Identification of the major tRNAPhe binding domain in the tetrameric structure of cytoplasmic phenylalanyl-tRNA synthetase from baker's yeast

AbstractNative cytoplasmic phenylalanyl-tRNA synthetase from baker's yeast is a tetramer of the α2β2 type. On mild tryptic cleavage it gives rise to a modified ∡2β′2 form that has lost the tRNAPhe binding capacity but is still able to activate phenylalanine. In this paper are presented data concerning peptides released by this limited proteolytic conversion as well as those arising from exhaustive tryptic digestion of the truncated β′ subunit. Each purified peptide was unambiguously assigned to a unique stretch of the β subunit amino acid sequence that was recently determined via gene cloning and DNA sequencing. Together with earlier results from affinity labelling studies the present data show that the Lys 172—Ile 173 bond is the unique target of trypsin under mild conditions and that the N-terminal domain of each β subunit (residues 1–172) contains the major tRNAPhe binding sites

Additive stacking for disaggregate electricity demand forecasting

Future grid management systems will coordinate distributed production and storage resources to manage, in a cost effective fashion, the increased load and variability brought by the electrification of transportation and by a higher share of weather dependent production. Electricity demand forecasts at a low level of aggregation will be key inputs for such systems. We focus on forecasting demand at the individual household level, which is more challenging than forecasting aggregate demand, due to the lower signal-to-noise ratio and to the heterogeneity of consumption patterns across households. We propose a new ensemble method for probabilistic forecasting which borrows strength across the households while accommodating their individual idiosyncrasies. In particular, we develop a set of models or “experts” which capture different demand dynamics, and we fit each of them to the data from each household. Then, we construct an aggregation of experts where the ensemble weights are estimated on the whole data set, the main innovation being that we let the weights vary with the covariates by adopting an additive model structure. In particular, the proposed aggregation method is an extension of regression stacking where themixture weights are modelled using linear combinations of parametric, smooth or random effects. The methods for building and fitting additive stacking models are implemented by the gamFactory R package, available at https://github.com/mfasiolo/gamFactory

Chitosan-coated nanoparticles: Effect of chitosan molecular weight on nasal transmucosal delivery

Drug delivery to the brain represents a challenge, especially in the therapy of central nervous system malignancies. Simvastatin (SVT), as with other statins, has shown potential anticancer properties that are difficult to exploit in the central nervous system (CNS). In the present work the physico-chemical, mucoadhesive, and permeability-enhancing properties of simvastatinloaded poly-e-caprolactone nanocapsules coated with chitosan for nose-to-brain administration were investigated. Lipid-core nanocapsules coated with chitosan (LNCchit) of different molecular weight (MW) were prepared by a novel one-pot technique, and characterized for particle size, surface charge, particle number density, morphology, drug encapsulation efficiency, interaction between surface nanocapsules with mucin, drug release, and permeability across two nasal mucosa models. Results show that all formulations presented adequate particle sizes (below 220 nm), positive surface charge, narrow droplet size distribution (PDI < 0.2), and high encapsulation efficiency. Nanocapsules presented controlled drug release and mucoadhesive properties that are dependent on the MW of the coating chitosan. The results of permeation across the RPMI 2650 human nasal cell line evidenced that LNCchit increased the permeation of SVT. In particular, the amount of SVT that permeated after 4 hr for nanocapsules coated with low-MW chitosan, high-MW chitosan, and control SVT was 13.9 ± 0.8 µg, 9.2 ± 1.2 µg, and 1.4 ± 0.2 µg, respectively. These results were confirmed by SVT ex vivo permeation across rabbit nasal mucosa. This study highlighted the suitability of LNCchit as a promising strategy for the administration of simvastatin for a nose-to-brain approach for the therapy of brain tumors

Modulation of the Suppression Efficiency and Amino Acid Identity of an Artificial Yeast Amber Isoleucine Transfer RNA in Escherichia coli by a G-U Pair in the Anticodon Stem

International audienc

Optimal control of a quantum sensor: A fast algorithm based on an analytic solution

Quantum sensors can show unprecedented sensitivities, provided they are controlled in a very specific, optimal way. Here, we consider a spin sensor of time-varying fields in the presence of dephasing noise, and we show that the problem of finding the optimal pulsed control field can be mapped to the determination of the ground state of a spin chain. We find an approximate but analytic solution of this problem, which provides a \emph{lower bound} for the sensor sensitivity, and a pulsed control very close to optimal, which we further use as initial guess for realizing a fast simulated annealing algorithm. We experimentally demonstrate the sensitivity improvement for a spin-qubit magnetometer based on a nitrogen-vacancy center in diamond.Comment: Main text: 8 pages, 5 figures. Supplemental Material: 4 pages, 2 figure