Development of a coupled model to simulate and assess arsenic contamination and impact factors in the Jinsha River Basin, China.

With the increasing severity of arsenic (As) pollution, quantifying the environmental behavior of pollutant based on numerical model has become an important approach to determine the potential impacts and finalize the precise control strategies. Taking the industrial-intensive Jinsha River Basin as typical area, a two-dimensional hydrodynamic water quality model coupled with Soil and Water Assessment Tool (SWAT) model was developed to accurately simulate the watershed-scale distribution and transport of As in the terrestrial and aquatic environment at high spatial and temporal resolution. The effects of hydro-climate change, hydropower station construction and non-point source emissions on As were quantified based on the coupled model. The result indicated that higher As concentration areas mainly centralized in urban districts and concentration slowly decreased from upstream to downstream. Due to the enhanced rainfall, the As concentration was significantly higher during the rainy season than the dry season. Hydro-climate change and the construction of hydropower station not only affected the dissolved As concentration, but also affected the adsorption and desorption of As in sediment. Furthermore, As concentration increased with the input of non-point source pollution, with the maximum increase about 30%, resulting that non-point sources contributed important pollutant impacts to waterways. The coupled model used in pollutant behavior analysis is general with high potential application to predict and mitigate water pollution.

Identify Regioselective Residues of Ginsenoside Hydrolases by Graph-Based Active Learning from Molecular Dynamics.

Identifying the catalytic regioselectivity of enzymes remains a challenge. Compared to experimental trial-and-error approaches, computational methods like molecular dynamics simulations provide valuable insights into enzyme characteristics. However, the massive data generated by these simulations hinder the extraction of knowledge about enzyme catalytic mechanisms without adequate modeling techniques. Here, we propose a computational framework utilizing graph-based active learning from molecular dynamics to identify the regioselectivity of ginsenoside hydrolases (GHs), which selectively catalyze C6 or C20 positions to obtain rare deglycosylated bioactive compounds from Panax plants. Experimental results reveal that the dynamic-aware graph model can excellently distinguish GH regioselectivity with accuracy as high as 96-98% even when different enzyme-substrate systems exhibit similar dynamic behaviors. The active learning strategy equips our model to work robustly while reducing the reliance on dynamic data, indicating its capacity to mine sufficient knowledge from short multi-replica simulations. Moreover, the model's interpretability identified crucial residues and features associated with regioselectivity. Our findings contribute to the understanding of GH catalytic mechanisms and provide direct assistance for rational design to improve regioselectivity. We presented a general computational framework for modeling enzyme catalytic specificity from simulation data, paving the way for further integration of experimental and computational approaches in enzyme optimization and design.

NR4A3 prevents diabetes induced atrial cardiomyopathy by maintaining mitochondrial energy metabolism and reducing oxidative stress.

BACKGROUND: Atrial cardiomyopathy (ACM) is responsible for atrial fibrillation (AF) and thromboembolic events. Diabetes mellitus (DM) is an important risk factor for ACM. However, the potential mechanism between ACM and DM remains elusive. METHODS: Atrial tissue samples were obtained from patients diagnosed with AF or sinus rhythm (SR) to assess alterations in NR4A3 expression, and then two distinct animal models were generated by subjecting Nr4a3-/- mice and WT mice to a high-fat diet (HFD) and Streptozotocin (STZ), while db/db mice were administered AAV9-Nr4a3 or AAV9-ctrl. Subsequently, in vivo and in vitro experiments were conducted to assess the impact of NR4A3 on diabetes-induced atrial remodeling through electrophysiological, biological, and histological analyses. RNA sequencing (RNA-seq) and metabolomics analysis were employed to unravel the downstream mechanisms. FINDINGS: The expression of NR4A3 was significantly decreased in atrial tissues of both AF patients and diabetic mice compared to their respective control groups. NR4A3 deficiency exacerbated atrial hypertrophy and atrial fibrosis, and increased susceptibility to pacing-induced AF. Conversely, overexpression of NR4A3 alleviated atrial structural remodeling and reduced AF induction rate. Mechanistically, we confirmed that NR4A3 improves mitochondrial energy metabolism and reduces oxidative stress injury by preserving the transcriptional expression of Sdha, thereby exerting a protective influence on atrial remodeling induced by diabetes. INTERPRETATION: Our data confirm that NR4A3 plays a protective role in atrial remodeling caused by diabetes, so it may be a new target for treating ACM. FUNDING: This study was supported by the major research program of National Natural Science Foundation of China (NSFC) No: 82370316 (to Q-S. W.), No. 81974041 (to Y-P. W.), and No. 82270447 (to Y-P. W.) and Fundation of Shanghai Hospital Development Center (No. SHDC2022CRD044 to Q-S. W.).

