Hello everybody, thanks for the invitation to speak here, the background's been set up perfectly for me by Yeejang to talk about epilepsy and by my time to talk about connectomics. So I'll skip most of this except just to say that hopefully you're convinced by now that epilepsy is a real societal problem. There's a lot of patience with epilepsy and it's a major economic burden amongst other reasons why it's a bad thing. So focal seizures, a particular type of seizures, focal epilepsy is a particular type of epilepsy and patients with focal epilepsy have seizures which are essentially mainly constrained to just a part of the brain, just an epileptic focus and the longstanding thought was that this is caused by focal abnormalities to the brain tissue and in many cases this is the truth so it can be caused by a brain tumor for example just a focal part of the brain, abnormal tissue and this can lead to focal seizures that are spatially constrained in their onset. However recent literature has suggested that it's actually a bit more complicated than this and that epilepsy is an emergent phenomenon of abnormal networks which are more widespread than just the observed seizure focus which is spatially constrained and so there's been lots of literature lately saying that these abnormal networks can be what's leading to the seizures. So in this talk I'm going to talk about structural networks and in particular structural networks inferred by diffusion tensor imaging. So quick summary of what I mean by this. So what we can do is we can infer network nodes using a passillation scheme here and that can be obtained from passillation of the structural T1 image and we can also infer connections between those nodes using diffusion tractography for a diffusion image. These two can be registered to the same space and then connections between one node and another can be inferred and we can generate a connection matrix something like this where we have nodes and connections between them and these can be represented in a matrix. One thing you'll notice is that not all nodes are the same so here's the superior frontal gyrus so this is a very large node and at the front here you've got a temporal pole which is one of the smallest nodes and so to make fair comparisons it's not quite so easy. So we have different nodal properties and we have also different edge properties so connections so there's different ways in which we can define the edge in the network or the connection network. This can be done by a connection weight which is kind of normalising between the density of streamlines which takes into account the node size and the number of streamlines connecting or we can just use the number of streamlines which is done in a lot of studies or the length of the streamlines. There's any number of ways that you can use to define your edge strength if you like and what a lot of studies do is they take this weighted connection matrix they set a threshold and then binarise it but all of these lie around the assumption that edge weight or strength or a weighted measure is representative of what's underneath it what's going on and as you saw in the previous slide there's many studies have shown that there's alterations in these edge strengths or these connectivities between patients and controls and my question here that I'm trying to investigate is what's driving this is it alterations in the gray matter node size or is it alterations in this white matter connectivity or is it a combination of both so just as I've said that the all nodes are not equal somewhere bigger than others all nodes that are the the same node in different subjects can also be different so the same node can be bigger in one subject than another and this can also influence the connectivity so I'm interested in finding out what's driving these network changes that have been found in the literature so we begin by just looking at the the node size so in this case we've looked at the surface area of each of the 82 nodes in this case picked a widely used passillation scheme that desiccant kill any free surfer a passillation scheme and we've looked at 22 patients with temporal lobe epilepsy or focal epilepsy taught to be constrained to the left temporal lobe and 39 age and gender matched controls and we essentially just plot the t statistics or the afar apart these two distributions are for each of the different regions and we see widespread atrophy in pretty much the whole brain and this is because patients have smaller brains than controls in terms of the total surface area this has been reported before in literature this is not particularly new this is we've known this since probably the late 80s when early MRI studies were done in this but it's just important to to highlight this the fact that there are differences in nodal properties in patients that may be driving the connectivity changes that seen in previous studies so what about if we just look at the connectivity values so we can look at the number of streamlines as a measure as he's done in many studies and here we look at the so this is a t statistic of each of the edges in the network and you can see it follows a pretty normal distribution there's no huge outliers here on either side if you were to plot the this is equivalent distribution for the surface areas it would have been very skewed to the left because they were shrinked a lot smaller in patients and when we plot the top 10% of these just an arbitrary number we can see that there is no obvious spatial profile to the left hemisphere as we may expect and these are not significant after correction for false discovery rates so multiple comparisons and so using this measure of just the number of streamlines the connectivity is actually not different really I would say I've been seeing this what we can do is look at a different measure that's widely used in the literature and this is the connectivity weight and many of you will be familiar with this study I would expect yes no it's one of the key studies that kicked out of the whole structural connectomics revolution that we're seeing in the recent years and it essentially takes a connectivity weight which is a function of the density of the number of streamlines for the surface area normalized by the length of the streamlines that interconnect those two areas and Hagman used this in his study in healthy subjects which I think is fine there's nothing wrong with that and I tried this in the patients to see if this would result in significant differences and it does even after FDR and so we find that in a subset of the connections there are when using this measure this definition of connectivity weight as many studies do in the literature we find these significant differences but I would argue that these differences are actually really being driven by the alterations in the nodes in the network rather than necessarily the connections between them and so I would say if you're if you're doing any connectomic studies looking at differences between groups so whether it's male or female it's well known that females have smaller brains than men generally or younger and old all the people have thinner cortices than males or disease cohorts where there's no differences I would say one should really take this into account and at least be aware of this in any structural connectomic studies so that's all well and good but that doesn't really tell us anything useful for treating epilepsy clinically with with consultant neurologist in the hospital and I'd just like to draw your attention to this number that Yi Jiang touched upon in her talk so around 30 to 50 percent of patients who have focal epilepsy will go into surgery they have their outcome is not good so they will still have seizures even after having this invasive surgery to remove what is thought to be the epileptic causing part of the brain and the reasons for this are not fully known but it's been suggested in recent years that epilepsy is a dynamic disease there's seizures come and go seizures evolve over time they may start off with fast oscillations or high amplitude spikes they may come and go at different times of day so some