Frequent Outliers Research Articles

A comparative analysis of supply chain factors impacting COVID-19 vaccine security in high-income countries (HICs) and low-income and middle-income countries (LMICs)

IntroductionThe COVID-19 pandemic focused attention on the importance of vaccine security to national security. Demand for vaccines far exceeded supply when the first COVID-19 vaccines were released. Growing data suggest...

IMPROVING GNSS POSITIONING RELIABILITY AND ACCURACY BASED ON FACTOR GRAPH OPTIMIZATION IN URBAN ENVIRONMENT

Abstract. Global navigation satellite system (GNSS) can provide global, precise, and continuous positioning in open-sky environments. However, urban environments with frequent outliers and cycle slips degrade the traditional Extended Kalman Filter (EKF) positioning performance. The susceptibility of EKF to outliers is attributed to its inherent structure. To mitigate, the GNSS positioning based on the Factor Graph Optimization (FGO) structure is adopted. FGO can enhance time correlation among observations and enable the updating of historical information, thereby improving resistance against outliers. In this study, we proposed a single-differenced GNSS-FGO model instead of the double-differenced model to preserve the sparsity of FGO, and outlier detection and PAR methods are employed to ensure urban positioning performance. To evaluate the proposed structure, experiments are conducted in both urban and open-sky environments. The results demonstrate the improvement of positioning accuracy and reliability, compared to EKF.

Strontium isotope compositions of Late Permian evaporites from the northernmost Thuringian Basin (Germany) indicate continental influence on the marine Zechstein Sea

In the Late Permian Zechstein Sea of Central Europe, up to 2000 m of evaporitic rocks were deposited in at least four consecutive cycles. The age of these evaporitic rocks could not yet be precisely determined, because they are virtually fossil-free and do not contain radiometrically datable volcanic layers. A chemostratigraphic age of the succession can be determined by comparing 87Sr/86Sr ratios of marine gypsum and anhydrite to the worldwide marine strontium evolution curve. Unfortunately, published 87Sr/86Sr data of the Zechstein succession are characterized by frequent outliers towards higher ratios, making an age assignment challenging. The scatter in 87Sr/86Sr ratios might be induced by different processes like the contribution of meteoric water to the brine, in-situ Rb decay, or post-depositional hydrothermal or diagenetic overprint. Here, we present a dataset of 26 new gypsum and anhydrite 87Sr/86Sr ratios from drill cores situated at “Alter Stolberg” in the northernmost Thuringian Basin. Evaporites of the Werra-, Staßfurt-, and Leine cycles were sampled. The close proximity of the drillings allows a very accurate assignment of the stratigraphic position of each sample, so that trends and outliers in 87Sr/86Sr ratios can easily be recognized. While the entire Werra Formation obviously revealed non-marine 87Sr/86Sr ratios, the lowermost 87Sr/86Sr ratios in the Staßfurt and Leine Formations can be assumed to represent marine ratios and allow estimating a chemostratigraphic age of 257‒254 Ma. The combination of the 87Sr/86Sr data with the mineral composition of the samples suggests a contribution of meteoric water, probably river water, to the Zechstein Sea as the main reason for the observed increase in 87Sr/86Sr ratios. Additional in-situ Rb decay, related to the riverine input of clay minerals, cannot be excluded. Modelling the amounts of sea water and meteoric water in the brine indicates that 83‒99% of meteoric water would be necessary to explain the highest 87Sr/86Sr ratios observed in the Werra Formation.Graphical abstract

GNSS precise positioning for smartphones based on the integration of factor graph optimization and solution separation

Currently, the global navigation satellite system on smartphones suffers from frequent outliers (faults) in the measurements, limiting their precise positioning applications. To mitigate, the factor-graph optimization (FGO) method is used, where the Doppler and time-differenced carrier-phase (TDCP) measurements determine the velocities. However, their estimations are vulnerable since the Doppler measurements are inaccurate, while the TDCP measurements suffer from frequent cycle slips. Statistical testing methods can be used to exclude measurement outliers, but they have high volumes of false alarms and missed detections. In this study, we propose an improved scheme of FGO with a solution separation (SS) test to improve smartphone precise positioning. After excluding measurement outliers with the SS test for TDCP and time-differenced pseudorange observation models, we apply an improved FGO scheme for positioning. We conducted an experiment with Xiaomi Mi 8 and Google Pixel 5, showing that our method outperforms the conventional FGO and Kalman filtering methods.

