Listeria monocytogenes infection in ruminants: Is there a link to the environment, food and human health? A review.

Listeria (L.) monocytogenes is widely distributed in the environment, but also has the ability to cause serious invasive disease in ruminants and humans. This review provides an overview of listeriosis in ruminants and discusses our insufficient understanding of reservoirs and possible cycling ofL. monocytogenes between animal and human hosts, food and the environment. It indicates gaps in our knowledge of the role of genetic subtypes in L. monocytogenes ecology and virulence as well as risk factors, in vivo diagnostics and pathogenesis of listeriosis in ruminants. Filling these gaps will contribute to improving the control of L. monocytogenes and enhancing disease prevention. As the prevalence of listeriosis in ruminants in Switzerland is likely to be underestimated, propositions concerning improvement options for surveillance of listeriosis in ruminants are provided.

Scilla monophylla Link vel: Scilla tingitana Schousb. Scilla pumila Brot.

von Carl Sprenger

Antonie Link, Hüftbild

Signatur des Originals: S 36/F01119

Adolf Link, Kniestück

Signatur des Originals: S 36/F05227

Antonie Link, Rollenbild, Brustbild, in: Richard Strauss: Prinz Methusalem

Signatur des Originals: S 36/F09099

Else Link, Brustbild

Signatur des Originals: S 36/F10911

Else Link, Rollenbild, Brustbild, in: Richard Wagner: Die Walküre

Signatur des Originals: S 36/F10912

Antonie Link, Kniestück

Signatur des Originals: S 36/G01450

Antonie Link, Kniestück

Signatur des Originals: S 36/G01451

Community health workers: The critical link to sustainable, healthy societies in low-resource settings---A model for the Baylor Shoulder to Shoulder Foundation

Community health workers (CHWs) can serve as a bridge between healthcare providers and communities to positively impact social determinants of health and, thus, the overall health of the population. The potential to effect lasting change is particularly significant within resource-poor settings with limited access to formally trained health care providers such as the small, rural village of Santa Ana Intibucá, Honduras and surrounding areas—located on the geographically and politically isolated border of Honduras and El Salvador. The Baylor Shoulder to Shoulder Foundation (BSTS) works in conjunction with Santa Ana's volunteer health committee to bring a health brigade that has provided health care and public health projects to the area at least twice a year since 2001. They have also hired a full-time Honduran physician, a Honduran in-country administrative director, and built a clinic; yet, no community health worker program exists. This CHW program model is the response to a clear need for a CHW program within the area served by BSTS and presents a CHW program model specific to Santa Ana Intibucá and surrounding areas to be implemented by BSTS. Methods used to develop this model include reviewing the literature for recommendations from leading authorities as well as successfully implemented CHW programs in comparable regions. This information was incorporated into existing knowledge and materials currently being used in the area. Using the CHW model proposed here, each brigade, in conjunction with the communities served, can help develop new modules to respond to the specific health priorities of the region at that time, incorporating consistent modes of contact with the local physician and the CHWs to provide refresher courses, training in new topics of interest, and to be reminded of the importance of community health workers' role as the critical link to healthy societies. With cooperation, effort, and support, the brigade can continue to help integrate a sustainable CHW system in which communities may be able to maximize the care they receive while also learning to care for their own health and the future of their communities.^

Mean dynamic topography and oceanographic parameters estimated from an inverse model and satellite geodesy, with link to model result in one single NetCDF file (392 MB), including the inverse data error covariance

Geostrophic surface velocities can be derived from the gradients of the mean dynamic topography-the difference between the mean sea surface and the geoid. Therefore, independently observed mean dynamic topography data are valuable input parameters and constraints for ocean circulation models. For a successful fit to observational dynamic topography data, not only the mean dynamic topography on the particular ocean model grid is required, but also information about its inverse covariance matrix. The calculation of the mean dynamic topography from satellite-based gravity field models and altimetric sea surface height measurements, however, is not straightforward. For this purpose, we previously developed an integrated approach to combining these two different observation groups in a consistent way without using the common filter approaches (Becker et al. in J Geodyn 59(60):99-110, 2012, doi:10.1016/j.jog.2011.07.0069; Becker in Konsistente Kombination von Schwerefeld, Altimetrie und hydrographischen Daten zur Modellierung der dynamischen Ozeantopographie, 2012, http://nbn-resolving.de/nbn:de:hbz:5n-29199). Within this combination method, the full spectral range of the observations is considered. Further, it allows the direct determination of the normal equations (i.e., the inverse of the error covariance matrix) of the mean dynamic topography on arbitrary grids, which is one of the requirements for ocean data assimilation. In this paper, we report progress through selection and improved processing of altimetric data sets. We focus on the preprocessing steps of along-track altimetry data from Jason-1 and Envisat to obtain a mean sea surface profile. During this procedure, a rigorous variance propagation is accomplished, so that, for the first time, the full covariance matrix of the mean sea surface is available. The combination of the mean profile and a combined GRACE/GOCE gravity field model yields a mean dynamic topography model for the North Atlantic Ocean that is characterized by a defined set of assumptions. We show that including the geodetically derived mean dynamic topography with the full error structure in a 3D stationary inverse ocean model improves modeled oceanographic features over previous estimates.