"All in One" Strategy for Achieving Superprotonic Conductivity by Incorporating Strong Acids into a Robust Imidazole-Linked Covalent Organic Framework.

The fabrication of solid-state proton-conducting electrolytes possessing both high performance and long-life reusability is significant but challenging. An "all-in-one" composite, H3PO4@PyTFB-1-SO3H, including imidazole, sulfonic acid, and phosphoric acid, which are essential for proton conduction, was successfully prepared by chemical post-modification and physical loading in the rationally pre-synthesized imidazole-based nanoporous covalent organic framework (COF), PyTFB-1. The resultant H3PO4@PyTFB-1-SO3H exhibits superhigh proton conductivity with its value even highly up to 1.15 × 10-1 S cm-1 at 353 K and 98% relative humidity (RH), making it one of the highest COF-based composites reported so far under the same conditions. Experimental studies and theoretical calculations further confirmed that the imidazole and sulfonic acid groups have strong interactions with the H3PO4 molecules and the synergistic effect of these three groups dramatically improves the proton conductivity properties of H3PO4@PyTFB-1-SO3H. This work demonstrated that by aggregating multiple proton carriers into one composite, effective proton-conducting electrolyte can be feasibly achieved.

Enhanced Lightweight YOLOX for Small Object Wildfire Detection in UAV Imagery.

Target detection technology based on unmanned aerial vehicle (UAV)-derived aerial imagery has been widely applied in the field of forest fire patrol and rescue. However, due to the specificity of UAV platforms, there are still significant issues to be resolved such as severe omission, low detection accuracy, and poor early warning effectiveness. In light of these issues, this paper proposes an improved YOLOX network for the rapid detection of forest fires in images captured by UAVs. Firstly, to enhance the network's feature-extraction capability in complex fire environments, a multi-level-feature-extraction structure, CSP-ML, is designed to improve the algorithm's detection accuracy for small-target fire areas. Additionally, a CBAM attention mechanism is embedded in the neck network to reduce interference caused by background noise and irrelevant information. Secondly, an adaptive-feature-extraction module is introduced in the YOLOX network's feature fusion part to prevent the loss of important feature information during the fusion process, thus enhancing the network's feature-learning capability. Lastly, the CIoU loss function is used to replace the original loss function, to address issues such as excessive optimization of negative samples and poor gradient-descent direction, thereby strengthening the network's effective recognition of positive samples. Experimental results show that the improved YOLOX network has better detection performance, with mAP@50 and mAP@50_95 increasing by 6.4% and 2.17%, respectively, compared to the traditional YOLOX network. In multi-target flame and small-target flame scenarios, the improved YOLO model achieved a mAP of 96.3%, outperforming deep learning algorithms such as FasterRCNN, SSD, and YOLOv5 by 33.5%, 7.7%, and 7%, respectively. It has a lower omission rate and higher detection accuracy, and it is capable of handling small-target detection tasks in complex fire environments. This can provide support for UAV patrol and rescue applications from a high-altitude perspective.

A quantum synthetic aperture radar image denoising algorithm based on grayscale morphology.

The quantum denoising technology efficiently removes noise from images; however, the existing algorithms are only effective for additive noise and cannot remove multiplicative noise, such as speckle noise in synthetic aperture radar (SAR) images. In this paper, based on the grayscale morphology method, a quantum SAR image denoising algorithm is proposed, which performs morphological operations on all pixels simultaneously to remove the noise in the SAR image. In addition, we design a feasible quantum adder to perform cyclic shift operations. Then, quantum circuits for dilation and erosion are designed, and the complete quantum circuit is then constructed. For a 2n×2n quantum SAR image with q grayscale levels, the complexity of our algorithm is O (n+q). Compared with classical algorithms, it achieves exponential improvement and also has polynomial-level improvements than existing quantum algorithms. Finally, the feasibility of our algorithm is validated on IBM Q.

Accelerated and precise identification of antioxidants and pesticides using a smartphone-based colorimetric sensor array.

