La Golondrina : The Swallow

https://digitalcommons.library.umaine.edu/mmb-ps/1545/thumbnail.jp

Robust elastic 2D/3D geometric graph matching

We present an algorithm for geometric matching of graphs embedded in 2D or 3D space. It is applicable for registering any graph-like structures appearing in biomedical images, such as blood vessels, pulmonary bronchi, nerve fibers, or dendritic arbors. Our approach does not rely on the similarity of local appearance features, so it is suitable for multimodal registration with a large difference in appearance. Unlike earlier methods, the algorithm uses edge shape, does not require an initial pose estimate, can handle partial matches, and can cope with nonlinear deformations and topological differences. The matching consists of two steps. First, we find an affine transform that roughly aligns the graphs by exploring the set of all consistent correspondences between the nodes. This can be done at an acceptably low computational expense by using parameter uncertainties for pruning, backtracking as needed. Parameter uncertainties are updated in a Kalman-like scheme with each match. In the second step we allow for a nonlinear part of the deformation, modeled as a Gaussian Process. Short sequences of edges are grouped into superedges, which are then matched between graphs. This allows for topological differences. A maximum consistent set of superedge matches is found using a dedicated branch-and-bound solver, which is over 100 times faster than a standard linear programming approach. Geometrical and topological consistency of candidate matches is determined in a fast hierarchical manner. We demonstrate the effectiveness of our technique at registering angiography and retinal fundus images, as well as neural image stacks.Peer ReviewedPostprint (author’s final draft

Superpixel-based Two-view Deterministic Fitting for Multiple-structure Data

This paper proposes a two-view deterministic geometric model fitting method, termed Superpixel-based Deterministic Fitting (SDF), for multiple-structure data. SDF starts from superpixel segmentation, which effectively captures prior information of feature appearances. The feature appearances are beneficial to reduce the computational complexity for deterministic fitting methods. SDF also includes two original elements, i.e., a deterministic sampling algorithm and a novel model selection algorithm. The two algorithms are tightly coupled to boost the performance of SDF in both speed and accuracy. Specifically, the proposed sampling algorithm leverages the grouping cues of superpixels to generate reliable and consistent hypotheses. The proposed model selection algorithm further makes use of desirable properties of the generated hypotheses, to improve the conventional fit-and-remove framework for more efficient and effective performance. The key characteristic of SDF is that it can efficiently and deterministically estimate the parameters of model instances in multi-structure data. Experimental results demonstrate that the proposed SDF shows superiority over several state-of-the-art fitting methods for real images with single-structure and multiple-structure data.Comment: Accepted by European Conference on Computer Vision (ECCV

Nanocatalizadores de platino soportados sobre un sistema proteína de capa-s/partículas poliméricas : obtención, caracterización y comportamiento en la reacción de reduccción de p-nitrofenol

En este trabajo se prepararon y caracterizaron bionanocatalizadores de platino soportados sobre un template formado por proteínas de capa-S y nanopartículas poliméricas. Las proteínas de capa-S utilizadas fueron aisladas de L. kefiri y las nanopartículas poliméricas fueron a base de poliuretano y acrílico, sintetizados mediante el método del prepolímero y por polimerización en emulsión, respectivamente. Una vez obtenidos los catalizadores, se lo redujo con H2 gaseoso a temperatura ambiente. Todos los sistemas fueron caracterizados por FTIR y microscopía electrónica de transmisión para evaluar la eficiencia de la síntesis de las nanopartículas poliméricas, la morfología del template proteína de capa-S/nanopartículas poliméricas y la distribución de tamaños de las partículas metálicas. Los catalizadores se emplearon en la reacción de reducción del p-nitrofenol con NaBH4, la cual fue seguida espectrofotométricamente, midiendo la absorción del reactivo a 400 nm. Se obtuvieron conversiones de entre 80 y 100% para tiempos de reacción de entre 1 y 1.5 h, obteniéndose los mejores resultados con el catalizador soportado sobre el template capa-S de L. kefiri 83111/acrílico. La excelente performance alcanzada se asigna a la capacidad del template proteínas de capa-S/nanopartículas poliméricas de actuar como guía del crecimiento y ensamblaje de las nanopartículas de platino.Fil: Huggias, Sofía . Universidad Nacional de La PlataFil: Bolla; Patricia A.. Universidad Nacional de La PlataFil: Serradell;María A.. Universidad Nacional de La PlataFil: Peruzzo, Pablo J.. Universidad Nacional de La PlataFil: Casella, Mónica L.. Universidad Nacional de La Plat

How to use mixed precision in ocean models : Exploring a potential reduction of numerical precision in NEMO 4.0 and ROMS 3.6

ceived funding from the EU ESiWACE H2020 Framework Programme under grant agreement no. 823988, from the Severo Ochoa (SEV-2011-00067) program of the Spanish Government and from the Ministerio de Economia y Competitividad under contract TIN2017-84553-C2-1-R.Mixed-precision approaches can provide substantial speed-ups for both computing- and memory-bound codes with little effort. Most scientific codes have overengineered the numerical precision, leading to a situation in which models are using more resources than required without knowing where they are required and where they are not. Consequently, it is possible to improve computational performance by establishing a more appropriate choice of precision. The only input that is needed is a method to determine which real variables can be represented with fewer bits without affecting the accuracy of the results. This paper presents a novel method that enables modern and legacy codes to benefit from a reduction of the precision of certain variables without sacrificing accuracy. It consists of a simple idea: we reduce the precision of a group of variables and measure how it affects the outputs. Then we can evaluate the level of precision that they truly need. Modifying and recompiling the code for each case that has to be evaluated would require a prohibitive amount of effort. Instead, the method presented in this paper relies on the use of a tool called a reduced-precision emulator (RPE) that can significantly streamline the process. Using the RPE and a list of parameters containing the precisions that will be used for each real variable in the code, it is possible within a single binary to emulate the effect on the outputs of a specific choice of precision. When we are able to emulate the effects of reduced precision, we can proceed with the design of the tests that will give us knowledge of the sensitivity of the model variables regarding their numerical precision. The number of possible combinations is prohibitively large and therefore impossible to explore. The alternative of performing a screening of the variables individually can provide certain insight about the required precision of variables, but, on the other hand, other complex interactions that involve several variables may remain hidden. Instead, we use a divide-and-conquer algorithm that identifies the parts that require high precision and establishes a set of variables that can handle reduced precision. This method has been tested using two state-of-the-art ocean models, the Nucleus for European Modelling of the Ocean (NEMO) and the Regional Ocean Modeling System (ROMS), with very promising results. Obtaining this information is crucial to build an actual mixed-precision version of the code in the next phase that will bring the promised performance benefits

