True Bias Research Articles

On the ability of the LR method to detect bias when there is pedigree misspecification and lack of connectedness

BackgroundCross-validation techniques in genetic evaluations encounter limitations due to the unobservable nature of breeding values and the challenge of validating estimated breeding values (EBVs) against pre-corrected phenotypes, challenges which the Linear Regression (LR) method addresses as an alternative. Furthermore, beef cattle genetic evaluation programs confront challenges with connectedness among herds and pedigree errors. The objective of this work was to evaluate the LR method's performance under pedigree errors and weak connectedness typical in beef cattle genetic evaluations, through simulation.MethodsWe simulated a beef cattle population resembling the Argentinean Brangus, including a quantitative trait selected over six pseudo-generations with a heritability of 0.4. This study considered various scenarios, including: 25% and 40% pedigree errors (PE-25 and PE-40), weak and strong connectedness among herds (WCO and SCO, respectively), and a benchmark scenario (BEN) with complete pedigree and optimal herd connections.ResultsOver six pseudo-generations of selection, genetic gain was simulated to be under- and over-estimated in PE-40 and WCO, respectively, contrary to the BEN scenario which was unbiased. In genetic evaluations with PE-25 and PE-40, true biases of − 0.13 and − 0.18 genetic standard deviations were simulated, respectively. In the BEN scenario, the LR method accurately estimated bias, however, in PE-25 and PE-40 scenarios, it overestimated biases by 0.17 and 0.25 genetic standard deviations, respectively. In herds facing WCO, significant true bias due to confounding environmental and genetic effects was simulated, and the corresponding LR statistic failed to accurately estimate the magnitude and direction of this bias. On average, true dispersion values were close to one for BEN, PE-40, SCO and WCO, showing no significant inflation or deflation, and the values were accurately estimated by LR. However, PE-25 exhibited inflation of EBVs and was slightly underestimated by LR. Accuracies and reliabilities showed good agreement between true and LR estimated values for the scenarios evaluated.ConclusionsThe LR method demonstrated limitations in identifying biases induced by incomplete pedigrees, including scenarios with as much as 40% pedigree errors, or lack of connectedness, but it was effective in assessing dispersion, and population accuracies and reliabilities even in the challenging scenarios addressed.

A study of referral bias in NMOSD and MOGAD cohorts

BackgroundNeuromyelitis optica spectrum disorder (NMOSD) and myelin oligodendrocyte glycoprotein antibody–associated disease (MOGAD) are rare disorders often seen in highly specialized services or tertiary centres. We aimed to assess if cohort characteristics depend on the origin of the referral catchment areas serviced by our centre (i.e. local, regional or national). MethodsRetrospective cohort study using a national referral service database including local (Oxfordshire), regional (Oxfordshire and neighbouring counties), and national patients. We included patients with the diagnosis of NMOSD, seronegative NMOSD or MOGAD, followed at the Oxford Neuromyelitis Optica Service. ResultsWe included 720 patients (331 with MOGAD, 333 with aquaporin-4 antibody (AQP4)-NMOSD, and 56 with seronegative NMOSD. The distribution of diagnoses was similar across referral cohorts. There were no significant differences in the proportion of pediatric onset patients, sex, or onset phenotype; more White AQP4-NMOSD patients were present in the local than in the national cohort (81 % vs 52 %). Despite no differences in follow-up time, more relapsing MOGAD disease was present in the national than in the local cohort (42.9 % vs. 24 %, p = 0.029). ConclusionThis is the first study assessing the impact of potential referral bias in cohorts of NMOSD or MOGAD. The racial difference in the AQP4-NMOSD cohorts likely reflects the variation in the population demographics rather than a referral bias. The over representation of relapsing MOGAD patients in the national cohort probably is a true referral bias and highlights the need to analyze incident cohorts when describing disease course and prognosis. It seems reasonable therefore to compare MOGAD and NMOSD patients seen withing specialised centres to general neurology services, provided both use similar antibody assays.

