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Q: Javascript selectors how to only get first ranking a refs Hi there I'm fiarly new to coding. and doing some javascipt layouting. but I need to make some variables. and don't know how. I want to make a dropdown menu. for this site. bertconinx.com now in order to do the thing I want to do I need to be albe to select a href without having to change any html. let the browser go to the link and then execute some javascript so what I need to do is find a way how to select the specific a href. I need to select the head menu intem without selecting any submen Items. because they should be dropdowns on a clickevent.
I got as far as this..
a href="http://bertconinx.com/category/portrets/">PortretsWeddings

should load first and then should dropdown his submenu clickevents.
but slecting the a href seems trick I've tried.
var MenuEvent = document.getElementsByClassName("menu-item").getElementsByTagName("a");

But it does not work.
Any Ideas on how to select them?
thanks.

A: The documentNode.getElementsByClassName returns a list of items, so while you can call .getElementsByTagName on each one, you can't call the function on the list directly.  What you need to do is something like
// initialize an array to hold the anchors we get, and get all menu items on the document
var firstAnchors = [],
    menuItems = document.getElementsByClassName('menu-item');
// iterate through the menu items and their specific anchors, adding only the first one of each menu item to our list
for(var menuItem of menuItems){
    firstAnchors.push(menuItem.getElementsByTagName('a')[0]);
}
// now, our anchors should be here
console.log(firstAnchors);

A: var parentMenus = [],
    parentMenueElems =  document.getElementById("menu-main-nav").children;
for(var parentMenueElem of parentMenueElems){

   if(!parentMenueElem){continue;}

   var parentMenue = {
       "name": parentMenueElem.firstElementChild.text,
       "anchor": parentMenueElem.firstElementChild
   };
   parentMenus.push(parentMenue);
}
console.log(parentMenus)

OutPut:
[
 {"name":"Portrets","anchor":element},
 {"name":"Weddings","anchor":element},
 {"name":"Wild","anchor":element},
 {"name":"Commercial Work","anchor":element},
 {"name":"About","anchor":element}
]
Example: element --> Portrets

Now you can perform any event on element
parentMenus[2].anchor.click();


Unfortunately I don't have a digital camera. Well… even if I did, my old pics would still be just bits of paper….
Anyway — I have spent the past several days scanning in all the photos I can find around the house — and I've only just begun. What an exhausting job!!!


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Shadow Hours
Michael is a recovering addict. Back on the wagon, he's now responsible for a young, beautiful, and pregnant wife. He's working the graveyard shift at a gas station to support his new family, but the job drives him crazy. Then a wealthy stranger, Stuart, enters Michael's life, taking Michael through a tour of the seediest and slimiest parts of L.A. underbelly. Is Stuart leading Michael to hell, or salvation?
Director: Isaac H. Eaton
Actors: Balthazar Getty, Brad Dourif, Clayton Landey, Corin Nemec, Frederic Forrest, Johnny Whitworth, Michael Dorn, Peter Greene, Peter Weller, Rebecca Gayheart
Keywords:123movies 9movies Balthazar Getty Brad Dourif Clayton Landey Corin Nemec Frederic Forrest Free gomovies gostream Johnny Whitworth losmovie Michael Dorn movie25 movie4k movietv Peter Greene Peter Weller primewire putlocker putlockers putlockertv Rebecca Gayheart Shadow Hours Online shockshare sitename solarmovie Watch Shadow Hours watch32 yesmovie
Muhme Mehle
Country: East Germany
A biographical film of Ben Hogan, from his start as a golf caddy to being considered one of golf's greatest players.
The thirteen-part series recounted the lives of the titular Fox family, who lived in Clapham in South London and had gangland connections.
Oxalá
A man exiled in Paris makes various trips to Portugal after the Carnation Revolution. Each trip is represented by a woman.
Bwana Devil
British railway workers in Kenya are becoming the favorite snack of two man-eating lions. Head engineer Bob Hayward becomes obsessed with trying to kill the beasts before they maul everyone…
Genre: Action, Drama
Iago and a comrade-in-arms are outside the Venice home of Desdemona's father, who does not yet know that she has eloped with Othello. Iago confides to his friend — who…
Genre: Drama, TV Movie
Hussy
Beaty, a prostitute, and Emory, a lighting/sound technician, fall in love while working at the same London cabaret, but their relationship isn't easy.
Suspicious River
A young married woman (Parker) sells her body, not just for money, to guests at the motel where she works as a receptionist.
Bill, a retarded man, ventures out into the world for the first time, having spent most of his life in a dreary inner city Institution. He is taken in by…
The Case Is Closed
A pre-teenager servant boy dies of carbon monoxide poisoning on a cold winter night. He was employed by a young working Calcutta couple with a small boy of their own….
Captain Horatio Hornblower R.N.
A naval adventure, based on the novels by C.S. Forester, about the heroic, 19th-century British seafarer. The story sails with his ship, the Lydia, through battles with Spain and then…
Genre: Action, Adventure, Drama, War
The Steel Lady
Surviving a plane crash in the Sahara, four oilmen find and manage to repair a German Afrika Corps tank which had been buried in the sand since WWII.
Trailer: Shadow Hours

redpajama_set_name: RedPajamaCommonCrawl


text:
Texas Railroad Commission seeks to close eminent domain loophole, but critics say rule too vague
Ultimately, it's crucial that the state has some mechanism in place to review whether or not private pipeline companies meet the legal requirements as a common carrier public use pipeline prior to eminent domain authority being conferred.
By Terri Hall l July 30, 2014
The Texas Railroad Commission, that regulates the oil and gas industry, recently proposed a new rule to close the so-called 'T-4' loophole that's long allowed private pipeline companies to claim common carrier status by simply checking a box on a one-page form and gain eminent domain authority. In 2011, the Texas Supreme Court said in its Denbury Green decision that companies need to prove they're truly a public use pipeline, known as a common carrier, before they obtain or exercise eminent domain powers. It quickly became an issue with the Keystone Pipeline, which TransCanada claimed to be a Texas common carrier and used eminent domain for its private international pipeline.
The proposed rule would give the Commission the ability to revoke a permit if a company violates the law, giving the Commission enforcement powers that it currently doesn't have. It would also mandate permits must be renewed on an annual basis. The legislature contemplated at least four bills to fix the problem last year, but none of them passed. While the Railroad Commission rule makes an effort to quell the controversy, it stops short of fixing the problem.
The rule is still too vague, not specifying what documents would be required to prove a company meets the statutory requirement of a common carrier, and lacks a mechanism for landowners to challenge a pipeline that affects their property. For instance, there would be no hearing process before the Commission. The Commission would determine, without public comment or hearing from landowners, whether or not a pipeline meets the classification sought within 45 days of a completed permit application. The rule would apply to new permits, amendments, renewals, and cancellations of existing permits. So existing permit holders would eventually be required to submit to the new renewal process and be subject to the requirements to prove its a common carrier.
The Railroad Commission has a reputation of being too cozy with the industry its supposed to regulate. Commissioners rely heavily on the industry to fund their campaigns. Property rights groups prefer an un-biased, genuine third party, like the State Office of Administrative Hearings, verify whether or not a pipeline meets the statutory requirements of a common carrier.
It appears landowners would still have their day in court to challenge any determination by the Commission, which is costly, but an important protection for property owners to have their day in court preserved. Ultimately, it's crucial that the state has some mechanism in place to review whether or not private pipeline companies meet the legal requirements as a common carrier public use pipeline prior to eminent domain authority being conferred. Since there is nothing in place today, it's forced landowners into expensive legal battles in court – in essence to do the state's job for them. Needless to say, it's not working for landowners or for the companies since it creates a needless, cumbersome and expensive process that protracts the final decision months and even years.
The proposed rule appears in the Texas Register with public comments to be accepted through 12 noon on August 25 on the Commission's web site. Select the rule to amend the T4 pipeline permit procedures and comments should refer to Gas Utilities Docket No. 10366.


text:
The Gifted Earns a Season 2 at Fox
More mutants!
Megan Vick Jan. 3, 2018, 4:00 p.m. PT
The Giftedis coming back for another round!
Fox announced that the X-Men universe superhero show is getting a Season 2 during the first day of the Television Critics Association winter press tour. The Gifted is ranked as the No. 3 new drama of the the 2017-18 television season with an average 2.0/7 in Live + 7 ratings and 8.3 million viewers across platforms.
Looking for your next binge? TV Guide editors handpick the shows that are actually worth watching and deliver daily recommendations straight to your inbox.
The Gifted Is the Best Show You Aren't Watching
It was created by Matt Nix and is coproduced by Marvel Television. X-Men film producers Bryan Singer, Lauren Shuler Donner, Simon Kinberg along with Marvel's Jeph Loeb and Jim Chory also serve as executive producers.
It centers on a group of super-powered mutants trying to survive in a world where the X-Men are in hiding and the government is boldly rounding mutants up with complete disregard for due process.
The series starsTrue Blood alum Stephen Moyer, Amy Acker and Emma Dumont.
The Gifted airs Mondays at 9/8c on Fox.


