My Web Home
“饥不择食”，是说人饿的时候，食欲强烈。但是如果抓到什么吃什么，急于填饱肚子对健康是非常有害的，因为有些食物是不宜空腹食用的，否则会给你的健康埋下隐患。
一、牛奶、豆浆
“饥不择食”，是说人饿的时候，食欲强烈。但是如果抓到什么吃什么，急于填饱肚子对健康是非常有害的，因为有些食物是不宜空腹食用的，否则会给你的健康埋下隐患。
这两种食物中含有大量的蛋白质，空腹饮用，蛋白质将“被迫”转化为热能消耗掉，起不到营养滋补作用。正确的饮用方法是与点心、面饼等含面粉的食品同食，或餐后两小时再喝，或睡前喝均可。
二、酸奶
空腹饮用酸奶，会使酸奶的保健作用减弱，而饭后两小时饮用，或睡前喝，既有滋补保健、促进消化作用，又有排气通便作用。
三、白酒
空腹饮酒会刺激胃黏膜，久之易引起胃炎、胃溃疡等疾病。另外，人空腹时，本身血糖就低，此时饮酒，人体很快出现低血糖，脑组织会因缺乏葡萄糖的供应而发生功能性障碍，出现头晕、心悸、出冷汗及饥饿感，严重者会发生低血糖昏迷。
四、茶
空腹饮茶能稀释胃液，降低消化功能，还会引起“茶醉”，表现为心慌、头晕、头痛、乏力、站立不稳等。
五、糖
糖是一种极易消化吸收的食品，空腹大量吃糖，人体短时间内不能分泌足够的胰岛素来维持血糖的正常值，使血液中的血糖骤然升高容易导致眼疾。而且糖属酸性食品，空腹吃糖还会破坏机体内的酸碱平衡和各种微生物的平衡，对健康不利。
六、柿子、西红柿
含有较多的果胶、单宁酸，上述物质与胃酸发生化学反应生成难以溶解的凝胶块，易形成胃结石。
七、香蕉
香蕉中有较多的镁元素，空腹吃香蕉会使人体中的镁骤然升高而破坏人体血液中的镁钙平衡，对心血管产生抑制作用，不利于身体健康。
八、山楂、桔子
含有大量的有机酸、果酸、山楂酸、枸橼酸等，空腹食用，会使胃酸猛增，对胃黏膜造成不良刺激，使胃胀满、嗳气、吐酸水。
九、大蒜
大蒜含有强烈辛辣味的大蒜素，空腹食蒜，会对胃黏膜、肠壁造成强烈的刺激，引起胃肠痉挛、绞痛。
十、白薯
白薯中含有单宁和胶质，会刺激胃壁分泌更多胃酸，引起烧心等不适感。
十一、冷饮
空腹状态下暴饮各种冷冻食品，会刺激胃肠发生挛缩，久之将导致各种酶促化学反应失调，诱发肠胃疾病。
有时候真想去他的房子车子，去他的三险一金，去他的结婚生子，去他的工作应酬，去他的居住证。爱哪儿哪，爱谁谁，走出去，走很远的路，认识很多的人，付出很多的爱，最后，嘎贝儿一声，客死异乡，墓碑上刻着：这家伙去另一个世界继续牛逼了…
现发放百度网盘邀请码10个，需要的拿走
http://pan.baidu.com/netdisk/beinvited?invite_code=a48e914967e637a8c2b623fced251c50
http://pan.baidu.com/netdisk/beinvited?invite_code=c2ee24eec9aab458233128630f302fa5
http://pan.baidu.com/netdisk/beinvited?invite_code=b34817f683463c601764c66d84f11721
http://pan.baidu.com/netdisk/beinvited?invite_code=e32f6be33189f72306a98c2b4d42f52b
http://pan.baidu.com/netdisk/beinvited?invite_code=11710a84c41302076564cfaf40b4ecc9
http://pan.baidu.com/netdisk/beinvited?invite_code=42882ef5486c172f5cfab38f22cb4fff
http://pan.baidu.com/netdisk/beinvited?invite_code=9434980ddde35da75e7c54bea77c1e70
http://pan.baidu.com/netdisk/beinvited?invite_code=467269d4e6e745ae45862446d47a3fce
http://pan.baidu.com/netdisk/beinvited?invite_code=4541b1cda43b697aa815ef3dd50b86ff
http://pan.baidu.com/netdisk/beinvited?invite_code=9d55ba5f8cce636c96fdfeb45e2a8165
The object of differential 2D expression analysis is to find those spots which show expression difference between groups, thereby signifying that they may be involved in some biological process of interest to the researcher. Due to chance, there will always be some difference in expression between groups. However, it is the size of this difference in comparison to the variance (i.e. the range over which expression values fall) that will tell us if this expression difference is significant or not. Thus, if the difference is large but the variance is also large, then the difference may not be significant. On the other hand, a small difference coupled with a very small variance could be significant. We use the one way Anova test (equivalent t-test for two groups) to formalise this calculation. The tests return a p-value that takes into account the mean difference and the variance and also the sample size. The p-value is a measure of how likely you are to get this spot data if no real difference existed. Therefore, a small p-value indicates that there is a small chance of getting this data if no real difference existed and therefore you decide that the difference in group expression data is significant. By small we usually mean 0.05.
