Fibre directions at a branch-stem junction in Norway spruce: a microscale investigation using X-ray computed tomography

The connection between branch and trunk in a tree must be strong enough to transfer all loads acting on the branch, and it is well known that such branch-stem connections are indeed very strong. In this paper, X-ray computer tomography is employed to investigate the local fibre orientation in the close surrounding of a knot in a Norway spruce specimen to better understand the origins of the mechanical strength of the branch-trunk connection. First, a wood specimen containing an entire knot from pith to bark was imaged with a voxel size of 52 µm. Subsequently, smaller specimens were cut from this original specimen and imaged again with increasingly higher resolution over four levels. With the highest resolution level (2.6 µm voxel size), the tracheids with smallest lumen were successfully traced. The results revealed how the direction of the fibre paths that start below the knot curve around it as the paths progress upwards to the region just above the knot, where the paths divide into two: one set of paths integrating with the knot on its top side and the other set continuing up along the trunk. Fibres that integrate with the knot at its top follow paths just before they continue into the knot, with a radius of curvature of only about 1 mm in both vertical and horizontal directions. No abrupt change of fibre pattern between latewood and earlywood is observed; rather, a continuous change of fibre direction across annual layers can be seen. The detailed characterisation of the local fibre structure around the knot provides new data that can explain the remarkable strength of the branch-trunk connection.

An Open-Source Whole Slide Image Registration Workflow at Cellular Precision Using Fiji, QuPath and Elastix

Image analysis workflows for Histology increasingly require the correlation and combination of measurements across several whole slide images. Indeed, for multiplexing, as well as multimodal imaging, it is indispensable that the same sample is imaged multiple times, either through various systems for multimodal imaging, or using the same system but throughout rounds of sample manipulation (e.g. multiple staining sessions). In both cases slight deformations from one image to another are unavoidable, leading to an imperfect superimposition Redundant and thus a loss of accuracy making it difficult to link measurements, in particular at the cellular level. Using pre-existing software components and developing missing ones, we propose a user-friendly workflow which facilitates the nonlinear registration of whole slide images in order to reach sub-cellular resolution level. The set of whole slide images to register and analyze is at first defined as a QuPath project. Fiji is then used to open the QuPath project and perform the registrations. Each registration is automated by using an elastix backend, or semi-automated by using BigWarp in order to interactively correct the results of the automated registration. These transformations can then be retrieved in QuPath to transfer any regions of interest from an image to the corresponding registered images. In addition, the transformations can be applied in QuPath to produce on-the-fly transformed images that can be displayed on top of the reference image. Thus, relevant data can be combined and analyzed throughout all registered slides, facilitating the analysis of correlative results for multiplexed and multimodal imaging.

Exploring cell-specific miRNA regulation with single-cell miRNA-mRNA co-sequencing data

Background Existing computational methods for studying miRNA regulation are mostly based on bulk miRNA and mRNA expression data. However, bulk data only allows the analysis of miRNA regulation regarding a group of cells, rather than the miRNA regulation unique to individual cells. Recent advance in single-cell miRNA-mRNA co-sequencing technology has opened a way for investigating miRNA regulation at single-cell level. However, as currently single-cell miRNA-mRNA co-sequencing data is just emerging and only available at small-scale, there is a strong need of novel methods to exploit existing single-cell data for the study of cell-specific miRNA regulation. Results In this work, we propose a new method, CSmiR (Cell-Specific miRNA regulation) to combine single-cell miRNA-mRNA co-sequencing data and putative miRNA-mRNA binding information to identify miRNA regulatory networks at the resolution of individual cells. We apply CSmiR to the miRNA-mRNA co-sequencing data in 19 K562 single-cells to identify cell-specific miRNA-mRNA regulatory networks for understanding miRNA regulation in each K562 single-cell. By analyzing the obtained cell-specific miRNA-mRNA regulatory networks, we observe that the miRNA regulation in each K562 single-cell is unique. Moreover, we conduct detailed analysis on the cell-specific miRNA regulation associated with the miR-17/92 family as a case study. The comparison results indicate that CSmiR is effective in predicting cell-specific miRNA targets. Finally, through exploring cell–cell similarity matrix characterized by cell-specific miRNA regulation, CSmiR provides a novel strategy for clustering single-cells and helps to understand cell–cell crosstalk. Conclusions To the best of our knowledge, CSmiR is the first method to explore miRNA regulation at a single-cell resolution level, and we believe that it can be a useful method to enhance the understanding of cell-specific miRNA regulation.

Pattern of Repetitive Element Transcription Segregate Cell Lineages during the Embryogenesis of Sea Urchin Strongylocentrotus purpuratus

Repetitive elements (REs) occupy a significant part of eukaryotic genomes and are shown to play diverse roles in genome regulation. During embryogenesis of the sea urchin, a large number of REs are expressed, but the role of these elements in the regulation of biological processes remains unknown. The aim of this study was to identify the RE expression at different stages of embryogenesis. REs occupied 44% of genomic DNA of Strongylocentrotus purpuratus. The most prevalent among these elements were the unknown elements—in total, they contributed 78.5% of REs (35% in total genome occupancy). It was revealed that the transcription pattern of genes and REs changes significantly during gastrulation. Using the de novo transcriptome assembly, we showed that the expression of RE is independent of its copy number in the genome. We also identified copies that are expressed. Only active RE copies were used for mapping and quantification of RE expression in the single-cell RNA sequencing data. REs expression was observed in all cell lineages and they were detected as population markers. Moreover, the primary mesenchyme cell (PMC) line had the greatest diversity of REs among the markers. Our data suggest a role for RE in the organization of developmental domains during the sea urchin embryogenesis at the single-cell resolution level.

