What's going on guys? It's your boy Jovani from spy options trading. It's Monday Monday Monday Monday tg. I am You guys know this already. I love Mondays guys. I love Mondays, especially when I've trade and make a good trade There's some bad news.  I followed my plan to the tee and It just didn't come out my way this time But today was the type of trading day that you hope for that every day traders dream or Every day trader hopes for And that is whenever it just runs and runs and runs and runs and runs Yes, I'm am a little disappointed as to the exact outcome I'll let you watch the video here in just a little bit, but I'm not disappointed because I actually Followed through with my plan and that at the end of the day makes me a better day trader guys My trading schedule for the rest of the week.  I'm actually heading out of town To Houston, Texas on Thursday, so I won't be trading Thursday or Friday Friday of course does not necessarily, you know any different but Thursday for sure and in the rest of the week You know tomorrow Tuesday and Wednesday Little on the busy side, but I'll see if I can sit in front of the computer and watch there There is sort of a very clear silver lining if you will on today's trade I Have a pretty good darn accurate Strategy on my hands. I don't know exactly what I'm trying to Insinuate here other than just pointing out It's a fact a working fact. I have a really good Accurate very accurate very consistent strategy It hasn't given me a whole lot of signals the last two weeks three weeks, but I'm still trading in and it's very accurate I'm really happy about this for the first time in a long time I actually feel like I have a good strategy a good plan and I'm following through with that plan and I'm not I'm not losing as much and That's I think that's key. I think that's the goal here's you don't lose and you're gonna eventually start making money. So Not yet. I'm not gonna upscale my trades yet But if I can learn to make even 1% 2% Consistently it doesn't matter game over that that's that's that's everything. That's the whole goal if I can make a dollar By trading a hundred dollars 1% All I need to do is just scale up my trades. It's that simple. It's literally that simple. That's what I'm doing here I've been getting a lot of comments a lot of emails about man, Giovanni, like you're you're barely making any money You know, you should go for bigger gains more profit more green Yes, but no As long as I can stay consistent and make consistent money with five hundred dollars It's game over guys. Then I just need to trade with five thousand dollars fifty thousand dollars five million dollar Five million dollars, okay, maybe that's a stretch But anyways you guys get the point guys, let me show you today's trade. I won't keep you much longer Let me show you today's trade guys. It was an awesome one in my opinion comment down below what you thought about today's trade Let me know what you made money on what you lost money on guys today. Thank you so much for watching. Here's today's trade Good morning, everybody today is March 11 2019. I haven't been trading a whole lot I've been really busy at my day job and just life in general But today I'm here in front of my computer looking for signals Markets are up here. I'm hoping that we get a signal today to the upside. Let's just be patient I hope everybody had an awesome weekend. I know I did and I might not be trading a whole lot this week I have a couple things. I'm traveling towards the end of this week. So we'll see. Thank you so much, everybody Getting a signal here on spy coming in trying to Produce here I'm went ahead and bought I bought for 276 calls that expire in two days about them at $1.20 Maybe a pot too soon. We'll see it. We'll see. Let's not panic just yet It's finally somewhat hitting in our direction here. I'm up just a tad bit. Let's see if we can continue going It's having a really tough time Trying to climb from here. I'm barely in the green Let's just hold off here Here we go. Maybe maybe this is the push. Here we go. Finally Like it. Here we go. That's what we were waiting for Alrighty I sold two at 129 and I'm gonna hold on to the other two here remember I am Being extremely disciplined here if it gets down to a dollar twenty on the option price I'm gonna sell the other two for even money and There's no question of Wondering if I'm gonna win basically is I already have I Went ahead and sold The other two at a dollar twenty the bid got down to a dollar twenty So I couldn't risk actually turning a win into a loss there So and I'm happy with that it could blow up from here might not who knows made my money And I'm good with that if you haven't already subscribed subscribe to my channel here Don't forget to hit that bell notification as well so you get notified the moment that I upload a video and Thank you so much everybody for your love your support. I may not be trading a whole lot the rest of this week I'm traveling towards the end of the week. So I'll keep you guys updated. Thank you everybody so much Have a great day. I almost forgot to show you today's trades guys. I bought four calls at a dollar twenty and I sold two at a dollar twenty nine and the other two at even money for a dollar twenty So there you have it guys. I'm sorry didn't show you that I had to re-record this section. See you guys later


A former military administrator and now a chieftain of the People's Democratic Party in Kagako, the local government area of Kaduna State, Major General Ishaku Uthman has called on the Zamfara State Governor Billo Matawali to arrest and prosecute the alleged bandit's kingpin, Ado Alliro, installed as Sergeant Fulani Yandoto in the state. General Uthman said the immediate suspension of Emeo or Benin Yandoto in Saffi Luka government area was not enough to serve as a deterrent to criminals operating in the state, but the arrest of the real culprit and punishment for his crimes. The Emeo or Benin Yandoto also had on Saturday conferred a chieftain's title on an alleged notorious bandit's kingpin as a way to stop the incessant attacks on the community by bandits.


