bluuebunny/crossref_metadata_2025_split huggingface dataset:
Predictive Modeling of Adaptive Behavior Trajectories in Autism: Insights from a Clinical Cohort Study
Authors: Brandes-Aitken Annie , Lazerwitz Maia , Eash Ally , Hattangadi Neil , Mukherjee Pratik , Marco Elysa , Shapiro Kevin 
Published: April 2024

Abstract
Research aimed at understanding how baseline clinical and demographic characteristics influence outcomes over time is critically important to inform individualized therapeutic programs for children with neurodevelopmental differences. This study characterizes adaptive behavior trajectories in children receiving medical and behavioral therapy within a network of care centers with a shared data-gathering mechanism for intake and longitudinal assessments. We then take the further step of utilizing intake data to develop machine-learning models which predict differences in those trajectories. Specifically, we evaluated data from 1,225 autistic children, aged 20–90 months, using latent class growth mixture modeling (LCGMM) with scores on the Vineland Adaptive Behavior Scales, 3rd Edition, as the primary outcome measure. The LCGMM analysis revealed two distinct clusters of adaptive behavior trajectories. The “Improved” group (\(\ge\)66% of the sample) exhibited greater developmental change in adaptive behavior, while the “Stable” group (\(\le\)33% of the sample) showed little change over time relative to age-matched normative data. For a subset of 729 children, we used machine learning algorithms to forecast adaptive behavior trajectories using clinical and sociodemographic data collected at the initial assessment, comparing elastic net GLM, support vector machine, and random forest. The best-performing random forest model predicted adaptive behavior trajectory with an accuracy rate of 77%. The strongest predictors in our model were socioeconomic status, history of developmental regression, child temperament, paternal age at the time of the child’s birth, baseline autism symptom severity, parent concerns about development, presence of ADHD symptoms, and parent concerns about mood. Notably, the inclusion of cumulative hours of applied behavioral analysis and developmental therapies in the machine learning models did not yield significant changes in performance metrics, indicating that increased therapy hours did not predict greater improvement. These findings extend our understanding of adaptive behavior development in autistic children and underscore the value of gathering comprehensive patient information at intake to tailor clinical care.
Efficacy of monotherapy or combination therapy against bloodstream infection caused by carbapenem-resistant Klebsiella pneumoniae
Authors: Jiang Lushun , Fang Jiaheng , Zhang Kanghui , Shen Jiaying , Guo Wenhui , Xu Liqian , Yu Wei 
Published: April 2024

Abstract

Objectives: The aim of this study was to summarize available dataon monotherapy or combination therapy for bloodstream infection (BSI) caused by carbapenem-resistant Klebsiella pneumoniae (CRKP).
Methods: A systematic review and meta-analysis was conducted to compare monotherapy with combination therapy in patients with BSI-CRKP. Mortality was the primary outcome.
Results: A total of 1675 patients in 13 studies were included. The pooled risk ratio (RR) of mortality were 0.79 (95%CI: 0.60-1.05) between combination therapy and monotherapy. However, no significant difference in mortality was observed. The RR of polymyxins-based and tigecycline-based combination therapy were 0.72 (95%CI: 0.47-1.12) and 0.67 (95%CI: 0.42-1.09), respectively. Similarly, the difference is statistically insignificant. Further analysis showed the lower mortality associated with polymyxins+tigecycline-based combination therapies than that with tigecycline combined with other antibiotics (RR=0.66, 95%CI: 0.52-0.83, P=0.00).
Conclusions: Combination therapy against BSI-CRKP has survival benefit, especially for polymyxins+tigecycline-containing therapies.
Unraveling Hematopoietic Stem Cell Expansion: A Microfluidic 3D Co-culture Approach on Demineralized Bone Matrix
Authors: kojabad Amir Asri , Atashbar Maryam , Atashi Amir , soleimani masoud 
Published: April 2024

