Comparing the CORAL and random forest approaches for modelling the in vitro cytotoxicity of silica nanomaterials

Nanotechnology is one of the most important technological developments of the twenty-first century. In silico methods such as quantitative structure-activity relationships (QSARs) to predict toxicity promote the safe-by-design approach for the development of new materials, including nanomaterials. In this study, a set of cytotoxicity experimental data corresponding to 19 data points for silica nanomaterials was investigated to compare the widely employed CORAL and Random Forest approaches in terms of their usefulness for developing so-called “nano-QSAR” models. “External” leave-one-out cross-validation (LOO) analysis was performed to validate the two different approaches. An analysis of variable importance measures and signed feature contributions for both algorithms was undertaken in order to interpret the models developed. CORAL showed a more pronounced difference between the average coefficient of determination (R2) between training and LOO (0.83 and 0.65 for training and LOO respectively) compared to Random Forest (0.87 and 0.78 without bootstrap sampling, 0.90 and 0.78 with bootstrap sampling), which may be due to overfitting. The aspect ratio and zeta potential from amongst the nanomaterials’ physico-chemical properties were found to be the two most important variables for the Random Forest and the average feature contributions calculated for the corresponding descriptors were consistent with the clear trends observed in the dataset: less negative zeta potential values and lower aspect ratio values were associated with higher cytotoxicity. In contrast, CORAL failed to capture these trends

Winnow based identification of potent hERG inhibitors in silico: comparative assessment on different datasets

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Measurement of the inclusive and dijet cross-sections of b-jets in pp collisions at sqrt(s) = 7 TeV with the ATLAS detector

The inclusive and dijet production cross-sections have been measured for jets containing b-hadrons (b-jets) in proton-proton collisions at a centre-of-mass energy of sqrt(s) = 7 TeV, using the ATLAS detector at the LHC. The measurements use data corresponding to an integrated luminosity of 34 pb^-1. The b-jets are identified using either a lifetime-based method, where secondary decay vertices of b-hadrons in jets are reconstructed using information from the tracking detectors, or a muon-based method where the presence of a muon is used to identify semileptonic decays of b-hadrons inside jets. The inclusive b-jet cross-section is measured as a function of transverse momentum in the range 20 < pT < 400 GeV and rapidity in the range |y| < 2.1. The bbbar-dijet cross-section is measured as a function of the dijet invariant mass in the range 110 < m_jj < 760 GeV, the azimuthal angle difference between the two jets and the angular variable chi in two dijet mass regions. The results are compared with next-to-leading-order QCD predictions. Good agreement is observed between the measured cross-sections and the predictions obtained using POWHEG + Pythia. MC@NLO + Herwig shows good agreement with the measured bbbar-dijet cross-section. However, it does not reproduce the measured inclusive cross-section well, particularly for central b-jets with large transverse momenta.Comment: 10 pages plus author list (21 pages total), 8 figures, 1 table, final version published in European Physical Journal

Observation of associated near-side and away-side long-range correlations in √sNN=5.02 TeV proton-lead collisions with the ATLAS detector

Two-particle correlations in relative azimuthal angle (Δϕ) and pseudorapidity (Δη) are measured in √sNN=5.02  TeV p+Pb collisions using the ATLAS detector at the LHC. The measurements are performed using approximately 1  μb-1 of data as a function of transverse momentum (pT) and the transverse energy (ΣETPb) summed over 3.1<η<4.9 in the direction of the Pb beam. The correlation function, constructed from charged particles, exhibits a long-range (2<|Δη|<5) “near-side” (Δϕ∼0) correlation that grows rapidly with increasing ΣETPb. A long-range “away-side” (Δϕ∼π) correlation, obtained by subtracting the expected contributions from recoiling dijets and other sources estimated using events with small ΣETPb, is found to match the near-side correlation in magnitude, shape (in Δη and Δϕ) and ΣETPb dependence. The resultant Δϕ correlation is approximately symmetric about π/2, and is consistent with a dominant cos⁡2Δϕ modulation for all ΣETPb ranges and particle pT

Comparing the CORAL and Random Forest approaches for modelling the in vitro cytotoxicity of silica nanomaterials.

