Reduction In Error Variance Research Articles

DigiSpan: Development and Evaluation of a Computer-Based, Adaptive Test of Short-Term Memory and Working Memory.

This article describes DigiSpan, a new computer-controlled auditory test of forward and reverse digit span, designed to be administered by clinicians, and presents normative and test-retest reliability data for adults. DigiSpan mimics conventional live-voice tests in that it commences with trials that ascend in length until a stopping criterion is met, giving rise to a conventional scaled score. It then administers five additional adaptive trials, the length of which depends on the correctness of the response to the previous trial. Each of these two segments of the measurement gives rise to a scaled score. The ascending and adaptive scores are averaged to give an overall score and subtracted to produce an internal measure of consistency, and hence reliability. Young adults with an Mage of 25 years (N = 163) were tested, of whom 65 were retested on a separate day. The scaled scores from the conventional ascending trials were highly consistent with existing normative data based on live-voice tests. Combination of the conventional scaled score with a scaled score based on the adaptive trials led to 44% reduction in error variance for forward memory span and 20% reduction for reverse memory span. The average of these (32%) is similar to but (insignificantly) less than the 42% reduction in error variance that can be predicted based on adding the five adaptive trials. Replacing live-voice production of digits by a clinician with recorded, computer-controlled production has not affected the difficulty of the test. Adding five additional trials around the sequence length that a test participant can just remember has produced a decrease in measurement error. In addition, the availability of separate scaled scores for the ascending and adaptive phases enables the reliability of the combined score to be checked, for both forward and reverse measurements. The combination of standardized delivery, increased accuracy, internal reliability check, and fast automated scoring makes the test highly suitable for clinical use.

Research on Inter-Frame Feature Mismatch Removal Method of VSLAM in Dynamic Scenes

Visual Simultaneous Localization and Mapping (VSLAM) estimates the robot’s pose in three-dimensional space by analyzing the depth variations of inter-frame feature points. Inter-frame feature point mismatches can lead to tracking failure, impacting the accuracy of the mobile robot’s self-localization and mapping. This paper proposes a method for removing mismatches of image features in dynamic scenes in visual SLAM. First, the Grid-based Motion Statistics (GMS) method was introduced for fast coarse screening of mismatched image features. Second, an Adaptive Error Threshold RANSAC (ATRANSAC) method, determined by the internal matching rate, was proposed to improve the accuracy of removing mismatched image features in dynamic and static scenes. Third, the GMS-ATRANSAC method was tested for removing mismatched image features, and experimental results showed that GMS-ATRANSAC can remove mismatches of image features on moving objects. It achieved an average error reduction of 29.4% and 32.9% compared to RANSAC and GMS-RANSAC, with a corresponding reduction in error variance of 63.9% and 58.0%, respectively. The processing time was reduced by 78.3% and 38%, respectively. Finally, the effectiveness of inter-frame feature mismatch removal in the initialization thread of ORB-SLAM2 and the tracking thread of ORB-SLAM3 was verified for the proposed algorithm.

A nonlinear data-driven approach to bias correction of XCO2 for NASA's OCO-2 ACOS version 10

Abstract. Measurements of column-averaged dry air mole fraction of CO2 (termed XCO2) from the Orbiting Carbon Observatory-2 (OCO-2) contain systematic errors and regional-scale biases, often induced by forward model error or nonlinearity in the retrieval. Operationally, these biases are corrected for by a multiple linear regression model fit to co-retrieved variables that are highly correlated with XCO2 error. The operational bias correction is fit in tandem with a hand-tuned quality filter which limits error variance and reduces the regime of interaction between state variables and error to one that is largely linear. While the operational correction and filter are successful in reducing biases in retrievals, they do not allow for throughput or correction of data in which biases become nonlinear in predictors or features. In this paper, we demonstrate a clear improvement in the reduction in error variance over the operational correction by using a set of nonlinear machine learning models, one for land and one for ocean soundings. We further illustrate how the operational quality filter can be relaxed when used in conjunction with a nonlinear bias correction, which allows for an increase in sounding throughput by 14 % while maintaining the residual error in the operational correction. The method can readily be applied to future Atmospheric CO2 Observations from Space (ACOS) algorithm updates, to OCO-2's companion instrument OCO-3, and to other retrieved atmospheric state variables of interest.

