sRNA EsrE Is Transcriptionally Regulated by the Ferric Uptake Regulator Fur in Escherichia coli

Bingbing Hou,Xichen Yang,Hui Xia,Haizhen Wu,Jiang Ye,Huizhan Zhang 한국미생물·생명공학회 2020 Journal of microbiology and biotechnology Vol.30 No.1

Small RNAs (sRNAs) are widespread and play major roles in regulation circuits in bacteria. Previously, we have demonstrated that transcription of esrE is under the control of its own promoter. However, the regulatory elements involved in EsrE sRNA expression are still unknown. In this study, we found that different cis-regulatory elements exist in the promoter region of esrE. We then screened and analyzed seven potential corresponding trans-regulatory elements by using pull-down assays based on DNA affinity chromatography. Among these candidate regulators, we investigated the relationship between the ferric uptake regulator (Fur) and the EsrE sRNA. Electrophoresis mobility shift assays (EMSAs) and β-galactosidase activity assays demonstrated that Fur can bind to the promoter region of esrE, and positively regulate EsrE sRNA expression in the presence of Fe2+.

Inhibition of Wnt/β-catenin signaling by monensin in cervical cancer

Fu Bingbing,Fang Lixia,Wang Ranran,Zhang Xueling 대한약리학회 2024 The Korean Journal of Physiology & Pharmacology Vol.28 No.1

The challenging clinical outcomes associated with advanced cervical cancer underscore the need for a novel therapeutic approach. Monensin, a polyether antibiotic, has recently emerged as a promising candidate with anti-cancer properties. In line with these ongoing efforts, our study presents compelling evidence of monensin's potent efficacy in cervical cancer. Monensin exerts a pronounced inhibitory impact on proliferation and anchorage-independent growth. Additionally,monensin significantly inhibited cervical cancer growth in vivo without causing any discernible toxicity in mice. Mechanism studies show that monensin's anti-cervical cancer activity can be attributed to its capacity to inhibit the Wnt/β -catenin pathway, rather than inducing oxidative stress. Monensin effectively reduces both the levels and activity of β -catenin, and we identify Akt, rather than CK1, as the key player in-volved in monensin-mediated Wnt/β-catenin inhibition. Rescue studies using Wnt activator and β-catenin-overexpressing cells confirmed that β-catenin inhibition is the mechanism of monensin’s action. As expected, cervical cancer cells exhibiting heightened Wnt/ β-catenin activity display increased sensitivity to monensin treat-ment. In conclusion, our findings provide pre-clinical evidence that supports further exploration of monensin's potential for repurposing in cervical cancer therapy, par - ticularly for patients exhibiting aberrant Wnt/ β -catenin activation

A hybrid method for dynamic stiffness identification of bearing joint of high speed spindles

Yongsheng Zhao,Bingbing Zhang,Guoping An,Zhifeng Liu,Ligang Cai 국제구조공학회 2016 Structural Engineering and Mechanics, An Int'l Jou Vol.57 No.1

Bearing joint dynamic parameter identification is crucial in modeling the high speed spindles for machining centers used to predict the stability and natural frequencies of high speed spindles. In this paper, a hybrid method is proposed to identify the dynamic stiffness of bearing joint for the high speed spindles. The hybrid method refers to the analytical approach and experimental method. The support stiffness of spindle shaft can be obtained by adopting receptance coupling substructure analysis method, which consists of series connected bearing and joint stiffness. The bearing stiffness is calculated based on the Hertz contact theory. According to the proposed series stiffness equation, the stiffness of bearing joint can be separated from the composite stiffness. Then, one can obtain the bearing joint stiffness fitting formulas and its variation law under different preload. An experimental set-up with variable preload spindle is developed and the experiment is provided for the validation of presented bearing joint stiffness identification method. The results show that the bearing joint significantly cuts down the support stiffness of the spindles, which can seriously affects the dynamic characteristic of the high speed spindles.

