Temporal relationship between instantaneous pressure gradients and peak‐to‐peak systolic ejection gradient in congenital aortic stenosis

ObjectiveWe sought to identify a time during cardiac ejection when the instantaneous pressure gradient (IPG) correlated best, and near unity, with peak‐to‐peak systolic ejection gradient (PPSG) in patients with congenital aortic stenosis. Noninvasive echocardiographic measurement of IPG has limited correlation with cardiac catheterization measured PPSG across the spectrum of disease severity of congenital aortic stenosis. A major contributor is the observation that these measures are inherently different with a variable relationship dependent on the degree of stenosis.DesignHemodynamic data from cardiac catheterizations utilizing simultaneous pressure measurements from the left ventricle (LV) and ascending aorta (AAo) in patients with congenital valvar aortic stenosis was retrospectively reviewed over the past 5 years. The cardiac cycle was standardized for all patients using the percentage of total LV ejection time (ET). Instantaneous gradient at 5% intervals of ET were compared to PPSG using linear regression and Bland‐Altman analysis.ResultsA total of 22 patients underwent catheterization at a median age of 13.7 years (interquartile range [IQR] 10.3‐18.0) and median weight of 51.1 kg (IQR 34.2‐71.6). The PPSG was 46.5 ± 12.6 mm Hg (mean ± SD) and correlated suboptimally with the maximum and mean IPG. The midsystolic IPG (occurring at 50% of ET) had the strongest correlation with the PPSG (PPSG = 0.97(IPG50%)–1.12, R2 = 0.88), while the IPG at 55% of ET was closest to unity (PPSG = 0.997(IPG55%)–1.17, R2 = 0.87).ConclusionsThe commonly measured maximum and mean IPG are suboptimal estimates of the PPSG in congenital aortic stenosis. Using catheter‐based data, IPG at 50%–55% of ejection correlates well with PPSG. This may allow for a more accurate estimation of PPSG via noninvasive assessment of IPG.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140042/1/chd12514.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/140042/2/chd12514_am.pd

Time-lapsing biodiversity: an open source method for measuring diversity changes by remote sensing

Understanding biodiversity changes in time is crucial to promptly provide management practices against diversity loss. This is overall true when considering global scales, since human-induced global change is expected to make significant changes on the Earth's biota. Biodiversity management and planning is mainly based on field observations related to community diversity, considering different taxa. However, such methods are time and cost demanding and do not allow in most cases to get temporal replicates. In this view, remote sensing can provide a wide data coverage in a short period of time. Recently, the use of Rao's Q diversity as a measure of spectral diversity has been proposed in order to explicitly take into account differences in a neighbourhood considering abundance and relative distance among pixels. The aim of this paper was to extend such a measure over the temporal dimension and to present an innovative approach to calculate remotely sensed temporal diversity. We demonstrated that temporal beta-diversity (spectral turnover) can be calculated pixel-wise in terms of both slope and coefficient of variation and further plotted over the whole matrix / image. From an ecological and operational point of view, for prioritisation practices in biodiversity protection, temporal variability could be beneficial in order to plan more efficient conservation practices starting from spectral diversity hotspots in space and time. In this paper, we delivered a highly reproducible approach to calculate spatio-temporal diversity in a robust and straightforward manner. Since it is based on open source code, we expect that our method will be further used by several researchers and landscape managers

ORTHORECTIFICATION OF A LARGE DATASET OF HISTORICAL AERIAL IMAGES: PROCEDURE AND PRECISION ASSESSMENT IN AN OPEN SOURCE ENVIRONMENT

The availability of data time series spanning a long period is crucial for landscape change analysis. A suitable dataset, both in terms of time span and information content, must be available for the use with a GIS.In Italy, one of the most important historical source of land cover analysis is the GAI (Gruppo Aereo Italiano) photogrammetric survey (“Volo GAI”) commissioned in 1954 by the Italian national mapping agency, Istituto Geografico Militare Italiano (IGMI).The survey covers the whole Italy, but so far only some Regions, namely Lombardia and Veneto, have carried out the image rectification and the successive analyses to map land cover and use.This work describes the process of image orthorectification of the Volo GAI images for the Province of Trento (Provincia Autonoma di Trento).Image orthorectification must be performed to transform the images in maps available for analysis. This procedure corrects the geometry according to the terrain surface described by a Digital Terrain Model (DTM) to create an image compatible with the cartographic projection in use.To this end, the orthorectification modules available in GRASS GIS have been used, with the advantage of using the same GIS environment which will be used for the landscape analysis. The dataset covering the whole Province contains almost 100 images, this paper presents the preliminary results of the orthorectification of a quarter of the images. A reduced dataset has been used to test the results obtained using different settings with respect to: digital image resolution, DTM resolution and number of Ground Control Points (GCPs) used for the external orientation.These preliminary tests show that for the average quality of the Volo GAI images scan resolution beyond 600 DPI and DTM resolution above 10 m do not provide significant improvements for orthorectification images. The minimum number of GCPs to guarantee the requested accuracy can vary from image to image, depending on the image quality and recognizable features position, but it is usually in the 15–20 points range

Positional errors in species distribution modelling are not overcome by the coarser grains of analysis

