bluuebunny/crossref_metadata_2025_split huggingface dataset:
Robot-Assisted Versus Fluoroscopy-Assisted Kyphoplasty in the Treatment of Osteoporotic Vertebral Compression Fracture: A Retrospective Study
Authors: Yuan Wei , Meng Xiaotong , Cao Wenhai , Zhu Yue 
Published: December 2020

Study design: A retrospective study. Objectives: To compare the clinical and radiological outcomes of robot assisted (RA) and fluoroscopy assisted (FA) percutaneous kyphoplasty (PKP) in treating single/double segment osteoporotic vertebral compression fracture (OVCF). Methods: Patients with single/double segment OVCF receiving either RA or FA PKP were evaluated retrospectively at our spine center from April 2018 to October 2019. The operation time, fluoroscopy frequency, fluoroscopy exposure time, total radiation dose, visual analogue scale (VAS), local kyphosis angle (LKA), height of fractured vertebra (HFV) and complications were compared between the single/double RA group and the FA group. Results: A total of 96 cases were included in this study, with 59 cases of single segment OVCF and 37 cases of double segment OVCF. For single/double segment OVCF, both RA and FA PKP could relieve pain and reduce fracture. The RA group showed lower fluoroscopy frequency, shorter fluoroscopy exposure time during operation for surgeons, better correction in LKA and HFV, lower rate of cement leakage, but more fluoroscopy frequency, fluoroscopy exposure time and radiation dose for patients compared with the FA group (P < 0.05), while the single RA group showed longer operation time compared with the FA group (P < 0.05). Conclusions: For single/double segment OVCF, RA has more advantages in correcting vertebra fracture, reducing intraoperative radiation exposure for surgeons, and reducing the cement leakage rate, but it increases intraoperative radiation for patients compared with FA PKP. And FA has shorter operation time in treating single segment OVCF than RA PKP.
Multidimensional Understanding of Homosexuality: A Qualitative Integration of Perspectives
Authors: Sen Srijita , Das Prothama , Dutta Susmita , Das Sudeshna , Banerjee Debanjan , Ray Deepshikha 
Published: December 2020

Background: Though DSM no longer considers homosexuality as a clinical condition, it still remains a contentious issue across social, legal, and religious paradigms. Collectivistic and traditional societies (eg, India) are more reticent in accepting the multifaceted nature of sexuality. This study thereby tries to arrive at a collective understanding about homosexuality. Methods: The study was conducted in the following 3 parts: Focus group discussion (FDG) to unravel the collective understanding of homosexuality in heterosexual young adults. In-depth personal interviews with 3 homosexual persons. In-depth personal interview with 3 heterosexual peers of homosexual persons. Results: Qualitative analysis of the FGDs revealed that the participants share a collective opinion that “lack of acceptance and negative stereotyping of homosexuality” to be a predominant social phenomenon in India. Interpretative phenomenological analysis (IPA) of the personal interviews of the homosexual persons revealed experience of social ostracization and unique personal journey toward self-acceptance and adaptation. IPA of the personal interviews of the heterosexual peers disseminate that these people have the agonizing vicarious experience of seeing their friends being discriminated against and also personal experiences of social rejection on account of having a homosexual friend. Conclusion: This study is unique in that it tried to recognize homosexuality from multiple perspectives. Findings suggest that heteronormative hegemony operates insidiously and pervades the boundary of the self-generating “self-doubt.” Understanding these dimensions might help address their unmet needs and identities as well as mitigate stigma surrounding the same.
Multi-view feature selection for identifying gene markers: a diversified biological data driven approach
Authors: Acharya Sudipta , Cui Laizhong , Pan Yi 
Published: December 2020

