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Background: Postoperative urinary retention (POUR), a common condition after prolapse surgery with potential serious sequelae if left untreated, lacks a clearly established optimal timing for catheter removal. This study aimed to develop and validate a predictive model for postoperative urinary retention lasting > 2 and > 4 days after prolapse surgery. Methods: We conducted a retrospective review of 1,122 patients undergoing prolapse surgery. The dataset was divided into training and testing cohorts. POUR was defined as the need for continuous intermittent catheterization resulting from a failed spontaneous voiding trial, with passing defined as two consecutive voids ≥ 150 mL and a postvoid residual urine volume ≤ 150 mL. We performed logistic regression and the predicted model was validated using both training and testing cohorts. Results: Among patients, 31% and 12% experienced POUR lasting > 2 and > 4 days, respectively. Multivariable logistic model identified 6 predictors. For predicting POUR, internal validation using cross-validation approach showed good performance, with accuracy lasting > 2 (area under the curve [AUC] 0.73) and > 4 days (AUC 0.75). Split validation using pre-separated dataset also showed good performance, with accuracy lasting > 2 (AUC 0.73) and > 4 days (AUC 0.74). Calibration curves demonstrated that the model accurately predicted POUR lasting > 2 and > 4 days (from 0 to 80%). Conclusions: The proposed prediction model can assist clinicians in personalizing postoperative bladder care for patients undergoing prolapse surgery by providing accurate individual risk estimates. [ABSTRACT FROM AUTHOR]

Internal tandem duplication (ITD) of the FMS-like tyrosine kinase (FLT3) gene is associated with poor clinical outcomes in patients with acute myeloid leukemia. Although recent methods for detecting FLT3-ITD from next-generation sequencing (NGS) data have replaced traditional ITD detection approaches such as conventional PCR or fragment analysis, their use in the clinical field is still limited and requires further information. Here, we introduce ITDetect, an efficient FLT3-ITD detection approach that uses NGS data. Our proposed method allows for more precise detection and provides more detailed information than existing in silico methods. Further, it enables FLT3-ITD detection from exome sequencing or targeted panel sequencing data, thereby improving its clinical application. We validated the performance of ITDetect using NGS-based and experimental ITD detection methods and successfully demonstrated that ITDetect provides the highest concordance with the experimental methods. The program and data underlying this study are available in a public repository. [ABSTRACT FROM AUTHOR]

Background: Although anti-apoptotic proteins of the B-cell lymphoma-2 (BCL2) family have been utilized as therapeutic targets in acute myeloid leukaemia (AML), their complicated regulatory networks make individualized therapy difficult. This study aimed to discover the transcriptional signatures of BCL2 family genes that reflect regulatory dynamics, which can guide individualized therapeutic strategies. Methods: From three AML RNA-seq cohorts (BeatAML, LeuceGene, and TCGA; n = 451, 437, and 179, respectively), we constructed the BCL2 family signatures (BFSigs) by applying an innovative gene-set selection method reflecting biological knowledge followed by non-negative matrix factorization (NMF). To demonstrate the significance of the BFSigs, we conducted modelling to predict response to BCL2 family inhibitors, clustering, and functional enrichment analysis. Cross-platform validity of BFSigs was also confirmed using NanoString technology in a separate cohort of 47 patients. Results: We established BFSigs labeled as the BCL2, MCL1/BCL2, and BFL1/MCL1 signatures that identify key anti-apoptotic proteins. Unsupervised clustering based on BFSig information consistently classified AML patients into three robust subtypes across different AML cohorts, implying the existence of biological entities revealed by the BFSig approach. Interestingly, each subtype has distinct enrichment patterns of major cancer pathways, including MAPK and mTORC1, which propose subtype-specific combination treatment with apoptosis modulating drugs. The BFSig-based classifier also predicted response to venetoclax with remarkable performance (area under the ROC curve, AUROC = 0.874), which was well-validated in an independent cohort (AUROC = 0.950). Lastly, we successfully confirmed the validity of BFSigs using NanoString technology. Conclusions: This study proposes BFSigs as a biomarker for the effective selection of apoptosis targeting treatments and cancer pathways to co-target in AML. [ABSTRACT FROM AUTHOR]

Motivation Pathway analyses have led to more insight into the underlying biological functions related to the phenotype of interest in various types of omics data. Pathway-based statistical approaches have been actively developed, but most of them do not consider correlations among pathways. Because it is well known that there are quite a few biomarkers that overlap between pathways, these approaches may provide misleading results. In addition, most pathway-based approaches tend to assume that biomarkers within a pathway have linear associations with the phenotype of interest, even though the relationships are more complex. Results To model complex effects including non-linear effects, we propose a new approach, Hierarchical structural CoMponent analysis using Kernel (HisCoM-Kernel). The proposed method models non-linear associations between biomarkers and phenotype by extending the kernel machine regression and analyzes entire pathways simultaneously by using the biomarker-pathway hierarchical structure. HisCoM-Kernel is a flexible model that can be applied to various omics data. It was successfully applied to three omics datasets generated by different technologies. Our simulation studies showed that HisCoM-Kernel provided higher statistical power than other existing pathway-based methods in all datasets. The application of HisCoM-Kernel to three types of omics dataset showed its superior performance compared to existing methods in identifying more biologically meaningful pathways, including those reported in previous studies. Availability and implementation The HisCoM-Kernel software is freely available at http://statgen.snu.ac.kr/software/HisCom-Kernel/. The RNA-seq data underlying this article are available at https://xena.ucsc.edu/ , and the others will be shared on reasonable request to the corresponding author. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Objective: To develop and validate a prediction model for bothersome stress urinary incontinence after prolapse surgery and to compare it with an existing clinical prediction model (CUPIDO model). Design: Retrospective cohort study. Setting: Two tertiary hospitals in South Korea. Population: A total of 1142 patients who underwent prolapse surgery with or without a concomitant midurethral sling. Methods: To construct a prediction model, we performed logistic regression using both exhaustive and stepwise variable selection, validating the model both internally and externally. Main Outcome Measures: Bothersome stress urinary incontinence defined as the presence of bothersome symptoms of stress urinary incontinence and/or subsequent continence procedure one year after surgery. Results: Postoperative bothersome stress urinary incontinence occurred in 10% of patients. A model containing six predictors (age, diabetes mellitus, subjective urinary incontinence, prolapse reduction stress test result, type of prolapse surgery, and a concomitant midurethral sling) showed excellent performance for predicting bothersome stress urinary incontinence (area under the curve 0.74, 95% confidence interval 0.62–0.86) and outperformed the CUPIDO model (area under the curve 0.63, 95% confidence interval 0.49–0.76; DeLong's test P = 0.014). Conclusions: This prediction model might be a useful tool to guide patient decision making regarding a concomitant continence procedure at the time of prolapse surgery. The predictive value of this model needs to be validated further in cohorts with different characteristics. The proposed prediction model for bothersome stress urinary incontinence after prolapse surgery outperforms an existing model. The proposed prediction model for bothersome stress urinary incontinence after prolapse surgery outperforms an existing model. Linked article This article is commented on by J. Marinus van der Ploeg & Ewout W. Steyerberg, pp. 1165 in this issue. To view this minicommentary visit https://doi.org/10.1111/1471-0528.17067. [ABSTRACT FROM AUTHOR]

