Two Antimycin A Analogues from Marine-Derived Actinomycete Streptomyces lusitanus

Two new antimycin A analogues, antimycin B1 and B2 (1–2), were isolated from a spent broth of a marine-derived bacterium, Streptomyces lusitanus. The structures of 1 and 2 were established on the basis of spectroscopic analyses and chemical methods. The isolated compounds were tested for their anti-bacterial potency. Compound 1 was found to be inactive against the bacteria Bacillus subtilis, Staphyloccocus aureus, and Loktanella hongkongensis. Compound 2 showed antibacterial activities against S. aureus and L. hongkongensis with MIC values of 32.0 and 8.0 μg/mL, respectively

Ernie-Gram BiGRU Attention: An Improved Multi-Intention Recognition Model for Air Traffic Control

In recent years, the emergence of large-scale pre-trained language models has made transfer learning possible in natural language processing, which overturns the traditional model architecture based on recurrent neural networks (RNN). In this study, we constructed a multi-intention recognition model, Ernie-Gram_Bidirectional Gate Recurrent Unit (BiGRU)_Attention (EBA), for air traffic control (ATC). Firstly, the Ernie-Gram pre-training model is used as the bottom layer of the overall architecture to implement the encoding of text information. The BiGRU module that follows is used for further feature extraction of the encoded information. Secondly, as keyword information is very important in Chinese radiotelephony communications, the attention layer after the BiGRU module is added to realize the extraction of keyword information. Finally, two fully connected layers (FC) are used for feature vector fusion and outputting intention classification vector, respectively. We experimentally compare the effects of two different tokenizer tools, the BERT tokenizer tool and Jieba tokenizer tool, on the final performance of the Bert model. The experimental results reveal that although the Jieba tokenizer tool has considered word information, the effect of the Jieba tokenizer tool is not as good as that of the BERT tokenizer tool. The final model’s accuracy is 98.2% in the intention recognition dataset of the ATC instructions, which is 2.7% higher than the Bert benchmark model and 0.7–3.1% higher than other improved models based on BERT

Enhancing Air Traffic Control Communication Systems with Integrated Automatic Speech Recognition: Models, Applications and Performance Evaluation

In air traffic control (ATC), speech communication with radio transmission is the primary way to exchange information between the controller and the pilot. As a result, the integration of automatic speech recognition (ASR) systems holds immense potential for reducing controllers’ workload and plays a crucial role in various ATC scenarios, which is particularly significant for ATC research. This article provides a comprehensive review of ASR technology’s applications in the ATC communication system. Firstly, it offers a comprehensive overview of current research, including ATC corpora, ASR models, evaluation measures and application scenarios. A more comprehensive and accurate evaluation methodology tailored for ATC is proposed, considering advancements in communication sensing systems and deep learning techniques. This methodology helps researchers in enhancing ASR systems and improving the overall performance of ATC systems. Finally, future research recommendations are identified based on the primary challenges and issues. The authors sincerely hope this work will serve as a clear technical roadmap for ASR endeavors within the ATC domain and make a valuable contribution to the research community

Earthquake event knowledge graph construction and reasoning

Efficient decision-making in earthquake emergencies plays a crucial role in ensuring individual safety, protecting personal property, and maintaining societal stability. However, traditional approaches to earthquake emergency decision-making rely on manual analysis or rule-based methods, which often struggle to fully leverage the wealth of information and uncover hidden data connections. Consequently, the efficiency of earthquake emergency decision-making is compromised. To address this issue, this study proposes a method for constructing an earthquake event knowledge graph and utilizing it for decision-making in earthquake emergencies. Firstly, specialized earthquake event knowledge ontology is developed, tailored to the unique characteristics of earthquake event data. Secondly, structured instances of earthquake event knowledge are extracted from text using transfer learning techniques, enabling the construction of the earthquake event knowledge graph. Thirdly, the earthquake event knowledge is represented as multidimensional vectors using knowledge graph representation learning technology. This facilitates the identification of similar earthquake events through inference based on vector similarity computation. In conclusion, the results of a case-based study demonstrate the effectiveness of the proposed method in providing accurate outcomes, facilitating earthquake event matching, enabling the retrieval and reuse of historical earthquake event knowledge, and serving as a valuable reference for earthquake emergency decision-making

