Browse Articles

Safety helmet detection methods in heavy machinery factory

Electrical Alternance in a Patient With Type 1 Myotonic Dystrophy: What Diagnosis?

A weakly supervised method for surgical scene components detection with visual foundation model

Purpose: Detection of crucial components is a fundamental problem in surgical scene understanding. Limited by the huge cost of spatial annotation, current studies mainly focus on the recognition of three surgical elements ⟨instrument, verb, target⟩, while the detection of surgical components ⟨instrument, target⟩ remains highly challenging. Some efforts have been made to detect surgical components, yet their limitations include: (1) Detection performance highly depends on the amount of manual spatial annotations; (2) No previous study has investigated the detection of targets. Methods: We introduce a weakly supervised method for detecting key components by novelly combining the surgical triplet recognition model and the foundation model of Segment Anything Model (SAM). First, by setting appropriate prompts, we used SAM to generate candidate regions for surgical components. Then, we preliminarily localize components by extracting positive activation areas in class activation maps from the recognition model. However, using instrument’s class activation as a position attention guide for target recognition leads to positional deviations in the target’s resulting positive activation. To tackle this issue, we propose RDV-AGC by introducing an Attention Guide Correction (AGC) module. This module adjusts the attention guidance for target according to the instrument’s forward direction. Finally, we match the initial localization of instruments and targets with the candidate areas generated by SAM, achieving precise detection of components in the surgical scene. Results: Through ablation studies and comparisons to similar works, our method has achieved remarkable performance without requiring any spatial annotations. Conclusion: This study introduced a novel weakly supervised method for detecting surgical components by integrating the surgical triplet recognition model with visual foundation model.

Optimization of large-diameter deep-hole stope blasting technology based on numerical simulation: a case study of the Dongguashan copper mine

Letter by Luo et al Regarding Article, “Half-Life and Clearance of Cardiac Troponin I and Troponin T in Humans”

Region sampling NeRF-SLAM based on Kolmogorov-Arnold network

Currently, NeRF-based SLAM is rapidly developing in reconstructing and bitwise estimating indoor scenes. Compared with traditional SLAM, the advantage of the NeRF-based approach is that the error returns to the pixel itself, the optimization process is WYSIWYG, and it can also be differentiated for map representation. Still, it is limited by its MLP-based implicit representation to scale to larger and more complex environments. Inspired by the quadtree in ORB-SLAM2 and the recently proposed Kolmogorov-Arnold network, our approach replaces the MLP with a KAN network based on Gaussian functions, combines quadtree-based regional pixel sampling and random sampling, delineates the scene by voxels, and supports dynamic scaling to realize a high-fidelity reconstruction of large scenes for a SLAM system. Exposure compensation and VIT loss are also introduced to alleviate the necessity of NeRF on dense coverage, which significantly improves the ability to reconstruct sparse outdoor view environments stable. Experiments on three different types of datasets show that our approach reduces the trajectory error accuracy of indoor datasets from centimeter-level to millimeter-level compared to existing NeRF-based SLAM and achieves stable reconstruction in complex outdoor environments, considering the performance while ensuring efficiency.

Assessment of bone status and bone turnover in pediatric patients with familiar hypomagnesemia with hypercalciuria and nephrocalcinosis

Electronic Provider Notification to Facilitate the Recognition and Management of Severe Aortic Stenosis: A Randomized Clinical Trial

