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Autism spectrum disorders (ASD) are a heterogeneous group of neurodevelopmental disorders with a complex inheritance pattern. While many rare variants in synaptic proteins have been identified in patients with ASD, little is known about their effects at the synapse and their interactions with other genetic variations. Here, following the discovery of two de novo SHANK2 deletions by the Autism Genome Project, we identified a novel 421 kb de novo SHANK2 deletion in a patient with autism. We then sequenced SHANK2 in 455 patients with ASD and 431 controls and integrated these results with those reported by Berkel et al. 2010 (n = 396 patients and n = 659 controls). We observed a significant enrichment of variants affecting conserved amino acids in 29 of 851 (3.4%) patients and in 16 of 1,090 (1.5%) controls (P = 0.004, OR = 2.37, 95% CI = 1.23-4.70). In neuronal cell cultures, the variants identified in patients were associated with a reduced synaptic density at dendrites compared to the variants only detected in controls (P = 0.0013). Interestingly, the three patients with de novo SHANK2 deletions also carried inherited CNVs at 15q11-q13 previously associated with neuropsychiatric disorders. In two cases, the nicotinic receptor CHRNA7 was duplicated and in one case the synaptic translation repressor CYFIP1 was deleted. These results strengthen the role of synaptic gene dysfunction in ASD but also highlight the presence of putative modifier genes, which is in keeping with the "multiple hit model" for ASD. A better knowledge of these genetic interactions will be necessary to understand the complex inheritance pattern of ASD.
Causes of maladaptation
(2019)
Evolutionary biologists tend to approach the study of the natural world within a framework of adaptation, inspired perhaps by the power of natural selection to produce fitness advantages that drive population persistence and biological diversity. In contrast, evolution has rarely been studied through the lens of adaptation's complement, maladaptation. This contrast is surprising because maladaptation is a prevalent feature of evolution: population trait values are rarely distributed optimally; local populations often have lower fitness than imported ones; populations decline; and local and global extinctions are common. Yet we lack a general framework for understanding maladaptation; for instance in terms of distribution, severity, and dynamics. Similar uncertainties apply to the causes of maladaptation. We suggest that incorporating maladaptation‐based perspectives into evolutionary biology would facilitate better understanding of the natural world. Approaches within a maladaptation framework might be especially profitable in applied evolution contexts – where reductions in fitness are common. Toward advancing a more balanced study of evolution, here we present a conceptual framework describing causes of maladaptation. As the introductory article for a Special Feature on maladaptation, we also summarize the studies in this Issue, highlighting the causes of maladaptation in each study. We hope that our framework and the papers in this Special Issue will help catalyze the study of maladaptation in applied evolution, supporting greater understanding of evolutionary dynamics in our rapidly changing world.
Background: Intracerebral haemorrhage growth is associated with poor clinical outcome and is a therapeutic target for improving outcome. We aimed to determine the absolute risk and predictors of intracerebral haemorrhage growth, develop and validate prediction models, and evaluate the added value of CT angiography.
Methods: In a systematic review of OVID MEDLINE—with additional hand-searching of relevant studies' bibliographies— from Jan 1, 1970, to Dec 31, 2015, we identified observational cohorts and randomised trials with repeat scanning protocols that included at least ten patients with acute intracerebral haemorrhage. We sought individual patient-level data from corresponding authors for patients aged 18 years or older with data available from brain imaging initially done 0·5–24 h and repeated fewer than 6 days after symptom onset, who had baseline intracerebral haemorrhage volume of less than 150 mL, and did not undergo acute treatment that might reduce intracerebral haemorrhage volume. We estimated the absolute risk and predictors of the primary outcome of intracerebral haemorrhage growth (defined as >6 mL increase in intracerebral haemorrhage volume on repeat imaging) using multivariable logistic regression models in development and validation cohorts in four subgroups of patients, using a hierarchical approach: patients not taking anticoagulant therapy at intracerebral haemorrhage onset (who constituted the largest subgroup), patients taking anticoagulant therapy at intracerebral haemorrhage onset, patients from cohorts that included at least some patients taking anticoagulant therapy at intracerebral haemorrhage onset, and patients for whom both information about anticoagulant therapy at intracerebral haemorrhage onset and spot sign on acute CT angiography were known.
