LEADER 05682nam 2200637 450 001 9910137216403321 005 20230621135621.0 010 $a9782889195060$b(ebook) 035 $a(CKB)3710000000520111 035 $a(SSID)ssj0001666229 035 $a(PQKBManifestationID)16454510 035 $a(PQKBTitleCode)TC0001666229 035 $a(PQKBWorkID)14999885 035 $a(PQKB)11667616 035 $a(WaSeSS)IndRDA00056247 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/42563 035 $a(EXLCZ)993710000000520111 100 $a20160829d2015 uy 0 101 0 $aeng 135 $aur||#|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aCa2+ and Ca2+-interlocked membrane guanylate cyclase modulation of neuronaland cardiovascular signal transduction /$ftopic editors: Rameshwar K. Sharma, Wolfgang Baehr,Clint L. Makino and Teresa Duda 210 $cFrontiers Media SA$d2015 210 31$aFrance :$cFrontiers Media SA,$d2015 215 $a1 online resource (185 pages) $cillustrations; digital, PDF file(s) 225 0 $aFrontiers Research Topics 300 $aBibliographic Level Mode of Issuance: Monograph 320 $aIncludes bibliographical references. 330 $aThe development of the field of membrane guanylate cyclase transduction system has been colorful, filled with exceptional historical events in cellular signaling research. From denial to resurgence, the field has branched in multiple directions. The signal transduction characteristics and signaling elements are unique. The field has established cyclic GMP as an ubiquitous intracellular second messenger, playing a critical role in the control of many physiological processes, including cardiac vasculature, smooth muscle relaxation, blood volume, cellular growth, sensory transduction, neural plasticity, learning and memory. Unlike the three-component design of its predecessor: adenylate cyclase, G-protein and G-protein coupled receptor, the membrane guanylate cyclase transduction system consists of a single entity, a trans-membrane-spanning protein that serves as both a receptor and a signal transducer. Membrane guanylate cyclases exist in multiple forms. Each form translates the captured signal at a structurally conserved core catalytic site that resides in the intracellular domain. Yet the mechanism of capturing the signal is unique to each form. The surface receptor form uses its extracellular domain to capture hormonal signals; the Ca2+-modulated ROS-GC employs its intracellular domains; and the olfactory receptor ONE-GC captures odorant signals at its extracellular domain and amplifies them at multiple intracellular domains. The composition of the hormone receptor form differs from the ROS-GC and ONE-GC forms, consisting of a single polypeptide, that is both a signal receptor and the transducer. In contrast, both ROS-GC and ONE-GC are multi-component systems. A Ca2+ sensing subunit(s) captures the signal and transmits it to a companion guanylate cyclase, that transduces it. Moreover, the modes of signal transduction vary in ROS-GC and ONE-GC. ROS-GC is a direct transducer of Ca2+ signals but the Ca2+ sensors in ONE-GC only amplify the odorant signal received and transmitted by its extracellular domain. An additional refinement is that ROS-GC1 is a bimodal Ca2+ switch, turned ?OFF? as intracellular [Ca2+] rises above 75 nM, but then turned back ?ON? when [Ca2+] exceeds 345 nM. These modes occur uniquely in the outer segments and synapses of cones in rodent retinas. In a new paradigm change, the dogma has been shattered that the ANF hormone receptor guanylate cyclase, ANF-RGC, is the specific transducer of ANF alone. It is now known that ANF-RGC also transduces a Ca2+ signal. Ca2+ captured by its sensor neurocalcin ? (NC?) directly activates the catalytic module of ANF-RGC. Accordingly, and impressively, targeted gene-deletion mouse model studies demonstrate that both pathways are linked with blood pressure regulation. Their disruption causes hypertension. Thus the ANF-RGC combines features of hormone receptor and ROS-GC forms of membrane guanylate cyclases. These studies also broaden the classification of the Ca2+ sensors. NC?, classified as a neuronal calcium sensor, is more widespread. The general theme of this Research Topic is to present a comprehensive coverage of the expanding role being played by this beautifully designed transduction machinery. The reviews will cover its history to its present status, move on to theoretical and experimental investigations propelling the field in future directions, and provide illustrations where the field contributes to clinical medicine. 