The effect of cover crop and crop rotation on soil water storage and on sorghum yield

Rotação de cultura e cultura de cobertura constituem importantes meios para melhoria do rendimento de culturas em áreas de sequeiro como a região "Coastal Bend" do Estado do Texas. Um ensaio foi conduzido em 1995, como parte de um experimento de longa duração (7 anos), com o objetivo de investigar o efeito da aveia (Avena sativa L.) como cultura de cobertura, e da rotação de cultura, no armazenamento da água do solo e no rendimento do sorgo (Sorghum bicolor L.). O delineamento experimental adotado foi o de blocos ao acaso, em parcelas subdivididas, com quatro repetições. As rotações foram alocadas nas parcelas, e a cultura de cobertura, nas subparcelas. A cultura de cobertura reduziu o rendimento do sorgo. Este efeito foi atribuído à reduzida concentração de N disponível do solo. Por atraso no extermínio e incorporação da aveia, seu resíduo, com elevada relação C/N, atuou como dreno, pela imobilização, em lugar de ser fonte de N para o sorgo. As rotações sorgo/algodão e sorgo/soja proporcionaram efeito positivo no rendimento de sorgo. Este efeito foi atribuído à maior concentração de N nestas seqüências.Crop rotation and cover crop can be important means for enhancing crop yield in rainfed areas such as the lower Coastal Bend Region of Texas, USA. A trial was conducted in 1995 as part of a long-term cropping experiment (7 years) to investigate the effect of oat (Avena sativa L.) cover and rotation on soil water storage and yield of sorghum (Sorghum bicolor L.). The trial design was a RCB in a split-plot arrangement with four replicates. Rotation sequences were the main plots and oat cover crop the subplots. Cover crop reduced sorghum grain yield. This effect was attributed to a reduced concentration of available soil N and less soil water storage under this treatment. By delaying cover termination, the residue with a high C/N acted as an N sink through competition and/or immobilization instead of an N source to sorghum plants. Crop rotation had a significantly positive effect on sorghum yield and this effect was attributed to a significantly larger amount of N concentration under these rotation sequences

Integration of nanofiltration and reverse osmosis technologies in polyphenols recovery schemes from winery and olive mill wastes by aqueous-based processing

More sustainable waste management in the winery and olive oil industries has become a major challenge. Therefore, waste valorization to obtain value-added products (e.g., polyphenols) is an efficient alternative that contributes to circular approaches and sustainable environmental protection. In this work, an integration scheme was purposed based on sustainable extraction and membrane separation processes, such as nanofiltration (NF) and reverse osmosis (RO), for the recovery of polyphenols from winery and olive mill wastes. Membrane processes were evaluated in a closed-loop system and with a flat-sheet membrane configuration (NF270, NF90, and Duracid as NF membranes, and BW30LE as RO membrane). The separation and concentration efficiency were evaluated in terms of the total polyphenol content (TPC), and by polyphenol families (hydroxybenzoic acids, hydroxycinnamic acids, and flavonoids), using high-performance liquid chromatography. The water trans-membrane flux was dependent on the trans-membrane pressure for the NF and RO processes. NF90 membrane rejected around 91% of TPC for the lees filters extracts while NF270 membrane rejected about 99% of TPC for the olive pomace extracts. Otherwise, RO membranes rejected more than 99.9% of TPC for both types of agri-food wastes. Hence, NF and RO techniques could be used to obtain polyphenol-rich streams, and clean water for reuse purposesPeer ReviewedPostprint (published version

Lineage Analysis of Circulating Trypanosoma cruzi Parasites and Their Association with Clinical Forms of Chagas Disease in Bolivia

