First Trimester Maternal Plasma Aberrant miRNA Expression Associated with Spontaneous Preterm Birth

Spontaneous Preterm Delivery (sPTD) is one of the leading causes of perinatal mortality and morbidity worldwide. The present case–control study aims to detect miRNAs differentially expressed in the first trimester maternal plasma with the view to identify predictive biomarkers for sPTD, between 32^0/7 and 36^6/7 weeks, that will allow for timely interventions for this serious pregnancy complication. Small RNA sequencing (small RNA-seq) of five samples from women with a subsequent sPTD and their matched controls revealed significant down-regulation of miR-23b-5p and miR-125a-3p in sPTD cases compared to controls, whereas miR-4732-5p was significantly overexpressed. Results were confirmed by qRT-PCR in an independent cohort of 29 sPTD cases and 29 controls. Statistical analysis demonstrated that miR-125a is a promising early predictor for sPTL (AUC: 0.895; 95% CI: 0.814-0.972; p < 0.001), independent of the confounding factors tested, providing a useful basis for the development of a novel non-invasive predictive test to assist clinicians in estimating patient-specific risk.     

µ-Opioid Receptors Expressed by Intrinsically Photosensitive Retinal Ganglion Cells Contribute to Morphine-Induced Behavioral Sensitization

Opioid drugs are the most effective tools for treating moderate to severe pain. Despite their analgesic efficacy, long-term opioid use can lead to drug tolerance, addiction, and sleep/wake disturbances. While the link between opioids and sleep/wake problems is well-documented, the mechanism underlying opioid-related sleep/wake problems remains largely unresolved. Importantly, intrinsically photosensitive retinal ganglion cells (ipRGCs), the cells that transmit environmental light/dark information to the brain’s sleep/circadian centers to regulate sleep/wake behavior, express μ-opioid receptors (MORs). In this study, we explored the potential contribution of ipRGCs to opioid-related sleep/circadian disruptions. Using implanted telemetry transmitters, we measured changes in horizontal locomotor activity and body temperature in mice over the course of a chronic morphine paradigm. Mice lacking MORs expressed by ipRGCs (McKO) exhibited reduced morphine-induced behavioral activation/sensitization compared with control littermates with normal patterns of MOR expression. Contrastingly, mice lacking MORs globally (MKO) did not acquire morphine-induced locomotor activation/sensitization. Control mice also showed morphine-induced hypothermia in both the light and dark phases, while McKO littermates only exhibited morphine-induced hypothermia in the dark. Interestingly, only control animals appeared to acquire tolerance to morphine’s hypothermic effect. Morphine, however, did not acutely decrease the body temperature of MKO mice. These findings support the idea that MORs expressed by ipRGCs could contribute to opioid-related sleep/wake problems and thermoregulatory changes.     

Indirect boron diffusion in amorphous silicon modeled by kinetic Monte Carlo

In this paper, we present an atomistic kinetic Monte Carlo (KMC) model for boron diffusion in amorphous silicon (a-Si). Boron diffusion is assumed to be indirect, mediated by dangling bonds (DBs) present in a-Si. The model shows a transient character due to a-Si relaxation modeled by DB annihilation. In addition, B produces clusters at higher concentrations, leading to an immobile B part. The model, when calibrated, has been successfully applied to experiments of thermal anneals of amorphized B marker layers and B implantation into preamorphized Si. In addition, we studied the contribution of B diffusion in a-Si in ultrashallow junction (USJ) formation of advanced technologies. We have also used the model to demonstrate how the contribution to diffusion of B in a-Si in current and future technologies can be higher than in c-Si.     

Stochastic methods for joint registration, restoration, and interpolation of multiple undersampled images.

Using a stochastic framework, we propose two algorithms for the problem of obtaining a single high-resolution image from multiple noisy, blurred, and undersampled images. The first is based on a Bayesian formulation that is implemented via the expectation maximization algorithm. The second is based on a maximum a posteriori formulation. In both of our formulations, the registration, noise, and image statistics are treated as unknown parameters. These unknown parameters and the high-resolution image are estimated jointly based on the available observations. We present an efficient implementation of these algorithms in the frequency domain that allows their application to large images. Simulations are presented that test and compare the proposed algorithms.     

Bayesian kernel methods for analysis of functional neuroimages

We propose an approach to analyzing functional neuroimages in which 1) regions of neuronal activation are described by a superposition of spatial kernel functions, the parameters of which are estimated from the data and 2) the presence of activation is detected by means of a generalized likelihood ratio test (GLRT). Kernel methods have become a staple of modern machine learning. Herein, we show that these techniques show promise for neuroimage analysis. In an on-off design, we model the spatial activation pattern as a sum of an unknown number of kernel functions of unknown location, amplitude, and/or size. We employ two Bayesian methods of estimating the kernel functions. The first is a maximum a posteriori (MAP) estimation method based on a Reversible-Jump Markov-chain Monte-Carlo (RJMCMC) algorithm that searches for both the appropriate model complexity and parameter values. The second is a relevance vector machine (RVM), a kernel machine that is known to be effective in controlling model complexity (and thus discouraging overfitting). In each method, after estimating the activation pattern, we test for local activation using a GLRT. We evaluate the results using receiver operating characteristic (ROC) curves for simulated neuroimaging data and example results for real fMRI data. We find that, while RVM and RJMCMC both produce good results, RVM requires far less computation time, and thus appears to be the more promising of the two approaches.     

