Why we can no longer ignore consecutive disasters

In recent decades, a striking number of countries have suffered from consecutive disasters: events whose impacts overlap both spatially and temporally, while recovery is still under way. The risk of consecutive disasters will increase due to growing exposure, the interconnectedness of human society, and the increased frequency and intensity of nontectonic hazard. This paper provides an overview of the different types of consecutive disasters, their causes, and impacts. The impacts can be distinctly different from disasters occurring in isolation (both spatially and temporally) from other disasters, noting that full isolation never occurs. We use existing empirical disaster databases to show the global probabilistic occurrence for selected hazard types. Current state‐of‐the art risk assessment models and their outputs do not allow for a thorough representation and analysis of consecutive disasters. This is mainly due to the many challenges that are introduced by addressing and combining hazards of different nature, and accounting for their interactions and dynamics. Disaster risk management needs to be more holistic and codesigned between researchers, policy makers, first responders, and companies

Protein kinase C-activating isophthalate derivatives mitigate Alzheimer's disease-related cellular alterations

Abnormal protein kinase C (PKC) function contributes to many pathophysiological processes relevant for Alzheimer's disease (AD), such as amyloid precursor protein (APP) processing. Phorbol esters and other PKC activators have been demonstrated to enhance the secretion of soluble APP alpha (sAPP alpha), reduce the levels of beta-amyloid (A beta), induce synaptogenesis, and promote neuroprotection. We have previously described isophthalate derivatives as a structurally simple family of PKC activators. Here, we characterised the effects of isophthalate derivatives HMI-1a3 and HMI-1b11 on neuronal viability, neuroinflammatory response, processing of APP and dendritic spine density and morphology in in vitro. HMI-1a3 increased the viability of embryonic primary cortical neurons and decreased the production of the pro-inflammatory mediator TNF alpha, but not that of nitric oxide, in mouse neuron-BV2 microglia co-cultures upon LPS- and IFN-gamma-induced neuroinflammation. Furthermore, both HMI-1a3 and HMI-1b11 increased the levels of sAPPa relative to total sAPP and the ratio of A beta 42/A beta 40 in human SH-Sv5v neuroblastoma cells. Finally, bryostatin-1, but not HMI-1a3, increased the number of mushroom spines in proportion to total spine density in mature mouse hippocampal neuron cultures. These results suggest that the PKC activator HMI-1a3 exerts neuroprotective functions in the in vitro models relevant for AD by reducing the production of TNF alpha and increasing the secretion of neuroprotective sAPPa.Peer reviewe

Three dimensional photoacoustic tomography in Bayesian framework

The image reconstruction problem (or inverse problem) in photoacoustic tomography is to resolve the initial pressure distribution from detected ultrasound waves generated within an object due to an illumination by a short light pulse. Recently, a Bayesian approach to photoacoustic image reconstruction with uncertainty quantification was proposed and studied with two dimensional numerical simulations. In this paper, the approach is extended to three spatial dimensions and, in addition to numerical simulations, experimental data are considered. The solution of the inverse problem is obtained by computing point estimates, i.e., maximum a posteriori estimate and posterior covariance. These are computed iteratively in a matrix-free form using a biconjugate gradient stabilized method utilizing the adjoint of the acoustic forward operator. The results show that the Bayesian approach can produce accurate estimates of the initial pressure distribution in realistic measurement geometries and that the reliability of these estimates can be assessed

Moderate and heavy metabolic stress interval training improve arterial stiffness and heart rate dynamics in humans

Traditional continuous aerobic exercise training attenuates age-related increases of arterial stiffness, however, training studies have not determined whether metabolic stress impacts these favourable effects. Twenty untrained healthy participants (n = 11 heavy metabolic stress interval training, n = 9 moderate metabolic stress interval training) completed 6 weeks of moderate or heavy intensity interval training matched for total work and exercise duration. Carotid artery stiffness, blood pressure contour analysis, and linear and non-linear heart rate variability were assessed before and following training. Overall, carotid arterial stiffness was reduced (p  0.05). This study demonstrates the effectiveness of interval training at improving arterial stiffness and autonomic function, however, the metabolic stress was not a mediator of this effect. In addition, these changes were also independent of improvements in aerobic capacity, which were only induced by training that involved a high metabolic stress

Time-Frequency based Feature Selection for Discrimination of non stationary Biosignals.

This research proposes a generic methodology for dimensionality reduction upon time-frequency representations applied to the classification of different types of biosignals. The methodology directly deals with the highly redundant and irrelevant data contained in these representations, combining a first stage of irrelevant data removal by variable selection, with a second stage of redundancy reduction using methods based on linear transformations. The study addresses two techniques that provided a similar performance: the first one is based on the selection of a set of the most relevant time?frequency points, whereas the second one selects the most relevant frequency bands. The first methodology needs a lower quantity of components, leading to a lower feature space; but the second improves the capture of the time-varying dynamics of the signal, and therefore provides a more stable performance. In order to evaluate the generalization capabilities of the methodology proposed it has been applied to two types of biosignals with different kinds of non-stationary behaviors: electroencephalographic and phonocardiographic biosignals. Even when these two databases contain samples with different degrees of complexity and a wide variety of characterizing patterns, the results demonstrate a good accuracy for the detection of pathologies, over 98%.The results open the possibility to extrapolate the methodology to the study of other biosignals