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ABSTRACT Call and Response Experiments in Sampling the Environment

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In ACM SenSys’04pp. 25-38, November 3-5, 2004, Baltimore, MarylandCall and Response:Experiments in Sampling theEnvironmentMaxim A.Batalin1,2,Mohammad Rahimi1,3,Y an Yu1,3,Duo Liu1,4,Aman Kansal1,4,Gaurav S.Sukhatme1,2,William J.Kaiser1,4,Mark Hansen1,5,Gregory J.Pottie1,4,Mani Srivastava1,4,Deborah Estrin1,31Center for Embedded Networked Sensing,UCLA,Los Angeles,CA90095 2Department of Computer Science,USC,Los Angeles,CA900893Department of Computer Science,UCLA,Los Angeles,CA900954Department of Electrical Engineering,UCLA,Los Angeles,CA900955Department of Statistics,UCLA,Los Angeles,CA90095{maxim@,mhr@,yanyu@,duoliu@,gaurav@,kaiser@,cocteau@,pottie@,destrin@}ABSTRACTMonitoring of environmental phenomena with embedded net-worked sensing confronts the challenges of both unpredictable variability in the spatial distribution of phenomena,coupled with demands for a high spatial sampling rate in three di-mensions.For example,low distortion mapping of critical solar radiation properties in forest environments may re-quire two-dimensional spatial sampling rates of greater than 10samples/m2over transects exceeding1000m2.Clearly, adequate sampling coverage of such a transect requires an impractically large number of sensing nodes.This paper describes a new approach where the deployment of a com-bination of autonomous-articulated and static sensor nodes enables sufficient spatiotemporal sampling density over large transects to meet a general set of environmental mapping demands.To achieve this we have developed an embedded networked sensor architecture that merges sensing and articulation with adaptive algorithms that are responsive to both variability in environmental phenomena discovered by the mobile sensors and to discrete events discovered by static sensors.We be-gin by describing the class of important driving applications, the statistical foundations for this new approach,and task allocation.We then describe our experimental implementa-tion of adaptive,event aware,exploration algorithms,which exploit our wireless,articulated sensors operating with de-terministic motion over large areas.Results of experimental Permission to make digital or hard copies of all or part of this work forpersonal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on thefirst page.To copy otherwise,to republish,to post on servers or to redistribute to lists,requires prior specific permission and/or a fee.SenSys’04,November3–5,2004,Baltimore,Maryland,USA. Copyright2004ACM1-58113-879-2/04/0011...$5.00.measurements and the relationship among sampling meth-ods,event arrival rate,and sampling performance are pre-sented.Categories and Subject DescriptorsI.2.9[Artificial Intelligence]:Robotics—autonomous ve-hicles,sensors;C.3[Computer Systems Organization]: Special-Purpose and Application-Based Systems—real time and embedded systems; C.2.4[Computer Communica-tion Networks]:Distributed Systems—distributed applica-tions;J.7[Computer Applications]:Computers In Other Systems—command and control,process control,real time General TermsAlgorithms,Design,Experimentation,Management,Mea-surement,Performance,ReliabilityKeywordsmobile robotics,sensor network,adaptive sampling,task al-location,distributed1.INTRODUCTIONA broad class of environmental monitoring objectives in fundamental science,environmental resource management, and public health protection demand distributed sensing ca-pabilities[1].For example,the potential global impact of climate change creates a requirement for understanding the interaction between the forest canopy and the atmosphere. Understanding critical phenomena,for example the nature of carbonflux from the atmosphere to forest biomass,re-quires direct experimental characterization of spatiotempo-rally distributed phenomena[2].This includes measurement of solar radiation(driving fundamental photosynthesis and growth),atmospheric water vapor,temperature,and chem-ical composition.Distributed measurements are requiredsince these phenomena are sensitive to(and in turn influ-ence)the heterogeneous structure of the natural environ-ment.1.1High Fidelity Environmental FieldMonitoringMany environmental monitoring applications share the challenge for achieving highfidelity characterization capa-bility for environmentalfield variables.This is convention-ally achieved with high spatiotemporal sampling rate.For example,solar radiation and atmospheric properties must be mapped in natural environments over a spatial extent comparable to those of the forest structure(where a mea-surement transect height may exceed50m and the width re-quired to adequately span the heterogeneous structure may exceed100m).At the same time,some phenomena dis-play a characteristic spatial variability on the scale of cen-timeters,reflecting thefine scale of natural structure and important phenomena.Thus,in considering highfidelity sampling for these phenomena over a two-dimensional plane with the required spatial extent of over1000m2and resolu-tion of greater than10sample/m2requires an impractically large number of sensing elements with10,000measurement points.Measurement distortion may also result from an improper spatial-sampling distribution(specifically due to mismatch between the spatial structure of the phenomena and sensor node placement).This introduces yet further challenges for environmental monitoring by limiting the capability for dis-tributed sensor node deployment planning.Specifically,the inherent unpredictability and variability of environmental structure and phenomena precludes the possibility of achiev-ing adequate spatial sampling density by advance planning. The conventional solution for reducing this source of dis-tortion has been to increase spatiotemporal sampling rate. However,this results in excessive disturbance to the sensed environment.Clearly,fixed sensor distributions,alone,are not adequate for many important environmental monitoring phenomena.Since it is the presence of dynamic physical structures(for example the growth of foliage or environmental erosion)that lead to unpredictable and variable sensor coverage require-ments,sensor networked systems must exploit wide-ranging and high-spatial-resolution sensor node mobility in order to ensure adequate coverage.1.2Coordinated Fixed and Mobile SensingNodes for High Fidelity CoverageThis paper describes a new Networked Infomechanical Systems(NIMS)architecture that combines bothfixed and mobile sensor nodes to achieve a spatiotemporal environ-ment coverage that is dramatically advanced over that of either system alone.Mobility allows the networked sensor system to always seek the most efficient spatiotemporal sam-pling distribution to achieve a specified accuracy of environ-mental variable reconstruction.Further,mobility also per-mits the NIMS system to respond to initially unpredictable and variable environmental evolution.While it is shown here that this architecture enables mo-bile sensors that adapt to variable environments,their cov-erage at any instant is restricted to their effective area of regard.Thus,temporal measurement distortion will appear in the presence of rapidly changingphenomena.Figure1:The NIMS system exploits suspended aerial infrastructure to enable sustainable and pre-cise transport of mobile nodes within complex three-dimensional environments.Phenomena are mapped within a spatially extensive transect where sensing nodes have precise command over horizontal and vertical motion.The NIMS architecture introduces coordination between fixed and mobile devices.Sparsely distributedfixed sensor nodes,each remaining vigilant over a localized area of re-gard,provide a distributed event detection service for the combined network.We experimentally demonstrate that proper task allocation of mobile devices permits both high spatial sampling rate and low event response time.The NIMS architecture coordinating mobile andfixed de-vices provides the ability to accommodate highfidelity sens-ing with limited resource constraints.For example,typi-cal environmental monitoring characterization tasks require costly sensing modalities where the cost is measured in terms of energy,mass,volume,monetary expense,or other value). Examples in natural environmental monitoring are tasks that require gas phase analysis or multi-spectral imaging. Thus,while the demand for dense spatial coverage is high, the resource cost associated with supporting a vast num-ber of individual sensor elements for adequate spatiotempo-ral coverage may be excessively high and the disturbance to the environment due to their introduction may be pro-hibitive.However,it is demonstrated here that the com-bination of low spatial-densityfixed sensors(providing the required assets for mobile node task allocation)and highly capable mobile devices results in both adequate spatiotem-poral coverage as well reduced overall resource cost.The NIMS architecture for mobile and adaptive sampling must include the attributes of1)wide range mobility within three-dimensional volumes,2)precise and high resolution position and orientation control,3)long term autonomous, sustainable operation,and4)mobile operation with minimal disturbance to the environment with regards to physical sur-faces or structures or to emitted acoustic noise.The combi-nation of these requirements are met by NIMS infrastructure-supported mobility,as shown in Figure1.Here,infrastruc-Figure2:Map of solar radiation intensity