bluuebunny/crossref_metadata_2025_split huggingface dataset:
Spatial Single-Cell Mapping of Transcriptional Differences Across Genetic Backgrounds in Mouse Brains
Authors: Hemminger Zachary , Sanchez-Tam Gabriela , De Ocampo Haley , Wang Aihui , Underwood Thomas , Xie Fangming , Zhao Qiuying , Song Dongyuan , Li Jingyi Jessica , Dong Hongwei , Wollman Roy 
Published: October 2024

AbstractGenetic variation can alter brain structure and, consequently, function. Comparative statistical analysis of mouse brains across genetic backgrounds requires spatial, single-cell, atlas-scale data, in replicates—a challenge for current technologies. We introduceAtlas-scaleTranscriptomeLocalization usingAggregateSignatures (ATLAS), a scalable tissue mapping method. ATLAS learns transcriptional signatures from scRNAseq data, encodes themin situwith tens of thousands of oligonucleotide probes, and decodes them to infer cell types and imputed transcriptomes. We validated ATLAS by comparing its cell type inferences with direct MERFISH measurements of marker genes and quantitative comparisons to four other technologies. Using ATLAS, we mapped the central brains of five male and five female C57BL/6J (B6) mice and five male BTBR T+ tf/J (BTBR) mice, an idiopathic model of autism, collectively profiling over 40 million cells across over 400 coronal sections. Our analysis revealed over 40 significant differences in cell type distributions and identified 16 regional composition changes across male-female and B6-BTBR comparisons. ATLAS thus enables systematic comparative studies, facilitating organ-level structure-function analysis of disease models.
Mutations that prevent phosphorylation of the BMP4 prodomain impair proteolytic maturation of homodimers leading to lethality in mice
Authors: Kim Hyung-seok , Sanchez Mary L. , Silva Joshua , Schubert Heidi L. , Dennis Rebecca , Hill Christopher P. , Christian Jan L. 
Published: October 2024

AbstractBone morphogenetic protein4 (BMP4) plays numerous roles during embryogenesis and can signal either alone as a homodimer, or together with BMP7 as a more active heterodimer. BMPs are generated as inactive precursor proteins that dimerize and are cleaved to generate the bioactive ligand and inactive prodomain fragments. In humans, heterozygous mutations within the prodomain of BMP4 are associated with birth defects. We studied the effect of two of these mutations (p.S91C and p.E93G), which disrupt a conserved FAM20C phosphorylation motif, on ligand activity. We compared the activity of ligands generated from BMP4, BMP4S91Cor BMP4E93GinXenopusembryos and found that these mutations reduce the activity of BMP4 homodimers but not BMP4/7 heterodimers. We generatedBmp4S91CandBmp4E93Gknock-in mice and found thatBmp4S91C/S91Cmice die by E11.5 and display reduced BMP activity in multiple tissues including the heart. MostBmp4E93G/E93Gmice die before weaning andBmp4−/E93Gmutants die prenatally with reduced or absent eyes, heart and ventral body wall closure defects. Mouse embryonic fibroblasts (MEFs) isolated fromBmp4S91CandBmp4E93Gembryos show accumulation of BMP4 precursor protein, reduced levels of cleaved BMP ligand and reduced BMP activity relative to MEFs from wild type littermates. BecauseBmp7is not expressed in MEFs, the accumulation of unprocessed BMP4 precursor protein in mice carrying these mutations most likely reflects an inability to cleave BMP4 homodimers, leading to reduced levels of ligand and BMP activity in vivo. Our results suggest that phosphorylation of the BMP4 prodomain is required for proteolytic activation of BMP4 homodimers, but not heterodimers.Summary StatementMutations associated with birth defects in humans that prevent phosphorylation of the BMP4 prodomain preclude proteolytic activation of the precursor protein
Machine learning detects hidden treatment response patterns only in the presence of comprehensive clinical phenotyping
Authors: Auger Stephen D. , Scott Gregory 
Published: October 2024

ABSTRACTInferential statistics traditionally used in clinical trials can miss relationships between clinical phenotypes and treatment responses. We simulated a randomised clinical trial to explore how gradient boosting (XGBoost) machine learning (ML) compares with traditional analysis when ‘ground truth’ treatment responsiveness depends on the interaction of multiple phenotypic variables. As expected, traditional analysis detected a significant treatment benefit (outcome measure change from baseline = 4.23; 95% CI 3.64–4.82). However, recommending treatment based upon this evidence would lead to 56.3% of patients failing to respond. In contrast, ML correctly predicted treatment response in 97.8% (95% CI 96.6– 99.1) of patients, with model interrogation showing the critical phenotypic variables and the values determining treatment response had been identified. Importantly, when a single variable was omitted, accuracy dropped to 69.4% (95% CI 65.3–73.4). This proof of principle underscores the significant potential of ML to maximise the insights derived from clinical research studies. However, the effectiveness of ML in this context is highly dependent on the comprehensive capture of phenotypic data.
Transition of dimuonium through foil
Authors: Alizzi Abdaljalel , Uskov Artem , Silagadze Z. K. 
Published: October 2024

