Matrix elements of U(2n) generators in a multishell spin-orbit basis. II. The two-shell nonzero shift ACCs and U(2n) generator MEs

This is the second in a series of articles whose ultimate goal is the evaluation of the matrix elements (MEs) of the U(2n) generators in a multishell spin-orbit basis. This extends the existing unitary group approach to spin-dependent configuration interaction (CI) and many-body perturbation theory calculations on molecules to systems where there is a natural partitioning of the electronic orbital space. As a necessary preliminary to obtaining the U(2n) generator MEs in a multishell spin-orbit basis, we must obtain a complete set of adjoint coupling coefficients for the two-shell composite Gelfand-Paldus basis. The zero-shift coefficients were obtained in the first article of the series. in this article, we evaluate the nonzero shift adjoint coupling coefficients for the two-shell composite Gelfand-Paldus basis. We then demonstrate that the one-shell versions of these coefficients may be obtained by taking the Gelfand-Tsetlin limit of the two-shell formulas. These coefficients,together with the zero-shift types, then enable us to write down formulas for the U(2n) generator matrix elements in a two-shell spin-orbit basis. Ultimately, the results of the series may be used to determine the many-electron density matrices for a partitioned system. (C) 1998 John Wiley & Sons, Inc.

Matrix elements of U(2n) generators in a multishell spin-orbit basis. III. General formulas

This is the third and final article in a series directed toward the evaluation of the U(2n) generator matrix elements (MEs) in a multishell spin/orbit basis. Such a basis is required for many-electron systems possessing a partitioned orbital space and where spin-dependence is important. The approach taken is based on the transformation properties of the U(2n) generators as an adjoint tensor operator of U(n) x U(2) and application of the Wigner-Eckart theorem. A complete set of adjoint coupling coefficients for the two-shell composite Gelfand-Paldus basis (which is appropriate to the many-electron problem) were obtained in the first and second articles of this series. Ln the first article we defined zero-shift coupling coefficients. These are proportional to the corresponding two-shell del-operator matrix elements. See P. J. Burton and and M. D. Gould, J. Chem. Phys., 104, 5112 (1996), for a discussion of the del-operator and its properties. Ln the second article of the series, the nonzero shift coupling coefficients were derived. Having obtained all the necessary coefficients, we now apply the formalism developed above to obtain the U(2n) generator MEs in a multishell spin-orbit basis. The methods used are based on the work of Gould et al. (see the above reference). (C) 1998 John Wiley & Sons, Inc.

Extended GCD and Hermite normal form algorithms via lattice basis reduction

Extended gcd calculation has a long history and plays an important role in computational number theory and linear algebra. Recent results have shown that finding optimal multipliers in extended gcd calculations is difficult. We present an algorithm which uses lattice basis reduction to produce small integer multipliers x(1), ..., x(m) for the equation s = gcd (s(1), ..., s(m)) = x(1)s(1) + ... + x(m)s(m), where s1, ... , s(m) are given integers. The method generalises to produce small unimodular transformation matrices for computing the Hermite normal form of an integer matrix.

Structural basis of autoregulation of phenylalanine hydroxylase

Phenylalanine hydroxylase converts phenylalanine to tyrosine, a rate-limiting step in phenylalanine catabolism and protein and neurotransmitter biosynthesis. It is tightly regulated by the substrates phenylalanine and tetrahydrobiopterin and by phosphorylation. We present the crystal structures of dephosphorylated and phosphorylated forms of a dimeric enzyme with catalytic and regulatory properties of the wild-type protein. The structures reveal a catalytic domain flexibly linked to a regulatory domain. The latter consists of an N-terminal autoregulatory sequence (containing Ser 16, which is the site of phosphorylation) that extends over the active site pocket, and an alpha-beta sandwich core that is, unexpectedly, structurally related to both pterin dehydratase and the regulatory domains of metabolic enzymes. Phosphorylation has no major structural effects in the absence of phenylalanine, suggesting that phenylalanine and phosphorylation act in concert to activate the enzyme through a combination of intrasteric and possibly allosteric mechanisms.

