bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Three-Dimensional Extension of the Slope Chain Code: Analyzing the Tortuosity of the Flagellar Beat of Human Sperm
Authors: Bribiesca-Sánchez Andrés , Guzmán Adolfo , Montoya Fernando , Díaz-Guerrero Dan S. , Hernández Haydeé O. , Hernández-Herrera Paul , Darszon Alberto , Corkidi Gabriel , Bribiesca Ernesto 
Published: December 2023

Abstract
In the realm of 3D image processing, accurately representing the geometric nuances of line curves is crucial. Building upon the foundation set by the Slope Chain Code (SCC), which adeptly represents intricate two-dimensional curves using an array capturing the exterior angles at each vertex, this study introduces an innovative 3D encoding method tailored for polygonal curves. This 3D encoding employs parallel slope and torsion chains, ensuring invariance to common transformations like translations, rotations, and uniform scaling, while also demonstrating robustness against mirror imaging and variable starting points. A hallmark feature of this method is its ability to compute tortuosity, a descriptor of curve complexity or winding nature. By applying this technique to biomedical engineering, we delved into the flagellar beat patterns of human sperm. These insights underscore the versatility of our 3D encoding across diverse computer vision applications.
Correlation analysis and predictive value of insulin resistance index on heart function in newly diagnosed Type 2 diabetes mellitus patients
Authors: Li Shengnan , Chen Run , Cao Yan , Hu Zhongshun , Ni Danfeng , Du Zihui , Zhang Chaoxue 
Published: December 2023

Abstract
Background In recent years, simpler and more practical indicators based on routine biochemical tests or anthropometric measurements have been widely utilized for the assessment of insulin sensitivity. However, limited research has been conducted to investigate the predictive value of these novel simplified measures in relation to subclinical left ventricular systolic dysfunction.Methods A total of 160 newly diagnosed patients with type 2 diabetes mellitus (T2DM) and 70 healthy subjects matched by age and sex were included in the study. Left ventricular function parameters were assessed using AFI echocardiography. Four indicators of insulin resistance (IR) were computed: Triglyceride to high-density lipoprotein cholesterol ratio (TG/HDL-c), the product of fasting triglycerides and glucose levels(TyG), TYG multiplied by the body mass index(TyG-BMI)and the Insulin resistance metabolic score(METS-IR). The binary logistic regression analysis identified clinical and ultrasonic risk factors associated with abnormal GLPS-AVG in patients with T2DM. Develop a multiple-index-based log P model for integrated application. The diagnostic efficacy of the log P model in predicting left ventricular systolic function impairment was assessed using ROC analysis.Results Competing risk regression revealed that BMI, IVSD, SPB, LA and LVPWD were significant risk factors for the reduction of GLPS-AVG in individuals with T2DM. Additionally, two IR index models were found to be closely associated with abnormal GLPS-AVG: TyG-BMI (6.227,p = 0.000); METS-IR(7.436,p = 0.000). ROC analysis results indicate that TyG-BMI, METS-IR, IVSD, SBP, LA and a combination of five other indexes have demonstrated certain efficacy in predicting and evaluating diabetic heart function reduction. Its ROC-AUC (0.95CI) are 0.750 (0.564 ~ 0.934), 0.774 (0.582 ~ 0.944), 0.702 (0.461 ~ 0.948), 0.737 (0.478 ~ 0.983), 0.726 (0.483 ~ 0.951), 0.878 (0.770 ~ 0.987) respectively.Conclusion Approximately 20% of newly diagnosed patients with T2DM exhibit early-stage left ventricular systolic dysfunction.The Log P model exhibited the highest predictive efficiency when applied in combination, with significantly higher sensitivity, specificity and accuracy than each individual application.
Research on Recognition and Localization of Cucumber Based on Complex Environment
Authors: Junjie Kang , Jian-hong Zheng , Long Xue , Yi-wen Chen , LIU Mu-hua , Jing Li 
Published: December 2023

