IDF 2006 Spring报道
2型糖尿病合并肥胖血糖和体重管理规范PPT精选课件
18.5
-
-
-
体重正常
18.5~23.9
-
增加
高
超重
24 -27.9
增加
高
极高
肥胖
28
高
极高
极高
*相关疾病指高血压、糖尿病、血脂异常和危险因素聚集 **体重过低可能预示有其他健康问题
中华人民共和国卫生部疾病控制司.中国成人超重和肥胖症预防控制指南(试行).2003 年4 月.
肥胖对2型糖尿病的影响
运动治疗
运动遵循的原则
运动时间
运动方式及强度
注意事项
运动治疗
循序渐进：从轻微、短时间开始，逐渐增加运动量，延长运动时间 合适的心率：不超过(170－年龄)次/分钟 达到微微出汗的程度 第二天起床后不感觉疲劳 运动时能说话，但不能唱歌
*校正年龄、性别、降糖治疗方案、糖尿病病程、吸烟和显著相互作用
2
2
2
P=0.0028
P<0.001
P<0.001
P<0.001
P<0.001
P=0.008
与体重正常人群比较的HR*
NS
NS
*
Presentation title
T2DM合并肥胖患者减重的获益
05
03
text
01
02
04
降低血糖， 改善血糖控制1
≥11.1
糖尿病和肥胖的诊断标准
诊断标准
目标值
BMI(kg/m2) 超重 肥胖
≥24 ≥28
或
腰围(cm) 腹型肥胖 男性 女性
≥90 ≥85
糖尿病的诊断标准1,2
肥胖的诊断标准2,3
蜜源植物盐肤木的研究
蜜源植物盐肤木的研究韩加敏1,2 朱欣3 谭宏伟4 王小平5 樊莹6 董坤2 董霞7（1 铜仁市畜牧技术推广站，铜仁 554300；2 云南农业大学动物科学技术学院，昆明 650201；3 贵州省草地技术试验推广站，贵阳 550025；4 重庆市畜牧技术推广总站，重庆 401121；5 重庆市彭水县畜牧发展中心，彭水 409600；6 贵州省畜禽遗传资源管理站，贵阳 550001；7 云南农业大学食品科学技术学院，昆明 650201）摘要：【目的】通过对盐肤木开花规律、开花期、蜜蜂采集习性及蜜腺和花粉超微结构的研究，以评价其蜜源价值，丰富盐肤木蜜源的理论研究，为其开发利用提供依据。
【方法】调查重庆市彭水县、贵州省纳雍县盐肤木的开花规律、开花期、蜜蜂采集习性等，对数据进行统计分析；对蜜腺和花粉样品采用扫描电镜进行超微结构的研究。
【结果】彭水、纳雍盐肤木花期为8~9月，雌花单花7~9 d、雄花单花5~7 d，局部区域单株花期长，其花期随海拔下降而推迟，不同的海拔使盐肤木花期得以延续，一些地区总体花期可达2个月左右；雌、雄花均有泌蜜孔，雌花在子房颜色玫红且柱头黄色时泌蜜孔开口最大（21.25 μm2），雄花从花瓣未下卷时到花瓣下卷后蜜腺不断长大，开口面积分别为18.23 μm2和23.19 μm2。
盐肤木花粉嫩黄色，赤道面观如卵形，花粉大小约38.93（31.99~46.13）μm×20.53（18.07~23.89）μm，具3条萌发沟，沟长达两极。
中蜂在盐肤木花期出巢及携粉回巢高峰是11:00时，回巢高峰是11∶00时至12∶00时；雌花在13∶00时、雄花在10∶00时至11∶00时访花蜜蜂数量达到峰值，访花蜜蜂数量达到高峰；蜜蜂采集盐肤木花粉的最适温度为26.10 ℃~27.10 ℃，最适湿度为60.77%~67.43%。
【结论】盐肤木单株花期及整体花期长，雌、雄花均具有较强的吸引蜜蜂的能力，12点之前在雄花序上的采集蜂更多。
“FPD International 2006” ,“CEATEC JAPAN 2006”会议报道
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基于微流控的植物根部-微生物相互作用研究进展
基于微流控的植物根部-微生物相互作用研究进展陈登博1,付玉明1,2∗,冯佳界1,2(1.北京航空航天大学生物与医学工程学院,北京100191;2.北京航空航天大学空天生物技术与医学工程国际联合研究中心,北京100191)摘要:基于微流控技术研究空间环境下植物的根-菌互作,有利于揭示植物-微生物稳态对空间环境效应的响应与适应机制㊂介绍了微流控技术中关于根-菌互作的成像技术,重点阐述了微流控技术针对不同栽培基质的成像以及对根际化学环境的操控/采样功能的优势,分析了芯片技术针对不同根系形态需求的研究,并对微流控技术在空间环境根-菌互作研究中的应用进行展望㊂关键词:微流控芯片;植物-微生物相互作用;根部生理学;空间生命保障中图分类号:Q948.12㊀文献标识码:A㊀文章编号:1674-5825(2022)06-0845-08收稿日期:2022-04-24;修回日期:2022-09-19基金项目:国家自然科学基金(31870852)第一作者:陈登博,男,硕士研究生,研究方向为空间生命保障技术与纳米生物技术㊂E-mail:chendengbo@∗通讯作者:付玉明,男,博士,副教授,研究方向为航天居室环境-微生物组-人体健康轴研究㊂E-mail:fuyuming@Research Progress of Microfluidics-based Plant-Microbe InteractionCHEN Dengbo 1,FU Yuming1,2∗,FENG Jiajie 1,2(1.School of Biological Science and Medical Engineering,Beihang University,Beijing 100191,China;2.International Joint Research Center of Aerospace Biotechnology &Medical Engineering,Beihang University,Beijing 100191,China)Abstract :The study of plant-microbe interactions in space environment based on microfluidic tech-nology is conducive to revealing the response and adaptation mechanism of plant-microbe homeostasis to the space environment.In this paper,the imaging technology of root-bacteria interaction in mi-crofluidic technology was introduced,the advantages of microfluidic technology for imaging different cultivation substrates and manipulating /sampling the rhizosphere chemical environment were dis-cussed,and the researches of microfluidic technology for different root morphological requirements were analyzed.In addition,the application of microfluidic technology in the study of root-bacteria interaction in space environment was prospected.Key words :microfluidic chip;plant-microbe interaction;root physiology;space life support1㊀引言㊀㊀植物栽培是地面和受控生态生命保障系统的重要组成部分㊂植物的根系有固定植株㊁吸收水分和养分等重要功能,根际微生物在植物根表或近根部位生长繁殖,是植物微生物组的重要组成部分㊂植物脱落物或分泌物可到达根际微区,在根系周围形成丰富而复杂的化学环境[1],是植物在长期进化过程中形成的一种适应外界环境变化的重要机制[2]㊂这些植物脱落物或分泌物为微生物提供营养,以此构建和调节根际微生物菌群[3];另一方面,根际微生物也会深度参与调解植物生理活动[4-5]㊂因此,植物与微生物的根际相互作用(简称根-菌互作)是植物学和微生物学第28卷㊀第6期2022年㊀12月㊀㊀㊀㊀㊀㊀㊀㊀㊀载㊀人㊀航㊀天Manned Spaceflight㊀㊀㊀㊀㊀㊀㊀㊀㊀Vol.28㊀No.6Dec.2022研究的热点问题㊂传统的根-菌互作研究所用的栽培方式难以实时营造对根际研究所需化学环境,且由于需要将植物根部取出进行采样和成像观察,使得采样和成像不具有实时性(时间分辨率较低),难以复现动态的互作过程㊂并且根毛可增加根表面积,为根部探索更大空间,在根生理学研究中具有重要地位,但却因为尺度过小而难以采样和成像等㊂因此,根-菌相互作用的实时化㊁可视化和操控性研究是一项新的挑战㊂近年来,控制小体积流体的微流控芯片技术(或称为芯片实验室)为生物学研究的实时化和可视化提供了新方法,在根-菌互作研究中展现出巨大潜力㊂微流控技术在根-菌互作研究中具有三大优势:①透明的芯片可实现根-菌互作的实时成像;②可实现对根际环境的多次采样;③可对根际化学环境实现准确操控,以研究化学环境对互作的影响㊂目前最广泛采用的芯片构建流程及材料为:按照所需的芯片设计图纸,以光刻机制作与其互补的光刻胶材质或3D打印制作塑料材质的模板(Template/mold),以聚二甲基硅氧烷(Polydimethylsiloxane,PDMS)浇注到模板上成型后剥离,再以等离子体氧化PDMS的需封装面(即有芯片通道的面)以活化其表面基团,最后放置玻璃片至封装面上键合以完成封装[6]㊂相对于二氧化硅㊁热固性塑料㊁热塑性塑料等其他可选的芯片材质,PDMS的价格低廉㊁偏软质㊁制作模板后可快速批量浇注制取等优势,使其成为主流芯片制作流程中常用材料[6]㊂等离子体氧化封装方式是不可逆的,即封装后很难将PDMS从玻璃片上拆卸;若实验有拆卸需求,可考虑可逆的封装方式,直接在室温下依赖PDMS和玻璃片间的范德华力封装,但这样封装不严密,在外力和内压下容易因意外拆卸开[7]㊂高等植物可以再生氧气㊁食物和水,是生物再生生命保障系统(Bioregenerative Life Support Sys-tem,BLSS)的功能核心[8]㊂而空间特殊环境(微重力㊁辐射㊁磁场㊁密闭㊁微生物多样性受限等)对根-菌互作的影响尚不明晰,前期搭载实验表明植物对微生物病害的敏感性可能增加[9]㊂而微流控技术体积小㊁性价比高,对于空间研究也独具优势㊂本文综述了基于微流控的植物根部发育和根-菌互作的研究,阐述微流控芯片针对不同栽培基质的成像及对根际化学环境的操控/采样功能的优势,分析了芯片针对不同根系形态需求的研究,并对微流控技术在空间环境根-菌互作研究中的重要作用进行展望㊂2㊀根-菌互作芯片的成像技术㊀㊀主流微流控芯片的材质(PDMS㊁玻璃片等)透光性好,对根-菌互作的成像观察独具优势㊂若能结合荧光等生物发光技术和一些高级成像技术,将可以更全面地还原根-菌互作过程㊂图1㊀针对根-菌互作的芯片Fig.1㊀Chip for root bacteria interaction Massalha等[10-11]构建的微流控系统TRIS (Tracking Root Interactions