bluuebunny/crossref_metadata_2025_split huggingface dataset:
NG2C: pretenuring garbage collection with dynamic generations for HotSpot big data applications
Authors: Bruno Rodrigo , Oliveira Luís Picciochi , Ferreira Paulo 
Published: November 2017

Big Data applications suffer from unpredictable and unacceptably high pause times due to Garbage Collection (GC). This is the case in latency-sensitive applications such as on-line credit-card fraud detection, graph-based computing for analysis on social networks, etc. Such pauses compromise latency requirements of the whole application stack and result from applications' aggressive buffering/caching of data, exposing an ill-suited GC design, which assumes that most objects will die young and does not consider that applications hold large amounts of middle-lived data in memory.
To avoid such pauses, we propose NG2C, a new GC algorithm that combines pretenuring with user-defined dynamic generations. By being able to allocate objects into different generations, NG2C is able to group objects with similar lifetime profiles in the same generation. By allocating objects with similar lifetime profiles close to each other, i.e. in the same generation, we avoid object promotion (copying between generations) and heap fragmentation (which leads to heap compactions) both responsible for most of the duration of HotSpot GC pause times.
NG2C is implemented for the OpenJDK 8 HotSpot Java Virtual Machine, as an extension of the Garbage First GC. We evaluate NG2C using Cassandra, Lucene, and GraphChi with three different GCs: Garbage First (G1), Concurrent Mark Sweep (CMS), and NG2C. Results show that NG2C decreases the worst observable GC pause time by up to 94.8% for Cassandra, 85.0% for Lucene and 96.45% for GraphChi, when compared to current collectors (G1 and CMS). In addition, NG2c has no negative impact on application throughput or memory usage.
Electroencephalographic connectivity measures predict learning of a motor sequencing task
Authors: Wu Jennifer , Knapp Franziska , Cramer Steven C. , Srinivasan Ramesh 
Published: November 2017

Individuals vary significantly with respect to rate and degree of improvement with motor practice. While the regions that underlie motor learning have been well described, neurophysiological factors underlying differences in response to motor practice are less well understood. The present study examined both resting-state and event-related EEG coherence measures of connectivity as predictors of response to motor practice on a motor sequencing task using the dominant hand. Thirty-two healthy young right-handed participants underwent resting EEG before motor practice. Response to practice was evaluated both across the single session of motor practice and 24 h later at a retention test of short-term motor learning. Behaviorally, the group demonstrated statistically significant gains both in single-session “motor improvement” and across-session “motor learning.” A resting-state measure of whole brain coherence with primary motor cortex (M1) at baseline robustly predicted subsequent motor improvement (validated R2 = 0.55) and motor learning (validated R2 = 0.68) in separate partial least-squares regression models. Specifically, greater M1 coherence with left frontal-premotor cortex (PMC) at baseline was characteristic of individuals likely to demonstrate greater gains in both motor improvement and motor learning. Analysis of event-related coherence with respect to movement found the largest changes occurring in areas implicated in planning and preparation of movement, including PMC and frontal cortices. While event-related coherence provided a stronger prediction of practice-induced motor improvement (validated R2 = 0.73), it did not predict the degree of motor learning (validated R2 = 0.16). These results indicate that connectivity in the resting state is a better predictor of consolidated learning of motor skills. NEW & NOTEWORTHY Differences in response to motor training have significant societal implications across a lifetime of motor skill practice. By evaluating both resting-state and event-related measures of brain function, our findings highlight interindividual differences in brain connectivity providing unique insights into differences in response to motor training. These findings have wide-ranging implications in settings ranging from advanced professional motor training to rehabilitation after brain injury.
A computational analysis of signal fidelity in the rostral nucleus of the solitary tract
Authors: Boxwell Alison , Terman David , Frank Marion , Yanagawa Yuchio , Travers Joseph B. 
Published: November 2017

