Background There is a substantial gap in provision of adequate surgical care in many low- and mid... more Background There is a substantial gap in provision of adequate surgical care in many low- and middle-income countries. This study aimed to identify the economic burden of unmet surgical need for the common condition of appendicitis. Methods Data on the incidence of appendicitis from 170 countries and two different approaches were used to estimate numbers of patients who do not receive surgery: as a fixed proportion of the total unmet surgical need per country (approach 1); and based on country income status (approach 2). Indirect costs with current levels of access and local quality, and those if quality were at the standards of high-income countries, were estimated. A human capital approach was applied, focusing on the economic burden resulting from premature death and absenteeism. Results Excess mortality was 4185 per 100 000 cases of appendicitis using approach 1 and 3448 per 100 000 using approach 2. The economic burden of continuing current levels of access and local quality wa...
Individual tree attributes, such as stem volume and biomass, are usually predicted by using tradi... more Individual tree attributes, such as stem volume and biomass, are usually predicted by using traditional field-derived allometric models. However, these models are derived from data collected from small areas and lack a level of detail of tree components (e.g., stem, branches, and leaves). Remote sensing techniques such as the Quantitative Structure Modelling (QSM) applied on high-density LiDAR data emerge as a promising solution for obtaining extensive and detailed tree attribute estimates. We used a high-density LiDAR data on board of a Unmanned Aerial Vehicle (UAV) to evaluate the performance of the QSM approach in estimating field-derived individual tree attributes such as the diameter at breast height (dbh), tree height (ht), and volume (v), as well as the stem (SAGB), branch (BAGB), and total (TAGB) aboveground biomass of eucalyptus trees. QSM was used in two different approaches: (i) using dbh and h derived from QSM and then applied into the field-based equations for estimatio...
Canopy height is a fundamental parameter for determining forest ecosystem functions such as biodi... more Canopy height is a fundamental parameter for determining forest ecosystem functions such as biodiversity and above-ground biomass. Previous studies examining the underlying patterns of the complex relationship between canopy height and its environmental and climatic determinants suffered from the scarcity of accurate canopy height measurements at large scales. NASA’s mission, the Global Ecosystem Dynamic Investigation (GEDI), has provided sampled observations of the forest vertical structure at near global scale since late 2018. The availability of such unprecedented measurements allows for examining the vertical structure of vegetation spatially and temporally. Herein, we explore the most influential climatic and environmental drivers of the canopy height in tropical forests. We examined different resampling resolutions of GEDI-based canopy height to approximate maximum canopy height over tropical forests across all of Malaysia. Moreover, we attempted to interpret the dynamics unde...
Selective logging can cause significant impacts on the residual stands, affecting biodiversity an... more Selective logging can cause significant impacts on the residual stands, affecting biodiversity and leading to environmental changes. Proper monitoring and mapping of the impacts from logging activities, such as the stumps, felled logs, roads, skid trails, and forest canopy gaps, are crucial for sustainable forest management operations. The purpose of this study is to assess the indicators of selective logging impacts by detecting the individual stumps as the main indicators, evaluating the performance of classification methods to assess the impacts and identifying forest gaps from selective logging activities. The combination of forest inventory field plots and unmanned aerial vehicle (UAV) RGB and overlapped imaged were used in this study to assess these impacts. The study area is located in Ulu Jelai Forest Reserve in the central part of Peninsular Malaysia, covering an experimental study area of 48 ha. The study involved the integration of template matching (TM), object-based ima...
Lidar point clouds have been frequently used in forest inventories. The higher point density has ... more Lidar point clouds have been frequently used in forest inventories. The higher point density has provided better representation of trees in forest plantations. So we developed a new approach to fill this gap in the integrated crop-livestock-forest system, the sampling forest inventory, which uses the principles of individual tree detection applied under different plot arrangements. We use a UAV-lidar system (GatorEye) to scan an integrated crop-livestock-forest system with Eucalyptus benthamii seed forest plantations. On the high density UAV-lidar point cloud (>1400 pts. m2), we perform a comparison of two forest inventory approaches: Sampling Forest Inventory (SFI) with circular (1380 m2 and 2300 m2) and linear (15 trees and 25 trees) plots and Individual Tree Detection (ITD). The parametric population values came from the approach with measurements taken in the field, called forest inventory (FI). Basal area and volume estimates were performed considering the field heights and ...
