Toughening of a carbon-fibre composite using electrospun poly(Hydroxyether of Bisphenol A) nanofibrous membranes through inverse phase separation and inter-domain etherification

The interlaminar toughening of a carbon fibre reinforced composite by interleaving a thin layer (~20 microns) of poly(hydroxyether of bisphenol A) (phenoxy) nanofibres was explored in this work. Nanofibres, free of defect and averaging several hundred nanometres, were produced by electrospinning directly onto a pre-impregnated carbon fibre material (Toray G83C) at various concentrations between 0.5 wt % and 2 wt %. During curing at 150 °C, phenoxy diffuses through the epoxy resin to form a semi interpenetrating network with an inverse phase type of morphology where the epoxy became the co-continuous phase with a nodular morphology. This type of morphology improved the fracture toughness in mode I (opening failure) and mode II (in-plane shear failure) by up to 150% and 30%, respectively. Interlaminar shear stress test results showed that the interleaving did not negatively affect the effective in-plane strength of the composites. Furthermore, there was some evidence from DMTA and FT-IR analysis to suggest that inter-domain etherification between the residual epoxide groups with the pendant hydroxyl groups of the phenoxy occurred, also leading to an increase in glass transition temperature (~7.5 °C).

Incorporating domain knowledge with video and voice data analysis in news broadcasts

This paper addresses the area of video annotation, indexing and retrieval, and shows how a set of tools can be employed, along with domain knowledge, to detect narrative structure in broadcast news. The initial structure is detected using low-level audio visual processing in conjunction with domain knowledge. Higher level processing may then utilize the initial structure detected to direct processing to improve and extend the initial classification.

The structure detected breaks a news broadcast into segments, each of which contains a single topic of discussion. Further the segments are labeled as a) anchor person or reporter, b) footage with a voice over or c) sound bite. This labeling may be used to provide a summary, for example by presenting a thumbnail for each reporter present in a section of the video. The inclusion of domain knowledge in computation allows more directed application of high level processing, giving much greater efficiency of effort expended. This allows valid deductions to be made about structure and semantics of the contents of a news video stream, as demonstrated by our experiments on CNN news broadcasts.

Damage identification of civil infrastructure in the time domain using FEM calibrated ARMAX model

Due to environmental loads, mechanical damages, structural aging and human factors, civil infrastructure inevitably deteriorate during their service lives. Since their damage may claim human lives and cause significant economic losses, how to identify damages and assess structural conditions timely and accurately has drawn increasingly more attentions from structural engineering community worldwide. In this study, a fast and sensitive time domain damage identification method will be developed. First, a high quality finite element model is built and the structural responses are simulated under different damage scenarios. Based on the simulated data, an Auto Regressive Moving Average Exogenous (ARMAX) model is then developed and calibrated. The calibrated ARMAX model can be used to identify damage in different scenarios through model updating process using clonal selection algorithm (CSA). The identification results demonstrate the performance of the proposed methodology, which has the potential to be used for damage identification in practices.

Assessing health literacy : a new domain for collaboration between language testers and health professionals

Health literacy, defined as an individual's capacity to process health information in order to make appropriate health decisions, is the focus of increasing attention in medical fields due to growing awareness that suboptimal health literacy is associated with poorer health outcomes. To explore this issue, a number of instruments, reported to have high internal consistency and strong correlations with general literacy tests, have been developed. However, their validity as measures of the target construct is seldom explored using multiple sources of evidence. The current study, involving collaboration between health professionals and language specialists, set out to assess the validity of the Rapid Estimate of Adult Literacy in Medicine (REALM), which describes itself as a “reading recognition” test that measures ability to pronounce common medical and lay terms. Drawing on a sample of 310 respondents, including both native and non-native speakers of English, investigations were undertaken to probe the REALM's validity as a measure of understanding the selected terms and to consider associations between scores on this widely used test and those derived from other recognized health literacy tests. Results suggest that the REALM is underrepresenting the health literacy construct and that the test may also be biased against non-native speakers of English. The study points to an expanded role for language testers, working in collaboration with experts from medical disciplines, in developing and evaluating health literacy tools.

Underdetermined blind source separation by parallel factor analysis in time-frequency domain

This paper presents a new time-frequency approach to the underdetermined blind source separation using the parallel factor decomposition of third-order tensors. Without any constraint on the number of active sources at an auto-term time-frequency point, this approach can directly separate the sources as long as the uniqueness condition of parallel factor decomposition is satisfied. Compared with the existing two-stage methods where the mixing matrix should be estimated at first and then used to recover the sources, our approach yields better source separation performance in the presence of noise. Moreover, the mixing matrix can be estimated at the same time of the source separation process. Numerical simulations are presented to show the superior performance of the proposed approach to some of the existing two-stage blind source separation methods that use the time-frequency representation as well.

