bluuebunny/crossref_metadata_2025_split huggingface dataset:
Community-Level Economic Distress, Race, and Risk of Adverse Outcomes After Heart Failure Hospitalization Among Medicare Beneficiaries
Authors: Mentias Amgad , Desai Milind Y. , Vaughan-Sarrazin Mary S. , Rao Shreya , Morris Alanna A. , Hall Jennifer L. , Menon Venu , Hockenberry Jason , Sims Mario , Fonarow Gregg C. , Girotra Saket , Pandey Ambarish 
Published: November 2021

Background:
Socioeconomic disadvantage is a strong determinant of adverse outcomes in patients with heart failure. However, the contribution of community-level economic distress to adverse outcomes in heart failure may differ across races and ethnicities.

Methods:
Patients of self-reported Black, White, and Hispanic race and ethnicity hospitalized with heart failure between 2014 and 2019 were identified from the Medicare MedPAR Part A 100% Files. We used patient-level residential ZIP code to quantify community-level economic distress on the basis of the Distressed Community Index (quintile 5: economically distressed versus quintiles 1–4: nondistressed). The association of continuous and categorical measures (distressed versus nondistressed) of Distressed Community Index with 30-day, 6-month, and 1-year risk-adjusted mortality, readmission burden, and home time were assessed separately by race and ethnicity groups.

Results:
The study included 1 611 586 White (13.2% economically distressed), 205 840 Black (50.6% economically distressed), and 89 199 Hispanic (27.3% economically distressed) patients. Among White patients, living in economically distressed (versus nondistressed) communities was significantly associated with a higher risk of adverse outcomes at 30-day and 1-year follow-up. Among Black and Hispanic patients, the risk of adverse outcomes associated with living in distressed versus nondistressed communities was not meaningfully different at 30 days and became more prominent by 1-year follow-up. Similarly, in the restricted cubic spline analysis, a stronger and more graded association was observed between Distressed Community Index score and risk of adverse outcomes in White patients (versus Black and Hispanic patients). Furthermore, the association between community-level economic distress and risk of adverse outcomes for Black patients differed in rural versus urban areas. Living in economically distressed communities was significantly associated with a higher risk of mortality and lower home time at 1-year follow-up in rural areas but not urban areas.

Conclusions:
The association between community-level economic distress and risk of adverse outcomes differs across race and ethnic groups, with a stronger association noted in White patients at short- and long-term follow-up. Among Black patients, the association of community-level economic distress with a higher risk of adverse outcomes is less evident in the short term and is more robust and significant in the long-term follow-up and rural areas.
Effects of Sleep on Language and Motor Consolidation: Evidence of Domain General and Specific Mechanisms
Authors: Ben-Zion Dafna , Gabitov Ella , Prior Anat , Bitan Tali 
Published: November 2021

Abstract
The current study explores the effects of time and sleep on the consolidation of a novel language learning task containing both item-specific knowledge and the extraction of grammatical regularities. We also compare consolidation effects in language and motor sequence learning tasks, to ask whether consolidation mechanisms are domain general. Young adults learned to apply plural inflections to novel words based on morphophonological rules embedded in the input, and learned to type a motor sequence using a keyboard. Participants were randomly assigned into one of two groups, practicing each task during either the morning or evening hours. Both groups were retested 12 and 24 hours post-training. Performance on frequent trained items in the language task stabilized only following sleep, consistent with a hippocampal mechanism for item-specific learning. However, regularity extraction, indicated by generalization to untrained items in the linguistic task, as well as performance on motor sequence learning, improved 24 hours post-training, irrespective of the timing of sleep. This consolidation process is consistent with a frontostriatal skill-learning mechanism, common across the language and motor domains. This conclusion is further reinforced by cross-domain correlations at the individual level between improvement across 24 hours in the motor task and in the low-frequency trained items in the linguistic task, which involve regularity extraction. Taken together, our results at the group and individual levels suggest that some aspects of consolidation are shared across the motor and language domains, and more specifically, between motor sequence learning and grammar learning.
Data citation and the citation graph
Authors: Buneman Peter , Dosso Dennis , Lissandrini Matteo , Silvello Gianmaria 
Published: November 2021

