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Projection and backprojection are fundamental operators in tomographic image reconstruction. Existing approaches-namely pixel-driven (PD), ray-driven (RD), and distance-driven (DD)-are subject to well-known trade-offs among interpolation artifacts, geometric flexibility, and computational cost. This study aims to introduce a unified framework that systematically models and resolves the intrinsic limitations of current projection topologies. Within this framework, a novel topology called vector-driven (VD) is proposed to improve geometric flexibility without sacrificing computational efficiency. A three-level hierarchical formulation-comprising topology, algorithm, and implementation-is also developed to isolate the structural origins of artifacts and performance bottlenecks in projection operators. The proposed VD projection and backprojection method represents all tomographic system components as vectors in 3D space and performs non-orthogonal projections that preserve geometric symmetry between forward and backward operations. Experimental validation was conducted using multiple geometric configurations, and quantitative image quality metrics (SSIM, RMSE, PSNR, NCC, UIQI) were compared against PD, RD, and DD baselines. VD produced artifact-free output across all tested geometries and reached near-perfect image-quality scores matching the leading RD-PD baseline combination. VD attained the shortest wall-clock time in both directions, running about 37% faster than an optimised PD algorithm in forward projection and reaching near parity in backprojection, while remaining 31%-57% faster than De Man's DD and 62%-82% faster than Siddon's RD in both directions. This advantage reflects VD's balanced profile of low arithmetic workload of 13.7-24.0 GFLOP per call (giga floating-point operations), high pipeline efficiency with 3.6-4.4 instructions per cycle (IPC), and low DRAM traffic of about 5 GB read per call. VD unifies forward and backward projection within a single vector-based model and, in the configurations tested, performs competitively with established projection topologies in both accuracy and computational cost.

Biologic therapies targeting interleukin-17 (IL-17) and interleukin-23 (IL-23) pathways have transformed psoriasis management, with IL-17 inhibitors widely regarded as achieving faster clearance. However, the clinical significance of differences in clearance times remains unclear. To compare time to achieve a 75% reduction in the Psoriasis Area and Severity Index (PASI75) between IL-17 and IL-23 inhibitors using data from head-to-head clinical trials. A PubMed search identified trials reporting PASI75 response times for IL-17 and IL-23 inhibitors. Graphs from 12 studies were analyzed using Engauge Digitizer Software to estimate the time until half of participants reached PASI75. IL-17 inhibitors achieved PASI75 faster than IL-23 inhibitors, with average response times of 4.5 weeks versus 7 weeks, respectively. The fastest IL-17 agent (bimekizumab) reached PASI75 in 2.5 weeks, while the fastest IL-23 agent (risankizumab) required 5.5 weeks. Differences between classes were smaller than anticipated, particularly for newer IL-23 inhibitors targeting the p19 subunit. IL-17 inhibitors generally achieve faster psoriasis clearance than IL-23 inhibitors, but the clinical relevance of this difference is limited. Safety and long-term efficacy profiles should guide treatment selection, highlighting the importance of personalized patient care.

