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信令（Signaling）
我们把这些消息称为信令（Signaling）。 - 信令分类 - 信令主要有以下几种分类方式： 按信令的功能分： 线路信令：具有监视功能，用来监视主被叫的摘、挂机状态及设备忙闲。
1.7K10编辑于 2024-02-28 - 来自专栏物联网思考
ble4.2 L2CAP层信令通道包详解（SIGNALING PACKET FORMATS）
可以看出BLE主要使用0x0004、0x0005、0x0006三个信道；0x0004用于ATT协议，0x0005用于L2CAP信令，0x0006用于安全管理。
1.5K20发布于 2021-09-15 - 来自专栏desperate633
Java线程通信（Thread Signaling）利用共享对象实现通信忙等（busy waiting）wait(), notify() and notifyAll()信号丢失（Missed Sign
线程通信的目的就是让线程间具有互相发送信号通信的能力。 而且，线程通信可以实现，一个线程可以等待来自其他线程的信号。举个例子，一个线程B可能正在等待来自线程A的信号，这个信号告诉线程B数据已经处理好了。
98220发布于 2018-08-22 - 来自专栏Y大宽
手画综合信号通路（部分）
自噬 ○1 mTOR Signaling ○2 AMPK Signaling ○3PI3K/Akt Signaling ○4 p53 Signaling ○5Hedgehog signaling 自噬 生物氧化 ○1PKA Signaling ○2PIK3/AKT Signaling ○3AMPK Signaling生物氧化 ? 脂类代谢 ○1 AMPK Signaling ○2 PKA Signaling ○3 PPAR Signaling ○4 Dopamine Receptor Signaling ○5Aryl Hydrocarbon Receptor Signaling ○6Sphingosine-1-phosphate Signaling ○7Serotonin Receptor Signaling脂类代谢
91840发布于 2020-06-15 - 来自专栏charlieroro
如何在kubernetes中实现分布式可扩展的WebSocket服务架构
WebRTC signaling 是WebRTC协议的前置步骤，它依赖signaling server在需要建立WebRTC连接的客户端之间转发协商协议。 客户端和signaling server之间的连接通常使用WebSockets。 每个signaling实例需要了解系统中的其他实例，这可以通过kubernetes中的Headless Service关联signaling deployment，然后调用Kubernetes Endpoints 实现步骤如下： 通过kubernetes API来发现signaling实例，并实现rendezvous哈希逻辑。 当返回的实例与当前客户端注册的不一致，则负载均衡器只会断开与该客户端相关的 负载均衡器-signaling 之间的WebSocket，并重新建立一条到正确的signaling实例的 负载均衡器-signaling
2.4K50编辑于 2023-09-14 - 来自专栏随意记录
本地 k8s 部署
app: signaling-0 replicas: 1 template: metadata: labels: app: signaling- 0 spec: containers: - name: signaling image: code.com:6543/signaling-server :latest，监听端口（containerPort）是 21116，将 signaling 这个配置挂载到了 /opt/signaling_server/config/signaling/production.toml [signaling] data_center = "beijing" service_name = "signaling" signaling_port = 21116 /config/signaling/production.toml。
2.2K41编辑于 2022-04-11 - 来自专栏生信菜鸟团
CellChat细胞通讯（二）可视化篇
This hierarchical plot consist of two components: the left portion shows autocrine and paracrine signaling to fibroblast and the right portion shows signaling to immune cells vertex.receiver = c(1,2,3,4) # netVisual_aggregate(cellchat, signaling = pathways.show, vertex.receiver = vertex.receiver, layout = = pathways.show, layout = "circle") netVisual_aggregate(cellchat, signaling = pathways.show, layout pathways (where each sector in the chord diagram is a ligand, receptor or signaling pathway.)
