Cosmetics, Vol. 5, Pages 10: Personal Care Products Are Only One of Many Exposure Routes of Natural Toxic Substances to Humans and the Environment

Cosmetics, Vol. 5, Pages 10: Personal Care Products Are Only One of Many Exposure Routes of Natural Toxic Substances to Humans and the Environment

Cosmetics doi: 10.3390/cosmetics5010010

Authors: Thomas Bucheli Bjarne Strobel Hans Hansen

The special issue “A Critical View on Natural Substances in Personal Care Products” is dedicated to addressing the multidisciplinary special challenges of natural ingredients in personal care products (PCP) and addresses also environmental exposure. In this perspective article, we argue that environmental exposure is probably not so much dominated by PCP use, but in many cases by direct emission from natural or anthropogenically managed vegetation, including agriculture. In support of this hypothesis, we provide examples of environmental fate and behaviour studies for compound classes that are either listed in the International Nomenclature of Cosmetics Ingredients (INCI) or have been discussed in a wider context of PCP applications and have been classified as potentially harmful to humans and the environment. Specifically, these include estrogenic isoflavones, the carcinogenic ptaquiloside and pyrrolizidine alkaloids, saponins, terpenes and terpenoids, such as artemisinin, and mycotoxins. Research gaps and challenges in the domains of human and environmental exposure assessment of natural products common to our currently rather separated research communities are highlighted.

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Corrigendum to “CC_TRS: Continuous Clustering of Trajectory Stream Data Based on Micro Cluster Life”

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A Comparison Study on Rule Extraction from Neural Network Ensembles, Boosted Shallow Trees, and SVMs

One way to make the knowledge stored in an artificial neural network more intelligible is to extract symbolic rules. However, producing rules from Multilayer Perceptrons (MLPs) is an NP-hard problem. Many techniques have been introduced to generate rules from single neural networks, but very few were proposed for ensembles. Moreover, experiments were rarely assessed by 10-fold cross-validation trials. In this work, based on the Discretized Interpretable Multilayer Perceptron (DIMLP), experiments were performed on 10 repetitions of stratified 10-fold cross-validation trials over 25 binary classification problems. The DIMLP architecture allowed us to produce rules from DIMLP ensembles, boosted shallow trees (BSTs), and Support Vector Machines (SVM). The complexity of rulesets was measured with the average number of generated rules and average number of antecedents per rule. From the 25 used classification problems, the most complex rulesets were generated from BSTs trained by “gentle boosting” and “real boosting.” Moreover, we clearly observed that the less complex the rules were, the better their fidelity was. In fact, rules generated from decision stumps trained by modest boosting were, for almost all the 25 datasets, the simplest with the highest fidelity. Finally, in terms of average predictive accuracy and average ruleset complexity, the comparison of some of our results to those reported in the literature proved to be competitive.

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Two- and Three-Dimensional Fast Intrawall Imaging with Microwave Tomographic Algorithm

A fast and efficient microwave tomographic algorithm is proposed for 2-D and 3-D real-time intrawall imaging. The exploding reflection model is utilized to simplify the imaging formulation, and the half-space Green’s function is expanded in the spectral domain to facilitate the easy implementation of the imaging algorithm with the fast Fourier transform (FFT) and inverse fast Fourier transform (IFFT). The linearization of the inversion scheme and employment of FFT/IFFT in the imaging formula make the algorithm suitable for various applications pertaining to the inspection of a large probed region and allow real-time processing. Representative numerical and experimental results are presented to show the effectiveness and efficiency of the proposed algorithm for real-time intrawall characterization.

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Involvement of P2X7 Receptor in Proliferation and Migration of Human Glioma Cells

Previous studies have demonstrated that activation of P2X7 receptors (P2X7R) results in the proliferation and migration of some types of tumor. Here, we asked whether and how the activated P2X7R contribute to proliferation and migration of human glioma cells. Results showed that the number of P2X7R positive cells was increasing with grade of tumor. In U87 and U251 human glioma cell lines, both expressed P2X7R and the expression was enhanced by 3′-O-(4-benzoylbenzoyl) ATP (BzATP), the agonist of P2X7R, and siRNA. Our results also showed that 10 μM BzATP was sufficient to induce the proliferation of glioma cell significantly, while the cell proliferation reached the peak with 100 μM BzATP. Also, the migration of U87 and U251 cells was significantly increased upon BzATP treatment. However, the number of apoptotic cells of U87 and U251 was not significantly changed by BzATP. In addition, the expression of ERK, p-ERK, and proliferating cell nuclear antigen (PCNA) protein was increased in BzATP-treated U87 and U251 glioma cells. PD98059, an inhibitor of the MEK/ERK pathway, blocked the increased proliferation and migration of glioma cells activated by BzATP. These results suggest that ERK pathway is involved in the proliferation and migration of glioma cells induced by P2X7R activation.

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Target Tracking via Particle Filter and Convolutional Network

We propose a more effective tracking algorithm which can work robustly in a complex scene such as illumination, appearance change, and partial occlusion. The algorithm is based on an improved particle filter which used the efficient design of observation model. Predefined convolutional filters are used to extract the high-order features. The global representation is generated by combining local features without changing their structures and space arrangements. It not only increases the feature invariance, but also maintains the specificity. The extracted feature from convolution network is introduced into particle filter algorithm. The observation model is constructed by fusing the color feature of the target and a set of features from templates which are extracted by convolutional networks without training in our paper. It is fused with the features extracted from convolutional network for tracking. In the process of tracking, the template is updated in real time, and then the robustness of the algorithm is improved. Experiments show that the algorithm can achieve an ideal tracking effect when the targets are in a complex environment.

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Grape Seed Proanthocyanidin Extract Prevents Ovarian Aging by Inhibiting Oxidative Stress in the Hens

Oxidative stress is an important inducement in ovarian aging which results in fecundity decline in human and diverse animals. As a potent antioxidant, grape seed proanthocyanidin extract (GSPE) was investigated to ameliorate chicken ovarian aging in this study. Firstly, ovarian antioxidant capacity of hens at different ages (90, 150, 280, and 580 days old) was compared to elucidate its age-related changes. Subsequently, a D-gal-induced (2.5 mg/mL) aging ovarian model was established and the cultured ovarian tissues were treated with GSPE at 5 μg/mL for 72 h to evaluate the putative attenuating effects of GSPE on ovarian aging. Meanwhile, ovaries of D280 (young) and D580 (old) were treated with GSPE for 72 h in culture to verify the protective effects of GSPE on natural aging ovary. The results showed that GSPE could rescue the antioxidant capacity decline by increasing the antioxidase activities and their gene expression in either D-gal-induced or natural aging ovaries. Moreover, GSPE could maintain the homeostasis between cell proliferation and apoptosis in the D-gal-induced and natural aging ovaries, as well as alleviate D-gal-induced nucleus chromatin condensation in the ovarian granulosa cells. In conclusion, GSPE treatment can effectively prevent the ovarian aging process in hens by reducing oxidative stress.

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