IDCube Lite: Free Interactive Discovery Cube software for multi- and hyperspectral applications.
Ontology highlight
ABSTRACT:
SUBMITTER: Mishra D
PROVIDER: S-EPMC8409277 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:In many fields of science and technology we are confronted with complex networks. Making sense of these networks often require the ability to visualize and explore their intermingled structure consisting of nodes and links. To facilitate the identification of significant connectivity patterns, many methods have been developed based on the rearrangement of the nodes so as to avoid link criss-cross. However, real networks are often embedded in a geometrical space and the nodes code for an intrinsic physical feature of the system that one might want to preserve. For these spatial networks, it is therefore crucial to find alternative strategies operating on the links and not on the nodes. Here, we introduce Vizaj a javascript web application to render spatial networks based on optimized geometrical criteria that reshape the link profiles. While optimized for 3D networks, Vizaj can also be used for 2D networks and offers the possibility to interactively customize the visualization via several controlling parameters, including network filtering and the effect of internode distance on the link trajectories. Vizaj is further equipped with additional options allowing to improve the final aesthetics, such as the color/size of both nodes and links, zooming/rotating/translating, and superimposing external objects. Vizaj is an open-source software which can be freely downloaded and updated via a github repository. Here, we provide a detailed description of its main features and algorithms together with a guide on how to use it. Finally, we validate its potential on several synthetic and real spatial networks from infrastructural to biological systems. We hope that Vizaj will help scientists and practitioners to make sense of complex networks and provide aesthetic while informative visualizations.
| S-EPMC10035906 | biostudies-literature
Project description:High-throughput next-generation sequencing technologies pose increasing demands on the efficiency, accuracy and usability of data analysis software. In this article, we present ZOOM Lite, a software for efficient reads mapping and result visualization. With a kernel capable of mapping tens of millions of Illumina or AB SOLiD sequencing reads efficiently and accurately, and an intuitive graphical user interface, ZOOM Lite integrates reads mapping and result visualization into a easy to use pipeline on desktop PC. The software handles both single-end and paired-end reads, and can output both the unique mapping result or the top N mapping results for each read. Additionally, the software takes a variety of input file formats and outputs to several commonly used result formats. The software is freely available at http://bioinfor.com/zoom/lite/.
| S-EPMC2896157 | biostudies-literature
Project description:Experiments involving high-throughput sequencing are widely used for analyses of chromatin function and gene expression. Common examples are the use of chromatin immunoprecipitation for the analysis of chromatin modifications or factor binding, enzymatic digestions for chromatin structure assays, and RNA sequencing to assess gene expression changes after biological perturbations. To investigate the pattern and abundance of coverage signals across regions of interest, data are often visualized as profile plots of average signal or stacked rows of signal in the form of heatmaps. We found that available plotting software was either slow and laborious or difficult to use by investigators with little computational training, which inhibited wide data exploration. To address this need, we developed SeqPlots, a user-friendly exploratory data analysis (EDA) and visualization software for genomics. After choosing groups of signal and feature files and defining plotting parameters, users can generate profile plots of average signal or heatmaps clustered using different algorithms in a matter of seconds through the graphical user interface (GUI) controls. SeqPlots accepts all major genomic file formats as input and can also generate and plot user defined motif densities. Profile plots and heatmaps are highly configurable and batch operations can be used to generate a large number of plots at once. SeqPlots is available as a GUI application for Mac or Windows and Linux, or as an R/Bioconductor package. It can also be deployed on a server for remote and collaborative usage. The analysis features and ease of use of SeqPlots encourages wide data exploration, which should aid the discovery of novel genomic associations.
| S-EPMC5133382 | biostudies-literature
Project description:In this paper, a novel dual band frequency reconfigurable antenna using an origami magic cube is proposed for wireless sensor network (WSN) applications. The proposed origami antenna consists of a meandered monopole folded onto three sides of the magic cube. A microstrip open-ended stub is loaded on the meandered monopole. The proposed origami magic cube can be mechanically folded and unfolded. The proposed antenna operates at 1.57 GHZ and 2.4 GHz in the folded state. In the unfolded state, the proposed antenna operates at 900 MHz and 2.3 GHz. The resonant frequency of the second band can be tunable by varying the length and position of the open stub. The origami magic cube is built on paper. Its performance is numerically and experimentally demonstrated from S-parameters and radiation patterns. The measured 10 dB impedance bandwidth of the proposed origami antenna is 18% (900-1120 MHz) and 15% (2.1-2.45 GHz) for the unfolded state and 20% (1.3-1.6 GHz) and 14% (2.3-2.5 GHz) for the folded state. The measured peak gain at 900 MHz and 2.3 GHz are 1.1 dBi and 2.32 dBi, respectively, in the unfolded state. The measured peak gain at 1.5 GHz and 2.4 GHz are 3.28 dBi and 1.98 dBi, respectively, in the folded state.