A positive is a significant result, i.e. the p-value is less than your cut off value, normally 0.05. A false positive is when you get a significant difference when, in reality, none exists. As I mentioned above, the p-value is the chance that this data could occur given no difference actually exists. So, choosing a cut off of 0.05 means there is a 5% chance that we make the wrong decision.
When we set a p-value threshold of, for example, 0.05, we are saying that there is a 5% chance that the result is a false positive. In other words, although we have found a statistically significant result, in reality, there is no difference in the group means. While 5% is acceptable for one test, if we do lots of tests on the data, then this 5% can result in a large number of false positives. For example, if there are 200 spots on a gel and we apply an ANOVA or t-test to each, then we would expect to get 10 false positives by chance alone. This is known as the multiple testing problem.
While there are a number of approaches to overcoming the problems due to multiple testing, they all attempt to assign an adjusted p-value to each test, or similarly, reduce the p-value threshold. Many traditional techniques such as the Bonferroni correction are too conservative in the sense that while they reduce the number of false positives, they also reduce the number of true discoveries. The False Discovery Rate approach is a more recent development. This approach also determines adjusted p-values for each test. However, it controls the number of false discoveries in those tests that result in a discovery (i.e. a significant result). Because of this, it is less conservative that the Bonferroni approach and has greater ability (i.e. power) to find truly significant results.
Another way to look at the difference is that a p-value of 0.05 implies that 5% of all tests will result in false positives. An FDR adjusted p-value (or q-value) of 0.05 implies that 5% of significant tests will result in false positives. The latter is clearly a far smaller quantity.
q-values are the name given to the adjusted p-values found using an optimised FDR approach. The FDR approach is optimised by using characteristics of the p-value distribution to produce a list of q-values. In what follows I will tie up some ideas and hopefully this will help clarify some of the ideas about p and q values.
It is usual to test many hundreds or thousands of spot variables in a proteomics experiment. Each of these tests will produce a p-value. The p-values take on a value between 0 and 1 and we can create a histogram to get an idea of how the p-values are distributed between 0 and 1. Some typical p-value distributions are shown below. On the x-axis we have histogram bars representing p-values. Each has a width of 0.05 and so in the first bar (red or green) we have those p-values that are between 0 and 0.05. Similarly, the last bar represents those p-values between 0.95 and 1.0, and so on. The height of each bar gives an indication of how many values are in the bar. This is called a density distribution because the area of all the bars always adds up to 1. Although the two distributions appear quite different, you will notice that they flatten off towards the right of the histogram. The red (or green) bar represents the significant values, if you set a p-value threshold of 0.05.
