bluuebunny/crossref_metadata_2025_split huggingface dataset:
Targeted sequencing in DLBCL, molecular subtypes, and outcomes: a Haematological Malignancy Research Network report
Authors: Lacy Stuart E. , Barrans Sharon L. , Beer Philip A. , Painter Daniel , Smith Alexandra G. , Roman Eve , Cooke Susanna L. , Ruiz Camilo , Glover Paul , Van Hoppe Suzan J. L. , Webster Nichola , Campbell Peter J. , Tooze Reuben M. , Patmore Russell , Burton Cathy , Crouch Simon , Hodson Daniel J. 
Published: March 2020

Abstract
Based on the profile of genetic alterations occurring in tumor samples from selected diffuse large B-cell lymphoma (DLBCL) patients, 2 recent whole-exome sequencing studies proposed partially overlapping classification systems. Using clustering techniques applied to targeted sequencing data derived from a large unselected population-based patient cohort with full clinical follow-up (n = 928), we investigated whether molecular subtypes can be robustly identified using methods potentially applicable in routine clinical practice. DNA extracted from DLBCL tumors diagnosed in patients residing in a catchment population of ∼4 million (14 centers) were sequenced with a targeted 293-gene hematological-malignancy panel. Bernoulli mixture-model clustering was applied and the resulting subtypes analyzed in relation to their clinical characteristics and outcomes. Five molecular subtypes were resolved, termed MYD88, BCL2, SOCS1/SGK1, TET2/SGK1, and NOTCH2, along with an unclassified group. The subtypes characterized by genetic alterations of BCL2, NOTCH2, and MYD88 recapitulated recent studies showing good, intermediate, and poor prognosis, respectively. The SOCS1/SGK1 subtype showed biological overlap with primary mediastinal B-cell lymphoma and conferred excellent prognosis. Although not identified as a distinct cluster, NOTCH1 mutation was associated with poor prognosis. The impact of TP53 mutation varied with genomic subtypes, conferring no effect in the NOTCH2 subtype and poor prognosis in the MYD88 subtype. Our findings confirm the existence of molecular subtypes of DLBCL, providing evidence that genomic tests have prognostic significance in non-selected DLBCL patients. The identification of both good and poor risk subtypes in patients treated with R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) clearly show the clinical value of the approach, confirming the need for a consensus classification.
YWHAE/14-3-3ε expression impacts the protein load, contributing to proteasome inhibitor sensitivity in multiple myeloma
Authors: Xu Yan , Fulciniti Mariateresa , Samur Mehmet K. , Ho Matthew , Deng Shuhui , Liu Lanting , Wen Kenneth , Yu Tengteng , Chyra Zuzana , Dereibal Sanika , Zhang Li , Yao Yao , Chakraborty Chandraditya , Morelli Eugenio , Li Na , Lopez Michael A. , Perini Tommaso , Mu Shidai , An Gang , Alonso Rafael , Bianchi Giada , Tai Yu-Tzu , Anderson Kenneth C. , Qiu Lugui , Munshi Nikhil C. 
Published: March 2020

