RUDN University biologist developed new model for analyzing photosynthesis in vivo
Measurements of the efficiency of photosynthesis in living systems are necessary because they allow us to estimate the carbon cycle, and therefore the impact on the climate. To study photosynthesis in vivo, the vegetation absorption coefficient is used – a value that shows how deep the incident radiation penetrates the canopy. It depends on biochemical, structural and external factors, so its evaluation is very difficult. Alexei Solovchenko, an employee of RUDN University, and his colleagues from the USA and Israel have found a new way to assess this indicator.
First, biologists calculated the ratio of absorption and transmission coefficients for individual leaves and canopy in general. Measuring these coefficients for the canopy “in sum” is difficult, but for a single leaf it is simple, so knowing the ratio between them, you can calculate the absorption and transmission of canopy, knowing the coefficients for a single leaf. Then the researchers of RUDN University obtained an equation that connects the canopy absorption coefficient to the pigments absorption coefficient – primarily chlorophyll – in leaves. It turned out that the canopy, unlike a single leaf, can absorb light in the infrared range, and also, the absorption coefficients of pigments for plants with different densities of canopy, may differ. Therefore, biologists had to make appropriate changes to the final model.
The researchers tested this mathematical model describing the canopy absorption coefficient on crops with different types of photosynthesis – corn (C4 photosynthesis), soybeans and rice (C3 photosynthesis), measuring the spectra of absorbed and reflected solar radiation.
The model showed that in the blue spectral region, the canopy of rice reflects more than the canopy of other crops. Scientists believe it is because rice grows in water. Also, absorption curves for plants with C3 type of photosynthesis (soybeans and rice) obtained with the model differed from those of plants with C4 type of photosynthesis (corn), due to biochemical differences.
Thus, the model created by biologists can "predict" the absorption of light by different types of plants with different types of photosynthesis, different canopy architectures and different pigment content in the leaf.
The article was published in Remote Sensing of Environment.
Imagine a world where everyone has enough food, clean water, access to education, and decent work. A world where nature is protected and the future of our planet is cared for. These are the Sustainable Development Goals—to achieve a sustainable future for all! To this end, in 2015, the United Nations (UN) defined 17 Sustainable Development Goals (SDGs). The SDGs are a global plan that helps countries and people work together towards a better future. All 193 UN member states have joined the plan.
Researchers from the Faculty of Artificial Intelligence at RUDN University conducted a large-scale study that revealed systemic errors in large language models (LLMs) when diagnosing depression based on text. This work, carried out in collaboration with colleagues from AIRI, Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Ivannikov Institute for System Programming of the Russian Academy of Sciences, Moscow Institute of Physics and Technology, and MBZUAI, not only identifies the problem but also lays the foundation for the creation of more reliable and secure tools for detecting depression and anxiety.
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Imagine a world where everyone has enough food, clean water, access to education, and decent work. A world where nature is protected and the future of our planet is cared for. These are the Sustainable Development Goals—to achieve a sustainable future for all! To this end, in 2015, the United Nations (UN) defined 17 Sustainable Development Goals (SDGs). The SDGs are a global plan that helps countries and people work together towards a better future. All 193 UN member states have joined the plan.
Researchers from the Faculty of Artificial Intelligence at RUDN University conducted a large-scale study that revealed systemic errors in large language models (LLMs) when diagnosing depression based on text. This work, carried out in collaboration with colleagues from AIRI, Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Ivannikov Institute for System Programming of the Russian Academy of Sciences, Moscow Institute of Physics and Technology, and MBZUAI, not only identifies the problem but also lays the foundation for the creation of more reliable and secure tools for detecting depression and anxiety.
Alexandra Sentyabreva, a junior researcher at the Laboratory of Cell Technologies and Tissue Engineering at RUDN Research Institute of Molecular and Cellular Medicine at the Russian University of People's Friendship, won the competition for young scientists at the All-Russian Scientific Conference “Topical Issues of Morphogenesis in Norm and Pathology.” She was awarded the Academician A.P. Avtsyn Prize.