Similarity of common land use types among Twitter users and the Travel Tracker survey individuals.

<p>A-C: Violin plots of similarity of common land use types between Twitter users and the Travel survey individuals compared to the control group (a random sample from the land use map of Chicago for rank one (A), rank two (B) and rank three (C). Each sample is made of 10,000 individuals in case of Twitter and the travel survey and 10,000 random land use parcel in the case of the random map sample.</p

Twitter temporal signatures.

<p>A-D: Twitter users’ temporal signatures aggregated by land use type for all users during weekdays (A-B) and weekends (C-D). Weekdays were defined as Mondays to Fridays while Weekends include Saturdays and Sundays. Signatures were normalized by the total number of tweets counts in a land use class to allow comparisons.</p

Scatter plots of temporal signatures of individual key locations.

<p>A-B: Distribution of individual clusters in a 2D space defined by the temporal activity (percentage of tweets relative to the total number of tweets in the cluster) during different hours of the day. A: morning vs. evening. B: morning vs. afternoon. Clusters with similar land use attributes have a similar distribution of tweets within the twenty-four cycle. Hexagonal binning was used to display the common (mode) land use attribute in each bin.</p

Confusion matrix of Twitter land use classification.

<p>Confusion matrix of Twitter land use classification.</p

Semantics of top tweeted-from locations.

<p>A-B: Count of unique users grouped by land use types and ranks of their top ten key locations; absolute count (A) and normalized count (B). C-D: Count of surveyed individuals who reported their stay times at different locations during the day grouped by land use types and ranks (based on the duration of stay); absolute count (C) and normalized count (D). Data were extracted from the travel survey of Chicago and present an estimate of the preferential return of Chicago residents at the time of the survey.</p

Spatial uncertainty.

<p>A: Box plots of the distribution of spatial uncertainty index grouped by rank; an index value of one indicates that all the tweets in a cluster are in the close proximity of a single land use parcel. Notice the strong left-skewed distribution, which indicates that the majority of the parcels are uniquely associated with a particular parcel. B: Log-log distribution of number of parcels per unique users grouped by activity types.</p

Rapidly Measuring Spatial Accessibility of COVID-19 Healthcare Resources: A Case Study of Illinois, USA

AbstractThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causing the coronavirus disease 2019 (COVID-19) pandemic, has infected millions of people and caused hundreds of thousands of deaths. While COVID-19 has overwhelmed healthcare resources (e.g., healthcare personnel, testing resources, hospital beds, and ventilators) in a number of countries, limited research has been conducted to understand spatial accessibility of such resources. This study fills this gap by rapidly measuring the spatial accessibility of COVID-19 healthcare resources with a particular focus on Illinois, USA. Specifically, the rapid measurement is achieved by resolving computational intensity of an enhanced two-step floating catchment area (E2SFCA) method through a parallel computing strategy based on cyberGIS (cyber geographic information science and systems). The study compared the spatial accessibility measures for COVID-19 patients to those of general population, identifying which geographic areas need additional healthcare resources to improve access. The results also help delineate the areas that may face a COVID-19-induced shortage of healthcare resources caused by COVID-19. The Chicagoland, particularly the southern Chicago, shows an additional need for resources. Our findings are relevant for policymakers and public health practitioners to allocate existing healthcare resources or distribute new resources for maximum access to health services.</div

Image_1_Construction of disulfidptosis-based immune response prediction model with artificial intelligence and validation of the pivotal grouping oncogene c-MET in regulating T cell exhaustion.png

BackgroundGiven the lack of research on disulfidptosis, our study aimed to dissect its role in pan-cancer and explore the crosstalk between disulfidptosis and cancer immunity.MethodsBased on TCGA, ICGC, CGGA, GSE30219, GSE31210, GSE37745, GSE50081, GSE22138, GSE41613, univariate Cox regression, LASSO regression, and multivariate Cox regression were used to construct the rough gene signature based on disulfidptosis for each type of cancer. SsGSEA and Cibersort, followed by correlation analysis, were harnessed to explore the linkage between disulfidptosis and cancer immunity. Weighted correlation network analysis (WGCNA) and Machine learning were utilized to make a refined prognosis model for pan-cancer. In particular, a customized, enhanced prognosis model was made for glioma. The siRNA transfection, FACS, ELISA, etc., were employed to validate the function of c-MET.ResultsThe expression comparison of the disulfidptosis-related genes (DRGs) between tumor and nontumor tissues implied a significant difference in most cancers. The correlation between disulfidptosis and immune cell infiltration, including T cell exhaustion (Tex), was evident, especially in glioma. The 7-gene signature was constructed as the rough model for the glioma prognosis. A pan-cancer suitable DSP clustering was made and validated to predict the prognosis. Furthermore, two DSP groups were defined by machine learning to predict the survival and immune therapy response in glioma, which was validated in CGGA. PD-L1 and other immune pathways were highly enriched in the core blue gene module from WGCNA. Among them, c-MET was validated as a tumor driver gene and JAK3-STAT3-PD-L1/PD1 regulator in glioma and T cells. Specifically, the down-regulation of c-MET decreased the proportion of PD1+ CD8+ T cells.ConclusionTo summarize, we dissected the roles of DRGs in the prognosis and their relationship with immunity in pan-cancer. A general prognosis model based on machine learning was constructed for pan-cancer and validated by external datasets with a consistent result. In particular, a survival-predicting model was made specifically for patients with glioma to predict its survival and immune response to ICIs. C-MET was screened and validated for its tumor driver gene and immune regulation function (inducing t-cell exhaustion) in glioma.</p

HydroShare: Advancing Hydrology through Collaborative Data and Model Sharing

HydroShare is an online, collaborative system for open sharing of hydrologic data, analytical tools, and models.  It supports the sharing of and collaboration around “resources” which are defined by standardized content types for data formats and models commonly used in hydrology.  These include time series, geographic grids and shapes, multidimensional space-time data as well as models and model instances. This poster illustrates the HydroShare collaborative environment and web based services developed to support the sharing and processing of hydrologic data and models.  With HydroShare you can: Share your data and models with colleagues; Manage who has access to the content that you share; Share, access, visualize and manipulate a broad set of hydrologic data types and models; Use the web services application programming interface (API) to program automated and client access; Publish data and models and obtain a citable digital object identifier (DOI); Aggregate your resources into collections; Discover and access data and models published by others; Use web apps to visualize, analyze and run models on data in HydroShare.  The capability to assign DOIs to HydroShare resources means that they are permanently citable helping researchers who share their data get credit for the data published.  Models, and Model Instances, which in HydroShare are a model application to a specific site with its input and output data can also receive DOI's.  Collections allow multiple resources from a study to be aggregated together providing a comprehensive archival record of the research outcomes, supporting transparency and reproducibility, thereby enhancing trust in the findings.  Reuse to support additional research is also enabled.  HydroShare supports web apps to act on resources for cloud (server) based visualization and analysis, including large scale geographic and digital elevation model analysis at the CyberGIS center at the National Center for Supercomputing Applications (NCSA) and execution of SWAT and RHESSys models.<br