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Blue-grain lines were created on the basis of the spring bread wheat variety Saratovskaya 29 (S29) with chromosome 4B or 4D replaced with chromosome 4Th from Thinopyrum ponticum. The leaf pubescence of the two lines differs from S29 and from each other. In this work, we studied the effect of these substitutions on the manifestation of this trait. To quantify pubescence, the LHDetect2 program was used to determine trichome length and number on the leaf fold microphotographs. The key gene Hl1 on chromosome 4B and another unidentified gene with a weak effect determine the leaf pubescence of the recipient S29. Their interaction leads to the formation of trichomes of up to 300 microns in length. Replacement of both copies of chromosome 4B with two copies of wheatgrass chromosome 4Th modifies leaf pubescence in line S29_4Th(4B) so that the leaf pubescence characteristic of S29 becomes more sparse, and trichomes of up to 600–700 µm in length are formed. Additionally, we described modification of pubescence in the substitution line S29_4Th(4D) where chromosome 4D that does not carry any pubescence gene was replaced. Under this substitution, trichomes of up to 400 µm in length were formed and the average length of trichomes on the underside of the leaf was reduced. The replacement of the Hl1 gene in the lines was also confirmed by the allelic state of the linked microsatellite marker Xgwm538. Thus, as a result of the studies, a new leaf pubescence gene introgressed from Th. ponticum into bread wheat was identified. We designated it as Hl1th. For the purpose of selection, we propose to use the unlicensed informative microsatellite markers Xgwm538 and Xgwm165, allowing chromosomes 4A, 4B, 4D and 4Th to be distinguished.

Analysis of hyperspectral images is of great interest in plant studies. Nowadays, this analysis is used more and more widely, so the development of hyperspectral image processing methods is an urgent task. This paper presents a hyperspectral image processing pipeline that includes: preprocessing, basic statistical analysis, visualization of a multichannel hyperspectral image, and solving classification and clustering problems using machine learning methods. The current version of the package implements the following methods: construction of a confidence interval of an arbitrary level for the difference of sample averages; verification of the similarity of intensity distributions of spectral lines for two sets of hyperspectral images on the basis of the Mann–Whitney U-criterion and Pearson’s criterion of agreement; visualization in two-dimensional space using dimensionality reduction methods PCA, ISOMAP and UMAP; classification using linear or ridge regression, random forest and catboost; clustering of samples using the EM-algorithm. The software pipeline is implemented in Python using the Pandas, NumPy, OpenCV, SciPy, Sklearn, Umap, CatBoost and Plotly libraries. The source code is available at: https://github.com/igor2704/Hyperspectral_images. The pipeline was applied to identify melanin pigment in the shell of barley grains based on hyperspectral data. Visualization based on PCA, UMAP and ISOMAP methods, as well as the use of clustering algorithms, showed that a linear separation of grain samples with and without pigmentation could be performed with high accuracy based on hyperspectral data. The analysis revealed statistically significant differences in the distribution of median intensities for samples of images of grains with and without pigmentation. Thus, it was demonstrated that hyperspectral images can be used to determine the presence or absence of melanin in barley grains with great accuracy. The flexible and convenient tool created in this work will significantly increase the efficiency of hyperspectral image analysis.

Crop field monitoring using unmanned aerial vehicles (UAVs) is one of the most important technologies for plant growth control in modern precision agriculture. One of the important and widely used tasks in field monitoring is plant stand counting. The accurate identification of plants in field images provides estimates of plant number per unit area, detects missing seedlings, and predicts crop yield. Current methods are based on the detection of plants in images obtained from UAVs by means of computer vision algorithms and deep learning neural networks. These approaches depend on image spatial resolution and the quality of plant markup. The performance of automatic plant detection may affect the efficiency of downstream analysis of a field cropping pattern. In the present work, a method is presented for detecting the plants of five species in images acquired via a UAV on the basis of image segmentation by deep learning algorithms (convolutional neural networks). Twelve orthomosaics were collected and marked at several sites in Russia to train and test the neural network algorithms. Additionally, 17 existing datasets of various spatial resolutions and markup quality levels from the Roboflow service were used to extend training image sets. Finally, we compared several texture features between manually evaluated and neural-network-estimated plant masks. It was demonstrated that adding images to the training sample (even those of lower resolution and markup quality) improves plant stand counting significantly. The work indicates how the accuracy of plant detection in field images may affect their cropping pattern evaluation by means of texture characteristics. For some of the characteristics (GLCM mean, GLRM long run, GLRM run ratio) the estimates between images marked manually and automatically are close. For others, the differences are large and may lead to erroneous conclusions about the properties of field cropping patterns. Nonetheless, overall, plant detection algorithms with a higher accuracy show better agreement with the estimates of texture parameters obtained from manually marked images.

