bluuebunny/crossref_metadata_2025_split huggingface dataset:
Expanding the Diversity of Texts and Applications: Findings from the Section on Clinical Natural Language Processing of the International Medical Informatics Association Yearbook
Authors: Névéol Aurélie , Zweigenbaum Pierre ,   Section Editors for the IMIA Yearbook Section on Clinical Natural Language Processing
Published: August 2018

Objectives: To summarize recent research and present a selection of the best papers published in 2017 in the field of clinical Natural Language Processing (NLP).
Methods: A survey of the literature was performed by the two editors of the NLP section of the International Medical Informatics Association (IMIA) Yearbook. Bibliographic databases PubMed and Association of Computational Linguistics (ACL) Anthology were searched for papers with a focus on NLP efforts applied to clinical texts or aimed at a clinical outcome. A total of 709 papers were automatically ranked and then manually reviewed based on title and abstract. A shortlist of 15 candidate best papers was selected by the section editors and peer-reviewed by independent external reviewers to come to the three best clinical NLP papers for 2017.
Results: Clinical NLP best papers provide a contribution that ranges from methodological studies to the application of research results to practical clinical settings. They draw from text genres as diverse as clinical narratives across hospitals and languages or social media.
Conclusions: Clinical NLP continued to thrive in 2017, with an increasing number of contributions towards applications compared to fundamental methods. Methodological work explores deep learning and system adaptation across language variants. Research results continue to translate into freely available tools and corpora, mainly for the English language.
Sensor, Signal, and Imaging Informatics in 2017
Authors: Hsu William , Deserno Thomas , Kahn Charles ,   Section Editors for the IMIA Yearbook Section on Sensor, Signal and Imaging Informatics
Published: August 2018

Objective: To summarize significant contributions to sensor, signal, and imaging informatics literature published in 2017.
Methods: PubMed® and Web of Science® were searched to identify the scientific publications published in 2017 that addressed sensors, signals, and imaging in medical informatics. Fifteen papers were selected by consensus as candidate best papers. Each candidate article was reviewed by section editors and at least two other external reviewers. The final selection of the four best papers was conducted by the editorial board of the International Medical Informatics Association (IMIA) Yearbook.
Results: The selected papers of 2017 demonstrate the important scientific advances in management and analysis of sensor, signal, and imaging information.
Conclusion: The growth of signal and imaging data and the increasing power of machine learning techniques have engendered new opportunities for research in medical informatics. This synopsis highlights cutting-edge contributions to the science of Sensor, Signal, and Imaging Informatics.
SNOMED CT Concept Hierarchies for Sharing Definitions of Clinical Conditions Using Electronic Health Record Data
Authors: Willett Duwayne , Kannan Vaishnavi , Chu Ling , Buchanan Joel , Velasco Ferdinand , Clark John , Fish Jason , Ortuzar Adolfo , Youngblood Josh , Bhat Deepa , Basit Mujeeb 
Published: August 2018

Background Defining clinical conditions from electronic health record (EHR) data underpins population health activities, clinical decision support, and analytics. In an EHR, defining a condition commonly employs a diagnosis value set or “grouper.” For constructing value sets, Systematized Nomenclature of Medicine–Clinical Terms (SNOMED CT) offers high clinical fidelity, a hierarchical ontology, and wide implementation in EHRs as the standard interoperability vocabulary for problems.
Objective This article demonstrates a practical approach to defining conditions with combinations of SNOMED CT concept hierarchies, and evaluates sharing of definitions for clinical and analytic uses.
Methods We constructed diagnosis value sets for EHR patient registries using SNOMED CT concept hierarchies combined with Boolean logic, and shared them for clinical decision support, reporting, and analytic purposes.
Results A total of 125 condition-defining “standard” SNOMED CT diagnosis value sets were created within our EHR. The median number of SNOMED CT concept hierarchies needed was only 2 (25th–75th percentiles: 1–5). Each value set, when compiled as an EHR diagnosis grouper, was associated with a median of 22 International Classification of Diseases (ICD)-9 and ICD-10 codes (25th–75th percentiles: 8–85) and yielded a median of 155 clinical terms available for selection by clinicians in the EHR (25th–75th percentiles: 63–976). Sharing of standard groupers for population health, clinical decision support, and analytic uses was high, including 57 patient registries (with 362 uses of standard groupers), 132 clinical decision support records, 190 rules, 124 EHR reports, 125 diagnosis dimension slicers for self-service analytics, and 111 clinical quality measure calculations. Identical SNOMED CT definitions were created in an EHR-agnostic tool enabling application across disparate organizations and EHRs.
Conclusion SNOMED CT-based diagnosis value sets are simple to develop, concise, understandable to clinicians, useful in the EHR and for analytics, and shareable. Developing curated SNOMED CT hierarchy-based condition definitions for public use could accelerate cross-organizational population health efforts, “smarter” EHR feature configuration, and clinical–translational research employing EHR-derived data.
Internet Gaming Disorder – Depression – Prokrastination
Authors: Pechler S. 
Published: August 2018

