Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19 and is spread personto-person through close contact. We aimed to investigate the effects of physical distance, face masks, and eye protection on virus transmission in health-care and non-health-care (eg, community) settings. MethodsWe did a systematic review and meta-analysis to investigate the optimum distance for avoiding person-toperson virus transmission and to assess the use of face masks and eye protection to prevent transmission of viruses. We obtained data for SARS-CoV-2 and the betacoronaviruses that cause severe acute respiratory syndrome, and Middle East respiratory syndrome from 21 standard WHO-specific and COVID-19-specific sources. We searched these data sources from database inception to May 3, 2020, with no restriction by language, for comparative studies and for contextual factors of acceptability, feasibility, resource use, and equity. We screened records, extracted data, and assessed risk of bias in duplicate. We did frequentist and Bayesian meta-analyses and random-effects metaregressions. We rated the certainty of evidence according to Cochrane methods and the GRADE approach. This study is registered with PROSPERO, CRD42020177047. FindingsOur search identified 172 observational studies across 16 countries and six continents, with no randomised controlled trials and 44 relevant comparative studies in health-care and non-health-care settings (n=25 697 patients). Transmission of viruses was lower with physical distancing of 1 m or more, compared with a distance of less than 1 m (n=10 736, pooled adjusted odds ratio [aOR] 0•18, 95% CI 0•09 to 0•38; risk difference [RD] -10•2%, 95% CI -11•5 to -7•5; moderate certainty); protection was increased as distance was lengthened (change in relative risk [RR] 2•02 per m; p interaction =0•041; moderate certainty). Face mask use could result in a large reduction in risk of infection (n=2647; aOR 0•15, 95% CI 0•07 to 0•34, RD -14•3%, -15•9 to -10•7; low certainty), with stronger associations with N95 or similar respirators compared with disposable surgical masks or similar (eg, reusable 12-16-layer cotton masks; p interaction =0•090; posterior probability >95%, low certainty). Eye protection also was associated with less infection (n=3713; aOR 0•22, 95% CI 0•12 to 0•39, RD -10•6%, 95% CI -12•5 to -7•7; low certainty). Unadjusted studies and subgroup and sensitivity analyses showed similar findings.Interpretation The findings of this systematic review and meta-analysis support physical distancing of 1 m or more and provide quantitative estimates for models and contact tracing to inform policy. Optimum use of face masks, respirators, and eye protection in public and health-care settings should be informed by these findings and contextual factors. Robust randomised trials are needed to better inform the evidence for these interventions, but this systematic appraisal of currently best available evidence might inform interim guidance.Funding World Health Organization.

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems—patient classification, fundamental biological processes and treatment of patients—and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.

Almost all organisms live in environments that have been altered, to some degree, by human activities. Because behaviour mediates interactions between an individual and its environment, the ability of organisms to behave appropriately under these new conditions is crucial for determining their immediate success or failure in these modified environments. While hundreds of species are suffering dramatically from these environmental changes, others, such as urbanized and pest species, are doing better than ever. Our goal is to provide insights into explaining such variation. We first summarize the responses of some species to novel situations, including novel risks and resources, habitat loss/fragmentation, pollutants and climate change. Using a sensory ecology approach, we present a mechanistic framework for predicting variation in behavioural responses to environmental change, drawing from models of decision-making processes and an understanding of the selective background against which they evolved. Where immediate behavioural responses are inadequate, learning or evolutionary adaptation may prove useful, although these mechanisms are also constrained by evolutionary history. Although predicting the responses of species to environmental change is difficult, we highlight the need for a better understanding of the role of evolutionary history in shaping individuals’ responses to their environment and provide suggestion for future work.

