Initial evaluation of some introduced forage plants for herbage productivity at two sites in Ghana

No abstract

Reaching the End-Game for GWAS: Machine Learning Approaches for the Prioritization of Complex Disease Loci.

Genome-wide association studies (GWAS) have revealed thousands of genetic loci that underpin the complex biology of many human traits. However, the strength of GWAS - the ability to detect genetic association by linkage disequilibrium (LD) - is also its limitation. Whilst the ever-increasing study size and improved design have augmented the power of GWAS to detect effects, differentiation of causal variants or genes from other highly correlated genes associated by LD remains the real challenge. This has severely hindered the biological insights and clinical translation of GWAS findings. Although thousands of disease susceptibility loci have been reported, causal genes at these loci remain elusive. Machine learning (ML) techniques offer an opportunity to dissect the heterogeneity of variant and gene signals in the post-GWAS analysis phase. ML models for GWAS prioritization vary greatly in their complexity, ranging from relatively simple logistic regression approaches to more complex ensemble models such as random forests and gradient boosting, as well as deep learning models, i.e., neural networks. Paired with functional validation, these methods show important promise for clinical translation, providing a strong evidence-based approach to direct post-GWAS research. However, as ML approaches continue to evolve to meet the challenge of causal gene identification, a critical assessment of the underlying methodologies and their applicability to the GWAS prioritization problem is needed. This review investigates the landscape of ML applications in three parts: selected models, input features, and output model performance, with a focus on prioritizations of complex disease associated loci. Overall, we explore the contributions ML has made towards reaching the GWAS end-game with consequent wide-ranging translational impact

Radon: a universal baseline indicator at sites with contrasting physical settings

The primary goal of World Meteorological Organisation Global Atmosphere Watch (WMO‐GAW) baseline stations is systematic global monitoring of chemical composition of the atmosphere, requiring a reliable, consistent and unambiguous approach for the identification of baseline air. Premier stations in the GAW baseline network span a broad range of physical settings, from remote marine to high‐altitude continental sites, necessitating carefully tailored site‐specific requirements for baseline sampling, data selection, and analysis. Radon‐222 is a versatile and unambiguous terrestrial tracer, widely‐used in transport and mixing studies. Since the majority of anthropogenic pollution sources also have terrestrial origins, radon has become a popular addition to the ‘baseline selection toolkit’ at numerous GAW stations as a proxy for ‘pollution potential’. In the past, detector performance and postprocessing methods necessitated the adoption of a relaxed (e.g. 100 mBq m‐3) radon threshold for minimal terrestrial influence, intended to be used in conjunction with other baseline criteria and analysis procedures, including wind speed, wind direction, particle number, outlier rejection and filtering. However, recent improvements in detector sensitivity, stability and post‐processing procedures have reduced detection limits below 10 mBq m‐3 at Cape Grim and to 25 mBq m‐3 at other baseline stations. Consequently, for suitably sensitive instruments (such as the ANSTO designed and built two‐filter dual‐flow‐loop detectors), radon concentrations alone can be used to unambiguously identify air masses that have been removed from terrestrial sources (at altitude or over ice), or in equilibrium with the ocean surface, for periods of >2‐3 weeks (radon ≤ 40 mBq m‐3). Potentially, radon observations alone can thus provide a consistent and universal (site independent) means for baseline identification. Furthermore, for continental sites with complex topography and meteorology, where true ‘baseline’ conditions may never occur, radon can be used to indicate the least terrestrially‐perturbed air masses, and provide a means by which to apply limits to the level of ‘acceptable terrestrial influence’ for a given application. We demonstrate the efficacy of the radon‐based selection at a range of sites in contrasting physical settings, including: Cape Grim (Tasmania), Cape Point (South Africa), Mauna Loa (Hawaii), Jungfraujoch (Switzerland) and Schneefernerhaus (Germany).Bureau of Meteorology and CSIRO Oceans and Atmosphere,Climate Science Centre

The rationale and plan for creating a World Antimalarial Resistance Network (WARN)

Drug resistant malaria was a major factor contributing to the failure of a worldwide campaign to eradicate malaria in the last century, and now threatens the large investment being made by the global community in the rollout of effective new drug combinations to replace failed drugs. Four related papers in this issue of Malaria Journal make the case for creating the World Antimalarial Resistance Network (WARN), which will consist of four linked open-access global databases containing clinical, in vitro, molecular and pharmacological data, and networks of reference laboratories that will support these databases and related surveillance activities. WARN will serve as a public resource to guide antimalarial drug treatment and prevention policies and to help confirm and characterize the new emergence of new resistance to antimalarial drugs and to contain its spread