obtained in a forest ecosystem by a NIMS system transport-ing a light intensity sensor within the canopy.So-lar radiation intensity is indicated in contours as it varies spatially according to horizontal and vertical sensor displacement.ture is adapted to the environment and uniquely enables each of the attributes above.1.3Motivating ApplicationAn example of a fundamental phenomenon that controls processes within natural ecosystems is theflux of solar ra-diation.Solar radiation is spatiallyfiltered by the complex ecosystem structure and ultimately controls photosynthesis and growth[3].The characterization of solar radiation spa-tiotemporal patterns is of primary interest to understanding growth,evolution,and global change trends[2].NIMS provides thefirst method for extensive spatiotem-poral mapping of phenomena,including solar radiation in the ecosystem.Figure2displays a map of the photosyn-thetically active radiation(PAR)spectral region of solar il-lumination.This two dimensional PAR map was acquired by a NIMS system operating in a mixed conifer forest within the James San Jacinto Mountain Reserve[4].Note the char-acteristic scale of variability for solar radiation is less than 1m with a need to characterize a spatial extent that equals the dimensions of the forest canopy itself.Figure3shows a NIMS system deployed in the forest re-serve for continuous operation.This system includes sup-porting cable infrastructure,a horizontally mobile embed-ded computing platform payload,image sensing,and a ver-tically mobile meteorological sensor system carrying water vapor,temperature,and PAR sensing capability.Wireless networking is incorporated to linkfixed nodes(distributed on the surface or suspended)with the vertically and horizon-tally mobile elements.The NIMS infrastructure is elevated in the environment and thus lies above environmental ob-stacles to solar radiation.NIMS systems have exploited this and have been deployed with battery energy sources supplied solely by solar photovoltaic cell energy harvesting.Energy is transported as needed along the NIMS cable infrastruc-ture by an articulated cable system.The NIMS system is deployed in a transect of length70m and average height of 15m with a total area of over1,000m2.The experimental NIMS system operates with a linear speed range for node motion of0.1to1m/second.Thus,the Figure3:A NIMS system deployed at the James San Jacinto Mountain Reserve ().This image shows the NIMS cable infrastructure,horizontal transport node(carrying an embedded computing platform, image sensor,and vertical transport control,and vertically mobile meteorological sensor node.time required to map an entire1,000m2transect with0.1 m2resolution with a simple’raster’regular scanning sched-ule will exceed104to105seconds.Phenomena that vary at a characteristic rate exceeding this scanning rate may not be accurately mapped.However,without knowledge of the spatial variability of measured phenomena,the sampling system must sample at high spatial rate in order to achieve low measurement distortion.1.4Event-Aware Adaptive Sampling Systems Thus,as will be described below,the requirements for the measurement system to respond to variable environmental structure and to accurately map environmental variables at minimum time and resource cost has lead to the introduc-tion of a NIMS architecture that combines mobile adaptive sampling with task allocation methods controlling node mo-tion.These rely onfixed sensor networks as well as mobile elements to ensure that measurement is most efficient and then to focus exploration and mapping at the point in a transect for which change in environmental variables has occurred.This paperfirst describes the Adaptive Sampling,Task Allocation,and also Event-Aware Adaptive Sampling meth-ods and algorithms.A new Fidelity Driven Sampling adap-tive method is introduced and tested with experimental data. Then,experimental methods including system hardware and software architecture are discussed.This includes descrip-tion of a laboratory-scale NIMS system(NIMS-LS)employed for algorithm and verification of combined software and mo-bile embedded hardware system implementations.Finally, experimental results obtained with NIMS-LS demonstrat-ing the marked benefits of combined adaptive sampling and task allocation are described.It is important to note that Related Work is discussed in each Section below.2.ADAPTIVE SAMPLING ALGORITHM Sampling configurations(e.g.sample density)for em-bedded networked sensor systems are generally limited by considerations related to the availability of communication channel resources or energy constraints.This has spawned a number of recursive estimation techniques that make de-cisions in a hierarchical fashion[5].For example,local clus-ter heads accumulate information and propagate this ac-cording to a value metric.By introducing infrastructure-supported mobility,it is now possible to characterize envi-ronmental variablefields with much greater spatiotemporal density than was previously practical.While this enables new applications,it also creates new choices for estimators and for optimizing sampling.A new algorithm,Fidelity Driven Sampling will be described that exploits mobile sam-pling tofirst stratify the environment into regions requiring varying degrees of sample density,then samples in these re-gions while tracking estimated sampling error(fidelity).Fi-delity Driven Sampling has the advantage over conventional raster(or other pre-planned sampling strategies)of actively seeking to minimize error without prior knowledge of the variablefield.2.1MethodologyOptimal designs for estimators have been studied exten-sively in the statistics literature;see[6,7,8,9].These approaches often assume a parametric form for the esti-mate.Given a generic learning algorithm,the problem of optimal design is much harder.Optimal designs for sim-ple kernel methods were studied by[10]where it was found that the best placement of points depends on the second derivative of the function;places with high curvature should have relatively more points thanflat regions.[11]study designs for local linear smoothing,ultimately proposing a batch-sequential algorithm.It should be noted that image compression methods are not directly applicable here since these are based on post-processing of acquired data,whereas fidelity-driven adaptive sampling specifically selects actual measurement sample sites according to data characteristics. The NIMS adaptive sampling algorithms are designed with the goal of sequentially constructing a sampling pattern where at each step,samples are chosen that improve the estimate of the sensedfield.[12]studies such sampling plans in the context of kernel smoothing.Points are added one at a time, chosen so as to reduce an estimate of the integrated squared error.[13,14]study the same problem,but with neural networks as their estimators.They use the term“active learning”to describe the adaptive sampling process.[13] selects points to minimize an information criterion,while [14]studies the mean squared error.In each case,it is as-sumed the bias is negligible and attention is focused on the variance of the estimator.[14]also introduces the notion of path constraints on the samples,a topic that will be impor-tant in our ultimate deployment of adaptive sampling.The Adaptive Sampling algorithm reported here,relies on a mean squared error estimation approach and an under-lying learning algorithm based on a local linear smoother. Further,a variable bandwidth is assumed based on nearest neighbors.This algorithm,termed Fidelity Driven Sampling Algorithm1Pseudocode description of the Fidelity Driven Sampling algorithmC:rectangular cell(scanning area)PREDICT FRAME(C)while there is a strata with error higher than threshold doextract strata with highest bias erroradd sampling pointsif current strata mean square error>threshold error thencompute vertical and horizontal mean square errorif horizontal bias error>vertical bias error thendivide strata horizontallyadd stratum to the working queueelsedivide strata verticallyadd stratum to the working queuereturn local polynomialfit(FDS),attempts to reduce mean square error at each sam-pling point by adjusting point density and location.It will be further described and evaluated below.Mean squared error can be decomposed into a bias com-ponent and a variance component.In our application of solar illumination mapping,measurement noise is a negli-gible fraction of the overall signal,and hence our greatest concern will be bias.In other applications,bias may not be dominant,and other estimation methods will be needed. In these other regimes,attention will likely focus on tasks other thanfield estimation;or if a snapshot of thefield is de-sired,a longer learning process involving repeated measures in time will be required.This is all the subject of future work.Throughout the sampling process,FDS maintains an es-timate of thefield being observed.In this paper,a local linearfitting routine is chosen,with its bandwidth varying to include afixed number of nearest ing this estimate,the FDS loop identifies regions or strata exhibit-ing a high degree of misfit.At each step in the sampling process,FDS adds points to that stratum with the largest error.In so doing,the FDS algorithm reduces the distance between neighbors and effectively lowers the bandwidth of the local linearfit within the stratum.The algorithm con-tinues adding points to poorfitting strata until either an overall sample budget is exhausted or a desiredfidelity limit is achieved.Local linearfits