayesian hierarchical models to characterize antibody responses after scrub typhus infection and used the joint distributions of the peak antibody titers and decay rates to estimate population-level incidence rates in the cross-sectional serosurveys. Median responses persisted above an optical density (OD) of 1.8 for 23.6 months for IgG and an OD of 1 for 4.5 months for IgM. Among 18- to 29-year-olds, the seroincidence was 10 per 1,000 person-years (95% CI, 5-19) in Tamil Nadu, India, and 14 per 1,000 person-years (95% CI: 10-20) in the Kathmandu Valley, Nepal. When seroincidence was calculated with antibody decay ignored, the disease burden was underestimated by more than 50%. The approach can be deployed prospectively, coupled with existing serosurveys, or leverage banked samples to efficiently generate scrub typhus seroincidence estimates.

BackgroundHepatitis-E virus (HEV), an etiologic agent of acute inflammatory liver disease, is a significant cause of morbidity and mortality in South Asia. HEV is considered endemic in Nepal; but data on population-level infection transmission is sparse.MethodsWe conducted a longitudinal serosurvey in central Nepal to assess HEV exposure. At each visit, capillary blood samples were collected and analyzed for the presence of anti-HEV IgG antibodies. The study took place between February 2019 and April 2021, with up to 4 visits per participant approximately 6 months apart.ResultsWe collected 2513 samples from 923 participants aged 0-25 years, finding a seroprevalence of 4.8% and a seroincidence rate of 10.9 per 1000 person-years. Young adults and individuals consuming surface water faced the highest incidence of infection. Geospatial analysis identified potential HEV clusters, suggesting a need for targeted interventions.SignificanceOur findings demonstrate that HEV is endemic in Nepal and that the risk of infection increases with age.

BACKGROUND: Environmental surveillance, using detection of Salmonella Typhi DNA, has emerged as a potentially useful tool to identify typhoid-endemic settings; however, it is relatively costly and requires molecular diagnostic capacity. We sought to determine whether S. Typhi bacteriophages are abundant in water sources in a typhoid-endemic setting, using low-cost assays. METHODOLOGY: We collected drinking and surface water samples from urban, peri-urban and rural areas in 4 regions of Nepal. We performed a double agar overlay with S. Typhi to assess the presence of bacteriophages. We isolated and tested phages against multiple strains to assess their host range. We performed whole genome sequencing of isolated phages, and generated phylogenies using conserved genes. FINDINGS: S. Typhi-specific bacteriophages were detected in 54.9% (198/361) of river and 6.3% (1/16) drinking water samples from the Kathmandu Valley and Kavrepalanchok. Water samples collected within or downstream of population-dense areas were more likely to be positive (72.6%, 193/266) than those collected upstream from population centers (5.3%, 5/95) (p=0.005). In urban Biratnagar and rural Dolakha, where typhoid incidence is low, only 6.7% (1/15, Biratnagar) and 0% (0/16, Dolakha) river water samples contained phages. All S. Typhi phages were unable to infect other Salmonella and non-Salmonella strains, nor a Vi-knockout S. Typhi strain. Representative strains from S. Typhi lineages were variably susceptible to the isolated phages. Phylogenetic analysis showed that S. Typhi phages belonged to the class Caudoviricetes and clustered in three distinct groups. CONCLUSIONS: S. Typhi bacteriophages were highly abundant in surface waters of typhoid-endemic communities but rarely detected in low typhoid burden communities. Bacteriophages recovered were specific for S. Typhi and required Vi polysaccharide for infection. Screening small volumes of water with simple, low-cost (~$2) plaque assays enables detection of S. Typhi phages and should be further evaluated as a scalable tool for typhoid environmental surveillance.

