ANCIENT JEWISH HIST
ANCIENT JEWISH HIST JEWISH 0182A
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This 58 page Class Notes was uploaded by Karlee Zulauf on Friday September 4, 2015. The Class Notes belongs to JEWISH 0182A at University of California - Los Angeles taught by Staff in Fall. Since its upload, it has received 87 views. For similar materials see /class/177849/jewish-0182a-university-of-california-los-angeles in Jewish Studies at University of California - Los Angeles.
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Date Created: 09/04/15
International Political Economy Globalization and Costs Topics What is International Political Economy IPE Varied uses of the term The central concept of cost Activities Trade Money Investment International Institutions Globalization Political Economy Political Economy has multiple meanings Study of the politics of administering the economy Example how election cycles affect economic growth The economics of political action Example how do states fund their activties Use of concepts from economics to study politics Example the PD and other ideas from game theory For this course I will use International Political Economy or IPE to mean the politics of world trade finance and investment Cost Central to all of IPE is the idea of cost One notion of cost concerns how much someone pays in money or in exchange for a gain a good or service Money and exchange are imperfect measures of opportunity cost The next best gain that could have been obtained with whatever a person or state gave up to receive the gain that he or she chose instead By assumption persons and states minimize opportunity costs Whenever a person or state sees the chance to acquire the same gain while giving up less of the next best gain the person or state seizes the chance Trade and Money International trade is the exchange of goods by buyers and sellers subordinate to different states Buyers and sellers may be private citizens or firms owned by private citizens They may be state organizations eg UCLA admits a foreign student Monetary policy addresses the consequences of each state s practice of Issuing Its own currency In many cases private banks issued currency until the state monopolized this right Some states today do not issue their own currency often using the dollar instead During the 19905 Russia issued rubles but most transactions were in dollars Numerous countries declare that the value of their currencies in international trade will be equal to some fixed proportion of the value of a dollar in international trade Money increases trade transactions can occur even when the buyer does not possess the exact good wanted by the seller Investment Implying Development Investment means the use of goods to produce other goods Foreign investment means producing goods in another country Foreign direct investment FDI means export of production goods to a foreign country where local labor uses them to produce more goods to be sold in that country or in world trade Portfolio investment means the purchase of production goods in a foreign country usually by lending the money for a local person to purchase them but possibly by purchasing stock in a local firm They can be hard to tell apart which is it if a US firm buys stock in a foreign firm and then ships equipment from a factory in the US to a factory in the foreign country that is now owned by the US firm Investment is related to development or the emergence of more highly productive economic activities in a region where people are engaged in activities of lower productivity Trade money and investment are all intertwined Institutions of IPE Since World War II a separate institution has been created to manage each topic of IPE For trade the General Agreement on Trade and Tariffs GATT has become the World Trade Organization VVTO For money the International Monetary Fund or IMF For investment and development the International Bank for Reconstruction and Development or World Bank Complexity IPE is complex Multiple levels of analysis in which politics within each state exercises interactive and mutual influences on politics among states Multiple forms of interaction Trade Money Investment Multiple feedback loops Figure 91 in Nau 322 tries to trace this complex interaction Globalization Nau never defines globalization Apparently he means however a transition from a world of subsistence economies in which people produced everything they needed in local communities to a world with a single global economy in which products move between localities 273 Globalization in my view is another of these terms like terrorism customarily used without any precise de nMon Nau relies on analysis by Thomas Friedman who is a journalist not a scholar As in the case of terrorism something is happening the question is what is it Ancient Trade Nau s world of local subsistence economies has not existed for a long time if ever While economic life has certainly changed it has for many centuries relied on longdistance trade Very lightweight very valuable items moved along land routes The exceptions were human slaves and livestock able to move themselves Bulk goods moved along water routes seas rivers lakes canals By 400 BCE Mediterranean cargo ships could carry up to 500 tons each AncientAthens fed itself on grain produced in southern Russia Romans traded down the Red Sea and along the Arabian coastline as far as southern India Malay sailors carried products onward to China A Jewish community is perhaps mistakenly recorded to have been active in Guangzhou before 900 CE when its members were massacred Russia began as management of river shipping between the Baltic and Constantinople Languages resembling English spread throughout a zone extending from northern India across Iran and Russia to the British Isles plausibly because of trade routes connecting the Baltic with India more than 5000 years ago Reduction of Transportation Costs After 1400 replacement of singlemasted with fullrigged ships enabled first a doubling and ultimately a sextupling of maximum cargo loads Full rigging made it possible to steer larger ships using a rudder attached to the stern Before 1900 replacement of sail by steam power and wooden by metal hulls roughly quadrupled maximum cargo loads Since 1945 introduction of container ships enabled concentration of cargo among single vessels Each technology sharply reduced the cost of foreign goods relative to domestic goods Sea transport is much cheaper per unit of a good than land transport Loading and unloading are the biggest element of costs Because crew size varies more with technology than with ship size enlargement of cargo loads reduced labor cost per unit Container ships reduce costs of loading and unloading and also cost less to build per unit of cargo transported Effects of Reducing Transportation Costs By the assumption of minimization people and states offered comparable goods at lower cost will shift to the cheapergood When transportation costs fall people shift from Somestic to foreign goods buy abroad instead of buying omestic State policy faces new challenges Domestic sellers suffer losses in the short term Domestic buyers receive gains Buyers and sellers come into conflict that the state must manage The new conflicts partly displace and partly add to conflicts within the state that occurred in the world with more restricted international trade that preceded the decline of transportation costs Comparative Advantage Political effects of trade can be seen in the concept of comparative advantage An Englishman of Sephardi descent David Ricardo originated the concept of comparative advantage in 1817 Two years after final defeat of Napoleon at Waterloo Napoleon had organized the Continental System forbidding English ships to call at European ports Ricardo imagined why the English victory was beneficial to England and to continental powers Ricardo s Example simplified Ricardo imagined a world economy consisting of two states Portugal and England using labor to produce two goods wool and wine Suppose arbitrarily that one hour of labor in Portugal can produce three pounds of wool or four bottles of wine Suppose arbitrarily that one hour of labor in England can produce two pounds of wool or two bottles of wine Portugal produces both wine and wool with less labor more efficiently than England Here Production Cost is defined as input that could be used to produce something else Ricardo used labor as an example of input But inputs could be anything involved in producing Production without Trade Suppose England and Portugal each have 100 hours of labor available for producing either wool or wine Suppose each devotes half of its available hours of labor to either product Then England produces 100 pounds of wool 50 hours x 2 poundshour and 100 bottles of wine 50 hours x 2 bottleshour Portugal produces 150 pounds of wool 50 hours times 3 poundshour and 200 bottles of wine 50 hours x 4 bottles per unit The world consisting of England and Portugal produces 250 pounds of wool and 300 bottles of wine Ricardian Trade Even though Portugal produces both wine and wool more cheaply than England both countries gain if each specializes If Portugal produces only wine each hour of labor produces four bottles of wine If England produces only wool each hour of labor produces two pounds of wool Effects of Specialization One hundred hours of labor in Portugal produces 400 bottles of wine One hundred hours of labor in England produces 200 pounds of wool World production is 200 pounds of wool and 400 bottles of wine The world has lost 50 pounds of wool 200 instead of 250 pounds but gained 100 bottles of wine 400 instead of 300 bottles Gains from Specialization As long as a people are willing to pay half a pound of wool for one bottle of wine both countries gain by trading wool for wine In England people pay a whole pound of wool for a bottle of wine In Portugal people pay three quarters of a pound of wool for a bottle of wine Consumer satisfaction increases Incomplete Specialization Suppose people want more wool than the reduced total world production of 200 pounds Portugal need not entirely halt wool production If Portugal devotes 20 hours of labor to wool production it produces 60 pounds of wool at the cost of 80 bottles of wine World wool production increases to 260 pounds while world wine production falls to 320 bottles from complete specialization Compared to the world without trade there are 1 extra pounds of wool and 20 extra bottles of wme Terms of Trade Terms of trade depend not only on the production costs but on the demand in each country Suppose that each country demands no less woolldand wine than it produces in the notrade wor Producing no wine but twice as much wool as it does in the no trade world Enfgland trades wool for at least 100 bottles of wine rom Portugal Producing twofifths as much wool as in the no trade world Portugal trades wine for at least 90 pounds of wool from England Savings and Earnings from Trade England can save a tenth of a pound of wool on each bottle of wine 10090100 Portugal can earn an extra 15 pound of wool 9075100 for each bottle of wine A penny saved is a penny earned What actual deal emerges between England and Portugal can be computed but certainly some possible deal saves or earns for each country General Principle As long as relative production costs or opportunity costs differ from one country to another all countries can save or earn by specializing and trading World output of all products increases This is true even if one country produces all products more cheaply than the other country I Ii Mlcroarray Consumem i LECTURE 2A SOURCES OF UNWANTED VARIABILITY SLIDE 1 hunmaseese SLIDE z There are three primary Svurces Dfunwanudnmse The rsuswhatwe WI term sz nmsequot mdLhIS Is Lhzway I k V Same a elements Dfnmse m wur expenmem farm me Same aseese m exmpIe diabetes and um geneue backgwundmxghtbe differentbeween me wlxdaumwx be me meam memeIeeme we mums black SLIDE 3 disease Ta 5 lug extent disease Is Samele de nzd SLIDE a hkd mu m yvu ta have a aseese which m rm Is agmn a term Impasea an yDu because at We smer ldenu es as a PageIuME easily recognized and you go all the way down to actually complex genetics like diabetes and cancer and asthma on the other end of the spectrum which you inherit as part of your genetic background and that is clearly a disease state because it causes your body direct harm SLIDE 5 So what causes this spectrum On the left side of the slide you see that normal variation is caused by SNPs and as you move towards the right end of the spectrum the term mutation applies and in general a mutation is a much more devastating event to genomic DNA SLIDE 6 Mutations could be gaping holes or huge deletions in