Privacy Protection Framework with Defined Policies for Service-Oriented Architecture

Service-Oriented Architecture (SOA) is a computer systems design concept which aims to achieve reusability and integration in a distributed environment through the use of autonomous, loosely coupled, interoperable abstractions known as services. In order to interoperate, communication between services is very important due to their autonomous nature. This communication provides services with their functional strengths, but also creates the opportunity for the loss of privacy. In this paper, a Privacy Protection Framework for Service-Oriented Architecture (PPFSOA) is described. In this framework, a Privacy Service (PS) is used in combination with privacy policies to create privacy contracts that outline what can and cannot be done with a consumer’s personally identifiable information (PII). The privacy policy consists of one-to-many privacy rules, with each rule created from a set of six privacy elements: collector, what, purpose, retention, recipient and trust. The PS acts as an intermediary between the service consumer and service provider, to establish an unbiased contract before the two parties begin sending PII. It is shown how many Privacy Services work together to form the privacy protection framework. An examination of what current approaches to protecting privacy in an SOA environment is also presented. Finally, the operations the PS must perform in order to fulfill its tasks are outlined

Menstrual irregularity and bone mass in premenopausal women: Cross-sectional associations with testosterone and SHBG

Background. There have been few studies examining the associations between menstrual irregularity, androgens and bone mass in population-based samples of premenopausal women. This study aimed to describe the associations between menstrual pattern, testosterone, sex hormone binding globulin (SHBG) and bone mass in a population-based sample of premenopausal women. Methods. Cross-sectional study (N = 382, mean age 31.5 years). Menstrual pattern was assessed by questionnaire, bone mass measured by quantitative ultrasound (QUS) and androgen status was assessed by levels of serum testosterone, SHBG and the free androgen index (FAI). Results. Women with irregular cycles (n = 41, 11%) had higher free androgen index (FAI, P = 0.01) and higher QUS measurements including speed of sound (SOS, 1%, P < 0.05), quantitative ultrasound index (QUI, 7%, p < 0.05), and broadband ultrasound attenuation (BUA, 7%, p = 0.10). These associations persisted after adjustment for age and body mass index (BMI). After further adjustment for hormonal factors (either testosterone, SHBG or FAI), the strength of the associations was moderately attenuated, however, women with irregular cycles still had a 6% increase in mean QUS. Total testosterone, FAI and SHBG were also associated with QUS measures (testosterone and FAI, r +0.11 to +0.21, all p < 0.05; SHBG r -0.14 to -0.16, all p < 0.05) and the associations remained significant after adjustment. Conclusion. Irregular menstrual cycles were associated with higher bone mass in this population-based sample of premenopausal women suggesting menstrual disturbance should continue to be evaluated but may be less harmful for bone mass. The association between menstrual irregularity and bone mass was partially mediated by markers of androgen status especially free testosterone

Identifying differentially methylated genes using mixed effect and generalized least square models

<p>Abstract</p> <p>Background</p> <p>DNA methylation plays an important role in the process of tumorigenesis. Identifying differentially methylated genes or CpG islands (CGIs) associated with genes between two tumor subtypes is thus an important biological question. The methylation status of all CGIs in the whole genome can be assayed with differential methylation hybridization (DMH) microarrays. However, patient samples or cell lines are heterogeneous, so their methylation pattern may be very different. In addition, neighboring probes at each CGI are correlated. How these factors affect the analysis of DMH data is unknown.</p> <p>Results</p> <p>We propose a new method for identifying differentially methylated (DM) genes by identifying the associated DM CGI(s). At each CGI, we implement four different mixed effect and generalized least square models to identify DM genes between two groups. We compare four models with a simple least square regression model to study the impact of incorporating random effects and correlations.</p> <p>Conclusions</p> <p>We demonstrate that the inclusion (or exclusion) of random effects and the choice of correlation structures can significantly affect the results of the data analysis. We also assess the false discovery rate of different models using CGIs associated with housekeeping genes.</p

Automatic de-identification of textual documents in the electronic health record: a review of recent research

<p>Abstract</p> <p>Background</p> <p>In the United States, the Health Insurance Portability and Accountability Act (HIPAA) protects the confidentiality of patient data and requires the informed consent of the patient and approval of the Internal Review Board to use data for research purposes, but these requirements can be waived if data is de-identified. For clinical data to be considered de-identified, the HIPAA "Safe Harbor" technique requires 18 data elements (called PHI: Protected Health Information) to be removed. The de-identification of narrative text documents is often realized manually, and requires significant resources. Well aware of these issues, several authors have investigated automated de-identification of narrative text documents from the electronic health record, and a review of recent research in this domain is presented here.</p> <p>Methods</p> <p>This review focuses on recently published research (after 1995), and includes relevant publications from bibliographic queries in PubMed, conference proceedings, the ACM Digital Library, and interesting publications referenced in already included papers.</p> <p>Results</p> <p>The literature search returned more than 200 publications. The majority focused only on structured data de-identification instead of narrative text, on image de-identification, or described manual de-identification, and were therefore excluded. Finally, 18 publications describing automated text de-identification were selected for detailed analysis of the architecture and methods used, the types of PHI detected and removed, the external resources used, and the types of clinical documents targeted. All text de-identification systems aimed to identify and remove person names, and many included other types of PHI. Most systems used only one or two specific clinical document types, and were mostly based on two different groups of methodologies: pattern matching and machine learning. Many systems combined both approaches for different types of PHI, but the majority relied only on pattern matching, rules, and dictionaries.</p> <p>Conclusions</p> <p>In general, methods based on dictionaries performed better with PHI that is rarely mentioned in clinical text, but are more difficult to generalize. Methods based on machine learning tend to perform better, especially with PHI that is not mentioned in the dictionaries used. Finally, the issues of anonymization, sufficient performance, and "over-scrubbing" are discussed in this publication.</p

