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Background: An influenza forecasting system is critical to influenza epidemic preparedness. Low temperature has long been recognized as a condition favoring influenza epidemic, yet it fails to justify the summer influenza peak in tropics/subtropics. Recent studies have suggested that absolute humidity (AH) had a U-shape relationship with influenza survival and transmission across climate zones, indicating that a unified influenza forecasting system could be established for China with various climate conditions. Methods: Our study has generated weekly influenza forecasts by season and type/subtype in northern and southern China from 2011 to 2021, using a forecasting system combining an AH-driven susceptible-infected-recovered-susceptible (SIRS) model and the ensemble adjustment Kalman filter (EAKF). Model performance was assessed by sensitivity and specificity in predicting epidemics, and by accuracies in predicting peak timing and magnitude. Results: Our forecast system can generally well predict seasonal influenza epidemics (mean sensitivity>87.5%; mean specificity >80%). The average forecast accuracies were 82% and 60% for peak timing and magnitude at 3–6 weeks ahead for northern China, higher than those of 42% and 20% for southern China. The accuracy was generally better when the forecast was made closer to the actual peak time. Discussion: The established AH-driven forecasting system can generally well predict the occurrence of seasonal influenza epidemics in China.

Abstract Investigations using hot compression tests on a new high-strength weathering steel revealed specific deformation behaviors across different conditions. These tests were performed at temperatures ranging from 850 to 1050 °C and at strain rates from 0.01 to 5 s−1. Results indicated that a decrease in the deformation temperature combined with an increase in strain rate notably enhanced both the maximum stress and strain achieved. Notably, above 900 °C and with strain rates below 0.1 s−1, the flow stress of the material reached a steady state at certain strain levels. At a strain rate of 1 s−1, irrespective of the temperature, the steel shows a continuous strain hardening behavior, achieving no stable flow stress state. Notably, when the true strain exceeds 0.8, an unusual increase in flow stress occurs, predominantly due to secondary work hardening effects. The microstructural changes in the deformed samples were examined using electron backscatter diffraction (EBSD), which helped elucidate the softening mechanisms inherent in this high-strength steel. Further, processing maps developed from true strains of 0.1–0.9, derived from the experimental flow stress data, suggest controlling the strain within 0.2–0.4 to minimize instability during hot working.

Abstract Chemotherapeutic drugs often fail to provide long-term efficacy due to their lack of specificity and high toxicity. To enhance the biosafety and reduce the side effects of these drugs, various nanocarrier delivery systems have been developed. In this study, we loaded the anticancer drug doxorubicin (DOX) and an MRI contrast agent into silica nanoparticles, coating them with pH-responsive and tumor cell-targeting polymers. These polymers enable the carrier to achieve targeted delivery and controlled drug release in acidic environments. This integrated diagnostic and therapeutic strategy successfully achieved both the diagnosis and treatment of liver cancer. Additionally, we demonstrated that the nanocarrier inhibits autophagic flux in liver cancer cells by targeting the autophagy-lysosome pathway and regulating the nuclear translocation of TFEB, thereby promoting tumor cell death. This novel diagnostic-integrated nanocarrier is expected to be a promising tool for targeted liver cancer treatment.

Currently, ground truth fusion image, fused image contrast, and naturalness are rarely considered in existing infrared and visible image fusion (IVF) methods. In this article, we proposed a pseudosupervised generative adversarial network (GAN) with single scale retinex (SSR) embedding for IVF. First, a pseudoground truth fusion image conception and its computation method was proposed to solve ground truth fusion image shortage problem. Second, a novel SSR module embedded residual GAN was designed to improve fusion image contrast and naturalness. Finally, a special dense and mixed modal inputting strategy was also proposed for better modal mixed feature extraction. Extensive experimental results on public IVF datasets verified the superior performance of our proposed approach over other representative methods. It was demonstrated that the fused image details, contrast, and naturalness were significantly improved.

Microlens arrays (MLAs) made from fused silica possess broad applications in wavefront sensing, optical focusing, beam shaping, etc. However, it is challenging to fabricate the hard-brittle silica MLAs with high accuracy and low cost by conventional manufacturing techniques efficiently. In this work, a novel two-step method, which consists of rapid evaporative ablation and precision melting polishing by CO2 lasers, is proposed to fabricate high-quality MLAs. The first step is to rapidly ablate and engrave micro-pillar arrays (MPAs) with flat groove bottom by high-energy density CO2 lasers. The second step is to precisely polish and shape the flat-topped MPAs into the dome-topped MLAs with good surface roughness (Sa) by low-energy density CO2 lasers. The whole preparation process could be achieved through a set of machining system. Firstly, the geometric dimension (period, p and height, h) of the microlens is determined by the Monte Carlo ray tracing method. Secondly, to diminish the groove depth inconsistency caused by heat accumulation, the multiple ablation compensation strategy is developed. The MPAs (p = 200–400 μm), of which maximum height error is 7.1%, are prepared in 1 s by the proposed compensation strategy. Then, the MPAs are smoothed into the MLAs by low-energy density CO2 lasers, and the average Sa is improved from 960 nm to 32.56 nm. Finally, the optical performances of the MLAs are verified from the effectiveness of imaging, focusing and homogenization. The proposed two-step processing method paves a new way to fabricate high-performance MLAs on hard-brittle fused silica with low cost and high efficiency.

The complex underground environment of coal coalmines, influenced by lighting, coal dust, and water mist, often results in collected images with blurred details and missing textures, leading to decreased image resolution and posing significant limitations to the intelligent development of coal coalmine safety monitoring. Image super-resolution reconstruction, an essential image processing technology, aims to recover clear high-resolution images from low-resolution coalmine images, thereby significantly enhancing the reliability of intelligent monitoring and safety management in coal coalmines. To address issues such as the loss of edge texture information and blurring of details in coalmine images, a coalmine image super-resolution reconstruction method integrating multi-dimensional features and residual attention networks is proposed. First, a multi-branch network is employed to parallelly integrate dynamic convolution and channel attention mechanisms, capturing different spatial statistical characteristics through “horizontal-channel” and “vertical-channel” interactions. Secondly, a recursive sparse self-attention mechanism is designed to aggregate representative feature maps under linear complexity, adaptively selecting weight distribution and reducing information redundancy during computation. Finally, the basic unit of deep feature extraction is constructed based on the standard multi-head self-attention mechanism and residual connection, with the obtained feature information and shallow features jointly input into the reconstruction module via skip connections to complete super-resolution reconstruction of coalmine images. Experimental results indicate that the proposed method significantly outperforms existing mainstream algorithms in both objective evaluation metrics and subjective visual analysis. In tests on the coalmine dataset, LPIPS (Learned Perceptual Image Patch Similarity) decreases by an average of 10.97% and 9.91%, while PSNR (Peak Signal-to-Noise Ratio) increases by an average of 4.10% and 2.30% for 2x and 4x scaling factors, respectively, demonstrating the method's effectiveness in restoring the structure and texture details of coalmine images.

Image feature extraction and matching is a critical technique for video and image stitching, visual localization and navigation in underground coal mines. However, underground tunnelling environmental factors, such as low light, uneven illumination, and repetitive textures, result in low-contrast images with indistinct texture information, making feature point extraction difficult and increasing the rate of mismatches. To address these issues, an coalmine image matching method based on multi-level feature fusion enhancement and weighted grid statistics is proposed. Firstly, multi-level feature detection network based on deformable convolution layers and stacked network is adopted for ensuring the rotational invariance of features. Secondly, both the extracted feature points and descriptors are encoded and projected into a high-dimensional space using a feature enhancement module, and the Transformer is then used to enhance the distinguishability of the features. Finally, to address the perceptual confusion problem caused by repetitive textures in underground scenes, a multi-stage matching optimization strategy based on weighted grids is used. This strategy can combine matching quality factors and motion smoothness constraints to filter the mismatches. A lot of experiments were conducted on an underground actual dataset, as well as on two public datasets including LOL and HPatches. The experimental results show that the proposed feature extraction and matching method has higher accuracy and robustness. Specifically, compared with the ORB, SIFT, ASLFeat and Superpoint algorithms, the proposed feature extraction method achieved average accuracy improvements of 33.07%, 69.78%, 17.65% and 33.52% respectively. Compared with the feature matching methods including FLANN, BF+KNN, BF+RANSAC and BF+GMS, the proposed feature matching method achieved average accuracy improvements of 19.66%, 23.26%, 4.16% and 18.46%.