Improved monitoring of cryptogam gas-exchange and volatile emissions during desiccation-rehydration cycles with a within-chamber hydration method

Desiccation-rehydration studies in cryptogams constitute an important tool to understand the relation of key physiological traits with species stress tolerance and environmental adaptability. Real-time monitoring of responses has been limited by the design of commercial or custom measuring cuvettes and difficulties in experimental manipulation. We developed a within-chamber rehydration method that allows to rewater the samples rapidly, without the need to open the chamber and take out the sample for manual rehydration by the investigator. Data is collected in real-time and simultaneously with an infrared gas-analyzer (LICOR-7000), a chlorophyll fluorometer (Maxi Imaging-PAM) and a proton transfer reaction time-of-flight mass-spectrometer (PTR-TOF-MS) for volatile organic compound emissions. The system was tested on four cryptogam species with contrasting ecological distributions. No major errors or kinetics disruptions were found during system testing and measurements. Our within-chamber rehydration method improved accuracy, as measurement periods were not lacking, and repeatability of the protocol by reducing error variance in sample manipulation. This method provides an improved technique to conduct desiccation-rehydration measurements, contributing to the standardization and accuracy of current existing methodologies. A close real-time and simultaneous monitoring of photosynthesis, chlorophyll fluorescence and volatile organic compound emission data, offers a novel perspective in the analysis of the cryptogam stress responses that is yet to be fully explored.

Ocean drifter velocity data assimilation, Part 1: Formulation and diagnostic results

Numerical ocean circulation forecasts diverge from reality due to non-deterministic processes. Through data assimilation, ocean forecasting systems routinely make corrections to scalar ocean variables of temperature and salinity derived from ocean observations. Velocity observations, however, are vector quantities that require different processing. To assimilate existing ocean velocity observations, which generally are depth limited, vertical error covariances are needed to relate the velocity errors throughout the water column. In this paper we show a velocity assimilation approach that relies on historical observations and geostrophy to relate velocity, temperature, and salinity. Geopotential calculations transform temperature and salinity covariances into corresponding geopotential covariances. This paper describes the methods and examines details of the solution process for ocean velocity data assimilation. The approach employs two separate three-dimensional variational (3DVAR) analyses combined to create one set of initial conditions during each daily forecast cycle. The first 3DVAR analysis utilizes temperature, salinity, and sea surface height anomaly data and reduces the background error variance based on these observations. The second 3DVAR analysis uses the reduced error variance and performs velocity data assimilation using surface drifter data, and these observations further reduce the error variance of temperature and salinity for the next cycle. The second 3DVAR analysis for velocity data assimilation utilizes the new full depth error covariances. We show diagnostic results from a Gulf of Mexico simulation during extensive drifter deployments in the summer of 2020. Results reveal that the two-step data assimilation cycle applies vector velocity innovations to create increments that correct the background field towards a geostrophic balance in temperature and salinity.

A-203 Evaluation of WMS-IV Logical Memory Performance (Storage vs. Retrieval) in a Memory Disorders Clinic

Abstract Objective: To determine whether poor free recall on WMS-IV Logical Delay (LM-II) results from limitations in encoding/storage and/or retrieval operations. We examined performance using the recently published 20-item 5-option multiple choice recognition measure (LM-REC). Participants and Method: 664 outpatient neuropsychological referrals, 60% women, 84% white non-Hispanic, average age 60.4±15.7 and education 14.9±2.9 years. Participants were administered a comprehensive battery (HRNES-R). Protocols were screened for performance invalidity. LM-REC was administered following the LM-II. Differences between scaled scores on LM-REC and LM-II were examined to differentiate between storage and retrieval difficulties. Results: LM-REC scores exceeded LM-II scores by 10 or more points (one standard deviation) in 44.7% of the sample, 15 or more points in 26.8%, and 20 or more in 16.8% of sample. Sample was divided into two groups based on degree of recognition benefit. Group 1 (n=297) had less than 10-point benefit, and Group 2 (n=226) had a benefit 315. Average scaled score on LM-II was 97.7±12.5, LM-REC was 101.5±12.2, normal range. Group 2 free recall on LM-II was 73±12.4, moderate impairment range, but its mean LM-REC score was 95.3±11.4, Borderline Range. Conclusions: LM-REC reduced error variance and measurement bias associated with guessing using the True-False WMS-IV recognition measure. A substantial subset of neuropsychological referrals performed poorly on free recall (LM-II) due to a retrieval problem. Free recall underestimated their storage efficiency in 44.7% of cases. Precise delineation of this information has important implications regarding the underlying neural substrate, pathology, and recommendations for intervention.

