Measures of the Dynamics of G Protein Interaction With the Ribosome With Applications to Antibiotic Screening

Ribosomes catalyze protein synthesis via the translation cycle, in which the translation initiation is recognized as a key step to regulate the process. The functional complexes of the bacterial ribosome undergo large conformational changes during the initiation of protein synthesis. Dramatic progress in the elucidation of ribosome structure by both X-ray crystallography and cryoelectron microscopy (cryo-EM) has provided some of the best evidence for such changes. At the same time, detailed rate studies of initiation, even though for the most part incomplete, have shown this process to be complex, multistep reactions, raising the question of the extent to which specific structural changes can be assigned to specific steps described in the proposed kinetic mechanism. By using fluorescence stopped-flow, quenched flow and FRET approaches to elucidate the kinetic mechanism of initiation, particularly the formation of a 70S initiation, we have found that following GTP hydrolysis by IF2 bound within a 70S complex, the G-domain moves toward L11-NTD, leading to increased FRET efficiency, and that Pi is released following such movement. Our results also showed that two G-proteins, IF2 and EF-Tu, can bind to the ribosome simultaneously during the transition from initiation to elongation. In vitro fluorescence assays were also developed to identify biologically active thiopeptide precursor compounds as potential new antibiotics. It is shown that some of these precursors represent novel compounds with respect to their ability to bind to ribosomes. These findings provide not only insight into the mechanism of action of thiopeptide compounds, but also demonstrate the potential of such assays for identifying novel lead compounds that might be missed using conventional inhibitory screening protocols

Non-contrast computed tomography-based radiomics for staging of connective tissue disease-associated interstitial lung disease

Rationale and introductionIt is of significance to assess the severity and predict the mortality of patients with connective tissue disease-associated interstitial lung disease (CTD-ILD). In this double-center retrospective study, we developed and validated a radiomics nomogram for clinical management by using the ILD-GAP (gender, age, and pulmonary physiology) index system.Materials and methodsPatients with CTD-ILD were staged using the ILD-GAP index system. A clinical factor model was built by demographics and CT features, and a radiomics signature was developed using radiomics features extracted from CT images. Combined with the radiomics signature and independent clinical factors, a radiomics nomogram was constructed and evaluated by the area under the curve (AUC) from receiver operating characteristic (ROC) analyses. The models were externally validated in dataset 2 to evaluate the model generalization ability using ROC analysis.ResultsA total of 245 patients from two clinical centers (dataset 1, n = 202; dataset 2, n = 43) were screened. Pack-years of smoking, traction bronchiectasis, and nine radiomics features were used to build the radiomics nomogram, which showed favorable calibration and discrimination in the training cohort {AUC, 0.887 [95% confidence interval (CI): 0.827–0.940]}, the internal validation cohort [AUC, 0.885 (95% CI: 0.816–0.922)], and the external validation cohort [AUC, 0.85 (95% CI: 0.720–0.919)]. Decision curve analysis demonstrated that the nomogram outperformed the clinical factor model and radiomics signature in terms of clinical usefulness.ConclusionThe CT-based radiomics nomogram showed favorable efficacy in predicting individual ILD-GAP stages

Synthesis and functional activity of tRNAs labeled with fluorescent hydrazides in the D-loop

We describe an optimized procedure for replacing the dihydrouridine residues of charged tRNAs with Cy3 and Cy5 dyes linked to a hydrazide group, and demonstrate that the labeled molecules are functional in ribosomal activities including 30S initiation complex formation, EF–Tu-dependent binding to the ribosome, translocation, and polypeptide synthesis. This procedure should be straightforwardly generalizable to the incorporation of other hydrazide-linked fluorophores into tRNA or other dihydrouridine-containing RNAs. In addition, we use a rapid turnover FRET experiment, measuring energy transfer between Cy5-labeled tRNAfMet and Cy3-labeled fMetPhe-tRNAPhe, to obtain direct evidence supporting the hypothesis that the early steps of translocation involve movements of the flexible 3′-single-stranded regions of the tRNAs, with the considerable increase in the distance separating the two tRNA tertiary cores occurring later in the process

