bluuebunny/crossref_metadata_2025_split huggingface dataset:
A lesson from a magnificent eruption: year 2021, La Palma, Canary Islands
Authors: Marques Fernando , Catalao Joao , Soler Vicente 
Published: April 2023

AbstractThe exceptionally dynamic eruption of La Palma in 2021 represents a unique window to look deep into the lithosphere and the mechanics of an eruptive process. Magma ascent is powered by overpressure (dynamic/lithostatic pressure > 1), meaning that thermodynamically estimated pressure cannot be directly converted to depth. The problem is that the amount of overpressure is still unknown. We use classical rock mechanics and fluid flow laws to analyse geophysical and thermodynamic data, and infer high values of overpressure (> 1.5). A large pressure gradient between surface vents and magma reservoirs was established in the early days of eruption, which violently sucked magma from the deeper sources and greatly increased magma velocity that induced brittle behaviour of the lithosphere at depths of 40 km (deepest earthquake). Supersonic explosions and lava fountains hundreds of meters tall were the surface witness of the inferred high overpressure. The non-acknowledgement of significant overpressure in the volcanic process has two undesirable effects: (1) the lithospheric structure, magma ponding levels, volumes, and plumbing system may be misplaced by several kilometres; (2) it hampers the understanding of the mechanics of the whole process and, therefore, of a better knowledge of hazard and risk.
Copula-based Risk Aggregation with Trapped Ion Quantum Computers
Authors: Zhu Daiwei , Shen Weiwei , Giani Annarita , Majumder Saikat Ray , Neculaes Bogdan , Johri Sonika 
Published: April 2023

Abstract
Copulas are mathematical tools for modeling joint probability distributions. In the past 60 years they have become an essential analysis tool on classical computers in various fields. The recent finding that copulas can be expressed as maximally entangled quantum states has revealed a promising approach to practical quantum advantages: performing tasks faster, requiring less memory, or, as we show, yielding better predictions. Studying the scalability of this quantum approach as both the precision and the number of modeled variables increase is crucial for its adoption in real-world applications. In this paper, we successfully apply a Quantum Circuit Born Machine (QCBM) based approach to modeling 3-and 4-variable copulas on trapped ion quantum computers. We study the training of QCBMs with different levels of precision and circuit design on a simulator and a state-of-the-art trapped ion quantum computer. We observe decreased training efficacy due to the increased complexity in parameter optimization as the models scale up. To address this challenge, we introduce an annealing-inspired strategy that dramatically improves the training results. In our end-to-end tests, various configurations of the quantum models make a comparable or better prediction in risk aggregation tasks than the standard classical models.
Cases of paternal inheritance and recombination of mictochondria in peas (Pisum L.)
Authors: Bulgakova Vera S. , Shatskaya Natalia V. , Kosterin Oleg E. , Vasiliev Gennadiy V. 
Published: April 2023

Abstract
Pea has been hitherto considered to have strict maternal inheritance of mitochondria, as most of flowering plants. Using a CAPS marker we detected a case of paternal inheritance of mitochondrial DNA (mtDNA) in the second generation of a cross between a wild and a cultivated representatives of the common pea (Pisum sativum L.). Heteroplasmy for mitochondrial and plastid DNA was detected in arrays of high throughput sequence reads and confirmed by analysis of CAPS markers in an accession of Pisum fulvum Sm. and probable heteroplasmy for mtDNA marker was detected in an accession of the wild P. sativum subsp, elatius. A case of mtDNA recombination in one of accessions of the same subspecies from Turkey was observed as a ca 13 km long insert originating from a distant evolutionary lineage. Thus, three phenomena described in this work, occasional paternal leakage of mitochondria, naturally occurring heteroplasmy and recombination of diverged mitochondrial genomes in pea, elucidated microevolutionary processes which shaped diversity in this important crop.
DREG： A large-scale biological knowledge graph for Drug Repurposing
Authors: Feng Xin , Ma Zhansen , Xin Yongxian , Yu Cuinan , Xin Ruihao 
Published: April 2023

