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| And I saw another sign in heaven, great and marvellous, seven angels having the seven last plagues; for in them is filled up the wrath of God.
|
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| ᏅᏩᏓᎴᏃ ᎤᏰᎸᏛ ᎦᎸᎳᏗ ᎠᎩᎪᎲᎩ, ᎦᎸᏉᏗ ᎠᎴ ᎤᏍᏆᏂᎪᏗ, ᎾᏍᎩ ᎦᎵᏉᎩ ᎢᏯᏂᏛ ᏗᏂᎧᎿᎭᏩᏗᏙᎯ ᏓᏂᏰᎲᎩ ᎦᎵᏉᎩ ᎢᏳᏓᎴᎩ ᎤᎵᏍᏆᎸᏗ ᎤᏕᏔᏙᏗ; ᎾᏍᎩᏰᏃ ᎤᏁᎳᏅᎯ ᎤᏔᎳᏬᎯᏍᏗ ᎨᏒ ᎠᏂᎧᎵᏏᏏᎭ.
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Nv-wa-da-le-no u-ye-lv-dv ga-lv-la-di a-gi-go-hv-gi, ga-lv-quo-di a-le u-s-qua-ni-go-di, na-s-gi ga-li-quo-gi i-ya-ni-dv di-ni-ka-hna-wa-di-do-hi da-ni-ye-hv-gi ga-li-quo-gi i-yu-da-le-gi u-li-s-qua-lv-di u-de-ta-do-di; na-s-gi-ye-no U-ne-la-nv-hi u-ta-la-wo-hi-s-di ge-sv a-ni-ka-li-si-si-ha.
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| And I saw as it were a sea of glass mingled with fire: and them that had gotten the victory over the beast, and over his image, and over his mark, and over the number of his name, stand on the sea of glass, having the harps of God.
|
|---|
| ᎠᎴ ᎠᎩᎪᎲᎩ ᎥᏓᎸ ᎠᏓᎨᏗ ᏗᎬᏩᎸᏌᏛ ᎾᏍᎩᏯᎢ ᎠᏥᎸ ᎤᏓᏑᏱ ᎨᏒᎩ; ᎠᎴ ᎾᏍᎩ Ꮎ [ᎥᎦᏥᎪᎥᎩ] ᏅᎩᏗᎦᏅᏌᏗ ᎤᎾᏓᎵᏁᎯᏕᎸᎯ, ᎠᎴ ᎾᏍᎩ Ꮎ ᏣᎦᏟᎶᏍᏔᏅᎯ, ᎠᎴ ᎤᏤᎵ ᏧᏬᏪᎶᏙᏗ, ᎠᎴ ᎾᏍᎩ ᏚᏙᎥ ᎢᎦᎢ ᎠᏎᏍᏗ ᎨᏒᎢ, ᎠᏂᏙᎾᎥᎩ ᎥᏓᎸ ᎠᏓᎨᏗ ᏗᎬᏩᎸᏌᏛ ᎾᏍᎩᏯᎢ, ᎤᏁᎳᏅᎯ ᏧᏤᎵ ᏗᎧᏃᎩᏍᏙᏗ ᏓᏂᏁᎲᎩ
|
|---|
A-le a-gi-go-hv-gi v-da-lv a-da-ge-di di-gv-wa-lv-sa-dv na-s-gi-ya-i a-tsi-lv u-da-su-yi ge-sv-gi; a-le na-s-gi na [v-ga-tsi-go-v-gi] nv-gi-di-ga-nv-sa-di u-na-da-li-ne-hi-de-lv-hi, a-le na-s-gi na tsa-ga-tli-lo-s-ta-nv-hi, a-le u-tse-li tsu-wo-we-lo-do-di, a-le na-s-gi du-do-v i-ga-i a-se-s-di ge-sv-i, a-ni-do-na-v-gi v-da-lv a-da-ge-di di-gv-wa-lv-sa-dv na-s-gi-ya-i, U-ne-la-nv-hi tsu-tse-li di-ka-no-gi-s-do-di da-ni-ne-hv-gi
|
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|
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| And they sing the song of Moses the servant of God, and the song of the Lamb, saying, Great and marvellous are thy works, Lord God Almighty; just and true are thy ways, thou King of saints.
|
|---|
| ᎠᎴ ᏚᏂᏃᎩᏒᎩ ᎼᏏ ᎤᏁᎳᏅᎯ ᎤᏤᎵ ᎤᏅᏏᏓᏍᏗ ᏧᏃᎩᏛ, ᎠᎴ ᎤᏃᏕᎾ ᎠᎩᎾ ᎤᏤᎵ ᏗᎧᏃᎩᏍᏗ, ᎯᎠ ᎾᏂᏪᏍᎬᎩ, ᎦᎸᏉᏗᏳ ᎠᎴ ᎤᏍᏆᏂᎪᏗᏳ ᏗᏣᎸᏫᏍᏓᏁᏗ ᎨᏒ ᏣᎬᏫᏳᎯ ᎠᎴ ᏣᏁᎳᏅᎯ ᏫᎾᏍᏛᎾ ᏣᎵᏂᎩᏛ; ᎣᏍᏛ ᎠᎴ ᏚᏳᎪᏛ ᎢᎭᏛᏁᎵᏙᎯ ᏂᎯ ᏣᎬᏫᏳᎯ ᎤᎾᏓᏅᏘ ᎤᎾᏤᎵᎦ.
|
|---|
A-le du-ni-no-gi-sv-gi Mo-si U-ne-la-nv-hi u-tse-li u-nv-si-da-s-di tsu-no-gi-dv, a-le U-no-de-na a-gi-na u-tse-li di-ka-no-gi-s-di, hi-a na-ni-we-s-gv-gi, Ga-lv-quo-di-yu a-le u-s-qua-ni-go-di-yu di-tsa-lv-wi-s-da-ne-di ge-sv Tsa-gv-wi-yu-hi a-le Tsa-ne-la-nv-hi Wi-na-s-dv-na tsa-li-ni-gi-dv; O-s-dv a-le du-yu-go-dv i-ha-dv-ne-li-do-hi ni-hi Tsa-gv-wi-yu-hi u-na-da-nv-ti u-na-tse-li-ga.
|
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|
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| Who shall not fear thee, O Lord, and glorify thy name? for thou only art holy: for all nations shall come and worship before thee; for thy judgments are made manifest.
|
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| ᎦᎪ ᏂᏣᎾᏰᏍᎬᎾ ᎨᏎᏍᏗ ᏂᎯ ᏣᎬᏫᏳᎯ, ᎠᎴ ᏂᎦᎸᏉᏗᏍᎬᎾ ᎨᏎᏍᏗ ᏕᏣᏙᎥᎢ? ᏂᎯᏰᏃ ᏨᏒᎯᏳ ᏂᏍᎦᏅᎾ, ᏂᎦᏗᏳᏰᏃ ᎤᎾᏓᏤᎵᏛ ᏴᏫ ᏓᏁᏩᏗᏒ ᏛᏂᎷᏥ ᎠᎴ ᏂᏣᏛᏅ ᎢᎬᏱᏢ ᏛᎾᏓᏙᎵᏍᏔᏂ; ᏂᎯᏰᏃ ᏗᏧᎪᏙᏗ ᎨᏒ ᎬᏂᎨᏒ ᏄᎵᏍᏔᏅ.
|
|---|
Ga-go ni-tsa-na-ye-s-gv-na ge-se-s-di ni-hi Tsa-gv-wi-yu-hi, a-le ni-ga-lv-quo-di-s-gv-na ge-se-s-di de-tsa-do-v-i? ni-hi-ye-no tsv-sv-hi-yu ni-s-ga-nv-na, ni-ga-di-yu-ye-no u-na-da-tse-li-dv yv-wi da-ne-wa-di-sv dv-ni-lu-tsi a-le ni-tsa-dv-nv i-gv-yi-tlv dv-na-da-do-li-s-ta-ni; ni-hi-ye-no di-tsu-go-do-di ge-sv gv-ni-ge-sv nu-li-s-ta-nv.
|
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|
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| And after that I looked, and, behold, the temple of the tabernacle of the testimony in heaven was opened:
|
|---|
| ᎣᏂᏃ ᎢᏴᏛ ᏓᏆᎧᎾᏅᎩ, ᎠᎴ ᎬᏂᏳᏉ ᏗᎦᎳᏫᎢᏍᏗᏱ ᏗᎧᎿᎭᏩᏛᏍᏗ ᏗᏗᏱ ᎦᎵᏦᏛ ᎤᎵᏍᏚᎢᏛ ᎨᏒᎩ ᎦᎸᎳᏗ;
|
|---|
O-ni-no i-yv-dv da-qua-ka-na-nv-gi, a-le gv-ni-yu-quo di-ga-la-wi-i-s-di-yi di-ka-hna-wa-dv-s-di di-di-yi ga-li-tso-dv u-li-s-du-i-dv ge-sv-gi ga-lv-la-di;
|
|---|
|
|---|
| And the seven angels came out of the temple, having the seven plagues, clothed in pure and white linen, and having their breasts girded with golden girdles.
|
|---|
| ᎦᎵᏉᎩᏃ ᎢᏯᏂᏛ ᏗᏂᎧᎿᎭᏩᏗᏙᎯ ᏗᎦᎳᏫᎢᏍᏗᏱ ᏓᏳᏂᏄᎪᏨᎩ, ᏓᏂᏰᎲᎩ ᎦᎵᏉᎩ ᎤᏕᏯᏙᏗ, ᏚᎾᏄᏮᎩ ᏂᏚᏓᏅᎦᎸᎾ ᎠᎴ ᏧᏁᎬ ᏙᎴᏛ ᏗᏄᏬ, ᎠᎴ ᏗᏂᏁᏥᏱ ᏚᎾᏓᏠᎲᎩ ᎠᏕᎸ-ᏓᎶᏂᎨ ᏗᎪᏢᏅᎯ ᏗᏓᏠᏍᏗ.
|
|---|
Ga-li-quo-gi-no i-ya-ni-dv di-ni-ka-hna-wa-di-do-hi di-ga-la-wi-i-s-di-yi da-yu-ni-nu-go-tsv-gi, da-ni-ye-hv-gi ga-li-quo-gi u-de-ya-do-di, du-na-nu-wv-gi ni-du-da-nv-ga-lv-na a-le tsu-ne-gv do-le-dv di-nu-wo, a-le di-ni-ne-tsi-yi du-na-da-tlo-hv-gi a-de-lv--da-lo-ni-ge di-go-tlv-nv-hi di-da-tlo-s-di.
|
|---|
|
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| And one of the four beasts gave unto the seven angels seven golden vials full of the wrath of God, who liveth for ever and ever.
|
|---|
| ᎠᏏᏴᏫᏃ ᎾᏍᎩ Ꮎ ᏅᎩ ᎢᏯᏂᏛ ᎨᏒ ᏗᏅᏃᏛ ᎦᎵᏉᎩ ᎢᏯᏂᏛ ᏗᏂᎧᎿᎭᏩᏗᏙᎯ ᏚᏁᎸᎩ ᎦᎵᏉᎩ ᎫᎫ ᎠᏕᎸᏓᎶᏂᎨ ᏗᎪᏢᏔᏅᎯ, ᏗᎧᎵᎢ ᎤᏁᎳᏅᎯ ᎤᏔᎳᏬᎯᏍᏗ ᎨᏒᎢ, ᎾᏍᎩ ᏂᎪᎯᎸ ᎠᎴ ᏂᎪᎯᎸ ᏤᎭ.
|
|---|
A-si-yv-wi-no na-s-gi na nv-gi i-ya-ni-dv ge-sv di-nv-no-dv ga-li-quo-gi i-ya-ni-dv di-ni-ka-hna-wa-di-do-hi du-ne-lv-gi ga-li-quo-gi gu-gu a-de-lv-da-lo-ni-ge di-go-tlv-ta-nv-hi, di-ka-li-i U-ne-la-nv-hi u-ta-la-wo-hi-s-di ge-sv-i, na-s-gi ni-go-hi-lv a-le ni-go-hi-lv tse-ha.
|
|---|
|
|---|
| And the temple was filled with smoke from the glory of God, and from his power; and no man was able to enter into the temple, till the seven plagues of the seven angels were fulfilled.
|
|---|
| ᏗᎦᎳᏫᎢᏍᏗᏱᏃ ᎤᎧᎵᏨᎩ ᏧᎧᎵᏨᎩ ᏧᎦᏒᏍᏗ ᎤᏁᎳᏅᎯ ᎠᏥᎸᏉᏗᏳ ᎨᏒ ᎠᎴ ᎤᎵᏂᎩᏗᏳ ᎨᏒ ᏅᏓᏳᏓᎴᏅᎯ; ᎥᏝ ᎠᎴ ᎩᎶ ᏫᎬᏩᏴᏍᏗ ᏱᎨᏎ ᏗᎦᎳᏫᎢᏍᏗᏱ, ᎬᏂ ᎾᏍᎩ Ꮎ ᎦᎵᏉᎩ ᏧᏕᏯᏙᏗ ᎨᏒ ᎦᎵᏉᎩ ᎢᏯᏂᏛ ᏗᏂᎧᎿᎭᏩᏗᏙᎯ ᏥᏚᎾᏒᎦᎸ ᏱᏚᎵᏍᏆᏛ.
|
|---|
Di-ga-la-wi-i-s-di-yi-no u-ka-li-tsv-gi tsu-ka-li-tsv-gi tsu-ga-sv-s-di U-ne-la-nv-hi a-tsi-lv-quo-di-yu ge-sv a-le u-li-ni-gi-di-yu ge-sv nv-da-yu-da-le-nv-hi; v-tla a-le gi-lo wi-gv-wa-yv-s-di yi-ge-se di-ga-la-wi-i-s-di-yi, gv-ni na-s-gi na ga-li-quo-gi tsu-de-ya-do-di ge-sv ga-li-quo-gi i-ya-ni-dv di-ni-ka-hna-wa-di-do-hi tsi-du-na-sv-ga-lv yi-du-li-s-qua-dv.
|
|---|


