The QUT-NOISE-TIMIT corpus for the evaluation of voice activity detection algorithms

The QUT-NOISE-TIMIT corpus consists of 600 hours of noisy speech sequences designed to enable a thorough evaluation of voice activity detection (VAD) algorithms across a wide variety of common background noise scenarios. In order to construct the final mixed-speech database, a collection of over 10 hours of background noise was conducted across 10 unique locations covering 5 common noise scenarios, to create the QUT-NOISE corpus. This background noise corpus was then mixed with speech events chosen from the TIMIT clean speech corpus over a wide variety of noise lengths, signal-to-noise ratios (SNRs) and active speech proportions to form the mixed-speech QUT-NOISE-TIMIT corpus. The evaluation of five baseline VAD systems on the QUT-NOISE-TIMIT corpus is conducted to validate the data and show that the variety of noise available will allow for better evaluation of VAD systems than existing approaches in the literature.

Extracellular matrix of the human cyclic corpus luteum

Extracellular matrix regulates many cellular processes likely to be important for development and regression of corpora lutea. Therefore, we identified the types and components of the extracellular matrix of the human corpus luteum at different stages of the menstrual cycle. Two different types of extracellular matrix were identified by electron microscopy; subendothelial basal laminas and an interstitial matrix located as aggregates at irregular intervals between the non-vascular cells. No basal laminas were associated with luteal cells. At all stages, collagen type IV α1 and laminins α5, β2 and γ1 were localized by immunohistochemistry to subendothelial basal laminas, and collagen type IV α1 and laminins α2, α5, β1 and β2 localized in the interstitial matrix. Laminin α4 and β1 chains occurred in the subendothelial basal lamina from mid-luteal stage to regression; at earlier stages, a punctate pattern of staining was observed. Therefore, human luteal subendothelial basal laminas potentially contain laminin 11 during early luteal development and, additionally, laminins 8, 9 and 10 at the mid-luteal phase. Laminin α1 and α3 chains were not detected in corpora lutea. Versican localized to the connective tissue extremities of the corpus luteum. Thus, during the formation of the human corpus luteum, remodelling of extracellular matrix does not result in basal laminas as present in the adrenal cortex or ovarian follicle. Instead, novel aggregates of interstitial matrix of collagen and laminin are deposited within the luteal parenchyma, and it remains to be seen whether this matrix is important for maintaining the luteal cell phenotype.

Bending and bundling of metal-free vertically aligned ZnO nanowires due to electrostatic interaction

Bending and bundling was observed from vertically aligned arrays of ZnO nanowires with flat (0001) top surfaces, which were synthesized using a vapor-phase method without metal catalysts. Sufficient evidence was found to exclude electron-beam bombardment during scanning electron microscopy as a cause for bending and bundling. We attribute the bending and bundling to electrostatic interactions due to charged (0001) polar surfaces, and also discussed the threshold surface charge densities for the bending and bundling based on a simple cantilever-bending model. Some growth features were indicative of the operation of electrostatic interactions during the growth.

Catalyst-free growth and optical properties of vertically aligned ZnO Nanorod Arrays on Si substrates covered with thin buffer layers

Vertically aligned ZnO nanorods have been grown on silicon substrates pre-coated with thin, less than 10 nm, textured ZnO seeding layers via a vapor-solid mechanism. The ZnO seeding layers, which were essential for vertical alignment of ZnO nanorods without using any metal catalyst, were prepared by decomposing zinc acetate. The structure and the luminescence properties of the ZnO nanorods synthesized onto ZnO seeding layers were investigated and their morphologies were compared with those of single-crystalline GaN substrates and silicon substrates covered with sputtered ZnO flms. Patterning of ZnO seed layers using photolithography allowed the fabrication of patterned ZnO-nanorod arrays.

