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An exploratory study of associations between judgement bias, demographic and behavioural characteristics, and detection task performance in medical detection dogs
Medical detection dogs search for diseases from remote samples (biodetection) and assist patients with chronic conditions (medical alert assistance). There is scarce information on how dogs’ decision-making tendencies relate to task performance. This study explored the relationships between medical detection dog demographics, responses in a behavioural test battery, ‘optimistic’ or ‘pessimistic’ decisions in a judgement bias task, and their performance in detection tasks. A sample of 58 trainee and trained medical detection dogs were studied in a Go/NoGo spatial judgement bias test. For trainee dogs (n = 39), training outcome (pass/fail) and trainer ratings of behavioural traits; yielding a composite score of ability in detection tasks, were used as markers of task performance. For trained biodetection dogs (n = 27), scent sensitivity and specificity scores derived during training and testing trials were used. Older dogs (p < 0.001), those showing higher ‘Confidence’ (p = 0.009), ‘Food orientation’ (p = 0.014) and ‘Playfulness’ (p = 0.005) in the test battery, and those who made more ‘optimistic’ decisions in the judgement bias task (p = 0.002), had higher detection task ability scores. For trained dogs, latency to approach ambiguous stimuli was positively correlated with scent specificity levels (n = 25, p = 0.021), suggesting that more ‘pessimistic’ dogs tended to be more specific. Our findings suggest relationships between behaviour in judgement bias tests and other learning and discrimination tasks, which may reflect underlying individual or personality differences in affective and/or cognitive processes that influence dogs’ style of searching and performance ability in medical detection tasks. Future research is needed to explore these associations further and investigate the value of judgement bias tasks in predicting later search performance in medical and other types of search dogs.
Methanol to Olefins (MTO): Understanding and Regulating Dynamic Complex Catalysis
Trends and disparities in ischemic stroke mortality and location of death in the United States: A comprehensive analysis from 1999–2020
Background Stroke remains the fifth leading cause of mortality in the United States, with significant geographical and racial disparities in outcomes. Understanding trends in location of death for ischemic stroke patients is crucial for improving end-of-life care and addressing healthcare inequities. Methods & Findings This retrospective study used Centers for Disease Control and Prevention’s Wide-ranging Online Data for Epidemiologic Research (CDC WONDER) data to examine ischemic stroke mortality, stratified by urbanization level and race. Age-adjusted mortality rates were calculated using the 2000 US standard population. Age-adjusted ischemic stroke mortality rates increased across all urbanization levels since 2009, with the most pronounced rises in non-metropolitan areas. An increasing proportion of deaths occurred at home, shifting from inpatient medical facilities. Significant disparities were observed in access to specialized end-of-life stroke care, particularly for racial minorities and rural residents. Black/African American individuals and those in rural settings were more likely to die in less specialized environments due to healthcare access barriers. Conclusions The findings highlight a critical shift in the patterns of mortality and end-of-life care preferences among ischemic stroke patients over the past two decades. These findings highlight significant shifts in the patterns of mortality and location of death among ischemic stroke patients over the past two decades, with notable differences across urbanization levels and racial groups. The increasing proportion of home deaths and persistent disparities in location of death suggest a need for further research to understand the underlying factors driving these trends and their implications for end-of-life care quality and access.
