Study on Pathogeny Identification and Biological Characteristics of Anthracnose in Cassava

[Objective] The research aimed to isolate and identify the anthracnose pathogen of Cassava and study its biological charcteristics.[Method] Two isolates of anthracnose （CCGHN01 and CCGHN03） in Cassava were isolated from the diseased leaves collected from Hainan of China.They were identified through the morphological observation of conidia and ITS sequence analysis.And the morphological characteristics were studied.[Result] The morphological observation of conidia and ITS sequence analysis indicated that the two isolates were Colletotrichum gloeosporioides.The morphological characteristics study results showed that optimum medium for the growth of two strains was PSA,the optimum temperatures were 26 ℃ and 30 ℃ respectively,the optimum pH was 8.0,the optimum light conditions were alternative light and dark and complete darkness respectively.For the conidia germination of two strains,the optimum temperatures were 28 ℃ and 30 ℃ respectively and the lethal temperature was 55 ℃ remaining 10 min.[Conclusion] The research laid the foundation for further control of anthracnose in Cassava.

Isolation and Identification of Colletotrichum gloeosporioides in Pears and Its Biological Characteristics

[Objective] This study aimed to explore the biological characteristics of Col etotrichum gloeosporioides in pears. [Method] Twenty-five C. gloeosporioides strains were isolated and identified from the diseased samples. Their pathogenicity was identified by inoculating the surface of punctured pears with fungal discs. The effects of different temperatures, pH values, carbon sources and nitrogen sources on the growth of C. gloeosporioides mycelia were explored by incubating fungal discs on the center of plates. [Result] Among the twenty-five C. gloeosporioides strains, three had strong pathogenicity, and eighteen had intermediate pathogenicity, and four strains had weak pathogenicity. Those highly-pathogenic strains had darker colonies, with dense mycelia, whereas those lowly-pathogenic ones had white colonies, with sparse mycelia. Those with fast-growing colonies showed strong pathogenicity, while those with slowly-growing colonies displayed weak pathogenicity. There was no relationship between conidia yield and pathogenicity. The optimum temperature for the growth of C. gloeosporioides mycelia was 25-30 ℃, and the optimum pH was 5.0-7.0. C. gloeosporioides could make use of various carbon sources （monosaccharide and disaccharide）, inorganic and organic nitrogen sources, and the optimal carbon source and nitrogen source were sucrose and beef extract, respectively. [Conclusion] Our study benefits further understanding of C. gloeospori-oides and helps to control pear anthracnose more effectively.

Identification and Biological Characteristics of Blank Spot Pathogen from Lilium lancifolium

With the increasing planting area of Lilium lancifolium, the leaf fungal dis-ease of L. lancifolium is becoming more and more serious. In June and July of 2014, the excessive rainfal leads to the serious outbreak of leaf disease of L. lanci-folium. In mid-June, the wilting rate of L. lancifolium in seriously-infected field was even up to 50%-70%. In some fields, the shoots of L. lancifolium even al wilted. The pathogen was isolated from the infected leaves of L. lancifolium. Its pathogenici-ty, spore morphology, 18S rDNA sequence and biological characteristics were stud-ied. The results showed the isolated pathogen was Alternaria alternate. The lethal temperature of mycelial growth was 55 ℃. The optimum pH was 6-7. Among the tested carbon sources and nitrogen sources, the optimum carbon source was mal-tose, and the optimum nitrogen source was yeast extract.

Establishment of Molecular Biological Method for Identification of Bacteria by 16S rDNA and gyrB Gene

[Objectives]The paper was to establish a molecular biological method for identification of bacterial strains.[Methods]The thalli of standard bacterial strains existing in the laboratory were collected and genomic DNA was extracted for amplification of 16S rDNA and gyrB gene.The 16S rDNA and gyrB gene sequences were obtained after sequencing.Sequences were aligned and analyzed via EzBioCloud and NCBI database,and phylogenetic trees were constructed to determine the species relationship of strains.Meantime,they were compared with known strains.[Results]This method could identify 5 standard strains accurately to the species level.The 16S rDNA and gyrB gene sequences were aligned and analyzed in EzBioCloud database and NCBI database.The strain with the max score was consistent with the known strain.And the query cover and ident were both above 99%.[Conclusions]The established molecular biological method for identification of bacterial strains by 16S rDNA and gyrB gene has good accuracy,which effectively solves the problem that the laboratory identification of bacteria relies on traditional methods and the accuracy can not be guaranteed,and further improves the identification ability of laboratory bacterial strains.

