• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    Spatial search weighting information contained in cell velocity distribution

    2024-02-29 09:19:44YikaiMa馬一凱NaLi李娜andWeiChen陳唯
    Chinese Physics B 2024年2期
    關鍵詞:李娜

    Yikai Ma(馬一凱), Na Li(李娜), and Wei Chen(陳唯),?

    1State Key Laboratory of Surface Physics and Department of Physics,Fudan University,Shanghai 200438,China

    2China National Center for Bioinformation,Beijing 100101,China

    3National Genomics Data Center,Beijing Institute of Genomics,Chinese Academy of Sciences,Beijing 100101,China

    Keywords: cell migration,foraging efficiency,random walk,spatial search weight

    1.Introduction

    Cell migration is a topic of significant interest in the fields of biology, medicine, and physics.[1–3]Physicists primarily study cell migration behavior using methods such as generalized Langevin equations and models of nonlinear dynamics.[4–9]It is evident that the random walk of cells differs significantly from the passive walk of particles driven by thermal fluctuations,as observed in the mean square displacement and probability distribution of cell velocities.[8,10–13]It is a natural idea that the migration behavior of organisms may be related to their efficiency in foraging and searching.Therefore, researchers have conducted numerous studies on the relationship between biological movement patterns and search efficiency.[14–18]However,the majority of studies in this field have primarily focused on individual trajectory characteristics.There is a relative scarcity of research examining the relationship between collective characteristics of biological group movement and foraging efficiency.We noticed that there are significant differences in the average trajectory velocities of individual cells,[19]implies that cells within a community exhibit variability.In this paper,we aim to explore how the distribution ratio of high-speed cells and low-speed cells in such cell communities affects the foraging efficiency of the cell community.Our work indicates that,through considering weights of spatial search, the experimentally obtained velocity distribution corresponds to an optimal search strategy.We speculate that this specific spatial search weighting is an evolutionary outcome associated with the historical survival environment,ultimately manifested in the distribution of cell velocities.

    The article is organized as follows: Firstly, Section 2 presents the experimental methods and results.Then, in Section 3, a simulation model is established based on the experimental data.Next, Section 4 calculates the search efficiency under different cell speed distributions using the established framework, and Section 5 discusses the results.Section 6 introduces spatial search weights as a model improvement.Subsequently, Section 7 recalculates the search efficiency within the updated model, and Section 8 provides a comprehensive analysis of the results.Finally,Section 9 summarizes the findings and contributions of the study.

    2.Experimental results

    Dictyostelium discoideum cells(Dicty)were used as the model cells for our study.The methods used for preparing Dicty cells and acquiring data are similar to those described in Ref.[20].In the experiment,starving KAx-3 Dicty cells were dispersed onto nutrient-free agar surfaces after a 2-hour starvation period in 5?C.The planting density is 30 cells/mm2,and the samples were kept at room temperature throughout the entire observation period.In the absence of food, Dicty cells exhibit random movement on agar surfaces as they search for sustenance.The movement trajectories of cells were continuously captured using a microscope equipped with a digital camera (Olympus CX23, objective 4×; Canon EOS 100D).The images are captured with a spatial resolution 5184×3456 and a time interval ?t=20 s.

    We employed a home-made IDL program to track the positions of cells at each time, allowing us to obtain spatial trajectoriesr(t) of the cells.We quantified the spatial distance?rtraveled by the cells during a given time interval ?t.The instantaneous velocityvtof the cells was calculated using the formulavt=?r/?t.For each obtained cell trajectory,we can define the trajectory velocityvof the cell using

    Here,the timeTrepresents the time duration of the cell movement trajectory.The difference betweenvandvtlies in the following: the difference invtrepresents the instantaneous fluctuations in the velocity of individual cells,while the difference invrepresents the inherent velocity differences among different cells observed in the cell community.Based on the calculatedvfrom different cell trajectories,we can calculate the number of cells with different trajectory velocitiesvin the cell community, and accordingly, plot the probability density distribution curveP(v), which represents the proportion of cells with different trajectory velocityvin the cell community.TheP(v)curve is depicted in Fig.1.

    From Fig.1, it shows that within the same cell colony,cells spontaneously divide into subgroups with different speeds during their spatial search for food.The speeds of high-speed and low-speed cells can differ by an order of magnitude.The peak on the left side indicates that, in the Dicty cell colony, most cells have small velocities, and only a few cells are able to move quickly over long distances.

    The shape of theP(v) curve resembles the Maxwell–Boltzmann distribution of an ideal gas.Therefore, we have developed a phenomenological model (Eq.(2)) based on the Boltzmann formula to describe the shape of theP(v)curve:

    wherekis the normalization coefficient.From they-log plot of the curve,the insert in Fig.1,it is evident that the decrease in the high-speed segment follows a linear decay rather than a quadratic decay.Hence, the exponential term in Eq.(2) is expressed as exp(-v/v0) instead of exp(-(v/v0))2.When fitting the actual data,we discovered that the value ofαis approximately 2.Consequently, in subsequent fittings, we fixα= 2 and only adjustv0as the sole adjustable parameter.This approach provides an advantage in subsequent simulation work where we require a series of velocity distributions with continuous changes.By only modifying the parameterv0,we can achieve different widths of the distribution and most probable speeds ofP(v)curves.