Zusammenfassung
Ziel der Arbeit: Diskussion der Zusammenhänge zwischen Internet Gaming Disorder (IGD) und Prokrastination als maladaptive Strategien zur Emotionskontrolle sowie deren Einfluss auf Depressionen. Methodik: Übersichtsarbeit auf Basis der bekannten Studienergebnisse. Ergebnisse: Die Korrelationen zwischen dem Auftreten von Depressionen und suchtartigem Gebrauch des Internets konnten in zahlreichen Studien nachgewiesen werden. Trotz uneinheitlich verankerter Definition des Begriffs Internetsucht, Internet Gaming Disorder und exzessivem Spielverhalten zeigen sich durchgehend Verbindungen zu Prokrastination und depressiven Symptomen. Prokrastination und IGD können als Vulnerabilitätsfaktoren für Depressionen betrachtet werden. Schlussfolgerungen: Exzessiver Medienkonsum und Prokrastinationsverhalten können als maladaptive Strategie von Patienten mit Depressionen verstanden werden. Unklar bleibt aber inwieweit die Depression als Ursache des exzessiven Medienkonsums angesehen werden kann oder ob sie direkte Folge des hohen Medienkonsums ist. Die Trias aus Depression, IGD und Prokrastination sollte aus diesem Grund weiter untersucht werden.
EKT bei depressiven Störungen
Authors: Sartorius A. 
Published: August 2018

ZusammenfassungDie Elektrokrampftherapie oder Elektrokonvulsionstherapie (EKT) gilt bei der Behandlung depressiver Störungen nicht nur als Routinetherapie, sondern bei schweren oder therapieresistenten oder wahnhaften Depressionen als Therapie der Wahl. Aufgrund bestehender Vorurteile und Stigmatisierungen ist die EKT in dieser Indikation in Deutschland im Vergleich zu anderen Industrieländern immer noch massiv unterrepräsentiert, obwohl sie mittlerweile ein modernes, sicheres, nebenwirkungsarmes und gut verträgliches Verfahren darstellt. In diesem Übersichtsartikel werden auch die Indikationen für schizodepressive Episoden, depressive Episoden mit führender stuporös-katatoner Symptomatik oder Depressionen mit vorbestehenden kognitiven Störungen oder begleitenden Parkinson-Syndromen diskutiert. Ebenso wird ein kurzer Überblick über die Durchführung einer EKT in Bezug auf Vorbereitung, Narkose und Stimulation, Überwachung, technische Parameter und Übergang in eine Erhaltungs-EKT gegeben.
Stylet slow-pull versus standard suction for endoscopic ultrasound-guided fine-needle aspiration of solid pancreatic lesions: a multicenter randomized trial
Authors: Saxena Payal , El Zein Mohamad , Stevens Tyler , Abdelgelil Ahmed , Besharati Sepideh , Messallam Ahmed , Kumbhari Vivek , Azola Alba , Brainard Jennifer , Shin Eun , Lennon Anne , Canto Marcia , Singh Vikesh , Khashab Mouen 
Published: August 2018

