掌纹屈肌线纹型的量化研究

本文提出一组定量描述掌纹屈肌线纹型的特征向量,以描述和区分传统皮纹学中定义的6种掌纹屈肌线纹型。用该特征向量对151例(302只手掌掌纹)样本的6种屈肌线纹型进行了描述,并用Fisher判别分析进行了多类判别。结果发现,6种屈肌线纹型的分类判别正确率达到96.0%,其中桥贯型、悉尼型、通贯型、叉贯型的分类正确率为100%。为检验判别效果,用"刀切法"原则进行了回代交叉核实,验证后的正确率亦为96.0%,验证效果较好。结论认为,所采用的特征向量与相应的判别方法结合,能描述并区分不同的屈肌线纹型,并可得到较高分类正确率。     

The time-into-intensity-mapping network

We employ a special combination of different networks in order to process (transient) spatiotemporal patterns. In a first layer, feature analyzing cells translate instantaneous spatial patterns into activities of cells symbolizing the presence of certain feature values. A second layer maps the time sequence of symbols into a spatial activity pattern of the so-called TIM-cells. A third layer recognizes predefined activity patterns. We demonstrate the behaviour of the network using gaussian patterns in (1 + 1) space-time dimensions.     

特征代谢通路上的基因表达相关性及共调控表达模式

利用随机森林-通路分析法,通过袋外样本OOB的分类错误率筛选特征代谢通路,在特征通路上作基因表达相关性研究并对通路上的基因采用MAP(Mining attribute profile)算法挖掘不同实验条件下基因的共调控表达模式,对共调控表达模式进行聚类.分析结果显示同一特征代谢通路上的基因表达倾向相似,有2条特征代谢通路存在共表达模式.其中一条通路含108个表达模式,对这些模式进行聚类,其最低聚类的相似系数仍高达0.623.说明同一特征代谢通路上的基因共表达模式在不同实验条件下仍具有高度的相似性.对以通路作为基因模块进行复杂疾病的研究具有借鉴意义.     

A model of a human recognition system with ‘thinking’

The human system of pattern recognition is explored. This system utilizes the method of syntactic feature comparison, activation of the patterns containing the feature, and sorting among the activated patterns. Both the pattern recognition process without thinking and with thinking are discussed, and examples are given. Contrary to the usual concept, the more complicated pattern has a better chance to be recognized correctly.A portion of this paper has been presented at the 1977 International Cybernetic Conference in Washington     

Feature selection for DNA methylation based cancer classification

Molecular portraits, such as mRNA expression or DNA methylation patterns, have been shown to be strongly correlated with phenotypical parameters. These molecular patterns can be revealed routinely on a genomic scale. However, class prediction based on these patterns is an under-determined problem, due to the extreme high dimensionality of the data compared to the usually small number of available samples. This makes a reduction of the data dimensionality necessary. Here we demonstrate how phenotypic classes can be predicted by combining feature selection and discriminant analysis. By comparing several feature selection methods we show that the right dimension reduction strategy is of crucial importance for the classification performance. The techniques are demonstrated by methylation pattern based discrimination between acute lymphoblastic leukemia and acute myeloid leukemia.     

DNAfan: a software tool for automated extraction and analysis of user-defined sequence regions

SUMMARY: DNAfan (DNA Feature ANalyzer) is a tool combining sequence-filtering and pattern searching. DNAfan automatically extracts user-defined sets of sequence fragments from large sequence sets. Fragments are defined by annotated gene feature keys and co- or non-occurring patterns within the feature or close to it. A gene feature parser and a pattern-based filter tool localizes and extracts the specific subset of sequences. The selected sequence data can subsequently be retrieved for analyses or further processed with DNAfan to find the occurrence of specific patterns or structural motifs. DNAfan is a powerful tool for pattern analysis. Its filter features restricts the pattern search to a well-defined set of sequences, allowing drastic reduction in false positive hits. AVAILABILITY: http://bighost.ba.itb.cnr.it:8080/Framework.     

Analysis of a model for banded vegetation patterns in semi-arid environments with nonlocal dispersal

Journal of Mathematical Biology - Vegetation patterns are a characteristic feature of semi-arid regions. On hillsides these patterns occur as stripes running parallel to the contours. The...     

