True Average Research Articles

P-166 An artificial intelligence-powered tool to score fresh donor oocytes and predict blastulation: interim analysis of a prospective investigation conducted at 3 laboratories

Abstract Study question Is the Magenta-score associated with fresh donor oocytes’ blastulation competence, and can it be used to predict blastocyst rates per cohort of inseminated oocytes (BRpC)? Summary answer Developmentally-competent fresh donor oocytes showed significantly higher Magenta-score, although the number of blastocysts obtained was within the predicted range in only 63% of the cycles. What is known already Currently, many couples undergo donor oocyte treatments. However, despite donors being theoretically fertile, not all oocytes are developmentally competent, and many do not reach the blastocyst stage. Even if subjective and ineffective to predict oocyte competence, visual oocyte morphological assessment is still routinely used to evaluate oocytes. Lately, the rise of artificial intelligence (AI) resulted in promising tools to assess oocytes more objectively in a standardized method, possibly providing novel insights into the prediction of their competence. In this context, Magenta-score (Future Fertility), an AI-based image analysis tool, can potentially prove useful to optimize the management of oocyte donation treatments. Study design, size, duration A Blinded cohort study (June-2023/December-2023) was conducted to assess the primary outcome: association between fresh donor-oocytes’ Magenta-Score and their subsequent blastocyst development (assumed to be 50%). A sample size of 779 achieves 80%-power to detect the difference between the null-hypothesis point-biserial-correlation of 0 and the alternative hypothesis of 0.1 using a two-sided test with 5%-significance. Secondary outcomes were differences (i) predicted - true BRpCs, and (ii) predicted - true numbers of blastocysts obtained. Participants/materials, setting, methods Interim-analysis of 514 fresh-oocytes obtained from 63 donors at 3 centers and allocated (7.7±3, range:3-18) to 67 recipients. Pictures of denuded mature oocytes were acquired before ICSI. Magenta-scores were generated blindly (providing only donor age and total number of mature oocytes in cohort to the software). The software also estimated BRpCs and range of blastocysts obtainable from each cohort. Donors’ characteristics, ovarian stimulation/treatment cycle parameters, and sperm analyses were tested as confounders. Main results and the role of chance The only visual oocyte anomaly associated with lower Magenta-score was irregular shape (N = 484,5.4±2.2 vs N = 30,3.3±2.1, Mann-Whitney-U<0.01). Oocytes that developed into blastocysts (N = 194) had significantly higher Magenta-scores versus oocytes that did not reach this milestone (N = 320) (5.8±2.1 vs 4.9±2.3, Mann-Whitney-U<0.01; Odds-Ratio adjusted for sperm motility and incubator [standard/time-lapse]=1.26, 95%CI:1.13-1.34, p < 0.01; power=99.6%). No association was noted with day of blastulation or morphological quality. Data were concordant across centers. The average predicted-BRpC was 39.4±11.4% versus a true-BRpC of 40.6±22.6% (Person’s correlation: -0.24; mean difference: -1.1±27.6%, 95%CI from -7.8% to + 5.6%, p = 0.74). The average predicted number of blastocysts per oocyte cohort was 3.3±1.6 versus a true average of 2.9±1.7 (Person’s correlation: 0.25; mean difference: +0.4±2.0, 95%CI from -0.04 to + 0.9, p = 0.07). In 21% (N = 14) and 31% (N = 21) of cycles, the tool predicted an equal or lower number of blastocysts, respectively. In 5 cycles, no blastocyst was obtained, but the tool did not predict this unexpected outcome. When testing the predicted range of blastocysts obtained, the true number was within the range in 63% (N = 42) of cases and higher than the maximum predicted number in 13% (N = 9) of cases. No confounder was identified on the BRpC in this population of fresh donor cycles. Limitations, reasons for caution Of note, this is an interim analysis of an ongoing study. Not all mature oocytes recovered from a donor were utilized, thus not all oocytes in a cohort were imaged and included in analysis. Other putative confounders on BRpC should be considered in future studies with a larger sample size. Wider implications of the findings Effective management of an oocyte donation program is critical to comply with expected high success rates while minimizing the number of surplus blastocysts produced. The integration of genomic data, recipient couples’ characteristics, and the benefits of objective standardized AI-powered oocyte scoring is the most promising workflow to optimize this task. Trial registration number Not applicable