Abstract
Background and study aim Standard endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) procedures involve use of no-suction or suction aspiration techniques. A new aspiration method, the stylet slow-pull technique, involves slow withdrawal of the needle stylet to create minimum negative pressure. The aim of this study was to compare the sensitivity of EUS-FNA using stylet slow-pull or suction techniques for malignant solid pancreatic lesions using a standard 22-gauge needle.
Patients and methods Consecutive patients presenting for EUS-FNA of pancreatic mass lesions were randomized to the stylet slow-pull or suction techniques using a 22-gauge needle. Both techniques were standardized for each pass until an adequate specimen was obtained, as determined by rapid on-site cytology examination. Patients were crossed over to the alternative technique after four nondiagnostic passes.
Results Of 147 patients screened, 121 (mean age 64 ± 13.8 years) met inclusion criteria and were randomized to the stylet slow-pull technique (n = 61) or the suction technique (n = 60). Technical success rates were 96.7 % and 98.3 % in the slow-pull and suction groups, respectively (P > 0.99). The sensitivity for malignancy of EUS-FNA was 82 % in the slow-pull group and 69 % in the suction group (P = 0.10). The first-pass diagnostic rate (42.6 % vs. 38.3 %; P = 0.71), acquisition of core tissue (60.6 % vs. 46.7 %; P = 0.14), and the median (range) number of passes to diagnosis (2 1
2
3 vs. 1 1
2; P = 0.71) were similar in the slow-pull and suction groups, respectively.
Conclusions The stylet slow-pull and suction techniques both offered high and comparable diagnostic sensitivity with a mean of 2 passes required for diagnosis of solid pancreatic lesions. The endosonographer may choose either technique during FNA.
Floquet quantum criticality
Authors: Berdanier William , Kolodrubetz Michael , Parameswaran S. A. , Vasseur Romain 
Published: August 2018

Significance
Periodically driven “Floquet” systems are nonequilibrium systems whose time translation symmetry can give rise to a rich dynamical phase structure. In the presence of quenched disorder, they can avoid thermalizing to a bland infinite temperature state through a phenomenon known as many-body localization (MBL). The ability of these driven MBL phases to host phenomena forbidden in equilibrium, such as “time crystallinity,” has gained widespread interest in recent years. In this work, we consider the question of the criticality that emerges at the transitions between distinct Floquet MBL phases. By providing a universally applicable picture and applying it to a prototypical driven system, the driven Ising chain, we identify critical points and give an understanding of Floquet criticality in general.
Expansion of cancer stem cell pool initiates lung cancer recurrence before angiogenesis
Authors: Li Lei , Li Jiang-Chao , Yang Hong , Zhang Xu , Liu Lu-Lu , Li Yan , Zeng Ting-Ting , Zhu Ying-Hui , Li Xiao-Dong , Li Yan , Xie Dan , Fu Li , Guan Xin-Yuan 
Published: August 2018

Significance
Latent tumor cells are the crucial reason of tumor recurrence and the death of cancer patients. Preventing latent tumor relapse can prolong patients’ survival and have a long time surviving with latent tumor cells. Here, we describe a lung cancer suspensive tumor model in mouse and find that a high level of cancer stem cells undergoing asymmetric cell division in latent tumor is the key issue to reactivate a suspensive tumor. The results clearly delineate the state of latent tumor in vivo. A high level of serum IGF-1 can induce the suspensive-to-progressive tumor transition though promoting CSCs symmetric division, which illuminate a key checkpoint of cancer relapse before angiogenesis, highlighting a potential therapeutic target for preventing tumor recurrence.
RETRACTED: Survey on Encryption Approaches for Secure Face Biometrics
Authors: Kavya R , George Angitha 
Published: August 2018

Abstract
Biometric system is twisting up logically basic since they give strong moreover, compelling techniques for character affirmations. The face recognition is the most trademark methods for biometric conspicuous verification and give versatile biometric affirmation and is a development prepared for serving an extensive variety of security application. This paper deals with the examination of different encryption approaches like AES, DES, and RSA that can be used for go up against face recognition security suggests and moreover analyze DNA cryptography. With the examination of these systems, AES gives sublime security while considering face affirmation.
Improving protein function prediction using protein sequence and GO-term similarities
Authors: Makrodimitris Stavros , van Ham Roeland C H J , Reinders Marcel J T 
Published: August 2018

Abstract

Motivation
Most automatic functional annotation methods assign Gene Ontology (GO) terms to proteins based on annotations of highly similar proteins. We advocate that proteins that are less similar are still informative. Also, despite their simplicity and structure, GO terms seem to be hard for computers to learn, in particular the Biological Process ontology, which has the most terms (&gt;29 000). We propose to use Label-Space Dimensionality Reduction (LSDR) techniques to exploit the redundancy of GO terms and transform them into a more compact latent representation that is easier to predict.

