Understanding the statistics of fluctuation driven flows in the boundary layer of magnetically confined plasmas is desired to accurately model the lifetime of the vacuum vessel components. Mirror Langmuir probes (MLPs) are a novel diagnostic that uniquely allow us to sample the plasma parameters on a time scale shorter than the characteristic time scale of their fluctuations. Sudden large-amplitude fluctuations in the plasma degrade the precision and accuracy of the plasma parameters reported by MLPs for cases in which the probe bias range is of insufficient amplitude. While some data samples can readily be classified as valid and invalid, we find that such a classification may be ambiguous for up to 40% of data sampled for the plasma parameters and bias voltages considered in this study. In this contribution, we employ an autoencoder (AE) to learn a low-dimensional representation of valid data samples. By definition, the coordinates in this space are the features that mostly characterize valid data. Ambiguous data samples are classified in this space using standard classifiers for vectorial data. In this way, we avoid defining complicated threshold rules to identify outliers, which require strong assumptions and introduce biases in the analysis. By removing the outliers that are identified in the latent low-dimensional space of the AE, we find that the average conductive and convective radial heat fluxes are between approximately 5% and 15% lower as when removing outliers identified by threshold values. For contributions to the radial heat flux due to triple correlations, the difference is up to 40%.

Identification of errors or anomalous values, collectively considered outliers, assists in exploring data or through removing outliers improves statistical analysis. In biomechanics, outlier detection methods have explored the ‘shape’ of the entire cycles, although exploring fewer points using a ‘moving-window’ may be advantageous. Hence, the aim was to develop a moving-window method for detecting trials with outliers in intra-participant time-series data. Outliers were detected through two stages for the strides (mean 38 cycles) from treadmill running. Cycles were removed in stage 1 for one-dimensional (spatial) outliers at each time point using the median absolute deviation, and in stage 2 for two-dimensional (spatial–temporal) outliers using a moving window standard deviation. Significance levels of the t-statistic were used for scaling. Fewer cycles were removed with smaller scaling and smaller window size, requiring more stringent scaling at stage 1 (mean 3.5 cycles removed for 0.0001 scaling) than at stage 2 (mean 2.6 cycles removed for 0.01 scaling with a window size of 1). Settings in the supplied Matlab code should be customised to each data set, and outliers assessed to justify whether to retain or remove those cycles. The method is effective in identifying trials with outliers in intra-participant time series data.

Gene expression data have been presented as non-normalized (2-Ct*109) in all but the last six rows; this allows for the back-calculation of the raw threshold cycle (Ct) values so that interested individuals can readily estimate the typical range of expression of each gene. Values representing aberrant levels for a particular parameter (z-score>2.5) have been highlighted in bold. When there was a statistically significant difference (student’s t-test, p<0.05) between the outlier and non-outlier averages for a parameter (instead using normalized gene expression data), the lower of the two values has been underlined. All samples hosted Symbiodinium of clade C only unless noted otherwise. The mean Mahalanobis distance did not differ between Pocillopora damicornis and P. acuta (student’s t-test, p>0.05). SA = surface area. GCP = genome copy proportion. Ma Dis = Mahalanobis distance. “.” = missing data.

These files are supplements to the paper titled 'A Robust Two-step Method for Detection of Outlier Sets'.This paper identifies and addresses the need for a robust method that identifies sets of points that collectively deviate from typical patterns in a dataset, which it calls "outlier sets'', while excluding individual points from detection. This new methodology, Outlier Set Two-step Identification (OSTI) employs a two-step approach to detect and label these outlier sets. First, it uses Gaussian Mixture Models for probabilistic clustering, identifying candidate outlier sets based on cluster weights below a predetermined threshold. Second, OSTI measures the Inter-cluster Mahalanobis distance between each candidate outlier set's centroid and the overall dataset mean. OSTI then tests the null hypothesis that this distance does not significantly differ from its theoretical chi-square distribution, enabling the formal detection of outlier sets. We test OSTI systematically on 8,000 synthetic 2D datasets across various inlier configurations and thousands of possible outlier set characteristics. Results show OSTI robustly and consistently detects outlier sets with an average F1 score of 0.92 and an average purity (the degree to which outlier sets identified correspond to those generated synthetically, i.e., our ground truth) of 98.58%. We also compare OSTI with state-of-the-art outlier detection methods, to illuminate how OSTI fills a gap as a tool for the exclusive detection of outlier sets.

