dumb1224

The definition of haplotypes is definitely incorrect am I misunderstanding anything?

Exactly. Old parts of the system will be working if you leave them undisturbed. Mechanics have very good intuitions of this sort of thing.

I read about before there's proper engineering / physics theory about this too, it's like a car as a machine is a linear/smooth physics system with multiple weaknesses. Overtime longtime period of running many places might weaken but it still evolves into a slightly different smooth system, until you introduce a replacement which cause a mis-match of impedance or something like that.

Depending on the disease, even in cancer there's myeloma which may cause bone metastasis in many parts of the body with very focal lesions. Radiologists can't assess each and an every one of them, or even to find them all. So AI can definitely help in these scenarios.

I tried that AI diagnosis for my 15 old Ford C MAx too, however with a diagnostic problem the issue is unless you've got the ground truth, there's simply no way to verify any tool / human with a metric that you can compare and decide on future tasks.

The AI might be very good at diagnosing all minor issues, but might not lead to a successful repair, whereas human mechanics are extremely good on 80% of major issues that's not the ground truth, but will lead to successful repairs (that might not address the root but simply patch it). So it comes down to manage expectation / outcomes.

I don't think it's anyone (lay person)'s fault. As a chinese person moved to European in the early 2000s for the longest time I wouldn't touch a chinese product (although I'm very proud of the progress made in terms of industrialisation).

Things changed in 2012 when I bought a 'cheap-ish' Huawei phone, it was nothing of a flagship, no performance but it was really solid product. Very light and well built too. How ironic that was my first ever experience of buying a product exported to Europe.

High amount variance of quality is a daily reality and as any chinese consumer we are very used to that since the beginning of e-commerce. However there are multiple aspects to the quality feel. Production of high quality items is one thing (e.g one can do better QA etc), what I heard from some local car garages in China is that standardisation in the industry is still quite poor. E.g Parts to replace and bolts and nuts are not as standardised say as the German counter parts (purely from a mechanics point of view).

Having said that a lot of German car suppliers are in China, and the German car manufacturing industry evolved over a significantly longer period of time.

Probably morden production. When I was a kid in the 90s he's a giant figure in the books taught often in school (his birthplace is very close to my city too which makes him a local national hero) and well known as model figure. But not anything in the media. Maybe even earlier times before me.

It used to be a lot of them roaming in the residential area, out of necessity since household items were precious. Related is also the profession of a tinker to mend woks and pots and the scissor sharpener https://donwagner.dk/tinkers/tinkers-Zhongwen.html

Used to hear their shout in the street but largely disappeared in the 90s.

I did my CS undergrad in China but was already in the UK early 2000s. I was also abit surprised there's little mention of TCP/IP which is kinda considered classics if there's anything taught in CS at all. Java was definitly the new dominating force in industry and academia at that time.

However it depends on the resources the univ got. In some places there were other less Comp sci / software engineering focused degrees but got a little content overlap (I guess for financial benefits to enroll more students) such as e-commerce / digital degrees. They shared some courses with CS but not all.

The art of Hendrix's playing, then, is partly in how he harnessed that sound and integrated it into his voice. And of course, he's a force of nature when he does so.

One thing for me to notice is his playing does not require a rhythm guitarist. I discovered that what worked well is Mitch Mitchell as a Jazz drummer his playing was heavily influenced by classics. In a way it complemented Jimi's guitar tone so well.

I got the area wrong. Greater London is apparently 1500 km^2 so the total area of my administrative city is 4 times the size of that (with a total population of 3.4 mil)

Forgot to add context, my hometown is under zhejiang province (as different regions have different population structures).

It's a common source of confusion. The administrative definition of a 'city' is the equivalent of its metropolitan area + all satelite 'towns' and their suburbs (including farm lands).

My hometown has a population of 3.4 million (prefecture level city or 3rd tier as people call it). But it has an area about 6000 km^2, easily reaching the total size of London. At its core the central town has roughly a population of 700,000. And there are 4 more towns after the central one, each has smaller villages and suburbs under them. People living in these towns wouldn't consider they are living in the same city.