Motion-Inclusive Treatment Planning to Assess Normal Tissue Dose for Central Lung Stereotactic Body Radiation Therapy

PurposeFor lung SBRT, 4DCT is often used to delineate target volumes, whereas organs at risk (OARs) are typically outlined on either average intensity projection (AIP) or mid-ventilation (MidV = 30% phase) images. AIP has been widely adopted as it represents a true average, but image blurring often precludes accurate contouring of critical structures such as central airways. Here we compare AIP vs MidV planning for centrally located tumors via respiratory motion-inclusive (RMI) plans to better evaluate dose delivered throughout the breathing cycle. MethodsIndependently contoured and optimized AIP and MidV plans were created for 16 treatments and rigidly copied to each of the 10 breathing phase-specific CT image sets. Resulting dose distributions were deformably registered back to the MidV image set (used as reference due to clearer depiction of anatomy compared to motion-blurred AIP) and averaged to create RMI plans. Doses to central OARs were compared between plans. ResultsMean absolute dose differences were low for all comparisons (range 0.01 – 2.87 Gy), however, individual plans exhibited differences > 20 Gy. Dose differences >5 Gy were observed most often for plan comparisons involving AIP-based plans (MidV vs AIP 23, AIP RMI vs AIP 12, MidV RMI vs AIP RMI 7, and MidV RMI vs MidV 8 times). Inclusion of respiratory motion reduced large dose differences. Standard OAR thresholds were exceeded up to five times for each plan comparison scenario and always involved PBT D4cc tolerance dose. AIP-based contours were larger by on average 3-15%. ConclusionLarge dose differences were observed when plans with AIP-based contours were compared with MidV-based contours indicating that observed dose differences were likely due to contoured volume differences rather than the effect of motion. Because of blurring with AIP images, MidV RMI-based planning may offer a more accurate method to determine dose to critical OARs in the presence of respiratory motion.

The Use of One Sample t-Test in the Real Data

The t-statistic is the ratio of the departure of the estimated value of a parameter from its hypothesized value to its standard error. The term "t-statistic" is abbreviated from "hypothesis test statistic". It was first derived as a posterior distribution in 1876 by Helmertand Lüroth. The purpose of this research is to study the t-test, especially the one sample t-test to determine if the sample data come from the same population. The grade points average (GPA) of the students for the second, third, and fourth grades of the Department of Mathematics Education, Tishk International University are used. The one sample t test is used to predict the GPA of the students for the second, third, and the fourth grades respectively, in addition to the overall average scores for the three grades. The 95% confidence interval for the true population average is also conducted.

Quantum mean-square predictors and thermodynamics

Thermodynamic quantities, such as heat and work, are not functions of state, but rather of the process undergone by a physical system. Assessing them can therefore be difficult, since it requires probing the system at least twice. This is even more so when these quantities are to be assessed at the stochastic level. In this letter we show how to obtain optimal estimates of thermodynamic quantities solely from indirect measurement unravellings of an auxiliary system. The method always yields the true average. And the mean-squared error of the prediction is directly proportional to how well the method estimates the variance. As an application, we study energy fluctuations in a driven system, and in an avoided crossing work protocol.

Batch Selection via In Vitro/In Vivo Correlation in Pharmacokinetic Bioequivalence Testing.

Pharmacokinetic differences between manufacturing batches, well established for inhaled drug products, preclude control of patient risk in the customary two-way (single batch) pharmacokinetic bioequivalence crossover design if batches are randomly chosen. European regulators have recommended selecting a "typical" in vitro batch to represent each product in pharmacokinetic bioequivalence testing. We explored the feasibility of this approach to control patient risk (the "false equivalence", or Type I, error rate). The probability of achieving a Test/Reference 90% confidence interval within (0.80, 1.25) for a true (non-equivalent) value of 1.25 was simulated for a two-way crossover design using the median in vitro batch across a range of number of in vitro batches, in vitro/in vivo correlation (IVIVC) quality (correlation coefficient, r, of zero to one), and within-subject between-batch pharmacokinetic variability. Even under extremely optimistic conditions, e.g., r=0.95 and >100 batches per product screened in vitro, patient risk for typical between-batch variability levels remained at least threefold higher than the 5% regulatory expectation for the significance level (the false equivalence error rate) of the pharmacokinetic bioequivalence test. This elevated error rate in bioequivalence decision-making occurs because of incomplete confidence that the true product average has been identified, and, importantly, omission of this uncertainty from the bioequivalence confidence interval.