We present a set of novel algorithms which we call sequenceMiner, that detect and characterize anomalies in large sets of high-dimensional symbol sequences that arise from recordings of switch sensors in the cockpits of commercial airliners. While the algorithms we present are general and domain-independent, we focus on a specific problem that is critical to determining system-wide health of a fleet of aircraft. The approach taken uses unsupervised clustering of sequences using the normalized length of he longest common subsequence (nLCS) as a similarity measure, followed by a detailed analysis of outliers to detect anomalies. In this method, an outlier sequence is defined as a sequence that is far away from a cluster. We present new algorithms for outlier analysis that provide comprehensible indicators as to why a particular sequence is deemed to be an outlier. The algorithm provides a coherent description to an analyst of the anomalies in the sequence when compared to more normal sequences. The final section of the paper demonstrates the effectiveness of sequenceMiner for anomaly detection on a real set of discrete sequence data from a fleet of commercial airliners. We show that sequenceMiner discovers actionable and operationally significant safety events. We also compare our innovations with standard HiddenMarkov Models, and show that our methods are superior

Geographic coverage

National, regional

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2020 Vietnam COVID-19 High Frequency Phone Survey of Households (VHFPS) uses a nationally representative household survey from 2018 as the sampling frame. The 2018 baseline survey includes 46,980 households from 3132 communes (about 25% of total communes in Vietnam). In each commune, one EA is randomly selected and then 15 households are randomly selected in each EA for interview. We use the large module of to select the households for official interview of the VHFPS survey and the small module households as reserve for replacement. After data processing, the final sample size for Round 2 is 3,935 households.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire for Round 2 consisted of the following sections

Section 2. Behavior Section 3. Health Section 5. Employment (main respondent) Section 6. Coping Section 7. Safety Nets Section 8. FIES

Cleaning operations

Data cleaning began during the data collection process. Inputs for the cleaning process include available interviewers’ note following each question item, interviewers’ note at the end of the tablet form as well as supervisors’ note during monitoring. The data cleaning process was conducted in following steps: • Append households interviewed in ethnic minority languages with the main dataset interviewed in Vietnamese. • Remove unnecessary variables which were automatically calculated by SurveyCTO • Remove household duplicates in the dataset where the same form is submitted more than once. • Remove observations of households which were not supposed to be interviewed following the identified replacement procedure. • Format variables as their object type (string, integer, decimal, etc.) • Read through interviewers’ note and make adjustment accordingly. During interviews, whenever interviewers find it difficult to choose a correct code, they are recommended to choose the most appropriate one and write down respondents’ answer in detail so that the survey management team will justify and make a decision which code is best suitable for such answer. • Correct data based on supervisors’ note where enumerators entered wrong code. • Recode answer option “Other, please specify”. This option is usually followed by a blank line allowing enumerators to type or write texts to specify the answer. The data cleaning team checked thoroughly this type of answers to decide whether each answer needed recoding into one of the available categories or just keep the answer originally recorded. In some cases, that answer could be assigned a completely new code if it appeared many times in the survey dataset.
• Examine data accuracy of outlier values, defined as values that lie outside both 5th and 95th percentiles, by listening to interview recordings. • Final check on matching main dataset with different sections, where information is asked on individual level, are kept in separate data files and in long form. • Label variables using the full question text. • Label variable values where necessary.

Number of outlier years (>2 standard errors above or below the mean) per time period.