Precise assessments of forest species’ composition help analyze biodiversity patterns, estimate w... more Precise assessments of forest species’ composition help analyze biodiversity patterns, estimate wood stocks, and improve carbon stock estimates. Therefore, the objective of this work was to evaluate the use of high-resolution images obtained from Unmanned Aerial Vehicle (UAV) for the identification of forest species in areas of forest regeneration in the Amazon. For this purpose, convolutional neural networks (CNN) were trained using the Keras–Tensorflow package with the faster_rcnn_inception_v2_pets model. Samples of six forest species were used to train CNN. From these, attempts were made with the number of thresholds, which is the cutoff value of the function; any value below this output is considered 0, and values above are treated as an output 1; that is, values above the value stipulated in the Threshold are considered as identified species. The results showed that the reduction in the threshold decreases the accuracy of identification, as well as the overlap of the polygons o...
This is an open access article under the terms of the Creative Commons Attribution License, which... more This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
The file includes the longitudinal and radiological data for a retrospective incidental meningiom... more The file includes the longitudinal and radiological data for a retrospective incidental meningioma cohort (2007-2018). The study setting was The Walton Centre NHS Foundation Trust which serves a catchment area of 3.5 million people in the North west of England and Wales. Based on this data set, an online risk calculator has been created that could help guide monitoring strategies for patients with an incidental asymptomatic meningioma (www.impact-meningioma.com). It should be noted that this calculator has not been externally validated for clinical use and clinicians should exercise their own clinical judgement when making management decisions with patient under their care.
Urban trees and forests provide multiple ecosystem services (ES), including temperature regulatio... more Urban trees and forests provide multiple ecosystem services (ES), including temperature regulation, carbon sequestration, and biodiversity. Interest in ES has increased amongst policymakers, scientists, and citizens given the extent and growth of urbanized areas globally. However, the methods and techniques used to properly assess biodiversity and ES provided by vegetation in urban environments, at large scales, are insufficient. Individual tree identification and characterization are some of the most critical issues used to evaluate urban biodiversity and ES, given the complex spatial distribution of vegetation in urban areas and the scarcity or complete lack of systematized urban tree inventories at large scales, e.g., at the regional or national levels. This often limits our knowledge on their contributions toward shaping biodiversity and ES in urban areas worldwide. This paper provides an analysis of the state-of-the-art studies and was carried out based on a systematic review o...
IOP Conference Series: Earth and Environmental Science, 2021
Tropical forests play a significant role in regulating the average global atmospheric temperature... more Tropical forests play a significant role in regulating the average global atmospheric temperature encompassing 25 % of the carbon present in the terrestrial biosphere. However, the rapid change in climate, arising from unsustainable human practices, can significantly affect their carbon uptake capability in the future. For understanding these deviations, it is important to identify and quantify the large-scale canopy height variations arising from previous anthropogenic disturbances. With the advent of NASA GEDI spaceborne LiDAR (light detection and ranging), it is now possible to acquire three-dimensional vertical structural data of forests globally. In this study, we evaluate the applicability of GEDI for analyzing relative canopy height variations of secondary tropical forests of different age groups located across multiple geographical regions of peninsular Malaysia. The results for RH98 GEDI metric trends for the lowland and hill forests category across 4 different disturbance ...
