Molecular dynamics study of contact mechanics: contact area and interfacial separation from small to full contact

We report a molecular dynamics study of the contact between a rigid solid with a randomly rough surface and an elastic block with a flat surface. We study the contact area and the interfacial separation from small contact (low load) to full contact (high load). For small load the contact area varies linearly with the load and the interfacial separation depends logarithmically on the load. For high load the contact area approaches to the nominal contact area (i.e., complete contact), and the interfacial separation approaches to zero. The present results may be very important for soft solids, e.g., rubber, or for very smooth surfaces, where complete contact can be reached at moderate high loads without plastic deformation of the solids.Comment: 4 pages,5 figure

Faktor-faktor Psikologis Penentu Niat Ibu-ibu Rumah Tangga Di Indonesia Untuk Membeli Produk Tiruan/palsu

This paper aims to examine housewives self-reported intention to buy counterfeit products by employing the Theory of Planned Behavior (TPB) as the research framework. In total, 600 housewives completed a survey questionnaire measuring their responses to seven constructs in the TPB. Structural equation modeling (SEM) was used as the technique for data analysis with two step approach. The results of this study showed that overall the variables in the TPB model (attitude, subjective norms, perceived control behaviors) were able to explain and predict housewives intention to buy counterfeit products. Attitudes toward behavior have a greater influence on intentions to buy. Additional variables that are included: values were able to explain and predict attitudes toward buying behavior, past behavior was able to explain and predict perceived behavioral control, and social status could explain and predict housewives intention to buy counterfeit products

The Effective Potential of the N=0* Yang-Mills Theory

We study the \N=4 SYM theory with SU(N) gauge group in the large N limit, deformed by giving equal mass to the four adjoint fermions. With this modification, a potential is dynamically generated for the six scalars in the theory, \phi^i. We show that the resulting theory is stable (perturbatively in the 't Hooft coupling), and that there are some indications that =0 is the vacuum of the theory. Using the AdS/CFT correspondence, we compare the results to the corresponding supergravity computation, i.e. brane probing a deformed AdS_5 x S^5 background, and we find qualitative agreement.Comment: 12 pages, 2 figures, version to appear in JHE

Securitization, bank vigilance, leverage and sudden stops

Accounts of the recent financial crisis claim that the practice of securitizing bank loans had led banks to be less vigilant in their lending habits. Securitization, the argument goes, gives the originators of the loans worse incentives to screen potential borrowers and monitor them as compared to traditional direct lending. But, unless investors are pricing securities irrationally, wouldn't contract theory suggest that banks should always prefer the contract that allows them to commit to higher vigilance? This paper addresses this problem by introducing a model in which securitization leads to laxer lending standards, even though it is chosen optimally by banks and investors. I construct a model where investment is performed through intermediaries (banks) that choose the volume of lending and a variable level of effort in screening potential borrowers, set the lending standards, and can finance their activities either by eliciting deposits or selling securities. Securitization allows the banks to credibly communicate to investors information about the borrowers, which depositors cannot access. Securitization has two effects: at fixed leverage, securitization gives banks better incentives to screen borrowers and leads to higher lending standards; however, it also allows banks to choose a higher level of leverage, which in turn degrades the screening effort. In equilibrium, securitization leads to lower vigilance, but is still preferred because it allows the banks to intermediate more funds. Paradoxically, the method of finance that allows banks to better communicate information about borrowers leads in equilibrium to less information being produced. The model also provides a natural explanation for why securitization is not observed below a strict credit rating cutoff (FICO 620), and why securitization activity can discontinuously stop as a continuous function of overall economic conditions

Tissue-resident immune cells in health and disease: their heterogeneity and associated gene signatures

