Effect of dietary olive leaves and/or α-tocopheryl acetate supplementation on microbial growth and lipid oxidation of turkey breast fillets during refrigerated storage

Thirty turkeys were allocated to five groups of six birds each. One group that served as control was fed a basal diet, while the others were fed diets supplemented with olive leaves at 5 or 10 g/kg, or α-tocopheryl acetate at 150 or 300 mg/kg. Following slaughter, fillets from breast were stored at 4 °C in the dark for 12 days, and lipid oxidation and microbial growth were assessed. Results showed that the incorporation of olive leaves in turkey diets increased (P < 0.05) the oxidative stability of raw breast fillets during refrigerated storage. Dietary olive leaves at 10 g/kg were more effective (P < 0.05) in inhibiting lipid oxidation compared to 5 g/kg, but inferior to the supplementation of 300 mg α-tocopheryl acetate/kg. In turn, α-tocopheryl acetate supplementation at 150 mg/kg was equal to olive leaves at 5 g/kg but inferior to olive leaves at 10 g/kg. Total viable counts, lactic acid bacteria, Enterobacteriaceae and psychrotrophic bacteria counts were all increased (P < 0.05) in breast fillets of all groups throughout the refrigerated storage. Diet supplementation with α-tocopheryl acetate had no effect on the bacterial counts recorded in the control group, but diet supplementation with olive leaves resulted in decrease (P < 0.05) of all bacterial counts at day 2 of storage and thereafter; during this period, olive leaves at the level of 10 g/kg were more effective in inhibiting bacterial growth compared to the level of 5 g/kg.     

Band-like electron transport in organic transistors and implication of the molecular structure for performance optimization

The Hall effect and an increase of field-effect mobility with decreasing temperature is observerd in n-channel single-crystal organic field-effect transistors (OFETs). A quantitative analysis of these findings, together with results on different p-channel transistors, indicate the importance of the semiconductor molecular polarizability and the structure of the charge transport layers in the crystal for the observation of band-like transport in OFETs.     

Insights into the molecular organization of the neuron by cryo-electron tomography

Despite great progress in the identification and characterization of the key molecular players in neuronal function, remarkably little is known about their supramolecular organization. Cryo-electron tomography (cryo-ET), providing three-dimensional views of the molecular components of the cell in their native, fully hydrated environment, is uniquely positioned to elucidate the native architecture of the molecular machinery of the neuron. In our laboratory, we employ cryo-ET to study neuronal morphology in a variety of experimental systems and develop methods to extract quantitative and functional information from tomographic data. This approach has allowed us to shed light onto the intricate organization of the molecules of the synaptic cleft and the presynaptic cytomatrix, providing evidence for their functional roles. Also, cryo-ET of cultured neurons is beginning to open new perspectives on neuronal ultrastructure and the architecture of synaptic complexes in situ. Here, we will review these findings and discuss future directions towards the elucidation of the molecular landscape of the neuron.     

ATM traffic engineering for ABR service provisioning

The Available Bit Rate (ABR) service is being designed as a low-cost transport service over ATM, which will be using the bandwidth left available after servicing connections of another, high-priority class. The implementation of the ABR service requires large buffers at each multiplexing/switching stage to keep cell-loss rates down to a minimum, and a feedback mechanism from the network to the terminals in order for the latter to adjust their traffic profiles according to the prevailing congestion conditions. Thus, an enhanced set of traffic control functions is necessary to support this new service. In this paper the main traffic analysis and control problems related with the ABR service are addressed, modelled and answered on the basis of effective rates defined for the multiplexed connections. Emphasis is given to a simple CAC scheme which consists in allocating peak rates to the high-priority class and effective rates to the ABR class. An adaptive shaping mechanism is then required to enforce the contracted effective rates for the ABR streams. Producing ON/OFF streams facilitates the control functions by allowing the use of approximate closedform calculations.This work has been carried out partly in the framework of the RACE EXPLOIT project.     

Parameterized complex dielectric functions of CuIn1−xGaxSe2: applications in optical characterization of compositional non‐uniformities and depth profiles in materials and solar cells

In‐situ spectroscopic ellipsometry (SE) was employed to extract the complex dielectric functions ε = ε₁ + iε₂ over the spectral range of 0.75–6.5 eV for a set of polycrystalline CuIn_1−xGa_xSe₂ (CIGS) thin films with different alloy compositions x = [Ga]/{[In] + [Ga]}. For highest possible accuracy in ε for each CIGS thin film, specialized SE procedures were adopted including (i) deposition to a thickness of ~600 Å on smooth native oxide covered crystal silicon wafers, which minimizes the surface roughness on the film and thus the required corrections in data analysis, and (ii) measurement in‐situ, which minimizes ambient contamination and oxidation of the film surface. Assuming an analytical form for each of the ε spectra for these CIGS films, oscillator parameters were obtained in best fits, and these parameters were fit in turn to polynomials in x. With the resulting database of polynomial coefficients, the ε spectra for any composition of CIGS can be generated from the single parameter, x. In addition to enabling accurate contactless determination of bulk and surface roughness layer thicknesses of CIGS films by high speed multichannel SE, the database enables characterization of the composition and its profile with depth into these films, and even how the depth profile varies spatially within the plane of the films. In this study, depth profile parameters were found to correlate spatially with solar cell performance parameters. As a result, SE provides the capability of contactless compositional analysis of production‐scale CIGS photovoltaic modules at high speed. Copyright © 2016 John Wiley & Sons, Ltd.     

