Energy Metabolites as Biomarkers in Ischemic and Dilated Cardiomyopathy

With more than 25 million people affected, heart failure (HF) is a global threat. As energy production pathways are known to play a pivotal role in HF, we sought here to identify key metabolic changes in ischemic- and non-ischemic HF by using a multi-OMICS approach. Serum metabolites and mRNAseq and epigenetic DNA methylation profiles were analyzed from blood and left ventricular heart biopsy specimens of the same individuals. In total we collected serum from n = 82 patients with Dilated Cardiomyopathy (DCM) and n = 51 controls in the screening stage. We identified several metabolites involved in glycolysis and citric acid cycle to be elevated up to 5.7-fold in DCM (p = 1.7 × 10⁻⁶). Interestingly, cardiac mRNA and epigenetic changes of genes encoding rate-limiting enzymes of these pathways could also be found and validated in our second stage of metabolite assessment in n = 52 DCM, n = 39 ischemic HF and n = 57 controls. In conclusion, we identified a new set of metabolomic biomarkers for HF. We were able to identify underlying biological cascades that potentially represent suitable intervention targets.     

Preparation of Iron Filler-Based Photocomposites and Application in 3D Printing

The introduction of metallic fillers to polymers via the photopolymerization approach can endow the composite materials with some unique properties, but the relevant research is still scarce due to the issue of light penetration and inner filter effect. Herein, for the first time the fabrication of photocomposites based on fine iron powder (i.e., a typical kind of metallic filler) is reported in this work. The free radical polymerization of two different acrylate monomers, poly(ethylene glycol) diacrylate and trimethylolpropane triacrylate, is performed in the presence of iron filler under mild conditions (i.e., light emitting diode (LED)@405 nm irradiation at room temperature under air). And the real-time Fourier transform infrared spectroscopy reveals remarkable photopolymerization kinetics of acrylates with high final conversions and fast polymerization rates despite the increasing contents of iron filler in the composites. Interestingly, the 3D printing technique is applied to the iron filler-based composites to produce tridimensional patterns with excellent spatial resolution. This work not only paves the way for the investigation of photocomposites based on metallic fillers through photochemical methods, but also broadens the potential application prospects.     

Biomimetic Polymer Nanostructures by Injection Molding

The nanometer scale topography of self‐assembling structural protein complexes in animals is believed to induce favorable cell responses. An important example of such nanostructured biological complexes is fibrillar collagen that possesses a cross‐striation structure with a periodicity of 69 nm and a peak‐to‐valley distance of 4–6 nm. Bovine collagen type I was assembled into fibrillar structures in vitro and sedimented onto solid supports. Their structural motif was transferred into a nickel replica by physical vapor deposition of a small‐grained metal layer followed by galvanic plating. The resulting inverted nickel structure was found to faithfully present most of the micrometer and nanometer scale topography of the biological original. This nickel replica was used as a die for the injection molding of a range of different thermoplastic polymers. Total injection molding cycle times were in the range of 30–45 seconds. One of the polymer materials investigated, polyethylene, displayed poor replication of the biological nanotopographical motif. However, the majority of the polymers showed very high replication fidelity as witnessed by their ability to replicate the cross‐striation features of less than 5 nm height difference. The latter group of materials includes poly(propylene), poly(methyl methacrylate), poly(L ‐lactic acid), polycaprolactone, and a copolymer of cyclic and linear olefins (COC). This work suggests that the current limiting factor for the injection molding of nanometer scale topography in thermoplastic polymers lies with the grain size of the initial metal coating of the mold rather than the polymers themselves.

     

7-(Dialkylamino)coumarin-4-yl]methyl-Caged Compounds as Ultrafast and Effective Long-Wavelength Phototriggers of 8-Bromo-Substituted Cyclic Nucleotides

[7-(Dimethylamino)coumarin-4-yl]methyl (DMACM) and [7-(diethylamino)coumarin-4-yl]methyl (DEACM) esters of 8-bromoadenosine 3',5'-cyclic monophosphate (8-Br-cAMP) and 8-bromoguanosine 3',5'-cyclic monophosphate (8-Br-cGMP) are described as novel caged compounds for 8-bromo-substituted cyclic nucleotides. Synthesis is accomplished by treatment of the free acids of the cyclic nucleotides with the corresponding 7(dialkylamino)-substituted 4(diazomethyl)coumarins. Irradiation of the DMACM- and DEACM-caged cyclic nucleotides with UV light stimulates the release of the cyclic nucleotides within roughly a nanosecond. The new caged compounds are resistant to hydrolysis in aqueous buffers and exhibit long-wavelength absorption properties with maxima at 400 nm, high extinction coefficients, and high quantum yields (0.15-0.31). Their favorable properties render these compounds the most efficient and rapid phototriggers of 8-bromo-substituted cyclic nucleotides known. The usefulness of the compounds for physiological studies under nondamaging light conditions was examined in HEK293 cells expressing the alpha subunit of the cyclic-nucleotide-gated (CNG) channel of cone photoreceptors (CNGA3) and of olfactory neurons (CNGA2) by using confocal laser scanning microscopy and the patch clamp technique.     

