Spatio-temporal modelling of dam deformation using independent component analysis

Modelling dam deformation based on monitoring data plays an important role in the assessment of a dam’s safety. Traditional dam deformation modelling methods generally utilise single monitoring point. It means it is necessary to model for each monitoring point and the spatial correlation between points will not be considered using traditional modelling methods. Spatio-temporal modelling methods provide a way to model the dam deformation with only one functional expression and analyse the stability of dam in its entirety. Independent component analysis (ICA) is a statistical method of blind source separation (BSS) and can separate original signals from mixed observables. In this paper, ICA is introduced as a spatio-temporal modelling method for dam deformation. In this method, the deformation data series of all points were processed using ICA as input signals, and a few output independent signals were used to model. The real data experiment with displacement measurements by wire alignment of Wuqiangxi Dam was conducted and the results show that the output independent signals are correlated with physical responses of causative factors such as temperature and water level respectively. This discovery is beneficial in analysing the dam deformation. In addition, ICA is also an effective dimension reduced method for spatio-temporal modelling in dam deformation analysis applications

Targeting the Src Pathway Enhances the Efficacy of Selective FGFR Inhibitors in Urothelial Cancers with FGFR3 Alterations.

Selective FGFR inhibitors such as infigratinib (BGJ398) and erdafitinib (JNJ-42756493) have been evaluated in clinical trials for cancers with FGFR3 molecular alterations, particularly in urothelial carcinoma patients. However, a substantial proportion of these patients (up to 50%) display intrinsic resistance to these drugs and receive minimal clinical benefit. There is thus an unmet need for alternative therapeutic strategies to overcome primary resistance to selective FGFR inhibitors. In this study, we demonstrate that cells expressing cancer-associated activating FGFR3 mutants and the FGFR3-TACC3 fusion showed primary resistance to infigratinib in long-term colony formation assays in both NIH-3T3 and urothelial carcinoma models. We find that expression of these FGFR3 molecular alterations resulted in elevated constitutive Src activation compared to wildtype FGFR3 and that cells co-opted this pathway as a means to achieve intrinsic resistance to infigratinib. Targeting the Src pathway with low doses of the kinase inhibitor dasatinib synergistically sensitized multiple urothelial carcinoma lines harbouring endogenous FGFR3 alterations to infigratinib. Our data provide preclinical rationale that supports the use of dasatinib in combination with selective FGFR inhibitors as a means to overcome intrinsic drug resistance in the salvage therapy setting in urothelial cancer patients with FGFR3 molecular alterations

Identifying component modules

A computer-based system for modelling component dependencies and identifying component modules is presented. A variation of the Dependency Structure Matrix (DSM) representation was used to model component dependencies. The system utilises a two-stage approach towards facilitating the identification of a hierarchical modular structure. The first stage calculates a value for a clustering criterion that may be used to group component dependencies together. A Genetic Algorithm is described to optimise the order of the components within the DSM with the focus of minimising the value of the clustering criterion to identify the most significant component groupings (modules) within the product structure. The second stage utilises a 'Module Strength Indicator' (MSI) function to determine a value representative of the degree of modularity of the component groupings. The application of this function to the DSM produces a 'Module Structure Matrix' (MSM) depicting the relative modularity of available component groupings within it. The approach enabled the identification of hierarchical modularity in the product structure without the requirement for any additional domain specific knowledge within the system. The system supports design by providing mechanisms to explicitly represent and utilise component and dependency knowledge to facilitate the nontrivial task of determining near-optimal component modules and representing product modularity

Biomimetic intrafibrillar mineralization of type I collagen with intermediate precursors-loaded mesoporous carriers

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Human Bocavirus NS1 and NS1-70 Proteins Inhibit TNF-α-Mediated Activation of NF-κB by targeting p65.

Human bocavirus (HBoV), a parvovirus, is a single-stranded DNA etiologic agent causing lower respiratory tract infections in young children worldwide. Nuclear factor kappa B (NF-κB) transcription factors play crucial roles in clearance of invading viruses through activation of many physiological processes. Previous investigation showed that HBoV infection could significantly upregulate the level of TNF-α which is a strong NF-κB stimulator. Here we investigated whether HBoV proteins modulate TNF-α-mediated activation of the NF-κB signaling pathway. We showed that HBoV NS1 and NS1-70 proteins blocked NF-κB activation in response to TNF-α. Overexpression of TNF receptor-associated factor 2 (TRAF2)-, IκB kinase alpha (IKKα)-, IκB kinase beta (IKKβ)-, constitutively active mutant of IKKβ (IKKβ SS/EE)-, or p65-induced NF-κB activation was inhibited by NS1 and NS1-70. Furthermore, NS1 and NS1-70 didn't interfere with TNF-α-mediated IκBα phosphorylation and degradation, nor p65 nuclear translocation. Coimmunoprecipitation assays confirmed the interaction of both NS1 and NS1-70 with p65. Of note, NS1 but not NS1-70 inhibited TNF-α-mediated p65 phosphorylation at ser536. Our findings together indicate that HBoV NS1 and NS1-70 inhibit NF-κB activation. This is the first time that HBoV has been shown to inhibit NF-κB activation, revealing a potential immune-evasion mechanism that is likely important for HBoV pathogenesis

Differential cargo mobilisation within Weibel-Palade bodies after transient fusion with the plasma membrane.