    Due to the noticeable discrepancy in cell velocities within the colony, we aim to comprehend the variations in random walking patterns exhibited by cells with varying velocities.To divide the cells into five groups according to their cell trajectory velocitiesv,we plotted mean square displacement(MSD)curves for these five cell groups.The results are shown in the following Fig.2.

    Fig.1.The distribution of Dicty cell trajectory velocities.The insert in the figure is presented in a y-logarithmic plot, and the red solid line represents the fitting line of Eq.(2)with the fitting parameter value v0=0.72μm/min.

    Fig.2.The variation of〈r2〉(MSD)with time t for 5 groups of cells with different trajectory velocities v(dots).From bottom to top,each curve corresponds to velocity groups v=1.1±0.2, 2.1±0.2, 3.1±0.3, 4.1±0.4,5.2±0.6 μm/min.The solid line represents the fitting line of Eq.(3), and the fitting parameter values for each curve are listed in Table 1.

    Many papers have discussed the difference between the〈r2〉of Dicty cells and Eq.(3).[8,10]In our results, all the fitted curves and experimental data agree well, except for the〈r2〉curve corresponding to the lowest speed that exhibits noticeable deviations in the short-range limit.Therefore, we consider Eq.(3) as a first-order approximation that adequately captures the essential characteristics of random cell movement in our experiments.The primary advantage of using Eq.(3)is its ability to directly derive the two critical characteristics of random walking behavior: persistent timeτand diffusion coefficientD.Persistent timeτrepresents the duration during which cell movement tends to be in a straight line,or,in other words, the time it takes to forget the initial movement direction.The fitting results of the parametersτandDfor each〈r2〉curve are listed in Table 1.

    Table 1.The persistent time τ and diffusion coefficient D for each group of cells with different trajectory velocities v.

    Table 1 illustrates the distinction in walking patterns among various cells.It can be observed that as the trajectory speedvof cells increases, the diffusion coefficientDalso increases correspondingly.The persistent timeτof the cells follows a similar trend, except for a slight deviation inτfor the last group(with a cell proportion of less than 8%).

    To have a more intuitive understanding of the diffusion speed and turning frequency of cell groups with different speeds, figure 3 shows the trajectory of different-speed cell groups.The highest velocity we measured was approximately 9.2 μm/min.Hence, we divided the velocity into three categories on the trajectory plot:(0–3),(3–6),(>6)μm/min.Figure 3 provides a visual representation of this correlation.It illustrates that highly mobile cells exhibit trajectories with long persistent length,characterized by a longer persistent time(τ)and are represented by green trajectories.On the other hand,cells with a slower speed of movement display more curved and coiled trajectories, which are represented by trajectories with smaller persistent times(τ).By combining the information from Table 1 and Fig.3,we can conclude that cell velocities represented correspond to specific motion models(v,τ,D)indeed.

    3.Problem presentation and model approach

    The cell colony originates from a single spore.Why do they exhibit different distributions of motion patterns(v,τ,D)instead of sharing a single motion pattern? We consider that cell movement is for food searching.Therefore, we hypothesize that cell colonies may move with specific motion patterns to maximize the spatial search efficiency of the entire cell colony.From Fig.3, it can be observed that high-speed cell movement covers a greater distance, but their trajectories are straighter and resulting in more blank spaces between them.On the other hand,low-speed cells have smaller persistent timeτand more curved trajectories, which fill in the blanks between high-speed cell trajectories.Intuitively, a combination of specific high and low-speed cells might be more favorable for cell spatial search.The observed cell speed distributionP(v) in our experiment may represent an optimized motion pattern for cell spatial search efficiency.

    To validate our hypothesis, we developed a numerical model and utilized the framework of the OU process to simulate the movement trajectories of cell colonies.[21]The simulations considered different cell speed distributions represented byP(v), which corresponded to distinct motion patterns (v,τ,D) as indicated in Table 1.By examining these simulated movement trajectories,we could evaluate their spatial search efficiencies.By comparing the spatial search efficiencies of the cell colony trajectories associated with different speed distributionsP(v), we investigated whether the experimentally obtainedP(v)aligned with the highest spatial search efficiency.

    4.Simulation model establishment

    By continuously changing the value ofv0in Eq.(2), we can generate different velocity distributionsP(v),as shown in Fig.4.

    From Fig.4, it can be observed that a smallerv0represents a smaller proportion of high-speed cells in the colony,while a largerv0represents a more even velocity distribution.Therefore,the velocityv0can be used as a characteristic quantity to describe theP(v)distribution.It is worthy to note that directly using the distribution generated by Eq.(2)would lead to a problem: different distributions correspond to different average velocities of the cell colony.Therefore, we need to truncate the highest velocity of theP(v) curve generated by Eq.(2),in order that the average velocity of the simulated cell colony remains unchanged.After this treatment, the normalizedP(v)result that satisfies velocity normalization andP(v)probability normalization is shown in Fig.4.

    Fig.4.The result of velocity distribution curve P(v) after being normalized by average velocity.The left curve corresponds to v0 =0.72μm/min,and the right curve corresponds to v0=0.90μm/min.

    We simulate a cell population to generate a specific velocity distribution curveP(v) based on the cell seeding density, cell diameter, and microscope field of view size used in our experiment.Periodic boundary condition is used to keep the total number of cells in the field of view constant.We use the OU process to simulate the motion trajectories of all cells,aiming to match the characteristic values(v,D,τ)of cell grouping with Table 1.The width of the cell trajectories was set to match the size of the cells.

