How are close residues of protein structures distributed in primary sequence?

Structurally neighboring residues are categorized according to their separation in the primary sequence as proximal (1-4 positions apart) and otherwise distal, which in turn is divided into near (5-20 positions), far (21-50 positions), very far ( > 50 positions), and interchain (from different chains of the same structure). These categories describe the linear distance histogram (LDH) for three-dimensional neighboring residue types. Among the main results are the following: (i) nearest-neighbor hydrophobic residues tend to be increasingly distally separated in the linear sequence, thus most often connecting distinct secondary structure units. (ii) The LDHs of oppositely charged nearest-neighbors emphasize proximal positions with a subsidiary maximum for very far positions. (iii) Cysteine-cysteine structural interactions rarely involve proximal positions. (iv) The greatest numbers of interchain specific nearest-neighbors in protein structures are composed of oppositely charged residues. (v) The largest fraction of side-chain neighboring residues from beta-strands involves near positions, emphasizing associations between consecutive strands. (vi) Exposed residue pairs are predominantly located in proximal linear positions, while buried residue pairs principally correspond to far or very far distal positions. The results are principally invariant to protein sizes, amino acid usages, linear distance normalizations, and over- and underrepresentations among nearest-neighbor types. Interpretations and hypotheses concerning the LDHs, particularly those of hydrophobic and charged pairings, are discussed with respect to protein stability and functionality. The pronounced occurrence of oppositely charged interchain contacts is consistent with many observations on protein complexes where multichain stabilization is facilitated by electrostatic interactions.

Temporal fluctuations in coherence of brain waves.

As a measure of dynamical structure, short-term fluctuations of coherence between 0.3 and 100 Hz in the electroencephalogram (EEG) of humans were studied from recordings made by chronic subdural macroelectrodes 5-10 mm apart, on temporal, frontal, and parietal lobes, and from intracranial probes deep in the temporal lobe, including the hippocampus, during sleep, alert, and seizure states. The time series of coherence between adjacent sites calculated every second or less often varies widely in stability over time; sometimes it is stable for half a minute or more. Within 2-min samples, coherence commonly fluctuates by a factor up to 2-3, in all bands, within the time scale of seconds to tens of seconds. The power spectrum of the time series of these fluctuations is broad, extending to 0.02 Hz or slower, and is weighted toward the slower frequencies; little power is faster than 0.5 Hz. Some records show conspicuous swings with a preferred duration of 5-15s, either irregularly or quasirhythmically with a broad peak around 0.1 Hz. Periodicity is not statistically significant in most records. In our sampling, we have not found a consistent difference between lobes of the brain, subdural and depth electrodes, or sleeping and waking states. Seizures generally raise the mean coherence in all frequencies and may reduce the fluctuations by a ceiling effect. The coherence time series of different bands is positively correlated (0.45 overall); significant nonindependence extends for at least two octaves. Coherence fluctuations are quite local; the time series of adjacent electrodes is correlated with that of the nearest neighbor pairs (10 mm) to a coefficient averaging approximately 0.4, falling to approximately 0.2 for neighbors-but-one (20 mm) and to < 0.1 for neighbors-but-two (30 mm). The evidence indicates fine structure in time and space, a dynamic and local determination of this measure of cooperativity. Widely separated frequencies tending to fluctuate together exclude independent oscillators as the general or usual basis of the EEG, although a few rhythms are well known under special conditions. Broad-band events may be the more usual generators. Loci only a few millimeters apart can fluctuate widely in seconds, either in parallel or independently. Scalp EEG coherence cannot be predicted from subdural or deep recordings, or vice versa, and intracortical microelectrodes show still greater coherence fluctuation in space and time. Widely used computations of chaos and dimensionality made upon data from scalp or even subdural or depth electrodes, even when reproducible in successive samples, cannot be considered representative of the brain or the given structure or brain state but only of the scale or view (receptive field) of the electrodes used. Relevant to the evolution of more complex brains, which is an outstanding fact of animal evolution, we believe that measures of cooperativity are likely to be among the dynamic features by which major evolutionary grades of brains differ.

