Reverse engineering of model transformations for reusability

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-08789-4_14Proceedings of 7th International Conference, ICMT 2014, Held as Part of STAF 2014, York, UK, July 21-22, 2014Reuse techniques are key for the industrial adoption of Model-Driven Engineering (MDE). However, while reusability has been successfully applied to programming languages, its use is scarce in MDE and, in particular, in model transformations. In previous works, we developed an approach that enables the reuse of model transformations for different meta-models. This is achieved by defining reusable components that encapsulate a generic transformation template and expose an interface called concept declaring the structural requirements that any meta-model using the component should fulfil. Binding the concept to one of such meta-models induces an adaptation of the template, which becomes applicable to the meta-model. To facilitate reuse, concepts need to be concise, reflecting only the minimal set of requirements demanded by the transformation. In this paper, we automate the reverse engineering of existing transformations into reusable transformation components. To make a transformation reusable, we use the information obtained from its static analysis to derive a concept that is minimal with respect to the transformation and maximizes its reuse opportunities, and then evolve the transformation accordingly. The paper describes a prototype implementation and an evaluation using transformations from the ATL zoo.This work has been funded by the Spanish Ministry of Economy and Competitivity with project “Go Lite” (TIN2011-24139

Current Understanding of the Anatomy, Physiology, and Magnetic Resonance Imaging of Neurofluids: Update From the 2022 “<scp>ISMRM</scp> Imaging Neurofluids Study group” Workshop in Rome

Neurofluids is a term introduced to define all fluids in the brain and spine such as blood, cerebrospinal fluid, and interstitial fluid. Neuroscientists in the past millennium have steadily identified the several different fluid environments in the brain and spine that interact in a synchronized harmonious manner to assure a healthy microenvironment required for optimal neuroglial function. Neuroanatomists and biochemists have provided an incredible wealth of evidence revealing the anatomy of perivascular spaces, meninges and glia and their role in drainage of neuronal waste products. Human studies have been limited due to the restricted availability of noninvasive imaging modalities that can provide a high spatiotemporal depiction of the brain neurofluids. Therefore, animal studies have been key in advancing our knowledge of the temporal and spatial dynamics of fluids, for example, by injecting tracers with different molecular weights. Such studies have sparked interest to identify possible disruptions to neurofluids dynamics in human diseases such as small vessel disease, cerebral amyloid angiopathy, and dementia. However, key differences between rodent and human physiology should be considered when extrapolating these findings to understand the human brain. An increasing armamentarium of noninvasive MRI techniques is being built to identify markers of altered drainage pathways. During the three‐day workshop organized by the International Society of Magnetic Resonance in Medicine that was held in Rome in September 2022, several of these concepts were discussed by a distinguished international faculty to lay the basis of what is known and where we still lack evidence. We envision that in the next decade, MRI will allow imaging of the physiology of neurofluid dynamics and drainage pathways in the human brain to identify true pathological processes underlying disease and to discover new avenues for early diagnoses and treatments including drug delivery.Evidence level: 1Technical Efficacy: Stage

Frequency drift in MR spectroscopy at 3T

Purpose: Heating of gradient coils and passive shim components is a common cause of instability in the B-0 field, especially when gradient intensive sequences are used. The aim of the study was to set a benchmark for typical drift encountered during MR spectroscopy (MRS) to assess the need for real-time field-frequency locking on MRI scanners by comparing field drift data from a large number of sites.Method: A standardized protocol was developed for 80 participating sites using 99 3T MR scanners from 3 major vendors. Phantom water signals were acquired before and after an EPI sequence. The protocol consisted of: minimal preparatory imaging; a short pre-fMRI PRESS; a ten-minute fMRI acquisition; and a long post-fMRI PRESS acquisition. Both pre- and post-fMRI PRESS were non-water suppressed. Real-time frequency stabilization/adjustment was switched off when appropriate. Sixty scanners repeated the protocol for a second dataset. In addition, a three-hour post-fMRI MRS acquisition was performed at one site to observe change of gradient temperature and drift rate. Spectral analysis was performed using MATLAB. Frequency drift in pre-fMRI PRESS data were compared with the first 5:20 minutes and the full 30:00 minutes of data after fMRI. Median (interquartile range) drifts were measured and showed in violin plot. Paired t-tests were performed to compare frequency drift pre- and post-fMRI. A simulated in vivo spectrum was generated using FID-A to visualize the effect of the observed frequency drifts. The simulated spectrum was convolved with the frequency trace for the most extreme cases. Impacts of frequency drifts on NAA and GABA were also simulated as a function of linear drift. Data from the repeated protocol were compared with the corresponding first dataset using Pearson's and intraclass correlation coefficients (ICC).Results: Of the data collected from 99 scanners, 4 were excluded due to various reasons. Thus, data from 95 scanners were ultimately analyzed. For the first 5:20 min (64 transients), median (interquartile range) drift was 0.44 (1.29) Hz before fMRI and 0.83 (1.29) Hz after. This increased to 3.15 (4.02) Hz for the full 30 min (360 transients) run. Average drift rates were 0.29 Hz/min before fMRI and 0.43 Hz/min after. Paired t-tests indicated that drift increased after fMRI, as expected (p &lt; 0.05). Simulated spectra convolved with the frequency drift showed that the intensity of the NAA singlet was reduced by up to 26%, 44 % and 18% for GE, Philips and Siemens scanners after fMRI, respectively. ICCs indicated good agreement between datasets acquired on separate days. The single site long acquisition showed drift rate was reduced to 0.03 Hz/min approximately three hours after fMRI.Discussion: This study analyzed frequency drift data from 95 3T MRI scanners. Median levels of drift were relatively low (5-min average under 1 Hz), but the most extreme cases suffered from higher levels of drift. The extent of drift varied across scanners which both linear and nonlinear drifts were observed.</p

¿Cómo hacer un análisis cuantitativo de datos de tipo descriptivo con el paquete estadístico SPSS?

En la actualidad es difícil hablar de procesos estadísticos de análisis cuantitativo de datos sin hacer referencia a la informática aplicada a la investigación. Estos recursos informáticos se basan a menudo en paquetes de programas informáticos que tienen por objetivo ayudar al/la investigador/a en la fase de análisis de datos. En estos momentos uno de los paquetes más perfeccionados y completos es el SPSS (Statistical Package for the Social Sciences). El SPSS es un paquete de programas para llevar a cabo el análisis estadístico de los datos. Constituye una aplicación estadística muy potente, de la que se han ido desarrollando diversas versiones desde sus inicios, en los años setenta. En esta ficha las salidas de ordenador que se presentan corresponden a la versión 11.0.1. No obstante, aunque la forma ha ido variando desde sus inicios, su funcionamiento sigue siendo muy similar entre las diferentes versiones. Antes de iniciarnos en la utilización de las aplicaciones del SPSS es importante familiarizarse con algunas de las ventanas que más usaremos. Al entrar al SPSS lo primero que nos encontramos es el editor de datos. Esta ventana visualiza, básicamente, los datos que iremos introduciendo. El editor de datos incluye dos opciones: la vista de los datos y la de las variables. Estas opciones pueden seleccionarse a partir de las dos pestañas que se presentan en la parte inferior. La vista de datos contiene el menú general y la matriz de datos. Esta matriz está estructurada ubicando los casos en las filas y las variables en las columnas