Improved descriptions of soil hydrology in crop models: The elephant in the room?

Soil-crop simulation models are widely used to assess the impacts of soil management and climate change on soil water balance, solute transport and crop production. In this context, it is important that hydrological processes in the soil-crop system are accurately modelled. We suggest here that empirical treatments of soil water flow, water uptake by plant mots and transpiration limit the applicability of crop models and increase prediction errors. We further argue that this empiricism is to a large extent unnecessary, as parsimonious physics-based descriptions of these water flow processes in the soil-crop system are now available. Recent reviews and opinion articles, whilst strongly advocating the need for improvements to crop models, fail to mention the significant role played by accurate treatments of soil hydrology. It seems to us that empirical models of soil water flow have become the elephant in the room

Potential of natural language processing for metadata extraction fromenvironmental scientific publications

Summarizing information from large bodies of scientific literature is anessential but work-intensive task. This is especially true in environmentalstudies where multiple factors (e.g., soil, climate, vegetation) cancontribute to the effects observed. Meta-analyses, studies thatquantitatively summarize findings of a large body of literature, rely onmanually curated databases built upon primary publications. However, giventhe increasing amount of literature, this manual work is likely to requiremore and more effort in the future. Natural language processing (NLP)facilitates this task, but it is not clear yet to which extent theextraction process is reliable or complete. In this work, we explore threeNLP techniques that can help support this task: topic modeling, tailoredregular expressions and the shortest dependency path method. We apply thesetechniques in a practical and reproducible workflow on two corpora ofdocuments: the Open Tension-diskInfiltrometer Meta-database (OTIM) and the Meta corpus. The OTIM corpus contains the sourcepublications of the entries of the OTIM database of near-saturated hydraulicconductivity from tension-disk infiltrometer measurements(https://github.com/climasoma/otim-db, last access: 1 March 2023). The Meta corpus is constituted ofall primary studies from 36 selected meta-analyses on the impact ofagricultural practices on sustainable water management in Europe. As a firststep of our practical workflow, we identified different topics from theindividual source publications of the Meta corpus using topic modeling.This enabled us to distinguish well-researched topics (e.g., conventionaltillage, cover crops), where meta-analysis would be useful, from neglectedtopics (e.g., effect of irrigation on soil properties), showing potentialknowledge gaps. Then, we used tailored regular expressions to extractcoordinates, soil texture, soil type, rainfall, disk diameter and tensionsfrom the OTIM corpus to build a quantitative database. We were able toretrieve the respective information with 56 % up to 100 % of allrelevant information (recall) and with a precision between 83 % and100 %. Finally, we extracted relationships between a set of driverscorresponding to different soil management practices or amendments (e.g.,"biochar", "zero tillage") and target variables (e.g., "soilaggregate", "hydraulic conductivity", "crop yield") from thesource publications' abstracts of the Meta corpus using the shortestdependency path between them. These relationships were further classifiedaccording to positive, negative or absent correlations between the driverand the target variable. This quickly provided an overview of the differentdriver-variable relationships and their abundance for an entire body ofliterature. Overall, we found that all three tested NLP techniques were ableto support evidence synthesis tasks. While human supervision remainsessential, NLP methods have the potential to support automated evidencesynthesis which can be continuously updated as new publications becomeavailable

Effective Treatment of Respiratory Alphaherpesvirus Infection Using RNA Interference

BACKGROUND: Equine herpesvirus type 1 (EHV-1), a member of the Alphaherpesvirinae, is spread via nasal secretions and causes respiratory disease, neurological disorders and abortions. The virus is a significant equine pathogen, but current EHV-1 vaccines are only partially protective and effective metaphylactic and therapeutic agents are not available. Small interfering RNAs (siRNA's), delivered intranasally, could prove a valuable alternative for infection control. siRNA's against two essential EHV-1 genes, encoding the viral helicase (Ori) and glycoprotein B, were evaluated for their potential to decrease EHV-1 infection in a mouse model. METHODOLOGY/PRINCIPAL FNDINGS: siRNA therapy in vitro significantly reduced virus production and plaque size. Viral titers were reduced 80-fold with 37.5 pmol of a single siRNA or with as little as 6.25 pmol of each siRNA when used in combination. siRNA therapy in vivo significantly reduced viral replication and clinical signs. Intranasal treatment did not require a transport vehicle and proved effective when given up to 12 h before or after infection. CONCLUSIONS/SIGNIFICANCE: siRNA treatment has potential for both prevention and early treatment of EHV-1 infections