Replanting trees helps with avoiding desertification, reducing the chances of soil erosion and fl... more Replanting trees helps with avoiding desertification, reducing the chances of soil erosion and flooding, minimizing the risks of zoonotic disease outbreaks, and providing ecosystem services and livelihood to the indigenous people, in addition to sequestering carbon dioxide for mitigating climate change. Consequently, it is important to explore new methods and technologies that are aiming to upscale and fast-track afforestation and reforestation (A/R) endeavors, given that many of the current tree planting strategies are not cost effective over large landscapes, and suffer from constraints associated with time, energy, manpower, and nursery-based seedling production. UAV (unmanned aerial vehicle)-supported seed sowing (UAVsSS) can promote rapid A/R in a safe, cost-effective, fast and environmentally friendly manner, if performed correctly, even in otherwise unsafe and/or inaccessible terrains, supplementing the overall manual planting efforts globally. In this study, we reviewed the ...
BACKGROUND Flow aneurysms (FAs) associated with brain arteriovenous malformations (AVMs) are thou... more BACKGROUND Flow aneurysms (FAs) associated with brain arteriovenous malformations (AVMs) are thought to arise from increased haemodynamic stress due to high flow shunting. This study aims to describe the changes in conservatively managed FAs after successful AVM treatment. METHODS Patients with symptomatic AVMs and associated FAs who underwent successful treatment of the AVM between 2008-2017, were included. FA dimensions were measured on surveillance angiography to assess longitudinal changes. RESULTS 32 patients were identified with 48 FAs. 16 (33%) FAs were treated endovascularly; 18 (38%) FAs were treated surgically; 14 (29%) FAs (11 patients) were monitored. FAs demonstrated a decrease in size from 5.0mm to 3.8mm (24%; p=0.016) and 4.9mm to 3.6mm (27%; p=0.013), in height and width respectively over median 35 months. However, on subgroup analysis, only class IIb aneurysms demonstrated a significant decrease in size (51% reduction in largest diameter, p = 0.046) and only 3 FAs (21%) resolved. There were no haemorrhages observed during follow-up. CONCLUSION While conservatively managed FAs demonstrated a reduction in size after the culprit AVM was treated, this was only significant in FAs located close to AVM nidus (class IIb). There were no haemorrhages during median 35 months follow-up, however, long term data is lacking. Our data supports close observation of all conservatively managed aneurysms and a tailored approach based on the proximity to the nidus and observed changes in size.
Applications of unmanned aerial vehicles (UAVs) have proliferated in the last decade due to the t... more Applications of unmanned aerial vehicles (UAVs) have proliferated in the last decade due to the technological advancements on various fronts such as structure-from-motion (SfM), machine learning, and robotics. An important preliminary step with regard to forest inventory and management is individual tree detection (ITD), which is required to calculate forest attributes such as stem volume, forest uniformity, and biomass estimation. However, users may find adopting the UAVs and algorithms for their specific projects challenging due to the plethora of information available. Herein, we provide a step-by-step tutorial for performing ITD using (i) low-cost UAV-derived imagery and (ii) UAV-based high-density lidar (light detection and ranging). Functions from open-source R packages were implemented to develop a canopy height model (CHM) and perform ITD utilizing the local maxima (LM) algorithm. ITD accuracy assessment statistics and validation were derived through manual visual interpreta...
Compassion has been one of the greatest virtues of healthcare professionals. In the early phase o... more Compassion has been one of the greatest virtues of healthcare professionals. In the early phase of the pandemic, a lot of caution was essential, and restrictions were imposed on the hospital visitation of the COVID-19 patients by their family members. The healthcare system was overburdened, and the healthcare workers were apprehensive about the new virus and the rising mortality. Compassion and family-centered care took a step back as survival of the pandemic became the ultimate goal of mankind. "COVID-19 patients admitted to the critical care units, their loved ones and the healthcare professionals caring for these patients took the brunt of the emotional and psychological impacts of the pandemic." However, as we have moved more than a year into the pandemic, knowledge and resources we gained may be leveraged to provide family-centered critical care for COVID-19 patients. Family presence in intensive care units (ICUs) has been associated with higher satisfaction with care, collaboration with the medical team, shared decision-making, reduced delirium, and optimized end-of-life care of COVID-19 patients. The policymakers should review the restrictions, consider a holistic approach, and take appropriate actions to provide safe family-centered critical care for COVID-19 patients.
