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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.

A prospective observational study (ClinicalTrial ID: NCT05771415) was conducted to compare placental oxygenation in low-risk, uncomplicated term pregnancies measured by near-infrared spectroscopy (NIRS) in relation to the placental maturity grade determined by ultrasound assessment according to the Grannum scale. We included 34 pregnancies divided into two groups according to placental maturation. For each pregnancy, measurements were taken at the site above the central part of the placenta (test) and at the site outside of the placenta on the lower abdomen (control). Student’s t-test was used to compare tissue oxygenation index (TOI) values among the study groups. The normality of distribution was proven by the Kolmogorov‒Smirnov test. In women with low placental maturity grade, the mean TOI value above the placenta was 70.38 ± 3.72, which was lower than the respective value in women with high placental maturity grade (77.99 ± 3.71; p < 0.001). The TOI values above the placenta and the control site were significantly different in both groups (70.38 ± 3.72 vs 67.83 ± 3.21 and 77.99 ± 3.71 vs 69.41 ± 3.93; p < 0.001). The results offer a new perspective on placental function based on specific non-invasive real-time oxygenation measurements. Unfortunately, and because of technical limitations, NIRS cannot yet be implemented as a routine clinical tool.

Abstract Improving the calculation of land‐atmosphere fluxes of heat and water vapor in mountain terrain requires better resolution of thermally driven diurnal winds (i.e., valley, slope winds) due to differential heating by terrain and radiative fluxes. In this study, the Weather Research and Forecasting model is used to simulate flow in large‐eddy simulation (LES) mode over the complex terrain of the Fortress Mountain and Marmot Creek research basins, Kananaskis Valley, Canadian Rockies, Alberta in mid‐summer. The model was used to examine the temporal and spatial evolution of local winds and near‐surface boundary layer processes with variability in topography and elevation. Numerically resolving complex terrain wind flow effects require smaller grid cell size. However, the use of terrain‐following coordinates in most numerical weather prediction models results in large numerical errors when flow over steep terrain is simulated. These errors propagate through the domain and can result in numerical instability. To avoid this issue when simulating flow over steep terrain a local smoothing approach was used, where smoothing is applied only where slope exceeds some predetermined threshold. LES results from local smoothing were compared with a mesoscale model and LES with global smoothing. Simulations are evaluated using sounding data and meteorological stations. The differences in flow patterns and reversals in two mountain basins suggest that valley geometry and volume is relevant to the break up of inversion layers, removal of cold‐air pools, and strength of thermally driven winds.

Wind energy is anticipated to play a central role in enabling a rapid transition from fossil fuels to a system based largely on renewable power. For wind power to fulfill its expected role as the backbone – providing nearly half of the electrical energy – of a renewable-based, carbon-neutral energy system, critical challenges around design, manufacture, and deployment of land and offshore technologies must be addressed. During the past 3 years, the wind research community has invested significant effort toward understanding the nature and implications of these challenges and identifying associated gaps. The outcomes of these efforts are summarized in a series of 10 articles, some under review by Wind Energy Science (WES) and others planned for submission during the coming months. This letter explains the genesis, significance, and impacts of these efforts.

Abstract This study evaluates the efficacy of current satellite observing systems to detect methane point sources from typical oil and gas production (O&G) facilities using a novel very high-resolution methane concentration dataset generated using a microscale model. Transport and dispersion of typical methane emissions from seven well pads were simulated and the column enhancements for pseudo satellite pixel sizes of 3, 1, and 0.05 km were examined every second of the 2-h simulations (7200 realizations). The detectability of plumes increased with a pixel resolution, but two orders of magnitude change in emission rates at the surface results only in about 0.4%, 1.6%, and 47.8% enhancement in the pseudo-satellite retrieved methane column at 3, 1, and 0.05 km, respectively. Average methane emission rates estimated by employing the integrated mass enhancement (IME) method to column enhancements at 0.05 km showed an underestimation of the mean emissions by 0.2–6.4%. We show that IME derived satellite-based inversions of methane emissions work well for large persistent emission sources (e.g., super emitters), however, the method is ill-suited to resolve short-term emission fluctuations (