Design of Groundwater Level Monitoring Networks for Maximum Data Acquisition at Minimum Travel Cost

Groundwater monitoring networks represent the main source of information about water levels and water quality within aquifers. In this paper, a method is proposed for the optimal design of monitoring networks to obtain groundwater-level data of high spatial relevance at a low cost. It uses the estimate error variance reduction obtained with the static Kalman filter as optimization criteria, while simultaneously evaluating the optimal routes to follow through the traveling salesman problem. It was tested for a network of 49 wells in the Calera aquifer in Zacatecas, Mexico. The study area was divided into three zones, and one working day (8 h) was taken to visit each one, with an average speed of 40 km/h and a sampling time of 0.5 h. An optimal network of 26 wells was obtained with the proposal, while 21 wells should be monitored if the optimal routing is neglected. The average standard error using 49 wells of the original network was 35.01 m, an error of 38.35 m was obtained for 21 wells (without optimal routing) and 38.36 m with the 26 wells selected using the proposal. However, the latter produce estimates closer to those obtained with the 49 wells. Following the proposal, more field data can be acquired, reducing costs.

Load Asymmetry Angle Estimation Using Multiple view Videos.

A robust computer vision-based approach is developed to estimate the load asymmetry angle defined in the revised NIOSH lifting equation (RNLE). The angle of asymmetry enables the computation of a recommended weight limit for repetitive lifting operations in a workplace to prevent lower back injuries. An open-source package OpenPose is applied to estimate the 2D locations of skeletal joints of the worker from two synchronous videos. Combining these joint location estimates, a computer vision correspondence and depth estimation method is developed to estimate the 3D coordinates of skeletal joints during lifting. The angle of asymmetry is then deduced from a subset of these 3D positions. Error analysis reveals unreliable angle estimates due to occlusions of upper limbs. A robust angle estimation method that mitigates this challenge is developed. We propose a method to flag unreliable angle estimates based on the average confidence level of 2D joint estimates provided by OpenPose. An optimal threshold is derived that balances the percentage variance reduction of the estimation error and the percentage of angle estimates flagged. Tested with 360 lifting instances in a NIOSH-provided dataset, the standard deviation of angle estimation error is reduced from 10.13° to 4.99°. To realize this error variance reduction, 34% of estimated angles are flagged and require further validation.

Targeted observation analysis of the tides and currents in a Coastal Marine Proving Ground

Tidal characteristics in the well-known Coastal Marine Proving Ground near Chudao Island located in Shandong Province, China, are firstly investigated based on Princeton Ocean Model (POM) with a generalized coordinate system. Numerical results having been validated by available observations, the ensemble transform–based sensitivity method that calculates the gradient of forecast error variance reduction is used to identify sensitive areas of the water level and the current velocity in the Marine Proving Ground and its vicinity. Sensitive areas of the water level are mainly distributed around Chudao Island, the spatial range of which distributes smaller than that of the current velocity. When sensitivities of the water level and the current velocity are considered together, the coincidence areas serve as the most appropriate areas for adaptively deploying observation instruments. We found that a particular area west of Chudao Island is the most appropriate area for the hydrological observations in the Marine Proving Ground, which provide an insight into rational targeted observation analysis in tide-dominated shallow water areas.

Ensemble-Based Targeted Observation Method Applied to Radar Radial Velocity Observations on Idealized Supercell Low-Level Rotation Forecasts: A Proof of Concept

Abstract The potential future installation of a multifunction phased-array radar (MPAR) network will provide capabilities of case-specific adaptive scanning. Knowing the impacts adaptive scanning may have on short-term forecasts will influence scanning strategy decision-making in hopes to produce the most optimal ensemble forecast while also benefiting human severe weather warning decision-making. An ensemble-based targeted observation algorithm is applied to an observing system simulation experiment (OSSE) where the impacts of synthetic idealized supercell radial velocity observations are estimated before the observations are “collected” and assimilated. The forecast metric of interest is the low-level rotation forecast metric (0–1-km updraft helicity), a surrogate for tornado prediction. It is found that the ensemble-based targeted observation approach can reasonably estimate the true error variance reduction when an effective method that treats sampling error is applied, the period of model forecast is associated with less degrees of nonlinearity, and the observation information content relative to the background forecast is larger. In some scenarios, a subset of a full-volume scan assimilation produces better forecasts than all observations within the full volume. Assimilating the full-volume scan increases the number of potential spurious correlations arising between the forecast metric and radial velocity observation induced state perturbations, which may degrade the forecast metric accuracy.

