bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deciphering the patterns and timing of diversification of the genusMelanastera(Hemiptera: Psylloidea: Liviidae) in the Neotropics
Authors: Serbina Liliya Štarhová , Burckhardt Daniel , Dušátková Lenka Petráková , Queiroz Dalva L. , Goldenberg Renato , Schuler Hannes , Percy Diana M. , Malenovský Igor 
Published: December 2024

AbstractEven after decades of research on diversification in the Neotropics, our understanding of the evolutionary processes shaping Neotropical clades is still incomplete. In the current study, we used different divergence times and likelihood-based methods to investigate the influence of biogeography and host associations on the diversification of the most species-rich Neotropical psyllid genusMelanastera(Liviidae) using molecular phylogenetic data from seven gene fragments (four mitochondrial and three nuclear). The putatively monophyletic group of NeotropicalMelanasteraspecies has an estimated crown node age of 20.2 Ma (ML, CI 20.2–30.6) or 23.2 Ma (BI, 95% HPD 16.6–32.6), with diversification occurring mainly in the Upper Miocene, although some species groups diversified in the Pliocene and Pleistocene. Biogeographic analysis suggests that the NeotropicalMelanasteraoriginated from the Pacific region of South and Central America. We detected a shift in diversification rates that likely occurred either at the time of origin ofMelanasteraor during the main colonisation of the Atlantic and Amazon Forests, followed by a subsequent slowdown in speciation rates. State-dependent speciation and extinction models revealed a significant relationship between this diversification shift and the shift ofMelanasterato the plant families Melastomataceae and Annonaceae, reflecting the impact of host switching on speciation rates in this group. This period also coincides with several independent dispersal events from the Atlantic and Amazon Forests to other parts of the Neotropics. Taken together, the results of the current study suggest that diversification ofMelanasterawas facilitated by major shifts to new host families, which may have promoted the dispersal ofMelanasterainto new adaptive zones with subsequent processes of local speciation.
A Deep Learning Model for Accurate Segmentation of theDrosophila melanogasterBrain from Micro-CT Imaging
Authors: McDaniel Jacob. F. , Marsh Mike , Schoborg Todd 
Published: December 2024

AbstractThe use of microcomputed tomography (Micro-CT) for imaging biological samples has burgeoned in the past decade, due to increased access to scanning platforms, ease of operation, isotropic three-dimensional image information, and the ability to derive accurate quantitative data. However, manual data analysis of Micro-CT images can be laborious and time intensive. Deep learning offers the ability to streamline this process, but historically has included caveats—namely, the need for a large amount of training data, which is often limited in many Micro-CT studies. Here we show that accurate deep learning models can be trained using only 1-3 Micro-CT images of the adultDrosophila melanogasterbrain using Dragonfly’s pre-trained neural networks and minimal user knowledge. We further demonstrate the power of our model by showing that it can accurately segment the brain across different tissue contrast stains, scanner models, and genotypes. Finally, we show how the model can assist in identifying morphological similarities and differences between mutants based on volumetric quantification, facilitating a rapid assessment of novel phenotypes. Our models are freely available and can be refined based on individual user needs.SummaryMicro-CT data can be automatically segmented and quantified using a deep learning model trained on as few as 3 samples, facilitating rapid comparison of developmental phenotypes.
Structural basis for ring-opening fluorescence by the RhoBAST RNA aptamer
Authors: Siwik Shea H. , Wierzba Aleksandra J. , Lennon Shelby R. , Olenginski Lukasz T. , Palmer Amy E. , Batey Robert T. 
Published: December 2024

AbstractTagging RNAs with fluorogenic aptamers has enabled imaging of transcripts in living cells, thereby revealing novel aspects of RNA metabolism and dynamics. While a diverse set of fluorogenic aptamers has been developed, a new generation of aptamers are beginning to exploit the ring-opening of spirocyclic rhodamine dyes to achieve robust performance in live mammalian cells. These fluorophores have two chemical states: a colorless, cell-permeable spirocyclic state and a fluorescent zwitterionic state. Recently, the developed dye SpyRho555 almost exclusively adopts the closed state in solution and becomes fluorescent in complex with the RhoBAST aptamer. To understand the basis for RhoBAST-SpyRho555 fluorogenicity, we have determined crystal structures of RhoBAST in complex with 5-carboxytetramethylrhodamine and a SpyRho555 analogue, MaP555. RhoBAST is organized by a perfect four-way junction that positions two loops to form the dye-binding pocket. The core of the ligand resides between a tri-adenine floor and a single guanine base, largely driven by π-stacking interactions. Importantly, the unpaired guanine interacts with the 3-position group of MaP555 to stabilize the open conformation, supported by mutagenesis data, and may play an active role in promoting the open conformation of the dye.Graphical Abstract
Sleep patterns predicting stress resilience are dependent on sex
Authors: Bush Brittany J , Mohamed Affra , Andrews Eva-Jeneé , Cain Gabrielle , Fawole Ayobami , Johnson Hadiya , Arocho Ashton , Qiao Zhimei , Paul Ketema N , Ehlen J. Christopher 
Published: December 2024

