Modelling of effects of ultrastructural morphology on the hygroelastic properties of wood fibres

Wood fibres constitute the structural framework of e.g. wood, paper, board and composites, where stiffness and dimensional stability are of importance. An analytical modelling approach has been used for prediction of hygroelastic response, and assessment of the stresses in thick-walled cylinder models of wood fibres. A wood fibre was idealised as a multilayered hollow cylinder made of orthotropic material with helical orientation. The hygroelastic response of the layered assembly due to axisymmetric loading and moisture content changes was obtained by solving the corresponding boundary value problem of elasticity. A simple solution scheme based on the state space approach and the transfer matrix method was employed. This was combined with an analytical ultrastructural homogenisation method, used to link hygroelastic properties of constituent wood polymers to properties of each layer. Predicted hygroelastic response captured experimentally measured behaviour. Fibres that were constrained not to twist showed a stiffer response than fibres allowed twisting under uniaxial loading. It was also shown that the ultrastructure, i.e. the microfibril angle, will control the hygroexpansion in the same way as it controls the compliance of the cell wall. Qualitative failure trends comparable with experimental observations could be established with stress analysis and a simple plane-stress failure criterion.     

Stochastic molecular descriptors for polymers. 4. Study of complex mixtures with topological indices of mass spectra spiral and star networks: The blood proteome case

The Quantitative Structure-Property Relationships (QSPRs) based on Graph or Network Theory are important for predicting the properties of polymeric systems. In the three previous papers of this series (Polymer 45 (2004) 3845-3853; Polymer 46 (2005) 2791-2798; and Polymer 46 (2005) 6461-6473) we focused on the uses of molecular graph parameters called topological indices (TIs) to link the structure of polymers with their biological properties. However, there has been little effort to extend these TIs to the study of complex mixtures of artificial polymers or biopolymers such as nucleic acids and proteins. In this sense, Blood Proteome (BP) is one of the most important and complex mixtures containing protein polymers. For instance, outcomes obtained by Mass Spectrometry (MS) analysis of BP are very useful for the early detection of diseases and drug-induced toxicities. Here, we use two Spiral and Star Network representations of the MS outcomes and defined a new type of TIs. The new TIs introduced here are the spectral moments (π_k) of the stochastic matrix associated to the Spiral graph and describe non-linear relationships between the different regions of the MS characteristic of BP. We used the MARCH-INSIDE approach to calculate the π_k(SN) of different BP samples and S2SNet to determine several Star graph TIs. In the second step, we develop the corresponding Quantitative Proteome-Property Relationship (QPPR) models using the Linear Discriminant Analysis (LDA). QPPRs are the analogues of QSPRs in the case of complex biopolymer mixtures. Specifically, the new QPPRs derived here may be used to detect drug-induced cardiac toxicities from BP samples. Different Machine Learning classification algorithms were used to fit the QPPRs based on π_k(SN), showing J48 decision tree classifier to have the best performance. These results suggest that the present approach captures important features of the complex biopolymers mixtures and opens new opportunities to the application of the idea supporting classic QSPRs in polymer sciences.     

Unconventional Charge Density Wave Arising from Electron–Phonon Interaction

We report of a theoretical study on quasi-one dimensional unconventional charge density wave (UCDW) driven by electron–phonon interaction. Within mean field theory, we find that the wavevector dependence of the coupling leads to a momentum dependent single particle gap on the Fermi surface. The presence of small energy single particle excitations around the gap nodes significantly changes the optical conductivity compared to the conventional CDW result. In addition to that, the collective phase excitation arising from fluctuation of the order parameter leads to further qualitative changes of the conductivity and results in an effective mass that is nonmonotonic in temperature.      

A survey on approaches to gridification

The Grid shows itself as a globally distributed computing environment, in which hardware and software resources are virtualized to transparently provide applications with vast capabilities. Just like the electrical power grid, the Grid aims at offering a powerful yet easy‐to‐use computing infrastructure to which applications can be easily ‘plugged’ and efficiently executed. Unfortunately, it is still very difficult to Grid‐enable applications, since current tools force users to take into account many details when adapting applications to run on the Grid. In this paper, we survey some of the recent efforts in providing tools for easy gridification of applications and propose several taxonomies to identify approaches followed in the materialization of such tools. We conclude this paper by describing common features among the proposed approaches, and by pointing out open issues and future directions in the research and development of gridification methods. Copyright © 2007 John Wiley & Sons, Ltd.     

