Speech Dereverberation Based on Integrated Deep and Ensemble Learning Algorithm

Reverberation, which is generally caused by sound reflections from walls, ceilings, and floors, can result in severe performance degradation of acoustic applications. Due to a complicated combination of attenuation and time-delay effects, the reverberation property is difficult to characterize, and it remains a challenging task to effectively retrieve the anechoic speech signals from reverberation ones. In the present study, we proposed a novel integrated deep and ensemble learning algorithm (IDEA) for speech dereverberation. The IDEA consists of offline and online phases. In the offline phase, we train multiple dereverberation models, each aiming to precisely dereverb speech signals in a particular acoustic environment; then a unified fusion function is estimated that aims to integrate the information of multiple dereverberation models. In the online phase, an input utterance is first processed by each of the dereverberation models. The outputs of all models are integrated accordingly to generate the final anechoic signal. We evaluated the IDEA on designed acoustic environments, including both matched and mismatched conditions of the training and testing data. Experimental results confirm that the proposed IDEA outperforms single deep-neural-network-based dereverberation model with the same model architecture and training data

3,4-Di-O-Caffeoylquinic Acid Inhibits Angiotensin-II-Induced Vascular Smooth Muscle Cell Proliferation and Migration by Downregulating the JNK and PI3K/Akt Signaling Pathways

We previously reported 3,4-di-O-caffeoylquinic acid (CQC) protected vascular endothelial cells against oxidative stress and restored impaired endothelium-dependent vasodilatation. Here, we further investigated its anti-atherosclerotic effect against angiotensin II (Ang II) evoked proliferation and migration of cultured rat vascular smooth muscle cells (rVSMC). The results showed CQC (1–20 μM) clearly inhibited Ang-II-stimulated BrdU incorporation and cell migration of rVSMC in a concentration-dependent manner but without significant cytotoxicity. Western blot analysis revealed Ang II increased the phosphorylation levels of Akt and mitogen-activated protein kinases (MAPKs;p38, ERK1/2 and JNK) in rVSMC. In the presence of phosphatidylinositol 3-kinase (PI3K) inhibitor wortmannin and three individual MAPK inhibitors SB203580, PD98059 and SP600125, both Ang-II-induced cell proliferation and migration were significantly attenuated, although to differing extents, suggesting the PI3K and MAPK signal pathways all participated in regulating rVSMC proliferation and migration. Also, the CQC pretreatment markedly suppressed Ang-II-induced phosphorylation of Akt and JNK rather than ERK1/2, although it failed to affect p38 phosphorylation. In conclusion, our data demonstrate CQC may act by down-regulating Akt, JNK and part of the ERK1/2 pathways to inhibit Ang-II-induced rVSMC proliferation and migration. The anti-atherosclerotic effect of CQC is achieved either by endothelial cells protection or by VSMC proliferation/migration inhibition, suggesting this compound may be useful in preventing vascular diseases

Collective Privacy Recovery: Data-sharing Coordination via Decentralized Artificial Intelligence

Collective privacy loss becomes a colossal problem, an emergency for personal freedoms and democracy. But, are we prepared to handle personal data as scarce resource and collectively share data under the doctrine: as little as possible, as much as necessary? We hypothesize a significant privacy recovery if a population of individuals, the data collective, coordinates to share minimum data for running online services with the required quality. Here we show how to automate and scale-up complex collective arrangements for privacy recovery using decentralized artificial intelligence. For this, we compare for first time attitudinal, intrinsic, rewarded and coordinated data sharing in a rigorous living-lab experiment of high realism involving >27,000 real data disclosures. Using causal inference and cluster analysis, we differentiate criteria predicting privacy and five key data-sharing behaviors. Strikingly, data-sharing coordination proves to be a win-win for all: remarkable privacy recovery for people with evident costs reduction for service providers.Comment: Contains Supplementary Informatio

5-ALA mediated photodynamic therapy induces autophagic cell death via AMP-activated protein kinase

Photodynamic therapy (PDT) has been developed as an anticancer treatment, which is based on the tumor-specific accumulation of a photosensitizer that induces cell death after irradiation of light with a specific wavelength. Depending on the subcellular localization of the photosensitizer, PDT could trigger various signal transduction cascades and induce cell death such as apoptosis, autophagy, and necrosis. In this study, we report that both AMP-activated protein kinase (AMPK) and mitogen-activated protein kinase (MAPK) signaling cascades are activated following 5-aminolevulinic acid (ALA)-mediated PDT in both PC12 and CL1-0 cells. Although the activities of caspase-9 and -3 are elevated, the caspase inhibitor zVAD-fmk did not protect cells against ALA-PDT-induced cell death. Instead, autophagic cell death was found in PC12 and CL1-0 cells treated with ALA-PDT. Most importantly, we report here for the first time that it is the activation of AMPK, but not MAPKs that plays a crucial role in mediating autophagic cell death induced by ALA-PDT. This novel observation indicates that the AMPK pathway play an important role in ALA-PDT-induced autophagy

