Advancing neuroscience through real-time processing of big data:Transition from open-loop to closed-loop paradigms

The brain functions as a closed-loop system that continuously generates behavior in response to the external environment and adjusts actions based on the outcomes.Traditional research methodologies in neuroscience,especially those employed in brain imaging experiments,have mainly adopted an open-loop paradigm(Grosenick et al.,2015).Functional neural circuits are analyzed offline and subsequently tested through manipulation of neuronal activities within specific regions or with genetic markers.By establishing a closed-loop research paradigm,functional ensembles can be detected and tested in real time with temporal sequences.These functional ensembles,rather than brain regions or genetically labeled neural populations,serve as fundamental units of neural networks,offering valuable insights into the dissection of neural circuits.The closed-loop research paradigm also enables the capture of high-dimensional activities of internal brain dynamics and precise elucidation of physiological processes such as learning,decision-making,and sleep.

4种植物源性成分多重real-time PCR检测方法的建立及其在食用淀粉中的应用

建立一种可同时快速检测红薯、木薯、马铃薯、玉米源性成分的多重实时聚合酶链式反应(real-time polymerase chain reaction,real-time PCR)方法。分别以红薯g3pdh基因、木薯g3pdh基因、马铃薯UGPase基因、玉米zSSIIb基因为靶基因设计特异性引物和TaqMan探针,以18S rRNA基因为内参基因,建立多重real-time PCR方法,开展方法学验证,并对不同掺入比例模拟样品和实际淀粉样品进行检测。结果显示,该方法具有高通量、特异性强、灵敏度高等优点。与15种非目标源性均无交叉反应;对目标DNA的检测灵敏度可达到3×10^(-3) ng/μL,且具有良好的线性关系和扩增效率;对淀粉样品的检出限可达0.1%,对50份实际样品进行检测,结果与参比方法一致,说明建立的多重real-time PCR法可用于食用淀粉种类掺假鉴别检测。

ICON/MIGHTI与TIMED/SABER探测温度数据的对比

ICON卫星为临近空间环境特性研究、建模和预报提供了新数据.通过对ICON/MIGHTI与TIMED/SABER在90~105 km高度探测温度数据的比较,计算两者的年平均温度偏差和均方根误差,同时分析月平均温度偏差在不同月份中随高度和纬度的分布情况,为MIGHTI和SABER温度探测数据在临近空间大气建模和预报应用提供参考依据.结果表明,MIGHTI和SABER的温度垂直廓线变化趋势基本吻合,数值上有所差异.在12°S-42°N范围内,MIGHTI探测温度与SABER相比,在90~93 km时偏低,偏差最大值约2.5 K,在93~105 km偏高,偏差的绝对值最大约10 K.在不同季节,白天的温度偏差通常高于夜晚.SABER和MIGHTI的月平均温度偏差随季节和纬度的变化显著,夏季时的月平均温度偏差最大,且温度的均方根误差最大.

TimeGAN-Informer长时机场能见度预测

能见度的预测对机场的业务决策、保障飞机的安全起降具有重要的意义。针对现有能见度预测模型预测时间较短的问题,提出一种基于TimeGAN Informer(Time Generative Adversarial Network-Informer)的机场能见度预测方法。利用2018—2022年气象和污染物数据,通过相关系数法和递归特征消除法提取出能见度的主要影响因素,使用TimeGAN时间序列生成对抗网络对数据进行扩充,并将Informer长时间序列预测模型应用于能见度预测。结果显示:当预测步长为1 d、2 d、3 d时,TimeGAN Informer的绝对误差(Mean Absolute Error,MAE)分别为2.42、3.13、3.57,比Informer分别降低了0.29、0.27、0.28,比长短时记忆网络(Long Short-Term Memory,LSTM)分别降低了0.28、0.49、0.63;均方根误差(Root Mean Square Error,RMSE)分别为3.03、3.7、4.09,比Informer分别降低了0.38、0.22、0.24,比长短时记忆网络(LSTM)分别降低了0.3、0.5、1.04;百分误差小于30%的分别占测试样本集的78.07%、70.68%、63.84%。尽管随着步长的增加预测效果变差,但在预测步长为3 d时,多数样本的预测误差仍小于30%,实现了对机场区域较为准确的长时能见度预测。

