The tomato yellow leaf curl virus C4 protein alters the expression of plant developmental genes correlating to leaf upward cupping phenotype in tomato

Tomato yellow leaf curl virus (TYLCV), a monopartite begomovirus in the family Geminiviridae, is efficiently transmitted by the whitefly, Bemisia tabaci, and causes serious economic losses to tomato crops around the world. TYLCV-infected tomato plants develop distinctive symptoms of yellowing and leaf upper cupping. In recent years, excellent progress has been made in the characterization of TYLCV C4 protein function as a pathogenetic determinant in experimental plants, including Nicotiana benthamiana and Arabidopsis thaliana. However, molecular mechanism leading to disease symptom development in natural host plant tomato has yet to be characterized. The aim of the current study was to generate transgenic tomato plants expressing the TYLCV C4 gene and evaluate differential gene expression through comparative transcriptome analysis between the transgenic C4 plants and the transgenic green fluorescent protein (Gfp) gene control plants. Transgenic tomato plants expressing the TYLCV C4 developed phenotypes, including leaf upward cupping and yellowing that are similar the disease symptom expressed on tomato plants infected with TYLCV. In a total of 241 differentially expressed genes identified in the transcriptome analysis, a series of plant development-related genes, including transcription factors, glutaredoxins, protein kinases, R-genes and microRNA target genes, were significantly altered. These results provide further evidence to support the important function of the C4 protein in begomovirus pathogenicity. These transgenic tomato plants could serve as basic genetic materials for further characterization of plant receptors that are interacting with the TYLCV C4.

APLIKASI SISTEM PAKAR DALAM MENDIAGNOSIS PENYAKIT KULIT PADA MANUSIA BERBASIS VISUAL

Health is a blessing that is very important for humans, most of us, many of us don't really care about skin diseases caused by fungi such as tinea versicolor, water fleas, ringworm and scabies. In general, people are quite aware of how to deal with the symptoms of skin diseases suffered. But it would be better to include medical participation in detecting a disease symptom, because many disease symptoms are considered trivial by some people but can be fatal to human skin. So it is necessary to make an application based on medical knowledge to diagnose skin diseases in humans which is used as a tool in obtaining information about skin diseases in humans and provide recommendations as the first action that must be taken to repeat skin diseases in humans. The knowledge base is structured in such a way into a database with several tables. Drawing conclusions in this expert system using the forward chaining inference method. The expert system will provide questions to the user in the form of symptoms of several diseases and the user will answer these questions. Until the user will get a solution from the results of the question earlier.

Inclusion of environmentally themed search terms improves Elastic Net regression nowcasts of regional Lyme disease rates

Lyme disease is the most widely reported vector-borne disease in the United States. 95% of human cases are reported in the Northeast and upper Midwest. Human cases typically occur in the spring and summer months when an infected nymph Ixodid tick takes a blood meal. Current federal surveillance strategies report data on an annual basis, leading to nearly a year lag in national data reporting. These lags in reporting make it difficult for public health agencies to assess and plan for the current burden of Lyme disease. Implementation of a nowcasting model, using historical data to predict current trends, provides a means for public health agencies to evaluate current Lyme disease burden and make timely priority-based budgeting decisions. The objective of this study was to develop and compare the performance of nowcasting models using free data from Google Trends and Centers of Disease Control and Prevention surveillance reports for Lyme Disease. We developed two sets of elastic net models for five regions of the United States first using monthly proportional hit data from 21 disease symptoms and tick related terms and second using monthly proportional hit data from all terms identified via Google correlate plus 21 disease symptom and vector terms. Elastic net models using the larger term list were highly accurate (Root Mean Square Error: 0.74, Mean Absolute Error: 0.52, R2: 0.97) for four of the five regions of the United States. Including these more environmental terms improved accuracy 1.33-fold while reducing error 0.5-fold compared to predictions from models using disease symptom and vector terms alone. Models using Google data similar to this could help local and state public health agencies accurately monitor Lyme disease burden during times of reporting lag from federal public health reporting agencies.