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Abstract. Climate change is increasing the frequency and intensity of wildfires globally, with significant impacts on society and the environment. However, our understanding of the global distribution of extreme fires remains skewed, primarily influenced by media coverage and regional research concentration. This inaugural State of Wildfires report systematically analyses fire activity worldwide, identifying extreme events from the March 2023–February 2024 fire season. We assess the causes, predictability, and attribution of these events to climate change and land use, and forecast future risks under different climate scenarios. During the 2023–24 fire season, 3.9 million km2 burned globally, slightly below the average of previous seasons, but fire carbon (C) emissions were 16 % above average, totaling 2.4 Pg C. This was driven by record emissions in Canadian boreal forests (over 9 times the average) and dampened by reduced activity in African savannahs. Notable events included record-breaking wildfire extent and emissions in Canada, the largest recorded wildfire in the European Union (Greece), drought-driven fires in western Amazonia and northern parts of South America, and deadly fires in Hawai’i (100 deaths) and Chile (131 deaths). Over 232,000 people were evacuated in Canada alone, highlighting the severity of human impact. Our analyses revealed that multiple drivers were needed to cause areas of extreme fire activity. In Canada and Greece a combination of high fire weather and an abundance of dry fuels increased the probability of fires by 4.5-fold and 1.9–4.1-fold, respectively, whereas fuel load and direct human suppression often modulated areas with anomalous burned area. The fire season in Canada was predictable three months in advance based on the fire weather index, whereas events in Greece and Amazonia had shorter predictability horizons. Formal attribution analyses indicated that the probability of extreme events has increased significantly due to anthropogenic climate change, with a 2.9–3.6-fold increase in likelihood of high fire weather in Canada and a 20.0–28.5-fold increase in Amazonia. By the end of the century, events of similar magnitude are projected to occur 2.22–9.58 times more frequently in Canada under high emission scenarios. Without mitigation, regions like Western Amazonia could see up to a 2.9-fold increase in extreme fire events. For the 2024–25 fire season, seasonal forecasts highlight moderate positive anomalies in fire weather for parts of western Canada and South America, but no clear signal for extreme anomalies is present in the forecast. This report represents our first annual effort to catalogue extreme wildfire events, explain their occurrence, and predict future risks. By consolidating state-of-the-art wildfire science and delivering key insights relevant to policymakers, disaster management services, firefighting agencies, and land managers, we aim to enhance society’s resilience to wildfires and promote advances in preparedness, mitigation, and adaptation.

Abstract. This paper describes the rationale and the protocol of the first component of the third simulation round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a, http://www.isimip.org, last access: 2 November 2023) and the associated set of climate-related and direct human forcing data (CRF and DHF, respectively). The observation-based climate-related forcings for the first time include high-resolution observational climate forcings derived by orographic downscaling, monthly to hourly coastal water levels, and wind fields associated with historical tropical cyclones. The DHFs include land use patterns, population densities, information about water and agricultural management, and fishing intensities. The ISIMIP3a impact model simulations driven by these observation-based climate-related and direct human forcings are designed to test to what degree the impact models can explain observed changes in natural and human systems. In a second set of ISIMIP3a experiments the participating impact models are forced by the same DHFs but a counterfactual set of atmospheric forcings and coastal water levels where observed trends have been removed. These experiments are designed to allow for the attribution of observed changes in natural, human, and managed systems to climate change, rising CH4 and CO2 concentrations, and sea level rise according to the definition of the Working Group II contribution to the IPCC AR6.

