Cultural-based visual expression: Emotional analysis of human face via Peking Opera Painted Faces (POPF)

© 2015 The Author(s) Peking Opera as a branch of Chinese traditional cultures and arts has a very distinct colourful facial make-up for all actors in the stage performance. Such make-up is stylised in nonverbal symbolic semantics which all combined together to form the painted faces to describe and symbolise the background, the characteristic and the emotional status of specific roles. A study of Peking Opera Painted Faces (POPF) was taken as an example to see how information and meanings can be effectively expressed through the change of facial expressions based on the facial motion within natural and emotional aspects. The study found that POPF provides exaggerated features of facial motion through images, and the symbolic semantics of POPF provides a high-level expression of human facial information. The study has presented and proved a creative structure of information analysis and expression based on POPF to improve the understanding of human facial motion and emotion

Realization of a Tunable Artificial Atom at a Supercritically Charged Vacancy in Graphene

The remarkable electronic properties of graphene have fueled the vision of a graphene-based platform for lighter, faster and smarter electronics and computing applications. One of the challenges is to devise ways to tailor its electronic properties and to control its charge carriers. Here we show that a single atom vacancy in graphene can stably host a local charge and that this charge can be gradually built up by applying voltage pulses with the tip of a scanning tunneling microscope (STM). The response of the conduction electrons in graphene to the local charge is monitored with scanning tunneling and Landau level spectroscopy, and compared to numerical simulations. As the charge is increased, its interaction with the conduction electrons undergoes a transition into a supercritical regime 6-11 where itinerant electrons are trapped in a sequence of quasi-bound states which resemble an artificial atom. The quasi-bound electron states are detected by a strong enhancement of the density of states (DOS) within a disc centered on the vacancy site which is surrounded by halo of hole states. We further show that the quasi-bound states at the vacancy site are gate tunable and that the trapping mechanism can be turned on and off, providing a new mechanism to control and guide electrons in grapheneComment: 18 pages and 5 figures plus 14 pages and 15 figures of supplementary information. Nature Physics advance online publication, Feb 22 (2016

SMART: Unique splitting-while-merging framework for gene clustering

Copyright @ 2014 Fa et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Successful clustering algorithms are highly dependent on parameter settings. The clustering performance degrades significantly unless parameters are properly set, and yet, it is difficult to set these parameters a priori. To address this issue, in this paper, we propose a unique splitting-while-merging clustering framework, named “splitting merging awareness tactics” (SMART), which does not require any a priori knowledge of either the number of clusters or even the possible range of this number. Unlike existing self-splitting algorithms, which over-cluster the dataset to a large number of clusters and then merge some similar clusters, our framework has the ability to split and merge clusters automatically during the process and produces the the most reliable clustering results, by intrinsically integrating many clustering techniques and tasks. The SMART framework is implemented with two distinct clustering paradigms in two algorithms: competitive learning and finite mixture model. Nevertheless, within the proposed SMART framework, many other algorithms can be derived for different clustering paradigms. The minimum message length algorithm is integrated into the framework as the clustering selection criterion. The usefulness of the SMART framework and its algorithms is tested in demonstration datasets and simulated gene expression datasets. Moreover, two real microarray gene expression datasets are studied using this approach. Based on the performance of many metrics, all numerical results show that SMART is superior to compared existing self-splitting algorithms and traditional algorithms. Three main properties of the proposed SMART framework are summarized as: (1) needing no parameters dependent on the respective dataset or a priori knowledge about the datasets, (2) extendible to many different applications, (3) offering superior performance compared with counterpart algorithms.National Institute for Health Researc

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

The abundant marine bacterium Pelagibacter simultaneously catabolizes dimethylsulfoniopropionate to the gases dimethyl sulfide and methanethiol

Marine phytoplankton produce ~109 tons of dimethylsulfoniopropionate (DMSP) per year1,2, an estimated 10% of which is catabolized by bacteria through the DMSP cleavage pathway to the climatically active gas dimethyl sulfide (DMS)3,4. SAR11 Alphaproteobacteria (order Pelagibacterales), the most abundant chemoorganotrophic bacteria in the oceans, have been shown to assimilate DMSP into biomass, thereby supplying this cell’s unusual requirement for reduced sulfur5,6. Here we report that Pelagibacter HTCC1062 produces the gas methanethiol (MeSH) and that simultaneously a second DMSP catabolic pathway, mediated by a cupin-like DMSP lyase, DddK, shunts as much as 59% of DMSP uptake to DMS production. We propose a model in which the allocation of DMSP between these pathways is kinetically controlled to release increasing amounts of DMS as the supply of DMSP exceeds cellular sulfur demands for biosynthesis

Benefits and risks of the hormetic effects of dietary isothiocyanates on cancer prevention

The isothiocyanate (ITC) sulforaphane (SFN) was shown at low levels (1-5 µM) to promote cell proliferation to 120-143% of the controls in a number of human cell lines, whilst at high levels (10-40 µM) it inhibited such cell proliferation. Similar dose responses were observed for cell migration, i.e. SFN at 2.5 µM increased cell migration in bladder cancer T24 cells to 128% whilst high levels inhibited cell migration. This hormetic action was also found in an angiogenesis assay where SFN at 2.5 µM promoted endothelial tube formation (118% of the control), whereas at 10-20 µM it caused significant inhibition. The precise mechanism by which SFN influences promotion of cell growth and migration is not known, but probably involves activation of autophagy since an autophagy inhibitor, 3-methyladenine, abolished the effect of SFN on cell migration. Moreover, low doses of SFN offered a protective effect against free-radical mediated cell death, an effect that was enhanced by co-treatment with selenium. These results suggest that SFN may either prevent or promote tumour cell growth depending on the dose and the nature of the target cells. In normal cells, the promotion of cell growth may be of benefit, but in transformed or cancer cells it may be an undesirable risk factor. In summary, ITCs have a biphasic effect on cell growth and migration. The benefits and risks of ITCs are not only determined by the doses, but are affected by interactions with Se and the measured endpoint