基于TimeGAN数据增强的复杂过程故障分类方法

针对传统基于重构的故障分类方法在故障样本稀疏或失衡情况下效果不佳、故障子空间区分能力弱的问题,提出基于TimeGAN数据增强的复杂过程故障分类方法.针对小子样故障,使用TimeGAN对历史故障数据进行数据增强,生成与历史数据分布相似的虚拟故障样本;采用马氏距离评估虚拟样本的质量,剔除不可信样本,构造平衡的故障样本集.将故障样本映射到高维核空间,并在核空间中提取故障子空间.设计故障分类策略并定义4种故障分类性能评估指标以定量衡量算法的分类性能.Tennessee Eastman应用结果表明,所提数据增强方法可以有效扩充故障样本,进而提高故障重构率.与WGAN-GP和SMOTE方法进行对比,发现基于TimeGAN数据增强的故障分类方法具有更好的分类性能.

A frequency servo SoC with output power stabilization loop technology for miniaturized atomic clocks

A frequency servo system-on-chip(FS-SoC)featuring output power stabilization technology is introduced in this study for high-precision and miniaturized cesium(Cs)atomic clocks.The proposed power stabilization loop(PSL)technique,incorporating an off-chip power detector(PD),ensures that the output power of the FS-SoC remains stable,mitigating the impact of power fluctuations on the atomic clock's stability.Additionally,a one-pulse-per-second(1PPS)is employed to syn-chronize the clock with GPS.Fabricated using 65 nm CMOS technology,the measured phase noise of the FS-SoC stands at-69.5 dBc/Hz@100 Hz offset and-83.9 dBc/Hz@1 kHz offset,accompanied by a power dissipation of 19.7 mW.The Cs atomic clock employing the proposed FS-SoC and PSL obtains an Allan deviation of 1.7×10^(-11) with 1-s averaging time.

Rapid and stable calcium-looping solar thermochemical energy storage via co-doping binary sulfate and Al–Mn–Fe oxides

Solar thermochemical energy storage based on calcium looping(CaL)process is a promising technology for next-generation concentrated solar power(CSP)systems.However,conventional calcium carbonate(CaCO_(3))pellets suffer from slow reaction kinetics,poor stability,and low solar absorptance.Here,we successfully realized high power density and highly stable solar thermochemical energy storage/release by synergistically accelerating energy storage/release via binary sulfate and promoting cycle stability,mechanical strength,and solar absorptance via Al–Mn–Fe oxides.The energy storage density of proposed CaCO_(3)pellets is still as high as 1455 kJ kg^(-1)with only a slight decay rate of 4.91%over 100 cycles,which is higher than that of state-of-the-art pellets in the literature,in stark contrast to 69.9%of pure CaCO_(3)pellets over 35 cycles.Compared with pure CaCO_(3),the energy storage power density or decomposition rate is improved by 120%due to lower activation energy and promotion of Ca^(2+)diffusion by binary sulfate.The energy release or carbonation rate rises by 10%because of high O^(2-)transport ability of molten binary sulfate.Benefiting from fast energy storage/release rate and high solar absorptance,thermochemical energy storage efficiency is enhanced by more than 50%under direct solar irradiation.This work paves the way for application of direct solar thermochemical energy storage techniques via achieving fast energy storage/release rate,high energy density,good cyclic stability,and high solar absorptance simultaneously.

Brain Time Stack图像融合技术在CT中的应用

目的:分析Brain Time Stack图像融合技术在CT中的应用。方法:选取2021年3月—2022年9月衡水市第四人民医院收治的50例CT检查患者作为研究对象。所有患者进行CT检查并进行Brain Time Stack后处理。比较四组不同部位CT值、标准差(SD)、信噪比(SNR)。比较四组图像主观质量评分。分析不同部位CT值、SD、SNR与图像主观质量评分的相关性。结果:B组的延髓、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值明显低于A组;C组的延髓、脑室、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值高于A组;D组延髓、额叶灰质、颞肌肌肉CT值明显低于A组,脑室、额叶白质、小脑外侧CT值明显高于A组;C组延髓、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值明显高于B组;D组延髓、脑室、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值明显高于B组;D组延髓、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉CT值明显低于C组;D组脑室CT值明显高于C组,差异有统计学意义(P<0.05)。B组、C组、D组延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SD值明显低于A组;C组延髓、脑室、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SD值均明显高于B组;C组额叶灰质SD明显低于B组;D组延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧、肌肉SD均明显低于B组、C组,差异有统计学意义(P<0.05)。B组、C组、D组延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SNR均明显高于A组;C组、D组延髓、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SNR值明显高于B组;C组、D组脑室SNR明显低于B组;D组延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧、颞肌肌肉SNR明显高于C组,差异有统计学意义(P<0.05)。D组图像主观质量评分最高,差异有统计学意义(P<0.05)。延髓、脑室、额叶灰质、额叶白质、小脑内侧、小脑外侧及颞肌肌肉SD与主观质量评分呈明显负相关,SNR与主观质量评分间呈明显正相关,差异有统计学意义(P<0.05)。结论:利用Brain Time Stack图像融合技术对头部CT扫描检查图像处理,动脉期结合前一期及后一期的图像数据在处理后具有更好的质量和更少的噪音。