    Based on the simulation results, we obtained the celltrajectories images,as shown in Fig.5(a).

    The cell trajectory images generated through simulation are presented in black and white format: the grayscale value of the area covered by the cell trajectories was set as 1 (representing the searched area),while the uncovered area was set as zero(representing the unsearched area).We utilized Eq.(4)to calculate the proportion of white regions in the image,represented asCR.

    whereg(i)represents the grayscale value of eachipixel in the image, andArepresents the total number of pixels in the image.The relationship betweenCR, which represents the proportion of the area covered by cell trajectories,and the random walking timetis plotted in Fig.5(b).

    Thus, we can define the search efficiency by the growth rate of theCR(t)curve.However,as seen from the fitted curve in Fig.5(b),CR(t)cannot be well fit to the exponential modely=1-exp(-t/t0).The difference between the fit and simulation results becomes more pronounced when dealing with higher cell density in experimental studies or larger cell size in simulations.Therefore, we cannot quantitatively measure the spatial search efficiency of the cell colony using the characteristic timet0of the exponential model.Hence,we directly defineE,the average value ofCRwithin the search timeTsas given in Eq.(5),as the characteristic value of the spatial search efficiency of the cell colony.

    A larger value ofEindicates higher search efficiency.As shown in Fig.6,afterCR(t)reaches saturation,the monotonicity and overall trend ofEare not affected by the length of the search timeTs.Therefore,Ecan be considered as a reliable physical quantity to define the search efficiency.

    Fig.5.(a) Simulated cell motion trajectory graph, simulated for 5000 steps.(b)The relationship between the proportion of white area in the image CR and time t in panel(a).The black line represents the numerical calculation result of the image.The red dashed line represents the fitting curve of y=1-exp(-t/t0).

    5.Discussion of the model results

    Using the above method,we simulated the movement trajectories of cell colonies under differentP(v)distributions by continuously changing the values ofv0in Eq.(2)and obtained differentCR(t)curves as shown in Fig.6(a).The spatial search efficiencyEfor eachCR(t) curve is calculated according to Eq.(5).TheE(v0)curve is plotted in Fig.6(b).Among them,v0=0.72 μm/min corresponds to the velocity distribution of cells in the experiment.From Fig.6(b), it can be observed that the spatial search efficiencyEmonotonously varies withv0.[22]This is contrary to our initial expectation.

    Fig.6.Simulation of spatial coverage ratio CR(t)derived from different P(v) distributions obtained from Eq.(2).From top to bottom, the curves correspond to v0 =0.24, 0.48, 0.72, 0.96, 1.20, 1.44 μm/min.(b)Relationship between cell spatial search efficiency E and v0.

    Recalling our definition of spatial search efficiency, the monotonous increase inEis actually natural: the more highspeed cells there are, the more distance the cell colony naturally covers in the same time period.Therefore, more new areas are covered, which leads that the corresponding spatial search efficiency is higher.However,this does not align with our initial intuition.

    Intuitively, we believe that the trajectory graph of cells moving in a straight line at high speeds may not necessarily be the optimal choice: because cells tend to move straight at high speeds,there are often many blank spaces between cell trajectories, which is not conducive to cell food search.However,in our previous model,an increased number of blank spaces in the movement trajectory does not affect the spatial search efficiency: the exploration of distant regions by high-speed cells compensates for those missed blanks in the nearby locations.But in the real world,the significance of search results at different distances is obviously different for cells.The distribution characteristics of food can significantly affect the search strategy of organisms.[23]Finding food closer to the colony is more meaningful for the bacterial population.However, our previous model did not take this into account.

    6.Introduction of spatial search weights W(r)

    As mentioned before,we need to consider the differences in spatial search weights corresponding to different positions in the cell movement trajectory in space,with higher weights in places closer to the initial distribution of the colony.Then we will construct a function to describe such a distribution of spatial weights.Considering that the spatial distribution range of cells is within the size range of cell spores,the spatial search weight for finding food within this region should be the highest and consistent,and then the weight should start to decrease gradually as the distance from the colony center increases.According to the above assumption,we construct the cell spatial search weight functionW(r) as given in Eq.(6).Here, we have not used a simple Gaussian distribution to consider that the spatial search weight within the initial spatial distribution region should be similar.

    Here,ris the radial distance from the center of the cell colony.The parametersr0andrgcorrespond to the width of the equally weighted central region and the rate of weight decrease with distance,respectively.

    Fig.7.The cell spatial search weights calculated according to Eq.(6)for r0 = 600 and rg = 100, as well as their distribution in a twodimensional space(a)and with respect to the spatial distribution of the cell colony(b).The center point in panel(b)corresponds to the center of the spatial distribution of the cell colony.

    According to Eq.(6),the distribution of cell spatial search weights is shown in Fig.7.We should multiply this weight distributionW(r)(Fig.7(b))with the image of the distribution trajectory of cells(Fig.5(a)).This will yield a new weighted trajectory image.

    We simulate the cell motion trajectory again using the above method.Initially,the cells are distributed in a finite central area with a size ofr0and the periodic boundary conditions are removed.The motion trajectories of the cells are recalculated under differentv0(corresponding to differentP(v)).According to the weighted modified trajectory image obtained from each simulation, as shown in Fig.8, which is technically obtained by multiplying the trajectory map (similar to Fig.5(a)) by the spatial distribution (Fig.7(b)).The characteristic valueEof the current spatial search efficiency is recalculated.and we can recalculate the equivalent coverage rateCR(t)curve based on Eq.(4).At this time,the gray valueg(i)in Eq.(4)is just as we expected after being modified byW(r).