Abstract
Lighting variations, leaf occlusion, and fruit overlap make it difficult for mobile picking robots to detect and locate cucumber fruits in complex environments. This paper proposes a novel detection method based on the YOLOv4-tiny-SCE model for cucumbers in a complex environment. It combines the attention mechanism and adaptive spatial feature pyramid method to improve the detection effect of blocked and overlapping cucumbers. Additionally, the method also incorporates a loss function and clustering algorithm to enhance the accuracy and robustness of cucumber detection. On this basis, the 3D spatial coordinate model of cucumber is established using a Realsense depth camera to obtain the target image. To validate the cucumber detection and location method based on the YOLOv4-tiny-SCE model, a comparison experiment between YOLOv4-tiny-SCE and other lightweight models is conducted on the dataset. The results indicate that the YOLOv4-tiny-SCE model achieves an average detection accuracy of 99.7%. The average detection time per image is 0.006s, and there is a 2.5% increase in the F1 score. The average positioning errors of cucumber in X, Y, and Z three-dimensional space are 1.77mm, 2.9mm and 1.8 mm, respectively. This method balances target detection accuracy and model size, which is helpful in realizing the detection and location of cucumbers on low-performance airborne terminals in the future.
Asymmetry of Winter Precipitation Event Predictions in South China
Authors: Zhen Shixin , Hou Zhaolu , Li Jianping , Diao Yina , Zhang Yazhou 
Published: December 2023

Abstract

Winter precipitation anomalies in South China (SC) frequently result in severe disasters. However, the evaluation of prediction performance and distinctions between positive precipitation anomaly events (PPA, wet condition) and negative precipitation anomaly events (NPA, dry condition) in current operational models remains incomplete. This study employed the Climate Forecast System version 2 (CFSv2) to assess winter precipitation prediction accuracy in SC from 1983 to 2021. Differences in predicting PPA and NPA events and the underlying physical mechanisms were explored. The results indicate that CFSv2 can effectively predict interannual variations in winter precipitation in SC, as there is a significant time correlation coefficient of 0.68 (0.62) between observations and predictions, with a lead time of 0 (3) months. The model revealed an intriguing asymmetry in prediction skills: PPA outperformed NPA in both deterministic and probabilistic prediction. The higher predictability of PPA, as indicated by the perfect model correlation and signal-to-noise ratio, contributed to its superior prediction performance when compared to NPA. Physically, tropical signals from the ENSO and extratropical signals from the Arctic Sea ice anomaly, were found to play pivotal roles in this asymmetric feature. ENSO significantly impacts PPA events, whereas NPA events are influenced by a complex interplay of factors involving ENSO and Arctic Sea ice, leading to low NPA predictability. The capability of the model to replicate Arctic Sea ice signals is limited, but it successfully predicts ENSO signals and reproduces their related circulation responses. This study highlights the asymmetrical features of precipitation prediction, aiding in prediction models improvement.
Impact of the COVID-19 Pandemic on Utilization and Cost for Care of Pediatric ALL
Authors: Hoover Alex , Watson Dave , Reimche Paige , Tanner Lynn , Gilchrist Laura , Finch Mike , Messinger Yoav , Turcotte Lucie 
Published: December 2023

Abstract
Objective
Acute lymphoblastic leukemia (ALL) is the most common childhood malignancy and requires a unique pattern of healthcare utilization including an acute/emergent presentation and an intensive initial 8 months of therapy followed by two years of outpatient treatment. The COVID-19 pandemic caused massive global disruptions in healthcare use and delivery. This report aims to examine the effects of the COVID-19 pandemic on the presentation, diagnosis and continued management of childhood ALL in regard to utilization and cost of care.
Results
Utilizing a commercial insurance claims database, 529 pediatric patients were identified who were diagnosed with ALL and completed their initial 8 months of treatment between January 2016 and December 2021. New diagnoses were evaluated by era and demographics. Utilization was measured by COVID-related era as number of inpatient and outpatient encounters, inpatient days, and cumulative cost. None of these cost or utilization factors changed significantly during or shortly after the pandemic. These findings reinforce that the necessary care for pediatric ALL is largely inflexible and was unwavering despite the massive shifts in the healthcare system caused by the COVID-19 pandemic. This provides a valuable benchmark as we further examine the factors that influence the pandemic’s impact on health equity and access to care, especially in vulnerable pediatric populations. This is the first investigation of the effect of the COVID-19 pandemic on utilization and cost of care in pediatric cancer.
Bio-stimulated synthesis of Zinc oxide Nanoparticles from Andrographis echioides (L.) Nees., characterization and its anti diabetic activity
Authors: Palempalli Uma Maheswari Devi. , Draksha Niveditha , Mathakala Vani , chitta Suredsh kumar 
Published: December 2023