your genetic code or it could be caused by point mutations which in just looking at the sequence look exactly like SNPs but they will actually change the amino acid coding sequence They could be out of frame mutations so they ll turn the resultant proteins into a garbled mess of amino acids Thus in general mutations are more severe events and SNPs are more benign events but when you have a whole pattern of SNPs which comes together with another pattern of SNPs to form a disease state that is now classified as mutation and falls in the middle of this spectrum Moving forward to the right of this spectrum you have SNPs which change the amount of RNA that is produced They can be in regulatory regions They change the composition of the transcripts that are produced meaning they change amino acids and these are generally termed nonsynonymous SNPs and these are going to be the SNPs which are going to obscure our expression profiling results They are normal variants They change transcript levels within a range of normal which differ from person to person to person and in general don t cause mutations but cause variations between people Now say you have a mutation which changes the level of the transcript as well That change will become buried in all of the normal variations So how do you get around that And again this is just to reinforce the point that these regulatory SNPs can come together in one combination and form a pattern of normal variation but when they come together in a nonadvantageous combination say in an offspring they could cause a disease phenotype And this becomes very difficult to parse out because it is along the spectrum of normal human variation So what we are really interested in doing is identifying those transcript changes which are consistently found in people with a disease state and may sometimes be found in people without the disease state but if you look across a large number of individuals there is no consistent theme in terms of that SNP noise tracking with a normal or unaffective person s phenotype So you ll have random uctuations in the unaffected person or population but you will have no fluctuation in the mutation Thus the takeaway message here is to identify expression correlates of disease samples in cohorts that are consistent across the state and thus cannot be attributed to random SNP noise Page 2 of 16 LECTURE 2B SNPs SLIDE 1 I would like to focus this short minilecture on contrasting human SNPs to mouse and rat SNPs and how these are actually quite different when designing expression profiling experiments SLIDE 2 As we mentioned the distinction between SNPs and mutations is a gradient from things that clearly cause disease to things that are just normal variation but the important point to keep in mind here in designing expression profiling experiments is that SNPs can actually influence the expression of disease For example a patient might have cystic fibrosis and all the patients might share the same deltaF508 mutations but they can respond differently to the environment and their response to the environment can be modulated by SNPs For example allergens in the environment can predispose their lungs to having some problems with additional asthma but that asthma is caused by a SNP say in the lLl3 So you just have to be careful in identifying the variables that you are studying Is it environment Is it a single mutation that is shared or is SNPs that are in uence that mutation SLIDE 3 So there are some additional things that we consider quite often when designing human experiments in addition to SNPs which are for example sex Ylinked genes are not present in fem ales and in fact when we expression profile males or females we see the Ylinked genes expressed in males and not in females which is what you would expect In addition many genes are hormone responsive and that depends on the tissue and depends on the gene and depends on the age of the patient So all those things need to be considered when designing both your control groups and your experimental groups Then there is age Gene expression is different between puberty at different points in development both for hum an mouse and rats or any organisms you would like to study Another intriguing example is X inactivation For example most people don t realize that if you take monozygotic twin females people think of them genetically identical They are genetically identical but their expression profiles can often be very different Why is that Because X inactivation is not necessarily the same in monozygotic twins We found a number of female monozygotic twins where one was using almost the paternal X chromosome and the other was using the maternal X chromosome 10 of the genes then are different in which ones are expressed between those identical twins So it is important to even think of things like that and drug metabolism SNPs What drugs are being used on a patient and is there a variation in the response to those drugs And finally identifying candidate genes for SNPs can be a goal of expression profiling For example we are doing in our lab a lot of exercise studies on normal volunteers where their muscles are then biopsied and we look for differentially expressed genes based on different types of exercise And those differentially expressed genes become candidates for SNP discovery so we can identify the polymorphisms or SNPs that dictate whether somebody will be a sprinter or a weightlifter or a long distance runner SLIDE 4 So is it the same situation in mouse in rat Is it as complicated and are there as many sources of SNP noise It depends Most mouse strains are typically inbred They are generally used a model to reduce genetic variation through sisterbrother matings There is relatively little SNP noise between the same inbred mouse strain So you can go get some black six mice and pretty much rest assure that they are pretty much congenic or genetically identical and relatively little SNP noise should be present However because of that same brothersister mating and the congenic nature of the strains if you look at different strains there has been a great deal of founder effect and there is very different genetic backgrounds then between different inbred mouse strains and in fact there is an exaggerated SNP noise difference when looking at different mouse strains So for example on our sample submission form for expression profiling in our consortium we are emphatically asking what is the mouse background that you are using and are all your experiments done on the same mouse strain because if they are not that could tremendously complicate analysis Transgenics and knockouts are a bit different with regards to mice Transgenics you can assume are in fact a congenic strain and inbred and the control strain is the so called background strain in which the transgenic was produced This is different from knockouts or knockins The reason is because they are Page 3 of 16 generated in different ways Transgenics you are adding a gene to a genetic background so you are injecting oocytes with a specific gene construct and you know the genetic background of that oocyte or that zygote So you can get the control strain the background strain as it is called Knockouts are generated in a different way with transfection of ES cells and creation of chimeras and part of the process of making knockouts or knockins is using different mouse background strains So it gets more complicated with knockouts and it actually is problematic to get congenic strains of knockouts It is just something you have to be aware of when designing experiments and make sure you have a handle on what actually the background strain is whether it has been outbred or if it has been inbred back to being congenic with some background strain Just make sure you ask those questions and know what is going on SLIDE 5 So in general with mouse the experimental design is such that you typically mix the tissue samples from multiple mice and can pretty much assume that those mice are very similar You assume inbred strains have relatively little SNP noise Outbred knockout strains should be mixed to normalized SNP noise and make sure that you try to get a congenic strain SLIDE 6 Kits are maintained as relatively outbred strains There are some specific strains of rat for example Hooded or Norway brown but those them selves are outbred They are isolates that are then outbred so within the strain rats can be quite genetically different and between strains they are even more genetically different This situation in rats is not all that dissimilar to hum ans where sure most hum ans are considered outbred but you do have isolated populations that are considered more inbred whether it s Indian Eskimos or the Ashkenazi Jewish population or even Caucasians versus Africans or AfricanAmericans So it is similar in rats where they are generally outbred so you need to get a handle on that and make sure you are studying a relatively good number of rats Now whether you expression profile them individually or mixed we will go over in the next session So rats are generally used as a source of genetic variation to actually study that and so as I mentioned it is often a good idea to mix tissues from multiple rats to normalize out the SNP noise Unless you are trying to study SNP noise then you don t want to mix them SLIDE 7 So just to conclude this short section SNP noise for expression profiling you need to get a handle on your variables and whether that is a reduction of variables or an isolation of a certain variable is up to the experimental design and up to you to keep track of sex age strain Humans and rats have substantial SNP noise that must be taking into account whereas inbred mouse strains have relatively little SNP noise but be careful of knockouts because they are rarely congenic Page 4 of 16 LECTURE ZC METHODS TO OVERCOME UNWANTED VARIABLES SLIDE 1 In this session we are going to go into quite a bit more detail on experimental design We are going to talk about methods to overcome unwanted variables or isolating variables and particularly address the issue of whether you want to do lots of individual profiles or you want to do mixed samples on a limited number of profiles We will go over the statistical analysis about the different options within this consortium and show how your experimental design ends up influencing how you can interpret your data in the end SLIDE 2 So let s talk about a couple more sources of variability In the last two minilectures you heard about SNP noise and what that means and how it depends on whether you are studying humans rats or mice Here let s address a couple more variables One is tissue heterogeneity and noise due to that Now let s compare and contrast tissues versus cell cultures Tissues have mixed cell populations and obviously between different regions of the same tissue or different individuals the same tissue you can have different ratios of those different cell populations So of course your expression profiles will re ect that You have the additional complication with patients in that you have pathology So let s take cystic fibrosis again In lungs with cystic fibrosis patients you might want to compare them to normal lungs but recognize that there are a lot of differences going on in that tissue a lot of cellular changes in addition to just the CFT mutation and the loss of the cystic fibrosis transmembrane regulation protein You have inflammatory cells You have bacteria even invading the lung So when you are looking at pathological tissue keep track of what is primary and what is secondary and what you want to study in that experiment Now it is tempting to say that cell cultures are the best system to expression profile and indeed you often have a single cell type on the dish But there is a whole other plethora of variables and in fact in looking at these variables we typically discourage people from cell culture experiment both from our own personal experience and because of a number of variables that are difficult to control For example just take serum If you take different serum lots say fetal or even neonate or different types of serum different organisms those are all a tremendous variable There is different amount of growth factors even if you take the same fetal bovine serum Different fetal bovines are outbred so you have all sorts of variability within that serum You have problems with cell density problems with maintaining temperature and C02 And in general we find that maintaining all those extrinsic variables in cell culture can be quite difficult and so often we go back to the tissue to look at primary instead of secondary problems that you can t contro SLIDE 3 Let s talk about one other thing which is noise due to the disease So for example