bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dynamical study of infochemical influences on tropic interaction of diffusive plankton system
Authors: Tiwari Satish Kumar , Singh Ravikant , Thakur Nilesh Kumar 
Published: February 2021

AbstractWe propose a model for tropic interaction among the infochemical-producing phytoplankton and non-info chemical-producing phytoplankton and microzooplankton. Volatile information-conveying chemicals (infochemicals) released by phytoplankton play an important role in the food webs of marine ecosystems. Microzooplankton is an ecologically important grazer of phytoplankton for coexistence of a large number of phytoplankton species. Here, we discuss how information transferred by dimethyl sulfide shapes the interaction of phytoplankton. Phytoplankton deterrents may lead to propagation of IPP bloom. The interaction between IPP and microzooplankton follows the Beddington–DeAngelis-type functional response. Analytically, we discuss boundedness, stability and Turing instability of the model system. We perform numerical simulation for temporal (ODE model) as well as a spatial model system. Our numerical investigation shows that microzooplankton grazing refuse of IPP leads to oscillatory dynamics. Increasing diffusion coefficient of microzooplankton shows Turing instability. Time evolution also plays an important role in the stability of system dynamics. The results obtained in this paper are useful to understand the dominance of algal bloom in coastal and estuarine ecosystem.
Assessment of groundwater contamination risk by BTEX from residual fuel soil phase
Authors: Zanello Victoria , Scherger Leonardo E. , Lexow Claudio 
Published: February 2021

AbstractThe aim of this work is to assess the risk of groundwater contamination associated with BTEX dissolution from fuels as a residual phase. Numerical simulations of sixty scenarios were carried out with the software HYDRUS 2D/3D. Groundwater contamination risk was analyzed given the combination of different porous media textures (silt loam, sandy loam and clay), water fluxes (0.5%, 1% or 3% Rainfall), water table depths (1.5, 2.5, 5 or 8 m below ground surface) and biodegradation rate (active or null). Risk was calculated comparing leachate concentrations to the aquifer and limits established by an international guideline for human drinking water. In all cases, benzene and toluene had the highest mobility in the dissolved phase. Contrary, xylene and ethylbenzene tended to concentrate close to the source zone. These two compounds predominantly concentrated in the solid phase. Calculated risk was proportional to the water flux rate and inversely proportional to the unsaturated thickness. Without biodegradation, in fine-grained sediments risk was very high for shallow aquifers (> 1.5 m depth) and moderate or low for deeper aquifers. However, in sandy loam sediments risk was classified as very high for aquifers up to 8 m deep. When biodegradation was considered, leached concentrations were greatly reduced in the three textures. BTEX concentration in Bahía Blanca City´s aquifer showed acceptable agreement with simulated scenarios. The most sensitive parameters to model results were biodegradation > foc > water table depth > Ks. This study is important for assessing the risks and developing management strategies for fuel contaminated sites.
DeepCAPE: A Deep Convolutional Neural Network for the Accurate Prediction of Enhancers
Authors: Chen Shengquan , Gan Mingxin , Lv Hairong , Jiang Rui 
Published: February 2021

Abstract
The establishment of a landscape of enhancers across human cells is crucial to deciphering the mechanism of gene regulation, cell differentiation, and disease development. High-throughput experimental approaches, which contain successfully reported enhancers in typical cell lines, are still too costly and time-consuming to perform systematic identification of enhancers specific to different cell lines. Existing computational methods, capable of predicting regulatory elements purely relying on DNA sequences, lack the power of cell line-specific screening. Recent studies have suggested that chromatin accessibility of a DNA segment is closely related to its potential function in regulation, and thus may provide useful information in identifying regulatory elements. Motivated by the aforementioned understanding, we integrate DNA sequences and chromatin accessibility data to accurately predict enhancers in a cell line-specific manner. We proposed DeepCAPE, a deep convolutional neural network to predict enhancers via the integration of DNA sequences and DNase-seq data. Benefitting from the well-designed feature extraction mechanism and skip connection strategy, our model not only consistently outperforms existing methods in the imbalanced classification of cell line-specific enhancers against background sequences, but also has the ability to self-adapt to different sizes of datasets. Besides, with the adoption of auto-encoder, our model is capable of making cross-cell line predictions. We further visualize kernels of the first convolutional layer and show the match of identified sequence signatures and known motifs. We finally demonstrate the potential ability of our model to explain functional implications of putative disease-associated genetic variants and discriminate disease-related enhancers. The source code and detailed tutorial of DeepCAPE are freely available at https://github.com/ShengquanChen/DeepCAPE.
Global Profiling of 2-Hydroxyisobutyrylome in Common Wheat
Authors: Zhang Ning , Zhang Lingran , Li Linjie , Geng Junyou , Zhao Lei , Ren Yan , Dong Zhongdong , Chen Feng 
Published: February 2021