Abstract Background & aims The benefit of postoperative adjuvant transcatheter arterial chemoembolization (pTACE) for patients with hepatocellular carcinoma (HCC), especially those with Child-Pugh (CP) B, remains controversial. This study aimed to assess the survival benefit of pTACE for HCC patients with CP B. Methods Data from 297 HCC patients with CP B7 or B8 were analyzed, dividing them into groups with and without pTACE (70, 23.6% vs. 227, 76.4%). Propensity score matching (PSM) was used to control for confounding bias, and competing-risk regression was applied to address bias from non-cancer-specific death (NCSD). Results Preliminary findings suggest that pTACE did not increase the incidence of severe complications in HCC patients with CP B7 or B8. Survival analysis indicated that the group receiving pTACE had better overall survival and recurrence-free survival than the group without pTACE after PSM. Furthermore, competitive risk analysis revealed that pTACE was an independent prognostic factor associated with reduced cancer-specific death incidence (subdistribution hazard ratio [SHR] 0.644, 95%CI: 0.378–0.784, P = 0.011) and recurrence (SHR 0.635, 95% CI: 0.379–0.855, P = 0.001). Importantly, pTACE did not increase NCSD. Subgroup analysis corroborated these results. Conclusion Adjuvant TACE demonstrates the potential to significantly enhance the long-term prognosis of HCC patients with CP B7 or B8 following hepatectomy, particularly those with multiple tumors, large tumor size, macrovascular or microvascular invasion, and narrow resection margin. Hence, pTACE should be considered for patients at high risk of recurrence following thorough evaluation.

The choroid plexus (ChP) serves as the principal origin of cerebrospinal fluid (CSF). CSF hypersecretion due to ChP inflammation has emerged as an important pathogenesis of hydrocephalus recently. Nevertheless, the precise mechanisms of ChP inflammation and the ensuing CSF hypersecretion in hydrocephalus remain ill-defined. In the present study, we elucidate the critical role of macrophages in the pathogenesis of ChP inflammation. Specifically, we identify the chemokine CCL2, released by ChP epithelial cells, recruits CCR2+ monocytes to the ChP thereby inciting hydrocephalus pathogenesis. The accumulated ChP macrophages increase the inflammation in ChP epithelial cells through TNF-α/TNFR1/NF-κB signaling cascade, thereby leading to CSF hypersecretion. Strikingly, augmentation of ChP‒CCL2 using an adeno-associated viral approach (AAV) exacerbates macrophage recruitment, activation, and ventriculomegaly in rat PHH models. Systemic application of Bindarit, a specific CCL2 inhibitor, significantly inhibits ChP macrophage infiltration and activation and reduces CSF secretion rate. Furthermore, the administration of CCR2 antagonist (INCB 3284) reduces ChP macrophage accumulation and ventriculomegaly. This study not only unveils the ChP CCL2‒CCR2 signaling in the pathophysiology of hydrocephalus but also unveils Bindarit as a promising therapeutic choice for the management of posthemorrhagic hydrocephalus.

Small regulatory RNAs (sRNAs) are non-coding RNA molecules that fine-tune various cellular processes and respond to various environmental stimuli. In Bacillus subtilis, the regulatory mechanisms and specific targets of several sRNAs remain largely unknown. In this study, we identified and characterized S612 as a self-terminating sRNA in B. subtilis. The expression of S612 is regulated by external signals, including nutrient availability and salt concentration. Overexpression of S612 induced filamentous cells with extensive cellular elongation and complete inhibition of sporulation, indicating its potential to control cell morphology and spore formation. S612 directly targets and downregulates genes through post-transcriptional base pairing with mRNAs, including ylmD, trpE, ycxC, yycS, rapH, and amyE, some of which are involved in cell membrane integrity, cell wall synthesis, and sporulation initiation. Therefore, we propose that S612 is an important post-transcriptional regulator of cell morphology and sporulation.

Objectives: The COVID-19 pandemic significantly disrupted the global influenza seasonal patterns due to nonpharmaceutical interventions. This study aims to describe the influenza seasonal characteristics in the first two seasons after lifting COVID-19 NPIs and assess shifts before, during, and after the pandemic. Methods: We analyzed country-specific weekly influenza data (2011-2024) from WHO FluNet and collected COVID-19 NPI timing from official announcements. The study was divided into pre-pandemic, pandemic, and post-pandemic phases, estimating epidemic onset, peak week, peak intensity, and duration by climate zones. Results: In temperate countries, peak intensity after the pandemic decreased by 8.4 %, while duration increased by 1.8 weeks, and onset and peak were delayed by 18.5 and 22.8 weeks compared to regular seasonal pattern before the pandemic. Subtropical countries experienced a 17.2 % decrease in peak intensity, a 2.4-week decrease in duration, and delays in onset and peak by 13.5 and 2.3 weeks. Tropical countries had a 10 % decrease in peak intensity, a 3-week reduction in duration, and a 6.6-week delay in onset with no significant change in peak time. Conclusion: Influenza seasonality shifted significantly after the pandemic, with epidemic durations returning to typical patterns but peak intensities remained low. Robust surveillance after an infectious disease pandemic is crucial to inform prevention and control strategies.

Affected by factors such as few light sources and uneven illumination in underground coalmines, underground images have some problems such as low illumination, presenting many dark areas, blurring or missing detail information, excessive darkness generating noise, etc. Traditional image enhancement methods are prone to some shortcomings such as color distortion and loss of detail information in low-light image enhancement. Furthermore, a deep-learning low-light image enhancement method can solve the problem of low-light image brightness enhancement to a certain extent, but its model generalization ability is poor in real-world scenarios. Aiming at above mentioned problems, taking the advantage of Transformer's strong generalization ability, a low illumination image enhancement algorithm based on Transformer model is proposed. Firstly, the Swin v2 module is combined with the convolution module to construct the multiplicative and additive maps of the underground low illuminance image, and superimposed with the original image for fitting, in order to solve the problems of blurring or missing detail information, and over-darkness generating noise. At the same time, the attention mechanism of the fusion multi-scale module is used to perform color processing on the superimposed fitted image to solve the problems of limited image brightness enhancement, the existence of many dark areas, and color distortion. The experiments verify that the performances of this paper's algorithm on the objective quality metrics Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM), are improved by 34.76%, 55.73%; 47.32%, 52.76%; 22.52%, 25.7%; 19.615%, 12.285%; 5.81%, 2.625%, compared to LIME, Zero-DCE, RetiNexNet, MBLLEN, and KIND algorithms. Meanwhile, the qualitative analysis results show that the proposed method can significantly enhance the low illumination image of the underground coalmine, the image brightness reaches the visible range, the color is more realistic compared to other methods, and the image detail information is clearer. The study shows that the proposed algorithm has a good performance in terms of the degree of image noise, color distortion, contrast, structural similarity, and brightness, which is relatively superior overall.

