bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mesolimbic dopamine adapts the rate of learning from action
Authors: Coddington Luke T. , Lindo Sarah E. , Dudman Joshua T. 
Published: June 2021

AbstractRecent success in training artificial agents and robots derives from a combination of direct learning of behavioral policies and indirect learning via value functions. Policy learning and value learning employ distinct algorithms that optimize behavioral performance and reward prediction, respectively. In animals, behavioral learning and the role of mesolimbic dopamine signaling have been extensively evaluated with respect to reward prediction; however, to date there has been little consideration of how direct policy learning might inform our understanding. Here we used a comprehensive dataset of orofacial and body movements to understand how behavioral policies evolve as naive, head-restrained mice learned a trace conditioning paradigm. Individual differences in initial dopaminergic reward responses correlated with the emergence of learned behavioral policy, but not the emergence of putative value encoding for a predictive cue. Likewise, physiologically-calibrated manipulations of mesolimbic dopamine produced multiple effects inconsistent with value learning but predicted by a neural network-based model that used dopamine signals to set an adaptive rate, not an error signal, for behavioral policy learning. This work provides strong evidence that phasic dopamine activity can regulate direct learning of behavioral policies, expanding the explanatory power of reinforcement learning models for animal learning.
Automated Chest Radiographs Triage Reading by a Deep Learning Referee Network
Authors: López-González Rafael , Sánchez-García Jose , Fos-Guarinos Belén , García-Castro Fabio , Alberich-Bayarri Ángel , Soria-Olivas Emilio , Muñoz-Núñez Carlos , Martí-Bonmatí Luis 
Published: June 2021

AbstractChest radiographs are often obtained as a screening for early diagnosis tool to rule out abnormalities mainly related to different cardiovascular and respiratory diseases. Reading and reporting numerous chest radiographs is a complex and time-consuming task. This research proposes and evaluates a deep learning (DL) approach based on convolutional neural networks (CNN) combined with a referee fully connected neural network as a computer-aided diagnosis tool in chest X-ray triage and worklist prioritization. The CNN models were trained with a combination of three large scale databases: ChestX-ray14, CheXpert and PadChest. The final database contained 327,176 images labeled with findings obtained by natural language processing (NLP) techniques applied to the radiology reports. The dataset was split in 16 different balanced binary partitions, which were used to train 16 finding-specific classification CNNs. Afterwards, a normal vs abnormal partition of the dataset was created, being abnormal the presence of at least one pathologic change. This final partition was used to train a fully connected neural network as referee that was fed with all the 16 previously trained outcomes. The Area Under the Curve (AUC) analysis evaluated and compared the performance of the models. The system was successfully implemented and evaluated with a test set of 3400 images. The AUC of the normal vs abnormal classification was 0.94. The highest AUC of the finding-specific classifiers was 0.99 for hernia. The proposed system can be used to assist radiologists identifying abnormal exams, allowing a time-efficiency triage approach.
Reward Processing in Children with Psychotic-like Experiences
Authors: Harju-Seppänen Jasmine , Irizar Haritz , Bramon Elvira , Blakemore Sarah-Jayne , Mason Liam , Bell Vaughan 
Published: June 2021

AbstractAlterations to striatal reward pathways have been identified in individuals with psychosis. They are hypothesised to be a key mechanism that generate psychotic symptoms through the production of aberrant attribution of motivational salience and are proposed to result from accumulated childhood adversity and genetic risk, making the striatal system hyper-responsive to stress. However, few studies have examined whether children with psychotic-like experiences (PLEs) also exhibit these alterations, limiting our understanding of how differences in reward processing relate to hallucinations and delusional ideation in childhood. Consequently, we examined whether PLEs and PLE-related distress were associated with reward-related activation in the nucleus accumbens (NAcc). The sample consisted of children (N = 6,718) from the Adolescent Brain Cognitive Development (ABCD) study aged 9-10 years who had participated in the Monetary Incentive Delay (MID) task in functional MRI. We used robust mixed-effects linear regression models to investigate the relationship between PLEs and NAcc activation during the reward anticipation and reward outcome stages of the MID task. Analyses were adjusted for gender, household income, ethnicity, depressive symptoms, movement in the scanner, pubertal development, scanner ID, subject and family ID. There was no reliable association between PLEs and alterations to anticipation-related or outcome-related striatal reward processing. We discuss the implications for developmental models of psychosis and suggest a developmental delay model of how PLEs may arise at this stage of development.
Progression of COVID19 Pandemic in India: A Concurrent Linear Regression Analysis Approach
Authors: Chaurasia Aalok Ranjan , Singh Brijesh P. , Singh Ravendra 
Published: June 2021

