cxr: A toolbox for modelling species coexistence in R

Recent developments in modern coexistence theory (MCT) have advanced our understanding of how species interactions among themselves and with the environment influence community dynamics. Although the formulation of MCT is mathematically clear, its application to empirical cases is still challenging, which precludes its adoption by a large range of ecologists and evolutionary biologists interested in broad questions related to community assembly and the maintenance of species diversity. We developedcxr, anrpackage that provides a complete toolbox for calculating species vital rates and interaction parameters, from which the user can obtain estimates of coexistence outcomes based on stabilizing niche differences and average fitness differences. Our aim is to offer a highly versatile package to accommodate different research needs. This means that the user can define population models, use different optimization algorithms and include the effect of external covariates on species interactions, which may include environmental variables (e.g. temperature, precipitation, salinity) and biotic controls (e.g. predation, pollination, mycorrhizae). To illustrate the functionality and versatility ofcxr, we provide a complete set of population dynamic models and a dataset from a highly diverse grassland community. By building bridges between MCT formulation and its implementation, we provide tools to obtain a deeper mechanistic understanding of how species interactions determine basic patterns such as species abundances and dominance, which are core information for many applied fields, such as conservation, restoration and invasion biology. Finally, the package is not limited taxonomically to any particular group. The application of tools derived from MCT to a wide range of different systems can create feedbacks between empirical and theoretical studies in a way that stimulates a better understanding of the processes maintaining biodiversity

The spatial configuration of biotic interactions shapes coexistence-area relationships in an annual plant community

The increase of species richness with area is a universal phenomenon on Earth. However, this observation contrasts with our poor understanding of how these species-area relationships (SARs) emerge from the collective effects of area, spatial heterogeneity, and local interactions. By combining a structuralist approach with five years of empirical observations in a highly-diverse Mediterranean grassland, we show that spatial heterogeneity plays a little role in the accumulation of species richness with area in our system. Instead, as we increase the sampled area more species combinations are realized, and they coexist mainly due to direct pairwise interactions rather than by changes in single-species dominance or by indirect interactions. We also identify a small set of transient species with small population sizes that are consistently found across spatial scales. These findings empirically support the importance of the architecture of species interactions together with stochastic events for driving coexistence- and species-area relationships. Local patterns of species coexistence across scales could determine the shape of species-area relationships. Here the authors apply a structuralist approach to empirical data on annual plant communities to assess how species interactions shape coexistence- and species-area relationships

Fine scale prediction of ecological community composition using a two-step sequential Machine Learning ensemble

