bluuebunny/crossref_metadata_2025_split huggingface dataset:
Transcriptome-Wide Root Causal Inference
Authors: Strobl Eric V. , Gamazon Eric R. 
Published: July 2024

ABSTRACTRoot causal genes correspond to the first gene expression levels perturbed during pathogenesis by genetic or non-genetic factors. Targeting root causal genes has the potential to alleviate disease entirely by eliminating pathology near its onset. No existing algorithm discovers root causal genes from observational data alone. We therefore propose the Transcriptome-Wide Root Causal Inference (TWRCI) algorithm that identifies root causal genes and their causal graph using a combination of genetic variant and unperturbed bulk RNA sequencing data. TWRCI uses a novel competitive regression procedure to annotate cis and trans-genetic variants to the gene expression levels they directly cause. The algorithm simultaneously recovers a causal ordering of the expression levels to pinpoint the underlying causal graph and estimate root causal effects. TWRCI outperforms alternative approaches across a diverse group of metrics by directly targeting root causal genes while accounting for distal relations, linkage disequilibrium, patient heterogeneity and widespread pleiotropy. We demonstrate the algorithm by uncovering the root causal mechanisms of two complex diseases, which we confirm by replication using independent genome-wide summary statistics.
Evaluation of false positive and false negative errors in targeted next generation sequencing
Authors: Moon Youngbeen , Kim Young-Ho , Kim Jong-Kwang , Hong Chung Hwan , Kang Eun-Kyung , Choi Hye Won , Lee Dong-eun , Kim Tae-Min , Heo Seong Gu , Han Namshik , Hong Kyeong-Man 
Published: July 2024

AbstractBackgroundAlthough next generation sequencing (NGS) has been adopted as an essential diagnostic tool in various diseases, NGS errors have been the most serious problem in clinical implementation. Especially in cancers, low level mutations have not been easy to analyze, due to the contaminating normal cells and tumor heterozygosity.ResultsIn targeted NGS (T-NGS) analyses for reference-standard samples containing mixtures of homozygote H. mole DNA with blood genomic DNA at various ratios from four certified NGS service providers, large differences in the lower detection limit of variants (16.3 times, 1.51∼24.66%) and the false positive (FP) error rate (4280 times, 5.814 x 10−4∼1.359 x 10−7) were found. Employment of the commercially available Dragen system for bioinformatic analyses reduced FP errors in the results from companies BB and CC, but the errors originating from the NGS raw data persisted. Bioinformatic conditional adjustment to increase sensitivity (less than 2 times) led to a much higher FP error rate (610∼8200 times). In addition, problems such as biased preferential reference base calls during bioinformatic analysis and high-rate FN errors in HLA regions were found in the NGS analysis.ConclusionT-NGS results from certified NGS service providers can be quite various in their sensitivity and FP error rate, suggesting the necessity of further quality controls for clinical implementation of T-NGS. The present study also suggests that mixtures of homozygote and heterozygote DNAs can be easily employed as excellent reference-standard materials for quality control of T-NGS.
A deep learning-based multiscale integration of spatial omics with tumor morphology
Authors: Schmauch Benoît , Herpin Loïc , Olivier Antoine , Duboudin Thomas , Dubois Rémy , Gillet Lucie , Schiratti Jean-Baptiste , Di Proietto Valentina , Le Corre Delphine , Bourgoin Alexandre , Taïeb Pr. Julien , Emile Pr. Jean-François , Fridman Pr. Wolf H. , Pronier Elodie , Laurent-Puig Pr. , Durand Eric Y. 
Published: July 2024

