A biological nano-foam: the wall of coniferous bisaccate pollen (data sets)
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ABSTRACT:
SUBMITTER: Alexandra Pacureanu
PROVIDER: EMPIAR-10903 | biostudies-other |
REPOSITORIES: biostudies-other
Publications
A biological nanofoam: The wall of coniferous bisaccate pollen.
Cojocaru Ruxandra R Mannix Oonagh O Capron Marie M Miller C Giles CG Jouneau Pierre-Henri PH Gallet Benoit B Falconet Denis D Pacureanu Alexandra A Stukins Stephen S
Science advances 20220209 6
The outer layer of the pollen grain, the exine, plays a key role in the survival of terrestrial plant life. However, the exine structure in different groups of plants remains enigmatic. Here, modern and fossil coniferous bisaccate pollen were examined to investigate the detailed three-dimensional structure and properties of the pollen wall. X-ray nanotomography and volume electron microscopy are used to provide high-resolution imagery, revealing a solid nanofoam structure. Atomic force microscop ...[more]
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Project description:The outer layer of the pollen grain, the exine, plays a key role in the survival of terrestrial plant life. However, the exine structure in different groups of plants remains enigmatic. Here, modern and fossil coniferous bisaccate pollen were examined to investigate the detailed three-dimensional structure and properties of the pollen wall. X-ray nanotomography and volume electron microscopy are used to provide high-resolution imagery, revealing a solid nanofoam structure. Atomic force microscopy measurements were used to compare the pollen wall with other natural and synthetic foams and to demonstrate that the mechanical properties of the wall in this type of pollen are retained for millions of years in fossil specimens. The microscopic structure of this robust biological material has potential applications in materials sciences and also contributes to our understanding of the evolutionary success of conifers and other plants over geological time.
| S-EPMC8827650 | biostudies-literature
Project description:We are still far from being able to predict organisms' shapes purely from their genetic codes. While it is imperative to identify which encoded macromolecules contribute to a phenotype, determining how macromolecules self-assemble independently of the genetic code may be equally crucial for understanding shape development. Pollen grains are typically single-celled microgametophytes that have decorated walls of various shapes and patterns. The accumulation of morphological data and a comprehensive understanding of the wall development makes this system ripe for mathematical and physical modeling. Therefore, pollen walls are an excellent system for identifying both the genetic products and the physical processes that result in a huge diversity of extracellular morphologies. In this piece, I highlight the current understanding of pollen wall biology relevant for quantification studies and enumerate the modellable aspects of pollen wall patterning and specific approaches that one may take to elucidate how pollen grains build their beautifully patterned walls.
| S-EPMC9292386 | biostudies-literature
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Project description:Reactome (http://www.reactome.org) is an open-source, expert-authored, peer-reviewed, manually curated database of reactions, pathways and biological processes. We provide an intuitive web-based user interface to pathway knowledge and a suite of data analysis tools. The Pathway Browser is a Systems Biology Graphical Notation-like visualization system that supports manual navigation of pathways by zooming, scrolling and event highlighting, and that exploits PSI Common Query Interface web services to overlay pathways with molecular interaction data from the Reactome Functional Interaction Network and interaction databases such as IntAct, ChEMBL and BioGRID. Pathway and expression analysis tools employ web services to provide ID mapping, pathway assignment and over-representation analysis of user-supplied data sets. By applying Ensembl Compara to curated human proteins and reactions, Reactome generates pathway inferences for 20 other species. The Species Comparison tool provides a summary of results for each of these species as a table showing numbers of orthologous proteins found by pathway from which users can navigate to inferred details for specific proteins and reactions. Reactome's diverse pathway knowledge and suite of data analysis tools provide a platform for data mining, modeling and analysis of large-scale proteomics data sets. This Tutorial is part of the International Proteomics Tutorial Programme (IPTP 8).
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Project description:Persistent homology (PH) is a popular tool for topological data analysis that has found applications across diverse areas of research. It provides a rigorous method to compute robust topological features in discrete experimental observations that often contain various sources of uncertainties. Although powerful in theory, PH suffers from high computation cost that precludes its application to large data sets. Additionally, most analyses using PH are limited to computing the existence of nontrivial features. Precise localization of these features is not generally attempted because, by definition, localized representations are not unique and because of even higher computation cost. Such a precise location is a sine qua non for determining functional significance, especially in biological applications. Here, we provide a strategy and algorithms to compute tight representative boundaries around nontrivial robust features in large data sets. To showcase the efficiency of our algorithms and the precision of computed boundaries, we analyze the human genome and protein crystal structures. In the human genome, we found a surprising effect of the impairment of chromatin loop formation on loops through chromosome 13 and the sex chromosomes. We also found loops with long-range interactions between functionally related genes. In protein homologs with significantly different topology, we found voids attributable to ligand-interaction, mutation, and differences between species.
