Gaussian processes retrieval of crop traits in Google Earth Engine based on Sentinel-2 top-of-atmosphere data.

The unprecedented availability of optical satellite data in cloud-based computing platforms, such as Google Earth Engine (GEE), opens new possibilities to develop crop trait retrieval models from the local to the planetary scale. Hybrid retrieval models are of interest to run in these platforms as they combine the advantages of physically- based radiative transfer models (RTM) with the flexibility of machine learning regression algorithms. Previous research with GEE primarily relied on processing bottom-of-atmosphere (BOA) reflectance data, which requires atmospheric correction. In the present study, we implemented hybrid models directly into GEE for processing Sentinel-2 (S2) Level-1C (L1C) top-of-atmosphere (TOA) reflectance data into crop traits. To achieve this, a training dataset was generated using the leaf-canopy RTM PROSAIL in combination with the atmospheric model 6SV. Gaussian process regression (GPR) retrieval models were then established for eight essential crop traits namely leaf chlorophyll content, leaf water content, leaf dry matter content, fractional vegetation cover, leaf area index (LAI), and upscaled leaf variables (i.e., canopy chlorophyll content, canopy water content and canopy dry matter content). An important pre-requisite for implementation into GEE is that the models are sufficiently light in order to facilitate efficient and fast processing. Successful reduction of the training dataset by 78% was achieved using the active learning technique Euclidean distance-based diversity (EBD). With the EBD-GPR models, highly accurate validation results of LAI and upscaled leaf variables were obtained against in situ field data from the validation study site Munich-North-Isar (MNI), with normalized root mean square errors (NRMSE) from 6% to 13%. Using an independent validation dataset of similar crop types (Italian Grosseto test site), the retrieval models showed moderate to good performances for canopy-level variables, with NRMSE ranging from 14% to 50%, but failed for the leaf-level estimates. Obtained maps over the MNI site were further compared against Sentinel-2 Level 2 Prototype Processor (SL2P) vegetation estimates generated from the ESA Sentinels' Application Platform (SNAP) Biophysical Processor, proving high consistency of both retrievals (R 2 from 0.80 to 0.94). Finally, thanks to the seamless GEE processing capability, the TOA-based mapping was applied over the entirety of Germany at 20 m spatial resolution including information about prediction uncertainty. The obtained maps provided confidence of the developed EBD-GPR retrieval models for integration in the GEE framework and national scale mapping from S2-L1C imagery. In summary, the proposed retrieval workflow demonstrates the possibility of routine processing of S2 TOA data into crop traits maps at any place on Earth as required for operational agricultural applications.

Quantifying Fundamental Vegetation Traits over Europe Using the Sentinel-3 OLCI Catalogue in Google Earth Engine.

Thanks to the emergence of cloud-computing platforms and the ability of machine learning methods to solve prediction problems efficiently, this work presents a workflow to automate spatiotemporal mapping of essential vegetation traits from Sentinel-3 (S3) imagery. The traits included leaf chlorophyll content (LCC), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fractional vegetation cover (FVC), being fundamental for assessing photosynthetic activity on Earth. The workflow involved Gaussian process regression (GPR) algorithms trained on top-of-atmosphere (TOA) radiance simulations generated by the coupled canopy radiative transfer model (RTM) SCOPE and the atmospheric RTM 6SV. The retrieval models, named to S3-TOA-GPR-1.0, were directly implemented in Google Earth Engine (GEE) to enable the quantification of the traits from TOA data as acquired from the S3 Ocean and Land Colour Instrument (OLCI) sensor.Following good to high theoretical validation results with normalized root mean square error (NRMSE) ranging from 5% (FAPAR) to 19% (LAI), a three fold evaluation approach over diverse sites and land cover types was pursued: (1) temporal comparison against LAI and FAPAR products obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) for the time window 2016-2020, (2) spatial difference mapping with Copernicus Global Land Service (CGLS) estimates, and (3) direct validation using interpolated in situ data from the VALERI network. For all three approaches, promising results were achieved. Selected sites demonstrated coherent seasonal patterns compared to LAI and FAPAR MODIS products, with differences between spatially averaged temporal patterns of only 6.59%. In respect of the spatial mapping comparison, estimates provided by the S3-TOA-GPR-1.0 models indicated highest consistency with FVC and FAPAR CGLS products. Moreover, the direct validation of our S3-TOA-GPR-1.0 models against VALERI estimates indicated with regard to jurisdictional claims in good retrieval performance for LAI, FAPAR and FVC. We conclude that our retrieval workflow of spatiotemporal S3 TOA data processing into GEE opens the path towards global monitoring of fundamental vegetation traits, accessible to the whole research community.

