Benchmarking algorithms for genomic prediction of complex traits

The usefulness of Genomic Prediction (GP) in crop and livestock breeding programs has led to efforts to develop new and improved GP approaches including non-linear algorithm, such as artificial neural networks (ANN) (i.e. deep learning) and gradient tree boosting. However, the performance of these algorithms has not been compared in a systematic manner using a wide range of GP datasets and models. Using data of 18 traits across six plant species with different marker densities and training population sizes, we compared the performance of six linear and five non-linear algorithms, including ANNs. First, we found that hyperparameter selection was critical for all non-linear algorithms and that feature selection prior to model training was necessary for ANNs when the markers greatly outnumbered the number of training lines. Across all species and trait combinations, no one algorithm performed best, however predictions based on a combination of results from multiple GP algorithms (i.e. ensemble predictions) performed consistently well. While linear and non-linear algorithms performed best for a similar number of traits, the performance of non-linear algorithms vary more between traits than that of linear algorithms. Although ANNs did not perform best for any trait, we identified strategies (i.e. feature selection, seeded starting weights) that boosted their performance near the level of other algorithms. These results, together with the fact that even small improvements in GP performance could accumulate into large genetic gains over the course of a breeding program, highlights the importance of algorithm selection for the prediction of trait value

Ice nucleating particles carried from below a phytoplankton bloom to the arctic atmosphere

Author Posting. © American Geophysical Union, 2019. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 46(14), (2019): 8572-8581, doi: 10.1029/2019GL083039.As Arctic temperatures rise at twice the global rate, sea ice is diminishing more quickly than models can predict. Processes that dictate Arctic cloud formation and impacts on the atmospheric energy budget are poorly understood, yet crucial for evaluating the rapidly changing Arctic. In parallel, warmer temperatures afford conditions favorable for productivity of microorganisms that can effectively serve as ice nucleating particles (INPs). Yet the sources of marine biologically derived INPs remain largely unknown due to limited observations. Here we show, for the first time, how biologically derived INPs were likely transported hundreds of kilometers from deep Bering Strait waters and upwelled to the Arctic Ocean surface to become airborne, a process dependent upon a summertime phytoplankton bloom, bacterial respiration, ocean dynamics, and wind‐driven mixing. Given projected enhancement in marine productivity, combined oceanic and atmospheric transport mechanisms may play a crucial role in provision of INPs from blooms to the Arctic atmosphere.We sincerely thank the U.S. Coast Guard and crew of the Healy for assistance with equipment installation and guidance, operation of the underway and CTD systems, and general operation of the vessel during transit and at targeted sampling stations. We would also like to thank Allan Bertram, Meng Si, Victoria Irish, and Benjamin Murray for providing INP data from their previous studies. J. M. C., R. P., P. L., L. T., and E. B. were funded by the National Oceanic and Atmospheric Administration (NOAA)’s Arctic Research Program. J. C. was supported by the NOAA Experiential Research & Training Opportunities (NERTO) program. T. A. and N. C. were supported through the NOAA Earnest F. Hollings Scholarship program. A. P. was funded by the National Science Foundation under Grant PLR‐1303617. Russel C. Schnell and Michael Spall are acknowledged for insightful discussions during data analysis and interpretation. There are no financial conflicts of interest for any author. INP data are available in the supporting information, while remaining DBO‐NCIS data presented in the manuscript are available online (at https://www2.whoi.edu/site/dboncis/).2020-01-1

Introduction to “Binary Binds”: Deconstructing Sex and Gender Dichotomies in Archaeological Practice

YesGender archaeology has made significant strides toward deconstructing the hegemony of binary categorizations. Challenging dichotomies such as man/woman, sex/gender, and biology/culture, approaches informed by poststructuralist, feminist, and queer theories have moved beyond essentialist and universalist identity constructs to more nuanced configurations. Despite the theoretical emphasis on context, multiplicity, and fluidity, binary starting points continue to streamline the spectrum of variability that is recognized, often reproducing normative assumptions in the evidence. The contributors to this special issue confront how sex, gender, and sexuality categories condition analytical visibility, aiming to develop approaches that respond to the complexity of theory in archaeological practice. The papers push the ontological and epistemological boundaries of bodies, personhood, and archaeological possibility, challenging a priori assumptions that contain how sex, gender, and sexuality categories are constituted and related to each other. Foregrounding intersectional approaches that engage with ambiguity, variability, and difference, this special issue seeks to “de-contain” categories, assumptions, and practices from “binding” our analytical gaze toward only certain kinds of persons and knowledges, in interpretations of the past and practices in the present

Global data on earthworm abundance, biomass, diversity and corresponding environmental properties

