So today's webinar is titled, Linking Metabolomics to Diseases Using Human Genetics, a CLSA study. This webinar will be presented by Yuen Heng Chen, PhD candidate in the Department of Human Genetics at McMagel University, who is under the supervision of professors, Brett Richards and Celia Greenwood. His research focuses on understanding the interplay between metabolites and diseases using genomics approaches. He is also interested in the impact of sex on the genetic variant metabolite associations. Okay, so now I'll turn it over to you. And yeah, and thanks everyone for your presentation of my webinar. And thanks for Celia Chen for giving me this opportunity to present my work on the metabolomics and genetics. So today's talk will be centered around this study entitled Genomic Atlas of the Plastametablon prioritizes metabolites implicated in human diseases. So specifically I will talk about the general background relate to metabolites and diseases and briefly describe the method that were used in this study and the dataset that were used. And then I will talk about the details for this research study, including the research aims, designs, results and conclusion. Let's start with the background formation. So let's start with the main focus of the study, metabolites. So what are metabolites? So metabolites are small molecules that either the intermediate or the end product of in the metabolic reactions. The definition shows, depending on their chemicals features or about function, they can be usually grouped into categories such as lipids, amino acid, carbohydrates, vitamins, energy substrates, nucleotides, xenobiotics and more. They are important because they are involved in the essential biological process in the organisms and contribute to the development of numerous phenotypes. Some well-known metabolites include like glucose and fructose from candy, like caffeine from coffee and vitamin C from the fruits. So manufacturers can influence metabolites. Genetics is one of the major endogenous contributors. So genetic variants, they can influence the transformer levels and also protein levels. And those proteins, especially those enzyme transporters, directly metabolites and metabolites play a key role that regulate the levels of metabolites, either in the circulating system or in specific tissues. Exogenous factors like food, drugs or environmental factors like even side exposure, they can also influence metabolite levels. Lastly, the gut bacteria have been found to, they can further metabolize the food or those rich the gut and the produce metabolites, which can be re-observed by human body and influence metabolite levels. So then the next question is how a metabolite measured? So one technique that widely used is called high-performance liquid chromatography mass spectrometry, HPLC, LC, MS. This is a technique that can measure hundreds of thousands of metabolites at the same time and is also the one chosen by CELSA to measure the metabolites. So in brief, so the blood samples or plasma samples first go through this HPLC step for chemical separation and then the separated chemicals will be ionized and detected by the MS part. So then the mass spectrometry or the spectrum generated by mass bag can be used to identify or and quantify those metabolites, such as cholesterol and glycine. So as I mentioned, this technique can measure over 1,000 metabolite simultaneously. So to the next question is, why do we need to study metabolites? Why do we need to spend so much effort to collect samples and develop those two O's to measure metabolite levels? I already partially mentioned that one of the reasons is that those metabolites, they are essential for the metabolic process in the human body and they also contribute to the development of the phenotypes. Additionally, studies have found the association between those metabolites and many diseases, such as branched-chain amino acids with obesity, saturated fatty acids with liver disease, some bio-acid with COVID-19 infection in lung, some bio-acid were explored as the treatment. And lastly, cholesterol with coronary heart disease. So those associations has been found in the animal models and also in cohort studies from human. But this still, those association fund is still many steps away from the real clinical practice and it's partially because of two major challenges. One is called reverse causation and the second one is called confounding and it will explain those two concept in detail here. So in, for example, if we find a metabolite that are associated with increased risk for obesity, there is also a possibility that this association observed that those higher levels of the metabolites in obese individuals is could it because of obesity itself led to the changes of metabolites instead of this metabolite increase occurred before the obesity because obesity as a physiological change, it come with a lot of, for example, more adipose tissue, more adiposeides and those changes can lead to in alteration in metabolism too, which can lead to changes in metabolite levels. So that's the challenge for reverse causation. Another challenge confounding is in this example, we find metabolites to be associated with cardiovascular disease. We also know that obesity is also important factor that could influence both cardiovascular disease and metabolites, so it is a confounder. So if that's the case, then the association we observed between the metabolite and cardiovascular disease is just occurred just because obesity, not because this metabolites have any direct effect on cardiovascular disease. So that's the situation or challenge called confounding. So then how can we address those challenges that will lead to the message I'm going to talk about. So one or the golden standard to solve or address those challenges is randomized study. So a general design for randomized control trial include when eligible participant got recruited, they will be randomized randomly assigned to arms, intervention arm and the placebo arm or the control arm. So here I'm using vitamin D and multiple sclerosis as an example. So if we want to study whether given people, vitamin D supplements can reduce their risk for multiple sclerosis, we can design these randomized control trial and give the intervention arm, the people in the intervention arm the supplement of vitamin D and which can lead to increased cellular level of vitamin D. And in the control group, we just give them placebo and their vitamin D level will remain the same. So then we after follow up them for many years, we will see whether the incidence of multiple sclerosis is higher or the same between those two arms. The advantage of randomized control trial in brief, of course there's a lot more advantage but specifically for the two challenges I brought up is they can reduce the bias related to the confounding by the randomization step. So if we think obesity could be the confounder by randomization, the intervention group and the control group will have equal number or like highly celebrated number of BMI or obese individuals to make obesity less or unlikely to influence the different incidents we may observe for the multiple sclerosis. Another advantage for randomized control trial is that we are sure that intervention like the supplementation of vitamin D happens before the incidence of multiple sclerosis. So the timeline is very clear which avoid various causation issue. Of course randomized control trial have some limitations such as having costly, it usually require long duration to see enough or to get enough cases for the comparison and also sometimes it's unethical to test the potentially harm or harmful intervention. For example, if you want to know the causal effect of saturated fatty acids, we cannot give people saturated fatty acid directly if we know it's really a harmful thing. So yeah, so then some scientists come up with this new genetic epidemiological approach. What I say new is has been there for at least 10 years but it's become quite popular for the past five years because of availability of the data. So this method is called Mendelian Remediation. So I would call it MR for the rest of my talking. So basically MR is a genetic epidemiology approach where we try to mimic the design of a randomized control trial. So similarly in the MR study, we have the population. We assume that the Leo's oxygen variant were randomly assigned. So that led to two groups. The group for them who have the GLEO which we know it's associated with higher level of vitamin D and another group individual with CLEO where their vitamin D level is lower or which similar to no change. And then by checking the incidence of the disease within those two groups that have different alleles of this gene variants, more specifically we are checking whether the GLEO is also associated with the risk for multiple sclerosis. We can infer the effect of our exposure vitamin D level and the outcome multiple sclerosis risk. So actually this study, the MR study has been done to investigate the vitamin D effect on risk of multiple sclerosis. So this study has been published on plus Madison. So in this forest plot, I'm showing you the effect of single genetic variant. There's four of their effect on them through the exposure, which is vitamin D on the risk for multiple sclerosis as well as also there is met analysis results. So here it shows the per standard deviation decrease of the vitamin D level. We observe the higher risk for multiple sclerosis. So which kind of support the conclusion that the vitamin D is causal for multiple sclerosis risk especially for those individuals with lowers and average vitamin D levels, they have higher risk. So there are three key assumptions for MR study. So the first assumption is that the genetic variant, they are associated with our exposure. In my case, here is metabolites. And the second assumption is that the genetic variants are not associated with the confounder of exposure and outcome. And lastly, the genetic variants are not only associated with the outcome through the exposure. With those three assumptions, we can use the genetic associations between the variant and exposures as instrument variable to infer the causal effect of exposure on outcomes such as metabolites to disease risk. So just to summarize this method. So MR kind of uses measured variation in genes of known phenotypes to examine the causal effect of a modifiable exposure such as vitamin D on disease such as multiple sclerosis. So the advantages of MR, especially to address those two challenges are so they can avoid confounding by simulating the randomization process. So according to the Mendo second law, the independent storm and so alleles will be sorted into gametes independently and randomly. So which means the individual who carry the GLEO as I mentioned that increase or led to higher level of AMD, they should be similar in terms of their potential confounders or other features as those carry C alleles. So the confounders should be, you know, evenly distributed in those two subcommittee. So another advantage is that it can avoid reverse correlation is partially because genetic function, they usually happened before the occurrence of the disease. So the generic variants, since we know they're related to metabolites, they are also near the gene that encode an enzyme or a transporter for vitamin D. They are likely to first influence the vitamin D levels than influencing the disease outcomes. So one missing part I didn't mention when I describe MR is how can we find the association between the specific Leo of the variant with either the vitamin D levels and with the risk for multiple sclerosis. So to find those associations, we perform a type of analysis called genome-wide association studies. So for example, for vitamin D, we had good for people, we recruit them, we genotype their genotypes and we also measured the vitamin D levels. Then we can check the association between all the vaginal variants and the specific Leos of those variants and whether the dosage of specific Leo and the social ways may be higher or lower level of vitamin D. And here is just one example of a positive association between the GLEO and vitamin D levels. As for the case control or for diseases, we really have a cohort with both cases and controls. And