EDAC-modified chitosan/imidazolium-polysulfone composite beads for removal of Cr(VI) from aqueous solution.

To enhance the stability and adsorption performance of chitosan in Cr(VI)-contaminated acidic wastewater, a novel EDAC-modified-EDTA-crosslinked chitosan derivative (CSEC) was synthesized via a one-pot method with chitosan, 1-Ethyl-3-(3-dimethylaminopropyl) carbodiimide hydrochloride (EDAC), and Na2EDTA as raw materials. To further improve the mechanical strength and separation performance of CSEC, a novel composite bead (CSEP) of CSEC and imidazolium-functionalized polysulfone (IMPSF) was prepared through a phase inversion method. The chemical composition and microstructure of CSEC and CSEP were characterized by FESEM, FTIR, NMR and XPS techniques. The maximum adsorption capacities of CSEC and CSEP for Cr(VI) were 145.96 and 135.82 mg g-1 at pH 3, respectively, and the equilibrium time for Cr(VI) adsorption by CSEC and CSEP was 5 min and 8 h, respectively. The adsorption process of Cr(VI) by both CSEC and CSEP was exothermic and spontaneous. Compared to CSEC, CSEP has significantly enhanced resistance to interference from coexisting anions. The removal mechanism of Cr(VI) by CSEP might involve redox reaction as well as electrostatic attraction between Cr(VI) oxyanions and various nitrogen cations, including protonated amino groups, guanidinium groups, protonated tertiary amine groups, and imidazolium cations. The CSEP beads have potential application value in the treatment of acidic wastewater containing Cr(VI).

Prevalence and prognostic value of malnutrition in patients with IPF using three scoring systems.

BACKGROUND: To describe the nutritional status of IPF patients, report clinical associations and evaluate the prognostic value of them in IPF. METHODS: 264 IPF patients diagnosed with IPF at the Second Xiangya Hospital of Central South University between January 2011 and January 2021 were recruited. Three different scoring systems, including nutritional risk index (NRI), controlling nutritional status (CONUT) score, and prognostic nutritional index (PNI) were used to describe the nutritional status of IPF patients. RESULT: This study investigated the prevalence of malnutrition in 264 IPF patients, of which the percentage with malnutrition varied from 37.5 % with the NRI, to 47.4 % with the CONUT score, and to 6.4 % with the PNI. The moderate to severe malnutrition ranged from 10.2 % to 31.1 % across these indices, with PNI identifying only 4.9 % in this category. Worsening malnutrition status was associated with significantly higher incidence of all-cause mortality and IPF death regard of the malnutrition index as NRI (p < 0.05). When the normal nutrition of NRI was used as a reference, patients in the moderate to severe risk remained at a higher risk of all-cause death (HR = 2.06(1.25-3.41)) and IPF death(HR = 2.36(1.35-4.15)). The adjusted multivariate analysis, identified age(HR = 1.13(1.08-1.20)), DLCO <60, % predicted (HR = 3.31(1,24-9.42)) and the use of anti-fibrotic drugs (HR = 0.25(0.10-0.60)) as independent predictors of mortality. CONCLUSIONS: Malnutrition is common among patients with IPF and the baseline as diagnosis of IPF is strongly related to increased mortality.

Multi-focus image fusion with parameter adaptive dual channel dynamic threshold neural P systems.

