Protecting participant privacy while maintaining content and context: Challenges in qualitative data De‐identification and sharing

The Library Assessment for Research and Scholarship Lab investigates qualitative research support across disciplines. In 2018–2019, the lab conducted 29 interviews with faculty, librarians, and doctoral students who engaged in qualitative research to understand their needs during the research lifecycle. At the conclusion of this project, the qualitative data will be deposited in a repository where it can be made available for future secondary use. The deposited data will include de‐identified versions of the complete interview transcripts. This poster supplements existing de‐identification standards, details drafting and revising protocol for de‐identification of our data, and discusses the de‐identification process we used for the qualitative data. Existing de‐identification literature and standards are limited and not widely uniform in qualitative research. In developing de‐identification protocol, our lab recognized several potential challenges in the process and created procedures to ensure future data usability. There is inherent tension between keeping privacy intact and sharing undistorted qualitative data. We aim to address some of the hazards with de‐identification best practices, demonstrating methodology for producing high quality de‐identified qualitative data. In offering up a test case with suggested methods to better protect participants’ identities, this work will lend itself to sustainable qualitative data sharing and reuse.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163391/2/pra2415_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163391/1/pra2415.pd

A new FDI potential index : design and application to the EU regions.

The aim of this paper is to propose a new way of computing the FDI Potential Index to address the issue of FDI attractiveness at the EU regional level. This new index employs a sound way of selecting the variables involved in its construction, for which a factor analysis is performed. Accordingly, six factors (“economic potential”, “market size”, “labour situation”, “technological progress”, “labour regulation” and “competitiveness”) are identified. Next, by applying the methodology of composite indicators and considering different weighting and aggregation schemes, three versions (un-weighted linear, weighted linear and weighted geometric) of the new FDI Potential Index are computed. Afterwards, the comparison of the weighted linear version of the Potential Index with the conventional FDI Performance Index allows us to apply the United Nations Conference on Trade and Development (UNCTAD) FDI typology. The results reveal considerable heterogeneity among EU regions in terms of FDI attractiveness, and that regions belonging to the same group of the UNCTAD classification are highly concentrated from a geographical perspective. In view of these findings, we compute an additional version of both the FDI Potential and Performance indices, in which the geographical location of each region plays a key role. Based on these spatial indices, some general policy implications are drawn

Electron Capture Dissociation Mass Spectrometry of Tyrosine Nitrated Peptides

In vivo protein nitration is associated with many disease conditions that involve oxidative stress and inflammatory response. The modification involves addition of a nitro group at the position ortho to the phenol group of tyrosine to give 3-nitrotyrosine. To understand the mechanisms and consequences of protein nitration, it is necessary to develop methods for identification of nitrotyrosine-containing proteins and localization of the sites of modification.Here, we have investigated the electron capture dissociation (ECD) and collision-induced association (CID) behavior of 3-nitrotyrosine-containing peptides. The presence of nitration did not affect the CID behavior of the peptides. For the doubly-charged peptides, addition of nitration severely inhibited the production of ECD sequence fragments. However, ECD of the triply-charged nitrated peptides resulted in some singly-charged sequence fragments. ECD of the nitrated peptides is characterized by multiple losses of small neutral species including hydroxyl radicals, water and ammonia. The origin of the neutral losses has been investigated by use of activated ion (AI) ECD. Loss of ammonia appears to be the result of non-covalent interactions between the nitro group and protonated lysine side-chains

Genetic Comparison of a Croatian Isolate and CEPH European Founders

Human isolates have been postulated as a good resource for the identification of QTL due to reduced genetic diversity and a more homogeneous environment. Isolates may also have increased linkage disequilibrium (LD) due to small effective population size and, either loss or increase in frequency of alleles that are rare in the general population from which they originate. Here we investigate the difference in allele and genotype frequencies, LD and homozygous tracts between an isolate—several villages from the island of Vis in Croatia—and an outbred population of European origin: the Hapmap CEPH founders. Using the HumanHap300 v1 Genotyping BeadChip, we show that our population does not differ greatly from the reference CEU outbred population despite having a slightly higher proportion of monomorphic loci, a slightly higher long-range LD, and a greater proportion of individuals with long homozygous tracts. We conclude that genotyping arrays should perform equally well in our isolate as in outbred European populations for disease mapping studies and that SNP–trait associations discovered in our well-characterized Croatian isolate should be valid in the general European population from which they descend. Genet. Epidemiol. 34: 140–145, 2010. © 2009 Wiley-Liss, Inc

