2,770 research outputs found
Multi-Channel Stochastic Variational Inference for the Joint Analysis of Heterogeneous Biomedical Data in Alzheimer's Disease
The joint analysis of biomedical data in Alzheimer's Disease (AD) is
important for better clinical diagnosis and to understand the relationship
between biomarkers. However, jointly accounting for heterogeneous measures
poses important challenges related to the modeling of the variability and the
interpretability of the results. These issues are here addressed by proposing a
novel multi-channel stochastic generative model. We assume that a latent
variable generates the data observed through different channels (e.g., clinical
scores, imaging, ...) and describe an efficient way to estimate jointly the
distribution of both latent variable and data generative process. Experiments
on synthetic data show that the multi-channel formulation allows superior data
reconstruction as opposed to the single channel one. Moreover, the derived
lower bound of the model evidence represents a promising model selection
criterion. Experiments on AD data show that the model parameters can be used
for unsupervised patient stratification and for the joint interpretation of the
heterogeneous observations. Because of its general and flexible formulation, we
believe that the proposed method can find important applications as a general
data fusion technique.Comment: accepted for presentation at MLCN 2018 workshop, in Conjunction with
MICCAI 2018, September 20, Granada, Spai
Approximation of conformal mappings using conformally equivalent triangular lattices
Consider discrete conformal maps defined on the basis of two conformally
equivalent triangle meshes, that is edge lengths are related by scale factors
associated to the vertices. Given a smooth conformal map , we show that it
can be approximated by such discrete conformal maps . In
particular, let be an infinite regular triangulation of the plane with
congruent triangles and only acute angles (i.e.\ ). We scale this
tiling by and approximate a compact subset of the domain of
with a portion of it. For small enough we prove that there exists a
conformally equivalent triangle mesh whose scale factors are given by
on the boundary. Furthermore we show that the corresponding discrete
conformal maps converge to uniformly in with error of
order .Comment: 14 pages, 3 figures; v2 typos corrected, revised introduction, some
proofs extende
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Enhanced Delivery of Rituximab Into Brain and Lymph Nodes Using Timed-Release Nanocapsules in Non-Human Primates.
Tumor metastasis into the central nervous system (CNS) and lymph nodes (LNs) is a major obstacle for effective therapies. Therapeutic monoclonal antibodies (mAb) have revolutionized tumor treatment; however, their efficacy for treating metastatic tumors-particularly, CNS and LN metastases-is poor due to inefficient penetration into the CNS and LNs following intravenous injection. We recently reported an effective delivery of mAb to the CNS by encapsulating the anti-CD20 mAb rituximab (RTX) within a thin shell of polymer that contains the analogs of choline and acetylcholine receptors. This encapsulated RTX, denoted as n-RTX, eliminated lymphoma cells systemically in a xenografted humanized mouse model using an immunodeficient mouse as a recipient of human hematopoietic stem/progenitor cells and fetal thymus more effectively than native RTX; importantly, n-RTX showed notable anti-tumor effect on CNS metastases which is unable to show by native RTX. As an important step toward future clinical translation of this technology, we further analyzed the properties of n-RTX in immunocompetent animals, rats, and non-human primates (NHPs). Our results show that a single intravenous injection of n-RTX resulted in 10-fold greater levels in the CNS and 2-3-fold greater levels in the LNs of RTX, respectively, than the injection of native RTX in both rats and NHPs. In addition, we demonstrate the enhanced delivery and efficient B-cell depletion in lymphoid organs of NHPs with n-RTX. Moreover, detailed hematological analysis and liver enzyme activity tests indicate n-RTX treatment is safe in NHPs. As this nanocapsule platform can be universally applied to other therapeutic mAbs, it holds great promise for extending mAb therapy to poorly accessible body compartments
Nicotinic acetylcholine receptor expression in human airway correlates with lung function
Nicotine and its derivatives, by binding to nicotinic acetylcholine receptors (nAChRs) on bronchial epithelial cells, can regulate cellular signaling and inflammatory processes. Delineation of nAChR subtypes and their responses to nicotine stimulation in bronchial epithelium may provide information for therapeutic targeting in smoking-related inflammation in the airway. Expression of nAChR subunit genes in 60 bronchial epithelial biopsies and immunohistochemical staining for the subcellular locations of nAChR subunit expression were evaluated. Seven human bronchial epithelial cell lines (HBECs) were exposed to nicotine in vitro for their response in nAChR subunit gene expression to nicotine exposure and removal. The relative normalized amount of expression of nAChR α4, α5, and α7 and immunohistochemical staining intensity of nAChR α4, α5, and β3 expression showed significant correlation with lung function parameters. Nicotine stimulation in HBECs resulted in transient increase in the levels of nAChR α5 and α6 but more sustained increase in nAChR α7 expression. nAChR expression in bronchial epithelium was found to correlate with lung function. Nicotine exposure in HBECs resulted in both short and longer term responses in nAChR subunit gene expression. These results gave insight into the potential of targeting nAChRs for therapy in smoking-related inflammation in the airway.postprin