AbstractBackgroundIn recent years, to investigate challenging bioinformatics problems, the utilization of multiple genomic and proteomic sources has become immensely popular among researchers. One such issue is feature or gene selection and identifying relevant and non-redundant marker genes from high dimensional gene expression data sets. In that context, designing an efficient feature selection algorithm exploiting knowledge from multiple potential biological resources may be an effective way to understand the spectrum of cancer or other diseases with applications in specific epidemiology for a particular population.ResultsIn the current article, we design the feature selection and marker gene detection as a multi-view multi-objective clustering problem. Regarding that, we propose an Unsupervised Multi-View Multi-Objective clustering-based gene selection approach called UMVMO-select. Three important resources of biological data (gene ontology, protein interaction data, protein sequence) along with gene expression values are collectively utilized to design two different views. UMVMO-selectaims to reduce gene space without/minimally compromising the sample classification efficiency and determines relevant and non-redundant gene markers from three cancer gene expression benchmark data sets.ConclusionA thorough comparative analysis has been performed with five clustering and nine existing feature selection methods with respect to several internal and external validity metrics. Obtained results reveal the supremacy of the proposed method. Reported results are also validated through a proper biological significance test and heatmap plotting.
Convex hulls in hamming space enable efficient search for similarity and clustering of genomic sequences
Authors: Campo David S. , Khudyakov Yury 
Published: December 2020

Abstract
Background
In molecular epidemiology, comparison of intra-host viral variants among infected persons is frequently used for tracing transmissions in human population and detecting viral infection outbreaks. Application of Ultra-Deep Sequencing (UDS) immensely increases the sensitivity of transmission detection but brings considerable computational challenges when comparing all pairs of sequences. We developed a new population comparison method based on convex hulls in hamming space. We applied this method to a large set of UDS samples obtained from unrelated cases infected with hepatitis C virus (HCV) and compared its performance with three previously published methods.

Results
The convex hull in hamming space is a data structure that provides information on: (1) average hamming distance within the set, (2) average hamming distance between two sets; (3) closeness centrality of each sequence; and (4) lower and upper bound of all the pairwise distances among the members of two sets. This filtering strategy rapidly and correctly removes 96.2% of all pairwise HCV sample comparisons, outperforming all previous methods. The convex hull distance (CHD) algorithm showed variable performance depending on sequence heterogeneity of the studied populations in real and simulated datasets, suggesting the possibility of using clustering methods to improve the performance. To address this issue, we developed a new clustering algorithm, k-hulls, that reduces heterogeneity of the convex hull. This efficient algorithm is an extension of the k-means algorithm and can be used with any type of categorical data. It is 6.8-times more accurate than k-mode, a previously developed clustering algorithm for categorical data.

Conclusions
CHD is a fast and efficient filtering strategy for massively reducing the computational burden of pairwise comparison among large samples of sequences, and thus, aiding the calculation of transmission links among infected individuals using threshold-based methods. In addition, the convex hull efficiently obtains important summary metrics for intra-host viral populations.
Semi-supervised learning for somatic variant calling and peptide identification in personalized cancer immunotherapy
Authors: Sherafat Elham , Force Jordan , Măndoiu Ion I. 
Published: December 2020

Abstract
Background
Personalized cancer vaccines are emerging as one of the most promising approaches to immunotherapy of advanced cancers. However, only a small proportion of the neoepitopes generated by somatic DNA mutations in cancer cells lead to tumor rejection. Since it is impractical to experimentally assess all candidate neoepitopes prior to vaccination, developing accurate methods for predicting tumor-rejection mediating neoepitopes (TRMNs) is critical for enabling routine clinical use of cancer vaccines.

Results
In this paper we introduce Positive-unlabeled Learning using AuTOml (PLATO), a general semi-supervised approach to improving accuracy of model-based classifiers. PLATO generates a set of high confidence positive calls by applying a stringent filter to model-based predictions, then rescores remaining candidates by using positive-unlabeled learning. To achieve robust performance on clinical samples with large patient-to-patient variation, PLATO further integrates AutoML hyper-parameter tuning, classification threshold selection based on spies, and support for bootstrapping.

