Friday, February 24th
Levine Hall, Penn Engineering
3330 Walnut Street, Philadelphia, PA 19104
5:15 - 6pm Dinner (Chipotle sponsored by The New York Times)
6 - 6:30pm Makin friends (team formation & games)
6:30 - 7:20pm
SUPER Beginner: Git It Gurl (Git Workshop)
6:30 - 7:20pm
Intermediate: How to Become an Open Sourcerer
7:30 - 8:20pm
SUPER Beginner: Web Dev (HTML & CSS Workshop)
7:30 - 8:20pm
Intermediate: Web Dev (JavaScript, JQuery, Bootstrap Workshop)
8:30 - 9:20pm
SUPER Beginner: Hosting your Website (Github Pages Workshop)
8:30 - 9:20pm
Intermediate: Back that End Up (Backend Firebase Workshop)
9:30pm Get HYPED for tomorrow!!!!! EEEEEE!!!!
Saturday, February 25th
3401 Grays Ferry Ave, Philadelphia, PA 19146
8:30 - 9am Breakfast (from Living Room Cafe)
8:45 - 9am Opening Ceremony
9am Begin building!
9:15am Intel Hardware Tech Talk
11am Fresh-Made Juice Bar
12pm Lunch Panel (food from Jake's Sandwich Board)
1-3pm Photobooth
2pm Professional Photo Shoot
3pm Lil Pop Shop (sponsored by Softheon)
6pm Dinner (from Herban Quality Eats)
8pm Sugar Philly (sponsored by Rough Draft Ventures)
9:15pm Project Submission
9:20pm Demos
10:20pm Closing Ceremonies


{% extends "checker/base.html" %} {% set title = _('Summary of your answers') %} {% import "macros/review.html" as Review %} {% block inner_content %}


Name: {{food.name}}
Quantity: {{food.quantity}}
cost: ₹ {{food.cost}}
discription goes here
specification goes here
reviews goes here


The color control panel is used to adjust the currently active
color parameter. The currently active color parameter may be set
by two different methods:
(1) by selecting any of the object areas
such that the color parameter defining this area's color will become
the currently active color parameter, or
(2) by selecting the
respective color at the bottom of the color control panel which shows
the colors of all adjustable color parameters of the current demo
display.
Colors are adjusted by pointing into a chromaticity chart or by adjusting color coordinate sliders. Coordinates may only be set such that the resulting color is actually displayable on the current display device.
The color control panel can show any of the following color systems:
Selection between these color systems is done by pressing the respective button on top of the color control panel.
Chromaticity charts are shown in a 2D-chart with a separate luminance control slider. Each chart also contains a marked region indicating those chromaticity values which are displayable by the current output device at the currently defined luminance level. The currently active color may be adjusted by pressing the left mouse button and moving the cursor within the valid color region. Depending on the state of the Follow invalid colors flag it is also possible to move outside the currently valid color region as long as the target chromaticity can be displayed at a lower luminance level.
3D-values are shown as 3 sliders. Note that the XYZ-sliders are hard to use since only displayable colors may be set and the slider version of the color control panel does not have a visible rangle control.
Numerical values for any of the above listed color systems may be shown in the bottom region of the color control panel. It also is possible to directly enter numerical values into the respective text entry fields.
To add/remove a numerical color coordinates field to the color control panel open the Options menu on the main menu bar, select the options subgroup Color and open the submenu Numerical coordinates fields. Add or remove a coordinate system by activating or clearing the respective flag.
To push a color onto the color board press the right mouse button within the demo display area and then select Copy to color board from the popup menu. This copies the color of the currently active color parameter to the color board.
To set the currently active color parameter to the last color of the color board use the Paste from color board entry of the display area's popup menu. This also removes the color from the color board.
To set the currently active color parameter to any color contained in the color board simply press the left mouse button on top of the respective field of the color board. This does not remove the color from the color board.
To enable/disable showing the colors currently contained in the color board within the color chart open the Options menu on the main menu bar, select options subgroup Color and modify the flag Show color board in charts.
This panel shows all those parameters of a display object which control geometric properties. Depending on the type of parameter the geometry control panel provides different tools to modify parameter values. Numerical parameters have sliders and text value controls, string parameters have text control fields, and binary flags have switches. Parameters which are only allowed to assume a finite number of integer control codes have these codes listed for selection together with their labels.
This panel shows all those parameters of a display object which control timing properties. Control tools are identicl to those of the geometry control panel.
Note, however, that most timing properties are only relevant during real time experimental runs and are not used in display demonstration mode.


class documentation
class DescartesCoordinateSystem(CoordinateSystem):
Class implementing a 2D Descartes coordinate system object.
| Method | __init__ |
Initializes the coordinate system. |
| Method | bbox |
Sets the bounding box of the coordinate system |
| Method | bounds |
Sets the lower and upper bounds of the X and Y values |
| Method | draw |
Draws the coordinate system. |
| Method | local |
Converts local coordinates to the context coordinate system (given by the bounding box). |
| Property | bbox |
Returns the bounding box of the coordinate system |
| Property | bounds |
Returns the lower and upper bounds of the X and Y values |
| Method | _recalc |
Recalculates some cached scale factors used within the class |
| Instance Variable | _bbox |
Undocumented |
| Instance Variable | _bounds |
Undocumented |
| Instance Variable | _ox |
Undocumented |
| Instance Variable | _ox2 |
Undocumented |
| Instance Variable | _oy |
Undocumented |
| Instance Variable | _oy2 |
Undocumented |
| Instance Variable | _sx |
Undocumented |
| Instance Variable | _sy |
Undocumented |
Inherited from AbstractCairoDrawer
(via CoordinateSystem
):
| Instance Variable | context |
Undocumented |
| Method | _mark |
Marks the given point with a small circle on the canvas. Used primarily for debugging purposes. |
Initializes the coordinate system.
| Parameters | |
| context | the context on which the coordinate system will be drawn. |
| bbox | the bounding box that will contain the coordinate system. |
| bounds | minimum and maximum X and Y values in a 4-tuple. |


Strictly's Kelvin Fletcher has a secret dance past as he emerges as favourite to win
The former Emmerdale star has revaled his gruelling training regime
by Jane Lavender, Louise Randell, Will Trigger, https://www.facebook.com/dailymirror/, https://www.mirror.co.uk/authors/louise-randell/Former Emmerdale star Kelvin Fletcher has wowed judges and fans with his slick dance floor moves.
The 35-year-old is now one of the favourites to walk away with the Glitterball trophy.
Kelvin has been pushing himself to the limit in his bid for victory with 5am work-outs and 12 hour training sessions with partner Oti Mabuse.
The actor has also increased his calories intake to 3,000 a day and makes sure he drinks up to four litres of water a day.
Kelvin said: ""I’m on my feet all day. It’s quite tiring, so I’ve always been quite pro carbs – I think they’re a great thing. I probably eat about 300-350g of carbs a day.
"About 160g of protein, about 60-80g of fat, which I think is about 2,500-3,000 calories.
"Carbs get a bad press, but they’re pretty important. I’ve stayed pretty lean, I’m about five kilos lighter than I was a couple of months ago.
"Which is fine, I think – it helps with the dancing."
Kelvin is taught his dance moves on Monday and he makes sure he knows the routine by Tuesday lunch time so he and Oti can spend the rest of the week perfecting it.
And it's certainly been paying off with he and Oti regularly close to the top of the leaderboard.
Kelvin's revealing outfits and hunky shape have also won him a legion of new fans.
But it's now emerged that the dad-of-two has a secret dance past.
Before his starring role in Emmerdale, Kelvin was a regular at ballet classes from the age of eight.
Kelvin, who played Andy Sugden in the ITV soap, says he was bullied as a child because he took dance lessons.
He told Attitude Magazine: "I was eight years old and was playing for my local rugby club.
"I was having trials for my town team, but Tuesday I was doing 'good toes, naughty toes.'
"I was always like that. I always liked dancing and you'd go to class and find you had an hour acting and an hour of ballet.
"I didn't mind the poofter jibes."
Kelvin was a late replacement in the BBC competition after Made In Chelsea's Jamie Laing suffered a foot injury.


Original Issue: #245
Hold down the mouse button, or longpress via touch anywhere on the page to trigger a 'longpress' event that shows up in the developer console.


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MEAN.JS is a web application boilerplate, which means you should start changing everything :-)
This sample application tracks users and articles.
MongoDB is a NoSQL database. MongoDB's great manual is the place to get started with NoSQL and MongoDB.
Express is a NodeJS server-side application web framework. Check out The ExpressJS API reference for more information.
AngularJS is client-side web application framework. The Thinkster Popular Guide and Egghead Videos are great resources.
Node.js is a JavaScript run-time, popular for being a web server platform. Node's website and this stackOverflow thread offer excellent starting points to get to grasps with node.
Once you're familiar with the foundation technology, check out the MEAN.JS Documentation:
The ID is {{saludo}}
The ID is {{datos1}}
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The button has been clicked {{id}} times.
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Sections
About
Code for San Francisco is a group of people, first and foremost. We also call ourselves technologists, planners, designers, doers, thinkers, and activists. Each week, we actively work together to improve the City and County of San Francisco, often using technology to support our efforts. By connecting people, organizations, resources, tools, and networks to build for San Francisco, we will all thrive.
Code for San Francisco is a safe space for experimentation, learning and practicing skills, taking chances, and asking questions, all to affect and encourage positive change. Whenever possible, we proactively reach out to those who are left out, excluded, ignored, or discriminated against. Those people, in our opinion, represent our most desirable source for experiences, knowledge, and thoughts. We fail without their involvement.
What happens at a hack night?
Code for San Francisco starts every Wednesday at 6 PM at Code for America's HQ. The event is free, and the invitation is open to anyone, especially folks who are not programmers.
Here's what happens at Code for San Francisco every week (see event page each week for details):
6:00pm – Socializing and food
As people arrive, we gather on the first floor of Code for America's offices.
Because we’re meeting during the dinner hour, food is always provided, usually pizza. We want to make sure people are happy and fed, and eating together is a great way to break the ice and bond with other attendees. This is time for everyone to get to know each other.
6:30pm – Welcoming remarks
We get started around 6:30pm with a brief welcome and introduction to the event.
Next, we hold an open floor for announcements for civic tech and open government related things. This could be plugging other events, mentioning newsworthy articles, and job announcements in the government and civic tech space.
Following this present projects are encouraged to give a short 30 second pitch to share: project background for new members, any updates, and specific needs they have for the evening.
Short presentations (< 5 minutes) also happen during this section. Often times these are projects presenting their work, but could be on anything civic tech related. These are not always scheduled ahead of time, but we do encourage you to reach out to us before the event to ensure we have time for everyone interested.
We record these announcements in our weekly meeting notes and agenda Google Doc. Here’s an example from our 11/1/2017 hack night.
7:00pm – Presentation with Q&A
Occasionally we will have longer form presentations that range from 15 minutes to 45 minutes, with an open question & answer (Q&A) session after.
Presenters can be any government agency, non-profit, company or group who've made use of open government data or built a civic technology application. Anyone can propose a talk, and we often reach out to presenters who would be a good fit. For every speaker, we provide some clear speaker guidelines.
~7:30pm - 9:30pm – Hack time
Once the opening wraps up, the format of the event opens up to allow people to get together and:
- Work on projects
- Start something new
- Discuss civic tech in the bay area and at large
Anyone is welcome to start a new project. The only requirement is that you take ownership of the group and make an honest attempt to keep showing up every week.
When 9:30pm rolls around, we all head home and do it all over again next week.
Everyone is welcome!
Not a techie? That's ok! We encourage non-technical folks to pair up and learn from our community's designers and developers. We've seen time and time again, the most successful projects come from teams with a diverse background.
Remember, there's much more to making a great project than just coding. Not the least of which, there is also:
- User research
- Product management
- Project management
- Community engagement
- Promotion
How are you organized?
Code for San Francisco is led by a team of volunteer organizers.
Leadership Council
Every month, we hold an open Leadership Council Meeting to discuss new ideas and ways to make the hack night better. Anyone is welcome to join! More info »
Finances
Code for San Francisco is hosted at Code for America and supported by our amazing sponsors.
Our Partners
Code for San Francisco partners with San Francisco government, Bay Area non-profits, and companies to make our city work better. Here’s some of the organizations we work with and are supported by:


