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Protocol for evaluation of a virtual wheelchair simulator in assessing mobility skills and cognitive abilities in diverse populations: A multicentric mixed-methods pilot study
Background Current wheelchair acquisition, prescription, and training programs often require comprehensive assessments integrating both power mobility skills and cognitive abilities. While wheelchair simulators offer promise for these assessments, but they have not been fully validated. Objective This study aims to develop and refine a protocol for evaluating the feasibility, reliability and preliminary validity of virtual wheelchair simulator metrics in assessing users’ current power mobility skills and cognitive abilities, following STARD guidelines. Reference standards include the self-report Wheelchair Skill Test (WST), Power Mobility Road Test (PMRT) and the Montreal Cognitive Assessment (MoCA). Methods This multicentric, mixed-methods pilot study will recruit participants with mobility disabilities, a control group of individuals without disabilities, and healthcare professionals to use a virtual wheelchair simulator. Healthcare professionals will evaluate the simulator’s assessments and provide expert feedback on the protocol. Quantitative data will include simulator-derived performance metrics compared to reference standards, and physiological data (e.g., heart rate, skin conductance, temperature, inter-beat-intervals, accelerometer and eye-gaze tracking). Qualitative data (semi-structured interviews) will capture user experiences and insights for protocol refinement. The Quality of Experience (QoE) evaluation framework will assess cognitive workload (NASA-TLX and PAAS), usability (System Usability Scale), immersion (IGroup Presence Questionnaire), and emotion (Self-Assessment Manikin). Data analysis will include correlation analysis, regression models, thematic analysis, and statistical tests (e.g., independent t-tests, Mann-Whitney U tests) to compare simulator-based performance across groups. Discussion This pilot study seeks to fill critical gaps in current wheelchair training and prescription methods by exploring the use of a virtual simulator to objectively assess both cognitive abilities and power mobility skills. Integrating the QoE assessment framework will provide insights into user interactions, ensuring that the simulator supports tailored training and improve user outcomes in mobility, and safety. Future research may extend this protocol to clinical settings to further evaluate its applicability and effectiveness.
Chloroplast whole genome assembly and phylogenetic analysis of Persicaria criopolitana reveals its new taxonomic status
Abstract Persicaria criopolitana (Polygonaceae), a dominant annual herb in wetland ecosystems, is ecologically and horticulturally significant. Despite its prevalence, genomic resources for clarifying its phylogenetic relationships and supporting conservation efforts remain limited. The complete chloroplast genome of P. criopolitana was sequenced, assembled, and annotated. Comparative genomic analyses with other Persicaria species were conducted to identify structural variations and evolutionary dynamics. Phylogenetic relationships were reconstructed using maximum-likelihood methods based on whole chloroplast genome sequences. The chloroplast genome (159,427 bp) exhibits a conserved quadripartite structure, encoding 131 genes, including 86 protein-coding, 37 tRNA, and 8 rRNA genes. Key features include: 208 simple sequence repeats (SSRs) were detected, predominantly mononucleotide motifs. A pronounced preference for A/U-ending codons, with leucine as the most frequent amino acid. A 62-bp extension of the ndhF gene into the inverted repeat (IRb) region within the small single-copy (SSC) region.Phylogenetic resolution: P. criopolitana clusters within Persicaria sect. Polygonum, demonstrating distant divergence from sect. Cephalophilon.This study provides the first complete chloroplast genome resource for P. criopolitana, resolving its taxonomic position and revealing adaptive genomic signatures. These findings advance molecular tools for species identification, inform conservation strategies, and elucidate evolutionary mechanisms in Persicaria.