Genetic variation in SH3-domain GRB2-like (endophilin)-interacting protein 1 has a major impact on fat mass

Objective:

The SH3-domain GRB2-like (endophilin)-interacting protein 1 (SGIP1) gene has been shown to be differentially expressed in the hypothalamus of lean versus obese Israeli sand rats (Psammomys obesus), and is suspected of having a role in regulating food intake. The purpose of this study was to assess the role of genetic variation in SGIP1 in human disease.
Subjects:

We performed single-nucleotide polymorphism (SNP) genotyping in a large family pedigree cohort from the island of Mauritius. The Mauritius Family Study (MFS) consists of 400 individuals from 24 Indo-Mauritian families recruited from the genetically homogeneous population of Mauritius. We measured markers of the metabolic syndrome, including diabetes and obesity-related phenotypes such as fasting plasma glucose, waist:hip ratio, body mass index and fat mass.
Results:

Statistical genetic analysis revealed associations between SGIP1 polymorphisms and fat mass (in kilograms) as measured by bioimpedance. SNP genotyping identified associations between several genetic variants and fat mass, with the strongest association for rs2146905 (P=4.7 × 10−5). A strong allelic effect was noted for several SNPs where fat mass was reduced by up to 9.4% for individuals homozygous for the minor allele.
Conclusions:

Our results show association between genetic variants in SGIP1 and fat mass. We provide evidence that variation in SGIP1 is a potentially important determinant of obesity-related traits in humans.

FEM calibrated ARMAX model updating method for time domain damage identification

Due to environmental loads, mechanical damages, structural aging and human factors, civil infrastructure inevitably deteriorate during their service lives. Since their damage may claim human lives and cause significant economic losses, how to identify damages and assess structural conditions timely and accurately has drawn increasingly more attentions from structural engineering community worldwide. In this study, a fast and sensitive time domain damage identification method will be developed. To do this, a finite element model of a steel pipe laid on the soil is built and the structural responses are simulated under different damage scenarios. Based on the simulated data, an Auto Regressive Moving Average Exogenous (ARMAX) model is then built and calibrated. The calibrated ARMAX model is used to identify different damage scenarios through model updating process using clonal selection algorithm (CSA). The results demonstrate the application potential of the proposed method in identifying the pipeline conditions. To further verify its performance, laboratory tests of a steel pipe laid on the soil with and without soil support (free span damage) are carried out. The identification results of pipe-soil system show that the proposed method is capable of identifying damagein a complex structural system. Therefore, it can be applied to identifying pipeline conditions.

The P-Loop domain of yeast Clp1 mediates interactions between CF IA and CPF factors in Pre-mRNA 3′ end formation

 Cleavage factor IA (CF IA), cleavage and polyadenylation factor (CPF), constitute major protein complexes required for pre-mRNA 3' end formation in yeast. The Clp1 protein associates with Pcf11, Rna15 and Rna14 in CF IA but its functional role remained unclear. Clp1 carries an evolutionarily conserved P-loop motif that was previously shown to bind ATP. Interestingly, human and archaean Clp1 homologues, but not the yeast protein, carry 5' RNA kinase activity. We show that depletion of Clp1 in yeast promoted defective 3' end formation and RNA polymerase II termination; however, cells expressing Clp1 with mutant P-loops displayed only minor defects in gene expression. Similarly, purified and reconstituted mutant CF IA factors that interfered with ATP binding complemented CF IA depleted extracts in coupled in vitro transcription/3' end processing reactions. We found that Clp1 was required to assemble recombinant CF IA and that certain P-loop mutants failed to interact with the CF IA subunit Pcf11. In contrast, mutations in Clp1 enhanced binding to the 3' endonuclease Ysh1 that is a component of CPF. Our results support a structural role for the Clp1 P-loop motif. ATP binding by Clp1 likely contributes to CF IA formation and cross-factor interactions during the dynamic process of 3' end formation.

Novel Z-Domain precoding method for blind separation of spatially correlated signals

In this paper, we address the problem of blind separation of spatially correlated signals, which is encountered in some emerging applications, e.g., distributed wireless sensor networks and wireless surveillance systems. We preprocess the source signals in transmitters prior to transmission. Specifically, the source signals are first filtered by a set of properly designed precoders and then the coded signals are transmitted. On the receiving side, the Z-domain features of the precoders are exploited to separate the coded signals, from which the source signals are recovered. Based on the proposed precoders, a closed-form algorithm is derived to estimate the coded signals and the source signals. Unlike traditional blind source separation approaches, the proposed method does not require the source signals to be uncorrelated, sparse, or nonnegative. Compared with the existing precoder-based approach, the new method uses precoders with much lower order, which reduces the delay in data transmission and is easier to implement in practice.

