Explore the vast range of titles available.

Ground penetrating radar (GPR) data inversion, especially in parallel-layered homogeneous media with multiple subsurface targets, still faces challenges in accurately reconstructing geometric structures due to weak reflections and complex target–medium interactions. To address these limitations, this paper proposes a novel multi-scale inversion framework named GPRGAN-SCSE (Ground Penetrating Radar Generative Adversarial Network with Spatial-Channel Squeeze and Excitation). Built upon the Pix2Pix Generative Adversarial Network (Pix2PixGAN), the proposed model incorporates a Spatial-Channel Squeeze and Excitation (SCSE) module into a residual U-Net generator to adaptively enhance target features embedded in layered media. Furthermore, a tri-scale discriminator ensemble is designed to enforce structural consistency and suppress layer-induced artifacts. The network is optimized using a composite loss integrating adversarial loss, L1 loss, and gradient difference loss to jointly improve structural continuity and boundary sharpness. Experiments conducted on a simulation dataset of parallel-layered homogeneous media with multiple targets demonstrate that GPRGAN-SCSE substantially outperforms existing inversion networks. The proposed method reduces the MAE by 63.8% and achieves a Structural Similarity Index (SSIM) of 99.96%, effectively improving the clarity of subsurface edges and the fidelity of geometric contours. These results confirm that the proposed framework provides a robust and high-precision solution for non-destructive subsurface imaging under layered media conditions.

ObjectiveTo characterise the contributing factors that affect medical students’ subspecialty choice and to estimate the extent of influence of individual factors on the students’ decision-making process.DesignSystematic review and meta-analysis.MethodsA systematic search of the Cochrane Library, ERIC, Web of Science, CNKI and PubMed databases was conducted for studies published between January 1977 and June 2018. Information concerning study characteristics, influential factors and the extent of their influence (EOI) was extracted independently by two trained investigators. EOI is the percentage level that describes how much each of the factors influenced students’ choice of subspecialty. The recruited medical students include students in medical school, internship, residency training and fellowship, who are about to or have just made a specialty choice. The estimates were pooled using a random-effects meta-analysis model due to the between-study heterogeneity.ResultsData were extracted from 75 studies (882 209 individuals). Overall, the factors influencing medical students’ choice of subspecialty training mainly included academic interests (75.29%), competencies (55.15%), controllable lifestyles or flexible work schedules (53.00%), patient service orientation (50.04%), medical teachers or mentors (46.93%), career opportunities (44.00%), workload or working hours (37.99%), income (34.70%), length of training (32.30%), prestige (31.17%), advice from others (28.24%) and student debt (15.33%), with significant between-study heterogeneity (p<0.0001). Subgroup analyses revealed that the EOI of academic interests was higher in developed countries than that in developing countries (79.66% [95% CI 70.73% to 86.39%] vs 60.41% [95% CI 43.44% to 75.19%]; Q=3.51, p=0.02). The EOI value of prestige was lower in developed countries than that in developing countries (23.96% [95% CI 19.20% to 29.47%] vs 47.65% [95% CI 34.41% to 61.24%]; Q=4.71, p=0.01).ConclusionsThis systematic review and meta-analysis provided a quantitative evaluation of the top 12 influencing factors associated with medical students’ choice of subspecialty. Our findings provide the basis for the development of specific, effective strategies to optimise the distribution of physicians among different departments by modifying these influencing factors.

Adolescent idiopathic scoliosis is the most common spinal disorder in adolescents with a prevalence of 0.5–5.2% worldwide. The traditional methods for scoliosis screening are easily accessible but require unnecessary referrals and radiography exposure due to their low positive predictive values. The application of deep learning algorithms has the potential to reduce unnecessary referrals and costs in scoliosis screening. Here, we developed and validated deep learning algorithms for automated scoliosis screening using unclothed back images. The accuracies of the algorithms were superior to those of human specialists in detecting scoliosis, detecting cases with a curve ≥20°, and severity grading for both binary classifications and the four-class classification. Our approach can be potentially applied in routine scoliosis screening and periodic follow-ups of pretreatment cases without radiation exposure. Junlin Yang et al. develop deep learning algorithms that diagnose adolescent idiopathic scoliosis with its accuracy superior to human specialists. This method reduces patients’ exposure to radiation and unnecessary referrals when used for routine scoliosis screening and periodic follow-ups.