A New Constructive Heuristic Driven by Machine Learning for the Traveling Salesman Problem

Recent systems applying Machine Learning (ML) to solve the Traveling Salesman Problem (TSP) exhibit issues when they try to scale up to real case scenarios with several hundred vertices. The use of Candidate Lists (CLs) has been brought up to cope with the issues. A CL is defined as a subset of all the edges linked to a given vertex such that it contains mainly edges that are believed to be found in the optimal tour. The initialization procedure that identifies a CL for each vertex in the TSP aids the solver by restricting the search space during solution creation. It results in a reduction of the computational burden as well, which is highly recommended when solving large TSPs. So far, ML was engaged to create CLs and values on the elements of these CLs by expressing ML preferences at solution insertion. Although promising, these systems do not restrict what the ML learns and does to create solutions, bringing with them some generalization issues. Therefore, motivated by exploratory and statistical studies of the CL behavior in multiple TSP solutions, in this work, we rethink the usage of ML by purposely employing this system just on a task that avoids well-known ML weaknesses, such as training in presence of frequent outliers and the detection of under-represented events. The task is to confirm inclusion in a solution just for edges that are most likely optimal. The CLs of the edge considered for inclusion are employed as an input of the neural network, and the ML is in charge of distinguishing when such edge is in the optimal solution from when it is not. The proposed approach enables a reasonable generalization and unveils an efficient balance between ML and optimization techniques. Our ML-Constructive heuristic is trained on small instances. Then, it is able to produce solutions—without losing quality—for large problems as well. We compare our method against classic constructive heuristics, showing that the new approach performs well for TSPLIB instances up to 1748 cities. Although ML-Constructive exhibits an expensive constant computation time due to training, we proved that the computational complexity in the worst-case scenario—for the solution construction after training—is O(n2logn2), n being the number of vertices in the TSP instance.

Efficient Probabilistic Approach to Range-Only SLAM With a Novel Likelihood Model

The range-only simultaneous localization and mapping (RO-SLAM) problem is considered in this article. This is a difficult problem since bearing measurements are not available. Also, the frequent outliers induced by the sensor nature (typically sonar or radio pulses) render the RO-SLAM problem more challenging. The non-Gaussian distribution found in the RO-SLAM problem makes that the landmarks cannot be directly updated using the approach based on the Gaussian assumption. We propose a region-based particle filter and a proper likelihood model to realize the reduction of particle numbers and allow the introduction of new landmarks without delayed processing. A transition mode is designed from the loosely coupled way to the tightly coupled way and used to estimate the pose of the robot and locations of landmarks. Furthermore, two detection methods are, respectively, used to remove the outliers generated by the range-only sensors according to the distribution status of the landmark estimation. The proposed algorithm is evaluated through simulations and experiments with a range-only sensor, i.e., Ultrawideband (UWB). Furthermore, the obtained results are compared with the classical algorithm. The simulation and experiment results verify the validity and superiority of the proposed RO-SLAM algorithm.

Robust Outlier-Adaptive Filtering for Vision-Aided Inertial Navigation

With the advent of unmanned aerial vehicles (UAVs), a major area of interest in the research field of UAVs has been vision-aided inertial navigation systems (V-INS). In the front-end of V-INS, image processing extracts information about the surrounding environment and determines features or points of interest. With the extracted vision data and inertial measurement unit (IMU) dead reckoning, the most widely used algorithm for estimating vehicle and feature states in the back-end of V-INS is an extended Kalman filter (EKF). An important assumption of the EKF is Gaussian white noise. In fact, measurement outliers that arise in various realistic conditions are often non-Gaussian. A lack of compensation for unknown noise parameters often leads to a serious impact on the reliability and robustness of these navigation systems. To compensate for uncertainties of the outliers, we require modified versions of the estimator or the incorporation of other techniques into the filter. The main purpose of this paper is to develop accurate and robust V-INS for UAVs, in particular, those for situations pertaining to such unknown outliers. Feature correspondence in image processing front-end rejects vision outliers, and then a statistic test in filtering back-end detects the remaining outliers of the vision data. For frequent outliers occurrence, variational approximation for Bayesian inference derives a way to compute the optimal noise precision matrices of the measurement outliers. The overall process of outlier removal and adaptation is referred to here as “outlier-adaptive filtering”. Even though almost all approaches of V-INS remove outliers by some method, few researchers have treated outlier adaptation in V-INS in much detail. Here, results from flight datasets validate the improved accuracy of V-INS employing the proposed outlier-adaptive filtering framework.