This paper presents a study of the passage of dimuonium through the foil of ordinary matter. First, we provide an overview of how dimuonium is planned to be produced for such a type of experiment and how it is expected to interact with the ordinary atoms—predominantly electromagnetically via the screened Coulomb potential of the atomic nuclei. Then, we describe the transport equations that represent the evolution of dimuonium states during the passage and their solution methods. Finally, for three different foils (beryllium, aluminium, and lead), we present the results of this study. To estimate the impact of uncertainties in the potential of a target atom, we study 15 different approximations of the atomic potential and show that the corresponding atomic-potential-model-dependent error in the yields of the low-lying states of dimuonium is quite small within the framework of the applied Born approximation. The convergence of the results after truncation of the infinite system of transport equations to the finite number of quantum states of dimuonium is also studied, and good convergence for the yields of low-lying states is demonstrated.

Published by the American Physical Society
2024
Numerical simulation for investigating crevice corrosion of carbon steel in neutral/alkaline concrete construction
Authors: Hu Minglei , Dai Kunjie , Zhang Wei , Xu Ke , Shi Jingkun , Fu Xiaoqian , Ji Yucheng , Dong Chaofang 
Published: October 2024

Purpose
This study aims to investigate the crevice corrosion behavior of carbon steel in neutral/alkaline environments utilizing a transient multi-physics field model.

Design/methodology/approach
The crevice corrosion of carbon steel with different solution pH and crevice width was modeled, incorporating mass transfer, homogeneous phase and localized electrochemical reactions. The extent of crevice corrosion was evaluated by the geometric deformation of the model mesh. The hydro-chemical state inside the crevice was discussed through the Cl− concentration and potential distribution of the solution.

Findings
Results revealed that the formation of pitting corrosion near the crevice mouth was accelerated in a neutral solution. When pH = 8 and pH = 9, the carbon steel matrix was dissolved and the Cl− content within the solution was significantly reduced due to the higher concentration of hydroxide ions (OH−).

Originality/value
The crevice corrosion behavior of carbon steel in neutral/alkaline environments is closely associated with solution pH rather than the crevice width. The inhibition of crevice corrosion in alkaline environments was proved by finite element simulation. These findings provide valuable insights that can be applied in engineering applications to prevent and mitigate crevice corrosion in neutral/alkaline environments.
Effects of virtual try-on technology as an innovative e-commerce tool on consumers' online purchase intentions
Authors: Sekri Khawla , Bouzaabia Olfa , Rzem Haifa , Juárez-Varón David 
Published: October 2024

PurposeThe purpose of this study is to investigate the role of using augmented reality (AR) in the form of virtual try-on technology in consumers' purchase decision-making process.Design/methodology/approachThe study, executed in a beauty industry context, uses the value-based adoption model (VAM). Data were collected by means of a survey carried out on 238 Tunisian women. Subjects performed an experimental task using the virtual try-on (VTO) application in the L’Oréal website. Web-administered questionnaires were used to collect the data, which was processed using an exploratory factor analysis and partial least squares structural equation modeling.FindingsThe findings shows that perceived value is positively related to purchase intentions and it was affected by both perceived benefits and perceived costs. In particular, perceived benefit (perceived usefulness) was found to have a strong positive effect on perceived value. Moreover, it turns out that perceived enjoyment does not have a significant effect on the perceived value. In terms of perceived costs, perceived intrusiveness was found to limit perceived value. The results also show a significant relationship between AR characteristics and perceived benefits. For personal traits, personal innovativeness is found positively influencing perceived usefulness, but it shows no significant effect on perceived enjoyment.Practical implicationsCompanies should highlight the benefits for consumers (interactivity, informativeness and usefulness) and attempt to reduce the costs (intrusiveness) related to the use of VTO AR technology, which can play a substantial role in determining the perceived value and purchase intentions.Originality/valueThe existent literature, which examines the AR in e-tailing, shows weak acknowledgment of theories related to consumer barriers to AR adoption in e-tailing, they overlook the role of consumer psychology and individual differences in AR acceptance. Thus, this study contributes to the literature by enhancing the understanding of the roles that AR based VTO technology plays in determining consumers’ online purchase intentions by extending the application of perceived value theory and taking into account its characteristics and personal traits that play a role in weakening or strengthening the customer's benefits and cost perceptions.
E-commerce behavioral intention of Vietnam Gen Z by using the UTAUT2 model
Authors: Cao Phong Thanh 
Published: October 2024