AbstractSleep disturbances and stress have a well-established link with neuropsychiatric illness; however, the nature of this relationship remains unclear. Recently, studies using the mouse social-defeat stress model revealed a causal role for non-rapid eye movement (NREM) sleep in the maladaptive behavioral responses to stress. These results suggest a novel function for NREM sleep; as a response by cortical neurons to mitigate the maladaptive effects of stress. A major limitation in many social defeat studies has been the exclusion of females. Women exhibit a greater prevalence of both affective disorders and sleep disturbances compared to men, thus there is a clear need to understand sleep – stress interactions in females. The present study adapts recently developed female social-defeat stress models to allow social-defeat and EEG in male – female pairs. Our findings duplicated the behavioral responses that occurred in other female, nondiscriminatory, and male models of social-defeat stress. Analysis of electroencephalographic (EEG) recordings, before exposure to stress, revealed that susceptibility to the behavioral effects of stress was associated with increased post-defeat NREM sleep—exclusively in females. In males, increased NREM sleep after social defeat stress occurred only in resilient mice. A potential cause of these sleep differences was also identified prior to stress exposure; we identified sex differences in recovery from NREM-sleep loss, thus, suggesting a sex-difference in the homeostatic process regulating sleep. These contrasting responses reveal sexual dimorphism in both NREM sleep predicting resilience and NREM sleep changes induced by social-defeat stress. When considered in the context of existing human literature, these findings suggest that sex is a major factor influencing the interaction of sleep with maladaptive behavioral responses to stress.
An infection prediction model developed from inpatient data can predict out-of-hospital COVID-19 infections from wearable data when controlled for dataset shift
Authors: Feng Ting , Mariani Sara , Conroy Bryan , Damiano Robert , Silva Ikaro , Swearingen Dennis , McFarlane Daniel C. 
Published: December 2024

ABSTRACTThe COVID-19 pandemic highlighted the importance of early detection of illness and the need for health monitoring solutions outside of the hospital setting. We have previously demonstrated a real-time system to identify COVID-19 infection before diagnostic testing1, that was powered by commercial-off-the-shelf wearables and machine learning models trained with wearable physiological data from COVID-19 cases outside of hospitals. However, these types of solutions were not readily available at the onset nor during the early outbreak of a new infectious disease when preventing infection transmission was critical, due to a lack of pathogen-specific illness data to train the machine learning models. This study investigated whether a pretrained clinical decision support algorithm for predicting hospital-acquired infection (predating COVID-19) could be readily adapted to detect early signs of COVID-19 infection from wearable physiological signals collected in an unconstrained out-of-hospital setting. A baseline comparison where the pretrained model was applied directly to the wearable physiological data resulted a performance of AUROC = 0.52 in predicting COVID-19 infection. After controlling for contextual effects and applying an unsupervised dataset shift transformation derived from a small set of wearable data from healthy individuals, we found that the model performance improved, achieving an AUROC of 0.74, and it detected COVID-19 infection on average 2 days prior to diagnostic testing. Our results suggest that it is possible to deploy a wearable physiological monitoring system with an infection prediction model pretrained from inpatient data, to readily detect out-of-hospital illness at the emergence of a new infectious disease outbreak.
Large-kernel convolutional neural networks for wide parameter-space searches of continuous gravitational waves
Authors: Joshi Prasanna M. , Prix Reinhard 
Published: December 2024

The sensitivity of wide parameter-space searches for continuous gravitational waves (CWs) is limited by their high computational cost. Deep learning is being studied as an alternative method to replace various aspects of a CW search. In previous work [], new design principles were presented for deep neural network (DNN) search of CWs and such DNNs were trained to perform a targeted search with matched-filtering sensitivity. In this paper, we adapt these design principles to build a DNN architecture for wide parameter-space searches in 10 days of data from two detectors (H1 and L1). We train a DNN for each of the benchmark cases: six all-sky searches and eight directed searches at different frequencies in the search band of 20–1000  Hz. We compare our results to the DNN sensitivity achieved from Dreissigacker and Prix [] and find that our trained DNNs are more sensitive in all the cases. The absolute improvement in detection probability ranges from 6.5% at 20 Hz to 38% at 1000 Hz in the all-sky cases and from 1.5% at 20 Hz to 59.4% at 500 Hz in the directed cases. An all-sky DNN trained on the entire search band of 20–1000 Hz shows a high sensitivity at all frequencies, providing a proof of concept for training a single DNN to perform the entire search. We also study the generalization of the DNN performance to signals with different signal amplitude, frequency, and the dependence of the DNN sensitivity on sky position.