If there are no significant changes in the experiment, you will expect to see a distribution more like that on the left above while an experiment with significant changes will look more like that on the right. So, even if there are no significant changes in the experiment, we still expect, by chance, to get p-values < 0.05. These are false positives, and shown in red. Even in an experiment with significant changes (in green), we are still unsure if a p-value < 0.05 represents a true discovery or a false positive. Now, the q-value approach tries to find the height where the p-value distribution flattens out and incorporates this height value into the calculation of FDR adjusted p-values. We can see this in the histogram below. This approach helps to establish just how many of the significant values are actually false positives (the red portion of the green bar).
Now, the q-values are simply a set of values that will lie between 0 and 1. Also, if you order the p-values used to calculate the q-values, then the q-values will also be ordered. This can be seen in the following screen shot from Progenesis SameSpots. Notice that q-values can be repeated.
To interpret the q-values, you need to look at the ordered list of q-values. There are 839 spots in this experiment. If we take spot 52 as an example, we see that it has a p-value of 0.01 and a q-value of 0.0141. Recall that a p-value of 0.01 implies a 1% chance of false positives, and so with 839 spots, we expect between 8 or 9 false positives, on average, i.e. 839*0.01 = 8.39. In this experiment, there are 52 spots with a value of 0.01 or less, and so 8 or 9 of these will be false positives. On the other hand, the q-value is a little greater at 0.0141, which means we should expect 1.41% of all the spots with q-value less than this to be false positives. This is a much better situation. We know that 52 spots have a q-value less than 0.0141 and so we should expect 52*0.0141 = 0.7332 false positives, i.e. less than one false positive. Just to reiterate, false positives according to p-values take all 839 values into account when determining how many false positives we should expect to see while q-values take into account only those tests with q-values less the threshold we choose. Of course, it is not always the case that q-values will result in less false positives, but what we can say is that they give a far more accurate indication of the level of false positives for a given cut-off value.
