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The Molnieboi Spur is located at the northwestern margin of the Katun Range, the high-mountain part of the Altai Mountains. Unique geological and geophysical characteristics of the Molnieboi Spur made it an attractive target for complex botanical studies including botanical, soil, geological, geochemical, geophysical, radiation, and soil gas surveys and analyses. In this paper, we present the first version of the geographic information system (GIS) application for the Molnieboi Spur developed using the software QGIS. A digital elevation model for the study area was derived from a detailed topographic map. The database was filled with tabular data on about 100 parameters including: eight botanical characteristics of the Lonicera caerulea local population, two cytogenetic indices of Lonicera caerulea seeds, five types of biochemical parameters of Lonicera caerulea leaves and fruits, three types of geochemical characteristics of the local soils, three types of radiation parameters of the local soils and Lonicera caerulea plants, and one soil gas parameter. The results of the magnetometric survey were inserted as a raster image. A visual analysis of the maps produced allows one to better understand the spatial relationships between various natural components of the Molnieboi Spur.
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Geophysical and geochemical anomalies may have a mutagenic effect on plants growing in active fault zones being the factors of evolutionary transformation of plant populations. To test this assumption we evaluated the mutation activity of a Lonicera caerulea natural population in one of the active fault zones in the Altai Mountains. We derived principal cytogenetic indices (i.e., mitotic, prophase, metaphase, anaphase, and telophase indices as well as proportion and range of abnormal mitoses) for meristematic cells of Lonicera caerulea seedlings. We found that the local geological and geophysical environment (i.e., mineralogical composition of rocks and anomalies of the magnetic field) increases the mitotic activity and the number of abnormal mitoses in the meristematic cells. The results may help to clarify the role of environmental conditions of tectonically active regions in microevolutionary processes.
Resting-state functional magnetic resonance imaging (rs-fMRI)-derived functional connectivity patterns have been extensively utilized to delineate global functional organization of the human brain in health, development, and neuropsychiatric disorders. In this paper, we investigate how functional connectivity in males and females differs in an age prediction framework. We first estimate functional connectivity between regions-of-interest (ROIs) using distance correlation instead of Pearsons correlation. Distance correlation, as a multivariate statistical method, explores spatial relations of voxel-wise time courses within individual ROIs and measures both linear and nonlinear dependence, capturing more complex information of between-ROI interactions. Then, a novel non-convex multi-task learning (NC-MTL) model is proposed to study age-related gender differences in functional connectivity, where age prediction for each gender group is viewed as one task. Specifically, in the proposed NC-MTL model, we introduce a composite regularizer with a combination of non-convex $ell_{2,1-2}$ and $ell_{1-2}$ regularization terms for selecting both common and task-specific features. Finally, we validate the proposed NC-MTL model along with distance correlation based functional connectivity on rs-fMRI of the Philadelphia Neurodevelopmental Cohort for predicting ages of both genders. The experimental results demonstrate that the proposed NC-MTL model outperforms other competing MTL models in age prediction, as well as characterizing developmental gender differences in functional connectivity patterns.
We describe the current state and future plans for a set of tools for scientific data management (SDM) designed to support scientific transparency and reproducible research. SDM has been in active use at our MRI Center for more than two years. We designed the system to be used from the beginning of a research project, which contrasts with conventional end-state databases that accept data as a project concludes. A number of benefits accrue from using scientific data management tools early and throughout the project, including data integrity as well as reuse of the data and of computational methods.
We present Clinica (www.clinica.run), an open-source software platform designed to make clinical neuroscience studies easier and more reproducible. Clinica aims for researchers to i) spend less time on data management and processing, ii) perform reproducible evaluations of their methods, and iii) easily share data and results within their institution and with external collaborators. The core of Clinica is a set of automatic pipelines for processing and analysis of multimodal neuroimaging data (currently, T1-weighted MRI, diffusion MRI and PET data), as well as tools for statistics, machine learning and deep learning. It relies on the brain imaging data structure (BIDS) for the organization of raw neuroimaging datasets and on established tools written by the community to build its pipelines. It also provides converters of public neuroimaging datasets to BIDS (currently ADNI, AIBL, OASIS and NIFD). Processed data include image-valued scalar fields (e.g. tissue probability maps), meshes, surface-based scalar fields (e.g. cortical thickness maps) or scalar outputs (e.g. regional averages). These data follow the ClinicA Processed Structure (CAPS) format which shares the same philosophy as BIDS. Consistent organization of raw and processed neuroimaging files facilitates the execution of single pipelines and of sequences of pipelines, as well as the integration of processed data into statistics or machine learning frameworks. The target audience of Clinica is neuroscientists or clinicians conducting clinical neuroscience studies involving multimodal imaging, and researchers developing advanced machine learning algorithms applied to neuroimaging data.
Measuring similarity between molecules is an important part of virtual screening (VS) experiments deployed during the early stages of drug discovery. Most widely used methods for evaluating the similarity of molecules use molecular fingerprints to encode structural information. While similarity methods using fingerprint encodings are efficient, they do not consider all the relevant aspects of molecular structure. In this paper, we describe a quantum-inspired graph-based molecular similarity (GMS) method for ligand-based VS. The GMS method is formulated as a quadratic unconstrained binary optimization problem that can be solved using a quantum annealer, providing the opportunity to take advantage of this nascent and potentially groundbreaking technology. In this study, we consider various features relevant to ligand-based VS, such as pharmacophore features and three-dimensional atomic coordinates, and include them in the GMS method. We evaluate this approach on various datasets from the DUD_LIB_VS_1.0 library. Our results show that using three-dimensional atomic coordinates as features for comparison yields higher early enrichment values. In addition, we evaluate the performance of the GMS method against conventional fingerprint approaches. The results demonstrate that the GMS method outperforms fingerprint methods for most of the datasets, presenting a new alternative in ligand-based VS with the potential for future enhancement.
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