Big Data and Analysis of Data Transfers for International Research Networks Using NetSage

Modern science is increasingly data-driven and collaborative in nature. Many scientific disciplines, including genomics, high-energy physics, astronomy, and atmospheric science, produce petabytes of data that must be shared with collaborators all over the world. The National Science Foundation-supported International Research Network Connection (IRNC) links have been essential to enabling this collaboration, but as data sharing has increased, so has the amount of information being collected to understand network performance. New capabilities to measure and analyze the performance of international wide-area networks are essential to ensure end-users are able to take full advantage of such infrastructure for their big data applications. NetSage is a project to develop a unified, open, privacy-aware network measurement, and visualization service to address the needs of monitoring today's high-speed international research networks. NetSage collects data on both backbone links and exchange points, which can be as much as 1Tb per month. This puts a significant strain on hardware, not only in terms storage needs to hold multi-year historical data, but also in terms of processor and memory needs to analyze the data to understand network behaviors. This paper addresses the basic NetSage architecture, its current data collection and archiving approach, and details the constraints of dealing with this big data problem of handling vast amounts of monitoring data, while providing useful, extensible visualization to end users

Superspreading: Mechanisms and Molecular Design

The intriguing ability of certain surfactant molecules to drive the superspreading of liquids to complete wetting on hydrophobic substrates is central to numerous applications that range from coating flow technology to enhanced oil recovery. Despite significant experimental efforts, the precise mechanisms underlying superspreading remain unknown to date. Here, we isolate these mechanisms by analyzing coarse-grained molecular dynamics simulations of surfactant molecules of varying molecular architecture and substrate affinity. We observe that for superspreading to occur, two key conditions must be simultaneously satisfied: the adsorption of surfactants from the liquid–vapor surface onto the three-phase contact line augmented by local bilayer formation. Crucially, this must be coordinated with the rapid replenishment of liquid–vapor and solid–liquid interfaces with surfactants from the interior of the droplet. This article also highlights and explores the differences between superspreading and conventional surfactants, paving the way for the design of molecular architectures tailored specifically for applications that rely on the control of wetting

The Ubiquitin-Proteasome System: Potential Therapeutic Targets for Alzheimer’s Disease and Spinal Cord Injury

The ubiquitin-proteasome system (UPS) is a crucial protein degradation system in eukaryotes. Herein, we will review advances in the understanding of the role of several proteins of the UPS in Alzheimer’s disease (AD) and functional recovery after spinal cord injury (SCI). The UPS consists of many factors that include E3 ubiquitin ligases, ubiquitin hydrolases, ubiquitin and ubiquitin-like molecules, and the proteasome itself. An extensive body of work links UPS dysfunction with AD pathogenesis and progression. More recently, the UPS has been shown to have vital roles in recovery of function after SCI. The ubiquitin hydrolase (Uch-L1) has been proposed to increase cellular levels of mono-ubiquitin and hence to increase rates of protein turnover by the UPS. A low Uch-L1 level has been linked with Ab accumulation in AD and reduced neuroregeneration after SCI. One likely mechanism for these beneficial effects of Uch-L1 is reduced turnover of the PKA regulatory subunit and consequently, reduced signaling via CREB. The neuron-specific F-box protein Fbx2 ubiquitinates b-secretase thus targeting it for proteasomal degradation and reducing generation of Ab. Both Uch-L1 and Fbx2 improve synaptic plasticity and cognitive function in mouse AD models. The role of Fbx2 after SCI has not been examined, but abolishing ß-secretase reduces neuronal recovery after SCI, associated with reduced myelination. UBB+1, which arises through a frame-shift mutation in the ubiquitin gene that adds 19 amino acids to the C-terminus of ubiquitin, inhibits proteasomal function and is associated with increased neurofibrillary tangles in patients with AD, Pick’s disease and Down’s syndrome. These advances in understanding of the roles of the UPS in AD and SCI raise new questions but, also, identify attractive and exciting targets for potential, future therapeutic interventions

Dynamic Dystroglycan Complexes Mediate Cell Entry of Lassa Virus.