Electrical investigation of p-type 4H-SiC heavily doped by ion implantation

In the last decades, an increasing interest in the research field of wide bandgap semiconductors was observed, mostly due to the progressive approaching of silicon-based devices to their theoretical limits. 4H-SiC is an example among these, and is a mature compound for applications. The main advantages offered 4H-SiC in comparison with silicon are an higher breakdown field, an higher thermal conductivity, a higher operating temperature, very high hardness and melting point, biocompatibility, but also low switching losses in high frequencies applications and lower on-resistances in unipolar devices. Then, 4H-SiC power devices offer great performance improvement; moreover, they can work in hostile environments where silicon power devices cannot function. Ion implantation technology is a key process in the fabrication of almost all kinds of SiC devices, owing to the advantage of a spatially selective doping. This work is dedicated to the electrical investigation of several differently-processed 4H-SiC ion- implanted samples, mainly through Hall effect and space charge spectroscopy experiments. It was also developed the automatic control (Labview) of several experiments. In the work, the effectiveness of high temperature post-implant thermal treatments (up to 2000°C) were studied and compared considering: (i) different methods, (ii) different temperatures and (iii) different duration of the annealing process. Preliminary p + /n and Schottky junctions were also investigated as simple test devices. 1) Heavy doping by ion implantation of single off-axis 4H-SiC layers The electrical investigation is one of the most important characterization of ion-implanted samples, which must be submitted to mandatory post-implant thermal treatment in order to both (i) recover the lattice after ion bombardment, and (ii) address the implanted impurities into lattice sites so that they can effectively act as dopants. Electrical investigation can give fundamental information on the efficiency of the electrical impurity activation. To understand the results of the research it should be noted that: (a) To realize good ohmic contacts it is necessary to obtain spatially defined highly doped regions, which must have conductivity as low as possible. (b) It has been shown that the electrical activation efficiency and the electrical conductivity increase with the annealing temperature increasing. (c) To maximize the layer conductivity, temperatures around 1700°C are generally used and implantation density high till to 10 21 cm -3 . In this work, an original approach, different from (c), is explored by the using very high annealing temperature, around 2000°C, on samples of Al + -implant concentration of the order of 10 20 cm -3 . Several Al + -implanted 4H-SiC samples, resulting of p-type conductivity, were investigated, with a nominal density varying in the range of about 1-5∙10 20 cm -3 and subjected to two different high temperature thermal treatments. One annealing method uses a radiofrequency heated furnace till to 1950°C (Conventional Annealing, CA), the other exploits a microwave field, providing a fast heating rate up to 2000°C (Micro-Wave Annealing, MWA). In this contest, mainly ion implanted p-type samples were investigated, both off-axis and on-axis <0001> semi-insulating 4H-SiC. Concerning p-type off-axis samples, a high electrical activation of implanted Al (50-70%) and a compensation ratio below 10% were estimated. In the work, the main sample processing parameters have been varied, as the implant temperature, CA annealing duration, and heating/cooling rates, and the best values assessed. MWA method leads to higher hole density and lower mobility than CA in equivalent ion implanted layers, resulting in lower resistivity, probably related to the 50°C higher annealing temperature. An optimal duration of the CA treatment was estimated in about 12-13 minutes. A RT resistivity on the lowest reported in literature for this kind of samples, has been obtained. 2) Low resistivity data: variable range hopping Notwithstanding the heavy p-type doping levels, the carrier density remained less than the critical one required for a semiconductor to metal transition. However, the high carrier densities obtained was enough to trigger a low temperature impurity band (IB) conduction. In the heaviest doped samples, such a conduction mechanism persists till to RT, without significantly prejudice the mobility values. This feature can have an interesting technological fall, because it guarantee a nearly temperature- independent carrier density, it being not affected by freeze-out effects. The usual transport mechanism occurring in the IB conduction is the nearest neighbor hopping: such a regime is effectively consistent with the resistivity temperature behavior of the lowest doped samples. In the heavier doped samples, however, a trend of the resistivity data compatible with a variable range hopping (VRH) conduction has been pointed out, here highlighted for the first time in p-type 4H-SiC. Even more: in the heaviest doped samples, and in particular, in those annealed by MWA, the temperature dependence of the resistivity data is consistent with a reduced dimensionality (2D) of the VRH conduction. In these samples, TEM investigation pointed out faulted dislocation loops in the basal plane, whose average spacing along the c-axis is comparable with the optimal length of the hops in the VRH transport. This result suggested the assignment of such a peculiar behavior to a kind of spatial confinement into a plane of the carrier hops. 3) Test device the p + -n junction In the last part of the work, the electrical properties of 4H-SiC diodes were also studied. In this case, a heavy Al + ion implantation was realized on n-type epilayers, according to the technological process applied for final devices. Good rectification properties was shown from these preliminary devices in their current-voltage characteristics. Admittance spectroscopy and deep level transient spectroscopy measurements showed the presence of electrically active defects other than the dopants ones, induced in the active region of the diodes by ion implantation. A critical comparison with the literature of these defects was performed. Preliminary to such an investigation, it was assessed the experimental set up for the admittance spectroscopy and current-voltage investigation and the automatic control of these measurements.

