bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deep Visual Proteomics Unveils Precision Medicine Insights in Composite Small Lymphocytic and Classical Hodgkin Lymphoma
Authors: Zheng Xiang , Pedersen Lars Møller , Bzorek Michael , Mund Andreas , Gjerdrum Lise Mette Rahbek , Mann Matthias 
Published: June 2024

AbstractCoexistence of two cancer types in the same organ presents challenges for clinical decision-making, calling for personalized treatment strategies. Deep Visual Proteomics (DVP) combines AI driven single cell type analysis with laser microdissection and ultrasensitive mass spectrometry. In a composite case of classical Hodgkin lymphoma (cHL) and small lymphocytic lymphoma (SLL) in a single patient, we investigated the potential of DVP to inform precision oncology. We quantified the proteomic landscapes in the cHL and SLL to a depth of thousands of proteins. Our analysis revealed distinct proteome profiles in cHL and SLL populations, highlighting their clonal unrelatedness. Our data suggested standardized chemotherapy and interleukin-4 inhibition as potential strategies to manage chemo-resistance – instead of bone marrow transplantation. DVP highlighted minichromosome maintenance protein and proteasome inhibitors for cHL and H3K27 methylation and receptor tyrosine kinase inhibitors for SLL as subtype-specific treatments. Thus cell-type specific insights of DVP can guide personalized oncological treatments.
Deep Learning-Based Multimodal Clustering Model for Endotyping and Post-Arthroplasty Response Classification using Knee Osteoarthritis Subject-Matched Multi-Omic Data
Authors: Rockel Jason S. , Sharma Divya , Espin-Garcia Osvaldo , Hueniken Katrina , Sandhu Amit , Pastrello Chiara , Sundararajan Kala , Potla Pratibha , Fine Noah , Lively Starlee S. , Perry Kimberly , Mohamed Nizar N. , Syed Khalid , Jurisica Igor , Perruccio Anthony V. , Rampersaud Y. Raja , Gandhi Rajiv , Kapoor Mohit 
Published: June 2024

AbstractBackgroundPrimary knee osteoarthritis (KOA) is a heterogeneous disease with clinical and molecular contributors. Biofluids contain microRNAs and metabolites that can be measured by omic technologies. Deep learning captures complex non-linear associations within multimodal data but, to date, has not been used for multi-omic-based endotyping of KOA patients. We developed a novel multimodal deep learning framework for clustering of multi-omic data from three subject-matched biofluids to identify distinct KOA endotypes and classify one-year post-total knee arthroplasty (TKA) pain/function responses.Materials and MethodsIn 414 KOA patients, subject-matched plasma, synovial fluid and urine were analyzed by microRNA sequencing or metabolomics. Integrating 4 high-dimensional datasets comprising metabolites from plasma (n=151 features), along with microRNAs from plasma (n=421), synovial fluid (n=930), or urine (n=1225), a multimodal deep learning variational autoencoder architecture with K-means clustering was employed. Features influencing cluster assignment were identified and pathway analyses conducted. An integrative machine learning framework combining 4 molecular domains and a clinical domain was then used to classify WOMAC pain/function responses post-TKA within each cluster.FindingsMultimodal deep learning-based clustering of subjects across 4 domains yielded 3 distinct patient clusters. Feature signatures comprising microRNAs and metabolites across biofluids included 30, 16, and 24 features associated with Clusters 1-3, respectively. Pathway analyses revealed distinct pathways associated with each cluster. Integration of 4 multi-omic domains along with clinical data improved response classification performance, with Cluster 3 achieving AUC=0·879 for subject pain response classification and Cluster 2 reaching AUC=0·808 for subject function response, surpassing individual domain classifications by 12% and 15% respectively.InterpretationWe have developed a deep learning-based multimodal clustering model capable of integrating complex multi-fluid, multi-omic data to assist in KOA patient endotyping and test outcome response to TKA surgery.FundingCanada Research Chairs Program, Tony and Shari Fell Chair, Campaign to Cure Arthritis, University Health Network Foundation.
Correlations in microbial abundance data reveal host-bacteria and bacteria-bacteria interactions jointly shaping theC. elegansmicrobiome
Authors: Martini K. Michael , Boddu Satya Spandana , Taylor Megan N. , Nemenman Ilya , Vega Nic M. 
Published: June 2024