then we check whether specific Leos and the variants are more frequent in the case group than the control group. Then that observation can lead to the identification of the association between this Leo and this variant and the disease liability. So using GWAS, we can find the association between the genome variants with metabolites and with disease risks. So that's conclude the two methods I used in my study. So lastly, for this introduction, I will talk about the data I used. So I think everyone here knows what is CSA. So basically CSA recued over 50,000 individuals to collect their information for research use. And specifically they have this comprehensive cohort which have about 30,000 individuals. They get more data from those individuals. They also collect the blood samples. They also the urine samples and measured metabolites and genotype data using those biological samples. So one important consideration when we try to use this comprehensive cohort is that this cohort is more educated. They have generally higher household income. They're more Canadian born and really have better general health compared to the general population of Canada. So I've been more detail about this comprehensive cohort. So they're clinical phenotypes such as, you know, body mass intakes, disease risk, or sorry, disease prevalence has been collected. They're socioeconomic status, data are also collected, lifestyle behavior data were also collected. And importantly, they have their genotype data collected and also imputed for use from metabolites about 9,500 individuals a month from this comprehensive cohort, you know, have metabolomics data. And by the HPOC MS technique over 30,000 individuals 1,500 metabolites were measured by this company called Metabolone. A few more words about the genomic data. So being specifically for this comprehensive cohort about 26,000 individuals have genomic data, 50% females, 93% were identified as European SS3 individuals. Genotyping step, they measured about 800,000 variants. And then those genotyped variants were imputed using the top meta reference panel to which can lead to identification of over 300 million genetic variants that can be used for genetic research. From a tablet or from a metabolomics data, so the EDTA plasma samples were sent to the company for them to measure the metabolables. So as I mentioned, the metabolome, they used this HPOC MS technique. So they measured 13, 14 biochemicals include 1,071 compounds with no identity. So they can, based on the mass spectrum, they know what chemical those compounds are. And they also have about 243 compounds that they can separate, but they just cannot map them to any known structures. So those metabolites are unnamed biochemicals and usually characterized as like partially characterized or unknown metabolites in the data provided by metabolome. So for the metabolomics data, metabolome provided the data after different normalization and imputation steps. So the data after batch normalization or QC matrix normalization has been provided. So both approach aims to address the potential batch effect with the measurement metabolables. So then data without or with imputed values, with minimum values detected for the given metabolites were both provided because some metabolites that could have relatively low abundance in the plasma. So it could be below the detection limit. And then the data, imputed values based on the minimum values for the metabolites were provided for those metabolites. So finally, we reach to the part I would talk about the research aims, design and results and collusion for my research project. So for this project, I have two aims. The first aim is I try to identify the genetic determinants for circulating metabolites using GWAS. And second aim is using the association I identified between varying the metabolites, I then applied MR to infer the causal effect of those metabolites and 12 treats and diseases. So let's start with the M1, the GWAS part. So for the GWAS, I performed separate GWAS for each metabolites and also some metabolite ratios. I used up to 8,299 unrelated individuals in grouping S history from the San Jose cohort. I surveyed 1,091 metabolites that prison in over 50% of the individuals and also 309 metabolite ratios. So after GWAS, I will have the association between all the genetic variants and the metabolites. Then to identify those conditional independent genome-wide significant associations for the metabolites and metabolite ratios, I performed this conditional and joint analysis to identify the leading variant or the leading association for the variant and the metabolites in each genetic regions. So here I'm showing Manhattan plot. So by performing GWAS for metabolites, I identified 1,702 independent variant metabolite associations from 690 metabolites. And they come from about 248 low side or genetic regions. And that's what's showing in this Manhattan plot. So the X axis for this plot is a chromosome and the different genetic positions and the Y axis just showing the p-value or negative log 10 transformative p-value for the association between the genetic variant and metabolites. So each line of the dots is actually a locus. So all those association happens in this specific or chosen genetic regions. And each dot is a variant metabolite associations. So for example, for this locus, we can see many genetic variant in this locus associated with some amino acids, which indicated by the color of the dot and some lipids. And from this plot, the key message is that we find the association between variants from all the autosomes with many, many different types of metabolites. And those metabolites come from different categories, which kind of one advantage of those metabolomics measurement approach because we can simultaneously check the association with many different type of metabolites. So the second part I'm trying to show here is I explored the novelty of the association I fund as well as the general heritability of the metabolites. So on the left panel, you can see this is the number of no one or novel associations for different metabolites under or belonging to different categories of metabolites as shown in the X-axis and the Y-axis, just the counts for the association for those metabolites. And the dark blue one, I think is no one association. And the light blue one as those novel association identified in the current study. So for example, for lipids, we find about 150 novel association and another 150 no one association that have been reported in the literature. And interestingly, we also find maybe 100 metabolites, they do not have the association, sorry, that some lipids that do not have significant associations identified in this study. And this kind of the light green part, which is for the metabolite without significant associations with generarians seems to be have a higher proportion for iso-xenobiotics, which are the metabolites that usually you obtained from exogenous source, such as food or medicine. So here on the right panel, I'm showing you the heritability estimated for different metabolites. And this is a volume plot on the X-axis, just the categories of the metabolites and Y-axis, just the estimate heritability. So in general, we find around like 20% of the variants of metabolites can be explained by genetics, that's the medium heritability. And for some categories of metabolites like xenobiotics or peptide, their medium heritability is lower compared to others, whereas cofactors and vitamins and nucleotides, they seem to have higher medium heritability. So last feature related to genetic architecture or last two features related to genetic architecture, I want to talk about here is polygenicity and pleiotropy. So polygenicity is about the number of low side associated with each metabolites. So it basically saying, whether this metabolite is only determined by one or two low side, or they are influenced by many, many different genes. And so that's something tried to reflect or tried to be reflected by the polygenicity. And in this panel here, we can see that, so the X-axis is the number of low side associated with, associated pyramid tablet and Y-axis is heritability estimated for that corresponding metabolites. So each dot is a metabolites. So we can see most of the, I wouldn't say most, but a majority of the metabolites, they have fewer than two low side. As we can see, most dots is for here. So which means the polygenicity from metabolites at least based on the evidence from the current study is moderate or is not a super or in other words, metabolites are not very polygenic. And we also see this positive correlation between the number of low side for metabolites and heritability. So which just showing that the more polygenic the metabolites are, usually their heritability is higher. Then for the pleotropy feature. So pleotropy is a measure about how many metabolites are associated with each locus. So on the X-axis for this plot here, it's a cumulative percentage acts. So basically we are showing, for example here, about 50% of the locus or low side, they associate with fewer than three metabolites. That's how we read this plot. And interestingly, there are some locus that associate with up to 79 metabolites. So in that case, the gene for that locus, they could be a hop gene for some, for many metabolic process and influence many metabolites. It could be worth further exploration for how that gene involved in the metabolism of those metabolites and how can we use targeting that gene for those process. So that's finished the part related to the metabolite GWAS. Here I'm showing through the results for metabolite ratio's GWAS. So how the metabolite ratio were constructed. So that's this part. We first identified the metabolites that share enzyme or transporter. And then for those metabolite peers, we construct a ratio. And then using the ratio as the treat or as the phenotype for GWAS. So the reason why we are doing or constructing the metabolite ratio, the first is because by constructing the ratio, we can reduce the variance of the measurements and increase the statistical power. And secondly, those material ratio associations may help us capture some metabolic reactions. For example, maybe there are some variant for this enzyme or transporter that really led to this variation of the metabolite ratio in the population. That's something we want to discover. And on the panel B here, I'm showing the association we identified between different metabolite peers. So each band, for the circular plot, each band is one metabolite for the corresponding categories. And each line that link to metabolites is just for the two metabolites for this metabolite ratio. So the darkness of the filling color of this line indicate how strong the association are. So we can see that for lipids and amoon acids, there are a lot of association for metabolite ratios, but they are kind of more, seems to be within their categories. Whereas for energy substrates, we find genetic variants that contribute to the ratio between genetic variants, sorry, energy substrates and many different types of metabolites, which somehow I think is quite met our expectation as many different reactions that involving different metabolites usually need this energy substrates. So by running the GWAS for metabolite ratios, we identified 16 additional associations that were not captured by single metabolite GWAS. Here I'm just showing one example. So we found this RS247227 variant is associated with the ratio for caffeine and paraffin. So, and from the literature, we know that paraffin I think is just one of the derivative of caffeine. And we also find the closest gene for this genevarium is actually the enzyme that catalyze this process. So which kind of supporting, you know, when we are attention to conduct this metamler ratio GWAS or supporting the value of conducting metamler ratio GWAS. Based on those variant metabolite associations or variant metamler ratio associations, we next tried to identify the effector genes that mediated the effect of variant on the metabolites. So specifically, we identified the protein coding genes that near this variant. And then we check whether this variant also influenced the transcript level, which is EQTL or splicing variant of this gene, which is SQLTL first. And