Evolution and treatment of vitamin B12 deficiency as a risk factor for (cognitive and functional) neurodegenerative diseases in institutionalized elderly = Evolución y tratamiento de la deficiencia de vitamina B12 como factor de riesgo de enfermedades neurodegenerativas (cognitivas y funcionales) en las personas mayores institucionalizadas.

Prevalence of vitamin B12 deficiency is very common in elderly people and can reach values as high as 40.5% of the population. It can be the result of the interaction among several factors. Vitamin B12 deficiencies have been associated with neurological, cognitive deterioration, haematological abnormalities and cardiovascular diseases that have an important influence on the health of the elderly and their quality of life. It is necessary to approach the problems arisen from the lack of data relative to them. The main objective of this thesis was to analyse the evolution of vitamin B12 status and related parameters, lipid and haematological profiles and their relationship to health risk factors, and to functional and cognitive status over one year and to determine the effect of an oral supplementation of 500 μg of cyanocobalamin for a short period of 28 days. An additional objective was to analyze the possible effects of medicine intakes on vitamin B status. Three studies were performed: a) a one year longitudinal follow-up with four measure points; b) an intervention study providing an oral liquid supplement of 500 μg of cyanocobalamin for a 28 days period; and c) analysis of the possible effect of medication intake on vitamin B status using the ATC classification of medicines. The participants for these studies were recruited from nursing homes for the elderly in the Region of Madrid. Sixty elders (mean age 84 _ 7y, 19 men and 41 women) were recruited for Study I and 64 elders (mean age 82 _ 7y, 24 men and 40 women) for Study II. For Study III, baseline data from the initially recruited participants of the first two studies were used. An informed consent was obtained from all participants or their mentors. The studies were approved by the Ethical Committee of the University of Granada. Blood samples were obtained at each examination date and were analyzed for serum cobalamin, holoTC, serum and RBC folate and total homocysteine according to laboratory standard procedures. The haematological parameters analyzed were haematocrit, haemoglobin and MCV. For the lipid profile TG, total cholesterol, LDL- and HDLcholesterol were analyzed. Anthropometric measures (BMI, skinfolds [triceps and subscapular], waist girth and waist to hip ratio), functional tests (hand grip, arm and leg strength tests, static balance) and MMSE were obtained or administered by trained personal. The vitamin B12 supplement of Study II was administered with breakfast and the medication intake was taken from the residents’ anamnesis. Data were analyzed by parametric and non-parametric statistics depending on the obtained data. Comparisons were done using the appropriate ANOVAs or non-parametric tests. Pearsons’ partial correlations with the variable “time” as control were used to define the association of the analyzed parameters. XIII The results showed that: A) Over one year, in relationship to vitamin B status, serum cobalamin decreased, serum folate and mean corpuscular volumen increased significantly and total homocysteine concentrations were stable. Regarding blood lipid profile, triglycerides increased and HDL-cholesterol decreased significantly. Regarding selected anthropometric measurements, waist circumference increased significantly. No significant changes were observed for the rest of parameters. B) Prevalence of hyperhomocysteinemia was high in the elderly studied, ranging from 60% to 90 % over the year depending on the cut-off used for the classification. LDL-cholesterol values were high, especially among women, and showed a tendency to increase over the year. Results of the balance test showed a deficiency and a tendency to decrease; this indicates that the population studied is at high risk for falls. Lower extremity muscular function was deficient and showed a tendency to decrease. A highly significant relationship was observed between the skinfold of the triceps and blood lipid profile. C) Low cobalamin concentrations correlated significantly with low MMSE scores in the elderly studied. No correlations were observed between vitamin B12 status and functional parameters. D) Regarding vitamin B12 status, holo-transcobalamin seems to be more sensitive for diagnosis; 5-10% of the elderly had a deficiency using serum cobalamin as a criterion, and 45-52% had a deficiency when using serum holotranscobalamin as a criterion. E) 500 μg of cyanocobalamin administered orally during 28 days significantly improved vitamin B12 status and significantly decreased total homocysteine concentrations in institutionalized elderly. No effect of the intervention was observed on functional and cognitive parameters. F) The relative change (%) of improvement of vitamin B12 status was higher when using serum holo-transcobalamin as a criterion than serum cobalamin. G) Antiaenemic drug intake normalized cobalamin, urologic drugs and corticosteroids serum folate, and psychoanaleptics holo-transcobalamin levels. Drugs treating pulmonary obstruction increased total homocysteine concentration significantly. H) The daily mean drug intake was 5.1. Fiftynine percent of the elderly took medication belonging to 5 or more different ATC groups. The most prevalent were psycholeptic (53%), antiacid (53%) and antithrombotic (47%) drugs.