Climate change contributes to the increased frequency and intensity of wildfires globally, with significant impacts on society and the environment. However, our understanding of the global distribution of extreme fires remains skewed, primarily influenced by media coverage and regionalised research efforts. This inaugural State of Wildfires report systematically analyses fire activity worldwide, identifying extreme events from the March 2023–February 2024 fire season. We assess the causes, predictability, and attribution of these events to climate change and land use and forecast future risks under different climate scenarios. During the 2023–2024 fire season, 3.9×106 km2 burned globally, slightly below the average of previous seasons, but fire carbon (C) emissions were 16 % above average, totalling 2.4 Pg C. Global fire C emissions were increased by record emissions in Canadian boreal forests (over 9 times the average) and reduced by low emissions from African savannahs. Notable events included record-breaking fire extent and emissions in Canada, the largest recorded wildfire in the European Union (Greece), drought-driven fires in western Amazonia and northern parts of South America, and deadly fires in Hawaii (100 deaths) and Chile (131 deaths). Over 232 000 people were evacuated in Canada alone, highlighting the severity of human impact. Our analyses revealed that multiple drivers were needed to cause areas of extreme fire activity. In Canada and Greece, a combination of high fire weather and an abundance of dry fuels increased the probability of fires, whereas burned area anomalies were weaker in regions with lower fuel loads and higher direct suppression, particularly in Canada. Fire weather prediction in Canada showed a mild anomalous signal 1 to 2 months in advance, whereas events in Greece and Amazonia had shorter predictability horizons. Attribution analyses indicated that modelled anomalies in burned area were up to 40 %, 18 %, and 50 % higher due to climate change in Canada, Greece, and western Amazonia during the 2023–2024 fire season, respectively. Meanwhile, the probability of extreme fire seasons of these magnitudes has increased significantly due to anthropogenic climate change, with a 2.9–3.6-fold increase in likelihood of high fire weather in Canada and a 20.0–28.5-fold increase in Amazonia. By the end of the century, events of similar magnitude to 2023 in Canada are projected to occur 6.3–10.8 times more frequently under a medium–high emission scenario (SSP370). This report represents our first annual effort to catalogue extreme wildfire events, explain their occurrence, and predict future risks. By consolidating state-of-the-art wildfire science and delivering key insights relevant to policymakers, disaster management services, firefighting agencies, and land managers, we aim to enhance society's resilience to wildfires and promote advances in preparedness, mitigation, and adaptation. New datasets presented in this work are available from https://doi.org/10.5281/zenodo.11400539 (Jones et al., 2024) and https://doi.org/10.5281/zenodo.11420742 (Kelley et al., 2024a).

Investigations on acute carbon monoxide (CO) poisoning struggle to highlight a relevant discriminant criterion related to CO poisoning severity for predicting complications, such as delayed neurological syndromes. In this context, it remains difficult to demonstrate the superiority of one method of oxygen (O2) administration over others or to identify the optimal duration of normobaric 100% oxygen (NBO) treatment. Myoglobin, as hemoglobin, are a potential binding site for CO, which could be a source of extravascular CO storage that impacts the severity of CO poisoning. It is not possible in routine clinical practice to estimate this potential extravascular CO storage. Indirect means of doing so that are available in the first few hours of poisoning could include, for example, the carboxyhemoglobin half-life (COHbt1/2), which seems to be influenced itself by the level and duration of CO exposure affecting this store of CO within the body. However, before the elimination of CO can be assessed, the COHbt1/2 toxicokinetic model must be confirmed: research still debates whether this model mono- or bi-compartmental. The second indirect mean could be the assessment of a potential COHb rebound after COHb has returned to 5% and NBO treatment has stopped. Moreover, a COHb rebound could be considered to justify the duration of NBO treatment. On an experimental swine model exposed to moderate CO poisoning (940 ppm for ±118 min until COHb reached 30%), we first confirm that the COHb half-life follows a bi-compartmental model. Secondly, we observe for the first time a slight COHb rebound when COHb returns to 5% and oxygen therapy is stopped. On the basis of these two toxicokinetic characteristics in favor of extravascular CO storage, we recommend that COHbt1/2 is considered using the bi-compartmental model in future clinical studies that compare treatment effectiveness as a potential severity criterion to homogenize cohorts of the same severity. Moreover, from a general toxicokinetic point of view, we confirm that a treatment lasting less than 6 hours appears to be insufficient for treating moderate CO poisoning.