The pigment composition of plant seed coat affects important properties such as resistance to pathogens, pre-harvest sprouting, and mechanical hardness. The dark color of barley (Hordeum vulgare L.) grain can be attributed to the synthesis and accumulation of two groups of pigments. Blue and purple grain color is associated with the biosynthesis of anthocyanins. Gray and black grain color is caused by melanin. These pigments may accumulate in the grain shells both individually and together. Therefore, it is difficult to visually distinguish which pigments are responsible for the dark color of the grain. Chemical methods are used to accurately determine the presence/absence of pigments; however, they are expensive and labor-intensive. Therefore, the development of a new method for quickly assessing the presence of pigments in the grain would help in investigating the mechanisms of genetic control of the pigment composition of barley grains. In this work, we developed a method for assessing the presence or absence of anthocyanins and melanin in the barley grain shell based on digital image analysis using computer vision and machine learning algo rithms. A protocol was developed to obtain digital RGB images of barley grains. Using this protocol, a total of 972 images were acquired for 108 barley accessions. Seed coat from these accessions may contain anthocyanins, melanins, or pigments of both types. Chemical methods were used to accurately determine the pigment content of the grains. Four models based on computer vision techniques and convolutional neural networks of different architectures were developed to predict grain pigment composition from images. The U-Net network model based on the EfficientNetB0 topology showed the best performance in the holdout set (the value of the “accuracy” parameter was 0.821).

The spike shape and morphometric characteristics are among the key characteristics of cultivated cereals, being associated with their productivity. These traits are often used for the plant taxonomy and authenticity of hexaploid wheat species. Manual measurement of spike characteristics is tedious and not precise. Recently, the authors of this study developed a method for wheat spike morphometry utilizing 2D image analysis. Here, this method is applied to study variations in spike size and shape for 190 plants of seven hexaploid (2n = 6x = 42) species and one artificial amphidiploid of wheat. Five manually estimated spike traits and 26 traits obtained from digital image analysis were analyzed. Image-based traits describe the characteristics of the base, center and apex of the spike and common parameters (circularity, roundness, perimeter, etc.). Estimates of similar traits by manual measurement and image analysis were shown to be highly correlated, suggesting the practical importance of digital spike phenotyping. The utility of spike traits for classification into types (spelt, normal and compact) and species or amphidiploid is shown. It is also demonstrated that the estimates obtained made it possible to identify the spike characteristics differing significantly between species or between accessions within the same species. The present work suggests the usefulness of wheat spike shape analysis using an approach based on characteristics obtained by digital image analysis.

IntroductionPubescence is an important phenotypic trait observed in both vegetative and generative plant organs. Pubescent plants demonstrate increased resistance to various environmental stresses such as drought, low temperatures, and pests. It serves as a significant morphological marker and aids in selecting stress-resistant cultivars, particularly in wheat. In wheat, pubescence is visible on leaves, leaf sheath, glumes and nodes. Regarding glumes, the presence of pubescence plays a pivotal role in its classification. It supplements other spike characteristics, aiding in distinguishing between different varieties within the wheat species. The determination of pubescence typically involves visual analysis by an expert. However, methods without the use of binocular loupe tend to be subjective, while employing additional equipment is labor-intensive. This paper proposes an integrated approach to determine glume pubescence presence in spike images captured under laboratory conditions using a digital camera and convolutional neural networks.MethodsInitially, image segmentation is conducted to extract the contour of the spike body, followed by cropping of the spike images to an equal size. These images are then classified based on glume pubescence (pubescent/glabrous) using various convolutional neural network architectures (Resnet-18, EfficientNet-B0, and EfficientNet-B1). The networks were trained and tested on a dataset comprising 9,719 spike images.ResultsFor segmentation, the U-Net model with EfficientNet-B1 encoder was chosen, achieving the segmentation accuracy IoU = 0.947 for the spike body and 0.777 for awns. The classification model for glume pubescence with the highest performance utilized the EfficientNet-B1 architecture. On the test sample, the model exhibited prediction accuracy parameters of F1 = 0.85 and AUC = 0.96, while on the holdout sample it showed F1 = 0.84 and AUC = 0.89. Additionally, the study investigated the relationship between image scale, artificial distortions, and model prediction performance, revealing that higher magnification and smaller distortions yielded a more accurate prediction of glume pubescence.

Crop field monitoring using unmanned aerial vehicles (UAVs) is one of the most important technologies for plant growth control in modern precision agriculture. One of the important and widely used tasks in field monitoring is plant stand counting. The accurate identification of plants in field images provides estimates of plant number per unit area, detects missing seedlings, and predicts crop yield. Current methods are based on the detection of plants in images obtained from UAVs by means of computer vision algorithms and deep learning neural networks. These approaches depend on image spatial resolution and the quality of plant markup. The performance of automatic plant detection may affect the efficiency of downstream analysis of a field cropping pattern. In the present work, a method is presented for detecting the plants of five species in images acquired via a UAV on the basis of image segmentation by deep learning algorithms (convolutional neural networks). Twelve orthomosaics were collected and marked at several sites in Russia to train and test the neural network algorithms. Additionally, 17 existing datasets of various spatial resolutions and markup quality levels from the Roboflow service were used to extend training image sets. Finally, we compared several texture features between manually evaluated and neural-network-estimated plant masks. It was demonstrated that adding images to the training sample (even those of lower resolution and markup quality) improves plant stand counting significantly. The work indicates how the accuracy of plant detection in field images may affect their cropping pattern evaluation by means of texture characteristics. For some of the characteristics (GLCM mean, GLRM long run, GLRM run ratio) the estimates between images marked manually and automatically are close. For others, the differences are large and may lead to erroneous conclusions about the properties of field cropping patterns. Nonetheless, overall, plant detection algorithms with a higher accuracy show better agreement with the estimates of texture parameters obtained from manually marked images. [ABSTRACT FROM AUTHOR]

