Risk of End-Stage Liver Disease in HIV-Viral Hepatitis Coinfected Persons in North America From the Early to Modern Antiretroviral Therapy Eras

Background. Human immunodeficiency virus (HIV)–infected patients coinfected with hepatitis B (HBV) and C (HCV) viruses are at increased risk of end-stage liver disease (ESLD). Whether modern antiretroviral therapy has reduced ESLD risk is unknown

A new Self-learning Algorithm for Dynamic Classification of Water Bodies

In many applications of remote sensing data land-water masks play an important role. In this context they can be a helpful orientation to distinguish dark areas (e.g. cloud shadows, topographic shadows, burned areas, coniferous forests) and water areas. However, water bodies cannot always be classified exactly on basis of available remote sensing data. This fact can be caused by a variety of different physical and biological factors (e.g. chlorophyll, suspended particles, surface roughness, turbid and shallow water and dynamic of water bodies) as well as atmospheric factors (e.g. haze and clouds). On the other hand the best available static water masks also show deficiencies. These are essentially caused by the fact that land-water masks represent only a temporal snapshot of the water bodies distributed worldwide and therefore these masks cannot reflect their dynamic behavior. This paper presents a dynamic self-learning water masking approach for AATSR remote sensing data in the context of integrating high-quality water masks in processing chains for deriving value-added remote sensing data products. As an advantage to conventional water masking algorithms, the proposed approach operates on basis of a static water mask as data base for deriving an optimized dynamic water mask. Significant research effort was spent to develop and validate a dynamic self-learning algorithm and a processing scheme for operational derivation of actual land-water masks as basis for operational interpretation of remote sensing data. Based on this concept actual activities and perspectives for contributions to operational monitoring systems will be presented

Effects of ENU dosage on mouse strains.

The germline supermutagen, N-ethyl-N-nitrosourea (ENU), has a variety of effects on mice. ENU is a toxin and carcinogen as well as a mutagen, and strains differ in their susceptibility to its effects. Therefore, it is necessary to determine an appropriate mutagenic, non-toxic dose of ENU for strains that are to be used in experiments. In order to provide some guidance, we have compiled data from a number of laboratories that have exposed male mice from inbred and non-inbred strains or their F(1) hybrids to ENU. The results show that most F(1) hybrid animals tolerate ENU well, but that inbred strains of mice vary in their longevity and in their ability to recover fertility after treatment with ENU