bluuebunny/crossref_metadata_2025_split huggingface dataset:
A global dataset of publicly available dengue case count data
Authors: Clarke J. , Lim A. , Gupte P. , Pigott D. M. , van Panhuis W. G. , Brady O. J. 
Published: March 2024

AbstractOpenDengue is a global database of dengue case data collated from public sources and standardised and formatted to facilitate easy reanalysis. Dataset version 1.2 of this database contains information on over 56 million dengue cases from 102 countries between 1924 and 2023, making it the largest and most comprehensive dengue case database currently available. Over 95% of records are at the weekly or monthly temporal resolution and subnational data is available for 40 countries. To build OpenDengue we systematically searched databases, ministry of health websites, peer reviewed literature and Pro-MED mail reports and extracted denominator-based case count data. We undertake standardisation and error checking protocols to ensure consistency and resolve discrepancies. We meticulously documented the extraction process to ensure records are attributable and reproducible. The OpenDengue database remains under development with plans for further disaggregation and user contributions are encouraged. This new dataset can be used to better understand the long-term drivers of dengue transmission, improve estimates of disease burden, targeting and evaluation of interventions and improving future projections.
Mendelian randomization study on insulin resistance and risk of hypertension and cardiovascular disease
Authors: Zhang Fangfang , Yu Zhimin 
Published: March 2024

AbstractObservational studies have suggested that insulin resistance (IR) is associated with hypertension and various cardiovascular diseases. However, the presence of a causal relationship between IR and cardiovascular disease remains unclear. Here, we applied Mendelian randomization (MR) approaches to address the causal association between genetically determined IR and the risk of cardiovascular diseases. Our primary genetic instruments comprised 53 SNPs associated with IR phenotype from a GWAS of up to 188,577 participants. Genetic association estimates for hypertension and venous thromboembolism (VTE) were extracted from UK Biobank, estimates for atrial fibrillation (AF) were extracted from the hitherto largest GWAS meta-analysis on AF, estimates for heart failure were extracted from HERMES Consortium, estimates for peripheral artery disease (PAD) and aortic aneurysm were extracted from the FinnGen Study. The main analyses were performed using the random-effects inverse-variance weighted approach, and complemented by sensitivity analyses and multivariable MR analyses. Corresponding to 55% higher fasting insulin adjusted for body mass index, 0.46 mmol/L lower high-density lipoprotein cholesterol and 0.89 mmol/L higher triglyceride, one standard deviation change in genetically predicted IR was associated with increased risk of hypertension (odds ratio (OR) 1.06, 95% CI 1.04–1.08; P = 1.91 × 10–11) and PAD (OR 1.90, 95% CI 1.43–2.54; P = 1.19 × 10–5). Suggestive evidence was obtained for an association between IR and heart failure (OR per SD change in IR: 1.19, 95% CI 1.01–1.41, P = 0.041). There was no MR evidence for an association between genetically predicted IR and atrial fibrillation, VTE, and aortic aneurysm. Results were widely consistent across all sensitivity analyses. In multivariable MR, the association between IR and PAD was attenuated after adjustment for lipids (P = 0.347) or BMI (P = 0.163). Our findings support that genetically determined IR increases the risk of hypertension and PAD.
Preclinical systolic dysfunction relating to ankle-brachial index among high-risk PAD population with preserved left ventricular ejection fraction
Authors: Lin Yueh-Hung , Sung Kuo-Tzu , Tsai Cheng-Ting , Lai Yau-Huei , Lo Chi-In , Yu Fa-Chang , Lan Wei-Ran , Hung Ta-Chuan , Kuo Jen-Yuan , Hou Charles Jia-Yin , Yen Chih-Hsuan , Peng Ming-Cheng , Yeh Hung-I. , Wu Ming-Ting , Hung Chung-Lieh 
Published: March 2024

