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Characterization of class I and II ADP-ribosylation factors (Arfs) in live cells: GDP-bound class II Arfs associate with the ER–Golgi intermediate compartment independently of GBF1. Mol. Biol. Cell 19, 3488–3500 (2008). Article CAS PubMed PubMed Central Google Scholar Duijsings, D. et...
Characterization of class I and II ADP-ribosylation factors (Arfs) in live cells: GDP-bound class II Arfs associate with the ER–Golgi intermediate compartment independently of GBF1. Mol. Biol. Cell 19, 3488–3500 (2008). Article CAS PubMed PubMed Central Google Scholar Duijsings, D. et...
In the wake of the adoption of the Kunming-Montreal Global Biodiversity Framework (henceforth GBF;15), which requires signatory countries to conserve at least 30% of their terrestrial, inland water, coastal, and marine areas by 2030 (Target 3, also known as '30 × 30'), effective area...
VNGBF $0.00 Vanguard Funds PLC0.00% SLF.PR.J:CA $0.00 Sun Life Financial Inc. PFD SHS SER 9Q0.00% AGNCL $0.00 AGNC Investment Corp. 7.75% DP PFD G0.00% NQDMXUS15T:IND $0.00 NASDAQ DM Ex US Telecommunications TR Index0.00% NQSG50LM:IND $0.00 Nasdaq Singapore Industrials Large Mid...
the structure of the underlying genetic regulatory modules. Decomposing the TRN into a small set of recurring regulatory patterns, called network motifs (NM), facilitates the inference. Identifying NMs defined by specific transcription factors (TF) establishes the framework structure of a TRN and ...
the structure of the underlying genetic regulatory modules. Decomposing the TRN into a small set of recurring regulatory patterns, called network motifs (NM), facilitates the inference. Identifying NMs defined by specific transcription factors (TF) establishes the framework structure of a TRN and ...
www.nature.com/scientificreports OPEN The viral capping enzyme nsP1: a novel target for the inhibition of chikungunya virus infection received: 16 April 2016 accepted: 18 July 2016 Published: 22 August 2016 L. Delang1, C. Li2,3, A. Tas4, G. Quérat5, I. C. Albulescu4...
Drug combinations can expand therapeutic options and address cancer’s resistance. However, the combinatorial space is enormous precluding its systematic exploration. Therefore, synergy prediction strategies are essential. We here present an approach to