Optimization of a wearable speed monitoring device for welding applications

The heat input is a parameter used to ensure quality of submerged metal arc welding, one of the most common metal joining procedures in the manufacturing industry. This parameter is dependent on the current and voltage, defined by the specific welding procedure, along with the welding speed, a feature that is strongly dependent on the hand movement of the welding operator. A wearable speed monitoring device (SMD) was developed by fusing optical and inertial sensor data to measure the handheld device speed of the welder. This work aims at tuning the parameters affecting the performance of SMD prototype by testing it with four batches of common electrodes in a real industrial environment. The outcomes were compared with the results from the traditional manual calculation, namely, a true coarse average (TCA) velocity. At first, observations were performed to identify any scope of improvement for the SMD. Secondly, an optimization of the main parameters was performed by minimizing a cost function involving the TCA velocities for each batch. A final assessment was then performed with the optimized parameters evaluating the achieved results; a comparative analysis showed a reduction of the relative average errors of 32%, 1.4%, and 4.2% for 2.5, 4, and 5 mm electrode sets, respectively. The system was found already optimized for the 3.25-mm electrodes, using baseline parameters with an error of about 12%. The methodology employed in this study can be further utilized in scenarios in which speed monitoring is employed for light-driven applications with a moving reference.

How Often Does Active Learning Actually Occur? Perception versus Reality

Substantial evidence suggests active learning pedagogies are superior to lecturing, but little evidence exists on the prevalence of such methods. Watts and Schaur (2011) find, based on self-reported data, the median instructor lectures 83 percent of the time. We analyze audio recordings of 535 total classes from 30 different instructors to show instructors greatly underestimate how often they use passive learning pedagogies such as lecturing. Survey results show instructors estimate they lecture approximately 78.5 percent of class time; our data reveals the true average is 89 percent. This gap between perception and reality is statistically significant.

1 Assessing the predictive adequacy of simple and complex mathematical models

Abstract The establishment of credibility for a mathematical model’s (MM) predictive ability is an essential component for improving the MM because it stimulates the evolutionary thinking (i.e., the next generation of the model) of mental conceptualizations, assumptions, and boundaries of the MM. Its predictive adequacy is commonly assessed through its ability to precisely or accurately predict observed (real) values. The precision component measures how closely the model predicted values are of each other or whether a defined pattern of predictions exists. The accuracy component, on the other hand, measures how closely the average of the model predicted values are to the actual (true) average. Many statistics exist to determine precision and accuracy of MM such as mean bias, resistant coefficient of determination, coefficient of determination, modeling efficiency, concordance correlation coefficient (CCC), the mean square error of prediction, Kleijnen’s statistic (regression of the difference between predicted and observed on their sum), and Altman and Bland’s limits of agreement statistics among many more. However, for complex models that use skewed data or repeated data in which the data is not independent (e.g., multiple measurements on the same subject), simple statistics may not suffice. For instance, four methods to compute CCC exist (moment, variance components, U-statistics, and generalized estimating equations—GEE), but only the last two methods are resilient to lightly skewed data. Another type of complexity arises when meta-analytical approaches are used at the model development phase or the model evaluation phase. In general, meta-analytical approaches remove errors (i.e., variation) associated with random variables that are believed to be known. Under these circumstances, MM tends to overperform (i.e., they have greater predictive adequacy) and their future performance may be deceitful when trying to forecast at scenarios in which the random variable(s) is(are) indeterminable or unquantifiable.

