To examine age-related effects on high-level consciously controlled and low-level automatically controlled inhibitory processes, the Simon task was combined with the masked prime task in a hybrid procedure. Young and older adults responded to the identity of targets (left/right key-press to left-/right-pointing arrows) that appeared on the left/right of the screen and were preceded by left-/right-pointing backward-masked arrow primes at fixation. Responses were faster and more accurate when the target was congruent with its location than incongruent (Simon effect), and when the target was incompatible with the prime than compatible (negative compatibility effect; NCE). The Simon effect was disproportionately larger, and the NCE disproportionately delayed, in older adults compared to young adults, indicating both high- and low-level inhibitory control deficits with aging. Moreover, the two effects were additive in young adults, but interactive in older adults, providing support for the dedifferentiation hypothesis of aging. Specifically, older adults’ prime-related inhibitory control appeared improved on incongruent relative to congruent trials, suggesting that impaired automatic control was substituted by high-level, non-automatic processes.
aging; inhibition; cognitive control; masked priming; negative compatibility effect; Simon effect; dedifferentiation
It is now clear that non-consciously perceived stimuli can bias our decisions. Although previous researches highlighted the importance of automatic and unconscious processes involved in voluntary action, the neural correlates of such processes remain unclear. Basal ganglia dysfunctions have long been associated with impairment in automatic motor control. In addition, a key role of the medial frontal cortex has been suggested by administrating a subliminal masked prime task to a patient with a small lesion restricted to the supplementary motor area (SMA). In this task, invisible masked arrows stimuli were followed by visible arrow targets for a left or right hand response at different interstimuli intervals (ISI), producing a traditional facilitation effect for compatible trials at short ISI and a reversal inhibitory effect at longer ISI. Here, by using fast event-related fMRI and a weighted parametric analysis, we showed BOLD related activity changes in a cortico-subcortical network, especially in the SMA and the striatum, directly linked to the individual behavioral pattern. This new imaging result corroborates previous works on subliminal priming using lesional approaches. This finding implies that one of the roles of these regions was to suppress a partially activated movement below the threshold of awareness.
Evolutionary search algorithms have become an essential asset in the algorithmic toolbox for solving high-dimensional optimization problems in across a broad range of bioinformatics problems. Genetic algorithms, the most well-known and representative evolutionary search technique, have been the subject of the major part of such applications. Estimation of distribution algorithms (EDAs) offer a novel evolutionary paradigm that constitutes a natural and attractive alternative to genetic algorithms. They make use of a probabilistic model, learnt from the promising solutions, to guide the search process. In this paper, we set out a basic taxonomy of EDA techniques, underlining the nature and complexity of the probabilistic model of each EDA variant. We review a set of innovative works that make use of EDA techniques to solve challenging bioinformatics problems, emphasizing the EDA paradigm's potential for further research in this domain.
Execution of a response that has been primed by a backward-masked stimulus is inhibited (negative compatibility effect; NCE). Three experiments investigated the locus of this inhibition. Masked primes (left- or right-pointing arrows) were followed either by an arrow or a circle target. Arrow targets always required a left- or right-hand response, but the experiments differed in the response required to circles: press neither, either or both response keys (i.e. nogo, free choice and bimanual, respectively). Arrow targets showed the usual NCEs. Circle targets showed NCEs in the form of a response bias away from the primed response in the nogo and free-choice tasks; primes and targets differed on these trials, ruling out a perceptual explanation of the NCE. The bimanual task showed no such bias, suggesting that the NCE is located at a level of abstract response codes rather than specific muscle commands.
inhibition; masked motor priming; nogo; free-choice; bimanual responses
One of the goals of systems biology is to reverse engineer in a comprehensive fashion the arrow diagrams of signal transduction systems. An important tool for ordering pathway components is genetic epistasis analysis, and here we present a strategy termed Alternative Inputs (AIs) to perform systematic epistasis analysis. An alternative input is defined as any genetic manipulation that can activate the signaling pathway instead of the natural input. We introduced the concept of an “AIs-Deletions matrix” that summarizes the outputs of all combinations of alternative inputs and deletions. We developed the theory and algorithms to construct a pairwise relationship graph from the AIs-Deletions matrix capturing both functional ordering (upstream, downstream) and logical relationships (AND, OR), and then interpreting these relationships into a standard arrow diagram. As a proof-of-principle, we applied this methodology to a subset of genes involved in yeast mating signaling. This experimental pilot study highlights the robustness of the approach and important technical challenges. In summary, this research formalizes and extends classical epistasis analysis from linear pathways to more complex networks, facilitating computational analysis and reconstruction of signaling arrow diagrams.