Error-Aware Data Clustering for In-Network Data Reduction in Wireless Sensor Networks.

A wireless sensor network (WSN) deploys hundreds or thousands of nodes that may introduce large-scale data over time. Dealing with such an amount of collected data is a real challenge for energy-constraint sensor nodes. Therefore, numerous research works have been carried out to design efficient data clustering techniques in WSNs to eliminate the amount of redundant data before transmitting them to the sink while preserving their fundamental properties. This paper develops a new error-aware data clustering (EDC) technique at the cluster-heads (CHs) for in-network data reduction. The proposed EDC consists of three adaptive modules that allow users to choose the module that suits their requirements and the quality of the data. The histogram-based data clustering (HDC) module groups temporal correlated data into clusters and eliminates correlated data from each cluster. Recursive outlier detection and smoothing (RODS) with HDC module provides error-aware data clustering, which detects random outliers using temporal correlation of data to maintain data reduction errors within a predefined threshold. Verification of RODS (V-RODS) with HDC module detects not only random outliers but also frequent outliers simultaneously based on both the temporal and spatial correlations of the data. The simulation results show that the proposed EDC is computationally cheap, able to reduce a significant amount of redundant data with minimum error, and provides efficient error-aware data clustering solutions for remote monitoring environmental applications.

Defining Hsp33's Redox-regulated Chaperone Activity and Mapping Conformational Changes on Hsp33 Using Hydrogen-deuterium Exchange Mass Spectrometry.

Living organisms regularly need to cope with fluctuating environments during their life cycle, including changes in temperature, pH, the accumulation of reactive oxygen species, and more. These fluctuations can lead to a widespread protein unfolding, aggregation, and cell death. Therefore, cells have evolved a dynamic and stress-specific network of molecular chaperones, which maintain a "healthy" proteome during stress conditions. ATP-independent chaperones constitute one major class of molecular chaperones, which serve as first-line defense molecules, protecting against protein aggregation in a stress-dependent manner. One feature these chaperones have in common is their ability to utilize structural plasticity for their stress-specific activation, recognition, and release of the misfolded client. In this paper, we focus on the functional and structural analysis of one such intrinsically disordered chaperone, the bacterial redox-regulated Hsp33, which protects proteins against aggregation during oxidative stress. Here, we present a toolbox of diverse techniques for studying redox-regulated chaperone activity, as well as for mapping conformational changes of the chaperone, underlying its activity. Specifically, we describe a workflow which includes the preparation of fully reduced and fully oxidized proteins, followed by an analysis of the chaperone anti-aggregation activity in vitro using light-scattering, focusing on the degree of the anti-aggregation activity and its kinetics. To overcome frequent outliers accumulated during aggregation assays, we describe the usage of Kfits, a novel graphical tool which allows easy processing of kinetic measurements. This tool can be easily applied to other types of kinetic measurements for removing outliers and fitting kinetic parameters. To correlate the function with the protein structure, we describe the setup and workflow of a structural mass spectrometry technique, hydrogen-deuterium exchange mass spectrometry, that allows the mapping of conformational changes on the chaperone and substrate during different stages of Hsp33 activity. The same methodology can be applied to other protein-protein and protein-ligand interactions.

