Yeah, we on boss talk one-on-one. Yeah, we gonna top three all this about time dead or live That's something that we do on boss talk top three artists dead or live I don't care you could say Had a little bit. Oh, you love padded notes. You know, I'm sorry.  I'm gonna put me in But the greatest artist of all time number one who often to say Michael Jackson Was coming at me about that for the longest you the one got that nigga over that mad at Mike Mike It ain't nothing tougher than Mike Nothing You see that ain't no way in the first of all These the kind of stats might got right here. That's the killer ain't nobody gonna never I'd sell it now That's not gonna happen ever Who's gonna take it on This the coldest part about Mike man, I had the great I had all the records before stream Then they just added me to that too y'all y'all like I'm gonna miss the he missed that it hard So it's over from that and then I tell people this here man imagine this man imagine you being 17 You 18 I'm 16. We're in the print. This is we love women these girls They hollering they screaming they going crazy. You 17 you going through what you 18? But they hollering for the nine y'all Wow Yeah, I'm talking about they falling out. Yeah The women they get it. They taking their clothes out. They going crazy. I'm talking about they they lose in they mind You 17. Yeah, you 18.  I don't already passed up these Sammy Davis is um Smoky Robinson now our main one brown James Brown I'm finna pass him by the time I make it to 17 man. I'm I can it's over my whole childhood I gotta play with drafts market I'm a monster monster. You're right man.  That's your boy Mike Hey, man, what's your favorite Michael song? I'm All of them. I'm a Mike fan So when you were younger, did you ever dress up in the jacket? I didn't get the jacket Hey, that's still me and my mama You can go get that jacket. I would not buy you something behind That jacket was everything I didn't get it. Number two. Number two. Yeah, I'm gonna get you Number two for me would be Oh, let me get back on number two for me would be Ice Cube is very influential. You like ice cubes. Somebody gave us ice cube. Wow. Here's your number two Cuz he really the first rapper that I thought was man. This nigga is a bad bad See, I liked it run Peter. I liked all that LL But I ain't like I'm like I like that nigga cute I'm gonna say pop but cube had something done something to me before He's a business man. It's that once upon a time in the project. Yo Cube is the toughest man Like from as far as the dude who could and I man, so he was very influential to me So too and number three me gotta be you and I don't blame I was gonna see if he was gonna forget to add No, yeah, I ain't I didn't even think about that twice me really number one You know, just I'm just going I just had to I just had to say Mike Cuz even over Mike My son in cuz I like to remind him to two times a year Mike the coldest ever never even Chris Brown. That's you know, that's that that's baby Mike That baby Mike Cuz you know, I'll be came on and talk about Chris Brown Don't do that. Don't do that. I'll be saying it's a listen I could bet right man. Mike is so influence. You know, it ain't even about the John. It's about man This is Mike yeah, the glove booms. Yeah, that might you gotta be a love if you if you born in the In the 90s You that might not Register know if you born in the 70s a 70 you understand what the hell Yeah, you know what I'm talking about because you remember thriller comes out every year and Halloween that will forever be a Halloween. I just love it man. I grew up with and I always look at that like that man And as far as a rapper though, you know, I just always love ice cube. I love you know, I'm saying so


So once I went, those days, today I just came back from Kailash, from Kailash 7 to Australia and back. So today when I travelled there's a whole jing-bang going with me, too much organization. We got all kinds of boots, we got clothes, we got this thing and gloves and the works and cars and the works. Can't imagine, too much organization. At that time I just went by myself, by a local bus. Generally I sat on top of the bus and went because I didn't want to miss the mountains.  They started 4-4-13 in the morning from Haridwar and go straight to Geeta Gaurikanth or to Badrinath. They have to get there so they don't stop anywhere. These buses used to be called as Bukhartaal buses. That means hunger strike buses because they don't stop anywhere for food. The driver opens up his rolled up chapati and starts eating as he's driving. So you are sitting there wondering where is the lunch, where is the lunch? No lunch. So they called Bukhartaal. So I went on top of the bus because I don't want to miss even a bit of the mountain. And electric wires will come. He'll just out-bidgely.  So I went there without any kind of things. That's not the first time I went. I think about 26 years I went continuously every year. And just my jeans and my t-shirt and I have a canvas jacket which I used to ride in South India. It's very good for the rain.  So I just went up and I went to Kanti Sarovar. It was around 3.30 in the afternoon. I just sat there and I didn't know what hit me. I was just sat there with my eyes open. It's an incredible glacier lake. As nature is phenomenally beautiful. My eyes are open. My mouth was shut. But I could hear my voice singing loudly around me. Very loudly, like it's on a big microphone. I'm just looking. It's my voice clearly singing this chant. For the first time I'm hearing it but it's in my voice. But my mouth is clearly shut. So I sat there listening to this and that's what we chanted just now. What it means is sound. Nad means sound. Sound is not an appropriate translation. But in English language there is no appropriate word for nad. Nad is more than sound. Probably because there is no appropriate translations in English language, similar things have happened. I think... are there any Bible readers? Anybody? Nobody? I think they've... I'm not read but I only heard from people that it seems the first part of the Bible is about first there was a word and the word was with God and the word is God. Something like this, is that right? Somebody knows? Something like that. See right now, whatever I'm uttering right now I'm only making sounds. But you are interpreting these sounds as words, isn't it? Yes? I'm actually making only sound. Suppose I spoke in a language that you do not understand. The word would be sound and the sound would be word, isn't it? In your experience, even now it is so. But you think they are words, they are not words, they are just sounds. So somewhere when it came to English language sound would sound too abstract. So somebody made a word out of it.  But in the existence a sound is just a sound, isn't it? If there is nobody here in this hall, if I speak, it doesn't matter in which language I speak. As far as the walls are concerned, it's just a sound.  So, nath goes beyond sound. There's no appropriate word, but something more than sound. Because sound is not seen as a physical quantity. It is seen as the last bastion of physicality. In yoga, we are looking at the sound as the last citadel of physicality. Beyond that there is no physical. It means sound is everything and sound is life. All the life forms are sound. Sound means a vibration. Today modern physics is proving everything is just a reverberation in the existence. And what is karma and what is dharma means, what entangles you is karma. What liberates you is dharma. Both are sound. What is bondage and what is liberation. That is also sound. What is referred to as Shiva and Shakti, that is also sound. Everything is sound. Whatever you know as physical creation is a sound because everything has a vibration. Where there's a vibration, there is a sound. Whether you can hear it or not is the only question, but there is a sound. Wherever there is a vibration, there is a sound.