Pathological axis fracture secondary to a solitary bone plasmacytoma: Two cases and a literature review.

BACKGROUND: Solitay bone plasmocytoma (SBP) account for just 5-10% of all plasma cell neoplasms. They are infrequent in the cervical spine, especially involving the C0-C2 segment. In this article we conducted a literature review and present the diagnosis, management and long term course of two patients with SBP of C2 causing cervical instability. METHODS: We assessed the clinical records of two patients with SBP in C2 and cervical instability attributed to SP-B involving C2. Both patients presented with progressive, severe cervicalgia, and the "sensation" of skull instability. Magnetic resonance imaging revealed an extensive, infiltrative lesion involving C2 vertebral body and lateral masses, consistent with a plasmacytoma. RESULTS: Both patients underwent emergency posterior surgical stabilization with craniocervical fixation; this was accompanied by a C2 transpedicular biopsy. Postoperatively, patients exhibited no focal neurological deficits and rapidly became pain free. They additional recieved 25 sessions of local conventional radiation therapy. Both patients are doing well as respective 2 and 7-year follow-up. CONCLUSION: Although rare, unstable SBP may present atypical cervical location that readily responds to surgical descompression/fusion and radiotherapy.

Monitoring Cropland Phenology on Google Earth Engine Using Gaussian Process Regression.

Monitoring cropland phenology from optical satellite data remains a challenging task due to the influence of clouds and atmospheric artifacts. Therefore, measures need to be taken to overcome these challenges and gain better knowledge of crop dynamics. The arrival of cloud computing platforms such as Google Earth Engine (GEE) has enabled us to propose a Sentinel-2 (S2) phenology end-to-end processing chain. To achieve this, the following pipeline was implemented: (1) the building of hybrid Gaussian Process Regression (GPR) retrieval models of crop traits optimized with active learning, (2) implementation of these models on GEE (3) generation of spatiotemporally continuous maps and time series of these crop traits with the use of gap-filling through GPR fitting, and finally, (4) calculation of land surface phenology (LSP) metrics such as the start of season (SOS) or end of season (EOS). Overall, from good to high performance was achieved, in particular for the estimation of canopy-level traits such as leaf area index (LAI) and canopy chlorophyll content, with normalized root mean square errors (NRMSE) of 9% and 10%, respectively. By means of the GPR gap-filling time series of S2, entire tiles were reconstructed, and resulting maps were demonstrated over an agricultural area in Castile and Leon, Spain, where crop calendar data were available to assess the validity of LSP metrics derived from crop traits. In addition, phenology derived from the normalized difference vegetation index (NDVI) was used as reference. NDVI not only proved to be a robust indicator for the calculation of LSP metrics, but also served to demonstrate the good phenology quality of the quantitative trait products. Thanks to the GEE framework, the proposed workflow can be realized anywhere in the world and for any time window, thus representing a shift in the satellite data processing paradigm. We anticipate that the produced LSP metrics can provide meaningful insights into crop seasonal patterns in a changing environment that demands adaptive agricultural production.

Green LAI Mapping and Cloud Gap-Filling Using Gaussian Process Regression in Google Earth Engine.