De Novo Design of Proteins for Autocatalytic Isopeptide Bond Formation
Study protocol for the EYEdentify project: An examination of gaze behaviour in autistic adults using a virtual reality-based paradigm
Introduction Autism Spectrum Condition (ASC) is characterised by difficulties in social communication and interaction, which may pose significant challenges to daily functioning throughout life. While current diagnostic methods for ASC often rely on measures based on subjective reports, there is a growing need for objective, quantifiable measures to support current clinical assessment of ASC. Eye-tracking technology records eye and gaze movements in real time and provides a direct and objective method for assessing social attention. Integrating eye-tracking within virtual reality (VR) environments presents a novel approach for capturing gaze behaviour in dynamic, ecologically valid social scenarios. This study aims to investigate whether VR-based eye information can reveal group differences in gaze behaviour between autistic adults and neurotypical controls in simulated social interactions. Methods This case-control study will include 140 adults diagnosed with ASC and 50 neurotypical controls, matched by age and gender. Participants will engage in six VR-based social scenarios, which vary in social complexity and the presence of non-social distractors. Eye information will be measured using eye-tracking technology integrated into a head-mounted display. Gaze behaviour will be analysed through fixation-based metrics on parameters including number of fixations, mean fixation time, and dwell time, on predetermined Areas of Interest. Analysis Statistical analyses will assess between-group differences in gaze behaviour as well as correlations between gaze metrics and clinical measures of social functioning, social cognition and symptom severity. Discussion This study utilises VR-based eye-tracking to investigate novel paradigms for assessing gaze behaviour in ASC in immersive, interactive environments and aims to advance the current understanding of visual social attention in ASC. Positive outcomes from this study may support further research into VR-based eye-tracking to supplement existing clinical assessment methods.
Rational Design for Monodisperse Gallium Nanoparticles by In Situ Monitoring with Small-Angle X-ray Scattering
Maternal counseling for preterm deliveries, assessing an effective method of counseling: A randomized trial
Objective To assess the acquired knowledge of mothers about prematurity outcomes when employing two distinct approaches to prenatal counseling among those experiencing preterm labor. A secondary aim was to assess the anxiety levels of trial subjects after the antenatal consultation. Study design Ninety-two pregnant women admitted between 23 and 34 weeks of gestation with threatened preterm labor were randomized in to two groups to receive either verbal counseling (Group 1) or verbal counseling supplemented with written and pictorial information (Group 2). Mothers completed a validated anxiety inventory and demographic questionnaire before counseling and an anxiety inventory and knowledge questionnaire after the counseling. There was a minimum two-hour gap between the counseling and completion of the questionnaire. Results Of the 92 women who completed the knowledge questionnaire, 45 (49%) were in Group 1 and 47(51%) in Group 2. Forty-three participants in group 1 and 45 participants in group 2 had their pre and post anxiety scores analyzed. There was a trend of increased recall rates in group 2 for short-term problems, long-term problems, intervention, and incidence rates, but it did not reach statistically significant level. There was an overall decrease in State Trait Anxiety Inventory (STAI) scores of participants after counseling (p = 0.002) but no statistically difference in change of STAI scores between the two groups (p = 0.981). Conclusion Based on the results of our study, regardless of the method of counseling there was no difference in knowledge assessment and comprehension of information. However, there was an overall decrease in anxiety level of mothers following any type of counseling. Trial registration ClinicalTrials.gov NCT02707237
In vitro and in silico pharmaco-nutritional assessments of some lesser-known Nigerian nuts: Persea americana, Tetracarpidium conophorum, and Terminalia catappa
Together with their nutritional qualities, the biosafety, antidiabetic, antioxidant, and anti-inflammatory effects of Tetracarpidium conophorum nuts, Persea americana seeds, and Terminalia cattapa kernels were evaluated in vitro and in silico. RBC membrane stabilisation for anti-inflammatory characteristics, antioxidant activities by ABTS, DPPH, H2O2, and nitric oxide scavenging assays, and α-glucosidase and α-amylase inhibitory assays conducted in vitro were used to evaluate the anti-diabetic activity. With an IC50 value of 208 μg/mL, P. americana showed the maximum amount of inhibition, according to the results, while T. catappa showed a somewhat lower degree of inhibition at 236 μg/mL. P. americana exhibited the highest degree of α-amylase inhibition, with an IC50 value of 312 µg/mL. T. catappa showed the strongest DPPH radical scavenging activity, while T. conophorum showed the highest ABTS radical scavenging activity. T. catappa showed the strongest effectiveness in neutralising hydrogen peroxide. In tests using human red blood cells, T. catappa showed the strongest inhibition of RBC hemolysis. While P. americana showed higher concentrations of copper, manganese, potassium, and calcium, T. catappa showed higher magnesium concentrations. T. catappa had considerably higher levels of ash, proteins, lipids, and carbohydrates than T. conophorum, which had the highest quantity of crude fibre, according to proximate analysis. Molecular docking experiments have revealed that plant extracts from P. americana, T. conophorum, and T. catappa have substantial binding affinities towards α-glucosidase and amylase. Pseudococaine, M-(1-methylbutyl) phenylmethylcarbamate, o-xylene, and 1-deoxynojirimycin were the four compounds that showed binding affinities that were higher than those of acarbose. Acarbose and nitrate were not as compatible with docking scores as compared to the compounds dimethyl phthalate, pseudococaine, M-(1-Methylbutyl)phenyl methylcarbamate, 2-chloro-3-oxohexanedioic acid, and methyl 2-chloro-5-nitrobenzoate. These results suggest that these plant extracts hold great potential for the creation of therapeutic medications that specifically target oxidative stress-related diseases like diabetes.