The immune system is comprised of numerous cell types, molecules and pathways whose primary purpose is to regulate tissue homeostasis and protect an individual from disease. Researchers have tried to identify and characterise components of the immune system and their interactions as modulating aspects of this system is a major goal of the pharmaceutical industry. This work has described the significant heterogeneity of immune cell types which are defined by their microenvironment, extending from their lineage commitment in specialised tissues, e.g. bone marrow and thymus, to their activation states in disease. A facet of this heterogeneity are tissues-resident immune cells (TRICs) which have tissue specific homeostatic functions, and differ from their tissue naïve counterparts at the transcriptomic and epigenetic level. Furthermore, these cells display unique activation states in disease, making them a target for tissue-specific therapies. Hence, in this thesis, I have sought to expand on the current knowledge of TRICs in health and disease, investigating their heterogeneity and how to define them using various computational approaches. Initially in chapter two a single well defined TRIC population, microglia, the tissue-resident macrophage of the brain is investigated. Microglia are the dominant immune cell type of the brain and are strongly implicated in neurodegenerative disease. These cells exhibit great heterogeneity depending on the brain region they reside in, also influencing their activation states. Fifteen studies had previously sought to define the functional profile of microglia in humans and mice, but these ‘gene signatures’ showed poor agreement overall. To address this issue a core human microglia signature conserved across brain regions was derived. Accordingly, data derived from intact brain tissue and pooled cells derived transcriptomic data from various brain regions was collated. This included nine datasets across three resources, the Genotype-Tissue Expression (GTEx) project, the Allen Brain Atlas (ABA) and a study of central nervous system (CNS) cells from Zhang et al. From each dataset, a microglial signature was derived using gene coexpression network (GCN) analysis to capture genes sharing a common expression profile across samples and which likely represented the same biology. The final human microglia signature comprised of 249 genes which were present in three or more of the dataset-derived microglial signatures. This gene set was validated using various sources of evidence. The average expression of signature genes correlated with microglial numbers and was significantly higher in myeloid populations relative to other immune and CNS cell types. Furthermore, the proteins encoded by signature genes positively stained for microglia in different brain regions. The signature provides a means to understand the homeostatic state of these cells and a baseline against which their divergence in disease may be measured. Accordingly, the signature was used to analyse microglia in a transcriptomic dataset generated from post-mortem brain tissue of individuals of different ages and from Alzheimer’s patients in four regions of human brain. This helped untangle the qualitative (activation states) and quantitative (cell proportions) differences in microglia between conditions and brain regions. Microglial cell numbers correlated with neuroinflammation and tau pathology in a region-dependent manner. The activation state of these cells was characterised by the downregulation of homeostatic genes (CX3CR1 and P2RY12) and upregulation of TREM2-TYROBP pathway genes which have been implicated in Alzheimer’s disease through genome-wide association studies (GWAS). In chapter three, the analysis of TRICs was expanded upon from microglia to other TRIC populations. Currently, several immune cell types have been defined by selected markers and cytokine/chemokine profiles in the context of different diseases and tissues. However, a comprehensive unbiased analysis of these phenotypes in the context of other immune cells is required to appreciate the breadth of cellular heterogeneity, revealing commonalities and further subdivisions of known cell types. Given the availability of transcriptomic atlases of immune- and tissue-derived cells, there is a considerable scope for further analyses of TRIC biology. Hence, publicly available transcriptomic datasets from mouse including that derived from pooled-cells of marker-defined cell types and that from unbiased single cell RNA-Seq (scRNA-Seq) were considered. The former was taken from the Immunological genome project (ImmGen) resource, which comprised of 128 combinations of marker-defined cell types from 26 tissues. The relationship between these cells was studied, as were the gene signatures associated with them. Comparing cell types based on their transcriptome showed the relative similarity between lymphoid cell types relative to the heterogeneous myeloid cell populations. Using GCN analysis, 157 gene modules associated with either cell lineages, cell types, cell subsets or TRICs were identified. Interestingly, it was difficult to distinguish certain marker-defined cell types from others, either suggesting that cell types could be defined by a few genes or current markers may encompass overlapping cell populations. As a complimentary unbiased approach, we also analysed immune subsets defined by the Tabula Muris Atlas, which included scRNA-Seq data derived from twelve tissues. Forty-three cell clusters were identified which were associated with forty-four gene clusters. The analyses highlighted gene clusters associated with the different cell lineages, cell types and TRICs, many of which significantly overlapped with those from the ImmGen GCN analysis. Some gene signatures were unique to TRICs or common across them, indicative of a tissue-dependent/independent biology. As expected, the greatest number, eleven signatures were associated with macrophages, eight of which agreed with cell types identified in the literature based on certain associated genes. To aid these analyses, novel approaches including annotating cells from a reference set of known cell types based on their transcriptomic profile; and capturing gene coexpression patterns using GCNs were developed, both of which are ongoing challenges unique to scRNA-Seq due to particular technical and biological variations. Building upon the work described in chapter three, chapter four involved extending similar deconvolution analyses to human TRIC gene signatures from bulk tissue transcriptomic data taken from the GTEx resource. First, a set of reference immune signatures (RIS) was derived from a downsampled collection of all the 28 tissues of the GTEx, thus representing nine classifications of immune cell types. In the case of macrophages, monocytes and dendritic cells, combined gene signatures were identified, thus highlighting the resolution of bulk-transcriptomics for signature derivation. Finally, the RIS aided in deriving TRIC signatures individually from the 21 tissues of the GTEx considered for downstream analyses. As expected, signatures of macrophages-monocyte-dendritic cells were found in every tissue and across tissues 1,012 genes were associated with these cells, the highest number relative to other cell classifications. Interestingly, genes that were most commonly associated with a given cell type across tissues included many known markers for them, as found for macrophage-monocyte-dendritic cells, neutrophils, T cells, NK cells and B cells. Subsequently, each TRIC signature was compared with those derived from mouse in chapter three. Thirty-nine gene clusters overlapped between species and were associated with 12 TRICs. Seven TRIC populations and their associated genes were supported through literature. In conclusion, this work has sought to examine the heterogeneity of TRICs, the transcriptomic signatures associated with them and the computational approaches to best derive them from tissue and cell level data. The work also shows the potential of using these TRIC signatures to explore disease states and the associated response of immune cells in this context

