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Abstract: Room impulse responses (RIRs) are used very widely to characterize the acoustic conditions of rooms, such as in the derivation of reverberation time, early decay time and clarity index. This study investigates the subjective decay rate (or reverberance) of RIRs when directly listened to (rather than convolved with a dry signal such as speech or music). Through a subjective experiment, it investigates the effects of gain (or listening level) and background noise level on the reverberance of RIRs that had been measured in three concert auditoria. The task of the experiment was to match the decay rate of RIRs to that of a reference RIR by ear, by adjusting the RIRs’ exponential decay rate. Based on objective loudness modeling, gain should have a positive effect on reverberance, and background noise has a negative effect. This is confirmed in the results of the experiment. Furthermore, the objectively calculated loudness decay function provides an effective predictor of subjective decay rate, which performs better than conventional early decay time or reverberation time for the RIRs tested. [Copyright &y& Elsevier]

Abstract: The Bayesian network is a popular tool for describing relationships between data entities by representing probabilistic (in)dependencies with a directed acyclic graph (DAG) structure. Relationships have been inferred between biological entities using the Bayesian network model with high-throughput data from biological systems in diverse fields. However, the scalability of those approaches is seriously restricted because of the huge search space for finding an optimal DAG structure in the process of Bayesian network learning. For this reason, most previous approaches limit the number of target entities or use additional knowledge to restrict the search space. In this paper, we use the hierarchical clustering and order restriction (H-CORE) method for the learning of large Bayesian networks by clustering entities and restricting edge directions between those clusters, with the aim of overcoming the scalability problem and thus making it possible to perform genome-scale Bayesian network analysis without additional biological knowledge. We use simulations to show that H-CORE is much faster than the widely used sparse candidate method, whilst being of comparable quality. We have also applied H-CORE to retrieving gene-to-gene relationships in a biological system (The ‘Rosetta compendium’). By evaluating learned information through literature mining, we demonstrate that H-CORE enables the genome-scale Bayesian analysis of biological systems without any prior knowledge. [Copyright &y& Elsevier]

Abstract: We propose a new support vector data description (SVDD) incorporating the local density of a training data set by introducing a local density degree for each data point. By using a density-induced distance measure based on the degree, we reformulate a conventional SVDD. Experiments with various real data sets show that the proposed method more accurately describes training data sets than the conventional SVDD in all tested cases. [Copyright &y& Elsevier]

Abstract: In this paper, the conventional k-modes-type algorithms for clustering categorical data are extended by representing the clusters of categorical data with k-populations instead of the hard-type centroids used in the conventional algorithms. Use of a population-based centroid representation makes it possible to preserve the uncertainty inherent in data sets as long as possible before actual decisions are made. The k-populations algorithm was found to give markedly better clustering results through various experiments. [Copyright &y& Elsevier]

Abstract: By using a kernel function, data that are not easily separable in the original space can be clustered into homogeneous groups in the implicitly transformed high-dimensional feature space. Kernel k-means algorithms have recently been shown to perform better than conventional k-means algorithms in unsupervised classification. However, few reports have examined the benefits of using a kernel function and the relative merits of the various kernel clustering algorithms with regard to the data distribution. In this study, we reformulated four representative clustering algorithms based on a kernel function and evaluated their performances for various data sets. The results indicate that each kernel clustering algorithm gives markedly better performance than its conventional counterpart for almost all data sets. Of the kernel clustering algorithms studied in the present work, the kernel average linkage algorithm gives the most accurate clustering results. [Copyright &y& Elsevier]

A new cluster validity index is proposed that determines the optimal partition and optimal number of clusters for fuzzy partitions obtained from the fuzzy c-means algorithm. The proposed validity index exploits an overlap measure and a separation measure between clusters. The overlap measure, which indicates the degree of overlap between fuzzy clusters, is obtained by computing an inter-cluster overlap. The separation measure, which indicates the isolation distance between fuzzy clusters, is obtained by computing a distance between fuzzy clusters. A good fuzzy partition is expected to have a low degree of overlap and a larger separation distance. Testing of the proposed index and nine previously formulated indexes on well-known data sets showed the superior effectiveness and reliability of the proposed index in comparison to other indexes. [Copyright &y& Elsevier]

