Simulación y control predictivo del sistema de transporte de gas en una gran zona industrial

El artículo describe un enfoque para el control operativo y de supervisión de un sistema de transmisión de gas para grandes zonas industriales utilizando un modelo de control predictivo, así como métodos analíticos y de simulación. El control operativo y de supervisión del sistema de transporte de gas cubre el horizonte temporal de varias horas a varios días e implica la realización de varias acciones cíclicamente repetidas. Los autores proponen un modelo predictivo de series de tiempo del parámetro de consumo de gas considerando las condiciones climáticas de temperatura, que se amplía con base en la contabilidad de las relaciones de correlación entre los volúmenes de consumo de cada consumidor. Los métodos de control utilizados en la actualidad, reaccionando a las desviaciones actuales del régimen planificado, a priori no permiten lograr los mejores resultados. Se puede lograr un aumento significativo en la estabilidad del control y una reducción en el costo del combustible y los recursos energéticos utilizando el método de control basado en modelos predictivos. En este caso, el modelo de objeto de control se utiliza para predecir su comportamiento dentro del horizonte de tiempo seleccionado, y las acciones de control óptimas se seleccionan sobre esta base. El proceso de predecir y seleccionar acciones de control se repite periódicamente, cambiando constantemente los límites del horizonte temporal. El método descrito para cambiar el diagrama de flujo consiste en cambiar todos los flujos al mismo tiempo o en una transición preventiva y suave basada en la introducción de un diagrama de flujo ponderado para varios modos estacionarios, siempre que su desajuste se minimice en intervalos de tiempo vecinos. correspondiente a los intervalos de constancia de las solicitudes de consumo

Human Genetics in Rheumatoid Arthritis Guides a High-Throughput Drug Screen of the CD40 Signaling Pathway

Although genetic and non-genetic studies in mouse and human implicate the CD40 pathway in rheumatoid arthritis (RA), there are no approved drugs that inhibit CD40 signaling for clinical care in RA or any other disease. Here, we sought to understand the biological consequences of a CD40 risk variant in RA discovered by a previous genome-wide association study (GWAS) and to perform a high-throughput drug screen for modulators of CD40 signaling based on human genetic findings. First, we fine-map the CD40 risk locus in 7,222 seropositive RA patients and 15,870 controls, together with deep sequencing of CD40 coding exons in 500 RA cases and 650 controls, to identify a single SNP that explains the entire signal of association (rs4810485, P = 1.4×10(−9)). Second, we demonstrate that subjects homozygous for the RA risk allele have ∼33% more CD40 on the surface of primary human CD19+ B lymphocytes than subjects homozygous for the non-risk allele (P = 10(−9)), a finding corroborated by expression quantitative trait loci (eQTL) analysis in peripheral blood mononuclear cells from 1,469 healthy control individuals. Third, we use retroviral shRNA infection to perturb the amount of CD40 on the surface of a human B lymphocyte cell line (BL2) and observe a direct correlation between amount of CD40 protein and phosphorylation of RelA (p65), a subunit of the NF-κB transcription factor. Finally, we develop a high-throughput NF-κB luciferase reporter assay in BL2 cells activated with trimerized CD40 ligand (tCD40L) and conduct an HTS of 1,982 chemical compounds and FDA–approved drugs. After a series of counter-screens and testing in primary human CD19+ B cells, we identify 2 novel chemical inhibitors not previously implicated in inflammation or CD40-mediated NF-κB signaling. Our study demonstrates proof-of-concept that human genetics can be used to guide the development of phenotype-based, high-throughput small-molecule screens to identify potential novel therapies in complex traits such as RA

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

TRY plant trait database - enhanced coverage and open access

10.1111/gcb.14904GLOBAL CHANGE BIOLOGY261119-18