Abstract 180: Some Like it Hot; Radiation Safety in the Catheterization Lab

Objective: Ionizing radiation is an unseen and often unappreciated safety concern for patients and cath lab staff. Radiation exposure has both deterministic and stochastic effects, with patient risk estimated by peak Skin Dose and Air kerma Area product. In our lab exposure guidelines have been set for radiation safety (fluoro time < 60 min, 4,000 mGray and 20,000 cGray cm 2 ). A prior audit of practice 2011-2013 found that 17.1% (758/4,439) of patients undergoing interventional procedures exceeded these limits. Safer radiation practices were implemented including: intra procedure operator feedback, decrease in cine frame rate and run length, promote mapping and less angled views. However changing patient characteristics (increased obesity, prior CABG) and an increase in structural procedures warrant reexamination of practice. Procedure: The frequency of guideline excessive radiation exposure for cardiac cath lab patients was tracked over a 12 month period from 11/2016-11/2017. The frequency of radiation exposure exceeding our guidelines was tracked overall and by operator (16), and by specific intervention. Results: Over the one year period 10.2% of interventions [174/1,702] received excessive skin or total body radiation. Rates exceeding guidelines by operator ranged from 4% to 26% with operators performing structural or peripheral procedures having the highest patient radiation exposures. Structural procedures resulted in exposures as high as 123 min fluoro time, TAD of 226,385 cGray cm2, 11,129 mGray skin dose [patient had post procedure skin damage]. Factors associated with high radiation exposures included: increasing obesity, prior surgical revascularization, coronary anomalies, multivessel procedure or high complexity procedure, difficulties in imaging requiring highly angled or multiple views, procedural complications. Interventions associated with limiting exposure included: limiting fellow participation, decrease in frame rate, interim stills with limited cine runs. The default settings of X-Ray equipment have been modified to encourage safe and appropriate use of radiation in the cardiac catheterization lab (however, physician overide of settings is still possible). Conclusions: The changes made in imaging equipment and procedures 5 years ago have been largely sustained and have resulted in less frequent radiation outliers. Patient factors (e.g. obesity, prior CABG) complex and multivessel interventions, structural and vascular procedures are potentially frequently associated with excess radiation. Changing default settings and implementing radiation safety practices, including staging where appropriate, can significantly decrease radiation exposure to patients and staff. Cumulative and target organ specific patient doses of radiation should be tracked and more readily accessible in the patient’s medical record.

Electrocardiographic patterns and long-term training-induced time changes in 2484 elite football players

High-level physical training induces cardiac structural and functional changes, including 12-lead electrocardiogram modifications. The purpose of this cross-sectional longitudinal study was to establish a quantitative electrocardiographic profile in highly trained football players. Initial and serial annual electrocardiogram monitoring over subsequent years allowed us to investigate the long-term effects of exercise on cardiac conduction and electrophysiological remodelling. Between 2005 and 2015, serial evaluations, including 12-lead electrocardiograms, were performed in 2484 elite male football players from the French Professional Football League. A total of 6247 electrocardiograms were performed (mean 2.5±1.8 electrocardiograms/player). Heart rate (beats/min), atrioventricular delay (PR, ms), intraventricular conduction delay (QRS, ms), corrected QT delay (QTc) and electrical left ventricular hypertrophy (LVH) (Sokolow-Lyon index, mm) were measured, and the fixed effect of time was evaluated using panel data analysis (β [95% confidence interval] change between two visits). According to European Society of Cardiology and Seattle criteria, 15% of the electrocardiogram intervals were considered abnormal. We observed 17% sinus bradycardia<50 beats/min (mean heart rate 60±11 beats/min), 8% first-degree atrioventricular block>200ms (mean PR 170±27ms), 1.5% QRS>120ms (mean QRS 87±19ms) and 3% prolonged QT interval (mean QTc using Bazett's formula [QTcB] 395±42ms). Electrical LVH (mean Sokolow-Lyon index 34±10mm) was noted in 37% of players. Over time, electrocardiogram changes were noted, with a significant remodelling trend in terms of decreased heart rate (-0.41 [-0.55 to -0.26] beats/min), QRS duration (-2.4 [-2.7 to -2.1] ms) and QTcB delay (-1.2 [-1.9 to -0.5] ms) (all P<0.001). This study describes usual electrocardiographic training-induced changes in a large series of football players over the follow-up timeframe. The most frequent outliers were electrical LVH and sinus bradycardia. These results have important implications for optimizing electrocardiogram interval measurements in initial screening and during follow-up of football players, with potential cost-effective implications.