Abstract

Background
Hematopoietic stem cell transplantation (HSCT) is a well-established therapy for various hematological malignancies. Umbilical cord blood (UCB) HSCs offer an alternative source but are limited by a small number of CD34 + cells, delaying hematopoietic and immunologic recovery. Recent evidence underscores the importance of closely recapitulating the bone marrow niche for ex vivo stem cell expansion..
Methods
We describe a novel 3D microfluidic chip for dynamic co-culture of MSCs and HSCs within a demineralized bone matrix (DBM) scaffold. Co-culture was conducted in both dynamic and static 3D conditions without exogenous cytokines for seven days.
Results
Scanning electron microscopy images revealed that CD34 + CD38- cells primarily lodged beneath the MSCs layer rather than on its surface. MSCs repopulated the DBM scaffold and sustained HSC expansion for seven days. The proportion of CD34 + cells increased significantly (1.35-fold), with CD34 + CD38- cells nearly doubling in the microfluidic 3D co-culture compared to the control group. Microfluidic conditions promoted CXCR4 transcription (4.7-fold), colony-forming potency (3.6-fold), and osteogenic properties of DBM (9.4-fold) compared to the control group.
Conclusion
Indirect and direct crosstalk among HSCs, MSCs, and extracellular matrix (ECM) proteins can enhance UCB HSC expansion potency in an engineered bone marrow chip. Our microfluidic-based chip represents a significant step toward overcoming current limitations in UCB HSC numbers.
Investigations for increasing the 3D-forming potential of high-density fiberboards
Authors: Krüger Robert , Buchelt Beate , Herold Jan , Wagenführ André 
Published: April 2024

Abstract

The trend in modern interior design leans towards curved and shaped surfaces. This cannot be achieved with flat materials without additional effort. Materials from renewable resources, such as wood-based materials, are material- and energy-intensively processed to enable larger deformations. Therefore, this study deals with methods to increase the deformation potential of adhesive-free high-density fiberboards. One method is plasticizing in a saturated steam atmosphere, which is well known from the bending of solid wood. The second is the application of a special kerf pattern that geometrically increases the deformability. The combination of both methods was also investigated. Uniaxial tensile tests were performed to evaluate the deformation potential of the methods used. The strain along and transverse to the tensile direction as well as the modulus of elasticity and Poisson's ratio were determined as results. All the methods investigated lead to an increase in the maximum strain along the tensile load; steaming by a factor of 2, kerf patterning by a factor of 4 and the combination by a factor of 10 as compared to solid fiberboard. The application of the kerf pattern causes an auxetic material behavior with a negative Poisson's ratio. The combination of both methods reduces the modulus of elasticity by a factor of more than 100. Overall, the investigated methods are suitable for increasing the deformation potential of fiberboards with regard to the forming of 3D-shaped surfaces.
Chemical and Microbial Safety Criteria for Egyptian Ras Cheeses
Authors: Awad Sameh , Elsaadany Khaled , Ibrahim Amel 
Published: April 2024

Abstract

Up until now, most of the Ras cheese has been produced by small dairy sectors in Egypt using raw milk. This research aimed to determine the pathogens, and undesirable microorganisms in traditional Egyptian Ras cheese using classical methods and real-time PCR. Forty samples of Ras cheese were analysed for chemical and microbiological properties. The results revealed a wider range in the chemical composition of the collected samples. Most of the Ras cheese samples were within the Egyptian Standard for chemical compositions. There were only two samples of Ras cheese that had more moisture than the Egyptian Standard recommended.. The results of the microbiological analysis of the Ras cheese reveal that the coliform count ranged between 1 and 5.20 log CFU/g, with an average of 1.70 log CFU/g. Anaerobic spore-forming bacteria (Clostridium perfringens) were found in 29 out of 40 Ras cheese samples. All Egyptian cheese samples contained more yeast and mold than the Egyptian Standard recommends. All cheese samples were free of Listeria monocytogenes and Salmonella spp. The real-time PCR showed that 5% of samples were positive for methicillin-resistant Staphylococcus aureus, and all the analysed samples were free of E. coli O157:H7. According to this study's findings, most Ras cheese samples contained higher levels of Staphylococcus aureus, molds, and yeasts than what Egyptian standards recommended. The results of this study should be used to develop specific procedures for risk management along the milk production chain.
Exploring the Dynamics of Women's Field Hockey Performance: A Comparative Analysis of the Impact of Opponent Quality between Olympic Games and Chinese National Games
Authors: Huo Kelin , Yang Xudong , Zhang Ziyu , Li Baitu , Zhou Changjing 
Published: April 2024