13.2K42编辑于 2022-10-31 - 来自专栏生信宝典
Venn网络展示富集分析结果
DUSP1 HALLMARK_TNFA_SIGNALING_VIA_NFKB IRS2 HALLMARK_TNFA_SIGNALING_VIA_NFKB KLF9 HALLMARK_TNFA_SIGNALING_VIA_NFKB CEBPD HALLMARK_TNFA_SIGNALING_VIA_NFKB DNAJB4 HALLMARK_TNFA_SIGNALING_VIA_NFKB CFLAR HALLMARK_TNFA_SIGNALING_VIA_NFKB TSC22D1 HALLMARK_TNFA_SIGNALING_VIA_NFKB KLF6 HALLMARK_TNFA_SIGNALING_VIA_NFKB RHOB HALLMARK_TNFA_SIGNALING_VIA_NFKB MAFF HALLMARK_TNFA_SIGNALING_VIA_NFKB PTX3 HALLMARK_TNFA_SIGNALING_VIA_NFKB NR4A3 HALLMARK_TNFA_SIGNALING_VIA_NFKB ATF3 HALLMARK_TNFA_SIGNALING_VIA_NFKB GFPT2 HALLMARK_TNFA_SIGNALING_VIA_NFKB RCAN1 HALLMARK_TNFA_SIGNALING_VIA_NFKB
64700编辑于 2022-01-18 - 来自专栏生信菜鸟团
多组cellchat细胞通讯批量分析
= i,signaling.name = paste(i,"sigaling"), ,pt.title=10,vertex.receiver = vertex.receiver, layout = "circle") title(main = paste0(i,' signaling')) print(p) } = i, title = paste0(i, " signaling pathway", " Contribution of each L-R pair")) print(p) } role analysis on the aggregated cell-cell communication network from all signaling pathways #> Signaling = c("MIF")) #> Visualizing differential outgoing and incoming signaling changes from NL to LS #> The
1.3K10编辑于 2025-02-10 - 来自专栏生信学习
cellchat-(2)信号通路可视化-上
# 信号通路水平 netVisual_aggregate( cellchat, signaling = "ITGB2", #pathway_name vertex.receiver = c( 1, 3, 5), #指定受体细胞 layout = "hierarchy" #指定输出层次图 ) # 单个配体受体水平 extractEnrichedLR(cellchat, signaling 热图 热图也是从整个信号通路水平展示不同细胞间的相互作用： netVisual_heatmap( cellchat, signaling = "ITGB2", color.heatmap = "Reds") 图片 去掉signaling参数后就是所有细胞间的所有相互作用，类似于第一节中总的相互作用网络图。 计算信号通路中各受体配体对的贡献 netAnalysis_contribution(cellchat, signaling = "ITGB2") 图片 这样就能看到不同配体受体对对于该通路的贡献了。
2.3K20编辑于 2023-08-06 - 来自专栏R语言可视化
可视化—KEGG富集图中如何展示特定的通路
#显示特定的通路selected_pathways <- c( "B cell receptor signaling pathway", "T cell receptor signaling pathway ", "Fc epsilon RI signaling pathway", "PD-L1 expression and PD-1 checkpoint pathway in cancer", "Th1 and Th2 cell differentiation", "IL-17 signaling pathway", "Th17 cell differentiation", "MAPK signaling pathway", "PI3K-Akt signaling pathway", "Ras signaling pathway")kk_filtered <- kk@result %>% filter
2.1K01编辑于 2024-11-01 - 来自专栏空间转录组数据分析
通过空间行为（optimal transport）推断空间细胞间通讯信号方向(COMMOT)
='mouse', signaling_type='Secreted Signaling', database='CellChat') print(df_cellchat.shape) # filter 1 Tgfb2 Tgfbr1_Tgfbr2 TGFb Secreted Signaling 2 Tgfb3 Tgfbr1_Tgfbr2 TGFb Secreted Signaling 3 Tgfb1 Acvr1b_Tgfbr2 TGFb Secreted Signaling 4 Tgfb1 Acvr1c_Tgfbr2 TGFb Secreted Signaling Now The signaling results are stored as spot-by-spot matrices in the obsp slots. /adata.h5ad") Determine the spatial direction of a signaling pathway, for example, the PSAP pathway.