Tundra ecosystems in the Arctic store up to 40% of global below-ground organic carbon but are exposed to the fastest climate warming on Earth. However, accurately monitoring landscape changes in the Arctic is challenging due to the complex interactions among permafrost, micro-topography, climate, vegetation, and disturbance. This complexity results in high spatiotemporal variability in permafrost distribution and active layer depth (ALD). Moreover, these key tundra processes interact at different scales, and an observational mismatch can limit our understanding of intrinsic connections and dynamics between above and below-ground processes. Consequently, this could limit our ability to model and anticipate how ALD will respond to climate change and disturbances across tundra ecosystems. In this paper, we studied the fine-scale heterogeneity of ALD and its connections with land surface characteristics across spatial and spectral scales using a combination of ground, unoccupied aerial system, airborne, and satellite observations. We showed that airborne sensors such as AVIRIS-NG and medium-resolution satellite Earth observation systems like Sentinel-2 can capture the average ALD at the landscape scale. We found that the best observational scale for ALD modeling is heavily influenced by the vegetation and landform patterns occurring on the landscape. Landscapes characterized by small-scale permafrost features such as polygon tussock tundra require high-resolution observations to capture the intrinsic connections between permafrost and small-scale land surface and disturbance patterns. Conversely, in landscapes dominated by water tracks and shrubs, permafrost features manifest at a larger scale and our model results indicate the best performance at medium resolution (5 m), outperforming both higher (0.4 m) and lower resolution (10 m) models. This transcends our study to show that permafrost response to climate change may vary across dominant ecosystem types, driven by different above- and below-ground connections and the scales at which these connections are happening. We thus recommend tailoring observational scales based on landforms and characteristics for modeling permafrost distribution, thereby mitigating the influences of spatial-scale mismatches and improving the understanding of vegetation and permafrost changes for the Arctic region.

Fire is an integral component of ecosystems globally and a tool that humans have harnessed for millennia. Altered fire regimes are a fundamental cause and consequence of global change, impacting people and the biophysical systems on which they depend. As part of the newly emerging Anthropocene, marked by human-caused climate change and radical changes to ecosystems, fire danger is increasing, and fires are having increasingly devastating impacts on human health, infrastructure, and ecosystem services. Increasing fire danger is a vexing problem that requires deep transdisciplinary, trans-sector, and inclusive partnerships to address. Here, we outline barriers and opportunities in the next generation of fire science and provide guidance for investment in future research. We synthesize insights needed to better address the long-standing challenges of innovation across disciplines to (i) promote coordinated research efforts; (ii) embrace different ways of knowing and knowledge generation; (iii) promote exploration of fundamental science; (iv) capitalize on the "firehose" of data for societal benefit; and (v) integrate human and natural systems into models across multiple scales. Fire science is thus at a critical transitional moment. We need to shift from observation and modeled representations of varying components of climate, people, vegetation, and fire to more integrative and predictive approaches that support pathways toward mitigating and adapting to our increasingly flammable world, including the utilization of fire for human safety and benefit. Only through overcoming institutional silos and accessing knowledge across diverse communities can we effectively undertake research that improves outcomes in our more fiery future.

California has experienced a rapid increase in burned area over the past several decades. Although fire behavior is known to be closely tied to ecosystem impacts, most analysis of changing fire regimes has focused solely on area burned. Here we present a standardized database of wildfire behavior, including daily fire rate-of-spread and fire radiative power for large, multiday wildfires in California during 2012-2018 using remotely-sensed active fire observations. We observe that human-ignited fires start at locations with lower tree cover and during periods with more extreme fire weather. These characteristics contribute to more explosive growth in the first few days following ignition for human-caused fires as compared to lightning-caused fires. The faster fire spread, in turn, yields a larger ecosystem impact, with tree mortality more than three times higher for fast-moving fires (>1 km day-1) than for slow moving fires (

This paper describes the rationale and the protocol of the first component of the third simulation round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a, http://www.isimip.org, last access: 2 November 2023) and the associated set of climate-related and direct human forcing data (CRF and DHF, respectively). The observation-based climate-related forcings for the first time include high-resolution observational climate forcings derived by orographic downscaling, monthly to hourly coastal water levels, and wind fields associated with historical tropical cyclones. The DHFs include land use patterns, population densities, information about water and agricultural management, and fishing intensities. The ISIMIP3a impact model simulations driven by these observation-based climate-related and direct human forcings are designed to test to what degree the impact models can explain observed changes in natural and human systems. In a second set of ISIMIP3a experiments the participating impact models are forced by the same DHFs but a counterfactual set of atmospheric forcings and coastal water levels where observed trends have been removed. These experiments are designed to allow for the attribution of observed changes in natural, human, and managed systems to climate change, rising CH4 and CO2 concentrations, and sea level rise according to the definition of the Working Group II contribution to the IPCC AR6.