Abstract

This study analyzed the performance of the Chinese women’s national field hockey team during the 2020 Tokyo Olympics and 2021 National Games to assess the impact of opposition quality on performance. Game statistics were collected using notational analysis software for 76 Olympic and 40 National Games matches. Non-parametric Mann-Whitney U tests were used to compare tournaments. No significant differences existed for 35 out of 38 metrics, except Offense in 25-Yd Area (P = 0.013), Handball Style (P = 0.000) and Entry into Arc - Right Lane (P = 0.017). When exclusively considering Chinese national team’s observations, superior National Games performance did emerge for Shot (P = 0.046), Goal from Short Corner (P = 0.044), Into the Arc (P = 0.046), Entry into Arc - Q3 (P = 0.009), Dribble into the Arc (P = 0.046), Handball Style into Arc (P = 0.041), Forehand Shot (P = 0.033), and Small Skill Shot (P = 0.014). The study underscores the influence of opposition quality on team performance, with a need for targeted improvements in arc penetration efficacy, conversion rates of shots to goals, and adaptation of tactical approaches against stronger defenses. The research points towards the need for strategic high-performance programs, improved domestic league quality, and a structured youth development system to elevate the overall standard of Chinese field hockey to achieve global competitiveness.
Quantifying trade-offs between therapeutic efficacy and resistance dissemination for enrofloxacin dose regimens in cattle
Authors: Deb Liton Chandra , Timsina Archana , Lenhart Suzanne , Foster Derek , Lanzas Cristina 
Published: April 2024

Abstract

The use of antimicrobial drugs in food-producing animals increases the selection pressure on pathogenic and commensal bacteria to become resistant. This study aims to evaluate the existence of trade-offs between treatment effectiveness, cost, and the dissemination of resistance in gut commensal bacteria. We developed a within-host ordinary differential equation model to track the dynamics of antimicrobial drug concentrations and bacterial populations in the site of infection (lung) and the gut. The model was parameterized to represent enrofloxacin treatment for bovine respiratory disease (BRD) caused by Pastereulla multocida in cattle. Three approved enrofloxacin dosing regimens were compared for their effects on resistance on P. multocida and commensal E. coli: 12.5 mg/kg and 7.5 mg/kg as a single dose, and 5 mg/kg as three doses. Additionally, we explored non-approved regimes. Our results indicated that both 12.5 mg/kg and 7.5 mg/kg as a single dose scenario increased the most the treatment costs and prevalence of P. multocida resistance in the lungs, while 5 mg/kg as three doses increased resistance in commensal E. coli bacteria in the gut the most out of the approved scenarios. A proposed scenario (7.5 mg/kg, two doses 24 hours apart) showed low economic costs, minimal P. multocida, and moderate effects on resistant E. coli. Overall, the scenarios that decrease P. multocida, including resistant P. multocida did not coincide with the scenarios that decrease resistant E. coli the most, suggesting a trade-off between both outcomes. The sensitivity analysis indicates that bacterial populations were the most sensitive to drug conversion factors into plasma (𝛽), elimination of the drug from the colon (𝜗), fifty percent sensitive bacteria (P. multocida) killing effect (Ls50), fifty percent of bacteria (E. coli) above ECOFF killing effect (Cr50), and net drug transfer rate in the lung (𝛾) parameters.
A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle accelerators
Authors: Rautela Mahindra , Williams Alan , Scheinker Alexander 
Published: April 2024

Abstract

Particle accelerators are complex systems that focus, guide, and accelerate intense charged particle beams to high energy. Beam diagnostics present a challenging problem due to limited non-destructive measurements, computationally demanding simulations, and inherent uncertainties in the system. We propose a two-step unsupervised deep learning framework named as Conditional Latent Autoregressive Recurrent Model (CLARM) for learning the spatiotemporal dynamics of charged particles in accelerators. CLARM consists of a Conditional Variational Autoencoder (CVAE) transforming six-dimensional phase space into a lower-dimensional latent distribution and a Long Short-Term Memory (LSTM) network capturing temporal dynamics in an autoregressive manner. The CLARM can generate projections at various accelerator modules by sampling and decoding the latent space representation. The model also forecasts future states (downstream locations) of charged particles from past states (upstream locations). The results demonstrate that the generative and forecasting ability of the proposed approach is promising when tested against a variety of evaluation metrics.
Perioperative Complications and Outcomes of 12 Patients Bridged to Heart Transplantation by Extracorporeal Membrane Oxygenation
Authors: Fang Xing , Yan Jueyue , Xu Zhipeng , Zhang Jingchen , Li Tong 
Published: April 2024