Femoral component rotations in different gap tensions in total knee arthroplasty: A prospective randomized controlled trial

BackgroundIn total knee arthroplasty (TKA), femoral component rotation is an important factor in the flexion stability and biomechanics of the patellofemoral joint. However, it remains unclear how much tension is appropriate when performing TKA using the gap technique. MethodsOne hundred fifty TKAs that used the gap technique were randomized into one of the three groups. Gravity group (n=50) included patients who underwent TKA using only the weight of the patient's lower leg. In 20-lbf group (n=50) and 30-lbf group (n=50), a gap-tensioning device was set at 20lbf and 30lbf respectively. The femoral component rotation was measured based on the clinical transepicondylar axis (cTEA) on postoperative CT and any outliers (a femoral component rotation diverging from the cTEA over 3°) were evaluated. ResultsThe mean femoral component rotation was −0.82°±2.44° (95% confidence interval [CI], −1.52° to −0.13°) in gravity group, −0.40°±2.22° (95% CI, −1.03° to 0.23°) in 20-lbf group, and 1.37°±2.70° (95% CI, 0.61° to 2.14°) in 30-lbf group. The mean femoral component rotation in 30-lbf group was significantly different from that in gravity group (p<0.001) and 20-lbf group (p<0.001). There were more outliers in 30-lbf group (18% in gravity group, 18% in 20-lbf group, and 36% in 30-lbf group; p=0.043). ConclusionsThe use of a tensioning device set at 30lbf resulted in an externally rotated femoral component and frequent outliers.

Ensembles of low degree archeomagnetic field models for the past three millennia

We introduce ensembles of time-varying archeomagnetic field models, consisting of a reference model, a mean model and a thousand individual models. We present a set of three such ensembles, built from archeomagnetic, volcanic and sedimentary data sets, that cover the past three millennia. These ensembles can be used to describe the field at any location from the core surface to the magnetosphere, and assess the way uncertainties due to the limited distribution and quality of the data affect any of its component or parameter, such as individual Gauss coefficients. They provide alternative – and, we argue, more complete – descriptions of the archeomagnetic field to those provided by previously published archeomagnetic field models, being better suited to existing and emerging needs, such as those of geomagnetic data assimilation. We present the data sets we rely on – essentially the same as those used by other recent archeomagnetic field models – and describe how errors affecting the data, and errors due to non-modelled small spatial scales of the field, are taken into account. We next explain our modeling strategy and motivation for building low degree spherical harmonic degree ensembles of models. We carry on a number of end-to-end simulations to both illustrate the usefulness of such ensembles and point at the type of errors one should expect. Practical illustrations of what can be done with these three ensembles of models, with examples of geomagnetic inferences, are also described. Northern high-latitude flux patches, for instance, appear to be the most robust features of all. These patches tend to fluctuate, but clearly have some favored locations, resulting in the same clear signature with three tongues (over Northern America, Europe and Asia) in the time-averaged field at the core-mantle boundary, similar to what had been found in earlier models. Inferences about the field behavior in the Southern hemisphere are more difficult to draw. Still, some suggestions that the well-known present South Atlantic reversed patch could have arisen as early as in 1500 A.D. are found in some of the ensembles. We otherwise confirm that most of the current archeomagnetic field model limitations are related to a number of sediment cores, identified as producing frequent outliers in the modeling process. We provide evidence that such cores are likely affected by timing errors and timing delays between magnetization lock-in and sediment deposition, that future more advanced treatment should be able to handle. All source files for the three ensembles of models, together with appropriate Matlab applications can be downloaded from http://geomag.ipgp.fr/download/ARCHEO_FM.zip.

Validation of six satellite-retrieved land surface emissivity products over two land cover types in a hyper-arid region