Findings: Of 4191 studies identified, 77 were eligible for inclusion. Overall, 36 (47%) cohorts provided data on 5435 eligible patients. 5076 of these patients were not taking anticoagulant therapy at symptom onset (median age 67 years, IQR 56–76), of whom 1009 (20%) had intracerebral haemorrhage growth. Multivariable models of patients with data on antiplatelet therapy use, data on anticoagulant therapy use, and assessment of CT angiography spot sign at symptom onset showed that time from symptom onset to baseline imaging (odds ratio 0·50, 95% CI 0·36–0·70; p<0·0001), intracerebral haemorrhage volume on baseline imaging (7·18, 4·46–11·60; p<0·0001), antiplatelet use (1·68, 1·06–2·66; p=0·026), and anticoagulant use (3·48, 1·96–6·16; p<0·0001) were independent predictors of intracerebral haemorrhage growth (C-index 0·78, 95% CI 0·75–0·82). Addition of CT angiography spot sign (odds ratio 4·46, 95% CI 2·95–6·75; p<0·0001) to the model increased the C-index by 0·05 (95% CI 0·03–0·07).
Interpretation: In this large patient-level meta-analysis, models using four or five predictors had acceptable to good discrimination. These models could inform the location and frequency of observations on patients in clinical practice, explain treatment effects in prior randomised trials, and guide the design of future trials.
Funding: UK Medical Research Council and British Heart Foundation.
Directed and elliptic flow of charged pions and protons in Pb + Pb collisions at 40 and 158 A GeV
(2003)
Directed and elliptic flow measurements for charged pions and protons are reported as a function of transverse momentum, rapidity, and centrality for 40 and 158A GeV Pb + Pb collisions as recorded by the NA49 detector. Both the standard method of correlating particles with an event plane, and the cumulant method of studying multiparticle correlations are used. In the standard method the directed flow is corrected for conservation of momentum. In the cumulant method elliptic flow is reconstructed from genuine 4, 6, and 8-particle correlations, showing the first unequivocal evidence for collective motion in A+A collisions at SPS energies.
Background: The German Consortium for Hereditary Breast and Ovarian Cancer (GC-HBOC) has established a multigene panel (TruRisk®) for the analysis of risk genes for familial breast and ovarian cancer. Summary: An interdisciplinary team of experts from the GC-HBOC has evaluated the available data on risk modification in the presence of pathogenic mutations in these genes based on a structured literature search and through a formal consensus process. Key Messages: The goal of this work is to better assess individual disease risk and, on this basis, to derive clinical recommendations for patient counseling and care at the centers of the GC-HBOC from the initial consultation prior to genetic testing to the use of individual risk-adapted preventive/therapeutic measures.
Objectives: We explore the importance of SARS-CoV-2 sentinel surveillance testing in primary care during a regional COVID-19 outbreak in Austria.
Design: Prospective cohort study.
Setting: A single sentinel practice serving 22 829 people in the ski-resort of Schladming-Dachstein.
Participants: All 73 patients presenting with mild-to-moderate flu-like symptoms between 24 February and 03 April, 2020.
Intervention: Nasopharyngeal sampling to detect SARS-CoV-2 using real-time reverse transcriptase-quantitative PCR (RT-qPCR).
Outcome measures: We compared RT-qPCR at presentation with confirmed antibody status. We split the outbreak in two parts, by halving the period from the first to the last case, to characterise three cohorts of patients with confirmed infection: early acute (RT-qPCR reactive) in the first half; and late acute (reactive) and late convalescent (non-reactive) in the second half. For each cohort, we report the number of cases detected, the accuracy of RT-qPCR, the duration and variety of symptoms, and the number of viral clades present.
Results: Twenty-two patients were diagnosed with COVID-19 (eight early acute, seven late acute and seven late convalescent), 44 patients tested SARS-CoV-2 negative and 7 were excluded. The sensitivity of RT-qPCR was 100% among all acute cases, dropping to 68.1% when including convalescent. Test specificity was 100%. Mean duration of symptoms for each group were 2 days (range 1–4) among early acute, 4.4 days (1–7) among late acute and 8 days (2–12) among late convalescent. Confirmed infection was associated with loss of taste. Acute infection was associated with loss of taste, nausea/vomiting, breathlessness, sore throat and myalgia; but not anosmia, fever or cough. Transmission clusters of three viral clades (G, GR and L) were identified.
