Dynamic causal modeling of load-dependent modulation of effective connectivity within the verbal working memory network

Neuroimaging studies have consistently shown that working memory (WM) tasks engage a distributed neural network that primarily includes the dorsolateral prefrontal cortex, the parietal cortex, and the anterior cingulate cortex. The current challenge is to provide a mechanistic account of the changes observed in regional activity. To achieve this, we characterized neuroplastic responses in effective connectivity between these regions at increasing WM loads using dynamic causal modeling of functional magnetic resonance imaging data obtained from healthy individuals during a verbal n-back task. Our data demonstrate that increasing memory load was associated with (a) right-hemisphere dominance, (b) increasing forward (i.e., posterior to anterior) effective connectivity within the WM network, and (c) reduction in individual variability in WM network architecture resulting in the right-hemisphere forward model reaching an exceedance probability of 99% in the most demanding condition. Our results provide direct empirical support that task difficulty, in our case WM load, is a significant moderator of short-term plasticity, complementing existing theories of task-related reduction in variability in neural networks. Hum Brain Mapp, 2013. © 2013 Wiley Periodicals, Inc.

Catching transcriptional regulation by thermostatistical modeling

Gene expression is frequently regulated by multiple transcription factors (TFs). Thermostatistical methods allow for a quantitative description of interactions between TFs, RNA polymerase and DNA, and their impact on the transcription rates. We illustrate three different scales of the thermostatistical approach: the microscale of TF molecules, the mesoscale of promoter energy levels and the macroscale of transcriptionally active and inactive cells in a cell population. We demonstrate versatility of combinatorial transcriptional activation by exemplifying logic functions, such as AND and OR gates. We discuss a metric for cell-to-cell transcriptional activation variability known as Fermi entropy. Suitability of thermostatistical modeling is illustrated by describing the experimental data on transcriptional induction of NF?B and the c-Fos protein.

Mode-locked fiber lasers with significant variability of generation regimes

We demonstrate a great variability of single-pulse (with only one pulse/wave-packet traveling along the cavity) generation regimes in fiber lasers passively mode-locked by non-linear polarization evolution (NPE) effect. Combining extensive numerical modeling and experimental studies, we identify multiple very distinct lasing regimes with a rich variety of dynamic behavior and a remarkably broad spread of key parameters (by an order of magnitude and more) of the generated pulses. Such a broad range of variability of possible lasing regimes necessitates developing techniques for control/adjustment of such key pulse parameters as duration, radiation spectrum, and the shape of the auto-correlation function. From a practical view point, availability of pulses/wave-packets with such different characteristics from the same laser makes it imperative to develop variability-aware designs with control techniques and methods to select appropriate application-oriented regimes. © 2014 The Authors.