Kissing bugs (Rhodnius prolixus) are vectors of Trypanosoma cruzi, posing risks to military personnel and working dogs in endemic regions. This study quantified time-dependent knockdown of R. prolixus on commercial 300-series (80% blockage) netting treated with dinotefuran (neonicotinoid) or esfenvalerate (pyrethroid) under controlled laboratory conditions. Laboratory-reared adult R. prolixus were exposed to 8.2 × 8.2 cm net clippings in modified WHO cone bioassays. Four insects per replicate were exposed for 3, 6, 9, 12, or 15 minutes (five replicates per exposure × treatment; N = 300, including untreated controls). Knockdown was assessed at 1-hour post-exposure. Data were analyzed with two-way ANOVA with post-hoc pairwise comparisons (emmeans), three-parameter log-logistic models to estimate KD₅₀, and probit regression to calculate time-to-response metrics (KT₅₀/KT₉₀) with confidence intervals and model diagnostics. Knockdown increased with exposure time for both insecticides, with negligible effect in controls. Dinotefuran acted significantly faster, achieving ≥85% knockdown by 6 minutes, whereas esfenvalerate required approximately 12 minutes to reach similar levels; both reached 100% knockdown by 15 minutes. ANOVA indicated significant main effects of insecticide and exposure time, as well as a significant interaction (P ≤ 0.018), reflecting differing temporal dynamics. Pairwise comparisons identified a significant advantage for dinotefuran at 9 minutes (P = 0.001). Log-logistic modeling yielded KD₅₀ ≈ 4.0 min for dinotefuran versus ≈ 211 min for esfenvalerate, although estimates were imprecise. Probit regression corroborated the faster action of dinotefuran (KT₅₀ ≈ 0.18 min; KT₉₀ ≈ 6.1 min) compared with esfenvalerate (KT₅₀ ≈ 1.9 min; KT₉₀ ≈ 12.9 min), with good model fit. Although both dinotefuran and esfenvalerate treated nets have a knockdown effect on R. prolixus, Dinotefuran-treated netting produced substantially faster knockdown of R. prolixus than esfenvalerate-treated netting, consistent with their distinct neurophysiological modes of action. These findings support the use of neonicotinoid-treated netting as a rapid-acting barrier in military operational environments where brief insect contact occurs or pyrethroid efficacy may be compromised.

Escherichia coli is a highly diverse bacterial species that includes avian pathogenic E. coli (APEC), one of the most prevalent causative agents of disease in poultry worldwide. Rapid and accurate discrimination of E. coli strains is essential for outbreak management, antimicrobial resistance surveillance, and vaccine development. In this study, we compared the performance of Fourier Transform Infrared (FTIR) spectroscopy using the IR Biotyper system with Nanopore and Illumina whole-genome sequencing (WGS) for typing 200 E. coli isolates, originating from four poultry rearing farms in the Netherlands. From each farm, we sampled 10 one-day-old meat type rearing chicks, and from every chick, we isolated 5 E. coli strains. FTIR clustering showed strong concordance with WGS-based classifications, particularly serotyping and core-genome similarity determined by PopPUNK analysis (Adjusted Rand Index 0.75-0.92). While Nanopore and Illumina sequencing provided the highest genetic resolution, FTIR offered a faster (max 6 vs 12-28 days for 200 isolates) and more cost-effective alternative for assessing clonality. Across all methods, multiple strains were detected per farm, whereas most birds carried a single dominant E. coli strain. Our findings demonstrate that FTIR provides a reliable and scalable phenotypic method for rapid strain discrimination in E. coli, complementing WGS in diagnostic, surveillance, and epidemiological settings where speed and throughput are critical. Escherichia coli is a major pathogen in poultry and a potential zoonotic risk for humans. Rapid and accurate discrimination of avian pathogenic E. coli (APEC) strains is critical for outbreak management, antimicrobial resistance surveillance, and the design of effective autogenous vaccines. In this study, we compared Fourier Transform Infrared (FTIR) spectroscopy with Nanopore and Illumina whole-genome sequencing for strain typing of E. coli isolates originating from poultry. The results show that FTIR provides comparable clustering accuracy to genomic approaches at a fraction of the time and costs. This work demonstrates that FTIR can serve as a practical, high-throughput alternative for routine monitoring of E. coli in veterinary diagnostics and food safety of poultry meat, enabling faster decision-making and more targeted interventions across the poultry production chain.

