Harbin Institute of Technology

About: Harbin Institute of Technology is a education organization based out in Harbin, China. It is known for research contribution in the topics: Microstructure & Control theory. The organization has 88259 authors who have published 109297 publications receiving 1603393 citations. The organization is also known as: HIT.

Brewery wastewater treatment using air-cathode microbial fuel cells.

Abstract: Effective wastewater treatment using microbial fuel cells (MFCs) will require a better understanding of how operational parameters and solution chemistry affect treatment efficiency, but few studies have examined power generation using actual wastewaters. The efficiency of wastewater treatment of a beer brewery wastewater was examined here in terms of maximum power densities, Coulombic efficiencies (CEs), and chemical oxygen demand (COD) removal as a function of temperature and wastewater strength. Decreasing the temperature from 30 degrees C to 20 degrees C reduced the maximum power density from 205 mW/m2 (5.1 W/m3, 0.76 A/m2; 30 degrees C) to 170 mW/m2 (20 degrees C). COD removals (R COD) and CEs decreased only slightly with temperature. The buffering capacity strongly affected reactor performance. The addition of a 50-mM phosphate buffer increased power output by 136% to 438 mW/m2, and 200 mM buffer increased power by 158% to 528 mW/m2. In the absence of salts (NaCl), maximum power output varied linearly with wastewater strength (84 to 2,240 mg COD/L) from 29 to 205 mW/m2. When NaCl was added to increase conductivity, power output followed a Monod-like relationship with wastewater strength. The maximum power (P max) increased in proportion to the solution conductivity, but the half-saturation constant was relatively unaffected and showed no correlation to solution conductivity. These results show that brewery wastewater can be effectively treated using MFCs, but that achievable power densities will depend on wastewater strength, solution conductivity, and buffering capacity.

Cyclin D–CDK4 kinase destabilizes PD-L1 via cullin 3–SPOP to control cancer immune surveillance

Abstract: Treatments that target immune checkpoints, such as the one mediated by programmed cell death protein 1 (PD-1) and its ligand PD-L1, have been approved for treating human cancers with durable clinical benefit. However, many patients with cancer fail to respond to compounds that target the PD-1 and PD-L1 interaction, and the underlying mechanism(s) is not well understood. Recent studies revealed that response to PD-1-PD-L1 blockade might correlate with PD-L1 expression levels in tumour cells. Hence, it is important to understand the mechanistic pathways that control PD-L1 protein expression and stability, which can offer a molecular basis to improve the clinical response rate and efficacy of PD-1-PD-L1 blockade in patients with cancer. Here we show that PD-L1 protein abundance is regulated by cyclin D-CDK4 and the cullin 3-SPOP E3 ligase via proteasome-mediated degradation. Inhibition of CDK4 and CDK6 (hereafter CDK4/6) in vivo increases PD-L1 protein levels by impeding cyclin D-CDK4-mediated phosphorylation of speckle-type POZ protein (SPOP) and thereby promoting SPOP degradation by the anaphase-promoting complex activator FZR1. Loss-of-function mutations in SPOP compromise ubiquitination-mediated PD-L1 degradation, leading to increased PD-L1 levels and reduced numbers of tumour-infiltrating lymphocytes in mouse tumours and in primary human prostate cancer specimens. Notably, combining CDK4/6 inhibitor treatment with anti-PD-1 immunotherapy enhances tumour regression and markedly improves overall survival rates in mouse tumour models. Our study uncovers a novel molecular mechanism for regulating PD-L1 protein stability by a cell cycle kinase and reveals the potential for using combination treatment with CDK4/6 inhibitors and PD-1-PD-L1 immune checkpoint blockade to enhance therapeutic efficacy for human cancers.

${\cal H}_{\infty}$ Estimation for Uncertain Systems With Limited Communication Capacity

Abstract: This paper investigates the problem of robust Hinfin estimation for uncertain systems subject to limited communication capacity The parameter uncertainty belongs to a given convex polytope and the communication limitations include measurement quantization, signal transmission delay, and data packet dropout, which appear typically in a network environment The problem of Hinfin filter design is first solved for a nominal system subject to the aforementioned information limitations, which is then extended to the uncertain case based on the notion of quadratic stability To further reduce the overdesign in the quadratic framework, this paper also proposes a parameter-dependent filter design procedure, which is much less conservative than the quadratic approach The quadratic and parameter-dependent approaches provide alternatives for designing robust Hinfin filters with different degrees of conservativeness and computational complexity Two examples, including a mass-spring system, are utilized to illustrate the design procedures proposed in this paper

Deep learning for event-driven stock prediction

Abstract: We propose a deep learning method for event-driven stock market prediction. First, events are extracted from news text, and represented as dense vectors, trained using a novel neural tensor network. Second, a deep convolutional neural network is used to model both short-term and long-term influences of events on stock price movements. Experimental results show that our model can achieve nearly 6% improvements on S&P 500 index prediction and individual stock prediction, respectively, compared to state-of-the-art baseline methods. In addition, market simulation results show that our system is more capable of making profits than previously reported systems trained on S&P 500 stock historical data.