PO-152 The effects of 4 weeks training mediates apelin on the p-AMPK(Thr172)/AMPK ratio in skeletal muscle of mice: There is no full text article associated with this abstract

Objective To investigate the effects of 4 weeks aerobic exercise mediates apelin on the p-AMPK(Thr172)/AMPK ratio in skeletal muscle of mice. Methods The C57BL/6J wild type mice(n=40) were randomly divided into four groups: control group (WC), exercise group (WE), apelin injection control group (AC) and apelin injection exercise group (AE), with 10 mice in each group. Apelin injection group mice were intraperitoneally injected with apelin (0.1 μmol/kg/day) for 4 weeks. At the same time, the exercise groups mice underwent 60min/day treadmill running with a slope of  5°at the speed of 15m/min for 2 weeks, and the speed was adjusted to 20m/min in the later 2 weeks. 48 h after the final exercise session quadriceps muscles were harvest. The protein expression of apelin, APJ, AMPKα and p-AMPKα (Thr172) in skeletal muscle was determined by Western Blot. Results (1) Compared with WC group, the protein expression of apelin , APJ and p-AMPKα (Thr172)/AMPKα ratio  in AC group skeletal muscle of mice were increased; (2) Compared with WE group , the p-AMPKα (Thr172) / AMPKα ratio in AE group skeletal muscle of mice were  increased. Conclusions Apelin supplementation for 4 weeks can up-regulate AMPK protein activity in skeletal muscle both in sedentary group and exercise group

COOOL: A Learning-To-Rank Approach for SQL Hint Recommendations

Query optimization is a pivotal part of every database management system (DBMS) since it determines the efficiency of query execution. Numerous works have introduced Machine Learning (ML) techniques to cost modeling, cardinality estimation, and end-to-end learned optimizer, but few of them are proven practical due to long training time, lack of interpretability, and integration cost. A recent study provides a practical method to optimize queries by recommending per-query hints but it suffers from two inherited problems. First, it follows the regression framework to predict the absolute latency of each query plan, which is very challenging because the latencies of query plans for a certain query may span multiple orders of magnitude. Second, it requires training a model for each dataset, which restricts the application of the trained models in practice. In this paper, we propose COOOL to predict Cost Orders of query plans to cOOperate with DBMS by Learning-To-Rank. Instead of estimating absolute costs, COOOL uses ranking-based approaches to compute relative ranking scores of the costs of query plans. We show that COOOL is theoretically valid to distinguish query plans with different latencies. We implement COOOL on PostgreSQL, and extensive experiments on join-order-benchmark and TPC-H data demonstrate that COOOL outperforms PostgreSQL and state-of-the-art methods on single-dataset tasks as well as a unified model for multiple-dataset tasks. Our experiments also shed some light on why COOOL outperforms regression approaches from the representation learning perspective, which may guide future research

PO-145 Effects of HIF-1α on Nrf2-ARE antioxidant signal in mice skeletal muscle after acute exhaustive exercise

