or a heterozygous mix, or you could have little b, little b. And in typical RNA-seq or expression analyses, we don't care which allele of the gene is being expressed. We just count from either allele. But there are reasons why it's interesting to look at the expression of the two alleles. So here I'm using some language. So I'm showing this gene now along the genome. And let's say upstream of the gene is a SNP or variant, or it could be even a copy number variant, which is somehow regulatory for the gene. And so sometimes that's called an RSNP or regulatory SNP. And then inside of the gene, there could be another SNP, just called a transcribe SNP or an eSNP. And it could be interesting to take into account which allele is being expressed. So if this is a regulatory SNP for this gene, if you look across from the homozygous to the heterozygous and then the homozygous of the alternate allele, if you see a change in the number of transcripts, that's indicating that that is an EQTL, an expression quantitative trait. Locus, the pair of the SNP and the gene, it describes a locus. And that might tell you about what that SNP is doing. So it's important for understanding non-coding genetic variation. And if you look across these different types of individuals and you see a change, that's called an EQTL. You can also see that within this middle group. So within a heterozygous individual, you can count the difference between the two alleles if you can observe it. So this is a little diagram. This on the y-axis is whether there is a lelec imbalance. And so on the top two panels, there is no lelec imbalance. So you can see the number of reads are the same. And that's because there's no genetic variation in this regulatory element here. So in both cases, it's the same. And on the bottom, there's some lelec imbalance because let's say this SNP, this little blue or a brown line, is somehow disrupting the binding of this yellow transcription factor. So then there's less of the transcription. So on the bottom two diagrams, there is a lelec imbalance in the system. And there's true biological lelec imbalance. And then left to right is whether there's a SNP inside the transcribed region, so this TSNP. And that's whether we can observe the lelec imbalance. So there needs to be genetic variation in the coding, in the transcribed region. It doesn't have to be in the coding region. It could be in the UTR. But if there is genetic variation there, we can see it. And if there's both genetic variation in the regulatory regions and genetic variation in the transcribed region, then we can detect an imbalance. So I hope that diagram is not too complicated. And then I also wanted to mention that it is a context-specific property. So it's not that within an individual there will always be a lelec imbalance, but it actually differs by over time and by cell type. So this is a diagram showing like, depending on what elements are accessible, so they're epigenetic state, and depending on what transcription factors happen to be in the nucleus, you might see or not see a lelec imbalance. And this is, we'll see this today. So we'll see an example of dynamic lelec imbalance. And then another member of the LUB lab, Wynson Mu, worked on a package to detect a lelec imbalance across cell types and single cell data. Let's see. I'm just gonna, I see a question from Ryan. If you see a lelec imbalance, it could be explained by an EQTL, but also by something like splicing QTL. Yes. So I'm giving a super quick intro to a lelec imbalance. And we're gonna kind of focus on this kind of regulatory region type of imbalance, but in the text introduction of this workshop, we talk about there's a bunch of other things that could be going on, like imprinting or splicing QTLs. And so we're kind of, I'm just showing a cartoon, but there's all these other interesting things that you can detect and those are kind of covered in the text. So why do we have this, why a workshop today? There are many workflows for detecting a lelec imbalance and they have lots of advantages. And so a very popular one is called WASP. And often a lelec imbalance is considered at the SNP level or at the gene level. And it does not account for isoforms at all. So it just looks to see if there's a stack of reads at a SNP and if those are imbalanced. And there's a number of advantages of that approach, but one that we, a disadvantage that we focused on is that there could be across the two alleles different imbalance for different promoters. And that's kind of what we're gonna focus on today. So for example, here, there's a SNP here, which is producing less expression of this promoter, transcripts starting at that promoter, and then suppose this variant on the other allele on the other chromosome is producing less expression for transcripts from that promoter. And these dotted lines would represent like chromatin contacts. So maybe this is some complex looping thing here, but we do see these examples and we wanted to be able to observe this isoform level regulation as well as the gene level. So let's see, coming up on like nine minutes, our input for like, what type of data do we need to run this? Oh, and then let me just try to respond to that. So Ryan asked a question about those are, yeah, these are alternative promoters like for the same gene. And this, we see this happening in real data. So that's kind of what we're focused on. We wanna be able to detect that as well as gene level imbalance. So we're gonna assume that there are nasic reeds and we're kind of focusing on genes where there's some heterozygosity. So we need to have measured, there needs to be a heterozygos snip in the transcribed region of the gene or else we just don't see the imbalance. There could be imbalance, but we're stuck with the total count. We can't disambiguate from the reeds. And so our pipeline that we've worked on with a number of collaborators, I'll have a slide that acknowledges all the collaborators in a minute. You start with a FASTA for the genome and like a strain specific VCF. So we've done a lot of work with model organisms and F1 crosses. I'll mention that if you wanna do human allelic imbalance, we are very interested to support that, but right now our pipeline is really for when isogenic. So all the samples are from the same F1 or it could be a heterozygous human donor, but you're kind of doing something over time in a single donor. We're working on generalizing this. So you start with this FASTA and VCF and you build a diploid transcript sequence using this tool called GTG tools from the Churchill lab.  So it's got two versions of every transcript. And here we're using salmon to quantify expression of the two alleles. And you also generate bootstrap distributions for the allelic data. We import to R with a new function in the Fishpond package called import allelic counts. And then we do differential analysis, which we'll see today. So just I wanna pause and acknowledge a lot of people we've been working on this project, including a team that's for the real data analysis, this mouse osteoblast data set. We've done methods development with Rob Patrick's group, including Nor who's here today and Mohsen and Biostats mentorship and community at UNC and the financial support. Okay, so I'm gonna off from the slides and switch over to this pipeline. And I'm gonna go fast because I went slower than I thought on the slides. So let me just zoom in real quick and then I'll hand off soon. So there's a lot of text above, which is kind of redundant with what I just was talking about. And then I'm jumping in here at when we talking about how to import this data. So we've quantified with salmon to the two alleles of the organism and we have bootstrap data that tells us about the uncertainty. So now we create transcripts from some transcript database. We assume we have all these different samples and then we basically import the data and there's an option about what level of analysis do you wanna do the allelic comparisons. So you can do it at the gene level. That's kind of, we've done that and it gives you very similar results to WASP or you can also do it at like a sub gene resolution. And so you can do it at the isoform level, but we found that it's preferable to do it at the transcript start site level that you can find interesting things. And the way you do that is you just, we've added a function that helps you like group isoforms by their transcription start site. And also we have like a little wiggle parameter like plus or minus 50 base pairs. We group those together because we're interested in potential imbalance that's affecting like specific promoter regions. So after doing this, this kind of grouping of the isoforms to the TSS level, you import the data using this new function called import allelic counts. And it'll build a, what we call a wide summarized experiment. It's wide because it has first samples and their like reference allele and then come the alternative allele. So it's double wide compared to a normal summarized experiment. And then the last thing before I pass off to Yuffie, I just wanna very quickly describe the data set we're looking at. So it's a really cool data set from our collaborators. So Cheryl and Gary who are, who among other things are mouse geneticists. And Cheryl is also, Cheryl Eckert-Bicknell is also a studies osteoblast. So the bone cells that generate the bone density and the matrix in the bone. And this is a differentiation time course where they start with precursor cells to the mature osteoblasts and then they differentiate them over 18 days. So we have nine time points of an F1 mouse and we can track how the two alleles for every gene, how they track over time. And this is, so we're gonna release all the data in like very shortly. This is the data that we have here today is a subset of that is just chromosome one. Yeah, so I think I'll switch over now and I'll just be around if there's any questions I'll just be in the background. Why are we switching over? Does anybody have any questions for Mike? Including like what is allelic? I totally missed that. I know it's not a common analysis. So I'm sure there'd be people in the room who haven't done it before. So very basic questions are totally welcome about allelic analysis or why you do this. Okay, so this workshop is on orchestra. If anybody wants to try to log in and follow along. Thank you, Mike for the amazing introduction. So if you wanna follow along the code, you can open up the RStudio right now. So in this workshop, we have pre-package to the data set. As Mike has mentioned, it's the F1 osteoblast data set that is short down to only chromosome one and the one is on gene level and the one is on transcript starting site level. So first of all, we just wanna explore like what the data set looks like. And here we first load up this gene level data set as a summarized experiment data set. And then we can see that in the assay, we have counts, abundance, and there's supposed to be length and then in wraps one to 30s. And then that is the bootstrap replicates from salmon that is supposed to capture the inferential uncertainty. And the role name will be the gene name because we're looking at gene level. We can also look at the metadata. There's three pieces of informations and their alleles, which A2 will be the reference here. That would be the black six and then A1 will be the alternative. It's either one to nine or cats. So we have two different cross over here. And then the day column capture the different time points. As Mike has mentioned, we have nine different time points from day two to day 18 every other day. So why do we need, okay, let me just go down here and show you the assay name as I just described there, the read counts, abundance calculated from TPM and the length will be the transcript length that I've reached out of all the transcripts within the given gene. And then we have 30 different bootstrap replicates. We think that 30 is a good number because it doesn't take too long to run in salmon. And I also do a pretty good job of capturing the uncertainty. And I had a question here that why do we need bootstrap replicates? And I feel like I just said to you guys that it's used to capture inferential uncertainty. So essentially when, I think you guys probably familiar with that that