14,934 research outputs found

    Arctic tundra shrubification can obscure increasing levels of soil erosion in NDVI assessments of land cover derived from satellite imagery

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    This research was supported by the St Andrews World Leading Scholarship.Monitoring soil erosion in the Arctic tundra is complicated by the highly fragmentated nature of the landscape and the limited spatial resolution of even high-resolution satellite data. The expansion of shrubs across the Arctic has led to substantial changes in vegetation composition that alter the spectral reflectance and directly affect vegetation indices such as the normalized difference vegetation index (NDVI), which is widely applied for environmental monitoring. This change can mask soil erosion if datasets with too coarse spatial resolutions are used, as increases in NDVI driven by shrub expansion can obscure concurrent increases in barren land cover. Here we created land cover maps from a multispectral uncrewed aerial vehicle (UAV) and land cover survey and assessed satellite imagery from PlanetScope, Sentinel-2 and Landsat-8 for several areas in north-eastern Iceland. Additionally, we used a novel application of the Shannon evenness index (SHEI) to evaluate levels of pixel mixing. Our results show that shrub expansion can lead to spectral confusion, which can obscure soil erosion processes and emphasize the importance of considering spatial resolution when monitoring highly fragmented landscapes. We demonstrate that remote sensing data with a resolution < 3 m greatly improves the amount of information captured in an Icelandic tundra environment. The spatial resolution of Landsat data (30 m) is inadequate for environmental monitoring in our study area. We found that the best platform for monitoring tundra land cover is Sentinel-2 when used in combination with multispectral UAV acquisitions for validation. Our study has the potential to improve environmental monitoring capabilities by introducing the use of SHEI to assess pixel mixing and determine optimal spatial resolutions. This approach combined with comparing remote sensing imagery of different spatial and time scales significantly advances our comprehension of land cover changes, including greening and soil degradation, in the Arctic tundra.Publisher PDFPeer reviewe

    Discharge Age and Weight for Very Preterm Infants in Six Countries: 2012-2020

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    BACKGROUND Postmenstrual age for surviving infants without congenital anomalies born at 24-29 weeks' gestational age from 2005 to 2018 in the USA increased 8 days, discharge weight increased 316 grams, and median discharge weight z-score increased 0.19 standard units. We asked whether increases were observed in other countries. METHODS We evaluated postmenstrual age, weight, and weight z-score at discharge of surviving infants without congenital anomalies born at 24-29 weeks' gestational age admitted to Vermont Oxford Network member hospitals in Austria, Ireland, Italy, Switzerland, the UK, and the USA from 2012 to 2020. RESULTS After adjustment, the median postmenstrual age at discharge increased significantly in Austria (3.6 days, 99% CI [1.0, 6.3]), Italy (4.0 days [2.3, 5.6]), and the USA (5.4 days [5.0, 5.8]). Median discharge weight increased significantly in Austria (181 grams, 99% CI [95, 267]), Ireland (234 [143, 325]), Italy (133 [83, 182]), and the USA (207 [194, 220]). Median discharge weight z-score increased in Ireland (0.24 standard units, 99% CI [0.12, 0.36]) and the USA (0.15 [0.13, 0.16]). Discharge on human milk increased in Italy, Switzerland, and the UK, while going home on cardiorespiratory monitors decreased in Austria, Ireland, and USA and going home on oxygen decreased in Ireland. CONCLUSIONS In this international cohort of neonatal intensive care units, postmenstrual discharge age and weight increased in some, but not all, countries. Processes of care at discharge did not change in conjunction with age and weight increases

    ROOTING OF ARAUCARIA MINI-CUTTINGS IN DIFFERENT ENVIRONMENTS AND SUBSTRATES

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    Araucaria is a native conifer, with high economic importance, especially in the southern region of the Brazil. Considering the difficulties of producing clonal plants of the species, the objective of the study was to determine the influence of different environments and substrates on root formation of Araucaria angustifolia minicuttings. Orthotropics shoots were collected in a mini clonal garden. Minicuttings were prepared with 10 ± 1 cm in length, keeping 1/3 of the needles, and immersed in a hydroalcoholic solution of 3,000 mg L-1 of indolbutyric acid for 10 seconds. Then they were planted in 210 cm³ tubes, testing four different substrates, which are, based on pine bark, vermiculite and charcoal (S1); based on pine bark and vermiculite (S2); based on pine bark, peat and coconut fiber (S3) and based on pine bark, vermiculite, charcoal and carbonized rice husk (S4). These minicuttings were maintained in three different environments for rooting: Automated Greenhouse (CVA) with 80% reduction in luminosity and mist irrigation, Simple Greenhouse House (CVS) with 84% reduction in luminosity and microsprinkler irrigation, and Mini-tunnel (EST) with 90% light reduction and micro sprinkler irrigation. After 120 days, minicuttings survival and rooting were determined. CVA provided better rooting of minicuttings, whereas EST resulted in high mortality and no root formation. There was no influence of the substrates on the evaluated variables. Thus, the use of greenhouses with automated irrigation by misting is recommended for rooting araucaria minicuttings, regardless of the substrate used, although overall rooting rates are not yet considered viable

