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Abstract Introduction The interleukin-23p19 subunit inhibitor, guselkumab, has demonstrated improvements in clinical and patient-reported outcome (PRO) measures in patients with moderate-to-severe psoriasis. Understanding the relationship among clinical response, PRO measures and baseline characteristics could help clinicians individualize treatment plans. The objective of this analysis was to examine changes in signs, symptoms and quality-of-life (QoL) PRO measures in patients who maintained complete skin clearance through ≥ 3 years in the phase 3 VOYAGE 1 trial. Methods A descriptive post hoc analysis of data from VOYAGE 1 was conducted to compare baseline characteristics of patients who maintained complete skin clearance (Psoriasis Area and Severity Index [PASI] = 0 for ≥ 156 consecutive weeks) versus patients who did not. Mean scores for individual domains of the Dermatology Life Quality Index (DLQI) and Psoriasis Symptom and Sign Diary (PSSD) were evaluated in patients who maintained complete skin clearance, and baseline characteristics of patients who achieved PRO scores of DLQI = 0/1 and PSSD = 0 were compared with those who did not. Results Of the 329 patients included in this post hoc analysis, 73 (22.2%) maintained PASI = 0 for ≥ 156 weeks. This group had a numerically lower proportion of patients at baseline with obesity, depression or previous biologic treatment and a higher proportion who had never smoked. Patients who maintained PASI = 0 generally achieved positive DLQI and PSSD outcomes, though some impact of residual disease was observed, largely related to the DLQI “Symptoms and feelings” sub-scale and PSSD components “Dryness,” “Redness” and “Itch.” Patients reporting continued disease impact (despite sustaining PASI = 0) had greater disease severity at baseline versus those achieving DLQI = 0/1 and PSSD = 0. Conclusion Clinical measures alone do not capture the full patient experience. While both QoL and clinical symptoms are responsive to highly effective treatment, a subset of patients with complete clinical response is still impacted by their psoriasis. Further investigation into this population is warranted. Trial registration ClinicalTrials.gov, NCT02207231.

Abstract Japanese quails (Coturnix japonica) have a high reproductive rate because they reach sexual maturity very early. This short rearing time results in increasing interest among breeders and consumers. The aim of the study was a comparative analysis of two genotypes and sexes of Japanese and Pharaoh quails and their impact on body weight, carcass composition, meat quality, digestive system morphometry and leg bone dimensions. The study involved 40 birds (10 females and 10 males), Japanese quail and Pharaoh quail, 42 days old. Quail genotype had an effect (P

For many safety-related applications such as hazard mapping or road management, well-documented avalanche events are crucial. Nowadays, despite the variety of research directions, the available data are mostly restricted to isolated locations where they are collected by observers in the field. Webcams are becoming more frequent in the Alps and beyond, capturing numerous avalanche-prone slopes. To complement the knowledge about avalanche occurrences, we propose making use of this webcam imagery for avalanche mapping. For humans, avalanches are relatively easy to identify, but the manual mapping of their outlines is time intensive. Therefore, we propose supporting the mapping of avalanches in images with a learned segmentation model. In interactive avalanche segmentation (IAS), a user collaborates with a deep-learning model to segment the avalanche outlines, taking advantage of human expert knowledge while keeping the effort low thanks to the model's ability to delineate avalanches. The human corrections to the segmentation in the form of positive clicks on the avalanche or negative clicks on the background result in avalanche outlines of good quality with little effort. Relying on IAS, we extract avalanches from the images in a flexible and efficient manner, resulting in a 90 % time saving compared to conventional manual mapping. The images can be georeferenced with a mono-photogrammetry tool, allowing for exact geolocation of the avalanche outlines and subsequent use in geographical information systems (GISs). If a webcam is mounted in a stable position, the georeferencing can be re-used for all subsequent images. In this way, all avalanches mapped in images from a webcam can be imported into a designated database, making them available for the relevant safety-related applications. For imagery, we rely on current data and data archived from webcams that cover Dischma Valley near Davos, Switzerland, and that have captured an image every 30 min during the daytime since the winter of 2019. Our model and the associated mapping pipeline represent an important step forward towards continuous and precise avalanche documentation, complementing existing databases and thereby providing a better base for safety-critical decisions and planning in avalanche-prone mountain regions.