Photolytic Access to Oxaspirodecanes and Chromenes from Vinyldiazo Ester Cycloaddition with <i>p</i> -Quinones: A Vinylcarbene Is Not Involved
Optimizing CNN for pavement distress detection via edge-enhanced multi-scale feature fusion
Traditional crack detection methods initially relied on manual observation, followed by instrument-assisted techniques. Today, road surface inspection leverages deep learning to achieve automated crack detection. However, in the domain of deep learning-based road surface damage classification, the heterogeneous and complex nature of road environments introduces significant background noise and unstructured features. These factors often undermine the robustness and generalization capability of models, thereby adversely affecting classification accuracy. To address this challenge, this research incorporates edge priors by integrating traditional edge detection techniques with deep convolutional neural networks (DCNNs). This paper proposes an innovative mechanism called Edge-Enhanced Multi-Scale Feature Fusion (EE-MSFF), which enhances edge information through multi-scale feature extraction, thereby mitigating the impact of complex backgrounds and improving the model’s focus on crack regions. Specifically, the proposed mechanism leverages classical edge detection operators such as Sobel, Prewitt, and Laplacian to perform multi-scale edge information extraction during the feature extraction phase of the model. This process captures both local edge features and global structural information in crack regions, thereby enhancing the model’s resistance to interference from complex backgrounds. By employing multi-scale receptive fields, the EE-MSFF mechanism facilitates hierarchical fusion of feature maps, guiding the model to learn edge information that is correlated with crack regions. This effectively strengthens the model’s ability to perceive damaged pavement features in complex environments, improving classification accuracy and stability. In this study, the model underwent systematic training and validation on both the complex-background dataset RDD2020 and the simple-background dataset Concrete_Data_Week3. Experimental results demonstrate that the proposed model achieved a classification accuracy of 88.68% on the RDD2020 dataset and 99.5% on the Concrete_Data_Week3 dataset, where background interference is minimal. Furthermore, ablation studies were conducted to analyze the independent contributions of each module, highlighting the performance improvements associated with the integration of multi-scale edge features.
Fluorinated Ribonucleocarbohydrate Nanoparticles Allow Ultraefficient mRNA Delivery and Protein Expression in Tumor-Associated Myeloid Cells
A knowledge tracing approach with dual graph convolutional networks and positive/negative feature enhancement network
Knowledge tracing models predict students’ mastery of specific knowledge points by analyzing their historical learning performance. However, existing methods struggle with handling a large number of skills, data sparsity, learning differences, and complex skill correlations. To address these issues, we propose a knowledge tracing method based on dual graph convolutional networks and positive/negative feature enhancement. We construct dual graph structures with students and skills as nodes, respectively. The dual graph convolutional networks independently process the student and skill graphs, effectively resolving data sparsity and skill correlation challenges. By integrating positive/negative feature enhancement and spectral embedding clustering optimization modules, the model efficiently combines student and skill features, overcoming variations in learning performance. Experimental results on public datasets demonstrate that our proposed method outperforms existing approaches, showcasing significant advantages in handling complex learning data. This method provides new directions for educational data mining and personalized learning through innovative graph learning models and feature enhancement techniques.