Adolescence is a period notable for increased risk-taking behaviors, including substance use (SU). Longitudinal work has linked behavioral disinhibition, particularly impulsive dispositions and externalizing tendencies with SU, but the underlying neurobiological manifestations remain less well-defined. This study examined whether individual differences in reward-related striatal activity and impulsivity predicted mental health (externalizing symptoms) and SU outcomes (cannabis, nicotine, alcohol) over a year later. Adolescents (n = 140; M age = 14.9 years) from a larger longitudinal cohort completed a Monetary Incentive Delay (MID) fMRI task at baseline along with a measure of self-reported impulsivity. At follow-up, they reported externalizing symptoms and days of cannabis, e-cigarette, and alcohol use. Task behavior [response times [RTs], hit rates [HRs]] and striatal responses to anticipatory gain cues were extracted. Serial mediation models tested whether impulsivity and externalizing mediated an association between striatal activity and subsequent SU. Behaviorally, gain cues elicited faster target-related RTs and higher HRs (vs. loss or neutral trials), and performance scaled with incentive magnitude. Gain (vs. neutral) cues elicited greater bilateral caudate activity where more left caudate activity correlated with faster RTs and lower impulsivity. Serial mediation revealed that less left striatal activity during reward anticipation linked with higher impulsivity, which predicted more subsequent externalizing symptoms that, in turn, linked with more cannabis [indirect effect = -0.01, 95%CI (-0.04, -0.001)] and e-cigarette use days [indirect effect = -0.02, 95% CI (-0.05, -0.004)]. No indirect or direct effects emerged for alcohol use. These findings suggest blunted striatal activity may reflect reduced motivational drive for lower-intensity rewards (e.g., fictitious MID monetary gains), which contribute to SU vulnerability via heightened behavioral disinhibition in pursuit of higher-intensity stimulation. Intervention strategies that upregulate everyday reward value and strengthen self-regulation may offer utility in reducing teen SU.

Laparoscopic cholecystectomy (LAP-C) is the definitive treatment for symptomatic cholelithiasis, yet bile duct injury (BDI), primarily caused by anatomical misidentification, remains a formidable risk. The Critical View of Safety (CVS) achievement will prevent BDI, but its consistent attainment is technically demanding in inflamed or distorted anatomy. The body-first dissection technique has been proposed as an alternative approach to improve CVS achievement, though high-quality prospective evidence remains limited. This randomized controlled trial (RCT) was designed to systematically compare the two methods. In this single-center, single-blinded RCT, 88 patients undergoing elective LAP-C were enrolled, after exclusion 80 patients were randomized into two groups: Group A (body-first technique) and Group B (conventional technique). The primary endpoints were the rate and quality of CVS achievement, and the duration of surgery. Secondary outcomes included intraoperative complications, recovery of gastrointestinal function, postoperative pain and nausea, inflammatory marker C-reactive protein (CRP) levels, and patient satisfaction assessed by the RAND-36 questionnaire at discharge, one month, and three months, and a p-value < 0.05 was considered statistically significant. The body-first approach demonstrated a higher rate of satisfactory CVS than the conventional approach (92.5% vs. 75.0%); however, the difference did not reach statistical significance on two-sided Fisher's exact testing (p=0.066). On multivariable logistic regression, the conventional technique was independently associated with significantly lower odds of CVS achievement (adjusted odds ratio (OR) 0.10, 95% CI 0.02-0.66, p=0.017). Operative duration was comparable between groups (76.3 vs. 84.8 minutes, p=0.438). After multivariable adjustment, surgical technique was not independently associated with operative duration (&#x3b2; = 6.54 minutes, SE=6.48; p=0.316), whereas increasing operative difficulty (Modified Nassar grade) was the only independent predictor of longer operative duration (&#x3b2;=13.32 minutes per one-grade increase, 95% CI 7.11-19.53; p < 0.001). The body-first group had significantly lower postoperative pain (OR: 5.74, 95% CI: 2.15-15.3, p=0.001), faster gastrointestinal recovery (OR: 3.12, 95% CI: 1.25-7.75, p=0.014). The body-first technique confers a higher rate of achieving CVS, promotes faster gastrointestinal recovery, and reduces postoperative pain compared to the conventional technique. The benefits in safety and recovery make it a favorable alternative to the conventional technique in LAP-C.

