bluuebunny/crossref_metadata_2025 huggingface dataset:
The Digital Analytic Patient Reviewer (DAPR) for COVID-19 Data Mart Validation
Authors: Park Heekyong , Wang Taowei David , Wattanasin Nich , Castro Victor M. , Gainer Vivian , Goryachev Sergey , Murphy Shawn
Published: June 2021

Objective

To provide high-quality data for COVID-19 research, we validated COVID-19 clinical indicators and 22 associated computed phenotypes, which were derived by machine learning algorithms, in the Mass General Brigham (MGB) COVID-19 Data Mart.

Materials and Methods

Fifteen reviewers performed a manual chart review for 150 COVID-19 positive patients in the data mart. To support rapid chart review for a wide range of target data, we offered the Digital Analytic Patient Reviewer (DAPR). DAPR is a web-based chart review tool that integrates patient notes and provides note search functionalities and a patient-specific summary view linked with relevant notes. Within DAPR, we developed a COVID-19 validation task-oriented view and information extraction logic, enabled fast access to data, and considered privacy and security issues.

Results

The concepts for COVID-19 positive cohort, COVID-19 index date, COVID-19 related admission, and the admission date were shown to have high values in all evaluation metrics. For phenotypes, the overall specificities, PPVs, and NPVs were high. However, sensitivities were relatively low. Based on these results, we removed 3 phenotypes from our data mart. In the survey about using the tool, participants expressed positive attitudes towards using DAPR for chart review. They assessed the validation was easy and DAPR helped find relevant information. Some validation difficulties were also discussed.

Discussion and Conclusion

DAPR’s patient summary view accelerated the validation process. We are in the process of automating the workflow to use DAPR for chart reviews. Moreover, we will extend its use case to other domains.
Heterologous ChAdOx1 nCoV-19 and BNT162b2 prime-boost vaccination elicits potent neutralizing antibody responses and T cell reactivity
Authors: Groß Rüdiger , Zanoni Michelle , Seidel Alina , Conzelmann Carina , Gilg Andrea , Krnavek Daniela , Erdemci-Evin Sümeyye , Mayer Benjamin , Hoffmann Markus , Pöhlmann Stefan , Beil Alexandra , Kroschel Joris , Jahrsdörfer Bernd , Schrezenmeier Hubert , Kirchhoff Frank , Münch Jan , Müller Janis A.
Published: June 2021

Heterologous COVID-19 vaccination regimens combining vector- and mRNA-based vaccines are already administered, but data on solicited adverse reactions, immunological responses and elicited protection are limited. We aimed to evaluate the reactogenicity, humoral and cellular immune responses towards different SARS-CoV-2 variants after a heterologous ChAdOx1 nCoV-19 BNT162b2 prime-boost vaccination and analyzed a cohort of 26 individuals aged 25-46 (median 30.5) years that received a ChAdOx1 nCoV-19 prime followed by a BNT162b2 boost after an 8- week interval. Self-reported solicited symptoms after ChAdOx1 nCoV-19 prime were in line with previous reports and less severe after the BNT162b2 boost. Antibody titers increased significantly over time resulting in strong neutralization titers two weeks after the BNT162b2 boost. Neutralizing activity against the prevalent strain B.1.1.7 (Alpha) and immune-evading VOC B.1.351 (Beta) was ∼4-fold higher than in individuals receiving homologous BNT162b2 vaccination. No difference was seen in neutralization of VOI B.1.617 (Kappa). In addition, the heterologous vaccination induced CD4+ and CD8+ T cells reactive to SARS-CoV-2 spike peptides of all analyzed variants; Wuhan-Hu-1, B.1.1.7, B.1.351, and P.1 (Gamma). In conclusion, heterologous ChAdOx1 nCoV-19 / BNT162b2 prime-boost vaccination regimen is not associated with serious adverse events and results in a potent humoral immune response and elicits T cell reactivity. Variants B.1.1.7, B.1.351 and B.1.617.1 are potently neutralized by sera of all participants and reactive T cells recognize spike peptides of all tested variants. These results suggest that this heterologous vaccination regimen is at least as immunogenic and protective as homologous vaccinations.
U-RISC: an ultra-high-resolution electron microscopy dataset challenging existing deep learning algorithms
Authors: Shi Ruohua , Wang Wenyao , Li Zhixuan , He Liuyuan , Sheng Kaiwen , Ma Lei , Du Kai , Jiang Tingting , Huang Tiejun
Published: June 2021

