bluuebunny/crossref_metadata_2025_split huggingface dataset:
Global patterns in river water storage dependent on residence time
Authors: Collins Elyssa L. , David Cédric H. , Riggs Ryan , Allen George H. , Pavelsky Tamlin M. , Lin Peirong , Pan Ming , Yamazaki Dai , Meentemeyer Ross K. , Sanchez Georgina M. 
Published: April 2024

AbstractAccurate assessment of global river flows and stores is critical for informing water management practices, but current estimates of global river flows exhibit substantial spread and estimates of river stores remain sparse. Estimates of river flows and stores are hampered by uncertainties in land runoff, an unobserved quantity that provides water input to rivers. Here we leverage global river flow observations and an ensemble of land surface models to generate a globally gauge-corrected monthly river flow and storage dataset. We estimate a global river storage mean (± monthly variability) of 2,246 ± 505 km3 and a global continental flow of 37,411 ± 7,816 km3 yr−1. Our global river water storage time series demonstrates that flow wave residence time is a fundamental driver that can double or halve river water stores and their variability. We also reconcile the wide range in previous estimates of monthly variability in global river flows. We identify previously underappreciated freshwater sources to the ocean from the Maritime Continent (Indonesia, Malaysia and Papua New Guinea) amounting to 1.6 times the Congo River and illustrate our capability of detecting severe anthropogenic water withdrawals.
Virtual reality-empowered deep-learning analysis of brain cells
Authors: Kaltenecker Doris , Al-Maskari Rami , Negwer Moritz , Hoeher Luciano , Kofler Florian , Zhao Shan , Todorov Mihail , Rong Zhouyi , Paetzold Johannes Christian , Wiestler Benedikt , Piraud Marie , Rueckert Daniel , Geppert Julia , Morigny Pauline , Rohm Maria , Menze Bjoern H. , Herzig Stephan , Berriel Diaz Mauricio , Ertürk Ali 
Published: April 2024

AbstractAutomated detection of specific cells in three-dimensional datasets such as whole-brain light-sheet image stacks is challenging. Here, we present DELiVR, a virtual reality-trained deep-learning pipeline for detecting c-Fos+ cells as markers for neuronal activity in cleared mouse brains. Virtual reality annotation substantially accelerated training data generation, enabling DELiVR to outperform state-of-the-art cell-segmenting approaches. Our pipeline is available in a user-friendly Docker container that runs with a standalone Fiji plugin. DELiVR features a comprehensive toolkit for data visualization and can be customized to other cell types of interest, as we did here for microglia somata, using Fiji for dataset-specific training. We applied DELiVR to investigate cancer-related brain activity, unveiling an activation pattern that distinguishes weight-stable cancer from cancers associated with weight loss. Overall, DELiVR is a robust deep-learning tool that does not require advanced coding skills to analyze whole-brain imaging data in health and disease.
SMS121, a new inhibitor of CD36, impairs fatty acid uptake and viability of acute myeloid leukemia
Authors: Åbacka Hannah , Masoni Samuele , Poli Giulio , Huang Peng , Gusso Francesco , Granchi Carlotta , Minutolo Filippo , Tuccinardi Tiziano , Hagström-Andersson Anna K. , Lindkvist-Petersson Karin 
Published: April 2024

AbstractAcute myeloid leukemia (AML) is the most common form of acute leukemia in adults and the second most common among children. AML is characterized by aberrant proliferation of myeloid blasts in the bone marrow and impaired normal hematopoiesis. Despite the introduction of new drugs and allogeneic bone marrow transplantation, patients have poor overall survival rate with relapse as the major challenge, driving the demand for new therapeutic strategies. AML patients with high expression of the very long/long chain fatty acid transporter CD36 have poorer survival and very long chain fatty acid metabolism is critical for AML cell survival. Here we show that fatty acids are transferred from human primary adipocytes to AML cells upon co-culturing. A drug-like small molecule (SMS121) was identified by receptor-based virtual screening and experimentally demonstrated to target the lipid uptake protein CD36. SMS121 reduced the uptake of fatty acid into AML cells that could be reversed by addition of free fatty acids and caused decreased cell viability. The data presented here serves as a framework for the development of CD36 inhibitors to be used as future therapeutics against AML.
Breast problems and associated factors among lactating women in Northeast Ethiopia, 2022
Authors: Shitie Anguach , Adimasu Abebe , Tsegaye Delelegn , Belete Dagne , Mislu Esuyawkal , Assfaw Mandefro , Kettema Wondimnew Gashaw 
Published: April 2024

