A Cooperative Hunting Method for Multi-USV Based on the A* Algorithm in an Environment with Obstacles.

A single unmanned surface combatant (USV) has poor mission execution capability, so the cooperation of multiple unmanned surface ships is widely used. Cooperative hunting is an important aspect of multi USV collaborative research. Therefore, this paper proposed a cooperative hunting method for multi-USV based on the A* algorithm in an environment with obstacles. First, based on the traditional A* algorithm, a path smoothing method based on USV minimum turning radius is proposed. At the same time, the post order traversal recursive algorithm in the binary tree method is used to replace the enumeration algorithm to obtain the optimal path, which improves the efficiency of the A* algorithm. Second, a biomimetic multi USV swarm collaborative hunting method is proposed. Multiple USV clusters simulate the hunting strategy of lions to pre-form on the target's path, so multiple USV clusters do not require manual formation. During the hunting process, the formation of multiple USV groups is adjusted to limit the movement and turning of the target, thereby reducing the range of activity of the target and improving the effectiveness of the algorithm. To verify the effectiveness of the algorithm, two sets of simulation experiments were conducted. The results show that the algorithm has good performance in path planning and target search.

Efficacy and safety of CO2 laser in the treatment of chronic wounds: A Retrospective Matched Cohort Trial.

OBJECTIVES: Treating chronic cutaneous wounds is challenging, and debridement is a central concept in treating them. Studies have shown that CO2 laser debridement can control local infection and promote the wound healing process. The present study aimed to investigate the efficacy and safety of fully ablative CO2 laser debridement compared to routine surgical debridement in the treatment of chronic wounds. METHODS: The retrospective cohort study was conducted on patients with chronic (>1 month) cutaneous wounds (≥1 cm2 ) between December 1, 2017, and December 1, 2020, in the Wound Healing Center at Shanghai Ruijin Hospital, China. Patients treated with CO2 laser debridement with a DEKA SmartXide2 C80 (DEKA) (the CO2 laser group) were compared with matched control patients with similar baseline characteristics who had undergone routine surgical debridement (the routine group). The primary outcome was time-to-heal (days) for chronic wounds in two groups, and secondary outcomes included the wound area and BWAT (Bates-Jensen wound assessment tool) score before treatment, and at 1, 2, 3, and 4 weeks after treatment. RESULTS: The study included 164 patients (82 in the CO2 laser group and 82 matched in the routine group). The time-to-heal for patients in the CO2 laser group (41.30 ± 17.11) was significantly shorter than that of the patients in the routine group (48.51 ± 24.32) (p = 0.015). At 3 and 4 weeks after treatment, the absolute wound area of the CO2 laser group was significantly smaller than that of the routine group. Also, the CO2 laser group exhibited a significantly lower relative area at 2, 3, and 4 weeks after treatment. The CO2 laser group yielded significantly lower BWAT scores at 2, 3, and 4 weeks after treatment. Additionally, the relative BWAT score was significantly lower in the CO2 laser group than the relative scores in the routine group at 2, 3, and 4 weeks after treatment. No adverse events related to the treatments were observed in either group during the study period. CONCLUSIONS: The present study has shown that fully ablative CO2 laser debridement has several advantages over routine sharp surgical debridement. It is superior at ameliorating wound status and reducing wound area, and it also significantly reduces the time-to-heal for chronic wounds, without causing any adverse events.

A Hybrid Multi-Target Path Planning Algorithm for Unmanned Cruise Ship in an Unknown Obstacle Environment.

To solve the problem of traversal multi-target path planning for an unmanned cruise ship in an unknown obstacle environment of lakes, this study proposed a hybrid multi-target path planning algorithm. The proposed algorithm can be divided into two parts. First, the multi-target path planning problem was transformed into a traveling salesman problem, and an improved Grey Wolf Optimization (GWO) algorithm was used to calculate the multi-target cruise sequence. The improved GWO algorithm optimized the convergence factor by introducing the Beta function, which can improve the convergence speed of the traditional GWO algorithm. Second, based on the planned target sequence, an improved D* Lite algorithm was used to implement the path planning between every two target points in an unknown obstacle environment. The heuristic function in the D* Lite algorithm was improved to reduce the number of expanded nodes, so the search speed was improved, and the planning path was smoothed. The proposed algorithm was verified by experiments and compared with the other four algorithms in both ordinary and complex environments. The experimental results demonstrated the strong applicability and high effectiveness of the proposed method.

