Scott : A method for representing graphs asrooted trees for graph canonization

International audienceGraphs increasingly stand out as an essential data structurein the field of data sciences. To study graphs, or sub-graphs, that char-acterize a set of observations, it is necessary to describe them formally,in order to characterize equivalence relations that make sense in thescope of the considered application domain. Hence we seek to define acanonical graph notation, so that two isomorphic (sub) graphs have thesame canonical form. Such notation could subsequently be used to indexand retrieve graphs or to embed them efficiently in some metric space.Sequential optimized algorithms solving this problem exist, but do notdeal with labeled edges, a situation that occurs in important applicationdomains such as chemistry. We present in this article a new algorithmbased on graph rewriting that provides a general and complete solution tothe graph canonization problem. Although not reported here, the formalproof of the validity of our algorithm has been established. This claim isclearly supported empirically by our experimentation on synthetic com-binatorics as well as natural graphs. Furthermore, our algorithm supportsdistributed implementations, leading to efficient computing perspectives

Screensaver: an open source lab information management system (LIMS) for high throughput screening facilities

<p>Abstract</p> <p>Background</p> <p>Shared-usage high throughput screening (HTS) facilities are becoming more common in academe as large-scale small molecule and genome-scale RNAi screening strategies are adopted for basic research purposes. These shared facilities require a unique informatics infrastructure that must not only provide access to and analysis of screening data, but must also manage the administrative and technical challenges associated with conducting numerous, interleaved screening efforts run by multiple independent research groups.</p> <p>Results</p> <p>We have developed Screensaver, a free, open source, web-based lab information management system (LIMS), to address the informatics needs of our small molecule and RNAi screening facility. Screensaver supports the storage and comparison of screening data sets, as well as the management of information about screens, screeners, libraries, and laboratory work requests. To our knowledge, Screensaver is one of the first applications to support the storage and analysis of data from both genome-scale RNAi screening projects and small molecule screening projects.</p> <p>Conclusions</p> <p>The informatics and administrative needs of an HTS facility may be best managed by a single, integrated, web-accessible application such as Screensaver. Screensaver has proven useful in meeting the requirements of the ICCB-Longwood/NSRB Screening Facility at Harvard Medical School, and has provided similar benefits to other HTS facilities.</p

Re:Chicago

https://via.library.depaul.edu/museum-publications/1012/thumbnail.jp

HelmCoP: An Online Resource for Helminth Functional Genomics and Drug and Vaccine Targets Prioritization

A vast majority of the burden from neglected tropical diseases result from helminth infections (nematodes and platyhelminthes). Parasitic helminthes infect over 2 billion, exerting a high collective burden that rivals high-mortality conditions such as AIDS or malaria, and cause devastation to crops and livestock. The challenges to improve control of parasitic helminth infections are multi-fold and no single category of approaches will meet them all. New information such as helminth genomics, functional genomics and proteomics coupled with innovative bioinformatic approaches provide fundamental molecular information about these parasites, accelerating both basic research as well as development of effective diagnostics, vaccines and new drugs. To facilitate such studies we have developed an online resource, HelmCoP (Helminth Control and Prevention), built by integrating functional, structural and comparative genomic data from plant, animal and human helminthes, to enable researchers to develop strategies for drug, vaccine and pesticide prioritization, while also providing a useful comparative genomics platform. HelmCoP encompasses genomic data from several hosts, including model organisms, along with a comprehensive suite of structural and functional annotations, to assist in comparative analyses and to study host-parasite interactions. The HelmCoP interface, with a sophisticated query engine as a backbone, allows users to search for multi-factorial combinations of properties and serves readily accessible information that will assist in the identification of various genes of interest. HelmCoP is publicly available at: http://www.nematode.net/helmcop.html

Machine learning meets volcano plots: Computational discovery of cross-coupling catalysts

The application of modern machine learning to challenges in atomistic simulation is gaining attraction. We present new machine learning models that can predict the energy of the oxidative addition process between a transition metal complex and a substrate for C-C cross-coupling reactions. In turn, this quantity can be used as a descriptor to estimate the activity of homogeneous catalysts using molecular volcano plots. The versatility of this approach is illustrated for vast libraries of organometallic catalysts based on Pt, Pd, Ni, Cu, Ag, and Au combined with 91 ligands. Out-of-sample machine learning predictions were made on a total of 18 062 compounds leading to 557 catalyst candidates falling into the ideal thermodynamic window. This number was further refined by searching for candidates with an estimated price lower than 10 US$ per mmol. The 37 catalyst finalists are dominated by palladium phosphine ligand combinations but also include the earth abundant transition metal (Cu) with less common ligands. Our results indicate that modern statistical learning techniques can be applied to the computational discovery of readily available and promising catalyst candidates

Iron Behaving Badly: Inappropriate Iron Chelation as a Major Contributor to the Aetiology of Vascular and Other Progressive Inflammatory and Degenerative Diseases

The production of peroxide and superoxide is an inevitable consequence of aerobic metabolism, and while these particular "reactive oxygen species" (ROSs) can exhibit a number of biological effects, they are not of themselves excessively reactive and thus they are not especially damaging at physiological concentrations. However, their reactions with poorly liganded iron species can lead to the catalytic production of the very reactive and dangerous hydroxyl radical, which is exceptionally damaging, and a major cause of chronic inflammation. We review the considerable and wide-ranging evidence for the involvement of this combination of (su)peroxide and poorly liganded iron in a large number of physiological and indeed pathological processes and inflammatory disorders, especially those involving the progressive degradation of cellular and organismal performance. These diseases share a great many similarities and thus might be considered to have a common cause (i.e. iron-catalysed free radical and especially hydroxyl radical generation). The studies reviewed include those focused on a series of cardiovascular, metabolic and neurological diseases, where iron can be found at the sites of plaques and lesions, as well as studies showing the significance of iron to aging and longevity. The effective chelation of iron by natural or synthetic ligands is thus of major physiological (and potentially therapeutic) importance. As systems properties, we need to recognise that physiological observables have multiple molecular causes, and studying them in isolation leads to inconsistent patterns of apparent causality when it is the simultaneous combination of multiple factors that is responsible. This explains, for instance, the decidedly mixed effects of antioxidants that have been observed, etc...Comment: 159 pages, including 9 Figs and 2184 reference

Use of Computed Tomography in the Assessment of Severity of Aortic Valve Stenosis

The workhorse in the diagnosis of aortic stenosis (AS) has been transthoracic echocardiography (TTE) with clear-cut validated threshold values for grading it mild, moderate, or severe. However, up to one-third of patients may present with discordant findings on echo sonogram and may need further evaluation with other imaging modalities such as computed tomography (CT). CT is useful in determining aortic valve area (AVA) by planimetry and outperforms TTE in identifying severe AS in bicuspid aortic valve (BAV), but it is not routinely ordered for those purposes. It has been widely used in helping, determining, and grading the severity of AS by calculating aortic valve calcium (AVC) load with a scoring system. AVC scores of 2000 AU or more for men and 1300 AU for women are highly indicative of severe AS and have been associated with the poor outcomes. AVC score will underestimate AS in a minority of circumstances where the process is driven more by fibrosis than calcification. CT use is limited by its recent adoption into medical practice and, therefore, is still not universally available in every center. It requires additional training for providers and low-dose radiation exposure may be a concern for some patients