Experimental and Numerical Evidence of the Clustering Effect of Structures on Their Response during an Earthquake: A Case Study of Three Identical Towers in the City of Grenoble, France

In this article, interpretation of an equivalent to a macroseismic intensity survey, performed in three identical stand-alone buildings located in Grenoble, France, after an M L 4.1 earthquake, reveals a clustering effect, resulting in different levels of perception of seismic loading by inhabitants. The clustering effect is confirmed using numerical simulation; the variation of the seismic response of the building in the middle of the cluster depends on the azimuth of the seismic source relative to the building cluster. The major effect is the splitting of its resonance frequency, accompanied by a decrease in vibration amplitude. We conclude that clustering has an impact on urban effects, calling into question the validity of seismic design, which considers buildings in urban areas as stand-alone constructions, and the interpretation of macroseismic inten- sity surveys conducted in dense urban areas

Reference Limits of Plasma Fibrinogen

Peer Reviewe

Earthquake Early Warning System for Structural Drift Prediction Using Machine Learning and Linear Regressors

In this work, we explored the feasibility of predicting the structural drift from the first seconds of P-wave signals for On-site Earthquake Early Warning (EEW) applications. To this purpose, we investigated the performance of both linear least square regression (LSR) and four non-linear machine learning (ML) models: Random Forest, Gradient Boosting, Support Vector Machines and K-Nearest Neighbors. Furthermore, we also explore the applicability of the models calibrated for a region to another one. The LSR and ML models are calibrated and validated using a dataset of ∼6,000 waveforms recorded within 34 Japanese structures with three different type of construction (steel, reinforced concrete, and steel-reinforced concrete), and a smaller one of data recorded at US buildings (69 buildings, 240 waveforms). As EEW information, we considered three P-wave parameters (the peak displacement, Pd, the integral of squared velocity, IV2, and displacement, ID2) using three time-windows (i.e., 1, 2, and 3 s), for a total of nine features to predict the drift ratio as structural response. The Japanese dataset is used to calibrate the LSR and ML models and to study their capability to predict the structural drift. We explored different subsets of the Japanese dataset (i.e., one building, one single type of construction, the entire dataset. We found that the variability of both ground motion and buildings response can affect the drift predictions robustness. In particular, the predictions accuracy worsens with the complexity of the dataset in terms of building and event variability. Our results show that ML techniques perform always better than LSR models, likely due to the complex connections between features and the natural non-linearity of the data. Furthermore, we show that by implementing a residuals analysis, the main sources of drift variability can be identified. Finally, the models trained on the Japanese dataset are applied the US dataset. In our application, we found that the exporting EEW models worsen the prediction variability, but also that by including correction terms as function of the magnitude can strongly mitigate such problem. In other words, our results show that the drift for US buildings can be predicted by minor tweaks to models

Mitochondrial effects of dexamethasone imply both membrane and cytosolic-initiated pathways in HepG2 cells

Glucocorticoid treatment is often linked to increased whole-body energy expenditure and hypermetabolism. Glucocorticoids affect mitochondrial energy production, notably in the liver, where they lead to mitochondrial uncoupling reducing the efficacy of oxidative phosphorylation. However, the signaling pathways involved in these phenomena are poorly understood. Here we treated HepG2 cells with dexamethasone for different times and, by using different combinations of inhibitors, we showed that dexamethasone treatment leads to recruitment of two main signaling pathways. The first one involves a G-protein coupled membrane glucocorticoid binding site and rapidly decreases complexes I and II activities while complex III activity is upregulated in a p38MAPK dependent mechanism. The second one implies the classical cytosolic glucocorticoid receptor and triggers long-term transcriptional increases of respiration rates and of complex IV activity and quantity. We concluded that mitochondria are the target of multiple dexamethasone-induced regulatory pathways that are set up gradually after the beginning of hormone exposure and that durably influence mitochondrial oxidative phosphorylation

Comparison of spheroids formed by rat glioma stem cells and neural stem cells reveals differences in glucose metabolism and promising therapeutic applications

