四十岁的男人

1． 四十岁的男人，如果还没有结婚，就别结了。无论你是一次未娶还是多次失败，你的身心已经到了不健康的地步，再把这种不健康带给别人是不负责任的。如果可以做到清心寡欲，独自漫步，也能活的自在。

2． 四十岁的男人，如果一事无成，就难成了。十几年的教育，二十几年的社会，这些都没整出点动静来，就不要为难自己了。当然，没出息的也有没出息的活法：好吸的抽两根小烟，好喝的咪两口小酒，好赌的打两副小牌，好色的看两张小碟。放低目标、摆平心态，日子也照样能过的舒舒服服。

3． 四十岁的男人，如果还没去过K房或者桑拿，最好抽空去一次。倘若有美色、美酒当前却依旧坐怀不乱，那么，你就真正达到了不惑的境界。不惑是一种高度，是一种层次，这其中的美妙待我四十岁时再与你细细道来。

4． 四十岁的男人，不要再像二、三十岁那样，目不斜视、直勾勾地盯着女士着装暴露的部位。纵使心里波涛起伏，也要注意形象得体。非礼勿视、若非要视、最好斜视。

5． 四十岁的男人，如果活得不开心，那估计是要把这份不开心带到棺材里去了。要放宽心胸，成就大小与金钱无关，是否风光无需载入史册。要记住：芸芸众生、人来人往，不是谁都能傲视天下，这其中有命、有运。走过、路过即可，不要耿耿于怀，更不要愤世嫉俗。

6． 四十岁的男人，成就过一两件事情很不容易，什么事情都没做成过更不容易。对于那些一事无成者虽然为时已晚，但大器晚成的例子从古至今、跨越东西也比比皆是。只要还相信自己，就值得再去放手一搏。

7． 四十岁的男人，别把自己的身材搞得一塌糊涂。臀部下垂和腹部隆起不是四十岁男人的专利。腾出些时间适当运动一下，譬如打打球、跑跑步都能对身心的抗老化起到积极的作用。

8． 四十岁的男人，当官的要清廉不垮，经商的要纳税守法，打工的切忌伸手乱拿。二、三十岁犯个什么错还能洗心革面、重新来过，四十岁再来次失足落马可就苦海无边、回天乏术了。

9． 四十岁的男人，应多尽些孝道，事业再怎么大，工作再怎么忙也要抽出些时间陪老人们吃吃饭、聊聊天，传承中华民族优秀精神的同时，也传递一个家庭互爱的传统，给后辈做出榜样。要记住：你怎么对待你的父母，你的后代就会怎么对待你。

10． 四十岁的男人，生活在一个瞬间万变、规则不明的巨大的时代反差里；在一个前不着村、后不着店的年龄中承担着心理和生理的双重变化难免有诸多的焦虑和烦恼。要注重内心修炼，要辨明是非取舍，要懂得照顾自己。

11． 四十岁的男人，纵使有钱到了令人发指的地步，也不要去包养什么二奶小蜜。不管是谁征服了谁，最终使坏的都是金钱。而金钱这东西虽然没有长腿，却和有腿的跑得一样快。除此以外，在这个时代，只要你身旁站着一个比你老婆光鲜靓丽却不是你老婆的女人你就会变得猥琐起来，在旁人看来这不是钱色交易就是权色交易。

12． 四十岁的男人，大多已为人父，最好有一两件精彩的人生故事与你的子女分享。这些故事无需惊天动地、鬼哭神泣，但要让你的子女们知道他们有一个并不庸俗的父亲，身上流淌着不寻常的血液。这个信念在他们未来的人生当中会起到至关重要的作用。

13． 四十岁的男人，要多参加几次葬礼。人生至此已行将过半，在中途先参观一下终点可以让自己消除恐惧的心理，驱赶过一天算一天的麻痹，得以更加感激生命、感激生活、感激岁月。

14． 四十岁的男人，最好每年做两次体检。医疗保险和人寿保险至少要各买一份，要未雨绸缪的为妻子和子女做些安排。四十岁是男人病症高发期，万一不幸需要先行一步，至少能给亲人们留下一份嘱托和希望。

