bluuebunny/crossref_metadata_2025_split huggingface dataset:
ST segment elevation for diagnosis of pericardial local blood clots after coronary artery bypass grafting: a case report
Authors: wang aiwen , Yuan Zhuo , Bi Shuzhen , Chen Huanzhen , Bo Xingpeng , Zong Liang 
Published: April 2024

Abstract

Backgroud: Local blood clots in the pericardium are a common complication after coronary artery bypass grafting (CABG). However, it is very rare to clinical present with persistent ST-segment elevation. To date, there have been no reported cases. Case presentation: A 54-year-old male was admitted due to exertional angina. Three-vessel coronary artery disease was initially diagnosed based on Coronary angiography. The patient underwent a selective three coronary artery bypass graft (CABG) and extracted the tube the next day. But, on the second postoperative day, type I respiratory failure and type 5 myocardial infarction were diagnosed based on elevated ST segment, high-sensitivity troponin T and decreased P02 (55mmHg). The patient was given intravenous nitroglycerin and invasive mechanical ventilation. Despite these interventions, the patient's condition deteriorated on the third postoperative day. Cardiac tamponade was diagnosed based on clinical presentation and echocardiography. He underwent an emergency procedure for retrograde pericardial effusion drainage, which led to a rapid improvement in his clinical symptoms. Unfortunately, the patient subsequently developed acute hepatic and renal failure. A rethoracotomy was performed on the fifth postoperative day, which showed a large amount of blood clots on the pericardial surface and partial occlusion of the inferior vena cava reflux. The patient 's condition improved significantly and the elevated ST segment returned to normal after the surgery. On the tenth day after reoperation, laboratory tests such as liver function, renal function, and troponin T returned to normal. The patient was discharged fourteen days after the reoperation. Conclusion: Persistent ST-segment elevation as a rare but easily detectable clinical presentation of of pericardial local blood clots after CABG.
Development and validation of a nomogram for predicting preterm birth among pregnant women who had Antenatal care follow-up at University of Gondar Comprehensive Specialized Hospital using maternal and fetal characteristics: Retrospective follow-up study
Authors: Gessese Rewina Tilahun , Geremew Bisrat Misganaw , Nigatu Solomon Gedlu , Wubneh Solomon Berehe , Tesfie Tigabu Kidie 
Published: April 2024

Abstract

Background: - Preterm complications are the leading cause of death in children under the age of 5. Estimating the probability of a pregnant woman being at risk of preterm delivery would help to initiate preventive measures to reduce preterm delivery. The available risk prediction models used non-feasible predictors and did not consider fetal characteristics. This study aimed to develop an easily interpretable nomogram based on maternal and fetal characteristics.
Methods: - A retrospective follow-up study was conducted with a total of 1039 pregnant women who were enrolled from June 1, 2021, to June 1, 2022, at the University of Gondar Comprehensive Specialized Hospital. Stata version 17 was used for data analysis. Important predictors were selected by the least absolute shrinkage and selection operator and entered into multivariable logistic regression. Statistically and clinically significant predictors were used for the nomogram’s development. Model performance was assessed by the area under the receiver operating curve (AUROC) and calibration plot. Internal validation was done through the bootstrapping method, and decision curve analysis was performed to evaluate the clinical and public health impacts of the model
Result: - The incidence proportion of preterm birth among pregnant women was 14.15% (95%CI: 12.03, 16.27). Antepartum hemorrhage, preeclampsia, polyhydramnios, anemia, human immune virus, malpresentation, premature rupture of membrane, and diabetic mellitus were used to develop a nomogram. The nomogram had a discriminating power AUROC of 0.79 (95% CI: 0.74, 0.83) and 0.78 (95% CI: 0.73, 0.82) on the development and validation sets. The calibration plots exhibited optimal agreement between the predicted and observed values; the Hosmer-Lemeshow test yielded a P-value of 0.602. The decision curve analysis revealed that the nomogram would add net clinical benefits at threshold probabilities less than 0.8.
Conclusion: - The developed nomogram had good discriminative performance and good calibration. Using this model could help identify pregnant women at a higher risk of preterm delivery and provide interventions like corticosteroid and progesterone administration, cervical cerclage, and nutritional support.
Brain Tumor Identification using Transfer Learning with Sugeno-Fuzzy Integral
Authors: Govil Nikhil , Choudhary Shailee Lohmor , Dixit Rinku Sharma , Anand Saurabh , Rachapudi Venubabu , Patro S Gopal Krishna , N Channa Keshava Naik , Muzammil Khursheed , Islam Saiful , Khan Wahaj Ahmad 
Published: April 2024