Abstract Background While several validated sports nutrition knowledge questionnaires exist, none are specifically designed to assess the sports nutrition knowledge (SNK) of Sri Lankan track and field athletes. This study aims to validate the Sri Lankan Sports Nutrition Knowledge Questionnaire (SLn-SNKQ), tailored for this athlete group, to provide more accurate and meaningful insights for research and practice. This will enable health professionals and coaches to confidently assess athletes’ nutrition knowledge, which directly influences their food choices. Methods The validity of the questionnaire was established through a multi-step approach. Content validity was achieved via ratings from nutrition experts, all of whom had specialized training in human nutrition and experience of working with athletes. Face validity was evaluated through in-depth telephone interviews with elite or highly trained athletes, using a retrospective think-out-loud protocol to gather feedback on the clarity and relevance of the questions. Construct validity involved nutrition-trained doctors (NTG), non-nutrition-trained professionals (NNTG), and elite-level athletes’ groups (AG). Internal consistency was assessed using Cronbach’s alpha, and test-retest reliability was evaluated. Results The final tool comprised 123 individual statements or prompts (items) that were organized into 32 broader questions, spanning 12 sub-sections. Content validity was confirmed by fully integrating 49 out of 70 comments and partially integrating four comments received from nutrition experts for each sub-section. Face validity was established by fully integrating 33 out of 40 comments received from 16 elite and highly trained athletes. Construct validity was confirmed, indicating significant differences in the total scores achieved as a percentage of the SLn-SNKQ among the NTG (462.5, 92.5%), NNTG (223.5, 44.7%), and AG (235, 47.0%; p 0.7). Conclusions The SLn-SNKQ has been validated and demonstrates robust psychometric properties, offering a reliable tool for assessing SNK among Sri Lankan track and field athletes.

INTRODUCTION: Salmonella Typhi and Salmonella Paratyphi, fecal-oral transmitted bacterium, have temporally and geographically heterogeneous pathways of transmission. Previous work in Kathmandu, Nepal implicated stone waterspouts as a dominant transmission pathway after 77% of samples tested positive for Salmonella Typhi and 70% for Salmonella Paratyphi. Due to a falling water table, these spouts no longer provide drinking water, but typhoid fever persists, and the question of the diseases dominant pathway of transmission remains unanswered. METHODS: We used environmental surveillance to detect Salmonella Typhi and Salmonella Paratyphi A DNA from potential sources of transmission. We collected 370, 1L drinking water samples from a population-based random sample of households in the Kathmandu and Kavre Districts of Nepal between February and October 2019. Between November 2019 and July 2021, we collected 380, 50mL river water samples from 19 sentinel sites on a monthly interval along the rivers leading through the Kathmandu and Kavre Districts. We processed drinking water samples using a single qPCR and processed river water samples using differential centrifugation and qPCR at 0 and after 16 hours of liquid culture enrichment. A 3-cycle threshold (Ct) decrease of Salmonella Typhi or Salmonella Paratyphi, pre- and post-enrichment, was used as evidence of growth. We also performed structured observations of human-environment interactions to understand pathways of potential exposure. RESULTS: Among 370 drinking water samples, Salmonella Typhi was detected in 7 samples (1.8%) and Salmonella Paratyphi A was detected in 4 (1.0%) samples. Among 380 river water samples, Salmonella Typhi was detected in 171 (45%) and Salmonella Paratyphi A was detected in 152 (42%) samples. Samples located upstream of the Kathmandu city center were positive for Salmonella Typhi 12% of the time while samples from locations in and downstream were positive 58% and 67% of the time respectively. Individuals were observed bathing, washing clothes, and washing vegetables in the rivers. IMPLICATIONS: These results suggest that drinking water was not the dominant pathway of transmission of Salmonella Typhi and Salmonella Paratyphi A in the Kathmandu Valley in 2019. The high degree of river water contamination and its use for washing vegetables raises the possibility that river systems represent an important source of typhoid exposure in Kathmandu.

