Showing papers by "University of East Anglia published in 2021"

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)

Abstract: In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

Towards complete and error-free genome assemblies of all vertebrate species

Abstract: High-quality and complete reference genome assemblies are fundamental for the application of genomics to biology, disease, and biodiversity conservation. However, such assemblies are available for only a few non-microbial species1-4. To address this issue, the international Genome 10K (G10K) consortium5,6 has worked over a five-year period to evaluate and develop cost-effective methods for assembling highly accurate and nearly complete reference genomes. Here we present lessons learned from generating assemblies for 16 species that represent six major vertebrate lineages. We confirm that long-read sequencing technologies are essential for maximizing genome quality, and that unresolved complex repeats and haplotype heterozygosity are major sources of assembly error when not handled correctly. Our assemblies correct substantial errors, add missing sequence in some of the best historical reference genomes, and reveal biological discoveries. These include the identification of many false gene duplications, increases in gene sizes, chromosome rearrangements that are specific to lineages, a repeated independent chromosome breakpoint in bat genomes, and a canonical GC-rich pattern in protein-coding genes and their regulatory regions. Adopting these lessons, we have embarked on the Vertebrate Genomes Project (VGP), an international effort to generate high-quality, complete reference genomes for all of the roughly 70,000 extant vertebrate species and to help to enable a new era of discovery across the life sciences.

Mutual potentiation of plant immunity by cell-surface and intracellular receptors

Abstract: The plant immune system involves cell-surface receptors that detect intercellular pathogen-derived molecules, and intracellular receptors that activate immunity upon detection of pathogen-secreted effector proteins that act inside the plant cell. Immunity mediated by surface receptors has been extensively studied1, but that mediated by intracellular receptors has rarely been investigated in the absence of surface-receptor-mediated immunity. Furthermore, interactions between these two immune pathways are poorly understood. Here, by activating intracellular receptors without inducing surface-receptor-mediated immunity, we analyse interactions between these two distinct immune systems in Arabidopsis. Pathogen recognition by surface receptors activates multiple protein kinases and NADPH oxidases, and we find that intracellular receptors primarily potentiate the activation of these proteins by increasing their abundance through several mechanisms. Likewise, the hypersensitive response that depends on intracellular receptors is strongly enhanced by the activation of surface receptors. Activation of either immune system alone is insufficient to provide effective resistance against the bacterial pathogen Pseudomonas syringae. Thus, immune pathways activated by cell-surface and intracellular receptors in plants mutually potentiate to activate strong defences against pathogens. These findings reshape our understanding of plant immunity and have broad implications for crop improvement. In Arabidopsis, two distinct types of immunity—that mediated by cell-surface receptors and that mediated by intracellular receptors—interact with and mutually enhance each other to provide effective defence against pathogens.

International consensus statement on allergy and rhinology: rhinosinusitis 2021

Abstract: I. Executive summary BACKGROUND: The 5 years since the publication of the first International Consensus Statement on Allergy and Rhinology: Rhinosinusitis (ICAR-RS) has witnessed foundational progress in our understanding and treatment of rhinologic disease. These advances are reflected within the more than 40 new topics covered within the ICAR-RS-2021 as well as updates to the original 140 topics. This executive summary consolidates the evidence-based findings of the document. Methods ICAR-RS presents over 180 topics in the forms of evidence-based reviews with recommendations (EBRRs), evidence-based reviews, and literature reviews. The highest grade structured recommendations of the EBRR sections are summarized in this executive summary. Results ICAR-RS-2021 covers 22 topics regarding the medical management of RS, which are grade A/B and are presented in the executive summary. Additionally, 4 topics regarding the surgical management of RS are grade A/B and are presented in the executive summary. Finally, a comprehensive evidence-based management algorithm is provided. Conclusion This ICAR-RS-2021 executive summary provides a compilation of the evidence-based recommendations for medical and surgical treatment of the most common forms of RS.

