A multiscale multisurface constitutive model for the thermo-plastic behavior of polyethylene

We present a multiscale model bridging length and time scales from molecular to continuum levels with the objective of predicting the yield behavior of amorphous glassy polyethylene (PE). Constitutive pa- rameters are obtained from molecular dynamics (MD) simulations, decreasing the requirement for ad- hoc experiments. Consequently, we achieve: (1) the identification of multisurface yield functions; (2) the high strain rate involved in MD simulations is upscaled to continuum via quasi-static simulations. Validation demonstrates that the entire multisurface yield functions can be scaled to quasi-static rates where the yield stresses are possibly predicted by a proposed scaling law; (3) a hierarchical multiscale model is constructed to predict temperature and strain rate dependent yield strength of the PE.

Exploring novel targeted therapeutic strategies against Acute Myeloid Leukemia cells based on inhibition of bromodomain proteins and CDC20 activity

Acute myeloid leukemia (AML) is a haematological malignancies arising from the accumulation of undifferentiated myeloid progenitors with an uncontrolled proliferation. The genomic landscape of AML revealed that the disease is characterized by high level of heterogeneity and is subjected to clonal evolution driven by selective pressure of chemotherapy. In this study, we investigated the therapeutic effects of the inhibition of BRD4 and CDC20 in vitro and ex vivo. We demonstrated that inhibition of BRD4 with GSK1215101A in AML cell lines was effective under hypoxia. It induced the activation of antioxidant response both, at transcriptomic and metabolomic levels, driven by enrichment of NRF2 pathway under normoxic and hypoxic condition. Moreover, the combined treatment with Omaveloxolone, a drug inducing NRF2 activation and NF-κB inhibition, potentiated the effects on apoptosis and colony forming capacity of stem progenitor cells. Lastly, gene expression profiling data revealed that combination treatment induced major changes in genes related to cell cycle, together with enrichment of cell differentiation pathways and negative regulation of WNT, in normoxia and hypoxia. Regarding CDC20, we observed its up-regulation in AML patients. Treatment with two different inhibitors, Apcin and proTAME, was effective in primary AML cells and in AML cell lines, through induction of apoptosis and mitotic arrest. The lack of correlation between proliferation markers and CDC20 levels in AML cell subpopulations supports the idea of alternative CDC20 functions, independent from its essential role during mitosis. CDC20-KD experiments conducted in AML cell lines revealed a mild effect on apoptosis induction, but no significant change in cell cycle progression. In summary, these results allowed the identification of a new strategy combination to improve the effects of BRD4 inhibition on LSC residing in the BM hypoxic niche, and provide some new evidence regarding the potential role of CDC20 as a new target for AML treatment.

Assessing the fit of unidimensional IRT models for binary data under model misspecification

Model misspecification affects the classical test statistics used to assess the fit of the Item Response Theory (IRT) models. Robust tests have been derived under model misspecification, as the Generalized Lagrange Multiplier and Hausman tests, but their use has not been largely explored in the IRT framework. In the first part of the thesis, we introduce the Generalized Lagrange Multiplier test to detect differential item response functioning in IRT models for binary data under model misspecification. By means of a simulation study and a real data analysis, we compare its performance with the classical Lagrange Multiplier test, computed using the Hessian and the cross-product matrix, and the Generalized Jackknife Score test. The power of these tests is computed empirically and asymptotically. The misspecifications considered are local dependence among items and non-normal distribution of the latent variable. The results highlight that, under mild model misspecification, all tests have good performance while, under strong model misspecification, the performance of the tests deteriorates. None of the tests considered show an overall superior performance than the others. In the second part of the thesis, we extend the Generalized Hausman test to detect non-normality of the latent variable distribution. To build the test, we consider a seminonparametric-IRT model, that assumes a more flexible latent variable distribution. By means of a simulation study and two real applications, we compare the performance of the Generalized Hausman test with the M2 limited information goodness-of-fit test and the Likelihood-Ratio test. Additionally, the information criteria are computed. The Generalized Hausman test has a better performance than the Likelihood-Ratio test in terms of Type I error rates and the M2 test in terms of power. The performance of the Generalized Hausman test and the information criteria deteriorates when the sample size is small and with a few items.