Results
We compare proteins using a sequence similarity profile (SSP) to a set of annotated training proteins. We introduce two new LSDR methods, one based on the structure of the GO, and one based on semantic similarity of terms. We show that these LSDR methods, as well as three existing ones, improve the Critical Assessment of Functional Annotation performance of several function prediction algorithms. Cross-validation experiments on Arabidopsis thaliana proteins pinpoint the superiority of our GO-aware LSDR over generic LSDR. Our experiments on A.thaliana proteins show that the SSP representation in combination with a kNN classifier outperforms state-of-the-art and baseline methods in terms of cross-validated F-measure.

Availability and implementation
Source code for the experiments is available at https://github.com/stamakro/SSP-LSDR.

Supplementary information
Supplementary data are available at Bioinformatics online.
Improving the prediction of protein–nucleic acids binding residues via multiple sequence profiles and the consensus of complementary methods
Authors: Su Hong , Liu Mengchen , Sun Saisai , Peng Zhenling , Yang Jianyi 
Published: August 2018

AbstractMotivationThe interactions between protein and nucleic acids play a key role in various biological processes. Accurate recognition of the residues that bind nucleic acids can facilitate the study of uncharacterized protein–nucleic acids interactions. The accuracy of existing nucleic acids-binding residues prediction methods is relatively low.ResultsIn this work, we introduce NucBind, a novel method for the prediction of nucleic acids-binding residues. NucBind combines the predictions from a support vector machine-based ab-initio method SVMnuc and a template-based method COACH-D. SVMnuc was trained with features from three complementary sequence profiles. COACH-D predicts the binding residues based on homologous templates identified from a nucleic acids-binding library. The proposed methods were assessed and compared with other peering methods on three benchmark datasets. Experimental results show that NucBind consistently outperforms other state-of-the-art methods. Though with higher accuracy, similar to many other ab-initio methods, cross prediction between DNA and RNA-binding residues was also observed in SVMnuc and NucBind. We attribute the success of NucBind to two folds. The first is the utilization of improved features extracted from three complementary sequence profiles in SVMnuc. The second is the combination of two complementary methods: the ab-initio method SVMnuc and the template-based method COACH-D.Availability and implementationhttp://yanglab.nankai.edu.cn/NucBindSupplementary informationSupplementary data are available at Bioinformatics online.
Resolution-exchanged structural modeling and simulations jointly unravel that subunit rolling underlies the mechanism of programmed ribosomal frameshifting
Authors: Chang Kai-Chun , Salawu Emmanuel Oluwatobi , Chang Yuan-Yu , Wen Jin-Der , Yang Lee-Wei 
Published: August 2018

Abstract

Motivation
Programmed ribosomal frameshifting (PRF) is widely used by viruses and bacteria to produce different proteins from a single mRNA template. How steric hindrance of a PRF-stimulatory mRNA structure transiently modifies the conformational dynamics of the ribosome, and thereby allows tRNA slippage, remains elusive.

Results
Here, we leverage linear response theories and resolution-exchanged simulations to construct a structural/dynamics model that connects and rationalizes existing structural, single-molecule and mutagenesis data by resolution-exchanged structural modelling and simulations. Our combined theoretical techniques provide a temporal and spatial description of PRF with unprecedented mechanistic details. We discover that ribosomal unfolding of the PRF-stimulating pseudoknot exerts resistant forces on the mRNA entrance of the ribosome, and thereby drives 30S subunit rolling. Such motion distorts tRNAs, leads to tRNA slippage, and in turn serves as a delicate control of cis-element’s unwinding forces over PRF.

Availability and implementation
All the simulation scripts and computational implementations of our methods/analyses (including linear response theory) are included in the bioStructureM suite, provided through GitHub at https://github.com/Yuan-Yu/bioStructureM.