Optimizing Functional Network Representation of Multivariate Time Series

By combining complex network theory and data mining techniques, we provide objective criteria for optimization of the functional network representation of generic multivariate time series. In particular, we propose a method for the principled selection of the threshold value for functional network reconstruction from raw data, and for proper identification of the network's indicators that unveil the most discriminative information on the system for classification purposes. We illustrate our method by analysing networks of functional brain activity of healthy subjects, and patients suffering from Mild Cognitive Impairment, an intermediate stage between the expected cognitive decline of normal aging and the more pronounced decline of dementia. We discuss extensions of the scope of the proposed methodology to network engineering purposes, and to other data mining tasks.

A new feature extraction method for signal classification applied to cat spinal cord signals

In the spinal cord of the anesthetized cat, spontaneous cord dorsum potentials (CDPs) appear synchronously along the lumbo-sacral segments. These CDPs have different shapes and magnitudes. Previous work has indicated that some CDPs appear to be specially associated with the activation of spinal pathways that lead to primary afferent depolarization and presynaptic inhibition. Visual detection and classification of these CDPs provides relevant information on the functional organization of the neural networks involved in the control of sensory information and allows the characterization of the changes produced by acute nerve and spinal lesions. We now present a novel feature extraction approach for signal classification, applied to CDP detection. The method is based on an intuitive procedure. We first remove by convolution the noise from the CDPs recorded in each given spinal segment. Then, we assign a coefficient for each main local maximum of the signal using its amplitude and distance to the most important maximum of the signal. These coefficients will be the input for the subsequent classification algorithm. In particular, we employ gradient boosting classification trees. This combination of approaches allows a faster and more accurate discrimination of CDPs than is obtained by other methods.

Bus line classification using neural networks

Grouping urban bus routes is necessary when there are evidences of significant differences among them. In Jiménez et al. (2013), a reduced sample of routes was grouped into clusters utilizing kinematic measured data. As a further step, in this paper, the remaining urban bus routes of a city, for which no kinematic measurements are available, are classified. For such purpose we use macroscopic geographical and functional variables to describe each route, while the clustering process is performed by means of a neural network. Limitations caused by reduced training samples are solved using the bootstrap method.

Guiding functional connectivity estimation by structural connectivity in MEG: an application to discrimination of mild cognitive impaired conditions

Whole brain resting state connectivity is a promising biomarker that might help to obtain an early diagnosis in many neurological diseases, such as dementia. Inferring resting-state connectivity is often based on correlations, which are sensitive to indirect connections, leading to an inaccurate representation of the real backbone of the network. The precision matrix is a better representation for whole brain connectivity, as it considers only direct connections. The network structure can be estimated using the graphical lasso (GL), which achieves sparsity through l1-regularization on the precision matrix. In this paper, we propose a structural connectivity adaptive version of the GL, where weaker anatomical connections are represented as stronger penalties on the corre- sponding functional connections. We applied beamformer source reconstruction to the resting state MEG record- ings of 81 subjects, where 29 were healthy controls, 22 were single-domain amnestic Mild Cognitive Impaired (MCI), and 30 were multiple-domain amnestic MCI. An atlas-based anatomical parcellation of 66 regions was ob- tained for each subject, and time series were assigned to each of the regions. The fiber densities between the re- gions, obtained with deterministic tractography from diffusion-weighted MRI, were used to define the anatomical connectivity. Precision matrices were obtained with the region specific time series in five different frequency bands. We compared our method with the traditional GL and a functional adaptive version of the GL, in terms of log-likelihood and classification accuracies between the three groups. We conclude that introduc- ing an anatomical prior improves the expressivity of the model and, in most cases, leads to a better classification between groups.