Abstract
As a novel post-translational modification (PTM), lysine 2-hydroxyisobutyrylation (Khib) is considered to regulate gene transcriptional activities in eukaryotic cells; however, the functions of Khib-modified proteins in plants remain unknown. Here, we report that Khib is an evolutionarily-conserved PTM in wheat and its progenitors. A total of 3348 Khib sites on 1074 proteins are identified in common wheat (Triticum aestivum L.) by using affinity purification and mass spectroscopy of 2-hydroxyisobutyrylome. Bioinformatic data indicate that Khib-modified proteins participate in a wide variety of biological and metabolic pathways. Immunoprecipitation confirms that Khib-modified proteins are present endogenously. A comparison of Khib and other main PTMs shows that Khib-modified proteins are simultaneously modified by multiple PTMs. Using mutagenesis experiments and co-immunoprecipitation assays, we demonstrate that Khib on K206 of phosphoglycerate kinase (PGK) is a key regulatory modification for its enzymatic activity, and mutation on K206 affects the interactions of PGK with its substrates. Furthermore, Khib modification of low-molecular-weight proteins is a response to the deacetylase inhibitors nicotinamide and trichostatin. This study provides evidence to promote our current understanding of Khib in wheat plants, including the cooperation between Khib and its metabolic regulation.
eTumorMetastasis: A Network-Based Algorithm Predicts Clinical Outcomes Using Whole-Exome Sequencing Data of Cancer Patients
Authors: Milanese Jean-Sébastien , Tibiche Chabane , Zaman Naif , Zou Jinfeng , Han Pengyong , Meng Zhigang , Nantel Andre , Droit Arnaud , Wang Edwin 
Published: February 2021

Abstract
Continual reduction in sequencing cost is expanding the accessibility of genome sequencing data for routine clinical applications. However, the lack of methods to construct machine learning-based predictive models using these datasets has become a crucial bottleneck for the application of sequencing technology in clinics. Here, we develop a new algorithm, eTumorMetastasis, which transforms tumor functional mutations into network-based profiles and identifies network operational gene (NOG) signatures. NOG signatures model the tipping point at which a tumor cell shifts from a state that doesn’t favor recurrence to one that does. We show that NOG signatures derived from genomic mutations of tumor founding clones (i.e., the ‘most recent common ancestor’ of the cells within a tumor) significantly distinguish the recurred and non-recurred breast tumors as well as outperform the most popular genomic test (i.e., Oncotype DX). These results imply that mutations of the tumor founding clones are associated with tumor recurrence and can be used to predict clinical outcomes. As such, predictive tools could be used in clinics to guide treatment routes. Finally, the concepts underlying the eTumorMetastasis pave the way for the application of genome sequencing in predictions for other complex genetic diseases. eTumorMetastasis pseudocode and related data used in this study are available at https://github.com/WangEdwinLab/eTumorMetastasis.
PM2RA: A Framework for Detecting and Quantifying Relationship Alterations in Microbial Community
Authors: Liu Zhi , Mi Kai , Xu Zhenjiang Zech , Zhang Qiankun , Liu Xingyin 
Published: February 2021

