bluuebunny/crossref_metadata_2025_split huggingface dataset:
Establishment of a corneal ulcer prognostic model based on machine learning
Authors: Wang Meng-Tong , Cai You-Ran , Jang Vlon , Meng Hong-Jian , Sun Ling-Bo , Deng Li-Min , Liu Yu-Wen , Zou Wen-Jin 
Published: July 2024

AbstractCorneal infection is a major public health concern worldwide and the most common cause of unilateral corneal blindness. Toxic effects of different microorganisms, such as bacteria and fungi, worsen keratitis leading to corneal perforation even with optimal drug treatment. The cornea forms the main refractive surface of the eye. Diseases affecting the cornea can cause severe visual impairment. Therefore, it is crucial to analyze the risk of corneal perforation and visual impairment in corneal ulcer patients for making early treatment strategies. The modeling of a fully automated prognostic model system was performed in two parts. In the first part, the dataset contained 4973 slit lamp images of corneal ulcer patients in three centers. A deep learning model was developed and tested for segmenting and classifying five lesions (corneal ulcer, corneal scar, hypopyon, corneal descementocele, and corneal neovascularization) in the eyes of corneal ulcer patients. Further, hierarchical quantification was carried out based on policy rules. In the second part, the dataset included clinical data (name, gender, age, best corrected visual acuity, and type of corneal ulcer) of 240 patients with corneal ulcers and respective 1010 slit lamp images under two light sources (natural light and cobalt blue light). The slit lamp images were then quantified hierarchically according to the policy rules developed in the first part of the modeling. Combining the above clinical data, the features were used to build the final prognostic model system for corneal ulcer perforation outcome and visual impairment using machine learning algorithms such as XGBoost, LightGBM. The ROC curve area (AUC value) evaluated the model’s performance. For segmentation of the five lesions, the accuracy rates of hypopyon, descemetocele, corneal ulcer under blue light, and corneal neovascularization were 96.86, 91.64, 90.51, and 93.97, respectively. For the corneal scar lesion classification, the accuracy rate of the final model was 69.76. The XGBoost model performed the best in predicting the 1-month prognosis of patients, with an AUC of 0.81 (95% CI 0.63–1.00) for ulcer perforation and an AUC of 0.77 (95% CI 0.63–0.91) for visual impairment. In predicting the 3-month prognosis of patients, the XGBoost model received the best AUC of 0.97 (95% CI 0.92–1.00) for ulcer perforation, while the LightGBM model achieved the best performance with an AUC of 0.98 (95% CI 0.94–1.00) for visual impairment.
Broadening bandgaps in a multi-resonant piezoelectric metamaterial plate via bandgap merging phenomena
Authors: Li Yuhao , Liu Zhiyuan , Zhou Hao , Yi Kaijun , Zhu Rui 
Published: July 2024

AbstractLocally resonant metamaterials usually have narrow bandgaps, which significantly limits their applications in realistic engineering environments. In this paper, an optimization method based on the genetic algorithm is proposed to broaden bandgaps in multi-resonant piezoelectric metamaterial through the merging of multiple separated bandgaps. Using the effective medium theory, the equivalent bending stiffness and dispersion relationship of a metamaterial plate are first obtained. Then, the criteria for determining the bandgap ranges for the two cases with and without damping are provided and analyzed. Furthermore, based on the bandgap merging phenomena, an optimization method for widening the bandgap is proposed based on the genetic algorithm. By investigating the bandgap widening effects in cases without and with damping, it is found that, when there is no damping, the bandgap can only be slightly widened; while after introducing damping into the transfer functions, the bandgap can be significantly widened by more than 200%. The bandgap widening effects are verified by comparing with finite element simulation results.
Setting the morphologic quality limits enabling accurate classification of charred archaeological grape seeds
Authors: Landa Vlad , Shapira Yekaterina , Eliyahu-Behar Adi , Ben-Arie Reut Levitan , Weiss Ehud , Reuveni Yuval , Drori Elyashiv 
Published: July 2024

