bluuebunny/crossref_metadata_2025_split huggingface dataset:
Tele-Rehabilitation for Older Adults: Assessing Effectiveness in Home Recovery After Hip and Pelvic Fractures
Authors: ALI KHAN AKHTAR , Xiaohe Ling , Li Wenkai , Xia Chengyan , Huang Hui 
Published: February 2025

Abstract

Background and Objectives: With the increasing incidence of hip and pelvic fractures among the aging population, traditional rehabilitation methods may lack accessibility, necessitating the exploration of tele-rehabilitation as an effective alternative. This study aimed to evaluate the feasibility and effectiveness of tele-rehabilitation (TR) in older patients with hip or pelvic fractures during home rehabilitation.
Methods:A prospective observational study was conducted on 70 patients admitted to a home rehabilitation service for femoral or pelvic fracture between January and November 2022. Tele-rehabilitation was provided using tablets and exercise apps via videoconferencing. Demographic characteristics, functional levels, therapy session numbers, TR usage, reasons for non-acceptance, and patient experience were collected.
Results: Of the 70 patients, 40 (57.1%) received TR. Patients who did not accept TR were older, frailer, and more likely to be female. The TR group achieved significant improvements in functional independence measure (FIM) and De Morton's Mobility Index (DEMMI) scores. On average, participants had 14.8 (± 7.5) home visits, but only 3.6 (± 2.2) sessions were delivered via TR. The TR group also had a shorter length of stay in the home rehabilitation service. Twenty-two patients (55%) completed the System Usability Scale (SUS), and the mean score was 76 (± 18), indicating satisfactory usability.
Conclusion: TR was acceptable to a significant portion of hip and pelvic fracture patients and could achieve clinically important functional gains. However, face-to-face visits remained the preferred mode of delivery. Further research is needed to address the barriers to TR uptake and improve its utilization.
Long read whole genome sequencing-based discovery of complex structural variants and their role in aetiology of non-syndromic autism spectrum disorder in India
Authors: Shah Jhanvi , Mondal Debasrija , Jain Deepika , Mhatre Priti , Patel Ketan , Iyer Anand , Pandya Manoj , Menghani Bhargavi , Dave Gayatri , Sheth Jayesh , Sheth Frenny , Ramdas Shweta , Sheth Harsh 
Published: February 2025

Abstract

Background: Despite having heritability estimates of 80%, approximately 50% cases of autism spectrum disorders (ASD) remain without a genetic diagnosis. Complex structural variants (SVs) detected using long-read genome sequencing are a relatively new class of variants implicated in neurodevelopmental disorders. Short read sequencing (SRS) and chromosomal microarray (CMA) are unable to resolve these SVs due to their inherent technological limitations. This study was aimed to detect and delineate the role of SVs in children with non-syndromic ASDs using long read whole genome sequencing (lrWGS) in whom prior traditional genetic tests did not yield a definitive genetic diagnosis.
Methods: A total of 23 patients with no prior genetic diagnosis from karyotyping, Fragile-X analysis, CMA and short read whole exome sequencing (srWES) were selected for lrWGS using Oxford Nanopore based sequencing platform. All samples were sequenced at an average coverage of ~10x. Contigs generated from high accuracy base calling were aligned against GRCh38/hg38 human reference genome build. SVs were called using five variant callers- Sniffles2, cuteSV, NanoVar SVIM and npInv, and annotated using AnnotSV. Concordant calls across at least three variant callers were filtered and prioritized for downstream analysis. Candidate variants were validated by orthogonal methods.
Results: A total of 46 low pass long read sequencing runs were performed for the selected 23 samples (two runs/ sample). The N50 read length of 6.74±3.31 kb was obtained across the runs, and on average, approximately 176,432 calls were made across all callers for each sample. The average number of deletions, duplications, insertions, inversions and translocations were 47,375, 2,498, 62,657, 1084 and 62,817, respectively per sample. Of 23 cases, a candidate SV, an inversion of approximately 2.7 Mb in size encompassing SNAP25-AS1 gene was observed. This gene is likely to be involved in the synaptic pathway and has previously been associated with autism.
Conclusion: This is the first study from India to assess the role of complex SVs in the aetiology of non-syndromic ASDs. Despite the small sample size, the study results suggest the limited role of complex SVs in the aetiology of non-syndromic ASD. Dearth of data supporting the role of complex SVs in non-syndromic ASDs in other cohorts from around the world further supports our conclusion. Hence, inclusion of lrWGS in clinical diagnosis for non-syndromic ASD is currently not supported.
Vector Potential Index: Bridging competence and contribution as an integrative measure of relative transmission capability
Authors: Bauer Amely M. , Burkett-Cadena Nathan D. , Reeves Lawrence E. , Alto Barry W. , Campbell Lindsay P. 
Published: February 2025

