Enhancement of nitrate removal at the sediment-water interface by carbon addition plus vertical mixing

Author Posting. © The Author(s), 2014. This is the author's version of the work. It is posted here by permission of Elsevier for personal use, not for redistribution. The definitive version was published in Chemosphere 136 (2015): 305-310, doi:10.1016/j.chemosphere.2014.12.010.Wetlands and ponds are frequently used to remove nitrate from effluents or runoffs. However, the efficiency of this approach is limited. Based on the assumption that introducing vertical mixing to water column plus carbon addition would benefit the diffusion across the sediment–water interface, we conducted simulation experiments to identify a method for enhancing nitrate removal. The results suggested that the sediment-water interface has a great potential for nitrate removal, and the potential can be activated after several days of acclimation. Adding additional carbon plus mixing significantly increases the nitrate removal capacity, and the removal of total nitrogen (TN) and nitrate-nitrogen (NO3--N) is well fitted to a first-order reaction model. Adding Hydrilla verticillata debris as a carbon source increased nitrate removal, whereas adding Eichhornia crassipe decreased it. Adding ethanol plus mixing greatly improved the removal performance, with the removal rate of NO3--N and TN reaching 15.0-16.5 g m-2 d-1. The feasibility of this enhancement method was further confirmed with a wetland microcosm, and the NO3--N removal rate maintained at 10.0-12.0 g m-2 d-1 at a hydraulic loading rate of 0.5 m d-1.The present work was supported by the State Oceanic Administration of China (Demonstration project of coastal wetland restoration, north coast of Hangzhou Wan bay), the National Science Foundation of China under Grant No. 51378306 and 41471393, and Science and Technology Planning Project of Zhejiang Province No.2014F50003

Modulation of Excited State Property Based on Benzo[a, c]phenazine Acceptor: Three Typical Excited States and Electroluminescence Performance

Throwing light upon the structure-property relationship of the excited state properties for next-generation fluorescent materials is crucial for the organic light emitting diode (OLED) field. Herein, we designed and synthesized three donor-acceptor (D-A) structure compounds based on a strong spin orbit coupling (SOC) acceptor benzo[a, c]phenazine (DPPZ) to research on the three typical types of excited states, namely, the locally-excited (LE) dominated excited state (CZP-DPPZ), the hybridized local and charge-transfer (HLCT) state (TPA-DPPZ), and the charge-transfer (CT) dominated state with TADF characteristics (PXZ-DPPZ). A theoretical combined experimental research was adopted for the excited state properties and their regulation methods of the three compounds. Benefiting from the HLCT character, TPA-DPPZ achieves the best non-doped device performance with maximum brightness of 61,951 cd m−2 and maximum external quantum efficiency of 3.42%, with both high photoluminescence quantum efficiency of 40.2% and high exciton utilization of 42.8%. Additionally, for the doped OLED, PXZ-DPPZ can achieve a max EQE of 9.35%, due to a suppressed triplet quenching and an enhanced SOC

Near-infrared photoactivatable control of Ca signaling and optogenetic immunomodulation

The application of current channelrhodopsin-based optogenetic tools is limited by the lack of strict ion selectivity and the inability to extend the spectra sensitivity into the near-infrared (NIR) tissue transmissible range. Here we present an NIR-stimulable optogenetic platform (termed Opto-CRAC ) that selectively and remotely controls Ca2+ oscillations and Ca2+-responsive gene expression to regulate the function of non-excitable cells, including T lymphocytes, macrophages and dendritic cells. When coupled to upconversion nanoparticles, the optogenetic operation window is shifted from the visible range to NIR wavelengths to enable wireless photoactivation of Ca2+-dependent signaling and optogenetic modulation of immunoinflammatory responses. In a mouse model of melanoma by using ovalbumin as surrogate tumor antigen, Opto-CRAC has been shown to act as a genetically-encoded photoactivatable adjuvant to improve antigen-specific immune responses to specifically destruct tumor cells. Our study represents a solid step forward towards the goal of achieving remote control of Ca2+-modulated activities with tailored function

Trends in the Levels of Serum Lipids and Lipoproteins and the Prevalence of Dyslipidemia in Adults with Newly Diagnosed Type 2 Diabetes in the Southwest Chinese Han Population during 2003–2012