Modeling the Direct Synthesis of Dimethyl Ether using Artificial Neural Networks

Artificial neural networks (ANNs) are designed and implemented to model the direct synthesis of dimethyl ether (DME) from syngas over a commercial catalyst system. The predictive power of the ANNs is assessed by comparison with the predictions of a lumped model parameterized to fit the same data used for ANN training. The ANN training converges much faster than the parameter estimation of the lumped model, and the predictions show a higher degree of accuracy under all conditions. Furthermore, the simulations show that the ANN predictions are also accurate even at some conditions beyond the validity range.     

Tetranorsesquiterpenoids as Attractants of Yucca Moths to Yucca Flowers

Journal of Chemical Ecology - The obligate pollination mutualism between Yucca and yucca moths is a classical example of coevolution. Oviposition and active pollination by female yucca moths occur...     

Performance models of storage contention in cloud environments

We propose simple models to predict the performance degradation of disk requests due to storage device contention in consolidated virtualized environments. Model parameters can be deduced from measurements obtained inside Virtual Machines (VMs) from a system where a single VM accesses a remote storage server. The parameterized model can then be used to predict the effect of storage contention when multiple VMs are consolidated on the same server. We first propose a trace-driven approach that evaluates a queueing network with fair share scheduling using simulation. The model parameters consider Virtual Machine Monitor level disk access optimizations and rely on a calibration technique. We further present a measurement-based approach that allows a distinct characterization of read/write performance attributes. In particular, we define simple linear prediction models for I/O request mean response times, throughputs and read/write mixes, as well as a simulation model for predicting response time distributions. We found our models to be effective in predicting such quantities across a range of synthetic and emulated application workloads.     

Simultaneous nonrigid registration of multiple point sets and atlas construction

Group-wise registration of a set of shapes represented by unlabeled point-sets is a challenging problem since, usually this involves solving for point correspondence in a nonrigid motion setting. In this paper, we propose a novel and robust algorithm that is capable of simultaneously computing the mean shape represented by a probability density function from multiple unlabeled point-sets and registering them non-rigidly to this emerging mean shape. This algorithm avoids the correspondence problem by minimizing the Jensen-Shannon (JS) divergence between the point sets. We motivate the use of the JS divergence by pointing out its close relationship to hypothesis testing. We derive the analytic gradient of the cost function in order to efficiently achieve the optimal solution. JS-divergence is symmetric with no bias toward any of the given shapes to be registered and whose mean is being sought. A by product of the registration process is a probabilistic atlas defined as the convex combination of the probability densities of the input point sets being aligned. Our algorithm can be especially useful for creating atlases of various shapes present in images as well as for simultaneously (rigidly or non-rigidly) registering 3D range data sets without having to establish any correspondence. We present experimental results on real and synthetic data.     

Secure video communication using firefly optimization and visual cryptography

In recent years, we face an increasing interest in protecting multimedia data and copyrights due to the high exchange of information. Attackers are trying to get confidential information from various sources, which brings the importance of securing the data. Many researchers implemented techniques to hide secret information to maintain the integrity and privacy of data. In order to protect confidential data, histogram-based reversible data hiding with other cryptographic algorithms are widely used. Therefore, in the proposed work, a robust method for securing digital video is suggested. We implemented histogram bit shifting based reversible data hiding by embedding the encrypted watermark in featured video frames. Histogram bit shifting is used for hiding highly secured watermarks so that security for the watermark symbol is also being achieved. The novelty of the work is that only based on the quality threshold a few unique frames are selected, which holds the encrypted watermark symbol. The optimal value for this threshold is obtained using the Firefly Algorithm. The proposed method is capable of hiding high-capacity data in the video signal. The experimental result shows the higher capacity and video quality compared to other reversible data hiding techniques. The recovered watermark provides better identity identification against various attacks. A high value of PSNR and a low value of BER and MSE is reported from the results.