AbstractCompositional structure of host-associated microbiomes is potentially affected by interactions among the microbes and between the microbes and the host. To quantify the relative importance of these contributions to the microbiome composition and variation, here we analyze absolute abundance (count) data for a minimal eight-species native microbiome in theCaenorhabditis elegansintestine. We find that a simple neutral model only considering migration, birth, death, and competition for space among the bacteria can capture the means and variances of bacterial abundance, but not the experimental bacteria-bacteria covariances. We find that either bacteria-bacteria interactions or correlations among bacterial population dynamics parameters induced by the host can qualitatively recapitulate the observed correlations among bacterial taxa. However, neither model is uniquely or completely sufficient to explain the data. Further, we observe that different interactions are required to explain (co)variance data in microbiota associated with different host genotypes, suggesting different community dynamics associated with these host types. Finally, we find that many of these signals are obscured when data are converted to proportions from counts, consistent with a growing literature on the limitations of compositional data for inference of population dynamics. We end with discussing the limitations of Lotka-Volterra type assumptions for microbial community data analysis revealed by our results.
Relationship between tourism development and the socioeconomic progress of tourist destinations
Authors: Marti Luisa , Puertas Rosa 
Published: June 2024

Purpose
The purpose of this paper is to analyse whether tourism destination competitiveness is associated with human development using contingency tables. Also, by means of a cluster analysis, it seeks to identify homogeneous groups of tourists.

Design/methodology/approach
The analysis relies on the Travel and Tourism Development Index and Human Development Index for 2021 and is applied to 81 countries with a high/very high level of human progress and 36 countries with a medium/low level.

Findings
The results show that infrastructure is the tourism dimension that has the greatest influence on human development for all countries, while conditions for doing business and sustainability are relevant to residents’ quality of life only in destinations with a high/very high level of progress. Cluster analysis points to India as a tourist destination that is markedly different from the other countries in its human progress, presenting much higher values for price competitiveness, infrastructure and natural and cultural resources.

Originality/value
It analyses the association between each one of the dimensions of tourism development and the Human Development Index.
Avatars’ impacts in retail: a study within regulatory engagement theory
Authors: ben Saad Sihem , Choura Fatma 
Published: June 2024

PurposeThis study aims to test the impact of avatars on consumer psychological states, engagement, and purchase intention in the online retail environment with reference to the Regulatory Engagement Theory.Design/methodology/approachOne-factor between-subject experimental design was used to test research hypotheses. Two versions of an experimental merchant website have been designed for the purpose of the study: with and without the avatar. Participants were randomly assigned to experimental conditions and responded to an online questionnaire displayed during the visit. SEM analyses with AMOS 24 and SPSS Macro Conditional Process Analysis for bootstrapping were used to test the hypotheses.FindingsThe results confirm the avatar’s positive impact on perceived enjoyment and immersion, both positively affect consumer engagement and purchase intention. The mediating role of psychological states in the impact of avatars on consumer engagement is also confirmed.Originality/valueThis study advances the interactive marketing literature by focusing on avatars as an emerging interactive technology in the virtual retail context. Unlike previous studies on virtual agents, which primarily focused on their utilitarian role in online customer support, this study investigates how avatars influence consumers' psychological states, engagement, and purchase intention.
Why employees speak up: unveiling motives for constructive voice
Authors: Kiura Mary , Leach Rebecca B. 
Published: June 2024

