Genetic polymorphisms and laboratory variables as predictors of blood pressure response to antihypertensive drugs

Hypertension is one of the major risk factors for cardiovascular morbidity. The advantages of antihypertensive therapy have been clearly demonstrated, but only about 30% of hypertensive patients have their blood pressure (BP) controlled by such treatment. One of the reasons for this poor BP control may lie in the difficulty in predicting BP response to antihypertensive treatment. The average BP reduction achieved is similar for each drug in the main classes of antihypertensive agents, but there is a marked individual variation in BP responses to any given drug. The purpose of the present study was to examine BP response to four different antihypertensive monotherapies with regard to demographic characteristics, laboratory test results and common genetic polymorphisms. The subjects of the present study are participants in the pharmacogenetic GENRES Study. A total of 208 subjects completed the whole study protocol including four drug treatment periods of four weeks, separated by four-week placebo periods. The study drugs were amlodipine, bisoprolol, hydrochlorothiazide and losartan. Both office (OBP) and 24-hour ambulatory blood pressure (ABP) measurements were carried out. BP response to study drugs were related to basic clinical characteristics, pretreatment laboratory test results and common polymorphisms in genes coding for components of the renin-angiotensin system, alpha-adducin (ADD1), beta1-adrenergic receptor (ADRB1) and beta2-adrenergic receptor (ADRB2). Age was positively correlated with BP responses to amlodipine and with OBP and systolic ABP responses to hydrochlorothiazide, while body mass index was negatively correlated with ABP responses to amlodipine. Of the laboratory test results, plasma renin activity (PRA) correlated positively with BP responses to losartan, with ABP responses to bisoprolol, and negatively with ABP responses to hydrochlorothiazide. Uniquely to this study, it was found that serum total calcium level was negatively correlated with BP responses to amlodipine, whilst serum total cholesterol level was negatively correlated with ABP responses to amlodipine. There were no significant associations of angiotensin II type I receptor 1166A/C, angiotensin converting enzyme I/D, angiotensinogen Met235Thr, ADD1 Gly460Trp, ADRB1 Ser49Gly and Gly389Arg and ADRB2 Arg16Gly and Gln27Glu polymorphisms with BP responses to the study drugs. In conclusion, this study confirmed the relationship between pretreatment PRA levels and response to three classes of antihypertensive drugs. This study is the first to note a significant inverse relation between serum calcium level and responsiveness to a calcium channel blocker. However, this study could not replicate the observations that common polymorphisms in angiotensin II type I receptor, angiotensin converting enzyme, angiotensinogen, ADD1, ADRB1, or ADRB2 genes can predict BP response to antihypertensive drugs.

Hydrologic drought prediction under climate change: Uncertainty modeling with Dempster-Shafer and Bayesian approaches

Representation and quantification of uncertainty in climate change impact studies are a difficult task. Several sources of uncertainty arise in studies of hydrologic impacts of climate change, such as those due to choice of general circulation models (GCMs), scenarios and downscaling methods. Recently, much work has focused on uncertainty quantification and modeling in regional climate change impacts. In this paper, an uncertainty modeling framework is evaluated, which uses a generalized uncertainty measure to combine GCM, scenario and downscaling uncertainties. The Dempster-Shafer (D-S) evidence theory is used for representing and combining uncertainty from various sources. A significant advantage of the D-S framework over the traditional probabilistic approach is that it allows for the allocation of a probability mass to sets or intervals, and can hence handle both aleatory or stochastic uncertainty, and epistemic or subjective uncertainty. This paper shows how the D-S theory can be used to represent beliefs in some hypotheses such as hydrologic drought or wet conditions, describe uncertainty and ignorance in the system, and give a quantitative measurement of belief and plausibility in results. The D-S approach has been used in this work for information synthesis using various evidence combination rules having different conflict modeling approaches. A case study is presented for hydrologic drought prediction using downscaled streamflow in the Mahanadi River at Hirakud in Orissa, India. Projections of n most likely monsoon streamflow sequences are obtained from a conditional random field (CRF) downscaling model, using an ensemble of three GCMs for three scenarios, which are converted to monsoon standardized streamflow index (SSFI-4) series. This range is used to specify the basic probability assignment (bpa) for a Dempster-Shafer structure, which represents uncertainty associated with each of the SSFI-4 classifications. These uncertainties are then combined across GCMs and scenarios using various evidence combination rules given by the D-S theory. A Bayesian approach is also presented for this case study, which models the uncertainty in projected frequencies of SSFI-4 classifications by deriving a posterior distribution for the frequency of each classification, using an ensemble of GCMs and scenarios. Results from the D-S and Bayesian approaches are compared, and relative merits of each approach are discussed. Both approaches show an increasing probability of extreme, severe and moderate droughts and decreasing probability of normal and wet conditions in Orissa as a result of climate change. (C) 2010 Elsevier Ltd. All rights reserved.