Abstract

Green synthesis is based on plant compounds' ability to reduce metal ions and stabilise them into nanoparticles, and it is gaining a lot of attention in recent years. It has been shown to be a reliable and alternate method for the development of novel Nanoparticles. Andrographis echioides (L.) Nees is a used to synthesize zinc oxide nanoparticles. UV-visible spectroscopy, FTIR, Zeta potential, particle size analysis, XRD, SEM, AFM analysis were adopted to characterise the capped ZnONPs. The intense surface Plasmon resonance bands of ZnONPs at 340 nm confirmed the green synthesis of ZnONPs. The various functional groups of A.echioidesinvolved in the synthesis and crystalline nature of ZnO Nanoparticles were identified using FTIR and X-ray diffraction respectively. The average particle size was found to be 4.0 nm, and the zeta potential value was -11 mV. Administration of ZnONPs significantly reduced the blood glucose level and HbA1C when compared with standard drug glibenclamide in STZ- induced in-vivo models. The antidiabetic potential of ZnONPs was confirmed by numerous biochemical parameters like serum lipid profiles, liver and renal functional markers and regeneration β- cells of islets of Langerhans in STZ- induced diabetic rats. Thus, the ZnONPs synthesized from A. echioideswere found to be effective anti-hyperglycemic agent in the management of diabetes.
Moisture as key for understanding the fluorescence of lignocellulose in wood
Authors: Peters Frank B. , Rapp Andreas O. 
Published: December 2023

Abstract
The fluorescence behaviour of lignocellulose in Pinus sylvestris L. was studied under the influence of moisture. Fluorescence excitation-emission-matrices (EEMs) of the solid wood surfaces were recorded. Two emission peaks were identified, one attributed to lignocellulose, the other to pinosylvin. The 2 peaks were successfully modelled with PARAFAC2-deconvolution. Lignocellulose showed excitation-dependent emission. Its emission was quenched and blue-shifted by moisture, while pinosylvin showed none of these properties. The quenching efficiency was proportional to the moisture content (linear Stern-Volmer plot), a phenomenon first demonstrated for wood in this study. Potential mechanisms for the moisture quenching are discussed, with clustering-triggered emission best explaining most of the observed peculiarities. The strong influence of moisture on the fluorescence of pine wood suggests that carbohydrates play an important role in lignocellulose fluorescence.
Genotype-Phenotype Correlation in CLCN4-Related Developmental and Epileptic Encephalopathy
Authors: Sahly Ahmed N. , Sierra-Marquez Juan , Bungert-Plümke Stefanie , Franzen Arne , Mougharbel Lina , Berrahmoune Saoussen , Dassi Christelle , Poulin Chantal , Sr Myriam , Guzman Raul E. , Myers Kenneth A. 
Published: December 2023

Abstract

CLCN4-related disorder is a rare X-linked neurodevelopmental condition with a pathogenic mechanism yet to be elucidated. CLCN4 encodes the vesicular 2Cl-/H+ exchanger ClC-4, and CLCN4 pathogenic variants frequently result in altered ClC-4 transport activity. The precise cellular and molecular function of ClC-4 remains unknown; however, together with ClC-3, ClC-4 is thought to have a role in the ion homeostasis of endosomes and intracellular trafficking. We reviewed our research database for patients with CLCN4 variants and epilepsy, and performed thorough phenotyping. We examined the functional properties of the variants in mammalian cells using patch-clamp electrophysiology, protein biochemistry, and confocal fluorescence microscopy. Three male patients with developmental and epileptic encephalopathy were identified, with differing phenotypes. Patients #1 and #2 had normal growth parameters and normal-appearing brains on MRI, while patient #3 had microcephaly, microsomia, complete agenesis of the corpus callosum and cerebellar and brainstem hypoplasia. The p.Gly342Arg variant of patient #1 significantly impaired ClC-4’s heterodimerization capability with ClC-3 and suppressed anion currents. The p.Ile549Leu variant of patient #2 and p.Asp89Asn variant of patient #3 both shift the voltage dependency of transport activation by 20 mV to more hyperpolarizing potentials, relative to the wild-type, with p.Asp89Asn favouring higher transport activity. We concluded that p.Gly342Arg carried by patient #1 and the p.Ile549Leu expressed by patient #2 impair ClC-4 transport function, leading to epilepsy and developmental impairment without clear brain malformation. In contrast, p.Asp89Asn variant results in a gain-of-transport function in ClC-4, causing severe brain malformation with associated developmental impairment, epilepsy, microcephaly, and microsomia.
Comparative Assessment of Eight Empirical and Four Hybrid Machine Learning Models for Estimating Daily Reference Evapotranspiration in Sub-Humid and Semi-Arid Climates
Authors: Acharki Siham , Raza Ali , Alshehri Fahad , Tegos Aristoteles , Amharref Mina , Bernoussi Abdes Samed , Singh Sudhir Kumar , Elbeltagie Ahmed 
Published: December 2023