Multi-focus image fusion (MFIF) is an important technique that aims to combine the focused regions of multiple source images into a fully clear image. Decision-map methods are widely used in MFIF to maximize the preservation of information from the source images. While many decision-map methods have been proposed, they often struggle with difficulties in determining focus and non-focus boundaries, further affecting the quality of the fused images. Dynamic threshold neural P (DTNP) systems are computational models inspired by biological spiking neurons, featuring dynamic threshold and spiking mechanisms to better distinguish focused and unfocused regions for decision map generation. However, original DTNP systems require manual parameter configuration and have only one stimulus. Therefore, they are not suitable to be used directly for generating high-precision decision maps. To overcome these limitations, we propose a variant called parameter adaptive dual channel DTNP (PADCDTNP) systems. Inspired by the spiking mechanisms of PADCDTNP systems, we further develop a new MFIF method. As a new neural model, PADCDTNP systems adaptively estimate parameters according to multiple external inputs to produce decision maps with robust boundaries, resulting in high-quality fusion results. Comprehensive experiments on the Lytro/MFFW/MFI-WHU dataset show that our method achieves advanced performance and yields comparable results to the fourteen representative MFIF methods. In addition, compared to the standard DTNP systems, PADCDTNP systems improve the fusion performance and fusion efficiency on the three datasets by 5.69% and 86.03%, respectively. The codes for both the proposed method and the comparison methods are released at https://github.com/MorvanLi/MFIF-PADCDTNP.

Seizure Detection of EEG Signals Based on Multi-Channel Long- and Short-Term Memory-Like Spiking Neural Model.

Seizure is a common neurological disorder that usually manifests itself in recurring seizure, and these seizures can have a serious impact on a person's life and health. Therefore, early detection and diagnosis of seizure is crucial. In order to improve the efficiency of early detection and diagnosis of seizure, this paper proposes a new seizure detection method, which is based on discrete wavelet transform (DWT) and multi-channel long- and short-term memory-like spiking neural P (LSTM-SNP) model. First, the signal is decomposed into 5 levels by using DWT transform to obtain the features of the components at different frequencies, and a series of time-frequency features in wavelet coefficients are extracted. Then, these different features are used to train a multi-channel LSTM-SNP model and perform seizure detection. The proposed method achieves a high seizure detection accuracy on the CHB-MIT dataset: 98.25% accuracy, 98.22% specificity and 97.59% sensitivity. This indicates that the proposed epilepsy detection method can show competitive detection performance.

Causal Brain Network Predicts Surgical Outcomes in Patients With Drug-Resistant Epilepsy: A Retrospective Comparative Study.

Network neuroscience, especially causal brain network, has facilitated drug-resistant epilepsy (DRE) studies, while surgical success rate in patients with DRE is still limited, varying from 30%  âˆ¼  70 %. Predicting surgical outcomes can provide additional guidance to adjust treatment plans in time for poorly predicted curative effects. In this retrospective study, we aim to systematically explore biomarkers for surgical outcomes by causal brain network methods and multicenter datasets. Electrocorticogram (ECoG) recordings from 17 DRE patients with 58 seizures were included. Ictal ECoG within clinically annotated epileptogenic zone (EZ) and non-epileptogenic zone (NEZ) were separately computed using six different algorithms to construct causal brain networks. All the brain network results were divided into two groups, successful and failed surgeries. Statistical results based on the Mann-Whitney-U-test show that: causal connectivity of α -frequency band ( 8  âˆ¼  13 Hz) in EZ calculated by convergent cross mapping (CCM) gains the most significant differences between the surgical success and failure groups, with a P value of 7.85e-08 and Cohen's d effect size of 0.77. CCM-defined EZ brain network can also distinguish the successful and failed surgeries considering clinical covariates (clinical centers, DRE types) with [Formula: see text]. Based on the brain network features, machine learning models were developed to predict the surgical outcomes. Among them, the SVM classifier with Gaussian kernel function and Bayesian optimization demonstrates the highest average accuracy of 84.48% by 5-fold cross-validation, further indicating that the CCM-defined EZ brain network is a reliable biomarker for predicting DRE surgical outcomes.

Electroacupuncture activates the peroxisome proliferators-activated receptor pathway to improve the phenotype of cerebral palsy.

