bluuebunny/crossref_metadata_2025_split huggingface dataset:
Linear versus deep learning methods for noisy speech separation for EEG-informed attention decoding
Authors: Das Neetha , Zegers Jeroen , Van hamme Hugo , Francart Tom , Bertrand Alexander 
Published: January 2020

AbstractObjectiveA hearing aid’s noise reduction algorithm cannot infer to which speaker the user intends to listen to. Auditory attention decoding (AAD) algorithms allow to infer this information from neural signals, which leads to the concept of neuro-steered hearing aids. We aim to evaluate and demonstrate the feasibility of AAD-supported speech enhancement in challenging noisy conditions based on electroencephalography (EEG) recordings.ApproachThe AAD performance with a linear versus a deep neural network (DNN) based speaker separation was evaluated for same-gender speaker mixtures using 3 different speaker positions and 3 different noise conditions.Main resultsAAD results based on the linear approach were found to be at least on par and sometimes even better than pure DNN-based approaches in terms of AAD accuracy in all tested conditions. However, when using the DNN to support a linear data-driven beamformer, a performance improvement over the purely linear approach was obtained in the most challenging scenarios. The use of multiple microphones was also found to improve speaker separation and AAD performance over single-microphone systems.SignificanceRecent proof-of-concept studies in this context each focus on a different method in a different experimental setting, which makes it hard to compare them. Furthermore, they are tested in highly idealized experimental conditions, which are still far from a realistic hearing aid setting. This work provides a systematic comparison of a linear and non-linear neuro-steered speech enhancement model, as well as a more realistic validation in challenging conditions.
Short hydrogen bonds enhance non-aromatic protein-related fluorescence
Authors: Stephens Amberley D. , Qaisrani Muhammad Nawaz , Ruggiero Michael T. , Miron Gonzalo Diaz , Morzan Uriel N. , González Lebrero Mariano C. , Jones Saul T.E. , Poli Emiliano , Bond Andrew D. , Woodhams Philippa J. , Kleist Elyse M. , Grisanti Luca , Gebauer Ralph , Zeitler J. Axel , Credgington Dan , Hassanali Ali , Schierle Gabriele S. Kaminski 
Published: January 2020

AbstractFluorescence in biological systems is usually associated with the presence of aromatic groups. Here, we show that specific hydrogen bonding networks can significantly affect fluorescence employing a combined experimental and computational approach. In particular, we reveal that the single amino acid L-glutamine, by undergoing a chemical transformation leading to the formation of a short hydrogen bond, displays optical properties that are significantly enhanced compared to L-glutamine itself.Ab initiomolecular dynamics simulations highlight that these short hydrogen bonds prevent the appearance of a conical intersection between the excited and the ground states and thereby significantly decrease non-radiative transition probabilities. Our findings open the doors for the design of new photoactive materials with biophotonic applications.Significance statementIntrinsic fluorescence of non-aromatic amino acids is a puzzling phenomenon with an enormous potential for technological and biomedical applications. The physical origins of this effect, however, remain elusive. Herein, we demonstrate how specific hydrogen bond networks can modulate fluorescence. We highlight the key role played by short hydrogen bonds in the networks on the ensuing fluorescence and we provide a detailed molecular mechanism to explain this unusual non-aromatic optical properties. Our findings should benefit the design of novel optically active biomaterials for applications in biosensing and imaging.
Disentangling neocortical alpha/beta and hippocampal theta/gamma oscillations in human episodic memory formation
Authors: Griffiths Benjamin J. , Martín-Buro María Carmen , Staresina Bernhard P. , Hanslmayr Simon 
Published: January 2020