Abstract
Improving reference evapotranspiration (RET) estimation accuracy contributes to effective water resource management, irrigation planning, and climate change assessments in agricultural systems. The widely recommended FAO-56 Penman-Monteith (PM-FAO56) model for RET estimation often faces limitations due to incomplete meteorological data availability. To address this, we evaluate the ability of eight empirical models, four machine learning (ML) models and their hybrid models to estimate daily RET in Gharb and Loukkos irrigated perimeters in Morocco. These ML and hybrid models include Random Forest (RF), M5 Pruned (M5P), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), RF-M5P, RF-XGBoost, RF-LightGBM and XGBoost-LightGBM. Additionally, six input combinations (based on Tmax, Tmin, RHmean, Rs and U2) were designed, with PM-FAO56 model considered as a target to models. Four statistical indicators including Kling Gupta Efficiency index (KGE), Coefficient of determination (R2), Mean Squared Error (RMSE), and Root relative squared error (RRSE) were applied to assess the models’ performance, across both training and testing phases. The findings reveal that Valiantzas 2013 (VAL2013b) model outperformed the other empirical models for all station, exhibiting high KGE and R2 (0.95–0.97), low RMSE (0.32–0.35 mm.day-1) and RRSE (8.14–10.30%). Additionally, the Hargreaves and Samani 1985 (HargS1985) model performed well in Gharb's stations, while the Valiantzas 2013 (VAL2013a) model showed good results in Loukkos' stations. Besides, the ML model’s performance RET estimation was higher when Tmax, Tmin, RHmean, Rs and U2 were used as inputs (combination 6). Among the ML and hybrid models, the XGBoost-LightGBM and RF-LightGBM achieved the highest accuracy (on average RMSE 0.015–0.097 mm.day-1), closely followed by the LightGBM and XGBoost models. However, M5P model had the lowest estimation accuracy RMSE ranged from 0.022 to 0.108 mm.day-1 on average. In summary, our study highlights the potential of ML models for RET estimation in subhumid and semi-arid areas, providing vital insights for improving water resource management, helping climate change research and optimizing irrigation scheduling for optimal agricultural water usage in the region.
Revisiting a COVID-19 seroprevalence cohort survey among health care workers and their household members in Kinshasa, DR Congo, 2020-2022
Authors: Madinga Joule , Mbala Placide , Nkuba Antoine-Jeremy , Baketana Leonel , Matungulu Elysé , Vanlerberghe Veerle , Lupola Patrick Mutombo , Seghers Caroline-Aurore , Smekens Tom , Ariën Kevin K. , Damme Wim Van , Kalk Andreas , Peeters Martine , Muyembe Jean-Jacques , Ahuka Steve 
Published: December 2023

Abstract
Serological surveys provide the most direct measurement to define the immunity landscape for many infectious diseases, including COVID-19, yet this methodology remains underexploited to clarify transmission dynamics. This is specifically the case in the context of the Democratic Republic of Congo, where COVID-19 case presentation was apparently largely oligo- or asymptomatic, and vaccination coverage remained extremely low. A cohort of 635 health care workers from 5 health zones of Kinshasa and 670 of their household members was followed up between July 2020 and January 2022, with 6- to 8-week intervals in the first year and 4- and 8-month intervals in the last year. At each visit, information on risk exposure and a blood sample were collected. Serology was defined as positive when binding antibodies against SARS-CoV-2 spike and nucleocapsid proteins were simultaneously present. The anti-SARS-CoV-2 antibody seroprevalence was high at baseline, at 17.3% (95% CI 14.4–20.6) and 7.8% (95% CI 5.5–10.8) for health care workers and household members, respectively, and fluctuated over time, between 9% and 62.1%. Seropositivity was heterogeneously distributed over the health zones (p < 0.001), ranging from 12.5% (95% CI 6.6–20.8) in N’djili to 33.7% (95% CI 24.6–43.8) in Bandalungwa at baseline for health care workers. Seropositivity was associated with increasing rounds aOR 1.75 (95% CI 1.66–1.85), with increasing age aOR 1.11 (95% CI 1.02–1.20), being a female aOR 1.35 (95% CI 1.10–1.66) and being a health care worker aOR 2.38 (95% CI 1.80–3.14). There was no evidence that health care workers brought the COVID-19 infection back home, with increased seropositivity risk among household members in subsequent surveys. There was much seroreversion and seroconversion detected over the different surveys, and health care workers had a 40% lower probability of seroreverting than household members (aOR 0.60 (95% CI 0.42–0.86)). Based on the WHO guidelines on the potential use of sero-surveys, the results of this cohort were revisited, and evidence provided by such studies in a ‘new disease’ epidemic and in a setting with low molecular testing capacities, such as COVID-19 in DRCongo, was insufficient to guide policy makers for defining control strategies.
Alveolar Proteinosis: Bronchoalveolar Lavage Experience and Current Literature Review
Authors: Aydin Sercan , Yavuz Hasan , Ergonul Ayse Gul , Akcam Tevfik Ilker , Turhan Kutsal , Cakan Alpaslan , Cagirici Ufuk 
Published: December 2023

