A solvable model of the genesis of amino-acid sequences via coupled dynamics of folding and slow genetic variation

We study the coupled dynamics of primary and secondary structure formation (i.e. slow genetic sequence selection and fast folding) in the context of a solvable microscopic model that includes both short-range steric forces and and long-range polarity-driven forces. Our solution is based on the diagonalization of replicated transfer matrices, and leads in the thermodynamic limit to explicit predictions regarding phase transitions and phase diagrams at genetic equilibrium. The predicted phenomenology allows for natural physical interpretations, and finds satisfactory support in numerical simulations.Comment: 51 pages, 13 figures, submitted to J. Phys.

The effect of drought and subsequent precipitation pulse on productivity, species composition, and carbon fluxes of the herbaceous understorey in a cork oak woodland

In the Iberian Peninsula, the cork oak woodlands are of great ecological and socio-economic importance. These savanna-type woodlands are characterized by an herbaceous understorey, dominated by C3 annual species. The productivity and related ecosystem processes of this understorey are highly dependent on timing and magnitude of precipitation events. 

Climate change scenarios for the Iberian Peninsula suggest not only increasing air temperatures, but also the possibility of decreasing spring precipitation, accompanied by an increase in the interval between precipitation events, which might cause drought conditions to occur, with more severe effects on productivity and ecosystem processes. 

An experiment was carried out in southern Portugal to assess the effect of a drought treatment, with a subsequent extreme precipitation event, on the herbaceous component of managed cork oak woodland. The facility consisted of five plots with rainout shelters (2.5 x 2.5 m), which exclude normal rainfall inputs (drought treatment), and five non-sheltered control plots (control treatment). In the drought treatment, 193 mm of precipitation was excluded in the period from mid March to mid May, with a subsequent precipitation pulse of 50 mm in the middle of May. Variables measured included productivity, plant community composition, soil respiration and soil microbial activity. 

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Hierarchical Self-Programming in Recurrent Neural Networks

We study self-programming in recurrent neural networks where both neurons (the `processors') and synaptic interactions (`the programme') evolve in time simultaneously, according to specific coupled stochastic equations. The interactions are divided into a hierarchy of $L$ groups with adiabatically separated and monotonically increasing time-scales, representing sub-routines of the system programme of decreasing volatility. We solve this model in equilibrium, assuming ergodicity at every level, and find as our replica-symmetric solution a formalism with a structure similar but not identical to Parisi's $L$-step replica symmetry breaking scheme. Apart from differences in details of the equations (due to the fact that here interactions, rather than spins, are grouped into clusters with different time-scales), in the present model the block sizes $m_i$ of the emerging ultrametric solution are not restricted to the interval $[0,1]$, but are independent control parameters, defined in terms of the noise strengths of the various levels in the hierarchy, which can take any value in [0,\infty\ket. This is shown to lead to extremely rich phase diagrams, with an abundance of first-order transitions especially when the level of stochasticity in the interaction dynamics is chosen to be low.Comment: 53 pages, 19 figures. Submitted to J. Phys.

The XY Spin-Glass with Slow Dynamic Couplings

We investigate an XY spin-glass model in which both spins and couplings evolve in time: the spins change rapidly according to Glauber-type rules, whereas the couplings evolve slowly with a dynamics involving spin correlations and Gaussian disorder. For large times the model can be solved using replica theory. In contrast to the XY-model with static disordered couplings, solving the present model requires two levels of replicas, one for the spins and one for the couplings. Relevant order parameters are defined and a phase diagram is obtained upon making the replica-symmetric Ansatz. The system exhibits two different spin-glass phases, with distinct de Almeida-Thouless lines, marking continuous replica-symmetry breaking: one describing freezing of the spins only, and one describing freezing of both spins and couplings.Comment: 7 pages, Latex, 3 eps figure

Nociceptive stimulation induces expression of Arc/Arg3.1 in the spinal cord with a preference for neurons containing enkephalin