CO2 Adsorption on the B12N12 Nanocage Encapsulated with Alkali Metals: A Density Functional Study

Haiyan Zhu,Qiyan Zhang,Qinfu Zhao,He Zhao,Yifan Feng,Bingbing Suo,Huixian Han,Qi Song,Yawei Li,Wenli Zou,Haiyan Zhu 성균관대학교(자연과학캠퍼스) 성균나노과학기술원 2019 NANO Vol.14 No.3

Density functional theory (DFT) calculations have been carried out to study the capacity of the B12N12 nanocage encapsulated with alkali metals (Li, Na, K) for the CO2 adsorption and activation. It is found that after encapsulating alkali metals, the alkali metal atoms are closer to one side of clusters instead of exactly lying at the center, and a considerable charge transfers from the inner alkali metal atoms to the B12N12 cage. Besides, the HOMO–LUMO gap (HLG) values of Li@B12N12, Na@B12N12 and K@B12N12 are decreased to about 6 eV, being much smaller than that of the pristine B12N12. Although the geometry structure parameters and the energy differences of M06-2X are slightly different from the ones of ωB97X-D, some identical results of two kinds of functional can be obtained. CO2 can be adsorbed chemically and physically on majority bonds of all the clusters, except for some bonds with large change in bond length and bond indices. The encapsulation of alkali-metal atoms may enhance the physical and chemical adsorption of CO2 on the surface of the clusters, in which Na@B12N12 and K@B12N12 are the most powerful physical and chemical adsorbent for CO2, respectively.

A fault diagnosis method using Interval coded deep belief network

Jiahui Tang,Jimei Wu,Bingbing Hu,Chang Guo,Jialing Zhang 대한기계학회 2020 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.34 No.5

The rolling bearing, though a widely used component in rotating machinery, is also failure-prone. There are some difficulties in extracting valuable fault features from the vibration signal generated by the rolling bearing operation. Based on the interval coded deep belief network, an improved method for fault diagnosis is introduced to deal with the existing problems. Interval code is an encoding method to characterize fault features by using bearing vibration amplitude distribution. This method can also reduce the complexity of the original data and integrate input dimension. The deep belief network can remove the dependence from manual feature extraction and quickly extract the fault features from coding results. This proposed method is employed in the fault diagnosis experiments of rolling bearing to evaluate the performance. From the obtained experiment results, it can be concluded that the interval coded deep belief network boosts performance, which can eliminate the limitation of the input data dimension in the traditional deep learning model.

Pharmacokinetic difference of berberine between normal and chronic visceral hypersensitivity irritable bowel syndrome rats and its mechanism

Zipeng Gong,Ying Chen,Ruijie Zhang,Qing Yang,Yajie Wang,Yan Guo,Bingbing Zhou,Xiaogang Weng,Xuchen Liu,Yujie Li,Xiaoxin Zhu,Yu Dong 대한약학회 2015 Archives of Pharmacal Research Vol.38 No.10

Berberine is one of active alkaloids from Rhizomacoptidis in traditional Chinese medicine. The pharmacokineticsof berberine in rat plasma were compared betweennormal and chronic visceral hypersensitivity irritable bowelsyndrome rats (CVH-IBS) established by mechanical colonirritation using angioplasty balloons for 2 weeks after oraladministration of berberine hydrochloride (25 mg/kg) withthe equivalent dose of 22 mg/kg for berberine according tobody weight. Immunohistochemical analysis of c-fos andmyosin light chain kinase (MLCK) and immunofluorescenceanalysis of MLCK in rat colon were conducted. Quantificationof berberine in rat plasma was achieved by using a sensitiveand rapidUPLC-MS/MSmethod. Plasma samples werecollected at 15 different points in time and the pharmacokineticparameters were analyzed by WinNonlin software. Thegreat different pharmacokinetic behavior of berberine wasobserved between normal and CVH-IBS model rats. Compared with normal group, T1/2 and AUC(0–t) of berberinein the model group were significantly increased, respectively(573.21 ± 127.53 vs 948.22 ± 388.57 min; 8,657.19 ±1,562.54 vs 11,415.12 ± 1,670.72 min.ng/ml). Cl/F of berberinein the model group significantly decreased, respectively(13.89 ± 1.69 vs 9.19 ± 2.91 L/h/kg). Additionally,the expressions of c-fos and MLCK in model group werehigher than those in normal group. The pharmacokinetic behaviorof berberine was significantly altered in CVH-IBSpathological conditions, which indicated the dosage modificationof berberine hydrochloride in CVH-IBS were necessary. Especially, improved exposure to berberine in ratplasma inCVH-IBSmodel rats was attributed to increased theexpression of MLCK.