Hard Decision-Based Cooperative Localization for Wireless Sensor Networks.

Reliable and accurate localization of objects is essential for many applications in wireless networks. Especially for large-scale wireless sensor networks (WSNs), both low cost and high accuracy are targets of the localization technology. However, some range-free methods cannot be combined with a cooperative method, because these range-free methods are characterized by low accuracy of distance estimation. To solve this problem, we propose a hard decision-based cooperative localization method. For distance estimation, an exponential distance calibration formula is derived to estimate distance. In the cooperative phase, the cooperative method is optimized by outlier constraints from neighboring anchors. Simulations are conducted to verify the effectiveness of the proposed method. The results show that localization accuracy is improved in different scenarios, while high node density or anchor density contributes to the localization. For large-scale WSNs, the hard decision-based cooperative localization is proved to be effective.

Estrogen inhibits osteoclasts formation and bone resorption via microRNA-27a targeting PPARγ and APC.

Inhibition of osteoclasts formation and bone resorption by estrogen is very important in the etiology of postmenopausal osteoporosis. The mechanisms of this process are still not fully understood. Recent studies implicated an important role of microRNAs in estrogen-mediated responses in various cellular processes, including cell differentiation and proliferation. Thus, we hypothesized that these regulatory molecules might be implicated in the process of estrogen-decreased osteoclasts formation and bone resorption. Western blot, quantitative real-time polymerase chain reaction, tartrate-resistant acid phosphatase staining, pit formation assay and luciferase assay were used to investigate the role of microRNAs in estrogen-inhibited osteoclast differentiation and bone resorption. We found that estrogen could directly suppress receptor activator of nuclear factor B ligand/macrophage colony-stimulating factor-induced differentiation of bone marrow-derived macrophages into osteoclasts in the absence of stromal cell. MicroRNA-27a was significantly increased during the process of estrogen-decreased osteoclast differentiation. Overexpressing of microRNA-27a remarkably enhanced the inhibitory effect of estrogen on osteoclast differentiation and bone resorption, whereas which were alleviated by microRNA-27a depletion. Mechanistic studies showed that microRNA-27a inhibited peroxisome proliferator-activated receptor gamma (PPARγ) and adenomatous polyposis coli (APC) expression in osteoclasts through a microRNA-27a binding site within the 3'-untranslational region of PPARγ and APC. PPARγ and APC respectively contributed to microRNA-27a-decreased osteoclast differentiation and bone resorption. Taken together, these results showed that microRNA-27a may play a significant role in the process of estrogen-inhibited osteoclast differentiation and function.

Up-regulation of Wip1 involves in neuroinflammation of retinal astrocytes after optic nerve crush via NF-κB signaling pathway.

OBJECTIVE: To evaluate the expression and possible roles of Wip1 in retinal astrocytes after optic nerve crush (ONC). METHODS: Expressions of Wip1, GFAP, and p-p65 in ONC model were analyzed by Western blot and immunofluorescence. The mRNA expressions of the pro-inflammatory cytokines (IL-8, TNF-α, IL-6 and IL-1ß) were analyzed by RT-PCR. RESULTS: Wip1 was up-regulated at 14 days after ONC by Western blot and immunofluorescence. The changes of Wip1 were striking in astrocytes. Furthermore, the protein expression level of p-p65 was paralleled with Wip1 in a time-dependent manner by ONC. In addition, co-localization of Wip1 with Phospho-NF-κB-p65 (p-p65) was detected. Finally, the mRNA expressions of the pro-inflammatory cytokines (IL-8, TNF-α, IL-6 and IL-1ß) were significantly increased in retina after ONC. CONCLUSIONS: These data were consistent with the hypothesis that Wip1 was implicated in neuroinflammation of retinal astrocytes after ONC via NF-κB signaling pathway.

