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Development and validation of a patient knowledge questionnaire for rheumatoid arthritis (PKQ-RA-11) in Danish and German
Get PDFObjectivePatient education is a cornerstone of rheumatology care, enabling patients to effectively and safely manage their condition. Standardized patient knowledge assessments are essential for benchmarking care quality and tailoring education to individual needs. This study aimed to develop and validate Danish and German versions of a patient knowledge questionnaire (PKQ) for rheumatoid arthritis (RA).MethodDanish and German adaptations from the English version involved a forward-and-backward translation process. Face validity was assessed with patients with RA in Denmark and Germany. Subsequently, the generated PKQ-RA-11 versions were tested in Danish and German RA patients.ResultsThe face-validity assessment included 20 patients (10 Danish, 10 German). Adjustments in the Danish version included rephrasing options and aligning with digital patient education content. The German version followed the refined Danish version with necessary cultural adjustments. PKQ-RA-11 comprises 11 multiple-choice questions with a scoring system to minimize guessing. The final PKQ-RA-11 was completed by 175 Danish and 174 German patients; mean completion time was 7.5 and 7.4 minutes, respectively. Mean ± sd baseline PKQ-RA-11 scores were 7.9 ± 1.6 for Danish and 6.2 ± 2.5 for German participants. Longitudinal data from Denmark indicated an increase in knowledge scores following patient education, shown by a mean score of 8.6 ± 1.5, demonstrating the tool’s responsiveness to changes in patient understanding of RA.ConclusionPKQ-RA-11 is a standardized tool for assessing disease-related knowledge in individuals with RA. It can be used to provide objective and transparent measures of patient understanding in educational programmes, clinical practice, or research
Exploring filter placement in convolutional layer topologies based on ResNet for image classification
No full textIn this paper we investigate the impact that altering the convolutional layer topology has upon the performance of computer vision tasks using a variety of widely used benchmark image datasets. Despite the widespread convention in convolutional neural networks, of incrementally doubling the filter count at each layer, there is little evidence substantiating the superiority of this method over other possible topologies. Our research reveals that a contrarian strategy—reducing the filters by half—can achieve performance on par with, if not superior to, this usual approach. We have extended our investigation to include a variety of novel topological structures. These empirical results challenge the prevailing assumption, that the sequential doubling of number of filters in the network configuration will always yield the best results with all datasets. Our findings advocate for a more nuanced approach to neural network design, incorporating a flexible approach to filter topologies into workflows. This could potentially have a significant impact upon the architectural standards in deep learning for visual recognition tasks
MIBiG 4.0: Advancing biosynthetic gene cluster curation through global collaboration
Get PDFSpecialized or secondary metabolites are small molecules of biological origin, often showing potent biological activities with applications in agriculture, engineering and medicine. Usually, the biosynthesis of these natural products is governed by sets of co-regulated and physically clustered genes known as biosynthetic gene clusters (BGCs). To share information about BGCs in a standardized and machine-readable way, the Minimum Information about a Biosynthetic Gene cluster (MIBiG) data standard and repository was initiated in 2015. Since its conception, MIBiG has been regularly updated to expand data coverage and remain up to date with innovations in natural product research. Here, we describe MIBiG version 4.0, an extensive update to the data repository and the underlying data standard. In a massive community annotation effort, 267 contributors performed 8304 edits, creating 557 new entries and modifying 590 existing entries, resulting in a new total of 3059 curated entries in MIBiG. Particular attention was paid to ensuring high data quality, with automated data validation using a newly developed custom submission portal prototype, paired with a novel peer-reviewing model. MIBiG 4.0 also takes steps towards a rolling release model and a broader involvement of the scientific community. MIBiG 4.0 is accessible online at https://mibig.secondarymetabolites.org/
A vision-guided deep learning framework for dexterous robotic grasping using Gaussian processes and transformers
Get PDFRobotic manipulation of objects with diverse shapes, sizes, and properties, especially deformable ones, remains a significant challenge in automation, necessitating human-like dexterity through the integration of perception, learning, and control. This study enhances a previous framework combining YOLOv8 for object detection and LSTM networks for adaptive grasping by introducing Gaussian Processes (GPs) for robust grasp predictions and Transformer models for efficient multi-modal sensory data integration. A Random Forest classifier also selects optimal grasp configurations based on object-specific features like geometry and stability. The proposed grasping framework achieved a 95.6% grasp success rate using Transformer-based force modulation, surpassing LSTM (91.3%) and GP (91.3%) models. Evaluation of a diverse dataset showed significant improvements in grasp force modulation, adaptability, and robustness for two- and three-finger grasps. However, limitations were observed in five-finger grasps for certain objects, and some classification failures occurred in the vision system. Overall, this combination of vision-based detection and advanced learning techniques offers a scalable solution for flexible robotic manipulation
Evaluating the accessibility and inclusivity of voluntary carbon markets for rural enterprises in the UK
No full textThe effect of deferring feedback on rule-based and information-integration category learning
Get PDFPrevious work has shown that deferring feedback significantly impairs two-dimensional information-integration category learning, often thought to recruit an implicit learning system, but leaves intact unidimensional rule-based learning, commonly assumed to engage an explicit system. These results were taken to support the influential COmpetition between Verbal and Implicit Systems (COVIS) dual-process theory. This conclusion has subsequently been challenged by the finding that this dissociation disappears when the number of relevant dimensions is matched between tasks. However, as well as replacing a unidimensional rule-based task with a two-dimensional conjunction task, the a different set of stimuli were used making it unclear which of these alterations was driving the difference in results. The current paper directly examined how both category structure and stimulus type influence the deferred feedback effect. We replicated both the original sets of results but found that deferred feedback also impaired information-integration learning to a greater extent than a conjunction task when the original stimuli were used. These results suggest that the impact of deferred feedback on category learning is more complicated than previously documented, as our findings cannot be easily explained by either COVIS or single-system accounts. Furthermore, our results highlight the critical role that the choice of stimuli has on categorization behavior and emphasize the importance of testing findings across different stimuli to ensure their robustness