15． 四十岁的男人，要远离二十岁的女孩，无论她多么楚楚动人，无论她多么飞蛾扑火。她尚处桃李年华，而你理当知天命、知是非。若真心欢喜，就不要给她一条泥泞坎坷、异常艰辛的道路。

16． 四十岁的男人，要有肩膀，要扛得起风雨，要承担得起生活的重负。即使再苦再累，也要给妻子一床温暖的被褥，给孩子一个避风的港湾，给家庭一份不悔的承诺。

17． 四十岁的男人，应该去读一些佛法、圣经之类的书籍。要理解，这浩荡人世之外的玄妙；要领悟，这生命一次又一次的轮回；要看到，这滚滚红尘背后的因果；要畏惧，有一双神秘的眼睛，一直在注视着我们。

18．四十岁的男人，就算到四十了也别怕。如果碰到有80后嘲讽年龄，用王朔老师的一句话回击最淋漓至尽：你们牛B什么，不就年轻么？老子也年轻过，可你们老过么?

文章引用自： 朱威廉

Trends in Health and Aging

Trend Analysis

• Trend analysis is a statistical approach to evaluate the linear or nonlinear relationships between two variables. When we talk about the secular trend, one of those variables is time.
• Usually, I tabulate the estimates by grouped time periods at first
• Then, use a regression model [log(rate)] to evaluate trends with calendar time
• Or, Age-Period-Cohort model [log(rate)] to evaluate calendar time trends accounting for age or cohort effects
• We can also use ANOVA (ANalysis Of VAriance) with contrast coefficients (polynomial contrasts, orthogonal polynomial coefficient)
• NCHS (2018, No. 179). National Center for Health Statistics Guidelines for Analysis of Trends in "Series 2. Data Evaluation and Methods Research"
• Yin (2016). Statistical Methods and Software Package for Medical Trend Analysis in Health Rate Review Process
• HealthKnowledge: Time-trend analysis, time series designs
• NCI Software: Joinpoint is statistical software for the analysis of trends using joinpoint models. 
• Kim (2000). Permutation tests for joinpoint regression with applications to cancer rates
• Chandler (2011). Statistical methods for Trend Detection and Analysis in the Environmental Sciences

SUDAAN and Collinearity

Tips: SUDAAN and Collinearity

Dear B,

I have another question about SUDAAN. The following article states that -

"Although SUDAAN controls for collinearity by default, separate regression analyses were performed in each model with SAS (SAS Institute, Inc, Cary, NC) for statistical diagnosis."

Q1: Does SUDAAN really control for collinearity by default?
Q2: If not, how do I test it?

Again, I really appreciate your help.
X

Dear X,

Q1: Does SUDAAN really control for collinearity by default?

SUDAAN does not control for collinearity. When betas (coefficients) get too big or too small (e.g. in logistic) SUDAAN will output whatever answer it computes and also issues a warning message about the possible existence of collinear variables. As far as I know, no software available controls for multicollinearity either. There are statistics that you can request in some software (e.g. SAS) to assess multicollinearity and some actions that you can take in order to alleviate the problem.

Q2: If not, how do I test it?

As you may know, multicollinearity in logistic regression (I am assuming that you are working with logistic regression) models is a result of strong correlations between independent variables.

Effect of multicollinearity:

The existence of multicollinearity inflates the variances of the parameter estimates. That may result, particularly for small and moderate sample sizes, in lack of statistical significance of individual independent variables while the overall model may be strongly significant.

Multicollinearity may also result in wrong signs and magnitudes of regression coefficient estimates, and consequently in incorrect conclusions about relationships between independent and dependent variables.

How to detect multicollinearity?

1-Start with examining the correlations (continuous and ordinal variables) and associations (nominal variables) between independent variables.

However, in some situation, when no pair of variables is highly correlated, but several variables are involved in interdependencies, it may not be sufficient.

2-It is better to use multicollinearity diagnostic statistics produced by linear regression analysis (PROC REG with options VIF TOL in SAS). 

For nominal independent variables, create dummy variables for each category except one (it will become a reference category). Use the dependent variable from logistic regression analysis or any other variable that is not one of the independent variables, as a dependent variable in the linear regression. The collinearity diagnostic statistics are based on the independent variables only, so the choice of the dependent variable does not matter.

