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.     

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.     

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.      

Nonlinear filtering of multivariate images under robust errorcriterion

A class of nonlinear filters for multivariate data is introduced. A robust error criterion is minimized. Approximate algorithms for computing the filter output are developed. A polynomial signal model is used in applications where the signal amplitude has to be retained with high fidelity. Simulated data and RGB color image data are used in experiments.     

Performance bounds for multistep prediction-based blindequalization

Blind equalization attempts to remove the interference caused by a communication channel without using any known training sequences. Blind equalizers may be implemented with linear prediction-error filters (PEFs). For many practical channel types, a suitable delay at the output of the equalizer allows for achieving a small estimation error. The delay cannot be controlled with one-step predictors. Consequently, multistep PEF-based algorithms have been suggested as a solution to the problem. The derivation of the existing algorithms is based on the assumption of a noiseless channel, which results in zero-forcing equalization. We consider the effects of additive noise at the output of the multistep PEF. Analytical error bounds for two PEF-based blind equalizers in the presence of noise are derived. The obtained results are verified with simulations. The effect of energy concentration in the channel impulse response on the error bound is also addressed     

Hydrophobic patterning of functional porous pigment coatings by inkjet printing

Paper-based analytical devices provide novel platforms for functional sensing applications, such as medical diagnostics and environmental monitoring. They are based on porous hydrophilic material, which transports the sample liquid by capillary action. The directional flow of aqueous liquids can be controlled by selective hydrophobising of pores. Earlier research in this field has concentrated on highly porous cellulose papers as base substrates, with no significant interest shown for pigment coatings. Such coatings can produce significantly thinner porous layers, thus requiring smaller sample volumes. This study investigates the hydrophobic patterning of custom-designed porous pigment coatings by functional inkjet printing. Tested coatings consisted of reference ground calcium carbonate and porous functionalised calcium carbonate (FCC) pigments combined with various binders, including microfibrillated cellulose. The hydrophobising custom-made inks contain polystyrene or alkyl ketene dimer (AKD) in p-xylene. The patterning is demonstrated by reaction arrays and simple channels. With polystyrene ink, successful hydrophobic barriers could be generated on all tested pigment/binder coatings, although generally requiring printing of multiple layers of barrier material. With AKD ink, hydrophobic patterns could be created successfully on coatings containing an organic binder, but not on coatings with inorganic sodium silicate as binder. The AKD ink generated hydrophobic barriers using fewer ink layers compared with polystyrene ink. Interestingly, AKD ink could hydrophobise the FCC pigment alone without binder, presumably due to hydroxyl groups on the pigment surface. Hydrophobic patterning of the pigment coatings is seen to require large amounts of hydrophobising agent, likely related to the high specific surface area.     

Elimination of enteric bacteria in biological-chemical wastewater treatment and tertiary filtration units

The occurrence and removal of salmonellae and faecal indicators in four conventional municipal wastewater treatment plants (MWTP) were investigated. In addition, we tested the efficiency of a semi-technical scale biological nutrient removal process and three pilot-scale tertiary filtration units in microbial removal. All influent samples collected from MWTPs contained salmonellae from 93 to 11,000 MPN/100 ml and indicator bacteria from about 10(7) to 10(8) CFU/100 ml. The reductions in salmonella numbers achieved in full-scale biological-chemical wastewater treatment and semi-technical scale biological nutrient removal processes were usually between 94% and virtually 100% (99.9%) and indicator bacteria reductions between 2 and 3 log units. Microbial numbers in MWTP effluents could be modelled as a function of effluent residual organic matter, suspended solids and total phosphorus concentrations. Pilot-scale tertiary treatment by rapid sand contact filter, chemical contact filter and biological-chemical contact filter reduced salmonella numbers below the detection limit and faecal coliform numbers on average by 99%, 39% and 71%, respectively. A total of 32 Salmonella serovars were identified among 197 Salmonella isolates from municipal wastewaters. Of the isolates, 32% were resistant to nalidixic acid, indicating reduced sensitivity to ciprofloxacin, the drug of choice in the treatment of salmonellosis. In addition, 18% of the isolates were multiresistant. Our results, especially antibiotic resistant Salmonella strains, indicate that conventional municipal wastewater treatment without efficient tertiary treatment, like filtration or disinfection, may constitute a risk for public health.     

Design,implementation, and evaluation of learning algorithms for dynamic real-time network monitoring

Network monitoring is necessary so as to ensure high reliability and availability in telecom networks. One of the main challenges posed by state-of-the-art monitoring tools is the creation of network baselines. Such baselines include thresholds that can be used to determine whether monitored values (with a given context, e.g., time) represent normal network operation or not. The size and complexity of current (and future) networks make it infeasible to manually determine and set baselines for each network operator and metric, let alone adapting the thresholds to changes in network conditions. This leads to the use of default baselines and/or setting baselines only once and never changing them throughout the lifetime of network elements. This does not only cause inefficient operation but could have implications for network reliability and availability. In this paper, we present the design, implementation, and evaluation of DARN: a collection of analytics and machine learning-based algorithms aimed at ensuring that network baselines are automatically adapted to different metric evolution. DARN has been comprehensively evaluated on a deployment with real traffic to confirm accuracy of generated baselines, a 22% improvement in accuracy due to baseline adaptation and a 72% reduction in false alarms.