Wavelet Based and Statistical EEG Analysis in Patients with Schizophrenia

Schizophrenia, which is considered a serious mental disorder and a psychological illness, is quite common in society today. Schizophrenia manifests itself with disordered thought development, hallucinations, and different behaviours and reactions. In this study, EEG data were collected and analyzed on a total of 84 subjects diagnosed with normal and schizophrenia. EEG data used as a diagnostic tool with low-resolution level has been used to distinguish between schizophrenic and normal individuals. First of all, statistical methods were used in the analyzes, and also frequency properties of the data were extracted by Wavelet analysis. As a result of the analysis, statistical findings include characteristics that distinguish between diagnosed schizophrenia and normal individuals. In addition, the findings obtained as a result of the Wavelet analysis were determined to distinguish between normal and schizophrenic individuals. While the mean used in statistical analysis takes the value 1.6 for normal individuals, it takes the value 2.9 for individuals diagnosed with schizophrenia. Also, in the results of Continuous Wavelet (CW) Analysis, very important findings were obtained in terms of detection in scale 16 and 64 bands.

APExpose_DE, an air quality exposure dataset for Germany 2010–2019

Exposure to poor air quality is considered a major influence on the occurrence of cardiovascular and respiratory diseases. Air pollution has also been linked to the severity of the effects of epidemics such as COVID-19 caused by the SARS-CoV-2 virus. Epidemiological studies require datasets of the long-term exposure to air pollution. We present the APExpose_DE dataset, a long-term (2010–2019) dataset providing ambient air pollution metrics at yearly time resolution for NO2, NO, O3, PM10 and PM2.5 at the NUTS-3 spatial resolution level for Germany (corresponding to the Landkreis or Kreisfreie Stadt in Germany, 402 in total).

Numerical solutions of higher order boundary value problems via wavelet approach

This paper presents a numerical scheme based on Haar wavelet for the solutions of higher order linear and nonlinear boundary value problems. In nonlinear cases, quasilinearization has been applied to deal with nonlinearity. Then, through collocation approach computing solutions of boundary value problems reduces to solve a system of linear equations which are computationally easy. The performance of the proposed technique is portrayed on some linear and nonlinear test problems including tenth, twelfth, and thirteen orders. Further convergence of the proposed method is investigated via asymptotic expansion. Moreover, computed results have been matched with the existing results, which shows that our results are comparably better. It is observed from convergence theoretically and verified computationally that by increasing the resolution level the accuracy also increases.

Functional Genomics of Axons and Synapses to Understand Neurodegenerative Diseases

Functional genomics studies through transcriptomics, translatomics and proteomics have become increasingly important tools to understand the molecular basis of biological systems in the last decade. In most cases, when these approaches are applied to the nervous system, they are centered in cell bodies or somatodendritic compartments, as these are easier to isolate and, at least in vitro, contain most of the mRNA and proteins present in all neuronal compartments. However, key functional processes and many neuronal disorders are initiated by changes occurring far away from cell bodies, particularly in axons (axopathologies) and synapses (synaptopathies). Both neuronal compartments contain specific RNAs and proteins, which are known to vary depending on their anatomical distribution, developmental stage and function, and thus form the complex network of molecular pathways required for neuron connectivity. Modifications in these components due to metabolic, environmental, and/or genetic issues could trigger or exacerbate a neuronal disease. For this reason, detailed profiling and functional understanding of the precise changes in these compartments may thus yield new insights into the still intractable molecular basis of most neuronal disorders. In the case of synaptic dysfunctions or synaptopathies, they contribute to dozens of diseases in the human brain including neurodevelopmental (i.e., autism, Down syndrome, and epilepsy) as well as neurodegenerative disorders (i.e., Alzheimer’s and Parkinson’s diseases). Histological, biochemical, cellular, and general molecular biology techniques have been key in understanding these pathologies. Now, the growing number of omics approaches can add significant extra information at a high and wide resolution level and, used effectively, can lead to novel and insightful interpretations of the biological processes at play. This review describes current approaches that use transcriptomics, translatomics and proteomic related methods to analyze the axon and presynaptic elements, focusing on the relationship that axon and synapses have with neurodegenerative diseases.

From System Modeling to System Analysis: The Impact of Resolution Level and Resolution Distribution in the Computer-Aided Investigation of Biomolecules

The ever increasing computer power, together with the improved accuracy of atomistic force fields, enables researchers to investigate biological systems at the molecular level with remarkable detail. However, the relevant length and time scales of many processes of interest are still hardly within reach even for state-of-the-art hardware, thus leaving important questions often unanswered. The computer-aided investigation of many biological physics problems thus largely benefits from the usage of coarse-grained models, that is, simplified representations of a molecule at a level of resolution that is lower than atomistic. A plethora of coarse-grained models have been developed, which differ most notably in their granularity; this latter aspect determines one of the crucial open issues in the field, i.e. the identification of an optimal degree of coarsening, which enables the greatest simplification at the expenses of the smallest information loss. In this review, we present the problem of coarse-grained modeling in biophysics from the viewpoint of system representation and information content. In particular, we discuss two distinct yet complementary aspects of protein modeling: on the one hand, the relationship between the resolution of a model and its capacity of accurately reproducing the properties of interest; on the other hand, the possibility of employing a lower resolution description of a detailed model to extract simple, useful, and intelligible information from the latter.