Encoding databases satisfying a given set of dependencies

Consider a relation schema with a set of dependency constraints. A fundamental question is what is the minimum space where the possible instances of the schema can be "stored". We study the following model. Encode the instances by giving a function which maps the set of possible instances into the set of words of a given length over the binary alphabet in a decodable way. The problem is to find the minimum length needed. This minimum is called the information content of the database. We investigate several cases where the set of dependency constraints consist of relatively simple sets of functional or multivalued dependencies. We also consider the following natural extension. Is it possible to encode the instances such a way that small changes in the instance cause a small change in the code. © 2012 Springer-Verlag

SPG10 is a rare cause of spastic paraplegia in European families

Background: SPG10 is an autosomal dominant form of hereditary spastic paraplegia (HSP), which is caused by mutations in the neural kinesin heavy chain KIF5A gene, the neuronal motor of fast anterograde axonal transport. Only four mutations have been identified to date.Objective: To determine the frequency of SPG10 in European families with HSP and to specify the SPG10 phenotype.Patients and methods: 80 index patients from families with autosomal dominant HSP were investigated for SPG10 mutations by direct sequencing of the KIF5A motor domain. Additionally, the whole gene was sequenced in 20 of these families.Results: Three novel KIF5A mutations were detected in German families, including one missense mutation (c.759G>T, p.K253N), one in frame deletion (c.768_770delCAA, p.N256del) and one splice site mutation (c.217G>A). Onset of gait disturbance varied from infancy to 30 years of age. All patients presented clinically with pure HSP, but a subclinical sensory--motor neuropathy was detected by neurophysiology studies.Conclusions: SPG10 accounts for approximately 3% of European autosomal dominant HSP families. All mutations affect the motor domain of kinesin and thus most likely impair axonal transport. Clinically, SPG10 is characterised by spastic paraplegia with mostly subclinical peripheral neuropathy

Identifiers in Registers - Describing Network Algorithms with Logic

We propose a formal model of distributed computing based on register automata that captures a broad class of synchronous network algorithms. The local memory of each process is represented by a finite-state controller and a fixed number of registers, each of which can store the unique identifier of some process in the network. To underline the naturalness of our model, we show that it has the same expressive power as a certain extension of first-order logic on graphs whose nodes are equipped with a total order. Said extension lets us define new functions on the set of nodes by means of a so-called partial fixpoint operator. In spirit, our result bears close resemblance to a classical theorem of descriptive complexity theory that characterizes the complexity class PSPACE in terms of partial fixpoint logic (a proper superclass of the logic we consider here).Comment: 17 pages (+ 17 pages of appendices), 1 figure (+ 1 figure in the appendix

A novel approach to phylogenetic tree construction using stochastic optimization and clustering

BACKGROUND: The problem of inferring the evolutionary history and constructing the phylogenetic tree with high performance has become one of the major problems in computational biology. RESULTS: A new phylogenetic tree construction method from a given set of objects (proteins, species, etc.) is presented. As an extension of ant colony optimization, this method proposes an adaptive phylogenetic clustering algorithm based on a digraph to find a tree structure that defines the ancestral relationships among the given objects. CONCLUSION: Our phylogenetic tree construction method is tested to compare its results with that of the genetic algorithm (GA). Experimental results show that our algorithm converges much faster and also achieves higher quality than GA

A Self-Consistent First-Principles Technique Having Linear Scaling

An algorithm for first-principles electronic structure calculations having a computational cost which scales linearly with the system size is presented. Our method exploits the real-space localization of the density matrix, and in this respect it is related to the technique of Li, Nunes and Vanderbilt. The density matrix is expressed in terms of localized support functions, and a matrix of variational parameters, L, having a finite spatial range. The total energy is minimized with respect to both the support functions and the elements of the L matrix. The method is variational, and becomes exact as the ranges of the support functions and the L matrix are increased. We have tested the method on crystalline silicon systems containing up to 216 atoms, and we discuss some of these results.Comment: 12 pages, REVTeX, 2 figure