Here are six files that provide details for all 44,120 identified single nucleotide polymorphisms (SNPs) or the 215 outlier SNPs associated with the evolution of rapid character displacement among replicate islands with (2Spp) and without competition (1Spp) between two Anolis species. On 2Spp islands, A. carolinensis occurs higher in trees and have evolved larger toe pads. Among 1Spp and 2Spp island populations, we identify 44,120 SNPs, with 215-outlier SNPs with improbably large FST values, low nucleotide variation, greater linkage than expected, and these SNPs are enriched for animal walking behavior. Thus, we conclude that these 215-outliers are evolving by natural selection in response to the phenotypic convergent evolution of character displacement. There are two, non-mutually exclusive perspective of these nucleotide variants. One is character displacement is convergent: all 215 outlier SNPs are shared among 3 out of 5 2Spp island and 24% of outlier SNPS are shared among all five out of five 2Spp island. Second, character displacement is genetically redundant because the allele frequencies in one or more 2Spp are similar to 1Spp islands: among one or more 2Spp islands 33% of outlier SNPS are within the range of 1Spp MiAF and 76% of outliers are more similar to 1Spp island than mean MiAF of 2Spp islands. Focusing on convergence SNP is scientifically more robust, yet it distracts from the perspective of multiple genetic solutions that enhances the rate and stability of adaptive change. The six files include: a description of eight islands, details of 94 individuals, and four files on SNPs. The four SNP files include the VCF files for 94 individuals with 44KSNPs and two files (Excel sheet/tab-delimited file) with FST, p-values and outlier status for all 44,120 identified single nucleotide polymorphisms (SNPs) associated with the evolution of rapid character displacement. The sixth file is a detailed file on the 215 outlier SNPs. Complete sequence data is available at Bioproject PRJNA833453, which including samples not included in this study. The 94 individuals used in this study are described in “Supplemental_Sample_description.txt” Methods Anoles and genomic DNA: Tissue or DNA for 160 Anolis carolinensis and 20 A. sagrei samples were provided by the Museum of Comparative Zoology at Harvard University (Table S2). Samples were previously used to examine evolution of character displacement in native A. carolinensis following invasion by A. sagrei onto man-made spoil islands in Mosquito Lagoon Florida (Stuart et al. 2014). One hundred samples were genomic DNAs, and 80 samples were tissues (terminal tail clip, Table S2). Genomic DNA was isolated from 80 of 160 A. carolinensis individuals (MCZ, Table S2) using a custom SPRI magnetic bead protocol (Psifidi et al. 2015). Briefly, after removing ethanol, tissues were placed in 200 ul of GH buffer (25 mM Tris- HCl pH 7.5, 25 mM EDTA, , 2M GuHCl Guanidine hydrochloride, G3272 SIGMA, 5 mM CaCl2, 0.5% v/v Triton X-100, 1% N-Lauroyl-Sarcosine) with 5% per volume of 20 mg/ml proteinase K (10 ul/200 ul GH) and digested at 55º C for at least 2 hours. After proteinase K digestion, 100 ul of 0.1% carboxyl-modified Sera-Mag Magnetic beads (Fisher Scientific) resuspended in 2.5 M NaCl, 20% PEG were added and allowed to bind the DNA. Beads were subsequently magnetized and washed twice with 200 ul 70% EtOH, and then DNA was eluted in 100 ul 0.1x TE (10 mM Tris, 0.1 mM EDTA). All DNA samples were gel electrophoresed to ensure high molecular mass and quantified by spectrophotometry and fluorescence using Biotium AccuBlueTM High Sensitivity dsDNA Quantitative Solution according to manufacturer’s instructions. Genotyping-by-sequencing (GBS) libraries were prepared using a modified protocol after Elshire et al. (Elshire et al. 2011). Briefly, high-molecular-weight genomic DNA was aliquoted and digested using ApeKI restriction enzyme. Digests from each individual sample were uniquely barcoded, pooled, and size selected to yield insert sizes between 300-700 bp (Borgstrom et al. 2011). Pooled libraries were PCR amplified (15 cycles) using custom primers that extend into the genomic DNA insert by 3 bases (CTG). Adding 3 extra base pairs systematically reduces the number of sequenced GBS tags, ensuring sufficient sequencing depth. The final library had a mean size of 424 bp ranging from 188 to 700 bp . Anolis SNPs: Pooled libraries were sequenced on one lane on the Illumina HiSeq 4000 in 2x150 bp paired-end configuration, yielding approximately 459 million paired-end reads ( ~138 Gb). The medium Q-Score was 42 with the lower 10% Q-Scores exceeding 32 for all 150 bp. The initial library contained 180 individuals with 8,561,493 polymorphic sites. Twenty individuals were Anolis sagrei, and two individuals (Yan 1610 & Yin 1411) clustered