Increasing atmospheric CO2 concentrations outweighs effects of stand density in determining growth and water use efficiency in Pinus ponderosa of the semi-arid grasslands of Nebraska (U.S.A.)

This study investigated the impacts of environmental (e.g., climate and CO2 level) and ecological (e.g., stand density) factors on the long-term growth and physiology of ponderosa pine (Pinus ponderosa) in a semi-arid north American grassland. We hypothesized that ponderosa pine long-term growth patterns were positively influenced by an increase in atmospheric CO2 concentrations and a decrease in stand density. To test this hypothesis, comparison of long-term trends in tree-ring width and carbon and oxygen stable isotopic composition of trees growing in dense and sparse forest stands were carried out at two sites located in the Nebraska National Forest. Results indicated that tree-ring growth increased over time, more at the sparse than at the dense stands. In addition, the carbon and oxygen isotopic ratios showed long-term increases in intrinsic water use efficiency (WUEi), with little difference between dense and sparse stands. We found a clear trend over time in ponderosa pine tree growth and WUEi, mechanistically linked to long-term changes in global CO2 concentration. The study also highlighted that global factors tend to outweigh local effects of stand density in determining long-term trends in ponderosa pine growth. Finally, we discuss the implications of these results for woody encroachment into grasslands of Nebraska and we underlined how the use of long-term time series is crucial for understanding those ecosystems and to guarantee their conservation

Higher Order Integrability in Generalized Holonomy

Supersymmetric backgrounds in M-theory often involve four-form flux in addition to pure geometry. In such cases, the classification of supersymmetric vacua involves the notion of generalized holonomy taking values in SL(32,R), the Clifford group for eleven-dimensional spinors. Although previous investigations of generalized holonomy have focused on the curvature \Rm_{MN}(\Omega) of the generalized SL(32,R) connection \Omega_M, we demonstrate that this local information is incomplete, and that satisfying the higher order integrability conditions is an essential feature of generalized holonomy. We also show that, while this result differs from the case of ordinary Riemannian holonomy, it is nevertheless compatible with the Ambrose-Singer holonomy theorem.Comment: 19 pages, Late

FlowerPhenoNet: Automated Flower Detection from Multi-View Image Sequences Using Deep Neural Networks for Temporal Plant Phenotyping Analysis

A phenotype is the composite of an observable expression of a genome for traits in a given environment. The trajectories of phenotypes computed from an image sequence and timing of important events in a plant’s life cycle can be viewed as temporal phenotypes and indicative of the plant’s growth pattern and vigor. In this paper, we introduce a novel method called FlowerPhenoNet, which uses deep neural networks for detecting flowers from multiview image sequences for high-throughput temporal plant phenotyping analysis. Following flower detection, a set of novel flower-based phenotypes are computed, e.g., the day of emergence of the first flower in a plant’s life cycle, the total number of flowers present in the plant at a given time, the highest number of flowers bloomed in the plant, growth trajectory of a flower, and the blooming trajectory of a plant. To develop a new algorithm and facilitate performance evaluation based on experimental analysis, a benchmark dataset is indispensable. Thus, we introduce a benchmark dataset called FlowerPheno, which comprises image sequences of three flowering plant species, e.g., sunflower, coleus, and canna, captured by a visible light camera in a high-throughput plant phenotyping platform from multiple view angles. The experimental analyses on the FlowerPheno dataset demonstrate the efficacy of the FlowerPhenoNet

Environmentally Marginalized Populations: the perfect storm for infectious disease pandemics, including COVID-19

COVID-19 has exacted a severe toll on the United States population’s physical and mental health and its effects have been felt most severely among people of color and low socioeconomic status. Using illustrative case studies, this commentary argues that in addition to COVID-19 health disparities created by psychosocial stressors such as the inability to socially distance and access quality healthcare, environmental justice communities have the additional burden of disproportionate exposure to toxic contaminants that contribute to their higher risk of COVID-19. Environmental contaminants including heavy metals and persistent organic pollutants found contaminating their nearby environments can alter the immune response, produce an inflammatory response, and induce systemic adverse health effects that, alongside social stressors, create the “perfect storm” in environmental justice communities for COVID-19

Instanton Cosmology and Domain Walls from M-theory and String Theory

The recent proposal by Hawking and Turok for obtaining an open inflationary universe from singular instantons makes use of low-energy effective Lagrangians describing gravity coupled to scalars and non-propagating antisymmetric tensors. In this paper we derive some exact results for Lagrangians of this type, obtained from spherical compactifications of M-theory and string theory. In the case of the S^7 compactification of M-theory, we give a detailed discussion of the cosmological solutions. We also show that the lower-dimensional Lagrangians admit domain-wall solutions, which preserve one half of the supersymmetry, and which approach AdS spacetimes near their horizons.Comment: 51 pages, Latex (3 times). Discussion and references adde