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At least 361 records · Page 20Linked to original sources
PubMed1993

Computer methods in neuroanatomy: determining mutual orientation of whole neuronal arbors.

In most neurons orientation can be recognized because their arbors are more or less polarized and/or flattened. These are morphological characteristics of great functional importance. This paper deals with three-dimensional display and mathematical definition of orientation planes and vectors in whole arbors. An orientation plane can be derived from the flattest rectangular prism with which it is possible to enclose the arbor, or may be found by best-fit least square determination (based on all digitized points of the arbor). Both approaches allow description and comparison, in quantitative terms, of the orientation of neurons under various normal, pathological or experimental conditions.

Computer Graphics↗
PubMed2000

Theoretical basis and computational methods for different test-day genetic evaluation methods.

In test-day (TD) models, records from individual test days are used to determine lactation production instead of aggregating records. Test-day models have recently gained considerable interest because they are more flexible in handling records from different recording schemes. Compared with only using records of complete lactations, they can reduce the generation interval through frequent genetic evaluations with the latest data. Test-day models can predict total production more accurately by accounting for time-dependent environmental effects. Test-day models may be separated into three groups: First, two-step models under which corrections are carried out at TD level and subsequently corrected TD records are processed in an aggregated form as lactation records. Second, fixed regression models assume that TD records within a lactation are repeated records. Because yields in the course of the lactation follow a curvilinear pattern, this curve can be considered by using suitable covariates. Third, random regression models additionally define the animal's genetic effect by using regression coefficients and allowing for covariances among them. The difference between random regression and fixed regression models is that the genetic merit of an individual is allowed to differ in the course of the lactation in random regression models. Random regressions are related to the approach of defining covariance functions for longitudinal data. Computationally, TD models are very demanding. For evaluations on a national scale, the size of the equation system can go to hundreds of millions of equations, depending on the size of the database and the specific model defined.

Animals↗
PubMed2005

A general computational method for converting normal spectra into derivative spectra.

The mathematical problem of converting a normal spectrum into the corresponding first- and second-derivative spectra is formulated as an integral equation of the first kind. Tikhonov regularization is then applied to solve the spectral conversion problem. The end result is a set of linear algebraic equations that takes in as input the original spectrum and produces as output the second-derivative spectrum, which is then integrated to yield the first-derivative spectrum. Noise amplification is kept under control by adjusting the regularization parameter (guided by generalized cross-validation) in the algebraic equations. The performance of this procedure is demonstrated by applying it to different types of spectral data taken from the literature.

Algorithms↗
PubMed2004

Understanding the selectivity of genistein for human estrogen receptor-beta using X-ray crystallography and computational methods.

We present X-ray crystallographic and molecular modeling studies of estrogen receptors-alpha and -beta complexed with the estrogen receptor-beta-selective phytoestrogen genistein, and coactivator-derived NR box peptides containing an LXXLL motif. We demonstrate that the ligand binding mode is essentially identical when genistein is bound to both isoforms, despite the considerably weaker affinity of this ligand for estrogen receptor-alpha. In addition, we examine subtle differences between binding site residues, providing an explanation for why genistein is modestly selective for the beta isoform. To this end, we also present the results of quantum chemical studies and thermodynamic arguments that yield insight to the nature of the interactions leading to estrogen receptor-beta selectivity. The importance of our analysis to structure-based drug design is discussed.

Computer Simulation↗
PubMed1996

Computational methods for defining the allowed conformational space of 16S rRNA based on chemical footprinting data.