Random bounce algorithm: real-time image processing for the detection of bats and birds

Wind energy plants generate an impact on wildlife with significant fatality rates for various bat and bird species, e.g. due to a collision with the rotor blades. Monitoring approaches, such as vision-based systems, are needed to reduce their mortality by means of an optimized turbine control strategy as soon as flying animals are detected. Since manual analysis of the video data is ineffective, automatic video processing with real-time capabilities is required. In this paper, we propose the random bounce algorithm (RBA) as a novel real-time image processing method for vision-based detection of bats and birds. The RBA is combined with object tracking in order to extract flight trajectories. Its performance is compared with connected components object detection. Results from a laboratory flight tunnel as well as from a field study at a 2 MW wind energy plant in Southern Germany will be presented and discussed. We have successfully detected and tracked objects both in laboratory experiments with many animals and in field experiments with individual animals at a frame rate of 10 fps.     

Indole-3-Glycerol Phosphate Synthase From Mycobacterium tuberculosis: A Potential New Drug Target

Tuberculosis (TB), caused by the pathogen Mycobacterium tuberculosis, affects millions of people worldwide. Several TB drugs have lost efficacy due to emerging drug resistance and new anti-TB targets are needed. Recent research suggests that indole-3-glycerol phosphate synthase (IGPS) in M. tuberculosis (MtIGPS) could be such a target. IGPS is a (β/α)₈-barrel enzyme that catalyzes the conversion of 1-(o-carboxyphenylamino)-1-deoxyribulose 5’-phosphate (CdRP) into indole-glycerol-phosphate (IGP) in the bacterial tryptophan biosynthetic pathway. M. tuberculosis over expresses the tryptophan pathway genes during an immune response and inhibition of MtIGPS allows CD4 T-cells to more effectively fight against M. tuberculosis. Here we review the published data on MtIGPS expression, kinetics, mechanism, and inhibition. We also discuss MtIGPS crystal structures and compare them to other IGPS structures to reveal potential structure-function relationships of interest for the purposes of drug design and biocatalyst engineering.     

Subsolidus Phase Relations and Transparent Conductors in the Cadmium-Indium-Tin Oxide System

Subsolidus phase relations have been determined in the CdO–InO_1.5–SnO₂ system at 1175°C. A cubic-bixbyite solution In_{2−2 x} (Cd,Sn)_{2 x} O₃ (0 < x < 0.34), a cubic spinel solution (1− x )CdIn₂O₄– x Cd₂SnO₄ (0 < x < 0.75), and an orthorhombic-perovskite solution Cd_{1− x} Sn_{1− x} In_{2 x} O₃ (0 < x < 0.045) having the GdFeO₃ structure have been discovered. The CdO phase field exists over a small range of InO_1.5 (<3%) and SnO₂ (<1%). Orthorhombic Cd₂SnO₄ (Sr₂PbO₄ structure) and rutile SnO₂ appear to be point compounds with negligible solubility. The vertical section between spinel CdIn₂O₄ and orthorhombic Cd₂SnO₄ was determined between 900° and 1175°C. The spinel phase field (1− x )CdIn₂O₄– x Cd₂SnO₄ was found to extend between x = 0 and x = 0.75 at 1175°C or x = 0.78 at 900°C. All of the phases in this system appear to allow small excess quantities of the donors In and/or Sn (vs cation stoichiometry) which may be the source of the electrons that give these oxides their n-type character. The electrical and optical properties of bulk and thin-film specimens in this system are compared and contrasted with each other and the relative merits of each are assessed.     

Policies for the transition towards a hydrogen economy: the EU case

This paper reviews the current EU policy framework in view of its impact on hydrogen and fuel cell development. It screens EU energy policies, EU regulatory policies and EU spending policies. Key questions addressed are as follows: to what extent is the current policy framework conducive to hydrogen and fuel cell development? What barriers and inconsistencies can be identified? How can policies potentially promote hydrogen and fuel cells in Europe, taking into account the complex evolution of such a potentially disruptive technology? How should the EU policy framework be reformed in view of a strengthened and more coherent approach towards full deployment, taking into account recent technology-support activities?     

Predicting the average size of blasted rocks in aggregate quarries using artificial neural networks

The prediction of the average size of fragments in blasted rock piles produced after blasting in aggregate quarries is essential for decresing the cost of crushing and secondary breaking. There are several conventional and advanced processes to estimate the size of blasted rocks. Among these, the empirical prediction of the expected fragmentation in most cases is carried out by Kuznetsov’s equation (Sov Min Sci 9:144–148, 1973), modified by Lilly (1986) and Cunningham (1987). The present research focuses on the effect of the engineering geological factors and blasting process on the blasted fragments using a more powerful, advanced computational tool, an artificial neural network. In particular, the blast database consists of the blastability index of limestone on the pit face, the quantities of the explosives and of the blasted rock pile, assessing the interaction of these parameters on the blasted rocks. The data were collected from two aggregate quarries, Drymos and Tagarades, near Thessaloniki, in the Central Macedonia region of Greece. This approach indicates significant performance stability, providing the fragmentation size with high accuracy.