Abstract
The dysbiosis of gut microbiota is associated with the pathogenesis of human diseases. However, observing shifts in the microbe abundance cannot fully reveal underlying perturbations. Examining the relationship alterations (RAs) in the microbiome between health and disease statuses provides additional hints about the pathogenesis of human diseases, but no methods were designed to detect and quantify the RAs between different conditions directly. Here, we present profile monitoring for microbial relationship alteration (PM2RA), an analysis framework to identify and quantify the microbial RAs. The performance of PM2RA was evaluated with synthetic data, and it showed higher specificity and sensitivity than the co-occurrence-based methods. Analyses of real microbial datasets showed that PM2RA was robust for quantifying microbial RAs across different datasets in several diseases. By applying PM2RA, we identified several novel or previously reported microbes implicated in multiple diseases. PM2RA is now implemented as a web-based application available at http://www.pm2ra-xingyinliulab.cn/.
Prevalence and Distribution of Major β-Thalassemia Mutations and HbE/β-Thalassemia Variant in Nepalese Ethnic Groups
Authors: Lama Raju , Yusof Wardah , Shrestha Tilak R. , Hanafi Sarifah , Bhattarai Matrika , Hassan Rosline , Zilfalil Bin Alwi 
Published: February 2021

Background:
Beta-thalassemia is a genetic disorder that is inherited in an autosomal recessive pattern. This genetic disease leads to a defective beta-globin hemoglobin chain causing partial or complete beta-globin chain synthesis loss. Beta-thalassemia major patients need a continuous blood transfusion and iron chelation to maintain the normal homeostasis of red blood cells (RBCs) and other systems in the body. Patients also require treatment procedures that are costly and tedious, resulting in a serious health burden for developing nations such as Nepal.

Methods:
A total of 61 individuals clinically diagnosed to have thalassemia were genotyped with multiplex amplification refractory mutation system–polymerase chain reaction (ARMS–PCR). Twenty-one major mutations were investigated using allele-specific primers grouped into six different panels.

Results:
The most common mutations found (23%) were IVS 1–5 (G-C) and Cd 26 (G-A) (HbE), followed by 619 deletion, Cd 8/9 (+G), Cd 16 (–C), Cd 41/42 (–TTCT), IVS 1–1 (G–T), Cd 19 (A-G), and Cd 17 (A-T) at 20%, 12%, 8%, 6%, 4%, 3%, and 1%, respectively.

Conclusion:
The results of this study revealed that Nepal’s mutational profile is comparable to that of its neighboring countries, such as India and Myanmar. This study also showed that thalassemia could be detected across 17 Nepal’s ethnic groups, especially those whose ancestors originated from India and Central Asia.
The neural substrates of risky rewards and losses in healthy volunteers and patient groups: a PET imaging study
Authors: Skandali Nikolina , Majuri Joonas , Joutsa Juho , Baek Kwangyeol , Arponen Eveliina , Forsback Sarita , Kaasinen Valtteri , Voon Valerie 
Published: February 2021

AbstractBackgroundRisk is an essential trait of most daily decisions. Our behaviour when faced with risks involves evaluation of many factors including the outcome probabilities, the valence (gains or losses) and past experiences. Several psychiatric disorders belonging to distinct diagnostic categories, including pathological gambling and addiction, show pathological risk-taking and implicate abnormal dopaminergic, opioidergic and serotonergic neurotransmission. In this study, we adopted a transdiagnostic approach to delineate the neurochemical substrates of decision making under risk.MethodsWe recruited 39 participants, including 17 healthy controls, 15 patients with pathological gambling and seven binge eating disorder patients, who completed an anticipatory risk-taking task. Separately, participants underwent positron emission tomography (PET) imaging with three ligands, [18F]fluorodopa (FDOPA), [11C]MADAM and [11C]carfentanil to assess presynaptic dopamine synthesis capacity and serotonin transporter and mu-opioid receptor binding respectively.ResultsRisk-taking behaviour when faced with gains positively correlated with dorsal cingulate [11C]carfentanil binding and risk-taking to losses positively correlated with [11C]MADAM binding in the caudate and putamen across all subjects.ConclusionsWe show distinct neurochemical substrates underlying risk-taking with the dorsal cingulate cortex mu-opioid receptor binding associated with rewards and dorsal striatal serotonin transporter binding associated with losses. Risk-taking and goal-directed control appear to dissociate between dorsal and ventral fronto-striatal systems. Our findings thus highlight the potential role of pharmacological agents or neuromodulation on modifying valence-specific risk-taking biases.
Possible Underlying Mechanisms of Severe Decadal Droughts in Arid Central Asia During the Last 530 Years: Results From the Last Millennium Climate Reanalysis Project Version 2.0
Authors: Peng Youbing 
Published: February 2021