Structural models for 16S ribosomal RNA have been proposed based on combinations of crosslinking, chemical protection, shape, and phylogenetic evidence. These models have been based for the most part on independent data sets and different sets of modeling assumptions. In order to evaluate such models meaningfully, methods are required to explicitly model the spatial certainty with which individual structural components are positioned by specific data sets. In this report, we use a constraint satisfaction algorithm to explicitly assess the location of the secondary structural elements of the 16S RNA, as well as the certainty with which these elements can be positioned. The algorithm initially assumes that these helical elements can occupy any position and orientation and then systematically eliminates those positions and orientations that do not satisfy formally parameterized interpretations of structural constraints. Using a conservative interpretation of the hydroxyl radical footprinting data, the positions of the ribosomal proteins as defined by neutron diffraction studies, and the secondary structure of 16S rRNA, the location of the RNA secondary structural elements can be defined with an average precision of 25 A (ranging from 12.8 to 56.3 A). The uncertainty in individual helix positions is both heterogeneous and dependent upon the number of constraints imposed on the helix. The topology of the resulting model is consistent with previous models based on independent approaches. The result of our computation is a conservative upper bound on the possible positions of the RNA secondary structural elements allowed by this data set, and provides a suitable starting point for refinement with other sources of data or different sets of modeling assumptions.

Base Sequence↗
PubMed1998

Conformational behavior of the HAV-VP3(110-121) peptidic sequence and synthetic analogs in membrane environments studied by CD and computational methods.

The present study was undertaken to examine the structural features that may be important to explain the immunogenicity of the (110-121) peptide sequence (FWRGDLVFDFQV) of VP3 capsid protein of hepatitis A virus. A conformational analysis of the preferred conformations by CD and molecular mechanics was carried out. Present results suggest that the interaction with liposomes as biomembrane model induces and stabilizes the amphipathic beta-structure of the peptide. To study the contribution of amino acid replacements at the RGD tripeptide as well as the influence of the peptide chain length on peptide conformation, solid-phase peptide synthesis of several peptide analogs was carried out and the peptide conformation was studied using CD spectroscopy. The results show that the RGD sequence is necessary to induce the beta-structure in the presence of liposomes.

Capsid↗
PubMed2002

A computational method for resequencing long DNA targets by universal oligonucleotide arrays.

Universal arrays contain all possible oligonucleotides of a certain length, typically 6-10 bases. They can determine in a single experiment all substrings of that length that occur along a target sequence. That information, also called the spectrum of the sequence, is not sufficient to uniquely reconstruct a sequence longer than a few hundred bases. We have devised a polynomial algorithm that reconstructs the sequence, given the spectrum and an additional reference sequence, homologous to the target sequence. Such a reference is available, for example, in the identification of single-nucleotide polymorphisms. The algorithm can handle errors in the spectrum as well as substitutions, insertions, and deletions in the target sequence. We present extensive simulation results, which show that the algorithm correctly reconstructs target sequences of >2,000 nucleotides from error-prone 8-mer spectra when realistic levels of single-nucleotide polymorphisms are present.

Algorithms↗
PubMed1983

Computer methods for the assessment of toxicity.

The prediction of the biological activity of chemical compounds by means of mathematical models is discussed. Biological activity of chemicals, including their toxicity on man and other biological organisms and systems, involves too complex phenomena to presently be predictable by fundamental models such as ab initio quantum mechanical models or statistical mechanical models. Hence one takes recourse to semi-empirical and empirical models which relate the variation in chemical structure of chemical compounds to the variation in their measured biological activity, e.g. toxicity in one or several test systems. These models are "calibrated" on series of similar compounds with "known" toxicity, the training set. Thereafter the models can--in fortunate cases--be used to predict the toxicity of compounds which are structurally similar to the training set compounds. The formulation and applicability of semi-empirical and empirical models relating chemical structure to biological activity is discussed. Causes and remedies for commonly encountered fallacies are presented.

Animals↗
PubMed2005

Three-dimensional models of human 2'-5' oligoadenylate synthetases: a new computational method for reconstructing an enzyme assembly.