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about download help
Vico is a programmers text editor for Mac OS X.
Some of the features are:
Click images to see a larger version.


{% extends "base.html" %} {% block title %}Page Not Found{% endblock %} {% block title_path %}Page Not Found {% endblock %} {% block content %}
We could not locate the page {{ request.path }}. This may mean that:


Home
Modules
Admins
merakiService#createAdmin
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Custom
merakiService#getNetworkIdForDeviceMac
Classes
merakiService
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MerakiService
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MerakiService
meraki-service.js
:
59
new MerakiService
()
Meraki Service


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Corey Keating
Corey's passion for technology stems back to his early experiments sequencing live music performances on a Commodore Amiga and mangling samples in an old, unreliable RT 6150 computer. An ardent lover of the outdoors and an avid mycophile, Corey finds constant sources of creative inspiration in the natural world.
He is currently doing doctoral work at Cornell University with an emphasis on experimental and electronic music. He has previously held positions with the Melos Music Collective and the Ira F. Brilliant Center for Beethoven Studies, where he was assistant editor. His interest in productive methodologies, work-flow enhancement, and collaborative partnerships led Corey to a Project Management position at CoLab, where he is able to foster greater understanding of the creative process.


Introducing Alarms
Introducing Cron AlarmsWe just launched a brand new feature, cron alarms and they are pretty freaking awesome. Free cron monitoring, all thanks to you.We get some great feedback here at cronasaservice.com, several users have asked us to add an alarm feature to monitor their jobs. Well here it is, we will…


Warehouse Keeper Sorint:
How was your hunting? In this area, there are some monsters that look just like the others, but are far stronger. We call them "elders," and I'd advise that you don't confront them until you're really ready. Remember, the older ginger gets, the hotter it becomes.
Private Warehouse
Clan Warehouse
Learn skills.
Quest


name
my name is Andriicountry
I am from Ukraine, from the city named Ternopil find itpassions
I like to read books, develop video gamesmusic
I like the Rock music of all kindsthat's my University, it is called
"Ternopil Ivan Puluj National Technical University"
and this one now is also mine,
"Opole University of Technology"
drawings
for me it is really relaxing to make some drawings on my digitizer. I am not a good artist, but as for me, the process itself is enjoyableother
I also like to play Airsoft, we have a bunch of nice locations in Ternopil and near, like open-casts, old factories and nice forestsgames
as for my passion to develop some games, it is same as the drawing. The process of creating a game is much more joyful than just playing a game. Of course, game development is also a hard process, which involves not only creativity, but also a huge amount of analythics, but i always can ask my friend to help me with mechanics, because he's a game designer. I also have fun participating in game jams. Especially the "One Hour Game Jam", can you imagine sich things could be made in one hour?


Bone breakthrough may lead to more durable airplane wings
| Source: | Cornell University |
| Summary: | Researchers have made a new discovery about how seemingly minor aspects of the internal structure of bone can be strengthened to withstand repeated wear and tear, a finding that could help treat patients suffering from osteoporosis. It could also lead to the creation of more durable, lightweight materials for the aerospace industry. |
Cornell researchers have made a new discovery about how seemingly minor aspects of the internal structure of bone can be strengthened to withstand repeated wear and tear, a finding that could help treat patients suffering from osteoporosis. It could also lead to the creation of more durable, lightweight materials for the aerospace industry.
The team's paper, "Bone-Inspired Microarchitectures Achieve Enhanced Fatigue Life," was published Nov. 18 in the Proceedings of the National Academy of Sciences. Co-authors include Cornell doctoral students Cameron Aubin and Marysol Luna; postdoctoral researcher Floor Lambers; Pablo Zavattieri and Adwait Trikanad at Purdue University; and Clare Rimnac at Case Western Reserve University.
For decades, scientists studying osteoporosis have used X-ray imaging to analyze the structure of bones and pinpoint strong and weak spots. Density is the main factor that is usually linked to bone strength, and in assessing that strength, most researchers look at how much load a bone can handle all at once.
But a team led by senior author Christopher J. Hernandez, associate professor in the Sibley School of Mechanical and Aerospace Engineering and in the Meinig School of Biomedical Engineering, is interested in long-term fatigue life, or how many cycles of loading a bone can bear before it breaks.
"The best way to understand the fatigue properties of material is to think about a part in your car that breaks every so often, so you have to take it to the shop. Well, why did it break? It was clearly strong enough, because it worked for months, years, just fine. But after cycling and cycling and cycling, tens of millions of cycles, it breaks," Hernandez said. "We've known about this property of materials for 150 years, and it's embedded in the design of everything we do. But not too many people had done this kind of study of the bone."
The internal architecture of bone consists of vertical plate-like struts that determine its strength when overloaded. The bone also has horizontal rod-like struts, which have little influence on strength and are essentially "window dressing." Hernandez and his team suspected that other aspects of architecture were important. Using new computer software, lead author Ashley Torres, M.A. '15, Ph.D. '18, MBA '19, was able to perform a deeper analysis of a bone sample and found that, when it comes to withstanding long-term wear and tear, the horizontal rod-like struts are critical for extending the bone's fatigue life.
"If you load the bone just once, it's all about how dense it is, and density is mostly determined by the plate-like struts," said Hernandez, who is also an adjunct scientist at the Hospital for Special Surgery, an affiliate of Weill Cornell Medicine. "But if you think about how many cycles of low-magnitude load something can take, these little sideways twiggy struts are what really matter. When people age, they lose these horizontal struts first, increasing the likelihood that the bone will break from multiple cyclic loads."
The team used a 3D printer to manufacture bone-inspired material made from a urethane methacrylate polymer. The researchers varied the thickness of the rods and were able to increase the material's fatigue life by up to 100 times.
Hernandez anticipates the reinforced microstructure lattices his team developed could be incorporated into just about any device, and would be particularly beneficial to the aerospace industry, where ultra-lightweight materials need to withstand tremendous and repeated strain.
"Every wind gust that an airplane hits causes a cycle of loading on it, so an airplane wing gets loaded thousands of times during every flight," Hernandez said. "If you want to make a durable device or a vehicle that is lightweight and will last a long time, then it really matters how many cycles of loading the part can take before it breaks. And the mathematical relationship we've derived in this study lets somebody who's designing one of these lattice structures balance the needs for stiffness and strength under a single load with the needs for tolerating many, many lower-level load cycles."
make a difference: sponsored opportunity
Story Source:
Materials provided by Cornell University.
Note: Content may be edited for style and length.
Journal Reference:
- Ashley M. Torres, Adwait A. Trikanad, Cameron A. Aubin, Floor M. Lambers, Marysol Luna, Clare M. Rimnac, Pablo Zavattieri, Christopher J. Hernandez. Bone-inspired microarchitectures achieve enhanced fatigue life. Proceedings of the National Academy of Sciences, 2019; 201905814 DOI: 10.1073/pnas.1905814116
Cite This Page:
| MLA | Cornell University. "Bone breakthrough may lead to more durable airplane wings." ScienceDaily. ScienceDaily, 21 November 2019. <www.sciencedaily.com/releases/2019/11/191121151212.htm>. |
| APA | Cornell University. (2019, November 21). Bone breakthrough may lead to more durable airplane wings. ScienceDaily. Retrieved November 21, 2019 from www.sciencedaily.com/releases/2019/11/191121151212.htm |
| Chicago | Cornell University. "Bone breakthrough may lead to more durable airplane wings." ScienceDaily. www.sciencedaily.com/releases/2019/11/191121151212.htm (accessed November 21, 2019). |


Namespace Sensus.iOS.Probes.Context
Classes
iOSBluetoothDeviceProximityProbe
Scans for the presence of other devices nearby that are running the current Protocol. When encountered, this Probe will read the device ID of other devices. This Probe also advertises the presence of the current device and serves requests for the current device's ID. This Probe reports data in the form of BluetoothDeviceProximityDatum objects. There are no caveats to the conditions under which an iOS device running this Probe will detect another device. Detection is possible if the other device is Android or iOS and if Sensus is foregrounded or backgrounded on the other device.
NOTE: The value of Id running on the other device must equal the value of Id running on the current device. When a Protocol is created from within the Sensus app, it is assigned a unique identifier. This value is maintained or changed depending on what you do:
When the newly created Protocol is copied on the current device, a new unique identifier is assigned to it. This breaks the connection between the Protocols.
When the newly created Protocol is shared via the app with another device, its identifier remains unchanged. This maintains the connection between the Protocols.
Thus, in order for this iOSBluetoothDeviceProximityProbe to operate properly, you must configure your Protocols in one of the two following ways:
Create your Protocol on one platform (either Android or iOS) and then share it with a device from the other platform for customization. The Id values of these Protocols will remain equal and this iOSBluetoothDeviceProximityProbe will detect encounters across platforms.
Create your Protocols separately on each platform and then set the Id field on one platform (using the "Set Study Identifier" button) to match the Id value of the other platform (obtained via "Copy Study Identifier").
See the Android subclass of BluetoothDeviceProximityProbe for additional information.
iOSBluetoothDeviceProximityProbeCentralManagerDelegate
iOS BLE central (scanner/client) delegate class. Receives events related to BLE scanning and
characteristic reading.
iOSBluetoothDeviceProximityProbePeripheralManagerDelegate
iOS BLE peripheral (advertiser/server) delegate class. Receives events related to BLE advertising and
characteristic reading.
iOSSoundProbe
Probes sound level (decibels) from microphone. http://developer.xamarin.com/recipes/ios/media/sound/record_sound


Open in TextMate in Finder for Tiger
This post has a great little droplet that can be added to the Finder toolbar to open either the selected items or the current Finder folder in TextMate.
Sadly, it doesn’t work in Tiger, and I haven’t made the jump to Leopard yet. If you’re a TextMate user who’s still on Tiger, and wants this feature, I tweaked Henrik’s AppleScript to play nice with Tiger.
Enjoy!
Update: Just fixed the script to work with folders and files that have spaces in the name.