then we checked whether this corresponding gene or no one to be involved in the metabolism of the metabolites by checking the HMDB, KAC, and POPCAM databases. So by combining those information, we kind of get or refine a list of potential effector genes that, you know, more likely to be relevant for this variant to metabolite associations. So the expression related relevant genes led to include about 550 genes and the biological relevant check identified about 250 genes. And the overlap part is 94, which is our effector genes. And most of those effector genes enzymes and another 7.4% of them are transporters. And of course, there are some other proteins like some binding proteins and circulating proteins that are part of the, or were thought to be the effector genes for the variant metabolism issues we found. So lastly, to see if this effector genes implicating in any phenotypes and if they can be used as drug targets we explored the linkage of those effector genes with the drugs and the phenotype changes in knockout mice and also human Mendelian traits or disease. So by checking the overlap part, we found 14 effector genes that all have all three sources of information. They are listed here. And these connections may help us identify the metabolites that can be used at the biomarkers for the related disease or maybe we can use the repurposed drug that linked to those effector genes to modulate the level of the corresponding metabolites or associated with traits. So that concludes the results for the M1 part. And here then we conduct analysis to identify the potential cause of metabolites for traits and disease using MR approach. So here is a general design for the MR analysis in this study. So for the exposure, we focused on those metabolite and metabolite ratios that have effector genes identified. Then for the outcomes, we focused on 12 traits from three categories. They are either agent related traits such as bone marrow density or metabolism related traits that body mass index and also some immune responsive related traits such as asthma. Then with exposure and outcome we performed a two sample MR analysis and identify the cause of metabolites and ratios for those outcomes. Buffer only corrected P value or thresholds were used to prioritize the associations and it's an active analysis like metabolic pleotropy check, reverse MR as well as MR result prioritization analysis as colonization were performed to prioritize the more likely or more that the cause of metabolites for the disease or traits tested. So here I'm just quickly show you the metabolites that we fund for the agent related traits. It's not for you to see the details. I will talk about two representative examples later but here just to show by MR approach we fund many metabolites that are related to aging traits relate to metabolism related traits and disease and also relate to immune related traits and diseases. So the two example I want to discuss more here. One is the R rotate with estimated bone marrow density. So through MR we find that the increasing level of genetic predicate level of R rotate is associated with lower estimated bone marrow density. And this is interesting because using an independent cohort we find that the higher level of R rotate is associated with higher risk for heat fracture. So this is consistent with our MR results. We're like both of those results suggesting that the increased level of R rotate is somehow involved in the impairment of bone health. Another example is alpha hydroxyl aloe verite. So from the MR we find that the increasing genetic predicate level of this metabolite associated with decreased BMI. Interestingly we find the effector gene for this variant metabolism association is called lactate dehydrogenase LDHA. And this gene if we knock out in the mouse model those heterozygous mass have decreased total fat amount which is kind of consistent with our observation that metabolites could be the mediator of the genetic effect on the obesity or anthropometric traits. Lastly, since we know many diseases and traits they can also be influenced by obesity. Here we try to perform analysis to differentiate the effect that whether our metabolites influence the outcomes such as asthma and BMD through the BMI or they just they can influence them independently. So this is the code approach at GWAS by subtraction which allowed us to differentiate or dissect the genetic genetic effect on the BMI dependent part and BMI independent part on those outcomes on the disease outcomes or trait outcomes. So here MR then with those association we performed MR again and we found for example the orotate for orotate the association with BMD is mostly through the part that independent from BMI. In another example for citrulline we find the relate to the asthma but at least it's partially it's affected partially through influencing the BMI. So there are some limitations of the study for example most available diseases and trait GWAS from individuals in European ancestry and this can limit the general stability of our finding to the non-European population. And secondly the metabolomics data relatively measurements so the finding we have for example we found this higher level of orotate associated with lower ABMD and we don't really know what exact level that higher level is. And it's to really apply those use of metabolite in clinical practice we need to additional experiments to really quantify the absolute measurement of those metabolites in the blood. And lastly MR because MR using the genetic variants as the instrument to check the effect of exposure and outcomes usually the genetic variants can only explain the part of the variants of your exposure that's kind of led to the low statistical power of MR approach. In conclusion we identify genetic terminates for citrulline metabolites and we also infer the causal effect of metabolite levels and ratios on 12 traits and diseases that are predominantly influenced by different mechanisms like aging metabolism and immune response. And I would like to thanks all the lab members from Dr. Brent Retru's group, Dr. Celia's group and also the collaborators that contribute to this research and also the funding agencies and also the CSA for sharing this data with research community. And thank you for listening. Thank you Yi Heng for your excellent presentation. I think you did a great job at taking a very complicated topic and making it very accessible. So I would now like to open it up for questions. So just a reminder, muting will remain on but you can enter your questions into the Q&A box at the bottom of the Zoom screen. I see a few questions have come in. Okay, so I will start with the first question from Yi Heng Chao. Thank you for the great presentation. Did you find the significance of any locus metabolite associations different from that reported in the literature? So for example, the significant association between metabolite A and locus B was reported insignificant in a previous study. Yes, definitely. But the reason why they are not significant it could be due to many different reasons, right? For example, they could be because of sample size. That's kind of one major difference. Also sometimes the technique they used to measure the metabolites could also limit how sensitive, how accurate you measure the metabolites. Okay, great. So the next question from Andrew Patterson. Thanks for a great presentation. In your Manhattan plot, I didn't see any results for the X or Y chromosomes. Yes, that's a good point. So first, I think for a CSA way, we do have X-chromolone, but at the time, we did not specifically perform X-chromolone analysis because X-chromolone is a very complex topic because the, sorry, I think there's the silencing because the silencing sometimes incomplete. So how to model the variant association with the metabolites sometimes is tricky. So at the time, we want to focus on autosomes first, but definitely X-chromolone should be studied for, to check their association with metabolites, especially should be investigating, you know, six specific design. Great. So another question from Andrew Patterson. Statistically, is there a difference between using the metabolite ratio as opposed to co-varying one metabolite for the other one? That's a good question. I don't really understand what you mean by co-varying. You mean creating the new phenotype using, instead of using a ratio, but using some other statistical way to aggregate their levels? Yeah, I haven't explored that. So unfortunately, I don't have the answer for that. That's okay. We'll see if he can comment on the question if he wants to clarify. Yeah. Okay, so Leela McKinnon writes, thanks for this great presentation. I thought your descriptions of the MR and GWAS methods were very clear and helped me understand them better. I have a very broad general question, which is what would you say the applications of this research are? What are some of the ways these findings could be used in a clinical setting? Yeah, that's a very good question. So as I mentioned, with the relative measurement from metabolomics, we don't really know, for example, which threshold we should set for or rotate should be labeled as high risk or to help us differentiate the high risk group for people with osteoporosis. But at least we kind of prioritize some of those metabolite targets that, you know, and their association with diseases. And then maybe next study could be, you know, get a smaller cohort and then we can more accurately measure those metabolites and then, you know, have a, then create a dosage dependent association between the absolute level of the metabolites and disease risk. And then in that case, maybe we can generalize those threshold for disease risk screening, et cetera, or maybe the orientate actually or the religion can be used as a drug target. So yeah, those kind of potential application for the findings. Okay, so I've got another question from Andrew Patterson. Did you analyze the unnamed metabolites? Wouldn't finding GWAS signals for them provide insight into the potential compounds slash roles? Yes, thanks for your question. Yeah, I did. So basically for my filtering step for to include metabolites, I didn't filter them based on whether they have no identity or not. I do include all of them when GWAS for all of them. However, the tricky part for analyzing unknown or for the mentality without identity is that you can find a strong association between this variant and the metabolites, but it's kind of tricky to know whether that metabolites like are they, you know, that associations because of what, right? So I mean, in my analysis, by integrating the biological evidence, we kind of try to focus on those variant and metabolization kind of supported by, you know, expression or metabolic evidence. But for those unknown metabolites, that part is kind of hard to obtain.  Okay, perfect. I think we have time for one last question and we've got one last question in the Q&A. Hi, Yi-Heng, thanks for your work and presentation. This is from Stephanie Chevalier. Can you provide some examples of energy metabolites which are not as obvious as others? Yeah, that sounds like a good question. I don't, I think the only one I remember, I think it's like phosphates. So somehow, they are grouped as energy substrates. Yeah. Okay, perfect. Thank you very much again. That was a wonderful presentation with lots of positive feedback. So before we wrap up, I would just like to remind everyone that the next deadline for the data access applications is April 10th. So please visit the website under data access to review the available data and we will also put a link in the chat. I'd also like everyone to remind everyone to complete their anonymous survey upon exiting the Zoom session. So if, yep, there comes a slide. So our next webinar will be factors associated with developing high nutrition risk, data from the CLSA. This will be presented on Tuesday, March 19th at noon by Dr. Christine Mills, the University of Waterloo. Registration details will be available on our website and we'll also post the link in the chat box. And finally, remember that the CLSA promotes this webinar using the hashtag CLSA webinar. We invite you to follow us on Twitter at CLSA underscore ELCP. Great. Thanks again for joining us today.