have been chosen because these can tie the notion of sampling with the resolution of structures ex-pected in the variablefield.In principle,any nonparametric procedure could be employed including thin plate splines or other radial basis functions.2.2The Fidelity Driven Sampling System Algorithm1shows pseudo code description of the Fidelity Driven Sampling adaptive algorithm.The NIMS robotic system continues adaptive sampling for the entire period of operation in the environmental mapping state.The FDS al-gorithm then calls the procedure P REDICT F RAME(C) which returns the estimate of the environmental variable field.The algorithm follows a procedure of stratifying aΠ(x,y,z)(c)The3D-case(d)The 3D-case projectiononto the transectFigure 4:Different SN topologies and corresponding projections onto the transect.sampling region and according to observed measurements in the region and for each strata,adjusting the number of sampling points.In addition,in the discussion below,the performance of Raster Scanning,Stratified Random Sampling,and the new Fidelity Driven Sampling algorithms are compared through simulation of each sampling method with actual experimen-tal data.It is important to note that Fidelity Driven Sam-pling provides an autonomous system that seeks to assign sampling points to achieve a specified threshold error value (or a specified stratification rank).By estimating error mag-nitudes,Fidelity Driven Sampling can actively adapt to achieve a sampling fidelity objective.This differs fundamentally from raster scan methods that are not informed of resid-ual errors.It is most important to note that Fidelity Driven Sampling,being adaptive,requires no prior knowledge of the environmental variable field characteristics and will rather report these characteristics.The successful demonstration of this is enclosed in Section VI.This P REDICT F RAME (C )procedure starts by insert-ing a root stratum in a queue.Here,the root stratum corresponds to the entire scanning area C (transect)-the entire region of study.P REDICT F RAME (C )then ini-tiates a loop that extracts strata with highest product of mean square error and area.It determines the sampling points to be added to the strata and then the mobile sen-sor moves to visit those points and sample corresponding data.After sampling points in the strata,it performs a lo-cal linear kernel regression and reevaluates the estimate of the phenomenon.Error is computed in terms of the abso-lute difference between estimated and sampled values.If the computed error in the strata falls below a threshold,P REDICT F RAME (C )exits the inner loop and proceeds to examine more strata or otherwise divides the strata into horizontal or vertical substrata depending on which division leads to the greatest reduction in error.Following Fidelity Driven Sampling operation (or raster scanning data acquisition)the returned variable field with its set of sample points was then supplied to a standardestimation algorithm (that performs an interpolation)and returns an environmental field map.This final result is referred to as a reconstruction of the variable field.Ex-perimental results and evaluation of reconstructions will be discussed below for varying field characteristics.Note that throughout the paper error metric used is integrated square error across the strata which represents the degree misfit of that strata but one can use more complex error functions that incorporate the cost of mobility.3.TASK ALLOCATION ALGORITHMTask Allocation (TA)is the problem of assigning available resources to tasks.For a comprehensive overview of TA for-mulations please refer to [15].There are two major subdi-visions:offline and online.The offline TA is the problem of assigning resources to different tasks if certain information (e.g.the distribution of task arrival times,relative task pri-ority)is known a priori .The assignment process proceeds offline.The offline TA problem,in its most general form,is equivalent to the conjunctive planning problem [16]which is NP-Complete.Our focus here is on online task allocation.In online TA,all information about the tasks becomes available only upon task arrival.The assignment of resources to tasks must be computed in real time.It has been shown [17]that greedy al-gorithms provide good approximate solutions to online TA.It has also been shown [17]that in some cases the greedy online TA solution is within a bounded limit of the optimal solution obtained by offline TA.Following the model in [18],we think of task assignment occurring in decision epochs .A decision epoch is a period of time during which only the tasks which have arrived since the end of the previous epoch are considered for assignment.Increasing the decision epoch to infinity converts the online TA into the offline TA prob-lem.We model the NIMS system as an online TA problem,since it is designed for real-life autonomous field applications in dynamic environments.Our work is related to the body of work on the problem of online multi-robot task allocation (MRTA).For an overview(a)(b)eAlgorithm2Event-Aware Adaptive Sampling Algorithm (x t,y t,z t):coordinates of current taskC t:a cell corresponding to current taskAS ON:flag,turns Adaptive Sampling Module on/offINSERT(T a,t):insert task t into unassigned tasks set T a based on time of arrivalCOMP UT E T ASK INF O(t):determines goal position and corresponding cell for tMOV E T O(x t,y t,z t):delivers the robot to given posi-tionP REDICT F RAME(C t):Algorithm1Task Allocation Module:if there is a new task t thenINSERT(T a,t)if T a=0andAS ON==false thent=T a(0)(x t,y t,z t,C t)=COMP UT E T ASK INF O(t)MOV E T O(x t,y t,z t)AS ON=trueAdaptive Sampling Module:if AS ON==true thenP REDICT F RAME(C t)AS ON=falseNote that a2D system is sometimes preferred because it is easier to setup in thefield and for some applications a 2D perspective is enough.As an example,consider study-ing sunlight intensity shining through a forest canopy.In this case a sensor network with illumination detectors can be placed on the ground.Suppose node k discovered an interesting reading(say an abnormal light value).The TA system then would guide the robot towards the goal point on l r computed by P ROJ l r.The mobile node then can study appropriate slice C k.The general3D-case system is investigated in this paper.3.2The Task Allocation SystemOur system consists of two algorithms-one running on the robot and another within the network.First we describe the algorithm that runs on every static node.It consists of two parts-Task Generation and Task Management.1.Task generation.If node’s sensor reading is abovea threshold a task is created and a notification mes-sage NEW TASK is broadcasted.The notification mes-sage contains the notifying node’s ID,location,sensor reading,and time stamp(time when the task was gen-erated).2.Task management.Each node maintains a set ofcurrently active tasks T a and non-active tasks T na.Ifa node receives NEW TASK message and the task is new(it is neither in T a nor in T na)then T a is updated and the message is redirected to the network.As we discuss later,when the robot fulfills its assigned task it sends a TASK DONE message containing the id of the node that generated the task and task’s time stamp.SensorSensorFixedMobileFigure6:The NIMS system architecture combines the adaptive sampling and Task Allocation(TA)al-gorithms providing interfaces to actuation and sens-ing.While the adaptive sampling relies on mo-bile sensor inputs,TA responds to events emanating from distributedfixed sensors.The adaptive sam-pling and TA algorithms are coordinated and hosted by the embedded Stargate platform shown in Fig-ure7If the task is in T a,then it is removed from T a,added to T na and the message is redirected to SN.Note that in practice the sizes of T a and T na arefixed(20 and30in our experiments).Both sets can be overwritten, so where is no overflow problem.At the same time,the size of T a should be set with care to avoid loss of data about currently active tasks and potentially failing to propagate this data to the rest of the system.Our system is a special case of the TA methodology de-scribed above-with only one resource(mobile node)for task assignment.We consider the problem of assigning tasks one at a time.In this case the greedy assignment is obviously optimal.Consider task assignment Equation1.Since there is only one mobile node,the next task with highest priority (according to criterion C)is assigned to the mobile node, no matter what the mobile node’s utility function might be. The task prioritization criterion selected here is C based on the time stamp associated with every task.The algorithm running on the mobile node is as follows.All incoming new tasks(specified by NEW TASK message) are sorted according to the criterion C(tasks with smaller time stamp get priority-a FIFO policy)and stored in a set of currently active tasks T a.Note that since the system does not have any prior knowledge about the spatiotemporal variation of event arrival,the priority of events are taken as uniform.Therefore,FIFO scheduling is appropriate.In our future work,as we will learn more about the nature of the phenomenon,we plan to incorporate that knowledge into the task allocation process.When the mobile node becomes available for reassignment,the task of highest priority is extracted from T a and assigned to it.Next,based on the task information the mobile node needs to compute a goal point.If the task’s position is p then the goal position will be P ROJ l r(p)in2D-case and P ROJΠr(p)in3D-case(see Fig-。

