Quantifying the impacts of ecological restoration on biodiversity and ecosystem services in agroecosystems: A global meta-analysis

Los apéndices, tablas y figuras que contiene el documento se localizan al final del mismo.Landscape transformation due to agriculture affects more than 40% of the planet’s land area and is the most important driver of losses of biodiversity and ecosystem services (ES) worldwide. Ecological restoration may significantly reduce these losses, but its effectiveness has not been systematically assessed in agroecosystems at the global level. We quantitatively meta-analyzed the results of 54 studies of how restoration actions reflecting the two contrasting strategies of land sparing and land sharing affect levels of biodiversity and ES in a wide variety of agroecosystems in 20 countries. Restoration increased overall biodiversity of all organism types by an average of 68%. It also increased the supply of many ES, in particular the levels of supporting ES by an average of 42% and levels of regulating ES by an average of 120% relative to levels in the pre-restoration agroecosystem. In fact, restored agroecosystems showed levels of biodiversity and supporting and regulating ES similar to those of reference ecosystems. Recovery levels did not correlate with the time since the last restoration action. Comparison of land sparing and land sharing as restoration strategies showed that while both were associated with similar biodiversity recovery, land sparing led to higher median ES response ratios. Passive and active restoration actions did not differ significantly in the levels of biodiversity or ES recovery. Biodiversity recovery positively correlated with ES recovery. We conclude that ecological restoration of agroecosystems is generally effective and can be recommended as a way to enhance biodiversity and supply of supporting and regulating ES in agricultural landscapes. Whether a land sharing or land sparing strategy is preferable remains an open question, and might be case dependent. Moreover, it is unclear whether crop production on restored land can meet future food production needs.Comunidad de MadridComisión Interministerial de Ciencia y Tecnología-CICY

Cardiovascular Magnetic Resonance and prognosis in cardiac amyloidosis

Background: Cardiac involvement is common in amyloidosis and associated with a variably adverse outcome. We have previously shown that cardiovascular magnetic resonance (CMR) can assess deposition of amyloid protein in the myocardial interstitium. In this study we assessed the prognostic value of late gadolinium enhancement (LGE) and gadolinium kinetics in cardiac amyloidosis in a prospective longitudinal study.Materials and methods: The pre-defined study end point was all-cause mortality. We prospectively followed a cohort of 29 patients with proven cardiac amyloidosis. All patients underwent biopsy, 2D-echocardiography and Doppler studies, I-123-SAP scintigraphy, serum NT pro BNP assay, and CMR with a T-1 mapping method and late gadolinium enhancement (LGE).Results: Patients with were followed for a median of 623 days (IQ range 221, 1436), during which 17 (58%) patients died. The presence of myocardial LGE by itself was not a significant predictor of mortality. However, death was predicted by gadolinium kinetics, with the 2 minute post-gadolinium intramyocardial T1 difference between subepicardium and subendocardium predicting mortality with 85% accuracy at a threshold value of 23 ms (the lower the difference the worse the prognosis). Intramyocardial T1 gradient was a better predictor of survival than FLC response to chemotherapy (Kaplan Meier analysis P = 0.049) or diastolic function (Kaplan-Meier analysis P = 0.205).Conclusion: In cardiac amyloidosis, CMR provides unique information relating to risk of mortality based on gadolinium kinetics which reflects the severity of the cardiac amyloid burden

End-systole and end-diastole detection in short axis cine MRI using a fully convolutional neural network with dilated convolutions

[EN] The correct assessment and characterization of heart anatomy and functionality is usually done through inspection of magnetic resonance image cine sequences. In the clinical setting it is especially important to determine the state of the left ventricle. This requires the measurement of its volume in the end-diastolic and end-systolic frames within the sequence trough segmentation methods. However, the first step required for this analysis before any segmentation is the detection of the end-systolic and end-diastolic frames within the image acquisition. In this work we present a fully convolutional neural network that makes use of dilated convolutions to encode and process the temporal information of the sequences in contrast to the more widespread use of recurrent networks that are usually employed for problems involving temporal information. We trained the network in two different settings employing different loss functions to train the network: the classical weighted cross-entropy, and the weighted Dice loss. We had access to a database comprising a total of 397 cases. Out of this dataset we used 98 cases as test set to validate our network performance. The final classification on the test set yielded a mean frame distance of 0 for the end-diastolic frame (i.e.: the selected frame was the correct one in all images of the test set) and 1.242 (relative frame distance of 0.036) for the end-systolic frame employing the optimum setting, which involved training the neural network with the Dice loss. Our neural network is capable of classifying each frame and enables the detection of the end-systolic and end-diastolic frames in short axis cine MRI sequences with high accuracy.Funding sources This work was partially supported by the Conselleria d'Innovació, Universitats, Ciència i Societat Digital, Generalitat Valenciana (grants AEST/2020/029 and AEST/2021/050) .Pérez-Pelegrí, M.; Monmeneu, JV.; López-Lereu, MP.; Maceira, AM.; Bodi, V.; Moratal, D. (2022). End-systole and end-diastole detection in short axis cine MRI using a fully convolutional neural network with dilated convolutions. Computerized Medical Imaging and Graphics. 99:1-8. https://doi.org/10.1016/j.compmedimag.2022.102085189

