Enterococcus faecalis provides protection during scavenging in carrion crow (Corvus corone)

The gut microbiota significantly influences host physiology and provides essential ecosystem services.While diet can affect the composition of the gut microbiota,the gut microbiota can also help the host adapt to specific dietary habits.The carrion crow(Corvus corone),an urban facultative scavenger bird,hosts an abundance of pathogens due to its scavenging behavior.Despite this,carrion crows infrequently exhibit illness,a phenomenon related to their unique physiological adaptability.At present,however,the role of the gut microbiota remains incompletely understood.In this study,we performed a comparative analysis using 16S rRNA amplicon sequencing technology to assess colonic content in carrion crows and 16 other bird species with different diets in Beijing,China.Our findings revealed that the dominant gut microbiota in carrion crows was primarily composed of Proteobacteria(75.51%)and Firmicutes(22.37%).Significant differences were observed in the relative abundance of Enterococcus faecalis among groups,highlighting its potential as a biomarker of facultative scavenging behavior in carrion crows.Subsequently,E.faecalis isolated from carrion crows was transplanted into model mice to explore the protective effects of this bacterial community against Salmonella enterica infection.Results showed that E.faecalis down-regulated the expression of pro-inflammatory cytokines tumor necrosis factor alpha(TNF-α),interferon gamma(IFN-γ),and interleukin 6(IL-6),prevented S.enterica colonization,and regulated the composition of gut microbiota in mice,thereby modulating the host’s immune regulatory capacity.Therefore,E.faecalis exerts immunoregulatory and anti-pathogenic functions in carrion crows engaged in scavenging behavior,offering a representative case of how the gut microbiota contributes to the protection of hosts with specialized diets.

A Bilevel Programming Approach for Optimization of Airport Ground Movement

This paper proposes an optimization model for the airport ground movement problem(GMP)based on bilevel programming to address taxi conflicts on the airport ground and to improve the operating safety and efficiency.To solve GMP,an iterative heuristic algorithm is designed.Instead of separately investigating each problem,this model simultaneously coordinates and optimizes the aircraft routing and scheduling.A simulation test is conducted on Nanjing Lukou International Airport(NKG)and the results show that the bilevel programming model can clearly outperform the widely used first-come-first-service(FCFS)scheduling scheme in terms of aircraft operational time under the precondition of none conflict.The research effort demonstrates that with the reduced operating cost and the improved overall efficiency,the proposed model can assist operations of the airports that are facing increasing traffic demand and working at almost maximum capacity.

Airport gate assignment problem with deep reinforcement learning

With the rapid development of air transportation in recent years,airport operations have attracted a lot of attention.Among them,airport gate assignment problem(AGAP)has become a research hotspot.However,the real-time AGAP algorithm is still an open issue.In this study,a deep reinforcement learning based AGAP(DRL-AGAP)is proposed.The optimization object is to maximize the rate of flights assigned to fixed gates.The real-time AGAP is modeled as a Markov decision process(MDP).The state space,action space,value and rewards have been defined.The DRL-AGAP algorithm is evaluated via simulation and it is compared with the flight pre-assignment results of the optimization software Gurobiand Greedy.Simulation results show that the performance of the proposed DRL-AGAP algorithm is close to that of pre-assignment obtained by the Gurobi optimization solver.Meanwhile,the real-time assignment ability is ensured by the proposed DRL-AGAP algorithm due to the dynamic modeling and lower complexity.

A greedy algorithm based on joint assignment of airport gates and taxiways in large hub airports

With the rapid development of civil aviation in recent years,the management and assignment of airport resources are becoming more and more difficult.Among the various airport resources,gates and taxiways are very important,therefore,many researchers focus on the airport gate and taxiway assignment problem.However,the joint assignment algorithm of airport gates and taxiways with realistic airport data has not been well studied.A greedy algorithm based on joint assignment of airport gates and taxiways using the data of a large hub airport in China is proposed.The objective is maximizing the ratio of fixed gates and minimizing the ratio of taxiway collisions.Simulation results show that it outperforms other assignment schemes.

Fluorescein angiography of retinal vascular involution after intravitreal injection of ranibizumab for retinopathy of prematurity

AIM: To describe the involution patterns of vessel growth of retina through fluorescein angiography(FA) of children, who had been under treatment up to 1 y previously intravitreal ranibizumab(IVR) as monotherapy for retinopathy of prematurity(ROP). METHODS: This is a retrospective study. The medical information and FA of 17 children(34 eyes) whose area of avascular retina from the ora serrata was more than two disc diameters(DD) were analyzed. RESULTS: Among 34 eyes, all were the presence of finger-shaped vessels and arteriolar-venular shunts(100%,34/34 eyes). Popcorn abnormalities were found in most of the eyes(94.1%, 32/34 eyes). Furthermore, in many cases(23.5%, 8/34 eyes), there were leakage persisting in the region of the junction between avascular and vascular retina. In contrast, just 2 eyes(5.9%) showed damage of retinal capillary bed and 3 eyes(8.8%) showed large area of retinal pigment epithelium(RPE) atrophy. CONCLUSION: Although IVR can be very effective in ROP, we should remain cautious as infants may remain avascular peripheral retinas and abnormal vessel. FA allows accurate visualization of vessel abnormalities in eyes with ROP, which will be helpful to affect assessment of disease activity and therapeutic effect.