do the patients and animals under study share the same primary problem An obvious example that was brought up a number of times is with patients with CFTR mutations They can have the same mutation and the same gene So there that is controlled and you shouldn t have much noise but we went over before about environmental noise and SNP noise An extreme example on the other hand is chronic obstructive pulmonary disease and you have many different etiologies and many different environmental effects which can cause COPD So you have a very heterogeneous population Chronic obstructive pulmonary disease is considered genetically heterogeneous so you have a lot of noise due to the different disease mechanisms Whereas patients with CFTR pretty much are considered genetically homogeneous where all patients involve mutations of the same gene So in this paradigm it can be okay to mix CF patients to normalize noise as far as tissue noise and SNP noise but it can be very problematic to mix COPD patient tissues because you don t even know if they share the same primary etiology Often in profiling experiments we search for the underlying genetic heterogeneity 7 these are term ed subclassification experiments SLIDE 4 So show me the noise Where is it Give me an example Show me a how you design an experiment and how you account for different sources of noise So here we are going to use an example of muscular dystrophy You can also see a written example which is downloadable from this site as the Sample Proposal Form Now muscular dystrophy patients have defects in muscle tissue There is usually a single genetic mutation leading to a single biochemical defect Now muscle has some advantages It is relatively Page 5 of 16 homogeneous It is about 50 or 30 of your body mass and as you can tell from going to the supermarket with meat when you buy different cuts of meat it is still recognizable as meat So it is somewhat homogeneous at least It is generally ash frozen which is nice because the standard pathological preparation for muscle is just immediately taking it from a patient and as soon as possible putting it in an isopentane cooled liquid nitrogen and ash freezing it very quickly which is ideal for RNA preparation Now let s take a specific example Duchenne muscular dystrophy the first positionally cloned gene All patients have mutations in the dystrophin gene and are lacking dystrophin in their muscles So like cystic fibrosis we have a genetically homogeneous population under study So in this case we would like to match for age of the patient because the disease is progressive so we want to limit our studies to one stage in the disease We want to control for sex In this case Duchenne dystrophy generally affects only males so we make sure everyone is a male And then we want to make sure the same muscle is sampled SLIDE 5 So how much noise is there in this system and where does the noise come from So if we look at this what we have done is taken a single muscle biopsy from a patient divided it into two parts and then expression profile those individually So this graph here is a scatter graph On one axis is profile from one muscle biopsy and on the other axis the yaxis is a profile from the second piece of the same muscle biopsy And you can see that things line up pretty well So in this case you have two different regions of the same biopsy that give similar expression profiles However let s take a different patient and do the same thing And here is the same experimental design but simply done with a different patient We again have two different regions of another biopsy but in this case look how different the profiles are You see considerable scatter So what this analysis tells us is that tissue heterogeneity may or may not be substantial source of noise in the interpretation If you are lucky you can take two regions of the same biopsy and find very similar expression profiles which imply that the cellular content is very similar between those two pieces However a second patient we did the same thing and you find very different profiles So tissue heterogeneity can be a significant source of variation SLIDE 6 So how do you visualize the source of variabilities So here what you typically do is what is called cluster analysis or hierarchical clustering and you define the extent of sharing between different profiles Now in this case I am going to show you unsupervised clustering where we are not telling the program that there are any variables between the samples that we know about We are just saying Here are all the profiles from a bunch of different Duchenne muscular dystrophy patients Some of them are mixed Some of are individual Some are just duplicates but tell me what is related to each other So the software itself in unsupervised clustering determines which profiles are most closely related So here is a large series of profiles from Duchenne muscular dystrophy patient muscle biopsies One I want to point out here first is in one instance we took the same RNA sample the same hybridization cocktail and just put it on two different arrays And that is these 3AD for duplicate and 3A Now you see the branches of this dendrogram the lower the branch point the more closely related the samples are And if you look across all these profiles the arrow here is pointing to two profiles that are very highly related And this isn t generally what we find We find that the actual procedure the actual hybridization of the chip the scanning of the chip and the use of the Affymetrix algorithms to determine if absolute intensity the level of variability is very low and based on hybridization intensity is extremely reproducible And in fact it has gotten quite a bit better over the last couple years Those Affymetrix production facilities have been upgraded and the chips have become more and more consistent So in general we find that experimental variability as far as the process of the experiment is very low and that is shown here in these particular duplicate arrays which is what we find in general SLIDE 7 Okay what other sources Now here as I mentioned previously we have taken two different regions of the same biopsy as shown in the scatter graphs Here we are seeing profiles of 6A and 6B which is patient six one region of the biopsy versus the second region of the same biopsy and we find that the branch point is relatively low And in fact the program looks at all these profiles and says Yes these two profiles are highly related and we say Good because they are from the same patient so we were hoping they were Page 6 of 16 highly related This also suggests that at least in this patient there isn t much tissue heterogeneity However we can look at two different regions of the same biopsy of a different patient and in fact as shown here these 1A and 1B profiles again from different regions of the same biopsy actually show up in entirely different regions of the dendrogram In fact they are branched all the way up at the top even before control individuals that are normal are branched So this is the same sample corresponding to the dendrogram previously and it can show that depending on your patient depending on your tissue it can either be a huge source or very little source of variability in your expression profile SLIDE 8 So let s continue this analysis and continue looking at this dendrogram Now if we divide five biopsies from five different patients into two parts each We mix equal amounts of the five biopsies into two pools Keep in mind that the two pools here are from the same patients but the two pools are derived from different regions of the biopsies So there is actually no RNA or tissue shared between the two pools So then we hybridize each pool to an individual chip in this case So where do those show up Well that is the arrow here and in this case it is control individuals You can see that even though we used different regions of the biopsies the branch point is extremely low It is almost all the way on top of the profiles and this suggests that when you ve mixed you have in fact normalized SN39P noise because there are multiple individuals here and you have normalized tissue heterogeneity And when you think about it that makes sense You are now taking many different regions of different biopsies and mixing them all together so any variability due to tissue heterogeneity should be normalized You are basically averaging it all out and the same with the multiple individuals Any SN39P noise inherent in this population of patients with Duchenne muscular dystrophy patients is also normalized out You can see that that has normalized out all that SN39P noise lIixing does normalize out all these different sources of variability Of course a critical point here is that if you are focusing your study on tissue heterogeneity or if you are studying SN39P noise you definitely don t want to mix patients because you are effectively normalizing that all out SLIDE 9 Okay so what is appropriate The bottom line is it depends It depends on what you want to study If you know the primary variable where we know mutations in the CFTR gene cause CF or in the dystrophin gene cause Duchenne dystrophy and we only care about what is shared and not what is different between these genetically homogeneous patients then it is perfectly appropriate to mix samples It saves cost time and samples If you are interested in variation between patients then certainly this is not appropriate It is statistically much more powerful to do many individual profiles Now there are some analytical considerations that must be kept in mind In other words once we generate either mixed profiles or individual profiles how we can analyze that data statistically and functionally in the end will differ depending on the approach SLIDE 10 Now one method that we have used quite extensively is iterative or pairwise comparisons between small numbers of profiles Now we have a very extensive description of this protocol and it is published and we refer you to the website where there is extensive amount of text and examples and data that shows the output of this and the website is given here It is microarraycnmcresearchorgPGAhgm Basically what we are doing is we are taking a low number of profiles let s say mixed control profiles just two of them and mixed Duchenne muscular dystrophy profiles just two of them and with those two controls and two Duchenne profiles it is possible to do four iterative comparisons We can do control one versus Duchenne one or control one versus Duchenne two etc etc You end up with four different comparisons and we simply say Show us all genes that consistently have foldchanges greater than two So two fold or greater changes in gene expression levels between these four iterative or pairwise comparisons So the output then is average foldchanges You do not get P values A big important variable to recognize when you are doing analysis by foldchanges is that you are creating a ratio a ratio of expression level between a control profile and a specific gene in a control profile and a muscular dystrophy or experimental profile If a gene is not expressed significantly above background in one or the other profile that low level of gene expression becomes your denominator in the ratio So you end up with a five fold increased expression say in you experimental In which case your control becomes the denominator When your denominator Page 7 of 16 approaches zero or background levels recognize that this yields an unstable ratio As you know from high school or even junior high in math if you start dividing by zero your ratio goes towards infinity So because of that the lower your denominator gets the closer it gets to zero you start generating an inaccurate and exaggerated ratio Fortunately Affym etrix software takes that into account and will report out what is called a tilde sign which says Look your denominator is approaching zero One of these profiles is right at background or even below background There is definitely a foldchange You are getting a huge increase in one relative to the other but take the foldchange with a grain of salt because we are dividing by zero You are making things go toward infinity so be careful of this SLIDE 11 Data analysis of large numbers of profiles is a different method You obviously can t do iterative comparisons if you have 30 profiles It would generate an exponential number of comparisons and it gets mathematically complex So in this case with large number of profiles you generally want to export absolute analyses which in Affym etrix are called signal values import these into some other data mining tool such as Gene Spring SpotFire or even Excel which are ones we commonly use and then you generate P values between groups of profiles So if we take say 10 