The areas where I feel Germany has to catch up compared to other countries where I lived and worked in the last 13 years are: climate, digitisation and bureaucracy.
I’d also think that it needs to take more responsibility in Europe and sort out its domestic demographic problems.
Among the most pressing things would be a pragmatic approach to reach net zero asap.
Yet Germany emits more greenhouse gas per capita than many other European nations.
The problem is that Germans aren’t really aware of this.
While Germans are rather susceptible to environmental issues, the energy mix is still too heavy on fossils.
Home grown obstacles are the nuclear exit, subsidy of the ailing lignite mining industry (exit 2038) and bureaucracy hindering the rollout of wind energy.
Now one would hope the election to be a turning point, especially after the catastrophic floods in July — and devastating heatwaves across the globe.
But even the Green Party failed to get this message across.
My impression is because it muddled it with a broader view of social transformation, rather than laying out clear priorities.
But perhaps it’s also a reflection of German’s aversion to change.
We are more conservative than many of us would admit.
Another area where Germany seriously trails is digitisation.
I don’t get it.
Yes, you can pay contactless here and there, but it’s patchy, meaning you’ll always carry a stash of cash.
In the U.K., I never had a wallet on me.
If you want to buy a ticket for the tram, you spend 5 minutes on a vending machine to produce a paper strip which you need to stamp in the tram. In London you just wave your phone at the entrance to pay contactless. I haven’t seen a single person yet to pay with their phone here.
One hindrance to digitisation is Germany’s obsession with data protection. Many would say it is a response to historic oppressive state run surveillance by the GDR and the Nazis.
Sounds alternativlos — a term often used by Angela Merkel to escape debate.
But compare that to the U.K. which doesn’t even have identity cards or a civil registration system.
In that regard, the U.K. keeps an arm length’s distance from its citizens.
Unlike Germany, where the first thing after moving is to report to the mayors’ office to register.
Alternativlos? Generally life in the U.K. felt much more liberal.
At the same time the UK’s approach to data protection and also its GDPR implementation is much more pragmatic.
The consequences are real. The U.K. gathered essential covid19 data which enabled it to discover treatments, run a smooth vaccination campaign and effective exit strategy. Its trove of data informed the world. (The initial response in March ‘20 was rubbish though).
Another hindrance to digitisation is an obsession with paper, ultimately down to wet signatures as proof of identity.
I got the notification for a mandatory TV license by mail (name and address supplied by the civil registration system—was there anything about data protection?).
There was the option to register the TV license online — just to receive a letter requesting my wet signature for the standing order mandate, including a prepaid return envelope. Not sure if that’s money well spent.
In consequence, I have filled more forms in writing in the last month than in 9 years in the U.K. (Typically with another form each to consent to data usage according to GDPR, essentially allowing someone to type the content of the form into a computer and store it)..
Before completely going off tangent, back to the elections.
I’m seriously hoping a new government will deliver more ambitious change in terms of climate, digitalisation and bureaucracy.
Further we need to provide leadership in Europe and balance out our demographic change.
As a scientist, climate action has the highest priority. There are only 6 years of current co2 emissions left to stay within 1.5c heating. It’s now or never.
On climate Angela Merkel’s legacy is probably most underwhelming, as this was supposed to be a key agenda item.
I’d hope for a chancellor that tackles these issues. An international perspective may also help solve some domestic problems.
Closest to that would be Anna Lena Baerbock from the Greens, despite a bumpy campaign.
Both SPD and CDU offer continuity, but we had enough of that.
Now obviously, there will have to be a coalition of some sorts, because we’ll be looking at a six party parliament once more.
Numerically it could be the ‘traffic light’ of the SPD 🔴 (who narrowly lead the polls) with the Green Party 🟢 and the FDP 🟡.
Not sure. The FDP have in the last decades become a single topic low tax party. They stepped out of coalition negotiations with the greens and conservatives 4 years ago, which would have been closer to their programme than the SPD.
Alternatives would be a 🔴🟢🔴 coalition of SPD and Green Party with the socialists. This has been rejected so far due to the socialist’s fringe views on NATO and other issues.
An alternative could be an SPD/Green minority government, which would be unstable.