Self-organizing hierarchic networks for pattern recognition in protein sequence.

We present a method based on hierarchical self-organizing maps (SOMs) for recognizing patterns in protein sequences. The method is fully automatic, does not require prealigned sequences, is insensitive to redundancy in the training set, and works surprisingly well even with small learning sets. Because it uses unsupervised neural networks, it is able to extract patterns that are not present in all of the unaligned sequences of the learning set. The identification of these patterns in sequence databases is sensitive and efficient. The procedure comprises three main training stages. In the first stage, one SOM is trained to extract common features from the set of unaligned learning sequences. A feature is a number of ungapped sequence segments (usually 4-16 residues long) that are similar to segments in most of the sequences of the learning set according to an initial similarity matrix. In the second training stage, the recognition of each individual feature is refined by selecting an optimal weighting matrix out of a variety of existing amino acid similarity matrices. In a third stage of the SOM procedure, the position of the features in the individual sequences is learned. This allows for variants with feature repeats and feature shuffling. The procedure has been successfully applied to a number of notoriously difficult cases with distinct recognition problems: helix-turn-helix motifs in DNA-binding proteins, the CUB domain of developmentally regulated proteins, and the superfamily of ribokinases. A comparison with the established database search procedure PROFILE (and with several others) led to the conclusion that the new automatic method performs satisfactorily.     

AStream: an R package for annotating LC/MS metabolomic data

AStream, an R-statistical software package for the curation and identification of feature peaks extracted from liquid chromatography mass spectrometry (LC/MS) metabolomics data, is described. AStream detects isotopic, fragment and adduct patterns by identifying feature pairs that fulfill expected relational patterns. Data reduction by AStream allows compounds to be identified reliably and subsequently linked to metabolite databases. AStream provides researchers with a fast, reliable tool for summarizing metabolomic data, notably reducing curation time and increasing consistency of results. AVAILABILITY: The AStream R package and a study example can be freely accessed at http://www.urr.cat/AStream/AStream.html.     

Adaptive pattern classification and universal recoding: II. Feedback,expectation, olfaction,illusions

Part I of this paper describes a model for the parallel development and adult coding of neural feature detectors. It shows how any set of arbitrary spatial patterns can be recoded, or transformed, into any other spatial patterns (universal recoding), if there are sufficiently many cells in the network's cortex. This code is, however, unstable through time if arbitrarily many patterns can perturb a fixed number of cortical cells. This paper shows how to stabilize the code in the general case using feedback between cellular sites. A biochemically defined critical period is not necessary to stabilize the code, nor is it sufficient to ensure useful coding properties.We ask how short term memory can be reset in response to temporal sequences of spatial patterns. This leads to a context-dependent code in which no feature detector need uniquely characterize an input pattern; yet unique classification by the pattern of activity across feature detectors is possible. This property uses learned expectation mechanisms whereby unexpected patterns are temporarily suppressed and/or activate nonspecific arousal. The simplest case describes reciprocal interactions via trainable synaptic pathways (long term memory traces) between two recurrent on-center off-surround networks undergoing mass action (shunting) interactions. This unit can establish an adaptive resonance, or reverberation, between two regions if their coded patterns match, and can suppress the reverberation if their patterns do not match. This concept yields a model of olfactory coding within the olfactory bulb and prepyriform cortex. The resonance idea also includes the establishment of reverberation between conditioned reinforcers and generators of contingent negative variation if presently avialable sensory cues are compatible with the network's drive requirements at that time; and a search and lock mechanism whereby the disparity between two patterns can be minimized and the minimal disparity images locked into position. Stabilizing the code uses attentional mechanisms, in particular nonspecific arousal as a tuning and search device. We suggest that arousal is gated by a chemical transmitter system—for example, norepinephrine—whose relative states of accumulation at antagonistic pairs of on-cells and off-cells through time can shift the spatial pattern of STM activity across a field of feature detectors. For example, a sudden arousal increment in response to an un-expected pattern can reverse, or rebound, these relative activities, thereby suppressing incorrectly classified populations. The rebound mechanism has formal properties analogous to negative afterimages and spatial frequency adaptation.Supported in part by the Advanced Research Projects Agency under ONR Contract No. N00014-76-C-0185     