Deploying and processing neural representations of signals

Neural representations (NR) have emerged in the last few years as a powerful tool to represent signals from several domains, such as images, 3D shapes, or audio. Indeed, deep neural networks have been shown capable of approximating continuous functions that describe a given signal with theoretical infinite resolution. This finding allows obtaining representations whose memory footprint is fixed and decoupled from the resolution at which the underlying signal can be sampled, something that is not possible with traditional discrete representations, e.g., grids of pixels for images or voxels for 3D shapes. During the last two years, many techniques have been proposed to improve the capability of NR to approximate high-frequency details and to make the optimization procedures required to obtain NR less demanding both in terms of time and data requirements, motivating many researchers to deploy NR as the main form of data representation for complex pipelines. Following this line of research, we first show that NR can approximate precisely Unsigned Distance Functions, providing an effective way to represent garments that feature open 3D surfaces and unknown topology. Then, we present a pipeline to obtain in a few minutes a compact Neural Twin® for a given object, by exploiting the recent advances in modeling neural radiance fields. Furthermore, we move a step in the direction of adopting NR as a standalone representation, by considering the possibility of performing downstream tasks by processing directly the NR weights. We first show that deep neural networks can be compressed into compact latent codes. Then, we show how this technique can be exploited to perform deep learning on implicit neural representations (INR) of 3D shapes, by only looking at the weights of the networks.

Modelling and classification with quantile-based distributions

In this work, we explore and demonstrate the potential for modeling and classification using quantile-based distributions, which are random variables defined by their quantile function. In the first part we formalize a least squares estimation framework for the class of linear quantile functions, leading to unbiased and asymptotically normal estimators. Among the distributions with a linear quantile function, we focus on the flattened generalized logistic distribution (fgld), which offers a wide range of distributional shapes. A novel naïve-Bayes classifier is proposed that utilizes the fgld estimated via least squares, and through simulations and applications, we demonstrate its competitiveness against state-of-the-art alternatives. In the second part we consider the Bayesian estimation of quantile-based distributions. We introduce a factor model with independent latent variables, which are distributed according to the fgld. Similar to the independent factor analysis model, this approach accommodates flexible factor distributions while using fewer parameters. The model is presented within a Bayesian framework, an MCMC algorithm for its estimation is developed, and its effectiveness is illustrated with data coming from the European Social Survey. The third part focuses on depth functions, which extend the concept of quantiles to multivariate data by imposing a center-outward ordering in the multivariate space. We investigate the recently introduced integrated rank-weighted (IRW) depth function, which is based on the distribution of random spherical projections of the multivariate data. This depth function proves to be computationally efficient and to increase its flexibility we propose different methods to explicitly model the projected univariate distributions. Its usefulness is shown in classification tasks: the maximum depth classifier based on the IRW depth is proven to be asymptotically optimal under certain conditions, and classifiers based on the IRW depth are shown to perform well in simulated and real data experiments.

Variational Quantum Splines: Moving Beyond Linearity for Quantum Activation Functions

Activation functions within neural networks play a crucial role in Deep Learning since they allow to learn complex and non-trivial patterns in the data. However, the ability to approximate non-linear functions is a significant limitation when implementing neural networks in a quantum computer to solve typical machine learning tasks. The main burden lies in the unitarity constraint of quantum operators, which forbids non-linearity and poses a considerable obstacle to developing such non-linear functions in a quantum setting. Nevertheless, several attempts have been made to tackle the realization of the quantum activation function in the literature. Recently, the idea of the QSplines has been proposed to approximate a non-linear activation function by implementing the quantum version of the spline functions. Yet, QSplines suffers from various drawbacks. Firstly, the final function estimation requires a post-processing step; thus, the value of the activation function is not available directly as a quantum state. Secondly, QSplines need many error-corrected qubits and a very long quantum circuits to be executed. These constraints do not allow the adoption of the QSplines on near-term quantum devices and limit their generalization capabilities. This thesis aims to overcome these limitations by leveraging hybrid quantum-classical computation. In particular, a few different methods for Variational Quantum Splines are proposed and implemented, to pave the way for the development of complete quantum activation functions and unlock the full potential of quantum neural networks in the field of quantum machine learning.

Óxido nítrico: propriedades e potenciais usos terapêuticos

Nitric oxide ( NO) is a substance that acts as a second-messenger and is associated with a number of important physiological functions such as regulation of the vascular tonus, immune modulation and neurotransmission. As a physiological mediator, alteration of its concentration level may cause pathophysiological disfunctions such as hypertension, septic shock and impotence. Possible therapeutic approaches are being developed to control NO levels in vivo. We review herein the main physical and chemical properties of NO, its biological functions and available chemical interventions to reduce and increment its physiological concentration levels. Recent developments in the field are also highlighted.