LA, GET READY FOR WORLDPRIDE!
LA Pride announces bid to host WorldPride 2025 in Los Angeles.
November 5, West Hollywood: Today, Christopher Street West (CSW) – the 501(c)3 non-profit that produces the annual LA Pride Festival and Parade – announced an official bid to host WorldPride in Los Angeles for 2025. As the first permitted pride parade in the world, LA Pride has been advocating for LGBTQ+ rights and progressive political change for 50 years in 2020.
“Los Angeles is a global city where we honor our unique past and work tirelessly to imagine a better future for all,” said Estevan Montemayor, CSW board president. “LA Pride celebrates the city’s diverse experiences ensuring all communities are represented, and we are proud to announce our bid to host WorldPride Los Angeles 2025. We are confident that the vibrant infrastructure of Los Angeles is the perfect host city to welcome the world for WorldPride 2025.”
Los Angeles is a city of immigrants representing a diverse array of languages, ethnicities, religions, and cultures who have created the fabric of its dynamic and vibrant community. Together, the community champions human rights for all people, regardless of race, gender, and sexuality. Viewed by many as the entertainment capital of the world, Los Angeles is gearing up to host multiple monumental events over the next decade garnering exciting attention and spotlighting the trailblazing city.
“Given the transformation our city continues to undergo as well as a perfect score of 100 on the HRC Equality Index, Los Angeles stands out as the ideal global destination to host WorldPride 2025,” stated Don Skeoch, Chief Marketing Officer of the Los Angeles Tourism & Convention Board.
LA Pride, also known as the LA Pride Festival and Parade, is an annual LGBTQ+ pride event that takes place in West Hollywood, California. The event is organized by the Christopher Street West Association, an LGBT non-profit human rights organization in the Greater Los Angeles Area.


Hi there!
My name is Andy from Moringa School
This site is my web page with the skill set I have been gaining from the skill. I have been learning skills in the following:
I am a young man from Makuyu,KENYA aspiring to bring light to my generation. I love singing,playing the piano and I am a nature person. I love going on nature walks as I find them refreshing


Shrewsbury Town 3 Bristol Rovers 4: Lewis Cox and David Verman analysis - VIDEO
by David VermanWatch Lewis Cox and David Verman analyse Shrewsbury Town's gut-wrenching 4-3 defeat to Bristol Rovers.
Watch Lewis Cox and David Verman analyse Shrewsbury Town's gut-wrenching 4-3 defeat to Bristol Rovers.