Abstract

1. Vectorial capacity (VC) is a powerful tool for estimating a species’ importance in the transmission of vector-borne pathogens and predicting impacts of specific control measures on the transmission of those pathogens. However, VC is rarely estimated for zoonotic multi-vector, multi-host vector-borne disease (VBD) systems, due to the inherent difficulties of measuring several parameters, especially biting rate and daily probability of survival. 2. We present the Vector Potential Index (VPI), a novel metric for evaluating and comparing the potential of blood-feeding arthropod vectors to contribute to zoonotic VBD transmission using West Nile virus (WNV) in the eastern United States as a model system. Taking a meta-analysis approach, the VPI combines vector competence and host use data obtained from scientific literature to assign relative and absolute VPI ranks across species and transmission cycles. 3. Our case study of WNV demonstrates that the VPI framework effectively quantifies vector species’ potential to contribute to enzootic and epizootic transmission cycles. Most species exhibited low vector potential and although Aedes species were the most competent WNV vectors in the laboratory, only Culex species were assigned higher VPI ranks. Additionally, the VPI suggests that the contribution of Culex salinarius to WNV transmission in the U.S. may be greater than previously assumed based on assessments of individual parameters. Relative and absolute VPI ranks assigned to species aligned with recent work reviewing their role as vectors in the transmission cycles, indicating that by jointly considering vector competence and host use, the VPI provides a realistic approximation of a vector species’ potential to contribute to VBD transmission in the natural environment. 4. The VPI is a practical and highly versatile metric that is useful either as a stand-alone application or integrated with existing approaches, where it can be used to evaluate and compare vector species across different VBD systems or spatiotemporal scales at the species, population, or community level. We recommend the objective and reproducible VPI as a powerful yet simple tool for scientists and public health practitioners, where this trait-based approach has considerable potential to provide new insights into disease systems and enhance VBD surveillance and intervention strategies.
Analyzing How AI impact Environmental Sustainability: Case Study for USA
Authors: Oluwaseun Ayodele 
Published: February 2025

Abstract

This study investigates the role of private investment in Artificial Intelligence (AI) in promoting environmental sustainability in the United States from 1990 to 2019. It also analyzes the impact of financial globalization, technological innovation, and urbanization by testing the Load Capacity Curve (LCC) hypothesis. The study employs stationarity tests, which indicate that the variables are free from unit root problems and exhibit mixed orders of integration. Using the Autoregressive Distributive Lag (ARDL) Model bound test, the study finds that the variables are cointegrated in the long run. The short-run and long-run estimations of the ARDL model confirm the existence of the LCC hypothesis in the United States, revealing a U-shaped relationship between income and load capacity factor. The results show that private investment in AI has a significant positive correlation with the load capacity factor, thus promoting environmental sustainability. Conversely, technological innovation and financial globalization exhibit a negative correlation with the load capacity factor in both the short and long run. To validate the ARDL estimation approach, the study employs Fully Modified OLS, Dynamic OLS, and Canonical Correlation Regression estimation methods, all of which support the ARDL results. Additionally, the Granger Causality test reveals a unidirectional causal relationship from private investment in AI, financial globalization, economic growth, technological innovation, and urbanization to the load capacity factor.
Using machine learning and satellite data to improve flood forecasting: the case of the Ouémé basin at the Bétérou outlet
Authors: Obada Ezéchiel , Biao E. I. , Zohou P. J. , Yarou H. , Hounnondaho F. Z. , Alamou E. A. 
Published: February 2025

ABSTRACT
Flooding is a major concern for the scientific community, and it has been exacerbated by climate change. Accurate prediction of these extreme events is crucial for adequate preparedness. This study investigates the potential of advanced artificial intelligence (AI) techniques to enhance the accuracy of flood prediction and provide actionable insights for flood management. This study focuses on the African context, where data are scarce and the weak capacity of governments to react to floods makes populations vulnerable. It adopted advanced recurrent neural network architectures such as the long short-term memory (LSTM) and the convolutional long short-term memory (ConvLSTM) models, focusing on hydrological modeling innovation. The results indicated a high performance of these models in simulated runoff. The coefficient of determination (R2) and Nash–Sutcliffe efficiency between observed and simulated runoff are approximately 0.96 and 0.95, respectively, for the ConvLSTM model and 0.95 and 0.95 for the LSTM model. This study generated the flooding risk area maps. These maps represent a significant decision-making tool for flood management. This research confirms the effectiveness of deep learning in hydrology and proposes an innovative methodological framework for sustainable water resource management in the African context.
A review on enhancing water productivities adaptive to the climate change
Authors: Muharomah Riani , Setiawan Budi Indra , Sands Gary R. , Juliana Imroatul Chalimah , Gunawan Taufik Ari 
Published: February 2025

