Outlier Exclusion Research Articles

Abstract 4131618: Association between Clinical Characteristics and Telomere Length - Factor Analysis of Mixed Data (FAMD)-based Cluster Analysis

Introduction: Telomere length is a marker of cellular aging and is correlated with various cardiovascular risk factors. Aim: To assess the association between clinical characteristics of patients and telomere length in an unselected cohort of patients under the care of primary care physicians. Methods: The LIPIDOGEN study was carried out in the primary care 2015. Patients recruited to LIPIDOGEN cohort (n=1788) were a random subset of patients of LIPIDOGRAM 2015 (n=13,724) study. Patients for LIPIDOGRAM2015/LIPIDOGEN study were recruited in all 16 administrative regions in Poland. Each patient was asked to fill the questionnaire on risk factors, chronic, treatment and lifestyle and also had saliva secured for DNA analyses. Factor Analysis for Mixed Data (FAMD) based cluster analysis was utilized to discern groups of patients with similar clinical profiles and correlate them with relative telomere length (rTL). RTL was measured using quantitative polymerase chain reaction. Results: DNA quality was sufficient for further analyses in 1606 patients. After outlier exclusion, 1,342 patients (59.3% female, mean age 50.2 years) were analyzed. FAMD revealed two distinct clinical characteristic patterns, explaining 41.6% of the total variance in the clinical variables. The first pattern, correlated with male gender and a higher prevalence of MetS, hypertension, diabetes, obesity, greater waist circumference, elevated non-HDL-C and remnant-C levels, was linked to shorter rTL (r = -0.13, p < 0.001). The second pattern, characterized mainly by lipid parameters, showed no significant association with comorbidities or rTL (r = -0.04, p = 0.14). Conclusions: Pattern of clinical characteristics marked by classical cardiovascular risk factors, is inversely related to telomere length, underlining the potential role of metabolic disturbances in cellular aging. The second lipid-centric pattern without significant comorbidities was not associated with telomere length.

B-114 Reference interval (RI) estimation for TSH in adults using an indirect method with unsupervised machine learning tool and R programming language

Abstract Background Thyroid-stimulating hormone (TSH) plays a fundamental role in regulating thyroid hormones T3 (triiodothyronine) and T4 (thyroxine). Understanding TSH test results is essential for effective management of thyroid disorders and depends, among other factors, on establishing specific reference intervals (RI) for the population being served. We aimed to estimate the reference intervals for TSH in the adult population served by a large diagnostic network in Brazil. Methods We employed data mining followed by estimation using an indirect method through the LabRI™ algorithm with parametric, non-parametric, and robust statistical treatments for outlier exclusion, a series of algorithms programmed in R Language. Included were TSH values from outpatients, aged 20 to 60 years, both sexes, from two analytical platforms. Used as a comparative RI recommended by the American Thyroid Association (ATA, 2014). Data were extracted from the Data Lake, from January to December 2022, from two laboratories in Brazil. Exclusion criteria: samples from hospital sources and referral laboratories; pregnant; some laboratory tests correlated with de TSH test; samples from patients using thyroid hormone and medications that may interfere with hormonal function and TSH laboratory analysis. Results After exclusion criteria, the sample totaled 26,909 adults of both sexes. After excluding 2.24% outliers, the estimated RI was 0.67-4.26 mIU/L, with the comparative RI chosen for this study being the one recommended by ATA of 0.40-4.00 mIU/L. The RI obtained in our study was similar to the Canadian RI harmonization study for TSH (0.60-4.48 mIU/L). Conclusions We estimated the RI, harmonized between Roche and Siemens platforms, to be 0.67-4.26 mIU/L. The RI obtained by the LabRI™ tool is equivalent to the Adeli study (2023) and is under discussion by the company's endocrinology committee for whether to use the estimated RI from our internal study or the one recommended by ATA (2014) in reports.

