Sure, I am actually the Director of Informatics and Biocomputing here at OACR. I work on community databases, knowledge bases, such as WIRM Base, and what I'll be talking about today is the reactome database of human biological pathways. So you've talked about an enrichment of gene lists, and now we're going to go one step further on to discover your own relationships or discover novel relationships among the genes in your gene set using pathway network analysis. So the main motivation for looking at biological pathways from the computational statistical point of view is a dramatic reduction in the data size, instead of having thousands of individual genes that may come out of your ONIX experiment and thousands of hypotheses about which ones may or may not be biologically significant. pathway network analysis allows you to reduce that to dozens of pathways and reduce the number of hypotheses so that you can find biological processes which are enriched in your set of interest without the penalty of the multiple testing correction. This also allows you to look at a set of rare events, such as the long tail of rare cancer mutations. Those of you who work in cancer know that if you look at mutations, there are only a few genes in any given tumor type which are recurrently mutated, and then there are hundreds of genes which are present that you're mutated in just a few percent of the population. You don't know whether those are passenger mutations which have no significance or driver mutations which in rare instances are contributing to the tumor phenotype pathway network analysis can help you sift out the ones which are significant for the ones which aren't and discover the processes in which they participate. A similar problem pops up in rare germline mutations such as autism spectrum disease which has a strong hereditary component but there are few of any genes which are commonly mutated in the disease instead of seems to be the driven by many many different rare mutated many different rare germline variants. And again pathway network analysis has been used very successfully in this case to identify the underlying biological processes from this long tail. Similarly, you can tell biological stories because you're working on well amitated, well understood biological pathways, there's literature backing up the patterns that you're seeing so that you can identify hidden patterns and long lists of anonymous sounding genes. You can extract the pathways and build them to create mechanistic or quantitative models to explain your experimental observation. You can predict the function of unannotated genes by identifying genes which are novel genes or poorly annotated genes which interact with well known genes and start to make guesses about what those are doing. And you can identify molecular signatures using pathway network analysis by finding different sets of patients or cell lines or whatever you're working on organisms which differ one from the other at the pathway level and relate that to their phenotype. So I've been talking about pathway network analysis a lot, I haven't actually said what it is. It's a very broad term, I define it broadly as any analytic technique that uses pathway or molecular interaction information to gain insights into a biological system. It's very rapidly evolving field, lot of different approaches and anything I say today will probably be out of date in a year. So let's talk about what the difference between pathways and networks is. So pathway is basically two ways of looking at the same data set. The pathway related view is the traditional view that you learned in biochemistry 101 from a Leninger where you have a linear or branching set of biological reactions involving proteins or lipids or small molecules or metabolites and they're organized end to end in a way that seems to have some directionality. You can, there's an upstream portion and a downstream portion and it relates to causality and it's a great way of understanding what the system is doing in a teleological way. It probably doesn't actually relate to the way cells see what's going on inside them, which is probably much more complex and branching and looping and interacting, but it's a good way for us to organize the knowledge. So in the pathway oriented view of the epidermal growth factor receptor, let me see if the mouse here is going to work for me. No, this mouse doesn't work. This one works. Is my cursor visible to you? Yeah, you can see it. We have a reaction center here. This is where the reaction occurs and we have two inputs into the reaction. We have the EGFR receptor and we have the EGFR ligand and they interact with each other to form the EGFR complex and this reaction is negatively regulated in this case by a protein called LR1G1 and in fact this is a very simplified view. There are many different regulators of this reaction. EGF and EGFR, then two copies of this associate with each other to form a dimer. So the input here is the receptor ligand monomer. There are two of them that come together to form the dimer. This then catalyzes a reaction in which ATP is converted into ADP and the output from that is the phosphorylated version of EGF, EGFR. You shouldn't have to take notes on this, this is just an illustration and it's positively regulated by a protein product from the SRC1 gene. So this pathway continues and continues and continues until you finally get stimulation of mitosis and cell growth. If we look at this in a network fashion, however, we've reduced each of these genes and protein products and small molecules into a series of interacting entities. In this type of view, there's our nodes, which are the circles, and there are edges, which are the various arrows and lines that indicate interaction. So in this view, EGF activates EGFR. These two are inhibited by LRG1 and I believe this is drawn incorrectly, but EGFR is activated by the EGFR ligands activated by SHC1. This arrow should probably be pointing the other way. Just notice that for the first time. The nice thing about a network is that you can start to put in less well understood interactions. So if we have, say, proteomics, co-immuniprecipitation data that says that KRT17 and EGF co-immuniprecipitate, we can put in interaction edges, which indicate that there is some sort of physical or genetic interaction going on here. We don't know exactly what, but it's useful to have that bit of information in there. So the advantage of the pathway-oriented view is it's easy to comprehend by people. The advantage of the network-oriented view is it's a simplified data model. It's machine readable, and you can do mathematical modeling on this type of network. So now we're going to get down to the practicalities of working with pathways and networks. So to do any type of pathway network analysis, you need two things. You need a list of genes, proteins, RNAs from your experimental system. And so these can be genes that are, these can be variants in a genetic screen that you've identified or associated with a phenotype. They can be up or down regulated genes in an expression, a data set. It can be microRNAs in a small RNA sequencing experiment, et cetera, and so forth and so on. And you need a database of pathways or networks. And then you need a tool that will combine the two and tell you how they're related. So I'm going to talk about databases, the databases first. So there are basically two types of sources for network pathway information. One are pathway databases, which are oriented towards this human view of biological networks. And the other are network databases, which are the machine, which give you the machine readable view. The primary examples of the former, the pathway databases, which is confusingly labeled here, reaction network databases, because that's a formal description, are reactome and cake databases. These databases describe biological processes as a series of biochemical reactions in the way that I showed you in the EGFR example. And they're able to represent many, if not most, of the events and states found in biology. Here, this is an example of the reactome database data model, which shows the fundamentals of the data model centered around a reaction. A reaction can be modulated by a regulatory gene or protein or other small molecule. There can be a catalyst activity associated with a reaction. And a reaction converts a set of inputs into a set of outputs. The inputs can be small molecules, such as sugar molecules and intermediary metabolism. They can be two proteins, which are dimerizing. They can be a protease and its substrate. And the inputs then produce a series of outputs. So if you're talking about a proteolytic cleavage event, the input would be one input, the uncleaved protein. The catalyst would be the protease involved. And the outputs are the two cleaved products of the original protein. If you're talking about a transmembrane signaling event, the inputs is the unactivated version of the protein. The outputs are the activated version, and so forth. And the other big database for pathway information is KEG. It's a curated database compiled from published material. It includes information on all sorts of small and large molecules across many, many different organisms, and it provides a map for how each of these are organized together. So here is a typical pathway diagram from KEG showing the cell cycle. And it's showing the various components of the cell cycle organized as a series of reactions in the way we showed you before. In this case, they use a little black dot, black or open dots instead of the squares to show the reactions. But it's essentially the same thing I showed you before. Reactome, similar to KEG, is a pathway database. The main difference between KEG and Reactome are a couple differences. One is that Reactome focuses on very deeply curated human reactions, human pathways, and other organisms come along for the ride, but are not directly curated. KEG attempts to do all organisms. So it's much broader but shallower. The other difference is that KEG has licensing restrictions. It's not free for commercial use. Reactome is completely open source and open access. So Reactome covers pathways for metabolism signaling, other biological processes. Every pathway and every reaction is traceable to one or more references in the primary literature. And there's an editorial process which ensures that there are multiple references for key observations. And each pathway gets peer reviewed at regular intervals so that we have a pretty good sense that what goes into the database is correct. Reactome also is interlinked with many other online databases and provides built-in data analysis and visualization tools. Here is an example of one of the visualization analysis tools in Reactome. It is a, and you'll be seeing, Robin will be showing you something like this during the workshop that follows. This is a Google map of a double-stranded break repair pathway. What you're seeing on the left side is a hierarchical list of pathways, sub-pathways, and sub-sub-pathways. On the right is a scrollable and panable window that you can kind of drag around and zoom in and out of, which shows you details on the pathway. And we've projected a gene set onto this pathway to show where the up-regulated and down-regulated genes in your experimental set are. So here is an up-regulated gene in the gene set. Here's a