Connectomics is a developing field aiming at reconstructing the connection of the neural system at nanometer scale. Computer vision technology, especially deep learning methods used in image processing, has promoted connectomic data analysis to a new era. However, the performance of the state-of-the-art methods still falls behind the demand of scientific research. Inspired by the success of ImageNet, we present the U-RISC, an annotated Ultra-high Resolution Image Segmentation dataset for Cell membrane, which is the largest cell membrane annotated Electron Microscopy (EM) dataset with a resolution of 2.18nm/pixel. Multiple iterative annotations ensured the quality of the dataset. Through an open competition, we reveal that the performance of current deep learning methods still has a considerable gap with human-level, different from ISBI 2012, on which the performance of deep learning is close to human. To explore the causes of this discrepancy, we analyze the neural networks with a visualization method, attribution analysis. We find that in U-RISC, it requires a larger area around a pixel to predict whether the pixel belongs to the cell membrane or not. Finally, we integrate currently available methods to provide a new benchmark (0.67, 10% higher than the leader of competition, 0.61) for cell membrane segmentation on U-RISC and propose some suggestions in developing deep learning algorithms. The U-RISC dataset and the deep learning codes used in this paper will be publicly available.
Quality of Cervical Cancer Screening at a Family Health Unit in Salvador-BA
Authors: Freitas Coelho Julita Maria , Da Silva Jamilly Souza , Fonsêca Santos Letícia , França Lyvia Mirelle Carneiro , Ponte Gláucia Alencar , De Assis Sampaio Lorena Moura , Galvão Caroline Ramalho , Galvão Lorena Ramalho , Santos Dantas Bruna Matos , Santos Conceição Sarah Dos , Dias Gomes Érica Velasco , Cirqueira Silva Maria Emília , Oliveira Ana Clara Silva , Silva Caroline Santos
Published: June 2021

Introduction

Cytopathological examination is the main method for the screening of cervical cancer, its occurrence can be prevented or minimized through screening, with good coverage, quality of collection and analysis and women’s adherence to the examination.

Objective

To describe the quality of cytopathological exams performed at a family health unit in Salvador - Bahia during the years 2015 and 2016.

Method

A cross-sectional study, of an exploratory descriptive character, was conducted, using a database from a previous study carried out in a health unit in Salvador / BA and approved by the Ethical Committee from State University of Feira de Santana.

Results

Preventive gynecological exams of 1,350 women were analyzed, but 330 (24.4%) of them could not be evaluated due to the absence of the report in the health unit and the presence of acellular or hypocellular material that prevented the analysis. From 1020 exams, 45 (3.3%) presented an unsatisfactory sample, without conditions for analysis, while 589 (43.19%) showed only squamous cells. From 392 (29.0%) reports with an adequate study sample, 150 (%) were women with a mean age of 38.49 years. These were located and composed the final sample of the present investigation.

Final Considerations

We suggest the need for investments in continuing education for professionals involved in performing cytopathological exams, in order to ensure the right of women to comprehensive and quality health care.
High Speed Photographic Detection of Particle Distribution During Aerosol and Smoke Generating Surgical Procedures: Effects of Surgical Site Evacuation
Authors: Bolk Kody G. , Dunham Michael E. , Hoffseth Kevin F. , Jung Jangwook P , Garcia Beatriz M. , Walvekar Rohan R.
Published: June 2021

Objective

Determine the effectiveness of evacuation systems designed to clear bioaerosols and smoke from the surgical field.

Study design

High-speed photographic evaluation of aerosol and smoke generated in simulated surgical fields.

Materials and methods

Surgical site aerosol clearance was evaluated using a model of the anterior neck and prototypes for surgical site evacuator ports created using 3D printing. A commercially available electrocautery handpiece fitted with an evacuator was tested on animal tissue for smoke clearance. Both systems were connected to a commercial vacuum powered evacuation system. High speed photography was used to record videos of the aerosols and plumes. Fields were recorded with and without evacuation.

Results

Efficient aerosol clearance from an open surgical field using an evacuator port is dependent upon the port design, airflow velocity, and placement relative to the aerosol generating site. The size and surface geometry of the surgical field are also important.

Surgical smoke generated with electrocautery is cleared from the field by the evacuation enclosure around the handpiece, even at high electrocautery power settings. Except for device noise, there appears to be no reason for using evacuator flow rates below the maximum setting.