An Updated Assessment of Near-Surface Temperature Change From 1850: The HadCRUT5 Data Set

Abstract: We present a new version of the Met Office Hadley Centre/Climatic Research Unit global surface temperature dataset, HadCRUT5. HadCRUT5 presents monthly average near-surface temperature anomalies, relative to the 1961-1990 period, on a regular 5° latitude by 5° longitude grid from 1850 to 2018. HadCRUT5 is a combination of sea-surface temperature measurements over the ocean from ships and buoys and near-surface air temperature measurements from weather stations over the land surface. These data have been sourced from updated compilations and the adjustments applied to mitigate the impact of changes in sea-surface temperature measurement methods have been revised. Two variants of HadCRUT5 have been produced for use in different applications. The first represents temperature anomaly data on a grid for locations where measurement data are available. The second, more spatially complete, variant uses a Gaussian process based statistical method to make better use of the available observations, extending temperature anomaly estimates into regions for which the underlying measurements are informative. Each is provided as a 200-member ensemble accompanied by additional uncertainty information. The combination of revised input datasets and statistical analysis results in greater warming of the global average over the course of the whole record. In recent years, increased warming results from an improved representation of Arctic warming and a better understanding of evolving biases sea-surface temperature measurements from ships. These updates result in greater consistency with other independent global surface temperature datasets, despite their different approaches to dataset construction, and further increase confidence in our understanding of changes seen.

Meta-analysis of the Parkinson's disease gut microbiome suggests alterations linked to intestinal inflammation.

Abstract: The gut microbiota is emerging as an important modulator of neurodegenerative diseases, and accumulating evidence has linked gut microbes to Parkinson’s disease (PD) symptomatology and pathophysiology. PD is often preceded by gastrointestinal symptoms and alterations of the enteric nervous system accompany the disease. Several studies have analyzed the gut microbiome in PD, but a consensus on the features of the PD-specific microbiota is missing. Here, we conduct a meta-analysis re-analyzing the ten currently available 16S microbiome datasets to investigate whether common alterations in the gut microbiota of PD patients exist across cohorts. We found significant alterations in the PD-associated microbiome, which are robust to study-specific technical heterogeneities, although differences in microbiome structure between PD and controls are small. Enrichment of the genera Lactobacillus, Akkermansia, and Bifidobacterium and depletion of bacteria belonging to the Lachnospiraceae family and the Faecalibacterium genus, both important short-chain fatty acids producers, emerged as the most consistent PD gut microbiome alterations. This dysbiosis might result in a pro-inflammatory status which could be linked to the recurrent gastrointestinal symptoms affecting PD patients.

A guide to the composition and functions of the extracellular matrix.

Abstract: Extracellular matrix (ECM) is a dynamic 3-dimensional network of macromolecules that provides structural support for the cells and tissues. Accumulated knowledge clearly demonstrated over the last decade that ECM plays key regulatory roles since it orchestrates cell signaling, functions, properties and morphology. Extracellularly secreted as well as cell-bound factors are among the major members of the ECM family. Proteins/glycoproteins, such as collagens, elastin, laminins and tenascins, proteoglycans and glycosaminoglycans, hyaluronan, and their cell receptors such as CD44 and integrins, responsible for cell adhesion, comprise a well-organized functional network with significant roles in health and disease. On the other hand, enzymes such as matrix metalloproteinases and specific glycosidases including heparanase and hyaluronidases contribute to matrix remodeling and affect human health. Several cell processes and functions, among them cell proliferation and survival, migration, differentiation, autophagy, angiogenesis, and immunity regulation are affected by certain matrix components. Structural alterations have been also well associated with disease progression. This guide on the composition and functions of the ECM gives a broad overview of the matrisome, the major ECM macromolecules, and their interaction networks within the ECM and with the cell surface, summarizes their main structural features and their roles in tissue organization and cell functions, and emphasizes the importance of specific ECM constituents in disease development and progression as well as the advances in molecular targeting of ECM to design new therapeutic strategies.

Deep Learning for Person Re-identification: A Survey and Outlook.

