Aegis is a first-person simulator that utilizes both virtual reality and motion tracking to provide immersive lessons on self-defense. From breaking out of one's grip to disarming an assailant carrying a weapon, each lesson is designed to educate users in safely handling situations where other individuals pose a direct threat.
The primary focus of self-defense is not to win the fight, but to hurt your opponent enough for you to escape. If you're faced with a possible dangerous situation, your first instinct should be de escalation and you should attempt to remove yourself from the area, while remaining alert to threats. If you are faced with an assault and have no escape, make as much noise as you can to attract attention and use principles taught by experienced self-defense instructors.
There are 5 key pressure points on the body that are sensitive to damage, which you can use to your advantage against your assailant. These points are the eyes, nose, throat, groin, and knees.
If caught in a hand-to-hand fight, do your best to dodge your opponent's attacks and try to grab onto their arm/leg if you can in order to deny them use of that limb. Then, when they are exposed, go for an easily accessible pressure point. You can also use their energy against them, punishing their advance with an attack of your own.
Guns are rarely used at close range, but one is put against your head, you have a chance of escaping it. A fact you
must realize is that once you touch the gun at all, it will go off. So, the best strategy is to first get yourself out
of the gun's line of fire. The best direction would be up , but left and right can work too (go in the direction
opposite your dominant hand). Move while grabbing the gun with both hands.
After the gun has fired, or when it's already been raised, push into your opponent's body and take yank the gun away
from their hand, putting as much pressure on their arm(s) as you can. After you yank the gun way, put distance between
yourself and your assailant while chambering a new round into the barrel to be ready to fire if necessary. Increase
distance and run if there is an escape route available.
For the purpose of this video, we will be using a plastic water bottle in place of a gun.
Knives are very dangerous in close quarters situations, even more so than guns. Part of that danger is their ability to
be concealed and used very quickly. In fact, most victims don't even see the knife during the attack and only realize
they've been stabbed after the assault ended. Due to this potential danger it is very important to watch the hands of
any suspicious people around you, in case they may be carrying a blade.
If you manage to see a knife, do your best to dodge the knife and grab the arm holding the knife. After that, push into
your opponent's body and either take their arm over your shoulder to deny them its use and get them to drop the knife,
or hit their elbow in order to get them to release the knife due to the instant surge of pressure. After the knife is
dropped, throw or kick it away to buy you time for when you run in the opposite direction.
For the purpose of this video, we will be using a plastic water bottle in place of a knife.


This is a video of some sort
this is the page and stuff
This is another video of some sort
this is more and stuff


public class Pixel
extends java.lang.Object
| Modifier and Type | Method and Description |
|---|---|
void |
darken()
This method darkens a pixel by taking the red, green, and blue values
and decreasing them.
|
int |
getBlue()
This method returns the blue value of the pixel.
|
java.awt.Color |
getColor()
This method returns all three colours of the pixel as a Colour object
|
double |
getDistance(java.awt.Color c)
This method returns the color distance between this pixel and a specified color.
|
double |
getDistance(int r,
int g,
int b)
This method returns the color distance between this pixel and a specified color.
|
double |
getDistance(Pixel p)
This method returns the color distance between this and another Pixel's color.
|
int |
getGreen()
This method returns the green value of the pixel.
|
int |
getRed()
This method returns the red value of the pixel.
|
byte |
getTransparency()
This method returns the transparency value of the pixel.
|
void |
lighten()
This method lightens a pixel by taking the red, green, and blue values
and increasing them
|
void |
setBlue(int b)
This method sets the blue value of the pixel.
|
void |
setColor(java.awt.Color c)
This method changes all three colours in one method by accepting
a Colour object as a paramter
|
void |
setColor(int r,
int g,
int b)
This method changes all three colours in one method
|
void |
setGreen(int g)
This method sets the green value of the pixel.
|
void |
setRed(int r)
This method sets the red value of the pixel.
|
void |
setTransparency(byte t)
This method sets the transparency value of the pixel.
|
java.lang.String |
toString()
This method returns a string representation of the Pixel object
|
public double getDistance(int r, int g, int b)
r
- the red of the colorg
- the green of the colorb
- the blue of the colorpublic double getDistance(java.awt.Color c)
c
- a Colourpublic double getDistance(Pixel p)
p
- the other pixelpublic int getRed()
public int getGreen()
public int getBlue()
public byte getTransparency()
public void setRed(int r)
r
- (the new red value)public void setGreen(int g)
g
- (the new green value)public void setBlue(int b)
b
- (the new blue value)public void setTransparency(byte t)
t
- (the new transparency value)public java.awt.Color getColor()
public void setColor(int r, int g, int b)
r
- (the new red value)g
- (the new green value)b
- (the new blue value)public void setColor(java.awt.Color c)
c
- (the Colour object representation of red, green, blue)public void lighten()
public void darken()
public java.lang.String toString()
toString
in class java.lang.Object


--- layout: archive ---
Rebecca is a design-researcher whose graduate studies focus on family-centered technologies and policies. She is particularly committed to qualitative and participatory design methods for more diverse and inclusive designs. Most recently she led a nationwide, NSF-funded study on families use of technologies during COVID-19 using the Asynchronous Remote Communities (ARC) method. Rebecca is a UX Research intern at Facebook (Summer 2021).
Prior to attending graduate school, she was a Project Manager and Design Researcher at the Engagement Lab at Emerson College, working on civic media technology and research. In 2018, she managed the Make the Breast Pump Not Suck Hackathon at the MIT Media Lab. which fostered interdisciplinary collaboration for equity-driven postpartum healthcare in the United States. In her free time, she enjoys making stained glass and visiting as many national parks as possible.


|
|||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object org.apache.velocity.tools.generic.SafeConfig org.apache.velocity.tools.generic.AbstractLockConfig
SafeConfig
instead
@Deprecated public abstract class AbstractLockConfig
Apologies to anyone who stepped up to use Tools 2.0-beta1
and made their tools extend this class. Please just extend
the SafeConfig class now. It is better named and no longer
requires implementation of SafeConfig.configure(ValueParser)
,
as that may not be needed by subclasses that may simply want
to know safeMode and/or lockConfig settings.
Sorry for any inconvenience, but this class will be removed before Tools 2.0 goes final. Please update your subclasses before that time. Thanks.
| Field Summary |
|---|
| Fields inherited from class org.apache.velocity.tools.generic.SafeConfig |
|---|
LOCK_CONFIG_KEY, OLD_LOCK_CONFIG_KEY, SAFE_MODE_KEY |
| Constructor Summary | |
|---|---|
AbstractLockConfig()
Deprecated. |
| Method Summary |
|---|
| Methods inherited from class org.apache.velocity.tools.generic.SafeConfig |
|---|
configure, configure, isConfigLocked, isSafeMode, setLockConfig, setSafeMode |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
|---|
public AbstractLockConfig()
|
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| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


To resolve collision between two circles, one of the circles must be "pushed" out of the other until the distance between their center points is greater than the sum of their radii. The circle should be moved out of collision along the vector between the center points of the two circles. Its length is 0. the sum of the radii is 0. change radii


CAF Confederation Cup draw postponed to Tuesday
Johannesburg - The CAF Confederation Cup group draw scheduled for Sunday has been postponed to Tuesday at the Cairo offices of the Confederation of African Football.
A CAF statement confirmed that the draw for the second-tier competition would take place at 12:00 SA time but did not explain the 48-hour delay.
The seeded draw will divide the 16 survivors from three qualifying rounds into four groups with the winners and runners-up progressing to the quarter-finals.
Horoya (Guinea), Renaissance Berkane (Morocco), twice African champions Enyimba (Nigeria) and Al Masry (Egypt) are the top seeds, based on results in the past five CAF seasons.
Hassania Agadir (Morocco), Zanaco (Zambia), Enugu Rangers (Nigeria) and Djoliba (Mali) are the second seeds.
The unseeded clubs include Paradou (Algeria), ESAE (Benin), Daring Club Motema Pembe (Democratic Republic of Congo), Pyramids (Egypt), San Pedro (Ivory Coast), Al Nasr (Libya), Nouadhibou (Mauritania) and Bidvest Wits (South Africa).
Horoya, Enugu and Motema Pembe are former winners of the now defunct African Cup Winners Cup.
Group fixtures will be confined to Sundays with the first series on December 1 followed by December 8 and 29, February 16 and 23 and March 8.


The Data Science With Python Workflow Cheat Sheet connects the documentation for the main Python packages within the data science workflow.
The Python Cheat Sheet is a key component of in learning the Python for data science efficiently. We teach the cheat sheet as part of our Business Analysis With Python (DS4B 101-P) Course that is part of the Data Science for Business With Python Track.
Learn more about the Data Science with Python workflow and cheat sheet in our article, "New Python Cheat Sheet: Data Science Workflow with Python"


Introduction
This tutorial will teach you step-by-step how to mine Primecoin on the Rackspace cloud computing platform. Rackspace offers a six month 50 dollar per month trial (300 dollar total!) for new users. A credit card is required for the trial and after that for buying pay-as-you-go server time. You will be called to verify the information you entered and to test if you apply for the trial. As Primecoin mining has scientific benefits you can tell them you'll use the server for scientific data crunching.
Registering an account
The first thing that you'll need is an account at the Rackspace cloud hosting platform. If you have not used Rackspace before you can register for the trial here. Otherwise you can register for a normal account here. Rackspace is known to ban miners from time to time so do not charge your account with too much credit.
For the trail, be sure to click the "Infrastructure Service Level" option on the registration screen, or you will be billed for higher-tier support. The promo code is entered automatically. The green check mark shows us that the promo code is accepted.
Local software requirements:
- Primecoin Wallet Software For generating a Primecoin adress and holding funds.
- Putty SSH software for Windows (link to installer)
Set-up a server
Introduction
As you login to the Rackspace control panel you see a very nice overview of the services they provide. Servers you create will be displayed here and you can manage them here as well. We'll need a server of course first, so click on Create Server.
Configuration
First we have to pick a name for the server. Pick something unique. Now select Ubuntu 13.10 as the operating system.
As Primecoin mining requires a lot of CPU power, we'll choose a CPU heavy configuration, that is relatively cheap compared to other configurations. This so we can save on RAM and storage availability costs. The price here is displayed in your local currency. Now click create server at the bottom of the page.
Rackspace will now set-up and start your server. You will see a password pop-up like the one below. Write the password down on a piece of paper, we'll need it in a few minutes.
The page you'll see now displays the specifics of the server you just started. The network specifics are displayed at the bottom of the page. Copy the IPv4 ip adress.
Connect to the server
- Install Putty
- Start Putty and paste the server ip adress in the connection field like the image below, and click Open
- Click Yes on the Security Error
- The text window will ask you for a username, enter: root
- Enter the password you wrote down and hit enter
- After a few seconds you will see text like the image below the OSX instructions
Mac and Linux users:
- Open Terminal.app located in /Applications/Utilities/ or a terminal
- Enter: ssh root@ *paste/write down your server ip here*
- Enter: root
- Enter the password you wrote down and hit enter
- After a few seconds you will see text like the image below
Setting-up the miner
Now copy over these commands. You can paste in Putty by right-clicking.
apt-get update && apt-get install -y yasm git make g++ build-essential libminiupnpc-dev libboost-all-dev libdb++-dev libgmp-dev libssl-dev dos2unix && git clone https://github.com/thbaumbach/primecoin && cd ~/primecoin/src && make -f makefile.unix
The command above will take several minutes to complete. When the command line shows the "#" symbol again you can continue. The next step is to set-up and start your miner. We'll use the Beeeeer.org pool, which requires no set-up. Replace the "your xpm address here" in the command with your real address and copy the command to the server and press enter. Adjust the -genproclimit flag with the number of 's you ordered. If you picked an eight core server edit it as "-genproclimit=8". You don't need to change the password field.
screen ./primeminer -poolip=<IP_ADDRESS> -poolport=1337 -pooluser="your xpm adress here" -poolpassword=XXXXX -genproclimit=8
Congratulations, you are now mining! The screen output should look like the picture below. The mining speed is accented in the red box. The speed displayed below is not the speed you will get. This will be much higher.
You can close the Putty or command line window now. When reconnecting with the server, type this to see the mining output again.
screen -DDR
Payout and stats
The pool will automatically pay-out to the used address if you reach 3.0 XPM. You can run multiple miners on the same address to speed up the mining process. Check your current balance on the pool by filling in your address in this url.
http://beeeeer.org/user/*copy your address here*
Noteworthy Links
A GPU miner is being worked on here.
Weekly updates on Primecoin development are here.
Participate on the official forum here.
I Mined Some Primecoins, How Can I Find Out If I Broke Some World Records Or Not?
There is a 'listprimerecords' command since v0.1.2. It can be accessed from Qt debug window/console. There are two modes of this command, one lists records of any prime chain types, one lists records of a specific prime chain type.
To list records of length 11 prime chains of any type in Primecoin:listprimerecords 11
To list records of length 10 prime chains of specific type in Primecoin:listprimerecords 10 1CC
listprimerecords 10 2CC
listprimerecords 10 TWN
In the output of listprimerecords, it shows the date, block height, digits, origin and its primorial form of all the record prime chains in Primecoin block chain. There is also an 'ismine' field for each record, it would show value 'true' if the miner key belongs to your wallet.
Refer to world records for the current records and milestones achieved by Primecoin network.
The world records for simultaneous primes and prime chains are maintained at
- Largest known simultaneous primes maintained by Jens Kruse Andersen
- Cunningham chain records maintained by Dirk Augustin
- Bi-twin chain record maintained by Henri Lifchitz


