Identification,Isolation and Genomic Characterization of Porcine Astrovirus in Shandong Province,China in 2021-2023

[Objective]The paper was to identify,isolate,and characterize porcine astrovirus in Shandong Province between 2021 and 2023.[Method]A total of 1025 samples of porcine diarrhea samples were collected from various regions of Shandong Province between January 2021 and October 2023.The samples were tested by RT-PCR,followed by sequencing and phylogenetic analyses of the polymerase.[Result]The total positive rate of PAstV was 34.6%(355/1025).The respective proportions of individuals infected with PAstV-1,PAstV-2,PAstV-4 and PAstV-5 were 25.4%(90/355),28.2%(100/355),35.2%(125/355)and 22.5%(80/355),respectively.Additionally,mixed infection was observed.Meanwhile,849 samples of healthy pigs were tested by RT-PCR,and the results demonstrated that the total positive rate of PAstV was 8.13%(69/849).Of these,the proportion of PAstV-1,PAstV-2 and PAstV-4 infection was 27.5%(19/69),37.7%(26/69)and 40.6%(28/69),and a mixed infection also existed.Further sequencing and characterization of some the selected isolates revealed low sequence identities(56.2%)with known PAstV strains,indicating the presence of novel types or genotypes of PAstVs.Furthermore,the isolation conditions of porcine astrovirus were optimized,resulting in the purification of a pure PAstV-4 strain(designated PAstV-4-GRF1).The virus was found to exhibit typical astroviral morphology,with nucleotide identity ranging from 89.9 to 95.4%with previously published PAstV-4 strains.Then,macrovirus transcriptome sequencing showed that 88.30%of the CRF1 samples were mammalian astroviruses.By species classification,PAstV 4 and PAstV 2 accounted for 21.79%and 0.32%,respectively.Phylogenetic tree analysis showed that the c15050 fragment was identical to the GRF-1 sequencing fragment of the isolated strain,and exhibited the highest homology with the Hunan PAstV-4 sequence MK460231 in China.[Conclusion]As the inaugural isolated PAstV-4 strain,it furnishes pivotal materialfor the investigation ofthe biological and pathogenic properties of this virus as well as for the prospectivedevelopment of relevant biological and diagnostic reagents.

Isolation and Identification of Pathogens Causing Foot Rot Disease of Bayinbuluk Sheep in Xinjiang

[Objective]The paper was to investigate the pathogenicity of Fusobacterium necrophrum in Bayinbuluk sheep of Xinjiang.[Method]The pathogens of 60 rotten limb tissue samples were isolated and identified from 2019 to 2021.The pathogenicity and bacteria were detected by using artificial infection test,bacterial identification instrument and high performance liquid mass spectrometer.[Result]A total of 3 pathogenic bacteria were isolated from foot rot disease of Bayinbuluk sheep,namely F.necrophrum,Staphylococcus aureus and Escherichia coli,indicating a mixed infection.All of the isolates could cause death in mice,and the fatality rate of mice was 50%.[Conclusion]Foot rot disease is more common in the Bayinbuluk sheep flock in Hejing County of Xinjiang,and the disease can be controlled effectively by doing a good job of sanitary disinfection in the enclosure.

Isolation and Identification of Black Spot Pathogen in Psidium guajava

[Objective]The aim was to isolate and identify the pathogen causing black spot disease in guava（Psidium guajava）,so as to determine its taxonomic status.[Method]The fungus was identified by determining its pathogenicity,observing its morphology characteristics and analyzing its ITS sequence.[Result]The pathogen causing black spot disease in guava was a strain of Guignardia mangiferae.[Conclusion]This study will provide theoretical basis for curing black spot disease of guava.

Isolation,Screening and Primary Identification of a Keratin-degrading Fungus

[Objective] The paper was to provide new germplasm sources for efficient and economical degradation and utilization of animal keratin.[Method] The keratin-degrading fungus was isolated,screened and primarily identified by using the combination method of traditional isolation and screening,solid culture-medium degradation and animal test.[Result] A strain of non-pathogenic filamentous fungi with high degradation efficiency was obtained,which was preliminarily identified to be a species in Mucoraceae.[Conclusion] The discovery of the strain enriched the family members of keratin-degrading fungus,and provided new germplasm resources for degradation and utilization of animal keratin.

