So hello everyone, sorry for being late, but I'm very happy today that we have Juljanos and sorry, I cannot pronounce your name. That's fine. Yes it. Yes it. Yes. To to have a bite size talk today, I will hand it over to you to introduce yourself if you if you want to. Yeah, so I am Juljanos, Juljanos Pfeubler. I am a postdoc at the Zuse Institute Berlin, and I was working a long time at my doing my PhD at the University of Tübingen and the Freie Universität Berlin, and was a long time open MS contributor and maintainer. And a lot of the pipeline that we will present today is is course based space and open MS and it will be about mass spectrometry. Okay, I can present also myself. Um, I am Jasset Percibero, I am the team coordinator of Freie Database, which is the largest proteomics database of NBLEDA, and I work with also one of the developer appointments. Yeah. And, yeah, if there are no further questions in the beginning. I also can start sharing my screen. Would that be okay. Franziska. Yes, right.  Let's see.  We see the presentation. Perfect. I think it will be about a next flow workflow this time. And it's not a super recent addition to the next to the NFCOR community platform, but it was there for a long time or a little bit longer time, but we just recently released a 1.1 version. And that is much more stable and much more up to NFCOR standards and we thought that would be a great time to introduce it. It is a workflow for, as the name implies, quantitative mass spectrometry data analysis. Just following all the NFCOR standards. It is meant to be very reproducible and also applicable for large scale analysis. For example, on those big public repositories like pride where yes it is from. Yeah, what are the areas of application of our workflow. It is planned as a most in one workflow for the analysis of quantitative mass spectrometry experiments in general. I don't mean metabolomics, proteomics, proteogenomics, but the current focus and the thing or the topic that we started with in the workflow is relative quantification of proteins or modified proteoforms based on mass spectrometry experiments. And that, and I will present first or ask the question, why do people do proteomics and how is it different from the usual genomics that we see here in NFCOR. Well, one one thing or one nice example that people always give is the difference between a caterpillar and a butterfly. So they share that exactly the same genome. Unless it's like some very slight mutations to the stage of life. They have a vastly different proteome, not only in the like amount of proteins that are expressed, but also the types of proteins that are expressed and how they are modified and all of this gives a much better representation of the actual phenotype of an organism or a cell. So the proteomics can be used in addition, or instead of genomics or even transcriptomics. And they are one technique to to get the quantities of proteins, for example, in the cell is mass spectrometry. And like I would say the most common technique to do that is via liquid chromatography coupled mass spectrometry. Which means you you digest your sample with the proteins first, you put it into an eppendorf tube. So you subject this to a liquid chromatography to split them up by some chemical physical chemical properties to make the analysis easier. So you squeeze them, put them into the mass spectrometer, and the mass spectrometer can then measure the ions and the amount of ions that are there based on their property or their behavior in a magnetic field. And what you get out is so called book, so called mass spectrum, where you, you can see the intensity or which is related to the amount of ions that were there for a specific mass or to be specific, a specific master charge ratio. And the problem with this is that those mass spectrometry experimental setups can be very complex. People add new types of mass spectrometers new types of experiments that they want to how they invent new labeling strategies and how to compare different samples and so on. So here I give a little overview over the most common strategies and quantitative proteomics and want to highlight which of them are supported by quantum as overall it's we only support relative quantification since absolute quantification can usually only be done with certain standards and a few people do it unless they have a really exciting project going on. But also in relative quantification you have two big subtypes, you can have labeled relative quantification and label free relative quantification. Label free is usually cheaper, because the labels are expensive, but the analysis is sometimes a bit more complex. And here you can have so called data independent acquisition data dependent acquisition. You can have it feature based spectral counting. Regarding labeling, you can label the proteins or the protein pieces the peptides in vitro or you feed your organism or cells, certain amino acids labeled amino acids in vivo. And our focus for labeled quantification was the team so called TNT and I track strategies, which are very similar in analysis types. So if you have any of any data set that was gathered with one of those green strategies here, quantum as will be should be very useful for you. Here's an overview of the pipeline. Everything starts with the spectra in MZML or in raw format. MZML is open. It's sometimes a bit more verbose bigger. It's an XML based format. But we can also read raw files from the thermo Fisher instruments through conversion. We do some pre processing on the spectra, as well as on the protein database that you give the pipeline to say which proteins you think that are in your sample and you would like to identify and quantify them. And then we have three different branches, let's say that depend on which strategy was the experiment was based on. We have this data dependent label free branch in blue data dependent isobaric labeling TNT or I track in red and data independent in green. The top ones are usually done by open MS tools. The framework for mass spec analysis, while the lower one is done by DIN. It's a separate package where we were in close collaboration with the author to make it as efficient as possible in a distributed environment, computing environment. I will go over the steps one by one. At first, a bit more details about the input. I mentioned the mass spectra already. The second one is an experimental design that you need. And we highly recommend to use the sample the data relationship format. It's a community developed and tab separated format that for the data sets, for example, that are in pride. And we annotated a lot of them manually for our reanalysis. It contains information about the contents of the samples like organism. Yeah, labeled or not the experimental setup, but also the biological question like which condition a sample belongs to the protein database is the usual easy faster format. And can be either directly downloaded from SwiftProt or Tramble or manually created by some proteogenomics studies you did before. It can be with or without so called decoy proteins that we need later for false discovery rate estimation. In the preprocessing we, as I said, convert and index all of our spectra and the default format will be our MZML. So everything you have else will be converted into MZML before keep that in mind. We combine information from the STRF and the next flow parameters. So currently it's a bit mixed where you can set certain parameters because we also wanted to support very simple designs.  We do some sanity checks conversion convert them into designs for the specific tools, but also units for specific tools or certain vocabulary for specific tools. And regarding the database, we can also generate the decoys for you. It's usually done by reversing or shuffling sequences in the database. Then we perform identification with common so called database search engines. Currently you can select between MSGF plus and Comet or both of them, in which case they will be combined probabilistically by an open MS tool called consensus ID. We then offer a re-scoring mechanism that uses more features than just the similarity between predicted spectrum and observed spectrum. This is currently only possible by the SVM based tool percolator, but we are heavily developing or trying to integrate deep learning based scores from, for example, MS2 re-score. The false discovery rate estimation is done based on the well established target decoy approach. We offer FDRs on multiple levels. PS, the peptide match, peptide spectrum match level, peptide level, protein level, protein group level, and on different scales, either for a specific sample only or for experiment for the whole experiment. And we can do the so-called PICT FDRs that were recently published and show a bit more sensitivity in large-scale experiments. For the quantification of the peptides, in label-free quantification we use the open MS proteomics LFQ tool, which is also the main part of the old NFCore proteomics LFQ pipeline, which means if you're using that one, this is fully integrated and superseded, so you may switch to quantum MS. And this performs the following tasks. It does the identification of quantifiable features in your mass spec data. This can be done targeted by looking for specific IDs or untargeted by just looking at isotopes and illusion shapes. Then does retention time alignment. It links the identifications to get the best matches over all samples. And