System)是一个研究根-菌互作的典型装置,如图1(a)所示,体现了生物荧光技术在芯片根-菌互作成像中的出色效果㊂TRIS系统采用PDMS-玻璃片材质,在灌有固体植物培养基的移液器吸头中令拟南芥发苗,在根长出吸头前移栽至芯片通道入口令其向芯片中生长,并使用注射泵将液体培养基和所感兴趣的根际菌(枯草芽孢杆菌作为植物有益菌,大肠杆菌作为有害菌)注射进芯片通道内,这些方法在根-菌互作的芯片研究中被普遍使用㊂为了实时显微观察,该装置直接安装在显微镜上㊂在无菌芯片中接种了表达红色荧光蛋白的枯草芽孢杆菌和表达绿色荧光蛋白的大肠杆菌,使用激光扫描共焦显微镜分别荧光成像并叠加图像,发现在接种后12h当中,枯草芽孢杆菌向根伸长区聚集并定殖,大肠杆菌却被排除在根表面之外,通过图像观察菌群行为动态,可推测出有益菌对植物针对病648载人航天第28卷原体的保护机制㊂除使用荧光标记的细菌之外,该研究还使用了仅在6个特定根区(皮层㊁脉管系统㊁根毛等)表达绿色荧光蛋白的6种荧光拟南芥株系,并与红色荧光蛋白的枯草芽孢杆菌图像叠加,观察到了杆菌接种后6h内向根伸长区的明显趋化行为,实现荧光标记的植物和细菌共同成像㊂在可见光(包括荧光)手段之外,电子显微镜和原子力显微镜等先进成像技术的分辨率更高,可在根-菌互作研究中作为更高级的㊁细胞器水平的成像手段㊂比如根毛就是一种微米级的根部结构,可以应用这两种高级成像手段㊂与光学显微镜不同,这两者都要求观察面暴露在外,而根却被封装在芯片中㊂由于等离子体氧化法的封装是不可逆的,很难打开封装以将根和根际区暴露在外㊂针对这一需求,Aufrecht等[12]设计了一种可拆卸的㊁针对根毛研究的芯片,PDMS并未化学键合到玻璃片上,而只是在高压灭菌时形成了较弱的物理键,且用琼脂固化围住PDMS以进一步固定及保湿,如图1(b)所示㊂其可在光学成像完成后拆卸开以供电镜等成像㊂针对根毛研究的目的,芯片被设计成了两层(Two-layer)式的阶梯状腔室,较高的腔室(200μm)容纳主根㊁两侧较低的腔室(20μm)容纳根毛,实测证明根毛生长时可自然粘附在PDMS面上,在拆卸过程中可保持在原位,利于后续的电子显微镜/原子力显微镜对根毛的成像研究㊂研究人员进一步使用该芯片跟踪了2种植物益生菌在拟南芥发育早期根部定殖情况[13],结果发现,无论细菌种类和接种浓度如何, 4天后细菌细胞在根表面的覆盖面积均为1%~ 2%,且根的发育情况很大程度上取决于细菌接种的种类和浓度㊂3㊀芯片技术对不透明栽培基质的成像优势㊀㊀芯片通道中装载液体基质时,其在光学上透明的性质有助于成像,但液体并不是自然界或人工栽培的主流基质,自然环境中的根-菌互作大多发生在土壤等固体基质中㊂若将土壤引入芯片,以解决土壤颗粒不透明导致的可见光成像困难等问题,生物荧光和某些显微光谱成像技术或可成为其研究手段㊂Mafla-Endara等[14]设计了土壤芯片,将土壤置于芯片通道入口处,以可见光观察土壤及微生物扩散进入通道的过程,以揭示土壤生态系统的形成过程㊂研究发现,土壤液体和真菌菌丝是土壤物质扩散的主要驱动力,土壤颗粒和微生物在充满液体的通道中扩散比在空气中快得多,且真菌菌丝可携带细菌穿过气体障碍而扩散定殖㊂芯片成像还可用于量化土壤颗粒的运动模式,对所得显微视频中2~6μm土壤颗粒使用自动追踪算法制作速度-位置热图,发现土壤颗粒被芯片内部的流水拖拽形成蜿蜒的运动模式,也使细菌很快地移动㊂虽未引入植物,该研究使用的土壤芯片已展现了对根-菌互作的可见光成像研究潜力㊂图2㊀EcoFAbs的应用[15]Fig.2㊀The applications of EcoFABs[15]也有研究尝试让植物根进入装载有固体基质的芯片,以研究基质中的根-菌互作㊂Gao等[15]描述了EcoFAB(Ecosystem Fabrication)芯片制作方法,可向通道内装载沙子或土壤作为基质,以期在更接近自然条件的微环境中研究根-菌互作,如图2所示㊂观察发现,虽然在亮场(可见光)下,沙子和土壤的不透明性质让埋在其中的根系和微生物不可见,但在荧光显微镜下,荧光标记的根际益生菌Pseudomonas simea在土中清晰可见,展现了荧748第6期㊀㊀㊀㊀陈登博,等.基于微流控的植物根部-微生物相互作用研究进展光技术克服土壤不透明性成像的潜力㊂这种益生菌在沙子中集中于植物根尖,而在土壤中集中于芯片开口处㊂研究表明沙子的贫营养迫使益生菌定殖于根尖以摄取分泌物,而土壤的富营养使芯片开口处的氧气成为益生菌的首要需求㊂值得注意的是,EcoFAB的实验流程认为可使用镊子将裸露的植物幼苗直接从发苗的固体培养基上移栽至芯片的孔道内[15];而几乎所有其他芯片-植物的结合研究都选择使用内有固体培养基的移液器吸头作为发苗载体,并模块化地整体移栽至芯片孔道内[10,13,16],以防止移栽过程对根的伤害㊂使用移液器的成活率明显高于使用镊子的移栽,虽然使用镊子的做法更接近自然条件,但对实验操作要求较高,很难不伤害根系㊂至于直接在灌注培养基的芯片中发苗的方法[17],由于植物的发芽率并非100%等原因,失败率相对更高㊂针对土壤颗粒对可见光的不透明性,Puce-taite等[18]推荐对土壤芯片使用可见光光谱之外的㊁先进的显微光谱成像技术,以克服土壤的不透明性,利于在微观尺度监测土壤微生物和相关的生物地球化学过程㊂这些非可见光的显微光谱成像技术包括红外吸收㊁拉曼散射和基于同步辐射的X射线显微光谱技术等,有时需要在土壤中加入稳定同位素或纳米贵金属粒子等辅助成像定位,在微生物鉴定㊁代谢物/污染物的定量/定位等方面各有优势,也可运用于基于固体基质芯片的根-菌互作研究中㊂4㊀芯片技术对根际化学环境的操控/采样功能优势㊀㊀利用微流控亦可在时空上快速操控/监测根周围的化学环境,研究根部对生物或非生物因素的动态响应,例如一系列以RootChip命名的芯片设计[19],如图3所示㊂最初Grossmann等[19]开发的RootChip被用于根对化学环境的响应研究,并以根内的葡萄糖荧光传感器开展荧光成像,成功发现细胞内糖水平的改变主要发生在灌注了葡萄糖的根尖㊂对于使用拟南芥的研究,RootChip可在几厘米内(<10cm)部署多个平行通道,以一次性开展多个植株的重复性实验㊂Fendrych等[20]采用竖直放置的vRootChip(v意为vertical,竖直以不影响根向地性)研究根部生长的基因通路,观察拟南芥根生长情况数天,发现无生长素存在时拟南芥的根生长速度会在30s内迅速下降;补充少量生长素后,根生长速度又会在2min内恢复;并通过向芯片中根际环境注入cvxIAA㊁ccvTIR1等人工配体,最终确认了以TIR1/AF-BAux/IAA共受体复合物为基础的一个调节根生长的非转录分支[20]㊂Guichard等[21]开发了根生长通道更长的RootChip-8S微流控装置,Denninger 等[22]用其跟踪观察了与根毛形成相关的细胞极化过程机理,发现基因GEF3在细胞极化过程中有作为细胞膜标志物的作用㊂图3㊀安装8个植物的RootChip[19]Fig.3㊀Image of a RootChip with eight mounted live plants[19]一些芯片设计甚至可令同一植株的根部的不同部位分别处于不同化学环境中,以在完全排除个体差异因素的前提下,直观对比不同化学环境对根双侧的影响或对特定根段的影响㊂面向根生理学或环境异质性研究,研究人员通常使用双流或多流汇总的方式,即多种液体从多个入口汇总到同一条芯片通道中,来营造分界式共存的液体化学环境㊂对于分根段施加不同的化学环境,Meier 等[23]在2010年开发了可对拟南芥施加多层流化学刺激的芯片,实际使用生长素类似物2,4-D和生长素抑制剂NPA,层流的方向与根垂直,以验证生长素和抑制剂对指定根段的影响㊂研究设置了3个进液口以达成3层的层流,以控制流量的手段成功制造了厚度10μm(约1个根细胞长度)的2,4-D层,这一厚度是被掺杂在2,4-D中的荧光微球所显示㊂因为使用了生长素调节剂偶联荧光蛋白的拟南芥株系,采用荧光显微镜观察到了2,4-D在短短几分钟后令10μm长的根段长出了848载人航天第28卷根毛,表明了生长素影响可在单个根细胞尺度上发生,也证明了微流控研究在很小尺度(~10μm)上的化学刺激对根影响的能力㊂值得一提的是,由于层流的方向与根垂直,验证了大/小的流量中根的生长没有显著区别,从而排除了剪切力(~10dyne/cm2)可能造成的额外影响㊂对于双侧施加不同的化学环境,Stanley等[16]设计了双流RootChip(Dual-flow-RootChip),令2种液体平行于根轴同时进入通道,形成不对称的化学环境,也描述了详细的芯片实验步骤[24]㊂研究分别采用NaCl㊁磷酸盐和聚乙二醇在双流Ro-otChip中模拟干旱等胁迫形式,在根双侧不对称处理,研究根毛生长情况,证明根在生理和转录水平上具有局部适应环境中异质条件的能力,也证明双流芯片方法有助于还原根与环境相互作用的决策过程[16]㊂研究表明,每个根毛细胞可以自主地对环境做出响应[16,23]㊂微流控芯片的采样功能有较大潜力㊂芯片的流出液是其内部环境的重要样品,通过收集芯片的流出液,即可完成植物根际微生物和根系分泌物的采集,从而进行根际微生物组与代谢组分析㊂但实际开展了采样并使用组学手段分析的研究并不多㊂其原因是关注复杂微生物群落研究较少,而对有限个菌株的行为,使用荧光标记等技术即可揭示,如Massalha等[10]和Aufrecht等[13]的研究;另外对于根际研究,很多根际菌定殖在根部表面甚至内部,难以随流出液流出㊂5㊀芯片技术对根系形态等特殊需求的优势㊀㊀植物根系具有多种形状和尺寸,可为之相应设计适合的微流控通道和腔体,以让植株正常生长或方便成像㊂为研究根系较粗的植物,Khan 等[25]使用3D打印的模具制备了腔体高度10mm 的PDMS材质芯片,如图4(a)所示,用于研究二穗短柄草(Brachypodium distachyon,根系直径1~ 3mm)的根细胞和分析渗透胁迫下的基因表达,发现了基因BdDi19在幼苗短期渗透胁迫期间有表达㊂此外,针对须根系统研究,相对于传统的单条直道的芯片设计,Chai等[26]采用多室设计的微流控芯片,如图4(b)所示,令水稻的分枝根生长到一组径向的花瓣形室中,用以研究渗透胁迫图4㊀应用于不同植物的芯片Fig.4㊀Chips for different plants (模拟干旱环境)对根系发育的影响,发现随着聚乙二醇(PEG6000,用于营造渗透胁迫)浓度的增加,根的生长变慢,根毛的数量和长度增加,根尖边缘细胞的发育和聚集增多㊂为了方便显微观察,微流控芯片的尺寸普遍设计得较小,并且使用拟南芥等小型草本物种,这让根-菌互作的长期化观察以及对个体较大的木本植物的研究成为挑战㊂Noirot-Gros等[27]设计的根系-微生物相互作用芯片(RMI-chip),如图4(c)所示,通道长达36mm,可以培养山杨(木本植物)幼苗的根超过1个月,并且可以连续使用显微镜观察根-菌互作㊂研究发现细菌需要在山杨根部表面形成生物膜才能持久定殖㊂RMI芯片加以修改或优化,可以用于长期观察生长缓慢的植物,或者短期研究生长较快的植物㊂此外,设计功能导向性很强的特殊结构芯片,如Massalha等[10]的TRIS系统还有一个双根通道版本,在同一腔室里生长2株拟南芥的根,并设计了分隔结构避免双根的物理接触,却允许微生物948第6期㊀㊀㊀㊀陈登博,等.