Browse Articles

Time series analysis of malaria in pregnancy, using wavelet and SARIMAX models

Malaria in pregnancy (MIP) remains a global health challenge, affecting approximately 40% of pregnant women. Despite malaria control efforts by the Nigerian Government and its partners, regional disparities in health outcomes and malaria incidence trends among pregnant women remain under-studied. This study objectives were to assess MIP variability compared to general malaria cases, and forecast short-term MIP incidence over two years. This was achieved by analyzing malaria in pregnancy (MIP) variability across Nigeria from January 2015 to January 2025, using wavelet coherence, patterns of transmission cycles and selecting best modelling approach by comparing ARIMA and SARIMAX models to assess temporal trends before the forecast of short-term MIP incidence. Findings showed significant regional variability, with Cross River peaking in 2017 and 2019, while Enugu recorded its lowest trough in 2017. Malaria peaks in southern states remained lower than troughs in northern regions. Strong cross-correlations between MIP and general malaria transmission cycles were observed in Kebbi, Niger, Yobe, and Ondo, indicating persistent trends, while South-South and South-East exhibited weaker correlations, likely due to intervention fluctuations. SARIMAX models captured MIP trends more effectively, except Kebbi, where ARIMA fit better, and Niger, where SARIMAX exaggerated forecasts due to sensitivity to exogenous variables. Thus, SARIMAX was adopted for Cross River, Enugu, Ondo, and Yobe; while ARIMA was used for Kebbi and Niger States. It was discovered that Cross River and Enugu exhibited intervention-driven malaria fluctuations, Ondo, Niger, and Yobe displayed unstable or cyclical trends, reinforcing the importance of climate-sensitive forecasting models and seasonal interventions for improving malaria prediction accuracy. South-South and South-East need improved healthcare access, North-Central and North-West require seasonality forecasting, while North-East demands urgent control measures. Targeted malaria interventions are crucial to support achievement of the Nigeria’s National Malaria Elimination Programme (NMEP) goals.

Mitochondrial subtypes in renal ischemia reperfusion injury guide delayed graft function and Long-Term graft prediction

Focused low-intensity hippocampal transcranial ultrasound stimulation (TUS) for sleep disturbances in patients with chronic tinnitus: A study protocol for a pilot randomized controlled trial

Background Sleep disturbances are very common in tinnitus sufferers with a high prevalence ranging from 50% to 77%. Untreated sleep disturbances and tinnitus can cause brain shrinkage and lead to cognitive impairments in late adulthood. Until now, non-pharmacological treatments are very few for older patients suffering from sleep disturbances and chronic tinnitus. Even though clinical trials of transcranial magnetic stimulation (TMS) have shown positive results in the treatment of either sleep disturbances or chronic tinnitus, the results are highly varied due to the superficial cortical target. Compared to TMS, focused low-intensity transcranial ultrasound stimulation (TUS) is a newly developed modality of non-invasive brain stimulation that offers promising therapeutic effects by transmitting acoustic energy into deep brain structures with a high spatial resolution (i.e., sub-millimeter), which sparks interest in managing the comorbidities in ageing populations. Methods and design Chinese individuals between the ages of 60 and 90 years, who are right-handed and have sleep disturbances and chronic tinnitus, will participate in this pilot randomized clinical trial (RCT). Eligible participants will be randomly assigned to two treatment groups (1:1 ratio): low-intensity TUS or sham TUS (i.e., placebo-controlled group). Each group will consist of 15 participants. Before the treatment, high-resolution T1-weighted magnetic resonance imaging (MRI) data will be used to create a computational head model for each participant. The head model will help identify the treatment target of the left hippocampus. The treatments schedule contains six sessions of low-intensity TUS, three times per week, lasting two weeks. Each session of treatment lasts for 80 seconds. Throughout the study, outcome measurements will be conducted at four time points, including baseline, 2nd week, 6th week, and 12th week. The primary outcomes include subjective sleep quality and severity of tinnitus. The secondary measurements include actigraphy, tinnitus handicap inventory and glymphatic function. Participants’ adherence to the program and any adverse event will be closely monitored throughout the duration of the clinical trial. Conclusions It is expected that a 2-week treatment of low-intensity TUS will show significant enhancement in sleep quality and the severity of tinnitus symptoms compared to sham TUS. This proposed clinical trial will provide high-level and valuable clinical evidence that could inform the effect size and personalized modeling of focused low-intensity TUS for different types of brain diseases. Trial registration ClinicalTrials.gov Identifier: NCT06776705.