Real-world evidence in Alzheimer’s disease: the ROADMAP Data Cube

INTRODUCTION:The ROADMAP project aimed to provide an integrated overview of European real-world data on Alzheimer's disease (AD) across the disease spectrum. METHODS:Metadata were identified from data sources in catalogs of European AD projects. Priority outcomes for different stakeholders were identified through systematic literature review, patient and public consultations, and stakeholder surveys. RESULTS:Information about 66 data sources and 13 outcome domains were integrated into a Data Cube. Gap analysis identified cognitive ability, functional ability/independence, behavioral/neuropsychiatric symptoms, treatment, comorbidities, and mortality as the outcomes collected most. Data were most lacking in caregiver-related outcomes. In general, electronic health records covered a broader, less detailed data spectrum than research cohorts. DISCUSSION:This integrated real-world AD data overview provides an intuitive visual model that facilitates initial assessment and identification of gaps in relevant outcomes data to inform future prospective data collection and matching of data sources and outcomes against research protocols

Therapeutic and Prognostic Implications of BRAF V600E in Pediatric Low-Grade Gliomas

Purpose BRAF V600E is a potentially highly targetable mutation detected in a subset of pediatric low-grade gliomas (PLGGs). Its biologic and clinical effect within this diverse group of tumors remains unknown. Patients and Methods A combined clinical and genetic institutional study of patients with PLGGs with long-term follow-up was performed (N = 510). Clinical and treatment data of patients with BRAF V600E mutated PLGG (n = 99) were compared with a large international independent cohort of patients with BRAF V600E mutated-PLGG (n = 180). Results BRAF V600E mutation was detected in 69 of 405 patients (17%) with PLGG across a broad spectrum of histologies and sites, including midline locations, which are not often routinely biopsied in clinical practice. Patients with BRAF V600E PLGG exhibited poor outcomes after chemotherapy and radiation therapies that resulted in a 10-year progression-free survival of 27% (95% CI, 12.1% to 41.9%) and 60.2% (95% CI, 53.3% to 67.1%) for BRAF V600E and wild-type PLGG, respectively (P < .001). Additional multivariable clinical and molecular stratification revealed that the extent of resection and CDKN2A deletion contributed independently to poor outcome in BRAF V600E PLGG. A similar independent role for CDKN2A and resection on outcome were observed in the independent cohort. Quantitative imaging analysis revealed progressive disease and a lack of response to conventional chemotherapy in most patients with BRAF V600E PLGG. Conclusion BRAF V600E PLGG constitutes a distinct entity with poor prognosis when treated with current adjuvant therapy. (C) 2017 by American Society of Clinical Oncolog

Therapeutic and Prognostic Implications of BRAF V600E in Pediatric Low-Grade Gliomas.

Purpose BRAF V600E is a potentially highly targetable mutation detected in a subset of pediatric low-grade gliomas (PLGGs). Its biologic and clinical effect within this diverse group of tumors remains unknown. Patients and Methods A combined clinical and genetic institutional study of patients with PLGGs with long-term follow-up was performed (N = 510). Clinical and treatment data of patients with BRAF V600E mutated PLGG (n = 99) were compared with a large international independent cohort of patients with BRAF V600E mutated-PLGG (n = 180). Results BRAF V600E mutation was detected in 69 of 405 patients (17%) with PLGG across a broad spectrum of histologies and sites, including midline locations, which are not often routinely biopsied in clinical practice. Patients with BRAF V600E PLGG exhibited poor outcomes after chemotherapy and radiation therapies that resulted in a 10-year progression-free survival of 27% (95% CI, 12.1% to 41.9%) and 60.2% (95% CI, 53.3% to 67.1%) for BRAF V600E and wild-type PLGG, respectively ( P \u3c .001). Additional multivariable clinical and molecular stratification revealed that the extent of resection and CDKN2A deletion contributed independently to poor outcome in BRAF V600E PLGG. A similar independent role for CDKN2A and resection on outcome were observed in the independent cohort. Quantitative imaging analysis revealed progressive disease and a lack of response to conventional chemotherapy in most patients with BRAF V600E PLGG. Conclusion BRAF V600E PLGG constitutes a distinct entity with poor prognosis when treated with current adjuvant therapy

Regulatory sites for splicing in human basal ganglia are enriched for disease-relevant information

Genome-wide association studies have generated an increasing number of common genetic variants associated with neurological and psychiatric disease risk. An improved understanding of the genetic control of gene expression in human brain is vital considering this is the likely modus operandum for many causal variants. However, human brain sampling complexities limit the explanatory power of brain-related expression quantitative trait loci (eQTL) and allele-specific expression (ASE) signals. We address this, using paired genomic and transcriptomic data from putamen and substantia nigra from 117 human brains, interrogating regulation at different RNA processing stages and uncovering novel transcripts. We identify disease-relevant regulatory loci, find that splicing eQTLs are enriched for regulatory information of neuron-specific genes, that ASEs provide cell-specific regulatory information with evidence for cellular specificity, and that incomplete annotation of the brain transcriptome limits interpretation of risk loci for neuropsychiatric disease. This resource of regulatory data is accessible through our web server, http://braineacv2.inf.um.es/

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)

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Publisher Copyright: © 2022, The Author(s).Background: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. Results: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. Conclusions: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.Peer reviewe