Karma means you're doing, your life is you're making. This is the greatest empowerment you can have. One question like we share that we meet people we meant to meet or is it a destiny, luck or fate? Have you just fallen in love with something? Huh? No, no because I don't want to disturb the romance. See, look at it this way, since you woke up today morning till this moment, let's leave the sleeping time. Since you came awake fully from that moment to this moment, your body has been doing its things, isn't it so? Physical activity is happening both outwardly and inwardly, happening or no? That's why we're alive, it's happening. Mental activity has been happening, emotional activity is happening, energy activity is happening. How much of these four dimensions of activity did you perform consciously from the moment you came awake till now? How much do you think, what percentage? What percentage? Well below one percent, believe me. When you perform activity consciously less than one percent, over ninety-nine percent is unconscious. Everything will look accidental, isn't it? Everything looks like divine intervention because ninety-nine percent of the time you're unconscious, do one thing. When you drive today to wherever you drive, ninety-nine percent of the time, close your eyes and drive.  Things will happen in a completely different way.  Everything that's accidental, we want to attribute it to some other force elsewhere. No, this must stop, we must understand, it is we. Is there no other force? Of course, you didn't make the creation, nor did I, all right? You did not create this creation, nor did I create this creation. There is, but that is a different dimension altogether. And what is happening with you right now is entirely you're making. From where you come from in India is the only culture on the planet which constantly told you, your life is your karma. Karma means you're doing. Your life is you're making, whatever may be happening, you may be able to logically figure out why it is so, you may not be able to figure out. But still you know one thing, if this is happening to me, this is my making. This is the greatest empowerment you can have. When you understand my life is my making, whatever happened till now, it doesn't matter. How will you make your tomorrow? I'm asking you. If you clearly, clearly know one hundred percent, my life is my making. How will you make the next moment? How will you make a tomorrow? How will you make your future in the most beautiful way? Isn't it so? That's what needs to happen.


Friends, countrymen, I return to you in the greatest hour of need, the year of our lord 2024, to talk about, what else but American Arcadia. Because now and then comes a narrative game that will glue me to the screen. 2023 was excellent at offering me those. In all of them different experiences, from the survival narrative of the pale beyond to the fantastic saga of Baldur's Gate 3 to the meta-narrative mind-bending horror of Alan Wake 2. I've played through more unforgettable narrative experiences in the last half year than across the two years prior. American Arcadia was the last game I played in 2023 and the first game I finished in 2024. I couldn't have wished for a better ending or a better beginning. This narrative-driven story is told in such a delectable way. Dual narratives, office-style cuts to the main characters and villains, comments as they tell their story, plenty of humor and a graphic style that prioritizes clarity and colour. While also being damn stylish, I am enamoured with the character models, whether in motion or across cutscenes. They're adorable and the lack of noses gives them such a unique identity. I love the farming. I love the framing even more. This idea that everything going on the screen is the visualisation of Angela and Trevor's interweaving story of escape in the face of surveillance from hell. That's right. Arcadia is the worst thing in the universe. The next step and evolution of reality television. The dome city that keeps everyone in a perpetual version of America's 70s. Every single moment of every single person's life always caught on camera. And there's our loved Trevor, the most normal, ordinary person in all of Arcadia. He isn't one for drama. He's not endlessly popular, doesn't have an innate charisma, well, outside of being voiced by Yuri Lowenthal and isn't a magnet for, as I said, drama.  It's not his fault he's grown up in an atmosphere that encourages the most toxic traits imaginable. We all know what version of reality reality shows sell, as different as Arcadia is marketed as being from other corporate products meant to entertain by packaging and selling of a horrible version of reality. That's one of the game's messages. Entertainment is driven by the same standards in this fictional version of America as it is in our own world. And those standards, I'm sorry to say, are about as glamorous as a gilded shite. Trevor's life isn't interesting. He's no one anyone would care to watch. His life is defined by a rich interiority, which does not have the sex appeal of a melodramatic human crash pandas existence. He knows who I'm talking about. So when a series of incidents start to break the ordered nature of his life, that spooks Trevor. When management calls him up and asks him to come up to the executive suit of his boss, that spooks him more. And when an unfamiliar voice tells him he's about to be made to disappear, that sets him off on a running spree that won't soon see an end. The voice in his ear? That is Angela. An activist working at Walton Media, the very corporation that does in charge of American Arcadia. The in-world show that follows the lives of all these poor, unsuspecting people living in their gilded cage. Angela is the best, using her position as an executive assistant to try and save the life of our lad, Trevor, as she aids the anti-Arcadia organization breakout, Angela's fun, chaotic and certified fresh. Her game sections are in the 2.5D of Trevor's runny nonsense but puzzle solvers in the first person, reminiscent in their control, though certainly not in atmosphere, of something like Call of the Sea. I give the latter game an arbitrary mention because American Arcadia sees reunited voice actors Yuri Lowenthal and Sissy Jones. Yuri Jones plays host Vivian Walton, a media personality with Teet. My fellow citizens, we have a new Edge Travel Grant winner. His name is Trevor Hills, a 28-year-old account manager with no friends and a boring life that nobody gives two shits about. I'm sorry, I just can't help myself. I know the rules, all grant winners must be publicly announced, blah blah blah, but honestly I don't see the point in it. Who cares about this guy? Anyway, let's take five and we'll record it in one go, okay? Especially behind closed doors, developer Out of the Blue has given Jones lightens for fun, her line delivery a thing of beauty. I love baddies with an attitude. Those characters that never lack in more than a mischievous spark or two, those who dominate a scene with at least a hint of gleeful malevolence. The secondary antagonists are the various Walton media security employees, both agents who make sure no native citizen of Arcadia gets anywhere they shouldn't, and the more fearsome interceptors in their tight sci-fi costumes. I couldn't believe it. Until then, interceptors were an urban legend, a high tech security force in Arcadia with a really cool sci-fi codename. It all started with the infamous leaked Arcadia 6 picture, you know? That one that Walton media has always denied to be true, the guy in the back was believed to be an interceptor. Most people just refer to him as the Tackleberry, because he looked like that guy from the police academy movies, you know, helmet, sunglasses, leather jacket and all that. Anyway, I had worked in Walton media for eight years and I never saw or heard a thing about those guys. Those lads really don't leave much to their imagination, do they? The chief of security, Marcus, gives a particularly memorable appearance. His arc culminates in such a fantastic twist that I was genuinely shocked into a laughter of disbelief. He makes for one of the most memorable sections in the entire game. You're sharing Angel's perspective, while Trevor is on your monitor, controlled by you, trying to hide from a gaggle of 70s-styled security people, blending into crowds, hiding behind timed fountains, and voltage boxes, cars and the like. You also have to respond to a series of drilling questions by Marcus. In effect, the game forces you to split your attention between the two scenes, between the two sections, the two ways of playing. American Arcadia. Each question by Marcus, each new member of Arcadia security looking for Trevor, they are to the tension in a masterful way. I felt a few times here, as well as elsewhere, the most frustrating moment I experienced across the entire game is owed entirely to my own impatience and thick-headedness. The game itself really did me no wrong. The other memorable scenes of the game include an impromptu house visit from Angel's vexing boss Kendra, as well as an incredible psychedelic sequence. Everything about the ending is pitch-perfect. I'd say more about it, but truthfully, Yatik Kroshaw said everything there is to say about why American Arcadia worked in a recent episode of semi-rumblematic, titled The Importance of an Ending. That's the video that persuaded me to pick up the game in the first place, and I can tell you how happy I am to have listened to Yatik's recommendation. Walton Media is a clear stand-in for Disney. Although its creator has a by and large cleaner historical record, then does all Walt. There's a mickey substitute, and Arcadia started out as a sort of Disneyland before the original Mr. Walton passed away, and his nasty businessman of a brother kicked to the curb. The ideology-driven scientist who made Arcadia a household name. So, clearly, this is inspired by the Truman Show, maybe even a pinch of The Running Man. Just based on the main activity Trevor's doing and ends on some interesting revelations later on, here in I speak of the Stephen King novel as opposed to the action film with Schwarzenegger. The slow introduction of Trevor to the truth behind the world he has always known is crafted with care, and feeds into a sense of disquiet that explodes as soon as Angelo directly contacts him. The friendship between the two comes by naturally across the course of the game, and is born of beautiful sentiment. Angelo is trying to undo, or at least undermine, the dystopian horror that Walton and Arcadia stand for, all while selling themselves as wholesome family entertainment. Trevor, he just wants to lead a normal life, to return to the kind of life he always believed he had, in fact. Their journey together and apart is going to leave a mark in my mind as one of the most charming stories of friendship and drives for liberation that I have encountered yet.  