Proline, an amino acid, plays an important role in plants, and it is involved in stress resistance and development. Earlier, to study the proline role in maintaining stress resistance in plants, we obtained genetically modified transgenic lines of tobacco (Nicotiana tabacum L.) with reduced activity of proline dehydrogenase (PDH, the proline degradation gene) and increased content of proline. Transgenic tobacco plants demonstrated greater resistance to high concentrations of NaCl, drought, low temperatures, and heavy metals vs. control plants. The visual assessment showed that the leaf pubescence in transgenic plants varied noticeably. Here we apply automated analysis of the tobacco leaf folds to estimate quantitative characteristics of pubescence in genetically modified tobacco plants and the control SR1 line under non-stress conditions. Our results showed differences in the number of trichomes and their length between transgenic and control plants. The trichome number significantly increased in transgenic plants (from 1.5 to 3 times). The largest differences in the trichome numbers were observed for trichomes with lengths from 0 to 380 µm. When assessing the trichome length, the opposite was observed. In all three transgenic lines, the trichome length was significantly lower than that of the control SR1 line. The data obtained indicate the effect of proline as an important metabolome component affecting the plant phenotype. Our results demonstrate perspectives of tobacco transgenic lines as promising genetic models for studying the proline role in plant morphogenesis.

Remote sensing using hyperspectral cameras is an important technology for non-destructive monitoring of plant pigment composition, which is closely related to their physiological state or infection with pathogens. The paper presents the experience of using Specim IQ, a mobile hyperspectral camera, to study common root rot (the pathogen is the fungus Bipolaris sorokiniana Shoem.) affecting the seedlings of four wheat varieties and to analyze the pulp of potato tubers of 82 lines and varieties. Spectral characteristics were obtained for seedlings and the most informative spectral features (indices) for root rot detection were determined based on the data obtained. Seedlings of control variants in the visible part of the spectrum show an increase in reflectance with a small peak in the green area (about 550 nm), then a decrease due to light absorption by plant pigments with an extremum at a wavelength of about 680 nm. Analysis of histograms of vegetation index values demonstrated that the TVI and MCARI indices are the most informative for detecting the pathogen on wheat seedlings according to hyperspectral survey data. For potato samples, regions of the spectrum were found that correspond to local maxima and minima of reflection. It was shown that the spectra of potato varieties have the greatest differences within wavelength ranges of 900-1000 nm and 400-450 nm, which in the former case may be associated with the level of water content, and in the latter, with the formation of melanin in the tubers. It was shown that according to the characteristics of the spectrum, the samples studied are divided into three groups, each characterized by increased or reduced intensity levels for the specified parts of the spectrum. In addition, minima in the reflection spectra corresponding to chlorophyll a were found for a number of varieties. The results demonstrate the capabilities of the Specim IQ camera for conducting hyperspectral analyses of plant objects.

The spike shape and morphometric characteristics are among the key characteristics of cultivated cereals, being associated with their productivity. These traits are often used for the plant taxonomy and authenticity of hexaploid wheat species. Manual measurement of spike characteristics is tedious and not precise. Recently, the authors of this study developed a method for wheat spike morphometry utilizing 2D image analysis. Here, this method is applied to study variations in spike size and shape for 190 plants of seven hexaploid (2n = 6x = 42) species and one artificial amphidiploid of wheat. Five manually estimated spike traits and 26 traits obtained from digital image analysis were analyzed. Image-based traits describe the characteristics of the base, center and apex of the spike and common parameters (circularity, roundness, perimeter, etc.). Estimates of similar traits by manual measurement and image analysis were shown to be highly correlated, suggesting the practical importance of digital spike phenotyping. The utility of spike traits for classification into types (spelt, normal and compact) and species or amphidiploid is shown. It is also demonstrated that the estimates obtained made it possible to identify the spike characteristics differing significantly between species or between accessions within the same species. The present work suggests the usefulness of wheat spike shape analysis using an approach based on characteristics obtained by digital image analysis. [ABSTRACT FROM AUTHOR]

