Nutritional strategies for promoting fat utilization and delaying the onset of fatigue during prolonged exercise

Carbohydrate ingestion before and during endurance exercise delays the onset of fatigue (reduced power output). Therefore, endurance athletes are recommended to ingest diets high in carbohydrate (70% of total energy) during competition and training. However, increasing the availability of plasma free fatty acids has been shown to slow the rate of muscle and liver glycogen depletion by promoting the utilization of fat. Ingested fat, in the form of long-chain (C 16-22 ) triacylglycerols, is largely unavailable during acute exercise, but medium-chain (C 8-10 ) triacylglycerols are rapidly absorbed and oxidized. We have shown that the ingestion of medium-chain triacylglycerols in combination with carbohydrate spares muscle carbohydrate stores during 2 h of submaximal (< 70% VO 2 peak) cycling exercise, and improves 40 km time-trial performance. These data suggest that by combining carbohydrate and medium-chain triacylglycerols as a pre-exercise supplement and as a nutritional supplement during exercise, fat oxidation will be enhanced, and endogenous carbohydrate will be spared. We have also examined the chronic metabolic adaptations and effects on substrate utilization and endurance performance when athletes ingest a diet that is high in fat (> 70% by energy). Dietary fat adaptation for a period of at least 2-4 weeks has resulted in a nearly two-fold increase in resistance to fatigue during prolonged, low- to moderate-intensity cycling (< 70% VO 2 peak). Moreover, preliminary studies suggest that mean cycling 20 km time-trial performance following prolonged submaximal exercise is enhanced by 80 s after dietary fat adaptation and 3 days of carbohydrate loading. Thus the relative contribution of fuel substrate to prolonged endurance activity may be modified by training, pre-exercise feeding, habitual diet, or by artificially altering the hormonal milieu or the availability of circulating fuels. The time course and dose-response of these effects on maximizing the oxidative contribution of fat for exercise metabolism and in exercise performance have not been systematically studied during moderate- to high-intensity exercise in humans.     

Engaging young students in scientific investigations: prompting for meaningful reflection

This study examined the verbal prompts a tutor used to promote reflection and young students’ responses to these prompts. Seven children (ages 8–12) participated in 260 min of one-on-one tutoring to learn scientific concepts related to gear movement; the tutor spontaneously provided these students with 763 prompts for reflection. Prompts reliably induced reflection: Students responded verbally 87% of the time. Turn-by-turn discourse analysis revealed seven distinct types of prompts and 11 distinct types of verbal responses. High-level prompts were strongly associated with high-level responses. A log-multiplicative association model with two dimensions (temporality and certainty) represented the relationships between prompt and response types; from this model, odds ratios estimated the strength of association between specific pairs of prompt and response types. Findings are discussed in terms of the effects that reflection may have on students’ developing understanding of scientific concepts.     

Controversial Rap Themes,Gender Portrayals and Skin Tone Distortion: A Content Analysis of Rap Music Videos

A content analysis of rap music videos aired on BET, MTV, and VH1 examined the occurrence of controversial themes, gender differences, and skin tone distortion. The results of this study found that current rap music videos have placed an emphasis on themes of materialism and misogyny. Additionally, men and women in the videos differ in their portrayal of these themes. Specifically, female characters are significantly more likely to appear as objects of sexuality. Men and women also differ in their appearance with more African American females appearing to have Eurocentric features. Implications and suggestions for future research are discussed.     

Making the case for a material-dialogic approach to science education

ABSTRACT

Science is concerned with understanding the world. As such, engaging with the materiality of that world is integral to both empirical experimentation and theorising within science. However, it has been recognised for some time that the way scientists learn about the world and the way that young people learn about science cannot be simply equated. This difference has been pronounced in recent decades by the dominance of constructivist and social-constructivist theories of learning, which focus on the development of concepts in the minds of people. Whilst these theories have yielded insight into the complexity of learning, the role of the material remains undertheorised, not only within practical science inquiry, but also in relation to the broader materiality of classrooms. Through a detailed critical literature review, this paper demonstrates the need for a stronger theoretical frame through which to understand the role of the material in the learning and pedagogy of science. Building upon the gaps and possibilities that this review reveals, we outline a new material-dialogic theory via a synthesis of Barad’s Agential Realism and Bakhtinian dialogic theory. The significance of this paper lies in offering a theoretical basis for more effective practice.     

Neural Semantic Personalized Ranking for item cold-start recommendation

Recommender systems help users deal with information overload and enjoy a personalized experience on the Web. One of the main challenges in these systems is the item cold-start problem which is very common in practice since modern online platforms have thousands of new items published every day. Furthermore, in many real-world scenarios, the item recommendation tasks are based on users’ implicit preference feedback such as whether a user has interacted with an item. To address the above challenges, we propose a probabilistic modeling approach called Neural Semantic Personalized Ranking (NSPR) to unify the strengths of deep neural network and pairwise learning. Specifically, NSPR tightly couples a latent factor model with a deep neural network to learn a robust feature representation from both implicit feedback and item content, consequently allowing our model to generalize to unseen items. We demonstrate NSPR’s versatility to integrate various pairwise probability functions and propose two variants based on the Logistic and Probit functions. We conduct a comprehensive set of experiments on two real-world public datasets and demonstrate that NSPR significantly outperforms the state-of-the-art baselines.     

Full Steam Ahead: A Conceptual Analysis of User-Supplied Tags on Steam

This article describes a conceptual analysis of user-generated tags applied to video games in the Steam video game distribution system. The research team scraped all user-generated tags available on Steam and then conducted a conceptual analysis of the tags, sorting them into categories and comparing them to the current version of the Video Game Metadata Schema. This analysis allowed the team to identify new metadata elements and terms useful to game players. We present a discussion covering the major issues in organizing the terms, as well as the implications for the future work in the area of video game metadata.