[Display omitted] • Combining LC-MS/MS and CODA/COCONUT predicted propolis-associated immune diseases. • Propolis displayed therapeutic effects against atopic dermatitis. • CODA/COCONUT provided compound-associated genes based on constituents of propolis. • Propolis regulated the expression of target genes predicted by the network analysis. • Active compounds of propolis and their combinatorial effects was discovered. Discovering new bioactivities and identifying active compounds of food materials are major fields of study in food science. However, the process commonly requires extensive experiments and can be technically challenging. In the current study, we employed network biology and cheminformatic approaches to predict new target diseases, active components, and related molecular mechanisms of propolis. Applying UHPLC-MS/MS analysis results of propolis to Context-Oriented Directed Associations (CODA) and Combination-Oriented Natural Product Database with Unified Terminology (COCONUT) systems indicated atopic dermatitis as a novel target disease. Experimental validation using cell- and human tissue-based models confirmed the therapeutic potential of propolis against atopic dermatitis. Moreover, we were able to find the major contributing compounds as well as their combinatorial effects responsible for the bioactivity of propolis. The CODA/COCONUT system also provided compound-associated genes explaining the underlying molecular mechanism of propolis. These results highlight the potential use of big data-driven network biological approaches to aid in analyzing the impact of food constituents at a systematic level. [ABSTRACT FROM AUTHOR]

Summary A destabilized tumor vasculature leads to limited drug delivery, hypoxia, detrimental tumor microenvironment, and even metastasis. We performed a side-by-side comparison of ABTAA (Ang2-Binding and Tie2-Activating Antibody) and ABA (Ang2-Blocking Antibody) in mice with orthotopically implanted glioma, with subcutaneously implanted Lewis lung carcinoma, and with spontaneous mammary cancer. We found that Tie2 activation induced tumor vascular normalization, leading to enhanced blood perfusion and chemotherapeutic drug delivery, markedly lessened lactate acidosis, and reduced tumor growth and metastasis. Moreover, ABTAA favorably altered the immune cell profile within tumors. Together, our findings establish that simultaneous Tie2 activation and Ang2 inhibition form a powerful therapeutic strategy to elicit a favorable tumor microenvironment and enhanced delivery of a chemotherapeutic agent into tumors. [ABSTRACT FROM AUTHOR]

Summary: Current antiangiogenic therapy is limited by its cytostatic nature and systemic side effects. To address these limitations, we have unveiled the role of RhoJ, an endothelial-enriched Rho GTPase, during tumor progression. RhoJ blockade provides a double assault on tumor vessels by both inhibiting tumor angiogenesis and disrupting the preformed tumor vessels through the activation of the RhoA-ROCK (Rho kinase) signaling pathway in tumor endothelial cells, consequently resulting in a functional failure of tumor vasculatures. Moreover, enhanced anticancer effects were observed when RhoJ blockade was employed in concert with a cytotoxic chemotherapeutic agent, angiogenesis-inhibiting agent, or vascular-disrupting agent. These results identify RhoJ blockade as a selective and effective therapeutic strategy for targeting tumor vasculature with minimal side effects. [Copyright &y& Elsevier]

