Equine herpesvirus-2 E10 gene product, but not its cellular homologue, activates NF-kappaB transcription factor and c-Jun N-terminal kinase.

We have previously reported on the death effector domain containing E8 gene product from equine herpesvirus-2, designated FLICE inhibitory protein (v-FLIP), and on its cellular homologue, c-FLIP, which inhibit the activation of caspase-8 by death receptors. Here we report on the structure and function of the E10 gene product of equine herpesvirus-2, designated v-CARMEN, and on its cellular homologue, c-CARMEN, which contain a caspase-recruiting domain (CARD) motif. c-CARMEN is highly homologous to the viral protein in its N-terminal CARD motif but differs in its C-terminal extension. v-CARMEN and c-CARMEN interact directly in a CARD-dependent manner yet reveal different binding specificities toward members of the tumor necrosis factor receptor-associated factor (TRAF) family. v-CARMEN binds to TRAF6 and weakly to TRAF3 and, upon overexpression, potently induces the c-Jun N-terminal kinase (JNK), p38, and nuclear factor (NF)-kappaB transcriptional pathways. c-CARMEN or truncated versions thereof do not appear to induce JNK and NF-kappaB activation by themselves, nor do they affect the JNK and NF-kappaB activating potential of v-CARMEN. Thus, in contrast to the cellular homologue, v-CARMEN may have additional properties in its unique C terminus that allow for an autonomous activator effect on NF-kappaB and JNK. Through activation of NF-kappaB, v-CARMEN may regulate the expression of the cellular and viral genes important for viral replication

Generic, Extensible, Configurable Push-Pull Framework for Large-Scale Science Missions

The push-pull framework was developed in hopes that an infrastructure would be created that could literally connect to any given remote site, and (given a set of restrictions) download files from that remote site based on those restrictions. The Cataloging and Archiving Service (CAS) has recently been re-architected and re-factored in its canonical services, including file management, workflow management, and resource management. Additionally, a generic CAS Crawling Framework was built based on motivation from Apache s open-source search engine project called Nutch. Nutch is an Apache effort to provide search engine services (akin to Google), including crawling, parsing, content analysis, and indexing. It has produced several stable software releases, and is currently used in production services at companies such as Yahoo, and at NASA's Planetary Data System. The CAS Crawling Framework supports many of the Nutch Crawler's generic services, including metadata extraction, crawling, and ingestion. However, one service that was not ported over from Nutch is a generic protocol layer service that allows the Nutch crawler to obtain content using protocol plug-ins that download content using implementations of remote protocols, such as HTTP, FTP, WinNT file system, HTTPS, etc. Such a generic protocol layer would greatly aid in the CAS Crawling Framework, as the layer would allow the framework to generically obtain content (i.e., data products) from remote sites using protocols such as FTP and others. Augmented with this capability, the Orbiting Carbon Observatory (OCO) and NPP (NPOESS Preparatory Project) Sounder PEATE (Product Evaluation and Analysis Tools Elements) would be provided with an infrastructure to support generic FTP-based pull access to remote data products, obviating the need for any specialized software outside of the context of their existing process control systems. This extensible configurable framework was created in Java, and allows the use of different underlying communication middleware (at present, both XMLRPC, and RMI). In addition, the framework is entirely suitable in a multi-mission environment and is supporting both NPP Sounder PEATE and the OCO Mission. Both systems involve tasks such as high-throughput job processing, terabyte-scale data management, and science computing facilities. NPP Sounder PEATE is already using the push-pull framework to accept hundreds of gigabytes of IASI (infrared atmospheric sounding interferometer) data, and is in preparation to accept CRIMS (Cross-track Infrared Microwave Sounding Suite) data. OCO will leverage the framework to download MODIS, CloudSat, and other ancillary data products for use in the high-performance Level 2 Science Algorithm. The National Cancer Institute is also evaluating the framework for use in sharing and disseminating cancer research data through its Early Detection Research Network (EDRN)

Inhibition of death receptor signals by cellular FLIP.

