Normal and anomalous classification of category exemplars in schizophrenia

Background Schizophrenia has been associated with semantic memory impairment and previous studies report a difficulty in accessing semantic category exemplars (Moelter et al. 2005 Schizophr Res 78:209–217). The anterior temporal cortex (ATC) has been implicated in the representation of semantic knowledge (Rogers et al. 2004 Psychol Rev 111(1):205–235). We conducted a high-field (4T) fMRI study with the Category Judgment and Substitution Task (CJAST), an analogue of the Hayling test. We hypothesised that differential activation of the temporal lobe would be observed in schizophrenia patients versus controls. Methods Eight schizophrenia patients (7M : 1F) and eight matched controls performed the CJAST, involving a randomised series of 55 common nouns (from five semantic categories) across three conditions: semantic categorisation, anomalous categorisation and word reading. High-resolution 3D T1-weighted images and GE EPI with BOLD contrast and sparse temporal sampling were acquired on a 4T Bruker MedSpec system. Image processing and analyses were performed with SPM2. Results Differential activation in the left ATC was found for anomalous categorisation relative to category judgment, in patients versus controls. Conclusions We examined semantic memory deficits in schizophrenia using a novel fMRI task. Since the ATC corresponds to an area involved in accessing abstract semantic representations (Moelter et al. 2005), these results suggest schizophrenia patients utilise the same neural network as healthy controls, however it is compromised in the patients and the different ATC activity might be attributable to weakening of category-to-category associations.

Special issue on advances in data mining and its applications

Data mining is the process to identify valid, implicit, previously unknown, potentially useful and understandable information from large databases. It is an important step in the process of knowledge discovery in databases, (Olaru & Wehenkel, 1999). In a data mining process, input data can be structured, seme-structured, or unstructured. Data can be in text, categorical or numerical values. One of the important characteristics of data mining is its ability to deal data with large volume, distributed, time variant, noisy, and high dimensionality. A large number of data mining algorithms have been developed for different applications. For example, association rules mining can be useful for market basket problems, clustering algorithms can be used to discover trends in unsupervised learning problems, classification algorithms can be applied in decision-making problems, and sequential and time series mining algorithms can be used in predicting events, fault detection, and other supervised learning problems (Vapnik, 1999). Classification is among the most important tasks in the data mining, particularly for data mining applications into engineering fields. Together with regression, classification is mainly for predictive modelling. So far, there have been a number of classification algorithms in practice. According to (Sebastiani, 2002), the main classification algorithms can be categorized as: decision tree and rule based approach such as C4.5 (Quinlan, 1996); probability methods such as Bayesian classifier (Lewis, 1998); on-line methods such as Winnow (Littlestone, 1988) and CVFDT (Hulten 2001), neural networks methods (Rumelhart, Hinton & Wiliams, 1986); example-based methods such as k-nearest neighbors (Duda & Hart, 1973), and SVM (Cortes & Vapnik, 1995). Other important techniques for classification tasks include Associative Classification (Liu et al, 1998) and Ensemble Classification (Tumer, 1996).

Clinical supervision: Its influence on client-rated working alliance and client symptom reduction in the brief treatment of major depression

Supervision of psychotherapists and counselors, especially in the early years of practice, is widely accepted as being important for professional development and to ensure optimal client outcomes. Although the process of clinical supervision has been extensively studied, less is known about the impact of supervision on psychotherapy practice and client symptom outcome. This study evaluated the impact of clinical supervision on client working alliance and symptom reduction in the brief treatment of major depression. The authors randomly assigned 127 clients with a diagnosis of major depression to 127 supervised or unsupervised therapists to receive eight sessions of problems-solving treatment. Supervised therapists were randomly assigned to either alliance skill- or alliance process-focused supervision and received eight supervision sessions. Before beginning treatment, therapists received one supervision session for brief training in the working alliance supervision approach and in specific characteristics of each case. Standard measures of therapeutic alliance and symptom change were used as dependent variables. The results showed a significant effect for both supervision conditions on working alliance from the first session of therapy, symptom reduction, and treatment retention and evaluation but no effect differences between supervision conditions. It was not possible to separate the effects of supervision from the single pretreatment session and is possible that allegiance effects might have inflated results. The scientific and clinical relevance of these findings is discussed.

CysView: protein classification based on cysteine pairing patterns

CysView is a web-based application tool that identifies and classifies proteins according to their disulfide connectivity patterns. It accepts a dataset of annotated protein sequences in various formats and returns a graphical representation of cysteine pairing patterns. CysView displays cysteine patterns for those records in the data with disulfide annotations. It allows the viewing of records grouped by connectivity patterns. CysView's utility as an analysis tool was demonstrated by the rapid and correct classification of scorpion toxin entries from GenPept on the basis of their disulfide pairing patterns. It has proved useful for rapid detection of irrelevant and partial records, or those with incomplete annotations. CysView can be used to support distant homology between proteins. CysView is publicly available at http://research.i2r.a-star.edu.sg/CysView/.