AbstractPeripheral artery disease (PAD) shares common clinical risk factors, for example, endothelial dysfunction, with preserved ejection fraction (LVEF) heart failure (HFpEF). Whether PAD is associated with preclinical systolic dysfunction and higher HF risk among individuals presenting preserved LVEF remains uncertain. We retrospectively included outpatients with at least one known or established cardiovascular (CV) risk factor with LVEF ≥ 50%. Patients were categorized into high risk and low risk of developing PAD (PAD vs Non-PAD) by ankle-brachial index (ABI) (≤ 0.90 or > 1.4) and further stratified based on their history of HFpEF (HFpEF vs. Non-HFpEF), resulting in the formation of four distinct strata. Preclinical systolic dysfunction was defined using dedicated speckle-tracking algorithm. A total of 2130 consecutive patients were enrolled in the study, with a median follow-up of 4.4 years. The analysis revealed a higher prevalence of high risk of developing PAD in patients with HFpEF compared to those without HFpEF (25.1% vs. 9.4%). Both high risk of developing PAD and HFpEF were independently associated with preclinical systolic dysfunction (global longitudinal strain, GLS ≥ − 18%) (odds ratio, OR: 1.38; 95% confidence interval, CI: 1.03–1.86). In comparison to patients at low risk of developing PAD without HFpEF (Non-PAD/Non-HFpEF group), those categorized as having a high risk of developing PAD with HFpEF (PAD/HFpEF group) exhibited the most impaired GLS and a heightened susceptibility to heart failure hospitalization (hazard ratio, HR: 6.51; 95% CI: 4.43–9.55), a twofold increased risk of all-cause mortality (HR: 2.01; 95% CI: 1.17–3.38), cardiovascular mortality (HR: 2.44; 95% CI: 1.08–5.51), and non-cardiovascular mortality (HR: 1.78; 95% CI: 0.82–3.84). A high risk of developing PAD was strongly linked to impaired preclinical systolic function and an increased likelihood for subsequent hospitalization for HF, all-cause mortality, CV mortality and non-CV mortality. There is a clear need for preventive strategies aimed at reducing hospitalizations for HF and mortality in this high-risk population.
Analysis and evaluation of explainable artificial intelligence on suicide risk assessment
Authors: Tang Hao , Miri Rekavandi Aref , Rooprai Dharjinder , Dwivedi Girish , Sanfilippo Frank M. , Boussaid Farid , Bennamoun Mohammed 
Published: March 2024

AbstractThis study explores the effectiveness of Explainable Artificial Intelligence (XAI) for predicting suicide risk from medical tabular data. Given the common challenge of limited datasets in health-related Machine Learning (ML) applications, we use data augmentation in tandem with ML to enhance the identification of individuals at high risk of suicide. We use SHapley Additive exPlanations (SHAP) for XAI and traditional correlation analysis to rank feature importance, pinpointing primary factors influencing suicide risk and preventive measures. Experimental results show the Random Forest (RF) model is excelling in accuracy, F1 score, and AUC (>97% across metrics). According to SHAP, anger issues, depression, and social isolation emerge as top predictors of suicide risk, while individuals with high incomes, esteemed professions, and higher education present the lowest risk. Our findings underscore the effectiveness of ML and XAI in suicide risk assessment, offering valuable insights for psychiatrists and facilitating informed clinical decisions.
Experimental and computational studies of crystal violet removal from aqueous solution using sulfonated graphene oxide
Authors: Oluwasina Olayinka Oluwaseun , Adelodun Adedeji Adebukola , Oluwasina Olugbenga Oludayo , Duarte Helio A. , Olusegun Sunday Joseph 
Published: March 2024

AbstractPositively charged contaminants can be strongly attracted by sulfanilic acid-functionalized graphene oxide. Here, sulfonated graphene oxide (GO-SO3H) was synthesized and characterized for cationic crystal violet (CV) adsorption. We further studied the effect of pH, initial concentration, and temperature on CV uptake. The highest CV uptake occurred at pH 8. A kinetic study was also carried out by applying the pseudo-first-order and pseudo-second-order models. The pseudo-second-order’s adsorption capacity (qe) value was much closer to the experimental qe (qeexp:0.13, qecal:0.12) than the pseudo-first-order model (qeexp:0.13, qecal:0.05). The adsorption performance was accomplished rapidly since the adsorption equilibrium was closely obtained within 30 min. Furthermore, the adsorption capacity was significantly increased from 42.85 to 79.23%. The maximum adsorption capacities of GO-SO3H where 97.65, 202.5, and 196.2 mg·g−1 for CV removal at 298, 308, and 328 K, respectively. The Langmuir and Freundlich adsorption isotherms were applied to the experimental data. The data fit well into Langmuir and Freundlich except at 298 K, where only Langmuir isotherm was most suitable. Thermodynamic studies established that the adsorption was spontaneous and endothermic. The adsorption mechanism was revealed by combining experimental and computational methods. These findings suggest that GO-SO3H is a highly adsorbent for removing harmful cationic dye from aqueous media.
Warming waters lead to increased habitat suitability for juvenile bull sharks (Carcharhinus leucas)
Authors: Mullins Lindsay , Cartwright John , Dykstra Steven L. , Evans Kristine , Mareska John , Matich Philip , Plumlee Jeffrey D. , Sparks Eric , Drymon J. Marcus 
Published: March 2024