Caracterización y actividad catalítica de bionanocatalizadores de platino soportados sobre sistemas proteínas de capa-s/poliuretano

Las subunidades de proteínas de capa-S poseen la capacidad de autoensamblarse sobre distintas superficies formando arreglos en la escala nanométrica. Este aspecto disparó el interés por el empleo de estas proteínas en la construcción biomolecular con prometedoras aplicaciones nanobiotecnológicas

Cholinergic dysfunction in isolated rapid eye movement sleep behaviour disorder links to impending phenoconversion

\ua9 2024 The Author(s). European Journal of Neurology published by John Wiley & Sons Ltd on behalf of European Academy of Neurology.Background and purpose: Most patients with isolated rapid eye movement sleep behaviour disorder (iRBD) progress to a parkinsonian alpha-synucleinopathy. However, time to phenoconversion shows great variation. The aim of this study was to investigate whether cholinergic and dopaminergic dysfunction in iRBD patients was associated with impending phenoconversion. Methods: Twenty-one polysomnography-confirmed iRBD patients underwent baseline 11C-donepezil and 6-Fluoro-(18F)-l-3,4-dihydroxyphenylalanine (18F-DOPA) positron emission tomography (PET). Potential phenoconversion was monitored for up to 8 years. PET images were analysed according to patients\u27 diagnoses after 3 and 8 years using linear regression. Time-to-event analysis was made with Cox regression, dividing patients into low and high tracer uptake groups. Results: Follow-up was accomplished in 17 patients. Eight patients progressed to either Parkinson\u27s disease (n = 4) or dementia with Lewy bodies (n = 4), while nine remained non-phenoconverters. Compared with non-phenoconverters, 8-year phenoconverters had lower mean 11C-donepezil uptake in the parietal (p = 0.032) and frontal cortex (p = 0.042), whereas mean 11C-donepezil uptake in 3-year phenoconverters was lower in the parietal cortex (p = 0.005), frontal cortex (p = 0.025), thalamus (p = 0.043) and putamen (p = 0.049). Phenoconverters within 3 years and 8 years had lower 18F-DOPA uptake in the putamen (p < 0.001). iRBD patients with low parietal 11C-donepezil uptake had a 13.46 (95% confidence interval 1.42;127.21) times higher rate of phenoconversion compared with those with higher uptake (p = 0.023). iRBD patients with low 18F-DOPA uptake in the most affected putamen were all phenoconverters with higher rate of phenoconversion (p = 0.0002). Conclusions: These findings suggest that cortical cholinergic dysfunction, particularly within the parietal cortex, could be a biomarker candidate for predicting short-term phenoconversion in iRBD patients. This study aligns with previous reports suggesting dopaminergic dysfunction is associated with forthcoming phenoconversion

Presynaptic Dopaminergic Imaging Characterizes Patients with REM Sleep Behavior Disorder Due to Synucleinopathy

Objective: To apply a machine learning analysis to clinical and presynaptic dopaminergic imaging data of patients with rapid eye movement (REM) sleep behavior disorder (RBD) to predict the development of Parkinson disease (PD) and dementia with Lewy bodies (DLB). Methods: In this multicenter study of the International RBD study group, 173 patients (mean age 70.5 ± 6.3 years, 70.5% males) with polysomnography-confirmed RBD who eventually phenoconverted to overt alpha-synucleinopathy (RBD due to synucleinopathy) were enrolled, and underwent baseline presynaptic dopaminergic imaging and clinical assessment, including motor, cognitive, olfaction, and constipation evaluation. For comparison, 232 RBD non-phenoconvertor patients (67.6 ± 7.1 years, 78.4% males) and 160 controls (68.2 ± 7.2 years, 53.1% males) were enrolled. Imaging and clinical features were analyzed by machine learning to determine predictors of phenoconversion. Results: Machine learning analysis showed that clinical data alone poorly predicted phenoconversion. Presynaptic dopaminergic imaging significantly improved the prediction, especially in combination with clinical data, with 77% sensitivity and 85% specificity in differentiating RBD due to synucleinopathy from non phenoconverted RBD patients, and 85% sensitivity and 86% specificity in discriminating PD-converters from DLB-converters. Quantification of presynaptic dopaminergic imaging showed that an empirical z-score cutoff of -1.0 at the most affected hemisphere putamen characterized RBD due to synucleinopathy patients, while a cutoff of -1.0 at the most affected hemisphere putamen/caudate ratio characterized PD-converters. Interpretation: Clinical data alone poorly predicted phenoconversion in RBD due to synucleinopathy patients. Conversely, presynaptic dopaminergic imaging allows a good prediction of forthcoming phenoconversion diagnosis. This finding may be used in designing future disease-modifying trials. ANN NEUROL 2024