Whole-brain patterns of 1H-magnetic resonance spectroscopy imaging in Alzheimer's disease and dementia with Lewy bodies

Acknowledgements We thank Craig Lambert for his help in processing the MRS data. The study was funded by the Sir Jules Thorn Charitable Trust (grant ref: 05/JTA) and was supported by the National Institute for Health Research (NIHR) Newcastle Biomedical Research Centre and the Biomedical Research Unit in Lewy Body Dementia based at Newcastle upon Tyne Hospitals National Health Service (NHS) Foundation Trust and Newcastle University and the NIHR Biomedical Research Centre and Biomedical Research Unit in Dementia based at Cambridge University Hospitals NHS Foundation Trust and the University of Cambridge.Peer reviewedPublisher PD

Performance Modeling and Analysis of a Massively Parallel DIRECT— Part 1

Modeling and analysis techniques are used to investigate the performance of a massively parallel version of DIRECT, a global search algorithm widely used in multidisciplinary design optimization applications. Several highdimensional benchmark functions and real world problems are used to test the design effectiveness under various problem structures. Theoretical and experimental results are compared for two parallel clusters with different system scale and network connectivity. The present work aims at studying the performance sensitivity to important parameters for problem configurations, parallel schemes, and system settings. The performance metrics include the memory usage, load balancing, parallel efficiency, and scalability. An analytical bounding model is constructed to measure the load balancing performance under different schemes. Additionally, linear regression models are used to characterize two major overhead sources—interprocessor communication and processor idleness, and also applied to the isoefficiency functions in scalability analysis. For a variety of highdimensional problems and large scale systems, the massively parallel design has achieved reasonable performance. The results of the performance study provide guidance for efficient problem and scheme configuration. More importantly, the generalized design considerations and analysis techniques are beneficial for transforming many global search algorithms to become effective large scale parallel optimization tools

Grey and white matter differences in Chronic Fatigue Syndrome : A voxel-based morphometry study

Conflicts of interest and source of funding The authors declare no conflicts of interest. This research was funded by the Medical Research Council (MR/J002712/1). AF is supported by Research Capability Funding from the Newcastle upon Tyne Hospitals NHS Foundation Trust and the Northumberland, Tyne and Wear NHS Foundation Trust.Peer reviewedPublisher PD

Phosphorylation of CLEC-2 is dependent on lipid rafts, actin polymerization,secondary mediators, and Rac

The C-type lectin-like receptor 2 (CLEC-2)activates platelets through Src and Syk tyrosine kinases via a single cytoplasmic YxxL motif known as a hem immunoreceptor tyrosine-based activation motif (hemITAM).Here, we demonstrate using sucrose gradient ultracentrifugation and methyl--cyclodextrin treatment that CLEC-2 translocates to lipid rafts upon ligand engagement and that translocation is essential for hemITAM phosphorylation and signal initiation. HemITAM phosphorylation, but not translocation, is also critically dependent on actin polymerization,Rac1 activation, and release of ADP and thromboxane A2 (TxA2). The role of ADP and TxA2 in mediating hosphorylation is dependent on ligand engagement and rac activation but is independent of platelet aggregation. In contrast,tyrosine phosphorylation of the GPVIFcR -chain ITAM, which has 2 YxxL motifs,is independent of actin polymerization and secondary mediators. These results reveal a unique series of proximal events in CLEC-2 phosphorylation involving actin polymerization, secondary mediators,and Rac activation

Algorithm XXX: VTDIRECT95: Serial and Parallel Codes for the Global Optimization Algorithm DIRECT

VTDIRECT95 is a Fortran 95 implementation of D.R. Jones' deterministic global optimization algorithm called DIRECT, which is widely used in multidisciplinary engineering design, biological science, and physical science applications. The package includes both a serial code and a data-distributed massively parallel code for different problem scales and optimization (exploration vs. exploitation) goals. Dynamic data structures are used to organize local data, handle unpredictable memory requirements, reduce the memory usage, and share the data across multiple processors. The parallel code employs a multilevel functional and data parallelism to boost concurrency and mitigate the data dependency, thus improving the load balancing and scalability. In addition, checkpointing features are integrated into both versions to provide fault tolerance and hot restarts. Important alogrithm modifications and design considerations are discussed regarding data structures, parallel schemes, error handling, and portability. Using several benchmark functions and real-world applications, the software is evaluated on different systems in terms of optimization effectiveness, data structure efficency, parallel performance, and checkpointing overhead. The package organization and usage are also described in detail

Caspase-8 controls the gut response to microbial challenges by Tnf-alpha-dependent and independent pathways

