Thermal Operators in Ising Percolation

We discuss a new cluster representation for the internal energy and the specific heat of the d-dimensional Ising model, obtained by studying the percolation mapping of an Ising model with an arbitrary set of antiferromagnetic links. Such a representation relates the thermal operators to the topological properties of the Fortuin-Kasteleyn clusters of Ising percolation and is a powerful tool to get new exact relations on the topological structure of FK clusters of the Ising model defined on an arbitrary graph.Comment: 17 pages, 2 figures. Improved version. Major changes in the text and in the notations. A missing term added in the specific heat representatio

Machine Learning-Driven Multiscale Modeling: Bridging the Scales with a Next-Generation Simulation Infrastructure

Interdependence across time and length scales is common in biology, where atomic interactions can impact larger-scale phenomenon. Such dependence is especially true for a well-known cancer signaling pathway, where the membrane-bound RAS protein binds an effector protein called RAF. To capture the driving forces that bring RAS and RAF (represented as two domains, RBD and CRD) together on the plasma membrane, simulations with the ability to calculate atomic detail while having long time and large length- scales are needed. The Multiscale Machine-Learned Modeling Infrastructure (MuMMI) is able to resolve RAS/RAF protein-membrane interactions that identify specific lipid-protein fingerprints that enhance protein orientations viable for effector binding. MuMMI is a fully automated, ensemble-based multiscale approach connecting three resolution scales: (1) the coarsest scale is a continuum model able to simulate milliseconds of time for a 1 μm2 membrane, (2) the middle scale is a coarse-grained (CG) Martini bead model to explore protein-lipid interactions, and (3) the finest scale is an all-atom (AA) model capturing specific interactions between lipids and proteins. MuMMI dynamically couples adjacent scales in a pairwise manner using machine learning (ML). The dynamic coupling allows for better sampling of the refined scale from the adjacent coarse scale (forward) and on-the-fly feedback to improve the fidelity of the coarser scale from the adjacent refined scale (backward). MuMMI operates efficiently at any scale, from a few compute nodes to the largest supercomputers in the world, and is generalizable to simulate different systems. As computing resources continue to increase and multiscale methods continue to advance, fully automated multiscale simulations (like MuMMI) will be commonly used to address complex science questions

IRF4 transcription factor-dependent CD11b+ dendritic cells in human and mouse control mucosal IL-17 cytokine responses.

Mouse and human dendritic cells (DCs) are composed of functionally specialized subsets, but precise interspecies correlation is currently incomplete. Here, we showed that murine lung and gut lamina propria CD11b+ DC populations were comprised of two subsets: FLT3- and IRF4-dependent CD24(+)CD64(-) DCs and contaminating CSF-1R-dependent CD24(-)CD64(+) macrophages. Functionally, loss of CD24(+)CD11b(+) DCs abrogated CD4+ T cell-mediated interleukin-17 (IL-17) production in steady state and after Aspergillus fumigatus challenge. Human CD1c+ DCs, the equivalent of murine CD24(+)CD11b(+) DCs, also expressed IRF4, secreted IL-23, and promoted T helper 17 cell responses. Our data revealed heterogeneity in the mouse CD11b+ DC compartment and identifed mucosal tissues IRF4-expressing DCs specialized in instructing IL-17 responses in both mouse and human. The demonstration of mouse and human DC subsets specialized in driving IL-17 responses highlights the conservation of key immune functions across species and will facilitate the translation of mouse in vivo findings to advance DC-based clinical therapies

Russia’s Legal Transitions: Marxist Theory, Neoclassical Economics and the Rule of Law

We review the role of economic theory in shaping the process of legal change in Russia during the two transitions it experienced during the course of the twentieth century: the transition to a socialist economy organised along the lines of state ownership of the means of production in the 1920s, and the transition to a market economy which occurred after the fall of the Soviet Union in the 1990s. Despite differences in methodology and in policy implications, Marxist theory, dominant in the 1920s, and neoclassical economics, dominant in the 1990s, offered a similarly reductive account of law as subservient to wider economic forces. In both cases, the subordinate place accorded to law undermined the transition process. Although path dependence and history are frequently invoked to explain the limited development of the rule of law in Russia during the 1990s, policy choices driven by a deterministic conception of law and economics also played a role.This is the author accepted manuscript. The final version is available from Springer via http://dx.doi.org/10.1007/s40803-015-0012-