It's one of the most enjoyable works of fiction in the medium I've come across, in recent memory at least, and its short length invites you to jump in without a moment's hesitation. You can be done with American Arcadia in anywhere between four and six hours. It's the length of two to three good movies, or a single Martin score, say the Irishman. Treat yourselves. A good time is awaiting. And if you enjoyed this video, please don't forget to smash that like button, subscribe, share the video with your friends on your socials, and so on and so forth. We'll have to see it. Meanwhile, I'm Philip Magnus, and I will see you again next time for more goodness as I review some of my favorite games from 2023, and prepare for another year in gaming, in books, and in many other sources of entertainment really. See you next time. Bye!


Welcome to Toffey TV, it is the Evert News Daily. US investment firm MSP Sport capital are considering a potential investment in Evert Nare story broke last night. Members from that group attended the Southampton game a couple of weeks ago when Evert lost 2-1. They have investments in other football teams There are four, including Augsburg in Germany and Esterel in Portugal. They are also a part of McLaren Formula 1 race and team as well. This is what Farad Mishiri basically spoke about on his video getting people involved within the club. We've got expertise in the sport and world to help with global sponsorships and things like that growing the business but also to help fund the end of Evert New Stadium which Mishiri has funded most of it but is obviously looking for money to bridge that final bit. He did say in that video the other day that he could fund it himself but he would prefer to get another partner in and these are like I said very experienced within the sport and world. The fact that they're involved in already in football and in Formula 1 and when you actually go into the people who are involved in it they have got extensive experience within important fields, within investments in sports and things like that. They have just purchased the X Games as well which is really big in the United States so they seem to know what they're doing. The report coming out of it is that they'll be given a place on the board or they'll want board representation which again is talking of you know we've as fans want that board strengthened so having someone on it from them I think will make a big difference as well and hopefully they can help drive make you know make everything bigger make everything better at what they do and hopefully drive revenues up as well which is massively important for Evert and you know given the the situation with FFP so this could be the kind of investment you would want in the football club someone a group who do have experience within sports and hopefully you know bring Evert closer in alignment to the top teams who have all of these kind of people with this kind of experience driving their football clubs for too long we haven't had that we haven't had that bandwidth had that experience within the club so you know we're looking for some positive news I'm trying to spin this as positively as I can so there you go. Onto the managerial search for Everton, Marcelo Bielsa has landed in the country for talks with Everton over the vacant manager's job he's flown in from Brazil he had three big suitcases with him so it looks like he's here for a few days he is the favorite if you believe reports obviously that's been you know bobbling around for a couple of days that story will he won't he will he's arrived here for talks. Sean Dice is another main contender for the managerial job and then today David Anciolotti's name was thrown in as well he's the former assistant manager at Everton of course he's at Real Madrid with father Carlo he's come into it whether that is actually real who knows we know it's been talked about it's in the betting as well so we don't know whether that is actually a real alternative but his name has been mentioned today along with Ralph Hassenhuttle whichever report you read one saying he wanted the other one saying he's ruled out of it Wayne Rooney still involved in the run as well so yeah so it'll be interesting to see how that develops with Bielsa over the next couple of days I imagine being interested in appointments anyway if Everton made it. A report today that Antony Gordon has told Everton he won't return to the club he won't play for Everton again this is amid Newcastle links he is Newcastle currently negotiating with Everton for the purchase of Antony Gordon Chelsea have been interested in him but have not made a bid or certainly aren't at the negotiating stage with Everton and Antony Gordon it looks like may well be on its way to St James's Park I think the big thing for Everton here is selling them it's fine to sell them but we have to see who we can bring in can that money be used to bring people in this window Everton need it you know I've been saying for a while we need the three players in this window you know two attackers and a midfield player who's got a bit of creativity about them that would suggest that Antony Gordon goes it becomes four four players in with only five days to go seems highly unlikely it's been done before of course it has but it's very very difficult there isn't even any real credible stories today about players coming in Marko on out of it was mentioned again and Joe Pedro was mentioned but Joe Pedro was actually injured at the moment so that would be a bit of a mad sign but just right now time is running out on Everton they need to strengthen this window and obviously five days to go no manager trying to convince players to come and join this when your bottom of the with joint bottom of the league is I imagine a very very difficult shell at the moment and when your hands are tied a little bit with FFP so Antony Gordon's sale is a could well hang in the balance on what Everton can do to bring replacements in and finally former Everton assistant manager and player Duncan Ferguson has been named manager of forest green rovers today the 51 year old scott's taking the job was offered a vegan burger but he refused to eat it on camera so he'd try it later very interesting for that's good news for Duncan obviously he left the club in the summer it's been waiting for the right opportunity to take a few rolls down already but he has taken this job they are bottom of league one they've got the way cut out a little bit but it's a good opportunity for him to get on the managerial ladder and show people what he is capable of it's up to him and his his first bit of business since becoming the manager today has taken a young Everton midfielder Tyler on Yango on loan for the rest of the season Fergus and obviously knows him work with him had him training with them every day when Carlo Antirotti was the manager and they and obviously Tyler was involved last season as well under frank lampard while Duncan was there he has been on loan for the first half of the season a bit and Albin now goes to join forest green rovers in league one and they will hopefully help Duncan move forest green away from danger so good luck to both of them that is it for the news daily make sure you give the video a thumbs up subscribe if you haven't and if you want to become a toffee tv premier member click the link in the description or the qr code that will be on the screen now see you later


Hi everyone, in this tutorial I'm gonna be showing you how to work with fractions or rational numbers in Python language. Instead of working with float point numbers, sometimes we need to work with fractions. Sometimes in proportions or probabilities it's more easier to solve problems using fractions instead of working with float point numbers. Python has built-in module fractions that help us to work with fractions. Also it has its own new type called fractions. With this type we can declare fractions and work with them. To create fractions first we need to import fractions type. So I'm gonna type from fractions import type called fraction. I'm gonna create first fraction. This fraction type wants two arguments from us.  Let's create one more fraction. This first fraction means one over five. Let's create another fraction for example three over four. And now we can use these fractions in our expressions. For example I'm gonna multiply these two fractions and I'm gonna print the result of this multiplying. And as you can see when we're multiplying one over five with three over four we're gonna get new fraction three over twenty. That's all in this tutorial. Don't forget to subscribe to our channel. See you in the next tutorial. Happy coding.


