bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impaired neurocognitive function in glioma patients: from pathophysiology to novel intervention strategies
Authors: Noll Kyle R. , Walbert Tobias , Wefel Jeffrey S. 
Published: October 2020

Purpose of review
This review succinctly summarizes the recent literature regarding etiological contributors to impaired neurocognitive function (NCF) in adult patients with glioma. A brief overview of intervention and prevention strategies is also provided.

Recent findings
A majority of patients with glioma exhibit NCF deficits, most frequently in memory and executive functioning. Impairments are often disabling and associated with reduced quality of life and survival. Cause is multifactorial and includes the tumour itself, treatments received and associated comorbidities. Although modern techniques such as brain mapping, dosing modifications and prophylactic medication aim to improve the NCF outcomes following neurosurgical resection and radiation therapy, a sizeable proportion of patients continue to evidence treatment-related NCF declines related to adverse effects to both local and distributed cerebral networks. Numerous patient and tumour characteristics, including genetic markers and sociodemographic factors, influence the pattern and severity of NCF impairment. Some rehabilitative and pharmacologic approaches show promise in mitigating NCF impairment in this population, though benefits are somewhat modest and larger scale intervention studies are needed.

Summary
Research regarding NCF in patients with glioma has dramatically proliferated, providing insights into the mechanisms underlying impaired NCF and pointing to potential interventions, though further work is needed.
COVID-19 era, Preventive effect of no going out against co-infection of the seasonal influenza virus and SARS-CoV-2
Authors: Hayashi Takuma , Yaegashi Nobuo , Konishi Ikuo 
Published: October 2020

AbstractIn the situation where expansion of coronavirus infectious disease-2019 (COVID-19) does not stop, there is concern about co-infection of people with the seasonal influenza infections from late autumn to winter 2020. Therefore, the importance of supplying vaccines against the seasonal influenza has been pointed out all over the world. As an example in Japan, the number of people infected with the seasonal influenza, hand-foot-and-mouth disease (HFMD), epidemic keratoconjunctivitis, and pharyngoconjunctival fever (PCF), which are the seasonal infectious diseases in the 2020 season, has decreased remarkably compared to the number of people infected each year. It is believed that the significant reduction in the number of people infected with these seasonal infectious diseases is a result of the pervasive hand washing, wearing masks and maintaining social distance in COVID-19 rea. To examine the correlation between the three factors of the number of people with each seasonal infectious disease, the mask wearing rate, and the outing rate, we created a three-dimensional scatter plot based on these three factors using principal component analysis. Our research findings demonstrated preventive effect of no going out against co-infection with the seasonal influenza and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
Oil Immersed Lossless Total Analysis System (OIL-TAS): Integrated RNA Extraction and Detection for SARS-CoV-2 Testing
Authors: Juang Duane S. , Juang Terry D. , Dudley Dawn M. , Newman Christina M. , Friedrich Thomas C. , O’Connor David H. , Beebe David J. 
Published: October 2020

AbstractThe coronavirus disease 2019 (COVID-19) pandemic exposed difficulties in scaling current quantitative PCR (qPCR)-based diagnostic methodologies for large-scale infectious disease testing. Bottlenecks include the lengthy multi-step process of nucleic acid extraction followed by qPCR readouts, which require costly instrumentation and infrastructure, as well as reagent and plastic consumable shortages stemming from supply chain constraints. Here we report a novel Oil Immersed Lossless Total Analysis System (OIL-TAS), which integrates RNA extraction and detection onto a single device that is simple, rapid, cost effective, uses minimal supplies and requires reduced infrastructure to perform. We validated the performance of OIL-TAS using contrived samples containing inactivated SARS-CoV-2 viral particles, which show that the assay can reliably detect an input concentration of 10 copies/μL and sporadically detect down to 1 copy/μL. The OIL-TAS method can serve as a faster, cheaper, and easier-to-deploy alternative to current qPCR-based methods for infectious disease testing.
Characterizing the genetic diversity of the Andean blueberry (Vaccinium floribundumKunth.) across the Ecuadorian Highlands
Authors: Vega-Polo Pamela , Cobo Maria M. , Argudo Andrea , Gutierrez Bernardo , Rowntree Jennifer , de Lourdes Torres Maria 
Published: October 2020