Measurement analysis of strata deformation caused by construction of quasi-rectangular shield

Bag-of-words is competitive with sum-of-embeddings language-inspired representations on protein inference

Inferring protein function is a fundamental and long-standing problem in biology. Laboratory experiments in this field are often expensive, and therefore large-scale computational protein inference from readily available amino acid sequences is needed to understand in more detail the mechanisms underlying biological processes in living organisms. Recently, studies have utilised mathematical ideas from natural language processing and self-supervised learning, to derive features based on protein sequence information. In the area of language modelling, it has been shown that learnt representations from self-supervised pre-training can capture the semantic information of words well for downstream applications. In this study, we tested the ability of sequence-based protein representations learnt using self-supervised pre-training on a large protein database, on multiple protein inference tasks. We show that simple baseline representations in the form of bag-of-words histograms perform better than those based on self-supervised learning, on sequence similarity and protein inference tasks. By feature selection we show that the top discriminant features help bag-of-words capture important information for data-driven function prediction. These findings could have important implications for self-supervised learning models on protein sequences, and might encourage the consideration of alternative pre-training schemes for learning representations that capture more meaningful biological information from the sequence alone.

Selective lowest and upper instrumented vertebra for the correction of Lenke type 6C adolescent idiopathic scoliosis

Advancing fishery dependent and independent habitat assessments using automated image analysis: A fisheries management agency case study

Advances in artificial intelligence and machine learning have revolutionised data analysis, including in the field of marine and fisheries sciences. However, many fisheries agencies manage sensitive or proprietary data that cannot be shared externally, which can limit the adoption of externally hosted artificial intelligence platforms. In this study, we develop and evaluate two residual network-based automatic image annotation models to process fishery specific habitat data to support ecosystem-based fisheries management in the Exmouth Gulf Prawn Managed Fishery in Western Australia. Using an extensive dataset of 13,128 manually annotated benthic habitat images, we train a grid-based annotation model and an image-level object detection model. Both models demonstrated high overall accuracy, with the grid-based model achieving 90.8% and the image-level model 92.9%. Patch-wise accuracy of the image-level model was 74.2%, highlighting its ability to classify broader spatial context without requiring point-based labelling. Precision and recall values for both models often exceeded 70% for dominant habitat classes such as unconsolidated substrate, macroalgae, and seagrass. The development of these models supports the potential for cost-effective, robust, and scalable in-house habitat classification for fishery or ecoregion specific habitat data to support timely decision-making. Further, the grid-based model uniquely integrates spatial precision with compatibility to existing manual data workflows, enabling seamless adoption within many existing fisheries monitoring programs. Despite limitations, such as a class imbalanced dataset, both models present a scalable, data secure solution for fisheries management agencies. This study establishes a foundation for integrating artificial intelligence driven image analysis of proprietary fisheries data, to further support responsive, standardised and data-informed decision making.

Thermal behavior of the Klein Gordon oscillator in a dynamical noncommutative space

Sex differences in the association of BMI and weight perception with depression and suicidality among Korean adolescents

This study aimed to investigate sex-based differences in the effects of weight perception on depression and suicidality in Korean adolescents with and without obesity. A multiple logistic regression analysis stratified by sex was conducted using the 2023 Korea Youth Risk Behavior Survey data of 51,462 middle and high school students. BMI-based obese adolescents comprised a higher proportion of male participants (22.5%) than female participants (17.4%). The rate of underweight perception was higher among male participants (32.4%) than among female participants (23.3%); however, the rate of overweight perception was higher among female participants (37.3%) than among male participants (36.0%). The risk of depression or suicidality was higher among female participants (30.7% and 18.2%, respectively) than among male participants (21.3% and 10.7%, respectively). In female participants without obesity, the risk of depression and suicidality with overweight perception increased by 1.106 times (95% confidence interval [CI] 1.020–1.199) and 1.295 times (95% CI 1.176–1.427), respectively, compared to that with underweight perception as the reference group. However, this difference was not statistically significant among male participants without obesity. The findings of this study suggest that to improve the mental health of adolescents, proper weight recognition education along with obesity prevention should be implemented, and sex-specific interventions should be considered.

An ensemble Kalman filter with rescaling disaggregation for assimilating terrestrial water storage into hydrological models

Abstract Assimilating satellite-based Terrestrial Water Storage (TWS) observations can improve the vertical summation of water storage states in hydrological models. However, it can degrade individual storage compartments or hydrological fluxes, limiting the applicability of TWS Data Assimilation (DA) for water management and flood monitoring. This issue arises from the ensemble-based TWS update disaggregation approach used by DA techniques like the Ensemble Kalman Filter (EnKF). Thus, this study makes two key contributions. First, we introduce a novel analysis method that provides quantitative and qualitative insights into how individual storage compartments are affected during TWS DA, by examining the sign and magnitude of the individual storage updates and their responses. Second, we propose a new disaggregation approach, EnKF-R, which “rescales” the individual storage of model compartments to match the updated TWS, avoiding the use of ensemble statistics within the disaggregation process. The EnKF-R approach was tested in two climatologically different river basins and validated against both synthetic and real independent data. Our results show that EnKF-R produces similar TWS estimates to the classical EnKF while reducing degradations in individual water storage compartments and with lower computational cost, making it a promising alternative. Limitations regarding spatial continuity and uncertainty estimation require further developments.

