Reducing Side Effects of Hiding Sensitive Itemsets in Privacy Preserving Data Mining

Data mining is traditionally adopted to retrieve and analyze knowledge from large amounts of data. Private or confidential data may be sanitized or suppressed before it is shared or published in public. Privacy preserving data mining (PPDM) has thus become an important issue in recent years. The most general way of PPDM is to sanitize the database to hide the sensitive information. In this paper, a novel hiding-missing-artificial utility (HMAU) algorithm is proposed to hide sensitive itemsets through transaction deletion. The transaction with the maximal ratio of sensitive to nonsensitive one is thus selected to be entirely deleted. Three side effects of hiding failures, missing itemsets, and artificial itemsets are considered to evaluate whether the transactions are required to be deleted for hiding sensitive itemsets. Three weights are also assigned as the importance to three factors, which can be set according to the requirement of users. Experiments are then conducted to show the performance of the proposed algorithm in execution time, number of deleted transactions, and number of side effects

The Interplay of Reovirus with Autophagy

Autophagy participates in multiple fundamental physiological processes, including survival, differentiation, development, and cellular homeostasis. It eliminates cytoplasmic protein aggregates and damaged organelles by triggering a series of events: sequestering the protein substrates into double-membrane vesicles, fusing the vesicles with lysosomes, and then degrading the autophagic contents. This degradation pathway is also involved in various disorders, for instance, cancers and infectious diseases. This paper provides an overview of modulation of autophagy in the course of reovirus infection and also the interplay of autophagy and reovirus

Reduction in magnetic coercivity of Co nanomagnets by Fe alloying

We measured the magnetic hysteresis and coercivity of individual Co and Co$_{0.8}$Fe$_{0.2}$ bilayer nano-sized island structures formed on Cu (111) substrate using spin-polarized scanning tunneling microscopy. From the hysteresis taken on various sizes of islands, we found that the alloyed islands are ferromagnetic with out-of-plane magnetic anisotropy, same as the pure islands. Coercivity of the alloy islands, which is dependent on their size, was significantly reduced to ≈40% of that of the pure islands. Based on the Stoner–Wohlfarth model, we evaluated the amount of magnetic anisotropic energy and anisotropy constant for both pure and alloy islands. Since tunneling spectra taken on the alloy islands show upward shifts of the valence electronic states as compared to the pure ones, fewer electrons populated in the valence band of the alloy islands are presumably responsible for the reduction in the magnetic anisotropic energy

Percutaneous Transhepatic Cholangiography and Drainage is an Effective Rescue Therapy for Biliary Complications in Liver Transplant Recipients Who Fail Endoscopic Retrograde Cholangiopancreatography

BackgroundWe attempted to evaluate both the factors that predispose a patient to biliary complications after liver transplantation and the results of percutaneous transhepatic cholangiography and drainage (PTCD) for the management of those complications.MethodsThis study retrospectively reviewed the cases of 81 patients who received liver transplants at Taipei Veterans General Hospital between February 2003 and June 2008. Biliary complications were diagnosed on the basis of clinical findings, laboratory data, and the results of imaging studies.ResultsA total of 18 patients (22.2%) developed biliary complications, and living donor liver transplantation (LDLT) was a significant risk factor (p = 0.035), compared to cadaveric liver transplantation. Eight patients with biliary complications received PTCD as the first treatment modality and 6 had successful results. An additional 10 patients received endoscopic retrograde cholangiopancreatography (ERCP) initially, but only 2 patients were effectively managed. One patient received conservative treatment after ERCP failure. One patient died from sepsis after ERCP. The remaining 6 patients with failed ERCP were successfully managed with PTCD. The overall mortality rate in these patients with biliary complications was 16.7%. No significant prognostic predictors were identified, including age, sex, biochemical data, and model for end-stage liver disease scores.ConclusionBiochemical markers cannot predict biliary complications preoperatively. LDLT increases the risk of biliary complications. PTCD is an effective rescue therapy when ERCP fails