AbstractThis paper uses concurrent linear regression analysis approach to describe the progression of COVID 19 pandemic in India during the period 15 March 2020 through 15 May 2021. The approach provides very good fit to the daily reported new confirmed cases of the disease. The paper suggests that, based on the parameter of the model, an early warning system may be developed and institutionalised to undertaken necessary measures to control the spread of the disease, thereby controlling the pandemic.
Muon: multimodal omics analysis framework
Authors: Bredikhin Danila , Kats Ilia , Stegle Oliver 
Published: June 2021

AbstractAdvances in multi-omics technologies have led to an explosion of multimodal datasets to address questions ranging from basic biology to translation. While these rich data provide major opportunities for discovery, they also come with data management and analysis challenges, thus motivating the development of tailored computational solutions to deal with multi-omics data.Here, we present a data standard and an analysis framework for multi-omics — MUON — designed to organise, analyse, visualise, and exchange multimodal data. MUON stores multimodal data in an efficient yet flexible data structure, supporting an arbitrary number of omics layers. The MUON data structure is interoperable with existing community standards for single omics, and it provides easy access to both data from individual omics as well as multimodal dataviews. Building on this data infrastructure, MUON enables a versatile range of analyses, from data preprocessing, the construction of multi-omics containers to flexible multi-omics alignment.
PGfinder, a novel analysis pipeline for the consistent, reproducible and high-resolution structural analysis of bacterial peptidoglycans
Authors: Patel Ankur V. , Turner Robert D. , Rifflet Aline , Acosta-Martin Adelina E. , Nichols Andrew , Awad Milena M. , Lyras Dena , Gomperts-Boneca Ivo , Bern Marshall , Collins Mark O. , Mesnage Stéphane 
Published: June 2021

AbstractMany software solutions are available for proteomics and glycomics studies, but none are ideal for the structural analysis of peptidoglycan, the essential and major component of bacterial cell envelopes. It is comprised of glycan chains and peptide stems, both containing unusual amino acids and sugars. This has forced the field to rely on manual analysis approaches, which are time-consuming, labour-intensive, and prone to error. The lack of automated tools has hampered the ability to perform high-throughput analyses and prevented the adoption of a standard methodology. Here, we describe a novel tool called PGfinder for the analysis of peptidoglycan structure and demonstrate that it represents a powerful tool to quantify PG fragments and discover novel structural features. Our analysis workflow, which relies on open-access tools, is a breakthrough towards a consistent and reproducible analysis of bacterial peptidoglycans. It represents a significant advance towards peptidoglycomics as a full-fledged discipline.
Heterogeneity and molecular programming of progenitors for motor neurons and oligodendrocytes
Authors: Xing Lingyan , Chai Rui , Wang Jiaqi , Lin Jiaqi , Li Hanyang , Wang Yueqi , Lai Biqin , Sun Junjie , Chen Gang 
Published: June 2021

AbstractThe pMN domain is a restricted domain in the ventral spinal cords, defined by the expression of olig2 gene. The fate determination of pMN progenitors is highly temporally and spatially regulated, with motor neurons and oligodendrocyte progenitor cells (OPCs) developing sequentially. Insight into the heterogeneity and molecular programs of pMN progenitors is currently lacking. With the zebrafish model, we identified multiple states of neural progenitors using single-cell sequencing: proliferating progenitors, common progenitors for both motor neurons and OPCs, and restricted precursors for either motor neurons or OPCs. We found specific molecular programs for neural progenitor fate transition, and manipulations of representative genes in the motor neuron or OPC lineage confirmed their critical role in cell fate determination. The transcription factor NPAS3 is necessary for the development of the OPC lineage and can interact with many known genes associated with schizophrenia. Deciphering progenitor heterogeneity and molecular mechanisms for these transitions will elucidate the formation of complex neuron-glia networks in the central nervous system during development, and understand the basis of neurodevelopmental disorders.
Test-retest reliability of regression dynamic causal modeling
Authors: Frässle Stefan , Stephan Klaas E. 
Published: June 2021

