Recent advances in nonlinear optics for bio‐imaging applications

Nonlinear optics,which is a subject for studying the interaction between intense light and materials,has great impact on various research fields.Since many structures in biological tissues exhibit strong nonlinear optical effects,nonlinear optics has been widely applied in biomedical studies.Especially in the aspect of bio-imaging,nonlinear optical techniques can provide rapid,label-free and chemically specific imaging of biological samples,which enable the investigation of biological processes and analysis of samples beyond other microscopy techniques.In this review,we focus on the introduction of nonlinear optical processes and their applications in bio-imaging as well as the recent advances in this filed.Our perspective of this field is also presented.

Effects of Distiller's Grain Substrate with Acid Regulation on the Growth of Tobacco Seedlings in Floating Breeding System

In order to reduce the use of peat resources and realize the sustainable development of tobacco, the pH of distiller s grain substrate was adjusted with humic acid, and the effects of various distiller s grain substrates on the growth of tobacco seedlings were studied to find out the method of cultivating high-quality tobacco seedlings by using distiller s grain substrate. The results showed that when 60% of humic acid was added to the distiller s grain substrate made from coarse distiller s grains, fine distiller s grains, perlite and vermiculite, both the pH and electrical conductivity decreased significantly and were similar to that of the conventional substrate. Moreover, the emergence rate of tobacco seedlings, the rate of strong tobacco seedlings, leaf number, stem height, root growth and quality of tobacco seedlings were good.

Generative adversarial networks for detecting contamination events in water distribution systems using multi-parameter,multi-site water quality monitoring

Contamination events in water distribution networks(WDNs)can have a huge impact on water supply and public health;increasingly,online water quality sensors are deployed for real-time detection of contamination events.Machine learning has been used to integrate multivariate time series water quality data at multiple stations for contamination detection;however,accurate extraction of spatial features in water quality signals remains challenging.This study proposed a contamination detection method based on generative adversarial networks(GANs).The GAN model was constructed to simultaneously consider the spatial correlation between sensor locations and temporal information of water quality indicators.The model consists of two networksda generator and a discriminatordthe outputs of which are used to measure the degree of abnormality of water quality data at each time step,referred to as the anomaly score.Bayesian sequential analysis is used to update the likelihood of event occurrence based on the anomaly scores.Alarms are then generated from the fusion of single-site and multi-site models.The proposed method was tested on a WDN for various contamination events with different characteristics.Results showed high detection performance by the proposed GAN method compared with the minimum volume ellipsoid benchmark method for various contamination amplitudes.Additionally,the GAN method achieved high accuracy for various contamination events with different amplitudes and numbers of anomalous water quality parameters,and water quality data from different sensor stations,highlighting its robustness and potential for practical application to real-time contamination events.

Storage, form, and influencing factors of karst inorganic carbon in a carbonate area in China

Carbonate rock chemical weathering carbon sinks reduce the rate of increase of the atmospheric CO_(2) concentration and global warming. However, uncertainty still exists in the estimation results of carbonate rock chemical weathering carbon sink fluxes(CCSF), and the contributions of climate change and ecological restoration to the CCSF are not clear. To this end, we compiled published site data on ion concentrations in different watersheds in China and used a classical thermodynamic dissolution model to reassess the potential and spatial and temporal patterns of the CCSF in China from 1991 to 2020. We quantified the contributions of temperature(MAT), precipitation(MAP), evapotranspiration(ET), soil water(SM), and the normalized difference vegetation index(NDVI) to the CCSF. The results revealed that(1) China's CCSF was 22.76 t CO_(2)km^(-2)yr^(-1), which was higher than the global average(15.77 t CO_(2)km^(-2)yr^(-1)). The total carbonate rock chemical weathering carbon sink(CCS) was 4772.67×10^(4)t CO_(2), contributing 14.91% of the global CCS through a carbonate rock area of 252.98×10^(4)km^(-2).(2) China's CCSF decreased gradually from southeast to northwest, with values of 33.14, 12.93, and7.27 t CO_(2)km^(-2)yr^(-1)in the southern karst, Qinghai-Tibetan karst, and northern karst regions, respectively.(3) The overall CCSF in China exhibited an increasing trend from 1991 to 2020, with a rate of increase of 0.16 t CO_(2)km^(-2)yr^(-1).(4) The contributions of the MAP, MAT, ET, SM, and NDVI to the CCSF were 63.3%, 3.02%, 27.5%, 3.1%, and 3.05%, respectively. Among them, the increase in precipitation was the main contributor to the increase in the CCSF in China over the last 30 years, while the enhancement of ET offset part of the positive contribution of the increase in precipitation to the CCSF. In conclusion, the results of this study provide a systematic quantification of the magnitude, the patterns, and the influencing factors of CCS over a long time series in China. The results are of great significance and provide a reference for the diagnosis and gap analysis of the national and global carbon neutrality capacities.

