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Recovery of cyanophycin granule polypeptide from activated sludge: carbon source dependence and aggregation-induced luminescence characteristics

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Frontiers of Environmental Science & Engineering 2024年第2期18卷 25-34页

作者： kui Zou Hongyuan liu Bo Feng Taiping Qing Peng Zhang College of Environment and Resources Xiangtan UniversityXiangtan 411105China

In the sewage treatment process,facilitating the conversion of pollutants into value-added resources holds great potential for reducing the amount of greenhouse gas emissions and promoting economic circulation.Cyanophycin granule polypeptide(CGP),a recently discovered high value-added biopolymer present in activated sludge,has provided new avenues for the recovery of resources.However,the mechanisms that regulate CGP synthesis and the characteristics of this biopolymer in activated sludge remain unclear thus far.This study investigated the synthesis of CGP,polyhydroxyalkanoates(PHA),and alginate-like exopolysaccharides(ALE)in various microbial aggregates under different carbon sources feeding conditions.Our results showed that the CGP yields was superior that of PHA and ALE when subjected to identical carbon source feeding conditions.Furthermore,biofilm was more conducive to CGP accumulation than floc sludge.Compared with glucose and methanol,sodium acetate significantly enriched the CGP synthetase-encoding gene(cphAabundance=~17419),resulting in the highest CGP yield(average 107.1 mg/g MLSS)in both biofilm and floc sludge.This study is the first to reported the characteristic fluorescence of CGP(Ex/Em=~360/450 nm)caused by the aggregated luminescence of arginine on the side chains.Overall,this study highlights the potential application of CGP as a fluorescent material and offers insights into CGP recovery from activated sludge in wastewater treatment plants.

关键词： Cyanophycin Polyhydroxyalkanoate Alginate-like exopolysaccharides Bioresource recovery Aggregation-induced luminescence

An eikonal equation-based earthquake location method by inversion of multiple phase arrivals

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Science China Earth Sciences 2024年第6期67卷 1802-1817页

作者： Gaoyue LAO Dinghui YANG Shaolin liu Guiju DONG Wenshuai WANG kui liu National Institute of Natural Hazards Ministry of Emergency Management of ChinaBeijing 100085China School of Earth Sciences Yunnan UniversityKunming 650091China Department of Mathematical Sciences Tsinghua UniversityBeijing 100084China School of Mathematics and Statistics Ningxia UniversityYinchuan 750021China

The precise determination of earthquake location is the fundamental basis in seismological community,and is crucial for analyzing seismic activity and performing seismic tomography.First arrivals are generally used to practically determine earthquake locations.However,first-arrival traveltimes are not sensitive to focal depths.Moreover,they cannot accurately constrain focal depths.To improve the accuracy,researchers have analyzed the depth phases of earthquake locations.The traveltimes of depth phases are sensitive to focal depths,and the joint inversion of depth phases and direct phases can be implemented to potentially obtain accurate earthquake locations.Generally,researchers can determine earthquake locations in layered models.Because layered models can only represent the first-order feature of subsurface structures,the advantages of joint inversion are not fully explored if layered models are used.To resolve the issue of current joint inversions,we use the traveltimes of three seismic phases to determine earthquake locations in heterogeneous models.The three seismic phases used in this study are the first P-,sPg-and PmP-waves.We calculate the traveltimes of the three seismic phases by solving an eikonal equation with an upwind difference scheme and use the traveltimes to determine earthquake locations.To verify the accuracy of the earthquake location method by the inversion of three seismic phases,we take the 2021 M_(S)6.4 Yangbi,Yunnan earthquake as an example and locate this earthquake using synthetic and real seismic data.Numerical tests demonstrate that the eikonal equation-based earthquake location method,which involves the inversion of multiple phase arrivals,can effectively improve earthquake location accuracy.

