QSAR studies on benzene sulfonamide carbonic anhydrase inhibitors: need of hydrophobic parameter for topological modeling of binding constants of sulfonamides to human CA-II

The binding constants (logK) of benzene sulfonamides to human CA-II have been modeled using a large series of distance-based topological indices. The need or otherwise of the hydrophobic parameter (logP) for such topological modeling of logK has been examined critically. In both the cases excellent results have been obtained. In multiparametric models involving indicator parameters we observed that introduction of hydrophobic parameter (logP) yields much improved statistics. The results are discussed on the basis of statistical parameters and also by using cross-validation method.     

QSAR study on toxicity to aqueous organisms using the PI index

We have attempted to develop quantitative structure-toxicity relationships (QSTRs) to predict hydrophobicity (logP) as well as toxicity (pEC50 microm) of benzene derivatives using recently introduced Padmakar-Ivan (PI) index. The results have shown that both logP as well as pEC50 of benzene derivatives can be modelled excellently in multiparametric models in that the PI index and some indicator parameters are involved. The predictive ability of the models is discussed on the basis of the cross-validation method. The superiority of the PI index over several other topological indices is critically examined.     

Novel estimation of lipophilic behaviour of polychlorinated biphenyls

A novel method has been developed for the estimation of lipophilic behaviour of polychlorinated biphenyls (PCBs) using the recently introduced Padmakar-Ivan (PI) index. The results obtained are compared with the earlier reported Abraham method. The statistical analyses showed that the proposed method based on the PI index is quite useful.     

Topological estimation of electronic absorption bands of arene absorption spectra as a tool for modeling their toxicity and environmental pollution

A novel application of distance-based topological indices : Wiener (W)-, Szeged (Sz)-, Padmakar-Ivan (PI)-, and Sadhana (Sd)-indices in modeling electronic absorption bands of arene absorption spectra has been described. It is demonstrated that all these indices correlate linearly with the logarithm of beta and para electronic absorption bands in several series of arene systems. The results have shown that our methodology is best suited for the estimation--lnlambda(beta), while comparatively less significant results are obtained in case of the estimation of lnlambda(p). The statistical analysis of the data have shown that PI index gives better results for modeling lnlambda(beta); while Sz index proved better for modeling lnlambda(p). The results are critically discussed on the basis of regression parameters and quality of correlation. Such a study will be useful as a tool for modeling toxicity of arene system as well as their environmental pollution.     

QSAR studies on 1,2-dithiole-3-thiones: modeling of lipophilicity, quinone reductase specific activity, and production of growth hormone

Studies on modeling of lipophilicity (logP) quinone reductase specific activity (logCDQR) and production of growth hormone (logCDGH) of 1,2-dithiole-3-thiones have been carried out using distance-based topological indices. The regression analysis of the data has shown that the set of compounds exhibit 'familial' relationships in that excellent results are obtained by dividing the data set into two or more classes (families).     

Correlations between the benzene character of acenes or helicenes and simple molecular descriptors

Aromatic stabilities of acenes and helicenes, which are responsible for their biological, environmental and cancerous behavior have been modeled using a newly introduced Sadhana (Sd) and A indices. The results are compared with those obtained from PI (Padmakar-Ivan) index. The regression analysis has shown that excellent results are obtained by considering acenes and helicenes as separate classes of isomeric benzenoid hydrocarbons and that A index is better index than both PI and Sd indices.     

Estimation of melting points of large set of persistent organic pollutants utilizing QSPR approach

The presence of polyhalogenated persistent organic pollutants (POPs), such as Cl/Br-substituted benzenes, biphenyls, diphenyl ethers, and naphthalenes has been identified in all environmental compartments. The exposure to these compounds can pose potential risk not only for ecological systems, but also for human health. Therefore, efficient tools for comprehensive environmental risk assessment for POPs are required. Among the factors vital for environmental transport and fate processes is melting point of a compound. In this study, we estimated the melting points of a large group (1419 compounds) of chloro- and bromo- derivatives of dibenzo-p-dioxins, dibenzofurans, biphenyls, naphthalenes, diphenylethers, and benzenes by utilizing quantitative structure—property relationship (QSPR) techniques. The compounds were classified by applying structure-based clustering methods followed by GA-PLS modeling. In addition, random forest method has been applied to develop more general models. Factors responsible for melting point behavior and predictive ability of each method were discussed.     