Epoch extraction from linear prediction residual for identification of closed glottis interval

In voiced speech analysis epochal information is useful in accurate estimation of pitch periods and the frequency response of the vocal tract system. Ideally, linear prediction (LP) residual should give impulses at epochs. However, there are often ambiguities in the direct use of LP residual since samples of either polarity occur around epochs. Further, since the digital inverse filter does not compensate the phase response of the vocal tract system exactly, there is an uncertainty in the estimated epoch position. In this paper we present an interpretation of LP residual by considering the effect of the following factors: 1) the shape of glottal pulses, 2) inaccurate estimation of formants and bandwidths, 3) phase angles of formants at the instants of excitation, and 4) zeros in the vocal tract system. A method for the unambiguous identification of epochs from LP residual is then presented. The accuracy of the method is tested by comparing the results with the epochs obtained from the estimated glottal pulse shapes for several vowel segments. The method is used to identify the closed glottis interval for the estimation of the true frequency response of the vocal tract system.

Ability of human chorionic gonadotropin beta-subunit to inhibit the steroidogenic response to lutropin.

Ability of the beta-subunit of human chorionic gonadotropin to inhibit the response to lutropin (luteinizing hormone, LH) was tested in the immature rat ovarian system and pregnant-mare-serum-gonadotropin-primed rat ovarian system with progesterone production being used as the response. Human chorionic gonadotropin beta-subunit was found to inhibit human and ovine lutropin-stimulated progesterone production. At a constant dose of lutropin, inhibition was dependent on the concentration of beta-subunit. When concentration of the beta-subunit was kept constant at 5.0 microgram/ml and the concentration of lutropin was varied, the inhibition was maximum at the saturating concentration of the native hormone. The alpha-subunit of the human chorionic gonadotropin did not inhibit the response to lutropin. The lutropin/beta-subunit ratio required to produce an inhibition of response was much lower than that required to bring about an observable inhibition of binding.

