Trends in Global Nanotechnology Regulation: The Public-Private Interplay

Over the last decade, concerns regarding potential exposure to nano materials gave rise to substantial new regulation intended to ensure safe development of nanotechnology applications. This Article examines the resultant regulatory system through empirical analysis of trends and patterns in global development of nanotechnology regulatory initiatives. It argues that rather than a government-driven process, it was private actors who set the regulatory wheels in motion. This Article shows that under conditions of scientific uncertainty, governments lacking technical and scientific knowledge to support risk-based regulation often leave a regulatory void. Consequently, businesses apply self-risk-management strategies to fill the gap, leading the way for public regulation to follow. Thus, it is the continual interplay between public and private regulations that shapes the current landscape and drives the regulatory innovation. This Article further shows that, while formal partnerships are rare, an informal and self-organized collaboration is occurring through reciprocal signals of intent and tacit understanding of the internal and external factors that drive both public and private regulations. It concludes that, due to some unique aspects of nanotechnology, private regulation has (and is likely to continue to have) a prominent role in regulating nanotechnologies

Random Forest Algorithm Improves Detection of Physiological Activity Embedded within Reflectance Spectra Using Stomatal Conductance as a Test Case

Plants transpire water through their tissues in order to move nutrients and water to the cells. Transpiration includes various mechanisms, primarily stomata movement, which controls the rate of CO2 and water vapor exchange between the tissues and the atmosphere. Assessment of stomatal conductance is available for gas exchange techniques at leaf level, yet these techniques are not scalable to the whole plant let alone a large vegetation area. Hyperspectral reflectance spectroscopy, which acquires hundreds of bands in a single scan, may capture a glimpse of the crop’s physiological activity and therefore meet the scalability challenge. In this study, classic chemometric analyses are used alongside advanced statistical learning algorithms in order to identify stomatal conductance cues in hyperspectral measurements of cotton plants experiencing a gradient of irrigation. Random forest of regression trees identified 23 wavelengths related to both structural properties of the plant as well as water content. Partial least squares regression succeeded in relating these wavelengths to stomatal conductance, but only partially (R2 < 0.2). An artificial neural network algorithm reported an R2 = 0.54 with an 89% error-free performance on the same data subset. This study discusses implementation of machine learning methodologies as a benchmark for deeper analysis of spectral information, such as required when searching for plant physiology-related attenuations embedded within reflectance spectra

Random Forest Algorithm Improves Detection of Physiological Activity Embedded within Reflectance Spectra Using Stomatal Conductance as a Test Case

Plants transpire water through their tissues in order to move nutrients and water to the cells. Transpiration includes various mechanisms, primarily stomata movement, which controls the rate of CO2 and water vapor exchange between the tissues and the atmosphere. Assessment of stomatal conductance is available for gas exchange techniques at leaf level, yet these techniques are not scalable to the whole plant let alone a large vegetation area. Hyperspectral reflectance spectroscopy, which acquires hundreds of bands in a single scan, may capture a glimpse of the crop’s physiological activity and therefore meet the scalability challenge. In this study, classic chemometric analyses are used alongside advanced statistical learning algorithms in order to identify stomatal conductance cues in hyperspectral measurements of cotton plants experiencing a gradient of irrigation. Random forest of regression trees identified 23 wavelengths related to both structural properties of the plant as well as water content. Partial least squares regression succeeded in relating these wavelengths to stomatal conductance, but only partially (R2 &lt; 0.2). An artificial neural network algorithm reported an R2 = 0.54 with an 89% error-free performance on the same data subset. This study discusses implementation of machine learning methodologies as a benchmark for deeper analysis of spectral information, such as required when searching for plant physiology-related attenuations embedded within reflectance spectra.</jats:p

Directed elimination of senescent cells by inhibition of BCL-W and BCL-XL

AbstractSenescent cells, formed in response to physiological and oncogenic stresses, facilitate protection from tumourigenesis and aid in tissue repair. However, accumulation of such cells in tissues contributes to age-related pathologies. Resistance of senescent cells to apoptotic stimuli may contribute to their accumulation, yet the molecular mechanisms allowing their prolonged viability are poorly characterized. Here we show that senescent cells upregulate the anti-apoptotic proteins BCL-W and BCL-XL. Joint inhibition of BCL-W and BCL-XL by siRNAs or the small-molecule ABT-737 specifically induces apoptosis in senescent cells. Notably, treatment of mice with ABT-737 efficiently eliminates senescent cells induced by DNA damage in the lungs as well as senescent cells formed in the epidermis by activation of p53 through transgenic p14ARF. Elimination of senescent cells from the epidermis leads to an increase in hair-follicle stem cell proliferation. The finding that senescent cells can be eliminated pharmacologically paves the way to new strategies for the treatment of age-related pathologies.</jats:p