ALBERT

Description: Autism spectrum disorders (ASDs) are childhood neurodevelopmental disorders with complex genetic origins. Previous studies focusing on candidate genes or genomic regions have identified several copy number variations (CNVs) that are associated with an increased risk of ASDs. Here we present the results from a whole-genome CNV study on a cohort of 859 ASD cases and 1,409 healthy children of European ancestry who were genotyped with approximately 550,000 single nucleotide polymorphism markers, in an attempt to comprehensively identify CNVs conferring susceptibility to ASDs. Positive findings were evaluated in an independent cohort of 1,336 ASD cases and 1,110 controls of European ancestry. Besides previously reported ASD candidate genes, such as NRXN1 (ref. 10) and CNTN4 (refs 11, 12), several new susceptibility genes encoding neuronal cell-adhesion molecules, including NLGN1 and ASTN2, were enriched with CNVs in ASD cases compared to controls (P = 9.5 x 10(-3)). Furthermore, CNVs within or surrounding genes involved in the ubiquitin pathways, including UBE3A, PARK2, RFWD2 and FBXO40, were affected by CNVs not observed in controls (P = 3.3 x 10(-3)). We also identified duplications 55 kilobases upstream of complementary DNA AK123120 (P = 3.6 x 10(-6)). Although these variants may be individually rare, they target genes involved in neuronal cell-adhesion or ubiquitin degradation, indicating that these two important gene networks expressed within the central nervous system may contribute to the genetic susceptibility of ASD.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2925224/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2925224/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Glessner, Joseph T -- Wang, Kai -- Cai, Guiqing -- Korvatska, Olena -- Kim, Cecilia E -- Wood, Shawn -- Zhang, Haitao -- Estes, Annette -- Brune, Camille W -- Bradfield, Jonathan P -- Imielinski, Marcin -- Frackelton, Edward C -- Reichert, Jennifer -- Crawford, Emily L -- Munson, Jeffrey -- Sleiman, Patrick M A -- Chiavacci, Rosetta -- Annaiah, Kiran -- Thomas, Kelly -- Hou, Cuiping -- Glaberson, Wendy -- Flory, James -- Otieno, Frederick -- Garris, Maria -- Soorya, Latha -- Klei, Lambertus -- Piven, Joseph -- Meyer, Kacie J -- Anagnostou, Evdokia -- Sakurai, Takeshi -- Game, Rachel M -- Rudd, Danielle S -- Zurawiecki, Danielle -- McDougle, Christopher J -- Davis, Lea K -- Miller, Judith -- Posey, David J -- Michaels, Shana -- Kolevzon, Alexander -- Silverman, Jeremy M -- Bernier, Raphael -- Levy, Susan E -- Schultz, Robert T -- Dawson, Geraldine -- Owley, Thomas -- McMahon, William M -- Wassink, Thomas H -- Sweeney, John A -- Nurnberger, John I -- Coon, Hilary -- Sutcliffe, James S -- Minshew, Nancy J -- Grant, Struan F A -- Bucan, Maja -- Cook, Edwin H -- Buxbaum, Joseph D -- Devlin, Bernie -- Schellenberg, Gerard D -- Hakonarson, Hakon -- 1U24MH081810/MH/NIMH NIH HHS/ -- HD055751/HD/NICHD NIH HHS/ -- HD055782-01/HD/NICHD NIH HHS/ -- HD35476/HD/NICHD NIH HHS/ -- M01 RR000064-340579/RR/NCRR NIH HHS/ -- M01 RR000064-350579/RR/NCRR NIH HHS/ -- M01 RR000064-35S10579/RR/NCRR NIH HHS/ -- M01 RR000064-35S10591/RR/NCRR NIH HHS/ -- M01 RR000064-35S10602/RR/NCRR NIH HHS/ -- M01 RR000064-35S20579/RR/NCRR NIH HHS/ -- M01 RR000064-35S20591/RR/NCRR NIH HHS/ -- M01 RR000064-35S20602/RR/NCRR NIH HHS/ -- M01 RR000064-360579/RR/NCRR