Hello, welcome to Combined Wing Chun. I'm Sui Fu David. Welcome to Wing Chun Dummy Training Techniques, also called Mu Yan Zhang in Chinese. In this video series, you're going to see 116 Wing Chun Wing Dummy Training Techniques, 116 moves from Yip Man's Wing Dummy Form. I'm going to show you each move step by step with the action cam so you have the best way to learn the Wing Chun Form at home. Make sure you subscribe and click the bell for notifications so you don't miss a single lesson in this series. Here we go. Alright, here's where it gets really interesting. So, application number eight is the Gan Down Style, right, into this one again. So, we were in the Wing Dummy, we ended up here, and then now we go here, and then we do the Cup Style Down Style, right. So, number is eight, nine. Eight, nine, right? So, now what you do this for is you don't, this is, people think, okay, I can do this, here's my Gan Down Style, here's my Cup Style, which is okay, this work with you, but why would I want to go back into the middle when I'm going to meet this punch? I want to stay on the outside. So, if I'm here, why not I just go Gan Down Style and then stay on the outside like that. So, I think the Wing Dummy Form in this is teaching us what to do if we do the Gan Down Style on the wrong side. So, what I mean by that is here, okay, I Gan Down Style on the wrong side, on the inside. The next punch is coming this way, which I don't want to meet, okay. So, whenever we have this thing on the inside, he's on the inside on the inside, the Wing Dummy Form is teaching us to do a Cup Style, to go good back on the outside. So, you Gan Style on the wrong side, immediately you need to Cup Style and come back on the outside. So, we're here, Gan Down Style, Gan Down Style, and then we have our Cup Style, Jam Style. It's a Cup Style, Jam Style. So, basically I'm meeting it on the wrong side, I need the Cup Style and Jam Style and then we have this here as an attack. Okay, so I need to do that. That's because I did it on the wrong side, I have to recover to get back on the outside. So, when you do a Cup Style, it can be a low hit or it can be a high one, in the dummy form it's actually a Jam Style, right. So, that's when you're doing it high. I need to do that and recover back on the outside. Now, also what happens is we can do it low too. So, let's say that I did the O-Bong Style like this, okay. So, remember if you did this one, I did this, but you can also do it this way. Punching Low.  Punching Low and then Gan Style here. So, the same motion, I'm doing a Cup Style, punching this way. Okay, so now I want to get, I don't want to stay here because this hand is here. So, I do a Cup Style and now I'm on the outside again. So, it is high, boom, right. Or it comes in low, now I'm about with the O-Bong Style and then I move it back on the outside. So, at this angle, let's say you're hitting high, boom. I'm open here, so I need to recover. Okay, or if it's low, boom. I'm open here, so I need to recover, so I go. Okay, so that's what the technique number 89 is teaching us how to do. It's what if you gun or you bomb on the inside, how to get back on the outside and get a better position. So, here's the final move in the first section of the Wind W Form. In the advanced Wind W 616 Move Form, it is the Zat Style and the Deng Style. So, Zat Style, Deng Style. Okay, so how to use it is, we ended up with the Cup Style and then the Gan Style, right. So, the next logical attack would be to Zat Style, pull it down and then attack. Okay, so it's very good because this pulls them into your attack, so it doubles the power. If I was just going to push out, that's one force, that's one directory of power. If I pull in, that's another directory of power, another line of force. If I add those two lines of force, it adds the power of a whole system together. And also kind of gives them a little shock. That's what they call the Zat Style. Also destroys the balance. So, that's why from here, the logical thing is to Zat Style and to hit. Because that's the fastest movement from here, Zat Style to hit. So, let's say that the punch is straight. I did the wrong side, right. I did a number nine. And then logically, the next step would be Zat Style and attack. So, in the form, it's a straight palm, but you can alternate anything you want. It could be a punch, it could be a spear, it could be a time zone, all those work. But the point there is that after this one, you have your hand on his arms like this. The next logical thing is to pull it down and punch the palm. So, that is the step number ten application. So, those are the first ten moves in Yip Man's Wing Chun 116 move form. And we showed you the applications of how to recover when you're in a bad position. And interpreting what that Wing Chun wind dummy form is for. So, I hope you enjoyed these lessons. If you like this video, make sure you comment, like and subscribe. And tell your friends, share it with your friends. So, until next time, just keep it David, trade hard and trade safe.


After a year of testing multiple men's shaving blades, the results are no surprise. Hey there, njroot22.com here with a men's shaving blade vlog slash review slash let's cut to the chase and there's nothing to analyze here. Do the blades work and are they affordable? The affordable thing is what got me into this experiment about a year ago just to set the standard here. We don't shave every day like a lot of guys do like just when they have a little bit of stubble and they have a perfect clean shave every day. So this test here and these results may not apply to you. I shave about 100 times a year maybe 110 times a year. I shave maybe twice a week on average and for the last god knows many decades I've been using the whatever the latest Gillette blade was the fusion fusion five and there's so many different fusion power fusion there's all these extra bells and whistles and price points that come along with it and you know obviously there's a power version which I like and I'll explain at the end of the video why the power version is good but it's not for the reason you think. So about a year ago I bought this Bicflex 5 pivot or whatever the heck it's called. I got it at like TJ Maxx at the checkout line you know they get you. It's five blades with a shaving trimmer attachment which I never use so I don't pay for but anyway it was like five bucks for four blades I gave it a shot and the first shave I had was phenomenal it was probably the best shave I had had I can recall in the last 10 or 20 years and I'm like wow I think I found a new shaver. Second shave was okay I don't remember it being as good as the first but then the third shave on this Bicflex 5 I cut my face to pieces I'm like what the f it didn't laugh it was two good shaves before I had to I wouldn't touch the blade again and I'm I just had two good shaves with this blade and I'm almost afraid to try it the third time after what happened when I got my face shredded to pieces so that led me to be curious about all these other blades they had and some of the other brands I've tried just so you know there's the off-brand five blade razors that either Walmart sells or Shoprite or any of these