Abstract
High protein load is a feature of multiple myeloma (MM), making the disease exquisitely sensitive to proteasome inhibitor (PIs). Despite the success of PIs in improving patient outcome, the majority of patients develop resistance leading to progressive disease; thus, the need to investigate the mechanisms driving the drug sensitivity vs resistance. With the well-recognized chaperone function of 14-3-3 proteins, we evaluated their role in affecting proteasome activity and sensitivity to PIs by correlating expression of individual 14-3-3 gene and their sensitivity to PIs (bortezomib and carfilzomib) across a large panel of MM cell lines. We observed a significant positive correlation between 14-3-3ε expression and PI response in addition to a role for 14-3-3ε in promoting translation initiation and protein synthesis in MM cells through binding and inhibition of the TSC1/TSC2 complex, as well as directly interacting with and promoting phosphorylation of mTORC1. 14-3-3ε depletion caused up to a 50% reduction in protein synthesis, including a decrease in the intracellular abundance and secretion of the light chains in MM cells, whereas 14-3-3ε overexpression or addback in knockout cells resulted in a marked upregulation of protein synthesis and protein load. Importantly, the correlation among 14-3-3ε expression, PI sensitivity, and protein load was observed in primary MM cells from 2 independent data sets, and its lower expression was associated with poor outcome in patients with MM receiving a bortezomib-based therapy. Altogether, these observations suggest that 14-3-3ε is a predictor of clinical outcome and may serve as a potential target to modulate PI sensitivity in MM.
Crucial role for lung iron level and regulation in the pathogenesis and severity of asthma
Authors: Ali Md. Khadem , Kim Richard Y. , Brown Alexandra C. , Mayall Jemma R. , Karim Rafia , Pinkerton James W. , Liu Gang , Martin Kristy L. , Starkey Malcolm R. , Pillar Amber L. , Donovan Chantal , Pathinayake Prabuddha S. , Carroll Olivia R. , Trinder Debbie , Tay Hock L. , Badi Yusef E. , Kermani Nazanin Z. , Guo Yi-Ke , Aryal Ritambhara , Mumby Sharon , Pavlidis Stelios , Adcock Ian M. , Weaver Jessica , Xenaki Dikaia , Oliver Brian G. , Holliday Elizabeth G. , Foster Paul S. , Wark Peter A. , Johnstone Daniel M. , Milward Elizabeth A. , Hansbro Philip M. , Horvat Jay C. 
Published: March 2020

Accumulating evidence highlights links between iron regulation and respiratory disease. Here, we assessed the relationship between iron levels and regulatory responses in clinical and experimental asthma.We show that cell-free iron levels are reduced in the bronchoalveolar lavage (BAL) supernatant of severe or mild–moderate asthma patients and correlate with lower forced expiratory volume in 1 s (FEV1). Conversely, iron-loaded cell numbers were increased in BAL in these patients and with lower FEV1/forced vital capacity (FVC) ratio. The airway tissue expression of the iron sequestration molecules divalent metal transporter 1 (DMT1) and transferrin receptor 1 (TFR1) are increased in asthma, withTFR1expression correlating with reduced lung function and increased Type-2 (T2) inflammatory responses in the airways. Furthermore, pulmonary iron levels are increased in a house dust mite (HDM)-induced model of experimental asthma in association with augmentedTfr1expression in airway tissue, similar to human disease. We show that macrophages are the predominant source of increased Tfr1 and Tfr1+macrophages have increasedIl13expression. We also show that increased iron levels induce increased pro-inflammatory cytokine and/or extracellular matrix (ECM) responses in human airway smooth muscle (ASM) cells and fibroblastsex vivoand induce key features of asthmain vivo, including airway hyper-responsiveness (AHR) and fibrosis, and T2 inflammatory responses.Together these complementary clinical and experimental data highlight the importance of altered pulmonary iron levels and regulation in asthma, and the need for a greater focus on the role and potential therapeutic targeting of iron in the pathogenesis and severity of disease.
Energy efficiency trade-offs in small to large electric vehicles
Authors: Weiss Martin , Cloos Kira Christina , Helmers Eckard 
Published: March 2020

Abstract
Background
As electric kick scooters, three-wheelers, and passenger cars enter the streets, efficiency trade-offs across vehicle types gain practical relevance for consumers and policy makers. Here, we compile a comprehensive dataset of 428 electric vehicles, including seven vehicle types and information on certified and real-world energy consumption. Regression analysis is applied to quantify trade-offs between energy consumption and other vehicle attributes.

Results
Certified and real-world energy consumption of electric vehicles increase by 60% and 40%, respectively, with each doubling of vehicle mass, but only by 5% with each doubling of rated motor power. These findings hold roughly also for passenger cars whose energy consumption tends to increase 0.6 ± 0.1 kWh/100 km with each 100 kg of vehicle mass. Battery capacity and vehicle mass are closely related. A 10 kWh increase in battery capacity increases the mass of electric cars by 15 kg, their drive range by 40–50 km, and their energy consumption by 0.7–1.0 kWh/100 km. Mass-produced state-of-the-art electric passenger cars are 2.1 ± 0.8 kWh/100 km more efficient than first-generation vehicles, produced at small scale.