Precession of the Orbit of Mercury

The influence of the planets of the Solar System on the precession of the orbit of Mercury is investigated in the framework of classical mechanics. The influence of each planet on the motion of Mercury is calculated in the context of a restricted problem of three bodies: the Sun, the planet in question, and Mercury. It is demonstrated that the average shift of the perihelion of the orbit of Mercury determined in the context of a planar circular restricted three-body problem is 556.5″ per century and agrees with the observed shift (570″) with a relative accuracy of 2.5%. It is also demonstrated that the observed perihelion shift of Mercury features oscillatory components with a total amplitude up to 20″ and periods from several years to several decades. Owing to the presence of these components, the perihelion shift rate calculated based on observations conducted for several tens or even hundreds of years may differ considerably from the true average rate. This is likely the reason why the calculated average perihelion shift rate is not completely consistent with observational data.

Comparing Maximum Dose to Central Airways in Mid-Ventilation Versus Average Intensity Projection-Based 4DCT Planning for Lung Stereotactic Body Radiation Therapy (SBRT)

Four-Dimensional Computed Tomography (4DCT) is typically used in radiation treatment planning for improved targeting of intrathoracic tumors. While tumor definition uses all in- and expiration image sets to account for the full range of respiratory motion, the definition of organs at risk (OARs) is less well defined. We investigated OAR dose differences for two commonly used image sets for SBRT dose calculation: average intensity projection (AIP) and mid-point in ventilation (midV). Determining accurate OAR doses is particularly important for proximal airways, as RTOG 0813 results have shown that increased dose to these structures can result in unacceptable, at times fatal toxicity. Fifteen 4DCT-based lung SBRT plans originally generated on 30% MidV image sets were transferred to associated AIP images. Fractionation schedules were 50 Gy in 5 fractions in 5, 48 Gy in 4 fractions in 9, and 40 Gy in 5 fractions in 1 plan. Internal gross tumor volumes (iGTV) were delineated on maximum intensity projection (MIP) images. OARs were contoured per RTOG 0813 protocol on both MidV and AIP datasets. Maximum doses were reported for each OAR (trachea, proximal bronchial tree (PBT), heart, and great vessels (GV)) on original MidV plans and after plan transfer to AIP image sets with field arrangements and monitor unit (MU) values unchanged. AIP volumes were, on average, greater by 4.26 ± 5.44 cc for trachea and 13.11 ± 12.21 for PBT, and reduced by 21.6 ± 70.42 for heart and 6.06 ± 41.14 cc for GV. Average superior-inferior movement of tumor and carina were 7.1 ± 4.1 and 7.6 ± 4.7 mm, respectively. The average absolute difference between AIP and MidV in maximum doses to trachea, PBT, heart, and GV were 1.01 ± 1.37, 1.68 ± 3.10, 1.45 ± 2.05, and 1.07 ± 1.21 Gy, respectively. Maximum dose to PBT was increased in 3 of 15 (20%) plans after transfer to AIP image sets, with an average increase of 3.03 ± 2.97 Gy (0.09 to 7.10). Of the remaining 12 plans (80%), this was decreased by an average of 1.35 ± 3.04 Gy (0.01 to 11.31). Fourteen (93%) plans exhibited an absolute difference in maximum dose of > 1 Gy to at least one OAR. Absolute difference in maximum dose to heart showed a positive correlation with carina motion (R2 = 0.61), and GV a weak positive correlation with tumor motion (R2 = 0.40). Our results show a difference in maximum dose to OARs between AIP and MidV image sets. These differences are confounded by tumor and carina motion and associated differences in contoured volumes. Although this study does not allow to identify the better image set, AIP images are more likely to account for a true positional average, and AIP-based plans are expected to be more representative of the true maximum OAR dose. To calculate correct OAR doses, both tumor and OARs require contouring and planning on all respiratory phases which might be necessary for critical central structures such as PBT, if they are in the high dose area.