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Q: How to get specific data of particular city? I have list of cities in database and want to show their specific data in web controllers.
Here, Attach the screenshot and the code which i used.
Controllerrs.py file
from odoo import http

class CurrentWeather(http.Controller):
    @http.route('/current_weather/', auth='public', website='True')
    def index(self, **kw):
        location = http.request.env['weather_location.weather_location']
        return http.request.render('current_weather.index', {
            'locations': location.search([])
            })

    @http.route('/current_weather/condition', auth='public', website='True')
    def index_current_condition(self, **kw):
        weather_condition = http.request.env['current_weather.current_weather']
        return http.request.render('current_weather.index_condition', {
            'weather_conditions': weather_condition.search([])
            })

Template.xml file
        <template id="index">
        <t t-call="website.layout">
            <t t-set="title">Locations</t>

                    <t t-foreach="locations" t-as="location">

                            <t t-esc="location.name"/>

                    </t>

        </t>
    </template>

When click on mumbai only data of mumbai will show how to do that?
Currently, data shown in all cities are same.

A: In your controller index_current_condition you need to think of a way to filter the weather_condition based on the city clicked on. This can be done via the search method of the ORM API.
With weather_condition.search([]) you are returning all records found in your model when passing a blank list. So here you need to use something along the lines of weather_condition.search([('city', '=', current_city)]), where city needs to be a field in your model current_weather.current_weather (and should be linked to your other model weather_location.weather_location) and current_city must be somehow dynamically passed to the controller.

redpajama_set_name: RedPajamaStackExchange


text:
Travel Events News / Tourism News
Aarhus will focus on the role of children during its time at European Capital of Culture
Two new European Capitals of Culture for 2017
Aarhus and Pafos have taken up the title of European Capital of Culture for 2017.
The cultural programme will officially begin on January 21st in Aarhus.
The opening ceremony for Pafos 2017 will take place on January 28th, with Christos Stylianides, commissioner for humanitarian aid and crisis management, and Nicos Anastasiades, president of Cyprus in attendance.
Commissioner Tibor Navracsics said: "The title of European Capital of Culture is a unique opportunity to bring communities together through culture and to foster strong local, European and international partnerships for the future.
"I wish Aarhus and Pafos every success for the coming year."
Both cities have come up with programmes which showcase centuries of culture while using different art forms to address the socio-economic problems facing Europe today.
'Rethink' is the central theme of Aarhus 2017.
The Danish city will show how arts, culture and the creative sector can help us to re-think and shape our basic social, urban, cultural and economic patterns of behaviour and find new solutions to common challenges.
A rooftop Viking saga performance, an art exhibition stretching across the city and the coastline, a Creativity World Forum and an international children's literary festival are just some of the many events which will bridge the past with creative ideas for the present and future.
Aarhus 2017 will launch its cultural programme with children at the heart of the celebrations.
Hundreds of children from the Central Denmark region will gather in Aarhus to imagine the future in a series of events entitled Land of Wishes.
As night falls during the opening ceremony, a spectacular show filled with pageantry, Viking spirits and gods in the sky will mark the start of the city's year as European Capital of Culture.
Linking Continents, Bridging Cultures is the common thread running through hundreds of events organised by Pafos 2017.
The first Cypriot city to host a European Capital of Culture embraces its experiences of multiculturalism and its geographical proximity to the Middle East and North Africa to strengthen relations between countries and cultures.
The opening ceremony for Pafos 2017 is inspired by one of the themes for the year's cultural programme: Myth and Religion.
New life will be given to the myth of Pygmalion and Galatea and other narratives from the history of Pafos in a unique spectacle of music and dance.
During the opening weekend on January 28th-29th, the city will be converted into an Open Air Factory with numerous shows and artistic performances.
In 2016, Wroclaw in Poland and San Sebastian in Spain were European Capitals of Culture.
Following Aarhus and Pafos in 2017, the future European Capitals of Culture will be Valletta (Malta) and Leeuwarden (Netherlands) in 2018, Plovdiv (Bulgaria) and Matera (Italy) in 2019 and Rijeka (Croatia) and Galway (Ireland) in 2020.
Timisoara (Romania), Elefsina (Greece) and Novi Sad (Serbia, candidate country) were recently recommended to become the three European Capitals of Culture 2021 and are awaiting their official nomination by the relevant authorities.
Antigua & Barbuda breaks US visitor record in 2016
Cathay Pacific signs codeshare deal with Air Canada
New EU-wide rules on drone operations
European Parliament approves no-deal aviation arrangements
European Commission gives approval to Air France-KLM investment in Virgin Atlantic
Qatar signs comprehensive air transport agreement with European Union
WTTC offers support as ETIAS approved in Europe
UNWTO deepens cooperation with European Union
Mobile roaming charges abolished for EU citizens
Tourism industry lobbies government for support during Brexit negotiations


The old saw says - 'Let a sleeping dog lie.' Experience knows better; experience says, If you want to convince do it yourself.
Philosophers are in the habit of setting themselves before life and experience.
For me it is not dubious that our thinking goes on for the most part without use of signs (words) and beyond that to a considerable degree unconsciously. For how, otherwise, should it happen that sometimes we "wonder" quite spontaneously about some experience? This "wondering" seems to occur when an experience comes into conflict with a world of concepts which is already sufficiently fixed in us. Whenever such a conflict is experienced hard and intensively it reacts back upon our thought world in a decisive way. The development of this thought world is in a certain sense a continuous flight from "wonder."
My own experience but confirms the opinion that the Musalman as a rule is a bully, and the Hindu as rule is a coward.
Christianity only hopes. It has hung its harp on the willows, and cannot sing a song in a strange land. It has dreamed a sad dream, and does not yet welcome the morning with joy. The mother tells her falsehoods to her child, but, thank heaven, the child does not grow up in its parent's shadow. Our mother's faith has not grown with her experience. Her experience has been too much for her. The lesson of life was too hard for her to learn.
As to science, we may well define it for our purpose as "methodical thinking directed toward finding regulative connections between our sensual experiences".
I find nothing in fables more astonishing than my experience in every hour. One moment of a man's life is a fact so stupendous as to take the luster out of fiction.
When good is near you, when you have life in yourself, it is not by any known or accustomed way; you shall not discern the foot-prints of any other; you shall not see the face of man; you shall not hear any name; the way, the thought, the good, shall be wholly strange and new. It shall exclude example and experience.
Their reward for enduring the awful experience was the right to tell people about it.
When a man after long years of searching chances upon a thought which discloses something of the beauty of this mysterious universe, he should not therefore be personally celebrated. He is already sufficiently paid by his experience of seeking and finding.
As regards to its use on the coinage we have actual experience by which to go. In all my life I have never heard any human being speak reverently of this motto on the coins or show any sign of having appealed to any high emotion in him. But I have literally hundreds of times heard it used as an occasion of, and incitement to, the sneering ridicule which it is above all things undesirable that so beautiful and exalted a phrase should excite.
Just as we believe by faith that the greatest happiness of the next life consists simply in the contemplation of this divine majesty, likewise we experience that we derive the greatest joy of which we are capable in this life from the same contemplation, even though it is much less perfect.
The scholar is not apt to make his most familiar experience come gracefully to the aid of his expression.


