bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mapping Vaccine Names in Clinical Trials to Vaccine Ontology using Cascaded Fine-Tuned Domain-Specific Language Models
Authors: Li Jianfu , Li Yiming , Pan Yuanyi , Guo Jinjing , Sun Zenan , Li Fang , He Yongqun , Tao Cui 
Published: September 2023

Abstract

Background
Vaccines have revolutionized public health by providing protection against infectious diseases. They stimulate the immune system and generate memory cells to defend against targeted diseases. Clinical trials evaluate vaccine performance, including dosage, administration routes, and potential side effects. ClinicalTrials.gov is a valuable repository of clinical trial information, but the vaccine data in them lacks standardization, leading to challenges in automatic concept mapping, vaccine-related knowledge development, evidence-based decision-making, and vaccine surveillance.
Results
In this study, we developed a cascaded framework that capitalized on multiple domain knowledge sources, including clinical trials, Unified Medical Language System (UMLS), and the Vaccine Ontology (VO), to enhance the performance of domain-specific language models for automated mapping of VO from clinical trials. The Vaccine Ontology (VO) is a community-based ontology that was developed to promote vaccine data standardization, integration, and computer-assisted reasoning. Our methodology involved extracting and annotating data from various sources. We then performed pre-training on the PubMedBERT model, leading to the development of CTPubMedBERT. Subsequently, we enhanced CTPubMedBERT by incorporating SAPBERT, which was pretrained using the UMLS, resulting in CTPubMedBERT + SAPBERT. Further refinement was accomplished through fine-tuning using the Vaccine Ontology corpus and vaccine data from clinical trials, yielding the CTPubMedBERT + SAPBERT + VO model. Finally, we utilized a collection of pre-trained models, along with the weighted rule-based ensemble approach, to normalize the vaccine corpus and improve the accuracy of the process. The ranking process in concept normalization involves prioritizing and ordering potential concepts to identify the most suitable match for a given context. We conducted a ranking of the Top 10 concepts, and our experimental results demonstrate that our proposed cascaded framework consistently outperformed existing effective baselines on vaccine mapping, achieving 71.8% on top 1 candidate’s accuracy and 90.0% on top 10 candidate’s accuracy.
Conclusion
This study provides a detailed insight into a cascaded framework of fine-tuned domain-specific language models improving mapping of VO from clinical trials. By effectively leveraging domain-specific information and applying weighted rule-based ensembles of different pre-trained BERT models, our framework can significantly enhance the mapping of VO from clinical trials.
High caffeine intake as a risk factor for tooth loss: A Study of the National Health and Nutrition Examination Survey (NHANES) 2017-2020
Authors: Chen Yu , Lin Lanling , Zhang Qian 
Published: September 2023

Abstract
Introduction: Tooth loss can have a wide range of adverse consequences on patients. Caffeine is the main active ingredient in the world's most popular beverages. This study aims to explore any possible connection between the two.
Methods: This study analyzed data from the NHANES collected from 2017 to March 2020. Multivariate logistic regression models to examine the association between the caffeine intake and tooth loss. Odds ratios (OR) with 95% confidence intervals (95%CI) was used to evaluate the significance of association.
Results: A total of 5,450 participants were finally included in the trial. Adjusted Multivariate logistic regression result showed that tooth loss was strongly linked with higher levels of caffeine intake (OR=1.74; 95% CI 1.40-2.17; p<0.001). Stratified logistic regression analysis showed that only in the subgroups of age (p=0.01) and PIR (p=0.02), the results showed statistical difference. Caffeine intake does not influence tooth loss in ≥ 60 group (p<0.05).
Conclusion: In conclusion, increased caffeine consumption may be a factor in tooth loss.
Application of Arnold’s Cat Map and Bülban Map for Image Encryption in Chaotic Cryptography
Authors: Attanayake Sarasi , Yapage Nihal 
Published: September 2023

