PointCloudExplore 2: Visual exploration of 3D gene expression

To better understand how developmental regulatory networks are defined inthe genome sequence, the Berkeley Drosophila Transcription Network Project (BDNTP)has developed a suite of methods to describe 3D gene expression data, i.e.,the output of the network at cellular resolution for multiple time points. To allow researchersto explore these novel data sets we have developed PointCloudXplore (PCX).In PCX we have linked physical and information visualization views via the concept ofbrushing (cell selection). For each view dedicated operations for performing selectionof cells are available. In PCX, all cell selections are stored in a central managementsystem. Cells selected in one view can in this way be highlighted in any view allowingfurther cell subset properties to be determined. Complex cell queries can be definedby combining different cell selections using logical operations such as AND, OR, andNOT. Here we are going to provide an overview of PointCloudXplore 2 (PCX2), thelatest publicly available version of PCX. PCX2 has shown to be an effective tool forvisual exploration of 3D gene expression data. We discuss (i) all views available inPCX2, (ii) different strategies to perform cell selection, (iii) the basic architecture ofPCX2., and (iv) illustrate the usefulness of PCX2 using selected examples

Integrating Data Clustering and Visualization for the Analysis of 3D Gene Expression Data

The recent development of methods for extracting precise measurements of spatial gene expression patterns from three-dimensional (3D) image data opens the way for new analyses of the complex gene regulatory networks controlling animal development. We present an integrated visualization and analysis framework that supports user-guided data clustering to aid exploration of these new complex datasets. The interplay of data visualization and clustering-based data classification leads to improved visualization and enables a more detailed analysis than previously possible. We discuss (i) integration of data clustering and visualization into one framework; (ii) application of data clustering to 3D gene expression data; (iii) evaluation of the number of clusters k in the context of 3D gene expression clustering; and (iv) improvement of overall analysis quality via dedicated post-processing of clustering results based on visualization. We discuss the use of this framework to objectively define spatial pattern boundaries and temporal profiles of genes and to analyze how mRNA patterns are controlled by their regulatory transcription factors

Benchmarking \u201cSmart City\u201d Technology Adoption in California: An Innovative Web Platform for Exploring New Data and Tracking Adoption

UC-ITS-2021-24In recent years, \u201csmart city\u201d technologies have emerged that allow cities, counties, and other agencies to manage their infrastructure assets more effectively, make their services more accessible to the public, and allow citizens to interface with new web-and mobile-based alternative service providers. This project developed an innovative user-friendly web interface for local and state policymakers that tracks and displays information on the adoption of such technologies in California across the policing, transportation, and water and wastewater sectors for a comprehensive set of local service providers: connectedgov.berkeley.edu. Contrary to conventional smart city indices, this platform allows users to view rates of adoption in maps that attribute adoption to the local public agencies or service providers actually procuring or regulating the technologies in question. Users can construct indices or view technologies one by one. Users can also explore the relationship between technology adoption and local service area conditions and demographics, or download the raw data and scripts used to collect it. This report illustrates the utility of the data the authors have collected, and the analytics one can perform using the web interface through an analysis of the rollout of three technologies in the transportation sector: electric vehicle (EV) chargers, transportation network company (TNC) service areas, and micromobility services across California

Resource-Efficient, Hierarchical Auto-Tuning of a Hybrid Lattice Boltzmann Computation on the Cray XT4

We apply auto-tuning to a hybrid MPI-pthreads lattice Boltzmann computation running on the Cray XT4 at National Energy Research Scientific Computing Center (NERSC). Previous work showed that multicore-specific auto-tuning can improve the performance of lattice Boltzmann magnetohydrodynamics (LBMHD) by a factor of 4x when running on dual- and quad-core Opteron dual-socket SMPs. We extend these studies to the distributed memory arena via a hybrid MPI/pthreads implementation. In addition to conventional auto-tuning at the local SMP node, we tune at the message-passing level to determine the optimal aspect ratio as well as the correct balance between MPI tasks and threads per MPI task. Our study presents a detailed performance analysis when moving along an isocurve of constant hardware usage: fixed total memory, total cores, and total nodes. Overall, our work points to approaches for improving intra- and inter-node efficiency on large-scale multicore systems for demanding scientific applications

