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Background and Aims: The relationship between SARS-CoV-2 infection and ICD shock remains unclear. We analyzed the characteristics of patients received shocks after SARS-CoV-2 infection to explore causes of these shocks to provide information for subsequent treatment. Methods: We retrospectively analyzed data from seven patients who hospitalized the First People’s Hospital of Yunnan Province between December 2022 and January 2023 after experiencing ICD shock or ventricular arrhythmias following SARS-CoV-2 infection. We collected baseline information i.e., age and sex, device type, arrhythmia type, comorbidities, etc to analyze the causes of electrical shocks. Results: All patients’ devices were implanted in our hospital. The patients’ mean age was 67±10 years. Four underwent implantation for primary prevention, and three for secondary prevention. These patients received 80 ICD shocks after SARS-CoV-2 infection, 71 (88.75%) nine (11.25%) of which were treatment for ventricular tachycardia and atrial fibrillation, respectively. There were 54 antitachycardia pacing(ATP) treatments, which forty-eight arrhythmic events were terminated through antitachycardia pacing and six not. Laboratory tests conducted upon admission revealed that six patients had blood potassium levels below 4.0 mmol/L. Five patients had blood calcium levels below 2.11 mmol/L. Four of seven patients had elevated troponin concentrations (0.030–0.297 ng/mL). All patients had significantly elevated N-terminal pro-B-type natriuretic peptide levels (608.8–25,758 pg/mL). Six patients had a QT interval of > 440 ms and a mean QT interval of 460±46 ms. Conclusion: SARS-CoV-2 infection may be associated with ICD shock. Clinicians should pay close attention to patients with implanted devices after SARS-CoV-2 infection and actively eliminate arrhythmogenic triggers to minimize the likelihood of ICD shock.

Sweetpotato (Ipomoea batatas) is one of the most important crops in China. To gain a clearer picture of the occurrence of diseases on sweetpotato, 50 fields (100 plants/field) were randomly surveyed in prominent sweetpotato growing areas of Lulong county, Hebei Province in the years 2021–2022. Plants showing chlorotic leaf distortion with mildly twisted young leaves and stunted vines were observed frequently. It was similar to the symptoms of chlorotic leaf distortion of sweetpotato (Clark et al. 2013). The incidence of disease with patch pattern ranged from 15% to 30%. Ten symptomatic leaves were excised, surface disinfested with 2% sodium hypochlorite for 1 min, rinsed three times in sterilized ddH2O, and cultured on potato dextrose agar (PDA) at 25°C. Nine fungal isolates were obtained. A pure culture of representative isolate FD10 obtained after serial hyphal tip transfer was examined for morphological and genetic characters. Colonies of isolate FD10 on PDA at 25°C were slow growing (4±0.1mm/day) with aerial, white-to-pink mycelium. Lobed colonies had greyish-orange pigmentation in reverse and conidia aggegated in false heads. Conidiophores were prostrate and short. Phialides were mostly monophialidic but occasionally polyphialidic. Polyphialidic openings often denticulate in a rectangular arrangement. Microconidia were abundant, long, oval to allantoid, mostly none or one septate, and 4.79 to 9.53 × 2.08 to 3.22 μm (n = 20). Macroconidia were fusiform to falcate with a beaked apical cell and a footlike basal cell, 3 to 5 septate, and 25.03 to 52.92 × 2.56 to 4.49 µm. Chlamydospores were absent. All in agreement with the morphology of Fusarium denticulatum (Nirenberg and O'Donnell 1998). Genomic DNA of isolate FD10 was extracted. The EF-1α and β-tubulin genes were amplified and sequenced (O’Donnell and Cigelnik 1997; O’Donnell et al. 1998). The obtained sequences were deposited in GenBank (accession nos. OQ555191 and OQ555192). BLASTn revealed that those sequences showed 99.86% (EF-1α) and 99.93% (β-tubulin) homology with the relative sequences of F. denticulatum type strain CBS407.97 (accession nos. MT011002.1 and MT011060.1), respectively. Furthermore, a neighbor-joining phylogenetic tree based on the EF-1α and β-tubulin sequences revealed that the isolate FD10 clustered with F. denticulatum. Based on morphological characteristics and sequence analysis, the isolate FD10 associated with chlorotic leaf distortion of sweetpotato was identified as F. denticulatum. Pathogenicity tests were performed by immersing ten 25-cm-long vine-tip cuttings of cultivar Jifen 1 origin from tissue culture in a conidial suspension (1 × 106 conidia per ml) of isolate FD10. Vines immersed in sterile distilled water used as a control. All inoculated plants in 25-cm plastic pots were incubated in a climate chamber at 28℃ and 80% RH for two and half months and the control were incubated in a separate climate chamber. Nine inoculated plants developed chlorotic terminals, moderate interveinal chlorosis and slight leaf distortion. No symptoms were observed on the control plants. The pathogen was reisolated from inoculated leaves and matched the morphological and molecular characteristics of the original isolates, thus fulfilling Koch’s postulates. To our knowledge, this is the first report of F. denticulatum causing chlorotic leaf distortion of sweetpotato in China. Its identification will promote the management of this disease in China.