Objective. To determine the trends of serum lipid levels and dyslipidemia in adults newly diagnosed with type 2 diabetes mellitus during 2003–2012 in Southwest China. Methods. Serum lipid measurements of 994 adults were obtained from 5 independent, cross-sectional studies (2003-2004, 2005-2006, 2007-2008, 2009-2010, and 2011-2012). The main outcome measures were mean serum total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and triglyceride levels; body mass index; hemoglobin A1C level; and the percentages of patients with dyslipidemia, hypertension, coronary heart disease, and cerebrovascular disease. Results. The mean total cholesterol and low-density lipoprotein cholesterol levels increased from 4.92 ± 1.15 to 5.30 ± 1.17 mmol/L (P = 0.039) and 2.72 ± 0.83 to 3.11 ± 1.09 mmol/L (P = 0.004), respectively, and the mean HDL cholesterol level declined from 1.22 ± 0.30 to 1.06 ± 0.24 mmol/L (P < 0.001). The percentages of patients with dyslipidemia increased gradually. The incidence of coronary heart and cerebrovascular diseases increased from 8.2% to 19.1% and 6.6% to 15.3%, respectively (P < 0.05). Conclusion. Unfavorable upward trends were observed in serum lipid levels and the prevalence of dyslipidemia, coronary heart disease, and cerebrovascular disease in adults newly diagnosed with type 2 diabetes mellitus in Southwest China during 2003–2012

Near-infrared photoactivatable control of Ca2+ signaling and optogenetic immunomodulation

The application of current channelrhodopsin-based optogenetic tools is limited by the lack of strict ion selectivity and the inability to extend the spectra sensitivity into the near-infrared (NIR) tissue transmissible range. Here we present an NIR-stimulable optogenetic platform (termed 'Opto-CRAC') that selectively and remotely controls Ca(2+) oscillations and Ca(2+)-responsive gene expression to regulate the function of non-excitable cells, including T lymphocytes, macrophages and dendritic cells. When coupled to upconversion nanoparticles, the optogenetic operation window is shifted from the visible range to NIR wavelengths to enable wireless photoactivation of Ca(2+)-dependent signaling and optogenetic modulation of immunoinflammatory responses. In a mouse model of melanoma by using ovalbumin as surrogate tumor antigen, Opto-CRAC has been shown to act as a genetically-encoded 'photoactivatable adjuvant' to improve antigen-specific immune responses to specifically destruct tumor cells. Our study represents a solid step forward towards the goal of achieving remote and wireless control of Ca(2+)-modulated activities with tailored function. DOI: http://dx.doi.org/10.7554/eLife.10024.00

The Elongator Complex Interacts with PCNA and Modulates Transcriptional Silencing and Sensitivity to DNA Damage Agents

Histone chaperones CAF-1 and Asf1 function to deposit newly synthesized histones onto replicating DNA to promote nucleosome formation in a proliferating cell nuclear antigen (PCNA) dependent process. The DNA replication- or DNA repair-coupled nucleosome assembly pathways are important for maintenance of transcriptional gene silencing and genome stability. However, how these pathways are regulated is not well understood. Here we report an interaction between the Elongator histone acetyltransferase and the proliferating cell nuclear antigen. Cells lacking Elp3 (K-acetyltransferase Kat9), the catalytic subunit of the six-subunit Elongator complex, partially lose silencing of reporter genes at the chromosome VIIL telomere and at the HMR locus, and are sensitive to the DNA replication inhibitor hydroxyurea (HU) and the damaging agent methyl methanesulfonate (MMS). Like deletion of the ELP3, mutation of each of the four other subunits of the Elongator complex as well as mutations in Elp3 that compromise the formation of the Elongator complex also result in loss of silencing and increased HU sensitivity. Moreover, Elp3 is required for S-phase progression in the presence of HU. Epistasis analysis indicates that the elp3Δ mutant, which itself is sensitive to MMS, exacerbates the MMS sensitivity of cells lacking histone chaperones Asf1, CAF-1 and the H3 lysine 56 acetyltransferase Rtt109. The elp3Δ mutant has allele specific genetic interactions with mutations in POL30 that encodes PCNA and PCNA binds to the Elongator complex both in vivo and in vitro. Together, these results uncover a novel role for the intact Elongator complex in transcriptional silencing and maintenance of genome stability, and it does so in a pathway linked to the DNA replication and DNA repair protein PCNA

Efficient Object Detection Framework and Hardware Architecture for Remote Sensing Images

