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Background Data on stoma reversal following restorative rectal resection (RRR) with a diverting stoma are conflicting. This study investigated a Danish population‐based cohort of patients undergoing RRR to evaluate factors predictive of stoma reversal during 3 years of follow‐up. Methods Patients from national registries with rectal cancer undergoing RRR or Hartmann's procedure with curative intent between May 2001 and April 2012 were included. Patients with a diverting stoma were followed from the time of primary rectal cancer resection to date of stoma reversal, death, emigration, or end of 3‐year follow‐up. The cumulative incidence proportion (CIP) of stoma reversal at 1 and 3 years was calculated, treating death as a competing risk. Factors predictive of stoma reversal were explored using Cox regression analysis. Results Of 6859 patients included, 35·7, 41·9 and 22·4 per cent respectively had a RRR with a diverting stoma, RRR without a stoma, and Hartmann's procedure with an end‐colostomy. In patients with a diverting stoma, the CIP of stoma reversal was 70·3 (95 per cent c.i. 68·4 to 72·1) per cent after 1 year, and 74·3 (72·5 to 76·0) per cent after 3 years. Neoadjuvant treatment (hazard ratio (HR) 0·75, 95 per cent c.i. 0·66 to 0·85), blood loss greater than 300 ml (HR 0·86, 0·76 to 0·97), anastomotic leak (HR 0·41, 0·33 to 0·50), T3 category (HR 0·63, 0·47 to 0·83), T4 category (HR 0·62, 0·42 to 0·90) and UICC stage IV (HR 0·57, 0·41 to 0·80) were possible predictors of delayed stoma reversal. Conclusion In one‐quarter of the patients the diverting stoma had not been reversed 3 years after the intended RRR procedure., In this Danish population‐based cohort study, the stoma reversal rate during 3 years of follow‐up was estimated among patients undergoing intended restorative rectal cancer resection, and characteristics predictive of stoma reversal were explored. One‐quarter of patients had not had their diverting stoma reversed, and more than one‐half of patients with anastomotic leakage still had the stoma after 3 years. Stoma reversal after rectal surgery

The Blume-Emery-Griffiths model with a random crystal field is studied in a random graph architecture, in which the average connectivity is a controllable parameter. The disordered average over the graph realizations is treated by replica symmetry formalism of order parameter functions. A self-consistent equation for the distribution of local fields is derived and numerically solved by a population dynamics algorithm. The results show that the average connectivity amounts to changes in the topology of the phase diagrams. Phase diagrams for representative values of the model parameters are compared with those obtained for fully connected mean field and renormalization group approaches., Comment: 14 pages, 6 figures, accepted for publication in Physica A

High lipid and high protein infant formula milk powders were stored at water activity of 0.11, 0.33 and 0.53 for up to fourteen weeks at 40 °C to investigate the effect of storage water activity on physicochemical properties and formation of volatiles to thereby recommend optimal storage water activity conditions. Water activity of the powders was determined during storage together with surface colour, glass transition temperature combined with dynamic headspace sampling followed by gas chromatography/mass spectrometry. The principal component analysis (PCA) showed that the optimal water activity for storage of high lipid infant formula milk powder, for which lipid oxidation was found to be the critical quality parameter, is aw = 0.33 with lowest lipid oxidation, while for high protein infant formula milk powder, for which protein degradation was found to be the critical quality parameter, aw = 0.11 is optimal to limit formation of Maillard reaction products.

We develop a novel method based in the sparse random graph to account the interplay between geometric frustration and disorder in cluster magnetism. Our theory allows to introduce the cluster network connectivity as a controllable parameter. Two types of inner cluster geometry are considered: triangular and tetrahedral. The theory was developed for a general, non-uniform intra-cluster interactions, but in the present paper the results presented correspond to uniform, anti-ferromagnetic (AF) intra-clusters interactions $J_{0}/J$. The clusters are represented by nodes on a finite connectivity random graph, and the inter-cluster interactions are random Gaussian distributed. The graph realizations are treated in replica theory using the formalism of order parameter functions, which allows to calculate the distribution of local fields and, as a consequence, the relevant observable. In the case of triangular cluster geometry, there is the onset of a classical Spin Liquid state at a temperature $T^{*}/J$ and then, a Cluster Spin Glass (CSG) phase at a temperature $T_{f}/J$. The CSG ground state is robust even for very weak disorder or large negative $J_{0}/J$. These results does not depend on the network connectivity. Nevertheless, variations in the connectivity strongly affect the level of frustration $f_{p}=-\Theta_{CW}/T_{f}$ for large $J_{0}/J$. In contrast, for the non-frustrated tetrahedral cluster geometry, the CSG ground state is suppressed for weak disorder or large negative $J_{0}/J$. The CSG boundary phase presents a re-entrance which is dependent on the network connectivity., Comment: 29 pages, 8 figures, submitted to PRE

