Climate teleconnections modulate global burned area

Climate teleconnections (CT) remotely influence weather conditions in many regions on Earth, entailing changes in primary drivers of fire activity such as vegetation biomass accumulation and moisture. We reveal significant relationships between the main global CTs and burned area that vary across and within continents and biomes according to both synchronous and lagged signals, and marked regional patterns. Overall, CTs modulate 52.9% of global burned area, the Tropical North Atlantic mode being the most relevant CT. Here, we summarized the CT-fire relationships into a set of six global CT domains that are discussed by continent, considering the underlying mechanisms relating weather patterns and vegetation types with burned area across the different world's biomes. Our findings highlight the regional CT-fire relationships worldwide, aiming to further support fire management and policy-making.We thank Lorea Garcia for her insights and useful suggestions in the interpretation of CT-fire relationships during the review process of the manuscript. This project received funding from the Spanish Ministry of Science and Innovation, project FIREPATHS (PID2020-116556RA-I00) (authors receiving funding: A.C. and M.R.) and the European Union’s Horizon 2020 research and innovation programme MSCA-ITN-2019— Innovative Training Networks under grant agreement No. 860787 (PyroLife) (authors receiving funding: A.C., M.T. and C.S.), and the European Horizon 2020 research and innovation programme under grant agreement No. 101037419 (FIRE-RES) (authors receiving funding: A.C., J.R., C.S. and S.d.M.)

Fire behavior modeling for operational decision-making

Simulation frameworks are necessary to facilitate decision-making to many fire agencies. An accurate estimation of fire behavior is required to analyze potential impact and risk. Applied research and technology together have improved the implementation of fire modeling, and decision-making in operational environments.Dr Cardil acknowledges the support of Technosylva USA and Wageningen University in his research stays in the USA and the Netherlands to develop this work. The authors of this paper acknowledges the support of the EUfunded PYROLIFE project (Reference: 860787; https://pyrolife.lessonsonfire.eu/), a project in which a new generation of experts will be trained in integrated wildfire management

Applications of drones in emerging economies: a case study of Malaysia

Drones or unmanned aerial vehicles (UAVs) are becoming increasingly popular for commercial and noncommercial uses – especially in fields of environment, surveillance, aerial photography, digital communications, search and rescue operations and military. Drones are in fact low cost aerial robots, that require little preparation and infrastructure and can be equipped with any number of sensors or cameras making them ideal for monitoring the environment. Environmental monitoring plays a major role in analyzing climate and management impacts on natural, agricultural systems, assessing, forecasting and even preventing natural disasters and enhancing hydrological cycle. Monitoring and data collection systems are based upon a combination of ground-based measurements and remote sensing sensors observations. These data however have spatiotemporal constraints. Drones offer an opportunity to bridge the existing gap between field observations and remote sensing by providing high spatial detail over relatively large areas in a cost-effective way. Drones have become popular in several developed countries in recent years. However, the use of drones is still in the infancy stage of development at developing countries such as Malaysia. This paper attempts to review the development of drone applications in Malaysia in order to identify future directions, applications, developments and challenges. We summarize that, to leverage the full potential of drones approaches in Malaysia, measurement protocols, retrieval algorithms, and processing and evaluation techniques need to be harmonized to ensure the sustainability and resiliency of the implementation

The long-term outcomes of epilepsy surgery

<div><p>Objective</p><p>Despite modern anti-epileptic drug treatment, approximately 30% of epilepsies remain medically refractory and for these patients, epilepsy surgery may be a treatment option. There have been numerous studies demonstrating good outcome of epilepsy surgery in the short to median term however, there are a limited number of studies looking at the long-term outcomes. The aim of this study was to ascertain the long-term outcome of resective epilepsy surgery in a large neurosurgery hospital in the U.K.</p><p>Methods</p><p>This a retrospective analysis of prospectively collected data. We used the 2001 International League Against Epilepsy (ILAE) classification system to classify seizure freedom and Kaplan-Meier survival analysis to estimate the probability of seizure freedom.</p><p>Results</p><p>We included 284 patients who underwent epilepsy surgery (178 anterior temporal lobe resections, 37 selective amygdalohippocampectomies, 33 temporal lesionectomies, 36 extratemporal lesionectomies), and had a prospective median follow-up of 5 years (range 1–27). Kaplan-Meier estimates showed that 47% (95% CI 40–58) remained seizure free (apart from simple partial seizures) at 5 years and 38% (95% CI 31–45) at 10 years after surgery. 74% (95% CI 69–80) had a greater than 50% seizure reduction at 5 years and 70% (95% CI 64–77) at 10 years. Patients who had an amygdalohippocampectomy were more likely to have seizure recurrence than patients who had an anterior temporal lobe resection (p = 0.006) and temporal lesionectomy (p = 0.029). There was no significant difference between extra temporal and temporal lesionectomies. Hippocampal sclerosis was associated with a good outcome but declined in relative frequency over the years.</p><p>Conclusion</p><p>The vast majority of patients who were not seizure free experienced at least a substantial and long-lasting reduction in seizure frequency. A positive long-term outcome after epilepsy surgery is possible for many patients and especially those with hippocampal sclerosis or those who had anterior temporal lobe resections.</p></div