with A. sagrei and were not used to define A. carolinesis’ SNPs. Anolis carolinesis reads were aligned to the Anolis carolinensis genome (NCBI RefSeq accession number:/GCF_000090745.1_AnoCar2.0). Single nucleotide polymorphisms (SNPs) for A. carolinensis were called using the GBeaSy analysis pipeline (Wickland et al. 2017) with the following filter settings: minimum read length of 100 bp after barcode and adapter trimming, minimum phred-scaled variant quality of 30 and minimum read depth of 5. SNPs were further filtered by requiring SNPs to occur in > 50% of individuals, and 66 individuals were removed because they had less than 70% of called SNPs. These filtering steps resulted in 51,155 SNPs among 94 individuals. Final filtering among 94 individuals required all sites to be polymorphic (with fewer individuals, some sites were no longer polymorphic) with a maximum of 2 alleles (all are bi-allelic), minimal allele frequency 0.05, and He that does not exceed HWE (FDR <0.01). SNPs with large He were removed (2,280 SNPs). These SNPs with large significant heterozygosity may result from aligning paralogues (different loci), and thus may not represent polymorphisms. No SNPs were removed with low He (due to possible demography or other exceptions to HWE). After filtering, 94 individual yielded 44,120 SNPs. Thus, the final filtered SNP data set was 44K SNPs from 94 indiviuals. Statistical Analyses: Eight A. carolinensis populations were analyzed: three populations from islands with native species only (1Spp islands) and 5 populations from islands where A. carolinesis co-exist with A. sagrei (2Spp islands, Table 1, Table S1). Most analyses pooled the three 1Spp islands and contrasted these with the pooled five 2Spp islands. Two approaches were used to define SNPs with unusually large allele frequency differences between 1Spp and 2Spp islands: 1) comparison of FST values to random permutations and 2) a modified FDIST approach to identify outlier SNPs with large and statistically unlikely FST values. Random Permutations: FST values were calculated in VCFTools (version 4.2, (Danecek et al. 2011)) where the p-value per SNP were defined by comparing FST values to 1,000 random permutations using a custom script (below). Basically, individuals and all their SNPs were randomly assigned to one of eight islands or to 1Spp versus 2Spp groups. The sample sizes (55 for 2Spp and 39 for 1Spp islands) were maintained. FST values were re-calculated for each 1,000 randomizations using VCFTools. Modified FDIST: To identify outlier SNPs with statistically large FST values, a modified FDIST (Beaumont and Nichols 1996) was implemented in Arlequin (Excoffier et al. 2005). This modified approach applies 50,000 coalescent simulations using hierarchical population structure, in which demes are arranged into k groups of d demes and in which migration rates between demes are different within and between groups. Unlike the finite island models, which have led to large frequencies of false positive because populations share different histories (Lotterhos and Whitlock 2014), the hierarchical island model avoids these false positives by avoiding the assumption of similar ancestry (Excoffier et al. 2009). References Beaumont, M. A. and R. A. Nichols. 1996. Evaluating loci for use in the genetic analysis of population structure. P Roy Soc B-Biol Sci 263:1619-1626. Borgstrom, E., S. Lundin, and J. Lundeberg. 2011. Large scale library generation for high throughput sequencing. PLoS One 6:e19119. Bradbury, P. J., Z. Zhang, D. E. Kroon, T. M. Casstevens, Y. Ramdoss, and E. S. Buckler. 2007. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23:2633-2635. Cingolani, P., A. Platts, L. Wang le, M. Coon, T. Nguyen, L. Wang, S. J. Land, X. Lu, and D. M. Ruden. 2012. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin) 6:80-92. Danecek, P., A. Auton, G. Abecasis, C. A. Albers, E. Banks, M. A. DePristo, R. E. Handsaker, G. Lunter, G. T. Marth, S. T. Sherry, G. McVean, R. Durbin, and G. Genomes Project Analysis. 2011. The variant call format and VCFtools. Bioinformatics 27:2156-2158. Earl, D. A. and B. M. vonHoldt. 2011. Structure Harvester: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conservation Genet Resour 4:359-361. Elshire, R. J., J. C. Glaubitz, Q. Sun, J. A. Poland, K. Kawamoto, E. S. Buckler, and S. E. Mitchell. 2011. A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS One 6:e19379. Evanno, G., S. Regnaut, and J. Goudet. 2005. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14:2611-2620. Excoffier, L., T. Hofer, and M. Foll. 2009. Detecting loci under selection in a hierarchically structured population. Heredity 103:285-298. Excoffier, L., G. Laval, and S. Schneider. 2005. Arlequin (version 3.0): An integrated software package for population genetics data analysis.