This is a simple example of using Entitytled with Play Framework.
Once the application has been compiled and the server started, your application can be accessed at: http://localhost:9000


class: center, middle, inverse, title-slide # Analysis of RNAseq data in R and Bioconductor (part 3)
###
http://rockefelleruniversity.github.io/RU_RNAseq/
--- ## What we will cover * Session 1: Alignment and counting * Session 2: Differential gene expression analysis * _Session 3: Visualizing results through PCA and clustering_ * Session 4: Gene Set Analysis * Session 5: Differential Transcript Utilization analysis --- ## Vizualising high dimensional data In todays session we will work with some of the RNAseq data of adult mouse tissues from Bing Ren's lab, Liver and Heart.  The **RangedSummarizedExperiment** object with the gene counts has been saved as a .RData object in **data** directory: **data/gC_TissueFull.RData** --- class: inverse, center, middle # RNAseq with multiple groups

 --- ## Vizualizing Biological Data Biological data is often considered to have high dimensionality. Common techniques used to visualize genomics data include dimension reduction and/or clustering followed by the graphical representation of data as a heatmap. These techniques makes interpretation of the data simpler to better identify patterns within our data i.e. reproducibility of replicates within groups and magnitude of changes in signal between groups.

 --- ## Multiple Groups In this experiment we will be comparing three tissues. This represent a more complex experimental design than the two group comparison we have often used i.e. between activated and naive t-cells. We still use some similar approaches, but there will also be some additional approaches that will help i.e. clustering and dimensional reduction techniques to interrogate this data. --- ## Loading in our data The data has already been mapped and counted with *Rsubread* and *SummarizedExperiment* respectively. The *RangedSummarizedExperiment* loaded in here has the counts for all 3 tissues. The grouping metadata is the Tissue variable. ```r load("data/gC_TissueFull.RData") geneCounts ``` ``` ## class: RangedSummarizedExperiment ## dim: 14454 6 ## metadata(0): ## assays(1): '' ## rownames(14454): 20671 27395 ... 26900 170942 ## rowData names(0): ## colnames(6): Sorted_Heart_1 Sorted_Heart_2 ... Sorted_Liver_1 ## Sorted_Liver_2 ## colData names(1): Tissue ``` --- ## DEseq2 input We can now set up our **DESeq2** object and run the **DESeq** workflow function ```r dds <- DESeqDataSet(geneCounts, design = ~Tissue) dds <- DESeq(dds) ``` --- ## Running DEseq2 To extract comparisons we can simply specify the tissues of interest to the results function. ```r LiverVskidney <- results(dds, c("Tissue", "Liver", "Kidney")) heartVsLiver <- results(dds, c("Tissue", "Heart", "Liver")) heartVskidney <- results(dds, c("Tissue", "Heart", "Kidney")) heartVskidney ``` ``` ## log2 fold change (MLE): Tissue Heart vs Kidney ## Wald test p-value: Tissue Heart vs Kidney ## DataFrame with 14454 rows and 6 columns ## baseMean log2FoldChange lfcSE stat pvalue padj ## <numeric> <numeric> <numeric> <numeric> <numeric> <numeric> ## 20671 64.5915 1.675554 0.400136 4.18746 2.82090e-05 1.11084e-04 ## 27395 354.2077 1.069639 0.276556 3.86771 1.09862e-04 3.93252e-04 ## 18777 517.6560 -1.976956 0.342107 -5.77876 7.52525e-09 4.66423e-08 ## 21399 107.3182 -0.373288 0.315755 -1.18221 2.37124e-01 3.56834e-01 ## 108664 370.2050 -2.062351 0.368121 -5.60237 2.11435e-08 1.24993e-07 ## ... ... ... ... ... ... ... ## 20592 150.6694 0.0434984 0.283835 0.153253 0.87819903 0.919818 ## 26908 264.7052 0.2109728 0.287845 0.732938 0.46359624 0.592105 ## 22290 124.0370 0.0786872 0.459615 0.171203 0.86406453 0.909363 ## 26900 375.4670 0.8709573 0.330544 2.634922 0.00841566 0.020596 ## 170942 10.3089 -1.3065830 0.873286 -1.496168 0.13460985 0.226951 ``` --- ## Comparing multiple results To identify genes specifically upregulated in Heart versus other tissues we just need to do a simple data merge. First we convert **DESeqResults** objects into a data.frame, remove NAs and extract most interesting columns to us. ```r heartVsLiverDF <- as.data.frame(heartVsLiver) heartVskidneyDF <- as.data.frame(heartVskidney) heartVsLiverDF <- heartVsLiverDF[!is.na(heartVsLiverDF$padj), ] heartVskidneyDF <- heartVskidneyDF[!is.na(heartVskidneyDF$padj), ] heartVsLiverDF <- heartVsLiverDF[, c("log2FoldChange", "padj")] heartVskidneyDF <- heartVskidneyDF[, c("log2FoldChange", "padj")] ``` --- ## Comparing multiple results We can then update the column names and merge our data.frame to have a single table of most useful information. The *by=0* means that it will use the rownames (which contain the Gene IDs) as the common feature. ```r colnames(heartVskidneyDF) <- paste0("HeartVsKidney", "_", colnames(heartVskidneyDF)) colnames(heartVsLiverDF) <- paste0("HeartVsLiver", "_", colnames(heartVsLiverDF)) fullTable <- merge(heartVsLiverDF, heartVskidneyDF, by = 0) fullTable[1:2, ] ``` ``` ## Row.names HeartVsLiver_log2FoldChange HeartVsLiver_padj ## 1 100017 -0.6209016 0.1688893 ## 2 100019 -0.6718217 0.1137017 ## HeartVsKidney_log2FoldChange HeartVsKidney_padj ## 1 0.1789343 0.7431574 ## 2 -0.1426205 0.7860500 ``` --- ## Comparing multiple results Now we can extract our genes are significantly upregulated in Heart in both conditions. ```r upInHeart <- fullTable$HeartVsLiver_log2FoldChange > 0 & fullTable$HeartVsKidney_log2FoldChange > 0 & fullTable$HeartVsLiver_padj < 0.05 & fullTable$HeartVsKidney_padj < 0.05 upInHeartTable <- fullTable[upInHeart, ] upInHeartTable[1:2, ] ``` ``` ## Row.names HeartVsLiver_log2FoldChange HeartVsLiver_padj ## 12 100038347 4.927612 2.564094e-49 ## 24 100038575 2.434627 1.389225e-02 ## HeartVsKidney_log2FoldChange HeartVsKidney_padj ## 12 4.362883 5.756669e-51 ## 24 3.184769 3.529865e-04 ``` --- ## Do genes overlap? We can also make a logical data.frame of whether a gene was upregulated in Heart for both Liver and Kidney comparisons. ```r forVenn <- data.frame(UpvsLiver = fullTable$HeartVsLiver_log2FoldChange > 0 & fullTable$HeartVsLiver_padj < 0.05, UpvsKidney = fullTable$HeartVsKidney_log2FoldChange > 0 & fullTable$HeartVsKidney_padj < 0.05) forVenn[1:3, ] ``` ``` ## UpvsLiver UpvsKidney ## 1 FALSE FALSE ## 2 FALSE FALSE ## 3 FALSE TRUE ``` --- ## Do genes overlap? We can use **limmas'** **vennDiagram()** to produce a venn diagram of overlap in upregulation in heart vs Liver or Kidney. ```r library(limma) vennDiagram(forVenn) ``` ![](RU_RNAseq_p3_files/figure-html/tissue10-1.png) --- class: inverse, center, middle # Visualizing Counts

 --- ## Visualizing RNAseq data One of the first steps of working with count data for visualization is commonly to transform the integer count data to log2 scale. To do this we will need to add some artificial value (pseudocount) to zeros in our counts prior to log transform (since the log2 of zero is infinite). The DEseq2 **normTransform()** will add a 1 to our normalized counts prior to log2 transform and return a **DESeqTransform** object. ```r normLog2Counts <- normTransform(dds) normLog2Counts ``` ``` ## class: DESeqTransform ## dim: 14454 6 ## metadata(1): version ## assays(1): '' ## rownames(14454): 20671 27395 ... 26900 170942 ## rowData names(26): baseMean baseVar ... deviance maxCooks ## colnames(6): Sorted_Heart_1 Sorted_Heart_2 ... Sorted_Liver_1 ## Sorted_Liver_2 ## colData names(2): Tissue sizeFactor ``` --- ## Visualizing RNAseq data We can extract our normalized and transformed counts from the **DESeqTransform** object using the **assay()** function. ```r matrixOfNorm <- assay(normLog2Counts) boxplot(matrixOfNorm, las = 2, names = c("Heart_1", "Heart_2", "Kidney_1", "Kidney_2", "Liver_1", "Liver_2")) ``` ![](RU_RNAseq_p3_files/figure-html/gnTM-1.png) --- ## Visualizing RNAseq data When visualizing our signal however we now will have a similar problem with smaller counts having higher variance. This may cause changes in smaller counts to have undue influence in visualization and clustering. ```r library(vsn) vsn::meanSdPlot(matrixOfNorm) ``` ![](RU_RNAseq_p3_files/figure-html/gnTMP-1.png) --- ## Visualizing RNAseq data We can apply an **rlog** transformation to our data using the **rlog()** function which will attempt to shrink the variance for genes based on their mean expression. ```r rlogTissue <- rlog(dds) rlogTissue ``` ``` ## class: DESeqTransform ## dim: 14454 6 ## metadata(1): version ## assays(1): '' ## rownames(14454): 20671 27395 ... 26900 170942 ## rowData names(27): baseMean baseVar ... dispFit rlogIntercept ## colnames(6): Sorted_Heart_1 Sorted_Heart_2 ... Sorted_Liver_1 ## Sorted_Liver_2 ## colData names(2): Tissue sizeFactor ``` --- ## Visualizing RNAseq data Again we can extract the matrix of transformed counts with the **assay()** function and plot the mean/variance relationship. If we look at the axis we can see the shrinkage of variance for low count genes. .pull-left[ ```r rlogMatrix <- assay(rlogTissue) vsn::meanSdPlot(rlogMatrix) ``` ![](RU_RNAseq_p3_files/figure-html/gRLM-1.png) ] .pull-right[ ```r library(vsn) vsn::meanSdPlot(matrixOfNorm) ``` ![](RU_RNAseq_p3_files/figure-html/gnTMP2-1.png) ] --- class: inverse, center, middle # Dimension reduction

 --- ## Dimension reduction Since we have often have measured 1000s of genes over multiple samples/groups we will often try and simplify this too a few dimensions or meta/eigen genes which represent major patterns of signal across samples found. We hope the strongest patterns or sources of variation in our data are correlated with sample groups and so dimension reduction offers a methods to method to visually identify reproducibility of samples. Common methods of dimension reduction include Principal Component Analysis, MultiFactorial Scaling and Non-negative Matrix Factorization.

