Unmanned aerial vehicle (UAV) formations for bearing-only passive detection are increasingly important in modern military confrontations, and the array of the formation is one of the decisive factors affecting the detection accuracy of the system. How to plan the optimal geometric array in bearing-only detection is a complex nondeterministic polynomial problem, and this paper proposed the distributed stochastic subgradient projection algorithm (DSSPA) with layered constraints to solve this challenge. Firstly, based on the constraints of safe flight altitude and fixed baseline, the UAV formation is layered, and the system model for bearing-only cooperative localisation is constructed and analysed. Then, the calculation formula for geometric dilution of precision (GDOP) in the observation plane is provided, this nonlinear objective function is appropriately simplified to obtain its quadratic form, ensuring that it can be adapted and used efficiently in the system model. Finally, the proposed distributed stochastic subgradient projection algorithm (DSSPA) combines the idea of stochastic gradient descent with the projection method. By performing a projection operation on each feasible solution, it ensures that the updated parameters can satisfy the constraints while efficiently solving the convex optimisation problem of array planning. In addition to theoretical proof, this paper also conducts three simulation experiments of different scales, validating the effectiveness and superiority of the proposed method for bearing-only detection array planning in UAV formations. This research provides essential guidance and technical reference for the deployment of UAV formations and path planning of detection platforms.

With decades of advance research and recent developments in the drug and medical device regulatory approval process, patient-reported outcomes (PROs) are becoming increasingly important in clinical trials. While clinical trial analyses typically treat scores from PROs as observed variables, the potential to use latent variable models when analyzing patient responses in clinical trial data presents novel opportunities for both psychometrics and regulatory science. An accessible overview of analyses commonly used to analyze longitudinal trial data and statistical models familiar in both psychometrics and biometrics, such as growth models, multilevel models, and latent variable models, is provided to call attention to connections and common themes among these models that have found use across many research areas. Additionally, examples using empirical data from a randomized clinical trial provide concrete demonstrations of the implementation of these models. The increasing availability of high-quality, psychometrically rigorous assessment instruments in clinical trials, of which the Patient-Reported Outcomes Measurement Information System (PROMIS®) is a prominent example, provides rare possibilities for psychometrics to help improve the statistical tools used in regulatory science.

Group A streptococcal or Streptococcus pyogenes infections have been increasing post-COVID-19 pandemic. We describe the epidemiology of S. pyogenes pharyngitis and invasive disease in Alberta, Canada 2018–2023. Positive pharyngitis specimens were identified from throat swabs collected from pharyngitis patients. Invasive S. pyogenes was defined as the isolation of S. pyogenes from a normally sterile site or severe skin infection. S. pyogenes isolates were emm typed. Pharyngitis and invasive disease displayed seasonal trends preceding the COVID-19 pandemic followed by a sharp decrease during COVID-19 intervention measures. After the lifting of interventions, rates of pharyngitis and invasive disease rose. There were 182 983 positive pharyngitis specimens between 2018 and 2023 for a positivity rate of 17.6%. The highest rates occurred in the 0–9 age group in 2023 (41.5%). Invasive disease increased in 2022–2023 driven by emm1 and 12 types. M1UK strain was the most frequent M1 type associated with invasive disease (59% of M1 isolates sequenced). Notably, out of 182 983 pharyngitis cases, there were 111 cases of invasive S. pyogenes detected for an invasive disease rate of 0.06%. This descriptive epidemiology of S. pyogenes pharyngitis and invasive S. pyogenes disease highlights the rapid increase in cases of S. pyogenes occurring in western Canada and illustrates the critical need for a vaccine.

Increasing evidence indicates that (poly)phenol consumption can have beneficial effects in human brain function(1). The overall aim of this project is to investigate whether consumption of a (poly)phenol rich cranberry drink improve mental health and cognitive function in university students via the gut-brain axis.