So welcome everybody to the BioXL webinar series. BioXL is an European Center of Excellence for Computational Biomolecular Research. Today we have a webinar on a prototype system for achieving integrative structure, PDB-DEV, and the presenter of today are Brinda Vallat, from Rochkaer University, a Benjamin Web from the University of California, San Francisco. I will host this webinar and together with me, it's also Julian Singh from University of Edinburgh. Now we go to, I want to introduce you to the presenter of today. So we have Brinda Vallat, Brinda is assistant research professor at the Institute for Quantitative Biomedicine. As a computational chemistry and her research interest is in modeling and analysis of micro-molecular structure. Her aim is to build tools for data standardization, validation, archiving, and from since 2015 she's working with Ellen Bergman and Andrei Sali to create the intrastructure for archiving integrative structure of biomolec- biologic and micro molecules. The second presenter of today is Benjamin Web, University of California, San Francisco. He's a scientific software engineering in the group Andrei Sali. He's the lead developer of Intregative Modeling Platform and here you can see the web page of the platform and this plot is used to elucidate the structure of large micro-molecular assembling. Now I will start to give the word to Brinda that she will start the presentation and then we will move to Ben and then we will come back to Brinda and at the end we will have the question section. Hello everyone, thank you very much Alessandra and Julianne for giving us this opportunity to participate in the BioExcel webinar series and talk about our work regarding the development of the PDB Dev prototype system for archiving integrative structures. What is integrative structure determination? Integrative structure determination is an innovative approach to modeling the structures of biological systems based on data produced by multiple experimental and theoretical methods. It is typically used for modeling the structures of complex macromolecular assemblies that are not amenable to traditional methods of structure determination such as x-ray crystallography, NMR spectroscopy and electron microscopy. Integrative modeling involves combining data from multiple experimental methods such as x-ray crystallography, electron microscopy, NMR spectroscopy, chemical cross-linking, small angle scattering, fluorescence resonance energy transfer etc. The experimental data are gathered and converted to spatial restraints and combined with known starting structures of molecular components using modeling algorithms to build the structures of complex macromolecular assemblies. Before I go into the details of archiving integrative structures I would like to give some background information regarding the protein data bank and how the project of archiving integrative structures began. The protein data bank is a single global repository for experimentally determined structures of biological macromolecules and their complexes. It was established in 1971 with seven structures. It is the first open access digital resource in biological sciences. It currently contains over 160,000 structures of macromolecules. It is managed by the worldwide protein data bank organization that consists of the RCSB protein data bank in the US, protein data bank Japan, protein data bank in Europe and the Biomag Res Bank. The PDB archives structures of macromolecules determined using x-ray crystallography, NMR spectroscopy and three-dimensional electron microscopy. The structures archived in the PDB have atomic coordinates and metadata. The PDB also archives certain structures that are determined using multiple methods. These methods usually use one of the traditional methods such as x-ray, NMR or 3D EM in combination with other methods such as neutron diffraction or small angle scattering. And these multi-method modeling studies result in structures at atomic resolution. Here is a plot of the multi-method structures in the PDB. You can see that these structures have been increasing in the past decade. So the next question is can the PDB archive integrative structures? There are some technical challenges to archiving integrative structures in the PDB at present. These challenges arise due to the following features of integrative modeling. Integrative modeling involves restraints from a multitude of experiments such as chemical cross-linking, small angle scattering, forced-resonance energy transfer, etc. And integrative models are multi-scale. They are not necessarily atomic and can consist of both atomic and coarse-grained representations. Integrative models can also have constitutionally and confirmationally diverse assemblies. And they can use starting structural models that come from experimental or computational methods. Due to these reasons, the current PDB infrastructure is unable to handle integrative structures. In order to address the challenges involved in archiving integrative structures, the Worldwide Protein Data Bank set up a task force consisting of researchers from different fields contributing to integrative structural biology. The first meeting of the task force took place in October 2014 at EBI in Cambridge. Here is the summary of recommendations from the task force that was published in 2015. The task force recommended that structural models, data, metadata, and workflows need to be archived. A flexible model representation needs to be adopted. Methods need to be developed for assessing model uncertainty. A federated network of archives needs to be created for structural models and experimental data, and publication standards need to be established for integrative modeling studies. This talk will focus on the implementation of the first two recommendations, which is archiving structural models, data, metadata, and workflows, and adopting a flexible model representation. With this background information, we can now state our objectives as follows. Our goals are to build a system for archiving integrative structural models based on the recommendations of the WWPDB Integrative Hybrid Methods task force, and to develop the tools required for archiving integrative structures. While building the archiving system, we will adhere to the fair guiding principles of scientific data management, where archived integrative structures will be findable, accessible, interoperable with other data resources, and reusable for future applications. What are the requirements for archiving integrative structures? The first requirement is the creation of data standards. Data standards are the primary requirement for archiving and enable automated data processing, as well as dissemination of data in a standard form for downstream applications. Data standards encompass definitions for the data that need to be archived, such as the chemistry of macromolecules and small molecules, and the definitions for multi-scale model coordinates. They also need to support the information required for model validation, such as the spatial restraints used as input data, and the starting structural models used. They also need to include descriptions for metadata information, such as authors, software, citation, samples, experiments, workflows, etc. The second requirement is the archiving system. We need to build the infrastructure required for data collection, processing, archiving, and data distribution. Data collection involves collecting the model coordinates associated with spatial restraints, the starting structural models, and the information regarding the modeling protocols. Data processing involves any added curation that may be carried out, model validation, and visualization of the structural models. Data distribution involves creating mechanisms for web services, data download, data set discovery, and search and report tools. Together, these constitute the data pipeline for the archiving system. So let's look at the first requirement, which is creation of data standards. I would like to introduce the PDBX-MMCIF data standards used by the PDB to archive structures of macromolecules determined using x-ray crystallography, NMR spectroscopy, and electron microscopy. PDBX-MMCIF specifies the standards for representing macromolecular structures. It is designed to be extensible in order to handle evolving methods and structural biology. How is PDBX-MMCIF different from the widely used and well known PDB format? Let us look at an example to understand the differences. The figures here show how atomic coordinate data are represented in the PDB format and in MMCIF. The PDB format has implicit definitions and is limited by fixed columns that cannot handle large structures. Large complexes with tens of thousands of atoms cannot be handled by the PDB format. On the other