Methods and processes to develop and deliver a theory-informed education program for sustained behaviour change in emergency nursing
Background HIRAID® (History including Infection risk, Red flags, Assessment, Interventions, Diagnostics, communication and reassessment) is an evidence-based framework that supports emergency nurses to optimise safety, quality, and patient experience of care. HIRAID® was the intervention in a modified stepped-wedge cluster randomised controlled trial (SW-cRCT) in a convenience sample of 29 Australian emergency departments (Australian New Zealand Clinical Trials Registry: ACTRN12621001456842). The aim of this paper is to describe the methods and processes used to develop and deliver a theory-informed education program to support behaviour change during HIRAID® implementation. Methods The HIRAID® education program was developed using: i) existing HIRAID® research using the Behaviour Change Wheel and Theoretical Domains Framework to identify enablers and barriers to HIRAID® use; ii) application of educational pedagogical theoretical frameworks (constructive alignment, backwards design, scaffolded learning); Bloom’s taxonomy of educational objectives, and active and collaborative learning; iii) Australian standards related to safety, quality, clinical governance, and emergency nursing; and iv) behavioural diagnostic data from study sites (n = 670 nurses). Results HIRAID® education program consisted of HIRAID® Provider and Instructor Courses and was delivered using a ‘train-the-trainer’ model. Fifteen HIRAID® Instructor Courses were held from February 2021 to March 2023 with 162 participants, and at November 2023 over 1300 emergency nurses had completed the HIRAID® Provider Course. Conclusions The theory-informed approach to the HIRAID® education program enabled development of a structured program and delivery in the dynamic and complex emergency department environment. The approach reported in this paper provides a blueprint for other researchers aiming to change behaviours in complex settings.
The immunosurveillance signature predicts the prognosis and immunotherapy sensitivity for colon adenocarcinoma
Association between continuity of primary care and preventable hospitalization in adults with asthma: A cohort study
Objective Hospitalization often indicates deteriorating health, longer treatment times, and higher healthcare costs. This study aimed to investigate associations between continuity of care (COC) and asthma-related hospitalizations using a rigorous methodology. Methods This retrospective cohort study was conducted using national health insurance claims data. The study included adults with a diagnosis of asthma between 2015 and 2016 in a primary care setting. The exposure was measured using continuity of care indices (COCIs) during the first two years after inclusion. Cohorts were categorized into two groups based on COCI levels. The primary outcome was the incidence of asthma-related hospitalizations, and the secondary outcomes were emergency department (ED) utilization, systemic corticosteroid use, and asthma-related medical costs. Results A total of 24,173 patients were eligible for analysis, 13,212 of whom were continuously cared for by primary doctors (the continuity group), and 10,961 non-continuously (the non-continuity group). During a 2 year-follow-up period, 230 patients (1.74%) were hospitalized in the continuity group and 404 (3.69%) in the non-continuity group. After adjusting for confounding covariates, patients in the non-continuity group were found to be at significantly higher risk of hospital admission (adjusted hazard ratio (aHR)=2.04 [95% confidence interval = 1.73 ~ 2.41]). In addition, the risk of ED visits, systemic corticosteroid use, and costs were higher for patients in the non-continuity group (aHR = 2.26 [1.32 ~ 3.87], adjusted OR=1.58 [1.35 ~ 1.82], and expβ = 1.41 [1.37 ~ 1.45], respectively). Conclusions In adult asthma patients at the early stages of illness, increased continuity of primary care was found to be associated with fewer hospitalizations, fewer ED visits, and lower healthcare expenditures.
Development of machine learning models for gait-based classification of incomplete spinal cord injuries and cauda equina syndrome
Examination of hyper-palatable foods and their nutrient characteristics using globally crowdsourced data
Hyper-palatable foods (HPF), characterized by specific nutrient combinations at moderate to high levels (e.g., fat and sodium), have been suggested to increase energy intake and obesity risk due to their strong reinforcing properties. The study examined patterns in HPF availability, nutrient characteristics, and overlap with ultra-processed foods (UPF) across countries from a globally crowdsourced, open-source dataset. Food data (N = 314229 food items) from 17 countries were analyzed. Compared to the US, foods from most European countries examined, as well as Canada and Mexico, were significantly less likely to be identified as HPF (ORs = 0.70 to 0.93) and had lower % of calories from fat, sugar, starchy carbohydrates, and sodium compared to HPF items from the US (ORs = 0.76 to 0.98). Across countries, items identified as HPF substantially exceeded HPF threshold criteria by 70–229%. Foods identified as being both HPF and ultra-processed foods ranged from 33% (Bulgaria) to 50% (US). Overall, findings from 17 countries across Europe, North America, and South America highlighted foods from the US as being more likely to be hyper-palatable relative to most other countries examined. Results highlighted variability in the degree to which foods met criteria as HFP and UPF across countries.