Bimekizumab, a dual interleukin (IL)-17A/IL-17F inhibitor, has demonstrated clinical efficacy in clinical trials. However, real-world evidence comparing outcomes according to previous biologic exposure remains limited. We evaluated the effectiveness of bimekizumab in biologic-na&#xef;ve and biologic-experienced patients with plaque psoriasis, including high-impact body areas. We retrospectively analyzed 98 patients treated with bimekizumab (23 biologic-na&#xef;ve, 75 biologic-experienced). Outcomes included Psoriasis Area and Severity Index (PASI), PASI90 and PASI100 response rates, and Physician Global Assessment (PGA) scores for scalp, nail, genital, palmoplantar, and pretibial psoriasis. Biologic-experienced patients were further stratified by the number of prior biologics. Despite higher baseline PASI values (18.04 vs. 13.29; p&#x2009;=&#x2009;0.021), biologic-na&#xef;ve patients achieved significantly faster responses. At week 4, PASI reduction (85.5% vs. 62.5%; p&#x2009;<&#x2009;0.001), PASI90 (50.0% vs. 13.3%; p&#x2009;=&#x2009;0.001), and PASI100 (50.0% vs. 11.7%; p&#x2009;=&#x2009;0.001) were significantly higher in biologic-na&#xef;ve patients. Similar findings were observed in high-impact areas. Differences persisted through week 16 but disappeared from week 24 onward. Nevertheless, biologic-na&#xef;ve patients maintained numerically higher response rates throughout follow-up. Previous biologic burden did not compromise long-term outcomes. Bimekizumab showed sustained effectiveness regardless of prior biologic exposure. Nevertheless, biologic-na&#xef;ve patients achieved faster and deeper responses, supporting earlier use of bimekizumab to maximize treatment benefit.

Endovascular therapy is preferred over open surgery due to its minimally invasive nature, faster recovery, and lower perioperative risk; however, fluoroscopy guided procedures are limited by radiation exposure, high equipment costs, and reliance on highly skilled operators. This study aims to develop and evaluate a lightweight, portable robotic system for autonomous guidewire navigation to improve safety, accessibility, and operator independence. A compact 400&#xa0;g robotic device was designed with millimeter scale positioning accuracy, servo current-based real-time haptic feedback, precise axial rotation, automated retraction advance control, and compatibility with standard endovascular tools. Miniature linear actuated servomotors replicate skilled manual maneuvers using impedance control. Upon tip contact, the system advances the guidewire by 1&#xa0;mm, measures current changes, classifies lesion stiffness (soft, medium, stiff), and adapts virtual mass-spring-damper gains to regulate push speed and applied force. Bench experiments were conducted using flexible tubing with inserts simulating 20-80% stenosis and two current thresholds (75 and 94&#xa0;mA). Retraction frequency increased with stenosis severity, validating the autonomous control strategy. Lesion stiffness classification achieved F-scores of 0.83, 0.77, and 0.95 for soft, medium, and stiff conditions, respectively, demonstrating reliable discrimination and adaptive force modulation. The proposed system enables autonomous and adaptive guidewire advancement with high classification accuracy using low-cost, current-based sensing and impedance control. Its lightweight and portable design reduces dependence on continuous manual operation and specialized imaging infrastructure, supporting safer and faster interventions and potential deployment in prehospital or resource-limited settings. This prototype advances the development of more accessible and operator-independent endovascular therapy.

This study aims to present a simplified and resource-efficient computational model for predicting activity-dependent conduction velocity changes in unmyelinated axons, serving as a complementary tool to Hodgkin-Huxley models. Our approach is based on the concept of 'memory', where the speed of action potentials is modulated by prior activity. We utilized microneurography data from 95 mechano-insensitive C-fibres of healthy human participants, including both sexes, across various stimulation protocols to optimize model parameters. The model incorporates linear long-term and non-linear short-term memory components, effectively predicting propagation speed by convolving the history of recorded action potentials with the memory function. The proposed one-dimensional and two-dimensional memory functions yielded low mean squared errors in predicting the propagation speed of subsequent action potentials. This computational framework provides insights into dynamics of unmyelinated axons under varying conditions, enhancing our understanding of signal processing along the axon and its short-term memory capabilities. Additionally our model demonstrates rapid computation times suitable for real-time applications in electrophysiological experiments. This study introduces a novel model that simulates activity-dependent conduction velocity changes in unmyelinated axons, which is crucial for effective signal processing during conduction. Unlike Hodgkin-Huxley models that are computationally intensive and complex, our approach leverages fibre 'memory' to capture how prior activity influences conduction. With fewer parameters required to fit diverse datasets, including patient data, our highly efficient model enables faster simulations than Hodgkin-Huxley models and facilitates the analysis of spike train propagation over long distances, and it is therefore suitable for modelling peripheral axons that extend up to 1&#xa0;m. KEY POINTS: Unmyelinated axons, which are present in the peripheral and central nervous system, exhibit conduction velocity changes influenced by previous fibre activity, creating a form of fibre 'memory'. This study presents a novel computational model that predicts conduction velocity changes in unmyelinated axons based on prior activity, providing a faster and more efficient addition to complex Hodgkin-Huxley models. The new model incorporates both linear long-term and non-linear short-term memory components, demonstrating rapid computation times suitable for real-time applications. The model effectively captures the dynamics of nerve fibres, enhancing our understanding of axonal signal processing. This work offers insights into how previous activity influences axonal behaviour, informing future research on neurological disorders associated with altered nerve function.