BACKGROUND: Symptomatic severe aortic stenosis (AS) remains undertreated with high resultant mortality despite increased growth and availability of aortic valve replacement (AVR) since the advent of transcatheter therapies. We evaluate the impact of electronic provider notifications (EPNs) on rates of AVR at 1 year. METHODS: In a pragmatic cluster randomized clinical trial conducted within a multicenter academic health system from March 2022 through November 2023, 285 providers who had ordered a transthoracic echocardiogram (TTE) with findings potentially indicative of severe AS with an aortic valve area ≤1.0 cm 2 were enrolled. Providers were randomly assigned to receive EPNs for each of their patients with severe AS on TTE or to usual care. Notifications highlighted the detection of severe AS and included patient-specific clinical guideline recommendations for its management. The primary end point was the proportion of patients with severe AS receiving AVR within 1 year of the index TTE. RESULTS: A total of 144 providers were randomized to intervention and 141 to control, resulting in 496 and 443 patients assigned to each group, respectively. The patient cohort had mean age of 77±11 years, was 47% female, and had a mean aortic valve area of 0.8±0.1 cm 2 . Rates of AVR within 1 year were 48.2% with EPNs versus 37.2% with usual care (odds ratio [OR], 1.62 [95% CI, 1.13–2.32]; P =0.009]) and 60.7% and 46.5%, respectively, among symptomatic patients (OR, 1.77 [95% CI, 1.17–2.65]; P =0.006). Notification treatment effect was highest with EPNs for patients >80 years of age (OR, 2.00 [95% CI, 1.17–3.41]; P =0.01), for women (OR, 2.78, [95% CI, 1.69–4.57]; P <0.001), and when the index TTE was performed within the inpatient setting (OR, 2.49 [95% CI, 1.44–4.31]; P <0.001). Within 1 year, the restricted mean survival time was longer with EPNs in all (12 days; P =0.04) and symptomatic patients (23 days; P =0.01). CONCLUSIONS: In this first study of EPNs for valvular heart disease, EPNs increased rates of AVR for severe AS, lessened sex and age disparities in AVR use, and improved survival time. EPNs may be a simple, scalable intervention to raise awareness of critical TTE findings and improve the quality of care for patients with severe AS. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT05230225.

Research on government subsidy strategy of biomass power supply chain considering channel encroachment

To enhance the comprehensive utilization of biomass straw, governments may implement incentive policies for members of the biomass supply chain. This study examines the strategic interaction between government subsidy strategies and farmers’ channel encroachment strategies within the biomass power supply chain. A game-theoretic model is employed to analyze eight government subsidy scenarios, leading to the following conclusions: In the absence of encroachment, subsidies provided to either middlemen or farmers contribute to increased profits for the respective recipients. Notably, the analysis indicates that under the encroachment scenario, government subsidies directed solely to middlemen may negatively affect the overall social welfare of the biomass power generation supply chain. Furthermore, as channel competition intensifies, the probability and extent of this negative impact on social welfare are likely to increase. Additionally, the equilibrium outcome of the game-theoretic model establishes that farmers will invariably choose encroachment as a means to trigger government subsidies, thereby maximizing their profits. These findings provide essential theoretical insights into farmers’ strategic behavior aimed at income enhancement and offer guidance for government subsidy policies to achieve optimal social welfare.

Dynamics of Eucalyptus and Sorghum biomass growth and nitrogen assessment at a Saharan sandy soil irrigated with treated wastewater

Response by Kristensen et al to Letter Regarding Article, “Half-Life and Clearance of Cardiac Troponin I and Troponin T in Humans”

The association between myopia and health-related quality of life among Chinese children in primary and secondary school: A cross-sectional study