New York Times Top Stories
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Kitchen 1
Back Hallway
Great Room 2
Great Room 1
Stair Landing
Walkway
Loft
Kitchen 2
Lower Level
Owner's Bath
Owner's Suite
Top of L.L. Stairs
Laundry
L.L. Bar
Main Bath


--- layout: archive title: Zulip Chat Archive permalink: /stream/113489-new-members/topic/difference.20between.20two.20forms.html ---
What is the difference between this one that compiles
example (h : p ∨ q) : q ∨ p := or.elim h (assume hp : p, show q ∨ p, from or.intro_right q hp) (assume hq : q, show q ∨ p, from or.intro_left p hq)
and this one that doesn't?
example : p ∨ q → q ∨ p := or.elim (assume hp : p, show q ∨ p, from or.intro_right q hp) (assume hq : q, show q ∨ p, from or.intro_left p hq)
there is a missing h
on the first line of the proof
Your second example is equivalent to this:
example (h : p ∨ q) : q ∨ p := or.elim (assume hp : p, show q ∨ p, from or.intro_right q hp) (assume hq : q, show q ∨ p, from or.intro_left p hq) h
and or.elim
takes its arguments in a different order than that
I don't quite get it. How should it look? I don't see why there should be an h
involved at all?
The type of or.elim
is a ∨ b → (a → c) → (b → c) → c
So the first argument of or.elim
should be something of the type p ∨ q
.
But in your second example, the first thing you feed to or.elim
is of the type p → q ∨ p
.
Here is the way to fix your second example.
example : p ∨ q → q ∨ p := λ h, or.elim h (_) (_)
Ah I see. Thanks.
{% endraw %}Last updated: Apr 24 2020 at 19:02 UTC


Semesterly Report
Starting Fall 2019, the Engineering Student Council has released a semesterly report viewable to anyone in the engineering community. We hope that these reports can better address as to what ESC has done in the past semesters.
Starting Fall 2019, the Engineering Student Council has released a semesterly report viewable to anyone in the engineering community. We hope that these reports can better address as to what ESC has done in the past semesters.


Mission Statement
Team Dirt, a Chapter of IMBA, is dedicated to stewardship of sustainable, multiple-use, and purpose built trails, to the preservation of mountain biking access through advocacy and education, and to the promotion of responsible trail use. Team Dirt is a nonprofit, volunteer organization that works in conjunction with the BLM, Starker Forests, OSU College of Forestry, the USFS and other land managers to build and maintain trails throughout Benton County.
Latest News
Please check out the Team Dirt Facebook page at TeamDirtIMBA.
Upcoming Events
Individual Donations
Financial donations are used to purchase essential tools, safety equipment, volunteer day food, and insurance.


Phase 0
Week 1
January 22nd, 2016
This is the first week of Phase 0 at Dev Bootcamp, and so far it has been pretty overwhelming and amazing. I went into this program knowing next to nothing and so far learned a ton in the command crash course and with git.
Diving staight into it, version control gives you control and access to all versions of your work. With version control, you can work on a single project and be able to jump around between a number of different versions of the same project. Then you can compare changes over time, and if others are working on the same project, you can also see who last modified the project and when they last modified it.
Git uses branches to help track changes, when creating and making changes to files, you can make a new branch when making changes so you have multiple versions of the same file. Git then provides a history of the content changes made and can also favilitate collaborative changes to files.
The best reason to use GitHub is the ease of use and the ability to easily share your repositories with others. Using git command-line interface makes it easy to make, edit, and store repostories onto GitHub.


SwiftFortuneWheel
Fortune spinning wheel that supports dynamic content and rich customization.
| Main Features | |
|---|---|
| 🏵 | Dynamic content, support image, and text |
| 🌈 | Appearance customization |
| 🎨 | Drawn and animated using CoreGraphics |
| 🧮 | Dynamic layout |
| 🚀 | Written in Swift |
Getting Started
Installation
When you are ready to install, follow the Installation Guide.
Documentation
You can find the docs here. Documentation is generated with jazzy and hosted on GitHub-Pages.
Requirements
| App name | Swif | Xcode | Platforms |
|---|---|---|---|
| current version | Swift 5.0 | Xcode 11 | iOS 9.0 |
Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.
Inspiration
Author
Sherzod Khashimov


Jerk Phace
My Portfolio
About Me
My Game
Game
Contact Information:
Email<EMAIL_ADDRESS>Phone:<PHONE_NUMBER>
Pager:<PHONE_NUMBER>
Address: 27437 Poop road, Porland Or


