bluuebunny/crossref_metadata_2025_split huggingface dataset:
Metastatic vs. Localized Disease As Inclusion Criteria That Can Be Automatically Extracted From Randomized Controlled Trials Using Natural Language Processing
Authors: Windisch Paul , Dennstädt Fabio , Koechli Carole , Förster Robert , Schröder Christina , Aebersold Daniel M. , Zwahlen Daniel R. 
Published: June 2024

AbstractBackgroundExtracting inclusion and exclusion criteria in a structured, automated fashion remains a challenge to developing better search functionalities or automating systematic reviews of randomized controlled trials in oncology. The question “Did this trial enroll patients with localized disease, metastatic disease, or both?” could be used to narrow down the number of potentially relevant trials when conducting a search.Methods600 trials from high-impact medical journals were classified depending on whether they allowed for the inclusion of patients with localized and/or metastatic disease. 500 trials were used to develop and validate three different models with 100 trials being stored away for testing.ResultsOn the test set, a rule-based system using regular expressions achieved an F1-score of 0.72 (95% CI: 0.64 - 0.81) for the prediction of whether the trial allowed for the inclusion of patients with localized disease and 0.77 (95% CI: 0.69 - 0.85) for metastatic disease. A transformer-based machine learning model achieved F1 scores of 0.97 (95% CI: 0.93 - 1.00) and 0.88 (95% CI: 0.82 - 0.94), respectively. The best performance was achieved by a combined approach where the rule-based system was allowed to overrule the machine learning model with F1 scores of 0.97 (95% CI: 0.94 - 1.00) and 0.89 (95% CI: 0.83 - 0.95), respectively.ConclusionAutomatic classification of cancer trials with regard to the inclusion of patients with localized and or metastatic disease is feasible. Turning the extraction of trial criteria into classification problems could, in selected cases, improve text-mining approaches in evidence-based medicine.
Magnetoencephalography-based interpretable automated differential diagnosis in neurodegenerative diseases
Authors: Klepachevskyi D , Romano A , Aristimunha B , Angiolelli M , Trojsi F , Bonavita S , Sorrentino G , Andreone V , Minino R , Lopez E Troisi , Polverino A , Jirsa V , Saudargienė A , Corsi M.-C. , Sorrentino P 
Published: June 2024

AbstractAutomating the diagnostic process steps has been of interest for research grounds and to help manage the healthcare systems. Improved classification accuracies, provided by ever more sophisticated algorithms, were mirrored by the loss of interpretability on the criteria for achieving accuracy. In other words, the mechanisms responsible for generating the distinguishing features are typically not investigated. Furthermore, the vast majority of the classification studies focus on the classification of one disease as opposed to matched controls. While this scenario has internal validity, concerning the appropriateness toward answering scientific questions, it does not have external validity. In other words, differentiating multiple diseases at once is a classification problem closer to many real-world scenarios. In this work, we test the hypothesis that specific data features hold most of the discriminative power across multiple neurodegenerative diseases. Furthermore, we perform an explorative analysis to compare metrics based on different assumptions (concerning the underlying mechanisms). To test this hypothesis, we leverage a large Magnetoencephalography dataset (N=109) merging four cohorts, recorded in the same clinical setting, of patients affected by multiple sclerosis, amyotrophic lateral sclerosis, Parkinson’s disease, and mild cognitive impairment. Our results show that it is possible to reach a balanced accuracy of 67,1% (chance level = 35%), based on a small set of (non-disease specific) features. We show that edge metrics (defined as statistical dependencies between pairs of brain signals) perform better than nodal metrics (considering region while disregarding the interactions. Moreover, phase-based metrics slightly outperform amplitude-based metrics. In conclusion, our work shows that a small set of phase-based connectivity metrics applied to MEG data successfully distinguishes across multiple neurological diseases.
Interval timing clock property in the rat granular retrosplenial cortex
Authors: Kurotani Tohru , Nixima Ken’ichi , Tanaka Tomohiro , Sakurai Yoshio , Okanoya Kazuo 
Published: June 2024