Around 30–50% of Trypanosoma cruzi infections in Latin America cause chronic Chagas disease 10–30 years after the primary infection due to lack of effective treatment. The major clinical complications associated with chronic Chagas disease are cardiac myositis (leading to cardiac failure), and autonomous neuroplexus degeneration of the digestive tract that can cause megacolon or megaesophagus. Therefore, there are three major clinical forms of Chagas disease; cardiac, digestive and indeterminate (asymptomatic). The parasites, which can infect humans as well as other mammals, are transmitted by species of triatomines commonly found in the Americas. The parasite is divided in at least six discrete typing units: TcI, TcIIa–e. In humans, the TcI is mainly observed in Central America and northern parts of South America while the TcIIb/d/e is confined mainly to the southern cone of Latin America. We determined which DTU were prevalent in chronic patients in Bolivia, where the three clinical forms and several DTUs of the parasites are present, in order to determine whether there was a link between a particular parasite DTU and a particular clinical outcome. We found a vast majority of TcIId but its kDNA polymorphism showed no association with any of the clinical manifestations of chronic Chagas

Long-term outcomes of the global tuberculosis and COVID-19 co-infection cohort

Background: Longitudinal cohort data of patients with tuberculosis (TB) and coronavirus disease 2019 (COVID-19) are lacking. In our global study, we describe long-term outcomes of patients affected by TB and COVID-19. Methods: We collected data from 174 centres in 31 countries on all patients affected by COVID-19 and TB between 1 March 2020 and 30 September 2022. Patients were followed-up until cure, death or end of cohort time. All patients had TB and COVID-19; for analysis purposes, deaths were attributed to TB, COVID-19 or both. Survival analysis was performed using Cox proportional risk-regression models, and the log-rank test was used to compare survival and mortality attributed to TB, COVID-19 or both. Results: Overall, 788 patients with COVID-19 and TB (active or sequelae) were recruited from 31 countries, and 10.8% (n=85) died during the observation period. Survival was significantly lower among patients whose death was attributed to TB and COVID-19 versus those dying because of either TB or COVID-19 alone (p<0.001). Significant adjusted risk factors for TB mortality were higher age (hazard ratio (HR) 1.05, 95% CI 1.03-1.07), HIV infection (HR 2.29, 95% CI 1.02-5.16) and invasive ventilation (HR 4.28, 95% CI 2.34-7.83). For COVID-19 mortality, the adjusted risks were higher age (HR 1.03, 95% CI 1.02-1.04), male sex (HR 2.21, 95% CI 1.24-3.91), oxygen requirement (HR 7.93, 95% CI 3.44-18.26) and invasive ventilation (HR 2.19, 95% CI 1.36-3.53). Conclusions: In our global cohort, death was the outcome in >10% of patients with TB and COVID-19. A range of demographic and clinical predictors are associated with adverse outcomes

Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Summary Background Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. Methods We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung’s disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. Findings We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung’s disease) from 264 hospitals (89 in high-income countries, 166 in middleincome countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in lowincome countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. Interpretation Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between lowincome, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030

Performance Evaluation of Parallel Structure from Motion (SfM) Processing with Public Cloud Computing and an On-Premise Cluster System for UAS Images in Agriculture

Thanks to sensor developments, unmanned aircraft systems (UASs) are now among the most promising modern technologies used to collect imagery datasets that can be utilized to develop agricultural applications. These datasets can grow exponentially due to the ultrafine spatial and high temporal resolution capabilities of UAS data. One of the main obstacles to processing UAS data is the intensive computational resource requirements. The structure from motion (SfM) is the most popular algorithm used to generate 3D point clouds, orthomosaic images and digital elevation models (DEMs) in agricultural applications. Recently, the SfM algorithm has been implemented in parallel to process big UAS data quicker for certain applications. This study evalu-ated the performance of parallel SfM processing on public cloud computing and on-premise cluster systems. The UAS datasets collected over cropping fields were used for evaluation. We used multiple computing nodes and centralized network storage with different network environments for the SfM workflow. In single-node processing, an instance with the most computing power in the cloud computing system performed approximately 20 and 35 percent faster than in the single-node processing with the most computing power in the on-premises cluster. The parallel processing results showed that the cloud-based system performed better in scalability in terms of speed-up and efficiency metrics, although the absolute processing time was faster in the on-premise cluster. The experimental results also showed that the public cloud computing system might be a good alternative computing environment in UAS data processing for agricultural applications.Thanks to sensor developments, unmanned aircraft systems (UASs) are now among the most promising modern technologies used to collect imagery datasets that can be utilized to develop agricultural applications. These datasets can grow exponentially due to the ultrafine spatial and high temporal resolution capabilities of UAS data. One of the main obstacles to processing UAS data is the intensive computational resource requirements. The structure from motion (SfM) is the most popular algorithm used to generate 3D point clouds, orthomosaic images and digital elevation models (DEMs) in agricultural applications. Recently, the SfM algorithm has been implemented in parallel to process big UAS data quicker for certain applications. This study evalu-ated the performance of parallel SfM processing on public cloud computing and on-premise cluster systems. The UAS datasets collected over cropping fields were used for evaluation. We used multiple computing nodes and centralized network storage with different network environments for the SfM workflow. In single-node processing, an instance with the most computing power in the cloud computing system performed approximately 20 and 35 percent faster than in the single-node processing with the most computing power in the on-premises cluster. The parallel processing results showed that the cloud-based system performed better in scalability in terms of speed-up and efficiency metrics, although the absolute processing time was faster in the on-premise cluster. The experimental results also showed that the public cloud computing system might be a good alternative computing environment in UAS data processing for agricultural applications