Reducing the Vulnerability of Electric Power Infrastructure Against Natural Disasters by Promoting Distributed Generation

Natural disasters cause significant damage to the electrical power infrastructure every year. Therefore, there is a crucial need to reduce the vulnerability of the electric power grid against natural disasters. Distributed generation (DG) represents small-scale decentralized power generation that can help reduce the vulnerability of the grid, among many other benefits. Examples of DG include small-scale photo-voltaic (PV) systems. Accordingly, the goal of this paper is to investigate the benefits of DG in reducing the vulnerability of the electric power infrastructure by mitigating against the impact of natural disasters on transmission lines. This was achieved by developing a complex system-of-systems (SoS) framework using agent-based modeling (ABM) and optimal power flow (OPF). N-1 contingency analysis and optimization were performed under two approaches: The first approach determined the minimum DG needed at any single location on the electric grid to avoid blackouts. The second approach used a genetic algorithm (GA) to identify the minimum total allocation of DG distributed over the electric grid to mitigate against the failure of any transmission line. Accordingly, the model integrates ABM, OPF, and GA to optimize the allocation of DG and reduce the vulnerability of electric networks. The model was tested on a modified IEEE 6-bus system as a proof of concept. The outcomes of this research are intended to support the understanding of the benefits of DG in reducing the vulnerability of the electric power grid. The presented framework can guide future research concerning policies and incentives that can strategically influence consumer decision to install DG and reduce the vulnerability of the electric power infrastructure

Comparison of the mismatch-specific endonuclease method and denaturing high-performance liquid chromatography for the identification of HBB gene mutations

<p>Abstract</p> <p>Background</p> <p>Beta-thalassemia is a common autosomal recessive hereditary disease in the Meditertanean, Asia and African areas. Over 600 mutations have been described in the beta-globin (<it>HBB</it>), of which more than 200 are associated with a beta-thalassemia phenotype.</p> <p>Results</p> <p>We used two highly-specific mutation screening methods, mismatch-specific endonuclease and denaturing high-performance liquid chromatography, to identify mutations in the <it>HBB </it>gene. The sensitivity and specificity of these two methods were compared. We successfully distinguished mutations in the <it>HBB </it>gene by the mismatch-specific endonuclease method without need for further assay. This technique had 100% sensitivity and specificity for the study sample.</p> <p>Conclusion</p> <p>Compared to the DHPLC approach, the mismatch-specific endonuclease method allows mutational screening of a large number of samples because of its speed, sensitivity and adaptability to semi-automated systems. These findings demonstrate the feasibility of using the mismatch-specific endonuclease method as a tool for mutation screening.</p

Assessing the socio-demographic, technical, economic and behavioral factors of Nordic electric vehicle adoption and the influence of vehicle-to-grid preferences

This study investigates the interconnected influence of socio-demographics, behavioral, economic, and technical factors associated with electric vehicle (EV) adoption interest and the influence of vehicle-to-grid mobility on preferences. Using hierarchical regression analysis, we examine the impacts of six dimensions relating to socio-demographic, technical, economic, and behavioral factors in a survey (n=4885) across the countries of Denmark, Finland, Iceland, Norway, and Sweden. Our results show that younger males, with higher income, a higher number of children, and who had experiences with EVs and generally hold sustainability values are positively related to potential EV adoption. Among electric mobility attributes, vehicle-to-grid capability and charging time are determined to be the influential predictors. Adding vehicle-to-grid capability can foster EV adoption in our analysis, considering it can add a revenue stream for EV owners. Individuals continue to use specific knowledge of conventional fuel vehicles when considering EVs and their attributes. Among all of our factors, the fuel economy and financial savings, and environmental value were the strongest predictors. In comparison, the driving range was ranked less critical to former EV owners than a conventional car and current EV owners. Battery life was ranked more important to conventional fuel vehicle owners than current and former EV owners. Finally, former EV owners considered vehicle-to-grid to be more important than current EV and conventional car owners, implying that vehicle-to-grid could be the marginal incentive that would be the “tipping point.