Conclusions
Experimental results on real datasets demonstrate that PLATO has improved performance compared to model-based approaches for two key steps in TRMN prediction, namely somatic variant calling from exome sequencing data and peptide identification from MS/MS data.
Analysis of associations between emotions and activities of drug users and their addiction recovery tendencies from social media posts using structural equation modeling
Authors: Jha Deeptanshu , Singh Rahul 
Published: December 2020

AbstractBackgroundAddiction to drugs and alcohol constitutes one of the significant factors underlying the decline in life expectancy in the US. Several context-specific reasons influence drug use and recovery. In particular emotional distress, physical pain, relationships, and self-development efforts are known to be some of the factors associated with addiction recovery. Unfortunately, many of these factors are not directly observable and quantifying, and assessing their impact can be difficult. Based on social media posts of users engaged in substance use and recovery on the forum Reddit, we employed two psycholinguistic tools, Linguistic Inquiry and Word Count and Empath and activities of substance users on various Reddit sub-forums to analyze behavior underlining addiction recovery and relapse. We then employed a statistical analysis technique called structural equation modeling to assess the effects of these latent factors on recovery and relapse.ResultsWe found that both emotional distress and physical pain significantly influence addiction recovery behavior. Self-development activities and social relationships of the substance users were also found to enable recovery. Furthermore, within the context of self-development activities, those that were related to influencing the mental and physical well-being of substance users were found to be positively associated with addiction recovery. We also determined that lack of social activities and physical exercise can enable a relapse. Moreover, geography, especially life in rural areas, appears to have a greater correlation with addiction relapse.ConclusionsThe paper describes how observable variables can be extracted from social media and then be used to model important latent constructs that impact addiction recovery and relapse. We also report factors that impact self-induced addiction recovery and relapse. To the best of our knowledge, this paper represents the first use of structural equation modeling of social media data with the goal of analyzing factors influencing addiction recovery.
Cerebral oxygenation in 45-degree trendelenburg position for robot-assisted radical prostatectomy: a single-center, open, controlled pilot study
Authors: Wiesinger Clemens , Schoeb Dominik Stefan , Stockhammer Mathias , Mirtezani Emir , Mitterschiffthaler Lukas , Wagner Helga , Knotzer Johann , Pauer Walter 
Published: December 2020

AbstractBackgroundWithin the last decade, robotically-assisted laparoscopic prostatectomy (RALP) has become the standard for treating localized prostate cancer, causing a revival of the 45° Trendelenburg position. In this pilot study we investigated effects of Trendelenburg position on hemodynamics and cerebral oxygenation in patients undergoing RALP.MethodsWe enrolled 58 patients undergoing RALP and 22 patients undergoing robot-assisted partial nephrectomy (RAPN) (control group) in our study. Demographic patient data and intraoperative parameters including cerebral oxygenation and cerebral hemodynamics were recorded for all patients. Cerebral function was also assessed pre- and postoperatively via the Mini Mental Status (MMS) exam. Changes in parameters during surgery were modelled by a mixed effects model; changes in the MMS result were evaluated using the Wilcoxon signed rank test.ResultsPreoperative assessment of patient characteristics, standard blood values and vital parameters revealed no difference between the two groups.ConclusionsApplying a 45° Trendelenburg position causes no difference in postoperative brain function, and does not alter cerebral oxygenation during a surgical procedure lasting up to 5 h. Further studies in larger patient cohorts will have to confirm these findings.Trial registrationGerman Clinical Trial Registry; DRKS00005094; Registered 12th December 2013—Retrospectively registered;https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00005094.
Surgical suture course for dental students with the Peyton-4-step approach versus the PDCA cycle using video assisted self-monitoring
Authors: Leitmann A. , Reinert Siegmar , Weise Hannes 
Published: December 2020