down-regulated one. I believe this was an mRNA set. And here is a complex of multiple genes that have some up-regulated and some down-regulated components in the gene set. And statistics from the enrichment analysis below. So in the very basic usage of this, you have a gene set. You have a set of genes like you've been using yesterday. You upload this into Reactome. It does an enrichment analysis for you. It tells you which pathways are enriched in your gene set. And then you can zoom into this and see how the altered genes are related to each other within each of the enriched pathways. In addition to Reactome and Keg, there are about 1,900 other sources for pathway information. Some of them very boutique, focusing in great depth on certain pathways, others more broad. There's a great resource called the Pathway Commons in which multiple databases submit their pathways in a uniform format called Biopax. And then this resource allows you to search through them. So if you're interested in a particular pathway, you can find the best curated and deepest one by doing some searches on Pathway Commons. It also provides some tools for comparing one group's view of a pathway to another, and they're doing simple enrichment analysis. OK, so those are pathways. Now we're going to talk about networks. So pathways are great for people, but they're really lousy for doing computation over it. Generally, it has to be simple. The data model has to be simplified in order to start to do statistics and make models that a computer can deal with. Typically, the networks that we work with in biology are interaction networks. And these are a collection of nodes in which the nodes are genes, proteins, lipids, RNAs, et cetera, connected by edges. And the edges describe the nature of the interaction between them. So there can be undirected edges, where there's a mutual reciprocal reaction. Two proteins are binding to each other. There's no particular directionality of that in network diagrams that would be indicated as a line without an arrow. There can be positive or negative regulatory reactions indicated by an arrow. So a typical example would be a transcription factor. Transcription factor is a relationship to a gene whose expression it activates. The transcription factor would be one node. The RNA would actually be the second node. And the transcription factor is activating the RNA by increasing its expression. A catalyst would have a positive regulatory edge, whereas a, say, a ubiquinolation reaction would have a negative regulatory effect. And that would be shown as a line with a little bar on the end, OK? So as you can tell from the examples I gave you, edges could be physical interactions. They can be functional relationships. They can be more abstract things like co-expression relationships. You know there's a genetic relationship between two genes because they go up or down in synchrony with each other. Basically, any sort of relation can be expressed with a simple model.  Transcription regulatory networks is a good example. Metabolic networks, where you have a series of enzymatic activities and their relationship to the small molecules whose reactions they catalyze. Protein-protein interactions is a major one. You'll see a lot of them. And then there are higher-order networks such as disease networks, where each of the nodes is a disease. And they're connected to each other on the basis of, for example, sharing the number of genes that they share in common through genetic diseases. Here's another example, a virus host network. I have a virus that can infect multiple hosts or a host that can be infected by multiple different viruses. You can show those relationships. Yeah, of course. Anytime. Want me to go back? Yeah. From that reference, can we get pointed to, for example, I would be interested in virus host networks? So we can find out in that reference. Yeah, the question is, does the Barbasi review talk about virus host networks? Yeah, I believe this figure was stolen out of that review. It was stolen out of that review. And it is one of these examples. It's actually a great review of how you build networks and what they can be used for. And Mark Vidal is the first author. He's sort of the father of high-throughput physical interaction screens. And Barbasi is a mathematician and cancer oncologist who uses network analysis in his work. So you need to get network data from a database, just like you do pathway information. Network databases can be built automatically or via curation. If they're curated, you have a team of curators. It goes into the literature and pulls out papers that report physical or genetic interactions. If they're built automatically, it's usually coming from high-throughput experiments. These do hybrid experiments, proteomic immunoprecipitation experiments, the co-expression data. And many network databases use both sources of information. Typically, a network database has more sense of coverage of biological systems. The largest pathway databases cover no more than half the genome. Network databases can cover much more, but a lot of the associations that they report are false positives from the high-throughput experiments. So you need to keep that in mind. And then the next point is the relationships and the underlying evidence are more tentative, typically, than the pathway databases, because the nature of the interactions is less well understood. There are, again, like the pathway databases, there are multiple sources for curated networks. Each of the network databases share with each other or steal from each other. So you have to take some of this with a grain of salt. They're not completely uniformly curated. The three which are recommended here are biogrid, intact, and mint. Each of them has a different number, has different curation standards. Biogrid covers multiple organisms, but is relatively shallow. It has 529,000 genes and 167,000 interactions. Intact is much deeper and focuses on human. So it has 60,000 genes and 203,000 interactions. You can see it's kind of a dramatic difference in the gene to interaction ratio. And mint is smaller, but even deeper still, 31,000 genes and 83,000 interactions. We'll just look at a typical interaction database. We'll give you a interface like this one. You search for a gene of interest, in this case P53 or TP53. And it came up with 7,708 interactions for P53 that have been mined out of the literature. But notice that most of these interactions are actually published high throughput experiments. So these are a series of co-immunoprecipitation experiments. This actually came out of supplementary data published in the paper. OK, so often your choice of the source of the data is not going to be dictated by the tool that you use. And some tools have been tuned to work with particular data sets. Others are more general and will give you the option of populating it with a data set that you have an interaction or a pathway data set that you've downloaded, or even one that you created yourself. And we'll give you some flexibility. OK, so I'm going to talk now about tools that one uses to implement pathway network analysis. And so this is a figure from a Nature Methods paper that I helped write with a bunch of other authors from the International Cancer Genome Consortium. And it just got accepted over the weekend. So I'm happy about that after about a year of trying to get it accepted to Nature Methods. We broke down a pathway network analysis into three different categories. The first is the one that you did yesterday, enrichment of fixed gene sets. And in this type of analysis, you start out with a series of predefined fixed gene sets, such as go subcellular location terms, or go biological process groups. And then you do an enrichment analysis. And then you can supplement this by using a pathway database colorization. So the example that I showed earlier in which we uploaded a gene list into Reactome, it found overrepresented pathways and then showed you a colorized picture. That's basically what we're showing here. The second type is de novo. And so we're not going to talk about fixed gene set enrichment anymore because you're already familiar with it from yesterday. The second type, which is what you're going to focus on in the workshop, is de novo subnetwork construction and clustering. In this style of analysis, you start out with a large interaction network. This is typically done on networks. And present the network with a list of genes that came out of your experiment. And the algorithms will attempt to identify non-random groupings of the genes in your gene set, pull them out of the network, and try to build a subnetwork around them which shows how they relate to each other. If your genes or proteins or other small molecules are related to each other, it will find non-random clustering and pull it out. If they're just a random gene set, they'll be scattered all around the interaction network and you won't get anything out that will make much sense. And what this allows you to do is to identify new relationships that do not necessarily correspond to classic pathways that are hidden inside your data set and to identify subtypes in your data set which relate to biology. Then the last and most sophisticated type of analysis is pathway-based modeling, where you go back, where the algorithm is actually built around a computational model of how each entity in the network is related to the others. So preserves the catalyst and write positive and negative regulatory relationships and attempts to predict, given a series of experimental observations, such as mutated genes or RNA expression changes, what the downstream effects of that combination of alterations will have. And so this will give you the ability, these attempt to predict the integrated effect of multiple alterations in a quantitative fashion. What are these three methods good for? Well, the enrichment of fixed gene sets should probably always be the first thing you turn to. A typical question that you can use, this is good for, is asking what biological processes are altered in whatever you're studying, in my cancer or in my perturbed cell line or in my cases versus controls. And it usually sets you off in the right direction. For de novo subnetwork construction clustering, you can ask if new previously undescribed pathways are altered in this cancer or data set and are there clinically relevant subtypes within the data set, can we distinguish one patient population from another patient population based on which subnetworks are activated or inactivated? And pathway based modeling, you can go down to the personal genome level given a working cancer. So all my examples are in cancer, sorry. In a particular patient who has a series of mutations and hypermethylation of a gene resulting in a series of RNA expression changes and protein changes, what is the downstream integrated effect of all those changes? Can I make predictions of what drugs I can give the patient to take advantage of these changes or reverse these alterations? So enrichment of gene sets, this is covered in module three. This was taken from a, oh, this is interesting, but it didn't fix this since last week when I gave the cancer genome talk. It's the most popular form of pathway network analysis, either to form great end-user tools. Cisco model is very well worked out. The disadvantages are there are many possible