Intercomparison and validation of the mixed layer depth fields of global ocean syntheses

Intercomparison and evaluation of the global ocean surface mixed layer depth (MLD) fields estimated from a suite of major ocean syntheses are conducted. Compared with the reference MLDs calculated from individual profiles, MLDs calculated from monthly mean and gridded profiles show negative biases of 10–20 m in early spring related to the re-stratification process of relatively deep mixed layers. Vertical resolution of profiles also influences the MLD estimation. MLDs are underestimated by approximately 5–7 (14–16) m with the vertical resolution of 25 (50) m when the criterion of potential density exceeding the 10-m value by 0.03 kg m−3 is used for the MLD estimation. Using the larger criterion (0.125 kg m−3) generally reduces the underestimations. In addition, positive biases greater than 100 m are found in wintertime subpolar regions when MLD criteria based on temperature are used. Biases of the reanalyses are due to both model errors and errors related to differences between the assimilation methods. The result shows that these errors are partially cancelled out through the ensemble averaging. Moreover, the bias in the ensemble mean field of the reanalyses is smaller than in the observation-only analyses. This is largely attributed to comparably higher resolutions of the reanalyses. The robust reproduction of both the seasonal cycle and interannual variability by the ensemble mean of the reanalyses indicates a great potential of the ensemble mean MLD field for investigating and monitoring upper ocean processes.

The role of lipid composition on the interaction between a tryptophan-rich protein and model bacterial membranes

The interaction between tryptophan-rich puroindoline proteins and model bacterial membranes at the air-liquid interface has been investigated by FTIR spectroscopy, surface pressure measurements and Brewster angle microscopy. The role of different lipid constituents on the interactions between lipid membrane and protein was studied using wild type (Pin-b) and mutant (Trp44 to Arg44 mutant, Pin-bs) puroindoline proteins. The results show differences in the lipid selectivity of the two proteins in terms of preferential binding to specific lipid head groups in mixed lipid systems. Pin-b wild type was able to penetrate mixed layers of phosphatidylethanolamine (PE) and phosphatidylglycerol (PG) head groups more deeply compared to the mutant Pin-bs. Increasing saturation of the lipid tails increased penetration and adsorption of Pin-b wild type, but again the response of the mutant form differed. The results provide insight as to the role of membrane architecture, lipid composition and fluidity, on antimicrobial activity of proteins. Data show distinct differences in the lipid binding behavior of Pin-b as a result of a single residue mutation, highlighting the importance of hydrophobic and charged amino acids in antimicrobial protein and peptide activity.

Impact of instantaneous sea ice removal in a coupled general circulation model

The impact of extreme sea ice initial conditions on modelled climate is analysed for a fully coupled atmosphere ocean sea ice general circulation model, the Hadley Centre climate model HadCM3. A control run is chosen as reference experiment with greenhouse gas concentration fixed at preindustrial conditions. Sensitivity experiments show an almost complete recovery from total removal or strong increase of sea ice after four years. Thus, uncertainties in initial sea ice conditions seem to be unimportant for climate modelling on decadal or longer time scales. When the initial conditions of the ocean mixed layer were adjusted to ice-free conditions, a few substantial differences remained for more than 15 model years. But these differences are clearly smaller than the uncertainty of the HadCM3 run and all the other 19 IPCC fourth assessment report climate model preindustrial runs. It is an important task to improve climate models in simulating the past sea ice variability to enable them to make reliable projections for the 21st century.

Processes controlling surface, bottom and lateral melt of Arctic sea ice in a state of the art sea ice model

We present a modelling study of processes controlling the summer melt of the Arctic sea ice cover. We perform a sensitivity study and focus our interest on the thermodynamics at the ice–atmosphere and ice–ocean interfaces. We use the Los Alamos community sea ice model CICE, and additionally implement and test three new parametrization schemes: (i) a prognostic mixed layer; (ii) a three equation boundary condition for the salt and heat flux at the ice–ocean interface; and (iii) a new lateral melt parametrization. Recent additions to the CICE model are also tested, including explicit melt ponds, a form drag parametrization and a halodynamic brine drainage scheme. The various sea ice parametrizations tested in this sensitivity study introduce a wide spread in the simulated sea ice characteristics. For each simulation, the total melt is decomposed into its surface, bottom and lateral melt components to assess the processes driving melt and how this varies regionally and temporally. Because this study quantifies the relative importance of several processes in driving the summer melt of sea ice, this work can serve as a guide for future research priorities.

STUDY OF THE EQUATORIAL ATLANTIC OCEAN MIXING LAYER USING A ONE-DIMENSIONAL TURBULENCE MODEL

The General Ocean Turbulence Model (GOTM) is applied to the diagnostic turbulence field of the mixing layer (ML) over the equatorial region of the Atlantic Ocean. Two situations were investigated: rainy and dry seasons, defined, respectively, by the presence of the intertropical convergence zone and by its northward displacement. Simulations were carried out using data from a PIRATA buoy located on the equator at 23 degrees W to compute surface turbulent fluxes and from the NASA/GEWEX Surface Radiation Budget Project to close the surface radiation balance. A data assimilation scheme was used as a surrogate for the physical effects not present in the one-dimensional model. In the rainy season, results show that the ML is shallower due to the weaker surface stress and stronger stable stratification; the maximum ML depth reached during this season is around 15 m, with an averaged diurnal variation of 7 m depth. In the dry season, the stronger surface stress and the enhanced surface heat balance components enable higher mechanical production of turbulent kinetic energy and, at night, the buoyancy acts also enhancing turbulence in the first meters of depth, characterizing a deeper ML, reaching around 60 m and presenting an average diurnal variation of 30 m.