Chromatophore activity during natural pattern expression by the squid Sepioteuthis lessoniana: contributions of miniature oscillation

Squid can rapidly change the chromatic patterns on their body. The patterns are created by the expansion and retraction of chromatophores. The chromatophore consists of a central pigment-containing cell surrounded by radial muscles that are controlled by motor neurons located in the central nervous system (CNS). In this study we used semi-intact squid (Sepioteuthis lessoniana) displaying centrally controlled natural patterns to analyze spatial and temporal activities of chromatophores located on the dorsal mantle skin. We found that chromatophores oscillated with miniature expansions/retractions at various frequencies, even when the chromatic patterns appear macroscopically stable. The frequencies of this miniature oscillation differed between "feature" and "background" areas of chromatic patterns. Higher frequencies occurred in feature areas, whereas lower frequencies were detected in background areas. We also observed synchronization of the oscillation during chromatic pattern expression. The expansion size of chromatophores oscillating at high frequency correlated with the number of synchronized chromatophores but not the oscillation frequency. Miniature oscillations were not observed in denervated chromatophores. These results suggest that miniature oscillations of chromatophores are driven by motor neuronal activities in the CNS and that frequency and synchrony of this oscillation determine the chromatic pattern and the expansion size, respectively.     

A neural model of feature attention in motion perception

We utilize a model of motion perception to link a physiological study of feature attention in cortical motion processing to a psychophysical experiment of motion perception. We explain effects of feature attention by modulatory excitation of neural activity patterns in a framework of biased competition. Our model allows us to qualitatively replicate physiological data concerning attentional modulation and to generate model behavior in a decision experiment that is consistent with psychophysical observations. Furthermore, our investigation makes predictions for future psychophysical experiments.     

Analysis of synonymous codon usage patterns in the genus Rhizobium

The codon usage patterns of rhizobia have received increasing attention. However, little information is available regarding the conserved features of the codon usage patterns in a typical rhizobial genus. The codon usage patterns of six completely sequenced strains belonging to the genus Rhizobium were analysed as model rhizobia in the present study. The relative neutrality plot showed that selection pressure played a role in codon usage in the genus Rhizobium. Spearman’s rank correlation analysis combined with correspondence analysis (COA) showed that the codon adaptation index and the effective number of codons (ENC) had strong correlation with the first axis of the COA, which indicated the important role of gene expression level and the ENC in the codon usage patterns in this genus. The relative synonymous codon usage of Cys codons had the strongest correlation with the second axis of the COA. Accordingly, the usage of Cys codons was another important factor that shaped the codon usage patterns in Rhizobium genomes and was a conserved feature of the genus. Moreover, the comparison of codon usage between highly and lowly expressed genes showed that 20 unique preferred codons were shared among Rhizobium genomes, revealing another conserved feature of the genus. This is the first report of the codon usage patterns in the genus Rhizobium.     

Adaptive pattern classification and universal recoding: I. Parallel development and coding of neural feature detectors

This paper analyses a model for the parallel development and adult coding of neural feature detectors. The model was introduced in Grossberg (1976). We show how experience can retune feature detectors to respond to a prescribed convex set of spatial patterns. In particular, the detectors automatically respond to average features chosen from the set even if the average features have never been experienced. Using this procedure, any set of arbitrary spatial patterns can be recoded, or transformed, into any other spatial patterns (universal recoding), if there are sufficiently many cells in the network's cortex. The network is built from short term memory (STM) and long term memory (LTM) mechanisms, including mechanisms of adaptation, filtering, contrast enhancement, tuning, and nonspecific arousal. These mechanisms capture some experimental properties of plasticity in the kitten visual cortex. The model also suggests a classification of adult feature detector properties in terms of a small number of functional principles. In particular, experiments on retinal dynamics, including amarcrine cell function, are suggested.Supported in part by the Advanced Research Projects Agency under ONR Contract No. N00014-76-C-0185     

Predicting and analyzing early wake-up associated gene expressions by integrating GWAS and eQTL studies

