Photosynthesis is the process by which plants harvest sunlight to produce sugars from carbon dioxide and water. It is the primary source of energy for all life on Earth; hence it is important to ...understand how this process responds to climate change and human impact. However, model-based estimates of gross primary production (GPP, output from photosynthesis) are highly uncertain, in particular over heavily managed agricultural areas. Recent advances in spectroscopy enable the space-based monitoring of sun-induced chlorophyll fluorescence (SIF) from terrestrial plants. Here we demonstrate that spaceborne SIF retrievals provide a direct measure of the GPP of cropland and grassland ecosystems. Such a strong link with crop photosynthesis is not evident for traditional remotely sensed vegetation indices, nor for more complex carbon cycle models. We use SIF observations to provide a global perspective on agricultural productivity. Our SIF-based crop GPP estimates are 50-75% higher than results from state-of-the-art carbon cycle models over, for example, the US Corn Belt and the Indo-Gangetic Plain, implying that current models severely underestimate the role of management. Our results indicate that SIF data can help us improve our global models for more accurate projections of agricultural productivity and climate impact on crop yields. Extension of our approach to other ecosystems, along with increased observational capabilities for SIF in the near future, holds the prospect of reducing uncertainties in the modeling of the current and future carbon cycle.
Thermal inertia is a physical property of soil at the land surface related to water content. We developed a method for estimating soil thermal inertia using two daily measurements of surface ...temperature, to capture the diurnal range, and diurnal time series of net radiation and specific humidity. The method solves for soil thermal inertia assuming homogeneous 1-D diffusion of heat near the land surface. The solution uses a boundary condition taken as the maximum likelihood estimate of ground heat flux made by a probabilistic uncertainty model of the partitioning of net radiation based on the theory of maximum entropy production (MEP model). We showed that by coupling the 1-D diffusion and MEP models of energy transfer at the land surface, the number of free parameters in the MEP model can be reduced from two (P — soil thermal inertia and I — thermal inertia of convective heat transfer to the atmosphere) to one (P is defined by I). A sensitivity analysis suggested that, for the purpose of estimating thermal inertia, the coupled model should be parameterized by the ratio P/I. The coupled model was demonstrated at two semi-arid sites in the southwest United States to estimate thermal inertia and these thermal inertia values were used to estimate soil moisture. We found 1) parameterizing the MEP model with a constant annual P/I value resulted in surface flux estimates which were similar to those made when daily P and I parameters were derived directly from measurements of ground heat flux (Nash-Sutcliffe efficiency > 0.95); 2) estimates of P made using the coupled model were superior to those made using the diffusion model with a common linear approximation of the ground heat flux boundary condition; and 3) thermal inertia was a better predictor of soil moisture in moderately wet conditions than in dry conditions due to a lack of sensitivity of thermal inertia to changes in soil moisture at low moisture contents.
In the southwest United States, the current prolonged warm drought is similar to the predicted future climate change scenarios for the region. This study aimed to determine patterns in vegetation ...response to the early 21st century drought across multiple biomes. We hypothesized that different biomes (forests, shrublands, and grasslands) would have different relative sensitivities to both climate drivers (precipitation and temperature) and legacy effects (previous-year's productivity). We tested this hypothesis at eight Ameriflux sites in various Southwest biomes using NASA Moderate-resolution Imaging Spectroradiometer Enhanced Vegetation Index (EVI) from 2001 to 2013. All sites experienced prolonged dry conditions during the study period. The impact of combined precipitation and temperature on Southwest ecosystems at both annual and sub-annual timescales was tested using Standardized Precipitation Evapotranspiration Index (SPEI). All biomes studied had critical sub-annual climate periods during which precipitation and temperature influenced production. In forests, annual peak greenness (EVImax) was best predicted by 9-month SPEI calculated in July (i.e., January-July). In shrublands and grasslands, EVImax was best predicted by SPEI in July through September, with little effect of the previous year's EVImax. Daily gross ecosystem production (GEP) derived from flux tower data yielded further insights into the complex interplay between precipitation and temperature. In forests, GEP was driven by cool-season precipitation and constrained by warm-season maximum temperature. GEP in both shrublands and grasslands was driven by summer precipitation and constrained by high daily summer maximum temperatures. In grasslands, there was a negative relationship between temperature and GEP in July, but no relationship in August and September. Consideration of sub-annual climate conditions and the inclusion of the effect of temperature on the water balance allowed us to generalize the functional responses of vegetation to predicted future climate conditions. We conclude that across biomes, drought conditions during critical sub-annual climate periods could have a strong negative impact on vegetation production in the southwestern United States.
Photosynthesis is the process by which plants harvest sunlight to produce sugars from carbon dioxide and water. It is the primary source of energy for all life on Earth; hence it is important to ...understand how this process responds to climate change and human impact. However, model-based estimates of gross primary production (GPP, output from photosynthesis) are highly uncertain, in particular over heavily managed agricultural areas. Recent advances in spectroscopy enable the space-based monitoring of sun-induced chlorophyll fluorescence (SIF) from terrestrial plants. Here we demonstrate that spaceborne SIF retrievals provide a direct measure of the GPP of cropland and grassland ecosystems. Such a strong link with crop photosynthesis is not evident for traditional remotely sensed vegetation indices, nor for more complex carbon cycle models. We use SIF observations to provide a global perspective on agricultural productivity. Our SIF-based crop GPP estimates are 50-75% higher than results from state-of-the-art carbon cycle models over, for example, the US Corn Belt and the Indo-Gangetic Plain, implying that current models severely underestimate the role of management. Our results indicate that SIF data can help us improve our global models for more accurate projections of agricultural productivity and climate impact on crop yields. Extension of our approach to other ecosystems, along with increased observational capabilities for SIF in the near future, holds the prospect of reducing uncertainties in the modeling of the current and future carbon cycle.