AbstractTo form an episodic memory, we must first process a vast amount of sensory information about a to-be-encoded event and then bind these sensory representations together to form a coherent memory. While these two cognitive capabilities are thought to have two distinct neural origins, with neocortical alpha/beta oscillations supporting information representation and hippocampal theta-gamma phase-amplitude coupling supporting mnemonic binding, evidence for a dissociation between these two neural markers is conspicuously absent. To address this, seventeen human participants completed a sequence-learning task that first involved processing information about three stimuli, and then binding these stimuli together into a coherent memory trace, all the while undergoing MEG recordings. We found that decreases in neocortical alpha/beta power during sequence perception, but not mnemonic binding, correlated with enhanced memory performance. Hippocampal theta/gamma phase-amplitude coupling, however, showed the opposite pattern; increases during mnemonic binding (but not sequence perception) correlated with enhanced memory performance. These results demonstrate that memory-related decreases in neocortical alpha/beta power and memory-related increases in hippocampal theta/gamma phase-amplitude coupling arise at distinct stages of the memory formation process. We speculate that this temporal dissociation reflects a functional dissociation in which neocortical alpha/beta oscillations could support the processing of incoming information relevant to the memory, while hippocampal theta-gamma phase-amplitude coupling could support the binding of this information into a coherent memory trace.
Automated Isoform Diversity Detector (AIDD): A pipeline for investigating transcriptome diversity of RNA-seq data
Authors: Plonski Noel-Marie , Johnson Emily , Frederick Madeline , Mercer Heather , Fraizer Gail , Meindl Richard , Casadesus Gemma , Piontkivska Helen 
Published: January 2020

AbstractBackgroundAs the number of RNA-seq datasets that become available to explore transcriptome diversity increases, so does the need for easy-to-use comprehensive computational workflows. Many available tools facilitate analyses of one of the two major mechanisms of transcriptome diversity, namely, differential expression of isoforms due to alternative splicing, while the second major mechanism - RNA editing due to post-transcriptional changes of individual nucleotides – remains under-appreciated. Both these mechanisms play an essential role in physiological and diseases processes, including cancer and neurological disorders. However, elucidation of RNA editing events at transcriptome-wide level requires increasingly complex computational tools, in turn resulting in a steep entrance barrier for labs who are interested in high-throughput variant calling applications on a large scale but lack the manpower and/or computational expertise.ResultsHere we present an easy-to-use, fully automated, computational pipeline (Automated Isoform Diversity Detector, AIDD) that contains open source tools for various tasks needed to map transcriptome diversity, including RNA editing events. To facilitate reproducibility and avoid system dependencies, the pipeline is contained within a pre-configured VirtualBox environment. The analytical tasks and format conversions are accomplished via a set of automated scripts that enable the user to go from a set of raw data, such as fastq files, to publication-ready results and figures in one step. A publicly available dataset of Zika virus-infected neural progenitor cells is used to illustrate AIDD’s capabilities.ConclusionsAIDD pipeline offers a user-friendly interface for comprehensive and reproducible RNA-seq analyses. Among unique features of AIDD are its ability to infer RNA editing patterns, including ADAR editing, and inclusion of Guttman scale patterns for time series analysis of such editing landscapes. AIDD-based results show importance of diversity of ADAR isoforms, key RNA editing enzymes linked with the innate immune system and viral infections. These findings offer insights into the potential role of ADAR editing dysregulation in the disease mechanisms, including those of congenital Zika syndrome. Because of its automated all-inclusive features, AIDD pipeline enables even a novice user to easily explore common mechanisms of transcriptome diversity, including RNA editing landscapes.
Pyphe: A python toolbox for assessing microbial growth and cell viability in high-throughput colony screens
Authors: Kamrad Stephan , Rodríguez-López Maria , Cotobal Cristina , Correia-Melo Clara , Ralser Markus , Bähler Jürg 
Published: January 2020

AbstractMicrobial fitness screens are a key technique in functional genomics. We present an all-in-one solution,pyphe, for automating and improving data analysis pipelines associated with large-scale fitness screens, including image acquisition and quantification, data normalisation, and statistical analysis.Pypheis versatile and processes fitness data from colony sizes, viability scores from phloxine B staining or colony growth curves, all obtained with inexpensive transilluminating flatbed scanners. We applypypheto show that the fitness information contained in late endpoint measurements of colony sizes is similar to maximum growth slopes from time series. We phenotype gene-deletion strains of fission yeast in 59,350 individual fitness assays in 70 conditions, revealing that colony size and viability provide complementary, independent information. Viability scores obtained from quantifying the redness of phloxine-stained colonies accurately reflect the fraction of live cells within colonies.Pypheis user-friendly, open-source and fully-documented, illustrated by applications to diverse fitness analysis scenarios.
Single platelet variability governs population sensitivity and initiates intrinsic heterotypic behaviours
Authors: Jongen Maaike S. A. , MacArthur Ben D. , Englyst Nicola A. , West Jonathan 
Published: January 2020