AbstractBreastfeeding is the cornerstone of child and maternal health. However, maternal breast problems during breastfeeding have been frequently reported as one of the reasons for early discontinuation of breastfeeding. Despite the importance of having knowledge on breast problems magnitude and its associated factors in the clinical practices and designing effective interventions, there is limited data on this topic. Therefore, this study aimed to assess the prevalence and associated factors of breast problem among postnatal lactating women in Legambo district, south wollo zone, North East Ethiopia, in 2022. A community-based cross sectional study was conducted among 610 lactating mothers in Legambo district. Multi-stage sampling was employed to select study participants. Interviewer administered, WHO B-R-E-A-S-T-Feeding, observational checklist and maternal self-reported breast problem questionnaires were used to collect the data. Epi-Data version 3.1 was used for data entry and export to SPSS version 25.0 for analysis. Descriptive statistics and bi-variable and multivariable analysis was carried out. On the multivariable logistic regression, variables with p-value < 0.5 were considered as they had statistically significant association with breast problem. The overall prevalence of breast problems among postnatal lactating women was 54.3% (95%, CI 49.3–59.3%). Primipara (AOR = 5.09; 95% CI 3.40–7.62), preterm infant (AOR = 2.12; 95% CI 1.22–3.66), home delivery (AOR = 3.67; 95% CI 1.62–8.30), ineffective breastfeeding techniques (AOR = 2.45; 95%CI 1.61–3.74), caesarean section delivery (AOR = 2.05;95%CI :1.15–3.64) and mixed type of feeding (AOR = 1.97:95%CI 1.34–2.89) were factors showed significant association. The prevalence of breast problems was 54.3%. Relevant factors related to an increase risks are being primipara, cesarean section delivery, home delivery, preterm birth, ineffective breastfeeding techniques and mixed type of feeding.
Central composite design and mechanism of antibiotic ciprofloxacin photodegradation under visible light by green hydrothermal synthesized cobalt-doped zinc oxide nanoparticles
Authors: Hassaan Mohamed A. , Meky Asmaa I. , Fetouh Howida A. , Ismail Amel M. , El Nemr Ahmed 
Published: April 2024

AbstractIn this research, different Co2+ doped ZnO nanoparticles (NPs) were hydrothermally synthesized by an environmentally friendly, sustainable technique using the extract of P. capillacea for the first time. Co-ZnO was characterized and confirmed by FTIR, XPS, XRD, BET, EDX, SEM, TEM, DRS UV–Vis spectroscopy, and TGA analyses. Dislocation density, micro strains, lattice parameters and volume of the unit cell were measured using XRD results. XRD suggests that the average size of these NPs was between 44.49 and 65.69 nm with a hexagonal wurtzite structure. Tauc plot displayed that the optical energy bandgap of ZnO NPs (3.18) slowly declines with Co doping (2.96 eV). Near complete removal of the ciprofloxacin (CIPF) antibiotic was attained using Green 5% of Hy-Co-ZnO in the existence of visible LED light which exhibited maximum degradation efficiency (99%) within 120 min for 30 ppm CIPF initial concentration. The photodegradation mechanism of CIPF using Green Hy-Co-ZnO NPs followed the Pseudo-first-order kinetics. The Green Hy-Co-ZnO NPs improved photocatalytic performance toward CIPF for 3 cycles. The experiments were designed using the RSM (CCD) method for selected parameters such as catalyst dosage, antibiotic dosage, shaking speed, and pH. The maximal CIPF degradation efficiency (96.4%) was achieved under optimum conditions of 39.45 ppm CIPF dosage, 60.56 mg catalyst dosage, 177.33 rpm shaking speed and pH 7.57.
Assessing agronomic performance, chocolate spot resistance, and heat tolerance for diverse Vicia faba genotypes under varying environmental conditions
Authors: El-Abssi Mostafa G. , Awaad Hassan A. , Qabil Naglaa , Mansour Elsayed 
Published: April 2024