Duchenne dystrophy profiles and 10 normal profiles we have enough data there to actually generate P values How significant are the foldchanges or the differences in gene expression for each individual gene GeneSpring typically does not give you fold changes You actually have to do some additional analysis to say Okay we have a highly significant P value to this particular gene but what does it mean as far as foldchange Another thing which we will go into in subsequent minilectures is that when you have a large number of profiles temporal clustering becomes incredibly powerful approach so in other words generating a series of profiles at different time points after a single stimulus or variable For example one experiment that we will go over later that we have done in our lab is to induce muscle degeneration in mice Once you have induced muscle degeneration experimentally you then harvest tissue at defined time points during the regeneration necrosis then regeneration and then you profile each of those time points enter then into your software of choice and develop temporal clusters Now we won t go into it in this lecture but be prepared for that for next lecture block and that is an incredibly powerful approach to define coordinately regulated genes genes down stream of transcription factors new downstream targets all sorts of things and we will go over that later SLIDE 12 So one or two last slides What is the sensitivity or specificity of mixed versus individual profiles So here again is another example We take five Duchenne dystrophy patients two regions of the same biopsy all profiled individually and take these ten different profiles then and put them into GeneSpring to generate P values relative to similar control samples On the other hand we take the same RNAs and mix them into two pools So we only have two profiles instead of ten but now we are going to do the iterative fold change or pairwise comparisons that generate foldchanges Now let s compare these two approaches Remember with GeneSpring you are going to generate P values between two groups of profiles whereas with the mixed pools of samples you are going to do pairwise comparisons and generate foldchanges Individual profiles are shown to the left here and if we take those ten different profiles which is five Duchenne dystrophy all done in duplicates and compare them to a series of normal controls by GeneSpring we end up with nearly 1500 genes that have a P value of less than 005 very statistically significant Now another point here is notice since you are doing so many comparisons you can actually have quite a few genes that randomly will show a P value of less than 005 in fact roughly 5 of all the genes under study So there should be some noise in this analysis at P less than that 005 So of these 1500 genes maybe quite a few of them are noise and many of them are actually true differences Approaches such as permutational pvalue testing help to eliminate these false positives On the right we see the other analysis protocol which is mixed profiles Here we are only doing two Duchenne and two control profiles but from different regions of the biopsy many individuals And you see many fewer genes that survived these greater than two fold change in gene expression in these four iterative comparisons and again this is explained in more detail on the website But here you see only 400 genes So which genes are shared between the two approaches And what you see is the large majority of the mixed profile changes are in fact shared with the individual profiles with a P value of less than 005 Now the percentages in the center and the right the 85 and the 15 refer to the mixed profile So we see that 85 of genes detected with greater than two foldchange Page 8 of 16 by the mixed profile approach remember which is a small number of chips and a mixed bunch of patients still you can see how specific that is In fact very few changes 15 are not included in the individual profiles of P less than 005 So by this analysis it seems that mixing is relatively specific but not very sensitive SLIDE 13 The conclusion here is that mixing is a low cost alternative or means by which unwanted variables such as in this case SN39P noise due to different patients and tissue heterogeneity due to variability Within the muscle biopsy can be normalized Mixing is generally specific but generally insensitive depending on the P value that is used to cut off and the foldchange cut off that are used for comparison Experimental design must consider all sources of variability and conduct its profiling experimental design to either normalize or at least account for these variables Page 9 of 16 LECTURE 2D SCREENING STRATEGY TO ID SLIDE 1 Based on the large amount of interindividual variability caused by SN39P noise or transcriptional flux that we have talked about in the first or second minilecture in this block in the third module we have put forth a strategy to identify candidate genes which may be correlated with these disease specific processes Remember that is what we are trying to figure out and get to rise above the noise caused by SNPs In the fourth minilecture we will talk about validation techniques How do we validate these candidates at the protein or functional levels the ultimate test of whether your experiment was a success We can use a pooling strategy to eliminate noise and get diseasespecific correlates to shine through without using really any statistical analyses SLIDE 2 So the first phase of this screening strategy is to use pooled samples on very large Affymetrix arrays to identify preliminary candidate genes In the second phase those candidate genes can printed down onto glass spotted arrays and a large number of samples are screened with this inexpensive array on a subset of the tens of thousands genes that we preliminary screen with Alternatively we can screen this independent sample set on larger arrays if we have the funds Now that costs have dropped dramatically for all large arrays this becomes a feasible and in fact encouraged approach SLIDE 3 So again phase one is candidate gene identification We take five individuals with a given phenotype and five matched individuals without that phenotype isolate RNA from each individual synthesize cDNA and cRNA from each sample and pool the members of each group of five in equimolar amounts to form pools Thereafter each pool is hybridized to an individual Affym etrix array and for example in humans we are using the Ul33A and B chip set representing N45000 transcripts For mouse we are using the complete U74 chip set and for at we are using the complete U34 chip set and each one of these is screening tens of thousands of different transcripts in a single hybridization Again we routinely identify at least 80 true positives through this strategy There are certain confounding factors which we have talked a little bit about which make this strategy suboptimal and primarily those would be heterogeneous diseasecausing processes So for example if there is one primary mutation which results in the same phenotype in three out of the five samples and the other two has a different mutation which results in a similar but non identical phenotype and both operate through independent processes basically you will not have a consolidated pathway which is going to rise about the noise and in fact they might actually negate each other So whichever way this falls out whether you obtain a very nice gene list of candidates which all make sense or you don t you know that if you don t there may be some heterogeneity going on and you have only hybridized four array sets so then you can actually go back and assume there ma be heterogeneous processes and flush this out with individual arrays So as a screening approach we feel this is a very valid screening approach SLIDE 4 So the schematic of what this looks like is presented here where five individuals illustrated at the top one throuin five have biopsies taken Those biopsies are actually split in half in this example and half of those biopsies go into one pool and the other half go into another pool And what this does is basically account for experimental variability and really isolates just the biological noise from the experimental noise and all of these labeling processes are independently done on each sample and finally they are pooled and labeled onto the GeneChips SLIDE 5 We have discussed the iterative comparison strategy in the previous minilecture Page 10 of16 SLIDE 6 The second phase is validation One option is to take these lists of change calls that are illustrated by steps 123 and 4 and print all those gene probes onto glass slides And we have talked a little bit about where those clones sets available from for example though research genetics you can just send them a list of clones and they will send you back the clones We can use our cDNA array printer to print those onto glass arrays in larger volumes meaning we can make a hundred slides at a time and we can screen a very large sample set or patient set on a large number of arrays and thereby statistically validate that these outliers are true outliers over a large number of samples And that is done through the standard correlation analysis which we will talk about in this subsequent lecture Again cost permitting validation should be done on the largest arrays possible on these new samples so that that data may be archived for additional use in other projects SLIDE 7 This was an example an array that is currently used in the lab This happens to be a 22000 element cDNA array SLIDE 8 So the phase two statistics are relatively standard correlation statistics which will be addressed in a subsequent lecture and there are many different ways you can look at this where you look at standard differences in means between two classes You can look at differences in the ratio of variances between two classes You can look at intergene interactions for example using relevance networks etc But the end goal of the phase two is to actually get a P value for each of the genes on this subset arrays say whether they really are correlated to the disease process or they aren t SLIDE 9 So in conclusion there are two phases to our experimental design and this is really based on all of the SNP noise discussion that we have already went through A very large screen using large arrays can be done in phase one on two pools a disease pool and a control pool as a training set These candidate gene can either be printed onto custom spotted arrays and much larger sample sets screened for statistical validation validation set or this can be done on large arrays as well Finally of those genes which show statistical correlations ie P values less than 005 with whatever correlation statistic we are using will be shunted into protein and functional validation as discussed next Page11of16 LECTURE 2E VALIDATION TECHNIQUES SLIDE 1 Welcome to minilecture five in block two In this minilecture we are going talk about how to validate the results that you have gotten through your screening strategy So how do we that There are three different levels at which we can validate We can validate the RNA level We can validate at the protein level which is taking those the next step and we can finally which is the gold standard validate through a functional assay to see whether this protein causes the disease or has something to do with the disease SLIDE 2 So if we look at the RNA level there are three standard techniques that are available today to really validate the array results Northern blots are the standard lowtech lab technique which every molecular biology lab has access to and can be very quickly and easily done Quantitative reverse transcription followed by PCR is a more robust technique in that it requires lower RNA input amounts but it is also a little more expensive and difficult to do And finally we can use arrays to screen a large number of independent samples for every gene in parallel So now you get validation results on every single one of your change calls in a single hybridization over many samples So all of those validation techniques at the RNA level really get at the question of are your array results real and do they hold up over a large number of samples in these two clinical groups The next step and this is almost really mandatory these days is to validate those results at the protein level as well So remember RNA is translated into protein and if that protein is not concordantly dysregulated as its transcript was then the RNA results are really meaningless And so either by Western blot or by immunohistochemistry we want to say Yes this upregulated transcript results in an upregulated protein which has something to do with the disease Finally we want to look