Motivation Drug repositioning reveals novel indications for existing drugs and in particular, diseases with no available drugs. Diverse computational drug repositioning methods have been proposed by measuring either drug-treated gene expression signatures or the proximity of drug targets and disease proteins found in prior networks. However, these methods do not explain which signaling subparts allow potential drugs to be selected, and do not consider polypharmacology, i.e. multiple targets of a known drug, in specific subparts. Results Here, to address the limitations, we developed a subpathway-based polypharmacology drug repositioning method, PATHOME-Drug, based on drug-associated transcriptomes. Specifically, this tool locates subparts of signaling cascading related to phenotype changes (e.g. disease status changes), and identifies existing approved drugs such that their multiple targets are enriched in the subparts. We show that our method demonstrated better performance for detecting signaling context and specific drugs/compounds, compared to WebGestalt and clusterProfiler, for both real biological and simulated datasets. We believe that our tool can successfully address the current shortage of targeted therapy agents. Availability and implementation The web-service is available at http://statgen.snu.ac.kr/software/pathome. The source codes and data are available at https://github.com/labnams/pathome-drug. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Considering difficulties in on-site ADAMTS13 testing and the performance instability of PLASMIC score according to ethnicity, we developed a prediction tool, MED-TMA (machine learning (ML) method for differential diagnosis (DDx) of thrombotic microangiopathy (TMA)) to support clinical decision. Data from 319 patients visiting 31 hospitals in Korea clinically diagnosed with primary TMA was randomly separated by 2:1 into two groups – the development dataset (D-set, n = 212), the validation dataset (V-set, n = 107). Feature elimination was conducted to select optimal clinical predictors. We developed the model with the selected features using ML methods, verifying using V-set. After the feature elimination using 19 clinical variables, five variables were selected with high importance value. Among nine ML methods, four ML methods were chosen considering the Area Under the Curves (AUC) and the correlation between the methods using D-set. We developed MED-TMA based on an optimized ensemble model with the selected four ML methods resulting in AUC values of 0.945 and 0.924 in D-set and V-set, respectively. In addition to the binary outcome, MED-TMA was capable of providing a probability for DDx of TMA. The ensemble approach driven MED-TMA showed comparable accurate and intuitive decision support for DDx of TMA to that of the existing models based on a single ML method. We provide a web-based nomogram for the appropriate use of effective but costly therapeutics to treat TMA patients (http://hematology.snu.ac.kr/medtma/). • For differential diagnosis of primary TMA, we propose a decision support tool named MED-TMA. • MED-TMA improves the performance through ensemble with various ML methods. • MED-TMA provides accurate results with using simple laboratory data of patients. • MED-TMA provides more intuitive suggestion compared with the existing models. [ABSTRACT FROM AUTHOR]

Background: Genome-wide association studies (GWAS) have been widely used to identify phenotype-related genetic variants using many statistical methods, such as logistic and linear regression. However, GWAS-identified SNPs, as identified with stringent statistical significance, explain just a small portion of the overall estimated genetic heritability. To address this 'missing heritability' issue, gene- and pathway-based analysis, and biological mechanisms, have been used for many GWAS studies. However, many of these methods often neglect the correlation between genes and between pathways. Methods: We constructed a hierarchical component model that considers correlations both between genes and between pathways. Based on this model, we propose a novel pathway analysis method for GWAS datasets, Hierarchical structural Component Model for Pathway analysis of Common vAriants (HisCoM-PCA). HisCoM-PCA first summarizes the common variants of each gene, first at the gene-level, and then analyzes all pathways simultaneously by ridge-type penalization of both the gene and pathway effects on the phenotype. Statistical significance of the gene and pathway coefficients can be examined by permutation tests. Results: Using the simulation data set of Genetic Analysis Workshop 17 (GAW17), for both binary and continuous phenotypes, we showed that HisCoM-PCA well-controlled type I error, and had a higher empirical power compared to several other methods. In addition, we applied our method to a SNP chip dataset of KARE for four human physiologic traits: (1) type 2 diabetes; (2) hypertension; (3) systolic blood pressure; and (4) diastolic blood pressure. Those results showed that HisCoM-PCA could successfully identify signal pathways with superior statistical and biological significance. Conclusions: Our approach has the advantage of providing an intuitive biological interpretation for associations between common variants and phenotypes, via pathway information, potentially addressing the missing heritability conundrum. [ABSTRACT FROM AUTHOR]

Although genome-wide association studies (GWAS) have successfully identified thousands of single nucleotide polymorphisms (SNPs) associated with common diseases, these observations are limited for fully explaining "missing heritability". Determining gene–gene interactions (GGI) are one possible avenue for addressing the missing heritability problem. While many statistical approaches have been proposed to detect GGI, most of these focus primarily on SNP-to-SNP interactions. While there are many advantages of gene-based GGI analyses, such as reducing the burden of multiple-testing correction, and increasing power by aggregating multiple causal signals across SNPs in specific genes, only a few methods are available. In this study, we proposed a new statistical approach for gene-based GGI analysis, "Hierarchical structural CoMponent analysis of Gene–Gene Interactions" (HisCoM-GGI). HisCoM-GGI is based on generalized structured component analysis, and can consider hierarchical structural relationships between genes and SNPs. For a pair of genes, HisCoM-GGI first effectively summarizes all possible pairwise SNP–SNP interactions into a latent variable, from which it then performs GGI analysis. HisCoM-GGI can evaluate both gene-level and SNP-level interactions. Through simulation studies, HisCoM-GGI demonstrated higher statistical power than existing gene-based GGI methods, in analyzing a GWAS of a Korean population for identifying GGI associated with body mass index. Resultantly, HisCoM-GGI successfully identified 14 potential GGI, two of which, (NCOR2 × SPOCK1) and (LINGO2 × ZNF385D) were successfully replicated in independent datasets. We conclude that HisCoM-GGI method may be a valuable tool for genome to identify GGI in missing heritability, allowing us to better understand the biological genetic mechanisms of complex traits. We conclude that HisCoM-GGI method may be a valuable tool for genome to identify GGI in missing heritability, allowing us to better understand biological genetic mechanisms of complex traits. An implementation of HisCoM-GGI can be downloaded from the website (http://statgen.snu.ac.kr/software/hiscom-ggi). [ABSTRACT FROM AUTHOR]

Background: Identification of multi-markers is one of the most challenging issues in personalized medicine era. Nowadays, many different types of omics data are generated from the same subject. Although many methods endeavor to identify candidate markers, for each type of omics data, few or none can facilitate such identification. Results: It is well known that microRNAs affect phenotypes only indirectly, through regulating mRNA expression and/or protein translation. Toward addressing this issue, we suggest a hierarchical structured component analysis of microRNA-mRNA integration ("HisCoM-mimi") model that accounts for this biological relationship, to efficiently study and identify such integrated markers. In simulation studies, HisCoM-mimi showed the better performance than the other three methods. Also, in real data analysis, HisCoM-mimi successfully identified more gives more informative miRNA-mRNA integration sets relationships for pancreatic ductal adenocarcinoma (PDAC) diagnosis, compared to the other methods. Conclusion: As exemplified by an application to pancreatic cancer data, our proposed model effectively identified integrated miRNA/target mRNA pairs as markers for early diagnosis, providing a much broader biological interpretation. [ABSTRACT FROM AUTHOR]

