Lattice Boltzmann simulations of soft matter systems

This article concerns numerical simulations of the dynamics of particles immersed in a continuum solvent. As prototypical systems, we consider colloidal dispersions of spherical particles and solutions of uncharged polymers. After a brief explanation of the concept of hydrodynamic interactions, we give a general overview over the various simulation methods that have been developed to cope with the resulting computational problems. We then focus on the approach we have developed, which couples a system of particles to a lattice Boltzmann model representing the solvent degrees of freedom. The standard D3Q19 lattice Boltzmann model is derived and explained in depth, followed by a detailed discussion of complementary methods for the coupling of solvent and solute. Colloidal dispersions are best described in terms of extended particles with appropriate boundary conditions at the surfaces, while particles with internal degrees of freedom are easier to simulate as an arrangement of mass points with frictional coupling to the solvent. In both cases, particular care has been taken to simulate thermal fluctuations in a consistent way. The usefulness of this methodology is illustrated by studies from our own research, where the dynamics of colloidal and polymeric systems has been investigated in both equilibrium and nonequilibrium situations.Comment: Review article, submitted to Advances in Polymer Science. 16 figures, 76 page

Multilocus Phylogenetic Study of the Scheffersomyces Yeast Clade and Characterization of the N-Terminal Region of Xylose Reductase Gene

Many of the known xylose-fermenting (X-F) yeasts are placed in the Scheffersomyces clade, a group of ascomycete yeasts that have been isolated from plant tissues and in association with lignicolous insects. We formally recognize fourteen species in this clade based on a maximum likelihood (ML) phylogenetic analysis using a multilocus dataset. This clade is divided into three subclades, each of which exhibits the biochemical ability to ferment cellobiose or xylose. New combinations are made for seven species of Candida in the clade, and three X-F taxa associated with rotted hardwood are described: Scheffersomyces illinoinensis (type strain NRRL Y-48827T  =  CBS 12624), Scheffersomyces quercinus (type strain NRRL Y-48825T  =  CBS 12625), and Scheffersomyces virginianus (type strain NRRL Y-48822T  =  CBS 12626). The new X-F species are distinctive based on their position in the multilocus phylogenetic analysis and biochemical and morphological characters. The molecular characterization of xylose reductase (XR) indicates that the regions surrounding the conserved domain contain mutations that may enhance the performance of the enzyme in X-F yeasts. The phylogenetic reconstruction using XYL1 or RPB1 was identical to the multilocus analysis, and these loci have potential for rapid identification of cryptic species in this clade

A limited-size ensemble of homogeneous CNN/LSTMs for high-performance word classification

The strength of long short-term memory neural networks (LSTMs) that have been applied is more located in handling sequences of variable length than in handling geometric variability of the image patterns. In this paper, an end-to-end convolutional LSTM neural network is used to handle both geometric variation and sequence variability. The best results for LSTMs are often based on large-scale training of an ensemble of network instances. We show that high performances can be reached on a common benchmark set by using proper data augmentation for just five such networks using a proper coding scheme and a proper voting scheme. The networks have similar architectures (convolutional neural network (CNN): five layers, bidirectional LSTM (BiLSTM): three layers followed by a connectionist temporal classification (CTC) processing step). The approach assumes differently scaled input images and different feature map sizes. Three datasets are used: the standard benchmark RIMES dataset (French); a historical handwritten dataset KdK (Dutch); the standard benchmark George Washington (GW) dataset (English). Final performance obtained for the word-recognition test of RIMES was 96.6%, a clear improvement over other state-of-the-art approaches which did not use a pre-trained network. On the KdK and GW datasets, our approach also shows good results. The proposed approach is deployed in the Monk search engine for historical-handwriting collections

Single-cell analysis tools for drug discovery and development

The genetic, functional or compositional heterogeneity of healthy and diseased tissues presents major challenges in drug discovery and development. Such heterogeneity hinders the design of accurate disease models and can confound the interpretation of biomarker levels and of patient responses to specific therapies. The complex nature of virtually all tissues has motivated the development of tools for single-cell genomic, transcriptomic and multiplex proteomic analyses. Here, we review these tools and assess their advantages and limitations. Emerging applications of single cell analysis tools in drug discovery and development, particularly in the field of oncology, are discussed

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

Enhancing cellular uptake of activable cell-penetrating peptide–doxorubicin conjugate by enzymatic cleavage

Nian-Qiu Shi, Wei Gao, Bai Xiang, Xian-Rong QiDepartment of Pharmaceutics, School of Pharmaceutical Sciences, Peking University, Beijing, People's Republic of ChinaAbstract: The use of activable cell-penetrating peptides (ACPPs) as molecular imaging probes is a promising new approach for the visualization of enzymes. The cell-penetrating function of a polycationic cell-penetrating peptide (CPP) is efficiently blocked by intramolecular electrostatic interactions with a polyanionic peptide. Proteolysis of a proteinase-sensitive substrate present between the CPP and polyanionic peptide affords dissociation of both domains and enables the activated CPP to enter cells. This ACPP strategy could also be used to modify antitumor agents for tumor-targeting therapy. Here, we aimed to develop a conjugate of ACPP with antitumor drug doxorubicin (DOX) sensitive to matrix metalloproteinase-2 and -9 (MMP-2/9) for tumor-targeting therapy purposes. The ACPP-DOX conjugate was successfully synthesized. Enzymatic cleavage of ACPP-DOX conjugate by matrix metalloproteinase (MMP)-2/9 indicated that the activation of ACPP-DOX occurred in an enzyme concentration–dependent manner. Flow cytometry and laser confocal microscope studies revealed that the cellular uptake of ACPP-DOX was enhanced after enzymatic-triggered activation and was higher in HT-1080 cells (overexpressed MMPs) than in MCF-7 cells (under-expressed MMPs). The antiproliferative assay showed that ACPP had little toxicity and that ACPP-DOX effectively inhibited HT-1080 cell proliferation. These experiments revealed that the ACPP-DOX conjugate could be triggered by MMP-2/9, which enabled the activated CPP-DOX to enter cells. ACPP-DOX conjugate may be a potential prodrug delivery system used to carry antitumor drugs for MMP-related tumor therapy.Keywords: activable cell-penetrating peptide, matrix metalloproteinase, proteinase-sensitive substrate, cellular uptake, antiproliferative, enzymatic cleavage, tumor extracellular environmen