Abstract Time‐lapse cameras have been widely used as a tool to monitor the timing of seasonal vegetation growth. These simple, relatively inexpensive systems can provide high‐frequency observations of leaf development and demography which are critical data sets needed to characterize plant phenology from species to landscapes. This is important for understanding how plants are responding to global changes, as well as for validating satellite‐derived phenology products. However, in remote regions including the high‐latitude Arctic, deploying time‐lapse cameras could be challenging. The remoteness and lack of widespread power and telecommunications infrastructure limit options for the installation, maintenance and retrieval of data and equipment, and make it difficult for cameras to survive in extreme weather (e.g. long cold winters). To improve our understanding of Arctic phenology, new technologies are required to address these challenges. Here, we present a novel, low‐power, compact, lightweight time‐lapse camera system, called power‐interval camera automation module (PiCAM). The PiCAM was designed with explicit consideration to simplify deployment (i.e. without a need for external power supplies) of camera systems and to address the challenges of camera survival in harsh Arctic environments. In this paper, we describe the design, setup and technical details of the PiCAM and provide a roadmap for how to build and operate these systems. As proof of concept, we deployed 26 PiCAMs at three low‐Arctic tundra sites on the Seward Peninsula, Alaska in early August 2021 for characterizing Arctic plant phenology. Of the 26 PiCAMs, 70% remained active at the point of our revisit in late July 2022 despite the extreme winter temperatures they experienced (< −30°C, heavy snow cover). We extracted key plant phenology metrics from the PiCAMs and captured strong differences across key Arctic plant species. We showed that the PiCAM has the potential to be widely used for monitoring plant phenology across the broader Arctic region, addressing the need for ground‐based understanding of Arctic phenological diversity to develop knowledge of plant response to climate change and to validate remote sensing products.

Long-term records of burned area are needed to understand wildfire dynamics, assess fire impacts on ecosystems and air quality, and improve fire forecasts. Here, we fuse multiple streams of remote sensing data to create a 24 year (1997–2020) dataset of monthly burned area as a component of the fifth version of the Global Fire Emissions Database (GFED5). During 2001–2020, we use the Moderate Resolution Imaging Spectroradiometer (MODIS) MCD64A1 burned area product and adjust for the errors of commission and omission. Adjustment factors are estimated based on region, land cover, and tree cover fraction, using spatiotemporally aligned burned area from Landsat or Sentinel-2. Burned area in croplands, peatlands, and deforestation regions is estimated from MODIS active fire detections. Along-Track Scanning Radiometer (ATSR) and Visible and Infrared Scanner (VIRS) active fire data are used to extend the time series back to 1997. The global annual burned area during 2001–2020 is estimated to be 774 ± 63 Mha yr−1 or 5.9 ± 0.5 % of ice-free land. Burned area declined by 1.21 ± 0.66 % yr−1, a cumulative decrease of 24.2 ± 13.2 % over 20 years. The global reduction is primarily driven by a decrease in fires in savannas, grasslands, and croplands. Forest, peat, and deforestation fires did not exhibit significant long-term trends. The GFED5 global burned area is 93 % higher than MCD64A1, 61 % higher than GFED4s, and in closer agreement with products from higher-resolution satellite sensors. These data may reduce discrepancies between fire emission estimates from activity-based and atmospheric-based approaches, and improve our understanding of global fire impacts on the carbon cycle and climate system. The GFED5 global burned area product is freely accessible at https://doi.org/10.5281/zenodo.7668423 (Chen et al., 2023).

Changing wildfire regimes in the western US and other fire-prone regions pose considerable risks to human health and ecosystem function. However, our understanding of wildfire behavior is still limited by a lack of data products that systematically quantify fire spread, behavior and impacts. Here we develop a novel object-based system for tracking the progression of individual fires using 375 m Visible Infrared Imaging Radiometer Suite active fire detections. At each half-daily time step, fire pixels are clustered according to their spatial proximity, and are either appended to an existing active fire object or are assigned to a new object. This automatic system allows us to update the attributes of each fire event, delineate the fire perimeter, and identify the active fire front shortly after satellite data acquisition. Using this system, we mapped the history of California fires during 2012-2020. Our approach and data stream may be useful for calibration and evaluation of fire spread models, estimation of near-real-time wildfire emissions, and as means for prescribing initial conditions in fire forecast models.

Burned area has increased across California, especially in the Sierra Nevada range. Recent fires there have had devasting social, economic, and ecosystem impacts. To understand the consequences of new extremes in fire weather, here we quantify the sensitivity of wildfire occurrence and burned area in the Sierra Nevada to daily meteorological variables during 2001–2020. We find that the likelihood of fire occurrence increases nonlinearly with daily temperature during summer, with a 1°C increase yielding a 19 to 22% increase in risk. Area burned has a similar, nonlinear sensitivity, with 1°C of warming yielding a 22 to 25% increase in risk. Solely considering changes in summer daily temperatures from climate model projections, we estimate that by the 2040s, fire number will increase by 51 ± 32%, and burned area will increase by 59 ± 33%. These trends highlight the threat posed to fire management by hotter and drier summers.