Neurons in the rostral nucleus of the solitary tract (rNST) convey taste information to both local circuits and pathways destined for forebrain structures. This nucleus is more than a simple relay, however, because rNST neurons differ in response rates and tuning curves relative to primary afferent fibers. To systematically study the impact of convergence and inhibition on firing frequency and breadth of tuning (BOT) in rNST, we constructed a mathematical model of its two major cell types: projection neurons and inhibitory neurons. First, we fit a conductance-based neuronal model to data derived from whole cell patch-clamp recordings of inhibitory and noninhibitory neurons in a mouse expressing Venus under the control of the VGAT promoter. We then used in vivo chorda tympani (CT) taste responses as afferent input to modeled neurons and assessed how the degree and type of convergence influenced model cell output frequency and BOT for comparison with in vivo gustatory responses from the rNST. Finally, we assessed how presynaptic and postsynaptic inhibition impacted model cell output. The results of our simulations demonstrated 1) increasing numbers of convergent afferents (2–10) result in a proportional increase in best-stimulus firing frequency but only a modest increase in BOT, 2) convergence of afferent input selected from the same best-stimulus class of CT afferents produced a better fit to real data from the rNST compared with convergence of randomly selected afferent input, and 3) inhibition narrowed the BOT to more realistically model the in vivo rNST data. NEW & NOTEWORTHY Using a combination of in vivo and in vitro neurophysiology together with conductance-based modeling, we show how patterns of convergence and inhibition interact in the rostral (gustatory) solitary nucleus to maintain signal fidelity. Although increasing convergence led to a systematic increase in firing frequency, tuning specificity was maintained with a pattern of afferent inputs sharing the best-stimulus compared with random inputs. Tonic inhibition further enhanced response fidelity.
Integrative Analysis of Histopathological Images and Genomic Data Predicts Clear Cell Renal Cell Carcinoma Prognosis
Authors: Cheng Jun , Zhang Jie , Han Yatong , Wang Xusheng , Ye Xiufen , Meng Yuebo , Parwani Anil , Han Zhi , Feng Qianjin , Huang Kun 
Published: November 2017

Abstract
In cancer, both histopathologic images and genomic signatures are used for diagnosis, prognosis, and subtyping. However, combining histopathologic images with genomic data for predicting prognosis, as well as the relationships between them, has rarely been explored. In this study, we present an integrative genomics framework for constructing a prognostic model for clear cell renal cell carcinoma. We used patient data from The Cancer Genome Atlas (n = 410), extracting hundreds of cellular morphologic features from digitized whole-slide images and eigengenes from functional genomics data to predict patient outcome. The risk index generated by our model correlated strongly with survival, outperforming predictions based on considering morphologic features or eigengenes separately. The predicted risk index also effectively stratified patients in early-stage (stage I and stage II) tumors, whereas no significant survival difference was observed using staging alone. The prognostic value of our model was independent of other known clinical and molecular prognostic factors for patients with clear cell renal cell carcinoma. Overall, this workflow and the shared software code provide building blocks for applying similar approaches in other cancers. Cancer Res; 77(21); e91–100. ©2017 AACR.
A Galaxy Implementation of Next-Generation Clustered Heatmaps for Interactive Exploration of Molecular Profiling Data
Authors: Broom Bradley M. , Ryan Michael C. , Brown Robert E. , Ikeda Futa , Stucky Mark , Kane David W. , Melott James , Wakefield Chris , Casasent Tod D. , Akbani Rehan , Weinstein John N. 
Published: November 2017

Abstract
Clustered heatmaps are the most frequently used graphics for visualization of molecular profiling data in biology. However, they are generally rendered as static, or only modestly interactive, images. We have now used recent advances in web technologies to produce interactive “next-generation” clustered heatmaps (NG-CHM) that enable extreme zooming and navigation without loss of resolution. NG-CHMs also provide link-outs to additional information sources and include other features that facilitate deep exploration of the biology behind the image. Here, we describe an implementation of the NG-CHM system in the Galaxy bioinformatics platform. We illustrate the algorithm and available computational tool using RNA-seq data from The Cancer Genome Atlas program's Kidney Clear Cell Carcinoma project. Cancer Res; 77(21); e23–26. ©2017 AACR.
An Image Analysis Resource for Cancer Research: PIIP—Pathology Image Informatics Platform for Visualization, Analysis, and Management
Authors: Martel Anne L. , Hosseinzadeh Dan , Senaras Caglar , Zhou Yu , Yazdanpanah Azadeh , Shojaii Rushin , Patterson Emily S. , Madabhushi Anant , Gurcan Metin N. 
Published: November 2017