A parallel randomised controlled trial was conducted in 72 young healthy final year university students assigned to consume 236 mL cranberry drink (442 mg polyphenols) or nutrient matched placebo, daily for 12 weeks. The primary outcome was mood, measured as Total Mood Disturbance (TMD), using the Profile of Mood States (POMS) questionnaire. Secondary outcomes included stress, anxiety and depression levels, measured using the Perceived Stress Scale (PSS), and the Hospital Anxiety Depression Scale (HADS) questionnaire, salivary cortisol levels and cognitive function measured using the Online General Cognitive Assessment Battery (CogniFit). All of these questionnaires were measured every 4 weeks. Blood and urine samples were collected to measure inflammatory markers, gut-brain-axis metabolites, short chain fatty acids (SCFA), and cranberry polyphenols metabolites. Faecal samples were also collected for measuring gut microbiome diversity and composition and faecal metabolomics. Diet was assessed using food frequency questionnaires (FFQ), 7-day food diaries (EPIC) and 24 h online dietary recalls (intake 24) during the study. Linear mixed-effect model (LMM) and ANCOVA were used to investigate the relationship between the 2 trial arms.

No significant differences were found between treatments for mood, stress levels, anxiety, depression, circulating short chain fatty acids or inflammatory markers, however a significantly lower diurnal area under the curve of salivary cortisol (p = 0.010) and significantly higher short-term memory (Z-score: p = 0.024; Raw-score: p = 0.034) was found at 12 weeks in the cranberry group compared with the placebo. Besides, significant differences between interventions were found in plasma gut-brain axis metabolites, including the kynurenine and bile acids pathway which were kynurenine (p = 0.023), kynurenic (p = 0.032), quinolinic (p = 0.018), and glycolithocholic acid (p = 0.04) as well as several plasma and urinary cranberry (poly)phenol metabolites were modulated by cranberry consumption.

In conclusion, daily cranberry (poly)phenol supplementation for 12 week did not improve mood, stress, anxiety, and depression symptoms in healthy university students, however it may modulate cortisol levels and some aspects of cognitive function via the gut-brain axis.

(Poly)phenol intake has been associated with reduced risk of non-communicable diseases in epidemiological studies1,2. However, there are no dietary assessment tools that have been developed to estimate (poly)phenol intake in the UK. This study aimed to develop a novel food frequency questionnaire (FFQ) to capture dietary (poly)phenol intake in the UK and assess its relative validity by comparison with 7-day diet diaries (7DD) and plasma and urine (poly)phenols metabolites.

The (poly)phenol FFQ was developed based on the EPIC-Norfolk FFQ validated for energy and nutrient intake estimation in the UK population3,4. Food items added to the FFQ based on total (poly)phenols content (≥ 1 mg/serving) and UK consumption. Participants aged 18 ∼ 29 years (n=255) completed the EPIC-Norfolk FFQ and a (poly)phenol FFQ. In a subgroup (n=60), 7DD, spot urine, and fasting plasma samples were collected. An in-house (poly)phenol database was used to estimate (poly)phenol intake from FFQs and 7DD. Plasma and urinary (poly)phenol metabolite levels were analysed using a validated LC-MS method5. The agreements between (poly)phenol intake estimated using the (poly)phenol and EPIC-Norfolk FFQ and 7DDs, as well as plasma and urinary biomarkers, were evaluated by intraclass correlation coefficients (ICC), weighted kappa, quartile classification, and Spearman correlations, and the associations were investigated using linear regression models adjusting for energy intake and multiple testing (FDR < 0.05).