AIM: This study explores the efficacy of electroacupuncture (EA) in treating cerebral palsy (CP) in Sprague-Dawley (SD) pups, specifically CP animal models, and its molecular mechanisms. METHODS: Gait analysis and Y-maze were used to detect the improvement of motor ability and cognitive function of CP rats after EA treatment. Transcription sequencing was used to determine the key pathway for EA to improve the symptoms of CP. PPAR agonists were used to verify the causal relationship between the pathway and the improvement of CP phenotype. RESULTS: The motor ability and cognitive function of CP pups were improved after EA treatment. The results of transcriptome sequencing suggest that the improvement of CP phenotype may be caused by the activation of PPAR pathway. PPAR pathway is widely activated in the epithelium of CP pups treated with EA, which is verified by qPCR. Rosiglitazone (Ros), a PPAR agonist, can improve CP phenotype while activating PPAR pathway, which proves the causal relationship between PPAR pathway activation and CP phenotype improvement. CONCLUSION: Our study demonstrated behavioral improvements and enhanced cognitive functions in CP models after EA treatment by activating PPAR pathway, suggesting new perspectives for CP rehabilitation, and providing theoretical support for acupuncture treatment of CP.

An enhanced consensus algorithm for blockchain.

Consensus plays a crucial role in blockchain technology, with the deleted proof of stake (DPoS) consensus mechanism commonly utilized in both public and hybrid chains. However, the current DPoS mechanism faces challenges such as low node engagement in voting and potential security risks posed by malicious nodes. In response, we propose the DL-DPoS (deep link-delegated proof of stake) mechanism, which builds upon the DPoS framework. The DL-DPoS incorporates the LINK incentive mechanism to encourage inactive nodes to participate in voting and leader selection. Furthermore, a comprehensive credit scoring system based on wealth, performance, and stability is introduced to enhance the security of elected nodes. The verification process is optimized to involve all nodes except the leader node, and mechanisms are in place to handle malicious attacks by degrading or removing offending nodes and redistributing their responsibilities to the LINK group. Performance testing of the DL-DPoS mechanism, conducted through blockchain simulation tests using the GO language, shows a 23% increase in throughput compared to DPoS, with over 95% node participation and improved distribution of rights and equity. These results indicate the enhanced performance, security, and stability of the DL-DPoS consensus mechanism.

Humoral and cellular immune responses following Omicron BA.2.2 breakthrough infection and Omicron BA.5 reinfection.

The emergence of novel Omicron subvariants has raised concerns regarding the efficacy of immunity induced by prior Omicron subvariants breakthrough infection (BTI) or reinfection against current circulating Omicron subvariants. Here, we prospectively investigated the durability of antibody and T cell responses in individuals post Omicron BA.2.2 BTI, with or without subsequent Omicron BA.5 reinfection. Our findings reveal that the emerging Omicron subvariants, including CH.1.1, XBB, and JN.1, exhibit extensive immune evasion induced by previous infections. Notably, the level of IgG and neutralizing antibodies were found to correlate with subsequent Omicron BA.5 reinfection. Fortunately, T cell responses recognizing both Omicron BA.2 and CH.1.1 peptides were observed. Furthermore, Omicron BA.5 reinfection may alleviate immune imprinting induced by WT-vaccination, bolster virus-specific ICS+ T cell responses, and promote the phenotypic differentiation of virus-specific memory CD8+ T cells. Antigen-updated or T cell-conserved vaccines are needed to control the transmission of diverse emerging SARS-CoV-2 variants.

Relative validity of an intelligent ordering system to estimate dietary intake among university students from a medical school in Shanghai, China.