different ways of slicing and dicing gene sets. So you have to choose which ones to use or look at multiple ones. The gene sets are typically heavily overlapping, and when you get an enrichment result, you'll get a series of hits in gene sets, which may actually be related to each other. So you have to do another round of inspection or analysis to merge things that are related together. So you may get, for example, cell cycle and cell cycle checkpoints coming up as two hits, and you have to realize that those are probably related to the same underlying changes. And finally, when you look at genes as a series of bags, there may be regulatory relationships within those bags that you're not able to see, such as an increase in an upstream activator or a pathway coupled with a decrease in an inhibitor. They'll both appear in the same bag, but you may not be able to see that without further inspection. So now we're going to talk about, in a little more detail, de novo subnetwork construction and clustering. Basically, you're applying a list of altered biological entities to a biological network and finding topologically unlikely configurations. Typically, as measured as finding clusters of altered genes that are closer to each other in the network, then you would expect by chance. And there are various ways of measuring this. Typically, the main way is the average shortest path among them, which is counting up the number of hops between each pair and then averaging those shortest paths. You can then extract the clusters of these unlikely configurations to make subnetworks, and then you can annotate them using gene-set enrichment, in fact, to identify which biological processes correspond to these clusters of gene networks that you've found. So clustering is the process of grouping the biological entities into small communities such that the members of these clusters or communities are more connected with each other than they are with entities that are outside the cluster. As you would expect, if the interaction network is any good, highly connected proteins share similar properties. Typically, they'll be members of the same pathway or they'll be members of the same molecular complex or they'll be members of the same set of genetic regulatory interactions. There are a large number of ways of clustering, and this is actually the main difference between the major difference between different network analysis algorithms. The oldest and most widely used clustering algorithm is one that was developed by Gervin and Newman a couple of decades ago for use in analyzing the worldwide web to find communities of related users. It's very accurate, but a little slow. Many algorithms now use a faster one, faster but less accurate algorithm called Markov clustering. Both Gervin, Newman, and Markov clustering have a problem with ascertainment bias. If you have a gene which has a lot of interactions going into it or coming out of it, like p53, p53 and other highly related genes will always form a cluster and it will come out as a false positive in your data set. And the only reason for this is because p53 has been so heavily studied that there are a lot of interactions in the literature. And so there have been a number of corrections applied to this. The one that, in practice, works quite well is an algorithm called HotNet written by Ben Raphael at Brown University, which models the network like a metallic lattice and then applies heat to different points of the network to indicate gene activity. So if you have done a microwave experiment and you find that a gene is up-regulated in the model, you make the up-regulated gene hot and down-regulated genes you make cold. And then the algorithm uses heat diffusion equations to diffuse this activity out among the network. And the way it helps with highly connected genes like p53 because p53 has many, many wires coming out of it and the heat diffuses more rapidly. So it corrects, to some extent, for the ascertainment bias. And it's kind of mysterious why it should work, but it actually produces biologically intuitive results. So people like it. I like it. These three algorithms and others have been incorporated into a large and ever-growing set of clustering algorithms. The two that I want to point out to you are both implemented as cytoscape applications. And since you've used cytoscape yesterday, you can start using them now. One is called hypermodules written by Yuri Reimond, who is a postdoc in Gary Bader's lab. And this identifies network clusters that correlate with clinical characteristics or other phenotypic characteristics. So if you are trying to find a cluster of genes which is associated with response to a drug in a patient or a cell line or a mouse, this will weight the clusters for those that are associated with that phenotype. Very useful. And then the Reactome Functional Interaction Network Cytoscape app, also known as Reactome FIViz, offers multiple clustering and correlation algorithms, including Hocknet and Markov clustering and Girdman Newman, designed to work specifically on the Reactome FI network. And it's kind of a Swiss Army knife for doing this type of de novo extraction and analysis. And you'll be using it later this morning. And you'll be using it in your exercise, and Robin will be demoing it. So here's a typical output of a network clustering algorithm. We started out with a much, much bigger network than the algorithm extracted the members of the gene list that you gave it and may also extract other genes which are related to the ones, highly connected with the ones you gave it, that aren't actually in your data set. And then each cluster gets marked in a different color. You can draw polygons around it and then annotate them according to what biological processes are enriched in them. I highly recommend a particular interaction network, the Reactome Functional Interaction Network. This is basically a repeat of things that I've said, but it is a good compromise between curated and uncurated gene sets. We're going to skip through this and just talk about how the FI network was constructed. We started out with a series of pathway databases and converted each pathway into a network based on an extraction procedure that's shown here, but I won't go through it, to create a set of annotated functional interactions. Covering around about a third of the genome. And then we added to this a set of high throughput data sets taken from a number of network interaction databases, the literature and other high throughput data sets. It includes things like the transcription factor mapped from encode, a bunch of protein-protein interactions in various species, the sharing of go-biological processes, and then used a machine learning technique, naive-based classifier, to remove most of the false positives from the set. And then we took the curated functional interactions and the predicted functional interactions and combined them together into a single, very large functional interaction network spanning about half of the human genome. And we update this every year as of the last release at the end of 2014. There were a total of 11,780 proteins in this data set, about half from pathways and half predicted from high throughput experiments, covering roughly 58% of the genome. And there are 336,000 functional interactions there.  So the way you do de novo subnetwork extraction and clustering is you start out with the functional interaction network, which the tools download for you behind the scenes. This is showing just a little bit, little corner of the network. It's much larger than this. Present it with your genes. These are up and down regulated genes, for example, in a expression, microwave expression experiment. The algorithm finds out where they are in the network. It connects them. And then optionally, if you wish it to do so, it will identify linker genes, which are interconnecting the genes in your gene set, forms a network with those, and then extracts a subnetwork which relates your genes and the linker genes to each other. And you proceed from there. Here's an example of this at work. This is from a couple of years ago when we started sequencing pancreatic cancers at OICR. Here's a typical. This is the first 52 genomes that we did. And this is a typical look at somatically mutated genes. There are a couple of genes which are highly frequently mutated. In the data set, KRAS is mutated in 90% of patients, P53 in about 60%. And then there's this long, long, long, long tail of things that are only mutated once or twice. This goes on for a long, long way to the right. Looking at this or doing gene set enrichment analysis doesn't tell you much, but you can do a subnetwork extraction and clustering to get a picture like this one. And what this is showing pretty clearly is that there are a series of highly interacting modules each centered around one of those high frequency driver genes. Here's KRAS. And then there's mutations in a whole series of other genes which interact with KRAS. And they're interacting with KRAS more frequently than you would expect, and with each other more frequently than you would expect by chance. And it also includes members of the ERB, FGF, and EGFR pathways, as well as some unexpected things such as axon guidance. The second largest module involves a number of infrequently mutated genes involving participants from the Hedgehog and TGF beta signaling pathway, and so on and so forth. The interesting thing about this is that we're now up to about 450 genes, and the module map has 450 patients that we've done sequencing on or combined our data set with other groups. And we get the same module map that we did when we had 50. So there's a lot of information embedded in this that you can make use of, even at very small numbers. Now we can then ask, if we map these modules onto patients, are there differences from one patient to one donor to another? So what we did is for each of our donors, we looked at the mutations in that particular donor, mapped them onto the modules, and scored each module according to how many genes in that module were mutated in that patient. And you get this really kind of cool thing out of this. What we're showing is we're showing patients going down and modules going across. The color indicates for each patient how many genes in that module were mutated in that patient. And we did some adjustment for a total number of mutations. And we get a very clear pattern of four different tumor types, one which is negative for modules 1, 2, and 10, one which is positive for 1, 2, and 10, one that's positive in 2, and 10 only, and so on and so forth. The next thing to, so we've actually looked at these, these are K-RAS negative patients.  