Intraspecific classification of cultivated plants is necessary for the conservation of biological diversity, study of their origin and their phylogeny. The modern cultivated wheat species originated from three wild diploid ancestors as a result of several rounds of genome doubling and are represented by di-, tetra- and hexaploid species. The identification of wheat ploidy level is one of the main stages of their taxonomy. Such classification is possible based on visual analysis of the wheat spike traits. The aim of this study is to investigate the morphological characteristics of spikes for hexa- and tetraploid wheat species based on the method of high-performance phenotyping. Phenotyping of the quantitative characteristics of the spike of 17 wheat species (595 plants, 3348 images), including eight tetraploids (Triticum aethiopicum, T. dicoccoides, T. dicoccum, T. durum, T. militinae, T. polonicum, T. timopheevii, and T. turgidum) and nine hexaploids (T. compactum, T. aestivum, i:ANK-23 (near-isogenic line of T. aestivum cv. Novosibirskaya 67), T. antiquorum, T. spelta (including cv. Rother Sommer Kolben), T. petropavlovskyi, T. yunnanense, T. macha, T. sphaerococcum, and T. vavilovii), was performed. Wheat spike morphology was described on the basis of nine quantitative traits including shape, size and awns area of the spike. The traits were obtained as a result of image analysis using the WERecognizer program. A cluster analysis of plants according to the characteristics of the spike shape and comparison of their distributions in tetraploid and hexaploid species showed a higher variability of traits in hexaploid species compared to tetraploid ones. At the same time, the species themselves form two clusters in the visual characteristics of the spike. One type is predominantly hexaploid species (with the exception of one tetraploid, T. dicoccoides). The other group includes tetraploid ones (with the exception of three hexaploid ones, T. compactum, T. antiquorum, T. sphaerococcum, and i:ANK-23). Thus, it has been shown that the morphological characteristics of spikes for hexaploid and tetraploid wheat species, obtained on the basis of computer analysis of images, include differences, which are further used to develop methods for plant classifications by ploidy level and their species in an automatic mode.

The color of the grain shell of cereals is an important feature that characterizes the pigments and metabolites contained in it. The grain shell is the main barrier between the grain and the environment, so its characteristics are associated with a number of important biological functions: moisture absorption, grain viability, resistance to pre-harvest germination. The presence of pigments in the shell affects various technological properties of the grain. Color characteristics, as well as the appearance of the grain shell are an important indicator of plant diseases. In addition, the color of the grains serves as a classifying feature of plants. Genetic control of the color formation of both grains and other plant organs is exerted by genes encoding enzymes involved in the biosynthesis of pigments, as well as regulatory genes. For a number of pigments, these genes are well understood, but for some pigments, such as melanin, which causes the black color of grains in barley, the molecular mechanisms of biosynthesis are still poorly understood. When studying the mechanisms of genetic control of grain color, breeders and geneticists are constantly faced with the need to assess the color characteristics of their shell. The technical means of addressing this problem include spectrophotometers, spectrometers, hyperspectral cameras. However, these cameras are expensive, especially with high resolution, both spatial and spectral. An alternative is to use digital cameras that allow you to get high-quality images with high spatial and color resolution. In this regard, recently, in the field of plant phenotyping, methods for evaluating the color and texture characteristics of cereals based on the analysis of two-dimensional images obtained by digital cameras have been intensively developed. This mini-review is devoted to the main tasks related to the analysis of color and texture characteristics of cereals, and to methods of their description based on digital images.

Being a low‐toxic and hydrophilic representative of TAM, OX063 has shown its suitability for in‐vivo and in‐cell EPR experiments and design of spin labels. Using 13C labeling, we investigated the course of oxidative degradation of OX063 into quinone‐methide (QM) under the influence of superoxide as well as further thiol‐promoted reduction of QM into TAM radical, which formally corresponds to substitution of a carboxyl function by a hydroxyl group. We found these transformations being quantitative in model reactions mimicking specific features of biological media and confirmed the presence of these reactions in the blood and liver homogenate of mice in vitro. The emergence of the trityl with the hydroxyl group can be masked by an initial TAM in EPR spectra and may introduce distortions into EPR‐derived oximetry data if they have been obtained for objects under hypoxia. 13C labeling allows one to detect its presence, considering its different hyperfine splitting constant on 13C1 (2.04 mT) as compared to OX063 (2.30 mT). The potential involvement of these reactions should be considered when using TAM in spin‐labeling of biopolymers intended for subsequent EPR experiments, as well as in the successful application of TAM in experiments in vivo and in cell. [ABSTRACT FROM AUTHOR]

New fluorinated dibenzo-1,2,5,6-tetrathiocines 1 and 2 were synthesized by a novel condensation of 1,2-disulfenyl chlorides under the influence of elemental Cu, and structurally defined in the solid state, solution, and gas phase by single-crystal XRD, variable-temperature 19F NMR and NOESY, and DFT calculations. Crystalline 1 and 2 exhibited the C2h chair and C2 twist molecular conformations, respectively. Unlike 1 displayed only C⋯S shortened intermolecular contacts, crystal structure of 2 featured F⋯π, F⋯F, F⋯S and S⋯S contacts. In toluene solutions at 296 K, both twist and chair conformers were observed in the ∼1 : 1 and ∼7 : 1 ratios for 1 and 2, respectively. For 1, the twist ↔ chair conformer interconversion was practically temperature-independent, and for 2 revealed ΔH ∼3.5 kJ mol−1 and ΔS ∼4.4 J K−1 mol−1. With DFT, the PESs for twist–chair transformation and twist–twist racemization were analyzed and the TSs exhibiting twist–halfchair and boat conformations, respectively, were found. Several additional stationary points on the PESs, corresponding to hidden intermediates and bifurcation points and featuring twistboat and halfchair conformations were detected. In chloroform solution under sunlight, 1 underwent 1,2,5,6-tetrathiocine → 1,2,3,6-tetrathiocine isomerization whose product was characterized by XRD. [ABSTRACT FROM AUTHOR]