The psychosocial outcomes following cosmetic surgery are largely unknown: A systematic review
No full textIntroduction: Cosmetic surgery is marketed and widely assumed to have positive psychosocial outcomes, particularly in relation to body image, self-esteem and mental health. The present systematic review aimed to conduct a timely, up-to-date assessment of the existing academic empirical literature, applying stringent inclusion criteria to summarise only the highest quality of evidence in the field. Methods: Databases (EBSCO; Cochrane Library; Scopus; ProQuest) were systematically searched. Screening was completed by two independent reviewers. Prospective studies that utilised a control cohort to examine at least one psychosocial outcome using a validated measure following cosmetic surgery were included. Risk of was double assessed using the Effective Public Health Practice Project Quality Assessment Tool. Results: Seventeen studies met inclusion criteria. Heterogeneity across research designs, control groups, measures, and statistical analyses was evident. Overall, the quality of studies was poor. Results suggest short-term improvements in some psychosocial outcomes following cosmetic surgery (particularly in relation to body-area-specific satisfaction, self-esteem, sexual well-being and physical well-being), with little and mixed evidence for outcomes such as mental health, holistic body image, quality of life and social functioning. Very few studies explored psychosocial outcomes beyond 6-months post-surgery. Conclusion: Current evidence regarding psychosocial outcomes following cosmetic surgery is weak. There is an urgent need to conduct high-quality research, which will require collaboration between surgeons, research psychologists and methodologists. Recommendations include pre-registration, larger sample sizes, longer-term follow-up and appropriate control group recruitment. Given the increasing uptake in cosmetic surgery globally, this should be a priority for the field
Effects of Traditional Asian Diet on dietary fibre requirement, gut microbiome composition, and faecal and urine metabolomes in healthy Asian women: a pilot study
No full textThe Traditional Asian Diet (TAD) is characterised by high dietary fibre and functional foods. This study investigated TAD’s effects on meeting fibre requirements, gut microbiome, and faecal and urine metabolomes. A four-week randomised controlled trial was conducted among healthy Asian women allocated into the TAD group (n = 11) following a newly developed TAD program and the control group (n = 11). Assessments included dietary intake, gut health (symptoms, faecal form, frequency), serum fatty acids binding protein-2 (FABP-2) levels, faecal microbiome via 16s rRNA sequencing, and faecal and urine metabolites which were analysed using gas chromatography-mass spectrometry (GC-MS) and nuclear magnetic resonance (NMR), respectively. The TAD group showed significant increases in dietary fibre (P<0.001), reduced fat (P<0.05), and improved faecal form (P=0.009) compared to the control group. The TAD group was enriched with Parabacteroides merdae, while Bacteroides uniformis was more abundant in the control group. Individuals with baseline Prevotella copri showed its enrichment following TAD and higher butyrate levels, unlike the control group. The TAD led to lower urine levels of creatinine, dimethylamine, and phenethylamine compared to the control diet. In conclusion, the TAD program has proven beneficial effects in achieving dietary fibre, enriching the beneficial microbiota and metabolites, reducing harmful metabolites, and improving faecal form compared to a control diet
Novel thermal and non-thermal technologies towards sustainability and microbiological food safety and quality
Get PDFThis Special Issue, “Novel Thermal and Non-thermal Technologies towards Sustainability and Microbiological Food Safety and Quality”, was launched to address these challenges and to showcase advances in both fundamental research and applied studies. The contributions collectively explore how emerging processing strategies can support safe, high-quality, and sustainable food production, offering insights into microbial inactivation, product functionality, and environmental impact
A deep learning framework for detecting cross-generational facial markers associated with stress in pigs
Get PDFMaternal stress during gestation can alter offspring physiology, behaviour, and immune function. In pigs, such ‘prenatal stress’ is known to increase stress sensitivity, but the potential to automatically detect such sensitivity has remained unexplored. Automatic detection of facial expression has successfully identified differences in pigs dependent on their stress status. This study progresses this work by demonstrating that, for the first time, using a deep learning framework applied to facial analysis, stress-linked phenotypes can be learned from one generation and detected in the next. Using a dataset of over 7000 facial images from 18 gestating sows and 53 of their daughters, we trained and evaluated five state-of-the-art deep learning architectures across six independent daughter cohorts. Attention-based models significantly outperformed CNN-based models, with the Vision Transformer (ViT) model achieving a mean accuracy of 0.78 and an average F1-score of 0.76. Grad-CAM visualisations showed that the ViT consistently attended to biologically relevant facial regions, such as the eyes and snout, whereas CNNs often focused on diffuse or non-informative areas, resulting in reduced low-stress recall and greater batch sensitivity. Models trained on maternal facial images successfully predicted stress responsiveness in daughters from unrelated lineages, indicating that the model captured generalisable facial cues of stress rather than familial resemblance. This approach supports previous work showing that machine vision can detect putatively stress-related alterations to facial expression in pigs. Future application of this approach could offer a scalable, non-invasive tool for early detection of stress in livestock production systems, opening new avenues for welfare-oriented precision livestock management and informed breeding strategies aimed at improving stress resilience