For the last decade, Gaussian process regression (GPR) proved to be a competitive machine learning regression algorithm for Earth observation applications, with attractive unique properties such as band relevance ranking and uncertainty estimates. More recently, GPR also proved to be a proficient time series processor to fill up gaps in optical imagery, typically due to cloud cover. This makes GPR perfectly suited for large-scale spatiotemporal processing of satellite imageries into cloud-free products of biophysical variables. With the advent of the Google Earth Engine (GEE) cloud platform, new opportunities emerged to process local-to-planetary scale satellite data using advanced machine learning techniques and convert them into gap-filled vegetation properties products. However, GPR is not yet part of the GEE ecosystem. To circumvent this limitation, this work proposes a general adaptation of GPR formulation to parallel processing framework and its integration into GEE. To demonstrate the functioning and utility of the developed workflow, a GPR model predicting green leaf area index (LAI G ) from Sentinel-2 imagery was imported. Although by running this GPR model into GEE any corner of the world can be mapped into LAI G at a resolution of 20 m, here we show some demonstration cases over western Europe with zoom-ins over Spain. Thanks to the computational power of GEE, the mapping takes place on-the-fly. Additionally, a GPR-based gap filling strategy based on pre-optimized kernel hyperparameters is also put forward for the generation of multi-orbit cloud-free LAI G maps with an unprecedented level of detail, and the extraction of regularly-sampled LAI G time series at a pixel level. The ability to plugin a locally-trained GPR model into the GEE framework and its instant processing opens up a new paradigm of remote sensing image processing.

Pigment epithelium derived factor (PEDF) expression in the male tract of Wistar rats.

Pigment epithelium derived factor (PEDF) expression has been described in many organs as showing neurotrophic, anti-angiogenic, anti-apoptotic, anti-inflammatory, anti-oxidant and pro-cell survival properties. However, references to its activity in the male reproductive system are scarce. We aimed to characterize the expression of PEDF in the male reproductive tract of Wistar rats by using RT-PCR, western blot and immunostaining and also evaluate the effect of flutamide in PEDF expression. We found that PEDF is expressed in the epididymis, prostate and seminal vesicles in Wistar rats, but notably not in the testes. Under the effect of flutamide PEDF expression decreased, recovering by suppressing the antiandrogen. The epididymis is an essential organ in sperm maturation-storages. The role of PEDF in this physiological process has not been fully elucidated yet, but considering that in other systems PEDF has anti-apoptotic, anti-oxidants and pro-cell survival properties, its expression along the epididymis could play a role in the protection of spermatozoa while they are stored.

Preliminary study of ventilation with 4 ml/kg tidal volume in acute respiratory distress syndrome: feasibility and effects on cyclic recruitment - derecruitment and hyperinflation.

INTRODUCTION: Cyclic recruitment-derecruitment and overdistension contribute to ventilator-induced lung injury. Tidal volume (Vt) may influence both, cyclic recruitment-derecruitment and overdistension. The goal of this study was to determine if decreasing Vt from 6 to 4 ml/kg reduces cyclic recruitment-derecruitment and hyperinflation, and if it is possible to avoid severe hypercapnia. METHODS: Patients with pulmonary acute respiratory distress syndrome (ARDS) were included in a crossover study with two Vt levels: 6 and 4 ml/kg. The protocol had two parts: one bedside and other at the CT room. To avoid severe hypercapnia in the 4 ml/kg arm, we replaced the heat and moisture exchange filter by a heated humidifier, and respiratory rate was increased to keep minute ventilation constant. Data on lung mechanics and gas exchange were taken at baseline and after 30 minutes at each Vt (bedside). Thereafter, a dynamic CT (4 images/sec for 8 sec) was taken at each Vt at a fixed transverse region between the middle and lower third of the lungs. Afterward, CT images were analyzed and cyclic recruitment-derecruitment was determined as non-aerated tissue variation between inspiration and expiration, and hyperinflation as maximal hyperinflated tissue at end-inspiration, expressed as % of lung tissue weight. RESULTS: We analyzed 10 patients. Decreasing Vt from 6 to 4 ml/kg consistently decreased cyclic recruitment-derecruitment from 3.6 (2.5 to 5.7) % to 2.9 (0.9 to 4.7) % (P <0.01) and end-inspiratory hyperinflation from 0.7 (0.3 to 2.2) to 0.6 (0.2 to 1.7) % (P = 0.01). No patient developed severe respiratory acidosis or severe hypercapnia when decreasing Vt to 4 ml/kg (pH 7.29 (7.21 to 7.46); PaCO2 48 (26 to 51) mmHg). CONCLUSIONS: Decreasing Vt from 6 to 4 ml/kg reduces cyclic recruitment-derecruitment and hyperinflation. Severe respiratory acidosis may be effectively prevented by decreasing instrumental dead space and by increasing respiratory rate.