Reference genes selection and validation in yam by real-time quantitative polymerase chain reaction
Intelligent connected adaptive signal control considering pedestrians based on the EXP-DDQN algorithm
With the increasing integration of Connected and Automated Vehicles (CAVs) and Human-Driven Vehicles (HDVs) in urban traffic systems, along with highly variable pedestrian crossing demands, traffic management faces unprecedented challenges. This study introduces an improved adaptive signal control approach using an enhanced dual-layer deep Q-network (EXP-DDQN), specifically tailored for intelligent connected environments. The proposed model incorporates a comprehensive state representation that integrates CAV-HDV car-following dynamics and pedestrian flow variability. Additionally, it features an improved MC Greedy exploration strategy and prioritized experience replay, enabling efficient learning and adaptability in highly dynamic traffic scenarios. These advancements allow the system to dynamically adjust green light durations, phase switches, and pedestrian phase activations, achieving a fine balance between efficiency, safety, and signal stability. Experimental evaluations underscore the model’s distinct advantages, including a 26.9% reduction in vehicle-pedestrian conflicts, a 31.83% decrease in queue lengths, a 32.52% reduction in delays compared to fixed-time strategies, and a 35.17% reduction in pedestrian crossing wait times. Furthermore, EXP-DDQN demonstrates significant improvements over traditional DQN and DDQN methods across these metrics. These results underscore the method’s distinct capability to address the complexities of mixed traffic scenarios, offering valuable insights for future urban traffic management systems.
Fungal diversity and network analysis in rhizosphere soil of Atractylodes macrocephala across different cultivation regions
First insight into oral health misinformation in Jordan; an analysis of web content and information-seeking behaviors
Background Integrating the internet into daily life has profoundly influenced the public's behavior of seeking health information. Free access to cyberspace created a fertile environment for the spread of oral health misinformation, which can have a detrimental impact on the public’s oral health. The prevalence of oral health misinformation in Jordan has not been investigated; therefore, it is crucial to understand how oral health misinformation originates and what contributes to its dissemination. Objectives This study aims to examine the prevalence of oral health misinformation published on web pages in Jordan and to offer insight into the public's information-seeking behaviors regarding oral health. Methods This study is a mixed methods infodemiological analysis of oral health misinformation. A systematic content analysis was executed on web pages published in the Arabic language in Jordan from 2019 to 2023. Results 704 web pages were retrieved, of which 320 relevant web pages were included in the content analysis. Among these, 193 web pages (60.3%) published oral health misinformation. Publishers without a professional background published 185 web pages (95.9%) of the total misinformation-expressing web pages. According to the dental field, the highest frequency of misinformation occurred in oral medicine-101 web pages (52.3%). The validity of published oral health information was significantly influenced by the publishers’ interest (P = 0.006), the articles’ main themes (P = 0.005), and the publishers' professional background (P < 0.001). Contextual analysis of oral health misinformation showed significant differences among dental fields (P = 0.019), with the most frequent occurences related to causes (18.8%), home remedies (15.7%), and treatment (15.5%). Geographical variations in interest in oral health searches were observed across Jordanian governorates (P < 0.001), and temporal trends in interest varied significantly across the five-year period(P = 0.019). Conclusion The findings of this study suggest a need for public health interventions to restrict the dissemination of oral health misinformation.
