Synthetic RGD-containing alpha-helical coiled coil peptides promote integrin-dependent cell adhesion.

Integrin receptors are the main mediators of cell adhesion to the extracellular matrix. They bind to their ligands by interacting with short amino acid sequences, such as the RGD sequence. Soluble, small RGD-based peptides have been used to block integrin-binding to ligands, thereby interfering with cell adhesion, migration and survival, while substrate-immobilized RGD sequences have been used to enhance cell binding to artificial surfaces. This approach has several important medical applications, e.g. in suppression of tumor angiogenesis or stimulation of bone formation around implants. However, the relatively weak affinity of short RGD-containing peptides often results in incomplete integrin inhibition or ineffective ligation. In this work, we designed and synthesized several new multivalent RGD-containing molecules and tested their ability to inhibit or to promote integrin-dependent cell adhesion when used in solution or immobilized on substrates, respectively. These molecules consist of an oligomeric structure formed by alpha-helical coiled coil peptides fused at their amino-terminal ends with an RGD-containing fragment. When immobilized on a substrate, these peptides specifically promoted integrin alphaVbeta3-dependent cell adhesion, but when used in solution, they blocked alphaVbeta3-dependent cell adhesion to the natural substrates fibronectin and vitronectin. One of the peptides was nearly 10-fold more efficient than fibronectin or vitronectin in promoting cell adhesion, and almost 100-fold more efficient than a linear RGD tripeptide in blocking adhesion. These results indicate that alpha-helical coiled coil peptides carrying an amino-terminal RGD motif can be used as soluble antagonists or surface-immobilized agonists to efficiently inhibit or promote integrin alphaVbeta3-mediated cell adhesion, respectively.

A pathogen-specific epitope inserted into recombinant secretory immunoglobulin A is immunogenic by the oral route.

Oral administration of rabbit secretory IgA (sIgA) to adult BALB/c mice induced IgA+, IgM+, and IgG+ lymphoblasts in the Peyer's patches, whose fusion with myeloma cells resulted in hybridomas producing IgA, IgM, and IgG1 antibodies to the secretory component (SC). This suggests that SC could serve as a vector to target protective epitopes into mucosal lymphoid tissue and elicit an immune response. We tested this concept by inserting a Shigella flexneri invasin B epitope into SC, which, following reassociation with IgA, was delivered orally to mice. To identify potential insertion sites at the surface of SC, we constructed a molecular model of the first and second Ig-like domains of rabbit SC. A surface epitope recognized by an SC-specific antibody was mapped to the loop connecting the E and F beta strands of domain I. This 8-amino acid sequence was replaced by a 9-amino acid linear epitope from S. flexneri invasin B. We found that cellular trafficking of recombinant SC produced in mammalian CV-1 cells was drastically altered and resulted in a 50-fold lower rate of secretion. However, purification of chimeric SC could be achieved by Ni2+-chelate affinity chromatoraphy. Both wild-type and chimeric SC bound to dimeric IgA, but not to monomeric IgA. Reconstituted sIgA carrying the invasin B epitope within the SC moiety triggers the appearance of seric and salivary invasin B-specific antibodies. Thus, neo-antigenized sIgA can serve as a mucosal vaccine delivery system inducing systemic and mucosal immune responses.

Importância da Programação Linear na Determinação de Informações de Gestão - Caso Moave, Sa