Identification and Biological Characteristics of Pineapple Anthracnose in Hainan Province

[Objective]The pathogen of pineapple anthracnose were isolated and identified,and the biological characteristics of the pathogen were studied.[Method]Pathogen was isolated and purified from the leaves collected from different areas in Hainan province which had been infected by pineapple anthracnose,and the biological characteristics were studied as well as morphological identification of the pathogen.[Results] The pathogen of pineapple anthracnose was identified as Colletorichum gloeosprioides Penz.through morphological characteristics.The results of biological characteristic test showed that the optimum temperature for mycelium growth and spore germination were 25-30 and 28-30 ℃,respectively;and the optimum pH value was 6.0-8.0;complete darkness was the best condition for growth of mycelium,while alternative light and darkness was most beneficial for spore germination;the best carbon sources for mycelium growth were glucose,D-fructose and maltose;except for L-arabinose and D-galactose,the other carbon sources all had promotion effect on spore germination;yeast extract,peptone and beef extract were proved to be beneficial for mycelium growth and spore germination.Aspartic acid was only beneficial for spore germination;spores were just able to germinate when the relative humidity（RH）was greater than 90%,spore germination was the highest under water drops condition.[Conclusion]The results laid preliminary basis for the control of pineapple anthracnose.

Identification and Pathogen Characteristics Analysis of a Co-infection with CSFV,PRRSV and PCV2

[Objective] The aim of this study was to identify swine diseases caused by CSFV,PRRSV and PCV2 and thus to analyze its pathogeny chracteristics.[Method] The tissues and viscera of the diseased swine were collected from Xiangtan of Hunan(Code of HN/XT)to extract DNA and RNA for PCR amplification and sequencing.Meanwhile,the virulent strains were isolated and identified by cell separation technology.[Result] The sequencing analysis results showed that the amino acid homology between CSFV,PRRSV,PCV2 and sequen...

Identification and Characterization of Lysobacter enzymogenes as a Biological Control Agent Against Some Fungal Pathogens

Strain OH11, a Gram-negative, nonspore forming, rod-shaped bacterium with powerful antagonistic activity, was isolated from rhizosphere of green pepper in Jiangsu Academy of Agricultural Sciences of China and characterized to determine its taxonomic position. 16S rRNA gene sequence analysis revealed that strain OH11 belongs to the Gammaproteobacteria and had the highest degree of sequence similarity to Lysobacter enzymogenes strain C3 （AY074793） （99%）, Lysobacter enzymogenes strain N4-7 （U89965） （99%）, Lysobacter antibioticus strain （AB019582） （97%）, and Lysobacter gummosus strain （AB16136） （97%）. Chemotaxonomic data revealed that strain OHI 1 possesses a quinine system with Q-8 as the predominant compound and C15：0 iso, C17：1 iso ω9c as the predominant iso-branched fatty acids, all of which corroborated the assignment of strain OH11 to the genus Lysobacter. Results of DNA-DNA hybridization and physiological and biochemical tests clearly showed that strain OH11 was classified as Lysobacter enzymogenes. Strain OH11 could produce protease, chitinase, and β-1,3-glucanase. It showed strong in vitro antifungal activity against Rhizoctonia solani, Sclerotinia scletotiorum, and several other phytopathogenic fungi. This is the first report of identification and characterization of Lysobacter enzymogenes as a biological control agent of plant diseases in China.

Isolates identification and characteristics of microorganisms in biotrickling filter and biofilter system treating H_2S and NH_3

A combination system of biotrickling filter （BTF） and biofilter （BF）, adopting surfactant-modified clinoptilolite and surfactant- modified wood chip as the media respectively, was applied to treat H2S and NH3 simultaneously. The identification and sole carbon sources utilization patterns of isolates in the combination system were studied by Biolog system. The isolates were identified as Bacillus sphaericus, Geobacillus themoglucosidasius （55℃） and Micrococcus luteus （ATCC 9341） in BTF, and Aspergillus sydowii （Bainier ＆ Sartory） Thom＆ Church in BE Among 95 substrate classes supplied by Biolog system, the carboxylic acids and methyl esters had the highest utilization extent for the four species, followed by the amino acids and peptides. The descending sequence of carbon sources utilization capability of isolates was A. sydowii （52.6%）, M. luteus （39.5%）, B. sphaericus （21.6%）, and G. thermoglucosidasius （17.7%）.