Performance analysis of blockchain for civil aviation business data based on M/G/1 queuing theory

An Ethereum blockchain based on proof of stake ( PoS) consensus mechanism is used to achieve the data sharing within the civil aviation service platform for both airport group management and passengers. Considering the Gas consumption of Ethereum, the dynamic batch-service capacity constraint by the Block Gas Limit and the priority mechanism depending on the different Gas Price of transactions, M/ G/1 queuing theory with batch-service is used to construct the service model of transactions confirmation process in the proposed blockchain system, where the effects of transactions arrival rate, block capacity, service rate and number of nodes on the average confirmation time of transactions with different priority are analyzed, and eventually a performance analysis model of blockchain for civil aviation business data is proposed. The simulation results prove the usability and accuracy of the model, which can provide both theoretical basis for data sharing of civil aviation using Ethereum blockchain and the further optimization of transactions confirmation time.

A general method for modified function projective synchronization

This paper investigates the modified function projective synchronization,which means that the drive system and the response system are synchronized up to a desired scale matrix of function. By the active control scheme,a general method for modified function projective synchronization is proposed. Numerical simulations on chaotic Rssler system and hyper-chaotic Chen system are presented to verify the effectiveness of the proposed scheme.

Pseudo-label based semi-supervised learning in the distributed machine learning framework

With the emergence of various intelligent applications,machine learning technologies face lots of challenges including large-scale models,application oriented real-time dataset and limited capabilities of nodes in practice.Therefore,distributed machine learning(DML) and semi-supervised learning methods which help solve these problems have been addressed in both academia and industry.In this paper,the semi-supervised learning method and the data parallelism DML framework are combined.The pseudo-label based local loss function for each distributed node is studied,and the stochastic gradient descent(SGD) based distributed parameter update principle is derived.A demo that implements the pseudo-label based semi-supervised learning in the DML framework is conducted,and the CIFAR-10 dataset for target classification is used to evaluate the performance.Experimental results confirm the convergence and the accuracy of the model using the pseudo-label based semi-supervised learning in the DML framework.Given the proportion of the pseudo-label dataset is 20%,the accuracy of the model is over 90% when the value of local parameter update steps between two global aggregations is less than 5.Besides,fixing the global aggregations interval to 3,the model converges with acceptable performance degradation when the proportion of the pseudo-label dataset varies from 20% to 80%.

Fouling corrosion in aluminum heat exchangers

Abstract Fouling deposits on aluminum heat exchanger reduce the heat transfer efficiency and cause corrosion to the apparatus. This study focuses on the corrosive behavior of aluminum cou- pons covered with a layer of artificial fouling in a humid atmosphere by their weight loss, Tafel plots, electrochemical impedance spectroscopy （EIS）, and scanning electron microscope （SEM） observations. The results reveal that chloride is one of the major elements found in the fouling which damages the passive film and initiates corrosion. The galvanic corrosion between the metal and the adjacent carbon particles accelerates the corrosive process. Furthermore, the black carbon favors the moisture uptake, and gives the dissolved oxygen greater chance to migrate through the fouling layer and form a continuous diffusive path. The corrosion rate decreasing over time is conformed to electrochemistry measurements and can be verified by Faraday＇s law. The EIS results indicate that the mechanism of corrosion can be interpreted by the pitting corrosion evolution mechanism, and that pitting was observed on the coupons by SEM after corrosive exposure.

Analysis of antibiotic usage for viral community-acquired pneumonia in adults

The rationale for the antibiotic treatment of viral community-acquired pneumonia(CAP)in adults was analyzed to develop a clinical reference standard for this condition.Clinical data from 166 patients diagnosed with viral pneumonia across 14 hospitals in Beijing from November 2010 to December 2017 were collected.The indications for medications were evaluated,and the rationale for the use of antibiotics was analyzed.A total of 163(98.3%)patients with viral pneumonia were treated with antibiotics.A combination of C-reactive protein(CRP)and procalcitonin(PCT)was used as markers to analyze the possible indications for antibiotic use.With threshold levels set at 0.25µg/L for PCT and 20 mg/L for CRP,the rate of unreasonable use of antibiotics was 55.2%.By contrast,at a CRP level threshold of 60 mg/L,the rate of antibiotic misuse was 77.3%.A total of 39 of the 163(23.9%)patients did not meet the guidelines for drug selection for viral CAP in adults.The unreasonable use of antibacterial drugs for the treatment of viral CAP in adults is a serious concern.Clinicians must reduce the unnecessary use of antibiotics.