AbstractThe rodent granular retrosplenial cortex (gRSC), densely interconnected with the hippocampal formation and the anterior thalamic nuclei, plays an important role in learning and memory. We had revealed that small pyramidal neurons in the superficial layers of the rat gRSC exhibit late-spiking (LS) firing properties. It has been suggested that neural circuits containing LS neurons can encode time intervals on the order of seconds, known as “interval timing”. To test the possibility that the rat gRSC is involved in the processing of interval timing, we employed a trace fear conditioning paradigm in which the conditioned stimulus (CS) and the unconditioned stimulus (US) were temporally separated. First, we examined the effect of cytotoxic lesions made in the RSC prior to trace fear conditioning. We found that intact rats exhibited freezing behavior after CS tone presentation, whereas lesioned rats did not exhibit such freezing behavior. Next, we conductedin vivochronic or acute recordings of neural activity from the rat gRSC in a test session conducted one week after the conditioning. In both recordings, we observed a distinct spike activity in which there was a transient increase in the firing rate around the presentation of the CS tone, followed by a rapid suppression and then ramping activity (a gradual elevation of the firing rate) until the next CS presentation. This “ramping activity” is thought to be one way in which interval timing is represented in the brain. Post stimulus histogram analysis revealed the existence of ramping activity in the gRSC, which reached its peak at various time intervals after the onset of the CS tone. Interestingly, this activity was specifically observed in response to the CS tone but not to the non-CS tone. Moreover, in naive rat gRSC (no trace fear conditioning), no such ramping activity was observed. These results indicate that gRSC neurons can encode time information on the order of tens to hundreds of seconds, integrating incoming sensory input with past memory traces.
Vimentin promotes collective cell migration through collagen networks via increased matrix remodeling and spheroid fluidity
Authors: Thanh Minh Tri Ho , Poudel Arun , Ameen Shabeeb , Carroll Bobby , Wu M. , Soman Pranav , Zhang Tao , Schwarz J.M. , Patteson Alison E. 
Published: June 2024

AbstractThe intermediate filament (IF) protein vimentin is associated with many diseases with phenotypes of enhanced cellular migration and aggressive invasion through the extracellular matrix (ECM) of tissues, but vimentin’s role in in-vivo cell migration is still largely unclear. Vimentin is important for proper cellular adhesion and force generation, which are critical to cell migration; yet the vimentin cytoskeleton also hinders the ability of cells to squeeze through small pores in ECM, resisting migration. To identify the role of vimentin in collective cell migration, we generate spheroids of wide-type and vimentin-null mouse embryonic fibroblasts (mEFs) and embed them in a 3D collagen matrix. We find that loss of vimentin significantly impairs the ability of the spheroid to collectively expand through collagen networks and remodel the collagen network. Traction force analysis reveals that vimentin null spheroids exert less contractile force than their wild-type counterparts. In addition, spheroids made of mEFs with only vimentin unit length filaments (ULFs) exhibit similar behavior as vimentin-null spheroids, suggesting filamentous vimentin is required to promote 3D collective cell migration. We find the vimentin-mediated collective cell expansion is dependent on matrix metalloproteinase (MMP) degradation of the collagen matrix. Further, 3D vertex model simulation of spheroid and embedded ECM indicates that wild-type spheroids behave more fluid-like, enabling more active pulling and reconstructing the surrounding collagen network. Altogether, these results signify that VIF plays a critical role in enhancing migratory persistence in 3D matrix environments through MMP transportation and tissue fluidity.
Phthalate monoesters affect membrane fluidity and cell-cell contacts in endometrial stromal cell lines
Authors: Lavogina Darja , Kask Keiu , Kopanchuk Sergei , Visser Nadja , Laws Mary , Flaws Jodi A. , Kallak Theodora Kunovac , Olovsson Matts , Damdimopoulou Pauliina , Salumets Andres 
Published: June 2024

AbstractPhthalate monoesters have been identified as endocrine disruptors in a variety of models, yet understanding of their exact mechanisms of action and molecular targets in cells remains incomplete. Here, we set to determine whether epidemiologically relevant mono(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP) can affect biological processes by altering cell plasma membrane fluidity or formation of cell-cell contacts. As a model system, we chose endometrial stromal cell lines, one of which was previously used in a transcriptomic study with MEHHP or MEHHP-containing mixtures. A short-term exposure (1 h) of membrane preparations to endocrine disruptors was sufficient to induce changes in membrane fluidity/rigidity, whereas different mixtures showed different effects at various depths of the bilayer. A longer exposure (96 h) affected the ability of cells to form spheroids and highlighted issues with membrane integrity in loosely assembled spheroids. Finally, in spheroids assembled from T-HESC cells, MEHHP interfered with the formation of tight junctions as indicated by the immunostaining ofzonula occludens1 protein. Overall, this study emphasized the need to consider plasma membrane, membrane-bound organelles, and secretory vesicles as possible biological targets of endocrine disruptors and offered an explanation for a multitude of endocrine disruptor roles documented earlier.
Engaging management accountants in corporate sustainability
Authors: Kurki Martina , Järvenpää Marko 
Published: June 2024

