不同的人，思维的能力不一样。同样一个人，不同成长阶段，思维能力也有显著的不同。从呀呀学语，到激昂愤慨，到思维缜密，到暮年迟钝。这其中，属青壮年时期的思维能力最强，灵魂最有活力。如果思维或者灵魂的活跃度能够定量表征的话，明显有一个类似高斯函数的变化规律。 以往讨论灵魂和思维，总是停留在定性的层次，很难得出明确的结论。那么，如果能把灵魂进行定量的话，就能把哲学问题转变为科学问题，这样的研究才能得出明确的结论。起初，希腊哲学涵盖的范围可以说是包罗万象，大有穷极宇宙之理的架势。随后，也是不断地分割出内容，分配到科学的定量研究去了，取得了惊人的进展。现在，哲学最后的神圣领地是否也会被割让出去呢？ Francis Crick 和 Christof Koch提出了整合信息理论（integrated information theory, IIT），从信息论的角度来定量估计思维意识（consciousness）的水平。他们用 表示意识水平，单位是衡量信息量的bit，公式见文后的图片。整合信息表示各个信息单元间的连接和交互，这种连接越强，整合信息量越大，思维意识的水平也越高。而且，这种整合信息的方式也要有极多的可能性，这个概念类似信息熵，只有可能状态越多，可能性越大，信息熵才越大，意识水平才越高。这种信息熵的概念类似于彭罗斯的相空间，能达到的状态越多，相空间越大。Koch总结了一句话，非常经典地表达了这个概念： “To be conscious, you must be a single, integrated entity with a large repertoire of highly differentiated states.” 人脑就是一个典型的具有非常大意识水平的物体，神经元连接错综复杂，整合的信息来自全身各种感觉器官，还有内省。而且状态变化极其多样，可以说人一生中每个时刻的感受经历，都是脑能达到的一种状态，所以，人脑的状态空间包含了人的所有经历，而且，每个人的经历不同，脑都能包含表达。在微观层次，即使是单个神经元，也不是像人工神经网络里描述的那种阈值冲动模式，实际上，单个神经元的响应是非线性的，特别是突触中的长时程增强效应，这种效应是记忆形成的基础。所以，人脑能够达到一个非常高的意识水平。 除了人脑，那其他动物，甚至石头呢？Christof Koch认为，所有物体都有意识！只不过是水平不同。的确，按照整合信息理论，所有物体都可以计算出一个 值出来，表征意识的水平。人和动物，其他物体的最大区别在于自我意识（selfconsciousness）。看来体会到“我”这个概念，很可能也是量变引起质变的一个例子。比如，几个水分子没有表现出什么人可以感知的属性，可以忽略不计。如果一大堆水分子聚集在一起，就出现了液态水的性质。你很难说是多少个水分子聚集，突然导致液态水性质的出现。 需要注意的是，人脑思维与物质活动是密切联系的，在大脑中，神经冲动有着巨大的相空间，脑与其他物质的联系就相比可以忽略了。特别是大脑与其他的大脑，物质联系极少，因此，人不能通过思维进行沟通。人脑意识的物理边界可以认为是模糊的，感觉系统的神经元实际上起到了连接外界环境的作用，这种连接位置就是模糊的人脑意识和外界环境边界。因此，思维活动与物质活动是一一对应的，除了脑，其他物质运动也都有“思维”。只要测量相空间的大小，就可以估计思维的空间大小。人也会有思维尺度差异，这个应该是可以测量的。虽然Cricket和Koch提出了测量的公式，但真正计算一个生物体的整合信息量是非常困难的。即使对于只有302个神经元的线虫，目前最强大的计算机也还无法在合理的时间范围内给出结果。这其中，可能还与缺乏一些基本的生物状态的参数有关。 尽管如此，整合信息理论在宏观的层面，给出了一个解释意识的框架，为定量地研究意识提供了一个新的视角和可检验的切入点。这在以往的研究中，是最为困难的一步，往往把意识和灵魂当做科学无法企及的。笛卡尔就干脆把世界分为绝对二元的，物质和意识是分开的。现在，是时候寻求统一了，如果在这个问题上取得进展，对我们的世界观肯定是革命性的。 此外，Christof Koch和Francis Crick在神经元的微观层次，还提出了neural correlates of consciousness (NCC)理论，在后面的博文中再详细介绍。 【1】Koch C. Neuroscience: The connected self . Nature, 2012, 482(7383): 31–31. 【2】Welberg L. Consciousness: Effective detection . Nature Reviews Neuroscience, 2012, 13(3): 155–155. 【3】 Christof Koch 的主页 【4】Christof Koch, A Theory of Consciousness, scientific american mind, 2009 CR-Complexity-09.pdf 这个文档中，Koch讨论了对科学和宗教的初步看法 religion-06.pdf
前两天有博主讨论 数学公式和引用率的问题 。我今天偶然看到一篇题目叫做“数学在生物中的应用和滥用”的发在Science上的2004年的老文章（英文原名 "Uses and Abuses of Mathematics in Biology",原文PDF在博文最下方下载）。觉得里面的东西在今天读来还是有些意义。 (本来想偷懒，少写点儿东西，贴一个PDF就好了。但是编辑把这个博文放到主页上了，我觉得还是把这篇文章大概总结一下，才对得起各位读者） 主要内容点： 1. 数学在生物学发展的历史上起到过重大的作用。譬如Hardy and Weinberg equilibrium 对种群遗传的公式描述和Fisher, Haldane, and Wright等人在20世纪初期对于生物数学做出了进一步的发展。这里顺便说一句，Fisher( 费希尔 ）这个名字可能对于大多数人来说是作为一个著名的统计学家出现的。他其实还是一个生物遗传学家。 2.在一般的教学中，往往是先教理论性，然后教应用性的。而现在的新新人类可能会先有应用性的经验，譬如在电脑游戏里不经意用到的统计学。 3.数理科学注重于抽象性，而生物系统较为复杂。最近的计算工具改革促进了生物研究的计算模型化。 4.作者把数学和计算在生物中的兴起 比喻成“牛顿力学”的初级阶段 。 5.计算工具的进步使得更多的生物学家们能够利用数学模型做研究，但是 很多人缺少数学基础 。他们不一定明白自己在搞啥。 6. 复杂的模型不一定是更有意义的 ，更接近现实的，更有预见力的。 对于不知道的参数做出假设是危险的 。作者举了世界卫生组织和纽约人口协会做的一个研究艾滋病在非洲传染机率的例子。这个研究应用了复杂的公式，但其中的 一个假设是没有意义的 ：一个人和 10个不同的人 性交后被传染机率跟和 同一个人性交10次 的机率是相同的。很显然，前者的机率应该是更高的。第二个例子是英国做的一个关于食用带骨牛肉对于 克雅氏病 传染率的研究。研究者运用了数学模型，其结论是如果禁止食用带骨牛肉，就最少可以避免一例感染。结果，英国政府就颁布了这个禁令。而这个研究里面对于一个参数 - 食用了一定量感染病毒的牛肉后会被传染的机率 - 做了没有根据的泛泛的假设。