Automated detection and classification of brain injury lesions on structural magnetic resonance imaging

El daño cerebral adquirido (DCA) es un problema social y sanitario grave, de magnitud creciente y de una gran complejidad diagnóstica y terapéutica. Su elevada incidencia, junto con el aumento de la supervivencia de los pacientes, una vez superada la fase aguda, lo convierten también en un problema de alta prevalencia. En concreto, según la Organización Mundial de la Salud (OMS) el DCA estará entre las 10 causas más comunes de discapacidad en el año 2020. La neurorrehabilitación permite mejorar el déficit tanto cognitivo como funcional y aumentar la autonomía de las personas con DCA. Con la incorporación de nuevas soluciones tecnológicas al proceso de neurorrehabilitación se pretende alcanzar un nuevo paradigma donde se puedan diseñar tratamientos que sean intensivos, personalizados, monitorizados y basados en la evidencia. Ya que son estas cuatro características las que aseguran que los tratamientos son eficaces. A diferencia de la mayor parte de las disciplinas médicas, no existen asociaciones de síntomas y signos de la alteración cognitiva que faciliten la orientación terapéutica. Actualmente, los tratamientos de neurorrehabilitación se diseñan en base a los resultados obtenidos en una batería de evaluación neuropsicológica que evalúa el nivel de afectación de cada una de las funciones cognitivas (memoria, atención, funciones ejecutivas, etc.). La línea de investigación en la que se enmarca este trabajo de investigación pretende diseñar y desarrollar un perfil cognitivo basado no sólo en el resultado obtenido en esa batería de test, sino también en información teórica que engloba tanto estructuras anatómicas como relaciones funcionales e información anatómica obtenida de los estudios de imagen. De esta forma, el perfil cognitivo utilizado para diseñar los tratamientos integra información personalizada y basada en la evidencia. Las técnicas de neuroimagen representan una herramienta fundamental en la identificación de lesiones para la generación de estos perfiles cognitivos. La aproximación clásica utilizada en la identificación de lesiones consiste en delinear manualmente regiones anatómicas cerebrales. Esta aproximación presenta diversos problemas relacionados con inconsistencias de criterio entre distintos clínicos, reproducibilidad y tiempo. Por tanto, la automatización de este procedimiento es fundamental para asegurar una extracción objetiva de información. La delineación automática de regiones anatómicas se realiza mediante el registro tanto contra atlas como contra otros estudios de imagen de distintos sujetos. Sin embargo, los cambios patológicos asociados al DCA están siempre asociados a anormalidades de intensidad y/o cambios en la localización de las estructuras. Este hecho provoca que los algoritmos de registro tradicionales basados en intensidad no funcionen correctamente y requieran la intervención del clínico para seleccionar ciertos puntos (que en esta tesis hemos denominado puntos singulares). Además estos algoritmos tampoco permiten que se produzcan deformaciones grandes deslocalizadas. Hecho que también puede ocurrir ante la presencia de lesiones provocadas por un accidente cerebrovascular (ACV) o un traumatismo craneoencefálico (TCE). Esta tesis se centra en el diseño, desarrollo e implementación de una metodología para la detección automática de estructuras lesionadas que integra algoritmos cuyo objetivo principal es generar resultados que puedan ser reproducibles y objetivos. Esta metodología se divide en cuatro etapas: pre-procesado, identificación de puntos singulares, registro y detección de lesiones. Los trabajos y resultados alcanzados en esta tesis son los siguientes: Pre-procesado. En esta primera etapa el objetivo es homogeneizar todos los datos de entrada con el objetivo de poder extraer conclusiones válidas de los resultados obtenidos. Esta etapa, por tanto, tiene un gran impacto en los resultados finales. Se compone de tres operaciones: eliminación del cráneo, normalización en intensidad