Abstract
This study examines the use of chaos theory to image encryption by proposing a unique algorithm for image encryption based on two chaotic maps: Arnold’s CatMap and Bülban Map. The main objectives of this work areto propose an algorithm that encrypts images using chaotic maps efficiently and effectively and in addition, to comprehend the mathematical features of the two chaotic maps. The encryption algorithm was created using version 2017b of MATLAB. The effectiveness of the map was then calculating by evaluating the cross-correlation, vertical, horizontal, and diagonal correlations, entropy, PSNR, and elapsed encryption time of the encrypted image. Further, a key sensitivity investigation was conducted to determine the key’s force resistance. The correlation values that were significantly closer to 0 confirmed the effectiveness of the encryption algorithm for each image format. Higher entropy levelsand lower PSNR values indicate that the encryption algorithm is effective. In addition, the trial-and-error method was used to determine the parameter range in which the Bülban map will display chaotic features. Based on the findings obtained for encryption using the two maps independently and the suggested hybrid map, the optimal algorithm has been proposed. Utilizing both Arnold’s cat and Bülban maps with iteration number 170 for Arnold’s Cat Map increases the reliability of image encryption compared to using only one of them.
A blended module (STAIRS) to promote functional and personal recovery in patients with a major depressive disorder in remission: study protocol of a concurrent mixed methods randomized controlled trial
Authors: Wedema David , Wardenaar Klaas J. , Alma Manna A. , Asselt Antoinette D. I. , Korevaar Eliza L. , Schoevers Robert A. 
Published: September 2023

Abstract
Background Despite the availability of a wide variety of evidence-based treatments for major depressive disorder (MDD), many patients still experience impairments in their lives after remission. Programs are needed that effectively support patients in coping with these impairments. The program Storytelling and Training to Advance Individual Recovery Skills (STAIRS) was developed to address this need and combines the use of peer contact, expert-by-experience guidance, family support and professional blended care. The aim of the planned study is (1) to assess the efficacy of the STAIRS program in patients with remitted MDD, (2) to investigate patients’ subjective experiences with STAIRS, and (3) to evaluate the program’s cost-effectiveness.Methods A concurrent mixed-methods randomized controlled trial design will be used. Patients aged between 18 and 65 years with remitted MDD (N = 140) will be randomized to either a group receiving care as usual (CAU) + the STAIRS-program or a control group receiving CAU + some basic psychoeducation. Quantitative efficacy data on functional and personal recovery and associated aspects will be collected using self-report questionnaires at the start of the intervention, immediately following the intervention, and at the six-month follow-up. Insights into patients’ experiences on perceived effects and the way in which different program elements contribute to this effect, as well as the usability and acceptability of the program, will be gained by conducting qualitative interviews with patients from the experimental group, who are selected using maximum variation sampling. Finally, data on healthcare resource use, productivity loss and quality of life will be collected and analysed to assess the cost-effectiveness and cost-utility of the STAIRS-program.Discussion Well-designed recovery-oriented programs for patients suffering from MDD are scarce. If efficacy and cost-effectiveness are demonstrated with this study and patients experience the STAIRS program as usable and acceptable, this program can be a valuable addition to CAU. The qualitative interviews may give insights into what works for whom, which can be used to promote implementation.Trial registration: This trial was registered at ClinicalTrials.gov on 1 July 2021, registration number NCT05440812.
Infrared: a declarative tree decomposition-powered framework for bioinformatics
Authors: Yao Hua-Ting , Marchand Bertrand , Berkemer Sarah J. , Ponty Yann , Will Sebastian 
Published: September 2023

