BOD1 Is Required for Cognitive Function in Humans and <i>Drosophila</i>

Here we report a stop-mutation in the BOD1 (Biorientation Defective 1) gene, which co-segregates with intellectual disability in a large consanguineous family, where individuals that are homozygous for the mutation have no detectable BOD1 mRNA or protein. The BOD1 protein is required for proper chromosome segregation, regulating phosphorylation of PLK1 substrates by modulating Protein Phosphatase 2A (PP2A) activity during mitosis. We report that fibroblast cell lines derived from homozygous BOD1 mutation carriers show aberrant localisation of the cell cycle kinase PLK1 and its phosphatase PP2A at mitotic kinetochores. However, in contrast to the mitotic arrest observed in BOD1-siRNA treated HeLa cells, patient-derived cells progressed through mitosis with no apparent segregation defects but at an accelerated rate compared to controls. The relatively normal cell cycle progression observed in cultured cells is in line with the absence of gross structural brain abnormalities in the affected individuals. Moreover, we found that in normal adult brain tissues BOD1 expression is maintained at considerable levels, in contrast to PLK1 expression, and provide evidence for synaptic localization of Bod1 in murine neurons. These observations suggest that BOD1 plays a cell cycle-independent role in the nervous system. To address this possibility, we established two Drosophila models, where neuron-specific knockdown of BOD1 caused pronounced learning deficits and significant abnormalities in synapse morphology. Together our results reveal novel postmitotic functions of BOD1 as well as pathogenic mechanisms that strongly support a causative role of BOD1 deficiency in the aetiology of intellectual disability. Moreover, by demonstrating its requirement for cognitive function in humans and Drosophila we provide evidence for a conserved role of BOD1 in the development and maintenance of cognitive features

Inference of Functional Relations in Predicted Protein Networks with a Machine Learning Approach

Background: Molecular biology is currently facing the challenging task of functionally characterizing the proteome. The large number of possible protein-protein interactions and complexes, the variety of environmental conditions and cellular states in which these interactions can be reorganized, and the multiple ways in which a protein can influence the function of others, requires the development of experimental and computational approaches to analyze and predict functional associations between proteins as part of their activity in the interactome. Methodology/Principal Findings: We have studied the possibility of constructing a classifier in order to combine the output of the several protein interaction prediction methods. The AODE (Averaged One-Dependence Estimators) machine learning algorithm is a suitable choice in this case and it provides better results than the individual prediction methods, and it has better performances than other tested alternative methods in this experimental set up. To illustrate the potential use of this new AODE-based Predictor of Protein InterActions (APPIA), when analyzing high-throughput experimental data, we show how it helps to filter the results of published High-Throughput proteomic studies, ranking in a significant way functionally related pairs. Availability: All the predictions of the individual methods and of the combined APPIA predictor, together with the used datasets of functional associations are available at http://ecid.bioinfo.cnio.es/. Conclusions: We propose a strategy that integrates the main current computational techniques used to predict functional associations into a unified classifier system, specifically focusing on the evaluation of poorly characterized protein pairs. We selected the AODE classifier as the appropriate tool to perform this task. AODE is particularly useful to extract valuable information from large unbalanced and heterogeneous data sets. The combination of the information provided by five prediction interaction prediction methods with some simple sequence features in APPIA is useful in establishing reliability values and helpful to prioritize functional interactions that can be further experimentally characterized.This work was funded by the BioSapiens (grant number LSHG-CT-2003-503265) and the Experimental Network for Functional Integration (ENFIN) Networks of Excellence (contract number LSHG-CT-2005-518254), by Consolider BSC (grant number CSD2007-00050) and by the project “Functions for gene sets” from the Spanish Ministry of Education and Science (BIO2007-66855). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Mapping Morphological and Structural Properties of Lead Halide Perovskites by Scanning Nanofocus XRD

Scanning nanofocus X-ray diffraction (nXRD) performed at a synchrotron is used to simultaneously probe the morphology and the structural properties of spin-coated CH$_3$NH$_3$PbI$_3$ (MAPI) perovskite films for photovoltaic devices. MAPI films are spin-coated on a Si/SiO$_2$/poly(3,4-ethylenedioxythiophene): polystyrene sulfonate (PEDOT:PSS) substrate held at different temperatures during the deposition in order to tune the perovskite film coverage. The films are then investigated using nXRD and scanning electron microscopy (SEM). The advantages of nXRD over SEM and other techniques are discussed. A method to visualize, selectively isolate, and structurally characterize single perovskite grains buried within a complex, polycrystalline film is developed. The results of nXRD measurements are correlated with solar cell device measurements, and it is shown that spin-coating the perovskite precursor solution at elevated temperatures leads to improved surface coverage and enhanced solar cell performance.This work was funded by the UK Engineering and Physical Sciences Research Council via grants EP/M025020/1 “High resolution mapping of performance and degradation mechanisms in printable photovoltaic devices,” EP/J017361/1 (Supersolar Solar Energy Hub) and the E-Futures Doctoral Training Center in Interdisciplinary Energy Research EP/G037477/1. This work was partially funded by the President of the UAE’s Distinguished Student Scholarship Program (DSS), granted by the Ministry of Presidential Affairs, UAE (M.A. PhD scholarship). This work was also partially funded by the Masdar Institute through the grant Novel Organic Optoelectronic Devices. The authors gratefully acknowledge Manfred Burghammer and Martin Rosenthal at the ID13 – the microfocus beamline at the ESRF for their assistance with the nXRD measurements. XMaS is a mid-range facility supported by the Engineering and Physical Sciences Research Council (EPSRC)

