Revised diagnosis of the genus Bangana Hamilton, 1822 (Pisces : Cyprinidae), with taxonomic and nomenclatural notes on the Chinese species

Most morphological characters diagnostic of the 13 Chinese species of the cyprinid genus Sinilabeo Rendahl, 1932, are identical to those of the genus Bangana Hamilton, 1822. Consequently, these 13 species are transferred to Bangana. A revised diagnosis is provided for the now-expanded genus Bangana, and a dichotomous key and taxonomic and nomenclatural notes are included for the following valid Chinese species: B. decora, B. dero, B. devdevi, B. discognathoides, B. lemassoni, B. lippa, B. rendahli, B. tonkinensis, B. tungting, B. wui, B. xanthogenys, B. yunnanensis, and B. zhui. Literature reports, by Chinese authors, of Sinilabeo dero from the upper Irrawady River basin, in Yunnan, are based on misidentifcations of B. devdevi. Sinilabeo cirrhinoides Wu and Lin in Wu, Lin, Chen, Chen and He, 1977, and S. laticeps Wu and Lin in Wu, Lin, Chen, Chen and He, 1977, are junior subjective synonyms of B. devdevi and B. lippa, respectively. Sinilabeo yunnanensis Wu, Lin, Chen, Chen and He, 1977, is an available name, and a lectotype is designated for the species. Bangana zhui ( Zheng and Chen, 1983) is a valid species distinct from B. yunnanensis.

Application of probabilistic neural network in the clinical diagnosis of cancers based on clinical chemistry data

As a recently developed and powerful classification tool, probabilistic neural network was used to distinguish cancer patients from healthy persons according to the levels of nucleosides in human urine. Two datasets (containing 32 and 50 patterns, respectively) were investigated and the total consistency rate obtained was 100% for dataset 1 and 94% for dataset 2. To evaluate the performance of probabilistic neural network, linear discriminant analysis and learning vector quantization network, were also applied to the classification problem. The results showed that the predictive ability of the probabilistic neural network is stronger than the others in this study. Moreover, the recognition rate for dataset 2 can achieve to 100% if combining, these three methods together, which indicated the promising potential of clinical diagnosis by combining different methods. (C) 2002 Elsevier Science B.V. All rights reserved.

DNA diagnosis by capillary electrophoresis and microfabricated electrophoretic devices

DNA diagnosis is experiencing an impressive progression towards the development of novel technology to identity various clinically relevant categories of genetic changes and to meet the exponential growth of genomics. The introduction of capillary electrophoresis has dramatically accelerated the completion of the first draft of the human DNA sequence in the Human Genome Project, and thus, has become the method of choice for analysis of various genetic variants. The recent development of microfabricated electrophoretic devices has led to the possibility of integrating multiple sample handling with the actual measurement steps required for automation of molecular diagnostics. This review highlights the most recent progress in capillary electrophoresis and electrophoretic microdevices for DNA-based diagnostics, including the important areas of genotyping for point mutation, single nucleotide polymorphisms, short tandem repeats and organism identification. The application of these techniques for infectious and genetic disease diagnosis, as well as forensic identification purpose, are covered. The promising development and the challenges for techinical problems are also discussed.

Artificial neural network classification based on high-performance liquid chromatography of urinary and serum nucleosides for the clinical diagnosis of cancer

Nucleosides in human urine and serum have frequently been studied as a possible biomedical marker for cancer, acquired immune deficiency syndrome (AIDS) and the whole-body turnover of RNAs. Fifteen normal and modified nucleosides were determined in 69 urine and 42 serum samples using high-performance liquid chromatography (HPLC). Artificial neural networks have been used as a powerful pattern recognition tool to distinguish cancer patients from healthy persons. The recognition rate for the training set reached 100%. In the validating set, 95.8 and 92.9% of people were correctly classified into cancer patients and healthy persons when urine and serum were used as the sample for measuring the nucleosides. The results show that the artificial neural network technique is better than principal component analysis for the classification of healthy persons and cancer patients based on nucleoside data. (C) 2002 Elsevier Science B.V. All rights reserved.

Artificial neural network applied to diagnosis of lung cancer

Artificial neural network(ANN) approach was applied to classification of normal persons and lung cancer patients based on the metal content of hair and serum samples obtained by inductively coupled plasma atomic emission spectrometry (ICP-AES) for the two groups. This method was verified with independent prediction samples and can be used as an aiding means of the diagnosis of lung cancer. The case of predictive classification with one element missing in the prediction samples was studied in details, The significance of elements in hair and serum samples for classification prediction was also investigated.

Hypothesis Formation and Evaluation in Medical Diagnosis

This thesis describes some aspects of a computer system for doing medical diagnosis in the specialized field of kidney disease. Because such a system faces the spectre of combinatorial explosion, this discussion concentrates on heuristics which control the number of concurrent hypotheses and efficient "compiled" representations of medical knowledge. In particular, the differential diagnosis of hematuria (blood in the urine) is discussed in detail. A protocol of a simulated doctor/patient interaction is presented and analyzed to determine the crucial structures and processes involved in the diagnosis procedure. The data structure proposed for representing medical information revolves around elementary hypotheses which are activated when certain disposing of findings, activating hypotheses, evaluating hypotheses locally and combining hypotheses globally is examined for its heuristic implications. The thesis attempts to fit the problem of medical diagnosis into the framework of other Artifcial Intelligence problems and paradigms and in particular explores the notions of pure search vs. heuristic methods, linearity and interaction, local vs. global knowledge and the structure of hypotheses within the world of kidney disease.

Automatic diagnosis of task faults in flexible manufacturing systems

Hardy, N. W., Barnes, D. P., Lee, L. H. (1989). Automatic diagnosis of task faults in flexible manufacturing systems. Robotica, 7 (1):25-35