then you can optionally also transfer identifications to features that do not have an identification or you can re-quantify parts of your MS experiment. If in all samples, but this one, or in the most samples, but this sample, there was a feature but you couldn't find one in this one, then you can extract the last part of the signal. In isobaric labeling, it's much easier because it's just based on the intensity of so-called reporter ions. We support most TMT and itrack plexus, which means the plex just tells you how many channels you can multiplex into one sample, which means this means how many samples you can have in one mass spec run, let's say. And we also support so-called SPS, which introduces a third fragmentation level for mass spec. Then when you have quantified the peptides, you usually want are interested in the actually proteins that they come from. Therefore, we have two different inference techniques implemented in the Bayesian one with the open MS tool epiphany, but also a simple rule-based aggregation of peptides to proteins. And regarding quantification, we support the common strategies top three peptides per protein. IBAC is a common strategy that normalizes by the length of the protein, for example. Those come from open MS, but we also have support for statistical post-processing tools like MS-STUTS and TRIGLA, which then they have much more elaborate statistical models, and they also include significance testing between comparisons of samples, conditions, contrasts. For the third branch that is based on PTIAN, the data-independent acquisition branch, we made it fully parallelizable via multi-step analysis. So first you do an in silico library prediction and the pre-analysis for every sample, and then only after you do an empirical, like data-based library generation and a final analysis on the full experiment. It is also compatible with MS-STUTS. This means you could have relatively, you have a, of course, the output will not be comparable in the quantities, but it will be comparable in the format compared to other branches of the workflow. And we also converted, as all the other branches, it can be converted into MCTAP, which is for smaller experiments, human-readable, TAP-based format for the quantities and identifications of such experiments. And you can use it immediately for upload to PRIDE, for example, or publication, which is usually recommended by the journals. A bit more details on our general outputs. As I said, we have this MCTAP for all the quantitative and identification-related information. The MCTAP in general contains, on the right side here, metadata, a protein section, a peptide section, peptide spectrum match section, and for metabolomics also, small molecule section. It's used by, it's a community standard, so it's used by a lot of projects, and it's very helpful to have it for upload or journals. Then we, from our statistical post-processing, we can get heat maps or volcano plots for the comparisons between conditions that we can specify in the parameters of the workflow, for example. But we also have a full PMultiQC report, which is based on a plugin that we wrote for multi-QC specifically for proteomics. It includes quality control heat map over all samples, but also detailed plots per sample, and a detailed and searchable table of the results that is connected to an SQL backend. Those are some examples of our outputs. On the first picture, you can see the experimental design that you have given and how it was interpreted by our tool. They can get very complex in proteomics experiments because you can also fractionize your data, fractionate your data or your samples. With the usual biological and technical replicates, you can get quite complex. In the lower part, you can see a heat map of some aggregated quality control metrics for specific samples, and things like how many percent of contaminants were identified, the average peptide intensity, how many miscleavages in your digestion, we could find what was the rate of identifications from the overall number of spectra, and so on. Then some more detailed information about specific samples, for example, the number of spectra on each level, like fragment spectra or service spectra, how many of them were identified by each of the search engines, how many were identified after consensus ID, and so on. Lastly, one application that this workflow already had was a reanalysis of a large part of PRIDE. We really sat down and were annotating with a large group of PRIDE into the sample-to-data relationship format, which meant a lot of looking into papers, contacting authors, and so on. But which also means that you can now just, if you want to reanalyze something in a different way, you can just download the data from PRIDE or give URLs, which next low, of course, handles to the FTP. And, yeah, reanalyze it with different settings because the SCRF is already available. We then reanalyzed each entry with our quantum S. The good thing is we could analyze many of them because we made it very robust, bare default settings, and also supporting a lot of different experiment types as you have seen. And then, yeah, in the end, we just combined and visualized the results, in this case, per data set or per tissue, because a lot of data sets are very specific for a certain tissue. And, yeah, we're currently writing a publication on that. That was one of the first applications, yes. And I think that's it from our side. We're happy to answer all of your questions. Thank you so much. I'm just going to remove the spotlights. So if there are any questions from the audience, you should now be able to unmute yourself and ask the question right away. So are there any questions from the audience? If not, I actually have a question, maybe a bit selfish. It's very nice to see that we have some pipelines at least that are not NGS based. So I was wondering what made you choose NextFlow and NFCore for making this pipeline? The first thing was the incredible integration of all those large scale, high performance computing clusters and clouds that we have not seen in other workflow managers. Of course, a little bit, yeah, bias, because I knew some people from NextFlow. But yeah, I think it turned out to be the best choice in hindsight anyway. So, yeah, the NFCore team was very helpful in implementing all of this. And the AWS tests were also super nice because we, as a university, barely have any capability to test it on Amazon Clouds or something that always costs. And yeah, I think it gets a better reach also to industry by supporting, yeah, clouds. And maybe in the same vein, did you find any problems that were specifically there because it is not NGS? And because we're often very geared towards NGS? Yes, of course, yeah, it's not big problems, but some of your templates, let's say, they have a lot of, not a lot, but what was it for example? I think you were in the beginning, you had a fast QC parameter that was always supposed to be always there. And we of course had to remove it. Now, whenever a template update comes, you have to remove it again and things like that. But yeah, minor things. Okay. Are there any more questions from anyone? I would have one. Hi, great talk, thank you. I was wondering what you mentioned the small molecule MS experiments as a future possible application of the quantum MS. How far is it thought out or where does this stand? Yeah, so implementation wise, so we just, we have a colleague that created such a workflow based on very similar tools that we already have. That means the open MS ones, but also some other tools like Sirius for small molecule kind of database search. The competitor language snake bank. And, but we at least we see that it's a very feasible workflow that we have. And now we want to see, it should be a rather simple translation of the workflow, but we also want to check with the existing metabolite or workflow. Yeah, to see if we can combine them. We still have to check how, how compatible the SDRF and the MC top would be so that we for everything that we want to include into quantum as we definitely want. We want to start from an SDRF and have us output an MC tab or another future community community standard file format. And we think it should all be possible, since there's also an MC top and for metabolomics and yes it I think SDRF should have no problems at all to have some metabolomic specific annotations there. Yeah, I think we have started already to support metabolomics with SDRF. I mean we have the first call around it, how to do it. And as you said I think this is really important point. We have tried to put in quantum as the starting point on the end of the workflow should be a standard file formats for anyone who wants to join quantum as this will be the case for other use cases like proteomics like immunopathomics or any other use case that want to jump into mass pack quantitation in quantum as truly start by one standard file format something that the data out there is in that file format until end up into another standard file format which is this in this case is MC top but it could be something in the future slightly different. Great. Thank you both for the elaborations. Thank you very much. Do we have any more questions from the audience. It doesn't seem so then I would like to thank again you Janus and yes it. And of course, as usual, also the John Zuckerberg initiative for funding our bite size talks. So thank you very much everyone and see you. Bye. Bye.


