Bioinformatics tools for analysing viral genomic data

The field of viral genomics and bioinformatics is experiencing a strong resurgence due to high-throughput sequencing (HTS) technology, which enables the rapid and cost-effective sequencing and subsequent assembly of large numbers of viral genomes. In addition, the unprecedented power of HTS technologies has enabled the analysis of intra-host viral diversity and quasispecies dynamics in relation to important biological questions on viral transmission, vaccine resistance and host jumping. HTS also enables the rapid identification of both known and potentially new viruses from field and clinical samples, thus adding new tools to the fields of viral discovery and metagenomics. Bioinformatics has been central to the rise of HTS applications because new algorithms and software tools are continually needed to process and analyse the large, complex datasets generated in this rapidly evolving area. In this paper, the authors give a brief overview of the main bioinformatics tools available for viral genomic research, with a particular emphasis on HTS technologies and their main applications. They summarise the major steps in various HTS analyses, starting with quality control of raw reads and encompassing activities ranging from consensus and de novo genome assembly to variant calling and metagenomics, as well as RNA sequencing

Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing

Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now demonstrated unprecedented energy-efficiency through a new chip architecture based on spiking neurons, low precision synapses, and a scalable communication network. Here, we demonstrate that neuromorphic computing, despite its novel architectural primitives, can implement deep convolution networks that i) approach state-of-the-art classification accuracy across 8 standard datasets, encompassing vision and speech, ii) perform inference while preserving the hardware's underlying energy-efficiency and high throughput, running on the aforementioned datasets at between 1200 and 2600 frames per second and using between 25 and 275 mW (effectively > 6000 frames / sec / W) and iii) can be specified and trained using backpropagation with the same ease-of-use as contemporary deep learning. For the first time, the algorithmic power of deep learning can be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step closer.Comment: 7 pages, 6 figure

Modeling Life as Cognitive Info-Computation

This article presents a naturalist approach to cognition understood as a network of info-computational, autopoietic processes in living systems. It provides a conceptual framework for the unified view of cognition as evolved from the simplest to the most complex organisms, based on new empirical and theoretical results. It addresses three fundamental questions: what cognition is, how cognition works and what cognition does at different levels of complexity of living organisms. By explicating the info-computational character of cognition, its evolution, agent-dependency and generative mechanisms we can better understand its life-sustaining and life-propagating role. The info-computational approach contributes to rethinking cognition as a process of natural computation in living beings that can be applied for cognitive computation in artificial systems.Comment: Manuscript submitted to Computability in Europe CiE 201

New approaches to measuring anthelminthic drug efficacy: parasitological responses of childhood schistosome infections to treatment with praziquantel

By 2020, the global health community aims to control and eliminate human helminthiases, including schistosomiasis in selected African countries, principally by preventive chemotherapy (PCT) through mass drug administration (MDA) of anthelminthics. Quantitative monitoring of anthelminthic responses is crucial for promptly detecting changes in efficacy, potentially indicative of emerging drug resistance. Statistical models offer a powerful means to delineate and compare efficacy among individuals, among groups of individuals and among populations.; We illustrate a variety of statistical frameworks that offer different levels of inference by analysing data from nine previous studies on egg counts collected from African children before and after administration of praziquantel.; We quantify responses to praziquantel as egg reduction rates (ERRs), using different frameworks to estimate ERRs among population strata, as average responses, and within strata, as individual responses. We compare our model-based average ERRs to corresponding model-free estimates, using as reference the World Health Organization (WHO) 90 % threshold of optimal efficacy. We estimate distributions of individual responses and summarize the variation among these responses as the fraction of ERRs falling below the WHO threshold.; Generic models for evaluating responses to anthelminthics deepen our understanding of variation among populations, sub-populations and individuals. We discuss the future application of statistical modelling approaches for monitoring and evaluation of PCT programmes targeting human helminthiases in the context of the WHO 2020 control and elimination goals

Influence of wiring cost on the large-scale architecture of human cortical connectivity

