bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pain reflects the informational value of nociceptive inputs
Authors: Coll Michel-Pierre , Walden Zoey , Bourgoin Pierre-Alexandre , Taylor Véronique , Rainville Pierre , Robert Manon , Nguyen Dang Khoa , Jolicoeur Pierre , Roy Mathieu 
Published: July 2023

AbstractPain perception and its modulation are fundamental to human learning and adaptive behavior. This study investigated the hypothesis that pain perception is tied to pain’s learning function. Thirty-one participants performed a threat conditioning task where certain cues were associated with a possibility of receiving a painful electric shock. The cues that signalled potential pain or safety were regularly changed, requiring participants to continually establish new associations. Using computational models, we quantified participants’ pain expectations and prediction errors throughout the task and assessed their relationship with pain perception and electrophysiological responses. Our findings suggest that subjective pain perception increases with prediction error, that is when pain was unexpected. Prediction errors were also related to physiological nociceptive responses, including the amplitude of the nociceptive flexion reflex and EEG markers of cortical nociceptive processing (N2-P2 evoked potential and gamma-band power). Additionally, higher pain expectations were related to increased late event-related potential responses and alpha/beta decreases in amplitude during cue presentation. These results further strengthen the idea of a crucial link between pain and learning and suggest that understanding the influence of learning mechanisms in pain modulation could help us understand when and why pain perception is modulated in health and disease.
sNucConv: A bulk RNA-seq deconvolution method trained on single-nucleus RNA-seq data to estimate cell-type composition of human subcutaneous and visceral adipose tissues
Authors: Sorek Gil , Haim Yulia , Chalifa-Caspi Vered , Lazarescu Or , Ziv Maya , Hagemann Tobias , Nono Nankam Pamela Arielle , Blüher Matthias , Liberty Idit F. , Dukhno Oleg , Kukeev Ivan , Yeger-Lotem Esti , Rudich Assaf , Levin Liron 
Published: July 2023

AbstractDeconvolution algorithms rely on single-cell RNA-sequencing (scRNA-seq) data applied onto bulk RNA-sequencing (bulk RNA-seq) to extract information on the cell-types composition and proportions comprising a certain tissue. Adipose tissues’ cellular composition exhibits enormous plasticity in response to weight changes and high variance at different anatomical locations (depots). However, adipocytes – the functionally unique cell type of adipose tissue, are not amenable to scRNA-seq, a challenge recently met by applying single-nucleus RNA-sequencing (snRNA-seq). Here we aimed to develop a deconvolution method to estimate the cellular composition of human visceral and subcutaneous adipose tissues (hVAT and hSAT, respectively) using snRNA-seq to assess the true cell-type proportions. To correlate deconvolution-estimated cell-type proportions to true (snRNA-seq -derived) proportions, we analyzed seven hVAT and 5 hSAT samples by both bulk RNA-seq and snRNA-seq. snRNA-seq uncovered 15 distinct cell types in hVAT and 13 in hSAT. Deconvolution tools – SCDC, MuSiC, and Scaden exhibited low performance in estimating cell-type proportions (median |R|= 0.12 for estimated vs. true correlations). Notably, estimation accuracy somewhat improved by decreasing the number of cell-types groups, which nevertheless remained low (|R|<0.42). We therefore developedsNuConv, a novel method that employs Scaden, a deep-learning tool, trained using snRNA-seq - based data corrected by i. snRNA-seq/bulk RNA-seq highly-correlated genes, ii. corrected estimated cell-type proportions based on individual cell-type regression models. Applying sNuConv on our bulk RNA-seq data resulted in cell-type proportion estimation accuracy with median R=0.93 (range:0.76–0.97) for hVAT, and median R=0.95 (range:0.92–0.98) for hSAT. The resulting model was depot-specific, reflecting depot-differences in gene expression patterns. Thus, we present sNuConv, a novel, AI-based, method to deduce the cellular landscape of hVAT and hSAT from bulk RNA-seq data, providing proof-of-concept for producing validated deconvolution algorithms for tissues un-amenable to single-cell RNA sequencing.
An In-Silico Testbed for Fast and Accurate MR Labeling of Orthopaedic Implants
Authors: Noetscher Gregory M , Serano Peter J. , Horner Marc , Prokop Alexander , Hanson Jonathan , Fujimoto Kyoko , Brown James E. , Nazarian Ara , Ackerman Jerome , Makaroff Sergey N 
Published: July 2023