Background: As one possible solution to the "missing heritability" problem, many methods have been proposed that apply pathway-based analyses, using rare variants that are detected by next generation sequencing technology. However, while a number of methods for pathway-based rare-variant analysis of multiple phenotypes have been proposed, no method considers a unified model that incorporate multiple pathways. Results: Simulation studies successfully demonstrated advantages of multivariate analysis, compared to univariate analysis, and comparison studies showed the proposed approach to outperform existing methods. Moreover, real data analysis of six type 2 diabetes-related traits, using large-scale whole exome sequencing data, identified significant pathways that were not found by univariate analysis. Furthermore, strong relationships between the identified pathways, and their associated metabolic disorder risk factors, were found via literature search, and one of the identified pathway, was successfully replicated by an analysis with an independent dataset. Conclusions: Herein, we present a powerful, pathway-based approach to investigate associations between multiple pathways and multiple phenotypes. By reflecting the natural hierarchy of biological behavior, and considering correlation between pathways and phenotypes, the proposed method is capable of analyzing multiple phenotypes and multiple pathways simultaneously. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel approach for recovering a 3-D pose from a single human body depth silhouette using nonrigid point set registration and body part tracking. In our method, a human body depth silhouette is presented as a set of 3-D points and matched to another set of 3-D points using point correspondences. To recognize and maintain body part labels, we initialize the first set of points to corresponding human body parts, resulting in a body part-labeled map. Then, we transform the points to a sequential set of points based on point correspondences determined by nonrigid point set registration. After point registration, we utilize the information from tracked body part labels and registered points to create a human skeleton model. A 3-D human pose gets recovered by mapping joint information from the skeleton model to a 3-D synthetic human model. Quantitative and qualitative evaluation results on synthetic and real data show that complex human poses can be recovered more reliably with lower errors compared to other conventional techniques for 3-D pose recovery. [ABSTRACT FROM AUTHOR]

Objective Manual evaluation of machine learning algorithms and selection of a suitable classifier from the list of available candidate classifiers, is highly time consuming and challenging task. If the selection is not carefully and accurately done, the resulting classification model will not be able to produce the expected performance results. In this study, we present an accurate multi-criteria decision making methodology (AMD) which empirically evaluates and ranks classifiers’ and allow end users or experts to choose the top ranked classifier for their applications to learn and build classification models for them. Methods and material Existing classifiers performance analysis and recommendation methodologies lack (a) appropriate method for suitable evaluation criteria selection, (b) relative consistent weighting mechanism, (c) fitness assessment of the classifiers’ performances, and (d) satisfaction of various constraints during the analysis process. To assist machine learning practitioners in the selection of suitable classifier(s), AMD methodology is proposed that presents an expert group-based criteria selection method, relative consistent weighting scheme, a new ranking method, called optimum performance ranking criteria, based on multiple evaluation metrics, statistical significance and fitness assessment functions, and implicit and explicit constraints satisfaction at the time of analysis. For ranking the classifiers performance, the proposed ranking method integrates Wgt.Avg.F-score, CPUTimeTesting, CPUTimeTraining, and Consistency measures using the technique for order performance by similarity to ideal solution (TOPSIS). The final relative closeness score produced by TOPSIS, is ranked and the practitioners select the best performance (top-ranked) classifier for their problems in-hand. Findings Based on the extensive experiments performed on 15 publically available UCI and OpenML datasets using 35 classification algorithms from heterogeneous families of classifiers, an average Spearman's rank correlation coefficient of 0.98 is observed. Similarly, the AMD method has showed improved performance of 0.98 average Spearman's rank correlation coefficient as compared to 0.83 and 0.045 correlation coefficient of the state-of-the-art ranking methods, performance of algorithms (PAlg) and adjusted ratio of ratio (ARR). Conclusion and implication The evaluation, empirical analysis of results and comparison with state-of-the-art methods demonstrate the feasibility of AMD methodology, especially the selection and weighting of right evaluation criteria, accurate ranking and selection of optimum performance classifier(s) for the user's application's data in hand. AMD reduces expert's time and efforts and improves system performance by designing suitable classifier recommended by AMD methodology. [ABSTRACT FROM AUTHOR]

Background: In family-based association analysis, each family is typically ascertained from a single proband, which renders the effects of ascertainment bias heterogeneous among family members. This is contrary to case–control studies, and may introduce sample or ascertainment bias. Statistical efficiency is affected by ascertainment bias, and careful adjustment can lead to substantial improvements in statistical power. However, genetic association analysis has often been conducted using family-based designs, without addressing the fact that each proband in a family has had a great influence on the probability for each family member to be affected. Method: We propose a powerful and efficient statistic for genetic association analysis that considered the heterogeneity of ascertainment bias among family members, under the assumption that both prevalence and heritability of disease are available. With extensive simulation studies, we showed that the proposed method performed better than the existing methods, particularly for diseases with large heritability. Results: We applied the proposed method to the genome-wide association analysis of Alzheimer’s disease. Four significant associations with the proposed method were found. Conclusion: Our significant findings illustrated the practical importance of this new analysis method. [ABSTRACT FROM AUTHOR]

Motivation: Individuals in each family are genetically more homogeneous than unrelated individuals, and family-based designs are often recommended for the analysis of rare variants. However, despite the importance of family-based samples analysis, few statistical methods for rare variant association analysis are available.Results: In this report, we propose a FAmily-based Rare Variant Association Test (FARVAT). FARVAT is based on the quasi-likelihood of whole families, and is statistically and computationally efficient for the extended families. FARVAT assumed that families were ascertained with the disease status of family members, and incorporation of the estimated genetic relationship matrix to the proposed method provided robustness under the presence of the population substructure. Depending on the choice of working matrix, our method could be a burden test or a variance component test, and could be extended to the SKAT-O-type statistic. FARVAT was implemented in C++, and application of the proposed method to schizophrenia data and simulated data for GAW17 illustrated its practical importance.Availability: The software calculates various statistics for the analysis of related samples, and it is freely downloadable from http://healthstats.snu.ac.kr/software/farvat.Contact: won1@snu.ac.kr or tspark@stats.snu.ac.krSupplementary information: supplementary data are available at Bioinformatics online. [ABSTRACT FROM PUBLISHER]

Acoustic source localization has many important applications particularly for military tracking foreign objects. Even though Wireless Sensor Networks (WSNs) have been developed, this localization problem remains a big challenge. A system for solving source localization must have the ability to deal with the problems of recorded convolved mixture signals while minimizing the high communication and computation cost. This paper introduces a distributed design for positioning multiple independent moving sources based on acoustic signals in which we focus on utilizing the relative information of magnitudes recorded at different sensors. The sensors perform preprocessing on the sensed data to capture the most important information before compressing and sending extracted data to the base. At the base, the data is uncompressed and the source locations are inferred via two clustering stages and an optimization method. Analysis and simulation results lead to the conclusion that our system provides good accuracy and needs neither much communication nor complex computation in a distributed manner. It works well when there exists high noise with Rayleigh multipath fading under Doppler effect and even when the number of independent sources is greater than the number of microphone sensors. [ABSTRACT FROM AUTHOR]

Simple Summary: An increased risk of relapse and death from minimally invasive radical hysterectomy has been reported in some patients with early cervical cancer. Thus, the development of an intuitive and precise decision-aid tool, which estimates recurrence and mortality rates by surgical approach, is necessary. To develop models predicting survival outcomes according to the surgical approach, we collected clinicopathologic and survival data of patients with 2009 FIGO stage IB cervical cancer who underwent a radical hysterectomy. Using only variables that could be obtained preoperatively, we developed various models predicting the probability of 5-year progression-free survival and overall survival. Among them, hybrid ensemble models, combined with logistic regression and multiple machine learning models, achieved the best predictive performance. The developed models are expected to help physicians' and patients' decision making related to the surgical approach for primary radical hysterectomy. We purposed to develop machine learning models predicting survival outcomes according to the surgical approach for radical hysterectomy (RH) in early cervical cancer. In total, 1056 patients with 2009 FIGO stage IB cervical cancer who underwent primary type C RH by either open or laparoscopic surgery were included in this multicenter retrospective study. The whole dataset consisting of patients' clinicopathologic data was split into training and test sets with a 4:1 ratio. Using the training set, we developed models predicting the probability of 5-year progression-free survival (PFS) and overall survival (OS) with tenfold cross validation. The developed models were validated in the test set. In terms of predictive performance, we measured the area under the receiver operating characteristic curve (AUC) values. The logistic regression models comprised of preoperative variables yielded AUCs of 0.679 and 0.715 for predicting 5-year PFS and OS rates, respectively. Combining both logistic regression and multiple machine learning models, we constructed hybrid ensemble models, and these models showed much improved predictive performance, with 0.741 and 0.759 AUCs for predicting 5-year PFS and OS rates, respectively. We successfully developed models predicting disease recurrence and mortality after primary RH in patients with early cervical cancer. As the predicted value is calculated based on the preoperative factors, such as the surgical approach, these ensemble models would be useful for making decisions when choosing between open or laparoscopic RH. [ABSTRACT FROM AUTHOR]