B-120 Reference Interval (RI) Estimation for Pediatric Magnesium using an Indirect Method with Unsupervised Machine Learning Tool and Programming Language

Abstract Background Magnesium is an important intracellular component present in all enzymatic reactions involving Adenosine Triphosphate (ATP) utilization, with the majority stored in bones. In the blood, it exists as a free fraction and another bound to proteins. Regulation occurs through renal processes, and for this reason, hypermagnesemia is associated with renal insufficiency, compromising the central nervous system. On the other hand, hypomagnesemia cases are related to pancreatitis, intestinal malabsorption, childhood malnutrition, among other factors. Following complaints from pediatric doctors that our reference intervals (RI), as suggested by the manufacturer, would yield falsely elevated results in healthy patients, we estimated a new RI in the population served Methods We employed data mining followed by estimation using an indirect method through the LabRI™ algorithm with parametric, non-parametric, and robust statistical treatments for outlier exclusion, implemented in the R language. Latent abnormal values of outpatient pediatric patients were considered. We defined three pediatric partitions for both sexes, aged 0 to 6 years, 7 to 12 years, and above 12 years, using the comparative RI described by the Roche manufacturer. Data were extracted from our Data Lake between January and December 2022 in the Federal District of Brazil, from a laboratory part of a large network. Exclusion criteria included: Potassium > 5.5 and < 3.0 mmol/L; Phosphorus > 8.0 and < 1.5 mg/dL; Total Calcium > 12 mg/dL and < 7 mg/dL; Ionized Calcium > 1.5 mmol/L and < 0.8 mmol/L; Creatinine > 2.0 mg/dL; C-reactive protein (CRP) > 10 mg/dL; Ferritin > 700 ng/mL; A1C > 7.0%; LDL Cholesterol > 200 mg/dL; Magnesium repeated within < 12 months, and samples of hospital origin. Results The evaluated population after exclusion criteria totaled 6,865 children, distributed as follows: 26.0% aged 0 to 6 years, 30.3% aged 7 to 12 years, and 43.7% above 12 years. Outliers were excluded in 1.5%, 0.9%, and 1.0%, respectively. In all partitions, there was a significant discrepancy compared to the reference intervals recommended by Roche. In our estimation, there was an expansion of the upper limit of the reference interval in all partitions, supporting the clinical opinion. The new reference intervals became: 0-6 yrs: from 1.7-2.3 to 2.0-2.7; 7-12 yrs: from 1.7-2.1 to 2.0-2.6; and >12 yrs: from 1.7-2.2 to 1.9-2.6. The standard deviation varied between 0.19 and 0.20, and the median between 2.20 and 2.32 Conclusions The manufacturer states in the package insert that they did not assess the RI for the pediatric population and that each laboratory should verify the RI in the population served. We approve the adoption of the estimated RI for the pediatric population calculated by the LabRI™ tool in the partitions of 0-6 years, 7-12 years, and >12 years, stating in the report “Reference interval established by an indirect method in 6865 children tested in the laboratory, partitioned by age group.”

Robust and automatic beamstop shadow outlier rejection: combining crystallographic statistics with modern clustering under a semi-supervised learning strategy.

During the automatic processing of crystallographic diffraction experiments, beamstop shadows are often unaccounted for or only partially masked. As a result of this, outlier reflection intensities are integrated, which is a known issue. Traditional statistical diagnostics have only limited effectiveness in identifying these outliers, here termed Not-Excluded-unMasked-Outliers (NEMOs). The diagnostic tool AUSPEX allows visual inspection of NEMOs, where they form atypical pattern: clusters at the low-resolution end of the AUSPEX plots of intensities or amplitudes versus resolution. To automate NEMO detection, a new algorithm was developed by combining data statistics with a density-based clustering method. This approach demonstrates a promising performance in detecting NEMOs in merged data sets without disrupting existing data-reduction pipelines. Re-refinement results indicate that excluding the identified NEMOs can effectively enhance the quality of subsequent structure-determination steps. This method offers a prospective automated means to assess the efficacy of abeamstop mask, as well as highlighting the potential of modern pattern-recognition techniques for automating outlier exclusion during data processing, facilitating future adaptation to evolving experimental strategies.

