Reclaiming human machine nature

Extending and modifying his domain of life by artifact production is one of the main characteristics of humankind. From the first hominid, who used a wood stick or a stone for extending his upper limbs and augmenting his gesture strength, to current systems engineers who used technologies for augmenting human cognition, perception and action, extending human body capabilities remains a big issue. From more than fifty years cybernetics, computer and cognitive sciences have imposed only one reductionist model of human machine systems: cognitive systems. Inspired by philosophy, behaviorist psychology and the information treatment metaphor, the cognitive system paradigm requires a function view and a functional analysis in human systems design process. According that design approach, human have been reduced to his metaphysical and functional properties in a new dualism. Human body requirements have been left to physical ergonomics or "physiology". With multidisciplinary convergence, the issues of "human-machine" systems and "human artifacts" evolve. The loss of biological and social boundaries between human organisms and interactive and informational physical artifact questions the current engineering methods and ergonomic design of cognitive systems. New developpment of human machine systems for intensive care, human space activities or bio-engineering sytems requires grounding human systems design on a renewed epistemological framework for future human systems model and evidence based "bio-engineering". In that context, reclaiming human factors, augmented human and human machine nature is a necessityComment: Published in HCI International 2014, Heraklion : Greece (2014

Artificial intelligence in mammographic phenotyping of breast cancer risk: A narrative review

BACKGROUND: Improved breast cancer risk assessment models are needed to enable personalized screening strategies that achieve better harm-to-benefit ratio based on earlier detection and better breast cancer outcomes than existing screening guidelines. Computational mammographic phenotypes have demonstrated a promising role in breast cancer risk prediction. With the recent exponential growth of computational efficiency, the artificial intelligence (AI) revolution, driven by the introduction of deep learning, has expanded the utility of imaging in predictive models. Consequently, AI-based imaging-derived data has led to some of the most promising tools for precision breast cancer screening. MAIN BODY: This review aims to synthesize the current state-of-the-art applications of AI in mammographic phenotyping of breast cancer risk. We discuss the fundamentals of AI and explore the computing advancements that have made AI-based image analysis essential in refining breast cancer risk assessment. Specifically, we discuss the use of data derived from digital mammography as well as digital breast tomosynthesis. Different aspects of breast cancer risk assessment are targeted including (a) robust and reproducible evaluations of breast density, a well-established breast cancer risk factor, (b) assessment of a woman\u27s inherent breast cancer risk, and (c) identification of women who are likely to be diagnosed with breast cancers after a negative or routine screen due to masking or the rapid and aggressive growth of a tumor. Lastly, we discuss AI challenges unique to the computational analysis of mammographic imaging as well as future directions for this promising research field. CONCLUSIONS: We provide a useful reference for AI researchers investigating image-based breast cancer risk assessment while indicating key priorities and challenges that, if properly addressed, could accelerate the implementation of AI-assisted risk stratification to future refine and individualize breast cancer screening strategies

Biological and molecular structure analyses of the controls on soil organic matter dynamics

Includes bibliographical references (page 170).The dynamics of soil organic carbon (SOC) are controlled by the interaction of biological, physical, and chemical parameters. These are best measured by a combination of techniques such as long-term field sites with a C3↔C4 plant switch. Acid hydrolysis and 14C- dating measure the mean residence time (MRT) of the resistant fraction. Long-term incubation allows the in situ biota to identify and decompose the labile SOC components. Statistical analysis (curve fitting) of the CO2 release curves, determines the pool size and of the two labile fractions (1). The effect of chemical structure is measured with pyrolysismolecular beam mass spectrometry (py-MBMS). The dynamics of charcoal, clay and silt are measured with both 13C and 14C

Stability of bimetallic Pd-Zn catalysts for the steam reforming of methanol

ZnO-supported palladium-based catalysts have been shown in recent years to be both active and selective towards the steam reforming of methanol, although they are still considered to be less active than traditional copper-based catalysts. The activity of PdZn catalysts can be significantly improved by supporting them on alumina. Here we show that the Pd/ZnO/Al2O3 catalysts have better long-term stability when compared with commercial Cu/ZnO/Al2O3 catalysts, and that they are also stable under redox cycling. The Pd/ZnO/Al2O3 catalysts can be easily regenerated by oxidation in air at 420 °C followed by re-exposure to reaction conditions at 250 °C, while the Cu/ZnO based catalysts do not recover their activity after oxidation. Reduction at high temperatures (>420 °C) leads to Zn loss from the alloy nanoparticle surface resulting in a reduced catalyst activity. However, even after such extreme treatment, the catalyst activity is regained with time on stream under reaction conditions alone, leading to highly stable catalysts. These findings illustrate that the nanoparticle surface is dynamic and changes drastically depending on the environment, and that elevated reduction temperatures are not necessary to achieve high CO2 selectivity