    Study designs in medical research and their key characteristics

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    Medical research study designs are many and varied. At first glance they may be difficult to distinguish. Knowledge of their specific strengths and limitations is useful for investigators planning new projects and for readers of the medical literature. This review has three aims: (i) to present an overview of medical research types, (ii) to attract attention to multiple characteristics of medical study designs, and (iii) to provide a concise educational resource for young researchers in health sciences. The goals are achieved by analyzing main characteristics of medical study designs

    A novel segmentation approach for crop modeling using a plenoptic light-field camera : going from 2D to 3D

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    OMICASCrop phenotyping is a desirable task in crop characterization since it allows the farmer to make early decisions, and therefore be more productive. This research is motivated by the generation of tools for rice crop phenotyping within the OMICAS research ecosystem framework. It proposes implementing the image process- ing technologies and artificial intelligence technics through a multisensory approach with multispectral information. Three main stages are covered: (i) A segmentation approach that allows identifying the biological material associated with plants, and the main contri- bution is the GFKuts segmentation approach; (ii) a strategy that allows the development of sensory fusion between three different cameras, a 3D camera, an infrared multispectral camera, and a thermal multispectral camera, this stage is developed through a complex object detection approach; and (iii) the characterization of a 4D model that generates topological relationships with the information of the point cloud, the main contribution of this strategy is the improvement of the point cloud captured by the 3D sensor, in this sense, this stage improves the acquisition of any 3D sensor. This research presents a development that receives information from multiple sensors, especially infrared 2D, and generates a single 4D model in geometric space [X, Y, Z]. This model integrates the color information of 5 channels and topological information, relating the points in space. Overall, the research allows the integration of the 3D information from any sensor\technology and the multispectral channels from any multispectral camera, to generate direct non-invasive measurements on the plant.Magíster en Ingeniería ElectrónicaMagíster en Inteligencia ArtificialMaestríahttps://orcid.org/ 0000-0002-1477-6825https://scholar.google.com/citations?user=cpuxcwgAAAAJ&amp;hl=eshttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=000155691

    Automated multiclass segmentation, quantification, and visualization of the diseased aorta on hybrid PET/CT–SEQUOIA

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    Background Cardiovascular disease is the most common cause of death worldwide, including infection and inflammation related conditions. Multiple studies have demonstrated potential advantages of hybrid positron emission tomography combined with computed tomography (PET/CT) as an adjunct to current clinical inflammatory and infectious biochemical markers. To quantitatively analyze vascular diseases at PET/CT, robust segmentation of the aorta is necessary. However, manual segmentation is extremely time-consuming and labor-intensive. Purpose To investigate the feasibility and accuracy of an automated tool to segment and quantify multiple parts of the diseased aorta on unenhanced low-dose computed tomography (LDCT) as an anatomical reference for PET-assessed vascular disease. Methods A software pipeline was developed including automated segmentation using a 3D U-Net, calcium scoring, PET uptake quantification, background measurement, radiomics feature extraction, and 2D surface visualization of vessel wall calcium and tracer uptake distribution. To train the 3D U-Net, 352 non-contrast LDCTs from (2-[18F]FDG and Na[18F]F) PET/CTs performed in patients with various vascular pathologies with manual segmentation of the ascending aorta, aortic arch, descending aorta, and abdominal aorta were used. The last 22 consecutive scans were used as a hold-out internal test set. The remaining dataset was randomly split into training (n = 264; 80%) and validation (n = 66; 20%) sets. Further evaluation was performed on an external test set of 49 PET/CTs. The dice similarity coefficient (DSC) and Hausdorff distance (HD) were used to assess segmentation performance. Automatically obtained calcium scores and uptake values were compared with manual scoring obtained using clinical softwares (syngo.via and Affinity Viewer) in six patient images. intraclass correlation coefficients (ICC) were calculated to validate calcium and uptake values. Results Fully automated segmentation of the aorta using a 3D U-Net was feasible in LDCT obtained from PET/CT scans. The external test set yielded a DSC of 0.867 ± 0.030 and HD of 1.0 [0.6–1.4] mm, similar to an open-source model with a DSC of 0.864 ± 0.023 and HD of 1.4 [1.0–1.8] mm. Quantification of calcium and uptake values were in excellent agreement with clinical software (ICC: 1.00 [1.00–1.00] and 0.99 [0.93–1.00] for calcium and uptake values, respectively). Conclusions We present an automated pipeline to segment the ascending aorta, aortic arch, descending aorta, and abdominal aorta on LDCT from PET/CT and to accurately provide uptake values, calcium scores, background measurement, radiomics features, and a 2D visualization. We call this algorithm SEQUOIA (SEgmentation, QUantification, and visualizatiOn of the dIseased Aorta) and is available at https://github.com/UMCG-CVI/SEQUOIA. This model could augment the utility of aortic evaluation at PET/CT studies tremendously, irrespective of the tracer, and potentially provide fast and reliable quantification of cardiovascular diseases in clinical practice, both for primary diagnosis and disease monitoring