Dynamic Characterization of Equatorial Plasma Bubble Based on Triangle Network‐Joint Slope Approach

AbstractThis paper introduces a Triangle Network‐Joint Slope (TN‐JS) approach to characterize the spatial and temporal dynamics of Equatorial Plasma Bubbles (EPBs) during geomagnetic storms. To collaboratively determine the EPB drift directions from multiple stations, a Delaunay triangle network is constructed, utilizing the distribution of Ionospheric Piercing Points (IPPs). The Time Difference of Arrival (TDOA) is extracted through cross‐correlating the Rate of Total Electron Content (ROT). The EPB drift direction can be approximately calculated by considering TDOA and IPP distances within each individual triangle of the network. This calculation is then refined through a joint statistical analysis. Using a reference station as the origin, the remaining stations within the network are projected along the estimated EPB drift direction. A spatial‐temporal color map illustrating regional ionospheric anomaly ROT observations is constructed. The EPB drift velocity among multiple stations can be collectively estimated by fitting the slope of this map, facilitating outlier exclusion. Accounting for satellite dynamic effects and the diverse orbit characteristics of GPS and BDS, corresponding IPP scan velocity compensation is performed and analyzed for EPB dynamic estimation. Using the geomagnetic storm event that occurred on September 8 as a case study, the spatial‐temporal kinetic properties of EPBs is characterized by analyzing Global Navigation Satellite System (GNSS) observations from 17 Hong Kong monitoring stations with the proposed TN‐JS approach. The results indicate during this magnetic event, that EPBs exhibit a westward drift trend with velocities ranging from a few tens to hundreds of meters per second in GPS and BDS observations.

Motor fuel and core inflation

We ask whether there exists a fixed exclusion method that is simpler to compute than the CPI excluding food and energy, yet also closely tracks the behavior of outlier exclusion measures such as the trimmed mean. We document that the CPI excluding motor fuel achieves both goals.

Anorexia nervosa and bulimia nervosa: a Mendelian randomization study of gut microbiota.

Anorexia nervosa (AN) and bulimia nervosa (BN) poses a significant challenge to global public health. Despite extensive research, conclusive evidence regarding the association between gut microbes and the risk of AN and BN remains elusive. Mendelian randomization (MR) methods offer a promising avenue for elucidating potential causal relationships. Genome-wide association studies (GWAS) datasets of AN and BN were retrieved from the OpenGWAS database for analysis. Independent single nucleotide polymorphisms closely associated with 196 gut bacterial taxa from the MiBioGen consortium were identified as instrumental variables. MR analysis was conducted utilizing R software, with outlier exclusion performed using the MR-PRESSO method. Causal effect estimation was undertaken employing four methods, including Inverse variance weighted. Sensitivity analysis, heterogeneity analysis, horizontal multivariate analysis, and assessment of causal directionality were carried out to assess the robustness of the findings. A total of 196 bacterial taxa spanning six taxonomic levels were subjected to analysis. Nine taxa demonstrating potential causal relationships with AN were identified. Among these, five taxa, including Peptostreptococcaceae, were implicated as exerting a causal effect on AN risk, while four taxa, including Gammaproteobacteria, were associated with a reduced risk of AN. Similarly, nine taxa exhibiting potential causal relationships with BN were identified. Of these, six taxa, including Clostridiales, were identified as risk factors for increased BN risk, while three taxa, including Oxalobacteraceae, were deemed protective factors. Lachnospiraceae emerged as a common influence on both AN and BN, albeit with opposing effects. No evidence of heterogeneity or horizontal pleiotropy was detected for significant estimates. Through MR analysis, we revealed the potential causal role of 18 intestinal bacterial taxa in AN and BN, including Lachnospiraceae. It provides new insights into the mechanistic basis and intervention targets of gut microbiota-mediated AN and BN.

A method for analyzing AFM force mapping data obtained from soft tissue cryosections

Atomic force microscopy (AFM) is a valuable tool for assessing mechanical properties of biological samples, but interpretations of measurements on whole tissues can be difficult due to the tissue’s highly heterogeneous nature. To overcome such difficulties and obtain more robust estimates of tissue mechanical properties, we describe an AFM force mapping and data analysis pipeline to characterize the mechanical properties of cryosectioned soft tissues. We assessed this approach on mouse optic nerve head and rat trabecular meshwork, cornea, and sclera. Our data show that the use of repeated measurements, outlier exclusion, and log-normal data transformation increases confidence in AFM mechanical measurements, and we propose that this methodology can be broadly applied to measuring soft tissue properties from cryosections.

