The effects of glycaemic index of mixed meals on postprandial appetite sensation, cognitive function; and metabolic responses during intermittent exercise

Glucose is the primary fuel for the brain and also important for exercising muscle. The purpose of the thesis was to investigate the effects of the glycaemic index (GI) of mixed meals on appetite, cognitive performances and metabolic responses during intermittent exercise in recreationally active adults. Study one investigated whether a low GI (LGI) breakfast (GI = 42.5) could suppress appetite and reduce energy intake (EI) of 12 recreationally active females (28.2 ± 8.0 years) more than a high GI (HGI) breakfast (GI = 73.5). Area under the curve of the appetite score (AS AUC) following LGI breakfast was significantly greater than the HGI trial during the 60-min postprandial (pp) period (2568 ± 1027 vs. 2198 ± 821 mm∙min, p = 0.025). The HGI breakfast facilitated a stronger appetite suppressing effect up to eight hours post breakfast than the LGI trial (18834 ± 3906 vs. 21278 ± 3610 mm∙min, p = 0.028). The EI on the LGI trial day was significantly higher than on the pre-trial day (2,215 ± 576 vs. 1,748 ± 464 kcal, corrected p = 0.008). Fourteen recreationally active males (34.5 ± 8.9 years) in study two consumed the LGI (GI = 41.3) and HGI (GI = 74.3) breakfasts in the laboratory and then prescribed LGI and HGI meals in the free living environment. In line with study one, the AS AUC was significantly smaller following HGI than LGI breakfast over the 60-min pp period (2,989 ± 1,390 vs. 3,758 ± 1,290 mm∙min, p = 0.027). The HGI meals (GI = 76.9) suppressed appetite more than the LGI meals (GI = 39.6) over 12 hours on the trial day (35,454 ± 9,730 vs. 41,244 ± 8,829 mm∙min, p = 0.009) although energy balance was not different between trials. Study three investigated whether following a LGI breakfast (GI = 42.2) providing 1 g CHO kg-1 BM could result in a better vigilance and attention than a HGI breakfast (GI = 72.4), and reduced lunch EI in 16 recreationally active males (24.4 ± 3.6 years). A significant trial x time effect in the interference time of the Stroop Colour Word Task (SCWT) (p = 0.039) showed that the LGI breakfast maintained the attentional performance up to 90-min pp. Both high pre-task glucose concentration ([Glucose]) at 15-min pp and low pre-task [Glucose] at 105-min pp in the HGI trial were associated with unfavourable outcomes in vigilance in the Rapid Information Processing Task (RIPT). The LGI pre-task [Glucose] returning back to fasting level at 60-min pp was associated positively with the response time. The pre-lunch AS was a significant predictor of the lunch EI per fat free mass which explained 21% and 26% of variance in the LGI and HGI trials respectively. No significant difference was found in the ad libitum lunch EI between trials. Sixteen recreationally active males (27.8 ± 7.7 years) in study four consumed a LGI (GI = 42) and a HGI breakfast (GI = 72.8) providing 1.2 g CHO kg-1 BM consumed 60 minutes prior to intermittent running on two separate mornings. Better attentional performance at 150-min pp was found following LGI than HGI breakfast. The significant trial x time interaction in the SCWT (p = 0.045) showed the shortest interference time performed after the last exercise session in the LGI trial. The amounts of CHO and fat being oxidized were comparable between trials during three sessions of 16-min intermittent running with an average intensity of 65% V̇O2max. In conclusion, the pre-meal appetite sensation is more predictive of the subsequent meal EI than the pre-meal [Glucose]. The meal strategy for weight management in recreationally active adults may focus on greater appetite suppression by selecting HGI foods whilst maintaining healthy eating guidelines. Recreationally active males performing sports requiring high levels of vigilance and selective attention with low physical activity levels can benefit up to 60–90 min pp from the LGI breakfast. Their attentional performance can benefit from the LGI breakfast with moderate to high intermittent intensities in the late exercise period at 150–min pp. Recreationally active adults should consider the timing of meal consumption in relation to performing intermittent exercise, in order to maximize the advantages from the LGI or HGI breakfasts for cognitive performance or appetite suppression. They may be more liberal in pre-exercise food choices if substrate oxidation during intermittent running is only of their concern