The thickness-dependent band gap and defect features of ultrathin ZrO2 films studied by spectroscopic ellipsometry.

The band gap and defect features of ultrathin ZrO2 films with varying thicknesses have been investigated by spectroscopic ellipsometry through the point-by-point data inversion method. The ε2-sprectra in the 3-6 eV range are extracted based on an optical model consisting of a Si substrate/effective ZrO2 film/air ambient structure where the effective ZrO2 film is a combination of interfacial layers and ZrO2. Evident widening of the band gap with a reducing size is observed when the effective ZrO2 films are below a critical thickness, somewhere between 8.80 nm and 17.13 nm. This is due to quantum-confinement and amorphous effects. Moreover, the sub-band-gap defects at interfacial layers and in bulk ZrO2 are identified and present strong thickness dependence as well. The interfacial defects at 3.26, 4.13, 4.43, and 4.77 eV mainly exist below the critical thickness and exhibit a significant suppression with increasing film thickness. The bulk defects at 4.15 eV and 4.46 eV dominate in ZrO2 films once they are over the critical thickness. The evolution of the band gap and defects is closely related to variance in the electronic structure of amorphous ZrO2. Our results may be helpful in understanding controversial problems concerning the size effect on ultrathin high-k oxide films and exploring the further miniaturization of electronic devices based on them.

A Novel Group Decision-Making Method Based on Sensor Data and Fuzzy Information.

Algal bloom is a typical phenomenon of the eutrophication of rivers and lakes and makes the water dirty and smelly. It is a serious threat to water security and public health. Most scholars studying solutions for this pollution have studied the principles of remediation approaches, but few have studied the decision-making and selection of the approaches. Existing research uses simplex decision-making information which is highly subjective and uses little of the data from water quality sensors. To utilize these data and solve the rational decision-making problem, a novel group decision-making method is proposed using the sensor data with fuzzy evaluation information. Firstly, the optimal similarity aggregation model of group opinions is built based on the modified similarity measurement of Vague values. Secondly, the approaches' ability to improve the water quality indexes is expressed using Vague evaluation methods. Thirdly, the water quality sensor data are analyzed to match the features of the alternative approaches with grey relational degrees. This allows the best remediation approach to be selected to meet the current water status. Finally, the selection model is applied to the remediation of algal bloom in lakes. The results show this method's rationality and feasibility when using different data from different sources.

MicroRNA-17-92a upregulation by estrogen leads to Bim targeting and inhibition of osteoblast apoptosis.

Anti-apoptotic effects of estrogen on osteoblasts are very important in the etiology of estrogen protection of the adult skeleton against bone loss. The mechanisms of this process are still not fully understood. Recent studies implicated an important role of microRNAs in estrogen-mediated responses in various cellular processes, including cell apoptosis and proliferation. Therefore, we hypothesized that these regulatory molecules might be involved with estrogen in protecting osteoblasts from apoptosis. Western blotting, quantitative real-time PCR, flow cytometry and luciferase assays were employed to investigate the role of microRNAs in this process. The microRNA cluster miR-17-92a, a post-transcriptional regulator, was significantly reduced during dexamethasone, etoposide and tumor necrosis factor alpha (TNF-α)-induced osteoblasts apoptosis. The repression of miR-17-92a was significantly attenuated by estrogen. To delineate the role of miR-17-92a in apoptosis, we silenced and overexpressed miR-17-92a in osteoblasts. We found that miR-17-92a depletion significantly enhanced dexamethasone-induced apoptosis and overexpressing miR-17-92a remarkably increased the anti-apoptotic effects of estrogen on osteoblasts. Mechanistic studies showed that miR-17-92a inhibited Bim expression through a microRNA-17-92a-binding site within the 3'-untranslated region of Bim. The post-transcriptional repression of Bim was further confirmed by a luciferase reporter assay. These results showed that miR-17-92a, plays a significant role in the process of estrogen protection of osteoblasts against apoptosis, by regulating Bim expression.