Abstract Smoke from wildfires or burning biomass directly affects air quality and weather through modulating cloud microphysics and radiation. A simple wildfire emission coupling of black carbon (BC) and organic carbon (OC) with microphysics was implemented using the Weather Research and Forecasting model's fire module. A set of large‐eddy simulations inspired by unique surface and upper atmospheric observations from the 2021 Santa Coloma de Queralt Fire (Spain) were conducted to investigate the influence of background conditions and interactions between atmospheric and fire processes such as fire smoke, ambient moisture, and latent heat release on the formation and evolution of pyroconvective clouds. While the microphysical impact of BC and OC emissions on the dynamics of fire behavior is minimal on short time scales (

In this study, we focus on the effects of fuel bed representation and fire heat and smoke distribution in a coupled fire-atmosphere simulation platform for two landscape-scale fires: the 2018 Camp Fire and the 2021 Caldor Fire. The fuel bed representation in the coupled fire-atmosphere simulation platform WRF-Fire currently includes only surface fuels. Thus, we enhance the model by adding canopy fuel characteristics and heat release, for which a method to calculate the heat generated from canopy fuel consumption is developed and implemented in WRF-Fire. Furthermore, the current WRF-Fire heat and smoke distribution in the atmosphere is replaced with a heat-conserving Truncated Gaussian (TG) function and its effects are evaluated. The simulated fire perimeters of case studies are validated against semi-continuous, high-resolution fire perimeters derived from NEXRAD radar observations. Furthermore, simulated plumes of the two fire cases are compared to NEXRAD radar reflectivity observations, followed by buoyancy analysis using simulated temperature and vertical velocity fields. The results show that while the improved fuel bed and the TG heat release scheme have small effects on the simulated fire perimeters of the wind-driven Camp Fire, they affect the propagation direction of the plume-driven Caldor Fire, leading to better-matching fire perimeters with the observations. However, the improved fuel bed representation, together with the TG heat smoke release scheme, leads to a more realistic plume structure in comparison to the observations in both fires. The buoyancy analysis also depicts more realistic fire-induced temperature anomalies and atmospheric circulation when the fuel bed is improved.

Accurate estimation of fuels is essential for wildland fire simulations as well as decision-making related to land management. Numerous research efforts have leveraged remote sensing and machine learning for classifying land cover and mapping forest vegetation species. In most cases that focused on surface fuel mapping, the spatial scale of interest was smaller than a few hundred square kilometers; thus, many small-scale site-specific models had to be created to cover the landscape at the national scale. The present work aims to develop a large-scale surface fuel identification model using a custom deep learning framework that can ingest multimodal data. Specifically, we use deep learning to extract information from multispectral signatures, high-resolution imagery, and biophysical climate and terrain data in a way that facilitates their end-to-end training on labeled data. A multi-layer neural network is used with spectral and biophysical data, and a convolutional neural network backbone is used to extract the visual features from high-resolution imagery. A Monte Carlo dropout mechanism was also devised to create a stochastic ensemble of models that can capture classification uncertainties while boosting the prediction performance. To train the system as a proof-of-concept, fuel pseudo-labels were created by a random geospatial sampling of existing fuel maps across California. Application results on independent test sets showed promising fuel identification performance with an overall accuracy ranging from 55% to 75%, depending on the level of granularity of the included fuel types. As expected, including the rare—and possibly less consequential—fuel types reduced the accuracy. On the other hand, the addition of high-resolution imagery improved classification performance at all levels.

The GEWEX Atmospheric Boundary Layer Studies (GABLS) 1, 2 and 3 are used to develop a methodology for the design and testing of Reynolds-averaged Navier–Stokes (RANS) atmospheric boundary layer (ABL) models for wind energy applications. The first two GABLS cases are based on idealized boundary conditions and are suitable for verification purposes by comparing with results from higher-fidelity models based on large-eddy simulation. Results from three single-column RANS models, of 1st, 1.5th and 2nd turbulence closure order, show high consistency in predicting the mean flow. The third GABLS case is suitable for the study of these ABL models under realistic forcing such that validation versus observations from the Cabauw meteorological tower are possible. The case consists on a diurnal cycle that leads to a nocturnal low-level jet and addresses fundamental questions related to the definition of the large-scale forcing, the interaction of the ABL with the surface and the evaluation of model results with observations. The simulations are evaluated in terms of surface-layer fluxes and wind energy quantities of interest: rotor equivalent wind speed, hub-height wind direction, wind speed shear and wind direction veer. The characterization of mesoscale forcing is based on spatially and temporally averaged momentum budget terms from Weather Research and Forecasting (WRF) simulations. These mesoscale tendencies are used to drive single-column models, which were verified previously in the first two GABLS cases, to first demonstrate that they can produce similar wind profile characteristics to the WRF simulations even though the physics are more simplified. The added value of incorporating different forcing mechanisms into microscale models is quantified by systematically removing forcing terms in the momentum and heat equations. This mesoscale-to-microscale modeling approach is affected, to a large extent, by the input uncertainties of the mesoscale tendencies. Deviations from the profile observations are reduced by introducing observational nudging based on measurements that are typically available from wind energy campaigns. This allows the discussion of the added value of using remote sensing instruments versus tower measurements in the assessment of wind profiles for tall wind turbines reaching heights of 200 m.

