SEARCH · PubMed Health
Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
A combination of experimental and bioinformatics approaches leads Burge and colleagues (Wang et al., 2004 [this issue of Cell]) to a global view of how an RNA segment may be selected or avoided in mature mRNAs due to biased distributions of exonic enhancers and silencers, a process vital for genome evolution, developmental control, and disease onset.
Artificial neural network (ANN) has been recently introduced as a tool for data analysis. In this study, Kohonen's self-organizing maps (SOMs), a special type of neural network, were applied to a set of PCDD/PCDF concentrations found in 54 human milk and 83 food samples, which were collected in a number of countries all over the world. Data were obtained from the scientific literature. The purpose of the study was to find a potential relationship between PCDD/PCDF congener profiles in human milk and the dietary habits of the different countries in which samples were collected. The comparison of the SOM component planes for human milk and foodstuffs indicates that those countries with a greater fish consumption show also higher PCDD/PCDF concentrations in human milk. SOMs enable both the visualization of sample units and the visualization of congener distribution.
Multivariate methods are utilized to compare nicotinic acid and dihydropyridine as a drug carrier. Nicotinic acid and dihydropyridine form ester groups on 10 beta-lactam antibiotics with an oxymethyl group forming a linkage between the antibiotic and the drug carrier (nicotinic acid or dihydropyridine). Calculated molecular properties are analyzed by self-organizing tree algorithm (SOTA), bivariate regression, cluster analysis, factor analysis, discriminant analysis, hierarchical classification, and principal coordinates analysis. Ten important pharmacological properties for each of the nicotinic acid and dihydropyridine antibiotic derivatives are numerically similar and highly correlated. Calculated molecular properties include molar refractivity, molar volume, parachor, index of refraction, partition coefficient (log P), polarizability, and polar surface area. Dermal permeability coefficients (Kp) for nicotinic acid derivatives are similar to values for dihydropyridine derivatives. Dermal permeabliity coefficients analyzed by hierarchical classification and SOTA analysis were shown to be closely interrelated and highly correlated. Ten properties of the nicotinic acid and dihydropyridine were compared by Passing-Bablok regression analysis and shown to be highly correlated (r=0.9879). Box plot analysis of 10 properties, inclusive of both groups of derivatives, showed narrow ranges in values. Cluster analysis of derivative properties showed the nicotinic acid derivatives to be highly similar to the dihydropyridine derivatives of the same antibiotics. Cluster analysis was performed by single linkage, complete linkage, and centroid linkage. Factor analysis showed the nicotinic acid derivatives to be interrelated and similar to the dihydropyridine derivatives. Discriminant analysis performed on all derivatives formed a single highly cohesive and non-differentiated cluster, demonstrating strong similarity among nicotinic acid and dihydropyridine derivatives. Principal coordinates analysis (determines similarity) of Kp values showed high similarity between the nicotinic acid and dihydropyridine antibiotic derivatives.
Enzyme structures determined in organic solvents show that most organic molecules cluster in the active site, delineating the binding pocket. We have developed algorithms to perform solvent mapping computationally, rather than experimentally, by placing molecular probes (small molecules or functional groups) on a protein surface, and finding the regions with the most favorable binding free energy. The method then finds the consensus site that binds the highest number of different probes. The probe-protein interactions at this site are compared to the intermolecular interactions seen in the known complexes of the enzyme with various ligands (substrate analogs, products, and inhibitors). We have mapped thermolysin, for which experimental mapping results are also available, and six further enzymes that have no experimental mapping data, but whose binding sites are well characterized. With the exception of haloalkane dehalogenase, which binds very small substrates in a narrow channel, the consensus site found by the mapping is always a major subsite of the substrate-binding site. Furthermore, the probes at this location form hydrogen bonds and non-bonded interactions with the same residues that interact with the specific ligands of the enzyme. Thus, once the structure of an enzyme is known, computational solvent mapping can provide detailed and reliable information on its substrate-binding site. Calculations on ligand-bound and apo structures of enzymes show that the mapping results are not very sensitive to moderate variations in the protein coordinates.