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英文摘要怎么写

英文摘要怎么写

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• Eliminating all repetitions.
Sometimes a statement is repeated for emphasis. This is not necessary in a summary
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Using phrases instead of clauses or sentences.
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Making a fair copy:
In order to escape the bees, the driver drove away quickly and after some time arrived in London. Parking his car outside a hotel, he went in to have a drink. When he was told that his car was covered with bees, the driver telephoned the police. A bee-keeper was sent for and soon found the queen bee near the wheels. He took the queen and the bees home in a box and the driver then drove away in peace. (80 words)
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In a short time, the beekeeper arrived. He found the unwelcome passenger hidden near the wheels at the back of the car. Very grateful to the motorist for this unexpected gift, the keeper took the queen and her thousands of followers home in a large box. Equally grateful, the motorist drove away in peace, at last free from the “black cloud” which had hung over his car (192 words)
CpG ODN协同小鼠肝癌细胞裂解物对小鼠原位移植性肝癌的抑制作用研究

CpG ODN协同小鼠肝癌细胞裂解物对小鼠原位移植性肝癌的抑制作用研究

doi：10.3969/j.issn.l000484X.2020.23.006CpG ODN协同小鼠肝癌细胞裂解物对小鼠原位移植性肝癌的抑制作用研究①孙鹏②李欢欢王卫芳窦瑶周晓晶(长春中医药大学临床医学院，长春130117)中图分类号R730.51文献标志码A文章编号1000-484X（2020）23-28424）4[摘要]目的:研究CpG ODN与小鼠肝癌细胞（H22）裂解物（TCL）联用对小鼠原位移植性肝癌的抑制作用及机制初探。

方法:制备H22细胞裂解物和建立BALB/c小鼠原位移植性肝癌模型。

将模型鼠随机分为四组PBS、TCL、CpG ODN和CpG ODN+TCL,观察各组小鼠肿瘤生长情况及小鼠的生存期。

采用细胞毒性淋巴细胞（CTL）杀伤实验观察各组小鼠淋巴细胞对肝癌细胞的杀伤情况。

用ELISA方法检测CpG ODN协助TCL诱导Th1型细胞因子分泌水平。

结果:与其他组相比CpG ODN+TCL明显抑制了肿瘤生长、延长了荷瘤鼠的生存期（P<0.05）o体内外实验显示，CpG ODN能协同TCL刺激小鼠产生Th1型细胞因子和诱导CTL反应。

结论：以CpG ODN为佐剂的TCL具有抑制小鼠原位移植性肝癌的作用，且这种作用可能由于CpG ODN协同TCL诱导小鼠产生的Th1型细胞因子和CTL反应实现的。

[关键词]肿瘤细胞裂解物;免疫刺激性寡聚脱氧核苷酸;肝癌;Thl型免疫反应Effect of CpG ODN on assisting TCL from liver cancer cells to induce immune response against orthotopic-transplantated liver cancer in miceSUN Peng,LI Huan-Huan,WANG Wei-Fang,DOU Yao,ZHOU Xiao-Jing.Clinical Medical College,Changchun University of Traditional Chinese Medicine,Changchun130117,China[Abstract]Objective:To study the effect of CpG ODN on assisting TCL from H22cells to induce immune response against or-thotopic-transplantated liver cancer in mice and to explore the mechanism.Methods:We prepared TCL from H22cells and built BALB/c mice with orthotopic-transplantated liver cancer.The model mice were injected divided into PBS group,TCL group,CpG ODN group and CpG ODN+TCL group randomly.The mice were monitored for tumor growth and recoding the survival.CTL killing experiment was used to observe the killing of liver cancer cells by lymphocytes in each group.ELISA was used to detect CpG ODN assisting TCL to induce Th1cytokine secretion level.Results:Compared with other groups,CpG ODN+TCL significantly inhibited the growth of orthotopic transplantation liver cancer cells and prolong survival in mice（P<0.05）.In vivo and vitro experiments,CTL response and Th1-biased cytokine also induced by CpG ODN adjuvanted TCL.Conclusion:TCL with CpG ODN as an adjuvant has the effect of inhibiting orthotopic liver cancer in mice,and this effect may be realized by CpG ODN and TCL-induced Th1cytokine and CTL response in mice.[Key words]TCL；CpG ODN;Liver cancer；Th1immune response肝癌是最常见的肝脏原发恶性肿瘤，其致死率极高［1］。

