A Study on the Main Income Affecting the Distribution of Pension Income in China: A Case Study of Beijing

In the past few years, a large number of laborers in China have withdrawn from the market, resulting in a slowing down in the growth rate of the current economy as well as the total economic volume. In this context, the total scale of pension income distribution will therefore enter a state of slow growth even if the level of pension income distribution has improved. In the long run, a “contradictory” phenomenon will inevitably appear between the slow growth of pension income distribution and the rapid growth of pension demand, that is, the level of pension income distribution will not be able to meet pension needs. Accordingly, this paper aims to identify the main factors that affect the level of pension income in China, and to discuss how to optimize the level of pension income. Results show that the growth rate of the dependency ratio of the elderly has been in negative numbers for a long time, which indicates a shortage of available labor in China, and on the other hand, it reflects the stabilization and deepening of China’s aging. What is more, there is a causal relationship between the growth rate of residents’ disposable income and the growth rate of the dependency ratio of the elderly and the growth rate of China’s pension fund expenditure. In addition, the impact of local fiscal revenue on pension fund expenditure is not significant, so there is no explanatory significance for this work

Semi-automatic Data Annotation System for Multi-Target Multi-Camera Vehicle Tracking

Multi-target multi-camera tracking (MTMCT) plays an important role in intelligent video analysis, surveillance video retrieval, and other application scenarios. Nowadays, the deep-learning-based MTMCT has been the mainstream and has achieved fascinating improvements regarding tracking accuracy and efficiency. However, according to our investigation, the lacking of datasets focusing on real-world application scenarios limits the further improvements for current learning-based MTMCT models. Specifically, the learning-based MTMCT models training by common datasets usually cannot achieve satisfactory results in real-world application scenarios. Motivated by this, this paper presents a semi-automatic data annotation system to facilitate the real-world MTMCT dataset establishment. The proposed system first employs a deep-learning-based single-camera trajectory generation method to automatically extract trajectories from surveillance videos. Subsequently, the system provides a recommendation list in the following manual cross-camera trajectory matching process. The recommendation list is generated based on side information, including camera location, timestamp relation, and background scene. In the experimental stage, extensive results further demonstrate the efficiency of the proposed system.Comment: 9 pages, 10 figure

Non-homology-based prediction of gene functions in maize (\u3ci\u3eZea mays\u3c/i\u3e ssp. \u3ci\u3emays\u3c/i\u3e)

Advances in genome sequencing and annotation have eased the difficulty of identifying new gene sequences. Predicting the functions of these newly identified genes remains challenging. Genes descended from a common ancestral sequence are likely to have common functions.As a result, homology is widely used for gene function prediction. This means functional annotation errors also propagate from one species to another. Several approaches based on machine learning classification algorithms were evaluated for their ability to accurately predict gene function from non-homology gene features. Among the eight supervised classification algorithms evaluated, random forest-based prediction consistently provided the most accurate gene function prediction. Non-homology-based functional annotation provides complementary strengths to homology-based annotation, with higher average performance in Biological Process GO terms, the domain where homology-based functional annotation performs the worst, and weaker performance in Molecular Function GO terms, the domain where the accuracy of homology-based functional annotation is highest. GO prediction models trained with homology-based annotations were able to successfully predict annotations from a manually curated “gold standard” GO annotation set. Non-homology-based functional annotation based on machine learning may ultimately prove useful both as a method to assign predicted functions to orphan genes which lack functionally characterized homologs, and to identify and correct functional annotation errors which were propagated through homology-based functional annotations

A transverse current rectification in graphene superlattice

A model for energy spectrum of superlattice on the base of graphene placed on the striped dielectric substrate is proposed. A direct current component which appears in that structure perpendicularly to pulling electric field under the influence of elliptically polarized electromagnetic wave was derived. A transverse current density dependence on pulling field magnitude and on magnitude of component of elliptically polarized wave directed along the axis of a superlattice is analyzed.Comment: 12 pages, 6 figure

Synthesis and biological evaluation of pentacyclic triterpenoid derivatives as potential novel antibacterial agents