Paeonia lactiflora Pall. is an ethnopharmacological medicine with a long history of human use for treating various inflammatory diseases in many Asian countries. Aim of the study : Duchenne muscular dystrophy (DMD) is an X-linked degenerative muscle disease affecting 1 in 3500 males and is characterized by severe muscle inflammation and a progressive decline in muscle function. This study aimed to elucidate the effects of an ethanol extract of the root of Paeonia lactiflora Pall. (PL) on the muscle function in the muscular dystrophy X-linked (mdx) mouse, the most commonly used animal model of DMD. Male mdx mice and wild-type controls aged 5 weeks were orally treated with PL for 4 weeks. The corticosteroid prednisolone was used as a comparator drug. Muscle strength and motor coordination were assessed via the grip-strength and rotarod tests, respectively. Muscle damage was evaluated via histological examination and assessment of plasma creatine-kinase activity. Proteomic analyses were conducted to identify the muscle proteins whose levels were significantly affected by PL (ProteomeXchange identifier: PXD028886). Muscle and plasma levels of these proteins, and their corresponding mRNAs were measured using western blotting and ELISA, and quantitative reverse transcription–polymerase chain reaction, respectively. The muscle strength and motor coordination of mdx mice were significantly increased by the oral treatment of PL. PL significantly reduced the histological muscle damage and plasma creatine-kinase activity. Proteomic analyses of the muscle showed that PL significantly downregulated the high mobility group box 1 (HMGB1) protein and Toll-like receptor (TLR) 4, thus suppressing the HMGB1–TLR4–NF-κB signaling, in the muscle of mdx mice. Consequently, the muscle levels of proinflammatory cytokines/chemokines, which play crucial roles in inflammation, were downregulated. PL improves the muscle function and reduces the muscle damage in mdx mice via suppressing the HMGB1-TLR4-NF-κB signaling and downregulating proinflammatory cytokines/chemokines. [Display omitted] • Paeonia lactiflora ethanol extract (PL) restores muscle function in mdx mice. • PL reduces muscle damage and plasma creatine kinase activity. • Muscle proteome analysis shows that PL downregulates HMGB1. • PL suppresses the HMGB1-TLR4-NF-κB signaling in the muscle. • PL downregulates cytokine and chemokine expression in the muscle. [ABSTRACT FROM AUTHOR]

The dried root of Paeonia lactiflora Pall. (Radix Paeoniae) has been traditionally used to treat various inflammatory diseases in many Asian countries. Cisplatin is a broad-spectrum anticancer drug used in diverse types of cancer. However, muscle wasting is a common side effect of cisplatin chemotherapy. This study aimed to elucidate the effects of an ethanol extract of the root of Paeonia lactiflora Pall. (Radix Paeoniae, RP) on cisplatin-induced muscle wasting along with its molecular mechanism. C57BL/6 mice were intraperitoneally injected with cisplatin and orally treated with RP. Megestrol acetate was used as a comparator drug. Skeletal muscle mass was measured as the weight of gastrocnemius and quadriceps muscles, and skeletal muscle function was measured by treadmill running time and grip strength. Skeletal muscle tissues were analyzed by RNAseq, western blotting, ELISA, and immunofluorescence microscopy. In mice treated with cisplatin, skeletal muscle mass and skeletal muscle function were significantly reduced. However, oral administration of RP significantly restored skeletal muscle mass and function in the cisplatin-treated mice. In the skeletal muscle tissues of the cisplatin-treated mice, RP downregulated NF-κB signaling and cytokine levels. RP also downregulated muscle-specific ubiquitin E3 ligases, resulting in the restoration of myosin heavy chain (MyHC) and myoblast determination protein (MyoD), which play crucial roles in muscle contraction and muscle differentiation, respectively. RP restored skeletal muscle function and mass in cisplatin-treated mice by restoring the muscle levels of MyHC and MyoD proteins via downregulation of muscle-specific ubiquitin E3 ligases as well as muscle NF-κB signaling and cytokine levels. Image 1 • Paeonia lactiflora extract restores muscle mass/function in cisplatin-treated mice. • It attenuates NF-κB signaling and cytokine levels in skeletal muscle. • It also downregulates muscle-specific ubiquitin E3 ligases, MuRF1 and MAFbx. • It restores myosin heavy chain and MyoD in skeletal muscle. • It may be used to develop therapeutics against chemotherapy-induced muscle wasting. [ABSTRACT FROM AUTHOR]

Abstract: We propose new support vector machines (SVMs) that incorporate the geometric distribution of an input data set by associating each data point with a possibilistic membership, which measures the relative strength of the self class membership. By using a possibilistic distance measure based on the possibilistic membership, we reformulate conventional SVMs in three ways. The proposed methods are shown to have better classification performance than conventional SVMs in various tests. [Copyright &y& Elsevier]