Before I came to ProScan, I think the speed at which I could report was probably typical of a radiologist of my level of experience and training. Since being at ProScan, I think the rate at which I can report MSK MRI has doubled or tripled at least to what I was before. And I'm definitely a lot more confident and a lot more accurate. I think the best feature of this fellowship is the high volume of reporting that you can do. A lot of fellowships just involve reviewing teaching cases, which is great, or listening to lectures, which is great. But the pinnacle of learning, or the greatest sort of way that you can learn radiology is by doing radiology. And reporting is what we do day to day. And so a fellowship where you do that to a high volume every day and get expert feedback on every case that you report is fantastic.


Hi everyone, Joe for Jaspy's casebreaks.com coming at you with 2020, 2021 Panini Donnerous Optic Basketball Hybrid Edition 5-Box Picker Team Number 2. It's 5 boxes right there. Very big thanks everyone here for making it happen on a Chew Tuesday. If your name is highlighted, that means your team get 5X entries for the $500 break credit and a single entry for the break credit for tonight. We did it. This was the last break. Thanks to Matthew who unlocked this break with the Timberwolves and unlocked the sort of night long promo. So big thanks to Matthew for that. Now let's do the hybrid boxes. Good luck. Open up all the boxes at once. Everyone watch the movie The Town. Ben Affleck. Hawkeye's in it. John Hamm. Blake Lively. See another Affleck, Casey perhaps. No Damon. They Rob Fenway. Great movie. If you like heist movies, you're going to like this one. 2020 Draft Class, so we're looking for Lamello's. We're looking for Anthony Edwards, Tyrese Maxie, Tyrese Halliburton, etc., etc. Low numbered cards, different parallels, etc., etc. All right. Thanks everyone. Devante Graham. Kyrie Irving. Some pulsars there. There's an Anthony Edwards. Rated rookie card. Just base, not hollow, but still nice for Matthew. First of many I hope. Oh, someone won Mega Millions. Someone in California. Nice Tyrese Maxie. Joe Lawson Sixers. Someone in Southern California. Where did I buy my lottery ticket? Someone in Santa Monica, California maybe? I don't know if autographs are guaranteed in this, right? You got two hollow prisms. No, autos aren't guaranteed, but we're looking for parallels. Oh, nice. Anthony Edwards and that pulsar. That's what we're looking for. Matthew. New Jersey. Damn. Where in New Jersey? I know people in New Jersey. Perusho. Audubio. And a nice rated rookie hollow, Tyrese Halliburne. That's going to be King's edition. Going to Andrew. Romelo Ball. Another Tyrese Maxie. Sixers. Joe. These pulsars are not numbered, but they look cool. There's the rookies, Anthony Edwards. Nice. Joe with the hornets. Rated rookie base. Got Anthony Edwards. The rookies blue pulsar. And a James Ramsey, 13 out of 19, pulsar, autograph. That's going to be for Andrew and the Kings. Dramon. We got Jamal Murray. Another Tyrese Maxie. Love seeing that. All right. Onwards, almost there. Pretty quick break. And then we can give away that break credit. There's Luca, Steph, blah, blah, blah, blah, blah, blah, blah, blah, blah, blah, blah, Precious, Precious Ucceua. We've got Otto Porter, Paul George, Desmond Bain, et cetera, et cetera. There's Nets Denwitte, Nets Durant, Cole Anthony. We like Cole Anthony too, right? Orlando, that's going to go to Joe. Don't see where New Jersey but tell me New Jersey, where Nets. All right. Listen, I'm not greedy, Rex. If I can just win like the $1,000 prize from Mega Millions, I'll take it. I'm OK with that. Cassius Stanley, Donovan Mitchell, Hukusevsky, Kawhi, LeBron, Kevin Porter Jr., Riller, Harden, and you know what? Devon Vasell had a really nice game the other night. No Wemby, Vasell and Sohan both went off. I think both almost went for 30 points each. So a little one here for you, Joe. Let's see what happens there. Maybe then we see the secondary market value go up for that. There's LeBron James for my Lakers. Joe Lawson will get that as well. And that's that, ladies and gentlemen. Thank you. Now, who's going to win that $500 break credit? Let's go back to the list. Let's grab all of your names. Then Joe gets 1, 2, 3, 4, 5. Then we'll grab this group of names. Joe gets 1, 2, 3, 4, 5. That's from Kirk to Joe. And there's Alan, Alan, Kevin. Joe, five times. 1, 2, 3, 4, 5. And then we'll go to Diego and then Matthew. 1, 2, 3, 4, 5. Let's gather this group right here. And then Joe, 1, 2, 3, 4, 5. And then Andy and Diego. And then Andrew, 1, 2, 3, 4, 5. And then this group right here. All right, should be 54 names. Yes, that's correct. Now let's gather all the names. New dice. And the name on top. After 7, wins the $500 break credit. Winner takes all. 1, 2, 3, 4, 5, 6, and 7th and final time. All right, good luck. Drum roll, drum roll, drum roll. After 7, a lot of sad faces here. But appreciate you getting in. Only one face can be the happiest. And that's going to be, not Andrew, Andrew. Very close, man. But that's going to be Joe Lawson. He had the odds on his side. But we've seen it go the other way. But this time it worked out, Joe. 500 bucks going your way. Thank you very much, everybody, for getting in. I'm Joe for jazby'scasebreaks.com. And I'll see you next time for the next one. Bye-bye.


