Crescimento de Hymenaea stignocarpa no Pantanal da Nhecolândia, MS.

Resumo

Guia metodológico para uso do Laser Scanner Terrestre (TLS) em ambiente florestal.

A reconstituição dos ambientes florestais por meio de varreduras Laser Scanner Terrestre (TLS) tem possibilitado, além da compreensão desses ambientes com a extração de variáveis qualitativas que podem ser usadas em levantamentos fitossociológicos, aplicações de caráter métrico, como é o caso da extração de variáveis dendrométricas e contagem de indivíduos. Sem dúvida, essa tecnologia tem ganhado espaço no meio florestal e, possivelmente, em poucos anos, passará a fazer parte dos protocolos de inventários florestais. O planejamento do levantamento de campo com o TLS é uma das etapas fundamentais na coleta de dados, tendo em vista que está diretamente relacionado à qualidade do produto final. Além disso, facilita etapas como o registro da nuvem de pontos, processamento e posterior obtenção de variáveis dendrométricas.bitstream/item/222740/1/CT-467-1897-final.pd

Fusing multi-season UAS images with convolutional neural networks to map tree species in Amazonian forests.

Remote sensing images obtained by unoccupied aircraft systems (UAS) across different seasons enabled capturing of species-specific phenological patterns of tropical trees. The application of UAS multi-season images to classify tropical tree species is still poorly understood. In this study, we used RGB images from different seasons obtained by a low-cost UAS and convolutional neural networks (CNNs) to map tree species in an Amazonian forest. Individual tree crowns (ITC) were outlined in the UAS images and identified to the species level using forest inventory data. The CNN model was trained with images obtained in February, May, August, and November. The classification accuracy in the rainy season (November and February) was higher than in the dry season (May and August). Fusing images from multiple seasons improved the average accuracy of tree species classification by up to 21.1 percentage points, reaching 90.5%. The CNN model can learn species-specific phenological characteristics that impact the classification accuracy, such as leaf fall in the dry season, which highlights its potential to discriminate species in various conditions. We produced high-quality individual tree crown maps of the species using a post-processing procedure. The combination of multi-season UAS images and CNNs has the potential to map tree species in the Amazon, providing valuable insights for forest management and conservation initiatives

Time course of risk factors associated with mortality of 1260 critically ill patients with COVID-19 admitted to 24 Italian intensive care units

94noopenPurpose: To evaluate the daily values and trends over time of relevant clinical, ventilatory and laboratory parameters during the intensive care unit (ICU) stay and their association with outcome in critically ill patients with coronavirus disease 19 (COVID-19). Methods: In this retrospective–prospective multicentric study, we enrolled COVID-19 patients admitted to Italian ICUs from February 22 to May 31, 2020. Clinical data were daily recorded. The time course of 18 clinical parameters was evaluated by a polynomial maximum likelihood multilevel linear regression model, while a full joint modeling was fit to study the association with ICU outcome. Results: 1260 consecutive critically ill patients with COVID-19 admitted in 24 ICUs were enrolled. 78% were male with a median age of 63 [55–69] years. At ICU admission, the median ratio of arterial oxygen partial pressure to fractional inspired oxygen (PaO2/FiO2) was 122 [89–175] mmHg. 79% of patients underwent invasive mechanical ventilation. The overall mortality was 34%. Both the daily values and trends of respiratory system compliance, PaO2/FiO2, driving pressure, arterial carbon dioxide partial pressure, creatinine, C-reactive protein, ferritin, neutrophil, neutrophil–lymphocyte ratio, and platelets were associated with survival, while for lactate, pH, bilirubin, lymphocyte, and urea only the daily values were associated with survival. The trends of PaO2/FiO2, respiratory system compliance, driving pressure, creatinine, ferritin, and C-reactive protein showed a higher association with survival compared to the daily values. Conclusion: Daily values or trends over time of parameters associated with acute organ dysfunction, acid–base derangement, coagulation impairment, or systemic inflammation were associated with patient survival.openZanella A.; Florio G.; Antonelli M.; Bellani G.; Berselli A.; Bove T.; Cabrini L.; Carlesso E.; Castelli G.P.; Cecconi M.; Citerio G.; Coloretti I.; Corti D.; Dalla Corte F.; De Robertis E.; Foti G.; Fumagalli R.; Girardis M.; Giudici R.; Guiotto L.; Langer T.; Mirabella L.; Pasero D.; Protti A.; Ranieri M.V.; Rona R.; Scudeller L.; Severgnini P.; Spadaro S.; Stocchetti N.; Vigano M.; Pesenti A.; Grasselli G.; Aspesi M.; Baccanelli F.; Bassi F.; Bet A.; Biagioni E.; Biondo A.; Bonenti C.; Bottino N.; Brazzi L.; Buquicchio I.; Busani S.; Calini A.; Calligaro P.; Cantatore L.P.; Carelli S.; Carsetti A.; Cavallini S.; Cimicchi G.; Coppadoro A.; Dall'Ara L.; Di Gravio V.; Erba M.; Evasi G.; Facchini A.; Fanelli V.; Feliciotti G.; Fusarini C.F.; Ferraro G.; Gagliardi G.; Garberi R.; Gay H.; Giacche L.; Grieco D.; Guzzardella A.; Longhini F.; Manzan A.; Maraggia D.; Milani A.; Mischi A.; Montalto C.; Mormina S.; Noseda V.; Paleari C.; Pedeferri M.; Pezzi A.; Pizzilli G.; Pozzi M.; Properzi P.; Rauseo M.; Russotto V.; Saccarelli L.; Servillo G.; Spano S.; Tagliabue P.; Tonetti T.; Tullo L.; Vetrugno L.; Vivona L.; Volta C.A.; Zambelli V.; Zanoni A.Zanella, A.; Florio, G.; Antonelli, M.; Bellani, G.; Berselli, A.; Bove, T.; Cabrini, L.; Carlesso, E.; Castelli, G. P.; Cecconi, M.; Citerio, G.; Coloretti, I.; Corti, D.; Dalla Corte, F.; De Robertis, E.; Foti, G.; Fumagalli, R.; Girardis, M.; Giudici, R.; Guiotto, L.; Langer, T.; Mirabella, L.; Pasero, D.; Protti, A.; Ranieri, M. V.; Rona, R.; Scudeller, L.; Severgnini, P.; Spadaro, S.; Stocchetti, N.; Vigano, M.; Pesenti, A.; Grasselli, G.; Aspesi, M.; Baccanelli, F.; Bassi, F.; Bet, A.; Biagioni, E.; Biondo, A.; Bonenti, C.; Bottino, N.; Brazzi, L.; Buquicchio, I.; Busani, S.; Calini, A.; Calligaro, P.; Cantatore, L. P.; Carelli, S.; Carsetti, A.; Cavallini, S.; Cimicchi, G.; Coppadoro, A.; Dall'Ara, L.; Di Gravio, V.; Erba, M.; Evasi, G.; Facchini, A.; Fanelli, V.; Feliciotti, G.; Fusarini, C. F.; Ferraro, G.; Gagliardi, G.; Garberi, R.; Gay, H.; Giacche, L.; Grieco, D.; Guzzardella, A.; Longhini, F.; Manzan, A.; Maraggia, D.; Milani, A.; Mischi, A.; Montalto, C.; Mormina, S.; Noseda, V.; Paleari, C.; Pedeferri, M.; Pezzi, A.; Pizzilli, G.; Pozzi, M.; Properzi, P.; Rauseo, M.; Russotto, V.; Saccarelli, L.; Servillo, G.; Spano, S.; Tagliabue, P.; Tonetti, T.; Tullo, L.; Vetrugno, L.; Vivona, L.; Volta, C. A.; Zambelli, V.; Zanoni, A