Reference intervals of circulating secretoneurin concentrations determined in a large cohort of community dwellers: the HUNT study.

Secretoneurin (SN) is a novel cardiac biomarker that associates with the risk of mortality and dysfunctional cardiomyocyte Ca2+ handling in heart failure patients. Reference intervals for SN are unknown. SN was measured with a CE-marked ELISA in healthy community dwellers from the fourth wave of the Trøndelag Health Study (HUNT4) conducted in 2017-2019. The common, sex and age specific 90th, 95th, 97.5th and 99th percentiles were calculated using the non-parametric method and outlier exclusion according to the Reed test. The applicability of sex and age specific reference intervals were investigated using Harris and Boyd test. We also estimated the percentiles in a subset with normal findings on echocardiographic screening. The total cohort included 887 persons (56.4 % women). After echocardiographic screening 122 persons were excluded, leaving a total of 765 persons (57.8 % women). The 97.5th percentile (95 % CI in brackets) of SN was 59.7 (57.5-62.1) pmol/L in the total population and 58.6 (57.1-62.1) pmol/L after echocardiography screening. In general, slightly higher percentiles were found in women and elderly participants, but less than 4 % in these subgroups had concentrations deviating from the common 97.5th percentile. Low BMI or eGFR was also associated with higher concentrations of SN. Upper reference limits for SN were similar amongst healthy adult community dwellers regardless of prescreening including cardiac echocardiography or not. Women and elderly showed higher concentrations of SN, but the differences were not sufficiently large to justify age and sex stratified upper reference limits.

Sources of Variance in Human Tear Proteomic Samples: Statistical Evaluation, Quality Control, Normalization, and Biological Insight.

Human tear fluid contains numerous compounds, which are present in highly variable amounts owing to the dynamic and multipurpose functions of tears. A better understanding of the level and sources of variance is essential for determining the functions of the different tear components and the limitations of tear samples as a potential biomarker source. In this study, a quantitative proteomic method was used to analyze variations in the tear protein profiles of healthy volunteers. High day-to-day and inter-eye personal variances were observed in the tear volumes, protein content, and composition of the tear samples. Several normalization and outlier exclusion approaches were evaluated to decrease variances. Despite the intrapersonal variances, statistically significant differences and cluster analysis revealed that proteome profile and immunoglobulin composition of tear fluid present personal characteristics. Using correlation analysis, we could identify several correlating protein clusters, mainly related to the source of the proteins. Our study is the first attempt to achieve more insight into the biochemical background of human tears by statistical evaluation of the experimentally observed dynamic behavior of the tear proteome. As a pilot study for determination of personal protein profiles of the tear fluids of individual patients, it contributes to the application of this noninvasively collectible body fluid in personal medicine.

Exacerbation or suppression? Digital transformation and shadow banking activities of non-financial firms

Using an unbalanced panel dataset from Chinese non-financial firms spanning 2008–2021, this research explores the relationship between digital transformation and shadow banking. Contrary to expectations, the evidence indicates that digital transformation accelerates shadow banking rather than suppressing it. A result resilient to potential endogeneity, proxy variables, and outlier exclusion. Furthermore, shadow banking exacerbates operational risk, a pattern particularly prominent among firms with underdeveloped digital capabilities, intimating that digital maturity may alleviate the detrimental impacts. This study elucidates the economic repercussions of shadow banking in the digital transformation epoch, yielding valuable insights.

Outlier exclusion procedures for reaction time analysis: The cures are generally worse than the disease.