Outcomes of astigmatic correction with and without two different cyclotorsion compensation methods in small incision lenticule extraction surgery.

PURPOSE: To compare the astigmatic correction outcomes of small incision lenticule extraction (SMILE) surgery with or without two different cyclotorsion compensation methods. METHODS: This is a prospective randomized clinical trial. Patients with myopic astigmatism that underwent SMILE surgery were randomly divided into static cyclotorsion compensated group (SCC group), slit-lamp group and control group. In the SCC and slit-lamp groups, the intraoperative cyclotorsion was manually compensated with different limbal marking methods. In the control group, the cyclotorsion was not compensated. Visual acuity and manifest refraction were measured preoperatively and postoperatively. Astigmatic outcomes were estimated with vector analysis. RESULTS: A total of 94 eyes from 94 patients were analyzed postoperatively at the 3-month follow-up. Their mean preoperative cylinder was -1.56±0.86 D (range: -4.25 to -0.25 D). The mean preoperative spherical equivalent was -5.95±1.72 D (range: -10.50 to -2.75 D). All groups showed favorable results in the correction of myopic astigmatism. No statistically differences were found among three groups in postoperative visual acuity, refractive outcomes or vector parameters. CONCLUSION: Cyclotorsion compensation with two different manual limbal marking methods was helpful in aligning the surgical position in SMILE, but it was not as effective as expected for the correction of myopic astigmatism under well controlled surgical positioning.

Effect of acupuncture at Shuitong and Shuijin points on sufentanil-induced cough in general anesthesia induction: a randomized controlled trial. / é’ˆåˆºæ°´é€šã€æ°´é‡‘ç©´å¯¹å ¨èº«éº»é†‰è¯±å¯¼æœŸèˆ’èŠ¬å¤ªå°¼è¯±å‘å‘›å’³çš„å½±å“ï¼šéšæœºå¯¹ç §è¯•éªŒ.

OBJECTIVES: To observe the therapeutic effect of acupuncture at Shuitong and Shuijin points on preventing sufentanil-induced cough and its influence on hemodynamics in general anesthesia induction. METHODS: A total of 80 patients scheduled for elective surgery undergoing general anesthesia were randomly divided into an observation group (40 cases) and a control group (39 cases,1 case eliminated). In the control group, the routine anesthesia was performed,with intravenous injection of 1% sufentanil citrate 0.5 µg/kg, 1% propofol (total amount was calculated according to 2 mg/kg) and cisatracurium besilate 0.2 mg/kg. In the observation group, before routine anesthesia induction, acupuncture was applied to Shuitong and Shuijin points on the right and the needles were retained for 30 min. During anesthesia induction, the complications i.e. cough and chest wall stiffness were observed, and the systolic blood pressure (SBP), heart rate (HR) and pulse oxygen saturation (SpO2) were monitored 5 min after the patients entered the operation room (T0),at the moment of intravenous injection of sufentanil (T1) and 2 min after sufentanil injection (T2) , 1 min before and after endotracheal intubation (T3,T4) of the two groups, respectively. RESULTS: During anesthesia induction,the condition of mild, moderate and severe cough in the observation group was superior to that of the control group (P<0.05), the total cases of cough and its total incidence were lower than those of the control group (P<0.05). Two cases of chest wall stiffness were present in each group, but without statistical difference between the two groups (P>0.05). In comparison of SBP, HR and SpO2 at T0, T1, T2, T3 and T4, the differences were not significant statistically between the two groups (P>0.05). SBP and HR increased at T2 when compared with those at T1 in the control group (P<0.05), but there was no statistical difference in SpO2 (P>0.05); while, the differences in SBP, HR and SpO2 were not significant at T2 when compared with those at T1 in the observation group (P>0.05). CONCLUSIONS: Acupuncture at Shuitong and Shuijin points can effectively prevent from sufentanil-induced cough, reduce the severity of cough and stabilize the hemodynamic indicators.

Red and processed meat intake and risk of esophageal adenocarcinoma: a meta-analysis of observational studies.