You can then look among these tumor types to see if there's a difference in phenotype. In some cases, you find it, in others, you don't. If you look in pancreatic cancer, the main finding is that patients with tumor type 1 have a much longer overall survival, but it's a very rare phenotype, as you can see here. It actually turns out that the patients who have mismatch repair deficiencies. But in other cases, such as, here's an example in breast cancer, and I know my picture has disappeared. I don't know, it was here last week when I gave this talk. That's odd. Anyway, there's supposed to be a picture of a, this is a same experiment done on estrogen receptor positive breast cancer, where we're looking at expression levels of genes. ER positive breast cancer, typically these patients have a very good prognosis, have long survival, have good response to estrogen receptor inhibitors, but a few patients don't do so well. And we did an analysis comparing across a series of 500 tumor microarrays and identified a module that's highly variable among the groups. And it's, I don't have the picture to show you, but it involves Aurora B. kinase plus the mitosis related pathways. And if the gene is in this module, or about 13 genes in the module, if they have high expression, the patients have much worse survival than those who have low expression in the members of this pathway. In fact, the difference is such that this group of patients has the same prognosis as patients with triple negative disease, which is the worst subtype. So we found a very strong biomarker of prognosis based on a network analysis. And at the time this was published, it was the strongest such biomarker that had been found. It was not obvious by looking at a gene at a time. OK, the last topic we're going to talk about is pathway-based modeling, where you apply, again, you apply a list of altered genes, proteins, or RNAs to biological pathways. These algorithms attempt to keep the detailed regulatory information that's present in the pathway database. And unlike any of the other techniques, these modeling techniques allow you to take different types of molecular alterations and look at their effects together. So for example, you could profile a patient population in which you have genomics data, expression data, copy number changes, methylation changes, small RNA changes, and put them together into the model and have it predict how together these changes are going to affect the pathway activities. And this is where pathway modeling starts to shade into systems, biology, cell modeling. There are multiple, this field has a long, long heritage. It goes back 40 years to partial differential equations performed on bacteria and yeast growing in fermentation chambers, where people use enzyme kinetics, KMs, and KIs to model the rate of consumption and production of metabolites. There's an online PDE system that you can use called CellNet Analyzer that works quite well. But it's really designed for biochemical systems, not for higher-ordered signaling or cell regulatory pathways. Also, these PDEs don't work very well when you get more than a couple of dozen of genes or proteins involved. They no longer become computationally tractable. The second class of pathway-based models are called network flow models, which model information flow through a pathway or pathways. These are best developed for the analysis of kinase cascades, net forest, and network kin are both Netherlands-based web services that are designed for analyzing protein kinase and phosphorylation data sets. Then there's a large class of network-based reconstruction methods designed at identifying the transcriptional hierarchy, the transcriptional regulatory hierarchy from sets of expression arrays. These enable you to find master regulators, for example, in a system of perturbations. You can identify the top-level transcription factor, which regulates all the changes below. And the exemplar of this is a RACNI from Andrea Califano's group at Columbia University is specifically designed for the case where you have RNA seek or array data across the same system where you have at least 100 perturbations. So for example, B cells that have been treated with 100 different drugs. And you've done expression profilings on that. You can present that to RACNI, and it will identify the transcription factors, which are driving all the variation across those perturbations. And lastly, the most recent type of models are probabilistic graph models, which attempt to build up what is called a Bayesian network, where you have a series of nodes, molecules, which influence the others, and attempts to propagate the influences from the top down. The most advanced version of this is a system called Paradigm that was developed by Josh Stewart at the University of California, Santa Cruz, and widely used for cancer analysis at this time. We're going to talk about Paradigm in a little bit more detail, because it's been built into a site escape app. So the way PGMs are designed to allow you to integrate multiple omics data types onto the same pathway. So you can take copy number changes, RNA, expression changes, mutations, proteomics data. You can explicitly look at activated, phosphorylated versus inactivated, non-phosphorylated versions of proteins. And it will identify, it will integrate these data together and make predictions on which pathways are changed and tell you whether they're up-regulated, down-regulated, or not changed. And then you can take those pathway activities and relate them to the phenotypes, clinical or experimental phenotypes of interest. Here's a simplified view of how Paradigm works. It starts out with a really a traditional pathway. Here's P53, and it has a negative regulator, MDM2. And downstream of P53, there are multiple other steps, and finally it leads to apoptosis. In Paradigm explicitly models the central dogma. So for MDM2, there's the MDM2 gene. It drives production of the MDM2 RNA, which drives production of MDM2 protein, which then is activated to become the active protein, which then negatively interacts with P53, which again goes from gene to RNA to protein to P53 active protein, which eventually leads to apoptosis. Each of these steps has a weight associated with it. It's actually the probability that a two-fold change in this entity will result in an end-fold change in the downstream product. So using this model, you can model a whole bunch of things. You could have a mutation in the MDM2 gene, which will reduce the weight. You can have an mRNA change in the RNA, which will reduce the amount of RNA and will propagate down to the hair. You can have a copy number change in P53. It could be a deleted. You can have some mass spec information that indicates that the protein has changed. And by using the model, it will integrate them together and tell you, well, when you have all these changes, is apoptosis increased, decreased, or not changed very much? And this actually works quite well. Here's an example from cancer analysis of gluoblastoma multiforme. Similar to the pancreatic map that I showed you before, these are different patients. Here are different. Each row here is the predicted activity of one of the genes in the model. And you can see clear differences from one patient to another. Here's a group of patients who have a decrease in GATA interleukin activity. If you were to look at the individual mutations or RNA expression changes, you'd actually find a lot of heterogeneity in here. But by a variety of methods, these changes all lead to the same decrease in GATA IL2. Similarly, they have an increase in EGFR. So it's a very nice general technique, quite powerful. They're good and bad news about paradigm. Bad news is that it's distributed in source code form. It's hard to compile. It doesn't provide you with any pre-formatted pathway models. You have to figure out how to put them in yourself. Documentation is almost non-existent, and it takes a long time to run. The good news is over the last year, the Reactome team has put together a side-escape app that incorporates paradigm. It's in beta testing now, and it's still under active development. But it automatically downloads and uses Reactome pathway-based models. And we've improved the performance of the original algorithm so you can now use it interactively. So the tool that Robin will be leading you through today, Reactome FIViz, in addition to the subnetwork extraction and clustering, which you'll be doing in your workshop exercise, it also has experimental paradigm support. So that's the end of the lecture. I've given you a whole bunch of URLs here. And look for a view article covering this in Nature Methods coming up sometime in the next few weeks. So I'm happy to answer any questions. Yes, Stephen? Yeah. Just maybe it's totally irrelevant. But with the big network that you put up before that, you might think we're in cancer. Notice the axon guidance came up, and it made me wonder, because I've seen axon guidance come up in a couple of different experiments. And it makes me wonder if axon guidance is actually relevant there, or if there are a whole lot of genes that have been documented and been related to axon guidance that actually have a whole lot of other functions that we don't know about yet. And I'm just wondering how much influence that has on this type of analysis in Europe. Yeah, so just to repeat for the recording, the question is, what is the relevance and meaning of axon guidance as module five for pancreatic cancer? Well, so this first gate came up quite a few years ago and I was quite surprised at axon guidance and actually argued with my co-authors that we just couldn't call this thing axon guidance, because the same genes are also involved in other biological processes, including angiogenesis. But it's driven really by Robo-slit interactions, which are key for chemotaxis. And it actually turns out that there's a correlation between mutations in this pathway and metastasis and tumor mobility. And you can actually show that you can cause the cells, pancreatic cell lines, to become more mobile by knocking out certain components of this. So I think that in this case, the main problem is that the name of the pathway is too specific. It's really not axon guidance, but more cell chemotaxis, organogenesis organization, that kind of something along those lines would probably be a better way of describing it. Axon guidance genes have also come up in quite a few other tumors in recent years, so it's a real observation. Yeah, Roger? So we have a lot of tools that are discussed in one. So for the simple users, not our practitioners, would you recommend that we try a bunch of them? And we'll probably get different answers. How do you handle this? Just look what's coming between what your findings are? So the question is, what do you do when there are multiple tools and they're giving you different answers? In my lab, our standard operating procedures, we try several tools. And then first of all, we try to validate the results before we put any confidence in them. And if we seem to be getting equally valid partial answers from each one, we then take the union or intersection, depending on the circumstance. Not very systematic. The systematic way is you benchmark everything against the gold standard set. But particularly in this field, network analysis, it's usually not possible to do that. OK, thank you very much. I'll be hanging around for about a half an hour.


I am playing with the St. James plug-in today to show you how to get some amazing ambient tones and it's as easy as one, two, three. I'm using pretty much every pedal that comes in the St. James plug-in. I'm using a compressor but not too hard to keep everything together. What really makes it for ambience for me is the delay. It's a full stereo delay with the tone control that I've set quite dark, a saturation I've got a little bit going in on the delay so less drive on the amp tone and more drive on the delay just so that it sits in its own space and with the feedback all the way up and the mix kind of high we get this.