PurposeThe study empirically explores employees' motives for engaging in constructive voice behaviors.Design/methodology/approachThe authors utilized qualitative research methods. The data were collected through individual semi-structured interviews with 26 workers in the renewable energy industry in Kenya. The data were analyzed using a phronetic iterative approach (Tracy, 2020).FindingsThe data revealed various motives that may drive constructive voice including, personal (e.g. material rewards and emotional gratification), relational (e.g. advocating for others and diluting opposition) and organizational motives (e.g. ensuring organizational survival and bolstering innovation). Additionally, the authors illustrated how these motives may evolve and/or jointly drive constructive voice.Originality/valueAlthough voice scholars are beginning to recognize the existence of other motives besides prosocial, the knowledge of such motives has remained theoretical. This is one of the first studies to empirically examine motives for constructive voice. By unveiling the motives, the findings demonstrate different pathways through which a voice opportunity transforms into a constructive voice behavior.
Higher education pre- to post-COVID-19: student and faculty perceptions
Authors: Probst Gaby , Zizka Laura 
Published: June 2024

Purpose
Higher education institutions (HEIs) seek innovative approaches to attract students. Since the COVID-19 pandemic, many HEIs have considered diversification through digitalization. From the initial urgency to implement technology at the start of the pandemic to a gradual acceptance over time, HEIs witnessed a shift in perceptions. This paper aims to explore the evolution of the use of technology in HEI classrooms from pre- to post-Covid-19 as seen through the eyes of students and faculty members.

Design/methodology/approach
Seven surveys were conducted from March 2020 to January 2023 with faculty members and students at one business HEI in Switzerland. This longitudinal study analyzed the perceptions of faculty and students regarding technology in the ever-changing HEI environments. These results have been used to make practical recommendations for future HEI classroom settings.

Findings
The findings reveal that the social aspect must be considered when implementing technology into HEIs. Faculty members and students need appropriate training and adequate resources to engage with the technology in and outside the classroom. The results suggest that one result of the pandemic was a greater acceptance of blended learning practices in traditional business education.

Originality/value
The pandemic has left long-lasting effects on teaching and learning. While many HEIs revert to traditional ways, the authors suggest the need to embrace technology that encourages engagement and authentic teaching and learning. HEIs must listen to their faculty members and students, to create more innovative learning environments.
Factors and challenges affecting Enactus members’ social entrepreneurial actions: a focus group study
Authors: Otache Innocent , Akubo Alewo Johnson , David Bosede Beatrice 
Published: June 2024

Purpose
There is a paucity of studies on factors influencing social entrepreneurial behaviours or actions. Against this background, this study aims to explore factors and challenges affecting Enactus members’ social entrepreneurial actions in Nigeria.

Design/methodology/approach
This study adopted a qualitative approach, and data were gathered through three focus group discussions involving 25 people (18 Enactus members, 4 faculty advisors and 3 business advisory board members). The study adopted a content analysis technique for data analysis.

Findings
The findings reveal that social mission orientation, desire to create social impact and social problems influence Enactus members’ social entrepreneurial actions. Other influencing factors include social entrepreneurial passion, moral obligation, social empathy and social support. In addition, the study finds that financial and time constraints affect Enactus members’ social entrepreneurial actions.

Practical implications
The findings have implications for social entrepreneurs/enterprises, governments/policymakers and Enactus members.

Originality/value
To the best of the authors’ knowledge, this is the first qualitative study that provides insights into the factors influencing a group of people towards social entrepreneurial actions.
Dynamics of tree stems and biomass in old‐growth and secondary forests along gradients in liana dominance, elevation and soil
Authors: Ngute Alain Senghor K. , Pfeifer Marion , Schoeman David S. , Gereau Roy E. , Mnendendo Hamidu R. , Lyatuu Herman M. , Seki Hamidu A. , Shirima Deo D. , Marshall Andrew R. 
Published: June 2024

Abstract

Lianas, or woody vines, are key components of many tropical forests and can have substantial impacts on the dynamics and functioning of these important ecosystems. Their competition with trees for resources, in particular light, can hamper the recovery of forests from disturbances. Yet, it is unclear how forest disturbance interacts with liana–tree ratio (LTR), topography and soil properties to shape tree dynamics and the trajectories of forest succession.