Abstract As extreme precipitation intensifies under climate change, traditional risk models based on the ‘100-year return period’ concept are becoming inadequate in assessing real-world risks. In response, this nationwide study explores shifting extremes under non-stationary warming using high-resolution data across the contiguous United States. Results reveal pronounced variability in 100-year return levels, with Coastal and Southern regions displaying the highest baseline projections, and future spikes are anticipated in the Northeast, Ohio Valley, Northwest, and California. Exposure analysis indicates approximately 53 million residents currently reside in high-risk zones, potentially almost doubling and tripling under 2 °C and 4 °C warming. Drought frequency also rises, with over 37% of major farmland vulnerable to multi-year droughts, raising agricultural risks. Record 2023 sea surface temperature anomalies suggest an impending extreme El Niño event, demonstrating the need to account for natural climate variability. The insights gained aim to inform decision-makers in shaping adaptation strategies and enhancing the resilience of communities in response to evolving extremes.

Abstract Background The causative agents of Acute Encephalitis Syndrome remain unknown in 68–75% of the cases. In Nepal, the cases are tested only for Japanese encephalitis, which constitutes only about 15% of the cases. However, there could be several organisms, including vaccine-preventable etiologies that cause acute encephalitis, when identified could direct public health efforts for prevention, including addressing gaps in vaccine coverage. Objectives This study employs metagenomic next-generation-sequencing in the investigation of underlying causative etiologies contributing to acute encephalitis syndrome in Nepal. Methods In this study, we investigated 90, Japanese-encephalitis-negative, banked cerebrospinal fluid samples that were collected as part of a national surveillance network in 2016 and 2017. Randomization was done to include three age groups (15-years). Only some metadata (age and gender) were available. The investigation was performed in two batches which included total nucleic-acid extraction, followed by individual library preparation (DNA and RNA) and sequencing on Illumina iSeq100. The genomic data were interpreted using Chan Zuckerberg-ID and confirmed with polymerase-chain-reaction. Results Human-alphaherpes-virus 2 and Enterovirus-B were seen in two samples. These hits were confirmed by qPCR and semi-nested PCR respectively. Most of the other samples were marred by low abundance of pathogen, possible freeze-thaw cycles, lack of process controls and associated clinical metadata. Conclusion From this study, two documented causative agents were revealed through metagenomic next-generation-sequencing. Insufficiency of clinical metadata, process controls, low pathogen abundance and absence of standard procedures to collect and store samples in nucleic-acid protectants could have impeded the study and incorporated ambiguity while correlating the identified hits to infection. Therefore, there is need of standardized procedures for sample collection, inclusion of process controls and clinical metadata. Despite challenging conditions, this study highlights the usefulness of mNGS to investigate diseases with unknown etiologies and guide development of adequate clinical-management-algorithms and outbreak investigations in Nepal.

Abstract Emotions are fundamental to human behaviour. How we feel, individually and collectively, determines how humanity evolves and advances into our shared future. The rapid digitalisation of our personal, social and professional lives means we are frequently using digital media to express, understand and respond to emotions. Although recent developments in Artificial Intelligence (AI) are able to analyse sentiment and detect emotions, they are not effective at comprehending the complexity and ambiguity of digital emotion expressions in knowledge-focused activities of customers, people, and organizations. In this paper, we address this challenge by proposing a novel AI framework for the adaptable, robust, and explainable detection of multi-granular assembles of emotions. This framework consolidates lexicon generation and finetuned Large Language Model (LLM) approaches to formulate multi-granular assembles of two, eight and fourteen emotions. The framework is robust to ambiguous emotion expressions that are implied in conversation, adaptable to domain-specific emotion semantics, and the assembles are explainable using constituent terms and intensity. We conducted nine empirical studies using datasets representing diverse human emotion behaviours. The results of these studies comprehensively demonstrate and evaluate the core capabilities of the framework, and consistently outperforms state-of-the-art approaches in adaptable, robust, and explainable multi-granular emotion detection.

