Regulation of energy metabolism in skeletal muscle cells by PPARδ activation, in vitro exercise and perilipin 2 ablation

The prevalence of type 2 diabetes (T2D) has increased worldwide during the last decades. Obesity and accumulation of lipids in skeletal muscles are strongly associated with T2D. Much focus has been on the possibility of increasing lipid utilization by exercise or pharmacologically to avoid lipid accumulation in muscle. This thesis aimed to study regulation of energy metabolism related to obesity and T2D in cultured human myotubes by peroxisome proliferator-activated receptor δ (PPARδ) activation and in vitro exercise. The lipid droplet (LD)-associated protein, perilipin 2 (PLIN2), is one of the PPARδ target genes, and to study the functional role of PLIN2 and LDs in muscle energy metabolism we also examined myotube cultures established from PLIN2+/+ and PLIN2-/- mice. The results presented in this thesis suggest that myotubes to some extent retain the phenotype of their donors, and that responses to in vitro exercise reflected the in vivo characteristics of the donors. While both exercise in vitro, activation of PPARδ and lack of PLIN2 increased lipid oxidation, only in vitro exercise had any impact on the insulin-stimulated responses, whereas both PPARδ activation and PLIN2 ablation shifted the cells from glucose to lipid metabolism

Disturbance observer based adaptive sliding mode control for continuous stirred tank reactor

The continuous stirred tank reactor (CSTR) typifies an important class of process control systems. Is is a nonlinear system and is sensitive to both external disturbances and system uncertainty. Given these challenges, a nonsingular terminal sliding mode observer is proposed to estimate any external disturbance. Then, a continuous adaptive sliding mode control method is combined with the proposed disturbance observer. This is found to reduce chattering and improve control accuracy when compared with other methods. A full Lyapunov stability proof of the resulting closed-loop system is performed and the effectiveness of the proposed approach is demonstrated by simulation experiments

Utilization of H-reversal Trajectory of Solar Sail for Asteroid Deflection

Near Earth Asteroids have a possibility of impacting with the Earth and always have a thread on the Earth. This paper proposes a way of changing the trajectory of the asteroid to avoid the impaction. Solar sail evolving in a H-reversal trajectory is utilized for asteroid deflection. Firstly, the dynamics of solar sail and the characteristics of the H-reversal trajectory are analyzed. Then, the attitude of the solar sail is optimized to guide the sail to impact with the object asteroid along a H-reversal trajectory. The impact velocity depends on two important parameters: the minimum solar distance along the trajectory and lightness number. A larger lightness number and a smaller solar distance lead to a higher impact velocity. Finally, the deflection capability of a solar sail impacting with the asteroid along the H-reversal is discussed. The results show that a 10 kg solar sail with a lead-time of one year can move Apophis out of a 600-m keyhole area in 2029 to eliminate the possibility of its resonant return in 2036

Adversarial learning for distant supervised relation extraction

Recently, many researchers have concentrated on using neural networks to learn features for Distant Supervised Relation Extraction (DSRE). These approaches generally use a softmax classifier with cross-entropy loss, which inevitably brings the noise of artificial class NA into classification process. To address the shortcoming, the classifier with ranking loss is employed to DSRE. Uniformly randomly selecting a relation or heuristically selecting the highest score among all incorrect relations are two common methods for generating a negative class in the ranking loss function. However, the majority of the generated negative class can be easily discriminated from positive class and will contribute little towards the training. Inspired by Generative Adversarial Networks (GANs), we use a neural network as the negative class generator to assist the training of our desired model, which acts as the discriminator in GANs. Through the alternating optimization of generator and discriminator, the generator is learning to produce more and more discriminable negative classes and the discriminator has to become better as well. This framework is independent of the concrete form of generator and discriminator. In this paper, we use a two layers fully-connected neural network as the generator and the Piecewise Convolutional Neural Networks (PCNNs) as the discriminator. Experiment results show that our proposed GAN-based method is effective and performs better than state-of-the-art methods

Personalized ranking metric embedding for next new POI recommendation

The rapidly growing of Location-based Social Networks (LBSNs) provides a vast amount of check-in data, which enables many services, e.g., point-of-interest (POI) recommendation. In this paper, we study the next new POI recommendation problem in which new POIs with respect to users' current location are to be recommended. The challenge lies in the difficulty in precisely learning users' sequential information and personalizing the recommendation model. To this end, we resort to the Metric Embedding method for the recommendation, which avoids drawbacks of the Matrix Factorization technique. We propose a personalized ranking metric embedding method (PRME) to model personalized check-in sequences. We further develop a PRME-G model, which integrates sequential information, individual preference, and geographical influence, to improve the recommendation performance. Experiments on two real-world LBSN datasets demonstrate that our new algorithm outperforms the state-of-the-art next POI recommendation methods

Icosahedral B\u3csub\u3e12\u3c/sub\u3e-containing core–shell structures of B\u3csub\u3e80\u3c/sub\u3e

Low-lying icosahedral (Ih) B12-containing structures of B80 are explored, and a number of core–shell isomers are found to have lower energy than the previous predicted B80 fullerene. The structural transformation of boron clusters from tubular structure to core–shell structure may occur at a critical size less than B80

Clinicopathological features and CCT2 and PDIA2 expression in gallbladder squamous/adenosquamous carcinoma and gallbladder adenocarcinoma

BACKGROUND: Gallbladder cancer (GBC) is a relatively uncommon carcinoma among gastrointestinal cancers and usually has a rather poor prognosis. The most common subtype of GBC is adenocarcinoma (AC), which accounts for about 90% of GBC. Squamous carcinoma/adenosquamous carcinoma (SC/ASC) are comparatively rare histopathological subtypes of GBC. The clinicopathological features and biological behaviors of SC/ASC have not been well-characterized. No molecular biomarkers are currently available for predicting the progression, metastasis, and prognosis of the SC/ASC subtype of GBC. METHODS: We examined the expression levels of CCT2 and PDIA3 by immunohistochemistry (IHC) staining in human GBC tissue samples collected from 46 patients with SC/ASC and evaluated the clinicopathological significance of both CCT2 and PDIA3 expression in the SC/ASC subtypes of GBC by Kaplan-Meier analysis and multivariate Cox regression analysis. For comparison, we included specimens from 80 AC patients in our study to investigate the specificity of CCT2 and PDIA3 expression in GBC subtypes. RESULTS: We found that the positive expression of CCT2 and PDIA3 was significantly associated with clinicopathological features of both SC/ASC and AC specimens, including high TNM stage and lymph node metastasis. Univariate analysis revealed that the two-year survival rate was significantly lower for patients with positive expression of CCT2 and PDIA3 than for those with negative expression. Multivariate analysis also indicated that the positive expression of CCT2 and PDIA3 was negatively correlated with poor postoperative patient survival and positively correlated with high mortality. CONCLUSIONS: Our study suggests that positive expression of CCT2 or PDIA3 is associated with tumor progression and the clinical behavior of gallbladder carcinoma. Therefore, CCT2 and PDIA3 could be potentially important diagnostic and prognostic biomarkers for both SC/ASC and AC subtypes of GBC