Intra-person Variation Research Articles

The puzzle of misinformation: Exposure to unreliable content in the United States is higher among the better informed

Healthy news consumption requires limited exposure to unreliable content and ideological diversity in the sources consumed. There are two challenges to this normative expectation: the prevalence of unreliable content online; and the prominence of misinformation within individual news diets. Here, we assess these challenges using an observational panel tracking the browsing behavior of N ≈ 140,000 individuals in the United States for 12 months (January–December 2018). Our results show that panelists who are exposed to misinformation consume more reliable news and from a more ideologically diverse range of sources. In other words, exposure to unreliable content is higher among the better informed. This association persists after we control for partisan leaning and consider inter- and intra-person variation. These findings highlight the tension between the positive and negative consequences of increased exposure to news content online.

A large-scale evaluation of intraperson isotopic variation within human bone collagen and bioapatite

This study investigated intraperson skeletal (herein referred to as either “intraperson” or “intraskeletal”) variation in stable isotope ratios for collagen (C and N) and bioapatite (C and O) extracted from five to six long bones from 27 modern individuals. The maximum intraperson variation observed for collagen was 0.78‰ for δ13Ccoll values and 1.12‰ for δ15Ncoll values, with a mean variation (± SD) of 0.33 ± 0.18‰ and 0.45 ± 0.27‰, respectively. For bioapatite, the maximum intraperson variation was 1.63‰ for δ13Cap values and 4.80‰ for δ18Oap values, with a mean variation (± SD) of 0.81 ± 0.32‰ and 1.00 ± 1.03‰, respectively. These results generally agree with previously reported data on intraskeletal isotopic variation. Using a two- and three-standard-deviations-from-the-mean model with analytical quality control data included, it is proposed that two bones with differing collagen δ13Ccoll values greater than 0.75‰ are probably from different individuals, and those that have differing values greater than 0.95‰ are from different individuals. Likewise, differing collagen δ15Ncoll values greater than 1.05‰ are probably different, and greater than 1.35‰ are different. For bioapatite, the proposed values change to 1.55‰ and 1.90‰ for δ13Cap, respectively; for δ18Oap values no limits were set due to the unexpectedly large variation found in the study population. We highly encourage researchers to use extreme caution when interpreting δ18O values from bone apatite. We also note that these parameters were evaluated on modern samples and therefore may not reflect intraperson variation in past societies. Finally, we demonstrate application of these interpretative limits to sort commingled human remains cases.

High-Fidelity Illumination Normalization for Face Recognition Based on Auto-Encoder

Nonuniform illumination is one of the main issues that hinder the accuracy of face recognition because it makes the intra-person variation more complicated. To minimize the intra-person differences caused by varying illuminations, this paper presents a normalization method based on Convolutional Auto-encoder (CAE). The CAE is employed to map the face images under various illumination conditions to a normalized one, generating preliminary results with blurry and insufficient facial details, which are tricky for recognition. To recover these details, a restoration method based on re-blurring strategy and frequency analysis is proposed to preserve the facial features lying in high-frequency components based on discrete cosine transform (DCT). Therefore, in our method, these components are extracted and re-introduced into the outputs of CAE to enhance the fidelity of outputs. Thus, the facial details are preserved to the largest degree and the following works such as recognition tasks are benefited. Experiments conducted on the AR, extended Yale B, and CAS-PEAL database demonstrate the effectiveness of our method.

Multi-view Person Re-identification in a Fisheye Camera Network with Different Viewing Directions

Person re-identification is a crucial component for multi-camera networks in different real-world applications such as surveillance, automation, and business analytics. Despite considerable recent progress, the performance in practice is still not satisfactory due to the high intra-person variation and significant complexity of the task, including differences in scale, viewing direction, and illumination. We propose a novel approach for person re-identification, which exploits multi-view information of a fisheye camera looking downwards from the ceiling. To handle this highly variable multi-view information, we build a generic pipeline for processing fisheye camera imagery based on geometric sensor modelling and deep learning. The proposed approach is evaluated on a re-mapped version of the publicly available Market-1501 dataset, and, in addition, on a new fisheye dataset. Significant improvements are shown in our experiments: our approach achieves more than 97% rank#1 recognition rate if applied on the re-mapped Market-1501 dataset; on the new fisheye dataset we find an improvement of about 12% compared to random-view fusion.