With banners that read, Black Lives Matter, Justice for Mois, and immigrants matter, thousands of protesters took to the streets of several Brazilian cities on February 5th. They were out on the streets to demand justice for Mois Cabangabe, a migrant worker living in Rio de Janeiro, who was murdered on January 24th. The demonstrations were organized by migrant communities, collectives, and popular movements. Millions who took place in the protests in nine states denounced precarious work, racism, and xenophobia against immigrants. As Brazilians, we know the weight of genocide against our people, the weight of extermination that our people constantly suffer, but this is more than just that, a case of xenophobia that is very explicit, in which an immigrant with a goal was murdered in the same way that Brazilians are constantly murdered. We are here today to ask for justice for the Brazilian government, because this case is very confusing, it is a guy who was worked for, honestly, with his blood, and they didn't pay and killed him, so it could also be without him. 24-year-old Mois Cabangabe, a Congolese migrant, was found dead on January 24th, after being beaten to death in Rio de Janeiro. Cabangabe had gone to the Trapacalía Kiosk in the Barre de Tijuca Beach to collect payment for working two days as a kitchen helper, according to family members. In response, men from the kiosk beat him for 15 minutes until his death. Cabangabe was found on a ladder, tied up, and already dead by the time police arrived. Family members only found out about the death on Tuesday morning, January 25th, almost 12 hours after the crime. A man who claimed to have participated in the murder turned himself into the police on February 1st. On the same day, the government of the state of Rio de Janeiro announced that it will suspend the operation permit of the Trapacalía Kiosk. The city government of Rio de Janeiro has since announced that it will transform the kiosk into a memorial to Mois and African culture. The owner of the kiosk has not yet been released and is expected to give a statement to the police on Tuesday. Brazil, according to Mois, according to Patria, because this can happen in Brazil in the 21st century, this is intolerable and acceptable in a country like Brazil, we want justice. No one is born foreign, no one is born new, everyone is equal, everyone is equal, we have the same blood, blood is red, everyone has this red blood, we want justice, everyone is working, everyone was honest, everyone in this country knows Moisés, everyone wants justice, we want justice. In a joint note, Caritas Rio de Janeiro, the United Nations High Commissioner for Refugees and the International Organization for Migration, detailed how Mois arrived in Brazil as a child, accompanied by his brothers. Once in the country, he and his family were recognized as refugees by the Brazilian government. The Embassy of the Democratic Republic of the Congo said that they are in contact with Moisés family who are demanding a statement from the Ministry of Foreign Affairs. The embassy is also demanding answers from the Brazilian government regarding investigations into other Congolese migrants killed in the country.


I don't know who said it, but I've certainly heard that the price for freedom is constant vigilance. And I think that's important to remember. Hello everyone, Dylan Schumacher, Citadel Defense, and today we're going to talk about good news. So in the city or the state of New York, they were proposing this bill for like a third-ish time, something like that. Again, try to link it in the description below here. But I believe it was bill A416, which basically said that the state of New York, if they even suspect that you might be a transmitter of a communicable disease, they could forcibly remove you and detain you and quarantine you against your will for up to 60 days before they actually had to go to court in order to keep you there. It's absolutely insane. It's literally the government saying we can put you in a camp whenever we want, depending on how we feel about it because we say so and you have to stay there. And we're just going to make that the law now. It's absolutely out of this world beyond the pale insane. This is like Australia level bad kind of stuff. And they'd proposed this law in New York. Now fortunately, it looks like it as of today, the 21st of December when I'm filming this, and this might come out a few days later. They have withdrawn that they've withdrawn that law. They said, no, we're not going to we're not going to propose it to vote on this any longer, which is good. I am only assuming that it was withdrawn because people said, Hey, this is crazy. And so hopefully that's what happened. And hopefully nothing like that would ever be proposed again. Anybody who would propose a law like that, anybody who would vote for a law like that, anybody who would do anything to support something like that should be tarred and feathered. That's just kind of unequivocal. That should that should definitely happen. Because that of course goes against the entire founding principle, founding law and purpose of this nation. I will say this until I am blue in the face. Our civil liberties keep us safe. And the job of government is to protect your rights. That's why we founded this United States government thing. The government protects your rights. The government doesn't determine what rights you have. The government protects your rights. Ask yourself, how good of a job are they really doing at that? So I wanted to stop and just share with you that this is a good thing, that this is not going further. We should be happy about that. And we should be ever vigilant. Because as you well know, more and more and more of these things are going to come down the pipe. As I'm making this video, I know of at least four major cities, Chicago, New York, Boston, and I think one on the west coast, maybe it's LA, maybe it's San Francisco, that are trying to enforce vaccine requirements, right? You're not allowed to shop here or go here or do anything, unless you can prove that you have a vaccine and on and on and on. So more and more of these gross, unbelievable, Nazi-like laws are going to be enforced and passed and put in place. And we need to be aware and we need to be vigilant. And if you live anywhere near those places, move while you still can. Do brave deeds and endure.


everyone Shaber 1000 here. I've got this John Deere LA-145 that won't start. Now this is different from the other one that I posted okay where it had a stick in it this is a little different because can you hear that? We're going to diagnose this and find out what's going on with it. Stick around. Okay guys sorry about the wind noise I do have a lapel mic on thought maybe that would help but let's raise the hood up on this now I suspect this right here let me get you in here a little bit tighter that right there is a starting solenoid starter solenoid whichever you prefer right there now of course the first thing you're going to always want to check is the battery I'm pretty sure this battery is good I'm pretty sure it's up you can test it with a multimeter okay but a lot a lot of people don't have that thing but I know what a lot of people do have is a screwdriver just get you an old screwdriver a nail whatever but if I try to crank that that solenoid is clicking now I'm gonna go ahead and shut this off because I don't want it to start this thing might start up so leave your ignition off and there's a pole here and a pole here let me I don't want my glasses on I should put my glasses on here so I can see what's going on in the camera okay so again sorry for the wind there's a pole here and pole here okay take your screwdriver piece of metal whatever a wrench and it's cranking over the battery is a little low but it's cranking but if I use the key nothing so I know what that is so I went ahead and ordered one and I'll get some details with that here in just a second now this here this is what I ordered let me back you out okay this is what it looks like all right these things are very easy to change now again a lot of you guys already know this but thanks for watching anyway this is for the people that don't know that might help them save them a little money it's going to bolt on just like that now it's aftermarket so you know it could look a little different here and the way it bolts on to see that one's kind of bolted on like that with it but it's okay all right so what we're going to do is we're going to go ahead and change this out and see if that makes a difference now what I'm going to be using this one here is bigger that's a half inch for the old one this new one is a 7 16 just got a couple of wrenches here first thing you're going to want to do is unhook this battery okay now I'm going to cut you off for a second I'm going to move you over here and point you down maybe you can see a little bit better okay the first thing we're going to do is unhook this battery you only have to unhook one side now preferably if you can get to it always unhook your negative cable first and I'm going to have to go get another wrench to hold that but always unhook your negative cable first that way if it touches any kind of metal at all it's not going to spark now if you do it over here in touch metal you're going to spark possibly cause a fire melt some wires ruin your wrench weld your wrench to the frame whatever always unhook your negative cable first now let me go get another wrench and we'll take that off now let's hold one end I know these need need to be cleaned real good but this summer I want to put so when I wouldn't rubbing the mic too bad I want to put a new battery in it and I'm gonna put this in my orange one and the one that's in the orange one I'll take in for a return so always make sure they're clean because sometimes they get that looks like green fur growing on it so now this new part at the end of this video I will put a list on the screen of all the tractors this one part will fit we'll work on okay so right now we're just going to put this on here it's not back on here so we don't lose it which I'll put new new nuts on it and everything like that this tractor's old it's I'm probably 10 years old I'd have to look it up but pretty sure it's at least 10 years old now over here let me see let me bring you over here and I guess I can work from the other side there we go I'll bring in a little tighter on that shot okay if you want you can take pictures of how these go but this is the purple wire also the first thing you can do too is clean your connections but I know for a fact this one's bad but if you want go ahead and clean all your connections first take a picture of this so purple's going to be on the right black's going to be on the left so all right now I'm going to go ahead now see I can touch them and nothing will happen because I unhooked positive cable so let's go ahead but yeah I know for a fact that that's what this issue's been going on for a long time I've got one here I was going to put on it which would work it would work fine but I didn't want to cut these original wires so I didn't use this so and it got to where it would do that and you tap on it and it would start then last time we had it running I tapped on it it would not start so I'm just going to put this back