AbstractSpatial Transcriptomics (spTx) offers unprecedented insights into the spatial arrangement of the tumor microenvironment, tumor initiation/progression and identification of new therapeutic target candidates. However, spTx remains complex and unlikely to be routinely used in the near future. Hematoxylin and eosin (H&E) stained histological slides, on the other hand, are routinely generated for a large fraction of cancer patients. Here, we present a novel deep learning-based approach for multiscale integration of spTx with tumor morphology (MISO). We trained MISO to predict spTx from H&E on a new unpublished dataset of 72 10X Genomics Visium samples, and derived a novel estimate of the upper bound on the achievable performance. We demonstrate that MISO enables near single-cell-resolution, spatially-resolved gene expression prediction from H&E. In addition, MISO provides an effective patient representation framework that enables downstream predictive tasks such as molecular phenotyping or MSI prediction.
An Open-Set Semi-Supervised Multi-Task Learning Framework for Context Classification in Biomedical Texts
Authors: Tang Difei , Tam Thomas Yu Chow , Luo Haomiao , Telmer Cheryl A. , Miskov-Zivanov Natasa 
Published: July 2024

AbstractObjectiveIn biomedical research, knowledge about the relationships between entities, including genes, proteins, and drugs, is vital for unraveling the complexities of biological processes and intracellular pathway mechanisms. Natural language processing (NLP) and text mining methods have shown great success in biomedical relation extraction (RE). However, extracted relations often lack contextual information like cell type, cell line, and intracellular location, which are crucial components of biological knowledge. Previous studies have treated this problem as a post hoc context-relation association task, which is limited by the absence of a golden standard corpus, leading to error propagation and decreased model performance. To address these challenges, we created CELESTA (Context Extraction through LEarning with Semi-supervised multi-Task Architecture), a framework for biomedical context classification, applicable to both open-set and close-set scenarios.MethodsTo capture the inherent relationships between biomedical relations and their associated contexts, we designed a multi-task learning (MTL) architecture that seamlessly integrates with the semi-supervised learning (SSL) strategies during training. Our framework addresses the challenges caused by the lack of labeled data by assuming that the unlabeled data contain both in-distribution (ID) and out-of-distribution (OOD) data points. Further, we created a large-scale dataset consisting of five context classification tasks by curating two large Biological Expression Language (BEL) corpora and annotating them with our new entity span annotation method. We developed an OOD detector to distinguish between ID and OOD instances within the unlabeled data. Additionally, we utilized the data augmentation method combined with an external database to enrich our dataset, providing exclusive features for models during training process.ResultsWe conducted extensive experiments on the dataset, demonstrating the effectiveness of the proposed framework in significantly improving context classification and extracting contextual information with high accuracy. The newly created dataset and code used for this work are publicly available on GitHub (https://github.com/pitt-miskov-zivanov-lab/CELESTA).
Year-round quantification, structure and dynamics of epibacterial communities from diverse macroalgae reveal a persistent core microbiota and strong host specificities
Authors: Brunet Maéva , Le Duff Nolwen , Barbeyron Tristan , Thomas François 
Published: July 2024

AbstractMacroalgae-bacteria interactions play pivotal ecological roles in coastal ecosystems. Previous characterization of surface microbiota from various macroalgae evidenced fluctuations based on host tissues, physicochemical and environmental parameters. However, the dynamics and degree of similarity of epibacterial communities colonizing phylogenetically distant algae from the same habitat are still elusive. We conducted a year-long monthly epimicrobiota sampling on five algal species inhabiting an English Channel rocky shore:Laminaria digitata,Ascophyllum nodosum,Fucus serratus(brown algae),Palmaria palmata(red alga) andUlvasp. (green alga). To go beyond relative compositional data and estimate absolute variations in taxa abundance, we combined qPCR measurements of 16S rRNA gene copies with amplicon metabarcoding. A core microbiome composed of 10 genera was consistently found year-round on all algae. Notably, the abundant genusGranulosicoccusstood out for being the only one present in all samples and displayed an important microdiversity. Algal host emerged as the primary driver of epibacterial community composition, before seasonality, and bacterial taxa specifically associated with one or several algae were identified. Moreover, the impact of seasons on the epimicrobiota varied depending on algal tissues. Overall, this study provides an extensive characterization of the microbiota of intertidal macroalgae and enhances our understanding of algal-bacteria holobionts.
STK19 facilitates the clearance of lesion-stalled RNAPII during transcription-coupled DNA repair
Authors: van den Heuvel Diana , Rodríguez-Martínez Marta , van der Meer Paula J. , Moreno Nicolas Nieto , Park Jiyoung , Kim Hyun-Suk , van Schie Janne J.M. , Wondergem Annelotte P. , D’Souza Areetha , Yakoub George , Herlihy Anna E. , Kashyap Krushanka , Boissière Thierry , Walker Jane , Mitter Richard , Apelt Katja , de Lint Klaas , Kirdök Idil , Ljungman Mats , Wolthuis Rob M.F. , Cramer Patrick , Schärer Orlando D. , Kokic Goran , Svejstrup Jesper Q , Luijsterburg Martijn S. 
Published: July 2024