Cancer stem cells (CSCs) are thought to be partially responsible for cancer resistance to current therapies and tumor recurrence. Dichloroacetate (DCA), a compound capable of shifting metabolism from glycolysis to glucose oxidation, via an inhibition of pyruvate dehydrogenase kinase was used. We show that DCA is able to shift the pyruvate metabolism in rat glioma CSCs but has no effect in rat neural stem cells. DCA forces CSCs into oxidative phosphorylation but does not trigger the production of reactive oxygen species and consecutive anti-cancer apoptosis. However, DCA, associated with etoposide or irradiation, induced a Bax-dependent apoptosis in CSCs in vitro and decreased their proliferation in vivo. The former phenomenon is related to DCA-induced Foxo3 and p53 expression, resulting in the overexpression of BH3-only proteins (Bad, Noxa, and Puma), which in turn facilitates Bax-dependent apoptosis. Our results demonstrate that a small drug available for clinical studies potentiates the induction of apoptosis in glioma CSCs

Pro-oxidant effect of ALA is implicated in mitochondrial dysfunction of HepG2 cells

Heme biosynthesis begins in the mitochondrion with the formation of delta-aminolevulinic acid (ALA). In acute intermittent porphyria, hereditary tyrosinemia type I and lead poisoning patients, ALA is accumulated in plasma and in organs, especially the liver. These diseases are also associated with neuromuscular dysfunction and increased incidence of hepatocellular carcinoma. Many studies suggest that this damage may originate from ALA-induced oxidative stress following its accumulation. Using the MnSOD as an oxidative stress marker, we showed here that ALA treatment of cultured cells induced ROS production, increasing with ALA concentration. The mitochondrial energetic function of ALA-treated HepG2 cells was further explored. Mitochondrial respiration and ATP content were reduced compared to control cells. For the 300 μM treatment, ALA induced a mitochondrial mass decrease and a mitochondrial network imbalance although neither necrosis nor apoptosis were observed. The up regulation of PGC-1, Tfam and ND5 genes was also found; these genes encode mitochondrial proteins involved in mitochondrial biogenesis activation and OXPHOS function. We propose that ALA may constitute an internal bioenergetic signal, which initiates a coordinated upregulation of respiratory genes, which ultimately drives mitochondrial metabolic adaptation within cells. The addition of an antioxidant, Manganese(III) tetrakis(1-methyl-4-pyridyl)porphyrin (MnTMPyP), resulted in improvement of maximal respiratory chain capacity with 300 μM ALA. Our results suggest that mitochondria, an ALA-production site, are more sensitive to pro-oxidant effect of ALA, and may be directly involved in pathophysiology of patients with inherited or acquired porphyria

The XMM-LSS Survey: A well controlled X-ray cluster sample over the D1 CFHTLS area

We present the XMM-LSS cluster catalogue corresponding to the CFHTLS D1 area. The list contains 13 spectroscopically confirmed, X-ray selected galaxy clusters over 0.8 deg2 to a redshift of unity and so constitutes the highest density sample of clusters to date. Cluster X-ray bolometric luminosities range from 0.03 to 5x10^{44} erg/s. In this study, we describe our catalogue construction procedure: from the detection of X-ray cluster candidates to the compilation of a spectroscopically confirmed cluster sample with an explicit selection function. The procedure further provides basic X-ray products such as cluster temperature, flux and luminosity. We detected slightly more clusters with a (0.5-2.0 keV) X-ray fluxes of >2x10^{-14} erg/s/cm^{-2} than we expected based on expectations from deep ROSAT surveys. We also present the Luminosity-Temperature relation for our 9 brightest objects possessing a reliable temperature determination. The slope is in good agreement with the local relation, yet compatible with a luminosity enhancement for the 0.15 < z< 0.35 objects having 1 < T < 2 keV, a population that the XMM-LSS is identifying systematically for the first time. The present study permits the compilation of cluster samples from XMM images whose selection biases are understood. This allows, in addition to studies of large-scale structure, the systematic investigation of cluster scaling law evolution, especially for low mass X-ray groups which constitute the bulk of our observed cluster population. All cluster ancillary data (images, profiles, spectra) are made available in electronic form via the XMM-LSS cluster database.Comment: 12 pages 5 figures, MNRAS accepted. The paper with full resolution cluster images is available at http://vela.astro.ulg.ac.be/themes/spatial/xmm/LSS/rel_pub_e.htm