基于微流控的植物根部-微生物相互作用研究进展细胞和信号分子的自由流动,以直观地显示细菌对不同基因型株系根部的定殖偏好㊂根据具体需求而设计开发出来的微流控芯片更能满足各种植物生长的特殊需求,也是微流控芯片的优势之一㊂图5㊀空间环境下微流控技术在根-菌互作研究中的运用Fig.5㊀Application of microfluidic technique in the study of root-bacteria interaction in spatial environment6㊀根-菌互作空间研究现状及展望㊀㊀高等植物是BLSS 的功能核心,但空间环境因素导致植物生长处于逆境,对植物的生长发育具有显著影响㊂在太空飞行等空间环境下发现在微重力下生长的植物表现出对植物病菌的敏感性增加[28],地面3D 回转模拟微重力效应下的实验也证明了在模拟微重力效应下病菌更易侵染植物[29-31]㊂一方面可能是因为微重力对细胞壁的重生和木质素的合成起到了抑制作用[32],从而利于病原真菌的侵染;另一方面推测是微重力影响了植物宿主与自身微生物的相互作用㊂虽然植物遗传适应相对较慢,但植物共生的微生物却能够很快地适应环境变化[33]㊂而植物根际微生物组是植物的第2套基因组的组成部分,在植物生长发育过程当中起着至关重要的作用㊂植物益生菌对植物具有保护机制,可以形成生物膜以及生产植物激素从而提高植物个体抵御非外来的微生物环境胁迫的免疫能力㊁诱导免疫抗性等多种手段,从而来增强其对宿主的免疫抗逆㊁抗病能力[34],且微生物是BLSS 中必然存在的一个链环,因此有必要研究空间环境下植物的根-菌互作㊂但是受控条件下植物根际微生物的结构变化以及潜在威胁微生物研究甚少㊂由于空间实验的空间有限,即使对于探空火箭等所拥有的超过10cm ˑ10cm ˑ10cm 体积的实验空间[35-36],对于使用传统栽培方式的根-菌互作研究也明显不够㊂而且,由于空间搭载机会的稀缺和昂贵,很多实验必须先期在地面开展,在回转仪等模拟的微重力环境下进行[37-38]㊂与真正的空间实验相似,回转仪可供实验的区域非常狭小,同样难以容纳传统栽培方式的植株㊂微流控技术可以成为空间生物学研究中很有前途的工具,已经运用在国际空间站或卫星搭载的太空实验上㊂如应用于国际空间站的一种新的不依赖培养物的微生物监测系统(the Lab-On-a-Chip Application Development Portable Test Sys-tem,LOCAD-PTS)[39],在15min 内定量分析了舱室表面的内毒素(革兰氏阴性细菌和真菌的标志)㊂在目前第一个长时间的活体生物立方体卫星实验中,Nicholson 等[40]开展生命有机体轨道空间环境生存性(Space Environment Survivability ofLiving Organisms,SESLO)实验6个月,测定了枯草芽孢杆菌孢子在空间环境中长期静止(14㊁91和181天)后的萌发㊁生长和代谢情况㊂但目前空间生物学研究中,未将微流控技术应用在植物根-菌互作研究上㊂而微流控芯片体积小,且目前已有一些微流控根-菌互作研究没有采用注射泵,同样可实现根际营养液的更新[15]㊂微流控芯片作为载体更能满足研究需求㊂因此,如图5所示,对于长期进化适应1G 重力的地球环境的植物而言,空间微重力环境属于典型的逆境环境,可能导58载人航天第28卷致植物菌群失调,但目前对其机理并不清楚㊂基于微流控技术能更直观地研究植物-微生物在空间极端环境下相互作用机理,并可以通过其机理精准调控植物根部菌群,使植物拥有更大的固碳能力和更强的抗逆特性㊂微流控技术在根-菌互作研究中的显著优势能进一步帮助研究者理解植物学和微生物学研究的热点问题㊂但在空间环境下基于微流控技术开展植物根-菌互作研究依然存在着许多问题:①空间环境下,植物根生长会改变方向,对基于微流控技术的根菌互作观察有一定影响;②在芯片设计的过程中还需要考虑表面张力会成为界面的主要力;③目前的微流控技术主要针对在透明基底上成像,这将偏离自然土壤系统中根际的群落结构㊂这些问题需要利用更有效的方法来解决㊂7㊀结语㊀㊀目前,已有研究将微流控技术运用于根-菌互作中,显著提高了实验效率与根菌研究结果的分辨率㊂然而迄今为止,国际上在空间环境下应用微流控技术研究植物-微生物相互作用仍是空白㊂微流控技术具有便于对根菌互作实时成像以及对根际化学环境的操控/采样等优势,能够精细刻画反映出空间环境下植物-微生物互作规律,有益于揭示植物-微生物稳态对空间环境效应的响应与适应机制,从而助力空间环境下植物健康稳定生产,为BLSS空间实际构建应用奠定基础㊂参考文献(References)[1]㊀Sasse J,Martinoia E,Northen T.Feed your friends:Do plantexudates shape the root microbiome?[J].Trends in PlantScience,2018,23(1):25-41.[2]㊀李月明,杨帆,韩沛霖,等.植物根系分泌物响应非生物胁迫机理研究进展[J].应用与环境生物学报,2022,28(4):1-10.Li Y M,Yang F,Han P L,et al.Research progress on themechanism of root exudates in response to abiotic stresses[J].Chinese Journal of Applied&Environmental Biology,2022,28(4):1-10.(in Chinese)[3]㊀Ahmad R A,Michael D J,Segun G.Synergistic plant-mi-crobes interactions in the rhizosphere:A potential headway forthe remediation of hydrocarbon polluted soils[J].Internation-al Journal of Phytoremediation,2019,21(1/7):71-83.[4]㊀Berendsen R L,Vismans G,Yu K,et al.Disease-inducedassemblage of a plant-beneficial bacterial consortium[J].Isme Journal,2018,12(6):1496-1507.[5]㊀Jacoby R,Peukert M,Succurro A,et al.The role of soil mi-croorganisms in plant mineral nutrition-current knowledge andfuture directions[J].Frontiers in Plant Science,2017,(9):1-8.[6]㊀Ren K,Zhou J,Wu H.Materials for microfluidic chip fabri-cation[J].Accounts of Chemical Research,2013,46(11):2396-2406.[7]㊀Mcdonald J C,Duffy D C,Anderson J R,et al.Fabricationof microfluidic systems in poly(dimethylsiloxane)[J].Elec-trophoresis:An International 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膳食纤维定义
.
中国营养学会对膳食纤维的定义
• 膳食纤维一般是指不易被消化酶消化的多糖类食 物成分，主要来自于植物的细胞壁，包含纤维素 、半纤维素、树脂、果胶及木质素等。
• 根据膳食纤维的水溶性不同，将其分为2个基本类 型。即水溶性纤维（SDF）与不溶性纤维（IDF）
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膳食纤维成分特性
果胶
•
果胶主链上的糖基是半乳醛酸，其侧链上是半乳糖和阿拉伯糖。
它是一种无定形的物质，存在于水果和蔬菜的软组织中，可在热溶液
中溶解，在酸性溶液中遇热形成胶态。
树胶
•
树胶的化学结构因来源不同而有差别。主要成分是葡萄糖醛酸、
半乳酸、阿拉伯糖及甘露糖所组成的多糖。它可分散于水中，具有黏
稠性，可起到增稠剂的作用。
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我国居民膳食纤维的摄入量
• 1989-1997年呈下降趋势
成年男性由17g/d下降到13.6g/d 成年女性由15.6g/d下降到11.7g/d
• 1997年-2006年膳食摄入基本稳定
男性 13g/d 女性 12.5g/d
.
低聚木糖
• 是由2-7个木糖分子以β（1-4）糖苷键结合而成的 功能性聚合糖。是一种高效益生元。 低聚木糖具 有良好的物化性质及生理功效
膳食纤维是 • 指能抗人体小肠消化吸收，而在人体大肠能部分或全部发
酵的可食用的植物性成分、碳水化合物及其相类似物质的 总和，包括多糖、寡糖和木质素以及相关的植物物质。
• 膳食纤维具有润肠通便、调节控制血糖浓度和降血脂等一 种或多种生理功能。
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美国谷物化学家协会（AACC）
• 膳食纤维是一种可以食用的植物性成分，而非动物成分， 主要包括纤维素、半纤维素、果胶及亲水胶体物质，如树 胶、海藻多糖等组分。
Proceedings of the 2006 Winter Simulation Conference
Proceedings of the 2006 Winter Simulation ConferenceL. F. Perrone, F. P. Wieland, J. Liu, B. G. Lawson, D. M. Nicol, and R. M. Fujimoto, eds.ABSTRACTAvailability management influences key supply chain per-formance metrics such as customer service level and inven-tory. The availability management process involves gener-ating Available-to-Promise (ATP) quantities, scheduling customer orders against the ATP, and fulfilling the orders. ATP generation is a push-side of the availability manage-ment process, and it allocates expected availability into ATP quantities based on product types, demand classes, supply classes, and ATP time periods as well as various availability management polices. This paper describes a simulation work done for IBM computer hardware busi-ness to evaluate how changes in ATP generation would impact supply chain