Using temporal data from the Kilombero Valley and the Udzungwa Mountains of Tanzania, we demonstrate how the dynamics of tree stems and biomass vary between secondary and old‐growth forests with changes in the dominance of lianas and environmental gradients.

Greater tree recruitment and mortality in secondary forests compared with old‐growth forests suggested rapid regeneration processes and faster turnover. However, no significant differences were found in the net annual changes in the number or biomass of trees between secondary and old‐growth forests.

Our findings also showed that higher LTRs were positively associated with stem mortality but also with tree biomass growth, indicating a nuanced ecological role of lianas in forest ecosystems, which warrants further investigation to fully understand the causal factors at play.

Net changes in tree stem numbers decreased significantly with elevation, implying climatic constraints on forest regeneration at higher elevations. Soil cation exchange capacity and organic carbon were found to significantly influence tree stem recruitment and net change in abundance, although their effects on biomass remained unclear.

Synthesis: Our findings indicate that the recovery of tropical forests from disturbance in terms of the number and biomass of tree stems may be predictable along environmental gradients. These insights have the potential to broaden our capacity to develop more nuanced strategies that identify when and where tropical forests may require restoration interventions, with a focus on structural recovery.
Corporate fraud and independent director's re‐appointment: Information hypothesis or favouritism hypothesis?
Authors: Lyu Xiaoliang , Zhang Xiaochen 
Published: June 2024

AbstractUsing the analytical framework of social identity theory, this paper explores how a special corporate governance arrangement in the Chinese capital market, i.e., independent directors' re‐appointment, affects corporate ethical behaviours. Using the bivariate probit model, we find that independent directors’ re‐appointment significantly increases corporate fraud propensity, indicating that the favouritism towards re‐appointed independent directors by firms generated from social identity plays a dominant role in the corporate governance outcome in a relationship‐based society. Our results remain consistent after using an exogenous shock to alleviate the endogenous problems. The policy implication of this paper is that the corporate arrangement of re‐appointed independent directors in the Chinese capital market may impair stakeholders’ benefit and weaken business ethics. Top‐level institutional design should be improved and consider thoroughly the effects of social identity on corporate governance outcomes.
Neuronal ceroid lipofuscinosis in a Schapendoes dog is caused by a missense variant in CLN6
Authors: Bellamy Kim K. L. , Skedsmo Fredrik S. , Hultman Josefin , Jansen Johan Høgset , Lingaas Frode 
Published: June 2024

AbstractNeuronal ceroid lipofuscinosis (NCL) is a group of neurodegenerative disorders that occur in humans, dogs, and several other species. NCL is characterised clinically by progressive deterioration of cognitive and motor function, epileptic seizures, and visual impairment. Most forms present early in life and eventually lead to premature death. Typical pathological changes include neuronal accumulation of autofluorescent, periodic acid‐Schiff‐ and Sudan black B‐positive lipopigments, as well as marked loss of neurons in the central nervous system. Here, we describe a 19‐month‐old Schapendoes dog, where clinical signs were indicative of lysosomal storage disease, which was corroborated by pathological findings consistent with NCL. Whole genome sequencing of the affected dog and both parents, followed by variant calling and visual inspection of known NCL genes, identified a missense variant in CLN6 (c.386T>C). The variant is located in a highly conserved region of the gene and predicted to be harmful, which supports a causal relationship. The identification of this novel CLN6 variant enables pre‐breeding DNA‐testing to prevent future cases of NCL6 in the Schapendoes breed, and presents a potential natural model for NCL6 in humans.
Unveiling the impact of territorial and network embeddedness on circular economy adoption in cooperatives
Authors: De Bernardi Chiara , Corsini Filippo , Annesi Nora , Frey Marco 
Published: June 2024