AIMS: Inconsistent results relating dietary red and processed meat intake and risk of esophageal adenocarcinoma (EAC) have been reported. This article summarizes and quantifies the current evidence in a meta-analysis of observational studies. METHODS: Electronic search of MEDLINE and EMBASE databases was performed to identify peer-reviewed manuscripts up to 31 May 2012. Random-effects models were used to pool study results. RESULTS: Ten manuscripts from 3 cohort studies and 7 case-control studies were identified. The summary relative risks (SRRs) of EAC for highest versus lowest intake categories were 1.31 (95 % confidence intervals [CIs]: 1.05-1.64) for red meat consumption, and 1.41 (95 % CIs 1.09-1.83) for processed meat consumption. Subgroup analyses indicated that these positive relations were seen in case-control studies, but not in cohort studies. Based on dose-response analysis, similar results were found; the SRRs were 1.45(95 % CIs 1.09-1.93) per 100 g/day of red meat intake and 1.37 (95 % CIs 1.03-1.81) per 50 g/day of processed meat intake. CONCLUSIONS: The results of this meta-analysis indicate that consumption of red and processed meat may be associated with increased risk of esophageal adenocarcinoma. More studies, particularly prospective studies, are needed.

Remote sensing image analysis and prediction based on improved Pix2Pix model for water environment protection of smart cities.

Background: As an important part of smart cities, smart water environmental protection has become an important way to solve water environmental pollution problems. It is proposed in this article to develop a water quality remote sensing image analysis and prediction method based on the improved Pix2Pix (3D-GAN) model to overcome the problems associated with water environment prediction of smart cities based on remote sensing image data having low accuracy in predicting image information, as well as being difficult to train. Methods: Firstly, due to inversion differences and weather conditions, water quality remote sensing images are not perfect, which leads to the creation of time series data that cannot be used directly in prediction modeling. Therefore, a method for preprocessing time series of remote sensing images has been proposed in this article. The original remote sensing image was unified by pixel substitution, the image was repaired by spatial weight matrix, and the time series data was supplemented by linear interpolation. Secondly, in order to enhance the ability of the prediction model to process spatial-temporal data and improve the prediction accuracy of remote sensing images, the convolutional gated recurrent unit network is concatenated with the U-net network as the generator of the improved Pix2Pix model. At the same time, the channel attention mechanism is introduced into the convolutional gated recurrent unit network to enhance the ability of extracting image time series information, and the residual structure is introduced into the downsampling of the U-net network to avoid gradient explosion or disappearance. After that, the remote sensing images of historical moments are superimposed on the channels as labels and sent to the discriminator for adversarial training. The improved Pix2Pix model no longer translates images, but can predict two dimensions of space and one dimension of time, so it is actually a 3D-GAN model. Third, remote sensing image inversion data of chlorophyll-a concentrations in the Taihu Lake basin are used to verify and predict the water environment at future moments. Results: The results show that the mean value of structural similarity, peak signal-to-noise ratio, cosine similarity, and mutual information between the predicted value of the proposed method and the real remote sensing image is higher than that of existing methods, which indicates that the proposed method is effective in predicting water environment of smart cities.

Stress load and volunteer motivation of patient volunteers in Fangcang shelter hospitals in Shanghai, China during the COVID-19 pandemic: a cross-sectional survey.

