News News: NEW DELHI: The second phase of polling covering 95 Lok Sabha constituencies across 12 states saw an overall 67.
Tesla is at risk of missing jobs targets at its New York state Gigafactory 2 and paying tens of millions of dollars in fines, Greentech Media reports.
Investors' biggest fear about the finance industry – that if the Federal Reserve really is done raising rates for the foreseeable future, banks' main profit-making engine will sputter – is becoming apparent.
Pete Buttigieg's increasingly popular presidential run has drawn the support of more than two dozen top Democratic fundraisers, according to a list CNBC obtained from campaign aides.
Zoom prices its initial public offering at $36 per share, sources told CNBC Wednesday.
Lok Sabha Elections 2019 News: SUMERBANDH/PHIRINGIA (KANDHAMAL): A day before the second phase polls, Maoists on Wednesday resorted to violence by gunning down a polling party membe.
News News: BHOPAL/ NEW DELHI: The stage is set for a high wattage contest for the Bhopal Lok Sabha seat, held by BJP since 1989, with the saffron party naming Sa.
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text:
Arizona Cardinals, Arizona Sports, Football, NBA, NBA Draft, NFL, NFL Draft, Phoenix Suns, Sports
Arizona Sports: What a Week To Be a Sports Fan in Zona With the Addition of CP3 and Kyler's Magic
Photos by Fredrick Breedmon and Jesse D. Garrabrant
First, it was the Suns having the number 1 overall pick two years ago and was followed up with a bigger splash when the Arizona Cardinals had the top pick in 2019. The verdict is still out on Deandre Ayton but he's far from a bust. You can say he's under-performed for the hype that he's created growing up being a man amongst boys. In 109 games Ayton is averaging 17 points and 10.7 Rebounds per game. The second number 1 pick in Arizona was Kyler Murray, who is playing at an MVP level in his second season in the league so you can say he's meeting expectations of a top pick.
(Photo by Christian Petersen/Getty Images)
When Kyler Murray took the field with 35 seconds left down by 4 he wasn't tripping. He knew he had Deandre Hopkins in the End Zond. The Cardinals pulled off one of the most improbable Hail Mary's of All-Time to DeAndre Hopkins into triple coverage. The two top offseason acquisitions in an article I published a few weeks ago were Deandre Hopkins number 1 and my runner-up was Stefon Diggs who put the Bills up by 4 with 30+ seconds left. Obviously, for Kyler, that is all the time he needed with no timeouts. As long as he can give Hopkins a chance at a jump ball the Cards had a chance. Kyler is a special player doing special things in his 2nd season.
A day after Kyler Murray lifted every Cardinals fans on Sunday the Suns made the first big move of the NBA offseason. They sent rising star Kelly Oubre Jr., who had a career season, and Ricky Rubio to get CP3. Chris Paul will bring toughness to a very 'soft' Deandre Ayton and he will take some pressure off of Devin Booker. Most notably at the end of games. In the past, Arizona sports teams have gone after the aging veterans. However, Chris Paul is different at age 35 then Shaq when he got here or Emmitt Smith when he arrived in the desert. CP3 can still play with anyone on the court. This will be his 16th season in the league and he has the resume of a first ballot hall of famer. Side note he's been filmed working out with Devin Booker a day after the trade was made. That work ethic will be contagious for Monty Williams and the Phoenix Suns heading into next season.
(Photo by Nathaniel S. Butler/NBAE via Getty Images)
Two number 1 picks in two years for the Cardinals and Suns gives Arizona sports fans some optimism. The face of Arizona Sports for the last 15 years has been Larry Fitzgerald and most recently Devin Booker but he's still a baby. There are many bright new faces in Arizona. DeAndre Hopkins and Kyler Murray look like they're going to be a problem for the NFL for as long as they're together. The Suns are the winningest franchise in Arizona Sports and haven't had an offseason pickup to be this excited about since Sir Charles Barkley came to town. They also haven't tasted the playoffs in a decade and were a game short of making it last season with an 8-0 bubble run that gave Suns fans something to be proud of.
Born and raised in AZ and grew up playing football and baseball! I miss the daily grind of playing football in the Trenches! The Trenches are where games are won or lost and the toughest players live there. Graduated with a degree in Speech Communication and am now writing about the Sports I once loved to play! The passion lives deep inside of me!	View all posts by NMG
November 18, 2020 Leave a commentArizona Cardinals, Deandre Ayton, Deandre Hopkins, Kyler Murray, Monty Williams, Phoenix Suns
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Jared and I got into it today about which candy is better, Snickers or Milky Way. He claimed that only 1 in 10 would respond Milky Way. A quick office poll revealed Snickers is preferred more, but only by a small margin. The initially tally was Snickers 9, Milky Way 7.
Seating placement hazardous to your career advancement?
I'm sure if you have worked in an office, you have noticed that there are some workspaces within the office that are ideal for seclusion, quiet, and hiding from the boss. There are also those that are the exact opposite; crammed in a corner or in the middle of everything, next to the office trash can, or inline with the door where every person walking in can see your screen. Needless to say, the guy sitting by the door, let's call him John, is most likely to be the least productive.
I found it fairly difficult today to find how to make Word's menus (file, edit, format, view, etc) display all of the menu items instead of a short list with an expandable button.
A large menu displays, at the bottom is Customize. Click that.
Go to the Options Tab and select the "Always Show Full Menu" checkbox.
I got an invitation on Facebook today to a High School reunion. This would have been great if it was for Centerville High School, class of '01. However, it was for a totally different school, class of '03. Now I wouldn't have been as upset about this if it was a Centerville reunion for the wrong class year, but this was totally off. Basically, the creator of this event decided that rather then sending this to only his high school friends, he would send it to his entire friend list.
There are almost 700 people invited to the event and said HS's website lists the current enrollment at 1,670, so the numbers don't quite match up. Lame – next time, invite the appropriate people and spare me my time.
Why do companies require you to change you password every 90 days? You're the only one who knows the password. If someone else knew your old password, wouldn't they have already gotten into you email, data, and files and screwed things up, alerting you to the fact that someone else now has your password.
If someone can get your current password, whats going to stop them from figuring out what you just reset it to? Especially when people are just changing 1 letter to get around truly having to change their password. One guy at work here just keeps adding the number 6 to his password, so he is currently typing password666666666. Awesome security.
if only I had something useful to say . . .
I knew Google was good, but not that good. I logged into WordPress today to see that people had come to my homepage by searching Google with certain phrases. I tried it myself and low and behold, searching for "live messenger draw" returns my silly post as the top result.
If only I had something worthwhile to say, who knows how many other people would stumble onto this site.
Notice that next to my name, there is an icon that reads "YOU". Thanks LinkedIn, I wasn't sure STEVE CONROY was me. It also tells who I am twice.


Thomas, Dr. G.L. Goldfish Pools, Water-Lilies and Tropical Fishes. Jersey City. T.F.H. Pub. 1958. 336 pps. Octavo. Hardcover. First edition. Illustrated in color and black & white. Fine copy.
Thomas, Isaiah. The History of Printing in America. With a Biography of Printers & an Account of Newspapers. New York. Weathervane. 1975. 650 pps. Octavo. Hardcover. Reprint. Fine copy.
Thomas, R. T. Britain and Vichy: The Dilemma of Anglo-French Relations, 1940-42. New York. St. Martin's Press. 1979. 230 pps. Octavo. Hardcover. First edition. Ex-libris, usual markings. Very good copy in very good dust jacket.
Thomas, Tony. The Films of Ronald Reagan. Secaucus. Citadel Press. 1980. 224 pps. 4to. Hardcover. First edition. Illustrated. Fine copy in fine dust jacket (in mylar).
Thomas, Tony. The Films of Ronald Reagan. Secaucus. Citadel Press. 1980. 224 pps. 4to. Hardcover. Stated first edition. Illustrated. Important reference work. Fine copy in near fine (spine tip wear) dust jacket (in mylar).
Thomas, W. Hugh (1935- ). The Murder of Adolf Hitler: The Truth about the Bodies in the Berlin Bunker. New York. St. Martin's Press. 1996. 305 pps. Octavo. Hardcover. First edition. Fine copy in fine dust jacket.
Thompson, Bob. The Pocket Encyclopedia of California Wines. New York. Simon & Schuster. 1985. 207 pps. Octavo. Hardcover. Revised edition. Illustrated. Fine copy in fine dust jacket (in mylar).