Graduation 2017
Let the Dutch Oven Bakery help you with all of your graduation needs. Please call or visit either of our locations to order today!
Let the Dutch Oven Bakery help you with all of your graduation needs. Please call or visit either of our locations to order today!
Our cakes are available in white, chocolate, marble, or red velvet and frosted with our own buttercream icing. Prices may vary depending on the decorations selected. If you're watching costs, consider having a decorated centerpiece cake with plain iced sheet cakes to serve from. Cakes are scored in 2x3 or 2x2 inch pieces with an icing border.
| Size | 2x3 Serves | 2x2 Serves | Decorated | Plain Iced |
|---|---|---|---|---|
| 1/4 Sheet | 16 | 24 | $21 | $16 |
| 1/3 Sheet | 20 | 32 | $26 | $20 |
| 1/2 Sheet | 32 | 48 | $35 | $27 |
| 2/3 Sheet | 40 | 64 | $47 | $37 |
| Full Sheet | 64 | 96 | $57 | $47 |
Our regular "Dutch Oven Cookies" come in 6 different flavors:
Our decorated cookies and cupcakes are frosted with our buttercream icing. Prices may vary.
| Product | Price |
|---|---|
| Dutch Oven Cookies | $4.59/dozen |
| 3" Decorated Cookies | $1.19 and up |
| 5" Decorated Cookies | $2.59 and up |
| Cupcakes | $1.29 and up |
Our buns are made from scratch! Available in white or wheat.
| Product | Price |
|---|---|
| Mini Buns | $2.99/dozen |
| Hamburger Buns | $3.89/dozen |
.25 extra per dozen for slicing
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Import, filter, normalize and plot cells from human hematopoiesis scATACseq dataset
Compute gene activity scores and check known markers
Run chromVAR to identify TF activity in each cell cluster
The data comes from Satpathy et al., Nat. Biotechnol. 2019 (DOI: 10.1038/s41587-019-0206-z).
Counts data is available from GEO (GSE: GSE129785
).
Download the files related to scATACseq of all human hematopoiesis cells.
mkdir -p data/Homework_3/scATAC/
curl https://ftp.ncbi.nlm.nih.gov/geo/series/GSE129nnn/GSE129785/suppl/GSE129785_scATAC-Hematopoiesis-All.cell_barcodes.txt.gz -o data/Homework_3/scATAC/barcodes.tsv.gz
curl https://ftp.ncbi.nlm.nih.gov/geo/series/GSE129nnn/GSE129785/suppl/GSE129785_scATAC-Hematopoiesis-All.mtx.gz -o data/Homework_3/scATAC/matrix.mtx.gz
curl https://ftp.ncbi.nlm.nih.gov/geo/series/GSE129nnn/GSE129785/suppl/GSE129785_scATAC-Hematopoiesis-All.peaks.txt.gz -o data/Homework_3/scATAC/features.tsv.gz
Notice how the count matrix is in a different format than previously seen.
You will frequently encounter this type of situation. Analysis packages often provide
import functions for the different input formats. Check Seurat
documentation if needed,
to identify which function can be used to import a mtx
format.
Seurat::Read10X('data/Homework_3/scATAC/') cnts <-
Does it work? Why? The most likely reason is that the count matrix is not
structured exactly like the one which comes straight out of a cellranger count
pipeline. It is a very frequent issue occurring when one attempts to analyse
public data.
The alternative, longer route is to import each file separately,
make sure they are compatible with each other, and merge them
manually into a SeuratChromatinAssay
.
## - Load required libraries
library(tidyverse)
library(plyranges)
library(Seurat)
library(Signac)
## - Import counts, barcodes and features separately
file.path('data/Homework_3/scATAC/', "barcodes.tsv.gz")
barcode <- file.path('data/Homework_3/scATAC/', "features.tsv.gz")
features <- file.path('data/Homework_3/scATAC/', "matrix.mtx.gz")
matrix <- Matrix::readMM('data/Homework_3/scATAC/matrix.mtx.gz')
cnts <- vroom::vroom('data/Homework_3/scATAC/barcodes.tsv.gz') %>% as.data.frame()
barcodes <- readLines('data/Homework_3/scATAC/features.tsv.gz')[-1] %>%
features <- str_replace('_', ':') %>%
str_replace('_', '-') %>%
GRanges() %>%
mutate(peak = as.character(.) %>% str_replace(':', '-'))
## - Make sure that the row/colnames for the count matrix match those of
# barcodes and features
colnames(cnts) <- rownames(barcodes) <- barcodes$Group_Barcode
rownames(cnts) <- names(features) <- features$peak
## - Get human gene annotations (hg19/GRCh37) to feed it into the future `SeuratChromatinAssay`
GetGRangesFromEnsDb(ensdb = EnsDb.Hsapiens.v75::EnsDb.Hsapiens.v75)
annotations <-seqlevelsStyle(annotations) <- 'UCSC'
genome(annotations) <- "hg19"
## - Create Chromatin Assay
Signac::CreateChromatinAssay(
assay <-counts = cnts,
ranges = features,
genome = "hg19",
min.cells = 10,
min.features = 10,
annotation = annotations
) Seurat::CreateSeuratObject(
hemato <-counts = assay,
assay = 'ATAC',
meta.data = barcodes
)
You will find that CreateChromatinAssay()
function takes a long time to run.
This is because it internally computes metrics from the count matrix. You can
check the cell metadata to see which ones were calculated (on top of the ones
which come from the barcodes
data frame).
head(hemato[[]])
colnames(hemato[[]])[!colnames(hemato[[]]) %in% colnames(barcodes)]
Is the fragments
file (which should exist for any scATACseq experiment) available
for this experiment? Check the related GEO webpage.
For which analysis are the fragments
essential, exactly? Can we still
perform normalization/clustering/annotation without them? And motif enrichment analysis?
Since we don’t have the fragments
file at hand, most of the QC steps are not
available (e.g. TSSEnrichment
, NucleosomeSignal
or fragment size distribution).
(Note that the fragments
file is actually available as raw data from GEO.
Feel free to download it and perform further QC).
Still, we do have other pre-computed metrics (e.g. FRIP
and depth
).
Check these metrics and filter the Seurat
object (cells and features)
as deemed appropriate.
## - Filter data
quantile(hemato$depth, seq(0, 1, 0.1))
quantile(hemato$nFeature_ATAC, seq(0, 1, 0.1))
quantile(hemato$nCount_ATAC, seq(0, 1, 0.1))
quantile(hemato$FRIP, seq(0, 1, 0.1))
subset(hemato, subset = depth > 5000 & depth < 200000)
hemato <- subset(hemato, subset = nCount_ATAC > 3000 & nCount_ATAC < 100000)
hemato <- subset(hemato, subset = nFeature_ATAC > 1000 & nFeature_ATAC < 20000)
hemato <- subset(hemato, subset = FRIP > 0.30 & FRIP < 20000)
hemato <-
## - Remove peaks with low coverage
hemato[rowSums(GetAssayData(hemato, slot = "counts")) > 10, ]
hemato <- hemato[rowSums(GetAssayData(hemato, slot = "counts") > 0) > 10, ] hemato <-
Now that the dataset is filtered, one can normalize (by using TF-IDF approach) then further reduce the dimensionality for visualization purposes.
## - Normalize data
Signac::RunTFIDF(hemato)
hemato <-
## - Reduce dimensionality
Signac::FindTopFeatures(hemato, min.cutoff = 'q50')
hemato <- Signac::RunSVD(hemato)
hemato <-
## - Label clusters according to the original publication
c(
clusters <-'Cluster1' = 'HSC',
'Cluster2' = 'MEP',
'Cluster3' = 'CMP',
'Cluster4' = 'LMPP',
'Cluster5' = 'CLP',
'Cluster6' = 'Pro-B',
'Cluster7' = 'Pre-B',
'Cluster8' = 'GMP',
'Cluster9' = 'MDP',
'Cluster10' = 'pDC',
'Cluster11' = 'cDC',
'Cluster12' = 'Mono.',
'Cluster13' = 'Mono.',
'Cluster14' = 'Nai. B',
'Cluster15' = 'Mem. B',
'Cluster16' = 'Plasma',
'Cluster17' = 'Baso.',
'Cluster18' = 'Imm. NK',
'Cluster19' = 'Mat. NK',
'Cluster20' = 'Mat. NK',
'Cluster21' = 'Nai. CD4',
'Cluster22' = 'Nai. CD4',
'Cluster23' = 'Nai. Treg',
'Cluster24' = 'Mem. CD4',
'Cluster25' = 'Treg',
'Cluster26' = 'Nai. CD8',
'Cluster27' = 'Nai. CD8',
'Cluster28' = 'Nai. CD8',
'Cluster29' = 'Mem. CD8',
'Cluster30' = 'Mem. CD8',
'Cluster31' = 'Gamm. Del. T'
)$renamed_clusters <- clusters[hemato$Clusters]
hemato
## - Visualize data
Seurat::RunUMAP(hemato, reduction = 'lsi', dims = 2:30)
hemato <- DimPlot(hemato, group.by = 'renamed_clusters') + coord_fixed(ratio = 1) p <-
What can you observe in the UMAP
projection of the dataset? Comment on the
separation of some cell types into different spatially-resolved clusters.
Here again, the absence of fragments
complicates the computation of gene activity scores.
Indeed, Signac’s GeneActivity()
function does require scATACseq fragment information.
Without actual fragments, can you think of a way to still compute a ‘gene activity’-like score, approximating ~ the sum of scATACseq fragments overlapping a gene?
One way to do this is to:
cell + gene
and sum up the peak counts for each pairSeurat
assay containing “gene activity”-like scores and normalize it## - Extend all the genes in hg19 so that it covers 1000 bp upstream of the genebody
ensembldb::genes(EnsDb.Hsapiens.v75::EnsDb.Hsapiens.v75) %>%
hg_genes_extended <- filter(gene_biotype == 'protein_coding') %>%
keepStandardChromosomes(pruning.mode = 'coarse') %>%
filter(seqnames != 'MT') %>%
resize(width = width(.) + 2000, fix = 'start') %>%
shift(1000 * ifelse(strand(.) == '+', -1, 1)) %>%
mutate(id = 1:length(.)) %>%
mutate(gene_name = make.unique(gene_name))
seqlevelsStyle(hg_genes_extended) <- 'UCSC'
## - Find overlaps between all the scATAC peaks and the extended genes
findOverlaps(query = granges(hemato), subject = hg_genes_extended)
ov <-
## - For each peak-gene pair, add peak name and gene name
as_tibble(ov) %>%
ov <- dplyr::rename(peaknb = queryHits, genenb = subjectHits) %>%
mutate(gene_name = hg_genes_extended$gene_name[genenb]) %>%
mutate(row = granges(hemato)$peak[peaknb])
This step requires to transform the original counts
SparseMatrix into a tidy
data.table
, to enable fast left_join
between (1) peak counts / cell and
(2) gene <-> peak relationship table (ov
)
## - Extract counts for the gene-associated peaks
GetAssayData(hemato, 'counts')[unique(ov$peaknb), ]
cnts <-
## - Transform the cnts SparseMatrix into a tidy dense long data.frame
broom::tidy(cnts)
tidy_cnts <-
## - Transform the dense long data.frame into a data.table for faster computation
dtplyr::lazy_dt(tidy_cnts)
tidy_cnts <-
## - For each peak/cell count value, associate the corresponding gene
left_join(tidy_cnts, ov, by = 'row')
joint_data <-class(joint_data)
## WATCH OUT: You cannot see what is stored into `joint_data` as it
## is a deferred data.table, i.e. it will only execute once you pipe it to
## `as_tibble()`. Once you pipe it, the computation starts and takes a lot of time.
## - Group the joint_table by gene + cell and sum up all the counts
group_by(joint_data, column, gene_name) %>%
gene_averaged_cnts <- summarize(n = sum(value)) %>%
as_tibble()
## - To transform the data.table into SparseMatrix, it needs to be numerical
data.matrix(gene_averaged_cnts)
gene_averaged_cnts_num <-
## - Transform into sparse matrix
Matrix::sparseMatrix(
gene_averaged_cnts_sparse <-i = gene_averaged_cnts_num[, 'gene_name'],
j = gene_averaged_cnts_num[, 'column'],
x = gene_averaged_cnts_num[, 'n'],
)
## - Add back the actual cell and genes names
colnames(gene_averaged_cnts_sparse) <- sort(unique(gene_averaged_cnts$column))
rownames(gene_averaged_cnts_sparse) <- sort(unique(gene_averaged_cnts$gene_name))
## - Create new assay in hemato
'GENE']] <- CreateAssayObject(counts = gene_averaged_cnts_sparse)
hemato[[
## - Normalize this new assay with SCTransform
SCTransform(hemato, assay = 'GENE')
hemato <-
## - Save the resulting object!
saveRDS(hemato, 'data/Homework_3/scATAC/hemato.rds')
Now the Seurat
object contains 3 assays, the first one corresponding to the original scATACseq counts,
and the second one containing gene activity pseudocounts, and the third one containing SCT
-normalized
data. One can now perform “gene differential expression”-like analysis using the SCT
assay!
## - Reduce dimensionality to visualize cells in 2D
RunPCA(hemato) %>% RunUMAP(reduction = 'pca', dims = 1:50)
hemato <-
## - Find gene markers for Basophils
DefaultAssay(hemato) <- "SCT"
FeaturePlot(hemato, features = 'CD14', reduction = "umap") # Or NCR1 (NKp46), CD8A, CD14, MS4A1
p <-Idents(hemato) <- hemato$renamed_clusters
Seurat::FindMarkers(
basophils_genes <-object = hemato,
assay = 'GENE',
ident.1 = 'Baso.',
ident.2 = unique(Idents(hemato)[Idents(hemato) != 'Baso.']),
min.pct = 0.15,
only.pos = TRUE
)
chromVAR
works with raw counts stored as a RangedSummarizedExperiment
. By now, you should be able to
easily extract the raw counts from a specific Seurat
assay. Store it in a RangedSummarizedExperiment
(~ equivalent to Seurat
assay but in Bioconductor
).
What are the dimensions of the RangedSummarizedExperiment
generated? What are the features and what are the columns?
library(SummarizedExperiment)
SummarizedExperiment(
sumExp <-assays = list('counts' = GetAssayData(hemato, assay = 'ATAC', slot = 'counts')),
rowRanges = GetAssayData(hemato, assay = 'ATAC', slot = 'ranges'),
colData = hemato[[]]
)dim(sumExp)
This step is important to correct GC bias associated with each peak.
chromVAR::addGCBias(sumExp, genome = BSgenome.Hsapiens.UCSC.hg19::BSgenome.Hsapiens.UCSC.hg19) sumExp <-
Using motifmatchr
package, one can map a list of motifs (e.g. from JASPAR database)
over a RangedSummarizedExperiment
(or anything which can be coerced to a GRanges
, actually).
First, you need to import motifs from a public database in R
. This can be done
(among other ways) with the TFBSTools
pacakge.
## - Import motifs from JASPAR database
TFBSTools::getMatrixSet(
motifs <-::JASPAR2020,
JASPAR2020list(species = 9606, all_versions = FALSE)
)names(motifs) <- TFBSTools::name(motifs)
Now map the subset of motifs of interest to the filtered peaks, using motifmatchr::matchMotifs()
.
Read matchMotifs()
documentation and run it on your sumExp
. What is the class of the generated
object? Its dimensions? What are the features and what are the columns?
library(BiocParallel)
register(MulticoreParam(workers = 16, progressbar = TRUE))
motifmatchr::matchMotifs(motifs, sumExp, genome = BSgenome.Hsapiens.UCSC.hg19::BSgenome.Hsapiens.UCSC.hg19)
motif_mappings <-class(motif_mappingss)
dim(motif_mappings)
Because there are so many motifs and so many peaks, and in interest of time,
we will perform chromVAR
analysis on a subset of motifs only. You can manually filter the motifs
list to motifs that may be relevant in our context (e.g. EOMES
, or GATA2
, …).
## - Filter to interesting motifs to keep analysis relatively quick
# Feel free to select any TF of interest!
motif_mappings[, c('EOMES', 'GATA2', 'IRF8', 'EBF1', 'SPI1', 'TCF3', 'CEBPA')] motif_mappings_sub <-
chromVAR
’s computeDeviations()
function combines (1) the peak counts / cell (stored here in sumExp
) and
(2) the TF mapping over each peak (stored here in motif_mappings
).
to assess the mapping deviation for each TF over each cell compared to other cells.
Run computeDeviations()
on the set of peaks and motifs. What is the class of the generated
object? Its dimensions? What are the features and what are the columns?
## - Find background signal
chromVAR::getBackgroundPeaks(object = sumExp)
bg <-## - For each motif, compute its mapping deviation over the filtered peaks
chromVAR::computeDeviations(object = sumExp, annotations = motif_mappings_sub, background_peaks = bg) motif_deviations <-
The motif_deviations
object can be added as a new assay to the
hemato
Seurat
object. This way, one can rely on Seurat
-based
plotting functions to plot cells in their preferred dimensional space, and color
them using motif deviation scores computed with chromVAR
.
'MOTIF']] <- Seurat::CreateAssayObject(counts = chromVAR::deviationScores(motif_deviations))
hemato[[DefaultAssay(hemato) <- 'MOTIF'
lapply(rownames(hemato), function(motif) {
list_p <-FeaturePlot(hemato, features = motif, reduction = "umap") +
scale_colour_gradientn(
colors = c('#190886', '#6F07F8', '#F954A5', '#FF9D66', '#edf118'),
limits = c(-5, 5), oob = scales::squish
+
) theme(aspect.ratio = 1)
})length(list_p) + 1]] <- DimPlot(hemato, group.by = 'renamed_clusters', reduction = "umap") + theme(aspect.ratio = 1)
list_p[[ cowplot::plot_grid(plotlist = list_p) p <-
Plot side-by-side:
EBF1
gene activity per cellEBF1
motif enrichment (i.e. TF activity) per cell c(
labels <-'HSC',
'MPP',
'LMPP',
'CLP',
'Pro-B',
'Pre-B',
'Nai. B',
'Mem. B',
'Plasma'
)$b_cells_annots <- ifelse(hemato$renamed_clusters %in% labels, hemato$renamed_clusters, NA)
hemato DimPlot(hemato, group.by = 'b_cells_annots', reduction = "umap") + theme(aspect.ratio = 1) + scale_colour_discrete(na.value = '#aaaaaa')
p1 <-DefaultAssay(hemato) <- 'SCT'
FeaturePlot(hemato, features = 'EBF1', reduction = "umap") +
p2 <- scale_colour_gradientn(
colors = c('#dadad8', '#f1eca3', '#e0c067', '#db8035', '#a3160c'),
limits = c(0, 3), oob = scales::squish
+
) theme(aspect.ratio = 1)
DefaultAssay(hemato) <- 'MOTIF'
FeaturePlot(hemato, features = 'EBF1', reduction = "umap") +
p3 <- scale_colour_gradientn(
colors = c('#190886', '#6F07F8', '#F954A5', '#FF9D66', '#edf118'),
limits = c(-5, 5), oob = scales::squish
+
) theme(aspect.ratio = 1)
cowplot::plot_grid(p1, p2, p3, ncol = 3) p <-