Risk estimation in relation to anxiety and depression for low probability negative events

Foundational cognitive models propose that people with anxiety and depression show risk estimation bias, but most literature does not compute true risk estimation bias by comparing people's subjective risk estimates to their individualized reality (i.e., person-level objective risk). In a diverse community sample (N = 319), we calculated risk estimation bias by comparing people's subjective risk estimates for contracting COVID-19 to their individualized objective risk. Person-level objective risk was consistently low and did not differ across symptom levels, suggesting that for low probability negative events, people with greater symptoms show risk estimation bias that is driven by subjective risk estimates. Greater levels of anxiety, depression, and COVID-specific perseverative cognition separately predicted higher subjective risk estimates. In a model including COVID-specific perseverative cognition alongside anxiety and depression scores, the only significant predictor of subjective risk estimates was COVID-specific perseverative cognition, indicating that symptoms more closely tied to feared outcomes may more strongly influence risk estimation. Finally, subjective risk estimates predicted information-seeking behavior and eating when anxious, but did not significantly predict alcohol or marijuana use, drinking to cope, or information avoidance. Implications for clinical practitioners and future research are discussed.

A-003 Point of Care BNP Values Obtained by the Abbott i-STAT are not Interchangeable With Laboratory Determined Values Using Beckman DXI 800 Analyzer

Abstract Background B-type natriuretic peptide (BNP) is used as a biomarker of heart failure. In our hospital, point of care (POCT) BNP test is performed on whole blood using the i-STAT (Abbott, Abbott Park, IL) while in clinical laboratory, the DXI 800 analyzer is used (Beckman, Brea, CA) for determination of BNP in EDTA plasma. Sometimes clinicians interchangeably use POCT BNP with laboratory BNP test for monitoring heart failure patients, but values are sometimes significantly different causing diagnostic confusion. We compared POCT BNP values in 88 patients with laboratory-based BNP values analyzed between less than one hour to less than 12 h. In addition, 12 specimens were simultaneously analyzed for POCT BNP and lab-based BNP. No additional blood was drawn from patients and medical record was not accessed. The study had exempt status: by the IRB of University of Kansas Medical Center. Methods POCT BNP values were obtained using the i-STAT analyzer following manufacturer’s protocol. It is a two-site enzyme-linked immunosorbent assay (ELISA) with an analytical measurement range of 15–5000 pg/mL. In the laboratory BNP concentrations were obtained using the DXI 800 analyzer. It is also a sandwich immunoassay with the analytical measurement range of 5–5000 pg/mL. Results Overall comparison using lab-based BNP as x-axis and POCT BNP as y-axis produced the following regression equation; y = 1.48 x − 23.45 (n = 88, r = 0.96) indicating that although POCT BNP values correlated well with lab-based BNP values, POCT values showed an average 48% positive bias. When specimens analyzed within 4 h were compared, the regression equation was: y = 1.40 x + 74.12 (n = 20, r = 91) and when specimens analyzed within 1 h were compared, the regression equation was: y = 1.56 + 59.0 (n = 23, r = 0.97). In contrast, when 12 specimens were analyzed simultaneously using POCT and lab-based BNP, using x-axis as the lab-based BNP and y-axis as the POCT BNP, the following regression equation was observed: y = 1.83 x − 102.8 (n = 12, r = 0.99). Conclusion Higher positive bias in specimens analyzed simultaneously indicates true positive bias with POCT BNP compared to lab-based BNP because lab-based BNP in real patients were ordered later after POCT BNP. To our knowledge i-STAT BNP values were never compared with DXI 800 BNP values. We communicated with our clinicians not to use lab-based BNP test and POCT BNP in monitoring patients being treated for heart failure interchangeably. POCT BNP can be used for initial screening to identify potential patients with heart failure.