Current Opinion in Environmental Science & Health, 2021
This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
The high dimensionality of data generated by Unmanned Aerial Vehicle(UAV)-Lidar makes it difficul... more The high dimensionality of data generated by Unmanned Aerial Vehicle(UAV)-Lidar makes it difficult to use classical statistical techniques to design accurate predictive models from these data for conducting forest inventories. Machine learning techniques have the potential to solve this problem of modeling forest attributes from remotely sensed data. This work tests four different machine learning approaches-namely Support Vector Regression, Random Forest, Artificial Neural Networks, and Extreme Gradient Boosting-on high-density GatorEye UAV-Lidar point clouds for indirect estimation of individual tree dendrometric metrics (fieldderived) such as diameter at breast height, total height, and timber volume. A total of 370 trees had their dbh and height measured for validation purposes. Using LAStools we generated normalized Light Detection and Ranging (Lidar) point clouds and created a raster canopy height model at a 0.5x0.5 m spatial resolution following the construction of a digital terrain model and a digital surface model. The R package 'lidR' was set with the functions tree_detection (local maximum filter algorithm) and lastrees. Subsequently, we applied the function tree_metrics to extract individual metrics. Machine learning techniques were applied to the derived metrics to estimate dendrometric field measures. The machine learning models (MLM) with optimal hyperparameters showed similar predictive performances for modeling the variables diameter, height, and volume. All models had a rRMSE below 15% (for diameter at breast height), 9% (for height) and 29% (for volume). The Support Vector Regression algorithm showed the best performance. Our work demonstrates that all tested machine learning models are adequate and robust to handle the high dimensionality of UAV-Lidar data for the estimation of individual attributes, with Support Vector Regression model being the best performer in terms of minimal error rates.
Abstract Tropical savanna ecosystems play a major role in the seasonality of the global carbon cy... more Abstract Tropical savanna ecosystems play a major role in the seasonality of the global carbon cycle. However, their ability to store and sequester carbon is uncertain due to combined and intermingling effects of anthropogenic activities and climate change, which impact wildfire regimes and vegetation dynamics. Accurate measurements of tropical savanna vegetation aboveground biomass (AGB) over broad spatial scales are crucial to achieve effective carbon emission mitigation strategies. UAV-lidar is a new remote sensing technology that can enable rapid 3-D mapping of structure and related AGB in tropical savanna ecosystems. This study aimed to assess the capability of high-density UAV-lidar to estimate and map total (tree, shrubs, and surface layers) aboveground biomass density (AGBt) in the Brazilian Savanna (Cerrado). Five ordinary least square regression models estimating AGBt were adjusted using 50 field sample plots (30 m × 30 m). The best model was selected under Akaike Information Criterion, adjusted coefficient of determination (adj.R2), absolute and relative root mean square error (RMSE), and used to map AGBt from UAV-lidar data collected over 1,854 ha spanning the three major vegetation formations (forest, savanna, and grassland) in Cerrado. The model using vegetation height and cover was the most effective, with an overall model adj-R2 of 0.79 and a leave-one-out cross-validated RMSE of 19.11 Mg/ha (33.40%). The uncertainty and errors of our estimations were assessed for each vegetation formation separately, resulting in RMSEs of 27.08 Mg/ha (25.99%) for forests, 17.76 Mg/ha (43.96%) for savannas, and 7.72 Mg/ha (44.92%) for grasslands. These results prove the feasibility and potential of the UAV-lidar technology in Cerrado but also emphasize the need for further developing the estimation of biomass in grasslands, of high importance in the characterization of the global carbon balance and for supporting integrated fire management activities in tropical savanna ecosystems. Our results serve as a benchmark for future studies aiming to generate accurate biomass maps and provide baseline data for efficient management of fire and predicted climate change impacts on tropical savanna ecosystems.