Uncertainty of Reference Pixel Soil Moisture Averages Sampled at SMAP Core Validation Sites

Abstract Despite extensive efforts to maximize ground coverage and improve upscaling functions within core validation sites (CVS) of the NASA Soil Moisture Active Passive (SMAP) mission, spatial averages of point-scale soil moisture observations often fail to accurately capture the true average of the reference pixels. Therefore, some level of pixel-scale sampling error from in situ observations must be considered during the validation of SMAP soil moisture retrievals. Here, uncertainties in the SMAP core site average soil moisture (CSASM) due to spatial sampling errors are examined and their impact on CSASM-based SMAP calibration and validation metrics is discussed. The estimated uncertainty (due to spatial sampling limitations) of mean CSASM over time is found to be large, translating into relatively large sampling uncertainty levels for SMAP retrieval bias when calculated against CSASM. As a result, CSASM-based SMAP bias estimates are statistically insignificant at nearly all SMAP CVS. In addition, observations from temporary networks suggest that these (already large) bias uncertainties may be underestimated due to undersampled spatial variability. The unbiased root-mean-square error (ubRMSE) of CSASM is estimated via two approaches: classical sampling theory and triple collocation, both of which suggest that CSASM ubRMSE is generally within the range of 0.01–0.02 m3 m−3. Although limitations in both methods likely lead to underestimation of ubRMSE, the results suggest that CSASM captures the temporal dynamics of the footprint-scale soil moisture relatively well and is thus a reliable reference for SMAP ubRMSE calculations. Therefore, spatial sampling errors are revealed to have very different impacts on efforts to estimate SMAP bias and ubRMSE metrics using CVS data.

Detection of soil moisture anomalies based on Sentinel-1

Active and passive microwave remote sensing has evolved into a well-accepted and widely used method for the spatially continuous, coarse to medium resolution mapping of the surface soil moisture content (SMC). Presently, the exploitation of high-resolution data is less mature. Sentinel-1 is a high-resolution Synthetic Aperture Radar (∼5 by 20 m), which acts as the basis for mapping of SMC in this study. There aren't any other comparable SAR missions currently available whose data can be accessed and used for free.One of the applications for SMC measurements is connected to the relationship between SMC anomalies and natural hazards such as droughts, flooding, or land-slides. A requirement for the detection and quantification of an anomaly is a long time-series (often 10–30 years) to derive a reference value. Herein lies one of the issues of Sentinel-1 based SMC mapping – at the time of writing, the Sentinel-1 time-series spanned a period of approximately 3.5 years.We introduce an approach to overcome this problem and enable the Sentinel-1 based SMC anomaly detection. The method is based on a cross-calibration between Sentinel-1 SMC estimations and coarse resolution (∼30 km) modelled SMC from the Global Land Data Assimilation System (GLDAS), which covers the time-span from 1948 to today. As a result, we can derive the long-term averages for each Sentinel-1 pixel.Results show that the proposed method allows a very accurate reproduction of the average SMC (for any given pixel) – if computed for the S1 time-span, the RMSE between estimated (GLDAS based) and true average S1 SMC is 0.7 %-Vol. Furthermore, the comparison with an in-situ time-series shows the correct detection of negative and positive anomalies, respectively. The method presented here may allow the integration of S1 data into, e.g., drought monitoring or flood forecasting applications.

Statistics of spatial averages and optimal averaging in the presence of missing data

We estimate statistics of spatial averages defined by linearly weighting available observations over a spatial domain, and derive an account of bias and variance in the presence of missing observations. With missing observations, the spatial average is a ratio of random variables, and estimators are derived by truncating Taylor series of functions of the ratio followed by taking suitable expectations (’delta method’). The resulting estimators are approximate, and perform well when compared with simulations. Previous authors have examined “optimal averaging” strategies for minimizing mean squared error (MSE) of a spatial average, and we extend the analysis to the case of missing observations. It is shown that to minimize variance primarily requires higher weights where local variance and covariance are small, whereas minimizing bias requires higher weights where observations lie nearer to true spatial average. Missing data increases variance and contributes to bias, and reducing both effects involves emphasizing locations with observations nearer to the spatial average. The framework is applied to spatially averaged rainfall over India. We estimate standard error of all-India rainfall as the combined effect of measurement uncertainty and bias, in the case where weights are chosen to minimize MSE. We also discuss different special cases of the estimators, and applications thereof.

Costs of examinations performed in a hospital laboratory in Chile.