Combination of searches for heavy spin-1 resonances using 139 fb−1 of proton-proton collision data at √s = 13 TeV with the ATLAS detector

A combination of searches for new heavy spin-1 resonances decaying into diferent pairings of W, Z, or Higgs bosons, as well as directly into leptons or quarks, is presented. The data sample used corresponds to 139 fb−1 of proton-proton collisions at √ s = 13 TeV collected during 2015–2018 with the ATLAS detector at the CERN Large Hadron Collider. Analyses selecting quark pairs (qq, bb, tt¯, and tb) or third-generation leptons (τν and τ τ ) are included in this kind of combination for the frst time. A simplifed model predicting a spin-1 heavy vector-boson triplet is used. Cross-section limits are set at the 95% confdence level and are compared with predictions for the benchmark model. These limits are also expressed in terms of constraints on couplings of the heavy vector-boson triplet to quarks, leptons, and the Higgs boson. The complementarity of the various analyses increases the sensitivity to new physics, and the resulting constraints are stronger than those from any individual analysis considered. The data exclude a heavy vector-boson triplet with mass below 5.8 TeV in a weakly coupled scenario, below 4.4 TeV in a strongly coupled scenario, and up to 1.5 TeV in the case of production via vector-boson fusion

Search for new phenomena with top-quark pairs and large missing transverse momentum using 140 fb−1 of pp collision data at \sqrt{s} = 13 TeV with the ATLAS detector

A search is conducted for new phenomena in events with a top quark pair and large missing transverse momentum, where the top quark pair is reconstructed in final states with one isolated electron or muon and multiple jets. The search is performed using the Large Hadron Collider proton-proton collision data sample at a centre-of-mass energy of \sqrt{s} = 13 TeV recorded by the ATLAS detector that corresponds to an integrated luminosity of 140 fb^{−1}. An analysis based on neural network classifiers is optimised to search for directly produced pairs of supersymmetric partners of the top quark (stop), and to search for spin-0 mediators, produced in association with a pair of top quarks, that decay into dark-matter particles. In the stop search, the analysis is designed to target models in which the mass difference between the stop and the neutralino from the stop decay is close to the top quark mass. This new search is combined with previously published searches in final states with different lepton multiplicities. No significant excess above the Standard Model background is observed, and limits at 95% confidence level are set. Models with neutralinos with masses up to 570 GeV are excluded, while for small neutralino masses models are excluded for stop masses up to 1230 GeV. Scalar (pseudoscalar) dark matter mediator masses as large as 350 (370) GeV are excluded when the coupling strengths of the mediator to Standard Model and dark-matter particles are both set to one. At lower mediator masses, models with production cross-sections as small as 0.15 (0.16) times the nominal predictions are excluded. Results of this search are also used to set constraints on effective four-fermion contact interactions between top quarks and neutrinos

Accuracy versus precision in boosted top tagging with the ATLAS detector

Abstract The identification of top quark decays where the top quark has a large momentum transverse to the beam axis, known as top tagging, is a crucial component in many measurements of Standard Model processes and searches for beyond the Standard Model physics at the Large Hadron Collider. Machine learning techniques have improved the performance of top tagging algorithms, but the size of the systematic uncertainties for all proposed algorithms has not been systematically studied. This paper presents the performance of several machine learning based top tagging algorithms on a dataset constructed from simulated proton-proton collision events measured with the ATLAS detector at √ s = 13 TeV. The systematic uncertainties associated with these algorithms are estimated through an approximate procedure that is not meant to be used in a physics analysis, but is appropriate for the level of precision required for this study. The most performant algorithms are found to have the largest uncertainties, motivating the development of methods to reduce these uncertainties without compromising performance. To enable such efforts in the wider scientific community, the datasets used in this paper are made publicly available.</jats:p

Combination of searches for resonant Higgs Boson pair production using pp collisions at √s = 13 TeV with the ATLAS detector

A combination of searches for a new resonance decaying into a Higgs boson pair is presented, using up to 139  fb−1 of pp collision data at √s = 13 TeV recorded with the ATLAS detector at the LHC. The combination includes searches performed in three decay channels: b ¯ b ⁢b ¯ b , b⁢ ¯ b ⁢τ+⁢τ−, and b⁢ ¯ bγγ ⁢. No excess above the expected Standard Model background is observed and upper limits are set at the 95% confidence level on the production cross section of Higgs boson pairs originating from the decay of a narrow scalar resonance with mass in the range 251 GeV–5 TeV. The observed (expected) limits are in the range 0.96–600 fb (1.2–390 fb). The limits are interpreted in the type-I two-Higgs-doublet model and the minimal supersymmetric standard model, and constrain parameter space not previously excluded by other searches

Search for light long-lived particles in pp collisions at √s = 13 TeV using displaced vertices in the ATLAS inner detector

A search for long-lived particles (LLPs) using 140 fb−1 of pp collision data with √s = 13 TeV recorded by the ATLAS experiment at the LHC is presented. The search targets LLPs with masses between 5 and 55 GeV that decay hadronically in the ATLAS inner detector. Benchmark models with LLP pair production from exotic decays of the Higgs boson and models featuring long-lived axionlike particles (ALPs) are considered. No significant excess above the expected background is observed. Upper limits are placed on the branching ratio of the Higgs boson to pairs of LLPs, the cross section for ALPs produced in association with a vector boson, and, for the first time, on the branching ratio of the top quark to an ALP and a u/c quark