AbstractThis study investigates the morphological changes in grape pips resulting from various charring conditions. Employing high-resolution scanning combined with morphometric measurements for morphological analysis, we aimed to understand the effects of charring on grape pips. Our morphometric analysis demonstrated significant alterations in seed shape above 250 °C. The length–width ratio and the occurrence of cracks notably changed, providing a basis for assessing charring conditions. In addition, applying a machine learning classification method, we determined that accurate classification of grape varieties by the morphometric analysis method is feasible for seeds charred at up to 250 °C and 8 h. Integrating the morphometric changes and temperature ranges suitable for classification, we developed a sorting model for archaeological seeds. By projecting length–width ratios onto a curve calculated from controlled conditions, we estimated charring temperatures. Approximately 50% of archaeological seeds deviated from the model, indicating drastic charring conditions. This sorting model facilitates a stringent selection of seeds fit for classification, enhancing the accuracy of our machine learning-based methodology. In conclusion, combining machine learning with morphometric sorting enables the identification of charred grape seeds suitable for identification by the morphometric method. This comprehensive approach provides a valuable tool for future research for the identification of charred grape seeds found in archaeological contexts, enhancing our understanding of ancient viticulture practices and grape cultivation.
Observational study of missing SOFA score data frequency in RCTs relative to ICU length of stay
Authors: Molinnus Denise , Beulertz Michael , Bickenbach Johannes , Marx Gernot , Benstoem Carina 
Published: July 2024

AbstractThe Sequential Organ Failure Assessment, also known as SOFA score, was introduced to assess organ dysfunction of critical ill patients. However, understanding the impact of missing SOFA scores in randomized controlled trials and how this affect the validity and applicability of the SOFA score as a surrogate endpoint for predicting mortality has been a matter of interest. To address this, a secondary analysis of a systematic review was conducted to quantify the relationship between SOFA scores and the prediction of mortality in critically ill adults in randomized controlled trials (RCTs). The systematic review being referred to included 87 RCTs with a total of 12,064 critically ill patients. This analysis focused on missing SOFA score data in relation to the length of stay on the intensive care unit (ICU) and the methods used to handle missing data. SOFA score measurements from the included studies were categorized into three time frames: Early (t ≤ 4 days), Intermediate (t = 5–10 days) and Late (t > 10 days) measurement. Only one study reported a complete data set for calculating the SOFA score for an Early measurement. When considering all methods used to address missing data, 32% of studies still had missing data for Early measurements, and this percentage increased to 64% for Late measurements. These findings suggested that, over time, the number of studies with incomplete data sets has been increasing. The longer a patient is treated on the ICU, the higher the number of missing data which can impact the validity of SOFA score analyses. There was no clear trend towards a specific method for compensating missing data. An exemplary calculation demonstrated that ignoring missing data may lead to an underestimated variability of the treatment effect. This, in turn, could bias the interpretation of study results by policy- and clinical decision-makers. Overall, there are several limitations that need to be considered when using SOFA score as a surrogate endpoint for mortality. When employed as an outcome, the SOFA score is frequently missing and most studies do not adequately describe the amount or nature of missing data, or the methods used to handle missing data in the analysis.
Effect of early endothelial function improvement on subclinical target organ damage in hypertensives
Authors: Huang Xiaodong , Huang Xianwei , Pan Mandong , Lin Jiyan , Xie Liangdi 
Published: July 2024