Isolation,Identification and in vitro Antimicrobial Susceptibility of Pathogenic Aeromonas veronii from Soft-shelled Turtles

In order to effectively control diseases caused by Aeromonas veronii,pathogenic bacteria were isolated and incubated from infected soft-shelled turtles with traditional bacterial isolation method. Four strains of pathogenic bacteria were isolated and identified with traditional biochemical identification method and modern molecular biological identification techniques. According to the results, four strains of pathogenic bacteria were identified as A. veronii biovar sobria. Drug sensitivity test and in vitro antimicrobial test against Bacillus amyloliquefaciens were performed with agar diffusion method. The results showed that B. amyloliquefaciens and several antibiotics such as ofloxacin and gentamicin exerted strong antimicrobial effects on four isolates. B. amyloliquefaciens could be used for the prevention and control of diseases caused by A. veronii in aquaculture.

Isolation and Identification of Anthracnose Pathogen from Fruit of Red Fuji Apple

The fruits of Red Fuji apple with anthracnose symptoms were collected and submitted to tissue isolation and culture. One strain of anthracnose pathogen （numbered as Acgl） was obtained, and it was identified by both morphological and molecular biological methods. According to the morphological characteristics of the colony and conidia and the results of rDNA-ITS sequence analysis, the Acgl strain was identified as Colletotrichum gloeosporioides.

Isolation and Identification of Antifungal Bacterial Strain KL-1 against Plant Wilt Disease

[Objective] The antifungal bacteria of plant wilt disease was screened and identified to provide foundation for the study on bio-control preparation of plant wilt disease.[Method] Confrontation culture method was adopted to screen the bio-control bacteria with good antifungal effect against plant wilt disease,Biolog bacteria automatic identification system and 16S rDNA sequence analysis method were selected to identify its taxonomic status,the biological safety of the strain towards cotton and mice was also determined.[Result] 12 bacterial strains were isolated from rhizosphere of cotton.Among those strains,5 isolates showed antifungal activity against F.decemcellulare Brick,F.oxysporum f.sp.Diathi,F.oxysporum f.sp.vasinfectum.The antifungal effect of KL-1 strain against three target strains of pathogen reached 69.09%,80.78% and 78.89% respectively.Identification results of Biolog bacteria automatic identification system and 16S rDNA sequence analysis method showed that KL-1strain was Bacillus amyloliquefaciens;primary determination results of biological safety also showed that the strain KL-1 was safe and non-toxic towards cotton and mice.[Conclusion] KL-1strain of B.amyloliquefaciens had antifungal effect against several pathogens of plant wilt diseases,which was safe and non-toxic towards cotton and mice,being the bio-control strain with research and development potential.

Two-Staged Method for Ice Channel Identification Based on Image Segmentation and Corner Point Regression

Identification of the ice channel is the basic technology for developing intelligent ships in ice-covered waters,which is important to ensure the safety and economy of navigation.In the Arctic,merchant ships with low ice class often navigate in channels opened up by icebreakers.Navigation in the ice channel often depends on good maneuverability skills and abundant experience from the captain to a large extent.The ship may get stuck if steered into ice fields off the channel.Under this circumstance,it is very important to study how to identify the boundary lines of ice channels with a reliable method.In this paper,a two-staged ice channel identification method is developed based on image segmentation and corner point regression.The first stage employs the image segmentation method to extract channel regions.In the second stage,an intelligent corner regression network is proposed to extract the channel boundary lines from the channel region.A non-intelligent angle-based filtering and clustering method is proposed and compared with corner point regression network.The training and evaluation of the segmentation method and corner regression network are carried out on the synthetic and real ice channel dataset.The evaluation results show that the accuracy of the method using the corner point regression network in the second stage is achieved as high as 73.33%on the synthetic ice channel dataset and 70.66%on the real ice channel dataset,and the processing speed can reach up to 14.58frames per second.

Dynamic Hand Gesture-Based Person Identification Using Leap Motion and Machine Learning Approaches

Person identification is one of the most vital tasks for network security. People are more concerned about theirsecurity due to traditional passwords becoming weaker or leaking in various attacks. In recent decades, fingerprintsand faces have been widely used for person identification, which has the risk of information leakage as a resultof reproducing fingers or faces by taking a snapshot. Recently, people have focused on creating an identifiablepattern, which will not be reproducible falsely by capturing psychological and behavioral information of a personusing vision and sensor-based techniques. In existing studies, most of the researchers used very complex patternsin this direction, which need special training and attention to remember the patterns and failed to capturethe psychological and behavioral information of a person properly. To overcome these problems, this researchdevised a novel dynamic hand gesture-based person identification system using a Leap Motion sensor. Thisstudy developed two hand gesture-based pattern datasets for performing the experiments, which contained morethan 500 samples, collected from 25 subjects. Various static and dynamic features were extracted from the handgeometry. Randomforest was used to measure feature importance using the Gini Index. Finally, the support vectormachinewas implemented for person identification and evaluate its performance using identification accuracy. Theexperimental results showed that the proposed system produced an identification accuracy of 99.8% for arbitraryhand gesture-based patterns and 99.6% for the same dynamic hand gesture-based patterns. This result indicatedthat the proposed system can be used for person identification in the field of security.