Toward a Fairness-Aware Scoring System for Algorithmic Decision-Making

Scoring systems, as a type of predictive model, have significant advantages in interpretability and transparency and facilitate quick decision-making. As such, scoring systems have been extensively used in a wide variety of industries such as healthcare and criminal justice. However, the fairness issues in these models have long been criticized, and the use of big data and machine learning algorithms in the construction of scoring systems heightens this concern. In this paper, we propose a general framework to create fairness-aware, data-driven scoring systems. First, we develop a social welfare function that incorporates both efficiency and group fairness. Then, we transform the social welfare maximization problem into the risk minimization task in machine learning, and derive a fairness-aware scoring system with the help of mixed integer programming. Lastly, several theoretical bounds are derived for providing parameter selection suggestions. Our proposed framework provides a suitable solution to address group fairness concerns in the development of scoring systems. It enables policymakers to set and customize their desired fairness requirements as well as other application-specific constraints. We test the proposed algorithm with several empirical data sets. Experimental evidence supports the effectiveness of the proposed scoring system in achieving the optimal welfare of stakeholders and in balancing the needs for interpretability, fairness, and efficiency

SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand Cores

The ever-growing complexity of reinforcement learning (RL) tasks demands a distributed RL system to efficiently generate and process a massive amount of data to train intelligent agents. However, existing open-source libraries suffer from various limitations, which impede their practical use in challenging scenarios where large-scale training is necessary. While industrial systems from OpenAI and DeepMind have achieved successful large-scale RL training, their system architecture and implementation details remain undisclosed to the community. In this paper, we present a novel abstraction on the dataflows of RL training, which unifies practical RL training across diverse applications into a general framework and enables fine-grained optimizations. Following this abstraction, we develop a scalable, efficient, and extensible distributed RL system called ReaLly Scalable RL (SRL). The system architecture of SRL separates major RL computation components and allows massively parallelized training. Moreover, SRL offers user-friendly and extensible interfaces for customized algorithms. Our evaluation shows that SRL outperforms existing academic libraries in both a single machine and a medium-sized cluster. In a large-scale cluster, the novel architecture of SRL leads to up to 3.7x speedup compared to the design choices adopted by the existing libraries. We also conduct a direct benchmark comparison to OpenAI's industrial system, Rapid, in the challenging hide-and-seek environment. SRL reproduces the same solution as reported by OpenAI with up to 5x speedup in wall-clock time. Furthermore, we also examine the performance of SRL in a much harder variant of the hide-and-seek environment and achieve substantial learning speedup by scaling SRL to over 15k CPU cores and 32 A100 GPUs. Notably, SRL is the first in the academic community to perform RL experiments at such a large scale.Comment: 15 pages, 12 figures, 6 table

Evaluation of Robust Feature Descriptors for Texture Classification

Texture is an important characteristic in real and synthetic scenes. Texture analysis plays a critical role in inspecting surfaces and provides important techniques in a variety of applications. Although several descriptors have been presented to extract texture features, the development of object recognition is still a difficult task due to the complex aspects of texture. Recently, many robust and scaling-invariant image features such as SIFT, SURF and ORB have been successfully used in image retrieval and object recognition. In this paper, we have tried to compare the performance for texture classification using these feature descriptors with k-means clustering. Different classifiers including K-NN, Naive Bayes, Back Propagation Neural Network , Decision Tree and Kstar were applied in three texture image sets - UIUCTex, KTH-TIPS and Brodatz, respectively. Experimental results reveal SIFTS as the best average accuracy rate holder in UIUCTex, KTH-TIPS and SURF is advantaged in Brodatz texture set. BP neuro network works best in the test set classification among all used classifiers

Evaluation of Robust Feature Descriptors for Texture Classification

Texture is an important characteristic in real and synthetic scenes. Texture analysis plays a critical role in inspecting surfaces and provides important techniques in a variety of applications. Although several descriptors have been presented to extract texture features, the development of object recognition is still a difficult task due to the complex aspects of texture. Recently, many robust and scaling-invariant image features such as SIFT, SURF and ORB have been successfully used in image retrieval and object recognition. In this paper, we have tried to compare the performance for texture classification using these feature descriptors with k-means clustering. Different classifiers including K-NN, Naive Bayes, Back Propagation Neural Network , Decision Tree and Kstar were applied in three texture image sets - UIUCTex, KTH-TIPS and Brodatz, respectively. Experimental results reveal SIFTS as the best average accuracy rate holder in UIUCTex, KTH-TIPS and SURF is advantaged in Brodatz texture set. BP neuro network works best in the test set classification among all used classifiers

Differentiation of fetal hematopoietic stem cells requires ARID4B to restrict autocrine KITLG/KIT-Src signaling.

