940 resultados para Molecular Simulation


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We report the synthesis, structure and properties of [2]rotaxanes prepared by the assembly of benzylic amide macrocycles around a series of amide and sulfide-/sulfoxide-/sulfone-containing threads. The efficacy of rotaxane formation is related to the hydrogen bond accepting properties of the various sulfur-containing functional groups in the thread, with the highest yields (up to 63% with a rigid vinyl spacer in the template site) obtained for sulfoxide rotaxanes. X-Ray crystallography of a sulfoxide rotaxane, 5, shows that the macrocycle adopts a highly symmetrical chair-like conformation in the solid state, with short hydrogen bonds between the macrocycle isophthalamide NH-protons and the amide carbonyl and sulfoxide S-O of the thread. In contrast, in the X-ray crystal structures of the analogous sulfide (4) and sulfone (6) rotaxanes the macrocycle adopts boat-like conformations with long intercomponent NH…O=SO and NH…S hydrogen bonds (in addition to several intercomponent amide-amide hydrogen bonds). Taking advantage of the different hydrogen bonding modes of the sulfur-based functional groups, a switchable molecular shuttle was prepared in which the oxidation level of sulfur determines the position of the macrocycle on the thread.

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Tumor cells in ascites are a major source of disease recurrence in ovarian cancer patients. In an attempt to identify and profile the population of ascites cells obtained from ovarian cancer patients, a novel method was developed to separate adherent (AD) and non-adherent (NAD) cells in culture. Twenty-five patients were recruited to this study; 11 chemonaive (CN) and 14 chemoresistant (CR). AD cells from both CN and CR patients exhibited mesenchymal morphology with an antigen profile of mesenchymal stem cells and fibroblasts. Conversely, NAD cells had an epithelial morphology with enhanced expression of cancer antigen 125 (CA125), epithelial cell adhesion molecule (EpCAM) and cytokeratin 7. NAD cells developed infiltrating tumors and ascites within 12-14 weeks after intraperitoneal (i.p.) injections into nude mice, whereas AD cells remained non-tumorigenic for up to 20 weeks. Subsequent comparison of selective epithelial, mesenchymal and cancer stem cell (CSC) markers between AD and NAD populations of CN and CR patients demonstrated an enhanced trend in mRNA expression of E-cadherin, EpCAM, STAT3 and Oct4 in the NAD population of CR patients. A similar trend of enhanced mRNA expression of CD44, MMP9 and Oct4 was observed in the AD population of CR patients. Hence, using a novel purification method we demonstrate for the first time a distinct separation of ascites cells into epithelial tumorigenic and mesenchymal non-tumorigenic populations. We also demonstrate that cells from the ascites of CR patients are predominantly epithelial and show a trend towards increased mRNA expression of genes associated with CSCs, compared to cells isolated from the ascites of CN patients. As the tumor cells in the ascites of ovarian cancer patients play a dominant role in disease recurrence, a thorough understanding of the biology of the ascites microenvironment from CR and CN patients is essential for effective therapeutic interventions.

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The mechanical properties of microfilament networks are systematically summarized at different special scales in this paper. We have presented the mechanical models of single microfilaments and microfilament networks at microscale. By adopting a coarse-grained simulation strategy, the mechanical stability of microfilaments related cellular structures are analysed. Structural analysis is conducted to microfilament networks to understand the stress relaxation under compression. The nanoscale molecular mechanisms of the microfilaments deformation is also summarized from the viewpoint of molecular dynamics simulation. This paper provides the fundaments of multiscale modelling framework for the mechanical behaviours simulation of hierarchical microfilament networks.

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In this paper, we present fully Bayesian experimental designs for nonlinear mixed effects models, in which we develop simulation-based optimal design methods to search over both continuous and discrete design spaces. Although Bayesian inference has commonly been performed on nonlinear mixed effects models, there is a lack of research into performing Bayesian optimal design for nonlinear mixed effects models that require searches to be performed over several design variables. This is likely due to the fact that it is much more computationally intensive to perform optimal experimental design for nonlinear mixed effects models than it is to perform inference in the Bayesian framework. In this paper, the design problem is to determine the optimal number of subjects and samples per subject, as well as the (near) optimal urine sampling times for a population pharmacokinetic study in horses, so that the population pharmacokinetic parameters can be precisely estimated, subject to cost constraints. The optimal sampling strategies, in terms of the number of subjects and the number of samples per subject, were found to be substantially different between the examples considered in this work, which highlights the fact that the designs are rather problem-dependent and require optimisation using the methods presented in this paper.

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Passenger experience has become a major factor that influences the success of an airport. In this context, passenger flow simulation has been used in designing and managing airports. However, most passenger flow simulations failed to consider the group dynamics when developing passenger flow models. In this paper, an agent-based model is presented to simulate passenger behaviour at the airport check-in and evacuation process. The simulation results show that the passenger behaviour can have significant influences on the performance and utilisation of services in airport terminals. The model was created using AnyLogic software and its parameters were initialised using recent research data published in the literature.

