Models using Cattaneo and Vernotte hyperbolic heat equation or derived from it (Double Phase Lag model and their various versions) are very common in the present thermal literature, especially for ...simulating heat transfer at the meso scale, such as in bio heat transfer. Such papers refer to so-called experimental validations made in several previous articles. We show in this short review that the corresponding experiments were biased, because of a deficient methodological approach based on too simple assumptions or on poor data reduction techniques.
•Non-Fourier models that are used for heat transfer at the meso space/time scales are now quite present literature.•Paper using these models, Cattaneo-Vernotte or the different versions of the double phase lag model, refer to so-called validations described in several earlier experimental papers.•The corresponding experiments were either based on too simple assumptions or relied on poor data reduction techniques.•The flaws and methodological biases in 6 experiments of the literature are detailed here.•A Fourier-based model has already been derived for one-dimensional thermal diffusion in materials composed of non-homogeneous inner structure.
Deep learning explainability is often reached by gradient-based approaches that attribute the network output to perturbations of the input pixels. However, the relevance of input pixels may be ...difficult to relate to relevant image features in some applications, e.g. diagnostic measures in medical imaging. The framework described in this paper shifts the attribution focus from pixel values to user-defined concepts. By checking if certain diagnostic measures are present in the learned representations, experts can explain and entrust the network output. Being post-hoc, our method does not alter the network training and can be easily plugged into the latest state-of-the-art convolutional networks. This paper presents the main components of the framework for attribution to concepts, in addition to the introduction of a spatial pooling operation on top of the feature maps to obtain a solid interpretability analysis. Furthermore, regularized regression is analyzed as a solution to the regression overfitting in high-dimensionality latent spaces. The versatility of the proposed approach is shown by experiments on two medical applications, namely histopathology and retinopathy, and on one non-medical task, the task of handwritten digit classification. The obtained explanations are in line with clinicians’ guidelines and complementary to widely used visualization tools such as saliency maps.
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•Feature attribution explains CNNs in terms of the input pixels.•The abstraction of feature attribution to higher level impacting factors is hard.•Concept attribution explains CNNs with high-level concepts such as clinical factors.•Nuclei pleomorphism is shown as a relevant factor in breast tumor classification.•Concept attribution can match clinical expectations to the interpretability of CNNs.
Reactive oxygen species (ROS), generated by cells as side products of biological reactions, function as secondary messengers by impacting a host of cellular networks involved in maintaining normal ...homeostatic growth as well as pathological disease states. Redox-sensitive proteins, such as the tumor suppressor protein p53, are susceptible to ROS-dependent modifications, which could impact their activities and/or biological functions.
p53 is a transcription factor that controls a wide variety of target genes and regulates numerous cellular functions in response to stresses that lead to genomic instability. Thus, redox modifications of p53 could impact cell fate signaling and could have profound effects on pathways fundamental to maintaining cell and tissue integrity.
Recent studies present evidence that ROS function upstream of p53 in some model systems, while in others ROS production could be a downstream effect of p53 activation.
In this review, we describe how ROS production regulates p53 activity and how p53 can, in turn, influence cellular ROS production.
To investigate the impact of systemic inhibition of interleukin 6 (IL-6) or signal transducer and activator of transcription (Stat3) in an experimental model of osteoarthritis (OA).
Expression of ...major catabolic and anabolic factors of cartilage was determined in IL-6-treated mouse chondrocytes and cartilage explants. The anti-IL-6-receptor neutralising antibody MR16-1 was used in the destabilisation of the medial meniscus (DMM) mouse model of OA. Stat3 blockade was investigated by the small molecule Stattic ex vivo and in the DMM model.
In chondrocytes and cartilage explants, IL-6 treatment reduced proteoglycan content with increased production of matrix metalloproteinase (MMP-3 and MMP-13) and a disintegrin and metalloproteinase with thrombospondin motifs (ADAMTS-4 and ADAMTS-5). IL-6 induced Stat3 and extracellular signal-regulated kinase (ERK) 1/2 signalling but not p38, c-Jun N-terminal kinase or Akt. In the DMM model, Stat3 was activated in cartilage, but neither in the synovium nor in the subchondral bone. Systemic blockade of IL-6 by MR16-1 alleviated DMM-induced OA cartilage lesions, impaired the osteophyte formation and the extent of synovitis. In the same model, Stattic had similar beneficial effects on cartilage and osteophyte formation. Stattic, but not an ERK1/2 inhibitor, significantly counteracted the catabolic effects of IL-6 on cartilage explants and suppressed the IL-6-induced chondrocytes apoptosis.
IL-6 induces chondrocyte catabolism mainly via Stat3 signalling, a pathway activated in cartilage from joint subjected to DMM. Systemic blockade of IL-6 or STAT-3 can alleviate DMM-induced OA in mice.
