1,678 research outputs found

    A Network of SCOP Hidden Markov Models and Its Analysis

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    <p>Abstract</p> <p>Background</p> <p>The Structural Classification of Proteins (SCOP) database uses a large number of hidden Markov models (HMMs) to represent families and superfamilies composed of proteins that presumably share the same evolutionary origin. However, how the HMMs are related to one another has not been examined before.</p> <p>Results</p> <p>In this work, taking into account the processes used to build the HMMs, we propose a working hypothesis to examine the relationships between HMMs and the families and superfamilies that they represent. Specifically, we perform an all-against-all HMM comparison using the HHsearch program (similar to BLAST) and construct a network where the nodes are HMMs and the edges connect similar HMMs. We hypothesize that the HMMs in a connected component belong to the same family or superfamily more often than expected under a random network connection model. Results show a pattern consistent with this working hypothesis. Moreover, the HMM network possesses features distinctly different from the previously documented biological networks, exemplified by the exceptionally high clustering coefficient and the large number of connected components.</p> <p>Conclusions</p> <p>The current finding may provide guidance in devising computational methods to reduce the degree of overlaps between the HMMs representing the same superfamilies, which may in turn enable more efficient large-scale sequence searches against the database of HMMs.</p

    Bio-inspired Attentive Segmentation of Retinal OCT Imaging

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    Albeit optical coherence imaging (OCT) is widely used to assess ophthalmic pathologies, localization of intra-retinal boundaries suffers from erroneous segmentations due to image artifacts or topological abnormalities. Although deep learning-based methods have been effectively applied in OCT imaging, accurate automated layer segmentation remains a challenging task, with the flexibility and precision of most methods being highly constrained. In this paper, we propose a novel method to segment all retinal layers, tailored to the bio-topological OCT geometry. In addition to traditional learning of shift-invariant features, our method learns in selected pixels horizontally and vertically, exploiting the orientation of the extracted features. In this way, the most discriminative retinal features are generated in a robust manner, while long-range pixel dependencies across spatial locations are efficiently captured. To validate the effectiveness and generalisation of our method, we implement three sets of networks based on different backbone models. Results on three independent studies show that our methodology consistently produces more accurate segmentations than state-of-the-art networks, and shows better precision and agreement with ground truth. Thus, our method not only improves segmentation, but also enhances the statistical power of clinical trials with layer thickness change outcomes

    Recognition memory, self-other source memory, and theory-of-mind in children with autism spectrum disorder.

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    This study investigated semantic and episodic memory in autism spectrum disorder (ASD), using a task which assessed recognition and self-other source memory. Children with ASD showed undiminished recognition memory but significantly diminished source memory, relative to age- and verbal ability-matched comparison children. Both children with and without ASD showed an “enactment effect”, demonstrating significantly better recognition and source memory for self-performed actions than other-person-performed actions. Within the comparison group, theory-of-mind (ToM) task performance was significantly correlated with source memory, specifically for other-person-performed actions (after statistically controlling for verbal ability). Within the ASD group, ToM task performance was not significantly correlated with source memory (after controlling for verbal ability). Possible explanations for these relations between source memory and ToM are considered

    Corporate constructed and dissent enabling public spheres: differentiating dissensual from consensual corporate social responsibility

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    I here distinguish dissensual from consensual corporate social responsibility (CSR) on the grounds that the former is more concerned to organize (or portray) corporate-civil society disagreement than it is corporate-civil society agreement. In doing so, I first conceive of consensual CSR, and identify a positive and negative view thereof. Second, I conceive of dissensual CSR, and suggest that it can be actualized through the construction of dissent enabling, rather than consent-oriented, public spheres. Following this, I describe four actor-centred institutional theories-i.e. a sociological, ethical, transformative and economic perspective, respectively-and suggest that an economic perspective is generally well suited to explaining CSR activities at the organizational level. Accordingly, I then use the economic perspective to analyse a dissent enabling public sphere that Shell has constructed, and within which Greenpeace participated. In particular, I explain Shell's employment of dissensual CSR in terms of their core business interests; and identify some potential implications thereof for Shell, Greenpeace, and society more generally. In concluding, I highlight a number of ways in which the present paper can inform future research on business and society interactions

    Multijet production in neutral current deep inelastic scattering at HERA and determination of α_{s}

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    Multijet production rates in neutral current deep inelastic scattering have been measured in the range of exchanged boson virtualities 10 5 GeV and –1 < η_{LAB}^{jet} < 2.5. Next-to-leading-order QCD calculations describe the data well. The value of the strong coupling constant α_{s} (M_{z}), determined from the ratio of the trijet to dijet cross sections, is α_{s} (M_{z}) = 0.1179 ± 0.0013 (stat.)_{-0.0046}^{+0.0028}(exp.)_{-0.0046}^{+0.0028}(th.)

    Liposomal Co-Entrapment of CD40mAb Induces Enhanced IgG Responses against Bacterial Polysaccharide and Protein

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    Background Antibody against CD40 is effective in enhancing immune responses to vaccines when chemically conjugated to the vaccine antigen. Unfortunately the requirement for chemical conjugation presents some difficulties in vaccine production and quality control which are compounded when multivalent vaccines are required. We explore here an alternative to chemical conjugation, involving the co-encapsulation of CD40 antibody and antigens in liposomal vehicles. Methodology/Principal Findings Anti-mouse CD40 mAb or isotype control mAb were co-entrapped individually in cationic liposomal vehicles with pneumococcal polysaccharides or diphtheria and tetanus toxoids. Retention of CD40 binding activity upon liposomal entrapment was assessed by ELISA and flow cytometry. After subcutaneous immunization of BALB/c female mice, anti-polysaccharide and DT/TT responses were measured by ELISA. Simple co-encapsulation of CD40 antibody allowed for the retention of CD40 binding on the liposome surface, and also produced vaccines with enhanced imunogenicity. Antibody responses against both co-entrapped protein in the form of tetanus toxoid, and Streptococcus pneumoniae capsular polysaccharide, were enhanced by co-encapsulation with CD40 antibody. Surprisingly, liposomal encapsulation also appeared to decrease the toxicity of high doses of CD40 antibody as assessed by the degree of splenomegaly induced. Conclusions/Significance Liposomal co-encapsulation with CD40 antibody may represent a practical means of producing more immunogenic multivalent vaccines and inducing IgG responses against polysaccharides without the need for conjugation
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