Litter effects: Comments on Golub and Sobin's “Statistical modeling of litter as a random effect in mixed models to manage “intralitter likeness””

The importance of litter effects (clustering of variance among offspring in rodents) has been known for decades. The standard approach was to treat the entire litter as a unit or to select one male and one female from each litter to prevent oversampling. These methods work but are imperfect. Treating the litter as a whole fails to use valuable interindividual differences among offspring, and selecting representative pups fails to use all the data available. Golub and Sobin [https://doi.org/10.1016/j.ntt.2019.106841] address this using a better method. They show that using litter as a random factor in mixed linear models resolves this conundrum. As they demonstrate, such models control for litter clustering by partitioning litter variance from error variance. This reduces error variance and increases the power of F-tests of the independent variable(s). In our experience, this is the optimal solution. But as good as mixed linear models are when used with litter as a random factor, if other aspects of the experimental design are not appropriate, this cannot compensate for threats to validity from small sample sizes, dams not strictly randomly assigned to groups, repeated measure covariance structures not appropriately modeled, interactions not properly sliced, or a posteriori group comparisons not controlled for multiple comparisons. Appropriate handling of litter is only one consideration of experimental design and statistical analysis that when used in combination lead to valid, reproducible data.

Spatiotemporal imputation of MAIAC AOD using deep learning with downscaling

Aerosols have adverse health effects and play a significant role in the climate as well. The Multiangle Implementation of Atmospheric Correction (MAIAC) provides Aerosol Optical Depth (AOD) at high temporal (daily) and spatial (1 km) resolution, making it particularly useful to infer and characterize spatiotemporal variability of aerosols at a fine spatial scale for exposure assessment and health studies. However, clouds and conditions of high surface reflectance result in a significant proportion of missing MAIAC AOD. To fill these gaps, we present an imputation approach using deep learning with downscaling. Using a baseline autoencoder, we leverage residual connections in deep neural networks to boost learning and parameter sharing to reduce overfitting, and conduct bagging to reduce error variance in the imputations. Downscaled through a similar auto-encoder based deep residual network, Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) GMI Replay Simulation (M2GMI) data were introduced to the network as an important gap-filling feature that varies in space to be used for missingness imputations. Imputing weekly MAIAC AOD from 2000 to 2016 over California, a state with considerable geographic heterogeneity, our full (non-full) residual network achieved mean R2 = 0.94 (0.86) [RMSE = 0.007 (0.01)] in an independent test, showing considerably better performance than a regular neural network or non-linear generalized additive model (mean R2 = 0.78–0.81; mean RMSE = 0.013–0.015). The adjusted imputed as well as combined imputed and observed MAIAC AOD showed strong correlation with Aerosol Robotic Network (AERONET) AOD (R = 0.83; R2 = 0.69, RMSE = 0.04). Our results show that we can generate reliable imputations of missing AOD through a deep learning approach, having important downstream air quality modeling applications.

Treatment of Phonological Disorder: A Feasibility Study With Focus on Outcome Measures.

In a feasibility study for a randomized controlled trial of treatments for phonological disorders conducted over a period of 8 months, we examined 6 clinically relevant outcome measures. We took steps to reduce error variance and to maximize systematic variance. Six children received traditional treatment (Van Riper, 1939), and 7 received expansion points (Smit, 2000), a treatment program with both phonological and traditional elements. Outcome measures, which were applied to both word list and conversational samples, included percentage of consonants correct (PCC; Shriberg & Kwiatkowski, 1982), PCC for late and/or difficult (L/D) consonants and number of L/D consonants acquired. In repeated-measures analyses of variance, all measures showed significant differences from pretreatment to posttreatment, and the word list measures were associated with very high power values. In analyses of covariance for between-groups contrasts, the adjusted expansion points mean exceeded the adjusted traditional treatment mean for every measure; however, no differences reached significance. For the L/D PCC (conversation) measure, the contrast between groups was associated with a large effect size. We recommend that practitioners use outcome measures related to a word list. We recommend that researchers consider using L/D PCC on the basis of conversational samples to detect differences among treatment groups. https://doi.org/10.23641/asha.5872677.