Highlights • We tested a model to explain nurses’ acceptance of an Electronic Patient Record (EPR). • Performance expectancy is the strongest direct determinant of actual EPR use. • Compatibility of ...the EPR is the most important determinant of nurses’ satisfaction. • Social influence has the strongest total effect on actual EPR use. • Self-efficacy and social influence do not affect nurses’ satisfaction with an EPR.
•We provide an eXtended Conditional Auto-Regressive Expected Shortfall model.•We estimate a regularized network of US financial companies based on this model.•We propose a calibration approach for ...uncovering relevant edges of the network.•Our approach is shown to provide useful information for portfolio risk management.
In this article, we first generalize the Conditional Auto-Regressive Expected Shortfall (CARES) model by introducing the loss exceedances of all (other) listed companies in the Expected Shortfall related to each firm, thus proposing the CARES-X model (where the ‘X’, as usual, stands for eXtended in the case of large-dimensional problems). Second, we construct a regularized network of US financial companies by introducing the Least Absolute Shrinkage and Selection Operator in the estimation step. Third, we also propose a calibration approach for uncovering the relevant edges between the network nodes, finding that the estimated network structure dynamically evolves through different market risk regimes. We ultimately show that knowledge of the extreme risk network links provides useful information, since the intensity of these links has strong implications on portfolio risk. Indeed, it allows us to design effective risk management mitigation allocation strategies.
When high-energy-density materials are subjected to thermal or mechanical insults at extreme conditions (shock loading), a coupled response between the thermo-mechanical and chemical behaviors is ...systematically induced. We develop a reaction model for the fast chemistry of 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) at the mesoscopic scale, where the chemical behavior is determined by underlying microscopic reactive simulations. The slow carbon cluster formation is not discussed in the present work. All-atom reactive molecular dynamics (MD) simulations are performed with the ReaxFF potential, and a reduced-order chemical kinetics model for TATB is fitted to isothermal and adiabatic simulations of single crystal chemical decomposition. Unsupervised machine learning techniques based on non-negative matrix factorization are applied to MD trajectories to model the decomposition kinetics of TATB in terms of a four-component model. The associated heats of reaction are fit to the temperature evolution from adiabatic decomposition trajectories. Using a chemical species analysis, we show that non-negative matrix factorization captures the main chemical decomposition steps of TATB and provides an accurate estimation of their evolution with temperature. The final analytical formulation, coupled to a diffusion term, is incorporated into a continuum formalism, and simulation results are compared one-to-one against MD simulations of 1D reaction propagation along different crystallographic directions and with different initial temperatures. A good agreement is found for both the temporal and spatial evolution of the temperature field.
Assessing the extent of metal contamination in estuarine surface sediments is hampered by the high heterogeneity of sediment characteristics, the spatial variability of trace element sources, ...sedimentary dynamics and geochemical processes in addition to the need of accurate reference values for deciphering natural to anthropogenic contribution. Based on 285 surface sediment samples from the Loire Estuary, the first high-resolution spatial distributions are presented for grain-size, particulate organic carbon (POC) and the eight metals/metalloids identified as priority contaminants (Cd, Zn, Pb, Cu, As, Cr, Ni, Hg) plus Ag (an urban tracer). Grain-size and/or POC are major factors controlling the spatial distribution of trace element concentrations. The V-normalized trace metal concentrations divided by the V-normalized concentrations in the basin geochemical background showed the highest Enrichment Factors for Ag and Hg (EF; up to 34 and 140, respectively). These results suggest a severe contamination in the Loire Estuary for both elements. Intra-estuarine Ag and Hg anomalies were identified by comparison between respective normalized concentrations in the Loire Estuary surface sediments and those measured in the surface sediments at the outlet of the Loire River System (watershed-derived). Anthropogenic intra-estuarine Ag and Hg stocks in the uppermost centimetre of the sediment compared with rough annual fluvial flux estimates suggest that the overall strong Enrichment Factors for Ag (EFAg) and and Hg (EFHg) in the Loire Estuary sediments are mainly due to watershed-derived inputs, highlighting the need of high temporal hydro-geochemical monitoring to establish reliable incoming fluxes. Significant correlations obtained between EFCd and EFAg, EFCu and POC and EFHg and POC revealed common behavior and/or sources. Comparison of trace element concentrations with ecotoxicological indices (Sediment Quality Guidelines) provides first standardized information on the sediment quality in the Loire Estuary. The overall mean Effect Range Median quotients (m-ERM-q) results suggested that the Loire Estuary is mainly characterized by slightly toxic sediments even if ecotoxicological impacts have been previously reported on biota.
•The first high-resolution mapping of metal contamination in the Loire Estuary•The strongest EF values are obtained for Ag and Hg in the Loire surface sediments.•Grain size and POC contents mainly control the spatial distribution of concentrations.•Hg and Ag anomalies originated from the Loire watershed and intra-estuarine sources.•The Loire Estuary is mainly characterized by slightly toxic or non-toxic sediments.
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