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Headquarters location
Longleaf Services (US)
Codasat Canada (Canada)
NBN International (Europe)
Footprint Books (Australia)[1]
www.cornellpress.cornell.edu
2008 conference booth
The Cornell University Press is a division of Cornell University housed in Sage House, the former residence of Henry William Sage. It was first established in 1869, making it the first university publishing enterprise in the United States, but was inactive from 1884 to 1930.[2][3]
The press was established in the College of the Mechanic Arts (as mechanical engineering was called in the 19th century) because engineers knew more about running steam-powered printing presses than literature professors.[4] Since its inception,[2] The press has offered work-study financial aid: students with previous training in the printing trades were paid for typesetting and running the presses that printed textbooks, pamphlets, a weekly student journal, and official university publications.[5]
Today, the press is one of the country's largest university presses.[6] It produces approximately 150 nonfiction titles each year in various disciplines, including anthropology, Asian studies, biological sciences, classics, history, industrial relations, literary criticism and theory, natural history, philosophy, politics and international relations, veterinary science, and women's studies.[3][7] Although the press has been subsidized by the university for most of its history, it is now largely dependent on book sales to finance its operations.[8]
In 2010, the Mellon Foundation, whose President Don Michael Randel is a former Cornell Provost, awarded to the press a $50,000 grant to explore new business models for publishing scholarly works in low-demand humanities subject areas. With this grant, a book series was published titled "Signale: Modern German Letters, Cultures, and Thoughts[9]." Only 500 hard copies of each book in the series will be printed, with extra copies manufactured on demand once the original supply is depleted.[8]
Other currently active series include "Expertise: Cultures and Technologies of Knowledge"[10] and Police/Worlds: Studies in security, crime and governance.
See also [ edit ]
Category:Cornell University Press books
References [ edit ]
^ "Cornell University Press - Ordering Information". Retrieved 14 March 2018.
^ a b Bishop, Morris (1962). A History of Cornell. Ithaca, NY: Cornell University Press. p. 127. ISBN 978-0-8014-0036-0.
^ a b "The History of the Cornell University Press". Cornell University Press. Retrieved 2006-01-01.
^ Bishop, Morris (1962). A History of Cornell. Ithaca, NY: Cornell University Press. p. 96. ISBN 978-0-8014-0036-0.
^ Bishop, Morris (1962). A History of Cornell. Ithaca, NY: Cornell University Press. pp. 175–76. ISBN 978-0-8014-0036-0.
^ "2009–10 Factbook"(PDF). Cornell University. Archived from the original(PDF) on 2006-06-26. Retrieved 2009-12-27.
^ "Cornell University Press: Information for Authors". Cornell University Press. Retrieved 2006-06-06.
^ a b Lam, Jackie (September 21, 2010). "In a Tough Market, University Press Aims to Streamline Production". Cornell Daily Sun. Archived from the original on December 14, 2011. Retrieved 2010-09-22.
^ "Signale: Modern German Letters, Cultures, and Thought". Cornell University Press. Retrieved 2020-11-21.
^ "Expertise: Cultures and Technologies of Knowledge". Cornell University Press. Retrieved 2020-11-21.
External links [ edit ]
Colleges:
Agriculture and Life Sciences
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Industrial and Labor Relations
Forestry (Defunct)
Graduate and Professional Schools:
Medical (Qatar, Sciences,Tri-Institutional)
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Fields and Arenas:
Barton Hall
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Robert Trent Jones Golf Course
"Give My Regards to Davy"
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Andrew Dickson White House
Bailey Hall
Balch Hall
Barnes Hall
Boyce Thompson Institute
Bradfield Hall
Brain Collection
Caldwell Hall
Computing and Communications Center
Comstock Hall
Fernow Hall
Hartung–Boothroyd Observatory
Ornithology Lab
Rice Hall
Risley Residential College
Sage Chapel
Sage Hall
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Telluride House
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Morrill Land Grant Act of 1862
Literary societies
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Sierra Hull – Secrets Songs & Tunes – DVD Preview
By admin / February 27, 2010 March 16, 2017
It's finally here and now available for PRE-SALE! Sierra's Acutab Mandolin Instruction Video entitled: "Secrets Songs & Tunes".
Official Release Date: March 16, 2010
Learn the mandolin solos from Sierra's debut CD, Secrets. 4 hours on 2 DVDs – with printed booklet.
This is the official preview video by acutab:
Sierra Hull – Secrets Songs & Tunes mandolin DVD
Sierra Hull "Secrets"
Sierra Hull Homepage: http://www.sierrahull.com/
Sierra's Blog: http://sierrahull.blogspot.com/
Youtube channel with more interesting videos: http://www.youtube.com/user/9firefly9
Listen to the complete album Secrets: http://airplaydirect.com/music/SierraHull/
Buy the CD (Amazon partnerlink)
Secrets – Sierra Hull


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Cortana is the name for the intelligent personal assistant and knowledge navigator from Microsoft. Cortana builds off Microsoft's previous voice technology called TellMe, which was purchased by Microsoft in 2009. Cortana competes against Android's Google Now and Apple's Siri voice assistants.
The name 'Cortana' comes from the fictional, artificially intelligent (AI) character in the Halo video game series. The first evidence of Cortana's existence was found in June 2013 in the Windows Phone Central forums, appearing as 'zCortana' from a developer device that was accidentally sold to a non-Microsoft employee.
Naming and Voicing
The name Cortana was reserved for the AI in Microsoft's Halo gaming series. While there was a desire to call Microsoft's next-gen personal assistant Cortana as an official name, it was not clear that the company could do so, due to numerous marketing and trademark concerns.
However, on April 4 2014, Microsoft finally unveiled Cortana to the public during Build 2014. Microsoft chose the name Cortana due to a popular groundswell of fans who requested the name.
Jen Taylor, who currently voices Cortana in the Halo games, also voices the knowledge navigator for Windows Phone 8.1.
Look and Features
The current look of Cortana is both futuristic and minimalist, being represented as an orb with animation. From a pre-release description of Cortana by an inside source:
"The personality of Cortana is currently represented by a circle graphic that expresses what it's doing (searching, identifying music Cortana is currently represented by a circle graphic that expresses what it's doing (searching, identifying music à la the current Bing music listener built into Windows Phone, etc.) by various animations like spinning, flipping, etc. …more animations [have] come in over time, and the color is based on your theme color."
Cortana can integrate with third-party apps via a new API introduced by Microsoft at their 2014 Build conference. For instance, users can say "Foursquare...checkin" and the personal assistant launches the Foursquare app and brings up locations around you to choose for checking in. Cortana can set reminders, track your flights, tell you the weather, send messages, and answer questions. Cortana can also make NFL and other predictions and speak Klingon.
Currently, Cortana is available on Windows devices, however Microsoft has confirmed that Cortana will soon be available on Android devices as well.
fresh look
Cortana beta debuts major UI overhaul and more on Android and iOS
Dan Thorp-Lancaster 3 years ago 22
Cortana is getting a big overhaul on Android and iOS, including a new conversational UI, connections to outside apps for podcasts and music, and much more.
AI battles!
Google Assistant vs. Cortana: Which is better for you?
Tom Westrick 3 years ago 17
Stuck choosing between Microsoft's Cortana and Google Assistant? Here's how they stack up with each other!
use it all the time
How to go all out with Cortana on Android
Daniel Rubino 3 years ago 11
Did you know you can set the new Cortana experience in Microsoft Launcher for Android as the default voice assistant? Today, I'll show you how and whether you still need the dedicated Cortana app.
Amazon teams up with Microsoft to integrate Alexa with Cortana
Harish Jonnalagadda 4 years ago 22
You'll soon be able to talk to Cortana by saying, "Alexa, open Cortana" from an Alexa-powered device like the Echo, and vice versa.
Hey, Cortana
Cortana comes to Skype Preview for Android
Dan Thorp-Lancaster 4 years ago 1
Microsoft has brought a little bit of Cortana magic to the Skype Preview app on Android.
There are a whole lot of people using Cortana every month
Russell Holly 4 years ago 16
Cortana may not be the biggest, but the number of people using it continues to grow at a healthy pace.
Cheerio Cortana
Cortana for Android takes on Google Assistant with redesign
Florence Ion 5 years ago 36
Spruce up your Android device with Cortana's digital assistant features. Cortana for Android is coming and it features all the same helpful functionality as the Windows 10 desktop app. This includes cross-platform reminders, missed call alerts, and daily summaries. You can even ask Cortana for help on various matters the same way you would with Google Now. Cortana also lets you manage...
YES CHIEF?
Cortana for Android will lets you manage notifications on PC
Rich Edmonds 5 years ago 15
Microsoft has released an update to Cortana for Android, which enables the support of notification synchronisation between Google's mobile OS and Windows 10.
Hey Chief
Cyanogen OS has Microsoft's Cortana pre-loaded
Microsoft has been opening up its platforms and services to other ecosystems and Cortana is the latest to be partnered with a competitive offering. The company has teamed up with the developers behind Cyanogen OS to have the personal assistant bundled with powered hardware.
new assistant
Cortana officially released for Android
John Callaham 6 years ago 58
Microsoft has officially launched Cortana, its digital assistant app, for Android in the US and China. The app has been in closed beta testing for some time and allows users to share information on their PC like their schedule, contacts, reminders and more on their Android device
Yo where you going?
Voice-activated 'Hey Cortana' comes to Android devices
Microsoft is bringing the voice-activated Hey Cortana to Android with their latest beta release. However, it's not nearly as good as it is on Windows Phone.
More ways to use Cortana
Cortana could be included in next version of Cyanogen OS
Kirt McMaster, the co-founder and CEO of Cyanogen, says that the company is working with Microsoft to integrate the Cortana digital assistant into the next version of Cyanogen OS.
Bye Google Now
Hands-on with the public beta of Cortana on Android
Mark Guim 6 years ago 54
Cortana for Android was released as a public beta earlier today. I installed it on the LG G4 to see how it works. Is it better on Android compared to Windows Phone? Go watch our hands-on video to find out.
Microsoft updates Cortana for Android, lets you replace Now
Microsoft has rolled out an update to Cortana for Android. The company has now made it possible for users to switch up their default assistant, in favor of Google Now or Microsoft's Cortana.
How to sync Android to Windows 10
Phil Nickinson 6 years ago 43
It's 2015, and you can actually get through life without ever physically connecting your phone to a computer. (We've done it. It's great. You should try it sometime.) But for some folks it's still the way to go. They just have to plug in every now and then.


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Seznam řek v Albánii (albánsky řeka ) obsahuje řeky, které mají na území Albánie délku 100 km a více a také některé kratší.