Published by the American Physical Society
2024
The role of daily mindfulness on work–home conflict: a daily study of women leaders
Authors: Walsh Megan M. , Carleton Erica , Ortynsky Mikaila , Choi Ellen , Hancock Amanda J. , Arnold Kara A. 
Published: December 2024

PurposeWe investigate how mindfulness can help women leaders manage the work–home conflict using boundary theory. In this daily diary study, we examine daily levels of mindfulness as an antecedent to daily self-control and perceptions of work–home conflict. We propose that higher levels of daily mindfulness act as a personal resource that fosters self-control capacity, and this leads to a greater ability to manage work–home conflict.Design/methodology/approachA total of 86 women enrolled in a 30-day online mindfulness training program and completed daily surveys after each daily mindfulness training session. Data was analysed using the multilevel structural equation modelling.FindingsResults demonstrate that higher levels of daily mindfulness predict lower levels of daily work–home interference, and this relationship is mediated by self-control. This research supports the role of mindfulness through self-control on work–home conflict for women in leadership. Given the relatively high workforce participation among women with caregiving responsibilities, identifying resources that can be cultivated in order to enable more women to stay engaged in the workforce shows promise.Originality/valueThis study adds to the nascent literature of gendered mental health and well-being in leadership. Notably, women leaders often play a supportive role for employees and co-workers. Our findings suggest mindfulness training can be a useful tool to increase self-control resources in times of crises to mitigate the work–home conflict.
The conflict coaching challenge: design and evaluation of an online conflict coach
Authors: Brigg Morgan , Druckman Daniel , Loode Serge , Thai Hannibal A. 
Published: December 2024

Purpose
This article reports on the development and evaluation of an online conflict coaching system. The authors develop and test a broadly applicable yet tailored Conflict Coach that extends the current practice of coaching. It provides diagnostics and advice across several emotions and dimensions of conflict drawn from research and conflict management practice.

Design/methodology/approach
The coach engages coachees through five emotions, divided into three levels of conflict intensity and ten dimensions of conflict to deliver analysis and advice “packets” derived from both research findings and practice. A Prolific survey used as a proxy for the coaching system was administered to 341 respondents. A variety of closed and open-ended questions were asked about the respondents’ chosen conflict, the packets of advice and about their overall experience.

Findings
The coach was judged as somewhat helpful, quite understandable, quite implementable and providing sufficient advice. A sampling of the findings signals the importance of dealing with anger and dislike and finding ways to communicate more effectively. Longer-term assessments indicated that the advice continued to be helpful and worth recommending to others. There are opportunities to further develop the prototype including through field testing and use of artificial intelligence.

Originality/value
To the best of the authors’ knowledge, the Conflict Coach is a first attempt to provide a widely available yet tailored online system for responding to conflict. It promises positive social and practice implications that extend upon and complement existing conflict management through increased accessibility to information and advice based on published research findings and practice. Research opportunities accompany its possible further development.
Future accounting professionals – how important is teaching for sustainability?
Authors: Gomes Sónia , Jorge Susana , Eugénio Teresa 
Published: December 2024

PurposeThis study aims to analyze Portuguese professional regulators’ perspectives on the importance given to sustainable development (SD) issues and their position on integrating SD in the curricula of accounting and management courses. Secondly, it seeks to analyze, from the student’s perspective, the importance of SD for their future professional performance as accountants or auditors.Design/methodology/approachInterviews were conducted with the heads of the two professional regulatory bodies in Portugal. A complimentary content analysis of professional journals and other types of publications, training activities, videos of conferences, meetings and training sessions available on the official websites of these bodies and social media was also carried out. Data on students’ perspectives were collected through a questionnaire sent to students enrolled in accounting and management courses at higher education institutions (HEIs) in Portugal in 2020.FindingsThere is evidence that further interaction between professional regulators and HEIs is still needed to integrate teaching sustainable development (TSD) in the academic curricula; moreover, regulators expressed concern about raising awareness of the topic among future professionals. In turn, students consider that knowledge of SD is fundamental to their professional future. These students’ interests may motivate HEIs to integrate TSD since this will improve their professional performance in companies and contribute to realizing SD in general.Research limitations/implicationsThis study carried out in the Portuguese context, contributes to the gap in the literature. Portugal is part of the EU and must comply with EU directives on sustainability reports. Also, it contributes to answering UN calls as proposed by UNESCO, about the importance of teaching this subject nowadays, more relevant than ever. By emphasizing the importance of TSD, the study aims to raise awareness among students and educators about the urgent need for sustainable practices in business. This awareness can influence public attitudes toward sustainability and contribute to a more informed society.Originality/valueThis study is innovative in that it seeks to assess, from the perspective of professional regulators, their position on integrating SD into the curricula of accounting and management courses. It also analyses the views of professional regulators and students on the importance of TSD for the performance of future professionals.
Customer intentions toward the adoption of WhatsApp chatbots for restaurant recommendations
Authors: Romero-Charneco Maria , Casado-Molina Ana-María , Alarcón-Urbistondo Pilar , Cabrera Sánchez Juan Pedro 
Published: December 2024

Purpose
Given the importance of chatbots in customer service in tourism, this paper aims to understand the drivers that predispose regular consumers of restaurant recommendation chatbots to continue using them.