Improving Temporal Event Scheduling through STEP Perpetual Learning

Currently, most machine learning applications follow a one-off learning process: given a static dataset and a learning algorithm, generate a model for a task. These applications can neither adapt to a dynamic and changing environment, nor accomplish incremental task performance improvement continuously. STEP perpetual learning, by continuous knowledge refinement through sequential learning episodes, emphasizes the accomplishment of incremental task performance improvement. In this paper, we describe how a personalized temporal event scheduling system SmartCalendar, can benefit from STEP perpetual learning. We adopt the interval temporal logic to represent events’ temporal relationships and determine if events are temporally inconsistent. To provide strategies that approach user preferences for handling temporal inconsistencies, we propose SmartCalendar to recognize, resolve and learn from temporal inconsistencies based on STEP perpetual learning. SmartCalendar has several cornerstones: similarity measures for temporal inconsistency; a sparse decomposition method to utilize historical data; and a loss function based on cross-entropy to optimize performance. The experimental results on the collected dataset show that SmartCalendar incrementally improves its scheduling performance and substantially outperforms comparison methods

Experiments on Mechanical Response and Energy Dissipation Behavior of Rockburst-Prone Coal Samples Under Impact Loading

To reveal the dynamic mechanical response and energy dissipation behavior of rockburst-prone coal samples under impact loading, the compressive experiments on Xinzhouyao coals (prone) and Machang coals (nonprone) under different impact loadings were carried out using the Split Hopkinson Pressure Bar (SHPB). The dynamic mechanical properties were studied, including dynamic elastic modulus, strain rate, peak stress, peak strain, dynamic increment factor, and energy dissipation. The results show that the dynamic elastic modulus, peak stress, and peak strain of both prone and nonprone coals perform an obvious correlation with the increase of strain rate. The strain rate strengthening effect on the dynamic elastic modulus and compressive strength of rockburst-prone coal samples are more significant, reflected by the greater increment with the increase of strain rate, while the dynamic increment factors of both prone and nonprone coals show apparent strain rate strengthening. The incident, reflected, and transmitted energy of both two coals linearly increases with the impact velocity, although the increased rate may be different. The dissipated energy of rockburst-prone coal samples increases faster, while the rate of the increase of the dissipated energy is more stable with strain rate. The results may provide an important reference for revealing the failure law of engineering-scaled coal mass suffered by rockburst

Ethyl Viologen as a Superoxide Quencher to Enhance the Oxygen Reduction Reaction in Li-O-2 Batteries

10.1021/acsaem.2c01501ACS APPLIED ENERGY MATERIALS579040-904

DataSheet_1_Non-contrast computed tomography-based radiomics for staging of connective tissue disease-associated interstitial lung disease.docx

Rationale and introductionIt is of significance to assess the severity and predict the mortality of patients with connective tissue disease-associated interstitial lung disease (CTD-ILD). In this double-center retrospective study, we developed and validated a radiomics nomogram for clinical management by using the ILD-GAP (gender, age, and pulmonary physiology) index system.Materials and methodsPatients with CTD-ILD were staged using the ILD-GAP index system. A clinical factor model was built by demographics and CT features, and a radiomics signature was developed using radiomics features extracted from CT images. Combined with the radiomics signature and independent clinical factors, a radiomics nomogram was constructed and evaluated by the area under the curve (AUC) from receiver operating characteristic (ROC) analyses. The models were externally validated in dataset 2 to evaluate the model generalization ability using ROC analysis.ResultsA total of 245 patients from two clinical centers (dataset 1, n = 202; dataset 2, n = 43) were screened. Pack-years of smoking, traction bronchiectasis, and nine radiomics features were used to build the radiomics nomogram, which showed favorable calibration and discrimination in the training cohort {AUC, 0.887 [95% confidence interval (CI): 0.827–0.940]}, the internal validation cohort [AUC, 0.885 (95% CI: 0.816–0.922)], and the external validation cohort [AUC, 0.85 (95% CI: 0.720–0.919)]. Decision curve analysis demonstrated that the nomogram outperformed the clinical factor model and radiomics signature in terms of clinical usefulness.ConclusionThe CT-based radiomics nomogram showed favorable efficacy in predicting individual ILD-GAP stages.</p