Examine Tolerance and Variance Inflation Factor for each variable. Since for each independent variable, Tolerance = 1 – Rsq, where Rsq is the coefficient of determination for the regression of that variable on all remaining independent variables, low values indicate high multivariate correlation. 

The Variance Inflation Factor (VIF) is 1/Tolerance, it is always >= 1 and it is the number of times the variance of the corresponding parameter estimate is increased due to multicollinearity as compared to as it would be if there were no multicollinearity. 

There is no formal cutoff value to use with VIF for determining presence of multicollinearity. Values of VIF exceeding 10 are often regarded as indicating multicollinearity, but in weaker models, which is often the case in logistic regression, values above 2.5 may be a cause for concern (Reference: P.D. Allison, Logistic Regression Using the SAS System, SAS Institute).

What to do about multicollinearity?

In some cases, variables involved in multicollinearity can be combined into a single variable. If combining variables does not make sense, then some variables causing multicollinearity need to be dropped from the model.

Examining the correlations between variables and taking into account practical aspects and importance of the variables help in making a decision what variables to drop from the model.

Hope it helps.

Best,

B

  

The Annals of Internal Medicine - Statistical Guidelines (from 2006)

The Annals of Internal Medicine - Statistical Guidelines

http://www.annals.org/shared/author_info_stats.html

Advice for Students: Taking Notes that Work - lifehack.org
lifehack.org - They offer up advice and tips on how to make the best use of your time and how to accomplish things without spending a fortune. They are also are platform agnostic and always recommend interesting and helpful apps for Windows, Mac, and Linux users to try out, though their focus is not on software. Content is upbeat, lively, and constantly changing. And when we say constantly we mean it, every day they seem to post yet another useful essay or article. Really great stuff, and written always in a straightforward non-hyped way, with almost no fluff.

The Rise and Fall of Epidemiology Revisited

The rise and fall of epidemiology, 1950 2000 A.D.
Kenneth J Rothman
Int. J. Epidemiology. 2007 36: 708-710.
http://ije.oxfordjournals.org/cgi/content/full/36/4/708?etoc

Commentary: Epidemiology still ascendant
Kenneth J Rothman
Int. J. Epidemiol. 2007 36: 710-711.
http://ije.oxfordjournals.org/cgi/content/full/36/4/710?etoc

Commentary: Epidemiology and futurology why did Rothman get it wrong?
Cesar G Victora
Int. J. Epidemiol. 2007 36: 712-713.
http://ije.oxfordjournals.org/cgi/content/full/36/4/712?etoc

Commentary: The rise and rise of corporate epidemiology and the narrowing
of epidemiology's vision
Neil Pearce
Int. J. Epidemiol. 2007 36: 713-717.
http://ije.oxfordjournals.org/cgi/content/full/36/4/713?etoc

Commentary: Epidemiology needs the patients to survive
J W W Coebergh
Int. J. Epidemiol. 2007 36: 717-719.
http://ije.oxfordjournals.org/cgi/content/full/36/4/717?etoc

Commentary: Is epidemiology really dead, anyway? * A look back at Kenneth
Rothman's 'The rise and fall of epidemiology, 1950 2000 AD'
Michel P Coleman
Int. J. Epidemiol. 2007 36: 719-723.
http://ije.oxfordjournals.org/cgi/content/full/36/4/719?etoc

Parables of the River - The end of Type 2 Diabetes?

The end of Type 2 Diabetes?

I REALLY like this story forwarded by LG (below the video). Here is ideo (Pablo & Bruno).

Parables of the River
I have been working on introductions to learning modules for a community organizing course I will be teaching online in the Fall. One of the things I wanted to include was what some community organizers call the “Parable of the River” (or sometimes a waterfall) that is often attributed to Saul Alinsky. I was searching across the Internet to find a good representation of the parable and found a wide range of different versions. (To avoid writing introductions, I seem to have ended up writing this post . . . .) Interestingly, it seems like there are versions of this parable with a different perspective than that used by community organizers. And this different version seems somewhat more prevalent among those oriented towards more traditional social service.