Abstract

Background: Cardiogenic shock (CS) is characterized by end-organ hypoperfusion resulting from low cardiac output. Venous-artery extracorporeal membrane oxygenation (VA-ECMO), is widely used in patients presenting with refractory cardiogenic shock. The decision to use VA-ECMO as a bridge to heart transplantation has become widely accepted as a short-term option. However, complications can develop within weeks, which makes it unsuitable for bridging to transplantation and even affects the prognosis.
Methods：Data were collected from 12 patients who received extracorporeal membrane oxygenation (ECMO) as a bridge to heart transplantation between October 2020 and October 2021. General perioperative characteristics, ECMO-related complications, and posttransplantation survival were summarized.
Results：The 12 patients were male, with an average age of 45.33 years and an average BMI of 22.87 kg/m2. The main primary diseases were myocardial infarction (7 cases), dilated cardiomyopathy (4 cases), and fulminant myocarditis (1 case); the average ECMO maintenance time before transplantation was 14.25 days, the longest preoperative maintenance time was 37 days, the overall ECMO perioperative survival rate was 75.00%, and the 30-day survival rate was 91.67%. The main complications in the perioperative period are gastrointestinal bleeding, distal limb ischemia of the perfusion tube and distal limb ischemia requiring fasciotomy during ECMO maintenance, bleeding after transplantation in the operative area requiring reoperation for bleeding, and poor healing at the ECMO cannulation insertion site.
Conclusion：ECMO as a bridge to heart transplantation is a feasible treatment option for patients with end-stage heart failure and refractory cardiogenic shock. We should be aware of the complications related to ECMO management.
Findings of tobacco ringspot virus in ornamentals in the Netherlands from 1997 to 2020 indicate a need for evaluation of its EU-quarantine status
Authors: Schoen Ruben , de Krom Christel , Westenberg Marcel , Botermans Marleen , Bruggen Anne Sophie van , Meekes Ellis , Didden Leonie , Hooftman Rien , Roenhorst Annelien 
Published: April 2024

Abstract

Occasional findings of Tobacco ringspot virus (TRSV) in the Netherlands since 1997, were reason for large-scale surveys for TRSV in vegetatively propagated ornamental plants and for its vector, Xiphinema americanum sensu lato. In total, TRSV was identified in ten ornamental species, comprising over 30 cultivars, while X. americanum s.l. was not found. In addition, TRSV was identified in four ornamental species originating from outside the Netherlands. This report describes a newly designed and validated real time RT-PCR and gives an overview of TRSV findings and interceptions, including phylogenetic analyses of (nearly) complete genome sequences from available isolates and NCBI GenBank accessions in relation to their metadata of host and origin. Overall, the results suggest that TRSV entered the Netherlands on multiple occasions and was further spread by vegetative propagation. Furthermore, TRSV seems to be more widespread in the European Union (EU) than previously assumed, indicating that the current regulatory measures do not fully safeguard the absence of TRSV. Alternative strategies to protect the crops at risk are discussed, and reconsideration of the regulatory status of TRSV in the EU is recommended.
Preoperative Prediction of Rectal Cancer Staging Combining MRI Deep Transfer Learning, Radiomics Features, and Clinical Factors: Accurate Differentiation from Stage T2 to T3
Authors: Fan Lifang , Wu Huazhang , Wu Yimin , Wu Shujian , Zhao Jinsong , Zhu Xiangming 
Published: April 2024