AbstractChocolate spot and heat stress devastatingly impact the production of faba bean, particularly under prevailing climatic changes and rising drastic environmental conditions. Hence, the adaptability of faba bean performance is a decisive objective of plant breeders to ensure its sustainable production. The present study aimed to evaluate the agronomic performance and stability of diverse eleven faba bean genotypes for yield characters, chocolate spot, and heat stress in eight different growing environments. The faba bean genotypes were evaluated at two sowing dates in two different locations during two growing seasons. The evaluated eleven faba bean genotypes were sown timely in autumn (25 October) and late sowing in early winter (25 November) in Bilbeis and Elkhatara during 2020 and 2021 growing seasons. The results exhibited substantial differences among the evaluated sowing dates, locations, and faba bean genotypes for all studied characters. The genotypes Sakha-3, Nubaria-3, Nubaria-5, Misr-3, and Wadi-1 were able to produce acceptable yield and quality characters under timely sowing in autumn and late sowing in early winter in all tested environments. Moreover, the genotypes Nubaria-3, Nubaria-4, Nubaria-5, Sakha-4, Giza-3, and Triple White exhibited better resistance to chocolate spot. The assessed faba bean genotypes were evaluated under late sowing to expose the plants to high temperature stress at flowering and throughout the anthesis and seed-filling stages. The genotypes Nubaria-5, Nubaria-3, Nubaria-4, Sakha-3, Sakha-4, Wadi-1, and Misr-3 possessed tolerance to heat stress more than the other genotypes. Different statistical methods were applied to study the stability of assessed genotypes such as joint regression, Additive Main Effect and Multiplicative Interaction (AMMI) analysis, AMMI stability value, Wricke's and Ecovalence values. The estimated stability parameters were consistent in depicting the stability of the assessed faba bean genotypes. The findings revealed that Sakha-1, Misr-3, Nubaria-4, and Nubaria-5 demonstrated stable and desirable performance across all tested environments. The heatmap was employed to classify the assessed faba bean genotypes into different groups based on agronomic performance, chocolate spot resistance and heat stress tolerance. Nubaria-3, Nubaria-4, Nubaria-5, and Misr-3 had the best performance for agronomic performance, chocolate spot resistance, and heat stress tolerance. The obtained results provide evidence of employing promising faba bean genotypes for improving the stability of agronomic performance, chocolate spot resistance, and heat stress tolerance in breeding programs principally under unprecedented climate fluctuations.
Digital quantification of the MMSE interlocking pentagon areas: a three-stage algorithm
Authors: Kim Namhee , Truty Timothy , Duke Han S. , Heo Moonseong , Buchman Aron S. , Bennett David A. , Tasaki Shinya 
Published: April 2024

AbstractThe Mini-Mental State Examination (MMSE) is a widely employed screening tool for the severity of cognitive impairment. Among the MMSE items, the pentagon copying test (PCT) requires participants to accurately replicate a sample of two interlocking pentagons. While the PCT is traditionally scored on a binary scale, there have been limited developments of granular scoring scale to assess task performance. In this paper, we present a novel three-stage algorithm, called Quantification of Interlocking Pentagons (QIP) which quantifies PCT performance by computing the areas of individual pentagons and their intersection areas, and a balance ratio between the areas of the two individual pentagons. The three stages of the QIP algorithm include: (1) detection of line segments, (2) unraveling of the interlocking pentagons, and (3) quantification of areas. A set of 497 PCTs from 84 participants including their baseline and follow-up PCTs from the Rush Memory and Aging Project was selected blinded about their cognitive and clinical status. Analysis of the quantified data revealed a significant inverse relationship between age and balance ratio (beta = − 0.49, p = 0.0033), indicating that older age was associated with a smaller balance ratio. In addition, balance ratio was associated with perceptual speed (r = 0.71, p = 0.0135), vascular risk factors (beta = − 3.96, p = 0.0269), and medical conditions (beta = − 2.78, p = 0.0389). The QIP algorithm can serve as a useful tool for enhancing the scoring of performance in the PCT.
Calculation method and analysis of residual stress in the strip bending roller straightening process
Authors: Zhu Xiaoyu , Wang XiaoGang , Wu Huihai 
Published: April 2024