AbstractOne limitation on the ability to monitor health in older adults using Magnetic Resonance (MR) imaging is the presence of implants, where the prevalence of implantable devices (orthopedic, cardiac, neuromodulation) increases in the population, as does the pervasiveness of conditions requiring MRI studies for diagnosis (musculoskeletal diseases, infections, or cancer). The present study describes a novel multiphysics implant modeling testbed using the following approaches with two examples:-anin-silicohuman model based on the widely available Visible Human Project (VHP) cryo-section dataset;-a finite element method (FEM) modeling software workbench from Ansys (Electronics Desktop/Mechanical) to model MR radio frequency (RF) coils and the temperature rise modeling in heterogeneous media.Thein-silicoVHP Female model (250 parts with an additional 40 components specifically characterizing embedded implants and resultant surrounding tissues) corresponds to a 60-year-old female with a body mass index (BMI) of 36. The testbed includes the FEM-compatiblein-silicohuman model, an implant embedding procedure, a generic parameterizable MRI RF birdcage two-port coil model, a workflow for computing heat sources on the implant surface and in adjacent tissues, and a thermal FEM solver directly linked to the MR coil simulator to determine implant heating based on an MR imaging study protocol. The primary target is MR labeling of large orthopaedic implants. The testbed has very recently been approved by the US Food and Drug Administration (FDA) as a medical device development tool (MDDT) for 1.5 T orthopaedic implant examinations.
Influence of gastronomic motivations, satisfaction and experiences on loyalty towards a destination
Authors: Moral-Cuadra Salvador , Martín Juan Carlos , Román Concepción , López-Guzmán Tomás 
Published: July 2023

PurposeThe main objective of this research is to establish an integrated model of gastronomy tourism to help some of the main public and private stakeholders design strategies to improve tourists' gastronomic experience and satisfaction, taking gastronomic motivations as a starting point. Furthermore, the difference between destination satisfaction and gastronomic satisfaction has been established in order to determine the degree to which each one influences loyalty towards the destination.Design/methodology/approachAfter detailing the theoretical framework for the development of the hypotheses, the study was carried out using a quantitative methodology based on structural equation modelling (SEM). The final sample consisted of 710 tourists who visited Córdoba, Spain – a world heritage city of international renown.FindingsResults indicate that gastronomic motivations, gastronomic experience and destination satisfaction have a direct influence on loyalty towards a destination. Also, destination satisfaction is found to play a mediating role in the relationship between gastronomic experience and loyalty towards the destination. Differences between destination and gastronomic satisfaction have been evidenced. For fostering a tourist's loyalty towards a destination, gastronomic satisfaction alone is not enough; other elements inherent to the destination itself are necessary for full loyalty, whether attitudinal or behavioural.Originality/valueCorrectly identifying tourist motivations can help managers of Destination Management Organizations (DMOs) to develop tailored marketing and communication campaigns that boost return visits to the destination or recommendations to family and friends. DMOs need to be aware that DMOs cannot overlook elements such as safety, hospitality or destination cleanliness at the expense of gastronomic satisfaction.
Navigating COVID-19: unraveling supply chain disruptions through best-worst method and fuzzy TOPSIS
Authors: Ali Irfan , Charles Vincent , Modibbo Umar Muhammad , Gherman Tatiana , Gupta Srikant 
Published: July 2023

PurposeThe COVID-19 pandemic has caused significant disruptions to global supply chains (SCs), affecting the production, distribution, and transportation of goods and services. To mitigate these disruptions, it is essential to identify the barriers that have impeded the seamless operation of SCs. This study identifies these barriers and assesses their impact on supply chain network (SCN).Design/methodology/approachTo determine the relative importance of different barriers and rank the affected industries, a hybrid approach was employed, combining the best-worst method (BWM) and the technique for order preference by similarity to an ideal solution (TOPSIS). To accommodate the inherent uncertainties associated with the pandemic, a triangular fuzzy TOPSIS was used to represent the linguistic variable ratings provided by decision-makers.FindingsThe study found that the airlines and hospitality industry was the most affected by the barriers, accounting for 46% of the total, followed by the healthcare industry (23%), the manufacturing industry (19%), and finally the consumer and retail industry (17%).Research limitations/implicationsThis study is limited to the four critical industries and nine identified barriers. Other industries and barriers may have different weights and rankings. Nevertheless, the findings offer valuable insights for decision-makers in SC management, aiding them in mitigating the impact of COVID-19 on their operations and enhancing their resilience against future disruptions.Originality/valueThis study enhances understanding of COVID-19’s impact on SCN and provides a framework for assessing disruptions using multi-criteria decision-making processes. The hybrid approach of BWM and TOPSIS in a fuzzy environment is unique and offers potential applicability in various evaluation contexts.
Evaluation of field labor and management productivity in the USA construction industry
Authors: Jahanger Qais K. , Trejo David , Louis Joseph 
Published: July 2023

PurposeThe health of an economy is heavily dependent on the productivity of the economy's major industries including construction. While most macro-measures of productivity in the USA construction industry indicate a decline, corresponding studies at the individual task level indicate an increase in productivity. Therefore, this paper aims to identify areas where productivity challenges exist and thus provide recommendations for improvement in the construction industry.Design/methodology/approachA model that relates the way construction projects are executed with the sources of data that inform productivity analyses is developed and presented. This effort/value-flow model informs the data analysis that is performed to determine productivity trends for management and field labor. Further analysis for field labor productivity using field data and management productivity was separately conducted. Management productivity was particularly difficult to gauge, resulting in the use of surrogate measures.FindingsIt was observed that while both field labor and management productivities at the industry level have been decreasing, the decrease in management productivity was five times that of field labor productivity. A similar trend was observed for management productivity at the project level.Originality/valueThe primary contribution of this paper to the body of knowledge and industry is the introduction of a holistic analysis of USA construction productivity. Recommendations to improve management productivity include the use of technology, especially project management software.
Family firms and product recalls: an event study for the US automobile industry
Authors: Martins António Miguel , Pires Cesaltina Pacheco 
Published: July 2023

PurposeThis study explores whether the unique organizational form of family firms helps to mitigate the negative effects caused by the announcement of product recalls.Design/methodology/approachThe authors use an event study, for a sample of 2,576 product recalls in the United States (US) automobile industry, between January 2010 and June 2021.FindingsThe authors found that stock market's reaction to a product recall announcement is less negative for family firms. This superior performance is partially driven by the family firms' long-term investment horizons and higher strategic emphasis on product quality. However, the relationship between family ownership and cumulative abnormal returns around product recall announcements is nonlinear as the impact of family ownership starts by being positive but becomes negative for higher levels of family ownership. The authors also find that family firm's chief executive officer (CEO) and managerial ownership influence positively the stock market reaction to product recall announcements.Practical implicationsThis work has several implications for family firms' management as well as for investors and financial analysts. First, as higher managerial ownership is associated with a greater emphasis on product quality, decreasing stock market losses when a product recall occurs, family firms should consider increasing equity-based compensation. Second, as there seems to exist an optimal proportion of family ownership, family firms should consider the risks of increasing too much their ownership share. Third, investors and financial analysts can use the results in the study to help them in their investment and trading decisions in the stock market.Originality/valueThe authors extend the knowledge of product recalls by studying the under-researched role of the flexible, internally focused culture of family businesses on the stock market reaction to product recalls.
Managing workuniverses with workers and robots: the implications of digital-driven spatiotemporal dynamics for HRM
Authors: Peng Hongxia 
Published: July 2023

PurposeThe increasing presence of traditional or new forms of robots at work demonstrates how the copresence of workers and robots might reframe work and workplaces and consequently arouse new human resource management (HRM) questions regarding how to manage the spatiotemporal change of work in organizations. Based on a spatiotemporal perspective, this conceptual article examines the implication of new spatiotemporal dynamics of work, which are generated by the interaction between workers and traditional or new forms of robots that are driven by advanced digital technologies, for HRM.Design/methodology/approachThe article begins by carrying out a selective review focusing on the studies that enhanced the comprehension of the digital-driven spatiotemporal dynamics of work. It then presents a spatiotemporal framework from which it examines the implications of digital-driven spatiotemporal work boundaries for HRM. The article ends by underscoring the theoretical and empirical importance of taking more interest in new spatiotemporal forms of work for developing the HRM of the future.FindingsBy developing the notion of workuniverses, which denotes the spatiotemporal boundaries generated by the act of working through the interaction between workers and different forms of robots, this research first develops a theoretical framework that discerns three forms of spatiotemporal dynamics forming workuniverses at different levels and two spatiotemporal arrays for managing the spatiotemporal change of work in organizations. The HRM questions and ethical concerns generated by the formation of workuniverses are then revealed through four focuses: the management ethics in workuniverses, individuals' spatiotemporal well-being, collective spatiotemporal coordination and spatiotemporal change management in workuniverses.Originality/valueThis research provides an original perspective, which is the spatiotemporal perspective, to examine the new spatiotemporal dynamics that form workuniverses and the HRM questions and concerns generated by the increasing interaction between workers and different forms of digital-driven robots.
Forensic examination of the hand
Authors: Hackman Lucina , Black Sue 
Published: July 2023

AbstractUsing anatomical feature comparison to consider potential matches from hand images to identify or eliminate suspects in an investigation, commonly relating to those involved in the production of images of child sexual abuse, is an emerging forensic methodology that has become increasingly utilized by police forces within the United Kingdom. This article gives an overview of the development of a process that is based on an understanding of anatomical variation and shows how new approaches can be introduced to forensic investigations. It describes the rationale that led to the development of the investigative methodology within the United Kingdom, and how it is implemented to ensure that comparisons and the subsequent reports are both methodologically sound and admissible in any of the courts within the UK legal jurisdictions.
The Berlin puzzle: Why European solidarity prevailed in the adoption of the Corona recovery fund
Authors: WAAS LARA , RITTBERGER BERTHOLD 
Published: July 2023

AbstractIn light of the German government's long‐held preference against EU‐wide fiscal burden‐sharing, a hallmark of the Euro crisis, its support for an EU‐wide debt‐instrument during the COVID‐19 pandemic constitutes a dramatic policy U‐turn. To make sense of the ‘Berlin puzzle’, we develop a theoretical mechanism that explores why an initially reluctant German government heeded to the call for transnational fiscal solidarity: First, to avoid a ‘common bad’ of a large‐scale economic contraction, proposals for an EU‐wide fiscal response became a political imperative. Second, the successful framing of the crisis as ‘nobody's fault’ rendered the call for European solidarity as the dominant standard of legitimacy to which all governments subscribed. Third, governments whose preferences were not aligned with this standard faced mounting normative pressure and isolation. As a result, governments changed their positions, but not their preferences. We probe this mechanism by carrying out a process‐tracing analysis of the German government's fiscal policy U‐turn in the crucial months preceding the adoption of the Next Generation EU (NGEU) recovery plan in July 2020. The paper contributes to the growing literature on fiscal burden‐sharing in the EU by demonstrating when and how member states can change their stance on transnational fiscal burden‐sharing.
Population pharmacokinetics of polymyxin B in patients with liver dysfunction
Authors: Li Xueyong , Cheng Yu , Chen Bo , Chen Yiying , Huang Yingbin , Zhang Bingqing , Que Wancai , Liu Maobai , Zhang Hui , Qiu Hongqiang 
Published: July 2023

AbstractAimsPolymyxin B (PMB) is widely used to treat infections caused by multidrug‐resistant Gram‐negative pathogens. Currently, the pharmacokinetic data of PMB in patients with liver dysfunction are limited. This study aimed to develop a population pharmacokinetic (PopPK) model of PMB in patients with liver dysfunction and identify the factors affecting PMB pharmacokinetics.MethodsWe conducted a retrospective pharmacokinetic study involving 136 adults with different levels of liver function. Nonlinear mixed effects modelling was used to develop a PopPK model of PMB. Monte Carlo simulation was used to design PMB dosage schedules across various liver and renal functions.ResultsPMB pharmacokinetic analyses included 401 steady‐state concentrations in 136 adult patients. A one‐compartment pharmacokinetic model with first‐order absorption and elimination was used to describe the data. The typical population value of PMB clearance was 2.43 L/h and the volume of distribution was 23.11 L. This study revealed that creatinine clearance (CrCL) and Child–Pugh class were significantly associated with PMB pharmacokinetic parameters; however, clinically relevant variations of dose‐normalized drug exposure were not significant. For patients with a minimum inhibitory concentration of ≤0.5 mg/L, the appropriate dose was 40–75 mg/12‐h. When the dose exceeded 100 mg/12‐h, the risk of nephrotoxicity increased significantly.ConclusionsThis study provided PMB pharmacokinetic information for patients with liver dysfunction. Patients with renal and liver dysfunctions may not require an initial dose adjustment. Rather than PopPK‐guided dose adjustment, therapeutic drug monitoring of PMB plays a more direct role in optimizing dosing regimens based on its therapeutic window.
An intelligent hierarchical residual attention learning‐based conjoined twin neural network for Alzheimer's stage detection and prediction
Authors: Shankar Venkatesh Gauri , Sisodia Dilip Singh , Chandrakar Preeti 
Published: July 2023

AbstractAlzheimer's disorder (AD) causes permanent impairment in the brain's memory of the cellular system, leading to the initiation of dementia. Earlier detection of Alzheimer's disease in the initial stages is challenging for researchers. Deep learning and machine learning‐based techniques can help resolve many issues associated with brain imaging exploration. Brain MR Images (Brain‐MRI) are used to detect Alzheimer's in computable research work. To correctly categorize the stages of Alzheimer's disease, discriminative features need to be extracted from the MR images. Recently, many studies have used deep learning methods for the early detection of this disorder. However, overfitting degrades the deep learning method's performance because the dataset's selection images are smaller and imbalanced. Some studies could not reach more discriminative and effectual attention‐aware features for Alzheimer's stage classification to increase the model performance. In this paper, we develop a novel hierarchical residual attention learning‐inspired multistage conjoined twin network (HRAL‐CTNN) to classify the stages of Alzheimer's. We used augmentation approaches to scale insufficient and imbalanced data. The HRAL‐CTNN is efficiently overcoming the issues of not obtaining efficient attention‐aware and generative features for Alzheimer's stage classification. The proposed model solved the problem of redundant features by extracting attentive discriminant features, and scaling imbalance data by data augmentation, after that training and validation using HRAL‐CTNN. The execution of this proposed work has been performed on the ADNI MRI dataset. This work achieved outstanding accuracy of 99.97  0.01% and F1 score of 99.30  0.02% for Alzheimer's stage classification. This model proposed by our group outperformed the existing related studies in terms of the model's performance score.
1063. Lathyrus oleraceus Lam.: Leguminosae
Authors: Rix Martyn , Nesbitt Mark , King Christabel 
Published: July 2023

SummaryA wild relative of the garden pea, formerly called Pisum sativum L., but now included in the genus Lathyrus, is illustrated, and its relationship to cultivated peas is discussed. Recent studies of the DNA of Pisum and Lathyrus have led to the change of name for this common species.