Shedder status: Does it really exist?

The concept of shedder status is not new, and studies have been conducted by multiple groups but with varying results. Hence, to investigate the validity of the shedder status concept, a total of 486 touch DNA samples were collected from 81 individuals to examine DNA recovery 15 min post-handwashing. We defined the shedder status of participants using the dual requirement of number of self-alleles detected and the frequency of occurrence. While some participants had consistently high or low allele deposition, other participants showed great intra-person variation. Significant differences in the number of self-alleles were also observed between genders, and for the activities of self-contact and mobile phone usage.

EFFECTIVE FACE FEATURE FOR HUMAN IDENTIFICATION

Face image is one of the most important parts of human body. It is easily use for identification process. People naturally identify one another through face images. Due to increase rate of insecurity in our society, accurate machine based face recognition systems are needed to detect impersonators. Face recognition systems comprise of face detector module, preprocessing unit, feature extraction subsystem and classification stage. Robust feature extraction algorithm plays major role in determining the accuracy of intelligent systems that involves image processing analysis. In this paper, pose invariant feature is extracted from human faces. The proposed feature extraction method involves decomposition of captured face image into four sub-bands using Haar wavelet transform thereafter shape and texture features are extracted from approximation and detailed bands respectively. The pose invariant feature vector is computed by fusing the extracted features. Effectiveness of the feature vector in terms of intra-person variation and inter-persons variation was obtained from feature plots.

Abstract 3473: Intra-person and inter-person biomarker variation for modeling longitudinal values of ovarian cancer early detection biomarkers.

Abstract In 2012, an estimated 22,280 women will be diagnosed with ovarian cancer in the United States and 15,500 women will die from this disease. Though localized ovarian cancers have a survival rate of 92%, only 15-20% of ovarian cancers are detected at this stage. Developing an effective strategy for early detection could reduce mortality. While individual values of CA125 are neither sufficiently sensitive nor specific for early detection, monitoring CA125 over time shows greater specificity. The risk of ovarian cancer algorithm, ROCA, analyzes the trend of annual values for CA125, utilizing each woman's own baseline to refer a small fraction (<1-2%) of women to transvaginal sonography. Using this strategy, no more than three operations are required for each case of ovarian cancer detected. Use of multiple biomarkers might increase sensitivity for preclinical disease. Based on a multiplex assay of 96 biomarkers, a four biomarker panel was identified of CA125, HE4, MMP-7 and CA 72-4 with greater sensitivity than CA125 alone. To estimate the utility of these biomarkers for longitudinal screening, we assessed five annual longitudinal control serum samples from 200 women who did not develop ovarian cancer from the MDACC SPORE normal risk ovarian cancer screening study (NROSS). Utilizing WinBUGS, the inter-person and intra-person CVs were evaluated. Upon log transformation for CA125, HE4 and MMP-7 and log(log()+1) transformation for CA72-4, both the inter-person and intra-person CVs follow a log-normal distribution. The median inter-person CVs for CA125, HE4, MMP-7 and CA 72-4 were 49%, 20%, 35% and 84% respectively and the median intra-person CVs were 15%, 25%, 21% and 21% respectively. The ratios of intra-person CVs to inter-person CVs derived for CA125, HE4, MMP-7 and log(CA 72-4) were 0.31, 1.25, 0.6 and 0.31. We anticipate that the lower the ratio, the higher the contribution of each individual marker to early detection during longitudinal measurements. The intra-person CV's should permit development of a multi-marker algorithm. Assessment of longitudinal samples derived from 50 cases of preclinical ovarian cancer and 10 controls per case (UKCTOCS trial) are underway in order to establish such a longitudinal multi-marker algorithm for assessment of ovarian cancer risk. Citation Format: Archana Raamanathan, Zhen Lu, Robert C. Bast, Steven J. Skates. Intra-person and inter-person biomarker variation for modeling longitudinal values of ovarian cancer early detection biomarkers. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3473. doi:10.1158/1538-7445.AM2013-3473

On non-invasive 2D and 3D Chromatic White Light image sensors for age determination of latent fingerprints

