bluuebunny/crossref_metadata_2025_split huggingface dataset:
Brain signatures of surprise in EEG and MEG data
Authors: Mousavi Zahra , Kiani Mohammad Mahdi , Aghajan Hamid 
Published: January 2020

AbstractThe brain is constantly anticipating the future of sensory inputs based on past experiences. When new sensory data is different from predictions shaped by recent trends, neural signals are generated to report this surprise. Existing models for quantifying surprise are based on an ideal observer assumption operating under one of the three definitions of surprise set forth as the Shannon, Bayesian, and Confidence-corrected surprise. In this paper, we analyze both visual and auditory EEG and auditory MEG signals recorded during oddball tasks to examine which temporal components in these signals are sufficient to decode the brain’s surprise based on each of these three definitions. We found that for both recording systems the Shannon surprise is always significantly better decoded than the Bayesian surprise regardless of the sensory modality and the selected temporal features used for decoding.Author summaryA regression model is proposed for decoding the level of the brain’s surprise in response to sensory sequences using selected temporal components of recorded EEG and MEG data. Three surprise quantification definitions (Shannon, Bayesian, and Confidence-corrected surprise) are compared in offering decoding power. Four different regimes for selecting temporal samples of EEG and MEG data are used to evaluate which part of the recorded data may contain signatures that represent the brain’s surprise in terms of offering a high decoding power. We found that both the middle and late components of the EEG response offer strong decoding power for surprise while the early components are significantly weaker in decoding surprise. In the MEG response, we found that the middle components have the highest decoding power while the late components offer moderate decoding powers. When using a single temporal sample for decoding surprise, samples of the middle segment possess the highest decoding power. Shannon surprise is always better decoded than the other definitions of surprise for all the four temporal feature selection regimes. Similar superiority for Shannon surprise is observed for the EEG and MEG data across the entire range of temporal sample regimes used in our analysis.
Electrochemical carbon fiber-based technique for simultaneous recordings of brain tissue PO2, pH, and extracellular field potentials
Authors: Hosford Patrick S. , Wells Jack A. , Christie Isabel N. , Lythgoe Mark , Millar Julian , Gourine Alexander V. 
Published: January 2020

AbstractA method for simultaneous electrochemical detection of brain tissue PO2(PtO2) and pH changes together with neuronal activity using a modified form of fast cyclic voltammetry with carbon fiber electrodes is described. This technique has been developed forin vivoapplications and recordings from discrete brain nuclei in experimental animals. The small size of the carbon fiber electrode (⍰7μm, length <100μm) ensures minimal disruption of the brain tissue and allows recordings from small brain areas. Sample rate (up to 4 Hz) is sufficient to resolve rapid changes in PtO2and pH that follow changes in neuronal activity and metabolism. Rapid switching between current and voltage recordings allows combined electrochemical detection and monitoring of extracellular action potentials. For simultaneous electrochemical detection of PtO2and pH, two consecutive trapezoidal voltage ramps are applied with double differential-subtraction of the background current. This enables changes in current caused by protons and oxygen to be detected separately with minimal interference between the two. The profile of PtO2changes evoked by increases in local neuronal activity recorded using the described technique was similar to that of blood oxygen level dependent responses recorded using fMRI. This voltammetric technique can be combined with fMRI and brain vessel imaging to study the metabolic mechanisms underlying neurovascular coupling response with much greater spatial and temporal resolution than is currently possible.
Invertebrate Metaxins 1 and 2: Widely Distributed Proteins Homologous to Vertebrate Metaxins Implicated in Protein Import into Mitochondria
Authors: Adolph Kenneth W. 
Published: January 2020

