16,986 research outputs found
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
Reliability of ultrasound strain elastography in the assessment of the quadriceps and patellar tendon in healthy adults
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
Bi-objective optimization of pylon-engine-nacelle assembly: weight vs. tip clearance criterion
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 m and 7 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 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 m. The plasma
model result is excluded in the measured separation range.Comment: 6 page
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