关键词： Earthquake location Eikonal equation Fast marching method Heterogeneous model

Structural and functional connectivity of the whole brain and subnetworks in individuals with mild traumatic brain injury:predictors of patient prognosis

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Neural Regeneration Research 2024年第7期19卷 1553-1558页

作者： Sihong Huang Jungong Han Hairong Zheng Mengjun Li Chuxin Huang Xiaoyan kui Jun liu Department of Radiology The Second Xiangya HospitalCentral South UniversityChangshaHunan ProvinceChina Department of Computer Science Aberystwyth UniversityAberystwythCeredigionUK Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhenGuangdong ProvinceChina School of Computer Science and Engineering Central South UniversityChangshaHunan ProvinceChina Department of Radiology Quality Control Center of Hunan ProvinceChangshaHunan ProvinceChina Clinical Research Center for Medical Imaging of Hunan Province ChangshaHunan ProvinceChina

Patients with mild traumatic brain injury have a diverse clinical presentation,and the underlying pathophysiology remains poorly understood.Magnetic resonance imaging is a non-invasive technique that has been widely utilized to investigate neuro biological markers after mild traumatic brain injury.This approach has emerged as a promising tool for investigating the pathogenesis of mild traumatic brain injury.G raph theory is a quantitative method of analyzing complex networks that has been widely used to study changes in brain structure and function.However,most previous mild traumatic brain injury studies using graph theory have focused on specific populations,with limited exploration of simultaneous abnormalities in structural and functional connectivity.Given that mild traumatic brain injury is the most common type of traumatic brain injury encounte red in clinical practice,further investigation of the patient characteristics and evolution of structural and functional connectivity is critical.In the present study,we explored whether abnormal structural and functional connectivity in the acute phase could serve as indicators of longitudinal changes in imaging data and cognitive function in patients with mild traumatic brain injury.In this longitudinal study,we enrolled 46 patients with mild traumatic brain injury who were assessed within 2 wee ks of injury,as well as 36 healthy controls.Resting-state functional magnetic resonance imaging and diffusion-weighted imaging data were acquired for graph theoretical network analysis.In the acute phase,patients with mild traumatic brain injury demonstrated reduced structural connectivity in the dorsal attention network.More than 3 months of followup data revealed signs of recovery in structural and functional connectivity,as well as cognitive function,in 22 out of the 46 patients.Furthermore,better cognitive function was associated with more efficient networks.Finally,our data indicated that small-worldness in the acute stage could serve as a predictor of longitudinal ch

关键词： cognitive function cross-section follow-up functional connectivity graph theory longitudinal study mild traumatic brain injury prediction small-worldness structural connectivity subnetworks whole brain network

Automated, economical, and environmentally-friendly asphalt mix design based on machine learning and multi-objective grey wolf optimization

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Journal of Traffic and Transportation Engineering(English Edition) 2024年第3期11卷 381-405页

作者： jian liu Fangyu liu Linbing Wang Department of Civil and Environmental Engineering Virginia Polytechnic Institute and State University School of Environmental CivilAgricultural and Mechanical EngineeringUniversity of Georgia Illinois Center for Transportation Department of Civil and Environmental EngineeringUniversity of Illinois Urbana-Champaign

The increasing impact of the greenhouse effect on ecosystems is prompting transportation agencies to seek methods for reducing CO2emissions during pavement construction and maintenance.Additionally,the laboratory mix design process,which involves selecting aggregate gradation and binder content,is time-consuming and labor-intensive.To accelerate the traditional mix design procedure,this study presented a mix design procedure that can automatically determine gradation and binder content based on machine learning （ML） and a meta-heuristic algorithm.Specifically,ML approaches were employed to model the relationship between volumetric properties (mixture bulk specific gravity（Gmb） and air void （VV）) and both mixture component properties and mixture proportion,based on a dataset collected from literature with 660 mixture designs.Integrated with the prediction of ML models and the modified multi-objective grey wolf optimization （MOGWO）algorithm,an automatic asphalt mix design was proposed to pursue three goals,including VV,cost,and CO2emission.The results indicated that least squares support vector regression （LSSVR） and e Xtreme gradient boosting （XGBoost） achieved the highest prediction accuracies （correlation coefficient:0.92 for VV and 0.96 for Gmb）.The MOGWO algorithm successfully found the 26 optimal mix designs for the case of VV vs.cost vs.CO2emission.Compared to the traditional laboratory design,the optimal mixture with VV of4%achieves a cost saving of 2.46%and a reduction of 4.03%in carbon emission.The volumetric properties of the mixtures output by the approach also align closely with values measured in a laboratory.

