COVNET : A cooperative coevolutionary model for evolving artificial neural networks

This paper presents COVNET, a new cooperative coevolutionary model for evolving artificial neural networks. This model is based on the idea of coevolving subnetworks. that must cooperate to form a solution for a specific problem, instead of evolving complete networks. The combination of this subnetwork is part of a coevolutionary process. The best combinations of subnetworks must be evolved together with the coevolution of the subnetworks. Several subpopulations of subnetworks coevolve cooperatively and genetically isolated. The individual of every subpopulation are combined to form whole networks. This is a different approach from most current models of evolutionary neural networks which try to develop whole networks. COVNET places as few restrictions as possible over the network structure, allowing the model to reach a wide variety of architectures during the evolution and to be easily extensible to other kind of neural networks. The performance of the model in solving three real problems of classification is compared with a modular network, the adaptive mixture of experts and with the results presented in the bibliography. COVNET has shown better generalization and produced smaller networks than the adaptive mixture of experts and has also achieved results, at least, comparable with the results in the bibliography

Injection-site reactions upon Kineret (anakinra) administration: experiences and explanations

Anakinra (Kineret), a recombinant form of human interleukin-1 (IL-1) receptor antagonist, is approved for the treatment of rheumatoid arthritis (RA) in combination with methotrexate. Kineret is self-administered by daily subcutaneous injections in patients with active RA. The mechanism of action of anakinra is to competitively inhibit the local inflammatory effects of IL-1. Kineret is generally safe and well tolerated and the only major treatment-related side effects that appear are skin reactions at the injection site. Due to the relatively short half-life of anakinra, daily injection of the drug is required. This, in combination with the comparably high rates of injection-site reactions (ISRs) associated with the drug, can become a problem for the patient. The present review summarises published data concerning ISRs associated with Kineret and provides some explanations as to their cause. The objective is also to present some clinical experiences of how the ISRs can be managed

Amyloid Oligomer Conformation in a Group of Natively Folded Proteins

Recent in vitro and in vivo studies suggest that destabilized proteins with defective folding induce aggregation and toxicity in protein-misfolding diseases. One such unstable protein state is called amyloid oligomer, a precursor of fully aggregated forms of amyloid. Detection of various amyloid oligomers with A11, an anti-amyloid oligomer conformation-specific antibody, revealed that the amyloid oligomer represents a generic conformation and suggested that toxic β-aggregation processes possess a common mechanism. By using A11 antibody as a probe in combination with mass spectrometric analysis, we identified GroEL in bacterial lysates as a protein that may potentially have an amyloid oligomer conformation. Surprisingly, A11 reacted not only with purified GroEL but also with several purified heat shock proteins, including human Hsp27, 40, 70, 90; yeast Hsp104; and bovine Hsc70. The native folds of A11-reactive proteins in purified samples were characterized by their anti-β-aggregation activity in terms of both functionality and in contrast to the β-aggregation promoting activity of misfolded pathogenic amyloid oligomers. The conformation-dependent binding of A11 with natively folded Hsp27 was supported by the concurrent loss of A11 reactivity and anti-β-aggregation activity of heat-treated Hsp27 samples. Moreover, we observed consistent anti-β-aggregation activity not only by chaperones containing an amyloid oligomer conformation but also by several A11-immunoreactive non-chaperone proteins. From these results, we suggest that the amyloid oligomer conformation is present in a group of natively folded proteins. The inhibitory effects of A11 antibody on both GroEL/ES-assisted luciferase refolding and Hsp70-mediated decelerated nucleation of Aβ aggregation suggested that the A11-binding sites on these chaperones might be functionally important. Finally, we employed a computational approach to uncover possible A11-binding sites on these targets. Since the β-sheet edge was a common structural motif having the most similar physicochemical properties in the A11-reactive proteins we analyzed, we propose that the β-sheet edge in some natively folded amyloid oligomers is designed positively to prevent β aggregation

Query-constraint-based mining of association rules for exploratory analysis of clinical datasets in the National Sleep Research Resource

Abstract Background Association Rule Mining (ARM) has been widely used by biomedical researchers to perform exploratory data analysis and uncover potential relationships among variables in biomedical datasets. However, when biomedical datasets are high-dimensional, performing ARM on such datasets will yield a large number of rules, many of which may be uninteresting. Especially for imbalanced datasets, performing ARM directly would result in uninteresting rules that are dominated by certain variables that capture general characteristics. Methods We introduce a query-constraint-based ARM (QARM) approach for exploratory analysis of multiple, diverse clinical datasets in the National Sleep Research Resource (NSRR). QARM enables rule mining on a subset of data items satisfying a query constraint. We first perform a series of data-preprocessing steps including variable selection, merging semantically similar variables, combining multiple-visit data, and data transformation. We use Top-k Non-Redundant (TNR) ARM algorithm to generate association rules. Then we remove general and subsumed rules so that unique and non-redundant rules are resulted for a particular query constraint. Results Applying QARM on five datasets from NSRR obtained a total of 2517 association rules with a minimum confidence of 60% (using top 100 rules for each query constraint). The results show that merging similar variables could avoid uninteresting rules. Also, removing general and subsumed rules resulted in a more concise and interesting set of rules. Conclusions QARM shows the potential to support exploratory analysis of large biomedical datasets. It is also shown as a useful method to reduce the number of uninteresting association rules generated from imbalanced datasets. A preliminary literature-based analysis showed that some association rules have supporting evidence from biomedical literature, while others without literature-based evidence may serve as the candidates for new hypotheses to explore and investigate. Together with literature-based evidence, the association rules mined over the NSRR clinical datasets may be used to support clinical decisions for sleep-related problems