| S-EPMC10275456 | biostudies-literature
Project description:MotivationThe study of complex biological relationships is aided by large and high-dimensional data sets whose analysis often involves dimension reduction to highlight representative or informative directions of variation. In principle, information theory provides a general framework for quantifying complex statistical relationships for dimension reduction. Unfortunately, direct estimation of high-dimensional information theoretic quantities, such as entropy and mutual information (MI), is often unreliable given the relatively small sample sizes available for biological problems. Here, we develop and evaluate a hierarchy of approximations for high-dimensional information theoretic statistics from associated low-order terms, which can be more reliably estimated from limited samples. Due to a relationship between this metric and the minimum spanning tree over a graph representation of the system, we refer to these approximations as MIST (Maximum Information Spanning Trees).ResultsThe MIST approximations are examined in the context of synthetic networks with analytically computable entropies and using experimental gene expression data as a basis for the classification of multiple cancer types. The approximations result in significantly more accurate estimates of entropy and MI, and also correlate better with biological classification error than direct estimation and another low-order approximation, minimum-redundancy-maximum-relevance (mRMR).AvailabilitySoftware to compute the entropy approximations described here is available as Supplementary Material.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC2672626 | biostudies-literature
Project description:The rice GA biosynthetic gene OsGA3ox1 has been proposed to regulate pollen development through the gametophytic manner, but cellular characterization of its mutant pollen is lacking. In this study, three heterozygotic biallelic variants, "-3/-19", "-3/-2" and "-3/-10", each containing one null and one 3bp-deletion allele, were obtained by the CRISPR/Cas9 technique for the functional study of OsGA3ox1. The three homozygotes, "-19/-19", "-2/-2" and "-10/-10", derived from heterozygotic variants, did not affect the development of most vegetative and floral organs but showed a significant reduction in seed-setting rate and in pollen viability. Anatomic characterizations of these mutated osga3ox1 pollens revealed defects in starch granule accumulation and pollen wall development. Additional molecular characterization suggests that abnormal pollen development in the osga3ox1 mutants might be linked to the regulation of transcription factors OsGAMYB, OsTDR and OsbHLH142 during late pollen development. In brief, the rice GA3ox1 is a crucial gene that modulates pollen starch granule accumulation and pollen wall development at the gametophytic phase.
| S-EPMC10561864 | biostudies-literature
Project description:BACKGROUND:Analysing large and high-dimensional biological data sets poses significant computational difficulties for bioinformaticians due to lack of accessible tools that scale to hundreds of millions of data points. RESULTS:We developed a novel machine learning command line tool called PyBDA for automated, distributed analysis of big biological data sets. By using Apache Spark in the backend, PyBDA scales to data sets beyond the size of current applications. It uses Snakemake in order to automatically schedule jobs to a high-performance computing cluster. We demonstrate the utility of the software by analyzing image-based RNA interference data of 150 million single cells. CONCLUSION:PyBDA allows automated, easy-to-use data analysis using common statistical methods and machine learning algorithms. It can be used with simple command line calls entirely making it accessible to a broad user base. PyBDA is available at https://pybda.rtfd.io.
| S-EPMC6849186 | biostudies-literature
Project description:CellProfiler Analyst allows the exploration and visualization of image-based data, together with the classification of complex biological phenotypes, via an interactive user interface designed for biologists and data scientists. CellProfiler Analyst 2.0, completely rewritten in Python, builds on these features and adds enhanced supervised machine learning capabilities (Classifier), as well as visualization tools to overview an experiment (Plate Viewer and Image Gallery).Availability and implementationCellProfiler Analyst 2.0 is free and open source, available at http://www.cellprofiler.org and from GitHub (https://github.com/CellProfiler/CellProfiler-Analyst) under the BSD license. It is available as a packaged application for Mac OS X and Microsoft Windows and can be compiled for Linux. We implemented an automatic build process that supports nightly updates and regular release cycles for the software.Contactanne@broadinstitute.orgSupplementary information: Supplementary data are available at Bioinformatics online.
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Project description:MOTIVATION:Localization microscopy data is represented by a set of spatial coordinates, each corresponding to a single detection, that form a point cloud. This can be analyzed either by rendering an image from these coordinates, or by analyzing the point cloud directly. Analysis of this type has focused on clustering detections into distinct groups which produces measurements such as cluster area, but has limited capacity to quantify complex molecular organization and nano-structure. RESULTS:We present a segmentation protocol which, through the application of persistence-based clustering, is capable of probing densely packed structures which vary in scale. An increase in segmentation performance over state-of-the-art methods is demonstrated. Moreover we employ persistent homology to move beyond clustering, and quantify the topological structure within data. This provides new information about the preserved shapes formed by molecular architecture. Our methods are flexible and we demonstrate this by applying them to receptor clustering in platelets, nuclear pore components, endocytic proteins and microtubule networks. Both 2D and 3D implementations are provided within RSMLM, an R package for pointillist-based analysis and batch processing of localization microscopy data. AVAILABILITY AND IMPLEMENTATION:RSMLM has been released under the GNU General Public License v3.0 and is available at https://github.com/JeremyPike/RSMLM. Tutorials for this library implemented as Binder ready Jupyter notebooks are available at https://github.com/JeremyPike/RSMLM-tutorials. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
| S-EPMC7162425 | biostudies-literature