Pourshamsaei, H., A. Nobakhti, and R. B. Jana (2021), Adaptive Proper Orthogonal Decomposition for large scale reliable soil moisture estimation, Measurement Science and Technology, doi:10.1088/1361-6501/ac16af

Chaouachi, Manel., Marzouk, Takwa., Khiari, Bilel., Ben, Cécile., Gentzbittel, Laurent., Win, Joe., Kamoun, Sophien., & Djebali, Naceur. (2021). Genome sequences of bacterial biocontrol agents isolated from Solanum lycopersicum and Medicago truncatula in Tunisia. Zenodo. doi: 10.5281/zenodo.5596089

Varshney, RK., Roorkiwal, M., Sun, S., Bajaj, P., Chitikineni, A., Thudi, M., Singh, NP., Du, X., Upadhyaya, HD., Khan, AW., Wang, Y., Garg, V., Fan, G., Cowling, WA., Crossa, J., Gentzbittel, L., Voss-Fels, KP., Valluri, VK., Sinha, P., Singh, VK ., Ben, C., Rathore, A., Punna, R., Singh, MK., Tar'an, B., Bharadwaj, C., Yasin, M., Pithia, MS., Singh, S., Soren , KR., Kudapa, H., Jarquín, D., Cubry, P., Hickey, LT., Dixit, GP., Thuillet, AC., Hamwieh, A., Kumar, S., Deokar, AA., Chaturvedi , SK., Francis, A., Howard, R., Chattopadhyay, D., Edwards, D., Lyons, E., Vigouroux, Y., Hayes, BJ., von Wettberg, E., Datta, SK., Yang, H., Nguyen, HT., Wang, J., Siddique, KHM., Mohapatra, T., Bennetzen, JL., Xu, X., Liu, X. A chickpea genetic variation map based on the sequencing of 3,366 genomes. Nature. 2021 Nov;599(7886):622-627. doi: 10. 1038/s41586-021-04066-1 Epub 2021 Nov 10. Erratum in: Nature. 2022 Apr;604(7905):E12. PMID: 34759320; PMCID: PMC8612933.

Javanrouh, A., Baneh, H. and Ghafouri-Kesbi, F., 2021. Different models for genetic evaluation of growth rate and efficiency-related traits in Iran-Black sheep. Journal of Livestock Science and Technologies, 9(2), pp.67-74.

Mandal, A., Baneh, H., Roy, R. and Notter, DR, 2021. Genetic diversity and population structure of Jamunapari goat in India using pedigree analysis. Tropical animal health and production, 53(2), pp.1-11.

Gasanov, M., Merkulov, D., Nikitin, A., Matveev, S., Stasenko, N., Petrovskaia, A., Pukalchik, M., Oseledets, I., 2021. A New Multi-objective Approach to Optimize Irrigation Using a Crop Simulation Model and Weather History, in: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, VV, Dongarra, JJ, Sloot, PMA (Eds.), Computational Science – ICCS 2021, Lecture Notes in Computer Science. Springer International Publishing, Cham, pp. 75–88.

Illarionova, S., Nesteruk, S., Shadrin, D., Ignatiev, V., Pukalchik, M., Oseledets, I., 2021 MixChannel: Advanced Augmentation for Multispectral Satellite Images. Remote Sensing 13, 2181.

Mandal, A., Baneh, H. and Notter, DR, 2021. Modeling the growth curve of Muzaffarnagari lambs from India. Livestock Science, 251, p.104621.

Nesteruk, S., Shadrin, D., Pukalchik, M., Somov, A., Zeidler, C., Zabel, P., Schubert, D., 2021. Image Compression and Plants Classification Using Machine Learning in Controlled-Environment Agriculture : Antarctic Station Use Case. IEEE Sensors J. 1–1.

Shadrin, D., Nikitin, A., Tregubova, P., Terekhova, V., Jana, R., Matveev, S., Pukalchik, M., 2021. An Automated Approach to Groundwater Quality Monitoring—Geospatial Mapping Based on Combined Application of Gaussian Process Regression and Bayesian Information Criterion. Water 13, 400.

Stasenko, N., Chernova, E., Shadrin, D., Ovchinnikov, G., Krivolapov, I., Pukalchik, M., 2021. Deep Learning for improving the storage process: Accurate and automatic segmentation of spoiled areas on apples, in: 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). Presented at the 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), IEEE, Glasgow, United Kingdom, pp. 1–6.

Vypirailenko, D., Kiseleva, E., Shadrin, D., Pukalchik, M., 2021. Deep learning techniques for enhancement of weeds growth classification, in: 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). Presented at the 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), IEEE, Glasgow, United Kingdom, pp. 1–6.

Yudina, E., Petrovskaia, A., Shadrin, D., Tregubova, P., Chernova, E., Pukalchik, M., Oseledets, I., 2021. Optimization of Water Quality Monitoring Networks Using Metaheuristic Approaches: Moscow Region Use Case. Water 13, 888.