Novel measurement of fabric bending anisotropy

PurposeUnder the widely used testing methods for fabric bending behavior, only one result for one direction can be obtained by using one fabric which is low efficient. To obtain fabric bending anisotropy, it is necessary to conduct a great many testing experiments. The purpose of this paper is to investigate a novel, efficient and visual method that can measure fabric bending anisotropy.Design/methodology/approachFabrics are first cut into special shapes with eight strips including four directions, 0°(warp direction), 90°(weft direction), 45 and 135°(true bias), then are put onto the self-designed instrument. After that a camera is used to take picture from the right above the prism. New parameters, projection area, projection length, projection length, falling height and falling index (S, L, H and I in short) are obtained. Furthermore, standard deviation of them (SDS, SDL, SDH and SDI in short) are extracted for the characterization of bending anisotropy.FindingsResults show that the new method has good feasibility and S, L and I can be used to express fabric bending property well. Of all the four new parameters, SDL has the highest correlation with SD of bending length, SDS the second and SDH the third. That is, SDL can characterize bending anisotropy best. Taken convenience of data acquisition and correlation into consideration, bending length L is the best parameter. Average L and SDL in four directions can be combined to express the comprehensive bending behavior of fabrics.Originality/valueThe new method can measure and characterize both the fabric bending property and bending anisotropy. Besides its high efficiency, it can display fabric bending or bending anisotropy visually and directly.

A True Residual Bias: Did the Exacerbation Truly Precede NSAID Exposure?

A True Residual Bias: Did the Exacerbation Truly Precede NSAID Exposure?

Accurate Fairness: Improving Individual Fairness without Trading Accuracy

Accuracy and individual fairness are both crucial for trustworthy machine learning, but these two aspects are often incompatible with each other so that enhancing one aspect may sacrifice the other inevitably with side effects of true bias or false fairness. We propose in this paper a new fairness criterion, accurate fairness, to align individual fairness with accuracy. Informally, it requires the treatments of an individual and the individual's similar counterparts to conform to a uniform target, i.e., the ground truth of the individual. We prove that accurate fairness also implies typical group fairness criteria over a union of similar sub-populations. We then present a Siamese fairness in-processing approach to minimize the accuracy and fairness losses of a machine learning model under the accurate fairness constraints. To the best of our knowledge, this is the first time that a Siamese approach is adapted for bias mitigation. We also propose fairness confusion matrix-based metrics, fair-precision, fair-recall, and fair-F1 score, to quantify a trade-off between accuracy and individual fairness. Comparative case studies with popular fairness datasets show that our Siamese fairness approach can achieve on average 1.02%-8.78% higher individual fairness (in terms of fairness through awareness) and 8.38%-13.69% higher accuracy, as well as 10.09%-20.57% higher true fair rate, and 5.43%-10.01% higher fair-F1 score, than the state-of-the-art bias mitigation techniques. This demonstrates that our Siamese fairness approach can indeed improve individual fairness without trading accuracy. Finally, the accurate fairness criterion and Siamese fairness approach are applied to mitigate the possible service discrimination with a real Ctrip dataset, by on average fairly serving 112.33% more customers (specifically, 81.29% more customers in an accurately fair way) than baseline models.

Discerning media bias within a network of political allies and opponents: Disruption by partisans

An individual’s opinions about media bias derive from their own independent assessment of media outputs combined with peer pressure from networked political allies and opponents. Here we generalize previous idealized, probabilistic models of the perception formation process, based on a network of Bayesian learners inferring the bias of a coin, by introducing obdurate agents (partisans), whose opinions stay fixed. It is found that even one partisan destabilizes an allies-only network, stopping it from achieving asymptotic learning and forcing persuadable agents to vacillate indefinitely (turbulent nonconvergence) between the true coin bias θ0 and the partisan’s belief θp. The dwell time td at the partisan’s belief increases, as the partisan fraction f increases, and decreases, when multiple partisans disagree amongst themselves. In opponents-only networks, asymptotic learning occurs, whether or not partisans are present. However, the counterintuitive tendency to reach wrong conclusions first, identified in previous work with zero partisans, does not persist in general for θ0≠θp in complete networks; it is a property of sparsely connected systems (e.g. Barabási–Albert networks with attachment parameter ≲10). In mixed networks containing allies and opponents, partisans drive counterintuitive outcomes, which depend sensitively, on where they reside. A strongly balanced triad exhibits intermittency with a partisan (sudden transitions between long intervals of static beliefs and turbulent nonconvergence) and asymptotic learning without a partisan. Counterintuitively, in an unbalanced triad, one of the persuadable agents achieves asymptotic learning at θ0, when the partisan is located favorably, but is driven away from θ0, when there is no partisan. The above results are interpreted briefly in terms of the social science theory of structural balance.