BACKGROUND: Dietary assessment methods have limitations in capturing real-time eating behaviour accurately. Equipped with automated dietary-data-collection capabilities, the "intelligent ordering system" (IOS) has potential applicability in obtaining long-term consecutive, relatively detailed on-campus dietary records among university students with little resource consumption. We investigated (1) the relative validity of IOS-derived nutrient/food intakes compared to those from the 7-day food diary (7DFD); (2) whether including a supplemental food frequency questionnaire (SFFQ) improves IOS accuracy; and (3) sex differences in IOS dietary intake estimation. METHODS: Medical students (n = 221; age = 22.2 ± 2.4 years; 38.5% male and 61.5% female) completed the 7DFD and SFFQ. During the consecutive 7-day survey period, students weighed and photographed each meal before and after consumption. Then, students reviewed their 3-month diet and completed the SFFQ, which includes eight underprovided school-canteen food items (e.g., dairy, fruits, nuts). Meanwhile, 9385 IOS dietary data entries were collected. We used Spearman coefficients and linear regression models to estimate the associations among the different dietary intake assessment methods. Individual- and group-level agreement was assessed using the Wilcoxon signed-rank test, cross-classification, and Bland‒Altman analysis. RESULTS: IOS mean daily energy, protein, fat, and carbohydrate intake estimations were significantly lower (-15-20%) than those of the 7DFD. The correlation coefficients varied from 0.52 (for added sugar) to 0.88 (for soybeans and nuts), with fruits (0.37) and dairy products (0.29) showing weaker correlations. Sixty-two (milk and dairy products) to 97% (soybeans and nuts) of participants were classified into the same or adjacent dietary intake distribution quartile using both methods. The energy and macronutrient intake differences between the IOS + SFFQ and 7DFD groups decreased substantially. The separate fruit intake measurements from each assessment method did not significantly differ from each other (p > 0.05). IOS and IOS + SFFQ regression models generally yielded higher R2 values for males than for females. CONCLUSION: Despite estimation differences, the IOS can be reliable for medical student dietary habit assessment. The SFFQ is useful for measuring consumption of foods that are typically unavailable in school cafeterias, improving the overall dietary evaluation accuracy. The IOS assessment was more accurate for males than for females.

The prognostic role of high-density lipoprotein cholesterol/C- reactive protein ratio in idiopathic pulmonary fibrosis.

BACKGROUND: The prognosis of idiopathic pulmonary fibrosis (IPF) patients is highly heterogeneous. Abnormalities in lipids and their metabolism play an important role in the development of IPF. AIM: To investigate the value of lipid parameters, C-reactive protein (CRP), and high-density lipoprotein cholesterol/C-reactive protein (HDL-C/CRP) ratio levels in the prognosis of IPF patients. DESIGN: An observational cohort study. METHODS: We collected baseline data of non-IPF controls and IPF patients, and IPF patients were followed up for 4 years. All-cause death or lung transplantation and IPF-related death were the outcome events. Receiver operating characteristic (ROC) curves and Cox proportional hazards models were used to analyze the predictive effect of lipid parameters, CRP and HDL-C/CRP ratio on the prognosis of IPF patients. RESULTS: IPF patients had lower HDL-C, HDL-C/CRP ratio and higher CRP compared to non-IPF controls. IPF patients who died or underwent lung transplantation were older and had worse pulmonary function, lower HDL-C, HDL-C/CRP ratio and higher CRP compared with surviving patients. HDL-C/CRP ratio was better than HDL-C and CRP in predicting all-cause death or lung transplantation. IPF patients with low HDL-C/CRP ratio had shorter survival times. HDL-C/CRP ratio and DLCO% of predicted were independent protective factors for all-cause death or lung transplantation and IPF-related death in IPF patients, while age and GAP stage ≥ 2 (HR = 4.927)were independent risk factors for all-cause death or lung transplantation. Age > 65 years (HR = 3.533) was an independent risk factor for IPF-related death. CONCLUSIONS: HDL-C/CRP ratio was a valid predictor of clinical outcomes in IPF patients, including all-cause death or lung transplantation and IPF-related death.

The long non-coding RNA CCAT1 promotes erlotinib resistance in cholangiocarcinoma by inducing epithelial-mesenchymal transition via the miR-181a-5p/ROCK2 axis.

Cholangiocarcinoma (CCA) is a common malignancy of the digestive system, and its treatment is greatly challenged by rising chemoresistance. Long non-coding RNAs (lncRNAs) have been shown to play critical roles in the development of drug resistance in tumors. However, the role of the lncRNA CCAT1 in erlotinib resistance in CCA remains unclear. In this investigation, we identified CCAT1 as a pivotal factor contributing to erlotinib resistance in CCA. Furthermore, we uncovered that lncRNA CCAT1 modulated epithelial-mesenchymal transition (EMT) through Rho-associated coiled-coil-forming protein kinase 2 (ROCK2), thereby conferring erlotinib resistance upon CCA cells. Mechanistically, we demonstrated that miR-181a-5p interacted with CCAT1 to modulate the expression of ROCK2. Collectively, these findings shed light on the significant role of CCAT1 in the development of erlotinib resistance in CCA. The functional suppression of CCAT1 holds promise in enhancing the sensitivity to erlotinib by reversing EMT through the miR-181a-5p/ROCK2 signaling pathway. These findings provide valuable insights into the mechanisms underlying erlotinib resistance in CCA and the potential strategies for its treatment.