First an example of a “community organizing” version of the parable:

Once upon a time there was a small village on the edge of a river. The people there were good and life in the village was good. One day a villager noticed a baby floating down the river. The villager quickly swam out to save the baby from drowning. The next day this same villager noticed two babies in the river. He called for help, and both babies were rescued from the swift waters. And the following day four babies were seen caught in the turbulent current. And then eight, then more, and still more!
The villagers organized themselves quickly, setting up watchtowers and training teams of swimmers who could resist the swift waters and rescue babies. Rescue squads were soon working 24 hours a day. And each day the number of helpless babies floating down the river increased. The villagers organized themselves efficiently. The rescue squads were now snatching many children each day. While not all the babies, now very numerous, could be saved, the villagers felt they were doing well to save as many as they could each day. Indeed, the village priest blessed them in their good work. And life in the village continued on that basis.

One day, however, someone raised the question, "But where are all these babies coming from? Let’s organize a team to head upstream to find out who’s throwing all of these babies into the river in the first place!"

Now a different version of this parable:

While walking along the banks of a river, a passerby notices that someone in the water is drowning. After pulling the person ashore, the rescuer notices another person in the river in need of help. Before long, the river is filled with drowning people, and more rescuers are required to assist the initial rescuer. Unfortunately, some people are not saved, and some victims fall back into the river after they have been pulled ashore. At this time, one of the rescuers starts walking upstream

“Where are you going?” the other rescuers ask, disconcerted. The upstream rescuer replies, “I’m going upstream to see why so many people keep falling into the river.” As it turns out, the bridge leading across the river up- stream has a hole through which people are falling. The upstream rescuer realizes that fixing the hole in the bridge will prevent many people from ever falling into the river in the first place.

In both parables, the key issue is that those trying to rescue the drowning people are making an error by focusing on the current emergency rather than on what is causing the emergency. As a result, they have no hope of actually solving the problem.

A key distinction between them is that in the first parable an agent is assumed to be causing the babies to fall in the river. In the second the problem is simply technical, with no agent attached. The bridge “has” a hole (note the passive voice).

This tendency to obscure the agents behind oppression and social harm may be a key difference between what I would term a “community organizing” approach and more familiar “social service” and “social science” approaches. From social service and social science perspectives there simply are these problems that need to be solved. The highest level of action is identifying and addressing the (usually impersonal) causes of shared problems.
Importantly, this approach generally obscures the activity of the agents who are perpetuating social challenges through their action or inaction.

Perhaps some of the tendency to avoid seeking out responsible agents is a result of the enormous challenges involved in identifying someone or some institution that one can definitively say is causing a particular problem. But maybe part of the problem is this focus on “causes” in the first place. In fact, the “cause” question can become a pretty complex, ultimately unsolveable existential challenge with no clear solution. Is the cause of pollution from a coal plant the owners of the plant, or bad government standards, or perverse incentives that make clean production unprofitable, or any of an innumerable set of other influences? What is the “cause” of the fact that so many poor kids have difficulty reading?
In my experience, as social scientists, most educational scholars tend to draw from the second version of this parable rather than the first. There “are” problems and we need technical solutions to solve them. In fact, to the many scholars who tend to avoid thinking about “causes,” even the limited insights of the second parable seem like a revelation.
In contrast, when organizers are looking for targets (see earlier post) they aren’t really worried about who or what is the “cause” of a problem. Instead, they try to figure out who can or should be made responsible for the problem now that we have it. In other words, the challenge for a community organizer is to identify the agent that can be induced to solve the problem, regardless of the vast chain of influences that produced it. The aim is to build a coherent link between specific agents and a specific social problem, and the substance of such a link can vary widely.

From an organizing perspective, many people and institutions have resources that are not fairly shared, and the aim is to find ways to force some subset of these agents to use their resources in more equitable ways.

To simplify the distinction I am making, here, one might say that social scientists and social service people tend to focus on “what” caused a problem and “how” to solve the problem, while organizers focus on “who” can solve the problem. And in many cases, answering “what” and “how” questions seem like pre-organizing issues. Sometimes, of course, getting people to figure out the answers to these questions themselves in a collective manner can be tools for engaging, educating, and organizing them, but often this does not seem to be the case.