patients have more seizures during nighttime or different times of month so it's I would argue that there's strong evidence to suggest that epilepsy is a dynamic disease in addition to aspects of structural properties of the brain such as the network that was described in the earlier studies and so if we could if we take both of these aspects into account the dynamics and the structure the goal really is to ultimately see if we can predict surgical outcomes for patients before we do the operation before we remove remove the part of the brain in this invasive clinical work that's done so here we've used the same data so 22 patients and we've included the connectivity so this is the connectivity matrix M into the model with the realistic time delays and we have a essentially the model is a it exists in a bi stable state so there's a stable fixed point and an oscillatory limit cycle around it and the likelihood of transitioning from this state to the epileptic form state is dependent upon these parameters which are inferred from the patient data so M which is the connectivity matrix the delays and also the degree of atrophy in the patients and this is all node specific and derived from the patient specific data and so the first thing we can do is simulate this model and see if it gives us something reasonable looking for a seizure so here you've got a time series of a subset of the network nodes so there's eight nodes here now overlaid on the brain and when one goes into a seizure state you'll see it pop up on the brain so there's the first one that's left temporal I think and then we've got right thalamus popping up and others as the seizure spreads quickly throughout the brain so that's great we can simulate the seizure spreading in the patients can we predict the surgical success rates or can we explain that so as I've said a few times it ranges between 30 to 50 percent the success rate the reason for this variance is one of the reasons is because it depends on where the resection is so if you have a temporal lobe seizure and you have surgery for that it's probably going to be like 70 percent if you have frontal lobe seizures it's probably going to be more like 50 percent so here we're only looking at temporal lobe patients so we would expect the success rate to be about 70 percent and here's the literature that explains all that and we essentially ask the model what happens if we remove those nodes from the network and resimulate the model do we get an improvement in the seizure likelihood following that and that simulated resection and if so in how many patients do we see that significant improvement and actually in 72.7 percent of the simulated patients we see that improvement so we're matching the clinical data in two regards first of all the seizure spreading and second of all the success distribution if you like and the reason for that is this initiation at nodes with high atrophy and at network hubs and the spreading through what we think are possibly healthy connections or normal connections so the model can reproduce this essentially likelihood this probability of having a successful outcome what about individually what about if we test this on individual patients can we predict their outcome well this is preliminary work that's currently in review on two patients that I'm showing here and patient A we've plotted the simulated seizure likelihood so which nodes go into that seizure state sooner than other nodes and we've plotted this spatially and the red nodes are the ones that go into the seizure state very quickly using the patient data again and on both panels and the black nodes are those that were actually clinically resected and you can see that in this patient they overlap quite a lot and in this patient they don't so the predictions from the model would be that this patient would have a very good outcome because you're essentially removing those nodes from the network that are causing the seizures and in this patient you would have a very poor outcome and we went back to the data we did this blind to the to the actual outcomes and we went back to the data and we found actually yes that patient does have a good outcome and actually that patient did have a poor outcome they had seizures even after chopping out that part of the brain as brutal as it sounds and then we did this for another 16 patients so we've got 18 patients in total and we get 83 accuracy which when you think that the clinicians only get around 70 accuracy I think we're doing pretty well there importantly the probably one of the best figures from this is 100 sensitivity and what that means is that in all the patients where we predict there will be a bad outcome they actually had a bad outcome and that's crucial because that means that we can go on and say to the the surgeon here's what we think you should be doing instead we think you should be operating on this part and maybe the surgeon will take that into account and investigate that a little bit further so to conclude there's kind of two bits of work here that I presented but I think it's and I think the first bit is is very important to bear in mind for many or most connectomic studies is that if especially if we know that there's differences in volumetric measurements or differences in surface area between subject groups if you if you're doing a connective study comparing groups I think that this is crucial that these aspects should be considered especially in these groups for example where we know there's differences and dynamical simulations may be prove may be useful for providing mechanistic interpretation so why are the seizures spreading in the model why are they spreading in the patients and why might seizures come back and accurate predictions are possible and we can investigate alternative surgery strategies with these techniques so with that I'd like to thank Nishan and Francis who were the students which did a lot of work on this they were master students with me in Singapore and in Newcastle and just do a shameless plug there for the conference that we're doing next year thank you for your attention thank you rather than DTI based structural connectivity what what do you see is that different or does it slavishly follow structure so I skimmed over that very briefly so this is actually using the functional connectivity between ECOG channels no I meant fmri functional connectivity I'd love to try it and so there's there's a bit studies showing that there are differences between patients and controls in functional connectivity as you might expect so sorry but if you've got a large well I don't know if you've got a data set of patients with resting state functional connectivity and you've got the last 200 patients who failed or you accumulate them from data sets across the world you could ask the question where the local connectivity it'll be something a lot closer than you get from ECOG but if you could get something it would be very very clinically useful yeah I agree could be useful typically fmri and resting state fmri is not clinically acquired so getting that data would be a big challenge and a better option might be to do MEG resting state MEG and source localize that so there was a paper out in brain last year or the year before I think it was Engelot which looked at that and he found similar things to was actually these kind of network changes that correlate with what are seizure focus seizure focus eye in good outcome patients good idea it's a I think that's a a way to take this absolutely thank you