Objective: To investigate proliferation, EGF and EGFR expression of villous trophoblast (VTB), decidual cells (DC), and extravillous trophoblast (EVTB) in the placentas from pregnancies complicated with preeclampsia (PE) and to compare them with placentas from normal pregnancies. Methods: Twenty-nine PE placentas and 19 control placentas were studied for EGF and EGFR immunohistochemical expression (noted as week, moderate or strong). Proliferation was expressed as the proliferation index. The CK7 antibody was used to distinguish DC from EVTB. Results: DC and EVTB proliferation was significantly higher in PE placentas. EGFR and EGF expression showed no significant difference. Conclusion: Higher DC and EVTB proliferation in PE could contribute to PE development.

Objectives: Osteoprotegerin (OPG) has an important role in bone remodeling, and it has been proposed that the OPG gene might be a candidate gene for osteoporosis predisposition. Several studies have already assessed the connection between OPG gene polymorphism and bone mineral density (BMD). In this study we wanted to analyze the association of two polymorphisms in the OPG gene with BMD and bone turnover markers in women with and without osteoporosis. Material and methods: In 22 postmenopausal women with osteoporosis (aged 65.6 ±12.6) and 59 women without osteoporosis (aged 60.8 ±8.7) we analyzed the association of two polymorphisms in the OPG gene with BMD, measured by dual energy absorptiometry and with bone turnover markers (crosslaps and osteoprotegerin). A163G, G209A, T245G and G1181C polymorphisms were determined. Results: No significant differences in age, anthropometry, number of fractures, osteocalcin and cross-laps were found between women with and without osteoporosis. Women with osteoporosis were significantly longer in postmenopause. Significantly more women with osteoporosis had AG polymorphism (p = 0.038) compared to women without osteoporosis, while no significant difference was found in prevalence of TT and GG polymorphism between patients with and without osteoporosis. No relationship was found between investigated polymorphism and bone turnover markers. A significant negative correlation between total hip BMD and crosslaps (p = 0.046) as well as between total hip T score and crosslaps (p = 0.044) was found in women without osteoporosis Conclusions : Postmenopausal women with osteoporosis had AG polymorphism more frequently than women without osteoporosis. Our results indicate that A163G polymorphism could have an impact on higher bone loss in postmenopausal women.

The National Center for Atmospheric Research (NCAR) recently updated the comprehensive wind power forecasting system in collaboration with Xcel Energy addressing users’ needs and requirements by enhancing and expanding integration between numerical weather prediction and machine-learning methods. While the original system was designed with the primary focus on day-ahead power prediction in support of power trading, the enhanced system provides short-term forecasting for unit commitment and economic dispatch, uncertainty quantification in wind speed prediction with probabilistic forecasting, and prediction of extreme events such as icing. Furthermore, the empirical power conversion machine-learning algorithms now use a quantile approach to data quality control that has improved the accuracy of the methods. Forecast uncertainty is quantified using an analog ensemble approach. Two methods of providing short-range ramp forecasts are blended: the variational doppler radar analysis system and an observation-based expert system. Extreme events, specifically changes in wind power due to high winds and icing, are now forecasted by combining numerical weather prediction and a fuzzy logic artificial intelligence system. These systems and their recent advances are described and assessed.