Conclusions: RT-qPCR testing in primary care can rapidly and accurately detect SARS-CoV-2 among people with flu-like illness in a heterogeneous viral outbreak. Targeted testing in primary care can support national sentinel surveillance of COVID-19.
Due to massive energetic investments in woody support structures, trees are subject to unique physiological, mechanical, and ecological pressures not experienced by herbaceous plants. Despite a wealth of studies exploring trait relationships across the entire plant kingdom, the dominant traits underpinning these unique aspects of tree form and function remain unclear. Here, by considering 18 functional traits, encompassing leaf, seed, bark, wood, crown, and root characteristics, we quantify the multidimensional relationships in tree trait expression. We find that nearly half of trait variation is captured by two axes: one reflecting leaf economics, the other reflecting tree size and competition for light. Yet these orthogonal axes reveal strong environmental convergence, exhibiting correlated responses to temperature, moisture, and elevation. By subsequently exploring multidimensional trait relationships, we show that the full dimensionality of trait space is captured by eight distinct clusters, each reflecting a unique aspect of tree form and function. Collectively, this work identifies a core set of traits needed to quantify global patterns in functional biodiversity, and it contributes to our fundamental understanding of the functioning of forests worldwide.
Members of the ATP‐binding cassette (ABC) transporter superfamily translocate a broad spectrum of chemically diverse substrates. While their eponymous ATP‐binding cassette in the nucleotide‐binding domains (NBDs) is highly conserved, their transmembrane domains (TMDs) forming the translocation pathway exhibit distinct folds and topologies, suggesting that during evolution the ancient motor domains were combined with different transmembrane mechanical systems to orchestrate a variety of cellular processes. In recent years, it has become increasingly evident that the distinct TMD folds are best suited to categorize the multitude of ABC transporters. We therefore propose a new ABC transporter classification that is based on structural homology in the TMDs:
The crystal structure of the bovine Rieske iron-sulfur protein indicates a sulfur atom (S-1) of the iron-sulfur cluster and the sulfur atom (Sgamma) of a cysteine residue that coordinates one of the iron atoms form hydrogen bonds with the hydroxyl groups of Ser-163 and Tyr-165, respectively. We have altered the equivalent Ser-183 and Tyr-185 in the Saccharomyces cerevisiae Rieske iron-sulfur protein by site-directed mutagenesis of the iron-sulfur protein gene to examine how these hydrogen bonds affect the midpoint potential of the iron-sulfur cluster and how changes in the midpoint potential affect the activity of the enzyme. Eliminating the hydrogen bond from the hydroxyl group of Ser-183 to S-1 of the cluster lowers the midpoint potential of the cluster by 130 mV, and eliminating the hydrogen bond from the hydroxyl group of Tyr-185 to Sgamma of Cys-159 lowers the midpoint potential by 65 mV. Eliminating both hydrogen bonds has an approximately additive effect, lowering the midpoint potential by 180 mV. Thus, these hydrogen bonds contribute significantly to the positive midpoint potential of the cluster but are not essential for its assembly. The activity of the bc1 complex decreases with the decrease in midpoint potential, confirming that oxidation of ubiquinol by the iron-sulfur protein is the rate-limiting partial reaction in the bc1 complex, and that the rate of this reaction is extensively influenced by the midpoint potential of the iron-sulfur cluster.
The growth of aerosol due to the aqueous phase oxidation of sulfur dioxide by ozone was measured in laboratory-generated clouds created in the Cosmics Leaving OUtdoor Droplets (CLOUD) chamber at the European Organization for Nuclear Research (CERN). Experiments were performed at 10 and −10 °C, on acidic (sulfuric acid) and on partially to fully neutralised (ammonium sulfate) seed aerosol. Clouds were generated by performing an adiabatic expansion – pressurising the chamber to 220 hPa above atmospheric pressure, and then rapidly releasing the excess pressure, resulting in a cooling, condensation of water on the aerosol and a cloud lifetime of approximately 6 min. A model was developed to compare the observed aerosol growth with that predicted using oxidation rate constants previously measured in bulk solutions. The model captured the measured aerosol growth very well for experiments performed at 10 and −10 °C, indicating that, in contrast to some previous studies, the oxidation rates of SO2 in a dispersed aqueous system can be well represented by using accepted rate constants, based on bulk measurements. To the best of our knowledge, these are the first laboratory-based measurements of aqueous phase oxidation in a dispersed, super-cooled population of droplets. The measurements are therefore important in confirming that the extrapolation of currently accepted reaction rate constants to temperatures below 0 °C is correct.