Langevin dynamics modeling of the water diffusion tensor in partially aligned collagen networks

In this work, a Langevin dynamics model of the diffusion of water in articular cartilage was developed. Numerical simulations of the translational dynamics of water molecules and their interaction with collagen fibers were used to study the quantitative relationship between the organization of the collagen fiber network and the diffusion tensor of water in model cartilage. Langevin dynamics was used to simulate water diffusion in both ordered and partially disordered cartilage models. In addition, an analytical approach was developed to estimate the diffusion tensor for a network comprising a given distribution of fiber orientations. The key findings are that (1) an approximately linear relationship was observed between collagen volume fraction and the fractional anisotropy of the diffusion tensor in fiber networks of a given degree of alignment, (2) for any given fiber volume fraction, fractional anisotropy follows a fiber alignment dependency similar to the square of the second Legendre polynomial of cos(θ), with the minimum anisotropy occurring at approximately the magic angle (θMA), and (3) a decrease in the principal eigenvalue and an increase in the transverse eigenvalues is observed as the fiber orientation angle θ progresses from 0◦ to 90◦. The corresponding diffusion ellipsoids are prolate for θ < θMA, spherical for θ ≈ θMA, and oblate for θ > θMA. Expansion of the model to include discrimination between the combined effects of alignment disorder and collagen fiber volume fraction on the diffusion tensor is discussed.

An English-translated parallel corpus for the CJK Wikipedia collections

In this paper, we describe a machine-translated parallel English corpus for the NTCIR Chinese, Japanese and Korean (CJK) Wikipedia collections. This document collection is named CJK2E Wikipedia XML corpus. The corpus could be used by the information retrieval research community and knowledge sharing in Wikipedia in many ways; for example, this corpus could be used for experimentations in cross-lingual information retrieval, cross-lingual link discovery, or omni-lingual information retrieval research. Furthermore, the translated CJK articles could be used to further expand the current coverage of the English Wikipedia.

An evaluation of corpus-driven measures of medical concept similarity for information retrieval

Measures of semantic similarity between medical concepts are central to a number of techniques in medical informatics, including query expansion in medical information retrieval. Previous work has mainly considered thesaurus-based path measures of semantic similarity and has not compared different corpus-driven approaches in depth. We evaluate the effectiveness of eight common corpus-driven measures in capturing semantic relatedness and compare these against human judged concept pairs assessed by medical professionals. Our results show that certain corpus-driven measures correlate strongly (approx 0.8) with human judgements. An important finding is that performance was significantly affected by the choice of corpus used in priming the measure, i.e., used as evidence from which corpus-driven similarities are drawn. This paper provides guidelines for the implementation of semantic similarity measures for medical informatics and concludes with implications for medical information retrieval.

Enhanced hydrogen separation by vertically-aligned carbon nanotube membranes with zeolite imidazolate frameworks as a selective layer

Vertically-aligned carbon nanotube (VACNT) membranes show very high permeation fluxes due to the inherent smooth and frictionless nature of the interior of the nanotubes. However, the hydrogen selectivities are all in the Knudsen range and are quite low. In this study we grew molecular sieve zeolite imidazolate frameworks (ZIFs) via secondary seeded growth on the VACNT membranes as a gas selective layer. The ZIF layer has a thickness of 5–6 μm and shows good contact with the VACNT membrane surface. The VACNT supported ZIF membrane shows much higher H2 selectivity than Ar (7.0); O2 (13.6); N2 (15.1) and CH4 (9.8). We conclude that tailoring metal–organic frameworks on the membrane surface can be an effective route to improve the gas separation performance of the VACNT membrane.

Vertically-aligned carbon nanotube membranes for hydrogen separation

Vertically-aligned carbon nanotube membranes have been fabricated and characterized and the corresponding gas permeability and hydrogen separation were measured. The carbon nanotube diameter and areal density were adjusted by varying the catalyst vapour concentration (Fe/C ratio) in the mixed precursor. The permeances are one to two magnitudes higher than the Knudsen prediction, while the gas selectivities are still in the Knudsen range. The diameter and areal density effects were studied and compared, the temperature dependence of permeation is also discussed. The results confirm the existence of non-Knudsen transport and that surface adsorption diffusion may affect the total permeance at relative low temperature. The permeance of aligned carbon nanotube membranes can be improved by increasing areal density and operating at an optimum temperature.