Gunman still at large after killing outside Chicago school
by The Associated PressCHICAGO — Authorities are still searching for a man who allegedly shot and killed his daughter’s mother’s new boyfriend of outside of a Chicago elementary school.
Police say the gunman approached Richard Buick in his SUV early Friday outside of Moving Everest Charter School in the city’s West Side. The shooter fired and fled. Authorities say his 6-year-old daughter and her mother, who was holding a baby, were walking into the school.
Teacher Scott Musso and school nurse Amanda Thorsen heard the shooting. They went outside and started to perform CPR while Buick was in the driver’s seat because they couldn’t get him out.
Buick’s foot slipped and hit the gas, jolting the SUV forward. Musso, a former firefighter and paramedic, told The Chicago Tribune that he began steering to avoid hitting anyone and that he eventually steered the car into a guardrail to stop it.
Buick was pronounced dead at the scene.
Chicago police spokesman Michael Carroll says the suspected shooter remains at large. Police haven’t released his name.
The Associated Press


HTML Basics
Lists
Ordered list
- first item
- second item
- third item
- fourth item
- fifth item
Undered list
Discriptions list
- Name
- Ivan
- Born
- 1993
- Birthplace
- Russia
- Color
- Dark blue
Nested list
- List item one
- List item two with subitem:
- Subitem 1
- Subsubitem 1
- Subsubitem 2
- Subitem 2
- Final list item
List with links
Paragraphs
We leaf to snug on no need. Sportsman do offending supported extremity breakfast by listening. Indulgence contrasted sufficient to unpleasant in in insensible favourable. Secure shy favour length all twenty denote. Any delicate you how kindness horrible outlived servants. Mrs assured add private married removed believe did she.
Course sir people worthy horses add entire suffer.
Advantages entreaties mr he apartments do. undefined.
Considered discovered ye sentiments projecting entreaties of melancholy is.
Up hung mr we give rest half. Way own uncommonly travelling now acceptance bed compliment solicitude.
Took sold add play may none him few.
Estate was tended ten boy nearer seemed.
At principle perfectly by sweetness do.
You high bed wish help call draw side.
Called though excuse length ye needed it he having.
Their saved linen downs tears son add music. Decisively advantages nor expression unpleasing she led met.
Tables
| First name |
Last name |
| Jogn |
Doe |
| Jogn |
Doe |
Image
Pre tag
if (time = 20) {
greeting = "Good day";
} else {
greeting = "Good evening";
}


Lorem ipsum dolor sit amet, consectetur adipisicing elit. Mollitia neque assumenda ipsam nihil, molestias magnam, recusandae quos quis inventore quisquam velit asperiores, vitae? Reprehenderit soluta, eos quod consequuntur itaque. Nam.
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If you would like to recommend a subject please send us a message through our contact form.


Hello {{n1}},
{{s1}}
Symptoms:
CNV can create a sudden deterioration of central vision, noticeable within a few weeks. Other symptoms which can occur include colour disturbances, and metamorphopsia (distortions in which straight lines appears wavy).
Haemorrhaging of the new blood vessels can accelerate the onset of symptoms of CNV. CNV may also include the feeling of pressure behind your eye.
Treatment:
CNV can be treated with photodynamic therapy (PDT) coupled with a photosensitive drug such as verteporfin (Visudyne). The drug is given intravenously. It is then activated in the eye by a laser light. The drug destroys the new blood vessels, and prevents any new vessels forming by forming thrombi.
A Cochrane Review published in 2016 found that people with severe myopia (short-sightedness or near-sightedness) may benefit from being given anti-VEGF treatment.
Symptoms:
The primary symptom of macular edema is blurry or wavy vision near or in the center of your field of vision. Colors might also appear washed out or faded.
Most people with macular edema will have symptoms that range from slightly blurry vision to noticeable vision loss.
Treatment:
First, tackle what's causing it, like high blood sugar or high blood pressure. Just keeping your levels close to normal can stop eye damage from happening or getting worse.
The next step is to heal your retina. You should see an ophthalmologist or a retina specialist for treatment. What's right for you depends on the type of DME you have.
Symptoms:
Distortion of straight lines in your field of vision (metamorphopsia)
Difficulty adapting from bright lights to low lights
Hazy or blurry vision
Blank spot in your central vision
Treatment:
You can’t prevent drusen from forming. Having some hard drusen is considered normal.
Early diagnosis of drusen with regular eye exams can help you find out if you’ve developed AMD. Not everyone with drusen will go on to develop AMD.
Treatment for drusen isn’t necessary unless you also have AMD. Early treatment of AMD can slow the progression of the disease and minimize vision loss.
Tips to maintain healthy retina:
- Poor diet containing insufficient nutrients can cause the health of the retina to degrade. Therefore, it’s important to have a healthy and a balanced that contains all the essential nutrients.
- Drinking plenty of water is important for ensuring a good retina health. When the body is properly hydrated, the tear glands are able to produce sufficient tears and properly moisturise the eyes. Also, drinking plenty of water helps in purifying blood and promoting blood flow.
- Regular exercise helps in maintaining a healthy weight and promoting blood circulation to all body parts, including the eyes.
- Regular smoking can impair blood flow to the eyes and retina which can lead to the onset of severe eye disorders such as macular degeneration. Although quitting smoking can be a tough habit to break, getting rid of it will certainly pay off.
- Regular eye check-up is the key to maintaining a healthy retina and overall eye health. Also, regular eye check-up can help your doctor diagnose the early symptoms of eye disorders.


abstract class MarkForDeletionDao
MarkForDeletionDao() |
suspend fun delete(id: String): Unit
Delete mark from database. Currently it does the same thing as unMarkForDeletion. |
|
abstract suspend fun markForDeletion(id: String): Unit |
|
abstract suspend fun unMarkForDeletion(id: String): Unit |


The checkout simulates the dash button by presenting an absolutely awesome UI. Check it out to checkout things...
In case you use Camunda for orchestrating the order fulfillment flow you can access Camunda Operate for this service to get an overview...
You want to see what is going on in your microservice world? Checkout this simple monitor showing you all events & commands sent around, and some BPMN view of the overall end-to-end business capability...


FILM TITLE: CHRISTOPHER COLUMBUS -- THE DISCOVERY DIRECTOR: JOHN GLEN COUNTRY: USA/SPAIN 1992 CINEMATOGRAPHY: ALAN MILLS WRITTEN BY: MARIO PUZO CAST: Marlon Brando, Tom Selleck, George Corraface, Rachel Ward, Robert Davi, Catherine Zeta Jones. SUPER FEATURES: The ship replicas. The ocean scenes.
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
While this is not the superb adventure film we would like it to be, it does have a few good things in it.
Rachel Ward, is not only the most attractive of all, of them, she smoothly delivers the best performance. She is not campy, she does not come of pretentious, and she is feminine enough, though she is a queen. Aside from Rachel Ward, there is Marlon Brando, the elder church statesman Torquemada, upon whose shoulders the whole voyage may have depended. And if so, the voyage was arranged to get rid of an over zealous Christian who was getting in their nerves with all the clever rhetoric. But the well meaning Torquemada ( he lets everyone choose their own destruction ) also knows that he has to deal with the fact that the queen happens to love this genoese. He does not disapprove of it, and allows the whole thing to go through. He blesses it at the departure, as a sign that even if he doesn't believe the young man, at least he has a heart for the visit to the unknown. And Marlon Brando, as this elder is very good, not jumpy, very quiet, and also smooth. There is a certain aura of control and calm when he is on.
Christopher Columbus -- The Discovery, is not a bad film. If it suffers a bit, it is that the film has to live with 'every day' Spain, for a while, and then has to live with insurrections and bad leaders of ships, the only people the young sea captain could come up with. These moments in the script are a bit difficult to recreate, in that the story is limited to setting things up in the beginning. Had it just happened, instead of immediately telling us that this character was going to be a problem might have made the film more surprising and enjoyable, rather than so predictable.
But it isn't a bad evening of history, and could be enjoyed for a few moments, even if it is so darn romantic in the whole thing, which many of those sailors had to be anyway....
SEE IT WHEN YOU HAVE NOTHING BETTER TO CHECK OUT. LET THE KIDS SEE IT BEFORE THEIR HISTORY CLASSES. 3 GIBLOONS
Copyright (c) Pedro Sena 1994
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Welcome to the homepage of our Google add-on!
This is an add-on, that was developed for teachers, who use google forms to create tests for students to assess them.
You, as a creator of the form, get an overview of the responses of students for each form
as a series of graphs in the Forms menu.
The link to the add-on can be found in the Instructions menu,
where you can also find the instructions for using it in your form.
Our Add-on uses Learning Locker Storage, which is a open source data repository to store learning activity.
For storing data, it uses learning activity statements generated by xAPI (Tin Can).
Our Add-on captures Google Forms data using Tin Can and then sends to Learning Locker.
For visualizing we use D3, which is a JavaScript library to manipulate data.