Role of CIP2A in carcinogenesis

Worldwide and notably in the developed countries, cancer is an increasing cause of morbidity and mortality, being the second most common cause of death after ischemic heart disease. Now and in the future new cancer cases need to be diagnosed earlier. Prognostic factors may be helpful in recognizing and handling those patients who need more aggressive therapy, and it is also desirable to predict treatment response accurately. Cancerous inhibitor of protein phosphatase 2A (CIP2A) is an oncoprotein predominantly expressed in malignant tissues and inhibiting protein phosphatase 2A (PP2A) activity; it is a promising target for cancer therapy. The aim of this thesis was to evaluate the prognostic role of CIP2A in solid cancers, and for this purpose to explore expression of CIP2A, and investigating regulation of CIP2A in order to gain insight into signalling pathways leading to alteration in prognosis. Patients diagnosed with gastric, serous ovarian, tongue, or colorectal cancer at Helsinki University Central Hospital were included. Tumour tissue microarrays assembled from specimens from these patients were prepared and stained immunohistochemically for CIP2A protein expression. Associations with clinicopathologic parameters and other biomarkers were explored, and survival analyses were done according to the Kaplan-Meier method. Study of the role of CIP2A in intracellular signalling in vitro involved gastric, ovarian, and tongue cancer cell lines. We found CIP2A to be highly expressed in gastric, ovarian, tongue, and colorectal cancer specimens. CIP2A was associated with clinicopathologic parameters characterizing an aggressive disease, namely advanced stage, high grade, p53 immunopositivity, and high proliferation index. CIP2A led to recognition of gastric, ovarian, and tongue cancer patients with poor prognosis, however, with a cancer type-specific cut-off level for prognostic significance. In tongue cancer, it served as an independent prognostic marker. In contrast, in colorectal cancer, CIP2A provided no prognostic value. In cancer cell lines, CIP2A was highly expressed at both protein and mRNA levels, and promoted cell proliferation and anchorage-independent growth. In gastric cancer, we demonstrated with a MYCER construct in mouse embryo fibroblasts that activation of MYC led to increased CIP2A mRNA expression, and hence we suggested that a positive feedback mechanism between CIP2A and MYC may potentiate and prolong the oncogenic activity of these proteins. We demonstrated in ovarian cancer an association between CIP2A and EGFR protein overexpression and EGFR gene amplification. In ovarian and tongue cancer cells we showed that depletion of EGFR downregulates CIP2A expression. In conclusion, high CIP2A expression occurred frequently among patients with aggressive disease. CIP2A may serve as a prognostic marker in gastric, ovarian, and tongue cancer and thus may help in tailoring therapy for cancer patients. The positive feedback mechanism between CIP2A and MYC, as well as the positive regulation of CIP2A by EGFR, are a few signalling pathways regulating and regulated by CIP2A. These and other mechanisms need to be studied further, however. CIP2A is a potential target for therapy, and its potential role as predictive marker and as a tumour marker in serum requires exploration.

Parametric study of MSW landfill settlement model

A newly developed and validated constitutive model that accounts for primary compression and time-dependent mechanical creep and biodegradation is used for parametric study to investigate the effects of model parameters on the predicted settlement of municipal solid waste (MSW) with time. The model enables the prediction of stress strain response and yield surfaces for three components of settlement: primary compression, mechanical creep, and biodegradation. The MSW parameters investigated include compression index, coefficient of earth pressure at-rest, overconsolidation ratio, and biodegradation parameters of MSW. A comparison of the predicted settlements for typical MSW landfill conditions showed significant differences in time-settlement response depending on the selected model input parameters. The effect of lift thickness of MSW on predicted settlement is also investigated. Overall, the study shows that the variation in the model parameters can lead to significantly different results; therefore, the model parameter values should be carefully selected to predict landfill settlements accurately. It is shown that the proposed model captures the time settlement response which is in general agreement with the results obtained from the other two reported models having similar features. (C) 2011 Elsevier Ltd. All rights reserved.

Effects of phase inhomogeneity and boundary conditions on the dynamic response of SMA wire actuators

This paper reports the simulation results from the dynamic analysis of a Shape Memory Alloy (SMA) actuator. The emphasis is on understanding the dynamic behavior under various loading rates and boundary conditions, resulting in complex scenarios such as thermal and stress gradients. Also, due to the polycrystalline nature of SMA wires, presence of microstructural inhomogeneity is inevitable. Probing the effect of inhomogeneity on the dynamic behavior can facilitate the prediction of life and characteristics of SMA wire actuator under varieties of boundary and loading conditions. To study the effect of these factors, an initial boundary value problem of SMA wire is formulated. This is subsequently solved using finite element method. The dynamic response of the SMA wire actuator is analyzed under mechanical loading and results are reported. Effect of loading rate, micro-structural inhomogeneity and thermal boundary conditions on the dynamic response of SMA wire actuator is investigated and the simulation results are reported.

Stress-Strain Prediction of Jointed Rocks using Artificial Neural Networks

The applicability of Artificial Neural Networks for predicting the stress-strain response of jointed rocks at varied confining pressures, strength properties and joint properties (frequency, orientation and strength of joints) has been studied in the present paper. The database is formed from the triaxial compression tests on different jointed rocks with different confining pressures and different joint properties reported by various researchers. This input data covers a wide range of rock strengths, varying from very soft to very hard. The network was trained using a 3 layered network with feed forward back propagation algorithm. About 85% of the data was used for training and remaining15% for testing the predicting capabilities of the network. Results from the analyses were very encouraging and demonstrated that the neural network approach is efficient in capturing the complex stress-strain behaviour of jointed rocks. A single neural network is demonstrated to be capable of predicting the stress-strain response of different rocks, whose intact strength vary from 11.32 MPa to 123 MPa and spacing of joints vary from 10 cm to 100 cm for confining pressures ranging from 0 to 13.8 MPa.