Comprehensive analysis of cuproptosis and m6A-Target gene signatures for diagnostic significance and immune microenvironment characterization in polycystic ovary syndrome
LRRT*: A robotic arm path planning algorithm based on an improved Levy flight strategy with effective region sampling RRT*
Aiming at the problems of blind sampling points and slow planning speed of path planning Rapidly-exploring Random Trees algorithm, an effective region sampling Levy Rapidly-exploring Random Trees algorithm (LRRT*) is proposed based on the improved Levy flight strategy. Divide the entire path planning process into two stages: quickly finding the initial path and optimizing the path. Goal oriented strategy is used to explore the path when finding the initial path quickly. The Levy flight strategy is used to regenerate nodes after obstacles are encountered to improve the quality of the expansion points. They can quickly plan a collision-free path. In the phase of optimizing the initial path using the effective region sampling method, each sampling is only sampled around the initial path. Meanwhile, node rejection strategy is introduced to reduce the number of collision detection and accelerate the convergence speed. In 2D and 3D environments, the LRRT* algorithm reduces the initial path planning time by 17.6% and 91.9% respectively compared to the RRT* algorithm, and shortens the average planning time by 12.3% and 65.5%, and the path smoothness is 3.4% and 79.4% shorter respectively. Applying the LRRT algorithm to a robotic arm allows for the planning of collision-free paths.
Temporal variation in discriminating Acacia species using optical and radar data
Atypical nucleus pulposus migration and calcification: A specific radiographic sign for lumbar rheumatoid spondylitis
Objective This study aimed to characterize a specific radiographic manifestation of lumbar rheumatoid spondylitis. Methods and materials The patients diagnosed with lumbar rheumatoid spondylitis who underwent posterior lumbar fusion surgery between 1/6/2019 and 30/4/2023 in the Department of Orthopedic Surgery in our hospital were retrospectively studied (RA group). The patients diagnosed with lumbar disc herniation with nucleus pulposus migration (cranial or caudal) were also collected for comparison (control group). The clinical data and radiographic manifestations were compared and analyzed. Results A total of 14 patients in the RA group and 36 patients in the control group were enrolled in the current study. In the RA group, seven patients had nucleus pulposus migration, among whom five patients exhibited distinct calcification (5/7, 71.4%). Of the 36 patients in the control group, 10 patients demonstrated migrated nucleus pulposus calcification (10/36, 27.8%). The migrated nucleus pulposus in the RA patients exhibited diffuse calcification, while the migrated nucleus pulposus in the control group exhibited spot-like or shell-like calcification. In addition, nucleus pulposus migration and calcification in the RA group were more likely to occur in older patients, affect the higher lumbar levels and combine with intervertebral space collapse compared with the control group. Conclusions Nucleus pulposus migration and calcification is specific in RA patients. It may be a characteristic radiographic sign to establish a lumbar rheumatoid spondylitis diagnosis. The potential mechanism is still unclear and need to be further explored.
Study on the evolution mechanism of damage to overburden fissures of repetitive mining of close coal seam groups by positive faults
Abstract For the safety of coal mines and the efficient use of gas resources, precise identification of gas transmission routes and enrichment zones is essential. The mining of the coal seam causes many fissures in the overburdened rock, and the evolution of mining fissures is intimately linked to gas transport channels.In order to study the characteristics of overburden fissures in the mining of close coal seam groups under the influence of faults, Shucheng Mining Area in Guizhou as a Research Context, Research using physical similarity simulation tests and numerical simulation methods. The results show that the location of the overburden rupture line interacts with the faults and together they construct a stable inverted triangular overburden stability structure; Given the existence of faults, the upper seam is subjected to greater stress during mining compared to the lower seam, which makes the fissure development process in the upper seam more active.Mining significantly affects the stress distribution of the overlying rock layer of the upper disc coal seam. The stress of the upper disc coal seam increases with the advancement of mining of the lower disc coal seam, and the stress suffered by the side with fewer fault gaps is greater. The results of the study can provide a theoretical basis for mining close coal seam groups in fault tectonic regions, and promote coal mines to achieve safe and efficient use of gas.
