Identification of Bioactive Compounds of L.: Permutation Test Allows Differentiation among "Triguero" and Hybrid Green Varieties.

In this study, we determined the phytochemical profile of the Spanish "triguero" asparagus landrace "verde-morado" ( L.), a wild traditional landrace, and the improved "triguero" HT-801, together with two commercial green asparagus varieties. For comparison, we used reverse-phase high-performance liquid chromatography coupled with diode array electrospray time-of-flight mass spectrometry (RP-HPLC-DAD-ESI-TOF/MS) followed by a permutation test applied using a resampling methodology valid under a relaxed set of assumptions, such as i.

Author Correction: SciPy 1.0: fundamental algorithms for scientific computing in Python.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

SciPy 1.0: fundamental algorithms for scientific computing in Python.

SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.

Group-level spatio-temporal pattern recovery in MEG decoding using multi-task joint feature learning.

Background: The use of machine learning models to discriminate between patterns of neural activity has become in recent years a standard analysis approach in neuroimaging studies. Whenever these models are linear, the estimated parameters can be visualized in the form of brain maps which can aid in understanding how brain activity in space and time underlies a cognitive function. However, the recovered brain maps often suffer from lack of interpretability, especially in group analysis of multi-subject data.

Word meaning in the ventral visual path: a perceptual to conceptual gradient of semantic coding.

The meaning of words referring to concrete items is thought of as a multidimensional representation that includes both perceptual (e.g., average size, prototypical color) and conceptual (e.

Data-driven HRF estimation for encoding and decoding models.

Despite the common usage of a canonical, data-independent, hemodynamic response function (HRF), it is known that the shape of the HRF varies across brain regions and subjects. This suggests that a data-driven estimation of this function could lead to more statistical power when modeling BOLD fMRI data. However, unconstrained estimation of the HRF can yield highly unstable results when the number of free parameters is large.

Machine learning for neuroimaging with scikit-learn.

Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g.

Multi-subject dictionary learning to segment an atlas of brain spontaneous activity.

Fluctuations in brain on-going activity can be used to reveal its intrinsic functional organization. To mine this information, we give a new hierarchical probabilistic model for brain activity patterns that does not require an experimental design to be specified. We estimate this model in the dictionary learning framework, learning simultaneously latent spatial maps and the corresponding brain activity time-series.