bluuebunny/crossref_metadata_2025_split huggingface dataset:
Isolated Lateral Chronic Exertional Compartment Syndrome of the Leg: A New Entity?
Authors: van Zantvoort Antonia P.M. , Hundscheid Henricus P.H. , de Bruijn Johan A. , Hoogeveen Adwin R. , Teijink Joep A.W. , Scheltinga Marc R.M. 
Published: December 2019

Background: Chronic exertional compartment syndrome (CECS) mostly occurs in the anterior or deep posterior compartments (ant-CECS and dp-CECS, respectively) of the leg. It is generally accepted that CECS of the third or lateral compartment (lat-CECS) always occurs together with ant-CECS. However, whether exertional leg pain (ELP) can be caused by an isolated form of lat-CECS is unknown. Purpose: To determine the existence of isolated lat-CECS and study whether history taking and a physical examination aid in discriminating between different subtypes of CECS. Study Design: Case series; Level of evidence, 4. Methods: Patients were eligible for this single-center study, conducted between January 2013 and February 2018, if they reported anterolateral ELP and completed a questionnaire scoring the frequency and intensity of pain, tightness, cramps, muscle weakness, and paresthesia during rest and exercise. They were asked to mark areas of altered foot skin sensation, if present, on a drawing. All patients underwent a dynamic intracompartmental pressure (ICP) measurement of the anterior and lateral compartments simultaneously. The diagnosis of CECS was confirmed by elevated ICP (Pedowitz criteria). There were 3 patient groups: (1) isolated ant-CECS with elevated ICP in the anterior compartment and normal ICP in the lateral compartment, (2) isolated lat-CECS with elevated ICP in the lateral compartment but normal ICP in the anterior compartment, and (3) ant-/lat-CECS with elevated ICP in both the anterior and lateral compartments. Results: A total of 73 patients with anterolateral ELP fulfilled study criteria (isolated ant-CECS: n = 26; isolated lat-CECS: n = 5; ant-/lat-CECS: n = 42). Group differences were not observed regarding age (isolated ant-CECS: median, 26 years [range, 13-68 years]; isolated lat-CECS: median, 20 years [range, 17-63 years]; ant-/lat-CECS: median, 28 years [range, 17-57 years]; χ2 (2) = 0.466; P = .79), sex (isolated ant-CECS: 50% male; isolated lat-CECS: 40% male; ant-/lat-CECS: 62% male; P = .49), or bilateral symptoms (isolated ant-CECS: 54%; isolated lat-CECS: 80%; ant-/lat-CECS: 69%; P = .40). However, cramps at rest were present in a portion of the patients with isolated ant-CECS (38%) and ant-/lat-CECS (57%) but not in those with isolated lat-CECS ( P = .032). Patient drawings of altered foot skin sensation did not contribute to the diagnosis ( P = .92). ICP values after provocation were all lower in patients with isolated ant-CECS and isolated lat-CECS compared with those with ant-/lat-CECS ( P < .05). Conclusion: Seven percent of patients with CECS and anterolateral ELP who had symptoms due to isolated lat-CECS in the presence of normal muscle pressure in the anterior compartment.
Recurrent Anterior Cruciate Ligament Tears in the National Football League: A Case-Control Study
Authors: Stuhlman Casey R. , Owens Christopher J. , Samuelson Eric M. , Vermillion Ryan P. , Shermansky Mark D. , King Kevin B. , Connor Patrick M. 
Published: December 2019