    Fig.8.The modified cell trajectory map considering spatial search weight W(r).

    The optimum spatial search efficiency corresponding to the black curve in Fig.9 is around 0.5μm/min.This does not matchv0=0.72μm/min(the fitted result of the experimental data).But this is natural because the form of ourW(r)is an arbitrary assumption.We do not know the specific form ofW(r)used by cells in the real world.The value ofrg=68 used in Eq.(6)is just an arbitrarily generated weight curve.The result of the black curve in Fig.9 only indicates that in our model,given a spatial search weight distributionW(r),there is indeed an optimal velocity distribution for a cell colony instead of a higher proportion of high-speed cells being better.

    7.The spatial search efficiency E modified by W(r)

    The simulation results show that under the limitation of spatial search weightW(r), the spatial search efficiencyEof cell colonies can indeed reach the maximum value under a specific velocity distributionP(v),as shown by the black curve in Fig.9.

    Fig.9.The relationship curve between the spatial search efficiency E with W(r)and the characteristic velocity v0 in P(v)of the cell colony.The inset is the corresponding spatial search weight W(r) curve.The black line and the red line correspond to rg=68 and rg=32 in Eq.(6),respectively.

    In fact, we can always detect whichW(r) can make the maximum spatial search efficiencyEcorresponding to the velocity distributionP(v,v0= 0.72 μm/min) obtained through experiments by continuously changingW(r).In the simulation, we fixr0in Eq.(6) as the average width of spores,r0≈600μm, and continuously change the value ofrgto obtain different spatial search weight curvesW(r).As shown by the red line in Fig.9, when a suitable spatial search weight curveW(r)is found,the best search efficiency occurs atv0=0.72 μm/min (experimental result).In comparison to the red and black curves in Fig.9, a wider flat top of theW(r)curve corresponds to a largerv0in Eq.(2),as expected.From Fig.9,it can be seen that the velocity distributionP(v)of cells in experiments, modulated under a specific spatial search weightW(r), indeed corresponds to the optimal search efficiencyEof cell colonies.This means that the information of the spatial search weight of cell colonies is essentially contained in the velocity distribution of cell colonies.

    8.Discussion

    The spatial search weight should be the result of cell historical evolution[24]and related to the distribution characteristics of food in their growth environment.[23]In areas with scarce food, the distribution ofW(r) of cells maybe wide,and cells need to go far to find food.In areas with abundant food,the distribution ofW(r)of cells maybe narrow,and cells are more likely to search for food in the vicinity of spore areas.Since the cells in our laboratory have the same historical origin and the same real environment, they should have the same characteristics of spatial search weight distributionW(r).However, the spatial search weight distributionW(r)of the same cell colony may still change.For example, as the duration of cell planting time progresses,the spatial search weight distributionW(r)may change(if there is a change,it is reasonable).In our experiments,we have observed that the velocity distributionP(v)of cells will change significantly with the length of cell planted time.Whether this change is a direct reflection of the aging effect of spatial search weight distributionW(r) is a question that we want to explore further.The measured velocity distribution of cell colonies may still be different under different experimental conditions.For example,they can be influenced by factors such as the initial cell seeding density or the concentration of artificially added cAMP in the culture dish,as the collective behavioral characteristics of the resulting biological population are often closely related to the efficiency of interactions among organisms.[25]How to obtain reliable spatial search weight distributionW(r) from the optimum velocity distributionP(v) of cell colonies is still a challenge.

    9.Summary

    In this article, we seek to understand the speed distribution of cell movement in cell communities from the perspective of cell search efficiency.Based on the definition of cell search efficiency, the specific speed distribution of cell communities can correspond to the optimal spatial search efficiency of the cell community.Experimental findings suggest that the speed distribution of Dicty cells,under spatial search weight modulation,always corresponds to the optimal spatial search efficiency of the cell community.Our model explains the relationship between the distribution of cell spatial search weights and the speed distribution of cell movement, showing their intrinsic correlation.The deep information contained in the current speed distribution of cells is the spatial search weight distribution information during cell spatial search.In fact, we also provide a possible method to infer the spatial search weight based on the speed distribution of movement:by continuously adjusting the spatial search weight distribution under given speed conditions,we can calculate the change in search efficiency with the spatial search weight.Hence,the spatial search weight that corresponds to the optimal search efficiency represents the actual weight of cell movement during spatial search.Our work opens up directions for future research,where different conditions such as density and planting time can be studied to investigate whether the spatial weight of cell search movement obtained will also change.

    Acknowledgement

    Project supported by the National Natural Science Foundation of China(Grant No.31971183).