Fire is the dominant disturbance agent in Alaskan and Canadian boreal ecosystems and releases large amounts of carbon into the atmosphere. Burned area and carbon emissions have been increasing with climate change, which have the potential to alter the carbon balance and shift the region from a historic sink to a source. It is therefore critically important to track the spatiotemporal changes in burned area and fire carbon emissions over time. Here we developed a new burned-area detection algorithm between 2001–2019 across Alaska and Canada at 500 m (meters) resolution that utilizes finer-scale 30 m Landsat imagery to account for land cover unsuitable for burning. This method strictly balances omission and commission errors at 500 m to derive accurate landscape- and regional-scale burned-area estimates. Using this new burned-area product, we developed statistical models to predict burn depth and carbon combustion for the same period within the NASA Arctic–Boreal Vulnerability Experiment (ABoVE) core and extended domain. Statistical models were constrained using a database of field observations across the domain and were related to a variety of response variables including remotely sensed indicators of fire severity, fire weather indices, local climate, soils, and topographic indicators. The burn depth and aboveground combustion models performed best, with poorer performance for belowground combustion. We estimate 2.37×106 ha (2.37 Mha) burned annually between 2001–2019 over the ABoVE domain (2.87 Mha across all of Alaska and Canada), emitting 79.3 ± 27.96 Tg (±1 standard deviation) of carbon (C) per year, with a mean combustion rate of 3.13 ± 1.17 kg C m−2. Mean combustion and burn depth displayed a general gradient of higher severity in the northwestern portion of the domain to lower severity in the south and east. We also found larger-fire years and later-season burning were generally associated with greater mean combustion. Our estimates are generally consistent with previous efforts to quantify burned area, fire carbon emissions, and their drivers in regions within boreal North America; however, we generally estimate higher burned area and carbon emissions due to our use of Landsat imagery, greater availability of field observations, and improvements in modeling. The burned area and combustion datasets described here (the ABoVE Fire Emissions Database, or ABoVE-FED) can be used for local- to continental-scale applications of boreal fire science.

The remote monitoring of vital signs via wearable devices holds significant potential for alleviating the strain on hospital resources and elder-care facilities. Among the various techniques available, photoplethysmography stands out as particularly promising for assessing vital signs such as heart rate, respiratory rate, oxygen saturation, and blood pressure. Despite the efficacy of this method, many commercially available wearables, bearing Conformité Européenne marks and the approval of the Food and Drug Administration, are often integrated within proprietary, closed data ecosystems and are very expensive. In an effort to democratize access to affordable wearable devices, our research endeavored to develop an open-source photoplethysmographic sensor utilizing off-the-shelf hardware and open-source software components. The primary aim of this investigation was to ascertain whether the combination of off-the-shelf hardware components and open-source software yielded vital-sign measurements (specifically heart rate and respiratory rate) comparable to those obtained from more expensive, commercially endorsed medical devices. Conducted as a prospective, single-center study, the research involved the assessment of fifteen participants for three minutes in four distinct positions, supine, seated, standing, and walking in place. The sensor consisted of four PulseSensors measuring photoplethysmographic signals with green light in reflection mode. Subsequent signal processing utilized various open-source Python packages. The heart rate assessment involved the comparison of three distinct methodologies, while the respiratory rate analysis entailed the evaluation of fifteen different algorithmic combinations. For one-minute average heart rates’ determination, the Neurokit process pipeline achieved the best results in a seated position with a Spearman’s coefficient of 0.9 and a mean difference of 0.59 BPM. For the respiratory rate, the combined utilization of Neurokit and Charlton algorithms yielded the most favorable outcomes with a Spearman’s coefficient of 0.82 and a mean difference of 1.90 BrPM. This research found that off-the-shelf components are able to produce comparable results for heart and respiratory rates to those of commercial and approved medical wearables.