Characteristics of Shale Reservoir and Sweet Spot Identification of the Lower Cambrian Niutitang Formation in Northwestern Hunan Province, China

The accumulation and productivity of shale gas are mainly controlled by the characteristics of shale reservoirs;study of these characteristics forms the basis for the shale gas exploitation of the Lower Cambrian Niutitang Formation(Fm),Southern China.In this study,core observation and lithology study were conducted along with X-ray diffraction(XRD)and electronic scanning microscopy(SEM)examinations and liquid nitrogen(N2)adsorption/desorption and CH4 isothermal adsorption experiments for several exploration wells in northwestern Hunan Province,China.The results show that one or two intervals with high-quality source rocks(TOC>2 wt%)were deposited in the deep-shelf facies.The source rocks,which were mainly composed of carbonaceous shales and siliceous shales,had high quartz contents(>40 wt%)and low clay mineral(<30 wt%,mainly illites)and carbonate mineral(<20 wt%)contents.The SEM observations and liquid nitrogen(N2)adsorption/desorption experiments showed that the shale is tight,and nanoscale pores and microscale fractures are well developed.BJH volume(VBJH)of shale ranged from 2.144×10^-3 to 20.07×10^-3 cm^3/g,with an average of 11.752×10^-3 cm3/g.Pores mainly consisted of opened and interconnected mesopores(2–50 nm in diameter)or macropores(>50 nm in diameter).The shale reservoir has strong adsorption capacity for CH4.The Langmuir volume(VL)varied from 1.63 to 7.39 cm^3/g,with an average of 3.95 cm^3/g.The characteristics of shale reservoir are controlled by several factors:(1)A deep muddy continental shelf is the most favorable environment for the development of shale reservoirs,which is controlled by the development of basic materials.(2)The storage capacity of the shale reservoir is positively related to the TOC contents and plastic minerals and negatively related to cement minerals.(3)High maturity or overmaturity leads to the growth of organic pores and microfractures,thereby improving the reservoir storage capacity.It can be deduced that the high percentage of residual gas in Niutitang Fm results from the strong reservoir storage capacity of adsorbed gas.Two layers of sweet spots with strong storage capacity of free gas,and they are characterized by the relatively high TOC contents ranging from 4 wt%to 8 wt%.

Modeling and identification for soft sensor systems based on the separation of multi-dynamic and static characteristics

Data-driven soft sensor is an effective solution to provide rapid and reliable estimations for key quality variables online. The secondary variables affect the primary variable in considerably different speed, and soft sensor systems exhibit multi-dynamic characteristics. Thus, the first contribution is improving the model in the previous study with multi-time-constant. The characteristics-separation-based model will be identified in substep way,and the stochastic Newton recursive(SNR) algorithm is adopted. Considering the dual-rate characteristics of soft sensor systems, the proposed model cannot be identified directly. Thus, two auxiliary models are first proposed to offer the intersample estimations at each update period, based on which the improved algorithm(DAM-SNR) is derived. These two auxiliary models function in switching mechanism which has been illustrated in detail. This algorithm serves for the identification of the proposed model together with the SNR algorithm, and the identification procedure is then presented. Finally, the laboratorial case confirms the effectiveness of the proposed soft sensor model and the algorithms.

Multi-time scale identification of key kinetic processes for lithium-ion batteries considering variable characteristic frequency

The electrification of vehicles puts forward higher requirements for the power management efficiency of integrated battery management systems as the primary or sole energy supply.In this paper,an efficient adaptive multi-time scale identification strategy is proposed to achieve high-fidelity modeling of complex kinetic processes inside the battery.More specifically,a second-order equivalent circuit model network considering variable characteristic frequency is constructed based on the high-frequency,medium-high-frequency,and low-frequency characteristics of the key kinetic processes.Then,two coupled sub-filters are developed based on forgetting factor recursive least squares and extended Kalman filtering methods and decoupled by the corresponding time-scale information.The coupled iterative calculation of the two sub-filter modules at different time scales is realized by the voltage response of the kinetic diffusion process.In addition,the driver of the low-frequency subalgorithm with the state of charge variation amount as the kernel is designed to realize the adaptive identification of the kinetic diffusion process parameters.Finally,the concept of dynamical parameter entropy is introduced and advocated to verify the physical meaning of the kinetic parameters.The experimental results under three operating conditions show that the mean absolute error and root-mean-square error metrics of the proposed strategy for voltage tracking can be limited to 13 and 16 mV,respectively.Additionally,from the entropy calculation results,the proposed method can reduce the dispersion of parameter identification results by a maximum of 40.72%and 70.05%,respectively,compared with the traditional fixed characteristic frequency algorithms.The proposed method paves the way for the subsequent development of adaptive state estimators and efficient embedded applications.