Abstract
The citation graph is a computational artifact that is widely used to represent the domain of published literature. It represents connections between published works, such as citations and authorship. Among other things, the graph supports the computation of bibliometric measures such as h-indexes and impact factors. There is now an increasing demand that we should treat the publication of data in the same way that we treat conventional publications. In particular, we should cite data for the same reasons that we cite other publications. In this paper we discuss what is needed for the citation graph to represent data citation. We identify two challenges: to model the evolution of credit appropriately (through references) over time and to model data citation not only to a data set treated as a single object but also to parts of it. We describe an extension of the current citation graph model that addresses these challenges. It is built on two central concepts: citable units and reference subsumption. We discuss how this extension would enable data citation to be represented within the citation graph and how it allows for improvements in current practices for bibliometric computations, both for scientific publications and for data.
Lead Groups in EU Foreign Policy: The Cases of Iran and Ukraine
Authors: Alcaro Riccardo , Siddi Marco 
Published: November 2021

Abstract
Since the creation of the EU, there have been instances in which a restricted number of member states has handled an issue of international security on behalf of the Union. This article argues that, while controversial, these ‘lead groups’ have been a valuable practice. They have been effective in generating intra-EU consensus on specific issues and spurring the EU into action, thereby enabling a European response in the context of conflict management and complex international negotiations. Lead groups are sub-optimal arrangements compensating for the in-built institutional shortcomings of unanimity-based decision-making in EU foreign policy. As such, they do not bring integration further. They have nonetheless shown significant potential in giving initiative and content to EU foreign policy. This is shown through the analysis of two case studies, the Anglo-Franco-German trio involved in Iran’s nuclear issue and the Franco-German duo brokering a truce between Russia and Ukraine.
How I manage infection risk and prevention in patients with lymphoid cancer
Authors: Law Nancy , Taplitz Randy A. 
Published: November 2021

Abstract
Infections are a common cause of morbidity and mortality in patients with lymphoid cancer. Because cancer therapeutics, including new targeted therapies and immunotherapies, are evolving, clinicians need to be aware of additional risk factors and infections that may arise in patients treated with these agents. This article highlights fundamental issues in treating patients with lymphoid cancer, including risk factors for infection, screening for infectious diseases, and recommendations for antimicrobial prophylaxis in patients with lymphoid cancers. We present 4 scenarios of patients with lymphoid cancers who have various infections, and we describe a treatment approach based on a combination of evidence-based data and experience because objective data are limited regarding infections, especially with newer agents. The goal of this discussion is to provide a framework for institutions and health care providers to help them develop their own approach to preventing and treating infections in patients with lymphoid cancer.
Classification of age-related macular degeneration using convolutional-neural-network-based transfer learning
Authors: Chen Yao-Mei , Huang Wei-Tai , Ho Wen-Hsien , Tsai Jinn-Tsong 
Published: November 2021

Abstract
Background
To diagnose key pathologies of age-related macular degeneration (AMD) and diabetic macular edema (DME) quickly and accurately, researchers attempted to develop effective artificial intelligence methods by using medical images.

Results
A convolutional neural network (CNN) with transfer learning capability is proposed and appropriate hyperparameters are selected for classifying optical coherence tomography (OCT) images of AMD and DME. To perform transfer learning, a pre-trained CNN model is used as the starting point for a new CNN model for solving related problems. The hyperparameters (parameters that have set values before the learning process begins) in this study were algorithm hyperparameters that affect learning speed and quality. During training, different CNN-based models require different algorithm hyperparameters (e.g., optimizer, learning rate, and mini-batch size). Experiments showed that, after transfer learning, the CNN models (8-layer Alexnet, 22-layer Googlenet, 16-layer VGG, 19-layer VGG, 18-layer Resnet, 50-layer Resnet, and a 101-layer Resnet) successfully classified OCT images of AMD and DME.