We investigate thermodynamic phase transitions of the joint presence of spin glass (SG) and random field (RF) using a random graph model that allows us to deal with the quenched disorder. Therefore, the connectivity becomes a controllable parameter in our theory, allowing us to answer what the differences are between this description and the mean-field theory i.e., the fully connected theory. We have considered the random network random field Ising model where the spin exchange interaction as well as the RF are random variables following a Gaussian distribution. The results were found within the replica symmetric (RS) approximation, whose stability is obtained using the two-replica method. This also puts our work in the context of a broader discussion, which is the RS stability as a function of the connectivity. In particular, our results show that for small connectivity there is a region at zero temperature where the RS solution remains stable above a given value of the magnetic field no matter the strength of RF. Consequently, our results show important differences with the crossover between the RF and SG regimes predicted by the fully connected theory.

The plastic character of brain synapses is considered to be one of the foundations for the formation of memories. There are numerous kinds of such phenomenon currently described in the literature, but their role in the development of information pathways in neural networks with recurrent architectures is still not completely clear. In this paper we study the role of an activity-based process, called pre-synaptic dependent homeostatic scaling, in the organization of networks that yield precise-timed spiking patterns. It encodes spatio-temporal information in the synaptic weights as it associates a learned input with a specific response. We introduce a correlation measure to evaluate the precision of the spiking patterns and explore the effects of different inhibitory interactions and learning parameters. We find that large learning periods are important in order to improve the network learning capacity and discuss this ability in the presence of distinct inhibitory currents.

The van Hemmen Fermionic Ising Spin Glass (vH FISG) model in the presence of a transverse and a random magnetic field is adopted to study the inverse freezing (IF) transition, without using the replica method to treat the disorder. In this model, the spin interactions are given by a combination of random variables that follow Gaussian distribution. The random field (RF) also follows a Gaussian distribution. The introduction of allow us to investigate the IF under the effects of a disorder which is not a source of frustration. A particularity of this fermionic formalism is that the chemical potential and the provide a magnetic dilution and quantum spin flip mechanism, respectively. The results show a reentrant transition from the spin glass (SG) to the paramagnetic (PM) phase in the absence of and . This reentrance appears for a certain range of , in which is found a PM phase (at low temperatures) with lower entropy than the SG state, characterising the IF. However, the IF is gradually suppressed when the ...

The ability of a metric attractor neural networks (MANN) to learn structured patterns is analyzed. In particular we consider collections of fingerprints, which present some local features, rather than being modeled by random patterns. The network retrieval proved to be robust to varying the pattern activity, the threshold strategy, the topological arrangement of the connections, and for several types of noisy configuration. We found that the lower the fingerprint patterns activity is, the higher the load ratio and retrieval quality are. A simplified theoretical framework, for the unbiased case, is developed as a function of five parameters: the load ratio, the finiteness connectivity, the density degree of the network, randomness ratio, and the spatial pattern correlation. Linked to the latter appears a new neural dynamics variable: the spatial neural correlation. The theory agrees quite well with the experimental results.

In this paper we investigate the three-state spins van Hemmen model with a crystalline field in the random network. The van Hemmen model is known for being treated without the use of replicas. The same method used by van Hemmen is utilized here to average over disordered exchange interactions. To deal with averages over the realizations of the lattice we utilized the replica symmetry formalism of order parameter functions. The order parameters calculated to detect which phase the system encounters are calculated numerically by means of a population dynamics algorithm. Firstly we obtained phase diagrams in low fixed temperature in the diagram crystalline field versus random field, we observe higher connectivities to give rise to segregation between high activity and low activity spin glass (SG) phases, also we verify the appearance of a tricritical point in the low crystalline field region. Finally to account the effects of high thermal fluctuations we have drawn phase diagrams in the temperature versus random field plane for fixed values of crystalline field. Here we observe an important modification of the phase plane topology by the increment of network connectivity. It is also notable the presence of a reentrant behavior in first and second order phase transitions when the crystalline field is sufficiently large.