Progressive chronic tissue loss disease in Siderastrea siderea on Florida’s coral reef

Stony coral tissue loss disease (SCTLD) has devastated numerous species of corals across the Western Atlantic but one reef coral, Siderastrea siderea, displays unusual tissue loss lesions. We examined the dynamics of lesions in S. siderea from the cellular to the ecological level and compared the disease with SCTLD in other coral species. We tagged and monitored six S. siderea colonies with bleached lesions in Fort Lauderdale and 17 S. siderea colonies with purple lesions in the Florida Keys for 18 months. Lesions on most colonies showed progressive tissue loss with an average change in healthy tissue of +5.5% in Fort Lauderdale (some bleached lesions resolved) and −51.1% in the Florida Keys. Case fatality rate was zero for colonies within Fort Lauderdale and 5.9% for colonies in the Florida Keys. The disease remained on S. siderea throughout the study in the Florida Keys but fluctuated through time in Fort Lauderdale. Lesion morphologies and disease pathogenesis differed between regions which could be due to different disease agents, environmental co-factors, intrinsic differences among colonies or different stages of the same disease. S. siderea is known to be a species complex which might also explain differences in lesion morphologies and disease pathogenesis. Aquaria studies found S. siderea with lesions transmitted disease to S. siderea and Orbicella faveolata and that S. siderea was also susceptible to SCTLD. Unlike SCTLD in other species, treatment with antibiotics did not stop lesion progression in S. siderea. Histology on lesions indicated a disease process regardless of lesion morphology and was consistent with SCTLD. We cannot completely rule out SCTLD but based on the other components of disease pathogenesis (rate of tissue loss, lesion morphology, colony mortality, response to antibiotics) we conclude this could be a different disease, which we term Siderastrea sidera chronic tissue loss disease, consistent with accepted disease nomenclature.

Predicting the occurrence of probable sarcopenia in middle-aged and elderly patients with coronary artery disease: development and validation of a clinical model

Can digital village construction promote sustainable agricultural development in China?

Agriculture is a major contributor to global greenhouse gas emissions. Consequently, studying the sustainable development of agricultural activities is crucial for achieving the United Nations’ Sustainable Development Goals. Utilizing panel data from 30 Chinese provinces from 2012 to 2022, this study measured the environmental, economic, and social dimensions of sustainable agricultural development (SAD) at the provincial level in China. Employing two-way fixed-effects and mediated effects models, the study empirically examined the driving effect of digital village construction (DVC) on SAD, along with the underlying mechanisms. The results reveal that the overall level of SAD in China has shown a gradual upward trend, although a pattern of higher SAD levels in the eastern regions compared to the west remains evident. DVC was found to exert a significant positive effect on SAD. Crucially, the market-based allocation of factors and agricultural product circulation were identified as significant mediating variables in this relationship. Heterogeneity analysis showed that the promoting effect of DVC is more significant in major grain-producing areas and in regions exhibiting higher SAD levels. Based on these findings, the study proposes targeted policy recommendations to provide practical strategies for different regions to advance DVC, narrow regional disparities, and enhance SAD levels.

Eco-friendly fabrication of ZnO quantum dots using Brassica rapa (L.): metabolomic profiling and antimicrobial efficacy against foodborne pathogens supported by in-silico insights

Lane-keeping ability evaluation for driving skill tests: A multi-indicator fusion approach

Traditional driver’s skill tests primarily assess whether candidates meet specific standards in prescribed tasks, which often fails to fully reflect their overall driving performance in real-world scenarios. This can lead to suboptimal driving outcomes. Lane-keeping ability is a key indicator for evaluating a driver’s overall competence, as it reflects their proficiency in vehicle control, road environment perception, and emergency handling. However, due to the complex and varied factors influencing lane-keeping ability, there is currently a lack of effective methods for assessing this skill during drive skill tests. To address this gap, this paper proposes a multi-indicator fusion (MIF) method for evaluating lane-keeping ability in driver skill tests. First, to accommodate real-world lane-keeping scenarios in drive skill tests, multidimensional indicators representing lane-keeping ability are extracted from real low-speed naturalistic driving data, considering both lateral and longitudinal safety and stability. Next, by analyzing the distribution characteristics of these indicators using the K-means clustering method, groups of indicators with similar characteristics are identified. Furthermore, the Youden index, Boxplot, and statistical measures are then employed to determine the threshold values for each indicator, enhancing the accuracy of the evaluation. Finally, a comprehensive evaluation model for lane-keeping ability is constructed using the Analytic Hierarchy Process (AHP) based on a combination of subjective and objective weightings. The proposed MIF-based lane-keeping assessment method for drive skill tests was effectively validated in terms of its rationality and feasibility using naturalistic driving data. This study provides valuable reference points for assessing lane-keeping ability in the context of future autonomous driving environments.