house brands they're all made in China they probably tried to reverse engineer the Gillette blades and they they were okay but then after just a couple shaves again they the first shave is always good but they go quickly downhill and and I don't get a very good shave it doesn't grow in as nice for some reason you could tell the difference it doesn't grow in as nice so I can't I can't really use those anymore so I figured I would try some of these L super duper cheapo Bic make something that has three blades that was okay and it you know you could tell at the second shave it was no good and I even bought this top care really L cheapo blade it disposable completely disposable I have no idea and I even the first shave on this one was a little bit rough but I got a smooth shave but and again it your hair just doesn't grow back your facial hair doesn't grow back as nice so and that's the difference between and here's another one real quick this is a Gillette Mach 3 or something it's a Gillette 3 blade fully disposable you use it a couple times and toss it out and this was okay this this shaved my face okay but the conclusion I have really is Gillette is is the absolute best brand you can buy and I really don't see any difference between the the fusion five blades there's power there's lou comfort strip I can't even remember all the brands I'll throw some graphics up here to show you that there's so many different brands and they have a new one now I believe it's relatively new it's called the Gillette 5 doesn't have any fancy bells and whistles it doesn't have a trimmer on it it's just five blades and this thing works what works best about the Gillette blades and oh and another reason I wanted to find blades was because the Gillette prices keep going up and up but they're getting to be like 40 bucks for or 50 bucks for an eight pack at some regular stores so and that that's getting up there in price but the benefit of having Gillette blades is they last a long time I can get at least a month's worth of shaves out of one Gillette blade and what's good about it is and I usually wear by change the blade when that lubra strip wears out that's sort of an indicator that it's time to change the blade but as the blade gets you know worn out I don't get cut with the Gillette 5 blade options I never get cut you can just tell the shave isn't as good it takes a little longer to shave you have to go over some spots two or three times to get a nice smooth shave but I even if the blade is really getting old and really at its last legs I never get cut with a Gillette blade for some reason the way it wears out it doesn't get jagged edges or it's just you just know that the shaving isn't as good and it's time to replace the blade whereas these other ones it's like roulette you know this even though this Gillette I'm sorry BIC flex 5 was only used twice it ripped my face to shreds and so the fact that I had to throw it out after two shaves in these whatever Walmart brand and only last two or three shaves before it's just a bad shave it tells you something that your cost per shave is actually probably better on the Gillettes and I've tried dollar shave club that just wasn't a good shave from the get go I don't know what the hype is they just use psychological conditioning and I've heard from other people that like Kirkland makes a good shave but I'm willing to to guess that after a couple shaves it's not going to be good whereas the Gillette holds its stamina for a long time so I don't recommend any other brands if you're going to be on vacation you just want to have a throwaway razor you don't feel like losing the handle I guess the Gillette three blade ones are fine or if you could find the BIC flex 5 at a discount store it is pretty cheap and it has a nice handle and that's one last thing that I guess differentiates a lot of these shavers for most people is the handle you want a good balance because you know having a good balance tool is plays a role in how good you shave if you have a rickety flimsy thing you may end up not holding it right and you can cut yourself just because you don't have a good grip on the razor so that's it I'm sticking with Gillette I may have to buy in bulk or or find a discount sale or coupons or you know on amazon they have these and I think they might be knockoffs but they're hard to tell that they're knockoffs you can often get them in plain brown packaging and they're and they're pretty good I usually have to buy you know I think it came out to about eight razors a year so and that's a little more than a month I get per blade but that's it Gillette is our winner still and until something changes we're not we're not going to fuss around with these cheaper versions of shavers you want to like I said when I bring one on vacation throw it out you know I really still like the first shave on this big I have two new blades left and if you ever want a nice shave it's great the first one but that's it thanks and have a good week


We have strategic missions where we show that the 3rd infantry division can actually support our greater NATO allies inside of Europe. Right now we're moving all this equipment, both wheels and tracks, moving to Port of Savannah, getting them on the vessels. Once we make the movement to the European theater, we're going to download them there. At that point in time, we're going to make a massive convoy movement to show our strategic readiness and combat readiness to our allies of how we move across from one port to a staging area. This training remains significant and sophisticated. There's no substitute for this type of training. You cannot do it digitally. You cannot telecommute to battle. We need the forces on the ground to guarantee our combat readiness. The reception and facilitation of the onward movement of our forces requires the support and cooperation of tens of thousands of service members and civilians from across the alliance. If the time ever comes, we will be ready to meet the call. This training demonstrates our ability to operate as part of a multinational interoperable team, conducting one of the most complex maneuvers that any army can attempt and oppose river crossing. With our Polish allies side by side, we bring the best of our armies together to accomplish this mission. The capabilities of the 2nd Armor Brigade Combat Team and the Combat Aviation Brigade of the 3rd Infantry Division and the Polish 12th Mechanized Brigade greatly complement one another. More than 3,500 service members from 8 nations converged on the Joint Multinational Readiness Center's Hohenfels Training Area this September for Combined Resolve 14. Now in its 8th year, Combined Resolve is a buying home multinational exercise that tests U.S. Army units and strengthens international interoperability.  Over the last 10 months, the Brigade excelled in supporting a very unique opportunity to execute a strategic mission within the UCOM area of responsibility, successfully assuring our European partners and allies while adding the deterrence of regional aggression. Selecting for this difficult mission was both humbling and rewarding as we excelled in representing this forward division, the United States Army, and our great nation.