hand, PDBX-MMCIF definitions are explicitly defined in the PDBX-MMCIF data dictionary. The MMCIF data files clearly include the names of the data items and are not dependent on columns organized based on fixed widths. This makes PDBX-MMCIF flexible and self-consistent. PDBX-MMCIF can handle large structures and thus overcomes the limitations of the PDB format. So we decided to extend the PDBX-MMCIF data standards for integrative modeling. Standard definitions for the chemistry of polymeric macromolecules and small molecules. Atomic model coordinates and supporting metadata information such as source organisms, samples, author citations, etc. are taken from the PDBX-MMCIF master dictionary. And the new definitions that are required for integrative modeling are created in the IHM extension dictionary. What are the different features of the new extension IHM extension dictionary? The dictionary has added definitions for multi-scale multi-state ordered ensembles. What does this mean? Ensembles are a set of models that correspond to multiple solutions. As I mentioned earlier, multi-scale refers to models that have both atomic and coarse-grained representations. Multi-state models correspond to models in multiple conformational or constitutional states such as bound and unbound states or open and closed states. Ordered models correspond to models that belong to a time-ordered path or another path such as models of macromolecules belonging to a biochemical pathway. In addition, we can have any combination of the above four features. For instance, we can have two multi-scale states each described by an ensemble or an ensemble of pairs of multi-scale states. So the IHM dictionary provides a flexible model representation that can handle multi-scale multi-state ordered ensembles. Another important aspect of the dictionary is that it provides definitions for starting structural models and spatial restraints from different experiments used as input in integrative modeling. These are important for model validation. The dictionary provides definitions for experimental and computational models of components that may be used as starting models. This includes template details for comparative models as well as definitions for coordinates of the starting models. The dictionary also contains specifications for describing the spatial restraints derived from experimental data such as chemical cross-linking, 2D electron microscopy class averages, 3D electron microscopy maps, small-angle scattering profiles, etc. In addition, the dictionary has provisions to capture the provenance information for starting models and spatial restraints. If the starting models or input experimental data are archived in an external repository, then the corresponding accession codes can be provided. If not, a digital object identifier can be included for the input data from which the restraints are derived. For example, if 3D EM maps are used, then the maps can be archived in the EMDB repository and the corresponding accession code information can be included along with the integrative model. So the IHM dictionary provides the required definitions to handle starting models and experimentally derived spatial restraints. The dictionary also provides a generic set of definitions to handle the steps involved in the modeling workflow. For example, an integrative modeling study might involve the following steps. Gathering experimental data and starting models, translating the experimental data into spatial restraints, configurational sampling to obtain an ensemble of solutions, and then analyzing and validating the ensemble. Generic definitions for capturing these steps in the modeling protocol are described in the IHM dictionary. To summarize the data standards in the IHM dictionary, the details of the molecular components such as proteins, nucleic acids, small molecules, and the corresponding references to the sequence and chemical information databases, all these are taken from the PDBX MMCIF dictionary. The definitions for the starting component models, along with references to models that may be available from structural model repositories, are provided in the IHM dictionary. Similarly, the definitions for spatial restraints derived from experiments, along with references to data reciting in experimental data repositories, are also provided in the IHM extension dictionary. Together, these are applied to build an integrative model, which can be any combination of multi-scale, multi-state ordered ensembles. The flexible model representation provided in the IHM dictionary allows for complex integrative models to be described in a standard way. The IHM dictionary is open source and available from GitHub. I'm sorry. Now we move on to the next requirement, which is the creation of an archiving system. We have developed PDBDEV, which is a prototype system for archiving integrative structures. The data standards for PDBDEV are provided by the IHM dictionary. If you recollect from this earlier slide, we identified that the archiving system requires tools for data collection, processing, archiving, and data distribution. We will now look at each of these aspects. PDBDEV data collection. As a user interested in depositing an integrative model in PDBDEV, what information would you need in order to submit the structure to PDBDEV? The following are the requirements for submission, atomic or cos-grain model coordinates, polymeric sequences of macromolecules, and chemical identifiers for small molecules, details of model representation, structural assemblies, and ensembles, starting models used and how they map to the models deposited, spatial restraints used as input, accession codes or digital object identifiers, pointing to the experimental data and structural models in other resources, details of the modeling protocol. Structures can be submitted at this link, which is also available from the PDBDEV website, and MMSIF file compliant with the data standards in the IHM dictionary is required. What are the current processing procedures in PDBDEV? The current procedures include ensuring that the data are complete, including the representation of the molecular system, the model coordinates, the starting structural models, the spatial restraints, and details of the modeling protocol. We check for compliance with the IHM dictionary, we visualize the structures, and then issue an accession code. We also allow for structures to be kept on hold before publication, visualization of integrative structures. There are two applications that enable visualization of integrative structures, the Chimera X desktop application and the MOLSTAR web application. Both these applications support the visualization of multi-scale structures with atomic and coarse-grained representations. The image shown here corresponds to the integrative structure of the 552 subunit nuclear pore complex from budding yeast archived in PDBDEV and visualized using Chimera X. This model was obtained using spatial restraints derived from 3D EM maps, chemical cross-linking data, and small-angle scattering profiles. PDBDEV data distribution. We have a newly redesigned dynamic responsive PDBDEV website. We provide a new functionality to search and retrieve archived structures in PDBDEV using keywords. There are also individual pages for each entry that provide information regarding the structure, author, citation, software, input data types with links to data in other repositories. Model coordinates can be downloaded from the website and models can be visualized using MOLSTAR. This slide shows a snapshot of the structures archived in PDBDEV. The structures vary from small and medium-sized complexes such as the nucleotide excision repair complex, the mitochondrial iron-sulfur complex, and the 16S RNA complex with methyl transphrase A to very large complexes such as the nuclear pore complex and the RNF ring domain complex with the nucleosome. This slide shows the PDBDEV status as of May 2020. There are 39 release structures in PDBDEV at present with an additional 11 entries that have been processed and are kept on hold for publication. The plot on the left shows the statistics for the different modeling software used. There are several structures that have been obtained using the integrator modeling platform Rosetta and Haddock, and there are additional structures that have been obtained from software such as TADBIT, FPAS, Explore, PatchDoc, iSpot, BioEN, DMD, etc. The plot on the right shows the statistics for the types of experimental data used to derive the input spatial restraints for integrative modeling. Chemical crosslinking is predominantly used, followed by electron microscopy, small-angle scattering, NMR spectroscopy, foster resonance energy transfer, electron pattern magnetic resonance spectroscopy, mutagenesis, etc. I would now