BACKGROUND: 2'-5' Oligoadenylate synthetases are interferon-induced enzymes important for antiviral cell defense. Tetramer formation of human OAS1 is essential for the catalytic activity of the enzyme. However, no structure of any oligomeric OAS1 protein has yet been solved and the structural organization of the catalytically active tetramers is not known. MATERIAL/METHODS: Using a novel conjunction of comparative modeling, molecular surface analysis, and sequence analysis, high-resolution models of the known isoforms of human OAS1 were prepared. RESULTS: The resulting models suggest different oligomeric states for the p40, p46, and p48 isoforms as well as a significant difference in enzymatic activity between the p46 and p40/p48 isoforms. CONCLUSIONS: The differences in enzymatic activity could result in different susceptibility to viral infection in cells expressing individual isoforms. The models are consistent with the published biochemical data on human OAS1. The results also suggest that the structure of the active OAS2 dimers would be equivalent to that of p40 OAS1 tetramers.

2',5'-Oligoadenylate Synthetase↗
PubMed1997

An improved computer method to prepare 3D magnetic resonance images of thoracic structures.

The mediastinal and cardiovascular anatomy is complex. We have developed a three-dimensional (3D) reconstruction system for the major mediastinal structures using magnetic resonance imaging data on a NeXT workstation. The program uses a combination of automatic and manual procedures to determine the contours of the cardiac structures. The geometric centers of the contours are connected by a 3D space curve, and the central axis of each cardiac structures is determined. The contours are projected on the perpendicular plane to the central axis and semiautomatically processed until the contours of one pixel are obtained. Then the surface rendering with transparency is performed. Compositing combines two images so that both appear in the composite, superimposed on each other. Demonstration of the various mediastinal lines and cardiovascular diseases by the composits of the partly transparent 3D images has promoted a better understanding of the complex mediastinal and cardiovascular anatomy and diseases.

Heart↗
PubMed1995

The Protein Disease Database of human body fluids: II. Computer methods and data issues.

The Protein Disease Database (PDD) is a relational database of proteins and diseases. With this database it is possible to screen for quantitative protein abnormalities associated with disease states. These quantitative relationships use data drawn from the peer-reviewed biomedical literature. Assays may also include those observed in high-resolution electrophoretic gels that offer the potential to quantitate many proteins in a single test as well as data gathered by enzymatic or immunologic assays. We are using the Internet World Wide Web (WWW) and the Web browser paradigm as an access method for wide distribution and querying of the Protein Disease Database. The WWW hypertext transfer protocol and its Common Gateway Interface make it possible to build powerful graphical user interfaces that can support easy-to-use data retrieval using query specification forms or images. The details of these interactions are totally transparent to the users of these forms. Using a client-server SQL relational database, user query access, initial data entry and database maintenance are all performed over the Internet with a Web browser. We discuss the underlying design issues, mapping mechanisms and assumptions that we used in constructing the system, data entry, access to the database server, security, and synthesis of derived two-dimensional gel image maps and hypertext documents resulting from SQL database searches.

Body Fluids↗
PubMed2003

Computational methods and evaluation of RNA stabilization reagents for genome-wide expression studies.

Gene expression studies require high quality messenger RNA (mRNA) in addition to other factors such as efficient primers and labeling reagents. To prevent RNA degradation and to improve the quality of gene array expression data, several commercial reagents have become available. We examined a conventional hot-phenol lysis method and RNA stabilization reagents, and generated comparative gene expression profiles from Escherichia coli cells grown on minimal medium. Our data indicate that certain RNA stabilization reagents induce stress responses and proper caution must be exercised during their use. We observed that the laboratory reagent (phenol/EtOH, 5:95, v/v) worked efficiently in isolating high quality mRNA and reproducibility was such that reliable gene expression profiles were generated. To assist in the analysis of gene expression data, we wrote a number of macros that use the most recent gene annotation and process data in accordance with gene function. Scripts were also written to examine the occurrence of artifacts, based on GC content, length of the individual open reading frame (ORF), its distribution on plus and minus DNA strands, and the distance from the replication origin.

Base Composition↗
PubMed1982

[Regression of the cerebellar syndrome under long-term administration of 5-HTP or the combination of 5-HTP and benserazide. 26 cases quantified and treated using computer methods].

