bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Systematic Evaluation of Machine Learning-based Biomarkers for Major Depressive Disorder across Modalities
Authors: Winter Nils R. , Blanke Julian , Leenings Ramona , Ernsting Jan , Fisch Lukas , Sarink Kelvin , Barkhau Carlotta , Thiel Katharina , Flinkenflügel Kira , Winter Alexandra , Goltermann Janik , Meinert Susanne , Dohm Katharina , Repple Jonathan , Gruber Marius , Leehr Elisabeth J. , Opel Nils , Grotegerd Dominik , Redlich Ronny , Nitsch Robert , Bauer Jochen , Heindel Walter , Groß Joachim , Andlauer Till F. M. , Forstner Andreas J. , Nöthen Markus M. , Rietschel Marcella , Hofmann Stefan G. , Pfarr Julia-Katharina , Teutenberg Lea , Usemann Paula , Thomas-Odenthal Florian , Wroblewski Adrian , Brosch Katharina , Stein Frederike , Jansen Andreas , Jamalabadi Hamidreza , Alexander Nina , Straube Benjamin , Nenadić Igor , Kircher Tilo , Dannlowski Udo , Hahn Tim 
Published: February 2023

AbstractBackgroundBiological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, Major Depressive Disorder (MDD), patients only marginally differ from healthy individuals on the group-level. Whether Precision Psychiatry can solve this discrepancy and provide specific, reliable biomarkers remains unclear as current Machine Learning (ML) studies suffer from shortcomings pertaining to methods and data, which lead to substantial over-as well as underestimation of true model accuracy.MethodsAddressing these issues, we quantify classification accuracy on a single-subject level in N=1,801 patients with MDD and healthy controls employing an extensive multivariate approach across a comprehensive range of neuroimaging modalities in a well-curated cohort, including structural and functional Magnetic Resonance Imaging, Diffusion Tensor Imaging as well as a polygenic risk score for depression.FindingsTraining and testing a total of 2.4 million ML models, we find accuracies for diagnostic classification between 48.1% and 62.0%. Multimodal data integration of all neuroimaging modalities does not improve model performance. Similarly, training ML models on individuals stratified based on age, sex, or remission status does not lead to better classification. Even under simulated conditions of perfect reliability, performance does not substantially improve. Importantly, model error analysis identifies symptom severity as one potential target for MDD subgroup identification.InterpretationAlthough multivariate neuroimaging markers increase predictive power compared to univariate analyses, single-subject classification – even under conditions of extensive, best-practice Machine Learning optimization in a large, harmonized sample of patients diagnosed using state-of-the-art clinical assessments – does not reach clinically relevant performance. Based on this evidence, we sketch a course of action for Precision Psychiatry and future MDD biomarker research.FundingThe German Research Foundation, and the Interdisciplinary Centre for Clinical Research of the University of Münster.
Phage T3 overcomes the BREX defence through SAM cleavage and inhibition of SAM synthesis
Authors: Andriianov Aleksandr , Triguis Silvia , Drobiazko Alena , Sierro Nicolas , Ivanov Nikolai V. , Selmer Maria , Severinov Konstantin , Isaev Artem 
Published: February 2023

AbstractBacteriophage T3 encodes a SAMase that through cleavage of S-adenosyl-methionine (SAM) circumvents the SAM-dependent Type I Restriction-Modification defence of the host bacteriumEscherichia coli. Here, we show that the SAMase also allows T3 to evade BREX defence. SAM degradation weakly affects BREX methylation of host DNA, but completely inhibits the defensive function of BREX, suggesting that SAM is required as a co-factor for BREX-mediated exclusion of phage DNA. The anti-BREX activity of the T3 SAMase is mediated by two independent mechanisms: enzymatic degradation of SAM and downregulation of SAM synthesis through direct inhibition of the host SAM synthase MetK. We determined a 2.8 Å cryo-EM structure of the eight-subunit T3 SAMase-MetK complex. Structure guided mutagenesis of the SAMase-MetK interface revealed that the interaction with MetK stabilizes the T3 SAMasein vivo, thus further stimulating its anti-BREX activity. This work provides insights in the versatility and intricacy of bacteriophage counter-defence mechanisms and highlights the role of SAM as an important co-factor of diverse phage-defence systems.
Unistrand piRNA clusters are an evolutionarily conserved mechanism to suppress endogenous retroviruses across theDrosophilagenus
Authors: van Lopik Jasper , Alizada Azad , Trapotsi Maria-Anna , Hannon Gregory J. , Bornelöv Susanne , Nicholson Benjamin Czech 
Published: February 2023