一篇典型的响应曲面法优化培养基的文献

一篇典型的响应曲面法优化培养基的文献

Process Biochemistry 40(2005)681–690Direct fermentation of starch to l (+)lactic acid in SSF by Lactobacillusamylophilus GV6using wheat bran as support and substrate:medium optimization using RSMB.J.Naveena a ,Md.Altaf a ,K.Bhadrayya b ,S.S.Madhavendra c ,Gopal Reddy a ,∗aDepartment of Microbiology,Osmania University,Hyderabad 500007,AP ,Indiab Swaroop Tech.Consultancy,Secunderabad 500003,AP ,Indiac Indian Institute of Chemical Technology (IICT),Hyderabad 500007,AP ,IndiaReceived 6November 2003;received in revised form 2January 2004;accepted 30January 2004AbstractWheat bran is an underexploited cheap byproduct obtained during the milling process of wheat.The ability of Lactobacillus amylophilus GV6to hydrolyze raw starch in wheat bran to produce l (+)lactic acid was studied in solid state fermentation (SSF),opening a novel method for the utilization of these agricultural byproducts.l (+)Lactic acid production by L.amylophilus has been reported by various groups from processed and unprocessed starch but there are no reports available from natural starches in SSF.All amylolytic wild strains reported so far have high yield efficiencies at low substrate concentrations whereas at high substrate concentrations lactic acid production was low.L.amylophilus GV6showed high yield efficiency at high substrate concentration in SSF.To improve and optimize l (+)lactic acid production by L.amylophilus GV6,response surface methodology (RSM)using central composite rotatable design was adopted in SSF.MINITAB-13was used for planning the experiments,data analysis,contour diagrams and response optimizations.The optimum media composition was obtained as peptone,0.9%;yeast extract,0.88%;tri-ammonium citrate,0.379%;NaH 2PO 4·2H 2O,0.769%and Tween-80,0.30ml.Under these conditions a maximum of 36g of lactic acid was produced per 100g of wheat bran having 54g of starch.The organism showed 90%yield efficiency based on substrate consumed.Successful optimization of the selected ingredients,led to 100%increase in lactic acid production,i.e.from 18to 36g.Due to its high potentiality in conversion of starch to l (+)lactic acid,L.amylophilus GV6can be exploited industrially for developing a novel technology using inexpensive renewable resources.©2004Elsevier Ltd.All rights reserved.Keywords:Lactobacillus amylophilus ;Wheat bran;Lactic acid;RSM;SSF1.IntroductionLactic acid fermentation has received extensive attention for a long time [1,2].Lactic acid has wide applications in food,pharmaceutical,leather,textile industries and as a chemical feed stock.It has two enantiomers l (+)and d (−)of which l (+)isomer is used by human metabolism due to the presence of l lactate dehydrogenase and is preferred for food.In view of recent developments l (+)lactic acid is in great demand due to its use as starting material to produce biodegradable polymers used in medical,industrial and con-sumer products [3–6].The lactic acid market is expected to∗Corresponding author.Tel.:+91-4027682246;fax:+91-4027090020.grow 8.6%annually and in US alone its demand is expected to be 49,600MT in future [7,8].Lactic acid is produced by chemical synthesis and mi-crobial fermentation.Chemical synthesis results in racemic mixture of lactic acid whereas specific stereo isomeric form can be obtained by microbial fermentation [9,10].Refined sugars,although expensive are the most commonly used substrates for producing lactic acid by fermentation [2].Lactic acid is also produced from cheaper substrate like starch in two-step process of saccharification followed by Lactobacillus fermentation [10].Use of a microorganism which directly ferments starch to l (+)lactic acid would eliminate saccharification cost and Lactobacillus amy-lophilus GV6is one such organism having high production efficiency in submerged fermentation as reported earlier682 B.J.Naveena et al./Process Biochemistry40(2005)681–690Solid-state fermentation(SSF)technology has gained re-newed attention from industry,due to its advantages andreported to be the most appropriate process for developingcountries[14,15].Use of SSF in direct conversion of starchto l(+)lactic acid is not yet exploited.It is known that mini-mal preprocessing and supplementing of inorganic nutrientsto inexpensive substrates,originated from waste materials(starch or cellulosic substances),would offer great advan-tage to increase lactic acid production[16–18].A number ofcheaply available raw substrates(brans)have been screenedfor single step production of lactic acid by L.amylophilusGV6in SSF.Wheat bran was selected as the best substrateand support in SSF in laboratory level fermentations[19].In the present study,to achieve success in faster yieldimprovements,the media components peptone,yeast ex-tract,tri-ammonium citrate,NaH2PO4·2H2O and Tween-80 were evaluated for lactic acid production in SSF and sta-tistically optimized to enhance productivity.Each variablewas tested based on our prior experience over a range andfixed in central composite rotatable design of RSM.The roleof each variable,their interactions and statistical analysis toobtain predicted yields of lactic acid was explained by ap-plying second-order polynomial model.The data was ana-lyzed statistically and response surface contour plots wereconstructed which indicated the possibility of enhancementin the production of lactic acid.The analysis was done usingMINITAB-13.2.Materials and methods2.1.MicroorganismLactobacillus amylophilus GV6,a facultatively anaero-bic,amylolytic lactic acid producing strain,isolated,char-acterized and maintained in this laboratory was used in thepresent SSF studies[11–13].2.2.Fermentation mediaSSF was carried out anaerobically at37◦C,pH6.75in120ml serum vials with prereduced and sterilized mediumcomposed of10g of wheat bran and appropriate volumeof moistening liquid(83%).The moistening liquid containssodium acetate0.2%,magnesium sulphate0.05%,calciumcarbonate8%,and thefive nutrients(peptone,yeast extract,tri-ammonium citrate,NaH2PO4·2H2O and Tween-80)se-lected for optimizations according to the requirement of ex-perimental design.N2is used as head space ctic acid extraction is done after5days of incubation.2.3.Substrate–microbe interaction by scanning electron microscopeFermented and unfermented wheat bran samples were and0.1M),dehydrated in alcohol and these dehydrated samples were placed on aluminium stubs gold coated in HUS-GB vacuum evaporator,observed in Hitachi S-520 scanning electron microscope(SEM).2.4.Extraction and estimation of lactic acidLactic acid formed after fermentation was extracted from fermented bran with50ml distilled water by vigor-ous shaking andfiltering through cheesecloth after allow-ing it to stand for3h.The extract was cold centrifuged (8000rpm for20min)and supernatant was taken for es-timating the lactic acid.Total lactic acid was estimated according to the method of Kimberley and Tailor[20]. The concentration of lactic acid is expressed as gram per10g of the fermented bran.Total starch content in bran was determined by standard acid hydrolysis method [21].2.5.Response surface methodology(RSM)The central composite rotatable design[22]was used to develop mathematical models that relate the variables in the experiments and optimize them for lactic acid production. According to this design,32experiments were performed. Details of response surface methodology can be found in Neter et al.