Conclusions
The experimental results further showed that, after transfer learning, the VGG19, Resnet101, and Resnet50 models with appropriate algorithm hyperparameters had excellent capability and performance in classifying OCT images of AMD and DME.
General deep learning model for detecting diabetic retinopathy
Authors: Chen Ping-Nan , Lee Chia-Chiang , Liang Chang-Min , Pao Shu-I , Huang Ke-Hao , Lin Ke-Feng 
Published: November 2021

Abstract
Background
Doctors can detect symptoms of diabetic retinopathy (DR) early by using retinal ophthalmoscopy, and they can improve diagnostic efficiency with the assistance of deep learning to select treatments and support personnel workflow. Conventionally, most deep learning methods for DR diagnosis categorize retinal ophthalmoscopy images into training and validation data sets according to the 80/20 rule, and they use the synthetic minority oversampling technique (SMOTE) in data processing (e.g., rotating, scaling, and translating training images) to increase the number of training samples. Oversampling training may lead to overfitting of the training model. Therefore, untrained or unverified images can yield erroneous predictions. Although the accuracy of prediction results is 90%–99%, this overfitting of training data may distort training module variables.

Results
This study uses a 2-stage training method to solve the overfitting problem. In the training phase, to build the model, the Learning module 1 used to identify the DR and no-DR. The Learning module 2 on SMOTE synthetic datasets to identify the mild-NPDR, moderate NPDR, severe NPDR and proliferative DR classification. These two modules also used early stopping and data dividing methods to reduce overfitting by oversampling. In the test phase, we use the DIARETDB0, DIARETDB1, eOphtha, MESSIDOR, and DRIVE datasets to evaluate the performance of the training network. The prediction accuracy achieved to 85.38%, 84.27%, 85.75%, 86.73%, and 92.5%.

Conclusions
Based on the experiment, a general deep learning model for detecting DR was developed, and it could be used with all DR databases. We provided a simple method of addressing the imbalance of DR databases, and this method can be used with other medical images.
Epidemic prediction of dengue fever based on vector compartment model and Markov chain Monte Carlo method
Authors: Lee Chien-Hung , Chang Ko , Chen Yao-Mei , Tsai Jinn-Tsong , Chen Yenming J. , Ho Wen-Hsien 
Published: November 2021

Abstract
Background
Dengue epidemics is affected by vector-human interactive dynamics. Infectious disease prevention and control emphasize the timing intervention at the right diffusion phase. In such a way, control measures can be cost-effective, and epidemic incidents can be controlled before devastated consequence occurs. However, timing relations between a measurable signal and the onset of the pandemic are complex to be discovered, and the typical lag period regression is difficult to capture in these complex relations. This study investigates the dynamic diffusion pattern of the disease in terms of a probability distribution. We estimate the parameters of an epidemic compartment model with the cross-infection of patients and mosquitoes in various infection cycles. We comprehensively study the incorporated meteorological and mosquito factors that may affect the epidemic of dengue fever to predict dengue fever epidemics.

Results
We develop a dual-parameter estimation algorithm for a composite model of the partial differential equations for vector-susceptible-infectious-recovered with exogeneity compartment model, Markov chain Montel Carlo method, and boundary element method to evaluate the epidemic periodicity under the effect of environmental factors of dengue fever, given the time series data of 2000–2016 from three cities with a population of 4.7 million. The established computer model of “energy accumulation-delayed diffusion-epidemics” is proven to be effective to predict the future trend of reported and unreported infected incidents. Our artificial intelligent algorithm can inform the authority to cease the larvae at the highest vector infection time. We find that the estimated dengue report rate is about 20%, which is close to the number of official announcements, and the percentage of infected vectors increases exponentially yearly. We suggest that the executive authorities should seriously consider the accumulated effect among infected populations. This established epidemic prediction model of dengue fever can be used to simulate and evaluate the best time to prevent and control dengue fever.