但是在当时是不知道这个参数到底是多少的。本文的作者用了另外一种办法做了估计，结论是禁止食用带骨牛肉只会减少一个传染例子的一千分之一 。事实证明，英国的群众的眼睛是雪亮的，大家都觉得这个禁令是无理取闹，极其可笑的。 7.历史上数学的用处在不同的生物学科中是不一样的。生态学对于数学的应用较晚，到20世纪六十年代才开始。之前大多数是描述性的。很多野外作业的生态学家 对于模型最开始是嗤之以鼻的 ，但是在今日，生态学是一个又有野外考察、实验观察，又有数学分析的学科。相比较，免疫学应用数学就更晚了，但是分子水平的计算和模型对于设计药物有很大的帮助。 8. 数学和生物的结合相对于物理和工程才刚刚开始 。有些‘滥用’是难免的。最大的问题是用了一大堆复杂的参数和细节，却对于一些关键性的参数做出猜测。想想爱因斯坦说的一句话吧： 尽管不要过度简单，但是模型 还是越简洁越好 。 一些有意思的原文摘录： “The virtue of mathematics in such a context is that it forces clarity and precision upon the conjecture, thus enabling meaningful comparison between the consequences of basic assumptions and the empirical facts.” “A point that arguably deserves more emphasis than it usually gets is that, in such exploration of mathematical models , the understanding emerging from complex computer-based simulations can often be substantially less complete than that from the analytic methods of classical applied mathematics and theoretical physics.” "...an increasingly large body of work ... are drawn from the alleged working of a mathematical model, without clear understanding of what is actually going on" “Sadly, examples of the application of statistical “confidence intervals” to distributions resulting from making arbitrary assumptions about essentially unknown parameters , and then endowing this with reality by passage through a computer, continue to proliferate.” “...there is as yet no agreed explanation for why there is so long, and so variable, an interval between infection with HIV and onset of AIDS...It may even be that the design of effective vaccines against protean agents like HIV or malaria will require such population-level understanding ." "More familiar in some areas than others are the benefits of mathematical studies that underpin pattern seeking and other software that is indispensable in elucidating genomes, and ultimately in understanding how living things assemble themselves . Very generally useful are still-unfolding advances that illuminate the frequently counterintuitive behavior of nonlinear dynamical systems of many kinds." "Mathematics, however, does not have the long-standing relation to the life sciences that it does to the physical sciences and engineering." "Particularly tricky are instances in which conventional statistical packages (often based on assumptions of an underlying Gaussian distribution—the central limit theorem) are applied to situations involving highly nonlinear dynamical processes (which can often lead to situations in which 'rare events' are significantly more common than Gaussian distributions suggest)" "Perhaps most common among abuses, and not always easy to recognize, are situations where mathematical models are constructed with an excruciating abundance of detail in some aspects, whilst other important facets of the problem are misty or a vital parameter is uncertain to within, at best, an order of magnitude. It makes no sense to convey a beguiling sense of “reality” with irrelevant detail, when other equally important factors can only be guessed at. Above all, remember Einstein’s dictum:'models should be as simple as possible,but not more so.'" 