Approach to designing rotating drum bioreactors for solid-state fermentation on the basis of dimensionless design factors

The development of large-scale solid-stale fermentation (SSF) processes is hampered by the lack of simple tools for the design of SSF bioreactors. The use of semifundamental mathematical models to design and operate SSF bioreactors can be complex. In this work, dimensionless design factors are used to predict the effects of scale and of operational variables on the performance of rotating drum bioreactors. The dimensionless design factor (DDF) is a ratio of the rate of heat generation to the rate of heat removal at the time of peak heat production. It can be used to predict maximum temperatures reached within the substrate bed for given operational variables. Alternatively, given the maximum temperature that can be tolerated during the fermentation, it can be used to explore the combinations of operating variables that prevent that temperature from being exceeded. Comparison of the predictions of the DDF approach with literature data for operation of rotating drums suggests that the DDF is a useful tool. The DDF approach was used to explore the consequences of three scale-up strategies on the required air flow rates and maximum temperatures achieved in the substrate bed as the bioreactor size was increased on the basis of geometric similarity. The first of these strategies was to maintain the superficial flow rate of the process air through the drum constant. The second was to maintain the ratio of volumes of air per volume of bioreactor constant. The third strategy was to adjust the air flow rate with increase in scale in such a manner as to maintain constant the maximum temperature attained in the substrate bed during the fermentation. (C) 2000 John Wiley & Sons, Inc.

Structural basis of recognition of monopartite and bipartite nuclear localization sequences by mammalian importin-alpha

Importin-alpha is the nuclear import receptor that recognizes cargo proteins which contain classical monopartite and bipartite nuclear localization sequences (NLSs), and facilitates their transport into the nucleus. To determine the structural basis of the recognition of the two classes of NLSs by mammalian importin-alpha, we co-crystallized an N-terminally truncated mouse receptor protein with peptides corresponding to the monopartite NLS from the simian virus 40 (SV40) large T-antigen, and the bipartite NLS from nucleoplasmin. We show that the monopartite SV40 large T-antigen NLS binds to two binding sites on the receptor, similar to what was observed in yeast importin-alpha. The nucleoplasmin NLS-importin-alpha complex shows, for the first time, the mode of binding of bipartite NLSs to the receptor. The two basic clusters in the NLS occupy the two binding sites used by the monopartite NLS, while the sequence linking the two basic clusters is poorly ordered, consistent with its tolerance to mutations. The structures explain the structural basis for binding of diverse NLSs to the sole receptor protein. (C) 2000 Academic Press.

Nitrogen dynamics and the physiological basis of stay-green in sorghum

Sorghum [Sorghum bicolor (L,) Moench] hybrids containing the stay-green trait retain more photosynthetically active leaves under drought than do hybrids that do not contain this trait. Since the Longevity and photosynthetic capacity of a leaf are related to its N status, it is important to clarify the role of N in extending leaf greenness in stay-green hybrids. Field studies were conducted in northeastern Australia to examine the effect of three water regimes and nine hybrids on N uptake and partitioning among organs. Nine hybrids varying in the B35 and KS19 sources of stay-green were grown under a fully irrigated control, post-flowering water deficit, and terminal water deficit. For hybrids grown under terminal water deficit, stay-green was viewed as a consequence of the balance between N demand by the grain and N supply during gain filling. On the demand side, grain numbers were 16% higher in the four stay-green than in the five senescent hybrids. On the supply side, age-related senescence provided an average of 34 and 42 kg N ha(-1) for stay-green and senescent hybrids, respectively. In addition, N uptake during grain filling averaged 116 and 82 kg ha(-1) in stay-green and senescent hybrids. Matching the N supply from these two sources with grain N demand found that the shortfall in N supply for grain filling in the stay-green and senescent hybrids averaged 32 and 41 kg N ha(-1) resulting in more accelerated leaf senescence in the senescent hybrids. Genotypic differences in delayed onset and reduced rate of leaf senescence were explained by differences in specific leaf nitrogen and N uptake during grain filling. Leaf nitrogen concentration at anthesis was correlated with onset (r = 0.751**, n = 27) and rate (r = -0.783**, n = 27) of leaf senescence ender terminal water deficit.

