图1:美丽的花儿，美丽的世界 美丽的花儿总会触动我们的视觉和嗅觉。然而，你可曾想过植物也有感觉？ 对于植物来说，感觉极其重要。与动物不同，植物既不能为寻找食物而四处奔走，也不能主动逃避虫害的肆虐，更不会擅自挪动位置找寻可遮风避雨的“居所”。任凭风吹雨打、烈日暴晒，植物只能逆来顺受。因此，植物更需要灵敏的感觉来“察言观色”，以便应对瞬息万变的外界环境。或许你会笑着说：“子非鱼，焉知鱼之乐？”确实，植物不像动物一样，有鼻子有眼，生耳朵，长嘴巴，甚至还有皮肤。没有这些感觉器官，它们怎么可能有感觉呢？那么，请你跟随我们一同走进植物的感觉世界，你会慢慢明白：原来植物的感觉这么有趣啊！ 植物的视觉 像人类一样，植物也会看。人类之所以能看到东西，是因为眼里有光感受器；植物在它们的茎干和叶子里，也有光感受器。那么，植物看到了什么？最简单的答案：植物看到了光。 植物的光感受器能识别出红光和蓝光，甚至是人类肉眼不能看到的光波，如光谱中的远红外光和紫外光。通过这些光感受器，植物还能识别光源的方向，准确判断光线强弱，调节生理活动来适应光照周期。 达尔文的后期研究成果向人们展示了植物的趋光性（向光性），即植物往往会偏向于强光一方，以获取更多光能进行光合作用。植物的趋光性主要源于细胞内的一类光感受器——向光素。向光素对蓝光很敏感，分布于植物茎尖处。当植物向光的茎干一侧感受到蓝光，就会产生信号连锁传导，终止植物生长素的活动。与此同时，背光处的茎干细胞继续生长，这样就使植物向光强的方向弯曲生长。 另一类光感受器叫光敏色素，它能感受红光和远红光。在不同的光谱下，光敏色素有两种可相互变换的类型，即红光吸收型和远红光吸收型，主要是方便植物可吸收不同波长的光波来进行生理活动。 尽管植物的向光素和光敏色素这两种光受体与动物眼睛的光受体截然不同，但有研究表明，另一种光受体——隐花色素普遍存在于高等真核生物中，它能帮助动植物识别蓝光和紫外光。有了隐花色素光受体，植物除了可建立体内特有的生物钟或生长节律，调控生长发育外，还靠它来知晓钟点时辰哩。 图2: 植物的晨光浴 植物的触觉 植物对周围环境的变化相当灵敏且容易触动。瞧，风轻轻地拂过树梢；小虫子慢慢地爬过叶片；藤蔓伸出长长的须尖儿，想找一高枝“落脚”。一次简单的触摸或轻摇都足以干扰植物的生长，这就是为什么风大的地方，植被总是低矮，呈垫状。 在一定程度上，植物能感受到机械损伤带来的感觉。触觉反应最明显的要数食肉植物“明星”——捕蝇草。捕蝇草的叶片高度特化，左右对称呈夹子状，酷似贝壳。其叶缘处排列着长长的刺毛，左右交错，闭合时，密封严实。当一只苍蝇不慎落入其内，捕虫夹将以不可思议的力量迅速闭合，将其牢牢困住，慢慢享用。聪明的捕蝇草知道什么时候该“关门”，这主要归功于捕虫夹边缘上的触毛可灵敏地觉察到猎物的“登门造访”。 捕蝇草对猎物的感觉与一只苍蝇爬在你的手臂上的感觉，有着惊人的相似。当皮肤上的触觉受体意识到苍蝇存在时，激活的电流沿神经传送到大脑，大脑收到信号做出应急反应。同样，当一只苍蝇蹭到捕蝇草的触毛时，产生的电流迅速传给叶子，激活了细胞膜上的离子通道，促使捕虫夹快速关闭，整个信息传递过程不超过 0.1 秒。 显然，大部分植物对外界碰触的反应没有捕蝇草这么快，但它们对外界机械刺激的反应是相似的。令人吃惊的是，动植物细胞对外界触碰的反应都由同一类蛋白质起作用。触碰受体深植于细胞膜内，当受到外界压力或变形时，它会通过细胞膜释放带电荷离子，形成细胞内外的电荷差，从而产生电流。这些触觉反应能保证植物对外界变化做出特定、适合的应对策略。 图3：小蟋蟀钻花心了，痒痒吗？ 植物的嗅觉 菟丝子是一类寄生植物，几乎没有叶绿体，只能靠吸食其他植物体内的营养来生活。为了寻找合适的栖身寄养之所，菟丝子靠嗅觉识别合适的寄主，堪称植物界的“警犬”。有趣的是，大部分植物都有嗅觉，唯独菟丝子对臭味却“独有钟情”。。 在嗅觉方面，植物与动物可相提并论。动物鼻子中的嗅觉受体能识别并结合空气中的目的分子，植物也有嗅觉受体并对挥发性化学物质及其敏感。追溯到 20 世纪 20 年代，美国农业部的研究者发现，果实自然成熟的过程会产生大量乙烯，气味香甜，可加快果实的成熟。乙烯不仅可确保一只果实熟透，还会诱使相邻果实集体成熟。果实的批量成熟对于植物的“传宗接代”来说颇为重要，因为乙烯的芳香可吸引大量动物前来品尝，帮助植物传播种子。 此外，气味还能帮助植物互通信息。 20 世纪 80 年代的一项研究表明，一棵受到毛毛虫咬食的树，不仅自己会合成害虫不喜欢的化合物，还会给其他健康树发出“警报”，使远处未受咬食的健康树合成同样的化合物。 图4：尚未成熟的西番莲果实 植物的味觉 人类的嗅觉和味觉往往紧密相连、各行其责：嗅觉捕捉挥发性气体，味觉识别可溶性物质。植物的嗅觉与味觉的关系同样紧密，最明显的例子就是植物对虫害或病原菌的应急反应。在受到外界侵害时，植物通过释放大量挥发性气体，为同伴发出警告，其中最主要的一种成分是茉莉酮酸甲酯。尽管茉莉酮酸甲酯是一种高效的空气传递的信号分子，但它在植物里没有活性。相反，当它通过叶面上的气孔向外发散时，就会变成水溶性的茉莉酮酸，吸附在细胞内特定的受体上，引发叶子的抵御反应。如同人类舌头上分布着功能不一的味蕾细胞一样，植物也有不同类型的可溶性分子受体。 植物的味觉不仅可以感受危险和干旱的来临，还能识别亲缘类群。由于味觉负责识别可溶性化学物，植物的大部分味觉反应都在根部悄悄进行。 2011 年的一项研究表明，植物能利用根际间的化学信号来识别周围与自己有亲缘关系的类群。在没有亲缘关系的相邻植物中，根际间也有信息交流。