Conclusions

Bioaerosol and smoke generated during surgery are potential sources of respiratory pathogens and pose a threat to operating room personnel. Surgical site evacuation can significantly reduce the volume of airborne particles in the field but requires careful design and deployment considerations.x
A molecular toolbox for ADP-ribosyl binding proteins
Authors: Sowa Sven T. , Galera-Prat Albert , Wazir Sarah , Alanen Heli I. , Maksimainen Mirko M. , Lehtiö Lari
Published: June 2021

Proteins interacting with ADP-ribosyl groups are often involved in disease-related pathways or in viral infections, which makes them attractive targets for the development of inhibitors. Our goal was to develop a robust and accessible assay technology that is suitable for high-throughput screening and applicable to a wide range of proteins acting as either hydrolysing or non-hydrolysing binders of mono- and poly-ADP-ribosyl groups. As a foundation of our work, we developed a C-terminal protein fusion tag based on a Gi protein alpha subunit peptide (GAP), which allows for site-specific introduction of cysteine-linked mono- and poly-ADP-ribosyl groups as well as chemical ADP-ribosyl analogs. By fusion of the GAP-tag and ADP-ribosyl binders to fluorescent proteins, we were able to generate robust FRET signals and the interaction with 22 previously described ADP-ribosyl-binders was confirmed. To demonstrate the applicability of this binding assay for high-throughput screening, we utilized it to screen for inhibitors of the SARS-CoV-2 nsp3 macrodomain and identified the drug suramin as a moderate yet unspecific inhibitor of this protein. To complement the binding technology, we prepared high-affinity ADP-ribosyl binders fused to a nanoluciferase, which enabled simple blot-based detection of mono- and poly-ADP-ribosylated proteins. These tools can be expressed recombinantly inE. coliusing commonly available agents and will help to investigate ADP-ribosylation systems and aid in drug discovery.
Species-specific molecular barriers to SARS-CoV-2 replication in bat cells
Authors: Aicher Sophie-Marie , Streicher Felix , Chazal Maxime , Planas Delphine , Luo Dongsheng , Buchrieser Julian , Nemcova Monika , Seidlova Veronika , Zukal Jan , Serra-Cobo Jordi , Pontier Dominique , Schwartz Olivier , Pikula Jiri , Dacheux Laurent , Jouvenet Nolwenn
Published: June 2021

Bats are natural reservoirs of numerous coronaviruses, including the potential ancestor of SARS-CoV-2. Knowledge concerning the interaction between coronaviruses and bat cells is sparse. We investigated the susceptibility of primary cells from Rhinolophus ferrumequinum and Myotis species, as well as of established and novel cell lines from Myotis myotis, Eptesicus serotinus, Tadarida brasiliensis and Nyctalus noctula, to SARS-CoV-2 infection. None of these cells were sensitive to infection, not even the ones expressing detectable levels of angiotensin-converting enzyme 2 (ACE2), which serves as the viral receptor in many mammalian species. The resistance to infection was overcome by expression of human ACE2 (hACE2) in three cell lines, suggesting that restriction to viral replication was due to a low expression of bat ACE2 (bACE2) or absence of bACE2 binding in these cells. Infectious virions were produced but not released from hACE2-transduced M. myotis brain cells. E. serotinus brain cells and M. myotis nasal epithelial cells expressing hACE2 efficiently controlled viral replication. This ability to control viral replication correlated with a potent interferon response. Our data highlight the existence of species-specific molecular barriers to viral replication in bat cells. These novel chiropteran cellular models are valuable tools to investigate the evolutionary relationships between bats and coronaviruses.

Author summary

Bats host ancestors of several viruses that cause serious disease in humans, as illustrated by the on-going SARS-CoV-2 pandemic. Progress in investigating bat-virus interactions have been hampered by a limited number of bat cell lines. We have generated primary cells and cell lines from several bat species that are relevant for coronavirus research. The varying susceptibilities of the cells to SARS-CoV-2 infection offered the opportunity to uncover some species-specific molecular restrictions to viral replication. All bat cells exhibited a potent entry-dependent restriction. Once this block was overcome by over-expression of human ACE2, which serves at the viral receptor, two bat cell lines controlled well viral replication, which correlated with the inability of the virus to counteract antiviral responses. Other cells potently inhibited viral release. Our novel bat cellular models contribute to a better understanding of the molecular interplays between bats and viruses.
Spatial regulation of coordinated excitatory and inhibitory synaptic plasticity at dendritic synapses
Authors: Ravasenga Tiziana , Ruben Massimo , Polenghi Alice , Petrini Enrica Maria , Barberis Andrea
Published: June 2021