Abstract
Pathology Image Informatics Platform (PIIP) is an NCI/NIH sponsored project intended for managing, annotating, sharing, and quantitatively analyzing digital pathology imaging data. It expands on an existing, freely available pathology image viewer, Sedeen. The goal of this project is to develop and embed some commonly used image analysis applications into the Sedeen viewer to create a freely available resource for the digital pathology and cancer research communities. Thus far, new plugins have been developed and incorporated into the platform for out of focus detection, region of interest transformation, and IHC slide analysis. Our biomarker quantification and nuclear segmentation algorithms, written in MATLAB, have also been integrated into the viewer. This article describes the viewing software and the mechanism to extend functionality by plugins, brief descriptions of which are provided as examples, to guide users who want to use this platform. PIIP project materials, including a video describing its usage and applications, and links for the Sedeen Viewer, plug-ins, and user manuals are freely available through the project web page: http://pathiip.org. Cancer Res; 77(21); e83–86. ©2017 AACR.
Cistrome Cancer: A Web Resource for Integrative Gene Regulation Modeling in Cancer
Authors: Mei Shenglin , Meyer Clifford A. , Zheng Rongbin , Qin Qian , Wu Qiu , Jiang Peng , Li Bo , Shi Xiaohui , Wang Binbin , Fan Jingyu , Shih Celina , Brown Myles , Zang Chongzhi , Liu X. Shirley 
Published: November 2017

Abstract
Cancer results from a breakdown of normal gene expression control, so the study of gene regulation is critical to cancer research. To gain insight into the transcriptional and epigenetic factors regulating abnormal gene expression patterns in cancers, we developed the Cistrome Cancer web resource (http://cistrome.org/CistromeCancer/). We conducted the systematic integration and modeling of over 10,000 tumor molecular profiles from The Cancer Genome Atlas (TCGA) with over 23,000 ChIP-seq and chromatin accessibility profiles from our Cistrome collection. The results include reconstruction of functional enhancer profiles, “super-enhancer” target genes, as well as predictions of active transcription factors and their target genes for each TCGA cancer type. Cistrome Cancer reveals novel insights from integrative analyses combining chromatin profiles with tumor molecular profiles and will be a useful resource to the cancer gene regulation community. Cancer Res; 77(21); e19–22. ©2017 AACR.
dcmqi: An Open Source Library for Standardized Communication of Quantitative Image Analysis Results Using DICOM
Authors: Herz Christian , Fillion-Robin Jean-Christophe , Onken Michael , Riesmeier Jörg , Lasso Andras , Pinter Csaba , Fichtinger Gabor , Pieper Steve , Clunie David , Kikinis Ron , Fedorov Andriy 
Published: November 2017

Abstract
Quantitative analysis of clinical image data is an active area of research that holds promise for precision medicine, early assessment of treatment response, and objective characterization of the disease. Interoperability, data sharing, and the ability to mine the resulting data are of increasing importance, given the explosive growth in the number of quantitative analysis methods being proposed. The Digital Imaging and Communications in Medicine (DICOM) standard is widely adopted for image and metadata in radiology. dcmqi (DICOM for Quantitative Imaging) is a free, open source library that implements conversion of the data stored in commonly used research formats into the standard DICOM representation. dcmqi source code is distributed under BSD-style license. It is freely available as a precompiled binary package for every major operating system, as a Docker image, and as an extension to 3D Slicer. Installation and usage instructions are provided in the GitHub repository at https://github.com/qiicr/dcmqi. Cancer Res; 77(21); e87–90. ©2017 AACR.
“Personalized Cancer Therapy”: A Publicly Available Precision Oncology Resource
Authors: Kurnit Katherine C. , Bailey Ann M. , Zeng Jia , Johnson Amber M. , Shufean Md. Abu , Brusco Lauren , Litzenburger Beate C. , Sánchez Nora S. , Khotskaya Yekaterina B. , Holla Vijaykumar , Simpson Amy , Mills Gordon B. , Mendelsohn John , Bernstam Elmer , Shaw Kenna , Meric-Bernstam Funda 
Published: November 2017

