A brute‐force approach to vegetation classification

Aim: Introduction of a novel approach to the classification of vegetation data (species by plot matrices). This approach copes with a large amount of noise, groups irregularly shaped in attribute space and species turnover within groups. Method: The proposed algorithm (Isopam) is based on the classification of ordination scores from isometric feature mapping. Ordination and classification are repeated in a search for either high overall fidelity of species to groups of sites, or high quantity and quality of indicator species for groups of sites. The classification is performed either as a hierarchical, divisive method or as non‐hierarchical partitioning. In divisive clustering, resulting groups are subdivided until a stopping criterion is met. Isopam was tested on 20 real‐world data sets. The resulting classifications were compared with solutions from eight widely used clustering algorithms. Results: When looking at the significance of species fidelities to groups of sites, and at quantity and quality of indicator species, Isopam often achieved high ranks as compared with other algorithms.     

Biosensor approach to psychopathology classification

We used a multi-round, two-party exchange game in which a healthy subject played a subject diagnosed with a DSM-IV (Diagnostic and Statistics Manual-IV) disorder, and applied a Bayesian clustering approach to the behavior exhibited by the healthy subject. The goal was to characterize quantitatively the style of play elicited in the healthy subject (the proposer) by their DSM-diagnosed partner (the responder). The approach exploits the dynamics of the behavior elicited in the healthy proposer as a biosensor for cognitive features that characterize the psychopathology group at the other side of the interaction. Using a large cohort of subjects (n = 574), we found statistically significant clustering of proposers' behavior overlapping with a range of DSM-IV disorders including autism spectrum disorder, borderline personality disorder, attention deficit hyperactivity disorder, and major depressive disorder. To further validate these results, we developed a computer agent to replace the human subject in the proposer role (the biosensor) and show that it can also detect these same four DSM-defined disorders. These results suggest that the highly developed social sensitivities that humans bring to a two-party social exchange can be exploited and automated to detect important psychopathologies, using an interpersonal behavioral probe not directly related to the defining diagnostic criteria.     

AI-based approach to automatic sleep classification

The primary goal of this paper is to introduce the potential of artificial intelligence (AI) methods to researchers in sleep classification. AI provides learning procedures for the construction of a sleep classifier, prescribing how to combine the observed parameters and how to derive the corresponding decision thresholds. A case study reporting a successful application of an automatic induction of decision trees and of a learning vector quantizer to this domain is presented. Received: 24 May 1993/Accepted in revised form: 8 September 1993     

A new approach to the species classification problem in floristic analysis

A new method of species (inverse) classification of vegetation data, i.e. classification of species into groups with similar ecological tolerances, is presented which overcomes the problems of species abundance distorting the results. The algorithm TWO-STEP is based on the use of an asymmetric measure of dissimilarity: where i, j are species, h is the stand, n is the total number of stands, and x_ih is the amount of species i in stand h. The algorithm uses the rows of the asymmetric dissimilarity matrix generated as above to form a second symmetric dissimilarity matrix using the measure: where m is the number of species and k the species. Flexible sorting is applied to generate a species classification. Comparison of results after applying the TWO-STEP algorithm and a standard alternative to an artificial data set demonstrates its efficacy. TWO-STEP also shows considerable advantages over previous analyses for a Queensland rainforest data set (quantitative) and an English heath (qualitative) data set. Normalization of species data appears advantageous for quantitative data only.     

A multiresolution approach to automated classification of protein subcellular location images

Background  

Fluorescence microscopy is widely used to determine the subcellular location of proteins. Efforts to determine location on a proteome-wide basis create a need for automated methods to analyze the resulting images. Over the past ten years, the feasibility of using machine learning methods to recognize all major subcellular location patterns has been convincingly demonstrated, using diverse feature sets and classifiers. On a well-studied data set of 2D HeLa single-cell images, the best performance to date, 91.5%, was obtained by including a set of multiresolution features. This demonstrates the value of multiresolution approaches to this important problem.     

A federated design for a neurobiological simulation engine: the CBI federated software architecture

Simulator interoperability and extensibility has become a growing requirement in computational biology. To address this, we have developed a federated software architecture. It is federated by its union of independent disparate systems under a single cohesive view, provides interoperability through its capability to communicate, execute programs, or transfer data among different independent applications, and supports extensibility by enabling simulator expansion or enhancement without the need for major changes to system infrastructure. Historically, simulator interoperability has relied on development of declarative markup languages such as the neuron modeling language NeuroML, while simulator extension typically occurred through modification of existing functionality. The software architecture we describe here allows for both these approaches. However, it is designed to support alternative paradigms of interoperability and extensibility through the provision of logical relationships and defined application programming interfaces. They allow any appropriately configured component or software application to be incorporated into a simulator. The architecture defines independent functional modules that run stand-alone. They are arranged in logical layers that naturally correspond to the occurrence of high-level data (biological concepts) versus low-level data (numerical values) and distinguish data from control functions. The modular nature of the architecture and its independence from a given technology facilitates communication about similar concepts and functions for both users and developers. It provides several advantages for multiple independent contributions to software development. Importantly, these include: (1) Reduction in complexity of individual simulator components when compared to the complexity of a complete simulator, (2) Documentation of individual components in terms of their inputs and outputs, (3) Easy removal or replacement of unnecessary or obsoleted components, (4) Stand-alone testing of components, and (5) Clear delineation of the development scope of new components.     