The feasibility of 2D-intensity and 3D-topography images from a non-invasive Chromatic White Light (CWL) sensor for the age determination of latent fingerprints is investigated. The proposed method might provide the means to solve the so far unresolved issue of determining a fingerprints age in forensics. Conducting numerous experiments for an indoor crime scene using selected surfaces, different influences on the aging of fingerprints are investigated and the resulting aging variability is determined in terms of inter-person, intra-person, inter-finger and intra-finger variation. Main influence factors are shown to be the sweat composition, temperature, humidity, wind, UV-radiation, surface type, contamination of the finger with water-containing substances, resolution and measured area size, whereas contact time, contact pressure and smearing of the print seem to be of minor importance. Such influences lead to a certain experimental variability in inter-person and intra-person variation, which is higher than the inter-finger and intra-finger variation.Comparing the aging behavior of 17 different features using 1490 time series with a total of 41,520 fingerprint images, the great potential of the CWL technique in combination with the binary pixel feature from prior work is shown. Performing three different experiments for the classification of fingerprints into the two time classes [0, 5h] and [5, 24h], a maximum classification performance of 79.29% (kappa=0.46) is achieved for a general case, which is further improved for special cases. The statistical significance of the two best-performing features (both binary pixel versions based on 2D-intensity images) is manually shown and a feature fusion is performed, highlighting the strong dependency of the features on each other. It is concluded that such method might be combined with additional capturing devices, such as microscopes or spectroscopes, to a very promising age estimation scheme.

Do We Really Need SEVEN New Aerosol Particle Sampling Conventions?

The rationale behind recently proposed aerosol particle sampling conventions is discussed. The conventions are tailored toward estimation of dose to specific areas of the human respiratory tract prior to possible dissolution or clearance. Allowance is made for inter- and intra-person variation. Particle size from sub- to supermicrometer size is covered. The intent is to spur the development of personal sampling equipment for improved correlation between workplace assessment and health response.

Sampling Conventions for Estimating Ultrafine and Fine Aerosol Particle Deposition in the Human Respiratory Tract

To provide targets for personal samplers designed for estimating particle deposition at distinct locations in the body, accounting if necessary for inter- and intra-person variation. Ultrafine and fine aerosol sampling conventions are proposed for approximating the deposition efficiency for five distinct loci of the respiratory tract. The 2 × 5 = 10 conventions represent averages over variation in physical activity level, posture, sex, and breathing mode. Recognizing three approximate relationships among the 10 deposition efficiencies, the number of independent conventions is reduced to only seven, namely three ultrafine and four fine aerosol conventions. The ultrafine and fine conventions are defined as ideal sampling efficiencies in terms of thermodynamic (independent of particle density) and aerodynamic diameter, respectively. Addition of measured mass, surface area, or particle count from aerosol collected by sampler pairs in agreement with convention can be used to estimate dose (prior to clearance) at a particular locus in the mean over breathing conditions ranging from sitting, to light, or to heavy exercise, normal or mouth breathing, and male or female, with aerosol density effects partially corrected automatically by the separate aerodynamic and thermodynamic sampling. Linear combinations of the conventions can be used for yet simpler sampling, though with limited distinctness as to deposition locus. Alternatively to estimating simply a mean, the large inter- and intra-person variation (relative standard deviation of the order of 100%) corresponding to the wide range of breathing conditions can be approximately corrected by using 'arrays' of samplers in agreement with convention, given suitable profiling of the individual whose dose is to be assessed. The intent behind the proposed conventions is not to eliminate the current aerosol penetration conventions, which have found international application in exposure assessment particularly for determining compliance with standards on acceptable aerosol levels. The aim is to promote personal sampler design leading to more sharply defined health research than can be done at present.

Hand Gesture Recognition Using Multivariate Fuzzy Decision Tree and User Adaptation

As an emerging human-computer interaction (HCI) technology, recognition of human hand gesture is considered a very powerful means for human intention reading. To construct a system with a reliable and robust hand gesture recognition algorithm, it is necessary to resolve several major difficulties of hand gesture recognition, such as inter-person variation, intra-person variation, and false positive error caused by meaningless hand gestures. This paper proposes a learning algorithm and also a classification technique, based on multivariate fuzzy decision tree (MFDT). Efficient control of a fuzzified decision boundary in the MFDT leads to reduction of intra-person variation, while proper selection of a user dependent (UD) recognition model contributes to minimization of inter-person variation. The proposed method is tested first by using two benchmark data sets in UCI Machine Learning Repository and then by a hand gesture data set obtained from 10 people for 15 days. The experimental results show a discernibly enhanced classification performance as well as user adaptation capability of the proposed algorithm.