The development of quantitative digital phenotyping methods for evaluation of wild potato (section Petota Dumort., genus Solanum L.) tuberization is required for annotation of genebank collections and selection of the suitable donor material for potato breeding. There are no available methods specifically designed for the quantitative analysis of wild potato tuber morphology. The current study is devoted to evaluation of wild potato tubers’ morphological characteristics using a digital image processing technique. For this purpose, the mobile application SeedSounter developed previously for grain analysis was specifically adapted for tuber phenotyping. The application estimates the number and shape of objects scattered on a standard sheet of white paper (i. e. A3 or A4). Twelve accessions from the VIR genebank collection belonging to nine Petota species were grown in pots protected with garden fabric during the growing season of cultivated potato (Novosibirsk region). Tubers were collected form plants of nine genotypes. Three genotypes did not produce tubers. The weight of tubers collected from each plant was measured. The tuber yield from each plant was analyzed using SeedCounter (http://wheatdb.org/seedcounter). The number of tubers per plant was counted; the following characteristics were extracted from the images of individual tubers: length, width, projected area, length to width ratio, сircularity, roundness, rugosity and solidity. One-way ANOVA showed a significant effect of genotype on all measured characteristics. A pairwise comparison of nine Petota accessions using all measured parameters revealed statistically significant differences between 86 % of pairs. The overall tuber yield volume for each plant was calculated as a sum of volumes of individual tubers; tuber volume was calculated from its length to width ratio and projected area. A strong correlation between the evaluated tuber yield volume and yield weight was shown. We propose tuber yield volume as a characteristic for a general evaluation of tuberization for wild potato, implementing the four-step scale from 0 to 3. According to this characteristic, the twelve wild potato accessions studied could be divided into four groups with different tuberization abilities. The evaluated tuberization ability is partially in accordance with previously obtained VIR data. The results presented demonstrate the possibility to use SeedCounter for wild potato collections phenotyping.

Abstract Background The three epidemiologically important Opisthorchiidae liver flukes Opisthorchis felineus, O. viverrini, and Clonorchis sinensis, are believed to harbour similar potencies to provoke hepatobiliary diseases in their definitive hosts, although their populations have substantially different ecogeographical aspects including habitat, preferred hosts, population structure. Lack of O. felineus genomic data is an obstacle to the development of comparative molecular biological approaches necessary to obtain new knowledge about the biology of Opisthorchiidae trematodes, to identify essential pathways linked to parasite-host interaction, to predict genes that contribute to liver fluke pathogenesis and for the effective prevention and control of the disease. Results Here we present the first draft genome assembly of O. felineus and its gene repertoire accompanied by a comparative analysis with that of O. viverrini and Clonorchis sinensis. We observed both noticeably high heterozygosity of the sequenced individual and substantial genetic diversity in a pooled sample. This indicates that potency of O. felineus population for rapid adaptive response to control and preventive measures of opisthorchiasis is higher than in O. viverrini and C. sinensis. We also have found that all three species are characterized by more intensive involvement of trans-splicing in RNA processing compared to other trematodes. Conclusion All revealed peculiarities of structural organization of genomes are of extreme importance for a proper description of genes and their products in these parasitic species. This should be taken into account both in academic and applied research of epidemiologically important liver flukes. Further comparative genomics studies of liver flukes and non-carcinogenic flatworms allow for generation of well-grounded hypotheses on the mechanisms underlying development of cholangiocarcinoma associated with opisthorchiasis and clonorchiasis as well as species-specific mechanisms of these diseases.

The size, shape, and color of wheat seeds are important traits that are associated with yield and flour quality (size, shape), nutritional value, and pre-harvest sprouting (coat color). These traits are under multigenic control, and to dissect their molecular and genetic basis, quantitative trait loci (QTL) analysis is used. We evaluated 114 recombinant inbred lines (RILs) in a bi-parental RIL mapping population (the International Triticeae Mapping Initiative, ITMI/MP) grown in 2014 season. We used digital image analysis for seed phenotyping and obtained data for seven traits describing seed size and shape and 48 traits of seed coat color. We identified 212 additive and 34 pairs of epistatic QTLs on all the chromosomes of wheat genome except chromosomes 1A and 5D. Many QTLs were overlapping. We demonstrated that the overlap between QTL regions was low for seed size/shape traits and high for coat color traits. Using the literature and KEGG data, we identified sets of genes in Arabidopsis and rice from the networks controlling seed size and color. Further, we identified 29 and 14 candidate genes for seed size-related loci and for loci associated with seed coat color, respectively.

A method for automation of imago quantifying and fecundity assessment in Drosophila with the use of mobile devices running Android operating system is proposed. The traditional manual method of counting the progeny takes a long time and limits the opportunity of making large-scale experiments. Thus, the development of computerized methods that would allow us to automatically make a quantitative estimate of Drosophila melanogaster fecundity is an urgent requirement. We offer a modification of the mobile application SeedCounter that analyzes images of objects placed on a standard sheet of paper for an automatic calculation of D. melanogaster offspring or quantification of adult flies in any other kind of experiment. The relative average error in estimates of the number of flies by mobile app is about 2% in comparison with the manual counting and the processing time is six times shorter. Study of the effects of imaging conditions on accuracy of flies counting showed that lighting conditions do not significantly affect this parameter, and higher accuracy can be achieved using high-resolution smartphone cameras (8 Mpx and more). These results indicate the high accuracy and efficiency of the method suggested. This article has an associated First Person interview with the first author of the paper.

