Sequence tag identification of intact proteins by matching tanden mass spectral data against sequence data bases.

Molecular and fragment ion data of intact 8- to 43-kDa proteins from electrospray Fourier-transform tandem mass spectrometry are matched against the corresponding data in sequence data bases. Extending the sequence tag concept of Mann and Wilm for matching peptides, a partial amino acid sequence in the unknown is first identified from the mass differences of a series of fragment ions, and the mass position of this sequence is defined from molecular weight and the fragment ion masses. For three studied proteins, a single sequence tag retrieved only the correct protein from the data base; a fourth protein required the input of two sequence tags. However, three of the data base proteins differed by having an extra methionine or by missing an acetyl or heme substitution. The positions of these modifications in the protein examined were greatly restricted by the mass differences of its molecular and fragment ions versus those of the data base. To characterize the primary structure of an unknown represented in the data base, this method is fast and specific and does not require prior enzymatic or chemical degradation.

A complexity measure for selective matching of signals by the brain.

We have previously derived a theoretical measure of neural complexity (CN) in an attempt to characterize functional connectivity in the brain. CN measures the amount and heterogeneity of statistical correlations within a neural system in terms of the mutual information between subsets of its units. CN was initially used to characterize the functional connectivity of a neural system isolated from the environment. In the present paper, we introduce a related statistical measure, matching complexity (CM), which reflects the change in CN that occurs after a neural system receives signals from the environment. CM measures how well the ensemble of intrinsic correlations within a neural system fits the statistical structure of the sensory input. We show that CM is low when the intrinsic connectivity of a simulated cortical area is randomly organized. Conversely, CM is high when the intrinsic connectivity is modified so as to differentially amplify those intrinsic correlations that happen to be enhanced by sensory input. When the input is represented by an individual stimulus, a positive value of CM indicates that the limited mutual information between sensory sheets sampling the stimulus and the rest of the brain triggers a large increase in the mutual information between many functionally specialized subsets within the brain. In this way, a complex brain can deal with context and go "beyond the information given."

Rapid mass spectrometric peptide sequencing and mass matching for characterization of human melanoma proteins isolated by two-dimensional PAGE.

We report a general mass spectrometric approach for the rapid identification and characterization of proteins isolated by preparative two-dimensional polyacrylamide gel electrophoresis. This method possesses the inherent power to detect and structurally characterize covalent modifications. Absolute sensitivities of matrix-assisted laser desorption ionization and high-energy collision-induced dissociation tandem mass spectrometry are exploited to determine the mass and sequence of subpicomole sample quantities of tryptic peptides. These data permit mass matching and sequence homology searching of computerized peptide mass and protein sequence data bases for known proteins and design of oligonucleotide probes for cloning unknown proteins. We have identified 11 proteins in lysates of human A375 melanoma cells, including: alpha-enolase, cytokeratin, stathmin, protein disulfide isomerase, tropomyosin, Cu/Zn superoxide dismutase, nucleoside diphosphate kinase A, galaptin, and triosephosphate isomerase. We have characterized several posttranslational modifications and chemical modifications that may result from electrophoresis or subsequent sample processing steps. Detection of comigrating and covalently modified proteins illustrates the necessity of peptide sequencing and the advantages of tandem mass spectrometry to reliably and unambiguously establish the identity of each protein. This technology paves the way for studies of cell-type dependent gene expression and studies of large suites of cellular proteins with unprecedented speed and rigor to provide information complementary to the ongoing Human Genome Project.

Electroencephalogram based Causality Graph Analysis in Behavior Tasks of Parkinson’s Disease Patients

Electroencephalographic (EEG) signals of the human brains represent electrical activities for a number of channels recorded over a the scalp. The main purpose of this thesis is to investigate the interactions and causality of different parts of a brain using EEG signals recorded during a performance subjects of verbal fluency tasks. Subjects who have Parkinson's Disease (PD) have difficulties with mental tasks, such as switching between one behavior task and another. The behavior tasks include phonemic fluency, semantic fluency, category semantic fluency and reading fluency. This method uses verbal generation skills, activating different Broca's areas of the Brodmann's areas (BA44 and BA45). Advanced signal processing techniques are used in order to determine the activated frequency bands in the granger causality for verbal fluency tasks. The graph learning technique for channel strength is used to characterize the complex graph of Granger causality. Also, the support vector machine (SVM) method is used for training a classifier between two subjects with PD and two healthy controls. Neural data from the study was recorded at the Colorado Neurological Institute (CNI). The study reveals significant difference between PD subjects and healthy controls in terms of brain connectivities in the Broca's Area BA44 and BA45 corresponding to EEG electrodes. The results in this thesis also demonstrate the possibility to classify based on the flow of information and causality in the brain of verbal fluency tasks. These methods have the potential to be applied in the future to identify pathological information flow and causality of neurological diseases.

Constellations and the unsupervised learning of graphs

In this paper, we propose a novel method for the unsupervised clustering of graphs in the context of the constellation approach to object recognition. Such method is an EM central clustering algorithm which builds prototypical graphs on the basis of fast matching with graph transformations. Our experiments, both with random graphs and in realistic situations (visual localization), show that our prototypes improve the set median graphs and also the prototypes derived from our previous incremental method. We also discuss how the method scales with a growing number of images.

