Expression and Roles of Individual HIF Prolyl 4-Hydroxylase Isoenzymes in the Regulation of the Hypoxia Response Pathway along the Murine Gastrointestinal Epithelium

The HIF prolyl 4-hydroxylases (HIF-P4H) control hypoxia-inducible factor (HIF), a powerful mechanism regulating cellular adaptation to decreased oxygenation. The gastrointestinal epithelium subsists in “physiological hypoxia” and should therefore have an especially well-designed control over this adaptation. Thus, we assessed the absolute mRNA expression levels of the HIF pathway components, Hif1a, HIF2a, Hif-p4h-1, 2 and 3 and factor inhibiting HIF (Fih1) in murine jejunum, caecum and colon epithelium using droplet digital PCR. We found a higher expression of all these genes towards the distal end of the gastrointestinal tract. We detected mRNA for Hif-p4h-1, 2 and 3 in all parts of the gastrointestinal tract. Hif-p4h-2 had significantly higher expression levels compared to Hif-p4h-1 and 3 in colon and caecum epithelium. To test the roles each HIF-P4H isoform plays in the gut epithelium, we measured the gene expression of classical HIF target genes in Hif-p4h-1^−/−, Hif-p4h-2 hypomorph and Hif-p4h-3^−/− mice. Only Hif-p4h-2 hypomorphism led to an upregulation of HIF target genes, confirming a predominant role of HIF-P4H-2. However, the abundance of Hif-p4h-1 and 3 expression in the gastrointestinal epithelium implies that these isoforms may have specific functions as well. Thus, the development of selective inhibitors might be useful for diverging therapeutic needs.     

芬兰涂料工业走进新时代

芬兰的涂料市场在斯堪的纳维亚半岛(瑞典、挪威、丹麦、冰岛的泛称)处于较低的地位,也就是说,那儿是一个成熟的市场,没有很大的发展潜力。在芬兰,有1000家以上的公司积极活跃在化工行业,但是,只有30家左右的公司从事涂料与油墨的生产。2002年,涂料产量占芬兰化学工业总产值的4%。根据芬兰涂料工业协会的统计,2002年涂料总销售量(包括出口量与非会员的销售量)上升至134920t,相应的产值是38000万欧元。涂料产品在芬兰的总的销售量估计在86460t或按人口计算平均每人16.6kg。1产出超过了国内的需求产出超过了国内的需求,而且部分国内的需求甚至…     

Experiences on data quality in automatic tissue classification

Automatic intensity-based tissue classification sets requirements for the quality of multispectral magnetic resonance (MR) images. Tests for evaluating the separability of tissue classes, and on the other hand class distances required to obtain reliable classification, are presented in this study. Intraslice, interslice and interpatient training schemes for 5-nn classification were considered. Interslice training was utilized in classification of images from 10 patients with ischemic stroke giving results of satisfactory but highly variable quality. Based on the experience with these data sets, similar tests are recommended before imaging a large patient series in order to avoid extra manual work and to obtain reliable classification results.     

Joint Angular- and Delay-Domain MIMO Propagation Parameter Estimation Using Approximate ML Method

In this paper, we derive an estimation method that jointly estimates the parameters of the concentrated propagation paths and the distributed scattering component that are frequently observed in multiple-input multiple-output (MIMO) channel sounding measurements. The joint angular-delay domain model leads to a correlation matrix with high dimensionality, which makes direct implementation of a maximum-likelihood (ML) estimator unfeasible. We derive low-complexity methods for computing approximate ML estimates that exploit the structure of the covariance matrices. We propose an iterative two-step procedure that alternates between the estimation of the parameters of the concentrated propagation paths and the parameters of the distributed scattering. For the distributed scattering, the estimator first optimizes the parameters describing their time-delay structure. Then, using the estimated time-delay parameters, the parameters of the angular distributions are optimized. We present simulation results and compare the estimated time-delay and angular distributions to the actual distributions, demonstrating that high-quality estimates are obtained. The large sample performance of the estimator is studied by establishing the Cramer-Rao lower bound (CRLB) and comparing it to the variances of the estimates. The simulations show that the variance of the proposed estimation technique reaches the CRLB for relatively small sample size for most parameters, and no bias is observed.     