Conclusion
Efficiency trade-offs in electric vehicles differ from those in conventional cars—the latter showing a strong dependency of fuel consumption on rated engine power. Mass-related efficiency trade-offs in electric vehicles are large and could be tapped by stimulating mode shift from passenger cars to light electric road vehicles. Electric passenger cars still offer potentials for further efficiency improvements. These could be exploited through a dedicated energy label with battery capacity as utility parameter.
Fish biomarkers from a different perspective: evidence of adaptive strategy of Abramis brama (L.) to chemical stress
Authors: Tenji Dina , Micic Bojana , Sipos Sandor , Miljanovic Branko , Teodorovic Ivana , Kaisarevic Sonja 
Published: March 2020

Abstract
Background
A prerequisite for long-term survival of populations under multi-stress conditions is their capacity to set up efficient adaptive strategies. However, changes in the activity of molecular biomarkers have been for decades considered as early signals of the deterioration of the fish health and evidence of stress-related adverse biological effects. The aim of this study was to show that such changes actually represent adaptive response of fish to chemical stress. Gene expression and enzyme activity level in liver and brain of specimens from two populations of Abramis brama from contrasted habitats (nature reserve and urban) were examined. Selected parameters included biomarkers of general stress, antioxidative defence, xenobiotic metabolism, endocrine disruption, glucose homeostasis, iron homeostasis, and neurotoxicity.

Results
Exposure of A. brama population from urban area to chemical stress was confirmed by assessment of chronic toxic pressure at fish habitats using Toxic Unit approach. The most pronounced response to chemical stress is observed through the activation of antioxidative defence mechanisms in brain and liver at gene and enzyme activity level, high biotransformation capacity of liver, and activation of mechanisms that will meet energy demands and compensate for the metabolic costs of the response to toxicants (higher expression of genes related to glucose homeostasis in the exposed population). Higher hepatosomatic index in the exposed population implies liver hypertrophy due to increased functional load caused by pollution. Body condition factors indicate good overall condition of both fish populations and confirm high efficacy of mobilized adaptation mechanisms in the exposed population.

Conclusions
The study provided the first data on basal expression of a number of genes in A. brama, potentially valuable for biomonitoring studies in absence of clear pollution gradient and/or reference sites (conditions). The study highlights importance of newly identified roles of various genes and proteins, typically considered as biomarkers of effects, and shows that changes in these parameters do not necessarily indicate the deterioration of the fish health. Such changes should be considered as adaptive response to chemical stress, rather than direct proof of ecological impact of pollution in situ.
PESM: predicting the essentiality of miRNAs based on gradient boosting machines and sequences
Authors: Yan Cheng , Wu Fang-Xiang , Wang Jianxin , Duan Guihua 
Published: March 2020

AbstractBackgroundMicroRNAs (miRNAs) are a kind of small noncoding RNA molecules that are direct posttranscriptional regulations of mRNA targets. Studies have indicated that miRNAs play key roles in complex diseases by taking part in many biological processes, such as cell growth, cell death and so on. Therefore, in order to improve the effectiveness of disease diagnosis and treatment, it is appealing to develop advanced computational methods for predicting the essentiality of miRNAs.ResultIn this study, we propose a method (PESM) to predict the miRNA essentiality based on gradient boosting machines and miRNA sequences. First, PESM extracts the sequence and structural features of miRNAs. Then it uses gradient boosting machines to predict the essentiality of miRNAs. We conduct the 5-fold cross-validation to assess the prediction performance of our method. The area under the receiver operating characteristic curve (AUC), F-measure and accuracy (ACC) are used as the metrics to evaluate the prediction performance. We also compare PESM with other three competing methods which include miES, Gaussian Naive Bayes and Support Vector Machine.ConclusionThe results of experiments show that PESM achieves the better prediction performance (AUC: 0.9117, F-measure: 0.8572, ACC: 0.8516) than other three computing methods. In addition, the relative importance of all features also further shows that newly added features can be helpful to improve the prediction performance of methods.
LCQS: an efficient lossless compression tool of quality scores with random access functionality
Authors: Fu Jiabing , Ke Bixin , Dong Shoubin 
Published: March 2020