NERBio: using selected word conjunctions, term normalization, and global patterns to improve biomedical named entity recognition

BACKGROUND: Biomedical named entity recognition (Bio-NER) is a challenging problem because, in general, biomedical named entities of the same category (e.g., proteins and genes) do not follow one standard nomenclature. They have many irregularities and sometimes appear in ambiguous contexts. In recent years, machine-learning (ML) approaches have become increasingly common and now represent the cutting edge of Bio-NER technology. This paper addresses three problems faced by ML-based Bio-NER systems. First, most ML approaches usually employ singleton features that comprise one linguistic property (e.g., the current word is capitalized) and at least one class tag (e.g., B-protein, the beginning of a protein name). However, such features may be insufficient in cases where multiple properties must be considered. Adding conjunction features that contain multiple properties can be beneficial, but it would be infeasible to include all conjunction features in an NER model since memory resources are limited and some features are ineffective. To resolve the problem, we use a sequential forward search algorithm to select an effective set of features. Second, variations in the numerical parts of biomedical terms (e.g., "2" in the biomedical term IL2) cause data sparseness and generate many redundant features. In this case, we apply numerical normalization, which solves the problem by replacing all numerals in a term with one representative numeral to help classify named entities. Third, the assignment of NE tags does not depend solely on the target word's closest neighbors, but may depend on words outside the context window (e.g., a context window of five consists of the current word plus two preceding and two subsequent words). We use global patterns generated by the Smith-Waterman local alignment algorithm to identify such structures and modify the results of our ML-based tagger. This is called pattern-based post-processing. RESULTS: To develop our ML-based Bio-NER system, we employ conditional random fields, which have performed effectively in several well-known tasks, as our underlying ML model. Adding selected conjunction features, applying numerical normalization, and employing pattern-based post-processing improve the F-scores by 1.67%, 1.04%, and 0.57%, respectively. The combined increase of 3.28% yields a total score of 72.98%, which is better than the baseline system that only uses singleton features. CONCLUSION: We demonstrate the benefits of using the sequential forward search algorithm to select effective conjunction feature groups. In addition, we show that numerical normalization can effectively reduce the number of redundant and unseen features. Furthermore, the Smith-Waterman local alignment algorithm can help ML-based Bio-NER deal with difficult cases that need longer context windows

Genetic population structure of the alpine species Rhododendron pseudochrysanthum sensu lato (Ericaceae) inferred from chloroplast and nuclear DNA

<p>Abstract</p> <p>Background</p> <p>A complex of incipient species with different degrees of morphological or ecological differentiation provides an ideal model for studying species divergence. We examined the phylogeography and the evolutionary history of the <it>Rhododendron pseudochrysanthum </it>s. l.</p> <p>Results</p> <p>Systematic inconsistency was detected between gene genealogies of the cpDNA and nrDNA. Rooted at <it>R. hyperythrum </it>and <it>R. formosana</it>, both trees lacked reciprocal monophyly for all members of the complex. For <it>R. pseudochrysanthum </it>s.l., the spatial distribution of the cpDNA had a noteworthy pattern showing high genetic differentiation (F_ST= 0.56-0.72) between populations in the Yushan Mountain Range and populations of the other mountain ranges.</p> <p>Conclusion</p> <p>Both incomplete lineage sorting and interspecific hybridization/introgression may have contributed to the lack of monophyly among <it>R. hyperythrum</it>, <it>R. formosana </it>and <it>R. pseudochrysanthum </it>s.l. Independent colonizations, plus low capabilities of seed dispersal in current environments, may have resulted in the genetic differentiation between populations of different mountain ranges. At the population level, the populations of Central, and Sheishan Mountains may have undergone postglacial demographic expansion, while populations of the Yushan Mountain Range are likely to have remained stable ever since the colonization. In contrast, the single population of the Alishan Mountain Range with a fixed cpDNA haplotype may have experienced bottleneck/founder's events.</p

