Menopausal Mother Nature

News about Climate Change and our Planet

machine learning

Traffic density, wind and air stratification influence concentrations of air pollutant NO2

(Leibniz Institute for Tropospheric Research (TROPOS)) Traffic density is the most important factor for much the air pollutant nitrogen dioxide (NO2). However, weather also has an influence, according to a study by TROPOS, which evaluated the influence of weather conditions on nitrogen dioxide concentrations in Saxony 2015 to 2018 on behalf of LfULG. It was shown that wind speed and the height of the lowest air layer are the most important factors that determine how much pollutants can accumulate locally.

Artificial intelligence could revolutionize sea ice warnings

(UiT The Arctic University of Norway) Today, large resources are used to provide vessels in the polar seas with warnings about the spread of sea ice. Artificial intelligence may make these warnings cheaper, faster, and available for everyone.

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Repairing thousands of disease-causing mutations

Researchers have created a new searchable library of base editors — an especially efficient and precise kind of genetic corrector. Using experimental data from editing more than 38,000 target sites in cells with 11 of the most popular base editors (BEs), they created a machine learning model that accurately predicts base editing outcomes. Called BE-Hive, the library is free and open to the public.

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Repairing thousands of disease-causing mutations

Researchers have created a new searchable library of base editors — an especially efficient and precise kind of genetic corrector. Using experimental data from editing more than 38,000 target sites in cells with 11 of the most popular base editors (BEs), they created a machine learning model that accurately predicts base editing outcomes. Called BE-Hive, the library is free and open to the public.

Uncategorized

Repairing thousands of disease-causing mutations

Researchers have created a new searchable library of base editors — an especially efficient and precise kind of genetic corrector. Using experimental data from editing more than 38,000 target sites in cells with 11 of the most popular base editors (BEs), they created a machine learning model that accurately predicts base editing outcomes. Called BE-Hive, the library is free and open to the public.

Get excited by neural networks

(Institute of Industrial Science, The University of Tokyo) Scientists at The University of Tokyo introduced a new method for inferring the energy of the excited states of electrons in materials using machine learning. By rapidly predicting these values, this work can help better understand material properties and develop new substances.

Smart farms of the future: Making bioenergy crops more environmentally friendly

(DOE/Lawrence Berkeley National Laboratory) Farmers have enough worries — between bad weather, rising costs, and shifting market demands — without having to stress about the carbon footprint of their operations. But now a new set of projects by scientists at Lawrence Berkeley National Laboratory (Berkeley Lab) could make agriculture both more sustainable and more profitable.

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A good egg: Robot chef trained to make omelettes

A team of engineers have trained a robot to prepare an omelette, all the way from cracking the eggs to plating the finished dish, and refined the ‘chef’s’ culinary skills to produce a reliable dish that actually tastes good.

DFG establishes 14 new priority programs

(Deutsche Forschungsgemeinschaft) Topics range from sensor-integrating machine elements and the systems ecology of soils to the theoretical principles of deep learning / Around €85 million for three years.