    猜你喜歡
    李娜
    Characteristics of cell motility during cell collision
    李娜作品
    大眾文藝(2022年22期)2022-12-01 11:52:58
    Nanosecond laser preheating effect on ablation morphology and plasma emission in collinear dual-pulse laser-induced breakdown spectroscopy
    《榜樣》:藝術創(chuàng)作的一次“出圈”表達
    Wave–activity relation containing wave–basic flow interaction based on decomposition of general potential vorticity?
    Application research of bamboo materials in interior design
    Relationship between characteristic lengths and effective Saffman length in colloidal monolayers near a water-oil interface?
    Analysis of the Effects of Introversion and Extroversion Personality Traits on Students’ English Reading And Writing Abilities with its Relevant Teaching Advice
    李娜作品
    藝術家(2017年2期)2017-11-26 21:26:20
    新年音樂會上的歡呼
    av网站在线播放免费| 日韩中文字幕欧美一区二区 | 亚洲精品久久久久久婷婷小说| 中文字幕精品免费在线观看视频| 日本一区二区免费在线视频| 高清视频免费观看一区二区| 天天躁夜夜躁狠狠躁躁| 啦啦啦中文免费视频观看日本| 一级毛片 在线播放| 最近中文字幕2019免费版| 国产一卡二卡三卡精品 | 欧美日韩亚洲高清精品| 日韩成人av中文字幕在线观看| 久久久久精品久久久久真实原创| 欧美日韩综合久久久久久| 赤兔流量卡办理| 亚洲美女搞黄在线观看| 老司机在亚洲福利影院| 日韩成人av中文字幕在线观看| 国产熟女午夜一区二区三区| 中文字幕人妻丝袜制服| 考比视频在线观看| 麻豆乱淫一区二区| 夫妻午夜视频| 在线观看一区二区三区激情| 亚洲国产精品一区三区| 青草久久国产| av有码第一页| 一区二区日韩欧美中文字幕| 丝袜在线中文字幕| av国产久精品久网站免费入址| 国产精品三级大全| 又大又黄又爽视频免费| 色94色欧美一区二区| 日韩精品免费视频一区二区三区| 最近的中文字幕免费完整| 亚洲,一卡二卡三卡| 又大又黄又爽视频免费| 中文天堂在线官网| 久久毛片免费看一区二区三区| 亚洲av综合色区一区| 91精品国产国语对白视频| 狂野欧美激情性xxxx| 亚洲精品久久久久久婷婷小说| 岛国毛片在线播放| 精品视频人人做人人爽| 丝袜在线中文字幕| 亚洲欧美清纯卡通| 久久精品国产综合久久久| bbb黄色大片| 免费观看av网站的网址| 尾随美女入室| 天天影视国产精品| 日韩一区二区三区影片| 国产免费一区二区三区四区乱码| 午夜激情久久久久久久| 日韩人妻精品一区2区三区| 欧美人与善性xxx| 国产又爽黄色视频| 丰满饥渴人妻一区二区三| 男男h啪啪无遮挡| 国产成人欧美在线观看 | 男男h啪啪无遮挡| 亚洲精品国产色婷婷电影| 一区二区av电影网| 欧美精品人与动牲交sv欧美| 三上悠亚av全集在线观看| 午夜久久久在线观看| 超色免费av| 中文字幕另类日韩欧美亚洲嫩草| 亚洲精品一二三| 亚洲伊人色综图| 少妇人妻精品综合一区二区| 色播在线永久视频| 成人18禁高潮啪啪吃奶动态图| 精品午夜福利在线看| 男人操女人黄网站| 制服丝袜香蕉在线| 看免费成人av毛片| 国产精品麻豆人妻色哟哟久久| 婷婷成人精品国产| 午夜福利影视在线免费观看| 国产成人av激情在线播放| 欧美精品av麻豆av| 午夜激情久久久久久久| 极品人妻少妇av视频| 午夜av观看不卡| 视频在线观看一区二区三区| 亚洲第一区二区三区不卡| 久久精品国产亚洲av涩爱| tube8黄色片| 三上悠亚av全集在线观看| 蜜桃国产av成人99| av天堂久久9| 久久久久精品久久久久真实原创| 美女大奶头黄色视频| 老司机影院毛片| 欧美黄色片欧美黄色片| 国产又色又爽无遮挡免| 午夜精品国产一区二区电影| 麻豆乱淫一区二区| 最近2019中文字幕mv第一页| 国产在视频线精品| 久久青草综合色| 亚洲欧洲精品一区二区精品久久久 | 曰老女人黄片| 伦理电影免费视频| 成人国语在线视频| 自拍欧美九色日韩亚洲蝌蚪91| 十分钟在线观看高清视频www| 18在线观看网站| 成年美女黄网站色视频大全免费| 欧美日韩综合久久久久久| av有码第一页| 岛国毛片在线播放| 不卡视频在线观看欧美| 欧美黄色片欧美黄色片| 国产精品香港三级国产av潘金莲 | 97人妻天天添夜夜摸| 亚洲四区av| 亚洲综合精品二区| 一级毛片我不卡| 亚洲,一卡二卡三卡| 亚洲情色 制服丝袜| 两个人看的免费小视频| 久久久久人妻精品一区果冻| 一级毛片电影观看| 欧美另类一区| 久久久久人妻精品一区果冻| av天堂久久9| 亚洲综合色网址| 国产精品秋霞免费鲁丝片| 汤姆久久久久久久影院中文字幕| 国产精品久久久人人做人人爽| av视频免费观看在线观看| 一级爰片在线观看| 黑人巨大精品欧美一区二区蜜桃| 欧美 日韩 精品 国产| 国产精品久久久久久精品电影小说| netflix在线观看网站| 中文字幕av电影在线播放| 国产成人啪精品午夜网站| 国产成人欧美在线观看 | 免费少妇av软件| 久久精品国产a三级三级三级| 国产深夜福利视频在线观看| 免费看av在线观看网站| 国产精品一区二区精品视频观看| 2021少妇久久久久久久久久久| 国产在视频线精品| 丰满少妇做爰视频| 日韩欧美精品免费久久| 99久久99久久久精品蜜桃| 国产成人a∨麻豆精品| netflix在线观看网站| 婷婷色综合www| 亚洲婷婷狠狠爱综合网| 咕卡用的链子| 一本大道久久a久久精品| 亚洲欧美中文字幕日韩二区| 亚洲欧美一区二区三区久久| av国产久精品久网站免费入址| 夜夜骑夜夜射夜夜干| a 毛片基地| 韩国av在线不卡| 婷婷成人精品国产| 97在线人人人人妻| 美女午夜性视频免费| 精品视频人人做人人爽| 亚洲欧美成人精品一区二区| 91精品三级在线观看| 国产一区二区三区综合在线观看| 高清视频免费观看一区二区| 国产伦理片在线播放av一区| 亚洲久久久国产精品| 亚洲欧美成人精品一区二区| 嫩草影院入口| 国产1区2区3区精品| 美国免费a级毛片| 水蜜桃什么品种好| 青春草国产在线视频| 亚洲精品一区蜜桃| 日韩 欧美 亚洲 中文字幕| 1024香蕉在线观看| 不卡视频在线观看欧美| 天天添夜夜摸| 天天操日日干夜夜撸| 久久久亚洲精品成人影院| 亚洲美女黄色视频免费看| 视频区图区小说| 日日撸夜夜添| 亚洲精品国产一区二区精华液| 多毛熟女@视频| 国产99久久九九免费精品| 国产亚洲av片在线观看秒播厂| 国产乱来视频区| av在线app专区| 女性生殖器流出的白浆| 精品国产乱码久久久久久小说| 精品一区二区三区av网在线观看 | 一级片免费观看大全| 色婷婷av一区二区三区视频| 国产一区二区 视频在线| 中文字幕精品免费在线观看视频| 国产成人欧美| 日韩欧美一区视频在线观看| 黑丝袜美女国产一区| 岛国毛片在线播放| 人体艺术视频欧美日本| 免费人妻精品一区二区三区视频| 国产极品天堂在线| 这个男人来自地球电影免费观看 | 国产精品三级大全| 丁香六月欧美| 99热全是精品| 少妇精品久久久久久久| 人妻一区二区av| 国产熟女欧美一区二区| 亚洲精品,欧美精品| 精品一区二区免费观看| 亚洲精品国产一区二区精华液| 欧美日韩视频高清一区二区三区二| 久久精品亚洲熟妇少妇任你| 欧美成人精品欧美一级黄| 久久婷婷青草| 国产精品一区二区在线观看99| 只有这里有精品99| 一边亲一边摸免费视频| 国语对白做爰xxxⅹ性视频网站| 欧美日韩精品网址| 久久人人97超碰香蕉20202| 免费看不卡的av| 欧美日韩一区二区视频在线观看视频在线| 欧美亚洲 丝袜 人妻 在线| 欧美亚洲 丝袜 人妻 在线| 日日撸夜夜添| 国产99久久九九免费精品| 欧美日韩av久久| 老熟女久久久| 高清视频免费观看一区二区| 国产熟女欧美一区二区| 午夜老司机福利片| 天美传媒精品一区二区| 99九九在线精品视频| 女人被躁到高潮嗷嗷叫费观| 亚洲人成网站在线观看播放| 美女脱内裤让男人舔精品视频| 波多野结衣一区麻豆| 国产精品人妻久久久影院| 91精品国产国语对白视频| 侵犯人妻中文字幕一二三四区| 一个人免费看片子| 黄色一级大片看看| 老鸭窝网址在线观看| 亚洲国产av新网站| 久久国产亚洲av麻豆专区| 一级a爱视频在线免费观看| 久久狼人影院| 免费观看人在逋| 亚洲色图 男人天堂 中文字幕| 国产视频首页在线观看| 超碰成人久久| 无遮挡黄片免费观看| 日韩大片免费观看网站| 欧美国产精品一级二级三级| 一边摸一边抽搐一进一出视频| 亚洲精品一区蜜桃| 久久鲁丝午夜福利片| 汤姆久久久久久久影院中文字幕| 精品午夜福利在线看| 天天躁日日躁夜夜躁夜夜| 晚上一个人看的免费电影| 亚洲国产精品国产精品| 成人亚洲精品一区在线观看| 街头女战士在线观看网站| 国产亚洲午夜精品一区二区久久| av电影中文网址| 