text:
Iftan Hagimohamed poses for a portrait at the New Holly Gathering Hall on November 12, 2016. Photo by Jovelle Tamayo
The Fear and Resolve of Those Most Threatened by a Trump Presidency
Seattle's Muslims and Mexican immigrants share their hopes and concerns following the election.
by Jovelle Tamayo
Wednesday, January 18, 2017 1:30am
News & CommentAward-Winning JournalismImmigration
During his campaign for the country's highest office, Donald Trump's rhetoric was thick with invective. He railed against the Washington establishment and the media, and the businessman-turned-politician also disparaged various women, mocked a disabled reporter, and spread racist propaganda from white nationalist groups. Most troubling, though, were his policy prescription, which sought to lay the blame for economic insecurities and national security concerns at the feet of two communities—Muslims and undocumented Mexican immigrants. To ward off terrorism, he suggested a ban on all Muslims traveling to the United States and would not rule out a registry for all Muslims living in the U.S. To bring about economic revival, he has promised to build a wall along our southern border and to deport all 11 million undocumented immigrants currently living in the U.S. to the other side of it. The effectiveness and practicality of these proposals—not to mention their constitutionality and civility—has been widely questioned by various experts. Nonetheless, Trump won. And while his win last week has many on the left anxious about the future, it is these two communities that have the most at stake under a Trump administration. We asked what they are thinking now and what they believe the future holds.
Hanni Hassan, 15, Cleveland High School
If the person that's running the whole United States doesn't care about us and hates us, everyone's going to feel free to say what they want. I never knew racism growing up. I felt like an American. I felt like all of that was in the past. But lately I started noticing people I used to smile at don't smile back—little stuff. People at the stores used to be nicer. I feel like we never had power because we're minorities. We're immigrants. Our parents are refugees. We're Muslims. It's supposed to be a free country for everyone, but it's just not. It hit me. I'm scared. I don't know what's going to happen. Instead of being upset that he's president, I'm trying to hope he gets some sense knocked into him and gets it together to do his best as president. I have a lot of hope and I'm praying everything goes well. I don't want us to go to war. I don't want us to fight each other.
Faduma Ahmed, 29, Somali immigrant, Seattle University
This should be a lesson in the work that needs to be consistently done. We took so many things for granted. We laughed at him, we mocked him, we told ourselves, "Hey, this will never happen so let's just chill back. We're going to be fine." We left so much of the work, the effort to other people. We didn't focus. We didn't come together. I'm disappointed, but this doesn't discourage me from being here, from participating, from doing what I can for the community. At the end of the day, it's the little steps—the details—that matter. I believe that Trump won't be able to take my rights away. Yes, he can make life difficult, but it won't be possible. I've been here for so long, and I'm going to be here long after he stops being president. So I'm not worried. I believe people can still come together, use this as a learning moment, and move on. But it's frustrating to know this actually happened on our watch.
Sami Ahmed, 16, Cleveland High School
In the Wake of a Trump Upset, 11 Reasons to Have Hope in Washington StateNov 9, 2016
How to Smoke Weed in a Scary New WorldNov 16, 2016
I was disappointed. I felt a lot of hate. A lot of racist people around the world finally came out of their shell. There's no reason to vote for him if you're not racist because he said he doesn't like Muslim people, he said he's going to deport Mexicans, and a lot of stuff. I'm going to do things as usual. Nothing will change for me, but I feel like I'll see different things—like more racist people will come out and actually call you the n-word or tell you that you can't be here because you're Muslim. But I'm proud to be a Muslim and I always will be. I won't change for anybody. FDT, that's all I have to say.
Zane Mohamed, 18, Franklin High School
They finally announced he won and I was in a state of shock, and didn't know how to react for a second. The next morning when I woke up and I went to school, everyone was speaking about it, even my teachers. You could tell it really hit them. We elected this guy—this racist, this Islamophobic, xenophobic guy to run the free world, and that's not what we stand for whatsoever. From members of my own community, I've noticed massive amounts of support. Throughout the different schools there's been a lot of walkouts. Franklin's [Black Student Union] even came together after the election to speak about our reactions. So there's this massive amount of support through the city.
Daniel Ruiz, 31, immigrated to the U.S. in 1992 from Mexico
I felt anger. I felt frustration. But mainly disappointment because I'm not only part of the immigrant community but I'm also part of the LGBTQ community. Especially with Pence, the VP, having very conservative views on the LGBTQ issues that we face today—he's jeopardizing our rights. He and Trump are giving voice to all the people who maybe, before, did not want to express how they truly felt about others who are different from them. But now, they feel courage to do it. I'm not frightened. But I am a little more conscious of my surroundings when I'm out on the streets or getting to my car from the store. But having these types of meetings and gatherings—it feels good to listen to other people and to know people have your back.
Hannah Omar, 19, Seattle Central Community College
I'm so confused because if everyone around you is talking like they don't like Donald Trump, how did he win? Everyone on social media is bashing him. Everybody, everywhere is bashing him. So how did he win? I still haven't come across a Trump supporter yet. I'm not off-guard anymore because I've seen a lot of posts on Twitter—people saying stuff that's not OK. I haven't witnessed anything yet, but I know it's coming because I'm Muslim and I'm Somali.
Iftan Hagimohamed, 32, Somali-American advocate and teacher
As adults we were coping with fear, and our own children were afraid. They weren't sure if they were going to be safe at school. I have three boys, and I had to do a lot of convincing that they would be safe and their teachers would keep them safe, the school staff would keep them safe and I would keep them safe. And not to let this fear of the unknown of what's going to happen drive them. I can't speak for the entire Muslim community because I'm sure there were a lot of Muslims that voted for Trump. But for the Muslims that are here in King County, we weren't very fond of Donald Trump because of everything he represents. In a way it's kind of scary, but we're hoping collectively, as a local government, we can secure the safety of our children and our families and our communities.
Mohamud Yussuf, 57, Somali-American organizer/activist and publisher of Runta News
I've always admired [Trump's] truth-talking, his up-front speaking. I know people were offended, but not me. I've always thought he's honest. I cannot say I was excited, but I was OK to see him win. What he says about Islam—it doesn't offend me. The guy just speaks his mind and says many things about many other people. He was also insulted and attacked, and he endured. This is the culture of this country—people speak and attack each other, but we move on. We are refugees and we have endured a lot. It's not new to us. We can still survive in any circumstance. It's not the end of the world. If he does bad, we'll still exist. But I don't think he'll do what he's said. No one can rip off anyone's rights that easily, and Muslims are part of this country. Muslims have a lot of history in this country, and he can't afford to lose his relationship with Muslim countries.
Alma Gutierrez, Co-founder of Colectiva Legal de Pueblo, undocumented immigrant from Mexico
I was very surprised when he was elected. I absolutely feel so afraid and I cannot believe it. I am in shock. I started crying because this isn't just about me—it's about more than 11 million people who might be feeling the same way I do. It's a disaster for family, community, the economy, everything. It's absolutely a disaster in my point of view. We have to take action and start working with our community to defend ourselves. We know we are undocumented but we still have rights. Mostly we have to defend our families and our communities. It's time to reunite, be together. It's time to be involved in our community and it's time to be educated, even if you're not part of a community of color or an immigrant.
Rukaya Souleyman, 14, Sudanese immigrant attending Garfield High School
I'm against him because he's racist and people think it's OK to be racist and sexist, and that it's OK for everyone else to do exactly what he did, which is not a good representation of America.
Jorge Cuellar,20, immigrant from Mexico and a junior at UW
As a queer, undocumented student, it has really taken a toll on me because we have worked so hard just to be at a higher institution, where we're supposed to better ourselves or take advantage of these opportunities that the United States was supposed to offer. So, having come this far, it now feels like it's been for nothing. My parents sound really, really sad when I talk to them over the phone. They sound hopeless because they have also worked so hard to make a living here, and to have someone who represents so much hate, xenophobia, and anti-immigrant sentiments … It's been very hard for my parents as well.
Varisha Khan, 21, journalism and political science student at UW
I think it's important to know and remember that this climate of fear and hatred didn't just start on Tuesday. It's been going on for a while. Actually, 2015 was the year of a record number of anti-Muslim hate crimes reported to CAIR, the Council on American-Islamic Relations. Never before in our nation's history have we seen a higher, more severe number of [hate] crimes than in 2015. And that's continued to grow and we're on track in 2016 to surpass that number, and it doesn't seem it will stop, especially with this election. It's not stopping anytime soon. If there's anything I could tell an American-Muslim child or a young person right now, who's having the same fears a lot of people have, is that you can practice your faith fully and be an American fully. There's no need to compromise either.
news@seattleweekly.com
Trump Is Going to Be President. Time to Organize.
Seattle Felt Very Far Away From the United States Last Night


Three cast members and the ending theme performer have been revealed for Okoshiyasu, Chitose-chan ("Welcome, Chitose-chan"), an upcoming TV anime based on the "healing" manga by Yukiko Natsume about a baby penguin taking in the sights and sounds of Kyoto.
In Okoshiyasu, Chitose-chan, all of the voices for the human characters will be provided by voice actors (and Kyoto natives) Kōji Yusa and Kotori Koiwai, while the narration will be provided by stage / screen actor Shinichi Tsutsumi. This is the first time Tsutsumi has performed narration for an anime.
Additionally, the ending theme, entitled "Nakuko mo Warau" ("Able to Make a Crying Child Laugh"), is performed by Nyanzonu Deshi.
The original Okoshiyasu, Chitose-chan manga is serialized in Kodansha's BE・LOVE josei manga magazine. The Okoshiyasu, Chitose-chan TV anime is directed by Kyo Yatate (Bananya) and features animation production by Gathering. The series will broadcast in Japan on Tokyo MX and KBS Kyoto on Fridays during the 25:35 time slot (actually at 1:35AM JST on Saturday morning) beginning on October 05, 2018.


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To achieve a complete user experience, the Lenovo V330 81AX features a screen display of 15.6" which brings all of your contents into view, to avoid missing out any detail from your document, or scene from your favorite movie. With a resolution of Full HD 1920x1080, it displays your visuals with full clarity, precision, colors, and details. In addition to this, it comes equipped with 256GB of SSD storage which does not only accommodate your data and files but also provides faster system boot-ups for you to e gratify into.
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Very functional and easy to use. Easy to carry around. Keyboard is a nice size and the pads clicks on touch and not stiff or hard. Love the backlight keys. Battery life is sufficient for the work I do on it. Screen is nice as well. I was long over due for a refresh. Overall, this was a good buy and I still love my purchase.
Arrived quickly. For the price, I was surprised to get backlit keys and Pro OS. Solid features. Keyboard is more comfortable than expected. Good item.
Enjoying the laptop so far. Make sure to go through the first steps of the laptop updating itself. The specs provide a strong base to handle all your work and home load. We can't forget this includes Windows Professional. At this price, it is a good deal. Very pleased with this purchase.
Surpassed my expectations. I'm very pleased with quality and configuration. The speed from the SSD makes all the difference. I see this machine lasting for several years. Five stars!