So I'm Vlatka and I'm out of voice at the moment. So I will be speaking about health without my personal health. If you have problems hearing me, you have to shout out because obviously I cannot know. I will keep very close to microphone and afterwards I would like countries to disinfect this microphone. Okay, so let's start. As you know, WHO World Health Organization is saying alarming things that air pollution is driving global health crisis, that it has certain numbers of millions that's worldwide, but most of the population living in the planet are exposed to unsafe limits for the air pollution. And what's of course interesting is that air pollution is a mixture of gases like SO2 and NOXs and particles, which then later on is very important for health, because for example particles on those we can have some heavy metals that are then influencing health or gases can make secondary particles and so on. The complexity of air pollution is important and it's kind of difficult as well for health to then make links. So what are particles? So the particles are small compounds, let's say, we separate them by mass or by size. And in this graph we see the concentration in total of particles, how much of those are PM10, so you see that in the right side that it's quite low and the most of the particles are actually smaller than PM2.5 or finer or ultra fine particles, which then means something else for health and this is that they can go very deep into lungs. So here we are. So this graph shows the position fraction per particle diameter. So as you see in upper respiratory tract you can have mostly bigger particles like PM10. Then as you go lower and lower smaller particles of PM2.5 or lower ultra fine particles are deposited. And then of course this means something for how long it will stay in your body basically. So for deposition in Avleoli, so these are the small tissue in the lungs that are making them transfer oxygen.  So two main things are that why particles are important. These are for ultra fine particles but the same goes for larger particles. It affects cellular level which means cells and it affects as well circulation. So in terms of a cellular level or if it affects on a cell it can do oxidative stress which basically means damages to a cell or it can do inflammation. And then of course this means later on that there will be certain dysfunction in vasoconstriction or so your blood vessels will constrict and dilate less flexible which then means of course that you will have problems with adapting to either low temperatures or high temperatures and there will be more chances that you will develop then for example stroke through this and so on. And another influence of this oxidative stress and inflammation is on blood coagulability which then makes a link with this vasoconstriction or dilatation. Basically your blood is coagulated more and your vessels are not as flexible so which is like a double problem. So this is on a cellular level and then on a circulation level is basically that particles are deposited into lungs and then as same as oxygen is going via red blood cells same particles can do so they can be transferred to blood and then via blood they can go into heart, into brain, into liver, bone marrow and so on. So there will be two ways or two mechanisms how this will affect your health. This lovely graph or table shows basically I wanted to express like what are the WHO limits or World Health Organization and what are EU limit values. So for example for SO2 you see how much higher EU limits are or for example particulate matter. It's 2.5 fold for PM2.5 or 2 fold for PM10 which brings another question. For NO2 basically WHO limits are the same as EU levels, limit levels and those are basically somehow saying that this limit of 40 micrograms per meter cube is the value that will avoid most severe exposure problems while for the PM2.5 and PM2.10 concentration there is discrepancy in between the limit values. So for example WHO is saying okay after we have certain amount of evidence that will show that after 10 micrograms per cubic meter there will be severe or more severe exposure and more severe health effects. That's why we cut off at this point while US has a different cut off point and EU is quite behind so 25 micrograms per meter square. Okay this is a number of studies showing non-linear correlation with how much you are exposed to PM2.5, concentrations of 2.5. Basically what I wanted to say with these two graphs is that for example in Lufthad and you have a cut off point or saying the good air quality of PM2.5 is something around 20 or 40 which is totally meaningless. It's basically, this is totally my opinion, saying the values are based on what, on policy that currently exists, that is currently under process of review and that is politically agreed. So it's not even scientifically proven. It doesn't, or like there is basically something we have to check also okay we are seeing the air quality is good or not but what this means like under which, what is then good and we have to define this, whether is it for health or is it just some scale that we developed and we agreed okay this will be a good or not. So this is maybe a point to take home and then think about later on when we want to think okay these measurements that we are doing for what, for environment, for health, for climate and then we can develop a scale or at least have a rationale why we are using this scale. In terms of exact numbers like affecting health, so I prepared this graphic on increased risk for health impacts and there is difference between long-term exposure and short-term exposure and we are different kind of meta-analysis, scientific agreement at the current level is that like every increase of 10 micrograms of PM 2.5 or 20 micrograms of PM 10 will increase for example mortality by 6.2 on a population level. So this might not seem much, 6.2, but if you take whole of Belgium and calculate that this or how many Belgians are exposed to higher concentrations than 10 micrograms per meter square, a meter cube, then you will get a pretty high number. And the same goes as well for the arrest, like infant mortality, for prevalence of bronchitis, for asthma attacks, for hospital admissions and so on.  A lot of texts may be in a code, but I thought, okay, you might like this slide. So this is something that WHO developed, basically it's showing a summary of all the evidence and then meta-analysed together forming parameters were taken in. So like mortality, different kind of mortality, infant mortality or adult mortality, then different kind of hospitalization for respiratory disease, then you have cardiovascular disease, hospitalization and so on, prevalence and incidence of bronchitis and asthma among adults and among children and then lost working days. And those are all numbers for if you see pollutant, PM or ozone. Very little is for NOx and there is a reason why. And relative risk means basically by each 10% micrograms increase, you will get additional, so 1.062 in the first line basically means 6.2% of increase. In all cause mortality age, 30 plus I think. So just for you so you can interpret this kind of data and then maybe even use it in your calculations. This is something from WHO saying that around third of all lung cancer or stroke on heart disease are basically attributed to air pollution. For lung cancer probably the rest of 2.3 are for smoking, for stroke or some other risk factors and so on. And this graph goes together with why are we speaking about PM mostly in NO3 or ozone because there are a lot of publications around PM. So it's more easy to segregate PM and do analysis on that. While for NOx much less is done. Mainly because it's kind of scientist found out that it needs to be adjusted at 4 PM and because it's quite difficult to measure, blah, they are less prone to do the scientific measurements around this. But this does not mean that we should not take it seriously. So we should not measure NOx or we should not think how this affects health. Basically absence of evidence is not evidence of absence. So as time progresses we develop the methods for including measurements of NOx on our health. The data will probably grow bigger of course and the knowledge. So this is one thing that Committee on Medical Effects of Air Pollution from UK did and this is I think the biggest report on NO2 and health and how much it affects our health. So basically like 23% of one policy reduction of all traffic pollutants would have influence on all cause mortality I think and so on. And one last thing is why are different numbers for premature deaths according to different sources? So basically the answer is in the methods that they are using for example global burden of disease uses different kind of concentrations and