Responsabilidade civil pelo uso do cartão magnético e senha eletrônica: identificação das responsabilidades do consumidor e da instituição financeira

Divulgação dos SUMÁRIOS das obras recentemente incorporadas ao acervo da Biblioteca Ministro Oscar Saraiva do STJ. Em respeito à Lei de Direitos Autorais, não disponibilizamos a obra na íntegra.Localização na estante: 347.735:347.44(81) N395

Deep-STORM: super-resolution single-molecule microscopy by deep learning

We present an ultra-fast, precise, parameter-free method, which we term Deep-STORM, for obtaining super-resolution images from stochastically-blinking emitters, such as fluorescent molecules used for localization microscopy. Deep-STORM uses a deep convolutional neural network that can be trained on simulated data or experimental measurements, both of which are demonstrated. The method achieves state-of-the-art resolution under challenging signal-to-noise conditions and high emitter densities, and is significantly faster than existing approaches. Additionally, no prior information on the shape of the underlying structure is required, making the method applicable to any blinking data-set. We validate our approach by super-resolution image reconstruction of simulated and experimentally obtained data.Comment: 7 pages, added code download reference and DOI for the journal versio

DeepSTORM3D: dense three dimensional localization microscopy and point spread function design by deep learning

Localization microscopy is an imaging technique in which the positions of individual nanoscale point emitters (e.g. fluorescent molecules) are determined at high precision from their images. This is the key ingredient in single/multiple-particle-tracking and several super-resolution microscopy approaches. Localization in three-dimensions (3D) can be performed by modifying the image that a point-source creates on the camera, namely, the point-spread function (PSF). The PSF is engineered using additional optical elements to vary distinctively with the depth of the point-source. However, localizing multiple adjacent emitters in 3D poses a significant algorithmic challenge, due to the lateral overlap of their PSFs. Here, we train a neural network to receive an image containing densely overlapping PSFs of multiple emitters over a large axial range and output a list of their 3D positions. Furthermore, we then use the network to design the optimal PSF for the multi-emitter case. We demonstrate our approach numerically as well as experimentally by 3D STORM imaging of mitochondria, and volumetric imaging of dozens of fluorescently-labeled telomeres occupying a mammalian nucleus in a single snapshot.Comment: main text: 9 pages, 5 figures, supplementary information: 29 pages, 20 figure

A Novel Role for CSRP1 in a Lebanese Family with Congenital Cardiac Defects

Despite an obvious role for consanguinity in congenital heart disease (CHD), most studies fail to document a monogenic model of inheritance except for few cases. We hereby describe a first-degree cousins consanguineous Lebanese family with 7 conceived children: 2 died in utero of unknown causes, 3 have CHD, and 4 have polydactyly. The aim of the study is to unveil the genetic variant(s) causing these phenotypes using next generation sequencing (NGS) technology. Targeted exome sequencing identified a heterozygous duplication in CSRP1 which leads to a potential frameshift mutation at position 154 of the protein. This mutation is inherited from the father, and segregates only with the CHD phenotype. The in vitro characterization demonstrates that the mutation dramatically abrogates its transcriptional activity over cardiac promoters like NPPA. In addition, it differentially inhibits the physical association of CSRP1 with SRF, GATA4, and with the newly described partner herein TBX5. Whole exome sequencing failed to show any potential variant linked to polydactyly, but revealed a novel missense mutation in TRPS1. This mutation is inherited from the healthy mother, and segregating only with the cardiac phenotype. Both TRPS1 and CSRP1 physically interact, and the mutations in each abrogate their partnership. Our findings add fundamental knowledge into the molecular basis of CHD, and propose the di-genic model of inheritance as responsible for such malformations

Democratising deep learning for microscopy with ZeroCostDL4Mic

Deep Learning (DL) methods are powerful analytical tools for microscopy and can outperform conventional image processing pipelines. Despite the enthusiasm and innovations fuelled by DL technology, the need to access powerful and compatible resources to train DL networks leads to an accessibility barrier that novice users often find difficult to overcome. Here, we present ZeroCostDL4Mic, an entry-level platform simplifying DL access by leveraging the free, cloud-based computational resources of Google Colab. ZeroCostDL4Mic allows researchers with no coding expertise to train and apply key DL networks to perform tasks including segmentation (using U-Net and StarDist), object detection (using YOLOv2), denoising (using CARE and Noise2Void), super-resolution microscopy (using Deep-STORM), and image-to-image translation (using Label-free prediction - fnet, pix2pix and CycleGAN). Importantly, we provide suitable quantitative tools for each network to evaluate model performance, allowing model optimisation. We demonstrate the application of the platform to study multiple biological processes. Deep learning methods show great promise for the analysis of microscopy images but there is currently an accessibility barrier to many users. Here the authors report a convenient entry-level deep learning platform that can be used at no cost: ZeroCostDL4Mic

Repurposing cilostazol for raynaud's phenomenon

Raynaud's Phenomenon (RP) results from exaggerated cold-induced vasoconstriction. RP patients suffer from vasospastic attacks and compromised digital blood perfusion leading to a triple color change at the level the fingers. Severe RP may cause ulcers and threaten tissue viability. Many drugs have been used to alleviate the symptoms of RP. These include calcium-channel blockers, cGMP-specific phosphodiesterase type 5 inhibitors, prostacyclin analogs, and angiotensin receptor blockers. Despite their variety, these drugs do not treat RP but rather alleviate its symptoms. To date, no drug for RP has been yet approved by the U.S Food and Drugs Administration. Cilostazol is a selective inhibitor of phosphodiesterase-III, originally prescribed to treat intermittent claudication. Owing to its antiplatelet and vasodilating properties, cilostazol is being repurposed as a potential drug for RP. This review focuses on the different lines of action of cilostazol serving to enhance blood perfusion in RP patients

3329240.pdf

Supplemental information including: network architecture, training information, performance evaluation and further comparisons