ABSTRACTMetaxin 1 and 2 genes, previously investigated in vertebrates, are shown to be widely distributed among invertebrates. But metaxin 3 is absent. The predicted proteins of the invertebrate metaxins were initially identified by homology with human metaxin 1 and 2 proteins, and by the presence of characteristic GST_Metaxin protein domains. Invertebrate metaxins were revealed for a variety of phyla, including Echinodermata, Cnidaria, Porifera, Chordata, Arthropoda, Mollusca, Brachiopoda, Placozoa, and Nematoda. Metaxins were also found in insects (Arthropoda) of different taxonomic orders: Diptera, Coleoptera, Lepidoptera, Hymenoptera, and Blattodea. Invertebrate and human metaxin 1 proteins have about 41% identical amino acids, while metaxin 2 proteins have about 49% identities. Invertebrate and vertebrate metaxins share the same characteristic protein domains, further strengthening the identification of the invertebrate proteins as metaxins. The domains are, for metaxin 1, GST_N_Metaxin1_like, GST_C_Metaxin1_3, and Tom37. For metaxin 2, they are GST_N_Metaxin2, GST_C_Metaxin2, and Tom37. Phylogenetic trees show that invertebrate metaxin 1 and metaxin 2 proteins are related, but form separate groups. The invertebrate proteins are also closely related to vertebrate metaxins, though forming separate clusters. These phylogenetic results indicate that all metaxins likely arose from a common ancestral sequence. The neighboring genes of the invertebrate metaxin 1 and 2 genes are largely different for different invertebrate species. This is unlike the situation with vertebrate metaxin genes, where, for example, the metaxin 1 gene is adjacent to the thrombospondin 3 gene. The dominant secondary structures predicted for the invertebrate metaxins are alpha-helical segments, with little beta-strand. The conserved pattern of helical segments is the same as that found for vertebrate metaxins 1, 2, and 3.
Genetic characterization of stem cell-like cancer cell in the peri-tumoral regions and proliferative lymphocyte in the peripheral blood of patients with glioblastoma
Authors: Yang Runwei , Huang Guanglong , Guo Jinglin , Li Yaomin , Song Haimin , Li Kaishu , Yi Guozhong , Lin Zhiying , Wang Xiran , Xie Sidi , Liu Zhifeng , Shi Xiaofeng , Li Ke , Qi Songtao , Liu Yawei 
Published: January 2020

AbstractThe glioblastoma (GBM) recurrence rate is high, despite multimodal treatment including surgery, radiotherapy, chemotherapy, and immunotherapy. Most recurrences occur at the resection margin that is located outside the GBM contrast-enhancing region (C region) of magnetic resonance imaging. However, the nature of the GBM cells that lie outside the C region is unclear. We used single-cell RNA sequencing (scRNA-seq) to compare 12 samples taken from inside and outside the C region from four patients with GBM and identified a cluster of GBM cells outside the C region that exhibited fragmented CNVs in chromosomes such as 1, 10, 12 and 19 (herein termed CNV 4). A transcription factor–transcription cofactor interaction network was constructed to uncover the transcriptional regulatory mechanism of these CNV 4 GBM cells that had prognostic significance (p < 0.05). Furthermore, a sub-cluster of these CNV 4 GBM cells possessed stem cell-like properties and had tumorigenic potential. In parallel, we analyzed peripheral blood mononuclear cells (PBMCs) from four patients with GBM, three patients with epilepsy and three healthy volunteers by scRNA-seq. We identified a novel subtype of proliferative immune cells only in patients with GBM. Overall, our findings indicate that CNV 4 cells might contribute to GBM recurrence and proliferative circulating immune cells are specific to patients with GBM. This study sheds light on a neglected aspect of GBM, and opens new avenues to explore GBM recurrence and immunotherapy.SignificanceWe show that a subset of GBM cells outside the C region possess the properties of stem cell and tumorigenesis potential, which might be responsible for cancer recurrence. Besides, proliferative lymphocytes are specific to the PBMCs of patients with GBM, which could help to develop novel immunotherapy.
Improving the sensitivity of cluster-based statistics for fMRI data
Authors: Geerligs Linda , Maris Eric 
Published: January 2020