Biological collections play a huge role in studying biological diversity as systematic storages of biological materials in all combinations and forms. Collection materials have generally been formed over hundreds of years and may describe a vast number of samples counted by billions. Great efforts are made to preserve these materials, as well as to obtain more and more samples. The Russian Federation occupies a huge land area, has a long coastline and huge natural resources, a variety of natural and ecological zones. In this regard, its territory is unique from the viewpoint of biodiversity and development of biological collections. Currently, a large number of collections are being developed in Russia, but there are a number of problems associated, first of all, with the lack of an integrated information resource on bioresource collections (BRC). In order to support the development of scientific infrastructure, the Federal Agency for Scientific Organizations (FASO of Russia) has been working on the development of unified approaches to the use of existing bioresource collections and the establishment of the integrated information system. The paper presents an information portal designed to provide uniform methods of work for all BRC organizations of the FASO of Russia: input, storage, updating and differentiated access to specific information about storage units and their characteristics. The information system “Bioresource Collections of Scientific Organizations” (IS BRC) has been developed as a Web­portal (www.biores.cytogen.ru) integrating databases on bioresource collections of the FASO of Russia and graphical user interface. Access control to the databases integrated into the IS BRC is performed through authorized program access for viewing records, their creation and editing on the basis of REST technology. The graphical user interface (GUI) provides the following features in accordance with the access rights: authorized access to the BRC database; viewing BRC database records; editing BRC database records; creating and deleting BRC database records; statistical data analysis in the BRC database; generation of summary reports on the BRC database; export of records content in PDF/RTF/JSON format. The graphical user interface was implemented using the DRUPAL 7.0 toolkit. Architecturally, the portal is concerned as a central node with a series of modules communicating through the unified interfaces. In this way, we solve the problem of connecting new data sources (collection databases) implemented in different DBMS. Given the fact that currently many organizations support access to the catalogues of their collections independently, the portal also provides external links to these Web resources. At the same time, some information on collections is stored within the BRC databases of the FASO of Russia’s portal in unified formats. The portal contains the following functional sections: the home page containing general information on bioresource collections, the catalog of collections, individual pages for each particular collection with a short description (information about curators, statistical information about the number of storage units in the collection and the number of publications, as well as a link to the catalog of storage units of this BRC). Currently the portal contains more than 13 thousand entities of 65 bioresource collections organizations of the FASO of Russia. It is still being extended.

Information about the yield of wheat crops makes it possible to correctly assess their productivity and choose apropriate agronomic procedures to maximize yield. However, determining yields based on manual ear counts is labor intensive. Recently UAVs demonstrated high efficiency for rapid yield estimation. This paper presents a software package WDS (Wheat Detection System) for ears counting in wheat crops based on RGB images obtained from UAVs. WDS creates the flight plan, for the acquired images carries out automatic georeferencing to the appropriate fragment of the field, counts ears using the neural network models, reconstructs the density of ears in the crop and visualizes it as a heat map in the interactive web application. Based on the field experiment the accuracy of ears counting in plots was assessed: Spearman and Pearson correlation coefficients between the ears density counted manually and using WDS were 0.618 and 0.541, respectively (p-value < 0.05). WDS avaliable at https://github.com/Sl07h/wheat_detection.

The fruit fly Drosophila melanogaster is a classic research object in genetics and systems biology. In the genetic analysis of flies, a routine task is to determine the offspring size and gender ratio in their populations. Currently, these estimates are made manually, which is a very time-consuming process. The counting and gender determination of flies can be automated by using image analysis with deep learning neural networks on mobile devices. We proposed an algorithm based on the YOLOv4-tiny network to identify Drosophila flies and determine their gender based on the protocol of taking pictures of insects on a white sheet of paper with a cell phone camera. Three strategies with different types of augmentation were used to train the network. The best performance (F1 = 0.838) was achieved using synthetic images with mosaic generation. Females gender determination is worse than that one of males. Among the factors that most strongly influencing the accuracy of fly gender recognition, the fly’s position on the paper was the most important. Increased light intensity and higher quality of the device cameras have a positive effect on the recognition accuracy. We implement our method in the FlyCounter Android app for mobile devices, which performs all the image processing steps using the device processors only. The time that the YOLOv4-tiny algorithm takes to process one image is less than 4 s.

