bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effects of Air Temperature on Transient Expression of Influenza Hemagglutinin in Nicotiana benthamiana: Analysis of Transgene Transcription and Plant Stress Responses
Authors: Pruksarojanakul Patthasarun , Atsumi Kazune , Oh Youngjun , Matoba Nobuyuki , Matsuda Ryo 
Published: January 2025

ABSTRACTPostinfiltration air temperature is known to affect the accumulation of recombinant protein in Agrobacterium‐mediated transient expression in Nicotiana benthamiana plants, including the number of days needed to reach maximum content and the rate of reduction thereafter. This study aimed to clarify whether the transcript levels of the transgenes and those of plant stress response markers (i.e., hypersensitive response and endoplasmic reticulum [ER] stress) could be primary determinants of the accumulation of recombinant influenza hemagglutinin (HA) at 21 or 26°C. We found no correlation between the transgene expression levels (HA, RdRP, and MP) and the number of days needed to reach the maximum HA protein content at both temperatures. Regardless of the accumulation compartment, HA protein content peaked earlier at 26°C than at 21°C. The rapid reduction of HA content after reaching the maximum, observed only in the ER at 26°C, correlated with severe necrosis and high transcript levels of two representative ER stress markers, bZIP60 and BiP. This correlation suggests that high postinfiltration air temperature affects HA accumulation primarily through ER stress, a key factor in the rapid reduction of HA content after the peak.
Optimization of Mucoadhesive Oral Films Containing Olive Leaf Extract and Microencapsulated Thyme Essential Oil With Potential Antimicrobial Activity
Authors: Goktas Kubra , Yalcin Dilek , Erdem Cansu , Bicakci Beyza Tutku , Bayraktar Oguz 
Published: January 2025

ABSTRACTIn this study, developing mucoadhesive oral films with optimized olive leaf extract (OLE) and microencapsulated thyme essential oil (TEO) contents was aimed to create a novel oral delivery system with desirable film properties as well as enhanced bioactivities. For this, TEO‐containing sodium alginate/okra gum microcapsules were first fabricated at optimized electrospray conditions, where optimization was performed for three parameters, which are the flow rate of the emulsion solution, the potential difference, and the distance between the nozzle of the sample container and the collector plate, through three‐level centered Box–Behnken design. Prior to the inclusion of the OLE in custom‐designed mucoadhesive films, polymeric components of the films were also optimized through a separate multi‐variable, three‐level Box–Behnken experimental design by which the effect of mixing ratios of Carbopol 934, hydroxypropyl methylcellulose and polyethylene glycol on viscosity, solution pH, film pH, disintegration time, thickness, and weight were elaboratively investigated. The resulting films, containing OLE and microencapsulated TEO, obtained through solvent casting were characterized for pH, viscosity, mass uniformity and thickness, dispersion, tensile strength, folding endurance, contact angle, in vitro mucoadhesion time, and ex vivo mucoadhesion force. Results showed that the film had good tensile strength, swelling capacity, compatibility with oral mucosa, and flexibility. It also demonstrated desirable stability and adhesion ability. Regarding bioactivity, the film was found to have desirable antioxidant capacity and it was non‐toxic to fibroblast cells when diluted to certain concentration. Moreover, antiviral studies indicated reductions of 32.39% and 54.29% against Poliovirus Type 1 and Murine Norovirus, respectively, while preliminary antibacterial tests showed no significant reduction in certain bacteria within 1 min, but a logarithmic reduction in Staphylococcus aureus was observed. Despite favorable mucoadhesion properties and physical attributes, further investigation is needed to enhance the antimicrobial effectiveness of the films for their potential utilization in drug delivery systems.
Drug Decriminalization and Fatal Traffic Crashes: Evidence From BM110 in Oregon
Authors: Gunadi Christian , Shi Yuyan 
Published: January 2025