In the past two decades some fundamental properties of cortical connectivity have been discovered: small-world structure, pronounced hierarchical and modular organisation, and strong core and rich-club structures. A common assumption when interpreting results of this kind is that the observed structural properties are present to enable the brain's function. However, the brain is also embedded into the limited space of the skull and its wiring has associated developmental and metabolic costs. These basic physical and economic aspects place separate, often conflicting, constraints on the brain's connectivity, which must be characterized in order to understand the true relationship between brain structure and function. To address this challenge, here we ask which, and to what extent, aspects of the structural organisation of the brain are conserved if we preserve specific spatial and topological properties of the brain but otherwise randomise its connectivity. We perform a comparative analysis of a connectivity map of the cortical connectome both on high- and low-resolutions utilising three different types of surrogate networks: spatially unconstrained (‘random’), connection length preserving (‘spatial’), and connection length optimised (‘reduced’) surrogates. We find that unconstrained randomisation markedly diminishes all investigated architectural properties of cortical connectivity. By contrast, spatial and reduced surrogates largely preserve most properties and, interestingly, often more so in the reduced surrogates. Specifically, our results suggest that the cortical network is less tightly integrated than its spatial constraints would allow, but more strongly segregated than its spatial constraints would necessitate. We additionally find that hierarchical organisation and rich-club structure of the cortical connectivity are largely preserved in spatial and reduced surrogates and hence may be partially attributable to cortical wiring constraints. In contrast, the high modularity and strong s-core of the high-resolution cortical network are significantly stronger than in the surrogates, underlining their potential functional relevance in the brain

Rh-POP Pincer Xantphos Complexes for C-S and C-H Activation. Implications for Carbothiolation Catalysis

The neutral Rh­(I)–Xantphos complex [Rh­(κ³-_P,O,P-Xantphos)­Cl]_<i>n</i>, <b>4</b>, and cationic Rh­(III) [Rh­(κ³-_P,O,P-Xantphos)­(H)₂]­[BAr^F₄], <b>2a</b>, and [Rh­(κ³-_P,O,P-Xantphos-3,5-C₆H₃(CF₃)₂)­(H)₂]­[BAr^F₄], <b>2b</b>, are described [Ar^F = 3,5-(CF₃)₂C₆H₃; Xantphos = 4,5-bis­(diphenylphosphino)-9,9-dimethylxanthene; Xantphos-3,5-C₆H₃(CF₃)₂ = 9,9-dimethylxanthene-4,5-bis­(bis­(3,5-bis­(trifluoromethyl)­phenyl)­phosphine]. A solid-state structure of <b>2b</b> isolated from C₆H₅Cl solution shows a κ¹-chlorobenzene adduct, [Rh­(κ³-_P,O,P-Xantphos-3,5-C₆H₃(CF₃)₂)­(H)₂(κ¹-ClC₆H₅)]­[BAr^F₄], <b>3</b>. Addition of H₂ to <b>4</b> affords, crystallographically characterized, [Rh­(κ³-_P,O,P-Xantphos)­(H)₂Cl], <b>5</b>. Addition of diphenyl acetylene to <b>2a</b> results in the formation of the C–H activated metallacyclopentadiene [Rh­(κ³-_P,O,P-Xantphos)­(ClCH₂Cl)­(σ,σ-(C₆H₄)­C­(H)CPh)]­[BAr^F₄], <b>7</b>, a rare example of a crystallographically characterized Rh–dichloromethane complex, alongside the Rh­(I) complex <i>mer</i>-[Rh­(κ³-_P,O,P-Xantphos)­(η²-PhCCPh)]­[BAr^F₄], <b>6</b>. Halide abstraction from [Rh­(κ³-_P,O,P-Xantphos)­Cl]_<i>n</i> in the presence of diphenylacetylene affords <b>6</b> as the only product, which in the solid state shows that the alkyne binds perpendicular to the κ³-POP Xantphos ligand plane. This complex acts as a latent source of the [Rh­(κ³-_P,O,P-Xantphos)]⁺ fragment and facilitates <i>ortho</i>-directed C–S activation in a number of 2-arylsulfides to give <i>mer</i>-[Rh­(κ³-_P,O,P-Xantphos)­(σ,κ¹-Ar)­(SMe)]­[BAr^F₄] (Ar = C₆H₄COMe, <b>8</b>; C₆H₄(CO)­OMe, <b>9</b>; C₆H₄NO₂, <b>10</b>; C₆H₄CNCH₂CH₂O, <b>11</b>; C₆H₄C₅H₄N, <b>12</b>). Similar C–S bond cleavage is observed with allyl sulfide, to give <i>fac</i>-[Rh­(κ³-_P,O,P-Xantphos)­(η³-C₃H₅)­(SPh)]­[BAr^F₄], <b>13</b>. These products of C–S activation have been crystallographically characterized. For <b>8</b> in situ monitoring of the reaction by NMR spectroscopy reveals the initial formation of <i>fac</i>-κ³-<b>8</b>, which then proceeds to isomerize to the <i>mer</i>-isomer. With the <i>para</i>-ketone aryl sulfide, 4-SMeC ₆H₄COMe, C–H activation <i>ortho</i> to the ketone occurs to give <i>mer</i>-[Rh­(κ³-_P,O,P-Xantphos)­(σ,κ¹-4-(COMe)­C₆H₃SMe)­(H)]­[BAr^F₄], <b>14</b>. The temporal evolution of carbothiolation catalysis using <i>mer</i>-κ³-<b>8</b>, and phenyl acetylene and 2-(methylthio)­acetophenone substrates shows initial fast catalysis and then a considerably slower evolution of the product. We suggest that the initially formed <i>fac</i>-isomer of the C–S activation product is considerably more active than the <i>mer</i>-isomer (i.e., <i>mer</i>-<b>8</b>), the latter of which is formed rapidly by isomerization, and this accounts for the observed difference in rates. A likely mechanism is proposed based upon these data

