Retinitis pigmentosa: evaluation of the vestibular system with cervical and ocular vestibular evoked myogenic potentials and the video head impulse test

OBJECTIVE: Retinitis pigmentosa (RP) represents a group of inherited disorders in which abnormalities of the photoreceptors lead to progressive visual loss. Night blindness, peripheral visual field loss, and eventual total blindness represent typical visual damage of such disease. No study has previously evaluated the presence of a "latent" vestibular deficit in patients with RP. STUDY DESIGN: Prospective study with caloric test, cervical vestibular evoked myogenic potentials (C-VEMPs), ocular vestibular evoked myogenic potentials (O-VEMPs), and video head impulse test (v-HIT). SETTING: Tertiary referral center. PATIENTS: 16 patients suffering from RP. INTERVENTION: Evaluation of vestibular dysfunction with caloric test, C-VEMPs, O-VEMPs, and the measurement of the vestibular-ocular reflex (VOR) using the v-HIT. RESULTS: Only five patients with RP showed normal values in all the vestibular tests performed. Three patients had an evident deficit at the caloric test, whereas eight (50%) of them had a normal caloric test but a pathological response in at least one of the other vestibular tests performed. No patient of the study showed a bilateral otolith or ampullary dysfunction. CONCLUSION: Our patients with RP unexpectedly showed pathological responses in at least one of the vestibular tests performed. Nowadays, in patients affected by RP, a vestibular diagnostic protocol must include VEMPs and v-HIT to confirm the vestibular damage and to identify selective damage of the vestibular nerve

Greenhouse gas emissions from urban area of Naples

Urban areas are among the main causes of greenhouse gases emissions on the planet, despite covering relatively small areas of the land. Recently, a number of projects aim at monitoring the dynamics of city emissions using micro meteorological measurements by applying the technique of eddy correlation for measuring the fluxes of carbon dioxide, water, methane and energy. In this perspective, a super-site for the measurement of atmospheric pollutants from urban sources has been established in Naples (Campania, Southern Italy), where the complex layout of the coast and surrounding mountains favours the development of combined sea breeze upslope winds and the evolution of return flows with several layers of pollutants and subsidence. At the super-site, an eddy covariance tower has been installed on the rooftop of the Meteorological Observatory of Largo San Marcellino, situated in the historical city centre: a fast response ultrasonic anemometer (Gill WindMaster) has been mounted on a 10-m mast, alongside three insulated inlet lines through which the air is sampled for gaseous pollutants and particulate matter. The height of the terrace is on average 35 m above the irregular street level, resulting in an overall measuring height of 45 m. Mixing ratios of CO2, CH4 and H2O are measured by an infrared spectrometer (10 Hz, Los Gatos Research). The results shown that the mean urban levels of CO2 are between 420-520 ppm; the mean levels of CH4 span between 1.85-2.48 ppm. These fluxes are representative of varying footprint source areas, covering the historical centre of Naples, the harbour, and some main traffic arteries of the city. The analysis of these measurements on long-term will allow to establish relationships between the fluxes of greenhouse gases and the other pollutant species measured

Relative importance of climatic variables, soil properties and plant traits to spatial variability in net CO2 exchange across global forests and grasslands

Compared to the well-known drivers of spatial variability in gross primary productivity (GPP), the relative importance of climatic variables, soil properties and plant traits to the spatial variability in net ecosystem exchange of CO2 between terrestrial ecosystem and atmosphere (NEE) is poorly understood. We used principal component regression to analyze data from 147 eddy flux sites to disentangle effects of climatic variables, soil properties and plant traits on the spatial variation in annual NEE and its components (GPP and ecosystem respiration (RE)) across global forests and grasslands. Our results showed that the largest unique contribution (proportion of variance only explained by one class of variables) to NEE variance came from climatic variables for forests (24%-30%) and soil properties for grasslands (41%-54%). Specifically, mean annual precipitation and potential evapotranspiration were the most important climatic variables driving forest NEE, whereas available soil water capacity, clay content and cation exchange capacity mainly influenced grassland NEE. Plant traits showed a small unique contribution to NEE in both forests and grasslands. However, leaf phosphorus content strongly interacted with soil total nitrogen density and clay content, and these combined factors represented a major contribution for grassland NEE. For GPP and RE, the majority of spatial variance was attributed to the common contribution of climate, soil and plant traits (50% - 62%, proportion of variance explained by more than one class of variables), rather than their unique contributions. Interestingly, those factors with only minor influences on GPP and RE variability (e.g., soil properties) have significant contributions to the spatial variability in NEE. Such emerging factors and the interactions between climatic variables, soil properties and plant traits are not well represented in current terrestrial biosphere models, which should be considered in future model improvement to accurately predict the spatial pattern of carbon cycling across forests and grasslands globally.Peer reviewe

Global maps of soil temperature

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world\u27s major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (−0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications

Global maps of soil temperature

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km² resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e., offset) between in-situ soil temperature measurements, based on time series from over 1200 1-km² pixels (summarized from 8500 unique temperature sensors) across all the world’s major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in-situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications

Global maps of soil temperature.

