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The methods of pressures prediction based on geophysical data

100%

Wiśniowski R. , Toczek P.

tom Vol. 32, no. 3

551--563

Prediction of reservoir pressure and so its values at an early stage of wellbore designing significantly increases the profitability of exploration and production wells design. The prediction of rock mass pressures can be performed in a number of ways. The prediction methods have been reviewed since the 1950s. The methods of empirical forecasting of pressures proposed by Eaton B.A. as well as Hottman and Johnson were described in the paper. The paper also refers to the prediction of rock mass pressures on the basis of seismic well log results obtained from works performed on the same geologic unit. A relation between basic parameters of rocks along the wellbore profile is shown. The Eaton method was used for empirical determining pressure for a planned well based on seismic data form the existing well.

Protein intrachain contact prediction with most interacting residues (MIR)

88%

Acuña R. , Lacroix Z. , Papandreou N. , Chomilier J.

tom Vol. 10, no. 4

227--242

The transition state ensemble during the folding process of globular proteins occurs when a sufficient number of intrachain contacts are formed, mainly, but not exclusively, due to hydrophobic interactions. These contacts are related to the folding nucleus, and they contribute to the stability of the native structure, although they may disappear after the energetic barrier of transition states has been passed. A number of structure and sequence analyses, as well as protein engineering studies, have shown that the signature of the folding nucleus is surprisingly present in the native three-dimensional structure, in the form of closed loops, and also in the early folding events. These findings support the idea that the residues of the folding nucleus become buried in the very first folding events, therefore helping the formation of closed loops that act as anchor structures, speed up the process, and overcome the Levinthal paradox. We present here a review of an algorithm intended to simulate in a discrete space the early steps of the folding process. It is based on a Monte Carlo simulation where perturbations, or moves, are randomly applied to residues within a sequence. In contrast with many technically similar approaches, this model does not intend to fold the protein but to calculate the number of non-covalent neighbors of each residue, during the early steps of the folding process. Amino acids along the sequence are categorized as most interacting residues (MIRs) or least interacting residues. The MIR method can be applied under a variety of circumstances. In the cases tested thus far, MIR has successfully identified the exact residue whose mutation causes a switch in conformation. This follows with the idea that MIR identifies residues that are important in the folding process. Most MIR positions correspond to hydrophobic residues; correspondingly, MIRs have zero or very low accessible surface area. Alongside the review of the MIR method, we present a new postprocessing method called smoothed MIR (SMIR), which refines the original MIR method by exploiting the knowledge of residue hydrophobicity. We review known results and present new ones, focusing on the ability of MIR to predict structural changes, secondary structure, and the improved precision with the SMIR method.

Empirical verification of certain method for forecasting non-stationary processes.

51%

Galanc T. , Mikuś J. , Molasy M.

tom Vol. 5, No. 1-2

131-144

The paper presents an empirical verification of the method for forecasting non-stationary processes. The forecast and the estimation of its error are obtained as a result of solving two forecast parameter estimation problems. The verification is presented by means of such a real process as the power system load. The analysis of predicting the quality changes, depending on the changes in values of its determinants, is carried out. The analysis proves that the prediction method being verified yields good results - the forecasts computed based on it are reliable, because their errors do not exceed 3%.