Background There is a substantial gap in provision of adequate surgical care in many low- and mid... more Background There is a substantial gap in provision of adequate surgical care in many low- and middle-income countries. This study aimed to identify the economic burden of unmet surgical need for the common condition of appendicitis. Methods Data on the incidence of appendicitis from 170 countries and two different approaches were used to estimate numbers of patients who do not receive surgery: as a fixed proportion of the total unmet surgical need per country (approach 1); and based on country income status (approach 2). Indirect costs with current levels of access and local quality, and those if quality were at the standards of high-income countries, were estimated. A human capital approach was applied, focusing on the economic burden resulting from premature death and absenteeism. Results Excess mortality was 4185 per 100 000 cases of appendicitis using approach 1 and 3448 per 100 000 using approach 2. The economic burden of continuing current levels of access and local quality wa...
Individual tree attributes, such as stem volume and biomass, are usually predicted by using tradi... more Individual tree attributes, such as stem volume and biomass, are usually predicted by using traditional field-derived allometric models. However, these models are derived from data collected from small areas and lack a level of detail of tree components (e.g., stem, branches, and leaves). Remote sensing techniques such as the Quantitative Structure Modelling (QSM) applied on high-density LiDAR data emerge as a promising solution for obtaining extensive and detailed tree attribute estimates. We used a high-density LiDAR data on board of a Unmanned Aerial Vehicle (UAV) to evaluate the performance of the QSM approach in estimating field-derived individual tree attributes such as the diameter at breast height (dbh), tree height (ht), and volume (v), as well as the stem (SAGB), branch (BAGB), and total (TAGB) aboveground biomass of eucalyptus trees. QSM was used in two different approaches: (i) using dbh and h derived from QSM and then applied into the field-based equations for estimatio...
Canopy height is a fundamental parameter for determining forest ecosystem functions such as biodi... more Canopy height is a fundamental parameter for determining forest ecosystem functions such as biodiversity and above-ground biomass. Previous studies examining the underlying patterns of the complex relationship between canopy height and its environmental and climatic determinants suffered from the scarcity of accurate canopy height measurements at large scales. NASA’s mission, the Global Ecosystem Dynamic Investigation (GEDI), has provided sampled observations of the forest vertical structure at near global scale since late 2018. The availability of such unprecedented measurements allows for examining the vertical structure of vegetation spatially and temporally. Herein, we explore the most influential climatic and environmental drivers of the canopy height in tropical forests. We examined different resampling resolutions of GEDI-based canopy height to approximate maximum canopy height over tropical forests across all of Malaysia. Moreover, we attempted to interpret the dynamics unde...
Selective logging can cause significant impacts on the residual stands, affecting biodiversity an... more Selective logging can cause significant impacts on the residual stands, affecting biodiversity and leading to environmental changes. Proper monitoring and mapping of the impacts from logging activities, such as the stumps, felled logs, roads, skid trails, and forest canopy gaps, are crucial for sustainable forest management operations. The purpose of this study is to assess the indicators of selective logging impacts by detecting the individual stumps as the main indicators, evaluating the performance of classification methods to assess the impacts and identifying forest gaps from selective logging activities. The combination of forest inventory field plots and unmanned aerial vehicle (UAV) RGB and overlapped imaged were used in this study to assess these impacts. The study area is located in Ulu Jelai Forest Reserve in the central part of Peninsular Malaysia, covering an experimental study area of 48 ha. The study involved the integration of template matching (TM), object-based ima...
Lidar point clouds have been frequently used in forest inventories. The higher point density has ... more Lidar point clouds have been frequently used in forest inventories. The higher point density has provided better representation of trees in forest plantations. So we developed a new approach to fill this gap in the integrated crop-livestock-forest system, the sampling forest inventory, which uses the principles of individual tree detection applied under different plot arrangements. We use a UAV-lidar system (GatorEye) to scan an integrated crop-livestock-forest system with Eucalyptus benthamii seed forest plantations. On the high density UAV-lidar point cloud (>1400 pts. m2), we perform a comparison of two forest inventory approaches: Sampling Forest Inventory (SFI) with circular (1380 m2 and 2300 m2) and linear (15 trees and 25 trees) plots and Individual Tree Detection (ITD). The parametric population values came from the approach with measurements taken in the field, called forest inventory (FI). Basal area and volume estimates were performed considering the field heights and ...