Nanotechnology is one of the most important technological developments of the 21st century. In silico methods to predict toxicity, such as quantitative structure-activity relationships (QSARs), promote the safe-by-design approach for the development of new materials, including nanomaterials. In this study, a set of cytotoxicity experimental data corresponding to 19 data points for silica nanomaterials were investigated, to compare the widely employed CORAL and Random Forest approaches in terms of their usefulness for developing so-called 'nano-QSAR' models. 'External' leave-one-out cross-validation (LOO) analysis was performed, to validate the two different approaches. An analysis of variable importance measures and signed feature contributions for both algorithms was undertaken, in order to interpret the models developed. CORAL showed a more pronounced difference between the average coefficient of determination (R²) for training and for LOO (0.83 and 0.65 for training and LOO, respectively), compared to Random Forest (0.87 and 0.78 without bootstrap sampling, 0.90 and 0.78 with bootstrap sampling), which may be due to overfitting. With regard to the physicochemical properties of the nanomaterials, the aspect ratio and zeta potential were found to be the two most important variables for Random Forest, and the average feature contributions calculated for the corresponding descriptors were consistent with the clear trends observed in the data set: less negative zeta potential values and lower aspect ratio values were associated with higher cytotoxicity. In contrast, CORAL failed to capture these trends

Search for new phenomena in final states with an energetic jet and large missing transverse momentum in pp collisions at √ s = 8 TeV with the ATLAS detector

Results of a search for new phenomena in final states with an energetic jet and large missing transverse momentum are reported. The search uses 20.3 fb−1 of √ s = 8 TeV data collected in 2012 with the ATLAS detector at the LHC. Events are required to have at least one jet with pT > 120 GeV and no leptons. Nine signal regions are considered with increasing missing transverse momentum requirements between Emiss T > 150 GeV and Emiss T > 700 GeV. Good agreement is observed between the number of events in data and Standard Model expectations. The results are translated into exclusion limits on models with either large extra spatial dimensions, pair production of weakly interacting dark matter candidates, or production of very light gravitinos in a gauge-mediated supersymmetric model. In addition, limits on the production of an invisibly decaying Higgs-like boson leading to similar topologies in the final state are presente

Evaluation of Force-Field Calculations of Lattice Energies on a Large Public Dataset, Assessment of Pharmaceutical Relevance, and Comparison to Density Functional Theory

Crystal lattice energy is a key property affecting the ease of processing pharmaceutical materials during manufacturing, as well as product performance. We present an extensive comparison of 324 force-field protocols for calculating the lattice energies of single component, organic molecular crystals (further restricted to Z′ less than or equal to one), corresponding to a wide variety of force-fields (DREIDING, Universal, CVFF, PCFF, COMPASS, COMPASSII), optimization routines, and other variations, which could be implemented as part of an automated workflow using the industry standard Materials Studio software. All calculations were validated using a large new dataset (SUB-BIG), which we make publicly available. This dataset comprises public domain sublimation data, from which estimated experimental lattice energies were derived, linked to 235 molecular crystals. Analysis of pharmaceutical relevance was performed according to two distinct methods based upon (A) public and (B) proprietary data. These identified overlapping subsets of SUB-BIG comprising (A) 172 and (B) 63 crystals, of putative pharmaceutical relevance, respectively. We recommend a protocol based on the COMPASSII force field for lattice energy calculations of general organic or pharmaceutically relevant molecular crystals. This protocol was the most highly ranked prior to subsetting and was either the top ranking or amongst the top 15 protocols (top 5%) following subsetting of the dataset according to putative pharmaceutical relevance. Further analysis identified scenarios where the lattice energies calculated using the recommended force-field protocol should either be disregarded (values greater than or equal to zero and/or the messages generated by the automated workflow indicate extraneous atoms were added to the unit cell) or treated cautiously (values less than or equal to −249 kJ/mol), as they are likely to be inaccurate. Application of the recommended force-field protocol, coupled with these heuristic filtering criteria, achieved an root mean-squared error (RMSE) around 17 kJ/mol (mean absolute deviation (MAD) around 11 kJ/mol, Spearman’s rank correlation coefficient of 0.88) across all 226 SUB-BIG structures retained after removing calculation failures and applying the filtering criteria. Across these 226 structures, the estimated experimental lattice energies ranged from −60 to −269 kJ/mol, with a standard deviation around 29 kJ/mol. The performance of the recommended protocol on pharmaceutically relevant crystals could be somewhat reduced, with an RMSE around 20 kJ/mol (MAD around 13 kJ/mol, Spearman’s rank correlation coefficient of 0.76) obtained on 62 structures retained following filtering according to pharmaceutical relevance method B, for which the distribution of experimental values was similar. For a diverse set of 17 SUB-BIG entries, deemed pharmaceutically relevant according to method B, this recommended force-field protocol was compared to dispersion corrected density functional theory (DFT) calculations (PBE + TS). These calculations suggest that the recommended force-field protocol (RMSE around 15 kJ/mol) outperforms PBE + TS (RMSE around 37 kJ/mol), although it may not outperform more sophisticated DFT protocols and future studies should investigate this. Finally, further work is required to compare our recommended protocol to other lattice energy calculation protocols reported in the literature, as comparisons based upon previously reported smaller datasets indicated this protocol was outperformed by a number of other methods. The SUB-BIG dataset provides a basis for these future studies and could support protocol refinement