When doing lots of tests, as in a proteomics experiment, it is more intuitive to interpret p and q values by looking at the entire list of values in this way rather that looking at each one independently. In this way, a threshold of 0.05 has meaning across the entire experiment. When deciding on a cut-off or threshold value, you should do this from the point of view of how many false positives will this result in, rather than just randomly picking a p- or q-value of 0.05 and saying that everything with a value less than this is significant.
[转载自iPc.me]
如果泰坦尼克号最后没有撞冰山，那么结局会是怎样？
Rose 是否会与Jack厮守终老。抑或如某位豆友所说：“本该是一次转头就忘的约炮，却因一次海难成为一生的怀念！”也许还有其他的结局……
若是在港片，结局会是这样：Rose本想选择和Jack在一起，但被Karl威胁，如果不回到他身边，他将会利用家族的势力，找人杀了Jack。Rose为保护Jack，狠心和Jack分手，忍辱回到Karl身边，每天过着以泪洗面的生活。却不想卑鄙的Karl仍不肯放过Jack，派杀手追杀他。Jack摔下悬崖，被神秘人所救，获得神秘人传授一身本事。每天苦练枪法，三年后武艺大成，出山报仇。夕阳下，白鸽飞起，Jack身着黑色风衣，手执双枪，一番扫射，将Karl及其爪牙打成筛子，终于报仇雪恨。Rose觉得自己对不起Jack，于是想独自走。Jack将 Rose紧紧拥入怀里，深情的说道;”发生这种事，大家都不想的。命运的事呢，是不能强求的，所谓吉人自有天相。呐，做人呢，最要紧的就是开心。饿不饿？我下面给你吃！”
若是在韩剧，结局会是这样：Rose毅然甩了高帅富未婚夫Karl，为爱选择穷困潦倒的Jack。Jack从此浪子回头，不再吃喝嫖赌，找了一份在酒店做侍应生的工作，业余时间还坚持练画。Rose 与家庭决裂，靠着卖以前的首饰，开了一家小花店卖花，两个人每天吃着泡菜，过着贫穷但却幸福的生活。就在Jack的画慢慢有了一定的名气，生活慢慢好转起来时，Rose却得了白血病。临死前，Rose躺在Jack怀里说道：“偶吧，你一定要幸福哦！我会在天堂守护着你。Jack,I’m flying! 说完便断了气。从此Jack终生不娶，每天活在回忆里，孤独终老。
若是在印度片，结局会是这样：Rose为选择帅气迷人的Jack，还是高帅富的Karl苦恼不已，最后还是在两人之间摇摆不定。于是Jack和Karl为爱决斗，在船上打起来了，打着打着，音乐突然响起，三人跳起舞，马上周围出现一大帮围观群众，大家一起在船上跳起了欢快的广场舞。
若是在台剧，结局会是这样：Rose和Jack在一起，几个月才发现Jack原来是国内某大集团的富二代，因为以前被女人骗过钱，受过情伤，认为一切接近他的女人都是为了他的钱。为了找寻真爱才装成一副穷逼样接近Rose。Rose觉得自己被Jack欺骗了感情，自尊心受到了极大的伤害。留下一封信后，毅然离开Jack。出门时神情恍惚，不小心出了车祸，从此失忆。被前未婚夫Karl所救后，误以为Karl才是真爱。Jack费劲千辛万苦找到Rose，Rose却不认得眼前的爱人。大结局时，Rose由于意外撞到头部，恢复记忆，终于认出Jack，两人终在一起。
若是在内地剧，结局会是这样：Jack和Rose结婚以后，依然死性不改，用着Rose在外面打工赚来的辛苦钱吃喝嫖赌，喝醉了还打Rose。一年后，Rose终于无法忍受这样的生活，离开了Jack，回到了高帅富Karl身边。Jack终于悔悟过来，但一切已无可挽回。于是Jack痛改前非，发奋图强，每天努力画画。二十年后，名利双收的Jack已是著名画家，但他仍对Rose念念不忘，但找了二十年，一直没找到Rose。在酒吧邂逅一个国外归来的少女，这个少女不仅美貌非凡，而且酷似Rose。气质非凡的大叔Jack对少女有着致命的诱惑力，而他内心也把少女当成了Rose的替代品。酒醉之后，两人干柴烈火，滚起了床单。翌日，少女父母从国外归来，Jack和少女前去别墅见少女父母。晴天霹雳，Jack发现开门的竟是Rose，从Rose的口中，Jack得到恐怖答案，当年Rose是怀着身孕离开Jack的，少女不但是Rose之女，更是Jack之女……
若是在英剧，结局会是这样：Jack和Rose在一起之后，过了一段幸福的生活，不过好景不长，Jack发现 Rose对他越来越冷淡，回家的时间也越来越晚，有时甚至整晚都不回家。Jack忍不住了，有天跟踪Rose，发现Rose上了一辆法拉利，并与车中帅哥激情热吻。Jack心如刀割，整日借酒浇愁。某晚，Jack在酒吧偶遇Rose以前的高帅富未婚夫Karl，或因两人都曾被同一女人伤害，所以相谈甚欢，喝了很多酒。翌日Jack酒醉醒来，发现自己赤裸躺在酒店床上，身边睡的竟是Karl。原来Karl发现自己竟在不经意之间爱上了情敌Jack，对其念念不忘。于是找帅哥勾引Rose出轨，用尽心思，只为得到Jack。俊美不凡的Jack突然发现自己原来也对帅气多金的Karl芳心暗许，于是，有情人终成基友。
若是在德剧，结局会是这样：罗丝、杰克还有卡尔一起下了船，卡尔问罗丝，要跟谁一起走？罗丝翻了个白眼说：“噢。”然后卡尔再问罗丝，要跟谁一起走？罗丝又翻了个白眼说：“噢。”整个戏就在问答的无限循环中结束。
若是在日剧，结局会是这样：三人演着演着就滚到了一起，啪啪啪~啪啪啪~啪啪啪~雅蠛蝶~
天涯海角 
近期评论