There are not only microbes in our bodies and I don't know about these surfaces but also in the environment. And microbes come from people, come from animals, come from plants, from soil, you already heard that. And a very nice example is studying children who have very strong contact with those microbes and these are children from farmers and so we go to villages where there's some farming children and some non-farming children. So in my part of the world that would be a typical farmhouse and there's a very close proximity of animals which live in that part below the wood and people. So if you go through the main door you would go the hallway and maybe go one door or maybe two doors and you would be right in the stable where they keep usually animals. So this would be where you would end. These are family-run businesses, so father and mother work in there and the children usually spend a lot of time with these parents. They help the parents, they play there because they like the animals, usually the cats and kittens are there. And so they get exposed to this incredible variety of microbial exposures that go along with this. This is important because the timing is the important factor here. Asthma, allergies, infections all develop very early in life and so if you want to protect from these diseases you want to have these exposures very early on and you need the animals, you need the plant and you need the milk that these people produce. And this is a sustained effect. So if you go at school age into these villages and you ask for Asthma, allergies, hay fever and atopic dermatitis in children age 6 to 12 you see all of them have much less if they were grown and raised on a farm. And there are even other farming populations which are even much more interesting. These are the Amish people in the U.S., they emigrated from Switzerland not far from here and they have a very traditional farm style and you can see also the little kids are very early on exposed to these animals and to these environments where they inhale and of course they also ingest because they have it on their hands. So we made a project comparing Swiss farm children, non-farm children, farm children and Amish children because they come from the same sort and you can see the Amish in light blue columns. They almost have nothing. They don't have hay fever, they have very little positive allergy tests and the little Asthma they have is very, very mild. So if we go back to our European populations we can have the same effect if these kids are exposed to both the milk and the stable environment in the first year of life and then Asthma goes down from 12% to 1% hay fever from 15% to 3% or positive allergy tests from 33% to 12%. So it's quite significant effect which have been shown in many studies. It's not only about Asthma and allergies, it's also about infections in the early life. Rhinitis and otitis. And one example is here the milk, if they consume their own raw milk you see much less of rhinitis and otitis and it's much better than boiled farm milk or pasteurized milk which means that there might be something in the process of what we're doing to milk. And so the question of course is what is it? We know that it's not the microbes at this stage. We know that this is things that are destroyed by heat. We think it's proteins and other substances and I think it calls for you guys who are in the milk industry to try to come up with new ways how to process that milk. The other question of course that we say is the stable. So what is in the stable? And what is it that these kids inhale or are exposed to? In these tables there are myriads of microbes, yeast, fungi, phages, viruses. But how can we measure them? And you can do it by culture or you can do exactly what Rob said we can do it by DNA. There's just an electron microscope picture of what you would find in these tables. They're fungi, they're bacteria, but you want to know exactly which are the ones. And so we used exactly the methods that Rob is doing. It's a DNA fingerprinting. It's like knowing which are the bacteria and microbes in these environments. And what we see is that there is a huge diversity. And with this diversity this is the red bars the more of these bacteria you have the less these children have asthma. And this is true for bacteria and for fungi. So it's not a magic bullet in these environments. But the question is it, is it the more the better? Or is in that diversity like in a minestrone, is there a certain composition which is particularly good? And we think of this as being a microbial cocktail. And they are gram positives, they are gram negatives, they are listeria. And there are also some fungi which is for example erothium and penicillium. So it's not the magic bullet, it's not the more the better. It's a certain consistency. We took this into animal models also like Rob has shown into mice. And we can completely prevent asthma by giving these mice to inhale either the bugs themselves or the extracts of these dusts. So in mice we can already prevent asthma, we know how it works. And I think for us the big question is how can we translate this to children that grow up in New York City and are not fortunate to live on the farms and maybe really prevent asthma and allergies.


Every Army needs frontline troops, and the NRC's essential foot soldiers are its on-site inspectors, also known as resident inspectors. We frequently describe them as the eyes and ears of the NRC, and with good reason. Each US nuclear power plant has at least two such resident inspectors. Multi-reactor sites would have more inspectors, and their work is at the core of the NRC's power reactor inspection program. They come from a variety of backgrounds. Some served in the US nuclear navy prior to joining the NRC. Others worked in the nuclear industry, and still others are recruited directly out of college. Most come to the agency with engineering and science degrees. Regardless of where they worked previously, prospective resident inspectors must undergo an extensive and rigorous training program designed to ensure they have the proper knowledge and skills to carry out their duties.  The exam is administered by an inspector qualification board, which consists of at least three members, and evaluates how well an individual can integrate and apply inspector competencies to field situations. A resident inspector's training does not come to an end once he or she is determined to be fully qualified. All inspectors are required to complete at least annual refresher training to maintain a high level of performance. A typical day for an NRC resident inspector on site includes coming in and reviewing operations logs, engineering logs, observing operations performance of tests and evaluations. We also do extensive reviews on risk-significant systems that are important to site safety. The on-site inspectors will come in at all hours in the day, on top of being available 24 hours a day, 7 days a week for emergency response. We will typically come in to observe activities performed on back-shift hours overnight and in early morning hours. We observe work activities and testing of plant equipment to ensure that the licensee is meeting their requirements as they relate to the federal regulations. The resident inspector program is not new. Indeed, it has been in place for decades. It was launched in 1978 prior to the 1979 accident at Three Mile Island to improve the NRC's inspection program. The goal was to do so by providing increased knowledge of conditions at plants, improved ability to independently verify the performance of plant personnel, and improved incident response capability. Resident inspectors live in the communities around the plants they help oversee. But in order to help maintain inspectors' objectivity, there are restrictions on their interactions with plant personnel. The first thing that we do to ensure objectivity of our inspectors is we impose a seven-year limit on the amount of time that they can be assigned to a particular plant. This is to ensure that they don't become too comfortable, too familiar, if you will, with the procedures and the processes that the licensees conduct on a daily basis. To ensure that they maintain a questioning attitude about those activities that they're inspecting and observing day to day. The second thing that we do is we ask that our inspectors maintain a professionally distant relationship from the licensee's employees. There should not be any appearance of collusion, any appearance of conflict of interest. They should not be in bowling leagues, for example, with people that they regulate. The third thing is that we try to restrict the inspection activity to those issues or areas or plants that they were not previously affiliated with when they were working directly for the industry. This maintains a certain amount of objectivity and prevents the appearance of lack of objectivity with our inspections. All told, the agency has about 150 resident inspectors, including some who are based at nuclear fuel production facilities. More information about the NRC's React Riverside process and its resident inspector program is available on the agency's website at www.nrc.gov.