Selection and Molecular Biological Identification of a Strain of Bacillus sp. Inhibiting the Growth of Saprolegnia ferax

Based on the theory of biological control of Saprolegnia ferax,antagonism test of nine strains of Bacillus sp. to S. ferax JL was carried out. Bacillus sp.BA1 was screened to have significantly inhibitory effects on the growth of S. ferax JL( P 【 0. 05). Then,the effects of Bacillus sp. BA1 on different sources of S. ferax were carried out. Results showed that BA1 also had significantly inhibitory effects on S. ferax 6#,10# and S2( P 【 0. 05). Sequence of 16 S r DNA of BA1 was analyzed; and homologous alignment analysis showed that BA1 had more than 99% similarity with Bacillus cereus. Therefore,it could be concluded that strain BA1 was B. cereus,which significantly inhibited the growth of S. ferax and could be used as the biological control agent for S. ferax diseases in aquaculture.

Network traffic identification in packet sampling environment

With the rapid growth of network bandwidth,traffic identification is currently an important challenge for network management and security.In recent years,packet sampling has been widely used in most network management systems.In this paper,in order to improve the accuracy of network traffic identification,sampled NetFlow data is applied to traffic identification,and the impact of packet sampling on the accuracy of the identification method is studied.This study includes feature selection,a metric correlation analysis for the application behavior,and a traffic identification algorithm.Theoretical analysis and experimental results show that the significance of behavior characteristics becomes lower in the packet sampling environment.Meanwhile,in this paper,the correlation analysis results in different trends according to different features.However,as long as the flow number meets the statistical requirement,the feature selection and the correlation degree will be independent of the sampling ratio.While in a high sampling ratio,where the effective information would be less,the identification accuracy is much lower than the unsampled packets.Finally,in order to improve the accuracy of the identification,we propose a Deep Belief Networks Application Identification(DBNAI)method,which can achieve better classification performance than other state-of-the-art methods.

Trace elements spatial distribution characteristics,risk assessment and potential source identification in surface water from Honghu Lake,China

Five trace elements including Zn, Cu, Cd, Cr and As were investigated in surface water from ten typical sampling sites in Honghu Lake. The consequence indicated that all of the detected trace element levels were within the allowed standard of China’s safe water guideline. The hazard quotients （HQ） and the hazard index （HI） value levels of all the five heavy metals in all sampling sites did not exceed the acceptable risk limits of non-carcinogenic value through the selected assessment method. Pearson’s correlation analysis and principal component analysis （PCA） indicated that Zn and Cu mainly originated from the natural alluviation and non-point agricultural sources, whereas Cr and As were mainly derived from industrial effluents. Moreover, Cd mainly originated from both non-point agricultural and industrial pollution sources. In addition, cluster analysis （CA） implied that cluster 1 （including S3, S5, S6 and S10） was considered the set of high pollution sites and cluster 2 （including S4 and S9） was identified as the set of moderate pollution sites.

Three-Step Damage Identification Method Based on Dynamic Characteristics

A three-step damage identification method based on dynamic characteristics is proposed to improve the structure reliability and security and avoid serious accident. In the proposed method, the frequency and difference of modal curvature(DMC) are used as damage indexes. Firstly, the detection of the occurrence of damage is addressed by the frequency or the square of frequency change. Then the damage location inside the structure is measured by the DMC. Finally, with the stiffness reduction rate as a damage factor, the amount of damage is estimated by the optimization algorithm. The three-step damage identification method has been validated by conducting the simulation on a cantilever beam and the shaking table test on a submerged bridge. The results show that the method proposed in this paper can effectively solve the damage identification problem in theory and engineering practice.