AbstractThe Ecuadorianpáramo, a high altitude tundra-like ecosystem, is a unique source of various ecosystem services and distinct biodiversity. Anthropogenic activities are associated with its fragmentation, which alters ecological factors and directly threatens resident species.Vaccinium floribundumKunth., commonly known as Andean blueberry ormortiño, is a wild shrub endemic to the Andean region and highly valued in Ecuador for its berries, which are widely used in food preparations and hold an important cultural value. Since it is a wild species,mortiñocould be vulnerable to environmental changes, resulting in a reduction of the size and distribution of its populations. To evaluate the extent of these effects on themortiñopopulations, we assessed the genetic diversity and population structure of the species along the Ecuadorian highlands. We designed and developed a set of 30 homologous SSR markers and used 16 of these to characterize 100mortiñoindividuals from 27 collection sites. Our results revealed a high degree of genetic diversity (HE=0.73) for the Ecuadorianmortiño, and a population structure analyses suggested the existence of distinct genetic clusters present in the northern, central and southern highlands. A fourth, clearly differentiated cluster was also found and included individuals from locations at higher elevations. We suggest that the population structure of the species could be explained by an isolation-by-distance model and can be associated to the geological history of the Andean region. Our results suggest that elevation could also be a key factor in the differentiation ofmortiñopopulations. This study provides an extensive overview of the species across its distribution range in Ecuador, contributing to a better understanding of its conservation status. These results can assist the development of conservation programs for this valuable biological and cultural resource and for thepáramoecosystems as a whole.
ezGeno: An Automatic Model Selection Package for Genomic Data Analysis
Authors: Lin Jun-Liang , Hsieh Tsung-Ting , Tung Yi-An , Chen Xuan-Jun , Hsiao Yu-Chun , Yang Chia-Lin , Liu Tyng-Luh , Chen Chien-Yu 
Published: October 2020

AbstractTo facilitate the process of tailor-making a deep neural network for exploring the dynamics of genomic DNA, we have developed a hands-on package called ezGeno that automates the search process of various parameters and network structure. ezGeno considers three different sets of search spaces, namely, the number of filters, dilation factors, and the connectivity between different layers. ezGeno can be applied to any kind of 1D genomic input such as genomic sequences, histone modifications, DNase feature data and so on. Combinations of multiple abovementioned 1D features are also applicable. Specifically, for the task of predicting TF binding using genomic sequences as the input, ezGeno can consistently return the best performing set of parameters and network structure, as well as highlight the important segments within the original sequences. For the task of predicting tissue-specific enhancer activity using both sequence and DNase feature data as the input, ezGeno also regularly outperforms the hand-designed models. In this study, we demonstrate that ezGeno is superior in efficiency and accuracy when compared to AutoKeras, a general open-source AutoML package. The average AUC of ezGeno is also consistently higher than the result of using a one-layer DeepBind model. With the flexibility of ezGeno, we expect that this package can provide future researchers not only support of model design in their analysis of genomic studies but also more insights into the regulatory landscape.AvailabilityThe ezGeno package can be freely accessed athttps://github.com/ailabstw/ezGeno.ContactDr. Chien-Yu Chen,chienyuchen@ntu.edu.tw
Scene context impairs perception of semantically congruent objects
Authors: Spaak Eelke , Peelen Marius V. , de Lange Floris P. 
Published: October 2020