Prediction is one of the last frontiers in ecology. Indeed, predicting fine-scale species composition in natural systems is a complex challenge as multiple abiotic and biotic processes operate simultaneously to determine local species abundances. On the one hand, species intrinsic performance and their tolerance limits to different abiotic pressures modulate species abundances. On the other hand there is growing recognition that species interactions play an equally important role in limiting or promoting such abundances within ecological communities. Here, we present a joint effort between ecologists and data scientists to use data-driven models to predict species abundances using reasonably easy to obtain data. We propose a sequential data-driven modeling approach that in a first step predicts the potential species abundances based on abiotic variables, and in a second step uses these predictions to model the realized abundances once accounting for species competition. Using a curated data set over five years we predict fine-scale species abundances in a highly diverse annual plant community. Our models show a remarkable spatial predictive accuracy using only easy-to-measure variables in the field, yet such predictive power is lost when temporal dynamics are taken into account. This result suggests that predicting future abundances requires longer time series analysis to capture enough variability. In addition, we show that these data-driven models can also suggest how to improve mechanistic models by adding missing variables that affect species performance such as particular soil conditions (e.g. carbonate availability in our case). Robust models for predicting fine-scale species composition informed by the mechanistic understanding of the underlying abiotic and biotic processes can be a pivotal tool for conservation, especially given the human-induced rapid environmental changes we are experiencing. This objective can be achieved by promoting the knowledge gained with classic modelling approaches in ecology and recently developed data-driven models. Author summary Prediction is challenging but recently developed Machine Learning techniques allow to dramatically improve prediction accuracy in several domains. However, these tools are often of little application in ecology due to the hardship of gathering information on the needed explanatory variables, which often comprise not only physical variables such as temperature or soil nutrients, but also information about the complex network of species interactions that modulate species abundances. Here we present a two-step sequential modelling framework that overcomes these constraints. We first infer potential species abundances by training models just with easily obtained abiotic variables and then use this outcome to fine-tune the prediction of the realized species abundances when taking into account the rest of the predicted species in the community. Overall, our results show a promising way forward for fine scale prediction in ecology.O.G. acknowledges support provided by the Ministerio de Ciencia, Innovacion y Universidades (RYC-2017-23666). O.G. and I.B. acknowledge financial support provided by the Secretaria de Estado de Investigacion, Desarrollo e Innovacion (CGL2017-92436-EXP, SIMPLEX and RTI2018-098888-A-I00, MeDiNaS). J.G. acknowledges financial support provided by the Ministerio de Ciencia, Innovacion y Universidades (PGC2018-093854-B-I00). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Biologic Therapy in Refractory Non-Multiple Sclerosis Optic Neuritis Isolated or Associated to Immune Mediated Inflammatory Diseases. A Multicenter Study

We aimed to assess the e cacy of biologic therapy in refractory non-Multiple Sclerosis (MS) Optic Neuritis (ON), a condition more infrequent, chronic and severe than MS ON. This was an open-label multicenter study of patients with non-MS ON refractory to systemic corticosteroids and at least one conventional immunosuppressive drug. The main outcomes were Best Corrected Visual Acuity (BCVA) and both Macular Thickness (MT) and Retinal Nerve Fiber Layer (RNFL) using Optical Coherence Tomography (OCT). These outcome variables were assessed at baseline, 1 week, and 1, 3, 6 and 12 months after biologic therapy initiation. Remission was defined as the absence of ON symptoms and signs that lasted longer than 24 h, with or without an associated new lesion on magnetic resonance imaging with gadolinium contrast agents for at least 3 months. We studied 19 patients (11 women/8 men; mean age, 34.8 13.9 years). The underlying diseases were Bechet?s disease (n = 5), neuromyelitis optica (n = 3), systemic lupus erythematosus (n = 2), sarcoidosis (n = 1), relapsing polychondritis (n = 1) and anti-neutrophil cytoplasmic antibody -associated vasculitis (n = 1). It was idiopathic in 6 patients. The first biologic agent used in each patient was: adalimumab (n = 6), rituximab (n = 6), infliximab (n = 5) and tocilizumab (n = 2). A second immunosuppressive drug was simultaneously used in 11 patients: methotrexate (n = 11), azathioprine (n = 2), mycophenolate mofetil (n = 1) and hydroxychloroquine (n = 1). Improvement of the main outcomes was observed after 1 year of therapy when compared with baseline data: mean SD BCVA (0.8 0.3 LogMAR vs. 0.6 0.3 LogMAR; p = 0.03), mean SD RNFL (190.5 175.4 m vs. 183.4 139.5 m; p = 0.02), mean SD MT (270.7 23.2 m vs. 369.6 137.4 m; p = 0.03). Besides, the median (IQR) prednisone-dose was also reduced from 40 (10?61.5) mg/day at baseline to. 2.5 (0?5) mg/day after one year of follow-up; p = 0.001. After a mean SD follow-up of 35 months, 15 patients (78.9%) achieved ocular remission, and 2 (10.5%) experienced severe adverse events. Biologic therapy is e ective in patients with refractory non-MS ON

Analysis of the common genetic component of large-vessel vasculitides through a meta- Immunochip strategy

