Syndecan-1 Depletion Has a Differential Impact on Hyaluronic Acid Metabolism and Tumor Cell Behavior in Luminal and Triple-Negative Breast Cancer Cells

Glycosaminoglycans (GAGs) and proteoglycans (PGs) are major components of the glycocalyx. The secreted GAG and CD44 ligand hyaluronic acid (HA), and the cell surface PG syndecan-1 (Sdc-1) modulate the expression and activity of cytokines, chemokines, growth factors, and adhesion molecules, acting as critical regulators of tumor cell behavior. Here, we studied the effect of Sdc-1 siRNA depletion and HA treatment on hallmark processes of cancer in breast cancer cell lines of different levels of aggressiveness. We analyzed HA synthesis, and parameters relevant to tumor progression, including the stem cell phenotype, Wnt signaling constituents, cell cycle progression and apoptosis, and angiogenic markers in luminal MCF-7 and triple-negative MDA-MB-231 cells. Sdc-1 knockdown enhanced HAS-2 synthesis and HA binding in MCF-7, but not in MDA-MB-231 cells. Sdc-1-depleted MDA-MB-231 cells showed a reduced CD24-/CD44+ population. Furthermore, Sdc-1 depletion was associated with survival signals in both cell lines, affecting cell cycle progression and apoptosis evasion. These changes were linked to the altered expression of KLF4, MSI2, and miR-10b and differential changes in Erk, Akt, and PTEN signaling. We conclude that Sdc-1 knockdown differentially affects HA metabolism in luminal and triple-negative breast cancer model cell lines and impacts the stem phenotype, cell survival, and angiogenic factors.     

Fuzzy clusterwise linear regression analysis with symmetrical fuzzy output variable

The traditional regression analysis is usually applied to homogeneous observations. However, there are several real situations where the observations are not homogeneous. In these cases, by utilizing the traditional regression, we have a loss of performance in fitting terms. Then, for improving the goodness of fit, it is more suitable to apply the so-called clusterwise regression analysis. The aim of clusterwise linear regression analysis is to embed the techniques of clustering into regression analysis. In this way, the clustering methods are utilized for overcoming the heterogeneity problem in regression analysis. Furthermore, by integrating cluster analysis into the regression framework, the regression parameters (regression analysis) and membership degrees (cluster analysis) can be estimated simultaneously by optimizing one single objective function. In this paper the clusterwise linear regression has been analyzed in a fuzzy framework. In particular, a fuzzy clusterwise linear regression model (FCWLR model) with symmetrical fuzzy output and crisp input variables for performing fuzzy cluster analysis within a fuzzy linear regression framework is suggested. For measuring the goodness of fit of the suggested FCWLR model with fuzzy output, a fitting index is proposed. In order to illustrate the usefulness of FCWLR model in practice, several applications to artificial and real datasets are shown.     

Photoreflectance Studies of SIMOX Materials

Photoreflectance (PR) was used to study SIMOX materials produced under various fabrication conditions. The position, amplitude and shape of the 3.4 eV PR response were monitored for three different sets of samples which provided information about the crystalline quality of the top silicon layer. Each sample of the first set underwent different annealing conditions. A second set of six samples was arranged such that one sample was removed at a different step of the process involving three implantation-anneal cycles. A third set of four samples was implanted with different doses of oxygen. In the first case the PR signal improved with longer annealing times and higher temperatures; in the second case the PR signal appeared to deteriorate with each cycle undergone by the samples, while in the third case the degradation of the structure increased with the increased implantation dosage. Transmission electron microscopy (TEM) was also performed in the last two cases, and its results supported the PR conclusions.     

Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm

The traditional production scheduling problem considers performance indicators such as processing time, cost, and quality as optimization objectives in manufacturing systems; however, it does not take energy consumption or environmental impacts completely into account. Therefore, this paper proposes an energy-efficient model for flexible flow shop scheduling (FFS). First, a mathematical model for a FFS problem, which is based on an energy-efficient mechanism, is described to solve multi-objective optimization. Since FFS is well known as a NP-hard problem, an improved, genetic-simulated annealing algorithm is adopted to make a significant trade-off between the makespan and the total energy consumption to implement a feasible scheduling. Finally, a case study of a production scheduling problem for a metalworking workshop in a plant is simulated. The experimental results show that the relationship between the makespan and the energy consumption may be apparently conflicting. In addition, an energy-saving decision is performed in a feasible scheduling. Using the decision method, there could be significant potential for minimizing energy consumption.     

Adaptive control for the discrete-time parametric-strict-feedback form

The design of a state feedback controller for a class of SISO non-linear discrete-time systems is proposed. Namely, an alternative adaptive scheme is stated for the parametric-strict-feedback form. The control objective is to achieve tracking of a reference signal. Backstepping control is combined with a parameter estimator based on an orthogonalization process which overcomes the drawback of over-parameterization. Without assuming any growth conditions on the non-linearities, global boundedness and convergence are obtained in the closed loop system.     

Electrostatic actuation as a self-testing method for silicon pressure sensors

The aim of this work is to investigate the possibility of using electrostatic forces to actuate the membrane of a pressure sensor in order to create self-testing features in the system. It is shown that the electrostatic pressure is small compared with the external pressure, unless large voltages or electrical charges are used. The interrelation between electrostatic forces and externally applied pressure is calculated for different electrical drive modes of the capacitor, in the case of a uniform membrane and for a MESA-type membrane.     

Fluorescent core-sheath fibers by electrospinning of a phenyleneethynylene/poly(styrene-co-maleimide) blend

Ultrafine fluorescent fibers were prepared by electrospinning of a phenyleneethynylene oligomer mixed in DMF with a poly(styrene-co-maleimide) (PSM). The microscopic characterization by SEM reveals that the fibers are smooth and entangled one to the others forming a non-woven mat with an average diameter of 490 nm. By TEM, a core-sheath type structure is observed which derives from phase segregation between the PSM interacting with the oligomer and the excess of PSM that acts as matrix, as supported by XRD and DSC analysis. The membranes are fluorescent emitting at 420 nm, in the blue region, a high energy light, opening the possibility of their application in optoelectronic devices such as fluorescent sensors. Preliminary assays have been performed for the detection of sodium ions.