Dear colleagues,

I take advantage of Tim's message about the fact that responsible people have 
resigned from X.
I  really enjoyed Twitter (which I discovered rather late) because it was a 
great tool for announcing news from my laboratory, but also for keeping abreast 
of recent publications or pre-publications related to my research interests. 
I notice that many scientists have deserted X in recent months.

Can anyone suggest user-friendly alternatives used by the scientific 
communities to announce recent publications or news in their fields?

Best wishes,

Marc
—
Marc GRAILLE, PhD
DR1-CNRS
Laboratoire de Biologie Structurale de la Cellule  (BIOC; Ex-Laboratoire de 
Biochimie)
UMR7654 du CNRS

Head of the team: “Translation and degradation of eukaryotic mRNAs”

ÉCOLE POLYTECHNIQUE 
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FRANCE 
📞: +33 (0)1 69 33 48 90


 : [email protected] <mailto:[email protected]> / 
Twitter : @GrailleLab <https://twitter.com/GrailleLab>
https://portail.polytechnique.edu/bioc/en/research/coupling-between-translation-and-mrna-degradation-eukaryotes
—



> Le 2 déc. 2023 à 10:15, Tim Grüne <[email protected]> a écrit :
> 
> Hi Mark,
> responsible people are resigning from X.
> Cheers,
> Tim
> 
> Am 01.12.2023 23:24, schrieb Mark J. van Raaij:
>> just came across this critique of that paper on Twitter:
>> This exciting paper shows AI design of materials, robotic synthesis.
>> 10s of new compounds in 17 days. But did they? This paper has very
>> serious problems in materials characterisation. In my view it should
>> never have got near publication. Hold on tight let's take a look 😱
>>               [1]
>> Robert Palgrave (@Robert_Palgrave) on X [1]
>> twitter.com [1]
>> but I'm not enough of an expert to judge - perhaps some
>> characterizations were wrong and a lot of the paper does stand.
>>> On 1 Dec 2023, at 20:51, Bryan Lepore <[email protected]> wrote:
>>> Adding to that literature list a bit outside :
>>> Merchant, A., Batzner, S., Schoenholz, S.S. _et al._
>>> Quote:
>>> "... we show that graph networks trained at scale can reach
>>> unprecedented levels of generalization, improving the efficiency of
>>> materials discovery by an order of magnitude. "
>>> Scaling deep learning for materials discovery.
>>> _Nature_ (2023), November
>>> https://doi.org/10.1038/s41586-023-06735-9
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>> Links:
>> ------
>> [1] https://twitter.com/Robert_Palgrave/status/1730358675523424344
> 
> -- 
> --
> Tim Gruene
> Head of the Centre for X-ray Structure Analysis
> Faculty of Chemistry
> University of Vienna
> 
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