Operators' Accessibility Studies using Virtual Reality

International audienceThe development of fusion plants is more and more challenging. Compared to previous fusion experimental devices, integration constraints, maintenance and safety requirements are key parameters in the ITER project. Components are designed in parallel and we must consider integration, assembly and maintenance issues, which might have a huge impact on the overall design. That also implies to consider the operator's feedback to assess the feasibility of accessibility or maintenance processes. Virtual reality (VR) provides tools to optimize such integration. In 2010, the CEA IRFM decided to upgrade its design tools, by using VR during the life cycle (from design to operation) of a fusion component. The VR platform is intensively used in the design and assembly studies of WEST components. In particular, feasibility of the assembly scenario is assessed by the operators involving in the real assembly work. To study this aspect, the use of static manikins is quite frequent in the industry. However, more complex studies, like the feasibility of assembly and maintenance tasks in complex and very confined environments, require enhanced features such as dynamic and biomechanically realistic virtual humans. We also study the contribution of tactile feedback to improve physical presence and interaction in the virtual environment (VE), which is very important for the validation of a given task's feasibility and the ergonomic evaluation of the posture and gesture of the operator. In particular, we show that adapted behavior in respect to physical elements of the VE can be obtained using a dynamic co-localized representation of the subject's body and a pseudo-haptic tactile feedback. In this paper, we present integration studies involving operators and recent advances in the assessment of maintenance feasibility

The effect of PD-L1 testing on the cost-effectiveness and economic impact of immune checkpoint inhibitors for the second-line treatment of NSCLC

Background: Immune checkpoint inhibitors improve outcomes compared with chemotherapy in lung cancer. Tumor PD-L1 receptor expression is being studied as a predictive biomarker. The objective of this study was to assess the cost-effectiveness and economic impact of second-line treatment with nivolumab, pembrolizumab, and atezolizumab with and without the use of PD-L1 testing for patient selection. Design: We developed a decision-analytic model to determine the cost-effectiveness of PD-L1 assessment and second-line immunotherapy versus docetaxel. The model used outcomes data from randomized clinical trials (RCTs) and drug acquisition costs from the United States. Thereafter, we used epidemiologic data to estimate the economic impact of the treatment. Results: We included four RCTs (2 with nivolumab, 1 with pembrolizumab, and 1 with atezolizumab). The incremental quality-adjusted life year (QALY) for nivolumab was 0.417 among squamous tumors and 0.287 among non-squamous tumors and the incremental cost-effectiveness ratio (ICER) were $155 605 and$187 685, respectively. The QALY gain in the base case for atezolizumab was 0.354 and the ICER was $215 802. Compared with treating all patients, the selection of patients by PD-L1 expression improved incremental QALY by up to 183$98 421. Patient selection also reduced the budget impact of immunotherapy. Conclusion: The use of PD-L1 expression as a biomarker increases cost-effectiveness of immunotherapy but also diminishes the number of potential life-years saved.info:eu-repo/semantics/publishedVersio

A global multilayer cloud identification with POLDER/PARASOL

The detection of multilayer cloud situations is important for satellite retrieval algorithms and for many climate related applications. In this paper, we describe an algorithm based on the exploitation of the POLarization and Directionality of the Earth’s Reflectance (POLDER) observations to identify monolayered and multilayered cloudy situations along with a confidence index. Our reference comes from the synergy of the active instruments of the A-Train satellite constellation. The algorithm is based upon a decision tree that uses a metric from information theory and a series of tests on POLDER Level-2 products. We obtain a multilayer flag as the final result of a tree classification which takes discrete values between 0 and 100. Values closest to zero (resp. a hundred) indicate a higher confidence in the monolayer (resp. multilayer) character. This indicator can be used as it is, or with a threshold level that minimizes the risk of misclassification, as a binary index to distinguish between monolayer and multilayer clouds. For almost fully covered and optically thick enough cloud scenes, the risk of misclassification ranges from 29% to 34% over the period 2006–2010 and the average confidences in the estimated monolayer and multilayer characters of the cloud scenes are 74.0% and 58.2% respectively. With the binary distinction, POLDER provides a climatology of the mono/multi-layer cloud character that exhibits some interesting features. Comparisons with the performance of the Moderate-Resolution Imaging Spectroradiometer (MODIS) multilayerflag are given

Improved information about the vertical location and extent of monolayer clouds from POLDER3 measurements in the oxygen A-band