Clinical and Imaging Characteristics of Herlyn-Werner-Wunderlich Syndrome: a Comprehensive Analysis.

PURPOSE: To describe the clinical and imaging characteristics of Herlyn-Werner-Wunderlich syndrome (HWWS). METHODS: This study presented an observational case series involving consecutive patients diagnosed with HWWS, whose medical records were retrospectively reviewed. From June 2012 to December 2022, there were a total of 85 patients with HWWS enrolled in our study. We obtained the medical history, including demographic characteristics, clinical presentation, treatment, complications, and radiologic examinations performed. Patients > 18 years of age (n = 58) were recontacted. RESULT: In our analysis, 27 patients were categorised as having complete obstruction, and 58 were categorised as having incomplete obstruction. The mean age at the onset of symptoms and diagnosis of complete obstruction was significantly younger than incomplete obstruction (P < 0.05). For complete obstruction, the median time between menarche and the onset of symptoms was 2.1 years, while for incomplete obstruction, it was 5.3 years. There was a significantly lower incidence of intermittent mucopurulent discharge, irregular vaginal haemorrhage, and occasional examination findings of complete obstruction than incomplete obstruction (P < 0.05). Complete obstruction was significantly associated with dysmenorrhea and pelvic endometriosis compared with incomplete obstruction (P < 0.05). CONCLUSIONS: There are distinct clinical differences between patients with complete obstruction of the hemivagina and those with incomplete obstruction. HWWS can manifest as various combinations of uterine anomalies, communications anomalies, and renal anomalies. Early recognition and treatment can avoid complications and preserve fertility. KEYSWORDS: Herlyn-Werner-Wunderlich syndrome (HWWS); complete obstruction; incomplete obstruction; obstructed hemivagina; congenital malformation.

Verapamil inhibits respiratory syncytial virus infection by regulating Ca2+ influx.

AIMS: The study evaluated the antiviral effect of Verapamil against respiratory syncytial virus (RSV) and investigated its underlying mechanism. MATERIALS AND METHODS: RSV-infected BALB/c mice were treated with Verapamil. Body weight, survival rates, viral load, lung damage, inflammatory factors, and the expression of RSV fusion (F) protein were analyzed. In cellular studies, intracellular Ca2+ and viral titers were measured in the presence of Verapamil, Calcium Chloride, and EGTA. A time-of-addition assay assessed the antiviral effect of Verapamil. KEY FINDINGS: Mice infected with RSV and treated with Verapamil exhibited a significant decrease in weight loss, an increase in survival rates, and reductions in viral titers, RSV F protein expression, inflammatory responses, and lung tissue injury. Verapamil reduced intracellular calcium levels, which correlated with reduced viral titers. The addition of calcium chloride reversed the anti-viral effects mediated by Verapamil, while EGTA potentiated them. The antiviral activity of Verapamil was observed during the early phase of RSV infection, likely by blocking Ca2+ channels and inhibiting virus replication. SIGNIFICANCE: Verapamil effectively inhibits RSV infection by blocking calcium channels and reducing intracellular calcium levels, thereby impeding viral replication. Thus, Verapamil shows promise as a treatment for RSV.

Role of NLRP3 inflammasome in central nervous system diseases.