QSAR study on para-substituted aromatic sulfonamides as carbonic anhydrase II inhibitors using topological information indices

A linear quantitative structure-activity relationship has been developed for a series of para-substituted aromatic sulfonamides by using topological index methodologies. The compounds were studied for their carbonic anhydrase II (CAII) inhibitory activity. A large series of topological indices were calculated and the stepwise regression method was used to derive the most significant model. Very good results were obtained using multi-parametric regressions and showed that the information approach used in the present work is quite useful for modeling carbonic anhydrase inhibition.     

QSAR study on (13)C NMR chemical shifts on carbinol carbon atoms

QSAR calculations of (13)C NMR chemical shifts (ppm, TMS=0) on carbinol carbon atoms have been attempted using a large set of distance based topological indices: Wiener (W)-, Szeged (Sz)-, PI (Padmakar-Ivan) and Connectivity ((m)chi, (m)chi(v)) indices. The regression analysis has shown that excellent results are obtained in multiparametric regression. The predictive power of the proposed models are discussed using cross-validated parameters.     

Biometeorological and autoregressive indices for predicting olive pollen intensity

This paper reports on modelling to predict airborne olive pollen season severity, expressed as a pollen index (PI), in Córdoba province (southern Spain) several weeks prior to the pollen season start. Using a 29-year database (1982–2010), a multivariate regression model based on five indices—the index-based model—was built to enhance the efficacy of prediction models. Four of the indices used were biometeorological indices: thermal index, pre-flowering hydric index, dormancy hydric index and summer index; the fifth was an autoregressive cyclicity index based on pollen data from previous years. The extreme weather events characteristic of the Mediterranean climate were also taken into account by applying different adjustment criteria. The results obtained with this model were compared with those yielded by a traditional meteorological-based model built using multivariate regression analysis of simple meteorological-related variables. The performance of the models (confidence intervals, significance levels and standard errors) was compared, and they were also validated using the bootstrap method. The index-based model built on biometeorological and cyclicity indices was found to perform better for olive pollen forecasting purposes than the traditional meteorological-based model.     

Study on quantitative structure-toxicity relationships of benzene derivatives acting by narcosis

Hydrophobicity (logP) as well as quantiative structure-toxicity relationships (QSTRs) of some benzene derivatives acting by narcosis have been established based on narcotic mechanisms of action and toxicity data to the fathead minnow, Daphnia magna and Vibrio fischeri using information-theoretic topological index (Id). Excellent results are obtained in multiparametric regression upon introduction of dummy parameters (indicator variables). Consistent increase in R(2)(A) values indicated that inspite of collinarity between Id and one of the indicator variables (I(3-6)) the proposed models are statistically significant.     

Comparison of cytologic and acridine-orange flow-cytometric detection of malignant cells in human body cavity fluids

Flow cytometrically (FCM) derived DNA and RNA profiles were studied in acridine orange (AO)-stained body cavity fluid (BCF) specimens obtained from 78 patients with various solid tissue and hematologic malignancies. The ploidy (DNA index), RNA content (RNA index), proliferative activity (% S + G2M) and DNA and RNA scattergram patterns were tested "double-blind" against the cytologic scoring of specimens as malignant, benign or reactive. It was determined that expression of an "abnormal" RNA index (greater than or equal to 2.8) and an elevated proliferative activity (% S + G2M greater than or equal to 7.4) was dependent on the presence of malignancy; 21 of 22 specimens having those abnormal indices had DNA aneuploidy and were cytologically scored as positive. The AO FCM sensitivity and specificity for detecting malignant cells (when measured against cytology scoring) were 61% and 90%, respectively, using the "abnormal" RNA index and % S + G2M cut-offs together with the cellular DNA aneuploidy marker. By supplementing the cytologic scoring with AO FCM DNA and RNA features, the sensitivity for detecting malignant cells was 94%, as compared to 72% for cytology alone. Two specimens gave false-positive FCM results: a tuberculous effusion with a tetraploid subpopulation and a reactive mesothelial proliferation that was diploid and negative cytologically. Scoring for malignancy based on the visual pattern of the DNA and RNA FCM scattergrams, while showing good correlation for aneuploid specimens, in some cases failed to identify diploid disease. The results demonstrate the usefulness of FCM DNA and RNA analysis for supplementing cytologic examination of BCF specimens for the purpose of detecting malignant cells.(ABSTRACT TRUNCATED AT 250 WORDS)     