AbstractBackgroundIn this prospective study the Peyton 4-step approach of demonstration–deconstruction–comprehension (verbalization by the learner), and performance by the learner was compared to the PDCA cycle/Deming-Circle (Plan–Do–Check (video assisted self-monitoring)–Act) as a teaching method for surgical suturing and nodes with end performance as the primary objective.MethodsStudents of the third clinical semester in dental medicine were randomly selected to one of the two teaching methods. They completed a first course during the third clinical semester and a subsequent course during the fourth clinical semester. The focus was on learning surgical suturing techniques. Before the course started a questionnaire was handed out to both groups to evaluate their initial level of performance. Each course ended with a practical test to review the content of the course. The evaluation followed standardized parameters. Some of the test tasks in test one were repeated in test two to measure a horizontal as well as vertical difference in performance level.Results53 students (Peyton: n = 28/18 female, 10 male; PDCA: n = 25/14 female, 11 male) have completed both courses. The evaluation of the subjective questionnaires showed that the members of the PDCA-groups achieved a higher subjective increase in performance. The objective results also indicated higher learning success in the PDCA-groups compared to the Peyton-Group.Discussion/ConclusionThis study demonstrated significant learning success for both groups in their own self-assessment as well as in the results of the practical exercises. Subsequently, the superiority of the PDCA cycle could be shown for almost all criteria for surgical suturing techniques. Several studies prioritize the teaching of practical skills according to Peyton and consider step 3 (“comprehension”) to be the essential factor. The PDCA cycle, which has its origins in industrial quality management, and its success can be understood from the perspective of learning theory in terms of Jean Piaget’s model of equilibration. The necessity of active reflection on the learning content through practice constitutes the key element for transfer into long-term memory.
An interpretable risk prediction model for healthcare with pattern attention
Authors: Kamal Sundreen Asad , Yin Changchang , Qian Buyue , Zhang Ping 
Published: December 2020

AbstractBackgroundThe availability of massive amount of data enables the possibility of clinical predictive tasks. Deep learning methods have achieved promising performance on the tasks. However, most existing methods suffer from three limitations: (1) There are lots of missing value for real value events, many methods impute the missing value and then train their models based on the imputed values, which may introduce imputation bias. The models’ performance is highly dependent on the imputation accuracy. (2) Lots of existing studies just take Boolean value medical events (e.g. diagnosis code) as inputs, but ignore real value medical events (e.g., lab tests and vital signs), which are more important for acute disease (e.g., sepsis) and mortality prediction. (3) Existing interpretable models can illustrate which medical events are conducive to the output results, but are not able to give contributions of patterns among medical events.MethodsIn this study, we propose a novel interpretablePatternAttention model withValueEmbedding (PAVE) to predict the risks of certain diseases. PAVE takes the embedding of various medical events, their values and the corresponding occurring time as inputs, leverage self-attention mechanism to attend to meaningful patterns among medical events for risk prediction tasks. Because only the observed values are embedded into vectors, we don’t need to impute the missing values and thus avoids the imputations bias. Moreover, the self-attention mechanism is helpful for the model interpretability, which means the proposed model can output which patterns cause high risks.ResultsWe conduct sepsis onset prediction and mortality prediction experiments on a publicly available dataset MIMIC-III and our proprietary EHR dataset. The experimental results show that PAVE outperforms existing models. Moreover, by analyzing the self-attention weights, our model outputs meaningful medical event patterns related to mortality.ConclusionsPAVE learns effective medical event representation by incorporating the values and occurring time, which can improve the risk prediction performance. Moreover, the presented self-attention mechanism can not only capture patients’ health state information, but also output the contributions of various medical event patterns, which pave the way for interpretable clinical risk predictions.AvailabilityThe code for this paper is available at:https://github.com/yinchangchang/PAVE.
SURF: identifying and allocating resources during Out-of-Hospital Cardiac Arrest
Authors: Rao Gaurav , Choudhury Salimur , Lingras Pawan , Savage David , Mago Vijay 
Published: December 2020

Abstract
Background
When an Out-of-Hospital Cardiac Arrest (OHCA) incident is reported to emergency services, the 911 agent dispatches Emergency Medical Services to the location and activates responder network system (RNS), if the option is available. The RNS notifies all the registered users in the vicinity of the cardiac arrest patient by sending alerts to their mobile devices, which contains the location of the emergency. The main objective of this research is to find the best match between the user who could support the OHCA patient.