Publisher Copyright: © 2021, The Author(s).Earthworms are an important soil taxon as ecosystem engineers, providing a variety of crucial ecosystem functions and services. Little is known about their diversity and distribution at large spatial scales, despite the availability of considerable amounts of local-scale data. Earthworm diversity data, obtained from the primary literature or provided directly by authors, were collated with information on site locations, including coordinates, habitat cover, and soil properties. Datasets were required, at a minimum, to include abundance or biomass of earthworms at a site. Where possible, site-level species lists were included, as well as the abundance and biomass of individual species and ecological groups. This global dataset contains 10,840 sites, with 184 species, from 60 countries and all continents except Antarctica. The data were obtained from 182 published articles, published between 1973 and 2017, and 17 unpublished datasets. Amalgamating data into a single global database will assist researchers in investigating and answering a wide variety of pressing questions, for example, jointly assessing aboveground and belowground biodiversity distributions and drivers of biodiversity change.Peer reviewe

Pathway-based predictive approaches for non-animal assessment of acute inhalation toxicity

New approaches are needed to assess the effects of inhaled substances on human health. These approaches will be based on mechanisms of toxicity, an understanding of dosimetry, and the use of in silico modeling and in vitro test methods. In order to accelerate wider implementation of such approaches, development of adverse outcome pathways (AOPs) can help identify and address gaps in our understanding of relevant parameters for model input and mechanisms, and optimize non-animal approaches that can be used to investigate key events of toxicity. This paper describes the AOPs and the toolbox of in vitro and in silico models that can be used to assess the key events leading to toxicity following inhalation exposure. Because the optimal testing strategy will vary depending on the substance of interest, here we present a decision tree approach to identify an appropriate non-animal integrated testing strategy that incorporates consideration of a substance's physicochemical properties, relevant mechanisms of toxicity, and available in silico models and in vitro test methods. This decision tree can facilitate standardization of the testing approaches. Case study examples are presented to provide a basis for proof-of-concept testing to illustrate the utility of non-animal approaches to inform hazard identification and risk assessment of humans exposed to inhaled substances

Global data on earthworm abundance, biomass, diversity and corresponding environmental properties

Earthworms are an important soil taxon as ecosystem engineers, providing a variety of crucial ecosystem functions and services. Little is known about their diversity and distribution at large spatial scales, despite the availability of considerable amounts of local-scale data. Earthworm diversity data, obtained from the primary literature or provided directly by authors, were collated with information on site locations, including coordinates, habitat cover, and soil properties. Datasets were required, at a minimum, to include abundance or biomass of earthworms at a site. Where possible, site-level species lists were included, as well as the abundance and biomass of individual species and ecological groups. This global dataset contains 10,840 sites, with 184 species, from 60 countries and all continents except Antarctica. The data were obtained from 182 published articles, published between 1973 and 2017, and 17 unpublished datasets. Amalgamating data into a single global database will assist researchers in investigating and answering a wide variety of pressing questions, for example, jointly assessing aboveground and belowground biodiversity distributions and drivers of biodiversity change

Point-of-care testing in paediatric settings in the UK and Ireland: A cross-sectional study

Background: Point-of-care testing (POCT) is diagnostic testing performed at or near to the site of the patient. Understanding the current capacity, and scope, of POCT in this setting is essential in order to respond to new research evidence which may lead to wide implementation. Methods: A cross-sectional online survey study of POCT use was conducted between 6th January and 2nd February 2020 on behalf of two United Kingdom (UK) and Ireland-based paediatric research networks (Paediatric Emergency Research UK and Ireland, and General and Adolescent Paediatric Research UK and Ireland). Results: In total 91/109 (83.5%) sites responded, with some respondents providing details for multiple units on their site based on network membership (139 units in total). The most commonly performed POCT were blood sugar (137/139; 98.6%), urinalysis (134/139; 96.4%) and blood gas analysis (132/139; 95%). The use of POCT for Influenza/Respiratory Syncytial Virus (RSV) (45/139; 32.4%, 41/139; 29.5%), C-Reactive Protein (CRP) (13/139; 9.4%), Procalcitonin (PCT) (2/139; 1.4%) and Group A Streptococcus (5/139; 3.6%) and was relatively low. Obstacles to the introduction of new POCT included resources and infrastructure to support test performance and quality assurance. Conclusion: This survey demonstrates significant consensus in POCT practice in the UK and Ireland but highlights specific inequity in newer biomarkers, some which do not have support from national guidance. A clear strategy to overcome the key obstacles of funding, evidence base, and standardising variation will be essential if there is a drive toward increasing implementation of POCT

Multiorgan MRI findings after hospitalisation with COVID-19 in the UK (C-MORE): a prospective, multicentre, observational cohort study