Circadian rhythms are endogenous 24-hour rhythmic oscillations affecting human behaviors, such as sleep, blood pressure and other biological processes, the disturbance of which lead to circadian rhythm sleep disorders (CRSDs). In this study, based on the data from genome-wide association studies (GWASs) and expression quantitative trait loci (eQTLs), we tried to identify novel gene expression patterns in brain tissues that were associated with early wake-up. First, the maximum-relevance-minimum-redundancy (mRMR) method was adopted to analyze the involved gene expression patterns, yielding a feature list. Second, the incremental feature selection (IFS) method and the Dagging algorithm were applied to extract important gene expression patterns, which yield the best performance for Dagging. As a result, 4374 gene expression patterns were obtained, and they were further used to build an optimal classifier with a good performance of a Matthews's correlation coefficient of 0.933. Furthermore, the most important 49 gene expression patterns were extensively analyzed. Four genes were found to be related to circadian rhythm, as reported in previous studies. As a first attempt in identifying the target genes whose expression levels are associated with sleep-wake rhythms through integrating GWAS and eQTL results, this study can motivate more investigations in this regard.This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang.     

A neural network model for selective attention in visual pattern recognition

A neural network model of the mechanism of selective attention in visual pattern recognition is proposed and simulated on a digital computer.When a complex figure consisting of two patterns or more is presented to the model, it is segmented into individual patterns, and each pattern is recognized separately. Even if one of the patterns to which the model is paying selective attention is affected by noise or defects, the model can recall the complete pattern from which the noise has been eliminated and the defects corrected. It is not necessary for perfect recall that the stimulus pattern should be identical in shape to the training pattern. Even though the pattern is distorted in shape or changed in size, it can be correctly recognized and the missing portions restored.The model consists of a hierarchical neural network which has efferent as well as afferent connections between cells. The afferent and the efferent signals interact with each other in the network: the efferent signals, that is, the signals for selective attention, have a facilitating effect on the afferent ones, and, at the same time, the afferent signals gate efferent signal flow. When some feature in the stimulus is not extracted in the afferent paths, the threshold for detection of that feature is automatically lowered by decreasing the efficiency of inhibition, and the model tries to extract even vague traces of the undetected feature.     

Long-range, pattern-dependent contextual effects in early human visual cortex

The standard view of neurons in early visual cortex is that they behave like localized feature detectors. Here we demonstrate that processing in early visual areas goes beyond feature detection by showing that neural responses are greater when a feature deviates from its context compared to when it does not deviate from its context. Using psychophysics, fMRI, and electroencephalography methodologies, we measured neural responses to an oriented Gabor ("target") embedded in various visual patterns as defined by the relative orientation of flanking stimuli. We first show using psychophysical contrast adaptation and fMRI that a target that differs from its context results in more neural activity compared to a target that is contained within an alternating sequence, suggesting that neurons in early visual cortex are sensitive to large-scale orientation patterns. Next, we use event-related potentials to show that orientation deviations affect the earliest sensory components of the target response. Finally, we use forced-choice classification of "noise" stimuli to show that we are more likely to "see" orientations that deviate from the context. Our results suggest that early visual cortex is sensitive to global patterns in images in a way that is markedly different from the predictions of standard models of cortical visual processing.     

On alpha-helices terminated by glycine. 2. Recognition by sequence patterns

Consensus sequence patterns were constructed to describe helix ends with a characteristic conformation caused by specific three-center hydrogen bonds. This special type of hydrogen bond pattern comprises about one third of all helices and mostly contains glycine with a positive torsion angle phi at the helix ends. After a simple clustering procedure 6 resulting consensus sequence patterns were able to identify 501 out of 575 helix ends in the Brookhaven Protein Data Bank, showing the above-mentioned features. The patterns did not detect any false segment, but numerous sequence segments not identified by structural criteria were recognized. It is likely that they are indeed helices terminated by glycine with a positive torsion angle phi.     

Global analysis of patterns of gene expression during <Emphasis Type="Italic">Drosophila</Emphasis>embryogenesis

Background  

Cell and tissue specific gene expression is a defining feature of embryonic development in multi-cellular organisms. However, the range of gene expression patterns, the extent of the correlation of expression with function, and the classes of genes whose spatial expression are tightly regulated have been unclear due to the lack of an unbiased, genome-wide survey of gene expression patterns.