伊人久久大香线蕉亚洲五| 一本一本久久a久久精品综合妖精| 亚洲精品aⅴ在线观看| 女人精品久久久久毛片| 精品久久久精品久久久| 飞空精品影院首页| 十八禁网站网址无遮挡| 一区二区三区精品91| 成人18禁高潮啪啪吃奶动态图| 老司机在亚洲福利影院| 亚洲欧美色中文字幕在线| 人体艺术视频欧美日本| 久久久久精品久久久久真实原创| 亚洲情色 制服丝袜| 麻豆乱淫一区二区| 午夜福利一区二区在线看| 亚洲欧美精品自产自拍| av片东京热男人的天堂| xxxhd国产人妻xxx| 久久久久国产精品人妻一区二区| 日韩一区二区视频免费看| 国产av精品麻豆| 男女边摸边吃奶| 国产精品偷伦视频观看了| 丰满乱子伦码专区| 高清欧美精品videossex| 亚洲伊人色综图| 久久国产精品大桥未久av| 成人国产麻豆网| 高清不卡的av网站| 在线观看www视频免费| 国产成人午夜福利电影在线观看| 电影成人av| 国产精品欧美亚洲77777| 女性被躁到高潮视频| 波多野结衣av一区二区av| 肉色欧美久久久久久久蜜桃| 久久久久国产一级毛片高清牌| 人妻人人澡人人爽人人| 99久久人妻综合| 久久精品人人爽人人爽视色| 99精品久久久久人妻精品| 国产亚洲最大av| 一级毛片我不卡| 国产在线一区二区三区精| 精品少妇黑人巨大在线播放| 成人黄色视频免费在线看| 日本爱情动作片www.在线观看| 999精品在线视频| 不卡视频在线观看欧美| 日本av免费视频播放| 在线观看国产h片| 一级黄片播放器| 国产成人午夜福利电影在线观看| 天天操日日干夜夜撸| 日本猛色少妇xxxxx猛交久久| 亚洲欧美一区二区三区国产| 日韩一区二区视频免费看| 欧美激情高清一区二区三区 | 国产av国产精品国产| 悠悠久久av| 欧美人与善性xxx| 日本av免费视频播放| 我的亚洲天堂| 亚洲成人一二三区av| a级片在线免费高清观看视频| 国产黄频视频在线观看| 久久ye,这里只有精品| 久久久久国产精品人妻一区二区| 日韩欧美一区视频在线观看| 色精品久久人妻99蜜桃| 国产亚洲午夜精品一区二区久久| a 毛片基地| 国产淫语在线视频| 日韩人妻精品一区2区三区| 国产精品三级大全| 国产精品一二三区在线看| 欧美日本中文国产一区发布| 国产国语露脸激情在线看| 免费日韩欧美在线观看| 一区二区日韩欧美中文字幕| 成人毛片60女人毛片免费| 国产亚洲午夜精品一区二区久久| 丰满饥渴人妻一区二区三| 老汉色av国产亚洲站长工具| 久久久久精品久久久久真实原创| 久久国产亚洲av麻豆专区| 国产野战对白在线观看| 最近最新中文字幕大全免费视频 | 99热国产这里只有精品6| 亚洲婷婷狠狠爱综合网| 精品亚洲成国产av| 一级毛片黄色毛片免费观看视频| av又黄又爽大尺度在线免费看| 国产精品久久久久成人av| 一个人免费看片子| 亚洲欧美一区二区三区黑人| 午夜久久久在线观看| 另类精品久久| 五月开心婷婷网| 一级毛片 在线播放| 高清欧美精品videossex| 日韩一区二区视频免费看| 婷婷色麻豆天堂久久| 天堂8中文在线网| 老熟女久久久| 永久免费av网站大全| 精品一区在线观看国产| a 毛片基地| 少妇被粗大的猛进出69影院| 这个男人来自地球电影免费观看 | 久久毛片免费看一区二区三区| 亚洲人成电影观看| 1024视频免费在线观看| 桃花免费在线播放| 亚洲综合精品二区| 亚洲成人免费av在线播放| 精品国产一区二区三区久久久樱花| 久久久精品国产亚洲av高清涩受| 校园人妻丝袜中文字幕| 国产在线一区二区三区精| 国产有黄有色有爽视频| 精品午夜福利在线看| 国产黄色视频一区二区在线观看| 欧美老熟妇乱子伦牲交| 亚洲色图 男人天堂 中文字幕| 亚洲 欧美一区二区三区| 日日啪夜夜爽| 欧美xxⅹ黑人| 国产激情久久老熟女| 日韩中文字幕欧美一区二区 | 久久婷婷青草| 最新的欧美精品一区二区| 青青草视频在线视频观看| 女人被躁到高潮嗷嗷叫费观| 欧美精品亚洲一区二区| 久久久精品94久久精品| av电影中文网址| 黄片播放在线免费| 精品少妇内射三级| 国产精品熟女久久久久浪| 只有这里有精品99| 欧美乱码精品一区二区三区| 精品国产国语对白av| 韩国精品一区二区三区| 老司机影院毛片| 国产国语露脸激情在线看| 青青草视频在线视频观看| 亚洲国产欧美日韩在线播放| 制服丝袜香蕉在线| 大片电影免费在线观看免费| av电影中文网址| 热99久久久久精品小说推荐| 91aial.com中文字幕在线观看| 亚洲国产欧美网| 国产又爽黄色视频| 国产一区二区三区av在线| 美国免费a级毛片| 久久精品aⅴ一区二区三区四区| 亚洲av电影在线进入| 国产精品一国产av| 午夜久久久在线观看| 性色av一级| 国产免费现黄频在线看| av一本久久久久| 高清在线视频一区二区三区| 老鸭窝网址在线观看| 啦啦啦中文免费视频观看日本| 