bluuebunny/crossref_metadata_2025_split huggingface dataset:
Autologous Stem Cell Transplantation for Children With Renal Tumors, and Adults With Wilms Tumor: Retrospective Analysis of the Japanese Transplant Registry Unified Management Program
Authors: Kobayashi Ryoji , Inoue Masami , Takahashi Yoshiyuki , Kikuta Atsushi , Ogawa Atsushi , Koga Yuhki , Koh Katsuyoshi , Hashii Yoshiko , Hara Junichi , Kato Koji , Tabuchi Ken , Matsumoto Kimikazu 
Published: March 2020

Background:
Almost all pediatric patients with renal tumors are diagnosed with nephroblastoma (Wilms tumor), clear cell sarcoma, or malignant rhabdoid tumor. The choice of treatment is important for relapsed and refractory patients with nephroblastoma. Furthermore, clear cell sarcoma of the kidney (CCSK) and malignant rhabdoid tumor of the kidney (MRTK) have a poor prognosis compared with nephroblastoma. Thus, stem cell transplantation (SCT) is sometimes selected to treat these tumors.

Patients and Methods:
The authors targeted a total of 84 patients with nephroblastoma, CCSK, and MRTK who underwent a first autologous SCT between 1992 and 2014, and were registered in the Japanese Transplant Registry Unified Management Program system. The authors retrospectively analyzed the SCT data for survival rate.

Results:
Five-year overall survival rates for nephroblastoma, CCSK, and MRTK were 72.4%±6.3%, 46.8%±13.8%, and 36.4%±14.5%, respectively. The event-free survival rates at 5 years were 64.9%±6.7%, 35.7%±12.8%, and 27.3%±13.4%, respectively. The relapse rates at 5 years were 25.3%±11.4%, 46.2%±28.4%, and 60.0%±43.1%, respectively.

Conclusion:
Although the survival rate for nephroblastoma was relatively high, those of CCSK and MRTK were poor.
In Vivo Aortic Magnetic Resonance Elastography in Abdominal Aortic Aneurysm
Authors: Dong Huiming , Russell Duncan S. , Litsky Alan S. , Joseph Matthew E. , Mo Xiaokui , White Richard D. , Kolipaka Arunark 
Published: March 2020

Objectives
Using maximum diameter of an abdominal aortic aneurysm (AAA) alone for management can lead to delayed interventions or unnecessary urgent repairs. Abdominal aortic aneurysm stiffness plays an important role in its expansion and rupture. In vivo aortic magnetic resonance elastography (MRE) was developed to spatially measure AAA stiffness in previous pilot studies and has not been thoroughly validated and evaluated for its potential clinical value. This study aims to evaluate noninvasive in vivo aortic MRE-derived stiffness in an AAA porcine model and investigate the relationships between MRE-derived AAA stiffness and (1) histopathology, (2) uniaxial tensile test, and (3) burst testing for assessing MRE's potential in evaluating AAA rupture risk.

Materials and Methods
Abdominal aortic aneurysm was induced in 31 Yorkshire pigs (n = 226 stiffness measurements). Animals were randomly divided into 3 cohorts: 2-week, 4-week, and 4-week-burst. Aortic MRE was sequentially performed. Histopathologic analyses were performed to quantify elastin, collagen, and mineral densities. Uniaxial tensile test and burst testing were conducted to measure peak stress and burst pressure for assessing the ultimate wall strength.

Results
Magnetic resonance elastography–derived AAA stiffness was significantly higher than the normal aorta. Significant reduction in elastin and collagen densities as well as increased mineralization was observed in AAAs. Uniaxial tensile test and burst testing revealed reduced ultimate wall strength. Magnetic resonance elastography–derived aortic stiffness correlated to elastin density (ρ = −0.68; P < 0.0001; n = 60) and mineralization (ρ = 0.59; P < 0.0001; n = 60). Inverse correlations were observed between aortic stiffness and peak stress (ρ = −0.32; P = 0.0495; n = 38) as well as burst pressure (ρ = −0.55; P = 0.0116; n = 20).