[23].The matrix for this design along with ob-served results are shown in Table1.2.6.Statistical analysisRSM is used to study the variables independently for their interactions and quadratic effects.MINITAB-13was used for analysis of observed data,contour diagrams,re-gression coefficients,t-statistics and response optimizations. Five variables were considered for application of RSM.The behaviour of the system was explained by the following second-order polynomial equation.Y=B0+B i X i+B ii X2i+B ij X i X j(1)in which Y is predicted response which is a dependent variable,ctic acid production;B0is an off set term (constant);B i is linear effect;B ij is quadratic effect when i=j and interaction effect when i<j;B ii is a squared term;X i is i th variable,which are called as independent variables.The second-order polynomial equations were used to esti-mate the response of the dependent variable,ctic acid. Later an experiment was run using the optimum values for variables given by response optimization for confirmation of the predicted value and the lactic acid production wasB.J.Naveena et al./Process Biochemistry40(2005)681–690683 Table1Central composite rotatable design for optimization offive nutrients(each atfive levels)and mathematically predicted values and experimental values for the production of l(+)lactic acid by Lactobacillus amylophilus GV6in SSFRun Peptone(x1)Yeast extract(x2)Triammoniumcitrate(x3)NaH2PO4·2H2O(x4)Tween-80(x5)Lactic acid production gram per10g fb aExperimental values Predicted values1(−1)0.875(−1)0.875(−1)0.4(−1)0.55(1)0.25 2.90290 2.7805 2(1)1.625(−1)0.875(−1)0.4(−1)0.55(−1)0.15 1.90190 1.7369 3(−1)0.875(1)1.625(−1)0.4(−1)0.55(−1)0.15 2.23210 2.0949 4(1)1.625(1)1.625(−1)0.4(−1)0.55(2)0.25 1.42890 1.4821 5(−1)0.875(−1)0.875(1)0.8(−1)0.55(−1)0.15 1.78200 1.6389 6(1)1.625(−1)0.875(1)0.8(−1)0.55(2)0.25 1.42230 1.4693 7(−1)0.875(1)1.625(1)0.8(−1)0.55(2)0.25 1.80990 1.8849 8(1)1.625(1)1.625(1)0.8(−1)0.55(−1)0.15 1.47280 1.5773 9(−1)0.875(−1)0.875(−1)0.4(1)1.45(−1)0.15 1.025400.8389 10(1)1.625(−1)0.875(−1)0.4(1)1.45(1)0.25 1.68280 1.6865 11(−1)0.875(1)1.625(−1)0.4(1)1.45(1)0.25 1.55160 1.5829 12(1)1.625(1)1.625(−1)0.4(1)1.45(−1)0.15 2.72520 2.7861 13(−1)0.875(−1)0.875(1)0.8(1)1.45(2)0.25 2.00480 2.0301 14(1)1.625(−1)0.875(1)0.8(1)1.45(−1)0.15 1.71130 1.7661 15(−1)0.875(1)1.625(1)0.8(1)1.45(−1)0.15 1.57110 1.6537 16(1)1.625(1)1.625(1)0.8(1)1.45(2)0.25 1.62050 1.8933 17(−2)0.500(0)1.250(0)0.6(0)1.00(0)0.200.88990 1.1117 18(2)2.000(0)1.250(0)0.6(0)1.00(0)0.20 1.32000 1.1029 19(0)1.250(−2)0.500(0)0.6(0)1.00(0)0.20 1.63540 1.9131 20(0)1.250(2)2.000(0)0.6(0)1.00(0)0.20 2.45590 2.1831 21(0)1.250(0)1.250(−2)0.2(0)1.00(0)0.20 1.79710 2.0627 22(0)1.250(0)1.250(2)1.0(0)1.00(0)0.20 2.07260 1.8119 23(0)1.250(0)1.250(0)0.6(0)0.10(0)0.20 1.54550 1.7237 24(0)1.250(0)1.250(0)0.6(2)1.90(0)0.20 1.80880 1.6349 25(0)1.250(0)1.250(0)0.6(0)1.00(−2)0.10 2.10300 2.2656 26(0)1.250(0)1.250(0)0.6(0)1.00(2)0.30 2.58505 2.4271 27(0)1.250(0)1.250(0)0.6(0)1.00(0)0.20 1.93460 1.9109 28(0)1.250(0)1.250(0)0.6(0)1.00(0)0.20 1.90360 1.9109 29(0)1.250(0)1.250(0)0.6(0)1.00(0)0.20 1.80970 1.9109 30(0)1.250(0)1.250(0)0.6(0)1.00(0)0.20 2.05280 1.9109 31(0)1.250(0)1.250(0)0.6(0)1.00(0)0.20 1.85600 1.9109 32(0)1.250(0)1.250(0)0.6(0)1.00(0)0.20 1.91300 1.9109 Coded values are given in parenthesis.Real values are in percent w/w.a fb,fermented bran.3.Results and discussion3.1.Substrate–microbe interactionAn amylolytic strain,L.amylophilus GV6is used for production of l(+)lactic acid in SSF using wheat bran as solid support and substrate having a particle size ranging 1.5–3mm.Smaller particle size is generally used as it pro-vides larger surface area,for diffusion of nutrients,both at the surface and in the pores of substrates having the same tortuosity[24]and wheat bran fulfilled the above require-ments in the present study.As the substrate and microbial interactions are unique,chemical composition and physi-cal properties of the substrate should be taken into consid-eration.Growth characteristics,physiology and production of metabolites by the organism are based on the extracel-lular enzymes in growth-associated metabolism.Interaction of the organism with wheat bran was observed using SEM.rough in texture with extensive compact and complex mesh (Fig.1).After inoculation there was extensive growth of microbial cells seen adsorbed on to the wheat bran parti-cles and dissolution of meshfibres were observed due to extra cellular amylopullulanase production leaving behind relatively smooth substrate particles(Fig.2).This observa-tion explains the conversion of raw starch to glucose,which in turn is converted to l(+)lactic acid by L.amylophilus GV6.There are certain reports explaining the effect of sup-port for Lactobacillus sps.for production of lactic acid.In-ert support of inorganic porous materials was used for lactic acid production by immobilized cells of Lactobacillus rham-nosus[25].Lactobacillus delbruckii(mutant DP3),Lacto-bacillus casei(ATCC11443)and L.amylophilus(NRRL B-4437)produced more lactic acid using glucose as carbon source in a trapped state on solid support(polypropylene chips)than in free form[26].In the present study inexpen-sive wheat bran served both as solid support and substrate684 B.J.Naveena et al./Process Biochemistry 40(2005)681–690Fig.1.Scanning electron microscope (SEM)photograph of unfermented wheat bran with compact starch fibres.3.2.Optimization for lactic acid productionSignificantly contributing variables analyzed from previ-ous experiments were used in RSM to check the best oper-ating parameters and decide optimum operating conditions for the fermentation process.The Student’s ‘t ’distribution and corresponding values,along with parameter estimate are given in Table 2.The probability (P )values were used as a tool to check the significance of each of the coefficients.The results show that among the independent variables (x 1),two variables (x 2)yeast extract and (x 3)tri-ammonium cit-rate have significant effect.Among the interactions(x 1x 4)peptone and NaH 2PO 4·2H 2O (x 1x 5)peptone and Tween-80(x 2x 4)yeast extract and NaH 2PO 4·2H 2O (x 2x 5)yeastextract and Tween-80(x 3x 4)tri-ammonium citrate and NaH 2PO 4·2H 2O are highly significant (Table 2).From Fig.3it is evident that due to dominating interaction effects of peptone and NaH 2PO 4·2H 2O higher levels of these nutri-ents give higher yields of lactic acid.It is also observed that decrease in peptone and increase in Tween-80concentrations is giving higher yield of lactic acid (Fig.4).Fig.5shows higher yield of lactic acid at both higher and lower levels of yeast extract and NaH 2PO 4·2H 2O whereas yield is promi-nent at higher levels of these variables.Maximum lactic acid production is observed at lower yeast extract concentration with increase in Tween-80concentration and vice versa due to interaction effect as shown in Fig.6.At higher concen-trations of both tri-ammonium citrate and NaH 2PO 4·2H 2OB.J.Naveena et al./Process Biochemistry 40(2005)681–690685Table 2Coefficients and t -values for l (+)lactic acid production in SSF using central composite rotatable design VariableDesignate Coefficients Standard error of coefficients t -value Probability Constant I 1.91090.1064917.9440.000Peptonex 1−0.00220.05450−0.0410.968Yeast extract x 20.06750.0545 1.2380.241(NH 4)3citrate x 3−0.06270.05450−1.1510.274NaH 2PO 4·2H 2O x 4−0.02220.054500.7380.476Tween-80x 50.04020.054500.7880.476Peptone ×peptonex 21−0.20090.04930−4.0760.002Yeast extract ×yeast extract x 220.03430.049300.6950.502(NH 4)3citrate ×(NH 4)3citrate x 230.00660.049300.1330.897NaH 2PO 4·2H 2O ×NaH 2PO 4·2H 2O x 24−0.05790.04930−1.1740.265Tween-80×Tween-80x 250.10880.04930 2.2080.049Peptone ×yeast extract x 1x 20.06750.06675 1.0110.334Peptone ×(NH 4)3citrate x 1x 3−0.06050.06675−0.9060.384Peptone ×NaH 2PO 4.2H 2O x 1x 40.25550.06675 3.8280.003Peptone ×Tween-80x 1x 5−0.20720.06675−3.1040.010Yeast extract ×(NH 4)3citrate x 2x 3−0.05440.06675−0.8150.432Yeast extract ×NaH 2PO 4·2H 2O x 2x 40.13180.06675 1.9750.074Yeast extract ×Tween-80x 2x 5−0.19890.06675−2.9800.013(NH 4)3citrate ×NaH 2PO 4·2H 2O x 3x 40.11880.06675 1.7800.103(NH 4)3citrate ×Tween-80x 3x 50.03990.066750.5980.562NaH 2PO 4·2H 2O ×Tween-80x 4x 5−0.02180.06675−0.3260.750S ,0.2670;R 2,6.7%.lactic acid production was observed to be high (Fig.7).Fig.8is showing the effect of Tween-80and tri-ammonium citrate on lactic acid production.Either lower or higher concentrations of tri-ammonium citrate and Tween-80is favouring higher yield of lactic acid.Overall it is observed that Tween-80is influencing the production of lactic acid to maximum extent.At higher concentrations of Tween-80the organism might be utilizing certain nutrients present in solid substrate whereas,when concentration of Tween-80is reduced it is dependent on supplemented nutrients.It is re-ported that use of Tween-80,a surfactant,increased thepro-duction of enzymes [27,28]considerably in turn increasing the biomass and this may lead to increase in lactic acid pro-duction.NaH 2PO 4·2H 2O is also observed to have influence on lactic acid production as seen in Figs.3,5,and 7where the organism requires inorganic phosphate for higher yields of lactic acid.It is reported that inorganic phosphate has effect on lactic acid production by Lactobacillus helvictus [29].By applying the multiple regression analysis on experi-mental data,a second-order polynomial model (1)explains the role of each variable and their second-order interactions686 B.J.Naveena et al./Process Biochemistry40(2005)681–690Fig.4.Response surface showing the effect of peptone and Tween-80concentrations on lactic acid production.Fig.5.Response surface showing the effect of yeast extract and NaH2PO4·2H2O concentrations on lactic acid production.B.J.Naveena et al./Process Biochemistry 40(2005)681–690687Fig.7.Response surface showing the effect of (NH 4)3citrate and NaH 2PO 4·2H 2O concentrations on lactic acid production.in producing lactic acid.Y =1.9109−0.0022x 1+0.0675x 2−0.0627x 3−0.0222x 4+0.0403x 5+0.0675x 1x 2−0.0605x 1x 3+0.2555x 1x 4−0.2072x 1x 5−0.0544x 2x 3+0.1318x 2x 4−0.1989x 2x 5+0.1188x 3x 4+0.0399x 3x 5−0.0218x 4x 5−0.2009x 21+0.0343x 22+0.0066x 23−0.0579x 24+0.1089x 25(2)The quadratic model in Eq.(2)with 20terms contains5linear terms,5quadratic terms and 10,two-factor inter-actions.Out of these,insignificant terms (on the basis of P -values which are more than 0.05)are neglected (Table 2).The model equation was modified to reduced fitted model(3).Y =1.9109−0.2009x 21+0.1088x 25+0.2555x 1x 4−0.2072x 1x 5−0.1989x 2x 5(3)This reduced fitted model is considerably simpler and fits the data almost as well the model (2)with all terms.Hence it can be used for further exploration and validation.Analysis of variance (ANOV A)was done by MINITAB-13.ANOV A (Table 3)indicates that the effect of interactions of variables and their quadratic effects are highly significant and the regression between independent variables and out put is also quite significant.The observed and predicted experimental results are given in Table 1.The quadratic688 B.J.Naveena et al./Process Biochemistry 40(2005)681–690Table 3Analysis of variance for the production of l (+)lactic acid by Lactobacillus amylophilus GV6in SSF Serial number Source DF SS MS F -value P -value 1Regression 20 5.12820.2564 3.600.0172Linear 50.25470.05290.710.6263Square 5 1.79330.3586 5.030.0124Interaction 10 3.08030.3080 4.320.0125Residual error 110.78420.07126Model 60.75010.125018.380.0037Pure error 50.03400.00688Total315.9123model in Eq.(2)was used to predict the output of lactic acid with planned parameters and compared with observed values.The coefficient of determination (R 2)for production of lactic acid is 0.8670(Table 2).This observed variation in production of lactic acid (86.7%)can be explained by the fitted model Eq.(2).The multiple regression (R )for the production of lactic acid is 0.9311.This value shows a good agreement between experimental observations and predicted values.It can be seen that while the highest out put was observed at run 1(2.9029g/10g of bacterial bran)(Table 1).The overlaid contour plot,response surface plot of yield for certain pairs of variables shown at Figs.6,8and 9in-dicated that there are regions where the out put could be more than 3.000g.By running the optimization program with MINITAB-13within the experimental range investi-gated,the following optimum values were obtained:peptone,0.9%;yeast extract,0.88%;tri-ammonium citrate,0.379%;0.51.01.52.00.51.01.52.0PeptoneYEHold values: Tri-Amm: 0.6 Sod Di H: 1.0 Tween-80: 0.2Contour Plot of Lactic acid(Lactic ac id(24Lower Bound Upper BoundWhite area:feasible region NaH 2PO 4·2H 2O,0.769%and Tween-80,0.30ml.The pre-dicted lactic acid production was 32g/100g of wheat bran having 54g of starch.Hence the parameters given by re-sponse optimization were used for confirmation of the pre-dicted value of 32g lactic acid/100g of wheat bran.The or-ganism produced 36g of l (+)lactic acid from 54g of starch present in 100g of wheat bran.Lactobacillus amylophilus JCM 1125produced 53.4g/l of lactic acid using 100g/l,liquefied starch as reported by Yumoto and Ikeda [10].Lactobacillus plantarum NCIM 2084produced 72.9g/l of lactic acid when provided with 100g/l of liquefied starch [30].L.amylophilus (NRRL B4437)produced 29g/l lactic acid from 45g/l of corn starch and L.amylovorous was used in conversion of 120g/l liquefied starch to 92.5g/l lactic acid in submerged fermen-tation [31,32].In the above reports,enzymatic hydrolysis of starch was done prior to Lactobacillus fermentationB.J.Naveena et al./Process Biochemistry40(2005)681–690689which is expensive.Wheat straw hemicellulose pretreat-ment through enzymes/acid released11–12g/l fermentable sugars and were used for production of lactic acid in co-culture fermentation using Lactobacillus brevis and Lac-tobacillus pentosus[33].L.casei subsp.casei CFTRI2022, L.helviticus CFTRI2026and Streptococcus thermophilus CFTRI2034were used in SSF with sugarcane pressmud as a substrate containing14%total sugars on dry weight basis and reported6.14%lactic acid production[34].At high starch concentrations,the lactic acid production was low [5,10,31,32].In the present study L.amylophilus GV6was found to produce36g of lactic acid from a high concen-tration of raw starch(54g)present in100g of wheat bran. Wheat bran was utilized as both support and substrate by L.amylophilus GV6in single step conversion of raw starch to l(+)lactic pared with other reported strains, L.amylophilus GV6showed higher amylolytic and lactic acid production efficiencies which ensured elimination of saccharification steps of raw starch.A detailed study was made tofind out the lactic acid production yield(i.e.gram of lactic acid produced per gram of starch utilized).The organism could produce90%lactic acid yield,which is comparable with that of submerged fermentation reported earlier for this organism[13].Expensive pure starch could be replaced with cheaper underutilized abundantly available byproduct like wheat bran.The replacement of pure starch with wheat bran will bring down the cost of substrate100 times and will make the whole process more economical in terms of substrate costs.When compared to sucrose and hydrolyzed starch,the substrate costs will be still lowered to many folds.Successful application of RSM to optimize the selected ingredients led to two-fold(100%)increase in lactic acid production.L.amylophilus GV6is found to be the most efficient in production of l(+)lactic acid both in submerged and solid state fermentations at high substrate concentrations as per the available reports.Also there are no reports on use of amylolytic Lactobacillus in SSF.Thus,L.amylophilus GV6with its90%lactic acid yield efficiency with cheap renewable natural starchy substrates could turn out to be a novel strain to develop a potential technology. 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学校特色课程种类内容的英语作文