AbstractBecause of the high dimensionality of neuroimaging data, identifying a statistical test that is both valid and maximally sensitive is an important challenge. Here, we present a combination of two approaches for fMRI data analysis that together result in substantial improvements of the sensitivity of cluster-based statistics. The first approach is to create novel cluster definitions that are sensitive to physiologically plausible effect patterns. The second is to adopt a new approach to combine test statistics with different sensitivity profiles, which we call the min(p) method. These innovations are made possible by using the randomization inference framework. In this paper, we report on a set of simulations that demonstrate (1) that the proposed methods control the false-alarm rate, (2) that the sensitivity profiles of cluster-based test statistics vary depending on the cluster defining thresholds and cluster definitions, and (3) that the min(p) method for combining these test statistics results in a drastic increase of sensitivity (up to five-fold), compared to existing fMRI analysis methods. This increase in sensitivity is not at the expense of the spatial specificity of the inference.
Regulation of mitophagy by the NSL complex underlies genetic risk for Parkinson’s disease at Chr16q11.2 and on the MAPT H1 allele
Authors: Soutar Marc P.M. , Melandri Daniela , O’Callaghan Benjamin , Annuario Emily , Monaghan Amy E. , Welsh Natalie J. , D’Sa Karishma , Guelfi Sebastian , Zhang David , Pittman Alan , Trabzuni Daniah , Pan Kylie S. , Kia Demis A. , Bictash Magda , Gandhi Sonia , Houlden Henry , Cookson Mark R. , Wood Nicholas W , Singleton Andrew B. , Hardy John , Whiting Paul J. , Blauwendraat Cornelis , Whitworth Alexander J. , Manzoni Claudia , Ryten Mina , Lewis Patrick A. , Plun-Favreau Hélène 
Published: January 2020

ABSTRACTParkinson’s disease (PD) is a common incurable neurodegenerative disease. The identification of genetic variants via genome-wide association studies (GWAS) has considerably advanced our understanding of the PD genetic risk. Understanding the functional significance of the risk loci is now a critical step towards translating these genetic advances into an enhanced biological understanding of the disease. Impaired mitophagy is a key causative pathway in familial PD, but its relevance to idiopathic PD is unclear. We used a mitophagy screening assay to evaluate the functional significance of risk genes identified through GWAS. We identified two new regulators of PINK1-mitophagy, KAT8 and KANSL1, previously shown to modulate lysine acetylation. We show that KAT8 and KANSL1 modulatePINK1gene expression and subsequent PINK1-mitophagy. These findings suggest PINK1-mitophagy is a contributing factor to idiopathic PD.KANSL1is located on chromosome 17q21 where the risk associated gene has long been considered to beMAPT. While our data does not exclude a possible association between theMAPTgene and PD, it provides strong evidence thatKANSL1plays a crucial role in the disease. Finally, these results enrich our understanding of physiological events regulating mitophagy and establish a novel pathway for drug targeting in neurodegeneration.
A latent variable approach to decoding neural population activity
Authors: Whiteway Matthew R , Averbeck Bruno , Butts Daniel A 
Published: January 2020

AbstractDecoding is a powerful approach for measuring the information contained in the activity of neural populations. As a result, decoding analyses are now used across a wide range of model organisms and experimental paradigms. However, typical analyses employ general purpose decoding algorithms that do not explicitly take advantage of the structure of neural variability, which is often low-dimensional and can thus be effectively characterized using latent variables. Here we propose a new decoding framework that exploits the low-dimensional structure of neural population variability by removing correlated variability that is unrelated to the decoded variable, then decoding the resulting denoised activity. We demonstrate the efficacy of this framework using simulated data, where the true upper bounds for decoding performance are known. A linear version of our decoder provides an estimator for the decoded variable that can be more efficient than other commonly used linear estimators such as linear discriminant analysis. In addition, our proposed decoding framework admits a simple extension to nonlinear decoding that compares favorably to standard feed-forward neural networks. By explicitly modeling shared population variability, the success of the resulting linear and nonlinear decoders also offers a new perspective on the relationship between shared variability and information contained in large neural populations.
Neurodegenerative disease-associated protein aggregates are poor inducers of the heat shock response in neuronal-like cells
Authors: Gil R. San , Cox D. , McAlary L. , Berg T. , Walker A. K. , Yerbury J. J. , Ooi L. , Ecroyd H. 
Published: January 2020