Use of spectral indices and photosynthetic parameters to evaluate the growth performance of hydroponic tomato at different salinity levels
Conventional methods for measuring plant physiological parameters are expensive and time-consuming, and this has promoted the use of optical and sensing techniques. Therefore, this study was conducted to investigate the effect of salinity on the performance of hydroponic tomato plants, based on optical and sensing techniques (i.e., spectral indices and photosynthetic parameters), as well as fruit yield. Four spectral vegetation indices-VIs (Moisture Stress Index “MSI”, Canopy Response Salinity Index “CRSI”, Normalized Difference Nitrogen Index “NDNI” and Green Leaf Index “GLI”) were calculated using spectral measurements collected from tomato plant leaves. Also, four photosynthetic parameters (Net photosynthetic rate “PN”, Water use efficiency “WUE”, Transpiration rate “Tr” and Total stomatal conductance “Gs”) were measured from the same tomato plant leaves. Measurements were recorded for tomato plants grown under three salinity levels (Salinity-1; 2.5 dS m-1), (Salinity-2; 4.0 dS m-1), and (Salinity-3; 6.5 dS m-1) at different growth stages represented by days after transplantation (DAT), as 35 DAT (vegetative stage), 50 DAT (1st cluster flower stage), 60 DAT (3rd cluster flower stage), 75 DAT (fruit development stage) and 85 DAT (fruit ripening stage). Results showed that tomato plants were significantly affected by the imposed salinity treatments. Where, tomato plants treated with salinity-1 was healthier compared to salinily-3 treated plants. This has been concluded from the results of the studied VIs, where the highest mean values of MSI (0.543) and CRSI (0.779) were associated with salinity-3, along with low values of GLI (0.353) and NDNI (0.220), indicating high salinity stress. However, the highest mean values of both NDNI (0.232) and GLI (0.386) were observed for salinity-1, indicated healthy condition. It also proven with the studied photosynthetic parameters, with the highest mean values of PN (9.8 µmol CO2 m-2 s-1),Gs (0.117 mmol H2O m-2 s-1) and Tr (2.236 mmol H2O m-2 s-1) were associated with salinity-1, While the lowest mean values of PN (8.3 µmol CO2 m-2 s-1), Gs (0.102 mmol H2O m-2 s-1) and Tr (1.902 mmol H2O m-2 s-1) were recorded for the plants treated with salinity-1. Moreover, the total tomato fruit yield also decreased significantly at salinity-3 compared to salinity-1.
The impact of perceived restorative destination environments on tourists’ willingness-To-Pay for environmental protection
Machine learning techniques for continuous genetic assignment of geographic origin of forest trees
Origin tracking is important to ensure use of the right seed source and trade with legally harvested timber. Additionally, it can help to reconstruct human-caused historical long-distance seed transfer and to spot mislabelling in forest field trials. So far, genetic assignment approaches were mostly discrete, assigning test samples to predefined groups. The main limitation of this approach is the justification of these discrete groups when genetic variation across the landscape is actually continuous. Here, we compare the accuracy of five continuous assignment methods. Specifically, we test a nearest neighbour method (NN), direct gaussian process regression (GPR-D) using the radial basis kernel function, grid based gaussian process regression (GPR-G) applying the Matérn kernel function, genomic prediction (GP) and deep learning (DL), using two genome-wide single nucleotide polymorphism (SNP) datasets of trees from across Europe. The first dataset comprises 30,000 SNPs from 865 European beech (Fagus sylvatica) trees, the second dataset consists of 381 SNPs from 1,883 pedunculate oak (Quercus robur) trees. The accuracy, as measured by the geographic distance between true and predicted locations, was highest for the GPR-G and DL methods with the beech dataset with a median distance of only 55 km and 76 km, respectively. For the oak data GPR-G and DL also performed best with median distances of 263 km and 278 km, respectively. The relative error (distance/max distance among tree pairs) was below 8% for 90% of all samples for the best method for both datasets. We detected 35 individuals and 10 groups as outliers in the beech data and 27 individuals and 18 groups in the oak data. These outliers may be caused by mislabelling or historical human-caused long distance seed transfer. We discuss the differences in performance of the approaches and highlight future applications and potential for further improvements.