on here like that let's go ahead and take this off so my guess what I'm I'm going to be thinking and I don't work me on it but I'm thinking this is going to be the bottom one right here so that's where the starter wire is going to go it's going to go here and the battery wire is going to go here purple will go here black will go there but we'll get to that and if it don't work you're not going to burn something up it just won't start for you so like I said these are half inch these are five sixteenths which I have out here somewhere I think I brought it and I had it my hand okay I gotta go find my five sixteenths hang on a second guys again back now just take these little screws out now listen guys if you break these off ain't no big deal just move it over drill you another little hole and put these back in it's not a big it's not a big deal if these break you can use self-tappers on these as long as it as long as it holds it in there and it's not flopping around what it does it has a connection in there okay and what happens is when you energize both of these okay that's what turns your motor over that's what puts power from here to that in order for that to do that your wire see the blacks the ground your purple wire when you turn the key over to start it it puts a puts 12 volts into this okay and what that does inside of here is a little electromagnet and it will pull something like a little washer in there it'll pull it up against the inside of these two posts and that's what makes contact and then when you let off the key this goes dead and then you're running okay so that's how that works in a nutshell I mean that's pretty much it I don't have a cutaway see it's just one of these you can use self-tappers if you have to if you break one of these off it's not the end of the world don't freak out now I'm going to give you the part number of this thing because this thing will fit I don't know 50 or 60 different lawn lawn tractors I'll put it on the screen but I'll give you a part the part number for this here in just a second and where it came from and how much it costs and all that but let's get this on here first make sure okay all right now like I said it's gonna put this back on here you can clean that up but it doesn't ground through that so it's not very important that you do so you can just put this on here and no it's not half-assing the job is done right get my fingers working right here find the threads yeah this came late because it came in the damn mail but it went I'll tell you where it went I always try to do the hard one first and get it out of the way this came from New Philadelphia Ohio about 45 minutes from hometown if that something like that okay came from New Philadelphia Ohio now and it left there left New Philadelphia and went up to Grand Rapids Michigan left Grand Rapids Michigan it went to New Hampshire it left New Hampshire came down here to Florida and it went to Jacksonville it left Jacksonville went to Gainesville went from Gainesville to here now I don't know why it would have to go into Michigan then over to New Hampshire to come down here to Florida you know so it went it went northwest and it traveled over to Northeast before it headed south and how it was traveling in those three states for a week before it headed this way and then like I said it went to Jacksonville Gainesville then down to here so if you ask me it's a lot of fuel and it wasn't it wasn't flowing of course it was ask me it's a lot of fuel wasted fuel when it could leave there and just go straight to Jacksonville and they're all distribution centers distribution centers okay so anyway it's not about that I just thought you'd be interested in that because it's kind of weird now we're gonna take has a lock lock washer on it now we're gonna put this one here right put this one on here and when you tighten these guys don't don't go crazy on them because you'll snap them off okay so I can get my fingers work right today but it happens right now remember this one's a 7 16ths and go ahead and tighten this down I don't think these two matter because all it does is make power go from one to the other so these two don't matter all right and like I said make sure you clean your connections up real good this one looked pretty good and I'm putting new stuff on it I may take a wire brush across that battery but like I said I'm getting a new battery for it probably March when mowing the season kicks in she likes to mow a lot actually we both do but she really likes it and I really like it but someday she'll I'll just hear the mower fire up she'll beat me to it so I want it to be right for her when she gets on it to use it your washer on or not on won't get enough go like that I'm going to switch these two around because like I said these two does not matter because all it's going to do all it does is just make contact between the two so I'm just going to go ahead these two big ones they do not matter because the power on this goes through this a little wire right here and then into this fuse which you know run your lights your charging system and all that so it's connected on to this so that's not going to matter let's go ahead and put this on here put our nut on some people called a bolt that's going on to a bolt it's a pretty nice day out today I think it's like it's pushing 70 degrees it might be 70 it's supposed to be 72 it might be 70 but it is breezy today a lot breezer than it was yesterday it's not cold I mean I'm pretty comfortable let's see that way it's a little closer looks a little better and I got a comment from an idiot that said you know you see you don't like to half-ass anything but that was when I was doing a door handle on the truck and apparently ran out of memory but I did explain to everything everybody and I showed how it goes on and so I had to comment about that nothing was half-assed just because my memory card got full you know I still did the job and did it right it's not half-ass you know how can you can screw running out a memory card on your camera to half-ass and a job saying I didn't well that's how I know they don't watch the damn thing all the way through when I explain and I show look this how it goes and pretty much if you take something apart that simple it's because it was like how's the clips go on I explained that but if you take something apart that damn simple and you put it back and opposite of what you did it's you know I mean pretty self-explanatory isn't it just go backwards from the way you did it the way you took it off I mean but as far as the job that's not half-assing anything the job was done it was done right and it works and it was done with very new parts so anyway freaking trolls then I had a guy comment one day we was going back and forth about something it was just like you know he's he's just a little smart ass I mean you can tell it keyboard warrior and his mommy's basement my give me another sandwich I got a comment to this dude hitting the dislike buttons I finally I finally deleted him and you know blocked him but the thing about it is you know he just he's flat said one day just message me or not message me but comment on something you're a fucking asshole you know why I don't need your shit you're done you know all because you know I'm proving a point and yeah I have a right to defend myself I don't have to take that shit from anybody you know when I'm doing the job and I'm doing it right I've got a fairly high IQ I would think of 144 is not too bad if you ask me but anyway then I had a guy comment well I got 184 IQ I see your IQ is not you don't even know what what an IQ is he says you only got 144 well that's borderline genius you know so but I got a right to defend myself you know what I mean and and that's what I want to do but we just call me a fucking asshole because I'm proving a point you you know and you can't win that's ridiculous okay so let's back you out here I remember the batteries a little low but let's just let's go and see if it'll start I got to get a new throttle lever for this thing I got charger here just in case I need to well just in case I need to boost it all right so we're gonna put this on here I'm gonna put this one on here all right let's just touch it real quick there we go see all right let's see if it'll start it's been sitting for a while might not start okay no problem I got a booster right here no big deal see this one's in here that one really needs clean but like I said when I put a new battery in it's getting all new I may put new cables on it but I may not need them and put this on here I want to kick it up to it's just 40 amps that should be enough to crank this I'm not gonna have to put a 200 amp boost but let's try it well maybe I am I know last time we started it the battery was dead because it sat for a long time and I just ran it around front and back around here and it kept that charge that good there we go one thing I don't like about this tractor it does not have an amp gauge but I check it every so often sounds like it needs the valves adjusted you hear that tapping sounds like a knock so there it is I got all the gaskets and everything to rebuild this engine I do not have the rings there's a few different size rings for this thing and I even run it through the the model type and code number still three different sets of rings three different sizes so for this engine I don't know how it can be a different size one 22 horse but I do I saw what I have to do let's take the damn engine apart and put a micrometer on the rings get the right rings see I think charge is up really good all right so that's that now let me show you some part numbers let's talk about the part okay guys the part I got is from Stens okay Tarot Dactyl would be proud of me wouldn't he a lot of you guys might know who I'm talking about but I love that guy he's really cool part number is 435-151 now that will fit a tractor a John Deere tractor from the 102 clear up to the Z335 E and more so what I'm going to do is I'm going to run it across the screen I'm going to bring it up kind of slow and you guys can pause and look for your for your particular tractor and so I'm going to do that right now so you guys can check your part numbers if you need to if not go ahead and skip forward to the outro there you guys go first thing to check check your battery if you don't have a multimeter like I said arc your arc a screwdriver cross them to post and it should crank and if it doesn't then you could have a dead battery try to jump start it see if you see where you go from there if you're if your ends are kind of nasty like this one is clean all your connections first clean them all up still don't do anything you know put a jumper box on it still don't do anything then you know arc across that solenoid now if that does not work there could be an issue with your starter it could be hanging up it could be you know the the cable on your starter there's one cable runs right down to the starter and go ahead and clean it you know take the nut off clean it real good and try that but I think chances are once you find if it's clicking like that and you don't have a dead battery usually if it's clicking your your connections are making a good connections always good to clean them anyway but usually that's that's telling you at least to the solenoid it is getting connection