[Human eRF1 Translation Regulation].

Eukaryotic translation release factor eRF1 is an important cellular protein that plays a key role in translation termination, nonsense-mediated mRNA decay (NMD), and readthrough of stop codons. The amount of eRF1 in the cell influences all these processes. The mechanism of regulation of eRF1 translation through an autoregulatory NMD-dependent expression circuit has been described for plants and fungi, but the mechanisms of regulation of human eRF1 translation have not yet been studied.

Separation of recombinant erythropoietin and human serum albumin without the use of sophisticated equipment.

A number of drugs based on recombinant erythropoietin contain human serum albumin as an auxiliary component. The presence of this protein hinders the proper control of the drug quality in accordance with the requirements of regulating agencies. We propose the novel method for separation of recombinant erythropoietin (epoetin beta) and human serum albumin.

Functional Activity of Isoform 2 of Human eRF1.

Eukaryotic release factor eRF1, encoded by the gene, recognizes stop codons and induces peptide release during translation termination. produces several different transcripts as a result of alternative splicing, from which two eRF1 isoforms can be formed. Isoform 1 codes well-studied canonical eRF1, and isoform 2 is 33 amino acid residues shorter than isoform 1 and completely unstudied.

No free lunch for avoiding clustering vulnerabilities in distributed systems.

Emergent design failures are ubiquitous in complex systems, and often arise when system elements cluster. Approaches to systematically reduce clustering could improve a design's resilience, but reducing clustering is difficult if it is driven by collective interactions among design elements. Here, we use techniques from statistical physics to identify mechanisms by which spatial clusters of design elements emerge in complex systems modelled by heterogeneous networks.

Human learning of hierarchical graphs.

Humans are exposed to sequences of events in the environment, and the interevent transition probabilities in these sequences can be modeled as a graph or network. Many real-world networks are organized hierarchically and while much is known about how humans learn basic transition graph topology, whether and to what degree humans can learn hierarchical structures in such graphs remains unknown. We probe the mental estimates of transition probabilities via the surprisal effect phenomenon: humans react more slowly to less expected transitions.