at that protein which is disregulated and say If we tone it down or tone it up does it mediate the disease And that is the final gold standard for a validation technique SLIDE 3 So validation at the RNA level The first thing we ll talk about is the Northern blot and again this is standard lowtech procedure in which total RNA is electrophoresed through an agargel It is size fractionated it is transferred onto a nitrocellulose or nylon membrane and it is probed with the gene you are interested in looking at and validating So the pros are it is very easy very cheap and very straightforward to do The protocols have been optimized and the cons are that you need a fairly large amount of starting RNA So in each well of your Northern blot you need at least 10 micrograms of total RNA and if you are only working from a very small chunk of flash frozen human tissue that could be more than you can get And in addition that could be an extremely valuable tissue that you don t want to chew up needlessly And so this is where this technique falters in that it is very difficult to make that decision as to whether to use your entire tissue on this validation technique And finally it is a lowthroughput assay meaning you can only look at one transcript or one probing of a Northern And so this requires multiple stripping and rehybridizations of the same blot if RNA quantity is limiting and this really is not going to work for hundreds or thousands of different probes SLIDE 4 So to get around the limiting amount of RNA input that is required on a Northern blot you can do quantitative reverse transcription followed by PCR And in general what is done here is you isolate a very small amount of total RNA For example one microgram of total RNA we will reverse transcribe it into a cDNA or a copied DNA strand and use that as an input into a PCR reaction which is quantitative meaning that given a set input amount of cDNA you know how much amplification you get out on the back end and you can compare that across tissues This is usually done with an internal standard for amplification which doesn t vary across the different tissues and it is really a nice robust technique which requires very little RNA input The cons are that it is very expensive You need up to 100000 dollars of hardware sometimes to do this and each single PCR probe set is fairly expensive to design and use and this is primarily because uorescence is involved So expensive assays are run perhaps prohibitively expensive for hundreds of Page 12 of16 differentially expressed gene and again it is a single transcript assay So it is relatively lowthroughput You end of spending a lot of money and a lot of time validating a lot of genes if you have a lot of genes SLIDE 5 What we chose to do was validate at the RNA level using arrays These arrays can be custom printed or stock arrays This is a very highthroughput assay which is the major pro of this validation technique It is relatively inexpensive on the order of between 50 to 600 dollars per hybridization per assay and you need relatively low RNA input When this technology first came about people were using 100 to 200 micrograms of total RNA and as you just heard that is way too much for a biopsy from a clinical sample You are going to chew throuin your whole biopsy and probably need more biopsies just to fulfill that requirement But recent advances in amplification schemes have resulted in an input requirement of often much less that a single microgram of total RNA So we are really getting the best of both worlds here a very highthroughput and inexpensive assay coupled with a very lowinput requirement for this assay The only con is that a large amount of hardware is required on the order of about 150000 dollars and some expertise is required to perform this technology But that is why you have the NTNDSNIMH Amicroarray Consortium After you have done your multiple parallel hybridizations on a number of different tissues you can then input those intensity values into a program and say Well in this group of 50 nondiseased samples versus this 50 diseased samples is there a significant difference in the expression level of one gene And that is what is illustrated on this panel on the left of this slide On the left panel you see tumors at the top which have genes turned on represented by green spots On the right you see a different disease class of tumor which have that gene turned off and they are red This is just a graphically illustration of this this is a hierarchical clustering program that can be downloaded as freeware from Stanford but what is graphically represented here is a significant P value There is a significant difference between the expression levels in this gene with these tissues with a P value of less than 005 and now you have something hard and fast that you can hang your hat on and move forward into more complex experiments SLIDE 6 Okay so now we have validated some genes at the RNA level in an independent sample set What s next We really want to ask a question Is it true that this dysregulated gene produces different amounts of protein And there are a lot of intermediate steps that happen between a transcript being exported from the nucleus and its eventual function So we really want to look at the mature protein and say s it differentially expressed between the two states as well There are two ways you can do this The first is by immunohistochemistry Simply taking a paraffin or a ash frozen biopsy from a clinical sample slicing it very thin putting it on a microscope slide and probing that with an antibody directed against the gene product or the protein of interest So a very elegant technique which gets you at tissue heterogeneity very nicely because you can say Alright my tumor only comprises a third of this tissue blot and that is the third that is lighting up with this antibody So immediately you have an assay for tissue heterogeneity which can lend more credence to your results or less credence to your results given how it falls out but basically a very elegant very nice technique Not quite highthroughput in that every tissue section has to be stained with an individual antibody and assayed under a microscope It is very difficult to get quantitative signals from this type of technology And another one of the cons of this is that only about 20 of antibodies currently available work on paraffin sections and this is what hospitals are full of The vast minority of samples are actually frozen as soon as they come out of a patient and you can get almost any antibody to work on a frozen section but only about 20 of them to work on a paraffin section SLIDE 7 Again this is a single transcript assay What we can do though is do this in parallel to make it a high throughput assay And so on the left you see the paraffin left that you have walked upstairs from the basement of your hospital with and a small bore punch can be taken of that paraffin blot from a single patient clinical sample and rearrayed into a recipient paraffin blot which has been prebored So now you can actually generate an array with your clinical samples That blot can now be sliced and hybridized with a single antibody and you can compare multiple tumors in the same paraffin blot And the close up on the right actually shows you that you can maintain histology even though you are taking these very small Page 13 of16 punches So now you ve got a highthroughput assay for validating your results Western blots can be also used to validate the protein level and that gets you at the same answer Although it doesn t get you at the answer to whether there is tissue heterogeneity in your sample SLIDE 8 Finally the gold standard is validation at the functional level This is again done on a genebygene basis It is fairly tedious and lowthroughput and really occupies a lot of time in the laboratory but this is what you ultimately want to do You want to show that this protein which is dysregulated can be blocked or turned on and that has an impact on the state And here this is a figure from a recent paper from our group which shows that in certain brain tumors you can block an over expressed protein with a neutralizing antibody and have the tumor lose its metastatic potential So now we have gotten at the gold standard and now you can actually take that into a clinic and say Okay well let s design therapeutics against that protein that blocks metastasis And this is a real life example of how array results can get you to the clinical end point of interest which is therapy Therapy is what you want to do Diagnostics is the other arm of what you want to do in the clinic and based on previous slides you see that now we ve got a set of genes which tell tumors apart So now we can use those sets of genes to go in and diagnose the patient in this case even before the metastatic disease happens and tell them whether they can go home and they are going to be fine or whether they need these directed new therapies that we are going to be developing SLIDE 9 So the overall conclusions for lecture block two are m that SN39Ps or SN39P noise can obscure disease specific expression changes and you really need to get around that by validation and specific rodents can either have SN39P noise or not depending whether they are inbred or outbred strains and those can also obscure disease specific expression changes M we can parse out the noise and get around it and rise above it through a two tiered strategy The first is the candidate gene identification phase or training set So we start with two pools of clinical samples five samples in each pool isolate RNA expression profiles and we a candidate gene list This list is going to contain a number of true positives and a number of false positives and we really want to verify which are the real ones and which are the noise that made it through that first filter The next step is to screen a very large number of new samples on arrays and see if the correlations hold up when blinded to the state of interest We can then take this more robust gene list forward validate those at the protein level and finally show that they are functionally relevant Page 14of16 LECTURE 2F QUALITY CONTROL In this minilecturelecture we will talk about the generation of highquality microarray data As you know microarrays are powerful tools for the study of gene expression The number of laboratories relying on this technology is constantly increasing and to avoid experiment redundancy it is becoming a comm on practice to share microarray profiles in the worldwide web through the implementation of expression profiles databases In many circumstances however the quality of data in these databases is poor due to the lack of quality control In this lecture I will list some of the quality control standards that each Affym etrix GeneChip microarray here at Children s National Medical Center in Washington DC needs to meet before it is made public to the scientific community Quality control checkpoints are applied at different levels during the generation of microarray data and the ones we mostly take care of are shown in this slide Now let s see them one by one Regarding RNA extraction we generally prefer to extract total RNA instead of messenger RNA to minimize the loss of transcripts during the procedure The minimal amount of total RNA that we work with is six micrograms when doing a singleround of amplificationlabeling and its integrity is generally analyzed in a 1 agar gel Intensity and size of the two ribosomal bands is the main characteristic that we look at If the sample does not show good ribosomal bands it is discarded The next checkpoint is at the level of the cRNA fold amplification During this reaction using a T7 RNA polymerase promoter the cDNA is biotin labeled and amplified into cRNA The cRNA amplification rate is measured and samples showing amplification rates below four are not accepted In addition what this slide shows is the use of replicates In our lab generating replicates means that each tissue is divided into two parts and each part is analyzed on a microarray As you can see in the slide the first biological sample 1A is divided into two parts lAa and lAb These two are the replicates of the same biological sample The reason for doing so is to build a statistical strength so that we can compute variances and P values based on real biological replicates rather than using the error model The error model is described in section three of Gene Spring tutorial When replicates are generated we also calculate the correlation coefficient or R2 value This is done by loading into excel the values deriving from each metrix file and based on the results we decide to keep or discard the microarray Acceptable szalues vary depending on the tissue used For microarrays