and usually what you'll find is it's it's a bad solenoid sometimes tap on it if you tap on it and it cranks fine then you know that's what it is but hopefully this helps somebody just kind of quick self-explanatory like I said just four wires the two top ones you know no big deal like I said take a picture if you want these aftermarket ones like I said may look a little different but if you got the two small ones and the two big ones it's going to fit your tractor you can also get these at part stores you know you can go to your local lawnmower shop or you know small engine shop I ordered this online I got a three eBay it came out of I don't know the guy's eBay store name but it came out of New Philadelphia Ohio and it was $15 free shipping you can spend you know $30 on these if you go through John Deere you're looking at 30 bucks but if you go you know this is the stands and if you go through stands now I don't know how much it'll cost through stands okay but this is a stands part like I said this was $15 so just shop around and you should be okay again sorry for the wind noise we got this we got it running now start it'll start for I am going to go ahead and put it on trickle charge for a couple hours I think it'll be fine like I said I will be putting a new battery in it and I'm going to put that battery into my orange one and when I do that I'm going to clean all the connections and all that stuff like you should do anytime you change a battery so that battery is old battery I forget well we've been together over four years and that battery that battery was sitting in a garage her original battery which I still have finally puked in it and she said well that battery is no good and I said well let me let me check it and I checked it with a test light test light was a little dim I thought hmm so I put it on the charger for a little bit and then I checked it with a multi meter and it came up 13 something 13 volts I was like well this battery is good at least for now and this was over three years ago and it's still going so but it is getting old so like I said it'll be good enough for my orange tractor orange tractors just to back up anyway so there you guys go hopefully again it helped and I'm sorry about the wind noise I'm sorry if I was rubbing the mic too much please bear with me I don't I never wore a lot but like out here I wanted to get my mouth closer to the mic so you guys could hear me better so anyway hopefully that helped hopefully you found your part number and like I said that's what I looked up I copied that off the line it said and many more you know so you know best thing you can do is just look your part over and if it looks like that chances are it's gonna be right okay you know make sure it's 12 volt blah blah blah I think you'll be okay but with that list you just saw I mean it's got your tractor will probably fall under that somewhere that also it's not just for John Deere these will work on MTD craftsman which craftsman MTD it will work on you know Murray's pretty much anything that has that two little wires and the two big wires pretty much fit any of those kind of tractors so you know I mean it'll fit I was going to take the one off my simplicity and put on here but I just went ahead and ordered one because that way I didn't have to do it twice because if I had to took it off took mine off put on hers and I would just put a new one on that one instead of pulling it off again I was like now I want to make sure she has a good one because I don't know how old that one is on the simplicity but it's the same one it'll fit that simplicity it fits fits hundreds of different tractors so what you what you want to do is just you know if you if you go to order one if you don't have a John Deere if you have a craftsman or a Murray or a simplicity or something like a if you have one of those you know just when you go to order it just you know make sure your tractor is covered you know because usually down below it will give you a list of compatible vehicles compatible tractors so make sure just make sure you look it over good and like I said don't be alarmed if it shaped a little different take a picture of yours if you have to take it off clean your stuff boom now if your stuff's really clean you know there's no shame in doing one wire at a time you know I'm bolting the old one leaving the wires attached bolt the new one on then take one wire off at a time put on your new one there's no shame in that even if it does need clean you know clean it as you take it off and put it back on real simple so you guys should be all right if you have any questions leave them in comments below and I'll get back to you as quick as I can and we'll try to get straightened out together so was that being said guys thanks for watching shape error the myth man alleged I'm gone for now hope this helped stens bye bye guys and take care


A bridge being struck by a cargo vessel seems to be a story as old as time, and a bit of a cliche, plainly difficult video subject. Although so far on this channel, I've mainly covered similar events in the USA. Today's disaster shows it can happen anywhere. It is the evening of 5th January 1975, and four cars have plunged into the Derwent River. Within the blink of an eye, the occupants of the vehicles became the victims of a sudden and catastrophic structural collapse. My name is John and welcome to Plainly Difficult. Today we're looking at the Tasman Bridge Disaster. We begin our story in the city of Hobart, Tasmania, Australia in the 1940s. The city is one of the most populous regions on the Australian island state of Tasmania. Hobart straddles the estuary of the river Derwent, where the city extends along both sides. Needless to say, this requires methods to cross from one side to the other. Much like other cities built along the river, ferry services and bridges supply the demand to travel from either the east or west of the city. The suburb of Clarence in the eastern side of the Derwent had developed at a much slower pace than the western city and its suburbs. Originally, the city had a bridge built in 1830, roughly 20km upstream from the eastern shore's suburbs. This led to a lopsided development of the city's major population centres. In 1942, Hobart and its suburbs had a population of 65,000 on the western shore and in comparison to Clarence on the east, they only had around 4,400. Because of this, in 1943 the city gained a new crossing, a floating arch bridge with a lift span near the western shore. This would spark major population growth in the eastern suburbs of the city, which would triple between 1943 and 1964 from 4,400 to about 28,000. The rather limiting and not particularly permanent looking float bridge was looked to be replaced in the 1960s. This led to the Tasman Bridge, a four-lane span built next to its predecessor. Due to this bigger bridge, population on the east side exploded, yet again jumping from 28,000 to over 40,000 in just 10 years. However, eventually, the city would have to rely solely on its bridge as the local government funding for ferry services dried up, forcing commuters onto the roads. By the early 1970s, as many as 25,000 cars crossed over the Tasman Bridge every day, making the crossing essential for the east side's population. Now let's take a look at the specs of the Tasman Bridge. With the float bridge being a bit long in the tooth, the new Tasman Bridge was conceived to provide a 24-hour connection, not requiring traffic to stop like it did for the original lift section to open for shipping. The 7 million pound bridge consisted of 21 spans supported by 10 foot wide reinforced concrete columns with a 350 foot wide navigational span between pier numbers 14 and 15. This meant shipping was intended to pass closer to the eastern bank of the river. To support the piers, piles were driven into the riverbed. One set under pier 7 was the deepest in Australia. The bridge measured at 4,650 feet with a waterway width of 3,500 feet. The bridge deck was 57 feet wide which accommodates four lanes of traffic and two walkways. Construction ran from April 1960 to December 1964 with the first ship passing under the construction in August 1964. Although the bridge actually had its official opening on Thursday the 18th of March 1965, the bridge would serve the city well for 10 years, allowing the eastern side to grow into a larger and larger suburban area. The bridge became essential in stitching the two sections of the city together, but just under 10 years after its official opening, a section of the bridge would fall. The disaster. The harbour at Hobart is one of the deepest in the world, and this influenced the Tasman bridges design with its navigational span. This needless to say meant that shipping was a common sight along the Derwent river. Both commercial shipping and cruise ships make their way under the bridge. One such vessel was the Lake Illawarra, a bulk carrier. She weighed in at 7,274 gross registered tons and 10,380 tons dead weight, measuring in with a length of 139 meters or 458 feet with a beam of 18 meters or 59 feet. The handy sized classified vessel has a top speed of 12.5 knots roughly 14 miles an hour, delivered by a single prop steam turbine powered driveline. She was in her second decade of service in the 1970s, being launched in 1958, and was an Australian built and operated vessel. She is a regular sight along the Derwent. And on the evening of the 5th January 1975, the vessel is navigating along the river towards the Tasman bridge. This evening her destination is upstream from the city of Hobart, the Rizdon Zinc Works Refinery in Lutana, Tasmania. And she has a complement of 42 crew aboard. She is carrying 10,000 tons of zinc or concentrate, which is probably pretty handy given her destination. In command this evening is 60 year old captain Bolslaw Pelk with Robert Bank, age 45 on the helm, making steering adjustments under the captain's orders. The Lake Illawarra by 9pm is roughly 1300 meters, just over 1400 yards from the Tasman bridge. Captain Pelk, after seeing there was no traffic on the river, ordered full shipping speed from the engine room, which would bring the vessel up to roughly 8 knots. Although picking up speed, the ship wasn't acting as it should. The Lake Illawarra started to move off course towards foul ground and shallow water. Pelk seeing this ordered a turn to starboard, but this didn't fix the problem. The ship was not in line with the bridge markers, which were there to guide ships through the opening. Bank received another order, steered you north, but the bow swung quickly, but it was not enough. Steer to 101 Pelk ordered. He was trying to line up the ship with the channel at the same time as trying to correct the swing. But this quite drastic adjustment swung the ship far to starboard. All of these movements were ordered whilst the ship was at full shipping speed. By now the distance to the bridge had almost halved. Stop engines ordered Pelk. Banks turned hard to port, but the Lake Illawarra continued to the bridge. At 300 meters, roughly 350 yards, Pelk ordered the engine room to put the engines full of stern. But still the Lake Illawarra advanced. Panic Pelk ordered double full stern, then dropped both anchors, quickly followed with triple full of stern. But it was too late. The ship was on a crash course to the