"Your mind is no longer your own!"
"Your mind is no longer your own!"
Front Line DPS: Orkon not only preys upon enemies with his sharp claws, but infects them with a parasite that can control their movement
| Health | 80 | Health | 144 | |
| Physical Attack | 8 | Physical Attack | 11.2 | |
| Magic Attack | 0 | Magic Attack | 0 | |
| Physical Armor | 2.3 | Physical Armor | 4.2 | |
| Magic Armor | 2.5 | Magic Armor | 4.5 |
None
Orkon releases a parasite that enters the body of an enemy unit, allowing you to control their movement by tapping the screen. At the same time, the affected enemy will be hypnotized. After 9 seconds, the parasite will emerge from its host and attack nearby enemy units, dealing AoE Physical Damage. Parasites do not allow you to activate enemy ultimates, and some units are immune to parasites
Orkon knocks a nearby enemy into the air, then smashes the ground, dealing AoE Physical Damage over a small area
Orkon enters battle mode, initially gaining HP equivalent to 15% of his Max Health, and greatly increasing his Physical Attack and Lifesteal. However, while in this mode, his HP will continually be drained. This ability can be cancelled at any time by tapping again on Orkon's avatar, but the initial 15% HP boost will disappear upon cancellation (If he has less than 15% HP remaining, he will be left with 1 HP).  Each time he successfully grasps an enemy, Orkon will improve one of his stats. After one successful grasp, his Attack Speed will increase. After two successful grasps, his Physical Attack will increase, and he will reduce the amount of Physical and Magic Damage he takes by 30%. After his third successful grasp, he will be immune to all Control Abilities
After taking Damage, Orkon will gradually replenish some Health over the next 10 seconds. The effect can stack
*Key items required to fuse up to Orange+2 and fully equip. Not all items required for fusions listed. Signature Item not included.


|
DashingPlatforms
1.0
|
Public Member Functions | |
| MailboxProcessor (MessageHandlerList *handlerList, MailboxOwnerHandle &mailboxOwnerHandle) | |
| virtual | ~MailboxProcessor () |
| void | processMailbox (int numberThreads=1) |
| MailboxProcessor::MailboxProcessor | ( | MessageHandlerList * | handlerList, |
| MailboxOwnerHandle & | mailboxOwnerHandle | ||
| ) |
Constructor. Associate a Mailbox with its Message Handler List
References MailboxProcessor().
Referenced by MailboxProcessor().
|
virtual |
Virtual Destructor.
| void MailboxProcessor::processMailbox | ( | int | numberThreads = 1 | ) |
Reads messages from the queue and calls the appropriate message handler.
If the queue is empty it will block. After the message has been processed it will be deleted (or it will be checked in to the OPM if it is an OPM managed object). So applications SHOULD NOT DELETE messages when they handle them. If the mailbox has not be activated by the application, then getMessage() will return NULL, and this method will return.
| numberThreads | if more than 1 thread is given as a parameter, then a ThreadManager thread pool will be created to service the Mailbox queue. NOTE!! If the developer chooses to use a ThreadPool for processing the the Mailbox queue, the developer is responsible for ensuring that all of the Mailbox Handlers are THREAD SAFE!!! |
References ThreadManager::createThreadPool(), MessageBase::deleteMessage(), MessageHandlerList::find(), MailboxOwnerHandle::getMessage(), and MessageBase::getMessageId().
Referenced by ResourceMonitor::processMailbox(), ProcessManager::processMailbox(), FaultManager::processMailbox(), and DiscoveryManager::processMailbox().


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Basic Checklists¶
Running a Processed Setup¶
Prepackaged runs are common for testing SCHISM on a new system or rerunning a model from another user delivered as a bundle. In this case the user will have obtained a complete set up as a “tarball” (tar.gz) file or copies the run using the rsync command (don’t use copy command cp). See Linux hints.
The steps are:
☐ Set up the environment and compile schism
☐ Untar the archived run
☐ If the atmospheric files are packaged separately, reestablish the linkage using the instructions in the readme file.
☐ Compile the model using instructions on the VIMS website or precompliled binaries for Windows*
- ☐ Most clusters use a job manager and you submit to queue. Usually you need to adapt
☐ schism.sh to point to executable and load dependencies
☐ launch files such as launch.pbs (user name, number of cores and cpus per core, name of run)
☐ Create a directory called outputs
☐ Take care of any ‘gotchas’ such as making sure there is a directory named ‘outputs’ and that stack limits are disabled.
* Windows is good for learning but not typical for full Delta runs
Simple Hindcast Runs¶
☐ Choose run dates¶
See hotstart section on this topic
☐ Apply template to existing mesh¶
☐ Clone BayDeltaSCHISM from GitHub. Get a fresh copy or git stash any changes.
☐ Note the git hash code or tag in a readme.
☐ Copy BayDeltaSCHISM/templates/bay_delta to study preprocessing directory mystudy/bay_delta
☐ Obtain an SMS .2dm or SCHISM hgrid.gr3 file (e.g. bay_delta_111_prop605.2dm and add it to bay_delta_schism)
☐ Change main_bay_delta.yaml by pointing the mesh_inputfile to the name of the
☐ Make sure your flow transect file (flow_station_xsect.yaml) contains what you want
☐ Invoke the preprocessor with the command: $ prepare_schism main_bay_delta.yaml
☐ Copy the prepared items (.in, ,gr3, .ll, .prop, .nml) to the launch place.
☐ Collect time series inputs¶
☐ elev2d.bat
☐ download NOAA files for coastal stations at SF, Point Reyes, Monterey
☐ validate that the datum is NAVD88
☐ runs genelev2D.py
☐ multi_clip
☐ Hotstart (can be concurrent with barotropic)¶
☐ Nudging (can be concurrent with barotropic)¶
☐ Barotropic warmup¶
☐ interpolate_variables7 (or later) script to create uv3D.th.
☐ move the uv3D.th to the study directory
☐ delete all the files outputs/* or move /outputs to /outputs.tropic and create an empty /outputs
☐ Baroclinic simulation¶
☐ finalize your station.in output request


Create or edit Workloads
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{{w}}
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Clear
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Ho Yau Chik
Evelin Susan Fung Zhong
Vincent Garcia
Yangchen Gruben
Esra Gumrukculer
Shuting Guo
Ayse Yaren Kaya
Prithi Khalique
Soyeon Kim
Seowon Kim
Jahyun Lee
Emily Hue Foo Li
Khe Nguyen
Vincent Privitera
Emil Waldron


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Ecclesiastes 1:14
I have seen all the works that are done under the sun; and, behold, all is vanity and vexation of spirit.


URGENT DONATION REQUEST TO HELP HURRICANE VICTIMS
Assalamu ‘Alaikum (May Peace Be Upon You),
Alhamdulillah, our community is blessed to have weathered the storm here in the Triangle. Others around the state were not so fortunate, and we pray for those who have lost lives, homes and businesses, and are struggling to rebuild in the aftermath of Florence. Those of us who were spared the worst have a duty to help our community members in the areas that have been devastated by the storm.
IAR's Social and Welfare Committee, along with other North Carolina mosques and non-profit organizations, have joined together to share resources in coordinating a statewide relief effort to assist government agencies and relief organizations.
We have ongoing communication with government agencies and relief organizations assessing the extent of the damage and need.
We have coordinated a three-part effort:
1. SUPPLY DRIVE
Member organizations will be collecting donations this week in a supply drive.
The items can be dropped off Monday-Friday (9/17-9/21) at your neighborhood Triangle-area Mosques and on-campus locations designated by the corresponding Muslim Student Associations. All locations are collecting at all times Monday-Friday.
FINAL COLLECTION: Islamic Association of Raleigh - Saturday September 22nd, 2018 (9AM - 3PM)
Details about what is needed can be found here.
2. VOLUNTEER
Volunteers will be helping out in impact zones through our partnership with Triangle Muslim Aid (TMA), government and nonprofit organizations. As the storm moves off, we are being updated on the needs in impact zones. More information on needs and assignments will be provided by TMA to those who sign up below.
Sign up here if you're interested in volunteering.
3. MONETARY DONATIONS
If you'd prefer to give a monetary donation, please do so here.
We are working with NC state agencies to get aid to impact zones as soon as, and as efficiently as, possible.


Did you know that...
The most common cause of staph infections. It is a spherical bacterium, frequently part of the skin flora found in the nose and on skin. About 20% of the population are long-term carriers of ''S. aureus''. ''S. aureus'' can cause a range of illnesses from minor skin infections, such as pimples, impetigo (may also be caused by ''Streptococcus pyogenes''), boils (furuncles), cellulitis folliculitis, carbuncles, scalded skin syndrome and abscesses, to life-threatening diseases such as pneumonia, meningitis, osteomyelitis, endocarditis, toxic shock syndrome (TSS), bacteremia and septicemia.
Bacterial cells of the bacteria (Staphylococcus aureus) which is one of the causal agents of mastitis in dairy cows. Its large capsule protects the organism from attack by the cow’s immunological defenses.d infections. Abbreviated to ''S. aureus'' or ''Staph aureus'' in medical literature, ''S. aureus'' should not be confused with the similarly named and similarly dangerous (and also medically relevant) species of the genus ''Streptococcus''.
Its incidence is from skin, soft tissue, respiratory, bone, joint, endovascular to wound infections. It is still one of the five most common causes of nosocomial infections, often causing postsurgical wound infections. Abbreviated to ''S. aureus'' or ''Staph aureus'' in medical literature, ''S. aureus'' should not be confused with the similarly named and similarly dangerous (and also medically relevant) species of the genus ''Streptococcus''.