Abstract: Person re-identification (Re-ID) aims at retrieving a person of interest across multiple non-overlapping cameras. With the advancement of deep neural networks and increasing demand of intelligent video surveillance, it has gained significantly increased interest in the computer vision community. By dissecting the involved components in developing a person Re-ID system, we categorize it into the closed-world and open-world settings. We first conduct a comprehensive overview with in-depth analysis for closed-world person Re-ID from three different perspectives, including deep feature representation learning, deep metric learning and ranking optimization. With the performance saturation under closed-world setting, the research focus for person Re-ID has recently shifted to the open-world setting, facing more challenging issues. This setting is closer to practical applications under specific scenarios. We summarize the open-world Re-ID in terms of five different aspects. By analyzing the advantages of existing methods, we design a powerful AGW baseline, achieving state-of-the-art or at least comparable performance on twelve datasets for four different Re-ID tasks. Meanwhile, we introduce a new evaluation metric (mINP) for person Re-ID, indicating the cost for finding all the correct matches, which provides an additional criterion to evaluate the Re-ID system. Finally, some important yet under-investigated open issues are discussed.

Perturbations in Gut Microbiota Composition in Psychiatric Disorders. A Review and Meta-analysis

Abstract: Importance Evidence of gut microbiota perturbations has accumulated for multiple psychiatric disorders, with microbiota signatures proposed as potential biomarkers. However, no attempts have been made to evaluate the specificity of these across the range of psychiatric conditions. Objective To conduct an umbrella and updated meta-analysis of gut microbiota alterations in general adult psychiatric populations and perform a within- and between-diagnostic comparison. Data sources Cochrane Library, PubMed, PsycINFO, and Embase were searched up to February 2, 2021, for systematic reviews, meta-analyses, and original evidence. Study selection A total of 59 case-control studies evaluating diversity or abundance of gut microbes in adult populations with major depressive disorder, bipolar disorder, psychosis and schizophrenia, anorexia nervosa, anxiety, obsessive compulsive disorder, posttraumatic stress disorder, or attention-deficit/hyperactivity disorder were included. Data extraction and synthesis Between-group comparisons of relative abundance of gut microbes and beta diversity indices were extracted and summarized qualitatively. Random-effects meta-analyses on standardized mean difference (SMD) were performed for alpha diversity indices. Main outcomes and measures Alpha and beta diversity and relative abundance of gut microbes. Results A total of 34 studies provided data and were included in alpha diversity meta-analyses (n = 1519 patients, n = 1429 control participants). Significant decrease in microbial richness in patients compared with control participants were found (observed species SMD = -0.26; 95% CI, -0.47 to -0.06; Chao1 SMD = -0.5; 95% CI, -0.79 to -0.21); however, this was consistently decreased only in bipolar disorder when individual diagnoses were examined. There was a small decrease in phylogenetic diversity (SMD = -0.24; 95% CI, -0.47 to -0.001) and no significant differences in Shannon and Simpson indices. Differences in beta diversity were consistently observed only for major depressive disorder and psychosis and schizophrenia. Regarding relative abundance, little evidence of disorder specificity was found. Instead, a transdiagnostic pattern of microbiota signatures was found. Depleted levels of Faecalibacterium and Coprococcus and enriched levels of Eggerthella were consistently shared between major depressive disorder, bipolar disorder, psychosis and schizophrenia, and anxiety, suggesting these disorders are characterized by a reduction of anti-inflammatory butyrate-producing bacteria, while pro-inflammatory genera are enriched. The confounding associations of region and medication were also evaluated. Conclusions and relevance This systematic review and meta-analysis found that gut microbiota perturbations were associated with a transdiagnostic pattern with a depletion of certain anti-inflammatory butyrate-producing bacteria and an enrichment of pro-inflammatory bacteria in patients with depression, bipolar disorder, schizophrenia, and anxiety.

SARS-CoV-2 Variants of Interest and Concern naming scheme conducive for global discourse.

Abstract: A group convened and led by the Virus Evolution Working Group of the World Health Organization reports on its deliberations and announces a naming scheme that will enable clear communication about SARS-CoV-2 variants of interest and concern.

Update of P2X receptor properties and their pharmacology: IUPHAR Review 30

Abstract: The known seven mammalian receptor subunits (P2X1-7) form cationic channels gated by ATP. Three subunits compose a receptor channel. Each subunit is a polypeptide consisting of two transmembrane regions (TM1 and TM2), intracellular N- and C-termini, and a bulky extracellular loop. Crystallization allowed the identification of the 3D structure and gating cycle of P2X receptors. The agonist-binding pocket is located at the intersection of two neighbouring subunits. In addition to the mammalian P2X receptors, their primitive ligand-gated counterparts with little structural similarity have also been cloned. Selective agonists for P2X receptor subtypes are not available, but medicinal chemistry supplied a range of subtype-selective antagonists, as well as positive and negative allosteric modulators. Knockout mice and selective antagonists helped to identify pathological functions due to defective P2X receptors, such as male infertility (P2X1), hearing loss (P2X2), pain/cough (P2X3), neuropathic pain (P2X4), inflammatory bone loss (P2X5), and faulty immune reactions (P2X7).