The growth of aerosol due to the aqueous phase oxidation of sulfur dioxide by ozone was measured in laboratory-generated clouds created in the Cosmics Leaving OUtdoor Droplets (CLOUD) chamber at the European Organization for Nuclear Research (CERN). Experiments were performed at 10 and −10 °C, on acidic (sulfuric acid) and on partially to fully neutralised (ammonium sulfate) seed aerosol. Clouds were generated by performing an adiabatic expansion – pressurising the chamber to 220 hPa above atmospheric pressure, and then rapidly releasing the excess pressure, resulting in a cooling, condensation of water on the aerosol and a cloud lifetime of approximately 6 min. A model was developed to compare the observed aerosol growth with that predicted using oxidation rate constants previously measured in bulk solutions. The model captured the measured aerosol growth very well for experiments performed at 10 and −10 °C, indicating that, in contrast to some previous studies, the oxidation rates of SO2 in a dispersed aqueous system can be well represented by using accepted rate constants, based on bulk measurements. To the best of our knowledge, these are the first laboratory-based measurements of aqueous phase oxidation in a dispersed, super-cooled population of droplets. The measurements are therefore important in confirming that the extrapolation of currently accepted reaction rate constants to temperatures below 0 °C is correct.
Size-resolved measurements of atmospheric aerosol and cloud condensation nuclei (CCN) concentrations and hygroscopicity were conducted at the remote Amazon Tall Tower Observatory (ATTO) in the central Amazon Basin over a full seasonal cycle (Mar 2014–Feb 2015). In a companion part 1 paper, we presented an in-depth CCN characterization based on annually as well as seasonally averaged time intervals and discuss different parametrization strategies to represent the Amazonian CCN cycling in modelling studies (M. Pöhlker et al., 2016b). The present part 2 study analyzes the aerosol and CCN variability in original time resolution and, thus, resolves aerosol advection and transformation for the following case studies, which represent the most characteristic states of the Amazonian atmosphere:
1. Near-pristine (NP) conditions, defined as the absence of detectable black carbon (< 0.01 µg m−3), showed their highest occurrence (up to 30 %) in the wet season (i.e., Mar–May). On average, the NP episodes are characterized by a bimodal aerosol size distribution (strong Aitken mode: DAit = 70 nm, NAit = ~ 200 cm−3 vs. weaker accumulation mode: Dacc = 170 nm, Nacc = ~ 60 cm−3), a mostly organic particle composition, and relatively low hygroscopicity levels (κAit = 0.12 vs. κacc = 0.18). The NP CCN efficiency spectrum shows that the CCN population is sensitive to changes in supersaturation (S) over a wide S range.
2. Long-range transport (LRT) conditions frequently mix Saharan dust, African combustion smoke, and sea spray aerosols into the Amazonian wet season atmosphere. The LRT episodes (i.e., Feb–Apr) are characterized by an accumulation mode dominated size distribution (DAit = 80 nm, NAit = 120 cm−3 vs. Dacc = 180 nm, Nacc = 300 cm−3), a clearly increased abundance of dust and salt compounds, and relatively high hygroscopicity levels (κAit = 0.18, κacc = 0.34). The LRT CCN efficiency spectrum shows that the CCN population is highly sensitive to changes in S in the low S regime.
3. Biomass burning (BB) conditions dominate the Amazonian dry season. A selected characteristic BB episode shows a very strong accumulation mode (DAit = 70 nm, NAit = ~ 140 cm−3 vs. Dacc = 170 nm, Nacc = ~ 3400 cm−3), particles with very high organic fractions (> 90 %), and correspondingly low hygroscopicity levels (κAit = 0.14, κacc = 0.17). The BB CCN efficiency spectrum shows that the CCN population is highly sensitive to changes in S in the low S regime.