This spring/summer, I've had the opportunity to create 6 prom/leavers' ball dresses. 4 yr 11's and 2 yr 13's! Loved each and everyone of them. However, I will only be blogging the 3 most original ones (the ones I influenced most design wise, as I also sometimes just recreate pictures given to me). Hope you enjoy this series.
First of is 16 year old Debbie. She'd been telling me about making her prom dress for a while but we made no concrete plans e.g fabric shopping, face to face consultation; but she did send me a lot of style ideas via bb lol. She wanted a black, long sleeve, straight fitted, shiny/sequined dress with a high slit and an open back as seen on Ms Cheryl Cole.
For some reason, Debbie insisted on not showing her arms, but I'm of the opinion that it's nicely proportioned to her lovely figure.
Debbie came round to get her hair done by Jadoy on prom day; very classy and ideal style btw! We did a quick styling after and a photo shoot. She looked absolutely beautiful even with very minimal make up on.
What Debbie had to say: "My original idea for my prom dress was a long sleeve tight fitting dress, but as we went on a search for material, new ideas arose and eventually some lace was to be included, I had my mind set on the long sleeves but Wura, as always did an amazing job by altering it to look better with the lace. It was different from my original idea but was definitely better. I felt like a princess on that day in my dress as it was elegant and classy. I was comfortable all through the night and had ease when putting it on and taking it off. Loads of people asked where I got it from and was proud to say it was made by WuraFola ;) "
Awww thanks Debbie, how sweet! lool.... Hope you guys like it. Watch out for part 2!


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The story for the Swedish magazine VU is about an American artist, who brought the house (threatened by demolition) of the American civil rights activist Rosa Parks from Detroit to Berlin.
I decided to tell the story in two vectors: 1. A visit of the niece of Rosa Parks in Berlin and 2. A visit of the house by pupils of a elementary school named after Rosa Parks.


text:
\section{Introduction}
The path from initial screening to marketable drugs takes around 20 years \cite{paul_how_2010}, with cost being anywhere from \$0.5 to \$2.6 billion dollars \cite{avorn_26_2015}. Given the remarkable impediments associated with drug design, it is necessary to develop more precise strategies to propose drug candidates. Computational chemistry approaches to drug design, often called computer-aided drug design, received a lot of attention in the past 30 years. The growing availability of high-throughput screening (HTS) data holds a great potential to accelerate the development of efficient new compounds.

Pharmacologically active drug compounds act by binding to their target (which is most commonly a protein) and altering their function by either stimulating or inhibiting it. Broadly speaking, the current drug design approaches can be divided into two categories. The first one is ligand-based (or indirect) approaches that rely on existing molecules known to be a binding partner of some target and try to mimic their properties. These approaches include molecular fingerprint similarity search, pharmacophore modeling, or quantitative structure-activity relationship (QSAR). The second approach is structure-based (or direct) where a 3D structure of a desired target (or a target that is highly similar to the desired one) is known. New molecules are then designed to bind to the target based on its structural properties. The methods that fall in this category include molecular docking or molecular dynamic simulations.

Deep generative models (DGM) have attracted a lot of attention in recent years. Given their success in image and text generation \cite{guzel_turhan_recent_2018, aggarwal_generative_2021-1}, there is an ongoing effort to apply these approaches to the medical field as well. Current applications include medical image analysis, data augmentation, generation of new DNA, RNA, or protein sequences with specific properties and missing regions in known protein structures \cite{lan_generative_2020-1}.

Despite the optimism, designing novel drugs is particularly difficult due to the graphical structure of molecules, chemical validity, and feasibility of synthesis of generated molecules. Several major simplifications have been used so far to reduce the complexity of the task. For example, instead of working with and generating full molecular structures, molecules can be described by their properties in binary strings. However, to fully automate the drug design process, it is necessary to incorporate complex information, such as chirality and structural conformations, into the molecular representations.

Strikingly, 75-97\% of novel drugs fail human clinical trials. The primary reason for failing a trial is a lack of demonstrated efficiency and the second most common reason is safety concerns \cite{wong_estimation_2019}.  Therefore, optimizing for efficient physiological response and low toxicity in addition to favorable chemical properties is at the core of the approaches that could lead to successful drug design. This is encompassed in the field of drug response prediction.

In this review article, we describe the current DGM approaches for drug design with the aim of bridging the fields of drug design and drug response prediction to allow for designing more efficient and more powerful pipelines. We first briefly introduce the most common neural architectures that are used for drug design and drug response prediction. We also list the most common databases and tools, and compare current molecular data representations. We describe state-of-the-art techniques for small molecule design, gene therapy, and protein sequence generation. Finally, we conclude our review with remarks on the most challenging aspects of drug design and considerations about the direction of future research.

\begin{figure*}[h!]
    \centering
    \includegraphics[width=2\columnwidth]{img/models.png}
    \caption{Generative Models. A. Generative Adversarial Network (GAN). B. Autoencoder. C. Variational Autoencoder (VAE). D. Adversarial Autoencoder (AAE).}
    \label{fig:models}
\end{figure*}

\section{Overview of generative models}
Deep generative models have achieved great success ranging from generation of high-quality images to large corpora of meaningful text.  Formally, deep generative models are neural networks trained to model a distribution of training samples and are able to sample from the learned distribution to generate new samples.  Explicit generative models specify the full data distribution and allow to express likelihoods that a given sample comes from the distribution. On the other hand, implicit generative models only specify a procedure of drawing samples to generate new data but do not provide a way to express a full distribution.

\subsection{GANs}
Generative Adversarial Networks (GANs) are implicit density models introduced in 2014 \cite{goodfellow_generative_2014}. The idea behind generating samples with GANs is to have two networks - one is a generator model that creates new samples from a random vector and the other is the discriminator (or the critic) that aims to distinguish newly generated samples from the real ones. These two parts are competing with  each other. The generator gets better at generating realistically looking samples while the discriminator gets better at classifying those that are real and those that are fake. After both networks have been trained, only the generator is used to create realistic inputs from a random starter vector.

\subsection{Autoencoders}
A standard autoencoder (AE) has two parts - an encoder and a decoder. The inputs are passed through an encoder that outputs some representation of these inputs to the latent space. From there, the decoder can reconstruct the representation back such that it is as close as possible to the original input. Most commonly, the latent space is chosen such that it has fewer dimensions than the inputs. This leads to compression of the samples and extraction of the most useful information. The encoded latent space, which contains all the information that is necessary to retrieve the original input, can then be used for downstream tasks. For example, a classifier can be fitted on top of a learned latent space. Compared to Principal Component Analysis (PCA), autoencoders can perform non-linear dimensionality reduction. Autoencoders can also be used for denoising where a noisy input is reconstructed without the unwanted noise.

\subsection{VAEs}
Variational Autoencoders (VAEs) are regularized probabilistic autoencoders \cite{kingma_auto-encoding_2014}. Similar to a standard autoencoder, they contain an encoder and a decoder. However, instead of encoding each sample to some point a latent space, the encoder defines a whole distribution. From this distribution, vectors are being sampled and fed into a decoder that reconstructs them back to be as close as possible to the original input. To give the latent space distribution some specific properties, it is regularized during training. Most commonly, we force the latent space to follow a normal distribution since that provides a closed form for many equations and is simple to work with. The loss function for VAEs thus contains two terms – a reconstruction error and a regularization term which is a Kullback-Leibler divergence between the learned latent distribution and a specified prior (normal distribution).

\subsection{AAEs}
Adversarial Autoencoders (AAEs) are regularized autoencoders that adapt GANs training technique \cite{makhzani_adversarial_2016}. Similar to VAEs, they impose certain properties on the learned latent space such as being normally distributed but they use a different regularization technique. Apart from an encoder and a decoder, there is an auxiliary adversary network which is trained to distinguish samples from the latent space and samples drawn from a chosen distribution. The encoder tries to fool the adversary by producing samples that are indistinguishable from the chosen distribution and this serves as regularization of the latent space. One of the benefits of the AAEs compared to VAEs is that the chosen prior distribution that the encoder tries to mimic can be any distribution, even a non-parametric one. Depending on the choice of the encoder, the AAE can be either a deterministic model that maps all the inputs to the same space in the latent space or have a probabilistic encoder that outputs parameters for  a full latent posterior distribution.

\subsection{Transformers}
Transformer is an attention-based architecture that gained popularity in recent years due to its successful use in language models \cite{devlin_bert_2019, brown_language_2020}. Transformer has two functional parts: a block of stacked encoders and a block of decoders (Figure 2). Encoding part produces a representation of the input that tells us how different parts of the input sequence are related to each other. "Relevancy" is determined by the attention formula.  This approach allows us to establish connections between parts of the sequence that can be far from each other. This makes it attractive for use in protein structure prediction, where functional groups at different sides of the sequence can interact with each other, dictating the 3D conformation of the sequence.

\begin{figure}
    \centering
    \includegraphics[width=\columnwidth]{img/transf.png}
    \caption{Transformer architecture.}
    \label{fig:transf}
\end{figure}

\section{Databases and tools}
In this section, we highlight some of the most useful databases and tools for drug design and drug response prediction.