AbstractEndothelial dysfunction is acknowledged as a marker for subclinical target organ damage (STOD) in hypertension, though its therapeutic potential has not yet been clarified. This study assessed whether early endothelial function improvement (EEFI) reduced STOD in patients with essential hypertension (EH). We conducted a retrospective cohort analysis of 456 EH patients initially free from STOD. Endothelial function was assessed using brachial artery flow-mediated dilation (FMD), with values ≤ 7.1% indicating dysfunction. Patients were initially categorized by endothelial status (dysfunction: n = 180, normal: n = 276), and further divided into improved or unimproved groups based on changes within three months post-enrollment. During a median follow-up of 25 months, 177 patients developed STOD. The incidence of STOD was significantly higher in patients with initial dysfunction compared to those with normal function. Kaplan–Meier analysis indicated that the improved group had a lower cumulative incidence of STOD compared to the unimproved group (p < 0.05). Multivariable Cox regression confirmed EEFI as an independent protective factor against STOD in EH patients (p < 0.05), regardless of their baseline endothelial status, especially in those under 65 years old, non-smokers, and with low-density lipoprotein cholesterol levels ≤ 3.4 mmol/L. In conclusion, EEFI significantly reduces STOD incidence in EH patients, particularly in specific subgroups, emphasizing the need for early intervention in endothelial function to prevent STOD.
Affordable web-based foot–ankle exercise program proves effective for diabetic foot care in a randomized controlled trial with economic evaluation
Authors: Cruvinel-Júnior Ronaldo H. , Ferreira Jane S. S. P. , Veríssimo Jady L. , Monteiro Renan L. , Silva Érica Q. , Suda Eneida Y. , Sacco Isabel C. N. 
Published: July 2024

AbstractThe aim of this study was to shed light on a crucial issue through a comprehensive evaluation of the cost-effectiveness and cost-utility of a cutting-edge web-based foot–ankle therapeutic exercise program (SOPeD) designed for treating modifiable risk factors for ulcer prevention in individuals with diabetes-related peripheral neuropathy (DPN). In this randomized controlled trial, 62 participants diagnosed with DPN were assigned to the SOPeD software or received usual care for diabetic foot. Primary outcomes were DPN symptoms and severity, foot pain and function, and quality-adjusted life years (QALYs). Between-group comparisons provided 95% confidence intervals. The study also calculated incremental cost-effectiveness and cost-utility ratios (ICERs), analyzed direct costs from a healthcare perspective, and performed a sensitivity analysis to assess uncertainty. The web-based intervention effectively reduced foot pain, improved foot function and showed favorable cost-effectiveness, with ICERs ranging from (USD) $5.37–$148.71 per improvement in different outcomes. There is a high likelihood of cost-effectiveness for improving DPN symptoms and severity, foot pain, and function, even when the minimum willingness-to-pay threshold was set at $1000.00 USD. However, the intervention did not prove to be cost-effective in terms of QALYs. This study reveals SOPeD's effectiveness in reducing foot pain, improving foot function, and demonstrating cost-effectiveness in enhancing functional and clinical outcomes. SOPeD stands as a potential game-changer for modifiable risk factors for ulcers, with our findings indicating a feasible and balanced integration into public health systems. Further studies and considerations are vital for informed decisions to stakeholders and the successful implementation of this preventive program on a larger scale.Trial Registration: ClinicalTrials.gov, NCT04011267. Registered on 8 July 2019.
Interpretable machine learning for the prediction of death risk in patients with acute diquat poisoning
Authors: Li Huiyi , Liu Zheng , Sun Wenming , Li Tiegang , Dong Xuesong 
Published: July 2024

AbstractThe aim of this study was to develop and validate predictive models for assessing the risk of death in patients with acute diquat (DQ) poisoning using innovative machine learning techniques. Additionally, predictive models were evaluated through the application of SHapley Additive ExPlanations (SHAP). A total of 201 consecutive patients from the emergency departments of the First Hospital and Shengjing Hospital of China Medical University admitted for deliberate oral intake of DQ from February 2018 to August 2023 were analysed. The initial clinical data of the patients with acute DQ poisoning were collected. Machine learning methods such as logistic regression, random forest, support vector machine (SVM), and gradient boosting were applied to build the prediction models. The whole sample was split into a training set and a test set at a ratio of 8:2. The performances of these models were assessed in terms of discrimination, calibration, and clinical decision curve analysis (DCA). We also used the SHAP interpretation tool to provide an intuitive explanation of the risk of death in patients with DQ poisoning. Logistic regression, random forest, SVM, and gradient boosting models were established, and the areas under the receiver operating characteristic curves (AUCs) were 0.91, 0.98, 0.96 and 0.94, respectively. The net benefits were similar across all four models. The four machine learning models can be reliable tools for predicting death risk in patients with acute DQ poisoning. Their combination with SHAP provides explanations for individualized risk prediction, increasing the model transparency.
Key propagation pathways of extreme precipitation events revealed by climate networks
Authors: Li Kaiwen , Huang Yu , Liu Kai , Wang Ming , Cai Fenying , Zhang Jianxin , Boers Niklas 
Published: July 2024

