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Bipolar disorder (BD) is a heritable mental illness with complex etiology. While the largest published genome-wide association study identified 64 BD risk loci, the causal SNPs and genes within these loci remain unknown. We applied a suite of statistical and functional fine-mapping methods to these loci, and prioritized 22 likely causal SNPs for BD. We mapped these SNPs to genes, and investigated their likely functional consequences by integrating variant annotations, brain cell-type epigenomic annotations, brain quantitative trait loci, and results from rare variant exome sequencing in BD. Convergent lines of evidence supported the roles of SCN2A, TRANK1, DCLK3, INSYN2B, SYNE1, THSD7A, CACNA1B, TUBBP5, PLCB3, PRDX5, KCNK4, AP001453.3, TRPT1, FKBP2, DNAJC4, RASGRP1, FURIN, FES, YWHAE, DPH1, GSDMB, MED24, THRA, EEF1A2, and KCNQ2 in BD. These represent promising candidates for functional experiments to understand biological mechanisms and therapeutic potential. Additionally, we demonstrated that fine-mapping effect sizes can improve performance and transferability of BD polygenic risk scores across ancestrally diverse populations, and present a high-throughput fine-mapping pipeline (https://github.com/mkoromina/SAFFARI).
The performance of the electromagnetic calorimeter of the ALICE experiment during operation in 2010-2018 at the Large Hadron Collider is presented. After a short introduction into the design, readout, and trigger capabilities of the detector, the procedures for data taking, reconstruction, and validation are explained. The methods used for the calibration and various derived corrections are presented in detail. Subsequently, the capabilities of the calorimeter to reconstruct and measure photons, light mesons, electrons and jets are discussed. The performance of the calorimeter is illustrated mainly with data obtained with test beams at the Proton Synchrotron and Super Proton Synchrotron or in proton-proton collisions at s√=13 TeV, and compared to simulations.
The performance of the electromagnetic calorimeter of the ALICE experiment during operation in 2010–2018 at the Large Hadron Collider is presented. After a short introduction into the design, readout, and trigger capabilities of the detector, the procedures for data taking, reconstruction, and validation are explained. The methods used for the calibration and various derived corrections are presented in detail. Subsequently, the capabilities of the calorimeter to reconstruct and measure photons, light mesons, electrons and jets are discussed. The performance of the calorimeter is illustrated mainly with data obtained with test beams at the Proton Synchrotron and Super Proton Synchrotron or in proton-proton collisions at √s = 13 TeV, and compared to simulations.
New particle formation in the upper free troposphere is a major global source of cloud condensation nuclei (CCN)1,2,3,4. However, the precursor vapours that drive the process are not well understood. With experiments performed under upper tropospheric conditions in the CERN CLOUD chamber, we show that nitric acid, sulfuric acid and ammonia form particles synergistically, at rates that are orders of magnitude faster than those from any two of the three components. The importance of this mechanism depends on the availability of ammonia, which was previously thought to be efficiently scavenged by cloud droplets during convection. However, surprisingly high concentrations of ammonia and ammonium nitrate have recently been observed in the upper troposphere over the Asian monsoon region5,6. Once particles have formed, co-condensation of ammonia and abundant nitric acid alone is sufficient to drive rapid growth to CCN sizes with only trace sulfate. Moreover, our measurements show that these CCN are also highly efficient ice nucleating particles—comparable to desert dust. Our model simulations confirm that ammonia is efficiently convected aloft during the Asian monsoon, driving rapid, multi-acid HNO3–H2SO4–NH3 nucleation in the upper troposphere and producing ice nucleating particles that spread across the mid-latitude Northern Hemisphere.
Besides transcription, RNA decay accounts for a large proportion of regulated gene expression and is paramount for cellular functions. Classical RNA surveillance pathways, like nonsense-mediated decay (NMD), are also implicated in the turnover of non-mutant transcripts. Whereas numerous protein factors have been assigned to distinct RNA decay pathways, the contribution of long non-coding RNAs (lncRNAs) to RNA turnover remains unknown. Here we identify the lncRNA CALA as a potent regulator of RNA turnover in endothelial cells. We demonstrate that CALA forms cytoplasmic ribonucleoprotein complexes with G3BP1 and regulates endothelial cell functions. A detailed characterization of these G3BP1-positive complexes by mass spectrometry identifies UPF1 and numerous other NMD factors having cytoplasmic G3BP1-association that is CALA-dependent. Importantly, CALA silencing impairs degradation of NMD target transcripts, establishing CALA as a non-coding regulator of RNA steady-state levels in the endothelium.
Global water models (GWMs) simulate the terrestrial water cycle, on the global scale, and are used to assess the impacts of climate change on freshwater systems. GWMs are developed within different modeling frameworks and consider different underlying hydrological processes, leading to varied model structures. Furthermore, the equations used to describe various processes take different forms and are generally accessible only from within the individual model codes. These factors have hindered a holistic and detailed understanding of how different models operate, yet such an understanding is crucial for explaining the results of model evaluation studies, understanding inter-model differences in their simulations, and identifying areas for future model development. This study provides a comprehensive overview of how state-of-the-art GWMs are designed. We analyze water storage compartments, water flows, and human water use sectors included in 16 GWMs that provide simulations for the Inter-Sectoral Impact Model Intercomparison Project phase 2b (ISIMIP2b). We develop a standard writing style for the model equations to further enhance model improvement, intercomparison, and communication. In this study, WaterGAP2 used the highest number of water storage compartments, 11, and CWatM used 10 compartments. Seven models used six compartments, while three models (JULES-W1, Mac-PDM.20, and VIC) used the lowest number, three compartments. WaterGAP2 simulates five human water use sectors, while four models (CLM4.5, CLM5.0, LPJmL, and MPIHM) simulate only water used by humans for the irrigation sector. We conclude that even though hydrologic processes are often based on similar equations, in the end, these equations have been adjusted or have used different values for specific parameters or specific variables. Our results highlight that the predictive uncertainty of GWMs can be reduced through improvements of the existing hydrologic processes, implementation of new processes in the models, and high-quality input data.