Abstract
Background
Advanced sequencing machines dramatically speed up the generation of genomic data, which makes the demand of efficient compression of sequencing data extremely urgent and significant. As the most difficult part of the standard sequencing data format FASTQ, compression of the quality score has become a conundrum in the development of FASTQ compression. Existing lossless compressors of quality scores mainly utilize specific patterns generated by specific sequencer and complex context modeling techniques to solve the problem of low compression ratio. However, the main drawbacks of these compressors are the problem of weak robustness which means unstable or even unavailable results of sequencing files and the problem of slow compression speed. Meanwhile, some compressors attempt to construct a fine-grained index structure to solve the problem of slow random access decompression speed. However, they solve the problem at the sacrifice of compression speed and at the expense of large index files, which makes them inefficient and impractical. Therefore, an efficient lossless compressor of quality scores with strong robustness, high compression ratio, fast compression and random access decompression speed is urgently needed and of great significance.

Results
In this paper, based on the idea of maximizing the use of hardware resources, LCQS, a lossless compression tool specialized for quality scores, was proposed. It consists of four sequential processing steps: partitioning, indexing, packing and parallelizing. Experimental results reveal that LCQS outperforms all the other state-of-the-art compressors on all criteria except for the compression speed on the dataset SRR1284073. Furthermore, LCQS presents strong robustness on all the test datasets, with its acceleration ratios of compression speed increasing by up to 29.1x, its file size reducing by up to 28.78%, and its random access decompression speed increasing by up to 2.1x. Additionally, LCQS also exhibits strong scalability. That is, the compression speed increases almost linearly as the size of input dataset increases.

Conclusion
The ability to handle all different kinds of quality scores and superiority in compression ratio and compression speed make LCQS a high-efficient and advanced lossless quality score compressor, along with its strength of fast random access decompression. Our tool LCQS can be downloaded from https://github.com/SCUT-CCNL/LCQSand freely available for non-commercial usage.
A deep learning-based framework for lung cancer survival analysis with biomarker interpretation
Authors: Cui Lei , Li Hansheng , Hui Wenli , Chen Sitong , Yang Lin , Kang Yuxin , Bo Qirong , Feng Jun 
Published: March 2020

Abstract
Background
Lung cancer is the leading cause of cancer-related deaths in both men and women in the United States, and it has a much lower five-year survival rate than many other cancers. Accurate survival analysis is urgently needed for better disease diagnosis and treatment management.

Results
In this work, we propose a survival analysis system that takes advantage of recently emerging deep learning techniques. The proposed system consists of three major components. 1) The first component is an end-to-end cellular feature learning module using a deep neural network with global average pooling. The learned cellular representations encode high-level biologically relevant information without requiring individual cell segmentation, which is aggregated into patient-level feature vectors by using a locality-constrained linear coding (LLC)-based bag of words (BoW) encoding algorithm. 2) The second component is a Cox proportional hazards model with an elastic net penalty for robust feature selection and survival analysis. 3) The third commponent is a biomarker interpretation module that can help localize the image regions that contribute to the survival model’s decision. Extensive experiments show that the proposed survival model has excellent predictive power for a public (i.e., The Cancer Genome Atlas) lung cancer dataset in terms of two commonly used metrics: log-rank test (p-value) of the Kaplan-Meier estimate and concordance index (c-index).