CREIT on which they are looking the concentrations. They are using different kind of pollution so not just outdoor air pollution but also indoor air pollution and they are coding their health outcomes differently. So for EEA or European Environmental Agency they are not coding it so they are just taking all of the causes together while global burden of disease takes only four. Strokes, ischemic heart disease, COPD, some lung cancer and so on. So these basically mean that you should not compare the number of deaths from different sources rather look at as a relative number in this pile of numbers and not comparing the numbers. And to finally conclude that people in Europe are breathing a little bit cleaner air than five years ago so there is things that we can do and that we should do to improve our health. Nature-wise is there a simple explanation in this case? Is it that not being part of the meta-vehicle industry? Maybe. No, I think it's back then when Europe developed directive which was in 2002 if I'm not mistaken. There was just not so much evidence that they would and there were not so much evidence in Europe so this is one of the problems where for example database that exists in US it's so huge through Medicare and they are much better in just analyzing strokes together than they pair it with the data from the measurement stations and then see how it correlates to the hospital admissions and so on. And I think nowadays in Europe we have a big pool of data so I think now when air quality directive revision starts this year that there will be a big scientific community that will then want to influence this limit values but I think there is also work to do from the citizens. They need to be also aware of their role to demand lower limit values. On that note as well most of the countries in Europe are even struggling with reaching the EU level limit values now so I think 12 countries are being, steps are taken by commission to put them on court for breaching the limit so I don't know there will be a big opposition I think from the member states as well. Thank you.


And moving outside of Africa now, Sicilian mafia boss Giovanni Brusca, whose grizzly crimes include having a child's body dissolved in acid, has been released from prison, dubbed to the people's lair. Brusca has confessed to his role in over 100 killings, including the assassination of Italy's top anti-mafia prosecutor Giovanni Falcon. But Brusca became an informant helping prosecutors hunt down fellow mobsters. His release after 25 years in jail has outraged his victims' relatives. He will now be on parole for four years.


All right, very good morning. It is Wednesday, the 6th of October. I hope you're doing well. In terms of the briefing today, you're going to talk about just rising inflationary pressures, what's causing them yields, the 10-year Treasury yield, the 30-year yield in the US, both moving to their highest levels since June, as you can see here. The US 10-year break-even rate just popping out of that range that had been constricting some of the upside yield movement over the last few months and going back to the highest levels that we had really since late May, early June. What does that generally mean for markets? Well, despite the slight reprieve that we saw in equity markets yesterday, in terms of the major three indices, the S&P finished up around a percent. So did the Dow Jones Industrial Average. The Nasdaq was actually up about one and a quarter percent, flipping some of that underperformance that was seen on the Monday move. But this morning, we've kind of recommenced that trend a little bit and equity index futures despite that high close and Wall Street are just getting a little bit apprehensive again about the whole yield environment and the subsequent reaction effect. We've had the RBNZ, the New Zealand Central Bank, hike rates now overnight and the expectation, of course, that others soon to be following following these heightened inflation fears at the moment. And so equity index futures a little lower this morning. Asia stocks also generally lower. The Nikkei in Japan was down in excess of around one and a quarter percent and faded its opening gains and a brief foray into 28,000 territory. Auto names among the laggards amid ongoing production disruptions with the PM Kishida's new cabinet beginning on shaky ground. Latest polls actually having had him now call for a latest election. Polls have showed that his approval rating is at just 55% heading into the upcoming election. And there's a bit of context for Japanese politics. And that would be the lowest for a new leader in 13 years. So more political kind of uncertainties emanating out of Japan, just weighing on their local stock markets, things of underperformance. And then this morning, we've just had German factory orders come out. Just give you a bit of a flavor of what that looked like. This is the last 12 months of prints and German factory orders this morning at 7am in Germany plunged 7.7% month a month in August of 2021. Economists were only looking for a decline of around 2%. And so much lower than expected orders for motor vehicles. So you can see there's a very recurring theme here globally from Japan to Germany, particularly those two countries specifically being large automotive kind of manufacturing centers, orders for motor vehicles and parts fell 12% in Germany in August and metal production and processing went down about 9.6%. So as I said, a little bit of an apprehensive open here for European trade, the DAX is already down about 233, the NASDAQ underperforming a little bit down 137 and S&P down 33 at the moment. What this is translating to again, just given some of the overall emphasis that we're seeing on the general inflation expectations moving higher, not only is this then being a bit of a negative equities about tightening of policy, particularly more prevalent than for the likes of tech companies who are more, I guess fragile to developments on that front. But the dollar continues to extend on gains. So the Dixie is trading higher again, adding to a lot of yesterday's assent. And as that trades higher, it's just we're weighing on these major pairs here this morning, Euro dollars down 27 top left and cables down 54 at the moment. And that dollar strength weighing on the yellow metal, which is inverse to the dollar trading down 10 bucks at the moment at around 1751. And obviously T notes down, they've recovered a little bit was down as an excess of 10 ticks overnight. A few other things as well, just on a surface level before I really get going, just wanted a little chat about Bitcoin. And the reason why is because we've had some latest commentary come out in regard to the SEC chief, Gary Gensler, who has said the US will not ban crypto currencies. Now, as you can see here, this is looking at Bitcoin over the course of really the last six weeks or so. And we've had some quite seesaw commentary really, because what I've just mentioned there from Gensler, the US will not ban crypto currencies. He stated that the government's focus is on ensuring the industry adheres to investor and consumer protection rules, anti money laundering regulation and tax laws, very different from him saying, you know, this is the day of reckoning or the Coinbase spat with the SEC, which had contributed to a lot of the rationale behind some of the weight that was experienced at the beginning of the month of September and also mid September in the likes of Bitcoin. And so we've had two quite elevated green spots here, as you can see on the most right hand side of the chart here. And that's been not only has Gensler kind of ruled out banning crypto, we've heard pretty much the same from the Fed Chair Jerome Powell, who said back on the 30th of September on a congressional hearing that he had quote, no intention of barring crypto currencies. And I was reading a research note out of Bank of America, and again, to just add a layer of context, their strategists were talking about how more regulation, people have often thought more regulation would be negative for crypto in general. But Bank of America's take is the opposite. In fact, more regulation