Background: Anterior cruciate ligament (ACL) injuries are common in American football players. The risk of subsequent ACL reinjury to either the ipsilateral or the contralateral knee in National Football League (NFL) draftees with a history of successful ACL reconstruction before entering the NFL remains unknown. Hypothesis: NFL athletes with a history of successful ACL reconstruction before being drafted will likely demonstrate increased risk of subsequent ACL injury when compared with a control cohort consisting of players of similar positions and draft class. Study Design: Cohort study; Level of evidence, 3. Methods: Detailed orthopaedic evaluations of NFL Combine participants from 2006 to 2012 were obtained to identify players entering the NFL draft with a history of successful ACL reconstruction. A control cohort was created in a 2:1 ratio, consisting of players matched by position and draft class. Results: Of the 2016 players invited to the NFL Combine during the study period, 100 met the inclusion criteria. A total of 26 subsequent ACL reinjuries (12 ipsilateral, 14 contralateral) occurred in 25 players (25%) while playing in the NFL, with injuries occurring at a mean of 22.1 months after the NFL draft. In comparison, 18 of the 200 (9%) carefully matched cohort players without history of prior ACL injury sustained a new ACL injury during this time period ( P < .001). Conclusion: NFL athletes with a history of successful ACL reconstruction before being drafted into the NFL have a significantly higher rate of subsequent ACL reinjury while playing in the NFL when compared with a carefully matched cohort of players without a history of prior ACL injury.
Investigating a Quantitative Measure of Student Self-authorship for Undergraduate Medical Education
Authors: Fallar Robert , Hanss Basil , Sefcik Roberta , Goodson Lucy , Kase Nathan , Katz Craig 
Published: December 2019

Construct: In this study, the authors investigated the validity of a quantitative measure of self-authorship among medical students. Self-authorship is a cognitive-structural theory incorporating the ability to define one’s beliefs, identity, and social relations to operate in a complex, ambiguous environment. Background: Competency-based medical education (CBME) provides learners with the opportunity to self-direct their education at an appropriate pace to develop and exhibit required behaviors while incorporating functioning relationships with supervisors and trainers. Students must develop skills to adjust and succeed in this educational climate. Self-authorship is a theoretical lens that is relevant to identifying the development of the skills necessary to succeed in a CBME curriculum. Understanding the level of attained self-authorship by medical students can provide important information about which professional characteristics are more prevalent among those who are more self-authored and about how students succeed in medical school. Although there are calls in the extant literature for the application of self-authorship in medical education, there is no quantitative measure to assess its development among medical students. Approach: The authors developed a survey to measure self-authorship, including a free text question regarding the thought process around a hypothetical ethical situation during training. Data were collected in 2014 and 2015 from undergraduate medical students and analyzed using factor analysis and qualitative analysis of the free text. Validity evidence was sought regarding content, internal structure, and relationships to other variables. Results: Analysis supports the use of a 22-item instrument to assess 3 constructs of self-authorship: asserting independence and autonomy, knowledge processing, and sense of self in ethical situations. Content analysis of text responses supported the ability of the instrument to separate development, or a lack thereof, of self-authorship. Conclusions: The authors identified an instrument that measures multidimensional, higher-order characteristics that intersect with self-authorship. This instrument can be useful at a macro level for curricular and student assessment of self-authorship. Development of these characteristics can help foster success in a CBME environment and support curricular efforts in this regard. Understanding a student’s level of self-authorship can help identify areas for support as well as allow for comparisons of different student characteristics.
Dorsal Hippocampus ERK2 Signaling Mediates Anxiolytic-Related Behavior in Male Rats
Authors: Sierra-Fonseca Jorge A. , Parise Lyonna F. , Flores-Ramirez Francisco J. , Robles Eden H. , Garcia-Carachure Israel , Iñiguez Sergio D. 
Published: December 2019

Background Anxiety disorders are the most common neuropathologies worldwide, but the precise neuronal mechanisms that underlie these disorders remain unknown. The hippocampus plays a role in mediating anxiety-related responses, which can be modeled in rodents using behavioral assays, such as the elevated plus maze. Yet, the molecular markers that underlie affect-related behavior on the elevated plus maze are not well understood. Methods We used herpes simplex virus vector delivery to overexpress extracellular signal-regulated kinase-2, a signaling molecule known to be involved in depression and anxiety, within the dorsal hippocampus of adult Sprague-Dawley male rats. Three days post virus delivery, we assessed anxiety-like responses on the elevated plus maze or general locomotor activity on the open field test. Results When compared to controls, rats overexpressing extracellular signal-regulated kinase-2 in the dorsal hippocampus displayed an anxiolytic-like phenotype, per increases in time spent in the open arms, and less time in the closed arms, of the elevated plus maze. Furthermore, no changes in locomotor activity as a function of virus infusion were observed on the open field test between the experimental groups. Conclusion This investigation demonstrates that virus-mediated increases of extracellular signal-regulated kinase-2 signaling, within the hippocampus, plays a critical role in decreasing anxiogenic responses on the rat elevated plus maze. As such, our data provide construct validity, at least in part, to the molecular mechanisms that mediate anxiolytic-like behavior in rodent models for the study of anxiety.
GPRED-GC: a Gene PREDiction model accounting for 5 ′- 3′ GC gradient
Authors: Techa-Angkoon Prapaporn , Childs Kevin L. , Sun Yanni 
Published: December 2019

