611 research outputs found
A cluster analysis of patterns of objectively measured physical activity in Hong Kong
published_or_final_versio
Face Hallucination With Finishing Touches
Obtaining a high-quality frontal face image from a low-resolution (LR) non-frontal face image is primarily important for many facial analysis applications. However, mainstreams either focus on super-resolving near-frontal LR faces or frontalizing non-frontal high-resolution (HR) faces. It is desirable to perform both tasks seamlessly for daily-life unconstrained face images. In this paper, we present a novel Vivid Face Hallucination Generative Adversarial Network (VividGAN) for simultaneously super-resolving and frontalizing tiny non-frontal face images. VividGAN consists of coarse-level and fine-level Face Hallucination Networks (FHnet) and two discriminators, i.e., Coarse-D and Fine-D. The coarse-level FHnet generates a frontal coarse HR face and then the fine-level FHnet makes use of the facial component appearance prior, i.e., fine-grained facial components, to attain a frontal HR face image with authentic details. In the fine-level FHnet, we also design a facial component-aware module that adopts the facial geometry guidance as clues to accurately align and merge the frontal coarse HR face and prior information. Meanwhile, two-level discriminators are designed to capture both the global outline of a face image as well as detailed facial characteristics. The Coarse-D enforces the coarsely hallucinated faces to be upright and complete while the Fine-D focuses on the fine hallucinated ones for sharper details. Extensive experiments demonstrate that our VividGAN achieves photo-realistic frontal HR faces, reaching superior performance in downstream tasks, i.e., face recognition and expression classification, compared with other state-of-the-art methods
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
Deep learning with noisy labels is practically challenging, as the capacity
of deep models is so high that they can totally memorize these noisy labels
sooner or later during training. Nonetheless, recent studies on the
memorization effects of deep neural networks show that they would first
memorize training data of clean labels and then those of noisy labels.
Therefore in this paper, we propose a new deep learning paradigm called
Co-teaching for combating with noisy labels. Namely, we train two deep neural
networks simultaneously, and let them teach each other given every mini-batch:
firstly, each network feeds forward all data and selects some data of possibly
clean labels; secondly, two networks communicate with each other what data in
this mini-batch should be used for training; finally, each network back
propagates the data selected by its peer network and updates itself. Empirical
results on noisy versions of MNIST, CIFAR-10 and CIFAR-100 demonstrate that
Co-teaching is much superior to the state-of-the-art methods in the robustness
of trained deep models.Comment: NIPS 2018 camera-ready versio
Increased entropy of signal transduction in the cancer metastasis phenotype
Studies into the statistical properties of biological networks have led to
important biological insights, such as the presence of hubs and hierarchical
modularity. There is also a growing interest in studying the statistical
properties of networks in the context of cancer genomics. However, relatively
little is known as to what network features differ between the cancer and
normal cell physiologies, or between different cancer cell phenotypes. Based on
the observation that frequent genomic alterations underlie a more aggressive
cancer phenotype, we asked if such an effect could be detectable as an increase
in the randomness of local gene expression patterns. Using a breast cancer gene
expression data set and a model network of protein interactions we derive
constrained weighted networks defined by a stochastic information flux matrix
reflecting expression correlations between interacting proteins. Based on this
stochastic matrix we propose and compute an entropy measure that quantifies the
degree of randomness in the local pattern of information flux around single
genes. By comparing the local entropies in the non-metastatic versus metastatic
breast cancer networks, we here show that breast cancers that metastasize are
characterised by a small yet significant increase in the degree of randomness
of local expression patterns. We validate this result in three additional
breast cancer expression data sets and demonstrate that local entropy better
characterises the metastatic phenotype than other non-entropy based measures.