Aims Sub-therapeutic use of antibiotics as a growth promoter in animal diets has either been banned or voluntarily withdrawn from use in many countries to help curb the emergence of antibiotic-resistant pathogens. Probiotics may be an alternative to antibiotics as a growth promoter. We investigated the effects of a novel probiotic strain, Bacillus amyloliquefaciens H57 (H57) on the performance and microbiome-associated metabolic potential. Methods and Results Broiler chickens were fed either sorghum- or wheat-based diets supplemented with the probiotic H57. The growth rate, feed intake, and feed conversion in supplemented birds were compared with those in non-supplemented control. Caecal microbial metabolic functions were studied with shotgun metagenomic sequencing. H57 supplementation significantly increased the growth rate and daily feed intake of meat chickens relative to the non-supplemented controls without any effect on feed conversion ratio. In addition, relative to the non-supplemented controls, gene-centric metagenomics revealed that H57 significantly altered the functional capacity of the caecal microbiome, with amino acid and vitamin synthesis pathways being positively associated with H57 supplementation. Conclusions Bacillus amyloliquefaciens H57 improves the performance of meat chickens or broilers and significantly modifies the functional potential of their caecal microbiomes, with enhanced potential capacity for amino acid and vitamin biosynthesis.

The evolution of the Internet of Things technology has boosted the drastic increase in network traffic demand. Caching and multicasting are effective approaches to alleviate the backhaul burden of networks and reduce service latency. However, existing works do not jointly exploit the advantages of these two approaches. In this paper, we propose COCAM, a cooperative video edge caching and multicasting approach based on multi-agent deep reinforcement learning to minimize the transmission number in the multi-clouds scenario with limited storage capacity in each mobile edge server. Specifically, by integrating a cooperative transmission model with the caching model, we provide a concrete formulation of the joint problem. Then, We cast this decision-making problem as a multi-agent extension of the Markov decision process and propose a multi-agent actor-critic algorithm in which each agent learns a local caching policy and observes the information of neighboring agents as part of the environment. Finally, to validate the COCAM algorithm, we conduct extensive experiments on a real-world dataset. The results show that our proposed algorithm outperforms other baseline algorithms in terms of the number of video transmissions.

Recently, we have observed an explosion in the intellectual capacity of user equipment, coupled by a meteoric rise in the need for very demanding services and applications. The majority of the work leverages edge computing technologies to accomplish the quick deployment of microservices, but disregards their inter-dependencies. In addition, while constructing the microservice deployment approach, several research disregard the significance of system context extraction. The microservice deployment issue (MSD) is stated as a max-min problem by concurrently evaluating the system cost and service quality. This research first analyzes an attention-based microservice representation approach for extracting system context. The attention-modified soft actor-critic method is proposed to the MSD issue. The simulation results reveal the ASAC algorithm’s priorities in terms of average system cost and system reward.