Hey, this is Brett and this 2017 Ram 2500 Crew Cab Short Box Laramie Sport is stock number 12747Z. We're here at Summit Automotive in Fond du Lac, Wisconsin, you're new and used heavy-duty truck and Ram headquarters. This 2017 Ram 2500 has the 6.7 liter Cummins diesel engine. This truck has been fully-safetyed and inspected by our service shop. It has a fresh oil and filter change. All the fluids have been checked and topped off. And this truck is 100% ready to go. We're going to go all the way around in this video. Inside underneath, start it up and take a look under the hood. Delmonico red pearl coat is the color and we shoot all of our videos in 4K. So if you have HD video capabilities on your computer, tablet, smartphone, or television, I highly recommend turning them on right now because it is your best way to check out the quality, condition, options, and cleanliness of the truck before seeing it in person. And if you liked the video and how we do things here at Summit Automotive, we do videos here each and every day. Consider subscribing to our YouTube channel in the upper right hand part of the screen. And then click the bell notifications and you'll get updates on the videos we do each and every day here at Summit Automotive. This one comes with fuel off-road painted and polished aluminum 18-inch rims and it has Cooper Discover AT3 tires. These are LT29570R18s and they have, I'd say, 90% of the tread left on them, lots of tread left on those tires. This one does have a BDS suspension lift kit. It has BDS Nitro Series NX2 shocks and looks like probably about a 4-6-inch suspension lift kit on this truck. Front fender is in excellent condition as the projector lamp headlamps, LED running lights, factory fog lights, and the front bumper parking sensors. The front bumper is in really nice condition. I didn't see any major dents or dings on there. The Sport package gives you the gloss black grille and Ram logo on the front. And the hood is in very nice shape as well. I didn't see any major dents or dings. Passengers side front fender looks really good. And the passenger side rim, no scuffs or scrapes, just a little frosty from being outside. Delmonico Red has a lot of metal flake in it. This is where you're going to want to get those 4K capabilities on. You can see all that metal flake in that paint, really a cool looking color. You get the blacked out Cummins and Ram 2500 logos on the side as well, part of that Sport package. And as you go down this side of the truck, take note of how clean that body is, how reflective and mirror-like that paint is. We take these HD videos so if you are far away or even if you're close by and just cannot make the trip down, you can still see the truck. Hear the truck and have confidence in the vehicle you're looking at before you make the trip or before you get here. So that when you do get here, there's absolutely no surprises. And you can make a smart and informed buying decision from wherever you're at. And if this video helps you make that decision, let your salesman know that you saw the video that it was helpful and that Brett sent you. Back rim is in excellent shape as well. And like I said, these rims are just a little frosty from being outside so they are a gloss black on there. Back tires are like new as well. You can still see the little wear knobs on there from them being new. I'm not going to say they're brand new, but they're very, very new. Frame and underbody is in excellent shape, very little to no corrosion. And it's like that all the way underneath. Has all the remaining factory exhaust and it does have power running boards that come down. Little rockers look pretty good. And coming around to the back of the vehicle, notice you get the 4x4 off-road suspension package. And coming to the back, the only major imperfection I saw on the entire truck is on the back bumper here. There is one little ding right there, but it is a used truck. Has the backup parking sensors, full towing package, which includes a receiver hitch, 4-pin and 7-pin wiring. And the tailgate is in very nice condition. Notice the gloss black 4x4 and RAM logo on the back here as well. Comes with an access soft roll-up tonneau cover. No major rips or tears on there. And it does come with the spray and bed liner. This one also has LED bed lights and you turn those on with your cargo lamps inside the truck. Speaking of cargo lamps, it does have the cargo cam up there. So you can check out what's going on in the bed of the truck. And as you go down the driver's side, just as clean as that passenger side, no major dents or dings on the box. And for full disclosure, this back room is in a very nice shape as well. Down the rest of the side of the truck, the doors look pretty good. And seeing dents or dings does have the heated fold-up tow mirrors, built-in directional signals. They fold up like so and in like that. Pretty sure they're powerful then, which I can just a second. You can see that those AMP research running boards doing a nice job. They work nicely.  There are no rips, there are no tears on the seats. They're in very nice condition. Memory driver's seat, lumbar, weather tech floor mats, auto headlamps, power pedals, power windows, power locks, power mirrors. Get that leather bolster and wood grain trim. And like I said, these mirrors do power fold in. I always like showing both sides so that you know that both sides are working properly. We'll hop inside, check out the Miles radio and everything that this truck has to offer on the interior. Get the Laramie animation at the beginning there, 7-inch LCD display. And you can see that this one does have a digital speedometer, compass, outside temp display, trailer brake gain, range to empty. And you can change those corners to read whatever you want. Instrument cluster is very nice and clean, comes with the heated leather wrap steering wheel. Cruise controls on the right, Bluetooth and information center controls on the left and audio controls on the back of the steering wheel. This one does come with the six speed automatic transmission with the optional tap shift. And it does have the Alpine premium sound system. Comes with the 8.4 3C radio. This one does have the factory navigation system on it. As you can see there, and it also has AM, FM and Sirius XM radio capabilities. You also have your heated and cooled seat buttons, backup cam and cargo cam, which I'll show you right here. So there's your backup cam and then you can check out the cargo cam as well. Really nice features. And then you have your different climate controls here right now. It's off, we turn it on. You can see of your dual climate controls. Now, if you don't like doing the controls up there, you can do the climate controls down here with the more tactile buttons and knobs as well as a volume and tune control. Turned out four-wheel drive, it does have the push button start. Factory brake controller, downhill assist control, stability control, tow haul mode.  And it does have five auxiliary up-fit switches, keyless entry with the remote start. And this one does have a 115 volt, 150 watt plug-in. You can get the dual gloveboxes there, passenger side, floor mat and seat are in excellent condition. Smells extremely clean inside this truck. You do get those speakers in the ceiling part of that alpine sound system, headliners in nice shape. And it does come with the power sunroof. Map lights, power sign rear window button and home link buttons for your garage door security systems and lane systems. And finally, 911 and assist buttons in the rearview mirror. We'll take a quick look at the back seats and then we'll check out under the hood. The back seats are just as clean as the front seats. No rips or tears back here. These back seats are heated on the outboard side. No rips or tears, power sliding rear window with the built-in rear defrost. Seats are in fantastic condition.  You've got muddy boots or tools, put them on there. You're never going to get your seats dirty. Or if you've got pets, put both sides down. They've got a nice flat place to lay.  It does come with a winter front and a receiver hitch reducer. Weathertech floor mat back here. And there's factory floor mats underneath there. And you get in-floor storage with removable containers for easy cleaning. You can put ice and drinks in there.  You get those heated seat buttons and a 12 volt power point back here as well. Same trim on the back doors and you do get child safety locks on the back doors as well. Bottoms of the doors all look very, very good. And we'll start it up and take a look under the hood. Looking at those headlights, looks like somebody put HIDs in here. Starts right up, no check engine lights or anything like that. I would personally like to thank you for checking out the video today. And hopefully from this HD video, you will have been able to verify the quality and condition of this truck all the way around. Look, they even put LED fog lamps in this thing and HIDs. And under the hood, we have the 6.7 liter Cummins diesel engine. Engine bay is very clean, runs very smooth. And once again, this truck has been fully safe and inspected by our service shop as a fresh oil and filter change. All the fluids have been checked and topped off as four, like the tires. There is your emissions sticker. And this one is 100% ready to go. I would highly recommend this truck to make quality and condition standpoint and just overall appearance. Really a good looking truck. 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Hello everyone. I'm going to talk about peer-to-peer OS and Flatpak updates, which is something that we've been working on endless over the last year and a bit. I'll define some terminology first because these terms are a bit vague sometimes. Flatpak, you've probably all heard of, it's a Linux application sandboxing and distribution framework. For the purposes of this talk, we just need to know that it's basically something that uses OS Tree which provides things that users care about. We don't need to do any more details about how Flatpak works. The OS in this case is Endless OS, which is a Debian-based operating system. Again, you don't need to know any more details about it apart from the fact that it's based on OS Tree. OS Tree is very briefly git for operating system file trees, but I'll go into more details about what that means on the next slide. Peer-to-peer updates, we care about updating from other computers on the local network and from a USB stick that's been pre-prepared by someone else with updates for you. The overall goal is to be able to distribute system updates and to distribute new versions and new and different applications to other people without them having to download them over the internet. OS Tree, which is the core of how this is all done and the core of how Endless OS is implemented and the core of how Flatpak is implemented, it's like git. It's a content-addressed file system where you dump files in, they are hashed by their content and stored as a path based on that. Each of them has a checksum. Each object can be a file, it can be a directory tree that contains a hierarchy of files or it can be a comet object which contains some combination of files and directory trees. It's got refs, which are basically human readable names which point to a comet just like a git branch does and they can change over time so you can point them from an old comet to a new comet to a new comet. It's got remotes, just like git remotes, they are a little configuration saying here's some repository on the internet somewhere which you can download updated refs from. Each remote has a name which you choose locally. By convention it's always the same but it doesn't have to be and as I said before, Flatpak is based on OS Tree. So in the Flatpak world you've got apps. Each app is a ref in OS Tree terminology and when you deploy an app that is a comet from OS Tree with a load of files that