Strong agreements were observed between hydroxycinnamic acids estimated from (poly)phenol FFQ and 7DDs (kappa=0.75), fair agreements were found between 9 (poly)phenol groups, including total (poly)phenol intake (kappa=0.41), while the agreements for the rest of 17 classes and subclasses of (poly)phenols were poor (kappa: 0.07 ∼ 0.39). Strong positive associations were found in 9 (poly)phenols estimated from 7DDs, including dihydroflavonols, flavones, ellagitannins, hydroxyphenylacetic acids, total stilbenes, resveratrol, total other (poly)phenols, tyrosols, and alkylphenols with stdBeta from 0.62 (95% CI: 0.42 ∼ 0.82) to 0.95 (0.86 ∼ 1.00) (FDR p < 0.05). (Poly)phenol FFQs estimated (poly)phenol intake exhibited positive associations with 99 urinary metabolites (stdBeta: 0.27 (0.06 ∼ 0.48) to 0.88 (0.72 ∼ 1.03)) and 25 plasma metabolites (stdBeta:

0.39 (0.17 ∼ 0.62) to 0.83 (0.64 ∼ 1.02)) (FDR p < 0.05). The agreement between (poly)phenol FFQs and the EPIC-Norfolk FFQs was moderate (ICC 0.51 ∼ 0.69) for all (poly)phenol intake after adjusting for energy intake. Compared with the EPIC-Norfolk FFQs estimated (poly)phenol intake, stronger and more agreements and associations were found in (poly)phenol FFQs estimated (poly)phenol with 7DDs and biomarkers.

(Poly)phenols estimated from (poly)phenol FFQ exhibited fair agreements and moderate to strong associations with 7DDs and biomarkers in the target population, indicating the novel questionnaire may be a promising tool to assess (poly)phenol intake. A representative UK population group is warranted for to test the further usability.

This paper develops a novel full-state-constrained intelligent adaptive control (FIAC) scheme for a class of uncertain nonlinear systems under full state constraints, unmodeled dynamics and external disturbances. The key point of the proposed scheme is to appropriately suppress and compensate for unmodeled dynamics that are coupled with other states of the system under the conditions of various disturbances and full state constraints. Firstly, to guarantee that the time-varying asymmetric full state constraints are obeyed, a simple and valid nonlinear error transformation method has been proposed, which can simplify the constrained control problem of the system states into a bounded control problem of the transformed states. Secondly, considering the coupling relationship between the unmodeled dynamics and other states of the controlled system such as system states and control inputs, a decoupling approach for coupling uncertainties is introduced. Thereafter, owing to the employed dynamic signal and bias radial basis function neural network (BIAS-RBFNN) improved on traditional RBFNN, the adverse effects of unmodeled dynamics on the controlled system can be suppressed appropriately. Furthermore, the matched and mismatched disturbances are reasonably estimated and circumvented by a mathematical inequality and a disturbance observer, respectively. Finally, numerical simulations are provided to demonstrate the effectiveness of the proposed FIAC strategy.

The study applies a two-dimensional adaptive mesh refinement (AMR) method to estimate the coordinates of the locations of the centre of vortices in steady, incompressible flow around a square cylinder placed within a channel. The AMR method is robust and low cost, and can be applied to any incompressible fluid flow. The considered channel has a blockage ratio of $1/8$. The AMR is tested on eight cases, considering flows with different Reynolds numbers ($5\le Re\le 50$), and the estimated coordinates of the location of the centres of vortices are reported. For all test cases, the initial coarse meshes are refined four times, and the results are in good agreement with the literature where a very fine mesh was used. Furthermore, this study shows that the AMR method can capture the location of the centre of vortices within the fourth refined cells, and further confirms an improvement in the estimation with more refinements.