Genetic Determinants of Circulating Sphingolipid Concentrations in European Populations

Sphingolipids have essential roles as structural components of cell membranes and in cell signalling, and disruption of their metabolism causes several diseases, with diverse neurological, psychiatric, and metabolic consequences. Increasingly, variants within a few of the genes that encode enzymes involved in sphingolipid metabolism are being associated with complex disease phenotypes. Direct experimental evidence supports a role of specific sphingolipid species in several common complex chronic disease processes including atherosclerotic plaque formation, myocardial infarction (MI), cardiomyopathy, pancreatic beta-cell failure, insulin resistance, and type 2 diabetes mellitus. Therefore, sphingolipids represent novel and important intermediate phenotypes for genetic analysis, yet little is known about the major genetic variants that influence their circulating levels in the general population. We performed a genome-wide association study (GWAS) between 318,237 single-nucleotide polymorphisms (SNPs) and levels of circulating sphingomyelin (SM), dihydrosphingomyelin (Dih-SM), ceramide (Cer), and glucosylceramide (GluCer) single lipid species (33 traits); and 43 matched metabolite ratios measured in 4,400 subjects from five diverse European populations. Associated variants (32) in five genomic regions were identified with genome-wide significant corrected p-values ranging down to 9.08 x 10(-66). The strongest associations were observed in or near 7 genes functionally involved in ceramide biosynthesis and trafficking: SPTLC3, LASS4, SGPP1, ATP10D, and FADS1-3. Variants in 3 loci (ATP10D, FADS3, and SPTLC3) associate with MI in a series of three German MI studies. An additional 70 variants across 23 candidate genes involved in sphingolipid-metabolizing pathways also demonstrate association (p = 10(-4) or less). Circulating concentrations of several key components in sphingolipid metabolism are thus under strong genetic control, and variants in these loci can be tested for a role in the development of common cardiovascular, metabolic, neurological, and psychiatric diseases

BICEPP: an example-based statistical text mining method for predicting the binary characteristics of drugs

<p>Abstract</p> <p>Background</p> <p>The identification of drug characteristics is a clinically important task, but it requires much expert knowledge and consumes substantial resources. We have developed a statistical text-mining approach (BInary Characteristics Extractor and biomedical Properties Predictor: BICEPP) to help experts screen drugs that may have important clinical characteristics of interest.</p> <p>Results</p> <p>BICEPP first retrieves MEDLINE abstracts containing drug names, then selects tokens that best predict the list of drugs which represents the characteristic of interest. Machine learning is then used to classify drugs using a document frequency-based measure. Evaluation experiments were performed to validate BICEPP's performance on 484 characteristics of 857 drugs, identified from the Australian Medicines Handbook (AMH) and the PharmacoKinetic Interaction Screening (PKIS) database. Stratified cross-validations revealed that BICEPP was able to classify drugs into all 20 major therapeutic classes (100%) and 157 (of 197) minor drug classes (80%) with areas under the receiver operating characteristic curve (AUC) > 0.80. Similarly, AUC > 0.80 could be obtained in the classification of 173 (of 238) adverse events (73%), up to 12 (of 15) groups of clinically significant cytochrome P450 enzyme (CYP) inducers or inhibitors (80%), and up to 11 (of 14) groups of narrow therapeutic index drugs (79%). Interestingly, it was observed that the keywords used to describe a drug characteristic were not necessarily the most predictive ones for the classification task.</p> <p>Conclusions</p> <p>BICEPP has sufficient classification power to automatically distinguish a wide range of clinical properties of drugs. This may be used in pharmacovigilance applications to assist with rapid screening of large drug databases to identify important characteristics for further evaluation.</p