One limitation of a focus on causes and solutions without focusing on agents is that each agent will be linked to different resources and different possible actions. In other words, different agents imply different solutions. Perhaps more problematically, failing to focus on the identification of realistic agents of change often creates an enormous unbridgeable gulf between theoretical solutions and actual solutions.

Here is a somewhat relevant example that indicates some of the differences between the social science approach and the organizing approach: We have been working on the beginnings of an effort to transform dental care for low-income urban children. For a range of reasons, we want to fight for a school-based dental treatment program. And we have identified an agent and avenue of change—the state health department and the state health insurance program. But there is no clear established “blue chip” model or “solution” to fight for. So we have stepped back, and I have been working with the state dental school and local district officials to get a pilot school-based services project funded. A local “proof of concept” effort would provide the basis for a program blueprint that we could then fight for on a state level. To a large extent, however, this social science investigation work is “pre-organizing.”

Two final observations:
First, there is a key problem with this parable in both of its versions. It represents those who are harmed as powerless victims, often babies. But people are rarely entirely powerless, and organizers never approach people as if they were powerless or babies. It seems odd that this central parable used by many organizers contains such a disempowering metaphor at its core.

Second, it is interesting to note that in a version that Stanley Cohen says he got from Alinsky, “a fisherman is rescuing drowning people from a river. Finally, he leaves the next body to float by while he sets off upstream ‘to find out who the hell is pushing these poor folks into the water.’ According to Cohen, Alinsky used this story to make a further ethical point: ‘While the fisherman was so busy running along the bank to find the ultimate source of the problem, who was going to help those poor wretches who continued to float down the river?’”

An interesting website

A new kind of surveillance system?

http://whoissick.org/sickness/

Endocrine Regulation of Energy Metabolism by the Skeleton Cell -- Lee et al.

Endocrine Regulation of Energy Metabolism by the Skeleton

http://www.cell.com/content/article/fulltext?uid=PIIS0092867407007015

The regulation of bone remodeling by an adipocyte-derived hormone implies that bone may exert a feedback control of energy homeostasis. To test this hypothesis we looked for genes expressed in osteoblasts, encoding signaling molecules and affecting energy metabolism. We show here that mice lacking the protein tyrosine phosphatase OST-PTP are hypoglycemic and are protected from obesity and glucose intolerance because of an increase in β-cell proliferation, insulin secretion, and insulin sensitivity. In contrast, mice lacking the osteoblast-secreted molecule osteocalcin display decreased β-cell proliferation, glucose intolerance, and insulin resistance. Removing one Osteocalcin allele from OST-PTP-deficient mice corrects their metabolic phenotype. Ex vivo, osteocalcin can stimulate CyclinD1 and Insulin expression in β-cells and Adiponectin, an insulin-sensitizing adipokine, in adipocytes; in vivo osteocalcin can improve glucose tolerance. By revealing that the skeleton exerts an endocrine regulation of sugar homeostasis this study expands the biological importance of this organ and our understanding of energy metabolism.

Updated Physical Activity Guidelines

INDIANAPOLIS – All healthy adults ages 18 to 65 years need moderate-intensity aerobic physical activity for at least 30 minutes on five days each week or vigorous-intensity aerobic physical activity for at least 20 minutes on three days each week, according to updated physical activity guidelines released today by the American College of Sports Medicine (ACSM) and the American Heart Association (AHA).

Further, adults will benefit from performing activities that maintain or increase muscular strength and endurance for at least two days each week. It is recommended that 8-10 exercises using the major muscle groups be performed on two non-consecutive days. To maximize strength development, a resistance (weight) should be used for 8-12 repetitions of each exercise resulting in willful fatigue.

1. Moderate-intensity physical activity has been clarified.

2. Vigorous-intensity physical activity has been explicitly incorporated into the recommendation.

3. Specified: Moderate- and vigorous-intensity activities are complementary in producing health benefits, and a variety of activities can be combined to meet the recommendation.

4. Specified: Aerobic activity is needed in addition to routine activities of daily life.

5. More is better.

6. Short bouts of exercise are OK.

7. A muscle-strengthening recommendation is now included.

8. Wording has been clarified.

For detail click here

How to test whether the change among surveys in one group is equal to the change in the other groups?

