Privacy Preserving Data Mining For Horizontally Distributed Medical Data Analysis

To build reliable prediction models and identify useful patterns, assembling data sets from databases maintained by different sources such as hospitals becomes increasingly common; however, it might divulge sensitive information about individuals and thus leads to increased concerns about privacy, which in turn prevents different parties from sharing information. Privacy Preserving Distributed Data Mining (PPDDM) provides a means to address this issue without accessing actual data values to avoid the disclosure of information beyond the final result. In recent years, a number of state-of-the-art PPDDM approaches have been developed, most of which are based on Secure Multiparty Computation (SMC). SMC requires expensive communication cost and sophisticated secure computation. Besides, the mining progress is inevitable to slow down due to the increasing volume of the aggregated data. In this work, a new framework named Privacy-Aware Non-linear SVM (PAN-SVM) is proposed to build a PPDDM model from multiple data sources. PAN-SVM employs the Secure Sum Protocol to protect privacy at the bottom layer, and reduces the complex communication and computation via Nystrom matrix approximation and Eigen decomposition methods at the medium layer. The top layer of PAN-SVM speeds up the whole algorithm for large scale datasets. Based on the proposed framework of PAN-SVM, a Privacy Preserving Multi-class Classifier is built, and the experimental results on several benchmark datasets and microarray datasets show its abilities to improve classification accuracy compared with a regular SVM. In addition, two Privacy Preserving Feature Selection methods are also proposed based on PAN-SVM, and tested by using benchmark data and real world data. PAN-SVM does not depend on a trusted third party; all participants collaborate equally. Many experimental results show that PAN-SVM can not only effectively solve the problem of collaborative privacy-preserving data mining by building non-linear classification rules, but also significantly improve the performance of built classifiers

A study of health effects of long-distance ocean voyages on seamen using a data classification approach

Background: Long-distance ocean voyages may have substantial impacts on seamen’s health, possibly causing malnutrition and other illness. Measures can possibly be taken to prevent such problems from happening through preparing special diet and making special precautions prior or during the sailing if a detailed understanding can be gained about what specific health effects such voyages may have on the seamen. Methods: We present a computational study on 200 seamen using 41 chemistry indicators measured on their blood samples collected before and after the sailing. Our computational study is done using a data classification approach with a support vector machine-based classifier in conjunction with feature selections using a recursive feature elimination procedure. Results: Our analysis results suggest that among the 41 blood chemistry measures, nine are most likely to be affected during the sailing, which provide important clues about the specific effects of ocean voyage on seamen’s health. Conclusions: The identification of the nine blood chemistry measures provides important clues about the effects of long-distance voyage on seamen’s health. These findings will prove to be useful to guide in improving the living and working environment, as well as food preparation on ships

An analysis of microbiota-targeted therapies in patients with avian influenza virus subtype H7N9 infection

BACKGROUND: Selective prophylactic decontamination of the digestive tract is a strategy for the prevention of secondary nosocomial infection in patients with avian influenza virus subtype H7N9 infection. Our aim was to summarize the effectiveness of these therapies in re-establishing a stable and diverse microbial community, and reducing secondary infections. METHODS: Comprehensive therapies were dependent on the individual clinical situation of subjects, and were divided into antiviral treatment, microbiota-targeted therapies, including pro- or pre-biotics and antibiotic usage, and immunotherapy. Quantitative polymerase chain reaction and denaturing gradient gel electrophoresis (DGGE) were used for real-time monitoring of the predominant intestinal microbiome during treatment. Clinical information about secondary infection was confirmed by analyzing pathogens isolated from clinical specimens. RESULTS: Different antibiotics had similar effects on the gut microbiome, with a marked decrease and slow recovery of the Bifidobacterium population. Interestingly, most fecal microbial DGGE profiles showed the relative stability of communities under the continual suppression of the same antibiotics, and significant changes when new antibiotics were introduced. Moreover, we found no marked increase in C-reactive protein, and no cases of bacteremia or pneumonia, caused by probiotic use in the patients, which confirmed that the probiotics used in this study were safe for use in patients with H7N9 infection. Approximately 72% of those who subsequently suffered exogenous respiratory infection by Candida species or multidrug-resistant Acinetobacter baumannii and Klebsiella pneumoniae were older than 60 years. The combination of probiotics and prebiotics with antibiotics seemed to fail in these patients. CONCLUSIONS: Elderly patients infected with the influenza A (H7N9) virus are considered a high-risk group for developing secondary bacterial infection. Microbiota restoration treatment reduced the incidence of enterogenous secondary infection, but not exogenous respiratory infection. The prophylactic effects of microbiota restoration strategies for secondary infection were unsatisfactory in elderly and critically ill patients

