Muscle fiber typology substantially influences time to recover from high-intensity exercise

Human fast-twitch muscle fi- bers generate high power in a short amount of time but are easily fatigued, whereas slow-twitch fibers are more fatigue resistant. The transfer of this knowledge to coaching is hampered by the invasive nature of the current evaluation of muscle typology by biopsies. Therefore, a noninvasive method was developed to estimate muscle typology through proton magnetic resonance spectroscopy in the gastrocnemius. The aim of this study was to investigate whether male subjects with an a priori-determined fast typology (FT) are character- ized by a more pronounced Wingate exercise-induced fatigue and delayed recovery compared with subjects with a slow typology (ST). Ten subjects with an estimated higher percentage of fast-twitch fibers and 10 subjects with an estimated higher percentage of slow-twitch fibers underwent the test protocol, consisting of three 30-s all-out Wingate tests. Recovery of knee extension torque was evaluated by maximal voluntary contraction combined with electrical stimulation up to 5 h after the Wingate tests. Although both groups delivered the same mean power across all Wingates, the power drop was higher in the FT group (—61%) compared with the ST group (—41%). The torque at maximal voluntary contraction had fully recovered in the ST group after 20 min, whereas the FT group had not yet recovered 5 h into recovery. This noninvasive estimation of muscle typology can predict the extent of fatigue and time to recover following repeated all-out exercise and may have applications as a tool to individualize training and recovery cycles. NEW & NOTEWORTHY A one-fits-all training regime is present in most sports, though the same training implies different stimuli in athletes with a distinct muscle typology. Individualization of training based on this muscle typology might be important to optimize per- formance and to lower the risk for accumulated fatigue and potentially injury. When conducting research, one should keep in mind that the muscle typology of participants influences the severity of fatigue and might therefore impact the results

From Pokémon go to hashtags: how digital and social media is changing the church

What impact is social and digital media having on religion? Here Bex Lewis explores its impact on the Church. She finds that as the Church starts engaging its followers online it has become a space to debate issues, connect to others and reach people and, in turn, humanise the Church. Church activities are being taken back out into the online world and Church leaders have taken to digital platforms to speak out on social and political debates from Brexit to international terrorism. Public conversations have always been key for public theology but the Information Age we’re living in means that many of these public conversations will today take place in the digital sphere

Review of latest developments of ions sources

International audienc

Combining Explanation and Argumentation in Dialogue

Explanation and argumentation can be used together in such a way that evidence, in the form of arguments, is used to support explanations. In a hybrid system, the interlocking of argument and explanation compounds the problem of how to differentiate between them. The distinction is imperative if we want to avoid the mistake of treating something as fallacious while it is not. Furthermore, the two forms of reasoning may influence dialogue protocol and strategy. In this paper a basis for solving the problem is proposed using a dialogue model where the context of the dialogue is used to distinguish argument from explanation

GANIL status report

International audienc

Learning Curves for Mutual Information Maximization

An unsupervised learning procedure based on maximizing the mutual information between the outputs of two networks receiving different but statistically dependent inputs is analyzed (Becker and Hinton, Nature, 355, 92, 161). For a generic data model, I show that in the large sample limit the structure in the data is recognized by mutual information maximization. For a more restricted model, where the networks are similar to perceptrons, I calculate the learning curves for zero-temperature Gibbs learning. These show that convergence can be rather slow, and a way of regularizing the procedure is considered.Comment: 13 pages, to appear in Phys.Rev.

Argument Revision

Understanding the dynamics of argumentation systems is a crucial component in the de-velopment of computational models of argument that are used as representations of belief. To that end, in this paper, we introduce a model of Argument Revision, presented in terms of the contraction and revision of a system of structured argumentation. Argument Revi-sion is influenced by the AGM model of belief revision, but with certain key differences. Firstly, Argument Revision involves modifying the underlying model (system of argumen-tation) from which beliefs are derived, allowing for a finer-grained approach to modifying beliefs. Secondly, the richer structure provided by a system of argumentation permits a determination of minimal change based on quantifiable effects on the system as opposed to qualitative criteria such as entrenchment orderings. Argument Revision does, however, retain a close link to the AGM approach to belief revision. A basic set of postulates for rational revisions and contractions in Argument Revision is proposed; these postulates are influenced by, and capture the spirit of, those found in AGM belief revision. After specifying a determination of minimal change, based on measurable effects on the system, we conclude the paper by going on to show how Argument Revision can be used as a strategic tool by a participant in a multi-agent dialogue, assisting with commitment retraction and dishonesty. In systems of argumentation that contain even small knowledge bases, it is difficult for a dialogue participant to fully assess the impact of seemingly trivial changes to that knowledge base, or other parts of the system; we demonstrate, by means of an example, that Argument Revision solves this problem through a determination of minimal change that is justifiable and intuitive

Discovering Restricted Regular Expressions with Interleaving

Discovering a concise schema from given XML documents is an important problem in XML applications. In this paper, we focus on the problem of learning an unordered schema from a given set of XML examples, which is actually a problem of learning a restricted regular expression with interleaving using positive example strings. Schemas with interleaving could present meaningful knowledge that cannot be disclosed by previous inference techniques. Moreover, inference of the minimal schema with interleaving is challenging. The problem of finding a minimal schema with interleaving is shown to be NP-hard. Therefore, we develop an approximation algorithm and a heuristic solution to tackle the problem using techniques different from known inference algorithms. We do experiments on real-world data sets to demonstrate the effectiveness of our approaches. Our heuristic algorithm is shown to produce results that are very close to optimal.Comment: 12 page