Abstract
Brain tumor identification is essential in determining the cause and treatment of brain tumors, which are abnormal cell growths in the brain. The identification of brain cancers early and accurately is critical for prompt management and better patient outcomes. Significant advancement has been made in the invention of computer-aided detection systems that use sophisticated imaging methods and ML algorithms for automated brain tumor diagnosis in recent years. We provide a strategy for classifying brain tumor images into Pituitary, Glioma, and Meningioma tumors using a Sugeno fuzzy integral ensemble approach with three transfer learning approaches, namely ResNet-164, SqueezeNet, and DenseNet-201. In terms of accuracy, the proposed fuzzy ensemble strategies exceed each separate transfer learning approach. The proposed DenseNet-201 combined with SFI ensemble model has an accuracy rating of 99.19%. This framework was used to detect brain tumors in the current study, but it might potentially be built and used for medical imaging assessments of other illnesses. This solution improves the diagnostic process's efficiency and automation in the healthcare business, saving time and improving accuracy in brain tumor detection.
A Lightweight Deeplab V3+ Network Integrating Deep Transitive Transfer Learning and Attention Mechanism for Burned Area Identification
Authors: Liu Lizhi , Chen Erxue , Li Zengyuan , Guo Ying , Zhang Qiuliang , Wang Bing , Li Yu , Liu Yang 
Published: April 2024

Abstract
Complete and accurate burned area map data are needed to document spatial and temporal patterns of fires, to quantify their drivers, and to assess the impacts on human and natural systems. To achieve the the purpose of identifying burned area accurately and efficiency from remote sensing images, a lightweight deep learning model is proposed based on Deeplab V3+, which employs the combination of attention mechanism and deep transitive transfer learning (DTTL) strategy. The lightweight MobileNet V2 network integrated with Convolutional Block Attention Module (CBAM) is designed as the backbone network to replace the traditional time-consuming Xception of Deeplab V3+. The attention mechanism is introduced to enhance the recognition ability of the proposed deep learning model, and the deep transitive transfer learning strategy is adopted to solve the problem of incorrect identification of the burned area and discontinuous edge details caused by insufficient sample size during the extraction process. For the process of DTTL, the improved Deeplab V3 + network was first pre-trained on ImageNet. Sequentially, WorldView-2 and the Sentinel-2 dataset were employed to train the proposed network based on the ImageNet pre-trained weights. Experiments were conducted to extract burned area from remote sensing images based on the trained model, and the results show that the proposed methodology can improve extraction accuracy with OA of 92.97% and Kappa of 0.819, which is higher than the comparative methods, and it can reduce the training time at the same time. We applied this methodology to identify the burned area in Western Attica region of Greece, and a satisfactory result was achieved with. OA of 93.58% and Kappa of 0.8265. This study demonstrates the effectiveness of the improved Deeplab V3 + in identifying forest burned area. which can provide valuable information for forest protection and monitoring.
A Geomagnetic Estimate of Heliospheric Modulation Potential Over the Last 175 Years
Authors: Owens Mathew J. , Barnard Luke A. , Muscheler Raimund , Herbst Konstantin , Lockwood Mike , Usoskin Ilya , Asvestari Eleanna 
Published: April 2024

