Talaromyces marneffei Outside Endemic Areas in India: an Emerging Infection with Atypical Clinical Presentations and Review of Published Reports from India

Mycopathologia - Talaromycosis is a disseminated disease caused by Talaromyces (Penicillium) marneffei, mainly seen in acquired immunodeficiency syndrome (AIDS) patients. Its distribution is...     

Molecular mapping and location of QTLs for drought-resistance traits in <Emphasis Type="Italic">indica</Emphasis> rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.) lines adapted to target environments

Drought is a major limitation for rice production in rainfed ecosystems. Identifying quantitative trait loci (QTLs) linked to drought resistance provides opportunity to breed high yielding rice varieties suitable for drought-prone areas. Although considerable efforts were made in mapping QTLs associated with drought-resistance traits in rice, most of the studies involved indica × japonica crosses and hence, the drought-resistance alleles were contributed mostly by japonica ecotypes. It is desirable to look for genetic variation within indica ecotypes adapted to target environment (TE) as the alleles from japonica ecotype may not be expressed under lowland conditions. A subset of 250 recombinant inbred lines (RILs) of F₈ generation derived from two indica rice lines (IR20 and Nootripathu) with contrasting drought-resistance traits were used to map the QTLs for morpho-physiological and plant production traits under drought stress in the field in TE. A genetic linkage map was constructed using 101 polymorphic PCR-based markers distributed over the 12 chromosomes covering a total length of 1,529 cM in 17 linkage groups with an average distance of 15.1 cM. Composite interval mapping analysis identified 22 QTLs, which individually explained 4.8–32.2% of the phenotypic variation. Consistent QTLs for drought-resistance traits were detected using locally adapted indica ecotypes, which may be useful for rainfed rice improvement.     

Hydrodynamic regulation of monocyte inflammatory response to an intracellular pathogen

Systemic bacterial infections elicit inflammatory response that promotes acute or chronic complications such as sepsis, arthritis or atherosclerosis. Of interest, cells in circulation experience hydrodynamic shear forces, which have been shown to be a potent regulator of cellular function in the vasculature and play an important role in maintaining tissue homeostasis. In this study, we have examined the effect of shear forces due to blood flow in modulating the inflammatory response of cells to infection. Using an in vitro model, we analyzed the effects of physiological levels of shear stress on the inflammatory response of monocytes infected with chlamydia, an intracellular pathogen which causes bronchitis and is implicated in the development of atherosclerosis. We found that chlamydial infection alters the morphology of monocytes and trigger the release of pro-inflammatory cytokines TNF-α, IL-8, IL-1β and IL-6. We also found that the exposure of chlamydia-infected monocytes to short durations of arterial shear stress significantly enhances the secretion of cytokines in a time-dependent manner and the expression of surface adhesion molecule ICAM-1. As a functional consequence, infection and shear stress increased monocyte adhesion to endothelial cells under flow and in the activation and aggregation of platelets. Overall, our study demonstrates that shear stress enhances the inflammatory response of monocytes to infection, suggesting that mechanical forces may contribute to disease pathophysiology. These results provide a novel perspective on our understanding of systemic infection and inflammation.     

Use of Growth Indices in Markov Chain Models for Crop Yield Forecasting

An improved Markov chain model has been developed for forecasting of sugarcane yields in which growth indices of biometrical characters based on data from two stages simultaneously have been utilised. Comparisons were also made with the models in use viz. the regression model and the first order Markov chain model.