ABSTRACT
Crop water requirements depend on climate, soil, and plant characteristics, necessitating responsive and adaptive irrigation systems for efficient water use. The objectives of this study include assessing the implementation of irrigation technology and its impact on water use efficiency, reviewing smart irrigation systems employed as irrigation management systems, and introducing evapotranspirative irrigation technology as a straightforward smart irrigation approach. Globally, research on irrigation technologies highlights significant potential for water conservation. Smart irrigation system, as a facet of irrigation system management, is considered a strategic approach for effective irrigation implementation. The adoption of micro-irrigation systems in cultivated crops shows promising results in enhancing water productivity and significantly increasing yield rates, but smallholder farmers resist due to high costs. This study introduces innovative approaches using simple automatic technology based on the principle of evapotranspiration, aiming to mitigate high costs. This technology is designed to distribute water optimally at the highest evapotranspiration rate during prolonged dry periods. The key success indicators focus on water productivity, encompassing crop water, irrigation water, and total water. The evapotranspirative irrigation system is pivotal in regulating evapotranspiration rates, resulting in reduced water evaporation and increased land and water productivities, making it adaptive to the impacts of climate change.
Recent advancements in the anaerobic treatment of molasses wastewater in the context of process parameters and valorization aspect
Authors: Penmetsa Suresh Raju , Gautam Pavan Kumar , Allam Bharat Kumar , Manika N. , Banerjee Sushmita 
Published: February 2025

ABSTRACT

Molasses wastewater, a byproduct of the sugar and ethanol industries, is characterized by high biochemical oxygen demand (BOD) and chemical oxygen demand (COD) concentrations, low pH, dark color, and high viscosity. The discharge of improperly treated wastewater containing molasses can have detrimental effects on aquatic life. Developing effective primary treatment technologies for molasses-laden effluent has always been challenging for the scientific community. Anaerobic treatment has proven to be very effective in reducing the strength of molasses-based effluents by significantly reducing BOD and COD levels. In addition to improving the stabilization of degraded organic matter, anaerobic treatment generates minimal sludge, requires less energy, and produces organic fertilizer, which can be used for soil amendment. A further advantage of anaerobic treatment systems for molasses wastewater is that they make green energy from methane and biohydrogen. The anaerobic treatment of molasses wastewater stands as a promising and innovative technology that not only presents economic advantages but also contributes significantly to environmental preservation. Ongoing research efforts are directed toward enhancing bioreactor efficiency and refining process parameters. The present review seeks to offer a comprehensive and detailed account of the recent advancements in the anaerobic degradation of molasses-laden wastewaters.
DIGITAL TWIN APPLICATION IN CONSTRUCTION COST MANAGEMENT
Authors: Che Haron Roziha , Muhd Zafir Nurnaqueisha Balqis 
Published: February 2025

In line with the current construction revolution, it is time for the construction industry to embrace innovation and technology. This is corresponding with the National Construction Policy 2030 (NCP 2030) that comes out with the aim to digitalize the entire construction industry towards the IR 4.0. The focus is to boost the nation's construction industry's competitiveness and recognition worldwide. The construction industry has undergone a significant transformation in recent years such as BIM, IoT including Digital Twin due to the incorporation of digital technologies. A digital twin is a virtual representation of a physical asset. It is still a relatively new concept in the construction industry, but it offers an innovative method for improving cost management strategies in construction projects. Applications of the digital twin in construction cost management have the potential to revolutionize conventional methods. Therefore, this study seeks to determine the level of understanding of construction industry players on the concept of digital twin applications in construction cost management by providing the concept and to explore the challenges and strategies in implementing the digital twin applications in construction cost management. This research employed a mixed-method approach by means of questionnaire survey and interview for data collection. 35 samples that consist of construction industry players from different organizations participated in this study. The data collected from the survey and interview are analyses through descriptive and content analysis. Overall, the findings find out the understanding of digital twin applications with its challenges and strategies to overcome it. This research contributes to the body of knowledge regarding digital twin applications and construction cost management.
Creation of Text Vignettes Based on Patient-Reported Data to Facilitate a Better Understanding of the Patient Perspective: Design Study
Authors: Kelly Sofia Charlotte , Wegener Emilie Kauffeldt , Kayser Lars 
Published: February 2025

Background
Patient-reported outcome (PRO) data refer to information systematically reported by patients, or on behalf of patients, without the influence of health care professionals. It is a focal point of the health care system’s ambition toward becoming more involving and personalized. It is recognized that PROs provide valuable data. However, despite this recognition, there are challenges related to both patients’ and clinicians’ accurate interpretations of the quantitative data. To overcome these challenges, this study explores text vignettes as a representation of PROs.