Interface Engineering with Ultralow Ruthenium Loading for Efficient Water Splitting

Developing high-performance and cost-effective bifunctional electrocatalysts for water splitting is the key to large-scale hydrogen production. How to achieve higher performance with a lower amount of noble metal is still a major challenge. Herein, using a facile wet-chemistry strategy, we report the ultralow amount loading of ruthenium (Ru) on porous nickel foam (NF) as a highly efficient bifunctional electrocatalyst for water splitting. Theoretical simulations reveal that the coupling effect of Ru and Ni can significantly reduce the d-band center of the composite. The Ru-modified NF exhibits a very high level of HER activity with only 0.3 wt% of Ru, far surpassing commercial Pt/C. It only requires an extremely low overpotential (eta(10)) of 10 mV to achieve a current density of 10 mA cm(-2). in alkaline solution and a quite low Tafel slope of 34 mV dec(-1). This catalyst also shows remarkable performance for overall water splitting with a low voltage of 1.56 V at 10 mA cm(-2). These findings indicate the potential of this material in water-alkali electrolyzers, providing a new approach for fabrication of low-cost advanced electrocatalysts

Fabrication of pH-Responsive Nanoparticles with an AIE Feature for Imaging Intracellular Drug Delivery

Here we have demonstrated a facile method for construction of self-assembled nanoparticles with excellent fluorescent properties by the synergetic combination of unique AIE effects and tadpole-shaped polymers. The introduction of acid-sensitive Schiff base bonds furnished the polymeric vesicles and micelles with unique pH responsiveness that can possess maximal drug-release controllability inside tumor cells upon changes in physical and chemical environments, but present good stability under physiological conditions. Having benefited from the efficient fluorescence resonance energy transfer (FRET), the DOX-loaded fluorescent aggregates were employed for intracellular imaging and self-localization in surveillance of systemic DOX delivery. Cytotoxicity assay of the DOX-loaded aggregates indicated a fast internalization and a high cellular proliferation inhibition to MCF-7 cells while the PEG-POSS-(TPE)₇ nanoparticles displayed no cytotoxicity, exhibiting excellent biocompatibility and biological imaging properties. These results indicated that these biodegradable nanoparticles, as a class of effective pH-responsive and visible nanocarriers, have the potential to improve smart drug delivery and enhance the antitumor efficacy for biomedical applications

Metabolomics Study of Roux-en‑Y Gastric Bypass Surgery (RYGB) to Treat Type 2 Diabetes Patients Based on Ultraperformance Liquid Chromatography–Mass Spectrometry

Roux-en-Y gastric bypass (RYGB) is one of the most effective treatments for long-term weight loss and diabetes remission; however, the mechanisms underlying these changes are not clearly understood. In this study, the serum metabolic profiles of 23 remission and 12 nonremission patients with type 2 diabetes mellitus (T2DM) were measured at baseline, 6- and 12-months after RYGB. A metabolomics analysis was performed based on ultra-performance liquid chromatography–mass spectrometry. Clinical improvements in insulin sensitivity, energy metabolism, and inflammation were related to metabolic alterations of free fatty acids (FFAs), acylcarnitines, amino acids, bile acids, and lipids species. Differential metabolic profiles were observed between the two T2DM subgroups, and patients with severity fat accumulation and oxidation stress may be more suitable for RYGB. Baseline levels of tryptophan, bilirubin, and indoxyl sulfate measured prior to surgery as well as levels of FFA 16:0, FFA 18:3, FFA 17:2, and hippuric acid measured at 6 months after surgery best predicted the suitability and efficacy of RYGB for patients with T2DM. These metabolites represent potential biomarkers that may be clinically helpful in individualized treatment for T2DM patients by RYGB

Global Metabolic Profiling Identifies a Pivotal Role of Proline and Hydroxyproline Metabolism in Supporting Hypoxic Response in Hepatocellular Carcinoma

Purpose: Metabolic reprogramming is frequently identified in hepatocellular carcinoma (HCC), which is the most common type of liver malignancy. The reprogrammed cellular metabolisms promote tumor cell survival, proliferation, angiogenesis, and metastasis. However, the mechanisms of this process remain unclear in HCC