Privacy Preserving Utility Mining: A Survey

In big data era, the collected data usually contains rich information and hidden knowledge. Utility-oriented pattern mining and analytics have shown a powerful ability to explore these ubiquitous data, which may be collected from various fields and applications, such as market basket analysis, retail, click-stream analysis, medical analysis, and bioinformatics. However, analysis of these data with sensitive private information raises privacy concerns. To achieve better trade-off between utility maximizing and privacy preserving, Privacy-Preserving Utility Mining (PPUM) has become a critical issue in recent years. In this paper, we provide a comprehensive overview of PPUM. We first present the background of utility mining, privacy-preserving data mining and PPUM, then introduce the related preliminaries and problem formulation of PPUM, as well as some key evaluation criteria for PPUM. In particular, we present and discuss the current state-of-the-art PPUM algorithms, as well as their advantages and deficiencies in detail. Finally, we highlight and discuss some technical challenges and open directions for future research on PPUM.Comment: 2018 IEEE International Conference on Big Data, 10 page

Understanding variation in transcription factor binding by modeling transcription factor genome-epigenome interactions

Despite explosive growth in genomic datasets, the methods for studying epigenomic mechanisms of gene regulation remain primitive. Here we present a model-based approach to systematically analyze the epigenomic functions in modulating transcription factor-DNA binding. Based on the first principles of statistical mechanics, this model considers the interactions between epigenomic modifications and a cis-regulatory module, which contains multiple binding sites arranged in any configurations. We compiled a comprehensive epigenomic dataset in mouse embryonic stem (mES) cells, including DNA methylation (MeDIP-seq and MRE-seq), DNA hydroxymethylation (5-hmC-seq), and histone modifications (ChIP-seq). We discovered correlations of transcription factors (TFs) for specific combinations of epigenomic modifications, which we term epigenomic motifs. Epigenomic motifs explained why some TFs appeared to have different DNA binding motifs derived from in vivo (ChIP-seq) and in vitro experiments. Theoretical analyses suggested that the epigenome can modulate transcriptional noise and boost the cooperativity of weak TF binding sites. ChIP-seq data suggested that epigenomic boost of binding affinities in weak TF binding sites can function in mES cells. We showed in theory that the epigenome should suppress the TF binding differences on SNP-containing binding sites in two people. Using personal data, we identified strong associations between H3K4me2/H3K9ac and the degree of personal differences in NFκB binding in SNP-containing binding sites, which may explain why some SNPs introduce much smaller personal variations on TF binding than other SNPs. In summary, this model presents a powerful approach to analyze the functions of epigenomic modifications. This model was implemented into an open source program APEG (Affinity Prediction by Epigenome and Genome, http://systemsbio.ucsd.edu/apeg)

Understanding Variation in Transcription Factor Binding by Modeling Transcription Factor Genome-Epigenome Interactions

Despite explosive growth in genomic datasets, the methods for studying epigenomic mechanisms of gene regulation remain primitive. Here we present a model-based approach to systematically analyze the epigenomic functions in modulating transcription factor-DNA binding. Based on the first principles of statistical mechanics, this model considers the interactions between epigenomic modifications and a cis-regulatory module, which contains multiple binding sites arranged in any configurations. We compiled a comprehensive epigenomic dataset in mouse embryonic stem (mES) cells, including DNA methylation (MeDIP-seq and MRE-seq), DNA hydroxymethylation (5-hmC-seq), and histone modifications (ChIP-seq). We discovered correlations of transcription factors (TFs) for specific combinations of epigenomic modifications, which we term epigenomic motifs. Epigenomic motifs explained why some TFs appeared to have different DNA binding motifs derived from in vivo (ChIP-seq) and in vitro experiments. Theoretical analyses suggested that the epigenome can modulate transcriptional noise and boost the cooperativity of weak TF binding sites. ChIP-seq data suggested that epigenomic boost of binding affinities in weak TF binding sites can function in mES cells. We showed in theory that the epigenome should suppress the TF binding differences on SNP-containing binding sites in two people. Using personal data, we identified strong associations between H3K4me2/H3K9ac and the degree of personal differences in NFκB binding in SNP-containing binding sites, which may explain why some SNPs introduce much smaller personal variations on TF binding than other SNPs. In summary, this model presents a powerful approach to analyze the functions of epigenomic modifications. This model was implemented into an open source program APEG (Affinity Prediction by Epigenome and Genome, http://systemsbio.ucsd.edu/apeg).</p

