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This study evaluated a novel smartphone-based soil image segmentation technique and subsequent machine learning (ML) optimization methodology with a set of soil images for rapidly predicting soil organic matter (SOM) with minimal soil processing. A smartphone and a custom-made box were used to capture images for 90 soil samples, collected from three different agroclimatic zones of West Bengal, India under three different illumination conditions. To offset the impact of variable illumination, the reflectance component of the image was recovered by removing the illumination from the image. Further, to deceive the ML model without distorting the soil image, an adversarial image was generated by adding Gaussian noise to the image. A Tree-based Pipeline Optimisation Tool was used to find an optimum ML stacking scheme using six different ML models. Model validation statistics indicated that reflectance image-extracted sub-colour space could predict SOM with reasonable accuracy (R2 = 0.88, RMSE = 0.28%) using original images in stack one. Moreover, the sub-colour space using perturbed images in stack one could sense noise, worsening the model validation (R2 = 0.79, RMSE = 0.36%). Conversely, seven out of eight tested colour spaces in stack two were unable to sense the image noise, producing higher validation performance than the original images. The proposed smartphone-based image acquisition setup combined with the computer vision and ML pipeline produced an important advance in affordable optical tool-based SOM prediction with significant time and cost savings. More research is warranted to extend this approach by incorporating field images of variable soil types taken under variable illuminations. • Smartphone captured soil images were used to predict SOM. • The image reflectance was recovered to remove variable illumination. • Adversarial images were generated by adding Gaussian noise to the original images. • SOM prediction model produced good validation accuracy using original images. • Inclusion of perturbed images was able to detect noise. [ABSTRACT FROM AUTHOR]

Abstract Background China and other developing countries in Asia follow similar economic growth patterns described by the flying geese (FG) model, which explains the "catching-up" process of industrialization in latecomer economies. Japan, newly industrialized economies, and China have followed this path, with similar economic development trajectories. Based on the FG model, we postulated a "flying S" hypothesis stating that if a country is located within an FG region and its energy matrix is relatively constant, its per capita CO 2 emission curve will mirror that of "leading geese" countries in the same FG group. Method Historical CO 2 emissions data were obtained from literature review and national reports and were calculated using bottom-up methods. A sigmoid-shaped, non-linear mixed effect model was applied to examine ex post data with 1000 simulated predictions to construct 95% empirical bands from these fits. By multiplying by estimated population, we predicted total emissions of selected FG countries. Results Per capita CO 2 emissions from the same FG group mirror each other, especially among second and third industrial sectors. We estimated an annual 18,252.24 million tons of CO 2 emissions (MtCO 2) (95% CI = 9458.88–23,972.88) in China and 8281.76 MtCO2 (95% CI = 2765.68–14,959.12) in India in 2030. Conclusion This study bridges the macroeconomic FG paradigm to study climate change and proposes a "flying S" hypothesis to predict greenhouse gas emissions in East Asia. By applying our theory to empirical data, we provide an alternative framework to predict CO 2 emissions in 2030 and beyond. Highlights • Per capita CO 2 emissions from developing countries in Asia follow economic growth patterns, described as flying geese model. • Per capita CO 2 emissions trajectory in one country can forecast the emissions in another, under two assumptions. • Our flying-S model provides business-as-usual scenario, predicting counterfactual CO 2 emissions without any intervention. • In 2030, we estimated more than 18 billion tons of CO 2 (BtCO 2) emission in China, and 8 BtCO 2 in India. [ABSTRACT FROM AUTHOR]

Abstract: Little is known about the neutralization properties of HIV-1 in India to optimally design and test vaccines. For this reason, a functional Env clone was obtained from each of ten newly acquired, heterosexually transmitted HIV-1 infections in Pune, Maharashtra. These clones formed a phylogenetically distinct genetic lineage within subtype C. As Env-pseudotyped viruses the clones were mostly resistant to IgG1b12, 2G12 and 2F5 but all were sensitive to 4E10. When compared to a large multi-subtype panel of Env-pseudotyped viruses (subtypes B, C and CRF02_AG) in neutralization assays with a multi-subtype panel of HIV-1-positive plasma samples, the Indian Envs were remarkably complex. With the exception of the Indian Envs, results of a hierarchical clustering analysis showed a strong subtype association with the patterns of neutralization susceptibility. From these patterns we were able to identify 19 neutralization cluster-associated amino acid signatures in gp120 and 14 signatures in the ectodomain and cytoplasmic tail of gp41. We conclude that newly transmitted Indian Envs are antigenically complex in spite of close genetic similarity. Delineation of neutralization-associated amino acid signatures provides a deeper understanding of the antigenic structure of HIV-1 Env. [Copyright &y& Elsevier]