Balance between the hematopoietic stem cell (HSC) duality to either possess self-renewal capacity or differentiate into multipotency progenitors (MPPs) is crucial for maintaining homeostasis of the hematopoietic stem/progenitor cell (HSPC) compartment. To retain the HSC self-renewal activity, KIT, a receptor tyrosine kinase, in HSCs is activated by its cognate ligand KITLG originating from niche cells. Here, we show that AT-rich interaction domain 4B (ARID4B) interferes with KITLG/KIT signaling, consequently allowing HSC differentiation. Conditional Arid4b knockout in mouse hematopoietic cells blocks fetal HSC differentiation, preventing hematopoiesis. Mechanistically, ARID4B-deficient HSCs self-express KITLG and overexpress KIT. As to downstream pathways of KITLG/KIT signaling, inhibition of Src family kinases rescues the HSC differentiation defect elicited by ARID4B loss. In summary, the intrinsic ARID4B-KITLG/KIT-Src axis is an HSPC regulatory program that enables the differentiation state, while KIT stimulation by KITLG from niche cells preserves the HSPC undifferentiated pool

Glycogen synthase kinase-3β inactivation inhibits tumor necrosis factor-α production in microglia by modulating nuclear factor κB and MLK3/JNK signaling cascades

<p>Abstract</p> <p>Background</p> <p>Deciphering the mechanisms that modulate the inflammatory response induced by microglial activation not only improves our insight into neuroinflammation but also provides avenues for designing novel therapies that could halt inflammation-induced neuronal degeneration. Decreasing glycogen synthase kinase-3β (GSK-3β) activity has therapeutic benefits in inflammatory diseases. However, the exact molecular mechanisms underlying GSK-3β inactivation-mediated suppression of the inflammatory response induced by microglial activation have not been completely clarified. Tumor necrosis factor-α (TNF-α) plays a central role in injury caused by neuroinflammation. We investigated the regulatory effect of GSK-3β on TNF-α production by microglia to discern the molecular mechanisms of this modulation.</p> <p>Methods</p> <p>Lipopolysaccharide (LPS) was used to induce an inflammatory response in cultured primary microglia or murine BV-2 microglial cells. Release of TNF-α was measured by ELISA. Signaling molecules were analyzed by western blotting, and activation of NF-κB and AP-1 was measured by ELISA-based DNA binding analysis and luciferase reporter assay. Protein interaction was examined by coimmunoprecipitation.</p> <p>Results</p> <p>Inhibition of GSK-3β by selective GSK-3β inhibitors or by RNA interference attenuated LPS-induced TNF-α production in cultured microglia. Exploration of the mechanisms by which GSK-3β positively regulates inflammatory response showed that LPS-induced IκB-α degradation, NF-κBp65 nuclear translocation, and p65 DNA binding activity were not affected by inhibition of GSK-3β activity. However, GSK-3β inactivation inhibited transactivation activity of p65 by deacetylating p65 at lysine 310. Furthermore, we also demonstrated a functional interaction between mixed lineage kinase 3 (MLK3) and GSK-3β during LPS-induced TNF-α production in microglia. The phosphorylated levels of MLK3, MKK4, and JNK were increased upon LPS treatment. Decreasing GSK-3β activity blocked MLK3 signaling cascades through disruption of MLK3 dimerization-induced autophosphorylation, ultimately leading to a decrease in TNF-α secretion.</p> <p>Conclusion</p> <p>These results suggest that inactivation of GSK-3β might represent a potential strategy to downregulate microglia-mediated inflammatory processes.</p

Television Meets Facebook: The Correlation between TV Ratings and Social Media

Abstract This study examines the relationship between social media site Facebook and TV ratings drawing from audience factors of integration model of audience behavior. Based on context of Taiwan television network programs, this study collected measures for Facebook likes, shares, comments, posts for three genres of television shows and their Nielsen ratings over a period of eleven weeks, resulting in the size of sample more than 130 observations. This study applied multiple regression models and determined that the key social media measures correlate with TV ratings. In essence, TV shows with higher number of posts and engagement are likely to relate to higher ratings, special in drama shows. Subsequently, this study constructed the TV prediction models with measures for Facebook via SVR. The results suggested that prediction models are a good forecasting of which MAPE was between 10% -20%, even less than 10%. This implies that TV network should be motivated to invest in social media and engage their audience and analysts can use social media as a mechanism of exante forecasting