Conclusions
Noninvasive in vivo aortic MRE successfully detected aortic wall stiffening, confirming the extracellular matrix remodeling observed in the histopathologic analyses. These mural changes diminished wall strength. Inverse correlation between MRE-derived aortic stiffness and aortic wall strength suggests that MRE-derived stiffness can be a potential biomarker for clinically assessing AAA wall status and rupture potential.
Clinical and High-Resolution CT Features of the COVID-19 Infection: Comparison of the Initial and Follow-up Changes
Authors: Xiong Ying , Sun Dong , Liu Yao , Fan Yanqing , Zhao Lingyun , Li Xiaoming , Zhu Wenzhen 
Published: March 2020

Objectives
In late December 2019, an outbreak of coronavirus disease (COVID-19) in Wuhan, China was caused by a novel coronavirus, newly named severe acute respiratory syndrome coronavirus 2. We aimed to quantify the severity of COVID-19 infection on high-resolution chest computed tomography (CT) and to determine its relationship with clinical parameters.

Materials and Methods
From January 11, 2020, to February 5, 2020, the clinical, laboratory, and high-resolution CT features of 42 patients (26–75 years, 25 males) with COVID-19 were analyzed. The initial and follow-up CT, obtained a mean of 4.5 days and 11.6 days from the illness onset were retrospectively assessed for the severity and progression of pneumonia. Correlations among clinical parameters, initial CT features, and progression of opacifications were evaluated with Spearman correlation and linear regression analysis.

Results
Thirty-five patients (83%) exhibited a progressive process according to CT features during the early stage from onset. Follow-up CT findings showed progressive opacifications, consolidation, interstitial thickening, fibrous strips, and air bronchograms, compared with initial CT (all P < 0.05). Before regular treatments, there was a moderate correlation between the days from onset and sum score of opacifications (R = 0.68, P < 0.01). The C-reactive protein, erythrocyte sedimentation rate, and lactate dehydrogenase showed significantly positive correlation with the severity of pneumonia assessed on initial CT (R
range, 0.36–0.75; P < 0.05). The highest temperature and the severity of opacifications assessed on initial CT were significantly related to the progression of opacifications on follow-up CT (P = 0.001–0.04).

Conclusions
Patients with the COVID-19 infection usually presented with typical ground glass opacities and other CT features, which showed significant correlations with some clinical and laboratory measurements. Follow-up CT images often demonstrated progressions during the early stage from illness onset.
Monitoring Disease Transmissibility of 2019 Novel Coronavirus Disease in Zhejiang, China
Authors: Chong Ka Chun , Cheng Wei , Zhao Shi , Ling Feng , Mohammad Kirran N. , Wang Maggie Haitian , Ying Zee Benny Chung , Wei Lei , Xiong Xi , Liu Hengyan , Wang Jiangxuan , Chen Enfu 
Published: March 2020

AbstractWe monitored the transmissibility of 2019 novel coronavirus disease in Zhejiang accounting the transmissions from imported cases. Even though Zhejiang is one of the worst-affected provinces, an interruption of disease transmission (i.e. instantaneous reproduction numbers <1) was observed in early/mid-February after an early social-distancing response to the outbreak.
A framework to identify structured behavioral patterns within rodent spatial trajectories
Authors: Donnarumma Francesco , Prevete Roberto , Maisto Domenico , Fuscone Simone , Irvine Emily M. , van der Meer Matthijs A. A. , Kemere Caleb , Pezzulo Giovanni 
Published: March 2020

ABSTRACTAnimal behavior is highly structured. Yet, structured behavioral patterns – or “statistical ethograms” – are not immediately apparent from the full spatiotemporal data that behavioral scientists usually collect. Here, we introduce a framework to quantitatively characterize rodent behavior during spatial (e.g., maze) navigation, in terms of movement building blocks ormotor primitives. The hypothesis that we pursue is that rodent behavior is characterized by a small number of motor primitives, which are combined over time to produce open-ended movements. We assume motor primitives to be organized in terms of two sparsity principles: each movement is controlled using a limited subset of motor primitives (sparse superposition) and each primitive is active only for time-limited, time-contiguous portions of movements (sparse activity). We formalize this hypothesis using a sparse dictionary learning method, which we use to extract motor primitives from rodent position and velocity data collected during spatial navigation, and successively to reconstruct past trajectories and predict novel ones. Three main results validate our approach. First, rodent behavioral trajectories are robustly reconstructed from incomplete data, performing better than approaches based on standard dimensionality reduction methods, such as principal component analysis, or single sparsity. Second, the motor primitives extracted during one experimental session generalize and afford the accurate reconstruction of rodent behavior across successive experimental sessions in the same or in modified mazes. Third, in our approach the number of motor primitives associated with each maze correlates with independent measures of maze complexity, hence showing that our formalism is sensitive to essential aspects of task structure. The framework introduced here can be used by behavioral scientists and neuroscientists as an aid for behavioral and neural data analysis. Indeed, the extracted motor primitives enable the quantitative characterization of the complexity and similarity between different mazes and behavioral patterns across multiple trials (i.e., habit formation). We provide example uses of this computational framework, showing how it can be used to identify behavioural effects of maze complexity, analyze stereotyped behavior, classify behavioral choices and predict place and grid cell displacement in novel environments.
graphsim: An R package for simulating gene expression data from graph structures of biological pathways
Authors: Kelly S. Thomas , Black Michael A. 
Published: March 2020