Abstract
High-throughput genomic and molecular profiling of tumors is emerging as an important clinical approach. Molecular profiling is increasingly being used to guide cancer patient care, especially in advanced and incurable cancers. However, navigating the scientific literature to make evidence-based clinical decisions based on molecular profiling results is overwhelming for many oncology clinicians and researchers. The Personalized Cancer Therapy website (www.personalizedcancertherapy.org) was created to provide an online resource for clinicians and researchers to facilitate navigation of available data. Specifically, this resource can be used to help identify potential therapy options for patients harboring oncogenic genomic alterations. Herein, we describe how content on www.personalizedcancertherapy.org is generated and maintained. We end with case scenarios to illustrate the clinical utility of the website. The goal of this publicly available resource is to provide easily accessible information to a broad oncology audience, as this may help ease the information retrieval burden facing participants in the precision oncology field. Cancer Res; 77(21); e123–6. ©2017 AACR.
TumorMap: Exploring the Molecular Similarities of Cancer Samples in an Interactive Portal
Authors: Newton Yulia , Novak Adam M. , Swatloski Teresa , McColl Duncan C. , Chopra Sahil , Graim Kiley , Weinstein Alana S. , Baertsch Robert , Salama Sofie R. , Ellrott Kyle , Chopra Manu , Goldstein Theodore C. , Haussler David , Morozova Olena , Stuart Joshua M. 
Published: November 2017

Abstract
Vast amounts of molecular data are being collected on tumor samples, which provide unique opportunities for discovering trends within and between cancer subtypes. Such cross-cancer analyses require computational methods that enable intuitive and interactive browsing of thousands of samples based on their molecular similarity. We created a portal called TumorMap to assist in exploration and statistical interrogation of high-dimensional complex “omics” data in an interactive and easily interpretable way. In the TumorMap, samples are arranged on a hexagonal grid based on their similarity to one another in the original genomic space and are rendered with Google's Map technology. While the important feature of this public portal is the ability for the users to build maps from their own data, we pre-built genomic maps from several previously published projects. We demonstrate the utility of this portal by presenting results obtained from The Cancer Genome Atlas project data. Cancer Res; 77(21); e111–4. ©2017 AACR.
The Digital Slide Archive: A Software Platform for Management, Integration, and Analysis of Histology for Cancer Research
Authors: Gutman David A. , Khalilia Mohammed , Lee Sanghoon , Nalisnik Michael , Mullen Zach , Beezley Jonathan , Chittajallu Deepak R. , Manthey David , Cooper Lee A.D. 
Published: November 2017

Abstract
Tissue-based cancer studies can generate large amounts of histology data in the form of glass slides. These slides contain important diagnostic, prognostic, and biological information and can be digitized into expansive and high-resolution whole-slide images using slide-scanning devices. Effectively utilizing digital pathology data in cancer research requires the ability to manage, visualize, share, and perform quantitative analysis on these large amounts of image data, tasks that are often complex and difficult for investigators with the current state of commercial digital pathology software. In this article, we describe the Digital Slide Archive (DSA), an open-source web-based platform for digital pathology. DSA allows investigators to manage large collections of histologic images and integrate them with clinical and genomic metadata. The open-source model enables DSA to be extended to provide additional capabilities. Cancer Res; 77(21); e75–78. ©2017 AACR.
Cannabis Use and Incidence of Testicular Cancer: A 42-Year Follow-up of Swedish Men between 1970 and 2011
Authors: Callaghan Russell C. , Allebeck Peter , Akre Olof , McGlynn Katherine A. , Sidorchuk Anna 
Published: November 2017