when you're doing the read mapping, a read could go to multiple places. It could go on different alleles, different transcripts, different genes. And especially when we're doing a little like level as a form analysis, there are more options where this read can go. So this uncertainty of where exactly the read is mapped to will decrease the statistical power downstream. So hopefully when we have 30 different bootstraps it will be able to capture the variance of this uncertainty instead of if you just have one number you might land on like the extreme side of this spectrum. So to visualize this bootstrap replicates, one option is to use the function gate trace and you can plot the histogram. So here we're plotting the first genes in the sample cast cross black six, day two of the reference allele. And you can see that it's not really, the histogram is not going very wide and kind of averaged out. And if you look at the mean and the variance of this count, you can see the mean and the variance are kind of close. I can show you a not very close example. One sec. Okay. And here you can see that the estimate counts is going from 100 to 350 instead of just like 80 to 120. And if you go to mean and you can see the mean and variance are like not similar at all. Since the bootstrap is following a percent distribution. So when the mean versus similar we consider this uncertainty is pretty low. So this is an example of high certainty. And the previous example is example of kind of low uncertainty. And another way of... Go ahead. Can I interrupt? Cause that's a great, I didn't see that example yet, but that's fantastic because so one, two, nine is another mouse strain. It's too similar to black six. So that there's more uncertainty because we can't tell the alleles apart. Yeah. So that's fantastic. Yeah. Thank you for that information. I actually didn't know that. Okay. So another way of visualizing the bootstrap distribution will be using plot in wraps. This is actually my favorite function, FYI. Cause I'm going to show like a lot of this plot later on. So the way to show the bootstrap rough kids is you just specify acts alleles. So essentially you're telling the function that you want to group by alleles. And then the left side will be reference alleles. And then the right side will be the alter alleles. And then if you see like the bar goes huge, it means this sample has a really big uncertainty during the mapping. And then those small bars are just low, low uncertainty. So this is an example of just grouping by alleles. Things we have multiple time points. And if you want a better visualization of how your sample does with time by alleles category, you can just specify covariance days.  So the first column will be the outer Leo and reference a Leo of day one for this cast cross black six and then this second column will be the day four. So in this particular way of plotting for V we're actually viewing days as a categorical variable. And then in a scenario that you want to actually view it on a continuous scale, you need to compute in RV first. So computing for RV essentially just compute the mean and the variance of the bootstrap. And then you can plot the same way using the plot in wraps function. And I highly recommend this shift X. So essentially it shifts your outer Leo by writes 2.6, 2.2. So when you have two time points that exactly the same it won't overlap with each other. You can still see both of them on the same plot. So that's like a little trick that I would recommend to any researchers. Thank you. So after exploring the basic how your data look like we move on to filtering out the informative features. So in a scenario that when two alleles have the exact same sequence you wouldn't be able to identify where the reads go. So in a scenario like that the bootstrap will have exactly same counts for those alleles. So essentially those features give us no information and then we want to get those out of our dataset. The way of doing that is you can just looking at one bootstrap here. We're looking at the first bootstrap. And then we, so this one will be the count our first bootstrap for the outer Leo and this one will be the reference Leo. And then we are asking are those counts the same for all the samples? If they are the same we filter them out. So essentially we're just keeping all the features that don't have exactly same counts in alleles. And then we can see that we're filtering out like 50 genes. So you might also want to check the systematic bias of your data set. So here we are plotting a rough log for a little like log for change against the rough total counts. So the idea is if you don't have a systematic bias the large counts LFC is supposed to center at zero. And if you does there will not be center at zero. And another way of viewing it is you can plot your allelic ratio. And then here we're looking at the large counts alleles if the ratio is center at 0.5. And the question for you is what will we see if one of the parental genome and genotype was poorly annotated in terms of this histogram? So when one of the parental genome was poorly annotated the reads will not be able to properly map to this poorly annotated genome. So in the scenario like that we will see a shift in this rough ratio histogram. The red line or the main will not be around 0.5. It will be like shifted. So that's a really good way to quickly check if your dataset is you expected essentially. So after we check some of the bias and filter out the nine formative features and then we move on to testing. So here we are using SWISH which is developed and maintained by Dr. Mike Love and Enchi. I think I saw Enchi in the room as well. So we're using SWISH which is used to sign well Cox and sign rec test to compare alleles and then we are introducing I think I'm ahead of myself. Okay. First of all, let me just start with we're switching to TSS level like data instead of a gene level data because there are a few additional plots that we found particularly useful with TSS level then gene level. So we're just gonna do the filtering and then as we did before and then why do we think the TSS level AI will be different from gene level AI? As Mike has mentioned before that in some scenarios that a little imbalance will be masked at gene level. So for example, if a gene had two different transcripts and the one is more abundant in out and the one is more abundant in reference then at gene level you will not be able to detect a little imbalance but they actually exist in every transcript. Okay. So we move on running to run SWISH. Let me just get it started because it takes a few seconds. So traditionally running SWISH has three different steps. The first one will be scaling the inferential replicates and then you have to filter out the rows with insufficient counts and then you calculate the statistics like log for change and test the statistics, et cetera. Here we're skipping the scaling counts step because essentially since we're comparing alleles we're comparing within the same sample. So to correct the sequencing desk you are cancelling out this number that you're using to correct it. So we actually don't need to scale it in a scenario that we're testing a little imbalance. And here we're using label keep to flag all the features with a minimum count of 10 in at least three samples. So that's another filtering step and then we compute infRV as we described above. We will be performing two types of allelic imbalance testing. The first one is global AI for consistent allelic imbalance across all samples. So essentially the idea is global AI is you're pulling all the time points together and to test if there exists allelic imbalance overall. And then another testing is dynamic AI testing for nonzero correlation between the log allelic full change and the continuous variable. So that one is you're testing if the correlation between the allelic full change and here we have time. If that correlation is significant if the allelic ratio itself is changing over time. Okay, so here we're performing the global test. You only need to specify alleles which is what you're trying to test and the pair is there. So essentially you're telling the switch function that you wanna compare alleles with in the same day. So it doesn't get messed up. And then here is the function that used to test the dynamic AI. You have to additionally put covariance. So that is telling the system that you wanna test the correlation of allelic full change against this variable day. And then for the correlation we're using Spiritson but you can also use Spiritson if you want. Oh, that is the reason why we specify day twice in the dynamic analysis. So we can take a quick look of the results how significant are the TSS groups at 5% FDR set and then we can see there are a lot more global AI than dynamic AI. So my take in this is since for the dynamic AI you're testing the correlation. So you're adding additional layer to it. Not only there exists the allelic imbalance this allelic imbalance need to be changed with time. So that's just putting more criteria than just the global AI testing. And I wanna show you really quick how the results look like. Oops, I'm not really used to this. Okay, so you have like many informations in this result essentially. So the TX ID is telling you since we're testing TSS groups it's telling you what transcripts are grouped in this transcript group and then what genes it belongs to and then the transcription starting site, the symbols and then you have all the test statistics like the mean for RV log to full change P value and Q values. Okay, and then after we get the test results most exciting step is to pop your results and to see how your data is doing. So we start with looking at MA plots. So this is a global MA plot. It is a very similar idea as the plot we were using to check the systematic bias. You're plotting the log to full change against the log 10 main. So here the log to full change is better estimated than the previous one because here where the switch actually factor into the 30 bootstrap replicates. So the inferential uncertainty was factor into the log to full change calculated over here. So in this plot the blue dots are the significant AI global features and then you can see when we have larger log to full change usually those plots are flag the blue a flag that significant and then we can also view the same thing for the dynamic AI and then which agrees with the results we just thought that they're a lot less dynamic AI than the global AI. And then the reason that the extreme log to full change here are not flagged as significance is as I mentioned that dynamic AI is actually testing the significance of correlation. Oh, and then you can specify here we're testing 5% FDR stat but you can specify like the threshold as you want. Okay, so to visualize the global and dynamic AI results we recommend download ensemble dataset from annotation hub I pre downloaded if you're using this RStudio and when you're running this annotation hub step it's gonna ask you to create a directory just say yes and then it will download. So we're introducing this new function called plot a little like gene was in fish pond. So what it does is it actually help you visualize the test results of one feature which including the gene model. Maybe I can move this one. So it takes a little longer too. Okay, here, oh, there we go. So looking at this plot we have gene over here that we're trying to exam. So we put it in gene name but you can also use simple name if you want. And then we also need the database. Here we are using ensemble but you can also use TXTV. So this is the gene model that we extract from ensemble database. And then I wanna point out that cause the resolution is not great. So the direction of the transcripts are from right to left. And then we have six different transcripts over here and transcript one, three, four, five, six are grouped together because the starting side are similar or same. And then transcript number two are its own group. And the first bar will be the negative log 10 Q values. And then we can see that both of them are actually pretty high in the Q values, pretty significant. And then the second bar is the log two full change. And then we can look at the log two full change with the allylic proportion. So