Object detection in remote sensing images on a satellite or aircraft has important economic and military significance and is full of challenges. This task requires not only accurate and efficient algorithms, but also high-performance and low power hardware architecture. However, existing deep learning based object detection algorithms require further optimization in small objects detection, reduced computational complexity and parameter size. Meanwhile, the general-purpose processor cannot achieve better power efficiency, and the previous design of deep learning processor has still potential for mining parallelism. To address these issues, we propose an efficient context-based feature fusion single shot multi-box detector (CBFF-SSD) framework, using lightweight MobileNet as the backbone network to reduce parameters and computational complexity, adding feature fusion units and detecting feature maps to enhance the recognition of small objects and improve detection accuracy. Based on the analysis and optimization of the calculation of each layer in the algorithm, we propose efficient hardware architecture of deep learning processor with multiple neural processing units (NPUs) composed of 2-D processing elements (PEs), which can simultaneously calculate multiple output feature maps. The parallel architecture, hierarchical on-chip storage organization, and the local register are used to achieve parallel processing, sharing and reuse of data, and make the calculation of processor more efficient. Extensive experiments and comprehensive evaluations on the public NWPU VHR-10 dataset and comparisons with some state-of-the-art approaches demonstrate the effectiveness and superiority of the proposed framework. Moreover, for evaluating the performance of proposed hardware architecture, we implement it on Xilinx XC7Z100 field programmable gate array (FPGA) and test on the proposed CBFF-SSD and VGG16 models. Experimental results show that our processor are more power efficient than general purpose central processing units (CPUs) and graphics processing units (GPUs), and have better performance density than other state-of-the-art FPGA-based designs

Wave Propagation in L-Shape Beams with Piezoelectric Shunting Arrays

Piezoelectric shunting arrays are employed to control the elastic wave propagation in L-shape beams. Unlike straight beams where longitudinal and flexural waves usually propagate independently, these waves are coupled in an L-shape beam. Based on transfer matrix method and Bloch theorem, dispersion curves and vibration transmissibility are evaluated and analyzed. A locally resonant gap is produced on the flexural and longitudinal waves, respectively, whose locations are nonoverlapped if the shunt damping is void. However, the longitudinal wave band gap can be completely overlaid by the flexural one when a proper shunting resistance is involved. With the decreasing of shunting inductance, the locations of longitudinal and flexural wave gaps both go up to higher frequencies which agree with the variation of resonant frequencies, but they are less affected by shunting resistance. As the resistance increases, the width of the band gaps grows, whereas the attainable maximum attenuation within the band gaps shows a significant decrease. Also, finite element simulations are performed to validate the numerical predictions, which demonstrate that the resulting transmissibility of displacements agree well with the band gaps

A Review of Fundamental Optimization Approaches and the Role of AI Enabling Technologies in Physical Layer Security

With the proliferation of 5G mobile networks within next-generation wireless communication, the design and optimization of 5G networks are progressing in the direction of improving the physical layer security (PLS) paradigm. This phenomenon is due to the fact that traditional methods for the network optimization of PLS fail to adapt new features, technologies, and resource management to diversified demand applications. To improve these methods, future 5G and beyond 5G (B5G) networks will need to rely on new enabling technologies. Therefore, approaches for PLS design and optimization that are based on artificial intelligence (AI) and machine learning (ML) have been corroborated to outperform traditional security technologies. This will allow future 5G networks to be more intelligent and robust in order to significantly improve the performance of system design over traditional security methods. With the objective of advancing future PLS research, this review paper presents an elaborate discussion on the design and optimization approaches of wireless PLS techniques. In particular, we focus on both signal processing and information-theoretic security approaches to investigate the optimization techniques and system designs of PLS strategies. The review begins with the fundamental concepts that are associated with PLS, including a discussion on conventional cryptographic techniques and wiretap channel models. We then move on to discuss the performance metrics and basic optimization schemes that are typically adopted in PLS design strategies. The research directions for secure system designs and optimization problems are then reviewed in terms of signal processing, resource allocation and node/antenna selection. Thereafter, the applications of AI and ML technologies in the optimization and design of PLS systems are discussed. In this context, the ML- and AI-based solutions that pertain to end-to-end physical layer joint optimization, secure resource allocation and signal processing methods are presented. We finally conclude with discussions on future trends and technical challenges that are related to the topics of PLS system design and the benefits of AI technologies

Dissimilarity Space Based Multi-Source Cross-Project Defect Prediction

Software defect prediction is an important means to guarantee software quality. Because there are no sufficient historical data within a project to train the classifier, cross-project defect prediction (CPDP) has been recognized as a fundamental approach. However, traditional defect prediction methods use feature attributes to represent samples, which cannot avoid negative transferring, may result in poor performance model in CPDP. This paper proposes a multi-source cross-project defect prediction method based on dissimilarity space (DM-CPDP). This method not only retains the original information, but also obtains the relationship with other objects. So it can enhances the discriminant ability of the sample attributes to the class label. This method firstly uses the density-based clustering method to construct the prototype set with the cluster center of samples in the target set. Then, the arc-cosine kernel is used to calculate the sample dissimilarities between the prototype set and the source domain or the target set to form the dissimilarity space. In this space, the training set is obtained with the earth mover&rsquo;s distance (EMD) method. For the unlabeled samples converted from the target set, the k-Nearest Neighbor (KNN) algorithm is used to label those samples. Finally, the model is learned from training data based on TrAdaBoost method and used to predict new potential defects. The experimental results show that this approach has better performance than other traditional CPDP methods