contain whatever the app needs. It's binary, it's icon, whatever. So how do we add peer-to-peer support? What we have to begin with with OS Tree are these refs. These refs are unique per repository so just like a git branch you've got a master branch for each git repository you care about but each repository has a master branch so if you want to update your master branch in OS Tree you're caring about. Same if you have a ref in OS Tree and you want to update it you need to know what ref that it actually is so if I have an app that I've produced locally called G-edit and there's also a G-edit produced by the G-edit developers and published separately they're going to have probably the same ref name but they probably refer to different content so what you need is a global namespace which disambiguates those and says that the G-edit over here that I've produced locally is mine and the G-edit over there is theirs and those should be considered a set of things so we need a global namespace for refs. How do we do that? We added something called collection IDs so these are basically a globally unique version of the remote name so you can uniquely identify each repository and clones of it mirrored around the world with a collection ID so if G-edit were to be published by the G-edit developers in their repository they would set a collection ID for that repository if that were to be mirrored by GNOME they would copy the same collection ID so all the refs for either are considered to be equal and then if I were to publish my own G-edit I would choose a different collection ID by publishing something which is not necessarily the same as what they're doing so if you take a collection ID and a ref and consider those as a tuple that becomes globally unique and you can use those to look up and query for updates for the refs you want wherever you have a question I can repeat it Is there a convention or some enforcement for making people not collide with the names of their collection IDs? There's no enforcement although if someone were to choose a collection ID that had already been chosen and it started to collide with things then errors would appear everywhere there is a convention to use reverse domain names same as most other things but this is documented Any other questions so far? Cool So summarised collection IDs are like a name for a remote configured globally rather than locally Does this mean that you could prepare a malicious version of one app put it on a USB stick walk over to somebody else's endless OS computer then essentially update the app with a malicious version? You would have to have the GPGK from the original Right, so there is a measure of enforcement I'll come on to that in a second but I'm very perceptive So yes, summary files are the other half of the problem Each repository has a summary file and that contains amongst other things a map of refs to Comet checksums so it just gives a complete list of what the repository contains and the comets for each of those refs and it contains some other metadata like the repository description and its name, localised name and the summary file is signed by the same key for the entire repository so you know the summary is authentic for that repository so that you can't have a man in the middle interject a summary that contains inquiry data or malicious data as you download over HTTP which you can do for OS tree So it's traditionally signed as one big blob of stuff and it lists the refs If I am on a local area network and I've got some refs from this repository over here and some refs from this repository over here and maybe some from my operating system vendor over here and I've installed all of those and I want to expose my local OS tree repository onto the network I've basically got things from three different summary files and I need to combine those into one I can't do that if I've got one signature for the entire file because A, I can't reproduce any of the signatures for many of the upstream vendors and B, I can't sign with three keys somehow for different bits of content so the solution there is to yeah, that's the problem the solution is to drop the signatures that reduces security you say so we reintroduce security by implementing it a different way splitting the ref mapping up and flipping around how the security is done so instead of having a summary file which binds a ref which is the name to a comet checksum you have a ref binding which is in the comet which binds the ref name to that comet so it's kind of backwards rather than forwards and you can do this because each comet in OS tree has some metadata sort of saying it's date and the author and maybe a comet message just like git comets do and this metadata is always signed by the person who built the repository so if you put some extra metadata in there that says this comet should be on this ref and maybe also this ref and this ref and then you sign the whole lot you can always check whether a comet that you've downloaded and looked at is actually meant to be on the ref that you thought you downloaded it from which means that when you get rid of the signature on the summary file although the summary file isn't trusted you can then verify from everything that you pull back from it so now the groundwork is in place those are the two big problems that were in way of doing peer-to-peer updates we can do them so with USB updates we essentially take an OS tree repository and put it in a well-known location on a USB stick and with the LAN updates we essentially just expose an OS tree repository on the local network with a web server with the LAN stuff how do you actually find the updates on your local network without going into every machine and saying like what refs do you have can I have all of them early up to date because that would result in a lot of unnecessary traffic so like with a LAN of 30 machines say in a small business or a school or whatever each of them have 100 refs like a couple of your operating system this apps that you've installed many of them will be at different versions how do you actually find which refs that you want and which are up to date and which machine has the latest update that everyone else can pull from the solution is to take a bloom filter of the refs on each OS tree and put it in a DNS SD record with the fahi and then when you're updating from that peer you will check whether the ref you want is in their bloom filter if it isn't, you don't care about that anymore if it potentially is because bloom filters aren't entirely deterministic you download the summary file from that peer and you check to see if it does actually contain the ref you want or whether it's a false match and if it does contain the ref you want you then download that comet check the GPG signatures are all correct and match the ref and then download the rest of the comet and then update from them the code for all of this is being done completely upstream in libOS tree and in flat pack and there are some co-owns in our update for endless OS the OS op data which is also free software and it's already been supported in various upstream repositories where the collection IDs have been added to their configuration so flat hub for example has a collection ID set so you can already use flat hub apps with peer-to-peer updates if the tooling you're using supports it I mean that's still being shipped out to distributions and probably hasn't been enabled in many places yet but the pieces are in place the components that we have in the endless OS op data they're the bits that if you wanted to implement this for yourself for your own distribution or platform they're the bits you'd have to replicate or adapt they're not like shipped by default by flat pack so we've got a web server and a DNS SD record generator for the LAN sharing which basically takes your local OS tree repository expose it over the network and also updates anavahi list of DNS SD records generates the bloom filter from the refs that you have and various bits of plumbing for that to integrate it with system D and do socket activation this has been worked on by quite a few people endless we've got Matthew, Rob, Dan, Cresimir and me and then lots of help and review and feedback from Colin Alex OS dream of flat pack and also a lot of reviews and merge testing done by the RH atomic bot and that's it we're hiring so if anyone wants to talk to me about that please do we're looking for desktop engineer and tooling engineers but the code is all there and has anybody got any questions I feel that was like a lightning approach to it so I can expand in detail on anything how does this DNS SD record compare to distributed hash tables to what sorry distributed hash tables I think distributed hash tables took up a lot more space and they weren't as easy to implement but yeah I can't remember the details now we wanted the bloom filters to take up there were various restrictions on being supported by different routers as they forward DNS packets some of the larger DNS packets just get dropped so we wanted the bloom filters to be really small distributed hash tables I think come out a bit bigger but yeah I don't know it's been a while since I actually looked at that bloom filters certainly do what we want they allow you to have more than enough in your local repository that you're advertising several thousand if I remember correctly before the probability of collisions becomes too high to make it worthwhile and they do mean that you can massively cut down the amount of requests you have to make like unicast from you to the computer you think has a ref by using the bloom filters to cull the ones that definitely don't have the refs you want does that answer a bit more detail yeah thanks Alex back back I was promised a color emoji in the slides mmm you promised an emoji you got two emojis while it was black it's a color issue a color emoji would have detracted from the color scheme of the slides I think so sorry so what about line updates why just restricted to line updates and not to updates over the entire internet I guess by using DNSSD you sort of restrict yourself technically but is there a reason for not thinking about when updates I mean the original use case we had for supporting LAN updates is because NLSOS is something that we want to run on computers which have restricted internet access and particularly the use case we were caring about was schools where teachers machine will have an internet connection and then there will be loads of student machines that don't and they can only connect to the teachers machine I can't immediately see there being an advantage in doing updates over a wider network because the cost of communication between all the peers would start to get very complicated and also didn't want to go into writing an entire distributed hash table file system that's not my thing this does the job for the use case that we cared about there's nothing stopping people from writing one in future so the underlying APIs in OS tree that allow my computer to say I want this ref and this ref and this ref find updates for them they will in parallel look on the internet on the local network and on any USB sticks that are plugged in so you can always write an extra provider which would I don't know look on BitTorrent or something or some other custom protocol as long as it can do implement there's a couple of virtual functions basically but it can do all of these things in parallel and it will take whichever updates appear first and look likely for some moderately well defined metric of what likely means so yeah it's a possibility in future if you want any other questions so thank you Philip and give him a wonderful applause thanks