could be positive for the long run, at least. Once rules are established, the uncertainty then on over how to invest in crypto will be lifted. And as such then, given it might be there's more protection mechanisms in place, more oversight, that will mean that there's more general broad uptake and adoption of crypto in general. And therefore long term is positive. And so what does that look like this morning? Well, we looked at the Bitcoin chart obviously at the beginning of the week. And that's still looking fairly bullish at the moment. We haven't come up quite yet to test that high that was seen back on the seventh to seventh looking at the Bitcoin future here. But certainly that's what's in sight at the moment. And definitely keeping an eye on that as we trade around the 52k mark in Bitcoin futures this morning. Otherwise, a few other things just to get up to speed on on the news front. We've had the latest about Jerome Powell's reappointment. I wouldn't see this as market moving for this morning, but certainly it's worth keeping up to speed with this. Because obviously it's super important factor for policy setting going forward over the foreseeable future. And Powell has the backing apparently of more than half of the Republicans who sit on the banking, the Senate banking committee support that could see him earn a second turn to lead the central bank despite some of the sharp criticism you might have been reading the likes of the Democrat Elizabeth Warren of late, who's been very critical about loosening of regulation, his leadership inside of trading, kind of what we've been seeing most recently with Clarida at the weekend. And so there are quite a few vocal opponents, let's say in a Democratic party to that. Biden is yet to announce his nomination, but the White House has come out overnight and basically stated that Biden's confidence or has confidence in the Fed share for the time being and Powell's term obviously expires in February. So this is a decision we're expecting shortly. The growing support from Republicans though, going back to what this headline suggests, means that Powell may be able to get confirmed again in the 50-50 obviously split that exists at the moment in the Senate, even if a handful of Democrats oppose him anyway. So the point being then is that for now, I mean I have been seeing quite a lot of predict it, which is a kind of political betting site, seeing the odds decline quite sharply for Powell over the last few weeks. And I think Lionel Brainard odds have continued to move higher, but honestly I think that's just a lot of hot air and I still firmly believe that Powell will secure a second term. It's just a bit of nervousness as we go into just waiting the confirmation at this point in time, which I'll see. The other thing we did have briefly mentioned, he had the RBNZ overnight, they did hike interest rates by a quarter percent to 0.5 percent. It's the first rate hike that they've done in seven years, if you can believe it. That was expected though and actually the Kiwi dollars moved lower if anything. There was a brief blip on the initial reaction and then it's trended lower and of course that's because you're trading against a strengthening counterparty in the US dollar at the moment, which is really dictating proceedings for the major currency pairs. One thing to be aware of is they did signal further increases will likely be needed to tame inflation, but again that's not too dissimilar from what they've been positioning for anyway in their forward guidance. But while another rate increase in November looks firmly odds on, I did see one bank commenting, one investment bank saying that risks are skewed towards something coming along to derail the RBNZ's hiking cycle before its completion. And this is something that I was talking about with the Bank of England and how aggressively the market has been priced and I think that's been pushed out given this energy crisis we've had most recently, but which Piers and I talked about this on a podcast and how the market was pricing for like a feb hike from the Bank of England and we were going through the reasons and rationale why that's perhaps then pricing for you know kind of economic perfection and that the risk then is to disruption which could then obviously influence not just that of when the expectation of timing of hikes but the strength of the local currency as well. Similar kind of theory holds true for New Zealand at this point in time as well. The other things to be aware of we've had the API oil event trees come out overnight. I'm not going to dwell too much on this at the moment but the numbers were a build of 951,000. The crude headline expectations were actually for a drawdown of 300,000. Cushing build 2 million. Gasoline 3.682 million. As far as oil is trading at the moment still remains very elevated this morning in the futures market. We've just popped up again close proximity to not just the R1 at 79.88 but also the $80 psychological handle. From Monday's briefing of course this is the date or weekly chart excuse me that we were looking at and you can see the levels that we had marked up just working quite nicely for the time being as you can see here that previous inflection point that we saw through October November of 2014. Support then turned resistance on that decline that we saw through that kind of whole China hard landing fear and OPEC trying to squeeze out the competitors in the US shale industry. So that was that price point here of around $80 of which the market's just finding a bit of a hiatus for the moment. So quite a key technical level to watch certainly we're in distance of that now so it's something we'll be watching out for today. Again whether or not just given the speed of acceleration crude oil has seen with this energy crisis at the moment and this talk of switching gas to oil and so forth and the cold winter weather looming whether or not then we see perhaps a little bit of a consolidation at these levels and then we look to continue to kick on on the break of that next level as I said don't really see until we really get higher up to the 84 level which would be up here. So eyes on oil as well continues to be the case. In terms of the actual Canada for today a couple things to look out for so we've already had the German industrial order data as I said and we've got Eurozone retail sales that's largely a non-event I wouldn't really look at that with too much interest. The main data point for this afternoon is going to be US ADP national employment just to give you a refresh of what that looks like over the last 12 readings. We've basically had as we know fairly soft labor reports coming out of the US it's been one of the reasons which is really despite the inflationary conditions which has stopped the Fed from being more aggressive with their tightening of policy going forward but this isn't really a case now I think of this number is going to really move the needle dramatically. I think markets are very much primed now for a November commencement of tapering in terms of that more detailed announcement and so looking at this figure today the ADP number is expected to come in at 428,000 so continued improvement over the last two readings that we've had and a fairly incremental value. We do have Feds Bostick speaking later who is a voter on the FMC speaking twice today at two and four thirty. A few other things you should be aware of as well is that the ruling conservative party hold the final day of their party conference their annual conference in Manchester today we're not expecting according to Politico any major announcements to come out at the PM but the Prime Minister Boris Johnson will be giving a speech later on this afternoon. All right that is it if you've made it this far thanks for sticking with me don't forget if you're not already done so please subscribe to the channel I will be covering not only these briefings every morning but on Friday non-fond payrolls live on the channel so once you subscribe hit that bell icon and you'll get a notification as soon as that session begins on Friday. All right take care guys and have a good session ahead.