\subsection{Databases of Chemical Compounds}
Table \ref{tab:chem} shows the most commonly used databases of binding affinities, bioactivity, and 3D structures of chemical compounds.

\begin{table*}
\centering
\scalebox{0.75}{

    \begin{tabular}{|l|l|l|l|l|}
    \hline
    \textbf{Database} & \textbf{Type} & \textbf{Note}                           & \textbf{Entries} & \textbf{Link}                \\ \hline
    BindingDB &
      Binding affinity &
      \begin{tabular}[c]{@{}l@{}}Database containing binding affinities \\ between proteins and ligands with \\ drug-like properties.\end{tabular} &
      1.5 M &
      \begin{tabular}[c]{@{}l@{}}\href{BindingDB.org}{BindingDB.org}\end{tabular} \\ \hline
    ZINC &
      Bioactivity, 3D structure &
      Commercially available molecules &
      21 M &
      \begin{tabular}[c]{@{}l@{}}\href{https://zinc.docking.org/}{zinc.docking.org}\end{tabular} \\ \hline
    PubChem &
      Bioactivity, 3D structure &
      \begin{tabular}[c]{@{}l@{}}Contains compounds,   \\ substances (mixtures,  extracts), \\ and high-throughput screenings (BioAssay)\end{tabular} &
      40 M &
       \begin{tabular}[c]{@{}l@{}}\href{https://pubchem.ncbi.nlm.nih.gov/}{pubchem.ncbi.nlm.nih.gov}\end{tabular} \\ \hline
    PDB &
      3D structure &
      \begin{tabular}[c]{@{}l@{}}Contains 3D structures of proteins \\ and nucleic acids.\end{tabular} &
      170 000 &
      \begin{tabular}[c]{@{}l@{}}\href{https://www.rcsb.org/}{rcsb.org}\end{tabular} \\ \hline
    PDBeChem          & 3D structure  & Ligands referenced in the PDB database. & 14 500           &  \href{https://www.ebi.ac.uk/pdbe-srv/pdbechem/}{ebi.ac.uk}   \\ \hline
    ChemDB            & 3D structure  & Commercially available molecules.       & 5 M              &  \href{http://chemdb.ics.uci.edu/}{chemdb.ics.uci.edu}     \\ \hline
    ChemSpider        & 3D structure  & Crowdsourced chemical database.         & 100 M            &  \href{http://www.chemspider.com/}{chemspider.com} \\ \hline
    \end{tabular}
    }
    \caption{Databases of chemical compounds.}
    \label{tab:chem}
\end{table*}

\begin{table*}
\centering
\scalebox{0.75}{
    \begin{tabular}{|l|l|l|l|l|}
    \hline
    \textbf{Database} &
      \textbf{Type} &
      \textbf{Note} &
      \textbf{Size} &
      \textbf{Link} \\ \hline
    NCI-60 &
      Drug response &
      \begin{tabular}[c]{@{}l@{}}Cytotoxic and cytostatic effects of different \\ drugs on human tumor   cell lines.\end{tabular} &
      \begin{tabular}[c]{@{}l@{}}60 cell lines\\  with 100 000 drugs\end{tabular} &
       \href{https://dtp.cancer.gov/discovery_development/nci-60/}{dtp.cancer.gov} \\ \hline
    CTRPv2 &
      Drug response &
      \begin{tabular}[c]{@{}l@{}}Cytotoxic and cytostatic effects of different\\  drugs on human tumor cell lines.\\  Provides different concentrations of drugs.\end{tabular} &
      \begin{tabular}[c]{@{}l@{}}850 cell lines\\  with 481 drugs\end{tabular} &
       \href{https://portals.broadinstitute.org/ctrp.v2.1/}{broadinstitute.org} \\ \hline
    GDSC &
      Drug response &
      \begin{tabular}[c]{@{}l@{}}Cytotoxic and cytostatic effects of different \\ drugs on human tumor cell lines.\end{tabular} &
      \begin{tabular}[c]{@{}l@{}}700 cell lines \\ with 138 drugs\end{tabular} &
      \href{https://www.cancerrxgene.org/}{cancerrxgene.org}\\ \hline
    CCLE &
      Drug response &
      \begin{tabular}[c]{@{}l@{}}Cytotoxic and cytostatic effects of different\\  drugs on human tumor cell lines.\end{tabular} &
      \begin{tabular}[c]{@{}l@{}}1000 cell lines \\ with 24 drugs\end{tabular} &
      \href{https://ccle.ucla.edu/}{ccle.ucla.edu} \\ \hline
    NCBI GEO &
      \begin{tabular}[c]{@{}l@{}}Gene expression\\ profiles\end{tabular} &
      \begin{tabular}[c]{@{}l@{}}Gene expression data from human and \\ animal tissues.\end{tabular} &
      – &
     \href{https://www.ncbi.nlm.nih.gov/geo/}{ncbi.nlm.nih.gov} \\ \hline
    CMap-L1000v1 &
      \begin{tabular}[c]{@{}l@{}}Gene expression\\ changes\end{tabular} &
      \begin{tabular}[c]{@{}l@{}}Expression changes (perturbations)\\  of 1000 landmark genes for different\\  concentrations and durations of drug treatments.\end{tabular} &
      \begin{tabular}[c]{@{}l@{}}1000 genes \\ with 2700 drugs\end{tabular} &
       \href{https://cmap.ihmc.us/}{cmap.ihmc.us} \\ \hline
    \end{tabular}
    }

\caption{Databases of cell line and tissue screenings.}
\label{tab:cell_lines}
\end{table*}

\subsection{Databases of Cell Line Screens}
In Table \ref{tab:cell_lines}, we present open source databases of drug response (sensitivity) data on cancer cell lines as well as gene expression data collected using high-throughput screenings for cells and tissues.

\subsection{Tools}
QSAR is a computational method for modeling relationships between structural properties of chemical compounds and their biological properties (such as enzyme activity, minimum effective dose, toxicity). This can often be used to validate the results achieved by deep nets and for benchmarking. However, QSAR is sometimes at risk of giving inaccurate predictions.

RDKit open-source chemoinformatics library (available from http://www.rdkit.org) can be used for a number of 2D and 3D molecular operations, from stereochemistry identification to detecting chemistry problems.

\section{Data Representations}
Given the diversity of problems and screenings that drug discovery can cover, data representations also differ significantly. We present some of the most common ones. For a detailed review of the molecular representations, see David et al. \cite{david_molecular_2020}. We stress that there exist  molecules that cannot be described using a standard notation.

\subsection{SMILES and Its Variants}

Simplified Molecular Input Line Entry System (SMILES) represents molecules as strings and, to date, is one of the most commonly used molecular representations.
The strings are created by traversing a molecular graph in a specific order (for example, RDKit uses a depth-first search algorithm). SMILES strings are not unique and one molecule can have several representations. Therefore, a canonical string is often chosen for a given molecule.

SMILES Arbitrary Target Specification (SMARTS) is an extension of SMILES that allows to encode more information about bond types, connectivity, and atomic properties in a string.

Another string representation, called SMIRKS allows for expressing reactants and products of a generic chemical reaction.

\subsection{Molecular Fingerprints}
Molecular fingerprints are another popular way of encoding molecular structures. In its simplest form, binary encoding indicates presence of absence of individual substructures within a molecule. However, any information about bonds and other properties is necessarily lost with this representation and it is not a one-to-one mapping.

Circular fingerprint considers connectivity of atoms up to a certain distance to solve the problem of non-uniqueness of many representations. Every atom receives a descriptor that indicates its closest neighbors. Examples of circular fingerprints include extended-connectivity fingerprints (ECFPs) such as ECFP4 or ECFP6 where the number indicates the radius around an atom to be considered. Other examples are Multilevel Neighborhoods of Atoms (MNA) or MolPrint2D Format (MPD).

A fingerprint that describes molecules' surface properties are called spectophores. Molecules that have similar shapes and 3D properties will retains a similar representations.

\subsection{Peptide and Protein Representations}
The simplest way of representing peptides or proteins is using amino acid sequences. Each individual amino acid, a building blocks of peptides, is assigned a letter and the final peptide is represented by a string of these letters.

There are other representations of proteins such as distance and contact maps that preserve information about protein structures. Protein contact maps represent distances between all pairs of amino acids in a 3D protein structure in a matrix.

\subsection{Graph Representations}
Molecular graphs can be also described directly using graphical representations.

Standard adjacency matrices can be used to represent a presence or absence of bonds between atoms in a graph.

A node feature matrix is a larger matrix can be used to indicate atom types, formal charges, or other properties of each atom.