Abstract

Galactic cosmic rays (GCRs) interact with the Earth's atmosphere to produce energetic neutrons and cosmogenic radionuclides, such as 14C. The atmosphere is partially shielded from the interstellar GCR spectrum by both the geomagnetic and solar magnetic field. Solar shielding is often expressed as the heliospheric modulation potential, φ, which consolidates information about the strength and structure of the solar magnetic field into a single parameter. For the period 1951 to today, φ can be estimated from ground-based neutron monitor data. Prior to 1950, 14C in tree rings can be used to estimate φ, and hence the solar magnetic field, back centuries or millennia. Bridging the gap in the φ record is therefore of vital importance for long-term solar reconstructions. One method is to model φ using the sunspot number (SN) record. However, the SN record is only an indirect measure of the Sun's magnetic field, introducing uncertainty, and the record suffers from calibration issues. We here present a new reconstruction of φ based on geomagnetic data, which spans both the entire duration of the neutron monitor record and stretches back to 1845, providing a significant overlap with the 14C data. We first modify and test an existing model of φ based on a number of heliospheric parameters, namely the open solar flux, FS, the heliospheric current sheet tilt angle, α, and the global heliospheric magnetic polarity, p. This modified model is applied to recently updated geomagnetic estimates of FS and cyclic variations of α and p. This approach is shown to produce an annual φ estimate in excellent agreement with that obtained from neutron monitors over 1951-2023. It also suggests that ionisation chamber estimates of φ -- which have previously been used to extend the instrumental estimate back from 1951 to 1933 -- are not well calibrated. Comparison of the new geomagnetic φ with 14C estimates of φ suggests the long-term trend is overestimated in the most recent 14C data, possibly due to hemispheric differences in the Suess effect, related to the release of carbon by the burning of fossil fuels. We suggest that the new geomagnetic estimate of φ will provide an improved basis for future calibration of long-term solar activity reconstructions.
Synergistic Effect of Isomorphism Substitution of Double Metal Ce and Ti in Y Zeolite by Post-Synthesis Treatment for Efficient Removal of Dye Contamination
Authors: Moosavifar M. , Azarmehr M. , Fathyunes L. 
Published: April 2024

Abstract
The cationic interaction of binary metals in zeolite frameworks is a motivating field. Isomorphous substitution of Ti and Ce in the framework of NaY zeolite has been performed using post-synthesis treatment. Dealumination of the zeolite was performed by EDTA treatment, followed by the incorporation of Ce and Ti to decorate the metals in the zeolite framework. It was achieved in two steps based on their solubility in the corresponding solvents. The stability of the zeolite framework was studied using metal loading. It has been observed that as the amount of metals increased in the zeolite, the d spacing of the crystal lattice changed. Besides, at higher loading, certain metals are introduced into the framework, and others as exchangeable cations extra-framework in the zeolite network. The spectroscopic studies were used to characterize the modified zeolite, and the catalysts were studied for their catalytic activity in the photodegradation of 4-NP. Various parameters such as temperature, reaction time, pH, catalyst amount, and 4-NP concentration were studied to optimize the process. The synthesized catalysts show good activity for the photodegradation process. The study investigated the synergetic effect of Ce and Ti on the yield of photodegradation. The mineralization of 4-NP was confirmed by COD experiments with a removal efficiency of 68.76%. The catalyst was recoverable and maintained its catalytic activity after six runs. This finding is particularly significant as it suggests that the catalyst has the potential for industrial application and cost-effectiveness.
Social isolation emanated psychological exhaustion and disconnectedness among physicians during the COVID-19 pandemic: Results of a qualitative study in Pakistan
Authors: Rashid Muhammad Abo ul Hassan , Manzoor Malik Maliha , Fischer Florian 
Published: April 2024