Isocyanates play an essential role in modern manufacturing processes, especially in polyurethane production. There are numerous synthesis strategies for isocyanates both under industrial and laboratory conditions, which do not prevent searching for alternative highly efficient synthetic protocols. Here, we report a detailed theoretical investigation of the mechanism of sulfur dioxide-catalyzed rearrangement of phenylnitrile oxide into phenyl isocyanate, which was first reported in 1977. The DLPNO-CCSD(T) method and up-to-date DFT protocols were used to perform a highly accurate quantum-chemical study of the rearrangement mechanism. An overview of various organic and inorganic catalysts has revealed other potential catalysts, such as sulfur trioxide and selenium dioxide. Furthermore, the present study elucidated how substituents in phenylnitrile oxide influence reaction kinetics. This study was performed by a self-organized collaboration of scientists initiated by a humorous post on the VK social network. [ABSTRACT FROM AUTHOR]

The structure of the ear is one of the most important features of cereals associated with such agronomically important traits as productivity, resistance to environmental factors and pests, threshebility. Ears differ in shape, size, density, awnedness, color, etc. Analysis of the ear traits requires visual inspection, manual measurements and is very time-consuming. The effectiveness of ears’ phenotyping can be improved by the introduction of an automated image processing technology, storage of information in databases, use of machine learning algorithms to analyze this information. This paper presents a new approach for collecting, storing and analyzing of information about morphometric characteristics of ears of wheat. Two protocols for obtaining digital images of the ear have been developed. The computer-aided information system SpikeDroidDB has been developed, which allows you to store digital images of the ear, annotate their phenotypic features (14 features, including plant variety description, links to parent genotypes, generation, planting number, ear morphology description). The interface provides a flexible query system to access the data. SpikeDroidDB represents an interconnected representation between genotype, phenotype, location, and growing conditions. The web interface of SpikeDroidDB is available at http://spikedroid.biores. cytogen.ru/ and allows you to work with the system as with desktop computers or mobile devices. We used SpikeDroidDB for the digitization and annotation of a collection of ears of F2 hybrids from crosses between the Australian cultivar of common wheat Triple Dirk and accession KU506 of Chinese wheat Triticum yunnanense. This experiment includes analysis of 104 plants, 230 spike images. The analysis of the variability of ears in form, length, and other traits allowed determination of the type of their genetic control: compactness is controlled by two recessive genes, awn type and hairi ness at the site of attachment of the spikelet to the axis is controlled by single dominant gene type, hairiness on the axis of the spike is controlled by two dominant genes.

Abstract Background The use of artificial data to evaluate the performance of aligners and peak callers not only improves its accuracy and reliability, but also makes it possible to reduce the computational time. One of the natural ways to achieve such time reduction is by mapping a single chromosome. Results We investigated whether a single chromosome mapping causes any artefacts in the alignments’ performances. In this paper, we compared the accuracy of the performance of seven aligners on well-controlled simulated benchmark data which was sampled from a single chromosome and also from a whole genome. We found that commonly used statistical methods are insufficient to evaluate an aligner performance, and applied a novel measure of a read density distribution similarity, which allowed to reveal artefacts in aligners’ performances. We also calculated some interesting mismatch statistics, and constructed mismatch frequency distributions along the read. Conclusions The generation of artificial data by mapping of reads generated from a single chromosome to a reference chromosome is justified from the point of view of reducing the benchmarking time. The proposed quality assessment method allows to identify the inherent shortcoming of aligners that are not detected by conventional statistical methods, and can affect the quality of alignment of real data.

Seed storage is important to farmers, breeders and for germplasm preservation. During storage, seeds accumulate damage at the structural and metabolic level, which disrupt their function and reduce resistance to adverse external conditions. In this regard, issues related to seed aging prove to be relevant for maintaining the viability of genetic collections. We analyzed morphological characteristics of grains and their coat color for 44 recombinant inbred lines (RILs) of bread wheat grown in four different seasons, 2003, 2004, 2009 and 2014. Our investigations were performed in 2020. For 19 RILs from the same seasons germination was evaluated. Our results demonstrate that genotype significantly affects the variability of all seed traits, and the year of harvesting affects about 80% of them (including all the traits of shape and size). To identify the trend between changes in grain characteristics and harvesting year, we estimated correlation coefficients between them. No significant trend was detected for the grain shape/size traits, while 90% of the color traits demonstrated such a trend. The most significant negative correlations were found between the harvesting year and the traits of grain redness: the greater the storage time, the more intensive is red color component for the grains. At the same time, it was shown that grains of longer storage time (earlier harvesting year) have lighter coat. Analysis of linear correlations between germination of wheat seeds of different genotypes and harvesting years and their seed traits revealed a negative linear relationship between the red component of coat color and germination: the redder the grains, the lower their germination rate. The results obtained demonstrate manifestations of metabolic changes in the coat of grains associated with storage time and their relationship with a decrease of seed viability.

Diseases of cereals caused by pathogenic fungi can significantly reduce crop yields. Many cultures are exposed to them. The disease is difficult to control on a large scale; thus, one of the relevant approaches is the crop field monitoring, which helps to identify the disease at an early stage and take measures to prevent its spread. One of the effective control methods is disease identification based on the analysis of digital images, with the possibility of obtaining them in field conditions, using mobile devices. In this work, we propose a method for the recognition of five fungal diseases of wheat shoots (leaf rust, stem rust, yellow rust, powdery mildew, and septoria), both separately and in case of multiple diseases, with the possibility of identifying the stage of plant development. A set of 2414 images of wheat fungi diseases (WFD2020) was generated, for which expert labeling was performed by the type of disease. More than 80% of the images in the dataset correspond to single disease labels (including seedlings), more than 12% are represented by healthy plants, and 6% of the images labeled are represented by multiple diseases. In the process of creating this set, a method was applied to reduce the degeneracy of the training data based on the image hashing algorithm. The disease-recognition algorithm is based on the convolutional neural network with the EfficientNet architecture. The best accuracy (0.942) was shown by a network with a training strategy based on augmentation and transfer of image styles. The recognition method was implemented as a bot on the Telegram platform, which allows users to assess plants by lesions in the field conditions.