1.6K50编辑于 2023-02-23 - 来自专栏R语言及实用科研软件
🤩 Cellchat | 空间转录组也可以做细胞通讯啦！~（一）（单个数据集篇）
", key = "annotation") # use Secreted Signaling # Only uses the Secreted Signaling from CellChatDB v1 # CellChatDB.use <- subsetDB(CellChatDB, search = list(c("Secreted Signaling"), c("CellChatDB v1")) We do not suggest to use it in this way because CellChatDB v2 includes "Non-protein Signaling" (i.e., metabolic and synaptic signaling) that can be only estimated from gene expression data network, e.g., bigger circle indicates larger incoming signaling par(mfrow=c(1,1)) netVisual_aggregate
1.2K10编辑于 2024-11-23 - 来自专栏单细胞天地
CellChat三部曲1：使用CellChat对单个数据集进行细胞间通讯分析
") # use Secreted Signaling # use all CellChatDB for cell-cell communication analysis # CellChatDB.use df.net <- subsetCommunication(cellchat, signaling = c("WNT", "TGFb"))通过向WNT和TGFb发出信号来调节推断的细胞通信。 # Signaling role analysis on the aggregated cell-cell communication network from all signaling pathways communication network from all signaling pathways # Signaling role analysis on the cell-cell communication # Signaling role analysis on the aggregated cell-cell communication network from all signaling pathways
39.1K613发布于 2021-08-20 - 来自专栏单细胞天地
CellChat 三部曲3：具有不同细胞类型成分的多个数据集的细胞通讯比较分析
#> Merge the following slots: 'data.signaling','net', 'netP','meta', 'idents', 'var.features' , 'DB', to dermal cells and right portion shows signaling to epidermal cells par(mfrow = c(1,2), xpd=TRUE) for = pathways.show, layout = "circle", edge.weight.max = weight.max[1], edge.width.max = 10, signaling.name = pathways.show, layout = "chord", signaling.name = paste(pathways.show, names(object.list)[i])) } # (object.list)[i])) } #> Plot the aggregated cell-cell communication network at the signaling pathway
9.6K12发布于 2021-09-15 - 来自专栏TSINGSEE青犀视频
TSINGSEE青犀视频开发Webrtc的PC Factory三个线程介绍说明
在我们的开发中，需要了解Webrtc的PC Factory拥有三个线程： Signaling_thread_: 这个是所有PC Factory和PC的对外接口，执行都会切换到signaling_thread _，而PC Observer所有的回调函数也都会在signaling_thread_执行。 webrtc::CreatePeerConnectionFactory( Nullptr/*network_thread_*/, nullptr/*worker_thread_*/, nullptr/*signaling_thread Factory的第二个参数也没有提供（worker_thread_），就会调用rtc::Thread::Create()来创建并启动耗时相关线程 E、如果PC Factory的第三个参数没有提供，因为signaling_thread _比较特殊，没有提供时，那么webrtc是不会创建新的线程，而是把当前的线程进行包装一下作为signaling_thread_来用。
1.1K20发布于 2021-07-08 - 来自专栏单细胞学习小组
day 10 GSVA和CellChat
assay = "RNA") cellchat@DB <- subsetDB(CellChatDB.human, search = "Secreted <em>Signaling</em> ") cellchat <- subsetData(cellchat) dim(cellchat@data.signaling) 开始计算cellchat <- identifyOverExpressedGenes 右侧和上方的条形图是该行/列通讯概率之和netVisual_heatmap(cellchat, signaling = pathways.show, color.heatmap = "Reds") #5 计算配体-受体对信号网络的贡献度netAnalysis_contribution(cellchat, signaling = pathways.show) #6 热图展示 分析细胞在信号网络中角色：发送者 pathwaysnetAnalysis_signalingRole_network(cellchat, signaling = pathways.show, width = 12, height =
50410编辑于 2024-07-01 - 来自专栏单细胞天地
CellChat三部曲2：使用CellChat 对多个数据集细胞通讯进行比较分析
role analysis on the aggregated cell-cell communication network from all signaling pathways #> Signaling role analysis on the aggregated cell-cell communication network from all signaling pathways patchwork = pathways.show, layout = "chord", signaling.name = paste(pathways.show, names(object.list)[i])) } # = pathways.show, group = group.cellType, title.name = paste0(pathways.show, " signaling network - ", level #> Plot the aggregated cell-cell communication network at the signaling pathway level #> Note:
30.7K46编辑于 2022-01-17 - 来自专栏随意记录
docker 镜像制作示例
run 会自动拉取镜像并启动容器： IMAGE_FULL_TAG=镜像仓库/xremote-server:demo CONTAINER_NAME="rustdesk-server-demo" HOST_SIGNALING_PROT =21116 HOST_RELAY_PROT=21117 CONTAINER_SIGNALING_PORT=21116 CONTAINER_RELAY_PORT21117 IP=127.0.0.1 docker run -itd --restart=always --net bridge \ --name "${CONTAINER_NAME}" \ -p "${HOST_SIGNALING_PROT }:${CONTAINER_SIGNALING_PORT}/udp" \ -p "${HOST_SIGNALING_PROT}:${CONTAINER_SIGNALING_PORT}
4.4K30发布于 2021-11-20 - 来自专栏生信技能树
从msigdb下载的hallmark基因集里面的基因数量上限是200？？
geneset$gs_name))) Var1 Freq 1 HALLMARK_NOTCH_SIGNALING 34 2 HALLMARK_ANGIOGENESIS 36 3 HALLMARK_HEDGEHOG_SIGNALING 50 7 HALLMARK_REACTIVE_OXYGEN_SPECIES_PATHWAY 58 8 HALLMARK_TGF_BETA_SIGNALING PATHWAY 215 36 HALLMARK_ESTROGEN_RESPONSE_EARLY 216 37 HALLMARK_IL2_STAT5_SIGNALING 220 41 HALLMARK_KRAS_SIGNALING_UP 220 42 HALLMARK_OXIDATIVE_PHOSPHORYLATION
1K10编辑于 2024-11-21
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