deriving for example from inbred mice the minimal acceptable value between replicates is 098 For any other tissue we don t accept values below 095 Another checkpoint is to look for saturated probe sets Each microarray according to protocol is stained with Streptavidin twice staining one and staining two The second staining staining two is done to enhance the signal and detect a low abundant transcripts However it can lead to a saturation of the probe sets The saturation is generated by what we call the seaming effect that is the situation in which the signal intensity measured by the laser is not correlated with the abundance with the real biological transcript and this happens when the signal reaches its maximal intensity The only way to check for saturation of probes is to plot a scatter graph with the data deriving from the first and second staining As you can see in this slide in the upper right corner each dot represents a probe set or a gene and for some of these the similarity between the first and second staining is lost Some dots are in fact outside the boundaries and this indicates the saturation effect The first and second staining are represented respectively on the x and y axis of this graft Identification of saturation probes is very tedious work and currently an automated script has been developed in our center for a fast and efficient detection and which can be downloaded from this portal srte Let s talk about scaling factors Scaling factors are computed by the Affymetrix software and are based on the average intensity and global scaling values This is one form of normalization linear normalization to be specific The total intensity of the entire array is determined and then each gene multiplied by the same scalar to bring every chip to the same total intensity Another excellent algorithm for scaling is available in the dCHIP suite of software which is downloadable through this portal In each microarray the signal of Page 15 of16 Supplemental Notes on the Role of Law School Grades in Labor Market Outcomes For New Black Lawyers May 2005 In Part VII of Systemic Analysis I argued that law school grades play a much larger role than previously thought in determining the labor market outcomes of lawyers in the early years of their careers I presented there a series of analyses based on the After the JD database which collected data on over four thousand lawyers who passed the bar in 1999 or 2000 and who were surveyed and studied in 2002 and 2003 These regressions use a variety of controls to assess the effect of law school grades and law school prestige on the selfreported earnings of attorneys in the study Grades I found were a consistently very strong predictor of earnings In my Reply to Critics I discuss whether the earnings I reported in Systemic Analysis for all lawyers hold equally well when we consider blacks alone I report that if anything grades appear to have even more impact on black lawyer earnings than on those of lawyers generally To illustrate the point I present Table 1 below which reports an identical analysis for all participants in the AJD whose data includes the relevant variables and for blacks only As the reader can see the raw and standardized coefficients for Law School GPA are substantially higher for the blacks only regression than it is for the all others sample The blacks only analysis also has a higher R2 than the regression for all the other lawyers suggesting that these variables are at least as powerful in explaining the earnings of blacks as they are for attorneys generally Table 2 below presents the same regression but includes attorneys working for federal state and local governments and includes a dummy variable for government attorneys The GPA coefficient for the blacks only sample is again larger than the coefficient for all the other lawyers However the difference between the black and the all others GPA coefficient is smaller in this table than in Table l GPA seems to play a larger role for black outcomes but the difference is quite small Of course both Table l and Table 2 are based on quite simple models but partly for that reason they provide a straightforward way to compare the relative importance of GPA for black lawyers compared with lawyers generally Tables 3 and 4 below replicate Table 73 and 74 from Systemic Analysis but include additional columns showing the results for blacks only Table 3 improves on Table 2 in a two important ways more variables are controlled for and prestige is treated as a categorical variable rather than a continuous variable Table 4 adds one further improvement GPA is standardized for each individual by comparing the GPAs of each respondent to other reported GPAs from the same law school In both Table 3 and Table 4 the coefficient for Law School GPA is larger for blacks than for the all others sample whether one is looking at raw coefficients parameter estimates or standardized coefficients This result is so consistent across all of the analyses that it seems fair to conclude that employers give greater weight to GPA in evaluating black candidates than white candidates 7 perhaps as I suggest in my Reply to Critics because racial preferences make school quality a less reliable indicator of student aptitude for blacks than for others who don t receive preferences Note that one difference between the black regressions and those for all other respondents in Tables 3 and 4 is that no values are reported for Tier 7 This is because the number of black respondents in Tiers 7 and 8 was so small that I had to combine them to have a reasonablysized comparison group Indeed a general weakness in replicating Tables 3 and 4 for the blacksonly sample is that the sample size is too small to get very reliable measures in terms of standard errors of the coefficients for individual tiers for space reasons I haven t reported standard errors in these tables but standard errors are correlated with pvalues and the pvalues are generally higher in the blacks only columns than in the all others columns The general pattern of these coefficients however closely tracks the coefficients for the all others sample though the premium for the top tiers seems to be higher for blacks In Systemic Analysis p 465 I used the parameter estimates from Table 74 to roughly estimate the tradeoff to blacks of foregoing racial preferences Readers should refresh their memory of that discussion to easily follow this one To duplicate this estimate using the black only regression in Table 4 we first focus on the parameter estimate for standardized law school GPA 149 7 the percentage change in earnings resulting from a one standard deviation improvement in GPA If the median black is currently about two standard deviations below the median white in grades this would imply that closing the whiteblack grade gap would raise black earnings by 2 149 or 298 In contrast the earnings premium of a Tier 1 school versus a Tier 3 school is 488 261 227 which represents the earnings loss ofa black student from going to a moderately elite rather than highly elite institution The net tradeoff favors grades over prestige by a larger margin than in the original paper Of course the coefficient measures for blacks are based on a smaller sample and thus have more measurement error than the measures for the all others sample If one only had the black sample one might pause before making broad claims about the gradesprestige tradeoff But in combination with the highly significant results from the larger sample one can be highly confident that these patterns are real Law School GPA plays an enormous role in determining the earnings of lawyers early in their careers and the implicit tradeoffs involved in racial preferences appear to lower not raise black earnings 1 If a coefficient is statistically significant say has a p 04 then we have a lot of confidence that even with measurement error the coefficient really is greater than zero But we d be much more confident that the true value of the coefficient is say between 35 and 45 if we had a lower standard error and a p 0001 Thus the coefficients in the all others are generally more precise than the coefficients in the blacks only sample 7 an important consideration in the types of calculations presented in this paragraph of the text Table 1 Table 5 in Reply to Critics Table 71 in Systemic Analysis Simple Regression of Earnings for Secondyear Associates in Private Firms Dependent variable Lawyer earnings Tier ofMetro Market Raw Law School GPA Asian Other Male Model Statistics N119 Source After the JD supra note 237 national sample and racial oversample unweighted NA Not applicable for the Black sample Table 2 Table 1 with Government Attorneys Added Dependent variable Lawyer earnings Tier ofMetro Market Government Raw Law School GPA Asian Other Male Model Statistics N179 Source After the JD supra note 237 national sample and racial oversample unweighted NA Not applicable for the Black sample Table 3 Regression of Earnings of Attorneys Completing Second Year of Practice from the AJD Sample Using Raw GPAs Blacks All Others Parameter Standard t Value pivalue Standardized Parameter Standard t Value pivalue Standardized Independent Variables Estimate Error Estimate Estimate Error Estimate Tier ofMetro Market 0069 0022 317 0002 0189 0118 0006 2078 lt0001 0 348 Private Sector 0274 0068 401 0000 0261 0370 0021 1763 lt0001 0292 Raw LaW School GPA 0472 0075 63 lt0001 0352 0353 0023 1552 lt0001 0250 School Prestige Tier1 0451 0106 425 lt0001 0296 0297 0052 573 lt0001 0179 School Prestige Tier2 0252 0104 243 0017 0161 0170 0050 341 0001 0120 School Prestige Tier3 0267 0090 297 0004 0207 0117 0048 241 0016 0088 School Prestige Tier4 0029 0081 036 0721 0024 0038 0047 079 0427 0031 School Prestige Tier5 0164 0088 186 0065 0113 0052 0047 109 0275 0042 School Prestige Tier6 0138 0102 136 0177 0084 0021 0050 042 0675 0013 School Prestige Tier 7 0064 0048 133 0183 0046 Asian NA NA NA NA NA 0035 0027 131 0189 0020 Hispanic NA NA NA NA NA 0005 0027 019 0851 0003 Other NA NA NA NA NA 0006 0036 016 0872 0002 Male 0065 0053 122 0224 0066 0047 0015 313 0002 0049 Has Children 0099 0078 127 0207 0084 0031 0020 157 0117 0028 Bar Year ofAdInission 0007 0098 007 0941 0004 0007 0021 032 0747 0005 Moot Court Participation 0087 0040 215 0033 0116 0001 0011 012 0906 0002 School Govt ParticipantLeader 0046 0036 126 0210 0066 0019 0013 153 0125 0023 Earnings Important as aGoal 0102 0030 342 0001 0191 0047 0009 495 lt0001 0076 Working Fulltime 0284 0325 087 0384 0047 0367 0057 649 lt0001 0100 Has Other Job 0066 0149 044 0660 0024 0006 0043 014 0891 0002 Associate or Staff Attorney 0031 0070 044 0657 0032 0172 0022 785 lt0001 0180 General Clerkship 0396 0334 119 0238 0065 0041 0072 057 0568 0009 Hours Billed 0120 0086 14 0164 0116 0149 0022 666 lt0001 0152 Hours Worked 0001 0002 078 0436 0042 0002 0001 379 0000 0058 33721 hys SCiMam Umergmdume 0154 0092 168 0095 0091 0213 0028 763 lt0001 0 116 Has MBA 0109 0166 066 0512 0035 0015 0056 028 0781 0004 Roman Catholic 0163 0099 164 0103 0095 0021 0021 103 0303 0017 Jewish 0067 0037 178 0075 0028 Married Currently 0046 0064 072 0471 0045 0021 0016 13 0195 0022 LaW in Family 0050 0044 113 0259 0058 0021 0010 205 0040 0031 Age 0087 0035 246 0015 0147 0025 0011 231 0021 0038 Model Statistics Adj RZ0636 N157 Adj RZ0594N1857 Source After the JD supra note 237 national sample and racial oversample unweighted For de nitions of key variables see text Median income of respondents is 80000 Used as part ofthe compaIison grou NA Not applicable for the Black sample Of the in nitely Ray Luo September 2003 Preface A rigorous and logical narrator historian writes a novel while illustrating concepts of neuroscience a la Mon oncle d Amem39que discussing literature and culture a la An Incomplete Education and recalling memories of childhood a la Remembrance of Things Past The novel is constructed to re ect the existing web of knowledge as a work of creative non ction and can e ectively be subtitled an intellectual voyage77 On a general level this book is an attempt to unify the art of written communication as a medium in a single volume combining the attributes of ction and non ction the goals of entertainment and education and the ideals of diversity and structure Don7t let that fool you The ambitions of this book are small It merely wishes to be just what it claims to be a book that is a sequence of intelligible words with no pictures no movie clips and no interactivity used to communicate ideas from one entity the author to another the reader coherently and e ectively A reader with mere curiosity should pick it up enjoy it without obstacles and be done with it saying l7ve just read an interesting book77 PREFA CE Contents Preface 1 Proust 2 Jelkes iii