SummaryTranscriptomic analysis is used to capture the molecular state of a cell or sample in many biological and medical applications. In addition to identifying alterations in activity at the level of individual genes, understanding changes in the gene networks that regulate fundamental biological mechanisms is also an important objective of molecular analysis. As a result, databases that describe biological pathways are increasingly uesad to assist with the interpretation of results from large-scale genomics studies. Incorporating information from biological pathways and gene regulatory networks into a genomic data analysis is a popular strategy, and there are many methods that provide this functionality for gene expression data. When developing or comparing such methods, it is important to gain an accurate assessment of their performance. Simulation-based validation studies are frequently used for this. This necessitates the use of simulated data that correctly accounts for pathway relationships and correlations. Here we present a versatile statistical framework to simulate correlated gene expression data from biological pathways, by sampling from a multivariate normal distribution derived from a graph structure. This procedure has been released as the graphsim R package on CRAN and GitHub (https://github.com/TomKellyGenetics/graphsim) and is compatible with any graph structure that can be described using the igraph package. This package allows the simulation of biological pathways from a graph structure based on a statistical model of gene expression.
Fast oscillations >40Hz localize the epileptogenic zone: an electrical source imaging study using high-density electroencephalography
Authors: Avigdor Tamir , Abdallah Chifaou , von Ellenrieder Nicolás , Hedrich Tanguy , Rubino Annalisa , Russo Giorgio Lo , Bernhardt Boris , Nobili Lino , Grova Christophe , Frauscher Birgit 
Published: March 2020

ABSTRACTObjectiveFast Oscillations (FO) >40 Hz are a promising biomarker of the epileptogenic zone (EZ). Evidence using scalp electroencephalography (EEG) remains scarce. We assessed if electrical source imaging of FO using 256-channel high-density EEG (HD-EEG) is useful for EZ identification.MethodsWe analyzed HD-EEG recordings of 10 focal drug-resistant epilepsy patients with seizure-free postsurgical outcome. We marked FO candidate events at the time of epileptic spikes and verified them by screening for an isolated peak in the time-frequency plot. We performed electrical source imaging of spikes and FO within the Maximum Entropy of the Mean framework. Source localization maps were validated against the surgical cavity.ResultsWe identified FO in five out of 10 patients who had a superficial or intermediate deep generator. The maximum of the FO maps was localized inside the cavity in all patients (100%). Analysis with a reduced electrode coverage using the 10-10 and 10-20 system showed a decreased localization accuracy of 60% and 40% respectively.ConclusionsFO recorded with HD-EEG localize the EZ. HD-EEG is better suited to detect and localize FO than conventional EEG approaches.SignificanceThis study acts as proof-of-concept that FO localization using 256-channel HD-EEG is a viable marker of the EZ.Highlights- Fast oscillations > 40Hz are able to correctly localize the epileptogenic zone.- HD-EEG is superior in detection and localization of fast oscillations compared to conventional EEG approaches.- Presence of fast oscillations on the scalp might point to a superficial epileptic generator.
Genomic characterization of the adolescent idiopathic scoliosis associated transcriptome and regulome
Authors: Makki Nadja , Zhao Jingjing , Liu Zhaoyang , Eckalbar Walter L. , Ushiki Aki , Khanshour Anas M. , Wu Zhuoxi , Rios Jonathan , Gray Ryan S. , Wise Carol A. , Ahituv Nadav 
Published: March 2020

