Artificial intelligence-based software (AID-FOREST) for tree detection: A new framework for fast and accurate forest inventorying using LiDAR point clouds

Forest inventories are essential to accurately estimate different dendrometric and forest stand parameters. However, classical forest inventories are time consuming, slow to conduct, sometimes inaccurate and costly. To address this problem, an efficient alternative approach has been sought and designed that will make this type of field work cheaper, faster, more accurate, and easier to complete. The implementation of this concept has required the development of a specifically designed software called "Artificial Intelligence for Digital Forest (AID-FOREST)", which is able to process point clouds obtained via mobile terrestrial laser scanning (MTLS) and then, to provide an array of multiple useful and accurate dendrometric and forest stand parameters. Singular characteristics of this approach are: No data pre-processing is required either pre-treatment of forest stand; fully automatic process once launched; no limitations by the size of the point cloud file and fast computations.To validate AID-FOREST, results provided by this software were compared against the obtained from in-situ classical forest inventories. To guaranty the soundness and generality of the comparison, different tree spe-cies, plot sizes, and tree densities were measured and analysed. A total of 76 plots (10,887 trees) were selected to conduct both a classic forest inventory reference method and a MTLS (ZEB-HORIZON, Geoslam, ltd.) scanning to obtain point clouds for AID-FOREST processing, known as the MTLS-AIDFOREST method. Thus, we compared the data collected by both methods estimating the average number of trees and diameter at breast height (DBH) for each plot. Moreover, 71 additional individual trees were scanned with MTLS and processed by AID-FOREST and were then felled and divided into logs measuring 1 m in length. This allowed us to accurately measure the DBH, total height, and total volume of the stems.When we compared the results obtained with each methodology, the mean detectability was 97% and ranged from 81.3 to 100%, with a bias (underestimation by MTLS-AIDFOREST method) in the number of trees per plot of 2.8% and a relative root-mean-square error (RMSE) of 9.2%. Species, plot size, and tree density did not significantly affect detectability. However, this parameter was significantly affected by the ecosystem visual complexity index (EVCI). The average DBH per plot was underestimated (but was not significantly different from 0) by the MTLS-AIDFOREST, with the average bias for pooled data being 1.8% with a RMSE of 7.5%. Similarly, there was no statistically significant differences between the two distribution functions of the DBH at the 95.0% confidence level.Regarding the individual tree parameters, MTLS-AIDFOREST underestimated DBH by 0.16 % (RMSE = 5.2 %) and overestimated the stem volume (Vt) by 1.37 % (RMSE = 14.3 %, although the BIAS was not statistically significantly different from 0). However, the MTLS-AIDFOREST method overestimated the total height (Ht) of the trees by a mean 1.33 m (5.1 %; relative RMSE = 11.5 %), because of the different height concepts measured by both methodological approaches. Finally, AID-FOREST required 30 to 66 min per ha-1 to fully automatically process the point cloud data from the *.las file corresponding to a given hectare plot. Thus, applying our MTLS-AIDFOREST methodology to make full forest inventories, required a 57.3 % of the time required to perform classical plot forest inventories (excluding the data postprocessing time in the latter case). A free trial of AID -FOREST can be requested at [email protected]

A Single-Run Next-Generation Sequencing (NGS) Assay for the Simultaneous Detection of Both Gene Mutations and Large Chromosomal Abnormalities in Patients with Myelodysplastic Syndromes (MDS) and Related Myeloid Neoplasms