Landslide Susceptible Locations in Western Ghats: Prediction through openModeller

Many shallow landslides are triggered by heavy rainfall on hill slopes resulting in enormous casualties and huge economic losses in mountainous regions. Hill slope failure usually occurs as soil resistance deteriorates in the presence of the acting stress developed due to a number of reasons such as increased soil moisture content, change in land use causing slope instability, etc. Landslides triggered by rainfall can possibly be foreseen in real time by jointly using rainfall intensity-duration and information related to land surface susceptibility. Terrain analysis applications using spatial data such as aspect, slope, flow direction, compound topographic index, etc. along with information derived from remotely sensed data such as land cover / land use maps permit us to quantify and characterise the physical processes governing the landslide occurrence phenomenon. In this work, the probable landslide prone areas are predicted using two different algorithms – GARP (Genetic Algorithm for Rule-set Prediction) and Support Vector Machine (SVM) in a free and open source software package - openModeller. Several environmental layers such as aspect, digital elevation data, flow accumulation, flow direction, slope, land cover, compound topographic index, and precipitation data were used in modelling. A comparison of the simulated outputs, validated by overlaying the actual landslide occurrence points showed 92% accuracy with GARP and 96% accuracy with SVM in predicting landslide prone areas considering precipitation in the wettest month whereas 91% and 94% accuracy were obtained from GARP and SVM considering precipitation in the wettest quarter of the year.

Prediction of extension test results of soft cemented clays with a simple constitutive model

In the present paper, the constitutive model is proposed for cemented soils, in which the cementation component and frictional component are treated separately and then added together to get overall response. The modified Cam clay is used to predict the frictional resistance and an elasto-plastic strain softening model is proposed for the cementation component. The rectangular isotropic yield curve proposed by Vatsala (1995) for the bond component has been modified in order to account for the anisotropy generally observed in the case of natural soft cemented soils. In this paper, the model proposed is used to predict the experimental results of extension tests on the soft cemented soils whereas compression test results are presented elsewhere. The model predictions compare quite satisfactorily with the observed response. A few input parameters are required which are well defined and easily determinable and the model uses associated flow rule.

Use of Vertex Index in Structure-Activity Analysis and Design of Molecules

The last few decades have witnessed application of graph theory and topological indices derived from molecular graph in structure-activity analysis. Such applications are based on regression and various multivariate analyses. Most of the topological indices are computed for the whole molecule and used as descriptors for explaining properties/activities of chemical compounds. However, some substructural descriptors in the form of topological distance based vertex indices have been found to be useful in identifying activity related substructures and in predicting pharmacological and toxicological activities of bioactive compounds. Another important aspect of drug discovery e. g. designing novel pharmaceutical candidates could also be done from the distance distribution associated with such vertex indices. In this article, we will review the development and applications of this approach both in activity prediction as well as in designing novel compounds.

Prediction of Shallow Landslide prone regions in Undulating Terrains

Genetic Algorithm for Rule-set Prediction (GARP) and Support Vector Machine (SVM) with free and open source software (FOSS) - Open Modeller were used to model the probable landslide occurrence points. Environmental layers such as aspect, digital elevation, flow accumulation, flow direction, slope, land cover, compound topographic index and precipitation have been used in modeling. Simulated output of these techniques is validated with the actual landslide occurrence points, which showed 92% (GARP) and 96% (SVM) accuracy considering precipitation in the wettest month and 91% and 94% accuracy considering precipitation in the wettest quarter of the year.

