Machine learning techniques are now well established in experimental particle physics, allowing detector data to be analyzed in new and unique ways. The identification of signals in particle ...observatories is an essential data processing task that can potentially be improved using such methods. This paper aims at exploring the benefits that a dedicated machine learning approach might provide to the classification of signals in dual-phase noble gas time projection chambers. A full methodology is presented, from exploratory data analysis using Gaussian mixture models and feature importance ranking to the construction of dedicated predictive models based on standard implementations of neural networks and random forests, validated using unlabeled simulated data from the LZ experiment as a proxy to real data. The global classification accuracy of the predictive models developed in this work is estimated to be >99.0%, which is an improvement over conventional algorithms tested with similar data. The results from the clustering analysis were also used to identify anomalies in the data caused by miscalculated signal properties, showing that this methodology can also be used for data monitoring.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Macrophage behavior upon biomaterial implantation conditions the inflammatory response and subsequent tissue repair. The hypothesis behind this work was that fibrinogen (Fg) and magnesium (Mg) ...biomaterials, used in combination (FgMg) could act synergistically to modulate macrophage activation, promoting a pro-regenerative phenotype. Materials were characterized by scanning electron microscopy, Fg and Mg degradation products were quantified by atomic absorption spectroscopy and ELISA. Whole blood immune cells and primary human monocyte-derived macrophages were exposed to the biomaterials extracts in unstimulated (M0) or pro-inflammatory LPS or LPS-IFNγ (M1) conditions. Macrophage phenotype was evaluated by flow cytometry, cytokines secreted by whole blood cells and macrophages were measured by ELISA, and signaling pathways were probed by Western blotting. The secretomes of macrophages preconditioned with biomaterials extracts were incubated with human mesenchymal stem/stromal cells (MSC) and their effect on osteogenic differentiation was evaluated via Alkaline Phosphatase (ALP) activity and alizarin red staining. Scaffolds of Fg, alone or in the FgMg combination, presented similar 3D porous architectures. Extracts from FgMg materials reduced LPS-induced TNF-α secretion by innate immune cells, and macrophage M1 polarization upon LPS-IFNγ stimulation, resulting in lower cell surface CD86 expression, lower NFκB p65 phosphorylation and reduced TNF-α secretion. Moreover, while biomaterial extracts per se did not enhance MSC osteogenic differentiation, macrophage secretome, particularly from cells exposed to FgMg extracts, increased MSC ALP activity and alizarin red staining, compared with extracts alone. These findings suggest that the combination of Fg and Mg synergistically influences macrophage pro-inflammatory activation and crosstalk with MSC.
Modulating macrophage phenotype by degradable and bioactive biomaterials is an increasingly explored strategy to promote tissue repair/regeneration. Fibrinogen (Fg) and magnesium (Mg)-based materials have been explored in this context. Previous work from our group showed that monocytes interact with fibrinogen adsorbed onto chitosan surfaces through TLR4 and that fibrinogen scaffolds promote in vivo bone regeneration. Also, magnesium ions have been reported to modulate macrophage pro-inflammatory M1 stimulation and to promote bone repair. Here we report, for the first time, the combination of Fg and Mg materials, hypothesizing that it could act synergistically on macrophages, directing them towards a pro-regenerative phenotype. As a first step towards proving/disproving our hypothesis we used extracts obtained from Fg, Mg and FgMg multilayer constructs. We observed that FgMg extracts led to a reduction in the polarization of macrophages towards a pro-inflammatory phenotype. Also, the secretome of macrophages exposed to extracts of the combination material promoted the expression of osteogenic markers by MSCs.
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Polynomial interpolation or regression models are an important tool in Derivative-free Optimization, acting as surrogates of the real function. In this work, we propose the use of these models in the ...multiobjective framework of directional direct search, namely the one of Direct Multisearch. Previously evaluated points are used to build quadratic polynomial models, which are minimized in an attempt of generating nondominated points of the true function, defining a search step for the algorithm. Numerical results state the competitiveness of the proposed approach.
Direct multisearch (DMS) is a derivative-free optimization class of algorithms, suited for computing approximations to the complete Pareto front of a given multiobjective optimization problem. In DMS ...class, constraints are addressed with an extreme barrier approach, only evaluating feasible points. It has a well-supported convergence analysis and simple implementations present a good numerical performance, both in academic test sets and in real applications. Recently, this numerical performance was improved with the definition of a search step based on the minimization of quadratic polynomial models, corresponding to the algorithm BoostDMS. In this work, we propose and numerically evaluate strategies to improve the performance of BoostDMS, mainly through parallelization applied to the search and to the poll steps. The final parallelized version not only considerably decreases the computational time required for solving a multiobjective optimization problem, but also increases the quality of the computed approximation to the Pareto front. Extensive numerical results will be reported in an academic test set and in a chemical engineering application.