We study magnetic properties of the 3-state spin ( S i = 0 and ± 1 ) spin glass (SG) van Hemmen model with ferromagnetic interaction J 0 under a random field (RF). The RF follows a bimodal distribution The combined effect of the crystal field D and the special type of on-site random interaction of the van Hemmen model engenders the unfolding of the SG phases for strong enough RF, i. e., instead of one SG phase, we found two SG phases. Moreover, as J 0 is finite, there is also the unfolding of the mixed phase (with the SG order parameter and the spontaneous magnetization simultaneously finite) in four distinct phases. The emergence of these new phases separated by first and second order line transitions produces a multiplication of triple and multicritical points.

The Random Field Ising Model (RFIM) following bimodal and Gaussian distributions for the RF is investigated using a finite connectivity technique. We focused on determining the order of the phase transition as well as the existence of a tricritical point as a function of the connectivity c for both types of RF distribution. Our results indicate that for the Gaussian distribution the phase transition is always second-order. For the bimodal distribution, there is indeed a tricritical point. However, its location is strongly dependent on c . The tricritical point is suppressed below a certain minimum value of connectivity.

IMPORTANCE Molecular diagnosis is difficult to achieve in disease groups with a highly heterogeneous genetic background, such as cerebellar ataxia (CA). In many patients, candidate gene sequencing or focused resequencing arrays do not allow investigators to reach a genetic conclusion. OBJECTIVES To assess the efficacy of exome-targeted capture sequencing to detect mutations in genes broadly linked to CA in a large cohort of undiagnosed patients and to investigate their prevalence. DESIGN, SETTING, AND PARTICIPANTS Three hundred nineteen index patients with CA and without a history of dominant transmission were included in the this cohort study by the Spastic Paraplegia and Ataxia Network. Centralized storage was in the DNA and cell bank of the Brain and Spine Institute, Salpetriere Hospital, Paris, France. Patients were classified into 6 clinical groups, with the largest being those with spastic ataxia (ie, CA with pyramidal signs [n = 100]). Sequencing was performed from January 1, 2014, through December 31, 2016. Detected variants were classified as very probably or definitely causative, possibly causative, or of unknown significance based on genetic evidence and genotype-phenotype considerations. MAIN OUTCOMES AND MEASURES Identification of variants in genes broadly linked to CA, classified in pathogenicity groups. RESULTS The 319 included patients had equal sex distribution (160 female [50.2%] and 159 male patients [49.8%]; mean [SD] age at onset, 27.9 [18.6] years). The age at onset was younger than 25 years for 131 of 298 patients (44.0%) with complete clinical information. Consanguinity was present in 101 of 298 (33.9%). Very probable or definite diagnoses were achieved for 72 patients (22.6%), with an additional 19 (6.0%) harboring possibly pathogenic variants. The most frequently mutated genes were SPG7 (n = 14), SACS (n = 8), SETX (n = 7), SYNE1 (n = 6), and CACNA1A (n = 6). The highest diagnostic rate was obtained for patients with an autosomal recessive CA with oculomotor apraxia-like phenotype (6 of 17 [35.3%]) or spastic ataxia (35 of 100 [35.0%]) and patients with onset before 25 years of age (41 of 131 [31.3%]). Peculiar phenotypes were reported for patients carrying KCND3 or ERCC5 variants. CONCLUSIONS AND RELEVANCE Exome capture followed by targeted analysis allows the molecular diagnosis in patients with highly heterogeneous mendelian disorders, such as CA, without prior assumption of the inheritance mode or causative gene. Being commonly available without specific design need, this procedure allows testing of a broader range of genes, consequently describing less classic phenotype-genotype correlations, and post hoc reanalysis of data as new genes are implicated in the disease. © 2018 American Medical Association. All rights reserved.