AbstractCoastal ecosystems are highly vulnerable to the impacts of climate change and other stressors, including urbanization and overfishing. Consequently, distributions of coastal fish have begun to change, particularly in response to increasing temperatures linked to climate change. However, few studies have evaluated how natural and anthropogenic disturbances can alter species distributions in conjunction with geophysical habitat alterations, such as changes to land use and land cover (LU/LC). Here, we examine the spatiotemporal changes in the distribution of juvenile bull sharks (Carcharhinus leucas) using a multi-decadal fishery-independent survey of coastal Alabama. Using a boosted regression tree (BRT) modeling framework, we assess the covariance of environmental conditions (sea surface temperature, depth, salinity, dissolved oxygen, riverine discharge, Chl-a) as well as historic changes to LU/LC to the distribution of bull sharks. Species distribution models resultant from BRTs for early (2003–2005) and recent (2018–2020) monitoring periods indicated a mean increase in habitat suitability (i.e., probability of capture) for juvenile bull sharks from 0.028 to 0.082, concomitant with substantial increases in mean annual temperature (0.058°C/yr), Chl-a (2.32 mg/m3), and urbanization (increased LU/LC) since 2000. These results align with observed five-fold increases in the relative abundance of juvenile bull sharks across the study period and demonstrate the impacts of changing environmental conditions on their distribution and relative abundance. As climate change persists, coastal communities will continue to change, altering the structure of ecological communities and the success of nearshore fisheries.
A new automatic geo-electric self-potential imaging technique for diverse sustainable development scenarios
Authors: Elhussein Mahmoud , Diab Zein E. 
Published: March 2024

AbstractThis study introduces a rapid and efficient inversion algorithm designed for the interpretation of self-potential responses originating from mineralized and ore sources and hydrothermal activity, specifically addressing spherical, vertical, and horizontal cylindrical structures. The algorithm leverages local wavenumber and correlation imaging techniques to enhance accuracy in modeling. The correlation factor (Cf value) is crucial in this approach, calculated as the correlation between the local wavenumber of the measured self-potential field and that of the computed field. The algorithm identifies the maximum correlation Cf value (CF-max) as indicative of the optimal true model parameters. To validate the proposed algorithm, it was applied to three theoretical examples—one with contamination from regional background and another with multiple sources with and without different types of noises (random Gaussian and white Gaussian noises). Additionally, the approach was tested on three distinct real field cases related to mining, ore investigation and hydrothermal activity in India, Germany and USA. Through a comprehensive analysis of results from theoretical and real-world scenarios, including comparisons with different available data and literature information, the study concludes that the method is effective, applicable to multiple sources, accurate, and does not necessitate prior knowledge of the source shape. This algorithm presents a promising advancement in the field of self-potential interpretation for mineral exploration and geothermal exploration.
Lifestyle behaviour changes associated with osteoarthritis: a prospective cohort study
Authors: Ng Norman , Parkinson Lynne , Brown Wendy J. , Moorin Rachael , Peeters G. M. E. E. Geeske 
Published: March 2024