Introduction: The multiorgan impact of moderate to severe coronavirus infections in the post-acute phase is still poorly understood. We aimed to evaluate the excess burden of multiorgan abnormalities after hospitalisation with COVID-19, evaluate their determinants, and explore associations with patient-related outcome measures. Methods: In a prospective, UK-wide, multicentre MRI follow-up study (C-MORE), adults (aged ≥18 years) discharged from hospital following COVID-19 who were included in Tier 2 of the Post-hospitalisation COVID-19 study (PHOSP-COVID) and contemporary controls with no evidence of previous COVID-19 (SARS-CoV-2 nucleocapsid antibody negative) underwent multiorgan MRI (lungs, heart, brain, liver, and kidneys) with quantitative and qualitative assessment of images and clinical adjudication when relevant. Individuals with end-stage renal failure or contraindications to MRI were excluded. Participants also underwent detailed recording of symptoms, and physiological and biochemical tests. The primary outcome was the excess burden of multiorgan abnormalities (two or more organs) relative to controls, with further adjustments for potential confounders. The C-MORE study is ongoing and is registered with ClinicalTrials.gov, NCT04510025. Findings: Of 2710 participants in Tier 2 of PHOSP-COVID, 531 were recruited across 13 UK-wide C-MORE sites. After exclusions, 259 C-MORE patients (mean age 57 years [SD 12]; 158 [61%] male and 101 [39%] female) who were discharged from hospital with PCR-confirmed or clinically diagnosed COVID-19 between March 1, 2020, and Nov 1, 2021, and 52 non-COVID-19 controls from the community (mean age 49 years [SD 14]; 30 [58%] male and 22 [42%] female) were included in the analysis. Patients were assessed at a median of 5·0 months (IQR 4·2–6·3) after hospital discharge. Compared with non-COVID-19 controls, patients were older, living with more obesity, and had more comorbidities. Multiorgan abnormalities on MRI were more frequent in patients than in controls (157 [61%] of 259 vs 14 [27%] of 52; p<0·0001) and independently associated with COVID-19 status (odds ratio [OR] 2·9 [95% CI 1·5–5·8]; padjusted=0·0023) after adjusting for relevant confounders. Compared with controls, patients were more likely to have MRI evidence of lung abnormalities (p=0·0001; parenchymal abnormalities), brain abnormalities (p<0·0001; more white matter hyperintensities and regional brain volume reduction), and kidney abnormalities (p=0·014; lower medullary T1 and loss of corticomedullary differentiation), whereas cardiac and liver MRI abnormalities were similar between patients and controls. Patients with multiorgan abnormalities were older (difference in mean age 7 years [95% CI 4–10]; mean age of 59·8 years [SD 11·7] with multiorgan abnormalities vs mean age of 52·8 years [11·9] without multiorgan abnormalities; p<0·0001), more likely to have three or more comorbidities (OR 2·47 [1·32–4·82]; padjusted=0·0059), and more likely to have a more severe acute infection (acute CRP >5mg/L, OR 3·55 [1·23–11·88]; padjusted=0·025) than those without multiorgan abnormalities. Presence of lung MRI abnormalities was associated with a two-fold higher risk of chest tightness, and multiorgan MRI abnormalities were associated with severe and very severe persistent physical and mental health impairment (PHOSP-COVID symptom clusters) after hospitalisation. Interpretation: After hospitalisation for COVID-19, people are at risk of multiorgan abnormalities in the medium term. Our findings emphasise the need for proactive multidisciplinary care pathways, with the potential for imaging to guide surveillance frequency and therapeutic stratification

From Africa to the Arctic: How aerosols affect cloud seeding and ecosystems

Satellite-based measurements of aerosols provide insight into relative aerosol concentrations and transport, but are not sufficient in creating an adequate information base for climate models to depend on. Future measurements and process-level studies are necessary to reduce the uncertainties in both the direct forcing of aerosols, the ability of aerosols to seed cloud droplets and ice crystals, and the role of aerosols on ecosystem health. African dust transport is thought to increase the primary productivity in the Amazon rain forest, and the Bodélé Depression in Chad has been identified as the single biggest source of this dust. Filters from the German sampling sight, ATTO (Amazon Tall Tower Observatory), were collected in order to identify diatoms and minerals characteristic of African dust, specifically those characteristic of the Bodélé Depression. The diatoms found in the ATTO samples were almost all fragments of crushed cells of fossilized fresh water planktonic diatom species characteristically found from the Bodélé Depression. Using the HYSPLIT model, the air mass back trajectory of the dust transport for the last week of February 2016 shows that air masses traversed Bodélé before arriving in Cayenne and ATTO highlighting the importance of Bodélé transport on the Amazon Basin. In addition to dust, sea-spray is another the major sources of atmospheric particles and contains large amounts of organic materials that are ejected into the atmosphere through breaking waves. This organic material is associated with phytoplankton cell exudates and has been observed to have a high ice-nucleating ability in micro layer samples. I analyzed seawater samples from INARCO I cruise using SEM coupled with Energy Dispersive X-Ray analysis (EDS) techniques to determine each aerosol’s mixing state, and biological and elemental composition. The initial analysis of the components with the INARCO seawater samples showed high numbers of pennate and centric diatoms. For the harmful algal bloom (HAB) site, the sample filter contained far more diatoms than any of the previous sampling sites and also showed efficient INP (Ice Nucleating Particle) results. This could suggest that high rates of biological activity lead to more efficient INP. After further characterization, more conclusions can be drawn on the transport of African dust into the Amazon and the ice nucleating characteristics of the INRACO I and II samples to understand the air-sea-ice interactions in the Arctic