Parisa Shamsesfandabadi,1 Arpeet Patel,2 Yun Liang,1 Matthew J Shepard,3 Rodney E Wegner1 1Radiation Oncology department, Allegheny Health Network, Pittsburgh, PA, USA; 2Drexel University College of Medicine, Philadelphia, PA, USA; 3Neurosurgery Department, Allegheny Health Network, Pittsburgh, PA, USACorrespondence: Parisa Shamsesfandabadi, Email parisa.shamsesfandabadi@ahn.orgAbstract: Radiation therapy, a common treatment for central nervous system cancers, can negatively impact cognitive function, resulting in radiation-induced cognitive decline (RICD). RICD involves a decline in cognitive abilities such as memory and attention, likely due to damage to brain white matter, inflammation, and oxidative stress. The multifactorial nature of RICD poses challenges including different mechanisms of injury (neurogenesis, oxidative stress and neuroinflammation, dendritic structure alterations and vascular effects) and confounding factors like advanced age, and pre-existing conditions. Despite these challenges, several potential solutions exist. Neuroprotective agents like antioxidants can mitigate radiation damage, while cognitive rehabilitation techniques such as cognitive training and memory strategies improve cognitive function. Advanced imaging techniques like magnetic resonance imaging (MRI) help identify vulnerable brain areas, and proton therapy offers precise targeting of cancer cells, sparing healthy tissue. Multidisciplinary care teams are crucial for managing RICD’s cognitive and psychological effects. Personalized medicine, using genetic and molecular data, can identify high-risk patients and tailor treatments accordingly. Emerging therapies, including stem cell therapy and regenerative medicine, offer hope for repairing or replacing damaged brain tissue. Addressing RICD is vital for cancer survivors, necessitating consideration of cognitive function and provision of appropriate support and resources for those experiencing cognitive decline.Keywords: radiation therapy, radiation-induced cognitive decline, RICD, cognitive decline, cognitive function, central nervous system cancers

The use of three-dimensional (3D) gait analysis to image femorotibial translation can aid in the diagnosis of pathology and provide additional insight into the severity of KOA (knee osteoarthritis). Femorotibial translation is of particular importance in patients undergoing UKA (unicompartmental knee arthroplasty), as the absence or elongation of ligamentous structures results in changes in the kinematic alignment. The aim of the study was to evaluate the parameters of femorotibial translation in patients with MOA (medial unicompartmental OA). An artificial model was employed to develop a method for calculating femorotibial translation in vitro. In a prospective cohort study, gait data using three-dimensional gait analysis were collected from 11 patients (68.73 ± 9.22 years) with severe OA scheduled for UKA and 29 unmatched healthy participants (22.07 ± 2.23 years). The discrete variables characterising femorotibial translation were compared for statistical significance (p < 0.05) using the Student’s t-test and the Mann–Whitney U-test. The results of the study validated an artificial model to mimic femorotibial translation. The comparison of patients scheduled for UKA and a healthy unmatched control group showed no statistically significant differences concerning femorotibial translation in all three planes (p > 0.05). However, the PROMs (patient-reported outcome measures), spatiotemporal, and kinematic parameters showed statistically significant differences between the groups (p < 0.001). The data presented here demonstrate typical changes in PROMs as well as spatiotemporal and kinematic outcomes for MOA as seen in knee OA. The results of the clinical gait analyses demonstrate individualised femorotibial translation. The extent of individual femorotibial translation may prove to be an important parameter for altered joint kinematics in patients with MOA, especially prior to UKA implantation.

