Quinic Acid-Mediated Induction of Hypovirulence and a Hypovirulence-Associated Double-Stranded RNA in Rhizoctonia Solani

This study is a part of a project focused on the relationship between dsRNA and hypovirulence in R. solani. Hypovirulence refers to a condition in which a pathogen has a drastically reduced ability to cause disease. Rhizoctonia solani is a soil-borne pathogen causing diseases in numerous plants. Phenyl acetic acid (PAA), a metabolite of phenylalanine, causes Rhizoctonia disease symptoms on potato in the absence of the pathogen itself. The amount of PAA produced by a hypovirulent isolate is 10% of that produced by virulent isolates. A 3.6 kb dsRNA (M2) has been shown to be associated with hypovirulence in R. solani. Polypeptide A (PA) encoded by the M2 dsRNA is phylogenetically related to the pentafunctional polypeptide AROM of the shikimate pathway and QUTR, repressor of the quinate pathway in fungi. A hypothesis has been proposed to explain the mechanism of the M2 dsRNA-mediated hypovirulence. Polypeptide A may interfere with both the shikimate and quinate pathways, leading to a decreased production of aromatic amino acids and PAA, thus leading to a decreased virulence. Results of this study 1) supported this hypothesis, and 2) verified the relationship between the M2 dsRNA and hypovirulence in R. solani. A protein band of the expected size (83 kDa) was detected only in M2-containing isolates. The hypovirulent isolate Rhs 1A1 has a constitutive quinate pathway whereas the \u27 virulent isolate Rhs IAP has an inducible quinate pathway. Moreover, Rhs IAP has a higher level of expression for the shikimate pathway than Rhs 1Al. Data also sowed that phenylalanine levels were positively correlated with virulence in Rhs IAP. The addition of quinate converted virulent Rhs IAP to hypovirulent, and induced the synthesis of 1) a polypeptide of the same size as pA and reacting with anti-pA antibodies, and 2) the respective M2-specific transcript. For the first time, the arom gene has been cloned from Basidiomycetes. The R. solani arom gene has five introns as compared to one intron found in arom genes from other fungi. The deduced R. solani AROM polypeptide contains all of the highly conserved motifs and enzyme domains found in AROM polypeptides from other fungal species

Understanding seismic velocity structure and its time-varying process beneath the Mississippi embayment through ambient noise analysis