In today's assignment class, we will be looking fully at intrinsic semiconductors. This is assignment 2 and we will be focusing on intrinsic semiconductors. So before we start looking at the problems, we just do a brief review. So intrinsic semiconductors or pure semiconductors are essentially single crystals. We say that there are no defects in the semiconductor because these defects can again create electrons and holes of their own. In the case of an intrinsic semiconductor, we say that the electron concentration in the conduction band that is n is equal to the hole concentration in the valence band that is p and it is equal to something which we denote as ni and ni we call the intrinsic carrier concentration. We also say that ni is a function of the band gap of the material e g and also a function of temperature. So typically ni is written as n c n v exponential minus e g over 2 k t. So the intrinsic carrier concentration depends exponentially on the band gap. The temperature term enters in this exponential factor but n c and n v which are the effective density of states at the valence band edge and the conduction band edge are also a function of temperature. So n c and n v are also a function of temperature. Typically they are proportional to temperature to the 3 over 2 but the exponential term is the one that dominates. We also saw the general equation for conductivity sigma is nothing but n e mu e and p e mu h. In the case of an intrinsic semiconductor, this just becomes ni e mu e plus mu h. So these are just a few points about intrinsic semiconductor. We will be using them today during the course of the assignment. So let us first look at problem 1. So what fraction of current in intrinsic silicon is carried by holes? So we have silicon and it is intrinsic which means n equal to p equal to ni and the question asks what fraction of current or what fraction of conductivity is defined by the holes. So if you just say n is equal to p is equal to ni that means there is a 50% contribution that is a very simplistic answer. The reason is the conductivity not only depends on n it also depends upon mu e and mu h which is the mobility of the electrons and holes. So we can write the conductivity equation n e mu e plus p e mu h. This represent the fraction carried by the electrons, fraction due to electrons. This is the fraction due to holes. So we can include the numbers for mu e and mu h. So fraction carried by the holes we can write this in the form of a ratio it is nothing but p e mu h divided by the total that is n e mu e plus p e mu h. So for an intrinsic semiconductor n is equal to p is equal to ni so these terms cancel e will also cancel. So this is nothing but mu h over mu e plus mu h. So the fraction of current carried by holes is directly proportional to the whole mobility. We can plug in the numbers for silicon here. So for silicon mu h is 450 centimeter square per volt per second mu e is 1350. So the mobility of the electrons is higher. So we can plug in these numbers and the fraction is 0.25. So even though we have equal concentration of electrons and holes they do not have the same mobility and this is because your electrons are moving in the conduction band and the holes are moving in the balance band and this ultimately determines what fraction dominates whether the electron conductivity dominates or the hole conductivity dominates. Whether when we see an extrinsic semiconductor we will find that n and p are not the same one is much higher than the other and then there one of the term dominates because of the difference in concentration. So let us now move to question 2. So we have a pure semiconductor or an intrinsic semiconductor the band gap is 1.25 e v. So the effective masses of the electrons and holes are also given. The effective mass of the electron me star is 0.1 times me where me is the mass of the electron and m h star is 0.5 me. This is the effective mass for the hole. Once again we have seen the concept of effective mass before. So effective mass does not mean a change in the actual mass of the electron or the hole. It just represents the cumulative of all the forces of the atoms in the lattice that basically acts on the electrons and holes. Once again these numbers are different because you have electrons that are moving in the conduction band and holes that are moving in the valence band. So the band gap is given. The effective mass values are given. The carrier scattering time is temperature dependent and that is given of the form. So tau which is your scattering time is a function of temperature and this is 1-10 to the minus t. So 1 times 10 to the minus 10 divided by temperature and the units are seconds. So the effective masses are given. The band gap is given and the temperature dependence of the carrier scattering time is also given. This we will use to calculate the mobilities. So we want to find the following at two temperatures. One is 77 kelvin and the other is 300 kelvin. So 300 kelvin is room temperature, 77 kelvin is typically your liquid nitrogen boiling point. So that is a low temperature. The first one we want to find is the concentration of electrons or holes. Because this is a pure semiconductor, what we want to find is the value of the intrinsic carrier concentration. So we can go back to the equation Ni is nothing but square root of Nc and Nv exponential minus Eg over 2 kT. So the problem is we do not have the values of Nc and Nv. These are the effective densities of states at the band edges but these we can calculate once we know the effective mass. So Nc which is the density of states at the conduction band edge is nothing but 2 pi Nv star kT over h square whole power 3 over 2 Nv we can do the same for the valence band edge 2 pi mh star over kT by h square whole power 3 over 2. So we have the values for Nc and Nv. Then we see both are temperature dependent they are proportional to T to the power 3 half. So once we calculate Nc and Nv for both the temperatures we can plug in here and calculate the value for Ni the band gap is also known. So typically you have to keep all of this in SI units. So you have to convert Eg from electron volts to joules and that we can do by just multiplying by 1.6 times 10 to the minus 19 kB is also in joules. So it is your Boltzmann's constant that has a standard value. So once we plug in the numbers I am just going to write the final answers but you can just go through and check. So Nc at 77 Kelvin is 1.03 times 10 to the 23 per meter cube. So if you remember the definition of the effective density of states is the total number of states per unit volume that is available for the electron to occupy or the hole to occupy. Nc Nv is 1.151 times 10 to the 24 per meter cube. We can do the same calculations for 300 Kelvin again just write down the answers. So Nc is higher 7.92 times 10 to the 23 Nv is 8.85 times 10 to the 24 per meter cube. So compared to 300 Kelvin Nc and Nv are higher this is because we have more density of states available at higher temperature simply because they are directly proportional to T to the 3 over 2. So we can substitute these values of Nc and Nv in this expression and calculate the value for Ni. So let me just write that down Ni at 77 Kelvin is 4.63 times 10 to the minus 18 meter cube. So that is a really small number Ni at 300 Kelvin is 8.56 times 10 to the 13 per meter cube. So I can also write this in centimeter cube or 8.56 times 10 to the 7 centimeter cube. So your Nc and Nv values if you look or off by one order of magnitude simply because you have a rise in temperature but because your Ni depends exponentially on the band gap there is a huge variation between 77 Kelvin which is your liquid nitrogen temperature and 300 Kelvin which is room temperature. So here you have a value that is 10 to the minus 18 and at room temperature you have a value for Ni that is close to 10 to the 13. So that overall there is a 31 orders of magnitude change as we go from liquid nitrogen to room temperature. This is why we say that out of these two terms Nc and Nv and the exponential term the exponential term is the one that dominates in determining the value for Ni. So this is part A where we want to calculate the concentration of electrons and holes. Part B we want to calculate the Fermi energy or the location of the Fermi level. So part B we want the location of Efi. This is again a standard expression Efi is Eg over 2 minus three fourths kT ln of Me star over Mh star. So if Me and Mh were equal then Efi will be exactly at the center of the band. If you remember Efi is nothing but a representation of the chemical potential or the amount of work that needs to be done in order to remove an electron from a semiconductor. So even though your electron and hole concentrations are the same so N is equal to P because you have different effective masses your Efi is slightly shifted from the center of the gap. We can once again plug in the numbers so 77 kelvin 300 kelvin Me star and Mh star values are given temperature is also known. So Efi here if you do the substitution is 0.633 electron volts at room temperature Efi is 0.656. Eg over 2 if you look at it is just 1.25 over 2 which is 0.625 electron volts. So the values are very close to the center of the band gap but they are slightly shifted and the shift becomes higher the higher the temperature. So this is 0.633 and this is 0.656 so slightly deviated away from the center of the band gap. This is part B in part C we want to calculate the electron and hole mobilities. So we want to calculate the values of Mu E and Mu H. So Mu E and Mu H are related to the effective mass of the electrons and holes and they are also related to the scattering time. So Mu E is nothing but E tau E over Me star and Mu H is E tau H over Mh star.  