Despite the importance of cognitive control in many cognitive tasks involving uncertainty, the computational mechanisms of cognitive control in response to uncertainty remain unclear. In this study, we develop biologically realistic neural network models to investigate the instantiation of cognitive control in a majority function task, where one determines the category to which the majority of items in a group belong. Two models are constructed, both of which include the same set of modules representing task-relevant brain functions and share the same model structure. However, with a critical change of a model parameter setting, the two models implement two different underlying algorithms: one for grouping search (where a subgroup of items are sampled and re-sampled until a congruent sample is found) and the other for self-terminating search (where the items are scanned and counted one-by-one until the majority is decided). The two algorithms hold distinct implications for the involvement of cognitive control. The modeling results show that while both models are able to perform the task, the grouping search model fit the human data better than the self-terminating search model. An examination of the dynamics underlying model performance reveals how cognitive control might be instantiated in the brain for computing the majority function.
cognitive control; uncertainty; majority function; algorithms; computational modeling; neural networks
Many multimeric transcription factors recognize DNA sequence patterns by cooperatively binding to bipartite elements composed of half sites separated by a flexible spacer. We developed a novel bipartite algorithm, bipartite pattern discovery (Bipad), which produces a mathematical model based on information maximization or Shannon's entropy minimization principle, for discovery of bipartite sequence patterns. Bipad is a C++ program that applies greedy methods to search the bipartite alignment space and examines the upstream or downstream regions of co-regulated genes, looking for cis-regulatory bipartite patterns. An input sequence file with zero or one site per locus is required, and the left and right motif widths and a range of possible gap lengths must be specified. Bipad can run in either single-block or bipartite pattern search modes, and it is capable of comprehensively searching all four orientations of half-site patterns. Simulation studies showed that the accuracy of this motif discovery algorithm depends on sample size and motif conservation level, but results were independent of background composition. Bipad performed equivalent with or better than other pattern search algorithms in correctly identifying Escherichia coli cyclic AMP receptor protein and Bacillus subtilis sigma factor binding site sequences based on experimentally defined benchmarks. Finally, a new bipartite information weight matrix for vitamin D3 receptor/retinoid X receptor α (VDR/RXRα) binding sites was derived that comprehensively models the natural variability inherent in these sequence elements.
This paper describes a novel algorithm to analyze genetic linkage data using pattern recognition techniques and genetic algorithms (GA). The method allows a search for regions of the chromosome that may contain genetic variations that jointly predispose individuals for a particular disease. The method uses correlation analysis, filtering theory and genetic algorithms (GA) to achieve this goal. Because current genome scans use from hundreds to hundreds of thousands of markers, two versions of the method have been implemented. The first is an exhaustive analysis version that can be used to visualize, explore, and analyze small genetic data sets for two marker correlations; the second is a GA version, which uses a parallel implementation allowing searches of higher-order correlations in large data sets. Results on simulated data sets indicate that the method can be informative in the identification of major disease loci and gene-gene interactions in genome-wide linkage data and that further exploration of these techniques is justified. The results presented for both variants of the method show that it can help genetic epidemiologists to identify promising combinations of genetic factors that might predispose to complex disorders. In particular, the correlation analysis of IBD expression patterns might hint to possible gene-gene interactions and the filtering might be a fruitful approach to distinguish true correlation signals from noise.