I'm giving, I'd like some clarification on that second set of that program. Forget not that the witness to the world of evil cannot speak, except for what has seen the need for evil in the world. That set this is actually interesting. Well, forget not that the witness to the world of evil cannot speak. In other words, the screen is just a screen. And it's only the purpose or in the mind looking for the world to bring witness to evil and guilt that makes it speak that way. In other words, as long as there's guilt in the mind and as long as guilt is the purpose of the mind, the mind still wants to hold on to the ego, then it calls forth automatically witnesses from the world that will support and reinforce that wish or belief. It's kind of like a self-fulfilling prophecy. If you really are anchored that you're unworthy and lacking and scarce and guilty, then the world will gladly bring witness to it. It's just, I guess another way to come at it would be, the Course talks about how that you send messengers for us. There are the messengers of fear and then there are the messengers of love. And the messengers of fear bring back messages of fear. Or another way to think of it is in terms of the body and the five senses. That there's nothing, it's not like things happen in the world, you see light and light gets reflected into the eye and then the image gets turned upside down and gets messes into the brain. It's the mind has the categories and has the judging and the guilt in it and then the five senses just bring forth witnesses to what the mind wants. To fill up the boxes. To fill up the boxes. So if we look at that one, forgetting out the witnesses to the world of evil cannot speak except for what has seen a need for evil in the world. The ego has seen a need for evil in the world. The mind that is invested in the ego sees a need for evil in the world because that way the world's kind of like the scapegoat. The world is the source of suffering. The world that contains all the causes of suffering. And there's no need to look at the mind because the world seems to be the cause of suffering. Now if you can trace it back and see, oh, it's just my belief in guilt that's producing, assuming we are bringing witnesses of a guilty world or guilty persons and fear and destruction and all this. If I can trace it back down and see that the belief in the ego is false, then I can accept a new purpose. And everything that I look upon will just fit into the box of forgiveness. I will not attempt to use scenarios and memories and things that are in the world to justify the ego anymore because there's no point in justifying it. There's no point in trying to protect the ego or keep it concealed and hidden. So one part of the Course Jesus says, you know, it's not that you shouldn't see the body, but it's just looking at your wish to murder. At one point it's saying, you know, it's not that you should try to see your brother's body as senseless, but it's saying, look at your wish to condemn him. That's what has to be examined. Sometimes when people read the Course they think, well, if everyone's spirit, then, you know, I just shouldn't see any body. But that's like saying, I shouldn't see any effects. Wait a minute, if you believe in a false cause, why shouldn't you see effects? If you believe in a false cause... It surely will see. Unreal or hallucinating effects. We'll keep going into this cause business here and tracing it in because, again, there's a lot of things in this world that seem to be great, okay, groovy, wonderful, and there's a lot of forms of attack that aren't seen as forms of attack at all. If the mind to see them as forms of attack and realize that any form of attack is really keeping the awareness of God out of one's mind, then it would be a piece of cake to give up the branches or to give up the world. But some forms of attack don't seem like forms of attack. In fact, they seem quite attractive, and that's the ones that we'll look at as we keep going. The witnesses to sin all stand within one little space, and it is here you find the cause of your perspective on the world. Once you are unaware of what the cause of everything the world appeared to thrust upon you, uninvited and unasked, must really be. Of one thing you are sure of all the many causes you perceived as bringing pain and suffering to you, your guilt was not among them, nor did you in any way request them for yourself. This is how all illusions came about. The one who makes them does not see himself as making them, and their reality does not depend on him. The dynamic growing up I never had, when I was feeling sick, angry, depressed, upset, usually they weren't even talked about anyway, but suddenly it wasn't taught to me that it was a guilty mind. That was the cause of all these things. There are many, many, myriad of forms. It seems almost automatic that we just say, you're hurt, what happened? What did you do? Did somebody do that, or did you fall? I mean almost like we start searching for the cause, or if somebody is sad, or somebody is crying. What happened? Did somebody hurt your feelings, or did you get hurt? Missing knows all these things about, did you go outside without your jacket on? Yeah. Or where were you? Did you catch the flu? Did somebody have the flu? On and on and on, they had nothing to do with anything. Just the whole gamut of things. Of one thing you were sure of all the many causes you perceived as bringing pain and suffering to you, your guilt was not among them. Maybe a more helpful question to ask would be, what are you feeling guilty about? But do you ask that to the person preceding the pain? I don't know. Let's say I see pain and it doesn't have to be in this body. Let's say I see my son experiencing what I perceived to be pain. Is that the guilt in me then? Yes. That's the sickness. If it looks like pain to you, then there's the wish to see pain. Because it could look like a call for love. I mean, that's another option. To deceive mine is so convinced. I mean, if it sees a mangled foot or something where it says that's got to hurt, that's based on its conclusion of the past. As if I've experienced that too. Or I know other people that have experienced that and I know that that has to hurt. The Course says in one of the workbook lessons, pain is a wrong perspective. All perception of pain. It's not like you need to perceive that there really is pain in the world and then find a way to hocus-pocus it away. You know, wave of magic wand. If you're seeing pain in the world, including the body or any place else, it's just a misperception. That's what has to be healed. Now, admittedly, you have to go into the metaphysics and certainly when I've even begun to talk about what I just said in groups around the country, it barely does it. What? That's not just acceptance. That's right. Easily. Lately. I see this as what you were just, the sentence you were just asking for clarification about.  And then the question is, why do I want to see pain? What gain do I think there is in that? I just have to be clear as to not assume that by doing, by calling for witnesses of pain that I'm writing the script.  If I see that as pain, then that's my perception. But that's not my perception that causes a fall at the bike. That certainly is what I'm seeing as a result. And that's what I want clarification on. That's a good one to go into because I have been in different places where they have taught that you are responsible for the starving children in Malaya. You're responsible for cancer.  Kind of like, and from that perspective, it would seem again as if guilt would be inevitable. If I am the cause of every pain in the world. And I am to blame. Again, that's where I am responsible. We have to get real clear on that. That pain is a wrong perspective. It's a distorted perception. And pain, another name for pain would be wrong-mindedness. And the correction for wrong-mindedness is right-mindedness. Right-mindedness doesn't doctor up pain. It sees the impossibility of it. First of all, you have to not project responsibility to the ego. So you have to come to see that the ego is a belief in the mind. And then you have to, as the Course says, quickly turn it over to the Holy Spirit. To withdraw the projection from out there, so to speak, to within. And to hold on to it in a personal sense. To believe that I keep, you know, caused the pain that I'm seeing in Corey. Or even, you know, I as mind caused the pain. That's still always in wrong-mindedness. So the clarity is coming back to see that there's nothing causative on the screen. And that everything is just images. And that's where the forgiveness really starts to come in.  That has to be questioned. There's nothing in the world that produces pain. The old thing about, like when they give you a bonk on the knee, you know, to check the reflexes, if you feel the pain there, it's not the hammer striking the knee. It's not the finger touching the burner that produces the pain. Guilt, wrong-mindedness, judgment, ordering of thoughts. That's where the guilt comes from. And then when the mind feels guilty, then it calls forth witnesses from the world. A child that seems to be in pain. A finger that seems to be in pain. A foot that seems to be in pain. Proceeding pain over in Ethiopia or in the world where that's just a witness that's being called forth from the guilt. So the script was written and the only pain comes from a misperception.