AbstractAdolescent idiopathic scoliosis (AIS), a sideways curvature of the spine, is the most common pediatric musculoskeletal disorder, affecting ∼3% of the population worldwide. However, its genetic bases and tissues of origin remain largely unknown. Several genome-wide association studies (GWAS) have implicated nucleotide variants in noncoding sequences that control genes with important roles in cartilage, muscle, bone, connective tissue and intervertebral discs (IVDs) as drivers of AIS susceptibility. Here, we set out to define the expression of AIS-associated genes and active regulatory elements by performing RNA-seq and ChIP-seq against H3K27ac in these tissues in mouse and human. Our study highlights genetic pathways involving AIS-associated loci that regulate chondrogenesis, IVD development and connective tissue maintenance and homeostasis. In addition, we identify thousands of putative AIS-associated regulatory elements which may orchestrate tissue-specific expression in musculoskeletal tissues of the spine. Quantification of enhancer activity of several candidate regulatory elements from our study identifies three functional enhancers carrying AIS-associated GWAS SNPs at theADGRG6andBNC2loci. Our findings provide a novel genome-wide catalog of AIS-relevant genes and regulatory elements and aid in the identification of novel targets for AIS causality and treatment.
Varmole: A biologically drop-connect deep neural network model for prioritizing disease risk variants and genes
Authors: Nguyen Nam D. , Wang Daifeng 
Published: March 2020

AbstractPopulation studies such as GWAS have identified a variety of genomic variants associated with human diseases. To further understand potential mechanisms of disease variants, recent statistical methods associate functional omic data (e.g., gene expression) with genotype and phenotype and link variants to individual genes. However, how to interpret molecular mechanisms from such associations, especially across omics is still challenging. To address this, we develop an interpretable deep learning method, Varmole to simultaneously reveal genomic functions and mechanisms while predicting phenotype from genotype. In particular, Varmole embeds multi-omic networks into a deep neural network architecture and prioritizes variants, genes and regulatory linkages via drop-connect without needing prior feature selections. Varmole is available as a Python package on GitHub athttps://github.com/daifengwanglab/Varmole.
Experimental evolution of nascent multicellularity: Recognizing a Darwinian transition in individuality
Authors: Rose Caroline J. , Hammerschmidt Katrin , Rainey Paul B. 
Published: March 2020

AbstractMajor evolutionary transitions in individuality, at any level of the biological hierarchy, occur when groups participate in Darwinian processes as units of selection in their own right. Identifying transitions in individuality can be problematic because apparent selection at one level of the biological hierarchy may be a by-product of selection occurring at another level. Here we discuss approaches to this “levels-of-selection” problem and apply them to a previously published experimental exploration of the evolutionary transition to multicellularity. In these experiments groups of the bacteriumPseudomonas fluorescenswere required to reproduce via life cycles involving soma- and germline-like phases. The rate of transition between the two cell types was a focus of selection, and might be regarded as a property of groups, cells, or even genes. By examining the experimental data under several established philosophical frameworks, we argue that in thePseudomonasexperiments, bacterial groups acquired Darwinian properties sufficient to allow the evolution of traits adaptive at the group level.
Novel Human Kidney Cell Subsets Identified by Mux-Seq
Authors: Schroeder Andrew W. , Sur Swastika , Rashmi Priyanka , Damm Izabella , Zarinsefat Arya , Kretzler Matthias , Hodgin Jeff , Hartoularos George , Sigdel Tara , Ye Jimmie Chun , Sarwal Minnie M. ,   for the Kidney Precision Medicine Project
Published: March 2020