AbstractRegression dynamic causal modeling (rDCM) is a novel and computationally highly efficient method for inferring effective connectivity at the whole-brain level. While face and construct validity of rDCM have already been demonstrated, here we assessed its test-retest reliability – a test-theoretical property of particular importance for clinical applications – together with group-level consistency of connection-specific estimates and consistency of whole-brain connectivity patterns over sessions. Using the Human Connectome Project (HCP) dataset for eight different paradigms (tasks and rest) and two different parcellation schemes, we found that rDCM provided highly consistent connectivity estimates at the group level across sessions. Second, while test-retest reliability was limited when averaging over all connections (range of mean ICC 0.24-0.42 over tasks), reliability increased with connection strength, with stronger connections showing good to excellent test-retest reliability. Third, whole-brain connectivity patterns by rDCM allowed for identifying individual participants with high (and in some cases perfect) accuracy. Comparing the test-retest reliability of rDCM connectivity estimates to measures of functional connectivity, rDCM performed favorably – particularly when focusing on strong connections. Generally, for all methods and metrics, task-based connectivity estimates showed greater reliability than those from the resting state. Our results underscore the potential of rDCM for human connectomics and clinical applications.AuthorSummaryTest-retest reliability is an important prerequisite for the validity of connectivity estimates in many situations, particularly in clinical applications. Here, using different datasets from the Human Connectome Project, we demonstrate that regression DCM (rDCM) yields good to excellent test-retest reliability when focusing on strong connections. Comparing this to the test-retest reliability of functional connectivity measures, rDCM performed favorably in most cases. Furthermore, we show that reliability is not homogeneously distributed: we identified several regions (primarily in frontal and temporal lobe) that were linked via highly-reliable connections, regardless of the paradigm. Finally, we demonstrate that individual connectivity profiles are sufficiently unique that participants can be identified with high accuracy. Our findings emphasize the potential of rDCM for robust inference on directed “connectivity fingerprints” from fMRI data.
Small aircraft detection using deep learning
Authors: Kiyak Emre , Unal Gulay 
Published: June 2021

Purpose
The paper aims to address the tracking algorithm based on deep learning and four deep learning tracking models developed. They compared with each other to prevent collision and to obtain target tracking in autonomous aircraft.

Design/methodology/approach
First, to follow the visual target, the detection methods were used and then the tracking methods were examined. Here, four models (deep convolutional neural networks (DCNN), deep convolutional neural networks with fine-tuning (DCNNFN), transfer learning with deep convolutional neural network (TLDCNN) and fine-tuning deep convolutional neural network with transfer learning (FNDCNNTL)) were developed.

Findings
The training time of DCNN took 9 min 33 s, while the accuracy percentage was calculated as 84%. In DCNNFN, the training time of the network was calculated as 4 min 26 s and the accuracy percentage was 91%. The training of TLDCNN) took 34 min and 49 s and the accuracy percentage was calculated as 95%. With FNDCNNTL, the training time of the network was calculated as 34 min 33 s and the accuracy percentage was nearly 100%.

Originality/value
Compared to the results in the literature ranging from 89.4% to 95.6%, using FNDCNNTL, better results were found in the paper.
A multi-attribute data mining model for rule extraction and service operations benchmarking
Authors: Amoozad Mahdiraji Hannan , Tavana Madjid , Mahdiani Pouya , Abbasi Kamardi Ali Asghar 
Published: June 2021

PurposeCustomer differences and similarities play a crucial role in service operations, and service industries need to develop various strategies for different customer types. This study aims to understand the behavioral pattern of customers in the banking industry by proposing a hybrid data mining approach with rule extraction and service operation benchmarking.Design/methodology/approachThe authors analyze customer data to identify the best customers using a modified recency, frequency and monetary (RFM) model and K-means clustering. The number of clusters is determined with a two-step K-means quality analysis based on the Silhouette, Davies–Bouldin and Calinski–Harabasz indices and the evaluation based on distance from average solution (EDAS). The best–worst method (BWM) and the total area based on orthogonal vectors (TAOV) are used next to sort the clusters. Finally, the associative rules and the Apriori algorithm are used to derive the customers' behavior patterns.FindingsAs a result of implementing the proposed approach in the financial service industry, customers were segmented and ranked into six clusters by analyzing 20,000 records. Furthermore, frequent customer financial behavior patterns were recognized based on demographic characteristics and financial transactions of customers. Thus, customer types were classified as highly loyal, loyal, high-interacting, low-interacting and missing customers. Eventually, appropriate strategies for interacting with each customer type were proposed.Originality/valueThe authors propose a novel hybrid multi-attribute data mining approach for rule extraction and the service operations benchmarking approach by combining data mining tools with a multilayer decision-making approach. The proposed hybrid approach has been implemented in a large-scale problem in the financial services industry.
How does mentoring influence protégés’ work engagement? Roles of perceived organizational support and family-like employee-organization relationship
Authors: Yang Fang , Liu Pingqing , Xu Shuang 
Published: June 2021

