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The following options and preferences can be specified when calling TANGO Serial Key from the command line. These options are ignored when installing the package. TANGO Product Key_OBJECT_DETAILS_URL = ” Indicates the URL to the Object Type Details page. This is a page where you can download the ID of the objects in order to include them in your TANGO Serial Key cluster. TANGO Crack_HOSTNAME = ‘localhost’ The hostname to use to connect to the TANGO hosts. If you are using a dynamic DNS service, this should be set to the address of that service. TANGO_HOSTS = ‘localhost’ The comma separated list of hosts from the specified url that is considered for discovery if you define TANGO_CACHE_HOSTS. See below. TANGO_CACHE_HOSTS = ‘localhost,127.0.0.1’ The comma separated list of hosts from the specified url that is considered for cache. See below. TANGO_REPLICATION_TIME = ‘5’ How often TANGO should publish status of the distributed object controls and run the SONAR task to detect if the observed servers are still up. TANGO_D_MEM_THRESHOLD = ’60’ If the system memory is below this value, then the distributed memory garbage collector should be enabled, with consideration to slow tasks. TANGO_S_MEM_THRESHOLD = ’60’ If the system memory is below this value, then the SONAR task should be aborted. TANGO_FULLSCAN_INTERVAL = ’60’ For each auto-detected server, the task should be started. This parameter defines the maximum duration of the scan. TANGO_SONAR_TIMEOUT = ’60’ If the task has been running for more than this value, the task should be aborted. TANGO_S_DELAY = ’60’ The task should be initiated after this delay. TANGO_QUIET = ‘true’ For each auto-detected server, the task
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• It creates server and client apps in a distributed way, hiding the low-level network details. • Libraries for Tango Server and client are loaded and used automatically. • You can use standard C++, Java or Python as embedded languages. • A small API for the server and the client is used to hide the low-level details of the network. • The package includes tools for writing C++/ Java/ Python code. • It provides you with tools to monitor and control the system. • ITAMTANGO provides a complete SDK.Phenotypic characterization of monocyte-derived dendritic cells grown in the presence of GM-CSF and IL-10. Dendritic cells (DC) represent a major cell type found in peripheral tissues and in the draining lymphoid organs. Whereas the characterization of murine DC has become a common research tool in the last years, no information is available about the phenotypic and functional features of human DC. In this report, we describe a method to obtain phenotypically and functionally differentiated monocyte-derived dendritic cells from human peripheral blood monocytes. The differentiation process was optimized in the presence of the cytokine cocktail GM-CSF (2.5 ng/mL) and IL-10 (0.5 ng/mL) for 6 days. The monocytes were cultured in serum-free medium for another 2 days and subsequently matured by the addition of standard maturation agents, such as Zymosan A, TLR-ligands and CD40L. Phenotypic characterization of differentiated DC showed up-regulation of surface markers CD14, CD83, CD86, CCR7 and CD11c as compared with the monocyte stage. Moreover, IL-12 and IL-15 production was detected in GM-CSF/IL-10 generated dendritic cells by flow cytometry, while cytokine ELISAs showed detectable amounts of IL-8, IL-12, IL-15, IL-23, IL-27 and TNF-alpha. In functional experiments, the DC maturation, as evaluated by surface molecule expression, was found to be similar to that obtained with LPS and CD40L. In the present study, we describe a protocol to obtain a functional and phenotypically well differentiated DC from human monocytes. The presented method can be used to enhance our knowledge about the role of this cell type in inflammatory and immune responses and as a tool to search for new 2f7fe94e24
————————————————– Using TANGO, is as easy as creating a client or a server. To see the complete list of supported languages, visit: —————————————————- Visual Dialogs TANGO Visual Dialogs give you the opportunity to query to the network and get information about the devices. The result of these queries are visualized by TANGO using a widget format. The widget follows a multi-select mode. For example, an agent could select a list of options for the first question and other similar dialogs will be activated after. Supporting Environments TANGO supports Windows, Linux and Mac OS X. ———————— Getting Started To get started with TANGO, we recommend the following: To configure a server: ————————- On Windows you need to install the service to keep this service running even you log off. There are two ways to do this: a) Create a batch file and modify the registry to run the CMD. b) You can run the service from the command line on Windows. For more details on how to configure the service please see the instructions on the get started page. To configure a client: ————————- On Windows you need to install the service to keep this service running even you log off. There are two ways to do this: a) Create a batch file and modify the registry to run the CMD. b) You can run the service from the command line on Windows. For more details on how to configure the service please see the instructions on the get started page. To use TANGO you need to have a TANGO server or a TANGO client installed on the machines that will use this system. ———————— TANGO The TANGO package is divided in two parts: *The server* This TANGO server provides a library and API allowing you to create new devices and behaviors for your applications. A server is both a client and a server. You register your device and your server listens to requests. When a client requests information, the server answers that information to the client. *The client* This TANGO client implements a nice dialog using TANGO with a multi
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