if we look at the first transcript group we can see the cast is more abundant than the black six. So we see a positive log two full change over here. And then the second transcript group we see the black six is a lot more abundant than cast. And then this we see a negative log two full change. And the last bar is isoform proportion that is also calculated from TPM. And then we can see, my guess is since we have like five different transcript groups in this transcript group and then this transcript group will be more abundant over here. So we can verify what we see in plot allylic gene with plot infRV reps. And one thing is plot infRV reps only takes index. So we kind of have to grab the index from the summarized the dataset. And then we renamed the label. So it shows black six and cast instead of just a one and a two. So here we're essentially looking at, so samples over here will be different days of cast samples. And then here the left the y-axis is samples. And it is showing the transcript group over here. So the solo transcript group, which in plot allylic gene we have cast is more abundant than B6. And then in the plot infRV reps plot, you can still see the cast is more abundant than black six, which kind of verifying agrees with which we saw in the previous plot. And then another new plot that we are introducing today is plot allylic heat map. So this function itself will call heat map. The one, so this is the same as the plot infRV plot that you have to grab the index from the summarized experiment. And then the only additional work that you have to do is you have to grab the Q value itself from the summarized the data after running switch. And then we kind of customize the label over here to better visualize the day. So when there's more red, it means the out is more abundant. And then when it's blue, the reference is more abundant. And then the green bar over here is the greener, the more significant Q value you got over here. So that is for the global AI. And then the same set of function can also be used to visualize dynamic AI. And then I personally like the plot infRV reps the best for the dynamic AI because you get this really neat trend with time plot. So what you do is you first plot infRV reps, again recommend shifting it a little bit.  And then here you can see that the black six is changing over time, but cast is not. So that means this allylic ratio is definitely change over time for this gene and this transcript group. And then within the same gene, let's look at the other transcript group. Here, both cast and black six are increasing but we can see like from day six to day eight, cast is actually increasing much more quickly than black six which also indicating a change in allylic ratio with time. And here's the question is what do you notice with the two infRV plot hint? Is the allylic imbalance in the same direction? It's kind of hard to fit them in, it's not. So this one is the black six is much more abundant in the first one and the cast is much more abundant in the second one. So plot allylic gene can also be used to visualize time series allylic imbalance data but the difference between dynamic AI and global AI is here we need to bring the time points together because we have nine different time points would not be able to fit them in one plot just not enough room. So here we banged day two to day six, day eight to day 12 and day 14 to day 18 together. So the strategy to bang the time points could either be depend on your biological motivation or actually look at the plot infRV reps to maximize the difference in this scenario. So let's run this. Okay, here we actually have just two different transcripts but interestingly they're going different directions in the allylic ratio. You can see four, let's just call this one the first one. The black six is increasing over time but the cast is decreasing over time but the other transcript group, the cast is increasing over time and blacks is decreasing over time which also agrees with the plot infVREP plot we were seeing. The good thing about infVREP plot is if there's not many transcript groups in this gene you can plot them in the same plot which is a really good visualization to compare to two transcripts. However, if you some genes they have multiple transcript groups if you have like four or five it's going to be really hard to fit them all in in one plot with plot infVREPs and then it will, in this scenario plot allylic ratio might be a better idea but I actually think the heat map will be the most suitable for a scenario like that if you want to view visualize your results across multiple samples and the multiple transcripts. So the difference between global AI and dynamic AI in terms of heat map is we're adding this time bar over here so the way to add time bar is quite easy just essentially just build a data frame that extracts the time variable out of your results and then we also customize the label here saying the time but if you have multiple samples you can actually customize it saying different things and then it will be a lot easy to visualize this heat map especially if you have multiple transcripts here we only have two but if you have multiple it will be much easier to visualize with heat map. So that's it for the code demo itself and a few take away or not take away like clarification is first of all we found that with seesaw it's very hard to fit all the information in one plot because we have many layers of information there is isoforms, alleles, different time points and there's many measurements there are uncertainty and then there's test statistics so we recommend you explore different plots because we don't think any one of the plot will be able to capture any information. So we also recommend you look at a line reads we looked at few in IGV and you can also view them in other genome browser and then that's a good way to examine the distribution of the reads and also as Mike has mentioned here we're primarily motivated by examining transcript groups that has a little imbalance because of the cis regulatory elements but the same concept can be applied to to group the transcripts based on your own biological motivations. With that I'd like to thank my amazing advisor Dr. Mike Love, our collaborator from Osu Blast and Samma and the financial support I'm happy to take questions too. Thank you. Anybody in the room have questions? Okay, there's, if you haven't been logged in there's a really long good conversation going on in the online chat I'll see if I can copy it and put it somewhere because it's not really any unresolved questions in there. Thank you, very nice talk. Can you go back to the very first box plot figure that you showed? The one you said you really liked. Yeah, so I was wondering so it looks like there's sort of two groups of samples ones that are really variable and then ones that have a pretty tight distribution. Do you know what's going on there? Just give it back to her, she's got an answer. Okay, Mike correct me if I'm wrong. I actually think that corresponding to the information Mike was just saying that we have two different crosses. We have one, two, nine cross black six which one, two, nine black six are more closely related and then cast cross black six which are more distinguishable. So when you see a very, I think when you see like this high variables we're looking at one, two, nine cross black six because the sequins, the alloys are less likely to be distinguished. So you have the bootstrap replicas capture more uncertainty instead of when you have very different sequences with cast and black six it's a lot easier for like just less certainly in that. Is that correct? Okay, I think it's correct. Thank you for the question though. So on the very last heat map that you presented, I think I just missed what was the significance of adding that time bar at the beginning? Cause I know the previous heat map also trapped changes over the days. I actually think that here we're since we only have one variable that is time but if you have multiple samples that capture different information if you like not only label just by days you can change the samples over here and at the time bar. So that's like two layers, two different layers of information in there but here we only since we only have one so it's kind of like repetitive. Yeah. Yeah, thanks for both of you for the, for this worship. There's a lot that I think fluidly bit over my head cause I'm I don't work in this area but what I'm trying to understand in my mind is like you have these two groups of samples because of the two alleles. How does this compare against let's say if you compute the mean and variance and just divide it to, right? Now you have a single curve for a particular isoform and you could use maybe the some of the methods we saw earlier have I see like trying to capture or like trendy and find those where there's a those isoforms where there's a significant trend or maybe the difference. Maybe there's a trend on the significant difference between the, sorry, a significant trend for the difference between the two groups of samples. How would that compare against what you're doing? I actually not sure if I understand it correctly. Are you referring to the dynamic AI which is calculate or Mike, do you have any input? I guess one thing that's unique about this analysis versus like trending trends in gene expression is the covariance structure across isoforms and alleles is really complicated. And so we're doing a nonparametric test to avoid any kind of parametric assumption on that, but like there is all kinds of uncertainty across isoforms and alleles. And it's hard to like compress it to a summary statistic and just use like a spline or something. So yeah, if you can collapse it to a level where there's no more uncertainty, then you could like throw away the uncertainty and use a point estimate and like go do a spline with that. But at this point, it's too, like at the level shown on the screen now, there's just so much uncertainty and the distributions are not well behaved. They're like spike at zero and then a long tail, like they're very nasty. And so we don't make any parametric assumptions on that. I have a question. This is Ryan. Could you use a similar statistical model to analyze the ratio between spliced and unspliced transcripts? I haven't thought about that. Obviously the null hypothesis would not be 0.5, but other than that. The, that would be more complicated. I wouldn't, I don't want to say like, yeah, use it. I think it's more complicated. We, the nice thing about the alleles is like, there's just a single SNP difference, whereas the difference between an unspliced and a spliced transcript and the counts you get has all kinds of biases. Like I don't know, there's a bunch of, there's a couple of papers right now about the, like there's one from Albert Quo and Casper and Stephanie right now on BioArchive about all the biases that affect the unspliced versus spliced counts. Here, we assume that there's no real bias difference between the allelic counts because there's only a single letter change. Yep, thanks. Okay, I guess we'll do one last one from online. From either, I'm sure it's pronounced Thady or Tati. For example, on day one, gene X is expressed at 10, three from A1 and some from A2, but on day 10, gene X is expressed at 20, 10 from A1 and 10 from A2. How could you quantify the contribution to gene expression changes from changes in A1 and A2? I'm kind of, I'm kind of, for example, when they want to express that 10, three from A1 and seven from day 10, gene X is expressed at 20, 10 from A1 and 10 from, how could you quantify the contribution to gene expression changes? I think at each time point, for example, on day one, when you have this allelic imbalance, three from A1, seven from A2, you have a little racial at day one and then on day 10, you like the allelic ratio is gone and then this changes can actually, first of all, be captured, use the infrap plot and then if you use the dynamic AI testing, it should be able to capture this change you are referring to if that answers your question. All right, with that, let's give another thanks to a good presentation by Yufi and Mike. We spilled a little bit over, but we now have a break until 3.30, so see you all at the next one.