Inflammatory chemokines can be selectively released from Weibel-Palade bodies (WPBs) during kiss-and-run exocytosis. Such selectivity may arise from molecular size filtering by the fusion pore, however differential intra-WPB cargo re-mobilisation following fusion-induced structural changes within the WPB may also contribute to this process. To determine whether WPB cargo molecules are differentially re-mobilised, we applied FRAP to residual post-fusion WPB structures formed after transient exocytosis in which some or all of the fluorescent cargo was retained. Transient fusion resulted in WPB collapse from a rod to a spheroid shape accompanied by substantial swelling (>2 times by surface area) and membrane mixing between the WPB and plasma membranes. Post-fusion WPBs supported cumulative WPB exocytosis. To quantify diffusion inside rounded organelles we developed a method of FRAP analysis based on image moments. FRAP analysis showed that von Willebrand factor-EGFP (VWF-EGFP) and the VWF-propolypeptide-EGFP (Pro-EGFP) were immobile in post-fusion WPBs. Because Eotaxin-3-EGFP and ssEGFP (small soluble cargo proteins) were largely depleted from post-fusion WPBs, we studied these molecules in cells preincubated in the weak base NH4Cl which caused WPB alkalinisation and rounding similar to that produced by plasma membrane fusion. In these cells we found a dramatic increase in mobilities of Eotaxin-3-EGFP and ssEGFP that exceeded the resolution of our method (∼ 2.4 µm2/s mean). In contrast, the membrane mobilities of EGFP-CD63 and EGFP-Rab27A in post-fusion WPBs were unchanged, while P-selectin-EGFP acquired mobility. Our data suggest that selective re-mobilisation of chemokines during transient fusion contributes to selective chemokine secretion during transient WPB exocytosis. Selective secretion provides a mechanism to regulate intravascular inflammatory processes with reduced risk of thrombosis

Synthesis of novel thieno[3,2-b]thienobis(silolothiophene) based low bandgap polymers for organic photovoltaics

Thieno[3,2-b]thienobis(silolothiophene), a new electron rich hexacyclic monomer has been synthesized and incorporated into three novel donor–acceptor low-bandgap polymers. By carefully choosing the acceptor co-monomer, the energy levels of the polymers could be modulated and high power conversion efficiencies of 5.52% were reached in OPV devices

Surveying adjustment datum and relative deformation accuracy analysis

In the surveying adjustment, unknown parameters are usually not direct observations, but the elements related to these direct observations. In order to determine the unknown parameters adequate known data should be provided, and these necessarily required known data are used to form the adjustment datum. Under different datums, different results will be obtained even with the same direct observations. However, in the practical adjustment calculation, the datum and its effect on the results are always ignored. In this paper, the adjustment datum is firstly discussed and defined as datum equations. Then an adjustment method based on the datum equations and least squares is presented. This method is a generic one, not only suited for the case in an ordinary datum but also in the gravity centre datum or a quasi-datum, and can be easily used to analyse different deformations. Based on this method, the transformation between different reference frames is derived. It shows that the calculation results, deformation and positioning accuracy under one kind of datum are relative and generic. A case study is further introduced and used to test this new method. Based on the case study, the conclusions are reached. It is found that the relative positional root mean square error of each point becomes bigger as the distance between the point and the datum increases, and the relative deformation offsets under different kinds of datum are helpful for reliable deformation analysis

Unsupervised Bayesian linear unmixing of gene expression microarrays

Background: This paper introduces a new constrained model and the corresponding algorithm, called unsupervised Bayesian linear unmixing (uBLU), to identify biological signatures from high dimensional assays like gene expression microarrays. The basis for uBLU is a Bayesian model for the data samples which are represented as an additive mixture of random positive gene signatures, called factors, with random positive mixing coefficients, called factor scores, that specify the relative contribution of each signature to a specific sample. The particularity of the proposed method is that uBLU constrains the factor loadings to be non-negative and the factor scores to be probability distributions over the factors. Furthermore, it also provides estimates of the number of factors. A Gibbs sampling strategy is adopted here to generate random samples according to the posterior distribution of the factors, factor scores, and number of factors. These samples are then used to estimate all the unknown parameters. Results: Firstly, the proposed uBLU method is applied to several simulated datasets with known ground truth and compared with previous factor decomposition methods, such as principal component analysis (PCA), non negative matrix factorization (NMF), Bayesian factor regression modeling (BFRM), and the gradient-based algorithm for general matrix factorization (GB-GMF). Secondly, we illustrate the application of uBLU on a real time-evolving gene expression dataset from a recent viral challenge study in which individuals have been inoculated with influenza A/H3N2/Wisconsin. We show that the uBLU method significantly outperforms the other methods on the simulated and real data sets considered here. Conclusions: The results obtained on synthetic and real data illustrate the accuracy of the proposed uBLU method when compared to other factor decomposition methods from the literature (PCA, NMF, BFRM, and GB-GMF). The uBLU method identifies an inflammatory component closely associated with clinical symptom scores collected during the study. Using a constrained model allows recovery of all the inflammatory genes in a single factor