Altered cardiac bradykinin metabolism in experimental diabetes caused by the variations of angiotensin converting enzyme and other peptidases

The peptidases angiotensin-converting enzyme (ACE) and neutral endopeptidase 24.11 (NEP) mediate most of the kinin catabolism in normal cardiac tissue and are the molecular targets of inhibitory drugs that favorably influence diabetic complications. We studied the variations of those kininases in the myocardium of rats in experimental diabetes. ACE and NEP activities were significantly decreased in heart membranes 4–8 weeks post-streptozotocin (STZ) injection. However, insulin-dependent diabetes did not modify significantly bradykinin (BK) half-life (t1/2) while the effect of both ACE (enalaprilat) and ACE and NEP (omapatrilat) inhibitors on BK degradation progressively decreased, which may be explained by the upregulation of other unidentified metallopeptidase(s). In vivo insulin treatment restored the activities of both ACE and NEP. ACE and NEP activities were significantly higher in hearts of young Zucker rats than in those of Sprague–Dawley rats. BK t1/2 and the effects of peptidase inhibitors on t1/2 varied accordingly. It is concluded that kininase activities are subjected to large and opposite variations in rat cardiac tissue in type I and II diabetes models. A number of tissue or molecular factors may determine these variations, such as remodeling of cardiac tissue, ectoenzyme shedding to the extracellular fluid and the pathologic regulation of peptidase gene expression

A versatile Cloud Computing environment to facilitate African-European partnership in research: EO AFRICA R&D Innovation Lab

The African Framework for Research, Innovation, Communities and Applications (EO AFRICA) is an ESA initiative in collaboration with the African Union Commission that aims to foster an African-European R&D partnership facilitating the sustainable adoption of Earth Observation and related space technologies in Africa. EO AFRICA R&D Facility is the flagship of EO AFRICA with the overarching goals of enabling an active research community and promoting creative and collaborative innovation processes by providing funding, advanced training, and computing resources. The Innovation Lab is a state-of-the-art Cloud Computing infrastructure provided by the Facility to 30 research projects of African-European research tandems and participants of the capacity development activities of the Space Academy. The Innovation Lab creates new opportunities for innovative research to develop EO algorithms and applications adapted to African challenges and needs, through interactive Virtual Research Environments (VREs) with ready-to-use research and EO analysis software, and facilitated access to a wide range of analysis-ready EO datasets by leveraging the host DIAS infrastructure. The Innovation Lab is a cloud-based, user-friendly, and versatile Platform as a service (PaaS) that allows the users to develop, test, run, and optimize their research code making full use of the Copernicus DIAS infrastructure and a tailor-made interactive computing environment for geospatial analysis. Co-located data and computing services enable fast data exploitation and analysis, which in turn facilitates the utilization of multi-spectral spatiotemporal big data and machine learning methods. Each user has direct access to all online EO data available on the host DIAS (CreoDIAS), especially for Africa, and if required, can also request archived data, which is automatically retrieved and made available within a short delay. The Innovation Lab also supports user-provided in-situ data and allows access to EO data on the Cloud (e.g., other DIASes, CNES PEPS, Copernicus Hub, etc.) through a unified and easy-to-use and open-source data access API (EODAG). Because all data access and analysis are performed on the server-side, the platform does not require a fast Internet connection, and it is adapted for low bandwidth access to enable active collaboration of African – European research tandems. As a minimum configuration, each user has access to computing units with four virtual CPUs, 32 GB RAM, 100 GB local SSD storage, and 1 TB network storage. To a limited extent and for specific needs (e.g., AI applications like Deep Learning), GPU-enabled computing units are also provided. The user interface of the Innovation Lab allows the use of interactive Jupyter notebooks through the JupyterLab environment, which is served by a JupyterHub deployment with improved security and scalability features. For advanced research code development purposes, the Innovation Lab features a web-based VS Code integrated development environment, which provides specialized tools for programming in different languages, such as Python and R. Code analytics tools are also available for benchmarking, code profiling, and memory/performance monitoring. For specific EO workflows that require exploiting desktop applications (e.g., ESA SNAP, QGIS) for pre-processing, analysis, or visualization purposes, the Innovation Lab provides a web-based remote desktop with ready-to-use EO desktop applications. The users can also customize their working environment by using standard package managers. As endorsed by the European Commission Open Science approach, data and code sharing and versioning are crucial to allow reuse and reproduction of the algorithms, workflows, and results. In this context, the Innovation Lab has tools integrated into its interactive development environment that provide direct access to code repositories and allow easy version control. Although public code repositories (e.g., Github) are advised for better visibility, the Innovation Lab also includes a dedicated code repository to support the users' particular needs (e.g., storage of sensitive information). The assets (e.g., files, folders) stored on the platform can be easily accessed and shared externally through the FileBrowser tool. Besides providing a state-of-the-art computing infrastructure, the Innovation Lab also includes other necessary services to ensure a comfortable virtual research experience. All research projects granted by the EO AFRICA R&D Facility receive dedicated technical support for the Innovation Lab facilities. Scientific support and advice from senior researchers and experts for developing geospatial computing workflows are also provided. Users are able to request support contacting a helpdesk via a dedicated ticketing and chat system. After a 6-month development and testing period, the Innovation Lab became operational in September 2021. The first field testing of the platform took place in November 2021 during a 3-day hackathon jointly organized by EO AFRICA R&D, GMES & Africa, and CURAT as part of the AfricaGIS 2021 conference. Forty participants utilized the platform to develop innovative solutions to food security and water resources challenges, such as the impact of the COVID-19 pandemic on agricultural production or linking the decrease in agricultural production to armed conflicts. The activity was successful and similar ones are expected to be organized during major GIS and EO conferences in Africa during the lifetime of the project. Thirty research projects of African-European research tandems granted by the Facility will utilize the Innovation Lab to develop innovative and open-source EO algorithms and applications, preferably as interactive notebooks, adapted to African solutions to African challenges in food security and water scarcity by leveraging cutting-edge cloud-based data access and computing infrastructure. The call for the first 15 research projects was published in November 2021, and the projects are expected to start using the Innovation Lab in February 2022. In parallel, the Innovation Lab provides the computing environment for the capacity development activities of the EO AFRICA R&D Facility, which are organized under the umbrella of EO AFRICA Space Academy. These capacity development activities include several MOOCs, webinars, online and face-to-face courses designed and tailored to improve the knowledge and skills of African researchers in the utilization of Cloud Computing technology to work with EO data. Selected participants of the capacity development activities will use the Innovation Lab during their training. Moreover, the instructors in the Facility use the Innovation Lab to develop the training materials for the Space Academy. Access to the Innovation Lab will also be granted to individual researchers and EO experts depending on the use case and resource availability. Application for access can be made easily through the EO AFRICA R&D web portal after becoming a member of the EO AFRICA Community.This study is funded by ESA Contract No. 4000133905/21/I-EF

