Wildfire spreading prediction using multimodal data and deep neural network approach.

Predicting wildfire spread behavior is an extremely important task for many countries. On a small scale, it is possible to ensure constant monitoring of the natural landscape through ground means. However, on the scale of large countries, this becomes practically impossible due to remote and vast forest territories.

Forest age estimation in northern Arkhangelsk region based on machine learning pipeline on Sentinel-2 and auxiliary data.

Tree age is one of the key characteristics of a forest, along with tree species and height. It affects management decisions of forest owners and allows researchers to analyze environmental characteristics in support of sustainable development. Although forest age is of primary significance, it can be unknown for remote areas and large territories.

Deep Learning in Precision Agriculture: Artificially Generated VNIR Images Segmentation for Early Postharvest Decay Prediction in Apples.

Food quality control is an important task in the agricultural domain at the postharvest stage for avoiding food losses. The latest achievements in image processing with deep learning (DL) and computer vision (CV) approaches provide a number of effective tools based on the image colorization and image-to-image translation for plant quality control at the postharvest stage. In this article, we propose the approach based on Generative Adversarial Network (GAN) and Convolutional Neural Network (CNN) techniques to use synthesized and segmented VNIR imaging data for early postharvest decay and fungal zone predictions as well as the quality assessment of stored apples.

Large-scale forecasting of Heracleum sosnowskyi habitat suitability under the climate change on publicly available data.

This research aims to establish the possible habitat suitability of Heracleum sosnowskyi (HS), one of the most aggressive invasive plants, in current and future climate conditions across the territory of the European part of Russia. We utilised a species distribution modelling framework using publicly available data of plant occurrence collected in citizen science projects (CSP). Climatic variables and soil characteristics were considered to follow possible dependencies with environmental factors.

Regulation-based probabilistic substance quality index and automated geo-spatial modeling for water quality assessment.

Natural environments are recognized as complex heterogeneous structures thus requiring numerous multi-scale observations to yield a comprehensive description. To monitor the current state and identify negative impacts of human activity, fast and precise instruments are in urgent need. This work provides an automated approach to the assessment of spatial variability of water quality using guideline values on the example of 1526 water samples comprising 21 parameters at 448 unique locations across the New Moscow region (Russia).

Generation of the NIR Spectral Band for Satellite Images with Convolutional Neural Networks.

The near-infrared (NIR) spectral range (from 780 to 2500 nm) of the multispectral remote sensing imagery provides vital information for landcover classification, especially concerning vegetation assessment. Despite the usefulness of NIR, it does not always accomplish common RGB. Modern achievements in image processing via deep neural networks make it possible to generate artificial spectral information, for example, to solve the image colorization problem.

Phylogenetic relationships and status of taxa of and s.str. (Ranunculaceae) in north-eastern European Russia.

We studied the allopatric complex (L.) Mill. s.

Radioresistance, DNA Damage and DNA Repair in Cells With Moderate Overexpression of RPA1.

Molecular responses to genotoxic stress, such as ionizing radiation, are intricately complex and involve hundreds of genes. Whether targeted overexpression of an endogenous gene can enhance resistance to ionizing radiation remains to be explored. In the present study we take an advantage of the CRISPR/dCas9 technology to moderately overexpress the gene that encodes a key functional subunit of the replication protein A (RPA).

Genetic analysis in earthworm population from area contaminated with radionuclides and heavy metals.

This study assessed the effects of environmental contamination by naturally occurring radionuclides and heavy metals on the genetic structure of a population of the earthworm Aporrectodea caliginosa. A. caliginosa were collected from four sites and characterized by amplified fragment length polymorphism (AFLP) analyses.

Artificial intelligence models to predict acute phytotoxicity in petroleum contaminated soils.

Environment pollutants, especially those from total petroleum hydrocarbons (TPH), have a highly complex chemical, biological and physical impact on soils. Here we study this influence via modelling the TPH acute phytotoxicity effects on eleven samples of soils from Sakhalin island in greenhouse conditions. The soils were contaminated with crude oil in different doses ranging from the 3.

New records of Lumbricidae and Collembola in anthropogenic soils of East European tundra.

The terrestrial environment of the East European tundra consists of a mosaic of habitat types. In addition to the natural habitat diversity, various human-influenced types may occur. In the town of Vorkuta, Komi Republic, Russia the manure-enriched soils near hydrogen sulfide springs were observed.

Population and molecular datasets for (Orchidaceae).

The paper presents data on the ecological and phytocoenotic conditions of habitats of the (L.) R. Br.

Bayesian optimization for seed germination.

Background: Efficient seed germination is a crucial task at the beginning of crop cultivation. Although boundaries of environmental parameters that should be maintained are well studied, fine-tuning can significantly improve the efficiency, which is infeasible to be done manually due to the high dimensionality of the parameter space.

Results: Traditionally seed germination is performed in climatic chambers with controlled environmental conditions.

Programming of Cell Resistance to Genotoxic and Oxidative Stress.

Different organisms, cell types, and even similar cell lines can dramatically differ in resistance to genotoxic stress. This testifies to the wide opportunities for genetic and epigenetic regulation of stress resistance. These opportunities could be used to increase the effectiveness of cancer therapy, develop new varieties of plants and animals, and search for new pharmacological targets to enhance human radioresistance, which can be used for manned deep space expeditions.

Differential Molecular Stress Responses to Low Compared to High Doses of Ionizing Radiation in Normal Human Fibroblasts.

Understanding the mechanisms producing low dose ionizing radiation specific biological effects represents one of the major challenges of radiation biology. Although experimental evidence does suggest that various molecular stress response pathways may be involved in the production of low dose effects, much of the detail of those mechanisms remains elusive. We hypothesized that the regulation of various stress response pathways upon irradiation may differ from one another in complex dose-response manners, causing the specific and subtle low dose radiation effects.