OBJECTIVES: This study aims to assess the volunteer motivation and stress load of patient volunteers in the Fangcang shelter hospitals (FSHs), examine their associations, and explore the potential influence factors of volunteer motivation. DESIGN: Cross-sectional online survey conducted from 21 April to 20 May 2022. SETTING: Questionnaires were collected from patient volunteers selected by random cluster sampling in the FSHs in Shanghai, China. PARTICIPANTS: 197 participants who met the inclusion criteria as patients who were asymptomatic or presenting with mild symptoms in the FSHs and who volunteered to assist with routine work under quarantined settings. OUTCOME MEASURES: We investigated sociodemographic information, stress load and volunteer motivation through an online survey using the Volunteer Function Inventory and the Stress Overload Scale. Comparisons between groups were conducted by applying t-tests or analysis of variance. The correlation between volunteer motivation and stress was analysed by Pearson correlation. Influencing factors of volunteer motivation were determined by multivariable linear regression models. A value of p<0.05 was used to declare statistical significance. RESULTS: The mean score of volunteer motivation of patient volunteers was 73.24 (SD 12.00), while that of stress load was 46.08 (SD 21.28). The mean scores of the personal vulnerability (PV) and event load (EL), two dimensions of stress load, were 26.99 (SD 12.46) and 19.09 (SD 9.63), respectively. The majority of the participants (136, 69.04%) were grouped in the low (PV)-low (EL) stress category. Participants' volunteer motivation was negatively correlated with stress load (r=-0.238, p<0.001), as well as PV (r=-0.188, p<0.01) and EL (r=-0.283, p<0.001). Multivariable linear regression analysis identified that the potential influencing factors of volunteer motivation were occupation (B=1.100, 95% CI 0.037 to 2.164, p=0.043), health condition (B=-3.302, 95% CI -5.287 to -1.317, p<0.001) and EL (B=-0.434, 95% CI -0.756 to -0.111, p=0.009). Participants who worked in the public sector, had better health conditions and had lower EL were more likely to have higher volunteer motivation. CONCLUSIONS: Our study suggested that reducing stress load might be a possible pathway to encourage and maintain volunteerism in the FSH context. Implications and suggestions for future research on patient volunteer recruitment and management could be drawn from our findings.

Research on the Construction of Grain Food Multi-Chain Blockchain Based on Zero-Knowledge Proof.

As the main food source of the world's population, grain quality safety is of great significance to the healthy development of human beings. The grain food supply chain is characterized by its long life cycle, numerous and complex business data, difficulty defining private information, and difficult managing and sharing. In order to strengthen the ability of information application processing and coordination of the grain food supply chain under many risk factors, an information management model suitable for the grain food supply chain is studied based on the blockchain multi-chain technology. First, the information on key links in the grain food supply chain is analyzed to obtain privacy data classifications. Second, a multi-chain network model of the grain food supply chain is constructed, and based on this model, the hierarchical encryption and storage mode of private data as well as the relay cross-chain communication mode, are designed. In addition, a complete consensus process, including CPBFT, ZKP, and KZKP algorithms, is designed for the global information collaborative consensus under the multi-chain architecture. Finally, the model is verified through performance simulation, theory analysis, and prototype system verification in terms of its correctness, security, scalability, and consensus efficiency. The results show that this research model effectively reduces the storage redundancy and deals with problems of data differential sharing in traditional single-chain research, as well as provides a secure data protection mechanism, a credible data interaction mechanism, and an efficient multi-chain collaborative consensus mechanism. By attempting to apply blockchain multi-chain technology to the grain food supply chain, this study provides new research ideas for the trusted protection of data and information collaborative consensus in this field.

Lake eutrophication prediction based on improved MIMO-DD-3Q Learning.

As for the problem that the traditional single depth prediction model has poor strain capacity to the prediction results of time series data when predicting lake eutrophication, this study takes the multi-factor water quality data affecting lake eutrophication as the main research object. A deep reinforcement learning model is proposed, which can realize the mutual conversion of water quality data prediction models at different times, select the optimal prediction strategy of lake eutrophication at the current time according to its own continuous learning, and improve the reinforcement learning algorithm. Firstly, the greedy factor, the fixed parameter of Agent learning training in reinforcement learning, is introduced into an arctangent function and the mean value reward factor is defined. On this basis, three Q estimates are introduced, and the weight parameters are obtained by calculating the realistic value of Q, taking the average value and the minimum value to update the final Q table, so as to get an Improved MIMO-DD-3Q Learning model. The preliminary prediction results of lake eutrophication are obtained, and the errors obtained are used as the secondary input to continue updating the Q table to build the final Improved MIMO-DD-3Q Learning model, so as to achieve the final prediction of water eutrophication. In this study, multi-factor water quality data of Yongding River in Beijing were selected from 0:00 on July 26, 2021 to 0:00 on September 5, 2021. Firstly, data smoothing and principal component analysis were carried out to confirm that there was a certain correlation between all factors in the occurrence of lake eutrophication. Then, the Improved MIMO-DD-3Q Learning prediction model was used for experimental verification. The results show that the Improved MIMO-DD-3Q Learning model has a good effect in the field of lake eutrophication prediction.