Introduction
Crimes are a reflection of the period and society in which they occur. What happened, the objects and people involved, the location, how it was recorded - or even if it was recorded - all tell us something about the values, attitudes, and power structures of the day.
This data visualisation looks at the almost 20 million words that were written in the New South Wales Police Gazette and Weekly Record of Crime from the beginning of 1860 up until the end of 1900* to see what it shows about how the people, places, and things, changed in NSW over that period. But this is not a law-and-order piece, as it's not so much interested in the crimes themselves, but rather the data in aggregate, and what else is documented in the process of recording them. Changing fashions, new technologies, new modes of transport, the establishment of new towns, increasing wealth, all this is recorded in the gazette - along with the racism of the day - as a by-product of reporting the crimes (and other police matters) that occurred. For example, what objects were valued enough (and existed at the time) in order to be stolen? How did people dress, both those committing the (alleged) crimes, and the items of clothing that were stolen? Where did people live? What materials were in use? What modes of transport were used? Crime as a proxy for societal change.
Disclaimer
This project merely counts the number of times certain words and phrases appear in the gazette in a given year, so it's obviously a massive oversimplification of what's actually occurring over the period (though it does normalise the count against the total number of words in the gazette that year). On top of that, with few exceptions it doesn’t check for duplicates, typos, nor exclude notices in the gazette that refer to incidents from interstate. Also, given the text used is the result of optical character recognition (OCR), and the original documents themselves are old, there's also - understandably - a number of errors in the original conversion ('G' mistaken for 'C', 'f' for 't', '3' for '8' etc, and vice versa).
Plus, over four decades, spellings change, as do the terms used, as well as the names of places, so all that's shown here should be taken with a grain of salt (and the ones with fewer mentions even more so). That said, although this approach has its shortcomings, it can be an effective way to provide a quick overview of large datasets, especially for terms that have a higher frequency. For example, below you can see the popularity of the moustache throughout the 1880s and early 90s, how the town of Gulgong flourished during the gold rush years, and how the cabbagetree hat - one of the first distinctively Australian hats - became less popular over time:
For this project, the terms have been loosely grouped into people (in pink), places (in green), and things (in purple), but it is in no way comprehensive, as the idea was more just to have a bit of a play around, and see what the data might show. Also, in order to best see the changes over time, most of the graphs are relative, and have been scaled so that the year in which the term appeared at the greatest rate - as shown by the small black dot - has the maximum height.
* The gazette was printed up until 1930, but this project only looks at the period from 1860 up until federation. Note: Most of the photos used in this project are from the library's Holtermann collection and their photostream on flickr.
- People
- Places
- Things
- Peak year
About the gazette
The gazette was initially called 'Reports of Crime, etc., etc., for Police Information' and was bi-weekly, published on Mondays and Thursdays, from 1860 (the earliest digitised issue the library seems to have is #45 from Monday 4th of June, 1860). The first issue under the title New South Wales Police Gazette and Weekly Record of Crime was published Wednesday 5th of March, 1862, and was roughly weekly from then on.
The gazette published a range of information relevant to the NSW police force, such as promotions, police dismissed from the force, released prisoners, deserters, corrections and updates to previous issues, as well as occasional supplements from other government gazettes. But the bulk is to do with the crimes committed and the often evocative descriptions of the (alleged) perpetrator(s) that follow. A typical record of a crime will follow a similar format and note what occurred, the rough time and date (if known), the location, as well as if there was a reward.
The general approach
The approach was quite simple: count how many times a word or phrase appears in a given year, and see how that changes over time. I didn't look for context, so with few exceptions the code makes no distinction between whether, for example, it was a Panama hat that was stolen, or the Panama hat was worn by the (alleged) suspect.
The fact that a Panama hat was mentioned, showed that a) it was around at the time and b) valued enough to be either worn or stolen.
The same approach was taken with places: it didn't matter whether the crime took place in Dubbo, or the person was originally from Dubbo, or the person was ultimately sentenced in Dubbo, merely that the 'Dubbo' appeared in the gazette in a given year.
All that matters is the fact that the particular word or phrase occurred in a given year. These tallies are then normalised against the total number of words in the gazette for that year, so that words or phrases that appear in years where more crimes and events were recorded (and hence there's more words) aren't given undue weight. The results are then graphed to show the changes over the years.
- People
- Places
- Things
-
tattoo
-
Balmain
-
boarding house
-
gold
No. of words in Gazette
vs Population
This project uses the words in the gazette as a rough proxy for NSW as a whole (though as you can see, although they both increase roughly in tandem initially, the size of the gazette doesn't correspond to population size overall)
- Words in Gazette
- Population of NSW
1860s
1870s
1880s
1890s
Note: Trove only has issues of the Gazette from June 1860, so the word count for 1860 as a whole is lower, but given I'm using the ratio, rather than the count, I've treated it the same as other years. However, you will see a few cases where 1860 has a spike, and that is probably due to the fact that it doesn't reflect a full year of reporting. Historical population data via the ABS
Compare Terms
See how the rate at which an object or term is mentioned changes over the period. e.g: horses vs bicycles, or whiskers vs moustache, or diamonds vs opals, or cigars vs cigarettes, or moleskin vs serge, or public-houses vs railway stations or drunk vs talkative, or coffee vs tea, or California hats vs straw hats.
- People
- Places
- Things
vs
- Peak year
1860s
1870s
1880s
1890s
Note: scale is relative to the maximum rate of the two terms


-
Jekyll Blog
Creating this blog led me to learn a few things about how to set up a site with Jekyll. It only makes sense that I get meta with my first proper contribution and tell you how I did it.
-
Welcome
Welcome to Reason About. This is a blog that brings fresh insights and information from about 30 new and continually learning web developers together in one space. Specifically, we are a group of soon-to-graduate full stack bootcamp students and instructors. More specifically, we are Galvanizers.
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Strange Case of Dr Jekyll and Mr Hyde
Strange Case of Dr Jekyll and Mr Hyde is the original title of a novella written by the Scottish author Robert Louis Stevenson that was first published in 1886. The work is commonly known today as The Strange Case of Dr. Jekyll and Mr. Hyde, Dr. Jekyll and Mr. Hyde, or simply Jekyll & Hyde. It is about a London lawyer named Gabriel John Utterson who investigates strange occurrences between his old friend, Dr. Henry Jekyll, and the evil Edward Hyde.
-
Strange Case of Dr Jekyll and Mr Hyde
Strange Case of Dr Jekyll and Mr Hyde is the original title of a novella written by the Scottish author Robert Louis Stevenson that was first published in 1886. The work is commonly known today as The Strange Case of Dr. Jekyll and Mr. Hyde, Dr. Jekyll and Mr. Hyde, or simply Jekyll & Hyde. It is about a London lawyer named Gabriel John Utterson who investigates strange occurrences between his old friend, Dr. Henry Jekyll, and the evil Edward Hyde.
-
Strange Case of Dr Jekyll and Mr Hyde
Strange Case of Dr Jekyll and Mr Hyde is the original title of a novella written by the Scottish author Robert Louis Stevenson that was first published in 1886. The work is commonly known today as The Strange Case of Dr. Jekyll and Mr. Hyde, Dr. Jekyll and Mr. Hyde, or simply Jekyll & Hyde. It is about a London lawyer named Gabriel John Utterson who investigates strange occurrences between his old friend, Dr. Henry Jekyll, and the evil Edward Hyde.


Enjoys designing and developing websites, as well as working with the UX/UI side of mobile applications. Enjoys learning from others and working in team focused environments.
Graduated with a Bachelor's of Science in Web and Mobile Computing at the Rochester Institute of Technology in Decemeber 2019.
At RIT she is an active member of Women in Computing and served as their webmaster for two years. WiC is an organization dedicated to promoting the success and advancement of women in their academic and professional careers, especially in technology. She frequently volunteers to teach young girls HTML and CSS at WiC events. She helped organize the all women's hackathon, WiCHacks, in 2019.
Has interned previously at EagleDream Technologies, Nationwide Mutual Insurance and Prudential Financial as a Web Application Developer.
Wireframing • InVision • User Testing
HTML/CSS • Javascript • Java • PHP • SQL • jQuery
Adobe Photoshop • InDesign • Illustrator • GIMP
Swift (iOS) • Wordpress • Android • Final Cut Pro