In this course, we have discussed about various technologies which provides more functional information, more protein-protein interaction and other biomolecular interactions which gives us the functional consequence of the proteins. We talked to you about different type of technology platforms including protein microarrays, label-free biosensors and even next generation sequencing and mass spectrometry, very free. The idea is that finally all of this data which we generate from the wet lab experiments has to be made available to the public and likewise the whole research community should start sharing the data. And as you have seen in the last few lectures, we are giving you the glimpse and idea that how you can start using the public databases and various resources from which you can download data and now you can use open-source softwares to analyze the data set. In the field of cancer research and especially many clinical research, a lot of data being generated which is very costly and very precious. The large funded program from different governments which aims to investigate thousands of patient sample analysis with a new field emerging which is known as proteogenomics where aim is to look at from the same patient sample can be analyzed both proteins and gene level. And if you could do integrate this information, then probably we are able to get more meaningful information from the same sample type. In today's lecture, one of my research scholar Mr. Deepthru is going to show you how proteogenomics by integrating data from the genome and proteome could provide us some novel insight from the literature reviews of published data sets. He will also talk about how proteogenomic approaches can help to resolve various issues of diagnosing different grades of cancer or looking at different subtype of cancer which is very difficult to understand without having a very good molecular level understanding. He will also explain how proteomics and genomic data correlation can provide a much broader and meaningful picture of progression of cancer. The different accident program funded by National Cancer Institute in US from NIH have really accelerated the whole field of proteogenomics research. Scientists are sharing data obtained from tissue microarrays, protein microarray platforms, next generation sequencer and mass spectrometry and making it publicly available. The cancer genometallus or TCGA is an excellent resource for you to get it started and do more investigation further from the raw data which you can obtain from this portal. And likewise now CPTAC and other ambitious programs are available from which one could accelerate different type of proteogenomics research. Today Deep will talk to you about the workflows of some of the cases studies which were recently published using the workflow of proteogenomics in the area of cancer. So, let me welcome Deep Dhruv for his today's lecture. After the completion of human genome project and introduction of genomics into the disease pathobiology, there was a hope that genomics can lead to can bring revolutionary change in the cancer diagnosis and can lead to a path to personalized medicine. But the success of personalized medicine with the help of genomics was not that much revolutionary. From overall cohort of patients only few patients were respond to the predicted therapy based on the genomic profile. There were some loopholes that were still present after the successful outcome of genomics. So, recent paper published from Zang group there is a clinical potential of mass spectrometry based proteogenomics. So, in this paper he has talked how the clinical potential of the mass spectrometry based proteogenomics can be introduced. The personalized medicine with the help of genomics was not that much successful due to number of reasons. If we can see that with the help of genomics solving the problem like cancer is like jumping from one hurdle to the last hurdle and we are not taking into account a number of conditions and parameters that is coming in between the two hurdles. So, we are getting a complete profile of the genomics different types of mutations, different aberrations, but in the same hand we are missing different epigenetic aberrations, transcriptional regulations, alternative splicens and protein proteomics profiling. So, all these important information need to be taken into account to understand the pathobiology of the cancer and then only this tool can be used for the diagnosis and treatment. So, the message from this slide is that all this information starting from DNA to mRNA to protein need to be considered to reach to the goal and to diagnose to bring a revolutionary change in the cancer and a cancer diagnosis and treatment.  So, cancer driver gene is defined as one whose mutation increase net cell growth. The total number of driver gene is unknown, but we assume that is considerably less than 19,000 which has been given by Tokeima et al in 2016. So, from driver DV repository you can see like the top driver genes includes TP53, EGFR, P10 and how this hallmark driver genes are important in the glioblastoma in the glioblastoma tumor genesis we all know. So, here is the mutation profiles of those driver genes where the top driver genes are P10, TP53, EGFR and we can see the mutational profile in terms of samples which is in the x axis. So, if we choose one of the top three cancer driver genes that may be EGFR and we can understand that what is the expression of this EGFR gene in glioblastoma. So, we found that the expression of the EGFR gene in glioblastoma is pretty high. So, one of the top three cancer driver gene in glioblastoma is EGFR and if we want to check the expression of EGFR in terms of in taking into account the other cancer we found in case of GBM EGFR is highly overexpressed in both primary solid tumor and recurrent solid tumor. So, till now the genomics has given a lot of information about glioblastoma, but if we taken into account the correlation between the exon and protein we will found that the driver's core related to protein and exon is also giving some new information. This panel is to display that driver's core distribution of exon and protein position which help researchers quickly find the region of the gene with abundant deletorious mutations. So, now we understand that we did not consider a lot of things between the genomics and the precision medicine that not all mutated genes are stably expressed as proteins and genes that are expressed can be post-translationally modified. Therefore, precision medicine that relies solely on genomic based assay will exclude a lot of potentially relevant information like MI RNA, micro RNA. So, to support the previous statements and to give you a complete glimpse how the powerful tool of proteogenomics can be very helpful to solve different kinds of cancer. So, in this study they have taken 169 ovarian tumor samples from TCGA metadata and they have they tried to analyze rather co-relate the genomics, transcriptomics, proteomics and phosphoproteomics. So, before going into the paper let me give a glimpse of this kind of mutation and how this mutation can lead to lethality of a cell. So, the diagram has been taken from Walsh et al 2015 where we can see the functioning of PARP enzyme and how PARP enzyme is helping in DNA repair of single-strand DNA break. If PARP enzyme is inhibited, so there is no repair takes place and which helps which rather lead to collapse replication fork and the BRCA deficiency do not allow homologous recombination to happen. In C, the deficiency in the HR homologous recombination and base extension repair together lead to synthetic lethality than the co-relation. So, the sample information tumors were selected by examining the associated TCGA metadata to select tumors. On the basis of putative homologous recombination deficiency, presence of germline or somatic BRCA1 or BRCA2 mutations, BRCA1 promoter methylation or homozygous deletion of p-tens were taken. So, this clustering will is giving us the complete landscape of what are the different pathways are involved and how protein and mRNA are playing role and what is the correlation between the protein and mRNA in this pathway. So, till now we understand that the protein and mRNA correlation is there and how this protein and mRNA correlation is also playing a role in terms of biological pathway, but now they also try to understand that how CNA that is copy number aberration in each tumor is playing a role with protein and mRNA correlation. The blue one are the complete profile of the data generated whereas, the black one is the data that is present that is already present in the database. So, from the CNA mRNA correlation and CNA protein correlation they found that 2 important 2 important protein that is CHD4 and CHD5 are having the maximum number of CNAs. So, when they further studied they found that these 2 proteins are involved in chromatic organization. So, to