The interplay between quenched disorder provided by a random field (RF) and network connectivity in the Blume-Capel (BC) model is the subject of this paper. The replica method is used to average over the network randomness. It offers an alternative analytic route to both numerical simulations and standard mean field approaches. The results reveal a rich thermodynamic scenario with multicritical points that are strongly dependent on network connectivity. In addition, we also demonstrate that the RF has a deep effect on the inverse melting transition. This highly nontrivial type of phase transition has been proposed to exist in the BC model as a function of network topology. Our results confirm that the topological mechanism can lead to an inverse melting transition. Nevertheless, our results also show that as the RF becomes stronger, the paramagnetic phase is affected in such way that the topological mechanism for inverse melting is disabled.

Hexanal formation and pentanal formation were found to be early signs of quality deterioration of both a high lipid infant formula milk powder and a high protein infant formula milk powder with similar lactose and water content; these aldehydes were sensitive predictors for shelf-life at ambient conditions using short term storage at temperatures up to 55 °C. Higher temperature loads such as 70 °C were found to induce rapid lactose crystallisation entailing a rising water activity, higher levels of free radicals, rapid browning and formation of Strecker aldehydes from protein degradation, of which all were found to be unsuitable for shelf-life prediction. Advanced glycation end products were not detected during storage at 25 °C for up to 18 weeks and the rate of oxygen consumption for the reconstituted milk drink was not affected by storage of the infant formulas at 25 °C; neither can be recommended for shelf-life prediction.

A synaptic connectivity model is assembled on a spiking neuron network aiming to build up a dynamic pattern recognition system. The connection architecture includes gap junctions and both inhibitory and excitatory chemical synapses based on Hebb’s hypothesis. The network evolution resulting from external stimulus is sampled in a properly defined frequency space. Neurons’ responses to different current injections are mapped onto a subspace using Principal Component Analysis. Departing from the base attractor, related to a quiescent state, different external stimuli drive the network to different fixed points through specific trajectories in this subspace.

Sparse-code networks have retrieval abilities which are strongly dependent on the firing threshold for the neurons. If the connections are spatially uniform, the macroscopic properties of the network can be measured by the overlap between neurons and learned patterns, and by the global activity. However, for nonuniform networks, for instance small-world networks, the neurons can retrieve fragments of patterns without performing global retrieval. Local overlaps are needed to describe the network. We characterize the structure type of the neural states using a parameter that is related to fluctuations of the local overlaps, with distinction between bump and block phases. Simulation of neural dynamics shows a competition between localized (bump), structured (block) and global retrieval. When the network topology randomness increases, the phase-diagram shows a transition from local to global retrieval. Furthermore, the local phase splits into a bump phase for low activity and a block phase for high activity. A theoretical approach solves the asymptotic limit of the model, and confirms the simulation results which predicts the change of stability from bumps to blocks when the storage ratio increases.

The dynamical activity of a neural network model composed of electrically connected map-based neurons is investigated. After detailing the behavior of the isolated neuron for a wide parameter range, collective network states are depicted using the activity, spatial correlation and time phase distribution as measures. A detailed discussion on the stability of global and partial synchronization states is presented.

The three-state Ising neural network with synchronous updating and variable dilution is discussed starting from the appropriate Hamiltonians. The thermodynamic and retrieval properties are examined using replica mean-field theory. Capacity-temperature phase diagrams are derived for several values of the pattern activity and different gradations of dilution, and the information content is calculated. The results are compared with those for sequential updating. The effect of self-coupling is established. Also the dynamics is studied using the generating function technique for both synchronous and sequential updating. Typical flow diagrams for the overlap order parameter are presented. The differences with the signal-to-noise approach are outlined., Comment: 21 pages Latex, 12 eps figures and 1 ps figure

In the regime of partial synchronization, networks of diffusively coupled Hindmarch–Rose neurons show coherent structures developing in a region of the phase space which is wider than in the correspondent single neuron. Such structures are kept, without important changes, during several bursting periods. In this work, we study the time evolution of these structures and their dynamical stability under damage. This system may model the behavior of ensembles of neurons coupled through a bidirectional gap junction or, in a broader sense, it could also account for the molecular cascades present in the formation of flash and short time memory.