The human gene parkin, known to cause familial Parkinson disease, as well as several other genes, likely involved in other neurodegenerative diseases or in cancer, encode proteins of the RBR family of ubiquitin ligases. Here, we describe the structural diversity of the RBR family in order to infer their functional roles. Of particular interest is a relationship detected between RBR-mediated ubiquitination and RNA metabolism: a few RBR proteins contain RNA binding domains and DEAH-box RNA helicase domains. Global protein domain graph analyses demonstrate that this connection is not RBR-specific, but instead many other proteins contain both ubiquitination and RNA-related domains. These proteins are present in animals, plants and fungi, suggesting that the link between these two cellular processes is ancient. Our results show that global bioinformatic approaches, involving comparative genomics and domain network analyses, may unearth novel functional relationships involving well-known and thoroughly studied groups of proteins.
Understanding the function of complex cortical circuits requires the simultaneous recording of action potentials from many neurons in awake and behaving animals. Practically, this can be achieved by extracellularly recording from multiple brain sites using single wire electrodes. However, in densely packed neural structures such as the human hippocampus, a single electrode can record the activity of multiple neurons. Thus, analytic techniques that differentiate action potentials of different neurons are required. Offline spike sorting approaches are currently used to detect and sort action potentials after finishing the experiment. Because the opportunities to record from the human brain are relatively rare, it is desirable to analyze large numbers of simultaneous recordings quickly using online sorting and detection algorithms. In this way, the experiment can be optimized for the particular response properties of the recorded neurons. Here we present and evaluate a method that is capable of detecting and sorting extracellular single-wire recordings in realtime. We demonstrate the utility of the method by applying it to an extensive data set we acquired from chronically implanted depth electrodes in the hippocampus of human epilepsy patients. This dataset is particularly challenging because it was recorded in a noisy clinical environment. This method will allow the development of "closed-loop" experiments, which immediately adapt the experimental stimuli and/or tasks to the neural response observed.
The Self Organising Tree Map (SOTM) neural network is investigated as a means of segmenting micro-organisms from confocal microscope image data. Features describing pixel and regional intensities, phase congruency and spatial proximity are explored in terms of their impact on the segmentation of bacteria and other micro-organisms. The significance of individual features is investigated, and it is proposed that, within the context of micro-biological image segmentation, better object delineation can be achieved if certain features are more dominant in the initial stages of learning. In this way, other features are allowed to become more/less significant as learning progresses: as more knowledge is acquired about the data being segmented. We argue that the efficiency and flexibility of the SOTM in adapting to, and preserving the topology of input space, makes it an appropriate candidate for implementing this idea. We propose a refinement to the competitive search strategy that allows for a more appropriate fusion of signal and proximal features, thereby promoting a segmentation that is more sensitive to the regional associations of different microbial matter. A refined stop criterion is also suggested such that the dynamically generated number of classes becomes more data dependant. Preliminary experiments are presented and it is found that favouring intensity characteristics in the early phases of learning, whilst relaxing proximity constraints in later phases of learning, offers a general mechanism through which we can improve the segmentation of microbial constituents.
A comprehensive approach to the assessment of any child with autism must be matched specifically to each individual child and family. This premise holds for medical therapies and special education services as well as psychopharmacologic interventions. Behavioral, as opposed to pharmacologic, treatment is the hallmark of effective intervention for autism. Physicians involved in the care of children with autism need to become familiar with educational law and intervention recommendations. Goals should include improved functional verbal and nonverbal communication and social skills, increased engagement in developmentally appropriate activities, improved fine and gross motor skills, and the development of independent academic and organizations skills, as well as replacement of problem behaviors with developmentally appropriate behaviors.. Medicating children with autism is difficult, but is often necessary for chronic behavioral difficulties. In the absence of clear and present guidelines, we have attempted to use evidence and clinical experience to suggest an algorithm based on symptom clusters. Although children with autism may be responsive to medications at lower doses and more susceptible to side effects than other children, medical intervention can produce a significant improvement in the quality of life for the child and family. Careful thought leading to correct identification of target behaviors can appropriately direct better alternatives for medication. Although these approaches are costly and time-consuming endeavors, the expenditure of such efforts is the only available pathway to improve the potential outcomes for individuals with autism as well as decrease the lifetime societal costs for each individual.