This is my take about the article from Phillip Starke about roadmaps, and particularly about avoiding exact dates when giving estimates.
I couldn’t agree more with Phillip about exact dates:
In recognizing that we cannot predict the future, I encourage you to avoid exact dates wherever possible.
You cannot predict the future, and while software development claims it’s a “science”, I often see some black magic when teams try to guess a delivery date.
Agile to the rescue
That’s one of the goals of Agile, to break the famous “Iron Triangle” of project management.
Since it is almost impossible to deliver a project on time, on budget and with full scope, Agile offers to take the scope as the main variable and prioritize the value delivered to the customer.
Then, in the Agile toolbox, we have some practices around estimating, such as the now famous Planning Poker, but it’s still based on gut feelings. Gut feeling is subjective and situative.
It’s all about communication (again)
Giving the customer a delivery date is often a source of stress, because you can’t be wrong. If you are late, either you’ll have to:
- Pay for the extra costs of the delay
- Or ask your customer to do so, which is not an easy ask
But how to easily engage in a fair discussion about the delivery dates.
After some missing deadlines on my projects, I started to simply give a range instead of an exact date.
I think we can deliver this project between the 1st and the 20th of June
Exact dates look like contracts, and they seem immutable, you can’t change them.
While giving a range of dates, you already express some doubts, and it shows your customer that there is already a part of uncertainty.
Statistics FTW!
While this first approach has improved my way of announcing delivery dates, since it sparks interesting discussions with the customer, it was perfectible.
You probably already know that you need to check the past to predict the future. Scrum tells us that with the Velocity: by measuring each increment on each Sprint, the promise is to forecast what we could ship on the next Sprint.
While I find it great in theory, it’s hard to apply, especially when starting on a new project, or when the team changes.
And I got my Ah Ah moment when I attended a Lean conference a while ago.
The idea is simple, and it’s effective to communicate delivery dates to the customer: just add a percentage to your estimates:
There is an 80% chance that we’ll reach the goal between 4 and 5 months.
Yes, simple as that.
And if the customer has a hard time understanding the uncertainty, I usually say: “There is a 100% chance that we’ll reach the goal between tomorrow and 20 years from now.”
Usually, we find an agreement 😜
Header photo by Harrison Haines