606 $aAnimal Biochemistry$2HILCC 606 $aHuman Anatomy & Physiology$2HILCC 606 $aHealth & Biological Sciences$2HILCC 610 $aGlaucoma 610 $aVisceral Pain 610 $aCalcium 610 $amembrane guanylate cyclase 610 $aANF-RGC 610 $aGene Therapy 610 $aCyclic GMP 610 $asynaptic plasticity 610 $atrafficking 610 $aROS-GC 615 7$aAnimal Biochemistry 615 7$aHuman Anatomy & Physiology 615 7$aHealth & Biological Sciences 700 $aRameshwar K Sharma$4auth$01364370 702 $aSharma$b Rameshwar K 702 $aBaehr$b Wolfgang 702 $aMakino$b Clint L 801 0$bPQKB 801 2$bUkMaJRU 912 $a9910137216403321 996 $aCa2+ and Ca2+-interlocked membrane guanylate cyclase modulation of neuronaland cardiovascular signal transduction$93385579 997 $aUNINA LEADER 04300nam 2200505 450 001 9910797960003321 005 20221202000507.0 010 $a1-78528-302-2 035 $a(CKB)3710000000552095 035 $a(EBL)4520768 035 $a(MiAaPQ)EBC4520768 035 $a(PPN)228045908 035 $a(EXLCZ)993710000000552095 100 $a20160706d2015 uy| 0 101 0 $aeng 135 $aur|n|---||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aLearning Hunk $evisualize and analyze your Hadoop data using Hunk /$fDmitry Anoshin, Sergey Sheypak 210 1$aBirmingham :$cPackt Publishing,$d2015. 215 $a1 online resource (156 p.) 225 1 $aCommunity experience distilled 300 $aIncludes index. 311 $a1-78217-482-6 327 $aCover; Copyright; Credits; About the Authors; About the Reviewer; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Meet Hunk; Big data analytics; The big problem; The elegant solution; Supporting SPL; Intermediate results; Getting to know Hunk; Splunk versus Hunk; Hunk architecture; Connecting to Hadoop; Advance Hunk deployment; Native versus virtual indexes; Native indexes; Virtual index; External result provider; Computation models; Data streaming; Data reporting; Mixed mode; Hunk security; One Hunk user to one Hadoop user; Many Hunk users to one Hadoop user 327 $aHunk user(s) to the same Hadoop user with different queuesSetting up Hadoop; Starting and using a virtual machine with CDH5; SSH user; MySQL; Starting the VM and cluster in VirtualBox; Big data use case; Importing data from RDBMS to Hadoop using Sqoop; Telecommunications - SMS, Call, and Internet dataset from dandelion.eu; Milano grid map; CDR aggregated data import process; Periodical data import from MySQL using Sqoop and Oozie; Problems to solve; Summary; Chapter 2: Explore Hadoop Data with Hunk; Setting up Hunk; Extracting Hunk to a VM; Setting up Hunk variables and configuration files 327 $aRunning Hunk for the first timeSetting up a data provider and virtual index for CDR data; Setting up a connection to Hadoop; Setting up a virtual index for data stored in Hadoop; Accessing data through a virtual index; Exploring data; Creating reports; The top five browsers report; Top referrers; Site errors report; Creating alerts; Creating a dashboard; Controlling security with Hunk; The default Hadoop security; One Hunk user to one Hadoop user; Summary; Chapter 3: Meeting Hunk Features; Knowledge objects; Field aliases; Calculated fields; Field extractions; Tags; Event type 327 $aWorkflow actionsMacros; Data model; Add auto-extracting fields; Adding GeoIP attributes; Other ways to add attributes; Introducing Pivot; Summary; Chapter 4: Adding Speed to Reports; Big data performance issues; Hunk report acceleration; Creating a virtual index; Streaming mode; Creating an acceleration search; What's going on in Hadoop?; Report acceleration summaries; Reviewing summary details; Managing report accelerations; Hunk accelerations limits; Summary; Chapter 5: Customizing Hunk; What we are going to do with the Splunk SDK; Supported languages; Solving problems; REST API 327 $aThe implementation planThe conclusion; Dashboard customization using Splunk Web Framework; Functionality; A description of time-series aggregated CDR data; Source data; Creating a virtual index for Milano CDR; Creating a virtual index for the Milano grid; Creating a virtual index using sample data; Implementation; Querying the visualization; Downloading the application; Custom Google Maps; Page layout; Linear gradients and bins for the activity value; Custom map components; Other components; The final result; Summary; Chapter 6: Discovering Hunk Integration Apps; What is Mongo?; Installation 327 $aInstalling the Mongo app 410 0$aCommunity experience distilled. 606 $aBig data 606 $aNon-relational databases 615 0$aBig data. 615 0$aNon-relational databases. 700 $aAnoshin$b Dmitry$0938705 702 $aSheypak$b Sergey 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910797960003321 996 $aLearning Hunk$93777278 997 $aUNINA