How to achieve intelligent, safe and efficient mining of rockburst coal seams is a major engineering and technical challenge facing deep coal mining. The core connotation of this issue lies in the intelligent, unmanned, and inherently safe mining of coal seams with rockburst. Through accurate perception and intelligent early warning of coal and rock burst risks, as well as self-adaptation and self-optimization of mining engineering, intelligent anti-burst and mining control technologies are formed to achieve a fundamental shift from engineering-induced disasters to engineering-prevented and mitigated disasters. By implementing measures such as mining source design, roof area fracturing, localized targeted management, and strengthening roadway support, a low-stress environment and safe roadway space are created for the safe and efficient mining of coal seams with rock burst. On this basis, a big data analysis software platform that integrates static geological, dynamic monitoring, working conditions, personnel positioning, production organization, historical data and other information, as well as an intelligent mining control system that integrates impact pressure decision-making information, has been developed. The anti-impact information source has been transformed into an intelligent mining control source, forming a new flexible intelligent mining model of "low pressure fast pushing, medium pressure slow mining, and high pressure production stop". Field application shows that in the intelligent anti-impact and control mining area, the degree of mine pressure in working face and roadway is smaller,, and the energy release of coal and rock is more stable, which is conducive to the reasonable release of impact mine production capacity.

Abstract Background In China, both percutaneous microwave/radiofrequency ablation liver partition plus portal vein embolization (PALPP) and transarterial chemoembolization (TACE) plus portal vein embolization (PVE) have been utilized in planned hepatectomy. However, there is a lack of comparative studies on the effectiveness of these two techniques for cases with insufficient future liver remnant (FLR). Methods Patients were categorized into either the PALPP group or the TACE + PVE group. Clinical data, including FLR growth rate, complications, secondary resection rate, and overall survival rate, were compared and analyzed for both groups retrospectively. Results Between December 2014 and October 2021, a total of 29 patients underwent TACE + PVE (n = 12) and PALPP (n = 17). In the TACE + PVE group, 7 patients successfully underwent two-stage hepatectomy, while in the PALPP group, 13 patients underwent the procedure (two-stage resection rate: 58.3% vs. 76.5%, P = 0.42). There were no significant differences in postoperative complications of one-stage procedures (11.8% vs. 8.3%, P > 0.05) and second-stage resection complication (0% vs. 46.2%, P = 0.05) between the TACE + PVE and PALPP groups. However, the PALPP group demonstrated a shorter time to FLR volume growth for second-stage resection (18.5 days vs. 66 days, P = 0.001) and KGR (58.5 ml/week vs. 7.7 ml/week, P = 0.001). Conclusions Compared with TACE + PVE, PALPP results in a more significant increase in FLR volume and a higher rate of two-stage resection without increasing postoperative complications.

Abstract. Background:. Although the treatment of peripheral T-cell lymphoma (PTCL) has undergone advancements during the past several years, the response rate and long-term effects with respect to patients with PTCL remain unsatisfactory—particularly for relapsed or refractory (R/R) patients. This phase II trial was designed to explore the efficacy and safety of an all-oral regimen of chidamide plus prednisone, cyclophosphamide, and thalidomide (CPCT) for R/R PTCL patients who could not tolerate the standard chemotherapy for a variety of reasons. Methods:. We conducted a multicenter phase II clinical trial in which we combined chidamide (30 mg twice weekly) with prednisone (20 mg daily after breakfast), cyclophosphamide (50 mg daily after lunch), and thalidomide (100 mg daily at bedtime) (the CPCT regimen) for a total of fewer than 12 cycles as an induction-combined treatment period, and then applied chidamide as single-drug maintenance. Forty-five patients were ultimately enrolled from August 2016 to April 2021 with respect to Chinese patients at nine centers. Our primary objective was to assess the overall response rate (ORR) after the treatment with CPCT. Results:. Of the 45 enrolled patients, the optimal ORR and complete response (CR)/CR unconfirmed (CRu) were 71.1% (32/45) and 28.9% (13/45), respectively, and after a median follow-up period of 56 months, the median progression-free survival (PFS) and overall survival (OS) were 8.5 months and 17.2 months, respectively. The five-year PFS and OS rates were 21.2% (95% confidence interval [CI], 7.9–34.5%) and 43.8% (95% CI, 28.3–59.3%), respectively. The most common adverse event was neutropenia (20/45, 44.4%), but we observed no treatment-related death. Conclusion:. The all-oral CPCT regimen was an effective and safe regimen for R/R PTCL patients who could not tolerate standard chemotherapy for various reasons. Trial Registration:. ClinicalTrials.gov, NCT02879526.

Background: The pathogens causing unexplained pneumonia in both HIV-infected or HIV-unfected patients are likely to be complex. This retrospective study aimed to characterize the etiology of pneumonia in HIV-infected and HIV-uninfected patients using bronchoalveolar lavage fluid (BALF) analysis with metagenomic next-generation sequencing (mNGS) and X-pert MTB/RIF. Methods: Between January 2022 and May2024, 141 HIV-infected and 104 HIV-uninfected patients admitted to Nanjing Second Hospital with pneumonia were included. BALF samples were collected and analyzed using mNGS to detect bacteria, fungi, viruses, tuberculosis (TB) and non-tuberculous mycobacteria (NTM), and X-pert for TB detection. Clinical data including CD4 T-cell counts, comorbidities, and ART status were collected and analyzed. Results: HIV-uninfected patients were found to be older and exhibited a higher prevalence of comorbidities compared to HIV-infected patients. Despite higher median CD4 T-cell counts in HIV-uninfected individuals (412 cells/μL vs. 31 cells/μL in HIV-infected), TB detection rates using X-pert and mNGS were lower than anticipated, particularly in HIV-infected patients. Mixed-pathogen infections were significantly more prevalent in HIV-infected patients, especially those with lower CD4 T-cell counts. ART use showed variable impacts on pathogen diversity, with longer treatment durations associated with reduced infection complexity but persistent immunodeficiency in some cases.In patients with pneumonia, whether HIV-infected or HIV-uninfected, pathogens often exhibit complexity, underscoring the critical role of timely mNGS and X-pert analysis of BALF for early pathogen detection.

Background Fetal arrhythmias frequently co-occur with congenital heart disease in fetuses. The peaks observed in M-mode fetal echocardiograms serve as pivotal diagnostic markers for fetal arrhythmias. However, speckles, artifacts, and noise pose notable challenges for accurate image analysis. While current deep learning networks mainly overlook cardiac cyclic information, this study concentrated on the integration of such features, leveraging contextual constraints derived from cardiac cyclical features to improve diagnostic accuracy. Methods This study proposed a novel deep learning architecture for diagnosing fetal arrhythmias. The architecture presented a loss function tailored to the cardiac cyclical information and formulated a diagnostic algorithm for classifying fetal arrhythmias. The training and validation processes utilized a dataset comprising 4440 patches gathered from 890 participants. Results Incorporating cyclic loss significantly enhanced the performance of deep learning networks in predicting peak points for diagnosing fetal arrhythmia, resulting in improvements ranging from 7.11% to 14.81% in F 1-score across different network combinations. Particularly noteworthy was the 18.2% improvement in the F 1-score for the low-quality group. Additionally, the precision of diagnosing fetal arrhythmia across four categories exhibited improvement, with an average improvement rate of 20.6%. Conclusion This study introduced a cyclic loss mechanism based on the cardiac cycle information. Comparative evaluations were conducted using baseline methods and state-of-the-art deep learning architectures with the fetal echocardiogram dataset. These evaluations demonstrated the proposed framework’s superior accuracy in diagnosing fetal arrhythmias. It is also crucial to note that further external testing is essential to assess the model’s generalizability and clinical value.