Design/methodology/approach
A total of 386 regular consumers of a chatbot via WhatsApp restaurant recommender responded to an online questionnaire (inspired by scales found in the literature on technology adoption). Structural equation modeling was used to test the hypotheses.

Findings
Significant predictors of intention to continue using these chatbots included “effort expectancy (EE),” “hedonic motivation (HM),” “price value (PV)” and “habit (HT).” Specifically, HT still has a long way to go in terms of its performance, and it will be possible to work on it. Furthermore, two variables, EE and HM, act as a bottleneck when it comes to explaining this recurrent usage intention. Factors such as “performance expectancy (PE),” “facilitating conditions (FC)” and “social influence (SI)” did not influence “behavioral intention (BI).” Likewise, the moderating variables, age and gender, are not significant. Finally, the predictive capability of the model is demonstrated. The study findings will enable the development of effective strategies to foster consumer loyalty to this new technology in the restaurant industry.

Originality/value
This study contributes, building on the suitability of the unified theory of acceptance and use of technology 2 model, to explain users’ intention to continue using chatbot tourism services in the context of an information search for an unplanned and varied purchase decision, namely, restaurant recommendation services. To the best of the authors’ knowledge, this is the first analysis of tourist’s intention to reuse a real and fully functional chatbot via mobile instant messaging.
Exploring the role of nursing in implementation of collaborative integrated models of care in the community: a scoping review
Authors: Prebble Gemma , Evans David 
Published: December 2024

PurposeAlthough the nursing role appears central in delivering collaborative models of care little is known about the experience of nurses within implementation of integrated nursing care.Design/methodology/approachA scoping review to identify what is known about the experience of nurses implementing integrated care in the community, incorporating thematic analysis utilising the Rainbow Model.FindingsAlthough noting a paucity of evidence, findings indicate implementation of integrated care models provides both challenges and opportunities to nurses across all systems levels. Thematic analysis highlights a disproportionate lack of focus on organisational integration of nursing services within integrated care. Exploration of tacit attributes and network development is suggested to support nursing delivery of services which challenge traditional organisational and professional boundaries.Research limitations/implicationsLimited research exploring experiences of nurses in implementing integrated care suggests more rigorous mixed methods or qualitative research is indicated, including case studies exploring organisational integration, nursing leadership strategies and how tacit attributes support collaborative working.Originality/valueUsing a systems approach, applying the Rainbow Model within scoping review analysis, enabled exploration of what is known about nursing within the contemporary context of integrated healthcare services.
Understanding customer opinions on IoT applications implemented in the retail industry worldwide and its implications for businesses in Vietnam
Authors: Ho Thanh , Nguyen Ho , Dinh Hao , Pham Hoa , Pham Phuong , Tham Uyen 
Published: December 2024

Purpose
This study aims to deeply understand customer experiences toward Internet of Things (IoT) applications in retail by developing machine learning models for aspect-based sentiment analysis (SA). It includes creating a related terms dictionary and proposing implications for retail businesses in Vietnam based on these analyses. The ultimate goal is to gain insights into customer opinions and assist administrators in formulating effective digital transformation and business strategies within the Vietnamese market.

Design/methodology/approach
Initially, this research uses qualitative methods to identify different aspects of customer experience at stores equipped with IoT applications. Then, quantitative methods were applied through classification machine learning models which were trained on the annotated data set to classify comments into aspects and sentiments. Finally, the classification results were analyzed and visualized to draw implications about customer opinions of these stores.

Findings
This study collected 77,042 customers’ comment from potential and actual customers who have ever shopped at retail stores with IoT applications deployed worldwide, identified ten new aspects of customer experience in this field and built a dictionary of related terms. Furthermore, this study contributed two efficient ensemble models with an accuracy of 81% and 89% for analyzing aspects and customer sentiments, respectively. This study also proposes implications for managers regarding the use of IoT technology in retail stores to improve shopping experiences for customers.

Originality/value
This study’s findings help managers develop appropriate digital transformation and business strategies for integrating IoT technology into retail stores, especially for retail businesses in the Vietnamese market based on the analysis results and proposed model.