Tabulka řek

Nezařazené řeky
Berisha, Bushtrica, Drino, Dunaveci, Fani, Fani i Madh, Fani i Vogël, Gomsiqja, Gostima, Gjadri, Gjanica, Gjolja, Ishmi, Kalasa, Kiri, Lana, Lesniqja, Nikaj, Qarishta, Pavllo, Rapuni, Shala, Shushica, Tërkuza, Tirana, Valbona, Zeza

Poznámky

Reference

Externí odkazy

Albánie
Albánie
Albánie
řeky
Zeměpisné rekordy

redpajama_set_name: RedPajamaWikipedia


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HomeLatest NewsLibya Latest NewsForces loyal to self-declared premier 'Ghwell' seized control of 3 GNA ministries in Tripoli
Forces loyal to self-declared premier 'Ghwell' seized control of 3 GNA ministries in Tripoli
January 13, 2017 Ahmed Rashed Libya Latest News
While the Chairman of the UN of the Libyan Presidential Council Fayez al-Serraj was in Cairo to discuss with al-Sisi the latest developments in Libya, Khalifa Ghwell, the self-declared prime minister, said that his forces have seized at least three ministries in Libya's capital, declaring what appeared to be a miniature coup after what he described as a yearlong failure of the current U.N.-backed premier.
Khalifa Ghwell said that his forces control the ministries of defense, labor and the "martyrs and the wounded."
Ghwell announced in a televised speech that all the past arrangements brokered by the U.N. are "invalid," and described al-Serraj's government as "expired."
In addition, he ordered his forces that he referred to as the "Presidential Guard," to secure the capital and warned other militias to stand down.
He also called for new talks among Libyan factions without foreign mediators.
Ghwell said, "We are the ones with legitimacy," and he added,"We extend our hands to our Libyan rivals."
He continued,"God's Shariah will rule among us," without elaborating.
Ghwell added that the conditions have gone "from bad to worse" in the year since al-Serraj's government was formed, as the country has fallen in to a financial crisis.
"We gave him a year, and when he failed, we decided to return (to power)" Ghwell said.
In October, armed groups backing Ghwell "took control of a complex used by a parliament aligned with the GNA, and Ghwell has made several public appearances in which he has challenged the GNA's authority,"said Reuters.
In 2014, the National Salvation government was formed by the outgoing parliament after a disputed transfer of power that led to the establishment of rival governments, each backed by an array of militias.
Last year, the U.N. brokered the establishment of a third government in Tripoli under the leadership of Fayez al-Serraj, a Western-backed technocrat, hoping he could unify Libya and lead the fight against Islamic extremists.
However,the GNA has largely failed to impose its authority and restore the country's unity, although the Islamic State has been ousted from its former stronghold of Sirte and oil production has recovered slightly.
Since the Libyan Revolution that overthrew the long-time dictator 2011 Muammar Gaddafi, violence has spread in Libya among the different Libyan factions.
The GNA, the third government formed, has been unable to win endorsement for a cabinet from a parliament based in the east of the country, as other power-brokers, as Egypt,UAE,and France,have continued to back a rival government as armed forces allied to them and under the command of Gen.Khalifa Haftar have made military gains on the ground.
Moreover, although the GNA has gradually taken control of some ministries in Tripoli, but it has been unable to improve security or deal with a liquidity crisis, steep inflation, and a collapse in public healthcare provision.
In addition, the Presidential Council is made up of nine members who should represent different geographical regions and political currents within Libya.
The Council has been divided as two of its number mostly boycotting proceedings, and at the same time, different members regularly issuing contradictory statements.
It is noteworthy that the deputy leader of the GNA Musa al-Koni resigned this month and he saying the U.N.-backed administration had failed to tackle urgent problems arising from years of conflict and political disarray.
Koni said in a press conference in Tripoli said, "I announce my resignation due to the failure of the Presidential Council, because it holds responsibility for the killing, kidnapping, and rape that happened over the past year."
On the other hand, a spokesman for Serraj's government dismissed the moves by Ghwell's forces, saying "they are trying to sow chaos but they have no means to control."
He said the ministries Ghwell claimed to have seized are either under maintenance, not controlled by al-Serraj's government, or were seized briefly before being let go, as reported by Reuters.
Ashraf Tulty said, "This is nothing more than a media hoax." He added, "They are trying to sabotage the only internationally-recognized government in Libya."
The claims and counter-claims could not be independently confirmed, but the incident showed the extreme weakness of the U.N.-backed Government of National Accord (GNA).
The UN backed government is facing many challenges on all fronts.
First,The disorder in Tripoli after the returning back to Ghwell forces in to the city.
Second, Haftar's growing power supported by Egypt,UAE,France and lately Russia enabled him to expand his power in the east.
In addition, al-Sisi,Haftar's close ally,has brokered recently amending the Skhirat agreement in a way that suits General Khalifa Haftar.
On December 13, 2016, Cairo hosted a conference attended by Libyan officials and representatives from the country's numerous factions, where they issued a declaration of principles and five proposed amendments to an agreement, brokered by the UN in 2015.
The conference concluded an agreement on amending the 8th article of the 2015 agreement that outline the authorities of the Libyan army chief commander.
The article constituted a major obstacle during signing Skhirat agreement as it included the exclusion of General Khalifa Haftar from leading the army.
Third, Al-Serraj has lost control over the oil crescent terminals after Haftar had taken over oil facilities in eastern Libya from Petroleum Facilities Guards, a rival militia force allied to the UN-backed government in the capital, Tripoli.
Khalifa Ghwell
militias
National Salvation Government
oil terminals
Petroleum Facilities Guards
Skhirat agreement
Column: U.S. in transition, Turkey getting mixed messages
What does Egypt's Al-Sisi need from Britain?
Libya Latest News
Libya: Is Sarraj's 'resignation' announcement fait accompli or a strategic decision?
October 20, 2020 MEO Staff Libya Latest News
Libya is preparing for a wary political solution. Despite a normative political solution to the Libyan conflict, a surprising resignation announcement and mass protests took place in the territory of the U.N.-backed Government of National [ More … ]
Libya's deepening split finds battleground at oil terminals
March 12, 2017 MEO Staff Libya Latest News
Struggle for Ras Lanuf refinery and nearby Sidr depot threatens to spiral into all-out conflict between Libya's east and west. Hundreds if not thousands of armed men are converging on Libya's main oil shipping terminals, [ More … ]
Egypt Sent Military Support to Haftar's Militias
October 25, 2016 Ahmed Rashed Egypt Latest News, Libya Latest News
Egypt has sent military aircraft to eastern Libya to support General Khalifa Haftar's militias, known as the Libyan National Army (LNA), according to al-Arabi al Jadeed. The planes carrying military equipment and weapons arrived at [ More … ]


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The tale of horror left its imprint. How would life play out for victim and perpetrator?
Fiona Sussman's second novel, The Last Time We Spoke, was inspired by a chilling New Zealand crime. To understand it, she immersed herself in Auckland's underbelly
Thu, May 19, 2016, 10:52
Fiona Sussman
Fiona Sussman: What realities lay behind the headlines? How did each party move forward and negotiate the realm of their loss, be it in the form of a loved one's life or their own liberty?
A chilling New Zealand crime would be the genesis for my second novel, The Last Time We Spoke. The unsettling parallels between the victim's circumstances and some of my own, as well as the youth of the perpetrators, were what first piqued my interest.
It was the relentless media coverage, however, which knitted the story into my consciousness. But then, as is so often the media way, after the story had been sucked dry of sensation, it was abruptly dropped to make way for the next piece of news. I could not move on so easily. The tale of horror had left its imprint, and I found myself contemplating how life would play out for both victim and perpetrator. What realities lay behind the headlines? How did each party move forward and negotiate the realm of their loss, be it in the form of a loved one's life or their own liberty? These questions formed the seed from which The Last Time We Spoke would grow.
I had little trouble conjuring characters Carla and Kevin Reid – a white, middle-class couple living on a farm in rural Auckland, and Jack, their teenage son. Their world was one with which I was familiar. But what of Ben Toroa, the gangster, who robs this family of their domestic bliss? His counterpoint world was largely foreign to me. I owed it authenticity.
And so, over a period of two years, I navigated the underbelly of urban Auckland, visiting prisons and youth units across the North Island, going out on police patrol, speaking with individuals at Victim Support, reading victim and gangster autobiographies and, on one occasion, meeting face-to-face with an ex-gang member and murderer. I explored the colonisation of New Zealand and the rich Maori heritage from which Ben and his family had become dislocated. I learnt the slang of youth gangs. I listened to their hip-hop music. When I started rapping to music in my car while waiting to collect my children from school, I knew I had become fully immersed in this world!
I met many Bens during my research; our prisons are full of them. They are not all the evil, cold-hearted sociopaths we are often led to believe, but rather young men who have lost their way.
On the face of it, everything about 18-year-old Jack and 16-year-old Ben is different. The very disparate circumstances surrounding their births are what first sets them apart. Jack is dearly wanted; Ben survives a failed abortion. Jack is born into a middle-class home; Ben, into poverty.
While the dissimilarities between the two are so readily apparent, it was their commonalities which really interested me. The infant boys share identical human needs: the need for food and care, for love and for connection to something bigger than themselves. Neither is born a criminal, but rather each with the capacity to love – a trait with the power to elevate, but which circumstance can crush.
Ironically, it will be their very similar human drives which see Jack and Ben's paths diverge – one towards university, the other towards prison. Both teenagers need a safe base from which to explore the world. Both seek camaraderie and a bond with others. Both hunger for a sense of self-worth. Jack will find all this within the nurturing embrace of his family; Ben must find it elsewhere. A gang meets the brief.
One mild night in March, the lives of the Reid family and Ben Toroa collide. Neither will ever be the same again. The Last Time We Spoke defies a knee-jerk reaction to an appalling crime. Instead it peels back the headlines to navigate the darkness, finding light in unexpected places.
The Last Time We Spoke by Fiona Sussman is published byAllison and Busby, £12.99
0:42 Terry Jones, Monty Python star, dies at the age of 77
5 Rob Delaney: 'As my career took off, my son got sick...then he died'


Paulie Roche comes from a specialised background in architectural acoustic, audiovisual and electroacoustic design. With a focus in arts and culture, he provides integrated design practices with logical and creative styles to deliver a project that will have an end user's experience in mind. He started life as a live sound engineer and went on to specialise in ambisonics at Salford University. Paulie was a key part of the team responsible for the acoustic, electroacoustic and soundsystem design for several venues including the London Olympic Stadium and Velodrome, Al Rayyan Stadium (World Cup 2022) and Wimbledon No.1 Court. Paulie's creative approach in his role at Hoare Lea sees him lead the soundscape and experience design of immersive public art works (including the Hive at Kew Gardens), with projects in development across the UK, Middle East, Asia and United States.