Plant leaf pubescence is one of the important features, which is responsible for microclimate formation near the epidermis. It is involved in protection against adverse biotic and abiotic environmental factors. In Solanaceae, to which belongs the potato Solanum tuberosum L., leaf pubescence appears as multicellular unbranched trichomes of diverse size and morphology. Pubescence of this plants promotes resistance to insect pests, in particular, Colorado potato beetle and aphid, which is a carrier of viral diseases. During the process of breeding and genetic experiments, there is a need to assess the intensity of leaf pubescence of potato plants. For this task, micrographs taken under a microscope are commonly used. They are used to count different types of trichomes on the leaf surface to characterize the intensity of potato leaf pubescence. This approach requires visual counting of trichomes under a microscope and is fairly laborious. This protocol describes a rapid technology for quantitative assessment of the characteristics of potato pubescence (the number of trichomes on the leaf surface and the average length of trichomes) to solve the problems of genetics and breeding of this plant. It consists of a preparation technology, digital imaging of leaf folds with an optical microscope in transmitted light and subsequent automatic processing of images using the LHDetect2 software.

Introduction: Pubescence is an important phenotypic trait observed in both vegetative and generative plant organs. Pubescent plants demonstrate increased resistance to various environmental stresses such as drought, low temperatures, and pests. It serves as a significant morphological marker and aids in selecting stress-resistant cultivars, particularly in wheat. In wheat, pubescence is visible on leaves, leaf sheath, glumes and nodes. Regarding glumes, the presence of pubescence plays a pivotal role in its classification. It supplements other spike characteristics, aiding in distinguishing between different varieties within the wheat species. The determination of pubescence typically involves visual analysis by an expert. However, methods without the use of binocular loupe tend to be subjective, while employing additional equipment is labor-intensive. This paper proposes an integrated approach to determine glume pubescence presence in spike images captured under laboratory conditions using a digital camera and convolutional neural networks. Methods: Initially, image segmentation is conducted to extract the contour of the spike body, followed by cropping of the spike images to an equal size. These images are then classified based on glume pubescence (pubescent/glabrous) using various convolutional neural network architectures (Resnet-18, EfficientNet-B0, and EfficientNet-B1). The networks were trained and tested on a dataset comprising 9,719 spike images. Results: For segmentation, the U-Net model with EfficientNet-B1 encoder was chosen, achieving the segmentation accuracy IoU = 0.947 for the spike body and 0.777 for awns. The classification model for glume pubescence with the highest performance utilized the EfficientNet-B1 architecture. On the test sample, the model exhibited prediction accuracy parameters of F1 = 0.85 and AUC = 0.96, while on the holdout sample it showed F1 = 0.84 and AUC = 0.89. Additionally, the study investigated the relationship between image scale, artificial distortions, and model prediction performance, revealing that higher magnification and smaller distortions yielded a more accurate prediction of glume pubescence. [ABSTRACT FROM AUTHOR]

Three alkoxyamines based on imidazoline radicals with a pyridine functional group—potential initiators of nitroxide-mediated, controlled radical polymerization—were synthesized. Electron Paramagnetic Resonance (EPR) measurements reveal biexponential kinetics for the thermolysis for diastereomeric alkoxyamines and monoexponential kinetics for an achiral alkoxyamine. For comparison, the thermolysis of all three alkoxyamines was studied by NMR in the presence of three different scavengers, namely tetramethylpiperidine-N-oxyl (TEMPO), thiophenol (PhSH), and β-mercaptoethanol (BME), and detailed analysis of products was performed. NMR differentiates between N-inversion, epimerization, and homolysis reactions. The choice of scavenger is crucial for making a reliable and accurate estimate of the true homolysis rate constant.

A series of new fluorinated 2,3-diaminophenazines were obtained by the oxidation of fluorinated o-phenylenediamines with iron trichloride in water acidified with HCl. Isomeric 2,3-diaminophenazines with F atoms in both benzene fragments were obtained when o-phenylenediamines contained an F atom at the ortho-position with respect to the amino group. The ratio of isomers depends on the number and position of F atoms in o-phenylenediamines. DFT calculations for the formation of fluorinated 2,3-diaminophenazines in question were consistent with the experimentally observed ratio of the isomers. The formation of the predominant isomer was attributed to the action of the amino group with the highest N-charge of protonated o-phenylenediamines at the most electron-depleted position of the most stable protonated diimine. New compounds were characterized using various spectroscopy techniques (IR, UV, 1H, 19F, 13C NMR, and mass). Fluorinated 2,3-diaminophenazines are heat-resistant and fluoresce from green to red depending on the number of F atoms in the ring bearing amino groups. The presence of F atoms decreases the emission intensity but at the same time bathochromically shifts the fluorescence maximum. In the absence of the F atom in the ring bearing amino groups, fluorinated 2,3-diaminophenazines exhibited fluorescence intensities comparable to those of a non-fluorinated analog. [ABSTRACT FROM AUTHOR]