Many fungal pathogens invade plants by means of specialized infection structures called appressoria. In the rice (Oryza sativa) blast fungus Magnaporthe grisea, the pathogenicity mitogen-activated protein (MAP) kinase1 (PMK1) kinase is essential for appressorium formation and invasive growth. In this study, we functionally characterized the MST7 and MST11 genes of M. grisea that are homologous with the yeast MAP kinase kinase STE7 and MAP kinase kinase kinase STE11. Similar to the pmk1 mutant, the mst7 and mst11 deletion mutants were nonpathogenic and failed to form appressoria. When a dominant MST7 allele with S212D and T216E mutations was introduced into the mst7 or mst11 mutant, appressorium formation was restored in the resulting transformants. PMK1 phosphorylation also was detected in the vegetative hyphae and appressoria of transformants expressing the MST7 S212D T216E allele. However, appressoria formed by these transformants failed to penetrate and infect rice leaves, indicating that constitutively active MST7 only partially rescued the defects of the mst7 and mst11 mutants. The intracellular cAMP level was reduced in transformants expressing the MST7 S212D T216E allele. We also generated MST11 mutant alleles with the sterile alpha motif (SAM) and Ras-association (RA) domains deleted. Phenotype characterizations of the resulting transformants indicate that the SAM domain but not the RA domain is essential for the function of MST11. These data indicate that MST11, MST7, and PMK1 function as a MAP kinase cascade regulating infection-related morphogenesis in M. grisea. Although no direct interaction was detected between PMK1 and MST7 or MST11 in yeast two-hybrid assays, a homolog of yeast STE50 in M. grisea directly interacted with both MST7 and MST11 and may function as the adaptor protein for the MST11-MST7-PMK1 cascade.

Rice blast fungus (Magnaporthe grisea) forms a highly specialized infection structure for plant penetration, the appressorium, the formation and growth of which are regulated by the Mst11-Mst7-Pmk1 mitogen-activated protein kinase cascade. We characterized the MST50 gene that directly interacts with both MST11 and MST7. Similar to the mst11 mutant, the mst50 mutant was defective in appressorium formation, sensitive to osmotic stresses, and nonpathogenic. Expressing a dominant active MST7 allele in mst50 complemented its defects in appressorium but not lesion formation. The sterile a-motif (SAM) domain of Mst50 was essential for its interaction with Mst11 and for appressorium formation. Although the SAM and Ras-association domain (RAD) of Mst50 were dispensable for its interaction with Mst7, deletion of RAD reduced appressorium formation and virulence on rice (Oryza sativa) seedlings. The interaction between Mst50 and Mst7 or Mst11 was detected by coimmunoprecipitation assays in developing appressoria. Mst50 also interacts with Ras1, Ras2, Cdc42, and Mgb1 in yeast two-hybrid assays. Expressing a dominant active RAS2 allele in the wild-type strain but not in mst50 stimulated abnormal appressorium formation. These results indicate that MST50 functions as an adaptor protein interacting with multiple upstream components and plays critical roles in activating the Pmk1 cascade for appressorium formation and plant infection in M. grisea.

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The present study aimed to evaluate the probiotic characteristics of certain microbial strains for potential use as feed additives. Three bacterial strains and a yeast previously isolated from different environments were investigated. The strains were subjected to molecular identification and established as Lactobacillus paracasei CP133, Lactobacillus plantarum CP134, Bacillus subtilis CP350 and Saccharomyces cerevisiae CP605. Lactobacillus sp. CP133 and CP134 exhibited antibiosis, antibiotic activity, and relative odor reduction ability. Bacillus subtilis CP350 was thermotolerant, reduced hydrogen sulfide gas and showed significant proteolytic activity, whereas Saccharomyces cerevisiae CP605 exhibited high acid and bile salt tolerance. In general, the isolates in this study demonstrated improved functional characteristics, particularly acid and bile tolerance and relative cell adhesion to HT-29 monolayer cell line. Results in this work provides multifunctional probiotic characteristics of the strains for potential development of probiotics and cleaning of the environment.

BackgroundThe oral cavity is the store house of different species of microorganisms that are continuously engaged in causing diseases in the mouth. The present study was conducted to evaluate the antibacterial potential of crude extracts of the aerial parts of Phytolacca americana and its natural compounds against two oral pathogens, Porphyromonas gingivalis and Streptococcus mutans, which are primarily responsible for periodontal inflammatory diseases and dental caries, as well as a nonpathogenic Escherichia coli.MethodsCrude extract and fractions from the aerial parts of P. americana (0.008–1.8 mg/mL) were evaluated for their potential antibacterial activity against two oral disease causing microorganisms by micro-assays. The standard natural compounds present in P. americana, kaempferol, quercetin, quercetin 3-glucoside, isoqueritrin and ferulic acid, were also tested for their antibacterial activity against the pathogens at 1–8 μg/mL.ResultsThe crude extract was highly active against P. gingivalis (100% growth inhibition) and moderately active against S. mutans (44% growth inhibition) at 1.8 mg/mL. The chloroform and hexane fraction controlled the growth of P. gingivalis with 91% and 92% growth inhibition at a concentration of 0.2 mg/mL, respectively. Kaempferol exerted antibacterial activity against both the pathogens, whereas quercetin showed potent growth inhibition activity against only S. mutans in a concentration dependent manner.ConclusionThe crude extract, chloroform fraction, and hexane fraction of P. americana possesses active natural compounds that can inhibit the growth of oral disease causing bacteria. Thus, these extracts have the potential for use in the preparation of toothpaste and other drugs related to various oral diseases.

Among all diseases affecting rice production, rice blast disease has the greatest impact. Thus, monitoring and precise prediction of the occurrence of this disease are important; early prediction of the disease would be especially helpful for prevention. Here, we propose an artificial-intelligence-based model for rice blast disease prediction. Historical data on rice blast occurrence in representative areas of rice production in South Korea and historical climatic data are used to develop a region-specific model for three different regions: Cheolwon, Icheon and Milyang. A rice blast incidence is then predicted a year in advance using long-term memory networks (LSTMs). The predictive performance of the proposed LSTM model is evaluated by varying the input variables (i.e., rice blast disease scores, air temperature, relative humidity and sunshine hours). The most widely cultivated rice varieties are also selected and the prediction results for those varieties are analyzed. Application of the LSTM model to the accumulated rice-blast disease score data confirms successful prediction of rice blast incidence. In all regions, the predictions are most accurate when all four input variables are combined. Rice blast fungus prediction using the proposed LSTM model is variety-based; therefore, this model will be more helpful for rice breeders and rice blast researchers than conventional rice blast prediction models.

The conventional approach for generating gene replacement constructs involves several sequencespecific cloning steps and is time-consuming. A ligation-PCR approach was developed to efficiently generate gene replacement constructs. Two vectors useful for this ligation-PCR approach and another vector suitable for improving the efficiency of knockout mutant screens were constructed.

We study the construction of confidence intervals for efficiency levels of individual firms in stochastic frontier models with panel data. The focus is on bootstrapping and related methods. We start with a survey of various versions of the bootstrap. We also propose a simple parametric alternative in which one acts as if the identity of the best firm is known. Monte Carlo simulations indicate that the parametric method works better than the percentile bootstrap, but not as well as bootstrap methods that make bias corrections. All of these methods are valid only for large time-series sample size (T ), and correspondingly none of the methods yields very accurate confidence intervals except when T is large enough that the identity of the best firm is clear. We also present empirical results for two well-known data sets. JEL Classifications: C15, C23, D24