Conclusions
In this work, we have proposed a segmentation-free survival analysis system that takes advantage of the recently emerging deep learning framework and well-studied survival analysis methods such as the Cox proportional hazards model. In addition, we provide an approach to visualize the discovered biomarkers, which can serve as concrete evidence supporting the survival model’s decision.
RASflow: an RNA-Seq analysis workflow with Snakemake
Authors: Zhang Xiaokang , Jonassen Inge 
Published: March 2020

Abstract
Background
With the cost of DNA sequencing decreasing, increasing amounts of RNA-Seq data are being generated giving novel insight into gene expression and regulation. Prior to analysis of gene expression, the RNA-Seq data has to be processed through a number of steps resulting in a quantification of expression of each gene/transcript in each of the analyzed samples. A number of workflows are available to help researchers perform these steps on their own data, or on public data to take advantage of novel software or reference data in data re-analysis. However, many of the existing workflows are limited to specific types of studies. We therefore aimed to develop a maximally general workflow, applicable to a wide range of data and analysis approaches and at the same time support research on both model and non-model organisms. Furthermore, we aimed to make the workflow usable also for users with limited programming skills.

Results
Utilizing the workflow management system Snakemake and the package management system Conda, we have developed a modular, flexible and user-friendly RNA-Seq analysis workflow: RNA-Seq Analysis Snakemake Workflow (RASflow). Utilizing Snakemake and Conda alleviates challenges with library dependencies and version conflicts and also supports reproducibility. To be applicable for a wide variety of applications, RASflow supports the mapping of reads to both genomic and transcriptomic assemblies. RASflow has a broad range of potential users: it can be applied by researchers interested in any organism and since it requires no programming skills, it can be used by researchers with different backgrounds. The source code of RASflow is available on GitHub: https://github.com/zhxiaokang/RASflow.

Conclusions
RASflow is a simple and reliable RNA-Seq analysis workflow covering many use cases.
SEQing: web-based visualization of iCLIP and RNA-seq data in an interactive python framework
Authors: Lewinski Martin , Bramkamp Yannik , Köster Tino , Staiger Dorothee 
Published: March 2020

Abstract
Background
RNA-binding proteins interact with their target RNAs at specific sites. These binding sites can be determined genome-wide through individual nucleotide resolution crosslinking immunoprecipitation (iCLIP). Subsequently, the binding sites have to be visualized. So far, no visualization tool exists that is easily accessible but also supports restricted access so that data can be shared among collaborators.

Results
Here we present SEQing, a customizable interactive dashboard to visualize crosslink sites on target genes of RNA-binding proteins that have been obtained by iCLIP. Moreover, SEQing supports RNA-seq data that can be displayed in a different window tab. This allows, e.g. crossreferencing the iCLIP data with genes differentially expressed in mutants of the RBP and thus obtain some insights into a potential functional relevance of the binding sites. Additionally, detailed information on the target genes can be incorporated in another tab.

Conclusion
SEQing is written in Python3 and runs on Linux. The web-based access makes iCLIP data easily accessible, even with mobile devices. SEQing is customizable in many ways and has also the option to be secured by a password. The source code is available at https://github.com/malewins/SEQing.
Modeling methylation dynamics with simultaneous changes in CpG islands
Authors: Grosser Konrad , Metzler Dirk 
Published: March 2020

AbstractBackgroundIn vertebrate genomes, CpG sites can be clustered into CpG islands, and the amount of methylation in a CpG island can change due to gene regulation processes. Thus, single regulatory events can simultaneously change the methylation states of many CpG sites within a CpG island. This should be taken into account when quantifying the amount of change in methylation, for example in form of a branch length in a phylogeny of cell types.ResultsWe propose a probabilistic model (the IWE-SSE model) of methylation dynamics that accounts for simultaneous methylation changes in multiple CpG sites belonging to the same CpG island. We further propose a Markov-chain Monte-Carlo (MCMC) method to fit this model to methylation data from cell type phylogenies and apply this method to available data from murine haematopoietic cells and from human cell lines. Combined with simulation studies, these analyses show that accounting for CpG island wide methylation changes has a strong effect on the inferred branch lengths and leads to a significantly better model fit for the methylation data from murine haematopoietic cells and human cell lines.ConclusionThe MCMC based parameter estimation method for the IWE-SSE model in combination with our MCMC based inference method allows to quantify the amount of methylation changes at single CpG sites as well as on entire CpG islands. Accounting for changes affecting entire islands can lead to more accurate branch length estimation in the presence of simultaneous methylation change.