The performance of species distribution models (SDMs) is known to be affected by analysis grain and positional error of species occurrences. Coarsening of the analysis grain has been suggested to compensate for positional errors. Nevertheless, this way of dealing with positional errors has never been thoroughly tested. With increasing use of fine-scale environmental data in SDMs, it is important to test this assumption. Models using fine-scale environmental data are more likely to be negatively affected by positional error as the inaccurate occurrences might easier end up in unsuitable environment. This can result in inappropriate conservation actions. Here, we examined the trade-offs between positional error and analysis grain and provide recommendations for best practice. We generated narrow niche virtual species using environmental variables derived from LiDAR point clouds at 5 x 5 m fine-scale. We simulated the positional error in the range of 5 m to 99 m and evaluated the effects of several spatial grains in the range of 5 m to 500 m. In total, we assessed 49 combinations of positional accuracy and analysis grain. We used three modelling techniques (MaxEnt, BRT and GLM) and evaluated their discrimination ability, niche overlap with virtual species and change in realized niche. We found that model performance decreased with increasing positional error in species occurrences and coarsening of the analysis grain. Most importantly, we showed that coarsening the analysis grain to compensate for positional error did not improve model performance. Our results reject coarsening of the analysis grain as a solution to address the negative effects of positional error on model performance. We recommend fitting models with the finest possible analysis grain and as close to the response grain as possible even when available species occurrences suffer from positional errors. If there are significant positional errors in species occurrences, users are unlikely to benefit from making additional efforts to obtain higher resolution environmental data unless they also minimize the positional errors of species occurrences. Our findings are also applicable to coarse analysis grain, especially for fragmented habitats, and for species with narrow niche breadth

Mapping the recreational value of coppices’ management systems in Tuscany

In recent decades the growing interest in forested areas has led to a higher level of appreciation and consideration regarding the various benefits and services provided by forests. Despite this, when it comes to acknowledging their economic value and their capacity to produce income, the production of timber seems to be the main or even the only function that is considered. However, by adopting a sustainable forest management approach, the value related to non-market forest functions could also be considered. The present paper aims to quantify the potential income related to the recreational value of coppice forest by considering three different management systems: traditional coppice, active conversion to high forest and the natural evolution of forest. In order to do so, a contingent valuation method was used, and 248 forest users were surveyed in the region of Tuscany, Italy. The surveys included a revised price-list method, and the results obtained showed the existence of willingness to pay (WTP) for the maintenance of forests. Users showed a strong preference for conversion to high forest, while natural evolution was the least preferred management option. People’s perception on this matter was also assessed based on their specific location, by georeferencing all of the respondents’ answers: considering this, it was observed that belonging to a municipality located in or close to the mountains (i.e., mountain and natural municipalities) influenced the users’ WTP to maintain natural evolution

Taking stock of nature: Essential biodiversity variables explained

In 2013, the Group on Earth Observations Biodiversity Observation Network (GEO BON) developed the framework of Essential Biodiversity Variables (EBVs), inspired by the Essential Climate Variables (ECVs). The EBV framework was developed to distill the complexity of biodiversity into a manageable list of priorities and to bring a more coordinated approach to observing biodiversity on a global scale. However, efforts to address the scientific challenges associated with this task have been hindered by diverse interpretations of the definition of an EBV. Here, the authors define an EBV as a critical biological variable that characterizes an aspect of biodiversity, functioning as the interface between raw data and indicators. This relationship is clarified through a multi-faceted stock market analogy, drawing from relevant examples of biodiversity indicators that use EBVs, such as the Living Planet Index and the UK Spring Index. Through this analogy, the authors seek to make the EBV concept accessible to a wider audience, especially to non-specialists and those in the policy sector, and to more clearly define the roles of EBVs and their relationship with biodiversity indicators. From this we expect to support advancement towards globally coordinated measurements of biodiversity

Double down on remote sensing for biodiversity estimation. A biological mindset

In the light of unprecedented planetary changes in biodiversity, real-time and accurate ecosystem and biodiversity assessments are becoming increasingly essential for informing policy and sustainable development. Biodiversity monitoring is a challenge, especially for large areas such as entire continents. Nowadays, spaceborne and airborne sensors provide information that incorporate wavelengths that cannot be seen nor imagined with the human eye. This is also now accomplished at unprecedented spatial resolutions, defined by the pixel size of images, achieving less than a meter for some satellite images and just millimeters for airborne imagery. Thanks to different modeling techniques, it is now possible to study functional diversity changes over different spatial and temporal scales. At the heart of this unifying framework are the “spectral species”—sets of pixels with a similar spectral signal—and their variability over space. The aim of this paper is to summarize the power of remote sensing for directly estimating plant species diversity, particularly focusing on the spectral species concept

Low-energy Coulomb excitation of 94Zr

Recent state-of-the-art Monte Carlo shell-model calculations predict shape coexistence in Zr isotopes. In this context, the 94Zr nucleus is particularly interesting since some experimental investigations have already suggested the possible coexistence of spherical and oblate shapes, however, no definitive conclusion on its deformation has been reported to date. As such, a dedicated experiment to study collectivity and configuration coexistence in 94Zr by means of a low-energy Coulomb excitation was performed. This study was performed at the INFN Legnaro National Laboratory with the GALILEO-SPIDER setup, which, in this instance, was further augmented with 6 Lanthanum Bromide scintillators (LaBr3:Ce) in order to to maximize the γ-ray detection efficiency