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PUResNet: prediction of protein-ligand binding sites using deep residual neural network
Jeevan Kandel1,
Hilal Tayara2 &
Kil To Chong ORCID: orcid.org/0000-0002-1952-00013,4
Predicting protein-ligand binding sites is a fundamental step in understanding the functional characteristics of proteins, which plays a vital role in elucidating different biological functions and is a crucial step in drug discovery. A protein exhibits its true nature after binding to its interacting molecule known as a ligand that binds only in the favorable binding site of the protein structure. Different computational methods exploiting the features of proteins have been developed to identify the binding sites in the protein structure, but none seems to provide promising results, and therefore, further investigation is required.
In this study, we present a deep learning model PUResNet and a novel data cleaning process based on structural similarity for predicting protein-ligand binding sites. From the whole scPDB (an annotated database of druggable binding sites extracted from the Protein DataBank) database, 5020 protein structures were selected to address this problem, which were used to train PUResNet. With this, we achieved better and justifiable performance than the existing methods while evaluating two independent sets using distance, volume and proportion metrics.
In living organisms, all biological processes involve proteins that are dynamic molecules with functions almost invariably dependent on the interactions with other molecules, which are affected in physiologically important ways through subtle, or striking changes in the protein conformation [1]. Such interactions occur in a specific site of a protein known as binding site, and any interacting molecule, ion, or protein is known as ligand. Elucidating the characteristics and function of a protein depends solely on its interaction with the ligand at a suitable binding site. The prediction of such binding sites is the first step towards understanding the functional properties of the proteins leading to drug discovery.
In recent years, numerous methods have been proposed to identify the potential druggable binding sites. Fpocket [2] is a geometry-based method, which is based on Voronoi tessellation and alpha spheres. The alpha sphere is a sphere that contacts four atoms on its boundary and contains no internal atom, which was introduced by Liang and Edelsbrunner [3]. LIGSITE [4] and POCKET [5] are based on a regular Cartesian grid, where if an area of solvent-accessible grid points are enclosed on both sides by the protein atoms, then it has a higher chance of being located in a pocket or cavity. EASYMIFs and SITEHOUND [6] are energy-based methods, where the molecular interaction fields (MIFs) are used to identify the probable binding sites through filtering and clustering. ConCavity [7] is a geometry-based method that combines evolutionary sequence conservation. COACH [8] is a consensus method based on a template in which the pocket is predicted by using a support vector machine (SVM). Other than the traditional methods for predicting the binding site, which are based on geometry, energy, evolutionary, consensus, and template, machine learning and deep learning methods have successfully emerged in recent years.
P2Rank [9] is a machine learning-based method for predicting ligand binding sites in the protein structures that imposes the random forest algorithm, where the 1D feature vector represents 35 numerical features and is trained on CHEN11 [10] dataset. DeepSite [11], kalasanty [12], DeepSurf [13] and DeepPocket [14] are deep learning approaches, which are based on 3D convolutional neural networks. In DeepSite and kalasanty methods, the protein structure is treated as a 3D image discretized into a grid of 1 × 1 × 1 Å3 sized voxel. DeepSite uses 16 × 16 × 16 voxels, whereas kalasanty uses 36 × 36 × 36 voxels to represent a protein structure. DeepSurf is a surface based learning approach where a new representation of the 3D protein surface is introduced, based on local voxel grids centered at sample points of the surface and uses 16 × 16 × 16 voxels. DeepPocket is a multi-step approach to get the final pocket location where first Fpocket is used to get the pockets and later classified whether they are binding site or not. All these methods shows promising results and uses scPDB [15] dataset. To improve result, filtering of scPDB dataset based on structural similarity is required which is not done by any mentioned deep learning methods. Although, DeepSite employs sequence similarity method to eliminate similar protein structure but we are more focused on structural similarity.
Our work is focused on improving the training data, so that our deep learning model can generalize more and provide better predictions. Therefore, we developed an independent training dataset, which is a subset of scPDB [15], a publicly available dataset released in 2017, containing 16034 entries, 4782 proteins, and 6326 ligands. Among 16034 protein structures present in scPDB, we selected 5020 structures. First, each of the protein structures from scPDB were grouped according to the UniProt ID [16], and then the Tanimoto coefficient [17] was calculated. Second, longest sequenced protein structure was selected from each UniProt ID cluster according to the Tanimoto coefficient (if Tanimoto coefficient \(\ge\) 80%, then it is regarded as a similar structure [17]). Finally, manual inspection was performed using PYMOL [18] and 5020 protein structures were selected out of 16034.
In this study, ResNet [19] architecture is used as a backbone for our model (PUResNet). ResNet is one of the popular deep learning architecture due to residual learning and identity mapping by shortcuts [19]. PUResNet comprises two blocks, encoder and decoder, where there is a skip connection between encoder and decoder as well as within the layers of encoder and decoder. Skip connections are used to address the vanishing gradient problem, which is the most common problem in training deep neural networks [20].
Protein structure is treated as a 3D image of the shape (36 × 36 × 36 × 18) which is input to PUResNet, and the output is the same as the input shape with a single channel (i.e., 36 × 36 × 36 × 1), where each voxel (point in 3D space) in the output has a probability that whether or not the voxel belongs to the cavity. Later, these predictions can be saved as mol2 files, which can be later visualized using the molecular modeling software (PYMOL).
In this study, new training dataset is developed, which is a subset of scPDB. scPDB dataset consists of protein structures belonging to 2050 different protein families [21]. The family Pkinase contains the highest number of protein structures (1486) whereas 555 protein families contain only a single structure. As an independent validation dataset, we selected COACH420 [22] test dataset, which consists of 420 protein structure with known ligands, and among them 122 protein structures were removed since they were present in our dataset. Finally, 298 protein structure with ligand were selected. Additionally, BU48 [23] dataset consisting of 48 pairs of bounded and unbounded protein structure, among which 31 pair were selected as an independent dataset, after removing protein structure contained in our training set.
304 protein structures that were erroneous while loading using openbabel [24, 25] were removed from scPDB dataset. Then, we followed the process of data cleaning, as depicted in Fig. 1. First, the grouping of protein structure according to the UniProtID was conducted using the Retrieve/ID mapping tool available online (https://www.uniprot.org/uploadlists/). A total of 5462 clusters of UniPort ID were obtained, of which 2964 contained a single protein structure and 2498 contained multiple protein structures. The cluster of UniPort ID (P00388), which had 19 protein structures, was the largest of all. Second, each protein structure in the cluster fingerprint was determined, where we used a substructure-based fingerprint calculation molecular access system (MACCS) [26], and then the Tanimoto index was calculated within each cluster.
For calculating Tanimoto index as shown in Fig. 2, protein structure having N amino acids, we obtained N-3+1 number of 3-mers (consecutive amino acid substrings of length three within a protein sequence obtained using frame size of three and stride of one), where each 3-mers is represented as a single molecule using openbabel and 167-bit MACCS key was obtained. Then, fingerprint for the protein structure is an array of size (N-3+1,167), where each row contains MACCS key for corresponding 3-mers. Let, A1 and A2 be fingerprint arrays for two protein structures. If the length of A1 and A2 is equal then the Tanimoto index is calculated between A1 and A2. Else if the length of A1 is greater than A2 then the Tanimoto index is calculated with the frame size of A2 with stride 1 and the maximum Tanimoto index is taken from calculated ones. Else Tanimoto index is calculated with the frame size of A1 with stride 1 and maximum Tanimoto index is taken from obtained values.
Flow diagram of data cleaning process
Flow diagram showing calculation of Tanimoto index
On an average, for each cluster having multiple protein structure, the Tanimoto index was found to be 80%, and therefore, we decided to select the longest sequenced protein structure from each cluster because of high similarity between the protein structure in the cluster [17]. The total number of selected protein structures was 5462 corresponding to unique UniPort IDs as a single cluster. For each protein structure in a single cluster, manual inspection was performed using PYMOL. Protein structures along with their binding sites were loaded in PYMOL, the chain with the binding site was retained, and the others were removed. As we treat the protein structure as a 3D image and classify each voxel as a binding site or not. Data imbalance occurs when data points are not equally distributed among classes. In our case, the number of voxels not belonging to the binding site is very high which makes our problem to be highly imbalanced. In a protein structure represented in 3D image, the ratio of voxels belonging to binding site to the voxels not belonging to binding site is about 0.001. Due to high data imbalance, the removal of chains without a binding site is necessary to address this problem. Although we cannot fully eradicate this problem, this step provides little leverage to the model. After that, the distance between the binding site coordinates to the center of the protein structure was calculated; if the distance between any coordinate of the binding site and the protein structure center is greater than 70 Å, then it is removed because such a binding site cannot be represented in voxels, and this will lead to training data without a binding site or a portion of the binding site. Finally, 5020 protein structures were selected for training, corresponding to 5020 Uniport ID and 1243 protein families, among which the Pkinase family contained 186 protein structures, and was largest of all.
We split our data into four folds by addressing the problem of data leakage during validation, based on the protein family, all the structures belonging to one family were kept in the same set of each fold (either on training or validation set). In each fold, the training set consisted of 3765 protein structures, whereas the validation set had 1255. We used k-fold [27, 28] training to tune the hyperparameters and validate PUResNet. After selecting the optimal parameters, the model was trained on the entire dataset for better performance.
Data representation
Here, the protein structure was treated as a 3D image of size 36 × 36 × 36 × 18, where a 3D cube of size 36 × 36 × 36 is placed at the center of a protein with 70 Å distance in each direction, and was described based on nine atomic features [29], such as hybridization, heavy atoms, heteroatoms, hydrophobic, aromatic, partial charge, acceptor, donor, and ring. Plotting of each atomic feature used in the study of the protein structure (1A80) is provided in Additional file 1. Finally, one protein structure was represented with 3D voxels of size 36 × 36 × 36 × 18.
To treat it as a binary segmentation problem where input size is 36 × 36 × 36 × 18 and output size is 36 × 36 × 36 × 1, each binding site was represented using same sized 3D voxels (36 × 36 × 36 × 1) placed at the protein center, and for each voxel, if the binding site was present, then the assigned value was 1 or else 0.
PUResNet is derived from the concept of U-Net [30] and ResNet. U-Net was originally developed for the segmentation of biomedical images, which are composed of convolutional and max-pooling layers in the encoder side and convolutional and up sampling layers in the decoder side. Moreover, there is a skip connection between the encoder and decoder blocks. Here, we propose a variant of U-Net that consists of three basic blocks (convolution, identity, and up-sampling blocks), as depicted in Additional file 2: Figure 1S, 2S and 3S which are based on the concept of ResNet. Unlike the 2D segmentation problem, which uses 2D convolution, we used 3D convolution to address our problem.
PUResNet is divided into two blocks, an encoder and a decoder, as depicted in Fig. 3, where the encoder is composed of a convolution block and an identity block (which has convolution layers as shown in Additional file 2: Figure 2S ), and the decoder is composed of an up-sampling block and an identity block. Instead of directly passing the skip connection to the decoder block like U-net, we first pass it through the identity block and then to the decoder, which leads to use of identity operation [19] in the skip connection. The idea behind designing this model is to address the vanishing gradient problem. To validate, whether skip connection helps in eliminating the vanishing gradient problem, we visualized the training process of two variants of PUResNet, one with skip connection and one without. As shown in Additional file 4: Figure 9S we can see that the accuracy of the PUResNet (learning rate = 10−5, kernel regularizer as L2 with value of 10−3, batch size of 5) without skip connections is almost constant which implies as the model is deep, gradients are either exploding or vanishing (shown in Additional file 4 Figure 10S). To counter this problem, skip connection inspired from ResNet architecture, are added in PUResNet which drastically changes the performance of the model as shown in Additional file 4: Figure 11S. One of the benefits of using skip connection is to eliminate exploding or vanishing gradients(as shown in Additional file 4: Figure 12S) in deep neural networks [20]. Here in PUResNet, there are 12 layers in the convolution block, 10 layers in the identity block, and 14 layers in the up sampling block. Altogether, there are 5 convolution blocks, 13 identity blocks, and 4 up sampling blocks. Number of filters used in each block is provided in Additional file 2: Table S1 and input/output size of each block is shown in Additional file 2: Figure S4. In total, there are 252 layers in PUResNet with 13,840,903 trainable parameters and 16,992 non-trainable parameters. Although PUResNet is deep but has a smaller number of parameters than kalasanty, which has 23 million parameters. A detailed explanation of this model is provided in Additional file 2.
Model PUResNet architecture showing both encoder and decoder block with skip connections
Our approach to optimize the hyperparameters was conducted through K-fold training, and we implemented the hit and trial approach using a heuristic method for optimizing the model. To select the value of K during the K-fold training, we assessed the validation and training curves for different values of K and found that K = 4 exhibits a smoother validation and training curve for our dataset. Hyperparameter optimization was conducted through selecting two sets of hyperparameters in such a way that the difference in values was high. K-fold training was conducted using the two sets of hyperparameters and determined which set had good performance, and then, the average value of the two sets was computed. After that, K-fold training was performed using individual values keeping others the same, and the results were obtained. If the performance was better than the previous result, then those values were selected and otherwise discarded. Further, we selected the top two results from K-fold training, which was conducted recursively until optimal parameters were obtained. Here, while selecting the optimal parameter, we considered every data point as the validation data using cross-validation so that our parameters were not biased towards a certain protein structure. Finally, after obtaining a set of optimal hyperparameters, we conducted K-fold cross-validation using K = 4, and the results were obtained. Final training was performed on the entire dataset with the obtained optimal parameters (learning rate = 10−4, kernel regularizer as L2 with value of 10−4, batch size of 5, number of trainable parameters 13,840,903, and others as default values as in keras [31]). Dice loss and binary crossentropy are widely used loss functions in the case of binary segmentation problems. To find out the performance of these loss functions, we carried out 4 fold experiment. As shown in Additional file 4: Figure 1S to 8S, we can observe that the dice loss has better performance than binary crossentropy (learning rate = 30−7, kernel regularizer as L2 with value of 10−5, batch size of 5). As expected, dice loss performs better in the case of highly a imbalanced dataset [32]. Therefore, we selected dice loss as our loss function.