Abstract
Background: During the COVID-19 pandemic, many healthcare providers remained on call in COVID-19 wards, isolation centres, and emergency departments, caring for both infected and non-infected patients. Physicians have faced various challenges during the pandemic, including the stress of working with limited resources, the physical burden of long work hours and fear of public abuse in terms of stigmatization. Therefore, this study focusses on social-psychological aspects of isolation period of physicians during the COVID-19 pandemic.
Methods: It is a qualitative study based on responses from 14 physicians working in two hospitals located in Islamabad, which is the capital city of Pakistan (Pakistan Institute of Medical Sciences [PIMS] and PolyclinicHospital). In-depth interviews were conducted with selected physicians by using an interview guide as a tool of data collection to explore the experiences of those physicians who suffered from COVID-19 and lived in isolation as their quarantine period. The data was analyzed by using thematic analysis technique.
Results: The study explored isolation experiences of physicians during the selected period of COVID-19 and significantly found that two major themes emerged, i.e. psychological exhaustion (depression, anxiety and loneliness were subthemes) and disconnectedness (relational detachment and lack of interaction were subthemes).
Conclusions: This pandemic crisis has significantly resulted in having adverse impact on the ability of physicians to approach both potential and existing patients. Particularly in times of such a crisis, adequate measures have to be taken to address mental health issues of the health workforce.
rtpcr: a package for statistical analysis of real-time PCR data in R
Authors: Mirzaghaderi Ghader 
Published: April 2024

Abstract
Background: Real-time polymerase chain reaction (real-time PCR), is widely used in biological research. Various analysis methods are employed on the real-time PCR data to measure the mRNA levels under different experimental conditions.
Results: ‘rtpcr’ package was developed for amplification efficiency calculation and statistical analysis of real-time PCR data in R. By accounting for up to two reference genes and amplification efficiency values, a general calculation methodology covering both the Livak and Pfaffl methods was used. Based on the experimental conditions, the functions of the ‘rtpcr’ package use a t-test (for experiments with a two-level factor) or analysis of variance (for cases where more than two levels or factors or a blocking factor exist) to calculate the fold change (FC) or relative expression (RE). The functions further provide standard deviations and confidence limits for means, apply statistical mean comparisons and present letter mean grouping. To facilitate function application, different data sets were used as examples and the outputs were explained. An outstanding feature of ‘rtpcr’ package is providing publication-ready bar plots with various controlling arguments for experiments with up to three different factors.
Conclusions:The ‘rtpcr’ is a useful and user-friendly package for analyzing real-time PCR data. It was built based on a general method with both calculational and graphical output capabilities.
Challenges Limiting Healthcare Data Utilization for Policy Decision-Making in Tanzania: A Qualitative Panel Discussion.
Authors: Chillo Omary , Kazyoba Paul , Giattas MaryRose , Mohamed Al-Mafazy , Magembe Grace , Kitali Erick 
Published: April 2024

Abstract
Background: Accurate and reliable healthcare data are essential for effective policy decision-making, resource allocation, and improved health outcomes. In Tanzania, healthcare data utilization remains low, with various obstacles limiting the effective use of health information for decision-making. This study aimed to identify and understand the key obstacles that limit healthcare data utilization for policy decision-making in Tanzania, based on a qualitative panel discussion.
Methods: A qualitative research approach was employed, focusing on a panel discussion with five experts in the field of health systems and Health Management Information Systems (HMIS) during the 8th Tanzania Health Summit. The panelists were purposively selected, representing diverse backgrounds and expertise in healthcare data utilization. Data were collected from the panelists' presentations and audience interaction, with 400 participants attending the session. A thematic analysis approach was used to identify the key obstacles limiting healthcare data utilization in Tanzania.
Results: The study identifies key obstacles limiting healthcare data utilization in Tanzania, which include unskilled professionals, multiple health information systems, poor data quality, competing donor priorities, poor communication, healthcare staff fatigue, and low working morale. These challenges hinder effective data-driven decision-making and healthcare service delivery. Addressing these obstacles requires a multifaceted approach involving collaboration among stakeholders, investment in capacity building, harmonization of health information systems, improved communication, and prioritization of healthcare worker well-being.
Conclusion: The findings of this study provide valuable insights into the challenges faced in healthcare data utilization for policy decision-making in Tanzania. Addressing these obstacles is critical for enhancing the capacity of healthcare professionals, policymakers, and other stakeholders to make informed decisions based on accurate, high-quality healthcare data. The study's results can serve as a foundation for targeted interventions and policy recommendations aimed at improving healthcare data utilization in Tanzania and in similar resource-limited settings.
Formalizing BPE Tokenization
Authors: Berglund Martin , Merwe Brink van der 
Published: April 2024