Sinus automaticity and sinoatrial conduction in severe symptomatic sick sinus syndrome

AbstractElectrophysiologic studies with recordings of sinus node electrograms were performed in 38 patients with severe symptomatic sick sinus syndrome. Thirty-two of the 38 patients had episodic tachyarrhythmias and 17 presented with syncope. The clinically documented sinus or atrial pause was 5.6 ± 2.8 s (mean ± SD).Patients were divided into three groups according to electrophysiologic findings. Group I consisted of nine patients with complete sinoatrial block. Sinus node electrograms were recorded during the episodes of long pauses. Seven patients had unidirectional exit block, with the atrial impulse being capable of retrograde penetration to the sinus node causing suppression of sinus automaticity; two had bidirectional sinoatrial block.Group II consisted of 22 patients with either 1:1 sinoatrial conduction (group IIa = 13 patients) or second degree sinoatrial exit block (group IIb = 9 patients) during spontaneous sinus rhythm. Sinoatrial exit block, ranging from 1 to >14 sinus beats, was observed during postpacing pauses that ranged from 1,650 to 37,000 ms (mean 7,286 ± 6,989). The maximal sinus node recovery time ranged from 770 to 5,580 ms (mean 3,004 ± 1,686) and was normal in 5 patients and prolonged in 17.Group III consisted of seven patient with no recordable sinus node electrogram, reflecting either a technical failure or a quiescence of sinus activity. The sinus node recovery time in these seven patients ranged from 1,190 to 4,260 ms (mean 2,949 ± 1, 121).Thus, abnormalities in both sinus node automaticity and sinoatrial conduction are responsible for the long sinus or atrial pauses in the sick sinus syndrome. However, complete sinoatrial exit block can occur and cause severe bradycardia with escape rhythm; repetitive sinoatrial exit block plays a major role in producing posttachycardia pauses

Age as a predisposing factor of respiratory alkalosis in accidental carbon monoxide poisoning

AbstractAimsThe purpose of this study was to determine the frequency of and identify the predisposing factors for respiratory alkalosis in patients with accidental carbon monoxide (CO) poisoning.MethodsPatients presenting to the emergency department with accidental CO poisoning were retrospectively identified and divided into Group A (no respiratory alkalosis) and Group B (respiratory alkalosis). Charts were reviewed for neurologic status, various demographic factors, and laboratory data.ResultsA total 96 patients, 37 (38.5%) men and 59 (61.5%) women, were identified. Of these, the 58 (60.4%) patients without respiratory alkalosis were placed in Group A and the 38 (39.6%) patients with respiratory alkalosis were placed in Group B. Independent multivariate predictors of CO poisoning presenting with respiratory alkalosis were age [odds ratio (OR), 1.04; 95% confidence interval (CI), 1.01–1.08] and respiratory rate (OR, 1.16; 95% CI, 1.01–1.33). The rates of respiratory alkalosis in patients younger than 15 years, 15–29 years, 30–44 years, 45–59 years, and older than 59 years were 17.4%, 32.4%, 51.9%, 75%, and 75%, respectively (p<0.01).ConclusionsRespiratory alkalosis in the patients with CO poisoning is not an uncommon finding, and as age increases, the percentage becomes higher. When emergency physicians are faced with patients presenting with respiratory alkalosis of undetermined cause, CO poisoning should be taken into consideration, especially in the elderly