Hi everybody, Joe for jazby's casebreaks.com. We did it. We knocked out our jazby's nine box quick hit football mixer Featuring some gold standard elite optic and more And pretty nice stuff all card ship All 32 teams are in very big. Thank you for this late-night rally everybody. Thanks to the people who bought their spots straight up Congrats again to the people who won their spots in the filler. Thanks for taking the risk on that all 32 are in Let's roll it. Let's randomize it snake eyes two times one and Two easy and guess what this unlocks our Thursday night football promo We'll be doing that break credit randomizer after we finish this break I think this break should take more than a half an hour or so two times for the teams and Two the Chronicles value packs might take the longest amount of time everything else would be relatively quick Colts down to Giants After two just snake eyes left eye right eye And then it's Colt down to Giants. All right. Yep. There we go And let's see what What you get Brody, you've got the blue horseshoes mark with the Seahawks Kevin M with the Saints James with the Steelers Riley with my Raiders Thomas with the Lions William Texans and Cowboys both the Texas teams Josh with the commanders That's El Canon in the chat Kevin with the Browns Ronnie with the Chiefs Richard with the Dolphins Royal with the Jaguars Josh with the Chargers Matthew with the Bears will with the Jets Dave Barrows with the Carolina Kevin M with the Titans Todd with the Falcons will with the Niners El Canon with the Patriots Ronnie with the Buccaneers James with the Bengals Rick with the Bills will with the Vikings Riley with the Rams Kevin with the Eagles Travis last spot mojo Baltimore Ravens Matt with the Packers Dave Barrows with the Cardinals Riley with the Broncos and Pascal with the G men Now we're gonna pause the video and feel free to trade during that trade window if you'd like trade at your own risk as always And we're gonna pause the video. So when we come back we'll see if there's any trades and we'll have the break stick around BRB All right. Welcome back everybody No deals were done in this break. So let's get on with it here on a Thursday night. Thanks for spending your Thursday with us Thanks for staying up staying up late with us as well another great late night rally We've had a lot of those over the last week or two. So I do appreciate that. All right. Remember these chronicles of Fat pack boxes for whatever reason they shipped us ship these to us Without it being shrink-wrapped usually most of these times they are 12 packs per box So there's one two three four five six right there and One two three four five six. We'll get the base heavier stuff out of the way a little bit first Yeah, Rick. I'm gonna do that after After I go off there. I'll find out all that break credit here are our Chronicles packs like as soon as I turn this around they'll be they'll go the other way, right? Absolutely or trade a lance, but Let's pull him off. Obviously There's your offensive rookie of the year Jamar Chase from last year reigning offensive review They're James with the bangles tray lance poor tray lance that goes to will in the Niners That's the copper parallel you kind of tell that's the that's the Exclusive to the set Dave Barrett. Do I golf? I've been trying to golf. I've been getting into it. I just started. It's seriously working I'm a total beginner. It's just started like two or three months ago three months ago now How long has it been guys? I've been I've been I've been boring the crew with my At least most of the crew with my golf journey rookie Najee Harris Yeah, well, that's poor trade lance Dave Harrell. Let's cut him some slack I think it was this fall But yeah, I've been trying I've been trying to get into it So I've been I've been hitting the driving range You know a couple times a week or so Practicing my swing My grip my grip is actually pretty good. I think I played a lot of tennis growing up and through high school, so I feel like that the the grip and some some mechanics sort of transfer over And so I'm just kind of working on nice Najee Harris black parallel right here that goes to James and Pittsburgh So I'm kind of working on that I've been starting on working on my starting with the irons The seven eight and nines Not working in anything else just yet. I'm just putting here and there Get me started Hopefully another few months. I'll be I'll graduate to Graduates is maybe some par three courses And then I could do that for a little while then graduate to some full golf courses and then probably the pros Pretty soon. I'll probably be a pro in no time probably by this time next year You'll be like hey is why why isn't Joe breaking anymore? Oh, sorry He's uh, he's he's working on getting his You know, he's on the corn fairy tour You know working his way to the PGA tour Joe doesn't know what a par three golf course is Do you know what par is Joe? First of all you have to know the game of golf and there are par threes Different par is oh fan. Do you want to use fancy words or executive course? There's a really nice one here in Manhattan Beach that builds himself as an executive course But it's really just It's really just nine holes or eight nine 18 holes 18 holes in a par three But yeah working on the irons. I'm not as good as Joe Pizzle I got it. I got it practice a lot more if I'm gonna Play golf with Joe and be respectable out there and not embarrass myself Right. Yeah, Nathan's like par threes are fun Get your iron iron work done there. Yeah, now that I'm kind of getting into it Just doing some Google searches. I just realized just how many Just how many par three is kind of dot Los Angeles or just how many golf courses there are first of all, but Which shouldn't be a surprise. It's a big city, right? But um But yeah That goes to Houston by the way, that'll be for will some upside for Davis Mills, I think former Stanford guy Person who has that goes and that's will and the Jets to Mack Jones is gonna go to Josh right? Yeah, I'll kind of on with the Mack Jones is There's an Ian book for a second. I thought that kind of like a downtown car, but no, that's a magnitude there Oh executive course smaller mainly par threes and some par fours There's one in Manhattan Beach. It's really nice. It's attached to a hotel But yeah, that's what I was told Nathan is just like my colleague Nick Jaspy who Who who runs this joint and does a lot of the buying and There's 20 21 Donner's optic football does a lot of the buying and posting of the brakes on the site and whatnot His older brother and I are really good friends, that's how I know the family and And Yeah, he's the really he's the one really pushing me to get into get into some golf and one of our regulars here one of our regulars here had an extra set of Some old tailor-made irons a little bit on the older side, but pretty pretty new they weren't really gifted me some irons and then I Put together like a hybrid and a putter and a wedge Another regular gift to me an iron or a driver And so now I do have a full I have a full set now spot some accessories. I love the excess rising in golf It's like yeah, I can get cool golf shoes now Yeah, I wonder how many slide I feel like the celebrities Dave Vero's just end up playing in those private country clubs though So I don't know how many celebrities I'm actually going to run into on a public course But if I make my way into a country club I feel like we probably have to sell a lot more of these nine box quick hit football mixers if we're if Jaspy Jaspy crew is going to get to To country club level but LA country club level that is Robert Woods to 299 Yeah, I was just gonna ask Oliver. Where where where are you gonna you're gonna be anywhere tomorrow 6 30 am t time tomorrow And I'll be We have a top golf here that's attached to a nine hole Par three there was an old golf course that they actually built the course on They kind of reconfigured that course Kind of remodeled and looks really nice apparently Skylinks and Long Beach nice Good luck Mike is sickie for the dolphins That's gonna be for Richard and the Mack Jones insert gifted rookie insert will go to El Canone Jordan Acons to 199 And a Jamar chase to 25 nice Yeah, that's the one thing about golf that I'm gonna that I'm really gonna have to to adjust Is is those early mornings there? Jordan Acons for the Texans will and the Jamar chase to 25 will be for James and the Bengals No, just just use my driver on those par threes. I haven't even touched the driver I'm gonna work on my irons first And try to do things the right way DK Metcalf to 99 Dallas Goddard to 199 and John Riggins to 299 for the Jets that'll be for will Kevin Wilson has the Dallas Goddard orange and The DK Metcalf will go to another bird team that'll be for Mark and the Seahawks I don't know when you work hours like we do Oliver That's that's 6 30 is daunting 24 out of 50 killing hill for the packers Matt and Carlos a boogie bashing is your rated rookie autograph Buffalo Rick Thomas with the bills Out of 25. All right next box. Maybe we'll do these select that blaster boxes here Just work Pacific time hours, but think of East Coast hours pretend 6 30 is 9 30 Just pretending doesn't tell your tell your body clock Rex Next thing you know you're gonna tell me I can just imagine a feast and a feast will appear on my table Just like in the movie hook doesn't work that way man Get real man. Get real There's that Wilson Jets.  Yo roofie.  There's Ian book red white and blue die cut No roof. He was dead right sorry spoiler alert Saints Kevin M Trevor Lawrence rookie card. That'll be for royal and the Jaguars and a terrace Marshall jr. Relic Carolina that'll be for Dave Barrow's It'll be for Dave Barrow's once again Carolina. There's Elijah more die cut Trey Hendrickson die cut James with the bangles James with the Steelers gets the Najee Harris will with the Texans gets the Davis mills and will also with the Jets and we got to Joe try and show Show Inka die cuts right there for the Buccaneers Start a rainbow Ronnie and that my friends are the two blaster boxes prison blasters What about Should we do with some elite first and then clearly? We'll close with the gold standard This has more packs, and then we'll do the two clearly boxes Close with the gold standard box and the next video will be the break credit randomizer giving away $3,500 a break credit thanks for our 30 that football promo In increments of a hundred so 35 winners of a hundred dollars each It's gonna make a lot of people a pretty happy All for doing what you would normally do join join our group breaks, which I appreciate Check in tomorrow. Who knows if there'll be another another promo tomorrow some new stuff on the site tomorrow I always try to mix things up every day on jasky's case rakes calm I always have some kind of promo or something special going on What games are we looking forward to this weekend? Let's let's talk a little talk a little football obviously, I'll be I'll be catching my My Raiders Titans It's gonna be a must-win game for both teams. I guess We got Texans at Bears. I guess for neutral fans. I don't know if anyone was really excited about Raiders Titans either Chiefs colds. I mean if the Colts were a little bit better I thought that that cold scheme that chiefs colds game could have been a good early season matchup I guess Bill's dolphins would be the big one, right? For the for neutral fans. Anyway, mine's Vikings Raven's Patriots bangles Jets Eagles commanders Saints Panthers Jaguars Charger, I guess maybe Trevor Lawrence Justin Herbert Rams Cardinals could be interesting at least for Los Angeles Falcon Seahawks. Yeah, I guess Aaron Rodgers in Facing Tom Brady. I guess that would be the that would be the one all of them Niners Broncos on Sunday night football I guess that Sunday matchup is not bad and then Monday night Cowboys Giants That's what Barros is looking for as a Cowboys guy and he does have Crack Raiders. Maybe maybe that's not I think Cardinals beat the Rams. You know what Cardinals actually kind of have the Rams number a lot and the Rams aren't quite aren't quite right Not yet. Anyway Al Canone's asking do I start Tua or Burrow this weekend? Well Tua is Tua is facing That Bill's defense. Oh Maybe it's the ramp. Maybe it's the Seahawks that have the Rams number. Those games are always weird I would I would lean all over leans towards Joe Burrow.  