Abstract
Motivation:
Many bioinformatics problems can be approached as optimization or controlled sampling tasks, and solved exactly and efficiently using Dynamic Programming (DP). However, such exact methods are typically tailored towards specific settings, complex to develop, and hard to implement and adapt to problem variations.
Methods:
We introduce the Infrared framework to overcome such hindrances for a large class of problems. Its underlying paradigm is tailored toward problems that can be declaratively formalized as sparse feature networks, a generalization of constraint networks. Classic Boolean constraints specify a search space, consisting of putative solutions whose evaluation is performed through a combination of features. Problems are then solved using generic cluster tree elimination algorithms over a tree decomposition of the feature network. Their overall complexities are linear on the number of variables, and only exponential on the treewidth of the feature network. For sparse feature networks, associated with low to moderate treewidths, these algorithms allow to find optimal solutions, or generate controlled samples, with practical empirical efficiency.
Results:
Implementing these methods, the Infrared software allows Python programmers to rapidly develop exact optimization and sampling applications based on a tree decomposition-based efficient processing. Instead of directly coding specialized algorithms, problems are declaratively modeled assets of variables over finite domains, whose dependencies are captured by constraints and functions. Such models are then automatically solved by generic DP algorithms. To illustrate the applicability of Infrared in bioinformatics and guide new users, we model and discuss variants of bioinformatics applications. We provide reimplementations (and extensions) for methods targeting RNA design, RNA sequence-structure alignment, parsimony-driven inference of ancestral traits in phylogenetic trees/networks, and coding sequence design demonstrate multidimensional Boltzmann sampling. Previous work together with novel results demonstrate the practical relevance of the framework, whose complexity is typically equivalent or better than specialized algorithms and implementations.
Availability:
Infrared is available at https://www.lix.polytechnique.fr/~will/Software/Infrared with extensive documentation, including various usage examples and API reference; it can beinstalled using Conda or from source.
Lead Oxide-Modified Graphite Electrodes for Electrochemical Degradation of Congo Red Dye in Aqueous Solution
Authors: Ohanele Emmanuela C. , Oguzie Emeka E. , Oguzie Kanayo L. , Ezema Fabian I. 
Published: September 2023

Abstract

Congo red dye (CR) dye in aqueous solution was decolorized by an electrolysis process using graphite and lead dioxide modified graphite (G/PbO2) as anode materials. The electrodeposited lead dioxide film was characterised by means of SEM EDS. Comparative performance assessment of the anode materials under different process parameters reveals that the lead dioxide film improved electro catalytic effect of the modified electrode. The adjustment of the deposition bath pH from 1.5 to 3 resulted to formation of uniform agglomeration and disappearance of particulates as well as the better adhesion of film to substrate. The degradation rate (DR) observed for the G/PbO2 was higher than for the unmodified graphite electrode. Increase in applied voltage from 25 to 30 V at 23mA/mm2, improved degradation efficiency (DE) from 84.7–91.32% for graphite and from 96.09–99.98% and for G/PbO2 respectively with 1 M KCl. The optimum degradation time of 45 mins was recorded for graphite anode system which reduced to 30 mins with G/PbO2 anode electrolytic cell. CR degraded to compounds with smalle r molecular weights and better stability as observed with GC MS analysis and computational total energy study respectively. The modification of the graphite electrode surface by electrodepositing PbO2 film improved the DE and optimum reaction time. These findings carry significant implications for the design of advanced electrocatalytic systems in environmental and wastewater treatment applications.
The Effect of Specific Antibiotic Subtypes on The Development of Childhood Disease
Authors: Chen Jessie , Choo-Kang Candice , Ni Jeffrey , Adam Kaavya , Watchmaker Brittney , Friedman Hannah , McGurn Andrew , Babinski Piotr , Markossian Talar , Boyd Bridget , Dugas Lara R. 
Published: September 2023

Abstract
Our study examines the relationship between antibiotic subtype, spectrum, and timing on the development of subsequent childhood disease. Our findings contribute to current knowledge on the effects of microbiome disruption by highlighting significant associations between antibiotic usage and the development of eczema and obesity. Notably, only eczema was linked to antibiotic use within the first year of life. When examining specific antibiotic subtypes, we discovered that broad-spectrum antibiotics (e.g., azithromycin, amoxicillin, amoxicillin-clavulanate, clindamycin) were associated with diseases in first-year-of-life and lifetime exposure groups. We suggest that broad-spectrum antibiotics disrupt the developing gut microbiome, making children more susceptible to disease development and atopy. For instance, clindamycin use within the first year of life was significantly associated with eczema (OR = 4.21, p < 0.002, C.I = 1.92–9.1), and amoxicillin-clavulanate use correlated with asthma (OR = 1.97, p < 0.002, C.I = 1.31–2.93). In conclusion, this study emphasizes the significance of considering the antibiotic spectrum and its impact on the developing gut microbiome when studying antibiotic-disease relationships. Future research should further explore these complex mechanisms and guide more judicious antibiotic prescribing practices, especially during early childhood, to mitigate potential risks associated with antibiotic use.
Collision Avoidance and Routing based on Location Access (CARLA) of mobile robots
Authors: El-sayyad Shimaa , Saleh Ahmed , Ali Hesham , Rabie Asmaa , Saraya Mohamed , Abdelsalam Mohamed 
Published: September 2023