Applying an Improved Dung Beetle Optimizer Algorithm to Network Traffic Identification

Network traffic identification is critical for maintaining network security and further meeting various demands of network applications.However,network traffic data typically possesses high dimensionality and complexity,leading to practical problems in traffic identification data analytics.Since the original Dung Beetle Optimizer(DBO)algorithm,Grey Wolf Optimization(GWO)algorithm,Whale Optimization Algorithm(WOA),and Particle Swarm Optimization(PSO)algorithm have the shortcomings of slow convergence and easily fall into the local optimal solution,an Improved Dung Beetle Optimizer(IDBO)algorithm is proposed for network traffic identification.Firstly,the Sobol sequence is utilized to initialize the dung beetle population,laying the foundation for finding the global optimal solution.Next,an integration of levy flight and golden sine strategy is suggested to give dung beetles a greater probability of exploring unvisited areas,escaping from the local optimal solution,and converging more effectively towards a global optimal solution.Finally,an adaptive weight factor is utilized to enhance the search capabilities of the original DBO algorithm and accelerate convergence.With the improvements above,the proposed IDBO algorithm is then applied to traffic identification data analytics and feature selection,as so to find the optimal subset for K-Nearest Neighbor(KNN)classification.The simulation experiments use the CICIDS2017 dataset to verify the effectiveness of the proposed IDBO algorithm and compare it with the original DBO,GWO,WOA,and PSO algorithms.The experimental results show that,compared with other algorithms,the accuracy and recall are improved by 1.53%and 0.88%in binary classification,and the Distributed Denial of Service(DDoS)class identification is the most effective in multi-classification,with an improvement of 5.80%and 0.33%for accuracy and recall,respectively.Therefore,the proposed IDBO algorithm is effective in increasing the efficiency of traffic identification and solving the problem of the original DBO algorithm that converges slowly and falls into the local optimal solution when dealing with high-dimensional data analytics and feature selection for network traffic identification.

Identification and evaluation of shale oil micromigration and its petroleum geological significance

Taking the Lower Permian Fengcheng Formation shale in Mahu Sag of Junggar Basin,NW China,as an example,core observation,test analysis,geological analysis and numerical simulation were applied to identify the shale oil micro-migration phenomenon.The hydrocarbon micro-migration in shale oil was quantitatively evaluated and verified by a self-created hydrocarbon expulsion potential method,and the petroleum geological significance of shale oil micro-migration evaluation was determined.Results show that significant micro-migration can be recognized between the organic-rich lamina and organic-poor lamina.The organic-rich lamina has strong hydrocarbon generation ability.The heavy components of hydrocarbon preferentially retained by kerogen swelling or adsorption,while the light components of hydrocarbon were migrated and accumulated to the interbedded felsic or carbonate organic-poor laminae as free oil.About 69% of the Fengcheng Formation shale samples in Well MY1 exhibit hydrocarbon charging phenomenon,while 31% of those exhibit hydrocarbon expulsion phenomenon.The reliability of the micro-migration evaluation results was verified by combining the group components based on the geochromatography effect,two-dimension nuclear magnetic resonance analysis,and the geochemical behavior of inorganic manganese elements in the process of hydrocarbon migration.Micro-migration is a bridge connecting the hydrocarbon accumulation elements in shale formations,which reflects the whole process of shale oil generation,expulsion and accumulation,and controls the content and composition of shale oil.The identification and evaluation of shale oil micro-migration will provide new perspectives for dynamically differential enrichment mechanism of shale oil and establishing a“multi-peak model in oil generation”of shale.

Isolation and Identification of a Flocculant Producing Strain TS-1

[Objective] The purpose was to select out and identify a flocculant producing strain which could produce high active flocculant.[Method] The strain producing high active flocculant was isolated out and purified through medium culture and the selected strain was identified through observing its culture characters and determining its physiological and biochemical property.[Result] Fourteen strains of bacteria with flocculant producing function were isolated from tested soil samples through isolation,purification and preliminary screening using dilution-spread plate method and plate streaking method.Five strains of flocculant producing bacteria showing higher flocculation activity were selected out after second screening and their flocculation rates were higher than 70%;the flocculation activity of one strain among them was still stable after multiple subculturings,its flocculation rate was always above 90% and it was marked as TS-1.TS-1 was encapsulated Gram-positive bacillus and there was no lipid in it,such as poly-β-hydroxybutyric acid.TS-1 was Bacillus amyloliquefaciens,so it was named Bacillus TS-1.[Conclusion] The strain selected out in this experiment could be used in the flocculation and biochemical treatment of wastewater from starch industry.