Conclusions
Given our developed model, government epidemic prevention teams can apply this platform before they physically carry out the prevention work. The optimal suggestions from these models can be promptly accommodated when real-time data have been continuously corrected from clinics and related agents.
Classifying chest CT images as COVID-19 positive/negative using a convolutional neural network ensemble model and uniform experimental design method
Authors: Chen Yao-Mei , Chen Yenming J. , Ho Wen-Hsien , Tsai Jinn-Tsong 
Published: November 2021

AbstractBackgroundTo classify chest computed tomography (CT) images as positive or negative for coronavirus disease 2019 (COVID-19) quickly and accurately, researchers attempted to develop effective models by using medical images.ResultsA convolutional neural network (CNN) ensemble model was developed for classifying chest CT images as positive or negative for COVID-19. To classify chest CT images acquired from COVID-19 patients, the proposed COVID19-CNN ensemble model combines the use of multiple trained CNN models with a majority voting strategy. The CNN models were trained to classify chest CT images by transfer learning from well-known pre-trained CNN models and by applying their algorithm hyperparameters as appropriate. The combination of algorithm hyperparameters for a pre-trained CNN model was determined by uniform experimental design. The chest CT images (405 from COVID-19 patients and 397 from healthy patients) used for training and performance testing of the COVID19-CNN ensemble model were obtained from an earlier study by Hu in 2020. Experiments showed that, the COVID19-CNN ensemble model achieved 96.7% accuracy in classifying CT images as COVID-19 positive or negative, which was superior to the accuracies obtained by the individual trained CNN models. Other performance measures (i.e., precision, recall, specificity, and F1-score) obtained bythe COVID19-CNN ensemble model were higher than those obtained by individual trained CNN models.ConclusionsThe COVID19-CNN ensemble model had superior accuracy and excellent capability in classifying chest CT images as COVID-19 positive or negative.
VPMBench: a test bench for variant prioritization methods
Authors: Ruscheinski Andreas , Reimler Anna Lena , Ewald Roland , Uhrmacher Adelinde M. 
Published: November 2021

Abstract
Background
Clinical diagnostics of whole-exome and whole-genome sequencing data requires geneticists to consider thousands of genetic variants for each patient. Various variant prioritization methods have been developed over the last years to aid clinicians in identifying variants that are likely disease-causing. Each time a new method is developed, its effectiveness must be evaluated and compared to other approaches based on the most recently available evaluation data. Doing so in an unbiased, systematic, and replicable manner requires significant effort.

Results
The open-source test bench “VPMBench” automates the evaluation of variant prioritization methods. VPMBench introduces a standardized interface for prioritization methods and provides a plugin system that makes it easy to evaluate new methods. It supports different input data formats and custom output data preparation. VPMBench exploits declaratively specified information about the methods, e.g., the variants supported by the methods. Plugins may also be provided in a technology-agnostic manner via containerization.

Conclusions
VPMBench significantly simplifies the evaluation of both custom and published variant prioritization methods. As we expect variant prioritization methods to become ever more critical with the advent of whole-genome sequencing in clinical diagnostics, such tool support is crucial to facilitate methodological research.
Genome-wide survey and identification of AP2/ERF genes involved in shoot and leaf development in Liriodendron chinense
Authors: Zong Yaxian , Hao Ziyuan , Tu Zhonghua , Shen Yufang , Zhang Chengge , Wen Shaoying , Yang Lichun , Ma Jikai , Li Huogen 
Published: November 2021

Abstract
Background
Liriodendron chinense is a distinctive ornamental tree species due to its unique leaves and tulip-like flowers. The discovery of genes involved in leaf development and morphogenesis is critical for uncovering the underlying genetic basis of these traits. Genes in the AP2/ERF family are recognized as plant-specific transcription factors that contribute to plant growth, hormone-induced development, ethylene response factors, and stress responses.

Results
In this study, we identified 104 putative AP2/ERF genes in the recently released L. chinense genome and transcriptome database. In addition, all 104 genes were grouped into four subfamilies, the AP2, ERF, RAV, and Soloist subfamilies. This classification was further supported by the results of gene structure and conserved motif analyses. Intriguingly, after application of a series test of cluster analysis, three AP2 genes, LcERF 94, LcERF 96, and LcERF 98, were identified as tissue-specific in buds based on the expression profiles of various tissues. These results were further validated via RT-qPCR assays and were highly consistent with the STC analysis. We further investigated the dynamic changes of immature leaves by dissecting fresh shoots into seven discontinuous periods, which were empirically identified as shoot apical meristem (SAM), leaf primordia and tender leaf developmental stages according to the anatomic structure. Subsequently, these three candidates were highly expressed in SAM and leaf primordia but rarely in tender leaves, indicating that they were mainly involved in early leaf development and morphogenesis. Moreover, these three genes displayed nuclear subcellular localizations through the transient transformation of tobacco epidermal cells.