 --- ## PCA We can see PCA in action with our data simply by using the DESeq2's **plotPCA()** function and our **DESeqTransform** object from our rlog transformation. We must also provide a metadata column to colour samples by to the **intgroup** parameter and we set the **ntop** parameter to use all genes in PCA (by default it is top 500). ```r plotPCA(rlogTissue, intgroup = "Tissue", ntop = nrow(rlogTissue)) ``` ![](RU_RNAseq_p3_files/figure-html/gPCA-1.png) --- ## PCA This PCA show the separation of samples by their group and the localization of samples with groups. Since PC1 here explains 51% of total variances between samples and PC2 explains 44%, the reduction of dimensions can be seen to explain much of the changes among samples in 2 dimensions. Of further note is the separation of samples across PC1 but the lack of separation of Heart and Kidney samples along PC2. ![](RU_RNAseq_p3_files/figure-html/gPCA3-1.png) --- ## PCA PCA is often used to simply visualize sample similarity, but we can extract further information of the patterns of expression corresponding PCs by performing the PCA analysis ourselves. We can use the **prcomp()** function with a transposition of our matrix to perform our prinicipal component analysis. The mappings of samples to PCs can be found in the **x** slot of the **prcomp** object. ```r pcRes <- prcomp(t(rlogMatrix)) class(pcRes) ``` ``` ## [1] "prcomp" ``` ```r pcRes$x[1:2, ] ``` ``` ## PC1 PC2 PC3 PC4 PC5 PC6 ## Sorted_Heart_1 -103.8999 52.45800 -0.2401797 25.22797 1.0609927 2.437217e-13 ## Sorted_Heart_2 -117.8517 46.07786 0.4445612 -23.96120 -0.7919848 2.426392e-13 ``` --- ## PCA We can now reproduce the previous plot from DEseq2 in basic graphics from this. ```r plot(pcRes$x, col = colData(rlogTissue)$Tissue, pch = 20, cex = 2) legend("top", legend = c("Heart", "Kidney", "Liver"), fill = unique(colData(rlogTissue)$Tissue)) ``` ![](RU_RNAseq_p3_files/figure-html/gPRcosmp-1.png) --- ## PCA Now we have constructed the PCA ourselves we can investigate which genes' expression profiles influence the relative PCs. The influence (rotation/loadings) for all genes to each PC can be found in the **rotation** slot of the **prcomp** object. ```r pcRes$rotation[1:5, 1:4] ``` ``` ## PC1 PC2 PC3 PC4 ## 20671 -0.005479648 0.003577376 -0.006875591 0.0048625659 ## 27395 -0.002020427 0.003325506 -0.002302364 -0.0045132749 ## 18777 0.004615068 -0.005413345 0.008975098 -0.0028857868 ## 21399 0.005568549 0.002485067 0.002615072 -0.0001255119 ## 108664 0.005729029 -0.004912143 0.009991580 -0.0031039532 ``` --- ## PCA To investigate the separation of Kidney samples along the negative axis of PC2 I can then look at which genes most negatively contribute to PC2. Here we order by the most negative values for PC2 and select the top 100. ```r PC2markers <- sort(pcRes$rotation[, 2], decreasing = FALSE)[1:100] PC2markers[1:10] ``` ``` ## 20505 22242 16483 56727 77337 57394 ## -0.06497365 -0.06001728 -0.05896360 -0.05896220 -0.05595648 -0.05505202 ## 18399 20495 22598 20730 ## -0.05307506 -0.05256188 -0.05205661 -0.05175604 ``` --- ## PCA To investigate the gene expression profiles associated with PC2 we can now plot the log2 foldchanges (or directional statistics) from our pairwise comparisons for our PC2 most influencial genes. ```r PC2_hVsl <- heartVsLiver$stat[rownames(heartVsLiver) %in% names(PC2markers)] PC2_hVsk <- heartVskidney$stat[rownames(heartVskidney) %in% names(PC2markers)] PC2_LVsk <- LiverVskidney$stat[rownames(LiverVskidney) %in% names(PC2markers)] ``` --- ## PCA From the boxplot it is clear to see that the top100 genes are all specifically upregulated in Kidney tissue. ```r boxplot(PC2_hVsl, PC2_hVsk, PC2_LVsk, names = c("Heart/Liver", "Heart/Kidney", "Liver/Kidney"), ylab = "log2FC") ``` ![](RU_RNAseq_p3_files/figure-html/gPRcompRotf-1.png) --- class: inverse, center, middle # Sample-to-Sample correlation

 --- ## Sample-to-Sample correlation Another common step in quality control is to assess the correlation between expression profiles of samples. We can assess correlation between all samples in a matrix by using the **cor()** function. ```r sampleCor <- cor(rlogMatrix) sampleCor ``` ``` ## Sorted_Heart_1 Sorted_Heart_2 Sorted_Kidney_1 Sorted_Kidney_2 ## Sorted_Heart_1 1.0000000 0.9835321 0.7144527 0.7189597 ## Sorted_Heart_2 0.9835321 1.0000000 0.7190675 0.7229253 ## Sorted_Kidney_1 0.7144527 0.7190675 1.0000000 0.9929131 ## Sorted_Kidney_2 0.7189597 0.7229253 0.9929131 1.0000000 ## Sorted_Liver_1 0.7156978 0.6883444 0.7344165 0.7336117 ## Sorted_Liver_2 0.7186525 0.6918428 0.7366287 0.7396193 ## Sorted_Liver_1 Sorted_Liver_2 ## Sorted_Heart_1 0.7156978 0.7186525 ## Sorted_Heart_2 0.6883444 0.6918428 ## Sorted_Kidney_1 0.7344165 0.7366287 ## Sorted_Kidney_2 0.7336117 0.7396193 ## Sorted_Liver_1 1.0000000 0.9714750 ## Sorted_Liver_2 0.9714750 1.0000000 ``` --- ## Sample-to-Sample correlation We can visualize the the correlation matrix using a heatmap following sample clustering. First, we need to convert our correlation matrix into a distance measure to be use in clustering by subtracting from 1 to give dissimilarity measure and converting with the **as.dist()** to a **dist** object. We then create a matrix of distance values to plot in the heatmap from this using **as.matrix()** function. ```r sampleDists <- as.dist(1 - cor(rlogMatrix)) sampleDistMatrix <- as.matrix(sampleDists) ``` --- ## Sample-to-Sample correlation We can use the **pheatmap** library's pheatmap function to cluster our data by similarity and produce our heatmaps. We provide our matrix of sample distances as well as our **dist** object to the **clustering_distance_rows** and **clustering_distance_cols** function. By default hierarchical clustering will group samples based on their gene expression similarity into a dendrogram with between sample similarity illustrated by branch length. ```r library(pheatmap) pheatmap(sampleDistMatrix, clustering_distance_rows = sampleDists, clustering_distance_cols = sampleDists) ``` ![](RU_RNAseq_p3_files/figure-html/gSampleDistc-1.png) --- ## Sample-to-Sample correlation We can use the **brewer.pal** and **colorRampPalette()** function to create a white to blue scale. We cover this in more depth for using with [**ggplot** scales](https://rockefelleruniversity.github.io/Plotting_In_R/r_course/presentations/slides/ggplot2.html#60). ```r library(RColorBrewer) blueColours <- brewer.pal(9, "Blues") colors <- colorRampPalette(rev(blueColours))(255) plot(1:255, rep(1, 255), col = colors, pch = 20, cex = 20, ann = FALSE, yaxt = "n") ``` ![](RU_RNAseq_p3_files/figure-html/gSampleDistca-1.png) --- ## Sample-to-Sample correlation We can provide a slightly nicer scale for our distance measure heatmap to the **color** parameter in the **pheatmap** function. ```r pheatmap(sampleDistMatrix, clustering_distance_rows = sampleDists, clustering_distance_cols = sampleDists, color = colors) ``` ![](RU_RNAseq_p3_files/figure-html/gSampleDistd-1.png) --- ## Sample-to-Sample correlation Finally we can add some column annotation to highlight group membership. We must provide annotation as a data.frame of metadata we wish to include with rownames matching column names. Fortunately that is exactly as we have set up from DEseq2. We can extract metadata from the DESeq2 object with **colData()** function and provide it to the **annotation_col** parameter. ```r annoCol <- as.data.frame(colData(dds)) pheatmap(sampleDistMatrix, clustering_distance_rows = sampleDists, clustering_distance_cols = sampleDists, color = colors, annotation_col = annoCol) ``` ![](RU_RNAseq_p3_files/figure-html/gSampleDiste-1.png) --- class: inverse, center, middle # Clustering Analysis