Recognition of functional receptors by viruses is a key determinant for their host range, tissue tropism, and disease potential. The highly pathogenic Lassa virus (LASV) currently represents one of the most important emerging pathogens. The major cellular receptor for LASV in human cells is the ubiquitously expressed and evolutionary highly conserved extracellular matrix receptor dystroglycan (DG). In the host, DG interacts with many cellular proteins in a tissue-specific manner. The resulting distinct supramolecular complexes likely represent the functional units for viral entry, and preexisting protein-protein interactions may critically influence DG's function in productive viral entry. Using an unbiased shotgun proteomic approach, we define the largely unknown molecular composition of DG complexes present in highly susceptible epithelial cells that represent important targets for LASV during viral transmission. We further show that the specific composition of cellular DG complexes can affect DG's function in receptor-mediated endocytosis of the virus. Under steady-state conditions, epithelial DG complexes underwent rapid turnover via an endocytic pathway that shared some characteristics with DG-mediated LASV entry. However, compared to steady-state uptake of DG, LASV entry via DG occurred faster and critically depended on additional signaling by receptor tyrosine kinases and the downstream effector p21-activating kinase. In sum, we show that the specific molecular composition of DG complexes in susceptible cells is a determinant for productive virus entry and that the pathogen can manipulate the existing DG-linked endocytic pathway. This highlights another level of complexity of virus-receptor interaction and provides possible cellular targets for therapeutic antiviral intervention.IMPORTANCE Recognition of cellular receptors allows emerging viruses to break species barriers and is an important determinant for their disease potential. Many virus receptors have complex tissue-specific interactomes, and preexisting protein-protein interactions may influence their function. Combining shotgun proteomics with a biochemical approach, we characterize the molecular composition of the functional receptor complexes used by the highly pathogenic Lassa virus (LASV) to invade susceptible human cells. We show that the specific composition of the receptor complexes affects productive entry of the virus, providing proof-of-concept. In uninfected cells, these functional receptor complexes undergo dynamic turnover involving an endocytic pathway that shares some characteristics with viral entry. However, steady-state receptor uptake and virus endocytosis critically differ in kinetics and underlying signaling, indicating that the pathogen can manipulate the receptor complex according to its needs. Our study highlights a remarkable complexity of LASV-receptor interaction and identifies possible targets for therapeutic antiviral intervention

Genetic Variation Shapes Protein Networks Mainly through Non-transcriptional Mechanisms

Variation in the levels of co-regulated proteins that function within networks in an outbred yeast population is not driven by variation in the corresponding transcripts

International Veterinary Epilepsy Task Force consensus proposal: Medical treatment of canine epilepsy in Europe

In Europe, the number of antiepileptic drugs (AEDs) licensed for dogs has grown considerably over the last years. Nevertheless, the same questions remain, which include, 1) when to start treatment, 2) which drug is best used initially, 3) which adjunctive AED can be advised if treatment with the initial drug is unsatisfactory, and 4) when treatment changes should be considered. In this consensus proposal, an overview is given on the aim of AED treatment, when to start long-term treatment in canine epilepsy and which veterinary AEDs are currently in use for dogs. The consensus proposal for drug treatment protocols, 1) is based on current published evidence-based literature, 2) considers the current legal framework of the cascade regulation for the prescription of veterinary drugs in Europe, and 3) reflects the authors’ experience. With this paper it is aimed to provide a consensus for the management of canine idiopathic epilepsy. Furthermore, for the management of structural epilepsy AEDs are inevitable in addition to treating the underlying cause, if possible

Regulation of human intestinal T-cell responses by type 1 interferon-STAT1 signaling is disrupted in inflammatory bowel disease

This work was supported by a research fellowship grant from the Crohn’s and Colitis in Childhood Research Association (CICRA) and a small project grant from Crohn’s and Colitis UK (CCUK). We would like to acknowledge Professor Ian Sanderson, who helped with the initial design of this work, and provided important support throughout. We would also like to thank Dr Gary Warne for his advice and assistance in the use of the sorting by flow cytometry. We would also like to thank Dr Raj Lahiri and Professor Graham Foster for the kind gift of the primers for the ISGs (2’5’ OAS and MxA)

Peak intensity prediction in MALDI-TOF mass spectrometry: A machine learning study to support quantitative proteomics

Timm W, Scherbart A, Boecker S, Kohlbacher O, Nattkemper TW. Peak intensity prediction in MALDI-TOF mass spectrometry: A machine learning study to support quantitative proteomics. BMC Bioinformatics. 2008;9(1):443.Background: Mass spectrometry is a key technique in proteomics and can be used to analyze complex samples quickly. One key problem with the mass spectrometric analysis of peptides and proteins, however, is the fact that absolute quantification is severely hampered by the unclear relationship between the observed peak intensity and the peptide concentration in the sample. While there are numerous approaches to circumvent this problem experimentally (e. g. labeling techniques), reliable prediction of the peak intensities from peptide sequences could provide a peptide-specific correction factor. Thus, it would be a valuable tool towards label-free absolute quantification. Results: In this work we present machine learning techniques for peak intensity prediction for MALDI mass spectra. Features encoding the peptides' physico-chemical properties as well as string-based features were extracted. A feature subset was obtained from multiple forward feature selections on the extracted features. Based on these features, two advanced machine learning methods (support vector regression and local linear maps) are shown to yield good results for this problem (Pearson correlation of 0.68 in a ten-fold cross validation). Conclusion: The techniques presented here are a useful first step going beyond the binary prediction of proteotypic peptides towards a more quantitative prediction of peak intensities. These predictions in turn will turn out to be beneficial for mass spectrometry-based quantitative proteomics