Gesture Spotting Using Fuzzy Garbage Model and User Adaptation

Thanks to the rapid advancement of human-computer interaction technologies it is becoming easier for the elderly and/or people with disabilities to operate various electrical systems. Operation of home appliances by using a set of predefined hand gestures is an example. However, hand gesture recognition may fail when the predefined command gestures are similar to some ordinary but meaningless behaviors of the user. This paper uses a gesture spotting method to recognize a designated gesture from other similar gestures. A fuzzy garbage model is proposed to provide a variable reference value to determine whether the user’s gesture is the command gesture or not. Further, the authors propose two-stage user adaptation to enhance recognition performance: that is, off-line (batch) adaptation for inter-person variation and on-line (incremental) adaptation for intra-person variation. For implementation of the two-stage adaptation method, a genetic algorithm (GA) and the steepest descent method are adopted for each stage. Experimental results were obtained for 5 different users with left and up command gestures.

Analyzing Intra-person Variation: Hybridizing the ACE Model with P-Technique Factor Analysis and the Idiographic Filter

Integrating idiographic and nomothetic approaches to the study of behavior has met with success via the idiographic filter (IF) which separates irrelevant inter-individual differences from relevant inter-individual similarities at the level of construct measurement in order to facilitate drawing conclusions regarding nomothetic relationships among the constructs. We propose an integration of the IF and the ACE behavior genetics models through the use of P-technique factor analysis and its dynamic factor analysis extensions and examine how it can strengthen the modeling of genetic and environmental effects in behavioral data representing intra-person variation, change, and process.

Methodologic Data: Important Foundation for Molecular and Biomarker Studies

In this issue of Cancer Epidemiology, Biomarkers & Prevention (CEBP) , we have included a Focus section featuring a series of papers with the themes of Biomarkers, -Omics & Systems Biology and Biospecimens & Biorepositories to highlight the importance and/or emergence of these topics in cancer

Intra-Person Variation of Urinary Biomarkers of Oxidative Stress and Inflammation

Oxidative stress and inflammation have been linked to many chronic diseases including cancer and cardiovascular diseases. Urinary levels of F(2)-isoprostanes (F(2)-IsoPs), 2,3-dinor-5,6-dihydro-15-F(2t)-IsoP (15-F(2t)-IsoP-M), a major metabolite of F(2)-IsoPs, prostaglandin E(2) metabolite (PGE-M), and leukotriene E(4) (LTE(4)) have been proposed as biomarkers for oxidative stress and inflammation. However, little information is available regarding the intra-person variation of these biomarkers, hindering their application in epidemiologic studies. We evaluated the intra-person variation of these four urinary biomarkers among 48 randomly chosen participants of a validation study of a population-based cohort, the Shanghai Men's Health Study. Four spot urine samples, collected during each season over a 1-year period, were measured for these biomarkers. The intraclass correlation coefficients for F(2)-IsoPs, 15-F(2t)-IsoP-M, PGE-M, and LTE(4) were 0.69, 0.76, 0.67, and 0.64, respectively. The Spearman correlation coefficients, derived by using bootstrap analysis of single spot measurements and the average of the other three seasonal measurements, were 0.47, 0.60, 0.61, and 0.57 for F(2)-IsoPs, 15-F(2t)-IsoP-M, PGE-M, and LTE(4). Except for high correlations between F(2)-IsoPs and 15-F(2t)-IsoP-M (r = 0.65), the other biomarkers were moderately correlated (r = 0.21-0.44). Our study results suggest that these four urinary biomarkers have relatively low intra-person variation over a 1-year period. Spot measurements of F(2)-IsoPs, 15-F(2t)-IsoP-M, PGE-M, and LTE(4) could be useful as biomarkers of oxidative stress and inflammation status for epidemiologic studies.