Background: Previous study on the relationship between myopia and health-related quality of life (HRQOL) among children was only conducted within hospital setting, and this relationship in school environment remained unknown. This study aimed to investigate the association between myopia and HRQOL among Chinese children aged 6–15 years in primary and secondary school. Methods: This cross-sectional study included 1,634 children, all of whom underwent routine eye examinations including cycloplegic autorefraction. The EQ-5D-Y was used to assess HRQOL. Multiple linear regression models were performed to investigate the association of myopia with EQ-5D-Y utility index (UI) values and visual analogue scale (VAS) scores. Results: Among all children, 695 (43.53%) were diagnosed with myopia ranging from -0.5 to -10.5 diopters; the mean age was 9.38 ± 2.23 years old; 838 (51.29%) were boys, and 796 (48.71%) were girls. Compared with emmetropic children, myopic children had a smaller proportion of problems with self-care and a larger proportion of problems with pain/discomfort and anxiety/depression. Children with myopia had significantly lower UI values [β = -0.008, 95% confidence interval (CI): -0.016, 0.000] and VAS scores (β = -1.300, 95%CI: -2.522, -0.078) compared to their emmetropic peers. The self-evaluation of eye health was positively associated with both UI values and VAS scores. Furthermore, decreases in UI values and VAS scores were associated to the onset of myopia, and were more pronounced in children with myopia progression. Conclusions: This study found a significant association between myopia and worse HRQOL in primary and secondary school children. These findings highlight that governments and society should pay attention to the HRQOL of myopic children.

Production of multi-subunit proteins in CHO cells by transposase-mediated integration of subunit-splitting vectors

Palliative Care and Advanced Cardiovascular Disease in Adults: Not Just End-of-Life Care: A Scientific Statement From the American Heart Association

Cardiovascular disease remains a leading cause of morbidity and mortality in adults despite recent scientific advancements. Although people are living longer lives, there may be an adverse impact on quality of life, necessitating a greater need for palliative care services and support. Palliative care for adults with advanced cardiovascular disease has the potential to significantly improve quality of life for individuals living with cardiovascular disease and their informal care partners. Effective communication, shared decision-making, age-friendly care principles, and advance care planning are vital components of palliative care and support comprehensive and holistic care throughout the advanced cardiovascular disease trajectory and across care settings. Current evidence highlights the benefits of palliative care in managing symptoms, reducing psychological distress, and supporting both people with cardiovascular disease and their care partners. However, significant gaps exist in palliative care research related to non–heart failure populations, care partner outcomes, and palliative care implementation in diverse populations. This scientific statement (1) discusses the application of effective communication, shared decision-making, age-friendly care, and advance care planning in advanced cardiovascular disease palliative care; (2) provides a summary of recent evidence related to palliative care and symptom management, quality of life, spiritual and psychological support, and bereavement support in individuals with advanced cardiovascular disease and their care partners; (3) discusses issues involving diversity, equity, and inclusion in cardiovascular disease palliative care; (4) highlights the ethical and legal concerns surrounding palliative care and implanted cardiac devices; and (5) provides strategies for palliative care engagement in adults with advanced cardiovascular disease for the care team.

Spatiotemporal dynamics and influencing factors of soil heterotrophic respiration in northeast China

Soil heterotrophic respiration (Rh) represents a primary pathway of carbon release from soil. Using meteorological data, DEM, soil organic carbon density, and other data, we simulated the Rh in Northeast China from 2001 to 2020 using the GSMSR model. We then analyzed its spatialtemporal distribution pattern and examined its spatial-temporal aggregation, differentiation characteristics, and influencing factors at the national level, employing methods such as standard deviation ellipse (a statistical method that describes the spread and direction of data points in space), cold-hot spot analysis, and geographically weighted regression. The results showed that: (1) From 2001 to 2020, the annual mean Rh of the terrestrial ecosystem in Northeast China ranged from 24.22 kgC/ha/year to 25.02 kgC/ha/year, with a very significant increasing trend at the rate of 0.04 kgC/ha/year. The total amount of carbon release from soil heterotrophic respiration ranged from 4.76 × 1011 to 5.02 × 1011 kilograms per year (kg/year), representing the annual carbon flux in the study region. And it had a significant increasing trend at the rate of 5.75 × 108 kg/year. (2) From the spatial differentiation and spatial clustering pattern, Rh was dominated by a northeast-southwest direction, its spatial distribution center was close to the northeast geographical center, and it had no obvious contraction or expansion trend on the whole. (3) In the northern and northeastern regions of the study area, vegetation cover directly influences local soil respiration rates. In most areas of the north, east, and south, per capita Gross Domestic Product directly affects soil respiration rates. It might provide a reference for the estimation of soil carbon loss and ecosystem carbon sink in this region.