Janus: an FPGA-based system for high-performance scientific computing

This paper describes JANUS, a modular massively parallel and reconfigurable FPGA-based computing system. Each JANUS module has a computational core and a host. The computational core is a 4x4 array of FPGA-based processing elements with nearest-neighbor data links. Processors are also directly connected to an I/O node attached to the JANUS host, a conventional PC. JANUS is tailored for, but not limited to, the requirements of a class of hard scientific applications characterized by regular code structure, unconventional data manipulation instructions and not too large data-base size. We discuss the architecture of this configurable machine, and focus on its use on Monte Carlo simulations of statistical mechanics. On this class of application JANUS achieves impressive performances: in some cases one JANUS processing element outperfoms high-end PCs by a factor ≈1000. We also discuss the role of JANUS on other classes of scientific applications.

Quantum simulation of a topological Mott insulator with Rydberg atoms in a Lieb lattice

We propose a realistic scheme to quantum simulate the so-far experimentally unobserved topological Mott insulator phase-an interaction-driven topological insulator-using cold atoms in an optical Lieb lattice. To this end, we study a system of spinless fermions in a Lieb lattice, exhibiting repulsive nearest-and next-to-nearest-neighbor interactions and derive the associated zero-temperature phase diagram within mean-field approximation. In particular, we analyze how the interactions can dynamically generate a charge density wave ordered, a nematic, and a topologically nontrivial quantum anomalous Hall phase. We characterize the topology of the different phases by the Chern number and discuss the possibility of phase coexistence. Based on the identified phases, we propose a realistic implementation of this model using cold Rydberg-dressed atoms in an optical lattice. The scheme, which allows one to access, in particular, the topological Mott insulator phase, robustly and independently of its exact position in parameter space, merely requires global, always-on off-resonant laser coupling to Rydberg states and is feasible with state-of-the-art experimental techniques that have already been demonstrated in the laboratory.

Multiclassificador inteligente de falhas no domínio do tempo em motores de indução trifásicos alimentados por inversores de frequência

Os motores de indução desempenham um importante papel na indústria, fato este que destaca a importância do correto diagnóstico e classificação de falhas ainda em fase inicial de sua evolução, possibilitando aumento na produtividade e, principalmente, eliminando graves danos aos processos e às máquinas. Assim, a proposta desta tese consiste em apresentar um multiclassificador inteligente para o diagnóstico de motor sem defeitos, falhas de curto-circuito nos enrolamentos do estator, falhas de rotor e falhas de rolamentos em motores de indução trifásicos acionados por diferentes modelos de inversores de frequência por meio da análise das amplitudes dos sinais de corrente de estator no domínio do tempo. Para avaliar a precisão de classificação frente aos diversos níveis de severidade das falhas, foram comparados os desempenhos de quatro técnicas distintas de aprendizado de máquina; a saber: (i) Rede Fuzzy Artmap, (ii) Rede Perceptron Multicamadas, (iii) Máquina de Vetores de Suporte e (iv) k-Vizinhos-Próximos. Resultados experimentais obtidos a partir de 13.574 ensaios experimentais são apresentados para validar o estudo considerando uma ampla faixa de frequências de operação, bem como regimes de conjugado de carga em 5 motores diferentes.