Precise assessments of forest species’ composition help analyze biodiversity patterns, estimate w... more Precise assessments of forest species’ composition help analyze biodiversity patterns, estimate wood stocks, and improve carbon stock estimates. Therefore, the objective of this work was to evaluate the use of high-resolution images obtained from Unmanned Aerial Vehicle (UAV) for the identification of forest species in areas of forest regeneration in the Amazon. For this purpose, convolutional neural networks (CNN) were trained using the Keras–Tensorflow package with the faster_rcnn_inception_v2_pets model. Samples of six forest species were used to train CNN. From these, attempts were made with the number of thresholds, which is the cutoff value of the function; any value below this output is considered 0, and values above are treated as an output 1; that is, values above the value stipulated in the Threshold are considered as identified species. The results showed that the reduction in the threshold decreases the accuracy of identification, as well as the overlap of the polygons o...
This is an open access article under the terms of the Creative Commons Attribution License, which... more This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
The file includes the longitudinal and radiological data for a retrospective incidental meningiom... more The file includes the longitudinal and radiological data for a retrospective incidental meningioma cohort (2007-2018). The study setting was The Walton Centre NHS Foundation Trust which serves a catchment area of 3.5 million people in the North west of England and Wales. Based on this data set, an online risk calculator has been created that could help guide monitoring strategies for patients with an incidental asymptomatic meningioma (www.impact-meningioma.com). It should be noted that this calculator has not been externally validated for clinical use and clinicians should exercise their own clinical judgement when making management decisions with patient under their care.
Urban trees and forests provide multiple ecosystem services (ES), including temperature regulatio... more Urban trees and forests provide multiple ecosystem services (ES), including temperature regulation, carbon sequestration, and biodiversity. Interest in ES has increased amongst policymakers, scientists, and citizens given the extent and growth of urbanized areas globally. However, the methods and techniques used to properly assess biodiversity and ES provided by vegetation in urban environments, at large scales, are insufficient. Individual tree identification and characterization are some of the most critical issues used to evaluate urban biodiversity and ES, given the complex spatial distribution of vegetation in urban areas and the scarcity or complete lack of systematized urban tree inventories at large scales, e.g., at the regional or national levels. This often limits our knowledge on their contributions toward shaping biodiversity and ES in urban areas worldwide. This paper provides an analysis of the state-of-the-art studies and was carried out based on a systematic review o...
IOP Conference Series: Earth and Environmental Science, 2021
Tropical forests play a significant role in regulating the average global atmospheric temperature... more Tropical forests play a significant role in regulating the average global atmospheric temperature encompassing 25 % of the carbon present in the terrestrial biosphere. However, the rapid change in climate, arising from unsustainable human practices, can significantly affect their carbon uptake capability in the future. For understanding these deviations, it is important to identify and quantify the large-scale canopy height variations arising from previous anthropogenic disturbances. With the advent of NASA GEDI spaceborne LiDAR (light detection and ranging), it is now possible to acquire three-dimensional vertical structural data of forests globally. In this study, we evaluate the applicability of GEDI for analyzing relative canopy height variations of secondary tropical forests of different age groups located across multiple geographical regions of peninsular Malaysia. The results for RH98 GEDI metric trends for the lowland and hill forests category across 4 different disturbance ...