State politics researchers commonly employ ordinary least squares (OLS) regression or one of its variants to test linear hypotheses. However, OLS is easily influenced by outliers and thus can produce misleading results when the error term distribution has heavy tails. Here we demonstrate that median regression (MR), an alternative to OLS that conditions the median of the dependent variable (rather than the mean) on the independent variables, can be a solution to this problem. Then we propose and validate a hypothesis test that applied researchers can use to select between OLS and MR in a given sample of data. Finally, we present two examples from state politics research in which (1) the test selects MR over OLS and (2) differences in results between the two methods could lead to different substantive inferences. We conclude that MR and the test we propose can improve linear models in state politics research.

BackgroundMeta-analysis of gene expression microarray datasets presents significant challenges for statistical analysis. We developed and validated a new bioinformatic method for the identification of genes upregulated in subsets of samples of a given tumour type (‘outlier genes’), a hallmark of potential oncogenes. MethodologyA new statistical method (the gene tissue index, GTI) was developed by modifying and adapting algorithms originally developed for statistical problems in economics. We compared the potential of the GTI to detect outlier genes in meta-datasets with four previously defined statistical methods, COPA, the OS statistic, the t-test and ORT, using simulated data. We demonstrated that the GTI performed equally well to existing methods in a single study simulation. Next, we evaluated the performance of the GTI in the analysis of combined Affymetrix gene expression data from several published studies covering 392 normal samples of tissue from the central nervous system, 74 astrocytomas, and 353 glioblastomas. According to the results, the GTI was better able than most of the previous methods to identify known oncogenic outlier genes. In addition, the GTI identified 29 novel outlier genes in glioblastomas, including TYMS and CDKN2A. The over-expression of these genes was validated in vivo by immunohistochemical staining data from clinical glioblastoma samples. Immunohistochemical data were available for 65% (19 of 29) of these genes, and 17 of these 19 genes (90%) showed a typical outlier staining pattern. Furthermore, raltitrexed, a specific inhibitor of TYMS used in the therapy of tumour types other than glioblastoma, also effectively blocked cell proliferation in glioblastoma cell lines, thus highlighting this outlier gene candidate as a potential therapeutic target. Conclusions/SignificanceTaken together, these results support the GTI as a novel approach to identify potential oncogene outliers and drug targets. The algorithm is implemented in an R package (Text S1).