Highlights of This Issue

Serum estradiol (E2) is used as a diagnostic biomarker in a variety of clinical settings. Specifically, E2 measurements are often used in studies exploring the role of estrogen in hormone-related cancers and conditions. An accurate and reliable measurement of E2 is critically important, especially when measuring the low E2 levels found in elderly men and postmenopausal women. Stanczyk and colleagues discuss the specificity and sensitivity limitations of direct E2 radioimmunoassays (RIAs) and demonstrate the unreliability of these assays to detect low serum E2 levels. The authors recommend the use of liquid or gas chromatography-tandem mass spectroscopy or conventional RIAs, in combination with preceding purification steps, for accurate and reliable E2 measurement.The use of biomarkers as indicators of exposure often requires that a single biomarker measurement accurately reflects an individual's exposure. Unfortunately, the temporal stability and variation of many blood and urine biomarkers have not been determined. To fill this knowledge gap, Kotsopoulos and colleagues evaluated the reproducibility of over 80 different blood and urine biomarkers using specimens collected from participants in the Nurses' Health Study over the course of 1 to 3 years. Using intraclass correlation coefficients, the authors report that for the majority of the biomarkers they evaluated, a single measurement can reliably estimate average levels over a 1- to 3-year period.F2-isoprostane and its major metabolite (15-F2t-IsoP-M), as well as leukotriene E4 and prostaglandin E2, are four potential urinary biomarkers for oxidative stress and inflammation. Wu and colleagues evaluated the intra-person variation of these four biomarkers by examining four urine samples from 48 randomly selected study participants over the course of one year. The authors report that each of these biomarkers displayed low intra-person variation during the year-long study period, indicating their future utility as reliable biomarkers for oxidative stress and inflammation.Pooling biomarker data from several different studies is a valuable tool, allowing researchers to make robust estimates of cancer risk for a particular biomarker. Ideally, standardized methods would have been used by each pooled study so that the biomarker data can be used in their original units. This ideal however, is often not realistic. In this issue, Key and colleagues present an approach that allows pooling biomarker studies even when they do not use standardized methods to measure biomarker levels. The technique uses study-specific quantiles or percentage increases and can provide substantial information on the relationship between a biomarker and cancer risk.

Androgen and Prostate Cancer: Is the Hypothesis Dead?

Data from animal, clinical, and prevention studies support the role of androgen in prostate cancer growth, proliferation, and progression. However, results serum-based epidemiologic studies in humans have been inconclusive. Part of the inconsistency in these findings stems from differences in study population, assay accuracy, intraperson variation, and limited sample size. Recently, data from a large pooled analysis of 18 prospective studies (3,886 cases and 6,438 healthy controls) showed no association between serum androgen and prostate cancer risk. It is not surprising that the pooled analysis did not find a positive link between circulating levels of total testosterone and prostate cancer risk because, individually, few of the 18 studies included in the pooled analysis reported a substantial positive association. The null result, however, does not pronounce a death sentence for the androgen hypothesis; rather, it underscores the importance of a better understanding of androgen action within the prostate, including the relationship between tissue and serum levels of androgen. In this commentary, we explain why circulating levels of testosterone may not reflect androgen action in the prostate and why tissue levels of androgen, in particular dihydrotestosterone, and the androgen receptor and its coregulators are critical to androgen action in the prostate and should be incorporated in future studies. It is timely to integrate system thinking into our research and use an interdisciplinary approach that involves different disciplines, including epidemiology, endocrinology, pathology, and molecular biology, to help dissect the complex interplay between sex steroids and genetic and lifestyle factors in prostate cancer etiology.