This study compares six satellite-retrieved land surface emissivity (LSE) products over gravel plains and sand dunes of the hyper-arid Namib desert in Namibia and validates them with in-situ measurements performed with the ‘emissivity box method’. The following products are compared: LSE derived by the Land Surface Analysis — Satellite Application Facility (LSA-SAF) for the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG), LSE products MOD11A2.C5, MOD11B1.C4.1, and MOD11B1.C5 derived for the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard EOS-Terra, LSE derived with the Temperature Emissivity Separation (TES) algorithm for the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) onboard EOS-Terra, and LSE derived with the TES algorithm for EOS-Terra/MODIS data. The LSA-SAF, MOD11A2.C5, and MOD11B1.C5 algorithms directly or indirectly utilize land cover classification and vegetation cover fraction data with the result that for arid regions their LSE are practically identical to the bare ground emissivities assigned to those classes. Over the gravel plains, mean LSA-SAF, ASTER-TES, and MODTES LSE are about 0.950 in the 11μm range, whereas mean MOD11A2.C5 and MOD11B1.C5 are about 1.5% (~1K) higher. The LSA-SAF algorithm misclassifies the sand dunes as ‘open & closed shrubland’, which results in an overestimated mean LSE (0.969). Since MOD11A2.C5 and MOD11B1.C5 utilize a similar classification and similar emissivity library data, their LSE estimates for the sand dunes are also too high (mean of 0.972 and 0.980, respectively). In contrast, the physics-based ASTER-TES and MODTES algorithms estimate mean sand dune LSE as 0.952 and 0.948, respectively. The physics-based MOD11B1.C4.1 algorithm produced noisy LSE estimates with frequent outliers at 5km resolution: spatial averaging yielded mean LSE of 0.950 and 0.954 for the gravel plains and the sand dunes, respectively. Based on a combined analysis of in-situ LSE and TES retrieved LSE, and also accounting for uncertainty in the fraction of dry grass (only gravel plains), for future work it is recommended to use SEVIRI ch10.8 emissivities of 0.941±0.004 for the sand dunes and 0.944±0.015 for the gravel plains, respectively. The results suggest that split window algorithms would benefit significantly from using physically based MODTES LSE.

Commonly performed procedures in clinical research: A benchmark for payment

Slow or insufficient enrollment in clinical research and a high demand for research participants raises questions about the need for and use of incentives to participate, including payment. Much of the available literature on payment to research participants focuses on ethical concerns, and rarely addresses guidelines, benchmarks, or formulas to assist investigators to assign or evaluate appropriate payment for individuals who take part in clinical research trials and procedures.Using four years of data collected about the inconvenience units assigned by intramural investigators to selected clinical research procedures conducted at the National Institutes of Health (NIH) Clinical Center, this study provides payment benchmarks for commonly performed procedures.Results were obtained from data collected on 36,273 incidents of payment made for procedures to research participants from August 2004 to August 2008. Analysis of the inconvenience units value assigned to specific procedures suggests that despite a wide distribution and frequent outliers, a convergence in practice around the center of distribution for most procedures does exist. As one of the first published studies reporting data reflecting payment amount for specific clinical research procedures, these data can guide investigators and institutional review boards as they establish and review an appropriate amount of payment to offer research participants. Our data may be useful in promoting payment standards for procedures, thereby complementing proposals or guidelines that advise payment calculations according to time and procedures.

Reducing the number of local linear models in neuro-fuzzy modeling: A split-and-merge clustering approach

In this paper, we propose a neuro-fuzzy modeling approach for identification of a generalized first-order Takagi–Sugeno (TS) fuzzy models with considerably low number of local linear models (LLMs). For this purpose, the main idea is to identify uneven linear operating regimes of a process by LLMs localized in rather flexible fuzzy subspaces (FSs). To this end, a split-and-merge clustering approach is introduced. Through the split procedure, a generalized TS model is identified whose LLMs are localized in simple FSs. Then, by merging these simple FSs, so to keep the generality of the model, more flexible FSs are produced to localize the LLMs. As an illustrative example, the proposed modeling method is utilized to approximate a benchmark nonlinear function. Moreover, the performance of our approach is compared with that of several well-known state-of-the-art approaches in identification of a steam generator. It is demonstrated that our identified model generalizes well, with considerable lower number of LLMs. Robustness of the proposed modeling method against additive noise and outliers is studied as well, where good resilience against noise and drop of performance in the face of frequent outliers is observed in prediction of Mackey–Glass time-series.

Smoothing delay jitter in networked control systems

Delay jitter is a critical factor that must be considered in many real-time applications that require accurate prediction of packet delivery times. Delay jitter can be smoothed by holding packets in a play-back buffer for a certain time called a play-back delay. In this paper, we extensively examine the new scheme that we have proposed in [1] to enhance the prediction of the play-back delay in Networked Control Systems (NCSs). Our newly proposed scheme differs from previous schemes by using the one way delay variation that we have studied its behavior by conducting extensive measurements between fifty strategically located wired and wireless connected hosts. The advantages of our scheme are that it predicts sudden delay spikes, provides a closer approximation to the round trip time (RTT), and exhibits less frequent outliers.