We apply ambient noise analysis to image shear wave velocity from near surface to uppermost mantle beneath the Mississippi embayment, and investigate the crustal response to climatological loadings. To further understand the generation mechanism of microseisms, we explore the azimuthal distribution of the signal-to-noise ratio and amplitude difference of crustal surface waves and estimate possible source locations in the ocean through back- projections.A shear wave velocity model with 0.5 0.5 resolution for the crust and uppermost mantle has been determined. We take advantage of the dense coverage and long-term deployments of 277 3-component broadband stations installed from 1990 to 2018 to image the shear wave velocity. Rayleigh group velocity dispersion curves extracted from ambient noise are inverted to obtain shear wave velocity at 5, 12, 24, and 43 km. We find that low velocity features characterize the Reelfoot Graben, Rough Creek Graben, Black Warrior basin, and southern Mississippi embayment in the upper 5 km of crust. High velocity features characterize the Ozark plateau, Ouachita mountains and Nashville dome. From 5 to 12 km, a low velocity anomaly is associated with the Missouri batholith. From 12 to 24 km, high velocity features characterize the Reelfoot-Rough Creek graben, and along the Appalachian-Ouachita thrust front. From 24 to 43 km, high velocity anomalies are commonly observed in the Mississippi embayment, and spatially correlated with the crustal thickness.Cross-correlation of the ambient seismic field is also used to estimate seasonal seismic velocity variations and to determine the underlying physical mechanisms. We process continuously recorded broadband data from 53 stations in 2014 to obtain daily and yearly cross- correlations and measure the Rayleigh wave phase velocity change over 4 frequency bands, 0.3-1, 0.5-1.2, 0.7-1.5, and 1-2 Hz. We then calculate the correlation coefficients between the velocity variations and the precipitation, water table fluctuation, temperature, atmospheric pressure and wind speed to find which external variable correlates most strongly with the observed changes. We observe high t/t (a proxy for velocity variation), the slowest velocity relative to annual average, from May to July and low t/t in September/October, and find the t/t variations correlate primarily with water table fluctuation. The correlation coefficients between water table fluctuation and t/t are independent of the interstation distance and frequency, but high coefficients are observed more often in the 0.3-1 Hz than 1-2 Hz band probably because high-frequency coherent signals attenuate faster than low-frequency ones. The t/t variations lag behind the water table fluctuation by about 20 days, which suggests the velocity changes can be attributed to the pore pressure diffusion effect. The maximum t/t variations decrease with frequency from 0.03% at 0.3-1 Hz to 0.02% at 1-2 Hz, and the differences between them might be related to different local sources or incident angles. The seasonal variations of t/t are azimuthally independent, and a large increase of noise amplitude only introduces a small increase to the t/t variation. The maximum t/t variations non-linearly decrease with the distance, which could be associated with the attenuation of coherent noise. At close distances, the maximum t/t holds a wide range of values, which is likely related to local structure. At larger distances, velocity variations sample a larger region so that it stabilizes to a more uniform value. We find that the observed changes in wave speed are in agreement with the prediction of a poroelastic model.The source distribution of ambient noise is of fundamental importance to understanding the generation mechanism of microseisms. Cross-correlations of ambient seismic noise from 277 broadband stations with at least 1-month recording between 1990 and 2018 are used to estimate source locations of primary and secondary microseisms inside the Mississippi embayment. We investigate source locations by analyzing the azimuthal distribution of the signal-to-noise ratio (SNR) and amplitude difference of crustal surface wave arrivals and by 2D F-K analysis. We also use 84 stations with continuous 1-year recording to explore seasonal variations of SNRs of the surface wave, which could be used to locate active sources in different seasons. We observe that (1) four azimuths could be identified in the azimuthal distribution of SNRs and reflect four different energy sources. Two energy sources are active in the Pacific and Atlantic ocean of northern hemisphere during winter and two relatively weak sources are active near Australia and South America in the southern hemisphere during summer. (2) Primary microseisms originate along the coastlines of southern Australia, Canada and Alaska, Newfoundland, and northeast South America. (3) Secondary microseisms could be generated in the deep water of northern and southern Pacific ocean, along coastlines of Canada and Alaska associated with reflections, and in the deep water of south of Greenland. (4) Azimuthal distribution of SNRs of sediment surface waves observed at 1-5s is negatively correlated with the geometry of the edge of the Mississippi embayment. The sediment surface waves could be induced by the basin-edge

VIRTUALIZED CLOUD PLATFORM MANAGEMENT USING A COMBINED NEURAL NETWORK AND WAVELET TRANSFORM STRATEGY

This study focuses on implementing a log analysis strategy that combines a neural network algorithm and wavelet transform. Wavelet transform allows us to extract the important hidden information and features of the original time series log data and offers a precise framework for the analysis of input information. While neural network algorithm constitutes a powerfulnonlinear function approximation which can provide detection and prediction functions. The combination of the two techniques is based on the idea of using wavelet transform to denoise the log data by decomposing it into a set of coefficients, then feed the denoised data into a neural network. The experimental outputs reveal that this strategy can have a better ability to identify the patterns among problems in a log dataset, and make predictions with a better accuracy. This strategy can help the platform maintainers to adopt corresponding actions to eliminate risks before the occurrence of serious damages

Period disambiguation with MaxEnt model

Abstract. This paper presents our recent work on period disambiguation, the kernel problem in sentence boundary identification, with the maximum entropy (Maxent) model. A number of experiments are conducted on PTB-II WSJ corpus for the investigation of how context window, feature space and lexical information such as abbreviated and sentence-initial words affect the learning performance. Such lexical information can be automatically acquired from a training corpus by a learner. Our experimental results show that extending the feature space to integrate these two kinds of lexical information can eliminate 93.52% of the remaining errors from the baseline Maxent model, achieving an F-score of 99.8227%.

An Improved Corpus Comparison Approach to Domain Specific Term Recognition

PACLIC / The University of the Philippines Visayas Cebu College Cebu City, Philippines / November 20-22, 200

Preliminary Exploration of the Mental Health Education Competency Survey of Primary and Middle School Head Teachers

Despite a recent focus on the mental health of students, primary and middle school mental health education in China has been hampered by a lack of resources fand inadequate professional training. This study assessed the mental health education competency of primary and middle school head teachers using the Mental Health Education Competency Questionnaire, a measure based on data from documentary analysis, behavioral event interviews (BEIs), and expert judgment. Factor, reliability, and validity analysis of this questionnaire were conducted. Through these analyses, seven structural dimensions of mental health education competency were found: mental health education skill, career growth, personality charm, occupational tendency, achievement feature, student perspective, and professional knowledge. This questionnaire will improve hiring and evaluation processes and, therefore, improve mental health education

Over-expression of an S-domain receptor-like kinase extracellular domain improves panicle architecture and grain yield in rice.

