Search

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.

In order to consider forest biomass as a real alternative for energy production, it is critical to obtain accurate estimates of its availability using non-destructive sampling methods. In this study, we estimate the biomass available in a Scots pine-dominated forest (Pinus sylvestris L.) located in Spain. The biomass estimates were obtained using LiDAR data combined with a multispectral camera and allometric equations. The method used to fuse the data was based on back projection, which assures a perfect match between both datasets. The results present estimates for each of the seven different biomass components: above ground, below ground, log, needles, and large, medium and small branches. The accuracy of the models varied between R2 values of 0.46 and 0.67 with RMSE% ranging from 15.72% to 35.43% with all component estimates below 20%, except for the model estimating biomass of big branches. The models in this study are suitable for the estimation of biomass and demonstrate that computation is possible at a fine scale for the different biomass components. These remote sensing methods are sufficiently accurate to develop biomass resource cartography for multiple energy uses.

The accurate prediction of forest above-ground biomass is nowadays key to implementing climate change mitigation policies, such as reducing emissions from deforestation and forest degradation. In this context, the coefficient of determination ($${R^2}$$) is widely used as a means of evaluating the proportion of variance in the dependent variable explained by a model. However, the validity of $${R^2}$$ for comparing observed versus predicted values has been challenged in the presence of bias, for instance in remote sensing predictions of forest biomass. We tested suitable alternatives, e.g. the index of agreement ($$d$$) and the maximal information coefficient ($$MIC$$). Our results show that $$d$$ renders systematically higher values than $${R^2}$$, and may easily lead to regarding as reliable models which included an unrealistic amount of predictors. Results seemed better for $$MIC$$, although $$MIC$$ favoured local clustering of predictions, whether or not they corresponded to the observations. Moreover, $${R^2}$$ was more sensitive to the use of cross-validation than $$d$$ or $$MIC$$, and more robust against overfitted models. Therefore, we discourage the use of statistical measures alternative to $${R^2}$$ for evaluating model predictions versus observed values, at least in the context of assessing the reliability of modelled biomass predictions using remote sensing. For those who consider $$d$$ to be conceptually superior to $${R^2}$$, we suggest using its square $${d^2}$$, in order to be more analogous to $${R^2}$$ and hence facilitate comparison across studies.

In the context of predicting forest attributes using a combination of airborne LIDAR and multispectral (MS) sensors, we suggest the inclusion of normalized difference vegetation index (NDVI) metrics along with the more traditional LIDAR height metrics. Here the data fusion method consists of back-projecting LIDAR returns onto original MS images, avoiding co-registration errors. The prediction method is based on non-parametric imputation (the most similar neighbor). Predictor selection and accuracy assessment include hypothesis tests and over-fitting prevention methods. Results show improvements when using combinations of LIDAR and MS compared to using either of them alone. The MS sensor has little explanatory capacity for forest variables dependent on tree height, already well determined from LIDAR alone. However, there is potential for variables dependent on tree diameters and their density. The combination of LIDAR and MS sensors can be very beneficial for predicting variables describing forests structural heterogeneity, which are best described from synergies between LIDAR heights and NDVI dispersion. Results demonstrate the potential of NDVI metrics to increase prediction accuracy of forest attributes. Their inclusion in the predictor dataset may, however, in a few cases be detrimental to accuracy, and therefore we recommend to carefully assess the possible advantages of data fusion on a case-by-case basis.

Numerous studies suggest the involvement of adenosine-5′-triphosphate (ATP) and similar nucleotides in the pathophysiology of asthma. Androgens, such as testosterone (TES), are proposed to alleviate asthma symptoms in young men. ATP and uridine-5′-triphosphate (UTP) relax the airway smooth muscle (ASM) via purinergic P2Y2 and P2Y4 receptors and K+ channel opening. We previously demonstrated that TES increased the expression of voltage-dependent K+ (KV) channels in ASM. This study investigates how TES may potentiate ASM relaxation induced by ATP and UTP. Tracheal tissues treated with or without TES (control group) from young male guinea pigs were used. In organ baths, tracheas exposed to TES (40 nM for 48 h) showed enhanced ATP- and UTP-evoked relaxation. Tetraethylammonium, a K+ channel blocker, annulled this effect. Patch-clamp experiments in tracheal myocytes showed that TES also increased ATP- and UTP-induced K+ currents, and this effect was abolished with flutamide (an androgen receptor antagonist). KV channels were involved in this phenomenon, which was demonstrated by inhibition with 4-aminopyridine. RB2 (an antagonist of almost all P2Y receptors except for P2Y2), as well as N-ethylmaleimide and SQ 22,536 (inhibitors of G proteins and adenylyl cyclase, respectively), attenuated the enhancement of the K+ currents induced by TES. Immunofluorescence and immunohistochemistry studies revealed that TES did not modify the expression of P2Y4 receptors or COX-1 and COX-2, while we have demonstrated that this androgen augmented the expression of KV1.2 and KV1.5 channels in ASM. Thus, TES leads to the upregulation of P2Y4 signaling and KV channels in guinea pig ASM, enhancing ATP and UTP relaxation responses, which likely limits the severity of bronchospasm in young males. [ABSTRACT FROM AUTHOR]