Distance center center (DCC) and discretized volume overlap (DVO) are the matrices used to evaluate model in different studies [9, 11, 12]. In this study, we propose new metrics, the Proportion of Ligand Inside (PLI) for the accountability of ligands and predicted binding sites.
Distance center center (DCC)
It is distance between center of predicted binding site to the center of actual binding site or ligand. If the distance is \(\le\) 4 Å, then it is determined to be correctly predicted site, which is used to measure the success rate of the model and defined as follows:
$$\begin{aligned} Success Rate = \frac{Number \, of \, sites \, having \, DCC\le 4A^o }{Total \, number \, of \, sites} \end{aligned}$$
Discretized volume overlap (DVO)
DCC metric does not consider the volume and shape of the predicted and actual binding sites or ligands. Therefore, DVO, which provides insight into the volume and shape, is the ratio between the volumetric intersection between the predicted(Vpbs) and actual binding site(Vabs) to their union. For predicted binding sites having DCC \(\le\) 4 Å, DVO was calculated as follows:
$$\begin{aligned} DVO=\frac{V_{pbs} \, \cap \, V_{abs}}{V_{pbs} \, \cup \, V_{abs}} \end{aligned}$$
Proportion of Ligand Inside (PLI)
In case of ligands using DVO metrics to find overlap does not provide a comprehensive idea of the overlap, the binding sites are usually larger than the ligand. The DVO metric is similar to a shape analysis between the two binding sites, but in case of ligands and binding sites, it is not appropriate. Therefore, we developed a new matrix to determine the proportion of ligand (VL) resides inside binding site(Vpbs). For predicted binding sites with a DCC less than or equal to 4 Å, PLI was calculate as follows:
$$\begin{aligned} PLI=\frac{V_{L} \, \cap \, V_{pbs}}{V_{L}} \end{aligned}$$
DCC was calculated by taking the center of the predicted and actual binding sites, and DVO by representing both the predicted and actual binding sites (for PLI Ligand) in a 3D grid of size 36x36x36. To calculate the F1 score, we considered a predicted binding site with a DCC less than or equal to 4 Å as true positive (TP), greater than 4 Å as false positive (FP) and no prediction as false negative (FN). In this problem, there is no true negative since every protein structure has a binding site.
K-Fold cross validation result
We conducted our experiment in 4 folds, where the entire dataset was divided into four parts, leaving one part as the validation set and the other as the training set; and thus, we obtained four different models. Each model was compared with the kalasanty, which we trained on each fold-keeping with obtained optimal (using our optimization technique) parameters (learning rate = 10−3, kernel regularizer as L2 with value of 10−4, batch size of 5 and others as default values as in keras [31]). Combining all folds, out of 5020 protein structures, kalasanty did not identify any binding site for 76 protein structures (i.e., 6% of total protein structure) and PUResNet did not identify any binding site for 122 protein structures (i.e., 10% of total protein structure). For 64% of protein structures, kalasanty returned a single binding site, whereas PUResNet returned a single binding site for 93% of protein structures. Here, we were able to achieve an average F1 score of 0.83, which is 0.22 more than that of kalasanty, as shown in Table 1. PUResNet achieved a 61% success rate, whereas kalasanty achieved 51%, as shown in Fig. 4. Average DVO (shown in Fig. 5) of kalasanty is 0.46, whereas that of PUResNet is 0.61 combining results of all fold. Therefore, PUResNet can predict the binding sites more precisely and accurately compared to kalasanty. More detailed results for each fold are provided in Additional file 3.
Table 1 KFold validation result
Success rate plot for different DCC values combining all fold (kalasanty vs PUResNet)
Histogram of DVO values combining all folds (kalasanty vs PUResNet)
Independent set result
The evaluation was conducted using the Coach420 and BU48 datasets individually to determine the performance of PUResNet and kalasanty. In the Coach420 dataset, kalasanty did not provide any output for 26 protein structures (i.e., 8% of total protein structure), whereas PUResNet did not provide any output for 19 protein structures (i.e., 6% of total protein structure), as shown in Table 2. PUResNet has a success rate of 53%, average DVO of 0.32, and average PLI of 0.87, whereas kalasanty has a success rate of 51%, average DVO of 0.30, and PLI of 0.82, as shown in Table 2 and Figs. 6, 7,8. Kalasanty has an F1 score of 0.64, whereas PUResNet has an F1 score of 0.66, as shown in Table 2.
In case of BU48 dataset, PUResNet did not provide any output for 3 protein structures (i.e., 4% of total protein structure), whereas kalasanty did not provide any output for 7 protein structures (i.e., 11% of total protein structure), as shown in Table 2. PUResNet has a success rate, average DVO, and average PLI of 62%, 0.31, and 0.89, respectively, whereas kalasanty has 57%, 0.30, and 0.82, respectively, as shown in Table 2 and Figs. 9, 10 and 11. F1 score was calculated to be 0.71 for both models, as shown in Table 2. Clearly, in both independent dataset PUResNet has better performance than kalasanty.
Table 2 Independent test results
Success rate plot for different DCC values in Coach420 dataset (PUResNet vs kalasanty)
Histogram of DVO values for protein structure having DCC \(\le\) 4 Å in Coach420
Histogram of PLI values for protein structure having DCC \(\le\) 4 Å in Coach420
Success rate plot for different DCC values in BU48 dataset (PUResNet vs kalasanty)
Histogram of DVO values for protein structure having DCC \(\le\) 4 Å in BU48
Histogram of PLI values for protein structure having DCC \(\le\) 4 Å in BU48
To better understand the performance of PUResNet, we further investigated each individual prediction made using PUResNet and kalasanty in the Coach420 and BU48 datasets. Figures 12 and 13 show the DCC values for individual protein structures predicted by kalasanty and PUResNet present in the Coach 420 and BU48 datasets, respectively. DCC values greater than or equal to 121.24 Å corresponds to the protein structures for which not even a single binding site was identified.
Scatter plot showing DCC values of Coach420 dataset predicted by kalasanty and PUResNet with different views(I-V), View I showing DCC values \(\le\) 20 Å from PUResNet and kalasanty, View II showing DCC values \(\le\) 10 Å from PUResNet and kalasanty, View III showing DCC values \(\ge\) 120 Å from PUResNet and \(\le\) 20 Å from kalasanty, View IV showing DCC values \(\ge\) 120 Å from kalasanty and \(\le\) 20Å from PUResNet and View V showing DCC values \(\ge\) 124 Å from kalasanty and PUResNet
Scatter plot showing DCC values of BU48 dataset predicted by kalasanty and PUResNet with different views (I–V), View I showing DCC values \(\le\) 20 Å from PUResNet and kalasanty, View II showing DCC values \(\le\) 10 Å from PUResNet and kalasanty, View III showing DCC values \(\ge\) 120 Å from PUResNet and \(\le\) 20 Å from kalasanty, View IV showing DCC values \(\ge\) 120 Å from kalasanty and \(\le\) 20 Å from PUResNet and View V showing DCC values \(\ge\) 120 Å from kalasanty and PUResNet
Out of 298 protein structures in the Coach420 dataset, both the models correctly predicted 137 protein structures, incorrectly predicted 100 protein structures, and for 11 protein structures, no site was predicted, as shown in Fig. 12 View I, II and V. Excluding the common predictions, kalasanty specifically provided output for eight protein structures (Fig. 12 View III) for which PUResNet did not provide any output. Among them, one protein structure was correctly predicted by kalasanty. Moreover, PUResNet predicted 14 protein structures (Fig. 12 View IV) for which no prediction was provided by kalasanty, and among them, four were correctly predicted. Additionally, 15 protein structures were correctly predicted by PUResNet, which were falsely predicted by kalasanty, whereas 12 protein structures were correctly predicted by kalasanty, which were falsely predicted by PUResNet. The average DVO for the common correctly predicted structures by both the models was 0.31, whereas the average PLI for PUResNet was 0.87, and that of kalasanty was 0.85.
Similarly, for BU48 dataset containing 62 protein structures (31 pairs of bound and unbound structures), 33 structures were correctly predicted, 14 were incorrectly predicted, and for one structure, no site was predicted, which was common among both the models, as shown in Fig. 13 View I, II and V. Excluding common predictions, 7 protein structures were correctly predicted by PUResNet; and among them, for two protein structures, kalasanty did not predict any site (Fig. 13 View IV), whereas 4 structures that were correctly predicted by kalasanty, among them for one PUResNet did not returned any site (Fig. 13 View III). For the three protein structures that were falsely predicted by PUResNet, kalasanty did not return any site. The average DVO for common correct prediction by each model is 0.28, whereas the average PLI of kalasanty and PUResNet is 0.86 and 0.87, respectively.
In the Coach420 dataset, protein structures 2zhz, 3h39, and 3gpl (shown in Fig. 14) have binding sites for the ATP(ADENOSINE-5'-TRIPHOSPHATE) ligand, which was completely missed by kalasanty, although there were 401 protein structures having ATP binding site in the scPDB dataset, whereas PUResNet predicted the binding site for all three structures, and among them, correct prediction was made for 3h39 and 3gpl (shown in Fig. 14). Protein Structure's (7est, 2w1a, 1a4k as shown in Fig. 14) binding site in both the model's prediction are different in shape and size.
Protein strucutre ( 2zhz, 3h39, 3gpl, 7est, 2w1a, 1a4k) from Coach420, showing predicted binding site by kalasanty(Blue region) and PUResNet (Red Region)
In BU48 dataset consisting of 31 pairs of bound and unbound structures, kalasanty completely missed to predict the unbound structures (1a6u,1krn,2ctv,2pk4 and 6ins) and bound structures (5cna and 1gca); however, PUResNet predicted all unbounded structure and did not predict bound structures (1rob, 6rsa and 5cna). For pairs ((1a6u, 1a6w), and (1gcg, 1gca) as shown in Fig. 15), PUResNet correctly predicted the binding sites, whereas kalasanty correctly predicted for 1gcg and 1a6w only. The binding site predicted by PUResNet for bound (1gca, 1a6w) and unbound (1a6u, 1gcg) structures has different shapes and sizes as shown in Fig. 15. Interestingly, for the pair (5cna, 2ctv), PUResNet was able to correctly predict the unbound 2ctv but kalasanty completely missed it. Therefore, we can conclude that the prediction made by PUResNet is distinct and better than that made by kalasanty.
Bound and Unbound pair ((1a6u,1a6w), (1gcg,1gca)), showing predicted binding site by kalasanty(Blue region) and PUResNet (Red Region)
To validate, whether our data cleaning process improves the performance of PUResNet, we performed an experiment in which PUResNet is trained on the original scPDB dataset. As shown in Additional file 4: Figure 11S,12S,13S,14S,15S, and 16S, we found out that in BU48 dataset as well as in Coach420 dataset, PUResNet trained on filtered dataset has better performance than PUResNet (learning rate = 30−5, kernel regularizer as L2 with value of 10−4, batch size of 10) trained on the original dataset.
We introduced a new deep learning model, PUResNet, to predict the ligand-binding sites on protein structures trained on a newly formed dataset, which is a subset of scPDB. We compared our results with those of kalasanty, which was previously mentioned to exhibit better performance than DeepSite, Fpocket, and Concavity. Our results suggest that PUResNet provides a better prediction than kalasanty. In K-fold experiment, PUResNet has a success rate of 61% whereas kalasanty has a success rate of 51%. The results from independent test sets (Coach420 and BU48) revealed that PUResNet exhibits better accuracy than kalasanty (PUResNet had success rates of 53% and 62%, respectively, in Coach420 and BU48, whereas kalasanty had 51% and 57%, respectively). It is important to note that although PUResNet is trained with approximately 1/3 of the dataset that was used to train kalasanty, we were able to exceed kalasanty in terms of performance while evaluating K-fold as well as in independent tests. The model was developed in Python using the Keras library. All the information regarding the use of the trained model is publicly available at https://github.com/jivankandel/PUResNet, along with the trained model and all datasets used in this work. Predicted sites are provided in a mol2 file and can be visualized using different software, such as PYMOL. This work can be further improved by using a sequence alignment tool before calculating similarity using our method which will remove the step of taking maximum over shifted sequences, representing the protein structures along with water molecules, as well as differentiating the surface residue and incorporating the depth of the residues.
All the codes and datasets related to this work are are publicly available at https://github.com/jivankandel/PUResNet. Training dataset: Cleaned training data: https://github.com/jivankandel/PUResNet/blob/main/scpdb_subset.zip. Independent dataset: BU48: https://github.com/jivankandel/PUResNet/blob/main/BU48.zip Coach420: https://github.com/jivankandel/PUResNet/blob/main/coach.zip. Model Code: https://github.com/jivankandel/PUResNet/blob/main/ResNet.py. Model weights: https://github.com/jivankandel/PUResNet/blob/main/whole_trained_model1.hdf. Usage Information: https://github.com/jivankandel/PUResNet/blob/main/README.md
PDB:
MIF:
Molecular Interaction Fields
SVM:
MACCS:
Molecular ACCess System
DCC:
Distance Center Center
DVO:
Discretized Volume Overlap
PLI:
Proportion of Ligand Inside
\(\mathrm{V}_{\mathrm{L}}\) :
Volume of Ligand
\(\mathrm{V}_{\mathrm{pbs}}\) :
Volume of Predicted Binding Site
\(V_{\mathrm{abs}}\) :
Volume of Actual Binding Site
TP:
True Positive
FP:
False Negative
Avg:
ATP:
ADENOSINE-5'-TRIPHOSPHATE
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Khanal J, Tayara H, Zou Q, Chong KT (2021) Identifying dna n4-methylcytosine sites in the rosaceae genome with a deep learning model relying on distributed feature representation. Comput Struct Biotechnol J 19:1612–1619
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Sudre CH, Li W, Vercauteren T, Ourselin S, Jorge Cardoso M (2017) Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Cardoso MJ, Arbel T, Carneiro G, Syeda-Mahmood T, Tavares JMRS, Moradi M, Bradley A, Greenspan H, Papa JP, Madabhushi A, Nascimento JC, Cardoso JS, Belagiannis V, Lu Z (eds) Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, Cham, pp 240–248
This work was supported in part by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2020R1A2C2005612) and in part by the Brain Research Program of the National Research Foundation (NRF) funded by the Korean government (MSIT) (No. NRF-2017M3C7A1044816) and supported by "Human Resources Program in Energy Technology" of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea. (No. 20204010600470).
Graduate School of Integrated Energy-AI, Jeonbuk National University, Jeonju, 54896, South Korea
Jeevan Kandel
School of International Engineering and Science, Jeonbuk National University, Jeonju, 54896, South Korea
Hilal Tayara
Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, 54896, South Korea
Kil To Chong
Advanced Electronics and Information Research Center, Jeonbuk National University, Jeonju, 54896, South Korea
JK introduced a deep residual neural network, designed and implemented the algorithms, and performed experiments. HT and KTC supervised the project. All authors contributed to the writing of the paper. All authors read and approved the final manuscript.
Correspondence to Hilal Tayara or Kil To Chong.
Feature visualization. Includes 3D plot of different features used in the study
Model description. Includes description of different model blocks with figures.
KFold training and validation results. Includes validation, training graph, success rate graph and histogram of DVO of different folds
Miscellaneous results.
Kandel, J., Tayara, H. & Chong, K.T. PUResNet: prediction of protein-ligand binding sites using deep residual neural network. J Cheminform 13, 65 (2021). https://doi.org/10.1186/s13321-021-00547-7
Ligand binding sites
Binding site prediction
Deep residual network


