Pattern formation without heating in an evaporative convection experiment

We present an evaporation experiment in a single fluid layer. When latent heat associated to the evaporation is large enough, the heat flow through the free surface of the layer generates temperature gradients that can destabilize the conductive motionless state giving rise to convective cellular structures without any external heating. The sequence of convective patterns obtained here without heating, is similar to that obtained in B\'enard-Marangoni convection. This work present the sequence of spatial bifurcations as a function of the layer depth. The transition between square to hexagonal pattern, known from non-evaporative experiments, is obtained here with a similar change in wavelength.Comment: Submitted to Europhysics Letter

A Simple Theory of Condensation

A simple assumption of an emergence in gas of small atomic clusters consisting of $c$ particles each, leads to a phase separation (first order transition). It reveals itself by an emergence of ``forbidden'' density range starting at a certain temperature. Defining this latter value as the critical temperature predicts existence of an interval with anomalous heat capacity behaviour $c_p\propto\Delta T^{-1/c}$. The value $c=13$ suggested in literature yields the heat capacity exponent $\alpha=0.077$.Comment: 9 pages, 1 figur

Penta-Hepta Defect Motion in Hexagonal Patterns

Structure and dynamics of penta-hepta defects in hexagonal patterns is studied in the framework of coupled amplitude equations for underlying plane waves. Analytical solution for phase field of moving PHD is found in the far field, which generalizes the static solution due to Pismen and Nepomnyashchy (1993). The mobility tensor of PHD is calculated using combined analytical and numerical approach. The results for the velocity of PHD climbing in slightly non-optimal hexagonal patterns are compared with numerical simulations of amplitude equations. Interaction of penta-hepta defects in optimal hexagonal patterns is also considered.Comment: 4 pages, Postscript (submitted to PRL

Ghosts of the past and dreams of the future: the impact of temporal focus on responses to contextual ingroup devaluation.

addresses: University of Exeter, Exeter, UK. [email protected]: Journal Article; Research Support, Non-U.S. Gov'tCopyright © 2012 SAGE Publications. Author's draft version; post-print. Final version published by Sage available on Sage Journals Online http://online.sagepub.com/The authors investigated the impact of temporal focus on group members' responses to contextual ingroup devaluation. Four experimental studies demonstrated that following an induction of negative ingroup evaluation, participants primed with a past temporal focus reported behavioral intentions more consistent with this negative appraisal than participants primed with a future temporal focus. This effect was apparent only when a negative (but not a positive) evaluation was induced, and only among highly identified group members. Importantly, the interplay between temporal focus and group identification on relevant intentions was mediated by individual self-esteem, suggesting that focus on the future may be conducive to separating negative ingroup appraisals from individual self-evaluations. Taken together, the findings suggest that high identifiers' responses to ingroup evaluations may be predicated on their temporal focus: A focus on the past may lock such individuals within their group's history, whereas a vision of the future may open up opportunities for change

Critical dimensions for random walks on random-walk chains

The probability distribution of random walks on linear structures generated by random walks in $d$-dimensional space, $P_d(r,t)$, is analytically studied for the case $\xi\equiv r/t^{1/4}\ll1$. It is shown to obey the scaling form $P_d(r,t)=\rho(r) t^{-1/2} \xi^{-2} f_d(\xi)$, where $\rho(r)\sim r^{2-d}$ is the density of the chain. Expanding $f_d(\xi)$ in powers of $\xi$, we find that there exists an infinite hierarchy of critical dimensions, $d_c=2,6,10,\ldots$, each one characterized by a logarithmic correction in $f_d(\xi)$. Namely, for $d=2$, $f_2(\xi)\simeq a_2\xi^2\ln\xi+b_2\xi^2$; for $3\le d\le 5$, $f_d(\xi)\simeq a_d\xi^2+b_d\xi^d$; for $d=6$, $f_6(\xi)\simeq a_6\xi^2+b_6\xi^6\ln\xi$; for $7\le d\le 9$, $f_d(\xi)\simeq a_d\xi^2+b_d\xi^6+c_d\xi^d$; for $d=10$, $f_{10}(\xi)\simeq a_{10}\xi^2+b_{10}\xi^6+c_{10}\xi^{10}\ln\xi$, {\it etc.\/} In particular, for $d=2$, this implies that the temporal dependence of the probability density of being close to the origin $Q_2(r,t)\equiv P_2(r,t)/\rho(r)\simeq t^{-1/2}\ln t$.Comment: LATeX, 10 pages, no figures submitted for publication in PR

Satb1 overexpression drives tumor-promoting activities in cancer-associated dendritic cells