We optimize jet mixing using large eddy simulations (LES) at a Reynolds number of $3000$. Key methodological enablers consist of Bayesian optimization, a surrogate model enhanced by deep learning and persistent data topology for physical interpretation. The mixing performance is characterized by an equivalent jet radius ($R_{eq}$) derived from the streamwise velocity in a plane located $8$ diameters downstream. The optimization is performed in a 22-dimensional actuation space that comprises most known excitations. This search space parameterizes the distributed actuation imposed on the bulk flow and at the periphery of the nozzle in the streamwise and radial directions. The momentum flux measures the energy input of the actuation. The optimization quadruples the jet radius $R_{eq}$ with a $7$-armed blooming jet after around $570$ evaluations. The control input requires $2\,\%$ momentum flux of the main flow, which is one order of magnitude lower than an ad hoc dual-mode excitation. Intriguingly, a pronounced suboptimum in the search space is associated with a double-helix jet, a new flow pattern. This jet pattern results in a mixing improvement comparable to the blooming jet. A state-of-the-art Bayesian optimization converges towards this double-helix solution. The learning is accelerated and converges to another better optimum by including a deep-learning-enhanced surrogate model trained along the optimization. Persistent data topology extracts the global and many local minima in the actuation space. These minima can be identified with flow patterns beneficial to the mixing.

Diet is an important modifiable lifestyle factor for human health, and plant-rich dietary patterns are associated with lower risk of non-communicable diseases in numerous studies. However, objective assessment of plant-rich dietary exposure in nutritional epidemiology studies remains challenging. This study aimed to develop and evaluate metabolic signatures of the most widely used plant-rich dietary patterns using a targeted metabolomics method comprising of 108 plant food metabolites.

A total of 218 healthy participants from the POLYNTAKE cohort were included, aged 51.5 ± 17.7 years, with 24h urine samples measured using ultra-high-performance liquid chromatography–mass spectrometry. The validation dataset employed three sample types to test the robustness of the signature, including 24h urine (ABP cohort, n = 88), plasma (POLYNTAKE cohort, n = 195), and spot urine (TwinsUK cohort, n = 198). Adherence to the plant-rich diet was assessed using a priori plant- rich dietary patterns. A combination of metabolites that evaluates the adherence and metabolic response to a specific diet was identified as metabolic signature. We applied linear regression analysis to select the metabolites significantly associated with dietary patterns (adjusting energy intake), and ridge regression to estimate penalized weights of each candidate metabolite. The correlation between metabolic signature and the dietary pattern was assessed by Spearman analysis (FDR < 0.05).

The metabolic signatures consisting of 42, 22, 35, 15, 33, and 33 predictive metabolites across different subclasses were found to be associated with adherence to Amended Mediterranean Score (A-MED), Original MED (O-MED), Dietary Approaches to Stop Hypertension (DASH), Mediterranean-DASH Intervention for Neurodegenerative Delay (MIND), healthy Plant-based Diet Index (hPDI) and unhealthy PDI (uDPI), respectively. The overlapping and distinct predictive metabolites across six dietary patterns predominantly consisted of phenolic acids (n = 38), including 14 cinnamic acids, 14 hydroxybenzoic acids, seven phenylacetic acids, and three hippuric acids. Six metabolites were included in all signatures, including two lignans: enterolactone-glucuronide, enterolactone-sulfate, and four phenolic acids: cinnamic acid, cinnamic acid-4'-sulfate, 2'- hydroxycinnamic acid, and 4-methoxybenzoic acid-3-sulfate. The established signatures were robustly correlated with dietary patterns in validation dataset (r = 0.13 - 0.40, FDR < 0.05).

We developed and evaluated a set of metabolic signatures that robustly reflected the adherence and metabolic response to plant-rich dietary patterns, suggesting the potential of these signatures to serve as an objective assessment of free-living eating habits.