Gene-Gene Interactions; Multifactorial Diseases; Pattern Recognition; Data Mining; Correlation Analysis; Parallel Genetic Algorithm
Sequence information and high-throughput methods to measure gene expression levels open the door to explore transcriptional regulation using computational tools. Combinatorial regulation and sparseness of regulatory elements throughout the genome allow organisms to control the spatial and temporal patterns of gene expression. Here we study the organization of cis-regulatory elements in sets of co-regulated genes. We build an algorithm to search for combinations of transcription factor binding sites that are enriched in a set of potentially co-regulated genes with respect to the whole genome. No knowledge is assumed about involvement of specific sets of transcription factors. Instead, the search is exhaustively conducted over combinations of up to four binding sites obtained from databases or motif search algorithms. We evaluate the performance on random sets of genes as a negative control and on three biologically validated sets of co-regulated genes in yeasts, flies and humans. We show that we can detect DNA regions that play a role in the control of transcription. These results shed light on the structure of transcription regulatory regions in eukaryotes and can be directly applied to clusters of co-expressed genes obtained in gene expression studies. Supplementary information is available at http://www.mit.edu/~kreiman/resources/cisregul/.
jClust is a user-friendly application which provides access to a set of widely used clustering and clique finding algorithms. The toolbox allows a range of filtering procedures to be applied and is combined with an advanced implementation of the Medusa interactive visualization module. These implemented algorithms are k-Means, Affinity propagation, Bron–Kerbosch, MULIC, Restricted neighborhood search cluster algorithm, Markov clustering and Spectral clustering, while the supported filtering procedures are haircut, outside–inside, best neighbors and density control operations. The combination of a simple input file format, a set of clustering and filtering algorithms linked together with the visualization tool provides a powerful tool for data analysis and information extraction.
Contact: firstname.lastname@example.org; email@example.com; firstname.lastname@example.org
Supplementary information: Supplementary data are available at Bioinformatics online.
► We search an optimal reconstruction resolution for given non-uniform point sets. ► We use a statistically-derived topology-controller to find the optimal resolution. ► The proposed topology-controller is derived from homology-based statistics. ► We can evaluate of the reconstruction process the need of visual inspection. ► We show qualitative comparisons and results of the proposed approach.
In this paper we present a novel algorithm to optimize the reconstruction from non-uniform point sets. We introduce a statistically-derived topology-controller for selecting the reconstruction resolution of a given non-uniform point set. Deriving information from homology-based statistics, our topology-controller ensures a stable and sound basis for the analysis process. By analyzing our topology-controller, we select an optimal reconstruction resolution which ensures both low reconstruction errors and a topological stability of the underlying signal. Our approach offers a valuable method for the evaluation of the reconstruction process without the need of visual inspection of the reconstructed datasets. By means of qualitative results we show how our proposed topology statistics provides complementary information in the enhancement of existing reconstruction pipelines in visualization.
Topology; Non-uniform representation; Reconstruction; Homology
A number of protein and peptide identification software tools based on MS data are available to the proteomics researchers. They all share a common functionality: they process MS data and present in their output peptides and proteins that best match with the input data. Even if restricting to sequence search engines one can observe heterogeneity of approaches, of algorithms, of input parameters, of the use of available sequence databases, of output information (scores, confidence levels, details of interpretation, etc.) and of possibilities to export results. The results obtained from different tools also vary both from the content and the form point of view. It is a challenge for the bio-informatics to help lab-researchers in manipulating results obtained from replicate analyses or from submissions made to multiple search engines.
Here we present our approach to represent side-by-side results from different MS/MS identification results. We expose elements of the difficulty to get appropriate exports from different search engines and to map the provided information, in order to align it in a single interface. We address questions such as: which export format from each tool is the most useful to perform alignment of results; how to align proteins and peptides coming from two different sequence databases (NCBInr and SwissProt, for instance); how to interpret protein grouping in separate queries; how to identify that proteins are the same if the sequence is not present in the result, or whether any of the database identifiers are different, etc.
As an illustrative example, we show how we convert outputs from Phenyx, Mascot, Sequest, or X!Tandem into the Phenyx result comparison feature and more. We will also show how this effort will contribute to and profit from the development of AnalysisXML, a HUPO PSI standard XML format to capture results from protein and peptide identification results.