like to give a brief tour of the PDBDEV website. Please bear with me for a moment while I move to the web browser application. This is the current PDBDEV website. Once you reach the website, you could do a keyword search or browse for all the current entries. For example, if you search for 3DEM, you get a list of all the entries that use 3DEM data in the modeling. You could define the results or you could display different results in the tablet form such as citation, release date, software, etc. If you go back to the homepage, you can also browse all the list of all the structures. There are currently 39 entries. Let's scroll. You can look at everything at one shot and get an idea of what's there. Let's pick an entry here, the nuclear pore complex. If you go to the entry page, you can get information about the structure itself, the kind of input data that was used, links to data in other resources. You could download the structure or view the structure, the 3D structure in MOLSTAR. Let's give it a minute for MOLSTAR to load the structure. This is the three-dimensional structure of the nuclear pore complex visualized using MOLSTAR. You could take a snapshot of the structure or MOLSTAR provides various tools that can be used to further analyze the structure. Going back to the homepage, there is a link to deposit new structures. User registration and login credentials are required to submit structures to PDB Dev. Let's go back to the presentation. With regard to the software tools, we've developed a Python IHM software library to support the IHM dictionary. This library has been developed by my co-presenter Benjamin Webb and it provides a mechanism to programmatically generate MMSIF files compliant with the IHM dictionary. We will now transfer the presentation to Ben who will describe the Python IHM software library. Over to Ben. Okay, thanks Brenda. Thanks Aledondra. So yeah, as Brenda said, I'm going to talk a little bit about the software, which we call Python IHM that we use to actually generate these models for deposition in PDB Dev. And as Aledondra said at the start of the talk, I'm a scientific software engineer. I work for Andres Shally at the University of California, San Francisco. So just to recap this slide that Brenda showed earlier, our objective in Andres' lab is to generate these integrative structures. And we developed a piece of software that we call the Integrative Modeling Platform or IMP that does this. It takes these various kinds of experimental physical and statistical data sources and combines those with some information. We have structural information about the individual components and builds a model that is as consistent as possible with all of that data. The IMP software, we developed it. We designed it as a Python and C++ toolbox. It's a bunch of little parts that fit together in various different ways because Integrative Modeling is a very broad field. There are lots of different problems that we try to solve. The complexes vary in size dramatically. As Brenda mentioned, there's a lot of different ways in which we can represent them. Lots of different sources of experimental information that we use and combine in different ways. And there's even some differences in how we actually do the sampling and scoring of candidate solutions. And so that's why we developed this software as a sort of a mix and match where we're putting these parts together to build these different protocols. So that software has been developed. We've used it for several years to generate a wide variety of these integrative structures. The code is all open source at GitHub. So our background is in modeling. But now we have to sort of put on a different hat. We have to think of ourselves now as depositors. And so we need to figure out how we're going to take our models and generate them in the format that would work with the deposition system that Brenda talked about. So we wanted to come up with this software that we could then tie into our software so that we could programmatically generate these IHM compliant MMSIF bars. We didn't want to be generating these things manually. We wanted to come out automatically as part of our modeling protocol. And as a bonus side effect, we kind of wanted to also be able to take those MMSIF files and read them back in or use them as inputs for further modeling, for example, and edit them to add additional information, and so on. So we wanted a piece of software that could read and write these MMSIF files that have this IHM data in them. So traditionally, we've generated PDB files, but we can't just take a PDB file and convert it into a file because, as Brenda said, the IHM dictionary is a superset of what's in PDB. So that would get us the coordinates, which is only a very small part of the problem. We don't have any of the experimental information that we use. We wouldn't have any of the modeling protocol, any of the additional metadata that's very useful in order for someone to then take that model and refine it or understand where it came from. So we have to think really about how it ties into the modeling procedure itself. So we need to take those, the actual coordinates, and also merge that in with all the other input information that we have to generate that complete representation of an integrative model in an MMSIF file. So the Python IHM software, it grew out of the software that we developed as part of IMP, and it's now, we've put it in a form whereby it's standalone and people, other people, other than us, can use it for their own modeling. So it's designed to take that same data model that Brenda talked about and to represent that data model as a hierarchy or a set of Python classes and then provide a mechanism to take that hierarchy of Python classes and write out an MMSIF file or even to take an MMSIF file and read it in and generate the Python classes from that MMSIF file. So the intention is that it plugs into modeling or visualization software so that it can be used as a transition layer between the internal representation in that modeling software, whether that be our own software IMP or other modeling software such as Haddock, for example. And or it could also be used in a standalone fashion whereby you take your input files and you run a little Python IHM script and generate the outputs that way. So the data model in Python IHM, similar to that in the dictionary itself, the hierarchy of Python class is that it's not exactly a one-to-one mapping, but it's it's it very closely corresponds to the dictionary, the IHM dictionary and the PDBX dictionary. So the Python classes roughly correspond to those tables in the MMSIF file that you saw earlier. And we end up with this hierarchy of Python classes where at the top you have a what we call a system class that represents the entire modeling system and that will point to various other classes. I won't go through all the classes here and they're all linked at the Python IHM website. But for example, a typical system will have one or more models. For example, you may have an ensemble of multiple models. Those models in turn will contain some structural information in the form of atoms or coarse-grained spheres, which then would refer back to the information about the chains and the primary sequence of those that coordinate information in the ACM unit and MC classes. Additionally, there are other classes that will tie in there. I've given two examples here is that there's a dataset class where you can list all of the input or output data that's associated with your system and protocol class where you can give some information about how the modeling was done. So on the right here is a fragment of an MMSIF file. This is a similar syntax what Brenda showed you earlier in the AtomSite table. These are the two tables that show the chains in a PDB file or PDBX, I should say NSF file, and the entities. So the structosim table here is listing essentially the chains in the file and the entity table is listing some information about the primary sequence of those chains. You'll see that the tables are linked by numeric or string identifiers. So the table on the right shows an entity that has an ID of one. The table on the left is a structosim, an ID of A which refers to an entity, that entity of ID one. The equivalent Python code is shown on the left here where we create a system object and then inside it we pack the entity. So we define the sequence of one of those entities and then we define the A chain. So the primary difference here is that we're using Python references or pointers throughout, so you don't have to manually keep track of the various identifiers that link these tables together in MSIF, which if you're trying to generate the file manually can become a housekeeping issue. The Python HM library takes care of all that for you by just