PointCloudExplore 2: Visual exploration of 3D gene expression

To better understand how developmental regulatory networks are defined in the genome sequence, the Berkeley Drosophila Transcription Network Project (BDNTP) has developed a suite of methods to describe 3D gene expression data, i.e., the output of the network at cellular resolution for multiple time points. To allow researchers to explore these novel data sets we have developed PointCloudXplore (PCX). In PCX we have linked physical and information visualization views via the concept of brushing (cell selection). For each view dedicated operations for performing selection of cells are available. In PCX, all cell selections are stored in a central management system. Cells selected in one view can in this way be highlighted in any view allowing further cell subset properties to be determined. Complex cell queries can be defined by combining different cell selections using logical operations such as AND, OR, and NOT. Here we are going to provide an overview of PointCloudXplore 2 (PCX2), the latest publicly available version of PCX. PCX2 has shown to be an effective tool for visual exploration of 3D gene expression data. We discuss (i) all views available in PCX2, (ii) different strategies to perform cell selection, (iii) the basic architecture of PCX2., and (iv) illustrate the usefulness of PCX2 using selected examples

Resource-Efficient, Hierarchical Auto-Tuning of a Hybrid Lattice Boltzmann Computation on the Cray XT4

We apply auto-tuning to a hybrid MPI-pthreads lattice Boltzmann computation running on the Cray XT4 at National Energy Research Scientific Computing Center (NERSC). Previous work showed that multicore-specific auto-tuning can improve the performance of lattice Boltzmann magnetohydrodynamics (LBMHD) by a factor of 4x when running on dual- and quad-core Opteron dual-socket SMPs. We extend these studies to the distributed memory arena via a hybrid MPI/pthreads implementation. In addition to conventional auto-tuning at the local SMP node, we tune at the message-passing level to determine the optimal aspect ratio as well as the correct balance between MPI tasks and threads per MPI task. Our study presents a detailed performance analysis when moving along an isocurve of constant hardware usage: fixed total memory, total cores, and total nodes. Overall, our work points to approaches for improving intra- and inter-node efficiency on large-scale multicore systems for demanding scientific applications

Integrating Data Clustering and Visualization for the Analysis of 3D Gene Expression Data

The recent development of methods for extracting precise measurements of spatial gene expression patterns from three-dimensional (3D) image data opens the way for new analyses of the complex gene regulatory networks controlling animal development. We present an integrated visualization and analysis framework that supports user-guided data clustering to aid exploration of these new complex datasets. The interplay of data visualization and clustering-based data classification leads to improved visualization and enables a more detailed analysis than previously possible. We discuss (i) integration of data clustering and visualization into one framework; (ii) application of data clustering to 3D gene expression data; (iii) evaluation of the number of clusters k in the context of 3D gene expression clustering; and (iv) improvement of overall analysis quality via dedicated post-processing of clustering results based on visualization. We discuss the use of this framework to objectively define spatial pattern boundaries and temporal profiles of genes and to analyze how mRNA patterns are controlled by their regulatory transcription factors

Additional file 3 of The Danish-American Research Exchange (DARE): a cross-sectional study of a binational research education program

Additional file 3: Supplemental Table 1. Qualitative data from DARE medical student alumni collected at two focus group meetings. Appendix 1. References for publications of DARE Fellows and Alumni, 2015-2020, Cohorts 1-5

Additional file 3 of The Danish-American Research Exchange (DARE): a cross-sectional study of a binational research education program

Additional file 3: Supplemental Table 1. Qualitative data from DARE medical student alumni collected at two focus group meetings. Appendix 1. References for publications of DARE Fellows and Alumni, 2015-2020, Cohorts 1-5

Multi-laboratory compilation of atmospheric carbon dioxide data for the year 2023-2024; obspack_ch4_1_NRT_v6.2_2024-06-27

This product is constructed using the Observation Package (ObsPack) framework [Masarie et al., 2014; www.earth-syst-sci-data.net/6/375/2014/]. The framework is designed to bring together atmospheric greenhouse gas (GHG) observations from a variety of sampling platforms, prepare them with specific applications in mind, and package and distribute them in a self-consistent and well-documented product. ObsPack products are intended to support GHG budget studies and represent a new generation of cooperative value-added GHG data products