The Sharing Economy and Collaborative Finance: the Case of P2p Lending in Vietnam

Peer-to-peer Online Lending (P2PO) has received increasing attention over the last years, not only because of its disruptive nature and its disintermediation of nearly all major banking functions, but also because of its rapid growth and expanding breadth of services. This model offers a new way of investing in addition to investing in traditional channels such as banking or financial company. The transaction process is done online, the personal information and terms of mobilization are completely transparent and secure in the best way. The strong development of P2PO also raises a number of issues that require careful attention to promote positive and to limit negative aspects. The research aims to highlight particular aspects of this new business model and to analyze the opportunities and risks for lenders and borrowers in Viet Nam. The research combines qualitative analysis and data survey to serve descriptive statistics about P2PO in Viet Nam. The research show the potential of online peer lending is enormous but the regulators will restrict the Sharing economy model in general and P2PO lending in particula

Deep Autoencoder for Recommender Systems: Parameter Influence Analysis

Recommender systems have recently attracted many researchers in the deep learning community. The state-of-the-art deep neural network models used in recommender systems are multilayer perceptron and deep autoencoder (DAE). In this work, we focus on the DAE model due to its superior capability to reconstruct the inputs, which works well for recommender systems. Existing works have similar implementations of DAE but the parameter settings are vastly different for similar datasets. In this work, we have built a flexible DAE model, named FlexEncoder that uses configurable parameters and unique features to analyze the parameter influences on the prediction accuracy of recommendations. Extensive evaluation on the MovieLens datasets are conducted, which drives our conclusions on the influences of DAE parameters. We find that DAE parameters strongly affect the prediction accuracy of the recommender systems, and the effect remains valid for bigger datasets in the same family

A comprehensive evaluation of polygenic score and genotype imputation performances of human SNP arrays in diverse populations

publishedVersio

Native interface of the SAM domain polymer of TEL

BACKGROUND: TEL is a transcriptional repressor containing a SAM domain that forms a helical polymer. In a number of hematologic malignancies, chromosomal translocations lead to aberrant fusions of TEL-SAM to a variety of other proteins, including many tyrosine kinases. TEL-SAM polymerization results in constitutive activation of the tyrosine kinase domains to which it becomes fused, leading to cell transformation. Thus, inhibitors of TEL-SAM self-association could abrogate transformation in these cells. In previous work, we determined the structure of a mutant TEL-SAM polymer bearing a Val to Glu substitution in center of the subunit interface. It remained unclear how much the mutation affected the architecture of the polymer, however. RESULTS: Here we determine the structure of the native polymer interface. To accomplish this goal, we introduced mutations that block polymer extension, producing a heterodimer with a wild-type interface. We find that the structure of the wild-type polymer interface is quite similar to the mutant structure determined previously. With the structure of the native interface, it is possible to evaluate the potential for developing therapeutic inhibitors of the interaction. We find that the interacting surfaces of the protein are relatively flat, containing no obvious pockets for the design of small molecule inhibitors. CONCLUSION: Our results confirm the architecture of the TEL-SAM polymer proposed previously based on a mutant structure. The fact that the interface contains no obvious potential binding pockets suggests that it may be difficult to find small molecule inhibitors to treat malignancies in this way

Antitumor activity of an anti-CD98 antibody.

CD98 is expressed on several tissue types and specifically upregulated on fast-cycling cells undergoing clonal expansion. Various solid (e.g., nonsmall cell lung carcinoma) as well as hematological malignancies (e.g., acute myeloid leukemia) overexpress CD98. We have identified a CD98-specific mouse monoclonal antibody that exhibits potent preclinical antitumor activity against established lymphoma tumor xenografts. Additionally, the humanized antibody designated IGN523 demonstrated robust tumor growth inhibition in leukemic cell-line derived xenograft models and was as efficacious as standard of care carboplatin in patient-derived nonsmall lung cancer xenografts. In vitro studies revealed that IGN523 elicited strong ADCC activity, induced lysosomal membrane permeabilization and inhibited essential amino acid transport function, ultimately resulting in caspase-3 and -7-mediated apoptosis of tumor cells. IGN523 is currently being evaluated in a Phase I clinical trial for acute myeloid leukemia (NCT02040506). Furthermore, preclinical data support the therapeutic potential of IGN523 in solid tumors

In vitro antifungal activity of Cinnamomum zeylanicum bark and leaf essential oils against Candida albicans and Candida auris

Candida infections are a significant source of patient morbidity and mortality. Candida albicans is the most common pathogen causing Candida infections. Candida auris is a newly described pathogen that is associated with multi-drug-resistant candidiasis and candidaemia in humans. The antifungal effects of various essential oils and plant compounds have been demonstrated against human pathogenic fungi. In this study, the effect of cinnamon leaf and bark essential oils (CEOs) was determined against both C. albicans and C. auris. The disc diffusion (direct and vapour) and broth microdilution method was used to determine antifungal activity of the EOs against selected strains (C. albicans ATCC 10231, C. albicans ATCC 2091 and C. auris NCPF 8971) whilst the mode of action and haemolysin activity of the CEOs were determined using electron microscopy and light microscopy. Direct and vapour diffusion assays showed greater inhibitory activity of bark CEO in comparison with leaf CEO. The minimum inhibitory concentrations (MICs) and minimum fungicidal concentrations (MFCs) of bark CEO for all tested strains was below 0.03% (v/v), which was lower than the MICs of the leaf CEO (0.06–0.13%, v/v) dependent on the strain and the MFCs at 0.25% (v/v). In the morphological interference assays, damage to the cell membrane was observed and both CEOs inhibited hyphae formation. The haemolysin production assay showed that CEOs can reduce the haemolytic activity in the tested C. albicans and C. auris strains. At low concentrations, CEOs have potent antifungal and antihaemolytic activities in vitro against C. albicans and C. auris. Key points • Essential oils from Cinnamomum zeylanicum Blume bark and leaf (CBEO and CLEO) demonstrated fungicidal properties at very low concentrations. • The antifungal activity of CBEO was greater than that of CLEO consistent with other recent published literature. • The mode of action of CBEO and CLEO was damage to the membrane of C. albicans and C. auris. • Both CBEO and CLEO inhibited the formation of hyphae and reduced haemolysin production in C. albicans and C. auris. [Figure not available: see fulltext.]

Development of a Prediction System for 3D Printed Part Deformation

The Additive Manufacturing (AM) process is applied in industrial applications. However, quality issues of the printed parts, including part distortion and cracks caused by high temperature and fast cooling, result in high residual stress. The theoretical calculation equation shows elastic behavior which is the linear behavior between strain and stress. However, in practice with the additive manufacturing process, strain and stress have nonlinear behavior. So, the prediction of the deformation of a printed part is inaccurate. The contribution of this research is the creation of an Inherent Strain (IS)-based part deformation prediction method during the Selective Laser Melting (SLM) process. To have the deformation in the design stage, we developed software for calculating the IS value and predicting the deformation. The difference between the calculated results and the experimental results is still there, so, we proposed an algorithm and developed an optimization module for the system to minimize this difference. In the final optimal printing process, the parameters are derived in order for the real printing process to have the required quality of the SLM printed part