Engineering scalable, cache and space efficient tries for strings

Storing and retrieving strings in main memory is a fundamental problem in computer science. The efficiency of string data structures used for this task is of paramount importance for applications such as in-memory databases, text-based search engines and dictionaries. The burst trie is a leading choice for such tasks, as it can provide fast sorted access to strings. The burst trie, however, uses linked lists as substructures which can result in poor use of CPU cache and main memory. Previous research addressed this issue by replacing linked lists with dynamic arrays forming a cache-conscious array burst trie. Though faster, this variant can incur high instruction costs which can hinder its efficiency. Thus, engineering a fast, compact, and scalable trie for strings remains an open problem. In this paper, we introduce a novel and practical solution that carefully combines a trie with a hash table, creating a variant of burst trie called HAT-trie. We provide a thorough experimental analysis which demonstrates that for large set of strings and on alternative computing architectures, the HAT-trie—and two novel variants engineered to achieve further space-efficiency—is currently the leading in-memory trie-based data structure offering rapid, compact, and scalable storage and retrieval of variable-length strings.     

Colloidal Stability of Zirconia-Doped Yttria–Silica Binary Aqueous Suspensions

Minor amounts of zirconia (up to 5 wt%) were fused with yttria and their effect on water solubility of yttria, and colloidal stability of yttria–silica aqueous binary suspensions, was examined. It is shown that incorporated zirconia reduces the water sensitivity of yttria while maintaining the hydrophilic nature of the surface. This fusion procedure makes yttria particles suitable for production of longlasting yttria–silica aqueous suspensions.     

Atomistic Modeling of Solidification Phenomena Using the Phase-Field-Crystal Model

In recent years, a fundamental understanding of solidification and its behavior has been gained through molecular dynamics simulations and the phase-field method, the first of which is limited to short time scales and the latter of which does not represent interface and elastoplastic properties accurately. Recently, the phase-field-crystal (PFC) method, a continuum method operating on atomistic length scales and diffusive time scales, has helped bridge the multiple scale gap between molecular dynamics and phase field. This review surveys the advances of PFC models in the context of various solidification phenomena.     

Complex dielectric functions of anodic bi-layer tantalum oxide

The optical properties and structure of anodic oxides are dependent on the anodization conditions. For tantalum oxide formed in dilute phosphoric acid, the anodic oxide forms as two chemically distinct layers, referred to as a bi-layer, where the inner layer is pure tantalum oxide and the outer layer contains incorporated phosphate. The complex dielectric functions of the individual inner and outer layers are determined using spectroscopic ellipsometry. The dielectric functions of the bi-layers are compared to mono-layer oxides formed in sodium sulfate, and the effects of hydrothermal sealing are explored. For bi-layer oxides formed to 70 V in phosphoric acid, the inner layer band gap is 4.37 ± 0.02 eV and the outer layer band gap is 3.86 ± 0.06 eV. Thin anodic oxides (∼6-15 nm) are best described by a mono-layer oxide model and exhibit higher optical absorption with a band gap of 3.98 ± 0.08 eV. This study shows that spectroscopic ellipsometry is a valuable tool in assessing processing-property relationships of multi-layer anodic films.     

A similarity learning approach to content-based image retrieval: application to digital mammography

In this paper, we describe an approach to content-based retrieval of medical images from a database, and provide a preliminary demonstration of our approach as applied to retrieval of digital mammograms. Content-based image retrieval (CBIR) refers to the retrieval of images from a database using information derived from the images themselves, rather than solely from accompanying text indices. In the medical-imaging context, the ultimate aim of CBIR is to provide radiologists with a diagnostic aid in the form of a display of relevant past cases, along with proven pathology and other suitable information. CBIR may also be useful as a training tool for medical students and residents. The goal of information retrieval is to recall from a database information that is relevant to the user's query. The most challenging aspect of CBIR is the definition of relevance (similarity), which is used to guide the retrieval machine. In this paper, we pursue a new approach, in which similarity is learned from training examples provided by human observers. Specifically, we explore the use of neural networks and support vector machines to predict the user's notion of similarity. Within this framework we propose using a hierarchal learning approach, which consists of a cascade of a binary classifier and a regression module to optimize retrieval effectiveness and efficiency. We also explore how to incorporate online human interaction to achieve relevance feedback in this learning framework. Our experiments are based on a database consisting of 76 mammograms, all of which contain clustered microcalcifications (MCs). Our goal is to retrieve mammogram images containing similar MC clusters to that in a query. The performance of the retrieval system is evaluated using precision-recall curves computed using a cross-validation procedure. Our experimental results demonstrate that: 1) the learning framework can accurately predict the perceptual similarity reported by human observers, thereby serving as a basis for CBIR; 2) the learning-based framework can significantly outperform a simple distance-based similarity metric; 3) the use of the hierarchical two-stage network can improve retrieval performance; and 4) relevance feedback can be effectively incorporated into this learning framework to achieve improvement in retrieval precision based on online interaction with users; and 5) the retrieved images by the network can have predicting value for the disease condition of the query.