Abstract Industrialization and urbanization contribute significantly to environmental pollution, particularly in water bodies. The consequences of this pollution are dire, impacting both the environment and public health. A serious hazard to life on Earth, contaminated water can cause food poisoning, diarrhea, respiratory diseases, and skin ailments, among other health problems. In countries like Bangladesh, where industrial activities, particularly in the garment sector, are prevalent, waste dumping into rivers and canals has reached alarming levels. This has resulted in a drastic decline in water quality, endangering aquatic life and ecosystems. To solve this important issue, we built an innovative real-time water quality monitoring system using the Internet of Things (IoT). Our system operates at three levels, collecting data from different layers of wastewater. Key parameters, including temperature, turbidity, total dissolved solids (TDS), and pH, are continuously monitored to assess water quality. The analysis revealed that wastewater temperature varied between 25 and 37 °C across different sites, with higher temperatures observed in industrial zones, indicating potential thermal pollution. Total dissolved solids (TDS) levels exhibited substantial variations, ranging from 170 to 360 ppm, surpassing standard thresholds and posing a threat to aquatic ecosystems. Turbidity readings ranged from 1.30 to 12.89 NTU, with higher turbidity observed in areas receiving industrial discharge, highlighting the impact of suspended solids on water clarity. pH values exceeded 7.0 across all sites, ranging from 9.5 to 10.32, indicating alkaline wastewater, particularly in regions with dyeing and textile industries. By integrating these specific metrics into our monitoring system, we provide valuable insights into industrial wastewater quality and its environmental implications. These findings underscore the urgent need for real-time monitoring and targeted interventions to address water pollution. Implementing sustainable water management strategies in industrial contexts is critical to protecting public health and preserving ecosystems for future generations.

Abstract Freshwater is unquestionably the most crucial resource essential for the sustenance and advancement of humankind. This invaluable entity surpasses all societal, economic, and environmental domains, consequently rendering it a ubiquitous good. Globally, it has been estimated that the industrial sector employs approximately 20% of the available freshwater. The principal aim within the industrial domain is to maximize production efficiency, rather than prioritizing the enhancement of water conservation and efficiency. Research suggests that a favorable association exists between the monetary investments made in technological improvements for industrial water treatment and reuse and a profitable return on investment that is sustained over a prolonged duration. This could plausibly explain the dearth of willingness exhibited by some corporations in dedicating resources to this vital issue. The objective of this study is to explicate the notion of sustainability concerning water management that can be operationalized in the context of Pakistan, while delving into the latest advancements in the sphere of sustainable management practices. This research endeavor shall serve as an instructive source for executives, entrepreneurs, and vested parties in various industrial domains to propel their endeavors toward sustainable practices while simultaneously achieving optimization and surpassing the benchmarks set by national regulations and international establishments. This investigation has illuminated the imperative of executing an all-encompassing water management strategy that incorporates the ecological, financial, and societal dimensions as the essential constituents of viability in industrial water utilization. This work ought to possess a worldwide scope, bearing in mind the ubiquitous character of industrial practices in the epoch of globalization.

Faced with declining donor funding for HIV, low- and middle-income countries must identify efficient and cost-effective ways to integrate HIV prevention programs into public health systems for long-term sustainability. In Zambia, donor support to the voluntary medical male circumcision (VMMC) program, which previously funded non-governmental organizations as implementing partners, is increasingly being directed through government structures instead. We developed a framework to understand how the behaviors of individual decision-makers within the government could be barriers to this transition. We interviewed key stakeholders from the national, provincial, and district levels of the Ministry of Health, and from donors and partners funding and implementing Zambia’s VMMC program, exploring the decisions required to attain a sustainable VMMC program and the behavioral dynamics involved at personal and institutional levels. Using pattern identification and theme matching to analyze the content of the responses, we derived three core decision-making phases in the transition to a sustainable VMMC program: 1) developing an alternative funding strategy, 2) developing a policy for early-infant (0–2 months) and early-adolescent (15–17 years) male circumcision, which is crucial to sustainable HIV prevention; and 3) identifying integrated and efficient implementation models. We formulated a framework showing how, in each phase, a range of behavioral dynamics can form barriers that hinder effective decision-making among stakeholders at the same level (e.g., national ministries and donors) or across levels (e.g., national, provincial and district). Our research methodology and the resulting framework offer a systematic approach for in-depth investigations into organizational decision-making in public health programs, as well as development programs beyond VMMC and HIV prevention. It provides the insights necessary to map organizational development and policy-making transition plans to sustainability, by explaining tangible factors such as organizational processes and systems, as well as intangibles such as the behaviors of policymakers and institutional actors.