Simple Summary: The recent advancement in high-throughput sequencing has become indispensable for immune-genomics and profiling the T- and B-cell receptor repertoires. Immune repertoire sequencing (IR-seq) and whole transcriptome sequencing (WTS) can be implemented to investigate and quantitatively characterize the complex pattern of the CDR3 region. We conducted T-cell diversity analysis result comparisons of these sequencing methods and suggest an intuitive approach to discriminate reliable TCR sequences and clonotype patterns from capturing errors. Although bulk-RNA sequencing is commonly used for cancer analysis, we confirmed capturing highly enriched TCR transcripts with IR-seq is more reliable for accurate immune repertoire discovery, and singleton read filtering criteria should be applied to capture true clonotypes from error-prone sequencing data. The use of such well-established data and analytical methodologies can broaden understanding of antigen specificity in immunity and enabling efficient therapeutic antibody finding. Analysis of the T-cell receptor (TCR) repertoire is essential to characterize the extensive collections of T-cell populations with recognizing antigens in cancer research, and whole transcriptome sequencing (WTS) and immune repertoire sequencing (IR-seq) are commonly used for this measure. To date, no standard read filtering method for IR measurement has been presented. We assessed the diversity of the TCR repertoire results from the paired WTS and IR-seq data of 31 multiple myeloma (MM) patients. To invent an adequate read filtering strategy for IR analysis, we conducted comparisons with WTS results. First, our analyses for determining an optimal threshold for selecting clonotypes showed that the clonotypes supported by a single read largely affected the shared clonotypes and manifested distinct patterns of mapping qualities, unlike clonotypes with multiple reads. Second, although IR-seq could reflect a wider TCR region with a higher capture rate than WTS, an adequate comparison with the removal of unwanted bias from potential sequencing errors was possible only after applying our read filtering strategy. As a result, we suggest that TCR repertoire analysis be carried out through IR-seq to produce reliable and accurate results, along with the removal of single-read clonotypes, to conduct immune research in cancer using high-throughput sequencing. [ABSTRACT FROM AUTHOR]

Gene–environment interaction (G×E) studies are one of the most important solutions for understanding the "missing heritability" problem in genome-wide association studies (GWAS). Although many statistical methods have been proposed for detecting and identifying G×E, most employ single nucleotide polymorphism (SNP)-level analysis. In this study, we propose a new statistical method, Hierarchical structural CoMponent analysis of gene-based Gene–Environment interactions (HisCoM-G×E). HisCoM-G×E is based on the hierarchical structural relationship among all SNPs within a gene, and can accommodate all possible SNP-level effects into a single latent variable, by imposing a ridge penalty, and thus more efficiently takes into account the latent interaction term of G×E. The performance of the proposed method was evaluated in simulation studies, and we applied the proposed method to investigate gene–alcohol intake interactions affecting systolic blood pressure (SBP), using samples from the Korea Associated REsource (KARE) consortium data. [ABSTRACT FROM AUTHOR]

We investigated the efficacy and safety of durvalumab (D) with or without tremelimumab (T) in addition to single-agent chemotherapy (CT) in patients with platinum-resistant recurrent ovarian cancer (PROC) lacking homologous recombination repair (HRR) gene mutations. KGOG 3045 was an open-label, investigator-initiated phase II umbrella trial. Patients with PROC without HRR gene mutations who had received ≥2 prior lines of therapy were enrolled. Patients with high PD-L1 expression (TPS ≥25%) were assigned to arm A (D + CT), whereas those with low PD-L1 expression were assigned to arm B (D + T75 + CT). After completing arm B recruitment, patients were sequentially assigned to arms C (D + T300 + CT) and D (D + CT). Overall, 58 patients were enrolled (5, 18, 17, and 18 patients in arms A, B, C, and D, respectively). The objective response rates were 20.0, 33.3, 29.4, and 22.2%, respectively. Grade 3–4 treatment-related adverse events were observed in 20.0, 66.7, 47.1, and 66.7 of patients, respectively, but were effectively managed. Multivariable analysis demonstrated that adding T to D + CT improved progression-free survival (adjusted HR, 0.435; 95% CI, 0.229–0.824; P = 0.011). Favorable response to chemoimmunotherapy was associated with MUC16 mutation (P = 0.0214), high EPCAM expression (P = 0.020), high matrix remodeling gene signature score (P = 0.017), and low FOXP3 expression (P = 0.047). Patients showing favorable responses to D + T + CT exhibited significantly higher EPCAM expression levels (P = 0.008) and matrix remodeling gene signature scores (P = 0.031) than those receiving D + CT. Dual immunotherapy with chemotherapy showed acceptable response rates and tolerable safety in HRR non-mutated PROC, warranting continued clinical investigation. • Efficacy and safety of chemotherapy plus durvalumab with or without tremelimumab were studied in PROC without HRR mutation. • Addition of tremelimumab improved progression-free survival on multivariable Cox HR analysis. • Biomarkers were explored with immunohistochemistry and whole-exome and transcriptome sequencing. • Mutation in MUC16 and high EPCAM were associated with favorable response in the overall cohort. • Arm-specific biomarker in tremelimumab group were EPCAM expression and matrix remodeling score. [ABSTRACT FROM AUTHOR]

Background: To evaluate the self-reported pain scores as a predictor of preterm birth (PTB) in symptomatic twin pregnancy and to develop a nomogram for the prediction model.Methods: We conducted a retrospective study of 148 cases of symptomatic twin pregnancies before 34 weeks of gestation visited at Seoul national university hospital from 2013 to 2018. With other clinical factors, self-reported pain score was evaluated by the numerical rating scale (NRS) pain scores for pain intensity. By multivariate analyses and logistic regression, we developed a prediction model for PTB within 7 days. Using the Cox proportional hazards model, the curves were plotted to show the predictability of the PTB according to NRS pain score, while adjusting the other covariates.Results: Twenty-three patients (15.5 %) delivered preterm within 7 days. By a logistic regression analysis, higher NRS pain score (OR 1.558, 95 % CI 1.093-2.221, P < 0.05), shorter cervical length (OR 3.164, 95 % CI 1.262-7.936, P < 0.05) and positive fibronectin results (OR 8.799, 95 % CI 1.101-70.330, P < 0.05) affect PTB within 7 days. Using the variables, the area under the receiver operating characteristic curve (AUROC) of the prediction model was 0.917. In addition, we developed a nomogram for the prediction of PTB within 7 days.Conclusions: Self-reported pain scores combined with cervical length and fetal fibronectin are useful in predicting impending PTB in symptomatic twin pregnancy. [ABSTRACT FROM AUTHOR]

