Three-dimensional force microscope: A nanometric optical tracking and magnetic manipulation system for the biomedical sciences

We report here the development of a three-dimensional (3D) magnetic force microscope for applying forces to and measuring responses of biological systems and materials. This instrument combines a conventional optical microscope with a free-floating or specifically bound magnetic bead used as a mechanical probe. Forces can be applied by the bead to microscopic structures of interest (specimens), while the reaction displacement of the bead is measured. This enables 3D mechanical manipulations and measurements to be performed on specimens in fluids. Force is generated by the magnetically permeable bead in reaction to fields produced by external electromagnets. The displacement is measured by interferometry using forward light scattered by the bead from a focused laser beam. The far-field interference pattern is imaged on a quadrant photodetector from which the 3D displacement can be computed over a limited range about the focal point. The bead and specimen are mounted on a 3D translation stage and feedback techniques are used to keep the bead within this limited range. We demonstrate the system with application to beads attached to cilia in human lung cell cultures

Gap-filling eddy covariance methane fluxes:Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET)

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Summery of Interface Meetings and Way Forward

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JMJD6 is a driver of cellular proliferation and motility and a marker of poor prognosis in breast cancer

10.1186/bcr3200Breast Cancer Research143-BCRR

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Not AvailableConservation agriculture (CA) is a key climate resilient and resource saving technology for higher productivity while reversing soil degradation in rainfed regions. In India, CA in the rice -wheat system of the Indo-Gangetic Plains (IGP) of south Asia has been extensively studied. However, relatively less attention was given to develop strategies to overcome the constraints in the adoption of CA in rainfed regions. Therefore, studies were initiated in rainfed regions under different cropping systems and soil types to standardize the best management practices and to address various constraints related to adoption of CA. Based on the results of experiments conducted in various agro ecosystems it has been found that the effect of CA on crop productivity and soil properties in different experiments are variable, depending on the management factors and duration of the study. Adoption of CA resulted in improvement in crop yield to the extent of 9-36.7% under different cropping systems, increase in net monetary returns by 1487% and rain water productivity by 4-25%. The water infiltration rate was increased by 53.2 -56.8 %, soil organic carbon content increased by 5-45.1% under different cropping systems at different soil depths. The available soil moisture content increased by 1.8-46.8% and the available soil nitrogen, phosphorus and potassium increased by 2.7-41.6,0.6-64.8 and 6.1-26.2%, respectively. The energy input under CA decreased by 0.9-57.6%, energy saving increased by 0.9- 34.88% and the energy use efficiency increased by 9.47-66.8%. The runoff and soil loss also decreased by 17.6-37.9% and 44.756.5%, respectively under CA as compared to conventional tillage (CT). Furthermore, we have observed that CA integrated with complementary practices like in situ moisture conservation (through permanent conservation furrow or permanent raised bed and furrow) in maize/horse gram-pigeonpea, maizepigeonpea system, weed and nutrient management practices in maize-pigeonpea, pearl millet-pigeonpea and cotton-pigeonpea improved the crop productivity and soil health in rainfed agro-ecosystems. Increase in crop residue retention either through manipulation of harvest height to 30-60 cm in cereals and live mulch with dhaincha in pigeonpea-castor system, improve soil health, resilience to climate change, productivity and profitability. These technologies have feasibility of adoption by the farmers.Not Availabl