另一项最新研究发现，当一排植物集体遭遇干旱时，只需 1 小时就可将信息传递给五排之外的植物，提醒它们关闭气孔，减少水分蒸发。不过，虽然身为邻居，没有根际间交流的植物却没有这个应急反应。 植物的听觉 关于植物对音乐的喜好，人们众说纷纭：植物喜欢古典乐，讨厌摇滚乐；喜欢轻音乐，讨厌重金属；喜欢舒缓平和的乐曲，讨厌欢快激昂的乐曲……奇怪的是，音乐对植物生长有益的观点却惊人地一致。 从生态适应性的角度来说，音乐与植物毫不相关，我们也没法希冀两者琴瑟和谐。然而，从理论上来说，植物能听到某些声音对于它们来说是有好处的。例如，虫子爬上叶面时的震动声，蜜蜂飞舞时的嗡嗡声，蚜虫翅膀的扑腾声，甚至由更小生物发出的极其微弱声响，都会给植物一种安全防范提示。更为离奇的是，植物还能通过识别其他植物身上发出的声音而获益。最近，瑞士伯尔尼植物科学研究所的研究人员捕捉到干旱来临时松树和橡树发出的超声波震动，这也许在提示其他植物做好应对干旱的准备。此外，意大利的科学家运用一套严谨的科学方法来研究植物的听力。前期研究表明，谷物类根尖会朝着有特殊节律震动的方向生长。更有趣的是，根尖还会发出声波。 然而，目前人们还没弄清楚植物是如何产生声波的，更不用说它们是如何捕捉声音。当然，假如这项研究成功了，到那时我们就会说，植物和动物一样，都具有五官功能。无论实验结果怎样，我们都可以毫不迟疑地说，植物在自己的世界里也有其独特的感觉体验。 图5：异彩纷呈的花花世界 备注：本博文的文字内容主要参考了以色列著名科普作家 Daniel Chamovitz的内容，结合自己的体验编译而成。文字部分已被《科学画报》采纳，刊登于2012年第12期的《自然奥秘》专栏。 Daniel Chamovitz： the director of the Manna Center for Plant Biosciences at Tel Aviv University, Israel. His new book is What a Plant Knows (Oneworld Publications/Scientific American/Farrar, Straus and Giroux). 另：为给部分对植物感兴趣的朋友更多信息，现将主要参考文献列入其下。 主要参考文献： Chamovitz D. 2012. Rooted in Sensation: Taste. New Scientist . 215 (2879 ): 37. Chamovitz D. 2012. Rooted in Sensation: Sight. New Scientist . 215 (2879 ): 35. Chamovitz D. 2012. Rooted in Sensation: Hearing. New Scientist . 215 (2879 ): 37. Chamovitz D. 2012. Rooted in Sensation: Smell. New Scientist . 215 (2879 ): 36. Chamovitz D. 2012. Rooted in Sensation: Taste. New Scientist . 215 (2879 ): 37. Gagliano, Monica; Mancuso, Stefano; Robert, Daniel. 2012. Towards understanding plant bioacoustics. Trends in plant science: 17(6) 323 - 325. Gen-ichiro Arimura, Kenji Matsui, and Junji Takabayashi. 2009. Chemical and Molecular Ecology of Herbivore-Induced Plant Volatiles: Proximate Factors and Their Ultimate Functions. Plant Cell Physiology. 50(5): 911-923 Falik O, Mordoch Y, Quansah L, Fait A, Novoplansky A. 2011. Rumor Has It…: Relay Communication of Stress Cues in Plants. PLoS ONE 6(11): e23625. Hines P. J., Sweet Smell of Communication.2006.ScienceSTKE. tw342. Pennisi E. 2006. Parasitic Weed Uses Chemical Cues to Find Host Plant. Science. 313 (5795):1867. Runyon J. B., Mescher M. C., De Moraes C. M. 2006. Volatile chemical cues guide host location and host selection by parasitic plants. Science. 313: 1964-1967. Runyon JB, Mescher MC, De Moraes CM. 2008. Parasitism by Cuscuta pentagona attenuates host plant defenses against insect herbivores. Plant Physiology.146: 987-995.