A semi-analytical algorithm for deriving the particle size distribution slope of turbid inland water based on OLCI data: A case study in Lake Hongze

The particle size distribution (PSD) slope (ξ) can indicate the predominant particle size, material composition, and inherent optical properties (IOPs) of inland waters. However, few semi-analytical methods have been proposed for deriving ξ from the surface remote sensing reflectance due to the variable optical state of inland waters. A semi-analytical algorithm was developed for inland waters having a wide range of turbidity and ξ in this study. Application of the proposed model to Ocean and Land Color Instrument (OLCI) imagery of the water body resulted in several important observations: (1) the proposed algorithm (754 nm and 779 nm combination) was capable of retrieving ξ with R2 being 0.72 (p < 0.01, n = 60), and MAPE and RMSE being 4.37% and 0.22 (n = 30) respectively; (2) the ξ in HZL was lower in summer than other seasons during the period considered, this variation was driven by the phenological cycle of algae and the runoff caused by rainfall; (3) the band optimization proposed in this study is important for calculating the particle backscattering slope (η) and deriving ξ because it is feasible for both algae dominant and sediment governed turbid inland lakes. These observations help improve our understanding of the relationship between IOPs and ξ, which are affected by different bio-optic processes and algal phenology in the lake environment

A study of health effects of long-distance ocean voyages on seamen using a data classification approach

Background: Long-distance ocean voyages may have substantial impacts on seamen’s health, possibly causing malnutrition and other illness. Measures can possibly be taken to prevent such problems from happening through preparing special diet and making special precautions prior or during the sailing if a detailed understanding can be gained about what specific health effects such voyages may have on the seamen. Methods: We present a computational study on 200 seamen using 41 chemistry indicators measured on their blood samples collected before and after the sailing. Our computational study is done using a data classification approach with a support vector machine-based classifier in conjunction with feature selections using a recursive feature elimination procedure. Results: Our analysis results suggest that among the 41 blood chemistry measures, nine are most likely to be affected during the sailing, which provide important clues about the specific effects of ocean voyage on seamen’s health. Conclusions: The identification of the nine blood chemistry measures provides important clues about the effects of long-distance voyage on seamen’s health. These findings will prove to be useful to guide in improving the living and working environment, as well as food preparation on ships

Mapping regions in Ste5 that support Msn5-dependent and -independent nuclear export

Careful control of the available pool of the MAPK scaffold Ste5 is important for mating pathway activation and prevention of inappropriate mating differentiation in haploid S. cerevisiae. Ste5 shuttles constitutively through the nucleus where it is degraded by a ubiquitin-dependent mechanism triggered by G1 CDK phosphorylation. Here we narrow down regions of Ste5 that mediate nuclear export. Four regions in Ste5 relocalize SV40-TAgNLS-GFP-GFP from nucleus to cytoplasm. One region is N-terminal, dependent on exportin Msn5/Ste21/Kap142, and interacts with Msn5 in two hybrid assays independently of mating pheromone, Fus3, Kss1, Ptc1, the NLS/PM, and RING-H2. A second region overlaps the PH domain and Ste11 binding site and two others are on the vWA domain and include residues essential for MAPK activation. We find no evidence for dependence on Crm1/Xpo1 despite numerous potential nuclear export sequences (NESs) detected by LocNES and NETNES1.1 predictors. Thus, Msn5 (homolog of human Exportin-5) and one or more exportins or adapter molecules besides Crm1/Xpo1 may regulate Ste5 through multiple recognition sites.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

An efficient data-driven particle PHD filter for multitarget tracking

In this paper we propose an efficient data-driven particle probability hypothesis density (PHD) filter for real-time multitarget tracking of nonlinear/non-Gaussian system in dense clutter environment. In specific, the input measurements are first classified into two sets, namely survival measurements and spontaneous birth measurements, after eliminating clutters by using existing historic state data of targets. Since most clutters do not participate in the complex weight computation of particle PHD filter, better real-time performance can be achieved. The tracking performance is also improved because the survival measurements are used for survival targets and the spontaneous birth measurements are used for spontaneous birth targets, resulting in less interference from each other and from clutters. Extensive simulations validate the improvement of both the real-time performance and tracking performance of the proposed data-driven particle PHD filter in comparison with the traditional particle PHD filter. ? 2005-2012 IEEE