Individual tree attribute estimation and uniformity assessment in fast-growing Eucalyptus spp. forest plantations using lidar and linear mixed-effects models

Fast-growing Eucalyptus spp. forest plantations and their resultant wood products are economically important and may provide a low-cost means to sequester carbon for greenhouse gas reduction. The development of advanced and optimized frameworks for estimating forest plantation attributes from lidar remote sensing data combined with statistical modeling approaches is a step towards forest inventory operationalization and might improve industry e ciency in monitoring and managing forest resources. In this study, we first developed and tested a framework for modeling individual tree attributes in fast-growing Eucalyptus forest plantation using airborne lidar data and linear mixed-e ect models (LME) and assessed the gain in accuracy compared to a conventional linear fixed-e ects model (LFE). Second, we evaluated the potential of using the tree-level estimates for determining tree attribute uniformity across di erent stand ages. In the field, tree measurements, such as tree geolocation, species, genotype, age, height (Ht), and diameter at breast height (dbh) were collected through conventional forest inventory practices, and tree-level aboveground carbon (AGC) was estimated using allometric equations. Individual trees were detected and delineated from lidar-derived canopy height models (CHM), and crown-level metrics (e.g., crown volume and crown projected area) were computed from the lidar 3-D point cloud. Field and lidar-derived crown metrics were combined for ht, dbh, and AGC modeling using an LME. We fitted a varying intercept and slope model, setting species, genotype, and stand (alone and nested) as random e ects. For comparison, we also modeled the same attributes using a conventional LFE model. The tree attribute estimates derived from the best LME model were used for assessing forest uniformity at the tree level using the Lorenz curves and Gini coe cient (GC).We successfully detected 96.6% of the trees from the lidar-derived CHM. The best LME model for estimating the tree attributes was composed of the stand as a random e ect variable, and canopy height, crown volume, and crown projected area as fixed e ects. The %RMSE values for tree-level height, dbh, and AGC were 8.9%, 12.1%, and 23.7% for the LFE model and improved to 7.3%, 7.1%, and 13.6%, respectively, for the LME model. Tree attributes uniformity was assessed with the Lorenz curves and tree-level estimations, especially for the older stands. All stands showed a high level of tree uniformity with GC values approximately 0.2. This study demonstrates that accurate detection of individual trees and their associated crown metrics can be used to estimate Ht, dbh, and AGC stocks as well as forest uniformity in fast-growing Eucalyptus plantations forests using lidar data as inputs to LME models. This further underscores the high potential of our proposed approach to monitor standing stock and growth in Eucalyptus—and similar forest plantations for carbon dynamics and forest product planninginfo:eu-repo/semantics/publishedVersio

Applications of drones in emerging economies: a case study of Malaysia

Drones or unmanned aerial vehicles (UAVs) are becoming increasingly popular for commercial and noncommercial uses - especially in fields of environment, surveillance, aerial photography, digital communications, search and rescue operations and military. Drones are in fact low cost aerial robots, that require little preparation and infrastructure and can be equipped with any number of sensors or cameras making them ideal for monitoring the environment. Environmental monitoring plays a major role in analyzing climate and management impacts on natural, agricultural systems, assessing, forecasting and even preventing natural disasters and enhancing hydrological cycle. Monitoring and data collection systems are based upon a combination of ground-based measurements and remote sensing sensors observations. These data however have spatiotemporal constraints. Drones offer an opportunity to bridge the existing gap between field observations and remote sensing by providing high spatial detail over relatively large areas in a cost-effective way. Drones have become popular in several developed countries in recent years. However, the use of drones is still in the infancy stage of development at developing countries such as Malaysia. This paper attempts to review the development of drone applications in Malaysia in order to identify future directions, applications, developments and challenges. We summarize that, to leverage the full potential of drones approaches in Malaysia, measurement protocols, retrieval algorithms, and processing and evaluation techniques need to be harmonized to ensure the sustainability and resiliency of the implementation