Assessing the implications of habitat transformations on human-large carnivore interactions outside protected areas

Abstract Humans and wildlife have coexisted spatially and temporally for many years. However, this is disturbed when human-induced changes constrain limited and shared resources, leading to increased competition for resources and negative human-wildlife interactions. This study aims to examine the implications of habitat changes on human–large carnivore interaction (HLCI) and identify priority areas for negative interaction. The study was conducted in the Terai Arc landscape in two study blocks constituting three wildlife corridors in the Corbett and Indo-Nepal transboundary landscapes. This study assessed the decadal changes in habitats and identified priority sites of negative interaction based on reported wildlife attacks due to Panthera tigris and Panthera pardus. The study mapped five spatial risk zones, from very low to very high, and determined the effects of habitat changes on HLCI. Changes in land-use patterns, vegetation health, and anthropogenic activity have significant effects on hotspots of negative HLCI. Our results will support decision-makers and park managers in streamlining, managing, and prioritizing local and regional mitigation efforts at the identified sites. This approach will help improve the co-existence scenario in the landscape.

Protocol to develop and pilot a primary mental healthcare intervention model to address the medium- to long-term Ebola associated psychological distress and psychosocial problems in Mubende District in Central Uganda (the Ebola+D project)

Ebola Virus Disease (EVD) presents significant global health challenges, including high mortality and substantial physical morbidity among patients and survivors. Beyond immediate health impacts, EVD survivors, frontline healthcare workers, and community members face profound mental health and psychosocial issues. Over 35 EVD outbreaks have occurred in Africa since 1976, often in the context of fragile health systems and chronic conflict, complicating the response to mental health needs. Uganda has experienced seven outbreaks, the latest from September 20, 2022, to January 11, 2023, affecting nine districts, with Mubende as the epicenter. The Mental Health Focus Area of the Medical Research Council/Uganda Virus Research Institute and London School of Hygiene and Tropical Medicine, Uganda Research Unit, in collaboration with Uganda’s Ministry of Health, has initiated the development and piloting of the Ebola+D mental health intervention to address the medium- and long-term mental health consequences of Ebola in the Mubende district. This intervention will be a collaborative stepped care model based on the successful HIV + D intervention in Uganda and the MANAS intervention in India. Participatory, theory-informed approaches will be employed in Mubende district to develop the Ebola+D mental health intervention. This will involve five phases: i) adaptation of the HIV + D collaborative stepped care mental health intervention into primary health care in Mubende district to produce the Ebola+D mental health intervention; ii) adaptation and translation of the Problem Solving Therapy for Primary Care (PST-PC) treatment manual to the local rural situation in Mubende district; iii) a pilot study to evaluate the acceptability, feasibility and impact of the Ebola+D mental health intervention on mental health outcomes; iv) a health economics component to examine the costs of the Ebola + D mental health intervention; and v) a qualitative component to explore the Ebola virus disease (EVD) associated negative beliefs and lived out experiences of affected members of the community. The findings from this study will inform future mental health and psychosocial interventions secondary to outbreaks of Emerging Viral Diseases (such as EVD) in low resourced settings such those in sub-Saharan Africa. Trial registration ClinicalTrials.gov NCT06093646

Mild hypothermia attenuates hepatic ischemia-reperfusion injury by regulating FoxO1/PPARα pathway

MSMCE: A novel representation module for classification of raw mass spectrometry data

Mass spectrometry (MS) analysis plays a crucial role in the biomedical field; however, the high dimensionality and complexity of MS data pose significant challenges for feature extraction and classification. Deep learning has become a dominant approach in data analysis, and while some deep learning methods have achieved progress in MS classification, their feature representation capabilities remain limited. Most existing methods rely on single-channel representations, which struggle to effectively capture structural information within MS data. To address these limitations, we propose a Multi-Channel Embedding Representation Module (MSMCE), which focuses on modeling inter-channel dependencies to generate multi-channel representations of raw MS data. Additionally, we implement a feature fusion mechanism by concatenating the initial encoded representation with the multi-channel embeddings along the channel dimension, significantly enhancing the classification performance of subsequent models. Experimental results on four public datasets demonstrate that the proposed MSMCE module not only achieves substantial improvements in classification performance but also enhances computational efficiency and training stability, highlighting its effectiveness in raw MS data classification and its potential for robust application across diverse datasets.

Identification of flood vulnerability areas using analytical hierarchy process techniques in the Wuseta watershed, Upper Blue Nile Basin, Ethiopia