Background: In pain processing, long term synaptic changes play an important role, especially during chronic pain. The immediate early gene Arc/Arg3.1 has been widely implicated in mediating long-term plasticity in telencephalic regions, such as the hippocampus and cortex. Accordingly, Arc/Arg3.1 knockout (KO) mice show a deficit in long-term memory consolidation. Here, we identify expression of Arc/Arg3.1 in the rat spinal cord using immunohistochemistry and in situ hybridizatio

Slowly evolving random graphs II: Adaptive geometry in finite-connectivity Hopfield models

We present an analytically solvable random graph model in which the connections between the nodes can evolve in time, adiabatically slowly compared to the dynamics of the nodes. We apply the formalism to finite connectivity attractor neural network (Hopfield) models and we show that due to the minimisation of the frustration effects the retrieval region of the phase diagram can be significantly enlarged. Moreover, the fraction of misaligned spins is reduced by this effect, and is smaller than in the infinite connectivity regime. The main cause of this difference is found to be the non-zero fraction of sites with vanishing local field when the connectivity is finite.Comment: 17 pages, 8 figure

Slowly evolving geometry in recurrent neural networks I: extreme dilution regime

We study extremely diluted spin models of neural networks in which the connectivity evolves in time, although adiabatically slowly compared to the neurons, according to stochastic equations which on average aim to reduce frustration. The (fast) neurons and (slow) connectivity variables equilibrate separately, but at different temperatures. Our model is exactly solvable in equilibrium. We obtain phase diagrams upon making the condensed ansatz (i.e. recall of one pattern). These show that, as the connectivity temperature is lowered, the volume of the retrieval phase diverges and the fraction of mis-aligned spins is reduced. Still one always retains a region in the retrieval phase where recall states other than the one corresponding to the `condensed' pattern are locally stable, so the associative memory character of our model is preserved.Comment: 18 pages, 6 figure

Diagonalization of replicated transfer matrices for disordered Ising spin systems

We present an alternative procedure for solving the eigenvalue problem of replicated transfer matrices describing disordered spin systems with (random) 1D nearest neighbor bonds and/or random fields, possibly in combination with (random) long range bonds. Our method is based on transforming the original eigenvalue problem for a $2^n\times 2^n$ matrix (where $n\to 0$) into an eigenvalue problem for integral operators. We first develop our formalism for the Ising chain with random bonds and fields, where we recover known results. We then apply our methods to models of spins which interact simultaneously via a one-dimensional ring and via more complex long-range connectivity structures, e.g. $1+\infty$ dimensional neural networks and `small world' magnets. Numerical simulations confirm our predictions satisfactorily.Comment: 24 pages, LaTex, IOP macro

Collective learning in schools described: building collective learning capacity

Processes of collective learning are expected to increase the professionalism of teachers and school leaders. Little is known about the processes of collective learning which take place in schools and about the way in which those processes may be improved. This paper describes a research into processes of collective learning at three primary schools. Processes of collective learning are described which took place in small teams in these schools. It is also pointed out which attempts can be made in order to reinforce these processes in the schools mentioned

Modelling topical photodynamic therapy treatment including the continuous production of Protoporphyrin IX

C L Campbell acknowledges financial support from an UK EPSRC PhD studentship (EP/K503162/1) and the Alfred Stewart Trust.Most existing theoretical models of photodynamic therapy (PDT) assume a uniform initial distribution of the photosensitive molecule, Protoporphyrin IX (PpIX). This is an adequate assumption when the prodrug is systematically administered; however for topical PDT this is no longer a valid assumption. Topical application and subsequent diffusion of the prodrug results in an inhomogeneous distribution of PpIX, especially after short incubation times, prior to light illumination. In this work a theoretical simulation of PDT where the PpIX distribution depends on the incubation time and the treatment modality is described. Three steps of the PpIX production are considered. The first is the distribution of the topically applied prodrug, the second in the conversion from the prodrug to PpIX and the third is the light distribution which affects the PpIX distribution through photobleaching. The light distribution is modelled using a Monte Carlo radiation transfer model and indicates treatment depths of around 2 mm during daylight PDT and approximately 3 mm during conventional PDT. The results suggest that treatment depths are not only limited by the light penetration but also by the PpIX distributionPostprintPeer reviewe