Abstract
Background
Gene is a key step in genome annotation. Ab initio gene prediction enables gene annotation of new genomes regardless of availability of homologous sequences. There exist a number of ab initio gene prediction tools and they have been widely used for gene annotation for various species. However, existing tools are not optimized for identifying genes with highly variable GC content. In addition, some genes in grass genomes exhibit a sharp 5 ′- 3′ decreasing GC content gradient, which is not carefully modeled by available gene prediction tools. Thus, there is still room to improve the sensitivity and accuracy for predicting genes with GC gradients.

Results
In this work, we designed and implemented a new hidden Markov model (HMM)-based ab initio gene prediction tool, which is optimized for finding genes with highly variable GC contents, such as the genes with negative GC gradients in grass genomes. We tested the tool on three datasets from Arabidopsis thaliana and Oryza sativa. The results showed that our tool can identify genes missed by existing tools due to the highly variable GC contents.

Conclusions
GPRED-GC can effectively predict genes with highly variable GC contents without manual intervention. It provides a useful complementary tool to existing ones such as Augustus for more sensitive gene discovery. The source code is freely available at https://sourceforge.net/projects/gpred-gc/.
Identifying protein-protein interface via a novel multi-scale local sequence and structural representation
Authors: Guo Fei , Zou Quan , Yang Guang , Wang Dan , Tang Jijun , Xu Junhai 
Published: December 2019

Abstract
Background
Protein-protein interaction plays a key role in a multitude of biological processes, such as signal transduction, de novo drug design, immune responses, and enzymatic activities. Gaining insights of various binding abilities can deepen our understanding of the interaction. It is of great interest to understand how proteins in a complex interact with each other. Many efficient methods have been developed for identifying protein-protein interface.

Results
In this paper, we obtain the local information on protein-protein interface, through multi-scale local average block and hexagon structure construction. Given a pair of proteins, we use a trained support vector regression (SVR) model to select best configurations. On Benchmark v4.0, our method achieves average Irmsd value of 3.28Å and overall Fnat value of 63%, which improves upon Irmsd of 3.89Å and Fnat of 49% for ZRANK, and Irmsd of 3.99Å and Fnat of 46% for ClusPro. On CAPRI targets, our method achieves average Irmsd value of 3.45Å and overall Fnat value of 46%, which improves upon Irmsd of 4.18Å and Fnat of 40% for ZRANK, and Irmsd of 5.12Å and Fnat of 32% for ClusPro. The success rates by our method, FRODOCK 2.0, InterEvDock and SnapDock on Benchmark v4.0 are 41.5%, 29.0%, 29.4% and 37.0%, respectively.

Conclusion
Experiments show that our method performs better than some state-of-the-art methods, based on the prediction quality improved in terms of CAPRI evaluation criteria. All these results demonstrate that our method is a valuable technological tool for identifying protein-protein interface.
Mild cognitive impairment understanding: an empirical study by data-driven approach
Authors: Liu Liyuan , Yu Bingchen , Han Meng , Yuan Shanshan , Wang Na 
Published: December 2019

Abstract
Background
Cognitive decline has emerged as a significant threat to both public health and personal welfare, and mild cognitive decline/impairment (MCI) can further develop into Dementia/Alzheimer’s disease. While treatment of Dementia/Alzheimer’s disease can be expensive and ineffective sometimes, the prevention of MCI by identifying modifiable risk factors is a complementary and effective strategy.