SUMMARYThe PIWI-interacting RNA (piRNA) pathway prevents endogenous genomic parasites, transposable elements, from damaging the genetic material of animal gonadal cells. Specific regions in the genome, called piRNA clusters, are thought to define each species’ piRNA repertoire and therefore its capacity to recognize and silence specific transposon families. The unistrand clusterflamenco(flam) is essential in the somatic compartment of theDrosophilaovary to restrictGypsy-family transposons from infecting the neighbouring germ cells. Disruption offlamresults in transposon derepression and sterility, yet it remains unknown whether this silencing mechanism is present more widely. Here, we systematically characterised 119Drosophilaspecies and identify five additionalflam-like clusters separated by up to 45 million years. Small RNA-sequencing validated these as bona-fide unistrand piRNA clusters expressed in somatic cells of the ovary, where they selectively target transposons of theGypsyfamily. Together, our study provides compelling evidence of a widely conserved transposon silencing mechanism that co-evolved with virus-likeGypsy-family transposons.
Radiation-induced persistent DNA damage response and late toxicity in cardiac tissue
Authors: Shigemori Kay , Jiang Yanyan , Martin Jennifer C , Hawkins Maria A , Ryan Anderson J , Parkes Eileen E 
Published: February 2023

AbstractBackgroundRadiotherapy treatment is a mainstay of cancer treatments including for thoracic malignancies such as lung or breast cancer. Cardiac toxicity is a recognised long-term complication of thoracic radiotherapy which persists despite improvements in therapeutic modalities. The mechanisms and potential therapeutic targets that could provide cardiac protection in the context of radiation therapy remain incompletely understood. Here we investigated early and late cardiac toxicity following irradiation using the A/J mouse model to identify potential molecular drivers.MethodsSingle doses of irradiation of either 13 or 15 Gy were delivered to female A/J mice aged 6 – 8 weeks using a SmART-PLAN system. ECG traces and analysis were performed on anaesthetised mice. Cardiac tissue was harvested up to 32 weeks following irradiation for histological analysis and second-harmonic imaging microscopy.ResultsCardiac RT resulted in cardiac conductivity abnormalities including prolonged QTc interval. Additionally, an increase in pericardial and perivascular fibrosis was noted with a marked increase in pericardial fibrosis at 15 Gy compared to 13 Gy. Persistent DNA damage response was identified in cardiomyocytes at 7 days post irradiation and polarisation of cardiac-infiltrating macrophages towards a CD206+ M2-like phenotype at the same timepoint.ConclusionsOur data introduce the A/J mouse as a novel model for the study of physiologically relevant early and late cardiac toxicities following irradiation. Early events that could contribute to long term toxicities include persistent DNA damage response and repolarisation of macrophages, further investigation of which could identify potential future cardioprotective therapeutic strategies.
Reconstructing the pressure field around a swimming fish using a physics-informed neural network
Authors: Calicchia Michael A. , Mittal Rajat , Seo Jung-Hee , Ni Rui 
Published: February 2023