The log-exponentiated Weibull regression model for interval-censored data

In interval-censored survival data, the event of interest is not observed exactly but is only known to occur within some time interval. Such data appear very frequently. In this paper, we are concerned only with parametric forms, and so a location-scale regression model based on the exponentiated Weibull distribution is proposed for modeling interval-censored data. We show that the proposed log-exponentiated Weibull regression model for interval-censored data represents a parametric family of models that include other regression models that are broadly used in lifetime data analysis. Assuming the use of interval-censored data, we employ a frequentist analysis, a jackknife estimator, a parametric bootstrap and a Bayesian analysis for the parameters of the proposed model. We derive the appropriate matrices for assessing local influences on the parameter estimates under different perturbation schemes and present some ways to assess global influences. Furthermore, for different parameter settings, sample sizes and censoring percentages, various simulations are performed; in addition, the empirical distribution of some modified residuals are displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be straightforwardly extended to a modified deviance residual in log-exponentiated Weibull regression models for interval-censored data. (C) 2009 Elsevier B.V. All rights reserved.

Influence diagnostics in nonlinear mixed-effects elliptical models

In this work we propose and analyze nonlinear elliptical models for longitudinal data, which represent an alternative to gaussian models in the cases of heavy tails, for instance. The elliptical distributions may help to control the influence of the observations in the parameter estimates by naturally attributing different weights for each case. We consider random effects to introduce the within-group correlation and work with the marginal model without requiring numerical integration. An iterative algorithm to obtain maximum likelihood estimates for the parameters is presented, as well as diagnostic results based on residual distances and local influence [Cook, D., 1986. Assessment of local influence. journal of the Royal Statistical Society - Series B 48 (2), 133-169; Cook D., 1987. Influence assessment. journal of Applied Statistics 14 (2),117-131; Escobar, L.A., Meeker, W.Q., 1992, Assessing influence in regression analysis with censored data, Biometrics 48, 507-528]. As numerical illustration, we apply the obtained results to a kinetics longitudinal data set presented in [Vonesh, E.F., Carter, R.L., 1992. Mixed-effects nonlinear regression for unbalanced repeated measures. Biometrics 48, 1-17], which was analyzed under the assumption of normality. (C) 2009 Elsevier B.V. All rights reserved.

Antiferromagnetic spherical spin-glass model

We study the thermodynamic properties and the phase diagrams of a multi-spin antiferromagnetic spherical spin-glass model using the replica method. It is a two-sublattice version of the ferromagnetic spherical p-spin glass model. We consider both the replica-symmetric and the one-step replica-symmetry-breaking solutions, the latter being the most general solution for this model. We find paramagnetic, spin-glass, antiferromagnetic and mixed or glassy antiferromagnetic phases. The phase transitions are always of second order in the thermodynamic sense, but the spin-glass order parameter may undergo a discontinuous change.

Interaction of a C-terminal peptide of Bos taurus diacylglycerol acyltransferase 1 with model membranes

Diacylglycerol acyltransferase 1 (DGAT1) catalyzes the final and dedicated step in the synthesis of triacylglycerol, which is believed to involve the lipids oleoyl coenzyme A (OCoA) and dioleoyl-sn-glycerol (DOG) as substrates. In this work we investigated the interaction of a specific peptide, referred to as SIT2, on the C-terminal of DGAT1 (HKWCIRHFYKP) with model membranes made with OCoA and DOG in Langmuir monolayers and liposomes. According to the circular dichroism and fluorescence data, conformational changes on SIT2 were seen only on liposomes containing OCoA and DOG. In Langmuir monolayers, SIT2 causes the isotherms of neat OCoA and DOG monolayers to be expanded, but has negligible effect on mixed monolayers of OCoA and DOG. This synergistic interaction between SIT2 and DOG + OCoA may be rationalized in terms of a molecular model in which SIT2 may serve as a linkage between the two lipids. Our results therefore provide molecular-level evidence for the interaction between this domain and the substrates OCoA and DOG for the synthesis of triacylglycerol. (C) 2009 Elsevier B.V. All rights reserved.