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Hanging out with @1967mustangman at our place. Come on over! 11/13/2009, 7:40:36 PM Favs: 0 Retweets: 0 link


CodeGen supports custom conditional expressions in field loops via the <IF CUSTOM_expression> template_code </IF> and <IF NOT_CUSTOM_expression> template_code </IF> tags. Custom field loop expressions test the presence or absence of some piece of text in a fields user text or long description.
There are two types of custom field loop expressions.
Multi-Line Custom Field Loop Expressions
Multi-line custom field loop expressions occur when the opening and closing conditional tags appear on separate lines in a template file, and delimit one or more entire lines of template code, like this:
<FIELD_LOOP>
[code]
<IF CUSTOM_expression>
conditional code
[<ELSE>
conditional code]
</IF[ CUSTOM_expression]>
[code]
</FIELD_LOOP>
Multi-line custom field loop expressions cause one or more lines of output code to be included or excluded, based on the evaluation of the expression.
In-Line Custom Field Loop Expressions
In-line custom field loop expressions occur when the opening and closing conditional tags appear on the same line in a template file, and delimit part of a line of template code, like this:
<FIELD_LOOP>
[code]
[code] <IF CUSTOM_expression> conditional code </IF[ CUSTOM_expression]> [code]
[code]
</FIELD_LOOP>
In-line custom field loop expressions cause part of an output line to be included or excluded based on the evaluation of the expression.
Nested Expressions
Multi-line expressions may be nested within one another, and in-line expressions may be nested within multi-line expressions. However, in-line expressions may not be nested inside one another.
Custom Field Loop Expression Example
An example of using a custom field loop expression might be if you wanted to arbitrarily exclude certain fields in a structure from being processed within a field loop. You could decide to add the text IGNORE_ME to the fields user text, or to the fields long description. You could then test the presence, or absence of this string with custom expression tags, like this:
<FIELD_LOOP>
<IF CUSTOM_IGNORE_ME>
code for fields WITH the custom text
</IF>
<IF CUSTOM_NOT_IGNORE_ME>
code for fields WITHOUT the custom text
</IF>
</FIELD_LOOP>
Copyright © 2021 Synergex International, Inc.


The Open Weed Science accepts contents from weed scientists around the globe to be published in the website, https://www.openpweedsci.org/.
We accept contents that improve our knowledge and reproducibility in weed research topics related to:
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Data analysis
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Data visualization
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Teaching materials
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Statistics
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Modeling
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Data science
To help ensure the quality of these materials, we ask that they be peer-reviewed by qualified members of the Open Weed Science community. Moreover, contributors could share data analysis published in a peer-reviewed journal.
How to contribute?
Section under develepment
You can E-mail: openweedsci@gmail.com


People with anxiety disorders respond to certain objects or situations with fear and dread or terror. Anxiety disorders include generalized anxiety disorder, social anxiety, panic disorders, and phobias.
Attention-deficit/hyperactivity disorder (ADHD) is one of the most common childhood mental disorders. It can continue through adolescence and adulthood. People diagnosed with ADHD may have trouble paying attention, controlling impulsive behaviors (may act without thinking about what the result will be), or be overly active.
Behavioral disorders involve a pattern of disruptive behaviors in children that last for at least 6 months and cause problems in school, at home, and in social situations. Behavioral symptoms can also continue into adulthood.
While bad moods are common, and usually pass in a short period, people suffering from mood disorders live with more constant and severe symptoms.  It is estimated that 1 in 10 adults suffer from some type of mood disorder, with the most common conditions being depression and bipolar disorder. With proper diagnosis and treatment, most of those living with mood disorders lead healthy, normal and productive lives.
Eating disorders involve obsessive and sometimes distressing thoughts and behaviors, including: overeating or undereating, feelings of depression or distress, and concern about weight, body shape, and poor self image.
Common types of eating disorders include anorexia, bulimia, and binge eating.
People with personality disorders have extreme and inflexible personality traits that cause problems in work, school, or social relationships. Personality disorders include antisocial personality disorder and borderline personality disorder.
A person can get PTSD after living through or seeing a traumatic event, such as war, a hurricane, physical abuse, or a serious accident. PTSD can make someone feel stressed and afraid after the danger is over. People with PTSD may experience symptoms like reliving the event over and over, sleep problems, become very upset if something causes memories of the event, constantly looking for possible threats, and changes in emotions like irritability, outbursts, helplessness, or feelings of numbness.
People with psychotic disorders hear, see, and believe things that aren’t real or true. They may also show signs of disorganized thinking, confused speech, and muddled or abnormal motor behavior. An example of a psychotic disorder is schizophrenia. People with schizophrenia may also have low motivation and blunted emotions.
Substance use disorders occur when frequent or repeated use of alcohol and/or drugs causes significant impairment, such as health problems, disability, and failure to meet major responsibilities at work, school, or home. Substance use problems can be fatal to the user or others. Examples include drunk driving fatalities and drug overdoses. Mental illnesses and substance use disorders often occur together. Sometimes one disorder can be a contributing factor to or can make the other worse. Sometimes they simply occur at the same time.


It’s no secret that the digital industry is booming. from exciting startups to global brands, to the new companies are reachin boomingesagencies, responding to the new possibilities available. however, the industry is exciting fast becoming overcr heaving with goloballife agencies offering similar services.
It’s no secret that the digital industry is booming. from exciting startups to global brands, to the new companies are reachin boomingesagencies, responding to the new possibilities available. however, the industry is exciting fast becoming overcr heaving with goloballife agencies offering similar services.
It’s no secret that the digital industry is booming. from exciting startups to global brands, to the new companies are reachin boomingesagencies, responding to the new possibilities available. however, the industry is exciting fast becoming overcr heaving with goloballife agencies offering similar services.


Select this option if you want IntelliJ IDEA to emulate the behavior when Subversion commands are executed
directly from the terminal in the interactive mode (dialogs will pop up where you can input credentials). This is
required to handle password/passphrase prompts for svn+ssh repositories, and trust invalid server certificates for
https repositories.


This is an example shows how IE6 can suddenly decide to ignore several lines of CSS when you switch the encoding of your HTML. You are looking at the ISO-8859-1 version. Check what happens when we switch to UTF-8 in IE6.
Read the article


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--- title: FAQs page_id: faqs layout: page nav_level: ---
No, we do not. We assist with the other costs and difficulties associated with getting an abortion, including transportation and accommodation.
Yes, we most definitely are. We are fully aware that people of all kinds experience unwanted pregnancies and are supportive of choice. We go out of our way to ensure that everyone feels comfortable, understood, and supported.
Unfortunately, those under the age of 18 must notify their parent or guardian in order to get an abortion under the law. For minors choosing to access abortion who do not want to tell their parents, the ACLU Judicial Bypass Project can help.
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These are my personal thoughts about the United States banning TikTok.
As you may have already heard, some congressional leaders have proposed the ban of a Chinese based social media application called TikTok.
There is fear, with some truth mixed in, that TikTok is a danger to United States national defense. There are many reasons congressional, military, and other policy leaders fear TikTok, but the common theme seems to be related to data collection on American citizens and the potential of manipulating viewpoints in favor of the Chinese government.
Continue reading


Southampton, United Kingdom, Jan 19 – Southampton will be at home to holders Arsenal in the fourth round of the FA Cup following a 2-0 win over coronavirus-hit Shrewsbury on Tuesday.
Dan N’Lundulu marked his first senior start for the Saints with a maiden goal for the club in the 17th minute at their St Mary’s ground before England’s James Ward-Prowse put the result beyond doubt a minute from time.
The Saints will have little time to enjoy this victory as their tie with Arsenal, a repeat of the 2003 FA Cup final won 1-0 by the Gunners, takes place at St Mary’s on Saturday.
Southampton manager Ralph Hasenhuttl, who made seven changes to the side that lost 2-0 to Premier League leaders Leicester, praised his youthful side while accepting a match against Arsenal would be “tougher” for the south coast club.