PurposeDrawing upon organizational support theory and family-like exchange perspective, this paper aims to investigate whether mentoring influences protégés’ work engagement, and the roles of perceived organizational support (POS) and family-like employee-organization relationship (FEOR) between mentoring and protégés’ work engagement.Design/methodology/approachMatched data were collected from 290 protégés and their mentors in two large state-owned enterprises in Northwest China. Multiple regression analyses and bootstrapping methods were used to test the hypotheses.FindingsThe results show that mentoring is positively related to protégés’ work engagement, and POS and FEOR play multiple mediation roles in the relationship between mentoring and protégés’ work engagement.Research limitations/implicationsThe primary contribution of this study is exploring the impact of mentoring on protégés’ work engagement. Additionally, this study uses organizational support and family-like exchange perspective to understand how mentoring influences protégés’ work engagement.Originality/valueDespite a few studies examining the effect of mentoring on protégés’ work engagement, but focusing excessively on organizational socialization and social exchange, as such, limited attention has been given to the role of emotions. This is, to the best of the authors’ knowledge, the first study to investigate the effect of emotional factors (including POS and FEOR) on the relationship between mentoring and protégés’ work engagement in Chinese organizational culture.
Organizational resilience as response to a crisis: case of COVID-19 crisis
Authors: Mokline Bechir , Ben Abdallah Mohamed Anis 
Published: June 2021

PurposeThe purpose of this paper is to explore the contribution of organizational resilience to the capacity of companies to cope with the COVID-19 crisis in a real organizational context through an empirical study of 16 Tunisian companies affected by the crisis.Design/methodology/approachThe authors adopt a qualitative study based on 16 semi-structured interviews to describe better the functioning of the processes of resilience in a real organizational context.FindingsThe real contribution consists in confirming a positive contribution of resilience to resistance in the face of the COVID-19 crisis in a Tunisian context through exceptional measures that have not previously been adopted by the companies concerned by this study.Originality/valueThe authors believe that they are the first to study the theme of resilience as a resistance mechanism in the face of the COVID-19 crisis in a Tunisian context. From this comes the originality of the research.
The relationship between higher education students' perceived employability, academic engagement and stress among students in China
Authors: Ma Yin , Bennett Dawn 
Published: June 2021

PurposeWith a focus on Chinese higher education students, the purpose of this study was to explore the relationship between students' perceived employability and their levels of academic engagement and stress.Design/methodology/approachThe study engaged 1,155 students from three universities in China. Students responded to an online survey, reporting their confidence in relation to their perceived employability, academic engagement and stress in life. The authors employed structural equation modelling to explore students' confidence in each employability attribute and to assess perceived employability relation to academic engagement and perceived stress.FindingsThe results suggest that self-perceptions of employability are positively associated with students' academic engagement and negatively associated with perceived stress. Perceived employability mediated the majority paths.Originality/valueThis is one of the few studies to examine perceived employability in line with academic engagement or stress and the first study to do so in China.
Tapping public sentiments on Twitter for tourism insights: a study of famous Indian heritage sites
Authors: Gulati Shruti 
Published: June 2021

PurposeTwitter is the most widely used platform with an open network; hence, tourists often resort to Twitter to share their travel experiences, satisfaction/dissatisfaction and other opinions. This study is divided into two sections, first to provide a framework for understanding public sentiments through Twitter for tourism insights, second to provide real-time insights of three Indian heritage sites i.e., the Taj Mahal, Red Fort and Golden Temple by extracting 5,000 tweets each (n = 15,000) using Twitter API. Results are interpreted using NRC emotion lexicon and data visualisation using R.Design/methodology/approachThis study attempts to understand the public sentiment on three globally acclaimed Indian heritage sites, i.e. the Taj Mahal, Red Fort and Golden temple using a step-by-step approach, hence proposing a framework using Twitter analytics. Extensive use of various packages of R programming from the libraries has been done for various purposes such as extraction, processing and analysing the data from Twitter. A total of 15,000 tweets from January 2015 to January 2021 were collected of the three sites using different key words. An exploratory design and data visualisation technique has been used to interpret results.FindingsAfter data processing, 12,409 sentiments are extracted. Amongst the three tourists' spots, the greatest number of positive sentiments is for the Taj Mahal and Golden temple with approximately 25% each. While the most negative sentiment can be seen for the Red Fort (17%). Amongst the positive emotions, the maximum joy sentiment (12%) can be seen in the Golden Temple and trust (21%) in the Red Fort. In terms of negative emotions, fear (13%) can be seen in the Red fort. Overall, India's heritage sites have a positive sentiment (20%), which surpasses the negative sentiment (13%). And can be said that the overall polarity is towards positive.Originality/valueThis study provides a framework on how to use Twitter for tourism insights through text mining public sentiments and provides real- time insights from famous Indian heritage sites.