4. Mixed pollution conditions show the superposition of African (i.e., volcanic) and Amazonian (i.e., biomass burning) aerosol emissions during the dry season. The African aerosols showed a broad monomodal distribution (D = 130 nm, N = ~ 1300 cm−3), with very high sulfate fractions (20 %), and correspondingly high hygroscopicity (κAit = 0.14, κacc = 0.22). This was superimposed by fresh smoke from nearby fires with one strong mode (D = 113 nm, Nacc = ~ 2800 cm−3), an organic-dominated aerosol, and sharply decreased hygroscopicity (κAit = 0.10, κacc = 0.20). These conditions underline the rapidly changing pollution regimes with clear impacts on the aerosol and CCN properties.
Overall, this study provides detailed insights into the CCN cycling in relation to aerosol-cloud interaction in the vulnerable and climate-relevant Amazon region. The detailed analysis of aerosol and CCN key properties and particularly the extracted CCN efficiency spectra with the associated fit parameters provide a basis for an in-depth analysis of aerosol-cloud interaction in the Amazon and beyond.
Size-resolved long-term measurements of atmospheric aerosol and cloud condensation nuclei (CCN) concentrations and hygroscopicity were conducted at the remote Amazon Tall Tower Observatory (ATTO) in the central Amazon Basin over a 1-year period and full seasonal cycle (March 2014–February 2015). The measurements provide a climatology of CCN properties characteristic of a remote central Amazonian rain forest site.
The CCN measurements were continuously cycled through 10 levels of supersaturation (S = 0.11 to 1.10 %) and span the aerosol particle size range from 20 to 245 nm. The mean critical diameters of CCN activation range from 43 nm at S = 1.10 % to 172 nm at S = 0.11 %. The particle hygroscopicity exhibits a pronounced size dependence with lower values for the Aitken mode (κAit = 0.14 ± 0.03), higher values for the accumulation mode (κAcc = 0.22 ± 0.05), and an overall mean value of κmean = 0.17 ± 0.06, consistent with high fractions of organic aerosol.
The hygroscopicity parameter, κ, exhibits remarkably little temporal variability: no pronounced diurnal cycles, only weak seasonal trends, and few short-term variations during long-range transport events. In contrast, the CCN number concentrations exhibit a pronounced seasonal cycle, tracking the pollution-related seasonality in total aerosol concentration. We find that the variability in the CCN concentrations in the central Amazon is mostly driven by aerosol particle number concentration and size distribution, while variations in aerosol hygroscopicity and chemical composition matter only during a few episodes.
For modeling purposes, we compare different approaches of predicting CCN number concentration and present a novel parametrization, which allows accurate CCN predictions based on a small set of input data.
Size-resolved long-term measurements of atmospheric aerosol and cloud condensation nuclei (CCN) concentrations as well as hygroscopicity were conducted at the remote Amazon Tall Tower Observatory (ATTO) in the central Amazon Basin over a one-year period and full seasonal cycle (March 2014 - February 2015). The presented measurements provide a climatology of CCN properties for a characteristic central Amazonian rain forest site.
The CCN measurements were continuously cycled through 10 levels of supersaturation (S = 0.11 to 1.10 %) and span the aerosol particle size range from 20 to 245 nm. The observed mean critical diameters of CCN activation range from 43 nm at S = 1.10 % to 172 nm at S = 0.11 %. The particle hygroscopicity exhibits a pronounced size dependence with lower values for the Aitken mode (κAit = 0.14 ± 0.03), elevated values for the accumulation mode (κAcc = 0.22 ± 0.05), and an overall mean value of κmean = 0.17 ± 0.06, consistent with high fractions of organic aerosol.
The hygroscopicity parameter κ exhibits remarkably little temporal variability: no pronounced diurnal cycles, weak seasonal trends, and few short-term variations during long-range transport events. In contrast, the CCN number concentrations exhibit a pronounced seasonal cycle, tracking the pollution-related seasonality in total aerosol concentration. We find that the variability in the CCN concentrations in the central Amazon is mostly driven by aerosol particle number concentration and size distribution, while variations in aerosol hygroscopicity and chemical composition matter only during a few episodes.
For modelling purposes, we compare different approaches of predicting CCN number concentration and present a novel parameterization, which allows accurate CCN predictions based on a small set of input data.