Circular dichroism assessment of an imidazole antifungal drug with plant based silver nanoparticles: Quantitative and DFT analysis

Circular dichroism (CD) methods have been developed for the analysis of luliconazole (LUC) using plant based silver nanoparticles (P-AgNPs). Cleaner and natural approach have found significant attention in recent times owing to their exceptional physicochemical characteristics. Utilizing FTIR, SEM, and XRD, the produced nanoparticles were analyzed. The produced P-AgNPs were then used to assay LUC in formulation drugs. Four CD methods are developed as zero order and second order derivative methods. Methods I and II are based on a normal CD scan (zero order) that produced calibration range from 2 - 16 μgmL-1 at 232 nm (positive band) and 299 nm (negative band), respectively. Methods III and IV are the second order derivative methods that are developed at 232 nm (negative band) and at 251 nm (positive band). Density functional theory study was done to comprehend the feasibility of the developed methods and to optimize the structure and energy gap that validated the experimental procedure. The LUC assay methods using the proposed CD approach are simple, sensitive and precise with a limit of detection for methods I, II, III and IV of 0.527, 0.428, 0.250 and 0.30 μgmL-1 and limit of quantification of 1.75, 1.42, 0.833 and 1.0 μgmL-1, respectively. For intra- and inter-day precision, the recovery data ranged from 99.48 to 101% and 99.37 to 101%, respectively. The methods were used in dosage forms that produced a relative standard deviation of less than 2% and the true bias (θL and θU) within ±2%, demonstrating the potential use of the developed methods.

Evaluating assumptions behind design-based estimators for unreported catches

Understanding a fishery’s impact on the marine ecosystem requires a quantification of total catches, which include unreported catches. For recent years in Norwegian waters, unreported catches have been estimated using data collected by the Norwegian Reference Fleet, a fisher self-sampling programme that regularly gathers data on catches of all species (including unwanted bycatches and discards). In this study, we focused on the use of design-based estimators for total catches in offshore fisheries, which have previously been used to estimate discards in the Norwegian coastal gillnet fisheries. After adapting the current methodology to the data available in offshore fisheries, we explored the assumptions behind both unit- and ratio-based estimators, and the effect of ignoring the cluster sampling design. Using a jack-knife resampling method to estimate the true bias in estimates of total catches and associated variability, we found that ignoring the cluster sampling design tended to underestimate the variability, which lead to occurrences where unreported catches were statistically detected when in fact there was too much uncertainty to make such a conclusion. Further validations suggested the cluster unit estimator is not unbiased as theoretically expected due to the sampling design favouring the selection of more active vessels. We therefore concluded that the unit and ratio cluster estimators are applied and compared, as per best practices.

Is chewable toothbrush effective in terms of dental plaque reduction in children? A systematic review and meta-analysis of randomized controlled trials.

To systematically evaluate randomized controlled trials (RCTs) on whether a chewable toothbrush (CTB) is more effective than a manual toothbrush (MTB) in terms of full-mouth dental plaque reduction in non-orthodontic children. Six databases were searched by two independent reviewers according to pre-specified eligibility criteria up to October 2022. No restrictions regarding language, date of publication and minimum follow-up period were imposed. The Cochrane Collaboration's Risk of Bias tool (RoB 2.0) was used for quality appraisal and GRADE for assessing the certainty of evidence. Random-effects pairwise meta-analysis compared the dental plaque change scores of CTB and MTB through mean differences (MDs) and associated confidence intervals (95% CI), and sensitivity analysis determined whether an individual study significantly affected the results. Seven eligible RCTs were retrieved, including data of 310 children aged 8-14 years. Five RCTs present some concerns and two have high RoB. No significant difference was detected between toothbrushes in terms of Turesky modification of the Quigley-Hein plaque index reduction (MD=-0.04, 95% CI: -0.26-0.18), with overall very low certainty on evidence. The pooled estimate remains unchanged when any RCT is omitted. There is very low-certainty evidence of no significant difference on plaque removal between MTB and CTB in non-orthodontic children. Due to unexplained high heterogeneity and low methodological quality of RCTs, we cannot determine the extent to which our finding reflects a true effect or bias arising from limitations of primary studies.