To evaluate the clinical value of bedside bimodal ultrasound in assessing early cerebral circulation in preterm infants from structural and functional perspectives, and to examine whether maternal recurrent spontaneous abortion (RSA), maternal active immunotherapy status, and ultrasound-derived markers were associated with neonatal cerebral hemodynamic trajectories and clinically significant intraventricular hemorrhage (IVH). In this single-center prospective cohort study, 120 preterm infants born to mothers with RSA were enrolled and stratified by maternal active immunotherapy status. Bedside bimodal ultrasound, including transcranial Doppler (TCD) and optic nerve sheath diameter (ONSD) measurement, was performed on postnatal days 1, 3, 5, and 7. The coefficient of variation of the middle cerebral artery pulsatility index (PI-CV) was calculated. Generalized estimating equations (GEE) were used for longitudinal analyses, and multivariable logistic regression was used to evaluate associations with clinically significant IVH (Papile grade &#x2265;II). Because the dataset was a limited tabular cohort, no random training/validation/test split and no class-balancing procedure were used; internal validation was performed using bootstrap resampling. Model performance was assessed using discrimination, calibration, clinical-utility, and likelihood-based model-quality metrics. PI-CV and ONSD decreased during the first postnatal week in both groups. GEE analysis using an AR(1) working correlation structure showed significant time effects and group&#x2009;&#xd7;&#x2009;time interactions for PI-CV and ONSD (all P&#x2009;<&#x2009;0.001), with a faster decline in the active immunotherapy group. ONSDmax and PI-CV at 24&#x2005;h remained associated with clinically significant IVH after clinical adjustment. The bimodal model had an AUC of 0.759 (95% CI, 0.674-0.844), the clinical-ultrasound model had an AUC of 0.799 (95% CI, 0.721-0.877), and the expanded explanatory model had an AUC of 0.817 (95% CI, 0.743-0.892). For the expanded model, the likelihood-ratio chi-square was 40.915, AIC was 139.301, BIC was 161.601, McFadden pseudo-R2 was 0.249, and the apparent Brier score was 0.174. Bootstrap internal validation using 1,000 resamples yielded optimism-corrected AUC/Brier scores of 0.762/0.190 for the clinical-ultrasound model and 0.774/0.203 for the expanded explanatory model. These results support exploratory risk stratification rather than definitive clinical deployment. Bedside bimodal ultrasound is a feasible noninvasive approach for dynamic assessment of early cerebral hemodynamics in preterm infants. The combination of ONSDmax and PI-CV may improve exploratory early IVH risk stratification. Maternal immunotherapy status was associated with distinct longitudinal cerebral hemodynamic and ONSD trajectories, reflected by faster modeled declines in PI-CV and ONSD during the first postnatal week. However, given the observational design and associational regression models, these findings do not establish a direct protective effect of maternal immunotherapy on IVH risk. External validation and longer-term outcome studies are required before clinical implementation.