Background: Studies have shown that abnormal temperature at night is a risk factor for respiratory health. However, there is limited evidence on the impact of hot and cold nights on cause-specific diseases such as pneumonia, which is a leading cause of morbidity and mortality in children. Methods: We collected daily data on pneumonia hospitalisations in children under five years from 2011 to 2017 in three low-, middle- and high-income countries (Bangladesh, China, and Australia). The intensity of hot and cold nights was measured by excess temperature. A space–time-stratified case-crossover analysis was used to estimate the association between hot and cold nights and childhood pneumonia hospitalisations. We further estimated the fraction of childhood pneumonia hospitalisations attributable to hot and cold nights. Results: Both hot and cold nights were associated with an increased risk of hospitalisations for childhood pneumonia in low-, middle-, and high-income countries, with a greater disease burden from hot nights. Specifically, the fraction of childhood pneumonia attributable to hot nights was the largest in Australia [21.2%, 95% confidence interval (CI): 11.8%-28.1%], followed by Bangladesh (15.2%, 95% CI: 4.1%–23.8%) and China (2.7%, 95% CI: 0.4%-4.7%). Additionally, the fraction of childhood pneumonia attributable to cold nights was 1.3% (95% CI: 0.4%-2.0%) in Bangladesh and 0.4% (95% CI: 0.1%-0.7%) in China. Conclusion: This multi-country study suggests that hot and cold nights are not only associated with a higher risk of pneumonia hospitalisations in children but also responsible for substantial fraction of hospitalisations, with a greater impact from hot nights.

Coal mine scene image recognition plays an important role in safety monitoring and equipment detection. However, traditional methods often depend on manually designed neural network architectures. These models struggle to handle the complex backgrounds, low illumination, and diverse objects commonly found in coal mine environments. Manual designs are not only inefficient but also restrict the exploration of optimal architectures, resulting to subpar performance. To address these challenges, we propose using a neural architecture search (NAS) to automate the design of neural networks. Traditional NAS methods are known to be computationally expensive. To improve this, we enhance the process by incorporating Particle Swarm Optimization (PSO), a scalable algorithm that effectively balances global and local searches. To further enhance PSO’s efficiency, we integrate the lifespan mechanism, which prevents premature convergence and enables a more comprehensive exploration of the search space. Our proposed method establishes a flexible search space that includes various types of convolutional layers, activation functions, pooling operations, and network depths, enabling a comprehensive optimization process. Extensive experiments show that the Lifespan-PSO NAS method outperforms traditional manually designed networks and standard PSO-based NAS approaches, offering significant improvements in both recognition accuracy (improved by 10%) and computational efficiency (resource usage reduced by 30%). This makes it a highly effective solution for real-world coal mine image recognition tasks via a PSO-optimized approach in terms of performance and efficiency.

This study introduces an organic light-emitting diode (OLED) light extraction method using a wavy-patterned polydimethylsiloxane (PDMS) substrate created via oxygen (O2) plasma treatment. A rapid fabrication process adjusted the flow, pressure, duration, and power of the O2 plasma treatment to replicate the desired wavy structure. This method allowed the treated samples to maintain over 90% total transmittance and enabled controlled haze adjustments from 10% to 70%. Finite-difference time-domain (FDTD) simulations were employed to determine optimal amplitudes and periods for the wavy structure to maximize optical performance. Further experiments demonstrated that bottom-emitting green fluorescent OLEDs constructed on these substrates achieved an external quantum efficiency (EQE) of 3.5%, representing a 97% improvement compared to planar PDMS OLEDs. Additionally, color purity variation was minimized to 0.044, and the peak wavelength shift was limited to 10 nm, ensuring consistent color purity and intensity even at wide viewing angles. This study demonstrates the potential of this cost-effective and efficient method in advancing high-quality display.

Artificial intelligence (AI) technologies have been widely applied to the automated detection of pipeline leaks. However, traditional AI methods still face significant challenges in effectively detecting the complete leak process. Furthermore, the deployment cost of such models has increased substantially due to the use of GPU-trained neural networks in recent years. In this study, we propose a novel leak detector, which includes a new model and a sequence labeling method that integrates prior knowledge with traditional reconstruction error theory. The proposed model combines the Kolmogorov–Arnold Network (KAN) with an autoencoder (AE). This model combines the Kolmogorov–Arnold Network (KAN) with an autoencoder (AE), forming a hybrid framework that effectively captures complex temporal dependencies in the data while exhibiting strong pattern modeling and reconstruction capabilities. To improve leak detection, we developed a novel unsupervised anomaly sequence labeling method based on traditional reconstruction error theory, which incorporates an in-depth analysis of the reconstruction error curve along with prior knowledge. This method significantly enhances the interpretability and accuracy of the detection process. Field experiments were conducted on real urban water supply pipelines, and a benchmark dataset was established to evaluate the proposed model and method against commonly used models and methods. The experimental results demonstrate that the proposed model and method achieved a high segment-wise precision of 93.1%. Overall, this study presents a transparent and robust solution for automated pipeline leak detection, facilitating the large-scale, cost-effective development of digital twin systems for urban pipeline leak emergency management.

Background: The climate change scenario has witnessed an increase in extreme temperature events (ETEs), including heat waves and cold spells, and a heightened occurrence of compounding with fine particulate matter (PM2.5). However, the impact of this phenomenon on the sensitivity to thyroid hormones (THs) in humans is unclear, especially in a group as specific as schizophrenia. Methods: A longitudinal study was constructed using longitudinal measurements of thyroid function in schizophrenia in the Anhui Mental Health Center. The latent growth mixture model was applied to assess the optimal trajectory of change in impaired THs sensitivity. We then used logistic regression to explore associations between heat waves, cold spells, and PM2.5 with impaired THs sensitivity trajectories in the total population and different gender and age subgroups. Furthermore, the effect of the frequency, intensity, and duration of ETEs in the above associations was explored, as well as an assessment of the interaction between ETEs and PM2.5. Results: Among 931 participants, we identified two classifications of trajectories of impaired THs sensitivity: “Low-stable” (n = 836, 89.80 %) and “Rise-slight down” (n = 95, 10.20 %). Logistic regression showed significant associations between each additional day of heat waves (≥3 days with temperature thresholds above the 95th percentile) and cold spells (≥3 days with temperature thresholds below the 5th percentile) and “Rise-slight down” trajectory, with odds ratios (95 % confidence intervals) of 1.06 (1.02, 1.10) and 1.19 (1.14, 1.24), respectively, and the strength of this association increased with the intensity and duration of ETEs. Subgroup analyses indicated that the association was more pronounced in males and the age group above 40 years. Furthermore, PM2.5 was found to interact with heat waves, with high concentrations exacerbating the effects of heat waves. Conclusions: Our findings suggest that mitigating both ETEs and PM2.5 exposures may bring health co-benefits in preventing thyroid impairment in schizophrenia.

Shallow-water benthic habitat classification of coral reefs based on satellite remote sensing is an important part of coral reef monitoring. Leveraging its potent capacity for feature learning, and generalization, deep learning emerges as a robust method for coral reef benthic habitat classification. Due to the complexity of the marine environment, it is difficult to produce high-quality pixel-by-pixel labels for deep learning-based methods, which makes it challenging to recover structural details of coral reef benthic habitats. Bathymetry data can provide spatial contextual information and geometric features, serving as auxiliary features to provide abundant structural information for benthic classification models. However, how to use bathymetry and what kind of bathymetry features to employ for assisting model learning remains to be explored. Therefore, a bathymetry feature fusion-weakly supervised coral reef benthic habitat classification model (BFFBHCM) is proposed. BFFBHCM is supervised by sparse benthic habitat samples with bathymetry and can generate dense, multi-scale bathymetry features. With the robust bathymetry-benthic feature fusion module (B-BFFM), BFFBHCM can consider both semantic and structural details of the benthic habitats, thus generating highly accurate benthic habitat classification results. Experiments were conducted using the NJUReef + dataset containing 10 coral reefs in the Spratly Islands, China, constructed based on in-situ data. Comprehensive experimental results demonstrate that the proposed BFFBHCM is insensitive to the vertical error in bathymetry, with an average mIoU 22.54 % higher than state-of-the-art methods. Furthermore, it outperforms the weakly-supervised method that excludes bathymetry by 10.14 %, and still exhibits generalization to coral reefs in different regions around the world.

