Direct Observation of Radical States and the Correlation with Performance Degradation in Organic Light-Emitting Diodes During Device Operation

Microscopic characterization of radical states in organic light‐emitting diodes (OLEDs) during device operation is useful for elucidating the degradation mechanism because the radical formation has been considered as non‐radiative recombination centers. Electron spin resonance (ESR) spectroscopy is suitable for such characterization because it can directly observe radicals in OLEDs. In this work, the detailed ESR investigation into the radical states in OLEDs during device operation is firstly reported using a typical light‐emitting Alq3‐based OLEDs. The simultaneous measurements of the ESR signal and the luminance of the same OLED are performed to study the direct correlation between the radical states and the performance degradation. These characteristics show that the luminance monotonically decreases and an ESR signal concomitantly increases as the duration of the device operation increases after operating the OLED. Using the analysis of density functional theory (DFT) calculation, the origin of the newly emerged ESR signal is ascribed to the cationic species due to decomposed Alq3 molecules. The elucidation of the radical species formed in OLEDs during device operation has been demonstrated at a molecular level for the first time. This ESR analysis would provide useful knowledge for understanding the degradation mechanism in the OLEDs at the molecular level

Simulating noise on a quantum processor: interactions between a qubit and resonant two-level system bath

Material defects fundamentally limit the coherence times of superconducting qubits, and manufacturing completely defect-free devices is not yet possible. Therefore, understanding the interactions between defects and a qubit in a real quantum processor design is essential. We build a model that incorporates the standard tunneling model, the electric field distributions in the qubit, and open quantum system dynamics, and draws from the current understanding of two-level system (TLS) theory. Specifically, we start with one million TLSs distributed on the surface of a qubit and pick the 200 systems that are most strongly coupled to the qubit. We then perform a full Lindbladian simulation that explicitly includes the coherent coupling between the qubit and the TLS bath to model the time dependent density matrix of resonant TLS defects and the qubit. We find that the 200 most strongly coupled TLSs can accurately describe the qubit energy relaxation time. This work confirms that resonant TLSs located in areas where the electric field is strong can significantly affect the qubit relaxation time, even if they are located far from the Josephson junction. Similarly, a strongly-coupled resonant TLS located in the Josephson junction does not guarantee a reduced qubit relaxation time if a more strongly coupled TLS is far from the Josephson junction. In addition to the coupling strengths between TLSs and the qubit, the model predicts that the geometry of the device and the TLS relaxation time play a significant role in qubit dynamics. Our work can provide guidance for future quantum processor designs with improved qubit coherence times.Comment: 8 pages, 5 figure

Changes in the gut microbiome influence the hypoglycemic effect of metformin through the altered metabolism of branched-chain and nonessential amino acids

AIMS: Although metformin has been reported to affect the gut microbiome, the mechanism has not been fully determined. We explained the potential underlying mechanisms of metformin through a multiomics approach. METHODS: An open-label and single-arm clinical trial involving 20 healthy Korean was conducted. Serum glucose and insulin concentrations were measured, and stool samples were collected to analyze the microbiome. Untargeted metabolomic profiling of plasma, urine, and stool samples was performed by GC-TOF-MS. Network analysis was applied to infer the mechanism of the hypoglycemic effect of metformin. RESULTS: The relative abundances of Escherichia, Romboutsia, Intestinibacter, and Clostridium were changed by metformin treatment. Additionally, the relative abundances of metabolites, including carbohydrates, amino acids, and fatty acids, were changed. These changes were correlated with energy metabolism, gluconeogenesis, and branched-chain amino acid metabolism, which are major metabolic pathways related to the hypoglycemic effect. CONCLUSIONS: We observed that specific changes in metabolites may affect hypoglycemic effects through both pathways related to AMPK activation and microbial changes. Energy metabolism was mainly related to hypoglycemic effects. In particular, branched-chain amino acid metabolism and gluconeogenesis were related to microbial metabolites. Our results will help uncover the potential underlying mechanisms of metformin through AMPK and the microbiome

The dynamic transcriptional and translational landscape of the model antibiotic producer Streptomyces coelicolor A3(2)

Individual Streptomyces species have the genetic potential to produce a diverse array of natural products of commercial, medical and veterinary interest. However, these products are often not detectable under laboratory culture conditions. To harness their full biosynthetic potential, it is important to develop a detailed understanding of the regulatory networks that orchestrate their metabolism. Here we integrate nucleotide resolution genome-scale measurements of the transcriptome and translatome of Streptomyces coelicolor, the model antibiotic-producing actinomycete. Our systematic study determines 3,570 transcription start sites and identifies 230 small RNAs and a considerable proportion (∼21%) of leaderless mRNAs; this enables deduction of genome-wide promoter architecture. Ribosome profiling reveals that the translation efficiency of secondary metabolic genes is negatively correlated with transcription and that several key antibiotic regulatory genes are translationally induced at transition growth phase. These findings might facilitate the design of new approaches to antibiotic discovery and development

Primary transcriptome and translatome analysis determines transcriptional and translational regulatory elements encoded in the Streptomyces clavuligerus genome

Determining transcriptional and translational regulatory elements in GC-rich Streptomyces genomes is essential to elucidating the complex regulatory networks that govern secondary metabolite biosynthetic gene cluster (BGC) expression. However, information about such regulatory elements has been limited for Streptomyces genomes. To address this limitation, a high-quality genome sequence of β-lactam antibiotic-producing Streptomyces clavuligerus ATCC 27 064 is completed, which contains 7163 newly annotated genes. This provides a fundamental reference genome sequence to integrate multiple genome-scale data types, including dRNA-Seq, RNA-Seq and ribosome profiling. Data integration results in the precise determination of 2659 transcription start sites which reveal transcriptional and translational regulatory elements, including -10 and -35 promoter components specific to sigma (σ) factors, and 5'-untranslated region as a determinant for translation efficiency regulation. Particularly, sequence analysis of a wide diversity of the -35 components enables us to predict potential σ-factor regulons, along with various spacer lengths between the -10 and -35 elements. At last, the primary transcriptome landscape of the β-lactam biosynthetic pathway is analyzed, suggesting temporal changes in metabolism for the synthesis of secondary metabolites driven by transcriptional regulation. This comprehensive genetic information provides a versatile genetic resource for rational engineering of secondary metabolite BGCs in Streptomyces