Replanting trees helps with avoiding desertification, reducing the chances of soil erosion and fl... more Replanting trees helps with avoiding desertification, reducing the chances of soil erosion and flooding, minimizing the risks of zoonotic disease outbreaks, and providing ecosystem services and livelihood to the indigenous people, in addition to sequestering carbon dioxide for mitigating climate change. Consequently, it is important to explore new methods and technologies that are aiming to upscale and fast-track afforestation and reforestation (A/R) endeavors, given that many of the current tree planting strategies are not cost effective over large landscapes, and suffer from constraints associated with time, energy, manpower, and nursery-based seedling production. UAV (unmanned aerial vehicle)-supported seed sowing (UAVsSS) can promote rapid A/R in a safe, cost-effective, fast and environmentally friendly manner, if performed correctly, even in otherwise unsafe and/or inaccessible terrains, supplementing the overall manual planting efforts globally. In this study, we reviewed the ...
BACKGROUND Flow aneurysms (FAs) associated with brain arteriovenous malformations (AVMs) are thou... more BACKGROUND Flow aneurysms (FAs) associated with brain arteriovenous malformations (AVMs) are thought to arise from increased haemodynamic stress due to high flow shunting. This study aims to describe the changes in conservatively managed FAs after successful AVM treatment. METHODS Patients with symptomatic AVMs and associated FAs who underwent successful treatment of the AVM between 2008-2017, were included. FA dimensions were measured on surveillance angiography to assess longitudinal changes. RESULTS 32 patients were identified with 48 FAs. 16 (33%) FAs were treated endovascularly; 18 (38%) FAs were treated surgically; 14 (29%) FAs (11 patients) were monitored. FAs demonstrated a decrease in size from 5.0mm to 3.8mm (24%; p=0.016) and 4.9mm to 3.6mm (27%; p=0.013), in height and width respectively over median 35 months. However, on subgroup analysis, only class IIb aneurysms demonstrated a significant decrease in size (51% reduction in largest diameter, p = 0.046) and only 3 FAs (21%) resolved. There were no haemorrhages observed during follow-up. CONCLUSION While conservatively managed FAs demonstrated a reduction in size after the culprit AVM was treated, this was only significant in FAs located close to AVM nidus (class IIb). There were no haemorrhages during median 35 months follow-up, however, long term data is lacking. Our data supports close observation of all conservatively managed aneurysms and a tailored approach based on the proximity to the nidus and observed changes in size.
Applications of unmanned aerial vehicles (UAVs) have proliferated in the last decade due to the t... more Applications of unmanned aerial vehicles (UAVs) have proliferated in the last decade due to the technological advancements on various fronts such as structure-from-motion (SfM), machine learning, and robotics. An important preliminary step with regard to forest inventory and management is individual tree detection (ITD), which is required to calculate forest attributes such as stem volume, forest uniformity, and biomass estimation. However, users may find adopting the UAVs and algorithms for their specific projects challenging due to the plethora of information available. Herein, we provide a step-by-step tutorial for performing ITD using (i) low-cost UAV-derived imagery and (ii) UAV-based high-density lidar (light detection and ranging). Functions from open-source R packages were implemented to develop a canopy height model (CHM) and perform ITD utilizing the local maxima (LM) algorithm. ITD accuracy assessment statistics and validation were derived through manual visual interpreta...
Compassion has been one of the greatest virtues of healthcare professionals. In the early phase o... more Compassion has been one of the greatest virtues of healthcare professionals. In the early phase of the pandemic, a lot of caution was essential, and restrictions were imposed on the hospital visitation of the COVID-19 patients by their family members. The healthcare system was overburdened, and the healthcare workers were apprehensive about the new virus and the rising mortality. Compassion and family-centered care took a step back as survival of the pandemic became the ultimate goal of mankind. "COVID-19 patients admitted to the critical care units, their loved ones and the healthcare professionals caring for these patients took the brunt of the emotional and psychological impacts of the pandemic." However, as we have moved more than a year into the pandemic, knowledge and resources we gained may be leveraged to provide family-centered critical care for COVID-19 patients. Family presence in intensive care units (ICUs) has been associated with higher satisfaction with care, collaboration with the medical team, shared decision-making, reduced delirium, and optimized end-of-life care of COVID-19 patients. The policymakers should review the restrictions, consider a holistic approach, and take appropriate actions to provide safe family-centered critical care for COVID-19 patients.