| S-EPMC5712905 | biostudies-literature
Project description:BackgroundAn important aspect of proteomic mass spectrometry involves quantifying and interpreting the isotope distributions arising from mixtures of macromolecules with different isotope labeling patterns. These patterns can be quite complex, in particular with in vivo metabolic labeling experiments producing fractional atomic labeling or fractional residue labeling of peptides or other macromolecules. In general, it can be difficult to distinguish the contributions of species with different labeling patterns to an experimental spectrum and difficult to calculate a theoretical isotope distribution to fit such data. There is a need for interactive and user-friendly software that can calculate and fit the entire isotope distribution of a complex mixture while comparing these calculations with experimental data and extracting the contributions from the differently labeled species.ResultsEnvelope has been developed to be user-friendly while still being as flexible and powerful as possible. Envelope can simultaneously calculate the isotope distributions for any number of different labeling patterns for a given peptide or oligonucleotide, while automatically summing these into a single overall isotope distribution. Envelope can handle fractional or complete atom or residue-based labeling, and the contribution from each different user-defined labeling pattern is clearly illustrated in the interactive display and is individually adjustable. At present, Envelope supports labeling with 2H, 13C, and 15N, and supports adjustments for baseline correction, an instrument accuracy offset in the m/z domain, and peak width. Furthermore, Envelope can display experimental data superimposed on calculated isotope distributions, and calculate a least-squares goodness of fit between the two. All of this information is displayed on the screen in a single graphical user interface. Envelope supports high-quality output of experimental and calculated distributions in PNG or PDF format. Beyond simply comparing calculated distributions to experimental data, Envelope is useful for planning or designing metabolic labeling experiments, by visualizing hypothetical isotope distributions in order to evaluate the feasibility of a labeling strategy. Envelope is also useful as a teaching tool, with its real-time display capabilities providing a straightforward way to illustrate the key variable factors that contribute to an observed isotope distribution.ConclusionEnvelope is a powerful tool for the interactive calculation and visualization of complex isotope distributions for comparison to experimental data. It is available under the GNU General Public License from http://williamson.scripps.edu/envelope/.
| S-EPMC2605472 | biostudies-literature
Project description:Live fluorescence imaging has demonstrated the dynamic nature of dendritic spines, with changes in shape occurring both during development and in response to activity. The structure of a dendritic spine correlates with its functional efficacy. Learning and memory studies have shown that a great deal of the information stored by a neuron is contained in the synapses. High precision tracking of synaptic structures can give hints about the dynamic nature of memory and help us understand how memories evolve both in biological and artificial neural networks. Experiments that aim to investigate the dynamics behind the structural changes of dendritic spines require the collection and analysis of large time-series datasets. In this paper, we present an open-source software called SpineS for automatic longitudinal structural analysis of dendritic spines with additional features for manual intervention to ensure optimal analysis. We have tested the algorithm on in-vitro, in-vivo, and simulated datasets to demonstrate its performance in a wide range of possible experimental scenarios.
| S-EPMC9300710 | biostudies-literature
Project description:BackgroundA considerable number of data mining approaches for biomedical data analysis, including state-of-the-art associative models, require a form of data discretization. Although diverse discretization approaches have been proposed, they generally work under a strict set of statistical assumptions which are arguably insufficient to handle the diversity and heterogeneity of clinical and molecular variables within a given dataset. In addition, although an increasing number of symbolic approaches in bioinformatics are able to assign multiple items to values occurring near discretization boundaries for superior robustness, there are no reference principles on how to perform multi-item discretizations.ResultsIn this study, an unsupervised discretization method, DI2, for variables with arbitrarily skewed distributions is proposed. Statistical tests applied to assess differences in performance confirm that DI2 generally outperforms well-established discretizations methods with statistical significance. Within classification tasks, DI2 displays either competitive or superior levels of predictive accuracy, particularly delineate for classifiers able to accommodate border values.ConclusionsThis work proposes a new unsupervised method for data discretization, DI2, that takes into account the underlying data regularities, the presence of outlier values disrupting expected regularities, as well as the relevance of border values. DI2 is available at https://github.com/JupitersMight/DI2.