Incomplete clinical records can be a significant hurdle in implant dentistry, transforming routine maintenance or a restorative task into a complex search. When the primary documentation-such as the implant passport or surgical report-is missing, the clinician is forced to rely on radiographic identification and trial-and-error, which increases the risk of component mismatch and patient dissatisfaction. The purpose of this study was to develop and validate an artificial intelligence (AI) algorithm capable of detecting dental implants on panoramic radiographs and classifying them by implant brand and prosthetic platform size. A dataset of 387 panoramic radiographs with 1004 dental implant images was randomly divided into training, validation, and test sets using an 80/10/10 stratified split across 25 independent partitions. Convolutional neural network (CNN) architectures were developed and trained using manually annotated images. Ground truth labels for implant brand and prosthetic platform size were obtained from patient records. Among the evaluated architectures, a Faster region-based CNN (R-CNN) combined with an EfficientNet-B7 backbone demonstrated the highest diagnostic performance. Implant detection achieved a mean Intersection over Union (IoU) of 77.65% &#xb1;11.54% and an accuracy of 99.45% &#xb1;0.70% with an average of 0.60 &#xb1;0.76 false negatives per split (mean &#xb1;standard deviation across 25 partitions). Implant brand classification accuracy was 98.93% &#xb1;0.91% (Callus Pro), 97.81% &#xb1;1.43% (Denti Root Form), 98.85% &#xb1;1.19% (NobelReplace Conical Connection partially machined collar [PMC]), and 96.79% &#xb1;1.83% for implants of unknown brand, yielding an overall brand-classification accuracy of 95.66% &#xb1;2.02%. Platform-size classification accuracy was 87.62% &#xb1;2.94% for narrow, 87.78% &#xb1;3.15% for regular, and 96.79% &#xb1;1.83% for unknown sizes. Combined brand-and-platform classification achieved an overall accuracy of 85.60% &#xb1;3.27%. The 2-stage CNN pipeline demonstrated clinically acceptable accuracy for automated dental implant detection and classification tasks on panoramic radiographs, supporting its potential integration into clinical workflows to improve diagnostic efficiency and standardization.

The growing bases of Artificial Intelligence (AI) applications ranging from diagnostic support to immersive training have rapidly advanced in the dental education field. Endodontics, by its very nature of relying so highly on a proper diagnosis and careful technical execution, is an indication through which AI may be best poised to succeed the most in specialty care. The aim of this systematic review was to assess the role of artificial intelligence (AI): machine learning (ML), deep learning (DL), virtual/augmented reality (VR/AR) and large language models (LLMs) related to endodontic education based on available evidence published until September 2025. This review was performed in accordance with the PRISMA 2020 guidelines. Publication databases were reviewed included PubMed, Scopus, Web of Science and Cochrane. Inclusion Criteria: Studies that evaluated any form of AI for didactic, preclinical or clinical education in endodontics and/or patient-centered education were included. Study characteristics, AI domains, applications and outcomes were extracted. Risk of bias and methodological quality were evaluated according to study design using RoB 2, ROBINS-I, AXIS, and AMSTAR-2 tools. Fifteen studies were included. Radiographic interpretation augmented by AI improved sensitivity and specificity to reduce false positive reporting especially for junior clinicians. In preclinical training, VR/AR simulations have shown to improve psychomotor skills, confidence and knowledge acquisition. LLMs can be useful in producing exam questions and case-based Q&A, although the accuracy and discriminatory ability varied. AI mediated Patient education interventions led to anxiety reduction and comprehension. There was heterogeneity of outcome measures, dataset bias; reliability and transparency issues. AI holds promise for use in diagnostic, didactic and preclinical endodontic education. They must be safely implemented in a controlled format, under the supervision of faculty and with objective evaluation metrics in place. AI provides quantifiable benefits in endodontic education by improving accuracy of diagnosis, assisting decision-making and facilitating dental students training using VR/AR simulation. Some interventions using AI in curricula may allow the student to acquire skills faster, feel more confident, and transfer these benefits to improved patient communication. But we need to make sure our integration is backed up with faculty monitoring, transparent AI models and rigorous validation before putting it in any production environment or relying on it too heavily for exam outcomes.