Predicting new-onset post-stroke depression from real-world data using machine learning algorithm

IntroductionPost-stroke depression (PSD) is a serious mental disorder after ischemic stroke. Early detection is important for clinical practice. This research aims to develop machine learning models to predict new-onset PSD using real-world data.MethodsWe collected data for ischemic stroke patients from multiple medical institutions in Taiwan between 2001 and 2019. We developed models from 61,460 patients and used 15,366 independent patients to test the models’ performance by evaluating their specificities and sensitivities. The predicted targets were whether PSD occurred at 30, 90, 180, and 365 days post-stroke. We ranked the important clinical features in these models.ResultsIn the study’s database sample, 1.3% of patients were diagnosed with PSD. The average specificity and sensitivity of these four models were 0.83–0.91 and 0.30–0.48, respectively. Ten features were listed as important features related to PSD at different time points, namely old age, high height, low weight post-stroke, higher diastolic blood pressure after stroke, no pre-stroke hypertension but post-stroke hypertension (new-onset hypertension), post-stroke sleep-wake disorders, post-stroke anxiety disorders, post-stroke hemiplegia, and lower blood urea nitrogen during stroke.DiscussionMachine learning models can provide as potential predictive tools for PSD and important factors are identified to alert clinicians for early detection of depression in high-risk stroke patients

Major interventions are associated with survival of out of hospital cardiac arrest patients - a population based survey

Background. The overall survival rate of out-of-hospital cardiac arrest (OHCA) in Taiwan or even in the whole of Asia is relatively low. Major interventions, such as target temperature management (TTM), coronary artery angiography, and extracorporeal membrane oxygenation (ECMO), have been associated with better patient outcome. However, studies in Taiwan revealing evidence of the benefits of these interventions are limited. Methods. A population-based study used an 8-year database to analyze overall survival and risk factors ˝among OHCA patients. All adult non-trauma OHCA patients were identified through diagnostic and procedure codes. Hospital survival and return of spontaneous circulation (ROSC) were primary and secondary outcomes. Logistic regression and Cox regression analyses were conducted. Results. There was a relationship between major interventions (including TTM, coronary artery angiography, and ECMO) and better hospital survival. Age, income, major interventions, and acute myocardial infarction history were associated with hospital survival. The adjusted hazard ratios (HRs) were 0.406 (95% CI, 0.295 to 0.558), 1.109 (95% CI, 1.027 to 1.197), 1.075 (95% CI, 1.002 to 1.154), 1.097 (95% CI, 1.02 to 1.181) and 0.799(95% CI, 0.677 to 0.942) for patients with major interventions, age≥50, medium low and low income, middle income, and acute myocardial infarction history, respectively. Conclusion. This population-based study in Taiwan revealed that older age (≥50), medium low and low income were associated with a lower rate of survival. Major interventions, including TTM, coronary angiography, and ECMO, were related to better survival

FASTSNP: an always up-to-date and extendable service for SNP function analysis and prioritization

Single nucleotide polymorphism (SNP) prioritization based on the phenotypic risk is essential for association studies. Assessment of the risk requires access to a variety of heterogeneous biological databases and analytical tools. FASTSNP (function analysis and selection tool for single nucleotide polymorphisms) is a web server that allows users to efficiently identify and prioritize high-risk SNPs according to their phenotypic risks and putative functional effects. A unique feature of FASTSNP is that the functional effect information used for SNP prioritization is always up-to-date, because FASTSNP extracts the information from 11 external web servers at query time using a team of web wrapper agents. Moreover, FASTSNP is extendable by simply deploying more Web wrapper agents. To validate the results of our prioritization, we analyzed 1569 SNPs from the SNP500Cancer database. The results show that SNPs with a high predicted risk exhibit low allele frequencies for the minor alleles, consistent with a well-known finding that a strong selective pressure exists for functional polymorphisms. We have been using FASTSNP for 2 years and FASTSNP enables us to discover a novel promoter polymorphism. FASTSNP is available at