Chaouachi, M., Marzouk, T., Jallouli, S., Elkahoui, S., Gentzbittel, L., Ben, C., & Djébali, N. (2021). Activity assessment of tomato endophytic bacteria bioactive compounds for the postharvest biocontrol of Botrytis cinerea. Postharvest Biology and Technology , 172 , 111389.
Spiridonov, D., M. Vasilyeva, E. T. Chung, Y. Efendiev, and R. B. Jana (2020), Multiscale model reduction of unsaturated flow problem in heterogeneous porous media with rough surface topography, Mathematics, 8(6), 904, doi: 10.3390/math8060904.

Shadrin, D., Pukalchik, M., Kovaleva, E., & Fedorov, M. (2020). Artificial intelligence models to predict acute phytotoxicity in petroleum contaminated soils. Ecotoxicology and Environmental Safety , 194 , 110410.

Gasanov, M., Petrovskaia, A., Nikitin, A., Matveev, S., Tregubova, P., Pukalchik, M., & Oseledets, I. Sensitivity analysis of soil parameters in crop model supported with high-throughput computing, ICCS, 2020

Shadrin, D., Pukalchik, M., Uryasheva, A., Rodichenko, N., & Tsetserukou, D. (2020). Hyper-spectral NIR and MIR data and optimal wavebands for detecting of apple trees diseases. arXiv preprint arXiv:2004.02325, ICLR 2020

Matvienko, I., Gasanov, M., Petrovskaia, A., Jana, R.B., Pukalchik, M., & Oseledets, I. (2020). Bayesian aggregation improves traditional single image crop classification approaches. arXiv preprint arXiv:2004.03468, ICLR 2020

Petrovskaia, A., Jana, RB, & Oseledets, IV (2020). A single image deep learning approach to restoration of corrupted remote sensing products. arXiv preprint arXiv:2004.04209, ICLR 2020

Shashkova, TI, Martynova, EU, Ayupova, AF, Shumskiy, AA, Ogurtsova, PA, Kostyunina, OV, ... & Zinovieva, NA (2020). Development of a low-density panel for genomic selection of pigs in Russia. Translational animal science , 4 (1), 264-274.


Kanapin, AA, Sokolkova, AB, Samsonova, AA, Shchegolkov, AV, Boldyrev, SV, Aupova, AF, ... & Samsonova, MG (2020). Genetic Variants Associated with Productivity and Contents of Protein and Oil in Soybeans. Biophysics , 65 (2), 241-249.
Nikitin, A., Fastovets, I., Shadrin, D., Pukalchik, M., & Oseledets, I. (2019). Bayesian optimization for seed germination. Plant methods , 15 (1), 43.

Pukalchik, M., Kydralieva, K., Yakimenko, O., Fedoseeva, E., & Terekhova, V. (2019). Outlining the potential role of humic products in modifying biological properties of the soil—a review. Frontiers in Environmental Science , 7 , 80.

Pukalchik, MA, Katrutsa, AM, Shadrin, D., Terekhova, VA, & Oseledets, IV (2019). Machine learning methods for estimation of the indicators of phosphogypsum influence in soil. Journal of soils and sediments , 19 (5), 2265-2276.

Jana, R.B., & Petrovskaia, A. (2019). 3D representation of soil structure using Generative Adversarial Networks. AGUFM , 2019 , H31I-1835.

Chernova, A., Mazin, P., Goryunova, S., Goryunov, D., Demurin, Y., Gorlova, L., ... & Khaitovich, P. (2019). Ultra-performance liquid chromatography-mass spectrometry for precise fatty acid profiling of oilseed crops. PeerJ , 7 , e6547.

Goryunova, SV, Goryunov, DV, Chernova, AI, Martynova, EU, Dmitriev, AE, Boldyrev, SV, ... & Demurin, YN (2019). Genetic and phenotypic diversity of the sunflower collection of the Pustovoit All-Russia Research Institute of Oil Crops (VNIIMK). Helia , 42 (70), 45-60.

Chernova, A., Gubaev, R., Mazin, P., Goryunova, S., Demurin, Y., Gorlova, L., ... & Khaytovich, P. (2019). UPLC–MS Triglyceride Profiling in Sunflower and Rapeseed Seeds. Biomolecules , 9 (1), 9.

Goryunov, DV, Anisimova, IN, Gavrilova, VA, Chernova, AI, Sotnikova, EA, Martynova, EU, ... & Goryunova, SV (2019). Association mapping of fertility restorer gene for CMS PET1 in sunflower. Agronomy , 9 (2), 49.