ABSTRACTOregon Ballot Measure 110 (BM 110) reduced the penalties for non‐commercial possession of a controlled substance, downgrading them from a felony or misdemeanor to a new Class E violation, punishable by a maximum $100 fine. In this paper, we investigate whether BM 110 was associated with changes in drug‐related fatal traffic crashes in Oregon after its implementation in February 2021. To do so, we used Fatality Analysis Reporting System (FARS) data from 2018 to 2021 to calculate population‐adjusted state‐level drug‐related fatal traffic crashes. We also employed a modified synthetic control method to create a “synthetic” Oregon, designed to closely resemble the state's pre‐policy sociodemographic characteristics and outcome trends while correcting for time‐invariant pre‐policy differences. The findings show that BM 110 was not associated with changes in drug‐related fatal traffic crashes per 100,000 population (0.114, 95% CI: −0.106, 0.334). These results suggest that the implementation of BM 110 did not change drug‐related fatal traffic crashes in Oregon in the early period following its adoption.
Advocating for Life: A Hospital's Guide to Suicide Prevention
Authors: Watson Ronnie D. , Walker Khirey B. 
Published: January 2025

ABSTRACTIntroductionWith a global increase in adolescent suicidal thoughts and behaviors (STBs), this study addresses the need for enhanced advocacy initiatives within psychiatric inpatient settings.MethodsThis study, conducted in the Midwest region of the United States, sampled a diverse group of healthcare professionals from psychiatric hospitals, including nurses, nurse practitioners, therapists, techs, and psychologists. Specifically, this study used reflexive thematic analysis to gather insights into the existing state of advocacy efforts, focusing on their alignment with the needs of adolescent patients and barriers to effective implementation.ResultsPreliminary findings revealed a significant gap in hospital‐wide advocacy programs tailored to the needs of adolescents facing STBs. Participants stressed the need for more personalized, dynamic advocacy strategies that go beyond traditional models, suggesting a move toward integrated, patient‐driven approaches.ConclusionThis study details several limitations with current psychiatric inpatient care practices, stressing the need for a paradigm shift toward more effective advocacy and suicide prevention strategies. Our results advocate for an approach that actively involves patients in their care journey.
Hybrid transformer‐based model for mammogram classification by integrating prior and current images
Authors: Jeny Afsana Ahsan , Hamzehei Sahand , Jin Annie , Baker Stephen Andrew , Van Rathe Tucker , Bai Jun , Yang Clifford , Nabavi Sheida 
Published: January 2025

AbstractBackgroundBreast cancer screening via mammography plays a crucial role in early detection, significantly impacting women's health outcomes worldwide. However, the manual analysis of mammographic images is time‐consuming and requires specialized expertise, presenting substantial challenges in medical practice.PurposeTo address these challenges, we introduce a CNN‐Transformer based model tailored for breast cancer classification through mammographic analysis. This model leverages both prior and current images to monitor temporal changes, aiming to enhance the efficiency and accuracy (ACC) of computer‐aided diagnosis systems by mimicking the detailed examination process of radiologists.MethodsIn this study, our proposed model incorporates a novel integration of a position‐wise feedforward network and multi‐head self‐attention, enabling it to detect abnormal or cancerous changes in mammograms over time. Additionally, the model employs positional encoding and channel attention methods to accurately highlight critical spatial features, thus precisely differentiating between normal and cancerous tissues. Our methodology utilizes focal loss (FL) to precisely address challenging instances that are difficult to classify, reducing false negatives and false positives to improve diagnostic ACC.ResultsWe compared our model with eight baseline models; specifically, we utilized only current images for the single model ResNet50 while employing both prior and current images for the remaining models in terms of accuracy (ACC), sensitivity (SEN), precision (PRE), specificity (SPE), F1 score, and area under the curve (AUC). The results demonstrate that the proposed model outperforms the baseline models, achieving an ACC of 90.80%, SEN of 90.80%, PRE of 90.80%, SPE of 90.88%, an F1 score of 90.95%, and an AUC of 92.58%. The codes and related information are available at https://github.com/NabaviLab/PCTM.ConclusionsOur proposed CNN‐Transformer model integrates both prior and current images, removes long‐range dependencies, and enhances its capability for nuanced classification. The application of FL reduces false positive rate (FPR) and false negative rates (FNR), improving both SEN and SPE. Furthermore, the model achieves the lowest false discovery rate and FNR across various abnormalities, including masses, calcification, and architectural distortions (ADs). These low error rates highlight the model's reliability and underscore its potential to improve early breast cancer detection in clinical practice.
Inclusivity in Practice Education: The Views and Experiences of Nurse Educators Supporting Nursing Students With Mental Health Needs in Mental Health Clinical Placements
Authors: Ramluggun Pras , Mansbridge Sarah , Bhundoo‐Swift Rita , Anjoyeb Mahmood , Rioga Margaret 
Published: January 2025