performance. The simulation work played an important role in making strategic business deci-sions that impacted customer services and inventory cost.1INTRODUCTIONThis work was motivated by supply chain processes of IBM’s Computer Hardware businesses. In IBM, businesses are being managed as On-Demand business, where business strategies, policies and processes are continually evaluated and changed to meet increasingly demanding needs of cus-tomers. These changes are called “business transforma-tions” in IBM. Various business transformation ideas are generated, evaluated and deployed to improve the effective-ness of the businesses especially in the area of supply chain. Availability Management Process (AMP) is one such area where transformation ideas are constantly evaluated and im-plemented. When a change in AMP is sought, the impact of such change has to be accurately assessed before they are implemented because the changes are typically expensive and time consuming to implement in large enterprises as IBM.The availability management involves generating availability outlook, scheduling customer orders against the availability outlook, and fulfilling the orders. Genera-tion of Availability Outlook is a push-side of the availabil-ity management process, and it allocates availability into ATP quantities based on various product and demand char-acteristics and planning time periods. Order Scheduling is a pull-side of availability management process, and it matches the customer orders against the Availability Out-look, determines when customer order can be shipped, and communicate the promised ship date to customers. Order fulfillment is executing the shipment of the order at the time of promised ship date. Even if an order is scheduled for shipment for a certain date based on the outlook of availability, the resources that are required to ship the product on the promised ship date may not actually avail-able when the ship date comes. A key role for effective availability management process is to coordinate and bal-ance the push-side and pull-side of ATP.Ball et al. (2004) gave an overview of the push-side (Availability Planning) and pull-side (Availability Promis-ing) of ATP with examples from Toshiba, Dell and Maxtor Corporation. They stressed the importance of coordinating the push and pull-side of availability management for sup-ply chain performance by making good use of available re-sources. Although ATP functions has been available in several commercial ERP and Supply Chain software such as SAP’s APO, i2’s Rhythm, Oracle’s ATP Server and Manugistics’ SCPO modules etc. for several years (see Ball et al. 2004 for details), those ATP tools are mostly fast search engines for availability database, and they schedule customer orders without any sophisticated quantitative methods. Research on the quantitative side of ATP is still at an early stage, and there are only a limited number of analytic models developed in supporting ATP.For the push-side of ATP, Ervolina and Dietrich (2000) developed an optimization model as the resource allocation tool, and described how the model is used for a complex Configured-to-Order (CTO) environment of the IBM Server business. They also stress how the push-side (Availability Promising) and pull-side (Availability Plan-ning) have to be work together for the overall availability management performance.For the pull-side of ATP, Chen et al. (2002) developed a Mixed-Integer Programming optimization model for aSIMULATING IMPACT OF AVAILABLE-TO-PROMISE GENERATION ON SUPPLY CHAIN PERFORMANCEYoung M. LeeIBM T.J. Watson Research Center1101 Kitchawan RoadYorktown Heights, NY 10598, U.S.A.process where order promising and fulfillment are handled in a predefined batching interval. Their model determines the committed order quantity for customer orders that ar-rive with requested delivery dates by simultaneously con-sidering material availability, production capacity as well as material compatibility constraints. They also studied how the batching interval affects supply chain performance with different degree of resource availability. Moses et al. (2004) also developed a model that computes optimal promised ship date considering not only availability but also other order-specific characteristics and existing com-mitments to the previous scheduled orders. Pan et al. (2004) also developed a heuristics-based order promising model but with E-commerce environment in mind. They modeled a process where customer orders arrive via Inter-net and as earliest possible shipment dates are computed in real-time and is promised to customers.All the previous work described above deal with either push-side of ATP or pull-side of ATP, but not together. There have not been any quantitative tool that looks at both the push and pull-side simultaneously as well as other dy-namic factors in supply chain, and