Abstract Fire is a crucial factor in terrestrial ecosystems playing a role in disturbance for vegetation dynamics. Process‐based fire models quantify fire disturbance effects in stand‐alone dynamic global vegetation models (DGVMs) and their advances have incorporated both descriptions of natural processes and anthropogenic drivers. Nevertheless, these models show limited skill in modeling fire events at the global scale, due to stochastic characteristics of fire occurrence and behavior as well as the limits in empirical parameterizations in process‐based models. As an alternative, machine learning has shown the capability of providing robust diagnostics of fire regimes. Here, we develop a deep‐learning‐based fire model (DL‐fire) to estimate daily burnt area fraction at the global scale and couple it within JSBACH4, the land surface model used in the ICON‐ESM. The stand‐alone DL‐fire model forced with meteorological, terrestrial and socio‐economic variables is able to simulate global total burnt area, showing 0.8 of monthly correlation (rm) with GFED4 during the evaluation period (2011–2015). The performance remains similar with the hybrid modeling approach JSB4‐DL‐fire (rm = 0.79) outperforming the currently used uncalibrated standard fire model in JSBACH4 (rm = −0.07). We further quantify the importance of each predictor by applying layer‐wise relevance propagation (LRP). Overall, land properties, such as fuel amount and water content in soil layers, stand out as the major factors determining burnt fraction in DL‐fire, paralleled by meteorological conditions over tropical and high latitude regions. Our study demonstrates the potential of hybrid modeling in advancing fire prediction in ESMs by integrating deep learning approaches in physics‐based dynamical models.

A 56-year-old woman was transferred to the intensive care unit (ICU) two days after an allogeneic stem cell transplantation (ASCT) when she presented acute respiratory distress due to the relapse of a SARS-CoV-2 infection. Following that, she received two intravenous doses of 100 mg remdesivir. Subsequently, the patient developed multiple instances of diarrhea, progressing to oliguria and acute kidney injury, necessitating continuous venovenous hemofiltration (CVVH). Despite the absence of signs of hypoxemia or cardiocirculatory failure requiring vasopressor intervention, a progressive lactic acidosis emerged. Two days after the onset of lactic acidosis, a significant rise in aminotransferases and lactate dehydrogenase occurred, in the absence of encephalopathy and coagulation disorders. Remdesivir therapy had been interrupted upon the initial signs of lactic acidosis. Despite an improvement in liver function tests and lactic acidosis, the patient’s condition deteriorated, ultimately leading to her demise on day 29 due to newly arising hematological complications.

A 68-year-old man with a medical history of hypertension was admitted to the emergency department for diffuse abdominal pain preceded by bloody diarrhea. Upon admission, neurological examination was normal, but he suddenly developed a left-sided hemiparesis. After a normal brain computed tomography, intravenous thrombolysis was administered for a suspicion of ischemic stroke. In the first laboratory investigations, hemoglobin was 16.9 g/dL, platelets 121 × 109/L (150–450), and serum creatinine 1.17 mg/dL. By the second hospital day, the platelet level dropped to 79 × 109/L, with haptoglobin at 0.12 g/L, 3% schistocytes, and normal ADAMTS13 activity (57%). Serum creatinine increased to 1.84 mg/dL with oliguria. The suspicion of thrombotic microangiopathy was supported by the identification of Shiga toxin genes stx1 and stx2 on a rectal swab and the isolation of an eaeA-negative Shiga toxin-producing E. coli O113:H4. The patient presented a generalized tonic-clonic seizure, and orotracheal intubation was required for decreased consciousness. Plasma exchange therapy was started, and eculizumab was given 6 days after symptoms onset. Brain magnetic resonance imaging (MRI) on day 13 showed symmetric hyperintensities within basal ganglia that disappeared on a second MRI on day 37. At 2-month follow-up, the patient had made a complete neurological and renal recovery and eculizumab therapy was stopped.

Abstract Background We report a case of acute respiratory distress associated with a histological pattern of acute fibrinous and organizing pneumonia, and discuss the possible responsibility of flecainide therapy. Case presentation A 61-year-old African woman developed a rapidly progressive dyspnea and required admission in the intensive care unit for orotracheal intubation and mechanical ventilation. Chest X-ray examination revealed bilateral infiltrates predominating in the basal part of both lungs. Lung computed tomography disclosed bilateral ground-glass opacities and septal thickening. After exclusion of the most common causes of infectious or immune pneumonia, a toxic origin was investigated and flecainide toxicity was considered. Lung biopsy was consistent with the unusual pattern of acute fibrinous and organizing pneumonia. Clinical and radiological improvement was noted after corticosteroid therapy, but the patient died from septic complications. Conclusion Flecainide-induced lung injury has rarely been reported in the literature and remains a diagnosis of exclusion. The histological pattern of acute fibrinous and organizing pneumonia has been previously observed with amiodarone. There are no firm guidelines for the treatment of acute fibrinous and organizing pneumonia, but some patients may positively respond to corticosteroids.