y normalización espacial. Identificación de puntos singulares. El objetivo de esta etapa es automatizar la identificación de puntos anatómicos (puntos singulares). Esta etapa equivale a la identificación manual de puntos anatómicos por parte del clínico, permitiendo: identificar un mayor número de puntos lo que se traduce en mayor información; eliminar el factor asociado a la variabilidad inter-sujeto, por tanto, los resultados son reproducibles y objetivos; y elimina el tiempo invertido en el marcado manual de puntos. Este trabajo de investigación propone un algoritmo de identificación de puntos singulares (descriptor) basado en una solución multi-detector y que contiene información multi-paramétrica: espacial y asociada a la intensidad. Este algoritmo ha sido contrastado con otros algoritmos similares encontrados en el estado del arte. Registro. En esta etapa se pretenden poner en concordancia espacial dos estudios de imagen de sujetos/pacientes distintos. El algoritmo propuesto en este trabajo de investigación está basado en descriptores y su principal objetivo es el cálculo de un campo vectorial que permita introducir deformaciones deslocalizadas en la imagen (en distintas regiones de la imagen) y tan grandes como indique el vector de deformación asociado. El algoritmo propuesto ha sido comparado con otros algoritmos de registro utilizados en aplicaciones de neuroimagen que se utilizan con estudios de sujetos control. Los resultados obtenidos son prometedores y representan un nuevo contexto para la identificación automática de estructuras. Identificación de lesiones. En esta última etapa se identifican aquellas estructuras cuyas características asociadas a la localización espacial y al área o volumen han sido modificadas con respecto a una situación de normalidad. Para ello se realiza un estudio estadístico del atlas que se vaya a utilizar y se establecen los parámetros estadísticos de normalidad asociados a la localización y al área. En función de las estructuras delineadas en el atlas, se podrán identificar más o menos estructuras anatómicas, siendo nuestra metodología independiente del atlas seleccionado. En general, esta tesis doctoral corrobora las hipótesis de investigación postuladas relativas a la identificación automática de lesiones utilizando estudios de imagen médica estructural, concretamente estudios de resonancia magnética. Basándose en estos cimientos, se han abrir nuevos campos de investigación que contribuyan a la mejora en la detección de lesiones. ABSTRACT Brain injury constitutes a serious social and health problem of increasing magnitude and of great diagnostic and therapeutic complexity. Its high incidence and survival rate, after the initial critical phases, makes it a prevalent problem that needs to be addressed. In particular, according to the World Health Organization (WHO), brain injury will be among the 10 most common causes of disability by 2020. Neurorehabilitation improves both cognitive and functional deficits and increases the autonomy of brain injury patients. The incorporation of new technologies to the neurorehabilitation tries to reach a new paradigm focused on designing intensive, personalized, monitored and evidence-based treatments. Since these four characteristics ensure the effectivity of treatments. Contrary to most medical disciplines, it is not possible to link symptoms and cognitive disorder syndromes, to assist the therapist. Currently, neurorehabilitation treatments are planned considering the results obtained from a neuropsychological assessment battery, which evaluates the functional impairment of each cognitive function (memory, attention, executive functions, etc.). The research line, on which this PhD falls under, aims to design and develop a cognitive profile based not only on the results obtained in the assessment battery, but also on theoretical information that includes both anatomical structures and functional relationships and anatomical information obtained from medical imaging studies, such as magnetic resonance. Therefore, the cognitive profile used to design