Abstract
In this paper, a novel robotic system methodology known as Collision Avoidance and Routing based on Location Access (CARLA) is proposed. It is intended for use in environments like hospitals or crisis situations where quick action and the preservation of human life are crucial, and when the primary actuator is to protect the lives of healthcare workers and to prevent the high potential for viral transmission among patients, such as in COVID-19 isolation hospitals. Our framework is primarily concerned with accuracy and quick responses. E.g. delivering tools or items in a specific district while avoiding collisions with other robots or any surrounding obstacles. The suggested CARLA can also be used for a variety of related applications, including those found in factories, warehouses, and any other situation where mobile robots must be chosen and routed using a distant centralized control. The Robot Knowledge Module (RKM), Robot Selection Module (RSM), and Route Reservation Module (RRM) are the three core modules that make up the proposed framework, which will be explained in detail throughout the paper. In comparison to conventional techniques used in similar situations, the simulation results using the suggested framework demonstrate the flexible and effective mobility of the robots in terms of the robots' path computation time and the request's success rate without collision.
Comparing Continent Ileostomy (CI) Conversion to Repair/Redo IPAA: Favorable Outcomes
Authors: Ecker Nils Karl Josef , Dinh Christian , Möslein Gabriela , Ecker Karl-Wilhelm 
Published: September 2023

Abstract
Purpose: This study aims to compare the outcomes of repair/redo ileal pouch-anal anastomosis (repair/redo-IPAA) with the conversion of IPAA to continent ileostomy (CI) in an effort to prevent the need for a permanent ileostomy (IS) following IPAA failure.
Methods: This research involved a retrospective analysis of surgical records, employing descriptive statistics and Kaplan-Meier survival analysis.
Results: Among 57 patients with an IPAA, up to three revisions were necessary due to complications or complete failure. Ultimately, repair/redo-IPAA preserved the IPAA in 14 patients (24.6%), conversion to CI salvaged the pouch in 21 patients (36.8%), and IS was unavoidable in 22 patients (38.6%). The cumulative probability of requiring conversion surgery was calculated to be 54.0% at 20 years, thereby reducing the cumulative risk of IS to 32.3%. The 20-year cumulative probability of pouch salvage by repair/redo IPAA was only 21.9%. However, this rate increased to 67.7% when conversion procedures were considered. Following repair/redo-IPAA, only 8.3% of patients reported evacuation frequencies of ≤4 during the day, and 16.7% were evacuation-free at night. In contrast, after conversion to CI, 98.0% of patients reported a maximum of four evacuations in a 24-hour period. After undergoing repair/redo IPAA, between half and two-thirds of patients reported experiencing incontinence or soiling, while complete continence was achieved in all patients following conversion to CI. Notably, the majority of patients expressed overall satisfaction with their respective procedures. A positive correlation was identified between very high subjective satisfaction and positive objective surgical outcomes exclusively in patients who underwent conversion to CI.
Conclusion: When complications or failure of IPAA occur, conversion to CI emerges as a highly viable alternative to repair/redo IPAA. This conclusion is supported by the observation that patient satisfaction appears to be closely tied to stable surgical outcomes. To reinforce these findings, further prospective studies are warranted.
Trial registration numbers: None
Longstanding auditory sensory and semantic differences in preterm born children
Authors: Retsa Chrysa , Turpin Hélène , Geiser Eveline , Ansermet François , Müller-Nix Carole , Murray Micah M. 
Published: September 2023