This dataset contains a list of outlier sample concentrations identified for 17 water quality constituents from streamwater sample collected at 15 study watersheds in Gwinnett County, Georgia for water years 2003 to 2020. The 17 water quality constituents are: biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), suspended sediment concentration (SSC), total nitrogen (TN), total nitrate plus nitrite (NO3NO2), total ammonia plus organic nitrogen (TKN), dissolved ammonia (NH3), total phosphorus (TP), dissolved phosphorus (DP), total organic carbon (TOC), total calcium (Ca), total magnesium (Mg), total copper (TCu), total lead (TPb), total zinc (TZn), and total dissolved solids (TDS). 885 outlier concentrations were identified. Outliers were excluded from model calibration datasets used to estimate streamwater constituent loads for 12 of these constituents. Outlier concentrations were removed because they had a high influence on the model fits of the concentration relations, which could substantially affect model predictions. Identified outliers were also excluded from loads that were calculated using the Beale ratio estimator. Notes on reason(s) for considering a concentration as an outlier are included.

Full title: Mining Distance-Based Outliers in Near Linear Time with Randomization and a Simple Pruning Rule Abstract: Defining outliers by their distance to neighboring examples is a popular approach to finding unusual examples in a data set. Recently, much work has been conducted with the goal of finding fast algorithms for this task. We show that a simple nested loop algorithm that in the worst case is quadratic can give near linear time performance when the data is in random order and a simple pruning rule is used. We test our algorithm on real high-dimensional data sets with millions of examples and show that the near linear scaling holds over several orders of magnitude. Our average case analysis suggests that much of the efficiency is because the time to process non-outliers, which are the majority of examples, does not depend on the size of the data set.

This product was developed as part of the project supported by the grant from and the National Oceanic and Atmospheric Administration’s Ocean Acidification Program under award NA18OAR0170430 to the Virginia Institute of Marine Science. The data product consists of water quality data for tidal 98 stations for 1984–2018. The source data used to generate this product were downloaded from the Chesapeake Bay Program’s (CBP) data hub. Out of the total of 255 monitoring stations in the Tidal Monitoring Program, we selected 98 with the long monitoring record (30 years or longer). The following variables were downloaded from the data hub at the native temporal and vertical resolution (between one and four cruises per month and approximately 10 depth levels sampled between 0 and 37 m) for 1984–2018: water temperature (T), salinity (S), pH, total alkalinity (TA), dissolved oxygen (DO) , and chlorophyll (Chl). All pH data prior to 1998 were removed because of the data quality concerns (Herrmann et al., 2020). Briefly, we found a dramatic difference in long-term trends between stations measured by institutions in the state of Virginia and stations measured by the state of Maryland, particularly from late spring to early fall. The boundary between the station groups runs east–west within the mesohaline portion of the bay, where the Potomac River estuary intersects the mainstem bay. The boundary separates strong negative linear trends to the south (Virginia stations) from neutral and weakly positive linear trends to the north (Maryland stations). For all variables, data entries marked with CBP’s “Problem” and “Qualifier” flags were removed. Additionally, all variables were scanned for extreme outliers: for each variable, data from all stations, depths, and times were combined into a single composite sample for which the 75th and 25th percentiles (i.e., the upper and lower quantiles) and the interquartile range (the difference between the upper and lower quantiles) were calculated. Extreme outliers were defined as the values falling outside of a certain number (censoring criterion) of interquartile ranges from the upper and lower quantiles.

This product was developed as part of the project supported by the grant from and the National Oceanic and Atmospheric Administration’s Ocean Acidification Program under award NA18OAR0170430 to the Virginia Institute of Marine Science. The data product consists of water quality data for tidal 98 stations for 1984–2018. The source data used to generate this product were downloaded from the Chesapeake Bay Program’s (CBP) data hub. Out of the total of 255 monitoring stations in the Tidal Monitoring Program, we selected 98 with the long monitoring record (30 years or longer). The following variables were downloaded from the data hub at the native temporal and vertical resolution (between one and four cruises per month and approximately 10 depth levels sampled between 0 and 37 m) for 1984–2018: water temperature (T), salinity (S), pH, total alkalinity (TA), dissolved oxygen (DO) , and chlorophyll (Chl). All pH data prior to 1998 were removed because of the data quality concerns (Herrmann et al., 2020). Briefly, we found a dramatic difference in long-term trends between stations measured by institutions in the state of Virginia and stations measured by the state of Maryland, particularly from late spring to early fall. The boundary between the station groups runs east–west within the mesohaline portion of the bay, where the Potomac River estuary intersects the mainstem bay. The boundary separates strong negative linear trends to the south (Virginia stations) from neutral and weakly positive linear trends to the north (Maryland stations). For all variables, data entries marked with CBP’s “Problem” and “Qualifier” flags were removed. Additionally, all variables were scanned for extreme outliers: for each variable, data from all stations, depths, and times were combined into a single composite sample for which the 75th and 25th percentiles (i.e., the upper and lower quantiles) and the interquartile range (the difference between the upper and lower quantiles) were calculated. Extreme outliers were defined as the values falling outside of a certain number (censoring criterion) of interquartile ranges from the upper and lower quantiles.