Background Gaur (Bos gaurus) is listed as a vulnerable species in the IUCN Red List of threatened species due to the rapid population decline caused by human pressures in their habitats. To develop successful conservation plans, it is essential to understand the relationship between Gaur and their habitats. This study aimed to investigate the factors influencing Gaur habitat use and identify and rank conservation threats in Chitwan National Park, Nepal. Methods Using line transect surveys, we recorded Gaur’s presence via direct sightings and indirect signs (dung, footprints, horns) over an area of 176 km2 in July and August 2022. We used binary logistic regression models to determine the collected ecological and anthropogenic factors influencing the occurrence of Gaur and the relative whole-site threat ranking method to rank conservation threats. Results The results revealed that the probability of Gaur occurrence increases with moderate to high canopy cover, riverine and Shorea robusta dominated forests, and nearest distance to road/path/firelines, while decreasing with the presence of predators. Uncontrolled fire, invasive species, human disturbances, and climate change were ranked as the most prevailing threats to Gaur in our study area. Conservation managers should implement effective habitat management interventions, such as construction of waterhole, firelines maintenance, grassland management, and control of invasive species in the potential habitats, to safeguard and maintain the sustainability of Gaur populations and associated herbivores. Future studies should consider larger geographical settings and multiple seasons, and habitat suitability assessments should be conducted to determine current and future suitable habitat refugia for Gaur and other threatened wildlife species at the landscape level.

Ascites represents an infrequent sequela of hypothyroidism, manifesting in fewer than 4% of affected individuals. Herein, we delineate a case characterized by profound hypothyroidism accompanied by substantial ascites, further complicated by cardiac insufficiency. A 29-year-old female, previously diagnosed with postradiation hypothyroidism subsequent to a diagnosis of Grave’s disease 11 years prior, presented with exacerbating dyspnoea, abdominal distension, and orthopnea. In January 2024, she was admitted with massive ascites, exhibiting clinical manifestations of both hypothyroidism and cardiac failure. Thyroid function tests were markedly abnormal, with a thyroid-stimulating hormone level of 77.65 mIU/L, triiodothyronine at 2.2 nmol/L, and thyroxine levels below 3.2 pmol/L. Echocardiographic evaluation revealed dilated cardiomyopathy with a significantly reduced systolic (ejection fraction of 25.9%) and diastolic function (E/A ratio of 0.87). Analysis of the ascitic fluid demonstrated a serum-ascites albumin gradient exceeding 1.1 g/L (3 g/L). Ultrasonography of the abdomen ruled out portal hypertension, while computed tomography of the abdomen confirmed extensive ascites without evidence of malignancy. Under the supervision of a specialist, the patient was administered a high dosage of levothyroxine (300 mcg), leading to a significant amelioration in both thyroid function parameters and her ascites. Subsequent thyroid function tests demonstrated a decrease in thyroid-stimulating hormone levels to 11.7 mIU/L and an increase in thyroxine levels to 15.6 pmol/L, indicating a positive response to the thyroid hormone replacement therapy. Subsequent echocardiographic assessment showed improvement in the ejection fraction to 26.9% and diastolic function (E/A ratio of 1.27). Myxedema ascites, though infrequent, is readily amenable to treatment. The serum-ascites albumin gradient exceeding 1.1 g/L may be indicative of hypothyroidism-associated ascites, although the paucity of studies renders it uncertain whether this is a characteristic feature. Further investigation into the etiology, diagnostic criteria, and management strategies for ascites in the context of hypothyroidism is warranted.

