Fluidized-carriers were supplemented into the aerobic tank of a full-scale wastewater treatment plant (WWTP) using an anaerobic/anoxic/aerobic (A2/O) system to improve the nitrogen removal efficiency ...in effluents. The effects of carrier supplementation on denitrification ability and the bacterial community structures were investigated over 10 months. The results showed that the average effluent concentration of total nitrogen (TN) was maintained at 9.46 ± 1.14 mg/L, which was lower than 15.17 ± 2.00 mg/L in the effluent without carrier supplementation, indicating that adding fluidized-carriers into the aerobic tank contributed to nitrogen removal efficiency. A thick biofilm was formed after 4 months, which provided a good anoxic-aerobic microenvironment to the microbes. Illumina sequencing analysis showed a higher bacterial diversity in the biofilm. The relative abundance of nitrifying bacteria, denitrifying bacteria, and aerobic denitrifying bacteria in the biofilms was 13.68–39%, 11.56–12.17%, and 9.76–12.50%, respectively, which was beneficial for nitrogen removal in the system. The most prevalent genera were Nitrospira, Bacillus, Thauera, Hyphomicrobium, Acinetobacter, Zoogloea, Pseudomonas, and Paracoccus, which can metabolize nitrogenous or aromatic compounds and were the major functional bacterial genera, suggesting that these organisms play key roles in biodegradation processes in the carrier-added A2/O wastewater treatment system.
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•Carriers in aerobic tank of full-scale A2/O system contributed to nitrogen removal.•The effluent TN was decreased significantly after carrier addition for 4 months.•Fluidized-carriers were beneficial for the accumulation of functional microorganisms.•Nitrospira and Bacillus genera developed in biofilm than that in suspended biomass.
Fluidized-carriers were supplemented into the aerobic tank of a full-scale wastewater treatment plant (WWTP) using an anaerobic/anoxic/aerobic (A
/O) system to improve the nitrogen removal efficiency ...in effluents. The effects of carrier supplementation on denitrification ability and the bacterial community structures were investigated over 10 months. The results showed that the average effluent concentration of total nitrogen (TN) was maintained at 9.46 ± 1.14 mg/L, which was lower than 15.17 ± 2.00 mg/L in the effluent without carrier supplementation, indicating that adding fluidized-carriers into the aerobic tank contributed to nitrogen removal efficiency. A thick biofilm was formed after 4 months, which provided a good anoxic-aerobic microenvironment to the microbes. Illumina sequencing analysis showed a higher bacterial diversity in the biofilm. The relative abundance of nitrifying bacteria, denitrifying bacteria, and aerobic denitrifying bacteria in the biofilms was 13.68-39%, 11.56-12.17%, and 9.76-12.50%, respectively, which was beneficial for nitrogen removal in the system. The most prevalent genera were Nitrospira, Bacillus, Thauera, Hyphomicrobium, Acinetobacter, Zoogloea, Pseudomonas, and Paracoccus, which can metabolize nitrogenous or aromatic compounds and were the major functional bacterial genera, suggesting that these organisms play key roles in biodegradation processes in the carrier-added A
/O wastewater treatment system.
Patients with acute kidney injury (AKI) frequently require kidney transplantation and supportive therapies, such as rehydration and dialysis. Here, we show that radiolabelled DNA origami ...nanostructures (DONs) with rectangular, triangular and tubular shapes accumulate preferentially in the kidneys of healthy mice and mice with rhabdomyolysis-induced AKI, and that rectangular DONs have renal-protective properties, with efficacy similar to the antioxidant
-acetylcysteine-a clinically used drug that ameliorates contrast-induced AKI and protects kidney function from nephrotoxic agents. We evaluated the biodistribution of DONs non-invasively via positron emission tomography, and the efficacy of rectangular DONs in the treatment of AKI via dynamic positron emission tomography imaging with
Ga-EDTA, blood tests and kidney tissue staining. DNA-based nanostructures could become a source of therapeutic agents for the treatment of AKI and other renal diseases.
Ulcerative colitis (UC) is a chronic nonspecific inflammatory disease of colon and rectum with unknown etiology, and the lesions are mainly confined to the mucosa and submucosa of large intestine. ...The main clinical features of UC include diarrhea, abdominal pain, bloody purulent stool and tenesmus, which seriously affect patients' quality of life. Most of UC patients would receive drug therapy with the exception of surgery for some severe cases. However, current drugs for the treatment of UC have certain limitations including difficulty of radical treatment, adverse reactions and drug resistance after long-term use and exorbitant price of some drugs. The research and development of new drugs for the treatment of UC is urgent, and natural alkaloids are an important source. This research paid close attention to the progress of natural alkaloids from diverse medicinal plants for treating UC in the last twenty years. The potential mechanisms for the natural alkaloids in the treatment of UC was closely related to its modulation of oxidative stress, immune response, intestinal flora and improvement of the gut barrier function. Remarkable effectiveness and safety of natural-derived alkaloids make them potential candidates of UC therapy.
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Glioma is the most common and malignant tumor of the central nervous system. Glioblastoma (GBM) is the most aggressive glioma, with a poor prognosis and no effective treatment because of its high ...invasiveness, metabolic rate, and heterogeneity. The tumor microenvironment (TME) contains many tumor-associated macrophages (TAMs), which play a critical role in tumor proliferation, invasion, metastasis, and angiogenesis and indirectly promote an immunosuppressive microenvironment. TAM is divided into tumor-suppressive M1-like (classic activation of macrophages) and tumor-supportive M2-like (alternatively activated macrophages) polarized cells. TAMs exhibit an M1-like phenotype in the initial stages of tumor progression, and along with the promotion of lysing tumors and the functions of T cells and NK cells, tumor growth is suppressed, and they rapidly transform into M2-like polarized macrophages, which promote tumor progression. In this review, we discuss the mechanism by which M1- and M2-polarized macrophages promote or inhibit the growth of glioblastoma and indicate the future directions for treatment.