Abstract
In this paper, we formalize practical byte pair encoding tokenization as it is used in large language models and other NLP systems. There are subtle differences between implementations, so we in particular formally define and investigate the semantics of the SentencePiece and HuggingFace tokenizers, and how they relate to each other. These differences depend on the details of how the dictionary of tokenization rules is constructed. Beyond this, we consider how tokenization can be performed in an incremental fashion, as well as doing it left-to-right using an amount of memory constant in the length of the string, enabling e.g. using a finite state string-to-string transducer.
Genome-wide analysis of the BoBZR1 family genes and transcriptome analysis in Brassica oleracea
Authors: Ullah Muhammad Asad , Shafiq Muhammad , Wahid Abdul , Ahmad Shahab 
Published: April 2024

Abstract

The BRASSINAZOLE-RESISTANT 1 genes play a crucial role as key regulators in Brassinosteroid (BR) signaling, which affects various plant developmental and stress-responsive aspects. Understanding regulatory mechanisms via BZR1 in modulating target genes has become a main point in research on plant BR signaling networks. Despite this, the BZR1 functioning in B. oleracea remained poorly elucidated. A complete genome-wide analysis identified 12 BZR1 genes in B. oleracea, categorized into three groups based on their gene motif and structural features. These BoBZR1s were found on eight different chromosomes. Synteny analysis between B. oleracea, Arabidopsis, and potato provided perception into their evolutionary characteristics. Promoter regions of BoBZR1 family genes in B. oleracea have shown specific cis-elements associated with hormones, stress, and plant development. The expressions analysis toward cuticular wax synthesis has revealed various expression levels of all BoBZR1 genes from wild to mutant type. Differential expressions of BoBZR1 genes were observed for all seven different tested tissues. The whole study involved systematic characterization of the BoBZR1 family, and expression patterns, in BR signaling and its extensive involvement in developmental processes in B. oleracea. Results establish a theoretical foundation for deeper investigation of BoBZR1 structure and functions in B. oleracea, specifically toward regulating plant stress.
A System for Automatic Seamless Connection of Historical Cadastral Maps
Authors: Lenc Ladislav , Baloun Josef , Martínek Jiří , Král Pavel 
Published: April 2024

Abstract

This paper presents a way to create a complex system for the automatic connection of individual map sheets into one seamless map. The goal is to provide a seamlessly connected map and allow its online presentation. This way, the maps will be easily available and allow much more efficient work for historians, archivists, and the general public. We also detail a set of methods for processing and analysis of historical cadastral maps and evaluate and compare the methods among them. The processed maps are hand-drawn and bring many challenges for machine processing. We focus on the following tasks: nomenclature detection and recognition; map frame detection; landmark detection, and map area segmentation. The tasks are solved utilising a combination of traditional computer vision techniques and neural networks. The experimental section concentrates on the comparison of different methods and the selection of the best candidates for the final system. Moreover, we have created two annotated datasets that are used for the evaluation of the presented methods. The datasets are publicly available for research purposes.
Consumption and the permanent income of households
Authors: Frish Roni 
Published: April 2024

Abstract
This study examines the household consumption utilizing data from the Israeli Consumer Expenditure Surveys and longitudinal administrative income records for 2004–2016. Three key findings challenge the predictions of the Permanent Income Hypothesis (PIH) and the Life-Cycle Hypothesis (LCH): First, households with higher income growth (2004–2016) exhibited lower consumption levels in the earlier period (2004–2007) and higher consumption in the later period (2013–2016). Second, households tend to consume a significant portion of transitory incomes immediately. Third, households without a pension plan show a marked decrease in consumption upon retirement.