The induction of synaptic plasticity at an individual dendritic glutamatergic spine can affect neighboring spines. This local modulation generates dendritic plasticity microdomains believed to expand the neuronal computational capacity. Here, we investigate whether local modulation of plasticity can also occur between glutamatergic synapses and adjacent GABAergic synapses. Using MNI-glutamate and DPNI-GABA double uncaging combined with electrophysiology, live-cell imaging and single-particle tracking, we find that the induction of LTP at an individual glutamatergic spine causes the depression of nearby GABAergic inhibitory synapses (within 3 microns), whereas more distant ones are potentiated. Notably, L-type calcium channels and calpain are required for this plasticity spreading. Overall, our data support a model whereby input-specific glutamatergic postsynaptic potentiation induces a spatially-regulated rearrangement of inhibitory synaptic strength in the surrounding area through short-range heterosynaptic interactions. Such local coordination of excitatory and inhibitory synaptic plasticity is expected to profoundly influence dendritic information processing and integration.
Drosophilaarchitectural protein CTCF is not essential for fly survival and is able to function independently of CP190
Authors: Kyrchanova Olga , Klimenko Natalia , Postika Nikolay , Bonchuk Artem , Zolotarev Nicolay , Maksimenko Oksana , Georgiev Pavel
Published: June 2021

CTCF is the most likely ancestor of proteins that contain large clusters of C2H2 zinc finger domains (C2H2) and is conserved among most bilateral organisms. In mammals, CTCF functions as the main architectural protein involved in the organization of topology-associated domains (TADs). In vertebrates andDrosophila, CTCF is involved in the regulation of homeotic genes. Previously, null mutations in thedCTCFgene were found to result in death during the stage of pharate adults, which failed to eclose from their pupal case. Here, we obtained several new nulldCTCFmutations and found that the complete inactivation of dCTCF appears to be limited to phenotypic manifestations of theAbd-Bgene and fertility of adult flies. Many modifiers that are not associated with an independent phenotypic manifestation can significantly enhance the expressivity of the nulldCTCFmutations, indicating that other architectural proteins are able to functionally compensate for dCTCF inactivation inDrosophila. We also mapped the 715–735 aa region of dCTCF as being essential for the interaction with the BTB (Broad-Complex, Tramtrack, and Bric a brac) and microtubule-targeting (M) domains of the CP190 protein, which binds to many architectural proteins. However, the mutational analysis showed that the interaction with CP190 was not essential for the functional activity of dCTCFin vivo.

Highlights

The dCTCF 715–735 aa region interacts with the BTB and M domains of CP190

Interaction with CP190 is not essential for dCTCF functionin vivo

NulldCTCFmutants are viable but show severely reduced fertility
Loss of Stk11 in basolateral amygdalar projection neurons impairs taste aversion learning by altering the temporal pattern of taste response plasticity in gustatory cortex
Authors: Nanu Roshan D. , Murdy Thomas J. , Levitan David , Scott Seneca , Nelson Sacha B. , Katz Donald B.
Published: June 2021

Gustatory cortex (GC) responds to tastes on the tongue with dynamic ensemble activity that represents first the presence, then the identity, and finally the hedonic value (palatability) of tastes. This final state of the taste response is uniquely altered by conditioned taste aversion (CTA) – a powerful one-trial learning paradigm in which a taste becomes aversive after association with gastric malaise – a process requiring coordination between GC and basolateral amygdala (BLA). Previously, we found that one key requirement for learning in this circuit is expression of the serine/threonine kinase 11 (Stk11) gene (a tumor suppression gene which has only recently been associated with learning; Levitan et al., 2020). When Stk11 is knocked out in BLA projection neurons (BLApn), CTA learning fails to occur. Here we have examined how learning-related response plasticity in GC taste responses is impacted by the knockout of Stk11 in BLApn. Contrary to the commonly held assumption that a lack of learning means a lack of such plasticity, but consistent with the fact that Stk11KO has been shown to increase the excitability of BLApn, our data reveal that the knockout of Stk11 in BLApn does not eliminate plasticity; rather, it shifts the impact of CTA training on GC taste responses to an earlier, learning-inappropriate epoch. Even naïve taste representations are altered—specifically, the pattern of similarities and differences among the different taste responses are rendered abnormal by Stk11 KO, and these relationships fail to change with training. Finally, the latency of behavior-related dynamic ensemble features of the GC taste response, which is also abnormal in naïve KO mice, is rendered disorganized by CTA. Together, these results suggest that Stk11 plays a role in governing the coordination of GC activity by BLA, and demonstrate that alterations in the function of BLApn caused by Stk11KO inhibit learning not by inhibiting plasticity but by changing its temporal properties.
Mesolimbic dopamine adapts the rate of learning from action
Authors: Coddington Luke T. , Lindo Sarah E. , Dudman Joshua T.
Published: June 2021

Recent 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

Chest 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

Alterations 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

This 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

Advances 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

Many 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

The 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

Abstract

Regression 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.