Association of daily sitting time and leisure-time physical activity with body fat among U.S.adults

Background:Prolonged sitting and reduced physical activity lead to low energy expenditures.However,little is known about the joint impact of daily sitting time and physical activity on body fat distribution.We investigated the independent and joint associations of daily sitting time and physical activity with body fat among adults.Methods:This was a cross-sectional analysis of U.S.nationally representative data from the National Health and Nutrition Examination Survey2011-2018 among adults aged 20 years or older.Daily sitting time and leisure-time physical activity(LTPA)were self-reported using the Global Physical Activity Questionnaire.Body fat(total and trunk fat percentage)was determined via dual X-ray absorptiometry.Results:Among 10,808 adults,about 54.6%spent 6 h/day or more sitting;more than one-half reported no LTPA(inactive)or less than 150 min/week LTPA(insufficiently active)with only 43.3%reported 150 min/week or more LTPA(active)in the past week.After fully adjusting for sociodemographic data,lifestyle behaviors,and chronic conditions,prolonged sitting time and low levels of LTPA were associated with higher total and trunk fat percentages in both sexes.When stratifying by LTPA,the association between daily sitting time and body fat appeared to be stronger in those who were inactive/insuufficiently active.In the joint analyses,inactive/insuufficiently active adults who reported sitting more than 8 h/day had the highest total(female:3.99%(95%confidence interval(95%CI):3.09%-4.88%);male:3.79%(95%CI:2.75%-4.82%))and trunk body fat percentages(female:4.21%(95%CI:3.09%-5.32%);male:4.07%(95%CI:2.95%-5.19%))when compared with those who were active and sitting less than 4 h/day.Conclusion:Prolonged daily sitting time was associated with increased body fat among U.S.adults.The higher body fat associated with 6 h/day sitting may not be offset by achieving recommended levels of physical activity.

Time series prediction of reservoir bank landslide failure probability considering the spatial variability of soil properties

Historically,landslides have been the primary type of geological disaster worldwide.Generally,the stability of reservoir banks is primarily affected by rainfall and reservoir water level fluctuations.Moreover,the stability of reservoir banks changes with the long-term dynamics of external disastercausing factors.Thus,assessing the time-varying reliability of reservoir landslides remains a challenge.In this paper,a machine learning(ML)based approach is proposed to analyze the long-term reliability of reservoir bank landslides in spatially variable soils through time series prediction.This study systematically investigated the prediction performances of three ML algorithms,i.e.multilayer perceptron(MLP),convolutional neural network(CNN),and long short-term memory(LSTM).Additionally,the effects of the data quantity and data ratio on the predictive power of deep learning models are considered.The results show that all three ML models can accurately depict the changes in the time-varying failure probability of reservoir landslides.The CNN model outperforms both the MLP and LSTM models in predicting the failure probability.Furthermore,selecting the right data ratio can improve the prediction accuracy of the failure probability obtained by ML models.

Prescribed Performance Tracking Control of Time-Delay Nonlinear Systems With Output Constraints

The problem of prescribed performance tracking control for unknown time-delay nonlinear systems subject to output constraints is dealt with in this paper. In contrast with related works, only the most fundamental requirements, i.e., boundedness and the local Lipschitz condition, are assumed for the allowable time delays. Moreover, we focus on the case where the reference is unknown beforehand, which renders the standard prescribed performance control designs under output constraints infeasible. To conquer these challenges, a novel robust prescribed performance control approach is put forward in this paper.Herein, a reverse tuning function is skillfully constructed and automatically generates a performance envelop for the tracking error. In addition, a unified performance analysis framework based on proof by contradiction and the barrier function is established to reveal the inherent robustness of the control system against the time delays. It turns out that the system output tracks the reference with a preassigned settling time and good accuracy,without constraint violations. A comparative simulation on a two-stage chemical reactor is carried out to illustrate the above theoretical findings.