Context-free and context-sensitive dynamics in recurrent neural networks

Continuous-valued recurrent neural networks can learn mechanisms for processing context-free languages. The dynamics of such networks is usually based on damped oscillation around fixed points in state space and requires that the dynamical components are arranged in certain ways. It is shown that qualitatively similar dynamics with similar constraints hold for a(n)b(n)c(n), a context-sensitive language. The additional difficulty with a(n)b(n)c(n), compared with the context-free language a(n)b(n), consists of 'counting up' and 'counting down' letters simultaneously. The network solution is to oscillate in two principal dimensions, one for counting up and one for counting down. This study focuses on the dynamics employed by the sequential cascaded network, in contrast to the simple recurrent network, and the use of backpropagation through time. Found solutions generalize well beyond training data, however, learning is not reliable. The contribution of this study lies in demonstrating how the dynamics in recurrent neural networks that process context-free languages can also be employed in processing some context-sensitive languages (traditionally thought of as requiring additional computation resources). This continuity of mechanism between language classes contributes to our understanding of neural networks in modelling language learning and processing.

Identifying rate-limiting nodes in large-scale cortical networks for visuospatial processing: an illustration using fMRI

With the advent of functional neuroimaging techniques, in particular functional magnetic resonance imaging (fMRI), we have gained greater insight into the neural correlates of visuospatial function. However, it may not always be easy to identify the cerebral regions most specifically associated with performance on a given task. One approach is to examine the quantitative relationships between regional activation and behavioral performance measures. In the present study, we investigated the functional neuroanatomy of two different visuospatial processing tasks, judgement of line orientation and mental rotation. Twenty-four normal participants were scanned with fMRI using blocked periodic designs for experimental task presentation. Accuracy and reaction time (RT) to each trial of both activation and baseline conditions in each experiment was recorded. Both experiments activated dorsal and ventral visual cortical areas as well as dorsolateral prefrontal cortex. More regionally specific associations with task performance were identified by estimating the association between (sinusoidal) power of functional response and mean RT to the activation condition; a permutation test based on spatial statistics was used for inference. There was significant behavioral-physiological association in right ventral extrastriate cortex for the line orientation task and in bilateral (predominantly right) superior parietal lobule for the mental rotation task. Comparable associations were not found between power of response and RT to the baseline conditions of the tasks. These data suggest that one region in a neurocognitive network may be most strongly associated with behavioral performance and this may be regarded as the computationally least efficient or rate-limiting node of the network.

A method for the design of MRI radiofrequency coils based on triangular and pulse basis functions

This paper presents a numerical technique for the design of an RF coil for asymmetric magnetic resonance imaging (MRI) systems. The formulation is based on an inverse approach where the cylindrical surface currents are expressed in terms of a combination of sub-domain basis functions: triangular and pulse functions. With the homogeneous transverse magnetic field specified in a spherical region, a functional method is applied to obtain the unknown current coefficients. The current distribution is then transformed to a conductor pattern by use of a stream function technique. Preliminary MR images acquired using a prototype RF coil are presented and validate the design method. (C) 2002 Elsevier Science B.V. All rights reserved.

Visualization of learning in multilayer perceptron networks using principal component analysis

This paper is concerned with the use of scientific visualization methods for the analysis of feedforward neural networks (NNs). Inevitably, the kinds of data associated with the design and implementation of neural networks are of very high dimensionality, presenting a major challenge for visualization. A method is described using the well-known statistical technique of principal component analysis (PCA). This is found to be an effective and useful method of visualizing the learning trajectories of many learning algorithms such as back-propagation and can also be used to provide insight into the learning process and the nature of the error surface.