学校特色课程种类内容的英语作文

学校特色课程种类内容的英语作文Special Classes at My SchoolHi there! My name is Emma and I'm in 5th grade. I really love going to school because we have so many fun special classes besides just the regular subjects like math, reading, and science. Today I want to tell you all about the awesome special courses we get to take at my elementary school.Art ClassOne of my absolute favorite special classes is art. We have art once a week and it's always a highlight of my week. In art, we get to work with all sorts of different materials and make really cool projects. Sometimes we paint with watercolors or acrylics. Other times we sculpt things out of clay or papier-mâché. We've made ceramic pots, weavings, collages, you name it!My art teacher, Mr. Martinez, is so enthusiastic about art and really encourages us to explore our creativity. He's taught us about different artistic styles and techniques from various cultures around the world. We've studied ancient Greek pottery, Native American weaving patterns, East Asian calligraphy and brush painting, and so much more. Getting to learn about thehistorical and cultural context behind different art forms makes art class really fascinating.One of my favorite art projects was when we learned about Mexican folk art and made brightly colored Alebrijes sculptures out of paper mache. An Alebrije is a fantastical creature formed by combining different animal features. Mine had a dog's body, peacock feathers, butterfly wings, and a lion's head. It turned out so vibrant and unique! Creating art from different world traditions helps me appreciate the diversity of human cultures.Music ClassAnother one of the special classes I look forward to is music. We have general music twice a week where we sing songs, learn about reading musical notation, and even get to play some instruments like xylophones and glockenspiels. Sometimes we put on little musical performances for our classmates too.My music teacher, Mrs. Patel, has taught us so many fun songs from all around the world in different languages. We've learned traditional African call-and-response chants, Mexican mariachi songs, Brazilian bossa nova rhythms, and American blues melodies. Getting exposed to such a wide variety of musical genres and styles from global traditions has really opened my ears in an exciting way.In addition to general music, starting in 3rd grade we can choose to be part of the school chorus, band, or orchestra as an after-school activity. I'm part of the chorus and we've performed cool arrangements of pop songs as well as classic pieces. Last year we did a awesome mashup of "Despacito" and Beethoven's "Ode to Joy" that was a huge hit at the spring concert!Physical EducationThen there's PE or physical education class. We have PE twice a week and it's basically a time for us to run around and get physical activity through games and sports. Of course we learn about health, exercise, and team-building too.In PE, we've played all sorts of fun games like kickball, dodgeball, capture the flag, and my personal favorite - handball. Handball is kind of like soccer but you use your hands instead of your feet to pass and shoot a small rubber ball into goals on each end. It's such an energetic game and makes for a great workout!Our PE teacher, Coach Taylor, also teaches us more "traditional" sports and the fundamentals of games like basketball, soccer, volleyball, baseball/softball, and football. We work on skills like dribbling, passing, shooting, batting, throwing, catching, and offensive/defensive strategies. Sometimes we havetournaments between classes which really amps up the excitement and competitive spirit.But PE class isn't just about playing games - we also learn about the science of physical fitness and why daily exercise is so important for our bodies and minds. Coach Taylor has taught us about nutrition, muscle groups, cardiovascular health, and the benefits of an active lifestyle. She makes sure we do lots of strength and conditioning activities in addition to just playing sports.World Cultures & LanguagesMy favorite special class is definitely World Cultures & Languages. For one semester each year, we spend time exploring the rich cultural traditions of a particular world region through music, dance, games, food, clothing, holidays and more. We've studied the vibrant cultures of West Africa, Japan, Mexico, India, and next semester we'll be learning about the indigenous peoples and traditions of Australia and the Pacific islands.In this class, we get to sample authentic cuisine, learn traditional dances and songs, try on cultural dress, and play classic games from that part of the world. We made Ghanaian Kente cloth weavings last year and played the West African Mancala game, which originated as a way for people to bondover storytelling. When we studied Japan, we had so much fun learning some Anime drawing techniques, playing JapaneseBingo, trying on kimonos and geta sandals, and celebrating the Hinamatsuri festival by displaying ornamental dolls.But one of the coolest parts is that we get beginner language lessons in addition to the cultural activities! We work on basic phrases, greetings, numbers, colors, and simple vocabulary in the languages spoken in those regions. I've learned simple conversational bits in Wolof, Japanese, Spanish, and Hindi so far. It's been really helpful for expanding my understanding of the world.Learning about the rich diversity of cultures across the globe helps me appreciate just how wonderfully unique and vibrant all the world's peoples and traditions are. It's opened my eyes to the fact that there are so many ways of living, being, creating, and expressing yourself through art, music, food, language and more. The world is such an endlessly fascinating place!Technology & STEMFinally, I have to talk about our Technology & STEM (science, technology, engineering, and math) class. In this class we get todo hands-on projects, experiments, and coding activities that bring STEM concepts to life in an engaging way.We've learned basic programming and created simple games and animations using kid-friendly coding languages like Scratch. We've also done design challenges where we have to plan, build and test things like roller coasters, bridges, parachutes and more out of everyday materials. Once we had to construct these crazy Rube Goldberg machines that used chain reactions to perform simple tasks in an overly complex way. It was hilarious seeing what silly contraptions everyone came up with!In STEM class, we get to explore scientific principles through fun, interactive demos and investigations too. We've made crystals, built simple circuits, experimented with chemical reactions, studied plant biology by growing our own gardens, and examined weather patterns. We even got to launch mini rockets and learn about aerodynamics!My STEM teacher, Mr. Watson, is always coming up with creative new ways to make science and technology feel like playtime rather than work. He definitely inspired me to pursue my interests in building and tinkering with gadgets. Who knows, maybe I'll be an engineer or coder when I grow up!Well, there you have it - those are some of the awesome special courses we get to take at my school. From art and music to world cultures, PE, and STEM, we really get exposed to such a rich variety of activities that make learning feel like an exciting adventure. The special classes allow us to explore our diverse talents and interests beyond just core academics. They've opened my eyes to the wider world and all it has to offer.I feel so lucky to attend a school that values giving kids a well-rounded, culturally literate education. The special courses have helped me discover new passions, better understand our amazingly multicultural global community, and develop a genuine love of learning. I look forward to finding out what other fascinating realms of knowledge await me in the years ahead. School really is the best!。