So it’s going to be messy. Whatever the result today is, there will be months of negotiations to form a coalition during which the actual programme will shape up — the risk being that it ends up a minimal consensus rather than the ambitious plan needed.
Also, since the race is close and some 40% voted by mail it may take quite some time to have reliable numbers as the after vote polls at 18:00 cest may not be representative.
Update 18:15: Messy indeed. Head to head race between SPD and CDU, both around 25%. Left party at 5% and at risk of not making the cut. Possibilities:
SPD - Green - FDP
CDU - Green - FDP
SPD - Green - Left (could fall short of majority)
SPD - CDU (not again)
A long night ahead.
Update 21:10. Projections show a 1% lead for SPD.
First statements show an emboldened Green Party and FDP.
It seems as if the 2 winners SPD or CDU are in fact the losers, because even if they lead a 3 party coalition government, they’ll be in the minority with 2 partners.
Also interesting is the age distribution. The future is green-yellow.
@harald_voeh has developed a model that tracks 62 different lineages across 315 local authorities in England. His model estimates total and lineage-specific incidence and growth rates.
The model also calculates lineage-specific relative growth rates and provides a fairly accurate reconstruction of the epidemic and its many subepidemics across the nation between Sep '20 and Apr' 21. We also included a provisional analysis until 15 May '21 to track B.1.617.2
Want to *see* how a tumour has evolved and grown? And also how different clones acquired characteristic transcriptional and histopathological features?
Jessica Svedlund developed a base-specific extension of the in situ sequencing protocol (BaSISS) to detect somatic mutations on a microscopy slide with fluorescently tagged padlock probes. 2/9
These signals are denoised and assembled into microscopic maps of subclonal growth using @LomakinAI's rigorous machine learning model. 3/9
Did the new SARS-CoV-2 B.1.1.7 lineage spread during the English national lockdown? Rising numbers and estimated higher R value suggest so. Together with our colleagues from COG-UK we took a closer look. >> virological.org/t/lineage-spec…
Fitting lineage-agnostic daily PCR test and viral genome data from COG-UK to 382 local authorities we find evidence that B.1.1.7 has spread in a staggering 200/246 of affected LTLAs during the November lockdown (R>1) while at the same time other lineages contracted (R<1). >>
The evidence is therefore overwhelming that B.1.1.7 was repeatedly capable to proliferate under lockdown measures sufficient to suppress other SARS-CoV-2 lineages. B.1.1.7 spread was not an isolated event of general failure of viral containment (both R>1). >>
We trained a neural network on 17k tumour slides with known genomics transcriptomics to assess how histopathology, molecular tumour characteristics and survival correspond. 1/8 nature.com/articles/s4301…
This analysis discovered histopathological patterns of 167 different mutations ranging from whole genome duplications to point mutations in cancer driver genes - about 1/4 mutations tested. 2/8
Further, around 40% of the transcriptome is correlated with histopathology reflecting tumour grade and composition. This is probably best illustrated at the example of infiltrating lymphocytes TILs, which can be identified and localised through their expression signature. 3/8
Tired of C19 preprints? Read this: My student @NadezdaVolkova1 & collaborators completely took apart how mutational signatures are sculpted by DNA damage and repair. We grew and sequenced > 2700 worms from 53 repair KO's exposed to 11 mutagens. Phew. 1/7 nature.com/articles/s4146…
Analysing all different combinations, including wildtype and no treatment, allows to map the mutagenic contributions of damage and repair: 9/11 mutagens produce different mutational signatures depending on which repair pathways are acting. This involves 32/53 repair genes. 2/7
We can pin down which elements of a mutational signature are caused by which type of DNA alteration (the same mutagen often produces a variety) – and which repair pathway is involved in mending each type of lesion (usually many pathways operate jointly) 3/7
Hello world. Here’s something interesting: @yufu0413 from my lab trained a deep convolutional neural net in cancer histopathology *and* genomics using 14M images from 17k H&E slides across 28 cancer types. The outcome is stunning. 1/5 biorxiv.org/cgi/content/sh…
The network can predict a good range of genomic alterations, including whole genome duplications. From H&E-images alone. 2/5
It also finds a lot of associations in bulk transcriptome data, deconvolves the signal to find areas on each slide corresponding to molecular cell types such as tumour infiltrating lymphocytes. Entirely automated. 3/5