The evolving tree-analysis and applications

In this paper, we enhance and analyze the Evolving Tree (ETree) data analysis algorithm. The suggested improvements aim to make the system perform better while still maintaining the simple nature of the basic algorithm. We also examine the system's behavior with many different kinds of tests, measurements and visualizations. We compare the ETree's performance against classical data analysis methods and very similar modern systems. We find that the ETree is a suitable method for unsupervised analysis of huge data sets.     

Nonlinear dynamical factor analysis for state change detection

Changes in a dynamical process are often detected by monitoring selected indicators directly obtained from the process observations, such as the mean values or variances. Standard change detection algorithms such as the Shewhart control charts or the cumulative sum (CUSUM) algorithm are often based on such first- and second-order statistics. Much better results can be obtained if the dynamical process is properly modeled, for example by a nonlinear state-space model, and then the accuracy of the model is monitored over time. The success of the latter approach depends largely on the quality of the model. In practical applications like industrial processes, the state variables, dynamics, and observation mapping are rarely known accurately. Learning from data must be used; however, methods for the simultaneous estimation of the state and the unknown nonlinear mappings are very limited. We use a novel method of learning a nonlinear state-space model, the nonlinear dynamical factor analysis (NDFA) algorithm. It takes a set of multivariate observations over time and fits blindly a generative dynamical latent variable model, resembling nonlinear independent component analysis. We compare the performance of the model in process change detection to various traditional methods. It is shown that NDFA outperforms the classical methods by a wide margin in a variety of cases where the underlying process dynamics changes.     

Platelet-Rich Plasma Promotes the Proliferation of Human Keratinocytes via a Progression of the Cell Cycle. A Role of Prolidase

Although the role of platelet-rich plasma (PRP) in tissue regeneration has been confirmed in many studies, the mechanism of this process is still not fully understood. Human keratinocytes (HaCaT) cells were used as an experimental model for studies on the effects of PRP on cell proliferation, migration, collagen biosynthesis, prolidase activity, and its expression and anabolic signaling. The activation of epidermal growth factor receptor (EGFR), β₁-integrin, and insulin-like growth factor-1 receptor (IGF-1R) by PRP were investigated by western blot and immunocytochemistry. It has been found that PRP induced keratinocytes migration and proliferation through activation of cell cycle progression and EGFR downstream signaling. Similar biological effects were achieved by an addition to the culture medium of prolidase (PEPD), a ligand of EGFR (PRP is a rich source of PEPD–2 ng/mL). PRP-dependent stimulation of collagen biosynthesis was accompanied by an increase in the expression of NF-κβ, IGF-1R-downstream signaling proteins, and PEPD activity. The data suggest that PRP activates a complex of growth factors and adhesion receptors that stimulate cell proliferation, migration, and collagen biosynthesis. PRP induces PEPD-dependent human keratinocyte proliferation through activation of the EGFR receptor. Our study provides a novel mechanism of PRP-dependent wound healing.     

Lipofilling in Breast Oncological Surgery: A Safe Opportunity or Risk for Cancer Recurrence?

Lipofilling (LF) is a largely employed technique in reconstructive and esthetic breast surgery. Over the years, it has demonstrated to be extremely useful for treatment of soft tissue defects after demolitive or conservative breast cancer surgery and different procedures have been developed to improve the survival of transplanted fat graft. The regenerative potential of LF is attributed to the multipotent stem cells found in large quantity in adipose tissue. However, a growing body of pre-clinical evidence shows that adipocytes and adipose-derived stromal cells may have pro-tumorigenic potential. Despite no clear indication from clinical studies has demonstrated an increased risk of cancer recurrence upon LF, these observations challenge the oncologic safety of the procedure. This review aims to provide an updated overview of both the clinical and the pre-clinical indications to the suitability and safety of LF in breast oncological surgery. Cellular and molecular players in the crosstalk between adipose tissue and cancer are described, and heterogeneous contradictory results are discussed, highlighting that important issues still remain to be solved to get a clear understanding of LF safety in breast cancer patients.     