Natural laterite fixed-bed columns intercalated with two types of layers (inert materials, such as fine sand and gravel, and adsorbent materials, such as activated carbon prepared from Balanites aegyptiaca (BA-AC)) were used for As(III) removal from an aqueous solution. Investigations were carried out to solve the problem of column clogging, which appears during the percolation of water through a natural laterite fixed-bed column. Experimental tests were conducted to evaluate the hydraulic conductivities of several fixed-bed column configurations and the effects of various parameters, such as the grain size, bed height, and initial As(III) concentration. The permeability data show that, among the different types of fixed-bed columns investigated, the one filled with repeating layers of laterite and activated carbon is more suitable for As(III) adsorption, in terms of performance and cost, than the others (i.e., non-intercalated laterite; non-intercalated activated carbon, repeating layers of laterite and fine sand; and repeating layers of laterite and gravel). A study was carried out to determine the most efficient column using breakthrough curves. The breakthrough increased from 15 to 85 h with an increase in the bed height from 20 to 40 cm and decreased from 247 to 32 h with an increase in the initial As(III) concentration from 0.5 to 2 mg/L. The Bohart–Adams model results show that increasing the bed height induced a decrease in the kAB and N0 values. The critical bed depths determined using the bed depth service time (BDST) model for As(III) removal were 15.23 and 7.98 cm for 1 and 20% breakthroughs, respectively. The results show that the new low-cost adsorptive porous system based on laterite layers with alternating BA-AC layers can be used for the treatment of arsenic-contaminated water.

Measurement(s) Wildfire half-daily perimeters and attributes Technology Type(s) Remote sensing Sample Characteristic - Organism Wildfires Sample Characteristic - Environment Ecosystems Sample Characteristic - Location California

‘Human-caused fires and natural fires could have different impacts. Here the authors report a geospatial analysis of lightning-ignited and human-ignited fires in California between 2012 and 2018, finding that the latter were more likely to develop under extreme conditions with larger ecosystem impacts.’

The Arctic is warming at over twice the rate of the rest of the Earth, resulting in significant changes in vegetation seasonality that regulates annual carbon, water, and energy fluxes. However, a crucial knowledge gap exists regarding the intricate interplay among climate, permafrost, and vegetation that generates high phenology variability across extensive tundra landscapes. This oversight has led to significant discrepancies in phenological patterns observed across warming experiments, long-term ecological observations, and satellite and modeling studies, undermining our ability to understand and forecast plant responses to climate change in the Arctic. To address this problem, we assessed plant phenology across three low-Arctic tundra landscapes on the Seward Peninsula, Alaska, using a combination of in-situ phenocam observations and high-resolution PlanetScope CubeSat data. We examined the patterns and drivers of phenological diversity across the landscape by (1) quantifying phenological diversity among dominant plant function types (PFTs) and (2) modeling the interrelations between plant phenology and fine-scale landscape features, such as topography, snowmelt, and vegetation. Our findings reveal that both spring and fall phenology varied significantly across Arctic PFTs, accounting for about 25%–44% and 34%–59% of the landscape-scale variation in the start of spring [SOS] and start of fall [SOF], respectively. Deciduous tall shrubs (e.g. alder and willow) had a later SOS (∼7 d behind the mean of other PFTs), but completed leaf expansion (within 2 weeks) considerably faster compared to other PFTs. We modeled the landscape-scale variation in SOS and SOF using Random Forest, which showed that plant phenology can be accurately captured by a suite of variables related to vegetation composition, topographic characteristics, and snowmelt timing (variance explained: 53%–68% for SOS and 59%–82% for SOF). Notably, snowmelt timing was a crucial determinant of SOS, a factor often neglected in most spring phenology models. Our study highlights the impact of fine-scale vegetation composition, snow seasonality, and landscape features on tundra phenological heterogeneity. Improved understanding of such considerable intra-site phenological variability and associated proximate controls across extensive Arctic landscapes offers critical insights for representation of tundra phenology in process models and associated impact assessments with climate change.