CONTENTS Chapter 1 Proust Of the in nitely many ways to begin this narrative I choose the one that leads to the most logical progression of causes and effects from my point of view Although the circumstances of my birth upbringing and education lead to an imperfect explanation of the events which I am about to describe they do account for much of my behavior and my perception of the behaviors of others after that crucial evening of September 30th a date wherein begins the chain of events that led logically from my prior existence to my current existence Any record of history must undoubtedly leave out some factor that explains a ne grain of some aspect of a motivation because no record can be complete else the reader can never hope to nish reading it much less for the historian to write it in the rst place Thus I must necessarily leave out much of the circumstances of my childhood in favor of recounting certain experiences of my youth when they are useful in explaining the mechanism in a progression of events I begin with that improbable catalyst that set off a necessarily sequence of reactions leading to my present demise the night of September 30th I say that it is improbable only because of the 365 times 23 or so op portunities for thinking seriously about the topic I chose or rather fell into the mood of that particular night to take the burden off my shoulders and contemplated what Marcel Proust was really talking about in his overture to Swamz s Way He begins as follows For a long time I used to go to bed early Sometimes when I had put out my candle my eyes would close so quickly that I had not even time to say to myself l7m falling asleep7 And half an hour 2 CHAPTER 1 PROUST later the thought that it was time to go to sleep would awaken me I would make as if to put away the book which I imagined was still in my hands and to blow out the light I had gone on thinking while I was asleep about what I had just been reading but these thoughts had taken a rather peculiar turn it seemed to me that I myself was the immediate subject of my book Thus as l was reading about Proust reading about himself while he fell asleep while I fell asleep I took the Archimedean stance if you will of sounding off an alarm in my mind crying out Eureka and saying to myself this boundary between consciousness and unconsciousness just the way l7ve expe rienced it its just the way Proust described it in the introduction to Swamz s Wag77 First there7s that bit about going to sleep early l7m all for going to sleep early when its possible Second the peculiar turns of thoughts had taken for me too an aura of comprehensible nonsense An idea taken to bed becomes so unique so exact and solves its problem so elegantly that were I to force myself in an effort equivalent to the conjuring of the idea I would immediately get up from bed to write it down Unfortunately if you do choose to write it down you would have much trouble deciding what exactly to write down as my semi annually kept journal would attest Perhaps it is true that Friedrich Kekule discovered the structure of benzene while dreaming of two snakes biting each others tails that Samuel Taylor Coleridge composed his masterpiece Kubla Khan in an opium induced dream state that Geoffrey Chaucer based his The House of Fame on a dream that Otto Loewi discovered neurotransmitters after an experiment to test whether stimulation of heart beat is electrical or chemical appeared twice in his dreams The rst time he scribbled some notes down and went back to sleep only to nd them indecipherable in the morning Not able to speak for them myself I can only say that for me personally most transiently promising visions arrived at in a dream are far less glamorous than they appear to be For example I once had an idea for a book that I believed would have changed the course of literary history and took the novel to a new height it has never been to before or so it seemed in an early morning half waking half dreaming state It goes as follows as I quote from my records Old man living in California second oldest man to ride a plane in the air force He talks to his wife in bed about following devil vs following hangman He meets another woman7 plays golf with her Her sudden suicide brings forth her only lost son He learns of her death by suicide His wife returns He talks of following the hangman Ultra realistic dream of a lm There is nothing much to write home about The monumental7 paradigm shifting7 Pulitzer Prize winning nature of the work could simply not be found And what is it about the devil and the hangman Here7s another seemingly great idea from my past reveries7 in the domain of ideas for motion pictures Film of a dream of bleeding in the left ear due to car crash or some other violent event Awaken to nd that ear plug in the left ear is hurting Example of adaptive nature of dream body tells brain what to dream ln fact7 l was even dreaming of the ear plug before I actually awoke to take it out Circular lmmaking l7ve also had7 via sleep and early morning half wakefulness slouching in bed7 an idea for a story I was to title Biography of John Gay A man who7 in uenced by Virginia Woolf and others7 wants to be a lesbian He changes his lifestyle cross dressing7 forcing others to declare him gay but he is not Like Mr Peter Bent7 he prefers to explore female sexuality7 but he is confused by his own sex7 which precludes him from lesbianism On the advice of Salvador Dali and others7 particularly how to write a novel types whose names are in a neat aisle at your local bookstore7 I kept ajournal of ideas and recollections of dreams This journal has never ceased to be a source of personal entertainment Unfortunately7 there has been no novels as of yet7 but just like Coleridge in writing Kubla Khan l7m hoping to catch a glimpse of the in nitely more beautiful realm of the dream and jot down some 54 or so of the original 300 or so complete lines from that vision in a dream77 revealed to me while incapacitated with a nite memory Here then7 nally7 is a vision I had rather recently7 an idea for another book A biography of Al Davis7 the man who loved the masses He wanted to win7 more than anything7 the heart of the universe around him He won lndeed7 the peculiar turns of thoughts of sleep are glorious7 incomprehensible7 and impossible to ignore afterwards 4 CHAPTER 1 PROUST Finally let us turn to that curious comment of Proust7s I myself was the immediate subject of my book77 Reading past the possibly misleading translation not only does Proust mean that he was imagining himself to be the protagonist of that novel he was reading but also that his wandering thoughts have taken over the contents of the book Curious fellow this Proust is for he actually imagined that imagination had taken over reality to become a reality Instead of clogging your mind further with speculative possibly spurious theses let us pause here to examine the career of Proust for to understand what his book says we darn well better understand its immediate subject I promise you l7ll return to that necessary sequence of reactions soon and explain the relevance of all this discussion on the biographical narrative you7ve purchased expecting a swash buckling adventure with high cholesterol romance Marcel Proust was the son of a professor of medicine and an educated Jewish woman He went to school at Lycee Condorcet but we can safely say that he was more enamored with his life than his studies so that beaches at Normandy became the model of his ctional Balbec military training at Orleans became Doncieres and visits to uncles and aunts in Auteuil became Combray Like most literary geniuses he was afflicted with a disease which in his case was the asthma that later con ned him to a room on Boulevard Haussmann for the latter half of his life while he continued revising his mas terpiece expanding it from three to seven volumes Although he also wrote a collection of various hodgepodge called Pleasures and Regrets an incom plete novel about himself called Jean Santeuz39l and various articles for the magazine Le Banquet he is really only famous for the enormous 4000 page plus masterpiece Remembrance of Things Past the rst volume of which is Swann s Way Proust7s claim to fame is the stream of consciousness style which takes the reader into the usually wandering mind of the author who claims to realistically portray her own consciousness but actually just gets to write whatever she feels like at the moment You probably know that people like James Joyce Virginia Woolf whom we will get to later in this book and William Faulkner were direct descendants of Papa Proust The remarkable thing about Proust is that he found an excuse a justi cation for his new style in the philosophy of Henri Bergson Whether he was convinced by the philosophy or the philosophy was convinced by him is debatable7 but it certainly is the case that his writing tries very hard to live up to the theory of real time The idea is that real time is experienced time7 not measured clock time One moment may seem longer than another because so many things happen Another moment may seem short because things happen so routinely Some other moment may even seem long because were so bored The point is the actual time on the clock doesn7t re ect our experience Children7s time goes by slowly because a smaller proportion of their lives have been lived The elderly seem to die so quickly because compared to their eighty years of existence7 one year seems so short On the side7 note that this means Einstein didn7t solve the whole problem of perceptual time7 only the physical one If the principle of lived time is to be taken seriously7 then di erent amounts of writing should represent di erent durations oftime7 and here7s the juggernaut from Proust the subject matter of the writing should re ect experienced time7 past or present Since Proust spent his days reminiscing on old times7 his lived time is mostly in the past Even the process of writing takes time7 but he never writes about that lnstead7 he writes about the interesting stu like love7 society7 disappointment7 and the taste of a piece of madeleine in lime tea Notice that Proust didn7t nd bullet proof justi cations for his tech niques For example7 why does he rely on immediate sensory experiences to remind him of the past You can easily bring up the past by talking about it It is only rarely that you remember your moms cooking habits by smelling the aroma of a stained undershirt Why does Proust rely on taste7 smell7 and other sensory experiences Mostly because he chose to For another example7 why does he write constantly about past a airs and aunt7s lime blossom7 and not at all on the mental processes involved in constructing his novel For goodness sake7 he spent his entire day locked up in a cork lined room writing his autobiographical excuse of a novel shouldn7t he write about being locked up in a cork lined room writing an autobiographical excuse of a novel The problem is that as soon as he does7 he7d be dwelling in the present7 and not the past7 which is not what he wants In order for Proust7s technique to work7 writing itself has to be a transparent medium that has no intrinsic value and takes zero amount of time to complete You may say that half of Proust7s life didn7t count because he was writing The other half includes numerous blank pages because he fell asleep The question that came to my mind on the night of September 30th and continued to plague me on the morning of October 1st was how should peo 6 CHAPTER 1 PROUST ple write about what they7re writing about If Proust never found justi ca tion for writing what he wrote then how do I nd justi cation for beginning to write what I write Above all the hoopla about realistic representations of life and experience lies a simple rule people write what they know about Proust knew about real time and intuition and monkey business about mem ory and how its the same as experience so he wrote about them I know about Proust so I should write about Proust My stream of consciousness is not about lemon tea and armpit odor its about ideas of Proust Henry Adams Ludwig Wittgenstein Orson Welles Virginia Woolf biology philos ophy neuroscience mathematics psychology movies relationships sports dysfunctional families television reruns etc lf 1 were to put my stream of consciousness on paper there would be lots of boring stuff like what was on TV at 1100 PM on September 30th I decided to weed out the useless boring stuff and save the slices of cake77 for a book And as the words slices of cake77 quoted no doubt from a book on Alfred Hitchcock somewhere echoed in my mind7s tongue I fell gently into sleep Little did I know I was going to wake up with a related but even more important question concerning life one