AbstractSevere persistent drought events during the last 530 years are examined in Arid Central Asia (ACA) by using the data with proxy assimilation from the Last Millennium Climate Reanalysis project version 2.0 (LMR v2.0). According to the results from the LMR v2.0, six severe decadal droughts occurred in ACA during the last 530 years and five of them overlap with the results based on the independent reconstructions of Asian summer precipitation, such as the droughts of 1519–1538 CE, 1545–1561 CE, 1621–1658 CE, 1822–1846 CE, and 1964–1983 CE. The climate anomaly patterns are characterized by opposite anomalies between the high‐latitude and midlatitude Eurasia during the first four well‐agreement droughts, with negative precipitation and positive surface level pressure and 500‐hPa geopotential height anomalies located over midlatitude Eurasia and opposite anomalies over high‐latitude Eurasia. Whereas, during the last well‐agreement drought, opposing pattern of the climate variation observed between in ACA and in the Mediterranean with negative precipitation anomalies and positive 500‐hPa geopotential height anomalies in ACA and opposite anomalies in the Mediterranean. The positive Arctic Oscillation/North Atlantic Oscillation influence is found to dominate the droughts of 1621–1658 CE via a northward migration of the westerlies. While the last drought of 1964–1983 CE produced by the combined impacts of the negative Atlantic Multidecadal Oscillation and Pacific Decadal Oscillation via the Silk Road pattern over the midlatitude Eurasian continent.
Imaging the P‐Wave Velocity Structure of Arctic Subsea Permafrost Using Laplace‐Domain Full‐Waveform Inversion
Authors: Kang Seung‐Goo , Jin Young Keun , Jang Ugeun , Duchesne Mathieu J. , Shin Changsoo , Kim Sookwan , Riedel Michael , Dallimore Scott R. , Paull Charles K. , Choi Yeonjin , Hong Jong Kuk 
Published: February 2021

AbstractClimate change in the Arctic has recently become a major scientific issue, and detailed information on the degradation of subsea permafrost on continental shelves in the Arctic is critical for understanding the major cause and effects of global warming, especially the release of greenhouse gases. The subsea permafrost at shallow depths beneath the Arctic continental shelves has significantly higher P‐wave velocities than the surrounding sediments. The distribution of subsea permafrost on Arctic continental shelves has been studied since the 1970s using seismic refraction methods. With seismic refraction data, the seismic velocity and the depth of the upper boundary of subsea permafrost can be determined. However, it is difficult to identify the lower boundary and the internal shape of permafrost. Here, we present two‐dimensional P‐wave velocity models of the continental shelf in the Beaufort Sea by applying the Laplace‐domain full‐waveform inversion method to acquired multichannel seismic reflection data. With the inverted P‐wave velocity model, we identify anomalous high seismic velocities that originated from the subsea permafrost. Information on the two‐dimensional distribution of subsea permafrost on the Arctic continental shelf area, including the upper and lower bounds of subsea permafrost, are presented. Also, the two‐dimensional P‐wave velocity model allows us to estimate the thawing pattern and the shape of subsea permafrost structures. Our proposed P‐wave velocity models were verified by comparison with the previous distribution map of subsea permafrost from seismic refraction analyses, geothermal modeling, and well‐log data.
Numerical and Analytical Modeling of Flow Partitioning in Partially Saturated Fracture Networks
Authors: Kordilla Jannes , Dentz Marco , Tartakovsky Alexandre M. 
Published: February 2021