A comparison of dissected follicle numbers and follicle counts on the ovarian surface for the evaluation of ovarian follicular populations in Bos indicus cows

Ovaries (n = 140) from 70 mixed-age multiparous, lactating Brahman cross (3/4-7/8 Bos indicus) cows were used to examine the hypothesis that counts of follicles visible on the surface of the ovaries of Bos indicus cows and their classification into diameter size classes, are closely correlated with numbers of follicles in those size classes found by complete dissection of the ovary. immediately after ovariectomy, mean diameters (long and short axes averaged) of all follicles greater than or equal to 2 mm visible on the surface of each ovary were measured. All follicles greater than or equal to 2 mm were dissected from the ovaries, excess stroma removed and follicle diameters measured under a stereomicroscope using an ocular graticule. For each ovary, follicles were classified in either small (8 mm) categories based on either diameters of surface or dissected follicles. Data for numbers of surface and dissected follicles (mean +/- SE) in small, medium, large categories and total follicle numbers, respectively, were 24.4 +/- 1.6 vs. 28.0 +/- 1.9, 1.6 +/- + 0.2 vs. 11.6 +/- 1.0, 0.5 +/- 0.1 vs. 0.7 +/- 0.1 and 26.4 +/- 1.6 vs. 40.4 +/- 2.5. Correlation coefficients (r) for counts of surface and dissected follicles in small, medium, large and total follicle numbers were 0.76, 0.40, 0.69 and 0.79, respectively. Medium size follicles presented only a small translucent area on the surface of the ovary, leading to an underestimate of numbers when categorised by surface evaluation. Counts of follicles visible on the surface of the ovaries of Bos indicus cows and their classification into size classes based on estimated diameter, are closely correlated with numbers of follicles in those size classes found at dissection of the ovary for small (8 mm) and total follicles but not for medium sized (4-8 mm) follicles. (C) 1997 Elsevier Science B.V.

A classification of subantarctic Heard Island vegetation

The vascular and bryophyte floras of subantarctic Heard Island were classified using cluster analysis into six vegetation communities: Open Cushion Carpet, Mossy Feldmark, Wet Mixed Herbfield, Coastal Biotic Vegetation, Saltspray Vegetation, and Closed Cushion Carpet. Multidimensional scaling indicated that the vegetation communities were not well delineated but were continua. Discriminant analysis and a classification tree identified altitude, wind, peat depth, bryophyte cover and extent of bare ground, and particle size as discriminating variables. The combination of small area, glaciation, and harsh climate has resulted in reduced vegetation variety in comparison to those subantarctic islands north of the Antarctic Polar Front Zone. Some of the functional groups and vegetation communities found on warmer subantarctic islands are not present on Heard Island, notably ferns and sedges and fernbrakes and extensive mires, respectively.

A new subfamily, Bothriocrotoninae n. subfam., for the genus Bothriocroton Keirans, King & Sharrad, 1994 status amend. (Ixodida : Ixodidae), and the synonymy of Aponomma Neumann, 1899 with Amblyomma Koch, 1844

Evidence suggesting polyphyly of the traditionally recognised tick genus Aponomma Neumann, 1899 is summarized. Continued recognition of this genus in its current concept leaves a polyphyletic genus Aponomma and a paraphyletic genus Amblyomma Koch, 1844. To improve the correlation between our understanding of phylogenetic relationships in metastriate ticks and their classification, a few changes in classification are proposed. The members of the 'indigenous Australian Aponomma' group (sensu Kaufman, 1972), A. auruginans Schulze, 1936, A. concolor Neumann, 1899, A. glebopalma Keirans, King & Sharrad, 1994, A. hydrosauri (Denny, 1843) and A. undatum (Fabricius, 1775), are transferred to Bothriocroton Keirans, King & Sharrad, 1994, which is raised to full generic rank. The remaining members of Aponomma are transferred to Amblyomma. Uncertainty remains on relationships of Bothriocroton to other metastriate lineages and on the systematic position of the two species formerly included in the 'primitive Aponomma' group, A. elaphense Price, 1959 and A. sphenodonti Dumbleton, 1943.

Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB

A 16S rRNA gene database (http://greengenes.bl.gov) addresses limitations of public repositories by providing chimera screening, standard alignment, and taxonomic classification using multiple published taxonomies. It was found that there is incongruent taxonomic nomenclature among curators even at the phylum level. Putative chimeras were identified in 3% of environmental sequences and in 0.2% of records derived from isolates. Environmental sequences were classified into 100 phylum-level lineages in the Archaea and Bacteria.