AbstractDroplet microfluidics combined with flow cytometry was used for high throughput single platelet function analysis. A large-scale sensitivity continuum was shown to be a general feature of human platelets from individual donors, with hypersensitive platelets coordinating significant sensitivity gains in bulk platelet populations and shown to direct aggregation in droplet-confined minimal platelet systems. Sensitivity gains scaled with agonist potency (convulxin>TRAP-14>ADP) and reduced the collagen and thrombin activation threshold required for platelet population polarization into pro-aggregatory and pro-coagulant states. The heterotypic platelet response results from an intrinsic behavioural program. The method and findings invite future discoveries into the nature of hypersensitive platelets and how community effects produce population level behaviours in health and disease.
A near-term iterative forecasting system successfully predicts reservoir hydrodynamics and partitions uncertainty in real time
Authors: Thomas R. Quinn , Figueiredo Renato J. , Daneshmand Vahid , Bookout Bethany J. , Puckett Laura K. , Carey Cayelan C. 
Published: January 2020

AbstractFreshwater ecosystems are experiencing greater variability due to human activities, necessitating new tools to anticipate future water quality. In response, we developed and deployed a real-time iterative water temperature forecasting system (FLARE – Forecasting Lake And Reservoir Ecosystems). FLARE is composed of: water quality and meteorology sensors that wirelessly stream data, a data assimilation algorithm that uses sensor observations to update predictions from a hydrodynamic model and calibrate model parameters, and an ensemble-based forecasting algorithm to generate forecasts that include uncertainty. Importantly, FLARE quantifies the contribution of different sources of uncertainty (driver data, initial conditions, model process, and parameters) to each daily forecast of water temperature at multiple depths. We applied FLARE to Falling Creek Reservoir (Vinton, Virginia, USA), a drinking water supply, during a 475-day period encompassing stratified and mixed thermal conditions. Aggregated across this period, root mean squared error (RMSE) of daily forecasted water temperatures was 1.13 C at the reservoir’s near-surface (1.0 m) for 7-day ahead forecasts and 1.62C for 16-day ahead forecasts. The RMSE of forecasted water temperatures at the near-sediments (8.0 m) was 0.87C for 7-day forecasts and 1.20C for 16-day forecasts. FLARE successfully predicted the onset of fall turnover 4-14 days in advance in two sequential years. Uncertainty partitioning identified meteorology driver data as the dominant source of uncertainty in forecasts for most depths and thermal conditions, except for the near-sediments in summer, when model process uncertainty dominated. Overall, FLARE provides an open-source system for lake and reservoir water quality forecasting to improve real-time management.Key PointsWe created a real-time iterative lake water temperature forecasting system that uses sensors, data assimilation, and hydrodynamic modelingOur water quality forecasting system quantifies uncertainty in each daily forecast and is open-source16-day future forecasted temperatures were within 1.4°C of observations over 16 months in a reservoir case study
ER stress-mediated BK dysfunction in the DRG underlies pain in a model of multiple sclerosis
Authors: Yousuf Muhammad Saad , Samtleben Samira , Lamothe Shawn M. , Friedman Timothy , Catuneanu Ana , Thorburn Kevin , Desai Mansi , Tenorio Gustavo , Schenk Geert J. , Ballanyi Klaus , Kurata Harley T. , Simmen Thomas , Kerr Bradley J. 
Published: January 2020