Income tax 2021-2022, figuring gross profit. Get ready to get refunds to the max, dive in into income tax 2021-2022. Most of this information can be found in Publication 334, Tax Guide for Small Business Tax Year 2021, income tax formula, looking at line one income, which would have a supplemental schedule that in essence being an income statement with income and expenses. Basically being deductions to net then rolling in to line one income, line one here on the tax formula as well as on the first page of the form 1040 we see here where we would have then the schedule see rolling into the schedule one, the schedule one rolling into page one, form 1040, line number eight we see here. This is the schedule see profit or loss from business basically and income statement. So now we're looking at the figuring of the gross profit. And I know what you're thinking, I don't want to figure that, it's gross. But it's not that bad, it's not that gross once you get into it. Note that the gross profit isn't the bottom line, we're not talking about the net income, we're talking about the gross profit basically income less the cost of goods sold we looked into the cost of goods sold calculation in a prior presentation. So after you figure the gross, after you figure the gross receipts from your business, which we looked at in chapter five and the cost of goods sold we looked at in the prior presentation you are ready to figure your gross profit which should be fairly easy at this point because you did the difficult part, you did the income, you did the cost of goods sold if applicable it would only be applicable if you had a company that had inventory either inventory that you're purchasing marking up and selling or manufacturing the inventory if you don't have those items then the figuring of the gross profit is super simple because you don't have any cost of goods sold and it would basically be the same as the income line. So you must determine gross profit before you deduct any business expenses. So gross profit is subtotal along the way to get to the end result of net income important subtotal from an accounting standpoint as well as from a tax standpoint because the relationship between the cost of goods sold and the income should be quite significant is quite significant for companies that primarily make money by selling inventory whether manufactured or whether just bought and marked up so that subtotal along the way to get to net income is important even though the cost to get sold is really just kind of like another kind of expense it's a really important expense so these expenses are discussed in chapter eight so business that sell products figuring your gross profit by first figuring your net receipts so you're going to figure the net receipts that's going to be your basically income line three on the schedule C by subtracting any returns and allowances line two so the receipts your income basically minus the returns and allowances which is kind of like a contra income line it acts like kind of a deduction but it's really like a reduction of income to get to your basically your income line so you're going to subtract out the cost of goods sold so once again on line schedule C subtracting any returns and allowances line two from gross receipts line one returns and allowances include cash or credit refunds you make to customers rebates and other allowances off the actual sales price next subtract the cost of goods sold line four from the net receipts line three the result is the gross profit from your business see it wasn't that gross to do it once you get into the work it's not that gross business that sell that sell services so if what if your service business well then it's even easier because you don't have any inventory therefore no cost of goods sold so you do not have to figure the cost of goods sold if the sale of merchandise is not an income producing factor for your business the gross profit is the same as the net receipts gross receipts minus any refund rebates or other allowances most professions and businesses that sell services rather than products can figure gross profit directly from the net receipts in this way so it's just an easy calculation no subtraction of the cost of goods sold because there is none because there's no inventory illustration this illustration of growth of the gross profit section of the income statement of a retail business shows how gross profit is figured so we're about to sell to show you an illustration of gross profit it might be a little gross so prepare yourself firm stomach here we go so we've got the income statement you're into December 31st, 2021 we've got the gross receipts minus the returns and allowances gives us the net receipts notice that those returns and allowances is a subtraction you might say that's acting like an expense but it's in the income section because we're kind of like reversing kind of like a sale that happened that happened instead of recording an expense that was something that was consumed to generate the revenue and then we're going to subtract out the cost of goods sold to get to that gross profit there it is there's the gross one there's the gross so then we have the inventory at the beginning of the year so this is our cost of goods sold calculation $37,845 plus the purchases this is what we are purchasing of basically inventory and then we're going to subtract out items withdrawn for personal use we're going to take some of that inventory and consume it ourselves that gives us the $283,250 for the purchases in essence then we got the cost of goods sold cost of goods I'm sorry goods available for sale cost of goods available for sale I typically call it and that will give us the $321,935 minus the Indian inventory which you would find on the balance sheet in essence and that would then give us the cost of goods that we sold $288,140 notice that this $288,140 basically page 2 of the schedule C should tie into then the cost of goods sold basically on page 1 of the schedule C of the schedule C I hope I said schedule C both times here of the schedule C now if you're using accounting software you might just have this already made for you because they used a perpetual accounting system and you would have to then possibly the cost of goods sold calculation because you might have the beginning inventory from the prior year you might then have the cost of goods sold because it came from your accounting software and then what you would then be backing and you might have the ending inventory from the accounting software on the balance sheet what you would be backing into then is going to be the goods available this calculator I'm sorry the purchases this would be X these aren't X's I'm just pointing those out those were done and then this would be X if you were to do like a you know algebra if you were to do algebra on it so items to check consider the following items before figuring your gross profit gross receipts at the end of each business day make sure your records balance with your actual cash and credit receipts for the day you may find it helpful to use cash registers to keep track of receipts you should also use a proper invoicing system and keep a separate bank account for your business so separating the business and personal we're getting into the bookkeeping side of things the bookkeeping practicing of stuff that's always the good idea to make your tax stuff a lot easier sales tax collection so check to make sure your records show the correct sales tax collected if you collect state and local sales tax imposed on you as the seller of goods or services from the buyer you must include the amount collected in gross receipts if you are required to collect state and local taxes imposed on the buyer and turn them over to state or local government you generally do not include these amounts in income and the reason for that is basically you you collect the sales tax when you're selling something and that's kind of imposed on the idea is that it's imposed on the client so that means that when you're collecting it it's being used as the tax collector in a similar way as if you're an employer and you have to withhold from your employee the payroll taxes you're basically just acting as the tax collector so if it's a sales tax you're charging the customer the tax and you're basically acting as the tax collector so that part of the revenue typically you would want to put on the books as a liability as opposed to as revenue and then when you pay it to the to the government it's going to be an expense because it never hit the income statement so in any case inventory at the beginning of the year compare this figure with last year's ending inventory the two amounts should usually be the same so that gets into our inventory calculation again which we saw in the cost of good sold calculation purchases if you take any inventory items for personal use use them use them yourself provide them to your family or give them be sure to remove them from the cost of good sold so we saw that in a prior presentation your details on how to adjust cost of good sold see the merchandise withdrawal from sale chapter 6 so inventory at the end of the year so check to make sure your procedures for taking inventory are adequate so at the end of the year you might have a perpetual inventory system you might have a periodic inventory system you want to make sure that you're counting your inventory either way at the end of the year to make sure that you're reporting it just from an accounting standpoint properly at that point in time so this procedure should ensure all items have been included in inventory and proper pricing techniques have been used use inventory forms and adding machine tapes as the only evidence for your inventory inventory forms are available at office supply stores these forms have columns for recording the description quantity unit price and value of each inventory each page has space to record who made the physical count who priced the items who made the extensions and who proof read the calculations you might have software that can help you with this as well remember you do want to do a physical count even if you have a perpetual inventory system so that you can properly value the inventory at that point these forms will help satisfy you that the total inventory is accurate they will also provide you with a permanent record of your inventory support its validity