Backgrounds: Recent large-scale genetic studies often involve clustered phenotypes such as repeated measurements. Compared to a series of univariate analyses of single phenotypes, an analysis of clustered phenotypes can be useful for substantially increasing statistical power to detect more genetic associations. Moreover, for the analysis of rare variants, incorporation of biological information can boost weak effects of the rare variants. Results: Through simulation studies, we showed that the proposed method outperforms other method currently available for pathway-level analysis of clustered phenotypes. Moreover, a real data analysis using a large-scale whole exome sequencing dataset of 995 samples with metabolic syndrome-related phenotypes successfully identified the glyoxylate and dicarboxylate metabolism pathway that could not be identified by the univariate analyses of single phenotypes and other existing method. Conclusion: In this paper, we introduced a novel pathway-level association test by combining hierarchical structured components analysis and penalized generalized estimating equations. The proposed method analyzes all pathways in a single unified model while considering their correlations. C/C++ implementation of PHARAOH-GEE is publicly available at http://statgen.snu.ac.kr/software/pharaoh-gee/. [ABSTRACT FROM AUTHOR]

Background: De novo stress urinary incontinence (SUI) may develop following pelvic organ prolapse surgery. Performing prophylactic continence surgery may reduce the risk of de novo SUI and subsequent continence surgery; however, it may increase the risk of complications. Therefore, many surgeons try to identify women at high risk for de novo SUI and perform continence surgery selectively. Recently, a model for predicting the risk of de novo SUI after prolapse surgery was developed using data from the Outcomes following vaginal Prolapse repair and midUrethral Sling (OPUS) trial; its prediction accuracy was significantly better than that of the stress test alone. However, few studies have verified its prediction accuracy in discrete populations. The aim of this study was to externally validate the prediction model for de novo SUI after prolapse surgery in Korean women. Methods: This retrospective cohort study included 320 stress-continent women who underwent prolapse surgery for pelvic organ prolapse quantification stage 2–4 anterior or apical prolapse and who completed a 1-year follow-up. Predicted probabilities by the de novo SUI online risk calculator were compared with observed outcomes and quantitated using the model's area under the curve and calibration plot. Subgroup analyses were also performed by the type of prolapse surgery. Results: The de novo SUI prediction model showed moderate discrimination in our study cohort; area under the curve (95% confidence interval) = 0.73 (0.67–0.78) in the whole cohort, 0.69 (0.61–0.78) in women who underwent native tissue repair or colpocleisis, and 0.74 (0.65–0.82) in those who underwent sacrocolpopexy. Calibration curves demonstrated that the model accurately predicted the observed outcomes of de novo SUI in women who underwent native tissue repair or colpocleisis but underestimated outcomes in those who underwent sacrocolpopexy. The predicted probability cutoff points corresponding to an actual risk of 50% were 40% in women who underwent native tissue repair or colpocleisis and 30% in those who underwent sacrocolpopexy. Conclusions: The de novo SUI prediction model is acceptable for use in Korean women and may aid in shared decision-making regarding prophylactic continence procedure at the time of prolapse surgery. [ABSTRACT FROM AUTHOR]

Fibronectin, a matrix glycoprotein aberrantly expressed in various tumor cells, is a known candidate biomarker for the early diagnosis of hepatocellular carcinoma (HCC). In this study, we investigated whether serum fibronectin levels could predict tumor recurrence in patients with early-stage HCC after curative treatment. A total of 83 patients who showed complete response after initial curative treatment were included. The levels of serum fibronectin at baseline and 4–6 weeks after initial treatment were analyzed with regard to their associations with recurrence. Multivariate logistic regression analyses were performed to construct a prognostic nomogram. Baseline fibronectin levels were not significantly correlated with tumor size, number, stage, and serum α-fetoprotein levels. However, decrease in serum fibronectin levels after treatment was significantly associated with reduced HCC recurrence in multivariate logistic regression (odds ratio, 0.009; p < 0.001). Furthermore, a nomogram consisting of gender and changes in serum fibronectin showed a good discriminatory capability for the prediction of HCC recurrence with an area under the receiver-operating curve of 0.87. In conclusion, changes in serum fibronectin levels may be a surrogate indicator for assessment of treatment response in patients with early HCC after curative treatment. [ABSTRACT FROM AUTHOR]

Background: A Mendelian transmission produces phenotypic and genetic relatedness between family members, giving family-based analytical methods an important role in genetic epidemiological studies—from heritability estimations to genetic association analyses. With the advance in genotyping technologies, whole-genome sequence data can be utilized for genetic epidemiological studies, and family-based samples may become more useful for detecting de novo mutations. However, genetic analyses employing family-based samples usually suffer from the complexity of the computational/statistical algorithms, and certain types of family designs, such as incorporating data from extended families, have rarely been used. Results: We present a Workbench for Integrated Superfast Association studies for Related Data (WISARD) programmed in C/C++. WISARD enables the fast and a comprehensive analysis of SNP-chip and next-generation sequencing data on extended families, with applications from designing genetic studies to summarizing analysis results. In addition, WISARD can automatically be run in a fully multithreaded manner, and the integration of R software for visualization makes it more accessible to non-experts. Conclusions: Comparison with existing toolsets showed that WISARD is computationally suitable for integrated analysis of related subjects, and demonstrated that WISARD outperforms existing toolsets. WISARD has also been successfully utilized to analyze the large-scale massive sequencing dataset of chronic obstructive pulmonary disease data (COPD), and we identified multiple genes associated with COPD, which demonstrates its practical value. [ABSTRACT FROM AUTHOR]

To compare survival outcomes of primary laparoscopic radical hysterectomy (LRH) and open radical hysterectomy (ORH) in patients with FIGO stage IB cervical cancer. We retrospectively identified stage IB1–IB2 cervical cancer patients who received either LRH (n = 343) or ORH (n = 222) at two tertiary institutional hospitals between 2000 and 2018. To adjust for confounders, we conducted Mahalanobis distance-based sample matching for stage, histology, cervical mass size, parametrial invasion, and lymph node metastasis. Then, survival outcomes were compared between the matched groups. Through the independent matching processes, we narrowed the study population to stage IB1 patients and stage IB1 patients with tumor size ≤2 cm on pre-operative MRI. After matching, LRH group showed poorer progression-free survival (PFS) than ORH group (3-year: 85.4% vs. 91.8%; P = 0.036), whereas no significant difference in overall survival (OS) was found. Regarding recurrence patterns, no significant differences in the incidences of pelvic, retroperitoneal lymph node and abdominal recurrences, or distant metastasis were observed between the two groups. Among the matched patients with stage IB1 who had cervical mass size ≤2 cm, the LRH and ORH groups showed similar PFS (3-year: 90.0% vs. 93.1%; P = 0.8) and OS (5-year: 98.6% vs. 96.4%; P = 0.6). Despite the retrospective design, our matched cohort study suggests that ORH might be preferable for the surgical treatment of FIGO stage IB cervical cancer. However, in stage IB1 patients with tumor size ≤2 cm, LRH might be applicable, as equivalent outcomes were found regardless of the surgical approach. Further prospective studies are warranted. • We conducted a matching study to investigate survival of laparoscopic radical hysterectomy (LRH) in stage IB cervical cancer. • After matching, LRH group showed a higher recurrence rate compared to open group, whereas overall survival was not different. • In stage IB1 patients with tumor size ≤2 cm, equivalent survival outcomes were observed regardless of the surgical approach. [ABSTRACT FROM AUTHOR]