these treatments integrates information personalized and evidence-based. Neuroimaging techniques represent an essential tool to identify lesions and generate this type of cognitive dysfunctional profiles. Manual delineation of brain anatomical regions is the classical approach to identify brain anatomical regions. Manual approaches present several problems related to inconsistencies across different clinicians, time and repeatability. Automated delineation is done by registering brains to one another or to a template. However, when imaging studies contain lesions, there are several intensity abnormalities and location alterations that reduce the performance of most of the registration algorithms based on intensity parameters. Thus, specialists may have to manually interact with imaging studies to select landmarks (called singular points in this PhD) or identify regions of interest. These two solutions have the same inconvenient than manual approaches, mentioned before. Moreover, these registration algorithms do not allow large and distributed deformations. This type of deformations may also appear when a stroke or a traumatic brain injury (TBI) occur. This PhD is focused on the design, development and implementation of a new methodology to automatically identify lesions in anatomical structures. This methodology integrates algorithms whose main objective is to generate objective and reproducible results. It is divided into four stages: pre-processing, singular points identification, registration and lesion detection. Pre-processing stage. In this first stage, the aim is to standardize all input data in order to be able to draw valid conclusions from the results. Therefore, this stage has a direct impact on the final results. It consists of three steps: skull-stripping, spatial and intensity normalization. Singular points identification. This stage aims to automatize the identification of anatomical points (singular points). It involves the manual identification of anatomical points by the clinician. This automatic identification allows to identify a greater number of points which results in more information; to remove the factor associated to inter-subject variability and thus, the results are reproducible and objective; and to eliminate the time spent on manual marking. This PhD proposed an algorithm to automatically identify singular points (descriptor) based on a multi-detector approach. This algorithm contains multi-parametric (spatial and intensity) information. This algorithm has been compared with other similar algorithms found on the state of the art. Registration. The goal of this stage is to put in spatial correspondence two imaging studies of different subjects/patients. The algorithm proposed in this PhD is based on descriptors. Its main objective is to compute a vector field to introduce distributed deformations (changes in different imaging regions), as large as the deformation vector indicates. The proposed algorithm has been compared with other registration algorithms used on different neuroimaging applications which are used with control subjects. The obtained results are promising and they represent a new context for the automatic identification of anatomical structures. Lesion identification. This final stage aims to identify those anatomical structures whose characteristics associated to spatial location and area or volume has been modified with respect to a normal state. A statistical study of the atlas to be used is performed to establish which are the statistical parameters associated to the normal state. The anatomical structures that may be identified depend on the selected anatomical structures identified on the atlas. The proposed methodology is independent from the selected atlas. Overall, this PhD corroborates the investigated research hypotheses regarding the automatic identification of lesions based on structural medical imaging studies (resonance magnetic studies). Based on these foundations, new research fields to improve the automatic identification of lesions in brain injury can be proposed.

