Towards machine-learning-driven effective mashup recommendations from big data in mobile networks and the Internet-of-Things

A large number of Web APIs have been released as services in mobile communications,but the service provided by a single Web API is usually limited.To enrich the services in mobile communications,developers have combined Web APIs and developed a new service,which is known as a mashup.The emergence of mashups greatly increases the number of services in mobile communications,especially in mobile networks and the Internet-of-Things(IoT),and has encouraged companies and individuals to develop even more mashups,which has led to the dramatic increase in the number of mashups.Such a trend brings with it big data,such as the massive text data from the mashups themselves and continually-generated usage data.Thus,the question of how to determine the most suitable mashups from big data has become a challenging problem.In this paper,we propose a mashup recommendation framework from big data in mobile networks and the IoT.The proposed framework is driven by machine learning techniques,including neural embedding,clustering,and matrix factorization.We employ neural embedding to learn the distributed representation of mashups and propose to use cluster analysis to learn the relationship among the mashups.We also develop a novel Joint Matrix Factorization(JMF)model to complete the mashup recommendation task,where we design a new objective function and an optimization algorithm.We then crawl through a real-world large mashup dataset and perform experiments.The experimental results demonstrate that our framework achieves high accuracy in mashup recommendation and performs better than all compared baselines.

A validated UPLC–MS/MS method for simultaneous determination of imatinib, dasatinib and nilotinib in human plasma

A sensitive, rapid, simple and economical ultra-performance liquid chromatography-tandem mass spectrometric method （UPLC-MS/MS） was developed and validated for simultaneous determination of imatinib, dasatinib and nilotinib in human plasma using gliquidone as internal standard （IS）. Liquid-liquid extraction method with ethyl acetate was used for sample pre-treatment. The separation was performed on an Xtimate Phenyl column using isocratic mobile phase consisting of A （aqueous phase： 0.15% formic acid and 0.05% ammonium acetate） and B （organic phase： aeetonitrile） （A：B=40：60, v/v）. The flow rate was 0.25 mL/min and the total run time was 6 min. The multiple reaction monitoring （MRM） transitions, m/z 494.5-394.5 for imatinib, 488.7-401.5 for dasatinib, 530.7-289.5 for nilotinib and 528.5-403.4 for IS, were chosen to achieve high selectivity in the simultaneous analyses. The method exhibited great improvement in sensitivity and good linearity over the concentration range of 2.6-5250.0 ng/mL for imatinib, 2.0-490.0 ng/mL for dasatinib, and 2.4-4700.0 ng/mL for nilotinib. The method showed acceptable results on sensitivity, specificity, recovery, precision, accuracy and stability tests. This UPLC-MS/MS assay was successfully used for human plasma samples analysis and no significant differences were found in imatinib steady-state trough concentrations among the SLC22A5 -1889T 〉 C or SLCOIB3 699G 〉 A genotypes （P 〉 0.05）. This validated method can provide support for clinical therapeutic drug monitoring and pharmacokinetic investigations of these three tyrosine kinase inhibitors （TKIs）.