Fitness assessment of document model

Document classification is a supervised machine learning process, where predefined category labels are assigned to documents based on the hypothesis derived from training set of labelled documents. Documents cannot be directly interpreted by a computer system unless they have been modelled as a collection of computable features. Rogati and Yang [M. Rogati and Y. Yang, Resource selection for domain-specific cross-lingual IR, in SIGIR 2004: Proceedings of the 27th annual international conference on Research and Development in Information Retrieval, ACM Press, Sheffied: United Kingdom, pp. 154-161.] pointed out that the effectiveness of document classification system may vary in different domains. This implies that the quality of document model contributes to the effectiveness of document classification. Conventionally, model evaluation is accomplished by comparing the effectiveness scores of classifiers on model candidates. However, this kind of evaluation methods may encounter either under-fitting or over-fitting problems, because the effectiveness scores are restricted by the learning capacities of classifiers. We propose a model fitness evaluation method to determine whether a model is sufficient to distinguish positive and negative instances while still competent to provide satisfactory effectiveness with a small feature subset. Our experiments demonstrated how the fitness of models are assessed. The results of our work contribute to the researches of feature selection, dimensionality reduction and document classification.

Real-Time Gene-Expression Extraction with Fast Classification (FC) Networks

Fast Classification (FC) networks were inspired by a biologically plausible mechanism for short term memory where learning occurs instantaneously. Both weights and the topology for an FC network are mapped directly from the training samples by using a prescriptive training scheme. Only two presentations of the training data are required to train an FC network. Compared with iterative learning algorithms such as Back-propagation (which may require many hundreds of presentations of the training data), the training of FC networks is extremely fast and learning convergence is always guaranteed. Thus FC networks may be suitable for applications where real-time classification is needed. In this paper, the FC networks are applied for the real-time extraction of gene expressions for Chlamydia microarray data. Both the classification performance and learning time of the FC networks are compared with the Multi-Layer Proceptron (MLP) networks and support-vector-machines (SVM) in the same classification task. The FC networks are shown to have extremely fast learning time and comparable classification accuracy.

Effectiveness of document representation for classification

Conventionally, document classification researches focus on improving the learning capabilities of classifiers. Nevertheless, according to our observation, the effectiveness of classification is limited by the suitability of document representation. Intuitively, the more features that are used in representation, the more comprehensive that documents are represented. However, if a representation contains too many irrelevant features, the classifier would suffer from not only the curse of high dimensionality, but also overfitting. To address this problem of suitableness of document representations, we present a classifier-independent approach to measure the effectiveness of document representations. Our approach utilises a labelled document corpus to estimate the distribution of documents in the feature space. By looking through documents in this way, we can clearly identify the contributions made by different features toward the document classification. Some experiments have been performed to show how the effectiveness is evaluated. Our approach can be used as a tool to assist feature selection, dimensionality reduction and document classification.

A classification of concurrency failures in java components

The Java programming language supports concurrency. Concurrent programs are hard to test due to their inherent non-determinism. This paper presents a classification of concurrency failures that is based on a model of Java concurrency. The model and failure classification is used to justify coverage of synchronization primitives of concurrent components. This is achieved by constructing concurrency flow graphs for each method call. A producer-consumer monitor is used to demonstrate how the approach can be used to measure coverage of concurrency primitives and thereby assist in determining test sequences for deterministic execution.

Developing pineapple fruit has a small transcriptome dominated by metallothionein

In a first step toward understanding the molecular basis of pineapple fruit development, a sequencing project was initiated to survey a range of expressed sequences from green unripe and yellow ripe fruit tissue. A highly abundant metallothionein transcript was identified during library construction, and was estimated to account for up to 50% of all EST library clones. Library clones with metallothionein subtracted were sequenced, and 408 unripe green and 1140 ripe yellow edited EST clone sequences were retrieved. Clone redundancy was high, with the combined 1548 clone sequences clustering into just 634 contigs comprising 191 consensus sequences and 443 singletons. Half of the EST clone sequences clustered within 13.5% and 9.3% of contigs from green unripe and yellow ripe libraries, respectively, indicating that a small subset of genes dominate the majority of the transcriptome. Furthermore, sequence cluster analysis, northern analysis, and functional classification revealed major differences between genes expressed in the unripe green and ripe yellow fruit tissues. Abundant genes identified from the green fruit include a fruit bromelain and a bromelain inhibitor. Abundant genes identified in the yellow fruit library include a MADS box gene, and several genes normally associated with protein synthesis, including homologues of ribosomal L10 and the translation factors SUI1 and eIF5A. Both the green unripe and yellow ripe libraries contained high proportions of clones associated with oxidative stress responses and the detoxification of free radicals.