A natural classification of the basic helix–loop–helix class of transcription factors

A natural (evolutionary) classification is provided for 242 basic helix–loop–helix (bHLH) motif-containing proteins. Phylogenetic analyses of amino acid sequences describe the patterns of evolutionary change within the motif and delimit evolutionary lineages. These evolutionary lineages represent well known functional groups of proteins and can be further arranged into five groups based on binding to DNA at the hexanucleotide E-box, the amino acid patterns in other components of the motif, and the presence/absence of a leucine zipper. The hypothesized ancestral amino acid sequence for the bHLH transcription factor family is given together with the ancestral sequences of the subgroups. It is suggested that bHLH proteins containing a leucine zipper are not a natural, monophyletic group.

Functional antioxidant responsive elements

Exposure of human and rodent cells to a wide variety of chemoprotective compounds confers resistance against a broad set of carcinogens. For a subset of the chemoprotective compounds, protection is generated by an increase in the abundance of protective enzymes like glutathione S-transferases (GST). Antioxidant responsive elements (AREs) mediate the transcriptional induction of a battery of genes which comprise much of this chemoprotective response system. Past studies identified a necessary ARE “core” sequence of RTGACnnnGC, but this sequence alone is insufficient to mediate induction. In this study, the additional sequences necessary to define a sufficient, functional ARE are identified through systematic mutational analysis of the murine GST Ya ARE. Introduction of the newly identified necessary nucleotides into the regions flanking a nonresponsive, ARE-like, GST-Mu promoter sequence produced an inducible element. A screen of the GenBank database with the newly identified ARE consensus identified 16 genes which contained the functional ARE consensus sequence in their promoters. Included within this group was an ARE sequence from the murine ferritin-L promoter that mediated induction when tested. In an electrophoretic mobility-shift assay, the ferritin-L ARE was bound by ARE–binding protein 1, a protein previously identified as the likely mediator of the chemoprotective response. A three-level ARE classification system is presented to account for the distinct induction strengths observed in our mutagenesis studies. A model of the ARE as a composite regulatory site, where multiple transcription factors interact, is presented to account for the complex characteristics of ARE-mediated chemoprotective gene expression.

A rapid classification protocol for the CATH Domain Database to support structural genomics

In order to support the structural genomic initiatives, both by rapidly classifying newly determined structures and by suggesting suitable targets for structure determination, we have recently developed several new protocols for classifying structures in the CATH domain database (http://www.biochem.ucl.ac.uk/bsm/cath). These aim to increase the speed of classification of new structures using fast algorithms for structure comparison (GRATH) and to improve the sensitivity in recognising distant structural relatives by incorporating sequence information from relatives in the genomes (DomainFinder). In order to ensure the integrity of the database given the expected increase in data, the CATH Protein Family Database (CATH-PFDB), which currently includes 25 320 structural domains and a further 160 000 sequence relatives has now been installed in a relational ORACLE database. This was essential for developing more rigorous validation procedures and for allowing efficient querying of the database, particularly for genome analysis. The associated Dictionary of Homologous Superfamilies [Bray,J.E., Todd,A.E., Pearl,F.M.G., Thornton,J.M. and Orengo,C.A. (2000) Protein Eng., 13, 153–165], which provides multiple structural alignments and functional information to assist in assigning new relatives, has also been expanded recently and now includes information for 903 homo­logous superfamilies. In order to improve coverage of known structures, preliminary classification levels are now provided for new structures at interim stages in the classification protocol. Since a large proportion of new structures can be rapidly classified using profile-based sequence analysis [e.g. PSI-BLAST: Altschul,S.F., Madden,T.L., Schaffer,A.A., Zhang,J., Zhang,Z., Miller,W. and Lipman,D.J. (1997) Nucleic Acids Res., 25, 3389–3402], this provides preliminary classification for easily recognisable homologues, which in the latest release of CATH (version 1.7) represented nearly three-quarters of the non-identical structures.

A fully automatic evolutionary classification of protein folds: Dali Domain Dictionary version 3

The Dali Domain Dictionary (http://www.ebi.ac.uk/dali/domain) is a numerical taxonomy of all known structures in the Protein Data Bank (PDB). The taxonomy is derived fully automatically from measurements of structural, functional and sequence similarities. Here, we report the extension of the classification to match the traditional four hierarchical levels corresponding to: (i) supersecondary structural motifs (attractors in fold space), (ii) the topology of globular domains (fold types), (iii) remote homologues (functional families) and (iv) homologues with sequence identity above 25% (sequence families). The computational definitions of attractors and functional families are new. In September 2000, the Dali classification contained 10 531 PDB entries comprising 17 101 chains, which were partitioned into five attractor regions, 1375 fold types, 2582 functional families and 3724 domain sequence families. Sequence families were further associated with 99 582 unique homologous sequences in the HSSP database, which increases the number of effectively known structures several-fold. The resulting database contains the description of protein domain architecture, the definition of structural neighbours around each known structure, the definition of structurally conserved cores and a comprehensive library of explicit multiple alignments of distantly related protein families.