Two-dimensional (2D) layered materials have been studied in depth during the past two decades due to their unique structure and properties. Transition metal (TM) intercalation of layered materials have been proven as an effective way to introduce new physical properties, such as tunable 2D magnetism, but the direct growth of atomically thin heteroatoms-intercalated layered materials remains untapped. Herein, we directly synthesize various ultrathin heteroatoms-intercalated 2D layered materials (UHI-2DMs) through flux-assisted growth (FAG) approach. Eight UHI-2DMs (V 1/3 NbS 2 , Cr 1/3 NbS 2 , Mn 1/3 NbS 2 , Fe 1/3 NbS 2 , Co 1/3 NbS 2 , Co 1/3 NbSe 2 , Fe 1/3 TaS 2 , Fe 1/4 TaS 2 ) were successfully synthesized. Their thickness can be reduced to the thinnest limit (bilayer 2D material with monolayer intercalated TM), and magnetic ordering can be induced in the synthesized structures. Interestingly, due to the possible anisotropy-stabilized long-range ferromagnetism in Fe 1/3 TaS 2 with weak interlayer coupling, the layer-independent magnetic ordering temperature of Fe 1/3 TaS 2 was revealed by magneto-transport properties. This work establishes a general method for direct synthesis of heteroatom-intercalated ultrathin 2D materials with tunable chemical and physical properties. The intercalation of heteroatoms has been demonstrated to be an effective approach to introduce new physical properties in 2D layered materials (2DMs). Here, the authors report a flux-assisted growth method to synthesize various ultrathin heteroatoms-intercalated 2DMs, showing evidence of anisotropy-stabilized long-range ferromagnetism in Fe 1/3 TaS 2 .

Population aging–weakening has become a critical constraint on rural sustainability in China’s Yellow River Basin (YRB), posing substantial challenges to ecological conservation and high-quality development. This study develops a multidimensional evaluation framework categorizing rural aging–weakening into four typologies: general development type (GDT), shallow aging–weakening type (SAT), medium aging–weakening type (MAT), and deep aging–weakening type (DAT). Then, the XGBoost model is used to assess the factors influencing the spatial diversity of aging–weakening types in the rural population at different spatial and temporal scales. The key findings reveal the following: (1) The proportion of aging–weakening areas increased from 65% (2000) to 72% (2020), exhibiting distinct regional trajectories. Upper reaches demonstrate severe manifestations (34% combined MAT/DAT in 2020), contrasting with middle reaches dominated by GDT/SAT (>80%). Lower reaches show accelerated deterioration (MAT/DAT surged from 10% to 31%). (2) Spatial differentiation primarily arises from terrain-habitat conditions, industrial capacity, urbanization, and agricultural income. While most factors maintained stable directional effects, agricultural income transitioned from positive to negative correlation post-2010. Upper/middle reaches are predominantly influenced by geographical environment, with the role of socioeconomic factors gradually increasing. Lower reaches exhibit stronger economic–environmental interactions. (3) This research provides actionable insights for differentiated regional strategies: upper reaches require ecological migration programs, middle areas need industrial transition support, while lower regions demand coordinated economic–environmental governance. Our typological framework offers methodological advancements for assessing demographic challenges in vulnerable watersheds, with implications extending to similar developing regions globally.

PurposeTo establish and validate a universal artificial intelligence (AI) platform for collaborative management of cataracts involving multilevel clinical scenarios and explored an AI-based medical referral pattern to improve collaborative efficiency and resource coverage.MethodsThe training and validation datasets were derived from the Chinese Medical Alliance for Artificial Intelligence, covering multilevel healthcare facilities and capture modes. The datasets were labelled using a three-step strategy: (1) capture mode recognition; (2) cataract diagnosis as a normal lens, cataract or a postoperative eye and (3) detection of referable cataracts with respect to aetiology and severity. Moreover, we integrated the cataract AI agent with a real-world multilevel referral pattern involving self-monitoring at home, primary healthcare and specialised hospital services.ResultsThe universal AI platform and multilevel collaborative pattern showed robust diagnostic performance in three-step tasks: (1) capture mode recognition (area under the curve (AUC) 99.28%–99.71%), (2) cataract diagnosis (normal lens, cataract or postoperative eye with AUCs of 99.82%, 99.96% and 99.93% for mydriatic-slit lamp mode and AUCs >99% for other capture modes) and (3) detection of referable cataracts (AUCs >91% in all tests). In the real-world tertiary referral pattern, the agent suggested 30.3% of people be ‘referred’, substantially increasing the ophthalmologist-to-population service ratio by 10.2-fold compared with the traditional pattern.ConclusionsThe universal AI platform and multilevel collaborative pattern showed robust diagnostic performance and effective service for cataracts. The context of our AI-based medical referral pattern will be extended to other common disease conditions and resource-intensive situations.