 --- ## Clustering genes and samples We can use the same methods of clustering samples to cluster genes with similar expression patterns together. Clustering genes will allow us to identify the major patterns of gene expressions within our data and to group genes with similar expression profiles for review and functional analysis. --- ## Clustering Inputs Minimizing the number of genes considered for clustering helps speed things up. To reduce the dataset we can subset to genes that are highly variable using something akin to an ANOVA test. With DESeq2 we can identify genes significantly changing across groups by comparing our models with and without our groups of interest using the **results** function. - Tissue model ``` ~ Tissue ``` - No groups model ``` ~ 1 ``` --- ## Likelihood-ratio test To run a LRT we must set the parameter of **reduced** to our alternative model of no groups. We also set the **test** parameter to **LRT** to allow us to compare the models. ```r dds2 <- DESeq(dds, test = "LRT", reduced = ~1) acrossGroups <- results(dds2) acrossGroups <- acrossGroups[order(acrossGroups$pvalue), ] acrossGroups[1:3, ] ``` ``` ## log2 fold change (MLE): Tissue Liver vs Heart ## LRT p-value: '~ Tissue' vs '~ 1' ## DataFrame with 3 rows and 6 columns ## baseMean log2FoldChange lfcSE stat pvalue padj ## <numeric> <numeric> <numeric> <numeric> <numeric> <numeric> ## 16483 124208.2 -2.27414 1.584829 1543.84 0.00000e+00 0.00000e+00 ## 17888 43684.0 -14.85201 0.654258 1524.73 0.00000e+00 0.00000e+00 ## 21956 16543.9 -15.03634 1.059070 1389.39 1.98399e-302 9.55889e-299 ``` --- ## Likelihood-ratio test We can use the **plotCounts()** to get review expression profile of a gene, one at a time. We define the gene of interest to **gene** parameter and **intgroup** to specify metadata column to group counts by. ```r plotCounts(dds2, gene = "17888", intgroup = "Tissue") ``` ![](RU_RNAseq_p3_files/figure-html/tissue12-1.png) --- ## Filter with LRT results Clustering is done on the counts. We can subset our rlog transformed gene expression matrix to those genes significant in our LRT test. This filters the ~45% of genes that are not chganing across our experiment. ```r sigChanges <- rownames(acrossGroups)[acrossGroups$padj < 0.01 & !is.na(acrossGroups$padj)] sigMat <- rlogMatrix[rownames(rlogMatrix) %in% sigChanges, ] nrow(rlogMatrix) ``` ``` ## [1] 14454 ``` ```r nrow(sigMat) ``` ``` ## [1] 8094 ``` --- ## Clustering genes and samples We can pass our filtered matrix of expression to the **pheatmap()** function and set the **scale** parameter to *row* to allow for clustering of relative changes in gene expression (this does a by gene Z-score). Additionally due to the large number of genes, we turn rowname off with the **show_rownames** function. ```r library(pheatmap) pheatmap(sigMat, scale = "row", show_rownames = FALSE) ``` ![](RU_RNAseq_p3_files/figure-html/he2-1.png) --- ## Clustering genes and samples ```r pheatmap(sigMat, scale = "row", show_rownames = FALSE) ``` ![](RU_RNAseq_p3_files/figure-html/he2a-1.png) --- ## Clustering genes and samples Now we have a visual representation of changes in gene expression across samples we can use the clustering to derive groups of genes with similar expression patterns. Gene with similar expression profiles may share functional roles and so we can use these groups to further evaluate our gene expression data. Many approaches to identifying clustered groups of genes exist including K-means, SOM and HOPACH. The pheatmap package has in built methods for K means and hierarchical clustering. For K means we can simply provide a desired number of clusters to the **kmeans_k** parameter. For the moment we will just pick 7. ```r library(pheatmap) set.seed(153) k <- pheatmap(sigMat, scale = "row", kmeans_k = 7) ``` --- ## Clustering genes and samples The resulting plot no longer shows our individual genes but the average relative expression of genes within a cluster. The heatmap rownames show the cluster name and importantly the number of genes within each cluster. ```r library(pheatmap) set.seed(153) k <- pheatmap(sigMat, scale = "row", kmeans_k = 7) ``` ![](RU_RNAseq_p3_files/figure-html/km2-1.png) --- ## Clustering genes and samples The **pheatmap()** function returns information on clustering. This is returned as a list, from which the K-means clustering the assignment of genes to clusters can be extracted. ```r names(k$kmeans) ``` ``` ## [1] "cluster" "centers" "totss" "withinss" "tot.withinss" ## [6] "betweenss" "size" "iter" "ifault" ``` ```r clusterDF <- as.data.frame(factor(k$kmeans$cluster)) colnames(clusterDF) <- "Cluster" clusterDF[1:10, , drop = FALSE] ``` ``` ## Cluster ## 20671 1 ## 27395 3 ## 18777 4 ## 21399 5 ## 108664 4 ## 319263 6 ## 76187 4 ## 70675 6 ## 73824 5 ## 100039596 4 ``` --- ## Clustering genes and samples We can now plot our full heatmap highlighting the membership of genes to clusters. We add an additional row annotation by providing a data.frame of desired annotation with rownames matching between our annotation data.frame and our rlog transformed matrix to the **annotation_row** parameter. ```r OrderByCluster <- sigMat[order(clusterDF$Cluster), ] pheatmap(OrderByCluster, scale = "row", annotation_row = clusterDF, show_rownames = FALSE, cluster_rows = FALSE) ``` --- ## Clustering genes and samples ```r OrderByCluster <- sigMat[order(clusterDF$Cluster), ] pheatmap(OrderByCluster, scale = "row", annotation_row = clusterDF, show_rownames = FALSE, cluster_rows = FALSE) ``` ![](RU_RNAseq_p3_files/figure-html/km4r-1.png) --- ## Identifying optimal clusters When doing clustering you will need to optimize the number of clusters selected. Methods exist to help identify the ideal number. One such method is to assess the **silhoutte** score at different successive cluster numbers and choose the cluster number with the highest mean **silhoutte** score. The **Silhouette** method evaluates the similarity of cluster members to the similarity between clusters as below. For all genes/samples, the dissimilarity for a cluster member to its own cluster , <math xmlns="http://www.w3.org/1998/Math/MathML"><msub><mi>a</mi><mi>i</mi></msub></math>, is calculated as the mean distance between a cluster member and all other members of that cluster. Further to this the minimun, mean dissimilarity of the cluster member to members of other clusters is calculated, <math xmlns="http://www.w3.org/1998/Math/MathML"><msub><mi>b</mi><mi>i</mi></msub></math>. <math xmlns="http://www.w3.org/1998/Math/MathML"> <msub> <mi>S</mi> <mi>i</mi> </msub> <mo>=</mo> <mo stretchy="false">(</mo> <msub> <mi>b</mi> <mi>i</mi> </msub> <mo>−</mo> <msub> <mi>a</mi> <mi>i</mi> </msub> <mo stretchy="false">)</mo> <mrow class="MJX-TeXAtom-ORD"> <mo>/</mo> </mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> <mo stretchy="false">(</mo> <msub> <mi>a</mi> <mi>i</mi> </msub> <mo>,</mo> <msub> <mi>b</mi> <mi>i</mi> </msub> <mo stretchy="false">)</mo> </math> --- ## Identifying number of clusters We can use the NbClust package to calculate the **Silhoutte** scores over successive cluster numbers. We supply the scaled matrix to the *NbClust* function and set the min and maximum cluster numbers to try using the **min.nc** and **max.nc** respectively. We can retrieve the optimal cluster number from the **Best.nc** slot of our result list. Here we see the number is lower at 3. Maybe a cluster for every sample group's unique gene expression signature. ```r library(NbClust) rowScaledMat <- t(scale(t(sigMat))) clusterNum <- NbClust(rowScaledMat, distance = "euclidean", min.nc = 2, max.nc = 12, method = "kmeans", index = "silhouette") clusterNum$Best.nc ``` ``` ## Number_clusters Value_Index ## 3.0000 0.4956 ``` --- ## Identifying number of clusters We can the use the **Best.partition** slot to extract the cluster membership as we did with pheatmap. We can arrange our matrix by using the **match** function between the row names of our matrix and the names of genes in our new cluster membership vector. ```r clusterNum$Best.partition[1:10] ``` ``` ## 20671 27395 18777 21399<PHONE_NUMBER>63 76187 70675 ## 2 2 1 3 1 3 1 3 ## 73824 100039596 ## 3 1 ``` ```r orderedCluster <- sort(clusterNum$Best.partition) sigMat <- sigMat[match(names(orderedCluster), rownames(sigMat)), ] ``` --- ## Identifying number of clusters We can now visualize the new clustering alongside our old clustering. In this case the order of genes is determined by our optimized clustering. While our labeled clusters were derived from our original pheatmap clustering. We can clearly see how these clusters overlap. ```r pheatmap(sigMat, scale = "row", annotation_row = clusterDF, show_rownames = FALSE, cluster_rows = FALSE) ``` ![](RU_RNAseq_p3_files/figure-html/kmsha-1.png) --- # Time for an exercise [Link_to_exercises](../../exercises/exercises/RNAseq_part4_exercise.html) [Link_to_answers](../../exercises/answers/RNAseq_part4_answers.html)


src
qfits_time.c File Reference
Functions
char *
qfits_get_datetime_iso8601
(void)
Returns the current date and time as a static string.


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Louis Tomlinson Releases New Song ‘Don’t Let It Break Your Heart’
by Jamie SamhanLouis Tomlinson has just released a new single from his upcoming album Walls.
On Saturday, the former One Direction member dropped “Don’t Let It Break Your Heart”.
“Without getting too deep… I think just honesty. I mean the lyrics, naturally, my style, are quite autobiographical anyway,” Tomlinson recently told Billboard’s “Pop Shop” podcast while talking about his new album.
He added, “I’m always trying to humanize myself as much as possible and make myself relatable to fans, and I think with honesty, you can do that, you know what I mean?”
Listen to the new track above. Walls will be released on Jan. 31, 2020.


Development Status#
This page just shows where the project is at and what needs to be worked on.
Todo List#
This sections contains a list of things to do. If this section is empty then either there are no more todos or todo_include_todos
is False
in config.py
.
Todo
Add pymodule page
Todo
Add more documentation on dependency management and best practices
Todo
Make more clear and less abrupt


JS Beginners
JavaScript, often abbreviated as JS, is a high-level, just-in-time compiled, multi-paradigm programming language that conforms to the ECMAScript specification.


ABOUT: Daryl Peter Paul Licawen Verzosa was born on February 23, 1996 in Bontoc, Mountain Province, Philippines. He is a dancer and choreographer known for his YouTube Channel, Daryl Verzosa. He is also known for his dance contribution and choreography for UC FUEGO, a dance organization that started on 2015 at University of Cordilleras in Baguio City, Philippines where he is studying, NO X’QZ (No excuse), a dance group in Baguio City, and Rhythmus Dance Studio, where he teaches students who are willing to learn dancing. At age seven, he began dancing which he then considered as a hobby.
Later on, Michael Jackson “The King of Pop” made a big impact in him and he started imitating and impersonating him. He pursued more his dancing when Michael Jackson passed away on June 2009 which led him to do dance covers of MJ on YouTube as a tribute. In 2010, he joined his first dance crew in town, the No X’QZ. Well known dancers like Brian Puspos, Ian Eastwood, Keone and Mari, and many more dancers serve as his motivation to share his knowledge and his passion to all the dancers. Now, he calls dancing as his passion.