AbstractHydrodynamic pressure is a physical quantity that is utilized by fish and many other aquatic animals to generate thrust and sense the surrounding environment. To advance our understanding of how fish react to unsteady flows, it is necessary to intercept the pressure signals sensed by their lateral line system. In this study, the authors propose a new, non-invasive method for reconstructing the instantaneous pressure field around a swimming fish from 2D particle image velocimetry (PIV) measurements. The method uses a physics-informed neural network (PINN) to predict an optimized solution for the velocity and pressure fields that satisfy in an ℒ2sense both the Navier Stokes equations and the constraints put forward by the measurements. The method was validated using a direct numerical simulation of a swimming mackerel,Scomber scombrus, and was applied to empirically obtained data of a turning zebrafish,Danio rerio. The results demonstrate that when compared to traditional methods that rely on directly integrating the pressure gradient field, the PINN is less sensitive to the spatio-temporal resolution of the velocity field measurements and provides a more accurate pressure reconstruction, particularly on the surface of the body.
Gust response analysis based on a nonlinear structural model and the unsteady aerodynamics of a flexible aircraft
Authors: Masrour Javad , Sadati Seyed Hossein , Shahravi Morteza 
Published: February 2023

Purpose
This study aims to simulate gust effects on the aeroelastic behavior of a flexible aircraft. The dynamic response of the system for different discreet gust excitations is obtained using numerical simulations.

Design/methodology/approach
Coupled dynamics, including rigid and flexible body coordinates, are considered for modeling the dynamic behavior of the aircraft. Wing is considered flexible and other parts are considered rigid. Wing is modeled with nonlinear Euler Bernoulli beam. Moreover, unsteady aerodynamics based on the Wagner function are used for aerodynamic loading, and the results are compared with those of quasi-steady aerodynamics.

Findings
Von Kármán continuous gust is applied to this aircraft. In addition, the discrete “1- cosine” gust with different gust lengths is applied to the aircraft, and the maximum and minimum accelerations are computed. It is shown that the nonlinear modeling of the system represents the actual behavior and causes limit cycle oscillation phenomena.

Originality/value
This methodology can yield a relatively simple dynamic model for high aspect ratio aircrafts to provide insights into the vehicles’ dynamics, which can be available early in the design cycle.
Consumers' social self-identity drivers on social commerce platforms-based food and beverage
Authors: Bazi Saleh , Attar Razaz Waheeb , Adam Nawal Abdalla , Hajli Nick 
Published: February 2023

PurposeThis study aims to investigate the latest breakthroughs in social commerce by examining the characteristics of consumers' social self-identity factors in the food and beverage sector. The objective is to examine how social self-identities factors contribute to exchanging information on social commerce platforms for food and beverage products.Design/methodology/approachThis study employed partial least squares structural equation modeling (PLS-SEM) to test the measurement and structural model. A total of 238 food and beverage website and apps users were questioned using a structural survey.FindingsContrary to expectations, one self-inner driver does not influence consumers' intentions to exchange social commerce information, while up-to-date information, validation and like-minded discussion have a strong impact on the social commerce exchange model.Originality/valueThe findings of this research offer new insights into the brand's abilities to induce social commerce. The findings also assist marketing managers in better managing brand content on these websites.
Self-construal, face concerns and conflict management strategies: a meta-analysis
Authors: Yamini Sara , Fousiani Kyriaki , Wisse Barbara 
Published: February 2023

PurposeIn this meta-analysis, the authors investigate the relationship between self-construal and conflict management strategies and shed light on the inconsistent findings in the literature. Moreover, they examine the mediating role of face concerns in this relationship. Importantly, the present meta-analysis is the first to test the assumptions of face-negotiation theory with the meta-analytic structural equation modeling (MASEM), which enabled the authors to test the hypothesized relationships in one single model.Design/methodology/approachThe authors used the method of MASEM to test the relationship between self-construal and conflict management and assess the mediating role of face concerns. In this regard, the authors employed one-stage meta-analytic structural equation modeling to perform MASEM and its moderators.FindingsTwo hundred fifty-four effect sizes based on thirty-three studies were pooled in this meta-analysis. The authors found that individuals with stronger independent self-construal and stronger self-face concerns were more likely to use forcing. Moreover, the relationship between independent self-construal and forcing was mediated by self-face concerns. Individuals with a stronger interdependent self-construal and individuals with stronger other-face concerns were more likely to use problem-solving and yielding. The relationship between interdependent self-construal and problem-solving and yielding was mediated by other-face concerns. Finally, interdependent self-construal also had an indirect effect, via other-face concern, on avoiding and compromising.Originality/valueThe present meta-analysis is the first effort that the authors are aware of to test the assumptions of face-negotiation theory (FNT) using MASEM method. The authors used one-stage meta-analytic structural equation modeling viewed as the state of the methods to perform MASEM and its moderators. They employed full information meta-analytic structural equation modeling to show the generalizability and heterogeneity of structural equation modeling parameters. They applied studentized deleted residuals to assess outlier analysis and also conducted different methods to perform MASEM to check the robustness of the findings resulted. Finally, the current study adds multiple methods of assessing for publication bias.
TikTok app usage behavior: the role of hedonic consumption experiences
Authors: Abbasi Amir Zaib , Ayaz Natasha , Kanwal Sana , Albashrawi Mousa , Khair Nadine 
Published: February 2023