CGI - CGI Support Functions
CGI support functions. SCOOP is used to create a CGI object. This object contains data and methods needed to support CGI activities. In turn, CGI uses COOKIE to support HTTP cookies. The WEB application can use SESSION to support persistant server-side sessions. HTMLTMPL to support templates, PSQL to provide database for filling in templates.
Logging is supported in the CGI class. This is not decoupled because there are very few ways to debug CGI applications.
USE_FCGI is set to true in FCGI.lss if Fast CGI is in use. So, include FCGI.INC before CGI.INC to prepare for Fast CGI.
Still need: Response codes, Headers/Responses
| Last-modified: | Content-type: application/octet-stream; name="filename" | Content-disposition: attachment; filename="genome.jpg" | | Content-length: 3002 | Location: | Status: 200 OK | 302 Found (301 302 303) | 400 Bad Request | 501 Not Implemented | local redirect - just respond with Location: | absolute URI redirect same | with doc, respond 302 Location and doc returned
Uses
BQ
CHARS
COOKIE
CRACK
DSERVE
FCGI
HASH
HTMLESC
HTMLTMPL
JSON
READFILE
SCOOP
SEQ
SESSION
UNIQUE
VDIFFER
See Also
SESSION
HTMLTMPL
PSQL
«INCLUDES»≡ -INCLUDE 'BQ.INC' -INCLUDE 'CHARS.INC' -INCLUDE 'COOKIE.INC' -INCLUDE 'CRACK.INC' -INCLUDE 'DSERVE.INC' -INCLUDE 'FCGI.INC' -INCLUDE 'HASH.INC' -INCLUDE 'HTMLESC.INC' -INCLUDE 'HTMLTMPL.INC' -INCLUDE 'JSON.INC' -INCLUDE 'READFILE.INC' -INCLUDE 'SCOOP.INC' -INCLUDE 'SEQ.INC' -INCLUDE 'SESSION.INC' -INCLUDE 'UNIQUE.INC' -INCLUDE 'VDIFFER.INC' @
«CGI»≡ * -PUBLIC CLASS:CGI_CLASS * CGI_CLASS = SEND(CLASS, 'NEW_CLASS', 'CGI_CLASS') DEFINE('CGI_INIT()A,C') * -PUBLIC CGI_CLASS:'INIT' * SEND(CGI_CLASS, 'ADD_METHOD', 'INIT', 'CGI_INIT') * -PUBLIC CGI_CLASS:'GET_COOKIE_JAR' * SEND(CGI_CLASS, 'ADD_METHOD', 'GET_COOKIE_JAR', CODE( + ' SEND = |.COOKIE_JAR :(RETURN)')) * -PUBLIC CGI_CLASS:'GET_HEADER' * SEND(CGI_CLASS, 'ADD_METHOD', 'GET_HEADER', CODE( + ' SEND = |.HEADER :(RETURN)')) DEFINE('CGI_SEND()S') * -PUBLIC CGI_CLASS:'SEND' * SEND(CGI_CLASS, 'ADD_METHOD', 'SEND', 'CGI_SEND') * -PUBLIC CGI_CLASS:'GET_LOG' * SEND(CGI_CLASS, 'ADD_METHOD', 'GET_LOG', CODE( + ' SEND = |.LOG :(RETURN)')) * -PUBLIC CGI_CLASS:'GET_CONTENT' * SEND(CGI_CLASS, 'ADD_METHOD', 'GET_CONTENT', CODE( + ' SEND = |.CONTENT :(RETURN)')) * -PUBLIC CGI_CLASS:'SET_CONTENT' * SEND(CGI_CLASS, 'ADD_METHOD', 'SET_CONTENT', CODE( + ' |.CONTENT = A1 :(RETURN)')) DEFINE('CGI_CLOSE()A') * -PUBLIC CGI_CLASS:'CLOSE' * SEND(CGI_CLASS, 'ADD_METHOD', 'CLOSE', 'CGI_CLOSE') * -PUBLIC CGI_CLASS:'GET_ENV' * SEND(CGI_CLASS, 'ADD_METHOD', 'GET_ENV', CODE( + ' SEND = |.ENV :(RETURN)')) * -PUBLIC CGI_CLASS:'GET_QUERY' * SEND(CGI_CLASS, 'ADD_METHOD', 'GET_QUERY', CODE( + ' SEND = |.QUERY :(RETURN)')) * :(CGI_OBJECT_END) * * CLOSE CGI OBJECT * CGI_CLOSE ENDFILE(|.OUT_UNIT) DETACH(|.OUT) ENDFILE(|.LOG_UNIT) DETACH(|.LOG) :(RETURN) * * INITIALIZE CGI OBJECT * CGI_INIT CGI_INIT = SUPER('INIT') * * CREATE LOG CHANNEL * |.LOG_UNIT = IO_FINDUNIT() :F(FRETURN) |.LOG = UNIT_NAME(|.LOG_UNIT) * * WITH ALL THE CHANGES GOING TO SYSTEMD, LOGGING APPEARS TO HAVE * SUFFERED. SPECIFICALLY, IF THE LOGGING IS ONLY FORWARDED TO * RSYSLOG, LOGGER LOCAL DOESN'T WORK. WE ENABLE RSYSLOG TO LISTEN TO * THE NETWORK, AND THEN LOGGER CAN LOG TO LOCALHOST. STRANGE, AND * PROBABLY NOT SUITABLE FOR PRODUCTION. * * IF USING SYSTEMD LOGGER, DON'T USE -n localhost * OUTPUT(|.LOG, |.LOG_UNIT, 'W', + '|/usr/bin/logger -t CGI') :F(FRETURN) *+ '|/usr/bin/logger -n localhost -t CGI') :F(FRETURN) * * CREATE OUT CHANNEL * |.OUT_UNIT = IO_FINDUNIT() :F(FRETURN) |.OUT = UNIT_NAME(|.OUT_UNIT) OUTPUT(|.OUT, |.OUT_UNIT, 'B', '/dev/stdout') :F(FRETURN) * * COLLECT ENVIRONMENT VARIABLES INTO INSTANCE VARIABLE ENV. WE ONLY * GATHER UP "KNOWN" ENVIRONMENT VARIABLES. * |.ENV = DIFFER(USE_FCGI) FCGI_ENV :S(CGI_ENV_2) * |.ENV = TABLE(38) A = %('AUTH_TYPE,CONTENT_LENGTH,CONTENT_TYPE,' + 'CONTEXT_DOCUMENT_ROOT,CONTEXT_PREFIX,DOCUMENT_ROOT,' + 'GATEWAY_INTERFACE,HTTP_ACCEPT,HTTP_ACCEPT_CHARSET,' + 'HTTP_ACCEPT_ENCODING,HTTP_ACCEPT_LANGUAGE,' + 'HTTP_CACHE_CONTROL,HTTP_CONNECTION,HTTP_COOKIE,' + 'HTTP_HOST,HTTP_REFERER,HTTP_USER_AGENT,LIB_PATH,PATH,' + 'PATH_INFO,PATH_TRANSLATED,QUERY_STRING,REMOTE_ADDR,' + 'REMOTE_HOST,REMOTE_IDENT,REMOTE_PORT,REQUEST_METHOD,' + 'REQUEST_SCHEME,REQUEST_URI,SCRIPT_FILENAME,SCRIPT_NAME,' + 'SERVER_ADDR,SERVER_ADMIN,SERVER_NAME,SERVER_PORT,' + 'SERVER_PROTOCOL,SERVER_SIGNATURE,SERVER_SOFTWARE') SEQ(' (|.ENV)<A> = HOST(HOST_GETENV, A); A ', .I) CGI_ENV_2 $|.LOG = 'FCGI_ENV IN CGI' $|.LOG = JSON_ENCODE(FCGI_ENV) $|.LOG = 'QUERY_STRING: ' (|.ENV)<'QUERY_STRING'> * * FOR POST METHOD, READ (|.ENV)<'CONTENT_LENGTH'>.  NOTE THAT THIS MAY BE VERY LARGE. THIS * MAY FAIL DUE TO MEMORY EXHAUSTION. WE KNOW THAT WE CAN READ A LARGE * NUMBER OF BYTES, BUT I HAVE TO DETERMINE THE LIMIT. * * FOR FCGI, USE FCGI_IN. FIXME: SHOULD TEST CONTENT_LENGTH * (|.ENV)<'QUERY_STRING'> = DIFFER(USE_FCGI) VDIFFER(FCGI_IN) + :S(CGI_INIT_3) T3 = (|.ENV)<'CONTENT_LENGTH'> IDENT(T3) :S(CGI_INIT_3) (INTEGER(T3) GT(T3)) :F(CGI_INIT_3) T1 = IO_FINDUNIT() :F(FRETURN) INPUT('T2', T1, 'B,' T3, '/dev/stdin') :F(FRETURN) (|.ENV)<'QUERY_STRING'> = T2 ENDFILE(T1) DETACH('T2') CGI_INIT_3 * * PARSE QUERY_STRING INTO QUERY TABLE. HASH() IS USED TO PARSE * QUERY_STRING, BREAKING INTO NAME=VALUE SEPARATED BY &. EACH VALUE IS * PROCESSED WITH URL_DECODE(). * * MULTI-VALUE FIELDS ARE SEPARATED BY 0. USED BY CHECKBOXES * |.QUERY = HASH((|.ENV)<'QUERY_STRING'>, '=', '&',,,, + 'URL_DECODE', CHARS_NUL) $|.LOG = 'QUERY TABLE' $|.LOG = JSON_ENCODE(|.QUERY) * * PARSE HTTP_COOKIE INTO A COOKIE JAR. THERE MAY NOT BE ANY COOKIES, * IN WHICH CASE WE CREATE AN EMPTY COOKIE JAR. THE VALUES OF THE * COOKIES ARE NOT VALIDATED. HASH() PARSES THE COOKIE STRING. AFTER * THE TABLE IS GENERATED, IT IS CONVERTED TO AN ARRAY. SEQ() ADDS * EACH ELEMENT INTO THE COOKIE JAR. * |.COOKIE_JAR = NEW_COOKIE_JAR() A = HASH((|.ENV)<'HTTP_COOKIE'>, '=', ';',,, + 'COOKIE_FIX_NAME', 'URL_DECODE') A = CONVERT(A, 'ARRAY') :F(CGI_INIT_2) SEQ(' PUT_COOKIE_IN_JAR(|.COOKIE_JAR,' + 'A, NEW_COOKIE(A)) ', .I) CGI_INIT_2 * * INITIALIZE HEADER. THE HEADER IS A TABLE() WITH AT LEAST CONTENT_TYPE * ENTRY. * |.HEADER = #'CONTENT_TYPE=TEXT/HTML,CHARSET=' * * INITIALIZE CONTENT * |.CONTENT = * * WE NEED ROUTING IMPLEMENTED. * * WE NEED TO DO FURTHER PROCESSING ON THE FOLLOWING CGI ENVIRONMENT * VARIABLES. * * <ISINDEX> value1+value2+.. * HTTP_ACCEPT * ='TEXT/HTML,APPLICATION/XHTML+XML,APPLICATION/XML;Q=0.9,*/*;Q=0.8' * HTTP_ACCEPT_ENCODING='GZIP, DEFLATE' * HTTP_ACCEPT_LANGUAGE='EN-US,EN;Q=0.5' * :(RETURN) * * OUTPUT THE HEADER AND CONTENT * CGI_SEND * * FIRST, OUTPUT THE CONTENT-TYPE. IF CHARSET HAS BEEN SPECIFIED, * ADD THIS TO THE CONTENT-TYPE. * S = 'Content-type: ' (|.HEADER)<'CONTENT_TYPE'> S = S '; charset=' VDIFFER((|.HEADER)<'CHARSET'>) S = S CHARS_CR CHARS_NL * * GENERATE THE COOKIE STRING AND OUTPUT IF WE HAVE COOKIES TO SEND. * S = S VDIFFER(COOKIE_STRING(|.COOKIE_JAR)) * * THE HEADER FINISHES WITH A CR/LF * S = S CHARS_CR CHARS_NL $|.OUT = IDENT(USE_FCGI) S DIFFER(USE_FCGI) FCGI_SEND(S) * * SEND CONTENT * $|.OUT = IDENT(USE_FCGI) |.CONTENT DIFFER(USE_FCGI) FCGI_SEND(|.CONTENT) DIFFER(USE_FCGI) FCGI_SEND('') DIFFER(USE_FCGI) FCGI_END(0, FCGI_REQUEST_COMPLETE) :(RETURN) * CGI_OBJECT_END * @
The startup of a CGI script should explicitly set the library directory (if needed). As well, the path to the interpreter should be absolute. These are basic security measures.
This unit test will test basic CGI object creation. Cookie jars and cookies. The session cookie and sessions. Simple logging. Templates.
Template sample has jQuery lashed in. Need AJAJ (AJAX with JSON) server support and some sample web page stuff.
Performance Notes
Caching compiled templates (in a separate process), pooling database connections, fastcgi would all help. This code can be 50% faster with these techniques.
Then Session verification.
Pick a simple application, write and deploy: fog the mirror would be fine. Signon, message board.
CGI and PSQL use SCOOP. HTMLTMPL, SESSION and COOKIE could as well, but predate SCOOP.
«unit_test»≡ #!/usr/bin/bash export "SNOLIB=/home/fred/snolib"; export "SNOPATH=/home/fred/snolib"; export "LD_LIBRARY_PATH=/home/fred/snolib" exec "/usr/local/bin/snobol4" "-b" "$0" "$@" -INCLUDE 'FCGI.INC' -INCLUDE 'CGI.INC' -INCLUDE 'READFILE.INC' -INCLUDE 'BQ.INC' -INCLUDE 'PSQL.INC' -INCLUDE 'HTMLTMPL.INC' -INCLUDE 'SESSION.INC' -INCLUDE 'JSON.INC' -INCLUDE 'DSERVE.INC' -INCLUDE 'ROUTING.INC' * -CMNT EMBED THE TEMPLATES NEEDED.  -EMBED FILE 'CGI.tmpl' <!doctype html>    <meta charset="UTF-8" /> <title>CGI test</title>    jQuery


The nested html tags extend the org.apache.struts.taglib.logic
tags to allow them to relate to each other in a nested nature. The fundamental
logic of the logic tags don't change, except in that all references to
beans and bean properties will be managed in a nested context.


Because the weekend can seem to turn into one long day, it is important to wear something comfortable. Even without OU gear, there are still ways to show your love for this amazing university:
Green set
A matching set is probably the easiest option for a homecoming outfit. Whether you want to go the green route or a more neutral color, there are many options. This green set from Lucy in the Sky is stretchy, comfortable and the perfect OU green color.
You can also match a pair of plain sweatpants you already have with a top in the same color. It is simple yet unique, and you can go crazy on the accessories. It’s also a cheaper alternative to buying new OU gear.
Iron-on OU patch
If your outfit is too simple or you can’t find the right OU shirt or pants, you can always iron on a festive patch. It is cheaper and more efficient, and your outfit will be unique.
Etsy offers a plethora of OU patches and other accessories to add to any homecoming outfit. It is such an easy and budget-friendly idea. DIY is always the way to go.
Face stickers and gems
Other great accessories for any homecoming outfit are face stickers and green gems/glitter. You can find stickers at any bookstore on Court Street and Amazon or Walmart usually has stick-on gems and glitter.
You can wear the stickers on your cheekbone or put green glitter in your hair.
OU sweatshirt from the bookstore
The bookstores on Court Street and the Athens Walmart offer some great Ohio University apparel for affordable prices. You can never go wrong with a big, comfy OU sweatshirt for this upcoming weekend. It is simple yet classic and will keep you warm early on Saturday morning.


The best way to contact me is email but there are a number of other ways:
@samelamin LinkedIn Google Plus* GitHub *Not visited frequently.


