968 resultados para human hair analysis


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Acknowledgments This work was funded by an Arts and Humanities Research Council (AH/K006029/1) grant awarded to Rick Knecht, Kate Britton and Charlotta Hillerdal (Aberdeen); an AHRC-LabEx award (AH/N504543/1) to KB, RK, Keith Dobney (Liverpool) and Isabelle Sidéra (Nanterre); the Carnegie Trust to the Universities of Scotland (travel grant to KB); and the Max Planck Institute for Evolutionary Anthropology. The onsite collection of samples was carried out by staff and students from the University of Aberdeen, volunteer excavators and the residents of Quinhagak. We had logistical and planning support for fieldwork by the Qanirtuuq Incorporated, Quinhagak, Alaska, and the people of Quinhagak, who we also thank for sampling permissions. Special thanks to Warren Jones and Qanirtuuq Incorporated (especially Michael Smith and Lynn Church), and to all Nunalleq project team members, in Aberdeen and at other institutions, particularly Charlotta Hillerdal and Edouard Masson-Maclean (Aberdeen) for comments on earlier versions of this manuscript, and also to Véronique Forbes, Ana Jorge, Carly Ameen and Ciara Mannion (Aberdeen) for their inputs. Thanks also to Michelle Alexander (York). Finally, thank you to Ian Scharlotta (Alberta) for inviting us to contribute to this special issue, to the Editor, and to three anonymous reviewers, whose suggestions and recommended changes to an earlier version of this manuscript greatly improved the paper.

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Mercury and Lead concentrations obtained by ICP-OAS analysis of human hair from riverside communities along the Orinoco river in the Amazon state (Venezuela) were compared with those from Caracas, Venezuela. Taking into account the characteristics of these two environments and the values of the average concentrations of Mercury and Lead, baselines were established suggesting that gold mining activity near the Orinoco river is responsible for the high levels of Mercury in hair from the Amazon state, whereas automobile activity is responsible for high levels of Lead in hair in Caracas.

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A new method for high-resolution analyses of hair surface charge density under ambient conditions is presented in this paper. Electrostatic force microscopy (EFM) is used here to analyze changes in surface charge density in virgin hair, bleached hair, and hair treated with a cationic polymer. The atomic force microscopy technique is used concomitantly to analyze morphological changes in hair roughness and thickness. The EFM images depict exactly how the polymer is distributed on the surface of the hair fiber. The EFM's powerful analytical tools enabled us to evaluate the varying degrees of interaction between the hair fiber surface charge density and the cationic polymer. The surface charge density and the polymer's distribution in the hair fibers are presented in the light of EFM measurements. © 2006 Society of Cosmetic Scientists and the Socièété Française de Cosmétologie.

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Background/purpose The continuous advancement in cosmetic science has led to an increasing demand for the development of non-invasive, reliable scientific techniques directed toward claim substantiation, which is of utmost relevance, to obtain data regarding the efficacy and safety of cosmetic products. Methods In this work, we used the optical coherence tomography (OCT) technique to produce in vitro transversal section-images of human hair. We also compared the OCT signal before and after chemical treatment with an 18% w/w ammonium thioglycolate solution. Results The mean diameter of the medulla was 29 +/- 7 mu m and the hair diameter was 122 +/- 16 mu m in our samples of standard Afro-ethnic hair. A three-dimensional (3D) image was constructed starting from 601 cross-sectional images (slices). Each slice was taken in steps of 6.0 mu m at eight frames per second, and the entire 3D image was constructed in 60 s. Conclusion It was possible to identify, using the A-scan protocol, the principal structures: the cuticle, cortex and medulla. After chemical treatment, it was not possible to identify the main structures of hair fiber due to index matching promoted by deleterious action of the chemical agent.