关键词： Asphalt mix design Machine learning MOGWO CO 2 emission Volumetric properties

An Industrial Intrusion Detection Method Based on Hybrid Convolutional Neural Networks with Improved TCN

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Computers, Materials & Continua 2024年第1期78卷 411-433页

作者： Zhihua liu Shengquan liu jian Zhang College of Computer Science and Technology Xinjiang UniversityXinjiang Uygur Autonomous RegoinUrumqi830000China

Network intrusion detection systems(NIDS)based on deep learning have continued to make significant advances.However,the following challenges remain:on the one hand,simply applying only Temporal Convolutional Networks(TCNs)can lead to models that ignore the impact of network traffic features at different scales on the detection performance.On the other hand,some intrusion detection methods considermulti-scale information of traffic data,but considering only forward network traffic information can lead to deficiencies in capturing multi-scale temporal features.To address both of these issues,we propose a hybrid Convolutional Neural Network that supports a multi-output strategy(BONUS)for industrial internet intrusion detection.First,we create a multiscale Temporal Convolutional Network by stacking TCN of different scales to capture the multiscale information of network traffic.Meanwhile,we propose a bi-directional structure and dynamically set the weights to fuse the forward and backward contextual information of network traffic at each scale to enhance the model’s performance in capturing the multi-scale temporal features of network traffic.In addition,we introduce a gated network for each of the two branches in the proposed method to assist the model in learning the feature representation of each branch.Extensive experiments reveal the effectiveness of the proposed approach on two publicly available traffic intrusion detection datasets named UNSW-NB15 and NSL-KDD with F1 score of 85.03% and 99.31%,respectively,which also validates the effectiveness of enhancing the model’s ability to capture multi-scale temporal features of traffic data on detection performance.

关键词： Intrusion detection industrial internet channel spatial attention multiscale features dynamic fusion multi-output learning strategy

Strategies for the development and approval of COVID-19 vaccines and therapeutics in the post-pandemic period

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Signal Transduction and Targeted Therapy 2024年第1期9卷 19-35页

作者： Danyi Ao Xuemei He jian liu Li Xu State Key Laboratory of Biotherapy West China HospitalSichuan University610041 SichuanPeople’s Republic of China

The spread of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)has resulted in significant casualties and put immense strain on public health systems worldwide,leading to economic recession and social unrest.In response,various prevention and control strategies have been implemented globally,including vaccine and drug development and the promotion of preventive measures.Implementing these strategies has effectively curbed the transmission of the virus,reduced infection rates,and gradually restored normal social and economic activities.However,the mutations of SARS-CoV-2 have led to inevitable infections and reinfections,and the number of deaths continues to rise.Therefore,there is still a need to improve existing prevention and control strategies,mainly focusing on developing novel vaccines and drugs,expediting medical authorization processes,and keeping epidemic surveillance.These measures are crucial to combat the Coronavirus disease(COVID-19)pandemic and achieve sustained,long-term prevention,management,and disease control.Here,we summarized the characteristics of existing COVID-19 vaccines and drugs and suggested potential future directions for their development.Furthermore,we discussed the COVID-19-related policies implemented over the past years and presented some strategies for the future.

关键词： prevention sustained approval

What benefit can be obtained from magnetic resonance imaging diagnosis with artificial intelligence in prostate cancer compared with clinical assessments?

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Military Medical Research 2024年第2期11卷 268-286页

作者： Li-Tao Zhao Zhen-Yu liu Wan-Fang Xie Li-Zhi Shao jian Lu Jie Tian jian-Gang liu School of Engineering Medicine Beihang UniversityBeijing 100191China School of Biological Science and Medical Engineering Beihang UniversityBeijing 100191China CAS Key Laboratory of Molecular Imaging Institute of AutomationBeijing 100190China University of Chinese Academy of Sciences Beijing 100080China Department of Urology Peking University Third HospitalPeking UniversityBeijing 100191China Key Laboratory of Big Data‑Based Precision Medicine(Beihang University) Ministry of Industry and Information Technology of the People's Republic of ChinaBeijing 100191China Beijing Engineering Research Center of Cardiovascular Wisdom Diagnosis and Treatment Beijing 100029China