Fossil CO2 emissions in the post-COVID-19 era

Abstract: Five years after the adoption of the Paris Climate Agreement, growth in global CO2 emissions has begun to falter. The pervasive disruptions from the COVID-19 pandemic have radically altered the trajectory of global CO2 emissions. Contradictory effects of the post-COVID-19 investments in fossil fuel-based infrastructure and the recent strengthening of climate targets must be addressed with new policy choices to sustain a decline in global emissions in the post-COVID-19 era. Growth in CO2 emissions has slowed since the Paris Agreement 5 years ago. The COVID-19 pandemic has caused a drop in emissions of about 7% in 2020 relative to 2019, but strong policy is needed to address underlying drivers and to sustain a decline in global emissions beyond the current crisis.

COVID-19 evolution during the pandemic - Implications of new SARS-CoV-2 variants on disease control and public health policies.

Abstract: In recent weeks, several new strains of SARS-CoV-2, the causative agent of COVID-19, have emerged. These variants have evolved an increased transmission rate compared to the original strains, which...

Impacts of COVID-19 and fiscal stimuli on global emissions and the Paris Agreement

Abstract: The global economy is facing a serious recession due to COVID-19, with implications for CO2 emissions. Here, using a global adaptive multiregional input–output model and scenarios of lockdown and fiscal counter measures, we show that global emissions from economic sectors will decrease by 3.9 to 5.6% in 5 years (2020 to 2024) compared with a no-pandemic baseline scenario (business as usual for economic growth and carbon intensity decline). Global economic interdependency via supply chains means that blocking one country’s economic activities causes the emissions of other countries to decrease even without lockdown policies. Supply-chain effects contributed 90.1% of emissions decline from power production in 2020 but only 13.6% of transport sector reductions. Simulations of follow-up fiscal stimuli in 41 major countries increase global 5-yr emissions by −6.6 to 23.2 Gt (−4.7 to 16.4%), depending on the strength and structure of incentives. Therefore, smart policy is needed to turn pandemic-related emission declines into firm climate action.

A systematic global stocktake of evidence on human adaptation to climate change

Abstract: Assessing global progress on human adaptation to climate change is an urgent priority. Although the literature on adaptation to climate change is rapidly expanding, little is known about the actual extent of implementation. We systematically screened >48,000 articles using machine learning methods and a global network of 126 researchers. Our synthesis of the resulting 1,682 articles presents a systematic and comprehensive global stocktake of implemented human adaptation to climate change. Documented adaptations were largely fragmented, local and incremental, with limited evidence of transformational adaptation and negligible evidence of risk reduction outcomes. We identify eight priorities for global adaptation research: assess the effectiveness of adaptation responses, enhance the understanding of limits to adaptation, enable individuals and civil society to adapt, include missing places, scholars and scholarship, understand private sector responses, improve methods for synthesizing different forms of evidence, assess the adaptation at different temperature thresholds, and improve the inclusion of timescale and the dynamics of responses. Determining progress in adaptation to climate change is challenging, yet critical as climate change impacts increase. A stocktake of the scientific literature on implemented adaptation now shows that adaptation is mostly fragmented and incremental, with evidence lacking for its impact on reducing risk.

COVID-19 detection and disease progression visualization: Deep learning on chest X-rays for classification and coarse localization

Abstract: Chest X-rays are playing an important role in the testing and diagnosis of COVID-19 disease in the recent pandemic. However, due to the limited amount of labelled medical images, automated classification of these images for positive and negative cases remains the biggest challenge in their reliable use in diagnosis and disease progression. We implemented a transfer learning pipeline for classifying COVID-19 chest X-ray images from two publicly available chest X-ray datasets1,2. The classifier effectively distinguishes inflammation in lungs due to COVID-19 and Pneumonia from the ones with no infection (normal). We have used multiple pre-trained convolutional backbones as the feature extractor and achieved an overall detection accuracy of 90%, 94.3%, and 96.8% for the VGG16, ResNet50, and EfficientNetB0 backbones respectively. Additionally, we trained a generative adversarial framework (a CycleGAN) to generate and augment the minority COVID-19 class in our approach. For visual explanations and interpretation purposes, we implemented a gradient class activation mapping technique to highlight the regions of the input image that are important for predictions. Additionally, these visualizations can be used to monitor the affected lung regions during disease progression and severity stages.