PurposeExpectations regarding the participation of management accountants (MAs) in the promotion of sustainability of multinational enterprises (MNEs) have been poorly realised. This raises the question of whether MAs are invited to join in sustainability promotion or does sustainability not fit the perceived professional role of MAs. We suggest that the development of individual-level engagement of corporate sustainability is required for MAs to start contributing to corporate sustainability.Design/methodology/approachWe utilise the psychological ownership theory to investigate how MAs’ professional role could develop to incorporate advancing sustainability. Our qualitative study is based on 32 interviews conducted in seven local business units of three different technology-oriented MNEs.FindingsWe reveal features connected to the professional role of MAs that may impede the activation of the routes to psychological ownership of corporate sustainability, thus undermining their involvement in corporate sustainability enhancement. Moreover, we show that MAs’ own perceptions of their professional role may impede the stimulation of the routes.Originality/valueFrom a managerial viewpoint, our study helps readers to understand how the routes to psychological ownership of corporate sustainability could be cultivated in the development of the future role of MAs. It also gives input for MA professional organisations and MA professional education providers to develop conditions that foster sustainability thinking among MAs. Moreover, by integrating the examination of MAs’ professional role with the psychological ownership theory, we broaden the theoretical scene both in management accounting and in business sustainability research.
Comparative analysis of convolutional neural network and DenseNet121 transfer learning in agriculture focusing on crop leaf disease identification
Authors: Santoso Heru Agus , Fandhi Safsalta Brylian , Febrianto Nanang , Wilujeng Saraswati Galuh , Haw Su-Cheng 
Published: June 2024

PurposePlant cultivation holds a pivotal role in agriculture, necessitating precise disease identification for the overall health of plants. This research conducts a comprehensive comparative analysis between two prominent deep learning algorithms, convolutional neural network (CNN) and DenseNet121, with the goal of enhancing disease identification in tomato plant leaves.Design/methodology/approachThe dataset employed in this investigation is a fusion of primary data and publicly available data, covering 13 distinct disease labels and a total of 18,815 images for model training. The data pre-processing workflow prioritized activities such as normalizing pixel dimensions, implementing data augmentation and achieving dataset balance, which were subsequently followed by the modeling and testing phases.FindingsExperimental findings elucidated the superior performance of the DenseNet121 model over the CNN model in disease classification on tomato leaves. The DenseNet121 model attained a training accuracy of 98.27%, a validation accuracy of 87.47% and average recall, precision and F1-score metrics of 87, 88 and 87%, respectively. The ultimate aim was to implement the optimal classifier for a mobile application, namely Tanamin.id, and, therefore, DenseNet121 was the preferred choice.Originality/valueThe integration of private and public data significantly contributes to determining the optimal method. The CNN method achieves a training accuracy of 90.41% and a validation accuracy of 83.33%, whereas the DenseNet121 method excels with a training accuracy of 98.27% and a validation accuracy of 87.47%. The DenseNet121 architecture, comprising 121 layers, a global average pooling (GAP) layer and a dropout layer, showcases its effectiveness. Leveraging categorical_crossentropy as the loss function and utilizing the stochastic gradien descent (SGD) Optimizer with a learning rate of 0.001 guides the course of the training process. The experimental results unequivocally demonstrate the superior performance of DenseNet121 over CNN.
Revisiting corporate governance mechanisms and real earnings management activities in emerging economies
Authors: Mansoori Ebrahim , Al-Abdallah Ghaith 
Published: June 2024

Purpose
This study aims to investigate the effects of main corporate governance (CG) mechanisms used in Iran on the relationship between managers’ rewards and real earnings management activities.

Design/methodology/approach
Panel data analysis is performed on 101 companies listed on the Tehran Stock Exchange during the past seven years (from 2015 to 2021).

Findings
The percentage of non-executive members of the company’s board of directors and the percentage of acquisition of the company’s largest shareholders have a negative significant effect on the relationship between abnormal operating cash flows and managers’ remuneration. Moreover, the separation of the CEO from the chairman and vice chairman of the board has also a negative significant effect on this relationship. However, concentration of ownership does not have a significant effect on the relationship between abnormal operating cash flows and managers’ rewards.

Practical implications
The study provides policymakers and governing bodies with a better understanding of the effects of the percentage of non-executive board members, concentration of ownership, percentage of major shareholders and duality of the role of CEO (or president) from the chairman and vice chairman of the board on the relationship between managers’ rewards and earnings management.