Chernova, AI, & Martynova, EU (2019). High-throughput technologies for sunflower oil improvement. Current Challenges in Plant Genetics, Genomics, Bioinformatics, and Biotechnology , 24 , 227.
Lytkin, K.; Nosulchak, V.; Agakhanov, M.; Matveikina, E.; Lushchay, E.; Karzhaev, D.; Raines, E.; Vasylyk, I.; Rybachenko, N.; Grigoreva, E.; Volkov, V.; Volynkin, V.; Gentzbittel, L.; Potokina, E. Development of a High-Density Genetic Map for Muscadine Grape Using a Mapping Population from Selfing of the Perfect-Flowered Vine 'Dixie'. Plants 2022, 11, 3231. 10.3390/plants11233231

Djouider, S. I., L. Gentzbittel, R. B. Jana, M. Rickauer, C. Ben, and M. Lazalli (2022), Contribution to improving chickpea (Cicer arietinum L.) efficiency in low-phosphorus farming systems: Assessment of the relationships between P and N nutrition, nodulation capacity and productivity performance in P-deficient field conditions, Agronomy, doi: 10.3390/agronomy12123150

Petrovskaia, A., R. B. Jana, and I. V. Oseledets (2022), A single image deep learning approach to restoration of corrupted remote sensing products. Sensors, doi: 10.3390/s22239273.

Matvienko, I., M. Gasanov, A. Petrovskaia, M. Kuznetsov, R. B. Jana, M. Pukalchik, and I. V. Oseledets, (2022), Bayesian aggregation improves traditional single image crop classification approaches, Sensors, doi: 10.3390/s22228600

Busari, I., D. Sahoo, R. B. Jana, and C. V. Privette III (2022), Chlorophyll-a Predictions in a Piedmont Lake in Upstate South Carolina using Machine Learning Approaches, Journal of South Carolina Water Resources, accepted.

Gubaev, R., Boldyrev, S., Martynova, E., Chernova, A., Kovalenko, T., Peretyagina, T., Goryunova, S., Goryunov, D., Mukhina, Z., Ben, C., Gentzbittel, L., Khaitovich, P., Demurin, Y. (2022) Genetic mapping of loci involved in oil tocopherol composition control in Russian sunflower (Helianthus annuus L.) lines. G3 Genes|Genomes|Genetics, Volume 12, Issue4 doi: 10.1093/g3journal/jkac036

Mandal, A., Baneh, H., Rout, PK, Notter, DR, 2022. Genetic analysis of sexual dimorphism in growth of Jamunapari goats of India. Journal of Animal Breeding, Genetics 139, 1-14.

Fartash AH, Ben C, Mazurier M, Ebrahimi A, Ghalandar M, Gentzbittel L, Rickauer R. (2023) Medicago truncatula quantitative resistance to a new strain of Verticillium alfalfae from Iran revealed by a Genome-wide association study Front. In Pl. Science - Research Topic "Legume Root Diseases", Volume 14. doi: 10.3389/fpls.2023.1125551

Sbeiti A, Mazurier M, Ben C, Rickauer M, Gentzbittel L. (2023) Temperature increase modifies susceptibility to Verticillium wilt in Medicago spp and may contribute to the emergence of more aggressive pathogenic strains. Front. In Pl. Science - Research Topic "Plant-Microbe Interactions for Agricultural Sustainability Facing Environmental Challenge", Volume 14. doi: 10.3389/fpls.2023.1109154

Poornima S, Pushpalatha M, Jana RB, Patti LA. Rainfall Forecast and Drought Analysis for Recent and Forthcoming Years in India. Water. 2023; 15(3):592. doi:

PAG 2020, San Diego- poster. Abstract: "Association studies for cultivated Sunflower oil improvement."

International Sunflower Conference, Serbia 2020 –oral presentation accepted but postponed until 2021_ Abstract: "Genome-wide association studies reveal new genetic loci associated with fatty acid composition in Sunflower" (Accepted, but the conference was postponed till next year)
Gordon research conference on quantitative genetics and genomics 2019, Italy- poster and travel grant. Abstract: "Cultivated sunflower high-throughput genotyping and lipidomic profiling."

Young scientists conference Agrobiotech2019, Moscow – oral presentation. Abstract: Use of high-throughput sequencing and high-throughput molecular phenotyping to improve economically significant characteristics of sunflower oil (On Russian).

Mapping of genetic associations for agronomically important traits in Russian collection of rapeseeds (Brassica napus). R. Gubaev, S. Goryunova, D. Goryunov, P. Mazin, S. Boldyrev, A. Chernova, A. Ayupova, E. Martynova, Y. Demurin, Z. Mukhina, P. Khaitovich. Proceedings of the 9th International Moscow Conference on Computational Molecular Biology МССМВ'19, Moscow, July 27-30, 2019, p 103.
-Development of molecular markers for the detection and characterization of Colletotrichum lupini strains, causal agent of anthracnosis in white lupin (Lupinus albus). (IN22-06)
Date of receipt: 30.11.2

- SNP panel for genotyping and genomic selection of sunflower according to the content of fatty acids in seed oil. (Patent No. 2717642)

- Database of oilseed crops genotypes (State Registration Certificate No. 2018620271)

- Database of oilseed crops phenotypes (State Registration Certificate No. 2019620084)

- Database of lipid composition of adipose and muscle tissue of pork (State Registration Certificate No. 2019622411)

- Database of genomic variability of breeding pigs (State Registration Certificate No. 2019622289).