Supplementary information
Supplementary data are available at Bioinformatics online.
A generalized framework for controlling FDR in gene regulatory network inference
Authors: Morgan Daniel , Tjärnberg Andreas , Nordling Torbjörn E M , Sonnhammer Erik L L 
Published: August 2018

AbstractMotivationInference of gene regulatory networks (GRNs) from perturbation data can give detailed mechanistic insights of a biological system. Many inference methods exist, but the resulting GRN is generally sensitive to the choice of method-specific parameters. Even though the inferred GRN is optimal given the parameters, many links may be wrong or missing if the data is not informative. To make GRN inference reliable, a method is needed to estimate the support of each predicted link as the method parameters are varied.ResultsTo achieve this we have developed a method called nested bootstrapping, which applies a bootstrapping protocol to GRN inference, and by repeated bootstrap runs assesses the stability of the estimated support values. To translate bootstrap support values to false discovery rates we run the same pipeline with shuffled data as input. This provides a general method to control the false discovery rate of GRN inference that can be applied to any setting of inference parameters, noise level, or data properties. We evaluated nested bootstrapping on a simulated dataset spanning a range of such properties, using the LASSO, Least Squares, RNI, GENIE3 and CLR inference methods. An improved inference accuracy was observed in almost all situations. Nested bootstrapping was incorporated into the GeneSPIDER package, which was also used for generating the simulated networks and data, as well as running and analyzing the inferences.Availability and implementationhttps://bitbucket.org/sonnhammergrni/genespider/src/NB/%2B Methods/NestBoot.m
Combining planned and discovered comparisons in observational studies
Authors: Rosenbaum Paul R 
Published: August 2018

Summary
In observational studies of treatment effects, it is common to have several outcomes, perhaps of uncertain quality and relevance, each purporting to measure the effect of the treatment. A single planned combination of several outcomes may increase both power and insensitivity to unmeasured bias when the plan is wisely chosen, but it may miss opportunities in other cases. A method is proposed that uses one planned combination with only a mild correction for multiple testing and exhaustive consideration of all possible combinations fully correcting for multiple testing. The method works with the joint distribution of $\kappa^{T}\left( \mathbf{T}-\boldsymbol{\mu}\right) /\sqrt {\boldsymbol{\kappa}^{T}\boldsymbol{\Sigma\boldsymbol{\kappa}}}$ and $max_{\boldsymbol{\lambda}\neq\mathbf{0}}$$\,\lambda^{T}\left( \mathbf{T} -\boldsymbol{\mu}\right) /$$\sqrt{\boldsymbol{\lambda}^{T}\boldsymbol{\Sigma \lambda}}$ where $\kappa$ is chosen a priori and the test statistic $\mathbf{T}$ is asymptotically $N_{L}\left( \boldsymbol{\mu},\boldsymbol{\Sigma}\right) $. The correction for multiple testing has a smaller effect on the power of $\kappa^{T}\left( \mathbf{T}-\boldsymbol{\mu }\right) /\sqrt{\boldsymbol{\kappa}^{T}\boldsymbol{\Sigma\boldsymbol{\kappa} }}$ than does switching to a two-tailed test, even though the opposite tail does receive consideration when $\lambda=-\kappa$. In the application, there are three measures of cognitive decline, and the a priori comparison $\kappa$ is their first principal component, computed without reference to treatment assignments. The method is implemented in an R package sensitivitymult.
The Shared Genetic Basis of Educational Attainment and Cerebral Cortical Morphology
Authors: Ge Tian , Chen Chia-Yen , Doyle Alysa E , Vettermann Richard , Tuominen Lauri J , Holt Daphne J , Sabuncu Mert R , Smoller Jordan W 
Published: August 2018

Abstract
Individual differences in educational attainment are linked to differences in intelligence, and predict important social, economic, and health outcomes. Previous studies have found common genetic factors that influence educational achievement, cognitive performance and total brain volume (i.e., brain size). Here, in a large sample of participants from the UK Biobank, we investigate the shared genetic basis between educational attainment and fine-grained cerebral cortical morphological features, and associate this genetic variation with a related aspect of cognitive ability. Importantly, we execute novel statistical methods that enable high-dimensional genetic correlation analysis, and compute high-resolution surface maps for the genetic correlations between educational attainment and vertex-wise morphological measurements. We conduct secondary analyses, using the UK Biobank verbal–numerical reasoning score, to confirm that variation in educational attainment that is genetically correlated with cortical morphology is related to differences in cognitive performance. Our analyses relate the genetic overlap between cognitive ability and cortical thickness measurements to bilateral primary motor cortex as well as predominantly left superior temporal cortex and proximal regions. These findings extend our understanding of the neurobiology that connects genetic variation to individual differences in educational attainment and cognitive performance.