Optimal control towards sustainable wastewater treatment plants based on multi-agent reinforcement learning

Wastewater treatment plants are designed to eliminate pollutants and alleviate environmental pollution. However, the construction and operation of WWTPs consume resources, emit greenhouse gases (GHGs) and produce residual sludge, thus require further optimization. WWTPs are complex to control and optimize because of high nonlinearity and variation. This study used a novel technique, multi-agent deep reinforcement learning, to simultaneously optimize dissolved oxygen and chemical dosage in a WWTP. The reward function was specially designed from life cycle perspective to achieve sustainable optimization. Five scenarios were considered: baseline, three different effluent quality and cost-oriented scenarios. The result shows that optimization based on LCA has lower environmental impacts compared to baseline scenario, as cost, energy consumption and greenhouse gas emissions reduce to 0.890 CNY/m3-ww, 0.530 kWh/m3-ww, 2.491 kg CO2-eq/m3-ww respectively. The cost-oriented control strategy exhibits comparable overall performance to the LCA driven strategy since it sacrifices environmental bene ts but has lower cost as 0.873 CNY/m3-ww. It is worth mentioning that the retrofitting of WWTPs based on resources should be implemented with the consideration of impact transfer. Specifically, LCA SW scenario decreases 10 kg PO4-eq in eutrophication potential compared to the baseline within 10 days, while significantly increases other indicators. The major contributors of each indicator are identified for future study and improvement. Last, the author discussed that novel dynamic control strategies required advanced sensors or a large amount of data, so the selection of control strategies should also consider economic and ecological conditions

Community Detection in Multiplex Networks

A multiplex network models different modes of interaction among same-type entities. In this article we provide a taxonomy of community detection algorithms in multiplex networks. We characterize the different algorithms based on various properties and we discuss the type of communities detected by each method. We then provide an extensive experimental evaluation of the reviewed methods to answer three main questions: to what extent the evaluated methods are able to detect ground-truth communities, to what extent different methods produce similar community structures and to what extent the evaluated methods are scalable. One goal of this survey is to help scholars and practitioners to choose the right methods for the data and the task at hand, while also emphasizing when such choice is problematic.Comment: 55 pages. Accepted for publication on ACM Computing Surveys in a shorter versio

A multimodal imaging approach enables in vivo assessment of antifungal treatment in a mouse model of invasive pulmonary aspergillosis

Aspergillus fumigatus causes life-threatening lung infections in immunocompromised patients. Mouse models are extensively used in research to assess the in vivo efficacy of antifungals. In recent years, there has been an increasing interest in the use of non-invasive imaging techniques to evaluate experimental infections. However, single imaging modalities have limitations concerning the type of information they can provide. In this study, magnetic resonance imaging and bioluminescence imaging were combined to obtain longitudinal information on the extent of developing lesions and fungal load in a leucopenic mouse model of IPA. This multimodal imaging approach was used to assess changes occurring within lungs of infected mice receiving voriconazole treatment starting at different time points after infection. Results showed that IPA development depends on the inoculum size used to infect animals and that disease can be successfully prevented or treated by initiating intervention during early stages of infection. Furthermore, we demonstrated that reduction of the fungal load is not necessarily associated with the disappearance of lesions on anatomical lung images, especially when antifungal treatment coincides with immune recovery. In conclusion, multimodal imaging allows to investigate different aspects of disease progression or recovery by providing complementary information on dynamic processes, which are highly useful for assessing the efficacy of (novel) therapeutic compounds in a time- and labor-efficient manner

Thermodynamics of SU(N) Yang-Mills theories in 2+1 dimensions II - The deconfined phase

We present a non-perturbative study of the equation of state in the deconfined phase of Yang-Mills theories in D=2+1 dimensions. We introduce a holographic model, based on the improved holographic QCD model, from which we derive a non-trivial relation between the order of the deconfinement phase transition and the behavior of the trace of the energy-momentum tensor as a function of the temperature T. We compare the theoretical predictions of this holographic model with a new set of high-precision numerical results from lattice simulations of SU(N) theories with N=2, 3, 4, 5 and 6 colors. The latter reveal that, similarly to the D=3+1 case, the bulk equilibrium thermodynamic quantities (pressure, trace of the energy-momentum tensor, energy density and entropy density) exhibit nearly perfect proportionality to the number of gluons, and can be successfully compared with the holographic predictions in a broad range of temperatures. Finally, we also show that, again similarly to the D=3+1 case, the trace of the energy-momentum tensor appears to be proportional to T^2 in a wide temperature range, starting from approximately 1.2 T_c, where T_c denotes the critical deconfinement temperature.Comment: 2+36 pages, 10 figures; v2: comments added, curves showing the holographic predictions included in the plots of the pressure and energy and entropy densities, typos corrected: version published in JHE

Deep learning for healthcare applications based on physiological signals: A review

Background and objective: We have cast the net into the ocean of knowledge to retrieve the latest scientific research on deep learning methods for physiological signals. We found 53 research papers on this topic, published from 01.01.2008 to 31.12.2017. Methods: An initial bibliometric analysis shows that the reviewed papers focused on Electromyogram(EMG), Electroencephalogram(EEG), Electrocardiogram(ECG), and Electrooculogram(EOG). These four categories were used to structure the subsequent content review. Results: During the content review, we understood that deep learning performs better for big and varied datasets than classic analysis and machine classification methods. Deep learning algorithms try to develop the model by using all the available input. Conclusions: This review paper depicts the application of various deep learning algorithms used till recently, but in future it will be used for more healthcare areas to improve the quality of diagnosi