This year, the idea of home has been challenged. It has felt increasingly unstable for many immigrants who have settled here, especially those who escaped conflict, only to face the fallout of Brexit Britain, or witness the nightmarish Grenfell Tower fire on news reports across the country. Home, and how it plays into the immigrant experience, has never felt more precious – and more insecure.
Home for everyone – but especially people of colour – is a political issue. Communities who might never before have felt as if they had a stake in society are learning how to establish something familiar in an unfamiliar place, and how to create space for themselves in a country that doesn't always say it has space for us: learning how to make a place feel like a home.
As the demographic of the UK changes we are witnessing people making their home here from disparate global communities, while house prices are simultaneously making long-term stability unmanageable for a generation and, tragically, the safety net of social housing is being slowly frayed to nothing.
For many immigrants in this country, home is a place where lives are rebuilt – familiar objects like plants, maps or an old school calendar take us back to who we are. Home is what we build, and how we find calm in the chaos.
From inside living rooms all across the UK, here are stories of how people in this country have worked to achieve calm after trauma, and what it means to give old trinkets and precious things from the past a new life in Britain.
The house has become much more modern as the boys have grown older. It used to have pictures of traditional Bangladeshi boats called noka, and metallic rickshaws, but we took them all to Oxfam because the boys said it was clutter! I think our sons will keep their houses very simple.
Our house is colourful, it is part of our culture to have colour – I think English homes are more simple. On the wall we have the Ka'bah, the house in Mecca, with its gold-plated doors. There is also a map of Mecca which shows how it has changed over the years, and a Qur'an in the cabinet. Anything religious can't be thrown away – it is who we are.
I'm from a place called Sturge Town in Jamaica. It was the second Free Village in Jamaica, and my family was one of the first families to settle there in the 1800s. I was brought up around greenery, jungle, breadfruit, mangoes. Here, I plant my apple trees, pear trees and plants to make it feel like home. I am growing as they grow.
I've been here since 1965, when there were hardly any non-white people on this street. The first night I first moved in, a neighbour brought me some boiling water to have tea – that was something we both could understand.
I find it funny when people come to this room and say it's like the West Indies. I think it's the atmosphere. I have my grandaughter's picture on the wall and Alexander Bustamante, the first prime minister of Jamaica. I also have a funny tapestry showing Jamaican rules on not taking people's house business out into the street. It reflects the Jamaican sense of humour. It sounds like my mother.
I like my house very clean. Jamaicans are like that – very fussy! Cleanliness is important. I think I'd want people to know I was living here and I loved my home – and that after all these years, I made good of myself. And that it's clean, of course.
I don't know if you can compare our home now to the one we left in Syria. We don't have gardens much in Syria, we have balconies and flats. My father built our home back there and he owned it. We could do what we wanted to change the house, but in social housing in this country you cannot do things like paint the outside walls. The government has provided this house and we really appreciate that, but social housing is different.
We've been here nearly two years and our home is about peaceful surroundings and being secure. We left conflict so it is important. We do this by not having too many things, and we have a picture of part of the Qur'an on the wall. It is typical of a Syrian home, along with shisha, rugs and glassware. We miss the accessories. If we find similar ones, we will re-buy them. But for now, we just need calm.
It's all the little details from Malaysia that I miss the most. I have this very old school calendar that you would usually see in what we call coffee shops over there. They are really specific, very busy kind of calendars that people hand out at Chinese New Year for free to advertise their business.
My living room has our classic sarong cloth, which I framed. It still has the price on it. But the things that remind me of my most Asian self are my collection of DVDs which are super important to me. I have movies from the classic film era of Malaysia and also modern films.
Oh, and my cow bells – they remind me of what home sounds like, goats and cows in the early morning.
I think the story that my living room is trying to tell is that I am doing my best to live in this city, which is financially difficult, and the ruthlessness of having to move all the time. I'm trying my best with the Ikea furniture.
Home is nostalgia to me now – if I go back, there are no goats and cows any more because that Malaysia has gone. Home is constantly shifting, and these days it lives inside me and my boxes.
I have a thing for collecting weird tat. Anything that has a story or a history, I always keep an eye out for it. I think I feel a connection to objects that need a home.
The Iranian carpet and a brown marble table are the big things that remind me of home and my grandparents' interior aesthetic. It's very 70s looking – marble for hot climates.
One thing I want is a cherry pitter. It's a very Iranian thing because we make a stew with cherries called albaloo polow. You sit there and pit a million cherries to make it. My living room looks really bizarre to my family. My mum looks round, casts judgment, and says: "Oh, so you put this here, have you, that's… interesting," then she'll move stuff when I'm not looking.
I think some immigrant communities want to replicate what they've left behind, and I do, but with a stronger need to create something new.