ABSTRACTNeuropathic pain is a common symptom of multiple sclerosis (MS) and current treatment options are ineffective. In this study, we investigated whether endoplasmic reticulum (ER) stress in dorsal root ganglia (DRG) contributes to pain hypersensitivity in the experimental autoimmune encephalomyelitis (EAE) mouse model of MS. Inflammatory cells and increased levels of ER stress markers are evident in post-mortem DRGs from MS patients. Similarly, we observed ER stress in the DRG of mice with EAE and relieving ER stress with a chemical chaperone, 4-phenylbutyric acid (4-PBA), reduced pain hypersensitivity.In vitro, 4-PBA and the selective PERK inhibitor, AMG44, normalize cytosolic Ca2+transients in putative DRG nociceptors. We went to assess disease-mediated changes in the functional properties of Ca2+-sensitive BK-type K+channels in DRG neurons. We found that the conductance-voltage (GV) relationship of BK channels was shifted to a more positive voltage, together with a more depolarized resting membrane potential in EAE cells. Our results suggest that ER stress in sensory neurons of MS patients and mice with EAE is a source of pain and that ER stress modulators can effectively counteract this phenotype.
BATMAN: fast and accurate integration of single-cell RNA-Seq datasets via minimum-weight matching
Authors: Mandric Igor , Hill Brian L. , Freund Malika K. , Thompson Michael , Halperin Eran 
Published: January 2020

AbstractSingle-cell RNA-Sequencing (scRNA-Seq) is a set of technologies used to profile gene expression at the level of individual cells. Although the throughput of scRNA-Seq experiments is steadily growing in terms of the number of cells, large datasets are not yet commonly used due to prohibitively high costs. Integrating multiple datasets into one can improve power in scRNA-Seq experiments, and efficient integration is very important for downstream analyses such as identifying cell-type-specific eQTLs. State-of-the-art scRNA-Seq integration methods are based on the mutual nearest neighbors paradigm and fail to both correct for batch effects and maintain the local structure of the datasets. In this paper, we propose a novel scRNA-Seq dataset integration method called BATMAN (BATch integration via minimum-weight MAtchiNg). Across multiple simulations and real datasets, we show that our method significantly outperforms state-of-the-art tools with respect to existing metrics for batch effects by up to 80% while retaining cell-to-cell relationships. BATMAN is available athttps://github.com/mandricigor/batman.
Neuroligin3 Splice Isoforms Shape Mouse Hippocampal Inhibitory Synaptic Function
Authors: Uchigashima Motokazu , Leung Ming , Watanabe Takuya , Cheung Amy , Watanabe Masahiko , Kawasawa Yuka Imamura , Futai Kensuke 
Published: January 2020

ABSTRACTSynapse formation is a dynamic process essential for neuronal circuit development and maturation. At the synaptic cleft, trans-synaptic protein-protein interactions constitute major biological determinants of proper synapse efficacy. The balance of excitatory and inhibitory synaptic transmission (E-I balance) stabilizes synaptic activity. Dysregulation of the E-I balance has been implicated in neurodevelopmental disorders including autism spectrum disorders. However, the molecular mechanisms underlying E-I balance remain to be elucidated. Here, we investigate Neuroligin (Nlgn) genes that encode a family of postsynaptic adhesion molecules known to shape excitatory and inhibitory synaptic function. We demonstrate that Nlgn3 protein differentially regulates inhibitory synaptic transmission in a splice isoform-dependent manner at hippocampal CA1 synapses. Distinct subcellular localization patterns of Nlgn3 isoforms contribute to the functional differences observed among splice variants. Finally, single-cell sequencing analysis reveals thatNlgn1andNlgn3are the majorNlgngenes and that expression ofNlgnsplice isoforms are highly diverse in CA1 pyramidal neurons.
Hesperetin inhibits foam cell formation in macrophages via activating LXRα signal in an AMPK dependent manner
Authors: Chen Xuanjing , Zou Dezhi , Chen Xiaoling , Wu Huanlin , Xu Danping 
Published: January 2020