NIH HHS/ -- M01 RR000064-360582/RR/NCRR NIH HHS/ -- M01 RR000064-360591/RR/NCRR NIH HHS/ -- M01 RR000064-36S10579/RR/NCRR NIH HHS/ -- M01 RR000064-36S10582/RR/NCRR NIH HHS/ -- M01 RR000064-36S10591/RR/NCRR NIH HHS/ -- M01 RR000064-370579/RR/NCRR NIH HHS/ -- M01 RR000064-370582/RR/NCRR NIH HHS/ -- M01 RR000064-370591/RR/NCRR NIH HHS/ -- M01 RR000064-37S10579/RR/NCRR NIH HHS/ -- M01 RR000064-37S10582/RR/NCRR NIH HHS/ -- M01 RR000064-37S10591/RR/NCRR NIH HHS/ -- M01 RR000064-380579/RR/NCRR NIH HHS/ -- M01 RR000064-380582/RR/NCRR NIH HHS/ -- M01 RR000064-380591/RR/NCRR NIH HHS/ -- M01 RR000064-390579/RR/NCRR NIH HHS/ -- M01 RR000064-390582/RR/NCRR NIH HHS/ -- M01 RR000064-390591/RR/NCRR NIH HHS/ -- M01-RR00064/RR/NCRR NIH HHS/ -- MH061009/MH/NIMH NIH HHS/ -- MH0666730/MH/NIMH NIH HHS/ -- MH64547/MH/NIMH NIH HHS/ -- MH69359/MH/NIMH NIH HHS/ -- NS049261/NS/NINDS NIH HHS/ -- P01 HD035476-03/HD/NICHD NIH HHS/ -- P01 HD035476-04/HD/NICHD NIH HHS/ -- P01 HD035476-04S1/HD/NICHD NIH HHS/ -- P01 HD035476-04S2/HD/NICHD NIH HHS/ -- P01 HD035476-05/HD/NICHD NIH HHS/ -- P50 HD055751/HD/NICHD NIH HHS/ -- P50 HD055751-01/HD/NICHD NIH HHS/ -- P50 HD055751-010002/HD/NICHD NIH HHS/ -- P50 HD055751-019003/HD/NICHD NIH HHS/ -- P50 HD055751-02/HD/NICHD NIH HHS/ -- P50 HD055751-020002/HD/NICHD NIH HHS/ -- P50 HD055751-03/HD/NICHD NIH HHS/ -- P50 HD055751-030002/HD/NICHD NIH HHS/ -- P50 HD055751-04/HD/NICHD NIH HHS/ -- P50 HD055782-01/HD/NICHD NIH HHS/ -- R01 MH057881/MH/NIMH NIH HHS/ -- R01 MH061009/MH/NIMH NIH HHS/ -- R01 MH061009-01A1/MH/NIMH NIH HHS/ -- R01 MH061009-01A1S1/MH/NIMH NIH HHS/ -- R01 MH061009-02/MH/NIMH NIH HHS/ -- R01 MH061009-03/MH/NIMH NIH HHS/ -- R01 MH061009-04A1/MH/NIMH NIH HHS/ -- R01 MH061009-05/MH/NIMH NIH HHS/ -- R01 MH061009-06/MH/NIMH NIH HHS/ -- R01 MH061009-07/MH/NIMH NIH HHS/ -- R01 MH061009-08/MH/NIMH NIH HHS/ -- R01 MH064547/MH/NIMH NIH HHS/ -- R01 MH064547-01/MH/NIMH NIH HHS/ -- R01 MH064547-01S1/MH/NIMH NIH HHS/ -- R01 MH064547-02/MH/NIMH NIH HHS/ -- R01 MH064547-02S1/MH/NIMH NIH HHS/ -- R01 MH064547-03/MH/NIMH NIH HHS/ -- R01 MH064547-04/MH/NIMH NIH HHS/ -- R01 MH064547-05/MH/NIMH NIH HHS/ -- R01 MH069359/MH/NIMH NIH HHS/ -- R01 MH069359-01A2/MH/NIMH NIH HHS/ -- R01 MH069359-02/MH/NIMH NIH HHS/ -- R01 MH069359-03/MH/NIMH NIH HHS/ -- R01 MH069359-04/MH/NIMH NIH HHS/ -- R01 MH069359-05/MH/NIMH NIH HHS/ -- R01 NS049261/NS/NINDS NIH HHS/ -- R01 NS049261-01A2/NS/NINDS NIH HHS/ -- R01 NS049261-02/NS/NINDS NIH HHS/ -- R01 NS049261-03/NS/NINDS NIH HHS/ -- R01 NS049261-04/NS/NINDS NIH HHS/ -- R01 NS049261-05/NS/NINDS NIH HHS/ -- U10 MH066766-02S1/MH/NIMH NIH HHS/ -- U10MH66766-02S1/MH/NIMH NIH HHS/ -- U19 HD035476-06/HD/NICHD NIH HHS/ -- U19 HD035476-07/HD/NICHD NIH HHS/ -- U19 HD035476-08/HD/NICHD NIH HHS/ -- U19 HD035476-09/HD/NICHD NIH HHS/ -- U19 HD035476-10/HD/NICHD NIH HHS/ -- U24 MH081810/MH/NIMH NIH HHS/ -- U24 MH081810-01/MH/NIMH NIH HHS/ -- U24 MH081810-02/MH/NIMH NIH HHS/ -- U24 MH081810-03/MH/NIMH NIH HHS/ -- U24 MH081810-04/MH/NIMH NIH HHS/ -- U54 MH066673/MH/NIMH NIH HHS/ -- U54 MH066673-01A10001/MH/NIMH NIH HHS/ -- U54 MH066673-020001/MH/NIMH NIH HHS/ -- U54 MH066673-030001/MH/NIMH NIH HHS/ -- U54 MH066673-040001/MH/NIMH NIH HHS/ -- U54 MH066673-05/MH/NIMH NIH HHS/ -- U54 MH066673-050001/MH/NIMH NIH HHS/ -- UL1 RR024134/RR/NCRR NIH HHS/ -- UL1 RR024134-03/RR/NCRR NIH HHS/ -- UL1-RR024134-03/RR/NCRR NIH HHS/ -- Medical Research Council/United Kingdom -- England -- Nature. 2009 May 28;459(7246):569-73. doi: 10.1038/nature07953. Epub 2009 Apr 28.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania 19104, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/19404257" target="_blank"〉PubMed〈/a〉

