Transformaciones estructurales de las fibras para conferirles nuevas propiedades.

En este artículo, procuraremos definir los medios teóricos que pueden ser ideados para obtener el resultado deseado, tanto en lo que se refiere a las fibras naturales a las que se quiere hacer adquirir las propiedades de las fibras sintéticas, como a lo que hace referencia a las fibras «químicas» tradicionales a las que se quiere comunicar ciertas cualidades de las fibras naturales.Peer Reviewe

Transformaciones estructurales de las fibras para conferirles nuevas propiedades (continuación).

En este artículo, procuraremos definir los medios teóricos que pueden ser ideados para obtener el resultado deseado, tanto en lo que se refiere a las fibras naturales a las que se quiere hacer adquirir las propiedades de las fibras sintéticas, como a lo que hace referencia a las fibras «químicas» tradicionales a las que se quiere comunicar ciertas cualidades de las fibras naturales.Peer Reviewe

Relaciones entre la estructura química y las propiedades de las fibras artificiales y sintéticas.

Las macromoléculas se organizan en el espacio, en conglomerados más o menos ordenados según su propia estructura. Estos agregados tienen una morfología bastante parecida a la de la fibra, en particular una longitud que sobrepasa, bastante, sus otras dos dimensiones. Son las fibrillas. Según RIBI [Nature, 168, 1082 (1951)] la fibrilla elemental será un elemento de estructura general, a hallar en casi la totalidad de las fibras, cualesquiera que sea su origen, elemento que pueda originarse cuando las macromoléculas lineales, se ordenan en un enrejado. Si su longitud permanece indefinida e invariable, parece que su sección tengan unas dimensiones bastantes constantes de fibra a fibra, del orden de 70 a 110º A de largo. No se dispone de ningún dato en cuanto a las propiedades intrínsecas de las fibrillas, pues ningún medio ha podido ser puesto en práctica para efectuar medidas directas a tan pequeña escala. Debemos pues proceder «por comparaciones», haciendo variar por ejemplo la estructura interna de las fibrillas y examinando la variación de los parámetros mensurables de la fibra, o recíprocamente sometiendo la fibra a esfuerzos diversos y examinando cómo se modifica la estructura fibrilar.Peer Reviewe

Relaciones entre la estructura química y las propiedades de las fibras artificiales y sintéticas.

Las operaciones de hilatura están destinadas a paralelizar las fibras en una cinta y a mantener la cohesión del conjunto mediante una torsión regular. La arquitectura final del hilo dependerá de la regularidad del paralelismo de las fibras, de la homogeneidad de distribución en longitud y en diámetro de las mismas, de que su forma natural sea rectilínea o no. Seguidamente, los hilos se convierten en tejidos o tricots, y la arquitectura del artículo se hace en extremo compleja.Peer Reviewe

Prior-based Coregistration and Cosegmentation

We propose a modular and scalable framework for dense coregistration and cosegmentation with two key characteristics: first, we substitute ground truth data with the semantic map output of a classifier; second, we combine this output with population deformable registration to improve both alignment and segmentation. Our approach deforms all volumes towards consensus, taking into account image similarities and label consistency. Our pipeline can incorporate any classifier and similarity metric. Results on two datasets, containing annotations of challenging brain structures, demonstrate the potential of our method.Comment: The first two authors contributed equall

Spectral Graph Convolutions for Population-based Disease Prediction

Exploiting the wealth of imaging and non-imaging information for disease prediction tasks requires models capable of representing, at the same time, individual features as well as data associations between subjects from potentially large populations. Graphs provide a natural framework for such tasks, yet previous graph-based approaches focus on pairwise similarities without modelling the subjects' individual characteristics and features. On the other hand, relying solely on subject-specific imaging feature vectors fails to model the interaction and similarity between subjects, which can reduce performance. In this paper, we introduce the novel concept of Graph Convolutional Networks (GCN) for brain analysis in populations, combining imaging and non-imaging data. We represent populations as a sparse graph where its vertices are associated with image-based feature vectors and the edges encode phenotypic information. This structure was used to train a GCN model on partially labelled graphs, aiming to infer the classes of unlabelled nodes from the node features and pairwise associations between subjects. We demonstrate the potential of the method on the challenging ADNI and ABIDE databases, as a proof of concept of the benefit from integrating contextual information in classification tasks. This has a clear impact on the quality of the predictions, leading to 69.5% accuracy for ABIDE (outperforming the current state of the art of 66.8%) and 77% for ADNI for prediction of MCI conversion, significantly outperforming standard linear classifiers where only individual features are considered.Comment: International Conference on Medical Image Computing and Computer-Assisted Interventions (MICCAI) 201

Sawtooth period changes with mode conversion current drive on Alcator C-Mod

DEFC0299ER54512. Reproduction,  translation,  publication,  use and disposal,  in whole or in part,  by or for the United States government is permitted. Submitted for publication to Plasma Physics and Controlled Fusion. Sawtooth period changes with mode conversion current drive on Alcator C-Mo

Human brain mapping: a systematic comparison of parcellation methods for the human cerebral cortex

The macro-connectome elucidates the pathways through which brain regions are structurally connected or functionally coupled to perform a specific cognitive task. It embodies the notion of representing and understanding all connections within the brain as a network, while the subdivision of the brain into interacting functional units is inherent in its architecture. As a result, the definition of network nodes is one of the most critical steps in connectivity network analysis. Although brain atlases obtained from cytoarchitecture or anatomy have long been used for this task, connectivity-driven methods have arisen only recently, aiming to delineate more homogeneous and functionally coherent regions. This study provides a systematic comparison between anatomical, connectivity-driven and random parcellation methods proposed in the thriving field of brain parcellation. Using resting-state functional MRI data from the Human Connectome Project and a plethora of quantitative evaluation techniques investigated in the literature, we evaluate 10 subject-level and 24 groupwise parcellation methods at different resolutions. We assess the accuracy of parcellations from four different aspects: (1) reproducibility across different acquisitions and groups, (2) fidelity to the underlying connectivity data, (3) agreement with fMRI task activation, myelin maps, and cytoarchitectural areas, and (4) network analysis. This extensive evaluation of different parcellations generated at the subject and group level highlights the strengths and shortcomings of the various methods and aims to provide a guideline for the choice of parcellation technique and resolution according to the task at hand. The results obtained in this study suggest that there is no optimal method able to address all the challenges faced in this endeavour simultaneously