ABSTRACTAimThis study aimed to explore the perspectives and experiences of Nursing Practice Supervisors and Assessors, referred to as Nurse Educators, regarding their support for students with mental health needs during mental health clinical placements.DesignA qualitative survey design was employed to delve into Nurse Educators' views and experiences at two Mental Health Trusts in the Southeast of England, UK.MethodsData were collected using Google Forms to create an anonymous online questionnaire. Thirty‐five Nurse Educators, comprising Practice Supervisors and Practice Assessors from two National Health Trusts, providing placements for two universities in the Southeast of England, responded to the survey. Thematic analysis was conducted to interpret the responses.ResultsFactors which facilitated and inhibited Nurse Educators efforts to supporting students' mental health needs during placements were identified. They encompassed personal, professional, and procedural issues which were intricately intertwined to support students with mental health needs. While most Nurse Educators expressed a willingness to support students with mental health needs, challenges regarding the reasonableness of adjustments and their impact on Nurse Educators' practice were widely reported.Recommendations include a review of learning arrangements, guidelines, and policies for students with mental health needs in clinical placements, as well as tailored training of students' inclusive needs for Nurse Educators.No patient or public contribution.
Performance Analysis of an Onboard PV System on a Demonstrator Light Commercial Vehicle in Hannover, Germany
Authors: Patel Neel , Sovetkin Evgenii , Pieters Bart , Bittkau Karsten , Ding Kaining , Peibst Robby , Fischer Hilke , Reinders Angèle 
Published: January 2025

ABSTRACTWe present an analysis of the performance data of a monitored PV system onboard a light commercial electric vehicle during parking and driving conditions in the Hannover region of Germany. The PV system's nominal power is 2180 WP with flat silicon modules on the vehicle's roof, rear, left, and right sides and other electronic components needed to charge the vehicle's high‐voltage (HV) battery. The analysis indicated that after 488.92 h of operation, the modules mounted on the vehicle roof produced 133.32 kWh of electricity during parking at the best possible orientation compared to 15.4, 30.67, and 22.99 kWh for the modules mounted on the rear, left, and right sides, respectively. During the trips, after 31.99 h of operation, 6.12, 0.68, 1.08, and 1.86 kWh of electricity were produced by the modules on the roof, rear, left, and right sides, respectively. The overall system efficiency was in the 60%–65% range. The aggregated usable electricity reaching the HV battery after multiple conversion stages generated by the system at the two parking locations was 129.39 kWh. PV electricity generated at the two parking locations enabled a range extension of approximately 530 km, which is 30% of the total distance driven during the measurement period between April and July 2021.
Delay Margin Analysis of Uncertain Linear Control Systems Using Probabilistic μ$$ \mu $$
Authors: Somers F. , Roos C. , Biannic J.‐M. , Sanfedino F. , Preda V. , Bennani S. , Evain H. 
Published: January 2025