Similarly, in an edge feature matrix, each row represents an edge in a graph and is a binary vector that indicates properties of that such (such as the type of a bond).

\begin{figure}
    \centering
    \includegraphics[width=\columnwidth]{img/repres.png}
    \caption{Example of molecular representations. Amino acid glycine represented in different formats.}
    \label{fig:repres}
\end{figure}

\section{Drug Design}
 \subsection{Small Molecule Design}
With many variations of generative architectures published in recent years, there are a number of ways to enforce properties onto generated structures. Desired drug features, such as growth inhibition of tumors (or GI50), can be used as input to encoder in Conditional Autoencoders \cite{joo_generative_2020}. Some work was also done to frame drug design as a 'translation' problem, applying transformer architecture for converting amino-acid sequence to SMILES representation of the molecule that can bind it \cite{grechishnikova_transformer_2020}. However, even with faster screening methods, docking validation still remains a bottleneck in this type of candidate generation.
Moreover, when mapping discrete representations into continuous space, "grammar" of chemical  syntax is hard to preserve. This can lead to unrealistic aromatic systems and incorrect atomic valences. Because of this, SMILES-generated molecules often require additional "correcting" architectures.

One possible approach for correction is sequence-to-sequence learning with attention, where the input is a wrong sequence and the target is the corrected one \cite{druchok_towards_2019}. Grammar VAE (GVAE) \cite{kusner_grammar_2017-1} was also used to generate SMILES that follow syntactic constraints given by a context-free grammar. Finally, syntax-directed VAE (SDVAE) \cite{dai_syntax-directed_2018}) can make use of attribute grammar to enforce syntactic and semantic constraints on generated SMILES.

While focusing on SMILES strings or molecular fingerprints simplifies generation of new molecules, a lot of information is lost using these approaches. For instance, chemically similar molecules can have very different SMILES strings (Figure \ref{fig:smiles_sim}. This representation prevents learning meaningful smooth embedding.

\begin{figure}[h!]
    \centering
    \includegraphics[width=\columnwidth]{img/smiles_sim.png}
    \caption{SMILES strings for these two very similar molecules show only 50.5\% similarity.  Adapted from \cite{jin_junction_2019} .}
    \label{fig:smiles_sim}
\end{figure}

Graph neural networks that capture graph structures by message passing between the nodes are attractive methods for generation of full molecular structures \cite{jiang_could_2021}. Given that graphs are discrete structures with arbitrary connectivity, graph generation is a difficult domain and relatively young domain of generative modeling.

One approach is to generate graph nodes and edges one by one. This was done using an LSTM-based autoregressive model \cite{li_learning_2018}.  Simonovsky and Komodakis, \cite{simonovsky_graphvae_2018} used GraphVAE to directly generate nodes (atoms) and predict an adjacency matrix indicating edges (bonds) of a graph. Another approach, which enhances chemical validity of generated molecular graphs, is to use smaller subgraphs as building blocks of a final molecular graph. Jin et al., \cite{jin_junction_2019} used a model called Junction Tree VAE (JT-VAE), which first generated a junction tree that serves as a scaffold for the molecule and then specifies the subgraphs as nodes of the junction tree. Their method of coarse-to-fine generation of molecules significantly outperformed previous SMILES-based approaches such as GrammarVAE \cite{kusner_grammar_2017-1}, Character VAE \cite{gomez-bombarelli_automatic_2018-1}, or graph generating GraphVAE in validity of generated molecules. Another model , DeLinker, which is a gated graph neural network (GGNN) that links two molecular fragments together to form more complex compounds \cite{imrie_deep_2020} also incorporates 3D structural information to constrain the linking process. However, DeLinker's performance was not compared to previous methods.

\subsection{Gene Therapy}
Generative models have a particular promise for the area of gene therapy, where adeno-associated virus (AAV) capsids serve as vectors for gene delivery. However, due to wide-spread immunity against common AAVs, capsids often need to be diversified such that antigens on their surface aren't recognized by the human immune system.
Previous approaches, like directed evolution or DNA shuffling between serotypes, often produced unstable results (Bryant et al., 2021; Ogden et al., 2019). Earlier this year Dyno Therapeutics used variational autoencoders to create novel and stable adeno-associated viral capsids \cite{sinai_generative_2021}. This case also emphasizes that, compared to supervised methods, unsupervised generation can potentially take advantage of evolutionary information.

\subsection{Proteins and Peptides}
Peptides are short sequences of amino acids that can be used as therapeutics. There are over 60 peptides approved for clinical use by the FDA. While many naturally-occurring peptides are approved for medical use, they often end up having some weaknesses, such as low membrane permeability, chemical instability and short half-life. Synthetic peptides generated by computational methods can overcome these weaknesses \cite{fosgerau_peptide_2014}.

Das et al. \cite{das_accelerated_2021}, compared usage of Wasserstein Autoencoder (use Mean Maximum Discrepancy as a regularization in their loss) and Beta Variational Autoencoders (Kullback-Leibler divergence as regularization) for the screening of peptides with antimicrobial properties. WAE managed to learn meaningful latent representation that captured correlation between evolutionary similarity and distance in the z-space (Beta Variational Autoencoders didn't have the same correlation in their latent space but showed comparatively good BLEU score, reconstruction error and perplexity). VAEs can also be used for generation of functional variants of peptides: Hawkins-Hooker et al., 2021, showed evidence  of conditional VAEs being able to increase solubility of luxA sequence from P. luminescens without losing core properties of the wild-type. Given the complexity of de novo design, diversification of already functional peptides can allow for a more reliable design of therapeutics.

Most drugs target receptors  and channels (both are classes of proteins) on the surface of the cell or soluble (not part of a membrane) proteins. Given this, knowing the exact conformation of the target can be of much help when designing ligands. Moreover, proteins can serve as a therapeutics themselves. A huge milestone in deriving 3D structure from an amino acid sequence was achieved by DeepMind in protein structure annual competition, Critical Assessment of Protein Structure Prediction (CASP) \cite{noauthor_alphafold_nodate}. While their earlier architecture in CASP13, AlphaFold-1, relied on ResNet-like architecture to predict amino acid residue distances \cite{senior_improved_2020-1}, a major breakthrough was achieved with AlphaFold-2 in CASP14, when the team transitioned to transformer-like architecture \cite{noauthor_alphafold_nodate}. Previously, to predict the structure of a protein, one would have to undergo expensive and laborious X-ray crystallography or cryo-electron microscopy. Combination of generative methods with the knowledge of precise target shape can profoundly cut time for drug screening (Callaway, Ewen, 2020).

\section{Drug response}
Apart from designing novel pharmaceutically active compounds, another important task is to be able to predict their efficiency. Ultimately, incorporating drug response directly as one of the optimization goals for generation of new molecules can lead to a more powerful drug design pipeline.

Recent research efforts have produced large-scale publicly available drug screening profiles (Table \ref{tab:cell_lines}). Most of them contain high-throughput screenings for anti-cancer drugs against cancer cell lines. These cell lines are most commonly immortalized human or animal cells which are grown outside an organism, on a Petri dish. Despite the tremendous value that cell lines provide to our understanding of disease progression and treatment \cite{mirabelli_cancer_2019} there are important drug response differences compared to full organisms. Therefore, there has been an ongoing effort to transfer findings from cell lines to actual application in humans where the amount of available data on drug responses is very limited.

The input features that are used for drug response prediction on cell lines consist of one or multiple of the following: expression levels or different genes, gene methylation status, and protein abundance. Less common are DNA mutation profiles (single nucleotide variations) and copy number variations. The nature of the problem -- expression profiles of hundreds or thousands of genes -- creates a high feature to sample ratio that complicates the prediction task.
 Initial approaches for drug response prediction included linear regression and simple clustering algorithms, SVM, and Random Forests. Traditional feature selection and dimensionality reduction approaches achieved some limited success \cite{adam_machine_2020, baptista_deep_2021-1}. While the task itself is not generative in nature, DGM models such as VAEs, conditional VAEs, and AAEs, have been used to perform dimensionality reduction, deconfounding or domain adaptation for better response prediction.

The first study to apply generative models to drug response predictions was described by Dincer et al. \cite{dincer_deepprofile_2018}. In this work, a VAE model has been used to learn relevant features from gene expression data of cancer cells treated with a drug. Compared to drug response data, there is a vast amount of unlabeled gene expression results which their model leveraged. First, the VAE was trained on gene expression data of AML patients and then, a much smaller sample size for which responses to various drugs were available were passed through the encoder. On the compressed data, a linear classifier was fit to predict a binary target if patients responded to therapy or not. This method improved performance over no feature reduction.

In another approach, a semi-supervised VAE called Dr.VAE divided the task of predicting drug response into two parts – first, it learns how a drug perturb gene expression of cells, and second, it relates gene expression changed to drug sensitivity \cite{rampasek_drvae_2019}. This means that instead of using only post-treatment gene expression data (as done above), the data is considered in pairs of pre- and post-treatment and there is a learnable function that describes this change. Dr.VAE aims to learn a joint distribution of gene expression pairs, latent representations, and a categorical variable that corresponds to the drug response. The marked improvement of this approach over baselines could be potentially even greater if combined with supplying the model with chemical properties of tested drugs. Combining gene expression data with embedded structure of chemical compounds was done by Kuenzi et al. \cite{kuenzi_predicting_2020} which used a simple feed-forward network.