To determine the total average costs related to laboratory examinations performed in a hospital laboratory in Chile. Retrospective study with data from July 2014 to June 2015. 92 examinations classified in ten groups were selected according to the analysis methodology. The costs were estimated as the sum of direct and indirect laboratory costs and indirect institutional factors. The average values obtained for the costs according to examination group (in USD) were: 1.79 (clinical chemistry), 10.21 (immunoassay techniques), 13.27 (coagulation), 26.06 (high-performance liquid chromatography), 21.2 (immunological), 3.85 (gases and electrolytes), 156.48 (cytogenetic), 1.38 (urine), 4.02 (automated hematological), 4.93 (manual hematological). The value, or service fee, returned to public institutions who perform laboratory services does not adequately reflect the true total average production costs of examinations.

Target geo-localization based on camera vision simulation of UAV

This paper presents a simulation study on estimating the Geo-Location of a target based on multiple image of the target taken from a gimbaled camera mounted on a unmanned aerial vehicle (UAV), which orbits around the target with a radius such that the target is always in the field of camera vision. The Camera Vision Simulation of the UAV is implemented by using an ortho Geo-TIFF (Geo-Spatial Tagged Information File Format) as imagery reference, positional and attitude attributes of UAV, Gimbal and Camera and internal characteristic of the simulated Camera. Target is localized using the simulation images taken from multiple bearing waypoints by applying the Geo-Location algorithm using the simulation parameters as reference. For improving the accuracy of the estimation, error reduction techniques like true average, moving average and recursive least square are also suggested and implemented.

One thousand somatic SNVs per skin fibroblast cell set baseline of mosaic mutational load with patterns that suggest proliferative origin.

Few studies have been conducted to understand post-zygotic accumulation of mutations in cells of the healthy human body. We reprogrammed 32 skin fibroblast cells from families of donors into human induced pluripotent stem cell (hiPSC) lines. The clonal nature of hiPSC lines allows a high-resolution analysis of the genomes of the founder fibroblast cells without being confounded by the artifacts of single-cell whole-genome amplification. We estimate that on average a fibroblast cell in children has 1035 mostly benign mosaic SNVs. On average, 235 SNVs could be directly confirmed in the original fibroblast population by ultradeep sequencing, down to an allele frequency (AF) of 0.1%. More sensitive droplet digital PCR experiments confirmed more SNVs as mosaic with AF as low as 0.01%, suggesting that 1035 mosaic SNVs per fibroblast cell is the true average. Similar analyses in adults revealed no significant increase in the number of SNVs per cell, suggesting that a major fraction of mosaic SNVs in fibroblasts arises during development. Mosaic SNVs were distributed uniformly across the genome and were enriched in a mutational signature previously observed in cancers and in de novo variants and which, we hypothesize, is a hallmark of normal cell proliferation. Finally, AF distribution of mosaic SNVs had distinct narrow peaks, which could be a characteristic of clonal cell selection, clonal expansion, or both. These findings reveal a large degree of somatic mosaicism in healthy human tissues, link de novo and cancer mutations to somatic mosaicism, and couple somatic mosaicism with cell proliferation.

Application of Triple Collocation in Ground-Based Validation of Soil Moisture Active/Passive (SMAP) Level 2 Data Products

The validation of the soil moisture retrievals from the recently launched National Aeronautics and Space Administration (NASA) Soil Moisture Active/Passive (SMAP) satellite is important prior to their full public release. Uncertainty in attempts to characterize footprint-scale surface-layer soil moisture using point-scale ground observations has generally limited past validation of remotely sensed soil moisture products to densely instrumented sites covering an area approximating the satellite ground footprint. However, by leveraging independent soil moisture information obtained from land surface modeling and/or alternative remote sensing products, triple collocation (TC) techniques offer a strategy for characterizing upscaling errors in sparser ground measurements and removing the impact of such error on the evaluation of remotely sensed soil moisture products. Here, we propose and validate a TC-based strategy designed to utilize existing sparse soil moisture networks (typically with a single sampling point per satellite footprint) to obtain an unbiased correlation validation metric for satellite surface soil moisture retrieval products. Application of this TC strategy at five SMAP core validation sites suggests that unbiased estimates of correlation between the satellite product and the true footprint average can be obtained - even in cases where ground observations provide only one single reference point within the footprint. An example of preliminary validation results from the application of this TC strategy to the SMAP Level 2 Soil Moisture Passive (beta release version) product is presented.