The transcriptional landscape of Arabidopsis thaliana pattern-triggered immunity

Abstract: Plants tailor their metabolism to environmental conditions, in part through the recognition of a wide array of self and non-self molecules. In particular, the perception of microbial or plant-derived molecular patterns by cell-surface-localized pattern recognition receptors (PRRs) induces pattern-triggered immunity, which includes massive transcriptional reprogramming1. An increasing number of plant PRRs and corresponding ligands are known, but whether plants tune their immune outputs to patterns of different biological origins or of different biochemical natures remains mostly unclear. Here, we performed a detailed transcriptomic analysis in an early time series focused to study rapid-signalling transcriptional outputs induced by well-characterized patterns in the model plant Arabidopsis thaliana. This revealed that the transcriptional responses to diverse patterns (independent of their origin, biochemical nature or type of PRR) are remarkably congruent. Moreover, many of the genes most rapidly and commonly upregulated by patterns are also induced by abiotic stresses, suggesting that the early transcriptional response to patterns is part of the plant general stress response (GSR). As such, plant cells' response is in the first instance mostly to danger. Notably, the genetic impairment of the GSR reduces pattern-induced antibacterial immunity, confirming the biological relevance of this initial danger response. Importantly, the definition of a small subset of 'core immunity response' genes common and specific to pattern response revealed the function of previously uncharacterized GLUTAMATE RECEPTOR-LIKE (GLR) calcium-permeable channels in immunity. This study thus illustrates general and unique properties of early immune transcriptional reprogramming and uncovers important components of plant immunity.

Common-red-signal analysis with 24-yr high-precision timing of the European Pulsar Timing Array: Inferences in the stochastic gravitational-wave background search

Abstract: We present results from the search for a stochastic gravitational-wave background (GWB) as predicted by the theory of General Relativity using six radio millisecond pulsars from the Data Release 2 (DR2) of the European Pulsar Timing Array (EPTA) covering a timespan up to 24 years. A GWB manifests itself as a long-term low-frequency stochastic signal common to all pulsars, a common red signal (CRS), with the characteristic Hellings-Downs (HD) spatial correlation. Our analysis is performed with two independent pipelines, \eprise and \tn+\ftwo, which produce consistent results. A search for a CRS with simultaneous estimation of its spatial correlations yields spectral properties compatible with theoretical GWB predictions, but does not result in the required measurement of the HD correlation, as required for GWB detection. Further Bayesian model comparison between different types of CRSs, including a GWB, finds the most favoured model to be the common uncorrelated red noise described by a power-law with $A = 5.13_{-2.73}^{+4.20} \times 10^{-15}$ and $\gamma = 3.78_{-0.59}^{+0.69}$ (95% credible regions). Fixing the spectral index to $\gamma=13/3$ as expected from the GWB by circular, inspiralling supermassive black-hole binaries results in an amplitude of $A =2.95_{-0.72}^{+0.89} \times 10^{-15}$. We implement three different models, BAYESEPHEM, LINIMOSS and EPHEMGP, to address possible Solar-system ephemeris (SSE) systematics and conclude that our results may only marginally depend on these effects. This work builds on the methods and models from the studies on the EPTA DR1. We show that under the same analysis framework the results remain consistent after the data set extension.