国产日韩欧美在线精品| 狠狠婷婷综合久久久久久88av| 又大又黄又爽视频免费| 久久人人爽av亚洲精品天堂| 热re99久久国产66热| 99久久99久久久精品蜜桃| 亚洲欧美清纯卡通| 在线 av 中文字幕| 在现免费观看毛片| 老司机亚洲免费影院| av不卡在线播放| 咕卡用的链子| 水蜜桃什么品种好| 久久久国产欧美日韩av| 欧美亚洲 丝袜 人妻 在线| 制服人妻中文乱码| av在线app专区| 亚洲一区二区三区欧美精品| 一二三四中文在线观看免费高清| 狠狠精品人妻久久久久久综合| 十八禁网站网址无遮挡| 亚洲av中文av极速乱| 国产精品三级大全| 97精品久久久久久久久久精品| 国产日韩欧美亚洲二区| h视频一区二区三区| 午夜福利,免费看| 国产免费现黄频在线看| 成人手机av| 久久久久精品国产欧美久久久 | 日本一区二区免费在线视频| 色播在线永久视频| 日韩制服丝袜自拍偷拍| 日韩电影二区| 国产在线免费精品| 久久精品人人爽人人爽视色| 十八禁网站网址无遮挡| 男女床上黄色一级片免费看| 深夜精品福利| 欧美日本中文国产一区发布| 国产一区二区三区综合在线观看| 亚洲久久久国产精品| 一本一本久久a久久精品综合妖精| kizo精华| 99国产精品免费福利视频| 国产探花极品一区二区| 久久久久精品人妻al黑| 国产成人精品福利久久| 大香蕉久久网| 欧美最新免费一区二区三区| 欧美最新免费一区二区三区| 天天添夜夜摸| 欧美日韩一区二区视频在线观看视频在线| 国产男女超爽视频在线观看| 99精国产麻豆久久婷婷| 中文字幕人妻丝袜制服| 制服丝袜香蕉在线| 免费高清在线观看视频在线观看| 一区二区三区精品91| 国产一区二区三区av在线| av福利片在线| 国产一区亚洲一区在线观看| 国精品久久久久久国模美| 999精品在线视频| 国产毛片在线视频| a级毛片在线看网站| 色综合欧美亚洲国产小说| 91精品伊人久久大香线蕉| 国产日韩欧美亚洲二区| 制服丝袜香蕉在线| 成年女人毛片免费观看观看9 | 99久久人妻综合| 80岁老熟妇乱子伦牲交| 国产成人精品福利久久| 欧美日韩亚洲综合一区二区三区_| 美女大奶头黄色视频| 国产精品国产三级专区第一集| 久久久久久久精品精品| 在现免费观看毛片| 嫩草影视91久久| 欧美黄色片欧美黄色片| 天堂中文最新版在线下载| 91aial.com中文字幕在线观看| 国产男女内射视频| 久久 成人 亚洲| 少妇 在线观看| 人人妻,人人澡人人爽秒播 | 亚洲情色 制服丝袜| 超碰97精品在线观看| 国产在线免费精品| 日韩大片免费观看网站| 老司机在亚洲福利影院| 亚洲av日韩精品久久久久久密 | 国产精品99久久99久久久不卡 | 精品免费久久久久久久清纯 | 久久天躁狠狠躁夜夜2o2o | 久久精品亚洲熟妇少妇任你| 亚洲欧美激情在线| 亚洲国产成人一精品久久久| 久久精品国产a三级三级三级| 91成人精品电影| 久久精品aⅴ一区二区三区四区| 亚洲人成网站在线观看播放| 精品少妇黑人巨大在线播放| 女人久久www免费人成看片| 丝袜美腿诱惑在线| 这个男人来自地球电影免费观看 | av免费观看日本| 国产一区二区 视频在线| 国产淫语在线视频| 久久天躁狠狠躁夜夜2o2o | 一边亲一边摸免费视频| 91老司机精品| 丰满少妇做爰视频| 国产日韩欧美视频二区| 亚洲av成人不卡在线观看播放网 | 在线亚洲精品国产二区图片欧美| 午夜福利免费观看在线| 久久久亚洲精品成人影院| 免费观看a级毛片全部| 国产在线免费精品| 亚洲在久久综合| 国产爽快片一区二区三区| 亚洲成人国产一区在线观看 | 99精国产麻豆久久婷婷| 美女扒开内裤让男人捅视频| 蜜桃国产av成人99| 成人黄色视频免费在线看| 九色亚洲精品在线播放| 纵有疾风起免费观看全集完整版| 黄色怎么调成土黄色| 成年人免费黄色播放视频| 日韩成人av中文字幕在线观看| 日本黄色日本黄色录像| 亚洲 欧美一区二区三区| www.av在线官网国产| 校园人妻丝袜中文字幕| 国产毛片在线视频| 亚洲情色 制服丝袜| 男女床上黄色一级片免费看| 黑人欧美特级aaaaaa片| 深夜精品福利| 日本av手机在线免费观看| 深夜精品福利| xxxhd国产人妻xxx| 在线精品无人区一区二区三| 久久热在线av| 99国产精品免费福利视频| av在线播放精品| 五月开心婷婷网| 久久韩国三级中文字幕| 亚洲中文av在线| 国产1区2区3区精品| 国产一区二区激情短视频 | 少妇精品久久久久久久| 亚洲美女黄色视频免费看| 亚洲国产欧美日韩在线播放| 99香蕉大伊视频| 麻豆乱淫一区二区| 麻豆av在线久日| 欧美国产精品va在线观看不卡| 亚洲av日韩在线播放| 精品少妇内射三级| 狠狠婷婷综合久久久久久88av| 久久精品亚洲熟妇少妇任你| 国产在线免费精品| av卡一久久| 久久久久精品人妻al黑| 丰满少妇做爰视频| 欧美日韩综合久久久久久|