AbstractInfiltration processes in fractured‐porous media remain a crucial, yet not very well understood component of recharge and vulnerability assessment. Under partially saturated condition flows in fractures, percolating fracture networks and fault zones contribute to the fastest spectrum of infiltration velocities via preferential pathways. Specifically, the partitioning dynamics at fracture intersections determine the magnitude of flow fragmentation into vertical and horizontal components, hence the bulk flow velocity and dispersion of fracture networks. Here we derive an approximate analytical solution for the partitioning process and validate it using smoothed particle hydrodynamics simulations. The transfer function is conceptually based on simulation results and laboratory experiments carried out in previous works. It allows efficient flow simulation through fracture networks with simple cubic structures and an arbitrary number of fractures and aperture sizes via linear response theory and convolution of a given input signal. We derive a nondimensional bulk flow velocity () and dispersion coefficient () to characterize fracture networks in terms of dimensionless horizontal and vertical time scales τm and τ0. The dispersion coefficient strongly depends on the horizontal time scale and converges toward a constant value of 0.08 within reasonable fluid and geometrical parameter ranges, while the nondimensional velocity exhibits a characteristic scaling. Given that hydraulic information is often only available at limited places within (fractured‐porous) aquifer systems (boreholes or springs), our study intends to provide an analytical concept to potentially reconstruct internal fracture network geometries from external boundary information, such as the dispersive properties of discharge (groundwater level fluctuations).
circST6GALNAC6 suppresses bladder cancer metastasis by sponging miR-200a-3p to modulate the STMN1/EMT axis
Authors: Tan Shuo , Kang Ye , Li Hu , He Hai-Qing , Zheng Long , Wu Shui-Qing , Ai Kai , Zhang Lei , Xu Ran , Zhang Xuan-Zhi , Zhao Xiao-Kun , Zhu Xuan 
Published: February 2021

AbstractBladder cancer (BCa) is an aggressive malignancy because of its distant metastasis and high recurrence rate. Circular RNAs (circRNAs) exert critical regulatory functions in cancer progression. However, the expression patterns and roles of circRNAs in BCa have not been well investigated. In this study, we first screened circRNA expression profiles using a circRNA microarray of paired BCa and normal tissues, and the expression of circST6GALNAC6 was confirmed by qRT-PCR and fluorescence in situ hybridization (FISH). MTT, colony formation and Transwell assays were performed to measure cell proliferation, migration and invasion. We investigated the regulatory effect of circST6GALNAC6 on miRNA and its target genes to explore the potential regulatory mechanisms of circST6GALNAC6 by chromatin immunoprecipitation (ChIP), RNA immunoprecipitation (RIP), MS2-tagged RNA affinity purification (MS2-TRAP), immunofluorescence (IF) and dual luciferase activity assays. A nude mouse xenograft model was used to examine the functions of circST6GALNAC6/STMN1 in tumour metastasis in vivo. We found that 881 circRNAs were significantly dysregulated in BCa tissues compared to normal tissues. circST6GALNAC6(hsa_circ_0088708) was downregulated in BCa tissues and cells. Overexpression of circST6GALNAC6 effectively inhibited the cell proliferation, migration, invasion and epithelial–mesenchymal transition (EMT) in vitro and suppressed BCa metastasis in vivo. Mechanistically, we showed that the SP1 transcription factor, which binds to the circST6GALNAC6 mRNA transcript, activates circST6GALNAC6 transcription. Next, we verified that circST6GALNAC6 serves as a sponge that directly binds miR-200a-3p to regulate stathmin (STMN1) expression. Furthermore, we found that STMN1 is involved in circST6GALNAC6/miR-200a-3p axis-regulated BCa EMT and metastasis. Thus, our findings indicate an important underlying mechanism in BCa metastasis by which SP1-induced circST6GALNAC6 sponges miR-200a-3p to promote STMN1/EMT signalling. This mechanism could provide pivotal potential prognostic biomarkers and therapeutic targets for BCa.
Dissociation between asymmetric value updating and perseverance in human reinforcement learning
Authors: Sugawara Michiyo , Katahira Kentaro 
Published: February 2021

AbstractThe learning rate is a key parameter in reinforcement learning that determines the extent to which novel information (outcome) is incorporated in guiding subsequent actions. Numerous studies have reported that the magnitude of the learning rate in human reinforcement learning is biased depending on the sign of the reward prediction error. However, this asymmetry can be observed as a statistical bias if the fitted model ignores the choice autocorrelation (perseverance), which is independent of the outcomes. Therefore, to investigate the genuine process underlying human choice behavior using empirical data, one should dissociate asymmetry in learning and perseverance from choice behavior. The present study addresses this issue by using a Hybrid model incorporating asymmetric learning rates and perseverance. First, by conducting simulations, we demonstrate that the Hybrid model can identify the true underlying process. Second, using the Hybrid model, we show that empirical data collected from a web-based experiment are governed by perseverance rather than asymmetric learning. Finally, we apply the Hybrid model to two open datasets in which asymmetric learning was reported. As a result, the asymmetric learning rate was validated in one dataset but not another.