Objective
This study aimed to develop data-informed text vignettes based on data from the Readiness and Enablement Index for Health Technology (READHY) instrument as another way of representing PRO data and to examine how these are perceived as understandable and relevant for both patients and clinicians.

Methods
The text vignettes were created from participant responses to the READHY instrument, which encompasses health literacy, health education, and eHealth literacy. The text vignettes were created from 13 individual text strings, each corresponding to a scale in the READHY instrument. This study consisted of 3 sequential parts. In part 1, individuals with chronic obstructive pulmonary disease completed the READHY instrument, providing data to be used to create vignettes based on cluster profiles from the READHY instrument. Part 2 focused on the development of scale-based strings representing all READHY dimensions, which were evaluated through iterative cognitive interviews. In part 3, clinicians and patients assessed the understanding and relevance of the text vignettes.

Results
Clinicians and patients both understood and related to the text vignettes. Patients viewed the text vignettes as an accurate reflection of their PRO responses, and clinicians perceived the text vignettes as aligned with their understanding of patients’ experiences.

Conclusions
Text vignettes can be developed using PRO instruments, with individual scales as input strings. This provides an opportunity to present numeric values in a text format that is understandable and recognizable to most patients and clinicians. Challenges with the vignette’s language and layout require customization and clinician training to ensure meaningful interpretation. Findings also support the need to expand the study and enhance clinical relevance with alternative or contextually relevant text vignettes.
Mortality Risk Prediction in Patients With Antimelanoma Differentiation–Associated, Gene 5 Antibody–Positive, Dermatomyositis–Associated Interstitial Lung Disease: Algorithm Development and Validation
Authors: Li Hui , Zou Ruyi , Xin Hongxia , He Ping , Xi Bin , Tian Yaqiong , Zhao Qi , Yan Xin , Qiu Xiaohua , Gao Yujuan , Liu Yin , Cao Min , Chen Bi , Han Qian , Chen Juan , Wang Guochun , Cai Hourong 
Published: February 2025

Background
Patients with antimelanoma differentiation–associated gene 5 antibody–positive dermatomyositis–associated interstitial lung disease (anti-MDA5+DM-ILD) are susceptible to rapidly progressive interstitial lung disease (RP-ILD) and have a high risk of mortality. There is an urgent need for a reliable prediction model, accessible via an easy-to-use web-based tool, to evaluate the risk of death.

Objective
This study aimed to develop and validate a risk prediction model of 3-month mortality using machine learning (ML) in a large multicenter cohort of patients with anti-MDA5+DM-ILD in China.

Methods
In total, 609 consecutive patients with anti-MDA5+DM-ILD were retrospectively enrolled from 6 hospitals across China. Patient demographics and laboratory and clinical parameters were collected on admission. The primary endpoint was 3-month mortality due to all causes. Six ML algorithms (Extreme Gradient Boosting [XGBoost], logistic regression (LR), Light Gradient Boosting Machine [LightGBM], random forest [RF], support vector machine [SVM], and k-nearest neighbor [KNN]) were applied to construct and evaluate the model.

Results
After applying inclusion and exclusion criteria, 509 (83.6%) of the 609 patients were included in our study, divided into a training cohort (n=203, 39.9%), an internal validation cohort (n=51, 10%), and 2 external validation cohorts (n=92, 18.1%, and n=163, 32%). ML identified 8 important variables as critical for model construction: RP-ILD, erythrocyte sedimentation rate (ESR), serum albumin (ALB) level, age, C-reactive protein (CRP) level, aspartate aminotransferase (AST) level, lactate dehydrogenase (LDH) level, and the neutrophil-to-lymphocyte ratio (NLR). LR was chosen as the best algorithm for model construction, and the model demonstrated excellent performance, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.866, a sensitivity of 84.8%, and a specificity of 84.4% on the validation data set and an AUC of 0.90, a sensitivity of 85.0%, and a specificity of 83.9% on the training data set. Calibration curves and decision curve analysis (DCA) confirmed the model’s accuracy and clinical applicability. Moreover, the model showed strong predictive performance in the external validation cohorts (cohort 1: AUC=0.836, 95% CI 0.754-0.916; cohort 2: AUC=0.915, 95% CI 0.871-0.959), indicating good generalizability. This model was integrated into a web-based tool to predict the 3-month mortality for patients with anti-MDA5+DM-ILD.

Conclusions
We successfully developed a robust clinical prediction model and an accompanying web tool to estimate the 3-month mortality risk for patients with anti-MDA5+DM-ILD.