AbstractVisual scene context is well-known to facilitate the recognition of scene-congruent objects. Interestingly, however, according to the influential theory of predictive coding, scene congruency should lead to reduced (rather than enhanced) processing of congruent objects, compared to incongruent ones, since congruent objects elicit reduced prediction error responses. We tested this counterintuitive hypothesis in two online behavioural experiments with human participants (N = 300). We found clear evidence for impaired perception of congruent objects, both in a change detection task measuring response times as well as in a bias-free object discrimination task measuring accuracy. Congruency costs were related to independent subjective congruency ratings. Finally, we show that the reported effects cannot be explained by low-level stimulus confounds, response biases, or top-down strategy. These results provide convincing evidence for perceptual congruency costs during scene viewing, in line with predictive coding theory.Statement of RelevanceThe theory of the ‘Bayesian brain’, the idea that our brain is a hypothesis-testing machine, has become very influential over the past decades. A particularly influential formulation is the theory of predictive coding. This theory entails that stimuli that are expected, for instance because of the context in which they appear, generate a weaker neural response than unexpected stimuli. Scene context correctly ‘predicts’ congruent scene elements, which should result in lower prediction error. Our study tests this important, counterintuitive, and hitherto not fully tested, hypothesis. We find clear evidence in favour of it, and demonstrate that these ‘congruency costs’ are indeed evident in perception, and not limited to one particular task setting or stimulus set. Since perception in the real world is never of isolated objects, but always of entire scenes, these findings are important not just for the Bayesian brain hypothesis, but for our understanding of real-world visual perception in general.
Negative controls of chemical probes can be misleading
Authors: Lee Jinyoung , Schapira Matthieu 
Published: October 2020

ABSTRACTChemical probes are selective modulators that are used in cell assays to link a phenotype to a gene and have become indispensable tools to explore gene function and discover therapeutic targets. While binding to off-targets can be acceptable or beneficial for drugs, it is a confounding factor for chemical probes, as the observed phenotype may be driven by inhibition of an unknown off-target instead of the targeted protein. A negative control – a close chemical analog of the chemical probe that is inactive against the intended target – is typically used to verify that the phenotype is indeed driven by targeted protein. Here, we compare the selectivity profiles of four unrelated chemical probes and their respective negative controls and find that the control is sometimes inactive against up to 80% of known off-targets, suggesting that a lost phenotype upon treatment with the negative control may be driven by loss of inhibition of the off-target. To extend this analysis, we inspect the crystal structures of 90 pairs of unrelated proteins, where both proteins within each pair is in complex with the same drug-like ligand, and estimate that in 50% of cases, methylation (a simple chemical modification often used to generate negative controls) of the ligand at a position that will preclude binding to one protein (intended target) will also preclude binding to the other (off-target). These results uncover a risk associated with the use of negative controls to confirm gene-phenotype associations. We propose that a best practice should rather be to verify that two chemically unrelated chemical probes targeting the same protein lead to the same phenotype.Abstract Figure
Towards understanding the regulation of histone H1 somatic subtypes with OMICs
Authors: Ponte Inma , Andrés Marta , Jordan Albert , Roque Alicia 
Published: October 2020

AbstractBackgroundHistone H1 is involved in the regulation of chromatin higher-order structure and compaction. In humans, histone H1 is a multigene family with seven subtypes differentially expressed in somatic cells. Which are the regulatory mechanisms that determine the variability of the H1 complement is a long-standing biological question regarding histone H1. We have used a new approach based on the integration of OMICs data to address this question.ResultsWe have examined the 3D-chromatin structure, the binding of transcription factors (TFs), and the expression of somatic H1 genes in human cell lines, using data from public repositories, such as ENCODE. Analysis of Hi-C, ChIP-seq, and RNA-seq data, have shown that transcriptional control has a greater impact on H1 regulation than previously thought. Somatic H1 genes located in TADs show higher expression than in boundaries. H1 genes are targeted by a variable number of transcription factors including cell cycle-related TFs, and tissue-specific TFs, suggesting a finetuned, subtype-specific transcriptional control. We describe, for the first time, that all H1 somatic subtypes are under transcriptional co-regulation. The replication-independent subtypes, which are encoded in different chromosomes, isolated from other histone genes are also co-regulated with the rest of the somatic H1 genes, indicating that transcriptional co-regulation extends beyond the histone cluster.ConclusionsTranscriptional control and transcriptional co-regulation explain, at least in part, the variability of H1 complement, the fluctuations of H1 subtypes during development, and also the compensatory effects observed, in model systems, after perturbation of one or more H1 subtypes.
Retroactivity induced operating regime transition in a phosphorylation-dephosphorylation cycle
Authors: Parundekar Akshay , Viswanathan Ganesh A 
Published: October 2020

