24 research outputs found
A Proximal Approach for a Class of Matrix Optimization Problems
In recent years, there has been a growing interest in mathematical models
leading to the minimization, in a symmetric matrix space, of a Bregman
divergence coupled with a regularization term. We address problems of this type
within a general framework where the regularization term is split in two parts,
one being a spectral function while the other is arbitrary. A Douglas-Rachford
approach is proposed to address such problems and a list of proximity operators
is provided allowing us to consider various choices for the fit-to-data
functional and for the regularization term. Numerical experiments show the
validity of this approach for solving convex optimization problems encountered
in the context of sparse covariance matrix estimation. Based on our theoretical
results, an algorithm is also proposed for noisy graphical lasso where a
precision matrix has to be estimated in the presence of noise. The nonconvexity
of the resulting objective function is dealt with a majorization-minimization
approach, i.e. by building a sequence of convex surrogates and solving the
inner optimization subproblems via the aforementioned Douglas-Rachford
procedure. We establish conditions for the convergence of this iterative scheme
and we illustrate its good numerical performance with respect to
state-of-the-art approaches
Second-trimester amniotic fluid proteins changes in subsequent spontaneous preterm birth
IntroductionThe global sequence of the pathogenesis of preterm labor remains unclear. This study aimed to compare amniotic fluid concentrations of extracellular matrix-related proteins (procollagen, osteopontin and IL-33), and of cytokines (IL-19, IL-6, IL-20, TNF alpha, TGF beta, and IL-1 beta) in asymptomatic women with and without subsequent spontaneous preterm delivery. Material and methodsWe used amniotic fluid samples of singleton pregnancy, collected by amniocentesis between 16 and 20 weeks' gestation, without stigmata of infection (i.e., all amniotic fluid samples were tested with broad-range 16 S rDNA PCR to distinguish samples with evidence of past bacterial infection from sterile ones), during a randomized, double-blind, placebo-controlled trial to perform a nested case-control laboratory study. Cases were women with a spontaneous delivery before 37 weeks of gestation (preterm group). Controls were women who gave birth at or after 39 weeks (full term group). Amniotic fluid concentrations of the extracellular matrix-related proteins and cytokines measured by immunoassays were compared for two study groups. : NCT00718705. ResultsBetween July 2008 and July 2011, in 12 maternal-fetal medicine centers in France, 166 women with available PCR-negative amniotic fluid samples were retained for the analysis. Concentrations of procollagen, osteopontin, IL-19, IL-6, IL-20, IL-33, TNF alpha, TGF beta, and IL-1 beta were compared between the 37 who gave birth preterm and the 129 women with full-term delivery. Amniotic fluid levels of procollagen, osteopontin, IL-19, IL-33, and TNF alpha were significantly higher in the preterm than the full-term group. IL-6, IL-20, TGF beta, and IL-1 beta levels did not differ between the groups. ConclusionsIn amniotic fluid 16 S rDNA PCR negative samples obtained during second-trimester amniocentesis, extracellular matrix-related protein concentrations (procollagen, osteopontin and IL-33), together with IL-19 and TNF alpha, were observed higher at this time in cases of later spontaneous preterm birth
Computational optical imaging with a photonic lantern
[EN] The thin and flexible nature of optical fibres often makes them the ideal technology to view biological processes in-vivo, but current microendoscopic approaches are limited in spatial resolution. Here, we demonstrate a route to high resolution microendoscopy using a multicore fibre (MCF) with an adiabatic multimode-to-single-mode "photonic lantern" transition formed at the distal end by tapering. We show that distinct multimode patterns of light can be projected from the output of the lantern by individually exciting the single-mode MCF cores, and that these patterns are highly stable to fibre movement. This capability is then exploited to demonstrate a form of single-pixel imaging, where a single pixel detector is used to detect the fraction of light transmitted through the object for each multimode pattern. A custom computational imaging algorithm we call SARA-COIL is used to reconstruct the object using only the pre-measured multimode patterns themselves and the detector signals.This work was funded through the "Proteus" Engineering and Physical Sciences Research Council (EPSRC) Interdisciplinary Research Collaboration (IRC) (EP/K03197X/1), by the Science and Technology Facilities Council (STFC) through STFC-CLASP grants ST/K006509/1 and ST/K006460/1, STFC Consortium grants ST/N000625/1 and ST/N000544/1. S.L. acknowledges support from the National Natural Science Foundation of China under Grant no. 61705073. DBP acknowledges support from the Royal Academy of Engineering, and the European Research Council (PhotUntangle, 804626). The authors thank Philip Emanuel for the use of his confocal image of A549 cells and Eckhardt Optics for their image of the USAF 1951 target. The authors sincerely thank the anonymous reviewers of this paper for their detailed and considered feedback which helped us to improve the quality of this paper significantly.Choudhury, D.; Mcnicholl, DK.; Repetti, A.; Gris-Sánchez, I.; Li, S.; Phillips, DB.; Whyte, G.... (2020). Computational optical imaging with a photonic lantern. Nature Communications. 11(1):1-9. https://doi.org/10.1038/s41467-020-18818-6S19111Wood, H. A. C., Harrington, K., Birks, T. A., Knight, J. C. & Stone, J. M. High-resolution air-clad imaging fibers. Opt. Lett. 43, 5311–5314 (2018).Akram, A. R. et al. In situ identification of Gram-negative bacteria in human lungs using a topical fluorescent peptide targeting lipid A. Sci. Transl. Med. 10, eaal0033 (2018).Shin, J., Bosworth, B. T. & Foster, M. A. Compressive fluorescence imaging using a multi-core fiber and spatially dependent scattering. Opt. Lett. 42, 109–112 (2017).Papadopoulos, I. N., Farahi, S., Moser, C. & Psaltis, D. 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A hybrid interior point - Deep learning approach for poisson image deblurring
In this paper we address the problem of deconvolution of an image corrupted with Poisson noise by reformulating the restoration process as a constrained minimization of a suitable regularized data fidelity function. The minimization step is performed by means of an interior-point approach, in which the constraints are incorporated within the objective function through a barrier penalty and a forward-backward algorithm is exploited to build a minimizing sequence. The key point of our proposed scheme is that the choice of the regularization, barrier and step-size parameters defining the interior point approach is automatically performed by a deep learning strategy. Numerical tests on Poisson corrupted benchmark datasets show that our method can obtain very good performance when compared to a state-of-the-art variational deblurring strategy
Learning image deblurring by unfolding a proximal interior point algorithm
Image reconstruction is frequently addressed by resorting to variational methods, which account for some prior knowledge about the solution. The success of these methods, however, heavily depends on the estimation of a set of hyperparameters. Deep learning architectures are, on the contrary, very generic and efficient, but they offer very limited control over their output. In this paper we present iRestNet, a neural network architecture which combines the benefits of both approaches. iRestNet is obtained by unfolding a proximal interior point algorithm. This enables enforcing hard constraints on the pixel range of the restored image thanks to a logarithmic barrier strategy, without requiring any parameter setting. Explicit expressions for the involved proximity operator, and its differential, are derived, which allows training iRestNet with gradient descent and backpropagation. Numerical experiments on image deblurring show that the proposed approach provides good image quality results compared to state-of-the-art variational and machine learning methods
An instrumental and computational pipeline for visualizing free viral particles
International audienceWe are presenting the application of an optical and computational pipeline FAMOUS for revealing the presence of free viral particles named "virions". The idea of such a protocol is to give rise to images of virions in their environment with a soft solution for recording the native image, contrary to the standard solution of imaging virions with electron microscopy (EM) for visualizing viral particles. The final aim of the current work is to observe free viral particles of SARS-CoV-2, the virions responsible for the worldwide pandemic of Covid-19. But such particles have diameters between 80 and 120 nm, a dimension smaller than the resolution limit of optical-only microscopy solutions. We have chosen to start with the biggest free virions, cytomegalovirus (CMV), a virus from the herpesvirus family also named "Human Herpes Virus 5". Two kinds of cultures were involved: a fluorescent culture (BAD) and a label-free one (VHLE), both being collected from infected cell culture. VHLE virions were first observed after secondary immunostaining and concentrated with magnetic nanoparticles and then without labelling. The optical protocol rests on a standard solution of multiphoton microscopy combined with a computational strategy based on the point-spread-function (PSF) recordings, its mathematical modeling and the restauration of the image resting on the PSF model. A test with free viral particles of SARS-CoV-2 is led, delivering an optical visualization of the free-viral particles. The visualization of objects aggregates obtained in both situations confirm the relevance of the pipeline FAMOUS for imaging free virions