Accurate energy consumption forecasting is crucial for reducing operational costs, achieving net-zero carbon emissions, and ensuring sustainable buildings and cities of the future. Despite the frequent use of Artificial Intelligence (AI) algorithms for learning energy consumption patterns and predictions in Building Science, relying solely on these techniques for energy demand prediction addresses only a fraction of the challenge. A drift in energy usage can lead to inaccuracies in these AI models and subsequently to poor decision-making and interventions. While drift detection techniques have been reported, a reliable and robust approach capable of explaining identified discrepancies with actionable insights has not been discussed in extant literature. Hence, this paper presents an Artificial Intelligence framework for energy consumption forecasting with explainable drift detection, aimed at addressing these challenges. The proposed framework is composed of energy embeddings, an optimized dimensional model integrated within a data warehouse, and scalable cloud implementation for effective drift detection with explainability capability. The framework is empirically evaluated in the real-world setting of a multi-campus, mixed-use tertiary education setting in Victoria, Australia. The results of these experiments highlight its capabilities in detecting concept drift, adapting forecast predictions, and providing an interpretation of the changes using energy embeddings.

Generative Artificial Intelligence (AI) is heralding a new era in AI for performing a spectrum of complex tasks that are indistinguishable from humans. Alongside language and text, Generative AI models have been built for all other modalities of digital data, image, video, audio, and code. The full extent of Generative AI and its opportunities, challenges, contributions, and risks are still being explored by academic researchers, industry practitioners, and government policymakers. While this deep understanding of Generative AI continues to evolve, the lack of fluency, literacy, and effective interaction with Generative and conventional AI technologies are common challenges across all domains. Tertiary education institutions are uniquely positioned to address this void. In this article, we present the human-centred design of a universal AI literacy module, followed by its four primary constructs that provide core competence in AI to coursework and research students and academic and professional staff in a tertiary education setting. In comparison to related work in AI literacy, our design is inclusive due to the collaborative approach between multiple stakeholder groups and is comprehensive given the descriptive formulation of the primary constructs of this module with exemplars of how they activate core operational competence across the four groups.

Abstract Big Data is steadily expanding beyond the boundaries of its foundational constructs of three primary Vs, Volume, Velocity and Variety, and two secondary Vs, Veracity and Value. The advent of 5G networks, Edge computing and IoT technologies has transformed Big Data into this modern context. With these new manifestations of Big Data, the focus is not only on the data itself but on the context that it applies to its immediate environment as well as the human and societal perception of this context. It is increasingly challenging for conventional AI algorithms to process and transform this data, analyse and visualise a broad spectrum of insights, and then formulate the explainability of such insights in terms of bias, transparency, safety, ethics, and causality. Self-structuring Artificial Intelligence (SSAI) addresses the limitations of conventional AI by adapting to the inherent structure of the data, incrementally learning and abstracting from this structure. SSAI has not been investigated in a cloud-based setting for generating explainable insights from these new types of Big Data. In this paper we propose a cloud-based architecture for explainable Big Data analytics using SSAI in highly-connected 5G and Edge computing environments. The proposed architecture is empirically evaluated on a commercial scale Big Data use case of Smart Grid for Smart Cities. The results of these experiments confirm the functionality and effectiveness of the proposed architecture.

Abstract Ganges and Brahmaputra, two of Asia’s most prominent rivers, have a crucial role in Southeast Asia’s geopolitics and economy and are home to one of the world’s biggest marine ecosystems. Irrigation-driven groundwater depletion and climate change affect the Ganges-Brahmaputra’s hydrology, threatening the stability of the Bay of Bengal. Here, we quantify, using results from a land reanalysis, the impacts of a changing climate and intensive irrigation on the surface water flowing into the Bay of Bengal. The effects of such activities mostly occurring in the Ganges basin, either intensified or lessened depending on the area by the climatic conditions, decrease freshwater flow into the bay by up to 1200 m3/s/year. While the increase in precipitation in the Ganges basin reduces the effects of groundwater depletion on the streamflow, the decrease in precipitation and the snowmelt decline in the Brahmaputra basin exacerbate streamflow reduction due to groundwater depletion at the delta.