Allosteric but non-obligatory coupling of multiple stimuli to the opening of channel pore allows TRP channels to serve as polymodal cellular sensors.
While there can be multiple stimuli for apoptosis of the RPE, the most likely stimulus in this model, is oxidative stress from cigarette smoke.
An important property of TRPV1t is that it can integrate the effect of multiple stimuli to produce additive effects on the Bz-insensitive NaCl CT responses (Lyall et al. 2004, 2005a, 2005b, 2005c, 2007).
However, capacity-based models could explain the report of multiple sequential targets by referring to chunking mechanisms that would allow multiple stimuli to be part of one larger representation in memory.
Furthermore, this method of automatic scene segmentation should then enhance learning in downstream areas of the brain as previously suggested (Miconi and VanRullen 2010), allowing transformation-invariant representations of multiple stimuli to be formed simultaneously.
MAPks play an active signaling role following multiple stimuli, some of which include irradiation, osmotic stress, inflammation, growth factors, and mechanical loading [10], [11], [12], [13].
Understanding both the effect of the environmental context on transcriptomic responses and the integration of multiple stimuli is essential to the development of predictive models for gene expression that can be generalized to wide ranges of agronomical settings and anticipated climatic conditions.
Multiple stimuli are capable of activating NF-κB including, but not limited to, proinflammatory cytokines (e.g., TNF-α), activating cellular receptors (e.g., TCR), viral proteins (e.g., EBV-LMP-1), DNA cleavage, chemotherapeutics (doxorubicin), and oxidative stress.
The aim of this work is to investigate how such higher layers may exploit the input layer dynamics formed from prior learning about categories in order to segment a visual scene composed of multiple stimuli and learn transformation-invariant representations of them as they move in lockstep across the input layer.
The main novelty of the present approach for dynamic GRN inference is the possibility to consider the effects of multiple stimuli, which permits the advanced, simultaneous analysis of multi-experiment time series expression data, in our case together with the option of soft integration of prior knowledge.