AuthorSummary

Test-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.
Features and mechanisms of canonical and noncanonical genomic imprinting
Authors: Hanna Courtney W. , Kelsey Gavin
Published: June 2021

Genomic imprinting is the monoallelic expression of a gene based on parent of origin and is a consequence of differential epigenetic marking between the male and female germlines. Canonically, genomic imprinting is mediated by allelic DNA methylation. However, recently it has been shown that maternal H3K27me3 can result in DNA methylation-independent imprinting, termed “noncanonical imprinting.” In this review, we compare and contrast what is currently known about the underlying mechanisms, the role of endogenous retroviral elements, and the conservation of canonical and noncanonical genomic imprinting.
Cloning Polymerase Chain Reaction (PCR) Products: TA Cloning
Authors: Green Michael R. , Sambrook Joseph
Published: June 2021

The nontemplate-dependent terminal transferase activity inherent in nonproofreading DNA polymerases such as Taq provides a highly efficient method to clone PCR products. The enzyme adds a single, unpaired residue—preferentially an adenosyl residue—to each 3′ end of a double-stranded amplified product. The unpaired terminal (A) residues can pair with a linear T vector that carries an unpaired 3′-thymidyl residue at each end. The two chief advantages of TA cloning are speed and lack of reliance on restriction enzymes. The major disadvantage is an inability to clone directionally. For this reason, it is important to pick and analyze several transformed clones when a particular orientation of the amplified fragment is required.
Electrophysiological Recording for Study of Xenopus Retinotectal Circuitry
Authors: Luo Yuhao , Shen Wanhua , Cline Hollis T.
Published: June 2021

The innervation of the optic tectum of Xenopus by retinal ganglion cells controls visual information processing and behavioral output. Several indicators can be used to evaluate the functional inputs/outputs of tectal neurons, such as spontaneous activity, visually evoked currents, temporal receptive fields, and spatial receptive fields. Analysis of multiple functional properties in the same neurons allows increased understanding of mechanisms underlying visual system function and plasticity. Patch-clamp recordings combined with gene expression or morpholino-mediated knockdown techniques have been especially powerful in the study of specific genes during development and circuit function. The protocol described here provides instructions for performing in vivo electrophysiological recordings from individual tectal neurons to study retinotectal circuitry in the developing Xenopus tectum.
BL-11C Micro-MX: a high-flux microfocus macromolecular-crystallography beamline for micrometre-sized protein crystals at Pohang Light Source II
Authors: Gu Do-Heon , Eo Cheolsoo , Hwangbo Seung-A , Ha Sung-Chul , Kim Jin Hong , Kim Hyoyun , Lee Chae-Soon , Seo In Deuk , Yun Young Duck , Lee Woulwoo , Choi Hyeongju , Kim Jangwoo , Lim Jun , Rah Seungyu , Kim Jeong-Sun , Lee Jie-Oh , Kim Yeon-Gil , Park Suk-Youl
Published: June 2021

BL-11C, a new protein crystallography beamline, is an in-vacuum undulator-based microfocus beamline used for macromolecular crystallography at the Pohang Accelerator Laboratory and it was made available to users in June 2017. The beamline is energy tunable in the range 5.0–20 keV to support conventional single- and multi-wavelength anomalous-dispersion experiments against a wide range of heavy metals. At the standard working energy of 12.659 keV, the monochromated beam is focused to 4.1 µm (V) × 8.5 µm (H) full width at half-maximum at the sample position and the measured photon flux is 1.3 × 1012 photons s−1. The experimental station is equipped with a Pilatus3 6M detector, a micro-diffractometer (MD2S) incorporating a multi-axis goniometer, and a robotic sample exchanger (CATS) with a dewar capacity of 90 samples. This beamline is suitable for structural determination of weakly diffracting crystalline substances, such as biomaterials, including protein, nucleic acids and their complexes. In addition, serial crystallography experiments for determining crystal structures at room temperature are possible. Herein, the current beamline characteristics, technical information for users and some recent scientific highlights are described.
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

Purpose

Customer 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/approach

The 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.

Findings

As 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/value

The 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

Purpose

Drawing 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/approach

Matched 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.

Findings

The 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/implications

The 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/value

Despite 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

Purpose

The 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/approach

The 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.

Findings

The 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/value

The 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

Purpose

With 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/approach

The 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.

Findings

The 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/value

This 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

Purpose

Twitter 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/approach

This 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.

Findings

After 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/value

This 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.