--- layout: archive title: Zulip Chat Archive permalink: /stream/113489-new-members/topic/leanpkg.20issues.20(no.20more.20issue).html ---
Hi,
I am having issues with lean on vscode; I have aleanpkg.path
doesn't exist error, and then a failed to start child process
once vscode tries to fix it. How do I get lean up and running?
How did you install lean? Did you see the installation instructions: https://github.com/leanprover-community/mathlib/tree/master/docs/install
No more issues now, Kevin has helped me by just getting lean project up and running and now no issues persist!
After having installed mathlib
on Linux, all seemed fine (i.e. I could use it) until today I tried to update:
./update-mathlib Traceback (most recent call last): File "./update-mathlib", line 6, in <module> from github import Github File "/home/dan/.local/lib/python3.7/site-packages/github/__init__.py", line 44, in <module> from github.MainClass import Github, GithubIntegration File "/home/dan/.local/lib/python3.7/site-packages/github/MainClass.py", line 57, in <module> import jwt ModuleNotFoundError: No module named 'jwt'
Anyone can advise? I think I used update-mathlib
before on this machine, but am not quite 100% positive.
Never mind, figured it out.
It was not a mathlib
issue.
Just to let you know that there is now leanproject
which supercedes leanpkg
and update-mathlib
. You can download it with pip install mathlibtools
or similar -- see the installation instructions for your OS.
Last updated: Apr 24 2020 at 19:02 UTC


This project will help you understand and enjoy your video better!
HI! let's import your video or Youtube link then choose the languages.
or
Auto Detect
Chinese
German
English
French
Italian
Japanese
Korean
Spanish
Thai


Programming Languages
commonly used features in a side-by-side format
| Scripting Languages I: | Node.js, PHP, Python, Ruby |
| Scripting Languages II: | Perl, Lua, Groovy |
| GUI Scripting: | JavaScript, Smalltalk, Tcl, AutoHotkey |
| Command Line Interpreters: | POSIX Shell, Cmd.exe, PowerShell |
| Forth and PostScript: | Forth, PostScript |
| Prolog and Erlang: | Prolog, Erlang |
| Lisp Dialects: | Common Lisp, Racket, Clojure, Emacs Lisp |
| C and Go: | C, Go |
| OO C Style Languages: | C++, Objective-C, Java, C# |
| Rust, Swift, Scala: | Rust, Swift, Scala |
| TypeScript, Dart, Hack: | TypeScript, Dart, Hack |
| Pascal Style Languages: | Pascal, Ada, PL/pgSQL |
| ML Dialects and Haskell: | SML, OCaml, F#, Haskell |
| Numerical Analysis & Statistics: | MATLAB, R, NumPy |
| Computer Algebra Software: | Mathematica, SymPy, GAP, Pari/GP |
| Gnuplot and GLPK: | Gnuplot, GLPK |
| Fortran and APL: | Fortran, APL |
| Relational Data Languages: | SQL, Awk, Pig |
Programming Tools
| Unix Shells: | Bash, Fish, Ksh, Tcsh, Zsh |
| Text Mode Editors: | Vim, Emacs, Nano |
| Version Control: | Git, Mercurial |
| Build Tools: | Make, Rake, Ant, MSBuild |
| Terminal Multiplexers: | Screen, Tmux |
| Relational Databases: | PostgreSQL, MySQL, SQLite |
| Lightweight Markup: | Markdown, reStructuredText, MediaWiki, AsciiDoc, Org-mode |
| 2D Vector Graphics: | PostScript, Processing, SVG |


Based on data scraped from en.wiktionary.org of 7600+ German nouns.
 For information on how these rules were
determined and quality evaluation of the rules refer
here.
Every rule provides a set of endings, which help identify particular
gender. A rule is represented as a table, where headers of the columns
are genders, and column contents are endings which help identify this
gender.
The rule coveres at least {{selected_rule.rule_coverage}} of all German nouns. On the nouns where it
applies, its accuracy is {{selected_rule.rule_accuracy}}%. {{selected_rule.rule_description}}
{{gender}}
{{ending}}
Be careful about the following endings:
{{confuse}}
For a given noun, determine its gender by the ending of the noun. As an answer,
enter "die", "der" or "das" which is a definite nominative article of the noun.
For details on how to determine the grammatical gender, refer to the theory section
of this exercise. To start the exercise, press "Next".


The purpose of this data visualisation is to give insight in the makeup of the television top 50 of the Netherlands. The top 50 consists of the 50 broadcasts with the most viewers.
| Jenny Hasenack |
|---|
| 10367519 |
| Minor programmeren |
| Data processing |
In the upper graph is shown per year which channels had how many contributions to the top 50. The user can choose to view the data of the channels separately (by selecting 'All') or to view the data of the channels grouped into public and commerical channels (by selecting 'Groups'). By clicking the colored rectangles on the legend of this graph, lines can be added and removed.
In the lower graph, the content top 50 of the selected year is shown. The year can be selected by either moving the cursor over the upper graph, or clicking one of the buttons left to the lower graph. By hovering over the circles in the lower graph, more information of that specific broadcast is shown. Placing the cursor on the graph and scrolling enables vertical zooming. The graph can also be dragged vertically. More insight in the makeup of this top 50 can be obtained by clicking one of the buttons 'Channel', 'Time' or 'Day'. When clicked, the circles will be colored according to their value on the respective button subject.


SkillsUSA Florida
at Manatee Technical College
"Gearing up for the right direction!"
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{% extends "base.html" %} {% load i18n %} {% block head_title %}{% trans 'Frequently Asked Questions' %}{% endblock head_title %} {% block content %}
- {% trans "Why have you started UncensoredDNS? Why run uncensored DNS servers?" %}
- {% trans "I am strongly against using DNS as a tool to filter content on the internet. Many people do not have the technical know-how to setup their own DNS server, so I created UncensoredDNS in 2009 as an uncensored alternative to the internet providers filtered DNS servers." %}
- {% trans "Why not just use OpenDNS/Google DNS/Cloudflare/Quad9?" %}
- {% trans "The internet should have more small decentral community-run non-profit services and fewer huge corporations running services with massive userbases. This is also true when picking email/IM/SoMe services." %}
- {% trans "Do the UncensoredDNS DNS servers log any personal information?" %}
- {% trans "Absolutely nothing is being logged, neither about the users nor the usage of this service. I do keep graphs of the total number of queries, but no personally identifiable information is saved. The data that is saved will never be sold or used for anything except capacity planning of the service." %}
- {% trans "Where are the servers located?" %}
- {% trans "See the details on the DNS Servers page. They are placed on fast uplinks in professional data centers. I am actively looking for places to host anycast nodes, especially outside of Denmark or even outside of Europe. Please contact me if you can help." %}
- {% trans "Are the IP addresses likely to change?" %}
- {% trans "The DNS server IP addresses will never change if I can prevent it. The anycast server in particular is unlikely to ever change since the v4 and v6 IPs are from UncensoredDNS' own provider independent IP ranges." %}
- {% trans "Who is behind this service?" %}
{% url 'contact' as contacturl %}
- {% blocktrans with contacturl=contacturl %}My name is Thomas Steen Rasmussen. I operate this DNS service as an individual with my own money. If you have questions or comments please see the contact page.{% endblocktrans %}
- {% trans "Have you ever been contacted by authorities or law enforcement about this service?" %}
- {% trans "No, I have never been contacted by authorities or law enforcement regarding this service. This message will be updated if it happens." %}
{% endblock content %}


Pot Of Quotes
"Wise men learn more from fools than fools from wise men"– Cato
{% block head %}
"Wise men learn more from fools than fools from wise men"– Cato
Some content taken from Wise Words and Quotes by Vern McLellan.
Copyright © 1998. Used by permission of Tyndale House Publisher, Inc.
All rights reserved.