Classification Based on Hybridization of Parametric and Nonparametric Classifiers

Parametric methods of classification assume specific parametric models for competing population densities (e.g., Gaussian population densities can lead to linear and quadratic discriminant analysis) and they work well when these model assumptions are valid. Violation in one or more of these parametric model assumptions often leads to a poor classifier. On the other hand, nonparametric classifiers (e.g., nearest-neighbor and kernel-based classifiers) are more flexible and free from parametric model assumptions. But, the statistical instability of these classifiers may lead to poor performance when we have small numbers of training sample observations. Nonparametric methods, however, do not use any parametric structure of population densities. Therefore, even when one has some additional information about population densities, that important information is not used to modify the nonparametric classification rule. This paper makes an attempt to overcome these limitations of parametric and nonparametric approaches and combines their strengths to develop some hybrid classification methods. We use some simulated examples and benchmark data sets to examine the performance of these hybrid discriminant analysis tools. Asymptotic results on their misclassification rates have been derived under appropriate regularity conditions.     

Quadratic nonnegative matrix factorization

In Nonnegative Matrix Factorization (NMF), a nonnegative matrix is approximated by a product of lower-rank factorizing matrices. Most NMF methods assume that each factorizing matrix appears only once in the approximation, thus the approximation is linear in the factorizing matrices. We present a new class of approximative NMF methods, called Quadratic Nonnegative Matrix Factorization (QNMF), where some factorizing matrices occur twice in the approximation. We demonstrate QNMF solutions to four potential pattern recognition problems in graph partitioning, two-way clustering, estimating hidden Markov chains, and graph matching. We derive multiplicative algorithms that monotonically decrease the approximation error under a variety of measures. We also present extensions in which one of the factorizing matrices is constrained to be orthogonal or stochastic. Empirical studies show that for certain application scenarios, QNMF is more advantageous than other existing nonnegative matrix factorization methods.     

Psychological work characteristics,psychological workload and associated psychological and cognitive requirements of train drivers

This study aimed to assess the psychological work characteristics and psychological workload of train drivers and to define the psychological and cognitive requirements of their work. A systematic literature search was performed, and expert interviews were conducted. The following work demands were specific to train drivers: high emotional and mental demands, small amount of autonomy and skill discretion. No evidence of a high emotional workload, a high mental workload or short-term stress reactivity was found. In general, the drivers' fatigue complaints and recovery needs after work were comparable to that of other workers. However, severe sleepiness and high need for recovery did affect a substantial proportion of train drivers. The ability to stay aware, to anticipate, to remain attentive and to cope with fatigue are psychological and cognitive skills that are required to adequately and safely perform the train drivers' job. Including these requirements in periodic assessments of train drivers is recommended.     

Chrysin,Apigenin and Acacetin Inhibit Tumor Necrosis Factor-Related Apoptosis—Inducing Ligand Receptor-1 (TRAIL-R1) on Activated RAW264.7 Macrophages

Expression level of Tumor Necrosis Factor—related apoptosis—inducing ligand (TRAIL) receptors is one of the most important factors of TRAIL-mediated apoptosis in cancer cells. We here report for the first time data concerning TRAIL-R1 and TRAIL-R2 receptor expression on RAW264.7 macrophages. Three substances belonging to flavones: chrysin, apigenin and acacetin which differ from their substituents at the 4'' position in the phenyl ring were used in assays because of the variety of biological activities (e.g., anticancer activity) of the polyphenol compounds. The expression of TRAIL-R1 and TRAIL-R2 death receptors on non-stimulated and LPS (lipopolysaccharide)-stimulated macrophages was determined using flow cytometry. We demonstrate that RAW264.7 macrophages exhibit TRAIL-R1 surface expression and that the tested compounds: chrysin, apigenin and acacetin can inhibit TRAIL-R1 death receptor expression level on macrophages.     

Graft copolymers from commercial chlorinated polypropylene via Cu(0)‐mediated atom transfer radical polymerization

Commercially available chlorinated polypropylene has been used as a macroinitiator for the Cu(0)‐mediated atom transfer radical polymerization of methyl methacrylate and tert‐butyl acrylate to obtain well‐defined graft copolymers. The relatively narrow molecular weight distribution in the graft copolymers and linear kinetic plots indicated the controlled nature of the copolymerization reactions. Both Fourier transform infrared and ¹H NMR studies confirmed that the graft reactions had taken place successfully. After graft copolymer formation, tert‐butyl groups of poly(tert‐butyl acrylate) side chains were completely converted into poly(acrylic acid) chains to afford corresponding amphiphilic graft copolymers. © 2016 Society of Chemical Industry