Towards a whole-system framework for wildfire monitoring using Earth observations.

Fire seasons have become increasingly variable and extreme due to changing climatological, ecological, and social conditions. Earth observation data are critical for monitoring fires and their impacts. Herein, we present a whole-system framework for identifying and synthesizing fire monitoring objectives and data needs throughout the life cycle of a fire event. The four stages of fire monitoring using Earth observation data include the following: (1) pre-fire vegetation inventories, (2) active-fire monitoring, (3) post-fire assessment, and (4) multi-scale synthesis. We identify the challenges and opportunities associated with current approaches to fire monitoring, highlighting four case studies from North American boreal, montane, and grassland ecosystems. While the case studies are localized to these ecosystems and regional contexts, they provide insights for others experiencing similar monitoring challenges worldwide. The field of remote sensing is experiencing a rapid proliferation of new data sources, providing observations that can inform all aspects of our fire monitoring framework; however, significant challenges for meeting fire monitoring objectives remain. We identify future opportunities for data sharing and rapid co-development of information products using cloud computing that benefits from open-access Earth observation and other geospatial data layers.

Sensors for Fire and Smoke Monitoring.

Mastery of fire is intimately linked to advances in human civilization, culture and technology [...].

Geocorrection of Airborne Mid-Wave Infrared Imagery for Mapping Wildfires without GPS or IMU.

The increase in annual wildfires in many areas of the world has triggered international efforts to deploy sensors on airborne and space platforms to map these events and understand their behaviour. During the summer of 2017, an airborne flight campaign acquired mid-wave infrared imagery over active wildfires in Northern Ontario, Canada. However, it suffered multiple position-based equipment issues, thus requiring a non-standard geocorrection methodology. This study presents the approach, which utilizes a two-step semi-automatic geocorrection process that outputs image mosaics from airborne infrared video input. The first step extracts individual video frames that are combined into orthoimages using an automatic image registration method. The second step involves the georeferencing of the imagery using pseudo-ground control points to a fixed coordinate systems. The output geocorrected datasets in units of radiance can then be used to derive fire products such as fire radiative power density (FRPD). Prior to the georeferencing process, the Root Mean Square Error (RMSE) associated with the imagery was greater than 200 m. After the georeferencing process was applied, an RMSE below 30 m was reported, and the computed FRPD estimations are within expected values across the literature. As such, this alternative geocorrection methodology successfully salvages an otherwise unusable dataset and can be adapted by other researchers that do not have access to accurate positional information for airborne infrared flight campaigns over wildfires.

A Bi-Spectral Microbolometer Sensor for Wildfire Measurement.

This study describes the development of a prototype bi-spectral microbolometer sensor system designed explicitly for radiometric measurement and characterization of wildfire mid- and long-wave infrared radiances. The system is tested experimentally over moderate-scale experimental burns coincident with FLIR reference imagery. Statistical comparison of the fire radiative power (FRP; W) retrievals suggest that this novel system is highly reliable for use in collecting radiometric measurements of biomass burning. As such, this study provides clear experimental evidence that mid-wave infrared microbolometers are capable of collecting FRP measurements. Furthermore, given the low resource nature of this detector type, it presents a suitable option for monitoring wildfire behaviour from low resource platforms such as unmanned aerial vehicles (UAVs) or nanosats.

Development of the User Requirements for the Canadian WildFireSat Satellite Mission.

In 2019 the Canadian Space Agency initiated development of a dedicated wildfire monitoring satellite (WildFireSat) mission. The intent of this mission is to support operational wildfire management, smoke and air quality forecasting, and wildfire carbon emissions reporting. In order to deliver the mission objectives, it was necessary to identify the technical and operational challenges which have prevented broad exploitation of Earth Observation (EO) in Canadian wildfire management and to address these challenges in the mission design. In this study we emphasize the first objective by documenting the results of wildfire management end-user engagement activities which were used to identify the key Fire Management Functionalities (FMFs) required for an Earth Observation wildfire monitoring system. These FMFs are then used to define the User Requirements for the Canadian Wildland Fire Monitoring System (CWFMS) which are refined here for the WildFireSat mission. The User Requirements are divided into Observational, Measurement, and Precision requirements and form the foundation for the design of the WildFireSat mission (currently in Phase-A, summer 2020).