Defect Detection Model Using Time Series Data Augmentation and Transformation

Time-series data provide important information in many fields,and their processing and analysis have been the focus of much research.However,detecting anomalies is very difficult due to data imbalance,temporal dependence,and noise.Therefore,methodologies for data augmentation and conversion of time series data into images for analysis have been studied.This paper proposes a fault detection model that uses time series data augmentation and transformation to address the problems of data imbalance,temporal dependence,and robustness to noise.The method of data augmentation is set as the addition of noise.It involves adding Gaussian noise,with the noise level set to 0.002,to maximize the generalization performance of the model.In addition,we use the Markov Transition Field(MTF)method to effectively visualize the dynamic transitions of the data while converting the time series data into images.It enables the identification of patterns in time series data and assists in capturing the sequential dependencies of the data.For anomaly detection,the PatchCore model is applied to show excellent performance,and the detected anomaly areas are represented as heat maps.It allows for the detection of anomalies,and by applying an anomaly map to the original image,it is possible to capture the areas where anomalies occur.The performance evaluation shows that both F1-score and Accuracy are high when time series data is converted to images.Additionally,when processed as images rather than as time series data,there was a significant reduction in both the size of the data and the training time.The proposed method can provide an important springboard for research in the field of anomaly detection using time series data.Besides,it helps solve problems such as analyzing complex patterns in data lightweight.

Real-time Rescue Target Detection Based on UAV Imagery for Flood Emergency Response

Timely acquisition of rescue target information is critical for emergency response after a flood disaster.Unmanned Aerial Vehicles(UAVs)equipped with remote sensing capabilities offer distinct advantages,including high-resolution imagery and exceptional mobility,making them well suited for monitoring flood extent and identifying rescue targets during floods.However,there are some challenges in interpreting rescue information in real time from flood images captured by UAVs,such as the complexity of the scenarios of UAV images,the lack of flood rescue target detection datasets and the limited real-time processing capabilities of the airborne on-board platform.Thus,we propose a real-time rescue target detection method for UAVs that is capable of efficiently delineating flood extent and identifying rescue targets(i.e.,pedestrians and vehicles trapped by floods).The proposed method achieves real-time rescue information extraction for UAV platforms by lightweight processing and fusion of flood extent extraction model and target detection model.The flood inundation range is extracted by the proposed method in real time and detects targets such as people and vehicles to be rescued based on this layer.Our experimental results demonstrate that the Intersection over Union(IoU)for flood water extraction reaches an impressive 80%,and the IoU for real-time flood water extraction stands at a commendable 76.4%.The information on flood stricken targets extracted by this method in real time can be used for flood emergency rescue.

Cross-Dimension Attentive Feature Fusion Network for Unsupervised Time-Series Anomaly Detection

Time series anomaly detection is crucial in various industrial applications to identify unusual behaviors within the time series data.Due to the challenges associated with annotating anomaly events,time series reconstruction has become a prevalent approach for unsupervised anomaly detection.However,effectively learning representations and achieving accurate detection results remain challenging due to the intricate temporal patterns and dependencies in real-world time series.In this paper,we propose a cross-dimension attentive feature fusion network for time series anomaly detection,referred to as CAFFN.Specifically,a series and feature mixing block is introduced to learn representations in 1D space.Additionally,a fast Fourier transform is employed to convert the time series into 2D space,providing the capability for 2D feature extraction.Finally,a cross-dimension attentive feature fusion mechanism is designed that adaptively integrates features across different dimensions for anomaly detection.Experimental results on real-world time series datasets demonstrate that CAFFN performs better than other competing methods in time series anomaly detection.

量子Loop代数U_(q)(L(sl_(2)))的单权模

用构造的方法解决量子Loop代数U_(q)(L(sl_(2)))具有一个一维权空间的单权模的结构问题,得到了任意一个具有一维权空间的单权模必同构于U_(q)(L(sl_(2)))的四类单权模之一.此外,还构造了一类权空间维数为2的既非最高权也非最低权的量子Loop代数U_(q)(L(sl_(2)))的单权模.