The diamondback moth (DBM), Plutella xylostella (L.) is considered as the most destructive pest of Brassicaceae crops world-wide. Its migratory capacities and development of insecticide resistance in many populations leads to more difficulties for population management. To control movement of populations and apparitions of resistance carried by resistant migrant individuals, populations must be identified using genetic markers. Here, seven different ISSR markers have been tested as a tool for population discrimination and genetic variations among 19 DBM populations from Canada, USA, Brazil, Martinique Island, France, Romania, Austria, Uzbekistan, Egypt, Benin, South Africa, Réunion Island, Hong Kong, Laos, Japan and four localities in Australia were assessed. Two classification methods were tested and compared: a common method of genetic distance analyses and a novel method based on an advanced statistical method of the Artificial Neural Networks' family, the Self-Organizing Map (SOM). The 188 loci selected revealed a very high variability between populations with a total polymorphism of 100% and a global coefficient of gene differentiation estimated by the Nei's index (Gst) of 0.238. Nevertheless, the largest part of variability was expressed among individuals within populations (AMOVA: 73.71% and mean polymorphism of 94% within populations). Genetic differentiation among the DBM populations did not reflect geographical distances between them. The two classification methods have given excellent results with less than 1.3% of misclassified individuals. The origin of the high genetic differentiation and efficiency of the two classification methods are discussed.
The optimal feeding profile of a fed batch process was designed by means of an evolutionary algorithm. The algorithm chromosomes include the real-valued parameters of a profile function, defined by previous knowledge. Each chromosome is composed of the parameters that define the feeding profile: the feed rates, the singular arc parameters and the switching times between the profile states. The feed profile design was tested on a fed-batch process simulation. The accepted profiles were smooth and similar to those derived analytically in other studies. Two selection functions, roulette wheel and geometric ranking, were compared. In order to overcome the problem of model mismatches, a novel optimization scheme was carried out. During its operation the process was sampled, the model was updated and the optimization procedure was applied. The on-line optimization showed improvement in the objective function for relatively low sample times. Choosing the sampling frequencies depends on the process dynamics and the time required for the measurements and optimization. Further study on experiments of fed-batch process demonstrated the use of complex, non-differentiable model and produced improved process performances using the optimal feeding profile.
DynDom is a program that analyses conformational change in proteins for dynamic domains, hinge axes, and hinge-bending regions. Here, a number of improvements and additions are reported which have been implemented in the new version 1.50. The most significant improvement is in the determination of the hinge-bending residues. A new routine also compares quantities relating to the main-chain dihedrals of bending residues with the hinge-bending motion. This version of the program can now be run from the DynDom website at: http://www.sys.uea.ac.uk/dyndom.
OBJECTIVE: An exploratory data analysis framework, based on minimal spanning tree, is proposed as a means to support the analysis of single trial (ST) electrophysiological signals. The core of this framework is the compact description of the input ST sample in a form of content-dependent ordered lists. Based on the established hierarchies, efficient ways to increase the SNR, extract prototypical responses, visualize possible self-organization trends in the sample and track the course of evoked response along the trial-to-trial dimension, are proposed. METHOD: Magnetoencephalographic auditory evoked responses were used for demonstrating and validating the introduced framework. RESULTS AND CONCLUSION: The results demonstrate the benefits, from this intelligent manipulation of STs, in understanding and enhancing the actual evoked signal. Specifically we find support for stimulus-induced phase-resetting hypothesis in the 3-20 Hz band, the existence of trials void of the prototypical evoked response, and an order across the single trial set hinting at an underlying process with long time scale.