The time evolution of an exactly solvable layered feedforward neural network with three-state neurons and optimizing the mutual information is studied for arbitrary synaptic noise (temperature). Detailed stationary temperature-capacity and capacity-activity phase diagrams are obtained. The model exhibits pattern retrieval, pattern-fluctuation retrieval and spin-glass phases. It is found that there is an improved performance in the form of both a larger critical capacity and information content compared with three-state Ising-type layered network models. Flow diagrams reveal that saddle-point solutions associated with fluctuation overlaps slow down considerably the flow of the network states towards the stable fixed-points., Comment: 17 pages Latex including 6 eps-figures

Langmuir waves and turbulence resulting from an electron beam-plasma instability play a fundamental role in the generation of solar radio bursts. We report recent theoretical advances in nonlinear dynamics of Langmuir waves. First, starting from the generalized Zakharov equations, we study the parametric excitation of solar radio bursts at the fundamental plasma frequency driven by a pair of oppositely propagating Langmuir waves with different wave amplitudes. Next, we briefly discuss the emergence of chaos in the Zakharov equations. We point out that chaos can lead to turbulence in the source regions of solar radio emissions.

The principle of adaptation in a noisy retrieval environment is extended here to a diluted attractor neural network of Q-state neurons trained with noisy data. The network is adapted to an appropriate noisy training overlap and training activity which are determined self-consistently by the optimized retrieval attractor overlap and activity. The optimized storage capacity and the corresponding retriever overlap are considerably enhanced by an adequate threshold in the states. Explicit results for improved optimal performance and new retriever phase diagrams are obtained for Q=3 and Q=4, with coexisting phases over a wide range of thresholds. Most of the interesting results are stable to replica-symmetry-breaking fluctuations., 22 pages, 5 figures, accepted for publication in PRE

Electron Spin Resonance spectroscopy (ESR) was used to measure the mobility of the spin probe TEMPO in O/W-emulsions. This allowed determination of temperature-dependent microviscosity of the liquid fraction in lipid globules. Six hydrogenated palm kernel oil (HPKO) based emulsions containing caseinate and different combinations of lactic acid ester of monoglyceride (LACTEM), unsaturated monoglycerides (GMU) or saturated monoglyceride (GMS) were studied. The non-solidified oil in emulsions made with LACTEM+GMU had a high microviscosity, whereas the emulsion made with GMS had a low microviscosity. Also the partitioning of TEMPO between the lipid and aqueous phases was found to be highly temperature dependent, most likely due to the change of solid fat content with temperature. This behaviour may mimic the partitioning of aroma compounds in emulsions. The spin probe 5-doxylstearic acid was used to study the mobility of the components at the lipid globule surfaces. At 5°C all emulsions had a very low surface mobility. At 25°C the mobility of the spin probe was found to be correlated to the surface protein load. Emulsions with GMU had a high protein surface coverage and low mobility of the spin probe on the droplet surfaces. Conversely, in presence of LACTEM and GMS, the protein surface loads decreased and high surface mobilities were observed. Based on these results it is argued that the high macroscopic viscosity and lipid agglomeration of emulsions containing GMU is due to a lipid globule-protein-network where the lipid globules are connected via caseinate.

In this work we study the effects of three different strategies to associate memories in a neural network composed by both excitatory and inhibitory spiking neurons, which are randomly connected through recurrent excitatory and inhibitory synapses. The system is intended to store a number of memories, associated to spatial external inputs. The strategies consist in the presentation of the input patterns through trials in: i) ordered sequence; ii) random sequence; iii) clustered sequences. In addition, an order parameter indicating the correlation between the trials' activities is introduced to compute associative memory capacities and the quality of memory retrieval.

Different areas of the brain are involved in specific aspects of the information being processed both in learning and in memory formation. For example, the hippocampus is important in the consolidation of information from short-term memory to long-term memory, while emotional memory seems to be dealt by the amygdala. On the microscopic scale the underlying structures in these areas differ in the kind of neurons involved, in their connectivity, or in their clustering degree but, at this level, learning and memory are attributed to neuronal synapses mediated by longterm potentiation and long-term depression. In this work we explore the properties of a short range synaptic connection network, a nearest neighbor lattice composed mostly by excitatory neurons and a fraction of inhibitory ones. The mechanism of synaptic modification responsible for the emergence of memory is Spike-Timing-Dependent Plasticity (STDP), a Hebbian-like rule, where potentiation/depression is acquired when causal/non-causal spikes happen in a synapse involving two neurons. The system is intended to store and recognize memories associated to spatial external inputs presented as simple geometrical forms. The synaptic modifications are continuously applied to excitatory connections, including a homeostasis rule and STDP. In this work we explore the different scenarios under which a network with short range connections can accomplish the task of storing and recognizing simple connected patterns.

