Online monitoring of water toxicity by use of bioluminescent reporter bacterial biochips

We describe a flow-through biosensor for online continuous water toxicity monitoring. At the heart of the device are disposable modular biochips incorporating agar-immobilized bioluminescent recombinant reporter bacteria, the responses of which are probed by single-photon avalanche diode detectors. To demonstrate the biosensor capabilities, we equipped it with biochips harboring both inducible and constitutive reporter strains and exposed it to a continuous water flow for up to 10 days. During these periods we challenged the biosensor with 2-h pulses of water spiked with model compounds representing different classes of potential water pollutants, as well as with a sample of industrial wastewater. The biosensor reporter panel detected all simulated contamination events within 0.5-2.5 h, and its response was indicative of the nature of the contaminating chemicals. We believe that a biosensor of the proposed design can be integrated into future water safety and security networks, as part of an early warning system against accidental or intentional water pollution by toxic chemicals.     

The new Israeli feed safety law: challenges in relation to animal and public health

The Israeli feed safety legislation, which came to prominence in the early 1970s, has undergone a major change from simple feed safety and quality regulations to a more holistic concept of control of feed safety and quality throughout the whole feed production chain, from farm to the end user table. In February 2014, a new law was approved by the Israeli parliament, namely the Control of Animal Feed Law, which is expected to enter into effect in 2017. The law is intended to regulate the production and marketing of animal feed, guaranteeing the safety and quality of animal products throughout the production chain. The responsibility on the implementation of the new feed law was moved from the Plant Protection Inspection Service to the Veterinary Services and Animal Health. In preparation for the law's implementation, we have characterized the various sources and production lines of feed for farm and domestic animals in Israel and assessed the current feed safety challenges in terms of potential hazards or undesirable substances. Moreover, the basic requirements for feed safety laboratories, which are mandatory for analyzing and testing for potential contaminants, are summarized for each of the contaminants discussed. © 2016 Society of Chemical Industry     

Mesoscale Nanostructures as a Bridge Between Homogeneous and Heterogeneous Catalysis

The environment that surrounds catalytically active metallic nanoclusters has an important role in tuning their catalytic reactivity and selectivity and can initiate novel reaction routes. In this review we will demonstrate two different approaches for utilization of the environment in bi-functional, mesoscale catalysts. In these catalytic systems, the molecules that surround the metallic nanoclusters have an active role as co-catalysts. In the first bi-functional system, the steric effects of metal-adsorbed ligands have been exploited for regulating the adsorption orientation of reactants. By favoring specific orientation of the reactants, the products selectivity was widely tuned. In the second bi-functional catalytic system, the catalytic properties of the metallic nanoclusters were controlled by their encapsulation within a polymeric matrix. The oxidation state, catalytic reactivity and stability of metallic nanoclusters were tuned by their encapsulation in polyamidoamine dendrimer molecules. Oxidation of dendrimer-encapsulated nanoclusters into highly oxidized metal-ions activated the catalyst toward a variety of reactions which were previously catalyzed by homogeneous catalysts. Moreover, by modifying the properties of the polymeric matrix, enhanced chemo-, diastereo- and enantio-selectivity were obtained. These two examples of mesoscale catalysts indicate the important role of the surrounding environment in tuning the catalytic reactivity and selectivity. In addition, it is demonstrated that these catalysts can function as a bridge between homogeneous and heterogeneous catalysis.     

Supplemental inulin does not enhance iron bioavailability to Caco-2 cells from milk- or soy-based,probiotic-containing,yogurts but incubation at 37 °C does

The in vitro effects of supplemental inulin (4%) on iron (Fe) availability in two different probiotic-containing yogurts were examined. Milk or soy-based yogurts, with and without inulin, were incubated (37 °C) for 48 h or without any incubation before comparison by an in vitro gastrointestinal digestion/Caco-2 cell culture model was used to assess iron bioavailability. The dialysable Fe fraction, cell ferritin formation, and cell associated Fe were monitored. Supplemental inulin decreased dialysable Fe only in non-incubated milk-based yogurt. In both yogurts incubation by itself increased dialysable Fe, and inulin increased the latter only in soy-based yogurt. Cellular ferritin concentration were higher after exposure to non-incubated milk-based than soy-based yogurt, although, after incubation the latter induced the highest ferritin formation. These data suggest that inulin does not have a direct effect on Fe bioavailability in the small intestine, and that probiotic bacteria play an enhancing role on Fe bioavailability.     

Example-based single document image super-resolution: a global MAP approach with outlier rejection

Regularization plays a vital role in inverse problems, and especially in ill-posed ones. Along with classical regularization techniques based on smoothness, entropy, and sparsity, an emerging powerful regularization is one that leans on image examples. In this paper, we propose an efficient scheme for using image examples as driving a powerful regularization, applied to the image scale-up (super-resolution) problem. In this work, we target specifically scanned documents containing written text, graphics, and equations. Our algorithm starts by assigning per each location in the degraded image several candidate high-quality patches. Those are found as the nearest-neighbors (NN) in an image-database that contains pairs of corresponding low- and high-quality image patches. The found examples are used for the definition of an image prior expression, merged into a global MAP penalty function. We use this penalty function both for rejecting some of the irrelevant outlier examples, and then for reconstructing the desired image. We demonstrate our algorithm on several scanned documents with promising results.     

Image denoising via sparse and redundant representations over learned dictionaries.

We address the image denoising problem, where zero-mean white and homogeneous Gaussian additive noise is to be removed from a given image. The approach taken is based on sparse and redundant representations over trained dictionaries. Using the K-SVD algorithm, we obtain a dictionary that describes the image content effectively. Two training options are considered: using the corrupted image itself, or training on a corpus of high-quality image database. Since the K-SVD is limited in handling small image patches, we extend its deployment to arbitrary image sizes by defining a global image prior that forces sparsity over patches in every location in the image. We show how such Bayesian treatment leads to a simple and effective denoising algorithm. This leads to a state-of-the-art denoising performance, equivalent and sometimes surpassing recently published leading alternative denoising methods.     

Stable recovery of sparse overcomplete representations in the presence of noise

Overcomplete representations are attracting interest in signal processing theory, particularly due to their potential to generate sparse representations of signals. However, in general, the problem of finding sparse representations must be unstable in the presence of noise. This paper establishes the possibility of stable recovery under a combination of sufficient sparsity and favorable structure of the overcomplete system. Considering an ideal underlying signal that has a sufficiently sparse representation, it is assumed that only a noisy version of it can be observed. Assuming further that the overcomplete system is incoherent, it is shown that the optimally sparse approximation to the noisy data differs from the optimally sparse decomposition of the ideal noiseless signal by at most a constant multiple of the noise level. As this optimal-sparsity method requires heavy (combinatorial) computational effort, approximation algorithms are considered. It is shown that similar stability is also available using the basis and the matching pursuit algorithms. Furthermore, it is shown that these methods result in sparse approximation of the noisy data that contains only terms also appearing in the unique sparsest representation of the ideal noiseless sparse signal.