Abstract
Background: Developing new drugs involves significant costs. However, repurposing existing drugs can identify potential application areas for treating new diseases. Current drug repurposing methods often require strong domain knowledge and numerous biological experiments. The knowledge graph approach which leverages existing drug research and development knowledge has emerged as a promising alternative.
Results: This paper introduces the DREG (Drug Repurposing Entity Knowledge graph) model, which is based on a large-scale knowledge graph. By aggregating multiple related knowledge networks, the model extracts association information between known drug and disease entities. It then calculates entity similarity to predict unknown drug-disease relationships. The DREG model's performance, with an MRR index of 0.308 and Hits@10 of 0.628, surpasses that of the best-performing model by 4.7% (MRR) and 18.1% (Hits@10). The DREG model's effectiveness rate in quantitative experiments can reach 80%. The recommended results have clinical value, as some are already undergoing clinical trials on ClinicalTrials.gov and the China Clinical Trial Center.
Conclusion: The DREG model can predict candidate drugs for repurposing, reducing the scope of human expert evaluation and treating new diseases.
Development of Machine Learning-Based Personalized Predictive Models for Early Detection of Hepatocellular Carcinoma in HBV-Related Cirrhosis Patients with Low Levels of Serum Alpha-Fetoprotein
Authors: Xu Yuan , Xu Jing-Yao , Hu Hui , Zhang Bei , Zhou Fan , Yang Xinlei , Xiao Ouyang 
Published: April 2023

Abstract
Background: The continuous increase in the incidence of HCC in China is an urgent issue, and early diagnosis and treatment are crucial. This study aims to create personalized predictive models by combining machine learning technology with demographic, medical history, and non-invasive biomarker data. These models will enhance the decision-making capabilities of clinical doctors for liver cell carcinoma (HCC) in HBV-related cirrhosis patients with low levels of serum alpha-fetoprotein (AFP).
Methods: A total of 6,980 patients were included for further analysis treated between January 2012 and December 2018 were assessed. The laboratory test and clinical data before treatment were gathered. The significant risk factors were selected, and the relative risk of each variable affecting HCC diagnosis was calculated with machine learning and univariate regression analysis. Finally, in order to establish machine learning models, the data set was partitioned into a validation set (20%) and training set (80%) at random.
Results:.This study identified 12 independent risk factors for HCC by using Gaussian naïve Bayes (GNB), extreme gradient boosting (XGBoost), random forest (RF), and least absolute shrinkage and selection operation (LASSO) regression models. Multivariate analysis showed that males, age >60 years, alkaline phosphate (ALP) >150 U/L, AFP >25 ng/mL, carcinoembryonic antigen (CEA) >5 ng/mL, and fibrinogen (Fbg) >4 g/L were risk factors, while hypertension, calcium <2.25 mmol/L, potassium ≤3.5 mmol/L, direct bilirubin (DB) >6.8 μmol/L, hemoglobin (HB) <110 g/L, and glutamic-pyruvic transaminase (GPT) >40 U/L were protective factors in HCC patients. Based on these factors, a nomogram was constructed and showed an area under the curve (AUC) of 0.746 (sensitivity=0.710, specificity=0.646), which was significantly higher than AFP AUC of 0.658 (sensitivity=0.462, specificity=0.766). Compared with several machine learning algorithms, XGBoost model had an AUC of 0.832 (sensitivity=0.745, specificity=0.766) and independent validation AUC of 0.829 (sensitivity=0.766, specificity=0.737), which performed the highest level in both the test set and the training set.
Conclusions: The proposed XGBoost for classifying HCC in patients with HBV-related cirrhosis with low-level AFP demonstrated promising ability for individualized prediction of HCC cases.
Efficacy and Adverse Effects of Remdesivir in Patients with COVID-19 Pneumonia: A Retrospective Study
Authors: Hariri Bassem Al , Barman Manish , Haider Vajeeha , Nashwan Abdulqadir J. , Alharafsheh Ahmad , Salehi Aram , Illahi Memon Noor , Mohamedali Mohamed Gaafar 
Published: April 2023