Several major invasive bacterial pathogens are encapsulated. Expression of a polysaccharide capsule is essential for survival in the blood, and thus for virulence, but also is a target for host antibodies and the basis for effective vaccines. Encapsulated species typically exhibit antigenic variation and express one of a number of immunochemically distinct capsular polysaccharides that define serotypes. We provide the sequences of the capsular biosynthetic genes of all 90 serotypes of Streptococcus pneumoniae and relate these to the known polysaccharide structures and patterns of immunological reactivity of typing sera, thereby providing the most complete understanding of the genetics and origins of bacterial polysaccharide diversity, laying the foundations for molecular serotyping. This is the first time, to our knowledge, that a complete repertoire of capsular biosynthetic genes has been available, enabling a holistic analysis of a bacterial polysaccharide biosynthesis system. Remarkably, the total size of alternative coding DNA at this one locus exceeds 1.8 Mbp, almost equivalent to the entire S. pneumoniae chromosomal complement.

Plasmodium berghei and Plasmodium chabaudi are widely used model malaria species. Comparison of their genomes, integrated with proteomic and microarray data, with the genomes of Plasmodium falciparum and Plasmodium yoelii revealed a conserved core of 4500 Plasmodium genes in the central regions of the 14 chromosomes and highlighted genes evolving rapidly because of stage-specific selective pressures. Four strategies for gene expression are apparent during the parasites' life cycle: (i) housekeeping; (ii) host-related; (iii) strategy-specific related to invasion, asexual replication, and sexual development; and (iv) stage-specific. We observed posttranscriptional gene silencing through translational repression of messenger RNA during sexual development, and a 47-base 3′ untranslated region motif is implicated in this process.

The use of 13C natural abundance (δ13C) to follow C input to soil has gained widespread acceptance. However, inorganic C present in the soil as carbonates will interfere with the measurement of soil organic 13C unless removed or excluded from measurement. We report a simple and convenient HCl‐fumigation method to remove inorganic C from soil. Soil samples are weighed in Ag‐foil capsules, arranged on a microtiter plate, wetted with water to approximately field capacity, and placed in a desiccator containing a beaker with concentrated (12 M) HCl. The carbonates are released as CO2 by the acid treatment in 6 to 8 h. The soil samples are then dried at 60°C prior to isotope determination. The advantages of the HCl‐fumigation method to remove inorganic C from the soil are that: (i) no water soluble C will be lost from the soil; (ii) a large number of samples can be processed simultaneously; (iii) the removal of inorganic C is rapid and complete; and (iv) the method could also be used to determine both organic and inorganic C content in the soil. A potential disadvantage, however, is that the HCl fumigation changed the 15N natural abundance of soil N.

Background: Stenotrophomonas maltophilia is a nosocomial opportunistic pathogen of the Xanthomonadaceae. The organism has been isolated from both clinical and soil environments in addition to the sputum of cystic fibrosis patients and the immunocompromised. Whilst relatively distant phylogenetically, the closest sequenced relatives of S. maltophilia are the plant pathogenic xanthomonads.

SUMMARY Balanced chromosomal abnormalities (BCAs) represent a reservoir of single gene disruptions in neurodevelopmental disorders (NDD). We sequenced BCAs in autism and related NDDs, revealing disruption of 33 loci in four general categories: 1) genes associated with abnormal neurodevelopment (e.g., AUTS2, FOXP1, CDKL5), 2) single gene contributors to microdeletion syndromes (MBD5, SATB2, EHMT1, SNURF-SNRPN), 3) novel risk loci (e.g., CHD8, KIRREL3, ZNF507), and 4) genes associated with later onset psychiatric disorders (e.g., TCF4, ZNF804A, PDE10A, GRIN2B, ANK3). We also discovered profoundly increased burden of copy number variants among 19,556 neurodevelopmental cases compared to 13,991 controls (p = 2.07×10−47) and enrichment of polygenic risk alleles from autism and schizophrenia genome-wide association studies (p = 0.0018 and 0.0009, respectively). Our findings suggest a polygenic risk model of autism incorporating loci of strong effect and indicate that some neurodevelopmental genes are sensitive to perturbation by multiple mutational mechanisms, leading to variable phenotypic outcomes that manifest at different life stages.