text:
Q: color to B/W javascript affect Is there any known/simple/open-source library that provide
a javascript function that will switch colored picture that is displayed in an html page into a black and white
That can be used in all the most used browsers (IE, FireFox, Chrome)?
I mean something like:
<html>
...
<img id="myPic" src="pic.jpg">
...
<script type="text/javascript">
function onEvent(){
  var pic = document.getElementById("myPic");
  magicFunctionToBlackAndWhite(pic);
}
</script>
</html>

looking for that magicFunctionToBlackAndWhite()

A: There's no one solution that works across all browsers, but you can combine different solutions:
For IE, use the following CSS: filter: Gray
A lot of other browsers supports canvas, so you should be able to use this javascript code for that.
Of course it should't be too much of a hassle to make an image b/w in a serverside language such as .net, so you could always have a javascript that in principle does something like the following:
var imgs = document.getElementsByTagName('img');
for(var i = 0; i < imgs.length; i++) {
    imgs[i].src = 'convertimage.aspx?img=' + imgs[i].src;
}

...and then have all of the magic happening on the server.

A: That might be somehow possible by using the <canvas> element, but I would recommend you to do the black and white versions yourself, either manually or through some sort of script. Trust me – it'll save you a lot of trouble.

redpajama_set_name: RedPajamaStackExchange


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Britain's Richie Woodhall won a unanimous 12-round decision over reigning champion Thulane "Sugar Boy" Malinga of South Africa to claim the WBC super middleweight title.
The victory over Malinga raised Woodhall's record to 22-1-0. Malinga, a 42-year-old South African, slipped to 42-11-0.


Guava Lemonade is the best chilled out in this summer..Guava is rich in vitamin C and regulates blood pressure levels. It helps to keep your brain functions positive by maintaining good blood flow and also by infusing it with Vit B3 and Vit B6. Guava is an easy avail fruit. So you can take this beverage often. Lets enjoy this drink in hot summer.
Slice guava and grind it in a mixer. Just drain it fully by adding little water to it.
Then mix sugar, water and lemon to it.
Mix until the sugar get dissolved fully.
Finally add ice cubes and serve.
Double filter the guava mixture to avoid the coarseness of seed.