All right elite time. Good luck Matt Judon to 99 and Craftsman relic Amon Ross St. Brown That's for the Lions. That'll be for Thomas You got a Mack Jones 399 second year Mack Jones is 2022 elite Got Isaiah likely to 999 a likely story and our autograph is gonna be Breece Hall 27 out of 199 turn of the century autograph for will where there's a will there's a way Joe mixed into 24 for the Bengals. That'll be for James Guy cut we got Britain Brown to 399. We got it one who to 999 That's right. Jason is gonna see and a Kenny Pickett. Look at that. Wow purple ink autograph Out of these two random boxes of elite that I grabbed What are the odds James? With Pittsburgh got randomized Pittsburgh gets the purple ink Kenny Pickett on card auto Yeah, that's nice. You might he might get some looks soon It's James Robinson 75 Joe Pizzles asking who Doesn't doesn't watch a lot of football I guess doesn't follow a lot of football very closely All right, see you see you next week Thanks. Good luck to your Eagles Ceromeo Dobbs to 999 DK Metcalf to 399 Hi guys and another auto poppin soon, and that's eight. Oh, no, it's not an auto Look at that. It's got trolled by that upside the backwards card That Aiden Hutchinson will go to the Lions Jamar chased 75 It's K. Dotten to 12 to 12. It's a low number there. Yeah, Joe P. Loves the soccer It was Britain Brown to 999 and we've got turn of the century autograph. That's Eric That's easy E right here Eric Azuc Anna 37 out of 49 Richard with the Dolphins and a Breeze Hall to 399 You know, it's gonna be funny Oliver and Diego is that Joe P's daughter is gonna end up being Like a star soccer player in high school. There's a Modsos Gardner to 999 and Wondale Robinson pen pals black ink autograph for the New York football Giants the G men Pascal things asking about Arizona folly Pizzle Kyle Phillips to 999 Yeah, Dave's if whenever you get to the new stadium down here Pop by the shop. We're not too far away here. We're Lawrence. Um, but really nice I've only been to Into the Coliseum the Rose Bowl And then the Raiders team the Legion the one in Vegas and then yeah, and then so far I haven't been to a lot of football seeings, but Devon White But yeah, so far is pretty nice and Charles Woodson Packers edition Leet coverage relic for Matt All right, that may be about 10 15 more minutes to go in this break I think I need a bigger container for this break to Space in the regular containers here. All right some clearly Donner's Is that right Joe P? You're giving away an all-expense paid trip to come down beginning of November for the home run derby and fall stars game Including a special tour guide from Joe P You're throwing some golf there Joe we got a Niko Collins autograph for the Texan that's gonna be for will There's also a Jalen Hertz to 49 See the background there and a rated rookie Zach Wilson. That's for the Jets. That's gonna be will Got 14 out of 25 des Fitzpatrick for the Titans Kevin M And we've got a Rashad Bateman autograph. That's out of 10 6 out of 10 That will be for Travis and the Ravens last-bought mojo well Oliver Sun wants to go to ASU is that where we want to go Joe Pizzle and Diego or is is U of a better I think Oliver I think our guy Evan went to Went to an Arizona school. I think he's a wildcat maybe and Arizona wildcat All right final box ladies and gentlemen ASU is a better party school I Think Evan's a wildcat. It looks like sounds like Diego is a Sun Devil Forks up he says All right ninth and final box Got Antonio Gibson to 75 Yeah, maybe Oliver Sun wants to get down David There's Jalen Wiedermeyer 28 out of 99 that is for the bills and it'd be for Rick T we've got a Kyle Pitz gold played a rally little fleck of red fabric in there as well Todd and the Falcons 256 out of 299 Javante Williams for my rivals the Denver Broncos Riley on the board 105 out of 299 a white gold Jersey Trevor Lawrence white gold relic 280 at a 299 that's for a row either and the Jaguars points 600 points will randomize that to one person in the break in their Sam Howell triple relic an autograph 40 out of 149 I'm a man. I'm 40 40 out of 149 and that's for El Canon and the commanders. There you go gang He wants to do communications with the sports focus. He wants to be a group breaker. Is that what I heard? All right, let's go back to the list here The quick hit football mixer list Let's gather everybody's names from David and to El Canon and everyone in between you dice new list name on top after Five two and a three five times we'll get 600 points one two three four and Fifth and final time and that is Dave Barrow 600 points going your way a little consolation prize for you Dave Thanks, everybody. I'm Joe for Jasper's case breaks calm We'll see you in the next video for the the break credit randomizer. Bye. Bye


I was just thinking at 5 p.m. on a given Tuesday, and we're here with Attila's arrest of Salandia, and we're talking about mysterious malware on Apple's brand new, well, almost brand new M1 chip. Welcome to the show, Attila. Thank you, Jay, for having me. I really appreciate you taking the time to make this show. You know, this is an important topic. Well, there are many topics that are important on your desk, so we gotta hit a couple of them anyway, but let's start out with the M1 chip. What's special about it? Well, you know, if you use your cell phone, you'll notice after a period of time, it gets a little warm, but it doesn't get all that hot. But if you compare that to, say, a PC, well, you know, use your PC for a while, and that fan starts to spin, it gets real nice and warm. That has a lot to do with the architecture of the CPU itself, and the M1 is so revolutionary, because if you look at the architecture, and this is on Apple's website, you can see it for yourself, they put everything all into one big, big pie. And that includes the CPU, it could use the RAM and the storage of the system, but it also includes something called the GPU, which is a graphics processing unit. And the GPU makes a big difference, not just in your day to day web browsing, but really when it comes to creative work. So a lot of the creative folk are really excited about the M1 chip, particularly video and photo editing folks. But if you really think about the other type of creative work that this is going to spread into over the next one to two years, it's going to be engineering, because an engineering laptop can run, you know, five to $10,000 easy versus an M1 chip powered MacBook can run between one to $2,000.   And it's about 20 times the processing power at roughly half the price. So that's a big leap forward. And so there's going to be a lot of excitement, you're going to hear a lot of that in the Apple community over the next I would assume next 12 months. And if Apple sales are any indicator, quarter over quarter, Apple is reporting record earnings and sales because of this phenomenon. There's they're making better and better products and this M1 chip that came out last November is a whole game changer for this reason. Now, one of the things that, you know, that I think of when you when you talk about this is that, you know, the world is moving and computer users and use all moving, you know, to new new functionalities. So for example, for example, database processing, very important, although that is frequently in the cloud or on some remote server, rather than in the unit on your desk. No word processing has to me word processing. And for that matter, spreadsheet processing is pretty much plateaued because how much functionality can you use? Can an ordinary human being use it? You know, it's like we don't need a whole lot of additional functionality. But where where it's really going places, and you alluded to this is in, you know, heavy calculations, you know, like for science and technology and engineering, and also in graphics, graphics or everything, because whatever functionality you're using graphics is in an ancillary function to that. If I make a financial statement or a spreadsheet, I want to see that in a pie form pie chart, I want to see that. And that's actually more important in terms of presenting the information. So what I'm saying is my perception anyway, is that we are moving in the direction of graphics graphics becomes more more important going forward. And the, you know, the classical office programs, they're stable, and we don't have to worry about them, because the computer is well, any computer is well able to handle it. All right, am I? Well, there's there's a byproduct here we haven't talked about. And this addresses your concern directly. So if I bring out my surface, I'm likely to get at best maybe four to six hours of real computing time using that device before I need to plug it in. And that's that's about enough of an airplane ride from here to the mainland. And that's great. But with this new M1 chip architecture, the MacBook Pros have been rated up to 24 hours of uptime. So that means that you can start typing a lot longer just because of this chip architecture. And remember, this is just M1. What about M2 M3? They're already working on the the next version of these chips. So I predict that we're going to have not just, you know, stronger processing power, but a lot longer battery life similar to what you would expect from, you know, your cell phone, you know, cell phones used to last a couple hours. Now they last a couple of days if they're set up correctly.  So it's really exciting, more expensive. Well, that's the thing. It keeps getting cheaper and cheaper. The reason that this architecture is so critical is because it involved a rewrite of the entire Apple operating system. So this new version called Big Sur is designed to take advantage of this new architecture called ARM. And that ARM architecture is the same kind of architecture that you've seen in cell phones. You've gotten kind of used to that. But that highly segmented, highly efficient architecture is not something that Microsoft can quickly emulate. It's not something that Intel and AMD can quickly, you know, switch things over because it involves a complete software rewrite to address unique hardware. That's where Apple, I think they merged the two, right? Because they have the supply chain control over both. And, you know, when Apple and AMD and Microsoft eventually do kind of collaborate, and I'm sure they probably are, to address this new problem. I mean, they could be years behind by that point. And meanwhile, Apple just keeps pulling further and further ahead.  I got PCs, I got Macs, you know, everything. But we have to look at what is it that our clients are using and how do we protect them? And of course, that segments into the new vulnerabilities we're seeing with the M1 chips, because of course, whenever people start using something new that works really well, everything starts to become more vulnerable because it becomes a target. You called it mysterious malware. What can you say? Well, you know, I don't speak the foreign languages like you do, Jay, but I can tell you this. It's pretty scary. Silver Sparrow is a piece of malware that was adapted specifically to take advantage of the M1 chip. It also works on the Intel versions of the MacBooks. But the reason it was so scary is because, first of all, it popped up right away.  