Chromosomal abnormalities and somatic mutations are found in patients with myelodysplastic syndromes (MDS) and myelodysplastic/myeloproliferative neoplasms (MDS/MPN) in around 50-80% of cases. The identification of these alterations is important for the accurate diagnosis and prognostic classification of these patients. Often, an apparently normal or failed karyotype might lead to an inadequate estimation of the prognostic risk, and several strategies should be combined to solve these cases. The aim of this study was to introduce a novel next-generation sequencing (NGS)-based strategy for the simultaneous detection of all the clinically relevant genetic alterations associated with these disorders. We validated this approach on a large cohort of patients by comparing our findings with those obtained with standard-of-care methods (i.e., karyotype and SNP-arrays). We show that our platform represents a significant improvement on current strategies in defining diagnosis and risk stratification of patients with MDS and myeloid-related disorders. Myelodysplastic syndromes (MDS) and myelodysplastic/myeloproliferative neoplasms are clonal disorders that share most of their cytogenetic and molecular alterations. Despite the increased knowledge of the prognostic importance of genetics in these malignancies, next-generation sequencing (NGS) has not been incorporated into clinical practice in a validated manner, and the conventional karyotype remains mandatory in the evaluation of suspected cases. However, non-informative cytogenetics might lead to an inadequate estimation of the prognostic risk. Here, we present a novel targeted NGS-based assay for the simultaneous detection of all the clinically relevant genetic alterations associated with these disorders. We validated this platform in a large cohort of patients by performing a one-to-one comparison with the lesions from karyotype and single-nucleotide polymorphism (SNP) arrays. Our strategy demonstrated an approximately 97% concordance with standard clinical assays, showing sensitivity at least equivalent to that of SNP arrays and higher than that of conventional cytogenetics. In addition, this NGS assay was able to identify both copy-neutral loss of heterozygosity events distributed genome-wide and copy number alterations, as well as somatic mutations within significant driver genes. In summary, we show a novel NGS platform that represents a significant improvement to current strategies in defining diagnosis and risk stratification of patients with MDS and myeloid-related disorder

Global overview of the management of acute cholecystitis during the COVID-19 pandemic (CHOLECOVID study)

Background: This study provides a global overview of the management of patients with acute cholecystitis during the initial phase of the COVID-19 pandemic. Methods: CHOLECOVID is an international, multicentre, observational comparative study of patients admitted to hospital with acute cholecystitis during the COVID-19 pandemic. Data on management were collected for a 2-month study interval coincident with the WHO declaration of the SARS-CoV-2 pandemic and compared with an equivalent pre-pandemic time interval. Mediation analysis examined the influence of SARS-COV-2 infection on 30-day mortality. Results: This study collected data on 9783 patients with acute cholecystitis admitted to 247 hospitals across the world. The pandemic was associated with reduced availability of surgical workforce and operating facilities globally, a significant shift to worse severity of disease, and increased use of conservative management. There was a reduction (both absolute and proportionate) in the number of patients undergoing cholecystectomy from 3095 patients (56.2 per cent) pre-pandemic to 1998 patients (46.2 per cent) during the pandemic but there was no difference in 30-day all-cause mortality after cholecystectomy comparing the pre-pandemic interval with the pandemic (13 patients (0.4 per cent) pre-pandemic to 13 patients (0.6 per cent) pandemic; P = 0.355). In mediation analysis, an admission with acute cholecystitis during the pandemic was associated with a non-significant increased risk of death (OR 1.29, 95 per cent c.i. 0.93 to 1.79, P = 0.121). Conclusion: CHOLECOVID provides a unique overview of the treatment of patients with cholecystitis across the globe during the first months of the SARS-CoV-2 pandemic. The study highlights the need for system resilience in retention of elective surgical activity. Cholecystectomy was associated with a low risk of mortality and deferral of treatment results in an increase in avoidable morbidity that represents the non-COVID cost of this pandemic

Nucleotide signalling during inflammation

Inflammatory conditions are associated with the extracellular release of nucleotides, particularly ATP. In the extracellular compartment, ATP predominantly functions as a signalling molecule through the activation of purinergic P2 receptors. Metabotropic P2Y receptors are G-protein-coupled, whereas ionotropic P2X receptors are ATP-gated ion channels. Here we discuss how signalling events through P2 receptors alter the outcomes of inflammatory or infectious diseases. Recent studies implicate a role for P2X/P2Y signalling in mounting appropriate inflammatory responses critical for host defence against invading pathogens or tumours. Conversely, P2X/P2Y signalling can promote chronic inflammation during ischaemia and reperfusion injury, inflammatory bowel disease or acute and chronic diseases of the lungs. Although nucleotide signalling has been used clinically in patients before, research indicates an expanding field of opportunities for specifically targeting individual P2 receptors for the treatment of inflammatory or infectious diseases