Abstract
Aiming at the problem that the DC bus voltage is easily affected by nonlinear noise of the converter, photovoltaic output power and load disturbance, a bidirectional DC/DC converter control ...strategy based on the improved Linear Active Disturbance Rejection Control is designed. Firstly, establish the mathematical model of the bidirectional DC/DC converter, and analysis of existing problems in traditional LADRC. On this basis, a new state variable is introduced to observe the differential of the total disturbance to improve its rapidity; Then a lead-lag correction link is connected on the total disturbance observation channel to suppress the problem of high-frequency noise amplification. Finally, the Bode diagram is used to analyze the performance indicators of the improved LADRC. The simulation results show that, compared with the traditional LADRC, the control strategy in this paper reduces the bus voltage amplitude fluctuation by 63%, the adjustment time by 59%, and the inductor current ripple by 66%, which is more superior to suppressing the DC bus voltage fluctuation.
Bayesian neural networks (BNNs) have been proposed to address the problem of model uncertainty in training. By introducing weights associated with conditioned probability distributions, BNN is ...capable to resolve overfitting issues commonly seen in conventional neural networks. Frequent usage of Gaussian random variables requires a properly optimized Gaussian Random Number Generator (GRNG). The high hardware cost of conventional GRNG makes the hardware realization of BNN challenging. In this paper, a new hardware acceleration architecture for variational inference in BNNs is proposed to facilitate the applicability of BNN in larger-scale applications. In addition, the proposed implementation introduced the RAM based Linear Feedback based GRNG (RLF-GRNG) for effective weight sampling in BNNs. The RAM based Linear Feedback method can effectively utilize RAM resources for parallel Gaussian random number generation while requiring limited and sharable control logic. Implementation on an Altera Cyclone V FPGA suggests that the RLF-GRNG utilizes much less RAM resources compared to other GRNG methods. Experiments results show that the proposed hardware implementation of a BNN can still attain similar accuracy compared to software implementation.
VIBNN Cai, Ruizhe; Ren, Ao; Liu, Ning ...
Proceedings of the Twenty-Third International Conference on Architectural Support for Programming Languages and Operating Systems,
03/2018
Conference Proceeding
Bayesian Neural Networks (BNNs) have been proposed to address the problem of model uncertainty in training and inference. By introducing weights associated with conditioned probability distributions, ...BNNs are capable of resolving the overfitting issue commonly seen in conventional neural networks and allow for small-data training, through the variational inference process. Frequent usage of Gaussian random variables in this process requires a properly optimized Gaussian Random Number Generator (GRNG). The high hardware cost of conventional GRNG makes the hardware implementation of BNNs challenging. In this paper, we propose VIBNN, an FPGA-based hardware accelerator design for variational inference on BNNs. We explore the design space for massive amount of Gaussian variable sampling tasks in BNNs. Specifically, we introduce two high performance Gaussian (pseudo) random number generators: 1) the RAM-based Linear Feedback Gaussian Random Number Generator (RLF-GRNG), which is inspired by the properties of binomial distribution and linear feedback logics; and 2) the Bayesian Neural Network-oriented Wallace Gaussian Random Number Generator. To achieve high scalability and efficient memory access, we propose a deep pipelined accelerator architecture with fast execution and good hardware utilization. Experimental results demonstrate that the proposed VIBNN implementations on an FPGA can achieve throughput of 321,543.4 Images/s and energy efficiency upto 52,694.8 Images/J while maintaining similar accuracy as its software counterpart.
Single-model imaging can hardly provide sufficient information to clearly describe cellular behaviors, thus dual-model probes have been intensively studied for multiplex bio-detection and ...bio-imaging. Here, we developed a series of branched Au nanostructures with different surface modifications, which enabled dark-field microscopy (DFM) and surface-enhanced Raman scattering (SERS) imaging, owning to their high electromagnetic fields around the nanostructures. Moreover, we found that nanostructures modified with DNA and Arg–Gly–Asp (RGD) peptide could be internalized by cells efficiently and well distributed in cells without aggregation. These results demonstrate the potential of the nanostructures in applications like cell imaging and drug delivery.
Bayesian Neural Networks (BNNs) have been proposed to address the problem of model uncertainty in training and inference. By introducing weights associated with conditioned probability distributions, ...BNNs are capable of resolving the overfitting issue commonly seen in conventional neural networks and allow for small-data training, through the variational inference process. Frequent usage of Gaussian random variables in this process requires a properly optimized Gaussian Random Number Generator (GRNG). The high hardware cost of conventional GRNG makes the hardware implementation of BNNs challenging. In this paper, we propose VIBNN, an FPGA-based hardware accelerator design for variational inference on BNNs. We explore the design space for massive amount of Gaussian variable sampling tasks in BNNs. Specifically, we introduce two high performance Gaussian (pseudo) random number generators:
1)
the RAM-based Linear Feedback Gaussian Random Number Generator (RLF-GRNG), which is inspired by the properties of binomial distribution and linear feedback logics; and
2)
the Bayesian Neural Network-oriented Wallace Gaussian Random Number Generator. To achieve high scalability and efficient memory access, we propose a deep pipelined accelerator architecture with fast execution and good hardware utilization. Experimental results demonstrate that the proposed VIBNN implementations on an FPGA can achieve throughput of 321,543.4 Images/s and energy efficiency upto 52,694.8 Images/J while maintaining similar accuracy as its software counterpart.
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