text:
Siberian scientists explored the Yamal crater, which is connected with the Bermuda Triangle
In Siberia, scientists explored the Yamal crater. A photo: copah.info.
From the height of a helicopter, the Yamal crater looks small hole in the ground. Approaching, you can consider going down icy walls, and water at the bottom. Around the eternal tundra and peculiar her majestic calm. It's hard to believe that several months ago there was a cataclysm that blew up not only the thickness breeds (in the literal sense), but also news feeds (figuratively). Specialists of the Institute of Petroleum Geology and Geophysics named after A. A. Trofimuka SB RAS recently returned from an expedition during which investigated the funnel in order to draw conclusions about the reasons what happened, according to the publication of the SB RAS "Science in Siberia".
Scientists performed geophysical research using methods tomography, magnetic exploration and radiometry, sounding formation in the near zone. The essence of the latter is next: a closed loop is laid out on the surface, then current starts, then abruptly turns off, and by another circuit measured response, which depends on the resistance of one or another structures underground. "With it, we watched the depth about 300 meters, while tomography shows only the first 50. These are two complementary methods, "- says the participant expedition candidate of technical sciences Vladimir Vladimirovich Potapov. As for the magnetic and radiation background, in the first case, as scientists say, there are no anomalies, and in the second – indicators slightly elevated, but this is due to the release of rocks.
The specialists worked four and a half days – not counting Of course, the time it took to deploy camps. "Something we just did not have time, we could not physically. Some they didn't do things for security reasons, "says Leading engineer Oleg Nikolaevich Kushnarenko. Deputy Director INGG SB RAS Doctor of Technical Sciences Igor Nikolaevich Yeltsov complements: "Our geophysicists had a great temptation to descend into this crater, but the institute did not sanction enthusiastic scientists (although there was a climber in the squad) such dangerous work. "
Now, as Vladimir Potapov notes, it's still difficult to say what will be the exact results because processing all The data obtained is a long process. Vladimir Potapov, participant expeditions, candidate of technical sciences:
In any case, I think there will not be one hundred percent confidence in about the reasons for what happened, "the geophysicist emphasizes," but the work, Nevertheless, it is necessary to continue, and not only to us – by all possible ways. Put forward hypotheses, test them, because, firstly, the crater itself is a very interesting scientific phenomenon, and, secondly, such things can significantly affect the development of infrastructure Yamal in principle.
In fact, scientists conducted reconnaissance work, which, however, able to very strongly advance the scientific community in understanding of what happened, and also give prerequisites for the next trips: what you need to pay closer attention to, moreover, not only on this crater, but also in places where it can potentially something similar happens.
Be that as it may, preliminary results have nevertheless been obtained. Experts speak of them carefully and with a lot of reservations, but one thing is completely clear: for one single main reason that served giant explosion, no. The whole complex worked, if you want, a bouquet of circumstances, folded in one "flower", each of which contributed, and it is not clear what served metaphorical last straw.
First, the Yamal crater is located at the intersection tectonic faults. "Despite the fact that the region itself seismically calm territory, there is an active tectonic a life. The zone we are considering is at the junction of two major faults that cross the peninsula, "comments Igor Yeltsov. Vladimir Potapov continues: "Of course, this speaks of next: there was a slightly higher temperature, simply because that along these "cracks" in the earth's crust heat rises from the center of our planet. It warmed up the environment. "
Secondly, as the chief researcher of the Institute points out Earth cryosphere SB RAS (Tyumen) doctor of geological and mineralogical Sciences Marina Oskarovna Leibman, a very warm summer happened – accordingly, heating was added also from above. "For Yamal in general recent seasons are characterized by elevated temperatures in the warm season, which causes, incidentally, a number of problems with the arrangement Bovanenkovskoye field, "Igor notes in parentheses Yeltsov.
However, here one of the main participants in the event enters the scene: gas hydrates. Their release, according to the scientist, is the working hypothesis the formation of the Yamal crater: "As it turned out, they" live "in the deepest layer, which is located on the peninsula in the first hundreds meters, and in the surface. "
"It may well be that there were some other factors that provoked what happened pneumocop. Each added a little bit – the gas exploded, and a funnel turned out, "says Vladimir Potapov.
By the way, in this respect, the assumption of The Yamal crater is connected with another hypothesis, concerning one incredible natural phenomenon – the Bermuda Triangle. Scientists – people are quite prosaic, and they did not like otherworldly explanation of the reasons for the disappearance of not only ships, but and airplanes. "There is a version that it concerns manifestations gas hydrates, – smiles Igor Yeltsov. – They start actively decompose, methane ice turns into gas, moreover, it occurs like an atomic reaction, and they start very large volumes stand out. Accordingly, warming up, the ocean boils, and ships drown in this water with a huge share of gas floating, but because of the turbulence of the methane-saturated atmosphere – and air. "
Unlike the Bermuda Triangle, the Yamal Crater is a phenomenon not unique. According to experts, in fact, people are things are known. True, it's difficult to talk about their frequency, because all this happens in very sparsely populated areas. "Like us told – stipulating that these are just rumors – on the peninsula there are lakes of anomalous depth. To check the information, even if presented in the form of semi-legends, we measured the depth nearby water bodies, but in all cases it amounted to no more three meters. Unusual, a figure of 10-15-20 would be considered meters, and in this case the nature of such lakes is unclear, given flat terrain. So we can assume that such cataclysms occurred tens of thousands of years ago, and now the remaining the funnels are filled with water, "notes Oleg Kushnarenko.
The expedition of scientists was complex, its initiators Director of INGG SB RAS Academician Mikhail Ivanovich Epov and Head of Gazprom mining Nadym "Sergei Nikolaevich Menshikov." I must say, this quite a rare case when a state corporation and scientific organization united and put together resources: we gave part money from other projects, collected equipment, spent a large amount measurements, and now we interpret the material. Our partners provided us with helicopters and special equipment, says Igor Yeltsov, further explaining. —Because it was about high methane concentrations and potential for ignition, common things such as electric drills could not work there, they should only be in special performance. "In addition, also participated in the trip and representative of Gazprom VNIIGAZ LLC, whose tasks included sampling, description of geomorphology and other related work.
According to scientists, the Yamal crater, formed though not close to the Bovanenkovo field, but still pretty dangerous distance, once again reminds: infrastructure (including gas pipelines, buildings, engineering structures) should be created with taking into account the presence of tectonic faults in order to minimize risks. To prevent such quite rare natural phenomena that have a similar power, it is impossible – and indeed, how?
Ekaterina Pustolyakova
Bermuda Triangle Water Time Siberia


text:
Q: How can I use a quote " or ' character within a string? am working on selenium webdriver using JAVA !
am trying to pass a string dynamically using the variable exp
String exp=",,,4'-TETRA; P-CHLORIDE";

d.findElement(By.xpath("a[contains(text(),\""+exp+"\")]//ancestor::table//parent::div")).sendKeys(Keys.ARROW_DOWN);

but its giving me an error :
Unable to locate element: {"method":"xpath","selector":

"a[contains(text(),\",,,4'-TETRA; P-CHLORIDE\")]//ancestor::table//parent::div"

}

A: Use '"+exp+"' instead of \""+exp+"\" :
  d.findElement(By.xpath("a[contains(text(),'"+exp+"')]//ancestor::table//parent::div")).sendKeys(Keys.ARROW_DOWN);

Update
d.findElement(By.xpath('a[contains(text(),"'+exp+'")]//ancestor::table//parent::div')).sendKeys(Keys.ARROW_DOWN);


text:
Son of sheriff's deputy charged in Mich. slaying
The son of a Michigan sheriff's deputy who police say admitted killing a 16-year-old girl has been charged with murder.
TRAVERSE CITY, Mich. – The son of a Michigan sheriff's deputy who police say admitted killing a 16-year-old girl has been charged with murder.
Seventeen-year-old Robert Schwander was charged as an adult Thursday with first-degree murder in the death of Carly Lewis.
He has pleaded not guilty. Defense attorney Clarence Gomery tells The Associated Press that Schwander has called Lewis' death an accident and recognizes the "seriousness and potential consequences."
Schwander faces life in prison if convicted.
Investigators say they believe Schwander killed Lewis June 2 in a vacant building and moved her body to a sand pile at a public works site two days later.
The Grand Traverse County sheriff's department issued a statement saying Schwander's father is on paid administrative leave and that the case is "especially difficult" for the department.
Donald Trump Warns of a Liberal Supreme Court if Hillary Clinton Wins
As Trade War Persists, Mnuchin Says China Talks Have 'Broken Down'
Summary Box: Automakers rebound, construction dips
Police to search home where mom, kids were stabbed
Man Seeks to Recreate Wedding to Capture Moments Missed by Photographer
Man cleared in NYC block-party police shootout


First and foremost, I am a husband to my wonderful husband, John. I am a father of our five-year-old German Shephard, Kaiser. I am an avid reader of Mysteries, Horror, and Suspense, and biographies. I am a gamer. My favorite ones are Dragon Age, Mass Effect, Star Trek Online, Skyrim, Assassin's Creed, Fallout, and Civilization Five. I love to cook and I love trying new recipes (hint hint). I am an avid music junkie from POPular music to Opera, to Showtunes, Gospel, Rock, Rap, and Hip Hop. I am also a Poet – a lot of which is offered on this web page for free. And I do some gay fiction writing from time to time.
I love connecting with people. As an ex- fundi, I grew up in a world where there was this invisible boundary set down between me and the world around me. I felt more like an observer than an actual participant in that world. Since I've left fundamentalism – it has been my constant endeavor to be a part of the world. To be a part of humanity. And when I write my poetry or my books, I draw from the experiences of being raised in that environment as well as the experiences of tasting the bouquet of humanity I'd been denied all those years ago.
It's a way to entertain people, writing is a way to connect to them, as well as a way for me to recover from the world I'd lived in all those years ago. Thank you for coming on my journey with me.
"The way the author showed today's contemporary life of a committed gay couple and mirrored it against the, not nearly distant enough, past is particularly well done" – Attention is Arbitrary.
"Fiction is frequently a black and white rendering of the real world. Good guys vs. bad guys, beginning and an end, undying love and deep-ingrained hate. I find comfort in that, many of us do. It's simple, straightforward, easy. A way to relax. Nothing wrong with that. But when a book transcends that, when it excels in showing the human existence as multifaceted and as colorful as it can be…that's when reading a story can make you into a better person.
Between Us, by F.E. Feeley Jr. - OJ He Say!


Learn about the past to better understand the present.
This definitive biography of one of the most significant figures in American history is long overdue. A look at both the inner and the outer life of the famed abolitionist and orator, this well-written portrait draws on new information to provide the most well-rounded examination of Douglass yet. It won the 2019 Pulitzer Prize for History.
So you've decluttered with the help of "The Life Changing Magic of Tidying Up," but how do you stop yourself from buying too much stuff in the first place? Flanders' memoir offers guidance on how to become a more thoughtful consumer so you can lighten your ecological footprint.
Now is a particularly poignant moment to be reading this absorbing history, with the recent killing of a journalist during rioting in Northern Ireland. "Say Nothing" makes it clear that while the Troubles may be past, they are far from ancient history.
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The Pulitzer Prize-winning writer expounds on other historians he admires.
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