evaluates the effective-ness of the overall availability management process. Some of the work described above use simulation experiments to measure the effectiveness of their solutions, but their simu-lation work was only capable of simulating very specific supply chain environment, focusing only one aspect of ATP process.In this paper, we describe a simulation work that evaluates how changes in ATP generation impact supply chain performance by simulating all three parts of the availability management (generating availability outlook, scheduling customer orders, and fulfilling the order).The rest of paper is organized as follows. In section 2, we describe an availability management process in an IBM’s hardware business which we conducted the simula-tion study for. In section 3, we describe the simulation study done for changes in ATP generation, its impacts and results. Section 4 provides conclusion and remarks.2AVAILABILITY MANAGEMENT PROCESSIn IBM hardware businesses, the availability management consists of three main tasks: (1) generating availability out-look, (2) scheduling customer orders against the availabil-ity outlook, and (3) fulfilling the orders. The business that we analyzed in this study is CCHW (Complex Configured Hardware) business, which manufactures rather expensive, server-type computers.For the CCHW business, customers place orders in advance of their actual needs, often a few months in ad-vance. Typically, CCHW customers place orders as early as 3 months before the requested delivery dates (also called due dates), and early delivery and payment are not al-lowed. For this environment, products usually consist of a hierarchy of complex components, and require a longer supply planning. Many buyers in this environment pur-chase products based on a careful financial planning, and they typically know when they want to receive the prod-ucts and make payment. Customer orders in this environ-ment are typically highly skewed toward the end of quar-ter, e.g, only a small portion of orders are placed in the first week of a quarter, and the orders gradual increase, and fi-nally as much as 60-70% of orders are placed in the last 2 weeks of a quarter.Generation of Availability Outlook, is a push-side of the availability management process, and it pre-allocates ATP quantities, and prepare searchable availability data-base for promising future customer orders. For the CCHW business, the availability outlook is allocated by weekly buckets, and the availability is planned in much longer ho-rizon, often a quarter (3 months) into the future. ATP quantity is also called Availability Outlook for this reason. The ATP quantity is typically generated based on product type, demand classes, supply classes, and outlook time buckets. The product type can be finished goods level for Make-to-Stock (MTS) business or component level for Make-to-Order (MTO) or CTO business. Demand classes can be geographic sales locations, sales channels, customer priority, sensitivity to delivery dates, profitability and de-mand quantity. Supply classes can be degree of constraints and value of products. Outlook time buckets are typically in weekly buckets, that means that ATP quantity is allo-cated for week 1, week 2 and week 3 and so on. Availabil-ity is pre-allocated into ATP bucket based on the dimen-sion described above, and rolled-forward daily or weekly. The ATP quantity is determined based on the availability of components, finished goods, WIP (Work-In-Process), MPS (master production schedule), supplier commitment, and production capacity/flexibility. When customer orders arrive, ATP is searched in various ways according to scheduling polices to determine the ship (delivery) date that can be promised to customers.Customer Order Scheduling is a pull-side of availabil-ity management, and it reacts to customer orders and de-termines ship date for the orders. The CCHW customers usually request orders to be shipped (or delivered) in speci-fied future dates. And they would like to know whether the requested ship date can be met or how long is the delay if the date can’t be met. Customer orders arrive with vari-ous information such as product types, the demand classes, customer classes and ship dates. The order scheduler then searches through the availability outlook database, and identifies the availability that meets the characteristics. The scheduling can also be done by an ATP engine that uses certain algorithm to optimize the scheduling consider-ing various resources, policies and constraints. The scheduler then reserves specific availability against each order, and decrements the availability according to the pur-chase quantity of the order. The ship date of the order isdetermined from the time bucket where the availability is reserved, and it is promised to customers. Depending on the business environment, various