Comparison of canopy shape and vegetation indices of citrus trees derived from UAV multispectral images for characterization of citrus greening disease

Citrus greening is a severe disease significantly affecting citrus production in the United States because the disease is not curable with currently available technologies. For this reason, monitoring citrus disease in orchards is critical to eradicate and replace infected trees before the spread of the disease. In this study, the canopy shape and vegetation indices of infected and healthy orange trees were compared to better understand their significant characteristics using unmanned aerial vehicle (UAV)-based multispectral images. Individual citrus trees were identified using thresholding and morphological filtering. The UAV-based phenotypes of each tree, such as tree height, crown diameter, and canopy volume, were calculated and evaluated with the corresponding ground measurements. The vegetation indices of infected and healthy trees were also compared to investigate their spectral differences. The results showed that correlation coefficients of tree height and crown diameter between the UAV-based and ground measurements were 0.7 and 0.8, respectively. The UAV-based canopy volume was also highly correlated with the ground measurements (R2 > 0.9). Four vegetation indices—normalized difference vegetation index (NDVI), normalized difference RedEdge index (NDRE), modified soil adjusted vegetation index (MSAVI), and chlorophyll index (CI)—were significantly higher in healthy trees than diseased trees. The RedEdge-related vegetation indices showed more capability for citrus disease monitoring. Additionally, the experimental results showed that the UAV-based flush ratio and canopy volume can be valuable indicators to differentiate trees with citrus greening disease.Citrus greening is a severe disease significantly affecting citrus production in the United States because the disease is not curable with currently available technologies. For this reason, monitoring citrus disease in orchards is critical to eradicate and replace infected trees before the spread of the disease. In this study, the canopy shape and vegetation indices of infected and healthy orange trees were compared to better understand their significant characteristics using unmanned aerial vehicle (UAV)-based multispectral images. Individual citrus trees were identified using thresholding and morphological filtering. The UAV-based phenotypes of each tree, such as tree height, crown diameter, and canopy volume, were calculated and evaluated with the corresponding ground measurements. The vegetation indices of infected and healthy trees were also compared to investigate their spectral differences. The results showed that correlation coefficients of tree height and crown diameter between the UAV-based and ground measurements were 0.7 and 0.8, respectively. The UAV-based canopy volume was also highly correlated with the ground measurements (R2 > 0.9). Four vegetation indices—normalized difference vegetation index (NDVI), normalized difference RedEdge index (NDRE), modified soil adjusted vegetation index (MSAVI), and chlorophyll index (CI)—were significantly higher in healthy trees than diseased trees. The RedEdge-related vegetation indices showed more capability for citrus disease monitoring. Additionally, the experimental results showed that the UAV-based flush ratio and canopy volume can be valuable indicators to differentiate trees with citrus greening disease

Descripción de fuentes de datos heterogéneas utilizando tecnologías semánticas

La Web Semántica, plantea potenciales oportunidades para dotar de significado a los contenidos web. Las ontologías constituyen una de las principales herramientas para especificar explícitamente los conceptos de un dominio concreto, sus propiedades y sus relaciones; de manera que la información se publique en formatos que sean entendibles por agentes máquinas que pueden localizar y gestionar de forma precisa la información. En esta tesis se presenta una aplicación para la generación un modelo ontológico común, el cual describe diferentes fuentes de datos mediante sus metadatos, específicamente se trabajó con fuentes de tipo Base de datos, CSV, XML y EXCEL. Para ello, se analizó diferentes ontologías de descripción de metadatos, entre las cuales se tienen DCAT, PHDD y DISCO. Estas tres fuentes se unieron en un solo modelo, sobre el cual se realizaron algunas modificaciones, siendo la más importante la incorporación de una estructura, la cual me permita describir los diferentes tipo de datos que tienen los atributos de las fuentes. Después se procedió a la creación de un modelo relacional común, donde se almacena temporalmente los metadatos extraídos, para su posterior mapeo con el modelo ontológico común. Finalmente se procedió a generar un archivo RDF sobre el modelo ontológico común y publicar el mismo para su explotación. Para validar experimentalmente el modelo creado, se planteó un escenario de integración de varias fuentes de datos, donde se realizó una comparación haciendo consultas al modelo ontológico común y haciendo una inspección de forma manual a los metadatos y datos de dichas fuentes, esto con la finalidad de obtener la utilidad del modelo ontológico común. Concluyendo que mediante el modelo ontológico común el usuario encargado de la integración puede extraer las posibles asignaciones semánticas en términos de integración de datos, de forma fácil y a un menor costo, ya que lo realiza sobre un solo repositorio común.The Semantic Web, propound potential opportunities to give meaning to web content. Ontologies are one of the main tools to explicitly specify the concepts of a particular domain, its properties and its relationships; so that the information is published in formats that are understandable by machine agents that can locate and manage the information accurately. This thesis presents an application for the generation of a common ontological model, which describes different data sources through its metadata, specifically worked with sources such as Database, CSV, XML and EXCEL. For this, different ontologies of metadata description were analyzed, among which DCAT, PHDD and DISCO are available. These three sources were united in a single model, on which some modifications were made, the most important being the incorporation of a structure, which allows me to describe the different types of data that have the attributes of the sources. Afterwards, a common relational model was created, where the extracted metadata is temporarily stored, for its subsequent mapping with the common ontological model. Finally, we proceeded to generate an RDF file on the common ontological model and publish the same for its exploitation. In order to experimentally validate the created model, an integration scenario was set up for several data sources, where a comparison was made by consulting the common ontological model and manually inspecting the metadata and data of said sources, this with the purpose to obtain the utility of the common ontological model. Concluding that through the common ontological model, the user in charge of integration can extract the possible semantic assignments in terms of data integration, easily and at a lower cost, since it is done on a single common repository.Ingeniero de SistemasCuenc

3D Characterization of Sorghum Panicles Using a 3D Point Cloud Derived from UAV Imagery

Sorghum is one of the most important crops worldwide. An accurate and efficient high-throughput phenotyping method for individual sorghum panicles is needed for assessing genetic diversity, variety selection, and yield estimation. High-resolution imagery acquired using an unmanned aerial vehicle (UAV) provides a high-density 3D point cloud with color information. In this study, we developed a detecting and characterizing method for individual sorghum panicles using a 3D point cloud derived from UAV images. The RGB color ratio was used to filter non-panicle points out and select potential panicle points. Individual sorghum panicles were detected using the concept of tree identification. Panicle length and width were determined from potential panicle points. We proposed cylinder fitting and disk stacking to estimate individual panicle volumes, which are directly related to yield. The results showed that the correlation coefficient of the average panicle length and width between the UAV-based and ground measurements were 0.61 and 0.83, respectively. The UAV-derived panicle length and diameter were more highly correlated with the panicle weight than ground measurements. The cylinder fitting and disk stacking yielded R2 values of 0.77 and 0.67 with the actual panicle weight, respectively. The experimental results showed that the 3D point cloud derived from UAV imagery can provide reliable and consistent individual sorghum panicle parameters, which were highly correlated with ground measurements of panicle weight