Geomorphological mapping with a small unmanned aircraft system (sUAS): feature detection and accuracy assessment of a photogrammetrically-derived digital terrain model

Sherpa Romeo green journal. Permission to archive accepted author manuscript.Small unmanned aircraft systems (sUAS) are a relatively new type of aerial platform for acquiring high-resolution remote sensing measurements of Earth surface processes and landforms. However, despite growing application there has been little quantitative assessment of sUAS performance. Here we present results from a field experiment designed to evaluate the accuracy of a photogrammetrically-derived digital terrain model (DTM) developed from imagery acquired with a low-cost digital camera onboard an sUAS. We also show the utility of the highresolution (0.1 m) sUAS imagery for resolving small-scale biogeomorphic features. The experiment was conducted in an area with active and stabilized aeolian landforms in the southern Canadian Prairies. Images were acquired with a Hawkeye RQ-84Z Aerohawk fixed-wing sUAS. A total of 280 images were acquired along 14 flight lines, covering an area of 1.95 km2. The survey was completed in 4.5 hours, including GPS surveying, sUAS setup and flight time. Standard image processing and photogrammetric techniques were used to produce a 1 m resolution DTM and a 0.1 m resolution orthorectified image mosaic. The latter revealed previously un-mapped bioturbation features. The vertical accuracy of the DTM was evaluated with 99 Real-Time Kinematic GPS points, while 20 of these points were used to quantify horizontal accuracy. The horizontal root mean squared error (RMSE) of the orthoimage was 0.18 m, while the vertical RMSE of the DTM was 0.29 m, which is equivalent to the RMSE of a bare earth LiDAR DTM for the same site. The combined error from both datasets was used to define a threshold of the minimum elevation difference that could be reliably attributed to erosion or deposition in the seven years separating the sUAS and LiDAR datasets. Overall, our results suggest that sUAS-acquired imagery may provide a low-cost, rapid, and flexible alternative to airborne LiDAR for geomorphological mapping.Ye