AbstractTaking thin gauge strip as an example, the deformation process of metal strip in bending roll straightening was studied. Based on the theory of discrete, curvature integral and elastic–plastic mechanics, the strip travel trajectory of the bending roll straightening process is analyzed, and the numerical analytical calculation method of the continuous straightening process of the strip bending roll is established. The results are verified by establishing MARC finite element simulation and designing straightening experiment. The effects of yield strength, plastic rate and bending amount on residual stress after straightening were studied. In the straightening process, with the increase of the amount of bending roll, the residual strain converges to the region Γ, and with the increase of the yield strength, the region Γ decreases. With the increase of the yield strength, the amount of bending roll and the plastic rate, the wave height increases. The results of the calculation of residual stress, finite element simulation and experiment are close and the trend is consistent. The results show that the logic of the calculation method is reasonable, and the prediction error is within the scope of engineering application, which is helpful to the realization of process intelligence in the process of bending roller straightening.
Studying removal of anionic dye by prepared highly adsorbent surface hydrogel nanocomposite as an applicable for aqueous solution
Authors: Aljeboree Aseel M. , Alkaim Ayad F. 
Published: April 2024

AbstractIn this study, a Sodium alginate-g-poly (acrylamide-clay)/TiO2 hydrogel nanocomposite [SA-g-p(AM-Bn)/TiO2] was synthesized using the biopolymer sodium alginate (SA), acrylamide (AM), and bentonite clay (Bn) as hybrid materials embedded with titanium dioxide nanoparticles (TiO2NPs) for the removal of toxic Congo Red (CR) dye from an aqueous solution. The [SA-g-p(AM-Bn)/TiO2] nanocomposite has been described on the basis of thermal stability, morphological analysis, estimation of functional group, and crystalline/amorphous character by TGA, EFSEM/EDX, TEM, FT-IR, and XRD analysis, respectively. The effects of operational parameters toward the CR dye adsorption on [SA-g-p(AM-Bn)/TiO2], including contact time, adsorbent dosage, initial concentration, initial pH, and temperature were investigated. The maximum adsorption efficiency was found to be 185.12 mg/g for [SA-g-p(AM-Bn)/TiO2] in 100 mg/L of solution CR at pH 6.0 within 1 h. The equilibrium isotherms, kinetics, and thermodynamics parameters of adsorption were examined, and results showed that the isotherm fitted the Freundlich model and the kinetics adsorption model of CR followed pseudo-first-order, thus indicating physisorption of anionic-CR onto the sorbent due to the development of an electrostatic attraction bond. Thermodynamic parameters for [SA-g-p(AM-Bn)/TiO2] have values (ΔG and ΔH) reflecting the spontaneous and endothermic nature of the adsorption processes. Moreover, [SA-g-p(AM-Bn)/TiO2] presented outstanding excellent reusability and recyclability with a relatively best removal percentage as compared to [SA-g-p(AM-Bn)] and suggested their applicability towards the textile industry and water purification purposes.
Predictive modeling of co-infection in lupus nephritis using multiple machine learning algorithms
Authors: Zhang Jiaqian , Chen Bo , Liu Jiu , Chai Pengfei , Liu Hongjiang , Chen Yuehong , Liu Huan , Yin Geng , Zhang Shengxiao , Wang Caihong , Xie Qibing 
Published: April 2024

AbstractThis study aimed to analyze peripheral blood lymphocyte subsets in lupus nephritis (LN) patients and use machine learning (ML) methods to establish an effective algorithm for predicting co-infection in LN. This study included 111 non-infected LN patients, 72 infected LN patients, and 206 healthy controls (HCs). Patient information, infection characteristics, medication, and laboratory indexes were recorded. Eight ML methods were compared to establish a model through a training group and verify the results in a test group. We trained the ML models, including Logistic Regression, Decision Tree, K-Nearest Neighbors, Support Vector Machine, Multi-Layer Perceptron, Random Forest, Ada boost, Extreme Gradient Boosting (XGB), and further evaluated potential predictors of infection. Infected LN patients had significantly decreased levels of T, B, helper T, suppressor T, and natural killer cells compared to non-infected LN patients and HCs. The number of regulatory T cells (Tregs) in LN patients was significantly lower than in HCs, with infected patients having the lowest Tregs count. Among the ML algorithms, XGB demonstrated the highest accuracy and precision for predicting LN infections. The innate and adaptive immune systems are disrupted in LN patients, and monitoring lymphocyte subsets can help prevent and treat infections. The XGB algorithm was recommended for predicting co-infection in LN.
Exponential growth of private coastal infrastructure influenced by geography and race in South Carolina, USA
Authors: Beauvais Jeffrey , Markley Scott N. , Byers James E. 
Published: April 2024