To evaluate the application of front-of-package (FOP) labelling regulations to menu labelling in the Canadian restaurant sector by assessing the proportion of menu items that would be required to display the 'high-in' FOP symbol if the policy were extended to the restaurant sector. Nutrition information of 18 760 menu items was collected from 141 chain restaurants in Canada. Menu items were evaluated using the mandatory FOP labelling regulations promulgated in Canada Gazette II by Health Canada in July of 2022. Chain restaurants with ≥20 establishments in Canada. Canadian chain restaurant menu items including beverages, desserts, entrées, sides and starters. Overall, 77 % of menu items in the Canadian restaurant sector would display a 'high-in' FOP symbol. Among these menu items, 43 % would display 'high-in' one nutrient, 54 % would display 'high-in' two and 3 % would display 'high-in' all three nutrients-of-concern. By nutrient, 52 % were 'high-in' sodium, and 24 and 47 % were 'high-in' total sugars and saturated fat, respectively. Given the poor nutritional quality of restaurant foods, the current regulations, if applied to restaurant foods, would result in most menu items displaying a FOP symbol. Therefore, expanding the Canadian FOP labelling regulations to the restaurant sector can be key to ensuring a healthy food environment for Canadians. Furthermore, menu labelling along with other multi-faceted approaches such as reformulation targets are necessary to improve the dietary intake of Canadians from restaurant foods.

To assess the current Na levels in a variety of processed food groups and categories available in the Argentinean market to monitor compliance with the National Law and to compare the current Na content levels with the updated Pan American Health Organisation (PAHO) regional targets. Observational cross-sectional study. We surveyed 3997 food products, and the Na content of 760 and 2511 of them was compared with the maximum levels according to the Argentinean law and the regional targets, respectively. All food categories presented high variability of Na content. More than 90 % of the products included in the National Sodium Reduction Law were found to be compliant. Food groups with high median Na, such as meat and fish condiments, leavening flour and appetisers are not included in the National Law. In turn, comparisons with PAHO regional targets indicated that more than 50 % of the products were found to exceed the regional targets for Na. This evidence suggests that it is imperative to update the National Sodium Reduction Law based on regional public health standards, adding new food groups and setting more stringent legal targets.

The development of artificial intelligence algorithms typically demands abundant high-quality data. In medicine, the datasets that are required to train the algorithms are often collected for a single task, such as image-level classification. Here, we report a workflow for the segmentation of anatomical structures and the annotation of pathological features in slit-lamp images, and the use of the workflow to improve the performance of a deep-learning algorithm for diagnosing ophthalmic disorders. We used the workflow to generate 1,772 general classification labels, 13,404 segmented anatomical structures and 8,329 pathological features from 1,772 slit-lamp images. The algorithm that was trained with the image-level classification labels and the anatomical and pathological labels showed better diagnostic performance than the algorithm that was trained with only the image-level classification labels, performed similar to three ophthalmologists across four clinically relevant retrospective scenarios and correctly diagnosed most of the consensus outcomes of 615 clinical reports in prospective datasets for the same four scenarios. The dense anatomical annotation of medical images may improve their use for automated classification and detection tasks. A workflow that segments anatomical structures in slit-lamp images and that annotates pathological features in each image improves the performance of a deep-learning algorithm for the diagnosis of ophthalmic disorders.

High sodium intake contributes to hypertension, a leading risk factor for cardiovascular disease. Over 50% of Canadians regularly consume prepackaged and restaurant foods, which account for more than 70% of dietary sodium. Canada currently lacks public health strategies to address sodium levels in restaurant menu items, while the UK’s voluntary sodium reduction program (with targets set through the National Salt Reduction Initiative [NSRI]) led to significant reductions in sodium. The objectives were to compare sodium levels in Canadian restaurant menu items in 2020 to the UK NSRI 2024 targets and analyze changes between 2016 and 2020. Data were obtained from the University of Toronto Menu-FLIP (Food Label Information and Price) database, which includes over 20,000 items from 141 Canadian chain restaurants. A total of 3,616 menu items from the top 20 Canadian chains were assessed, of which 1,914 items in categories with UK NSRI 2024 targets were identified and compared to those targets, and 607 items were matched between 2016 and 2020 and analyzed for sodium changes. More than half (56.6%, n  = 1,083/1,914) of items exceeded UK NSRI targets. Sodium (mg/100 g) showed a large decrease in 39.5% ( n  = 100/607) of items, a medium decrease in 15.8% ( n  = 63/607), little change in 28.9% ( n  = 182/607), a medium increase in 5.3% ( n  = 68/607), and a large increase in 10.5% ( n  = 194/607) from 2016 to 2020. The prevalence and magnitude of sodium changes varied by food category. Overall, there was a statistically significant but nutritionally insignificant reduction in sodium per serving from 2016 to 2020 (−24 ± 819 mg, p  < 0.01). Canadian restaurant menu items were high in sodium, with more than half surpassing the UK NSRI targets. The observed increases and decreases in sodium highlight the need for Health Canada to set and for industry to adopt sodium reduction targets for restaurant menu items, similar to those in the UK.