Data scale as cartography: a semi-automatic approach for thematic web map creation

Open government promises increased transparency by providing its citizens datasets about city processes. Open data portals have been emerging all over the world as mines of open geographic datasets. Thematic web maps are key to understanding these open geographic datasets. Current thematic web maps are created by programmers and/or cartographers, and thus are not designed to be easily reused with new geographic datasets. As a result, they pose several challenges to non-experts wanting to adapt them to new scenarios. This article introduces a semi-automatic approach for the creation of thematic web maps by and for users with no prior training in cartography. The approach relies on the mapping between Stevens’ data types and Bertin’s visual variables, to suggest (meaningful) thematic map visualizations for a given input geographic dataset. It was implemented as a web prototype in AngularJS and evaluated with 19 participants. Results from the user study suggest that despite facing a few challenges in accurately identifying Stevens’ data types, participants managed to successfully create web maps and correctly answer spatial questions. The prototype and insights gathered from the user study are relevant to making cartographic products more accessible to a broader population, and open geographic data more usable in the context of an open government.</p

Cu–Sn Bimetallic Catalyst for Selective Aqueous Electroreduction of CO₂ to CO

We report a selective and stable electrocatalyst utilizing non-noble metals consisting of Cu and Sn for the efficient and selective reduction of CO₂ to CO over a wide potential range. The bimetallic electrode was prepared through the electrodeposition of Sn species on the surface of oxide-derived copper (OD-Cu). The Cu surface, when decorated with an optimal amount of Sn, resulted in a Faradaic efficiency (FE) for CO greater than 90% and a current density of −1.0 mA cm^–2 at −0.6 V vs RHE, compared to the CO FE of 63% and −2.1 mA cm^–2 for OD-Cu. Excess Sn on the surface caused H₂ evolution with a decreased current density. X-ray diffraction (XRD) suggests the formation of Cu–Sn alloy. Auger electron spectroscopy of the sample surface exhibits zerovalent Cu and Sn after the electrodeposition step. Density functional theory (DFT) calculations show that replacing a single Cu atom with a Sn atom leaves the d-band orbitals mostly unperturbed, signifying no dramatic shifts in the bulk electronic structure. However, the Sn atom discomposes the multifold sites on pure Cu, disfavoring the adsorption of H and leaving the adsorption of CO relatively unperturbed. Our catalytic results along with DFT calculations indicate that the presence of Sn on reduced OD-Cu diminishes the hydrogenation capabilityi.e., the selectivity toward H₂ and HCOOHwhile hardly affects the CO productivity. While the pristine monometallic surfaces (both Cu and Sn) fail to selectively reduce CO₂, the Cu–Sn bimetallic electrocatalyst generates a surface that inhibits adsorbed H*, resulting in improved CO FE. This study presents a strategy to provide low-cost non-noble metals that can be utilized as a highly selective electrocatalyst for the efficient aqueous reduction of CO₂

Genome-wide identification and evolutionary analysis of the FGF gene family in buffalo

Fibroblast growth factors (FGFs) are important polypeptide growth factors that play a critical role in many developmental processes, including differentiation, cell proliferation, and migration in mammals. This study employs in silico analyses to characterize the FGF gene family in buffalo, investigating their genome-wide identification, physicochemical properties, and evolutionary patterns. For this purpose, genomic and proteomic sequences of buffalo, cattle, goat, and sheep were retrieved from NCBI database. We identified a total of 22 FGF genes in buffalo. Physicochemical properties observed through ProtParam tool showed notable features of these proteins including in-vitro instability, thermostability, hydrophilicity, and basic nature. Phylogenetic analysis grouped 22 identified genes into nine sub-families based on evolutionary relationships. Additionally, analysis of gene structure, motif patterns, and conserved domains using TBtools revealed the remarkable conservation of this gene family across selected species throughout the course of evolution. Comparative amino acid analysis performed through ClustalW demonstrated significant conservation between buffalo and cattle FGF proteins. Mutational analysis showed three non-synonymous mutations at positions R103 > G, P7 > L, and E98 > Q in FGF4, FGF6, and FGF19, respectively in buffalo. Duplication events revealed only one segmental duplication (FGF10/FGF22) in buffalo and two in cattle (FGF10/FGF22 and FGF13/FGF13-like) with Ka/Ks values Communicated by Ramaswamy H. Sarma</p

Completeness page of the web-application.

Calculated completeness of the study data, i.e., if all items have been completed for each subject. The hierarchical structure of the metadata is displayed similar to the analysis page. Colored bars indicate the completeness of each metadata element.</p

Table illustrating the five different categories the application distinguishes and their calculated statistics and charts.

<p>Table illustrating the five different categories the application distinguishes and their calculated statistics and charts.</p

Table of performance benchmarks.

<p>Table of performance benchmarks.</p

ODM Data Analysis—A tool for the automatic validation, monitoring and generation of generic descriptive statistics of patient data

<div><p>Introduction</p><p>A required step for presenting results of clinical studies is the declaration of participants demographic and baseline characteristics as claimed by the FDAAA 801. The common workflow to accomplish this task is to export the clinical data from the used electronic data capture system and import it into statistical software like SAS software or IBM SPSS. This software requires trained users, who have to implement the analysis individually for each item. These expenditures may become an obstacle for small studies. Objective of this work is to design, implement and evaluate an open source application, called ODM Data Analysis, for the semi-automatic analysis of clinical study data.</p><p>Methods</p><p>The system requires clinical data in the CDISC Operational Data Model format. After uploading the file, its syntax and data type conformity of the collected data is validated. The completeness of the study data is determined and basic statistics, including illustrative charts for each item, are generated. Datasets from four clinical studies have been used to evaluate the application’s performance and functionality.</p><p>Results</p><p>The system is implemented as an open source web application (available at <a href="https://odmanalysis.uni-muenster.de" target="_blank">https://odmanalysis.uni-muenster.de</a>) and also provided as Docker image which enables an easy distribution and installation on local systems. Study data is only stored in the application as long as the calculations are performed which is compliant with data protection endeavors. Analysis times are below half an hour, even for larger studies with over 6000 subjects.</p><p>Discussion</p><p>Medical experts have ensured the usefulness of this application to grant an overview of their collected study data for monitoring purposes and to generate descriptive statistics without further user interaction. The semi-automatic analysis has its limitations and cannot replace the complex analysis of statisticians, but it can be used as a starting point for their examination and reporting.</p></div

PDF page of the web-application.

This page consists of a PDF viewer to view the generated PDF in the browser. It contains all calculated statistics and can be downloaded for later usage.</p

Cover page of the web-application.

The image shows the first page presented to the user after starting ODM-DA. Besides allowing the upload of an ODM file for the analysis, the download of a test file and a link to the user manual are provided. In addition, the different analysis options can be specified.</p

Schematic structure of an ODM file.

<p>Hierarchical structure of the ODM’s metadata on the left side and clinical data on the right side. The added attributes should clarify the connection between metadata and clinical data elements.</p