The S-domain receptor kinase (SRK) comprises a highly polymorphic subfamily of receptor-like kinases (RLKs) originally found to be involved in the self-incompatibility response in Brassica. Although several members have been identified to play roles in developmental control and disease responses, the correlation between SRKs and yield components in rice is still unclear. The utility of transgenic expression of a dominant negative form of SRK, OsLSK1 (Large spike S-domain receptor like Kinase 1), is reported here for the improvement of grain yield components in rice. OsLSK1 was highly expressed in nodes of rice and is a plasma membrane protein. The expression of OsLSK1 responded to the exogenous application of growth hormones, to abiotic stresses, and its extracellular domain could form homodimers or heterodimers with other related SRKs. Over-expression of a truncated version of OsLSK1 (including the extracellular and transmembrane domain of OsLSK1 without the intracellular kinase domain) increased plant height and improve yield components, including primary branches per panicle and grains per primary branch, resulting in about a 55.8% increase of the total grain yield per plot (10 plants). Transcriptional analysis indicated that several key genes involved in the GA biosynthetic and signalling pathway were up-regulated in transgenic plants. However, full-length cDNA over-expression and RNAi of OsLSK1 transgenic plants did not exhibit a detectable visual phenotype and possible reasons for this were discussed. These results indicate that OsLSK1 may act redundantly with its homologues to affect yield traits in rice and manipulation of OsLSK1 by the dominant negative method is a practicable strategy to improve grain yield in rice and other crops

A new diagnostic tool for brain disorders: extracellular vesicles derived from neuron, astrocyte, and oligodendrocyte

Brain disorders are the leading cause of disability worldwide, affecting people’s quality of life and causing economic burdens. The current clinical diagnosis of brain disorders relies solely on individual phenotypes and lacks accurate molecular biomarkers. An emerging field of research centers around extracellular vesicles (EVs), nanoscale membrane vesicles which can easily cross the blood–brain barrier. EVs in the blood are derived from various tissues, including the brain. Therefore, purifying central nervous system (CNS)-derived EVs from the blood and analyzing their contents may be a relatively non-invasive way to analyze brain molecular alterations and identify biomarkers in brain disorders. Recently, methods for capturing neuron-derived EVs (NDEs), astrocyte-derived EVs (ADEs), and oligodendrocyte-derived EVs (ODEs) in peripheral blood were reported. In this article, we provide an overview of the research history of EVs in the blood, specifically focusing on biomarker findings in six major brain disorders (Alzheimer’s disease, Parkinson’s disease, schizophrenia, bipolar disorder, depression, and autism spectrum disorder). Additionally, we discuss the methodology employed for testing CNS-derived EVs. Among brain disorders, Alzheimer’s disease has received the most extensive attention in EV research to date. Most studies focus on specific molecules, candidate proteins, or miRNAs. Notably, the most studied molecules implicated in the pathology of these diseases, such as Aβ, tau, and α-synuclein, exhibit good reproducibility. These findings suggest that CNS-derived EVs can serve as valuable tools for observing brain molecular changes minimally invasively. However, further analysis is necessary to understand the cargo composition of these EVs and improve isolation methods. Therefore, research efforts should prioritize the analysis of CNS-derived EVs’ origin and genome-wide biomarker discovery studies

Comparisons of case-selection approaches based on allele sharing and/or disease severity index: application to the GAW14 simulated data

For mapping complex disease traits, linkage studies are often followed by a case-control association strategy in order to identify disease-associated genes/single-nucleotide polymorphisms (SNPs). Substantial efforts are required in selecting the most informative cases from a large collection of affected individuals in order to maximize the power of the study, while taking into consideration study cost. In this article, we applied and extended three case-selection strategies that use allele-sharing information method for families with multiple affected offspring to select most informative cases using additional information on disease severity. Our results revealed that most significant associations, as measured by the lowest p-values, were obtained from a strategy that selected a case with the most allele sharing with other affected sibs from linked families ("linked-best"), despite reduction in sample size resulting from discarding unlinked families. Moreover, information on disease severity appears to be useful to improve the ability to detect associations between markers and disease loci