A global analysis of subsidence, relative sea-level change and coastal flood exposure

Abstract: Climate-induced sea-level rise and vertical land movements, including natural and human-induced subsidence in sedimentary coastal lowlands, combine to change relative sea levels around the world’s coasts. Although this affects local rates of sea-level rise, assessments of the coastal impacts of subsidence are lacking on a global scale. Here, we quantify global-mean relative sea-level rise to be 2.6 mm yr−1 over the past two decades. However, as coastal inhabitants are preferentially located in subsiding locations, they experience an average relative sea-level rise up to four times faster at 7.8 to 9.9 mm yr−1. These results indicate that the impacts and adaptation needs are much higher than reported global sea-level rise measurements suggest. In particular, human-induced subsidence in and surrounding coastal cities can be rapidly reduced with appropriate policy for groundwater utilization and drainage. Such policy would offer substantial and rapid benefits to reduce growth of coastal flood exposure due to relative sea-level rise. Land subsidence and uplift influence the rate of sea-level rise. Most coastal populations live in subsiding areas and experience average rates of relative sea-level rise three to four times faster than due to climate change alone, indicating the need for policy to address subsidence.

Long-read sequence assembly: a technical evaluation in barley

Abstract: Sequence assembly of large and repeat-rich plant genomes has been challenging, requiring substantial computational resources and often several complementary sequence assembly and genome mapping approaches. The recent development of fast and accurate long-read sequencing by circular consensus sequencing (CCS) on the PacBio platform may greatly increase the scope of plant pan-genome projects. Here, we compare current long-read sequencing platforms regarding their ability to rapidly generate contiguous sequence assemblies in pan-genome studies of barley (Hordeum vulgare). Most long-read assemblies are clearly superior to the current barley reference sequence based on short-reads. Assemblies derived from accurate long reads excel in most metrics, but the CCS approach was the most cost-effective strategy for assembling tens of barley genomes. A downsampling analysis indicated that 20-fold CCS coverage can yield very good sequence assemblies, while even five-fold CCS data may capture the complete sequence of most genes. We present an updated reference genome assembly for barley with near-complete representation of the repeat-rich intergenic space. Long-read assembly can underpin the construction of accurate and complete sequences of multiple genomes of a species to build pan-genome infrastructures in Triticeae crops and their wild relatives.

Recent smell loss is the best predictor of COVID-19 among individuals with recent respiratory symptoms.

Abstract: In a preregistered, cross-sectional study, we investigated whether olfactory loss is a reliable predictor of COVID-19 using a crowdsourced questionnaire in 23 languages to assess symptoms in individuals self-reporting recent respiratory illness. We quantified changes in chemosensory abilities during the course of the respiratory illness using 0-100 visual analog scales (VAS) for participants reporting a positive (C19+; n = 4148) or negative (C19-; n = 546) COVID-19 laboratory test outcome. Logistic regression models identified univariate and multivariate predictors of COVID-19 status and post-COVID-19 olfactory recovery. Both C19+ and C19- groups exhibited smell loss, but it was significantly larger in C19+ participants (mean ± SD, C19+: -82.5 ± 27.2 points; C19-: -59.8 ± 37.7). Smell loss during illness was the best predictor of COVID-19 in both univariate and multivariate models (ROC AUC = 0.72). Additional variables provide negligible model improvement. VAS ratings of smell loss were more predictive than binary chemosensory yes/no-questions or other cardinal symptoms (e.g., fever). Olfactory recovery within 40 days of respiratory symptom onset was reported for ~50% of participants and was best predicted by time since respiratory symptom onset. We find that quantified smell loss is the best predictor of COVID-19 amongst those with symptoms of respiratory illness. To aid clinicians and contact tracers in identifying individuals with a high likelihood of having COVID-19, we propose a novel 0-10 scale to screen for recent olfactory loss, the ODoR-19. We find that numeric ratings ≤2 indicate high odds of symptomatic COVID-19 (4 < OR < 10). Once independently validated, this tool could be deployed when viral lab tests are impractical or unavailable.

Recent Advances in Gold(III) Chemistry: Structure, Bonding, Reactivity, and Role in Homogeneous Catalysis.

Abstract: Over the past decade the organometallic chemistry of gold(III) has seen remarkable advances. This includes the synthesis of the first examples of several compound classes that have long been hypothesized as being part of catalytic cycles, such as gold(III) alkene, alkyne, CO and hydride complexes, and important catalysis-relevant reaction steps have at last been demonstrated for gold, like migratory insertion and β-H elimination reactions. Also, reaction pathways that were already known, for example the generation of gold(III) intermediates by oxidative addition and their reductive elimination, are much better understood. A deeper understanding of fundamental organometallic reactivity of gold(III) has revealed unexpected mechanistic avenues, which can open when the barriers for reactions that for other metals would be regarded as "standard" are too high. This review summarizes and evaluates these developments, together with applications of gold(III) in synthesis and catalysis, with emphasis on the mechanistic insight gained in these investigations.