The pathophysiology of sepsis-associated acute kidney injury (S-AKI) is not well elucidated. Platelets have been reported to play a critical role in the pathogenesis of AKI, but the true mechanism remains unknown. Herein, we established a mouse model of S-AKI by cecal ligation and puncture (CLP). Ticagrelor was given 24 h before and after CLP by gastric gavage. Platelets were isolated and analyzed by the label-free proteome approach to identify platelet-derived damage-associated molecular patterns (DAMPs). Our results demonstrated that, among all differentially expressed proteins (DEPs), platelet-derived transthyretin (TTR) exerted effects in S-AKI. To examine the direct effects of platelet TTR on human renal proximal tubule epithelial (HK2) cells damage, platelets were co-cultured with HK2 cells. The results indicated that platelet TTR can cause reactive oxygen species production and apoptosis in HK2 cells. Further research found that platelet TTR can also result in increased levels of mRNA and protein for protein kinase B (AKT), phosphatidylinositol 3-kinase (PI3K), and extracellular regulated protein kinase (ERK), as analyzed by real-time quantitative polymerase chain reaction (RT-qPCR) and western blotting. In conclusion, platelet-derived TTR may be one kind of DAMPs that plays an important role in the development of S-AKI.

Mobile Edge Computing (MEC) has emerged as a promising computing paradigm in the 5G architecture, which can empower User Equipments (UEs) with computation and energy resources offered by migrating workloads from UEs to the nearby MEC servers. Although the issues of computation offloading and resource allocation in MEC have been studied with different optimization objectives, they mainly focus on facilitating the performance in the quasi-static system, and seldomly consider time-varying system conditions in the time domain. In this paper, we investigate the joint optimization of computation offloading and resource allocation in a dynamic multi-user MEC system. Our objective is to minimize the energy consumption of the entire MEC system, by considering the delay constraint as well as the uncertain resource requirements of heterogeneous computation tasks. We formulate the problem as a Mixed Integer Non-Linear Programming (MINLP) problem, and propose a value iteration based Reinforcement Learning (RL) method, named Q-Learning, to determine the joint policy of computation offloading and resource allocation. To avoid the curse of dimensionality, we further propose a Double Deep Q Network (DDQN) based method, which can efficiently approximate the value function of Q-learning. Simulation results demonstrate that the proposed methods significantly outperform other baseline methods in different scenarios, except the Exhaustion method. Especially, the proposed DDQN based method achieves very close performance with the Exhaustion method, and can significantly reduce the average of 20%, 35%, 53% energy consumption compared with Offloading Decision, Local First method, and Offloading First method, respectively when the number of UEs is 5.

Background Circulating tumor DNA (ctDNA) has potential as a specific, noninvasive, and cost‐effective new biomarker for patients with lung cancer. This study aimed to determine whether plasma ctDNA can be used to predict treatment outcomes in patients with lung cancer. Methods Pre‐ and in‐treatment blood samples were collected from 14 patients with lung cancer receiving chemotherapy. Based on next‐generation sequencing technology, we constructed a unique molecular identifier (UMI) library and performed targeted deep sequencing of 72 genes (15 000×). We used dVAF to evaluate the change level and trend of variant allele frequency (VAF). Results We identified MUC16, KMT2D, AMER1, and NTRK1 as the most‐frequently mutated genes in ctDNA associated with lung cancer. Furthermore, we showed that the change trend of dVAF in patients with lung cancer undergoing chemotherapy was closely related to the changes in both tumor volume and tumor biomarkers, including CEA, CA125, NSE, and CK (Cytokeratin). Moreover, the ctDNA analysis revealed disease progression of SCLC patients earlier than did computed tomography. Conclusions The dynamic detection of plasma ctDNA VAF has the potential value as a biomarker for evaluating the efficacy of chemotherapy in patients with SCLC and advanced NSCLC, and may predict the progression of lung cancer patients earlier than radiography., The dVAF level in chemotherapy patients was closely associated with tumor size. High levels of tumor biomarkers were detected in patients with dVAF > 0. The dynamic detection of plasma ctDNA VAF has the potential value as a biomarker for evaluating the efficacy of chemotherapy in patients with SCLC and advanced NSCLC. Furthermore, the dynamic detection of plasma ctDNA VAF may predict the progression of lung cancer patients earlier than radiography.

