Convergences among psychoanalytic and cognitive-behavioral theories of depression.

Discusses 2 types of depression based on disruptions of (1) interpersonal relatedness or (2) self-esteem, and considers the nature, etiology, and treatment of depression based on the phenomenology of the issues that cause individuals to become depressed. The interpersonal perspective of S. Arieti and J. Bemporad (1980), the object relations perspective of J. Bowlby (see record 1988-16459-001), and the psychoanalytic ego psychology and cognitive developmental theory perspective of S. J. Blatt (1992) are compared and contrasted with each other and with the cognitive-behavioral perspective of A. T. Beck (1983). Areas of agreement and differences among these 4 theoretical positions are discussed in terms of their implications for treatment. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

What is Initiative?

This paper presents some alternate theories for explaining the term 'initiative', as it is used in the design of mixed-initiative AI systems. Although there is now active research in the area of mixed initiative interactive systems, there appears to be no true consensus in the field as to what the term 'initiative' actually means. In describing different possible approaches to the modeling of initiative, we aim to show the potential importance of each particular theory for the design of mixed initiative systems. The paper concludes by summarizing some of the key points in common to the theories, and by commenting on the inherent difficulties of the exercise, thereby elucidating the limitations which are necessarily encountered in designing such theories as the basis for designing mixed-initiative systems.     

Decision trees for hierarchical multi-label classification

Hierarchical multi-label classification (HMC) is a variant of classification where instances may belong to multiple classes at the same time and these classes are organized in a hierarchy. This article presents several approaches to the induction of decision trees for HMC, as well as an empirical study of their use in functional genomics. We compare learning a single HMC tree (which makes predictions for all classes together) to two approaches that learn a set of regular classification trees (one for each class). The first approach defines an independent single-label classification task for each class (SC). Obviously, the hierarchy introduces dependencies between the classes. While they are ignored by the first approach, they are exploited by the second approach, named hierarchical single-label classification (HSC). Depending on the application at hand, the hierarchy of classes can be such that each class has at most one parent (tree structure) or such that classes may have multiple parents (DAG structure). The latter case has not been considered before and we show how the HMC and HSC approaches can be modified to support this setting. We compare the three approaches on 24 yeast data sets using as classification schemes MIPS’s FunCat (tree structure) and the Gene Ontology (DAG structure). We show that HMC trees outperform HSC and SC trees along three dimensions: predictive accuracy, model size, and induction time. We conclude that HMC trees should definitely be considered in HMC tasks where interpretable models are desired.     

Network representation with clustering tree features

This paper studies the relationship between a case-based decision theory (CBDT) and an ideal point model (IPM). We show that a case-based decision model (CBDM) can be transformed into an IPM under some assumptions. This transformation can allow us to visualize the relationship among data and simplify the calculations of distance between one current datum and the ideal point, rather than the distances between data. Our results will assist researchers with their product design analysis and positioning of goods through CBDT, by revealing past dependences or providing a reference point. Furthermore, to check whether the similarity function, presented in the theoretical part, is valid for empirical analysis, we use data on the viewing behavior of audiences of TV dramas in Japan and compare the estimation results under the CBDM that corresponds to a standard decision model with similarities and other various similarity functions and without a similarity function. Our empirical analysis shows that the CBDM with a similarity function, presented in this study, best fits the data.     

Nanoscale Microstructural and Chemical Analysis of SiO2–Zn1−xAlxO Nanocomposites: Towards a Better Understanding of Si and Al Substitution in ZnO

In this study, we report on the microstructure of SiO₂‐coated Al‐doped ZnO nanoparticles densified by spark plasma sintering(SPS), using a multiscale approach. Our observations show that it is possible to successfully prepare dense pellets while keeping the nanostructure with well‐defined Si‐rich grain boundaries. Although a very limited partial solubility of Si in the ZnO matrix has been observed, Si is mostly concentrated at the grain boundaries. More surprisingly, we evidenced some areas with nanoscale inhomogeneity of the Al concentration, which can locally strongly exceed the average composition of the matrix. It could explain the apparent discrepancy observed in the literature between the simultaneous presence of ZnAl₂O₄ in Al‐doped ZnO, which should be the signature of the doping level exceeding the solubility limit, and the concentration of carriers that still depends on the nominal Al concentration in ZnO even in the presence of ZnAl₂O₄.     

A novel chemometric classification for FTIR spectra of mycotoxin-contaminated maize and peanuts at regulatory limits

The rapid identification of mycotoxins such as deoxynivalenol and aflatoxin B₁ in agricultural commodities is an ongoing concern for food importers and processors. While sophisticated chromatography-based methods are well established for regulatory testing by food safety authorities, few techniques exist to provide a rapid assessment for traders. This study advances the development of a mid-infrared spectroscopic method, recording spectra with little sample preparation. Spectral data were classified using a bootstrap-aggregated (bagged) decision tree method, evaluating the protein and carbohydrate absorption regions of the spectrum. The method was able to classify 79% of 110 maize samples at the European Union regulatory limit for deoxynivalenol of 1750 µg kg^–1 and, for the first time, 77% of 92 peanut samples at 8 µg kg^–1 of aflatoxin B₁. A subset model revealed a dependency on variety and type of fungal infection. The employed CRC and SBL maize varieties could be pooled in the model with a reduction of classification accuracy from 90% to 79%. Samples infected with Fusarium verticillioides were removed, leaving samples infected with F. graminearum and F. culmorum in the dataset improving classification accuracy from 73% to 79%. A 500 µg kg^–1 classification threshold for deoxynivalenol in maize performed even better with 85% accuracy. This is assumed to be due to a larger number of samples around the threshold increasing representativity. Comparison with established principal component analysis classification, which consistently showed overlapping clusters, confirmed the superior performance of bagged decision tree classification.     

Case of Mycobacterium mucogenicum in a home hemodialysis patient

We present a case of a patient on home hemodialysis who developed Mycobacterium mucogenicum bacteremia. While infections with this particular organism are rare, disseminated infections have been reported and have been associated with significant morbidity and mortality. Diagnosis required appropriate cultures, understanding of natural habitat of organism and complete environmental analysis including blood, dialysis sample port, reverse osmosis and incoming water supply cultures. The patient was treated successfully with systemic antibiotics, removal of central venous catheter, patient education and complete exchange of the hemodialysis circuit.