We aimed to investigate whether mitochondrial dysfunction in extracellular cerebrospinal fluid (CSF), which is associated with autophagy and mitophagy, might be involved in neurological outcomes in adult patients with hemorrhagic moyamoya disease (MMD) whose pathogenesis related to poor outcomes is not well-known. CSF samples were collected from 43 adult MMD patients and analyzed according to outcomes at 3 months. Fluorescence-activated cell sorter analysis (FACS) and the JC-1 red/green ratio were used to assess mitochondrial cells and intact mitochondrial membrane potential (MMP). We performed quantitative real-time polymerase chain reaction and Western blotting analyses of autophagy and mitophagy-related markers, including HIF1α, ATG5, pBECN1, BECN1, BAX, BNIP3L, DAPK1, and PINK1. Finally, FACS analysis with specific fluorescence-conjugated antibodies was performed to evaluate the potential cellular origin of CSF mitochondrial cells. Twenty-seven females (62.8%) with a mean age of 47.4 ± 9.7 years were included in the study. Among 43 patients with hemorrhagic MMD, 23 (53.5%) had poor outcomes. The difference in MMP was evident between the two groups (2.4 ± 0.2 in patients with poor outcome vs. 3.5 ± 0.4 in patients with good outcome; p = 0.02). A significantly higher expression (2–ΔCt) of HIF1α, ATG5, DAPK1 followed by BAX and BNIP3L mRNA and protein was also observed in poor-outcome patients compared to those with good outcomes. Higher percentage of vWF-positive mitochondria, suggesting endothelial cell origins, was observed in patients with good outcome compared with those with poor outcome (25.0 ± 1.4% in patients with good outcome vs. 17.5 ± 1.5% in those with poor outcome; p < 0.01). We observed the association between increased mitochondrial dysfunction concomitant with autophagy and mitophagy in CSF cells and neurological outcomes in adult patients with hemorrhagic MMD. Further prospective multicenter studies are needed to determine whether it has a diagnostic value for risk prediction. [ABSTRACT FROM AUTHOR]

Availability is one of the most important requirements in production system. Keeping a persistent level of high availability in the Infrastructure-as-a-Service (IaaS) cloud computing is a challenge due to the complexity of service providing. By definition, the availability can be maintained by coupling with the fault tolerance approaches. Recently, many fault tolerance methods have been developed, but few of them adequately consider the fault detection aspect, which is critical to issue the appropriate recovery actions just in time. In this paper, based on a rigorous analysis on the nature of failures, we would like to introduce a method to early identify the faults occurring in the IaaS system. By engaging fuzzy logic algorithm and prediction technique, the proposed approach can provide better performance in terms of accuracy and reaction rate, which subsequently enhances the system reliability. [ABSTRACT FROM AUTHOR]

Multimodal emotion recognition has gained much traction in the field of affective computing, human–computer interaction (HCI), artificial intelligence (AI), and user experience (UX). There is growing demand to automate analysis of user emotion towards HCI, AI, and UX evaluation applications for providing affective services. Emotions are increasingly being used, obtained through the videos, audio, text or physiological signals. This has led to process emotions from multiple modalities, usually combined through ensemble-based systems with static weights. Due to numerous limitations like missing modality data, inter-class variations, and intra-class similarities, an effective weighting scheme is thus required to improve the aforementioned discrimination between modalities. This article takes into account the importance of difference between multiple modalities and assigns dynamic weights to them by adapting a more efficient combination process with the application of generalized mixture (GM) functions. Therefore, we present a hybrid multimodal emotion recognition (H-MMER) framework using multi-view learning approach for unimodal emotion recognition and introducing multimodal feature fusion level, and decision level fusion using GM functions. In an experimental study, we evaluated the ability of our proposed framework to model a set of four different emotional states (Happiness, Neutral, Sadness, and Anger) and found that most of them can be modeled well with significantly high accuracy using GM functions. The experiment shows that the proposed framework can model emotional states with an average accuracy of 98.19% and indicates significant gain in terms of performance in contrast to traditional approaches. The overall evaluation results indicate that we can identify emotional states with high accuracy and increase the robustness of an emotion classification system required for UX measurement. [ABSTRACT FROM AUTHOR]

This paper presents a robust foreground detection method capable of adapting to different motion speeds in scenes. A key contribution of this paper is the background estimation using a proposed novel algorithm, neighbor-based intensity correction (NIC), that identifies and modifies the motion pixels from the difference of the background and the current frame. Concretely, the first frame is considered as an initial background that is updated with the pixel intensity from each new frame based on the examination of neighborhood pixels. These pixels are formed into windows generated from the background and the current frame to identify whether a pixel belongs to the background or the current frame. The intensity modification procedure is based on the comparison of the standard deviation values calculated from two pixel windows. The robustness of the current background is further measured using pixel steadiness as an additional condition for the updating process. Finally, the foreground is detected by the background subtraction scheme with an optimal threshold calculated by the Otsu method. This method is benchmarked on several well-known data sets in the object detection and tracking domain, such as CAVIAR 2004, AVSS 2007, PETS 2009, PETS 2014, and CDNET 2014. We also compare the accuracy of the proposed method with other state-of-the-art methods via standard quantitative metrics under different parameter configurations. In the experiments, NIC approach outperforms several advanced methods on depressing the detected foreground confusions due to light artifact, illumination change, and camera jitter in dynamic scenes. [ABSTRACT FROM PUBLISHER]

Background Gene-gene interaction (GGI) is one of the most popular approaches for finding the missing heritability of common complex traits in genetic association studies. The multifactor dimensionality reduction (MDR) method has been widely studied for detecting GGIs. In order to identify the best interaction model associated with disease susceptibility, MDR compares all possible genotype combinations in terms of their predictability of disease status from a simple binary high(H) and low(L) risk classification. However, this simple binary classification does not reflect the uncertainty of H/L classification. Methods We regard classifying H/L as equivalent to defining the degree of membership of two risk groups H/L. By adopting the fuzzy set theory, we propose Fuzzy MDR which takes into account the uncertainty of H/L classification. Fuzzy MDR allows the possibility of partial membership of H/L through a membership function which transforms the degree of uncertainty into a [0,1] scale. The best genotype combinations can be selected which maximizes a new fuzzy set based accuracy measure. Results Two simulation studies are conducted to compare the power of the proposed Fuzzy MDR with that of MDR. Our results show that Fuzzy MDR has higher power than MDR. We illustrate the proposed Fuzzy MDR by analysing bipolar disorder (BD) trait of the WTCCC dataset to detect GGI associated with BD. Conclusions We propose a novel Fuzzy MDR method to detect gene–gene interaction by taking into account the uncertainly of H/L classification and show that it has higher power than MDR. Fuzzy MDR can be easily extended to handle continuous phenotypes as well. The program written in R for the proposed Fuzzy MDR is available at https://statgen.snu.ac.kr/software/FuzzyMDR . [ABSTRACT FROM AUTHOR]

Supporting safe and resilient authentication and integrity of digital images is of critical importance in a time of enormous creation and sharing of these contents. This paper presents an improved digital image watermarking model based on a coefficient quantization technique that intelligently encodes the owner’s information for each color channel to improve imperceptibility and robustness of the hidden information. Concretely, a novel color channel selection mechanism automatically selects the optimal HL4 and LH4 wavelet coefficient blocks for embedding binary bits by adjusting block differences, calculated between LH and HL coefficients of the host image. The channel selection aims to minimize the visual difference between the original image and the embedded image. On the other hand, the strength of the watermark is controlled by a factor to achieve an acceptable tradeoff between robustness and imperceptibility. The arrangement of the watermark pixels before shuffling and the channel into which each pixel is embedded is ciphered in an associated key. This key is utterly required to recover the original watermark, which is extracted through an adaptive clustering thresholding mechanism based on the Otsu’s algorithm. Benchmark results prove the model to support imperceptible watermarking as well as high robustness against common attacks in image processing, including geometric, non-geometric transformations, and lossy JPEG compression. The proposed method enhances more than 4 dB in the watermarked image quality and significantly reduces Bit Error Rate in the comparison of state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