Group Wisdom and Omnaural Perception : Discovering Frequency Differences by Averaging the Retrospective Time Estimates of Listeners

Clint Miller,Li Ying,Liang Yuanyuan,Zhang Jinhui,Wu Bingbing,Zhang Ying 보안공학연구지원센터 2014 International Journal of Bio-Science and Bio-Techn Vol.6 No.1

Prognostic Value of 18F-FDG PET/CT Radiomics in Extranodal Nasal-Type NK/T Cell Lymphoma

Luo Yu,Huang Zhun,Gao Zihan,Wang Bingbing,Zhang Yanwei,Bai Yan,Wu Qingxia,Wang Meiyun 대한영상의학회 2024 Korean Journal of Radiology Vol.25 No.2

Objective: To investigate the prognostic utility of radiomics features extracted from 18F-fluorodeoxyglucose (FDG) PET/CT combined with clinical factors and metabolic parameters in predicting progression-free survival (PFS) and overall survival (OS) in individuals diagnosed with extranodal nasal-type NK/T cell lymphoma (ENKTCL). Materials and Methods: A total of 126 adults with ENKTCL who underwent 18F-FDG PET/CT examination before treatment were retrospectively included and randomly divided into training (n = 88) and validation cohorts (n = 38) at a ratio of 7:3. Least absolute shrinkage and selection operation Cox regression analysis was used to select the best radiomics features and calculate each patient’s radiomics scores (RadPFS and RadOS). Kaplan–Meier curve and Log-rank test were used to compare survival between patient groups risk-stratified by the radiomics scores. Various models to predict PFS and OS were constructed, including clinical, metabolic, clinical + metabolic, and clinical + metabolic + radiomics models. The discriminative ability of each model was evaluated using Harrell’s C index. The performance of each model in predicting PFS and OS for 1-, 3-, and 5-years was evaluated using the time-dependent receiver operating characteristic (ROC) curve. Results: Kaplan–Meier curve analysis demonstrated that the radiomics scores effectively identified high- and low-risk patients (all P < 0.05). Multivariable Cox analysis showed that the Ann Arbor stage, maximum standardized uptake value (SUVmax), and RadPFS were independent risk factors associated with PFS. Further, β2-microglobulin, Eastern Cooperative Oncology Group performance status score, SUVmax, and RadOS were independent risk factors for OS. The clinical + metabolic + radiomics model exhibited the greatest discriminative ability for both PFS (Harrell’s C-index: 0.805 in the validation cohort) and OS (Harrell’s C-index: 0.833 in the validation cohort). The time-dependent ROC analysis indicated that the clinical + metabolic + radiomics model had the best predictive performance. Conclusion: The PET/CT-based clinical + metabolic + radiomics model can enhance prognostication among patients with ENKTCL and may be a non-invasive and efficient risk stratification tool for clinical practice.

Mixed Reinforcement Learning for Efficient Policy Optimization in Stochastic Environments

Yao Mu,Baiyu Peng,Ziqing Gu,Shengbo Eben Li,Chang Liu,Bingbing Nie,Jianfeng Zheng,Bo Zhang 제어로봇시스템학회 2020 제어로봇시스템학회 국제학술대회 논문집 Vol.2020 No.10

Reinforcement learning has the potential to control stochastic nonlinear systems in optimal manners successfully. We propose a mixed reinforcement learning (mixed RL) algorithm by simultaneously using dual representations of environmental dynamics to search the optimal policy. The dual representation includes an empirical dynamic model and a set of state-action data. The former can embed the designer’s knowledge and reduce the difficulty of learning, and the latter can be used to compensate the model inaccuracy since it reflects the real system dynamics accurately. Such a design has the capability of improving both learning accuracy and training speed. In the mixed RL framework, the additive uncertainty of stochastic model is compensated by using explored state-action data via iterative Bayesian estimator (IBE). The optimal policy is then computed in an iterative way by alternating between policy evaluation (PEV) and policy improvement (PIM). The effectiveness of mixed RL is demonstrated by a typical optimal control problem of stochastic non-affine nonlinear systems (i.e., double lane change task with an automated vehicle).