中国喀斯特生态系统健康诊断的方法、进展与展望

中国是碳酸盐岩型喀斯特面积最大、分布最广的国家,在喀斯特地区居住着约2亿的人口,经济规模约占全国的10%.尤其是以贵州为中心的西南地区,位于长江和珠江的上游,经济欠发达,但却是国家的重要生态安全屏障.喀斯特地貌重要而特殊,科学诊断其生态系统的健康不仅对生态修复和实现可持续发展至关重要,更是制定应对方案的基本前提.尽管以往开展了大量的研究工作,但是依然缺乏系统性和整体性认识.本文结合前人研究,进一步将喀斯特地貌演化发育总结为6个阶段:成岩阶段、成陆阶段、成山阶段、成丛阶段、成林阶段和成原阶段.在此基础上,又从碳酸盐岩风化成土速率的计算、土壤水的诊断、水土流失的评价、石漠化解译与演变、生态系统服务的科学计量5个方面进行系统的总结和梳理.尽管不同时代、不同学者的不同研究方法,其结果有着非常大的差异.但是,综合考虑已有的研究结果及其使用方法的优点和不足,归纳发现:(1)碳酸盐岩化学风化很快(30~130 mm ka^(−1)),但由于其酸不溶物含量低(5%以内),导致其成土速率极其缓慢,一般在5~50 t km^(−2)a^(−1)之间.(2)土壤少且分布不连续,但是土壤含水量却比较高,在0.2~0.4 m^(3)m^(−3)之间,这可能与周边基岩的汇水效应相关,且未来呈干化趋势.(3)土壤侵蚀模数低,一般在2~200 t km^(−2)a^(−1)之间,但由于坡陡、降雨量大且与岩土突变接触,导致其侵蚀风险极大,并存在一定比例的地下漏失现象.(4)石漠化的演变可分为单变式、渐进式和返变式3种经典类型,但是通过决策树和人机交互的石漠化解译方法相对高效和精准.(5)喀斯特生态系统服务功能的评价模型要基于成土速率来进行修正水土保持功能和基于岩溶地质碳汇修正气候调节功能.当前诊断喀斯特生态系统健康的指标、方法和模型亟须改进和创新,要充分体现喀斯特地区的特殊性和模型方法的匹配性,提高监测数据的时空分辨率,特别是亟须建立喀斯特社会-生态系统耦合模型,准确刻画出喀斯特生态系统对气候变化和人类活动的响应过程,提升诊断模型的准确度和可预测性.总之,本文系统总结了诊断中国喀斯特生态系统健康与可持续性的方法和取得的重大进展,并进一步从“岩-土-水-石-生”的角度提出了未来的研究方向和突破重点,以期为维护喀斯特地区的生态安全和可持续发展提供科技支撑.

Novel gold nanoparticles targeting somatostatin receptor subtype two with near-infrared light for neuroendocrine tumour therapy