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Drug resistance continues to be a major barrier to the delivery of curative therapies in cancer. Historically, drug resistance has been associated with over-expression of drug transporters, changes in drug kinetics or amplification of drug targets. However, the emergence of resistance in patients treated with new-targeted therapies has provided new insight into the complexities underlying cancer drug resistance. Recent data now implicate intratumoural heterogeneity as a major driver of drug resistance. Single cell sequencing studies that identified multiple genetically distinct variants within human tumours clearly demonstrate the heterogeneous nature of human tumours. The major contributors to intratumoural heterogeneity are (i) genetic variation, (ii) stochastic processes, (iii) the microenvironment and (iv) cell and tissue plasticity. Each of these factors impacts on drug sensitivity. To deliver curative therapies to patients, modification of current therapeutic strategies to include methods that estimate intratumoural heterogeneity and plasticity will be essential.

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The microenvironment plays a key role in the cellular differentiation of the two main cell lineages of the human breast, luminal epithelial, and myoepithelial. It is not clear, however, how the components of the microenvironment control the development of these cell lineages. To investigate how lineage development is regulated by 3-D culture and microenvironment components, we used the PMC42-LA human breast carcinoma cell line, which possesses stem cell characteristics. When cultured on a two-dimensional glass substrate, PMC42-LA cells formed a monolayer and expressed predominantly luminal epithelial markers, including cytokeratins 8, 18, and 19; E-cadherin; and sialomucin. The key myoepithelial-specific proteins α-smooth muscle actin and cytokeratin 14 were not expressed. When cultured within Engelbreth-Holm- Swarm sarcoma-derived basement membrane matrix (EHS matrix), PMC42-LA cells formed organoids in which the expression of luminal markers was reduced and the expression of other myoepithelial-specific markers (cytokeratin 17 and P-cadherin) was promoted. The presence of primary human mammary gland fibroblasts within the EHS matrix induced expression of the key myoepithelial-specific markers, α-smooth muscle actin and cytokeratin 14. Immortalized human skin fibroblasts were less effective in inducing expression of these key myoepithelial-specific markers. Confocal dual-labeling showed that individual cells expressed luminal or myoepithelial proteins, but not both. Conditioned medium from the mammary fibroblasts was equally effective in inducing myoepithelial marker expression. The results indicate that the myoepithelial lineage is promoted by the extracellular matrix, in conjunction with products secreted by breast-specific fibroblasts. Our results demonstrate a key role for the breast microenvironment in the regulation of breast lineage development.

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Tissue engineering is a multidisciplinary field with the potential to replace tissues lost as a result of trauma, cancer surgery, or organ dysfunction. The successful production, integration, and maintenance of any tissue-engineered product are a result of numerous molecular interactions inside and outside the cell. We consider the essential elements for successful tissue engineering to be a matrix scaffold, space, cells, and vasculature, each of which has a significant and distinct molecular underpinning (Fig. 1). Our approach capitalizes on these elements. Originally developed in the rat, our chamber model (Fig. 2) involves the placement of an arteriovenous loop (the vascular supply) in a polycarbonate chamber (protected space) with the addition of cells and an extracellular matrix such as Matrigel or endogenous fibrin (34, 153, 246, 247). This model has also been extended to the rabbit and pig (J. Dolderer, M. Findlay, W. Morrison, manuscript in preparation), and has been modified for the mouse to grow adipose tissue and islet cells (33, 114, 122) (Fig. 3)...

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Doping as one of the popular methods to manipulate the properties of nanomaterials has received extensive application in deriving different types of graphene derivates, while the understanding of the resonance properties of dopant graphene is still lacking in literature. Based on the large-scale molecular dynamics simulation, reactive empirical bond order potential, as well as the tersoff potential, the resonance properties of N-doped graphene were studied. The studied samples were established according to previous experiments with the N atom’s percentage ranging from 0.43%-2.98%, including three types of N dopant locations, i.e., graphitic N, pyrrolic N and pyridinic N. It is found that different percentages of N-dopant exert different influence to the resonance properties of the graphene, while the amount of N-dopant is not the only factor that determines its impact. For all the considered cases, a relative large percentage of N-dopant (2.98% graphitic N-dopant) is observed to introduce significant influence to the profile of the external energy, and thus lead to an extremely low Q-factor comparing with that of the pristine graphene. The most striking finding is that, the natural frequency of the defective graphene with N-dopant appears uniformly larger than that of the pristine defective graphene. While for the perfect graphene, the N-dopant shows less influence to its natural frequency. This study will enrich the current understanding of the influence of dopants on graphene, which will eventually shed lights on the design of different molecules-doped graphene sheet.