ObjectivesTo identify the long-term spatiotemporal trend of ozone-related chronic obstructive pulmonary disease (COPD) burden by sex and country and to explore potential drivers.MethodsWe retrieved data of ozone-related COPD death and disability adjusted life year (DALY) from the Global Burden of Disease 2019. We used a linear regression of natural logarithms of age-standardized rates (ASRs) with calendar year to examine the trends in ASRs and a panel regression to identify country-level factors associated with the trends.ResultsGlobal ozone-attributable COPD deaths increased from 117,114 to 208,342 among men and from 90,265 to 156,880 among women between 1990 and 2019. Although ASRs of ozone-related COPD death and DALY declined globally, they increased in low and low-middle Socio-demographic Index (SDI) regions, with faster rise in women. Elevated average maximum temperature was associated with higher ozone-attributable COPD burden, while more green space was associated with lower burden.ConclusionMore efforts are needed in low and low-middle SDI regions, particularly for women, to diminish inter-country inequality in ozone-attributable COPD. Global warming may exacerbate the burden. Expanding green space may mitigate the burden.

Green-blue spaces (GBS) are pivotal in mitigating thermal discomfort. However, their management lacks guidelines rooted in epidemiological evidence for specific planning and design. Here we show how various GBS types modify the link between non-optimal temperatures and cardiovascular mortality across different thermal extremes. We merged fine-scale population density and GBS data to create novel GBS exposure index. A case time series approach was employed to analyse temperature-cardiovascular mortality association and the effect modifications of type-specific GBSs across 1085 subdistricts in south-eastern China. Our findings indicate that both green and blue spaces may significantly reduce high-temperature-related cardiovascular mortality risks (e.g., for low (5%) vs. high (95%) level of overall green spaces at 99th vs. minimum mortality temperature (MMT), Ratio of relative risk (RRR) = 1.14 (95% CI: 1.07, 1.21); for overall blue spaces, RRR = 1.20 (95% CI: 1.12, 1.29)), while specific blue space types offer protection against cold temperatures (e.g., for the rivers at 1st vs MMT, RRR = 1.17 (95% CI: 1.07, 1.28)). Notably, forests, parks, nature reserves, street greenery, and lakes are linked with lower heat-related cardiovascular mortality, whereas rivers and coasts mitigate cold-related cardiovascular mortality. Blue spaces provide greater benefits than green spaces. The severity of temperature extremes further amplifies GBS's protective effects. This study enhances our understanding of how type-specific GBS influences health risks associated with non-optimal temperatures, offering valuable insights for integrating GBS into climate adaptation strategies for maximal health benefits.

In order to overcome the problems existed in the construction process of complex giant system of smart mine, the overall technical framework of smart mine construction was proposed, and a detailed description was made from the aspects of smart mine system model construction, underground space reconstruction and model dynamic update, machine vision measurement technology, radio frequency explosion-proof test method of gas environment, mine safety closed-loop control system, smart mine standard system, etc. The smart mine was divided into information sensing support layer, edge computing layer, cloud data center, multi-type network, intelligent mine production management and control platform, intelligent mine production system and intelligent mine operation and maintenance management system. The smart mine technology architecture based on the deep integration of multiple systems was built. Based on the knowledge map and information extraction of smart mine, a smart mine system model driven by data innovation, based on communication network and centered on data computing power was constructed. A 3D visual space model based on machine vision perception information and supplemented by other perception information was constructed, and a 3D vision and spatial reconstruction framework of mine underground scene was proposed to realize the reconstruction and dynamic update of the 3D space of underground mine. Based on machine vision technology, the identification algorithm of downhole equipment position and coal-rock interface was developed to realize the simultaneous aerial measurement of fully mechanized mining equipment group and coal-rock interface. The limitations and shortcomings of the current explosion-proof standards on the power of 5G base stations in the underground gas environment were discussed, and a special test device for radio-frequency electromagnetic energy explosion-proof was designed and developed, which provides a method for reference to improve the power threshold of underground 5G base stations. The technical framework of mine disaster closed-loop management and control system, which integrates comprehensive perception of disaster information, independent decision-making of prevention and control plan, and coordinated control of prevention and control equipment, was proposed to realize advanced prediction and early warning and coordinated prevention and control of underground disasters. The framework of intelligent coal mine standard system was constructed, the typical cases of intelligent construction of coal mine and metal mine in China were analyzed, and the development trend and suggestions of intelligent mine construction were put forward.

Monoterpenoids are typically present in the secretory tissues of higher plants, and their biosynthesis is catalyzed by the action of monoterpene synthases (MTSs). However, the knowledge about these enzymes is restricted in a few plant species. MTSs are responsible for the complex cyclization of monoterpene precursors, resulting in the production of diverse monoterpene products. These enzymatic reactions are considered exceptionally complex in nature. Therefore, it is crucial to understand the catalytic mechanism of MTSs to elucidate their ability to produce diverse or specific monoterpenoid products. In our study, we analyzed thirteen genomes of Dipterocarpaceae and identified 38 MTSs that generate a variety of monoterpene products. By focusing on four MTSs with different product spectra and analyzing the formation mechanism of acyclic, monocyclic and bicyclic products in MTSs, we observed that even a single amino acid mutation can change the specificity and diversity of MTS products, which is due to the synergistic effect between the shape of the active cavity and the stabilization of carbon-positive intermediates that the mutation changing. Notably, residues N340, I448, and phosphoric acid groups were found to be significant contributors to the stabilization of intermediate terpinyl and pinene cations. Alterations in these residues, either directly or indirectly, can impact the synthesis of single monoterpenes or their mixtures. By revealing the role of key residues in the catalytic process and establishing the interaction model between specific residues and complex monoterpenes in MTSs, it will be possible to reasonably design and engineer different catalytic activities into existing MTSs, laying a foundation for the artificial design and industrial application of MTSs.

C32H44O6, monoclinic, P21 (no. 4), a = 12.2561 Å, b = 10.3674(4) Å, c = 23.3835(8) Å, β = 98.463°, V = 2938.85(19) Å3, Z = 2, Rgt(F) = 0.0602, wRref(F2) = 0.1537, T = 273(2) K.

Background: Climate change has shifted and will continue to alter the patterns of daytime and nighttime temperatures. However, it remains unclear whether hot days and nights, as well as cold days and nights, have varying impacts on mental disorders. Methods: Daily hospital-based data on 1.2 million outpatient visits for mental disorders were obtained from 15 Chinese cities (2011-2015). The index of hot day excess, hot night excess, cold day excess, and cold night excess were used to measure the intensity of hot/cold days and nights. Firstly, a distributed lag non-linear model was used to estimate the risk of mental disorders associated with hot/cold days and nights for each city. Secondly, city-specific estimates were pooled by multivariate meta-analysis to obtain national effect estimates. Thirdly, the attributable fraction of mental disorders was estimated for the nation, genders, age groups, heating methods, and air conditioning penetration rates. Results: Our findings revealed that both hot and cold nights were associated with a higher risk of mental disorders compared to hot and cold days. The attributable fraction of mental disorders was higher for cold (night: 25.74%; day: 19.65%) in contrast to heat (night: 16.91%; day: 7.24%). Males, the elderly, cities using central heating, and cities with low air conditioning penetration rates were vulnerable to cold nights. Conclusions: This nationwide study highlights the adverse effects and significant burden of hot and cold nights on mental disorders. It underscores the potential for mitigating these impacts through the adoption of air conditioning, particularly in southern cities across China.