The widely expressed protein Fas is a member of the tumour necrosis factor receptor family which can trigger apoptosis. However, Fas surface expression does not necessarily render cells susceptible to Fas ligand-induced death signals, indicating that inhibitors of the apoptosis-signalling pathway must exist. Here we report the characterization of an inhibitor of apoptosis, designated FLIP (for FLICE-inhibitory protein), which is predominantly expressed in muscle and lymphoid tissues. The short form, FLIPs, contains two death effector domains and is structurally related to the viral FLIP inhibitors of apoptosis, whereas the long form, FLIP(L), contains in addition a caspase-like domain in which the active-centre cysteine residue is substituted by a tyrosine residue. FLIPs and FLIP(L) interact with the adaptor protein FADD and the protease FLICE, and potently inhibit apoptosis induced by all known human death receptors. FLIP(L) is expressed during the early stage of T-cell activation, but disappears when T cells become susceptible to Fas ligand-mediated apoptosis. High levels of FLIP(L) protein are also detectable in melanoma cell lines and malignant melanoma tumours. Thus FLIP may be implicated in tissue homeostasis as an important regulator of apoptosis

Thermoset Shape Memory Polymer Variable Stiffness 4D Robotic Catheters

Variable stiffness catheters are typically composed of an encapsulated core. The core is usually composed of a low melting point alloy (LMPA) or a thermoplastic polymer (TP). In both cases, there is a need to encapsulate the core with an elastic material. This imposes a limit to the volume of variable stiffness (VS) material and limits miniaturization. This paper proposes a new approach that relies on the use of thermosetting materials. The variable stiffness catheter (VSC) proposed in this work eliminates the necessity for an encapsulation layer and is made of a unique biocompatible thermoset polymer with an embedded heating system. This significantly reduces the final diameter, improves manufacturability, and increases safety in the event of complications. The device can be scaled to sub-millimeter dimensions, while maintaining a high stiffness change. In addition, integration into a magnetic actuation system allows for precise actuation of one or multiple tools

Technology readiness levels for machine learning systems

The development and deployment of machine learning (ML) systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. The lack of diligence can lead to technical debt, scope creep and misaligned objectives, model misuse and failures, and expensive consequences. Engineering systems, on the other hand, follow well-defined processes and testing standards to streamline development for high-quality, reliable results. The extreme is spacecraft systems, where mission critical measures and robustness are ingrained in the development process. Drawing on experience in both spacecraft engineering and ML (from research through product across domain areas), we have developed a proven systems engineering approach for machine learning development and deployment. Our Machine Learning Technology Readiness Levels (MLTRL) framework defines a principled process to ensure robust, reliable, and responsible systems while being streamlined for ML workflows, including key distinctions from traditional software engineering. Even more, MLTRL defines a lingua franca for people across teams and organizations to work collaboratively on artificial intelligence and machine learning technologies. Here we describe the framework and elucidate it with several real world use-cases of developing ML methods from basic research through productization and deployment, in areas such as medical diagnostics, consumer computer vision, satellite imagery, and particle physics

Effect of mechanical preconditioning on the electrical properties of knitted conductive textiles during cyclic loading

This paper presents, for the first time, the electrical response of knitted conductive fabrics to a considerable number of cycles of deformation in view of their use as wearable sensors. The changes in the electrical properties of four knitted conductive textiles, made of 20% stainless steel and 80% polyester fibers, were studied during unidirectional elongation in an Instron machine. Two tests sessions of 250 stretch–recovery cycles were conducted for each sample at two elongation rates (9.6 and 12 mm/s) and at three constant currents (1, 3 and 6 mA). The first session assessed the effects of an extended cyclic mechanical loading (preconditioning) on the electrical properties, especially on the electrical stabilization. The second session, which followed after a 5 minute interval under identical conditions, investigated whether the stabilization and repeatability of the electrical features were maintained after rest. The influence of current and elongation rate on the resistance measurements was also analyzed. In particular, the presence of a semiconducting behavior of the stainless steel fibers was proved by means of different test currents. Lastly, the article shows the time-dependence of the fabrics by means of hysteresis graphs and their non-linear behavior thanks to a time–frequency analysis. All knit patterns exhibited interesting changes in electrical properties as a result of mechanical preconditioning and extended use. For instance, the gauge factor, which indicates the sensitivity of the fabric sensor, varied considerably with the number of cycles, being up to 20 times smaller than that measured using low cycle number protocols