High-sensitivity Cardiac Troponin Elevation after Electroconvulsive Therapy: A Prospective, Observational Cohort Study.

BACKGROUND: While electroconvulsive therapy is widely regarded as a lifesaving and safe procedure, evidence regarding its effects on myocardial cell injury is sparse. The objective of this investigation was to determine the incidence and magnitude of new cardiac troponin elevation after electroconvulsive therapy using a novel high-sensitivity cardiac troponin I assay. METHODS: This was a prospective cohort study in adult patients undergoing electroconvulsive therapy in a single academic center (up to three electroconvulsive therapy treatments per patient). The primary outcome was new high-sensitivity cardiac troponin I elevation after electroconvulsive therapy, defined as an increase of high-sensitivity cardiac troponin I greater than 100% after electroconvulsive therapy compared to baseline with at least one value above the limit of quantification (10 ng/l). Twelve-lead electrocardiogram and high-sensitivity cardiac troponin I values were obtained before and 15 to 30 min after electroconvulsive therapy; in a subset of patients, an additional 2-h high-sensitivity cardiac troponin I value was obtained. RESULTS: The final study population was 100 patients and a total of 245 electroconvulsive therapy treatment sessions. Eight patients (8 of 100; 8%) experienced new high-sensitivity cardiac troponin I elevation after electroconvulsive therapy with a cumulative incidence of 3.7% (9 of 245 treatments; one patient had two high-sensitivity cardiac troponin I elevations), two of whom had a non-ST-elevation myocardial infarction (incidence 2 of 245; 0.8%). Median high-sensitivity cardiac troponin I concentrations did not increase significantly after electroconvulsive therapy. Tachycardia and/or elevated systolic blood pressure developed after approximately two thirds of electroconvulsive therapy treatments. CONCLUSIONS: Electroconvulsive therapy appears safe from a cardiac standpoint in a large majority of patients. A small subset of patients with preexisting cardiovascular risk factors, however, may develop new cardiac troponin elevation after electroconvulsive therapy, the clinical relevance of which is unclear in the absence of signs of myocardial ischemia.

Airborne Optical and Thermal Remote Sensing for Wildfire Detection and Monitoring.

For decades detection and monitoring of forest and other wildland fires has relied heavily on aircraft (and satellites). Technical advances and improved affordability of both sensors and sensor platforms promise to revolutionize the way aircraft detect, monitor and help suppress wildfires. Sensor systems like hyperspectral cameras, image intensifiers and thermal cameras that have previously been limited in use due to cost or technology considerations are now becoming widely available and affordable. Similarly, new airborne sensor platforms, particularly small, unmanned aircraft or drones, are enabling new applications for airborne fire sensing. In this review we outline the state of the art in direct, semi-automated and automated fire detection from both manned and unmanned aerial platforms. We discuss the operational constraints and opportunities provided by these sensor systems including a discussion of the objective evaluation of these systems in a realistic context.

A cost-benefit analysis of WildFireSat, a wildfire monitoring satellite mission for Canada.

In anticipation of growing wildfire management challenges, the Canadian government is investing in WildFireSat, an Earth observation satellite mission designed to collect data in support of Canadian wildfire management. Although costs of the mission can be reasonably estimated, the benefits of such an investment are unknown. Here we forecast the possible benefits of WildFireSat via an avoided cost approach. We consider five socio-economic components: suppression costs (fixed and variable), timber losses, property, asset and infrastructure losses, evacuation costs, and smoke related health costs. Using a Monte Carlo analysis, we evaluated a range of possible changes to these components based on expert opinions. The resulting Net Present Value (NPV) estimates depend on the presumed impact of using WildFireSat decision support data products, with pessimistic and conservative assumptions generating mission costs that typically exceed potential benefits by 1.16 to 1.59 times, while more optimistic assumptions generate benefits in excess of costs by 8.72 to 10.48 times. The analysis here excludes some possibly significant market and non-market impacts expected from WildFireSat due to data limitations; accounting for these additional impacts would likely generate positive NPVs under even cautious impact assumptions.