Search for R-parity-violating supersymmetry in events with four or more leptons in sqrt(s) =7 TeV pp collisions with the ATLAS detector

A search for new phenomena in final states with four or more leptons (electrons or muons) is presented. The analysis is based on 4.7 fb−1 of $\sqrt{s}=7\;\mathrm{TeV}$ proton-proton collisions delivered by the Large Hadron Collider and recorded with the ATLAS detector. Observations are consistent with Standard Model expectations in two signal regions: one that requires moderate values of missing transverse momentum and another that requires large effective mass. The results are interpreted in a simplified model of R-parity-violating supersymmetry in which a 95% CL exclusion region is set for charged wino masses up to 540 GeV. In an R-parity-violating MSUGRA/CMSSM model, values of m 1/2 up to 820 GeV are excluded for 10 < tan β < 40

Measurement of the cross-section of high transverse momentum vector bosons reconstructed as single jets and studies of jet substructure in pp collisions at √s = 7 TeV with the ATLAS detector

This paper presents a measurement of the cross-section for high transverse momentum W and Z bosons produced in pp collisions and decaying to all-hadronic final states. The data used in the analysis were recorded by the ATLAS detector at the CERN Large Hadron Collider at a centre-of-mass energy of √s = 7 TeV;{\rm Te}{\rm V}$and correspond to an integrated luminosity of$4.6\;{\rm f}{{{\rm b}}^{-1}}$. The measurement is performed by reconstructing the boosted W or Z bosons in single jets. The reconstructed jet mass is used to identify the W and Z bosons, and a jet substructure method based on energy cluster information in the jet centre-of-mass frame is used to suppress the large multi-jet background. The cross-section for events with a hadronically decaying W or Z boson, with transverse momentum${{p}_{{\rm T}}}\gt 320\;{\rm Ge}{\rm V}$and pseudorapidity$|\eta |\lt 1.9$, is measured to be${{\sigma }_{W+Z}}=8.5\pm 1.7$ pb and is compared to next-to-leading-order calculations. The selected events are further used to study jet grooming techniques

Molecular Fingerprint-Derived Similarity Measures for Toxicological Read-Across: Recommendations for Optimal Use

Computational approaches are increasingly used to predict toxicity, in part due to pressures to find alternatives to animal testing. Read-across is the “new paradigm” which aims to predict toxicity by identifying similar, data rich, source compounds. This assumes that similar molecules tend to exhibit similar activities, i.e. molecular similarity is integral to read-across. Various molecular fingerprints and similarity measures may be used to calculate molecular similarity. This study investigated the value and concordance of the Tanimoto similarity values calculated using six widely used fingerprints within six toxicological datasets. There was considerable variability in the similarity values calculated from the various molecular fingerprints for diverse compounds, although they were reasonably concordant for homologous series acting via a common mechanism. The results suggest generic fingerprint-derived similarities are likely to be optimally predictive for local datasets, i.e. following sub-categorisation. Thus, for read-across, generic fingerprint-derived similarities are likely to be most predictive after chemicals are placed into categories (or groups), then similarity is calculated within those categories, rather than for a whole chemically diverse dataset