Hybrid model for BOF oxygen blowing time prediction based on oxygen balance mechanism and deep neural network

The amount of oxygen blown into the converter is one of the key parameters for the control of the converter blowing process,which directly affects the tap-to-tap time of converter. In this study, a hybrid model based on oxygen balance mechanism (OBM) and deep neural network (DNN) was established for predicting oxygen blowing time in converter. A three-step method was utilized in the hybrid model. First, the oxygen consumption volume was predicted by the OBM model and DNN model, respectively. Second, a more accurate oxygen consumption volume was obtained by integrating the OBM model and DNN model. Finally, the converter oxygen blowing time was calculated according to the oxygen consumption volume and the oxygen supply intensity of each heat. The proposed hybrid model was verified using the actual data collected from an integrated steel plant in China, and compared with multiple linear regression model, OBM model, and neural network model including extreme learning machine, back propagation neural network, and DNN. The test results indicate that the hybrid model with a network structure of 3 hidden layer layers, 32-16-8 neurons per hidden layer, and 0.1 learning rate has the best prediction accuracy and stronger generalization ability compared with other models. The predicted hit ratio of oxygen consumption volume within the error±300 m^(3)is 96.67%;determination coefficient (R^(2)) and root mean square error (RMSE) are0.6984 and 150.03 m^(3), respectively. The oxygen blow time prediction hit ratio within the error±0.6 min is 89.50%;R2and RMSE are0.9486 and 0.3592 min, respectively. As a result, the proposed model can effectively predict the oxygen consumption volume and oxygen blowing time in the converter.

庆阳驴mtDNA D-loop区遗传多样性及起源分析

[目的]研究庆阳驴养殖群体的遗传多样性与母系起源,了解其遗传信息,为保护庆阳驴种质资源、选育和遗传改良工作提供理论依据。[方法]随机选取133头庆阳驴,对其线粒体DNA(mitochondrial DNA,mtDNA)D-loop区序列进行PCR扩增、测序及比对,并探讨庆阳驴的遗传多样性与母系起源。[结果]在获得的520 bp D-loop碱基序列中,AT含量(57.3%)高于GC含量(42.8%),表现出碱基的偏倚性;检测到38个变异位点,包含8个碱基对的转换;其核苷酸多样性(Pi)、单倍型多样性(Hd)、平均核苷酸差异(K)分别为0.01591、0.895和8.274,与欧洲家驴和中国家驴研究的平均值相比较低,说明该驴品种核苷酸变异较为贫乏。庆阳驴mtDNA D-loop区存在35个单倍型,单倍型之间的遗传距离为0.002~0.042。系统进化结果显示,庆阳驴存在2个线粒体支系,表明其具有2个母系起源,且遗传距离表明,庆阳驴与克罗地亚家驴之间的遗传距离较近。[结论]本研究从分子水平初步揭示庆阳驴核苷酸变异比较贫乏,杂交程度高,mtDNA遗传多态性正逐步丧失,应加强庆阳驴品种的遗传资源保护工作。

Mn-doped SrCoO_(3-δ) Perovskite Oxides for Ethylene Production via Chemical Looping Oxidative Dehydrogenation of Ethane

Chemical looping oxidative dehydrogenation (CL-ODH) is an economically promising method for convertingethane into higher value-added ethylene utilizing lattice oxygen in redox catalysts, also known as oxygen carriers. Inthis study, perovskite-type oxide SrCoO_(3-δ) and B-site Mn ion-doped oxygen carriers (SrCo_(1-x)MnxO_(3-δ), x=0.1, 0.2, 0.3)were prepared and tested for the CL-ODH of ethane. The oxygen-deficient perovskite SrCoO_(3-δ) exhibited high ethyleneselectivity of up to 96.7% due to its unique oxygen vacancies and lattice oxygen migration rates. However, its low ethyleneyield limits its application in the CL-ODH of ethane. Mn doping promoted the reducibility of SrCoO_(3-δ) oxygen carriers,thereby improving ethane conversion and ethylene yield, as demonstrated by characterization and evaluation experiments.X-ray diffraction results confirmed the doping of Mn into the lattice of SrCoO_(3-δ), while X-ray photoelectron spectroscopy(XPS) indicated an increase in lattice oxygen ratio upon incorporation of Mn into the SrCoO_(3-δ) lattice. Additionally, H2temperature-programmed reduction (H2-TPR) tests revealed more peaks at lower temperature reduction zones and a declinein peak positions at higher temperatures. Among the four tested oxygen carriers, SrCo0.8Mn0.2O_(3-δ) exhibited satisfactoryperformance with an ethylene yield of 50% at 710 °C and good stability over 20 redox cycles. The synergistic effect of Mnplays a key role in increasing ethylene yields of SrCoO_(3-δ) oxygen carriers. Accordingly, SrCo0.8Mn0.2O_(3-δ) shows promisingpotential for the efficient production of ethylene from ethane via CL-ODH.