For a family-based sample, the phenotypic variance-covariance matrix can be parameterized to include the variance of a polygenic effect that has then been estimated using a variance component analysis. However, with the advent of large-scale genomic data, the genetic relationship matrix (GRM) can be estimated and can be utilized to parameterize the variance of a polygenic effect for population-based samples. Therefore narrow sense heritability, which is both population and trait specific, can be estimated with both population- and family-based samples. In this study we estimate heritability from both family-based and population-based samples, collected in Korea, and the heritability estimates from the pooled samples were, for height, 0.60; body mass index (BMI), 0.32; log-transformed triglycerides (log TG), 0.24; total cholesterol (TCHL), 0.30; high-density lipoprotein (HDL), 0.38; low-density lipoprotein (LDL), 0.29; systolic blood pressure (SBP), 0.23; and diastolic blood pressure (DBP), 0.24. Furthermore, we found differences in how heritability is estimated—in particular the amount of variance attributable to common environment in twins can be substantial—which indicates heritability estimates should be interpreted with caution. [ABSTRACT FROM AUTHOR]

Background: As the number of large-scale studies involving multiple organizations producing data has steadily increased, an integrated system for a common interoperable format is needed. In response to the coronavirus disease 2019 (COVID-19) pandemic, a number of global efforts are underway to develop vaccines and therapeutics. We are therefore observing an explosion in the proliferation of COVID-19 data, and interoperability is highly requested in multiple institutions participating simultaneously in COVID-19 pandemic research. Results: In this study, a laboratory information management system (LIMS) approach has been adopted to systemically manage various COVID-19 non-clinical trial data, including mortality, clinical signs, body weight, body temperature, organ weights, viral titer (viral replication and viral RNA), and multiorgan histopathology, from multiple institutions based on a web interface. The main aim of the implemented system is to integrate, standardize, and organize data collected from laboratories in multiple institutes for COVID-19 non-clinical efficacy testings. Six animal biosafety level 3 institutions proved the feasibility of our system. Substantial benefits were shown by maximizing collaborative high-quality non-clinical research. Conclusions: This LIMS platform can be used for future outbreaks, leading to accelerated medical product development through the systematic management of extensive data from non-clinical animal studies. [ABSTRACT FROM AUTHOR]

Currently, a lot of recent electronic health records are based on XML documents. In order to integrate these heterogeneous XML medical documents efficiently, studies on finding structure and semantic similarity between XML Schemas have been exploited. The main problem is how to harvest the most appropriate relatedness to combine two schemas as a global XML Schema for reusing and referring purposes. In this paper, we propose the novel resemblance measure that concurrently considers both structural and semantic information of two specific healthcare XML Schemas. Specifically, we introduce new metrics to compute the datatype and cardinality constraint similarities, which improve the quality of the semantic assessment. On the basis of the similarity between each element pair, we put forward an algorithm to calculate the similarity between XML Schema trees. Experimental results lead to the conclusion that our methodology provides better similarity values than the others with regard to the accuracy of semantic and structure similarities. [ABSTRACT FROM AUTHOR]

Abstract: The visualization process is a transformation of information content into knowledge via a visual representation. Visualization experience, proposed herein, reflects human sensations arising during the visualization process. It provides a basis in which to objectively measure and evaluate human participation in the visualization process; and thereby provides methods of control. Visualization experience modeling allows leveraging on the natural environment to augment understanding, therefore improve decision making. The application emphasis in this paper is on the theoretical development of visualization experience in the visualization process as applied to Ambient Assisted Living and Clinical Decision Support Systems. [Copyright &y& Elsevier]

Abstract: Knowledge constantly grows in scientific discourse and is revised over time by different stakeholders, either collaboratively or through institutionalized efforts. The body of knowledge gets structured and refined as the Communities of Practice concerned with a field of knowledge develop a deeper understanding of the issues. As a result, the knowledge model moves from a loosely clustered terminology to a semi-formal or even formal ontology. Change history management in such evolving knowledge models is an important and challenging task. Different techniques have been introduced in the research literature to solve the issue. A comprehensive solution must address various multi-faceted issues, such as ontology recovery, visualization of change effects, and keeping the evolving ontology in a consistent state. More so because the semantics of changes and evolution behavior of the ontology are hard to comprehend. This paper introduces a change history management framework for evolving ontologies; developed over the last couple of years. It is a comprehensive and methodological framework for managing issues related to change management in evolving ontologies, such as versioning, provenance, consistency, recovery, change representation and visualization. The Change history log is central to our framework and is supported by a semantically rich and formally sound change representation scheme known as change history ontology. Changes are captured and then stored in the log in conformance with the change history ontology. The log entries are later used to revert ontology to a previous consistent state, and to visualize the effects of change on ontology during its evolution. The framework is implemented to work as a plug-in for ontology repositories, such as Joseki and ontology editors, such as Protege. The change detection accuracy of the proposed system Change Tracer has been compared with that of Changes Tab, Version Log Generator in Protege; Change Detection, and Change Capturing of NeOn Toolkit. The proposed system has shown better accuracy against the existing systems. A comprehensive evaluation of the methodology was designed to validate the recovery operations. The accuracy of Roll-Back and Roll-Forward algorithms was conducted using different versions of SWETO Ontology, CIDOC CRM Ontology, OMV Ontology, and SWRC Ontology. Experimental results and comparison with other approaches shows that the change management process of the proposed system is accurate, consistent, and comprehensive in its coverage. [Copyright &y& Elsevier]

Conventional procedures to ensure authorized data access by using access control policies are not suitable for cloud storage systems as these procedures can reveal valid access parameters to a cloud service provider. In this paper, we have proposed oblivious access control policy evaluation (O-ACE); a data sharing system, which obliviously evaluates access control policy on a cloud server and provisions access to the outsourced data. O-ACE reveals no useful information about the access control policy neither to the cloud service provider nor to the unauthorized users. Through the security analysis of O-ACE it has been observed that computational complexity to compromise privacy of the outsourced data is same as reverting asymmetric encryption without valid key pair. We have realized O-ACE for Google Cloud. Our evaluation results show the fact that O-ACE CPU utilization cost is 0.01-0.30 dollar per 1,000 requests. [ABSTRACT FROM AUTHOR]

Abstract: Most healthcare data are available in XML format, which mainly focuses on the structure level and lacks support for data representation. Therefore, a variety of medical applications and medical semantic search engines have difficulty understanding and integrating healthcare data in a highly heterogeneous environment. OWL (Web Ontology Language) and Semantic Web technologies provide an infrastructure that can solve these problems. The aim of our study is to present a mechanism to ease the interpretation and automate the semantic transformation of XML healthcare data into the OWL ontology (S-Trans), which allows an easier and better semantic communication among hospital information systems. On the basis of the XML schemas (XSD or DTD), we extract the document structure and add more descriptions for XML elements. Moreover, to classify the semantic level of duplicate elements in an XML schema, we propose novel metrics to measure the similarity between them. Experimental results show that the proposed method reliably predicts semantic similarity of duplicates and produces a better-quality OWL ontology. [Copyright &y& Elsevier]