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Background Sexually-transmitted pathogens often have severe reproductive health implications if treatment is delayed or absent, especially in females. The complex processes of disease progression, namely replication and ascension of the infection through the genital tract, span both extracellular and intracellular physiological scales, and in females can vary over the distinct phases of the menstrual cycle. The complexity of these processes, coupled with the common impossibility of obtaining comprehensive and sequential clinical data from individual human patients, makes mathematical and computational modelling valuable tools in developing our understanding of the infection, with a view to identifying new interventions. While many within-host models of sexually-transmitted infections (STIs) are available in existing literature, these models are difficult to deploy in clinical/experimental settings since simulations often require complex computational approaches. Results We present STI-GMaS (Sexually-Transmitted Infections – Graphical Modelling and Simulation), an environment for simulation of STI models, with a view to stimulating the uptake of these models within the laboratory or clinic. The software currently focuses upon the representative case-study of Chlamydia trachomatis, the most common sexually-transmitted bacterial pathogen of humans. Here, we demonstrate the use of a hybrid PDE–cellular automata model for simulation of a hypothetical Chlamydia vaccination, demonstrating the effect of a vaccine-induced antibody in preventing the infection from ascending to above the cervix. This example illustrates the ease with which existing models can be adapted to describe new studies, and its careful parameterisation within STI-GMaS facilitates future tuning to experimental data as they arise. Conclusions STI-GMaS represents the first software designed explicitly for in-silico simulation of STI models by non-theoreticians, thus presenting a novel route to bridging the gap between computational and clinical/experimental disciplines. With the propensity for model reuse and extension, there is much scope within STI-GMaS to allow clinical and experimental studies to inform model inputs and drive future model development. Many of the modelling paradigms and software design principles deployed to date transfer readily to other STIs, both bacterial and viral; forthcoming releases of STI-GMaS will extend the software to incorporate a more diverse range of infections.

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This paper considers two problems that frequently arise in dynamic discrete choice problems but have not received much attention with regard to simulation methods. The first problem is how to simulate unbiased simulators of probabilities conditional on past history. The second is simulating a discrete transition probability model when the underlying dependent variable is really continuous. Both methods work well relative to reasonable alternatives in the application discussed. However, in both cases, for this application, simpler methods also provide reasonably good results.

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In this paper, I present a number of leading examples in the empirical literature that use simulation-based estimation methods. For each example, I describe the model, why simulation is needed, and how to simulate the relevant object. There is a section on simulation methods and another on simulations-based estimation methods. The paper concludes by considering the significance of each of the examples discussed a commenting on potential future areas of interest.

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Homologous recombination is needed for meiotic chromosome segregation, genome maintenance, and tumor suppression. RAD51AP1 (RAD51 associated protein 1) has been shown to interact with and enhance the recombinase activity of RAD51. Accordingly, genetic ablation of RAD51AP1 leads to enhanced sensitivity to and also chromosome aberrations upon DNA damage, demonstrating a role for RAD51AP1 in mitotic homologous recombination. Here we show physical association of RAD51AP1 with the meiosis-specific recombinase DMC1 and a stimulatory effect of RAD51AP1 on the DMC1-mediated D-loop reaction. Mechanistic studies have revealed that RAD51AP1 enhances the ability of the DMC1 presynaptic filament to capture the duplex-DNA partner and to assemble the synaptic complex, in which the recombining DNA strands are homologously aligned. We also provide evidence that functional cooperation is dependent on complex formation between DMC1 and RAD51AP1 and that distinct epitopes in RAD51AP1 mediate interactions with RAD51 and DMC1. Finally, we show that RAD51AP1 is expressed in mouse testes, and that RAD51AP1 foci colocalize with a subset of DMC1 foci in spermatocytes. These results suggest that RAD51AP1 also serves an important role in meiotic homologous recombination.

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Molecular doping and detection are at the forefront of graphene research, a topic of great interest in physical and materials science. Molecules adsorb strongly on graphene, leading to a change in electrical conductivity at room temperature. However, a common impediment for practical applications reported by all studies to date is the excessively slow rate of desorption of important reactive gases such as ammonia and nitrogen dioxide. Annealing at high temperatures, or exposure to strong ultraviolet light under vacuum, is employed to facilitate desorption of these gases. In this article, the molecules adsorbed on graphene nanoflakes and on chemically derived graphene-nanomesh flakes are displaced rapidly at room temperature in air by the use of gaseous polar molecules such as water and ethanol. The mechanism for desorption is proposed to arise from the electrostatic forces exerted by the polar molecules, which decouples the overlap between substrate defect states, molecule states, and graphene states near the Fermi level. Using chemiresistors prepared from water-based dispersions of single-layer graphene on mesoporous alumina membranes, the study further shows that the edges of the graphene flakes (showing p-type responses to NO2 and NH3) and the edges of graphene nanomesh structures (showing n-type responses to NO2 and NH3) have enhanced sensitivity. The measured responses towards gases are comparable to or better than those which have been obtained using devices that are more sophisticated. The higher sensitivity and rapid regeneration of the sensor at room temperature provides a clear advancement towards practical molecule detection using graphene-based materials.

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The practice of medicine has always aimed at individualized treatment of disease. The relationship between patient and physician has always been a personal one, and the physician's choice of treatment has been intended to be the best fit for the patient's needs. The necessary pooling/grouping of disease families and their assignment to a number of drugs or treatment methods has, consequently, led to an increase in the number of effective therapies. However, given the heterogeneity of most human diseases, and cancer specifically, it is currently impossible for the treating clinician to effectively predict a patient's response and outcome based on current technologies, much less the idiosyncratic resistances and adverse effects associated with the limited therapeutic options.