The synaptic plasticity rules that sculpt a neural network architecture are key elements to understand cortical processing, as they may explain the emergence of stable, functional activity, while avoiding runaway excitation. For an associative memory framework, they should be built in a way as to enable the network to reproduce a robust spatio-temporal trajectory in response to an external stimulus. Still, how these rules may be implemented in recurrent networks and the way they relate to their capacity of pattern recognition remains unclear. We studied the effects of three phenomenological unsupervised rules in sparsely connected recurrent networks for associative memory: spike-timing-dependent-plasticity, short-term-plasticity and an homeostatic scaling. The system stability is monitored during the learning process of the network, as the mean firing rate converges to a value determined by the homeostatic scaling. Afterwards, it is possible to measure the recovery efficiency of the activity following each initial stimulus. This is evaluated by a measure of the correlation between spike fire timings, and we analysed the full memory separation capacity and limitations of this system.

A recent study on noiseless learning and retrieval in attractor neural networks above saturation, by Griniasty and Gutfreund, is extended to take account for imperfect learning by means of a temperature β −1 = T . Violations of the constraint imposed on the local stabilities are taken into account by various cost functions. The distribution of local stabilities and the fraction of errors during the learning stage are analysed. The retrieval dynamics for the sparsely connected network is studied showing high retrieval overlap in reduced retrieval regions for finite T .

The phase diagrams of the three-state Ghatak-Sherrington spin-glass (or random Blume-Capel) model are obtained in mean-field theory with replica symmetry in order to study the effects of a ferromagnetic bias and finite random connectivity in which each spin is connected to a finite number of other spins. It is shown that inverse melting from a ferromagnetic to a low-temperature paramagnetic phase may appear for small but finite disorder and that inverse freezing appears for large disorder. There can also be a continuous inverse ferromagnetic to spin-glass transition.

This study examined the quality of International Classification of Diseases-10 colorectal cancer (CRC) diagnosis coding in the Danish National Registry of Patients (DNRP), using the Danish Cancer Registry (DCR) as a reference. We included all patients in Denmark with a CRC diagnosis in the DNRP and/or in the DCR from 2001 through 2006. Data quality was evaluated by estimating completeness and positive predictive value (PPV) of data in different subcategories of patients. We estimated mortality and date of diagnosis, to evaluate the effect of potential differences in data quality. Overall completeness of data in the DNRP for CRC was 93.4% [95% confidence interval (CI): 93.1-93.7] and the PPV was 88.9% (95% CI: 88.5-89.2). Completeness and PPV improved during the study period. However, the completeness of data for patients75 years in the 2001-2003 period [88.8% (95% CI: 87.8-89.6)] was lower than average, and cancers in more unspecific locations and cancers in the colorectal junction also had lower estimates (below 90%). There were no differences in survival estimates in the DNRP compared to the DCR. In conclusion, this study shows high CRC data quality in the DNRP measured by completeness and PPV, except in a few subgroups.

A synaptic architecture featuring both excitatory and inhibitory neurons is assembled aiming to build up an associative memory system. The connections follow a hebbian-like rule. The network activity is analyzed using a multidimensional reduction method, Principal Component Analysis (PCA), applied to neuron firing rates. The patterns are discriminated and recognized by well defined paths that emerge within PCA subspaces, one for each pattern. Detailed comparisons among these subspaces are used to evaluate the network storage capacity. We show a transition from a retrieval to a non-retrieval regime as the number of stored patterns increases. When gap junctions are implemented together with the chemical synapses, this transition is shifted and a larger number of memories is associated to the network.