Aligned poly(L-lactic-co-ε-caprolactone) electrospun microfibers and knitted structure: a novel composite scaffold for ligament tissue engineering

We developed a novel technique involving knitting and electrospinning to fabricate a composite scaffold for ligament tissue engineering. Knitted structures were coated with poly(L-lactic-co-e-caprolactone) (PLCL) and then placed onto a rotating cylinder and a PLCL solution was electrospun onto the structure. Highly aligned 2-μm-diameter microfibers covered the space between the stitches and adhered to the knitted scaffolds. The stress–strain tensile curves exhibited an initial toe region similar to the tensile behavior of ligaments. Composite scaffolds had an elastic modulus (150 ± 14 MPa) similar to the modulus of human ligaments. Biological evaluation showed that cells proliferated on the composite scaffolds and they spontaneously orientated along the direction of microfiber alignment. The microfiber architecture also induced a high level of extracellular matrix secretion, which was characterized by immunostaining. We found that cells produced collagen type I and type III, two main components found in ligaments. After 14 days of culture, collagen type III started to form a fibrous network. We fabricated a composite scaffold having the mechanical properties of the knitted structure and the morphological properties of the aligned microfibers. It is difficult to seed a highly macroporous structure with cells, however the technique we developed enabled an easy cell seeding due to presence of the microfiber layer. Therefore, these scaffolds presented attractive properties for a future use in bioreactors for ligament tissue engineering.

The efficiency of corpus-based distributional models for literature-based discovery on large data sets

This paper evaluates the efficiency of a number of popular corpus-based distributional models in performing discovery on very large document sets, including online collections. Literature-based discovery is the process of identifying previously unknown connections from text, often published literature, that could lead to the development of new techniques or technologies. Literature-based discovery has attracted growing research interest ever since Swanson's serendipitous discovery of the therapeutic effects of fish oil on Raynaud's disease in 1986. The successful application of distributional models in automating the identification of indirect associations underpinning literature-based discovery has been heavily demonstrated in the medical domain. However, we wish to investigate the computational complexity of distributional models for literature-based discovery on much larger document collections, as they may provide computationally tractable solutions to tasks including, predicting future disruptive innovations. In this paper we perform a computational complexity analysis on four successful corpus-based distributional models to evaluate their fit for such tasks. Our results indicate that corpus-based distributional models that store their representations in fixed dimensions provide superior efficiency on literature-based discovery tasks.

Diffusion tensor of water in partially aligned fibre networks

Monte Carlo simulations were used to investigate the relationship between the morphological characteristics and the diffusion tensor (DT) of partially aligned networks of cylindrical fibres. The orientation distributions of the fibres in each network were approximately uniform within a cone of a given semi-angle (θ0). This semi-angle was used to control the degree of alignment of the fibres. The networks studied ranged from perfectly aligned (θ0 = 0) to completely disordered (θ0 = 90°). Our results are qualitatively consistent with previous numerical models in the overall behaviour of the DT. However, we report a non-linear relationship between the fractional anisotropy (FA) of the DT and collagen volume fraction, which is different to the findings from previous work. We discuss our results in the context of diffusion tensor imaging of articular cartilage. We also demonstrate how appropriate diffusion models have the potential to enable quantitative interpretation of the experimentally measured diffusion-tensor FA in terms of collagen fibre alignment distributions.

Uniform, dense arrays of vertically aligned, large-diameter single-walled carbon nanotubes

Precisely controlled reactive chemical vapor synthesis of highly uniform, dense arrays of vertically aligned single-walled carbon nanotubes (SWCNTs) using tailored trilayered Fe/Al2O3/SiO2 catalyst is demonstrated. More than 90% population of thick nanotubes (>3 nm in diameter) can be produced by tailoring the thickness and microstructure of the secondary catalyst supporting SiO2 layer, which is commonly overlooked. The proposed model based on the atomic force microanalysis suggests that this tailoring leads to uniform and dense arrays of relatively large Fe catalyst nanoparticles on which the thick SWCNTs nucleate, while small nanotubes and amorphous carbon are effectively etched away. Our results resolve a persistent issue of selective (while avoiding multiwalled nanotubes and other carbon nanostructures) synthesis of thick vertically aligned SWCNTs whose easily switchable thickness-dependent electronic properties enable advanced applications in nanoelectronic, energy, drug delivery, and membrane technologies.