Theories of embodied cognition suppose that perception, action, and cognition are tightly intertwined and share common representations and processes. Indeed, numerous empirical studies demonstrate interaction between stimulus perception, response planning, and response execution. In this paper, we present an experiment and a connectionist model that show how the Simon effect, a canonical example of perception–action congruency, can be moderated by the (cognitive representation of the) task instruction. To date, no representational account of this influence exists. In the experiment, a two-dimensional Simon task was used, with critical stimuli being colored arrows pointing in one of four directions (backward, forward, left, or right). Participants stood on a Wii balance board, oriented diagonally toward the screen displaying the stimuli. They were either instructed to imagine standing on a snowboard or on a pair of skis and to respond to the stimulus color by leaning toward either the left or right foot. We expected that participants in the snowboard condition would encode these movements as forward or backward, resulting in a Simon effect on this dimension. This was confirmed by the results. The left–right congruency effect was larger in the ski condition, whereas the forward–backward congruency effect appeared only in the snowboard condition. The results can be readily accounted for by HiTEC, a connectionist model that aims at capturing the interaction between perception and action at the level of representations, and the way this interaction is mediated by cognitive control. Together, the empirical work and the connectionist model contribute to a better understanding of the complex interaction between perception, cognition, and action.
stimulus–response congruency; task set; perception–action interaction; Wii balance board; connectionist modeling; Simon effect; top-down modulation
Motivation: Single-particle cryo electron microscopy (cryoEM) typically produces density maps of macromolecular assemblies at intermediate to low resolution (∼5–30 Å). By fitting high-resolution structures of assembly components into these maps, pseudo-atomic models can be obtained. Optimizing the quality-of-fit of all components simultaneously is challenging due to the large search space that makes the exhaustive search over all possible component configurations computationally unfeasible.
Results: We developed an efficient mathematical programming algorithm that simultaneously fits all component structures into an assembly density map. The fitting is formulated as a point set matching problem involving several point sets that represent component and assembly densities at a reduced complexity level. In contrast to other point matching algorithms, our algorithm is able to match multiple point sets simultaneously and not only based on their geometrical equivalence, but also based on the similarity of the density in the immediate point neighborhood. In addition, we present an efficient refinement method based on the Iterative Closest Point registration algorithm. The integer quadratic programming method generates an assembly configuration in a few seconds. This efficiency allows the generation of an ensemble of candidate solutions that can be assessed by an independent scoring function. We benchmarked the method using simulated density maps of 11 protein assemblies at 20 Å, and an experimental cryoEM map at 23.5 Å resolution. Our method was able to generate assembly structures with root-mean-square errors <6.5 Å, which have been further reduced to <1.8 Å by the local refinement procedure.
Availability: The program is available upon request as a Matlab code package.
Contact: email@example.com and firstname.lastname@example.org
Supplementary information: Supplementary data are available at Bioinformatics Online.
Noninvasive brain–computer interfaces (BCI) translate subject's electroencephalogram (EEG) features into device commands. Large feature sets should be down-selected for efficient feature translation. This work proposes two different feature down-selection algorithms for BCI: (a) a sequential forward selection; and (b) an across-group variance. Power rar ratios (PRs) were extracted from the EEG data for movement imagery discrimination. Event-related potentials (ERPs) were employed in the discrimination of cue-evoked responses. While center-out arrows, commonly used in calibration sessions, cued the subjects in the first experiment (for both PR and ERP analyses), less stimulating arrows that were centered in the visual field were employed in the second experiment (for ERP analysis). The proposed algorithms outperformed other three popular feature selection algorithms in movement imagery discrimination. In the first experiment, both algorithms achieved classification errors as low as 12.5% reducing the feature set dimensionality by more than 90%. The classification accuracy of ERPs dropped in the second experiment since centered cues reduced the amplitude of cue-evoked ERPs. The two proposed algorithms effectively reduced feature dimensionality while increasing movement imagery discrimination and detected cue-evoked ERPs that reflect subject attention.