文章摘要： In the physical sciences, mathematical theory and experimental investigation have always marched together. Mathematics has been less intrusive in the life sciences, possibly because they have until recently been largely descriptive, lacking the invariance principles and fundamental natural constants of physics. Increasingly in recent decades, however, mathematics has become pervasive in biology, taking many different forms: statistics in experimental design; pattern seeking in bioinformatics; models in evolution, ecology, and epidemiology; and much else. I offer an opinionated overview of such uses—and abuses. 全文请下载PDF: May 2004 Uses and abuses of mathematics in biology.pdf
Measuring Fractures – Quality and Quantity By BOB HARDAGE Click to Enlarge As has been emphasized in the three preceding articles of this series, when a shear (S) wave propagates through a rock unit that has aligned vertical fractures, it splits into two S waves – a fast-S (S1) mode and a slow-S (S2) mode. The S1 mode is polarized in the same direction as the fracture orientation; the S2 mode is polarized in a direction orthogonal to the fracture planes. This month we translate the principles established by laboratory experiments discussed in the preceding articles of this series into exploration practice. Figure 1 displays examples of S1 and S2 images along a profile that crosses an Austin Chalk play in central Texas. Click to Enlarge The Austin Chalk reflection in the S2 image occurs later in time than it does in the S1 image because of the velocity differences between the S1 and S2 modes that propagate through the overburden above the chalk. Subsurface control indicated fractures were present where the S2 chalk reflection dimmed but the S1 reflection did not. This difference in reflectivity strength of the S1 and S2 modes occurs because, as shown last month (June EXPLORER), when fracture density increases, the velocity of the slow-S mode becomes even slower. In this case, the S2 velocity in the high-fracture-density chalk zone reduces to almost equal the S-wave velocity of the chalk seal, which creates a small reflection coefficient at the chalk/seal boundary. When fracture density is small, S2 velocity in the chalk is significantly faster than the S-wave velocity in the sealing unit, and there are large reflection coefficients on both the S1 and S2 data profiles. Using this S-wave reflectivity behavior as a fracture-predicting tool, a horizontal well was sited to follow the track of a second S2 profile that exhibited similar dimming behavior for the Austin Chalk. The S2 seismic data and the drilling results are summarized on figure 2 . Data acquired in this exploration well confirmed fractures occurred across the two zones A and B where the S2 reflection dimmed and were essentially absent elsewhere. The seismic story summarized here is important whenever a rigorous fracture analysis has to be done across a prospect. If fractures are a critical component to the development of a reservoir, more and more