A silviculture-oriented spatio-temporal model for germination in Pinus pinea L. in the Spanish Northern Plateau based on a direct seeding experiment

Natural regeneration in Pinus pinea stands commonly fails throughout the Spanish Northern Plateau under current intensive regeneration treatments. As a result, extensive direct seeding is commonly conducted to guarantee regeneration occurrence. In a period of rationalization of the resources devoted to forest management, this kind of techniques may become unaffordable. Given that the climatic and stand factors driving germination remain unknown, tools are required to understand the process and temper the use of direct seeding. In this study, the spatio-temporal pattern of germination of P. pinea was modelled with those purposes. The resulting findings will allow us to (1) determine the main ecological variables involved in germination in the species and (2) infer adequate silvicultural alternatives. The modelling approach focuses on covariates which are readily available to forest managers. A two-step nonlinear mixed model was fitted to predict germination occurrence and abundance in P. pinea under varying climatic, environmental and stand conditions, based on a germination data set covering a 5-year period. The results obtained reveal that the process is primarily driven by climate variables. Favourable conditions for germination commonly occur in fall although the optimum window is often narrow and may not occur at all in some years. At spatial level, it would appear that germination is facilitated by high stand densities, suggesting that current felling intensity should be reduced. In accordance with other studies on P. pinea dispersal, it seems that denser stands during the regeneration period will reduce the present dependence on direct seeding

Detection of fibrosing interstitial lung disease-suspected chest radiographs using a deep learning-based computer-aided detection system: a retrospective, observational study

Objectives To investigate the effectiveness of BMAX, a deep learning-based computer-aided detection system for detecting fibrosing interstitial lung disease (ILD) on chest radiographs among non-expert and expert physicians in the real-world clinical setting.Design Retrospective, observational study.Setting This study used chest radiograph images consecutively taken in three medical facilities with various degrees of referral. Three expert ILD physicians interpreted each image and determined whether it was a fibrosing ILD-suspected image (fibrosing ILD positive) or not (fibrosing ILD negative). Interpreters, including non-experts and experts, classified each of 120 images extracted from the pooled data for the reading test into positive or negative for fibrosing ILD without and with the assistance of BMAX.Participants Chest radiographs of patients aged 20 years or older with two or more visits that were taken during consecutive periods were accumulated. 1251 chest radiograph images were collected, from which 120 images (24 positive and 96 negative images) were randomly extracted for the reading test. The interpreters for the reading test were 20 non-expert physicians and 5 expert physicians (3 pulmonologists and 2 radiologists).Primary and secondary outcome measures The primary outcome was the comparison of area under the receiver-operating characteristic curve (ROC-AUC) for identifying fibrosing ILD-positive images by non-experts without versus with BMAX. The secondary outcome was the comparison of sensitivity, specificity and accuracy by non-experts and experts without versus with BMAX.Results The mean ROC-AUC of non-expert interpreters was 0.795 (95% CI; 0.765 to 0.825) without BMAX and 0.825 (95% CI; 0.799 to 0.850) with BMAX (p=0.005). After using BMAX, sensitivity was improved from 0.744 (95% CI; 0.697 to 0.791) to 0.802 (95% CI; 0.754 to 0.850) among non-experts (p=0.003), but not among experts (p=0.285). Specificity and accuracy were not changed after using BMAX among either non-expert or expert interpreters.Conclusion BMAX was useful for detecting fibrosing ILD-suspected chest radiographs for non-expert physicians.Trial registration number jRCT1032220090

Additional file 1 of Artificial intelligence-based model for predicting pulmonary arterial hypertension on chest x-ray images

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