Abstract

Background This study evaluates the efficacy of integrating MRI deep transfer learning, radiomic signatures, and clinical variables to accurately preoperatively differentiate between stage T2 and T3 rectal cancer.
Methods We included 361 patients with pathologically confirmed stage T2 or T3 rectal cancer, divided into a training set (252 patients) and a test set (109 patients) at a 7:3 ratio. The study utilized features derived from deep transfer learning and radiomics, with Spearman rank correlation and the Least Absolute Shrinkage and Selection Operator (LASSO) regression techniques to reduce feature redundancy. Predictive models were developed using Logistic Regression (LR), Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM), selecting the best-performing model for a comprehensive predictive framework incorporating clinical data.
Results After removing redundant features, 24 key features were identified. In the training set, the area under the curve (AUC)values for LR, RF, DT, and SVM were 0.867, 0.834, 0.900, and 0.944, respectively; in the test set, they were 0.847, 0.803, 0.842, and 0.910, respectively. The combined model, using SVM and clinical variables, achieved AUCs of 0.946 in the training group and 0.920 in the validation group.
Conclusion The study confirms the utility of a combined model of MRI deep transfer learning, radiomic features, and clinical factors for preoperative classification of stage T2 vs. T3 rectal cancer, offering significant technological support for precise diagnosis and potential clinical application.
Dependency-aware Online Task Offloading based on Deep Reinforcement Learning for IoV
Authors: Liu Chunhong , Wang HuaiChen , Zhao Mengdi , Liu Jialei , Zhao XiaoYan , Yuan Peiyan 
Published: April 2024

Abstract

The integration of artificial intelligence with vehicle wireless communicationtechnology willing meet the intelligent communication needs of the Internetof Vehicles (IoV), supporting more sophisticated vehicle applications. However,making real-time dependency-aware task offloading decisions is difficult due tothe high mobility of vehicles and the dynamic nature of the network environment.This leads to additional application computation time and energy consumption,increasing the risk of offloading failures for computationally intensive andlatency-sensitive applications. In this paper, an offloading strategy for vehicleapplications that jointly considers latency and energy consumption in the basestation cooperative computing model is proposed. Firstly, we establish a collaborativeoffloading model involving multiple vehicles, multiple base stations,and multiple edge servers. Transferring vehicular applications to the applicationqueue of edge servers and prioritizing them based on their completion deadlines.Secondly, each vehicular application is modeled as a directed acyclic graph(DAG) task with data dependency relationships. Subsequently, we propose atask offloading method based on task dependency awareness in deep reinforcementlearning (DAG-DQN). Tasks are assigned to edge servers at different base stations, and edge servers collaborate to process tasks, minimizing vehicle applicationcompletion time and reducing edge server energy consumption. Finally,simulation results show that compared with the heuristic method, our proposedDAG-DQN method reduces task completion time by 16%, reduces system energyconsumption by 19%, and improves decision-making efficiency by 70%.
Changing Patterns of Severe Acute Malnutrition and Associated Socio-Demographic Factors Among Under-Five Children in Maharashtra
Authors: Tonpe Mayura , Mankar Dhananjay 
Published: April 2024

Abstract

Background:
Malnutrition poses a significant health concern, particularly among children under the age of five, on a global scale. Among the various forms of malnutrition, Severe Acute Malnutrition (SAM) stands out as one of the most devastating conditions, contributing to an 11-fold greater risk of mortality and morbidity in affected children than in well-nourished children.
Despite being regarded as one of India's most economically developed states, Maharashtra continues to experience a persistently high prevalence of SAM. Recognizing this, there is a necessity to delve into the evolving trends of SAM in Maharashtra and explore the factors influencing its prevalence.
Materials and Methods:
In this study, the focus was on children aged 6 to 59 months who were residents of the state of Maharashtra. The information was obtained from the National Family Health Survey-4 (NFHS-4, 2015-16) and NFHS-5 (2019-21) databases. Bivariate analysis was conducted to determine the association between socio-demographic variables and SAM between NFHS-4 and NFHS-5 in Maharashtra. The multivariable binary logistic regression analysis was used to study the significant factors associated with the rising trend in SAM between the two survey periods.
Results:
The analysis showed that the SAM prevalence in Maharashtra has increased from 9.4% to 10.9% since NFHS-4. Bivariate analysis revealed that factors such as the wealth index, total number of children, media exposure of mothers, birth order, birthplace, and birth weight of the child were significant factors during NFHS-4, whereas, during NFHS-5, birth order and child age were the only factors that were found to be significant. The multivariable logistic regression model showed that maternal education and birth weight were the only factors that had a consistently significant association with SAM between the two survey periods (p<0.05).
Conclusion:
This study showed that despite the nutrition-sensitive policies and efforts of the Maharashtra government, the level of SAM among children is increasing. The observations from the study underscore the necessity of implementing nutrition-specific measures that target the underlying social issues in the state. Addressing these social determinants is crucial to overcoming the challenges associated with SAM effectively.