Empirical vs Bayesian approach for estimating haplotypes from genotypes of unrelated individuals

BACKGROUND: The completion of the HapMap project has stimulated further development of haplotype-based methodologies for disease associations. A key aspect of such development is the statistical inference of individual diplotypes from unphased genotypes. Several methodologies for inferring haplotypes have been developed, but they have not been evaluated extensively to determine which method not only performs well, but also can be easily incorporated in downstream haplotype-based association analyses. In this paper, we attempt to do so. Our evaluation was carried out by comparing the two leading Bayesian methods, implemented in PHASE and HAPLOTYPER, and the two leading empirical methods, implemented in PL-EM and HPlus. We used these methods to analyze real data, namely the dense genotypes on X-chromosome of 30 European and 30 African trios provided by the International HapMap Project, and simulated genotype data. Our conclusions are based on these analyses. RESULTS: All programs performed very well on X-chromosome data, with an average similarity index of 0.99 and an average prediction rate of 0.99 for both European and African trios. On simulated data with approximation of coalescence, PHASE implementing the Bayesian method based on the coalescence approximation outperformed other programs on small sample sizes. When the sample size increased, other programs performed as well as PHASE. PL-EM and HPlus implementing empirical methods required much less running time than the programs implementing the Bayesian methods. They required only one hundredth or thousandth of the running time required by PHASE, particularly when analyzing large sample sizes and large umber of SNPs. CONCLUSION: For large sample sizes (hundreds or more), which most association studies require, the two empirical methods might be used since they infer the haplotypes as accurately as any Bayesian methods and can be incorporated easily into downstream haplotype-based analyses such as haplotype-association analyses

Endocrine therapy resistant ESR1 variants revealed by genomic characterization of breast cancer derived xenografts

To characterize patient-derived xenografts (PDXs) for functional studies, we made whole-genome comparisons with originating breast cancers representative of the major intrinsic subtypes. Structural and copy number aberrations were found to be retained with high fidelity. However, at the single-nucleotide level, variable numbers of PDX-specific somatic events were documented, although they were only rarely functionally significant. Variant allele frequencies were often preserved in the PDXs, demonstrating that clonal representation can be transplantable. Estrogen-receptor-positive PDXs were associated with ESR1 ligand-binding-domain mutations, gene amplification, or an ESR1/YAP1 translocation. These events produced different endocrine-therapy-response phenotypes in human, cell line, and PDX endocrine-response studies. Hence, deeply sequenced PDX models are an important resource for the search for genome-forward treatment options and capture endocrine-drug-resistance etiologies that are not observed in standard cell lines. The originating tumor genome provides a benchmark for assessing genetic drift and clonal representation after transplantation

Thioredoxin Glutathione Reductase as a Novel Drug Target: Evidence from Schistosoma japonicum

Background: Schistosomiasis remains a major public health concern affecting billions of people around the world. Currently, praziquantel is the only drug of choice for treatment of human schistosomiasis. The emergence of drug resistance to praziquantel in schistosomes makes the development of novel drugs an urgent task. Thioredoxin glutathione reductase (TGR) enzymes in Schistosoma mansoni and some other platyhelminths have been identified as alternative targets. The present study was designed to confirm the existense and the potential value of TGR as a target for development of novel antischistosomal agents in Schistosoma japonicum, a platyhelminth endemic in Asia. Methods and Findings: After cloning the S. japonicum TGR (SjTGR) gene, the recombinant SjTGR selenoprotein was purified and characterized in enzymatic assays as a multifunctional enzyme with thioredoxin reductase (TrxR), glutathione reductase (GR) and glutaredoxin (Grx) activities. Immunological and bioinformatic analyses confirmed that instead of having separate TrxR and GR proteins in mammalian, S. japonicum only encodes TGR, which performs the functions of both enzymes and plays a critical role in maintaining the redox balance in this parasite. These results were in good agreement with previous findings in Schistosoma mansoni and some other platyhelminths. Auranofin, a known inhibitor against TGR, caused fatal toxicity in S. japonicum adult worms in vitro and reduced worm and egg burdens in S. japonicum infected mice. Conclusions: Collectively, our study confirms that a multifunctional enzyme SjTGR selenoprotein, instead of separate Trx