Dark triad traits are associated with a weaker morally-good true self bias in self-perceptions

ABSTRACT We examined whether Dark Triad (DT) traits moderate people’s tendency to associate moral traits with their true self. We hypothesized that people high in DT traits would show a weaker tendency to view moral (vs. immoral) characteristics as central to their identity. Undergraduate participants (N = 345) rated the perceived identity centrality of positive/negative traits in domains of morality/competence, and completed measures of psychopathy, narcissism, and Machiavellianism. Positive moral (vs. immoral and positive competence) traits were seen as more identity central overall, but this effect was weaker among participants high in DT traits. Further, all DT traits negatively (positively) predicted the identity centrality of moral (immoral) traits. These findings extend work on true self-perceptions and moral identity in the Dark Triad.

Deep IMU Bias Inference for Robust Visual-Inertial Odometry With Factor Graphs

Visual Inertial Odometry (VIO) is one of the most established state estimation methods for mobile platforms. However, when visual tracking fails, VIO algorithms quickly diverge due to rapid error accumulation during inertial data integration. This error is typically modeled as a combination of additive Gaussian noise and a slowly changing bias which evolves as a random walk. In this work, we propose to train a neural network to learn the true bias evolution. We implement and compare two common sequential deep learning architectures: LSTMs and Transformers. Our approach follows from recent learning-based inertial estimators, but, instead of learning a motion model, we target IMU bias explicitly, which allows us to generalize to locomotion patterns unseen in training. We show that our proposed method improves state estimation in visually challenging situations across a wide range of motions by quadrupedal robots, walking humans, and drones. Our experiments show an average 15% reduction in drift rate, with much larger reductions when there is total vision failure. Importantly, we also demonstrate that models trained with one locomotion pattern (human walking) can be applied to another (quadruped robot trotting) without retraining.

Evaluating Interference of Lipemia on Routine Clinical Biochemical Tests.

Objective Lipemia is an important cause of preanalytical errors in laboratory results. They affect the specimen integrity and trustworthiness of laboratory results. The present study was to assess the impact of lipemia on routine clinical chemistry analytes. Methods Anonymous leftover serum samples with normal levels of routine biochemical parameters were pooled. Twenty such pooled serum samples were used for the study. The samples were spiked with commercially available intralipid solution (20%) to produce lipemic concentrations of 0, 400 (mild, 20 μL), 1,000 (moderate, 50 μL), and 2,000 mg/dL (severe, 100 μL). Glucose, renal function test, electrolytes, and liver function test were estimated in all the samples. Baseline data without the effect of interference was considered as true value and percentage bias for the spiked samples was calculated. Interference was considered significant if the interference bias percentage exceeded 10%. Result Parameters like glucose, urea, creatinine, direct bilirubin, sodium, potassium, and chloride showed negative interference at mild and moderate lipemic concentration and positive interference at severe lipemic concentration. Parameters like aspartate transaminase (AST) and alanine transaminase (ALT) showed negative interference at mild and positive interference at moderate and severe lipemic concentration. Whereas uric acid, total protein, albumin, total bilirubin, alkaline phosphatase, gamma-glutamyl transferase, calcium, magnesium, and phosphorous showed positive interference at all concentrations. Significant interference (> 10%) was shown for magnesium (mild lipemia), albumin, direct bilirubin, ALT, and AST at moderate lipemic concentration. All parameters showed significant interference at severe lipemic concentration. Conclusion All the study parameters are affected by lipemic interference at varying levels. Laboratory-specific data regarding lipemic interference at various concentrations on the clinical biochemistry parameters is needed.