Results
In this study, based on the data collected by Centers for Disease Control and Prevention (CDC) through the nationwide telephone survey, we apply a data-driven approach to re-exam the previously founded risk factors and discover new risk factors. We found that depression, physical health, cigarette usage, education level, and sleep time play an important role in cognitive decline, which is consistent with the previous discovery. Besides that, the first time, we point out that other factors such as arthritis, pulmonary disease, stroke, asthma, marital status also contribute to MCI risk, which is less exploited previously. We also incorporate some machine learning and deep learning algorithms to weigh the importance of various factors contributed to MCI and predicted cognitive declined.

Conclusion
By incorporating the data-driven approach, we can determine that risk factors significantly correlated with diseases. These correlations could also be expanded to another medical diagnosis besides MCI.
DDIGIP: predicting drug-drug interactions based on Gaussian interaction profile kernels
Authors: Yan Cheng , Duan Guihua , Pan Yi , Wu Fang-Xiang , Wang Jianxin 
Published: December 2019

AbstractBackgroundA drug-drug interaction (DDI) is defined as a drug effect modified by another drug, which is very common in treating complex diseases such as cancer. Many studies have evidenced that some DDIs could be an increase or a decrease of the drug effect. However, the adverse DDIs maybe result in severe morbidity and even morality of patients, which also cause some drugs to withdraw from the market. As the multi-drug treatment becomes more and more common, identifying the potential DDIs has become the key issue in drug development and disease treatment. However, traditional biological experimental methods, including in vitro and vivo, are very time-consuming and expensive to validate new DDIs. With the development of high-throughput sequencing technology, many pharmaceutical studies and various bioinformatics data provide unprecedented opportunities to study DDIs.ResultIn this study, we propose a method to predict new DDIs, namely DDIGIP, which is based on Gaussian Interaction Profile (GIP) kernel on the drug-drug interaction profiles and the Regularized Least Squares (RLS) classifier. In addition, we also use the k-nearest neighbors (KNN) to calculate the initial relational score in the presence of new drugs via the chemical, biological, phenotypic data of drugs. We compare the prediction performance of DDIGIP with other competing methods via the 5-fold cross validation, 10-cross validation and de novo drug validation.ConlusionIn 5-fold cross validation and 10-cross validation, DDRGIP method achieves the area under the ROC curve (AUC) of 0.9600 and 0.9636 which are better than state-of-the-art method (L1 Classifier ensemble method) of 0.9570 and 0.9599. Furthermore, for new drugs, the AUC value of DDIGIP in de novo drug validation reaches 0.9262 which also outperforms the other state-of-the-art method (Weighted average ensemble method) of 0.9073. Case studies and these results demonstrate that DDRGIP is an effective method to predict DDIs while being beneficial to drug development and disease treatment.
Drug repositioning based on individual bi-random walks on a heterogeneous network
Authors: Wang Yuehui , Guo Maozu , Ren Yazhou , Jia Lianyin , Yu Guoxian 
Published: December 2019

Abstract
Background
Traditional drug research and development is high cost, time-consuming and risky. Computationally identifying new indications for existing drugs, referred as drug repositioning, greatly reduces the cost and attracts ever-increasing research interests. Many network-based methods have been proposed for drug repositioning and most of them apply random walk on a heterogeneous network consisted with disease and drug nodes. However, these methods generally adopt the same walk-length for all nodes, and ignore the different contributions of different nodes.

Results
In this study, we propose a drug repositioning approach based on individual bi-random walks (DR-IBRW) on the heterogeneous network. DR-IBRW firstly quantifies the individual work-length of random walks for each node based on the network topology and knowledge that similar drugs tend to be associated with similar diseases. To account for the inner structural difference of the heterogeneous network, it performs bi-random walks with the quantified walk-lengths, and thus to identify new indications for approved drugs. Empirical study on public datasets shows that DR-IBRW achieves a much better drug repositioning performance than other related competitive methods.