Abstract
More than 10% of births are preterm, and the long-term consequences on sensory and semantic processing of non-linguistic information remain poorly understood. 17 very preterm-born children (born at <33 weeks gestational age) and 15 full-term controls were tested at 10 years old with an auditory object recognition task, while 64-channel auditory evoked potentials (AEPs) were recorded. Sounds consisted of living (animal and human vocalizations) and manmade objects (e.g. household objects, instruments, and tools). Despite similar recognition behavior, AEPs strikingly differed between full-term and preterm children. Starting at 50ms post-stimulus onset, AEPs from preterm children differed topographically from their full-term counterparts. Over the 108-224ms post-stimulus period, full-term children showed stronger AEPs in response to living objects, whereas preterm born children showed the reverse pattern; i.e. stronger AEPs in response to manmade objects. Differential brain activity between semantic categories could reliably classify children according to their preterm status. Moreover, this opposing pattern of differential responses to semantic categories of sounds was also observed in source estimations within a network of occipital, temporal and frontal regions. This study highlights how early life experience in terms of preterm birth shapes sensory and object processing later on in life.
A Machine Learning Approach to Determine the Semantic Versioning Type of npm Packages Releases
Authors: Abdalkareem Rabe , Chowdhury Md Atique Reza , Shihab Emad 
Published: September 2023

Abstract

Semantic versioning policy is widely used to indicate the level of changes in a package release. Unfortunately, there are many cases where developers do not respect the semantic versioning policy, leading to the breakage of dependent applications. To reduce such cases, we proposed using machine learning (ML) techniques to effectively predict the new release type, i.e., patch, minor, major, in order to properly determine the semantic versioning type. To perform our prediction, we mined and used a number of features about a release, such as the complexity of the changed code, change types, and development activities. We then used four ML classifiers. To evaluate the performance of the proposed ML classifiers, we conducted an empirical study on 31 JavaScript packages containing a total of approximately 6,260 releases. We started by extracting 41 release-level features from historical data of packages’ source code and repositories. Then, we used four machine learning classifiers, namely XGBoost, Random Forest, Decision Tree, and Logistic Regression. We found that the XGBoost classifiers performed the best achieving median ROC-AUC values of 0.78, 0.69, and 0.74 for major, minor, and patch releases, respectively. We also found that features related to the change types in a release are the best predictors group of features in determining the semantic versioning type. Finally, we studied the generalizability of determining the semantic versioning type by applying a cross-package validation. Our results showed that the general classifier achieved median ROC-AUC values of 0.76, 0.69, and 0.75 for major, minor, and patch releases.
Will fire-smart landscape management effectively buffer the effects of future climate changes and long-term land abandonment on fire regimes?
Authors: Sil Ângelo , Azevedo João C. , Fernandes Paulo M. , Honrado João P. 
Published: September 2023

Abstract
Context: Long-term farmland abandonment is increasing fuel hazard in many mountainous landscapes of the Mediterranean Basin. Combined with ongoing climate change, fire activity and fire regimes may change in the future, thus challenging the management of these regions.
Objectives: To assess the effects of fire-smart management strategies on landscape dynamics, fire regulation capacity (FRC) and fire regime under long-term land abandonment and climate change scenarios.
Methods: We applied the LANDIS-II model to simulate fire-landscape dynamics under climate change (RCP 4.5 and 8.5 scenarios) and long-term farmland abandonment (2020-2050) according to three fire-smart management strategies focused on fire prevention (compared with a business-as-usual (BAU) strategy based on fire suppression) in a fire-prone Mediterranean mountainous landscape of Portugal.
Results: Fire and land dynamics increased fire-adapted forests and agroforestry systems and decreased dominance of shrublands and croplands, thereby fostering landscape heterogeneity and fragmentation. FRC decreased over time, particularly under RCP 8.5 and BAU. Fire-smart strategies prevented larger and intense fires compared to BAU, although less effectively under RCP 8.5. Shifts in the historical fire regime are expected in the future (increasing burned area and fire frequency), more markedly under the BAU strategy.
Conclusions: Fire-smart strategies performed better than BAU in averting current fire regime intensification. Merging forest- and silvopasture-based strategies is a promising approach in taming the effects of climate and farmland abandonment on fire activity. Our work underlined the importance of decreasing landscape fuel hazard to buffer the impact of global change on fire regimes in the future management of fire-prone Mediterranean mountain landscapes.