Adaptive regression modeling of biomarkers of potential harm in a population of U.S. adult cigarette smokers and nonsmokers

<p>Abstract</p> <p>Background</p> <p>This article describes the data mining analysis of a clinical exposure study of 3585 adult smokers and 1077 nonsmokers. The analysis focused on developing models for four biomarkers of potential harm (BOPH): white blood cell count (WBC), 24 h urine 8-epi-prostaglandin F_2α(EPI8), 24 h urine 11-dehydro-thromboxane B₂(DEH11), and high-density lipoprotein cholesterol (HDL).</p> <p>Methods</p> <p>Random Forest was used for initial variable selection and Multivariate Adaptive Regression Spline was used for developing the final statistical models</p> <p>Results</p> <p>The analysis resulted in the generation of models that predict each of the BOPH as function of selected variables from the smokers and nonsmokers. The statistically significant variables in the models were: platelet count, hemoglobin, C-reactive protein, triglycerides, race and biomarkers of exposure to cigarette smoke for WBC (R-squared = 0.29); creatinine clearance, liver enzymes, weight, vitamin use and biomarkers of exposure for EPI8 (R-squared = 0.41); creatinine clearance, urine creatinine excretion, liver enzymes, use of Non-steroidal antiinflammatory drugs, vitamins and biomarkers of exposure for DEH11 (R-squared = 0.29); and triglycerides, weight, age, sex, alcohol consumption and biomarkers of exposure for HDL (R-squared = 0.39).</p> <p>Conclusions</p> <p>Levels of WBC, EPI8, DEH11 and HDL were statistically associated with biomarkers of exposure to cigarette smoking and demographics and life style factors. All of the predictors togather explain 29%-41% of the variability in the BOPH.</p

Awareness of cognitive decline trajectories in asymptomatic individuals at risk for AD

Background: Lack of awareness of cognitive decline (ACD) is common in late-stage Alzheimer’s disease (AD). Recent studies showed that ACD can also be reduced in the early stages. Methods: We described different trends of evolution of ACD over 3 years in a cohort of memory-complainers and their association to amyloid burden and brain metabolism. We studied the impact of ACD at baseline on cognitive scores’ evolution and the association between longitudinal changes in ACD and in cognitive score. Results: 76.8% of subjects constantly had an accurate ACD (reference class). 18.95% showed a steadily heightened ACD and were comparable to those with accurate ACD in terms of demographic characteristics and AD biomarkers. 4.25% constantly showed low ACD, had significantly higher amyloid burden than the reference class, and were mostly men. We found no overall effect of baseline ACD on cognitive scores’ evolution and no association between longitudinal changes in ACD and in cognitive scores. Conclusions: ACD begins to decrease during the preclinical phase in a group of individuals, who are of great interest and need to be further characterized. Trial registration: The present study was conducted as part of the INSIGHT-PreAD study. The identification number of INSIGHT-PreAD study (ID-RCB) is 2012-A01731-42

Low Cognitive Awareness, but Not Complaint, is a Good Marker of Preclinical Alzheimer's Disease

Subjective cognitive decline (SCD) may result from many conditions, including Alzheimer's disease (AD)