DEGREE OF DETERIORATION DUE TO FIRE IN LARGE CONCRETE HALLS

Present paper deals with the engineering consequences of the fire attack in three halls in Budapest, Hungary. Material and structural behaviour are analysed under high temperature, which is %are based on practical observations reached even 800 \circC in some cases. These fire attacks were again lessons, which are important to be analysed

Angiostrongylosis-related restrictive pneumopathy assessed by arterial blood gas analysis in a dog

Pulmonary angiostrongylosis was diagnosed by the Baermann method and larval identification from faecal and bronchoalveolar lavage samples in a five-month- old male mongrel dog with dyspnoea and cough. Arterial blood gas analysis indicated arterial hypoxaemia and restrictive pneumopathy. In addition to the palliative treatment, fenbendazole was administered (50 mg/kg/24 h per os) for 14 days. The respiratory signs subsided within a short time clinically, but serial arterial blood gas analysis demonstrated an ongoing ventilation disorder. Repeated haematology, thoracic radiography, bronchoscopy and blood gas analysis were performed to follow the course of the disease. The most severe eosinophilia was detected after the beginning of the anthelmintic therapy, and the arterial pO2 level was permanently low. Arterial blood gas analysis provided the most adequate information about the course of the pneumopathy and it greatly facilitated the patient’s medical management

An empirical analysis of training protocols for probabilistic gene finders

BACKGROUND: Generalized hidden Markov models (GHMMs) appear to be approaching acceptance as a de facto standard for state-of-the-art ab initio gene finding, as evidenced by the recent proliferation of GHMM implementations. While prevailing methods for modeling and parsing genes using GHMMs have been described in the literature, little attention has been paid as of yet to their proper training. The few hints available in the literature together with anecdotal observations suggest that most practitioners perform maximum likelihood parameter estimation only at the local submodel level, and then attend to the optimization of global parameter structure using some form of ad hoc manual tuning of individual parameters. RESULTS: We decided to investigate the utility of applying a more systematic optimization approach to the tuning of global parameter structure by implementing a global discriminative training procedure for our GHMM-based gene finder. Our results show that significant improvement in prediction accuracy can be achieved by this method. CONCLUSIONS: We conclude that training of GHMM-based gene finders is best performed using some form of discriminative training rather than simple maximum likelihood estimation at the submodel level, and that generalized gradient ascent methods are suitable for this task. We also conclude that partitioning of training data for the twin purposes of maximum likelihood initialization and gradient ascent optimization appears to be unnecessary, but that strict segregation of test data must be enforced during final gene finder evaluation to avoid artificially inflated accuracy measurements

Efficient decoding algorithms for generalized hidden Markov model gene finders

BACKGROUND: The Generalized Hidden Markov Model (GHMM) has proven a useful framework for the task of computational gene prediction in eukaryotic genomes, due to its flexibility and probabilistic underpinnings. As the focus of the gene finding community shifts toward the use of homology information to improve prediction accuracy, extensions to the basic GHMM model are being explored as possible ways to integrate this homology information into the prediction process. Particularly prominent among these extensions are those techniques which call for the simultaneous prediction of genes in two or more genomes at once, thereby increasing significantly the computational cost of prediction and highlighting the importance of speed and memory efficiency in the implementation of the underlying GHMM algorithms. Unfortunately, the task of implementing an efficient GHMM-based gene finder is already a nontrivial one, and it can be expected that this task will only grow more onerous as our models increase in complexity. RESULTS: As a first step toward addressing the implementation challenges of these next-generation systems, we describe in detail two software architectures for GHMM-based gene finders, one comprising the common array-based approach, and the other a highly optimized algorithm which requires significantly less memory while achieving virtually identical speed. We then show how both of these architectures can be accelerated by a factor of two by optimizing their content sensors. We finish with a brief illustration of the impact these optimizations have had on the feasibility of our new homology-based gene finder, TWAIN. CONCLUSIONS: In describing a number of optimizations for GHMM-based gene finders and making available two complete open-source software systems embodying these methods, it is our hope that others will be more enabled to explore promising extensions to the GHMM framework, thereby improving the state-of-the-art in gene prediction techniques