Flood Risk Analysis Based on Information Diffusion Theory

Due to the fact that the flood data series of small drainage basins is relatively short, available data are often not sufficient for flood risk analysis. This presents the problem of risk analysis using very small data samples. One method that can be applied is to regard the available small samples as fuzzy information and optimize them using information diffusion technology to yield analytical results with greater reliability. In this article a risk analysis method based on information diffusion theory is applied to create a new flood risk analysis model. Application of the model is illustrated taking the Jinhuajiang and Qujiang drainage basins as examples. This is a new attempt at applying information diffusion theory in flood risk analysis. Computations based on this analytical flood risk model can yield an estimated flood damage value that is relatively accurate. This study indicates that the aforementioned model exhibits fairly stable analytical results, even when using a small set of sample data. The results also indicate that information diffusion technology is highly capable of extracting useful information and therefore improves system recognition accuracy. This method can be easily applied and the analytical results produced are easy to understand. Results are accurate enough to act as a guide in disaster situations.     

An Efficient Regression Type Estimator in Survey Sampling

For the estimation of population mean in simple random sampling, an efficient regression-type estimator is proposed which is more efficient than the conventional regression estimator and hence than mean per unit estimator, ratio and product estimators and many other estimators proposed by various authors. Some numerical examples are included for illustration.     

Focus: A Multifaceted Battle Against Cancer: Extracting Knowledge from Chemical Imaging Data Using Computational Algorithms for Digital Cancer Diagnosis

Fourier transform infrared (FTIR) spectroscopic imaging is an emerging microscopy modality for clinical histopathologic diagnoses as well as for biomedical research. Spectral data recorded in this modality are indicative of the underlying, spatially resolved biochemical composition but need computerized algorithms to digitally recognize and transform this information to a diagnostic tool to identify cancer or other physiologic conditions. Statistical pattern recognition forms the backbone of these recognition protocols and can be used for highly accurate results. Aided by biochemical correlations with normal and diseased states and the power of modern computer-aided pattern recognition, this approach is capable of combating many standing questions of traditional histology-based diagnosis models. For example, a simple diagnostic test can be developed to determine cell types in tissue. As a more advanced application, IR spectral data can be integrated with patient information to predict risk of cancer, providing a potential road to precision medicine and personalized care in cancer treatment. The IR imaging approach can be implemented to complement conventional diagnoses, as the samples remain unperturbed and are not destroyed. Despite high potential and utility of this approach, clinical implementation has not yet been achieved due to practical hurdles like speed of data acquisition and lack of optimized computational procedures for extracting clinically actionable information rapidly. The latter problem has been addressed by developing highly efficient ways to process IR imaging data but remains one that has considerable scope for progress. Here, we summarize the major issues and provide practical considerations in implementing a modified Bayesian classification protocol for digital molecular pathology. We hope to familiarize readers with analysis methods in IR imaging data and enable researchers to develop methods that can lead to the use of this promising technique for digital diagnosis of cancer.     

Some Common Themes for Enzymes and Verbs

Enzymes are remarkable molecules which make metabolism possible. Their processing powers are considerable for not only are they catalysts they also contribute to information processing, integration, coherence and memory in the cell. This complex of attributes suggests that a complementary perspective to enzyme nature and activity is needed related to what enzymes and verbs have in common. The value of this kind of thinking is that it shifts the focus from objects and mechanisms to processes and information. In order to support this idea a number of features which enzymes and verbs share are discussed including, context-dependence, occurrence, cases, voice, mood and glue/integrative capacities. The paper concludes with some reflections on the utility of a view of enzymes as verbs.     

Ribonuclease P: the diversity of a ubiquitous RNA processing enzyme

Ribonuclease P is the endonuclease required for generating the mature tRNA 5'-end. The ribonucleoprotein character of this enzyme has now been proven in most organisms and organelles. Exceptions, however, are still the chloroplasts, plant nuclei and animal mitochondria where no associated RNAs have been detected to date. In contrast to the known RNA subunits, which are fairly well-conserved in size and structure among diverse phylogenetic groups, the protein contribution to the holoenzyme is highly variable in size and number of the individual components. The structure of the bacterial protein component has recently been solved. In contrast, the spatial arrangement of the multiple subunits in eukaryotic enzymes is still enigmatic. Substrate requirements of the enzymes or their catalytic RNA subunits are equally diverse, ranging from simple single domain mimics to an almost intact three-dimensional structure of the pre-tRNA substrate. As an example for an intermediate in the enzyme evolution, ribonuclease P from the Cyanophora paradoxa cyanelle will be discussed in more detail. This enzyme is unique, as it combines cyanobacterial and eukaryotic features in its function, subunit composition and holoenzyme topology.     

Protein processing as a regulatory mechanism in the synthesis of the photosynthetic antenna in Chloroflexus

Chloroflexus aurantiacus can be induced to shift from respiratory to photosynthetic energy production by introducing light and/or lowering the oxygen concentration of a culture. After induction, cells synthesize bacteriochlorophyll and proteins for the formation of a functional photosynthetic apparatus. Bacteriochlorophyll is detectable within 2 h after induction. Chlorosome polypeptides are detected after 8–12 h. Two proteins, M_r 60,000 and M_r 47,000, are present in both induced and noninduced cells and react specifically with antibodies against chlorosome polypeptides. Immunological data suggest that these proteins (M_r 60,000 and 47,000) are polyproteins which are transcribed and translated in the dark. When cells are exposed to light or low oxygen tension these proteins are processed into functional polypeptides required in the assembly of the chlorosome. The reaction center polypeptide (M_r 26,000) appears to be part of a separate genetic control system.Dedicated to Prof. G. Drews on occasion of his 60th birthday     

Hormone-dependent processing of the avian progesterone receptor

Avian progesterone receptor exists as two forms, A and B, with molecular weights of 79,000 and 110,000 daltons, respectively. The origin and significance of these two forms is an area of active investigation and debate. Monoclonal antibodies produced against these two forms were used to examine receptor stability in cytosol and changes in the receptor forms induced by hormone binding. The lability of hormone binding at elevated temperatures is well documented. Analysis by Western blotting showed the receptor was stable in freshly prepared oviduct cytosol for 2 hr at 37°C, while hormone binding was lost within 30 min. However, loss of receptor through degradation was seen when cytosol was prepared from frozen tissue or when homogenization was excessive. Progesterone was injected into diethylstilbestrol-stimulated chicks to examine, in vivo, effects of hormone treatment on receptor forms in the cytosol and nuclear fractions. Progesterone treatment caused a time- and dose-dependent conversion of the A receptor to a form (A′) with a slower electrophoretic mobility. The cytosolic progesterone receptor was divided equally between the B and A forms, while the nuclear receptor was predominantly A′. The amount of nuclear receptor was consistently less than cytosolic receptor. Receptor phosphorylation was analyzed by incubating tissue minces with [³²P]orthophosphate with or without progesterone followed by immune isolation of receptor forms. Progesterone treatment caused a time-dependent increase in cytosol receptor phosphorylation which was evident after 5 min of treatment. This phosphorylation was observed with both the A and B receptor forms. The results indicate that receptor phosphorylation is a very early event during progesterone action.