Ginkgolide C attenuates cerebral ischemia/reperfusion-induced inflammatory impairments by suppressing CD40/NF-κB pathway.

ETHNOPHARMACOLOGICAL RELEVANCE: Ginkgo biloba L. (Ginkgoaceae), a traditional Chinese medicine, has been applied for thousands of years for the treatment of cardio-cerebral vascular diseases in China. It is written in Compendium of Materia Medica that Ginkgo has the property of "dispersing poison", which is now referred to as anti-inflammatory and antioxidant. Ginkgolides are important active ingredients in Ginkgo biloba leaves and ginkgolide injection has been frequently applied in clinical practice for the treatment of ischemic stroke. However, few studies have explored the effect and mechanism of ginkgolide C (GC) with anti-inflammatory activity in cerebral ischemia/reperfusion injury (CI/RI). AIM OF THE STUDY: The present study aimed to demonstrate whether GC was capable of attenuating CI/RI. Furthermore, the anti-inflammatory effect of GC in CI/RI was explored around the CD40/NF-κB pathway. MATERIALS AND METHODS: In vivo, middle cerebral artery occlusion/reperfusion (MCAO/R) model was established in rats. The neuroprotective effect of GC was assessed by neurological scores, cerebral infarct rate, microvessel ultrastructure, blood-brain barrier (BBB) integrity, brain edema, neutrophil infiltration, and levels of TNF-α, IL-1ß, IL-6, ICAM-1, VCAM-1, and iNOS. In vitro, rat brain microvessel endothelial cells (rBMECs) were preincubated in GC before hypoxia/reoxygenation (H/R) culture. The cell viability, levels of CD40, ICAM-1, MMP-9, TNF-α, IL-1ß, and IL-6, and activation of NF-κB pathway were examined. In addition, the anti-inflammatory effect of GC was also investigated by silencing CD40 gene in rBMECs. RESULTS: GC attenuated CI/RI as demonstrated by decreasing neurological scores, reducing cerebral infarct rate, improving microvessel ultrastructural features, ameliorating BBB disruption, attenuating brain edema, inhibiting MPO activity, and downregulating levels of TNF-α, IL-1ß, IL-6, ICAM-1, VCAM-1, and iNOS. Coherently, in rBMECs exposed to H/R GC enhanced cell viability and downregulated levels of ICAM-1, MMP-9, TNF-α, IL-1ß, and IL-6. Furthermore, GC suppressed CD40 overexpression and hindered translocation of NF-κB p65 from the cytosol to the nucleus, phosphorylation of IκB-α, and activation of IKK-ß in H/R rBMECs. However, GC failed to protect rBMECs from H/R-induced inflammatory impairments and suppress activation of NF-κB pathway when CD40 gene was silenced. CONCLUSIONS: GC attenuates cerebral ischemia/reperfusion-induced inflammatory impairments by suppressing CD40/NF-κB pathway, which may provide an available therapeutic drug for CI/RI.

Spatio-temporal data prediction of multiple air pollutants in multi-cities based on 4D digraph convolutional neural network.

