Influence of Risk Factors for Male Infertility on Sperm Protein Composition

Male infertility is a common health problem that can be influenced by a host of lifestyle risk factors such as environment, nutrition, smoking, stress, and endocrine disruptors. These effects have been largely demonstrated on sperm parameters (e.g., motility, numeration, vitality, DNA integrity). In addition, several studies showed the deregulation of sperm proteins in relation to some of these factors. This review inventories the literature related to the identification of sperm proteins showing abundance variations in response to the four risk factors for male infertility that are the most investigated in this context: obesity, diabetes, tobacco smoking, and exposure to bisphenol-A (BPA). First, we provide an overview of the techniques used to identify deregulated proteins. Then, we summarise the main results obtained in the different studies and provide a compiled list of deregulated proteins in relation to each risk factor. Gene ontology analysis of these deregulated proteins shows that oxidative stress and immune and inflammatory responses are common mechanisms involved in sperm alterations encountered in relation to the risk factors.     

Anomalous kinetics of lath martensite formation in stainless steel

The kinetics of lath martensite formation in Fe–17·3 wt-%Cr–7·1 wt-%Ni–1·1 wt-%Al–0·08 wt-%C stainless steel was investigated with magnetometry and microscopy. Lath martensite forms during cooling, heating and isothermally. For the first time, it is shown by magnetometry during extremely slow isochronal cooling that transformation rate maxima occur, which are interrupted by virtually transformation free temperature regions. Microscopy confirms martensite formation after athermal nucleation of clusters followed by their time dependent growth. The observations are interpreted in terms of time dependent autocatalytic lath martensite formation followed by mechanical stabilisation of austenite during the transformation process.     

Estimation of Intrinsic Volumes from Digital Grey-Scale Images

Local algorithms are common tools for estimating intrinsic volumes from black-and-white digital images. However, these algorithms are typically biased in the design based setting, even when the resolution tends to infinity. Moreover, images recorded in practice are most often blurred grey-scale images rather than black-and-white. In this paper, an extended definition of local algorithms, applying directly to grey-scale images without thresholding, is suggested. We investigate the asymptotics of these new algorithms when the resolution tends to infinity and apply this to construct estimators for surface area and integrated mean curvature that are asymptotically unbiased in certain natural settings.