1. Scenery

We have a table below:

	NHES (S1)	N I (S2)	N II (S3)	N III (S4)	N IV (S5)	Change (C)
BMI Group	High Cholesterol (%> 240 mg/dl)
< style=""> (G1)	27.1	22.3	22.1	13.8	15.2	-11.9 (C1)
25.0 – (G2)	39.2	33.1	31.2	23.3	18.7	-20.5 (C2)
> 30 (G3)	38.9	33.1	31.5	23.0	17.9	-21.0 (C3)

Age and sex-adjusted trends in CVD risk factors, by level of obesity and survey year in the

We want to know: Ho: C1 = C3.

2. Algorithm Solution

Recall:

Y= α*(S5, G1) + β₁*(S1) + β₂*(S2) + β₃*(S3) + β₄*(S4) + β₅*(G2) + β₆*(G3)

+ β₇*(S1, G2) + β₈* (S1, G3) + β₉*(S2, G2) + β₁₀*(S2, G3) + β₁₁*(S3, G2)

+ β₁₂*(S3, G3) + β₁₃*(S3, G2) + β₁₄*(S3, G3)

And: Prevalence of high cholesterol in NHES (S1) among persons with normal BMI (G1)

[S1-G1] = α + β₁ = 27.1, and

[S5-G1] = α = 15.2, then

C1 = [S5-G1] - [S1-G1] = 15.2 – 27.1 = -11.9

Also:

[S1-G3] = α + β₁ + β₆ + β₈ = 38.9, and

[S5-G3] = α + β₆ = 17.9, then

C3 = [S5-G3] - [S1-G3] = -21.0

So:

When we test whether C1 = C3, we are going to test:

α - (α + β₁) = (α + β₆) – (α + β₁ + β₆ + β₈),

i.e. β₈ = 0

Same for others:

To test C1 = C2, we are going to test, β₇ = 0

3. Implementation

Using PROC RLOGIST:

proc rlogist data= all;

  nest survey3 strata3 psu3/psulev=3 MISSUNIT;

  weight mecwgt3;

  subpopn age >19;

  subgroup bmigrp agegrp sex survey3 WHITE BMIADHOC;

  levels 3 3 2 5 2 2;

  model High_chol = bmigrp agegrp sex survey3 bmigrp*survey3;

  reflevel survey3 =5 agegrp =1 bmigrp=1;

  pred_eff bmigrp=(1,0,0)*survey3=(1,0,0,0,-1) /name="Survey: first versus last in BMI<25";

  pred_eff bmigrp=(0,1,0)*survey3=(1,0,0,0,-1)/name="Survey: first versus last in 25<BMI<30";

  pred_eff bmigrp=(0,0,1)*survey3=(1,0,0,0,-1)/name="Survey: first versus last in BMI>30";

  PREDMARG BMIGRP SURVEY3 BMIGRP*SURVEY3;

  PRINT BETA P_BETA PREDMRG SEPRDMRG P_PMCON PRMGCON SEPMCON

  /PREDMRGFMT=f7.3 PRMGCONFMT=F7.3 SEPMCONFMT=F7.3;

RUN;

SUDAAN will give us these beta and p value for beta:

-------------------------------

variable beta p value

-------------------------------

BMIGRP,

BY NHES & NHANES

1, 1 0.00 .

1, 2 0.00 .

1, 3 0.00 .

1, 4 0.00 .

1, 5 0.00 .

2, 1 0.33 0.0483 <- b=""> ß7

2, 2 0.32 0.0414

2, 3 0.24 0.1284

2, 4 0.41 0.0130

2, 5 0.00 .

3, 1 0.37 0.0181 <- b=""> ß8

3, 2 0.38 0.0156

3, 3 0.30 0.0350

3, 4 0.44 0.0021

3, 5 0.00 .