External validation of a mammography-derived AI-based risk model in a U.S. breast cancer screening cohort of White and Black women

Despite the demonstrated potential of artificial intelligence (AI) in breast cancer risk assessment for personalizing screening recommendations, further validation is required regarding AI model bias and generalizability. We performed external validation on a U.S. screening cohort of a mammography-derived AI breast cancer risk model originally developed for European screening cohorts. We retrospectively identified 176 breast cancers with exams 3 months to 2 years prior to cancer diagnosis and a random sample of 4963 controls from women with at least one-year negative follow-up. A risk score for each woman was calculated via the AI risk model. Age-adjusted areas under the ROC curves (AUCs) were estimated for the entire cohort and separately for White and Black women. The Gail 5-year risk model was also evaluated for comparison. The overall AUC was 0.68 (95% CIs 0.64-0.72) for all women, 0.67 (0.61-0.72) for White women, and 0.70 (0.65-0.76) for Black women. The AI risk model significantly outperformed the Gail risk model for all wome

'Sexercise': Working out heterosexuality in Jane Fonda’s fitness books

This is an Author's Accepted Manuscript of an article published in Leisure Studies, 30(2), 237 - 255, 2011, copyright Taylor & Francis, available online at: http://www.tandfonline.com/10.1080/02614367.2010.523837.This paper explores the connection between the promotion of heterosexual norms in women’s fitness books written by or in the name of Jane Fonda during the 1980s and the commodification of women’s fitness space in both the public and private spheres. The paper is set in the absence of overt discussions of normative heterosexuality in leisure studies and draws on critical heterosexual scholarship as well as the growing body of work theorising geographies of corporeality and heterosexuality. Using the principles of media discourse analysis, the paper identifies three overlapping characteristics of heterosexuality represented in Jane Fonda’s fitness books, and embodied through the exercise regimes: respectable heterosexual desire, monogamous procreation and domesticity. The paper concludes that the promotion and prescription of exercise for women in the Jane Fonda workout books centred on the reproduction and embodiment of heterosexual corporeality. Set within an emerging commercial landscape of women’s fitness in the 1980s, such exercise practices were significant in the legitimation and institutionalisation of heteronormativity

Evaluating the Potential of Legumes to Mitigate N2_{2}O Emissions From Permanent Grassland Using Process-Based Models

A potential strategy for mitigating nitrous oxide (N$_{2}$O) emissions from permanent grasslands is the partial substitution of fertilizer nitrogen (N$_{fert}$) with symbiotically fixed nitrogen (N$_{symb}$) from legumes. The input of N$_{symb}$ reduces the energy costs of producing fertilizer and provides a supply of nitrogen (N) for plants that is more synchronous to plant demand than occasional fertilizer applications. Legumes have been promoted as a potential N$_{2}$O mitigation strategy for grasslands, but evidence to support their efficacy is limited, partly due to the difficulty in conducting experiments across the large range of potential combinations of legume proportions and fertilizer N inputs. These experimental constraints can be overcome by biogeochemical models that can vary legume‐fertilizer combinations and subsequently aid the design of targeted experiments. Using two variants each of two biogeochemical models (APSIM and DayCent), we tested the N$_{2}$O mitigation potential and productivity of full factorial combinations of legume proportions and fertilizer rates for five temperate grassland sites across the globe. Both models showed that replacing fertilizer with legumes reduced N$_{2}$O emissions without reducing productivity across a broad range of legume‐fertilizer combinations. Although the models were consistent with the relative changes of N$_{2}$O emissions compared to the baseline scenario (200 kg N ha$^{-1}$ yr$^{-1}$; no legumes), they predicted different levels of absolute N$_{2}$O emissions and thus also of absolute N$_{2}$O emission reductions; both were greater in DayCent than in APSIM. We recommend confirming these results with experimental studies assessing the effect of clover proportions in the range 30–50% and ≤150 kg N ha$^{-1}$ yr$^{-1}$ input as these were identified as best‐bet climate smart agricultural practices