AbstractOperating regimes characterizing the input-output behaviour of an activated phosphorylation-dephosphorylation reaction cycle (PdPC) such as a single MAPK cascade are dictated by the saturated/unsaturated state of the two underlying enzymatic reactions. Four combinations of the states of these two enzymatic reactions led to identification of distinct operating regimes,viz., Hyperbolic (H), Signal transducing (ST), Threshold-hyperbolic (TH) and Ultrasensitive (U). A single PdPC without an explicit feedback have been classically viewed as a module offering signal flow from upstream to downstream, that is, one-way communication. Recently it has been shown that load due to sequestration of the phosphorylated or unphosphorylated form of the substrate by corresponding downstream targets permits retroactive signalling that offers two-way communication. We systematically characterize the operating regimes of a single PdPC subject to retroactivity in either of the substrate forms. We identify five possible regime transitions that could be achieved by increasing the retroactivity strength on either of the two substrate forms. Remarkably, a retroactivity strength of 0.30 in the unphosphorylated form of the substrate is sufficient to induce a transition from ST to H regime indicating that the input-output behaviour of a PdPC is highly sensitive to the presence of a downstream load. Using sensitivity and rate-balance analysis, we show that modulation of the saturation levels of the two enzymatic reactions by increasing retroactivity is the fundamental mechanism governing operating regime transition.HighlightsCharacterization of operating regimes in the presence of retroactivity.Retroactivity can induce a transition between different operating regimes.Saturation levels of the two enzymatic reactions govern the regime transition.Sensitivity of the protein levels to retroactivity is dictated by saturation levels.
Extreme suction attachment performance from specialised insects living in mountain streams (Diptera: Blephariceridae)
Authors: Kang Victor , White Robin T. , Chen Simon , Federle Walter 
Published: October 2020

AbstractSuction is widely used by animals for strong controllable underwater adhesion but is less well understood than adhesion of terrestrial climbing animals. Here we investigate the attachment of an aquatic insect larva (Blephariceridae), which clings to rocks in torrential streams using the only known muscle-actuated suction organs in insects. We measured their attachment forces on well-defined rough substrates and found their adhesion was much less reduced by micro-roughness than terrestrial climbing insects.In vivovisualisation of the suction organs in contact with microstructured substrates revealed that they can mould around large asperities to form a seal. Moreover, we showed that spine-like microtrichia on the organ are stiff cuticular structures that only make tip contact on smooth and microstructured substrates. Our results highlight the performance and versatility of blepharicerid suction organs and introduce a new study system to explore biological suction.
Inference of high-resolution trajectories in single cell RNA-Seq data from RNA velocity
Authors: Zhang Ziqi , Zhang Xiuwei 
Published: October 2020

ABSTRACTTrajectory inference methods are used to infer cell developmental trajectories in a continuous biological process, for example, stem cell differentiation. Most of the current trajectory inference methods infer the developmental trajectories based on transcriptome similarity between cells, using single cell RNA-Sequencing (scRNA-Seq) data. These methods are often restricted to certain trajectory structures like linear structure or tree structure, and the directions of the trajectory can only be determined when the root cell is provided. On the other hand, RNA velocity inference method is shown to be a promising alternative in predicting short term cell developmental direction from the sequencing data. Here by we presentCellPath, a single cell trajectory inference method that infers developmental trajectories by integrating RNA velocity information.CellPathis able to find multiple high-resolution cell developmental paths instead of a single backbone trajectory obtained from traditional trajectory inference methods, and it no longer constrains the trajectory structure to be of any specific topology. The direction information provided by RNA-velocity also allowsCellPathto automatically detect the root cell and the direction of the dynamic process. We evaluateCellPathon both real and synthetic datasets, and show thatCellPathfinds more accurate and detailed trajectories compared to the state-of-the-art trajectory inference methods.
Nabo – a framework to define leukemia-initiating cells and differentiation in single-cell RNA-sequencing data
Authors: Dhapola Parashar , Eldeeb Mohamed , Ugale Amol , Olofzon Rasmus , Erlandsson Eva , Soneji Shamit , Bryder David , Göran Karlsson 
Published: October 2020