PurposeTikTok social media app has become one of the most popular forms of leisure and entertainment activities, but how hedonic consumption experiences (comprising fantasy, escapism, enjoyment, role projection, sensory, arousal and emotional involvement) of the TikTok app determine users' intention to use the app and its resulting impact on the actual usage behavior remains limited in the information systems literature, especially featuring the hedonic consumption perspective in entertainment industry.Design/methodology/approachThis study employs uses & gratification theory to answer the “why” via predicting the role of hedonic consumption experiences that serve as gratifications to trigger technology acceptance behavior (especially, in form of users' behavioral intention to use the TikTok app and its further impact on usage behavior). This study utilizes the partial least squares-structural equation modeling approach to perform data analyses on 258 TikTok app users.FindingsOur results provide a strong support such that users' playful consumption experiences (i.e. escapism, role projection, arousal, sensory experience and enjoyment) positively influence their intention to use the TikTok app and its resultant effect on users' actual usage of the app. In contrast, fantasy and emotional involvement fail to influence users' intention to use the TikTok app.Originality/valueTo the best of our knowledge, our investigation is one of the first studies to apply the hedonic consumption experiences as potential gratifications that derive users' intention and its subsequent influence on the actual usage of the TikTok app. Our study results would assist marketing and brand managers to redefine approaches and tactics to create effective strategies that implement essential determinants to increase behavioral intention among entertainment service providers.
Zomato: shaping the future of food
Authors: Ramadan Zahy , Kanso Jana 
Published: February 2023

Learning outcomes
The learning outcomes are as follows:
1. assess and evaluate the challenges affecting a mobile food aggregator platform’s business model and its effect on the customer journey in a developing market;
2. understand the relationship between the different stakeholders on such platform, namely, restaurants, end-users and the platform itself, and its implication on the customer journey; and
3. develop different potential marketing strategies under that business context that can be implemented and replicated in a given emerging market.

Case overview/synopsis
Zomato is an online restaurant aggregator and food delivery company that provides information, menus and user reviews of restaurants. While Zomato was growing exponentially in terms of number of users and listed restaurants, the platform had to constantly reinvent itself to stay competitive in light of increasingly aggressive competitors that were launching into the different markets it serves. Maya, the country manager of Zomato Lebanon, faced a key dilemma deciding which potential strategy to replicate from head office into the local market that would help her fend off rising competitors while still increasing the size of the market.

Complexity academic level
This case can be used mainly in senior undergraduate-level business school students. The case can be taught in courses covering marketing and strategy.

Supplementary materials
Teaching notes are available for educators only.

Subject code
CSS 8: Marketing.
Using image analysis to quantify defects and prioritize repairs in built-up roofs
Authors: Mostafa Kareem , Hegazy Tarek , Hunsperger Robert D. , Elias Stepanka 
Published: February 2023

Purpose
This paper aims to use convolutional neural networks (CNNs) to provide an objective approach to classify deteriorated building assets according to the type and extent of damage. This research supports automated inspection of buildings and focuses on roofing elements as one of the most critical and externally distressed elements in buildings.

Design/methodology/approach
In this paper, 5,000+ images of deteriorated roofs from several buildings were collected to design a CNN system that automatically identifies and sizes roofing defects. Experimenting with different CNN formulations, the best accuracy is achieved using two-stage CNNs. The first-stage CNN classifies images into defect/no defect, while the second stage classifies the defected images according to the damage type. Based on the image classification, optimization is used to prioritize roof repairs by maximizing the return from limited rehabilitation funds.