AbstractProtein aggregation that results in the formation of inclusions is strongly correlated with neuronal death and is a pathological hallmark common to many neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS) and Huntington’s disease. Cells are thought to dramatically up-regulate the levels of heat shock proteins during periods of cellular stress via induction of the heat shock response (HSR). Heat shock proteins are well-characterised molecular chaperones that interact with aggregation-prone proteins to either stabilise, refold, or traffic protein for degradation. The reason why heat shock proteins are unable to maintain the solubility of particular proteins in neurodegenerative disease is unknown. We sought to determine whether neurodegenerative disease-associated protein aggregates can induce the HSR. Here, we generated a neuroblastoma cell line that expresses a fluorescent reporter under conditions of HSR induction, for example heat shock. Using these cells, we show that the HSR is not induced by exogenous treatment with aggregated forms of Parkinson’s disease-associatedα-synuclein or the ALS-associated G93A mutant of superoxide dismutase-1 (SOD1G93A). Furthermore, flow cytometric analysis revealed that intracellular expression of SOD1G93Aor a pathogenic form of polyQ-expanded huntingtin (Htt72Q), similarly, results in no or low induction of the HSR. In contrast, expression of a non-pathogenic but aggregation-prone form of firefly luciferase (Fluc) did induce an HSR in a significantly greater proportion of cells. Finally, we show that HSR induction is dependent on the intracellular levels of the aggregation-prone proteins, but the pathogenic proteins (SOD1G93Aand Htt72Q) elicit a significantly lower HSR compared to the non-pathogenic proteins (Fluc). These results suggest that pathogenic proteins either evade detection or impair induction of the HSR in neuronal-like cells. Therefore, defective HSR induction may facilitate the initiation of protein aggregation leading to inclusion formation in neurodegenerative diseases.
Depletion of the AD risk geneSORL1selectively impairs neuronal endosomal traffic independent of amyloidogenic APP processing
Authors: Knupp Allison , Mishra Swati , Martinez Refugio , Braggin Jacquelyn E. , Szabo Marcell , Hailey Dale W. , Small Scott A. , Jayadev Suman , Young Jessica E. 
Published: January 2020

SummaryTheSORL1gene encodes for the protein SorLA, a sorting receptor involved in retromer-related endosomal traffic. ManySORL1genetic variants increase Alzheimer’s disease (AD) risk, and rare loss-of-function truncation mutations have been found to be causal of late-onset AD.SORL1is expressed in neurons and glia of the central nervous system and loss ofSORL1has been reported in AD tissue. To model the causal loss-of-function mutations, we used CRISPR/Cas9 technology to depleteSORL1in human induced pluripotent stem cells (hiPSCs) to test the hypothesis that loss ofSORL1contributes to AD pathogenesis by leading to endosome dysfunction. We report that loss ofSORL1in hiPSC-derived neurons leads to early endosome enlargement, a cellular phenotype that is indicative of ‘traffic jams’ and is now considered a hallmark cytopathology AD. We validate defects in neuronal endosomal traffic by showing decreased localization of amyloid-precursor protein (APP) in the trans-Golgi network (TGN), and increased localization of APP in early endosomes, a site of APP cleavage by the β secretase BACE1. Microglia, immune cells of the CNS, which play a role in AD pathology also expressSORL1. We therefore tested and found no effect ofSORL1depletion on endosome size or morphology in hiPSC-derived microglia, suggesting a selective effect on neuronal endosomal trafficking. Finally, because BACE1 dependent APP fragments can cause endosome enlargement, we treatedSORL1deficient hiPSC-derived neurons with BACE1 inhibitors and demonstrate that endosome enlargement occurs independent of amyloidogenic APP fragments. Collectively, these findings clarify where and howSORL1links to AD. Moreover, our data, together with recent findings, underscores how sporadic AD pathways that regulate endosomal trafficking, and autosomal-dominant AD pathways that regulate APP cleavage, independently converge on the defining cytopathology of AD.
Toward the optimized crowdsourcing strategy for OCR post-correction
Authors: Suissa Omri , Elmalech Avshalom , Zhitomirsky-Geffet Maayan 
Published: January 2020