Isolation of E.tenella JL Strain Single-oocyst and PCR Identification

[Objective] In order to get a purified strain to carry out the relative molecular biology research about E.tenella. [Method] The single-oocyst isolation method was improved and the isolated single-oocyst which was put into capsule was fed to chickens. At the same time, the collected oocysts were identified by PCR method. [Result] The oocysts were isolated from feces of 15 chickens among that of 20 chickens and the infection rate was 75%. The PCR results demonstrated that the single-oocyst strain was E.tenella. [Conclusion] The inoculation of single oocyst capsule was simple, besides, this method did not only save time but also declined inoculation difficulty, increased infection rate and provided good materials for biological research of coccidian.

Identification of Hyperparasitic Fungal Species Associated with Coffee Leaf Rust

[Objectives]The paper was to elucidate the specific hyperparasitic fungal species that are associated with coffee leaf rust.[Methods]Tissue isolation,sample humidification,and three-point inoculation techniques were employed to isolate,culture,and purify the hyperparasitic fungi responsible for coffee leaf rust.The purified strains were identified using traditional morphological techniques and molecular biology methods.[Results]Four strains were isolated,specifically BS21(Cladosporium cladosporioides),BS34(C.tenuissimum),BS62(C.cladosporioides),and BS75(C.colombiae).[Conclusions]The findings of this research will contribute novel insights into the biological control of coffee leaf rust.

Pollution source identification methods and remediation technologies of groundwater: A review

Groundwater is an important source of drinking water.Groundwater pollution severely endangers drinking water safety and sustainable social development.In the case of groundwater pollution,the top priority is to identify pollution sources,and accurate information on pollution sources is the premise of efficient remediation.Then,an appropriate pollution remediation scheme should be developed according to information on pollution sources,site conditions,and economic costs.The methods for identifying pollution sources mainly include geophysical exploration,geochemistry,isotopic tracing,and numerical modeling.Among these identification methods,only the numerical modeling can recognize various information on pollution sources,while other methods can only identify a certain aspect of pollution sources.The remediation technologies of groundwater can be divided into in-situ and ex-situ remediation technologies according to the remediation location.The in-situ remediation technologies enjoy low costs and a wide remediation range,but their remediation performance is prone to be affected by environmental conditions and cause secondary pollution.The ex-situ remediation technologies boast high remediation efficiency,high processing capacity,and high treatment concentration but suffer high costs.Different methods for pollution source identification and remediation technologies are applicable to different conditions.To achieve the expected identification and remediation results,it is feasible to combine several methods and technologies according to the actual hydrogeological conditions of contaminated sites and the nature of pollutants.Additionally,detailed knowledge about the hydrogeological conditions and stratigraphic structure of the contaminated site is the basis of all work regardless of the adopted identification methods or remediation technologies.

DC-FIPD: Fraudulent IP Identification Method Based on Homology Detection

Currently,telecom fraud is expanding from the traditional telephone network to the Internet,and identifying fraudulent IPs is of great significance for reducing Internet telecom fraud and protecting consumer rights.However,existing telecom fraud identification methods based on blacklists,reputation,content and behavioral characteristics have good identification performance in the telephone network,but it is difficult to apply to the Internet where IP(Internet Protocol)addresses change dynamically.To address this issue,we propose a fraudulent IP identification method based on homology detection and DBSCAN(Density-Based Spatial Clustering of Applications with Noise)clustering(DC-FIPD).First,we analyze the aggregation of fraudulent IP geographies and the homology of IP addresses.Next,the collected fraudulent IPs are clustered geographically to obtain the regional distribution of fraudulent IPs.Then,we constructed the fraudulent IP feature set,used the genetic optimization algorithm to determine the weights of the fraudulent IP features,and designed the calculation method of the IP risk value to give the risk value threshold of the fraudulent IP.Finally,the risk value of the target IP is calculated and the IP is identified based on the risk value threshold.Experimental results on a real-world telecom fraud detection dataset show that the DC-FIPD method achieves an average identification accuracy of 86.64%for fraudulent IPs.Additionally,the method records a precision of 86.08%,a recall of 45.24%,and an F1-score of 59.31%,offering a comprehensive evaluation of its performance in fraud detection.These results highlight the DC-FIPD method’s effectiveness in addressing the challenges of fraudulent IP identification.