A series of ursolic acid (UA), oleanolic acid (OA) and 18β-glycyrrhetinic acid (GA) derivatives were synthesized by introducing a range of substituted aromatic side-chains at the C-2 position after the hydroxyl group at C-3 position was oxidized. Their antibacterial activities were evaluated in vitro against a panel of four Staphylococcus strains. The results revealed that the introduction of aromatic side-chains at the C-2 position of GA led to the discovery of potent triterpenoid derivatives for inhibition of both drug sensitive and resistant S. aureus, while the other two series derivatives of UA and OA showed no significant antibacterial activity even at high concentrations. In particular, GA derivative showed good potency against all four strains of Staphylococcus (MIC = 1.25 - 5 μmol/L) with acceptable pharmacokinetics properties and low cytotoxicity in vitro. Molecular docking was also performed using S. aureus DNA gyrase structure to rationalize the observed antibacterial activity. Therefore, this series of GA derivatives have strong potential for the development of a new type of triterpenoid antibacterial agent

Characterization of transcriptome dynamics during watermelon fruit development: sequencing, assembly, annotation and gene expression profiles

<p>Abstract</p> <p>Background</p> <p>Cultivated watermelon [<it>Citrullus lanatus </it>(Thunb.) Matsum. & Nakai var. <it>lanatus</it>] is an important agriculture crop world-wide. The fruit of watermelon undergoes distinct stages of development with dramatic changes in its size, color, sweetness, texture and aroma. In order to better understand the genetic and molecular basis of these changes and significantly expand the watermelon transcript catalog, we have selected four critical stages of watermelon fruit development and used Roche/454 next-generation sequencing technology to generate a large expressed sequence tag (EST) dataset and a comprehensive transcriptome profile for watermelon fruit flesh tissues.</p> <p>Results</p> <p>We performed half Roche/454 GS-FLX run for each of the four watermelon fruit developmental stages (immature white, white-pink flesh, red flesh and over-ripe) and obtained 577,023 high quality ESTs with an average length of 302.8 bp. <it>De novo </it>assembly of these ESTs together with 11,786 watermelon ESTs collected from GenBank produced 75,068 unigenes with a total length of approximately 31.8 Mb. Overall 54.9% of the unigenes showed significant similarities to known sequences in GenBank non-redundant (nr) protein database and around two-thirds of them matched proteins of cucumber, the most closely-related species with a sequenced genome. The unigenes were further assigned with gene ontology (GO) terms and mapped to biochemical pathways. More than 5,000 SSRs were identified from the EST collection. Furthermore we carried out digital gene expression analysis of these ESTs and identified 3,023 genes that were differentially expressed during watermelon fruit development and ripening, which provided novel insights into watermelon fruit biology and a comprehensive resource of candidate genes for future functional analysis. We then generated profiles of several interesting metabolites that are important to fruit quality including pigmentation and sweetness. Integrative analysis of metabolite and digital gene expression profiles helped elucidating molecular mechanisms governing these important quality-related traits during watermelon fruit development.</p> <p>Conclusion</p> <p>We have generated a large collection of watermelon ESTs, which represents a significant expansion of the current transcript catalog of watermelon and a valuable resource for future studies on the genomics of watermelon and other closely-related species. Digital expression analysis of this EST collection allowed us to identify a large set of genes that were differentially expressed during watermelon fruit development and ripening, which provide a rich source of candidates for future functional analysis and represent a valuable increase in our knowledge base of watermelon fruit biology.</p

Non-homology-based prediction of gene functions in maize (Zea mays ssp. mays)

Advances in genome sequencing and annotation have eased the difficulty of identifying new gene sequences. Predicting the functions of these newly identified genes remains challenging. Genes descended from a common ancestral sequence are likely to have common functions. As a result, homology is widely used for gene function pre- diction. This means functional annotation errors also propagate from one species to another. Several approaches based on machine learning classification algorithms were evaluated for their ability to accurately predict gene function from non-homology gene features. Among the eight supervised classification algorithms evaluated, random- forest-based prediction consistently provided the most accurate gene function predic- tion. Non-homology-based functional annotation provides complementary strengths to homology-based annotation, with higher average performance in Biological Process GO terms, the domain where homology-based functional annotation performs the worst, and weaker performance in Molecular Function GO terms, the domain where the accuracy of homology-based functional annotation is highest. GO prediction models trained with homology-based annotations were able to successfully predict annotations from a manually curated “gold standard” GO annotation set. Non-homology-based functional annotation based on machine learning may ultimately prove useful both as a method to assign predicted functions to orphan genes which lack functionally characterized homologs, and to identify and correct functional annotation errors which were propagated through homology-based functional annotations