Dealing with vagueness in complex forest landscapes: A soft classification approach through a niche-based distribution model

The increasing interest in biodiversity conservation has led to the development of new approaches to facilitate ecologically based conservation policies and management plans. In this context, the development of effective methods for the classification of forest types constitutes a crucial issue as forests represent the most widespread vegetation structure and play a key role in ecosystem functioning. In this study a maximum entropy approach (Maxent) to forest type classification in a complex Mediterranean area, has been investigated. Maxent, a niche-based model of species/habitat distribution, allowed researchers to estimate the potential distribution of four forest types: Holm oak, Mixed oak, Mixed broadleaved and Riparian forests. The Maxent model's internal tests have proved a powerful tool for estimating the model's accuracy and analyzing the effects of the most important variables in the produced models. Moreover the comparison with a spectral response-based fuzzy classification, showed a higher accuracy in the Maxent outputs, demonstrating how the use of environmental variables, combined with spectral information in the classification of natural or semi-natural land cover classes, improves map accuracies. The modeling approach followed by this study, taking into account the uncertainty proper of the natural ecosystems and the use of environmental variables in land cover classification, can represent a useful approach to making more efficient and effective field inventories and to developing effective conservation policies.     

A wavelet optimization approach for ECG signal classification

Wavelets have proved particularly effective for extracting discriminative features in ECG signal classification. In this paper, we show that wavelet performances in terms of classification accuracy can be pushed further by customizing them for the considered classification task. A novel approach for generating the wavelet that best represents the ECG beats in terms of discrimination capability is proposed. It makes use of the polyphase representation of the wavelet filter bank and formulates the design problem within a particle swarm optimization (PSO) framework. Experimental results conducted on the benchmark MIT/BIH arrhythmia database with the state-of-the-art support vector machine (SVM) classifier confirm the superiority in terms of classification accuracy and stability of the proposed method over standard wavelets (i.e., Daubechies and Symlet wavelets).     

A single-cell sequencing approach to the classification of large, vacuolated sulfur bacteria

The colorless, large sulfur bacteria are well known because of their intriguing appearance, size and abundance in sulfidic settings. Since their discovery in 1803 these bacteria have been classified according to their conspicuous morphology. However, in microbiology the use of morphological criteria alone to predict phylogenetic relatedness has frequently proven to be misleading. Recent sequencing of a number of 16S rRNA genes of large sulfur bacteria revealed frequent inconsistencies between the morphologically determined taxonomy of genera and the genetically derived classification. Nevertheless, newly described bacteria were classified based on their morphological properties, leading to polyphyletic taxa. We performed sequencing of 16S rRNA genes and internal transcribed spacer (ITS) regions, together with detailed morphological analysis of hand-picked individuals of novel non-filamentous as well as known filamentous large sulfur bacteria, including the hitherto only partially sequenced species Thiomargarita namibiensis, Thioploca araucae and Thioploca chileae. Based on 128 nearly full-length 16S rRNA-ITS sequences, we propose the retention of the family Beggiatoaceae for the genera closely related to Beggiatoa, as opposed to the recently suggested fusion of all colorless sulfur bacteria into one family, the Thiotrichaceae. Furthermore, we propose the addition of nine Candidatus species along with seven new Candidatus genera to the family Beggiatoaceae. The extended family Beggiatoaceae thus remains monophyletic and is phylogenetically clearly separated from other related families.     

A cell-based approach to characterize antimicrobial compounds through kinetic dose response

The rapid spread of antibiotic resistance has created a pressing need for the development of novel drug screening platforms. Herein, we report on the use of cell-based kinetic dose response curves for small molecule characterization in antibiotic discovery efforts. Kinetically monitoring bacterial growth at sub-inhibitory concentrations of antimicrobial small molecules generates unique dose response profiles. We show that clustering of profiles by growth characteristics can classify antibiotics by mechanism of action. Furthermore, changes in growth kinetics have the potential to offer insight into the mechanistic action of novel molecules and can be used to predict off-target effects generated through structure–activity relationship studies. Kinetic dose response also allows for detection of unstable compounds early in the lead development process. We propose that this kinetic approach is a rapid and cost-effective means to gather critical information on antimicrobial small molecules during the hit selection and lead development pipeline.     

A hypertext-like approach to navigating through the GCG sequence analysis package

Programs of the recently released Unix version of the GeneticsComputing Group (GCG) sequence analysis package can now be accessedvia a user-friendly hypertext-like navigation system, HYGCG.The resultant system organizes the diverse suite of programsinto logical groups, and provides a guide and explanation ofcommands. In addition, for users unfamiliar with the Unix operatingsystem, the program also provides a similar interface to commonlyused Unix commands. Options for personal customization and expansionto accommodate GCG extensions and other software are also provided.This system should be useful especially to the inexperiencedor infrequent user as context-sensitive on-line help is providedwithin this simple and consistent approach. Written in the Clanguage and using the curses and termcap libraries, the systemis easily portable to most Unix environments and has been madefreely available via anonymous file transfer protocol (FTP)through the Internet global computer network. No modificationof the GCG package is needed.     

A hierarchical Bayesian approach for handling missing classification data

相似文献