by nature, the nature of references between Python objects. The concern and part of the decision we made when we designed the library was that we tried to make it lightweight. So if you're not careful, you end up duplicating the entire data structure that's in your modelling program in the Python HM classes. So we try to avoid duplicating the data instead. So for example, all the atomic information or the coarse-grained structural information isn't directly stored in the Python HM classes, but they kind of act as fathards, there's wrappers if you like. They delegate that information through to the native modelling software objects where possible. So we're not generating like an entire separate copy of the model using twice the memory and so on. So just a few features of the library and part of it is that the part of the reason for not having a perfect one-to-one mapping from tables to Python objects is that then the API of the library is fairly stable. So when the dictionary itself changes, we update things, changes a little bit about how the tables relate to each other and putting new information in and so on. The Python HM API doesn't change so that we don't have to then change all the way in which we're generating these Python scripts. You could just rerun the script and it will generate a new version of the file that corresponds to the new dictionary. It supports both the IHM extension that Brenda mentioned earlier. There's also a FRET dictionary to cover FRET data and that's also supported by the Python HM library. Of course, it does support a subset of PDBX just for all that structural information that that's also in standard PDB files. It is extensible so in principle you could add support for other MSIF dictionaries if you wanted to or even add custom categories. So in IMP, for example, we have a handful of small data items that just keep track of the information that's of only of interest to us as modelers as opposed to depositors. The MSIF reader is reasonably fast because it's accelerated. It's not the fastest MSIF reader in the world but it's relatively fast and because we have this stable API that sits on top of the data representation, we're not limited by the MSIF format itself. If you have another file format that conforms to the same data model, we can support that. For example, we support binary SIF which supports all the same data that's in MMSIF but it's a binary file and there's some basic support for actually reading the dictionaries themselves so you can read in MSIF and validate it against those dictionaries. So as I mentioned earlier, we try to use the library in three different ways. So one way would be to use it as part of an existing modeling software package. So as part of that software taking your input information and generating models, Python HM will also sit in there somewhere and generate MSIF files. Secondly, you could use it standalone if you have your models on your other dates, you can run through Python HM as a Python script and generate MSIF files and thirdly, you can use it as an input mode whereby you're taking MSIF file and reading in that file and creating that hierarchy of Python objects. So let's look at each of these three in turn. So first, we can use it as part of an existing modeling software platform. So we have it tied into our package, imp. So when we generate models with imp, imp's internal data model is mapped to the Python HM data model. So it ends up generating that top-level system objects in Python HM that contains all the information about the modeling. The idea is it doesn't generate the MSIF file itself but it generates that top-level object which you can then further manipulate. And there's a couple of examples here if you want to go and look at those later for two publications in the last couple of years where we use this software. So I just want to make a point here as an aside is that the full story is that for modeling, we're not usually using the raw experimental data. We usually use some sort of data that's been pre-processed in some fashion. So for example, if we're using cross-linking data, typically we get a CSV file from our experimental collaborators that just has a list of all the residues, the cross-link residues. So we have a list of approximate residues. That's not the raw experimental data. We don't have all the spectra that is really of interest if you want to reproduce that modeling. The imp software doesn't care about that. It only cares about that CSV file of the processed data. But when we come to deposition, we really want to put that original experimental data in. So we don't want that CSV file. We want the accession code of the cross-linking data. And maybe it's in a database like Pride or Massive, for example. And of course, there's other metadata that we want to put in the file that of course just simply wasn't available at the time we did the modeling. So the publication of the modeling course isn't available until after we've done the modeling. So it's obviously not there. So that's why we have this mechanism for then using the Python HM API to take that system object that we generate as part of our modeling procedure and tweak it and adjust it to add additional information before we actually write out the NSF file. To help you a little bit, it does have some support for extracting metadata such as PDB headers from the files automatically. So you can ask it to go in and pull some of that information out. So rather than just having it linked to say we'll use a particular PDB file, it can go and look in that headers and it can find out what the actual identifier was in the PDB database, which is more useful for someone who's trying to reproduce your work. So the second way in which we can use Python HM is standalone. If you've previously done some modeling and you've generated some sort of coordinate file and you also have some of the other information you used, you just have to write a little Python script that goes through and gathers all that information together, runs it through Python HM and generates that NSF file. You can do that after the fact. And there's another example here of a system we published fairly recently where we did that. And that's also in the Python HM documentation. And finally, we can use NSF in an input mode. So it does have full support for reading in those MSF files and populating the data model. So you might want to do that if you want to visualize the models, or we want to use them as input for further modeling. It's a chimera X that Brenda showed in one of her slides earlier. That actually does use Python HM internally. So when you're reading an MSF file with chimera X, what it's doing is it's running through Python HM and it's generating, it's extracting that hierarchy of Python objects from the MSF file and then it's then converting those into chimera X's own internal representation of the structure so that then you can display it. So just to conclude here before I hand back to Brenda, the Python IHM software, it's all open source and you can get it directly from GitHub. Or if you also, you can also install it with pip. So it's on PyPy. You can get the latest Python IHM code installed that way. It's a very permissive license. So it's designed so that there shouldn't be any issue in combining it with a typical software package, open source or not. It doesn't have any awkward dependencies. It's only using pure Python, a little bit of C for the reader if you want to do it that way. So you only need the Python standard library. It works with both Python 2 and Python 3. And of course, it's designed to be used not just with our software, but with any modeling package. As I mentioned, there's at least some support in Haddock to use it. Of course, we welcome contributions from anybody that wants to also develop the software. So with that, I'll pass back to Brenda to conclude. Thank you, Ben. I just want to quickly touch upon what we're currently working on. We are working on developing methods for validating integrative structures. And we're also working on building the infrastructure for automated data harvesting. We need automated tools for data harvesting because users have model industry and data in different formats. So we're creating an automated system for harvesting diverse data. Future perspectives. Our goal is to build all the tools required for archiving integrative structures. Once the mechanisms for collecting, processing, validating and archiving integrative structures are fully established through PDB Dev, these structures will be moved into the PDB archive and the tools will be integrated with the tools in the PDB. Another long-term goal is the creation of a federated network of interoperating resources that contribute to integrative structural biology. This goal poses additional challenges such as