A viscoelastic metamaterial beam for integrated vibration isolation and energy harvesting

Locally resonant metamaterials have low-frequency band gaps and the capability of converging vibratory energy in the band gaps at resonant cells.It has been demonstrated by several researchers that the dissipatioin of vibratory energy within the band gap can be improved by using viscoelastic materials.This paper designs an integrated viscoelastic metamaterial for energy harvesting and vibration isolation.The viscoelastic metamaterial is achieved by a viscoelastic beam periodically arrayed with spatial ball-pendulum nonlinear energy harvesters.The nonlinear resonator with an energy harvesting function is achieved by placing a free-rolling magnetic ball in a spherical cavity with an additional induction coil.The dynamic equations of viscoelastic metamaterials under transverse excitation are established,and the energy harvesting and vibration isolation characteristics within the dispersion relation of viscoelastic metamaterials are analyzed.The results show that the vibrations of the main body of the viscoelastic metamaterial beam are significantly suppressed in the frequency range of the local resonance band gap.At the same time,the elastic waves are limited in the nonlinear resonator with an energy harvesting function,which improves the energy output.Finally,an experimental platform of viscoelastic metamaterial vibration is established for validation purposes.

Dynamic analysis of a novel multilink-spring mechanism for vibration isolation and energy harvesting

Due to technical limitations,existing vibration isolation and energy harvesting(VIEH)devices have poor performance at low frequency.This paper proposes a new multilink-spring mechanism(MLSM)that can be used to solve this problem.The VIEH performance of the MLSM under harmonic excitation and Gaussian white noise was analyzed.It was found that the MLSM has good vibration isolation performance for low-frequency isolation and the frequency band can be widened by adjusting parameters to achieve a higher energy harvesting power.By comparison with two special cases,the results show that the MLSM is basically the same as the other two oscillators in terms of vibration isolation but has better energy harvesting performance under multistable characteristics.The MLSM is expected to reduce the impact of vibration on high-precision sensitive equipment in some special sites such as subways and mines,and at the same time supply power to structural health monitoring devices.

Molecular Identification of Mycobacterium Strains Responsible of Bovine Tuberculosis Cases in Bobo-Dioulasso Slaughterhouse, Burkina Faso

Bovine tuberculosis (bTB) is an endemic zoonosis significantly affects animal health in Burkina Faso. The primary causative agent is Mycobacterium tuberculosis (M. tuberculosis) complex, mainly M. bovis. Cattle are considered as natural reservoir of M. bovis. However, in Burkina Faso, the circulation of these strains remains poorly understood and documented. This study aimed to identify and characterize Mycobacterium strains from suspected carcasses during routine meat inspection at Bobo-Dioulasso refrigerated slaughterhouse. A prospective cross-sectional study was conducted from January 2021 to December 2022 on cases of seizures linked to suspected bovine tuberculosis. Microbiological and molecular analyzes were used for mycobacterial strain isolation and characterization. Out of 50 samples, 24% tested positive by microscopy and 12% by culture. Molecular analysis identified 6 strains of Mycobacteria, exclusively Mycobacterium bovis specifically the subspecies bovis (Mycobacterium bovis subsp bovis). In conclusion, M. bovis subsp bovis is the primary agent responsible for bovine tuberculosis in Bobo-Dioulasso. Continuous monitoring of mycobacterial strains is therefore necessary for the effective control of this pathology in the local cattle population.

Quaternion-Based Adaptive Trajectory Tracking Control of a Rotor-Missile with Unknown Parameters Identification

This paper investigates the adaptive trajectory tracking control problem and the unknown parameter identification problem of a class of rotor-missiles with parametric system uncertainties.First,considering the uncertainty of structural and aerodynamic parameters,the six-degree-of-freedom(6Do F) nonlinear equations describing the position and attitude dynamics of the rotor-missile are established,respectively,in the inertial and body-fixed reference frames.Next,a hierarchical adaptive trajectory tracking controller that can guarantee closed-loop stability is proposed according to the cascade characteristics of the 6Do F dynamics.Then,a memory-augmented update rule of unknown parameters is proposed by integrating all historical data of the regression matrix.As long as the finitely excited condition is satisfied,the precise identification of unknown parameters can be achieved.Finally,the validity of the proposed trajectory tracking controller and the parameter identification method is proved through Lyapunov stability theory and numerical simulations.