A methodological problem in most reaction time (RT) tasks is that some measured RTs may be outliers, being either too fast or too slow to reflect the task-related processing of interest. Numerous ad hoc procedures have been used to identify these outliers for exclusion from further analyses, but the accuracies of these methods have not been systematically compared. The present study compared the performance of 58 different outlier exclusion procedures (OEPs) using four huge datasets of real RTs. The results suggest that these OEPs are likely to do more harm than good, because they incorrectly identify outliers, increase noise, introduce bias, and generally reduce statistical power. The results suggest that RT researchers should not automatically apply any of these OEPs to clean their RT data prior to the main analyses. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A-386 Performance assessment of Point of care testing for blood glucose: a 5 year review of EQA data

Abstract Introduction WEQAS is the largest External Quality Assessment (EQA) provider for the Point of Care Testing (POCT) market within the UK supplying samples and assessment to over 30 000 sites per month with over 20 Programmes across the entire Health care sector. All the Programmes are accredited to ISO 17043.The aim of the programme is to provide support to POCT co-ordinators, to identify non compliant sites and improve the analytical performance of users. Most of the programmes are designed to allow the full assessment of all the devices within the organisation at either monthly, bimonthly or quarterly intervals. A Co-ordinator in each organisation is given a Group Administrator function for the EQA website and maintains the database for its own organisation. The role of performance surveillance is therefore devolved to each individual Co-ordinator at a local level and monitored nationally by the EQA organiser. The powerful database gives POCT Co-ordinators a wealth of information on method and analyser performance both within their own organisation and between organisations. Organisation performance summary reports, distribution letters, non-compliance reports, poor performance reports and cumulative reports are generated from one system. The POCT Users are also provided with a simple traffic light system with clear action limits. For the POCT glucose programme one sample is distributed each month with samples covering a range of 2 to 28 mmol/l annually. Methods A review of performance was undertaken between January 2018 and January 2023 for the POCT glucose Programmes for a selection of devices. Devices that were no longer available were excluded as were devices with <50 users in the group. This represented data from 10 360 devices per month in 2018 and 12 277 in 2023. Performance was assessed as the coefficient of variation (CV%) for the device group after outlier exclusion and calculated for a wide range of glucose concentrations. Results For devices with CV less than 5% in 2018 there was generally no further improvement in performance, however at a glucose of 2 mmol/L an improvement in performance of 0.5to 3.5% was observed for the other devices. In 2018 CVs ranged from 3.7 to 12% and 3.1 to 8.3%% at 2.5 and 26 mmol/L respectively compared with 4 to 10.3% and 3.3 to 7.2% in 2022. Conclusion Although there were numerous examples where EQA identified poor performance at an individual user level, there was little evidence to suggest that the performance of POCT glucose devices had improved significantly over the last 5 years.

Predicting orthophosphate in feces and manure from dairy cattle

Dairy cattle excreta are a valuable source of orthophosphate (Ortho-P), an inorganic form of phosphorus (P) that is readily available for microorganisms, plant growth, and development. There is, however, a growing environmental concern about the potential negative environmental impact of excessive amounts of Ortho-P excretion, which can lead to the eutrophication of water bodies. As a result, the development of mathematical equations to quantify and manage Ortho-P excretion on dairy farms could prove valuable for environmental sustainability. This study aimed to use literature data to develop empirical predictions for Ortho-P (g/kg dry matter [DM]) excretion using total P (TP [g/kg DM]) content of dairy cattle feces (Ortho-Pf) and manure (Ortho-Pm). Data sets from studies that evaluated and characterized the different forms of P in feces and manure from dairy cattle were compiled. After outlier exclusion, the final retained database for feces included 37 treatment means from 4 published papers while the manure comprised 23 treatment means from 7 published papers. A linear-mixed model was used to develop the predictive equations, incorporating the random effect of the study. A leave-one-out cross-validation procedure was used to evaluate the predictive ability of the developed models, whereby studies were regarded as folds. The fecal equation was determined as Ortho-Pf (g/kg DM) = −2.447 (0.572) + 0.966 (0.083) × TP (g/kg DM) (R2 = 0.79) and resulted in a root mean square prediction error as a percentage of mean observed value (RMSPE, %) of 32.8% and error due to random sources of 97.6%. Additionally, the manure equation was determined as Ortho-Pm (g/kg) = −0.204 (0.446) + 0.590 (0.065) × TP (g/kg) (R2 = 0.77) and had an RMSPE of 43.3% with a random error of 93.9%. Both models revealed minimal mean and slope biases on feces and manure data. Findings suggest that these sets of equations can be used to estimate excreted Ortho-P from total excreted P, helping nutritionists and farmers to understand the impact of dietary P changes on the environment. Further, these equations can be incorporated into extant models such as the Cornell Net Carbohydrate and Protein System (CNCPS) to aid in understanding and mitigating P and Ortho-P excretion from dairy cattle and to clarify the portion of P that migrates more rapidly into watersheds.

