Apolipoprotein M

Apolipoprotein M (apoM) is a 26-kDa protein that is mainly associated with high-density lipoprotein (HDL) in human plasma, with a small proportion present in triglyceride-rich lipoproteins (TGRLP) and low-density lipoproteins (LDL). Human apoM gene is located in p21.31 on chromosome 6 (chromosome 17, in mouse). Human apoM cDNA (734 base pairs) encodes 188-amino acid residue-long protein. It belongs to lipocalin protein superfamily. Human tissue expression array study indicates that apoM is only expressed in liver and in kidney and small amounts are found in fetal liver and kidney. In situ apoM mRNA hybridization demonstrates that apoM is exclusively expressed in the hepatocytes and in the tubule epithelial cells in kidney. Expression of apoM could be regulated by platelet activating factor (PAF), transforming growth factors (TGF), insulin-like growth factor (IGF) and leptin in vivo and/or in vitro. It has been demonstrated that apoM expression is dramatically decreased in apoA-I deficient mouse. Hepatocyte nuclear factor-1α (HNF-1α) is an activator of apoM gene promoter. Deficiency of HNF-1α mouse shows lack of apoM expression. Mutations in HNF-1α (MODY3) have reduced serum apoM levels. Expression of apoM is significantly decreased in leptin deficient (ob/ob) mouse or leptin receptor deficient (db/db) mouse. ApoM concentration in plasma is positively correlated to leptin level in obese subjects. These may suggest that apoM is related to the initiation and progression of MODY3 and/or obesity

An inversion algorithm for recovering a coefficient of Sturm-Liouville operator (Analysis of inverse problems through partial differential equations and related topics)

In this paper, an efficient algorithm for recovering a density of Sturm-Liouville operator from given two spectra is investigated. Based on Lidskii's theorem and Mercer's theorem, we build a sequence of trace formulae which bridge explicitly the density and eigenvalues in terms of nonlinear Fredholm integral equations. Due to intrinsic difficulties on ill-posedness of an inverse spectral problem, a truncated Fourier series regularization method is utilized for reconstructing the unknown density. Moreover, shifted Legendre polynomials are carried to balance the different order of trace formulae. Numerical results are presented to illustrated the effectiveness and stability of the proposed reconstruction algorithm

Addressing Wealth Inequality Problem in Blockchain-Enabled Knowledge Community with Reputation-Based Incentive Mechanism

An increasing number of online knowledge communities have started incorporating the cut-edge FinTech, such as the tokenbased incentive mechanism running on blockchain, into their ecosystems. However, the improper design of incentive mechanisms may result in reward monopoly, which has been observed to harm the ecosystems of exiting communities. This study is aimed to ensure that the key factors involved in users’ reward distribution can truly reflect their contributions to the community so as to increase the equity of wealth distribution. It is one of the first to comprehensively balance a user’s historical and current contributions in reward distribution, which has not received sufficient attention from extant research. The simulation analysis demonstrates that the proposed solution of amending the existing incentive mechanism by incorporating a refined reputation indicator significantly increases the equity of rewards distribution and effectively enlarges the cost of achieving reward monopoly

Industry Classification Based on Labor Mobility Network Mining

Industry classification is important for industry analysis and competitive intelligence. However, existing schemes and methods are limited by the small number of industry categories and the lagged information of firms’ business. In this paper, we propose a novel industry classification method by constructing the labor mobility network from the LinkedIn profiles. We also propose a hierarchical extension of the community detection algorithm to better discover latent industry clusters on the constructed network. The evaluation conducted on real-world datasets shows that our method outperforms the best existing industry classification scheme and the state-of-the-art method and improves their explanatory power by 8.31% and 3.97% respectively. Moreover, our method is effective in earlier revealing firms’ action of entering new industries

Improving spam filtering in enterprise email systems with blockchain-based token incentive mechanism

Spam has caused serious problems for email systems. To address this issue, numerous spam filter algorithms have been developed, all of which require extensive training on labeled spam datasets to obtain the desired filter performance. However, users\u27 privacy concerns and apathy make it difficult to acquire personalized spam data in real-world applications. When it comes to enterprise email systems, the problem worsens because enterprises are extremely sensitive to the possible disclosure of confidential information during the reporting of spam to the cloud. Targeting these obstacles, this study proposes a blockchain-based token incentive mechanism, with the aim of encouraging users to report spam while protecting business secrets and ensuring the transparency of reward rules. The proposed mechanism also enables a decentralized ecosystem for token circulation, fully utilizing the advantages of blockchain technologies. We developed a prototype of the proposed system, on which we conducted a user experiment to verify our design. Results indicate that the proposed incentive mechanism is effective and can raise the probability of spam reporting by more than 1.4 times

Análisis FINGRAMS de sistemas difusos basados en reglas bajo premisas de interpretabilidad y precisión

El  objetivo  de  este  proyecto  es  crear  un  nuevo  paradigma  para  el  análisis  de  comprensibilidad de sistemas difusos basado, se centra en identificar en base a la  metodología FINGRAMS la selección, o no selección, de reglas difusas provenientes de  un sistema difuso basado en reglas  (SBRD)  cuando están son optimizados mediante  un  proceso  genético  multiobjetivo  considerando  precisión,  interpretabilidad  y  relevancia.  El  sistema experto  propuesto  se  valida  utilizando  nueve  conjuntos  de  datos,  dos  algoritmos difusos lingüísticos y dos dispersos, cuatro medidas de interpretabilidad y  dos formulaciones de relevancia de la regla.  En esta preocupación, se desarrolla un sistema experto basado en reglas difusas para  analizar diferentes puntos de vista de Interpretabilidad, Precisión y Relevancia, y las  pruebas estadísticas.  Los resultados revelan que el rendimiento del sistema experto propuesto es superior al  de las reglas de baja relevanciaDepartamento de Ingeniería de Sistemas y AutomáticaMáster en Investigación en Ingeniería de Procesos y Sistemas Industriale