So once again we can calculate the values of tau. In this particular question tau E and tau H are both the same because we do not distinguish between electrons and holes. We only say it depends upon temperature. Once we calculate tau we can go ahead and calculate Mu E and Mu H and get it for the two different temperatures. So let me again write down this side is 77 Kelvin this side is 300 Kelvin. So tau if you calculate is 1.3 times 10 to the minus 12 seconds. We can then calculate Mu E which is 2.283 unit is meter square per volts per second. Sometimes centimeter square per volts per second are also there. It depends upon which you want to use. Mu H is 0.456 meter square volts per second and Mu H the mobility of the holes is lower because the whole effective mass is higher than that of the electron. We can do the same for 300 Kelvin. In this case tau is 3.3 times 10 to the minus 13 seconds. So higher the temperature smaller is the scattering time. So at low temperature this is minus 12 seconds this is minus 13 seconds. So one way to think about this is higher the temperature faster the electrons and holes are moving because they have higher thermal velocities. So they can scatter of the atoms quicker. Mu E is 0.586 meter square per volts per second Mu H 0.117 meter square per volts per second. So the last part of the question we want to calculate the electrical conductivity sigma is nothing but N i E Mu E plus Mu H. So N i we got in the first part of this question Mu E and Mu H we just calculated. So we can just plug in the numbers. So sigma at 77 Kelvin is very small because if you remember N i is very small. So 10 to the minus 36 ohm inverse meter inverse you can also have ohm inverse and centimeter inverse depending upon what your values the units for N i and Mu E and Mu H are. So the same thing we can do at room temperature and sigma is 9.6 minus 10 to the minus 6. So by looking at an intrinsic semiconductor at two different temperatures one thing we find is that the carrier concentration increases exponentially with temperature. Similarly the conductivity will also increase because the carrier concentration increases. This again is determined by the value of the band gap. So higher the value of E g more steeper is this dependence. So if instead of 1.25 we had done the same problem with say 2 electron volts your answers will also be different but the difference between 77 and 300 Kelvin will also be more pronounced. So that is something you can always work out can take the same values but change the value of E g to 2 electron volts and do this question and you can see the difference between 77 and 300 Kelvin. So let us now move to question 3. So question 3 we have gallium arsenide which is a direct band gap semiconductor with E g of 1.42 electron volts at 300 Kelvin. So gallium arsenide it has a higher band gap than silicon it is also a direct band gap material but that is not relevant for this question. It is at room temperature so temperature is 300 Kelvin. Take N c equal to N v equal to 5 times 10 to the 18 per centimeter cube and also independent of temperature. So the N c and N v values are given and just for this question we are assuming that both are same and that they are also independent of temperature. Simply speaking this is not true but as you see for this particular question this is a very valid assumption. So first we want to calculate the intrinsic carrier concentration at room temperature. So that is pretty straight forward you have seen the formula before. So N c N v exponential minus E g over 2 k T. So we can plug in the numbers the value of E g is given. So N i is 6.05 times 10 to the 6 per centimeter cube. So this is the intrinsic carrier concentration at room temperature N i is a pretty small number for comparison silicon has a value of N i of 10 to the 10 so 4 orders of magnitude higher but this is because gallium arsenide has a higher band gap. So the next part of the question says explain numerically how the carrier concentration can be doubled without adding dopants. So we want to keep this semiconductor your pure semiconductor but at the same time we want to increase the value of N i. So the new value of N i that we want is double of the N i value at room temperature. If you are not allowed to add dopants and if you look at this equation the only way to increase N i is to increase temperature because N c and N v are both temperature dependent terms N i also depends on temperature through this exponential term minus E g over 2 k T. So increasing temperature will once again increase N i. So the only way to increase N i without adding dopants is to increase temperature. So we want to know what the new temperature is when your value of N i is 2 times the N i at room temperature. So we will once again use this expression N c and N v are constant so it is not a function of temperature if it were a function of temperature that will also have to be taken into account but N c and N v are constant we know the new value of N i so it is 2 N i at room temperature square root of N c N v exponential minus E g over 2 k let me call this temperature T prime. So T prime is the only thing we want to know is the only unknown this is known these are all known we can put this and recalculate and this gives you the value of T prime to be 307.7 Kelvin. So you increase the temperature by 7 degrees so delta T is 7.7 Kelvin you can double the concentration of N i. So temperature equal to 300 is approximately 27 degrees so 300 Kelvin is 27 degree Celsius so 307 is 34.7 degree Celsius. So we find that even for a small increase in the value of N i so you are only doubling the value of N i you need to increase your temperature by 7 degrees. If you want the really high conductivities that we see in the extrinsic semiconductors can actually calculate that the temperature change must be much higher. This is one of the reason why intrinsic semiconductors are almost never used in the case of devices usually doped semiconductors are used because it is much more easier to control the dopant concentration and then control the carrier concentration and also the conductivity. So let us now go to problem 4. So problem 4 we want to calculate the intrinsic carrier concentration of germanium. So we have germanium with a band gap E g of 0.66 electron volts. This is lower than that of silicon in fact germanium was the first semiconductor that was used ME star is 0.56 ME and MH star is 0.40 ME. So we want to calculate N i so the equation is the same N i is N c N v exponential minus E g over 2 k T. So N c and N v are again related to ME star and MH star. So N c is 2 pi ME star k T over h square whole power 3 over 2 we can write a similar equation for MH star we saw that earlier during question 2. So once again we can calculate N c and N v plug it back in and get the value of N i. So we will do the numbers can write down the final answers N c is 1.05 times 10 to the 25 per meter cube N v which is the same equation except ME star is replaced by MH star N v is 6.33 times 10 to the 24 per meter cube. So N c and N v are known we can calculate N i. N i if you do is 2.36 times 10 to the 19 per meter cube I am just writing down the final answer the math can always be worked out or 2.36 times 10 to the 13 per centimeter cube. So we saw that gallium arsenide has a value of N i that is 4 times or 4 orders lower than that of silicon. Germanium on the other hand has a value of N i that is nearly 3 orders of magnitude higher than silicon. Once again the differences are all related to the band gap values. We can then calculate sigma is N i E mu E and mu H the value of mu E and mu H are given mu E is 3900 and mu H is 1900 centimeter square per volt per second. So that sigma is nothing but 0.022 ohm inverse and centimeter inverse if you also want to calculate the resistivity rho is 1 over sigma which is equal to 45.66 ohm centimeter. The question also asked to calculate the position of the Fermi level at room temperature. So that is again an application of the formula E f i is E g over 2 minus 3 fourths k t ln of M e star over M h star. So this again is very close but it is not exactly at the center of the band gap. So let us now look at problem 5. There is a particular semiconductor and it says that the effective density of states is a constant N c naught times temperature to the power 3 over 2 and same way N v is N v naught times temperature to the power 3 over 2. So the experimental values of N i at different temperatures are given. So we have temperature values of N i in centimeter cube so 200, 300, 400 and 500 and the values of N i 10 to the 7. So we can see that with increasing in temperature the value of N i also increases. So the question ask us to determine this product N c naught times N v naught and also the band gap. So both E g is not known and these two numbers are not known. You can go back to the original equation N i is N c N v exponential minus E g over 2 k T. N c and N v we can substitute these x terms so that this simplifies to T to the 3 over 2 square root of N c naught and N v naught which is a temperature independent term times exponential minus E g over 2 k T. So we can choose any two temperatures so we have four we can take any two temperatures and take the ratio of N i at these two temperatures. So N i some temperature T 1, N i at another temperature T 2 it is nothing but taking this ratio which is here this is a temperature independent term. So this becomes T 1 over T 2 whole to the power 3 over 2 exponential minus E g over 2 k 1 over T 1 minus 1 over T 2. So T 1 and T 2 values are known N i values are known for example your T 1 could be 200 Kelvin T 2 could be 300 Kelvin in which case the N i values are tabulated the only unknown here is E g. So I did these calculations taking 200 and 300 you can take it with any of the other temperatures we have also done and checked you substitute the values E g works out to be 1.25 electron volts. So this is the value of E g which is the band gap of the material once you know E g you can substitute E g for any of the temperatures and evaluate N c naught and N v naught this is a temperature independent term if you do that N c naught times N v naught is nothing but 1.188 times 10 to the 29 the units here are crucial N c and N v the square root of that should have the units of centimeter per centimeter cube or per meter cube in this particular question this also depends on T to the 3 over 2. So the units of this product is centimeter to the power minus 6 Kelvin to the power minus 3 that way when we substitute for square root units work out in the right way. So today we have looked at various problems related to intrinsic semiconductors the important thing to remember is that the intrinsic carrier concentration is a function of temperature and depends upon the band gap of the material.


