Sentence examples for nodes we identified from inspiring English sources

Taken together, these findings argue for a coordinated, temporal relationship between the master regulatory nodes we identified here.

After the first step, carried out for the whole set of 1,657 network nodes, we identified an initial group of more central proteins.

12– 14 In those nodes we identified tumor-reactive lymphocytes, secreting interferon (IFN -γ upon stIFN -γion with tupon antigen in addition to dostimulationt proliferation, with assessed ex vivo directumorfter dissection.

Using co-expression analysis of transcriptional profiling data from studies on blood, macrophages, as well as lymph nodes, we identified a large cluster (n = 619 probesets) that exhibited a correlated positive response upon infection across a number of pathogens or due to different immune stimuli.

From the root node we identified seven decisions that may be considered by a GP under such a scenario: ■ Strategy A: repeat the LFT panel and then perform a specific test for viral hepatitis if an abnormality is still present on re-testing.

By enumerating all possible regulatory configurations from the driver node, we identify two types of motifs associated with the toggle switch that is captured by the existence of bistable states.

When scanning the repressed node 119, we identified six interesting sub-nodes, covering most of the node: sub-node 102 (correlation 0.582), sub-node 79 (correlation 0.667), sub-node 76 (correlation 0.678), sub-node 63 (correlation 0.72), sub-node 54 (correlation 0.75) and sub-node 52 (correlation 0.75).

Using these significant sRNA as "seed" nodes, we particularly identified the YbeY-dependent Hfq-dependent sRNA-mRNA sub-network as well as YbeY-dependent Hfq-independent sRNA-mRNA sub-network from the CLR reverse-engineered full sRNAs network.

Starting with a genetic screen for mutants with abnormal nodes of Ranvier, we identified a mutation in sec63 that disrupts nodal NaV clusters in the PNS and the CNS of zebrafish larvae.

Our methodology allows to prove the existence of an evasive class of reputed nodes, which we identify as 'hidden influentials' after [22], who have a major role when it comes to spawn system-wide phenomena.

They showed that by varying the distance between nodes, we can identify regions in which the communication becomes unstable, the so-called "gray areas" or "transitional region".