An NMRA-like protein regulates gene expression in Phytophthora capsici to drive the infection cycle on tomato

Phytophthora spp. cause devastating disease epidemics on important crop plants and pose a grave threat to global crop production. Critically, Phytophthora pathogens represent a distinct evolutionary lineage in which pathogenicity has been acquired independently. Therefore, there is an urgent need to understand and disrupt the processes that drive infection if we aspire to defeat oomycete pathogens in the field. One area that has received little attention thus far in this respect is the regulation of Phytophthora gene expression during infection. Here, we characterize PcNMRAL1 (Phyca11_505845), a homolog of the Aspergillus nidulans nitrogen metabolite repression regulator NMRA and demonstrate a role for this protein in progression of the Phytophthora capsici infection cycle. PcNmrAL1 is coexpressed with the biotrophic marker gene PcHmp1 (haustorial membrane protein 1) and, when overexpressed, extends the biotrophic infection stage. Microarray analyses revealed that PcNmrAL1 overexpression in P. capsici leads to large-scale transcriptional changes during infection and in vitro. Importantly, detailed analysis reveals that PcNmrAL1 overexpression induces biotrophy-associated genes while repressing those associated with necrotrophy. In addition to factors controlling transcription, translation, and nitrogen metabolism, PcNMRAL1 helps regulate the expression of a considerable effector repertoire in P. capsici. Our data suggests that PcNMRAL1 is a transcriptional regulator that mediates the biotrophy to necrotrophy transition. PcNMRAL1 represents a novel factor that may drive the Phytophthora disease cycle on crops. This study provides the first insight into mechanisms that regulate infection-related processes in Phytophthora spp. and provides a platform for further studies aimed at disabling pathogenesis and preventing crop losses. </jats:p

Biofuels: A Hands-On Approach, Learning the Potential of Utilizing Non-Food Sources

The global energy economy is huge and thoughts of replacing large amounts of petroleum based fuels by massive levels of fermentation of grains are not realistic. On an energy basis what global agriculture produces for food will almost cover the energy demands if all of it is redirected to the production of fuels—either as alcohols for gasoline or as fat derivatives for diesel fuel. This means that chemical processes need to be developed that allow inclusion of non-food based agricultural and urban wastes as well as forest debris into the energy economy. These represent opportunities to capture new sources of energy that would otherwise not be captured. This project is based on the idea that every little bit helps, and focuses on a hands-on approach to isolating chemicals from fallen vegetation with an emphasis on adding to the transportation fuel pool. Hydrolysis of cellulosic wastes from various sources easily collected on our campus has been explored seeking ways to break them down to fermentable sugars. These sugars are then fermented to form alcohols suitable for inclusion in gasoline. Extraction of vegetable oils has also been explored. Finally an attempt has been made to quantify the impact such a strategy might have on global energy supplies if practiced on a wide-scale basis.https://scholar.dominican.edu/ug-student-posters/1001/thumbnail.jp

Dynamic system with no equilibrium and its chaos anti-synchronization

Recently, systems with chaos and the absence of equilibria have received a great deal of attention. In our work, a simple five-term system and its anti-synchronization are presented. It is special that the system has a hyperbolic sine nonlinearity and no equilibrium. Such a system generates chaotic behaviours, which are verified by phase portraits, positive Lyapunov exponent as well as an electronic circuit. Moreover, the system displays multistable characteristic when changing its initial conditions. By constructing an adaptive control, chaos anti-synchronization of the system with no equilibrium is obtained and illustrated via a numerical example

Development and validation of a Clostridium difficile infection risk prediction model

OBJECTIVE: The purpose of this study was to develop and validate a risk prediction model that could identify patients at high risk for Clostridium difficile infection (CDI) before they develop disease. DESIGN: Retrospective cohort. SETTING: Tertiary care medical center. PATIENTS: Patients admitted to the hospital for ≥48 hours from 1-1-2003 through 12-31-2003. METHODS: Data were collected electronically from the hospital’s Medical Informatics database and analyzed with logistic regression to determine variables that best predicted patients’ risk for development of CDI. Model discrimination and calibration were calculated. The model was bootstrapped 500 times to validate the predictive accuracy. A receiver operating characteristic (ROC) curve was calculated to evaluate potential risk cut-offs. RESULTS: 35,350 admissions with 329 CDI cases were included. Variables in the risk prediction model were age, CDI pressure, admissions in previous 60 days, modified Acute Physiology Score, days on high risk antibiotics, low albumin, admission to an ICU, and receipt of laxatives, gastric acid suppressors, or antimotility drugs. The calibration and discrimination of the model were very good to excellent (C index=0.88; Brier score 0.009). CONCLUSIONS: The CDI risk prediction model performed well. Further study is needed to determine if it could be used in a clinical setting to prevent CDI-associated outcomes and reduce costs