Improved testing inference in mixed linear models

Mixed linear models are commonly used in repeated measures studies. They account for the dependence amongst observations obtained from the same experimental unit. Often, the number of observations is small, and it is thus important to use inference strategies that incorporate small sample corrections. In this paper, we develop modified versions of the likelihood ratio test for fixed effects inference in mixed linear models. In particular, we derive a Bartlett correction to such a test, and also to a test obtained from a modified profile likelihood function. Our results generalize those in [Zucker, D.M., Lieberman, O., Manor, O., 2000. Improved small sample inference in the mixed linear model: Bartlett correction and adjusted likelihood. Journal of the Royal Statistical Society B, 62,827-838] by allowing the parameter of interest to be vector-valued. Additionally, our Bartlett corrections allow for random effects nonlinear covariance matrix structure. We report simulation results which show that the proposed tests display superior finite sample behavior relative to the standard likelihood ratio test. An application is also presented and discussed. (C) 2008 Elsevier B.V. All rights reserved.

Likelihood ratio tests for variance components in linear mixed models

Although the asymptotic distributions of the likelihood ratio for testing hypotheses of null variance components in linear mixed models derived by Stram and Lee [1994. Variance components testing in longitudinal mixed effects model. Biometrics 50, 1171-1177] are valid, their proof is based on the work of Self and Liang [1987. Asymptotic properties of maximum likelihood estimators and likelihood tests under nonstandard conditions. J. Amer. Statist. Assoc. 82, 605-610] which requires identically distributed random variables, an assumption not always valid in longitudinal data problems. We use the less restrictive results of Vu and Zhou [1997. Generalization of likelihood ratio tests under nonstandard conditions. Ann. Statist. 25, 897-916] to prove that the proposed mixture of chi-squared distributions is the actual asymptotic distribution of such likelihood ratios used as test statistics for null variance components in models with one or two random effects. We also consider a limited simulation study to evaluate the appropriateness of the asymptotic distribution of such likelihood ratios in moderately sized samples. (C) 2008 Elsevier B.V. All rights reserved.

On estimation and influence diagnostics for the Grubbs` model under heavy-tailed distributions

The Grubbs` measurement model is frequently used to compare several measuring devices. It is common to assume that the random terms have a normal distribution. However, such assumption makes the inference vulnerable to outlying observations, whereas scale mixtures of normal distributions have been an interesting alternative to produce robust estimates, keeping the elegancy and simplicity of the maximum likelihood theory. The aim of this paper is to develop an EM-type algorithm for the parameter estimation, and to use the local influence method to assess the robustness aspects of these parameter estimates under some usual perturbation schemes, In order to identify outliers and to criticize the model building we use the local influence procedure in a Study to compare the precision of several thermocouples. (C) 2008 Elsevier B.V. All rights reserved.

A note on influence diagnostics in nonlinear mixed-effects elliptical models

This paper provides general matrix formulas for computing the score function, the (expected and observed) Fisher information and the A matrices (required for the assessment of local influence) for a quite general model which includes the one proposed by Russo et al. (2009). Additionally, we also present an expression for the generalized leverage on fixed and random effects. The matrix formulation has notational advantages, since despite the complexity of the postulated model, all general formulas are compact, clear and have nice forms. (C) 2010 Elsevier B.V. All rights reserved.

Perspectives for Novel Mixed Diruthenium-Organic Drugs as Metallopharmaceuticals in Cancer Therapy

Ruthenium compounds have been actively studied as metallodrugs for cancer therapy. Representatives of ruthenium-based antitumor drugs are the classes of ruthenium(III)-chlorido-(N-ligand)complexes, including the drugs namely NAMI-A and KP1019 in clinical trials, and ruthenium(II)-arene organometallics, with some compounds currently undergoing advanced preclinical testing. An alternative approach for tumor-inhibiting metallodrugs is the coordination of metal ions to organic pharmaceuticals. The combination of antitumor-active ruthenium ion with biologically-active pro-ligands in single compounds can result in the enhancement of activity, for example through synergistic effects. In the present article, some developments in the ruthenium-based antitumor drugs field are briefly highlighted and recent studies on mixed diruthenium-organic drugs as metallopharmaceuticals in cancer therapy are described. Novel organic pharmaceuticals-containing diruthenium(II, III)complexes have shown promising antitumor activity for C6 rat glioma - a model for glioblastoma multiforme (GBA).

Designing Novel Antitrypanosomal Agents from a Mixed Graph-Theoretical Substructural Approach

Chagas disease is nowadays the most serious parasitic health problem. This disease is caused by Trypanosoma cruzi. The great number of deaths and the insufficient effectiveness of drugs against this parasite have alarmed the scientific community worldwide. In an attempt to overcome this problem, a model for the design and prediction of new antitrypanosomal agents was obtained. This used a mixed approach, containing simple descriptors based on fragments and topological substructural molecular design descriptors. A data set was made up of 188 compounds, 99 of them characterized an antitrypanosomal activity and 88 compounds that belong to other pharmaceutical categories. The model showed sensitivity, specificity and accuracy values above 85%. Quantitative fragmental contributions were also calculated. Then, and to confirm the quality of the model, 15 structures of molecules tested as antitrypanosomal compounds (that we did not include in this study) were predicted, taking into account the information on the abovementioned calculated fragmental contributions. The model showed an accuracy of 100% which means that the ""in silico"" methodology developed by our team is promising for the rational design of new antitrypanosomal drugs. (C) 2009 Wiley Periodicals, Inc. J Comput Chem 31: 882-894. 2010