Dynamically searching for a domain for protein function prediction

The availability of large amounts of protein-protein interaction (PPI) data makes it feasible to use computational approaches to predict protein functions. The base of existing computational approaches is to exploit the known function information of annotated proteins in the PPI data to predict functions of un-annotated proteins. However, these approaches consider the prediction domain (i.e. the set of proteins from which the functions are predicted) as unchangeable during the prediction procedure. This may lead to valuable information being overwhelmed by the unavoidable noise information in the PPI data when predicting protein functions, and in turn, the prediction results will be distorted. In this paper, we propose a novel method to dynamically predict protein functions from the PPI data. Our method regards the function prediction as a dynamic process of finding a suitable prediction domain, from which representative functions of the domain are selected to predict functions of un-annotated proteins. Our method exploits the topological structural information of a PPI network and the semantic relationship between protein functions to measure the relationship between proteins, dynamically select a suitable prediction domain and predict functions. The evaluation on real PPI datasets demonstrated the effectiveness of our proposed method, and generated better prediction results.

Multitasking: multiple, domain-specific cognitive functions in a virtual environment

Multitasking among three or more different tasks is a ubiquitous requirement of everyday cognition, yet rarely is it addressed in research on healthy adults who have had no specific training in multitasking skills. Participants completed a set of diverse subtasks within a simulated shopping mall and office environment, the Edinburgh Virtual Errands Test (EVET). The aim was to investigate how different cognitive functions, such as planning, retrospective and prospective memory, and visuospatial and verbal working memory, contribute to everyday multitasking. Subtasks were chosen to be diverse, and predictions were derived from a statistical model of everyday multitasking impairments associated with frontal-lobe lesions (Burgess, Veitch, de Lacy Costello, & Shallice, 2000b). Multiple regression indicated significant independent contributions from measures of retrospective memory, visuospatial working memory, and online planning, but not from independent measures of prospective memory or verbal working memory. Structural equation modelling showed that the best fit to the data arose from three underlying constructs, with Memory and Planning having a weak link, but with both having a strong directional pathway to an Intent construct that reflected implementation of intentions. Participants who followed their preprepared plan achieved higher scores than those who altered their plan during multitask performance. This was true regardless of whether the plan was efficient or poor. These results substantially develop and extend the Burgess et al. (2000b) model to healthy adults and yield new insight into the poorly understood area of everyday multitasking. The findings also point to the utility of using virtual environments for investigating this form of complex human cognition.

Toughening epoxy thermosets with block ionomers : the role of phase domain size

Herein we report a novel approach to toughen epoxy thermosets using a block ionomer, i.e., sulfonated polystyrene-block-poly(ethylene-co-butylene)-block- polystyrene (SSEBS). SSEBS was synthesized by sulfonation of SEBS with 67 wt % polystyrene (PS). Phase morphology of the epoxy/SSEBS blends can be controlled at either nanometer or micrometer scale by simply adjusting the sulfonation degree of SSEBS. It has been found that there exists a critical degree of sulfonation (10.8 mol %) forming nanostructures in these epoxy/SSEBS blends. Above this critical value, macrophase separation can be avoided and only microphase separation occurs, yielding transparent nanostructured blends. All epoxy/SSEBS blends display increased fracture toughness compared to neat epoxy. But the toughening efficiency varies with the phase domain size, and their correlation has been established over a broad range of length scales from nanometers to a few micrometers. In the nanostructured blends with SSEBS of high sulfonation degrees, the fracture toughness decreases with decreasing size of the phase domains. In the macrophase-separated blends, only a slight improvement in toughness can be obtained with SSEBS of low sulfonation degrees. The epoxy blend with submicrometer phase domains in the range 0.05-1.0 μm containing SSEBS of a moderate degree of sulfonation (5.8 mol %) displays the maximum toughness. This study has clearly clarified the role of phase domain size on toughening efficiency in epoxy thermosets.

Versican processing by a disintegrin-like and metalloproteinase domain with thrombospondin-1 repeats proteinases-5 and -15 facilitates myoblast fusion

Skeletal muscle development and regeneration requires the fusion of myoblasts into multinucleated myotubes. Because the enzymatic proteolysis of a hyaluronan and versican-rich matrix by ADAMTS versicanases is required for developmental morphogenesis, we hypothesized that the clearance of versican may facilitate the fusion of myoblasts during myogenesis. Here, we used transgenic mice and an in vitro model of myoblast fusion, C2C12 cells, to determine a potential role for ADAMTS versicanases. Versican processing was observed during in vivo myogenesis at the time when myoblasts were fusing to form multinucleated myotubes. Relevant ADAMTS genes, chief among them Adamts5 and Adamts15, were expressed both in developing embryonic muscle and differentiating C2C12 cells. Reducing the levels of Adamts5 mRNA in vitro impaired myoblast fusion, which could be rescued with catalytically active but not the inactive forms of ADAMTS5 or ADAMTS15. The addition of inactive ADAMTS5, ADAMTS15, or full-length V1 versican effectively impaired myoblast fusion. Finally, the expansion of a hyaluronan and versican-rich matrix was observed upon reducing the levels of Adamts5 mRNA in myoblasts. These data indicate that these ADAMTS proteinases contribute to the formation of multinucleated myotubes such as is necessary for both skeletal muscle development and during regeneration, by remodeling a versican-rich pericellular matrix of myoblasts. Our study identifies a possible pathway to target for the improvement of myogenesis in a plethora of diseases including cancer cachexia, sarcopenia, and muscular dystrophy.