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Mickey Mouse, one of the world's most recognizable cartoon characters, did not wear a shirt in his earliest incarnation in theatrical shorts and, for many years, Donald Duck did not wear pants and still rarely does so. Especially when one considers the era in which these figures were first created by the Walt Disney Studio, in the 1920s and 1930s, why are they portrayed without full clothing? The obvious answer, of course, is that they are animals, and animals do not wear clothes. But these are no ordinary animals: in most cases, they do wear clothing - some clothing, at least - and they walk on two legs, talk in a more or less intelligible fashion, and display a number of other anthropomorphic traits. If they are essentially animals, why do they wear clothing at all? On the other hand, if these characters are more human than animal, as suggested by other behavioral traits - they walk, talk, work, read, and so on - why are they not more often fully clothed? To answer these questions I undertook three major research strategies used to gather evidence: interpretive textual analysis of 321 cartoons; secondary analysis of interviews conducted with the animators who created the Disney characters; and historical and archival research on the Disney Company and on the times and context in which it functioned. I was able to identify five themes that played a large part in what kind of clothing a character wore; first, the character's gender and/or sexuality; second, what species or "race" the character was; third, the character's socio-economic status; fourth, the degree to which the character was anthropomorphized; and, fifth, the context in which the character and its clothing appeared in a particular scene or narrative. I concluded that all of these factors played a part in determining, to some extent, the clothing worn by particular characters at particular times. However, certain patterns emerged from the analysis that could not be explained by these factors alone or in combination. Therefore, my analysis also investigates the individual and collective attitudes and desires of the men in the Disney studio who were responsible for creating these characters and the cultural conditions under which they were created. Drawing on literature from the psychoanalytic approach to film studies, I argue that the clothing choices spoke to an idealized fantasy world to which the animators (most importantly, Walt Disney himself), and possibly wider society, wanted to return.

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Analysis of human behaviour through visual information has been a highly active research topic in the computer vision community. This was previously achieved via images from a conventional camera, but recently depth sensors have made a new type of data available. This survey starts by explaining the advantages of depth imagery, then describes the new sensors that are available to obtain it. In particular, the Microsoft Kinect has made high-resolution real-time depth cheaply available. The main published research on the use of depth imagery for analysing human activity is reviewed. Much of the existing work focuses on body part detection and pose estimation. A growing research area addresses the recognition of human actions. The publicly available datasets that include depth imagery are listed, as are the software libraries that can acquire it from a sensor. This survey concludes by summarising the current state of work on this topic, and pointing out promising future research directions.

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This work describes an analytical procedure for vanadium determination in human hair slurries by electrothermal AAS using longitudinal heating (LHGA) and transversal heating (THGA) graphite furnace atomizers. The samples were powdered using cryogenic grinding and the hair slurries containing 0.2% (m/v) were prepared in three different media for determination of vanadium: 0.14 mol L-1 HNO3, 0.1% (v/v) Triton X-100 and 0.1% (v/v) water soluble tertiary amines (CFA-C, pH 8). The limits of detection (LOD), limits of quantification (LOQ), and characteristic masses obtained were 0.28, 0.95 mu g L-1 and 35 pg (LHGA) and 0.34, 1.13 mu g L-1 and 78 pg (THGA), respectively. The accuracy of the analytical results obtained by the proposed procedure in both equipments was confirmed by a paired t-test at the 95% confidence level and compared with a conventional procedure based on acid digestion. (c) 2006 Elsevier B.V. All rights reserved.

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A fast method was optimized and validated in order to quantify amphetamine-type stimulants (amphetamine, AMP; methamphetamine, MAMP; fenproporex, FPX; 3,4-methylenedioxymethamphetamine, MDMA; and 3,4-methylenedioxyamphetamine, MDA) in human hair samples. The method was based in an initial procedure of decontamination of hair samples (50 mg) with dichloromethane, followed by alkaline hydrolysis and extraction of the amphetamines using hollow-fiber liquid-phase micro extraction (HF-LPME) in the three-phase mode. Gas chromatography-mass spectrometry (GC-MS) was used for identification and quantification of the analytes. The LoQs obtained for all amphetamines (around 0.05 ng/mg) were below the cut-off value (0.2 ng/mg) established by the Society of Hair Testing (SoHT). The method showed to be simple and precise. The intra-day and inter-day precisions were within 10.6% and 11.4%, respectively, with the use of only two deuteratecl internal standards (AMP-d5 and MDMA-d5). By using the weighted least squares linear regression (1/x(2)), the accuracy of the method was satisfied in the lower concentration levels (accuracy values better than 87%). Hair samples collected from six volunteers who reported regular use of amphetamines were submitted to the developed method. Drug detection was observed in all samples of the volunteers. (c) 2012 Elsevier B.V. All rights reserved.