Yankees pick up yet another win nine five the final Jackie talked about how it was important for the Yankees to take care of business against the low liais team today after a big series against the Astros didn't start out great for the Yankees but they found their way back to another victory. This game screamed trap game and then it screamed it even louder when the Yankees fell behind five to one because Montgomery had a rocky third inning but as you just mentioned Ryan the Yankees were able to crawl their way back into this game they were resilient they were persistent they were patient and they were better because they are better they have the best record in Major League Baseball the A's have the worst record in Major League Baseball and I think that was exemplified in that six run rally that the Yankees had yes the Yankees had three hits and a walk and hit batsman during that inning but that inning was fueled by two catchers interferences against Murphy the A's catcher and if you make mistakes like that against the Yankees they're going to pounce they're going to take advantage that's what they did and they were able to get their 23rd come from behind win props to Montgomery that he rebounded the pitch all the way into the seventh inning and then Alberto Bray who comes out of the bullpen thrilled to be a Yankee again and he closes it out


Hi everyone, Joe for jaspyscasebreaks.com with 2019-2020 Panini basketball Panini mosaic basketball sell-up pack break number 67 with a chance to win 10 spots in noir and There are a couple other breaks that filled in between but then noir ended up did selling out straight up Those straight-up spots sold out so that break will be a little bit later tonight Big thanks all of these folks for getting into the action. There are the teams right here So as always we're gonna do the break first We're gonna randomize names and teams at nine times each To an ninth and final time after nine we've got spiro down to Adam Five and four nine times for the teams a ninth and final time and the randomizer for the spots will be as Always different dice roll new list. All right, so there's the whole list of List right there That's that part list right there. It's a little pack break. I'm not gonna read up all the names preserve what little voice I have left tonight Steve with the pelicans and Dustin Grizzly's it's been a long week folks. We'll keep him in busy. Let's print. Let's rip. Good luck RJ bearer and the pink camo exclusive to these sets All right, nothing super significant there, but let's give away those noir spots ladies and gentlemen so New list new dice. Let's grab all the names from spiro down to Matthew And it's gonna be the top ten After Nine top ten after nine Good luck one Two and a ninth and final time after nine big. Thanks all these folks right here. I appreciate you giving it a shot Congrats to Diane Travis Alan Robert Travis TJ Leonard Adam Oliver Robert and Adam that group right there.  Thanks for watching. I'm Joe. I'll see you next time for the next one. Bye. Bye


Third party, another object from the perspective of those two frames, and thinking about the velocity of that object as perceived in the two frames. We'll also learn how to properly add velocities of objects to frame velocities in special relativity. Let's use a concrete example to motivate a kind of basic problem we can use going forward to think about the question of object velocities relative to moving frames of reference. So the example I will pick for this is a non-copyright violating space wars. Recently in a globular cluster fairly nearby two ships were engaged in a chase. The lead ship is moving away from the pursuing ship at a velocity given by the vector v. The pursuing ship fires a projectile straight at the lead ship along the line of motion and at a velocity vector u relative to the firing ship. With what velocity does the lead ship observe the projectile to move? Now I've illustrated this with a little graphic cartoon right here. We have the pursuing ship on the left, the projectile it's fired with the velocity of the projectile from its perspective drawn here in red. The ship it's pursuing over here on the right and the velocity of that ship being pursued relative to the pursuing ship given by the vector v. Now in the Galilean or Newtonian view of space and time the answer to the question with what velocity does the lead ship observe the projectile to move is rather straightforward. The observed velocity in its frame of reference u vector prime would be equal to the velocity of that ship with respect to the pursuing ship minus the velocity of the projectile with respect to the pursuing ship. That would also turn out to be completely wrong when the velocities in this question approach velocities near that of light. So for instance if the projectile is actually a beam of light imagine a laser beam a laser cannon mounted on the front of the pursuing ship it turns on the cannon the beam is emitted this is a beam of pure light it should move at the speed of light. We plug that into this calculation we get all kinds of wrong answers here. The lead ship doesn't doesn't see the laser beam approaching at the speed of light and we know that's just not consistent with observation as encoded in the postulates of special relativity. So what then is the correct addition of velocities in a problem like this? And that's the question we want to figure out in this lecture. We can begin by writing down the Lorenz transformation equations treating the pursuing ship as the rest frame the lead ship as the moving frame and the projectile as an object to be located or studied in either frame. The space-time coordinates of that object in each frame are given as follows for example if we have the space-time coordinates x and t in the rest frame we can get the space-time coordinates in the moving frame the s prime frame using this version of the Lorenz transformation equations yielding x prime and t prime the location and the time at which the location is observed for the projectile in the perspective of the lead ship. Now we can write differentials of space and time using calculus dx prime and dt prime and this will allow us to work toward obtaining equations with velocities. So for instance u prime is the first derivative of x prime with respect to t prime after all that would be the velocity of the object as observed in the lead ship or moving frame u would be the first derivative of x with respect to t that's the perspective of the projectiles velocity from the rest frame or the pursuing ship. Now if this particular step feels weirdly familiar to you In an earlier lecture I walked you through a brief example as to why the Lorenz transformation needs to be a linear transformation between moving frame coordinates and rest frame coordinates and we came dangerously close in that lecture to deriving the velocity transform albeit I was doing that for arbitrary powers x to the n and t to