Prediction of Ground Water Levels in the Uplands of a Tropical Coastal Riparian Wetland using Artificial Neural Networks

Artificial Neural Networks (ANNs) have been found to be a robust tool to model many non-linear hydrological processes. The present study aims at evaluating the performance of ANN in simulating and predicting ground water levels in the uplands of a tropical coastal riparian wetland. The study involves comparison of two network architectures, Feed Forward Neural Network (FFNN) and Recurrent Neural Network (RNN) trained under five algorithms namely Levenberg Marquardt algorithm, Resilient Back propagation algorithm, BFGS Quasi Newton algorithm, Scaled Conjugate Gradient algorithm, and Fletcher Reeves Conjugate Gradient algorithm by simulating the water levels in a well in the study area. The study is analyzed in two cases-one with four inputs to the networks and two with eight inputs to the networks. The two networks-five algorithms in both the cases are compared to determine the best performing combination that could simulate and predict the process satisfactorily. Ad Hoc (Trial and Error) method is followed in optimizing network structure in all cases. On the whole, it is noticed from the results that the Artificial Neural Networks have simulated and predicted the water levels in the well with fair accuracy. This is evident from low values of Normalized Root Mean Square Error and Relative Root Mean Square Error and high values of Nash-Sutcliffe Efficiency Index and Correlation Coefficient (which are taken as the performance measures to calibrate the networks) calculated after the analysis. On comparison of ground water levels predicted with those at the observation well, FFNN trained with Fletcher Reeves Conjugate Gradient algorithm taken four inputs has outperformed all other combinations.

Ground motion prediction equation considering combined dataset of recorded and simulated ground motions

Himalayan region is one of the most active seismic regions in the world and many researchers have highlighted the possibility of great seismic event in the near future due to seismic gap. Seismic hazard analysis and microzonation of highly populated places in the region are mandatory in a regional scale. Region specific Ground Motion Predictive Equation (GMPE) is an important input in the seismic hazard analysis for macro- and micro-zonation studies. Few GMPEs developed in India are based on the recorded data and are applicable for a particular range of magnitudes and distances. This paper focuses on the development of a new GMPE for the Himalayan region considering both the recorded and simulated earthquakes of moment magnitude 5.3-8.7. The Finite Fault simulation model has been used for the ground motion simulation considering region specific seismotectonic parameters from the past earthquakes and source models. Simulated acceleration time histories and response spectra are compared with available records. In the absence of a large number of recorded data, simulations have been performed at unavailable locations by adopting Apparent Stations concept. Earthquakes recorded up to 2007 have been used for the development of new GMPE and earthquakes records after 2007 are used to validate new GMPE. Proposed GMPE matched very well with recorded data and also with other highly ranked GMPEs developed elsewhere and applicable for the region. Comparison of response spectra also have shown good agreement with recorded earthquake data. Quantitative analysis of residuals for the proposed GMPE and region specific GMPEs to predict Nepal-India 2011 earthquake of Mw of 5.7 records values shows that the proposed GMPE predicts Peak ground acceleration and spectral acceleration for entire distance and period range with lower percent residual when compared to exiting region specific GMPEs. Crown Copyright (C) 2013 Published by Elsevier Ltd. All rights reserved.

Cybernetic modeling of adaptive prediction of environmental changes by microorganisms

Microorganisms exhibit varied regulatory strategies such as direct regulation, symmetric anticipatory regulation, asymmetric anticipatory regulation, etc. Current mathematical modeling frameworks for the growth of microorganisms either do not incorporate regulation or assume that the microorganisms utilize the direct regulation strategy. In the present study, we extend the cybernetic modeling framework to account for asymmetric anticipatory regulation strategy. The extended model accurately captures various experimental observations. We use the developed model to explore the fitness advantage provided by the asymmetric anticipatory regulation strategy and observe that the optimal extent of asymmetric regulation depends on the selective pressure that the microorganisms experience. We also explore the importance of timing the response in anticipatory regulation and find that there is an optimal time, dependent on the extent of asymmetric regulation, at which microorganisms should respond anticipatorily to maximize their fitness. We then discuss the advantages offered by the cybernetic modeling framework over other modeling frameworks in modeling the asymmetric anticipatory regulation strategy. (C) 2013 Published by Elsevier Inc.