The statistical mechanics of a two-state Ising spin-glass model with finite random connectivity, in which each site is connected to a finite number of other sites, is extended in this work within the replica technique to study the phase transitions in the three-state Ghatak-Sherrington (or random Blume-Capel) model of a spin glass with a crystal field term. The replica symmetry ansatz for the order function is expressed in terms of a two-dimensional effective-field distribution which is determined numerically by means of a population dynamics procedure. Phase diagrams are obtained exhibiting phase boundaries which have a reentrance with both a continuous and a genuine first-order transition with a discontinuity in the entropy. This may be seen as "inverse freezing", which has been studied extensively lately, as a process either with or without exchange of latent heat., Comment: 10 pages, 6 figures

Oxidative Stability of bread is increasingly being recognized as important for long shelf-life. Formation of free radicals in whole wheat flour and white flour during heating was compared using Electron Spin Resonance spectroscopy in order to identify the primary oxidative events. Heating lead to a higher content of free radicals in wholemeal wheat flour than in white flour, since components in the husk seem to make a major contribution. Two different pathways are suggested for the formation of free radicals in wheat flour. The activation energy for the radical formation in the flours was estimated by Arrhenius plot as 34 kJ mol−1 up to 453 K, and above 473 K higher activation energies were observed. The change in activation energies indicates a change in reaction mechanisms for oxidation probably involving species with different mobility, i.e. different molecular weight. The accumulated concentration of free radicals in heated flour increased during one month storage and it is slightly affected by the water activity in the storage container.

A break in replica symmetry is found above the critical line alpha c( kappa ) for storage with a finite minimal fraction of errors, within the first stage of the Parisi scheme, for noiseless networks with continuous synapses. The replica symmetry breaking solution yields the highest (most stable) minimal fraction of errors everywhere above alpha c( kappa ).

The storage properties of an optimal neural network with correlated patterns is studied allowing for a finite fraction of errors determined by the Gardner-Derrida cost function. A discontinuity in the probability distribution of the local stabilities is seen as a drastic decrease in the minimal fraction of errors. There is also an enlargement of the domain in which the replica symmetric solution is stable, allowing for higher storage capacities.

The Hopfield model of associative memory with the Hebb learning rule is studied for a finite number p of correlated patterns. The storage capacity α = P/N is considered in a network with further P − p embedded uncorrelated patterns, and the corresponding phase diagrams are exhibited. Numerical simulations are carried out to discuss the retrieval quality and the basins of attraction of the network.

We investigate the dynamical states of a two-dimensional network of Hindmarsh-Rose spiking neurons, in the vicinity of the current threshold where the single neuron becomes active. Each neuron is electrically coupled with neurons in its close neighborhood. The existence of multistable synchronization states is established and discussed. We also show that, provided adequate initial conditions, the collective behavior is able to keep the network in activity, even for current values far below the activity threshold of the single neuron. A phase diagram of the different network states is presented for a large interval of the coupling-current parameter space.

Layered neural networks are feedforward structures that yield robust parallel and distributed pattern recognition. Even though much attention has been paid to pattern retrieval properties in such systems, many aspects of their dynamics are not yet well characterized or understood. In this work we study, at different temperatures, the memory activity and information flows through layered networks in which the elements are the simplest binary odd non-monotonic function. Our results show that, considering a standard Hebbian learning approach, the network information content has its maximum always at the monotonic limit, even though the maximum memory capacity can be found at non-monotonic values for small enough temperatures. Furthermore, we show that such systems exhibit rich macroscopic dynamics, including not only fixed point solutions of its iterative map, but also cyclic and chaotic attractors that also carry information.

The Hindmarsh-Rose (HR) system of equations is a model that captures the essential of the spiking activity of biological neurons. In this work we present an exploratory numerical study of the time activities of two HR neurons interacting through electrical synapses. The knowledge of this simple system is a first step towards the understanding of the cooperative behavior of large neural assemblies. Several periodic and chaotic attractors where identified, as the coupling strength is increased from zero until the perfect synchronization regime. In addition to the known phase locking synchronization at weak coupling, electrical synapses also allow for both in-phase and antiphase synchronization from moderate to strong coupling. A regime where the system changes apparently randomly between in-phase and antiphase locking evolves to a bistability regime, where both in-phase and antiphase periodic attractors are locally stable. At the strong coupling regime in-phase chaotic evolution dominates, but windows with complex periodic behavior are also present.