Background: Subdural and subgaleal drains are equally effective after burrhole craniostomy for chronic subdural haematoma, however the optimal location of drains after minicraniotomy is not clear. As such we present the first study to assess this. Methods: Consecutive patients undergoing minicraniotomy for cSDH between 2019 and 2023 at a single institution were included. Subgaleal drains were placed exclusively by a single surgeon with the rest of the department utilising standard subdural drains. Cases were stratified by drain location. Primary outcomes included changes in functional status (Modified Rankin Score, mRS) at 3 months from preoperative baseline. Results: A total of 137 patients were included, of which 24.6% received subgaleal drains. Discharge home was higher in the subgaleal group compared to subdural group (79.4% vs 57.3%, p=0.02). Subgaleal drain location (p<0.0001) and better preoperative GCS (p=0.01) were predictors of improved 3 month mRS. Worse premorbid mRS (p=0.002), subdural drain (p=0.004), and decreased consciousness at presentation (p<0.002) were predictors of not being discharged home. Surgical recurrence was lower in the subgaleal group than the subdural group (2.9% vs 13.6%, p=0.12), but not statistically significant. Conclusions: Subgaleal drains are associated with shorter hospitalisation, greater chance of discharge home, and better functional outcomes than subdural drains.

Flexitarian, vegetarian and vegan diets are increasingly popular, particularly amongst young adults. This is the first randomised dietary intervention to investigate the health, wellbeing, and behavioural implications of consuming a basal vegetarian diet that additionally includes low-to-moderate amounts of red meat compared to one containing plant-based meat alternatives (PBMAs) in young adults (NCT04869163)(1). The objective for the current analysis is to measure adherence to the intervention, nutrition behaviours, and participants’ experience with their allocated dietary group. Eighty healthy young adults participated in this 10-week dietary intervention as household pairs. Household pairs were randomised to receive approximately three serves of beef and lamb meat (average of 390 g total cooked weight per person per week, flexitarian group) or PBMAs (350–400 g, vegetarian group) on top of a basal vegetarian diet. Participants were supported to adopt healthy eating behaviours, and this intervention was developed and implemented using a behaviour change framework(2). Diet adherence (eating allocated meat or PBMA, abstaining from animal-based foods not provided by researchers) was monitored daily, with total scores calculated at the end of the 10-week intervention period. Eating experiences were measured by the Positive Eating Scale and a purpose-designed exit survey, and a food frequency questionnaire measured dietary intake. Analyses used mixed effects modelling taking household clustering into account. The average total adherence score was 91.5 (SD = 9.0) out of a possible 100, with participants in the flexitarian group scoring higher (96.1, SD = 4.6, compared to 86.7, SD = 10.0; p < 0.001). Those receiving meat were generally more satisfied with this allocation compared to those receiving the PBMAs, even though a leading motivation for participants joining the study was an opportunity to try plant-based eating (35% expressed that that interest). Participants in both intervention groups had increased vegetable intake (p < 0.001), and reported more positive eating experiences (p = 0.020) and satisfaction with eating (p = 0.021) at the end of the 10-week intervention relative to baseline values. Behavioural methods to encourage engagement with the trial were successful, as participants demonstrated excellent adherence to the intervention. The flexitarian and vegetarian diets elicited different responses in adherence and eating experience. This holds relevance for the inclusion of red meat and PBMAs in healthy, sustainable dietary patterns beyond this study alone.

This paper proposes a theoretical insurance model to explain well-documented loss underreporting and to study how strategic underreporting affects insurance demand. We consider a utility-maximizing insured who purchases a deductible insurance contract and follows a barrier strategy to decide whether she should report a loss. The insurer adopts a bonus-malus system with two rate classes, and the insured will move to or stay in the more expensive class if she reports a loss. First, we fix the insurance contract (deductibles) and obtain the equilibrium reporting strategy in semi-closed form. A key result is that the equilibrium barriers in both rate classes are strictly greater than the corresponding deductibles, provided that the insured economically prefers the less expensive rate class, thereby offering a theoretical explanation to underreporting. Second, we study an optimal deductible insurance problem in which the insured strategically underreports losses to maximize her utility. We find that the equilibrium deductibles are strictly positive, suggesting that full insurance, often assumed in related literature, is not optimal. Moreover, in equilibrium, the insured underreports a positive amount of her loss. Finally, we examine how underreporting affects the insurer’s expected profit.