In this video, we will talk about bag of words and how we can use that to create automatic grading of SES. So, what is bag of words is simple. It actually picks the frequency of words, a lot of words occurring in the sentences. It picks each word or many times it occurs. So, we can put that in the bag of words frequency. And this also can be used to find the similar words like we saw to find the similar words. So, if you have similar words, find the similar words first and put that in the bag of words, because some students might write in a wrong spelling or something. And it is a sparse vector. I will talk about what is sparse vector in detail. So, what happens first is, remove the running letters, remove all this in and articles, proportions, conjection verse, if you move it or it is up to you how much you want to remove it. And correct the sentences spelling correctly, like using the similarity words, that is very important. And you have to create a dictionary. That dictionary should have all the words with the frequency of words occurring in the dictionary. Let us look at what is bag of words in detail in this slide. Sentence 1 says students interact with peers in class. Students are interacting with the peers in the class. Peer instruction increases students interest, something like that. Let us say bag of words of sentence 1. The words sent student occurred one time, interact occurred once, with occurred once, peer occurred once, in occurred once, class occurred once. That is a bag of word of sentence 1. Second sentence, some student wrote sentence 2, peer occurred once, instruction occurred once, increase occurred once, students occurred once, interest occurred once. See, I just wrote a word in a form given in the sentence. What you can do is, you can go lemma or stem form of it. So, the root form or stem form of it that reduces this the length of this vector. I will talk about how big the vector is going to be. It is not easy. So, that also possible, right? Let us look at that. So, if I come, if there are two sentences, I want to create a one bag of word for this two sentences. Student occurred twice to one time here, one time here, interact occurred once, with occurred once, peer occurred twice, in class once, all of them others occurred once. So, hope you understand this particular set I created from these two sentences. That is very, very basic. That is all about bag of words, very, very basic. You have to understand this how this set has been created from these sentences. I have just counted whether it is occurring once or twice and each word, all the individual words, the unique words are in the sentence, I have just counted how many times it occurred. That is a frequency of words occurring in the thing. Similar words can have a frequency can be increased. So, I have bag of word sentence for the two sentences. Now, I have the same two sentences. Consider this is my dictionary or this is a complete set I have. The set has eight words. The first word is student, second word is interact. Now, you understand I removed the, what to say, I removed the word in it. In is not here. What I did, I gave a position for each word. Instead of taking the word and putting its frequency, I have a dictionary. My dictionary has only eight words, just eight words. Students interact with peer class instruction increases interest. That is it. Just eight words, I have in my dictionary. The sentence is coming out of this dictionary. I need to find, I need to create the numerical form of the sentences. If I have this dictionary, this, I can say students is the first portion in this dictionary. So, one. Second portion is second word is interactive. Is it interact, exists in the sentence one? Yes, it exists. If it exists, mark it as one. If it does not exist, mark it as zero. With exists in sentence one, yes, it exists. Third one. Fourth one, peer. Is peer exist in the sentence? Yes, fourth one. Peer, spear is exist. Class exists, yes, class exists. Instruction, no, not exists. Sixth word is zero. Increases, not exist. So, my vector of sentence one is 1111000. So, if someone asks me, can you form the sentence? The sentence as students interact with peer class. That is it. That is the words this particular sentence has. It is not important about the order. The student occurred first. I will tell you about that. That is very important. But what are the words from the dictionary exist in this? So, like see, sentence one has a five words from this dictionary. The position of the word is also given 1111000. So, it is easy if he has one on zero for machine learning to classify because one or zero is very simple. Sentence two have a student in it. So, student is here. It is not the first position. That is what I am saying. The order of position is not important in back of words. Student is one, zero, zero, interest is not there, width is not there. Peer is there and the class is not there. Instructions increases, instruction is six. So, it is simple. What I am trying to explain is very simple. So, one, I am just putting the order. One, two, three, four, five, six, seven, eight. Hope you understand what I am talking. So, it is basically sentence one, sentence two and which words are occurring like in the sentence. Consider your many students writing these kinds of sentences, say five sentences they are writing. So, if you combine the word, you might get the bag of word dictionary of thousand or sometimes hundred thousands or sometimes it goes to even million because if you consider all the forms of the words, the dictionary will go big. The dictionary goes to the all the existing words in English plus different forms they occur. It is very, very huge. So, this can go to a big vector. But let us take its thousand words. Let us take it as a thousand words. If it is a thousand words and if it is thousand words and your students writes a sentence like this. So, consider there are six words in the sentence and there are a lot of zeros up to thousand. It is not just not three zero like zero. So, it is like up to thousand it is a zero. This vector is called a sparse vector you know it just only out of thousand only five words are there. So, it is not a complete vector like only sparse vector. So, in order to avoid this sparseness in the vector or the sentence formation, we can also use the index instead of the particular position. You can mark the index. For example, student occurred in the index number 1, interact occurred in index number 2, with occurred index number 3, pure index number 4 class and so forth 1 2 3 4 5 4 6 7 1. So, instead of writing each and every vector in a complete dictionary form like this, you can only say how many words that particular sentence are. This word only have a five sentence, this word only have a four sentence, something like that. It is simple to put that you know. It is very easy, this sparseness has gone, but even this is also of a complexity on its own. Let us see what is the problem there. So, which means you need to write a program, the first dictionary has been there with indexed then hashing the then you can take for sentence 1 and sentence 2, then we can only use the index that is enough for a representing that particular sentence. Why we are doing this sentence represented as a numbers, because when you give the words to a machine learning classifier like Ney Bayes or a Deshentry, they would not able to understand these words. Deshentry will not work here, Ney Bayes or some other classifiers SVM or something. They will not take these words and do it. Instead, you have to convert the words into particular numerical form. That numerical form can be converted using this kind of bag of words approach. Let us look at this where can be applied. Hope you understand there are two sentences and we can convert into words. Let us look at this. If you have 100 students writing essay and you have validated that essay by the human experts like two, three teachers, validated the essay into a very good, excellent, or average, poor, something like that. You have automatically graded them. If you want to create an automatic algorithm to grade these essays, what you do? So, consider what you learned in a previous slide. Can you use that knowledge to create a automated essay grading system? That is only that bag of words approach and similarity approaches. Can you think of it? Take a moment, think about it, write your steps, what you exactly do, after you do that, assume to continue. There is no response here. Instead, I want to explain that. Let us see how it goes. Student one wrote some sentence. I like coffee, something like that. Suppose the essay is about coffee. So, I like the coffee. I prefer roast level, tree or medium, four is good. Anyway, so and from some country, so Anjula's, something like that just can write it. Student says that coffee is not good for health. Maybe someone do not like coffee. Someone says coffee is very important, good for health, all these things coming. So, there are like 100 students wrote this kind of a essay about coffee. And you have a teacher grading that and say that this is a good average, good average per, only three grades, let us say the three grades, good average per you are creating it. Now, the idea is you have to create a bag of words. From all these 100 sentences, you have to identify all the unique words and put that in a one dictionary. So, the bag of word for the big sentence equals I like coffee, prefer roast, sorry for my handwriting, I am just prefer roast level. So, now you might see what is this I am talking about is I am talking about a bi-gram. So, if you put all the words together, you just actually could in a bi-gram. And a frequency can be used, but instead of using frequency, let us say this exact one set I am creating, one set of dictionary from all the existing words you are creating. Once you have that, I am going to do student 1, it is a id, student 1 as so some vector 1, 0, 1, 1, 0, 0, 1, 1, 0, some sparse vector with the label good. Student 2 have something like that. So, the idea is now what you have is your sentences, the students essays has been converted into a matrix with the label good average poor, there are three things you have it. So, there are three, it is not a binary classifier, so three multi-classic classifier, the three classes here. And you have a student id and you have the features, features not one, the feature is the length of this dictionary, the dictionary can go to 5000 or 10,000. That big dictionary you have, you have this, this is the basic form of automatically grading your essays. What you have to do, you have until students data and you have done all this grading, you have to take a 10th full course validation, train the system and test it on the 10 times, 10, 9, 30 times and you create a some accurate classifier, which automatically feed any essay, it will give you the average good or poor. So, you do not need to do it from the next time. It is very simple, basic form I was talking about, but it will not yield a good result, you know. But you can check up the latest papers and try to understand what they do. That is very interesting, if you can do that. And I just want to tell one more thing, any form of machine learning classifier we are doing in a supervised classification is, this is the form that is a matrix of features x1, x2, x3 and there is a weight we want to find out, weight associated with x1 till xn equal to the level, level can be multiclass like a binary class or multiclass. So, that is exactly what I will be, it is y, it is just y, level. So, what I am trying to say is, it is exactly the form of machine learning colleges, exactly what we did in matrix in our schooling time. So, that is exactly what are the basic form machine learning. If you can imagine any problem into this particular form unable to apply, you can understand that what is algorithm how it works, everything is easy to understand. So, if you want to go in detail about that form, not every matrix have the perfect solution. So, if you do not have perfect solution, you have to find the nearest solution. That is exactly what is happening in the machine learning. So, not every problem have perfect solution to put the weights to exactly match the words there. So, we have to identify the nearest possible solution that is global minimal solution, with least possible error. Some classifier is able to do it, some classifier not able to do it. So, that is why the way performance in the classifier varies based on the way they approach. Hope you understand what is bag of words and how to use bag of words for automatically grade essay. If you have students done assignments and you have graded them automatically or if you graded them using your teachers or your friends graded them the assignments or the marks, take that as the input and convert that into a bag of words like a big dictionary and create the, if you have understood this data is very good. So, it is a simple scripts can help you to convert them and you can check it out whether you can create automated grader system or not, it is very easy to do. If you are from this course start, if you are trying to understand programming, this is a very good simple example to learn and try it out. Because identifying the set of dictionary or their index not tough at all, it is very easy. It is in very possible in a pandas library. So, try it out and check it out whether it works or not. Thank you.