AbstractHomeowners in coastal environments often augment their access to estuarine ecosystems by building private docks on their personal property. Despite the commonality of docks, particularly in the Southeastern United States, few works have investigated their historical development, their distribution across the landscape, or the environmental justice dimensions of this distribution. In this study, we used historic aerial photography to track the abundance and size of docks across six South Carolina counties from the 1950s to 2016. Across our roughly 60-year study period, dock abundance grew by two orders of magnitude, mean length of newly constructed docks doubled, and the cumulative length of docks ballooned from 34 to 560 km. Additionally, we drew on census data interpolated into consistent 2010 tract boundaries to analyze the racial and economic distribution of docks in 1994, 1999, 2011, and 2016. Racial composition, measured as the percentage of a tract’s population that was White, positively correlated with dock abundance in each year. Median household income and dock abundance were only correlated in 2011. Taken together, these metrics indicate the growing desire for direct estuary access, however, that access does not appear to be equally spread across racial groups. Because docks enhance estuarine access and demarcate private property, our study provides longitudinal insights into environmental justice concerns related to disparate private property ownership. We found a persistent correlation between the racial characteristics of an area and dock abundance, strongly indicating that White South Carolinians have had disproportionately greater private water access for the past two decades.
Development of sensor system and data analytic framework for non-invasive blood glucose prediction
Authors: Rajeswari S. V. K. R. , Vijayakumar P. 
Published: April 2024

AbstractPeriodic quantification of blood glucose levels is performed using painful, invasive methods. The proposed work presents the development of a noninvasive glucose-monitoring device with two sensors, i.e., finger and wrist bands. The sensor system was designed with a near-infrared (NIR) wavelength of 940 nm emitter and a 900–1700 nm detector. This study included 101 diabetic and non-diabetic volunteers. The obtained dataset was subjected to pre-processing, exploratory data analysis (EDA), data visualization, and integration methods. Ambiguities such as the effects of skin color, ambient light, and finger pressure on the sensor were overcome in the proposed ‘niGLUC-2.0v’. niGLUC-2.0v was validated with performance metrics where accuracy of 99.02%, mean absolute error (MAE) of 0.15, mean square error (MSE) of 0.22 for finger, and accuracy of 99.96%, MAE of 0.06, MSE of 0.006 for wrist prototype with ridge regression (RR) were achieved. Bland–Altman analysis was performed, where 98% of the data points were within ± 1.96 standard deviation (SD), 100% were under zone A of the Clarke Error Grid (CEG), and statistical analysis showed p < 0.05 on evaluated accuracy. Thus, niGLUC-2.0v is suitable in the medical and personal care fields for continuous real-time blood glucose monitoring.
Investigation of the effectiveness of a classification method based on improved DAE feature extraction for hepatitis C prediction
Authors: Zhang Lin , Wang Jixin , Chang Rui , Wang Weigang 
Published: April 2024

AbstractHepatitis C, a particularly dangerous form of viral hepatitis caused by hepatitis C virus (HCV) infection, is a major socio-economic and public health problem. Due to the rapid development of deep learning, it has become a common practice to apply deep learning to the healthcare industry to improve the effectiveness and accuracy of disease identification. In order to improve the effectiveness and accuracy of hepatitis C detection, this study proposes an improved denoising autoencoder (IDAE) and applies it to hepatitis C disease detection. Conventional denoising autoencoder introduces random noise at the input layer of the encoder. However, due to the presence of these features, encoders that directly add random noise may mask certain intrinsic properties of the data, making it challenging to learn deeper features. In this study, the problem of data information loss in traditional denoising autoencoding is addressed by incorporating the concept of residual neural networks into an enhanced denoising autoencoder. In our experimental study, we applied this enhanced denoising autoencoder to the open-source Hepatitis C dataset and the results showed significant results in feature extraction. While existing baseline machine learning methods have less than 90% accuracy and integrated algorithms and traditional autoencoders have only 95% correctness, the improved IDAE achieves 99% accuracy in the downstream hepatitis C classification task, which is a 9% improvement over a single algorithm, and a nearly 4% improvement over integrated algorithms and other autoencoders. The above results demonstrate that IDAE can effectively capture key disease features and improve the accuracy of disease prediction in hepatitis C data. This indicates that IDAE has the potential to be widely used in the detection and management of hepatitis C and similar diseases, especially in the development of early warning systems, progression prediction and personalised treatment strategies.