Evolution and evaluation of a trainable cloud classifier

Neural network classifiers have recently been popular in image classification and remote sensing applications. In this paper a case study is reported, where the evolution started with a pure neural network based solution and reached a simplified classifier with a few neural network properties. This seems to be a typical evolution concerning neural networks. A multispectral cloud classifier was implemented to automate the interpretation of AVHRR (Advanced Very High Resolution Radiometer) images. It can be adapted to changing situations with new examples. This is a requirement in satellite image applications, hence changes in illumination, round the year, during day and night, and aging of electronics are possible. The classification is done in two phases, clouds are separated from the background and then only clouds are classified. The evaluation of the classifier is based on the comparison between the SYNOP observations and the satellite observations. Comparisons with other published results show that the classifier is working     

Subspace-based direction-of-arrival estimation using nonparametricstatistics

The problem of subspace estimation using multivariate nonparametric statistics is addressed. We introduce new high-resolution direction-of-arrival (DOA) estimation methods that have almost optimal performance in nominal conditions and are robust in the face of heavy-tailed noise. The extensions of the techniques for the case of coherent sources are considered as well. The proposed techniques are based on spatial sign and rank concepts. We show that spatial sign and rank covariance matrices can be used to obtain convergent estimates of the signal and noise subspaces. In the proofs, the noise is assumed to be spherically symmetric. Moreover, we illustrate how the number of signals may be determined using the proposed covariance matrix estimates and a robust estimator of variance. The performance of the algorithms is studied using simulations in a variety of noise conditions including noise that is not spherically symmetric. The results show that the algorithms perform near optimally in the case of Gaussian noise and highly reliably if the noise is non-Gaussian     

Effect of management history and temperature on the mineralization of methylene urea in soil

A soil incubation and a greenhouse study on processing tomato were used to test the effects of soil temperature and the size and activity of the soil microbial biomass (SMB) on the degradation (mineralization) rate of a slow-release N fertilizer, methylene urea (MU), a condensation product of urea and formaldehyde. The mineralization rates of three MUs: Short (S), Medium (M), and Long (L) with different water solubilities were measured at two temperatures in a soil with low (fallow, F) and high (cover crop, CC) microbial activity. In the greenhouse study, the fate of fertilizer N was followed using ¹⁵N-urea and ¹⁵N-MU. The fertilizer N efficiency calculated for urea using the ¹⁵N mass balance approach was 93 and 85% compared with 65 and 67% for MU-S in F and CC soils, respectively. During six months of incubation, 52 and 63% of MU-S N was mineralized at 20 and 30 °C, respectively. The accumulated N data suggested that the degradation of all three MU types followed first-order reaction kinetics. The reaction rates were similar for all three MUs and increased with increasing temperature. However, fitting discrete, non-accumulated data revealed that MU mineralization is more complex and cannot be modeled with simple exponential decay equations. The size and activity of SMB did not affect the mineralization rate of MU-N under laboratory or greenhouse conditions. Interestingly, Activity Index (AI), defined as the slowly available pool of MU-N, was not a reliable indicator for the mineralization rate and plant availability of MU-N.     

Performance of MIMO Radar With Angular Diversity Under Swerling Scattering Models

The performance of statistical multiple-input multiple-output (MIMO) radar configurations that use distributed antennas is analyzed in this paper. Statistical MIMO radars exploit angular diversity to mitigate the impact of radar cross section (RCS) fluctuations. The fluctuations can be modeled with the Swerling scattering model consisting of four different cases with either fast or slow target RCS fluctuations. In this paper, the performance of different statistical MIMO radar configurations is compared in the different Swerling cases. Both target detection and direction of arrival estimation tasks are considered. We derive the optimal test statistics for target detection for non-orthogonal waveforms in all the Swerling cases in single-pulse as well as multi-pulse scenarios. We derive a closed-form density function for the test statistics under null and alternate hypotheses in the Swerling cases 1 and 2. For orthogonal waveforms in cases 3 and 4, the density function is given as a convolution involving a transcendental function. A suboptimal detector having a closed-form density function in cases 3 and 4 when the waveforms are orthogonal is introduced as well. In the direction finding task, confidence bounds of the squared estimation error of the different configurations are compared. The comparison is done in terms of the confidence bounds as the CramÉr–Rao bounds are not defined for all the cases and configurations. The pros and cons of the angular diversity and each radar configuration are pointed out in different fluctuation scenarios.