Description: 〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Anagnostou, Evdokia -- England -- Nature. 2012 Nov 8;491(7423):196-7. doi: 10.1038/491196a.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/23135462" target="_blank"〉PubMed〈/a〉

Description: The Early Eocene Climate Optimum (EECO, which occurred about 51 to 53 million years ago), was the warmest interval of the past 65 million years, with mean annual surface air temperature over ten degrees Celsius warmer than during the pre-industrial period. Subsequent global cooling in the middle and late Eocene epoch, especially at high latitudes, eventually led to continental ice sheet development in Antarctica in the early Oligocene epoch (about 33.6 million years ago). However, existing estimates place atmospheric carbon dioxide (CO2) levels during the Eocene at 500-3,000 parts per million, and in the absence of tighter constraints carbon-climate interactions over this interval remain uncertain. Here we use recent analytical and methodological developments to generate a new high-fidelity record of CO2 concentrations using the boron isotope (delta(11)B) composition of well preserved planktonic foraminifera from the Tanzania Drilling Project, revising previous estimates. Although species-level uncertainties make absolute values difficult to constrain, CO2 concentrations during the EECO were around 1,400 parts per million. The relative decline in CO2 concentration through the Eocene is more robustly constrained at about fifty per cent, with a further decline into the Oligocene. Provided the latitudinal dependency of sea surface temperature change for a given climate forcing in the Eocene was similar to that of the late Quaternary period, this CO2 decline was sufficient to drive the well documented high- and low-latitude cooling that occurred through the Eocene. Once the change in global temperature between the pre-industrial period and the Eocene caused by the action of all known slow feedbacks (apart from those associated with the carbon cycle) is removed, both the EECO and the late Eocene exhibit an equilibrium climate sensitivity relative to the pre-industrial period of 2.1 to 4.6 degrees Celsius per CO2 doubling (66 per cent confidence), which is similar to the canonical range (1.5 to 4.5 degrees Celsius), indicating that a large fraction of the warmth of the early Eocene greenhouse was driven by increased CO2 concentrations, and that climate sensitivity was relatively constant throughout this period.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Anagnostou, Eleni -- John, Eleanor H -- Edgar, Kirsty M -- Foster, Gavin L -- Ridgwell, Andy -- Inglis, Gordon N -- Pancost, Richard D -- Lunt, Daniel J -- Pearson, Paul N -- England -- Nature. 2016 Apr 25;533(7603):380-4. doi: 10.1038/nature17423.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Ocean and Earth Science, National Oceanography Centre Southampton, University of Southampton Waterfront Campus, Southampton SO14 3ZH, UK. ; School of Earth and Ocean Sciences, Cardiff University, Park Place, Cardiff CF10 3AT, UK. ; School of Earth Sciences, Bristol University, Bristol BS8 1RJ, UK. ; School of Geographical Sciences, Bristol University, Bristol BS8 1SS, UK. ; Department of Earth Sciences, University of California, Riverside, California 92521, USA. ; Organic Geochemistry Unit, School of Chemistry, University of Bristol, Cantock's Close, Bristol BS8 1TS, UK. ; Cabot Institute, University of Bristol, Bristol BS8 1UJ, UK.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/27111509" target="_blank"〉PubMed〈/a〉