Generative models have also been used for deconfounding in drug response prediction. As mentioned above, there is often a mismatch between drug efficiency tested in cell lines and in humans. When using predictive models, they can detect cell-line specific patterns. If an autoencoder is applied directly, it will mainly learn to encode these non-relevant signals as they are usually the main source of variation in the dataset. Adversarial Deconfounding Autoencoder (AD-AE) was used to extract only the relevant signal from this data (i.e., expression patterns of cancerous cells independent of their source) \cite{dincer_adversarial_2020}. This model is most similar to AAE, but the adversary network tries to predict a confounder, such as sex, biological age or origin of cell, from embeddings. This forces the latent space to contain only confounder-invariant representations. AD-AE was also better at domain adaptation to different datasets which indicates that it learned more meaningful cancer responses to drugs. Given that there is much more gene expression data on cancer cell lines than on tumor cells, AD-AE has also been applied for domain adaptation from cancer cells to tumors \cite{he_code-ae_2021-1}.

Finally, drug response can serve as a direct optimization target for generating new molecules. While conditional VAE (CVAE) has been used in the past for drug design with specific physicochemical properties (see section Drug Design; \cite{lim_molecular_2018}), Joo et al., used a conditional VAE used to generate new drug candidates with the desired property of inhibiting cancer cell growth \cite{joo_generative_2020}. The model takes in molecular fingerprints and a one-hot vector which indicates the binarized concentration of a drug needed to inhibit 50\% of cells in a population (GI50). During inference, a label with desired GI50 value is passed to the decoder along with a random sample from a latent space. Generated molecular fingerprints did indeed resemble similarity to those of FDA-approved drugs for breast cancer indicating that the model learned the relationship between the structural properties encoded in the fingerprints and efficiency of drugs. However, generating a molecular fingerprint is not the same as generating a full molecular structure. In the future, more direct approaches that generate full molecular graphs directly can be used with similar optimization targets.

\section{Challenges and Future Directions}
With exceptionally high failure rates and a large amount of unlabeled data, drug design and drug response prediction represent attractive areas for applying generative models. However, as emphasized by some authors in the field, reduction in cost and time for drug screening using AI often does not lead to improved decisions \cite{bender_dangers_2021-1}.

Generation of new pharmaceutically active compounds is challenging for several reasons. A lot of information is lost by using molecular representations. Many of the available data formats (such as SMILES or amino acid strings), when used alone, ignore the complexity of biological systems when trying to predict drug performance. This complexity includes but is not limited to secondary targets, drug metabolites and concentration-dependent effects.

Generating a molecular fingerprint of a drug or a SMILES string is far from generating a full molecular structure. Moreover, \textit{chiral} molecules (molecules that are not identical to their mirror images) often have very different properties. Drug receptors can recognize only one \textit{enantiomer} (one mirror image) but not the other. Sometimes, they can even have opposing properties with one being an inhibitor while the other being an activator of the same receptor \cite{weiskopf_drug_2002}. This information is completely lost in SMILES strings. Better molecular representations or modelling of full molecular graphs could therefore substantially advance the field in the upcoming years.

AlphaFold represents an excellent example of how emphasizing relevance of data (protein structure) can help solve the most complex biological problems \cite{senior_improved_2020-1}. In fact, Alphafold's transformer achieved state-of-art results in protein folding with just training 170,000 samples.

Another problem of some studies is a lack of proper benchmarking and evaluation compared to previously published work. Several papers provided only proof-of-the-concept examples of validity of their approaches. Having rigorous, standardized, and well-known comparison standards for drug design, similar to benchmarks for image generation on CIFAR-10 or ImageNet, would enable more transparent evaluation of newly proposed methods. Examples of proposed benchmarks include GuacaMol \cite{brown_guacamol_2019} or MOSES \cite{polykovskiy_molecular_2018}.

\begin{figure}[h!]
    \centering
    \includegraphics[width=\columnwidth]{img/zhavor.png}
    \caption{Compound 1 is DDR1 kinase inhibitor produced by the model and Ponatinib is a compound from dataset most similar to it. Adapted from }
    \label{fig:zhavor}
\end{figure}

Furthermore, the field of generation is particularly vulnerable to IP issues. For example, DDR1 kinase inhibitors recently produced by the generative pipeline of Insilico \cite{zhavoronkov_deep_2019} were claimed to be "the AlphaGO moment of drug design". However, later a few researchers pointed out that the drug is almost identical to the one that appeared in the dataset of patented ligands they used, drug Iclusig (compound ponatinib) marketable by ARIAD Pharmaceuticals (Figure \ref{fig:zhavor}) \cite{walters_assessing_2020}. To prevent this from happening, the future research in de novo candidate generation should publish molecules from the training set that are most similar to the generated candidates.
This heavy reliance on data and previous research, however, points to one other problem. In recent years, there has been growing evidence on lack of reproducibility in biological research. Recent investigation into oncology drug design showed that two-thirds of published data on drug targets is not reproducible \cite{prinz_believe_2011}. Given wrong data, even state-of-the-art models won't be able to generate meaningful candidates.

\section*{Conclusions}
In this paper, we present the current advances in the fields of drug design and drug response prediction using deep generative models. While there is a clear intuition for why these methods can be applied to drug discovery, most of the progress is yet to be made. Only a small fraction of research discussed in this review went beyond proof-of-concept and was used in industry pipelines. Generative models face limitations similar to previous approaches used in drug discovery and drug response prediction, (like QSAR), namely, little understanding of actual mechanism of action and risk of inaccurate predictions. Even though large databases of unlabeled molecules are attractive for unsupervised generations, to really increase success rate of drug discovery, future research should also make an emphasis on relevancy and quality of data used in the training.

\bibliographystyle{unsrt}

redpajama_set_name: RedPajamaArXiv


The freight transport sector will significantly evolve by 2050. To avoid negative aspects of freight transport growth, low carbon logistics solutions (e.g. consolidation of goods or route optimization) reducing GHG emissions, transportation flows, traffic accidents and other related issues must be actively applied.
THE MISSION to search for Agents of Change which are environmentally, socially and economically conscious of future logistics challenges and opportunities and develop long-term, low-carbon, innovative and value adding investment strategies and partnerships.
THE GOAL to select the best candidates in three categories: "Business", "Municipality" and "NGO", which have demonstrated leadership and developed a low carbon logistics project with significant economic, environmental and social benefits on local, national or even multinational economies and communities.
The Awards will consist of national* and international stages. During the national contest stage, one winner in each category will be elected. At the second stage, the national winners will compete for an international winner's nomination.
Participation in the Award is an opportunity to gain recognition and to demonstrate your achievements to an international audience. The Award will help your business, municipality or NGO to be seen as an example of good practice and portray your success storyso others may follow.
Furthermore, the winners will may adorn themselves with the LOW CARBON LOGISTICS AWARD 2018 label, as special recognition confirming their achievements in implementing low carbon logistics solutions.
The winners will be presented with the awards during the Gala Dinner of the international conference "Green Cities: green logistics for greener cities", held in SZCZECIN, Poland on 13-14 September 2018.
*Applications are accepted only from entities or individuals acting in Germany, Lithuania, Poland or Sweden.
Application deadline: 15 th of July, 2018.
Is your municipality (or municipal institution) a pioneer in city logistics? Have you developed an effective cooperation project or a successful communication campaign?
You are invited to apply for the LOW CARBONLOGISTICS AWARD 2018: MUNICIPALITY PROJECT OF THE YEAR!
Are you actively advocating for low carbon logistics solutions in your city or community?
Apply for the LOW CARBON LOGISTICS AWARD 2018: NGO AND COMMUNITY PROJECT OF THE YEAR!
Application forms will be evaluated by an esteemed international jury which is composed of experts with both academic and entrepreneurial backgrounds. Highly experienced representatives in logistics and the business sector, experts working in the field of sustainable development, as well as academic professionals will jointly elect both national winners and the final international winners of each category.
Applicants are invited to fill in the form below.
Contact your country representative if you have any further questions.


The Constable collection from Tetrad combines real leather with velvet seat cushions. Hand studding details to the arms and seat front. Mixed scatter cushions in various designs are included.
Displayed in Galveston Bark with Coco Velvet Olive seat cushions. Scatter cushions included.


Find City meetings and events. Filter calendar events by category or department from the drop-down menus below. Select the event to learn more.
Planning Commission meeting scheduled for Tuesday, February 12, 2019, is canceled due to inclement weather.
The Planning Commission meets on the second and fourth Tuesday of every month at 7:00 p.m. in the City Hall Council Chambers. Meetings are held at Tumwater City Hall, Council Chambers, 555 Israel Rd. SW, Tumwater, WA (map – this is a link).
The Planning Commission acts as an advisory body to the Council on all land use, comprehensive planning issues, and any other interesting items which the Council feels may need Planning Commission review. The Commission is an integral part of the implementation of the Growth Management Act. The Commission was created in accordance with RCW Chapter 35.63 and Tumwater Municipal Code (TMC) Chapter 2.56.
Please contact Planning Manager Brad Medrud or Permit & Planning Technician Kerri Kinnaird at (360) 754-4180 or cdd@ci.tumwater.wa.us with any questions about the agenda or upcoming meetings.
Americans with Disabilities Act (ADA) accommodations provided upon request, with reasonable, adequate notice. Contact the City Clerk at cityclerk@ci.tumwater.wa.us or call (360) 754-4130.