evidence like that presented here is appearing that emphasizes the need to do prospect evaluation with elastic-wavefield seismic data that allow geology to be imaged with both P waves and S waves. The value of S-wave data is that the polarization direction of the S1 mode defines the azimuth of the dominant set of vertical fractures in a fracture population, and the reflection strength of the S2 mode, which is a qualitative indicator of S2 velocity, infers fracture density. The Earth fracture model assumed here is a rather simple one in which there is only one set of constant-azimuth vertical fractures. What do you do if there are two sets of fractures with the fracture sets oriented at different azimuths? That situation will be discussed in next month’s article. 转自AAPG 2011 july Explorer
除了甜蜜，honey的作用应该还有不少。以前有个室友，常服用蜂蜜，说是对他的便秘有神效。这个疗效不太象是伪科学，网上有很多信息，我自己也试验过几回：虽然我没有便秘，但是服用蜂蜜之后过一段时间再去方便，舒畅得真是一塌糊涂 方博士称：蜂蜜和其他糖类一样，主要就是给身体提供能量，并没有特殊的营养价值。有人也许会说，蜂蜜除了糖，不是还有其他成分吗？它们就没有营养价值？这些其他成分，例如蛋白质、维生素和矿物质，不能说没有营养价值，但是它们在蜂蜜中的含量太少，可以忽略不计。 【来源： http://discover.news.163.com/10/1223/10/6OJ4ABLE000125LI.html 】 我对蜂蜜的具体成分没有做过任何调查，不过上面那段话是非常不科学的。如何去认识那个错误？可以考虑一些常见药片的重量，再看看上面标识的有效成分（active pharmaceutical ingredient, API)含量。照方博士的思路，可以去找无数的制药公司打假，因为它们生产的药片中的API含量太少，可以忽略不计。 从PUBMED里面【 www.ncbi.nlm.nih.gov 】 输入几个关键词（honey, nutrition, health），可以找到不少相关资料。 下面是其中的一篇综述，感兴趣的可以去研读一下： J Am Coll Nutr. 2008 Dec;27(6):677-89. Honey for nutrition and health: a review. Bogdanov S, Jurendic T, Sieber R, Gallmann P. Swiss Bee Research Centre, Agroscope Liebefeld-Posieux Research Station ALP, Bern, Switzerland. Abstract Due to the variation of botanical origin honey differs in appearance, sensory perception and composition. The main nutritional and health relevant components are carbohydrates, mainly fructose and glucose but also about 25 different oligosaccharides. Although honey is a high carbohydrate food, its glycemic index varies within a wide range from 32 to 85, depending on the botanical source. It contains small amounts of proteins, enzymes, amino acids, minerals, trace elements, vitamins, aroma compounds and polyphenols. The review covers the composition, the nutritional contribution of its components, its physiological and nutritional effects. It shows that honey has a variety of positive nutritional and health effects, if consumed at higher doses of 50 to 80 g per intake.
在应用环介导等温扩增技术（LAMP）做相关检测的早期探索实验中，为了保证结论更为客观，同时也为了避免LAMP产物污染，一般采用实时浊度仪（Real time turbidimeter）来进行实验。LA-320c型实时浊度仪是专门用于LAMP反应的仪器，它是由LA-200行改进而来，其基本原理是：在实时浊度仪中，通过加热（60℃~65℃0.5℃）使LAMP反应进行，同时在反应管一侧装有650 nm的发光二极管（LED），控制入射光强度，在其相对一次装有光电二极管（Photodiode,PD），控制透射光强度，由于LAMP反应中生成副产物白色硫酸镁沉淀，造成光强度差异，浊度计算公式为：浊度=In(I PD /I LED )，其中I PD 为PD接收的光强度，I LED 为LED发射的光强度。在计算机联机软件上，通过调整反应基线和每6 s检测一次浊度，从而获得浊度曲线。当实时浊度判断曲线超过阈值（Threshold）0.1时，即判断为阳性结果，否则为阴性结果。 转载请注明出处： http://www.sciencenet.cn/m/user_content.aspx?id=317430 参考文献： Mori Y, Kitao M, Tomita N, et al. Real-time turbidimetry of LAMP reaction for quantifying template DNA .J Biochem Biophys Methods, 2004, 59(2):145-157.
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