//= ./html/header.html
//= ./html/problems.html //= ./html/how.html //= ./html/programm.html //= ./html/teacher.html //= ./html/guarantee.html //= ./html/review.html //= ./html/registration.html
//= ./html/footer.html


Mohamed Eldesouki
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--- title: Contact call_to_action: Map background_image_path: large_header: false show_in_navigation: true navigation_order: 3 map: latitude: -45.8787605 longitude:<PHONE_NUMBER> zoom: 13 ---
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Mushrooms - P. cubensis
I made a tea earlier this evening from 2.6 grams of small, dried, closed-capped cubensis by breaking them up into tiny pieces and steeping them in 208 degree (F) water twice for 8-10 minutes each time. These particular mushrooms are extremely potent, I learned several weeks ago by taking 3 grams and having my brain turned inside out. So I expected a strong experience from this amount. Instead of merely having a strong trip, however, I turned into a shadow of my former existence, except that the shadow was real and my former life was revealed to be the creating of a small mind. I lost myself to the waves of pure spirit that came crashing down.
It was only 20 minutes after drinking the tea that I had to close my eyes, and lost track of where I was, and soon after, who I was. I was squirming around on the floor for hours, not in control of my body, giving it all up to God's will, following the whim of the divine hand. I laughed like a maniac, I may have cried but it's not clear if that's the correct word for it. I saw through my everyday games like an adult knows about a child's world, and also knows the adult world, someone with duplicity of vision and the capacity for multiple simultaneous understanding. Too much went on in my mind to describe, all at the same time, as I made connections between seemingly disparate and distant things and saw the interconnectedness of all things clearly. I actually experienced this, although that is meaningless to say in words.
 I felt that one human life was an extremely short period of time, like a race run by children without any determinable purpose other than fun, or just the love of breathing the air, or doing something, anything at all. However, I saw that each life is lived with some meaning to it; what can be learned through one person's challenges is the lesson of the lifetime, and then death comes momentarily, until we are born again, and thousands of lifetimes pass in what is one afternoon for the gods that are revealed by the mushroom, as we make our ways slowly towards purity and love, taking one small step each hundred thousand years. I saw myself as one little addition to the tapestry of life, a hundred of my lifetimes like a single dot on the canvass of life, so insignificant, but still a part of the beauty.
I felt as if everything that was happening to me had happened once before, in a long deja-vu that must have lasted an hour or more. I endured quite a bit of suffering during this time. My horrible trip on LSD almost 20 years ago came back to me, with some of the same words being offered occasionally to my mind like bait for the fish of my fears: 'hiccoughs in the fabric of life', 'vomit'. I saw vaginas appear from above, pink and plastic-looking. I heard all kinds of hallucinations, voices talking, bugs vibrating, and more that I can't remember.
I asked my wife to help me get out of my deja-vu nightmare. She tired, but everything she did seemed like she had done the same thing last time I took these mushrooms, about three weeks ago - a very strange trip, where I wasn't sure what was happening during the trip, and even afterwards I couldn't really figure out what had happened. Finally, somehow, with her support, I got unstuck from the nightmare, and focused on following god's will each second.
I should mention that even during the horrible deja-va episode, I was fairly calm and accepting of my situation, far better than I felt 20 years ago during my bad trip. I was surprised that the mental instability I accessed with LSD was present to some degree with the mushrooms this time, as I had imagined that LSD was synthetic, and therefore could more easily lead to such mental states, whereas mushrooms tended to be linked to more spiritual experiences, rather than pure psychosis. However, mushrooms can lead to as much of a psychotic experience as LSD, I discovered, if I take enough.
What an experience - beyond normal human consciousness, beyond the limits of the skin and mind. I'm now at T + 6.5 hours and still tripping strongly as I write this, though I'm back in my body for the most part.
The next day: I feel tired from the experience, and from staying up until 5 AM, but it was one of the most powerful experiences in my life, and so fatigue is par for the course. I had some insights into my life and some poor decisions I had been making, and I am grateful for that, but to move only where the hand of god pushed me was the part of the trip that I will never forget.
This trip was fantastic, scary, overwhelming, emotional, and facilitated a great amount of learning for me. It was as strong or stronger than my other strongest mushroom trips, most of which were on 3.5 to 4 grams of cubensis. It might even compare in sheer power to the 8 hits of LSD I took 20 years ago when I freaked out, but this was so much gentler mentally and so much more powerful spiritually that I still prefer mushrooms to any other drug. I will be experimenting with them again soon, though for my next trip on these potent mushrooms I will probably take about 2 grams so that I have a more moderate experience.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


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"Here are the badges."


Hypervisors need the ability to bridge traffic between VMs and with the outside world. On Linux-based hypervisors, this used to mean using the built-in L2 switch (the Linux bridge), which is fast and reliable. So, it is reasonable to ask why Open vSwitch is used.
The answer is that Open vSwitch is targeted at multi-server virtualization deployments, a landscape for which the previous stack is not well suited. These environments are often characterized by highly dynamic end-points, the maintenance of logical abstractions, and (sometimes) integration with or offloading to special purpose switching hardware.
The following characteristics and design considerations help Open vSwitch cope with the above requirements.
The mobility of state: All network state associated with a network entity (say a virtual machine) should be easily identifiable and migratable between different hosts. This may include traditional "soft state" (such as an entry in an L2 learning table), L3 forwarding state, policy routing state, ACLs, QoS policy, monitoring configuration (e.g. NetFlow, IPFIX, sFlow), etc.
Open vSwitch has support for both configuring and migrating both slow (configuration) and fast network state between instances. For example, if a VM migrates between end-hosts, it is possible to not only migrate associated configuration (SPAN rules, ACLs, QoS) but any live network state (including, for example, existing state which may be difficult to reconstruct). Further, Open vSwitch state is typed and backed by a real data-model allowing for the development of structured automation systems.
Responding to network dynamics: Virtual environments are often characterized by high-rates of change. VMs coming and going, VMs moving backwards and forwards in time, changes to the logical network environments, and so forth.
Open vSwitch supports a number of features that allow a network control system to respond and adapt as the environment changes. This includes simple accounting and visibility support such as NetFlow, IPFIX, and sFlow. But perhaps more useful, Open vSwitch supports a network state database (OVSDB) that supports remote triggers. Therefore, a piece of orchestration software can "watch" various aspects of the network and respond if/when they change. This is used heavily today, for example, to respond to and track VM migrations.
Open vSwitch also supports OpenFlow as a method of exporting remote access to control traffic. There are a number of uses for this including global network discovery through inspection of discovery or link-state traffic (e.g. LLDP, CDP, OSPF, etc.).
Maintenance of logical tags: Distributed virtual switches (such as VMware vDS and Cisco's Nexus 1000V) often maintain logical context within the network through appending or manipulating tags in network packets. This can be used to uniquely identify a VM (in a manner resistant to hardware spoofing), or to hold some other context that is only relevant in the logical domain. Much of the problem of building a distributed virtual switch is to efficiently and correctly manage these tags.
Open vSwitch includes multiple methods for specifying and maintaining tagging rules, all of which are accessible to a remote process for orchestration. Further, in many cases these tagging rules are stored in an optimized form so they don't have to be coupled with a heavyweight network device. This allows, for example, thousands of tagging or address remapping rules to be configured, changed, and migrated.
In a similar vein, Open vSwitch supports a GRE implementation that can handle thousands of simultaneous GRE tunnels and supports remote configuration for tunnel creation, configuration, and tear-down. This, for example, can be used to connect private VM networks in different data centers.
Hardware integration: Open vSwitch's forwarding path (the in-kernel datapath) is designed to be amenable to "offloading" packet processing to hardware chipsets, whether housed in a classic hardware switch chassis or in an end-host NIC. This allows for the Open vSwitch control path to be able to both control a pure software implementation or a hardware switch.
There are many ongoing efforts to port Open vSwitch to hardware chipsets. These include multiple merchant silicon chipsets (Broadcom and Marvell), as well as a number of vendor-specific platforms. (The PORTING file discusses how one would go about making such a port.)
The advantage of hardware integration is not only performance within virtualized environments. If physical switches also expose the Open vSwitch control abstractions, both bare-metal and virtualized hosting environments can be managed using the same mechanism for automated network control.
In many ways, Open vSwitch targets a different point in the design space than previous hypervisor networking stacks, focusing on the need for automated and dynamic network control in large-scale Linux-based virtualization environments.
The goal with Open vSwitch is to keep the in-kernel code as small as possible (as is necessary for performance) and to re-use existing subsystems when applicable (for example Open vSwitch uses the existing QoS stack). As of Linux 3.3, Open vSwitch is included as a part of the kernel and packaging for the userspace utilities are available on most popular distributions.