Towards a universal “baseline” characterisation of air masses for high- and low-altitude observing stations using Radon-222

We demonstrate the ability of atmospheric radon concentrations to reliably and unambiguously identify local and remote terrestrial influences on an air mass, and thereby the potential for alteration of trace gas composition by anthropogenic and biogenic processes. Based on high accuracy (lower limit of detection 10–40 mBq m–3), high temporal resolution (hourly) measurements of atmospheric radon concentration we describe, apply and evaluate a simple two-step method for identifying and characterising constituent mole fractions in baseline air. The technique involves selecting a radon-based threshold concentration to identify the “cleanest” (least terrestrially influenced) air masses, and then performing an outlier removal step based on the distribution of constituent mole fractions in the identified clean air masses. The efficacy of this baseline selection technique is tested at three contrasting WMO GAW stations: Cape Grim (a coastal low-altitude site), Mauna Loa (a remote high-altitude island site), and Jungfraujoch (a continental high-altitude site). At Cape Grim and Mauna Loa the two-step method is at least as effective as more complicated methods employed to characterise baseline conditions, some involving up to nine steps. While it is demonstrated that Jungfraujoch air masses rarely meet the baseline criteria of the more remote sites, a selection method based on a variable monthly radon threshold is shown to produce credible “near baseline” characteristics. The seasonal peak-to-peak amplitude of recent monthly baseline CO2 mole fraction deviations from the long-term trend at Cape Grim, Mauna Loa and Jungfraujoch are estimated to be 1.1, 6.0 and 8.1 ppm, respectively

Over 1000 genetic loci influencing blood pressure with multiple systems and tissues implicated.

High blood pressure (BP) remains the major heritable and modifiable risk factor for cardiovascular disease. Persistent high BP, or hypertension, is a complex trait with both genetic and environmental interactions. Despite swift advances in genomics, translating new discoveries to further our understanding of the underlying molecular mechanisms remains a challenge. More than 500 loci implicated in the regulation of BP have been revealed by genome-wide association studies (GWAS) in 2018 alone, taking the total number of BP genetic loci to over 1000. Even with the large number of loci now associated to BP, the genetic variance explained by all loci together remains low (~5.7%). These genetic associations have elucidated mechanisms and pathways regulating BP, highlighting potential new therapeutic and drug repurposing targets. A large proportion of the BP loci were discovered and reported simultaneously by multiple research groups, creating a knowledge gap, where the reported loci to date have not been investigated in a harmonious way. Here, we review the BP-associated genetic variants reported across GWAS studies and investigate their potential impact on the biological systems using in silico enrichment analyses for pathways, tissues, gene ontology and genetic pleiotropy

The fidelity of dynamic signaling by noisy biomolecular networks

This is the final version of the article. Available from Public Library of Science via the DOI in this record.Cells live in changing, dynamic environments. To understand cellular decision-making, we must therefore understand how fluctuating inputs are processed by noisy biomolecular networks. Here we present a general methodology for analyzing the fidelity with which different statistics of a fluctuating input are represented, or encoded, in the output of a signaling system over time. We identify two orthogonal sources of error that corrupt perfect representation of the signal: dynamical error, which occurs when the network responds on average to other features of the input trajectory as well as to the signal of interest, and mechanistic error, which occurs because biochemical reactions comprising the signaling mechanism are stochastic. Trade-offs between these two errors can determine the system's fidelity. By developing mathematical approaches to derive dynamics conditional on input trajectories we can show, for example, that increased biochemical noise (mechanistic error) can improve fidelity and that both negative and positive feedback degrade fidelity, for standard models of genetic autoregulation. For a group of cells, the fidelity of the collective output exceeds that of an individual cell and negative feedback then typically becomes beneficial. We can also predict the dynamic signal for which a given system has highest fidelity and, conversely, how to modify the network design to maximize fidelity for a given dynamic signal. Our approach is general, has applications to both systems and synthetic biology, and will help underpin studies of cellular behavior in natural, dynamic environments.We acknowledge support from a Medical Research Council and Engineering and Physical Sciences Council funded Fellowship in Biomedical Informatics (CGB) and a Scottish Universities Life Sciences Alliance chair in Systems Biology (PSS). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

A self-report risk index to predict occurrence of dementia in three independent cohorts of older adults: The ANU-ADRI

