3,582 research outputs found
Chinese patients with sporadic Hirschsprung's disease are predominantly represented by a single RET haplotype.
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Inkjet-Printed rGO/binary Metal Oxide Sensor for Predictive Gas Sensing in a Mixed Environment
Funder: Alphasense LimitedAbstract: Selectivity for specific analytes and high‐temperature operation are key challenges for chemiresistive‐type gas sensors. Complementary hybrid materials, such as reduced graphene oxide (rGO) decorated with metal oxides enables realization of room‐temperature sensors with enhanced sensitivity. However, sensor training to identify target gases and accurate concentration measurement from gas mixtures still remain very challenging. This work proposes hybridization of rGO with CuCoO x binary metal oxide as a sensing material. Highly stable, room‐temperature NO2 sensors with a 50 ppb of detection limit is demonstrated using inkjet printing. A framework is then developed for machine‐intelligent recognition with good visibility to identify specific gases and predict concentration under an interfering atmosphere from a single sensor. Using ten unique parameters extracted from the sensor response, the machine learning‐based classifier provides a decision boundary with 98.1% accuracy, and is able to correctly predict previously unseen NO2 and humidity concentrations in an interfering environment. This approach enables implementation of an intelligent platform for printable, room‐temperature gas sensors in a mixed environment irrespective of ambient humidity
The interplay of microscopic and mesoscopic structure in complex networks
Not all nodes in a network are created equal. Differences and similarities
exist at both individual node and group levels. Disentangling single node from
group properties is crucial for network modeling and structural inference.
Based on unbiased generative probabilistic exponential random graph models and
employing distributive message passing techniques, we present an efficient
algorithm that allows one to separate the contributions of individual nodes and
groups of nodes to the network structure. This leads to improved detection
accuracy of latent class structure in real world data sets compared to models
that focus on group structure alone. Furthermore, the inclusion of hitherto
neglected group specific effects in models used to assess the statistical
significance of small subgraph (motif) distributions in networks may be
sufficient to explain most of the observed statistics. We show the predictive
power of such generative models in forecasting putative gene-disease
associations in the Online Mendelian Inheritance in Man (OMIM) database. The
approach is suitable for both directed and undirected uni-partite as well as
for bipartite networks
Hierarchical Equations of Motion Approach to Quantum Thermodynamics
We present a theoretical framework to investigate quantum thermodynamic
processes under non-Markovian system-bath interactions on the basis of the
hierarchical equations of motion (HEOM) approach, which is convenient to carry
out numerically "exact" calculations. This formalism is valuable because it can
be used to treat not only strong system-bath coupling but also system-bath
correlation or entanglement, which will be essential to characterize the heat
transport between the system and quantum heat baths. Using this formalism, we
demonstrated an importance of the thermodynamic effect from the tri-partite
correlations (TPC) for a two-level heat transfer model and a three-level
autonomous heat engine model under the conditions that the conventional quantum
master equation approaches are failed. Our numerical calculations show that TPC
contributions, which distinguish the heat current from the energy current, have
to be take into account to satisfy the thermodynamic laws.Comment: 9 pages, 4 figures. As a chapter of: F. Binder, L. A. Correa, C.
