BEGIN:VCALENDAR
VERSION:2.0
PRODID:www.dresden-science-calendar.de
METHOD:PUBLISH
CALSCALE:GREGORIAN
X-MICROSOFT-CALSCALE:GREGORIAN
X-WR-TIMEZONE:Europe/Berlin
BEGIN:VTIMEZONE
TZID:Europe/Berlin
X-LIC-LOCATION:Europe/Berlin
BEGIN:DAYLIGHT
TZNAME:CEST
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
DTSTART:19810329T030000
RRULE:FREQ=YEARLY;INTERVAL=1;BYMONTH=3;BYDAY=-1SU
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:CET
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
DTSTART:19961027T030000
RRULE:FREQ=YEARLY;INTERVAL=1;BYMONTH=10;BYDAY=-1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
UID:DSC-22629
DTSTART;TZID=Europe/Berlin:20260130T110000
SEQUENCE:1769755133
TRANSP:OPAQUE
DTEND;TZID=Europe/Berlin:20260130T120000
URL:https://dresden-science-calendar.org/calendar/de/detail/22629
LOCATION:TUD CRTD\, Fetscherstraße 10501307 Dresden
SUMMARY:Lakadamyali: Super-resolution imaging of chromatin in health and di
 sease
CLASS:PUBLIC
DESCRIPTION:Speaker: Melike Lakadamyali\nInstitute of Speaker: University o
 f Pennsylvania\, Perelman School of Medicine\nTopics:\n\n Location:\n  Nam
 e: TUD CRTD ()\n  Street: Fetscherstraße 105\n  City: 01307 Dresden\n  Ph
 one: +49 (0)351 458 82052\n  Fax: +49 (0)351 458 82059 \nDescription: <p>W
 e are pleased to announce an additional PoL Research Seminar with a talk b
 y <strong>Melike Lakadamyali</strong> from University of Pennsylvania\, Pe
 relman School of Medicine.&amp\;nbsp\;</p><p><strong>When?</strong> &amp\;
 nbsp\;Friday\, 30. January 2026\, 11:00 am<br><strong>Where? </strong>CRTD
  auditorium right</p><p><strong>Abstract:</strong></p><p>Super-resolution 
 microscopy has opened new possibilities for visualizing chromatin architec
 ture in situ at nanoscale resolution. Leveraging quantitative super-resolu
 tion imaging\, we revealed the heterogeneous nature of nucleosome folding 
 and demonstrated that chromatin structure at both nano- and meso-scales is
  highly plastic\, dynamically remodeling in response to chemical and mecha
 nical cues in health and disease. By combining biologically interpretable 
 feature extraction with machine learning\, we further showed that cells ca
 n be accurately classified into distinct states based solely on their mult
 i-scale chromatin organization\, while also identifying the specific chrom
 atin features that drive classification\, thus offering mechanistic insigh
 t into cell-state regulation.</p>
DTSTAMP:20260726T145424Z
CREATED:20260128T063906Z
LAST-MODIFIED:20260130T063853Z
END:VEVENT
END:VCALENDAR