Technology readiness levels for machine learning systems

The development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. Lack of diligence can lead to technical debt, scope creep and misaligned objectives, model misuse and failures, and expensive consequences. Engineering systems, on the other hand, follow well-defined processes and testing standards to streamline development for high-quality, reliable results. The extreme is spacecraft systems, with mission critical measures and robustness throughout the process. Drawing on experience in both spacecraft engineering and machine learning (research through product across domain areas), we’ve developed a proven systems engineering approach for machine learning and artificial intelligence: the Machine Learning Technology Readiness Levels framework defines a principled process to ensure robust, reliable, and responsible systems while being streamlined for machine learning workflows, including key distinctions from traditional software engineering, and a lingua franca for people across teams and organizations to work collaboratively on machine learning and artificial intelligence technologies. Here we describe the framework and elucidate with use-cases from physics research to computer vision apps to medical diagnostics

Technology Readiness Levels for Machine Learning Systems

The development and deployment of machine learning (ML) systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. The lack of diligence can lead to technical debt, scope creep and misaligned objectives, model misuse and failures, and expensive consequences. Engineering systems, on the other hand, follow well-defined processes and testing standards to streamline development for high-quality, reliable results. The extreme is spacecraft systems, where mission critical measures and robustness are ingrained in the development process. Drawing on experience in both spacecraft engineering and ML (from research through product across domain areas), we have developed a proven systems engineering approach for machine learning development and deployment. Our "Machine Learning Technology Readiness Levels" (MLTRL) framework defines a principled process to ensure robust, reliable, and responsible systems while being streamlined for ML workflows, including key distinctions from traditional software engineering. Even more, MLTRL defines a lingua franca for people across teams and organizations to work collaboratively on artificial intelligence and machine learning technologies. Here we describe the framework and elucidate it with several real world use-cases of developing ML methods from basic research through productization and deployment, in areas such as medical diagnostics, consumer computer vision, satellite imagery, and particle physics

Measurements of fiducial and differential cross sections for Higgs boson production in the diphoton decay channel at s√=8 TeV with ATLAS

Measurements of fiducial and differential cross sections are presented for Higgs boson production in proton-proton collisions at a centre-of-mass energy of s√=8 TeV. The analysis is performed in the H → γγ decay channel using 20.3 fb−1 of data recorded by the ATLAS experiment at the CERN Large Hadron Collider. The signal is extracted using a fit to the diphoton invariant mass spectrum assuming that the width of the resonance is much smaller than the experimental resolution. The signal yields are corrected for the effects of detector inefficiency and resolution. The pp → H → γγ fiducial cross section is measured to be 43.2 ±9.4(stat.) − 2.9 + 3.2 (syst.) ±1.2(lumi)fb for a Higgs boson of mass 125.4GeV decaying to two isolated photons that have transverse momentum greater than 35% and 25% of the diphoton invariant mass and each with absolute pseudorapidity less than 2.37. Four additional fiducial cross sections and two cross-section limits are presented in phase space regions that test the theoretical modelling of different Higgs boson production mechanisms, or are sensitive to physics beyond the Standard Model. Differential cross sections are also presented, as a function of variables related to the diphoton kinematics and the jet activity produced in the Higgs boson events. The observed spectra are statistically limited but broadly in line with the theoretical expectations