A Investigação Operacional vem demonstrando ser uma valiosa ferramenta de gestão nos dias de hoje em que se vive num mercado cada vez mais competitivo. Através da Programação Linear pode-se reproduzir matematicamente um problema de maximização dos resultados ou minimização dos custos de produção com o propósito de auxiliar os gestores na tomada de decisão. A Programação Linear é um método matemático em que a função objectivo e as restrições assumem características lineares, com diversas aplicações no controlo de gestão, envolvendo normalmente problemas de utilização dos recursos disponíveis sujeitos a limitações impostas pelo processo produtivo ou pelo mercado. O objectivo geral deste trabalho é o de propor um modelo de Programação Linear para a programação ou produção e alocação de recursos necessários. Optimizar uma quantidade física designada função objectivo, tendo em conta um conjunto de condicionalismos endógenas às actividades em gestão. O objectivo crucial é dispor um modelo de apoio à gestão contribuindo assim para afectação eficiente de recursos escassos à disposição da unidade económica. Com o trabalho desenvolvido ficou patente a importância da abordagem quantitativa como recurso imprescindível de apoio ao processo de decisão. The operational research has proven to be a valuable management tool today we live in an increasingly competitive market. Through Linear Programming can be mathematically reproduce a problem of maximizing performance or minimizing production costs in order to assist managers in decision making. The Linear Programming is a mathematical method in which the objective function and constraints are linear features, with several applications in the control of management, usually involving problems of resource use are available subject to limitations imposed by the production process or the market. The overall objective of this work is to propose a Linear Programming model for scheduling or production and allocation of necessary resources. Optimizing a physical quantity called the objective function, given a set of endogenous constraints on management thus contributing to efficient allocation of scarce resources available to the economic unit. With the work has demonstrated the importance of the quantitative approach as essential resource to support the decision process.

Exome sequencing identifies recurrent somatic MAP2K1 and MAP2K2 mutations in melanoma.

We performed exome sequencing to detect somatic mutations in protein-coding regions in seven melanoma cell lines and donor-matched germline cells. All melanoma samples had high numbers of somatic mutations, which showed the hallmark of UV-induced DNA repair. Such a hallmark was absent in tumor sample-specific mutations in two metastases derived from the same individual. Two melanomas with non-canonical BRAF mutations harbored gain-of-function MAP2K1 and MAP2K2 (MEK1 and MEK2, respectively) mutations, resulting in constitutive ERK phosphorylation and higher resistance to MEK inhibitors. Screening a larger cohort of individuals with melanoma revealed the presence of recurring somatic MAP2K1 and MAP2K2 mutations, which occurred at an overall frequency of 8%. Furthermore, missense and nonsense somatic mutations were frequently found in three candidate melanoma genes, FAT4, LRP1B and DSC1.

MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field.

The large spatial inhomogeneity in transmit B(1) field (B(1)(+)) observable in human MR images at high static magnetic fields (B(0)) severely impairs image quality. To overcome this effect in brain T(1)-weighted images, the MPRAGE sequence was modified to generate two different images at different inversion times, MP2RAGE. By combining the two images in a novel fashion, it was possible to create T(1)-weighted images where the result image was free of proton density contrast, T(2) contrast, reception bias field, and, to first order, transmit field inhomogeneity. MP2RAGE sequence parameters were optimized using Bloch equations to maximize contrast-to-noise ratio per unit of time between brain tissues and minimize the effect of B(1)(+) variations through space. Images of high anatomical quality and excellent brain tissue differentiation suitable for applications such as segmentation and voxel-based morphometry were obtained at 3 and 7 T. From such T(1)-weighted images, acquired within 12 min, high-resolution 3D T(1) maps were routinely calculated at 7 T with sub-millimeter voxel resolution (0.65-0.85 mm isotropic). T(1) maps were validated in phantom experiments. In humans, the T(1) values obtained at 7 T were 1.15+/-0.06 s for white matter (WM) and 1.92+/-0.16 s for grey matter (GM), in good agreement with literature values obtained at lower spatial resolution. At 3 T, where whole-brain acquisitions with 1 mm isotropic voxels were acquired in 8 min, the T(1) values obtained (0.81+/-0.03 s for WM and 1.35+/-0.05 for GM) were once again found to be in very good agreement with values in the literature.