Adding a Twist to Lateral Flow Immunoassays: A Direct Replacement of Antibodies with Helical Affibodies, from Selection to Application
Retraction: Evaluation of the winter landscape of the plant community of urban park green spaces based on the scenic beauty esitimation method in Yangzhou, China
Retraction: Application and effect evaluation of nursing quality target management in free flap transplantation for hand injury
Reorganizing the Pt Surface Water Structure for Highly Efficient Alkaline Hydrogen Oxidation Reaction
Collaborative management measures of subsurface drainage and bio-organic fertilizer application for coastal sunflower (Helianthus annuus L.) based on TOPSIS entropy weight method
Soil salinization has become a global resource and ecological issue, and sunflower planting has had a good improvement effect on saline-alkali land. The study explores the collaborative management measures of subsurface drainage and bio-organic fertilization with high-yield, high-quality, and environmentally friendly sunflowers through experiments. We designed three subsurface pipe spacings (10, 15, and 20 m) and six methods of combined application of organic fertilizer (organic fertilizer nitrogen 100%, organic fertilizer nitrogen 75% + inorganic fertilizer nitrogen 25%, organic fertilizer nitrogen and inorganic fertilizer nitrogen each 50%, organic fertilizer nitrogen 25% + inorganic fertilizer nitrogen 75%, 100% inorganic fertilizer nitrogen, and no fertilizer treatment). Nine evaluation indexes were selected for the four aspects of yield increase, quality improvement, soil improvement, and emission reduction, and an index system was constructed. In the evaluation model, the TOPSIS entropy weight method was calculated to compare and select the most suitable growth method of subsurface drainage and bio-organic fertilizer application for sunflower growth in saline-alkali land. The results showed that the best treatment was 75% organic fertilizer nitrogen + 25% inorganic fertilizer nitrogen, and the best spacing for the subsurface drainage was 10 m. Under this treatment, the relative application progress reached 0.574, and the yield, oleic acid content, soil organic matter content, soil salt reduction efficiency, and N2O emissions were 2.93 t/ha, 21.73%, 2.21%, 37.62%, and 9.86 kg/ha, respectively.
Can you hear me? Playback experiment highlights detection range differences between commonly used PAM devices: C-POD, F-POD and SoundTrap
Passive acoustic monitoring (PAM) is a valuable tool for monitoring acoustically active small cetaceans such as the harbour porpoise (Phocoena phocoena), with a range of devices commonly used across studies. However, to ensure comparability of findings, there is a need to compare the ability of devices to detect acoustic signals. Using a playback approach, we determined the detection probability and effective detection radius/area (EDR/EDA) for co-deployed C-POD (Cetacean POrpoise Detectors), F-POD (Full waveform capture POD) and SoundTrap acoustic monitoring devices. We conducted playbacks of harbour porpoise recordings across two transects at a range of distances from moored devices, while accounting for a range of variables likely to influence the detection probability of playbacks. Distance from the devices influenced the detection probability across all devices, and a significant difference between transects was also found for the C-POD, possibly due to different ambient noise conditions. The maximum detection distance of the playbacks for the SoundTrap and the F-POD was between 400 - 500m, and EDR was estimated at 297m (EDA 0.276 km2) and 241m (EDA 0.181 km2), respectively. The maximum detection distance for the C-POD was lower, at 300 - 400m, and an EDR of 220m (EDA 0.153 km2). A lower EDR was calculated for harbour porpoise buzzes compared to clicks across devices, due to lower source level of buzzes, suggesting that time spent foraging may be underestimated in PAM studies. The results highlight how detection ranges may differ across commonly used PAM devices, affecting comparability of detection rates across studies. EDR/EDA is an important prerequisite for PAM-derived density and abundance estimates. As such, understanding how devices differ is essential for comparing studies and appropriate planning of long-term acoustic monitoring projects, particularly where estimates of abundance are a key goal.