AbstractThe aim of this prospective cohort study was to compare changes in lifestyle behaviours over nine years in women who were and were not diagnosed with osteoarthritis (OA). Data were from the 1945–51 cohort of the Australian Longitudinal Study on Women’s Health (aged 50–55 in 2001) who completed written surveys in 2001, 2004, 2007 and 2010. The sample included 610 women who were, and 3810 women who were not diagnosed with OA between 2004 and 2007. Descriptive statistics were used to assess changes in lifestyle behaviours (weight, sitting time, physical activity, alcohol and smoking) in the two groups, over three survey intervals: from 2001–2004 (prior to diagnosis); from 2004–2007 (around diagnosis); and from 2007–2010 (following diagnosis). Compared with women without OA (28%), a greater proportion of women with OA (38%) made at least one positive lifestyle change (p < 0.001). These included losing > 5 kg (9.8% vs. 14.4%, p < 0.001), and reducing sitting time by an hour (29.5% vs. 39.1%, p < 0.001) following diagnosis. However, women with OA also made negative lifestyle changes (35% vs. 29%, p < 0.001), for example, gaining > 5 kg around the time of diagnosis (21.4% vs. 14.5%, p < 0.001) and increasing sitting time by an hour following diagnosis (38.4% vs. 32.3%, p = 0.003). More women with OA also started smoking following diagnosis (8.9% vs. 0.8%, p < 0.001). While some women made positive changes in lifestyle behaviours during and following OA diagnosis, others made negative changes. Consistent support from clinicians for managing OA symptoms may enable patients to make more positive changes in lifestyle behaviours.
An improved transformer-based concrete crack classification method
Authors: Ye Guanting , Dai Wei , Tao Jintai , Qu Jinsheng , Zhu Lin , Jin Qiang 
Published: March 2024

AbstractIn concrete structures, surface cracks are an important indicator for assessing the durability and serviceability of the structure. Existing convolutional neural networks for concrete crack identification are inefficient and computationally costly. Therefore, a new Cross Swin transformer-skip (CSW-S) is proposed to classify concrete cracks. The method is optimized by adding residual links to the existing Cross Swin transformer network and then trained and tested using a dataset with 17,000 images. The experimental results show that the improved CSW-S network has an extended range of extracted image features, which improves the accuracy of crack recognition. A detection accuracy of 96.92% is obtained using the trained CSW-S without pretraining. The improved transformer model has higher recognition efficiency and accuracy than the traditional transformer model and the classical CNN model.
Python farming as a flexible and efficient form of agricultural food security
Authors: Natusch D. , Aust P. W. , Caraguel C. , Taggart P. L. , Ngo V. T. , Alexander G. J. , Shine R. , Coulson T. 
Published: March 2024

AbstractDiminishing natural resources and increasing climatic volatility are impacting agri-food systems, prompting the need for sustainable and resilient alternatives. Python farming is well established in Asia but has received little attention from mainstream agricultural scientists. We measured growth rates in two species of large pythons (Malayopython reticulatus and Python bivittatus) in farms in Thailand and Vietnam and conducted feeding experiments to examine production efficiencies. Pythons grew rapidly over a 12-month period, and females grew faster than males. Food intake and growth rates early in life were strong predictors of total lifetime growth, with daily mass increments ranging from 0.24 to 19.7 g/day for M. reticulatus and 0.24 to 42.6 g/day for P. bivittatus, depending on food intake. Pythons that fasted for up to 4.2 months lost an average of 0.004% of their body mass per day, and resumed rapid growth as soon as feeding recommenced. Mean food conversion rate for dressed carcasses was 4.1%, with useable products (dressed carcass, skin, fat, gall bladder) comprising 82% of the mass of live animals. In terms of food and protein conversion ratios, pythons outperform all mainstream agricultural species studied to date. The ability of fasting pythons to regulate metabolic processes and maintain body condition enhances food security in volatile environments, suggesting that python farming may offer a flexible and efficient response to global food insecurity.
cyp51A mutations, protein modeling, and efflux pump gene expression reveals multifactorial complexity towards understanding Aspergillus section Nigri azole resistance mechanism
Authors: Sen Pooja , Vijay Mukund , Kamboj Himanshu , Gupta Lovely , Shankar Jata , Vijayaraghavan Pooja 
Published: March 2024