Objectives: Intestinal epithelial cells (IEC) express toll-like receptors (TLR) that facilitate microbial recognition. Stimulation of TLR ligands induces a transient increase in epithelial cell shedding, a mechanism that serves the antibacterial and antiviral host defence of the epithelium and promotes elimination of intracellular pathogens. Although activation of the extrinsic apoptosis pathway has been described during inflammatory shedding, its functional involvement is currently unclear. Design: We investigated the functional involvement of caspase-8 signalling in microbial-induced intestinal cell shedding by injecting Lipopolysaccharide (LPS) to mimic bacterial pathogens and poly(I:C) as a probe for RNA viruses in vivo. Results: TLR stimulation of IEC was associated with a rapid activation of caspase-8 and increased epithelial cell shedding. In mice with an epithelial cell-specific deletion of caspase-8 TLR stimulation caused Rip3-dependent epithelial necroptosis instead of apoptosis. Mortality and tissue damage were more severe in mice in which IECs died by necroptosis than apoptosis. Inhibition of receptor-interacting protein (Rip) kinases rescued the epithelium from TLR-induced gut damage. TLR3-induced necroptosis was directly mediated via TRIF-dependent pathways, independent of Tnf-α and type III interferons, whereas TLR4-induced tissue damage was critically dependent on Tnf-α. Conclusions: Together, our data demonstrate an essential role for caspase-8 in maintaining the gut barrier in response to mucosal pathogens by permitting inflammatory shedding and preventing necroptosis of infected cells. These data suggest that therapeutic strategies targeting the cell death machinery represent a promising new option for the treatment of inflammatory and infective enteropathies

Performance Modeling and Analysis of a Massively Parallel DIRECT— Part 2

Modeling and analysis techniques are used to investigate the performance of a massively parallel version of DIRECT, a global search algorithm widely used in multidisciplinary design optimization applications. Several highdimensional benchmark functions and real world problems are used to test the design effectiveness under various problem structures. In this second part of a twopart work, theoretical and experimental results are compared for two parallel clusters with different system scale and network connectivity. The first part studied performance sensitivity to important parameters for problem configurations and parallel schemes, using performance metrics such as memory usage, load balancing, and parallel efficiency. Here linear regression models are used to characterize two major overhead sources—interprocessor communication and processor idleness—and also applied to the isoefficiency functions in scalability analysis. For a variety of highdimensional problems and large scale systems, the massively parallel design has achieved reasonable performance. The results of the performance study provide guidance for efficient problem and scheme configuration. More importantly, the design considerations and analysis techniques generalize to the transformation of other global search algorithms into effective large scale parallel optimization tools

Using Hierarchical Data Mining to Characterize Performance of Wireless System Configurations

This paper presents a statistical framework for assessing wireless systems performance using hierarchical data mining techniques. We consider WCDMA (wideband code division multiple access) systems with two-branch STTD (space time transmit diversity) and 1/2 rate convolutional coding (forward error correction codes). Monte Carlo simulation estimates the bit error probability (BEP) of the system across a wide range of signal-to-noise ratios (SNRs). A performance database of simulation runs is collected over a targeted space of system configurations. This database is then mined to obtain regions of the configuration space that exhibit acceptable average performance. The shape of the mined regions illustrates the joint influence of configuration parameters on system performance. The role of data mining in this application is to provide explainable and statistically valid design conclusions. The research issue is to define statistically meaningful aggregation of data in a manner that permits efficient and effective data mining algorithms. We achieve a good compromise between these goals and help establish the applicability of data mining for characterizing wireless systems performance

Incremental Information Gain Analysis of Input Attribute Impact on RBF-Kernel SVM Spam Detection

The massive increase of spam is posing a very serious threat to email and SMS, which have become an important means of communication. Not only do spams annoy users, but they also become a security threat. Machine learning techniques have been widely used for spam detection. Email spams can be detected through detecting senders’ behaviour, the contents of an email, subject and source address, etc, while SMS spam detection usually is based on the tokens or features of messages due to short content. However, a comprehensive analysis of email/SMS content may provide cures for users to aware of email/SMS spams. We cannot completely depend on automatic tools to identify all spams. In this paper, we propose an analysis approach based on information entropy and incremental learning to see how various features affect the performance of an RBF-based SVM spam detector, so that to increase our awareness of a spam by sensing the features of a spam. The experiments were carried out on the spambase and SMSSpemCollection databases in UCI machine learning repository. The results show that some features have significant impacts on spam detection, of which users should be aware, and there exists a feature space that achieves Pareto efficiency in True Positive Rate and True Negative Rate