We show that increases in entropy can be used to identify genes and signalling
pathways implicated in breast cancer metastasis. Further exploration of such
integrated cancer expression and protein interaction networks will therefore be
a fruitful endeavour.Comment: 5 figures, 2 Supplementary Figures and Table
Kinship Index Variations among Populations and Thresholds for Familial Searching
Current familial searching strategies are developed primarily based on autosomal STR loci, since most of the offender profiles in the forensic DNA databases do not contain Y-STR or mitochondrial DNA data. There are generally two familial searching methods, Identity-by-State (IBS) based methods or kinship index (KI) based methods. The KI based method is an analytically superior method because the allele frequency information is considered as opposed to solely allele counting. However, multiple KIs should be calculated if the unknown forensic profile may be attributed to multiple possible relevant populations. An important practical issue is the KI threshold to select for limiting the list of candidates from a search. There are generally three strategies of setting the KI threshold for familial searching: (1) SWGDAM recommendation 6; (2) minimum KI≥KI threshold; and (3) maximum KI≥KI threshold. These strategies were evaluated and compared by using both simulation data and empirical data. The minimum KI will tend to be closer to the KI appropriate for the population of which the forensic profile belongs. The minimum KI≥KI threshold performs better than the maximum KI≥KI threshold. The SWGDAM strategy may be too stringent for familial searching with large databases (e.g., 1 million or more profiles), because its KI thresholds depend on the database size and the KI thresholds of large databases have a higher probability to exclude true relatives than smaller databases. Minimum KI≥KI threshold strategy is a better option, as it provides the flexibility to adjust the KI threshold according to a pre-determined number of candidates or false positive/negative rates. Joint use of both IBS and KI does not significantly reduce the chance of including true relatives in a candidate list, but does provide a higher efficiency of familial searching
Revisiting Date and Party Hubs: Novel Approaches to Role Assignment in Protein Interaction Networks
The idea of 'date' and 'party' hubs has been influential in the study of
protein-protein interaction networks. Date hubs display low co-expression with
their partners, whilst party hubs have high co-expression. It was proposed that
party hubs are local coordinators whereas date hubs are global connectors. Here
we show that the reported importance of date hubs to network connectivity can
in fact be attributed to a tiny subset of them. Crucially, these few, extremely
central, hubs do not display particularly low expression correlation,
undermining the idea of a link between this quantity and hub function. The
date/party distinction was originally motivated by an approximately bimodal
distribution of hub co-expression; we show that this feature is not always
robust to methodological changes. Additionally, topological properties of hubs
do not in general correlate with co-expression. Thus, we suggest that a
date/party dichotomy is not meaningful and it might be more useful to conceive
of roles for protein-protein interactions rather than individual proteins. We
find significant correlations between interaction centrality and the functional
similarity of the interacting proteins.Comment: 27 pages, 5 main figures, 4 supplementary figure
Isoforms of U1-70k control subunit dynamics in the human spliceosomal U1 snRNP
Most human protein-encoding genes contain multiple exons that are spliced together, frequently in alternative arrangements, by the spliceosome. It is established that U1 snRNP is an essential component of the spliceosome, in human consisting of RNA and ten proteins, several of which are post- translationally modified and exist as multiple isoforms. Unresolved and challenging to investigate are the effects of these post translational modifications on the dynamics, interactions and stability of the particle. Using mass spectrometry we investigate the composition and dynamics of the native human U1 snRNP and compare native and recombinant complexes to isolate the effects of various subunits and isoforms on the overall stability. Our data reveal differential incorporation of four protein isoforms and dynamic interactions of subunits U1-A, U1-C and Sm-B/B’. Results also show that unstructured post- ranslationally modified C-terminal tails are