AbstractThe paper explores the adoption and operationalization of the circular economy strategy within the ambit of cooperatives, anchoring this exploration in the embeddedness theory. In more detail, our study aims to discern whether territorial and network embeddedness act as catalysts for the adoption of a circular economy strategy and subsequently influence the operationalization of circular initiatives. Given that cooperatives intrinsically emphasize community bonds and mutualistic relationships, understanding the nuances of their embeddedness can elucidate the pathways in adopting and operationalizing circular economy strategies. The study draws from a survey encompassing 1116 Italian cooperatives to fathom how these organizations integrate and operationalize the circular economy in their routine operations. Data were scrutinized using a structural equation model. Outcomes reveal that both territorial and network embeddedness emerge as strong influencers of circular economy strategies, which subsequently sway the operationalization of circular initiatives. Our investigation, assessing also the mediating role of circular economy strategy adoption in relation to both territorial and network embeddedness on the assimilation of circular endeavors, offers enriched theoretical and managerial perspectives on the embeddedness theory and its practical implications.
The barriers to uptake of disaster risk management science in urban planning: A political economy analysis
Authors: Panwar Vikrant , Wilkinson Emily , Pelling Mark 
Published: June 2024

AbstractThere is increasing effort in science to support disaster risk management (DRM) and climate change adaptation in urban environments. It is now common for research calls and projects to reference coproduction methods and science uptake goals. This paper identifies lessons for researchers, research funders, and research users wishing to enable useful, useable, and used science based on the perspectives of research users in urban planning from low‐ and middle‐income countries. DRM‐supporting science is viewed by policy actors as: complicated and poorly communicated; presenting inadequate, partial, and outdated information; misaligned with policy cycles; and costly to access and inadequately positioned to overcome the policy barriers that hinder integration of DRM into urban planning. Addressing these specific concerns points to more systematic collection and organisation of data and enhancement of supporting administrative structures to facilitate better sight of human vulnerability and its link to development decision‐making and wider processes of urban risk creation.
Quantifying uncertainty in fatigue crack growth of SLM 316L through advanced predictive modeling
Authors: Haselibozchaloee Danial , Correia José A. F. O. , Braga Daniel F. O. , Cipriano Gonçalo , Reis Luis , Manuel Lance , Moreira Pedro M. G. P. 
Published: June 2024

AbstractOptimizing structural designs is crucial today, with additive manufacturing, particularly selective laser melting, gaining prominence. Thorough mechanical characterization of new materials remains vital. This paper investigates fatigue crack growth behavior in SLM 316L specimens under cyclic loading conditions. The study addresses result uncertainties by using CT specimens aligned along three building directions per ASTM E647 standards and a constant loading ratio (R = 0.1), necessitating mean value and confidence interval predictions. Departing from linear prediction models, innovative Bootstrap Polynomial and Power Regression Models and Bayesian Nonlinear Regression Model updated posterior distribution by Markov Chain Monte Carlo are employed. These approaches leverage bootstrapping to construct confidence intervals, offering robustness and flexibility in handling non‐normal data behavior and limited sample sizes. They provide tailored fits to data curvature, revealing limitations of linear prediction models in capturing observed nonlinear behavior, enhancing reliability in additive manufacturing applications, and advancing material science and engineering.
Metaheuristics for solving a flexible flow‐shop scheduling problem with s‐batching machines
Authors: Rocholl Jens , Mönch Lars 
Published: June 2024

AbstractA scheduling problem for a two‐stage flexible flow shop with s‐batching machines motivated by processes in additive manufacturing is considered. A batch is a group of jobs that are processed together on a single machine. Each job belongs to an incompatible family. Only jobs of the same family can be batched together. A maximum batch size is given. A setup time occurs between different batches. The jobs of a batch are processed in a serial manner, that is, the processing time of a batch is the sum of the processing times of the jobs forming the batch. Batch availability is assumed. A job has a weight, a due date, and a release date. The total weighted tardiness is considered as performance measure. We establish a mixed integer linear programming formulation for this scheduling problem. For large‐sized problem instances, an iterative decomposition approach (IDA) is proposed that uses a grouping genetic algorithm or an iterated local search (ILS) scheme to solve the single‐stage subproblems. Moreover, an alternative ILS scheme based on a disjunctive graph representation of the problem at hand is designed. Results of computational experiments based on randomly generated problem instances demonstrate that the IDA hybridized with ILS outperforms the two other schemes.