Terapêutica interdisciplinar para fratura cominutiva de côndilo por projétil de arma de fogo: enfoque miofuncional

TEMA: ferimentos causados por projéteis de arma de fogo apresentam alta incidência na região da cabeça e face. A articulação temporomandibular pode estar envolvida, além de estruturas anatômicas importantes como o nervo facial, necessitando de equipe multidisciplinar para efetuar tratamento adequado. PROCEDIMENTOS: apresentação de caso clínico de fratura condilar cominutiva causada por projétil de arma de fogo tratado de forma não-cirúrgica associado à terapia miofuncional orofacial. Paciente encaminhado para avaliação e procedimentos fonoaudiológicos após conduta da equipe de cirurgia bucomaxilofacial, sem remoção do projétil, alojado superficialmente, próximo da origem do músculo esternocleidomastóideo à direita, com fratura condilar cominutiva e lesão do nervo facial. Foram aspectos observados em avaliação: mordida aberta anterior, importante redução da amplitude dos movimentos mandibulares com desvios para o lado acometido, ausência de lateralidade contralateral, dor muscular, paralisia e parestesia em terço médio e superior da hemiface direita. Realizadas sessões de terapia miofuncional seguindo protocolo específico para traumas de face constando de: drenagem de edema; manipulações na musculatura levantadora da mandíbula ipsilateral; ampliação e correção dos movimentos mandibulares; procedimentos específicos referentes à paralisia facial e reorganização funcional direcionada. RESULTADOS: após oito semanas de terapia os resultados obtidos mostram restabelecimento de amplitude e da simetria dos movimentos mandibulares, reorganização da mastigação, adequação da deglutição e fala, remissão da sintomatologia dolorosa e remissão da paralisia do terço médio. CONCLUSÃO: o tratamento conservador da fratura por meio da terapia miofuncional orofacial resultou na reabilitação funcional da mandíbula e face dirigindo os movimentos e estimulando a adequação das funções estomatognáticas.

Avaliação de modelos geoidais gravimétricos determinados com funções covariâncias planas e esféricas (Estudo de Caso: Estado de São Paulo e adjacências)

The least squares collocation is a mathematical technique which is used in Geodesy for representation of the Earth's anomalous gravity field from heterogeneous data in type and precision. The use of this technique in the representation of the gravity field requires the statistical characteristics of data through covariance function. The covariances reflect the behavior of the gravity field, in magnitude and roughness. From the statistical point of view, the covariance function represents the statistical dependence among quantities of the gravity field at distinct points or, in other words, shows the tendency to have the same magnitude and the same sign. The determination of the covariance functions is necessary either to describe the behavior of the gravity field or to evaluate its functionals. This paper aims at presenting the results of a study on the plane and spherical covariance functions in determining gravimetric geoid models.

Delocalized water and fluoride contributions to Dyson orbitals for electron detachment from the hydrated fluoride anion

The experimental vertical electron detachment energy (VEDE) of aqueous fluoride, [F(-)(H(2)O)], is approximately 9.8 eV, but spectral assignment is complicated by interference between F(-) 2p and H(2)O 1b(1) orbitals. The electronic structure of [F(-)(H(2)O)] is analyzed with Monte Carlo and ab initio quantum-mechanical calculations. Electron-propagator calculations in the partial third-order approximation yield a VEDE of 9.4 eV. None of the Dyson orbitals corresponding to valence VEDEs consists primarily of F 2p functions. These results and ground-state atomic charges indicate that the final, neutral state is more appropriately described as [F(-)(H(2)O)(+)] than as [F(H(2)O)]. (C) 2010 American Institute of Physics. [doi: 10.1063/1.3431081]

A Novel Nonparametric Density Estimator

We present a novel nonparametric density estimator and a new data-driven bandwidth selection method with excellent properties. The approach is in- spired by the principles of the generalized cross entropy method. The pro- posed density estimation procedure has numerous advantages over the tra- ditional kernel density estimator methods. Firstly, for the first time in the nonparametric literature, the proposed estimator allows for a genuine incor- poration of prior information in the density estimation procedure. Secondly, the approach provides the first data-driven bandwidth selection method that is guaranteed to provide a unique bandwidth for any data. Lastly, simulation examples suggest the proposed approach outperforms the current state of the art in nonparametric density estimation in terms of accuracy and reliability.