Yeah, I don't know how to enter this one My body Hurts and it it has for a long time In my freshman year of college both of my wrists gave out it. It's a strange thing going from drumming and karate and writing and gaming to Suddenly finding the act of gripping a particularly heavy fork full of food at times too painful to bear I was fortunate in that professors allowed me to delay certain assignments or lowered required page counts on essays because The difference between 10 pages of typing in 15 could now be measured by the physical toll it took During my first winter break. I forced myself to learn the Dvorak keyboard layout because it requires less movement than QWERTY Also during that winter break I went to see Tron Legacy at the IMAX Theater in the Providence Place Mall Decided I needed to use the restroom right as the movie started Maybe I just wanted to get away from weirdly de-aged Kurt Russell and ran down the stairs a few at a time On the bottom step hit something wrong collapsed and ended up on crutches When I got back to campus where I lived in the third floor of a building with no elevator The ankle healed my wrists were temporarily addressed by wrappings that made me feel better While convincing everyone else I had just failed to unalive myself Eventually I got surgeries different ones for each because although the issues cropped up at the same time They had totally different causes ligament tore here and Attendant came loose here and was wearing itself out over my bone But I didn't really understand why I had to massage the scars and stretch and all of that stuff in the weeks after the surgeries I was young and sad and in pain It was never properly explained that if scar tissue was allowed to form it would hurt forever Whoops But that's just life right what doesn't kill you it makes you stronger But maybe not physically maybe not when you have to wear an elbow bracer an ebracer only use backpacks Because your messenger bags hurt my back in a way that the symmetrical weight from dual straps just don't Life is pain Maybe you grow from it or maybe it grows in you Hello by the way and welcome to the week air of you you can call me bad itself care and today I am talking about Julia Ducar now's sophomore feature titan Now to say I was excited for this movie would be a severe understatement the majority of Ducar now's debut 2016 is raw Is one of my favorite movies of the last 10 years? I have mentioned my affinity for coming-of-age stories about teenage girls something that is already Very weird to say and is going to be a whole lot weirder once I turn 30 But I promise it's innocuous and mostly just confusing as well as highly stylized ultra-violent cinematic experiences Like if assassination nation is my favorite movie, how could raw not be up there? The real reason is that I don't like the ending which feels like a punchline to a joke I didn't know was being set up and I didn't particularly enjoy on its own nor feel benefited the message of the film But what precedes that frustrating closer is visceral and beautiful and just Excited the hell out of me for what one would hope to be a long fruitful career for its writer-director But as is so often the case it was quite some time before I heard her name again This past June actually when her latest was announced as part of cans lineup I ignored all the descriptions and marketing and knew fuck all about what the film was other than the opinions of some Friends who'd seen it in Europe where it had premiered earlier or at press screenings over here and feelings were mixed And that was useful to know I tempered my sky-high expectations somewhat and I'm glad that I did Titan is a very different film than its predecessor which isn't Inherently a bad thing but raw is so specifically up my alley that in retrospect anything very different would never hit me Specifically as hard raw is a coming-of-age tale about a girl becoming a woman and all of the figurative and literal messiness that comes with it It follows in the footsteps of Ducarna's debut short film junior Which was the first role for raw star Garance Marie-Lyé Marie-Lyé has now played a character named Justine in all three of Ducarna's films She doesn't appear to be the same Justine which leads to the possibility that there are many more Marie-Lyé Performed Justine's to come which I think would be a kind of fun directorial trademark Which follows a 13 year old girl as she goes from being a gross tomboy or whatever to a beautiful girl Who has taken off her glasses let down her hair and started shedding her skin and leaving trails of goop behind her It's a more of your typical body horror than raw Which is less about the protagonist's body being torn apart than her drive to do that to others Titan returns to the former more and continues Ducarna's tradition of making gross movies about things that happen to women's bodies But because our new protagonist Alexia has fully grown her body is no longer changing of its own accord Enter pregnancy Now if you believe the word of its director Titan is a love story I'll be at a non-traditional one a woman who has become completely Disconnected from her own humanity gets a chance at redemption thanks to a broken man It's not necessarily romantic love a little more of a found family sort of thing kind of I Don't know it's a lot how a lot well the father of her child is a car Which we see in a sequence that is not explicit But to my puritanical American eyes was pretty shockingly sexual for a while I thought that this scene was either a dream or a coping mechanism It comes shortly after Alexia fucks a dude shit up with one of those Things that like looks like a chopstick that some people used to keep their hair in place that I can't be bothered to Look up the proper name of and either it wasn't real at all or it was her way of processing a traumatic event, but No, I it went down in universe exactly as depicted, which you might imagine causes some problems It occurs to me that making things super literal is part of why I didn't like raws ending Taitan handles it better as does the whole Murder thing well, I wouldn't call it a bloodbath Taitan's first path or so features some deeply unpleasant brutality What makes it hard to watch is less the actual violence than how Long some of these people suffer the first kill I mentioned felt like it went on for Minutes as the man convulses and eventually starts foaming at the mouth and I just wanted to stream Fucking cut already.  I'm reminded of the 10 hours I spent as Abby in the last of us part 2 after which I still wanted her exactly as dead as I did at the start All of that time in her shoes changed nothing for me And Titan probably does go a bit too far down that path for its own good I enjoyed the stylishness of the whole production enough that I never completely disconnected from it But if I really think about how I felt about Alexia after everything was said and done It wasn't much and what I do feel came primarily from first-time actor Agatha Roussell's wild performance, but if we're talking performance Vincent Landon gives one of the best I've seen in years as another guy named Vincent a fire captain who lost his young son a decade before Resulting in a divorce and then your usual depressive spiral He's also taking steroids in an effort to keep up with the youngsters on his team Things aren't great and as we meet him He has just been faced with a choice because Alexia while on the run saw photos of his missing son Adrian and decided that since she needed a place to go Why not that kid's parents? Obviously, she looks nothing like him, but she can do some things to make herself less feminine at least She shapes her head breaks her nose in another one of those excruciatingly long sequences of suffering I hated watching it and Binds her chest and belly with an ace bandage before going to the police posing as the long-lost boy and you could feel Vincent's understanding that this is not in fact his son and see the choice he makes to push forward anyway It is so subtly impactful the knowledge that he has a human-sized hole in his life And if this imposter wants to fill it, they'll do So Alexia becomes Adrian But even though she wants this second verse to be the same as the first it turns out to not be so easy and As her body bulges and tears from the thing growing in her stomach She finds herself becoming acclimated to this new world with a new father who cares far more than her actual parents ever seem to The opening scene which ends in young Alexia getting the titular titanium plate put into her skull is Fascinating because of how many different dynamics are set up so quickly and the film changes with Alexia Once she finds herself in Vincent's care the whole thing slows Way the heck down the stylized visuals are paired back and we find ourselves watching a Comparatively straightforward drama about two people becoming closer Ducornow has said that she has an existentialist approach to life believing that you have to be many in order to be one You have to go through metamorphosis to get closer to your essence and sure you will never completely reach that point But the journey is life That's what we're doing here and she reflects that in the construction of the movie as well as its contents It's another gutsy move because the change is jarring in a very different way than the violence is and I know that that transition Didn't work for everyone I'm not even really sure it worked for me so much as I just kind of accepted that it was happening and shrugged my shoulders It's where we get into seeing if Alexia can be redeemed whether Vincent's kindness and or brokenness that she's taking advantage of can get through the metaphorical titanium plate keeping her humanity locked away and This is actually pretty clever on Ducornow's part because as established Alexia is a straight-up monster I didn't really care about her whole deal But Vincent seems like a pretty good guy all things considered The initial concern that he might be trying to keep Alexia as some sort of pet quickly passes And we want her to be redeemed because quite frankly we don't want her to kill him So she needs to get better and regain her emotional humanity so that we don't have to worry so much about him Saying that out loud There's a lot to unpack and I have not read nearly enough feminist theory to do it justice. It is a little Messy and when you think about the film in terms of gender as opposed to more broadly about life It kind of starts to feel a little ugly There's been controversy around Alexia's whole stint as Adrienne and how it relates to the question of Transness because well I think it's pretty obvious that Ducornow did not set out to make a trans film certainly the idea comes to mind The most notable entry into the discourse is an article by Jude dry from Indie wire called titan twists trans tropes into perverse lifeless body horror and while the film did not hit me the way it hit her I Can't pretend like it doesn't play into that most dangerous of stereotypes gender presentation as deception used for personal gain Now I would bet a $200 donation to the transgender law center that this never occurred to Ducornow Though I made the donation just in case Because the broader reading falls apart under the barest scrutiny Alexia is not Adrienne because she feels like Adrienne or believes she is Adrienne She clearly doesn't want to be Adrienne She is doing the bare minimum to keep up the charade and as she becomes more comfortable She puts less and less effort into hiding her body when push finally comes to shove She wants to be known as herself and not her presentation I can totally understand why this would rub people the wrong way Especially given the images of her body cut up by that bandage. She's wrapped herself too tightly in But your mileage will vary I Do keep thinking about those bandages though Alexia is different from most of the monsters in body horror films in that she is not directly Changing in Tetsuo a man is rapidly being engulfed in machinery in titan The machinery is trying to escape a body that remains recognizably human throughout Virtually all body