A quantitative evaluation of cerebellar ataxia, with an ataxia score (total, static, kinetic) and the measurement of objective values related to the major symptoms, is proposed. 21 patients with heredo-ataxias were treated for 12 months with high doses (16 mg/kg/day) of D-L-5-HTP, L-5-HTP or the association D-L-5-HTP (16 mg/kg/day)-Benserazide(6 mg/kg/day). A computerized processing of the data obtained by regular examination was performed. The ataxia showed a significant regression at the 12th month, mainly in the static performances and in the speed of speech. L-5-HTP appeared to be more active than D-L-5-HTP. The regression of the cerebellar ataxia was also observed in non degenerative conditions such as multiple sclerosis and surgical injury of the anterior lobe vermis, showing that 5-HTP was active on the cerebellar syndrome in general. The regression of the cerebellar ataxia was very slow in inherited diseases and continued for 2 or 4 months after the treatment stopped. A serotoninergic cerebellar control of motricity is discussed.

5-Hydroxytryptophan↗
PubMed1994

Knowledge-based cephalometric analysis: a comparison with clinicians using interactive computer methods.

In modern orthodontic practice great reliance is placed on systematic and objective methods of characterizing craniofacial forms, using measurements based on both hard and soft tissue landmarks. Lateral skull X-ray images are routinely used in cephalometric analysis to provide quantitative measurements useful to clinical orthodontists. It is argued that a model- and knowledge-based methodology provides the best approach in successfully interpreting digitized lateral skull radiographs. A rule-based segmentation system, making use of an image appearance model, is used to extract image features from gray-level images. Complex image features and cephalometric landmarks are constructed from these segmented component features. A predictive model, defining picture structure, allows location hypotheses to be made for image features. The underlaying structure of the location model provides the basis for a geometric constraint model of use in discriminating between image feature candidates. A blackboard system is used to organize these tasks hierarchically, with individual knowledge sources grouped according to function and the individual stages of the adopted image interpretation cycle. Quantitative results demonstrate the superiority of this complex system over its component segmentation system run on its own. Comparisons with clinicians demonstrate both the strengths and the weaknesses of the present system. Comparisons with previous systems are favorable.

Cephalometry↗
PubMed2004

Transcription-based prediction of response to IFNbeta using supervised computational methods.

Changes in cellular functions in response to drug therapy are mediated by specific transcriptional profiles resulting from the induction or repression in the activity of a number of genes, thereby modifying the preexisting gene activity pattern of the drug-targeted cell(s). Recombinant human interferon beta (rIFNbeta) is routinely used to control exacerbations in multiple sclerosis patients with only partial success, mainly because of adverse effects and a relatively large proportion of nonresponders. We applied advanced data-mining and predictive modeling tools to a longitudinal 70-gene expression dataset generated by kinetic reverse-transcription PCR from 52 multiple sclerosis patients treated with rIFNbeta to discover higher-order predictive patterns associated with treatment outcome and to define the molecular footprint that rIFNbeta engraves on peripheral blood mononuclear cells. We identified nine sets of gene triplets whose expression, when tested before the initiation of therapy, can predict the response to interferon beta with up to 86% accuracy. In addition, time-series analysis revealed potential key players involved in a good or poor response to interferon beta. Statistical testing of a random outcome class and tolerance to noise was carried out to establish the robustness of the predictive models. Large-scale kinetic reverse-transcription PCR, coupled with advanced data-mining efforts, can effectively reveal preexisting and drug-induced gene expression signatures associated with therapeutic effects.

Adolescent↗
PubMed2004

Comparison of computational methods for identifying translation initiation sites in EST data.