Contextual visual localization: cascaded submap classification, optimized saliency detection, and fast view matching

In this paper, we present a novel coarse-to-fine visual localization approach: contextual visual localization. This approach relies on three elements: (i) a minimal-complexity classifier for performing fast coarse localization (submap classification); (ii) an optimized saliency detector which exploits the visual statistics of the submap; and (iii) a fast view-matching algorithm which filters initial matchings with a structural criterion. The latter algorithm yields fine localization. Our experiments show that these elements have been successfully integrated for solving the global localization problem. Context, that is, the awareness of being in a particular submap, is defined by a supervised classifier tuned for a minimal set of features. Visual context is exploited both for tuning (optimizing) the saliency detection process, and to select potential matching views in the visual database, close enough to the query view.

Weighted composition operators on the predual and dual Banach spaces of Beurling algebras on Z

Let vv be a weight sequence on ZZ and let ψ,φψ,φ be complex-valued functions on ZZ such that φ(Z)⊂Zφ(Z)⊂Z. In this paper we study the boundedness, compactness and weak compactness of weighted composition operators Cψ,φCψ,φ on predual Banach spaces c0(Z,1/v)c0(Z,1/v) and dual Banach spaces ℓ∞(Z,1/v)ℓ∞(Z,1/v) of Beurling algebras ℓ1(Z,v)ℓ1(Z,v).

Productivity and Credit Constraints: Firm-Level Evidence from Propensity Score Matching. Factor Markets Working Paper No. 3, September 2011

Drawing on a unique, farm-level panel dataset with 37,409 observations and employing a matching estimator, this paper analyses how farm access to credit affects farm input allocation and farm efficiency in the Central and Eastern European transition countries. We find that farms are asymmetrically credit constrained with respect to inputs. Farm use of variable inputs and capital investment increases up to 2.3% and 29%, respectively, per €1,000 of additional credit. Our estimates also suggest that farm access to credit increases total factor productivity up to 1.9% per €1,000 of additional credit, indicating that an improvement in access to credit results in an adjustment in the relative input intensities on farms. This finding is further supported by a negative effect of better access to credit on labour, suggesting that these two are substitutes. Interestingly, farms are found not to be credit constrained with respect to land.

Matching research and innovation policies in EU countries. Bruegel Working Paper 2015/16, December 2015

The European Union has prioritised the pursuit of innovation based growth and targeting of resources to promote research and development, but performance on innovation remains weak.With the lack of results comes fatigue, waning interest and mounting criticism about policy. Should the EU abandon its ambition to become the most innovative region in the world?We examine EU member state research and innovation policies. We assess whether the deployment of innovation policy instruments in EU countries matches their innovation capacity performance relative to other EU countries.We find a relative homogeneity of policy mixes in EU countries, despite the fairly wide and stable differences in their innovation capacities.Our analysis therefore provides a rationale for a more comprehensive review of innovation policy mixes to assess their adequacy in addressing country specific innovation challenges.

GRAFOS coloração, planaridade e "Matching"

A matemÆtica discreta Ø um dos ramos mais antigos da matemÆtica. Nos tempos mais recentes sofreu grandes avanos em especial na teoria dos grafos, a qual tornou-se numa poderosa ferramenta de anÆlise para entender e dar soluªo a vÆrios tipos de problemas complexos. O objectivo deste trabalho Ø contribuir para a obtenªo de possveis relaıes entre assuntos que partida poderamos pensar que sªo dspares (quando na realidade nªo o sªo), como coloraªo, planaridade e a existŒncia de matching em grafos. Esta dissertaªo Ø um trabalho de natureza reexiva, sobre a teoria dos grafos onde a ideia principal passa por questionarmos e discutirmos alguns temas pertinentes, deniıes e teoremas relacionando sempre com a planaridade dos grafos. DesenvolveremosumraciocnioecriaremosargumentosquefundamentemaexistŒncia de uma relaªo entre este tema e a coloraªo de grafos e a existŒncia de matching em grafos, utilizando exemplos e estabelecendo relaıes de causa e consequŒncia, deduzindo assim as respetivas conclusıes. Por vezes, os grafos nªo planares podem conter um aspeto visual um pouco complexo, devido aos vÆrios cruzamentos entre as suas arestas, originando assim um certo desencorajamento em utilizÆ-los como ferramenta para a soluªo de vÆrios problemas, quer sejam bÆsicos do quotidiano, ou mais complexos das mais vastas Æreas ligadas investigaªo. Um dos propsitos deste trabalho passa por desmisticar esta ideia e provar que existem muitas deniıes, propriedades, teoremas e algoritmos que podem ser aplicados em qualquer tipo de grafos, independentement da sua planaridade.

LOGITCPRPLOT: Stata module to graph component-plus-residual plot for logistic regression

logitcprplot can be used after logistic regression for graphing a component-plus-residual plot (a.k.a. partial residual plot) for a given predictor, including a lowess, local polynomial, restricted cubic spline, fractional polynomial, penalized spline, regression spline, running line, or adaptive variable span running line smooth

CPRPLOT2: Stata module to graph component-plus-residual plots for transformed regressors

cprplot2 is a variation of official Stata's cprplot and is used for graphing component-plus-residual plots (a.k.a. partial residual plots). Additional features (compared to cprplot): (1) cprplot2 can handle variables that enter the model repeatedly via different transformations (for example, polynomials). (2) cprplot2 can display component-plus-residual plots using the original units for transformed variables in the model. (3) A wrapper is provided to quickly display several component-plus-residual plots in a single image.

ALPHAWGT: Stata module to calculate Cronbach's alpha for weighted data

alphawgt computes the Cronbach's alpha statistic. It is the same than the official alpha (version 4.5.2, 09apr2002) except that fweights and aweights may be applied.