Abstract
Introduction: Remdesivir is an antiviral medication approved by the US Food and Drug Administration to combat COVID-19 infection in hospitalized patients. Although the adverse effects of Remdesivir are mainly unknown, data from randomized controlled trials have demonstrated its deleterious impact on several organ systems.
Purpose: This study aims to describe the safety and efficacy of Remdesivir administration in a cohort of 586 patients admitted to a tertiary hospital in Qatar for COVID-19-related reasons.
Methods: A retrospective study of 586 patients admitted with a diagnosis of COVID-19 and treated with Remdesivir were compared to 200 patients with COVID-19 who did not receive Remdesivir.
Results: The rate of mechanical ventilation admission to the intensive care unit was comparable across the two groups (2.35% vs. 2%, p =.75). Death rates were comparable between the two groups (0.02% vs. 0.03%, p =.43). There was a mean reduction in heart rate within the first three days of antiviral therapy. Negligible variations in serum AST, ALT, ALP, and eGFR levels were detected. Remdesivir-treated patients had a significantly shorter hospital stay.
Conclusion: Based on the limited data available regarding the adverse effects of Remdesivir, it is prudent to exercise caution by evaluating baseline parameters and avoiding concomitant use of potential cardio-, nephro-, or hepatotoxic drugs when using Remdesivir in patients hospitalized with COVID-19.
Direct C-H bond activation using visible light and a donor-acceptor polymer with tunable valence band
Authors: Li Xin-Hao , Zhang Shi-Nan , Li Qi-Yuan , Lin Yun-Xiao , Xia Si-Yuan , Xu Dong , Chen Jie-Sheng 
Published: April 2023

AbstractCurrently, converting hydrocarbons into high-value-added products through photocatalytic selective activation under mild conditions is attractive and challenging, motivating research efforts to explore inexpensive and efficient photocatalysts. Here, we report a donor-acceptor polymer with tunable valence band to trigger direct photocatalytic C-H bond activation for selective oxidation, halogenation and coupling reactions using visible light and air without the assistance of any sacrificial additives. Apart from the fast charge separation driven by donor-acceptor pairs, both experimental and theoretical results have demonstrated that the downshifted valence band of the polymer photocatalyst dominated the enhanced oxidation power to accelerate C-H bond activation. resulting in the best-in-class catalytic performance of paraxylene oxidation with high aldehyde selectivity (> 99%), outperforming the state-of-the-art metal-free homogeneous catalysts in the literature by a factor of 4.
Motivational Drivers for Health Professionals in a Large Quality Improvement Collaborative Project in Brazil: A Qualitative Study
Authors: Silva Eliane Pereira , Saturno-Hernández Pedro Jesus , Freitas Marise Reis , Gama Zenewton André Silva 
Published: April 2023

Abstract
Background
The success of quality improvement collaborative projects in healthcare depends on the engagement of professionals working in patient care, but the factors that modulates their motivation in these projects are still unclear. The objective of this study was to analyze barriers and facilitators to motivation of health professionals in a large project for implement evidence-based infection prevention practices in intensive care units of Brazilian hospitals.
Methods
This qualitative study was based on content analysis of semi-structured in-depth interviews with health professionals participating in the quality improvement collaborative project “Improving patient safety on a large scale in Brazil” involving 116 Brazilian hospitals. We randomly selected hospitals and conducted the interviews via videoconference with health professionals who participated in the project. The final sample size was defined using the principle of saturation. Data were encoded using the NVivo software, and analyses were based on the Capability, Opportunity, Motivation, and Behavior model and the Model for Understanding Success in Quality.
Results
We interviewed 28 health professionals from 12 hospitals in the five regions of Brazil. The barriers found were the non-involvement of senior managers, demand for immediate results, lack of supplies, excessive bureaucracy for changes, work overload, lack of work hours’ time allowed for the project, resistance of experienced healthcare professionals, belief that improvement increases workload, difficulty in engaging the night shift professionals, little involvement of physicians, lack of knowledge about quality improvement and collection of indicators. An active participation of senior managers in the rounds, institutional policy of continuous improvement, quality improvement team constant presence in the unit, team leader enthusiasm, understanding the reason for changes, teamwork, participating in change tests, learning in practice, celebrate small results and feedback on results were facilitators to motivation.
Conclusion
This study reinforced the importance of customizing large projects, involving the modulators of motivation, intervening in factors identified as barriers and facilitators, and verifying the interactions in the microsystem to ensure maximum motivation during collaborative projects.
Thermal Profile Modeling and Microstructural Evolution in Laser Processing of Inconel 625 Plates
Authors: Lawson Stephanie , Ghayoor Milad , Tabei Ali , Pasebani Somayeh 
Published: April 2023