A survey on feature weighting based K-Means algorithms

This is a pre-copyedited, author-produced PDF of an article accepted for publication in Journal of Classification [de Amorim, R. C., 'A survey on feature weighting based K-Means algorithms', Journal of Classification, Vol. 33(2): 210-242, August 25, 2016]. Subject to embargo. Embargo end date: 25 August 2017. The final publication is available at Springer via http://dx.doi.org/10.1007/s00357-016-9208-4 © Classification Society of North America 2016In a real-world data set there is always the possibility, rather high in our opinion, that different features may have different degrees of relevance. Most machine learning algorithms deal with this fact by either selecting or deselecting features in the data preprocessing phase. However, we maintain that even among relevant features there may be different degrees of relevance, and this should be taken into account during the clustering process. With over 50 years of history, K-Means is arguably the most popular partitional clustering algorithm there is. The first K-Means based clustering algorithm to compute feature weights was designed just over 30 years ago. Various such algorithms have been designed since but there has not been, to our knowledge, a survey integrating empirical evidence of cluster recovery ability, common flaws, and possible directions for future research. This paper elaborates on the concept of feature weighting and addresses these issues by critically analysing some of the most popular, or innovative, feature weighting mechanisms based in K-Means.Peer reviewedFinal Accepted Versio

Implementation of Olfactory Bulb Glomerular-Layer Computations in a Digital Neurosynaptic Core

We present a biomimetic system that captures essential functional properties of the glomerular layer of the mammalian olfactory bulb, specifically including its capacity to decorrelate similar odor representations without foreknowledge of the statistical distributions of analyte features. Our system is based on a digital neuromorphic chip consisting of 256 leaky-integrate-and-fire neurons, 1024 × 256 crossbar synapses, and address-event representation communication circuits. The neural circuits configured in the chip reflect established connections among mitral cells, periglomerular cells, external tufted cells, and superficial short-axon cells within the olfactory bulb, and accept input from convergent sets of sensors configured as olfactory sensory neurons. This configuration generates functional transformations comparable to those observed in the glomerular layer of the mammalian olfactory bulb. Our circuits, consuming only 45 pJ of active power per spike with a power supply of 0.85 V, can be used as the first stage of processing in low-power artificial chemical sensing devices inspired by natural olfactory systems