horror is ultimately about what makes someone human and at what point has someone crossed the line into something else entirely films like Tetsuo and the fly are Really blatant, but all of them grapple with it by exposing what humans are at their core meat and bone and blood When Alexia bleeds oil It made me think of how the human blood looked in the black and white Tetsuo Although what he bleeds as he transforms doesn't actually look anything like that But as we look to the future even that seems too limiting in a Q&A after the New York Film Festival premier Dukonau said all my work is about trying to ask myself What it is to be human and to broaden the spectrum of what we think humanity is is there humanity beyond the social construct And I can't think of an easier way to broaden that spectrum or make a person Contemplate those questions than to transform a body before their very eyes. It's like a magic trick human human Not so human We're all gonna draw our own lines, but where and why I'm not too interested in giving answers Dukonau added I don't think that art should give any answers whatsoever It should just raise more questions that allow you to debate afterwards internally or with others Now I've heard that a lot over the years and sometimes it feels genuine and other times sort of like a cop-out Like the artist doesn't have any answers and so the work itself isn't actually coherent because it's not grounded in anybody's reality But I don't think that's Dukonau. Her work is too Straightforward honestly at the end the plot is resolved. We know what happened So instead we are left to discuss the implications And I was surprised at how hopeful it all seemed a clear proclamation that humanity will continue that life Finds a way 6.9 out of 10 Thank you so much for watching. Thank you particularly to my patrons my mom Hammering Marco Kat Saracada Benjamin Schiff Anthony Cole Magnolia Denton Elliot Fowler Greg Lucina cojo Phil Bates Liam night Willow. I am the sword Raleigh Zimmerman Jacob Alexander and the folks who'd rather be read than said If you liked this video, that's great If not oh well if you want to see more, please subscribe. Hope to see in the next one


Thanks for waiting. It's time for my hunt. I'm HR 11 chat seems I was mistaken So do you use heavy-range weapon white have you tried out the light? No, I haven't I Have not the only weapons I've tried like extensively is bow and this is the heavy bow gun I do plan on trying to hammer next I'm gonna try my first melee weapon in terms of fighting. I need to create one. Yeah, that's great I'll post the the session ID and chat If you guys want to join Copy session I oh, I know you could do that all this time. I've been typing it out Ha, I didn't even know that was there. I used to go here be like Okay So Bounty complete, so let's go take care of that first wonderful Armorsphere, thank you. I Haven't even looked at investigations yet I've been getting them. They ain't get up to five-star now Bunch of new ones here So it goes up to three pages man You could you could really scroll past as fast with the scroll scroll. Well, I kind of like that Let's go take on my plan to runes Yeah, before I go out, I'm gonna I'm gonna I'm gonna try to hammer a little bit in the practice area There we go fertilize Okay, I'm not sure what I can rank up If anything, are you liking Marvel? Are you liking monster hunter better on PC than you did on ps4? I Like I like the aiming better I haven't got to the part where I got stuck on ps4 yet, so So far as just as fun because I got addicted to it on the ps4 too It was it was quite fun, and then I got tired of footprints, so Once I get over that hurdle did I think that question will be answered a little bit better All right, what do I need for this? I haven't discovered the stuff yet Okay, so I got three armor spheres level seven All right, let's do the level sevens Yeah, there's a bunch of storyline quests that require you to find a ton of footprints which to me It's okay for the first hundred times you do it But I got sick of it. I just want to hunt monsters. I didn't want to have to find footprints and people have told me They hated it too, and they found ways around it. I get back at the daily There we go logger voucher and cool All right, I think I have to go in my house to Why can't I go to my room? That's kind of weird. I can't go in my room Also, how do you get the I need to figure out how to get the mantles? That's something I never did on ps4, and I don't know why I can't get in my room I thought that entrance would work All right, I'll do that one next Mantles are from deliveries. Okay. I got to get on that about time. I get an update probably got another Am I playing on a max settings? No, I'm not Okay, let's run through the hunting basics first ready your weapon. I hate these icons so much. I can't tell what's what Iron hammer there we go. Let's try this out. There is a mighty close-range weapon that can stun monsters with blows to the head So standard combos three clicks Combo from charge to your control. Oh Charge big bang. Okay. I don't know how I did spinning blood in from that. There is a Meter somewhere for that. No, there isn't it's just three ticks. Got it. Okay. Have you been using the tail raider safaris? No I don't know what that is So it can look even better. Yeah, I'm not I'm not a max settings. I'm like mostly medium Low I got shadows turned up. Oh, there's one thing I need the LOD I learned that that's that's a killer framerate LOD bias Yes, turn that off fully sway off So you can see I got I got the recent resolution scaling on high volume rendering off Shallow quality low And I just turn a couple more things off and it's dropping feeling. Hey the sparrow gaming Everybody's showing some love in the chat Appreciate the support man by way too fast. It has It's gone by way too fast So How do you? So combo charge up from tier two So that's a tier two. I'm assuming I'm assuming that's the same thing So when they say charge tier two, what do they mean? That's as high as I can charge Where's rations? Okay, probably meat. I need meat on here the Well-done steak. So ammo and coatings. This will be fierce That's fine Let's put poison on three. Those are the four I use the most. Oh Sticky ammo, you know, I should put sticky ammo for I'd use that more Sticky and slice Yes, but sticky sticky here and then slice and then poison sticky Slice and poison I keep thinking poison is green And then we'll do the wyvern and Then the clusters Just put normal oil to it and there's three tiers of charging I Think it's charged in eat your charge and then charge again Eating a charge powers to hammer making the charge quicker Now that's something I didn't think would be a thing So what I just did was I customized the function menu this thing right here So you hit F1 and you press one But it doesn't actually drink it. So how do you drink it? You press it again? You click No, that's what I don't know how to use this shortcut menu Either drink is down at the bottom right. So check this out. So if I do F3 I'm sorry F1 3 Do I hit E here? Is it always E there F1 did a number key, but you have to hit something else Because it just it just breaks it out, but it doesn't put it on your shortcut bar where he is So if I do this Hold on Three see eight that Okay, so I guess if I'm full health If if okay, if it's full health, it won't work. I got it. Okay, so if I go control Okay, need a charge control again control again So you just do a combo you charge up combo charge up combo charge up combo Didn't right-click that. Oh, you have to right-click the hammer. Oh Create some distance to use a whetstone Freaking whetstone. I don't think I have it on my arm I'll have a shortcut bar Why do you need a whetstone a hammer chat? Okay? Is there a visual indicator of that though? I have a charge I meant Do I have to right-click during the charge? Yeah, I know the hammers lit up. So eat the Charge eat it. How do I so you eat the charge by right-clicking again? How do you eat the charge chat until you see either get knocked down? Oh, so if I charge again If I charge it again That's like tier two. I guess Think it's doing more damage That's interesting. I'll come back to that. That's interesting No, got my bone back Out of my loadouts Wait a minute Wait a minute. I saw like different loadouts There we go. There's my yes. There's my yes. Okay. Good old charge play. So whoa turn it off Turn it off, please How I turn that lock on off. Oh, no, what have you done? I turn it off. Oh No, I would like to turn that off, please because I think it was from melee that did that F was probably the lock on Yeah, so how do you unfocus? How you gonna do that? All right, I guess is for this is till day as a dash Dash there we go. All right. Whoo. I changed it to some crazy key Okay, so let's get back into the swing of things with this So I learned about Optimal range That video what so with normal ammo if I go back keep crap. I'm trying to sprint again. I go back here That's not optimal range, but the reticule will change When it's optimal range, I don't know if normal ammo can get too close No, you can't so if I go with like How'd I get demon ammo? Paralysis ammo This is the right bow bow gun All right, so piercing ammo Okay, so that's that's not optimal. Wow Pretty good range Piercing ammo is pretty good range now spread ammo. I gotta get real close Like that's not even close enough. It's like Right up on dudes That's how the range. Oh unless you try all the ammo in here. I didn't know that okay Paralysis ammo. Oh, I can't wait to get that That's some good range. Look at this Geez that is that's that's pretty long range Okay See demon ammo was probably close Trank ammo not really a trick almost closer Normal About sticky and stickies like mid-range. All right, let us go a quest. Oh, I've done the cluster ammo before Like all right, so let's look into This This cost 60 this 600 to send them. I did it. Okay, so the next thing is you guys said That the mantles were Deliveries and I've only done like a few deliveries I've only done like a few deliveries. So I guess I have to be on top of those Hey, if I dare Yeah, I only did like two actually aquasax a Sissy yaku hide and a shamosa. So let's hunt a sissy yaku. I don't want to go to that Do you have a sissy yaku? Do you have a sissy yaku? No, no investigations for sissy yaku Let's check and see if that's a Actually, let's do this first before I forget So all those estimation parts you see on the map are sometimes deliveries. Okay, uh Let's see if there's a sissy yaku an optimum optional I don't know for Yes, let's do this. Hey guys, if you want to join this is the code The session code is in chat I'm gonna put that in the title. I guess People can have that session Gotta remember to change it I need a mantle. I don't have a mantle yet. Yeah I have a better understanding of scout flies now that I did on the ps4 version. I don't have a clue what we're doing Like I didn't put two and two together about the footprints and the scout flies I don't know why I didn't know that but I just didn't It's like they're telling me to go this way chat I just came back from this way Or at least I got an investigation for the guy now. Excuse me They're gonna come back and attack me Is that him? That's not him, is he? I don't think I've ever fought this guy. Yeah, it is him Come here boy I would like to have a word sir You don't mind There we go Whoa, sir, I'm barely hitting the button Everybody's showing some love in the chat That tier three Oh, he does that I forgot Oh Okay, that's kind of annoying. I'm trying to I'm in control To switch to my weapon and it wasn't doing it. That was weird Okay, I stopped putting the weapon away Why am I shooting with control? I thought I put my weapon I don't know I thought control brought out your weapon This calls for a freaking sniper shot chat No, it doesn't Because you're aiming the slinger Didn't I always do that? Okay, my shield does block that I want to test that out The shield does block the blind which doesn't make sense, but hey I'm not gonna complain. I'm not an optimal I gotta back up for the pierce. There we go