Fight the Pandemic: Highlights From the 2020 IEEE 5-Minute Video Clip Contest [SP Competitions]
The annual IEEE 5-Minute Video Clip Contest (5-MICC) was launched by the IEEE Signal Processing Society (SPS), and the selected topic for the competition at IEEE ICIP 2020 was \u201cFight the Pandemic.\u201d The organizing committee selected three finalist videos and placed them online for public voting. The first one is about a visual analytic system for pandemic management, the second concerns machine learning screening of coronavirus disease (COVID-19) patients based on X-ray images, and the third deals with a COVID-19 test strip reader. Taking the public voting results from more than 800 participants into consideration, the panel of judges decided the final rankings of the three videos. In this article, we present an overview of the 5-MICC at ICIP 2020, describing the competition setup, the teams, and their approaches. We also share our experience and the feedback we received from the finalists
Low-frequency rotating and alternating magnetic field generators for biological applications: Design details of home-made setups
We describe a setup able to generate a rotating or alternating low-frequency (up to 10 Hz) magnetic field (up to 120 mT) for biological applications inside a 6 cm in diameter and 5 cm in height cylindrical space. It is suitable to insert several Petri dishes for cell death experiments and mice for in vivo ones. Since a particular attention was given to the field homogeneity, it is based on a six-pole electromagnet. The characteristics of the field are changed by varying the phase, amplitude or frequency of the electrical currents going through the coils, using a micro-controller-based human–machine interface and a RL chopper, which is described. The results of the magnetic simulations run to optimize the global design are provided. The cooling of the electromagnet is done using a chiller, and the sample temperature regulation using an air blower. All details on the mechanical, electrical and electronic elements, on the mounting, and on the programing are provided, so the setup can be reproduced. Suggestions for a further improvement of the electrical efficiency are given. This setup has already been used to demonstrate the possibility to kill cells using targeted magnetic nanoparticles and low-frequency magnetic fields
Ultrasound-induced Cavitation enhances the efficacy of Chemotherapy in a 3D Model of Pancreatic Ductal Adenocarcinoma with its microenvironment
Abstract Pancreatic ductal adenocarcinoma (PDAC) is supported by a complex microenvironment whose physical contribution to chemoresistance could be overcome by ultrasound (US) therapy. This study aims to investigate the ability of US-induced inertial cavitation in association with chemotherapy to alter tumor cell viability via microenvironment disruption. For this purpose, we used a 3D-coculture PDAC model partially mimicking the tumor and its microenvironment. Coculture spheroids combining DT66066 cells isolated from KPC-transgenic mice and murine embryonic fibroblasts (iMEF) were obtained by using a magnetic nanoshuttle method. Spheroids were exposed to US with incremental inertial cavitation indexes. Conditions studied included control, gemcitabine, US-cavitation and US-cavitation + gemcitabine. Spheroid viability was assessed by the reduction of resazurin and flow cytometry. The 3D-coculture spheroid model incorporated activated fibroblasts and produced type 1-collagen, thus providing a partial miniature representation of tumors with their microenvironment. Main findings were: (a) Gemcitabine (5 μM) was significantly less cytotoxic in the presence of KPC/iMEFs spheroids compared with KPC (fibroblast-free) spheroids; (b) US-induced inertial cavitation combined with Gemcitabine significantly decreased spheroid viability compared to Gemcitabine alone; (c) both cavitation and chemotherapy affected KPC cell viability but not that of fibroblasts, confirming the protective role of the latter vis-à-vis tumor cells. Gemcitabine toxicity is enhanced when cocultured spheroids of KPC and iMEF are exposed to US-cavitation. Although the model used is only a partial representation of PDAC, this experience supports the hypothesis that US-inertial cavitation can enhance drug penetration and cytotoxicity by disrupting PDAC microenvironment