Explainable machine learning for predicting conversion to neurological disease: Results from 52,939 medical records.

Objective: This study assesses the application of interpretable machine learning modeling using electronic medical record data for the prediction of conversion to neurological disease. Methods: A retrospective dataset of Cleveland Clinic patients diagnosed with Alzheimer's disease, amyotrophic lateral sclerosis, multiple sclerosis, or Parkinson's disease, and matched controls based on age, sex, race, and ethnicity was compiled. Individualized risk prediction models were created using eXtreme Gradient Boosting for each neurological disease at four timepoints in patient history. The prediction models were assessed for transparency and fairness. Results: At timepoints 0-months, 12-months, 24-months, and 60-months prior to diagnosis, Alzheimer's disease models achieved the area under the receiver operating characteristic curve on a holdout test dataset of 0.794, 0.742, 0.709, and 0.645; amyotrophic lateral sclerosis of 0.883, 0.710, 0.658, and 0.620; multiple sclerosis of 0.922, 0.877, 0.849, and 0.781; and Parkinson's disease of 0.809, 0.738, 0.700, and 0.651, respectively. Conclusions: The results demonstrate that electronic medical records contain latent information that can be used for risk stratification for neurological disorders. In particular, patient-reported outcomes, sleep assessments, falls data, additional disease diagnoses, and longitudinal changes in patient health, such as weight change, are important predictors.

Perioperative torsade de pointes: a systematic review of published case reports.

BACKGROUND: Torsade de pointes is a rare but potentially fatal arrhythmia. More than 40 cases of perioperative torsade de pointes have been reported in the literature; however, the current evidence regarding this complication is very limited. To improve our understanding, we performed a systematic review and meta-analysis of all published case reports of perioperative torsade de pointes. METHODS: MEDLINE was systematically searched for cases of perioperative torsade de pointes. We included patients of all age groups and cases that occurred from the immediate preoperative period to the third postoperative day. Patient and case characteristics as well as QT interval data were extracted. RESULTS: Forty-six cases of perioperative torsade de pointes were identified; 29 occurred in women (67%), and 2 episodes were fatal (case fatality rate: 4%). Craniotomies and cardiac surgery accounted for 40% of all cases. Preceding events identified by the authors were hypokalemia (12/46, 26%; 99% confidence interval [CI], 9%-43%) and bradycardia (7/46, 15%; 99% CI, 2%-28%). Drugs were implicated in approximately one third of the events (14/46, 30%; 99% CI, 13%-48%). The mean corrected QT (QTc) at baseline was 457 ± 67 milliseconds (minimum 320 milliseconds; maximum 647 milliseconds; data available in 27/46 patients). At the time of the event, the mean QTc increased to 575 ± 77 milliseconds (minimum 413 milliseconds; maximum 766 milliseconds; data available in 33/46 patients). On average, QTc increased by +118 milliseconds (99% CI, 70-166 milliseconds; P < 0.001) between baseline and after the torsade de pointes event. All patients, except for 2, had a substantial prolongation of their QTc interval at the time of the event. CONCLUSIONS: This systematic review identified several common risk factors for perioperative torsade de pointes. Given the nearly uniform presence of a substantial QTc interval prolongation at the time of a torsade de pointes episode, increased vigilance for perioperative QTc interval prolongation may be warranted.

The Microsoft HoloLens 2 Provides Accurate Biomechanical Measures of Performance During Military-Relevant Activities in Healthy Adults.