소프트 컴퓨팅 기법을 이용한 개인화된 손동작 인식 시스템

최근 하지가 불편한 노약자나 장애인이 집안의 다양한 가전기기를 손쉽게 제어하기 위한 비전 기반의 손동작 인식 기술이 발전해 왔다. 다수의 사용자가 하나의 손동작 인식 시스템을 사용할 경우 사용자마다 손동작 특성이 모두 다르기 때문에 특정 사용자의 인식률이 저하되는 문제가 발생한다. 또한 동일한 사용자라 하더라도 시간에 따라 손동작 특성이 변화할 수 있다. 사용자마다 다른 손동작 특성은 모델 학습 및 선택 기법을 사용해 효과적으로 다루어질 수 있다. 시간에 따라 변하는 사용자의 특성은 퍼지 개념을 이용해 효과적으로 다루어질 수 있다. 본 논문에서는 다변량 퍼지 의사 결정트리를 이용해 사용자 별 인식모델을 만드는 방법을 제시한다. 또한 새로운 사용자가 시스템을 사용할 경우 가장 적합한 모델을 선택해 인식에 사용하고 인식률을 측정한다. Recently, vision-based hand gesture recognition techniques have been developed for assisting elderly and disabled people to control home appliances. Frequently occurred problems which lower the hand gesture recognition rate are due to the inter-person variation and intra-person variation. The recognition difficulty caused by inter-person variation can be handled by using user dependent model and model selection technique. And the recognition difficulty caused by intra-person variation can be handled by using fuzzy logic. In this paper, we propose multivariate fuzzy decision tree learning and classification method for a hand motion recognition system for multiple users. When a user starts to use the system, the most appropriate recognition model is selected and used for the user.

Illumination-robust face recognition using ridge regressive bilinear models

The performance of face recognition is greatly affected by illumination changes because intra-person variation of the captured images under different lighting conditions can be much bigger than the inter-person variation. This paper proposes an illumination-robust face recognition by separating an identity factor and an illumination factor using symmetric bilinear models. The translation procedure in the bilinear model requires a repetitive computation of matrix inverse operations to reach the identity and illumination factors. This computation may result in a non-convergent case when the observation has noisy information or the model is overfitted. To alleviate this situation, we suggest a ridge regressive bilinear model that combines the ridge regression into the bilinear model. This provides a number of advantages: it stabilizes the bilinear model by shrinking the range of identity and illumination factors appropriately and improves the recognition performance. Experimental results show that the ridge regressive bilinear model significantly outperforms other existing methods such as the eigenface, quotient image, and the bilinear model in terms of the recognition rate under a variety of illuminations.

The Farm Family Exposure Study: Acquavella et al. Respond

We thank Mage for his comments. In our article on glyphosate in the Farm Family Exposure Study (FFES) (Acquavella et al. 2004), we used 24-hr urinary creatinine to assess the completeness of daily samples over 5 days for the 48 participating farmers. We erred by summarizing the results as micrograms per deciliter instead of micrograms per day. Using an expected daily excretion of 566 μg/day as the lower end of the normal range (Bingham et al. 1988; Forman 2003), only four 24-hr urine samples over 5 days were below that lower limit. Therefore, the completeness of urine collection for the applicators was exceptional. Further details of the urine collection and our assessment of completeness can be found in a related article (Baker et al. 2005). Mage criticizes us for not subtracting preapplication urine values in our assessment of systemic dose related to on-study applications. Indeed, seven of the applicators had detectable glyphosate in their urine on the day before their on-study application (Acquavella et al. 2004). Values were 1.1, 2.6, 3.9, 5.3, 8.3, 9.8, and 15.4 ppb. We intentionally did not correct for these initial values for two reasons. First, from an epidemiologic and public health standpoint, it is instructive to know the total dose for farmers during and after an application, which, for example, could then be compared to levels of toxicologic significance. Second, the overestimate caused by this practice is trivial for glyphosate in both an absolute and relative sense. Consider that glyphosate has a U.S. Environmental Protection Agency (EPA) reference dose of 2 mg/kg/day (U.S. EPA 1999), and the highest systemic dose we estimated in our study was 0.004 mg/kg/day. The requested corrections would be to the ten thousandths of a milligram per kilogram per day or less. Last, Mage calls the fact that we only evaluated one application per farm family a limitation that may vitiate our study. That is a strong indictment for a study that comprehensively assessed exposure for farm families related to a single application of three pesticides to an extent not seen before. We agree that characterizing intraperson variation in absorbed pesticide dose over several seasons would provide valuable information, but that was not the objective of the FFES. Nevertheless, Mage’s claim that we cannot reject the possibility that all 47 applicators have the same exposure distribution is refuted by our observations that absorbed dose was related to specific practices (e.g., not wearing gloves) and by similar findings in the literature that practices dictate absorbed dose (e.g., Arbuckle et al. 2002).