Findings
The developed CNNs reached 95% and 97% accuracy for the first and second phases, respectively, which is higher than achieved in previous literature efforts. Using the proposed model to automate inspection and condition assessment activities proved to be faster than conventional methods. Repair/replace strategy for a case study of 21 campus buildings based on their condition and budgetary constraints was suggested.

Research limitations/implications
Future research includes testing different data acquisition technologies (e.g. infrared imaging), performing severity-based classification and integrating with BIM for defect localization.

Originality/value
This study provides an objective approach to automate asset condition assessment and improve funding decisions using a combination of image analysis and optimization techniques. The proposed approach is applicable toward other asset types and components.
Scenario-based incident response training: lessons learnt from conducting an experiential learning virtual incident response tabletop exercise
Authors: Angafor Giddeon Njamngang , Yevseyeva Iryna , Maglaras Leandros 
Published: February 2023

PurposeThis paper aims to discuss the experiences designing and conducting an experiential learning virtual incident response tabletop exercise (VIRTTX) to review a business's security posture as it adapts to remote working because of the Coronavirus 2019 (COVID-19). The pandemic forced businesses to move operations from offices to remote working. Given that this happened quickly for many, some firms had little time to factor in appropriate cyber-hygiene and incident prevention measures, thereby exposing themselves to vulnerabilities such as phishing and other scams.Design/methodology/approachThe exercise was designed and facilitated through Microsoft Teams. The approach used included a literature review and an experiential learning method that used scenario-based, active pedagogical strategies such as case studies, simulations, role-playing and discussion-focused techniques to develop and evaluate processes and procedures used in preventing, detecting, mitigating, responding and recovering from cyber incidents.FindingsThe exercise highlighted the value of using scenario-based exercises in cyber security training. It elaborated that scenario-based incident response (IR) exercises are beneficial because well-crafted and well-executed exercises raise cyber security awareness among managers and IT professionals. Such activities with integrated operational and decision-making components enable businesses to evaluate IR and disaster recovery (DR) procedures, including communication flows, to improve decision-making at strategic levels and enhance the technical skills of cyber security personnel.Practical implicationsIt maintained that the primary implication for practice is that they enhance security awareness through practical experiential, hands-on exercises such as this VIRTTX. These exercises bring together staff from across a business to evaluate existing IR/DR processes to determine if they are fit for purpose, establish existing gaps and identify strategies to prevent future threats, including during challenging circumstances such as the COVID-19 outbreak. Furthermore, the use of TTXs or TTEs for scenario-based incident response exercises was extremely useful for cyber security practice because well-crafted and well-executed exercises have been found to serve as valuable and effective tools for raising cyber security awareness among senior leadership, managers and IT professionals (Ulmanová, 2020).Originality/valueThis paper underlines the importance of practical, scenario-based cyber-IR training and reports on the experience of conducting a virtual IR/DR tabletop exercise within a large organisation.
Mapping mentee outcomes in mentoring programs for managers
Authors: Gisbert-Trejo Nuria , Fernández-Ferrín Pilar , Albizu Eneka , Landeta Jon 
Published: February 2023

PurposeThis paper aims to identify, assess and classify the most significant mentee outcomes of formal mentoring processes aimed at managerial development.Design/methodology/approachAfter conducting an extensive literature review to identify the most significant mentee outcomes in the management field, a survey was conducted with a sample of 80 experienced mentees. An exploratory factor analysis (EFA) has been performed to simplify and contribute to operationalizing these results. Finally, focus group and nominal group techniques were conducted with practitioners to better understand the findings.FindingsA total of 27 mentee outcomes have been identified in the literature. EFA analysis has allowed us to downsize the 27 outcomes into four factors encompassing the most relevant mentee outcomes. The authors then provide an interpretation and discussion of the results.Originality/valueThis work compiles, reviews and systematizes existing studies on mentee outcomes in the field of management, classifying the results in a novel manner.