bridge. With the port anchor dropped and the engines of stern, the bow turned starboard. The ship slapped into the bridge. Meanwhile Captain Pelk and Banks had thought they had, although having hit the bridge, not hit it too hard. Banks even said thank god it didn't hit hard. But the bridge's fate was sealed. The roadway above had cracked. The electrical wiring that was used to connect the two parts of Hobart was sparking in the evening sky. Soon enough the realisation on the ship's bridge was that a collapse was imminent and would result in tons of concrete landing on the ship. 7,000 tons of roadway and concrete crashed onto the ship. The ship, with the impact of the bridge and now extra weight, began to sink. She momentarily settled on the riverbed, only to continue her descent as her weight of cargo pulled her down. Some crew started jumping overboard, but disaster was not only aboard the Lake Illawarra, but also on the Tasman Bridge. Darkness had consumed a crossing and four cars could be seen from the river's banks, headlight on, falling from the bridge into the water's all-consuming obscurity. Frank Manley and his family were making the crossing. As they went over the peak of the bridge, Manley slowed to what he thought was a broken-down car. His wife shouted, look, the bridge is gone. Manley slammed on the brakes of his Holden Monaro. The first two wheels went over the cliff edge that had now formed on the bridge. The car became beach-teetering on the edge of darkness. In a bizarre stroke of luck, he and his family had narrowly avoided death, but just look at how close he was. Murray Ling and his family managed to stop before the edge, but was rear-ended, pushing his car also onto the broken section. Luckily, his old Holden station wagon too did not topple down to the river. As many of the sailors bobbed in the water, one question hit them. What about the engine crew? These poor people had no warning issued to them. It would be later discovered that the engine room telegraphed still red for the stern. Emergency response was swift. Many had seen the ship crash into the bridge from the shore. Some locals using small boats raced to the crash scene and recovered many of the ship's crew. By morning, the full scale of the disaster became apparent. Multiple witnesses had reported up to 10 cars had gone off the bridge. The police in the morning would say we can only rely on reports about who had not arrived home by late night. Three unsupported spans and 127m section of roadway had disappeared under the collapse. Although disastrous, luckily due to the time of day and the fact that it was a Sunday, more vehicles were not involved. Over the coming days, police divers located and recovered four vehicles. As well as undertaking a comprehensive survey of the wreck of the Lake Illawarra on the 13th of January. In total, 12 had died, seven aboard the ship and five from the cars. The city was split in two and with 12 dead and millions of dollars of damage caused, a big question hovered over the disaster. And that was how and why. The investigation. Witnesses and survivors were painstakingly questioned and their stories corroborated. Engineers were asked for opinion on the bridge's design. The piers either side of the main shipping channel had been designed to withstand a blow from a 20,000 ton ship travelling at nine knots, but none of the piers on the bridge had been strengthened to withstand a blow from a passing ship. It would make the cost of such a bridge prohibitive, engineers said. A maritime court of inquiry started on the 30th of January, 1975 and investigated the collision. It would slam the crew, but mainly Captain Pelt. They found that the ship went out of control about two ship lengths or roughly 300 meters, just over 300 yards from the bridge. Due to a number of turns, the ship was going too slow and had lost its steerage way. The inquiry found Pelt at fault and suspended his license for six months. He was quoted as saying, I'm eager to get back to the sea. I've been at sea for 40 years and it has always been a cruel sea and no matter what people say it's a tough profession. But this wasn't going to be the case. A and L, the ship's operator, quietly retired Pelt in November 1975. Aftermath. After salvage being ruled out, the ship would stay sunk just below the bridge, although parts would be cut away for repair works on the bridge later on. The city would experience a drastic change in the way its suburbs experienced the region, with extended journey times and desperate for the little ferry services that ran across the river. The eastern suburbs became more independent post disaster as the detour using the older bridge to the north added several hours to a round trip. In March 1975, a joint Tasman Bridge Restoration Commission was appointed to rebuild the Tasman Bridge. In October, plans began. It would not only restore the crossing but also add an additional lane. The two halves of the city would be reunited with a temporary bridge opened a year after the disaster. But Hobart would have to wait in total two years to see the Tasman Bridge rebuilt, being completed in October 1977. Well, one big implementation post disaster was the use of pilots. Although not foolproof, the sunshine skyway would have a lot to say about that. It is definitely an improvement in safety. This is a plain difficult production, all videos on the channel are Creative Commons attribution share alike licensed. Plain difficult videos are produced by me, John, in a currently wet and miserable corner of southern London UK. I'd like to thank my Patrons and YouTube members for your financial support. If you're enjoying this outro song, you can check it out on my second channel made by John. I also have Instagram, Twitter and Spotify if you fancy checking out more of my content. And all that's left to say is Mr Music, play us out please.


Today on Nightline, we're updating our monthly bonus program, a look at last week's drivers dine in and drive, and some of our latest charity efforts. I'm Alec Lloyd for Nightline. We're adding a couple new ways for our drivers to put extra cash in their pockets. Our current monthly bonus program features requirements to earn more money per mile based on performance in regards to production and safety. We're not making any changes to that criteria, which allows drivers to make an extra five cents per mile. What we are doing is adding a fuel efficiency component that gives the opportunity to make another one cent per mile. In addition to that, drivers who qualify for safety, fuel, and earn at least half of the production bonus will receive an extra bonus of two cents per mile. With these updates, drivers have the potential to earn an extra eight cents per mile every single month. If you have any questions about these updates, reach out to your driver manager. This past week, we celebrated Drivers Dine In and Drive. This event happens on the first Friday of every quarter. Every single one of our terminal caters or cooks lunch for drivers, and when we say drivers, we mean all drivers. Whether you drive for night or you drive for someone else, you're welcome to come in and grab a meal. Our Gulfport terminal gave out some delicious deli sandwiches and crumble cookies. Our Phoenix terminal had a good old-fashioned cookout with burgers and brats. My mouth is watering, and I can't wait for the next one. I hope to see you there. Night Transportation is hoping to make a big difference in lives of children all across the country. In 2019, we started giving to Children's Miracle Network, and we are proud to have raised over $875,000 since the beginning of our partnership. CMN hospitals raise funds and awareness for 170 member hospitals and provide 32 million treatments to kids each year. All night employees are able to make donations via payroll. However, for all payroll contributions made during March and April, night will double the amount of donations given. So everything you give this month will have an even greater impact. Thank you to those who choose to donate to CMN hospitals. You are truly making a difference. We have the great privilege of supporting the Missing in America Project, a non-profit organization dedicated to finding, identifying, and connecting the remains of fallen military service members to their families before helping to give those who serve our country an honorable and dignified military burial. When asked about the importance of the organization, MIAP Vice President Clyde Tex Taylor said, Every veteran out there, myself included, signed a blank check. One thing we were promised was a dignified honorable military burial. Sitting on a funeral home shelf, they're not receiving that. All we are doing is fulfilling that obligation to them. This delivery of earns was made by one of our fleet of heroes trucks, and the shipment was unloaded by a volunteer group of military veterans employed at our Buckeye, Arizona terminal location. You can find out more about the Missing in America project and watch our full video covering the event in the description below. That horn means it's time to enter the speed zone. Facts flying in at 65 miles per hour. April 27th is Mechanic Appreciation Day. We will be celebrating with free breakfasts and lunches, and each tech will be getting a swag bag. Don't forget to thank a mechanic on the 27th. Congrats to our new million milers. Each of these drivers have driven the equivalent of a trip to the moon, back to the earth, back to the moon, and back to the earth again. Absolutely wild. Thank you for your commitment. This year's international road check will be May 17th through the 19th. Here are a few tips to keep in mind. Quality pre-chips should be made for all tractors and trailers at the start of the day. Have your tractor inspected at a shop prior to the event. Check all equipment before entering high enforcement DOT locations. There will be a focus on wheel ends, so check wheel hubs for leakage, rims for cracks, and damaged or missing studs. Also, check PSI with a tire gauge and overall conditions of tires. Are you subscribed to Night Transportation on YouTube? If not, what are you waiting for? Get notifications for these videos and all of our latest stories. We recently released a video showcasing Port Stryver Mike Sandford and another video showcasing our new educational assistance program. I'm Alec Lloyd. For the latest in all news night, this has been Nightline. Keep on trucking and good night.


Hypoxia is a common consequence of sepsis, which is the leading cause of death in intensive care unit, ICU, patients. This study examined whether the expression levels of certain genes regulated by hypoxia could be used as novel biomarkers for sepsis prognosis in ICU patients. The researchers found that the expression level of the gene encoding the enzyme hemoxygenase 1, HMOX1, was significantly higher in patients who developed sepsis or septic shock than those who did not. Furthermore, the gene's expression level was associated with the likelihood of developing sepsis or septic shock. Therefore, the researchers concluded that HMOX1 may serve as a useful biomarker for predicting the risk of sepsis and septic shock in ICU patients. This article was authored by Nikolaus S. Lotzios, Chrissy Kaskinidou, Edison Jahaj, and others. We are article.tv, links in the description below.