Quantitative analysis of 3-dimensional facial soft tissue photographic images: technical methods and clinical application.

BackgroundThe recent advent of 3D photography has created the potential for comprehensive facial evaluation. However, lack of practical true 3D analysis of the information collected from 3D images has been the factor limiting widespread utilization in orthodontics. Current evaluation of 3D facial soft tissue images relies on subjective visual evaluation and 2D distances to assess facial disharmony. The objectives of this project strive to map the surface and define boundaries of 3D facial soft tissue, modify mathematical functions to average multiple 3D facial images, and mathematically average 3D facial images allowing generation of color-coded surface deviation relative to a true average.MethodsCollaboration headed by UCLA Orthodontics with UCLA Neuroimaging was initiated to modify advanced brain mapping technology to accurately map the facial surface in 3D. 10 subjects were selected as a sample for development of the technical protocol. 3dMD photographic images were segmented, corrected using a series of topology correcting algorithms, and process to create close meshes. Shapes were mapped to a sphere using conformal and area preserving maps, and were then registered using a spherical patch mapping approach. Finally an average was created using 7-parameter procrustes alignment.ResultsSize-standardized average facial images were generated for the sample population. A single patient was then superimposed on the average and color-coded displacement maps were generated to demonstrate the clinical applicability of this protocol. Further confirmation of the methods through 3D superimposition of the initial (T0) average to the 4 week (T4) average was completed and analyzed.ConclusionsThe results of this investigation suggest that it is possible to average multiple facial images of highly variable topology. The immediate application of this research will be rapid and detailed diagnostic imaging analysis for orthodontic and surgical treatment planning. There is great potential for application to anthropometrics and genomics. This investigation resulted in establishment of a protocol for mapping the surface of the human face in three dimensions.

Modelling Spatial Variability in Concentrations of Single Pollutants and Composite Air Quality Indicators in Health Effects Studies

SummaryThe health impact resulting from a few days of elevated air pollution concentrations has been the focus of much recent research, most of which assesses the effects of single pollutants rather than composite air quality indicators. The average concentrations of these pollutants across the study region are typically estimated by averaging the measurements from the available network of monitors, and this simplistic approach has several deficiencies. Firstly, it is unlikely to be the average concentration across the region under study, owing to the likely non-random placement of the monitoring network. Secondly, the true spatial average is an unknown quantity, and hence the uncertainty in any estimate should be allowed for when estimating its health effects. This paper proposes a novel Bayesian hierarchical framework for addressing these problems, which consists of statistical models for estimating spatially representative measures of single pollutants and composite air quality indicators, and the health effects of these pollution measures while correctly allowing for their uncertainty. This methodological development is motivated by an epidemiological study of the effects of air pollution on respiratory mortality in Greater London, England, between 2003 and 2005. The key findings from this study are that traditional approaches are likely to underestimate the uncertainty in the health effects of air pollution compared with the approach proposed here and increased risks of between 1.4% and 3.1% are associated with 1-standard-deviation increases in the concentrations of ozone, particulate matter (PM10) and the composite air quality indicator that is adopted here.

ENHANCING CONSENSUS UNDER OPINION BIAS BY MEANS OF HIERARCHICAL DECISION MAKING

We study the role of hierarchical structures in a simple model of collective consensus formation based on the bounded confidence model with continuous individual opinions. For the particular variation of this model considered in this paper, we assume that a bias toward an extreme opinion is introduced whenever two individuals interact and form a common decision. As a simple proxy for hierarchical social structures, we introduce a two-step decision making process in which in the second step groups of like-minded individuals are replaced by representatives once they have reached local consensus, and the representatives in turn form a collective decision in a downstream process. We find that the introduction of such a hierarchical decision making structure can improve consensus formation, in the sense that the eventual collective opinion is closer to the true average of individual opinions than without it. In particular, we numerically study how the size of groups of like-minded individuals being represented by delegate individuals affects the impact of the bias on the final population-wide consensus. These results are of interest for the design of organizational policies and the optimization of hierarchical structures in the context of group decision making.