understand the complete biological pathway they took phospho peptides, proteins, transcripts and CNA and they found that these are the top pathways that is playing a role in this cancer pathobiology. So, out of which PDGFR beta which we all know is a angiogenic receptor is also showing an important correlation in terms of biological pathway. To understand the complete landscape of the cancer pathobiology they incorporated mRNA protein and phospho peptide data into one picture and where we can we see that a PGA for beta is upregulated in both mRNA and protein. So, this upregulation of the PGA for beta is not only giving a clue to a active angiogenesis, but also showing that what are the different downstream regulatory factors that are also up regulating or down regulating in terms of mRNA and protein. So, further they tried to do a DDN analysis. So, DDN analysis is differentially dependency network analysis where the proteins curated from the literature and from the C-bio portal. So, C-bio portal helps helps you to get the data out from the TCGA and they identified a subnetwork of 30 protein that displayed co-expression pattern differentiating from HRD from non-HRD patient. And from this DDN analysis they found that histone acetylation or deacetylation proteins are coming are playing are coming into the clusters and which includes HDAC1, RBP4, RBP7, EP300 and HUS1. So, from the last part of the study they understand that histone acetylation and deacetylation are playing an important role. So, this clue was enough to give an idea that acetylated peptides need to be studies. So, from the global proteome data they prepare acetylated peptide database search strategy and identify and quantify the acetylated peptides. From there they identified around 399 acetylated peptides and 50 acetylated significant peptide between HRD and non-HRD. So, as so from this 15 acetylated significant peptide they found that K12 and K16 that is acetylation of lysine in 12 and 16 were found. So, they validated the K12 and K16 using synthetic peptide and targeted analysis using SWAT MS. In the same thing they found that the K12 in terms of ITRAC data were upregulated in HRD negative and same thing has been validated in SWAT and they found the same upregulation in HRD negative. So, they went back and further search in the literature and they found that the acetylation of the H4 has previously reported to be involved in the choice of DNA double stand break DSB repair pathway. The relationship is regulated partially by HDAQ1 a protein also identified in DDN analysis. The potential role of HDAQ in modulating the choice of DSB repair pathway has been identified. So, the conclusion from the study we understand that the activation of PDGFR pathway in patient could potentially stratify selective enrollment in trial of anti angiogenic therapy. Recombinant human monoclonal antibody that blocks the angiogenesis by inhibiting VGFA has already been trial in patients. So, the PDGFR pathway the involvement of PDGFR pathway in this cancer is also giving this recombinant humanized monoclonal antibody role in limelight. Apart from this HRD acetylation K12 and K16 on histone H4 may provide an alternative biomarker of HRD. A rational for this selection of patient in future clinical trials of HDAC inhibitors alone or in combination with PRP inhibition can be also tried. So, the moral from the study we understand the ability of proteomics to complement genomics is providing additional insights into the pathway and processes that drives ovarian cancer biology. Not only the complete data which we are getting from the genomics is not enough to lead to a well-profiled diagnosis and treatment of cancer. So, all the important things like mRNA information, protein information and PTMs the post translational modification information need to be gathered and further correlated among themselves and then only we can reach to a conclusion and we can take this information and further validated in clinical trials. So, now we understand that how cancer driver mutation mRNA protein need to be taken into account to reach to the molecular target or cancer drug. From the last study we understand that how the group has only generated the proteomics data and they have tried to correlate the they are proteomics data with the already available mRNA, CNA data from the databases. So, firehouse can be used to download this kind of data like if we select a disease name that may be glioblastoma multi-form and we can see like all the data which are available in the TCGA can be downloaded from here. So, TCGA data version from 2016 from glioblastoma, clinical SNPs, methylation and mRNA sequencing data and reverse phase protein array data are already available. So, we can use this firehouse to download the data. So, now we are able to understand how proteogenomics and correlation of mRNA and protein can give us better insights of a particular disease, but to deal with this amount of big data prepare a panel which can help in the treatment or diagnosis of cancer. We need to think about different predictive and machine learning based analysis. I have taken an example of a paper a neural network approach to multi biomarker panel discovery by high throughput plasma proteomics profiling of breast cancer. Where in study and study we 40 cancer types and 40 controls were taken whereas in study C 20 cancer types and 20 controls were taken. Further they have done the proteomic analysis and they found that 246 proteins are common between 3 studies. After this analysis they have taken the data and tried to prepare a artificial neural networking model taking study A as a training set, study B as a testing set and study C for validation. So, in this kind of artificial neural networking in most of the cases for the training set maximum that means around 70 percent or more data need to be taken whereas for study B 30 percent data need to be taken. The model further validated with blind data set to check the efficiency of the model. In most of the cases the accuracy of the model need to be more than 80 or 85 percent. So, this artificial neural networking gives a panel base 3 panels with 5 markers and with the accuracy more than 85 percent. So, further this panels were taken forward and checked in large cohort of samples to validate the data. So, like this we can use artificial neural networking and different machine learning strategies to understand and predict top candidates that are playing key role in tumorogenesis and further development of the cancer. So, the main concept is the different protein understand the complete pathobiology and then only the landscape of disease can be drawn and from there we can understand and we can and that can lead to a drug target or precision medicine. So, by now you know that there is a huge amount of data that is available in the public repositories and databases which could be utilized and extracted for the further data analysis. The big research programs like Human Protein Atlas, the Cancer Genome Atlas or TCGA as well as different laboratories working worldwide including broad institute of Harvard and MIT have shared the data into various databases. All of these researchers and scientists are making their data publicly available. More recently now the Cancer Mone Short Project and International Cancer Proteogenome Consortium also aims to investigate the proteogenomic data from the same patients and intention is to make it publicly available and share with the entire scientific community. All you have to do is track the data, perform different type of analysis and then make meaningful insight. You can always define a unique question from the same sample and look at what is the best answer from the large number of sample data sets available to you. You can also compare the data from different laboratories or even integrate data obtained from different population. Look at the effect of the same disease in different geographical locations, different races, different age groups as well as the effect of different treatment or certain diseases which may have you know the recurrence nature. Many of these things could be investigated from these kind of publicly available data set. I hope these manuscript which we have discussed today have given you a very impressive glimpse of how genomics, transcriptomics, proteomics and sometimes even metabolomics together could provide you the much in depth information at the cellular level which was otherwise not possible few years ago. I hope you will be able to use some of these technologies and some of these data sets in your own research. In the next lecture, I will talk to you more about the various revolution which are happening in the field of omics in general of course more in the interactomics and proteomics and try to give you much more sense about what is exactly happening in this whole field which is really remarkable and revolutionary nature. Thank you.