Aplicação de técnicas inteligentes com análise no domínio do tempo para reconhecimento de defeitos em motores de indução trifásicos

Os motores de indução trifásicos são os principais elementos de conversão de energia elétrica em mecânica motriz aplicados em vários setores produtivos. Identificar um defeito no motor em operação pode fornecer, antes que ele falhe, maior segurança no processo de tomada de decisão sobre a manutenção da máquina, redução de custos e aumento de disponibilidade. Nesta tese são apresentas inicialmente uma revisão bibliográfica e a metodologia geral para a reprodução dos defeitos nos motores e a aplicação da técnica de discretização dos sinais de correntes e tensões no domínio do tempo. É também desenvolvido um estudo comparativo entre métodos de classificação de padrões para a identificação de defeitos nestas máquinas, tais como: Naive Bayes, k-Nearest Neighbor, Support Vector Machine (Sequential Minimal Optimization), Rede Neural Artificial (Perceptron Multicamadas), Repeated Incremental Pruning to Produce Error Reduction e C4.5 Decision Tree. Também aplicou-se o conceito de Sistemas Multiagentes (SMA) para suportar a utilização de múltiplos métodos concorrentes de forma distribuída para reconhecimento de padrões de defeitos em rolamentos defeituosos, quebras nas barras da gaiola de esquilo do rotor e curto-circuito entre as bobinas do enrolamento do estator de motores de indução trifásicos. Complementarmente, algumas estratégias para a definição da severidade dos defeitos supracitados em motores foram exploradas, fazendo inclusive uma averiguação da influência do desequilíbrio de tensão na alimentação da máquina para a determinação destas anomalias. Os dados experimentais foram adquiridos por meio de uma bancada experimental em laboratório com motores de potência de 1 e 2 cv acionados diretamente na rede elétrica, operando em várias condições de desequilíbrio das tensões e variações da carga mecânica aplicada ao eixo do motor.

Improving classification using a Confidence Matrix based on weak classifiers applied to OCR

This paper proposes a new feature representation method based on the construction of a Confidence Matrix (CM). This representation consists of posterior probability values provided by several weak classifiers, each one trained and used in different sets of features from the original sample. The CM allows the final classifier to abstract itself from discovering underlying groups of features. In this work the CM is applied to isolated character image recognition, for which several set of features can be extracted from each sample. Experimentation has shown that the use of CM permits a significant improvement in accuracy in most cases, while the others remain the same. The results were obtained after experimenting with four well-known corpora, using evolved meta-classifiers with the k-Nearest Neighbor rule as a weak classifier and by applying statistical significance tests.

Improving kNN multi-label classification in Prototype Selection scenarios using class proposals

Prototype Selection (PS) algorithms allow a faster Nearest Neighbor classification by keeping only the most profitable prototypes of the training set. In turn, these schemes typically lower the performance accuracy. In this work a new strategy for multi-label classifications tasks is proposed to solve this accuracy drop without the need of using all the training set. For that, given a new instance, the PS algorithm is used as a fast recommender system which retrieves the most likely classes. Then, the actual classification is performed only considering the prototypes from the initial training set belonging to the suggested classes. Results show that this strategy provides a large set of trade-off solutions which fills the gap between PS-based classification efficiency and conventional kNN accuracy. Furthermore, this scheme is not only able to, at best, reach the performance of conventional kNN with barely a third of distances computed, but it does also outperform the latter in noisy scenarios, proving to be a much more robust approach.

Dynamical properties of a strongly correlated model for quarter-filled layered organic molecular crystals

The dynamical properties of an extended Hubbard model, which is relevant to quarter-filled layered organic molecular crystals, are analyzed. We have computed the dynamical charge correlation function, spectral density, and optical conductivity using Lanczos diagonalization and large-N techniques. As the ratio of the nearest-neighbor Coulomb repulsion, V, to the hopping integral, t, increases there is a transition from a metallic phase to a charge-ordered phase. Dynamical properties close to the ordering transition are found to differ from the ones expected in a conventional metal. Large-N calculations display an enhancement of spectral weight at low frequencies as the system is driven closer to the charge-ordering transition in agreement with Lanczos calculations. As V is increased the charge correlation function displays a collective mode which, for wave vectors close to (pi,pi), increases in amplitude and softens as the charge-ordering transition is approached. We propose that inelastic x-ray scattering be used to detect this mode. Large-N calculations predict superconductivity with d(xy) symmetry close to the ordering transition. We find that this is consistent with Lanczos diagonalization calculations, on lattices of 20 sites, which find that the binding energy of two holes becomes negative close to the charge-ordering transition.