Giant cell arteritis (GCA) and Takayasu's arteritis (TAK) are major forms of large-vessel vasculitis (LVV) that share clinical features. To evaluate their genetic similarities, we analysed Immunochip genotyping data from 1,434 LVV patients and 3,814 unaffected controls. Genetic pleiotropy was also estimated. The HLA region harboured the main disease-specific associations. GCA was mostly associated with class II genes (HLA-DRB1/HLA-DQA1) whereas TAK was mostly associated with class I genes (HLA-B/MICA). Both the statistical significance and effect size of the HLA signals were considerably reduced in the cross-disease meta-analysis in comparison with the analysis of GCA and TAK separately. Consequently, no significant genetic correlation between these two diseases was observed when HLA variants were tested. Outside the HLA region, only one polymorphism located nearby the IL12B gene surpassed the study-wide significance threshold in the meta-analysis of the discovery datasets (rs755374, P?=?7.54E-07; ORGCA?=?1.19, ORTAK?=?1.50). This marker was confirmed as novel GCA risk factor using four additional cohorts (PGCA?=?5.52E-04, ORGCA?=?1.16). Taken together, our results provide evidence of strong genetic differences between GCA and TAK in the HLA. Outside this region, common susceptibility factors were suggested, especially within the IL12B locus

A genome-wide association study identifies risk alleles in plasminogen and P4HA2 associated with giant cell arteritis

Giant cell arteritis (GCA) is the most common form of vasculitis in individuals older than 50 years in Western countries. To shed light onto the genetic background influencing susceptibility for GCA, we performed a genome-wide association screening in a well-powered study cohort. After imputation, 1,844,133 genetic variants were analysed in 2,134 cases and 9,125 unaffected controls from ten independent populations of European ancestry. Our data confirmed HLA class II as the strongest associated region (independent signals: rs9268905, P = 1.94E-54, per-allele OR = 1.79; and rs9275592, P = 1.14E-40, OR = 2.08). Additionally, PLG and P4HA2 were identified as GCA risk genes at the genome-wide level of significance (rs4252134, P = 1.23E-10, OR = 1.28; and rs128738, P = 4.60E-09, OR = 1.32, respectively). Interestingly, we observed that the association peaks overlapped with different regulatory elements related to cell types and tissues involved in the pathophysiology of GCA. PLG and P4HA2 are involved in vascular remodelling and angiogenesis, suggesting a high relevance of these processes for the pathogenic mechanisms underlying this type of vasculitis

A Large-Scale Genetic Analysis Reveals a Strong Contribution of the HLA Class II Region to Giant Cell Arteritis Susceptibility

We conducted a large-scale genetic analysis on giant cell arteritis (GCA), a polygenic immune-mediated vasculitis. A case-control cohort, comprising 1,651 case subjects with GCA and 15,306 unrelated control subjects from six different countries of European ancestry, was genotyped by the Immunochip array. We also imputed HLA data with a previously validated imputation method to perform a more comprehensive analysis of this genomic region. The strongest association signals were observed in the HLA region, with rs477515 representing the highest peak (p = 4.05 × 10−40, OR = 1.73). A multivariate model including class II amino acids of HLA-DRβ1 and HLA-DQα1 and one class I amino acid of HLA-B explained most of the HLA association with GCA, consistent with previously reported associations of classical HLA alleles like HLA-DRB1∗04. An omnibus test on polymorphic amino acid positions highlighted DRβ1 13 (p = 4.08 × 10−43) and HLA-DQα1 47 (p = 4.02 × 10−46), 56, and 76 (both p = 1.84 × 10−45) as relevant positions for disease susceptibility. Outside the HLA region, the most significant loci included PTPN22 (rs2476601, p = 1.73 × 10−6, OR = 1.38), LRRC32 (rs10160518, p = 4.39 × 10−6, OR = 1.20), and REL (rs115674477, p = 1.10 × 10−5, OR = 1.63). Our study provides evidence of a strong contribution of HLA class I and II molecules to susceptibility to GCA. In the non-HLA region, we confirmed a key role for the functional PTPN22 rs2476601 variant and proposed other putative risk loci for GCA involved in Th1, Th17, and Treg cell function