Comparative analysis of long DNA sequences by per element information content using different contexts

BACKGROUND: Features of a DNA sequence can be found by compressing the sequence under a suitable model; good compression implies low information content. Good DNA compression models consider repetition, differences between repeats, and base distributions. From a linear DNA sequence, a compression model can produce a linear information sequence. Linear space complexity is important when exploring long DNA sequences of the order of millions of bases. Compressing a sequence in isolation will include information on self-repetition. Whereas compressing a sequence Y in the context of another X can find what new information X gives about Y. This paper presents a methodology for performing comparative analysis to find features exposed by such models. RESULTS: We apply such a model to find features across chromosomes of Cyanidioschyzon merolae. We present a tool that provides useful linear transformations to investigate and save new sequences. Various examples illustrate the methodology, finding features for sequences alone and in different contexts. We also show how to highlight all sets of self-repetition features, in this case within Plasmodium falciparum chromosome 2. CONCLUSION: The methodology finds features that are significant and that biologists confirm. The exploration of long information sequences in linear time and space is fast and the saved results are self documenting.

Genes for hereditary sensory and autonomic neuropathies: a genotype–phenotype correlation

Hereditary sensory and autonomic neuropathies (HSAN) are clinically and genetically heterogeneous disorders characterized by axonal atrophy and degeneration, exclusively or predominantly affecting the sensory and autonomic neurons. So far, disease-associated mutations have been identified in seven genes: two genes for autosomal dominant (SPTLC1 and RAB7) and five genes for autosomal recessive forms of HSAN (WNK1/HSN2, NTRK1, NGFB, CCT5 and IKBKAP). We performed a systematic mutation screening of the coding sequences of six of these genes on a cohort of 100 familial and isolated patients diagnosed with HSAN. In addition, we screened the functional candidate gene NGFR (p75/NTR) encoding the nerve growth factor receptor. We identified disease-causing mutations in SPTLC1, RAB7, WNK1/HSN2 and NTRK1 in 19 patients, of which three mutations have not previously been reported. The phenotypes associated with mutations in NTRK1 and WNK1/HSN2 typically consisted of congenital insensitivity to pain and anhidrosis, and early-onset ulcero-mutilating sensory neuropathy, respectively. RAB7 mutations were only found in patients with a Charcot-Marie-Tooth type 2B (CMT2B) phenotype, an axonal sensory-motor neuropathy with pronounced ulcero-mutilations. In SPTLC1, we detected a novel mutation (S331F) corresponding to a previously unknown severe and early-onset HSAN phenotype. No mutations were found in NGFB, CCT5 and NGFR. Overall disease-associated mutations were found in 19% of the studied patient group, suggesting that additional genes are associated with HSAN. Our genotype–phenotype correlation study broadens the spectrum of HSAN and provides additional insights for molecular and clinical diagnosis

Towards a Linear-Scaling DFT Technique: The Density Matrix Approach

A recently proposed linear-scaling scheme for density-functional pseudopotential calculations is described in detail. The method is based on a formulation of density functional theory in which the ground state energy is determined by minimization with respect to the density matrix, subject to the condition that the eigenvalues of the latter lie in the range [0,1]. Linear-scaling behavior is achieved by requiring that the density matrix should vanish when the separation of its arguments exceeds a chosen cutoff. The limitation on the eigenvalue range is imposed by the method of Li, Nunes and Vanderbilt. The scheme is implemented by calculating all terms in the energy on a uniform real-space grid, and minimization is performed using the conjugate-gradient method. Tests on a 512-atom Si system show that the total energy converges rapidly as the range of the density matrix is increased. A discussion of the relation between the present method and other linear-scaling methods is given, and some problems that still require solution are indicated.Comment: REVTeX file, 27 pages with 4 uuencoded postscript figure