The electrochemical oxidation of 5-hydroxymethylfurfural (HMF), a key biomass-derived platform molecule, offers a sustainable pathway to value-added chemicals. While ruthenium-based catalysts have been investigated for driving the complete oxidation of HMF to 2,5-furandicarboxylic acid (FDCA), the selective formation of partially oxidised intermediates, like 5-formyl-2-furancarboxylic acid (FFCA), a product with different functional groups, remains largely unexplored in alkaline media. In particular, the influence of catalyst crystallinity and defect density on selective FFCA electrosynthesis is still unclear. Here, we successfully synthesised and characterised two shape-controlled Ru nanostructures with distinct crystallinity. The first comprises Ru hourglass nanoparticles, monocrystalline and highly uniform, and the second consists of polymorphous nanoparticles, characterised by a combination of multiple Ru nanocrystals of uneven shapes. After supporting both materials on carbon, we evaluated their performance for HMF electrooxidation in alkaline electrolyte, comparing them to commercial RuC. Comprehensive structural and electrochemical characterisation enabled the correlation of morphology and crystallinity with catalytic behaviour. Both the hourglasses and polymorphous particles significantly outperformed the commercial RuC, achieving >95% HMF conversion with 66% selectivity toward FFCA and resulting in the first example of second-order kinetics for Ru-catalysed HMF electrooxidation. Stability tests show that while the polymorphous particles exhibited higher intrinsic activity, consistent with their defect-rich, polycrystalline structure, they suffered from faster deactivation. In contrast, the monocrystalline hourglass nanoparticles provided enhanced stability while maintaining high selectivity. These findings demonstrate that crystallinity engineering is a key strategy for balancing activity and durability in Ru-catalysed HMF electrooxidation in alkaline media.

Positive social comparative feedback during motor skill practice is hypothesized to enhance motor learning by triggering a dopaminergic response. Individual differences in dopamine-related genes impact dopamine neurotransmission and may influence responsiveness to motor practice conditions that target dopaminergic pathways. The purpose of this study was to examine the impact of dopamine genotype on learning of a motor sequence task under two different feedback conditions: response time only feedback or response time with positive social comparison. Fifty-two adults practiced a joystick-based motor sequence task over two consecutive days. On Day 1, participants were randomized to receive either 1) response time only feedback (i.e., "You completed the block in 80 seconds") or 2) positive social comparative feedback (i.e., "You completed the block in 80 seconds. You were faster than others"). Motor learning was assessed by retention performance, or the change in response time from the first block of Day 1 to the first block on Day 2. Saliva samples were used to genotype for dopamine receptors DRD1, DRD2 and DRD3 and COMT. Individual genes were scored (0-2) and summed to create a polygene score (0-8). Participants were then categorized as having Low (1-4) or High (5-8) dopamine neurotransmission. A significant interaction was found between time, summary polygene group and feedback group (p&#x2009;=&#x2009;0.048). The Low dopamine group showed greater improvements with response time only feedback versus positive social comparative feedback. The High dopamine group showed similar improvements in response time regardless of feedback type. This suggests that feedback targeting dopaminergic pathways may not be beneficial for individuals with lower dopamine neurotransmission. Our findings suggest that dopaminergic genetics may impact the efficacy of positive social comparative feedback on motor learning in low dopamine genotypes.

Interpretability remains a central challenge in the deployment of deep neural networks, particularly in safety-critical and decision-sensitive fields. This work proposes a unified framework for post-hoc global interpretability by transforming general neural network architectures-including the Residual Network and Transformer-into equivalent decision diagrams over real-valued inputs and multi-class outputs. These decision diagrams provide a transparent, structured view of the neural network's overall behavior, where each path encodes a tractable and interpretable decision rule. We identify a counterintuitive yet effective modification in the node merging process during diagram construction, which leads to faster entropy reduction and smaller equivalent intervals, thereby significantly reducing the diagram size while maintaining equivalence with the original network. The resulting representations not only support the exploration of logical properties, such as decision boundary tracing, equivalence checking, robustness analysis, and model counting, but also serve as globally interpretable surrogates for the original neural networks. Experiments validate the effectiveness and scalability of the proposed methods, highlighting their potential for reliable neural network analysis and verification.