How did it get there? Nobody knows. What's it doing? Nothing. So it was essentially, it opened up a back door, it stuck it onto the computer, and it just sat there and nobody knew what was going to happen. Now, luckily, Apple has since passed the systems. And if you feel that you're potentially infected with something like this, any up-to-date antivirus program can catch it and remove it. Obviously, on all of our clients, we ran a script that automatically patched and removed and checked for any of these infections. So having a good cybersecurity company in your corner can really help if you're a little nervous about this kind of scenario in your Apple environment. You know, we haven't used the pillory in the stock for a long time. But I think there's a place for it. With due regard for human rights, there's a place for it. And you take those hackers, put them in the pillory in the stock, maybe it'll help. Well, nobody, they're not getting caught, you know. No, that's the thing.  And they're highly organized. They're very smart. They pick the best of the best. There's a lot of money at stake. In fact, the reason that we're having to deal a lot with compliance, specifically with CMMC and State 171, these kind of maturity model frameworks, they're bringing them out at the federal level and it's going to trickle down to the state level, eventually is my prediction. But the reason that they're bringing this out is because billions of dollars of intellectual property, U.S. based property is being stolen every year. And it just keeps getting worse. And a lot of it has to do with not having strict enough standards and having good cyber hygiene, which leads to all these problems occurring. And that's how all the news articles get out. I mean, you mentioned that there was a camera leak. And of course, it was because a vendor did not do a good job in securing their infrastructure and allowed folks to tune in to cameras at Tesla and at hospitals. Yes, that makes 150,000 cameras to be specific. It's old news. I mean, the cameras for a long time at many companies have not been secured correctly. We've had to go on there ourselves and secure camera sisters after they become infected. This stuff is an ongoing problem. Cameras are Internet of Things devices and there's no security standards on them that's uniform. Do I care if somebody hacks my camera? Well, it depends. If you have it in your bathroom, for sure, yeah. Trust me, it's not the bathroom. Well, and that's the thing. One of the concerns that comes up with PCI compliance of having cameras that cashiers is that if you put your credit card onto the counter there, can the camera see it? And if it can, then yeah, that is a PCI compliance violation. So you've got to be careful of that. Interesting. Interesting. So we spoke a little before the show about, you know, the solar winds issue a few weeks ago. And it's very troubling. I mean, it seems to be a horrendous story about this every few weeks. The solar winds was scary in the sense that it was a hack that happened inside this country by actors outside this country. And now, according to this story that just came up today, it was not only the Russians, it was also the Chinese working, not in tandem, but in parallel. They're all on us. They're all working on us. What happened? What can we do? Solar winds, and you'll notice if you go through our blog, there's not really too much. I brought up about solar winds. We've had to deal with solar winds directly as a vendor, even though their Orion product was the one that was technically compromised. I suspect, and you know, I could get in trouble for saying this, but you know, I suspect that their other products are also compromised due to the kind of unusual activity we saw on them. And that just goes to show that they don't really have a good gauge on how bad the infection was. And for those viewers who have not heard of the solar winds attack, it's real simple. At some point in the, about a year ago, the solar winds products were, you know, left open and vulnerable and someone inserted some code that then was able to trickle out to all their systems. And you may have not have heard of solar winds because it's an industry product. And much like the, you know, the back end type of industry products, they have a lot of fingers in a lot of places. And this is specifically a management product. Think of it like, like a big wrench that you can use to control computers, thousands of computers across corporate and government networks. And that's exactly where solar winds specialized in, especially the Orion product.  It's a great tool for that. But unfortunately, if someone up the upstream is able to infect those computers, then now whoever's monitoring that has full access to whatever the technicians can see who are using the solar winds platform. So in this case, they can put in footholds inside of networks that we still don't know about. And that's not likely to go away anytime soon. So even though they pull the plug on Orion, I believe that that brand is tarnished. And since then, by the way, solar winds has spun off and says, oh, we're going to have another product called Enable. So we're no longer solar winds enable, we're just enable. And, you know, they want to distance themselves from the name. And, you know, it is unfortunate, but it is also one of those things that is not the first time we've seen supply chain attacks. Vendor reputation management is very difficult. It's a growing industry that I don't think even existed more than a few years ago, where they were looking at the vendor supply chain and making sure that they were secure up to the point of doing business with your company. And, of course, when it comes to doing business with the government, they decided to set up their own private way of ensuring the supply chain through the CMMC that's a cybersecurity maturity model certification program. And that's something that they promised to roll out last year. But of course, because of the pandemic and because of the complexity of the program itself, they're not going to be rolling that out until the next, next, I believe, is 2025. But that's going to be slowly rolled out to government contractors and anyone doing, you know, business with the Department of Defense as a requirement of the actual awards being given. And so there is quite a lot behind making that happen. And in fact, we just learned also that the SBA is going to be requiring a CMMC level one certification for anyone who is applying and wants to be approved through the 8A program. And that's the program that benefits those who are either a minority, a woman-owned or veteran-owned companies. Well, has the government been doing a good job on this? Because, you know, at the end of the day, if you ask the guy in the street, who is supposed to be protecting us, he's not going to say it's the manager or the solar man. He's going to say the federal government. We have all these intelligence agencies. We have cyber security left and right and up and down. They're protecting us. Are they? Yeah, that's a great question. It reminds me of an interview and also in our dealings with the FBI and Homeland Security. We've had the same experience. People like to use the word they. And the more you work with them, the less there is no they. It's just everyone working together to do the best job that they can. There's no, there's unlike this, you know, nefarious overlord, despite what you may have heard, dark, deep, you know, government shadow government that's. Sure, the deep state. But you know what, if I if I call the FBI one afternoon, today's a little late in the day, but say tomorrow, my name is Jay. And I want to collaborate with you guys. I think everybody has to work together to stop the cyber crime. Do you think I'd get past the receptionist? FBI is very good at collaborating with private sector. It's just you have to remember that they are working with critical infrastructure companies. So those that are energy, transportation, food and shipping, of course, and what they want and utilities. And they want to make sure that those companies are taken care of first. So if you call them up and said you had some sort of insider information that would show that you knew about access to a utility company, they do have a good process for this kind of thing.  They have a very structured approach to working with other departments within homeland security with the private sector. And that communication line is pretty well established. So yeah, Jay, you know, if after this you want to get in touch with some some contacts, I can put you in touch. It's, you know, they're all very open and very honest and everyone's doing their best to try to mitigate this problem. But they appear with you here today. What's that? Would they appear with you here today? Yeah, we could talk about that. Sure. They do try to have good community outreach because they do want to educate the the anyone that they can about common cyber threats and how to avoid them. Because what it really comes down to is security awareness training, that whole idea that much of this can be stopped through the human firewall. When it came down to this Mac and more distribution, you know, when it came down to Silver Sparrow, that's that's the that's the vulnerability specifically. They said, well, we don't know how it spread, but there's only so many ways. And it's, you know, someone clicked on email, they should know someone clicked on a website, they shouldn't know. And someone maybe went on the internet somewhere that they shouldn't know. And that the and that ends up flowing into the machine. And we see that over and over again, it's employee behavior that has the largest impact on an organization and on our society as a whole. This troubles me that we have Russia and China both working on us, you know, it's bad enough we have other geopolitical contentions around the world and both on Russia and China. But now to find that they're both working, you know, on hacking us to say nothing, not even to go to the question of what they're doing on voting rights. But I just got a call the other day from a lady who didn't know where to turn. She saw one of our videos on YouTube. And, you know, I just I felt really bad for her. And we tried to like help her out the best we could. But she called up because she was a victim of, I'm not sure if it was romance scam, which is one of the top, top types of scams out there.  But she had someone who was giving her death threats, if she wouldn't pay them in Apple gift cards or wire them money. I mean, that's the kind of, that's the kind of like horrendous activity that, that I mean, gets overlooked, right?  