Airway smooth muscle (ASM) contraction is determined by the increase in intracellular Ca2+ concentration ([Ca2+]i) caused by its release from the sarcoplasmic reticulum (SR) or by extracellular Ca2+ influx. Major channels involved in Ca2+ influx in ASM cells are L-type voltage-dependent Ca2+ channels (L-VDCCs) and nonselective cation channels (NSCCs). Transient receptor potential vanilloid 4 (TRPV4) is an NSCC recently studied in ASM. Mechanical stimuli, such as contraction, can activate TRPV4. We investigated the possible activation of TRPV4 by histamine (His)- or carbachol (CCh)-induced contraction in guinea pig ASM. In single myocytes, the TRPV4 agonist (GSK101) evoked an increase in [Ca2+]i, characterized by a slow onset and a plateau phase. The TRPV4 antagonist (GSK219) decreased channel activity by 94%, whereas the Ca2+-free medium abolished the Ca2+ response induced by GSK101. Moreover, GSK101 caused Na+ influx in tracheal myocytes. GSK219 reduced the Ca2+ peak and the Ca2+ plateau triggered by His or CCh. TRPV4 blockade shifted the concentration–response curve relating to His and CCh to the right in tracheal rings and reduced the maximal contraction. Finally, the activation of TRPV4 in single myocytes increased the Ca2+ refilling of the SR. We conclude that contraction of ASM cells after stimulation with His or CCh promotes TRPV4 activation, the subsequent influx of Ca2+ and Na+, and the opening of L-VDCCs. The entry of Ca2+ into ASM cells via TRPV4 and L-VDCCs contributes to optimal smooth muscle contraction. [ABSTRACT FROM AUTHOR]

Background. Multiple acyl-CoA dehydrogenase deficiency is an autosomal recessive disorder of the amino acid metabolism and fatty acid oxidation due to the deficiency of the electron transfer protein or electron transfer protein ubiquinone oxidoreductase. The clinical picture ranges from a severe neonatal lethal presentation to late myopathic forms responsive to riboflavin. Up to now, there is no effective treatment for the neonatal form, which exhibits severe metabolic acidosis, hyperammonemia, hypoketotic hypoglycemia, and rhabdomyolysis. We present the case of a child who has had a good long-term outcome after a typical neonatal onset, with a dramatic drop in ammonia levels during the initial metabolic decompensation crisis and adequate control even during intercurrent diseases thereafter with N-carbamylglutamate treatment.

In a climate change scenario, Mediterranean forest species such as pines may be endangered by rising temperatures and reduced precipitation, thus calling for studies on the transpiration and water balance in pines. In this paper, the response of young plants of Pinus sylvestris L., Pinus pinea L. and Pinus halepensis Mill. to different irrigation treatments has been studied. Significant differences were found in water potential, sap flow, leaf-level gas exchange and spectral variables. P. sylvestris had higher pre-dawn and midday water potentials, sap flow rates and leaf-level gas exchange rates compared to the other two species in well-watered conditions. Vapor pressure gradient correlated with stomatal conductance, net assimilation and transpiration, but the association between stomatal conductance and sap flow was weak. The environmental variables more strongly associated with sap flow were solar radiation and reference evapo-transpiration, especially in the well-watered plants, but those associations were weaker in the stressed plants. All three pine species showed the isohydric, drought-avoiding strategy common in the genus Pinus, maintaining relatively high water potentials in dry conditions. Nevertheless, P. halepensis showed a water-saving strategy, with a stomatal closure behavior under drought. Stomatal regulation was less strict in P. sylvestris, closer to a water-spending pattern, while P. pinea showed an intermediate behavior. Significant differences were recorded among species in spectral reflectance in the visible and infra-red regions. Photochemical Reflectance Index, Normalized Difference Vegetation Index and combinations of other ratios permitted the discrimination among the three pine species. These spectral variables showed association with sap flow rate, water potential and leaf-level gas exchange variables. Both cluster analysis and k-means classification discriminated Scots pine and Aleppo pine in two different groups. On the other hand, Stone pine showed differences in spectral behavior depending on the hydric status of the plants. Well-watered Stone pine plants had the same spectral behavior as Scots pine, while the plants subjected to drought stress were closer to Aleppo pine plants in spectral response. These findings may help to quantify the impacts of early and mid-summer water deficit on Mediterranean pines in future climate regimes.

Copyright of Revista Española de Quimioterapia is the property of Sociedad Espanola de Quimioterapia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)