Abstract Local interaction patterns play an important role in the structural and functional organization of complex networks. Here we propose a method for obtaining parsimonious decompositions of networks into higher order interactions which can take the form of arbitrary motifs. The method is based on a class of analytically solvable generative models which in combination with non-parametric priors allow us to infer higher order interactions from dyadic graph data without any prior knowledge on the types or frequencies of such interactions. We test the presented approach on simulated data for which we recover the set of underlying higher order interactions to a high degree of accuracy. For empirical networks the method identifies concise sets of atomic subgraphs from within thousands of candidates that cover a large fraction of edges and include higher order interactions of known structural and functional significance. Being based on statistical inference the method also produces a fit of the network to analytically tractable higher order models opening new avenues for the systematic study of higher order interactions.

Abstract Background Sex trafficking victims often have touchpoints with salons for waxing, styling, and other body modification services required by traffickers. Recently, some states have administered laws requiring salon professionals to receive intimate partner violence (IPV)-related training, with even fewer states mandating training on identifying sex trafficking. This study aimed to understand how salon professionals have witnessed evidence of violence, including IPV and sex trafficking, in the workplace and to explore the differences in their approach to each type of victim. Methods In-depth interviews were conducted with salon professionals (N = 10) and law enforcement professionals/policymakers (N = 5). Content and thematic analysis techniques were used. Results Salon professionals typically identified potential violence through signs such as bruises, odd behavior, and client disclosures, prompting them to engage in cautious conversations. Yet, few were trained to identify and intervene. Often, they responded to suspected violence by talking with the client, sharing concerns with salon leadership, directly intervening on the client’s behalf, or contacting the police. Law enforcement and salon professionals had suggestions about improving salon professionals’ recognition of and response to violence, including training on victim-focused resources, creating a safe environment, and building relationships with law enforcement. They also suggested strengthening community partnerships to increase resource advocacy and reporting. Conclusions One-on-one salon services may provide a unique opportunity to intervene and identify victims of violence, especially when empowered through additional training and collaborative partnerships with community-oriented policing initiates. Implementing training and community-based initiatives could aid salon professionals in gaining greater confidence in knowing what to do when serving a client who is a victim of IPV or sex trafficking.

Abstract We here introduce the Aquamarine (AQM) dataset, an extensive quantum-mechanical (QM) dataset that contains the structural and electronic information of 59,783 low-and high-energy conformers of 1,653 molecules with a total number of atoms ranging from 2 to 92 (mean: 50.9), and containing up to 54 (mean: 28.2) non-hydrogen atoms. To gain insights into the solvent effects as well as collective dispersion interactions for drug-like molecules, we have performed QM calculations supplemented with a treatment of many-body dispersion (MBD) interactions of structures and properties in the gas phase and implicit water. Thus, AQM contains over 40 global and local physicochemical properties (including ground-state and response properties) per conformer computed at the tightly converged PBE0+MBD level of theory for gas-phase molecules, whereas PBE0+MBD with the modified Poisson-Boltzmann (MPB) model of water was used for solvated molecules. By addressing both molecule-solvent and dispersion interactions, AQM dataset can serve as a challenging benchmark for state-of-the-art machine learning methods for property modeling and de novo generation of large (solvated) molecules with pharmaceutical and biological relevance.

Introduction: The Singapore Institute of Technology-University of Glasgow (SIT-UofG) Nursing Programme has traditionally taken a didactic teaching approach in the delivery of the Evidence-Based Practice (EBP) module. A hybrid approach was introduced using Game-Based Learning (GBL) to encourage active learning through gameplay. Methods: A Randomised Controlled Trial (RCT) was undertaken encompassing a cohort of 100 Nursing students taking the EBP module in their first year at the Singapore Institute of Technology (SIT) in the 2021/22 academic year. The experimental group (n=27) worked through the online GBL intervention and the EBP module, while the control group (n=27) took the EBP module alone. The GBL included five Learning Quests and three case studies. Results: High levels of satisfaction were reported by both the experimental group (n=22) and the control group (n=15) on the traditional content and delivery of the EBP module. High levels of engagement were reported by the experimental group on the GBL intervention; a one-sample statistics analysis confirming a significant level of engagement (p