----------------------------------

Life course epidemiology
by Yoav Ben-Shlomo

This edition of the International Journal of Epidemiology has four papers and accompanying commentaries that can be conveniently clustered under the heading of life course epidemiology. In the concluding chapter of ‘A life course approach to chronic disease epidemiology’, Diana Kuh and I raised several emerging and common themes that we felt needed to be addressed by future research. These were (i) understanding heterogeneity, (ii) going beyond repeat measures to understand trajectories, (iii) the role of accelerated postnatal weight and height gain and (iv) the use of life cohort cohorts and less conventional designs. All of these topics are addressed to some degree by these publications. ...
for full text article click here

Example of Application of GIS on Public Health

Last week, we had 7 people attended the training class of spatial analysis. Seems a lot of people are interested in this area. Attached please find a 4 years old project I involved about 'Temporal and spatial relationship of ozone and asthma'. One thing amazed me by GIS is geostatistics (maybe only thing, sorry), which can convert values among point, line, and area.
http://gis2.esri.com/library/userconf/proc03/p0911.pdf

How to send and receive large files through the Internet

There are several websites can send large files to our friends. One of them is TransferBIGFiles.com, a free service for sending and receiving large files through the Internet. No registration is needed to use the service. View a simple diagram of how the service works.

After you upload the file (up to 1GB), the recipient gets an email with a link to download the file. The file is good for 5 days or 20 downloads, whichever comes first. The optional settings let you add a note to the email, password protect the file and get an email confirmation of the download.

The folks behind the site share their experience in building TransferBigFiles.com in one weekend. Check out the statistics showing the number of transfers, file size, bandwidth and other interesting stats.

sasCommunity.org

http://sascommunity.org/wiki/Main_Page
User feedback to sasCommunity.org continues to be very positive (over 31,000 hits on the main page; over 1200 users and growing!). One of the important features of sasCommunity.org is the ability to organize articles into categories. Categories provide a great way to make sure your pages are found by interested sasCommunity visitors and provide an easy way to navigate between related articles

How to Read the New Recommendation Statement

Current Processes of the U.S. Preventive Services Task Force: Refining Evidence-Based Recommendation Development
by Janelle Guirguis-Blake, Ned Calonge, Therese Miller, Albert Siu, Steven Teutsch, Evelyn Whitlock for the U.S. Preventive Services Task Force. Ann Intern Med 2007;147 117-122 Open Access

How to Read the New Recommendation Statement: Methods Update from the U.S.
Preventive Services Task Force
by Mary B. Barton, Therese Miller, Tracy Wolff, Diana Petitti, Michael LeFevre, George Sawaya, Barbara Yawn, Janelle Guirguis-Blake, Ned Calonge, Russell Harris for the U.S. Preventive Services Task Force. Ann Intern Med 2007;147 123-127 Open Access

Dr. Richard P. Feynman (1918 -1988)


Nobelist Physicist, teacher, storyteller, bongo player
the hero of physics geeks everywhere!
http://amasci.com/feynman.html

"Science is the belief in the ignorance of the experts” - Richard P. Feynman
More definition of science: http://www.gly.uga.edu/railsback/1122sciencedefns.html

Water Will Rock You (We'll Rock You song)

Statistical Graphics Using ODS

http://support.sas.com/rnd/app/da/stat/odsgraph/

Graphics are an invaluable tool for data analysis, where one image can summarize pages of output. Graphics reveal patterns, point out differences, and provoke meaningful question about your data. SAS 9.1 now includes an experimental extension to the Output Delivery System (ODS) that provides high-quality graphics along with tabular output for many statistical procedures.

ODS Graphics gives you convenient access to commonly used graphics for a particular analysis. You no longer need to export your results to other graphical tools. With simple ODS commands and procedure options, you can generate the boxplots, scattergrams, and residual plots you need as well as several dozen other types of graphical displays. You can choose from several styles, or you can customize your graphics. You can direct the graphics to destinations such as HTML, RTF, and Postscript.

How to DYI a passport photo

How to DYI a passport photo

• U.S. Passport Photo Requirements: 2 x 2 inches (51 x 51 mm), the height of the head from top of hair to bottom of chin) should between 1 inch to 1 3/8 inches (25 mm - 35 mm).
• The easiest way to crop the US passport photo from a photo is to go to the Travel.State.Gov's "Already have a photo?" webpage, to crop one (a 600 pixels by 600 pixels photo automatically), and paste it on a 4 x 6 (1200 pixels by 1800 pixels) graph file.
• Guidelines for Producing High Quality Photographs for U.S. Travel Documents
• I usually follow the instructions of this website by Trinch to create a passport picture.
• On the US Department of State - Bureau of Consular Affairs website, you can 'Choose Photo', 'Crop Photo', and 'Save Photo' without need any software.
• Persofoto has more editing feature.
• 飞网中国 has a similar app in Chinese 
• Visa of photo for China: 1.89 x 1.30 inches (48 x 33 mm).