Hello everyone, welcome to theCUBE's presentation of the AWS Financial Services Partner Series. I'm your host, John Furrier, here in theCUBE Studios in Palo Alto. And today we're excited to have Nicole McQueen, who is the head of Global Technology Partnerships with Capital Markets for AWS and Chris Alicenia, Director of Business Development at FACTSET here to talk about the partnership between FACTSET and AWS Financial Services Team. Folks, thanks for coming on theCUBE today. Appreciate it. Thank you for being here. Thanks for having us.  I mean, pioneering data, data availability for acquisition of knowledge, all kinds of high-frequency trade, we know what's going on there on the cutting edge, but a lot's going on now and the role of data is more important than ever before. So this is a great conversation, looking forward to it. Let's set the table. The industry trends right now in the capital markets of data and data management is evolving very quickly. How do you guys see this industry market changing, evolving, what's this wave look like? Sure. Yeah, I can take that one. John, thanks for that. So for us, look, over the last decade, the velocity of change impacting financial services has been truly unprecedented, right? Rapidly evolving customer expectations, new industry players, powerful emerging technologies, new regulations have all really converged to form a new landscape. In addition, the COVID-19 pandemic really accelerated digital transformation as financial institutions responded to the whole new remote everything world. What we're seeing in capital markets is that to remain competitive, firms are mining both new and existing sources of information and engaging in transformation initiatives really targeted at lowering their cost structures. At the same time, they must comply with increasingly rigorous and complex regulatory standards in all of the countries in which they operate. AWS and our community of partners are helping capital markets firms power data-driven decision-making, accelerate their time to market, lower costs, strengthen security, and comply with regulations all while really striving to enrich our customer's experience. Chris, how do you see that fact said? What's your view? Yeah, I mean, for all the reasons Nicole described so well, the overarching trend we're seeing is the capital market firms are looking to forge a deeper relationship with fewer partners. I spend almost all my time at FactSet meeting with our clients, talking about their target operating model and where they're going, and this is where what I hear over and over again. As part of this consolidation and this deepening of relationships, those capital markets firms expect their partners to collaborate together on their behalf instead of dumping the burden of integration back on their firm. This is as true, if not more true, for data warehousing and data management than it is for any other critical component of the firm's target operating model. That's awesome. As a critical source of data and analytics to capital markets firms worldwide, AWS becomes a more important partner for FactSet every single day. I'm thrilled to be here today and excited to talk about more about why. I love it and appreciate that. And I think one of the things that's clear in the macro industry is speed, velocity, agility, time to insights, the competitive edge is really what people do and they want to do it fast. So I have to ask you guys, as you look at the cloud trends and some of the challenges you guys see with customers and turn them into opportunities, what are your customers focused on now? Is it integration? Is it the data management? Is it the cloud scale? How big of a role does data play in how your customers and these firms are thinking about how they're going to innovate for their customers and get that competitive advantage and even provide personalization experiences around that. So a lot to going on there with the customer as they start to operationalize and set their operating models for the future. Yeah. So John, I think to say data is foundational almost feels trite. It almost doesn't do it justice. But let's talk a little bit about the cloud part of it, right? The critical benefit of the cloud that these capital markets firms have never experienced before is the infinite elasticity of compute and storage. You're no longer constrained. If you know how much data you need to store and how much you want to compute, you want to do on it, the web, I'm sorry, the cloud, AWS gives you that ability to pivot quickly and react almost instantaneously, which was frankly never possible before. It's all about bringing them the data and the analytics into the data warehouse container or cloud solution that the client wants. This is increasingly kind of a hybrid strategy for the capital market firm as they're looking to deploy AWS and their cloud strategy more alongside traditional data warehousing solutions. So the mix is a little bit different from firm to firm, but everyone is challenging themselves with this question. And as you think about the new data that's coming into your workflows and the new needs you have as an organization, maybe around Gen AI, maybe around another product or another solution that you have to bring forth, you think about where does that data best fit? Is it in my cloud data warehouse? Is it in my traditional data warehouse? You know, what's sort of the hybrid solution there? FACSET is all about feeding than that hybrid solution with the data that's going to increase your productivity, increase your agility, and increase your efficiency at the end of the day. So when we think about sort of, what are the data that we're talking about there? Because, you know, again, you solve the problem or you think about the problem you wanna solve and then you immediately have to go back to what data do I need to solve that problem? From FACSET's perspective, it starts with our own source data. So there's about 30 foundational data sets inside of FACSET, like company fundamentals, estimates, ownership, macroeconomic data, that I would say kind of your core foundational data sets, but then very quickly, you move into more specialized data sets, like supply chain, geographic revenues, exposures, street account news, and corporate governance data like shark repellent that are critical to solving the problems you wanna solve. But then beyond the data that's in FACSET, because FACSET is so open, there are over 1,000 different third-party data partners across a really broad range of categories, whether it's indices, risk models, ratings. We think about combining FACSET data with third-party data in a complementary way to maximize the scope of the solution that we can bring to our capital markets partners and help them provide the most precise answers with the absolute best data in terms of what they're doing. The third leg of this beyond the FACSET data and the third-party data is all around portfolio analytics, right? Moving from the companies, the markets, the industries to your own funds, separately managed accounts, your portfolios, really any of your investment vehicles. How do we get the analytics that are around performance, risk, characteristics, reporting, peer comparisons? How do we bring that together into the same data set for fundamentals, for companies, for economics? It's all part of the same cloud data warehouse and available at your fingertips the same way. And then last but of course not least is the pricing data. For some users, that pricing data is real-time pricing ticking in on the fly, immediate and urgent. For others, it's extensive deep history of tick data to do some pretty robust and some pretty ambitious quantitative analysis. But ultimately this all has to blend seamlessly and it tailors into the S3 data warehouse to what the client wants to use and what the client's needs are. And that's really what we think of as FACSET data as a service. So, Nicole, you got to love the velocity of the data right now. FACSET's data rich, they got customers that have data that comes together, the data as a service. Why build something if you can just call a data API in the future? I mean, you guys have API with the cloud, Amazon's pioneered that model, now at scale comes the data.  