USCHO.com Fan Forum | board.uscho.com > College Hockey > Men's Division I > Great weekend for Colgate!
View Full Version : Great weekend for Colgate!
Can't wait to see the Mariucci trophy in the 'Gate trophy case. We're awfully young and inconsistent, but we'll see what this win does for the rest of the ECAC season. Go 'Gate.
What a weekend by Colgate. While I wish the Gophers would have won, but they didn't. Colgate just went about their business and beat #1 and #2. Congrats.
Finn was impressive. He should make for a fun team to watch.
I'm sure the Gophers are the better team - they were missing a couple players & a coach for the Colgate team. One thing about hockey - the best team doesn't always win. A hot goalie can get a team a lot of wins, and give them a bunch of confidence.
Colgate isn't bad, just very young and inconsistent. They will be fun for us to watch for the next couple of years.
I also wanted to say congratulations to the Colgate fans here. You guys most definitely deserved to win the tournament. If your team plays that way from here on out, they will make some noise this season. I cannot believe Finn is a freshman. Future is bright at Colgate for sure.
You could if you saw him in November at the HFH when he gave up three goals in seven shots.
One of which was highly reminiscent of a shot Robbie Bina once made on Jeff Frazee.
What this tells me is that the Coaching staff at Colgate has done an excellent job at developing their freshman goaltender. Our freshman goaltender, Alex Sakellaropoulos, also had a rough beginning when he had to step in for our injured starter in our first five games. When he got another starting role, in midseason, it was against one of the then top scoring teams, and the #1 team at that time, St. Cloud. He nearly shut them out (shots 34-1). Hats off to Finn and the Colgate coaching staff. Developing raw talent into winners is what good coaches do. By the way, you did the ECAC proud. Put the wins behind you, lots of tough competition on the horizon. No easy wins in the ECAC this year. Back to work.
Just a footnote. Troy Grosenick, our starting goalie last year had a real difficult start as a freshman as well. He went on to be a Hobey Baker finalist.
Not like Ralph to be rabidly to the point and truthful? You must not know him very well.


With passage of bills to reauthorize the ESEA in both the U.S. Senate and the House of Representatives (details here) the likelihood of getting an update to current law – known as No Child Left Behind (NCLB) – seems probable for the first time since NCLB "expired" in 2007. While few supporters stand behind all the original tenets of NCLB, some of its requirements helped improve education for students with disabilities.
However, few states are still operating under NCLB rules, most having obtained ESEA Flexibility from the U.S. Dept. of Education starting back in 2012. The new accountability plans states were allowed to develop under their ESEA Flexibility (to replace Adequate Yearly Progress or AYP) raised many questions about just how students with disabilities would be impacted, as we laid out in our 2013 report, ESEA Flexibility: Issues for Students with Disabilities.
Now Congress is moving toward a new ESEA that will completely eliminate many of the provisions of current law (maintained in ESEA Flexibility) that have worked to gain needed attention to the specialized services and supports necessary for students with disabilities to benefit. A few are recapped below.
INTERVENING IN LOW PERFORMING SCHOOLS. The overarching issue – for all historically under-performing groups of students including students with disabilities – is the lack of any requirement for states to take action in poor performing Title 1 schools in either the Senate or House ESEA bills. This concern has been best articulated by the Leadership Conference & 41 organizations in their letter to Senate HELP Committee leadership. The Education Trust also stands firmly opposed to this abandonment of accountability and encourages action via its All Kids Matter campaign. Intervention should be required in low performing schools and schools with significant gaps in the performance of student groups/categories.
MAKING STUDENT GROUPS COUNT. A major driver in the identification of low performing schools is the issue of "n" size. Schools and districts are held accountable only for the student groups that meet or exceed this minimum number of students.
For example, if a state established an "n" of 35, a school with only 20 students with disabilities in the tested grades would not be held accountable for this group of students. Under current law and both the Senate and House bills, states will continue to determine the minimum number of students necessary to hold schools and districts accountable for the performance of each student group (called "categories" in the Senate bill) directed only by a requirement for it to be statistically significant and not disclose personally identifiable information. This latitude in determining "n" size resulted in significant numbers of students with disabilities being left out of accountability systems. A 2013 report, The Inclusion of Students with Disabilities in School Accountability Systems, found that as many as 85% of students with disabilities in one state (AZ) were excluded from accountability systems due to "n" size.
One glimmer of hope in getting states to be more responsible in setting "n" size is contained in the Senate bill (Title I, Sec. 1016). It requires the U.S. Dept. of Education to publish a report on "best practices for determining valid, reliable, and statistically significant minimum numbers of students for each of the categories of students for the purposes of inclusion as categories of students in an accountability system." (The National Center for Education Statistics recommends an 'n' size of 10) Presumably states would look to this report to determine "n" sizes going forward. However, there is nothing binding about this and furthermore, it is not specified in either bill that the "n" size must be the same for all student groups/categories (as is the case in current law).
(3) While neither Senate nor House bills speak specifically to this, current NCLB regulations require that students with disabilities are assessed on the assessment for their enrolled grade level. This requirement helped to eliminate the practice of "out-of-level testing" (OOLT) of students with disabilities and resulted in schools and parents getting information on how students with disabilities perform vs. their same age/grade non-disabled peers. It will be important to maintain the prohibition on OOLT in any ESEA re-write.
Limitation. Senate bill limits the number of students with disabilities who can be assessed using an AA-AAS to one percent of all students assessed – which equates to roughly 9%-10% of students with disabilities. This provision is similar to a NCLB federal regulation and is designed to ensure that students with disabilities are not inappropriately assigned to an alternate assessment. The House bill contains no such limitation and would open the door for abuse of alternate assessment.
Prohibition. The Senate bill also contains a prohibition on any state developing any other alternate assessment for students with disabilities. Maintaining this provision in any ESEA re-write is critical to ensuring that students with disabilities – other than those with the most significant cognitive disabilities – participate in states' general assessment at their enrolled grade.
GRADUATION. Attention to the graduation rate of all students, including students with disabilities, was significantly enhanced via the promulgation of federal ESEA regulations in 2008. The new regulations ensured that graduation received enhanced focus, particularly in those high schools failing to graduate at least 60% of students in four years (called drop-out factories) by disaggregating graduation rates by student subgroups, setting annual graduation rate targets and linking those targets to Adequate Yearly Progress (AYP). Most of these advancements are lost in both the Senate and House ESEA bills. While both bills maintain the requirement to report graduation rates using the 4 Year Adjusted Cohort Graduation Rate (ACGR) for all students and student subgroups, neither incorporates serious consequences for failing to achieve annual targets or make significant improvements. More from AEE.
BOTTOM LINE: In their current form, neither the Senate nor House bill to reauthorize ESEA is good enough for students with disabilities. With these as starting points for conference, we can anticipate only an unacceptable produce to emerge.


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These are three good books for learning Arabic Language for the non-native speakers. But we should pay your attention that they are for who has slightly good knowledge about Arabic and how to pronounce it. We don't claim that you can consider them as an alternative to a teacher but they are good step for who wants to be excellent at Arabic. With these books, there is an Arabic-English glossary confined to the words mentioned in these books, and the author shows in its introduction how to find any word.


Membership Requirements - Wexford Credit Union Ltd.
Membership of Wexford Credit Union is open to anyone who lives or works within our common bond (as well as family members). Members who are now living outside the common bond, in either the Republic of Ireland or United Kingdom, may apply for membership of their child or step child, who is under the age of 18.
What is a "Common Bond"?
This is what unites all members of a credit union. The Common Bond defines the geographic area in which we can operate as a community credit union. Any person and their family members who are living or working in the area are eligible for membership.
In our case it consists of residence or employment in the parishes of Wexford (including Clonard), Glynn and Barntown.
1. Proof of identification e.g. passport, driving licence, national identity card, identification form with photo signed by a member of the Gardaí (ML10), Garda Age Card.
Utility bill e.g. telephone (including mobile), electric, gas, cable.
Drivers licence that contains a current address, has been issued in the previous 3 months and is not being used as your proof of identification.
3. PPS number verified on official documentation e.g. Drug Payment Scheme card, GP visit card, medical card, payslip, European Health Insurance Card, P60, P21, P45 etc.
Note: In compliance with the OECD Common Reporting Standard which came into effect on 1st January 2016, Wexford Credit Union Ltd. is required to seek information on the tax residency of all of its members. To this effect, all new members must sign a self-certification relating to their tax residency and provide their tax identification number, if tax resident outside of the Republic of Ireland. Failure to sign this self-certification & provide this tax identification number will mean that we cannot open account for you.
As a registered member and as per the Credit Union Act 1997 Section 75 and with the right of inspection allowed for under Section 71, any member of a credit union or any other person having an interest in the funds of a credit union may, at any reasonable hour, inspect the Register of Members at the registered office of Wexford Credit Union, the time and manner as may be determined by the board of directors and subject to terms and conditions.