The weirdest people in the world?

Behavioral scientists routinely publish broad claims about human psychology and behavior in the world's top journals based on samples drawn entirely from Western, Educated, Industrialized, Rich, and Democratic (WEIRD) societies. Researchers - often implicitly - assume that either there is little variation across human populations, or that these "standard subjects" are as representative of the species as any other population. Are these assumptions justified? Here, our review of the comparative database from across the behavioral sciences suggests both that there is substantial variability in experimental results across populations and that WEIRD subjects are particularly unusual compared with the rest of the species - frequent outliers. The domains reviewed include visual perception, fairness, cooperation, spatial reasoning, categorization and inferential induction, moral reasoning, reasoning styles, self-concepts and related motivations, and the heritability of IQ. The findings suggest that members of WEIRD societies, including young children, are among the least representative populations one could find for generalizing about humans. Many of these findings involve domains that are associated with fundamental aspects of psychology, motivation, and behavior - hence, there are no obvious a priori grounds for claiming that a particular behavioral phenomenon is universal based on sampling from a single subpopulation. Overall, these empirical patterns suggests that we need to be less cavalier in addressing questions of human nature on the basis of data drawn from this particularly thin, and rather unusual, slice of humanity. We close by proposing ways to structurally re-organize the behavioral sciences to best tackle these challenges.

The Weirdest People in the World?

Behavioral scientists routinely publish broad claims about human psychology and behavior in the world’s top journals based on samples drawn entirely from Western, Educated, Industrialized, Rich and Democratic (WEIRD) societies. Researchers - often implicitly - assume that either there is little variation across human populations, or that these “standard subjects” are as representative of the species as any other population. Are these assumptions justified? Here, our review of the comparative database from across the behavioral sciences suggests both that there is substantial variability in experimental results across populations and that WEIRD subjects are particularly unusual compared with the rest of the species - frequent outliers. The domains reviewed include visual perception, fairness, cooperation, spatial reasoning, categorization and inferential induction, moral reasoning, reasoning styles, self-concepts and related motivations, and the heritability of IQ. The findings suggest that members of WEIRD societies, including young children, are among the least representative populations one could find for generalizing about humans. Many of these findings involve domains that are associated with fundamental aspects of psychology, motivation, and behavior - hence, there are no obvious a priori grounds for claiming that a particular behavioral phenomenon is universal based on sampling from a single subpopulation. Overall, these empirical patterns suggests that we need to be less cavalier in addressing questions of human nature on the basis of data drawn from this particularly thin, and rather unusual, slice of humanity. We close by proposing ways to structurally re‐organize the behavioral sciences to best tackle these challenges.

Accuracy of Whole Blood Glucose Measurement When Venous Catheter Blood Samples Are Used on Glucose Meters

Previous studies have found positive bias and frequent outliers when central venous catheter (CVC) whole blood is used to dose glucose meters. We designed a study to determine whether positive bias and outliers with CVC whole blood glucose samples are due to exogenous glucose contamination of CVC samples, inherent bias and imprecision of glucose meters, or properties of CVC whole blood that interfere with the function of some glucose meters. We studied the relationship between venous whole blood and venous plasma glucose drawn by venipuncture to CVC whole blood and CVC plasma glucose in 50 hospitalized patients. In 27 patients whole blood glucose was measured on both the Accu-Chek Inform (Roche Diagnostics, Indianapolis, IN) and StatStrip (Nova Biomedical, Waltham, MA). By comparing CVC plasma to venous plasma glucose, we determined that contamination of CVC samples with exogenous glucose was uncommon. On the Inform meter outliers were approximately twice as common with CVC whole blood compared to venous whole blood. In 27 patients who had CVC whole blood analyzed by both Inform and StatStrip, outliers occurred approximately twice as often on the Inform compared to the StatStrip. Accounting for CVC samples contaminated with exogenous glucose, outliers on the StatStrip did not occur significantly more often using CVC whole blood compared to venous whole blood. Properties unique to CVC whole blood differentially affect glucose meter bias and imprecision. Device selection is critical in practices that wish to use CVC whole blood to monitor glucose concentration in hospitalized patients.