Brain–computer interface; EEG; Feature selection; Movement imagery; Event-related potentials
PromoterPlot () is a web-based tool for simplifying the display and processing of transcription factor searches using either the commercial or free TransFac distributions. The input sequence is a TransFac search (public version) or FASTA/Affymetrix IDs (local install). It uses an intuitive pattern recognition algorithm for finding similarities between groups of promoters by dividing transcription factor predictions into conserved triplet models. To minimize the number of false-positive models, it can optionally exclude factors that are known to be unexpressed or inactive in the cells being studied based on microarray or proteomic expression data. The program will also estimate the likelihood of finding a pattern by chance based on the frequency observed in a control set of mammalian promoters we obtained from Genomatix. The results are stored as an interactive SVG web page on our server.
A constrained non-rigid registration (CNRR) algorithm for use in prostate image-guided adaptive radiotherapy is presented in a coherent mathematical framework. The registration algorithm is based on a global rigid transformation combined with a series of local injective non-rigid multi-resolution cubic B-spline Free Form Deformation (FFD) transformations. The control points of the FFD are used to non-rigidly constrain the transformation to the prostate, rectum, and bladder. As well, the control points are used to rigidly constrain the transformation to the estimated position of the pelvis, left femur, and right femur. The algorithm was tested with both 3D conformal radiotherapy (3DCRT) and intensity-modulated radiotherapy (IMRT) dose plan data sets. The 3DCRT dose plan set consisted of 10 fan-beam CT (FBCT) treatment-day images acquired from four different patients. The IMRT dose plan set consisted of 32 cone-beam CT (CBCT) treatment-day images acquired from 4 different patients. The CNRR was tested with different combinations of anatomical constraints and each test significantly outperformed both rigid and non-rigid registration at aligning constrained bones and critical organs. The CNRR results were used to adapt the dose plans to account for patient positioning errors as well as inter-day bone motion and intrinsic organ deformation. Each adapted dose plan improved performance by lowering radiation distribution to the rectum and bladder while increasing or maintaining radiation distribution to the prostate.
Non-rigid image registration; B-spline free form deformation; Rigid registration; Image-guided radiotherapy; Adaptive prostate radiotherapy
In DNA microarray experiments, discovering groups of genes that share similar transcriptional characteristics is instrumental in functional annotation, tissue classification and motif identification. However, in many situations a subset of genes only exhibits consistent pattern over a subset of conditions. Conventional clustering algorithms that deal with the entire row or column in an expression matrix would therefore fail to detect these useful patterns in the data. Recently, biclustering has been proposed to detect a subset of genes exhibiting consistent pattern over a subset of conditions. However, most existing biclustering algorithms are based on searching for sub-matrices within a data matrix by optimizing certain heuristically defined merit functions. Moreover, most of these algorithms can only detect a restricted set of bicluster patterns.
In this paper, we present a novel geometric perspective for the biclustering problem. The biclustering process is interpreted as the detection of linear geometries in a high dimensional data space. Such a new perspective views biclusters with different patterns as hyperplanes in a high dimensional space, and allows us to handle different types of linear patterns simultaneously by matching a specific set of linear geometries. This geometric viewpoint also inspires us to propose a generic bicluster pattern, i.e. the linear coherent model that unifies the seemingly incompatible additive and multiplicative bicluster models. As a particular realization of our framework, we have implemented a Hough transform-based hyperplane detection algorithm. The experimental results on human lymphoma gene expression dataset show that our algorithm can find biologically significant subsets of genes.
We have proposed a novel geometric interpretation of the biclustering problem. We have shown that many common types of bicluster are just different spatial arrangements of hyperplanes in a high dimensional data space. An implementation of the geometric framework using the Fast Hough transform for hyperplane detection can be used to discover biologically significant subsets of genes under subsets of conditions for microarray data analysis.
