21 research outputs found
Widespread QSO-driven outflows in the early Universe⋆
No full textWe present the stacking analysis of a sample of 48 quasi-stellar objects (QSOs) at 4.5 < z < 7.1 detected by the Atacama Large Millimetre Array (ALMA) in the [CII] λ 158 μ m emission line to investigate the presence and the properties of massive, cold outflows associated with broad wings in the [CII] profile. The high sensitivity reached through this analysis allows us to reveal very broad [CII] wings tracing the presence of outflows with velocities in excess of 1000 km s −1 . We find that the luminosity of the broad [CII] emission increases with L AGN , while it does not significantly depend on the star formation rate of the host galaxy, indicating that the central active galactic nucleus (AGN) is the main driving mechanism of the [CII] outflows in these powerful, distant QSOs. From the stack of the ALMA cubes, we derive an average outflow spatial extent of ∼3.5 kpc. The average atomic neutral mass outflow rate inferred from the stack of the whole sample is Ṁ out ∼ 100 M ⊙ yr −1 , while for the most luminous systems it increases to ∼200 M ⊙ yr −1 . The associated outflow kinetic power is about 0.1% of L AGN , while the outflow momentum rate is ∼ L AGN / c or lower, suggesting that these outflows are either driven by radiation pressure onto dusty clouds or, alternatively, are driven by the nuclear wind and energy conserving but with low coupling with the interstellar medium. We discuss the implications of the resulting feedback effect on galaxy evolution in the early Universe
Cold molecular outflows in the local Universe and their feedback effect on galaxies
No full textWe study molecular outflows in a sample of 45 local galaxies, both star forming and active galactic nucleus (AGN), primarily by using CO data from the Atacama Large Millimeter/submillimeter Array (ALMA) archive and from the literature. For a subsample, we also compare the molecular outflow with the ionized and neutral atomic phases. We infer an empirical analytical function relating the outflow rate simultaneously to the star formation rate (SFR), LAGN, and galaxy stellar mass; this relation is much tighter than the relations with the individual quantities. The outflow kinetic power shows a larger scatter than in previous, more biased studies, spanning from 0.1 to 5 per cent of LAGN, while the momentum rate ranges from 1 to 30 times LAGN/c, indicating that these outflows can be both energy driven, but with a broad range of coupling efficiencies with the interstellar medium (ISM), and radiation pressure driven. For about 10 per cent of the objects, the outflow energetics significantly exceed the maximum theoretical values; we interpret these as ‘fossil outflows’ resulting from activity of a past strong AGN, which has now faded. We estimate that, in the stellar mass range probed here (), less than 5 per cent of the outflowing gas escapes the galaxy. The molecular gas depletion time associated with the outflow can be as short as a few million years in powerful AGN; however, the total gas (H2 + H i) depletion times are much longer. Altogether, our findings suggest that even AGN-driven outflows might be relatively ineffective in clearing galaxies of their entire gas content, although they are likely capable of clearing and quenching the central region
Too Influential or Too Inadequate? A Critical Discourse Analysis of Environmental Advertising
No full textThis study focused on how environmental advertising constructs messages and shapes reality for consumers. Guided by discourse theory, this study used critical discourse analysis to examine and thematize images of environmental advertisements, resulting in the four themes of personalizing, personification, time, and shock value over specifics. From these findings and their analysis, it became clear that today’s consumers of environmental advertising are in a predicament: these advertisements create unwarranted feelings of responsibility, blame, and pressure, while simultaneously falling short in offering substantive advice on how to make meaningful change. To address this twofold problem, this study created a guide to be a more critical consumer of media, as well as a suggested media campaign to offer a better approach to environmental advertising
Properties of the multiphase outflows in local (ultra)luminous infrared galaxies
No full textGalactic outflows are known to consist of several gas phases; however, the connection between these phases has been investigated little and only in a few objects. In this paper, we analyse Multi Unit Spectroscopic Explorer (MUSE)/Very Large Telescope (VLT) data of 26 local (U)LIRGs and study their ionized and neutral atomic phases. We also include objects from the literature to obtain a sample of 31 galaxies with spatially resolved multiphase outflow information. We find that the ionized phase of the outflows has on average an electron density three times higher than the disc (ne,disc ∼145 cm-3 versus ne,outflow ∼500 cm-3), suggesting that cloud compression in the outflow is more important than cloud dissipation. We find that the difference in extinction between outflow and disc correlates with the outflow gas mass. Together with the analysis of the outflow velocities, this suggests that at least some of the outflows are associated with the ejection of dusty clouds from the disc. This may support models where radiation pressure on dust contributes to driving galactic outflows. The presence of dust in outflows is relevant for potential formation of molecules inside them. We combine our data with millimetre data to investigate the molecular phase. We find that the molecular phase accounts for more than 60 per cent of the total mass outflow rate in most objects and this fraction is higher in active galactic nuclei (AGN)-dominated systems. The neutral atomic phase contributes of the order of 10 per cent, while the ionized phase is negligible. The ionized-to-molecular mass outflow rate declines slightly with AGN luminosity, although with a large scatter
Implementation, accuracy evaluation, and preliminary clinical trial of a CT-free navigation system for high tibial opening wedge osteotomy
No full textOBJECTIVE:
The objectives of this study are to design and evaluate a CT-free intra-operative planning and navigation system for high tibial opening wedge osteotomy. This is a widely accepted treatment for medial compartment osteoarthritis and other lower extremity deformities, particularly in young and active patients for whom total knee replacement is not advised. However, it is a technically demanding procedure. Conventional preoperative planning and surgical techniques have so far been inaccurate, and often resulting in postoperative malalignment representing either under- or over-correction, which is the main reason for poor long-term results. In addition, conventional techniques have the potential to damage the lateral hinge cortex and tibial neurovascular structures, which may cause fixation failure, loss of correction, or peroneal nerve paralysis. All these common problems can be addressed by the use of a surgical navigation system.