Radiosity with Well Distributed Ray Sets

In this paper we present a new radiosity algorithm, based on the notion of a well distributed ray set (WDRS). A WDRS is a set of rays, connecting mutually visible points and patches, that forms an approximate representation of the radiosity operator and the radiosity distribution. We propose an algorithm that constructs an optimal WDRS for a given accuracy and mesh. The construction is based on discrete importance sampling as in previously proposed stochastic radiosity algorithms, and on quasi Monte Carlo sampling. Quasi Monte Carlo sampling leads to faster convergence rates and the fact that the sampling is deterministic makes it possible to represent the well distributed ray set very efficiently in computer memory. Like previously proposed stochastic radiosity algorithms, the new algorithm is well suited for computing the radiance distribution in very complex diffuse scenes, when it is not feasible to explicitly compute and store form factors as in classical radiosity algorithms. Experiments show that the new algorithm is often more efficient than previously proposed Monte Carlo radiosity algorithms by half an order of magnitude and more.     

Adenovirus-Mediated Inducible Expression of a PD-L1 Blocking Antibody in Combination with Macrophage Depletion Improves Survival in a Mouse Model of Peritoneal Carcinomatosis

Immune checkpoint inhibitors (ICIs) have demonstrated remarkable efficacy in a growing number of malignancies. However, overcoming primary or secondary resistances is difficult due to pharmacokinetics issues and side effects associated with high systemic exposure. Local or regional expression of monoclonal antibodies (mAbs) using gene therapy vectors can alleviate this problem. In this work, we describe a high-capacity adenoviral vector (HCA-EFZP-aPDL1) equipped with a mifepristone-inducible system for the controlled expression of an anti-programmed death ligand 1 (PD-L1) blocking antibody. The vector was tested in an immune-competent mouse model of colorectal cancer based on implantation of MC38 cells. A single local administration of HCA-EFZP-aPDL1 in subcutaneous lesions led to a significant reduction in tumor growth with minimal release of the antibody in the circulation. When the vector was tested in a more stringent setting (rapidly progressing peritoneal carcinomatosis), the antitumor effect was marginal even in combination with other immune-stimulatory agents such as polyinosinic-polycytidylic acid (pI:C), blocking mAbs for T cell immunoglobulin, mucin-domain containing-3 (TIM-3) or agonistic mAbs for 4-1BB (CD137). In contrast, macrophage depletion by clodronate liposomes enhanced the efficacy of HCA-EFZP-aPDL1. These results highlight the importance of addressing macrophage-associated immunoregulatory mechanisms to overcome resistance to ICIs in the context of colorectal cancer.     

Well-Known and Novel Serum Biomarkers for Risk Stratification of Patients with Non-ischemic Dilated Cardiomyopathy

Non-ischemic dilated cardiomyopathy encompasses a wide spectrum of myocardial disorders, characterized by left ventricular dilatation with systolic impairment and increased risk of sudden cardiac death. In spite of all the therapeutic progress that has been made in recent years, dilated cardiomyopathy continues to be an important cause of cardiac transplant, being associated with an enormous cost burden for health care systems worldwide. Predicting the prognosis of patients with dilated cardiomyopathy is essential to individualize treatment. Late gadolinium enhancement-cardiac magnetic resonance imaging, microvolt T-wave alternans, and genetic testing have emerged as powerful tools in predicting sudden cardiac death occurrence and maximizing patient’s selection. Despite all these new diagnostic modalities, additional tests to complement or replace current tools are required for better risk stratification. Therefore, biomarkers are an easy and important tool that can help to detect patients at risk of adverse cardiovascular events. Additionally, identifying potential biomarkers involved in dilated cardiomyopathy can provide us important information regarding the diagnostic, prognostic, risk stratification, and response to treatment for these patients. Many potential biomarkers have been studied in patients with dilated cardiomyopathy, but only a few have been adopted in current practice. Therefore, the aim of our review is to provide the clinicians with an update on the well-known and novel biomarkers that can be useful for risk stratification of patients with non-ischemic dilated cardiomyopathy.