The integration of smartphones with conventional analytical approaches plays a crucial role in enhancing on-site detection platforms for point-of-care testing. Here, we developed a simple, rapid, and efficient three-channel colorimetric sensor array, leveraging the peroxidase (POD)-like activity of polydopamine-decorated FeNi foam (PDFeNi foam), to identify antioxidants using both microplate readers and smartphones for signal readouts. The exceptional catalytic capacity of PDFeNi foam enabled the quick catalytic oxidation of three typical peroxidase substrates (TMB, OPD and 4-AT) within 3 min. Consequently, we constructed a colorimetric sensor array with cross-reactive responses, which was successfully applied to differentiate five antioxidants (i.e., glycine (GLY), glutathione (GSH), citric acid (CA), ascorbic acid (AA), and tannic acid (TAN)) within the concentration range of 0.1-10 µM, quantitatively analyze individual antioxidants (with AA and CA as model analytes), and assess binary mixtures of AA and GSH. The practical application was further validated by discriminating antioxidants in serum samples with a smartphone for signal readout. In addition, since pesticides could be absorbed on the surface of PDFeNi foam through π-π stacking and hydrogen bonding, the active sites were differentially masked, leading to featured modulation on POD-like activity of PDFeNi foam, thereby forming the basis for pesticides discrimination on the sensor array. The nanozyme-based sensor array provides a simple, rapid, visual and high-throughput strategy for precise identification of various analytes with a versatile platform, highlighting its potential application in point-care-of diagnostic, food safety and environmental surveillance.

Robust links in photoactive covalent organic frameworks enable effective photocatalytic reactions under harsh conditions.

Developing heterogeneous photocatalysts for the applications in harsh conditions is of high importance but challenging. Herein, by converting the imine linkages into quinoline groups of triphenylamine incorporated covalent organic frameworks (COFs), two photosensitive COFs, namely TFPA-TAPT-COF-Q and TFPA-TPB-COF-Q, are successfully constructed. The obtained quinoline-linked COFs display improved stability and photocatalytic activity, making them suitable photocatalysts for photocatalytic reactions under harsh conditions, as verified by the recyclable photocatalytic reactions of organic acid involving oxidative decarboxylation and organic base involving benzylamine coupling. Under strong oxidative condition, the quinoline-linked COFs show a high efficiency up to 11831.6 µmol·g-1·h-1 and a long-term recyclable usability for photocatalytic production of H2O2, while the pristine imine-linked COFs are less catalytically active and easily decomposed in these harsh conditions. The results demonstrate that enhancing the linkage robustness of photoactive COFs is a promising strategy to construct heterogeneous catalysts for photocatalytic reactions under harsh conditions.

Strong Second-Harmonic Generation Induced by a Triphenylamine-Based Bismuth-Organic Framework for Photocatalytic Activity Enhancement.

Taking advantage of the well-defined geometry of metal centers and highly directional metal-ligand coordination bonds, metal-organic frameworks (MOFs) have emerged as promising candidates for nonlinear optical (NLO) materials. In this work, taking a photoresponsive carboxylate triphenylamine derivative as an organic ligand, a bismuth-based MOF, Bi-NBC, NBC = 4',4‴,4‴″-nitrilotris(([1,1'-biphenyl]-4-carboxylic acid)) is obtained. Structure determination reveals that it is a potential NLO material derived from its noncentrosymmetric structure, which is finally confirmed by its rarely strong second harmonic generation (SHG) effect. Theoretical calculations reveal that the potential difference around Bi atoms is large; therefore, it leads to a strong local built-in electric field, which greatly facilitates the charge separation and transfer and finally improves the photocatalytic performance. Our results provide a reference for the exploration of MOFs with NLO properties.

Baicalein suppresses Coxsackievirus B3 replication by inhibiting caspase-1 and viral protease 2A.