Incidental intracranial meningiomas: a systematic review and meta-analysis of prognostic factors and outcomes (vol 142, pg 211, 2019)

BackgroundIncidental discovery accounts for 30% of newly-diagnosed intracranial meningiomas. There is no consensus on their optimal management. This review aimed to evaluate the outcomes of different management strategies for these tumors.MethodsUsing established systematic review methods, six databases were scanned up to September 2017. Pooled event proportions were estimated using a random effects model. Meta-regression of prognostic factors was performed using individual patient data.ResultsTwenty studies (2130 patients) were included. Initial management strategies at diagnosis were: surgery (27.3%), stereotactic radiosurgery (22.0%) and active monitoring (50.7%) with a weighted mean follow-up of 49.5 months (SD = 29.3). The definition of meningioma growth and monitoring regimens varied widely impeding relevant meta-analysis. The pooled risk of symptom development in patients actively monitored was 8.1% (95% CI 2.7-16.1). Associated factors were peritumoral edema (OR 8.72 [95% CI 0.35-14.90]) and meningioma diameter ≥ 3 cm (OR 34.90 [95% CI 5.17-160.40]). The pooled proportion of intervention after a duration of active monitoring was 24.8% (95% CI 7.5-48.0). Weighted mean time-to-intervention was 24.8 months (SD = 18.2). The pooled risks of morbidity following surgery and radiosurgery, accounting for cross-over, were 11.8% (95% CI 3.7-23.5) and 32.0% (95% CI 10.6-70.5) respectively. The pooled proportion of operated meningioma being WHO grade I was 94.0% (95% CI 88.2-97.9).ConclusionThe management of incidental meningioma varies widely. Most patients who clinically or radiologically progressed did so within 5 years of diagnosis. Intervention at diagnosis may lead to unnecessary overtreatment. Prospective data is needed to develop a risk calculator to better inform management strategies

A prognostic model to personalize monitoring regimes for patients with incidental asymptomatic meningiomas

Background Asymptomatic meningioma is a common incidental finding with no consensus on the optimal management strategy. We aimed to develop a prognostic model to guide personalized monitoring of incidental meningioma patients. Methods A prognostic model of disease progression was developed in a retrospective cohort (2007–2015), defined as: symptom development, meningioma-specific mortality, meningioma growth or loss of window of curability. Secondary endpoints included non-meningioma-specific mortality and intervention. Results Included were 441 patients (459 meningiomas). Over a median of 55 months (interquartile range, 37–80), 44 patients had meningioma progression and 57 died (non-meningioma-specific). Forty-four had intervention (at presentation, n = 6; progression, n = 20; nonprogression, n = 18). Model parameters were based on statistical and clinical considerations and included: increasing meningioma volume (hazard ratio [HR] 2.17; 95% CI: 1.53–3.09), meningioma hyperintensity (HR 10.6; 95% CI: 5.39–21.0), peritumoral signal change (HR 1.58; 95% CI: 0.65–3.85), and proximity to critical neurovascular structures (HR 1.38; 95% CI: 0.74–2.56). Patients were stratified based on these imaging parameters into low-, medium- and high-risk groups and 5-year disease progression rates were 3%, 28%, and 75%, respectively. After 5 years of follow-up, the risk of disease progression plateaued in all groups. Patients with an age-adjusted Charlson comorbidity index ≥6 (eg, an 80-year-old with chronic kidney disease) were 15 times more likely to die of other causes than to receive intervention at 5 years following diagnosis, regardless of risk group. Conclusions The model shows that there is little benefit to rigorous monitoring in low-risk and older patients with comorbidities. Risk-stratified follow-up has the potential to reduce patient anxiety and associated health care costs