Background and Aims: The Australian National University AD Risk Index (ANU-ADRI, http://anuadri.anu.edu.au) is a self-report risk index developed using an evidence-based medicine approach to measure risk of Alzheimer's disease (AD). We aimed to evaluate the extent to which the ANU-ADRI can predict the risk of AD in older adults and to compare the ANU-ADRI to the dementia risk index developed from the Cardiovascular Risk Factors, Aging and Dementia (CAIDE) study for middle-aged cohorts. Methods: This study included three validation cohorts, i.e., the Rush Memory and Aging Study (MAP) (n = 903, age ≥53 years), the Kungsholmen Project (KP) (n = 905, age ≥75 years), and the Cardiovascular Health Cognition Study (CVHS) (n = 2496, age ≥65 years) that were each followed for dementia. Baseline data were collected on exposure to the 15 risk factors included in the ANU-ADRI of which MAP had 10, KP had 8 and CVHS had 9. Risk scores and C-statistics were computed for individual participants for the ANU-ADRI and the CAIDE index. Results: For the ANU-ADRI using available data, the MAP study c-statistic was 0.637 (95% CI 0.596-0.678), for the KP study it was 0.740 (0.712-0.768) and for the CVHS it was 0.733 (0.691-0.776) for predicting AD. When a common set of risk and protective factors were used c-statistics were 0.689 (95% CI 0.650-0.727), 0.666 (0.628-0.704) and 0.734 (0.707-0.761) for MAP, KP and CVHS respectively. Results for CAIDE ranged from c-statistics of 0.488 (0.427-0.554) to 0.595 (0.565-0.625). Conclusion: A composite risk score derived from the ANU-ADRI weights including 8-10 risk or protective factors is a valid, self-report tool to identify those at risk of AD and dementia. The accuracy can be further improved in studies including more risk factors and younger cohorts with long-term follow-up. © 2014 Anstey et al

Placental syncytiotrophoblast constitutes a major barrier to vertical transmission of Listeria monocytogenes.

Listeria monocytogenes is an important cause of maternal-fetal infections and serves as a model organism to study these important but poorly understood events. L. monocytogenes can infect non-phagocytic cells by two means: direct invasion and cell-to-cell spread. The relative contribution of each method to placental infection is controversial, as is the anatomical site of invasion. Here, we report for the first time the use of first trimester placental organ cultures to quantitatively analyze L. monocytogenes infection of the human placenta. Contrary to previous reports, we found that the syncytiotrophoblast, which constitutes most of the placental surface and is bathed in maternal blood, was highly resistant to L. monocytogenes infection by either internalin-mediated invasion or cell-to-cell spread. Instead, extravillous cytotrophoblasts-which anchor the placenta in the decidua (uterine lining) and abundantly express E-cadherin-served as the primary portal of entry for L. monocytogenes from both extracellular and intracellular compartments. Subsequent bacterial dissemination to the villous stroma, where fetal capillaries are found, was hampered by further cellular and histological barriers. Our study suggests the placenta has evolved multiple mechanisms to resist pathogen infection, especially from maternal blood. These findings provide a novel explanation why almost all placental pathogens have intracellular life cycles: they may need maternal cells to reach the decidua and infect the placenta

Processing of Retinal Signals in Normal and HCN Deficient Mice

This study investigates the role of two different HCN channel isoforms in the light response of the outer retina. Taking advantage of HCN-deficient mice models and of in vitro (patch-clamp) and in vivo (ERG) recordings of retinal activity we show that HCN1 and HCN2 channels are expressed at distinct retinal sites and serve different functions. Specifically, HCN1 operate mainly at the level of the photoreceptor inner segment from where, together with other voltage sensitive channels, they control the time course of the response to bright light. Conversely, HCN2 channels are mainly expressed on the dendrites of bipolar cells and affect the response to dim lights. Single cell recordings in HCN1−/− mice or during a pharmacological blockade of Ih show that, contrary to previous reports, Ikx alone is able to generate the fast initial transient in the rod bright flash response. Here we demonstrate that the relative contribution of Ih and Ikx to the rods' temporal tuning depends on the membrane potential. This is the first instance in which the light response of normal and HCN1- or HCN2-deficient mice is analyzed in single cells in retinal slice preparations and in integrated full field ERG responses from intact animals. This comparison reveals a high degree of correlation between single cell current clamp data and ERG measurements. A novel picture emerges showing that the temporal profile of the visual response to dim and bright luminance changes is separately determined by the coordinated gating of distinct voltage dependent conductances in photoreceptors and bipolar cells