Pigmentation and dermal conservative effects of the astonishing algae Sargassum Polycystum and Padina tenuis on guinea pigs, Human Epidermal Melanocytes (HEM) and Chang cells

Background: The preference for a fairer skin-tone has become a common trend among both men and women around the world. In this study, seaweeds Sargassum polycystum and Padina tenuis were investigated for their in vitro and in vivo potentials in working as skin whitening agents.Seaweed has been used as a revolutionary skin repairing agent in both traditional and modern preparations. The high antioxidant content is one of the prime reasons for its potent action. It has been employed in  traditional Chinese and Japanese medicine. For centuries, most medicalpractitioners in the Asian cultures have known seaweed as an organic  source of vitamins, minerals, fatty acids like omega-3 and omega-6 andantioxidants. The present objective of the study was to evaluate the potent dermal protective effect of the two seaweeds Sargassum polycystum and Padina tenuis on human cell lines and guinea pigs.Material and Methods: Seaweeds were extracted with ethanol and further fractionated with hexane, ethyl acetate and water. The extracts were tested for mushroom tyrosinase inhibitory activity, cytotoxicity in human epidermal melanocyte (HEM), and Chang cells. Extracts with potent melanocytotoxicity were formulated into cosmetic cream and tested on guinea pigs in dermal irritation tests and de-pigmentation assessments.Results: Both Sargassum polycystum and Padina tenuis seaweeds showed significant inhibitory effect on mushroom tyrosinase in the concentration tested. SPEt showed most potent cytotoxicity on HEM (IC50 of 36µg/ml), followed by SPHF (65µg/ml), and PTHF (78.5µg/ml). SPHF and SPEt reduced melanin content in skin of guinea pigs when assessed histologically.Conclusion: SPEt, SPHF and PTHF were able to inhibit HEM proliferation in vitro, with SPHF being most potent and did not cause any dermal irritation in guinea pigs. The results obtained indicate that SPHF is a promising  pharmacological or cosmetic agent.Key words: Hyper-pigmentation, Melanogenesis, Padina tenuis,  Sargassum polycystum, Tyrosinase, Whitening effect

Space-efficient Feature Maps for String Alignment Kernels

String kernels are attractive data analysis tools for analyzing string data. Among them, alignment kernels are known for their high prediction accuracies in string classifications when tested in combination with SVM in various applications. However, alignment kernels have a crucial drawback in that they scale poorly due to their quadratic computation complexity in the number of input strings, which limits large-scale applications in practice. We address this need by presenting the first approximation for string alignment kernels, which we call space-efficient feature maps for edit distance with moves (SFMEDM), by leveraging a metric embedding named edit sensitive parsing (ESP) and feature maps (FMs) of random Fourier features (RFFs) for large-scale string analyses. The original FMs for RFFs consume a huge amount of memory proportional to the dimension d of input vectors and the dimension D of output vectors, which prohibits its large-scale applications. We present novel space-efficient feature maps (SFMs) of RFFs for a space reduction from O(dD) of the original FMs to O(d) of SFMs with a theoretical guarantee with respect to concentration bounds. We experimentally test SFMEDM on its ability to learn SVM for large-scale string classifications with various massive string data, and we demonstrate the superior performance of SFMEDM with respect to prediction accuracy, scalability and computation efficiency.Comment: Full version for ICDM'19 pape

Combining work and child care: The experiences of mothers in Accra, Ghana

Work-family research has focused predominantly on Western women. Yet the forms of economic labour in which women are typically involved and the meaning of motherhood are context-specific. This paper aims to explore the experience of combining economic activity and child care of mothers with young children using urban Ghana as a case study. Semi-structured interviews (n=24) were conducted in three locations in the Accra Metropolitan Area. Transcripts were analysed using the general inductive approach. The results found women’s experience of role conflict to be bi-directional. With regard to role enhancement, economic activity allowed women to provide materially for their children. The combination of work and child care had negative consequences for women’s wellbeing. This research questions policy makers’ strategy of frequently targeting women in their roles either as generators of income, or as the primary care-takers of children by highlighting the reality of women’s simultaneous performance of these roles

Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier features

One-class support vector machine (OC-SVM) for a long time has been one of the most effective anomaly detection methods and extensively adopted in both research as well as industrial applications. The biggest issue for OC-SVM is yet the capability to operate with large and high-dimensional datasets due to optimization complexity. Those problems might be mitigated via dimensionality reduction techniques such as manifold learning or autoencoder. However, previous work often treats representation learning and anomaly prediction separately. In this paper, we propose autoencoder based one-class support vector machine (AE-1SVM) that brings OC-SVM, with the aid of random Fourier features to approximate the radial basis kernel, into deep learning context by combining it with a representation learning architecture and jointly exploit stochastic gradient descent to obtain end-to-end training. Interestingly, this also opens up the possible use of gradient-based attribution methods to explain the decision making for anomaly detection, which has ever been challenging as a result of the implicit mappings between the input space and the kernel space. To the best of our knowledge, this is the first work to study the interpretability of deep learning in anomaly detection. We evaluate our method on a wide range of unsupervised anomaly detection tasks in which our end-to-end training architecture achieves a performance significantly better than the previous work using separate training.Comment: Accepted at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) 201