Image registration is a fundamental task used in image processing to match two or more images taken at different times, from different sensors or from different viewpoints. The objective is to find in a huge search space of geometric transformations, an acceptable accurate solution in a reasonable time to provide better registered images. Exhaustive search is computationally expensive and the computational cost increases exponentially with the number of transformation parameters and the size of the data set. In this work, we present an efficient image registration algorithm that uses genetic algorithms within a multi-resolution framework based on the Non-Subsampled Contourlet Transform (NSCT). An adaptable genetic algorithm for registration is adopted in order to minimize the search space. This approach is used within a hybrid scheme applying the two techniques fitness sharing and elitism. Two NSCT based methods are proposed for registration. A comparative study is established between these methods and a wavelet based one. Because the NSCT is a shift-invariant multidirectional transform, the second method is adopted for its search speeding up property. Simulation results clearly show that both proposed techniques are really promising methods for image registration compared to the wavelet approach, while the second technique has led to the best performance results of all. Moreover, to demonstrate the effectiveness of these methods, these registration techniques have been successfully applied to register SPOT, IKONOS and Synthetic Aperture Radar (SAR) images. The algorithm has been shown to work perfectly well for multi-temporal satellite images as well, even in the presence of noise.
genetic algorithms; image registration; multi-resolution analysis; nonsubsampled contourlet transform; wavelet transform
New web-based technologies provide an excellent opportunity for sharing and accessing information and using web as a platform for interaction and collaboration. Although several specialized tools are available for analyzing DNA sequence information, conventional web-based tools have not been utilized for bioinformatics applications. We have developed a novel algorithm and implemented it for searching species-specific genomic sequences, DNA barcodes, by using popular web-based methods such as Google.
We developed an alignment independent character based algorithm based on dividing a sequence library (DNA barcodes) and query sequence to words. The actual search is conducted by conventional search tools such as freely available Google Desktop Search. We implemented our algorithm in two exemplar packages. We developed pre and post-processing software to provide customized input and output services, respectively. Our analysis of all publicly available DNA barcode sequences shows a high accuracy as well as rapid results.
Our method makes use of conventional web-based technologies for specialized genetic data. It provides a robust and efficient solution for sequence search on the web. The integration of our search method for large-scale sequence libraries such as DNA barcodes provides an excellent web-based tool for accessing this information and linking it to other available categories of information on the web.
To assess the ability of regression tree boosting to risk-adjust health care cost predictions, using diagnostic groups and demographic variables as inputs. Systems for risk-adjusting health care cost, described in the literature, have consistently employed deterministic models to account for interactions among diagnostic groups, simplifying their statistical representation, but sacrificing potentially useful information. An alternative is to use a statistical learning algorithm such as regression tree boosting that systematically searches the data for consequential interactions, which it automatically incorporates into a risk-adjustment model that is customized to the population under study.
Administrative data for over 2 million enrollees in indemnity, preferred provider organization (PPO), and point-of-service (POS) plans from Thomson Medstat's Commercial Claims and Encounters database.
The Agency for Healthcare Research and Quality's Clinical Classification Software (CCS) was used to sort 2001 diagnoses into 260 diagnosis categories (DCs). For each plan type (indemnity, PPO, and POS), boosted regression trees and main effects linear models were fitted to predict concurrent (2001) and prospective (2002) total health care cost per patient, given DCs and demographic variables.
Regression tree boosting explained 49.7–52.1 percent of concurrent cost variance and 15.2–17.7 percent of prospective cost variance in independent test samples. Corresponding results for main effects linear models were 42.5–47.6 percent and 14.2–16.6 percent.
The combination of regression tree boosting and a diagnostic grouping scheme, such as CCS, represents a competitive alternative to risk-adjustment systems that use complex deterministic models to account for interactions among diagnostic groups.