PurposeDigitization of historical documents is a challenging task in many digital humanities projects. A popular approach for digitization is to scan the documents into images, and then convert images into text using optical character recognition (OCR) algorithms. However, the outcome of OCR processing of historical documents is usually inaccurate and requires post-processing error correction. The purpose of this paper is to investigate how crowdsourcing can be utilized to correct OCR errors in historical text collections, and which crowdsourcing methodology is the most effective in different scenarios and for various research objectives.Design/methodology/approachA series of experiments with different micro-task’s structures and text lengths were conducted with 753 workers on the Amazon’s Mechanical Turk platform. The workers had to fix OCR errors in a selected historical text. To analyze the results, new accuracy and efficiency measures were devised.FindingsThe analysis suggests that in terms of accuracy, the optimal text length is medium (paragraph-size) and the optimal structure of the experiment is two phase with a scanned image. In terms of efficiency, the best results were obtained when using longer text in the single-stage structure with no image.Practical implicationsThe study provides practical recommendations to researchers on how to build the optimal crowdsourcing task for OCR post-correction. The developed methodology can also be utilized to create golden standard historical texts for automatic OCR post-correction.Originality/valueThis is the first attempt to systematically investigate the influence of various factors on crowdsourcing-based OCR post-correction and propose an optimal strategy for this process.
Improved analytical approach for studying the effect of diode power detector modelling on six-port communication receiver performance
Authors: Hazer Sahlabadi Mehran , Abdipour Abdolali , Mohammadi Abbas 
Published: January 2020

Purpose
The purpose of this paper is to study the effect of diode power detector modelling on six-port communication receiver performance.

Design/methodology/approach
Based on proposed and conventional squared diode model, six-port receiver’s demodulation and its error vector magnitude (EVM) performance due to hardware impairments are studied. Through considering both the models, the accuracy of proposed power detector model is compared to the squared model, and then both results are validated with envelope simulation (ENV) in advanced design system (ADS).

Findings
Comparing the numerical results with envelope simulation results proved that the proposed model is much more accurate than the conventional squared model for a wide range of input power levels.

Originality/value
Studying the receiver’s performance numerically, by considering the new proposed analytical approach for diode power detectors which is more accurate than the conventional squared model.
Design tools for electromagnetic-driven multi-physics systems using high performance computing
Authors: Lowther David , Ghorbanian Vahid , Mohammadi Mohammad Hossain , Ibrahim Issah 
Published: January 2020

Purpose
The design of electromagnetic systems for a variety of applications such as induction heating, electrical machines, actuators and transformers requires the solution of a multi-physics problem often involving thermal, structural and mechanical coupling to the electromagnetic system. This results in a complex analysis system embedded within an optimization process. The appearance of high-performance computing systems over the past few years has made coupled simulations feasible for the design engineer. When coupled with surrogate modelling techniques, it is possible to significantly reduce the wall clock time for generating a complete design while including the impact of the multi-physics performance on the device.

Design/methodology/approach
An architecture is proposed for linking multiple singe physics analysis tools through the material models and a controller which schedules the execution of the various software tools. The combination of tools is implemented on a series of computational nodes operating in parallel and creating a “super node” cluster within a collection of interconnected processors.

Findings
The proposed architecture and job scheduling system can allow a parallel exploration of the design space for a device.

Originality/value
The originality of the work derives from the organization of the parallel computing system into a series of “super nodes” and the creation of a materials database suitable for multi-physics interactions.
From principal cognitive complexity to teacher intent to leave
Authors: Da’as Rima’a , Schechter Chen , Qadach Mowafaq 
Published: January 2020

PurposeThe purpose of this paper is to test an innovative model for exploring the direct and indirect relationships between principals’ cognitive complexity (CC), schools’ absorptive capacity (ACAP), a teacher’s affective commitment and a teacher’s intent to leave.Design/methodology/approachData were collected from a survey of 1,664 teachers at 107 Arab elementary schools, randomly selected from the database of the Israeli educational system. To test the proposed model, multilevel structural equation modeling was conducted.FindingsThe analysis confirmed that schools’ ACAP and a teacher’s affective commitment are prominent mediators between principals’ CC and a teacher’s intent to leave.Practical implicationsUnderstanding the factors that contribute to a teacher’s intent to leave could help school principals and policy makers retain effective teachers in today’s schools.Originality/valueThis study adds to the body of research directed at identifying school principals’ characteristics, as well as work-related factors, which may decrease a teacher’s intent to leave and are amenable to leadership intervention.