| S-EPMC8425008 | biostudies-literature
Project description:BackgroundHierarchical clustering is an exploratory data analysis method that reveals the groups (clusters) of similar objects. The result of the hierarchical clustering is a tree structure called dendrogram that shows the arrangement of individual clusters. To investigate the row/column hierarchical cluster structure of a data matrix, a visualization tool called 'cluster heatmap' is commonly employed. In the cluster heatmap, the data matrix is displayed as a heatmap, a 2-dimensional array in which the colour of each element corresponds to its value. The rows/columns of the matrix are ordered such that similar rows/columns are near each other. The ordering is given by the dendrogram which is displayed on the side of the heatmap.ResultsWe developed InCHlib (Interactive Cluster Heatmap Library), a highly interactive and lightweight JavaScript library for cluster heatmap visualization and exploration. InCHlib enables the user to select individual or clustered heatmap rows, to zoom in and out of clusters or to flexibly modify heatmap appearance. The cluster heatmap can be augmented with additional metadata displayed in a different colour scale. In addition, to further enhance the visualization, the cluster heatmap can be interconnected with external data sources or analysis tools. Data clustering and the preparation of the input file for InCHlib is facilitated by the Python utility script inchlib_clust.ConclusionsThe cluster heatmap is one of the most popular visualizations of large chemical and biomedical data sets originating, e.g., in high-throughput screening, genomics or transcriptomics experiments. The presented JavaScript library InCHlib is a client-side solution for cluster heatmap exploration. InCHlib can be easily deployed into any modern web application and configured to cooperate with external tools and data sources. Though InCHlib is primarily intended for the analysis of chemical or biological data, it is a versatile tool which application domain is not limited to the life sciences only.
| S-EPMC4173117 | biostudies-literature
Project description:Hyperspectral imaging has proven significance in bio-imaging applications and it has the ability to capture up to several hundred images of different wavelengths offering relevant spectral signatures. To use hyperspectral imaging for in vivo monitoring and diagnosis of the internal body cavities, a snapshot hyperspectral video-endoscope is required. However, such reported systems provide only about 50 wavelengths. We have developed a four-dimensional snapshot hyperspectral video-endoscope with a spectral range of 400-1000 nm, which can detect 756 wavelengths for imaging, significantly more than such systems. Capturing the three-dimensional datacube sequentially gives the fourth dimension. All these are achieved through a flexible two-dimensional to one-dimensional fiber bundle. The potential of this custom designed and fabricated compact biomedical probe is demonstrated by imaging phantom tissue samples in reflectance and fluorescence imaging modalities. It is envisaged that this novel concept and developed probe will contribute significantly towards diagnostic in vivo biomedical imaging in the near future.
| S-EPMC4820774 | biostudies-literature
Project description:In lighting engineering, it has become necessary to develop new acquisition devices aligned with today’s technological needs, so that they can be easily integrated with IoT (Internet of things) systems. This allows recording lighting data in real-time either to perform analysis, estimate lighting quality measurements, or interact with intelligent lighting systems. Here a portable low-cost IoT hyperspectral acquisition device is presented, which can be used to perform measurements both indoors and outdoors. Since the device is part of IoT technologies, it has wireless communication and allows raw data to be sent to the cloud or to perform calculations directly within the device to be transmitted later. In addition, information on the hardware general design is provided and the device measurements uniformity is validated using the ASTM G-173-03 solar spectra. The proposed device can be used outdoors since its container is IP67 and it can simultaneously measure incident and diffuse radiation. On the other hand, the recorded data can be used for light source spectrum recovery, and to calculate light quality measurements. The hardware and software are customizable, which is an advantage over commercially available devices. The final cost of the device is 271.95 USD.
| S-EPMC9123436 | biostudies-literature