the m for instance here of course it's purely linear because it's based on the Lorenz transformation and so if some of this feels awkwardly familiar you may flip back to the earlier lecture on the Lorenz transformation and have a look and see where the roots of this were planted. So the differential of space in the moving frame dx prime is going to be equal to gamma times the quantity dx minus v dt and the differential of time in the moving frame is going to be equal to gamma times the quantity negative v over c square dx plus dt. Now we can take the ratio of dx prime over dt prime and this allows us to get the velocity u prime of the projectile as observed in the moving frame or the frame of the lead ship substituting in with our differentials for dx prime and dt prime we arrive at this rather unpleasant looking equation but one of the nice things about this is that the leading gamma factors the one over square root of one minus v squared over c squared terms they cancel out in both the numerator and the denominator and this leaves us with an equation that looks like this just in terms of the remaining differentials of dx and dt now if we divide the top and the bottom by dt the little unit of time that we're considering then we wind up with terms that go like dx over dt which is just u the velocity of the projectile in this case entirely along the x axis and so this equation takes the following form which at the end of things doesn't look horrible the velocity of the projectile as observed in the moving frame the frame of the lead ship is simply given by the velocity of the projectile as launched from the perspective of the rest frame the pursuing ship minus the velocity of the frame so the velocity difference between the lead ship and the pursuing ship divided by a quantity that goes like the motion one minus u v over c squared so we have arrived at a formula for combining the velocities of the moving frame with the velocity of the projectile as observed in the rest frame to allow us to compute the observed velocity of the projectile in the moving frame this equation is a substitution for the old Galilean transformation addition of velocities equation and is correct from the perspective of special relativity so let's plug in some numbers and see what we learn about projectile motion in the case where objects are also in relative motion to each other and observing that projectile as it moves and let's begin by picking a low velocity situation where the ships are not really moving apart from each other all that fast i've decided to pick the lead ship having a velocity of just one percent the speed of light or 0.01 c and i've picked a projectile velocity that's just three times bigger than that or three percent the speed of light 0.03 c from the perspective of the firing ship the pursuing ship now from the above equation we learn that the lead ship observes the projectile approaching it at a speed of 0.02 c now if you stare at this for a moment and recall the Galilean velocity transformation you'll note that this is exactly what we would have expected from the low speed case where all the velocities of objects in the problem are not really a large fraction of the speed of light although i've allowed them in this case to go up to a few percent the speed of light we actually get back exactly what would have been told to us by the velocity transformation in Newtonian slash Galilean relativity that is that u prime equals u minus v now that doesn't mean that this is exactly true at every decimal place there's some decimal place where the Newtonian Galilean approximation to space and time in motion breaks down compared to the more accurate special relativistic calculation so let's instead pick some bigger velocities let's now assume that the lead ship is racing away from the pursuing ship at half the speed of light and then from the perspective of the pursuing ship it fires this projectile at eight tenths the speed of light 0.8 c plugging those numbers in we find out that the lead ship observes the projectile to approach it at one half the speed of light and if you stare at that again for a moment play around with the numbers on your own you'll very quickly realize that this is definitely not what would have been predicted by the Newtonian or Galilean approach it's not simply u minus v in this case now interestingly we can look at the case of when the lead ship is flying toward the pursuer so now we turn the lead ship around and we aim it back at the pursuing ship and flip its velocity vector so that it's moving at negative 0.5 c from compared to its original direction of motion in that case we see that the lead ship that's now racing toward the projectile that's been fired at it doesn't observe that projectile to be moving in excess of the speed of light rather it observes it to be moving at 93 percent the speed of light and that's again a distinction from what the Newtonian or Galilean approach would have yielded the old relativistic approach from Galilean relativity would have predicted that the lead ship observes the projectile to be approaching at a speed that is far in excess of the speed of light but we also know from the postulates of special relativity that one consequence is that nothing can move faster than the speed of light and so we see that that's preserved here in the velocity transformation although the velocity of the ship is now aimed back at its pursuer and although the naive addition of velocities would give you something in excess of the speed of light the naive addition is not consistent with observations of space and time and the speed of light and using the special relativistic transformation we see that while it's true that the velocity of the projectile does appear to be larger than when the lead ship was racing away from it it does not exceed the speed of light but comes in at a pretty pretty fair fraction of the speed of light so let's summarize what we've learned about adding velocities in special relativity keeping in mind that the cases that I've built these equations from all involved an object velocity that was parallel or anti-parallel to the velocity of the frames if you have the velocity of the object in the rest frame and want to determine it in the moving frame then the left equation is what you want if on the other hand you have the object velocity in the moving frame and you want to determine it in the rest frame all that should change between the left equation and its corresponding equation on the right should be that you swap u and u prime and you flip the sign of all terms that involve v or v cubed or something like that you take v and send it to negative v and in fact that's the equation that's written here on the right you can always derive these directly from the Lorenz transformation or you can memorize one of them and remember how to transform it into the other by swapping the object velocities and flipping the sign of the frame velocities