Current Opinion in Environmental Science & Health, 2021
This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
The high dimensionality of data generated by Unmanned Aerial Vehicle(UAV)-Lidar makes it difficul... more The high dimensionality of data generated by Unmanned Aerial Vehicle(UAV)-Lidar makes it difficult to use classical statistical techniques to design accurate predictive models from these data for conducting forest inventories. Machine learning techniques have the potential to solve this problem of modeling forest attributes from remotely sensed data. This work tests four different machine learning approaches-namely Support Vector Regression, Random Forest, Artificial Neural Networks, and Extreme Gradient Boosting-on high-density GatorEye UAV-Lidar point clouds for indirect estimation of individual tree dendrometric metrics (fieldderived) such as diameter at breast height, total height, and timber volume. A total of 370 trees had their dbh and height measured for validation purposes. Using LAStools we generated normalized Light Detection and Ranging (Lidar) point clouds and created a raster canopy height model at a 0.5x0.5 m spatial resolution following the construction of a digital terrain model and a digital surface model. The R package 'lidR' was set with the functions tree_detection (local maximum filter algorithm) and lastrees. Subsequently, we applied the function tree_metrics to extract individual metrics. Machine learning techniques were applied to the derived metrics to estimate dendrometric field measures. The machine learning models (MLM) with optimal hyperparameters showed similar predictive performances for modeling the variables diameter, height, and volume. All models had a rRMSE below 15% (for diameter at breast height), 9% (for height) and 29% (for volume). The Support Vector Regression algorithm showed the best performance. Our work demonstrates that all tested machine learning models are adequate and robust to handle the high dimensionality of UAV-Lidar data for the estimation of individual attributes, with Support Vector Regression model being the best performer in terms of minimal error rates.
Abstract Tropical savanna ecosystems play a major role in the seasonality of the global carbon cy... more Abstract Tropical savanna ecosystems play a major role in the seasonality of the global carbon cycle. However, their ability to store and sequester carbon is uncertain due to combined and intermingling effects of anthropogenic activities and climate change, which impact wildfire regimes and vegetation dynamics. Accurate measurements of tropical savanna vegetation aboveground biomass (AGB) over broad spatial scales are crucial to achieve effective carbon emission mitigation strategies. UAV-lidar is a new remote sensing technology that can enable rapid 3-D mapping of structure and related AGB in tropical savanna ecosystems. This study aimed to assess the capability of high-density UAV-lidar to estimate and map total (tree, shrubs, and surface layers) aboveground biomass density (AGBt) in the Brazilian Savanna (Cerrado). Five ordinary least square regression models estimating AGBt were adjusted using 50 field sample plots (30 m × 30 m). The best model was selected under Akaike Information Criterion, adjusted coefficient of determination (adj.R2), absolute and relative root mean square error (RMSE), and used to map AGBt from UAV-lidar data collected over 1,854 ha spanning the three major vegetation formations (forest, savanna, and grassland) in Cerrado. The model using vegetation height and cover was the most effective, with an overall model adj-R2 of 0.79 and a leave-one-out cross-validated RMSE of 19.11 Mg/ha (33.40%). The uncertainty and errors of our estimations were assessed for each vegetation formation separately, resulting in RMSEs of 27.08 Mg/ha (25.99%) for forests, 17.76 Mg/ha (43.96%) for savannas, and 7.72 Mg/ha (44.92%) for grasslands. These results prove the feasibility and potential of the UAV-lidar technology in Cerrado but also emphasize the need for further developing the estimation of biomass in grasslands, of high importance in the characterization of the global carbon balance and for supporting integrated fire management activities in tropical savanna ecosystems. Our results serve as a benchmark for future studies aiming to generate accurate biomass maps and provide baseline data for efficient management of fire and predicted climate change impacts on tropical savanna ecosystems.
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