Predictive direct power control for dual-active-bridge multilevel inverter based on conservative power theory
This paper explores the feasibility of multilevel dual-active bridge-inverter (DABMI) applications for grid-connected applications of a modern Model of Predictive Direct Power Control (MPDPC) based on the conservative power theory (CPT). In the case of unbalanced grid voltages, the objective of the study is to promote continued active and reactive energy in MPDPC without reducing effciency such as transient response and current harmonics. The nature of the instantaneous p-q theory permits only one out of three control targets to be fulfilled. The proposed control approached directly regulates the instantaneous active and reactive power to achieve three particular control objectives namely sinusoidal and symmetrical grid current, cancelling twice of fundamental grid frequency reactive power ripples, and removing twice grid frequency active power ripple. The techniques of complicated Grid part sequence extraction are unnecessary and improved at no extra expense, as is the case with current MPDPC fault-tolerant approaches. The instantaneous power at the next sampling instant is predicted with the newly developed discrete-time model. Each possible switching state will then be evaluated in the cost function defined until the optimal state which lead to the minimum power errors is determined. In MATLAB/Simulink simulation, the proposed CPT-based MPDPC measures reliability and performance at balanced and unbalanced grid voltages then compared with the conventional and existing MPDPC The proposed method manages to achieve all of three control targets which generates sinusoidal grid currents and attenuates active and reactive power ripple of twice the grid frequency exactly at the same time without losing its critical effciency including transient reaction and current harmonics
Oncogenic mutation profiling in new lung cancer and mesothelioma cell lines
published_or_final_versio
A regularization projection algorithm for various problems with nonlinear mappings in Hilbert spaces
Study on micro fabricated stainless steel surface to anti-biofouling using electrochemical fabrication
The novel mTOR inhibitor RAD001 (Everolimus) induces antiproliferative effects in human pancreatic neuroendocrine tumor cells
Background/Aim: Tumors exhibiting constitutively activated PI(3) K/Akt/mTOR signaling are hypersensitive to mTOR inhibitors such as RAD001 (everolimus) which is presently being investigated in clinical phase II trials in various tumor entities, including neuroendocrine tumors (NETs). However, no preclinical data about the effects of RAD001 on NET cells have been published. In this study, we aimed to evaluate the effects of RAD001 on BON cells, a human pancreatic NET cell line that exhibits constitutively activated PI(3) K/Akt/mTOR signaling. Methods: BON cells were treated with different concentrations of RAD001 to analyze its effect on cell growth using proliferation assays. Apoptosis was examined by Western blot analysis of caspase-3/PARP cleavage and by FACS analysis of DNA fragmentation. Results: RAD001 potently inhibited BON cell growth in a dose-dependent manner which was dependent on the serum concentration in the medium. RAD001-induced growth inhibition involved G0/G1-phase arrest as well as induction of apoptosis. Conclusion: In summary, our data demonstrate antiproliferative and apoptotic effects of RAD001 in NET cells in vitro supporting its clinical use in current phase II trials in NET patients. Copyright (c) 2007 S. Karger AG, Basel
Semiparametric Multivariate Accelerated Failure Time Model with Generalized Estimating Equations
The semiparametric accelerated failure time model is not as widely used as
the Cox relative risk model mainly due to computational difficulties. Recent
developments in least squares estimation and induced smoothing estimating
equations provide promising tools to make the accelerate failure time models
more attractive in practice. For semiparametric multivariate accelerated
failure time models, we propose a generalized estimating equation approach to
account for the multivariate dependence through working correlation structures.
The marginal error distributions can be either identical as in sequential event
settings or different as in parallel event settings. Some regression
coefficients can be shared across margins as needed. The initial estimator is a
rank-based estimator with Gehan's weight, but obtained from an induced
smoothing approach with computation ease. The resulting estimator is consistent
and asymptotically normal, with a variance estimated through a multiplier
resampling method. In a simulation study, our estimator was up to three times
as efficient as the initial estimator, especially with stronger multivariate
dependence and heavier censoring percentage. Two real examples demonstrate the
utility of the proposed method
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