The COG database: new developments in phylogenetic classification of proteins from complete genomes

The database of Clusters of Orthologous Groups of proteins (COGs), which represents an attempt on a phylogenetic classification of the proteins encoded in complete genomes, currently consists of 2791 COGs including 45 350 proteins from 30 genomes of bacteria, archaea and the yeast Saccharomyces cerevisiae (http://www.ncbi.nlm.nih.gov/COG). In addition, a supplement to the COGs is available, in which proteins encoded in the genomes of two multicellular eukaryotes, the nematode Caenorhabditis elegans and the fruit fly Drosophila melanogaster, and shared with bacteria and/or archaea were included. The new features added to the COG database include information pages with structural and functional details on each COG and literature references, improvements of the COGNITOR program that is used to fit new proteins into the COGs, and classification of genomes and COGs constructed by using principal component analysis.

iProClass: an integrated, comprehensive and annotated protein classification database

The iProClass database is an integrated resource that provides comprehensive family relationships and structural and functional features of proteins, with rich links to various databases. It is extended from ProClass, a protein family database that integrates PIR superfamilies and PROSITE motifs. The iProClass currently consists of more than 200 000 non-redundant PIR and SWISS-PROT proteins organized with more than 28 000 superfamilies, 2600 domains, 1300 motifs, 280 post-translational modification sites and links to more than 30 databases of protein families, structures, functions, genes, genomes, literature and taxonomy. Protein and family summary reports provide rich annotations, including membership information with length, taxonomy and keyword statistics, full family relationships, comprehensive enzyme and PDB cross-references and graphical feature display. The database facilitates classification-driven annotation for protein sequence databases and complete genomes, and supports structural and functional genomic research. The iProClass is implemented in Oracle 8i object-relational system and available for sequence search and report retrieval at http://pir.georgetow n.edu/iproclass/.

TIGRFAMs: a protein family resource for the functional identification of proteins

TIGRFAMs is a collection of protein families featuring curated multiple sequence alignments, hidden Markov models and associated information designed to support the automated functional identification of proteins by sequence homology. We introduce the term ‘equivalog’ to describe members of a set of homologous proteins that are conserved with respect to function since their last common ancestor. Related proteins are grouped into equivalog families where possible, and otherwise into protein families with other hierarchically defined homology types. TIGRFAMs currently contains over 800 protein families, available for searching or downloading at www.tigr.org/TIGRFAMs. Classification by equivalog family, where achievable, complements classification by orthology, superfamily, domain or motif. It provides the information best suited for automatic assignment of specific functions to proteins from large-scale genome sequencing projects.

Measuring functional outcome in paediatric patients with burns: methodological considerations

Methodological criticisms of research undertaken in the area of paediatric burns are widespread. To date, quasi-experimental research designs have most frequently been used to examine the impact of impairments such as scarring and reduced ran e of motion on functional outcomes. Predominantly, these studies have utilised a narrow definition of functioning (e.g. school attendance) to determine a child's level of participation in activities post-burn injury. Until recently, there had been little attempt to develop and/or test a theoretical model of functional outcome with these children. Using a conceptual model of functional outcome based oil the International Classification of Functioning, Disability and Health, this review paper outlines the current state of the research literature and presents explanatory case study methodology as an alternative research design to further advance the Study of functional outcome post-burn injury. (C) 2004 Elsevier Ltd and ISBI. All rights reserved.

Accurate prediction of scorpion toxin functional properties from primary structures

Scorpion toxins are common experimental tools for studies of biochemical and pharmacological properties of ion channels. The number of functionally annotated scorpion toxins is steadily growing, but the number of identified toxin sequences is increasing at much faster pace. With an estimated 100,000 different variants, bioinformatic analysis of scorpion toxins is becoming a necessary tool for their systematic functional analysis. Here, we report a bioinformatics-driven system involving scorpion toxin structural classification, functional annotation, database technology, sequence comparison, nearest neighbour analysis, and decision rules which produces highly accurate predictions of scorpion toxin functional properties. (c) 2005 Elsevier Inc. All rights reserved.