    Beta Blockade Prevents Cardiac Morphological and Molecular Remodelling in Experimental Uremia

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    Heart failure and chronic kidney disease (CKD) share several mediators of cardiac pathological remodelling. Akin to heart failure, this remodelling sets in motion a vicious cycle of progressive pathological hypertrophy and myocardial dysfunction in CKD. Several decades of heart failure research have shown that beta blockade is a powerful tool in preventing cardiac remodelling and breaking this vicious cycle. This phenomenon remains hitherto untested in CKD. Therefore, we set out to test the hypothesis that beta blockade prevents cardiac pathological remodelling in experimental uremia. Wistar rats had subtotal nephrectomy or sham surgery and were followed up for 10 weeks. The animals were randomly allocated to the beta blocker metoprolol (10 mg/kg/day) or vehicle. In vivo and in vitro cardiac assessments were performed. Cardiac tissue was extracted, and protein expression was quantified using immunoblotting. Histological analyses were performed to quantify myocardial fibrosis. Beta blockade attenuated cardiac pathological remodelling in nephrectomised animals. The echocardiographic left ventricular mass and the heart weight to tibial length ratio were significantly lower in nephrectomised animals treated with metoprolol. Furthermore, beta blockade attenuated myocardial fibrosis associated with subtotal nephrectomy. In addition, the Ca++- calmodulin-dependent kinase II (CAMKII) pathway was shown to be activated in uremia and attenuated by beta blockade, offering a potential mechanism of action. In conclusion, beta blockade attenuated hypertrophic signalling pathways and ameliorated cardiac pathological remodelling in experimental uremia. The study provides a strong scientific rationale for repurposing beta blockers, a tried and tested treatment in heart failure, for the benefit of patients with CKD

    CALANGO: a phylogeny-aware comparative genomics tool for discovering quantitative genotype-phenotype associations across species

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    Living species vary significantly in phenotype and genomic content. Sophisticated statistical methods linking genes with phenotypes within a species have led to breakthroughs in complex genetic diseases and genetic breeding. Despite the abundance of genomic and phenotypic data available for thousands of species, finding genotype-phenotype associations across species is challenging due to the non-independence of species data resulting from common ancestry. To address this, we present CALANGO (comparative analysis with annotation-based genomic components), a phylogeny-aware comparative genomics tool to find homologous regions and biological roles associated with quantitative phenotypes across species. In two case studies, CALANGO identified both known and previously unidentified genotype-phenotype associations. The first study revealed unknown aspects of the ecological interaction between Escherichia coli, its integrated bacteriophages, and the pathogenicity phenotype. The second identified an association between maximum height in angiosperms and the expansion of a reproductive mechanism that prevents inbreeding and increases genetic diversity, with implications for conservation biology and agriculture

    Very metal-poor stars in the solar vicinity: kinematics and abundance analysis

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    Very metal-poor stars contain crucial information on the Milky Way's infancy. In our previous study \citep{Plotnikova_2022} we derived a mean age of ∼\sim 13.7 Gyr for a sample of these stars in the Sun's vicinity. In this work, we investigate the chemical and kinematics properties of these stars with the goal of obtaining some insights on their origin and their parent population. We did not find any Al-Mg anti-correlation, which lead us to the conclusion that these stars did not form in globular clusters, while the detailed analysis of their orbital parameters reveals that these stars are most probably associated with the pristine Bulge of the Milky Way. We then sketch a scenario for the formation of the Milky Way in which the first structure to form was the Bulge through rapid collapse. The other components have grown later on, with a significant contribution of accreted structures.Comment: 13 pages, 10 figures, accepted for publication in the Astrophysical Journa
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