And that's why they do it to somebody like that. Yeah. Yeah, exactly. So yeah, the big boys, the guys with the, you know, $100 million government contracts, they're just being lazy. What about everyone else who's just doing the best they can to survive a pandemic, to use their computer to work? And, you know, they're dealing with infected ad networks, they're dealing with, you know, incredible amount of sophisticated email targeted spearfishing attempts. And, you know, they're getting duped and exposed to these kinds of things. And this is why the training is going to be so important. I mean, everyone talks about, you know, why we make changes in society. Well, it's because people at one point decide that this is not going to be okay any longer. And we're going to do something about it. And you can only do that at the individual level, right? And that, that comes to any topic, whether it's cyber or climate or political, you name it. It's at the individual level that people can make a difference. Well, we have to be, we have to be more Akamaya about it. You know, one thing I wanted to ask you about it is, is my cell phone. Because I can download things on my cell phone and do all the time. I can email messaging this as well through a portal of some kind. And that means there's vulnerabilities on my cell phone. You're the guy, by the way, who showed me the way to Android. I don't know if you remember you counseled with me and showed me and I've been an Android user ever since. But query, you know, and people say, well, if they, if they hacked my cell phone, I just throw it in the trash. You know, I get another one and it's not a problem. You know, the cell phone is not really an exposure to me. But we know that's not true, is it? Well, an interesting study came out, this is about a year ago, where a company said, you know, let's just watch what kind of cell phone traffic is going on on these cell phone networks. And they looked at the big boys, I think it was AT&T, Verizon, T-Mobile, and they found that up to 60% of the traffic that was going on on cell phone networks, the data traffic, didn't look right. Meaning it was going to compromised ad networks. It was cell phones attacking other cell phones. It was cell phones attacking critical infrastructure. And these, this findings alarmed them. And it traces back to infected apps in the app store. And we've done several, you know, entries on this on how there are apps that are discovered to be vicious and you want to remove them from your device right away. But that all falls into that whole supply chain problem also. So remember SolarWinds, how it was a vulnerability that trickled down from the, from an original source and ended up inside the code, which then ended up at the customer. Right. It's the same problem with apps. And that's very difficult to mitigate. Apple does a decent job. You know, Google does a decent job. They all do the best they can. It is all best effort. But it also comes down to employee, I'm sorry, individual education. If you launch an app that is, let's say a game and it wants to know your location, that's, that's kind of odd. But you think, why would an app that's a video game or a little game like Solitaire or some harmless game, why don't we need to know where you're at? Why would it need access to your camera? Why does it need access to your contacts? Right. And most people just say, oh, yeah, sure. I want to play, I want to play the newest version of Bejeweled or Candy Crush or whatever. Right. But those are, those are malicious apps. They don't need access to your phone in that way. So don't let them through. Okay. So you say no when they want to know your location, but how do I know my phone is clean? How do I know my phone doesn't have some sort of trojan horse on it that will, you know, connect up with a nefarious network somewhere and do bad things not only to me but the community. How do I know my computer doesn't have that? Well, luckily there is some safeguards so you can enable trusted apps in the Google Play Store because you're an Android user. So you can go in there, you can change the trust rating. So you want to make sure that that's enabled on your phone. And then just in general, you can look at background processes and just kind of see what's going on. So for example, Internet of Things, they seem to use apps, but Internet IoT devices like the, you know, like smart turn on, turn off my lights kind of apps that are on your phone. Those will sometimes run in the background. You kind of wonder like, why are they running in the background? Why does it need access to my information and such? Those are kind of the kind of apps for you. If you have any suspicion, just remove them from your device altogether, return the device. There have been more IoT hacks probably this year than I've ever seen before. And there have been known vulnerabilities that in such things as smart TVs and FBI disclose some years back. I don't know about new ones, but, you know, the CISA website, that's a great place to go. The FBI website, great place to go. And believe it or not, a lot of times when we talk to the FBI, they say, you know, there's this great article from Bleeping Computer that really explains this problem very well. Bleepingcomputer.com is a great website to find out about how some of the internal workings of some of these vulnerabilities, how these hacks work. And of course, if you're using a product that's listed there, then you want to remove that product right away from your mobile device. Yeah, yeah, something about the price of liberty is eternal vigilance.   I mean, for example, you get married, all right, and the thing knows who your wife's family is right there.  Now it knows, you know, all the DNA for it. Not only that, but there's a whole genome thing going on. And apparently, some of these ancestry type sites have shared or lost data to organizations in China. Let's say China could be Russia as well, but let's say China. And they collect it. Now they know.  They know your family. God knows what else they know, because they have other sources, which they can integrate with that kind of medical information. And people say, I don't care. I don't care if they know I have a weak source. It doesn't matter to me. But what should it matter? Well, and add insult to injury. You paid for it too. Right, exactly. So it wasn't, it wasn't even so much that they kept your data, but you paid for it. One of the real concerns is how they, you know, that data is supposed to be kept private and yet it's being used for law enforcement. So that, that I think there were a few arrests last year based on some DNA, they were able to trace some DNA data back and forth. I'm sure if you Google, you'll see. I'm not sure if it was ancestors. It wasn't ancestry. It was one of the other big ones. And well, that, that is going to be a real risk. So it's, it's more than just, you know, maybe China has a copy of your ancestry record. You know, if they're, if you ever commit, commit a crime, or you know, someone who does, they may be able to track it using that information also. I think I lose out here is I, let's assume they had the, they had this ancestry type information for everybody in the country. So what, what are you going to do with it? Well, have you seen Gadica? No. It's a movie about this exact scenario. And in the future, or should I say in the future of the movie, they talk about how I created a class system. So some people had access to some resources who had good genes and those did not, did not have access to some resources.  Or even if, you know, if we have genetic manipulation prior to birth, and then you become, let's, you know, let's just say a more perfect version of a human being than, than the last generation that has maybe access to more things. You know, do we have that kind of society? These things don't happen overnight, but they do happen pretty quickly. And with any worry, you know, during the Trump administration, we came close to a dictatorship and that's still possible for sure. And in a dictatorship, if the government can control by the dictator, can get data on you, on your health, on your personal information, on your friends and relatives, on, on your geolocation, you're a great disadvantage. And that can be used by a wayward police organization to make your life miserable, as, has never been the case so much so before. So I, you know, I do worry about that. And I wonder what your thoughts are. This is another show sometime until I wonder what your thoughts are, at least in general, about what I can do, what I should do to limit that, you know, that, that, that, that hole in the boat. You gotta, you gotta be educated, use common sense. You know, that all this, all this stuff we talk about is all human nature amplified by technology. Right. I did a piece some time ago about the romance scammers, the, of the, of the, or the Bedouin romance scammers, and they dress up like Johnny Depp. And you can go into, where is it, where they have the, the city's carved into the rock faces. And, and they're all kind of sitting there.  And they've been doing it for hundreds of years. Now it's just digital. Right. They found a better way to do things. Folks have been working for years on a computer. Now we're using cameras, but, you know, nothing's really changed that much. So having a good foundation, having healthy skepticism, not be completely trusting of authority. These are all really good human traits to begin with. Have a free thinking mind. Be educated. I mean, one of the criticisms I remember back in economics class, one of the criticisms of capitalism was they had to be an educated consumer. Well, we're all educated consumers about the products that are around us. Why not be educated about the information that comes into a, comes across your screen. Right. Do you really trust it? Is it something you're going to act on? And especially if it's acting, if it's asking you to act on something right away, that's your big red flag right there. That's your big takeaway. If you want to write it down. Anything asking you to take immediate action right away? Yeah. That's, that's a big red flag. Don't, don't. We're going to put that on the final exam. So what about one last question and we'll have to go. You know, what about the government in that? I mean, this is an awful lot of hacking and, and fraud online fraud is especially during the pandemic here. I mean, these guys are stealing, you know, lots and lots and lots of money from ordinary people. I think it all could, using the power of the internet and programming and scripting and all that, to steal your money from you with all kinds of scams. I mean, it's everywhere. I don't think the government is doing jack about it. And I really think the government should do jack, but it takes, it takes money. It takes personnel. It takes somebody to meet them at the pass and, and then prosecute them. Are we doing that? I don't think we are. And if we're not doing it, what can we do? Well, you know, you just got to go back to the source.   There was a recent, one of the associations here was in locally in Hawaii had an AOAO. They had $250,000 stolen from them. And this was about two years ago. And they never got that money back. And I just heard today from one of our clients that, you know, they got about $26,000 attempted to be stolen, but their bank was much more vigilant, was able to stop the transaction. So I believe that over time, the banks are becoming a little bit more of a safety net, which is, which is good. But let's just assume the safety net does not exist. Don't be using fax machines to try and wire money. That's silly. Do things the proper way. Use reputable sources for, you know, for these kind of things.  These kind of things are going to happen. You have to assume that every citizen's personal information is out there and has been taken. So let's just start there. No, no one is, no one is 100% safe.  So you're not safe. So let's just start there. So using that common sense, having that, having that, you know, vigilance and a lot of the personal training is, is free. There's a lot of kind of like personal training available. If you're an individual for businesses, we do a lot of training, but there are plenty of other vendors out there you can talk to about making sure that your staff is trained. And the reason I like to harp on businesses more than individuals is because there's more money at stake. There's more businesses with millions in the bank than individuals with millions in the bank. And even though an individual could get wiped out, they could probably get back on their feet. But if a business gets wiped out, we're talking about a community destroying event. Everyone who's employed by that company can be out of work and may not get back on their feet. And unfortunately, I've seen that happen. And that's just not what we need in this kind of economic climate. No, especially in this kind of economic climate. Well, let's, let's circle back again a few weeks from now. And perhaps you can tell us that all these matters have been cleared up that we need not worry at all. Who knows? I've got a good job. What am I going to do? I would hate to see that. And tell us the rest. Cylinder, thank you so much for joining us today. Thanks, Jay. Stay safe out there.


