Gene cloning, molecular modeling, and phylogenetics of serine protease P32 and serine carboxypeptidase SCP1 from nematophagous fungi Pochonia rubescens and Pochonia chlamydosporia

The fungi Pochonia chlamydosporia and Pochonia rubescens are parasites of nematode eggs and thus are biocontrol agents of nematodes. Proteolytic enzymes such as the S8 proteases VCP1 and P32, secreted during the pathogenesis of nematode eggs, are major virulence factors in these fungi. Recently, expression of these enzymes and of SCP1, a new putative S10 carboxypeptidase, was detected during endophytic colonization of barley roots by these fungi. In our study, we cloned the genomic and mRNA sequences encoding P32 from P. rubescens and SCP1 from P. chlamydosporia. P32 showed a high homology with the serine proteases Pr1A from the entomopathogenic fungus Metarhizium anisopliae and VCP1 from P. chlamydosporia (86% and 76% identity, respectively). However, the catalytic pocket of P32 showed differences in the amino acids of the substrate-recognition sites compared with the catalytic pockets of Pr1A and VCP1 proteases. Phylogenetic analysis of P32 suggests a common ancestor with protease Pr1A. SCP1 displays the characteristic features of a member of the S10 family of serine proteases. Phylogenetic comparisons show that SCP1 and other carboxypeptidases from filamentous fungi have an origin different from that of yeast vacuolar serine carboxypeptidases. Understanding protease genes from nematophagous fungi is crucial for enhancing the biocontrol potential of these organisms.

Efficient Mitigation of Data and Control Flow Errors in Microprocessors

The use of microprocessor-based systems is gaining importance in application domains where safety is a must. For this reason, there is a growing concern about the mitigation of SEU and SET effects. This paper presents a new hybrid technique aimed to protect both the data and the control-flow of embedded applications running on microprocessors. On one hand, the approach is based on software redundancy techniques for correcting errors produced in the data. On the other hand, control-flow errors can be detected by reusing the on-chip debug interface, existing in most modern microprocessors. Experimental results show an important increase in the system reliability even superior to two orders of magnitude, in terms of mitigation of both SEUs and SETs. Furthermore, the overheads incurred by our technique can be perfectly assumable in low-cost systems.

Using multilevel modeling and mixed methods to make theoretical progress in microfoundations for strategy research

The microfoundations research agenda presents an expanded theoretical perspective because it considers individuals, their characteristics, and their interactions as relevant variables to help us understand firm-level strategic issues. However, microfoundations empirical research faces unique challenges because processes take place at different levels of analysis and these multilevel processes must be considered simultaneously. We describe multilevel modeling and mixed methods as methodological approaches whose use will allow for theoretical advancements. We describe key issues regarding the use of these two types of methods and, more importantly, discuss pressing substantive questions and topics that can be addressed with each of these methodological approaches with the goal of making theoretical advancements regarding the microfoundations research agenda and strategic management studies in general.