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The human movement analysis (HMA) aims to measure the abilities of a subject to stand or to walk. In the field of HMA, tests are daily performed in research laboratories, hospitals and clinics, aiming to diagnose a disease, distinguish between disease entities, monitor the progress of a treatment and predict the outcome of an intervention [Brand and Crowninshield, 1981; Brand, 1987; Baker, 2006]. To achieve these purposes, clinicians and researchers use measurement devices, like force platforms, stereophotogrammetric systems, accelerometers, baropodometric insoles, etc. This thesis focus on the force platform (FP) and in particular on the quality assessment of the FP data. The principal objective of our work was the design and the experimental validation of a portable system for the in situ calibration of FPs. The thesis is structured as follows: Chapter 1. Description of the physical principles used for the functioning of a FP: how these principles are used to create force transducers, such as strain gauges and piezoelectrics transducers. Then, description of the two category of FPs, three- and six-component, the signals acquisition (hardware structure), and the signals calibration. Finally, a brief description of the use of FPs in HMA, for balance or gait analysis. Chapter 2. Description of the inverse dynamics, the most common method used in the field of HMA. This method uses the signals measured by a FP to estimate kinetic quantities, such as joint forces and moments. The measures of these variables can not be taken directly, unless very invasive techniques; consequently these variables can only be estimated using indirect techniques, as the inverse dynamics. Finally, a brief description of the sources of error, present in the gait analysis. Chapter 3. State of the art in the FP calibration. The selected literature is divided in sections, each section describes: systems for the periodic control of the FP accuracy; systems for the error reduction in the FP signals; systems and procedures for the construction of a FP. In particular is detailed described a calibration system designed by our group, based on the theoretical method proposed by ?. This system was the “starting point” for the new system presented in this thesis. Chapter 4. Description of the new system, divided in its parts: 1) the algorithm; 2) the device; and 3) the calibration procedure, for the correct performing of the calibration process. The algorithm characteristics were optimized by a simulation approach, the results are here presented. In addiction, the different versions of the device are described. Chapter 5. Experimental validation of the new system, achieved by testing it on 4 commercial FPs. The effectiveness of the calibration was verified by measuring, before and after calibration, the accuracy of the FPs in measuring the center of pressure of an applied force. The new system can estimate local and global calibration matrices; by local and global calibration matrices, the non–linearity of the FPs was quantified and locally compensated. Further, a non–linear calibration is proposed. This calibration compensates the non– linear effect in the FP functioning, due to the bending of its upper plate. The experimental results are presented. Chapter 6. Influence of the FP calibration on the estimation of kinetic quantities, with the inverse dynamics approach. Chapter 7. The conclusions of this thesis are presented: need of a calibration of FPs and consequential enhancement in the kinetic data quality. Appendix: Calibration of the LC used in the presented system. Different calibration set–up of a 3D force transducer are presented, and is proposed the optimal set–up, with particular attention to the compensation of non–linearities. The optimal set–up is verified by experimental results.

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Differentiation between external contamination and incorporation of drugs or their metabolites from inside the body via blood, sweat or sebum is a general issue in hair analysis and of high concern when interpreting analytical results. In hair analysis for cannabinoids the most common target is Delta9-tetrahydrocannabinol (THC), sometimes cannabidiol (CBD) and cannabinol (CBN) are determined additionally. After repeated external contamination by cannabis smoke these analytes are known to be found in hair even after performing multiple washing steps. A widely accepted strategy to unequivocally prove active cannabis consumption is the analysis of hair extracts for the oxidative metabolite 11-nor-9-carboxy-THC (THC-COOH). Although the acidic nature of this metabolite suggests a lower rate of incorporation into the hair matrix compared to THC, it is not fully understood up to now why hair concentrations of THC-COOH are generally found to be much lower (mostly <10 pg/mg) than the corresponding THC concentrations. Delta9-Tetrahydrocannabinolic acid A (THCA A) is the preliminary end product of the THC biosynthesis in the cannabis plant. Unlike THC it is non-psychoactive and can be regarded as a 'precursor' of THC being largely decarboxylated when heated or smoked. The presented work shows for the first time that THCA A is not only detectable in blood and urine of cannabis consumers but also in THC positive hair samples. A pilot experiment performed within this study showed that after oral intake of THCA A on a regular basis no relevant incorporation into hair occurred. It can be concluded that THCA A in hair almost exclusively derives from external contamination e.g. by side stream smoke. Elevated temperatures during the analytical procedure, particularly under alkaline conditions, can lead to decarboxylation of THCA A and accordingly increase THC concentrations in hair. Additionally, it has to be kept in mind that in hair samples tested positive for THCA A at least a part of the 'non-artefact' THC probably derives from external contamination as well, because in condensate of cannabis smoke both THC and THCA A are present in relevant amounts. External contamination by side stream smoke could therefore explain the great differences in THC and THC-COOH hair concentrations commonly found in cannabis users.