Application of Graphene within Optoelectronic Devices and Transistors

Scientists are always yearning for new and exciting ways to unlock graphene's true potential. However, recent reports suggest this two-dimensional material may harbor some unique properties, making it a viable candidate for use in optoelectronic and semiconducting devices. Whereas on one hand, graphene is highly transparent due to its atomic thickness, the material does exhibit a strong interaction with photons. This has clear advantages over existing materials used in photonic devices such as Indium-based compounds. Moreover, the material can be used to 'trap' light and alter the incident wavelength, forming the basis of the plasmonic devices. We also highlight upon graphene's nonlinear optical response to an applied electric field, and the phenomenon of saturable absorption. Within the context of logical devices, graphene has no discernible band-gap. Therefore, generating one will be of utmost importance. Amongst many others, some existing methods to open this band-gap include chemical doping, deformation of the honeycomb structure, or the use of carbon nanotubes (CNTs). We shall also discuss various designs of transistors, including those which incorporate CNTs, and others which exploit the idea of quantum tunneling. A key advantage of the CNT transistor is that ballistic transport occurs throughout the CNT channel, with short channel effects being minimized. We shall also discuss recent developments of the graphene tunneling transistor, with emphasis being placed upon its operational mechanism. Finally, we provide perspective for incorporating graphene within high frequency devices, which do not require a pre-defined band-gap.Comment: Due to be published in "Current Topics in Applied Spectroscopy and the Science of Nanomaterials" - Springer (Fall 2014). (17 pages, 19 figures

Quantum Hall effect and Landau level crossing of Dirac fermions in trilayer graphene

We investigate electronic transport in high mobility (\textgreater 100,000 cm$^2$/V$\cdot$s) trilayer graphene devices on hexagonal boron nitride, which enables the observation of Shubnikov-de Haas oscillations and an unconventional quantum Hall effect. The massless and massive characters of the TLG subbands lead to a set of Landau level crossings, whose magnetic field and filling factor coordinates enable the direct determination of the Slonczewski-Weiss-McClure (SWMcC) parameters used to describe the peculiar electronic structure of trilayer graphene. Moreover, at high magnetic fields, the degenerate crossing points split into manifolds indicating the existence of broken-symmetry quantum Hall states.Comment: Supplementary Information at http://jarilloherrero.mit.edu/wp-content/uploads/2011/04/Supplementary_Taychatanapat.pd

NRF2-driven miR-125B1 and miR-29B1 transcriptional regulation controls a novel anti-apoptotic miRNA regulatory network for AML survival

Transcription factor NRF2 is an important regulator of oxidative stress. It is involved in cancer progression, and has abnormal constitutive expression in acute myeloid leukaemia (AML). Posttranscriptional regulation by microRNAs (miRNAs) can affect the malignant phenotype of AML cells. In this study, we identified and characterised NRF2-regulated miRNAs in AML. An miRNA array identified miRNA expression level changes in response to NRF2 knockdown in AML cells. Further analysis of miRNAs concomitantly regulated by knockdown of the NRF2 inhibitor KEAP1 revealed the major candidate NRF2-mediated miRNAs in AML. We identified miR-125B to be upregulated and miR-29B to be downregulated by NRF2 in AML. Subsequent bioinformatic analysis identified putative NRF2 binding sites upstream of the miR-125B1 coding region and downstream of the mir-29B1 coding region. Chromatin immunoprecipitation analyses showed that NRF2 binds to these antioxidant response elements (AREs) located in the 5′ untranslated regions of miR-125B and miR-29B. Finally, primary AML samples transfected with anti-miR-125B antagomiR or miR-29B mimic showed increased cell death responsiveness either alone or co-treated with standard AML chemotherapy. In summary, we find that NRF2 regulation of miR-125B and miR-29B acts to promote leukaemic cell survival, and their manipulation enhances AML responsiveness towards cytotoxic chemotherapeutics

Optoelectronics with electrically tunable PN diodes in a monolayer dichalcogenide

One of the most fundamental devices for electronics and optoelectronics is the PN junction, which provides the functional element of diodes, bipolar transistors, photodetectors, LEDs, and solar cells, among many other devices. In conventional PN junctions, the adjacent p- and n-type regions of a semiconductor are formed by chemical doping. Materials with ambipolar conductance, however, allow for PN junctions to be configured and modified by electrostatic gating. This electrical control enables a single device to have multiple functionalities. Here we report ambipolar monolayer WSe2 devices in which two local gates are used to define a PN junction exclusively within the sheet of WSe2. With these electrically tunable PN junctions, we demonstrate both PN and NP diodes with ideality factors better than 2. Under excitation with light, the diodes show photodetection responsivity of 210 mA/W and photovoltaic power generation with a peak external quantum efficiency of 0.2%, promising numbers for a nearly transparent monolayer sheet in a lateral device geometry. Finally, we demonstrate a light-emitting diode based on monolayer WSe2. These devices provide a fundamental building block for ubiquitous, ultra-thin, flexible, and nearly transparent optoelectronic and electronic applications based on ambipolar dichalcogenide materials.Comment: 14 pages, 4 figure