Etiology of preeclampsia (PE) remains unknown despite it being the leading cause of maternal and neonatal morbidity and mortality worldwide. PE placentas show signs of chronic oxidative stress, therefore we set to investigate the HIF-1α immunohistochemical expression in PE placentas. For the first time presented study reported on significantly higher HIF-1α immunohistochemical expression in all of the placental segments; villous trophoblast (VTB), decidual cells (DC) and extravillous trophoblast (EVTB) in the PE placentas compared to placentas from normal pregnancies. The further studies on the subject are necessary in order to understand the mechanisms behind elevated HIF-1α immunohistochemical levels in PE. [ABSTRACT FROM AUTHOR]

In this paper we examine the influence of the environment on the quality of olive oil. Land from olive groves in five locations in three towns along the coastline of Montenegro and the olive oil produced from these olive trees were investigated. Soil testing included the following parameters: pH, the contents of selected metals, polycyclic aromatic hydrocarbons (PAH), PCB, phenolic herbicides, triazines, carbamates, dithiocarbamates and chlorophenoxy compounds. Analysis of the soil in one location (Bar) showed the increased presence of arsenic (As) chromium, (Cr) and nickel (Ni) with regard to the maximum allowable concentration. Similar results were obtained at the second location (also in Bar) where an increased presence of chromium (Cr), nickel (Ni) and fluorine (F) was found. The tested oil samples obtained from these locations do not satisfy the quality standard prescribed norms. To be specific, the oil testing included the following parameters: the content of free fatty acids calculated as oleic acid; water and other volatile matter content; peroxide, saponification and the iodine number; reaction to rancidity; the refractive index (nD 40 °C), as well as the contents of metals and pesticides. These results demonstrate the need for the modernization of traditional olive production in Montenegro, which requires intensive national and EU funding and expertize. [ABSTRACT FROM AUTHOR]

Cu is an essential microelement for plants, but it can be toxic at high concentrations. The aquatic macrophytes that have been used as bioindicator species in this study are Phragmites australis, Ceratophyllum demersum and Lemna minor. The plants, water and sediments were tested for Cu content in six locations around Skadar Lake, Montenegro across four different periods of 2011. The content of Cu in the examined sediment was in the range of 23.9-54.4 mg kg-1. The largest proportion of Cu (64%) was associated with its oxidizable phase in the form of organic complexes. The concentration of Cu in the studied macrophytes declined in the following order: L. minor > P. australis > C. demersum. The highest content of Cu was detected in the root of L. minor (31.0 mg kg-1 dry weight) in October. During the research period, the concentration of Cu was constantly increasing in the leaves of P. australis and in the root and leaves L. minor, while other parts of the macrophytes recorded different trends. The highest correlation of Cu content with sediment was noted for P. australis. [ABSTRACT FROM AUTHOR]

The article provides information on the intrusions of machines and monsters into the human world which have become narratives of cultural and academic texts. It mentions that urgency to creatively respond to quickly altering conditions experienced by contemporary subjects which becomes greater. It states that actors are examining in contact with the real, lose their much/ness in unveiling their energies/forces, while accepting all consequences in experimental theatre.

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Objectives: Osteoprotegerin (OPG) has an important role in bone remodeling, and it has been proposed that the OPG gene might be a candidate gene for osteoporosis predisposition. Several studies have already assessed the connection between OPG gene polymorphism and bone mineral density (BMD). In this study we wanted to analyze the association of two polymorphisms in the OPG gene with BMD and bone turnover markers in women with and without osteoporosis., Material and Methods: In 22 postmenopausal women with osteoporosis (aged 65.6 ±12.6) and 59 women without osteoporosis (aged 60.8 ±8.7) we analyzed the association of two polymorphisms in the OPG gene with BMD, measured by dual energy absorptiometry and with bone turnover markers (crosslaps and osteoprotegerin). A163G, G209A, T245G and G1181C polymorphisms were determined., Results: No significant differences in age, anthropometry, number of fractures, osteocalcin and cross-laps were found between women with and without osteoporosis. Women with osteoporosis were significantly longer in postmenopause. Significantly more women with osteoporosis had AG polymorphism (p = 0.038) compared to women without osteoporosis, while no significant difference was found in prevalence of TT and GG polymorphism between patients with and without osteoporosis. No relationship was found between investigated polymorphism and bone turnover markers. A significant negative correlation between total hip BMD and crosslaps (p = 0.046) as well as between total hip T score and crosslaps (p = 0.044) was found in women without osteoporosis., Conclusions: Postmenopausal women with osteoporosis had AG polymorphism more frequently than women without osteoporosis. Our results indicate that A163G polymorphism could have an impact on higher bone loss in postmenopausal women.