Sensitivity of spin-aligned searches for neutron star-black hole systems using future detectors

Current searches for gravitational waves from compact-binary objects are primarily designed to detect the dominant gravitational-wave mode and assume that the binary components have spins which are aligned with the orbital angular momentum. These choices lead to observational biases in the observed distribution of sources. Sources with significant spin-orbit precession or unequal-mass-ratios, which have non-negligible contributions from sub-dominant gravitational-wave modes, may be missed; in particular, this may significantly suppress or bias the observed neutron star -- black hole (NSBH) population. We simulate a fiducial population of NSBH mergers and determine the impact of using searches that only account for the dominant-mode and aligned spin. We compare the impact for the Advanced LIGO design, A+, LIGO Voyager, and Cosmic Explorer observatories. We find that for a fiducial population where the spin distribution is isotropic in orientation and uniform in magnitude, we will miss $\sim 25\%$ of sources with mass-ratio $q > 6$ and up to $\sim 60 \%$ of highly precessing sources $(\chi_p > 0.5)$, after accounting for the approximate increase in background. In practice, the true observational bias can be even larger due to strict signal-consistency tests applied in searches. The observation of low spin, unequal-mass-ratio sources by Advanced LIGO design and Advanced Virgo may in part be due to these selection effects. The development of a search sensitive to high mass-ratio, precessing sources may allow the detection of new binaries whose spin properties would provide key insights into the formation and astrophysics of compact objects.

Fast and accurate isotope dilution mass spectrometry method for the assignment of trihalomethanes in water in an uncommon accuracy-based proficiency testing programme in Singapore

ABSTRACT In the light of the instability of trihalomethanes (THMs) in water, a fast and high accuracy method based on headspace gas chromatography-isotope dilution mass spectrometry (HS-GC-IDMS) technique was developed. The optimised method was validated using fortified water samples. Recoveries between 96.1% and 108.8% with an intermediate precision between 3.9% and 5.7% were achieved. The method was applied in the value assignment of THMs in water in an accuracy-based proficiency testing (PT) programme organised by the Health Sciences Authority (HSA) Singapore. The assigned values obtained for chloroform, bromodichloromethane, dibromochloromethane and bromoform were 73.6 ± 9.3 µg/L, 49.1 ± 5.2 µg/L, 47.7 ± 4.4 µg/L and 70.8 ± 6.8 µg/L, respectively. The PT programme was especially valuable and uncommon as it allowed local water testing laboratories to have access to traceable reference values for the benchmarking of their technical competencies. The procedure undertaken by the Laboratory to prepare a batch of PT sample comprising the unstable analytes, conduct homogeneity and stability testing, as well as value assignment, are presented. The between-samples relative standard deviations (RSDs) obtained in a feasibility batch of THMs concentrate in methanol were improved by four to six times compared to those from the first feasibility batch of THMs in water. Based on z-scores, evaluated using the assigned reference value determined by HS-GC-IDMS, 85% of the participating laboratories in the PT programme achieved satisfactory results. The provision of metrological assigned values heightened the testing laboratories’ awareness on the importance of accuracy-based PT programmes for evaluation of true method biases, which is particularly valuable for analytes with regulated limits.

Jointly estimating bias field and reconstructing uniform MRI image by deep learning

Bias field is one of the main artifacts that degrade the quality of magnetic resonance images. It introduces intensity inhomogeneity and affects image analysis such as segmentation. In this work, we proposed a deep learning approach to jointly estimate bias field and reconstruct uniform image. By modeling the quality degradation process as the product of a spatially varying field and a uniform image, the network was trained on 800 images with true bias fields from 12 healthy subjects. A network structure of bias field estimation and uniform image reconstruction was designed to compensate for the intensity loss. To further evaluate the benefit of bias field correction, a quantitative analysis was made on image segmentation. Experimental results show that the proposed BFCNet improves the image uniformity by 8.3% and 10.1%, the segmentation accuracy by 4.1% and 6.8% on white and grey matter in T2-weighted brain images. Moreover, BFCNet outperforms the state-of-the-art traditional methods and deep learning methods on estimating bias field and preserving image structure, and BFCNet is robust to different levels of bias field and noise.