Conclusions
Overall, we identified 104 AP2/ERF family members at the genome-wide level and discerned three candidate genes that might participate in the development and morphogenesis of the leaf primordium in L. chinense.
Effect of n-3 polyunsaturated fatty acids on ischemic heart disease and cardiometabolic risk factors: a two-sample Mendelian randomization study
Authors: Xu Bayi , Xu Zhixia , Xu Duanmin , Tan Xuerui 
Published: November 2021

Abstract
Background
The cardioprotective ability of n-3 polyunsaturated fatty acids (PUFAs) is controversial. Most studies suggest a specific role for PUFAs in cardioprotection from ischemic heart disease (IHD). However, few studies have used genetic biomarkers of n-3 PUFAs to examine their potential relationships with IHD. This study aimed to use Mendelian randomization to evaluate whether genetically-predicted n-3 PUFAs affect IHD and cardiometabolic risk factors (CRFs).

Methods
Genetic variants strongly (p < 5 × 10–8) and independently (r2 > 0.1) associated with n-3 PUFAs were derived from the CHARGE Consortium (including 8,866 subjects of European ancestry) and were used as instrumental variables (IVs) for evaluating the effect of n-3 PUFAs, including α-linolenic acid (ALA), docosapentaenoic acid (DPA), docosahexaenoic acid (DHA), and eicosapentaenoic acid (EPA). Data on the associations between the IVs and IHD, myocardial infarction, and CRFs (including diabetes, lipids, blood pressure, body mass index, and waist-to-hip ratio (WHR)) were obtained from the UK Biobank SOFT CAD GWAS with the CARDIoGRAMplusC4D 1000 Genomes-based GWAS (113,937 IHD cases and 339,115 controls), the Myocardial Infarction Genetics and CARDIoGRAM Exome consortia (42,335 MI cases and 78,240 controls), the DIAbetes Genetics Replication And Meta-analysis consortium (26,676 diabetes mellitus cases and 132,532 controls), the Global Lipids Genetics Consortium (n = 196,475), the International Consortium for Blood Pressure (n = 69,395), and the meta-analysis of GWAS for body fat distribution in the UK Biobank and Genetic Investigation of Anthropometric Traits (n = 694,649).

Results
Genetically-predicted higher ALA was associated with lower risk of IHD, type 2 diabetes (T2D), and lower serum lipids. The effect size per 0.05-unit increase (about 1 standard deviation) in plasma ALA level) was − 1.173 (95% confidence interval − 2.214 to − 0.133) for IHD. DPA and EPA had no association with IHD but were associated with a higher risk of T2D, higher levels of lipids or WHR. DHA had no association with IHD or CRFs.

Conclusions
Our study suggests a benefit of ALA for IHD and its main risk factors. DHA, DPA, and EPA had no association with IHD but were partly associated with increasing cardiometabolic risk factors.
An alternative splicing signature in human Crohn’s disease
Authors: Li Daowei , Liang Yuanzi , Lu Jia , Tan Yue 
Published: November 2021

Abstract
Background
Although hundreds of risk loci for Crohn’s disease (CD) have been identified, the underlying pathogenesis of CD remains unclear. Recently, evidence has shown that aberrant gene expression in colon tissues of CD patients is associated with the progression of CD. We reasoned that post-transcriptional regulation, especially alternative splicing (AS), may also play important roles in the pathogenesis of CD.

Methods
We re-analyzed public mRNA-seq data from the NCBI GEO dataset (GSE66207) and identified approximately 3000 unique AS events in CD patients compared to healthy controls.

Results
“Lysine degradation” and “Sphingolipid metabolism” were the two most enriched AS events in CD patients. In a validation study, we also sequenced eight subjects and demonstrated that key genes that were previously linked to CD, such as IRF1 and STAT3, also had significant AS events in CD.

Conclusion
Our study provided a landscape of AS events in CD, especially as the first study focused on a Chinese cohort. Our data suggest that dysregulation of AS may be a new mechanism that contributes to the pathogenesis of CD.