The central nervous system (CNS) is the most delicate system in human body, with the most complex structure and function. It is vulnerable to trauma, infection, neurodegeneration and autoimmune diseases, and activates the immune system. An appropriate inflammatory response contributes to defence against invading microbes, whereas an excessive inflammatory response can aggravate tissue damage. The NLRP3 inflammasome was the first one studied in the brain. Once primed and activated, it completes the assembly of inflammasome (sensor NLRP3, adaptor ASC, and effector caspase-1), leading to caspase-1 activation and increased release of downstream inflammatory cytokines, as well as to pyroptosis. Cumulative studies have confirmed that NLRP3 plays an important role in regulating innate immunity and autoimmune diseases, and its inhibitors have shown good efficacy in animal models of various inflammatory diseases. In this review, we will briefly discuss the biological characteristics of NLRP3 inflammasome, summarize the recent advances and clinical impact of the NLRP3 inflammasome in infectious, inflammatory, immune, degenerative, genetic, and vascular diseases of CNS, and discuss the potential and challenges of NLRP3 as a therapeutic target for CNS diseases.

Bioprotective Respirator Assembled by Defective Carbon Nitride for Long-Term Light Triggered Health Protection.

Wearing face masks is the best way to stop the spread of respiratory infections. However, if masks are not sterilized, changing them too frequently can actually increase the risk of cross-contamination. Herein, the construction of an antipathogen photocatalytic mask with carbon vacancy-modified carbon nitride nanosheets (g-C3N4-VC Ns) coated on the non-woven fabrics of the out layer of the mask, offering effective and long-term protection against damaging pathogens when exposed to light is reported. The introduced carbon vacancies are found capable of creating energy-disordered sites and inducing energetic electric force to overcome the Coulomb interactions between electron-hole pairs, thus promoting the electron-hole separation to achieve a high generation of reactive oxygen species (ROS). Thanks to its high activity in generating ROS upon exposure to light, the as-prepared photocatalytic mask shows high pathogen sterilization performance. This, in turn, prolongs the mask's protective lifetime, decreases the need for regular replacement, and decreases medical waste production. The work demonstrated here opens new viewpoints in designing pathogens biocidal protective devices for health protection, offering significant promise in specific environment self-protection.

In Silico Design of Heterogeneous Microvascular Trees Using Generative Adversarial Networks and Constrained Constructive Optimization.

OBJECTIVE: Designing physiologically adequate microvascular trees is of crucial relevance for bioengineering functional tissues and organs. Yet, currently available methods are poorly suited to replicate the morphological and topological heterogeneity of real microvascular trees because the parameters used to control tree generation are too simplistic to mimic results of the complex angiogenetic and structural adaptation processes in vivo. METHODS: We propose a method to overcome this limitation by integrating a conditional deep convolutional generative adversarial network (cDCGAN) with a local fractal dimension-oriented constrained constructive optimization (LFDO-CCO) strategy. The cDCGAN learns the patterns of real microvascular bifurcations allowing for their artificial replication. The LFDO-CCO strategy connects the generated bifurcations hierarchically to form microvascular trees with a vessel density corresponding to that observed in healthy tissues. RESULTS: The generated artificial microvascular trees are consistent with real microvascular trees regarding characteristics such as fractal dimension, vascular density, and coefficient of variation of diameter, length, and tortuosity. CONCLUSIONS: These results support the adoption of the proposed strategy for the generation of artificial microvascular trees in tissue engineering as well as for computational modeling and simulations of microcirculatory physiology.

Multi-level feature interaction image super-resolution network based on convolutional nonlinear spiking neural model.

Image super-resolution (ISR) is designed to recover lost detail information from low-resolution images, resulting in high-quality and high-definition high-resolution images. In the existing single ISR (SISR) methods based on convolutional neural networks (CNN), however, most of the models cannot effectively combine global and local information and are also easy to ignore the correlation between different hierarchical feature information. To address these problems, this study proposes a multi-level feature interactive image super-resolution network, which is constructed by the convolutional units inspired by nonlinear spiking mechanism in nonlinear spiking neural P systems, including shallow feature processing, deep feature extraction and fusion, and reconstruction modules. The different omni domain self-attention blocks are introduced to extract global information in the deep feature extraction and fusion stage and formed a feature enhancement module having a Transformer structure using a novel convolutional unit for extracting local information. Furthermore, to adaptively fuse features between different hierarchies, we design a multi-level feature fusion module, which not only can adaptively fuse features between different hierarchies, but also can better interact with contextual information. The proposed model is compared with 16 state-of-the-art or baseline models on five benchmark datasets. The experimental results show that the proposed model not only achieves good reconstruction performance, but also strikes a good balance between model parameters and performance.