Systems biology and the virtual physiological human

The virtual physiological human (VPH) initiative is intended to support the development of patient‐specific computer models and their application in personalised and predictive healthcare. The VPH, a core target of the European Commission's 7th Framework Programme, will serve as a ‘methodological and technological framework that, once established, will enable collaborative investigation of the human body as a single complex system’ (http://www.europhysiome.org/roadmap/). As such, the VPH initiative constitutes an integral part of the international Physiome Project (http://www.physiome.org.nz/), a worldwide public domain effort to develop a computational framework for the quantitative description of biological processes in living systems across all relevant levels of structural and functional integration, from molecule to organism, including the human (Kohl et al, 2000; Bassingthwaighte et al, 2009). So, what is the connection between this grand challenge and systems biology? To explore this, we must first agree on what we take systems biology to mean

Tick parasitism classification from noisy medical records

Much of the health information in the medical domain comes in the form of clinical narratives. The rich semantic information contained in these notes can be modeled to make inferences that assist the decision making process for medical practitioners, which is particularly important under time and resource constraints. However, the creation of such assistive tools is made difficult given the ubiquity of misspellings, unsegmented words and morphologically complex or rare medical terms. This reduces the coverage of vocabulary terms present in commonly used pretrained distributed word representations that are passed as input to parametric models that makes such predictions. This paper presents an ensemble architecture that combines indomain and general word embeddings to overcome these challenges, showing best performance on a binary classification task when compared to various other baselines. We demonstrate our approach in the context of the veterinary domain for the task of identifying tick parasitism from small animals. The best model shows 84.29% test accuracy, showing some improvement over models, which only use pretrained embeddings that are not specifically trained for the medical sub-domain of interest

Mitochondrial haplotypes reveal low diversity and restricted connectivity in the critically endangered batoid population of a Marine Protected Area

ACKNOWLEDGEMENTS This study was supported by NatureScot, Scottish Government project SP02B, a Heredity Fieldwork Grant of the Genetics Society, and Save Our Seas Foundation project SOSF 470. We would like to thank Leigh Taylor, Ronnie Campbell and Roger Eaton for skippering the sampling charters in the Marine Protected Area and all anglers who provided skate recapture data. Thanks to Fenella Wood and Danielle Sloan for assisting on charter trips. Further, thanks go to Marine Scotland Science (Francis Neat), the Centre for Environment Fisheries and Aquaculture Science (Vicky Bendall and Stewart Hetherington), and the University of St Andrews for providing tissue samples and Lauren Smith and Dan Wise for contributing samples of egg cases.Peer reviewedPublisher PD

Cosmogenic neutron production at the Sudbury Neutrino Observatory

Neutrons produced in nuclear interactions initiated by cosmic-ray muons present an irreducible background to many rare-event searches, even in detectors located deep underground. Models for the production of these neutrons have been tested against previous experimental data, but the extrapolation to deeper sites is not well understood. Here we report results from an analysis of cosmogenically produced neutrons at the Sudbury Neutrino Observatory. A specific set of observables are presented, which can be used to benchmark the validity of geant4 physics models. In addition, the cosmogenic neutron yield, in units of 10-4 cm2/(g·μ), is measured to be 7.28±0.09(stat)-1.12+1.59(syst) in pure heavy water and 7.30±0.07(stat)-1.02+1.40(syst) in NaCl-loaded heavy water. These results provide unique insights into this potential background source for experiments at SNOLAB

Steel and bone: Mesoscale modeling and middle-out strategies in physics and biology

Mesoscale modeling is often considered merely as a practical strategy used when information on lower-scale details is lacking, or when there is a need to make models cognitively or computationally tractable. Without dismissing the importance of practical constraints for modeling choices, we argue that mesoscale models should not just be considered as abbreviations or placeholders for more “complete” models. Because many systems exhibit different behaviors at various spatial and temporal scales, bottom-up approaches are almost always doomed to fail. Mesoscale models capture aspects of multi-scale systems that cannot be parameterized by simple averaging of lower-scale details. To understand the behavior of multi-scale systems, it is essential to identify mesoscale parameters that “code for” lower-scale details in a way that relate phenomena intermediate between microscopic and macroscopic features. We illustrate this point using examples of modeling of multi-scale systems in materials science (steel) and biology (bone), where identification of material parameters such as stiffness or strain is a central step. The examples illustrate important aspects of a so-called “middle-out” modeling strategy. Rather than attempting to model the system bottom-up, one starts at intermediate (mesoscopic) scales where systems exhibit behaviors distinct from those at the atomic and continuum scales. One then seeks to upscale and downscale to gain a more complete understanding of the multi-scale systems. The cases highlight how parameterization of lower-scale details not only enables tractable modeling but is also central to understanding functional and organizational features of multi-scale systems

Potential therapeutic implications of new insights into respiratory syncytial virus disease

Viral bronchiolitis is the most common cause of hospitalization in infants under 6 months of age, and 70% of all cases of bronchiolitis are caused by respiratory syncytial virus (RSV). Early RSV infection is associated with respiratory problems such as asthma and wheezing later in life. RSV infection is usually spread by contaminated secretions and infects the upper then lower respiratory tracts. Infected cells release proinflammatory cytokines and chemokines, including IL-1, tumor necrosis factor-α, IL-6, and IL-8. These activate other cells and recruit inflammatory cells, including macrophages, neutrophils, eosinophils, and T lymphocytes, into the airway wall and surrounding tissues. The pattern of cytokine production by T lymphocytes can be biased toward 'T-helper-1' or 'T-helper-2' cytokines, depending on the local immunologic environment, infection history, and host genetics. T-helper-1 responses are generally efficient in antiviral defense, but young infants have an inherent bias toward T-helper-2 responses. The ideal intervention for RSV infection would be preventive, but the options are currently limited. Vaccines based on protein subunits, live attenuated strains of RSV, DNA vaccines, and synthetic peptides are being developed; passive antibody therapy is at present impractical in otherwise healthy children. Effective vaccines for use in neonates continue to be elusive but simply delaying infection beyond the first 6 months of life might reduce the delayed morbidity associated with infantile disease