MATERIALS AND METHODS:
Surgical instruments are tracked optically with the SurgiGATE((R)) navigation system (PRAXIM MediVision, La Tronche, France). Following exposure, dynamical reference bases are attached to the femur, tibia, and proximal fragment of the tibia. A patient-specific coordinate system is then established, on the basis of registered anatomical landmarks. After intra-operative deformity measurement and correction planning, the osteotomy is performed under navigational guidance. The deformities are corrected by realigning the mechanical axis of the affected limb from the diseased medial compartment to the healthy lateral side. The wedge size, joint line orientation, and tibial plateau slope are monitored during correction. Besides correcting uni-planar varus deformities, the system provides the functionality to correct complex multi-planar deformities with a single cut. Furthermore, with on-the-fly visualization of surgical instruments on multiple fluoroscopic images, penetration of the hinge cortex and damage to the neurovascular structures due to an inappropriate osteotomy can be avoided.
RESULTS:
The laboratory evaluation with a plastic bone model (Synbone AG, Davos, Switzerland) shows that the error of deformity correction is <1.7 degrees (95% confidence interval) in the frontal plane and <2.3 degrees (95% confidence interval) in the sagittal plane. The preliminary clinical trial confirms these results.
CONCLUSION:
A novel CT-free navigation system for high tibial osteotomy has been developed and evaluated, which holds the promise of improved accuracy, reliability, and safety of this procedure
Deep Learning Models Compared to Experimental Variability for the Prediction of CYP3A4 Time-Dependent Inhibition.
No full textMost drugs are mainly metabolized by cytochrome P450 (CYP450), which can lead to drug-drug interactions (DDI). Specifically, time-dependent inhibition (TDI) of CYP3A4 isoenzyme has been associated with clinically relevant DDI. To overcome potential DDI issues, high-throughput in vitro assays were established to assess the TDI of CYP3A4 during the discovery and lead optimization phases. However, in silico machine learning models would enable an earlier and larger-scale assessment of TDI potential liabilities. For CYP inhibition, most modeling efforts have focused on highly imbalanced and small data sets. Moreover, assay variability is rarely considered, which is key to understand the model's quality and suitability for decision-making. In this work, machine learning models were built for the prediction of TDI of CYP3A4, evaluated prospectively, and compared to the variability of the experimental assay. Different modeling strategies were investigated to assess their influence on the model's performance. Through multitask learning, additional data sets were leveraged for model building, coming from public databases, in-house CYP-related assays, or other pharmaceutical companies (federated learning). Apart from the numerical prediction of inactivation rates of CYP3A4 TDI, three-class predictions were carried out, giving a negative (inactivation rate kobs 0.025 min-1) output. The final multitask graph neural network model achieved misclassification rates of 8 and 7% for positive and negative TDI, respectively. Importantly, the presented deep learning-based predictions had a similar precision to the reproducibility of in vitro experiments and thus offered great opportunities for drug design, early derisk of DDI potential, and selection of experiments. To facilitate CYP inhibition modeling efforts in the public domain, the developed model was used to annotate ∼16 000 publicly available structures, and a surrogate data set is shared as Supporting Information
Adapting Deep Learning QSPR Models to Specific Drug Discovery Projects
No full textMedicinal chemistry and drug design efforts can be assisted by machine learning (ML) models that relate the molecular structure to compound properties. Such quantitative structure–property relationship models are generally trained on large data sets that include diverse chemical series (global models). In the pharmaceutical industry, these ML global models are available across discovery projects as an “out-of-the-box” solution to assist in drug design, synthesis prioritization, and experiment selection. However, drug discovery projects typically focus on confined parts of the chemical space (e.g., chemical series), where global models might not be applicable. Local ML models are sometimes generated to focus on specific projects or series. Herein, ML-based global models, local models, and hybrid global-local strategies were benchmarked. Analyses were done for more than 300 drug discovery projects at Novartis and ten absorption, distribution, metabolism, and excretion (ADME) assays. In this work, hybrid global-local strategies based on transfer learning approaches were proposed to leverage both historical ADME data (global) and project-specific data (local) to adapt model predictions. Fine-tuning a pretrained global ML model (used for weights’ initialization, WI) was the top-performing method. Average improvements of mean absolute errors across all assays were 16% and 27% compared with global and local models, respectively. Interestingly, when the effect of training set size was analyzed, WI fine-tuning was found to be successful even in low-data scenarios (e.g., ∼10 molecules per project). Taken together, this work highlights the potential of domain adaptation in the field of molecular property predictions to refine existing pretrained models on a new compound data distribution