Abstract Background We report a case of platypnea–orthodeoxia syndrome observed in a complex clinical situation associating a bilateral pleural effusion, lobar pulmonary embolism, and a partial anomalous pulmonary venous return. Case presentation A 57-year-old Caucasian woman developed acute dyspnea in the postoperative course of an elective gynecological surgery for advanced stage ovarian cancer. Preoperative evaluation had failed to reveal any respiratory or cardiac problem. After evidence of a low arterial oxygen saturation, blood gas analysis from the central venous line correctly inserted in the right internal jugular vein revealed a higher oxygen saturation than in the arterial compartment. A thoracic computed tomography showed bilateral pleural effusion, lobar pulmonary embolism, and a drainage of a left pulmonary vein into the left innominate vein. This unique combination resulted in an uncommon cause of platypnea–orthodeoxia syndrome. Conclusion Often associated with right-to-left shunting, platypnea–orthodeoxia syndrome may be observed in complex clinical conditions with several factors influencing the ventilation/perfusion ratio. The paradoxical finding of a higher oxygen saturation in a central venous line than in an arterial line should prompt the clinician to look at the possibility of partial anomalous pulmonary venous return. No specific treatment is required in asymptomatic adults, except for an echocardiographic follow-up to detect the onset of pulmonary hypertension.

AbstractA 26-year-old man presented to the Emergency Department with lower urinary tract symptoms. He had a history of chronic ketamine abuse by sniffing over the last 6 years, with a recent increase in ketamine consumption. Acute kidney injury was related to bilateral hydronephrosis with dilatation of both ureters and irregularly thickened bladder walls. Laboratory investigations also revealed a marked hyponatremia and a major increase in liver enzymes consistent with a cholestatic injury. Finally, a pneumomediastinum was also diagnosed on the thoracic computed tomography. All these manifestations regressed after cessation of ketamine exposure. Chronic ketamine abuse may be associated with multiorgan toxicity. In particular, ketamine cystitis may be followed by obstructive complications leading to acute renal failure.

Copper deficiency is an acquired condition that can lead to neurologic dysfunctions, such as myelopathy, motor neuron impairment, polyneuropathy, cognitive impairment, and optic nerve neuropathy. Associated biological findings are low serum copper and ceruloplasmin levels with low copper urinary excretion. We report the case of a previously healthy 59-year-old man who presented a complex neurological picture starting with symptoms and radiological signs consistent with degenerative myelopathy in the presence of persisting low serum copper and ceruloplasmin despite oral and intravenous copper supplementation. Over time, his symptoms evolved into a motor neuron disease evocating an amyotrophic lateral sclerosis (ALS) phenotype. The potential role of copper deficiency is discussed, together with the difficulties in biomonitoring copper supplementation.

A 43-year-old woman with a medical history of splenectomy for immune thrombocytopenic purpura was diagnosed with Streptococcus pneumoniae bacteremia. Her initial complaints were fever and more importantly painful extremities that appeared cyanotic. During her hospitalisation, she never developed cardiocirculatory failure but presented acute kidney injury (AKI) with oliguria. Laboratory investigations confirmed AKI with serum creatinine 2.55 mg/dL which peaked at 6.49 mg/dL. There was also evidence for disseminated intravascular coagulation (DIC) with decreased platelet count, low fibrinogen levels, and high D-dimer levels. There were no signs of haemolytic anaemia. The initial ADAMTS13 activity was low (17%) but slowly recovered. Renal function progressively improved with supportive therapy, as opposed to the progressing skin necrosis. The association of DIC and low ADAMTS13 activity may have contributed to the severity of microthrombotic complications, even in the absence of thrombotic microangiopathy as thrombotic thrombocytopenic purpura (TTP) or pneumococcal-associated haemolytic uremic syndrome (pa-HUS).

Fire is an essential Earth system process that alters ecosystem and atmospheric composition. Here we assessed long-term fire trends using multiple satellite data sets. We found that global burned area declined by 24.3 ± 8.8% over the past 18 years. The estimated decrease in burned area remained robust after adjusting for precipitation variability and was largest in savannas. Agricultural expansion and intensification were primary drivers of declining fire activity. Fewer and smaller fires reduced aerosol concentrations, modified vegetation structure, and increased the magnitude of the terrestrial carbon sink. Fire models were unable to reproduce the pattern and magnitude of observed declines, suggesting that they may overestimate fire emissions in future projections. Using economic and demographic variables, we developed a conceptual model for predicting fire in human-dominated landscapes.