Sleep is essential for adolescents&rsquo; physical and psychological development. Sleep problems are common among adolescents with psychiatric disorders, but research on the prevalence of specific sleep disorders herein is lacking. To examine the prevalence of sleep disorders in adolescents with psychiatric disorders and to explore its relationship with sleep-hygiene-behaviors. In a cross-sectional pilot-study, 50 adolescents (16-24 years) receiving care at a specialized mental health institution completed the Holland Sleep Disorders Questionnaire and the Adolescent Sleep Hygiene Scale. The prevalence of sleep disorders and differences in sleep-hygiene-behaviors between adolescents with and without sleep disorder(s) were analyzed using cross-tabulations, <span class="CharOverride-4">&chi;</span>&sup2;-, and t-tests. 68% of adolescents scored above cut-off for at least one sleep disorder, and 54% for two or more. Insomnia disorder (60%) and circadian rhythm sleep-wake disorders (44%) were most prevalent. Comorbidity of multiple sleep disorders was high. Adolescents with sleep disorders reported significantly poorer sleep-hygiene, particularly pertaining substance use and pre-sleep emotional arousal. Sleep disorders are highly prevalent among adolescents with psychiatric disorders and are associated with poorer sleep-hygiene. Early detection and sleep disorder treatment, along with improving sleep-hygiene-behaviors, might be essential for healthy sleep quality and better/faster psychiatric recovery in adolescents.

Scanning ion conductance microscopy (SICM) has emerged as an important technique in the biomedical field due to its non-contact, nanoscale imaging capabilities, but the slow imaging speed limits its ability to capture dynamic biological processes. Undersampling combined with computational reconstruction presents a promising paradigm to accelerate imaging. However, existing compressed sensing (CS) and deep learning methods either yield suboptimal reconstruction quality or require abundant training data that are difficult to acquire for SICM. To address these challenges, we propose a zero-shot super-resolution (SR) framework that uses artificial neural networks to reconstruct high-fidelity images from undersampled SICM measurements. By exploiting internal image statistics, the method extracts training samples solely from the input itself to train an image-specific SR network, eliminating the reliance on external datasets. Comparative experiments demonstrate that the proposed method achieves superior reconstruction accuracy compared to bicubic interpolation, CS, and the baseline zero-shot SR (ZSSR) algorithms, while exhibiting robust performance across random initialization. Furthermore, it attains comparable reconstruction quality with significantly fewer sampling points than CS methods, thereby enabling faster SICM imaging. Reconstruction experiments under noisy conditions further demonstrate the effectiveness of the proposed method in practical SICM applications. This work offers a practical strategy for high-speed SICM imaging and suggests a promising pathway for enhancing imaging speed in other data-scarce scanning probe microscopy techniques.

Microvascular dysfunction contributes to foot complications in type 2 diabetes (T2D) and peripheral artery disease (PAD), yet has been characterized primarily in non-plantar regions. We evaluated microvascular reactivity and oxygenation at ulcer-prone plantar sites and responses to combined heat therapy and intermittent pneumatic compression (HT&#x2009;+&#x2009;IPC). Forty adults &#x2265;&#x2009;50&#x2009;years (Control n&#x2009;=&#x2009;20, T2D n&#x2009;=&#x2009;11, PAD with or without T2D n&#x2009;=&#x2009;9) were studied. Plantar hallux CVC (laser Doppler flux/MAP) and forefoot StO2 (near-infrared spectroscopy) were measured during local heating and reactive hyperemia. Responses to 60-min HT&#x2009;+&#x2009;IPC were evaluated using the contralateral foot as a control. At the plantar hallux, the increase in %CVCmax from baseline to the initial peak was attenuated in PAD compared with controls and T2D (p&#x2009;<&#x2009;0.01), whereas plateau responses were similar. At the forefoot, PAD showed faster deoxygenation during ischemia and slower reoxygenation during reactive hyperemia (p&#x2009;<&#x2009;0.01). During HT&#x2009;+&#x2009;IPC, popliteal blood flow increased similarly across groups; however, plantar StO2 increased in controls and T2D but remained below baseline in PAD (p&#x2009;<&#x2009;0.001). These preliminary findings indicate impaired plantar microvascular function in a heterogeneous PAD cohort, most of whom also had T2D. Larger studies are needed to confirm these findings and to elucidate the underlying mechanisms.