AbstractCholesterol efflux from macrophages is the first step of cholesterol reverse transport (RCT), whose increase inhibits cholesterol accumulation and foam cell formation to suppress atherogenesis. Liver X receptor alpha (LXRα) and adenosine monophosphate activated protein kinases (AMPK) both have the pivotal role in cholesterol homeostasis. However the association between these two molecules in cell model of atherosclerosis is poorly understood. Hesperetin has been reported to possess several protective effects for cardiovascular diseases, while little is known about the role of hesperetin and its underlying mechanism on macrophage foam cell formation. In this study, we sought to investigate the potential effects of hesperetin in cholesterol efflux by using human macrophage derived foam cells, focusing on liver X receptor alpha (LXRα) and adenosine monophosphate activated protein kinases (AMPK) implication. Hesperetin treatment concentration-dependently reduced foam cell formation, intracellular cholesterol level and cholesterol esterification rate, and enhanced cholesterol efflux in THP-1 macrophages. Hesperetin upregulated the protein levels of LXRα and its targets including ABCA1, ABCG1 as well as SR-BI, and phosphorylated-AMPK. Meanwhile, hesperetin-induced upregulation of LXRα expression was enhanced by AMPK agonist and inhibited by AMPK inhibitor. Furthermore, hesperetin increased mRNA level of LXRα and its target genes, all which were depressed by AMPKα1/α2 small interfering RNA (siRNA) transfection. In conclusion, we founded for the first time that hesperetin could active AMPK. And this activation upregulated LXRα and its targets including ABCA1, ABCG1 and SR-BI, which significantly inhibited foam cell formation and promoted cholesterol efflux in THP-1 macrophages. Our results highlight the therapeutic potential of hespretin for the possible reduction in foam cell formation. This new mechanism could contribute the anti-atherogenic effects of hesperetin.
Evaluating India’s energy targets using real options approach
Authors: Das Gupta Supratim , Mosiño Alejandro 
Published: January 2020

Purpose
The authors formulate India’s energy targets in light of pushing for renewable energy sources and reducing the dependence on imported coal. Share of imported coal in electricity generation has been approximately 10 per cent in recent years. While investments in renewables have grown in recent years as seen in installed capacities, coal-fired electricity generation has grown because of rising demand for electricity. The purpose of this study is to find a planner solution when high global coal prices force greater investments in renewable energies.

Design/methodology/approach
The authors use real options approach where global coal prices are the stochastic variable. They present an optimal stopping problem and solving the problem backward, the revenues from continuing with the current energy generation mix and those from replacing imported coal with wind and solar is compared for each period.

Findings
The “trigger price” for global coal prices when it is optimal for the social planner to invest in additional wind and solar capacities is found. Trigger prices is the threshold when investment must be undertaken whatever be the future evolution of coal prices; this gives the problem a value of waiting. India cannot afford to wait to invest if faced with strict short-term goals.

Originality/value
The work evaluates India’s domestic targets and its Paris Agreement goals in light of using more of wind and sun and replacing imported coal. Various data sources (government reports, research articles) are consulted to predict shares of electricity from various sources in future and the authors find the operating costs and the investment costs associated with switching to renewables.
Effects of CEO narcissism on decision-making comprehensiveness and speed
Authors: She Zhuolin , Li Quan , London Manuel , Yang Baiyin , Yang Bin 
Published: January 2020

Purpose
The purpose of this paper is to examine the relationships between CEO narcissism and strategic decision-making (SDM) processes (decision comprehensiveness and decision speed), and to explore the mediating role of top management team (TMT) members’ participation in decision making and the moderating role of TMT power distance.

Design/methodology/approach
Data were collected from a multisource, time-lagged survey of 103 CEOs and their corresponding TMT members in China. Structural equation modeling was used to test the hypothesized relationships.

Findings
The results indicated that CEO narcissism was negatively related to decision comprehensiveness and positively related to decision speed. These relationships were mediated by TMT members’ participation in decision making, especially when TMT power distance was high.

Practical implications
The results show the potential negative effects of CEOs’ narcissistic personality and suggest ways to attenuate it by increasing TMT participation and decreasing TMT power distance.

Originality/value
This study is an initial attempt to empirically examine how and under what conditions CEOs’ narcissism is a barrier to more comprehensive and more deliberate (slower) SDM.