Ground penetrating radar (GPR) has emerged as a pivotal tool for subsurface explorations, particularly in detecting subsurface defects that might endanger structural integrity. While GPR B-scan data visually depict underground conditions, it represents the time delay and amplitude of the returned electromagnetic (EM) waves, making them complex to interpret due to both their image-like appearance and their inherent waveform changes. To address this complexity, this article introduces the novel temporal–spatial synthesis network, designed to harness both temporal and spatial features for enhanced subsurface defect detection in GPR B-scan data. The echo state network, underpinned by reservoir computing, is utilized to fit the GPR data and capture its “temporal features,” emphasizing the temporal variations present in the EM waves. Concurrently, the convolutional neural network focuses on discerning “spatial features” from the B-scan images, spotlighting spatial patterns that possibly indicate subsurface defects. After extracting these temporal and spatial features, they are synthesized to form a comprehensive representation of the GPR data. The enhanced synthesized feature facilitates precise classification, resulting in heightened differentiation between normal and defect-contained subsurface areas. Experiments on real-world GPR datasets are conducted, with the results underscoring the efficacy of the proposed approach.

With the rapid growth of the mobile computing techniques, a wide variety of mobile edge computing (MEC) applications have emerged recently, aiming to provide computationally intensive and delay-sensitive network services. Through MEC, various complex tasks of mobile devices can be offloaded to the edge of network system for execution by edge servers, which greatly reduces the local computing burden. However, how to effectively allocate computational and communication resources in edge-cloud remains a challenging task, especially when multiple mobile users and edge servers are involved. In this paper, we propose a decomposition-based multi-objective optimization algorithm based on estimation-of-distribution models (MOEA/D-EoD) to deal with the task offloading and resource allocation problem in MEC. Especially, considering the features of multi-user and multi-server cloud-edge-end collaboration wireless MEC system, we construct a joint optimization model of task offloading and resource allocation, where limited communication and computational resource constraints are considered. To deal with the optimization model, we design an efficient decomposition-based algorithm, which incorporates two novel estimation-of-distribution models to deal with discrete and continuous decision variables of the problem. Experimental results obtained from benchmark test suites DTLZ and ZDT demonstrate that the proposed method exhibits significantly superior performance compared to other comparative algorithms. The proposed model and algorithm are simulated and tested on different test instances, and experimental results show the effectiveness and efficiency of our proposed method.

Semantic segmentation of high-resolution remote sensing images (HRRSIs) is a basic research in the field of remote sensing image processing. Many current CNN-based methods complete detailed segmentation by building an encoder–decoder network. However, the representative selection features of ground objects are often ignored and the semantic gap between high-level features and low-level features, resulting in redundant information and erroneous annotation results. In this article, we propose an original reinforcement bilateral network (ORBNet) to improve the performance of HRRSIs semantic segmentation. The ORBNet consists of two branches—the detail branch and the semantic branch, which are responsible for extracting low-level features and high-level features, respectively. The feature alignment and fusion (FAF) modules are used to align features at different levels between two branches and produce shallow features and deep features. Furthermore, we use the detail loss in the detail branch to supervise the generation of low-level features. The class-specific discriminative loss is used to help the semantic branch distinguish features of different ground objects. The spatial-channel attention (SCA) modules are used in the feature fusion stage to select representative features. We conducted extensive experiments on two open-source ISPRS remote sensing datasets, and the experimental results verified the superior performance of our ORBNet.

In this paper, a transfer learning technique was first utilized to obtain deep neural network models for different gate lengths in the avalanche breakdown regime. Once the characteristics of a gate-length metal-oxide-semiconductor field-effect transistor are measured and the data are used to obtain a deep neural network, behavioral models for different gate lengths are established by fewer hidden layers and less measured data at the desired biases and frequencies. Therefore, the training data through this method is significantly reduced and the accuracy of the testing dataset is comparable to that of the conventional artificial neural network technique. The predicted, measured, and simulated results based on the transit time theory at different bias voltages and frequencies are in good agreement. Multibias S-parameter behavioral models in the breakdown regime can be efficiently built using this technique. This approach can even be applied to other advanced technology nodes and can shorten the model building time for CMOS circuit design operating in the saturation and avalanche breakdown regions.

Large language model (LLM) processing, with natural language as its core, carries out information retrieval through intelligent Q&A. It has a wide range of application scenarios and is commonly considered a kind of generative AI. However, when LLMs handle generation tasks, the results generated by fundamental LLMs with an insufficient comprehensive performance, specifically in the vertical domain, are often inaccurate due to a poor generalization ability, resulting in the so-called “hallucination” phenomenon. To solve these problems, in this study, an enhanced retrieval scheme for LLM processing was developed, named the BM-RAGAM (BM25 retrieval-augmented generation attention mechanism), by constructing a vectorized knowledge base, utilizing a hybrid joint retrieval strategy of keyword matching through searching and a semantic-enhanced association with an attention mechanism and taking ocean-front- and eddy-related knowledge in oceanography as an example. This scheme realized accurate word-based matching with the BM25 algorithm and text generation through a semantic-enhanced association using RAG, and it was used to construct a vector database of the text knowledge on ocean fronts and eddies. The output was compared and analyzed with the fundamental LLM of Qwen2-72B using the proposed scheme, and an ablation experiment was conducted. The results show that the proposed scheme greatly reduced hallucination generation in the process of text generation, making its outputs more interpretable.

The spiral tube heat exchanger has the advantages of stable structure, high space utilization rate, and strong heat transfer ability. Supercritical carbon dioxide (s-CO2) has the advantages of high thermal conductivity, high specific heat capacity, and low viscosity. When the spiral tube heat exchanger uses supercritical carbon dioxide as the heat transfer medium, the two complement each other’s advantages and can further improve the heat transfer efficiency and optimize the space utilization rate. However, under the coupling influence of factors such as centrifugal force and buoyancy force, the heat transfer process of s-CO2 in the spiral tube is extremely complex. At present, there is no design criterion or empirical correlation with strong universality in the industrial field. Based on this, this paper mainly uses ANSYS FLUENT, a numerical simulation software suitable for numerical simulation, to study the influence of internal vortex field and secondary flow intensity on the thickness of thermal boundary layers through a combination of numerical simulation and theoretical analysis. At the same time, by changing various operating and structural parameters, the heat transfer characteristics of supercritical s-CO2 in the spiral tube heat exchanger are analyzed and summarized. In addition, we also established a new buoyancy factor, Fu, and tested that when Fu < 1.6 × 10−5, the effect of the buoyancy lift in the pipeline disappeared. At the same time, a correlation formula for predicting the heat transfer performance of spiral tube heat exchangers with a Nu number error less than 20% is established in this paper, which can provide solid theoretical guidance for industrial application.

Lake ice phenology serves as a sensitive indicator of climate change in the lake-rich Northeast China. In this study, the freeze-up date (FUD), break-up date (BUD), and ice cover duration (ICD) of 31 lakes were extracted from a time series of the land water surface temperature (LWST) derived from the combined MOD11A1 and MYD11A1 products for the hydrological years 2001 to 2021. Our analysis showed a high correlation between the ice phenology measures derived by our study and those provided by hydrological records (R2 of 0.89) and public datasets (R2 > 0.7). There was a notable coherence in lake ice phenology in Northeast China, with a trend in later freeze-up (0.21 days/year) and earlier break-up (0.19 days/year) dates, resulting in shorter ice cover duration (0.50 days/year). The lake ice phenology of freshwater lakes exhibited a faster rate of change compared to saltwater lakes during the period from HY2001 to HY2020. We used redundancy analysis and correlation analysis to study the relationships between the LWST and lake ice phenology with various influencing factors, including lake properties, local climate factors, and atmospheric circulation. Solar radiation, latitude, and air temperature were found to be the primary factors. The FUD was more closely related to lake characteristics, while the BUD was linked to local climate factors. The large-scale oscillations were found to influence the changes in lake ice phenology via the coupled influence of air temperature and precipitation. The Antarctic Oscillation and North Atlantic Oscillation correlate more with LWST in winter, and the Arctic Oscillation correlates more with the ICD.