Special AT-rich sequence-binding protein 1 (Satb1) governs genome-wide transcriptional programs. Using a conditional knockout mouse, we find that Satb1 is required for normal differentiation of conventional dendritic cells (DCs). Furthermore, Satb1 governs the differentiation of inflammatory DCs by regulating major histocompatibility complex class II (MHC II) expression through Notch1 signaling. Mechanistically, Satb1 binds to the Notch1 promoter, activating Notch expression and driving RBPJ occupancy of the H2-Ab1 promoter, which activates MHC II transcription. However, tumor-driven, unremitting expression of Satb1 in activated Zbtb46(+) inflammatory DCs that infiltrate ovarian tumors results in an immunosuppressive phenotype characterized by increased secretion of tumor-promoting Galectin-1 and IL-6. In vivo silencing of Satb1 in tumor-associated DCs reverses their tumorigenic activity and boosts protective immunity. Therefore, dynamic fluctuations in Satb1 expression govern the generation and immunostimulatory activity of steady-state and inflammatory DCs, but continuous Satb1 overexpression in differentiated DCs converts them into tolerogenic/pro-inflammatory cells that contribute to malignant progression.Fil: Tesone, Amelia J.. The Wistar Institute. Tumor Microenvironment and Metastasis Program; Estados UnidosFil: Rutkowski, Melanie R.. The Wistar Institute. Tumor Microenvironment and Metastasis Program; Estados UnidosFil: Brencicova, Eva. The Wistar Institute. Tumor Microenvironment and Metastasis Program; Estados UnidosFil: Svoronos, Nikolaos. The Wistar Institute. Tumor Microenvironment and Metastasis Program; Estados UnidosFil: Perales Puchal, Alfredo. The Wistar Institute. Tumor Microenvironment and Metastasis Program; Estados UnidosFil: Stephen, Tom L.. The Wistar Institute. Tumor Microenvironment and Metastasis Program; Estados UnidosFil: Allegrezza, Michael J.. The Wistar Institute. Tumor Microenvironment and Metastasis Program; Estados UnidosFil: Payne, Kyle K.. The Wistar Institute. Tumor Microenvironment and Metastasis Program; Estados UnidosFil: Nguyen, Jenny M.. The Wistar Institute. Tumor Microenvironment and Metastasis Program; Estados UnidosFil: Wickramasinghe, Jayamanna. The Wistar Institute. Center for Systems and Computational Biology; Estados UnidosFil: Tchou, Julia. University of Pennsylvania; Estados UnidosFil: Borowsky, Mark E.. Christiana Care Health System. Helen F. Graham Cancer Center; Estados UnidosFil: Rabinovich, Gabriel Adrián. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; ArgentinaFil: Kossenkov, Andrew V.. The Wistar Institute. Center for Systems and Computational Biology; Estados UnidosFil: Conejo Garcia, José R.. The Wistar Institute. Tumor Microenvironment and Metastasis Program; Estados Unido

Can we identify non-stationary dynamics of trial-to-trial variability?"

Identifying sources of the apparent variability in non-stationary scenarios is a fundamental problem in many biological data analysis settings. For instance, neurophysiological responses to the same task often vary from each repetition of the same experiment (trial) to the next. The origin and functional role of this observed variability is one of the fundamental questions in neuroscience. The nature of such trial-to-trial dynamics however remains largely elusive to current data analysis approaches. A range of strategies have been proposed in modalities such as electro-encephalography but gaining a fundamental insight into latent sources of trial-to-trial variability in neural recordings is still a major challenge. In this paper, we present a proof-of-concept study to the analysis of trial-to-trial variability dynamics founded on non-autonomous dynamical systems. At this initial stage, we evaluate the capacity of a simple statistic based on the behaviour of trajectories in classification settings, the trajectory coherence, in order to identify trial-to-trial dynamics. First, we derive the conditions leading to observable changes in datasets generated by a compact dynamical system (the Duffing equation). This canonical system plays the role of a ubiquitous model of non-stationary supervised classification problems. Second, we estimate the coherence of class-trajectories in empirically reconstructed space of system states. We show how this analysis can discern variations attributable to non-autonomous deterministic processes from stochastic fluctuations. The analyses are benchmarked using simulated and two different real datasets which have been shown to exhibit attractor dynamics. As an illustrative example, we focused on the analysis of the rat's frontal cortex ensemble dynamics during a decision-making task. Results suggest that, in line with recent hypotheses, rather than internal noise, it is the deterministic trend which most likely underlies the observed trial-to-trial variability. Thus, the empirical tool developed within this study potentially allows us to infer the source of variability in in-vivo neural recordings

Autonomous Bursting in a Homoclinic System

A continuous train of irregularly spaced spikes, peculiar of homoclinic chaos, transforms into clusters of regularly spaced spikes, with quiescent periods in between (bursting regime), by feeding back a low frequency portion of the dynamical output. Such autonomous bursting results to be extremely robust against noise; we provide experimental evidence of it in a CO2 laser with feedback. The phenomen here presented display qualitative analogies with bursting phenomena in neurons.Comment: Submitted to Phys. Rev. Lett., 14 pages, 5 figure