The present study aimed to explore the potential of artificial intelligence(AI)methodology based on magnetic resonance(MR)images to aid in the management of prostate cancer(PCa).To this end,we reviewed and summarized the studies comparing the diagnostic and predictive performance for PCa between AI and common clinical assessment methods based on MR images and/or clinical characteristics,thereby investigating whether AI methods are generally superior to common clinical assessment methods for the diagnosis and prediction fields of PCa.First,we found that,in the included studies of the present study,AI methods were generally equal to or better than the clinical assessment methods for the risk assessment of PCa,such as risk stratification of prostate lesions and the prediction of therapeutic outcomes or PCa progression.In particular,for the diagnosis of clinically significant PCa,the AI methods achieved a higher summary receiver operator characteristic curve(SROC-AUC)than that of the clinical assessment methods(0.87 vs.0.82).For the prediction of adverse pathology,the AI methods also achieved a higher SROC-AUC than that of the clinical assessment methods(0.86 vs.0.75).Second,as revealed by the radiomics quality score(RQS),the studies included in the present study presented a relatively high total average RQS of 15.2(11.0–20.0).Further,the scores of the individual RQS elements implied that the AI models in these studies were constructed with relatively perfect and standard radiomics processes,but the exact generalizability and clinical practicality of the AI models should be further validated using higher levels of evidence,such as prospective studies and open-testing datasets.

关键词： Clinically significant prostate cancer Adverse pathology Radiomics quality score Artificial intelligence Magnetic resonance imaging

Janus mesoporous nanoparticles enable building biological logic systems

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Science China Chemistry 2024年第1期67卷 316-318页

作者： Haitao Li jian liu State Key Laboratory of Catalysis Dalian Institute of Chemical PhysicsChinese Academy of SciencesDalian 116023China College of Chemistry and Chemical Engineering Inner Mongolia UniversityHohhot 010021China

This highlight is inspired by a recent article in Nature Chemistry[1],in which Zhao and co-workers from Fudan University developed an innovative emulsion-orientated anisotropic assembly method for the controllable fabrication of MSN&mPDA(mesoporous silica nanoparticles=MSN,mesoporous polydopamine=mPDA)Janus mesoporous nanoparticles and used them to build biological logic systems.The reported Janus mesoporous nanoparticles have delicate structures and are well-controlled,and the authors also demonstrated the unique performance of this material at the application end.

关键词： enable emulsion silica

Research on Anthropomorphic Obstacle Avoidance Trajectory Planning for Adaptive Driving Scenarios Based on Inverse Reinforcement Learning Theory

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Engineering 2024年第2期33卷 133-145页

作者： jian Wu Yang Yan Yulong liu Yahui liu School of Mechanical and Automotive Engineering Liaocheng UniversityLiaocheng 252000China 2 School of Vehicle and Mobility Tsinghua UniversityBeijing 100084China

The forward design of trajectory planning strategies requires preset trajectory optimization functions,resulting in poor adaptability of the strategy and an inability to accurately generate obstacle avoidance trajectories that conform to real driver behavior habits.In addition,owing to the strong time-varying dynamic characteristics of obstacle avoidance scenarios,it is necessary to design numerous trajectory optimization functions and adjust the corresponding parameters.Therefore,an anthropomorphic obstacle-avoidance trajectory planning strategy for adaptive driving scenarios is proposed.First,numerous expert-demonstrated trajectories are extracted from the HighD natural driving dataset.Subsequently,a trajectory expectation feature-matching algorithm is proposed that uses maximum entropy inverse reinforcement learning theory to learn the extracted expert-demonstrated trajectories and achieve automatic acquisition of the optimization function of the expert-demonstrated trajectory.Furthermore,a mapping model is constructed by combining the key driving scenario information that affects vehicle obstacle avoidance with the weight of the optimization function,and an anthropomorphic obstacle avoidance trajectory planning strategy for adaptive driving scenarios is proposed.Finally,the proposed strategy is verified based on real driving scenarios.The results show that the strategy can adjust the weight distribution of the trajectory optimization function in real time according to the“emergency degree”of obstacle avoidance and the state of the vehicle.Moreover,this strategy can generate anthropomorphic trajectories that are similar to expert-demonstrated trajectories,effectively improving the adaptability and acceptability of trajectories in driving scenarios.

关键词： Obstacle avoidance trajectory planning Inverse reinforcement theory Anthropomorphic Adaptive driving scenarios