Association between fish oil supplements use and serum per- and polyfluoroalkyl substances (PFAS): Results from the National Health and Nutrition Examination Survey

BackgroundPer- and polyfluoroalkyl substances (PFAS) are widespread pollutants and classified as potentially carcinogenic to humans. Although consumption of fish, seafood, and their byproducts is a known source of dietary PFAS exposure, little is known about the association between use of fish oil supplements and PFAS. Here, we examine associations between fish oil supplement use and serum PFAS concentrations. MethodsThis analysis includes adults, ages 25 years of age and older, surveyed as part of the National Health and Nutrition Examinations Survey (NHANES). Outcomes include five serum PFAS compounds: perfluorooctane sulfonic acid (PFOS), perfluorooctanoic acid (PFOA), perfluorononanoic acid (PFNA), perfluorohexane sulphonic acid (PFHxS) and perfluorodecanoic acid (PFDA). To determine the association between fish oil use and log-transformed PFAS concentrations, survey-weighted linear regression was used to estimate multivariate-adjusted ratios between supplement-users’ and non-users’ geometric mean serum PFAS concentrations. ResultsNo association was observed between fish oil use and PFAS. While results did not vary substantially by age, gender, study cycle, there was some indication of a potential inverse association in subgroups of interest. Specifically, an inverse association was observed between fish oil supplement use and PFOS levels in older adults, females, and in early calendar years; an inverse association was also observed between fish oil and PFNA in females and early calendar years. ConclusionsWhile fish oil users did not experience increased serum PFAS, there was an unexpected inverse association in some population subgroups. Further research will be needed to better understand whether this pattern reflects true differences, chance, or bias.

Medical Statistics as a Foundation of Biomedical Research

Biomedical research commonly deals with measured values in humans, laboratory animals, and cells. These collections of measured variables, termed data, are analyzed to achieve scientific objectives. In this review, we present an overview of medical statistics, including its concepts, methods, and tips, for biomedical research applications. We first introduce various concepts of medical statistics, such as true values (that would never be seen in the real world), measured values (actual measurements), bias, and random error. Medical statistics deals with the removal of bias and control of random errors to generate scientific evidence. As a statistical method, data description (or data visualization) is the first step that analysts use to examine data. Data descriptions reveal the patterns, trends, and relationships of the variables. Subsequently, data are analyzed using interval estimation (95% confidence intervals) and statistical hypothesis testing to guide conclusions. Herein, we address the limitations of hypothesis testing and the advantages of using 95% confidence intervals. Moreover, we provide a summary of multiple comparison procedures that allow the simultaneous testing of two or more null hypotheses.

Online Three-Axis Magnetometer Hard-Iron and Soft-Iron Bias and Angular Velocity Sensor Bias Estimation Using Angular Velocity Sensors for Improved Dynamic Heading Accuracy

This article addresses the problem of dynamic online estimation and compensation of hard-iron and soft-iron biases of three-axis magnetometers under dynamic motion in field robotics, utilizing only biased measurements from a three-axis magnetometer and a three-axis angular rate sensor. The proposed magnetometer and angular velocity bias estimator (MAVBE) utilizes a 15-state process model encoding the nonlinear process dynamics for the magnetometer signal subject to angular velocity excursions, while simultaneously estimating nine magnetometer bias parameters and three angular rate sensor bias parameters, within an extended Kalman filter framework. Bias parameter local observability is numerically evaluated. The bias-compensated signals, together with three-axis accelerometer signals, are utilized to estimate bias-compensated magnetic geodetic heading. Performance of the proposed MAVBE method is evaluated in comparison to the widely cited magnetometer-only TWOSTEP method in numerical simulations, laboratory experiments, and full-scale field trials of an instrumented autonomous underwater vehicle in the Chesapeake Bay, Maryland, USA. For the proposed MAVBE, (i) instrument attitude is not required to estimate biases, and the results show that (ii) the biases are locally observable, (iii) the bias estimates converge rapidly to true bias parameters, (iv) only modest instrument excitation is required for bias estimate convergence, and (v) compensation for magnetometer hard-iron and soft-iron biases dramatically improves dynamic heading estimation accuracy.