Formulations for Estimating Spatial Variations of Analysis Error Variance to Improve Multiscale and Multistep Variational Data Assimilation

When the coarse-resolution observations used in the first step of multiscale and multistep variational data assimilation become increasingly nonuniform and/or sparse, the error variance of the first-step analysis tends to have increasingly large spatial variations. However, the analysis error variance computed from the previously developed spectral formulations is constant and thus limited to represent only the spatially averaged error variance. To overcome this limitation, analytic formulations are constructed to efficiently estimate the spatial variation of analysis error variance and associated spatial variation in analysis error covariance. First, a suite of formulations is constructed to efficiently estimate the error variance reduction produced by analyzing the coarse-resolution observations in one- and two-dimensional spaces with increased complexity and generality (from uniformly distributed observations with periodic extension to nonuniformly distributed observations without periodic extension). Then, three different formulations are constructed for using the estimated analysis error variance to modify the analysis error covariance computed from the spectral formulations. The successively improved accuracies of these three formulations and their increasingly positive impacts on the two-step variational analysis (or multistep variational analysis in first two steps) are demonstrated by idealized experiments.

Microwave as a rapid cooking method for beef tenderness evaluation.

The work was intended to evaluate the possibility of using a microwave oven for cooking meat to be used in objective measurement protocols for meat tenderness and to optimize the conditions for this purpose. The use of a standardized microwave procedure allows a dramatic reduction in analysis time and may reduce error variance due to nonuniform cooking procedures.

Phishing suspiciousness in older and younger adults: The role of executive functioning.

Phishing is the spoofing of Internet websites or emails aimed at tricking users into entering sensitive information, with such goals as financial or identity theft. The current study sought to determine whether age is associated with increased susceptibility to phishing and whether tests of executive functioning can predict phishing susceptibility. A total of 193 cognitively intact participants, 91 younger adults and 102 older adults, were primarily recruited through a Psychology department undergraduate subject pool and a gerontology research registry, respectively. The Executive Functions Module from the Neuropsychological Assessment Battery and the Iowa Gambling Task were the primary cognitive predictors of reported phishing suspiciousness. Other predictors included age group (older vs. younger), sex, education, race, ethnicity, prior knowledge of phishing, prior susceptibility to phishing, and whether or not browsing behaviors were reportedly different in the laboratory setting versus at home. A logistic regression, which accounted for a 22.7% reduction in error variance compared to the null model and predicted phishing suspiciousness with 73.1% (95% CI [66.0, 80.3]) accuracy, revealed three statistically significant predictors: the main effect of education (b = 0.58, SE = 0.27) and the interactions of age group with prior awareness of phishing (b = 2.31, SE = 1.12) and performance on the Neuropsychological Assessment Battery Mazes test (b = 0.16, SE = 0.07). Whether or not older adults reported being suspicious of the phishing attacks used in this study was partially explained by educational history and prior phishing knowledge. This suggests that simple educational interventions may be effective in reducing phishing vulnerability. Although one test of executive functioning was found useful for identifying those at risk of phishing susceptibility, four tests were not found to be useful; these results speak to the need for more ecologically valid tools in clinical neuropsychology.

The role of respondents’ comfort for variance in stated choice surveys: evidence from a SCUBA diving case

Preference elicitation among outdoor recreational users is subject to measurement errors that depend, in part, on survey planning. This study uses data from a choice experiment survey on recreational SCUBA diving to investigate whether self-reported information on respondents’ comfort when they complete surveys correlates with the error variance in stated choice models of their responses. Comfort-related variables are included in the scale functions of the scaled multinomial logit models. The hypothesis was that higher comfort reduces error variance in answers, as revealed by a higher scale parameter and vice versa. Information on, e.g., sleep and time since eating (higher comfort) correlated with scale heterogeneity, and produced lower error variance when controlled for in the model. That respondents’ comfort may influence choice behavior suggests that knowledge of the respondents’ activity patterns could be used to plan the timing of interviews to decrease error variance in choices and, hence, generate better information.