The dynamics and the stationary states of an exactly solvable three-state layered feed-forward neural network model with asymmetric synaptic connections, finite dilution and low pattern activity are studied in extension of a recent work on a recurrent network. Detailed phase diagrams are obtained for the stationary states and for the time evolution of the retrieval overlap with a single pattern. It is shown that the network develops instabilities for low thresholds and that there is a gradual improvement in network performance with increasing threshold up to an optimal stage. The robustness to synaptic noise is checked and the effects of dilution and of variable threshold on the information content of the network are also established., Comment: Latex, 11 pages, 6 figures

We discuss, in this paper, the dynamical properties of extremely diluted, non-monotonic neural networks. Assuming parallel updating and the Hebb prescription for the synaptic connections, a flow equation for the macroscopic overlap is derived. A rich dynamical phase diagram was obtained, showing a stable retrieval phase, as well as a cycle two and chaotic behavior. Numerical simulations were performed, showing good agreement with analytical results. Furthermore, the simulations give an additional insight into the microscopic dynamical behavior during the chaotic phase. It is shown that the freezing of individual neuron states is related to the structure of chaotic attractors., 11 pages, 4 figures

The macroscopic dynamics of an extremely diluted threestate neural network based on mutual information and mean-field theory arguments is studied in order to establish the stability of the stationary states. Results are presented in terms of the pattern-recognition overlap, the neural activity, and the activity-overlap. It is shown that the presence of synaptic noise is essential for the stability of states that recognize only the active patterns when the full structure of the patterns is not recognizable. Basins of attraction of considerable size are obtained in all cases for a not too large storage ratio of patterns.

The categorization ability of fully connected neural network models, with either discrete or continuous Q-state units, is studied in this work in replica symmetric mean-field theory. Hierarchically correlated multistate patterns in a two level structure of ancestors and descendents (examples) are embedded in the network and the categorization task consists in recognizing the ancestors when the network is trained exclusively with their descendents. Explicit results for the dependence of the equilibrium properties of a Q=3-state model and a Q=infinity-state model are obtained in the form of phase diagrams and categorization curves. A strong improvement of the categorization ability is found when the network is trained with examples of low activity. The categorization ability is found to be robust to finite threshold and synaptic noise. The Almeida-Thouless lines that limit the validity of the replica-symmetric results, are also obtained.

In this work we analyze the interaction of isolated solitary structures and ion-acoustic radiation. If the radiation amplitude is small solitary structures persists, but when the amplitude grows energy transfer towards small spatial scales occurs. We show that transfer is particularly fast when a fixed point of a low dimensional model is destroyed., Comment: LaTex + 4 eps files

Perceptrons with graded input-output relations and a limited output precision are studied within the Gardner-Derrida canonical ensemble approach. Soft non- negative error measures are introduced allowing for extended retrieval properties. In particular, the performance of these systems for a linear and quadratic error measure, corresponding to the perceptron respectively the adaline learning algorithm, is compared with the performance for a rigid error measure, simply counting the number of errors. Replica-symmetry-breaking effects are evaluated., Comment: 26 pages, 10 ps figures

In the present paper we investigate the process of energy transfer in the Zakharov equations. Energy is initially injected into modes with small wave vectors. When the modulational instability threshold is exceeded, some additional modes with small wave vectors are excited and solitons are formed if one lies in a quasiintegrable regime and if the number of excited modes is large enough. These solitons are formed as a direct result of the modulational instability and in fact saturate the instability. However, use of a low-dimensional formalism based on collective variables shows that if the largest length scale of the linearly excited modes is much longer than the most unstable, these solitons may be greatly influenced as they interact with ion-acoustic waves. In those cases, full simulation of the space-time problem indicates that energy is progressively transferred to modes with very small length scales. Since we work with one spatial dimension, collapse is absent and energy transfer is due to the stochastic dynamics. @S1063-651X~98!02303-4#

After 1933 many scientists and university teachers were obliged to relinquish their posts in the universities of Germany because of national-socialist laws. Organizations-in-aid like the Academic Assistance Council in Great Britain tried to 'defend science and learning' raising funds and finding new openings for the expelled academics. But as immigration laws were tight and jobs were scarce in the host countries the AAC and the other organizations had to select the most qualified from among the applicants for support. -- The questions the article tries to answer are: What kind of criteria were applied in this selection? Who were the experts? How were the placements made? How did the applicants react to the decisions? Taking for example the AAC, it examines measures to assist a group of scientists who, having tried to settle down in England, finally emigrated to Turkey.