In response to the problem that current multi-city multi-pollutant prediction methods based on one-dimensional undirected graph neural network models cannot accurately reflect the two-dimensional spatial correlations and directedness, this study proposes a four-dimensional directed graph model that can capture the two-dimensional spatial directed information and node correlation information related to multiple factors, as well as extract temporal correlation information at different times. Firstly, A four-dimensional directed GCN model with directed information graph in two-dimensional space was established based on the geographical location of the city. Secondly, Spectral decomposition and tensor operations were then applied to the two-dimensional directed information graph to obtain the graph Fourier coefficients and graph Fourier basis. Thirdly, the graph filter of the four-dimensional directed GCN model was further improved and optimized. Finally, an LSTM network architecture was introduced to construct the four-dimensional directed GCN-LSTM model for synchronous extraction of spatio-temporal information and prediction of atmospheric pollutant concentrations. The study uses the 2020 atmospheric six-parameter data of the Taihu Lake city cluster and applies canonical correlation analysis to confirm the data's temporal, spatial, and multi-factor correlations. Through experimentation, it is verified that the proposed 4D-DGCN-LSTM model achieves a MAE reduction of 1.12%, 4.91%, 5.62%, and 11.67% compared with the 4D-DGCN, GCN-LSTM, GCN, and LSTM models, respectively, indicating the good performance of the 4D-DGCN-LSTM model in predicting multiple types of atmospheric pollutants in various cities.

Effect Observation of Electro-Acupuncture Anesthesia Combined with General Anesthesia in Elderly Patients Undergoing Gastrointestinal Tumor Resection.

Objective: To investigate the anesthetic effect of electro-acupuncture (EA) anesthesia combined with general anesthesia in elderly patients undergoing gastrointestinal tumor resection, and to analyze the effects of EA anesthesia on inflammatory factors, stress state and T lymphocyte subsets in elderly patients. Methods: Total of 118 elderly patients who underwent gastrointestinal tumor resection in our hospital from June 2018 to March 2021 were selected and divided into the control group (59 cases) and the observation group (59 cases) according to the random number method. General anesthesia was adopted in the control group and EA anesthesia combined with general anesthesia was adopted in the observation group. The anesthesia effect, stress state, levels of inflammatory factors, T-lymphocyte subsets and adverse reactions were compared. Results: The VAS score, agitation score and respiratory normalization time in the observation group were lower than those in the control group (p < 0.05). After surgery, the levels of serum Cor, ET, NE and DA in the observation group were lower than those in the control group (p < 0.05). At 24 h after surgery, the levels of serum TNF-α, IL-6 and IL-1ß in the observation group were lower than those in the control group (p < 0.05). At 24 h after surgery, the levels of C D 3 + , C D 4 + , and C D 4 + / / C D 8 + in the two groups were lower than those before surgery, and the levels of C D 3 + , C D 4 + , and C D 4 + / / C D 8 + in the observation group were higher than those in the control group (p < 0.05). During the hospitalization, the total incidence rate of adverse reactions after anesthesia in the observation group was lower than that in the control group (p < 0.05). Conclusion: EA anesthesia combined with general anesthesia has good anesthesia effect when used for gastrointestinal tumor resection in the elderly. It can stabilize the internal environment of patients, alleviate postoperative stress response and inflammatory response, and regulate the body immune function. Moreover, it has high safety and can significantly reduce the occurrence of postoperative adverse reactions.

Construction of rice supply chain supervision model driven by blockchain smart contract.

The outbreak of the COVID-19 and the Russia Ukraine war has had a great impact on the rice supply chain. Compared with other grain supply chains, rice supply chain has more complex structure and data. Using digital means to realize the dynamic supervision of rice supply chain is helpful to ensure the quality and safety of rice. This study aimed to build a dynamic supervision model suited to the circulation characteristics of the rice supply chain and implement contractualization, analysis, and verification. First, based on an analysis of key information in the supervision of the rice supply chain, we built a dynamic supervision model framework based on blockchain and smart contracts. Second, under the logical framework of a regulatory model, we custom designed three types of smart contracts: initialization smart contract, model-verification smart contract, and credit-evaluation smart contract. To implement the model, we combined an asymmetric encryption algorithm, virtual regret minimization algorithm, and multisource heterogeneous fusion algorithm. We then analyzed the feasibility of the algorithm and the model operation process. Finally, based on the dynamic supervision model and smart contract, a prototype system is designed for example verification. The results showed that the dynamic supervision model and prototype system could achieve the real-time management of the rice supply chain in terms of business information, hazard information, and personnel information. It could also achieve dynamic and credible supervision of the rice supply chain's entire life cycle at the information level. This new research is to apply information technology to the digital management of grain supply chain. It can strengthen the digital supervision of the agricultural product industry.