Hemicellulases and auxiliary enzymes for improved conversion of lignocellulosic biomass to monosaccharides

Background  

High enzyme loading is a major economic bottleneck for the commercial processing of pretreated lignocellulosic biomass to produce fermentable sugars. Optimizing the enzyme cocktail for specific types of pretreated biomass allows for a significant reduction in enzyme loading without sacrificing hydrolysis yield. This is especially important for alkaline pretreatments such as Ammonia fiber expansion (AFEX) pretreated corn stover. Hence, a diverse set of hemicellulases supplemented along with cellulases is necessary for high recovery of monosaccharides.     

Entomopathogenic Nematodes Are Not an Alternative to Fenamiphos for Management of Plant-Parasitic Nematodes on Golf Courses in Florida

With the cancellation of fenamiphos in the near future, alternative nematode management tactics for plant-parasitic nematodes (PPN) on golf courses need to be identified. The use of entomopathogenic nematodes (EPN) has been suggested as one possible alternative. This paper presents the results of 10 experiments evaluating the efficacy of EPN at managing PPN on turfgrasses and improving turf performance. These experiments were conducted at various locations throughout Florida over the course of a decade. In different experiments, different EPN species were tested against different species of PPN. Separate experiments evaluated multiple rates and applications of EPN, compared different EPN species, and compared single EPN species against multiple species of PPN. In a few trials, EPN were associated with reductions in certain plant-parasite species, but in other trials were associated with increases. In most trials, EPN had no effect on plant parasites. Because EPN were so inconsistent in their results, we conclude that EPN are not acceptable alternatives to fenamiphos by most turf managers in Florida at this time.     

Prediction of GTP interacting residues,dipeptides and tripeptides in a protein from its evolutionary information

Background  

Guanosine triphosphate (GTP)-binding proteins play an important role in regulation of G-protein. Thus prediction of GTP interacting residues in a protein is one of the major challenges in the field of the computational biology. In this study, an attempt has been made to develop a computational method for predicting GTP interacting residues in a protein with high accuracy (Acc), precision (Prec) and recall (Rc).