library(dplyr)
library(data.table)
library(pander)
library(ggplot2)
source('../code/rlib_doc.R')
panderOptions('table.split.table', Inf)
theme_set(theme_bw(base_size=12))
source('https://gist.githubusercontent.com/liangyy/43912b3ecab5d10c89f9d4b2669871c9/raw/8151c6fe70e3d4ee43d9ce340ecc0eb65172e616/my_ggplot_theme.R')
th$panel.border = element_rect(colour = th$axis.line$colour)
Here we are interested in surveying the performance of PRS and PTRS in four populations (African, British, Chinese, and Indian) in UK Biobank data. As most of the GWAS and transcritpome studies were done in Europeans, we are specifically interested in the out-of-sample performance of PRS/PTRS predictor obtained from European samples.
The analysis contained two stages: 1) training PRS/PTRS models; 2) test performance on held-out samples. For PRS, we built the LD-clumping based predictor (parameters: 250kb, R2 = 0.1). For PTRS, we learned the gene level effect size using S-PrediXcan with or without small variation. To implement the two-stage analysis pipeline, we split British individuals into three parts: 1) training set; 2) validation set; 3) test set. Training set was used to run GWAS followed by LD-clumping. Validation set was used to select the best-performing predictor (tuning hyperparameters). And test set was used to evaluate the predictor performance just as other populations.
After population assignment QC (removing individuals far from the mode of self-reported ancestry) and phenotype QC, the sample size of each population included in the analysis is shown below. As stated above, British population was further split into three parts with 5000 each for validation and testing along with the rest as training set.
pheno = fread('../output/query_phenotypes_cleaned_up.csv', header = T, sep = ',', data.table = F)
pheno %>% group_by(meaning) %>% summarize(nsample = n()) %>% pander(caption = 'Sample size by population in the analysis')
| meaning | nsample |
|---|---|
| African | 2835 |
| British | 356476 |
| Chinese | 1326 |
| Indian | 4789 |
We surveyed the same set of 17 quantitative traits as Martin et al. (2019). For each trait, we made one data splitting on British individuals. For each splitting, we ran all 17 traits. So that we had 17 x 17 PRS and PTRS predictors, where each trait had 17 independent replications (but for the same data splitting, the results across traits were not independent due to correlation between traits). The list of traits are listed below.
d = read.table('../external_data/martin_et_al_2019ng_table_s6_trait_description.tsv', sep = '\t', header = T)
traits = tolower(d$short)
d %>% pander(caption = '17 quantitative traits included in the analysis')
| short | long |
|---|---|
| BMI | body mass index |
| DBP | diastolic blood pressure |
| Hb | hemoglobin |
| Ht | Hematocrit |
| MCH | mean corpuscular hemoglobin |
| MCHC | mean corpuscular hemoglobin concentration |
| MCV | mean corpuscular volume |
| RBC | red blood cell count |
| SBP | systolic blood pressure |
| WBC | white blood cell count |
| Basophil | basophil count |
| Eosinophil | eosinophil count |
| Height | standing height |
| Lymphocyte | lymphocyte count |
| Monocyte | monocyte count |
| Neutrophil | neutrophil count |
| Platelet | platelet count |
Regarding the data sources of gene expression prediction models, we used GTEx v8 based models which are European based and MESA models which have African, Hispanic populations additionally. Specifically, we included GTEx v8 whole blood CTIMP models and mashr models and MESA models elastic net models which are from monocyte. The sample size of the models are listed below.
f2 = 'African American (AFA, n = 233), Hispanic (HIS, n = 352), and European (CAU, n = 578)'
f = do.call(rbind, sapply(strsplit(f2, '),')[[1]], function(x) {
x = strsplit(x, ' \\(')[[1]]
meta = strsplit(x[2], ', ')[[1]]
abbr = meta[1]
sample_size = stringr::str_remove(meta[2], 'n = ')
sample_size = as.numeric(stringr::str_remove(sample_size, '\\)'))
data.frame(population = stringr::str_remove(x[1], 'and '), abbreviation = abbr, sample_size = sample_size)
}, simplify = F))
rownames(f) = NULL
f = f %>% mutate(source = 'MESA', tissue = 'Monocyte')
f = rbind(f, data.frame(population = 'European', abbreviation = 'gtex_v8', sample_size = 573, source = 'GTEx_v8', tissue = 'Whole_Blood'))
f %>% pander('Sample size of gene expression prediction models in the analysis')
| population | abbreviation | sample_size | source | tissue |
|---|---|---|---|---|
| African American | AFA | 233 | MESA | Monocyte |
| Hispanic | HIS | 352 | MESA | Monocyte |
| European | CAU | 578 | MESA | Monocyte |
| European | gtex_v8 | 573 | GTEx_v8 | Whole_Blood |
Note that the non-European models were only used to calculate the predicted expression. The S-PrediXcan runs to obtain gene-level effect size and p-values were based on European models only. To improve clarity, we refer the latter as the part of PTRS predictor but not the former.
Let \(\hat{y}\) be the predicted/estimated outcome and \(y\) be the observed one. We measure the predictive power of \(\hat{y}\) using partial \(R^2\). Specifically, we compute sum of squared error (SSE) for \(\hat{y}^{\text{null}}\) and \(\hat{y}^{\text{full}}\) where null model is least squared predictor of \(y\) using covariates only and full model is least squared predictor using both covariates and \(\hat{y}\). So that \[R^2 = 1 - \frac{SSE^{\text{full}}}{SSE^{\text{null}}}\].
To measure transferability, we use the best performing model within each population. Note that it is different from the previous setting where we used the model with p-value cutoff that is best performing model in European. The current setting is consistent with Martin et al. (2019). Transferability is defined as the ratio of \(R^2\)’s for the population of interest over the British-validation.
To make it clear, here we describe how we obtain gene weight and how we decide if a gene should be included in the PTRS model. The gene weights are obtained from S-PrediXcan runs using GWAS summary statistics from British-training and GTEx V8 EUR/MESA CAU models (PredictDB). To determine which genes to include, we implemented the following three strategies:
naive
: P-value from the same S-PrediXcan runs.ldblock
: Top gene within each European LD block (Berisa and Pickrell (2016) formatted by GTEx GWAS subgroup).mashr
: For GTEx V8 data, we also trained MASHR models, so we used P-value from the S-PrediXcan runs using MASHR models in the same tissue.First of all, we are interested in PVE of predicted expression. It gives us a sense on how much variation could be captured by a linear predictor. In the case of genotype being predictor, PVE is heritability.
Here we calculate PVE using GCTA approach with first 20 PCs and some age and sex variables (age^2, age * sex, etc) as covariates. And the analysis was done one population at a time. For British, we used 5000 individuals (one of the test set). Here we compare the PVE results in all populations with predicted expression calculated from different models.
pops = c('African', 'British-test-1', 'Chinese', 'Indian')
models = c('AFHI', 'CAU', 'ctimp_Whole_Blood')
df_pve = list()
for(p in pops) {
for(m in models) {
filename = paste0('~/Desktop/tmp/gcta_regu/reml_from_hail_martin_et_al_traits_x_', m, '_x_', p, '.tsv')
df_pve[[length(df_pve) + 1]] = read.table(filename, header = T, sep = '\t') %>% mutate(model = m, population = p)
}
}
df_pve = do.call(rbind, df_pve)
df_pve %>% select(model, num_predictors) %>% filter(!duplicated(model)) %>% pander(caption = 'The rough number of predictors (genes) for each model')
| model | num_predictors |
|---|---|
| AFHI | 5554 |
| CAU | 4670 |
| ctimp_Whole_Blood | 7041 |
df_pve$h_sq[is.na(df_pve$h_sq)] = 0
df_pve$h_sq_se[is.na(df_pve$h_sq_se)] = 0
Models could differ by tissue, population, predictor, and etc. To simplify the comparison, we focus on three models: GTEx v8 whole blood CTIMP model, MESA AFA + HIS EN model, and MESA CAU EN model. These models has similar sample size.
for(p in pops) {
tmp = df_pve %>% filter(population == p) %>% reshape2::dcast(trait ~ model, value.var = 'h_sq')
pp = myggpairs(tmp %>% select(AFHI, CAU, ctimp_Whole_Blood), tmp$trait) +
th +
theme(legend.title = element_blank()) +
geom_abline(slope = 1, intercept = 0)
print(pp + ggtitle(p))
}
Take-away:
df_pve %>% ggplot() + geom_bar(aes(x = trait, y = h_sq, fill = population), stat = 'identity', position = position_dodge(0.5), width = 0.5) + facet_wrap(~model, ncol = 1) + theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) + th
Take-away:
Heritability estimates were from Neale’s lab using LDSC regression (on the basis of Europeans in UK Biobank).
df_h2 = readRDS('../analysis_output/hsq_neale_lab.rds')
df_pve_tmp = inner_join(df_pve, df_h2 %>% select(h2_observed, h2_observed_se, trait), by = 'trait')
df_pve_tmp %>% ggplot() +
geom_abline(slope = 0.2, intercept = 0, color = 'red') +
geom_point(aes(x = h2_observed, y = h_sq), alpha = .5, size = 0.1) +
geom_errorbar(aes(x = h2_observed, ymin = h_sq - 1.96 * h_sq_se, ymax = h_sq + 1.96 * h_sq_se), alpha = .5) +
geom_errorbarh(aes(xmin = h2_observed - 1.96 * h2_observed_se, xmax = h2_observed + 1.96 * h2_observed_se, y = h_sq), alpha = .5) +
facet_grid(model~population) +
th
Take-away:
collector = list()
for(i in 1 : 3) {
filename1 = paste0('~/Desktop/tmp/prs_r2/ptrs_r2_subset', i, '.txt') # I used the wrong name ptrs* for some runs ... but the runs themselves are correct
filename2 = paste0('~/Desktop/tmp/prs_r2/prs_r2_subset', i, '.txt')
if(file.exists(filename1)) {
tmp = read.table(filename1, header = T, sep = '\t')
} else if(file.exists(filename2)) {
tmp = read.table(filename2, header = T, sep = '\t')
}
collector[[length(collector) + 1]] = tmp %>% mutate(subset = i)
}
for(i in 4 : 17) {
for(t in traits) {
filename1 = paste0('~/Desktop/tmp/prs_r2/ptrs_r2_subset', i, '_x_', t, '.txt') # I used the wrong name ptrs* for some runs ... but the runs themselves are correct
filename2 = paste0('~/Desktop/tmp/prs_r2/prs_r2_subset', i, '_x_', t, '.txt')
if(file.exists(filename1)) {
tmp = read.table(filename1, header = T, sep = '\t')
} else if(file.exists(filename2)) {
tmp = read.table(filename2, header = T, sep = '\t')
}
collector[[length(collector) + 1]] = tmp %>% mutate(subset = i)
}
}
df_result_prs = do.call(rbind, collector)
df_result_prs$population = parse_pop(df_result_prs$population)
df_result_prs$pval_cutoff = factor(df_result_prs$pval_cutoff, levels = sort(unique(df_result_prs$pval_cutoff)))
for(pop in unique(df_result_prs$population)) {
p = df_result_prs %>% filter(population == pop) %>% ggplot() + geom_boxplot(aes(x = pval_cutoff, y = r2)) + facet_wrap(~trait, ncol = 4, scales = 'free_y') + theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) + ggtitle(pop) + th #
print(p)
}
Take-away:
df_median_prs = df_result_prs %>% group_by(population, trait, pval_cutoff) %>% summarize(median_r2 = median(r2)) %>% ungroup()
df_median_prs %>% ggplot() +
geom_point(aes(x = pval_cutoff, y = median_r2, color = population), alpha = 0.5, size = 5) +
geom_line(aes(x = pval_cutoff, y = median_r2, group = population, color = population)) +
facet_wrap(~trait, ncol = 3, scales = 'free_y') +
th +
theme(legend.position = 'bottom', axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
Take-away:
df_best_prs = df_median_prs %>% group_by(population, trait) %>% summarize(best_r2 = max(median_r2)) %>% ungroup()
df_best_prs = left_join(df_best_prs, df_best_prs %>% filter(population == 'British-validation'), by = 'trait', suffix = c('', '_reference'))
df_best_prs = df_best_prs %>% mutate(transferability = best_r2 / best_r2_reference)
df_best_prs %>% filter(population != 'British-validation') %>% ggplot() + geom_hline(yintercept = 1, linetype = 2) + geom_violin(aes(x = population, y = transferability)) + geom_boxplot(aes(x = population, y = transferability), width = 0.05) + th
Take-away:
Here, we load the results of
gtex = data.frame(
source = c('GTEx V8 Whole Blood'),
population_spredixcan = c('EUR'),
population_prediction_model = c('EUR'),
selection_of_gene = c('naive', 'ldblock', 'mashr')
)
mesa = data.frame(
source = c('MESA monocyte'),
population_spredixcan = c('CAU'),
population_prediction_model = c('CAU', 'AFHI')
)
overview_ptrs = rbind(
gtex,
mesa %>% mutate(selection_of_gene = 'naive'),
mesa %>% mutate(selection_of_gene = 'ldblock')
)
overview_ptrs %>% pander(caption = 'PTRS models being shown in this report')
| source | population_spredixcan | population_prediction_model | selection_of_gene |
|---|---|---|---|
| GTEx V8 Whole Blood | EUR | EUR | naive |
| GTEx V8 Whole Blood | EUR | EUR | ldblock |
| GTEx V8 Whole Blood | EUR | EUR | mashr |
| MESA monocyte | CAU | CAU | naive |
| MESA monocyte | CAU | AFHI | naive |
| MESA monocyte | CAU | CAU | ldblock |
| MESA monocyte | CAU | AFHI | ldblock |
type_folders = list(naive = 'ptrs_r2', ldblock = 'ptrs_ldblock_r2', mashr = 'ptrs_mashr_r2')
result = list()
# naive
for(i in 1 : 17) {
for(t in traits) {
for(type in names(type_folders)) {
filename = paste0('~/Desktop/tmp/', type_folders[[type]], '/ptrs-r2_subset', i, '_x_', t, '.txt')
tmp = read.table(filename, header = T, sep = '\t')
if('SSE.with' %in% colnames(tmp)) {
tmp = tmp %>% select(-SSE.wo, -SSE.with)
}
result[[length(result) + 1]] = tmp %>% mutate(subset = i, trait = t, model = 'GTExV8', type = type)
}
}
}
df_gtex = do.call(rbind, result)
df_gtex$population = parse_pop(df_gtex$population)
df_gtex$ptrs_col = stringr::str_remove(df_gtex$ptrs_col, 'pval_')
df_gtex$ptrs_col = factor(df_gtex$ptrs_col, levels = unique(df_gtex$ptrs_col))
mesa_models = c('AFHI', 'CAU')
type_folders = list(naive = 'ptrs_mesa_r2', ldblock = 'ptrs_mesa_ldblock_r2')
result = list()
for(m in mesa_models) {
for(i in 1 : 17) {
for(t in traits) {
for(type in names(type_folders)) {
filename = paste0('~/Desktop/tmp/', type_folders[[type]], '/', type_folders[[type]], '_', m, '/ptrs-r2_subset', i, '_x_', t, '.txt')
tmp = read.table(filename, header = T, sep = '\t')
result[[length(result) + 1]] = tmp %>% mutate(subset = i, trait = t, model = m, type = type)
}
}
}
}
df_mesa = do.call(rbind, result)
df_mesa = df_mesa %>% select(-SSE.wo, -SSE.with)
df_mesa$population = parse_pop(df_mesa$population)
df_mesa$ptrs_col = stringr::str_remove(df_mesa$ptrs_col, 'pval_')
df_mesa$ptrs_col = factor(df_mesa$ptrs_col, levels = unique(df_mesa$ptrs_col))
df_ptrs = rbind(df_gtex, df_mesa)
From the brief examination of \(R^2\) among replications, the variation is a little bigger than PRS (results not shown). But still British-validation and British-test are quite consistent. For the subsequent analysis, we use the median \(R^2\) for each (population, trait, cutoff, model, type) combination so that it is consistent with PRS. And similarly, we take the best performing model within each population (for each model and PTRS type).
df_median_ptrs = df_ptrs %>% group_by(population, trait, ptrs_col, model, type) %>% summarize(median_r2 = median(r2)) %>% ungroup()
df_best_ptrs = df_median_ptrs %>% group_by(population, trait, model, type) %>% summarize(best_r2 = max(median_r2)) %>% ungroup()
df_best_ptrs %>% filter(type != 'mashr') %>%
reshape2::dcast(population + trait + model ~ type, value.var = 'best_r2') %>%
ggplot() +
geom_abline(slope = 1, intercept = 0) +
geom_point(aes(x = naive, y = ldblock)) + facet_grid(population ~ model) +
th
Take-away:
df_best_ptrs %>% filter(model == 'GTExV8', type != 'ldblock') %>%
reshape2::dcast(population + trait + model ~ type, value.var = 'best_r2') %>%
ggplot() +
geom_abline(slope = 1, intercept = 0) +
geom_point(aes(x = naive, y = mashr)) + facet_wrap(~population, scales = 'free') +
th
Take-away:
Another important thing to examine is whether the population-matched prediction model could improve the predicitive power. To do so, we look at MESA results (PTRS-naive and PTRS-ldblock) and compare CAU to AFHI.
df_best_ptrs %>% filter(model != 'GTExV8') %>%
reshape2::dcast(population + trait + type ~ model, value.var = 'best_r2') %>%
ggplot() +
geom_abline(slope = 1, intercept = 0) +
geom_point(aes(x = CAU, y = AFHI)) + facet_grid(type~population, scales = 'free') +
th +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
Take-away:
For MESA results, we take the best performing model across all prediction models.
df_median_ptrs$source = 'GTExV8'
df_median_ptrs$source[df_median_ptrs$model != 'GTExV8'] = 'MESA'
df_best_ptrs_trans = df_median_ptrs %>% group_by(population, trait, source, type) %>% summarize(best_r2 = max(median_r2)) %>% ungroup()
df_best_ptrs_trans = left_join(df_best_ptrs_trans, df_best_ptrs_trans %>% filter(population == 'British-validation'), by = c('trait', 'source', 'type'), suffix = c('', '_reference'))
df_best_ptrs_trans = df_best_ptrs_trans %>% mutate(transferability = best_r2 / best_r2_reference)
df_best_ptrs_trans %>% filter(population != 'British-validation') %>% ggplot() + geom_hline(yintercept = 1, linetype = 2) + geom_violin(aes(x = population, y = transferability, color = type), position = position_dodge(width = 0.6)) + geom_boxplot(aes(x = population, y = transferability, fill = type), width = 0.05, position = position_dodge(width = 0.6)) + facet_wrap(~source, ncol = 1) + th
Take-away:
naive
, ldblock
, and mashr
approach perform similar to each other.df_best_ptrs_trans2 = df_median_ptrs %>% group_by(population, trait, source) %>% summarize(best_r2 = max(median_r2)) %>% ungroup()
df_best_ptrs_trans2 = left_join(df_best_ptrs_trans2, df_best_ptrs_trans2 %>% filter(population == 'British-validation'), by = c('trait', 'source'), suffix = c('', '_reference'))
df_best_ptrs_trans2 = df_best_ptrs_trans2 %>% mutate(transferability = best_r2 / best_r2_reference)
df_best_ptrs_combine = rbind(
df_best_ptrs_trans %>% select(population, trait, transferability, best_r2, source, type),
df_best_ptrs_trans2 %>% select(population, trait, transferability, best_r2, source) %>% mutate(type = 'collapse')
)
df_best_ptrs_combine %>% filter(population != 'British-validation') %>% ggplot() + geom_hline(yintercept = 1, linetype = 2) + geom_violin(aes(x = population, y = transferability, color = type), position = position_dodge(width = 0.6)) + geom_boxplot(aes(x = population, y = transferability, fill = type), width = 0.05, position = position_dodge(width = 0.6)) + facet_wrap(~source, ncol = 1) + th
Take-away:
prs = df_best_prs %>% select(population, trait, transferability, best_r2) %>% mutate(type = 'PRS')
ptrs = rbind(
df_best_ptrs_combine %>% filter(type == 'collapse') %>% mutate(type = paste0('PTRS-', source, '-', type)) %>% select(-source),
df_best_ptrs_trans %>% filter(type == 'naive') %>% mutate(type = paste0('PTRS-', source, '-', type)) %>% select(population, trait, transferability, best_r2, type)
)
rbind(
prs,
ptrs
) %>% filter(population != 'British-validation') %>%
ggplot() + geom_hline(yintercept = 1, linetype = 2) + geom_violin(aes(x = population, y = transferability, color = type), position = position_dodge(width = 0.6), width = 1) + geom_boxplot(aes(x = population, y = transferability, fill = type), width = 0.05, position = position_dodge(width = 0.6)) + th + theme(legend.position = 'bottom')
left_join(ptrs, prs %>% select(-type), by = c('trait', 'population'), suffix = c('_ptrs', '_prs')) %>% filter(population != 'British-validation') %>%
ggplot() + geom_abline(slope = 1, intercept = 0) +
geom_point(aes(x = transferability_prs, transferability_ptrs)) + facet_grid(type ~ population, scales = 'free') + th
Take-away:
inner_join(df_h2, prs, by = 'trait') %>%
ggplot() +
geom_point(aes(x = h2_observed, y = best_r2)) + facet_wrap(~population) + th +
ggtitle('PTRS R2 vs. heritability')
df_pve = df_pve %>% mutate(population = parse_pop(population))
df_pve$type = 'GTExV8'
df_pve$type[df_pve$model != 'ctimp_Whole_Blood'] = 'MESA'
df_pve_collapse = df_pve %>% group_by(trait, population, type) %>% summarize(pve = max(h_sq)) %>% ungroup()
df_pve_collapse = rbind(
df_pve_collapse,
df_pve %>% filter(model == 'AFHI') %>% mutate(type = paste0('MESA-', model)) %>% select(trait, population, type, h_sq) %>% rename(pve = h_sq),
df_pve %>% filter(model == 'CAU') %>% mutate(type = paste0('MESA-', model)) %>% select(trait, population, type, h_sq) %>% rename(pve = h_sq)
)
ptrs_collapse = ptrs %>% filter(type %in% c('PTRS-GTExV8-collapse', 'PTRS-MESA-collapse'))
ptrs_collapse$type_clean = 'GTExV8'
ptrs_collapse$type_clean[ptrs_collapse$type == 'PTRS-MESA-collapse'] = 'MESA'
ptrs_mesa = df_median_ptrs %>% filter(source == 'MESA') %>% group_by(population, trait, model) %>% summarize(best_r2 = max(median_r2)) %>% ungroup() %>% mutate(type = paste0('MESA-', model)) %>% select(-model) %>% mutate(type_clean = type)
ptrs_collapse = rbind(
ptrs_collapse %>% select(-transferability),
ptrs_mesa
)
inner_join(
df_pve_collapse,
ptrs_collapse,
by = c('type' = 'type_clean', 'trait', 'population')
) %>% ggplot() + geom_point(aes(x = pve, y = best_r2)) + facet_wrap(~population) + th + facet_grid(type ~ population) +
ggtitle('PTRS R2 vs. PVE \n MESA model takes the max between AFHI and CAU')
Take-away:
Berisa, Tomaz, and Joseph K Pickrell. 2016. “Approximately Independent Linkage Disequilibrium Blocks in Human Populations.” Bioinformatics 32 (2). Oxford University Press: 283.
Martin, Alicia R, Masahiro Kanai, Yoichiro Kamatani, Yukinori Okada, Benjamin M Neale, and Mark J Daly. 2019. “Clinical Use of Current Polygenic Risk Scores May Exacerbate Health Disparities.” Nature Genetics 51 (4). Nature Publishing Group: 584.