ABSTRACTMonte Carlo simulations have long been a widely used method in the industry for control system validation. They provide an accurate probability measure for sufficiently frequent phenomena but are often time‐consuming and may fail to detect very rare events. Conversely, deterministic techniques such as or IQC‐based analysis allow fast calculation of worst‐case stability margins and performance levels, but in the absence of a probabilistic framework, a control system may be invalidated on the basis of extremely rare events. Probabilistic ‐analysis has therefore been studied since the 1990s to bridge this analysis gap by focusing on rare but nonetheless possible situations that may threaten system integrity. The solution adopted in this paper implements a branch‐and‐bound algorithm to explore the whole uncertainty domain by dividing it into smaller and smaller subsets. At each step, sufficient conditions involving upper bound computations are used to check whether a given requirement–related to the delay margin in the present case–is satisfied or violated on the whole considered subset. Guaranteed bounds on the exact probability of delay margin satisfaction or violation are then obtained, based on the probability distributions of the uncertain parameters. The difficulty here arises from the exponential term classically used to represent a delay , which cannot be directly translated into the Linear Fractional Representation (LFR) framework imposed by ‐analysis. Two different approaches are proposed and compared in this paper to replace the set of delays . First, an equivalent representation using a rational function with unit gain and phase variations that exactly cover those of the original delays, resulting in an LFR with frequency‐dependent uncertainty bounds. Then, Padé approximations, whose order is chosen to handle the trade‐off between conservatism and complexity. A constructive way to derive minimal LFR from Padé approximations of any order is also provided as an additional contribution. The whole method is first assessed on a simple benchmark, and its applicability to realistic problems with a larger number of states and uncertainties is then demonstrated.
Application of artificial intelligence to ultrasound imaging for benign gynecological disorders: systematic review
Authors: Moro F. , Giudice M. T. , Ciancia M. , Zace D. , Baldassari G. , Vagni M. , Tran H. E. , Scambia G. , Testa A. C. 
Published: January 2025

ABSTRACTObjectiveAlthough artificial intelligence (AI) is increasingly being applied to ultrasound imaging in gynecology, efforts to synthesize the available evidence have been inadequate. The aim of this systematic review was to summarize and evaluate the literature on the role of AI applied to ultrasound imaging in benign gynecological disorders.MethodsWeb of Science, PubMed and Scopus databases were searched from inception until August 2024. Inclusion criteria were studies applying AI to ultrasound imaging in the diagnosis and management of benign gynecological disorders. Studies retrieved from the literature search were imported into Rayyan software and quality assessment was performed using the Quality Assessment Tool for Artificial Intelligence‐Centered Diagnostic Test Accuracy Studies (QUADAS‐AI).ResultsOf the 59 studies included, 12 were on polycystic ovary syndrome (PCOS), 11 were on infertility and assisted reproductive technology, 11 were on benign ovarian pathology (i.e. ovarian cysts, ovarian torsion, premature ovarian failure), 10 were on endometrial or myometrial pathology, nine were on pelvic floor disorder and six were on endometriosis. China was the most highly represented country (22/59 (37.3%)). According to QUADAS‐AI, most studies were at high risk of bias for the subject selection domain (because the sample size, source or scanner model was not specified, data were not derived from open‐source datasets and/or imaging preprocessing was not performed) and the index test domain (AI models were not validated externally), and at low risk of bias for the reference standard domain (the reference standard classified the target condition correctly) and the workflow domain (the time between the index test and the reference standard was reasonable). Most studies (40/59) developed and internally validated AI classification models for distinguishing between normal and pathological cases (i.e. presence vs absence of PCOS, pelvic endometriosis, urinary incontinence, ovarian cyst or ovarian torsion), whereas 19/59 studies aimed to automatically segment or measure ovarian follicles, ovarian volume, endometrial thickness, uterine fibroids or pelvic floor structures.ConclusionThe published literature on AI applied to ultrasound in benign gynecological disorders is focused mainly on creating classification models to distinguish between normal and pathological cases, and on developing models to automatically segment or measure ovarian volume or follicles. © 2025 The Author(s). Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.
How Do You Ride an Elevator? Passenger In-Cabin Behavior Analysis on a Smart-Elevator Platform
Authors: Robal Tarmo , Reinsalu Uljana 
Published: January 2025

Abstract
Modern elevators came into wide use some 150 years ago. With the advancement of technology, the main task of elevators has remained the same—transport people and goods in between floors—yet elevators have become more sophisticated with a trend toward smart-elevators. While increasing passenger comfort and travel experience, these systems also allow to get an insight into elevator passengers’ behavior during their travels. Have you ever wondered why some people always stand in the same place, or what is your favorite spot to stand in an elevator? This chapter focuses on exploring passengers’ in-cabin behavior while they travel between floors, enabled by a smart-elevator platform. To interpret collected real passengers’ data, a general passenger location analysis model and evaluation methods are developed and several scenarios designed to validate system readiness for behavioral studies. The latter three enablers are used to analyze real elevator passengers’ data to understand how passengers behave in cabins. The results show that passengers tend to choose their favorite position inside the cabin, permitted by the situation, and smart-elevators as a platform can be efficiently used to study passengers’ behavior to enhance cabin environment.
Leuven: A Smart City Experience
Authors: Heyrman Lieve , Guily Tim , Deneckère Rébecca , Kornyshova Elena , Elali Ramona 
Published: January 2025

