Hoosborn4116

For complex machine learning (ML) algorithms to gain widespread acceptance in decision making, we must be able to identify the features driving the predictions. Explainability models allow transparency of ML algorithms, however their reliability within high-dimensional data is unclear. To test the reliability of the explainability model SHapley Additive exPlanations (SHAP), we developed a convolutional neural network to predict tissue classification from Genotype-Tissue Expression (GTEx) RNA-seq data representing 16,651 samples from 47 tissues. Our classifier achieved an average F1 score of 96.1% on held-out GTEx samples. Using SHAP values, we identified the 2423 most discriminatory genes, of which 98.6% were also identified by differential expression analysis across all tissues. The SHAP genes reflected expected biological processes involved in tissue differentiation and function. Moreover, SHAP genes clustered tissue types with superior performance when compared to all genes, genes detected by differential expression analysis, or random genes. We demonstrate the utility and reliability of SHAP to explain a deep learning model and highlight the strengths of applying ML to transcriptome data.While persistence in a dormant state is crucial for the life cycle of Mycobacterium tuberculosis, no investigation regarding dormancy survival of different strains across different lineages was performed so far. We analyzed responses to oxygen starvation and recovery in terms of growth, metabolism, and transcription. FKBP inhibitor All different strains belonging to the Euro-American lineage (L4) showed similar survival and resuscitation characteristics. Different clinical isolates from the Beijing (L2), East African-Indian (L3), and Delhi/Central Asian (L1) lineage did not survive oxygen starvation. We show that dormancy survival is lineage-dependent. Recovery from O2 starvation was only observed in strains belonging to the Euro-American (L4) lineage but not in strains belonging to different lineages (L1, L2, L3). Thus, resuscitation from dormancy after oxygen starvation is not a general feature of all M. tuberculosis strains as thought before. Our findings are of key importance to understand infection dynamics of non-Euro-American vs Euro-American strains and to develop drugs targeting the dormant state.Many eye tracking studies use facial stimuli presented on a display to investigate attentional processing of social stimuli. To introduce a more realistic approach that allows interaction between two real people, we evaluated a new eye tracking setup in three independent studies in terms of data quality, short-term reliability and feasibility. Study 1 measured the robustness, precision and accuracy for calibration stimuli compared to a classical display-based setup. Study 2 used the identical measures with an independent study sample to compare the data quality for a photograph of a face (2D) and the face of the real person (3D). Study 3 evaluated data quality over the course of a real face-to-face conversation and examined the gaze behavior on the facial features of the conversation partner. Study 1 provides evidence that quality indices for the scene-based setup were comparable to those of a classical display-based setup. Average accuracy was better than 0.4° visual angle. Study 2 demonstrates that eye tracking quality is sufficient for 3D stimuli and robust against short interruptions without re-calibration. Study 3 confirms the long-term stability of tracking accuracy during a face-to-face interaction and demonstrates typical gaze patterns for facial features. Thus, the eye tracking setup presented here seems feasible for studying gaze behavior in dyadic face-to-face interactions. Eye tracking data obtained with this setup achieves an accuracy that is sufficient for investigating behavior such as eye contact in social interactions in a range of populations including clinical conditions, such as autism spectrum and social phobia.Accurate modelling of particle shrinkage during biomass pyrolysis is key to the production of biochars with specific morphologies. Such biochars represent sustainable solutions to a variety of adsorption-dependent environmental remediation challenges. Modelling of particle shrinkage during biomass pyrolysis has heretofore been based solely on theory and ex-situ experimental data. Here we present the first in-situ phase-contrast X-ray imaging study of biomass pyrolysis. A novel reactor was developed to enable operando synchrotron radiography of fixed beds of pyrolysing biomass. Almond shell particles experienced more bulk shrinkage and less change in porosity than did walnut shell particles during pyrolysis, despite their similar composition. Alkaline pretreatment was found to reduce this difference in feedstock behaviour. Ex-situ synchrotron X-ray microtomography was performed to study the effects of pyrolysis on pore morphology. Pyrolysis led to a redistribution of pores away from particle surfaces, meaning newly formed surface area may be less accessible to adsorbates.The permeability of a pore structure is typically described by stochastic representations of its geometrical attributes (e.g. pore-size distribution, porosity, coordination number). Database-driven numerical solvers for large model domains can only accurately predict large-scale flow behavior when they incorporate upscaled descriptions of that structure. The upscaling is particularly challenging for rocks with multimodal porosity structures such as carbonates, where several different type of structures (e.g. micro-porosity, cavities, fractures) are interacting. It is the connectivity both within and between these fundamentally different structures that ultimately controls the porosity-permeability relationship at the larger length scales. Recent advances in machine learning techniques combined with both numerical modelling and informed structural analysis have allowed us to probe the relationship between structure and permeability much more deeply. We have used this integrated approach to tackle the challengeenchmarked against full DBS simulations, a numerically upscaled Darcy flow model, and a Kozeny-Carman model. All numerical simulations were performed using GeoChemFoam, our in-house open source pore-scale simulator based on OpenFOAM. We found good agreement between the full DBS simulations and both the numerical and machine learning upscaled models, with the machine learning model being 80 times less computationally expensive. The Kozeny-Carman model was a poor predictor of upscaled permeability in all cases.