Characteristics of Implicit Customer Needs in Product Design and Methods for Their Identification

The identification of customer needs is one of the important first⁃step works for product design.Contrary to explicit ones,the implicit customer needs are less obvious,thus relying on the designers to discover.Up till now,there still lacks a rational or systematic method on the identification of implicit customer needs.Designers have to rely on their own intuition and personal experience to do the work,hindering the product design and development further on.Therefore,it is necessary to study the implicit customer needs and their identification methods.To this end,this paper first studies the characteristics of implicit needs,and clarifies the relationship between implicit needs and explicit needs.Based on this,the concept of customer needs life cycle is put forward.After that,two methods for the identification of implicit needs are proposed,including an active approach and a passive approach.For the active approach,it is suggested to exploit the functional characteristics of the product and the products in the same series or categories,for which a direct acquisition method and an evaluation data mining method are proposed,and a treadmill design case is studied.For the passive approach,it is suggested to exploit the scenario elements of product usage,for which a scenario elements questionnaire method and a scenario adaptation problem method are proposed,and a spinning bike design case is studied.The two design cases have demonstrated the process of customer needs identification and also verified the applicability of the proposed methods.

Non Ideal Schottky Barrier Diode’s Parameters Extraction and Materials Identification from Dark I-V-T Characteristics

Several parameters of a commercial Si-based Schottky barrier diode (SBD) with unknown metal material and semiconductor-type have been investigated in this work from dark forward and reverse I-V characteristics in the temperature (T) range of [274.5 K - 366.5 K]. Those parameters include the reverse saturation current (Is), the ideality factor (n), the series and the shunt resistances (Rs and Rsh), the effective and the zero bias barrier heights (ΦB and ΦB0), the product of the electrical active area (A) and the effective Richardson constant (A**), the built-in potential (Vbi), together with the semiconductor doping concentration (NA or ND). Some of them have been extracted by using two or three different methods. The main features of each approach have been clearly stated. From one parameter to another, results have been discussed in terms of structure performance, comparison on one another when extracted from different methods, accordance or discordance with data from other works, and parameter’s temperature or voltage dependence. A comparison of results on ΦB, ΦB0, n and NA or ND parameters with some available data in literature for the same parameters, has especially led to clear propositions on the identity of the analyzed SBD’s metal and semiconductor-type.

Impact Damage Identification of Aluminum Alloy Reinforced Plate Based on GWO-ELM Algorithm

As a critical structure of aerospace equipment,aluminum alloy stiffened plate will influence the stability of spacecraft in orbit and the normal operation of the system.In this study,a GWO-ELM algorithm-based impact damage identification method is proposed for aluminum alloy stiffened panels to monitor and evaluate the damage condition of such stiffened panels of spacecraft.Firstly,together with numerical simulation,the experimental simulation to obtain the damage acoustic emission signals of aluminum alloy reinforced panels is performed,to establish the damage data.Subsequently,the amplitude-frequency characteristics of impact damage signals are extracted and put into an extreme learning machine(ELM)model to identify the impact location and damage degree,and the Gray Wolf Optimization(GWO)algorithm is employed to update the weight parameters of the model.Finally,experiments are conducted on the irregular aluminum alloy stiffened plate with the size of 2200 mm×500 mm×10 mm,the identification accuracy of impact position and damage degree is 98.90% and 99.55% in 68 test areas,respectively.Comparative experiments with ELM and backpropagation neural networks(BPNN)demonstrate that the impact damage identification of aluminum alloy stiffened plate based on GWO-ELM algorithm can serve as an effective way to monitor spacecraft structural damage.

Identification of Mine Water Inrush Source Based on PCA-BP Neural Network

It is of great significance to analyze the chemical indexes of mine water and develop a rapid identification system of water source, which can quickly and accurately distinguish the causes of water inrush and identify the source of water inrush, so as to reduce casualties and economic losses and prevent and control water inrush disasters. Taking Ca2+, Mg2+, Na+ + K+, , , Cl-, pH value and TDS as discriminant indexes, the principal component analysis method was used to reduce the dimension of data, and the identification model of mine water inrush source based on PCA-BP neural network was established. 96 sets of data of different aquifers in Panxie mining area were selected for prediction analysis, and 20 sets of randomly selected data were tested, with an accuracy rate of 95%. The model can effectively reduce data redundancy, has a high recognition rate, and can accurately and quickly identify the water source of mine water inrush.

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.