Description: Past warm periods provide an opportunity to evaluate climate models under extreme forcing scenarios, in particular high ( 〉  800 ppmv) atmospheric CO2 concentrations. Although a post hoc intercomparison of Eocene ( ∼  50  Ma) climate model simulations and geological data has been carried out previously, models of past high-CO2 periods have never been evaluated in a consistent framework. Here, we present an experimental design for climate model simulations of three warm periods within the early Eocene and the latest Paleocene (the EECO, PETM, and pre-PETM). Together with the CMIP6 pre-industrial control and abrupt 4 ×  CO2 simulations, and additional sensitivity studies, these form the first phase of DeepMIP – the Deep-time Model Intercomparison Project, itself a group within the wider Paleoclimate Modelling Intercomparison Project (PMIP). The experimental design specifies and provides guidance on boundary conditions associated with palaeogeography, greenhouse gases, astronomical configuration, solar constant, land surface processes, and aerosols. Initial conditions, simulation length, and output variables are also specified. Finally, we explain how the geological data sets, which will be used to evaluate the simulations, will be developed.

Description: The GPHN gene codes for gephyrin, a key scaffolding protein in the neuronal postsynaptic membrane, responsible for the clustering and localization of glycine and GABA receptors at inhibitory synapses. Gephyrin has well-established functional links with several synaptic proteins that have been implicated in genetic risk for neurodevelopmental disorders such as autism spectrum disorder (ASD), schizophrenia and epilepsy including the neuroligins (NLGN2, NLGN4), the neurexins (NRXN1, NRXN2, NRXN3) and collybistin (ARHGEF9). Moreover, temporal lobe epilepsy has been linked to abnormally spliced GPHN mRNA lacking exons encoding the G-domain of the gephyrin protein, potentially arising due to cellular stress associated with epileptogenesis such as temperature and alkalosis. Here, we present clinical and genomic characterization of six unrelated subjects, with a range of neurodevelopmental diagnoses including ASD, schizophrenia or seizures, who possess rare de novo or inherited hemizygous microdeletions overlapping exons of GPHN at chromosome 14q23.3. The region of common overlap across the deletions encompasses exons 3–5, corresponding to the G-domain of the gephyrin protein. These findings, together with previous reports of homozygous GPHN mutations in connection with autosomal recessive molybdenum cofactor deficiency, will aid in clinical genetic interpretation of the GPHN mutation spectrum. Our data also add to the accumulating evidence implicating neuronal synaptic gene products as key molecular factors underlying the etiologies of a diverse range of neurodevelopmental conditions.