Multidimensional compound optimization is a new paradigm in the drug discovery process, yielding efficiencies during early stages and reducing attrition in the later stages of drug development. The success of this strategy relies heavily on understanding this multidimensional data and extracting useful information from it. This paper demonstrates how principled visualization algorithms can be used to understand and explore a large data set created in the early stages of drug discovery. The experiments presented are performed on a real-world data set comprising biological activity data and some whole-molecular physicochemical properties. Data visualization is a popular way of presenting complex data in a simpler form. We have applied powerful principled visualization methods, such as generative topographic mapping (GTM) and hierarchical GTM (HGTM), to help the domain experts (screening scientists, chemists, biologists, etc.) understand and draw meaningful decisions. We also benchmark these principled methods against relatively better known visualization approaches, principal component analysis (PCA), Sammon's mapping, and self-organizing maps (SOMs), to demonstrate their enhanced power to help the user visualize the large multidimensional data sets one has to deal with during the early stages of the drug discovery process. The results reported clearly show that the GTM and HGTM algorithms allow the user to cluster active compounds for different targets and understand them better than the benchmarks. An interactive software tool supporting these visualization algorithms was provided to the domain experts. The tool facilitates the domain experts by exploration of the projection obtained from the visualization algorithms providing facilities such as parallel coordinate plots, magnification factors, directional curvatures, and integration with industry standard software.
A new computational scheme integrating multi-center ab initio molecular orbitals for determining total energy and normal vibration of large cluster systems is presented. This method can be used to treat large cluster systems such as solvents by quantum mechanics. The geometry parameters, the total energies, the relative energies, and the normal vibrations for four models of water cluster, the hydrated hydronium ion complex, and the transition state of proton transfer are calculated by the present method and are compared with those obtained by the full ab initio MO method. The results agree very well. The scheme proposed in this article is also intended to be used in modeling computer cluster systems using parallel algorithms.
Muscle foods are an integral part of the human diet and during the last few decades consumption of poultry products in particular has increased significantly. It is important for consumers, retailers and food regulatory bodies that these products are of a consistently high quality, authentic, and have not been subjected to adulteration by any lower-grade material either by accident or for economic gain. A variety of methods have been developed for the identification and authentication of muscle foods. However, none of these are rapid or non-invasive, all are time-consuming and difficulties have been encountered in discriminating between the commercially important avian species. Whilst previous attempts have been made to discriminate between muscle foods using infrared spectroscopy, these have had limited success, in particular regarding the closely related poultry species, chicken and turkey. Moreover, this study includes novel data since no attempts have been made to discriminate between both the species and the distinct muscle groups within these species, and this is the first application of Raman spectroscopy to the study of muscle foods. Samples of pre-packed meat and poultry were acquired and FT-IR and Raman measurements taken directly from the meat surface. Qualitative interpretation of FT-IR and Raman spectra at the species and muscle group levels were possible using discriminant function analysis. Genetic algorithms were used to elucidate meaningful interpretation of FT-IR results in (bio)chemical terms and we show that specific wavenumbers, and therefore chemical species, were discriminatory for each type (species and muscle) of poultry sample. We believe that this approach would aid food regulatory bodies in the rapid identification of meat and poultry products and shows particular potential for rapid assessment of food adulteration.
