Lymphotoxin β receptor signaling promotes development of autoimmune pancreatitis

Little is known about the pathogenic mechanisms of autoimmune pancreatitis (AIP), an increasingly recognized, immune-mediated form of chronic pancreatitis. Current treatment options are limited and disease relapse is frequent. We investigated factors that contribute to the development of AIP and new therapeutic strategies.

Renal tissue oxygenation in essential hypertension and chronic kidney disease

Animal studies suggest that renal tissue hypoxia plays an important role in the development of renal damage in hypertension and renal diseases, yet human data were scarce due to the lack of noninvasive methods. Over the last decade, blood oxygenation level-dependent magnetic resonance imaging (BOLD-MRI), detecting deoxyhemoglobin in hypoxic renal tissue, has become a powerful tool to assess kidney oxygenation noninvasively in humans. This paper provides an overview of BOLD-MRI studies performed in patients suffering from essential hypertension or chronic kidney disease (CKD). In line with animal studies, acute changes in cortical and medullary oxygenation have been observed after the administration of medication (furosemide, blockers of the renin-angiotensin system) or alterations in sodium intake in these patient groups, underlining the important role of renal sodium handling in kidney oxygenation. In contrast, no BOLD-MRI studies have convincingly demonstrated that renal oxygenation is chronically reduced in essential hypertension or in CKD or chronically altered after long-term medication intake. More studies are required to clarify this discrepancy and to further unravel the role of renal oxygenation in the development and progression of essential hypertension and CKD in humans.

The performance of single- and multi-proxy transfer functions (testate amoebae, bryophytes, vascular plants) for reconstructing mire surface wetness and pH

Peatlands are widely exploited archives of paleoenvironmental change. We developed and compared multiple transfer functions to infer peatland depth to the water table (DWT) and pH based on testate amoeba (percentages, or presence/absence), bryophyte presence/absence, and vascular plant presence/absence data from sub-alpine peatlands in the SE Swiss Alps in order to 1) compare the performance of single-proxy vs. multi-proxy models and 2) assess the performance of presence/absence models. Bootstrapping cross-validation showing the best performing single-proxy transfer functions for both DWT and pH were those based on bryophytes. The best performing transfer functions overall for DWT were those based on combined testate amoebae percentages, bryophytes and vascular plants; and, for pH, those based on testate amoebae and bryophytes. The comparison of DWT and pH inferred from testate amoeba percentages and presence/absence data showed similar general patterns but differences in the magnitude and timing of some shifts. These results show new directions for paleoenvironmental research, 1) suggesting that it is possible to build good-performing transfer functions using presence/absence data, although with some loss of accuracy, and 2) supporting the idea that multi-proxy inference models may improve paleoecological reconstruction. The performance of multi-proxy and single-proxy transfer functions should be further compared in paleoecological data.

Estimating and quantifying uncertainties on level sets using the Vorob'ev expectation and deviation with Gaussian process models

Several methods based on Kriging have recently been proposed for calculating a probability of failure involving costly-to-evaluate functions. A closely related problem is to estimate the set of inputs leading to a response exceeding a given threshold. Now, estimating such a level set—and not solely its volume—and quantifying uncertainties on it are not straightforward. Here we use notions from random set theory to obtain an estimate of the level set, together with a quantification of estimation uncertainty. We give explicit formulae in the Gaussian process set-up and provide a consistency result. We then illustrate how space-filling versus adaptive design strategies may sequentially reduce level set estimation uncertainty.

Kernels and Designs for Modelling Invariant Functions: From Group Invariance to Additivity

We focus on kernels incorporating different kinds of prior knowledge on functions to be approximated by Kriging. A recent result on random fields with paths invariant under a group action is generalised to combinations of composition operators, and a characterisation of kernels leading to random fields with additive paths is obtained as a corollary. A discussion follows on some implications on design of experiments, and it is shown in the case of additive kernels that the so-called class of “axis designs” outperforms Latin hypercubes in terms of the IMSE criterion.