Objective Hypoxia or exercise could lead to oxidative stress. Hypoxia inducible factor-1 (HIF-1) is an oxygen sensor and the expression of its α subunit can be regulated by hypoxia. NF-E2-related factor 2 (Nrf2) is an important modifier of cellular responses to oxidative stress. A major mechanism in defense oxidative stress is the activation of the Nrf2-ARE antioxidant pathway. But whether the increase of HIF-1α could affect the Nrf2-ARE antioxidant signal, and further influence the oxidative stress status in vivo remains unknown. In this study, we wished to examine the effect of HIF-1α on Nrf2-ARE antioxidant pathway in mice skeletal muscle after acute exhaustion exercise. Methods HIF-1α high expression (H) and C57BL/6J mice(W) were used at 20 respectively and each kind of mice were randomly divided into two groups: control (C) and exercise (E). The treadmill exercise was preformed at the acute exhaustion exercise. On the day of acute exercise, mice allocated to perform treadmill running were subject to 5% incline and 5min at 10m/min, and then increased 3m/min every 3 minutes. Mice were sacrificed at the indicated time points following treadmill running.Nrf2, phosphor-Nrf2 (Ser40), nuclear Nrf2 protein were measured by Western Blot and Nrf2-ARE binding activity, the mRNA and proetin levels of Nrf2 target genes, key antioxidant enzymes (SOD1, SOD2, CAT, NQO-1) and ROS level, were also measured in skeletal muscles after the interventions. Results (1)The results showed that compared with WC, RNA and protein expression level of Nrf2 were increased in HC skeletal muscles. Nrf2-ARE binding activity, Nrf2 target gene SOD1, SOD2, NQO-1 mRNA expression and NQO-1 protein expression were also increased in HC skeletal muscles. Meanwhile, ROS level in HC skeletal muscles decreased significantly. (2) After the acute exhaustion exercise, high HIF-1α expression mice (HE) had higher expression of p-Nrf2(Ser 40) and nuclear Nrf2 protein than the wide type mice(WE). The mRNA expression of SOD1 and mRNA /protein of NQO-1 in HE increased as well. In contrast, ROS level decreased significantly in HE muscles. ConclusionsThe result indicated the proper high expression of HIF-1α could promote the antioxidant capacity of skeletal muscle in mice through Nrf2-ARE pathway

OR-035 Effect of Aerobic Training on the Exercise Capacity of Apelin Knock-out Mice: There is no full text article associated with this abstract

Objective Aerobic training is considered to be an effective way to enhance the body’s exercise capacity which is closely related to the improvement of skeletal muscle energy metabolism. And as a new myokine, apelin has been found to play a key role in regulating the energy metabolism of skeletal muscle. However, whether the loss of apelin gene affects exercise capacity and what role aerobic training play in it remains unknown. This study was designed to investigate the effect of apelin on exercise capacity during aerobic training and to provide a theoretical basis for the mechanism of aerobic exercise affecting exercise capacity. Methods Male C57BL/6J wild type mouse(n=20) and apelin knock-out mouse(n=20) were assigned by random allocation to four groups(n=10): wild type control(WC), wild type exercised(WE), apelin knock-out control(KC) and apelin knock-out exercised(KE). Exercise training consisted of treadmill running 60 minutes/day ×6 days/week for 4 weeks. The training intensity corresponded to the 70-75% maximum oxygen uptake of mice. The running speed was 15m/min with an incline of +5° in the first 2 weeks and subsequently adjusted to 20m/min according to the maximum oxygen uptake in the last 2 weeks. On the day after training, all groups were forced to perform a incremental exercise test to exhaustion. This test was started with an incline of +5°and a speed of 10 m/min for 5 min. After this initial phase, the speed was progressively increased by 3m/min every 3 min until animal exhausted. The maximum running speed, movement time and distance were recorded during the test. Results Compared with group WC, the maximum running speed, movement time and distance of group KC were significantly decreased(P<0.01). And the maximum running speed, movement time and distance of group KE were clearly higher than those of group KC(P<0.01). There is no significant difference between group WE and group WC, and between group KE and group WE. Conclusions The exercise capacity of mice was significantly decreased because of knocking out the apelin gene, and the exercise ability of apelin knock-out mice can be clearly enhanced by aerobic training

Structural Attack to Anonymous Graph of Social Networks

With the rapid development of social networks and its applications, the demand of publishing and sharing social network data for the purpose of commercial or research is increasing. However, the disclosure risks of sensitive information of social network users are also arising. The paper proposes an effective structural attack to deanonymize social graph data. The attack uses the cumulative degree of n-hop neighbors of a node as the regional feature and combines it with the simulated annealing-based graph matching method to explore the nodes reidentification in anonymous social graphs. The simulation results on two social network datasets show that the attack is feasible in the nodes reidentification in anonymous graphs including the simply anonymous graph, randomized graph and k-isomorphism graph