Half of human cancers harbor
mutations that render p53 inactive as a tumor suppressor. In these cancers, reactivation of mutant p53 (mutp53) through restoration of wild-type-like function constitutes ...a valuable anticancer therapeutic strategy. In order to search for mutp53 reactivators, a small library of tryptophanol-derived oxazoloisoindolinones was synthesized and the potential of these compounds as mutp53 reactivators and anticancer agents was investigated in human tumor cells and xenograft mouse models. By analysis of their anti-proliferative effect on a panel of p53-null NCI-H1299 tumor cells ectopically expressing highly prevalent mutp53, the compound SLMP53-2 was selected based on its potential reactivation of multiple structural mutp53. In mutp53-Y220C-expressing hepatocellular carcinoma (HCC) cells, SLMP53-2-induced growth inhibition was mediated by cell cycle arrest, apoptosis, and endoplasmic reticulum stress response. In these cells, SLMP53-2 restored wild-type-like conformation and DNA-binding ability of mutp53-Y220C by enhancing its interaction with the heat shock protein 70 (Hsp70), leading to the reestablishment of p53 transcriptional activity. Additionally, SLMP53-2 displayed synergistic effect with sorafenib, the only approved therapy for advanced HCC. Notably, it exhibited potent antitumor activity in human HCC xenograft mouse models with a favorable toxicological profile. Collectively, SLMP53-2 is a new mutp53-targeting agent with promising antitumor activity, particularly against HCC.
We present a new algorithm for solving large-scale unconstrained optimization problems that uses cubic models, matrix-free subspace minimization, and secant-type parameters for defining the cubic ...terms. We also propose and analyze a specialized trust-region strategy to minimize the cubic model on a properly chosen low-dimensional subspace, which is built at each iteration using the Lanczos process. For the convergence analysis we present, as a general framework, a model trust-region subspace algorithm with variable metric and we establish asymptotic as well as complexity convergence results. Preliminary numerical results, on some test functions and also on the well-known disk packing problem, are presented to illustrate the performance of the proposed scheme when solving large-scale problems.
RNA interference (RNAi) screening is a powerful technology for functional characterization of biological pathways. Interpretation of RNAi screens requires computational and statistical analysis ...techniques. We describe a method that integrates all steps to generate a scored phenotype list from raw data. It is implemented in an open-source Bioconductor/R package, cellHTS (http://www.dkfz.de/signaling/cellHTS). The method is useful for the analysis and documentation of individual RNAi screens. Moreover, it is a prerequisite for the integration of multiple experiments.
The solution of the Conic Quadratic Eigenvalue Complementarity Problem (CQEiCP) is first investigated without assuming symmetry on the matrices defining the problem. A new sufficient condition for ...existence of solutions of CQEiCP is presented, extending to arbitrary pointed, closed and convex cones a condition known to hold when the cone is the nonnegative orthant. We also address the symmetric CQEiCP where all its defining matrices are symmetric. We show that, assuming that two of its defining matrices are positive definite, this symmetric CQEiCP reduces to the computation of a stationary point of an appropriate merit function on a convex set. Furthermore, we discuss the use of the so called Spectral Projected Gradient (SPG) algorithm for solving CQEiCP when the cone of interest is the second-order cone (SOCQEiCP). A new algorithm is designed for the computation of the projections required by the SPG method to deal with SOCQEiCP. Numerical results are included to illustrate the efficiency of the SPG method and the new projection technique in practice.
In this paper, we address the solution of the symmetric eigenvalue complementarity problem (EiCP) by treating an equivalent reformulation of finding a stationary point of a fractional quadratic ...program on the unit simplex. The spectral projected-gradient (SPG) method has been recommended to this optimization problem when the dimension of the symmetric EiCP is large and the accuracy of the solution is not a very important issue. We suggest a new algorithm which combines elements from the SPG method and the block active set method, where the latter was originally designed for box constrained quadratic programs. In the new algorithm the projection onto the unit simplex in the SPG method is replaced by the much cheaper projection onto a box. This can be of particular advantage for large and sparse symmetric EiCPs. Global convergence to a solution of the symmetric EiCP is established. Computational experience with medium and large symmetric EiCPs is reported to illustrate the efficacy and efficiency of the new algorithm.
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