INTRODUCTION: Augmented reality systems, like the HoloLens 2 (HL2), have the potential to provide accurate assessments of mild traumatic brain injury (mTBI) symptoms in military personnel by simulating complex military scenarios while objectively measuring the user's movements with embedded motion sensors. The aim of this project was to determine if biomechanical measures of marching and squatting, derived from the HL2 motion sensors, were statistically equivalent, within 5%, to metrics derived from the gold-standard three-dimensional motion capture (MoCap) system. MATERIALS AND METHODS: Sixty-four adults (18-45 years; 34 males) completed a squatting and a marching task under single- (motor) and dual-task (motor + cognitive) conditions. Positional data from the HL2 and MoCap were simultaneously collected during the tasks and used to calculate and compare biomechanical outcomes. The HL2's augmented reality capabilities were utilized to deliver the cognitive component of the marching dual task. RESULTS: Equivalence testing indicated the HL2 and MoCap measures were within 5% in all squatting metrics-trial time, squat duration, squat velocity, squat depth, and dwell time. Marching metrics, including trial time, step count, stepping rate, and step interval, were also equivalent between the two systems. The mean reaction time for responses during the Stroop test was 810 (125) milliseconds per response. CONCLUSIONS: Biomechanical outcomes characterizing performance during two common military movements, squatting and marching, were equivalent between the HL2 and MoCap systems in healthy adults. Squatting and marching are two military-relevant tasks that require strength, motor coordination, and balance to perform, all of which are known to be affected by mTBI. Taken together, the data provide support for using the HL2 platform to deliver military-specific assessment scenarios and accurately measure performance during these activities. Utilizing objective and quantitative measures of motor function may enhance the management of military mTBI and reduce unnecessary risk to service members.

Postoperative QT interval prolongation in patients undergoing noncardiac surgery under general anesthesia.

BACKGROUND: Abnormal cardiac repolarization, indicated by a prolongation of the QT interval, increases the risk for torsades de pointes, a potentially life-threatening arrhythmia. Many perioperatively administered drugs and conditions prolong the QT interval. Despite several reports of perioperative torsades de pointes, systematic evidence regarding perioperative QT interval prolongation is limited. METHODS: Serial postoperative 12-lead electrocardiograms were obtained from 469 adult patients undergoing major noncardiac surgery under general anesthesia. Heart rate corrected QT-interval duration (Fridericia formula) was the primary outcome. All perioperatively administered drugs were recorded. Emphasis was placed on absolute QTc prolongation greater than 500 ms and relative increases of 30 and 60 ms. RESULTS: At the end of surgery, 80% of the patients (345 of 429) experienced a significant QTc interval prolongation (ΔQTc 23 ± 26 ms (mean and SD), 95% CI 20-25 ms, P less than 0.001). Approximately 51% (219 of 429) had a QTc greater than 440 ms, and 4% (16 of 429) a QTc greater than 500 ms. In 39% (166 of 429), the ΔQTc was greater than 30 ms, in 8% (34 of 429) >60 ms, and in greater than 0.5% (2 of 429) >100 ms. No changes in ΔQTc occurred at subsequent time points. One patient developed torsades de pointes with a ΔQTc: 29 ms (0.4% incidence rate). Several drugs had a large effect on ΔQTc: isoflurane, methadone, ketorolac, cefoxitin, zosyn, unasyn, epinephrine, ephedrine, and calcium. Postoperative body temperature had a weak negative correlation with ΔQTc (r = -0.15, P = 0.02); serum magnesium, potassium, and calcium concentrations were not correlated. CONCLUSION: Postoperative QT-interval prolongation is common. Several perioperatively administered drugs are associated with a substantial QT-interval prolongation. The exact cause and its clinical relevance are, however, unclear. Nevertheless, an association between postoperative QT prolongation and risk for torsades de pointes is likely.

ReSVA: A MATLAB method to co-register and mosaic airborne video-based remotely sensed data.

Airborne remotely sensed data (e.g. hyperspectral imagery, thermal videography, full frame RGB photography) often requires post-processing to be combined into a series of images or a mosaic for analysis. This is generally accomplished through the use of position and attitude hardware (i.e. Global Navigation Satellite System - GNSS / Inertial Measurement Unit - IMU) in combination with specialized software. Occasionally, hardware failure in the GNSS/IMU instrumentation occurs, however the data are still recoverable through a correction process, which allows image registration to mosaic the data. Here we present a simple and flexible MATLAB® code package that has been developed to combine video-based remotely sensed data. It first applies an iterative image registration process to align all frames, using pre-existing GPS information if supplied by the user, and then grids the frame data together to develop a final, single mosaic dataset that can be used for analysis. An example of this method using airborne infrared video data of a wildfire is shown as a demonstration.•MATLAB functions are easily adaptable to specific user needs and datasets.•The method outputs the combined data and positional information in three separate MATLAB variables that can be readily used for analysis in MATLAB or exported for use in other software.