Gogolin, J. Anders, and G. Adesso (eds.), "Thermodynamics in the quantum
regime - Recent Progress and Outlook", (Springer International Publishing
Random walk with barriers: Diffusion restricted by permeable membranes
Restrictions to molecular motion by barriers (membranes) are ubiquitous in
biological tissues, porous media and composite materials. A major challenge is
to characterize the microstructure of a material or an organism
nondestructively using a bulk transport measurement. Here we demonstrate how
the long-range structural correlations introduced by permeable membranes give
rise to distinct features of transport. We consider Brownian motion restricted
by randomly placed and oriented permeable membranes and focus on the
disorder-averaged diffusion propagator using a scattering approach. The
renormalization group solution reveals a scaling behavior of the diffusion
coefficient for large times, with a characteristically slow inverse square root
time dependence. The predicted time dependence of the diffusion coefficient
agrees well with Monte Carlo simulations in two dimensions. Our results can be
used to identify permeable membranes as restrictions to transport in disordered
materials and in biological tissues, and to quantify their permeability and
surface area.Comment: 8 pages, 3 figures; origin of dispersion clarified, refs adde
Hybridization in parasites: consequences for adaptive evolution, pathogenesis and public health in a changing world
[No abstract available
Depression and sickness behavior are Janus-faced responses to shared inflammatory pathways
It is of considerable translational importance whether depression is a form or a consequence of sickness behavior. Sickness behavior is a behavioral complex induced by infections and immune trauma and mediated by pro-inflammatory cytokines. It is an adaptive response that enhances recovery by conserving energy to combat acute inflammation. There are considerable phenomenological similarities between sickness behavior and depression, for example, behavioral inhibition, anorexia and weight loss, and melancholic (anhedonia), physio-somatic (fatigue, hyperalgesia, malaise), anxiety and neurocognitive symptoms. In clinical depression, however, a transition occurs to sensitization of immuno-inflammatory pathways, progressive damage by oxidative and nitrosative stress to lipids, proteins, and DNA, and autoimmune responses directed against self-epitopes. The latter mechanisms are the substrate of a neuroprogressive process, whereby multiple depressive episodes cause neural tissue damage and consequent functional and cognitive sequelae. Thus, shared immuno-inflammatory pathways underpin the physiology of sickness behavior and the pathophysiology of clinical depression explaining their partially overlapping phenomenology. Inflammation may provoke a Janus-faced response with a good, acute side, generating protective inflammation through sickness behavior and a bad, chronic side, for example, clinical depression, a lifelong disorder with positive feedback loops between (neuro)inflammation and (neuro)degenerative processes following less well defined triggers
Clinical characteristics of ovarian cancer classified by BRCA1, BRCA2, and RAD51C status.
We evaluated homologous recombination deficient (HRD) phenotypes in epithelial ovarian cancer (EOC) considering BRCA1, BRCA2, and RAD51C in a large well-annotated patient set. We evaluated EOC patients for germline deleterious mutations (n = 899), somatic mutations (n = 279) and epigenetic alterations (n = 482) in these genes using NGS and genome-wide methylation arrays. Deleterious germline mutations were identified in 32 (3.6%) patients for BRCA1, in 28 (3.1%) for BRCA2 and in 26 (2.9%) for RAD51C. Ten somatically sequenced patients had deleterious alterations, six (2.1%) in BRCA1 and four (1.4%) in BRCA2. Fifty two patients (10.8%) had methylated BRCA1 or RAD51C. HRD patients with germline or somatic alterations in any gene were more likely to be high grade serous, have an earlier diagnosis age and have ovarian and/or breast cancer family history. The HRD phenotype was most common in high grade serous EOC. Identification of EOC patients with an HRD phenotype may help tailor specific therapies.This work was supported by National Institutes of Health grants R01-CA122443, P50-CA136393, P30-CA15083, and the Fred C. and Katherine B. Andersen Foundation. We thank Gary Kenney, M.D. for pathology review of tumor tissue. We thank Craig Luccarini, Caroline Baynes from University of Cambridge for assisting our sample sequencing
Superconformal index, BPS monodromy and chiral algebras
We show that specializations of the 4d N = 2 superconformal index labeled by an integer N is given by Tr M-N where M is the Kontsevich-Soibelman monodromy operator for BPS states on the Coulomb branch. We provide evidence that the states enumerated by these limits of the index lead to a family of 2d chiral algebras A(N). This generalizes the recent results for the N = -1 case which corresponds to the Schur limit of the superconformal index. We show that this specialization of the index leads to the same integrand as that of the elliptic genus of compactification of the superconformal theory on S-2 x T-2 where we turn on 1/2 N units of U(1)(r) flux on S-2
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