Abstract

Objective: Bronchoalveolar lavage (BAL) is considered as the first-line therapy for pulmonary alveolar proteinosis (PAP). The points to be kept in mind about the disease and its treatment were reviewed with current research and presented together with our clinical experience on the effectiveness of the BAL procedure.
Methods: Patients with diagnosis of PAP were included. Total of 15 BALs were applied to 6 patients. Preoperative and 3rd postoperative hour arterial blood gas values were recorded. In addition to the partial oxygen pressures (PaO2), the ratio of the difference to the preoperative value was also evaluated and the lavage volumes of all patients were recorded.
Results: A general significant increase in PaO2 was observed compared to pre-treatment. The correlation between the PaO2 change and the lavage volume per side was interpreted as a remarkable result even though statistical significance was not determined.
Conclusion: An increase in PaO2 values post-lavage and a positive correlation between PaO2 change rate and lavage volume were observed. This may be a precursor to the idea that continuing lavage for a while after the lavage fluid has cleared improves the quality of the process. We believe that meta-analyses or studies with larger series are needed on this topic.
A Joint Ensemble Framework for the Detection of Acute Exacerbations in Chronic Obstructive Pulmonary Disease
Authors: Wang Xuchun , Zhang Yudong , Cui Yu , Ren Hao , Qiao Yuchao , Hao Chongqi , Qiu Lixia 
Published: December 2023

Abstract
Objective
The purpose of this study was to create a joint ensemble framework for identifying AECOPD and providing a plausible explanation of model predictions.
Methods
From MIMIC-III, we extracted and organized records for COPD and AECOPD patients. Furthermore, we integrated missing value imputation, joint feature selection, advanced ML algorithms, Bayesian optimization techniques, and the SHAP interpretable method to construct a joint optimized ensemble framework, serving as the predictive model for AECOPD risk identification. The efficacy of the model's prediction was evaluated using a composite score of six evaluation measures.
Results
CAD and 19 other variables significantly impacted AECOPD. Various resampling methods and classifiers yielded diverse prediction accuracies. LightGBM and LR models with NC processing showcased optimal combined performance pre-heterogeneous combination. The Voting ensemble with MWMOTE achieved superior balanced classification.
Conclusion
The joint ensemble framework improved AECOPD risk identification performance in clinically relevant data of COPD patients admitted in the ICU.
Feasibility of the manufacturing process of cork stoppers for spirits using vegetable and textiles fibers
Authors: Suffo Miguel , Martín-Alconchel Iván 
Published: December 2023

Abstract
Traditionally, cork stoppers composed of a plastic crown and a cork spike, usually agglomerated or technical, are used to seal the bottles. The use of synthetic plastic for crown caps derived from petroleum generates well-known environmental problems; therefore, it is of great interest to seek substitutes, deepen research, and develop new products and production methods focused on this powerful industry. This work aims to develop alternatives to the products currently available on the market, not only in terms of design and materials, but also by exploring alternative manufacturing methods such as compression molding and the possibility it offers for the incorporation of recycled materials into the product. The use of adhesives to bind other materials such as metals or wood provides numerous advantages in terms of durability, lightness, and design versatility. Through this work, adhesive binders will be tested to manufacture agrocomposites, which are blends of natural fibers and textiles functionalized as closures for spirits bottles. Likewise, the adhesion of the cork spike to the agrocomposite crown will be tested. The incorporation of materials previously considered as waste into resin matrix is being investigated. The designed mold has made it possible to manufacture prototypes with different resin-residue ratios.