Characterizing Head Impact Exposure in Men and Women During Boxing and Mixed Martial Arts.

BACKGROUND: The accumulation of subconcussive impacts has been implicated in permanent neurological impairment. A gap in understanding the relationship between head impacts and neurological function is the lack of precise characterization and quantification of forces that individuals experience during sports training and competition. PURPOSE: To characterize impact exposure during training and competition among male and female athletes participating in boxing and mixed martial arts (MMA) via an instrumented custom-fit Impact Monitoring Mouthguard (IMM). STUDY DESIGN: Cross-sectional study; Level of evidence, 3. METHODS: Twenty-three athletes (n = 4 women) were provided a custom-fit IMM. The IMM monitored impacts during sparring and competition. All training and competition sessions were videotaped. Video and IMM data were synchronized for post hoc data verification of true positives and substantiation of impact location. IMM data were collected from boxing and MMA athletes at a collaborating site. For each true-positive impact, peak linear acceleration and peak angular acceleration were calculated. Wilcoxon rank sum tests were used to evaluate potential differences in sport, activity type, and sex with respect to each outcome. Differences in impact location were assessed via Kruskal-Wallis tests. RESULTS: IMM data were collected from 53 amateur training sessions and 6 competitions (session range, 5-20 minutes). A total of 896 head impacts (men, n = 786; women, n = 110) were identified using IMM data and video verification: 827 in practice and 69 during competition. MMA and boxers experienced a comparable number of impacts per practice session or competition. In general, MMA impacts produced significantly higher peak angular acceleration than did boxing impacts (P < .001) and were more varied in impact location on the head during competitions. In terms of sex, men experienced a greater number of impacts than women per practice session. However, there was no significant difference between men and women in terms of impact magnitude. CONCLUSION: Characteristic profiles of head impact exposure differed between boxing and MMA athletes; however, the impact magnitudes were not significantly different for male and female athletes.

Severe bleeding in a woman heterozygous for the fibrinogen gammaR275C mutation.

The dysfibrinogen gammaR275C can be a clinically silent mutation, with only two out of 17 cases in the literature reporting a hemorrhagic presentation and four cases reporting a thrombotic presentation. We describe here a particularly severe presentation in 54-year-old female patient who required a hysterectomy at 47 years of age due to heavy menstrual bleeding. Coagulation studies revealed a prolonged prothrombin time and thrombin time, a normal fibrinogen antigen level, and a low fibrinogen activity level. Molecular analysis of the patient's DNA revealed a gamma chain gene mutation resulting in an amino acid substitution at residue 275 (gammaR275C). Protein sequencing of the fibrinogen gamma chain confirmed this mutation, which was named Fibrinogen Portland I. This case demonstrates that the gammaR275C mutation can lead to a severe hemorrhagic phenotype.

Intraneural ganglion cyst of the peroneal nerve in a four-year-old girl: a case report.

Intraneural ganglion cysts of the peroneal nerve are rare, usually occurring in adult men with a typical presentation of knee or proximal leg pain preceding motor weakness and/or sensory disturbances in the peroneal nervous distribution. A history of knee trauma and a palpable mass of the lateral knee in the region of the peroneal nerve are common. We present the unusual case of an intraneural ganglion cyst of the peroneal nerve in a 4-year-old girl. Although extremely rare in the pediatric population, the condition should be considered in the differential diagnosis of children presenting with new-onset foot deformities, foot drop, or clinical examinations consistent with a peroneal nerve lesion. Surgical treatment consisting of ganglion decompression with exploration and ligation of the articular branch of the peroneal nerve may result in improved functional recovery in the pediatric population compared with the adult population. Greater access to magnetic resonance imaging may allow diagnosis of cases that were not previously identified.