Economics Department
List of Faculty in the Economics Department
Faculty
Anderson David
Marie Petkus
Maria Apostolova
Sarthak Behara
Bruce K. Johnson
Ravi Radhakrishnan
Patten Mahler
Brian Rodgers
John Perry
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val fitnessFunctionProvider: FitnessFunctionProvider<TProgram, TOutput, TTarget>
Overrides EnvironmentFacade.fitnessFunctionProvider
Provides a function that can measure the fitness of a program.


This example shows how to programmatically control the AppBar and its commands.
The three buttons below are disabled or enabled when the AppBar is shown or hidden. To detect changes in the AppBar state, listen for the AppBar's beforeshow, aftershow, beforehide, or afterhide events.
The Show Bar button uses the show method to invoke the AppBar.
The Show Commands and Hide Commands buttons use the showCommands and hideCommands methods to control the visibility of commands.
This AppBar is set to sticky and is not dismissed when the user interacts with the application. If you need an AppBar to remain visible while the user interacts with their content (such as selecting multiple items), set the sticky property to true.


Test for support of EcmaScript strict mode
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KeyboardInput Module
The Keyboard Input module allows for display and selection of notes on a piano keyboard.
The user interface is created by instantiating a KeyboardInput object.
Here is an example describing how to use this module:
1) Add an HTMLtag for a small canvas in the web page source, with a unique 'id' attribute.
<canvas id="myUniqueID" width="1" height="1"/>
2) Define an XML string that will describe the interface's initial state. Here is an example:
var initXML = '<keyboardinput maxkeys="2" canvasEditable="true">' +
'<givenkeys>' +
'<givenkey pitchclass="4" register="5" />' +
'</givenkeys>' +
'<selectedkeys>' +
'<selectedkey pitchclass="5" register="2" />' +
'</selectedkeys>' +
'</keyboardinput>';
The maxkeys attribute indicates the maximum number of keys that can be selected at any given time. The given keys are keys that will be highlighted on the keyboard but will not be selectable/unselectable. The selected keys are keys that will be initially selected - these keys can be unselected.
For more details on the XML schema that the interface will accept and return, see the 'valid_input_xml.xsd' file in the module's XML directory.
3) Define a callback function that the interface will use to message back its current state. This function will be called whenever the user changes the interface's state (i.e. by selecting or unselecting keys on the keyboard).
var callBack = function(stateXML) {
//stateXML will contain the most up-to-date canvas state.
};
4) Create a KeyboardInput object using the canvas id from step 1, the XML string from step 2 and the callback function from step 3:
var musicCanvas = new KeyboardInput('myUniqueID', initXML, callBack, true);
Argument 4 specifies whether the interface should be editable after initialization. Use a false value to display a static interface.
Once instantiated, the KeyboardInput object will display an interactive keyboard on the web page. Whenever the user selects or unselects a key, the callback function is called and the most up-to-date canvas state is returned, as an XML string.
This module provides the following classes: