Mejoras en el diseño agronómico de la instalación de riego dirigidas al aumento de la productividad técnica y económica del agua en limonero temprano

El objetivo del trabajo fue incrementar la productividad en limonero temprano mediante mejoras en el diseño agronómico de la red de riego. El ensayo se llevó a cabo en una parcela experimental en Torre Pacheco (Murcia), en árboles adultos de limonero ‘Fino 49’ injertados sobre Citrus macrophylla Wester. Se han evaluado dos diseños del sistema de riego: diseño convencional (2L), con dos tuberías portagoteros (6 goteros árbol-1); y un diseño con mayor superficie mojada (3L), con tres tuberías (9 goteros árbol-1). Partiendo de los resultados agronómicos, se ha realizado un análisis económico comparativo entre dos diseños del sistema de riego. El diseño 3L fue el más productivo técnica y económicamente. El Producto Bruto Económico (PBE) fue un 14% superior en el diseño 3L respecto al 2L; el Producto Bruto Técnico (PBT) lo fue en un 6,2%. Este resultado se debe a dos motivos: por un lado y en mayor medida, al incremento en la proporción de limón de primer corte del diseño 3L y, por otro lado, a la disminución de limón derivado a la industria. El Margen Bruto (MB) sigue la misma pauta que el Producto Bruto (PB), ya que los costes diferenciales (CD) son de poca envergadura. El precio ponderado del kg de limón medio (PBE/PBT) es de 0,367 € kg-1 y 0,341 € kg-1, en 3L y 2L, respectivamente, y supone un ingreso de 2.631 € extras por hectárea y año a favor del sistema 3L. Por último, resaltar que el diseño 3L destaca principalmente por ser es más productivo económicamente, en relación al agua aplicada (€ m-3)

Aplicación de la oxifertirrigación para optimizar los recursos hídricos en cítricos, basado en la aplicación de peróxido de hidrógeno en el agua de riego

En este trabajo se estudia la respuesta fisiológica y agronómica de la aplicación de la oxifertirrigación química, basada en la aplicación de peróxido de hidrógeno como fuente de oxígeno a nivel radicular en cítricos. El ensayo tuvo lugar durante dos campañas (2018-2019 y 2019-2020) en árboles adultos de mandarino híbrido ‘Ortanique’ ubicados en una parcela experimental del IMIDA en Torre Pacheco (Murcia). Se establecieron dos tratamientos, un tratamiento ‘Control’ (0 ppm de H2O2) y otro identificado como ‘OXI’ (50-100 ppm de H2O2 durante todo el ciclo de cultivo). Ambos tratamientos recibieron la misma cantidad de agua y de fertilizante. El H2O2 se aplicó de forma continua con una bomba dosificadora a la red de riego. Los resultados más destacados mostraron que el estado hídrico de los árboles del tratamiento ‘OXI’ fue muy similar al control. Respecto a los parámetros de intercambio gaseoso, la aplicación de H2O2 estimuló una mayor apertura estomática en el mes de septiembre de ambas campañas. Sin embargo, las ligeras alteraciones fisiológicas no han supuesto cambios sustanciales en la biometría de la planta. En la segunda campaña, la aplicación de H2O2 en el riego favoreció la acumulación de N, K y Fe en hoja, lo que permitiría reducir la dosis de fertilizante. La respuesta productiva y la eficiencia en el uso del agua no presentaron una clara mejora a la aplicación de H2O2 en el agua de riego. Los mayores niveles de N en el tratamiento ‘OXI’ afectaron negativamente a la calidad del fruto, reduciendo el porcentaje de zumo y aumentando el porcentaje de corteza. En cambio, la aplicación de H2O2 disminuyó el índice de madurez de la fruta, lo que resulta interesante de cara a retrasar la recolección en variedades tardías

Riego de un cultivo de citricos con agua marina desalinizada. resultados preliminares en suelo y planta

La escasez de agua y la creciente presión sobre los recursos hídricos en las regiones semiáridas ha extendido la utilización para el riego de recursos hídricos no convencionales, como el agua marina desalinizada (AMD). Debido a su composición en Cl-, Na+ y B3+, el riego con AMD podría causar problemas agronómicos y afectar al suelo y a los cultivos a medio y largo plazo. En este estudio, se regó una parcela de mandarinos durante 20 meses con (i) agua proporcionada por la Comunidad de Regantes del Campo de Cartagena (CR), (ii) agua marina desalinizada (AMD) y (iii) mezcla de agua 50% CR y 50% AMD (AM). Se evaluó el efecto sobre la dinámica y acumulación de los iones tóxicos Cl-, Na+ y B3+ en el suelo y en la planta. La [B3+] del agua AMD fue superior a la de CR, acumulándose en el suelo, con una concentración un 25% superior a la encontrada con CR al final del ensayo. La [B3+] en la capa superficial del suelo se correlacionó con la [B3+] en el agua y con la [B3+] en la hoja. Aunque tras 20 meses los árboles regados con AMD tuvieron una [B3+] foliar un 25% superior a la de árboles regados con CR, no presentaron síntomas de toxicidad. Las [Cl-] y [Na+] del agua fueron similares en los tres tipos de agua, superando los umbrales a partir de los cuales pueden producir toxicidad en cítricos. Las concentraciones de Cl- y Na+ en hoja permanecieron por debajo del umbral de toxicidad establecido para cítricos. Los resultados obtenidos son preliminares ya que este estudio debería extenderse durante un periodo más largo para obtener datos más concluyentes acerca de los efectos a largo plazo de la utilización de AMD tanto en el suelo como en la planta

Artificial intelligence for dementia genetics and omics

Genetics and omics studies of Alzheimer's disease and other dementia subtypes enhance our understanding of underlying mechanisms and pathways that can be targeted. We identified key remaining challenges: First, can we enhance genetic studies to address missing heritability? Can we identify reproducible omics signatures that differentiate between dementia subtypes? Can high-dimensional omics data identify improved biomarkers? How can genetics inform our understanding of causal status of dementia risk factors? And which biological processes are altered by dementia-related genetic variation? Artificial intelligence (AI) and machine learning approaches give us powerful new tools in helping us to tackle these challenges, and we review possible solutions and examples of best practice. However, their limitations also need to be considered, as well as the need for coordinated multidisciplinary research and diverse deeply phenotyped cohorts. Ultimately AI approaches improve our ability to interrogate genetics and omics data for precision dementia medicine

A genetic link between risk for Alzheimer's disease and severe COVID-19 outcomes via the OAS1 gene

Recently, we reported oligoadenylate synthetase 1 (OAS1) contributed to the risk of Alzheimer’s disease, by its enrichment in transcriptional networks expressed by microglia. However, the function of OAS1 within microglia was not known. Using genotyping from 1313 individuals with sporadic Alzheimer’s disease and 1234 control individuals, we confirm the OAS1 variant, rs1131454, is associated with increased risk for Alzheimer’s disease. The same OAS1 locus has been recently associated with severe coronavirus disease 2019 (COVID-19) outcomes, linking risk for both diseases. The single nucleotide polymorphisms rs1131454(A) and rs4766676(T) are associated with Alzheimer’s disease, and rs10735079(A) and rs6489867(T) are associated with severe COVID-19, where the risk alleles are linked with decreased OAS1 expression. Analysing single-cell RNA-sequencing data of myeloid cells from Alzheimer’s disease and COVID-19 patients, we identify co-expression networks containing interferon (IFN)-responsive genes, including OAS1, which are significantly upregulated with age and both diseases. In human induced pluripotent stem cell-derived microglia with lowered OAS1 expression, we show exaggerated production of TNF-α with IFN-γ stimulation, indicating OAS1 is required to limit the pro-inflammatory response of myeloid cells. Collectively, our data support a link between genetic risk for Alzheimer’s disease and susceptibility to critical illness with COVID-19 centred on OAS1, a finding with potential implications for future treatments of Alzheimer’s disease and COVID-19, and development of biomarkers to track disease progression

Artificial intelligence for dementia genetics and omics

Genetics and omics studies of Alzheimer's disease and other dementia subtypes enhance our understanding of underlying mechanisms and pathways that can be targeted. We identified key remaining challenges: First, can we enhance genetic studies to address missing heritability? Can we identify reproducible omics signatures that differentiate between dementia subtypes? Can high‐dimensional omics data identify improved biomarkers? How can genetics inform our understanding of causal status of dementia risk factors? And which biological processes are altered by dementia‐related genetic variation? Artificial intelligence (AI) and machine learning approaches give us powerful new tools in helping us to tackle these challenges, and we review possible solutions and examples of best practice. However, their limitations also need to be considered, as well as the need for coordinated multidisciplinary research and diverse deeply phenotyped cohorts. Ultimately AI approaches improve our ability to interrogate genetics and omics data for precision dementia medicine. Highlights: We have identified five key challenges in dementia genetics and omics studies. AI can enable detection of undiscovered patterns in dementia genetics and omics data. Enhanced and more diverse genetics and omics datasets are still needed. Multidisciplinary collaborative efforts using AI can boost dementia research

Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease: a meta-analysis of genome-wide association studies

Background Genome-wide association studies (GWAS) in Parkinson's disease have increased the scope of biological knowledge about the disease over the past decade. We aimed to use the largest aggregate of GWAS data to identify novel risk loci and gain further insight into the causes of Parkinson's disease. Methods We did a meta-analysis of 17 datasets from Parkinson's disease GWAS available from European ancestry samples to nominate novel loci for disease risk. These datasets incorporated all available data. We then used these data to estimate heritable risk and develop predictive models of this heritability. We also used large gene expression and methylation resources to examine possible functional consequences as well as tissue, cell type, and biological pathway enrichments for the identified risk factors. Additionally, we examined shared genetic risk between Parkinson's disease and other phenotypes of interest via genetic correlations followed by Mendelian randomisation. Findings Between Oct 1, 2017, and Aug 9, 2018, we analysed 7·8 million single nucleotide polymorphisms in 37 688 cases, 18 618 UK Biobank proxy-cases (ie, individuals who do not have Parkinson's disease but have a first degree relative that does), and 1·4 million controls. We identified 90 independent genome-wide significant risk signals across 78 genomic regions, including 38 novel independent risk signals in 37 loci. These 90 variants explained 16–36% of the heritable risk of Parkinson's disease depending on prevalence. Integrating methylation and expression data within a Mendelian randomisation framework identified putatively associated genes at 70 risk signals underlying GWAS loci for follow-up functional studies. Tissue-specific expression enrichment analyses suggested Parkinson's disease loci were heavily brain-enriched, with specific neuronal cell types being implicated from single cell data. We found significant genetic correlations with brain volumes (false discovery rate-adjusted p=0·0035 for intracranial volume, p=0·024 for putamen volume), smoking status (p=0·024), and educational attainment (p=0·038). Mendelian randomisation between cognitive performance and Parkinson's disease risk showed a robust association (p=8·00 × 10−7). Interpretation These data provide the most comprehensive survey of genetic risk within Parkinson's disease to date, to the best of our knowledge, by revealing many additional Parkinson's disease risk loci, providing a biological context for these risk factors, and showing that a considerable genetic component of this disease remains unidentified. These associations derived from European ancestry datasets will need to be followed-up with more diverse data. Funding The National Institute on Aging at the National Institutes of Health (USA), The Michael J Fox Foundation, and The Parkinson's Foundation (see appendix for full list of funding sources)

Discovery and functional prioritization of Parkinson's disease candidate genes from large-scale whole exome sequencing.

BACKGROUND: Whole-exome sequencing (WES) has been successful in identifying genes that cause familial Parkinson's disease (PD). However, until now this approach has not been deployed to study large cohorts of unrelated participants. To discover rare PD susceptibility variants, we performed WES in 1148 unrelated cases and 503 control participants. Candidate genes were subsequently validated for functions relevant to PD based on parallel RNA-interference (RNAi) screens in human cell culture and Drosophila and C. elegans models. RESULTS: Assuming autosomal recessive inheritance, we identify 27 genes that have homozygous or compound heterozygous loss-of-function variants in PD cases. Definitive replication and confirmation of these findings were hindered by potential heterogeneity and by the rarity of the implicated alleles. We therefore looked for potential genetic interactions with established PD mechanisms. Following RNAi-mediated knockdown, 15 of the genes modulated mitochondrial dynamics in human neuronal cultures and four candidates enhanced α-synuclein-induced neurodegeneration in Drosophila. Based on complementary analyses in independent human datasets, five functionally validated genes-GPATCH2L, UHRF1BP1L, PTPRH, ARSB, and VPS13C-also showed evidence consistent with genetic replication. CONCLUSIONS: By integrating human genetic and functional evidence, we identify several PD susceptibility gene candidates for further investigation. Our approach highlights a powerful experimental strategy with broad applicability for future studies of disorders with complex genetic etiologies