Methods
For performing matching among the user and the AEDs, we used Bipartite Matching and Integer Linear Programming. However, these approaches take a longer processing time; therefore, a new method Preprocessed Integer Linear Programming is proposed that solves the problem faster than the other two techniques.

Results
The average processing time for the experimentation data was 1850 s using Bipartite matching, 32 s using the Integer Linear Programming and 2 s when using the Preprocessed Integer Linear Programming method. The proposed algorithm performs matching among users and AEDs faster than the existing matching algorithm and thus allowing it to be used in the real world.

Conclusion:
This research proposes an efficient algorithm that will allow matching of users with AED in real-time during cardiac emergency. Implementation of this system can help in reducing the time to resuscitate the patient.
Design of a companion bioinformatic tool to detect the emergence and geographical distribution of SARS-CoV-2 Spike protein genetic variants
Authors: Massacci Alice , Sperandio Eleonora , D’Ambrosio Lorenzo , Maffei Mariano , Palombo Fabio , Aurisicchio Luigi , Ciliberto Gennaro , Pallocca Matteo 
Published: December 2020

Abstract
Background
Tracking the genetic variability of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) is a crucial challenge. Mainly to identify target sequences in order to generate robust vaccines and neutralizing monoclonal antibodies, but also to track viral genetic temporal and geographic evolution and to mine for variants associated with reduced or increased disease severity. Several online tools and bioinformatic phylogenetic analyses have been released, but the main interest lies in the Spike protein, which is the pivotal element of current vaccine design, and in the Receptor Binding Domain, that accounts for most of the neutralizing the antibody activity.

Methods
Here, we present an open-source bioinformatic protocol, and a web portal focused on SARS-CoV-2 single mutations and minimal consensus sequence building as a companion vaccine design tool. Furthermore, we provide immunogenomic analyses to understand the impact of the most frequent RBD variations.

Results
Results on the whole GISAID sequence dataset at the time of the writing (October 2020) reveals an emerging mutation, S477N, located on the central part of the Spike protein Receptor Binding Domain, the Receptor Binding Motif. Immunogenomic analyses revealed some variation in mutated epitope MHC compatibility, T-cell recognition, and B-cell epitope probability for most frequent human HLAs.

Conclusions
This work provides a framework able to track down SARS-CoV-2 genomic variability.
Second law of thermodynamics in nonminimally coupled gravity
Authors: Azevedo R. P. L. , Avelino P. P. 
Published: December 2020

Abstract
In the present work we show that the second law of thermodynamics does not generally hold if the matter and gravitational fields are nonminimally coupled. We demonstrate this result by explicitly computing the evolution of the entropy of the matter fields in the case of a closed homogeneous and isotropic universe filled with dust and radiation, showing that, in this case, the sign of the entropy variation is determined by the evolution of the universe. The preservation of the second law of thermodynamics in these modified theories would require its generalization to account for a gravitational entropy contribution.
Giant deformations and soft-inflation in LCE balloons
Authors: Giudici Andrea , Biggins John S. 
Published: December 2020

Abstract
We propose that ballooning can be controlled, enriched and amplified by using rubbery networks of aligned molecular rods known as liquid crystal elastomers (LCEs). Firstly, LCEs are promising artificial muscles, showing large spontaneous deformations in response to heat and light. In LCE balloons, spontaneous deformations can trigger classic ballooning, either as phase separation (at constant volume) or a volume jump (at constant pressure), resulting in greatly magnified actuation strains. Secondly, even at constant temperature, LCEs have unusual mechanics augmented by soft modes of deformation in which the nematic director rotates within the elastomer. These soft modes enrich the mechanics of LCE balloons, which can also “balloon” between rotated and unrotated states, either during the classic instability, or as a separate pre-cursor, leading to successive instabilities during inflation.