Determinants of linear judgment: A meta-analysis of lens model studies

The mathematical representation of Brunswik s lens model has been usedextensively to study human judgment and provides a unique opportunity to conduct ameta-analysis of studies that covers roughly five decades. Specifically, we analyzestatistics of the lens model equation (Tucker, 1964) associated with 259 different taskenvironments obtained from 78 papers. In short, we find on average fairly high levelsof judgmental achievement and note that people can achieve similar levels of cognitiveperformance in both noisy and predictable environments. Although overall performancevaries little between laboratory and field studies, both differ in terms of components ofperformance and types of environments (numbers of cues and redundancy). An analysisof learning studies reveals that the most effective form of feedback is information aboutthe task. We also analyze empirically when bootstrapping is more likely to occur. Weconclude by indicating shortcomings of the kinds of studies conducted to date, limitationsin the lens model methodology, and possibilities for future research.

Speech prognosis and need of pharyngeal flap for non syndromic vs syndromic Pierre Robin Sequence

BACKGROUND: The aim of this retrospective study was to evaluate speech outcome and need of a pharyngeal flap in children born with nonsyndromic Pierre Robin Sequence (nsPRS) vs syndromic Pierre Robin Sequence (sPRS). METHODS: Pierre Robin Sequence was diagnosed when the triad microretrognathia, glossoptosis, and cleft palate were present. Children were classified at birth in 3 categories depending on respiratory and feeding problems. The Borel-Maisonny classification was used to score the velopharyngeal insufficiency. RESULTS: The study was based on 38 children followed from 1985 to 2006. For the 25 nsPRS, 9 (36%) pharyngeal flaps were performed with improvements of the phonatory score in the 3 categories. For the 13 sPRS, 3 (23%) pharyngeal flaps were performed with an improvement of the phonatory scores in the 3 children. There was no statistical difference between the nsPRS and sPRS groups (P = .3) even if we compared the children in the 3 categories (P = .2). CONCLUSIONS: Children born with nsPRS did not have a better prognosis of speech outcome than children born with sPRS. Respiratory and feeding problems at birth did not seem to be correlated with speech outcome. This is important when informing parents on the prognosis of long-term therapy

Strong minimax lower bounds for learning

Minimax lower bounds for concept learning state, for example, thatfor each sample size $n$ and learning rule $g_n$, there exists a distributionof the observation $X$ and a concept $C$ to be learnt such that the expectederror of $g_n$ is at least a constant times $V/n$, where $V$ is the VC dimensionof the concept class. However, these bounds do not tell anything about therate of decrease of the error for a {\sl fixed} distribution--concept pair.\\In this paper we investigate minimax lower bounds in such a--stronger--sense.We show that for several natural $k$--parameter concept classes, includingthe class of linear halfspaces, the class of balls, the class of polyhedrawith a certain number of faces, and a class of neural networks, for any{\sl sequence} of learning rules $\{g_n\}$, there exists a fixed distributionof $X$ and a fixed concept $C$ such that the expected error is larger thana constant times $k/n$ for {\sl infinitely many n}. We also obtain suchstrong minimax lower bounds for the tail distribution of the probabilityof error, which extend the corresponding minimax lower bounds.

On prediction of individual sequences

Sequential randomized prediction of an arbitrary binary sequence isinvestigated. No assumption is made on the mechanism of generating the bit sequence. The goal of the predictor is to minimize its relative loss, i.e., to make (almost) as few mistakes as the best ``expert'' in a fixed, possibly infinite, set of experts. We point out a surprising connection between this prediction problem and empirical process theory. First, in the special case of static (memoryless) experts, we completely characterize the minimax relative loss in terms of the maximum of an associated Rademacher process. Then we show general upper and lower bounds on the minimaxrelative loss in terms of the geometry of the class of experts. As main examples, we determine the exact order of magnitude of the minimax relative loss for the class of autoregressive linear predictors and for the class of Markov experts.