rules and policies are applied in this order scheduling process. Examples are first-come-first-served policy, customer priority-based scheduling, and revenue (or profit)-based scheduling etc. In a constraint environment, certain ceiling can also be im-posed to make sure the products are strategically distrib-uted to various demand classes.Order fulfillment is executing the shipment of the product at the time of promised ship date. Even if an order is scheduled with a specific promised ship date based on the availability outlook, the availability (ATP quantity) may not actually exist when the ship date comes. There are several reasons why the orders cannot be fulfilled at the promised date. One such reason is the quality of availabil-ity outlook generation. In CTO environment, availability outlook is often generated based on finished goods avail-ability, which is estimated based on supplier commitment on components and forecasted configuration of the finished goods. Since the component availability changes often and there is certain error in configuration forecast, the compo-nents that are required to assemble a certain finished good may not be available when it is time ship the product to customer. Another source for the fulfillment problem is due to IT system that supports the availability management process. The order scheduling is done based on the avail-ability outlook data in an IT system, which is typically re-freshed periodically since it is very expensive to update the database in real time. The availability information kept in the IT system (system availability) are not always synchro-nized with the actual availability (physical availability). Due to the potentially inaccurate view of the availability, unrealistic ship date can be promised to customer. There-fore, for certain customer orders the necessary ATP quan-tity may not be there when the promised ship date arrives, thus creating dissatisfied customers. The impact of IT on the fulfillment is discussed in detail by Lee (2006). There-fore, a key role for effective availability management proc-ess is to coordinate and balance the push-side and pull-side of ATP as well as IT resources. In this paper, we studied how the push-side ATP would affect the overall availabil-ity management process.3SIMULATING IMPACT OF ATP GENERATION For this study, we analyzed a situation where, one of IBM’s hardware businesses was interested in managing availability based on new demand class, and they didn’t know how the new demand class would impact their sup-ply chain performance, specifically on their customer ser-vices and inventory cost. The business wanted to change from a demand class#1 representing 4 geographic demand regions to a new demand class#2 representing 8 new geo-graphical demand regions. For this case, we developed a simulation model to evaluate the impact of the demand class change on supply chain performance. We modeled and simulated 4 different scenarios based on different ways of availability allocation and order scheduling as shown in Table 1.Scenario 1 is the existing (As-Is) availability manage-ment process, where availability outlook is allocated based on 19 Product Types, 4 Sources of Supply, 4 elements of Demand Class#1 and 13 Weekly buckets. When an order is generated, the order is assigned with attributes, e.g., a product type, a source of supply, a demand class and the customer requested ship date (also called due date). For the scenario 1, the simulation model tries to schedule each order by searching for availability for a specific product type, a source of supply and a demand class, and then the weekly bucket that corresponds to the customer requested ship date. If no availability is found, the model goes back to earlier weekly buckets until it find the availability. If availability is still not found, the simulation model looks for available in later weeks until it finds the availability. If no availability is found in any of 13 weekly buckets, the order is considered backlogged. For this case study, we simulated more than 100,000 orders which represent cus-tomer orders for the business for a year. From the simula-tion, we estimated the customer services and inventory holding costs.Table 1: Four Simulated ScenariosAllocation of ATP Constraint on Or-der Scheduling Scenario 1(As-Is)Product Type (19)Source of Supply (4)Demand Class1 (4)Weekly Buckets (13)No constraintScenario 2(To-Be)Product Type (19)Source of Supply (4)Demand Class2 (8)Weekly Buckets (13)No constraintScenario 3(To-Be)Product Type (19)Source of Supply a(4)Weekly Buckets (13)Ceiling imposed byProduct Type, De-mand Class2 andQuarterScenario 4(To-Be)Product Type (19)Source of Supply (4)Weekly Buckets (13)No constraintScenario 2 is the new (To-Be) availability manage-ment process that the business would like to evaluate. For this scenario, availability outlook