Waste copper-containing paint residue (WCPR) represents a typical hazardous waste containing both toxic organic substances and toxic heavy metals, but there are few reports on the recycling of heavy metals. The recovery of Cu from WCPR by H2SO4 leaching–extraction–stripping has the advantages of eco-friendliness, simplicity of operation, and high value-added product. The results show that under the optimal conditions, the leaching rate of Cu in WCPR is 94.31% (18.02 g/L), while the extraction and stripping rates of Cu in the leaching solution are 99.46 and 95.32%, respectively. Due to the high concentration of Cu2+ with fewer impurities in the stripping solution, the stripping solution is heated, evaporated, cooled, and crystallized to successfully produce high-purity dark blue CuSO4 crystal, accomplishing the high-value recycling of Cu in WCPR. In addition, the leach residue of WCPR contains acrylic resin and SiO2, which can be used in cement kilns for incineration, thus realizing the overall recycling and utilization of WCPR. HIGHLIGHTS The process of Cu recovery from waste copper-containing paint residue was first proposed to regenerate CuSO4.; Using leaching–extraction–stripping to recover Cu has the advantages of eco-friendliness and high value-added product.; The stripping solution without impurities can be used directly to regenerate CuSO4.; The resulting residue with no environmental risk can be cement kiln incineration.; The process can eliminate Cu pollution and recover Cu resource.;

Abstract Background Disease resilience is the ability of an animal to maintain productive performance under disease conditions and is an important selection target. In pig breeding programs, disease resilience must be evaluated on selection candidates without exposing them to disease. To identify potential genetic indicators for disease resilience that can be measured on selection candidates, we focused on the blood transcriptome of 1594 young healthy pigs with subsequent records on disease resilience. Transcriptome data were obtained by 3’mRNA sequencing and genotype data were from a 650 K genotyping array. Results Heritabilities of the expression of 16,545 genes were estimated, of which 5665 genes showed significant estimates of heritability (p 0.2). The correlation of heritability estimates with the corresponding estimates obtained for genes expressed in human blood was weak but a sizable number of genes with heritable expression levels overlapped. Genes with heritable expression levels were significantly enriched for biological processes such as cell activation, immune system process, stress response, and leukocyte activation, and were involved in various disease annotations such as RNA virus infection, including SARS-Cov2, as well as liver disease, and inflammation. To estimate genetic correlations with disease resilience, 3205 genotyped pigs, including the 1594 pigs with transcriptome data, were evaluated for disease resilience following their exposure to a natural polymicrobial disease challenge. Significant genetic correlations (p

Abstract From childhood to adolescence, the spatiotemporal development pattern of the human brain white matter connectome and its underlying transcriptomic and cellular mechanisms remain largely unknown. With a longitudinal diffusion MRI cohort of 604 participants, we map the developmental trajectory of the white matter connectome from global to regional levels and identify that most brain network properties followed a linear developmental trajectory. Importantly, connectome-transcriptomic analysis reveals that the spatial development pattern of white matter connectome is potentially regulated by the transcriptomic architecture, with positively correlated genes involve in ion transport- and development-related pathways expressed in excitatory and inhibitory neurons, and negatively correlated genes enriches in synapse- and development-related pathways expressed in astrocytes, inhibitory neurons and microglia. Additionally, the macroscale developmental pattern is also associated with myelin content and thicknesses of specific laminas. These findings offer insights into the underlying genetics and neural mechanisms of macroscale white matter connectome development from childhood to adolescence.

Abstract Background Platelet distribution width (PDW), but not platelet count, was found to more comprehensively reflect platelet activity. The present study, thus, aimed to evaluate the prognostic value of PDW to lymphocyte ratio (PDWLR) in patients with hepatocellular carcinoma (HCC) following hepatectomy. Methods Patients following hepatectomy were analyzed retrospectively. The Kaplan-Meier survival curves and Cox regression model were used to determine the prognostic value of PDWLR. Results 241 patients were analyzed eventually, and stratified into low and high PDWLR groups (≤ 9.66 vs. > 9.66). Results of comparing the baseline characteristics showed that high PDWLR was significantly associated with cirrhosis, and intraoperative blood loss (all P

Abstract The formation of biofilms is closely associated with persistent and chronic infections, and physiological heterogeneity such as pH and oxygen gradients renders biofilms highly resistant to conventional antibiotics. To date, effectively treating biofilm infections remains a significant challenge. Herein, we report the fabrication of micellar nanoparticles adapted to heterogeneous biofilm microenvironments, enabling nitric oxide (NO) release through two distinct photoredox catalysis mechanisms. The key design feature involves the use of tertiary amine (TA) moieties, which function as sacrificial agents to avoid the quenching of photocatalysts under normoxic and neutral pH conditions and proton acceptors at acidic pH to allow deep biofilm penetration. This biofilm-adaptive NO-releasing platform shows excellent antibiofilm activity against ciprofloxacin-resistant Pseudomonas aeruginosa (CRPA) biofilms both in vitro and in a mouse skin infection model, providing a strategy for combating biofilm heterogeneity and biofilm-related infections.

Abstract Background Gliomas are the most common malignant brain tumors, with powerful invasiveness and an undesirable prognosis. Actin related protein 2/3 complex subunit 5 (ARPC5) encodes a component of the Arp2/3 protein complex, which plays a significant role in regulating the actin cytoskeleton. However, the prognostic values and biological functions of ARPC5 in gliomas remain unclear. Methods Based on the TCGA, GEO, HPA, and UALCAN database, we determined the expression of ARPC5 in glioma. The results were verified by immunohistochemistry and Western blot analysis of glioma samples. Moreover, Kaplan-Meier curves, ROC curves, Cox regression analyses, and prognostic nomograms were used to observe the correlation between the ARPC5 expression and the prognosis of glioma patients. GO and KEGG enrichment analyses were conducted to identify immune-related pathways involved with the differential expression of ARPC5. Subsequently, the TCGA database was used to estimate the relationship between ARPC5 expression and immunity-related indexes, such as immune scores, infiltrating immune cells, and TMB. The TCIA database was used to assess the correlation between ARPC5 with immunotherapy. The association between ARPC5 and T cells marker CD3 was also evaluated through immunohistochemistry methods. The correlation between ARPC5 and T cell, as well as the prognosis of patients, was also evaluated using immunological methods. Moreover, the effect of ARPC5 on the biological characteristics of LN229 and U251 cells was determined by MTT, clone formation, and transwell migration assay. Results The high degree of ARPC5 was correlated with worse prognosis and unfavorable clinical characteristics of glioma patients. In the analysis of GO and KEGG, it is shown that ARPC5 was strongly correlated with multiple immune-related signaling pathways. The single-cell analysis revealed that ARPC5 expression was increased in astrocytes, monocytes and T cells. In addition, ARPC5 expression was strongly associated with immune scores, infiltrating immune cells, TMB, MSI, immune biomarkers, and immunotherapy. In experimental analysis, we found that ARPC5 was significantly overexpressed in gliomas and closely correlated with patient prognosis and CD3 expression. Functionally, the knockout of ARPC5 significantly reduced the proliferation and invasion of LN229 and U251 cells. Conclusions Our study revealed that the high expression level of ARPC5 may serve as a promising prognostic biomarker and be associated with tumor immunity in glioma.

Accurate land-sea classification of ATL03 datasets is the prerequisite for signal photons identification and higher-level data production. Current photon classification methods are mainly based on physical models and machine learning methods. Physical models necessitate a priori knowledge of the background noise rate, while machine learning methods require extensive manual intervention, which can be time-consuming. This work introduced a fast feature detection model (FFD model) for land-sea classification that eliminates the need for prior knowledge and is not constrained by time. The land-sea boundary usually experiences significant photon height changes and reflective intensity differences on both sides. The FFD model was developed to efficiently detect the inflection point of photon reflective intensity for preliminary land-sea classification. Minor classification errors were carefully corrected based on topographic geometric distribution in further refinement. The FFD model was applied to three typical land-sea boundary types to evaluate its classification accuracy. Validation of classification results with consistent Sentinel-2 images and manually tagging photons indicated that the FFD method exhibited substantial robustness to various land-sea boundary landforms. The FFD model can process nearly 105 raw photon classifications per second, with an average overall accuracy of 99.42 % for preliminary classification results. Coastal slope and surface reflection in land-sea boundary may have impact on classification accuracy. These effects were carefully eliminated in the refinement process, resulting in an average overall accuracy improvement to 99.86 %. Consequently, the FFD model represented a robust and efficient solution for automatic land-sea classification in coastal shallow water, coral reefs, and estuary areas.