SummaryTranscription-coupled DNA repair (TCR) removes bulky DNA lesions impeding RNA polymerase II (RNAPII) transcription. Recent studies have outlined the stepwise assembly of TCR factors CSB, CSA, UVSSA, and TFIIH around lesion-stalled RNAPII. However, the mechanism and factors required for the transition to downstream repair steps, including RNAPII removal to provide repair proteins access to the DNA lesion, remain unclear. Here, we identify STK19 as a new TCR factor facilitating this transition. Loss of STK19 does not impact initial TCR complex assembly or RNAPII ubiquitylation but delays lesion-stalled RNAPII clearance, thereby interfering with the downstream repair reaction. Cryo-EM and mutational analysis reveal that STK19 associates with the TCR complex, positioning itself between RNAPII, UVSSA, and CSA. The structural insights and molecular modeling suggest that STK19 positions the ATPase subunits of TFIIH onto DNA in front of RNAPII. Together, these findings provide new insights into the factors and mechanisms required for TCR.
Digital transformation, management efficiency and excess employees − evidence from Chinese listed firms
Authors: Zhang Mengyun , Pu Hongjing , Yu Tianmuzi , Qu Shuyang 
Published: July 2024

Purpose
The purpose of this study is to explore the relationship between digital transformation and excess employees. This research investigates the questions of when human−machine synergy can be achieved after a firm goes through digital transformation and whether there will be excess employees in the interim.

Design/methodology/approach
This paper takes A-share listed companies as research object in the period of 2011−2020 and a total of 24,718 samples are obtained. Hypothesis testing and regression analysis are performed in STATA.

Findings
This paper finds a human−machine mismatch in the short term, as evidenced by an increase in the rate of excess employees; however, with the progress of digital transformation, it can drive the achievement of human−machine synergy in the long term, and management efficiency plays a mediating role in this process. Further research showed that the effects of digital transformation on the number of employees, revenue generation per capita and profit generation per capita varied in both the short and long term. In addition, the characteristics of the company affect the relationship between digital transformation and excess employees.

Originality/value
This paper contributes to the understanding of the impacts of digital transformation on the human capital structure of companies at a micro level. It also provides insights into how to improve human capital demand structure through digitalization, thus providing insights into labor market changes.
Exploring the antecedent factors of continuous intention to use mobile money: insights from emerging markets
Authors: Qatawneh Nour , Al-Okaily Aws , Al-Okaily Manaf , Ur Rehman Shafique 
Published: July 2024

Purpose
The purpose of this study is to examine the factors that may have an influence on the continuous intention to use mobile money as one of the Financial Technology (FinTech) products beyond the COVID-19 pandemic lockdown period.

Design/methodology/approach
This study has empirically tested the expanded post-acceptance model (Extend-PAM) and the expectation-confirmation model (ECM) to explain the mobile money adoption in Jordan. Data collected were analyzed through partial least squares–structural equation modeling (PLS-SEM).

Findings
The results mainly showed that the quality of administrative services and trust significantly impact confirmation and perceived usefulness. In addition, perceived security and knowledge of mobile money as one of the FinTech services significantly influence users’ confirmation and perceived usefulness. Also, usefulness and satisfaction influence continuous intention. Significant relationships were noted among confirmation, perceived usefulness, satisfaction and continuous intention to use mobile money.