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The hair follicle cycle successively goes through the anagen, catagen, telogen, and latency phases, which correspond, respectively, to hair growth, arrest, shedding, and absence before a new anagen phase is initiated. Experimental observations collected over a period of 14 years in a group of 10 male volunteers, alopecic and nonalopecic, allowed us to determine the characteristics of scalp hair follicle cycles. On the basis of these observations, we propose a follicular automaton model to simulate the dynamics of human hair cycles. The automaton model is defined by a set of rules that govern the stochastic transitions of each follicle between the successive states anagen, telogen, and latency, and the subsequent return to anagen. The transitions occur independently for each follicle, after time intervals given stochastically by a distribution characterized by a mean and a variance. The follicular automaton model accounts both for the dynamical transitions observed in a single follicle and for the behavior of an ensemble of independently cycling follicles. Thus, the model successfully reproduces the evolution of the fractions of follicle populations in each of the three phases, which fluctuate around steady-state or slowly drifting values. We apply the follicular automaton model to the study of spatial patterns of follicular growth that result from a spatially heterogeneous distribution of parameters such as the mean duration of anagen phase. When considering that follicles die or miniaturize after going through a critical number of successive cycles, the model can reproduce the evolution to hair patterns similar to well known types of diffuse or androgenetic alopecia.

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Microarrays containing 1046 human cDNAs of unknown sequence were printed on glass with high-speed robotics. These 1.0-cm2 DNA "chips" were used to quantitatively monitor differential expression of the cognate human genes using a highly sensitive two-color hybridization assay. Array elements that displayed differential expression patterns under given experimental conditions were characterized by sequencing. The identification of known and novel heat shock and phorbol ester-regulated genes in human T cells demonstrates the sensitivity of the assay. Parallel gene analysis with microarrays provides a rapid and efficient method for large-scale human gene discovery.

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Falls are one of the greatest threats to elderly health in their daily living routines and activities. Therefore, it is very important to detect falls of an elderly in a timely and accurate manner, so that immediate response and proper care can be provided, by sending fall alarms to caregivers. Radar is an effective non-intrusive sensing modality which is well suited for this purpose, which can detect human motions in all types of environments, penetrate walls and fabrics, preserve privacy, and is insensitive to lighting conditions. Micro-Doppler features are utilized in radar signal corresponding to human body motions and gait to detect falls using a narrowband pulse-Doppler radar. Human motions cause time-varying Doppler signatures, which are analyzed using time-frequency representations and matching pursuit decomposition (MPD) for feature extraction and fall detection. The extracted features include MPD features and the principal components of the time-frequency signal representations. To analyze the sequential characteristics of typical falls, the extracted features are used for training and testing hidden Markov models (HMM) in different falling scenarios. Experimental results demonstrate that the proposed algorithm and method achieve fast and accurate fall detections. The risk of falls increases sharply when the elderly or patients try to exit beds. Thus, if a bed exit can be detected at an early stage of this motion, the related injuries can be prevented with a high probability. To detect bed exit for fall prevention, the trajectory of head movements is used for recognize such human motion. A head detector is trained using the histogram of oriented gradient (HOG) features of the head and shoulder areas from recorded bed exit images. A data association algorithm is applied on the head detection results to eliminate head detection false alarms. Then the three dimensional (3D) head trajectories are constructed by matching scale-invariant feature transform (SIFT) keypoints in the detected head areas from both the left and right stereo images. The extracted 3D head trajectories are used for training and testing an HMM based classifier for recognizing bed exit activities. The results of the classifier are presented and discussed in the thesis, which demonstrates the effectiveness of the proposed stereo vision based bed exit detection approach.