This paper addresses the issue of data governance in a cloud-based storage system. To achieve fine-grained access control over the outsourced data, we propose Self-Healing Attribute-based Privacy Aware Data Sharing in Cloud (SAPDS). The proposed system delegates the key distribution and management process to a cloud server without seeping out any confidential information. It facilitates data owner to restrain access of the user with whom data has been shared. User revocation is achieved by merely changing one attribute associated with the decryption policy, instead of modifying the entire access control policy. It enables authorized users to update their decryption keys followed by each user revocation, making it self-healing, without ever interacting with the data owner. Computation analysis of the proposed system shows that data owner can revoke n′ users with the complexity of O( n′). Besides this, legitimate users can update their decryption keys with the complexity of O(1). [ABSTRACT FROM AUTHOR]

Synchronization/desynchronization and clustering are important techniques in multi-node computing systems, especially for sensor networks (SN) which is broadly considered to be a type of multi-node computing environment. However, most of the existing algorithms' clustering criteria are limited to the node location information and ignore the nature and characteristics of the nodes as well as the requirements of the applications. In this paper, an autonomic concurrent General Criteria-based Clustering (GCC) method is proposed for multi-node computing systems. The GCC method is based on the neuron oscillator pulse-coupling model and its clustering criteria can come from any node-related data or properties. The cluster member nodes share similar physical or logical properties and represent those relationships in the form of Logical Clusters (LCs). Due to the neuron dynamic system basis of the method, there is concurrency that exists both on the whole network and on each individual node. The simulation shows that the GCC method can generate diverse logical clusters and synchronization/desynchronization coexistence results with acceptable time and energy usage. [ABSTRACT FROM AUTHOR]

In this work, we present an energy efficient hierarchical cooperative clustering scheme for wireless sensor networks. Communication cost is a crucial factor in depleting the energy of sensor nodes. In the proposed scheme, nodes cooperate to form clusters at each level of network hierarchy ensuring maximal coverage and minimal energy expenditure with relatively uniform distribution of load within the network. Performance is enhanced by cooperative multiple-input multiple-output (MIMO) communication ensuring energy efficiency for WSN deployments over large geographical areas. We test our scheme using TOSSIM and compare the proposed scheme with cooperative multiple-input multiple-output (CMIMO) clustering scheme and traditional multihop Single-Input-Single-Output (SISO) routing approach. Performance is evaluated on the basis of number of clusters, number of hops, energy consumption and network lifetime. Experimental results show significant energy conservation and increase in network lifetime as compared to existing schemes. [ABSTRACT FROM AUTHOR]

Target-specific antibodies can be rapidly enriched and identified from an antibody library using phage display. Large, naïve antibody libraries derived from synthetic or unimmunized sources can yield antibodies against virtually any antigen, whereas libraries from immunized sources tend to be smaller and are used exclusively against the antigen of immunization. In this study, 25 scFv libraries made from the spleens of immunized rabbits, each with a size ranging from 10 to higher than 10, were combined into a single large library with > 10 individual clones. Panning of this combined library yielded target-specific rabbit scFv clones against many non-immunizing antigens, including proteins, peptides, and a small molecule. Notably, specific scFv clones against a rabbit self-antigen (rabbit serum albumin) and a phosphorylated protein (epidermal growth factor receptor pTyr1173) could be isolated from the library. These results suggest that the immune library contained a significant number of unimmunized clones and that a sufficiently large immune library can be utilized similarly to a naïe library, i.e., against various non-immunizing antigens to yield specific antibodies. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we present a novel active contour (AC) model for medical image segmentation that is based on a convex combination of two energy functionals to both minimize the inhomogeneity within an object and maximize the distance between the object and the background. This combination is necessary because objects in medical images, e.g., bones, are usually highly inhomogeneous while distinct organs should generate distinct image configurations. Compared with the conventional Chan–Vese AC, the proposed model yields similar performance in a set of CT images but performs better in an MRI data set, which is generally in lower contrast. [Copyright &y& Elsevier]

Mobility is a good indicator of health status and thus objective mobility data could be used to assess the health status of elderly patients. Accelerometry has emerged as an effective means for long-term physical activity monitoring in the elderly. However, the output of an accelerometer varies at different positions on a subject's body, even for the same activity, resulting in high within-class variance. Existing accelerometer-based activity recognition systems thus require firm attachment of the sensor to a subject's body. This requirement makes them impractical for long-term activity monitoring during unsupervised free-living as it forces subjects into a fixed life pattern and impede their daily activities. Therefore, we introduce a novel single-triaxial-accelerometer-based activity recognition system that reduces the high within-class variance significantly and allows subjects to carry the sensor freely in any pocket without its firm attachment. We validated our system using seven activities: resting (lying/sitting/standing), walking, walking-upstairs, walking-downstairs, running, cycling, and vacuuming, recorded from five positions: chest pocket, front left trousers pocket, front right trousers pocket, rear trousers pocket, and inner jacket pocket. Its simplicity, ability to perform activities unimpeded, and an average recognition accuracy of 94% make our system a practical solution for continuous long-term activity monitoring in the elderly. [ABSTRACT FROM AUTHOR]

Abstract: Estimating location of mobile devices based on received signal strength (RSS) patterns is an attractive method to realize indoor positioning systems. Accuracy of RSS based location estimation, particularly in large target sites, is effected by several environmental factors. Especially the temporal or permanent absence of radio signals introduces null values rendering sparsity and redundancy in feature space. We present a visibility matrix based modular classification model which systematically caters for unavailable signals. This model is practically realized using two eminent classification methods: (1) multi-layer perceptron and (2) LVQ. In order to confirm robustness and applicability of this model, we developed two location systems at different sites. Experimental results in real-world environments demonstrate that modular classification model consistently achieves superior location accuracy. [Copyright &y& Elsevier]

Recently, social media have been used by researchers to detect depressive symptoms in individuals using linguistic data from users' posts. In this study, we propose a framework to identify social information as a significant predictor of depression. Using the proposed framework, we develop an application called the Socially Mediated Patient Portal (SMPP), which detects depression-related markers in Facebook users by applying a data-driven approach with machine learning classification techniques. We examined a data set of 4350 users who were evaluated for depression using the Center for Epidemiological Studies Depression (CES-D) scale. From this analysis, we identified a set of features that can distinguish between individuals with and without depression. Finally, we identified the dominant features that adequately assess individuals with and without depression on social media. The model trained on these features will be helpful to physicians in diagnosing mental diseases and psychiatrists in analysing patient behaviour. [ABSTRACT FROM AUTHOR]

The lack of Interoperable healthcare data presents a major challenge, towards achieving ubiquitous health care. The plethora of diverse medical standards, rather than common standards, is widening the gap of interoperability. While many organizations are working towards a standardized solution, there is a need for an alternate strategy, which can intelligently mediate amongst a variety of medical systems, not complying with any mainstream healthcare standards while utilizing the benefits of several standard merging initiates, to eventually create digital health personas. The existence and efficiency of such a platform is dependent upon the underlying storage and processing engine, which can acquire, manage and retrieve the relevant medical data. In this paper, we present the Ubiquitous Health Profile (UHPr), a multi-dimensional data storage solution in a semi-structured data curation engine, which provides foundational support for archiving heterogeneous medical data and achieving partial data interoperability in the healthcare domain. Additionally, we present the evaluation results of this proposed platform in terms of its timeliness, accuracy, and scalability. Our results indicate that the UHPr is able to retrieve an error free comprehensive medical profile of a single patient, from a set of slightly over 116.5 million serialized medical fragments for 390,101 patients while maintaining a good scalablity ratio between amount of data and its retrieval speed. [ABSTRACT FROM AUTHOR]