Myocarditis is an inflammatory disease of the cardiac muscle and one of the primary causes of dilated cardiomyopathy. Group B coxsackievirus (CVB) is one of the leading causative pathogens of viral myocarditis, which primarily affects children and young adults. Due to the lack of vaccines, the development of antiviral medicines is crucial to controlling CVB infection and the progression of myocarditis. In this study, we investigated the antiviral effect of baicalein, a flavonoid extracted from Scutellaria baicaleinsis. Our results demonstrated that baicalein treatment significantly reduced cytopathic effect and increased cell viability in CVB3-infected cells. In addition, significant reductions in viral protein 3D, viral RNA, and viral particles were observed in CVB3-infected cells treated with baicalein. We found that baicalein exerted its inhibitory effect in the early stages of CVB3 infection. Baicalein also suppressed viral replication in the myocardium and effectively alleviated myocarditis induced by CVB3 infection. Our study revealed that baicalein exerts its antiviral effect by inhibiting the activity of caspase-1 and viral protease 2A. Taken together, our findings demonstrate that baicalein has antiviral activity against CVB3 infection and may serve as a potential therapeutic option for the myocarditis caused by enterovirus infection.

Predictive risk factors for acute kidney injury after surgery for Stanford type A acute aortic dissection / 中国胸心血管外科临床杂志

@#Objective To identify the predictors of postoperative acute kidney injury in patients undergoing surgery for Stanford type A acute aortic dissection. Methods A total of 220 patients who underwent surgery for type A acute aortic dissection in Qingdao Municipal Hospital from September 2010 to September 2017 were divided into two groups including a group A and a group B based on whether acute kidney injury occurred or not after surgery. There were 40 patients with 29 males and 11 females with the mean age of 54.6±9.2 years in the group A, 180 patients with 133 males and 47 females with the mean age of 48.5±7.9 years in the group B. Univariate and multivariate analyses (logistic regression) were used to identify the predictive risk factors. Results Overall in-hospital mortality was 5.5%. In univariate analysis, there were statistically significant differences with respect to the age, preoperative creatinine, preoperative white blood cell, the European system for cardiac operative risk evaluation (EuroSCORE), total cardiopulmonary bypass (CPB) time, deep hypothermic circulatory arrest (DHCA) time, arch replacement, red blood cell transfusion intraoperative and in 24 hours postoperatively, postoperative mechanical ventilation time, ICU stay duration, hospital stay duration and in hospital mortality. Multivariate logistic analysis showed that preoperative creatinine, preoperative white blood cell, CPB time, and red blood cell transfusion intraoperative and in 24 hours postoperatively were the independent predictors for postoperative acute kidney injury. Conclusion The incidence of acute kidney injury is high after surgery for acute Stanford type A aortic dissection. It can be predicted based on above factors, for patients with these risk factors, more perioperative care strategies are needed in order to induce the incidence of acute kidney injury.

Main Factors Influencing FeCl-Induced Mouse Mesenteric Arteriole Thrombosis Model / 中国实验血液学杂志

<p><b>OBJECTIVE</b>To investigate the factors that influence FeCl-induced mouse mesenteric arteriole thrombosis model.</p><p><b>METHODS</b>Platelets were isolated from donor mice and labeled with Calcein-AM. Mice were transfused intravenously with Calcein-AM labeled platelets. The influence of mouse ages (3-6 weeks, 6-10 weeks and >10 weeks), transfused platelets counts (1×10, 1×10and 2×10platelets) and concentrations of FeCl(6%, 12%, 24% and 48%) on FeCl-induced thrombosis model were compared.</p><p><b>RESULTS</b>The occlusion time was 16 min for mice aged 3-6 weeks, which was shorter than that for 6 mice aged 6-10 weeks(25 min)(P<0.05) and that for mice aged >10 weeks(38 min)(P<0.01). The occlusion time resulting from transfusion of 1×10and 2×10of pletclets was 15-18 min, which was shorter than that of transfusion 1×10platelets (30 mins). The occlusion time resulting from transfusion of 6% and 12% FeClwas from 15 to 20 min, however the transfusion of 24% and 48% FeClall in all leads to vessel occlusion within 10 min.</p><p><b>CONCLUSION</b>The factors influencing the success of FeCl-induced mouse thrombosis model are more. Transfusion of 1×10to 2×10labeled platelets to 3-6 week-old mice, and 6% to 12% of FeClshould be used to induce thrombosis, and the experimental conditions should be optimized for this animal model, therefore, it is easier for us to set up a mouse mesenteric arteriole thrombosis model.</p>