I had some hiccups midway through where I thought I wasn't going to be able to publish because I wanted to publish on Wednesday which is kind of my horror comic vlog day but then they said that I needed to confirm my identity and my bank account and do all that stuff so it was like, ah please let me just make it go live because I was just so excited to share it with people especially you guys who have been with me since the beginning of its creation also side note, there are boats outside that are honking because there's a little bit of mist on the sea so I apologise for that. I thought in celebration of this we could just do like a mini sketchbook tour of all the horror stuff that I've done up to this point as in all the concept. I know you guys have probably seen a lot of it before but it would probably be nice to see it all in one go and me talk about it maybe. So the Kickstarter is live and it's super scary. I started this idea in June so the beginning opening sketches I was like you know what I want to do a horror comic and I just did some story beats where I wrote down the themes of the story what's going to be and certain scenes that I saw in my head but some of them aren't in the final story so it's kind of okay to read them. There was a house and it's dark and it seems lonely I was drawn to it. It had stairs down to the basement though the darkness it felt like it could go on forever. Footsteps on the landing edge and closer. I've never lived alone. So that was kind of I was going to have her narrate the story a little bit explaining it but I think with the final outcome it was more visually told. So here I was just drawing out characters and I was like oh what is this I had this image of a kitchen in my head for so long of just her sitting at a kitchen table as you'll see here. So I just drew out like kitchens and stuff and I started drawing out her character and that's when I came up with the name Emily is Burning. I don't know where it came from to be honest I just like the sound of it and I was like okay yeah that's good and I was drawing out different styles of her and then I thought like oh she's the character I like her and doing it and then obviously she doesn't look like that she looks very different to that so it's it's kind of cool I like this drawing where she's like I don't think I'm alone. Actually let me zoom in for you guys we'll zoom in a little bit more. Emily is burning this is the fake cover that I made where I didn't have a story or anything set out yet or no I like I had those ideas but it's nothing solid so I just did like a spooky cover like I was really into wanting to do some like spooky imagery so then I started drawing like the house that she may rent because I knew in my head the story was that she rented a new home or she moved into a new home somehow. If you've been watching this from the beginning you know that you know that she originally she was newly divorced and moving into a house by itself but I honestly don't know if that's the story now it's never mentioned in in the actual comic but you know it's not said where she's kind of come from just small houses and then I had that four of you know what lighthouse would be kind of cool but it just inspired me to think about a lighthouse as an option for somewhere she could be. I hate this drawing I was like yes lighthouse is and I was experimenting with copics and pencils but I don't like the actual drawing I did drew a bike here I don't know why and yeah and then I started really developing the style and look of the comic and I wanted it to look like that so each panel would be really hand drawn rough two two coloured and then I really got into like that style and I think yeah I did push this a lot but I think the final outcome of the style is a bit different to that but I think it is kind of this style then I was practicing drawing her and this feels like so long ago it's so strange to look back on it now as the Kickstarter has launched to be like I was at this point once and I felt like I don't know I felt like I was really excited about the project I was really enjoying it but it was hard I was thinking like oh can I do this at the time and now I'm looking back and I was like I'm like yes you can and then I had like a story idea of what she could say on the phone you can pause that if you want spoilers but it's not really worth it and then I was studying somebody else's style here and I was just trying to get to grips with what I wanted the overall look to be and it was hard it was tough but I was enjoying it like that was the funnest part for me because I would be working on that slice of life comic so it was really enjoyable to just let go and do different stuff and not be afraid of how it looked I remember really liking the pink cheeks so that's something that I bought into the final look of a comic different designs of the guy and different in situ situations that she would be in I remember thinking like I should draw something creepy because um you're doing a horror comic and what if everything you draw that's creepy it turns out to be not creepy and I did this and I didn't I didn't think it was that creepy but then Chris said it was creepy so maybe it is and you guys said it was a bit creepy so that's cool this is when I ran away with the tracing paper idea and I did really like that at the time but then it became too much too much work for not not enough payoff really because some of it was better than others and then I did all the thumbnails I wrote the script out so if this is a massive recap for everyone but I wrote the script out and then I doodle some thumbnails there and then I realized that I have to do all the thumbnails and so each page I did a thumbnail the panels of the flow of what it would look like I took those thumbnails and I created bigger thumbnails that would then turn into the final pages later on I again went back and drew some more styles I think this is kind of the style that I settled on because I really liked the way that looked and like the feeling it conveyed so that was the sort of thing I went with and it was really fun so then I did all the thumbnails final rough pages kind of bigger so these are exactly how the comic is but obviously the final pages are drawn a bit better and coloured and the lines are nicer so that's kind of the style I don't want us to show any more because then it will ruin it but then after that I just started drawing random stuff um this was for my Kickstarter this is the layout of one of the panels where she sees into the lighthouse for the first time and this took ages to just figure out the staircase even though I was using it reference image for the staircase it's still hard to translate that onto paper and I kept getting that wrong so I had to keep redoing it and rubbing it out and redoing it but it turned out pretty cool in the end I think so it was worth the time these are some sketches for a video for this squishy video where I did these girls which you might have seen you might not have these are some um Casey commissioned me to do a drawing for her and I was just figuring out like thumbnails of what to do and I won't show you guys the final one because obviously she needs to see it first and then I did some blogos designs rough concepts for a friend who's doing a who's doing like a food uh blog type thing so I was doing suit loads of rough logos for him this was for another commission Megae who's also a patron she asked me to draw her hen coop and I was just doing some rough studies and then I made up some chickens for some reason even though she sent me images um this was it for the typography which I've now decided that I'm going to do all digitally but like hand drawn digitally and this was the concept for the the cover but obviously the final cover is this I'll show you now on the screen but yeah um after that I just I was doodling this I think I was watching my bun art stream on twitch I don't know if you've heard of her I really like her stuff is by bun yeah so then I was just trying to draw different stuff for a little while to just get a break from the comic because obviously I was working on the final pages away from the sketchbook so when I would came back to my sketchbook if I was out and about I went to a summer production for a play and it was outside in the courtyard so I drew like the corner of the theater because I really like the way that looked and I only had like a few art supplies so it's not too colorful there's also this amazing tree there and I thought you know what I'm gonna sit here and draw this and as the actors were acting I sort of drew them a lot of them are my friends because I'm in the same theater company with them um this is my sister actually and yeah that was fun because they're all doing like different characters so and people had dogs they were sitting outside watching the the plays so I drew that dog but then he he moved and I was like no come back it looks really weird now it's just a dog in a play this was one of the characters in the play who is a woman who dresses like a man so she can be in plays in like Shakespearean times because obviously you weren't allowed to act if you're a woman here's some Kickstarter-y stuff there's some spoilers on this side so I don't want to show you but here's some banners that I made for the project and here's what I wanted the the banner for you know you see the image banner first to look like here I wanted to make icons for the projects the rewards that you could receive for Kickstarter so that was quite fun I really like doing that I did a version of this and then scanned it in and I didn't like it scanned in because the colors that seemed to washed out so I redrew it on the computer and then it was fine and then I was just I was thinking about what to call the reward tiers and I was thinking maybe I should name them after different horror books like the each tier but then it just didn't make sense so I didn't do that in the end this is for the clipped video that I did you guys didn't see I did a rough sketch in my sketchbook first of all and I really like the way that this looks because just the expression of his face is like years and I don't know I think it's quite sweet and that's the last thing because what's in here is for a video that's coming and yeah I still got a way to go to finish the sketchbook but most of it half of it has been taken up by I would say half of it has been taken up by horror comic stuff which is kind of cool really I don't know what you guys think but yeah we're finally here guys near the finishing lines and I really just want to thank you all for your help and support because everybody's been so excited to see this comic come to life and I'm really glad that it helped inspire some of you I think that's really cool and important at the end of the day like even if I print this I think it's really nice to know that other people are creating stuff because I've created something like I've sent a positive thing out into the universe so thanks for stopping by and checking in over these times but yeah that's where we're at guys and thank you for coming along in this journey obviously if you want a copy of the book just check out the Kickstarter below no pressure or anything like you've already done enough if you can't afford anything then that's completely cool maybe you could share it on your social medias that would be really helpful because maybe somebody else who likes horror or whatever of whatever reasons would like to get it and help me fund the project to get printed versions of this thanks for watching guys please like and subscribe for more content and I will see you next time bye


During his daily press briefing, Governor Andy Beshear said he would not strike down the 16-2 decision by the KHSAA to proceed with fall sports. All right. So I know there is one more topic that people want to hear from us on, and that's the decision by the Kentucky High School Athletic Association. Let me start by saying we're not going to overturn that decision. And it's not because I think that it is a good decision or a wise decision. But if we're going to defeat this virus, we need people other than me, or up here, all over Kentucky taking responsibility to make good and wise decisions. Governor Beshear would like to see athletes tested three times weekly and would continue to monitor the situation closely as teams move forward. Practice for fall sports will begin August 24th. Games can officially begin Labor Day week. For more info on this and other stories, check out mountain-topsports.com or visit our Facebook and Twitter page. With Mountain Top Sports, I'm Cody Davis.