ABSTRACTSingle-cell transcriptomics facilitates innovative approaches to define and identify cell types within tissues and cell populations. An emerging interest in the cancer field is to assess the heterogeneity of transformed cells, including the identification of tumor-initiating cells based on similarities to their normal counterparts. However, such cell mapping is often confounded by the large effects on total gene expression programs introduced by strong perturbations such as an oncogenic event. Here, we present Nabo, a novel computational method that allows mapping of cells from one population to the most similar cells in a reference population, independently of confounding changes to gene expression programs initiated by perturbation. We validated this method on multiple datasets from different sources and platforms and show that Nabo achieves higher rates of accuracy than conventional classification methods. Nabo is available as an integrated toolkit for preprocessing, cell mapping, differential gene expression identification, and visualization of single-cell RNA-Seq data. For exploratory studies, Nabo includes methods to help evaluate the reliability of cell mapping results. We applied Nabo on droplet-based single-cell RNA-Seq data of healthy and oncogene-induced (MLL-ENL) hematopoietic progenitor cells (GMLPs) differentiating in vitro. Despite a substantial cellular heterogeneity resulting from differentiation of GMLPs and the large transcriptional effects induced by the fusion oncogene, Nabo could pinpoint the specific cell stage where differentiation arrest occurs, which included an immunophenotypic definition of the tumor-initiating population. Thus, Nabo allows for relevant comparison between target and control cells, without being confounded by differences in population heterogeneity.
Constructing and analysing dynamic models with modelbase v1.0 - a software update
Authors: van Aalst Marvin , Ebenhöh Oliver , Matuszyńska Anna 
Published: October 2020

AbstractBackgroundComputational mathematical models of biological and biomedical systems have been successfully applied to advance our understanding of various regulatory processes, metabolic fluxes, effects of drug therapies and disease evolution or transmission. Unfortunately, despite community efforts leading to the development of SBML or the BioModels database, many published models have not been fully exploited, largely due to lack of proper documentation or the dependence on proprietary software. To facilitate synergies within the emerging research fields of systems biology and medicine by reusing and further developing existing models, an open-source toolbox that makes the overall process of model construction more consistent, understandable, transparent and reproducible is desired.Results and DiscussionWe provide here the update on the development ofmodelbase, a free expandable Python package for constructing and analysing ordinary differential equation-based mathematical models of dynamic systems. It provides intuitive and unified methods to construct and solve these systems. Significantly expanded visualisation methods allow convenient analyses of structural and dynamic properties of the models. Specifying reaction stoichiometries and rate equations, the system of differential equations is assembled automatically. A newly provided library of common kinetic rate laws highly reduces the repetitiveness of the computer programming code, and provides full SBML compatibility. Previous versions provided functions for automatic construction of networks for isotope labelling studies. Using user-provided label maps,modelbasev1.0 streamlines the expansion of classic models to their isotope-specific versions. Finally, the library of previously published models implemented inmodelbaseis continuously growing. Ranging from photosynthesis over tumour cell growth to viral infection evolution, all models are available now in a transparent, reusable and unified format usingmodelbase.ConclusionWith the small price of learning a new software package, which is written in Python, currently one of the most popular programming languages, the user can develop new models and actively profit from the work of others, repeating and reproducing models in a consistent, tractable and expandable manner. Moreover, the expansion of models to their label specific versions enables simulating label propagation, thus providing quantitative information regarding network topology and metabolic fluxes.