responsible for the dynamics of Sm-B/B’ and U1-C and that their interactions with the Sm core are controlled by binding to different U1-70k isoforms and their phosphorylation status in vivo. These results therefore provide the important functional link between proteomics and structure as well as insight into the dynamic quaternary structure of the native U1 snRNP important for its function.This work was funded by: BBSRC (OVM), BBSRC and EPSRC (HH and NM), EU Prospects (HH), European Science Foundation (NM), the Royal Society (CVR), and fellowship from JSPS and HFSP (YM and DAPK respectively)
Endogenous cholinergic inputs and local circuit mechanisms govern the phasic mesolimbic dopamine response to nicotine
Nicotine exerts its reinforcing action by stimulating nicotinic acetylcholine receptors (nAChRs) and boosting dopamine (DA) output from the ventral tegmental area (VTA). Recent data have led to a debate about the principal pathway of nicotine action: direct stimulation of the DAergic cells through nAChR activation, or disinhibition mediated through desensitization of nAChRs on GABAergic interneurons. We use a computational model of the VTA circuitry and nAChR function to shed light on this issue. Our model illustrates that the α4β2-containing nAChRs either on DA or GABA cells can mediate the acute effects of nicotine. We account for in vitro as well as in vivo data, and predict the conditions necessary for either direct stimulation or disinhibition to be at the origin of DA activity increases. We propose key experiments to disentangle the contribution of both mechanisms. We show that the rate of endogenous acetylcholine input crucially determines the evoked DA response for both mechanisms. Together our results delineate the mechanisms by which the VTA mediates the acute rewarding properties of nicotine and suggest an acetylcholine dependence hypothesis for nicotine reinforcement.Peer reviewe
A Formalism for the Systematic Treatment of Rapidity Logarithms in Quantum Field Theory
Many observables in QCD rely upon the resummation of perturbation theory to
retain predictive power. Resummation follows after one factorizes the cross
section into the rele- vant modes. The class of observables which are sensitive
to soft recoil effects are particularly challenging to factorize and resum
since they involve rapidity logarithms. In this paper we will present a
formalism which allows one to factorize and resum the perturbative series for
such observables in a systematic fashion through the notion of a "rapidity
renormalization group". That is, a Collin-Soper like equation is realized as a
renormalization group equation, but has a more universal applicability to
observables beyond the traditional transverse momentum dependent parton
distribution functions (TMDPDFs) and the Sudakov form factor. This formalism
has the feature that it allows one to track the (non-standard) scheme
dependence which is inherent in any scenario where one performs a resummation
of rapidity divergences. We present a pedagogical introduction to the formalism
by applying it to the well-known massive Sudakov form factor. The formalism is
then used to study observables of current interest. A factorization theorem for
the transverse momentum distribution of Higgs production is presented along
with the result for the resummed cross section at NLL. Our formalism allows one
to define gauge invariant TMDPDFs which are independent of both the hard
scattering amplitude and the soft function, i.e. they are uni- versal. We
present details of the factorization and resummation of the jet broadening
cross section including a renormalization in pT space. We furthermore show how
to regulate and renormalize exclusive processes which are plagued by endpoint
singularities in such a way as to allow for a consistent resummation.Comment: Typos in Appendix C corrected, as well as a typo in eq. 5.6
Prevalence of ocular and oculodermal melanocytosis in Spanish population with uveal melanoma
Producción CientíficaThe aim of this study was to determine the prevalence of ocular and oculodermal melanocytosis (ODM) among patients with uveal melanoma (UM) in a Spanish population.
METHODS:
Retrospective review of the medical records of patients with ODM among patients with UM.
RESULTS:
Ten (11 eyes) of 400 patients (2.7%) with UM associated had ODM. The mean age at diagnosis of UM among patients with ODM was 62 years. One patient had bilateral tumours. UM was diagnosed during a routine-examination in two cases. All tumours were medium (7/11) or large (4/11) in size, with a mean maximum base of 13 mm and height of 7 mm. No patient had extraocular extension or metastatic disease at diagnosis. Enucleation was done in five cases and I-125-brachytherapy in six. The mean follow-up was 43 months. One patient died because of metastasis 2 years after enucleation; one patient is currently on treatment of systemic metastasis 11 years after.
CONCLUSIONS:
ODM is more frequent in spanish population with UM than in American population. Despite the risk of UM in ODM, it is often diagnosed late when a conservative treatment is not indicated
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