Abstract
Thermal modeling is used in additive manufacturing laser processes to predict microstructural evolution of the materials under specified process conditions and parameters. The objective of this study was to develop, analyze and compare two predictive models: an analytical model and a numerical model for laser processing of materials of Inconel 625. These models were compared with experimental results for thermal profiling, and the effect of thermal profiles on microstructure of the experimental samples was explored. The three approaches; analytical modeling, numerical modeling, and experimental results were evaluated against thermal profile histories and correlated to microstructural evolution in laser processing. Maximum temperatures in the thermal profile of both models were shown in good agreement when compared to the experimental results. Cooling curves were also correlated with microstructure in terms of grain size, morphology, orientation, and texture evolution, with findings that match previously reported results. This research validates the proposed numerical model for examining optimal laser processing conditions for IN625 through both thermal history and microstructure comparison with experimental results using literature derived thermo-physical material properties.
Risk factors and clinical outcomes of cytomegalovirus diseases in hematologic malignancy patients without hematopoietic stem-cell transplantation: a case-control study
Authors: Yang Jinyoung , Lee Young Ho , Ko Jae-Hoon , Huh Kyungmin , Cho Sun Young , Chung Doo Ryeon , Peck Kyong Ran , Jung Chul Won , Kang Cheol-In 
Published: April 2023

Abstract
Backgrounds: This study aims to evaluate the risk factors and prognosis for CMV diseases in hematologic malignancy patients without HSCT.
Methods: We performed a case-control study (1:2) between 2012 and 2022. Adults with pathologic-confirmed CMV diseases (n=60) among hematologic malignancy patients were matched and compared to whom without CMV disease.
Results: Lymphoma was the most common underlying malignancy, and gastrointestinal tract involvement was the most common CMV disease. In the case group, high-dose steroid administration and transfusion within one month before diagnosis were higher (p<0.001). Steroid administration (aOR=5.78; 95% confidence interval: 1.25–26.68, p=0.024), red blood cell transfusion within one month (aOR=14.63; 2.75–77.76, p=0.002), low BMI (aOR=13.46, 2.07–87.45, p=0.006), and hypoalbuminemia (aOR=26.48, 5.93–118.17, p<0.001) were independent risk factors associated with CMV disease. The 30-day mortality was higher in the case group and CMV disease was significantly associated with all-cause mortality (aOR=14.41, 3.23–64.31, p<0.001).
Conclusions: In hematologic malignancy patients without HSCT, risk factors for CMV organ disease included high-dose steroid administration and RBC transfusion within one month, low BMI, and hypoalbuminemia. Overall mortality was significantly higher with CMV disease, and CMV disease occurrence was a significant risk factor for mortality.
Automatic Recommendation of Contents and Reinforcement Activities to Facilitate the Autonomous Learning in a Data Structure and Programming Course
Authors: Plaza Laura , Araujo Lourdes , López-Ostenero Fernando , Martínez-Romo Juan 
Published: April 2023

Abstract
Autonomous learning is increasingly recognized as an effective approach for achieving academic success, particularly in e-learning environments. It offers students greater flexibility, engagement, and motivation. A valuable tool for facilitating autonomous learning and tailoring materials and reinforcement activities to students' needs is a recommendation system based on their questions, doubts, and concerns expressed in the course forum. In this study, we propose the use of advanced natural language processing techniques to design and implement a recommender that supports students in autonomous learning. When a student posts a query in the course forum, our recommender system provides links to other discussion threads where related questions have been raised and additional activities to reinforce the study of topics that have been challenging. We have developed a content-based recommender that utilizes an algorithm capable of extracting key phrases, terms, and embeddings that describe the concepts in the student query and those present in other conversations and reinforcement activities with high precision. The recommender considers the similarity of the concepts extracted from the query and those covered in the course discussion forum and the exercise database to recommend the most relevant content for the student. Our results indicate that we can recommend both posts and activities with high precision (above 80%) using key phrases to represent the textual content.
Opportunities and Challenges in Clinical Learning of Midwifery Students in Public Universities of Tigray Region, Ethiopia, 2020: A Qualitative Study
Authors: Abreha Tomas Amare , W/tensay Kidisti Tesfay , Gebre Merhawi Birhane , Abrha Birhanu Abadi , Haile Gebrhud Berihu 
Published: April 2023