Yes it's only relevant if you know the reference at that time. The day today and the other satire shows aimed to follow certain general formula though so even if it's not funny for some its archetypical characters still fit in that genre of comedies in my opinion.

True that. So it's down to our preferences : )

Well depending on your taste of TV shows and the general culture.

When I moved to the UK early 2000s I could understand but can't appreciate that type of humour. I think its rooted in culture. Luckily that was the golden era of British comedies and there were great diversities so you can pick and choose what flavour you like.

Tumour evolution and progression is complex, being diagnosed early does not guarantee a linear growth. Even when it's biopsied at a timed interval you can't get a full picture of the cancer (invasive pattern etc) evolution trajectory. In some cases low grade tumours will be put on surveilance without radical treatment.

Diagnosis is complex too, you don't want the test to have low specificity. False positive is sometimes tolerated.

Cellular level computational simulation existed a very long time and it's more impressive by the day because of large collections of experimental datasets available.

However to infer or predict celular acitivities you need a ton of domain knowledge and experties about particular cell types, biological processes and specific environments. Typically the successful ones are human curated and validated (e.g large interaction networks based on literature).

In cancer it's even more unpredictable because of the lack of good (experimental) models, in-vivo or in-vitro, representing what actually happens the clinically and biologically underneath. Given the single cell resolution, its uncertainty will also amplify because of how heterogeneous inter- and intra- tumours are.

Having said that, a foundation model is definitely the future for futher development. But with all of these things, the bigger the model, the harder the validation process.

Not an expert in this field, but from their website https://www.hfnl.ustc.edu.cn/detail?id=23165 They seemed to have developed a vibration-based slicer scanner 'Blockface-VISoR' to scan 600μm 3d image but cut 400μm each time repeatly to reconstruct full 3d image. It says it's hydrogel treated sample.

P.S the visualisation tool to explore and navigate the slices is quite awesome too https://mesoanatomy.org/mesomouse/

In my hometown in China, same practice. However I find it not consistent for people from all over of China. When I get into a causal conversation about childhood with people from everywhere I had to do the conversion in my head (which school year what game came out e.g).

The common cancer treatment modalities: surgery, radiotherapy, chemotherapy, targeted drugs and their combinations are very effective first line treatments. I agree the statistics are much better.

In the field of cancer research it has been focusing more on drug / treatment resistance, heterogeneous response to the same treatment and development of less invasive methods both in treatment and assessment (imaging and monitoring). We have made huge progress in terms of deeper understanding of cancer biology and human disease mechanism in general.

However we have a very long way to understand when things progress outside of our control how to respond. E.g the key cancer drivers have been identified long ago but how biology and evolution modulate its response to external treatment has so much unknowns. That requires large effort to push the whole foundation of science to elucidate the details of these processes in my opinion.

Genotyping platforms are an entirely different beast I think.

The paper has a much clearer title 'Transcriptional regulation by PHGDH drives amyloid pathology in Alzheimer’s disease'. This is from the univ press media page, it's very common to use over-hyped titles to draw views.

Sorry my English in that sentence is probably flawed. I somehow still can't tell the difference very much.

I was doing machine learning but never dig into stats before. Then I tried to study Bayesian inference and regression by myself and finally I got what it really means and its importance. First I realised that ubiquity of 'likelihood' and 'likelihood function', then I realised it's just a way to parameterise the model parameters instead of input data. Then MLE is a way to get an estimate of maximum of that function, which is interpreted as the most likely setting to give rise to the data observed.

I know it's not statistically correct but I think it helped a lot in my understanding of other methods....

Agreed. In the 2000s it was all about BM25 in the NLP community. I hardly see any paper that did not mention it in my opinion.

You should call it rat race ;)

haha was looking for that!

Ban-kai 卍解

It is a bit of Russells paradox isn't it :D

If this is research then it shouldn't be true as it should belong to the 'most' research set.

Well not everyone starts experiment anew. Many also reuse accumulated datasets. For human data even more so.