This paper describes new advances in the exploitation of oxygen A-band measurements from POLDER3 sensor onboard PARASOL, satellite platform within the A-Train. These developments result from not only an account of the dependence of POLDER oxygen parameters to cloud optical thickness τ and to the scene's geometrical conditions but also, and more importantly, from the finer understanding of the sensitivity of these parameters to cloud vertical extent. This sensitivity is made possible thanks to the multidirectional character of POLDER measurements. In the case of monolayer clouds that represent most of cloudy conditions, new oxygen parameters are obtained and calibrated from POLDER3 data colocalized with the measurements of the two active sensors of the A-Train: CALIOP/CALIPSO and CPR/CloudSat. From a parameterization that is (μs, τ) dependent, with μs the cosine of the solar zenith angle, a cloud top oxygen pressure (CTOP) and a cloud middle oxygen pressure (CMOP) are obtained, which are estimates of actual cloud top and middle pressures (CTP and CMP). Performances of CTOP and CMOP are presented by class of clouds following the ISCCP classification. In 2008, the coefficient of the correlation between CMOP and CMP is 0.81 for cirrostratus, 0.79 for stratocumulus, 0.75 for deep convective clouds. The coefficient of the correlation between CTOP and CTP is 0.75, 0.73, and 0.79 for the same cloud types. The score obtained by CTOP, defined as the confidence in the retrieval for a particular range of inferred value and for a given error, is higher than the one of MODIS CTP estimate. Scores of CTOP are the highest for bin value of CTP superior in numbers. For liquid (ice) clouds and an error of 30 hPa (50 hPa), the score of CTOP reaches 50% (70%). From the difference between CTOP and CMOP, a first estimate of the cloud vertical extent h is possible. A second estimate of h comes from the correlation between the angular standard deviation of POLDER oxygen pressure σPO2 and the cloud vertical extent. This correlation is studied in detail in the case of liquid clouds. It is shown to be spatially and temporally robust, except for clouds above land during winter months. The analysis of the correlation's dependence on the scene's characteristics leads to a parameterization providing h from σPO2. For liquid water clouds above ocean in 2008, the mean difference between the actual cloud vertical extent and the one retrieved from σPO2 (from the pressure difference) is 5 m (−12 m). The standard deviation of the mean difference is close to 1000 m for the two methods. POLDER estimates of the cloud geometrical thickness obtain a global score of 50% confidence for a relative error of 20% (40%) of the estimate for ice (liquid) clouds over ocean. These results need to be validated outside of the CALIPSO/CloudSat track

Building capacity for clinical research in developing countries: the INDOX cancer research network experience

Transnational Organisations increasingly prioritise the need to support local research capacity in low and middle income countries in order that local priorities are addressed with due consideration of contextual issues. There remains limited evidence on the best way in which this should be done or the ways in which external agencies can support this process

Identification of a Proliferation Gene Cluster Associated with HPV E6/E7 Expression Level and Viral DNA Load in Invasive Cervical Carcinoma

Specific HPV DNA sequences are associated with more than 90% of invasive carcinomas of the uterine cervix. Viral E6 and E7 oncogenes are key mediators in cell transformation by disrupting TP53 and RB pathways. To investigate molecular mechanisms involved in the progression of invasive cervical carcinoma, we performed a gene expression study on cases selected according to viral and clinical parameters. Using Coupled Two-Way Clustering and Sorting Points Into Neighbourhoods methods, we identified a Cervical Cancer Proliferation Cluster composed of 163 highly correlated transcripts, many of which corresponded to E2F pathway genes controlling cell proliferation, whereas no primary TP53 targets were present in this cluster. The average expression level of the genes of this cluster was higher in tumours with an early relapse than in tumours with a favourable course (P=0.026). Moreover, we found that E6/E7 mRNA expression level was positively correlated with the expression level of the cluster genes and with viral DNA load. These findings suggest that HPV E6/E7 expression level plays a key role in the progression of invasive carcinoma of the uterine cervix via the deregulation of cellular genes controlling tumour cell proliferation. HPV expression level may thus correspond to a biological marker useful for prognosis assessment and specific therapy of the disease

Global burden of cancers attributable to infections in 2012:a synthetic analysis

Background Infections with certain viruses, bacteria, and parasites are strong risk factors for specifi c cancers. As new cancer statistics and epidemiological fi ndings have accumulated in the past 5 years, we aimed to assess the causal involvement of the main carcinogenic agents in diff erent cancer types for the year 2012. Methods We considered ten infectious agents classifi ed as carcinogenic to human beings by the International Agency for Research on Cancer. We calculated the number of new cancer cases in 2012 attributable to infections by country, by combining cancer incidence estimates (from GLOBOCAN 2012) with estimates of attributable fraction (AF) for the infectious agents. AF estimates were calculated from the prevalence of infection in cancer cases and the relative risk for the infection (for some sites). Estimates of infection prevalence, relative risk, and corresponding 95% CIs for AF were obtained from systematic reviews and pooled analyses. Findings Of 14 million new cancer cases in 2012, 2·2 million (15·4%) were attributable to carcinogenic infections. The most important infectious agents worldwide were Helicobacter pylori (770 000 cases), human papillomavirus (640 000), hepatitis B virus (420 000), hepatitis C virus (170 000), and Epstein-Barr virus (120 000). Kaposi’s sarcoma was the second largest contributor to the cancer burden in sub-Saharan Africa. The AFs for infection varied by country and development status—from less than 5% in the USA, Canada, Australia, New Zealand, and some countries in western and northern Europe to more than 50% in some countries in sub-Saharan Africa. Interpretation A large potential exists for reducing the burden of cancer caused by infections. Socioeconomic development is associated with a decrease in infection-associated cancers; however, to reduce the incidence of these cancers without delay, population-based vaccination and screen-and-treat programmes should be made accessible and available