VIS 2014 Schedule Browser
authored by
Connor Gramazio
, CS PhD Candidate @ Brown
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here
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Baltimore museum will acquire art only by women in 2020
The museum wants more female-identifying artists in its collection 'to rectify centuries of imbalance.'
The Baltimore Museum of Art (BMA) has announced that in 2020 it will only buy works by female-identifying artists. This commitment is an extension of "2020 Vision", BMA's year of exhibitions and programs dedicated to female-identifying artists and leaders.
The museum sees this as an opportunity to extend that commitment while also working to shift the scales within its collections, BMA explained, acknowledging that women artists are still underrepresented in the museum field and within museum collections.
More:
-
'To Future Women': US art exhibit of dreams for women to come
-
Art from rubbish: Noted artist Anatsui holds exhibition in Doha
-
The Truth in Numbers: Redefining Data Journalism Through Art
Announced November 14 by BAM's director Christopher Bedford, "something radical must be done to rectify centuries of imbalance," he said.
The Maryland museum acquired its first work by a female artist in 1916, two years after it was founded and three years before women gained the right to vote in the United States. Today, only four percent of the 95,000 pieces in its permanent collection were created by women.
Bedford says the museum is working to "correct our own canon" and address historical blind spots.
"We hope this will serve as a model and the first step towards better representation within our field. The BMA will explore works across genre, style, and medium, and will announce further details regarding the considerations and process for acquisitions in 2020 at a later date," the museum said in a statement provided to Al Jazeera.


Class NetPacketReader
Inherited Members
Namespace: LiteNetLib
Assembly: LiteNetLib.dll
Syntax
public sealed class NetPacketReader : NetDataReader
Methods
Recycle()
Declaration
public void Recycle()
public sealed class NetPacketReader : NetDataReader
public void Recycle()


{% extends "main.html" %}
{% block js %}
{% endblock %}
{% block section_title %}
{{ dist.project_name }}
version {{ dist.version }}
{% if distinfo.summary %}
{{ distinfo.summary }}
{% endif %}
{{location}}
{% endblock %} {% block content %}{% if dist.project_name|lower in pypi_update_cache %} {% if is_venv == False %}
Be sure that you are not currently in a virtual environment, updating system packages may cause your system to malfunction
Please make sure that updating this package will not affect your system! Please use a virtual environment
This package provides the following console scripts:
| Script Name | Module Name | Function/Method | {% for name, entry in entry_map['console_scripts'].items() %}
|---|---|---|
| {{ entry.name }} | {{ entry.module_name }} | {{ entry.attrs|join(",") }} |


Selected day: {{selectedTime | date:'mediumDate'}} Selected Time: {{selectedTime | date:'shortTime'}}


The latest illustrations from artist Michael Leunig 17 Nov 2019, 02:32 Best life A catastrophe. Modern hopscotch. Takes the biscuit. Get over it. Ode to Spring. Phoney love. One way. Boyhood poem. No problem. Best future. Cinderella. Cheese deposits. Carbon footprint scanner. Sporting life. With friends like these. Festival of the carts. The protector. Pot-bound. An opening. Pointing the way. Near-life experience. The Muddle Method. Problem solved. Ground down. Mental Health. A new development regarding development. Silent majority? Soul survival. Life. Inevitabilities.


We monetize platforms!
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- empower leading-edge technologies
- aggregate holistic systems
- evolve best-of-breed metrics
- cultivate impactful e-business
- target distributed web-readiness
- extend 24/365 interfaces
- whiteboard customized platforms
- incentivize integrated markets
- visualize seamless functionalities
- visualize leading-edge models
- exploit enterprise bandwidth


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Saw the addition of Take-off Assist Motors (TAMS) to main rocket design. Overhaul of Ground Operations to support new parrallel staging feature and payload was expanded to include a deployable glider.
View ProjectCorrected various stabilization problems in the Tyrannasoar and saw the addition of sensors and electronics to the payload. A new and more robust ignition system was also developed.
View ProjectOverhaul of team organization and shifted focus off propulsion and to the rocket as whole. Successfully flew on a new commercial M1575 hybrid motor.
View ProjectThe HyPE 1B2 rocket was the culmination of a 5 year project which started Rocket Project.
View Project


Introduction and Setup¶
pysqlc, works by (1) connecting to a database of your choice, and (2) executing a SQL
query on it. Authentication, utilization of cursors (and other SQL things) are done for you.
supported:
Python 2.7, 3.x
MySQL 5.7, 8.0
SQL Server 2012, 2016
Installation¶
At this point, pysqlc is not hosted on PyPI, so to install the library you need to download it from github. There are two ways you can do this, you can go to its github repo, download it/clone it locally, and install using using pip (locally):
>>> pip install .
or alternatively via pip and git directly:
>>> pip install git+https://github.com/sergegoussev/pysqlc.git
Quickstart¶
To use the library - you first have to connect to your database by initiating the DB
object, and then write SQL for whatever you are trying to do using DB.query("")
. That’s it!
When initiating the connection, you can either enter your login info manually, or utilize pysqlc’s environment variable approach (similar to the approach used by Google APIs). The below example to connect and extract some data from your database assumes you have setup an environmental variable for the library to use:
from pysqlc import DB
#connect to the database you want
db = DB(env_name="prod", database="database_name", )
#write your SQL query as a string
q = "SELECT * FROM table;"
#and get the data back using one simple function
result = db.query(q)
Ways to authenticate¶
To make connections, there are 2 options:
You can set up an environmental variable to not have to pass any login information every time you use the library (recommended); or
Choose to specify username/password manually.
To follow the environmental variable approach (works the same way as google cloud environmental variables, which points to a json on your computer), create a ‘SQL_LOGIN’ variable on your PC and point it to a .json file somewhere on your computer. The JSON file must follow the below structure with the name of the environment you are connecting to, and the various connection requirements:
{
"prod":{
"dbtype":"MySQL",
"host":"localhost",
"charset":"utf8mb4",
"username":"uid",
"password":"psw"
}
}
You can specify several environments, just add others as needed. Its recommended to set the default environment you will use as “prod” — this way you will not need to specify this environment every time when connecting.
specify either “MySQL” or “SQL Server” under dbtype;
“password” section can be ommited. If it is not included, the pysqlc will ask for the password every time the connection is made (or alternatively look for it to be passed into the DB object upon initiation):
Examples:
If password ommited from local variable json and not specified in code:
from pysqlc import DB db = DB(database='testdb') Enter password to login to the database server: #prompt '****' Successfully connected to testdb
If password ommited from local variable json, but specified in code:
from pysqlc import DB db = DB(database='testdb', password='pass') Successfully connected to testdb
If password included in json:
from pysqlc import DB db = DB(database='testdb') Successfully connected to testdb
Custom Configuration¶
Specifying environments¶
If you have setup your JSON
file to point towards multiple environments, you can specify the environment of interest upon initating the DB()
object by utilizing the env_name
parameter:
from pysqlc import DB
db = DB(env_name="dev", database='testdb')
Successfully connected to testdb
By default, env_name
is set to 'prod'
Troubleshooting
The order of arguments when specifying DB object is db(database="", env_name='prod', ...)
, hence make sure to specify the env_name
variable as the type of variable you are entering. For example if you specify db('dev')
for dev environment, pysqlc will look for the dev
database on the prod
(default) environment
Dev mode¶
pysqlc has the ability to print the result of every query statement to give you more visibility of what is going on. If you want to enable it, you can turn it on during initiation:
from pysqlc import DB
db = DB(env_name="dev", database='testdb', dev_mode=True)
By default, dev_mode=False
. The library will still print out major notifications (errors, DB connections, etc).
Note on values
input¶
When inputting data into the values
parameter, always passed in as a list (even if there is only one value):
q1 = "REPLACE INTO table (name) VALUES (%s);"
db.query(query=q1, values=['John Smith'], q_type='REPLACE')
#or multiple data values:
q2 = """
UPDATE table
SET number=%s
WHERE userid='%s';
"""
db.query(query=q2, values=[123, 'john-123'], q_type="UPDATE")


PKR rep says it was a joke when he spoke about telling lies to win elections in Saratok
by BY SULOK TAWIEKUCHING, Nov 8 ― PKR’s Krian State Assemblyman Ali Biju clarified in the Sarawak State Assembly today that he was only joking when he spoke about telling lies to win in an election in Saratok recently.
“Actually, this one ― (in Iban) enti enda bula, enda menang (if you don’t lie, you don’t win) is my joke,” he said during the debate on the state Budget 2020 tabled by Chief Minister Datuk Patinggi Abang Johari on October 4.
“Who would want to admit that they tell lies, in public and in a longhouse? No, such thing. If there are people who said that, it means they are joking,” Ali said.
He blamed the GPS State Assemblymen for taking his joke literally.
“They thought it was true. They took it literally. If they want to use this issue, go ahead. Let people laugh,” he said.
GPS State Assemblymen, during the debate, took a swipe at Ali for saying that “enti enda menang, enda menang”.
They asked him to clarify what did he mean by that remark.
Saribas State Assemblyman Datuk Abdul Wahab Aziz said “enti enda bula, enda menang” has become a tagline for Ali.
Layar State Assemblyman and GPS Youth leader Gerald Rentap said Ali cannot be joking when he muttered the remark.
He said Ali was the one who posted a video of himself on social media of his function in a longhouse.