Neuroendocrine tumours(NETs)are rare cancers with positive somatostatin receptor 2(SSTR2)expression,and treatment strategies for NETs are not satisfactory.Nanomaterial-mediated therapy targeting SSTR2 in NETs is very promising.This study firstly combined mesoporous silica-coated gold nanorods(AuNRs@mSiO_(2))and targeting-SSTR2 dodecane tetraacetic acidtyrosine3-octreotate(DOTA-TATE)into AuNRs@mSiO_(2)@DOTA-TATE to investigate NETs inhibition under near-infrared light.AuNRs@mSiO_(2)@DOTA-TATE showed good photothermal conversion efficiency.In vitro,under light irradiation,the cell viability significantly decreased with increasing AuNR@mSiO_(2)@DOTA-TATE concentration;in two successfully established neuroendocrine tumour organoids with SSTR2 expression,AuNRs@mSiO_(2)@DOTA-TATE with light inhibited tumours significantly better than AuNRs@mSiO_(2) with light.In vivo,the SSTR2-targeting ability and biodistribution of AuNRs@mSiO_(2)@DOTA-TATE were confirmed with AuNRs@mSiO_(2)@64Cu-DOTA-TATE under micro-positron emission tomography/computed tomography(micro-PET/CT);in the AuNRs@mSiO_(2)@DOTA-TATE with laser group,the tumour surface temperature increased rapidly,with tumour volumes similar to those in the octreotide group and significantly lower than those in other groups.There was no significant difference in mice body weight between the AuNRs@mSiO_(2)@DOTA-TATE with laser group and other groups.No significant inflammatory lesions or cell necrosis was found in the main organs.In summary,we presented a feasible strategy to construct AuNRs@mSiO_(2)@DOTA-TATE with good photothermal conversion efficiency,targetingSSTR2 ability,significant antitumour effects,and good biocompatibility,warranting further explorations of AuNRs@mSiO_(2)@DOTA-TATE for NETs therapy applications.

Anti-fatigue activity of Brassica rapa L.extract and correlation among biochemical changes in forced swimming mice

Brassica rapa L.is an edible and medical plant in China used for treating various diseases,especially in the Tibet region.In this study,the chemical constituents of Brassica rapa L.extract(BE)was clarified by UHPLC-MS and anti-fatigue ability of BE was assessed through the behavior test and weight-loaded forced swimming test in mice.Then we determined several biochemical parameters related to fatigue to explore possible reasons.Results indicate that BE could significantly enhance exercise performance in mice,and there was a significant improvement in both grip-strength and the time on rota-rods.Meanwhile,BE showed significant anti-fatigue effects by decreasing the blood lactic acid(BLA),blood urea nitrogen(BUN),and creatine kinase(CK)levels and increasing the lactate dehydrogenase(LDH)and malondialdehyde(MDA)levels.Increases in superoxide dismutase(SOD)and glutathione peroxidase(GSH-Px)were observed and reactive oxygen species(ROS)levels was regulated.Liver and muscle glycogen levels and adenosine triphosphate(ATP)levels were also significantly increased.Pearson correlation analysis indicates that fatigue-related indexes including metabolites and energy-related indexes were significantly correlated with antioxidant levels.In conclusion,BE possesses remarkable anti-fatigue activity,thereby shows great potential for improving exercise performance and alleviating physical fatigue and can be considered as a functional food supplement for further development.

Dynamic reconfiguration for TEG systems under heterogeneous temperature distribution via adaptive coordinated seeker

A thermoelectric generation(TEG)system has the weakness of relatively low thermoelectric conversion efficiency caused by heterogeneous temperature distribution(HgTD).Dynamic reconfiguration is an effective technique to improve its overall energy efficiency under HgTD.Nevertheless,numerous combinations of electrical switches make dynamic reconfiguration a complex combinatorial optimization problem.This paper aims to design a novel adaptive coordinated seeker(ACS)based on an optimal configuration strategy for large-scale TEG systems with series-paral-lel connected modules under HgTDs.To properly balance global exploration and local exploitation,ACS is based on'divide-and-conquer'parallel computing,which synthetically coordinates the local searching capability of tabu search(TS)and the global searching capability of a pelican optimization algorithm(POA)during iterations.In addition,an equivalent re-optimization strategy for a reconfiguration solution obtained by meta-heuristic algorithms(MhAs)is proposed to reduce redundant switching actions caused by the randomness of MhAs.Two case studies are carried out to assess the feasibility and superiority of AcS in comparison with the artificial bee colony algorithm,ant colony optimization,genetic algorithm,particle swarm optimization,simulated annealing algorithm,TS,and POA.Simulation results indicate that ACS can realize fast and stable dynamic reconfiguration of a TEG system under HgTDs.In addition,RTLAB platform-based hardware-in-the-loop experiments are carried out to further validate the hardware implemen-tation feasibility.