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0-5 and 5-15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications

Remote sensing based mapping of leaf nitrogen and leaf area index in European landscapes using the REGularized canopy reflectance (REGFLEC) model

Leaf biochemistry and biophysical parameters are important for simulating soil-vegetation-atmosphere exchanges of energy, water, CO2 and nitrogen. The accumulation of leaf nitrogen (N) in vegetation canopies is a major component of the ecosystem N balance, and leaf N concentration and leaf area index (LAI) are also important determinants of the maximum photosynthetic capacity and CO2 uptake by plants and trees. Even though agroand ecosystem-models can simulate leaf N uptake and LAI of vegetation canopies, regional modeling requires detailed spatial information about soil properties, N fertilization and atmospheric N deposition rates which are not easily accessible. In this study, high spatial resolution remote sensing data from the SPOT satellites were acquired within the NitroEurope project to prepare maps of leaf N and LAI for 5 European landscapes. Mapping was conducted using the REGFLEC model which is an automatic and image-based methodology recently developed for regional chlorophyll (Cab) and LAI estimation (ie. Houborg and Anderson, JARS 3, 2009). REGFLEC combines models for atmospheric correction (6S), canopy reflectance (ACRM) and leaf optics (PROSPECT). The only input information required are sensor characteristics, atmospheric properties (ie. derived from AIRS and MODIS satellite sensors) and maps of soil types and spectral vegetation classes within the study area. REGFLEC solves for the soil background reflectance and builds land cover specific look-up tables which are facilitating fast computation of Cab and LAI from spectral band reflectances or vegetation indices. Model performance previously proved very promising in Denmark (ie.Houborg and Boegh, Remote Sens. Env., 112, 2008) and in Maryland, USA (ie. Houborg et al., Remote Sens. Env., 113, 2009). In this study, REGFLEC performance is evaluated and discussed using field measurements of leaf N, SPADmeter data (SPAD 502 DL) and LAI (LAI-2000) in European landscapes located in Denmark, Poland, Scotland, the Netherlands and Italy. The inverse model estimations of soil reflectance parameters and canopy parameters are discussed in relation to the prevailing soil types and vegetation characteristics of land cover classes across the 5 European landscape

Evaluation of SEBS, METRIC-EEFlux, and QWaterModel Actual Evapotranspiration for a Mediterranean Cropping System in Southern Italy

Remote sensing-based evapotranspiration (ET) models with various levels of sophistication have emerged recently with the possibilities of user-defined model calibrations. Their application for water resources management and climate studies from regional to global scale has been rapidly increasing, which makes it important to validate field scale ET in a complex crop assemblage before operational use. Based on in situ flux-tower measurements by the eddy-covariance (EC) system, this study tested three single-source energy balance models for estimating daily ET from fennel/maize/ryegrass-clover cropland rotations in a Mediterranean context in southern Italy. The sensitivity of three user-friendly ET models (SEBS, QWaterModel, and METRIC-EEFlux) with reference to the EC system over a center pivot irrigated cropland is discussed in detail. Results in terms of statistical indicators revealed that SEBS and METRIC-EEFlux showed reasonable agreements with measured ET (r2 = 0.59SEBS, RMSE = 0.71 mm day−1; r2 = 0.65METRIC, RMSE = 1.13 mm day−1) in terms of trends and magnitudes. At 30 m spatial resolution, both models were able to capture the in-field variations only during the maize development stage. The presence of spurious scan lines due to sensor defects in Landsat L7 ETM+ can contribute to the qualities of the METRIC-Efflux’s ET product. In our observation, the QWaterModel did not perform well and showed the weakest congruency (r2 = 0.08QWaterModel) with ground-based ET estimates. In a nutshell, the study evaluated these automated remote sensing-based ET estimations and suggested improvements in the context of a generic approach used in their underlying algorithm for robust ET retrievals in rotational cropland ecosystems

Evaluation of SEBS, METRIC-EEFlux, and QWaterModel Actual Evapotranspiration for a Mediterranean Cropping System in Southern Italy

Remote sensing-based evapotranspiration (ET) models with various levels of sophistication have emerged recently with the possibilities of user-defined model calibrations. Their application for water resources management and climate studies from regional to global scale has been rapidly increasing, which makes it important to validate field scale ET in a complex crop assemblage before operational use. Based on in situ flux-tower measurements by the eddy-covariance (EC) system, this study tested three single-source energy balance models for estimating daily ET from fennel/maize/ryegrass-clover cropland rotations in a Mediterranean context in southern Italy. The sensitivity of three user-friendly ET models (SEBS, QWaterModel, and METRIC-EEFlux) with reference to the EC system over a center pivot irrigated cropland is discussed in detail. Results in terms of statistical indicators revealed that SEBS and METRIC-EEFlux showed reasonable agreements with measured ET (r2 = 0.59SEBS, RMSE = 0.71 mm day−1; r2 = 0.65METRIC, RMSE = 1.13 mm day−1) in terms of trends and magnitudes. At 30 m spatial resolution, both models were able to capture the in-field variations only during the maize development stage. The presence of spurious scan lines due to sensor defects in Landsat L7 ETM+ can contribute to the qualities of the METRIC-Efflux’s ET product. In our observation, the QWaterModel did not perform well and showed the weakest congruency (r2 = 0.08QWaterModel) with ground-based ET estimates. In a nutshell, the study evaluated these automated remote sensing-based ET estimations and suggested improvements in the context of a generic approach used in their underlying algorithm for robust ET retrievals in rotational cropland ecosystems