The Appropriateness of Outlier Exclusion Approaches Depends on the Expected Contamination: Commentary on André (2022)

In a recent article, André (2022) addressed the decision to exclude outliers using a threshold across conditions or within conditions and offered a clear recommendation to avoid within-conditions exclusions because of the possibility for large false-positive inflation. In this commentary, I note that André’s simulations did not include the situation for which within-conditions exclusion has previously been recommended—when across-conditions exclusion would exacerbate selection bias. Examining test performance in this situation confirms the recommendation for within-conditions exclusion in such a circumstance. Critically, the suitability of exclusion criteria must be considered in relationship to assumptions about data-generating mechanisms.

Using representational similarity analysis to reveal category and process specificity in visual object recognition

Cross-condition comparisons on neurodevelopmental conditions are central in neurodiversity research. In the realm of visual perception, the performance of participants with different category-specific disorders such as developmental prosopagnosia (problems with faces) and dyslexia (problems with words) have contributed to understanding of perceptual processes involved in word and face recognition. Alterations in face and word recognition are present in several neurodiverse populations, and improved knowledge about their relationship may increase our understanding of this variability of impairment. The present study investigates organizing principles of visual object processing and their implications for developmental disorders of recognition. Some accounts suggest that distinct mechanisms are responsible for recognizing objects of different categories, while others propose that categories share or even compete for cortical resources. We took an individual differences approach to estimate the relationship between abilities in recognition. Neurotypical participants (N = 97 after outlier exclusion) performed a match-to-sample task with faces, houses, and pseudowords. Either individual features or feature configurations were manipulated. To estimate the separability of visual recognition mechanisms, we used representational similarity analysis (RSA) where correlational matrices for accuracy were compared to predicted data patterns. Recognition abilities separated into face recognition on one hand and house/pseudoword recognition on the other, indicating that face recognition may rely on relatively selective mechanisms in neurotypicals. We also found evidence for a general visual object recognition mechanism, while some combinations of category (faces, houses, words) and processing type (featural, configural) likely rely on additional mechanisms. Developmental conditions may therefore reflect combinations of impaired and intact aspects of specific and general visual object recognition mechanisms, where featural and configural processes for one object category separate from the featural or configural processing of another. More generally, RSA is a promising approach for advancing understanding of neurodiversity, including shared aspects and distinctions between neurodevelopmental conditions of visual recognition.

Transitional Care Model in Germany: Findings from an economic analysis of a randomized controlled trial

IntroductionThe statutory discharge management system in German hospitals does not provide adequate transitional care for geriatric patients. The American Transitional Care Model (TCM), where a qualified professional supports the patient for a period before, during, and after discharge, could help to address this problem. In this study, we compared both approaches from an economic perspective. Our research questions were: (1) How do treatment costs per person differ when geriatric patients are supported according to the TCM compared with routine discharge management? (2) What are the intervention costs? MethodsThe present economic analysis was part of a randomized controlled trial conducted at a hospital in Germany. In this study, geriatric patients in the intervention group received care according to TCM and those in the control group received routine care. We obtained data from the hospital and a health insurance company. In a cost-cost analysis, we compared the treatment costs per patient incurred in both study groups. In cases where higher costs occurred in the intervention group, we also conducted a cost-utility analysis, using the 12-Item Short Form Survey questionnaire to collect quality of life data for the Quality Adjusted Life Years calculation. Intervention costs were calculated on the basis of staff salaries, working hours, and the cost of acquiring the necessary equipment. ResultsThe intervention group consisted of 109 geriatric patients, the control group of 119. The average quality of life score was slightly higher in the intervention group, but the difference was not significant. On average, the intervention group was less expensive. However, for individual cost types (e.g., rehabilitation), the intervention group incurred higher costs than the control group. The differences between the study groups were not significant for all cost types (p > 0.05). Intervention costs were estimated to be approximately 800 euros per patient. DiscussionThe TCM approach leads to savings. However, the amount of savings will depend on intervention costs, which vary in practice. After outlier exclusion, an unfavorable incremental cost-effectiveness ratio is observed for the TCM approach. ConclusionFrom an economic perspective, TCM in its present form provides no additional value to patient care compared with routine discharge management. However, we see an urgent need to optimize the existing discharge management system to ensure better transitional care for those affected. Therefore, we recommend that different TCM components should be tested in further studies to address the questions that could not be clarified in the present study.