Abstract
Leuven is the eighth-largest city in Belgium with more than 100,000 inhabitants. It is a leading region in the creation of starts-ups, research centers, and applications on health and climate. Numerous organizations in the Leuven region and the city administration itself are currently cooperating in order to work out advanced ideas around Smart City technology, which is also a crucial step in Leuven’s goal to become climate neutral by 2030. For this particular goal, the organization Leuven 2030 was founded. Leuven 2030 strives for a climate-neutral future for Leuven, representing inhabitants, companies, civil society organizations, knowledge institutions, and public authorities. They do this by combining science (scientific framework), social power (bringing people together around projects), and storytelling (inspiring stories about the steps taken).
Enhancing the Visitor Experience with Immersive Technologies and Gaming: The Monserrate Use Case
Authors: Portugal Carlos , Vaz David , Dias Miguel Sales , Trocado Pedro , Prata Alcina , Abreu Fernando Brito e 
Published: January 2025

Abstract
Immersive technologies, such as augmented reality (AR) and virtual reality (VR), are effective technologies being implemented across diverse fields. We propose an AR/VR-based Web app to be applied in the cultural tourism field with the objective of enhancing the visitors’ experience of the Monserrate Palace, part of a UNESCO cultural landscape located in Sintra, Portugal, managed by Parques de Sintra – Monte da Lua SA.
This chapter overviews the state of the art on immersive technologies in cultural heritage sites and the visitor experience at these sites. Then, it exposes the palace’s historical and cultural significance and some of the challenges it faces in engaging its visitors. The potential applications of immersive technologies in the palace are explored, including interactive storytelling and immersive experiences that allow for a higher level of visitor engagement. Next, it addresses potential challenges and limitations of deploying the proposed solution in the palace, such as technical constraints and preservation concerns. It then concludes with a reflection on the significant potential of AR and VR in enriching the immersive experience of Monserrate Palace, offering new possibilities for engaging with cultural heritages and creating a memorable and enjoyable visit for modern audiences.
Taxonomy of the Scientific Domain of Smart Life
Authors: Deneckère Rébecca , Kornyshova Elena , Sadouki Kaoutar , Brinkkemper Sjaak 
Published: January 2025

Abstract
The field of Smart Life represents a large spectrum of various application domains coming from very established ones as Smart energy and Smart home to very recent ones as Smart environment, Smart airport, or Smart Earth. We observed a substantial growth in the scientific literature with over 126,000 papers containing “Smart” in their titles in 2021. Despite this huge number, we did not identify a detailed classification of these fields in the existing literature. There is still a notable gap in the classification and systematization of these fields. To address this, we developed a generic taxonomy for Smart applications by conducting a systematic mapping study focused on state-of-the-art and research agenda-oriented papers (2341 scientific publications in total).
Evolution of the Scientific Domain of Smart Life
Authors: Deneckère Rébecca , Kornyshova Elena , Brinkkemper Sjaak 
Published: January 2025

Abstract
Research in “Smart” domains has proliferated over recent decades, branching into various subfields including Smart energy, Smart cities, and Smart homes and, more recently, Smart farming, Smart universities, and Smart tourism. Building on a bibliographic study of 2341 literature reviews and state-of-the-art papers, this chapter addresses the research question: How have Smart applications evolved through time? We show the growth of the Smart Life scientific domain reflecting a rising interest among researchers and society and provide an analysis of the increasing diversity of the research concepts in Smart Life. Finally, we present the evolution of four key Smart domains: Smart applications for enterprises, persons, environment, and society. Therefore, this chapter illustrates the surge in publications over the past decade underscoring the expanding relevance and impact of Smart Life in contemporary technological and societal contexts.