UNLABELLED: The importance of a diagnostic test that is simple and quick to identify Mycobacterium tuberculosis infection needs no emphasis. The tuberculin skin test (TST - 1 TU RT23) is the diagnostic tool for identifying M. tuberculosis infection at present. The test reaction on the skin is measured after 48-72 h. It is observed that often multi-modes are seen, when the reactions are drawn as a graph and the bimodality is seen very feebly. Because of the difficulties in the administration of TST, several serological tests were developed over three decades, but none of the studies showed the desired results. One study to evaluate purified protein derivative (PPD) antigen resulted in a claim of 80% sensitivity and 4% false-positivity rate (14), while other researchers were not able to obtain similar results. In addition, several problems were encountered due to the non-availability of antigens, and data analyses from an ELISA-based diagnostic test showed considerable overlap of distributions of optical density (OD) values among patients and healthy individuals (10). Classical statistical techniques cannot explain the cause of these overlaps. Hence, an attempt is made in this article to resolve these difficulties by the pattern recognition technique (PRT). The technique lies in splitting the data into clusters using a supervised algorithm. The data set is normally split into a training set, a test set and a validation set. The PRT gets "trained" through the training data set until the infected and uninfected groups of individuals are correctly classified. The training occurs based on an algorithm on the training set. On successful completion of the training, this technique is further tested and validated in the respective data sets. SETTING: A total of 273 finger-prick specimens were collected from five categories (Al, A2, B, C, D, E) of subjects not vaccinated with (bacille Calmette Guerin) (BCG) from Trivellore BCG Trial area adopted by the Tuberculosis Research Center, Chennai, India. OBJECTIVE: The study was conducted with the primary aim of evaluating purified antigens--r38 kDa, PPD and 30 kDa--for their usefulness as diagnostic tools and to test the applicability of the PRT in the evaluation of diagnostic tests. Individuals in two main categories (definitely not infected categories Al, A2 and D, and definitely infected categories B, E and C based on reaction to TST) were assembled for the purpose. RESULTS: The overall PRT performance of 30 kDa was 72.3% sensitivity and 90.9% specificity for identifying M. tuberculosis infection, while the r38 kDa antigen recorded a sensitivity of 73.8% and a specificity of 84.6%. In the case of PPD, the results were not promising. CONCLUSION: This paper on ELISA-based diagnostic tests attempts to implement an optimal decision support system through PRT that would identify the outcome (as infected or non-infected) based on the OD values. The PRT was able to predict the outcome for individual suspects. Further, Kullback-Leibler distance measurement has validated the PRT in distinguishing infected individuals from healthy subjects (based on the OD values).
Recent advances in molecular biology (e.g., cDNA microarray technology) enables the simultaneous monitoring of the expression level of thousands of genes. Due to the massive amount of complex data generated, sophisticated statistical approaches are necessary in order to properly address the experimental investigation. In this paper, we present statistical analysis of cDNA microarray data derived from bone regeneration experiments. Several interesting features from these data distinguish it from commonly used microarray experiment (i.e., separate hybridization of mRNA samples from reference and experimental tissues, selectively spotted cDNA sequences and 1060 systematically selected blank spots included in each array). Using this data set, we propose new methods for bioinformatic data normalization, as well as the modification and application of various other published methods in order to identify co-regulated gene expression patterns during the healing of a bone fracture. The proposed normalization methods perform effectively to eliminate the variations with a simple algorithm. Results from our cluster analysis revealed several clusters having distinct gene expression patterns during fracture healing. Our simulation study supports the reliability of the proposed methods.
We describe a useful and efficient tree building technique that is closely related to the maximum compatible subset method. Given a set of binary characters C and a degree bound d, the algorithm we describe counts the number of trees T that satisfy (1) the edges of T correspond to characters contained in C, and (2) the vertices of T have degree bounded by d. If the characters are weighted then we can efficiently determine which of these trees have maximum summed edge weight, where the weight of an edge equals the weight of the corresponding binary character in C. The complexity of the algorithms equals O(nk + ndK(d-1), where n is the number of taxa, k is the number of characters, and K is the number of distinct characters. A number of new tree consensus methods based on the algorithm are introduced, including one that uses edge weighted trees. As well, we illustrate the applicability of the algorithm for large sequence data sets by analyzing sequences from the "Out of Africa" mtDNA data set.