I implemented this and i got the voltage and current correct..But i cant seem to get real power and power factor correct... I tried all possible values for phase calibration but no avail.. Cant seem to pin point the problem...Getting negative real power and negative power factor!!!!!!
»
Re: Real power and power factor not correct
By convention, we regard real power as positive when importing, and negative when exporting. The difference is the phase of the current in relation to the voltage. If you flip your CT on its cable, you will invert the current signal and you should then read positive real power when importing (which I assume is what you are doing).
If you are using the standard sensors for voltage and current from the shop, the standard sketches should give you a power factor close to unity for a pure resistive load, but if you use other sensors, that won't necessarily be the case. As you have said nothing about what you have and which sketch you are using, I cannot help you any more. You should find all the information you need somewhere in Building Blocks.
Re: Real power and power factor not correct
I'm also the same, I change the direction of current sensor, but the power factor is not correct, I use Arduino Uno sketch, my voltage circuit is LMV321, the current sensor circuit I follow the instruction on this website. but my power is not correct.
Re: Real power and power factor not correct
Tell us more. There is not enough information about what you have and what you are trying to measure for us to help you.
Re: Real power and power factor not correct
the voltage, i follow instruction here http://openenergymonitor.org/emon/buildingblocks/acac-buffered-voltage-bias, the current, I follow instruction from http://openenergymonitor.org/emon/buildingblocks/how-to-build-an-arduino...
I use arduino Mega 2560 and here is my code
// EmonLibrary examples openenergymonitor.org, Licence GNU GPL V3
#include "EmonLib.h" // Include Emon Library
EnergyMonitor emon1; // Create an instance
void setup()
{
Serial.begin(9600);
emon1.voltage(15, 261, 5.5); // 2 -Voltage: input pin, calibration, phase_shift
emon1.current(1, 62.3); // Current: input pin, calibration.
}
void loop()
{
emon1.calcVI(20,2000); // Calculate all. No.of half wavelengths (crossings), time-out
emon1.serialprint(); // Print out all variables (realpower, apparent power, Vrms, Irms, power factor)
float realPower = emon1.realPower; //extract Real Power into variable
float apparentPower = emon1.apparentPower; //extract Apparent Power into variable
float powerFActor = emon1.powerFactor; //extract Power Factor into Variable
float supplyVoltage = emon1.Vrms; //extract Vrms into Variable
float Irms = emon1.Irms; //extract Irms into Variable
}
I compare the measured data with watt meter,and I get the not correct real watt & power factor, I try to change the "phase_shift" in voltage -Function but not correct. I use a 12 AC-AC transformer, YDCD sct013-000(00A)
Re: Real power and power factor not correct
44.39 222.62 3.34 -0.99
-725.62 732.34 220.48 3.32 -0.99
-725.09 731.75 220.64 3.32 -0.99
-735.97 742.54 222.21 3.34 -0.99
-736.14 743.10 222.49 3.34 -0.99
-737.36 743.32 222.44 3.34 -0.99
-728.75 735.22 221.26 3.32 -0.99
-732.48 739.14 221.89 3.33 -0.99
-722.96 729.62 219.58 3.32 -0.99
-724.73 731.26 220.38 3.32 -0.99
-722.53 729.27 219.82 3.32 -0.99
-727.39 734.53 220.40 3.33 -0.99
-740.18 746.82 222.79 3.35 -0.99
-722.13 728.88 220.04 3.31 -0.
the voltage & ampe is correct. But the real power is not correct, in my watt meter is real watt is 680W, power factor is 0,9
Re: Real power and power factor not correct
Did you follow steps 4 and 5 here?
http://openenergymonitor.org/emon/buildingblocks/calibration
Re: Real power and power factor not correct
1. Did you read this? I have now had to waste time deleting your spam.
2. You can connect your CT in exactly the same way as the voltage transformer, using the same op.amp.
3. Your power factor is 0.99, not 0.90. It represents a phase angle of just 8° I think that is very good if you have not calibrated your circuit.
4. In another thread, you have written that the calibration constant for the YHDC SCT-013-000 is not correct. Your calibration constant is correct for a burden resistor value of 32 Ω. Is that the value of your burden resistor?
Re: Real power and power factor not correct
Sorry, I'm new , so I dont know so much thing about this website.Thanks for your quick respone,I'll try soon
Re: Real power and power factor not correct
the value of my burden resistor for YHDC SCT-013-000 is 32 ohm
Re: Real power and power factor not correct
the circuit of the lmv321 for current sensor, I connect 2 out put of the sensor to the 2 output of the AC adapter in the circuit http://openenergymonitor.org/emon/buildingblocks/acac-buffered-voltage-bias
but the current is not correct even I change the calibration.
Re: Real power and power factor not correct
Now, what should I do to get the correct power factor, real watt, If not, my project is useless, waste of money & time.
Re: Real power and power factor not correct
Build your circuit according to this diagram:
When you have done that, follow exactly and in the correct order all the steps in the calibration instructions here.
If you cannot calibrate your circuit, there is something wrong with some of your components, and you need to measure and tell us:
1, The mains voltage,
2. The measured output voltage of your ac adapter,
3. The exact type of your ac adapter,
You also need to post your sketch - all of it.
Re: Real power and power factor not correct
thanks for your instructions.I have the correct real watt & power Factor, I've tried to build a new circuit follow instruction here http://openenergymonitor.org/emon/buildingblocks/acac-buffered-voltage-bias, the current, http://openenergymonitor.org/emon/buildingblocks/how-to-build-an-arduino...
With new circuit, I get correct power Factor, then to get the real watt = supply voltage * Irms*power Factor.
Many thanks for your instructions.
Re: Real power and power factor not correct
You do not need to do "real watt = supply voltage * Irms*power Factor"
If you look at emonLib.cpp:
real power, Vrms and Irms are the values that are calculated directly from the sampled voltage and current.
Then these values are used to calculate:
apparent power = Vrms × Irms
power factor = real power ÷ apparent power
Re: Real power and power factor not correct
here is the video with I test Energy monito with AC watt meter. I speak Vietnamese in the video. https://www.youtube.com/watch?v=4zhvSSiXF1k&list=UUxrCTbzN0S8pPrGRDJ_YA0w
Re: Real power and power factor not correct
I test my equipment with 2 case, the first is 680w, 3.4A, 0.9 power factor, the second is 1320w, 6,1A, 0,99 power factor (read on AC watt meter). my equipment is correct number.
Re: Real power and power factor not correct
this is my sketch,
// EmonLibrary examples openenergymonitor.org, Licence GNU GPL V3
#include "EmonLib.h" // Include Emon Library
EnergyMonitor emon1; // Create an instance
void setup()
{
Serial.begin(9600);
emon1.voltage(2, 234.26, 1.7); // Voltage: input pin, calibration, phase_shift
emon1.current(1, 111.1); // Current: input pin, calibration.
}
void loop()
{
emon1.calcVI(20,2000); // Calculate all. No.of half wavelengths (crossings), time-out
// emon1.serialprint(); // Print out all variables (realpower, apparent power, Vrms, Irms, power factor)
//
//extract Real Power into variable
float apparentPower = emon1.apparentPower; //extract Apparent Power into variable
float powerFActor = emon1.powerFactor; //extract Power Factor into Variable
float supplyVoltage = emon1.Vrms*0.981702307; //extract Vrms into Variable
float Irms = (emon1.Irms-.2)*.59;
float realPower = supplyVoltage*Irms*powerFActor; //extract Irms into Variable
Serial.print (" AC ");
Serial.print (powerFActor);
Serial.print (" ");
//Serial.print (apparentPower);
//Serial.print (" W ");
Serial.print (realPower);
Serial.print (" W ");
Serial.print (supplyVoltage);
Serial.print (" V ");
Serial.print (Irms);
Serial.print (" A ");
Serial.println();
delay(1000);
}
Re: Real power and power factor not correct
with arduino Uno, I get the correct real watt, power factor, but with arduino mega 2560, i get the wrong value. what's up
Re: Real power and power factor not correct
Look at emonlib.cpp, and check that it is reading the correct value for "SupplyVoltage". You will probably need to add a line to print this to the serial port. The value you get should be the processor Vcc in mV.
How wrong are the values that you get, i.e. what is the value displayed and what should the value be?
Re: Real power and power factor not correct
i'm sorry, i have mistake in soldering the circuit, so I get not correct value.Now,everything is correct. many thanks for your quick support.
Re: Real power and power factor not correct
When I use above sketch to measure 3 phase power, the Ampes & voltage is correct, but the power factor is not correct. ere is the data for power factor
Standard measure
1 0.35
0.5 0.64
0.8 0.84
Re: Real power and power factor not correct
That is hardly surprising, if you have not added another 2 voltage circuits for phases 2 & 3. There is a 120° phase shift between any pair of phases, therefore if you have only one voltage reference that is the same line as CT1, you will see that the "real power" (because it is not in fact the real power at all) in CT2 & CT3 is completely wrong and so is the power factor.
Try adding two more voltage inputs, or try using a sketch designed for 3-phase working. You can find one on Github. Follow the calibration instructions in the sketch exactly.


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Issue: CONS-TYPE-SPECIFIERForum: EDITORIAL
References: Issue PRETTY-PRINT-INTERFACE,
Issue ATOMIC-TYPE-SPECIFIER-LIST,
Issue RECURSIVE-DEFTYPE
Category: ADDITION
Edit history: 05-Feb-91, Version 1 by Pitman
15-Mar-91, Version 2 by Pitman
08-Apr-91, Version 3 by Pitman
(integrate amendment to option ADD from Mar-91 meeting)
Status: X3J13 passed option ADD on a vote of 12-1, March 1991.
Problem Description:
The description of the pretty printer dispatching mechanism is complicated
by the fact that dispatch cannot be described in terms of `type specifiers'.
In fact, the pretty printer dispatches on either a type specifier -or- a
Proposal (CONS-TYPE-SPECIFIER:ADD):
Permit the CONS type specifier to be a type specifier that specializes,
with the syntax:
(CONS [type-specifier-1 [type-specifier-2]])
Either type specifier may be *, which means the same as T.
Either the second type specifier or both type specifiers may be omitted.
Omitted type specifiers default to T.
Extend the CONS pretty printer dispatcher to treat (CONS) the same as CONS.
Test Case:
(TYPEP '(A B C) 'CONS) => T ; Already true
(TYPEP '(A B C) '(CONS T)) => T ; New with this proposal
(TYPEP '(A B C) '(CONS SYMBOL)) => T ; New with this proposal
(TYPEP '(A B C) '(CONS INTEGER)) => NIL ; New with this proposal
(TYPEP '(A B C) '(CONS T T)) => T ; New with this proposal
(TYPEP '(A B C) '(CONS SYMBOL SYMBOL)) => NIL ; New with this proposal
(TYPEP '(A B C) '(CONS SYMBOL (CONS SYMBOL (CONS SYMBOL))))
=> T ; New with this proposal
(TYPEP '(A B C) '(CONS SYMBOL (CONS SYMBOL (CONS SYMBOL NIL))))
=> NIL ; New with this proposal
(TYPEP '(A B C) '(CONS SYMBOL (CONS SYMBOL (CONS SYMBOL NULL))))
=> T ; New with this proposal
Rationale:
This will make the description of the pretty printer simpler with no
substantial overhead for other parts of the system.
This same approach was taken for EQL type specifiers in CLOS, so that
all method specializers could have corresponding type specifiers.
Current Practice:
Probably no one implements this.
Cost to Implementors:
Very small.
Cost to Users:
None.
Cost of Non-Adoption:
Extra conceptual overhead in the specification.
Benefits:
This new type specifier might be turn out to be useful in its own right in
some applications.
Aesthetics:
I think this improves the aesthetics.
Discussion:
This was discussed previously and discarded. But having looked at the
complexity it introduces in to the specification, I think it needs to be
reopened.
The question of what (TYPEP '(A B C) '(CONS)) returns was spun off as
a separate issue named ATOMIC-TYPE-SPECIFIER-LIST.
Barmar got briefly excited about the idea of
(deftype proper-list (&optional (element-type t))
`(or null (cons ,element-type proper-list)))
but others didn't like it. A separate issue RECURSIVE-DEFTYPE addresses
this.
Allan Wechsler (Symbolics) notes that
(deftype list-of-length (length)
'null
`(cons t (list-of-length ,(- length 1)))))
would in principle be reasonable under this proposal.