Originality/value
This paper integrates two key theories: the expanded post-acceptance model (Extend-PAM) and the expectation-confirmation model (ECM) in the post-adoption behavior of mobile money. Therefore, this study attempts to fill a literature gap by examining the antecedent factors that influence the continuous intention to use mobile money services in the post-consumption stage.
Proposing a 6R framework promoting circular strategies for platform organizations
Authors: Tewary Abhijeet , Upadhyay Parijat 
Published: July 2024

Purpose
This study aims to investigate the connection between the circular economy and sustainable operations management to identify the challenges and opportunities in platform organizations. The study looks at how the stated circular economy strategies (Reduce, Reuse, Repair, Refurbish, Repurpose and Recycle) are integrated across different industries, emphasizing how they align with the e-business model. The research evaluates their contribution to achieving Sustainable Development Goal 12, which focuses on responsible consumption and production.

Design/methodology/approach
A literature review has analyzed CE frameworks, business models and the role of sustainable operations management practices. This study utilized secondary data analysis of platform organizations and insights from case studies to identify patterns, strategies and outcomes. The study also involved practical examinations within organizations, specifically focusing on innovative start-ups.

Findings
The analysis uses the 6R framework (Reduce, Reuse, Repair, Refurbish, Repurpose and Recycle) to uncover valuable insights into organizational practices and highlight the role of platform organizations in promoting and achieving circular economy objectives. The research findings focus on the central importance of data regulation and governance while showcasing sustainable business practices through platform organizations.

Originality/value
This research is significant as it connects circular economy with platform organizations’ business models, emphasizing data regulation, resource efficiency, waste reduction and aligning business practices with Sustainable Development Goal 12.
Unlocking the promise of process mining: shaping perceptions and impact in the public sector
Authors: Racis Serena , Spano Alessandro 
Published: July 2024

PurposeWorldwide challenges impose public organizations to rethink their processes and satisfactorily meet citizens’ needs. Process mining (PM) techniques enable organizations to objectively analyse and improve their processes, by providing higher process transparency and efficiency. However, extant literature on PM applications in the public sector reveals there is still limited evidence on the opportunities and challenges perceived from PM introduction in the public sector, and on PM potential to enhance public sector digital transformation: this study aims to fill these gaps.Design/methodology/approachBased on Business Process Management and digital innovation fields of research, we administered a questionnaire to a sample of Italian civil servants working in different public organizations to investigate their perceptions of PM opportunities and challenges and the extent to which it can support public sector digital transformation. A three-level analysis was conducted to inspect findings with different levels of granularity, and results were analysed both descriptively and quantitatively.FindingsWe found a positive attitude towards PM introduction in the public sector, and perceived opportunities and challenges related to both the technical and the social systems. The triangulation between close-ended and open-ended questions suggests that PM could be the missing link between public sector digitalization and digital transformation. These findings can be used by policymakers to develop the best strategies to introduce PM into public organizations and support its adoption, and by researchers to further explore PM role in public sector digital transformation.Originality/valueDespite PM claiming to push digital transformation, it is not clear if it is also true for public sector organizations. This paper addresses this gap and it is among the first attempts to explore PM from civil servants’ viewpoint to investigate their perceptions of PM opportunities and challenges, as well as the variables that influence these perceptions.
Isogeometric collocation method to simulate phase-field crystal model
Authors: Masoumzadeh Reza , Abbaszadeh Mostafa , Dehghan Mehdi 
Published: July 2024

Purpose
The purpose of this study is to develop a new numerical algorithm to simulate the phase-field model.

Design/methodology/approach
First, the derivative of the temporal direction is discretized by a second-order linearized finite difference scheme where it conserves the energy stability of the mathematical model. Then, the isogeometric collocation (IGC) method is used to approximate the derivative of spacial direction. The IGC procedure can be applied on irregular physical domains. The IGC method is constructed based upon the nonuniform rational B-splines (NURBS). Each curve and surface can be approximated by the NURBS. Also, a map will be defined to project the physical domain to a simple computational domain. In this procedure, the partial derivatives will be transformed to the new domain by the Jacobian and Hessian matrices. According to the mentioned procedure, the first- and second-order differential matrices are built. Furthermore, the pseudo-spectral algorithm is used to derive the first- and second-order nodal differential matrices. In the end, the Greville Abscissae points are used to the collocation method.

Findings
In the numerical experiments, the efficiency and accuracy of the proposed method are assessed through two examples, demonstrating its performance on both rectangular and nonrectangular domains.

Originality/value
This research work introduces the IGC method as a simulation technique for the phase-field crystal model.
Aerodynamic discrepancies of high-speed trains meeting within two types noise barriers: considering modeling scale ratio
Authors: Yang Wei-Chao , Li Guo-Zhi , Deng E , Ouyang De-Hui , Lu Zhi-Peng 
Published: July 2024

Purpose
Sustainable urban rail transit requires noise barriers. However, these barriers’ durability varies due to the differing aerodynamic impacts they experience. The purpose of this paper is to investigate the aerodynamic discrepancies of trains when they meet within two types of rectangular noise barriers: fully enclosed (FERNB) and semi-enclosed with vertical plates (SERNBVB). The research also considers the sensitivity of the scale ratio in these scenarios.

Design/methodology/approach
A 1:16 scaled moving model test analyzed spatiotemporal patterns and discrepancies in aerodynamic pressures during train meetings. Three-dimensional computational fluid dynamics models, with scale ratios of 1:1, 1:8 and 1:16, used the improved delayed detached eddy simulation turbulence model and slip grid technique. Comparing scale ratios on aerodynamic pressure discrepancies between the two types of noise barriers and revealing the flow field mechanism were done. The goal is to establish the relationship between aerodynamic pressure at scale and in full scale.

Findings
The aerodynamic pressure on SERNBVB is influenced by the train’s head and tail waves, whereas for FERNB, it is affected by pressure wave and head-tail waves. Notably, SERNBVB's aerodynamic pressure is more sensitive to changes in scale ratio. As the scale ratio decreases, the aerodynamic pressure on the noise barrier gradually increases.

Originality/value
A train-meeting moving model test is conducted within the noise barrier. Comparison of aerodynamic discrepancies during train meets between two types of rectangular noise barriers and the relationship between the scale and the full scale are established considering the modeling scale ratio.
Nacelle optimisation through multi-fidelity neural networks
Authors: Sánchez-Moreno Francisco , MacManus David , Tejero Fernando , Sheaf Christopher 
Published: July 2024

Purpose
Aerodynamic shape optimisation is a complex problem usually governed by transonic non-linear aerodynamics, a high dimensional design space and high computational cost. Consequently, the use of a numerical simulation approach can become prohibitive for some applications. This paper aims to propose a computationally efficient multi-fidelity method for the optimisation of two-dimensional axisymmetric aero-engine nacelles.

Design/methodology/approach
The nacelle optimisation approach combines a gradient-free algorithm with a multi-fidelity surrogate model. Machine learning based on artificial neural networks (ANN) is used as the modelling technique because of its ability to handle non-linear behaviour. The multi-fidelity method combines Reynolds-averaged Navier Stokes and Euler CFD calculations as high- and low-fidelity, respectively.

Findings
Ratios of low- and high-fidelity training samples to degrees of freedom of nLF/nDOFs = 50 and nHF/nDOFs = 12.5 provided a surrogate model with a root mean squared error less than 5% and a similar convergence to the optimal design space when compared with the equivalent CFD-in-the-loop optimisation. Similar nacelle geometries and aerodynamic flow topologies were obtained for down-selected designs with a reduction of 92% in the computational cost. This highlights the potential benefits of this multi-fidelity approach for aerodynamic optimisation within a preliminary design stage.

Originality/value
The application of a multi-fidelity technique based on ANN to the aerodynamic shape optimisation problem of isolated nacelles is the key novelty of this work. The multi-fidelity aspect of the method advances current practices based on single-fidelity surrogate models and offers further reductions in computational cost to meet industrial design timescales. Additionally, guidelines in terms of low- and high-fidelity sample sizes relative to the number of design variables have been established.