The mathematics of taste By using ‘genetic programming’ to crossbreed algorithms, researchers help flavor companies figure out what their customers like. The design of aromas — the flavors of packaged food and drink and the scents of cleaning products, toiletries and other household items — is a multibillion-dollar business. The big flavor companies spend tens of millions of dollars every year on research and development, including a lot of consumer testing. But making sense of taste-test results is difficult. Subjects’ preferences can vary so widely that no clear consensus may emerge. Collecting enough data about each subject would allow flavor companies to filter out some of the inconsistencies, but after about 40 flavor samples, subjects tend to suffer “smell fatigue,” and their discriminations become unreliable. So companies are stuck making decisions on the basis of too little data, much of it contradictory. One of the biggest flavor companies in the world has turned to researchers in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) for help. To analyze taste-test results, the CSAIL researchers are using genetic programming, in which mathematical models compete with each other to fit the available data and then cross-pollinate to produce models that are more accurate still. The Swiss flavor company Givaudan asked CSAIL principal research scientist Una-May O’Reilly, postdoc Kalyan Veeramachaneni and the University of Antwerp’s Ekaterina Vladislavleva to help interpret the results of tests in which 69 subjects evaluated 36 different combinations of seven basic flavors, assigning each a score according to its olfactory appeal. For each subject, O’Reilly and her colleagues randomly generate mathematical functions that predict scores according to the concentrations of different flavors. Each function is assessed according to two criteria: accuracy and simplicity. A function that, for example, predicts a subject’s preferences fairly accurately using a single factor — say, concentration of butter — could prove more useful than one that yields a slightly more accurate prediction but requires a complicated mathematical manipulation of all seven variables. After all the functions have been assessed, those that provide poor predictions are winnowed out. Elements of the survivors are randomly recombined to produce a new generation of functions; those are then evaluated for accuracy and simplicity. The whole process is repeated about 30 times, until it converges on a set of functions that accord well with the preferences of a single subject. Because O’Reilly and her colleagues’ method produces profiles of individual test subjects’ tastes, it can sort them into distinct groups. It could be, for instance, that test subjects tend to have strong preferences for either cinnamon or nutmeg but not both. By marketing one product to cinnamon lovers and another to nutmeg lovers, a company could do much better than by marketing one product to both. “For every one of these 36 flavors, someone hated it and someone liked it,” O’Reilly says. “If you try to identify a flavor that the whole panel likes, you end up settling for a little bit less.” O’Reilly and her colleagues haven’t had an opportunity to empirically determine whether their models correctly predict subjects’ responses to new flavors. So to try to establish their model’s accuracy, they instead built another model. First, they developed a set of mathematical functions that represent subjects’ true taste preferences. Then they showed that, given the limitations of particular test designs, their algorithms could still divine those preferences. Although they developed the model purely to validate their approach, O’Reilly says, flavor researchers were intrigued by the possibility of using it to develop more accurate and efficient test protocols. “People have been playing with these techniques for decades,” says Lee Spector, a professor of computer science at Hampshire College and editor-in-chief of the journal Genetic Programming and Evolvable Machines , where the MIT researchers’ latest paper appears. “One of the reasons that they haven’t made a big splash until recently is that people haven’t really figured out, I think, where they can pay off big.” Taste preference, Spector says, “is a pretty brilliant area in which to apply the evolutionary methods — and it looks as though they’re working, also, so that’s exciting.” http://web.mit.edu/newsoffice/2012/what-smells-good-0124.html
标签: 味觉