I'll leave it up to you as to what your best possible learning strategy is for this but know that if you memorize one of these you can figure out the other from context and knowing how to trade the mathematics around now what if the object instead of having its velocity aligned parallel or anti-parallel to the frame velocities is moving in a direction that isn't solely parallel or anti-parallel to v so you might be tempted to assume that the object velocity in for instance the y direction assuming that the frames are moving only along the x and x prime axes you might be tempted to assume that the object velocity along the y direction and the z direction as observed in either frame is the same since in the Lorenz transformation coordinates y and y prime z and z prime are equal to each other if all the motion is along x and x prime and you'd be wrong you need to be very careful with these things why well think about it a second object velocity necessarily involves the time derivative of a coordinate is time absolute between two different frames of reference well we should feel pretty confident at this point that the answer is that it does not t does not equal t prime in special relativity because a time derivative is involved there's going to be a dy dt and there's going to be a dy prime dt prime and while y may be equal to y prime t is not equal to t prime so let's go through this i'm going to consider motion component along the y axis the frames are moving entirely along x so v and this is still directed entirely along the x and x prime axes but i'm going to allow the velocity of the object to develop a component u y or u y prime along the y and y prime axes respectively so let's look at what the transformation of u prime to u would be for the case of this component along the y axis and y prime axis so we know that in the rest frame u subscript y is just dy dt it's the change in the y coordinate with respect to time as observed in the rest frame now it's true that in the Lorenz transformation if the motion is entirely along x and x prime that y does equal y prime so we can replace dy with dy prime and no harm no foul that's mathematically allowed but if we're going to substitute for dt with dt prime we have to use the full glory of the differential form of the time equation in the Lorenz transformation and that means replacing dt with the quantity i show here in the denominator of this fraction now of course i can divide the top in the bottom by dt so that i get a u y prime in the numerator in the denominator gammas don't cancel out in this case though between the numerator and the denominator because y equals y prime y and y prime don't depend on a gamma factor to correct between them and as a result it's actually an easier derivation i feel than for the case of the object motion component along the direction of travel of the the frame relative to the rest frame but it's not perhaps quite as a memorable looking now similarly if we have u y prime and want u y all we have to do is swap u y prime and u y in these equations and replace v with minus v and so the corresponding equation that tells us what the velocity component in the moving frame looks like given the velocity component in the rest frame will be the one i show here and by the way if there's a component of motion along z and z prime you can obtain a similar equation it has pretty much exactly the same form as the one shown here with u y replaced by u z and u y prime replaced by u z prime um you can very quickly write that equation down but i just want to go through this because it's important to recognize that while it's true that y equals y prime and z equals z prime when the motion is entirely along x and x prime it is not true that u y is naively equal to u y prime and that's because a time derivative is involved and time does not pass the same way in the two frames when one is moving relative to the other finally let's take a look at one last special case and that is if the pursuing ship shoots a laser beam at the lead ship so what i've done is i've replaced the red projectile with a red squiggly line to represent an electromagnetic wave light being fired at the lead ship now the lead ship is still moving at a velocity v vector with respect to the pursuing ship i've put everything along the horizontal axis here but now the velocity of the projectile is c because this is a beam of light and so it will always and forever move at exactly the speed of light so the speed of this projectile is now exactly 2.998 times 10 to the eighth meters per second as viewed from all frames so if the pursuing ship had fired a weapon like this a laser beam a beam of light well we know that the second postulate of special relativity demands that all observers must see light moving at sea regardless of their state of motion so does this velocity addition relationship capture that postulate in all of its full glory well let's find out let's assume that the relative velocity of the lead ship to the pursuing ship is one half c and that the projectile speed as viewed in the rest frame of the pursuing ship is c the speed of light well plugging these numbers in we can start from the equation where we have the relative velocity of the two frames and the speed of the projectile in the rest frame and we can get the speed of the projectile as observed in the moving frame so all i've done is i've replaced u in this equation with c because the projectile is a speed up is a beam of light that's moving at the speed of light if you do some algebra you can simplify this equation to c minus v all over the quantity one minus v over c and if you do a little bit more algebra you'll find out that this is just equal to c minus v over the quantity one over c times c minus v and you play with this one step further you find out that this is just equal to c the speed of light so in fact we see that v entirely drops out of this equation once the projectile is a light beam the value of v doesn't matter at all the relative velocity between these two vessels can be any number and it won't affect the outcome of the calculation v could have been at a half c or negative a half c or point eight c or point nine nine nine nine c basically once u equals c v drops entirely out of the equation and we always recover that u prime equals c as well the second postulate of special relativity is fully obeyed by this velocity transformation equation so to review in this lecture we have learned how to think about object velocities in different frames of reference and how to go from the coordinates of an object that's in motion to its velocity in different frames we've then used that information to figure out how to properly add velocities of objects to frame velocities in special relativity we've looked at a couple of case studies of this and seen that everything seems to comport with the postulates of special relativity which themselves comport with observations of the natural world