Description: The estimation of heavy precipitation events is a particularly difficult task, especially over high mountainous terrain typically associated with scant availability of in situ observations. Therefore, quantification of precipitation variability in such data-limited regions relies on remote sensing estimates, due to their global coverage and near real-time availability. However, strong underestimation of precipitation associated with low-level orographic enhancement often limits the quantitative use of these data in applications. This study utilizes state-of-the-art numerical weather prediction simulations, toward the reduction of quantitative errors in satellite precipitation estimates and an insight on the nature of detection limitations. Satellite precipitation products based on different retrieval algorithms (Climate Prediction Center morphing method, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Cloud Classification System, and Global Satellite Mapping of Precipitation) are evaluated for their performance in a number of storm events over mountainous areas with distinct storm characteristics: Upper Blue Nile in Ethiopia and Alto Adige in NE Italy. High-resolution (1 and 2 km) simulations from the Regional Atmospheric Modeling System/Integrated Community Limited Area Modeling System are used to derive adjustments to the magnitude of satellite estimates. Finally, a microphysical investigation is presented for occurrences of erroneous precipitation detection from the satellite instruments. Statistical indexes showcase improvement in numerical weather prediction-adjusted satellite products and microphysical commodities among cases of no detection are discussed. ©2018. American Geophysical Union. All Rights Reserved.

Description: Hurricane Sandy (2012, referred to as Current Sandy) was among the most devastating storms to impact Connecticut’s overhead electric distribution network, resulting in over 15 000 outage locations that affected more than 500 000 customers. In this paper, the severity of tree-caused outages in Connecticut is estimated under future-climate Hurricane Sandy simulations, each exhibiting strengthened winds and heavier rain accumulation over the study area from large-scale thermodynamic changes in the atmosphere and track changes in the year ~2100 (referred to as Future Sandy). Three machine-learning models used five weather simulations and the ensemble mean of Current and Future Sandy, along with land-use and overhead utility infrastructure data, to predict the severity and spatial distribution of outages across the Eversource Energy service territory in Connecticut. To assess the influence of increased precipitation from Future Sandy, two approaches were compared: an outage model fit with a full set of variables accounting for both wind and precipitation, and a reduced set with only wind. Future Sandy displayed an outage increase of 42%–64% when using the ensemble of WRF simulations fit with three different outage prediction models. This study is a proof of concept for the assessment of increased outage risk resulting from potential changes in tropical cyclone intensity associated with late-century thermodynamic changes driven by the IPCC AR4 A2 emissions scenario.

Description: Despite recent advances, the link between the evolution of atmospheric CO2 and climate during the Eocene greenhouse remains uncertain. In particular, modelling studies suggest that in order to achieve the global warmth that characterised the early Eocene, warmer climates must be more sensitive to CO2 forcing than colder climates. Here, we test this assertion in the geological record by combining a new high-resolution boron isotope-based CO2 record with novel estimates of Global Mean Temperature. We find that Equilibrium Climate Sensitivity (ECS) was indeed higher during the warmest intervals of the Eocene, agreeing well with recent model simulations, and declined through the Eocene as global climate cooled. These observations indicate that the canonical IPCC range of ECS (1.5 to 4.5 °C per doubling) is unlikely to be appropriate for high-CO2 warm climates of the past, and the state dependency of ECS may play an increasingly important role in determining the state of future climate as the Earth continues to warm.