Fusion of data sets in multivariate linear regression with errors-in-variables

We consider the application of normal theory methods to the estimation and testing of a general type of multivariate regressionmodels with errors--in--variables, in the case where various data setsare merged into a single analysis and the observable variables deviatepossibly from normality. The various samples to be merged can differ on the set of observable variables available. We show that there is a convenient way to parameterize the model so that, despite the possiblenon--normality of the data, normal--theory methods yield correct inferencesfor the parameters of interest and for the goodness--of--fit test. Thetheory described encompasses both the functional and structural modelcases, and can be implemented using standard software for structuralequations models, such as LISREL, EQS, LISCOMP, among others. An illustration with Monte Carlo data is presented.

Equivalence of piecewise-linear approximation and Lagrangian relaxation for network revenue management

The network revenue management (RM) problem arises in airline, hotel, media,and other industries where the sale products use multiple resources. It can be formulatedas a stochastic dynamic program but the dynamic program is computationallyintractable because of an exponentially large state space, and a number of heuristicshave been proposed to approximate it. Notable amongst these -both for their revenueperformance, as well as their theoretically sound basis- are approximate dynamic programmingmethods that approximate the value function by basis functions (both affinefunctions as well as piecewise-linear functions have been proposed for network RM)and decomposition methods that relax the constraints of the dynamic program to solvesimpler dynamic programs (such as the Lagrangian relaxation methods). In this paperwe show that these two seemingly distinct approaches coincide for the network RMdynamic program, i.e., the piecewise-linear approximation method and the Lagrangianrelaxation method are one and the same.

A new compact linear programming formulation for choice network revenue management

The choice network revenue management model incorporates customer purchase behavioras a function of the offered products, and is the appropriate model for airline and hotel networkrevenue management, dynamic sales of bundles, and dynamic assortment optimization.The optimization problem is a stochastic dynamic program and is intractable. A certainty-equivalencerelaxation of the dynamic program, called the choice deterministic linear program(CDLP) is usually used to generate dyamic controls. Recently, a compact linear programmingformulation of this linear program was given for the multi-segment multinomial-logit (MNL)model of customer choice with non-overlapping consideration sets. Our objective is to obtaina tighter bound than this formulation while retaining the appealing properties of a compactlinear programming representation. To this end, it is natural to consider the affine relaxationof the dynamic program. We first show that the affine relaxation is NP-complete even for asingle-segment MNL model. Nevertheless, by analyzing the affine relaxation we derive a newcompact linear program that approximates the dynamic programming value function betterthan CDLP, provably between the CDLP value and the affine relaxation, and often comingclose to the latter in our numerical experiments. When the segment consideration sets overlap,we show that some strong equalities called product cuts developed for the CDLP remain validfor our new formulation. Finally we perform extensive numerical comparisons on the variousbounds to evaluate their performance.

Asymptotic robustness in multi-sample analysis of multivariate linear relations

Standard methods for the analysis of linear latent variable models oftenrely on the assumption that the vector of observed variables is normallydistributed. This normality assumption (NA) plays a crucial role inassessingoptimality of estimates, in computing standard errors, and in designinganasymptotic chi-square goodness-of-fit test. The asymptotic validity of NAinferences when the data deviates from normality has been calledasymptoticrobustness. In the present paper we extend previous work on asymptoticrobustnessto a general context of multi-sample analysis of linear latent variablemodels,with a latent component of the model allowed to be fixed across(hypothetical)sample replications, and with the asymptotic covariance matrix of thesamplemoments not necessarily finite. We will show that, under certainconditions,the matrix $\Gamma$ of asymptotic variances of the analyzed samplemomentscan be substituted by a matrix $\Omega$ that is a function only of thecross-product moments of the observed variables. The main advantage of thisis thatinferences based on $\Omega$ are readily available in standard softwareforcovariance structure analysis, and do not require to compute samplefourth-order moments. An illustration with simulated data in the context ofregressionwith errors in variables will be presented.