development of a common set of data standards and also tools for federated data exchange. In 2019, a workshop was held during the annual Biophysical Society meeting at Baltimore. The title of the workshop is Walking Towards Federating Structural Models and Data. The workshop focused on addressing the challenges involved in creating a federation of integrative structural biology resources. The outcomes of the workshop have been published in a recent paper. The creation of a global network of interoperating data resources will enable the growth of scientific research and also make it possible for the structural biology community to tackle very large structure determination challenges. I'd like to thank our team at Rutgers, including my mentors, Helen and John, who have provided invaluable guidance and support throughout the project. I'd like to thank my colleague Kathy Lawson for advising us during the development of the PDB Dev website. Maryam Fayazi was a grad student at Rutgers who implemented the front end for the PDB Dev website. Our collaborators at UCSF from Andre Shalie's group, Ben, the co-presenter today, has developed the Python IHM library and has also held immensely with the development of the IHM dictionary. Cy and Ignacia are working on developing methods for model validation. I'd like to thank Carl Kesselman, Hong Suda, and Sherbin at USC for collaborating with us on building the automated data harvesting system. I'd like to thank all the WWPDB directors for extending their support for this project, especially Stephen Burley, the director of RCSB PDB for his continued encouragement and support. I thank the Chimera X team, Tom Farron, and Tom Goddard from UCSF. MOLSTAR is actually a collaborative project between RCSB PDB and PDBE. I'd like to thank the principal developer of MOLSTAR, Alex Rose, at the UCSD division of RCSB PDB. I'm grateful to all my colleagues at RCSB PDB for their support. I thank the PDB Dev depositors for providing examples and working with us patiently while we build the system for archiving. I thank all the members of the WWPDB integrative hybrid methods task force for their recommendations and continued guidance. Funding, I'm grateful to the United States National Science Foundation for providing funding for this project. Thank you all for your kind attention. Please visit the PDB Dev website for exploring or depositing integrative structures. Now we move on to the QA session. I'm happy to answer any questions. Thank you, Brenda and Ben, for the very interesting talk. And thank you also both for getting up as early as you've had to to give us Europeans the chance to hear what you've been working on. For questions, if you just want to write your questions in the questions tab, we can get through a couple of questions, but probably not going to keep everyone too long. Our first question is from Alexandre Bovin. Alexandre, I've unmuted your mic if you'd like to ask a question yourself, otherwise I'll read it out in your study. Hi, Brenda and Ben, nice to listen to you today. Hi. So it's a question probably I asked it several times. I know the PDB people, for example, PDB, they have a kind of an editor of MMC files that they use internally. So are there any plans to provide some kind of friendly interface to users to fill in information into these integrative modeling MMC files? Not all users will be able to handle a Python API. Yes. Let me go back to this slide where I said we're building the infrastructure for automated data harvesting. That is exactly what we're doing. We will have a system for testing probably before the end of summer, and we should have it out for friendly users who could test the system. It will have an interface for providing all the data that is required and additional metadata as well. So that is the goal of that project. Okay. So it's not automated in a software workflow, but it's more an interactive way for the user to input data? Yes. It will be an interface where a user can come and put everything they want and click a button and finally generate an MMC file that can be submitted to PDB. Okay. Very good. Thanks. Thank you. Thank you for that answer. The next question we have is from Carol Burka. I have unmuted your microphone. Carol, if you would like to ask your question, please go right ahead. Otherwise, I'll read it for you. Okay. Thank you. It was a really nice presentation. May I ask about the ordered states which were shown on one of the first slides? What is their definition and where can I find it? Because I'm trying to look through the GitHub and I don't know where exactly I should look for them. You're talking about the multi-scale, multi-state ordered ensemble? Yep. Oh, the ordered states. The ordered states are actually defined as a graph of connected models with models being the nodes and the pathway or the ordering information as edges. It is in the, there is documentation in the GitHub. On the GitHub site, if you see dictionary documentation. Yep. Yeah. And if you go into that, there are some notes on how to define the ordered states. Okay. Did you? Yeah. Or you could also look at the dictionary, but then that will be too complicated. The documentation tells you where to look at. Okay. Thank you. You're welcome. Thanks for that. The next question we have is from Dikos Sumpasis. Dikos, I have unmuted your microphone. Please feel free to ask your question if you want. Otherwise, I'll read it for you. Can I speak now? Yes, you're welcome to speak. Well, I have a trivial problem for this excellent software development, which is I use a lot of old PDB files for my modeling stuff. Can I transfer them to to the new format MMC, which I hate as a theoretician, using your app, your Python IHM? I'm sure Ben will answer, but let's say I want to combine various protein segments from all proteins, which are in the PDB for years, but they are good enough for my purpose. Can I then combine them with using your system? But they are in the old format. They are in the old format. Yeah, I understand your question. Yeah, sure. So there are actually a couple of different tools for doing that. If you're actually carrying out integrative modeling, in which case you need to handle a lot more information regarding the integrative modeling itself, then Python IHM is the tool to use. Definitely, it can handle all that. It can take a PDB file, and you can create all the Python classes if you want to add any of the integrative modeling information, or just use the PDB file and convert it to MMC. But if it is not integrative modeling, if it is just a regular PDB file, there are also tools that are provided on the PDB website that can, there are software tools that can convert PDB to just plain PDB X and SIF. Okay. Can I integrate theoretical information? For example, concerning the hydration, which nobody can handle nowadays, but I can handle it. Can I combine a theoretical, can I take a model from your database and just input my theoretical prediction for the waters there? Theoretical prediction for waters, you mean? Yes, for the hydration of the system, because all the systems they are, most of them they are vacuum thoughts, but I work in the wet environment. Right. So the dictionary does allow for water molecules, I mean the solvent, and you can of course add that, add the coordinates for waters as long as you follow the definitions in the dictionary, but then you- No, they are not giving enough water information. I can predict what they don't see in the experiments. Right, that's because we archive what the users provide, so if the users did not model the water, then we don't have that information. I model the hydration theoretically, so can I input it to the system? Can you input it? Right now, no. Actually, if you added theoretically and then you want to submit it back to PDB Dev, then yeah, right now we don't support that. Okay, thanks a lot. I mean, that's a water-bounded brush. Yeah, yeah, right now we don't support that. Okay, thank you. Thank you for that. Alexandre, who asked the question previously, has also pointed at a couple of tools available online, I'm currently just copying them into the chat, but otherwise I think that that is all for questions for today, so I'd like to take this opportunity to thank again our speakers for taking the time to tell us about their very interesting work, and also to let the audience make the audience aware of the upcoming BioXL webinars, one on the Haddock 2.4 server, which is taking place on the 11th of June, and we've got a couple of webinars in autumn also lined up, and you'll hear more about them either during the Haddock webinar or if you go on bioxl.eu. There's a tad for webinars, and all of our upcoming webinars are there. Thank you very much for coming, everyone, and we all hope that you have a good day. Thank you. Thank you.