Yeah, I know, absolutely, John. Look, I think there's a lot of different ways we can take this as far as velocity, access to data, migrations to the cloud, what are financial services firms really focusing on? Right, when there's frankly so much to do. On the topic of data specifically, the explosion of data and the ability to store and analyze it cost effectively in the cloud has had a major impact on our industry. This has led financial services companies, frankly of all sizes, to explore the unlimited possibilities for using data to innovate and improve their businesses and their business outcomes. You know, I think the thing about the cloud, going to the next gen, FACSET has all this data, clients going to want the data. This is an example of where the future is going. Data as a service combined with a hybrid, you mentioned hybrid models, also key. I got to ask how cloud enables Chris FACSET to increase the efficiency and become more agile because I mean, a couple of dots are connecting, right? You see the data growth. I mean, budgets aren't increasing as fast as data is, but new development capabilities, new data advantages, mixing data. There's a lot of alchemy involved. How are you seeing the cloud specifically enable FACSET to increase the value efficiency and become more agile for your customers? All right, John. I mean that answer really starts back with our clients. So our clients have to be more agile. They have to be more dynamic than they've ever been before in the past. That's just a requirement for them to be successful and to not to survive, but to thrive going forward. So if we're going to be a trusted partner for that client, we can have a process that's arduous and littered with bottlenecks for them. Our partnership with AWS allows us to be more responsive than we frankly never thought we could have been in the past. Even that, when I say the past, I'm even talking 18, 24 months ago as what feels like the distant past. We can collaborate with the client in AWS sort of a tri-party conversation to solve their needs so much more quickly than was possible and really get them solutions running and up and running and evolving more important than just up and running, but evolving very quickly. And as you think about sort of what are the needs of GenAI and client solution workflows, I personally don't know that we have all of the answers today but we have a great platform to evolve quickly to meet those rapidly evolving needs. Nicole, what's your reaction to that? I mean, obviously they're moving in the cloud direction to get arduous tasks. Andy Jassy says heavy lifting, more time for creativity can emerge when you can automate a way and use AI and data effectively. Absolutely, so look, an example of how we're enabling our customers to increase efficiency and agility is accelerating speed to market. Financial services institutions can go from an idea to implementation quickly using our comprehensive portfolio of cloud services and purpose-built solutions from our industry partners such as FACSET. This accelerates innovation across the front, middle and back office. AWS offers a broad and deep set of over 200 services that financial services firms can use to experiment and create new cloud-native applications from infrastructure technologies like compute storage and databases to emerging technology such as the AI and machine learning technologies that we're discussing here to data lakes and analytics. We're continually accelerating our pace of innovation to deliver new technologies to help financial services customers transform their businesses. From a partnerships perspective, many of our partners offer their software services and solutions through AWS Marketplace, for example, where it's easy to find, buy and deploy software solutions in a matter of minutes. Our customers can also access over 1300 data sets and APIs relevant to the industry through AWS Data Exchange, right? So that has everything from insurance claims to ESG data to cryptocurrency data, all easily accessible through a single cloud interface. So an example of this actually is a customer of ours, Goldman Sachs, who has publicly highlighted AWS Data Exchange and AWS Service that FACSET is leveraging for data distribution today as a key component of their financial cloud strategy as it reduces that friction in sourcing financial data from both new and existing third-party providers, enabling them to ingest and process third-party data much more efficiently, cutting down that time spent on ETL and accelerating their time to value and insights. You know, it's so exciting, Nicole and Chris is the cube when we started 13 years ago, we were really evangelizing it. Big data is going to be big, it's huge. It's the new oil, remember those days back when big data was hot. It's kind of now 10 years later, a decade later was seeing the transformation really at such a tremendous scale. Mainly because the cloud, the AI stuff is hitting at the right time, the combination of the more compute, I love that compute and example, Chris, we had someone on the cube say, compute should be oxygen, it should be free. I'm sure Amazon would be like, okay, it's somewhat free. It gets freer as it gets cheaper, but you start to see real velocity, charatical changes. I got to ask you, Chris, at FACSET, because again, you guys have been grounded in data from your entire business models based upon it. You've got great clients, they've got applications, they've got needs. How important is the data management piece of this for the capital markets? It's evolved from the old school data warehouse days to get some queries in there, slow, now you've got data in the cloud, now you have cloud next generation where you have applications that are going to be impact data as code, data marketplaces. I mean, the creativity and the development of markets is going to change, so I have to ask you, as you look at the next generation, how important is the data management today, and what is the future with Genervai? Because Genervai is going to generate more data, it's going to generate more things. Take us through your view and vision. Yeah, so, so John, that's a great question. I would, I certainly wouldn't say that data management was ever easy, but let's specifically hone in on why it's getting harder every day and it's going to be harder in the future. That starts with the asset classes that capital markets firms are investing in. Not that long ago, the asset owners of the world were in classic 60% equity, 40% fixed income allocations. Now we're seeing the growth of private capital skyrocket and it might be 40% public equity, 40% private capital and 20% fixed income, but if you think about the growth of private capital, it puts the burden of data management so much more on the client. There is less data, data is not naturally concorded and aligned and it isn't even about sort of the same entities when you sort of think about real estate versus even companies, right, public and private companies. It's a different animal. How do you bring that data together? On top of that, the data that you need isn't structured the way that it has always been structured in the past. There's a much greater need for unstructured data both on the private company, the private assets, as well as bringing in unstructured data on the public assets. It's just increasing the amount and the nuance and the complexity of the data that's coming in and then your ability and your need to concord it is more important than ever because if it's sitting in a data silo and it's monolithic and it can't be combined with other data sets, then you're not going to get the best answer when you ask that question whether it's in Gen AI or whatever model