Reflections on Tiles (in Self-Assembly)

We define the Reflexive Tile Assembly Model (RTAM), which is obtained from the abstract Tile Assembly Model (aTAM) by allowing tiles to reflect across their horizontal and/or vertical axes. We show that the class of directed temperature-1 RTAM systems is not computationally universal, which is conjectured but unproven for the aTAM, and like the aTAM, the RTAM is computationally universal at temperature 2. We then show that at temperature 1, when starting from a single tile seed, the RTAM is capable of assembling n x n squares for n odd using only n tile types, but incapable of assembling n x n squares for n even. Moreover, we show that n is a lower bound on the number of tile types needed to assemble n x n squares for n odd in the temperature-1 RTAM. The conjectured lower bound for temperature-1 aTAM systems is 2n-1. Finally, we give preliminary results toward the classification of which finite connected shapes in Z^2 can be assembled (strictly or weakly) by a singly seeded (i.e. seed of size 1) RTAM system, including a complete classification of which finite connected shapes be strictly assembled by a "mismatch-free" singly seeded RTAM system.Comment: New results which classify the types of shapes which can self-assemble in the RTAM have been adde

Water Contaminants Associated With Unconventional Oil and Gas Extraction Cause Immunotoxicity to Amphibian Tadpoles

Chemicals associated with unconventional oil and gas (UOG) operations have been shown to contaminate surface and ground water with a variety of endocrine disrupting compounds (EDCs) inducing multiple developmental alteration in mice. However, little is known about the impacts of UOG-associated contaminants on amphibian health and resistance to an emerging ranavirus infectious disease caused by viruses in the genus Ranavirus, especially at the vulnerable tadpole stage. Here we used tadpoles of the amphibian Xenopus laevis and the ranavirus Frog virus 3 (FV3) as a model relevant to aquatic environment conservation research for investigating the immunotoxic effects of exposure to a mixture of 23 UOG-associated chemicals with EDC activity. Xenopus tadpoles were exposed to an equimass mixture of 23 UOG-associated chemicals (range from 0.1 to 10 µg/l) for 3 weeks prior to infection with FV3. Our data show that exposure to the UOG chemical mixture is toxic for tadpoles at ecological doses of 5 to 10 µg/l. Lower doses significantly altered homeostatic expression of myeloid lineage genes and compromised tadpole responses to FV3 through expression of TNF-α, IL-1β, and Type I IFN genes, correlating with an increase in viral load. Exposure to a subset of 6 UOG chemicals was still sufficient to perturb the antiviral gene expression response. These findings suggest that UOG-associated water pollutants at low but environmentally relevant doses have the potential to induce acute alterations of immune function and antiviral immunity

Optimization viewpoint on Kalman smoothing, with applications to robust and sparse estimation

In this paper, we present the optimization formulation of the Kalman filtering and smoothing problems, and use this perspective to develop a variety of extensions and applications. We first formulate classic Kalman smoothing as a least squares problem, highlight special structure, and show that the classic filtering and smoothing algorithms are equivalent to a particular algorithm for solving this problem. Once this equivalence is established, we present extensions of Kalman smoothing to systems with nonlinear process and measurement models, systems with linear and nonlinear inequality constraints, systems with outliers in the measurements or sudden changes in the state, and systems where the sparsity of the state sequence must be accounted for. All extensions preserve the computational efficiency of the classic algorithms, and most of the extensions are illustrated with numerical examples, which are part of an open source Kalman smoothing Matlab/Octave package.Comment: 46 pages, 11 figure

Observation of the thermal Casimir force

Quantum theory predicts the existence of the Casimir force between macroscopic bodies, due to the zero-point energy of electromagnetic field modes around them. This quantum fluctuation-induced force has been experimentally observed for metallic and semiconducting bodies, although the measurements to date have been unable to clearly settle the question of the correct low-frequency form of the dielectric constant dispersion (the Drude model or the plasma model) to be used for calculating the Casimir forces. At finite temperature a thermal Casimir force, due to thermal, rather than quantum, fluctuations of the electromagnetic field, has been theoretically predicted long ago. Here we report the experimental observation of the thermal Casimir force between two gold plates. We measured the attractive force between a flat and a spherical plate for separations between 0.7 $\mu$m and 7 $\mu$m. An electrostatic force caused by potential patches on the plates' surfaces is included in the analysis. The experimental results are in excellent agreement (reduced $\chi^2$ of 1.04) with the Casimir force calculated using the Drude model, including the T=300 K thermal force, which dominates over the quantum fluctuation-induced force at separations greater than 3 $\mu$m. The plasma model result is excluded in the measured separation range.Comment: 6 page