Amorphous structures induced in monocrystalline silicon by mechanical loading

Different amorphous structures have been induced in monocrystalline silicon by high pressure in indentation and polishing. Through the use of high-resolution transmission electron microscopy and nanodiffraction, it was found that the structures of amorphous silicon formed at slow and fast loading/unloading rates are dissimilar and inherit the nearest-neighbor distance of the crystal in which they are formed. The results are in good agreement with recent theoretical predictions. (C) 2004 American Institute of Physics.

Multiquantum eigenstates of a linear chain of coupled qubits

We present a technique to identify exact analytic expressions for the multiquantum eigenstates of a linear chain of coupled qubits. A choice of Hilbert subspaces is described that allows an exact solution of the stationary Schrodinger equation without imposing periodic boundary conditions and without neglecting end effects, fully including the dipole-dipole nearest-neighbor interaction between the atoms. The treatment is valid for an arbitrary coherent excitation in the atomic system, any number of atoms, any size of the chain relative to the resonant wavelength and arbitrary initial conditions of the atomic system. The procedure we develop is general enough to be adopted for the study of excitation in an arbitrary array of atoms including spin chains and one-dimensional Bose-Einstein condensates.

Spin exchange and superconductivity in a t-J(')-V model for two-dimensional quarter-filled systems

The effect of antiferromagnetic spin fluctuations on two-dimensional quarter-filled systems is studied theoretically. An effective t-J(')-V model on a square lattice which accounts for checkerboard charge fluctuations and next-nearest-neighbor antiferromagnetic spin fluctuations is considered. From calculations based on large-N theory on this model it is found that the exchange interaction J(') increases the attraction between electrons in the d(xy) channel only, so that both charge and spin fluctuations work cooperatively to produce d(xy) pairing.

Topological side-chain classification of beta-turns: Ideal motifs for peptidomimetic development

beta-turns are important topological motifs for biological recognition of proteins and peptides. Organic molecules that sample the side chain positions of beta-turns have shown broad binding capacity to multiple different receptors, for example benzodiazepines. beta-turns have traditionally been classified into various types based on the backbone dihedral angles (phi 2, psi 2, phi 3 and psi 3). Indeed, 57-68% of beta-turns are currently classified into 8 different backbone families (Type I, Type II, Type I', Type II', Type VIII, Type VIa1, Type VIa2 and Type VIb and Type IV which represents unclassified beta-turns). Although this classification of beta-turns has been useful, the resulting beta-turn types are not ideal for the design of beta-turn mimetics as they do not reflect topological features of the recognition elements, the side chains. To overcome this, we have extracted beta-turns from a data set of non-homologous and high-resolution protein crystal structures. The side chain positions, as defined by C-alpha-C-beta vectors, of these turns have been clustered using the kth nearest neighbor clustering and filtered nearest centroid sorting algorithms. Nine clusters were obtained that cluster 90% of the data, and the average intra-cluster RMSD of the four C-alpha-C-beta vectors is 0.36. The nine clusters therefore represent the topology of the side chain scaffold architecture of the vast majority of beta-turns. The mean structures of the nine clusters are useful for the development of beta-turn mimetics and as biological descriptors for focusing combinatorial chemistry towards biologically relevant topological space.

A quasi-chemical viscosity model for fully liquid slags in the Al2O3-CaO-'FeO'-SiO2 system

A structurally based viscosity model for fully liquid silicate slags has been proposed and applied to the Al2O3-CaO-'FeO'-SiO2 system at metallic iron saturation. The model links the slag viscosity to the internal structure of melts through the concentrations of various anion/cation structural units (SUs). The concentrations of structural units are equivalent to the second nearest neighbor bond concentrations calculated by the quasi-chemical thermodynamic model. This viscosity model describes experimental data over the entire temperature and composition range within the Al2O3-CaO-'FeO'-SiO2 system at metallic iron saturation and can be extended to other industrial slag systems.