BACKGROUND: Expressed Sequence Tag (EST) sequences are generally single-strand, single-pass sequences, only 200-600 nucleotides long, contain errors resulting in frame shifts, and represent different parts of their parent cDNA. If the cDNAs contain translation initiation sites, they may be suitable for functional genomics studies. We have compared five methods to predict translation initiation sites in EST data: first-ATG, ESTScan, Diogenes, Netstart, and ATGpr. RESULTS: A dataset of 100 EST sequences, 50 with and 50 without, translation initiation sites, was created. Based on analysis of this dataset, ATGpr is found to be the most accurate for predicting the presence versus absence of translation initiation sites. With a maximum accuracy of 76%, ATGpr more accurately predicts the position or absence of translation initiation sites than NetStart (57%) or Diogenes (50%). ATGpr similarly excels when start sites are known to be present (90%), whereas NetStart achieves only 60% overall accuracy. As a baseline for comparison, choosing the first ATG correctly identifies the translation initiation site in 74% of the sequences. ESTScan and Diogenes, consistent with their intended use, are able to identify open reading frames, but are unable to determine the precise position of translation initiation sites. CONCLUSIONS: ATGpr demonstrates high sensitivity, specificity, and overall accuracy in identifying start sites while also rejecting incomplete sequences. A database of EST sequences suitable for validating programs for translation initiation site prediction is now available. These tools and materials may open an avenue for future improvements in start site prediction and EST analysis.

Codon, Initiator↗
PubMed2006

Computational methods for comparison of large genomic and proteomic datasets reveal protein markers of metastatic cancer.

Large-scale genomic and proteomic analysis has provided a wealth of information on biologically relevant systems, and the ability to analyze this information is crucial to uncovering important biological relationships. However, it has proven difficult to compare large datasets from different sources due to different gene and protein identifiers assigned by individual laboratories and database systems. Here, we describe the design of a fully automated blast program (BlastPro) that facilitates rapid comparison of large protein-protein, nucleotide--nucleotide, or nucleotide--protein datasets from numerous, independent studies. Using this system, we compared several published genomic and proteomic databases for proteins that are upregulated in highly motile, metastatic tumor cells. Analysis of five independent studies comprised of greater than 1 x 10(6) genomic sequences and greater than 1,000 proteins revealed that the cytoskeletal-associated protein alpha-actinin is increased at both the mRNA and protein level in metastatic breast, prostate, and skin cancer cells. Interestingly, spatial analysis of alpha-actinin expression revealed that it is amplified 8-fold in the leading pseudopodium compared to the cell body compartment of migrating cells. These findings indicate that amplification of alpha-actinin and its localization to the leading pseudopodium are potential biomarkers of cancer progression to a more metastatic phenotype. Together, our results demonstrate that the BlastPro system can be used to compare large genomic and proteomic datasets to reveal important biological relationships including those associated with cancer progression.

Actinin↗
PubMed2000

Application of neural computing methods for interpreting phospholipid fatty acid profiles of natural microbial communities.

The microbial community compositions of surface and subsurface marine sediments and sediments lining burrows of marine polychaetes and hemichordates from the North Inlet estuary (near Georgetown, S.C. ) were analyzed by comparing ester-linked phospholipid fatty acid (PLFA) profiles with a back-propagating neural network (NN). The NNs were trained to relate PLFA inputs to sediment type outputs (e.g., surface, subsurface, and burrow lining) and worm species (e.g., Notomastus lobatus, Balanoglossus aurantiacus, and Branchyoasychus americana). Sensitivity analysis was used to determine which of the 60 PLFAs significantly contributed to training the NN. The NN architecture was optimized by changing the number of hidden neurons and calculating the cross-validation error between predicted and actual outputs of training and test data. The optimal NN architecture was found to be four hidden neurons with 60-input neurons representing the 60 PLFAs, and four output neurons coding for both sediment types and worm species. Comparison of cross-validation results using NNs and linear discriminant analysis (LDA) revealed that NNs had significantly fewer incorrect classifications (2.7%) than LDA (8.4%). For the NN cross-validation, both sediment type and worm species had 3 incorrect classifications out of 112. For the LDA cross-validation, sediment type and worm species had 7 and 12 incorrect classifications out of 112, respectively. Sensitivity analysis of the trained NNs revealed that 17 fatty acids explained 50% of variability in the data set. These PLFAs were highly different among sediments and burrow types, indicating significant differences in the microbiota.

Animals↗