基于D-loop序列和微卫星标记的4个黄颡鱼群体的遗传变异分析

为探究广东珠江、广西漓江、四川金沙江和云南西江4个水系黄颡鱼(Pelteobagrus fulvidraco)群体的遗传变异情况,本研究分别基于上述水系共108尾黄颡鱼的线粒体D-loop序列和12个微卫星标记对4个黄颡鱼群体进行群体内遗传多样性、群体间遗传距离和变异及群体结构分析。结果表明:共得到105个D-loop序列,其核苷酸组成中A+T含量占比为56.22%,G+C含量占比为43.78%。在105个有效序列中,多态位点数为746个(含缺和无效多态位点),单倍型数为51个。Hap2、Hap25、Hap15分别是广东珠江、广西漓江、四川金沙江群体的优势单倍型。4个黄颡鱼群体的单倍型多样性(H_(d))介于0.842~0.940,核苷酸多样性(π)介于0.002~0.083;四川金沙江群体Hd的最低,π也处于较低水平;而云南西江群体的Hd和π均最高;4个群体的Tajima′s D值均小于0。等位基因数(Na)、有效等位基因数(Ne)、观测杂合度(Ho)、期望杂合度(He)、多态信息含量(PIC)均为广西漓江群体最高,广东珠江群体次之,云南西江群体较低,四川金沙江群体最低。4个黄颡鱼群体的K2P遗传距离介于0.008~0.115之间,其中广西漓江与云南西江群体间的K2P遗传距离最近;广东珠江群体与其他3个群体的K2P遗传距离较远,与四川金沙江群体的K2P遗传距离最远。4个黄颡鱼群体间均存在极显著的遗传分化(P<0.01),群体间遗传分化指数(F_(st))介于0.114~0.959之间。4个黄颡鱼群体间Nei’s遗传距离和遗传一致性分别介于0.117~1.114之间和0.328~0.890之间,其中,广西漓江群体与云南西江群体的Nei’s遗传距离最近、遗传一致性最高,广东珠江与四川金沙江群体的Nei’s遗传距离最远、遗传一致性最低。在UPGMA系统聚类树中,4个黄颡鱼群体主要分为两支,其中广西漓江群体、云南西江群体最先聚为一支,然后与四川金沙江群体聚为一支,最后与广东珠江群体聚类。在NJ聚类树中,4个群体黄颡鱼的明显分为4个分支,广西漓江群体与云南西江群体呈镶嵌式分布。4个黄颡鱼群体中共享单倍型较少,但特有单倍型较多,其中云南西江和广西漓江群体相邻较近且共有单倍型较多。在基于Nei’s遗传距离进行的主坐标分析中,广西漓江群体和云南西江群体的个体之间遗传差异较小,广东珠江和四川金沙江群体的个体之间遗传差异较大。说明四川金沙江群体遗传多样性水平较低,云南西江群体遗传多样性水平较高,广西漓江群体与云南西江群体存在一定的基因交流。

Gigahertz frequency hopping in an optical phase-locked loop for Raman lasers

Raman lasers are essential in atomic physics,and the development of portable devices has posed requirements for time-division multiplexing of Raman lasers.We demonstrate an innovative gigahertz frequency hopping approach of a slave Raman laser within an optical phase-locked loop(OPLL),which finds practical application in an atomic gravimeter,where the OPLL frequently switches between near-resonance lasers and significantly detuned Raman lasers.The method merges the advantages of rapid and extensive frequency hopping with the OPLL’s inherent low phase noise,and exhibits a versatile range of applications in compact laser systems,promising advancements in portable instruments.