Along with the unprecedented boom of 5G, the Internet of Things (IoT), and artificial intelligence, smart cities are making a great clamor. When billions of IoT devices are streaming in the smart city, realizing low-latency and high-bandwidth services has a significant impact on the benefits of smart cities. Thus, managing resources efficiently and intelligently to meet various service requirements has become a challenge for smart cities. With the rapid development of machine intelligence, it can be considered that many machines constitute a machine society just like that of human beings. Inspired by the efficient and collaborative operation mechanism of human society, we propose a socialized learning scheme to address various needs in smart cities (e.g., reasonable resource allocation). Specifically, we present the socialized learning scheme from three perspectives. First, we design a cognitive paradigm of socialized learning to enlighten the long-term vision of applying learning-based resource management to smart cities, with comprehensive discussions of three dimensions consisting of architecture, decision, and knowledge. Second, we also probe the socialized learning methodology including socialized training and socialized inference. It highlights social relationships and introduces a series of technologies to realize social relations within and across layers. Finally, we give a socialized learning solution for solving the practical resource allocation problem in smart cities, thereby jointly improving decision accuracy and reducing training costs. Illustrative simulations are provided to show the effectiveness of the proposed scheme.

Per- and polyfluoroalkyl substances (PFASs), recognized worldwide as emerging pollutants, may pose a substantial threat to human health and our environment due to their stability, high concentrations, wide distribution, and easy accumulation. Ever since perfluorooctane sulfonate and perfluorooctanoic acid were recognized by the Stockholm Convention on Persistent Organic Pollutants, the public has become increasingly concerned about potential contamination and the environmental risks associated with PFASs. Ubiquitous PFAS contamination of drinking water, groundwater, surface water, and sediment has been detected, especially in areas with rapid industrial and economic development. Its accumulation in living organisms and foods has accentuated the importance of investigations into aquatic organisms at the bottom of the food chain, as the stability and integrity of the food web as well as the population quantity and structure of the aquatic ecosystem may be affected. This review provides a comprehensive summary of the toxic and toxicity-related effects of PFASs on aquatic plankton, aquatic invertebrates and microorganisms, the characteristics of different target aquatic organisms in toxicity investigations, and a feasibility evaluation of PFAS substitutes to provide valuable suggestions for further utilization and regulation of PFASs and their substitutes.

Background Non-communicable diseases (NCDs) pose a major challenge to health economic cost and residents’ health status. Community health workers (CHWs) are the gatekeeper of primary health care. Objective This study aimed to conduct a situational analysis of the current human resource and requirement of NCDs-related training in Chengdu with regard to address to understand the suggestions for improvement of challenges and barriers. Methods A descriptive online cross-sectional survey was conducted among CHWs (doctor and nurse) from 23 districts and counties in Chengdu. Sociodemographic and NCDs-related variables were collected. Univariate analysis and multiple response analysis were used to describe the characteristics of these variables. Results 711 doctors and 637 nurses completely responded. There were significant differences among gender, age, educational levels, professional title, working year, type of institution, urban circle and registration in general practice between doctors and nurses (P P P

Edge nodes (ENs) in Internet of Things commonly serve as gateways to cache sensing data while providing accessing services for data consumers. This paper considers multiple ENs that cache sensing data under the coordination of the cloud. Particularly, each EN can fetch content generated by sensors within its coverage, which can be uploaded to the cloud via fronthaul and then be delivered to other ENs beyond the communication range. However, sensing data are usually transient with time whereas frequent cache updates could lead to considerable energy consumption at sensors and fronthaul traffic loads. Therefore, we adopt age of information to evaluate data freshness and investigate intelligent caching policies to preserve data freshness while reducing cache update costs. Specifically, we model the cache update problem as a cooperative multi-agent Markov decision process with the goal of minimizing the long-term average weighted cost. To efficiently handle the exponentially large number of actions, we devise a novel reinforcement learning approach, which is a discrete multi-agent variant of soft actor-critic (SAC). Furthermore, we generalize the proposed approach into a decentralized control, where each EN can make decisions based on local observations only. Simulation results demonstrate the superior performance of the proposed SAC-based caching schemes.