PurposeIt is important to note that research specifically focusing on the application of UTAUT2 to Gen Z in Vietnam remains limited. Consequently, future research should aim to explore the application of UTAUT2 to Gen Z, thereby contributing to the practical theory and actual development of e-commerce in Vietnam.Design/methodology/approachTo gather data, the researcher utilized a quantitative approach by conducting surveys through Google Forms. Regression analysis was employed with SPSS and AMOS software to explore the relationship between constructs in the UTAUT2 model and e-commerce behavioral intention and use behavior. The present study included 32 observed variables, and the final sample size was 302 participants based on the actual situation.FindingsThe findings demonstrate the successful testing of UTAUT2 and yield positive results within the context of a developing country like Vietnam. Furthermore, the author acknowledges the influence of gender in the model, contributing to the diverse outcomes within this research domain. According to the research results, the UTAUT2 model explains that 69.5% of consumers exhibit behavioral intentions to use e-commerce. Therefore, it represents the predictive power of the model.Originality/valueThis study contributes significantly by concentrating on Generation Z’s e-commerce acceptance in Vietnam. It validates the UTAUT2 model within this demographic, underscores the influence of hedonic motivation and habit and reveals intriguing gender differences in behavioral intentions. These findings offer valuable insights for businesses and policymakers targeting Gen Z consumers.
Service virtuousness: implementing the very best of human qualities in service delivery
Authors: Yagil Dana 
Published: October 2024

PurposeIn light of the increasing prevalence of non-human services, organizations can benefit by emphasizing distinctively human qualities in their services. This paper suggests that cultivating virtues among employees can enhance the value of human-provided services.Design/methodology/approachThis conceptual paper incorporates ideas from management and service research examining the notion of virtue as well as human qualities that are associated with virtuousness. The paper builds upon service research, which emphasizes the important role of human elements in service interaction, along with extensive research in positive organizational scholarship, which characterizes virtues as the finest attributes of human nature.FindingsThe paper introduces “service virtuousness,” the voluntary and authentic expression of human virtues like compassion and creativity in service delivery, treating customers as individuals beyond their formal roles. A review of the literature reveals how virtuousness appears in services. The study also identifies when it is most beneficial, explores the challenges in its implementation, and suggests future research directions.Originality/valueThe paper offers a novel concept for service research grounded in core human values. Although service virtuousness does not focus on outcomes, it offers numerous benefits to all parties involved in the service setting. The theoretical framework for studying service virtuousness provides new opportunities to identify the conditions in which virtuousness is most beneficial in a service context.
Enablers and inhibitors of science commercialization: a predictive framework
Authors: Tikas Gaurav Dilip 
Published: October 2024

Purpose
This paper aims to propose a conceptual framework for measuring, comparing or predicting the science commercialization capability of academic research-driven organizations. Drawing on extant literature on innovation commercialization, two influential predictors were explored and the nature of their influence was proposed.

Design/methodology/approach
Based on the theoretical analysis of relevant literature on science commercialization, this conceptual paper proposes a predictive framework along with two critical antecedents – patronage and parochialism. The analysis further reveals their sub-dimensions and provides insights into how patronage and parochialism can be measured within empirical settings. The study concludes with theoretical propositions about how these two constructs influence science commercialization.

Findings
The study reveals that patronage can positively influence science commercialization and be reflected through mentorship, specialized knowledge-sharing and providing decision-making support to academic innovators pursuing science commercialization. Alternatively, parochialism can positively influence science commercialization and be reflected through academic innovators’ perception, purpose and persistence of efforts toward science commercialization initiatives.

Originality/value
The study contributes to extant literature on science commercialization in three major ways – a conceptual predictive model, theoretically grounded antecedents and their corresponding influence on science commercialization.
An efficient attention-based hybridized deep learning network with deep RBM features for customer behavior prediction in digital marketing
Authors: Sakthi B. , Sundar D. 
Published: October 2024

PurposeAn efficient customer behavior prediction model is designed using deep learning techniques. The necessary data used for the implementation are taken from standard datasets and presented to perform subsequent tasks. Here, deep restricted Boltzmann machines (RBM) features are retrieved from the input images. Further, the extracted deep RBM features are presented to the customer behavior prediction phase. Here, the attention-based hybrid deep learning (A-HDL) technique is designed based on the incorporation of a dilated deep temporal convolutional network (dilated-DTCN) and a weighted recurrent neural network (weighted RNN). Moreover, the weights in RNN are tuned using a modernized random parameter-based cheetah optimizer (MRPCO). Further, various experiments were performed on the implemented framework, and it secured an enhanced customer behavior prediction rate than the conventional models.Design/methodology/approachA novel hybrid deep network-based customer behavior prediction model was developed to predict the behavior of the customer so the companies yield more income by advertising their products based on the predicted results.FindingsWhen considering the first dataset, the designed customer behavior prediction mechanism produced 94% accuracy, which is higher than the conventional techniques such as long short-term memory (LSTM), DTCN, RNN and A-HDL with 88%, 87%, 89% and 93%.Originality/valueThe precision and the accuracy of the developed MRPCO-A-HDL-based customer behavior prediction model progressed than the conventional techniques and algorithms.
Digital twins and biodiversity: a conceptual framework of opportunities and challenge
Authors: Sun Yanqi , Xu Cheng 
Published: October 2024