Automatic left ventricle volume calculation with explainability through a deep learning weak-supervision methodology

[EN] Background and objective: Magnetic resonance imaging is the most reliable imaging technique to assess the heart. More specifically there is great importance in the analysis of the left ventricle, as the main pathologies directly affect this region. In order to characterize the left ventricle, it is necessary to extract its volume. In this work we present a neural network architecture that is capable of directly estimating the left ventricle volume in short axis cine Magnetic Resonance Imaging in the end-diastolic frame and provide a segmentation of the region which is the basis of the volume calculation, thus offering explain-ability to the estimated value. Methods: The network was designed to directly target the volumes to estimate, not requiring any labeled segmentation on the images. The network was based on a 3D U-net with extra layers defined in a scan-ning module that learned features like the circularity of the objects and the volumes to estimate in a weakly-supervised manner. The only targets defined were the left ventricle volumes and the circularity of the object detected through the estimation of the pi value derived from its shape. We had access to 397 cases corresponding to 397 different subjects. We randomly selected 98 cases to use as test set. Results: The results show a good match between the real and estimated volumes in the test set, with a mean relative error of 8% and a mean absolute error of 9.12 ml with a Pearson correlation coefficient of 0.95. The derived segmentations obtained by the network achieved Dice coefficients with a mean value of 0.79. Conclusions: The proposed method is capable of obtaining the left ventricle volume biomarker in the end-diastole and offer an explanation of how it obtains the result in the form of a segmentation mask without the need of segmentation labels to train the algorithm, making it a potentially more trustworthy method for clinicians and a way to train neural networks more easily when segmentation labels are not readily available.The authors acknowledge financial support from the Consel-leria d'Educacio, Investigacio, Cultura i Esport, Generalitat Valenciana (grants AEST/2019/037 and AEST/2020/029) , from the Agencia Valenciana de la Innovacion, Generalitat Valenciana (ref. INNCAD00/19/085) , and from the Centro para el Desarrollo Tecnologico Industrial (Programa Eurostars2, actuacion Interempresas Internacional) , Spanish Ministerio de Ciencia, Innovacion y Universidades (ref. CIIP-20192020) .Pérez-Pelegrí, M.; Monmeneu, JV.; López-Lereu, MP.; Pérez-Pelegrí, L.; Maceira, AM.; Bodi, V.; Moratal, D. (2021). Automatic left ventricle volume calculation with explainability through a deep learning weak-supervision methodology. Computer Methods and Programs in Biomedicine. 208:1-8. https://doi.org/10.1016/j.cmpb.2021.106275S1820

Magnetically induced CO2 methanation using exchange‐coupled spinel ferrites in cuboctahedron‐shaped nanocrystals

Magnetically induced catalysis can be promoted taking advantage of optimal heating properties from the magnetic nanoparticles to be employed. However, when unprotected, these heating agents that are usually air-sensitive, get sintered under the harsh catalytic conditions. In this context, we present, to the best of our knowledge, the first example of air-stable magnetic nanoparticles that: 1) show excellent performance as heating agents in the CO2 methanation catalyzed by Ni/SiRAlOx, with CH4 yields above 95 %, and 2) do not sinter under reaction conditions. To attain both characteristics we demonstrate, first the exchange-coupled magnetic approach as an alternative and effective way to tune the magnetic response and heating efficiency, and second, the chemical stability of cuboctahedron-shaped core–shell hard CoFe2O4–soft Fe3O4 nanoparticles.Xunta de Galicia | Ref. IN607 A 2018/5Xunta de Galicia | Ref. ED431C 2016-034Agencia Estatal de Investigación | Ref. CTM2017-84050-