AbstractBackgroundThe kidney is a highly complex organ that performs multiple functions necessary to maintain systemic homeostasis, with complex interplay from different kidney sub-structures and the coordinated response of diverse cell types, few known and likely many others, as yet undiscovered. Traditional global sequencing techniques are limited in their ability to identify unique and functionally diverse cell types in complex tissues.MethodsHerein we characterize over 45,000 cells from 10 normal human kidneys using unbiased single-cell RNA sequencing. We also apply, for the first time, an approach of multiplexing kidney samples (Mux-Seq), pooled from different individuals, to save input sample amount and cost. We applied the computational tool Demuxlet to assess differential expression across multiple individuals by pooling human kidney cells for scRNA sequencing, utilizing individual genetic variability to determine the identity of each cell.ResultsMultiplexed droplet single-cell RNA sequencing results were highly correlated with the singleplexed sample run data. One hundred distinct cell cluster populations in total were identified across the major cell types of the kidney, with varied functional states. Proximal tubular and collecting duct cells were the most heterogeneous, displaying multiple clusters with unique ontologies. Novel proximal tubular cell subsets were identified with regenerative potential. Trajectory analysis demonstrated evolution of cell states between intercalated and principal cells in the collecting duct.ConclusionsHealthy kidney tissue has been successfully analyzed to detect all known renal cell types, inclusive of resident and infiltrating immune cells in the kidney. Mux-Seq is a unique method that allows for rapid and cost-effective single cell, in depth, transcriptional analysis of human kidney tissue.Significance StatementUse of renal biopsies for single cell transcriptomics is limited by small tissue availability and batch effects. In this study, we have successfully employed the use of Mux-Seq for the first time in kidney. Mux-Seq allows the use of single cell technology at a much more cost-effective manner by pooling samples from multiple individuals for a single sequencing run. This is even more relevant in the case of patient biopsies where the input of tissue is significantly limited. We show that the data from overlapping tissue samples are highly correlated between Mux-Seq and traditional Singleplexed RNA seq. Furthermore, the results from Mux-Seq of 4 pooled samples are highly correlated with singleplexed data from 10 singleplex samples despite the inherent variability among individuals.
Combining tensor decomposition and time warping models for multi-neuronal spike train analysis
Authors: Williams Alex H. 
Published: March 2020

AbstractRecordings from large neural populations are becoming an increasingly popular and accessible method in experimental neuroscience. While the activity of individual neurons is often too stochastic to interrogate circuit function on a moment-by-moment basis, multi-neuronal recordings enable us to do so by pooling statistical power across many cells. For example, groups of neurons often exhibit correlated gain or amplitude modulation across trials, which can be statistically formalized in a tensor decomposition framework (Williams et al. 2018). Additionally, the time course of neural population dynamics can be shifted or stretched/compressed, which can be modeled by time warping methods (Williams et al. 2020). Here, I describe how these two modeling frameworks can be combined, and show some evidence that doing so can be highly advantageous for practical neural data analysis—for example, the presence of random time shifts hampers the performance and interpretability of tensor decomposition, while a time-shifted variant of this model corrects for these disruptions and uncovers ground truth structure in simulated data.
Polygenic adaptation and negative selection across traits, years and environments in a long-lived plant species (Pinus pinasterAit., Pinaceae)
Authors: de Miguel Marina , Rodríguez-Quilón Isabel , Heuertz Myriam , Hurel Agathe , Grivet Delphine , Jaramillo-Correa Juan-Pablo , Vendramin Giovanni G. , Plomion Christophe , Majada Juan , Alía Ricardo , Eckert Andrew J. , González-Martínez Santiago C. 
Published: March 2020

AbstractA decade of association studies in multiple organisms suggests that most complex traits are polygenic; that is, they have a genetic architecture determined by numerous loci distributed across the genome, each with small effect-size. Thus, determining the degree of polygenicity and its variation across traits, environments and years is useful to understand the genetic basis of phenotypic variation. In this study, we applied multilocus approaches to estimate the degree of polygenicity of fitness-related traits in a long-lived plant (Pinus pinasterAit., maritime pine) and to analyze how polygenicity changes across environments and years. To do so, we evaluated five categories of fitness-related traits (survival, height, phenology-related, functional, and biotic-stress response traits) in a clonal common garden network, planted in contrasted environments (over 12,500 trees). First, most of the analyzed traits showed evidence of local adaptation based onQST-FSTcomparisons. Second, we observed a remarkably stable degree of polygenicity, averaging 6% (range of 0-27%), across traits, environments and years. As previously suggested for humans, some of these traits showed also evidence of negative selection, which could explain, at least partially, the high degree of polygenicity. The observed genetic architecture of fitness-related traits in maritime pine supports the polygenic adaptation model. Because polygenic adaptation can occur rapidly, our study suggests that current predictions on the capacity of natural forest tree populations to adapt to new environments should be revised, which is of special relevance in the current context of climate change.