Brain structural circuitry shapes a richly patterned functional synchronization, supporting for complex cognitive and behavioural abilities. However, how coupling of structural connectome (SC) and functional connectome (FC) develops and its relationships with cognitive functions and transcriptomic architecture remain unclear. We used multimodal magnetic resonance imaging data from 439 participants aged 5.7–21.9 years to predict functional connectivity by incorporating intracortical and extracortical structural connectivity, characterizing SC–FC coupling. Our findings revealed that SC–FC coupling was strongest in the visual and somatomotor networks, consistent with evolutionary expansion, myelin content, and functional principal gradient. As development progressed, SC–FC coupling exhibited heterogeneous alterations dominated by an increase in cortical regions, broadly distributed across the somatomotor, frontoparietal, dorsal attention, and default mode networks. Moreover, we discovered that SC–FC coupling significantly predicted individual variability in general intelligence, mainly influencing frontoparietal and default mode networks. Finally, our results demonstrated that the heterogeneous development of SC–FC coupling is positively associated with genes in oligodendrocyte-related pathways and negatively associated with astrocyte-related genes. This study offers insight into the maturational principles of SC–FC coupling in typical development.

While poly (3-hydroxybutyrate) (PHB) holds promise as a bioplastic, its commercial utilization has been hampered by the high cost of raw materials. However, glycerol emerges as a viable feedstock for PHB production, offering a sustainable production approach and substantial cost reduction potential. Glycerol stands out as a promising feedstock for PHB production, offering a pathway toward sustainable manufacturing and considerable cost savings. The identification and characterization of strains capable of converting glycerol into PHB represent a pivotal strategy in advancing PHB production research. In this study, we isolated a strain, Ralstonia sp. RRA (RRA). The strain exhibits remarkable proficiency in synthesizing PHB from glycerol. With glycerol as the carbon source, RRA achieved a specific growth rate of 0.19 h−1, attaining a PHB content of approximately 50% within 30 h. Through third-generation genome and transcriptome sequencing, we elucidated the genome composition and identified a total of eight genes (glpR, glpD, glpS, glpT, glpP, glpQ, glpV, and glpK) involved in the glycerol metabolism pathway. Leveraging these findings, the strain RRA demonstrates significant promise in producing PHB from low-cost renewable carbon sources.

Abstract Hydrocephalus is one of the most common brain disorders and a life‐long incurable condition. An empirical “one‐size‐fits‐all” approach of cerebrospinal fluid (CSF) shunting remains the mainstay of hydrocephalus treatment and effective pharmacotherapy options are currently lacking. Macrophage‐mediated ChP inflammation and CSF hypersecretion have recently been identified as a significant discovery in the pathogenesis of hydrocephalus. In this study, a pioneering DNA nano‐drug (TSOs) is developed by modifying S2 ssDNA and S4 ssDNA with SPAK ASO and OSR1 ASO in tetrahedral framework nucleic acids (tFNAs) and synthesis via a one‐pot annealing procedure. This construct can significantly knockdown the expression of SPAK and OSR1, along with their downstream ion channel proteins in ChP epithelial cells, thereby leading to a decrease in CSF secretion. Moreover, these findings indicate that TSOs effectively inhibit the M0 to M1 phenotypic switch of ChP macrophages via the MAPK pathways, thus mitigating the cytokine storm. In in vivo post‐hemorrhagic hydrocephalus (PHH) models, TSOs significantly reduce CSF secretion rates, alleviate ChP inflammation, and prevent the onset of hydrocephalus. These compelling results highlight the potential of TSOs as a promising therapeutic option for managing hydrocephalus, with significant applications in the future.

Abstract Mitochondrial dysfunction contributes to the development of secondary brain injury (SBI) following intracerebral hemorrhage (ICH) and represents a promising therapeutic target. Celastrol, the primary active component of Tripterygium wilfordii, is a natural product that exhibits mitochondrial and neuronal protection in various cell types. This study aims to investigate the neuroprotective effects of celastrol against ICH‐induced SBI and explore its underlying mechanisms. Celastrol improves neurobehavioral and cognitive abilities in mice with autologous blood‐induced ICH, reduces neuronal death in vivo and in vitro, and promotes mitochondrial function recovery in neurons. Single‐cell nuclear sequencing reveals that the cyclic adenosine monophosphate (cAMP)/cAMP‐activated exchange protein‐1 (EPAC‐1) signaling pathways are impacted by celastrol. Celastrol binds to cNMP (a domain of EPAC‐1) to inhibit its interaction with voltage‐dependent anion‐selective channel protein 1 (VDAC1) and blocks the opening of mitochondrial permeability transition pores. After neuron‐specific knockout of EPAC1, the neuroprotective effects of celastrol are diminished. In summary, this study demonstrates that celastrol, through its interaction with EPAC‐1, ameliorates mitochondrial dysfunction in neurons, thus potentially improving SBI induced by ICH. These findings suggest that targeting EPAC‐1 with celastrol can be a promising therapeutic approach for treating ICH‐induced SBI.

Background: Studies have shown that short- and long-term exposure to particulate matter (PM) can increase the risk of asthma morbidity and mortality. However, the effect of medium-term exposure remains unknown. We aim to examine the effect of medium-term exposure to size-fractioned PM on asthma exacerbations among asthmatics with poor medication adherence. Methods: We conducted a longitudinal study in China based on the National Mobile Asthma Management System Project that specifically and routinely followed asthma exacerbations in asthmatics with poor medication adherence from April 2017 to May 2019. High-resolution satellite remote-sensing data were used to estimate each participant's medium-term exposure (on average 90 days) to size-fractioned PM (PM1, PM2.5, and PM10) based on the residential address and the date of the follow-up when asthma exacerbations (e.g., hospitalizations and emergency room visits) occurred or the end of the follow-up. The Cox proportional hazards model was employed to examine the hazard ratio of asthma exacerbations associated with each PM after controlling for sex, age, BMI, education level, geographic region, and temperature. Results: Modelling results revealed nonlinear exposure-response associations of asthma exacerbations with medium-term exposure to PM1, PM2.5, and PM10. Specifically, for emergency room visits, we found an increased hazard ratio for PM1 above 22.8 µg/m3 (1.060, 95 % CI: 1.025–1.096, per 1 µg/m3 increase), PM2.5 above 38.2 µg/m3 (1.032, 95 % CI: 1.010–1.054), and PM10 above 78.6 µg/m3 (1.019, 95 % CI: 1.006–1.032). For hospitalizations, we also found an increased hazard ratio for PM1 above 20.3 µg/m3 (1.055, 95 % CI: 1.001–1.111) and PM2.5 above 39.2 µg/m3 (1.038, 95 % CI: 1.003–1.074). Furthermore, the effects of PM were greater for a longer exposure window (90–180 days) and among participants with a high BMI. Conclusion: This study suggests that medium-term exposure to PM is associated with an increased risk of asthma exacerbations in asthmatics with poor medication adherence, with a higher risk from smaller PM.

We describe a case of necrotizing fasciitis in the United Kingdom in which Pseudomonas guariconensis was isolated from multiple blood culture and tissue samples. The organism carried a Verona integron-encoded metallo-β-lactamase gene and evidence of decreased susceptibility to β-lactam antimicrobial agents. Clinicians should use caution when treating infection caused by this rare pathogen.