Global water models (GWMs) simulate the terrestrial water cycle on the global scale and are used to assess the impacts of climate change on freshwater systems. GWMs are developed within different modelling frameworks and consider different underlying hydrological processes, leading to varied model structures. Furthermore, the equations used to describe various processes take different forms and are generally accessible only from within the individual model codes. These factors have hindered a holistic and detailed understanding of how different models operate, yet such an understanding is crucial for explaining the results of model evaluation studies, understanding inter-model differences in their simulations, and identifying areas for future model development. This study provides a comprehensive overview of how 16 state-of-the-art GWMs are designed. We analyse water storage compartments, water flows, and human water use sectors included in models that provide simulations for the Inter-Sectoral Impact Model Intercomparison Project phase 2b (ISIMIP2b). We develop a standard writing style for the model equations to enhance model intercomparison, improvement, and communication. In this study, WaterGAP2 used the highest number of water storage compartments, 11, and CWatM used 10 compartments. Six models used six compartments, while four models (DBH, JULES-W1, Mac-PDM.20, and VIC) used the lowest number, three compartments. WaterGAP2 simulates five human water use sectors, while four models (CLM4.5, CLM5.0, LPJmL, and MPI-HM) simulate only water for the irrigation sector. We conclude that, even though hydrological processes are often based on similar equations for various processes, in the end these equations have been adjusted or models have used different values for specific parameters or specific variables. The similarities and differences found among the models analysed in this study are expected to enable us to reduce the uncertainty in multi-model ensembles, improve existing hydrological processes, and integrate new processes.
Biogenic organic precursors play an important role in atmospheric new particle formation (NPF). One of the major precursor species is α-pinene, which upon oxidation can form a suite of products covering a wide range of volatilities. Highly oxygenated organic molecules (HOMs) comprise a fraction of the oxidation products formed. While it is known that HOMs contribute to secondary organic aerosol (SOA) formation, including NPF, they have not been well studied in newly formed particles due to their very low mass concentrations. Here we present gas- and particle-phase chemical composition data from experimental studies of α-pinene oxidation, including in the presence of isoprene, at temperatures (−50 and −30 ∘C) and relative humidities (20 % and 60 %) relevant in the upper free troposphere. The measurements took place at the CERN Cosmics Leaving Outdoor Droplets (CLOUD) chamber. The particle chemical composition was analyzed by a thermal desorption differential mobility analyzer (TD-DMA) coupled to a nitrate chemical ionization–atmospheric pressure interface–time-of-flight (CI-APi-TOF) mass spectrometer. CI-APi-TOF was used for particle- and gas-phase measurements, applying the same ionization and detection scheme. Our measurements revealed the presence of C8−10 monomers and C18−20 dimers as the major compounds in the particles (diameter up to ∼ 100 nm). Particularly, for the system with isoprene added, C5 (C5H10O5−7) and C15 compounds (C15H24O5−10) were detected. This observation is consistent with the previously observed formation of such compounds in the gas phase. However, although the C5 and C15 compounds do not easily nucleate, our measurements indicate that they can still contribute to the particle growth at free tropospheric conditions. For the experiments reported here, most likely isoprene oxidation products enhance the growth of particles larger than 15 nm. Additionally, we report on the nucleation rates measured at 1.7 nm (J1.7 nm) and compared with previous studies, we found lower J1.7 nm values, very likely due to the higher α-pinene and ozone mixing ratios used in the present study.
Biogenic organic precursors play an important role in atmospheric new particle formation (NPF). One of the major precursor species is α-pinene, which upon oxidation can form a suite of products covering a wide range of volatilities. Highly oxygenated organic molecules (HOMs) comprise a fraction of the oxidation products formed. While it is known that HOMs contribute to secondary organic aerosol (SOA) formation, including NPF, they have not been well studied in newly formed particles due to their very low mass concentrations. Here we present gas- and particle-phase chemical composition data from experimental studies of α-pinene oxidation, including in the presence of isoprene, at temperatures (−50 and −30 ∘C) and relative humidities (20 % and 60 %) relevant in the upper free troposphere. The measurements took place at the CERN Cosmics Leaving Outdoor Droplets (CLOUD) chamber. The particle chemical composition was analyzed by a thermal desorption differential mobility analyzer (TD-DMA) coupled to a nitrate chemical ionization–atmospheric pressure interface–time-of-flight (CI-APi-TOF) mass spectrometer. CI-APi-TOF was used for particle- and gas-phase measurements, applying the same ionization and detection scheme. Our measurements revealed the presence of C8−10 monomers and C18−20 dimers as the major compounds in the particles (diameter up to ∼ 100 nm). Particularly, for the system with isoprene added, C5 (C5H10O5−7) and C15 compounds (C15H24O5−10) were detected. This observation is consistent with the previously observed formation of such compounds in the gas phase. However, although the C5 and C15 compounds do not easily nucleate, our measurements indicate that they can still contribute to the particle growth at free tropospheric conditions. For the experiments reported here, most likely isoprene oxidation products enhance the growth of particles larger than 15 nm. Additionally, we report on the nucleation rates measured at 1.7 nm (J1.7 nm) and compared with previous studies, we found lower J1.7 nm values, very likely due to the higher α-pinene and ozone mixing ratios used in the present study.