Analysis of DNA-ploidy using laser scanning cytometer in brain tumors and its clinical application

There have been few reports on investigation of the cell kinetics of brain tumors using a laser scanning cytometer (LSC). We compared DNA analysis using a flow cytometer (FCM) with that by LSC using established cell lines of brain tumors, and we demonstrated the similarity of both analytical results. The DNA-index (DI) and proliferating index (PI) of various brain tumors were determined, and the results showed that these indices reflected the malignancy of the tumors. The DI values were higher for astrocytoma grade IV than for astrocytoma grade II, frequently showing an aneuploid pattern. The determination of DNA-ploidy using LSC, together with cryopathological diagnosis, was considered to be useful for deciding the area to be resected for glioma perioperatively and to be worth applying clinically.     

A Bayesian approach for temporally scaling climate for modeling ecological systems

With climate change becoming more of concern, many ecologists are including climate variables in their system and statistical models. The Standardized Precipitation Evapotranspiration Index (SPEI) is a drought index that has potential advantages in modeling ecological response variables, including a flexible computation of the index over different timescales. However, little development has been made in terms of the choice of timescale for SPEI. We developed a Bayesian modeling approach for estimating the timescale for SPEI and demonstrated its use in modeling wetland hydrologic dynamics in two different eras (i.e., historical [pre‐1970] and contemporary [post‐2003]). Our goal was to determine whether differences in climate between the two eras could explain changes in the amount of water in wetlands. Our results showed that wetland water surface areas tended to be larger in wetter conditions, but also changed less in response to climate fluctuations in the contemporary era. We also found that the average timescale parameter was greater in the historical period, compared with the contemporary period. We were not able to determine whether this shift in timescale was due to a change in the timing of wet–dry periods or whether it was due to changes in the way wetlands responded to climate. Our results suggest that perhaps some interaction between climate and hydrologic response may be at work, and further analysis is needed to determine which has a stronger influence. Despite this, we suggest that our modeling approach enabled us to estimate the relevant timescale for SPEI and make inferences from those estimates. Likewise, our approach provides a mechanism for using prior information with future data to assess whether these patterns may continue over time. We suggest that ecologists consider using temporally scalable climate indices in conjunction with Bayesian analysis for assessing the role of climate in ecological systems.     

Machine learning prediction of above-ground biomass in pure Calabrian pine (Pinus brutia Ten.) stands of the Mediterranean region,Türkiye

Above-ground biomass (AGB) is an important component for identifying carbon stocks, monitoring the impacts of climate change, and evaluating merchantable timber. Accurate prediction of forest AGB is central to the correct interpretation of these components and to produce usable data for planners and researchers. In this study, remotely sensed time-series data derived from Landsat 8 (reflectance (R) and vegetation indices (VI)), topographic (T) and climate (C) data were used as independent variables to predict AGB of pure Calabrian pine (Pinus brutia Ten.) stands using multiple regression analysis (MLR) and support vector machines (SVM) methods. The AGB modeling was done by using independent variables individually and by combining variables, and the AGB maps of the most successful models obtained from MLR and SVM methods were produced. It was determined that the most successful variable group was the VI when the independent variables were used one by one (MLR Training R² = 0.50, SVM Training R² = 0.67). The most successful predictions in AGB modeling were obtained with combining all independent variables and using the SVM method (Training R² = 0.85, Validation R² = 0.69). In the combination of independent variables, VI and C data made the greatest contribution to the success of the AGB prediction. The ‘green leaf index’ vegetation indices had the most significant effect on the modeling AGB. In this study, T and C in addition to spectral data has increased the AGB estimation performance. It has been found that the SVM method yielded higher model accuracy than MLR method in predicting AGB. Overall, the spectral data and the SVM method can contribute to improving the accuracy of AGB estimates and provide an effective approach towards the capability for forest ecosystem monitoring.