Abstract

Background: Clinical learning focuses on real problems in the context of professional practice in which learners are motivated by its relevance and active participation. Studies showed that midwifery students were challenged by the absence of a variety of cases in non-teaching hospitals, overcrowded teaching hospitals, absence of objective-based evaluation methods, and lack of supervision from clinical instructors. If the theory learned in class was applied in practice, it is helpful to produce skillful and competent midwifery professionals. The aim of this study was exploring opportunities and challenges for midwifery students in the clinical learning environment.
Methods: the study was conducted in public Universities of Tigray, Ethiopia. Phenomenology study design and purposive sampling technique were employed; four focused group discussions and five key informant interviews were conducted. Data was collected using an open-ended guide, transcribed verbatim, entered into ATLAS ti7 software, and translated. Then codes and themes were derived from the transcribed data, and finally analyzed thematically.
Results: a total of 33 participants in which 28 in four focused group discussions and five key informant interviews participated in this study. Based on the result, midwifery students were getting opportunities to practice when they were assigned to non-teaching hospitals, working with close supervision, having smooth relationships with staff, receiving constructive feedback, and evaluated based on their skills. Whereas, they were challenged by aggressive staff, poor follow up, overcrowded teaching hospitals, low usage of skills lab, and short time for clinical practice.
Conclusion: Midwifery students have positive attitude, and were getting opportunities to practice while they were assigned to a very conducive clinical learning environment with supportive and skillful clinical instructors/ preceptors.
However, they have negative attitude, and were challenged to work due to the poor attention given to midwifery students’ clinical learning. It is recommended that midwifery students have to practice in skills lab before they assigned for clinical practice and clinical instructors have to be integrated to teaching hospitals so as to educate students while their hands-on.
Methylation status, mRNA and protein expression of the SMAD4 gene in patients with non-melanocytic skin cancers
Authors: Ürün Yıldız Gürsel , Budak Metin , Keskin Elif Usturalı 
Published: April 2023

Abstract
Background
SMAD4 is a potent tumor suppressor. SMAD4 loss increases genomic instability and plays a critical role in the DNA damage response that leads to skin cancer development. We aimed to investigate SMAD4 methylation effects on mRNA and protein expression of SMAD4 in cancer and healthy tissues from patients with basal cell carcinoma (BCC), cutaneous squamous cell carcinoma (cSCC), and basosquamous skin cancer (BSC).
Methods and results
The study included 17 BCC, 24 cSCC and nine BSC patients. DNA and RNA were isolated from cancerous and healthy tissues following punch biopsy. Methylation-specific polymerase chain reaction (PCR) and real-time quantitative PCR methods were used to examine SMAD4 promoter methylation and SMAD4 mRNA levels, respectively. The percentage and intensity of staining of the SMAD4 protein were determined by immunohistochemistry. The percentage of SMAD4 methylation was increased in the patients with BCC (p = 0.007), cSCC (p = 0.004), and BSC (p = 0.018) compared to the healthy tissue. SMAD4 mRNA expression was decreased in the patients with BCC (p˂0.001), cSCC (p˂0.001), and BSC (p = 0.008). The staining characteristic of SMAD4 protein was negative in the cancer tissues of the patients with cSCC (p = 0.00). Lower SMAD4 mRNA levels were observed in the poorly differentiated cSCC patients (p = 0.001). The staining characteristics of the SMAD4 protein were related to age and chronic sun exposure.
Conclusions
The mRNA expression level of the SMAD4 gene and its changing protein expression are particularly important for the early diagnosis and prognosis of cSCC.
Trial Registration
The name of the trial register: SMAD4 Methylation and Expression Levels in Non-melanocytic Skin Cancers; SMAD4 Protein Positivity.