The role of discharge variability in the formation and preservation of alluvial sediment bodies

Extant, planform-based facies models for alluvial deposits are not fully fit for purpose, because they over-emphasise plan form whereas there is little in the alluvial rock record that is distinctive of any particular planform, and because the planform of individual rivers vary in both time and space. Accordingly, existing facies models have limited predictive capability. In this paper, we explore the role of inter-annual peak discharge variability as a possible control on the character of the preserved alluvial record. Data from a suite of modern rivers, for which long-term gauging records are available, and for which there are published descriptions of subsurface sedimentary architecture, are analysed. The selected rivers are categorized according to their variance in peak discharge or the coefficient of variation (CVQp = standard deviation of the annual peak flood discharge over the mean annual peak flood discharge). This parameter ranges over the rivers studied between 0.18 and 1.22, allowing classification of rivers as having very low ( 0.90) annual peak discharge variance. Deposits of rivers with very low and low peak discharge variability are dominated by cross-bedding on various scales and preserve macroform bedding structure, allowing the interpretation of bar construction processes. Rivers with moderate values preserve mostly cross-bedding, but records of macroform processes are in places muted and considerably modified by reworking. Rivers with high and very high values of annual peak discharge variability show a wide range of bedding structures commonly including critical and supercritical flow structures, abundant in situ trees and transported large, woody debris, and their deposits contain pedogenically modified mud partings and generally lack macroform structure. Such a facies assemblage is distinctively different from the conventional fluvial style recorded in published facies models but is widely developed both in modern and ancient alluvial deposits. This high-peak-variance style is also distinctive of rivers that are undergoing contraction in discharge over time because of the gradual annexation of the channel belt by the establishment of woody vegetation. We propose that discharge variability, both inter-annual peak variation and “flashiness” may be a more reliable basis for classifying the alluvial rock record than planform, and we provide some examples of three classes of alluvial sediment bodies (representing low, intermediate, and high/very high discharge variability) from the rock record that illustrate this point

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

The Surface Footprint of Bioturbation in a Prairie Sandhill Ecosystem: Resolving the Spatial Distribution and Geochemical Implications

An important disturbance regime in the grasslands of North America is the activity of fossorial mammals, whose digging activity creates small, prolific mounds of bare sub-soil within the grassland matrix. This thesis examined these mounds in a stabilizing sand dune ecosystem in the context of their spatial extent and distribution, and their implications on soil properties. Sub-decimeter-resolution aerial imagery from a small unmanned aircraft system (UAS) revealed that mounds are less spatially extensive than reported in other studies, and that the distribution of mounds is responsive to large-scale landscape features. Soil analyses revealed the mounds are essentially preserving small pockets of a dune’s pre-stabilization condition, mobilizing nearly pure sand poor in C and N but relatively rich in S to the surface, and retarding the build-up of organic matter. This suggests that mounds from fossorial mammals may play an under-recognized role in the dynamics of dune stabilization and succession

Management of ureteropelvic junction obstruction in childrend-a roundtable discussion

The investigation, management and follow-up of paediatric ureteropelvic junction obstruction is not standardized. The Young Pediatric Urology Committee of the European Society of Pediatric Urology interviewed five experts in the field on various aspects of management and compared this with published literature

Systematic Evaluation of Whole Genome Sequence-Based Predictions of Salmonella Serotype and Antimicrobial Resistance

Whole-genome sequencing (WGS) is used increasingly in public-health laboratories for typing and characterizing foodborne pathogens. To evaluate the performance of existing bioinformatic tools for in silico prediction of antimicrobial resistance (AMR) and serotypes of Salmonella enterica, WGS-based genotype predictions were compared with the results of traditional phenotyping assays. A total of 111 S. enterica isolates recovered from a Canadian baseline study on broiler chicken conducted in 2012-2013 were selected based on phenotypic resistance to 15 different antibiotics and isolates were subjected to WGS. Both SeqSero2 and SISTR accurately determined S. enterica serotypes, with full matches to laboratory results for 87.4 and 89.2% of isolates, respectively, and partial matches for the remaining isolates. Antimicrobial resistance genes (ARGs) were identified using several bioinformatics tools including the Comprehensive Antibiotic Resistance Database – Resistance Gene Identifier (CARD-RGI), Center for Genomic Epidemiology (CGE) ResFinder web tool, Short Read Sequence Typing for Bacterial Pathoge