Originality/value
Previous studies focus mainly on accrual-based earnings management. This study investigates real earnings management and provides empirical evidence on the most effective and significant CG dimensions in Iran. It embraces the fact that CG may have the same principal concept in different markets, but the mechanisms may vary significantly, thus opening the door for more comparative future research.
Digital inclusive finance and entrepreneurship in rural areas: evidence from China
Authors: Yu Chenwei , Hui Eddie Chi-man , Dong Zhaoyingzi 
Published: June 2024

PurposeThis paper aims to investigate the impact of digital inclusive finance on entrepreneurial activities in rural areas, as well as the underlying mechanisms and the variations of this impact across regions and household characteristics.Design/methodology/approachUtilizing the China Household Finance Survey Data, this paper applies the Two-Stage Least Squares with instrumental variables to assess the effect of digital inclusive finance on rural entrepreneurship.FindingsThe empirical findings indicate that digital inclusive finance significantly promotes entrepreneurial activities in rural areas by alleviating credit constraints, reducing financial information barriers, and altering risk attitudes for rural households. Additionally, this effect is more pronounced in the eastern region of China and for the “opportunity entrepreneurial activities.” Furthermore, the impact varies across rural households' income and consumption status, as well as the personal characteristics of household heads.Originality/valueFirstly, this study broadens our understanding of the mechanisms through which digital inclusive finance influences entrepreneurial activities, thereby filling a gap in existing entrepreneurship research. Secondly, the study's findings affirm the inclusive nature of digital finance, contributing significantly to the literature on regional equality and illuminating potential pathways toward achieving “common prosperity.”
Corporate network formation in Kuwait: board interlocks network analysis using exponential random graph models
Authors: Alebrahim Ebrahim , Alnahedh Mishari 
Published: June 2024

Purpose
Interlocking directorates are a common phenomenon across several markets around the world. Yet, the institutional environment and the role of corporate elites in forming board networks promote some developing markets as a unique setting to understand the corporate boards network structure. This study aims to first explore the board directors’ network of all publicly listed companies in Kuwait. This paper then evaluates the effects of exogenous factors and endogenous network structural processes on the likelihood of board interlock.

Design/methodology/approach
This study analyzes the interlocks network structures of 167 listed companies in 13 different market sectors in Kuwait relying on hand-collected directors’ data and using four measures of network centrality: betweenness, degree, closeness and eigenvector. The authors predict board interlocks using exponential random graph models (ERGM) and firm-level information from the Bloomberg database.

Findings
This study observes that both the firms and directors’ networks consist of 55 components, with the largest component containing about half of the total number of firms/directors. The firm’s network consists of one giant component of 85 firms, including all but one bank. This study shows the importance of endogenous network variables, such as the number of edges, centralization and triangles on the estimation of the factors that promote the board interlocks. Highly centralized firms are less likely to interlock with other firms, while two firms that are interlocked with a common third firm are more likely to interlock.

Originality/value
This paper is the first to analyze in-depth the structure of the directors network of companies in Kuwait. This study illustrates the complex map of interfirm and directors social networks in Kuwait. To the best of the authors’ knowledge, this study is among the first to exploit ERGM in the context of board interlocks to account for potential cross-dependencies and emergent network structures. Managers can identify the director interlock with other firms in the network and take advantage of the connection as a source of external knowledge and influence.
Understanding the frames before Fox: the development of unitary and pluralist views on organisations
Authors: Hodder Andy 
Published: June 2024

PurposeThis article examines the intellectual antecedents of Alan Fox’s frames of reference and contributes to academic work that seeks to unravel the pre-Donovan roots of British industrial relations. It examines the origins of the unitary and pluralist frames of reference with a particular focus on the work of Norman Ross.Design/methodology/approachThis article draws on published academic materials to examine the origins of the unitary and pluralist frames of reference.FindingsThe article identifies usage of the term “frame of reference” in industrial relations literature from the 1940s and demonstrates the origins of the unitary and pluralist conceptions of the firm in the works of Ross in the 1950s and 1960s.Originality/valueThe article provides a “fresh look” at the origins of the frames of reference.
Grey multi-criteria group consensus decision-making based on cobweb model
Authors: Guo Sandang , Guan Liuzhen , Li Qian , Jia Jing 
Published: June 2024

PurposeConsidering the bounded confidence of decision-makers (DMs), a new grey multi-criteria group consensus decision-making (GMCGCDM) model is established by using interval grey number (IGN), cobweb model, social network analysis (SNA) and consensus reaching process (CPR).Design/methodology/approachFirstly, the model analyzes the social relationship of DM under social networks and proposes a calculation method for DMs’ weights based on SNA. Secondly, the model defines a cobweb model to consider the preferences of decision-making alternatives in the decision-making process. The consensus degree is calculated by the area surrounded by the connections between each index value of DMs and the group. Then, the model coordinates the different opinions of various DMs to reduce the degree of bias of each DM and designs a consensus feedback mechanism based on bounded confidence to guide DMs to reach consensus.FindingsThe advantage of the proposed method is to highlight the practical application, taking the selection of low-carbon suppliers in the context of dual carbon as an example. Comparison analysis is performed to reveal the interpretability and applicability of the method.Originality/valueThe main contribution of this paper is to propose a new GMCGCDM model, which can not only expand the calculation method of DM’s weight and consensus degree but also reduce the time and cost of decision-making.