Abstract
Background: Given current drug policy reforms to decriminalize or legalize cannabis in numerous countries worldwide, the current study assesses the relation between cannabis use and the development of testicular cancer.
Methods: The study included a population-based sample (n = 49,343) of young men ages 18–21 years who underwent conscription assessment for Swedish military service in 1969–1970. The conscription process included a nonanonymous questionnaire eliciting information about drug use. Conscription information was linked to Swedish health and administrative registry data. Testicular cancers diagnosed between 1970 and 2011 were identified by International Classification of Diseases-7/8/9/10 testicular cancer codes in the Swedish National Patient Register, the Cancer Register, or the Cause of Death Register. Cox regression modeling was used to estimate the hazards associated with cannabis use and time to diagnosis of testicular cancer.
Results: No evidence was found of a significant relation between lifetime “ever” cannabis use and the subsequent development of testicular cancer [n = 45,250; 119 testicular cancer cases; adjusted HR (aHR), 1.42; 95% confidence interval (CI), 0.83–2.45]. “Heavy” cannabis use (defined as usage of more than 50 times in lifetime, as measured at conscription) was associated with the incidence of testicular cancer (n = 45,250; 119 testicular cancer cases; aHR 2.57; 95% CI, 1.02–6.50).
Conclusions: The current study provides additional evidence to the limited prior literature suggesting cannabis use may contribute to the development of testicular cancer.
Impact: Emerging changes to cannabis drug policy should consider the potential role of cannabis use in the development of testicular cancer. Cancer Epidemiol Biomarkers Prev; 26(11); 1644–52. ©2017 AACR.
The Future of Surveillance in the Context of Cancer Predisposition: Through the Murky Looking Glass
Authors: Malkin David , Nichols Kim E. , Schiffman Joshua D. , Plon Sharon E. , Brodeur Garrett M. 
Published: November 2017

Abstract
At least 10% of children with cancer harbor a disease-associated pathogenic variant in a known cancer predisposition gene. It is widely accepted that pathogenic variants affecting other genes, epigenetic factors, or abnormalities in additional gene products may contribute to the etiology of many more childhood cancers. Effective preventive measures exist for only a few cancer types associated with predisposing conditions, but the development and implementation of surveillance protocols aimed at reducing morbidity and mortality in at-risk children through the early detection of cancer has emerged as an important clinical tool. The articles in this Clinical Cancer Research series present international consensus generated recommendations for surveillance for a wide spectrum of cancer predisposition syndromes affecting children. In this article, we explore the challenges and opportunities for researchers and practitioners in the many fields affiliated with pediatric cancer, and we offer insights into what the future might hold as we continue our efforts to mitigate the impact of cancer susceptibility on children, their families and society. Clin Cancer Res; 23(21); e133–e7. ©2017 AACR.
See all articles in the online-only CCR Pediatric Oncology Series.
Prevalence of Aflatoxin-Associated TP53R249S Mutation in Hepatocellular Carcinoma in Hispanics in South Texas
Authors: Jiao Jingjing , Niu Weibo , Wang Ying , Baggerly Keith , Ye Yuanqing , Wu Xifeng , Davenport Dewitt , Almeda Jose Luis , Betancourt-Garcia Monica M. , Forse R. Armour , Stevenson Heather L. , Watt Gordon P. , McCormick Joseph B. , Fisher-Hoch Susan P. , Beretta Laura 
Published: November 2017

Abstract
We aimed to determine whether aflatoxin dietary exposure plays a role in the high incidence of hepatocellular carcinoma (HCC) observed among Hispanics in South Texas. We measured TP53R249S somatic mutation, hallmark of aflatoxin etiology in HCC, using droplet digital PCR and RFLP. TP53R249S mutation was detected in 3 of 41 HCC tumors from Hispanics in South Texas (7.3%). We also measured TP53R249S mutation in plasma cell-free DNA (cfDNA) from 218 HCC patients and 96 Hispanic subjects with advanced fibrosis or cirrhosis, from South Texas. The mutation was detected only in Hispanic and Asian HCC patients, and patients harboring TP53R249S mutation were significantly younger and had a shorter overall survival. The mutation was not detected in any Hispanic subject with advanced fibrosis or cirrhosis. Genes involved in cell-cycle control of chromosomal replication and in BRCA1-dependent DNA damage response were enriched in HCCs with TP53R249S mutation. The E2F1 family members, E2F1 and E2F4, were identified as upstream regulators. TP53R249S mutation was detected in 5.7% to 7.3% of Hispanics with HCC in South Texas. This mutation was associated with a younger age and worse prognosis. TP53R249S was however not detected in Hispanics in South Texas with cirrhosis or advanced fibrosis. Aflatoxin exposure may contribute to a small number of HCCs in Hispanics in South Texas, but the detection of TP53R249S mutation in plasma cfDNA is not a promising biomarker of risk assessment for HCC in subjects with cirrhosis or advanced fibrosis in this population. Cancer Prev Res; 11(2); 103–12. ©2017 AACR.