How to create R-packages

En el lenguaje de programación R (R Core Team 2019), los paquetes son la mejor manera de organizar, mantener y distribuir código y documentación. La motivación más directa para crear un paquete es la facilidad con que permiten compartir código con otros usuarios, pero de igual manera resulta extremadamente útil crear paquetes “de consumo propio”, por ejemplo en cuanto tengamos funciones propias que utilizamos en diferentes proyectos (véase De la Cruz (2019) para la creación de funciones en R). Incluir un paquete de R, bien documentado, en el material suplementario de un artículo o de un informe científico garantiza la reproducibilidad de los resultados y promueve la difusión de los mismos

A Prospective Study of the Serological, Clinical, and Epidemiological Features of a SARS-CoV-2 Positive Pediatric Cohort

Background: SARS-CoV-2 was a global pandemic. Children develop a mild disease and may have a different rate of seroconversion compared to adults. The objective was to determine the number of seronegative patients in a pediatric cohort. We also reviewed the clinical–epidemiological features associated with seroconversion. Methods: A multicenter prospective observational study during September–November 2020, of COVID-19, confirmed by reverse transcription-polymerase chain reaction. Data were obtained 4–8 weeks after diagnosis. Blood samples were collected to investigate the humoral response, using three different serological methods. Results: A total of 111 patients were included (98 symptomatic), 8 were admitted to hospital, none required an Intensive Care Unit visit. Median age: 88 months (IQR: 24–149). Median time between diagnosis and serological test: 37 days (IQR: 34–44). A total of 19 patients were non-seroconverters when using three serological techniques (17.1%; 95% CI: 10.6–25.4); most were aged 2–10 years (35%, p < 0.05). Univariate analysis yielded a lower rate of seroconversion when COVID-19 confirmation was not present amongst household contacts (51.7%; p < 0.05). Conclusions: There was a high proportion of non-seroconverters. This is more commonly encountered in childhood than in adults. Most seronegative patients were in the group aged 2–10 years, and when COVID-19 was not documented in household contacts. Most developed a mild disease. Frequently, children were not the index case within the family

Identification of a peripheral blood gene signature predicting aortic valve calcification

International audienceCalcific aortic valve disease (CAVD) is a significant cause of illness and death worldwide. Identification of early predictive markers could help optimize patient management. RNA-sequencing was carried out on human fetal aortic valves at gestational weeks 9, 13, and 22 and on a case-control study with adult noncalcified and calcified bicuspid and tricuspid aortic valves. In dimension reduction and clustering analyses, diseased valves tended to cluster with fetal valves at week 9 rather than normal adult valves, suggesting that part of the disease program might be due to reiterated developmental processes. The analysis of groups of coregulated genes revealed predominant immune-metabolic signatures, including innate and adaptive immune responses involving lymphocyte T-cell metabolic adaptation. Cytokine and chemokine signaling, cell migration, and proliferation were all increased in CAVD, whereas oxidative phosphorylation and protein translation were decreased. Discrete immune-metabolic gene signatures were present at fetal stages and increased in adult controls, suggesting that these processes intensify throughout life and heighten in disease. Cellular stress response and neurodegeneration gene signatures were aberrantly expressed in CAVD, pointing to a mechanistic link between chronic inflammation and biological aging. Comparison of the valve RNA-sequencing data set with a case-control study of whole blood transcriptomes from asymptomatic individuals with early aortic valve calcification identified a highly predictive gene signature of CAVD and of moderate aortic valve calcification in overtly healthy individuals. These data deepen and broaden our understanding of the molecular basis of CAVD and identify a peripheral blood gene signature for the early detection of aortic valve calcification