Within vs. between-subject effects of intranasal oxytocin on the neural response to cooperative and non-cooperative social interactions

The neuropeptide oxytocin (OT) plays a critical role in modulating social behavior across a wide range of vertebrate species. In humans, intranasal oxytocin (INOT) has been shown to modulate various aspects of social behavior, such as empathy, trust, in-group preference, and memory of socially relevant cues. Most INOT studies employ cross-sectional designs despite the enhanced statistical power and reduction in error variance associated with individual differences characteristic of within-subject designs. Using the Prisoner Dilemma task, which models a real-life dyadic social interaction, our group has systematically explored the effect of INOT on social cooperation and non-cooperation using a cross-sectional design. In the current study, we investigated if the main findings from our cross-sectional study could be replicated in a within-subject design using the same paradigm and whether new findings would emerge. We found OT to attenuate the ventral tegmental area response to reciprocated cooperation in women, an effect that is also present in our cross-sectional sample. However, other cross-sectional findings, especially those found in men, were not observed in this within-subject study. We hypothesize that the discrepancy can be explained by differing OT effects based on the degree of stimulus novelty/familiarity. Our within-subject study also revealed new effects not found previously in our cross-sectional study. Most importantly, OT treatment on scan 2 blocked amygdala habituation to unreciprocated cooperation found in a group that received placebo on both scans among men. Our results suggest that exogenous OT reduces the salience of positive social interactions among women and prevents habituation to negative social interactions among men. These findings may have implications for the potential clinical utility of OT as a treatment for psychiatric disorders.

VIRGIL ELECTRONIC CLINICAL OUTCOME ASSESSMENTS (ECOA) PLATFORM: IMPROVING SIGNAL DETECTION IN ALZHEIMER'S DISEASE CLINICAL TRIALS

The impact of Alzheimer's disease (AD) globally is astounding; the number of people living with the disease is predicted to quadruple by year 2050 to 106 million. Nonetheless, drug development for AD has had among the lowest success rate for any therapeutic area. The high failure rate in AD trials has, in part, been due to imprecision in endpoint measurements, which introduces noise. Clinical outcome assessments (COAs) that use traditional paper-based administrations are known to be cost- and time-inefficient, but they also are subject to human error, which creates variability in measurement. The Virgil Investigative Study Platform uses electronic source data collection with real-time clinical guidance to standardize outcome measurements and improve data quality. In the present study, we compared paper-based administrations against administration with Virgil to determine the extent to which the use of electronic assessments (eCOA) minimized scoring errors in primary and co-primary endpoints of AD trials. Paper-based assessments from a recent clinical trial of mild cognitive impairment (MCI) were compared against eCOA administrations of the same scales in separate MCI trials. All studies are phase II/III multinational trials. Score discrepancies in CDR, ADAS-Cog, ADCS-ADL-MCI, and MMSE were identified via review of audio recordings and worksheets by the same cohort of expert calibrated reviewers. For each scale, discrepancies were compared between paper-based and Virgil administrations. Percentages of reviews with at least one scoring discrepancy, as well as two or more discrepancies, were significantly and substantially lower in Virgil administrations compared to paper-based for all scales. Paper-based administrations create unnecessary variability around endpoint measurements, which can contribute to inconclusive results. The Virgil eCOA platform with real-time clinical guidance, auto-calculation of scores, and prompts for missing data and out-of-range errors can help standardize scale administration and scoring, thereby substantially reducing error variance and improving signal detection.

Peptidic β-sheet binding with Congo Red allows both reduction of error variance and signal amplification for immunoassays

Although conventional enzyme-linked immunosorbent assays (ELISA) and related assays have been widely applied for the diagnosis of diseases, many of them suffer from large error variance for monitoring the concentration of targets over time, and insufficient limit of detection (LOD) for assaying dilute targets. We herein report a readout mode of ELISA based on the binding between peptidic β-sheet structure and Congo Red. The formation of peptidic β-sheet structure is triggered by alkaline phosphatase (ALP). For the detection of P-Selectin which is a crucial indicator for evaluating thrombus diseases in clinic, the ‘β-sheet and Congo Red’ mode significantly decreases both the error variance and the LOD (from 9.7ng/ml to 1.1 ng/ml) of detection, compared with commercial ELISA (an existing gold-standard method for detecting P-Selectin in clinic). Considering the wide range of ALP-based antibodies for immunoassays, such novel method could be applicable to the analysis of many types of targets.