Apparent strain localization and shear wave dispersion in elastic fault gouge with microrotations

Shear deformation of fault gouge or other particulate materials often results in observed strain localization, or more precisely, the localization of measured deformation gradients. In conventional elastic materials the strain localization cannot take place therefore this phenomenon is attributed to special types of non-elastic constitutive behaviour. For particulate materials however the Cosserat continuum which takes care of microrotations independent of displacements is a more appropriate model. In elastic Cosserat continuum the localization in displacement gradients is possible under some combinations of the generalized Cosserat elastic moduli. The same combinations of parameters also correspond to a considerable dispersion in shear wave propagation which can be used for independent experimental verification of the proposed mechanism of apparent strain localization in fault gouge.

Inducible nitric oxide synthase in pityriasis lichenoides lesions

Pityriasis lichenoides (PL) is an inflammatory skin disease of unknown etiology. Nitric oxide (NO) has emerged as an important mediator of many physiological functions. The importance of NO-mediated signaling in skin diseases has been reported by several studies. A review of clinical records and histopathological slides of 34 patients diagnosed with PL was performed. Three different groups of skin biopsies including PL chronica (24 patients), PL et varioliformis acuta (10 patients) and 15 normal skin samples were subjected to the immunohistochemistry technique for inducible nitric oxide synthase (iNOS) detection. Normal skin group exhibited a few number of iNOS-positive cells in the dermis and rare positive cells in the upper epidermis, unlike abundant epidermal and dermal iNOS expression observed in both PL groups. According to our results, we hypothesize that NO produced by iNOS could participate in PL pathogenesis. Abnormal and persistent responses to unknown antigens, probably a pathogen, associated with NO immunoregulatory functions could contribute to the relapsing course observed in PL. NO anti-apoptotic effect on T-cell lymphocytes could play a role on maintenance of reactive T cells, leading to a T-cell lymphoid dyscrasia. Di Giunta G, Goncalves da Silva AM, Sotto MN. Inducible nitric oxide synthase in pityriasis lichenoides lesions.J Cutan Pathol 2009; 36: 325-330. (C) Blackwell Munksgaard 2008.

MicroRNAs Differentially Expressed in ACTH-Secreting Pituitary Tumors

Context: MicroRNAs (miRNAs) are small noncoding RNAs, functioning as antisense regulators of gene expression by targeting mRNA and contributing to cancer development and progression. More than 50% of miRNA genes are located in cancer-associated genomic regions or in fragile sites of the genome. Objective: The aim of the study was to analyze the differential expression of let-7a, miR-15a, miR-16, miR-21, miR-141, miR-143, miR-145, and miR-150 in corticotropinomas and normal pituitary tissue and verify whether their profile of expression correlates with tumor size or remission after treatment. Material and Methods: ACTH-secreting pituitary tumor samples were obtained during transphenoidal surgery from patients with Cushing disease and normal pituitary tissues from autopsies. The relative expression of miRNAs was measured by real-time PCR using RNU44 and RNU49 as endogenous controls. Relative quantification of miRNA expression was calculated using the 2(-Delta Delta Ct) method. Results: We found underexpression of miR-145 (2.0-fold; P = 0.04), miR-21 (2.4-fold; P = 0.004), miR-141 (2.6-fold; P = 0.02), let-7a (3.3-fold; P = 0.003), miR-150 (3.8-fold; P = 0.04), miR-15a (4.5-fold; P = 0.03), miR-16 (5.0-fold; P = 0.004), and miR-143 (6.4-fold; P = 0.004) in ACTH-secreting pituitary tumors when compared to normal pituitary tissues. There were no differences between miRNA expression and tumor size as well as miRNA expression and ratio of remission after surgery, except in patients presenting lower miR-141 expression who showed a better chance of remission. Conclusion: Our results support the possibility that altered miRNA expression profile might be involved in corticotrophic tumorigenesis. However, the lack of knowledge about miRNA target genes postpones full understanding of the biological functions of down-regulated or up-regulated miRNAs in corticotropinomas. (J Clin Endocrinol Metab 94: 320-323, 2009)

Exploring a literary gender: the chivalry novels

Exploring a literary gender: the chivalry novels This article deals with a series of aspects related to the thematic universe of the chivalry novels, focusing on the production of the literary work from a historical perspective. The text analyzes the advent and evolution of the genre chivalry novels, presents some central elements of its internal structure, concluding with some of the social functions of this kind of literature.