Interventions to reduce social isolation and loneliness during COVID-19 physical distancing measures: A rapid systematic review.

Abstract: Background A significant proportion of the worldwide population is at risk of social isolation and loneliness as a result of the COVID-19 pandemic. We aimed to identify effective interventions to reduce social isolation and loneliness that are compatible with COVID-19 shielding and social distancing measures. Methods and findings In this rapid systematic review, we searched six electronic databases (Medline, Embase, Web of Science, PsycINFO, Cochrane Database of Systematic Reviews and SCOPUS) from inception to April 2020 for systematic reviews appraising interventions for loneliness and/or social isolation. Primary studies from those reviews were eligible if they included: 1) participants in a non-hospital setting; 2) interventions to reduce social isolation and/or loneliness that would be feasible during COVID-19 shielding measures; 3) a relevant control group; and 4) quantitative measures of social isolation, social support or loneliness. At least two authors independently screened studies, extracted data, and assessed risk of bias using the Downs and Black checklist. Study registration: PROSPERO CRD42020178654. We identified 45 RCTs and 13 non-randomised controlled trials; none were conducted during the COVID-19 pandemic. The nature, type, and potential effectiveness of interventions varied greatly. Effective interventions for loneliness include psychological therapies such as mindfulness, lessons on friendship, robotic pets, and social facilitation software. Few interventions improved social isolation. Overall, 37 of 58 studies were of “Fair” quality, as measured by the Downs & Black checklist. The main study limitations identified were the inclusion of studies of variable quality; the applicability of our findings to the entire population; and the current poor understanding of the types of loneliness and isolation experienced by different groups affected by the COVID-19 pandemic. Conclusions Many effective interventions involved cognitive or educational components, or facilitated communication between peers. These interventions may require minor modifications to align with COVID-19 shielding/social distancing measures. Future high-quality randomised controlled trials conducted under shielding/social distancing constraints are urgently needed.

Working landscapes need at least 20% native habitat

Abstract: G. Benedetti, A. Peralta, A. Zarate, C. Evequoz, M. Muller, M. Piotti, and Y. Astafurova provided important assistance in searching for legislation in support of NWL. D. Caceres, C. Levers, and two anonymous referees critically reviewed previous versions of the manuscript. We appreciate funding from Agencia Nacional de Promocion Cientifica y Tecnologica (PICT 2015–2333, PICT‐2018‐00941), Natural Environment Research Council (Grant code: NE/N014472/1), Universidad Nacional de Rio Negro (PI 40‐B‐567), and the 2017–2018 Belmont Forum and BiodivERsA joint call for research proposals (under the BiodivScen ERA‐Net COFUND programme and with the funding organizations AEI, NWO, ECCyT and NSF).

The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances

Abstract: Time Series Classification (TSC) involves building predictive models for a discrete target variable from ordered, real valued, attributes. Over recent years, a new set of TSC algorithms have been developed which have made significant improvement over the previous state of the art. The main focus has been on univariate TSC, i.e. the problem where each case has a single series and a class label. In reality, it is more common to encounter multivariate TSC (MTSC) problems where the time series for a single case has multiple dimensions. Despite this, much less consideration has been given to MTSC than the univariate case. The UCR archive has provided a valuable resource for univariate TSC, and the lack of a standard set of test problems may explain why there has been less focus on MTSC. The UEA archive of 30 MTSC problems released in 2018 has made comparison of algorithms easier. We review recently proposed bespoke MTSC algorithms based on deep learning, shapelets and bag of words approaches. If an algorithm cannot naturally handle multivariate data, the simplest approach to adapt a univariate classifier to MTSC is to ensemble it over the multivariate dimensions. We compare the bespoke algorithms to these dimension independent approaches on the 26 of the 30 MTSC archive problems where the data are all of equal length. We demonstrate that four classifiers are significantly more accurate than the benchmark dynamic time warping algorithm and that one of these recently proposed classifiers, ROCKET, achieves significant improvement on the archive datasets in at least an order of magnitude less time than the other three.