SuperData: Russia to surpass France as third-largest games market in Europe
Growth of mobile, esports, and free-to-play PC games expected to offset end of generation slowdown
by Haydn TaylorRussia is projected to become the third-largest European market this year, growing 57% to surpass France in terms of overall revenue.
That's according to a joint report published by SuperData and Wargaming, which examines the games industry in the Commonwealth of Independent States.
Currently Russia is the fourth-biggest video game market in Europe, contributing 8% ($1.7 billion) of revenue in 2018.
As the current console generation draws to a close, there is a projected decline in market activity across Europe.
Despite this, the Russian game market is expected to grow by 57%, thanks primarily mobile which saw a 51% increase from 2017 to 2018, grossing $561 million.
PC gaming remains the most popular platform in Russia however. Last year, there was an average of 79 million monthly active PC users; this figure is set to grow by 3% in 2019.
Almost half (45%) of PC games revenue comes from free-to-play titles, whereas the overall European games market see just 20% of total revenue come from free-to-play.
The rapid growth of esports is also contributing to Russia's development. According to the report, 64% of Russian gamers watch gaming video content; the most popular esports in Russia are DOTA 2, and Counter-Strike: Global Offensive, which dominate eight out of the top ten streaming channels.
In 2018, the top ten Russian language channels on Twitch accrued 165 million hours watched.


About Us
This website provides comprehensive and up-to-date news as it fetches the data from api and builds the page, so you'll always be in touch with the world happenings. You can subscribe to our premium content to receive news letter weekly.
Get all the latest and breaking news from all the fields over the globe on our website
contact details - write to us at<EMAIL_ADDRESS>Designed By
Vaibhavi Ambarkar
Contact - ambarkarvaibhavi@gmail.com


Referencing a dictionary of binary values: {"A",1: "B":2, "C": 4, "D": 8, ...} resolves two-way mappings "A,C" <=> 5 .
More...
#include <Flags.h>
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typedef std::string | key_t |
| |
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typedef unsigned int | value_t |
| |
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typedef drain::Dictionary2< key_t, value_t > | dict_t |
| |
|
| static value_t | getValue (const dict_t &dict, const key_t &key, char separator=',') |
| | Computes bitwise OR function on the numeric or alphabetic value(s) presented by a string. More...
|
| |
| static std::string | getKeys (const dict_t &dict, value_t, char separator=',') |
| | Given an integer, retrieves dictionary keys corresponding to each index of set bits. More...
|
| |
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static std::ostream & | keysToStream (const dict_t &dict, value_t value, std::ostream &ostr, char separator=',') |
| | List keys in their numeric order.
|
| |
| static value_t | getFreeBit (const dict_t &dict) |
| | Return an interger (bit vector) with a new, previously unused value. More...
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| |
| static value_t | add (dict_t &dict, const dict_t::key_t &key, value_t i=0) |
| | Add a new entry in the dictionary. More...
|
| |
Referencing a dictionary of binary values: {"A",1: "B":2, "C": 4, "D": 8, ...} resolves two-way mappings "A,C" <=> 5 .
◆ add()
| FlagResolver::value_t add |
( |
dict_t & |
dict, |
|
|
const dict_t::key_t & |
key, |
|
|
value_t |
i = 0 |
|
) |
| |
|
static |
Add a new entry in the dictionary.
- Parameters
-
◆ getFreeBit()
| FlagResolver::value_t getFreeBit |
( |
const dict_t & |
dict | ) |
|
|
static |
Return an interger (bit vector) with a new, previously unused value.
Traverses the values in dict, and determines a new bit corresponding to a new value.
◆ getKeys()
| std::string getKeys |
( |
const dict_t & |
dict, |
|
|
value_t |
v, |
|
|
char |
separator = ',' |
|
) |
| |
|
static |
Given an integer, retrieves dictionary keys corresponding to each index of set bits.
List keys in their numeric order.
◆ getValue()
| FlagResolver::value_t getValue |
( |
const dict_t & |
dict, |
|
|
const key_t & |
key, |
|
|
char |
separator = ',' |
|
) |
| |
|
static |
Computes bitwise OR function on the numeric or alphabetic value(s) presented by a string.
- Parameters
-
| key | - String containing keys ("note,error") or values ("4,8,32") or a mixture of them. |
| lenient | - If true, bypassed unknown keys silently, otherwise throws exception |
The documentation for this class was generated from the following files:
- drain/util/Flags.h
- drain/util/Flags.cpp


Whether a business is a small or large entity, what is doing can be molded by an econosystem which is directly influenced by the time evolving microeconomic and macroeconomic indicators, not necessary in a explicit and linear fashion. Staggering amounts of these data economic indicators are accessible as never before from public sources of open data. Thanks to low cost computing power, the processing capabilities of these economic indicator are accessible to users around the world. It's time to enable business entities with comprehensible and efficient algorithms & analytical tools scattering useful intelligence from these data economic indicators.
This called process "Enterprising the Data Economic Indicators" is fundamentally different than what has come before, this process is based on the industrialization of learning through the ever-wider deployment of Machine Learning Tools and as companion to the formal economic models used to inform economic policy in concert with experimentation regarding the design of regulatory frameworks.
Completing and Challenging DSGE Models:
The need to augment the current dynamic stochastic general equilibrium (DSGE) models with a Machine Learning framework that can help us understand the associations(linear & nonlinear) between these indicators that shape the econosystem in an economy appears a reasonable addition to take the lessons of the recent crisis 2007-2008 into account. Following the unexpected sharp decline in real economic activity in the US and around the world in the autumn of 2008 and spring of 2009, a debated question "are DSGE models still useful in policy analysis?" has been addressed in this article: On the future of macroeconomic models.. The article promotes a clear message: "the current DSGE models are flawed, but they contain the right foundations and must be improved rather than discarded". Holistic approach is encouraged, different types of macro models are needed and have to interact.
Machine Learning models are also flexible enough to be used for many purposes, very effective in selecting important indicators from large number of candidate indicators(impossible to do with DSGE framework) and are designed to capture information that is true across the majority of repeated samples. Importantly enough, these flexible machine learning techniques have the potential to capture complex nonlinear interaction between the indicators using only algorithmic concepts and considering the interaction mechanisms as unknown and to be discovered by processing the datasets associated to these indicators.
The nonlinear aspect of these Machine Learning models is very crucial as the monetary policy, mainly conducted by central banks, has an important role in shaping microeconomic & macroeconomic developments into an economical systems affecting both national and around the world levels in an implicit manner with nonlinear feedbacks. For instance, unconventional monetary policy including credit easing, quantitative easing, forward guidance, and signaling.
Only changes in interest rates with a suitable supply-side credit conditions(credit standards) may have a direct effect on the valuation of financial assets and their expected returns, as well as on the consumption and investment decisions of households and corporations. Therefore sound established decisions by credit standards can in turn have desired consequences as a stable financial system enjoying gross domestic product (GDP) growth, more jobs, and controlled inflation.
It is important to evaluate quantitatively how these decisions end up affecting the various economic indicators and their inherent predictability as an EconoSystem to address the economy’s performance. A key goal of adding machine learning framework to DSGE framework is to provide insights about the transmission mechanism of monetary policy and to assess the predictive power of a number of economic indicators representing the state of an economy during a specific period of time. These most predictive indicators have then to be modeled accordingly to the fabrics of the DSGE models, dynamic, stochastic, and characterize the general equilibrium of the economy. This machine learning interactive process with DSGE models makes them less imperialistic and accept sharing the scene with other general purpose approaches to economical modelization.
How to Proceed:
Our quantitative analysis uses a set of mathematical models to evaluate the historical data related to most popular economic indicators in public databases for patterns that help to answer the questions in the "Main Points Section" in terms of numerical values. These numerical values are presented into a form guided by this statement "Translating and analyzing big data using charts, graphics, and images is becoming more and more necessary for decision-making." promoted by the Association of Public Data Users.
We provide:
Interactive Charting features the temporal dynamics of the considered interest rates in this work and to facilitate their comparisons. To find out more about the temporal dynamic of a specific interest rate, a double click on its name in the legend creates
its associated curve and then add up for multiple comparisons.
Economic Indicators Network is the results of our mathematical modeling and subsequent computation to study the numerical behavior of these interest rates. The obtained numerical values are used to explore and gain new insights into the impact of the temporal decision that determined them and to estimate and highlight specific interconnection levels between them. If there exist groups of interest rates highly interconnected but with few links to other interest rates. This kind of structure carry out much information about the network to answer our question quantitatively.
Random Walks on Interest Rates Network, by considering the interest rates network as a Graph (discrete mathematics), the theory of random walk on this graph help to extract basic properties determined by the spectrum of the graph as its community structures and stationary distribution.
Interpretation of stationary distribution values
More the stationary distribution value of a given indicator is higher more this indicator is affecting significantly the values of the considered economic indicators. The indicator in the lowest stationary distribution class have more difficulty to influence significantly other indicator values than the indicator in the highest stationary distribution class.
Detailed Numerical Results: United States of America Economy Case