Challenges in data-driven geospatial modeling for environmental research and practice.

Machine learning-based geospatial applications offer unique opportunities for environmental monitoring due to domains and scales adaptability and computational efficiency. However, the specificity of environmental data introduces biases in straightforward implementations. We identify a streamlined pipeline to enhance model accuracy, addressing issues like imbalanced data, spatial autocorrelation, prediction errors, and the nuances of model generalization and uncertainty estimation.

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.

Absence of enterotypes in the human gut microbiomes reanalyzed with non-linear dimensionality reduction methods.

Enterotypes of the human gut microbiome have been proposed to be a powerful prognostic tool to evaluate the correlation between lifestyle, nutrition, and disease. However, the number of enterotypes suggested in the literature ranged from two to four. The growth of available metagenome data and the use of exact, non-linear methods of data analysis challenges the very concept of clusters in the multidimensional space of bacterial microbiomes.

Integrative bioinformatics and artificial intelligence analyses of transcriptomics data identified genes associated with major depressive disorders including .

Major depressive disorder (MDD) is a common mental disorder and is amongst the most prevalent psychiatric disorders. MDD remains challenging to diagnose and predict its onset due to its heterogeneous phenotype and complex etiology. Hence, early detection using diagnostic biomarkers is critical for rapid intervention.

AI-enabled prediction of video game player performance using the data from heterogeneous sensors.

The emerging progress of video gaming and eSports lacks the tools for ensuring high-quality analytics and training in professional and amateur eSports teams. We report on an Artificial Intelligence (AI) enabled solution for predicting the eSports player in-game performance using exclusively the data from sensors. For this reason, we collected the physiological, environmental, and the smart chair data from professional and amateur players.

Generative adversarial networks for reconstruction of three-dimensional porous media from two-dimensional slices.

In many branches of earth sciences, the problem of rock study on the microlevel arises. However, a significant number of representative samples is not always feasible. Thus the problem of the generation of samples with similar properties becomes actual.

Effects of age, gender, and hemisphere on cerebrovascular hemodynamics in children and young adults: Developmental scores and machine learning classifiers.

A constant blood supply to the brain is required for mental function. Research with Doppler ultrasonography has important clinical value and burgeoning potential with machine learning applications in studies predicting gestational age and vascular aging. Critically, studies on ultrasound metrics in school-age children are sparse and no machine learning study to date has used color duplex ultrasonography to predict age and classify age-group.

Diagnosis of Schizophrenia Based on the Data of Various Modalities: Biomarkers and Machine Learning Techniques (Review).

Schizophrenia is a socially significant mental disorder resulting frequently in severe forms of disability. Diagnosis, choice of treatment tactics, and rehabilitation in clinical psychiatry are mainly based on the assessment of behavioral patterns, socio-demographic data, and other investigations such as clinical observations and neuropsychological testing including examination of patients by the psychiatrist, self-reports, and questionnaires. In many respects, these data are subjective and therefore a large number of works have appeared in recent years devoted to the search for objective characteristics (indices, biomarkers) of the processes going on in the human body and reflected in the behavioral and psychoneurological patterns of patients.

SCRAMBLER: A Tool for CRISPR Array Reconstruction and Its Application for Analysis of the Structure of Prokaryotic Populations.

CRISPR arrays are prokaryotic genomic loci consisting of repeat sequences alternating with unique spacers acquired from foreign nucleic acids. As one of the fastest-evolving parts of the genome, CRISPR arrays can be used to differentiate closely related prokaryotic lineages and track individual strains in prokaryotic communities. However, the assembly of full-length CRISPR arrays sequences remains a problem.

Analysis of Video Game Players' Emotions and Team Performance: An Esports Tournament Case Study.

Video gaming and eSports is a quickly developing industry already involving billions of players worldwide. Gaming and eSports tournaments require strong mental abilities to avoid severe stress and other negative consequences upon completing the game. In this article, we report on the impact of emotions on a team performance.

[Resting-state fMRI in preoperative non-invasive mapping in patients with left hemisphere glioma].

Maximum resection and preservation of neurological function are main principles in surgery of brain tumors, especially glial neoplasms with diffuse growth. Therefore, exact localizing of eloquent brain areas is an important component in surgical planning ensuring optimal resection with minimal postoperative neurological deficit. Functional MRI is used to localize eloquent brain areas adjacent to the tumor.

Bayesian Generative Models for Knowledge Transfer in MRI Semantic Segmentation Problems.

Automatic segmentation methods based on deep learning have recently demonstrated state-of-the-art performance, outperforming the ordinary methods. Nevertheless, these methods are inapplicable for small datasets, which are very common in medical problems. To this end, we propose a knowledge transfer method between diseases via the Generative Bayesian Prior network.

Application of sorting and next generation sequencing to study 5΄-UTR influence on translation efficiency in Escherichia coli.

Yield of protein per translated mRNA may vary by four orders of magnitude. Many studies analyzed the influence of mRNA features on the translation yield. However, a detailed understanding of how mRNA sequence determines its propensity to be translated is still missing.