Saliva-derived transcriptomic signature for gastric cancer detection using machine learning and leveraging publicly available datasets

Abstract Saliva, a non-invasive, self-collected liquid biopsy, holds promise for early gastric cancer (GC) screening. This study aims to assess the potential of saliva as a proxy for malignant gastric transformation and its diagnostic value through transcriptomic profiling. Leveraging transcriptomic data from the Gene Expression Omnibus (GEO), we constructed and validated predictive models through machine learning algorithms within the tidymodels framework. Tissue-based models were validated on independent tissue datasets, and subsequently applied to saliva. Additionally, an independent saliva-derived model was created and evaluated using sensitivity, specificity, accuracy, area under the curve (AUC), and likelihood ratio (LR) metrics. Tissue-derived models demonstrated excellent performance, with AUC values exceeding 0.9, but did not translate effectively to saliva, suggesting distinct molecular landscapes between tissue and saliva in GC. The saliva-specific model using support vector machine (SVM) achieved the highest performance, with an AUC of 0.87 (95% CI 0.72–0.97), a sensitivity of 0.79 (95% CI 0.58–0.95) and a specificity of 0.70 (95% CI 0.40–0.90). While saliva may not mirror tissue gene expression profile, it represents a promising non-invasive predictive tool for the early detection of GC. Further research is warranted to optimize saliva-derived molecular signatures, increasing their sensitivity and specificity for early cancer detection and advance the use of liquid biopsies in personalized medicine for improved screening, diagnostic and prognostic capabilities.

Blood Pressure Lowering Effects of a Novel Long-Acting NPR1 Agonist, XXB750, in Healthy Participants: A Randomized, First-in-Human Clinical Study

Correction: NPR1-like genes in Theobroma cacao: Evolutionary insights and potential in enhancing resistance to Phytophthora megakarya

Status, sources and health risk assessment of PAHs, NPAHs and OPAHs in road dust of Xinjiang, China

Assessment of the Bacterial communities associated with Anopheles gambiae larval habitats in Southern Ghana

Mosquito breeding habitats are ecosystems that comprise a complex, intimately associated micro-organism. This study aimed to determine the bacteria communities associated with Anopheles larval habitats and correlate their prevalence to the absence or presence of mosquito larvae. The 16S rRNA profiles of bacterial communities in Anopheles-positive breeding habitats (productive and semi-productive habitats) and negative habitats (non-productive) from Southern Ghana were analyzed using the Oxford Nanopore’s MinION platform with water and larval samples. A total of 15 bacterial taxa were identified across all habitats based on productivity. Significantly, mosquito-positive breeding habitats (productive and semi-productive) had more bacterial diversity compared to mosquito-negative habitats (non-productive). Comparison of the composition of bacteria in the different habitat types revealed that non-productive habitats had a higher prevalence of Epsilonproteobacteria (58.1%), while Gammaproteobacteria (33.2%) and Betaproteobacteria (30.5%) were dominant in the productive and semi-productive habitats. Gammaproteobacteria and Betaproteobacteria were the most abundant bacterial classes in Anopheles larvae. Comparing the water samples to larvae microbiomes revealed distinct composition. Betaproteobacteria (58.5%) and Cytophagia (10.7%) were predominately present in the water samples, whilst Betaproteobacteria (47.9%) and Gammaproteobacteria (21.6%) were dominant in the larval samples. This study revealed a higher bacterial composition may play a role in Anopheles mosquitoes’ attractiveness to a breeding habitat. These findings contribute to the understanding of which bacteria, directly or indirectly, can be linked to the absence or presence of mosquito larvae in breeding habitats and set the basis for the identification of specific bacterial taxa that could be harnessed for vector control in the future.