is generated based on 19 Product Types, 4 Sources of Supply and 13 Weekly buck-ets. But, in addition, it is generated based on 8 elements of Demand Class#2, which represent new geographic demand regions.Scenario 3 is another new (To-Be) availability man-agement process that the business would like to evaluate.For this scenario, availability outlook is generated based on 19 Product Types (19), 4 Sources of Supply (4) and 13 Weekly Buckets. It is not generated based on neither De-mand Class#1 nor Demand Class#2. However, in this case a constraint is imposed when scheduling order. The con-straint is a ceiling, which is a maximum allowed quantity for scheduling a specific product type and a specific De-mand Class#2. The ceiling is usually imposed with a pre-determined flexibility, 2% etc.Scenario 4 is another new (To-Be) availability man-agement process that is similar to the scenario 3, but there isn’t any ceiling imposed for the scheduling.For some of key data used in the simulation model are as follows. Customer orders are highly skewed toward the end of 13 week period. The number of orders in the first week of the quarter starts with about 4% of quarterly vol-ume, gradually increases, and for last two week of the quarter the number of weekly order goes up to about 15% of quarterly orders. In addition to the weekly skew of or-ders, the weekly demand itself has a variability. The vari-ability of component supply is also modeled. The cus-tomer requested ship date (due date) is also skewed in that a large portion of orders arriving early part of the quarter request orders to be shipped latter part of the quarter, and the orders arriving in the latter part of the quarter request the orders to be shipped within a few weeks before the end of the current quarter.One of the key performance metrics we wanted to measure for this study was scheduling delay. For this busi-ness, customer orders come with requested arrival dates. Since the transportation lead time is known in advance based on the service level agreement with carriers, it is easy to figure out when the order should be shipped so that the product arrives at customer’s place on the requested ar-rival date. The scheduling delay here, therefore, is defined as the difference between scheduled ship date and re-quested ship date. The figures 1, 2, 3, 4 show the schedul-ing delays for the four scenarios for one product type. Similar results were obtained for other 18 product types. It is clear to see in the figure 1 and 2 that the scheduling de-lay gets worse when the demand class is changed from one that has less members (Demand Class#1) to one that has more members (Demand Class#2). This is expected be-cause when availability buckets are bigger it is easier to schedule orders against them than when the availability buckets are smaller. As it can be seen in the Figure 3, the scheduling delay is substantially reduced when the demand class is dropped from the availability allocation. However, the ceiling creates significant constraint in scheduling to-ward the end of quarter. As expected, when the ceiling is dropped (Figure 4) the scheduling delay at the end of quar-ter disappears. The scheduling delays for the four scenar-ios are summarized in Table 2.Another key performance metrics for this case study was inventory holding cost. We assumed here that the holding a product for one year costs 20% of the sales value. Table 3 and Figure 5 compare inventory holding costs of the four scenarios. The scenario 2 would cost $2.827 million more than the scenario 1 (As-Is). However, the scenario 3 and 4 would generate a substantial saving as compared with the As-Is scenario, $3.730 million and $4.462 million respectively. According to the simulation results shown below, the scenario 3 and 4 appear to be good candidates for ATP generation methods, and the business is evaluating feasibility of implementing the sce-narios.Figure 3: Order Scheduling Delay of Scenario 3 (To-Be)Table 2: Order Scheduling Delay for 4 Scenarios Order Schedul-ing Delay Sce.1: As-Is Sce.2: To-Be Sce.3: To-Be Sce.4: To-BeWeek 0 72.10% 70.74% 78.25% 78.26% Week 1 12.25% 11.57% 10.38% 10.42% Week 2 4.64% 4.85% 2.73% 2.74% Week 3 2.71% 2.99% 2.66% 2.70% Week 4 2.87% 2.97% 3.03% 3.18% Week 5 2.18% 2.04% 1.50% 1.61% Week 6 1.33% 1.23% 0.57% 0.75% Week 7 0.62% 0.78% 0.16% 0.19% Week 8 0.14% 0.59% 0.03% 0.02% Week9 0.12% 0.28% 0.02% 0.01% Week 10 0.12% 0.25% 0.05% 0.03% Week 11 0.17% 0.33% 0.09% 0.02% Week 12 0.23% 0.46% 0.11% 0.03% > Week12 0.52% 0.95% 0.41% 0.04%Table 3. Inventory Holding Costs for 4 Scenarios Sce.1: As-Is Sce.2: To-Be Sce.3: To-Be Sce.4:To-BeInventory Holding Cost $13.135 million $15.962 million $9.405 million $8.673 million Inventory Holding Cost Sav-ing (wrt As-Is) -- -$2.827 million $3.730 million $4.462 million The simulation results from this case study clearly show that demand class and the number of demand class negatively impacts customer service and inventory. More generally, the larger the ATP quantity, the better the supply chain performance is.The availability management simulation tool was developed using the simulation engine of IBM WBI Modeler ® (IBM Corporation). 