AbstractThe comprehensive understanding of propagation patterns of extreme precipitation events (EPEs) is essential for early warning of associated hazards such as floods and landslides. In this study, we utilize climate networks based on an event synchronization measure to investigate the propagation patterns of EPEs over the global land masses, and identify 16 major propagation pathways. We explain them in association with regional weather systems, topographic effects, and travelling Rossby wave patterns. We also demonstrate that the revealed propagation pathways carry substantial EPE predictability in certain areas, such as in the Appalachian, the Andes mountains. Our results help to improve the understanding of key propagation patterns of EPEs, where the global diversity of the propagated patterns of EPEs and corresponding potential predictability provide prior knowledge for predicting EPEs, and demonstrate the power of climate network approaches to study the spatiotemporal connectivity of extreme events in the climate system.
Identification and characterization of specific motifs in effector proteins of plant parasites using MOnSTER
Authors: Calia Giulia , Porracciolo Paola , Chen Yongpan , Kozlowski Djampa , Schuler Hannes , Cestaro Alessandro , Quentin Michaël , Favery Bruno , Danchin Etienne G. J. , Bottini Silvia 
Published: July 2024

AbstractPlant pathogens cause billions of dollars of crop loss every year and are a major threat to global food security. Identifying and characterizing pathogens effectors is crucial towards their improved control. Because of their poor sequence conservation, effector identification is challenging, and current methods generate too many candidates without indication for prioritizing experimental studies. In most phyla, effectors contain specific sequence motifs which influence their localization and targets in the plant. Therefore, there is an urgent need to develop bioinformatics tools tailored for pathogen effectors. To circumvent these limitations, we have developed MOnSTER a specific tool that identifies clusters of motifs of protein sequences (CLUMPs). MOnSTER can be fed with motifs identified by de novo tools or from databases such as Pfam and InterProScan. The advantage of MOnSTER is the reduction of motif redundancy by clustering them and associating a score. This score encompasses the physicochemical properties of AAs and the motif occurrences. We built up our method to identify discriminant CLUMPs in oomycetes effectors. Consequently, we applied MOnSTER on plant parasitic nematodes and identified six CLUMPs in about 60% of the known nematode candidate parasitism proteins. Furthermore, we found co-occurrences of CLUMPs with protein domains important for invasion and pathogenicity. The potentiality of this tool goes beyond the effector characterization and can be used to easily cluster motifs and calculate the CLUMP-score on any set of protein sequences.
Deep metagenomic sequencing unveils novel SAR202 lineages and their vertical adaptation in the ocean
Authors: He Changfei , Fucich Daniel , Sosa Ana , Wang Hualong , Kan Jinjun , Liu Jihua , Xu Yongle , Jiao Nianzhi , Gonsior Michael , Chen Feng 
Published: July 2024

AbstractSAR202 bacteria in the Chloroflexota phylum are abundant and widely distributed in the ocean. Their genome coding capacities indicate their potential roles in degrading complex and recalcitrant organic compounds in the ocean. However, our understanding of their genomic diversity, vertical distribution, and depth-related metabolisms is still limited by the number of assembled SAR202 genomes. In this study, we apply deep metagenomic sequencing (180 Gb per sample) to investigate microbial communities collected from six representative depths at the Bermuda Atlantic Time Series (BATS) station. We obtain 173 SAR202 metagenome-assembled genomes (MAGs). Intriguingly, 154 new species and 104 new genera are found based on these 173 SAR202 genomes. We add 12 new subgroups to the current SAR202 lineages. The vertical distribution of 20 SAR202 subgroups shows their niche partitioning in the euphotic, mesopelagic, and bathypelagic oceans, respectively. Deep-ocean SAR202 bacteria contain more genes and exhibit more metabolic potential for degrading complex organic substrates than those from the euphotic zone. With deep metagenomic sequencing, we uncover many new lineages of SAR202 bacteria and their potential functions which greatly deepen our understanding of their diversity, vertical profile, and contribution to the ocean’s carbon cycling, especially in the deep ocean.
Astrocytes enhance plasticity response during reversal learning
Authors: Squadrani Lorenzo , Wert-Carvajal Carlos , Müller-Komorowska Daniel , Bohmbach Kirsten , Henneberger Christian , Verzelli Pietro , Tchumatchenko Tatjana 
Published: July 2024