Conclusions
Using individual random walk-lengths for different nodes of heterogeneous network indeed boosts the repositioning performance. DR-IBRW can be easily generalized to prioritize links between nodes of a network.
Autoencoder-based cluster ensembles for single-cell RNA-seq data analysis
Authors: Geddes Thomas A. , Kim Taiyun , Nan Lihao , Burchfield James G. , Yang Jean Y. H. , Tao Dacheng , Yang Pengyi 
Published: December 2019

Abstract
Background
Single-cell RNA-sequencing (scRNA-seq) is a transformative technology, allowing global transcriptomes of individual cells to be profiled with high accuracy. An essential task in scRNA-seq data analysis is the identification of cell types from complex samples or tissues profiled in an experiment. To this end, clustering has become a key computational technique for grouping cells based on their transcriptome profiles, enabling subsequent cell type identification from each cluster of cells. Due to the high feature-dimensionality of the transcriptome (i.e. the large number of measured genes in each cell) and because only a small fraction of genes are cell type-specific and therefore informative for generating cell type-specific clusters, clustering directly on the original feature/gene dimension may lead to uninformative clusters and hinder correct cell type identification.

Results
Here, we propose an autoencoder-based cluster ensemble framework in which we first take random subspace projections from the data, then compress each random projection to a low-dimensional space using an autoencoder artificial neural network, and finally apply ensemble clustering across all encoded datasets to generate clusters of cells. We employ four evaluation metrics to benchmark clustering performance and our experiments demonstrate that the proposed autoencoder-based cluster ensemble can lead to substantially improved cell type-specific clusters when applied with both the standard k-means clustering algorithm and a state-of-the-art kernel-based clustering algorithm (SIMLR) designed specifically for scRNA-seq data. Compared to directly using these clustering algorithms on the original datasets, the performance improvement in some cases is up to 100%, depending on the evaluation metric used.

Conclusions
Our results suggest that the proposed framework can facilitate more accurate cell type identification as well as other downstream analyses. The code for creating the proposed autoencoder-based cluster ensemble framework is freely available from https://github.com/gedcom/scCCESS
DEEPSEN: a convolutional neural network based method for super-enhancer prediction
Authors: Bu Hongda , Hao Jiaqi , Gan Yanglan , Zhou Shuigeng , Guan Jihong 
Published: December 2019

Abstract
Background
Super-enhancers (SEs) are clusters of transcriptional active enhancers, which dictate the expression of genes defining cell identity and play an important role in the development and progression of tumors and other diseases. Many key cancer oncogenes are driven by super-enhancers, and the mutations associated with common diseases such as Alzheimer’s disease are significantly enriched with super-enhancers. Super-enhancers have shown great potential for the identification of key oncogenes and the discovery of disease-associated mutational sites.

Results
In this paper, we propose a new computational method called DEEPSEN for predicting super-enhancers based on convolutional neural network. The proposed method integrates 36 kinds of features. Compared with existing approaches, our method performs better and can be used for genome-wide prediction of super-enhancers. Besides, we screen important features for predicting super-enhancers.

Conclusion
Convolutional neural network is effective in boosting the performance of super-enhancer prediction.
Predicting synthetic lethal interactions in human cancers using graph regularized self-representative matrix factorization
Authors: Huang Jiang , Wu Min , Lu Fan , Ou-Yang Le , Zhu Zexuan 
Published: December 2019

Abstract
Background
Synthetic lethality has attracted a lot of attentions in cancer therapeutics due to its utility in identifying new anticancer drug targets. Identifying synthetic lethal (SL) interactions is the key step towards the exploration of synthetic lethality in cancer treatment. However, biological experiments are faced with many challenges when identifying synthetic lethal interactions. Thus, it is necessary to develop computational methods which could serve as useful complements to biological experiments.

Results
In this paper, we propose a novel graph regularized self-representative matrix factorization (GRSMF) algorithm for synthetic lethal interaction prediction. GRSMF first learns the self-representations from the known SL interactions and further integrates the functional similarities among genes derived from Gene Ontology (GO). It can then effectively predict potential SL interactions by leveraging the information provided by known SL interactions and functional annotations of genes. Extensive experiments on the synthetic lethal interaction data downloaded from SynLethDB database demonstrate the superiority of our GRSMF in predicting potential synthetic lethal interactions, compared with other competing methods. Moreover, case studies of novel interactions are conducted in this paper for further evaluating the effectiveness of GRSMF in synthetic lethal interaction prediction.

Conclusions
In this paper, we demonstrate that by adaptively exploiting the self-representation of original SL interaction data, and utilizing functional similarities among genes to enhance the learning of self-representation matrix, our GRSMF could predict potential SL interactions more accurately than other state-of-the-art SL interaction prediction methods.