that doesn7t just affect artistic expression but the ethics of existence itself But before that happened the plot of this narrative says it has had enough and takes over the command to send a phone call from nowhere Chapter 2 J elkes Actually it wasn7t a real phone call but a ringing in my head but I had to get out of that chapter some how by any means necessary The other promise about discussing the ethics of existence however will indeed be ful lled So here7s what has happened so far l7m getting ready to go to bed when Proust interrupted my stream of consciousness with his stream of consciousness about how best to put streams of consciousness down on paper Then following my own stream I will refrain from repeating the phrase of consciousness from here on I began to question Proust7s motivation for discussing streams but ended up justifying both his method and my own reckless streaming here in this book Now here7s the key transition point Following Proust7s technique I am now justi ed to shoot straight past what ever happened that night and the subsequent morning and the morning after that morning and so and so forth until we arrive at October 15th or so in the span of 170 words I say October 15th 07quot so because it doesn7t really matter exactly which date it was or even which year as long as you get the idea that l Zipped right past a couple of weeks You may recall my calling September 30th a crucial evening and an improbably catalyst wherein began a chain of logical events As unlikely as it sounds those statements still hold true for something quite de nite did happen after I went to sleep in addition to the telephone ringing Why then did I choose to stream past it recklessly and I am stimulating your interest here and leave you to contemplate on something that may or may not have happened Well let me get back down from that fantastic level and assume my profession on the realistic level as a writer of ction and ll you in on the limited details of what may have happened before we dwell into 8 CHAPTER 2 JELKES that mysterious character who provides the name to the title of this chapter That night was the beginning of what I believe to be an experiment performed on my nervous system Soon after I fell asleep7 my subconscious mind xed on the idea of having to x on something7 anything The rst perceptible change in my mental behavior was that I felt like I had to control my breathing7 that if I was not consciously aware of the breathing7 I would miss out some how It seems to me I was trying to direct my attention at more worth while endeavors7 but frequently failed because I returned untimely often to xing on my own breathing Those of you who have this kind of syndrome77 could tell me exactly what is wrong7 but my own conclusion was that my mind was performing an experiment in psychology lf consciousness was something different from mere attention7 then xing it if it existed at all on some repetitive circumstance would provide some sort of heightened experience You may laugh now7 but yes7 l was trying to have an out of this world experience by being aware of my breathing lf consciousness is merely daily living7 then my excessive attention would be pointless The control condition would be living without xed consciousness while the experimental condition would be living with xed consciousness Needless to say7 the whole experiment was a failure7 because I assumed that there was some mode of living that is qualitatively distinct from daily existence My excuse was to study consciousness7 but I knew quite well that this thing called consciousness has no objective existence that is quanti able Why then do we have the concept of consciousness Let us return to that point in the future First7 let us examine why it is that we want to xate at all You may think I am crazy at this point7 but let me assure you that the need to concentrate for what ever reason is universal lrrational it may be7 there is a human predilection for xation that often has no direct bene t for the individual who performs it7 either physiologically or evolutionarily l have been discussing the need for7 or impulse of7 xation which the gen eral reader may or may not have ever experienced To ground the discussion in more solid footing7 let us instead discuss the related topics of the need for7 or impulse of7 belief and the need for7 or impulse of connection Returning to that October 15th morning after nights of fruitless experimentation7 I de cided to go to the theatre I called up my friend Dybbuk this time with a real phone and booked the tickets to see Tennessee Williams7s The Night of the Iguana that night Following the streamer7s approach again7 we returned home after both experiencing an epiphany on account of the play and took turns sitting on each others laps for a while You might have gathered that Dybbuk and l have a boyfriend girlfriend7 girlfriend boyfriend relationship gtlltgtllt Tennessee Williams The Night of the Iguana So many circumstances in the world and in life seem indeterminant7 free to take on whatever direction the moment will dictate But upon further re ection7 everything seems to have been determined7 win or lose People have hidden agendas for doing certain things that they don7t realize hurts others Sometimes they do it even if it did hurt others Such things are subtle for example favoring teams from a certain region of the country as a referee This happenstance is determined by the will of the people7 often the ugly will of the people The world7 having started out with no moral obligation but the survival of the reproductively ttest7 have taken on the evolutionary principle of spiritual selection77 as opposed to natural and sexual selection People will choose to favor those that contribute to their inclusive tness7 where7 in this case7 the tness extends not only to family members7 but to anyone or anything that one feels is in someway similar to oneself or even just likeable by oneself Thus there is no ultimate principle of morality7 especially in regard to a survival based arena such as sports In the real world7 morality is clouded by this prejudice of inclusive tness Therefore even the most harmless rules have some hidden agenda This in turn is the element that determines events Thus while things seems to surprise us7 we fail to recognize that our inner nature determines the outcome of events We7 like members of a basketball team7 try our hardest7 put forth our heart and effort to accomplish something great But at the end7 whether we do or do not succeed comes down to a large degree to the referee because only they determine whether such was or was not a foul Life then is like a game of basketball and people in it are either teammates or opponents People of power are the referees whom you cannot antagonize without picking up technical fouls Coaches are the philosophical forces morality or goodness versus selection or barbarism The only open ended issue in all this is the way you play the game The rules of the court are the rules of society But you may choose to follow your own rules You will of course be penalized7 10 CHAPTER 2 JELKES but your freedom may outweigh the penalty For example7 you may choose to engage in something based entirely on merit alone You will be hard pressed to nd such an activity7 however and the more you engage in it7 the more you7ll realize the inadequacies of it We may live the life we like In the end7 however7 the outcome has already been determined by our human natures The determinism of life does not take away from our ability to analyze it The forms of analysis have certain avors to them There is quantitative analysis like mathematics7 experimental analysis like biology7 philosophical analysis like rationalization7 literary analysis like art7 emotional analysis like personal crises7 skill based analysis like learning to master piano7 re ective analysis like remembering the past7 or even ignoring most of what we observe as a form of ignorance based analysis Above even the forms of analysis are forms of thinking To reach the goal of understanding the world and every thing including us in it7 we must be open to all kinds of analysis7 all kinds of thinking Better yet7 we must come up with different ways of thinking Some times7 the most dif cult part of all is the goal itself What should one do7 not out of habit7 but out of the concern over what one should do with one7s life This is what gets dif cult because the very words you are reading is a form of analysis the philosophical kind What should one do Well7 I can answer it here philosophically7 but it is the combined thinking at different levels such as life experience7 opportunities7 luck7 ulterior motives that propels us to specialize7 or on the ontological level7 to differentiate ls life simply differen tiation One might think so from the scienti c perspective7 but religiously speaking7 emotionally speaking7 truthfully from my heart7 that notion does not stick So how can I determine what life7s purpose is without agreement from my various perspectives7 or different ways of thinking In particular7 how can I resolve this issue7 not with another higher level of abstraction7 but with a tangible7 yet remarkably consistent answer It seems impossible not to indulge in philosophy as an answer to all these problems7 but l7m digging a deeper and deeper hole for myself Perhaps it is better to stop I hope very much that I am not hitting a dead spot here I often wonder whether people like you who are reading what I write actually sympathize with what I am trying to express For myself7 whenever I re read my own writing7 I feel like laughing because7 by golly7 that thing I was thinking about the other day7 its just like it is written down here Other times7 it is not so funny7 because I realize how extraordinary it is for communication7 even between you and I7 to be reasonably understandable But I hope for the best Not before one last point can I stop The seeming determinism of life 11 does not prevent those of us with a formula of success from accomplishing what we intend For example if you are right handed and want to write left handed you can do so eventually if you keep sticking with it If you want to make money motivate yourself constantly seek opportunities constantly and you can eventually do it I however have no rst hand experience If you want to be the best or at least a very good writer in the world practice often enough and gain enough feedback and you will one day reach that goal Although limits due to physical talent or mental makeup determined by childhood experiences exist no limit can stop you from at least accomplishing much of your goal as long as you plot all your actions towards attaining it The only constant in attempting to raise your inclusive tness take a goal directed approach supported by actions directly relevant to that goal It sounds like a cliche but please realize how many times one thinks she is taking a goal directed approach when in fact she is not For example she wants to loose weight But when the NCAA tourney game comes on she wants to watch it Well that takes away from the exercise which takes away from losing weight Things you do indirectly affect your goals If you want to study for the GRE but then you turn on the radio or television even just to pass time Well then you are not studying are you When you do study you tell yourself you are working towards that goal but you fail to recall that everything else you do contributes to working towards or away from that goal This is not to mention that we have multiple goals Thus in a general sense the goal directed approach is absolute In practice achieving the goal directed standard of action or at least near the standards limit is quite dif cult This is what makes life dif cult and essentially probabilistic and ironically determined in a sense We strive to limit our imperfections But some of us are more imperfect than others Wink wink I promise this will be the very last point or really a rather crude sort of advice Feel free to ignore if it doesn7t concern you There are those of us who suffer from problems of motivation One knows she should be getting something done but instead are driven to do some other thing that is not constructive often because of some impinging tease For example you might have been watching TV when you should have been studying for an exam Most of the time there is no justi cation for it Sometimes however you could argue that it is an once in a lifetime event or that you need a break I put it in quotation marks because there is no such thing as a break from living or even and you7re really getting desperate here that your hormone levels do not allow you to continue your goal directed behavior
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