Freeware:

• GIMP
• Paint.net
• PhotoFiltre 7

Guidelines on diabetes, pre-diabetes, and cardiovascular diseases

This link is to a 72-page review from a distinguished European group of authors.

It offers about everything you need to review clinical care of adult diabetic patients, at least insofar as their CV disease goes. Expect to find dozens of systematic tables, several good graphics, and 711
citations!

Physical activity and health Even low intensity exercise such as walking is associated with better health Evidence that physical activity improves health is convincing,1 but we lack knowledge about how to increase physical activity in individuals and populations. Taking part in sport may improve health, but sport is only taken up by a small proportion of the adult population, and mainly by the better educated. In this week's BMJ, a systematic review by Ogilvie and colleagues assesses the effect of interventions to improve walking on how much people walk, physical activity, fitness, disease risk factors, and wellbeing.2 It found that interventions tailored to people's needs, which targeted the most sedentary or those motivated to change, can increase walking by up to 30-60 minutes each week. Few studies included in the review assessed clinical benefits from the increased walking, and this remains to be shown in randomised controlled trials.

So what is the evidence so far on the effects of interventions on other types of physical activity? A recent Cochrane review of randomised controlled trials found that trials promoting physical activity in general significantly increased self reported physical activity (standardised mean increase of 0.31, 95% confidence interval 0.12 to 0.50), and fitness (0.40, 0.0.9 to 0.70).3 The review by Ogilvie and colleagues also included non-randomised studies, which, although considered weaker forms of evidence, are necessary to assess the effect of population level interventions such as bike lanes, walking paths, and recreational areas.

One non-randomised community intervention in Odense, Denmark, promoted bicycling through many initiatives and increased the number of bicycle trips by more than 20% over five years.4 At the same time, the number of accidents involving cyclists was 20% lower than in the rest of the country.

Another study found that children who cycled to school were 8% more fit than children who used other modes of transport including walking.5 It concluded that a 10-15 minute session of cycling twice a day would be enough to increase aerobic fitness in children.5

Observational studies have consistently shown that children who walk or cycle to school engage in more physical activity (other than the travel activity) than those who travel by other means.6 7 This extra activity may reflect selection (children who are generally more active choose active transport) or it may be that children who are encouraged to take up active transport go on to engage in other activities. However, because of the lack of cycle lanes in many countries it may be difficult to promote increased cycling for safety reasons.

A weakness in many of the trials of walking interventions is the lack of assessment of health gains; however, epidemiological studies suggest that health benefits of active transport are substantial. The nurses health study found that women who increased both walking distance and speed had a lower risk of cardiovascular disease, type 2 diabetes , and all cause mortality.8 9 The risk in the upper quintile of walking was around half that seen in the sedentary group. Similarly, another study found a 30% lower mortality rate in participants who cycled to work than in non-cyclists after adjusting for general physical activity level, socioeconomic background, and smoking.

Ogilvie and colleagues' study shows that interventions can increase the amount of walking. It has not yet been proved that the lower rates of disease and mortality seen in people who walk is caused by walking itself, but even this low intensity type of exercise probably improves metabolic control and other health parameters. The challenge now is to make politicians work for an environment that promotes walking, and to call on doctors to encourage patients to walk, especially those with disorders such as hypertension, metabolic syndrome, or raised fasting insulin.


Lars Bo Andersen, professor Norwegian School of Sport Sciences, Department of Sports Medicine, Box 4014, 0806, Oslo, Norway

Ggobi: the data visualization system.

http://www.ggobi.org/

GGobi is an open source visualization program for exploring high-dimensional data. It provides highly dynamic and interactive graphics such as tours, as well as familiar graphics such as the scatterplot, barchart and parallel coordinates plots. Plots are interactive and linked with brushing and identification.

Bootstrap sampling & complex survey for 2007-04-20 Presentation

<<Bootstrap sampling & complex survey 2007.pdf>>