you're in. It has to be perfectly blended together to maximize the quality of your analytic conclusion. That's always been true, but that's just a much greater burden than it was in the past. Nicole, the scale what he just said is amazing because that's really the core challenge and the opportunity if you get that right, there's a lot of things going into it. It's almost a new workflow, a new methodology. And so you're going to need more resources. You can't hire more PhDs. This is where the cloud comes in. This is where you guys have a lot of focus. Take it through that piece because this is, again, game changing. This moves the needle if you get it right. Absolutely. Look, I completely agree with Chris and you, John, that data management, it can seem daunting, it's enormous, it's huge, it's exponentially challenging and growing. And it's even more so, frankly, when you're trying to kind of break down and deal with huge, complex legacy infrastructures that companies have built over decades, right? It's a very daunting task, but the growth in technical users, the need to evaluate new data sets quickly and the ever-growing data available require companies to seek out better solutions. And we are here to support our partners and our customers in achieving that. It's really about harnessing data and using analytics and machine learning to remove that friction, right? And improve the experiences for our partners and our mutual end customers. That's awesome, and this is a great conversation. And again, by the way, we're on the cutting edge of going to the next level. We're already kind of here. You guys have to address this at FactSet because you guys have a lot of data. And again, I've been saying on theCUBE, if you have data right now, if you've done some good data hygiene, you're going to be have a tailwind with AI, Genevieve AI, and others. So congratulations, FactSet, and the others on this partnership. I see great practicality to it, as well as setting the table to take advantage of the growth. Final question on this is that as people look at the data, you're starting to see a power law. Chris, you mentioned specialty models. You're going to start to see data sets. Maybe why should I reinvent the wheel? I'll just call another data set. If it's got high-quality data, it could be smaller and more acute, more better. You're starting to see kind of this mindset of data as code. What data sets, I mean data sets are valuable. Are you guys thinking about making this more available? How should, what are customers seeing today from you guys on the data sets they have and the service you have, the marketplace? Give us an order of magnitude scope of what's the data sets look like? Are they available? Can they be developed on? What's the, take us through that piece of it. It's now an important piece of it. Yeah, it's really important, John. And just to back up a second on one of the things that you said before, when you talked about data hygiene, every element of your data hygiene that is sloppy, is imperfect, think of that as a crack in the foundation. And if you have too many cracks in the foundation, what you're trying to do is going to come down on you like a house of cards. So it's getting that data right and cleaning up those cracks and having as perfect hygiene as you can have to minimize the risks and to minimize the fallout. But then on top of that, how do you bring new data sets? How do you bring any type of data really into that environment elegantly and as easily as possible? So from a fax at an AWS perspective, we put 90 of our financial and alternative data sets sort of spoon fed them for immediate, almost instantaneous access through AWS. Those are the things that are really standardized. Now, you build on top of that with the analytics that aren't standardized. So when we started talking about performance attribution on portfolios, stress tests on risk for portfolios versus a range of different stress test type events, there's a degree of customization in that. That's not instantaneous, but that can happen in hours and days as opposed to months and quarters, right? So that's really the process and the workflow that we built up that allows you to transform this data and derive value much faster than you've ever been able to before. You know, it's Chris that's interesting you mentioned on hygiene, I think it was going to be around for a while. It's going to be a conversation that's going to change. But it's interesting. If you have these data sets available, think about what you could do, right? You could start thinking about things differently. You can bring in an element and bring in other external data and actually look at the role of that data point in a product, in an analysis, in an insight. You get better visibility faster. I mean, this is a new AI kind of benefit, Nicole. This is kind of what we're seeing. No, for sure. For sure. Yeah, no, totally plus one. Yeah. You know, I think, look, from an AWS perspective, you know, our innovative data and AI and machine learning services right there, the goal of those is to allow financial services institutions really to automate and optimize processes and capture data more quickly and more efficiently to your point exactly. You know, make it more usable faster. Yeah. Well, I really appreciate this conversation. It's on the cutting edge, it's relevant, it's cool, but it's really practical. You got a great example here with fact set. Thanks, Chris, for taking the time. Nicole, this is a perfect storm of innovation and opportunity with data as the script flips, but the faster the time to value. While we wrap up here, take this last minute to share what's going on. And fact set, is everything here ready to go? Put the plug in. Yeah, John, I mean, the way that we think about these questions as they're coming in from our clients is we really look at it. I listen to the question and as I'm processing the question, I'm thinking in the back of my head, what data do I need to drive the best possible answer to this question? Everything that we're talking about today is real. It's in production, it's ready to go. We're doing this for mutual clients of fact set in AWS and we want to get started. Well, I appreciate the honor to host this conversation as we look for highlights in the industry where you see data rich, data advantage, kind of be a tailwind into the growth. And AI and good data makes things better. And then as new things emerge, net new competitive advantages, new solutions for customers emerge and new opportunities come out of it. So it's a real example, Nicole and Chris, thanks for sharing your thoughts and commentary here in theCUBE, really appreciate it. Thank you for having us. Yeah, this is great. Thanks, thanks for having us, John. This is theCUBE here in Palo Alto, bringing in the folks and talk about the financial services industry and the partner series with AWS fact set and it was together, making things a reality now and today shipping, thanks for watching and we'll be back with more after this break.


My name is Sally Halper. I'm the head of content strategy and services here at the British Library. My job involves leading the development of the library's contemporary content strategy, which covers everything that's published since 1945. My local library was in Leeds. It was the old branch library for Headingley, which was called Bennett Road Library, and it was amazing. So in my early days working at the British Library, I helped to answer an inquiry about chemical formulae for ancient perfumes for a gentleman who was looking to restart his grandfather's business, which had been a big manufacturer of perfumes here in the UK. For me, it's the joy of helping people to find answers to their questions, not just giving them the answer, but enabling them to find the information for themselves. Working directly with the public and also getting to the end of that journey with them and seeing how pleased they are with the end result that they can achieve.