This qualitative study aims to analyse the personal qualification, attitudes and the pedagogical concepts of German teachers as experts in their profession regarding basic life support (BLS) education in secondary schools. Thirteen (n = 13) secondary school teachers participated in semi-structured expert interviews and were interviewed for at least 20 to 60 min regarding BLS student education. Interviews were semi-structured with guiding questions addressing (1) personal experience, (2) teacher qualification for BLS and (3) implementation factors (e.g., personal, material and organisational). Audio-recorded interviews were analysed by content analysis, generating a coding system. School teachers provided a heterogeneous view on implementation-related processes in BLS education. Many teachers were educated in first aid, acknowledge its importance, but had no experience in teaching BLS. They want to assure being competent for teaching BLS and need tailored trainings, materials, pedagogical information and the incorporation into the curriculum. Also, the management of time constraints, unwilling colleagues, or young students being overwhelmed were commonly mentioned considerations. Concluding, teachers reported to be willing to teach BLS but a stepwise implementation framework incorporating practice-oriented qualification and educational goals is missing.

This paper demonstrates the observation of wind shear and veer directly from the operational response of a wind turbine equipped with blade load sensors. Two independent neural-based observers, one for shear and one for veer, are first trained using a machine-learning approach and then used to produce estimates of these two wind characteristics from measured blade load harmonics. The study is based on a dataset collected at an experimental test site featuring a highly instrumented 8 MW wind turbine, an IEC-compliant (International Electrotechnical Commission) met mast, and a vertical profiling lidar reaching above the rotor top. The present study reports the first demonstration of the measurement of wind veer with this technology and the first validation of shear and veer with respect to lidar measurements spanning the whole rotor height. Results are presented in terms of correlations, exemplary time histories, and aggregated statistical metrics. Measurements of shear and veer produced by the observers are very similar to the ones obtained with the widely adopted profiling lidar while avoiding its complexity and associated costs.

We propose a deep learning approach for high-resolution species distribution modelling (SDM) at large scale combining point-wise, crowd-sourced species observation data and environmental data with Sentinel-2 satellite imagery. What makes this task challenging is the great variety of controlling factors for species distribution, such as habitat conditions, human intervention, competition, disturbances, and evolutionary history. Experts either incorporate these factors into complex mechanistic models based on presence-absence data collected in field campaigns or train machine learning models to learn the relationship between environmental data and presence-only species occurrence. We extend the latter approach here and learn deep SDMs end-to-end based on point-wise, crowd-sourced presence-only data in combination with satellite imagery. Our method, dubbed Sat-SINR, jointly models the spatial distributions of 5.6k plant species across Europe and increases the spatial resolution by a factor of 100 compared to the current state of the art. We exhaustively test and ablate multiple variations of combining geo-referenced point data with satellite imagery and show that our deep learning-based SDM method consistently shows an improvement of up to 3 percentage points across three metrics. We make all code publicly available at https://github.com/ecovision-uzh/sat-sinr.

The current study sought to better understand how leisure boredom is associated with alcohol use and how peer factors moderated the relationship between state and trait leisure boredom and past month alcohol use. The 2004 to 2008 multi-cohort study sample included 3,837 high school students (50% female; 91% mixed race; M[subscript age] = 14 years; SD = 0.83) in the Cape Town area of South Africa. Results of generalized multilevel models found peer factors (time spent with peers, injunctive friend norms, descriptive peer norms) and trait, but not state, leisure boredom significantly predicted past month alcohol use. Findings can inform alcohol prevention efforts and suggest both peer factors and trait leisure boredom are worthy targets for intervention. Specifically, supporting adolescents to effectively navigate experiences of leisure boredom may, in turn, reduce alcohol use.