Genome Wide Association Identifies PPFIA1 as a Candidate Gene for Acute Lung Injury Risk Following Major Trauma

Acute Lung Injury (ALI) is a syndrome with high associated mortality characterized by severe hypoxemia and pulmonary infiltrates in patients with critical illness. We conducted the first investigation to use the genome wide association (GWA) approach to identify putative risk variants for ALI. Genome wide genotyping was performed using the Illumina Human Quad 610 BeadChip. We performed a two-stage GWA study followed by a third stage of functional characterization. In the discovery phase (Phase 1), we compared 600 European American trauma-associated ALI cases with 2266 European American population-based controls. We carried forward the top 1% of single nucleotide polymorphisms (SNPs) at p<0.01 to a replication phase (Phase 2) comprised of a nested case-control design sample of 212 trauma-associated ALI cases and 283 at-risk trauma non-ALI controls from ongoing cohort studies. SNPs that replicated at the 0.05 level in Phase 2 were subject to functional validation (Phase 3) using expression quantitative trait loci (eQTL) analyses in stimulated B-lymphoblastoid cell lines (B-LCL) in family trios. 159 SNPs from the discovery phase replicated in Phase 2, including loci with prior evidence for a role in ALI pathogenesis. Functional evaluation of these replicated SNPs revealed rs471931 on 11q13.3 to exert a cis-regulatory effect on mRNA expression in the PPFIA1 gene (p = 0.0021). PPFIA1 encodes liprin alpha, a protein involved in cell adhesion, integrin expression, and cell-matrix interactions. This study supports the feasibility of future multi-center GWA investigations of ALI risk, and identifies PPFIA1 as a potential functional candidate ALI risk gene for future research

Septins restrict inflammation and protect zebrafish larvae from Shigella infection

Shigella flexneri, a Gram-negative enteroinvasive pathogen, causes inflammatory destruction of the human intestinal epithelium. Infection by S. flexneri has been well-studied in vitro and is a paradigm for bacterial interactions with the host immune system. Recent work has revealed that components of the cytoskeleton have important functions in innate immunity and inflammation control. Septins, highly conserved cytoskeletal proteins, have emerged as key players in innate immunity to bacterial infection, yet septin function in vivo is poorly understood. Here, we use S. flexneri infection of zebrafish (Danio rerio) larvae to study in vivo the role of septins in inflammation and infection control. We found that depletion of Sept15 or Sept7b, zebrafish orthologs of human SEPT7, significantly increased host susceptibility to bacterial infection. Live-cell imaging of Sept15-depleted larvae revealed increasing bacterial burdens and a failure of neutrophils to control infection. Strikingly, Sept15-depleted larvae present significantly increased activity of Caspase-1 and more cell death upon S. flexneri infection. Dampening of the inflammatory response with anakinra, an antagonist of interleukin-1 receptor (IL-1R), counteracts Sept15 deficiency in vivo by protecting zebrafish from hyper-inflammation and S. flexneri infection. These findings highlight a new role for septins in host defence against bacterial infection, and suggest that septin dysfunction may be an underlying factor in cases of hyper-inflammation

The deep space quantum link: prospective fundamental physics experiments using long-baseline quantum optics

The National Aeronautics and Space Administration's Deep Space Quantum Link mission concept enables a unique set of science experiments by establishing robust quantum optical links across extremely long baselines. Potential mission configurations include establishing a quantum link between the Lunar Gateway moon-orbiting space station and nodes on or near the Earth. This publication summarizes the principal experimental goals of the Deep Space Quantum Link. These goals, identified through a multi-year design study conducted by the authors, include long-range teleportation, tests of gravitational coupling to quantum states, and advanced tests of quantum nonlocality

International Asteroid Warning Network Timing Campaign : 2019 XS

Peer reviewe

The Gut Microbiome and Aquatic Toxicology: An Emerging Concept for Environmental Health

The microbiome plays an essential role in the health and onset of diseases in all animals, including humans. The microbiome has emerged as a central theme in environmental toxicology, as microbes interact with the host immune system in addition to its role in chemical detoxification. Pathophysiological changes in the gastrointestinal tissue caused by ingested chemicals, and metabolites generated from microbial biodegradation, can lead to systemic adverse effects. This critical review dissects what we know about the impacts of environmental contaminants on the microbiome of aquatic species, with special emphasis on the gut microbiome. We highlight some of the known major gut epithelium proteins in vertebrate hosts that are targets for chemical perturbation, proteins that also directly cross‐talk with the microbiome. These proteins may act as molecular initiators for altered gut function, and we propose a general framework for an adverse outcome pathway that considers gut dysbiosis as a major contributing factor to adverse apical endpoints. We present two case studies, nanomaterials and hydrocarbons with special emphasis on the Deepwater Horizon oil spill, to illustrate how investigations into the microbiome can improve understanding of adverse outcomes. Lastly, we present strategies to functionally relate chemical‐induced gut dysbiosis with adverse outcomes, as this is required to demonstrate cause‐effect relationships. Further investigations into the toxicant‐microbiome relationship may prove to be a major breakthrough for improving animal and human health. This article is protected by copyright. All rights reserve