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Evaluation of Sustainable Management Practices to Promote Healthy Growth of Woody Ornamentals
No full textWoody ornamental nursery industry is an important agriculture sector in the United States. Major losses in this industry are caused by soilborne pathogens and insect pests such as ambrosia beetles and flatheaded borer. Biotic (soilborne pathogens) and abiotic (flood and drought) stress factors are considered to predispose field nursery tree crops to beetle and borer attacks. The goal of this study was evaluation of sustainable management practices to promote healthy growth of woody ornamentals. Legume cover crop (crimson clover) significantly reduced root rot diseases of red maple caused by Phytophthora nicotianae, Phytopythium vexans or Rhizoctonia solani, improved crop growth, increased soil organic matter and total nitrogen and stimulated antagonistic Pseudomonad population count. In another study, mixture of legume (crimson clover) and grass (triticale) cover crop demonstrated superior control of soilborne diseases of red maple caused by above mentioned pathogens compared to sole use of crimson clover and triticale and the high seeding rate (1.5×low seeding rate) of cover crop was effective than low seeding rate. Exposure of plants to biotic stress (soilborne pathogen), or drought stress did not predispose trees to flatheaded borer and ambrosia beetle attacks. When exposed to flooding, ambrosia beetle attacks were recorded only in flood intolerant tree species (flowering dogwood and redbud), while there was no attack in flood tolerant red maples. Preventative application of Acibenzolar-S-methyl (ASM, both drench and foliar applications) reduced ambrosia beetle attacks, and colonization in simulated flood stressed dogwood. These findings can be included in integrated disease and insect pest management programs
Hydrodynamic Atmospheric Escape in HD 189733 b: Signatures of Carbon and Hydrogen Measured with the Hubble Space Telescope
Get PDFOne of the most well-studied exoplanets to date, HD 189733 b, stands out as an archetypal hot Jupiter with many observations and theoretical models aimed at characterizing its atmosphere, interior, host star, and environment. We report here on the results of an extensive campaign to observe atmospheric escape signatures in HD 189733 b using the Hubble Space Telescope and its unique ultraviolet capabilities. We have found a tentative, but repeatable in-transit absorption of singlyionized carbon (C ii, 5.2% ± 1.4%) in the epoch of June–July/2017, as well as a neutral hydrogen (H i) absorption consistent with previous observations. We model the hydrodynamic outflow of HD 189733 b using an isothermal Parker wind formulation to interpret the observations of escaping C and O nuclei at the altitudes probed by our observations. Our forward models indicate that the outflow of HD 189733 b is mostly neutral within an altitude of ∼2 Rp and singly ionized beyond that point. The measured in-transit absorption of C ii at 1335.7 Å is consistent with an escape rate of ∼1.1 × 1011 g s−1, assuming solar C abundance and an outflow temperature of 12,100 K. Although we find marginal neutral oxygen (O i) in-transit absorption, our models predict an in-transit depth that is only comparable to the size of measurement uncertainties. A comparison between the observed Lyα transit depths and hydrodynamics models suggests that the exosphere of this planet interacts with a stellar wind at least 1 order of magnitude stronger than solar
Identification of Pathogenicity Factors in Erwinia tracheiphila, Causal Agent of Bacterial Wilt
No full textCucurbits, collectively classified into Cucurbitaceae family, have about 1000 plant species that includes different important crops. More than 200 diseases affecting cucurbits are caused by a wide range of pathogens, including fungi, bacteria, Phytoplasma, and viruses and nematodes. Bacterial wilt is the most devastating disease among the several bacterial infections and can reduce yield by up to 80%. A major obstacle in the development of better management strategies for controlling bacterial wilt is a lack of host resistance in commercial cultivars and inadequate understanding of host-bacteria interactions and pathogen biology. A forward genetic approach employing transposon mutagenesis was employed to create a random mutant library in Erwinia tracheiphila strain Hca1-5N with help of an IPTG (isopropylthio-β-galactoside) inducible suicide vector, pSNCmTn5ME-mNeonGreen. Using transposable genetic elements that integrate into a recipient genome, transposon mutagenesis creates random insertion mutations that are easy to be identified. Random mutations were employed to establish a saturated mutant library that may be used in the future to evaluate mutants for varying levels of virulence
Machine Learning for Prediction of Amino Acid Side Chain in Proteins
No full textOne of the challenges and a very significant part of a protein structure of a prediction in three-dimensional is a side chain prediction. This area of research has a large importance, due to its various applications in protein design. In past few years, a lot methodologies and techniques have been crafted for side chain prediction such as DLPacker, FASPR, SCWRL4 and OPUS-Rota4. However, current methods are not enough in speed and accuracy. In this research, we addressed the problem from a different perspective. We employed machine learning approaches to pack the side chain of protein molecules given only the backbone. We analyzed 32,000 protein molecules to extract important geometrical features that can distinguish between different orientations of side chain rotamers. We designed multiple machine learning models and compared the performance of these models against each other. Four machine learning models were built: Random Forest, XG-Boost, Decision Trees, Logistic Regression. Further, we implemented a stacking model that uses all previous machine learning models. The results of our experiment show that Random Forest and Stacking are the most effective models to overcome this problem, as they have the highest total average accuracy, 70.3 and 69.9%, respectively. Given an accuracy of the existing state-of-the-art approaches, we have got a new improved accuracy. For instance, FASPR, a highest state-of-the-art approach with accuracy, has reached 69.1%