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[LATEST] Evidyon 2.14 - Tuesday May 12
Posted: Tue May 12, 2009 10:04 am
http://www.evidyon.com/download/install-evidyon.msi
If you're new, check out the basic instructions.
In this update:
If you're new, check out the basic instructions.
In this update:
- Button sounds
- Walking sounds
- Memory leak fixes
- "Lacertus" removed (you'll have to re-bind keys)
- Labels on all screens
- Right-click to split a stack of items. Clicking an item in inventory will re-stack it.
- Various bug-fixes


--- title: About ---
Nassau, Bahamas
<EMAIL_ADDRESS>Call me Theo. Some years ago - never mind how long precisely - having little or no money in my purse, and nothing particular to interest me on shore, I thought I would sail about a little. I could not imagine how much I would learn.
I have returned to the island nation of the Bahamas and while I do not know what the story of my life will be, I am determined to write it myself.


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Mind-Unwind
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Our Latest Games
Take a look at some of our recent mind refreshing games
Chess
Hangman
2048
Memory-Game
Hextris
Tic-Tac-Toe
New Brain-Game on its way!!!


{% extends "base.html" %} {% block page_title %}Books Catalogue - Single Item{% endblock %} {% block page_heading_title %}
| Author | {{ book.author }} |
|---|---|
| ISBN | {{ book.isbn }} |
| Genre | {{ book.genre }} |
| Publisher | {{ book.publisher }} |
| Summary | {{ book.summary }} |
There appears to be a problem, we can't find that book.
{% endif %} {% endblock %}


All in one micro-service framework with minimum dependencies and simplistic design. Most suitable for distributed applications and applications that serve multiple endpoints. The main concept behind Kaptan framework is using services as parts to build application.
Documentation is generated by TypeDoc and it's available at GitHub Pages of this repository.
| Package | Repository | Description |
|---|---|---|
kaptan-http | ibrahimduran/node-kaptan-http | Simple HTTP service with utilities. |
You can install Kaptan framework and its services easily from NPM using your favorite tool.
$ npm install --save kaptan
# or
$ yarn add kaptan
TypeScript users don't need to install any package for type definitions, they're served in the main package.
First parameter of Kaptan.constructor
is label for the application. Having a label prevents mess in logs when you have multiple instances of kaptan.
import { Kaptan } from 'kaptan';
const kaptan = new Kaptan('my-app');
Or feel free to extend Kaptan
class:
import { Kaptan } from 'kaptan';
class MyApp extends Kaptan {
constructor() {
super();
// (lines below are only visible to who believes in unicorns)
// My secret code - start
// My secret code - end
}
}
const app = new MyApp();
Services can be installed from NPM. You can use Kaptan.use
method to add service and service options.
import { CustomService } from 'kaptan-foo-bar';
kaptan.use(CustomService, { XYZ: false });
This will fire up application and create service instances.
kaptan.start()
.then(() => console.log('Application started!'))
.catch(err => console.error(err));
Services are created by extending Service class. Service constructor takes kaptan intance as first parameter and options object as second. Also kaptan
, logger
and options
properties get set in extended Service constructor.
import { Service } from 'kaptan';
class MyService extends Service {
constructor(kaptan, options) {
super(kaptan, {
XYZ: 'some_default_val',
...options
});
// theese are automatically set
console.log(this.kaptan);
console.log(this.options);
console.log(this.logger);
// service dependencies/injections
const fooBar = kaptan.services.spawn('FooBar');
}
async start() {
}
async stop() {
}
}
You can use kaptan.services.spawn
method to access service instances.
Kaptan framework comes with a built-in Network service to simplify networking. All features of network service is async/await - promise compatible. It currently supports three protocols for sending packets; Request/Response for sending request and receiving response synchronously (using promises) and Raw for sending data without waiting for response.
Informations below are just a brief description for understanding the concept. Don't forget to check documentation to get more information about classes or methods.
Kaptan package exports Network namespace which contains Network service. So you'll need to add Network.Network
as service.
import { Network } from 'kaptan';
kaptan.use(Network.Network, { PORT: 5000 });
Network.Packet
is basically a data holder class with serialization logic in it. Network.PacketHandler
instances can be passed to sockets and network service itself. By default, every one of packet handlers run when a packet arrives to the local socket but you can add filters to the handler using the Network.PacketFilter
class.
Also you can use literal objects to create PacketHandler
and PacketFilter
instances in order simplify usage.
const filter = Network.PacketFilter.from({ ref: 'myPacket' });
const handler = new Network.PacketHandler({
filter: filter,
onParsed(socket, packet) {
}
onReceive(socket, raw) {
}
});
Network.Address
is a common class that contains IP address utilities. Network.Socket
is a wrapper for net's socket object. You can pass packet handlers to sockets as mentioned above. There are two main methods in socket; wait(filter)
and send(packet)
. Since wait
method returns a promise, you can create synchronized communications between server and client with power of async/await syntax of ES2017.
const filter = new Network.PacketFilter()
.require('message');
const response = await socket.wait(filter);
socket.send({ data: { message: response.data.message } }); // mirror message
More about network will be available here soon..
Generated using TypeDoc


cd "C:\Program Files\OpenVPN\easy-rsa"
init-config.bat
vars.bat
file:set DH_KEY_SIZE=2048 set KEY_SIZE=4096 set KEY_COUNTRY=DK set KEY_PROVINCE=Copenhagen set KEY_CITY=Copenhagen set KEY_ORG=esignatur set KEY_EMAIL=${your_email_address} set KEY_CN=${your_openvpn_server_hostname_FQDN} set KEY_NAME=MakeUnique set KEY_OU=esignatur set PKCS11_MODULE_PATH=changeme set PKCS11_PIN=1234... the above are sample data - change it so it reflect your setup.
vars.bat
clean-all.bat
build-ca.bat
build-key-server.bat server
build-dh.bat
build-key.bat ${ClientName}
- change ClientName for each client to generate unique client certificates.vars.bat
command before running the build-key.bat ${ClientName}
commandserver.ovpn
to the OpenVPN config
folder:
port 1194 proto tcp4 dev tun ca "C:\\Program Files\\OpenVPN\\easy-rsa\\keys\\ca.crt" cert "C:\\Program Files\\OpenVPN\\easy-rsa\\keys\\server.crt" key "C:\\Program Files\\OpenVPN\\easy-rsa\\keys\\server.key" dh "C:\\Program Files\\OpenVPN\\easy-rsa\\keys\\dh2048.pem" server <IP_ADDRESS> <IP_ADDRESS> ifconfig-pool-persist "C:\\Program Files\\OpenVPN\\log\\ipp.txt" client-config-dir "C:\\Program Files\\OpenVPN\\config\\ccd" push "dhcp-option DNS <IP_ADDRESS>" push "dhcp-option DNS <IP_ADDRESS>" duplicate-cn keepalive 10 120 persist-key persist-tun status "c:\\Program Files\\OpenVPN\\log\\openvpn-status.log" log "c:\\Program Files\\OpenVPN\\log\\server.log" verb 4
sudo apt -y install network-manager-openvpn network-manager-openvpn-gnome
Place the following content in a file in c:\Program Files\OpenVPN\config
. The file suffix must be .ovpn
but the filename itself is up to you.
You must place the relevant content of the CA certificate, user certificate and user key between the -----BEGIN CERTIFICATE----- and -----END CERTIFICATE----- markers.
The -----BEGIN.. and -----END.. must be kept, so copy the entire content.
Change the ${remote-server-name}
variable to the IP-address or server name of your OpenVPN server.
client dev tun proto tcp remote ${remote-server-name} 1194 resolv-retry infinite ns-cert-type server keepalive 5 10 nobind persist-tun persist-key verb 3 <ca> -----BEGIN CERTIFICATE----- -----END CERTIFICATE----- <ca> <cert> -----BEGIN CERTIFICATE----- -----END CERTIFICATE----- <cert> <key> -----BEGIN PRIVATE KEY----- -----END PRIVATE KEY----- <key>
The RDP gateway is <IP_ADDRESS> (this is the server IP on the other end of the OpenVPN tunnel)
Once the server and at least one client is set up so that traffic flows you should really change the Remote Desktop Shadow and Remote Desktop User Mode rules for both TCP and UDP connection so that:
... that way, only RDP connections comming via OpenVPN is allowed.
Before changing this, make sure that you have a Plan B if anything goes wrong. If it breaks you will loose connectivity to the server. So only do this on a pretty virgin server which is easy to re-install :-/


My First html/css Page
This is some text.
Enter user's data:
Name:
Surname
Choose your gender:
Male
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Discription:
Submit
Baisc table
Name
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Gender


 If so, scratch that plan, and get creative.
The possibilities are as grand as your imagination.
Whatever your circumstances, it is your perspective that will determine how you will experience your situation.
If you are going to be alone this Thanksgiving – plan ahead to give yourself a good time. Here are some ideas:
- Make yourself a delicious feast for one.
- Call around and find others who will be alone and host a gathering.

- See if there is a restaurant or community group that has a large table for people who would otherwise be alone to share a meal together.
- Be bold and ask a friend if you can be included in their gathering.
- Volunteer with a community program that will be providing a nice meal to those in need.
- Create a sacred ritual for yourself that connects you to the true spirit of Thanksgiving. Take the time to deeply experience your gratitude for the blessings that are present in your life.
If you are dreading being with your family this Thanksgiving – do some inner preparation. For example:
- Think about past family gatherings that have been difficult for you and see if you can identify what dynamic is most challenging for you and why. How might you respond differently this year?
- Keep a strategy in your back pocket for when things get tough. For example, changing the subject, going for a walk, taking a bathroom break.
- Make a game of avoiding your triggers. If Uncle Joe gets to you every time, avoid him.
- If there are kids around, hang out with them.
- Help in the kitchen and feel good about being of service to your host.
 Take charge of your own experience.
 Don’t create a self-fulfilling prophecy for yourself built on a bad attitude.
If this is a tender time for you because you have recently lost a loved one – pay particular attention to your needs. Be kind and gentle with yourself. Maybe being with others who try too hard to compensate for your loss might not be what you need this year. If you need to be alone with your grief, then let it rip.  Be true to your own experience — whatever it is. But, know that you have choices and choose wisely what will serve you best.
If things aren’t shaping up to your liking this Thanksgiving, do something about it. It’s not too late to choose to make the most of any situation. My wish is that you be very good to yourself. And, if you aren’t going to be with people you love and enjoy, then take a good look at yourself in the mirror and love yourself upside down and sideways!
Happy Thanksgiving!
If you would like to know more about me and my work, please explore my website here.
— Published on November 17, 2019


Los Angeles sunsets are the most beautiful thing to watch. With miles of picturesque coastlines and expansive landscapes, there’s no shortage of places to watch the sun go down in the most breathtaking fashion.
Here are some of my favorite spots to watch sunset in Santa Monica, LAX, and Palos Verdes areas.