PurposeThe intersection of digital twins and ecological diversity offers a new and complex landscape that requires interdisciplinary investigation. Recognizing the urgent need for a nuanced approach to the ethical, technological and practical challenges in this area, this paper presents a conceptual framework that serves as a roadmap for future research and policymaking.Design/methodology/approachThis study employs a conceptual methodology deeply rooted in interdisciplinary perspectives. A systematic literature review was conducted using the Scopus database to identify relevant articles. The selected articles were rigorously analyzed to derive key dimensions, which were then validated through expert panel reviews and a pilot study.FindingsOur framework identifies seven critical dimensions: Data Acquisition and Simulation, Impact Assessment, Ecological Protection and Management, Ethical and Legal Considerations, Social and Cultural Impacts, Technological Feasibility and Limitations and Policies and Regulations. These dimensions provide a comprehensive structure for understanding and addressing the intersection of digital twins and biodiversity conservation.Originality/valueThis study contributes a foundational guide for sustainable and ethical engagement between digital twins and biodiversity conservation. It offers a novel, interdisciplinary framework that integrates diverse perspectives and provides practical insights for scholars, policymakers and practitioners in this emerging field.
Brand livestream mode options: AI vs KOL livestream in the platform supply chain
Authors: Yu Xiaoxue , Li Tao , Tan Qi , Liu Bin , Li Hui 
Published: October 2024

PurposeDriven by the rapid expansion of online retail and the surge in livestream commerce, the impact of different livestream mode on brand and platform performance has become a critical issue. This paper analyzes the impact of artificial intelligence (AI) and key opinion leader (KOL) livestream on the profitability of brands and the platform, incorporating the effects of horizontal interactions to identify the optimal livestream mode.Design/methodology/approachThis paper develops a model of a platform supply chain involving two brands and a platform, where each brand independently decides whether to utilize KOL or AI livestream. Applying Stackelberg game approach, the study derives equilibria for various livestream scenarios, identifying the optimal livestream mode for both parties. Additionally, the model is extended to incorporate asymmetric market potential and network externality to evaluate their impact on a brand’s choice of livestream mode.FindingsSeveral interesting and important results are derived in this paper. Firstly, it is found that AI livestream enables brands to leverage network externality and mitigate the market disadvantage, thereby gaining a competitive advantage. Secondly, while KOL livestream promotes trust, the medium KOL commission rates could cause brands to be trapped in a prisoner’s dilemma, and excessively high commission rates may render them less profitable. Thirdly, the KOL commission rate, network externality intensity, horizontal interactions and market disadvantage are critical determinants influencing a brand’s choice of livestream mode.Originality/valueThis study is the first to investigate the effects of horizontal interactions, asymmetric market potential and asymmetric network externality on livestream mode selection by brands within a platform supply chain. The research provides valuable insights into optimizing livestream strategies to enhance brand profitability.
Meritocratic beliefs in the United States, Finland, and China: A multidimensional approach using latent class analysis
Authors: Zhu Li 
Published: October 2024

AbstractThis study employs latent class analysis (LCA) as a novel methodology to investigate the multidimensional nature of meritocratic beliefs, addressing the limitations of traditional unidimensional approaches. Using data from the International Social Survey Program 2009 for the United States, Finland, and China, this study demonstrates several advantages of this multidimensional approach. First, LCA effectively identifies dual consciousness, where individuals simultaneously endorse meritocratic and structuralist explanations of social stratification. The analysis reveals three distinct narratives explaining social stratification: purely meritocratic beliefs, predominantly meritocratic beliefs, and dual consciousness. While all three subtypes consider merits highly important, they differ in their perceived importance of structural factors. Second, LCA facilitates cross‐national comparisons, unveiling qualitative typological variations in meritocratic beliefs across countries. Unique country‐specific subtypes or patterns emerge: Finland exhibits purely meritocratic beliefs, the United States shows predominantly meritocratic beliefs, and China demonstrates a dominance of dual consciousness. Although dual consciousness exists in all three countries, its prevalence varies significantly—dominant in China, moderate in the United States, and least in Finland. Third, this study reveals that the effect of education on meritocratic beliefs varies across the three countries. Education strengthens individual meritocratic beliefs in the United States, weakens them in Finland, and shows no significant effect in China. These findings highlight both within‐country and across‐country heterogeneity of meritocratic beliefs, underscoring the importance of a multidimensional approach.