Electrophysiological correlates of response time outliers: Outlier related potentials.

In response time (RT) research, RTs which largely deviate from the RT distribution are considered "outliers". Outliers are typically excluded from RT analysis building upon the implicit assumption that cognitive processing is distorted in outlier trials. The present study aims to test this assumption by comparing cognitive processing indexed by event-related potentials (ERP) of trials with outliers and valid trials in two different tasks. To this end, we compared stimulus- and response-locked ERPs for outliers identified by nine different methods with valid trials, using cluster-based permutation tests. Consistently across outlier exclusion methods and tasks, the late positive complex (P3) associated with response-related processes was reduced in outliers. Analyses of response-locked ERPs related this P3 attenuation to a slower and temporally more extended increase of the P3, possibly indexing reduced evidence accumulation speed in outliers. P3 peak amplitude in response-locked ERPs was similar between outliers and valid trials, suggesting that the absolute amount of evidence required for a response remained comparable. Furthermore, in addition to these more general ERP correlates of outliers, the contingent negative variation (CNV) ERP component was reduced in outliers as a function of preparatory demands of the task. Hence, electrophysiological correlates, and thus cognitive processing, are altered in outliers compared to valid trials. In order to avoid distortion of observed ERP differences between conditions, the RT outlier distribution should be considered for the analysis of ERPs in combined ERP and RT studies.

Bridge Deformation Analysis Using Time-Differenced Carrier-Phase Technique

Historically, monitoring possible deformations in suspension bridges has been a crucial issue for structural engineers. Therefore, to understand and calibrate models of the “load-structure-response”, it is essential to implement suspension bridge monitoring programs. In this work, due to increasing GNSS technology development, we study the movement of a long-span bridge structure using differenced carrier phases in adjacent epochs. Many measurement errors can be decreased by a single difference between consecutive epochs, especially from receivers operating at 10 Hz. Another advantage is not requiring two receivers to observe simultaneously. In assessing the results obtained, to avoid unexpected large errors, the outlier and cycle-slip exclusion are indispensable. The final goal of this paper is to obtain the relative positioning and associated standard deviations of a stand-alone geodetic receiver. Short-term movements generated by traffic, tidal current, wind, or earthquakes must be recoverable deformations, as evidenced by the vertical displacement graphs obtained through this approach. For comparison studies, three geodetic receivers were positioned on the Assut de l’Or Bridge in València, Spain. The associated standard deviation for the north, east, and vertical positioning values was approximately 0.01 m.

Methods for analyse performance in distributed systems

The purpose of this article is to analyze various methods for measuring central tendency in statistics, including arithmetic mean, median, winsorized mean, outlier exclusion method, Hodges-Lehmann estimator, and quantile estimation and much more. The advantages and disadvantages of each of these methods are discussed, as well as their practical applications in performance analysis in distributed systems. In particular, we focus on the importance of selecting an appropriate measure of central tendency that is robust to outliers and accurately reflects the distribution of the data. We also provide examples of how these methods can be applied to real-world datasets to gain insights into the underlying patterns and trends. Overall, this article provides a comprehensive overview of the different techniques for measuring central tendency and offers practical guidance for researchers and analysts looking to make informed decisions about perfomance analysis.