Fluctuating local moments, itinerant electrons and the magnetocaloric effect: the compositional hypersensitivity of FeRh

We describe an ab-initio Disordered Local Moment Theory for materials with quenched static compositional disorder traversing first order magnetic phase transitions. It accounts quantitatively for metamagnetic changes and the magnetocaloric effect. For perfect stoichiometric B2-ordered FeRh, we calculate the transition temperature of the ferromagnetic-antiferromagnetic transition to be $T_t =$ 495K and a maximum isothermal entropy change in 2 Tesla of $|\Delta S|= 21.1$ J~K$^{-1}$~kg$^{-1}$. A large (40\%) component of $|\Delta S|$ is electronic. The transition results from a fine balance of competing electronic effects which is disturbed by small compositional changes - e.g. swapping just 2\% Fe of `defects' onto the Rh sublattice makes $T_t$ drop by 290K. This hypersensitivity explains the narrow compositional range of the transition and impurity doping effects.Comment: 11 pages, 4 figure

Internal rationality, learning and imperfect information

We construct, estimate and explore the monetary policy consequences of a New Keynesian (NK) behavioural model with bounded-rationality and heterogeneous agents. We radically depart from most existing models of this genre in our treatment of bounded rationality and learning. Instead of the usual Euler learning approach, we assume that agents are internally rational (IR) given their beliefs of aggregate states and prices. The model is inhabited by fully rational (RE) and IR agents where the latter use simple heuristic rules to forecast aggregate variables exogenous to their micro-environment. We find that IR results in an NK model with more persistence and a smaller policy space for rule parameters that induce stability and determinacy. In the most general form of the model, agents learn from their forecasting errors by observing and comparing them with those under RE making the composition of the two types endogenous. In a Bayesian estimation with fixed proportions of RE and IR agents and a general heuristic forecasting rule we find that a pure IR model fits the data better than the pure RE case. However, the latter with imperfect rather than the standard perfect information assumption outperforms IR (easily) and RE-IR composites (slightly), but second moment comparisons suggest that the RE-IR composite can match data better. Our findings suggest that Kalman-filtering learning with RE can match bounded-rationality in matching persistence seen in the data

Designing Robust Monetary Policy Using Prediction Pools

How should a forward-looking policy maker conduct monetary policy when she has a finite set of models at her disposal, none of which are believed to be the true data generating process? In our approach, the policy makerfirst assigns weights to models based on relative forecasting performance rather than in-sample fit, consistent with her forward-looking objective. These weights are then used to solve a policy design prob- lem that selects the optimized Taylor-type interest-rate rule that is robust to model uncertainty across a set of well-established DSGE models with and without financial frictions. We find that the choice of weights has a significant impact on the robust optimized rule which is more inertial and aggressive than either the non-robust single model counterparts or the optimal robust rule based on backward-looking weights as in the common alternative Bayesian Model Averaging. Importantly, we show that a price-level rule has excellent welfare and robustness properties, and therefore should be viewed as a key instrument for policy makers facing uncertainty over the nature of financial frictions

Use of the Neurospora tyrosinase gene as a reporter gene in transformation experiments

Reporter gene systems have been developed to allow investigators to visually identify transformed cells and to follow the transcriptional activity from promoter regions preceding the reporter genes. These reporter genes encode protein products which can be easily assayed and which can be strained for in the transformed cell

Relaxation paths for single modes of vibrations in isolated molecules

A numerical simulation of vibrational excitation of molecules was devised, and used to excite computational models of common molecules into a prescribed, pure, normal vibration mode in the ground electronic state, with varying, controlable energy content. The redistribution of this energy (either non-chaotic or irreversible IVR) within the isolated, free molecule is then followed in time with a view to determining the coupling strength between modes. This work was triggered by the need to predict the general characters of the infrared spectra to be expected from molecules in interstellar space, after being excited by photon absorption or reaction with a radical. It is found that IVR from a pure normal mode is very "restricted" indeed at energy contents of one mode quantum or so. However, as this is increased, or when the excitation is localized, our approach allows us to isolate, describe and quantify a number of interesting phenomena, known to chemists and in non-linear mechanics, but difficult to demonstrate experimentally: frequency dragging, mode locking or quenching or, still, instability near a potential surface crossing, the first step to generalized chaos as the energy content per mode is increased.Comment: 25 pages, 15 figures; accepted by J. Atom. Phys.

Motif affinity and mass spectrometry proteomic approach for the discovery of cellular AMPK targets: identification of mitochondrial fission factor as a new AMPK substrate

AMP-activated protein kinase (AMPK) is a key cellular energy sensor and regulator of metabolic homeostasis. Although it is best known for its effects on carbohydrate and lipid metabolism, AMPK is implicated in diverse cellular processes, including mitochondrial biogenesis, autophagy, and cell growth and proliferation. To further our understanding of energy homeostasis through AMPK-dependent processes, the design and application of approaches to identify and characterise novel AMPK substrates are invaluable. Here, we report an affinity proteomicstrategy for the discovery and validation of AMPK targets using an antibody to isolate proteins containing the phospho-AMPK substrate recognition motif from hepatocytes that had been treated with pharmacological AMPK activators. We identified 57 proteins that were uniquely enriched in the activator-treated hepatocytes, but were absent in hepatocytes lacking AMPK. We focused on two candidates, cingulin and mitochondrial fission factor (MFF), and further characterised/validated them as AMPK-dependent targets by immunoblotting with phosphorylation site-specific antibodies. A small-molecule AMPK activator caused transient phosphorylation of endogenous cingulin at S137 in intestinal Caco2 cells. Multiple splice-variants of MFF appear to express in hepatocytes and we identified a common AMPK-dependent phospho-site (S129) in all the 3 predominant variants spanning the mass range and a short variant-specific site (S146). Collectively, our proteomic-based approach using a phospho-AMPK substrate antibody in combination with genetic models and selective AMPK activators will provide a powerful and reliable platform for identifying novel AMPK-dependent cellular targets

The CCFM Monte Carlo generator CASCADE 2.2.0

CASCADE is a full hadron level Monte Carlo event generator for ep, \gamma p and p\bar{p} and pp processes, which uses the CCFM evolution equation for the initial state cascade in a backward evolution approach supplemented with off - shell matrix elements for the hard scattering. A detailed program description is given, with emphasis on parameters the user wants to change and variables which completely specify the generated events