AbstractAstrocytes play a key role in the regulation of synaptic strength and are thought to orchestrate synaptic plasticity and memory. Yet, how specifically astrocytes and their neuroactive transmitters control learning and memory is currently an open question. Recent experiments have uncovered an astrocyte-mediated feedback loop in CA1 pyramidal neurons which is started by the release of endocannabinoids by active neurons and closed by astrocytic regulation of the D-serine levels at the dendrites. D-serine is a co-agonist for the NMDA receptor regulating the strength and direction of synaptic plasticity. Activity-dependent D-serine release mediated by astrocytes is therefore a candidate for mediating between long-term synaptic depression (LTD) and potentiation (LTP) during learning. Here, we show that the mathematical description of this mechanism leads to a biophysical model of synaptic plasticity consistent with the phenomenological model known as the BCM model. The resulting mathematical framework can explain the learning deficit observed in mice upon disruption of the D-serine regulatory mechanism. It shows that D-serine enhances plasticity during reversal learning, ensuring fast responses to changes in the external environment. The model provides new testable predictions about the learning process, driving our understanding of the functional role of neuron-glia interaction in learning.
The isomer distribution of C6H6 products from the propargyl radical gas-phase recombination investigated by threshold-photoelectron spectroscopy
Authors: Hrodmarsson Helgi Rafn , Garcia Gustavo A. , Bourehil Lyna , Nahon Laurent , Gans Bérenger , Boyé-Péronne Séverine , Guillemin Jean-Claude , Loison Jean-Christophe 
Published: July 2024

AbstractThe resonance-stabilization of the propargyl radical (C3H3) makes it among the most important reactive intermediates in extreme environments and grants it a long enough lifetime to recombine in both terrestrial combustion media and cold molecular clouds in space. This makes the propargyl self-reaction a pivotal step in the formation of benzene, the first aromatic ring, to eventually lead to polycyclic aromatic hydrocarbons in a variety of environments. In this work, by producing propargyl radicals in a flow tube where propyne reacted with F atoms and probing the reaction products by mass-selected threshold-photoelectron spectroscopy (TPES), we identified eight C6H6 products in total, including benzene. On top of providing the first comprehensive measurements of the branching ratios of the eight identified C6H6 isomers in the propargyl self reaction products (4 mbar, 298 K conditions), this study also highlights the advantages and disadvantages of using isomer-selective TPES to identify and quantify reaction products.
Detecting dynamical causality via intervened reservoir computing
Authors: Zhao Jintong , Gan Zhongxue , Huang Ruixi , Guan Chun , Shi Jifan , Leng Siyang 
Published: July 2024

AbstractAn abundance of complex dynamical phenomena exists in nature and human society, requiring sophisticated analytical tools to understand and explain. Causal analysis through observational time series data is essential in comprehending complex systems when controlled experiments are not feasible or ethical. Although data-based causal discovery methods have been widely used, there is still a lack of direct ways more aligned with the intuitive definition of causality, i.e., whether interventions on one element lead to changes in the subsequent development of others. To solve this problem, we propose the method of intervened reservoir computing (IRC) based on constructing a neural network replica of the original system and applying interventions to it. This approach enables controlled trials, thus observing the intervened evolution, in the digital twins of the underlying systems. Simulated and real-world data are used to test our approach and demonstrate its accuracy in inferring causal networks. Given the importance of causality in understanding complex dynamics, we anticipate that IRC could serve as a powerful tool for various disciplines to decipher the intrinsic mechanisms of natural systems from observational data.