Resultados inmediatos y alejados del switch arterial en pacientes condextrotransposición de grandes arterias: experiencia de 20 años / Early and late results of the switch operation for d-transposition of the great vessels. clinical experience in 108 patients operated on between 1992 and 2012

Objetivos: Comunicar los resultados de la operación de switch arterial en pacientes portadores de dextro Transposición de Grandes Arterias (D-TGA) y evaluar su evolución en el tiempo. Pacientes y Método: Estudio retrospectivo de pacientes sometidos a switch arterial entre mayo de 1992 y noviembre de 2012. Se comparó período 1 (1992 a 2002) con período 2 (2003 a 2012). Se definió D-TGA simple aquella sin lesiones asociadas y D-TGA compleja aquella con asociación de comunicación interventricular o coartación aórtica. Resultados: Un total de 108 pacientes componen la serie, 44 en el período 1 y 64 en el período 2, sin diferencias demográficas y anatómicas entre ambos períodos. Setenta tenían D-TGA simple y 38 D-TGA compleja. La mortalidad operatoria en el período 1 fue 33% versus 8,4 por ciento en el período 2 (p< 0.025); hubo tendencia a mayor mortalidad en pacientes con anatomía coronaria compleja. Se observó una disminución del riesgo relativo de mortalidad de 68,7 por ciento en el período 2. El 28,7 por ciento presentaron complicaciones postoperatorias, sin diferencias entre ambos períodos. Un paciente falleció en forma alejada. La mediana de seguimiento fue de 60 meses y la supervivencia de 84,25 por ciento a 10 y 20 años. Se realizaron 16 re intervenciones, principalmente plastías percutáneas de ramas pulmonares. La mayoría de los pacientes se mantenían asintomáticos. La mortalidad operatoria de los últimos 5 años fue 2,6 por ciento. Conclusiones: La mortalidad operatoria ha disminuido significativamente a lo largo de 20 años; persiste un mayor riesgo en ciertos patrones de anatomía coronaria. La supervivencia alejada y libre de reintervenciones es muy favorable.

Aim: to report the results of the arterial Switch operation in patients with D-transposition of the great vessels (D-TGA) and to evaluate their late course. Patients and Methods: A retrospective review of the clinical data on patients consecutively operated on for D-TGA using the switch procedure. Results obtained in patients operated on between 1992 and 2002 (Period 1) were compared to those obtained in patients undergoing their operation between 2013 and 2012. Patients with D-TGA and no complications were compared to those who had interventricular septal defect or aortic coarctation associated their TGA. Results: 44 patients belonged in Period 1 and 64 in Period 2 (total 108). Demographic and anatomical characteristics were similar in both periods. Simple D-TGA was present in 70 patients and complex D-TGA in 38. Operative mortality was higher in Period 1 compared to Period 2 (33 per cent vs. 8.4 percent, p<0.025). A higher, albeit not statiscally significant mortality was observed in patients with complex as opposed to simple D-TGA. A 68.7 per cent relative reduction mortality risk was observed in Period 2. Complications developed in 28.7 per cent of patients, with no difference between periods. Only 1 patient died during late follow up. Median follow up was 60 months and survival rate was 84.3 percent at 10 y 20 years. Sixteen patients required re-intervention, mainly to perform percutaneous plastic procedures on pulmonary artery branches. Most patients had an asymptomatic course during follow up. Surgical mortality for the last 5 years was 2.6 percent. Conclusion: surgical mortality for the arterial switch operation in patients with D-TGA has significantly decreased along a 20 year period. An increased surgical risk persists for patients with certain forms of coronary artery anomalies. Late survival free of re-intervention was the rule in these patients.