Synchronization in Sunspot Models

This note illustrates how agents' beliefs about economic outcomes can dynamically synchronize and de-synchronize to produce business-cycle-like fluctuations in a simple macroeconomic model. I consider a simple macroeconomic model with multiple equilibria, which are different ways that sunspots can forecast future output in a self-fulfilling manner. Agents are assumed to learn to use the sunspot variable through econometric learning. I show that if different agents have different interpretations of the sunspot, this leads to a complex nonlinear dynamic of synchronization of beliefs about the equilibrium being played. Depending on the extent of disagreement on the interpretation of the sunspot, the economy will be more or less volatile. The dispersion of the agents' beliefs is inversely related to volatility, since low dispersion implies that output is very sensitive to extrinsic noise (the sunspot). When disagreement crosses a critical threshold, the sunspot is practically ignored and the output is stable. The equation describing the evolution of the economy can be interpreted as a nonlinear-stochastic version of the Kuramoto model, a prototypical model of synchronization phenomena, and simulations confirm that the qualitative features of the model are in agreement with results from the Kuramoto literature

Fluid flow at the interface between elastic solids with randomly rough surfaces

I study fluid flow at the interface between elastic solids with randomly rough surfaces. I use the contact mechanics model of Persson to take into account the elastic interaction between the solid walls and the Bruggeman effective medium theory to account for the influence of the disorder on the fluid flow. I calculate the flow tensor which determines the pressure flow factor and, e.g., the leak-rate of static seals. I show how the perturbation treatment of Tripp can be extended to arbitrary order in the ratio between the root-mean-square roughness amplitude and the average interfacial surface separation. I introduce a matrix D(Zeta), determined by the surface roughness power spectrum, which can be used to describe the anisotropy of the surface at any magnification Zeta. I present results for the asymmetry factor Gamma(Zeta) (generalized Peklenik number) for grinded steel and sandblasted PMMA surfaces.Comment: 16 pages, 14 figure

Crystal Violet-Impregnated Slippery Surface to Prevent Bacterial Contamination of Surfaces

Biofilms which are self-organized communities can contaminate various infrastructural systems. Preventing bacterial adhesion on surfaces is more desirable than cleaning or disinfection of bacteria-contaminated surfaces. In this study, a 24 h bacterial adhesion test showed that “slippery surfaces” had increased resistance to bacterial contamination compared to polydimethylsiloxane and superhydrophobic surfaces. However, it did not completely inhibit bacterial attachment, indicating that it only retards surface contamination by bacteria. Hence, a strategy of killing bacteria with minimal bacterial adhesion was developed. A crystal violet-impregnated slippery (CVIS) surface with bactericidal and slippery features was produced through a simple dipping process. The CVIS surface had a very smooth and lubricated surface that was highly repellent to water and blood contamination. Bactericidal tests against Escherichia coli and Staphylococcus aureus showed that the CVIS surface exhibited bactericidal activity in dark and also showed significantly enhanced bactericidal activity (>3 log reduction in bacteria number) in white light