Risk adjustment; case mix; health care cost; boosting; data mining
Valid cueing has been shown to accelerate target identification and improve decision accuracy, however the precise nature and extent to which biasing influences the successive stages of target processing remain unclear. The present event-related potential (ERP) study used a “hybrid” task that combined features of standard cued-attention and task-switching paradigms in order to explore the effects of expectation on both identification and categorization of centrally-presented stimuli. Subjects made semantic judgments (living/nonliving) on word targets (“bunny”), and perceptual judgments (right/left) on arrow targets (“≪≪<”). Target expectancy was manipulated using cues that were valid (60% of trials), invalid (10%), or neutral (30%). Invalidly-cued targets required task-set switching before categorization could commence, and resulted in RT costs relative to validly- or neutrally-cued targets. Additionally, benefits from valid-cueing were observed for word targets. Invalid cueing of both arrow and word targets modulated early posterior visual potentials (P1/N1) and elicited a subsequent anterior P3a (270 ms). The temporal relationship of these effects suggests that the P3a indexed domain-general task-set switching processes recruited in response to the detection of unexpected perceptual information. Subsequent to the P3a and immediately preceding the behavioral response, validly-cued targets elicited enhanced stimulus-specific waveforms (arrows: parietal positivity [P290], words: inferior temporal negativity [late ITN: 400–600 ms]). The degree of neural enhancement relative to the invalid and neutral conditions mirrored the magnitude of corresponding RT benefits, suggesting that these waveforms indexed categorization and/or decision processes. Together, these results suggest that valid cueing increases the neural efficiency of initial stimulus identification, facilitating transmission of information to subsequent categorization stages, where increased neural activity leads to behavioral benefits.
categorization; attention; P3a; N400; N1; P1
Hematopoietic stem cell transplantation (HSCT) is a medical procedure in the field of hematology and oncology, most often performed for patients with certain cancers of the blood or bone marrow. A lot of patients have no suitable HLA-matched donor within their family, so physicians must activate a “donor search process” by interacting with national and international donor registries who will search their databases for adult unrelated donors or cord blood units (CBU). Information and communication technologies play a key role in the donor search process in donor registries both nationally and internationaly. One of the major challenges for donor registry computer systems is the development of a reliable search algorithm. This work discusses the top-down design of such algorithms and current practice. Based on our experience with systems used by several stem cell donor registries, we highlight typical pitfalls in the implementation of an algorithm and underlying data structure.
Working Memory Capacity (WMC) is thought to be related to executive control and focused memory search abilities. These two hypotheses make contrasting predictions regarding the effects of retrieval on forgetting. Executive control during memory retrieval is believed to lead to retrieval induced forgetting (RIFO) because inhibition of competing memory traces during retrieval renders them temporarily less accessible. According to this suggestion, superior executive control should increase RIFO. Alternatively, superior focused search abilities could diminish RIFO, because delimiting the search set reduces the amount of competition between traces and thus the need for inhibition. Some evidence suggests that high WMC is related to more RIFO, which is inconsistent with the focused search hypothesis.
Using the RIFO paradigm, we created distinct and overlapping categories to manipulate the amount of competition between them. This overlap increased competition between some categories while exclusive use of weak exemplars ensured negligible effects of output interference and integration.
Low WMC individuals exhibited RIFO within and between overlapping categories, indicating the effect of resolving competition during retrieval. High WMC individuals only exhibited between-category RIFO, suggesting they experienced reduced competition resolution demands. Low WMC Individuals exhibited the strongest RIFO and no retrieval benefits when interference resolution demands were high.
Our findings qualify the inhibitory explanation for RIFO by incorporating the focused search hypothesis for materials that are likely to pose extraordinary challenges at retrieval. The results highlight the importance of considering individual differences in retrieval-induced effects and qualify existing models of these effects.
In in a typical "left-to-right" phylogenetic tree, the vertical order of taxa is meaningless, as only the branch path between them reflects their degree of similarity. To make unresolved trees more informative, here we propose an innovative Evolutionary Algorithm (EA) method to search the best graphical representation of unresolved trees, in order to give a biological meaning to the vertical order of taxa.
Starting from a West Nile virus phylogenetic tree, in a (1 + 1)-EA we evolved it by randomly rotating the internal nodes and selecting the tree with better fitness every generation. The fitness is a sum of genetic distances between the considered taxon and the r (radius) next taxa. After having set the radius to the best performance, we evolved the trees with (λ + μ)-EAs to study the influence of population on the algorithm.
The (1 + 1)-EA consistently outperformed a random search, and better results were obtained setting the radius to 8. The (λ + μ)-EAs performed as well as the (1 + 1), except the larger population (1000 + 1000).
The trees after the evolution showed an improvement both of the fitness (based on a genetic distance matrix, then close taxa are actually genetically close), and of the biological interpretation. Samples collected in the same state or year moved close each other, making the tree easier to interpret. Biological relationships between samples are also easier to observe.