Outlier loci detected with ARLEQUIN 3.5 at α = 0.01 in a hierarchical analysis in which geographical populations were nested within group of populations experiencing selection for the same reproductive mode (OP vs CP). FCT between CP and OP populations (and significance as outlier) are shown, as well as expected heterozygosity (HE). Outlier detection analyses were also performed among OP and CP populations to ensure these loci were not outlier at this hierarchical level. The position on chromosomes is also given.Outlier loci identified by genome scans.

We present a set of novel algorithms which we call sequenceMiner, that detect and characterize anomalies in large sets of high-dimensional symbol sequences that arise from recordings of switch sensors in the cockpits of commercial airliners. While the algorithms we present are general and domain-independent, we focus on a specific problem that is critical to determining system-wide health of a fleet of aircraft. The approach taken uses unsupervised clustering of sequences using the normalized length of he longest common subsequence (nLCS) as a similarity measure, followed by a detailed analysis of outliers to detect anomalies. In this method, an outlier sequence is defined as a sequence that is far away from a cluster. We present new algorithms for outlier analysis that provide comprehensible indicators as to why a particular sequence is deemed to be an outlier. The algorithm provides a coherent description to an analyst of the anomalies in the sequence when compared to more normal sequences. The final section of the paper demonstrates the effectiveness of sequenceMiner for anomaly detection on a real set of discrete sequence data from a fleet of commercial airliners. We show that sequenceMiner discovers actionable and operationally significant safety events. We also compare our innovations with standard HiddenMarkov Models, and show that our methods are superior

README file included.

Effect sizes calculated using mean difference for burnt-unburnt study designs and mean change for before-after desings. Outliers, as defined in the methods section of the paper, were excluded prior to calculating effect sizes.

Clinical Examples of the Various Categories of Each Characteristic of Outlier.

Clinical discoveries largely depend on dedicated clinicians and scientists to identify and pursue unique and unusual clinical encounters with patients and communicate these through case reports and case series. This process has remained essentially unchanged throughout the history of modern medicine. However, these traditional methods are inefficient, especially considering the modern-day availability of health-related data and the sophistication of computer processing. Outlier analysis has been used in various fields to uncover unique observations, including fraud detection in finance and quality control in manufacturing. We propose that clinical discovery can be formulated as an outlier problem within an augmented intelligence framework to be implemented on any health-related data. Such an augmented intelligence approach would accelerate the identification and pursuit of clinical discoveries, advancing our medical knowledge and uncovering new therapies and management approaches. We define clinical discoveries as contextual outliers measured through an information-based approach and with a novelty-based root cause. Our augmented intelligence framework has five steps: define a patient population with a desired clinical outcome, build a predictive model, identify outliers through appropriate measures, investigate outliers through domain content experts, and generate scientific hypotheses. Recognizing that the field of obstetrics can particularly benefit from this approach, as it is traditionally neglected in commercial research, we conducted a systematic review to explore how outlier analysis is implemented in obstetric research. We identified two obstetrics-related studies that assessed outliers at an aggregate level for purposes outside of clinical discovery. Our findings indicate that using outlier analysis in clinical research in obstetrics and clinical research, in general, requires further development.