AbstractBlack Aspergillus species are the most common etiological agents of otomycosis, and pulmonary aspergillosis. However, limited data is available on their antifungal susceptibility profiles and associated resistance mechanisms. Here, we determined the azole susceptibility profiles of black Aspergillus species isolated from the Indian environment and explored the potential resistance mechanisms through cyp51A gene sequencing, protein homology modeling, and expression analysis of selected genes cyp51A, cyp51B, mdr1, and mfs based on their role in imparting resistance against antifungal drugs. In this study, we have isolated a total of 161 black aspergilli isolates from 174 agricultural soil samples. Isolates had variable resistance towards medical azoles; approximately 11.80%, 3.10%, and 1.24% of isolates were resistant to itraconazole (ITC), posaconazole (POS), and voriconazole (VRC), respectively. Further, cyp51A sequence analysis showed that non-synonymous mutations were present in 20 azole-resistant Aspergillus section Nigri and 10 susceptible isolates. However, Cyp51A homology modeling indicated insignificant protein structural variations because of these mutations. Most of the isolates showed the overexpression of mdr1, and mfs genes. Hence, the study concluded that azole-resistance in section Nigri cannot be attributed exclusively to the cyp51A gene mutation or its overexpression. However, overexpression of mdr1 and mfs genes may have a potential role in drug resistance.
Identification of a hyperinflammatory sepsis phenotype using protein biomarker and clinical data in the ProCESS randomized trial
Authors: DeMerle Kimberley M. , Kennedy Jason N. , Chang Chung-Chou H. , Delucchi Kevin , Huang David T. , Kravitz Max S. , Shapiro Nathan I. , Yealy Donald M. , Angus Derek C. , Calfee Carolyn S. , Seymour Christopher W. 
Published: March 2024

AbstractSepsis is a heterogeneous syndrome and phenotypes have been proposed using clinical data. Less is known about the contribution of protein biomarkers to clinical sepsis phenotypes and their importance for treatment effects in randomized trials of resuscitation. The objective is to use both clinical and biomarker data in the Protocol-Based Care for Early Septic Shock (ProCESS) randomized trial to determine sepsis phenotypes and to test for heterogeneity of treatment effect by phenotype comparing usual care to protocolized early, goal-directed therapy(EGDT). In this secondary analysis of a subset of patients with biomarker sampling in the ProCESS trial (n = 543), we identified sepsis phenotypes prior to randomization using latent class analysis of 20 clinical and biomarker variables. Logistic regression was used to test for interaction between phenotype and treatment arm for 60-day inpatient mortality. Among 543 patients with severe sepsis or septic shock in the ProCESS trial, a 2-class model best fit the data (p = 0.01). Phenotype 1 (n = 66, 12%) had increased IL-6, ICAM, and total bilirubin and decreased platelets compared to phenotype 2 (n = 477, 88%, p < 0.01 for all). Phenotype 1 had greater 60-day inpatient mortality compared to Phenotype 2 (41% vs 16%; p < 0.01). Treatment with EGDT was associated with worse 60-day inpatient mortality compared to usual care (58% vs. 23%) in Phenotype 1 only (p-value for interaction = 0.05). The 60-day inpatient mortality was similar comparing EGDT to usual care in Phenotype 2 (16% vs. 17%). We identified 2 sepsis phenotypes using latent class analysis of clinical and protein biomarker data at randomization in the ProCESS trial. Phenotype 1 had increased inflammation, organ dysfunction and worse clinical outcomes compared to phenotype 2. Response to EGDT versus usual care differed by phenotype.
Investigation of the performance of a horizontal-axis dual rotor wind turbine
Authors: Salah Dina Ahmed Hosni , Nosier Mahmoud Abed El-Rasheed , Hamed Ashraf Mostafa 
Published: March 2024

AbstractRecent years have seen a rise in interest in wind energy as a useful alternative to harmful energies like fossil fuels. The dual rotor wind turbine (DRWT) offers more rapid rates of wind energy extraction. The current study intends to compare the performance of the turbine with and without the addition of a second rotor. Additionally, it examines how tip speed ratio and phase shift angle will affect DRWT performance. Realizable k-shear stress transport turbulence models are used to solve the three-dimensional, turbulent, stable, and incompressible flow equations for the performance of dual-rotor wind turbines. Domain-independence tests and an impartial mesh test are run to assess the results and ensure their accuracy. The researcher relies on previous studies while constructing the single rotor wind turbine model. This model uses an S826 airfoil. The front and rear rotors are given streamlined representations using ANSYS, according to the researcher. The independent mesh test indicates that the mesh density has 11.5 million elements. The experiment's results show that the DRWT has a significant effect on the efficiency of wind energy.