A Wipe Transition Detection Approach Using Macroblock Type Information for MPEG Videos

A new wipe transition detection approach was proposed. By analyzing the spatial-temporal characteristics of an ideal wipe production model, the concept of wipe transition strip (TS) was introduced. The macroblock type information of P-frames is used to extract TS regions. An improved TS region accumulation technique is performed for detecting and verifying wipe transitions. The experimental results indicate that the proposed approach is capable of detecting various wipe transitions quickly and accurately.

Protective Effect of Silent Mating Type Information Regulation 2 Homolog 1 on TGF-β1 Pathway via mTOR in Diabetic Nephropathy

Objective: To demonstrate whether the expression of silent mating type information regulation 2 homolog 1 (SIRT1) affects the level of TGF-β1 and Smad3 in HEK293 cells through regulating mTOR. Methods: First, recombinant plasmids DNA (rSIRT1) and siRNA targeting SIRT1 were constructed which were transfected into Human Embryonic Kidney 293 cell (HEK293) cells, respectively. Then, the generation of intracellular ROS in cells was examined by flow cytometry using the oxidation-sensitive probe. Last, the expressions of TGF-β1, smad3, P53, mTOR, p-mTOR, LC3-I and LC3-II in cells were detected to observe the effect of SIRT1 on TGF-β1 Pathway by western blot analysis. Results: We demonstrated that overexpressing of SIRT1 may decrease TGF-β1 and Smad3 expression in HEK293 cells through regulating mTOR. In addition, the result is the opposite when SIRT1 was silent in HEK293 cells. Conclusions: SIRT1 is closely related to TGF-β1/Smad3 pathway that correlates with the regulation of mTOR and ROS generation and causes diabetic nephropathy. The available evidence implies that SIRT1 has great potential as a clinical target for the prevention and treatment of renal fibrosis in the development of DN.

Cardiovascular and nonalcoholic fatty liver disease:Sharing common ground through SIRT1 pathways

As a non-communicable disease,cardiovascular disorders have become the lea-ding cause of death for men and women.Of additional concern is that cardio-vascular disease is linked to chronic comorbidity disorders that include nonal-coholic fatty liver disease(NAFLD).NAFLD,also termed metabolic-dysfunction-associated steatotic liver disease,is the greatest cause of liver disease throughout the world,increasing in prevalence concurrently with diabetes mellitus(DM),and can progress to nonalcoholic steatohepatitis that leads to cirrhosis and liver fi-brosis.Individuals with metabolic disorders,such as DM,are more than two times likely to experience cardiac disease,stroke,and liver disease that includes NAFLD when compared individuals without metabolic disorders.Interestingly,cardiovascular disorders and NAFLD share a common underlying cellular me-chanism for disease pathology,namely the silent mating type information regu-lation 2 homolog 1(SIRT1;Saccharomyces cerevisiae).SIRT1,a histone deacetylase,is linked to metabolic pathways through nicotinamide adenine dinucleotide and can offer cellular protection though multiple avenues,including trophic factors such as erythropoietin,stem cells,and AMP-activated protein kinase.Translating SIRT1 pathways into clinical care for cardiovascular and hepatic disease can offer significant hope for patients,but further insights into the complexity of SIRT1 pathways are necessary for effective treatment regimens.

Targeting the core of neurodegeneration:FoxO,mTOR,and SIRT1

The global increase in lifespan noted not only in developed nations,but also in large developing countries parallels an observed increase in a significant number of noncommunicable diseases,most notable neurodegenerative disorders.Neurodegenerative disorders present a number of challenges for treatment options that do not resolve disease progression.Furthermore,it is believed by the year 2030,the services required to treat cognitive disorders in the United States alone will exceed$2 trillion annually.Mammalian forkhead transcription factors,silent mating type information regulation 2 homolog 1(Saccharomyces cerevisiae),the mechanistic target of rapamycin,and the pathways of autophagy and apoptosis offer exciting avenues to address these challenges by focusing upon core cellular mechanisms that may significantly impact nervous system disease.These pathways are intimately linked such as through cell signaling pathways involving protein kinase B and can foster,sometimes in conjunction with trophic factors,enhanced neuronal survival,reduction in toxic intracellular accumulations,and mitochondrial stability.Feedback mechanisms among these pathways also exist that can oversee reparative processes in the nervous system.However,mammalian forkhead transcription factors,silent mating type information regulation 2 homolog 1,mechanistic target of rapamycin,and autophagy can lead to cellular demise under some scenarios that may be dependent upon the precise cellular environment,warranting future studies to effectively translate these core pathways into successful clinical treatment strategies for neurodegenerative disorders.

Novel nervous and multi-system regenerative therapeutic strategies for diabetes mellitus with mTOR

Throughout the globe,diabetes mellitus（DM） is increasing in incidence with limited therapies presently available to prevent or resolve the significant complications of this disorder.DM impacts multiple organs and affects all components of the central and peripheral nervous systems that can range from dementia to diabetic neuropathy.The mechanistic target of rapamycin（m TOR） is a promising agent for the development of novel regenerative strategies for the treatment of DM.m TOR and its related signaling pathways impact multiple metabolic parameters that include cellular metabolic homeostasis,insulin resistance,insulin secretion,stem cell proliferation and differentiation,pancreatic β-cell function,and programmed cell death with apoptosis and autophagy.m TOR is central element for the protein complexes m TOR Complex 1（m TORC1） and m TOR Complex 2（m TORC2） and is a critical component for a number of signaling pathways that involve phosphoinositide 3-kinase（PI 3-K）,protein kinase B（Akt）,AMP activated protein kinase（AMPK）,silent mating type information regulation 2 homolog 1（Saccharomyces cerevisiae）（SIRT1）,Wnt1 inducible signaling pathway protein 1（WISP1）,and growth factors.As a result,m TOR represents an exciting target to offer new clinical avenues for the treatment of DM and the complications of this disease.Future studies directed to elucidate the delicate balance m TOR holds over cellular metabolism and the impact of its broad signaling pathways should foster the translation of these targets into effective clinical regimens for DM.

STRONG CODING THEOREM AND ASYMPTOTIC ERROR EXPONENT OF ARBITRARILY VARYING SOURCE

Csiszar's strong coding theorem for discrete memoryless scarce is generalized to arbitrarily varying source.We also determine the asymptotic error exponent for arbitrarily wrying source.

Type-Augmented Link Prediction Based on Bayesian Formula

Knowledge graphs(KGs)play a pivotal role in various real-world applications,but they are frequently plagued by incomplete information,which manifests in the form of missing entities.Link prediction,which aims to infer missing entities given existing facts,has been mostly addressed by maximizing the likelihood of observed triplets at the instance level.However,they ignore the semantic information most KGs contain and the prior knowledge implied by the semantic information.To address this limitation,we propose a Type-Augmented Link Prediction(TALP)approach,which builds a hierarchical feature model,computes type feature weights,trains them to be specific to different relations,encodes weights into prior probabilities and convolutional encodes instance-level information into likelihood probabilities;finally,combining them via Bayes rule to compute the posterior probabilities of entity prediction.Our proposed TALP approach achieves significantly better performance than existing methods on link prediction benchmark datasets.