4CONCLUDING REMARKSATP generation, as a part of availability management process, directly influences key supply chain performance such as customer services and inventory. Simulation is a very useful tool to estimate how different ATP generation method would affect impact the supply chain performance. In this paper, we described a simulation work that was de-veloped for IBM’s computer hardware business to evaluate various alternatives in ATP generation method. The model simultaneously simulates the three main compo-nents of availability management process; generating availability outlook, scheduling customer orders and ful-filling the orders, as well as the effect of other dynamics in the supply chain. The simulation study has been useful in making important decision on ATP generation methods. ACKNOWLEDGMENTThe author would like to thank Brad Howland, Joe DeMarco, and Barun Gupta of IBM Integrated Supply Chain (ISC) group for sharing their knowledge and experience in IBM’s availability management processes. REFERENCESBall, M.O., C-Y Chen, and Z-Y. Zhao. 2004. Available ToPromise, Handbook of Quantitative Analysis: Model-ing in an-E-Business Era, eds. D. Simchi-Levi, D. Wu, Z.M. Shen, Kluwer, 446-483.Chen, C-Y, Z. Zhao, and M. Ball. 2002. A model for batchadvanced available-to-promise. Production and Opera-tions Management, 11-4, 424-440.Ervolina, T., and B. Dietrich. 2001. Moving Toward Dyanamic Available to Promise. In Supply Chain Management: Status and Future Research, S. Gass and A. Jones (eds) preprints produced by R. H. Smith School of Business, U. of MD and Manufacturing Eng. Lab., NIST.Lee, Y. M. 2006. Analyzing the Effectiveness of Avail-ability Management Process. In Trends in Supply Chain Design and Management: Technologies and Methodologies. Jung, H., F.F. Chen, and B. Jeong (eds.). Springer, forthcoming.Pan, Y., and L. Shi. 2004. A Stochastic On-Line Model for Shipment Date Quoting with On-Line Delivery Guar-antees. In Proceedings of the 2004 Winter Simulation Conference, Eds. Ingalls, R.G., M.D. Rossetti, J. S.Smith, and B. A. Peters.Moses, S., H. Grand, L. Gruenwald, and S. Pulat. 2004.Real-time due-date promising by build-to-order envi-ronments. International Journal of Production Re-search, 42-20, 4353-4375.AUTHOR BIOGRAPHYYOUNG M. LEE works in the mathematical science de-partment of IBM’s T.J. Watson Research Center, U.S.A., in the areas of supply chain simulation and optimization. Prior to joining IBM, he had worked for BASF, where he had founded and managed the Mathematical Modeling Group, and led development of numerous optimization and simula-tion models for various logistics and manufacturing proc-esses. Recently at IBM, he developed several complex simu-lation models that are instrumental in analyzing and improving business processes, supply chain and IT solutions. He has a B.S., a M.S., and a Ph.D. degree in Chemical Engi-neering from Columbia University in the City of New York. His research interest includes simulation and optimization of supply chain, manufacturing and business processes. His email address is <ymlee@>.。
2006第三届精瑞住宅科学技术奖新闻发布会
新 闻 发 布 会 现 场
宅产业技术创新 ， 产业现代化等等一些
概念 ， 都是对于科技进步非常重要的方 设 置上 更符 合行 业 的发展 另一 方面
面。另外 ， 精瑞奖同其他 的国家设奖比 吸引更多的全国优秀企业和项 目来参与
较 有一个特点 就是主要奖励中国住 我们的评奖。申报的来源有几个渠道
舟 中国建 筑设计 研究 院 总建筑 师赵冠
谦 北京天宏原方董事长兼总经理蔡放
等。 中国科技奖励杂志 中国建设报 中
华工商时报 经济参考报 中国不动产
杂志以及阳光卫视等媒体参加了此次发
布会。
全国工商联房地产商会秘书长陈雪
舟 对这 个奖 项 的具体 情况 进 行 了介绍
他说 “ 精瑞奖是由国家批准的设立的
会 上 ， 体就精 瑞 媒 常重要 的。国家科技奖励工作办公 室 度， 因为我很看好这个奖的公益性和它 行了提 问， 各有关专家 到现在 已经批准 了 １ ２项社会设奖 ， 的合，对行业领域的发展是非 获奖来说 ，我之所 以会是这么一个态
全 田工商联房地产商会会长聂梅生
结以往经验 的基础上 ． 一方面是在奖项 精瑞奖是在科技类 ，是科技部批 准设
５ ２
Ｓ 热
还有像詹天佑土木工 立的 ，一个奖项关键要看其是否货真 设了一个华夏奖。
我们 现在加 强对 社会 力量 设奖 的 价实， 是否含金量高， 这样才能体现它 程 奖。
一
全圈工商联房地 产商会的秘 书长张 舟
中圉 建筑设计研究院总设计师赵冠谦
北京夭鸿圆方建筑设计有限
理蔡放
维普资讯
的权威。
管理 ， 加上国家的大力支持 积极引导．
spring框架
简介
Spring：
Spring是一个开源框架，它由Rod Johnson创建。它是为了解决企业应用开发的复杂性而创建的。Spring使 用基本的JavaBean来完成以前只可能由EJB完成的事情。然而，Spring的用途不仅限于服务器端的开发。从简单 性、可测试性和松耦合的角度而言，任何Java应用都可以从Spring中受益。
教育中心概述
Spring认证框架是一个开放源代码的J2EE应用程序框架，由Rod Johnson发起，是针对bean的生命周期进行 管理的轻量级容器（lightweight container）。 Spring是Java EE编程领域的一个轻量级开源框架，该框架 由一个叫Rod Johnson的程序员在 2002年最早提出并随后创建，是为了解决企业级编程开发中的复杂性，实现敏 捷开发的应用型框架。
Spring的一个最大的目的就是使JAVA EE开发更加容易。同时，Spring之所以与Struts、Hibernate等单层 框架不同，是因为Spring致力于提供一个以统一的、高效的方式构造整个应用，并且可以将单层框架以最佳的组 合揉和在一起建立一个连贯的体系。可以说Spring是一个提供了更完善开发环境的一个框架，可以为POJO(Plain Ordinary Java Object)对象提供企业级的服务。
Spring是一个轻量级的控制反转(IoC)和面向切面(AOP)的容器框架。
轻量——从大小与开销两方面而言Spring都是轻量的。完整的Spring框架可以在一个大小只有1MB多的JAR 文件里发布。并且Spring所需的处理开销也是微不足道的。此外，Spring是非侵入式的：典型地，Spring应用 中的对象不依赖于Spring的特定类。
并且，在请求的处理过程中，还存在许多操作不简便的做法！