Photosynthesis is the process by which plants harvest sunlight to produce sugars from carbon dioxide and water. It is the primary source of energy for all life on Earth; hence it is important to ...understand how this process responds to climate change and human impact. However, model-based estimates of gross primary production (GPP, output from photosynthesis) are highly uncertain, in particular over heavily managed agricultural areas. Recent advances in spectroscopy enable the space-based monitoring of sun-induced chlorophyll fluorescence (SIF) from terrestrial plants. Here we demonstrate that spaceborne SIF retrievals provide a direct measure of the GPP of cropland and grassland ecosystems. Such a strong link with crop photosynthesis is not evident for traditional remotely sensed vegetation indices, nor for more complex carbon cycle models. We use SIF observations to provide a global perspective on agricultural productivity. Our SIF-based crop GPP estimates are 50-75% higher than results from state-of-the-art carbon cycle models over, for example, the US Corn Belt and the Indo-Gangetic Plain, implying that current models severely underestimate the role of management. Our results indicate that SIF data can help us improve our global models for more accurate projections of agricultural productivity and climate impact on crop yields. Extension of our approach to other ecosystems, along with increased observational capabilities for SIF in the near future, holds the prospect of reducing uncertainties in the modeling of the current and future carbon cycle.
Artificial hydrocarbon networks (AHN) – a supervised learning method inspired on organic chemical structures and mechanisms – have shown improvements in predictive power and interpretability in ...comparison with other well-known machine learning models. However, AHN are very time-consuming that are not able to deal with large data until now. In this paper, we introduce the stochastic parallel extreme artificial hydrocarbon networks (SPE-AHN), an algorithm for fast and robust training of supervised AHN models in high-dimensional data. This training method comprises a population-based meta-heuristic optimization with defined individual encoding and objective function related to the AHN-model, an implementation in parallel-computing, and a stochastic learning approach for consuming large data. We conducted three experiments with synthetic and real data sets to validate the training execution time and performance of the proposed algorithm. Experimental results demonstrated that the proposed SPE-AHN outperforms the original-AHN method, increasing the speed of training more than 10,000x times in the worst case scenario. Additionally, we present two case studies in real data sets for solar-panel deployment prediction (regression problem), and human falls and daily activities classification in healthcare monitoring systems (classification problem). These case studies showed that SPE-AHN improves the state-of-the-art machine learning models in both engineering problems. We anticipate our new training algorithm to be useful in many applications of AHN like robotics, finance, medical engineering, aerospace, and others, in which large amounts of data (e.g. big data) is essential.
Deep brain stimulation has been used in the treatment of refractory obsessive-compulsive disorder (OCD). Our principal objective was to determine the safety and effectiveness of deep brain ...stimulation of the inferior thalamic peduncle in the treatment of refractory OCD.
An open protocol was performed from March 2003 to April 2007 in 5 patients with OCD refractory to conventional treatments. Bilateral stereotactic implantation of tetrapolar electrodes was aimed at the inferior thalamic peduncle and corroborated by electrophysiological responses and magnetic resonance imaging. All patients were off stimulation for 1 month after implantation. In the on-stimulation period, parameters were set at 5 V, 450 microseconds, 130 Hz in bipolar and continuous mode. Clinical changes were evaluated every 3 months for 12 months by means of the Yale-Brown Obsessive Compulsive Scale and the Global Assessment of Functioning scale. Statistical significance was assessed by the Friedman and Wilcoxon tests.
The mean Yale-Brown Obsessive Compulsive Scale score decreased from 35 to 17.8 (P < 0.001), and the mean Global Assessment of Functioning scale score improved from 20% to 70% (P < 0.0001). The neuropsychological battery did not show significant changes, and there were no side effects related to electrical stimulation in the chronic period.
We conclude that inferior thalamic peduncle stimulation is a safe procedure and may be an effective alternative in the treatment of those OCD cases refractory to conventional treatments.
Functional neuroimaging studies carried out on healthy volunteers while performing different
n-back tasks have shown a common pattern of bilateral frontoparietal activation, especially of the ...dorsolateral prefrontal cortex (DLPFC). Our objective was to use functional magnetic resonance imaging (fMRI) to compare the pattern of brain activation while performing two similar
n-back tasks which differed in their presentation modality. Thirteen healthy volunteers completed a verbal 2-back task presenting auditory stimuli, and a similar 2-back task presenting visual stimuli. A conjunction analysis showed bilateral activation of frontoparietal areas including the DLPFC. The left DLPFC and the superior temporal gyrus showed a greater activation in the auditory than in the visual condition, whereas posterior brain regions and the anterior cingulate showed a greater activation during the visual than during the auditory task. Thus, brain areas involved in the visual and auditory versions of the
n-back task showed an important overlap between them, reflecting the supramodal characteristics of working memory. However, the differences found between the two modalities should be considered in order to select the most appropriate task for future clinical studies.
