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Background: Asthma is a chronic airway inflammatory disease characterized by an imbalance in both Th1 and Th2 cytokines. Exhaled nitric oxide (NO) is elevated in asthma, and is a potentially useful non-invasive marker of airway inflammation. However, the origin and underlying mechanisms of intersubject variability of exhaled NO are not yet fully understood. We have previously described NO gas phase release from normal human bronchial epithelial cells (NHBEs, tracheal origin). However, smaller airways are the major site of morbidity in asthma. We hypothesized that IL-13 or cytomix (IL-1 beta, TNF-alpha, and IFN-gamma) stimulation of differentiated small airway epithelial cells (SAECs, generation 10-12) and A549 cells (model cell line of alveolar type II cells) in culture would enhance NO gas phase release. Methods: Confluent monolayers of SAECs and A549 cells were cultured in Transwell plates and SAECs were allowed to differentiate into ciliated and mucus producing cells at an air-liquid interface. The cells were then stimulated with IL-13 (10 ng/mL) or cytomix (10 ng/mL for each cytokine). Gas phase NO release in the headspace air over the cells was measured for 48 hours using a chemiluminescence analyzer. Results: In contrast to our previous result in NHBE, baseline NO release from SAECs and A549 is negligible. However, NO release is significantly increased by cytomix (0.51 +/- 0.18 and 0.29 +/- 0.20 pl.s(-1).cm(-2), respectively) reaching a peak at approximately 10 hours. iNOS protein expression increases in a consistent pattern both temporally and in magnitude. In contrast, IL-13 only modestly increases NO release in SAECs reaching a peak (0.06 +/- 0.03 pl.s-1.cm(-2)) more slowly (30 to 48 hours), and does not alter NO release in A549 cells. Conclusion: We conclude that the airway epithelium is a probable source of NO in the exhaled breath, and intersubject variability may be due, in part, to variability in the type (Th1 vs Th2) and location (large vs small airway) of inflammation.

The practical use of Zn metal anodes in aqueous zinc batteries is impeded by the growth of dendrites, anode corrosion, and hydrogen evolution reaction in aqueous electrolytes. In this study, a simple, energy‐efficient, and scalable approach is reported to mitigate these detrimental issues effectively. Using 1‐hexanethiol (HT), a hydrophobic self‐assembled mercaptan layer (SAML) with a highly ordered structure is in situ created on the surface of the Zn anode. This ultrathin interfacial structure guides uniform Zn deposition and shields the Zn anode from water and oxygen‐induced corrosion, thus effectively inhibiting dendrite formation and side reactions. Consequently, the HT‐Zn electrode showcases impressive electrochemical stability and reversibility, and the as‐assembled HT‐Zn||I2 full cell delivers increased specific capacity (from 112 to 155 mAh g−1 at 1 A g−1) and ultra‐stable cyclability (zero capacity decay during the extended 1500 cycles at 4 A g−1). To validate the effectiveness of this simple and scalable method, a large‐sized pouch cell is prepared, which can be stably operated for 1000 cycles with a capacity decay of merely 0.0098% per cycle and Coulombic efficiency exceeding 99.1%. The presented SAML strategy highlights the potential of molecular engineering in improving the performance of aqueous zinc batteries. [ABSTRACT FROM AUTHOR]

Benefiting from large-scale pretrained vision language models (VLMs), the performance of visual question answering (VQA) has approached human oracles. However, finetuning such models on limited data often suffers from overfitting and poor generalization issues, leading to a lack of model robustness. In this paper, we aim to improve input robustness from an information bottleneck perspective when adapting pretrained VLMs to the downstream VQA task. Input robustness refers to the ability of models to defend against visual and linguistic input variations, as well as shortcut learning involved in inputs. Generally, the representations obtained by pretrained VLMs inevitably contain irrelevant and redundant information for a specific downstream task, resulting in statistically spurious correlations and insensitivity to input variations. To encourage representations to converge to a minimal sufficient statistic in multimodal learning, we propose Correlation Information Bottleneck (CIB), which seeks a tradeoff between compression and redundancy in representations by minimizing the mutual information (MI) between inputs and representations while maximizing the MI between outputs and representations. Moreover, we derive a tight theoretical upper bound for the mutual information between multimodal inputs and representations, incorporating different internal correlations that guide models to learn more robust representations and facilitate modality alignment. Extensive experiments consistently demonstrate the effectiveness and superiority of the proposed CIB in terms of input robustness and accuracy. [ABSTRACT FROM AUTHOR]

As an important choice of strategic transformation of energy enterprises, digital transformation has a profound impact on the stock price fluctuation of enterprises. From the perspective of dynamic capacity and environmental regulation, analyzes influences of digital transformation upon energy companies' share movement volatility, constructs a theoretical model that considers digital transformation and stock price volatility as the primary effects, dynamic capabilities as the mediator, and environmental regulation as the moderator. In addition, the study employs data from China's A-share listed energy enterprises from 2013 to 2020, utilizing a fixed-effect model to perform an empirical test. The findings demonstrate a significant positive correlation between the digital transformation of energy enterprises and the volatility of stock prices, indicating that the greater the extent of digital transformation, the higher the volatility of enterprise stock prices. Among the dimensions of dynamic capability, only adaptability and innovation ability appears to mediate the relation between digital transformation and stock price fluctuation. Moreover, environmental regulation positively moderates the relationship between digital transformation and the learning ability dimension. Finally, from the macro and micro levels, this study puts forward the policies and supportive measures to stabilize the stock price of energy enterprises, and suggestions on how to implement the digital transformation strategy reasonably according to their own development status and characteristics to provide valuable insights for encouraging the digital transformation among energy firms. [ABSTRACT FROM AUTHOR]

Tunnel junctions (TJs) offer a unique approach to utilizing nonequilibrium tunneling injection of holes and have demonstrated potential applications in ultraviolet (UV) emitters. However, high operating voltage caused by the wide bandgap of the III‐nitrides has impeded the further promotion of TJ. Here, 275 nm n+‐Al0.45Ga0.55N/p+‐Al0.5Ga0.5N ultrathin tunnel junction (UTJ) deep‐UV light‐emitting diodes (LEDs) are developed for minimizing the electrical losses and achieve, to the current knowledge, the lowest operating voltage (5.7 V) at 300 mA reported to date. This study discovers that Mg‐Si co‐doping occurs in the n+‐Al0.45Ga0.55N layer during the growth of n+‐Al0.45Ga0.55N/p+‐Al0.5Ga0.5N UTJ due to the memory and diffusion effect of Mg, which leads to the formation of Ohmic contact between high‐work‐function Ni/Au and Mg‐Si co‐doped n+‐ Al0.45Ga0.55N layer in UTJ. The Zener diode, fluorine resin, and optimally designed glass lens are incorporated into the chip encapsulation to enhance the reliability and optical performance of the device. Furthermore, a high‐efficiency sterilization deep‐UV light source is developed integrated with 120 UTJ deep‐UV LED chips. Complete surface inactivation at a long irradiation distance (20 cm) is achieved within only seconds using the high‐power sterilization deep‐UV light source. These results indicate that UTJ is promising in developing deep UV LEDs and their integrated light sources. [ABSTRACT FROM AUTHOR]

Central Bank Digital Currency (CBDC) pledges to realize a vast array of new functionalities, such as frictionless consumer payment and money-transfer systems, as well as precise supervision of money circulation, thereby enabling a number of new financial instruments and monetary policy levers. This study proposes, from a system feedback loop and cybernetics perspective, a Dynamic Issuance Mechanism (DIM) for CBDC that can theoretically enhance the vitality of economic operations. In accordance with this mechanism, the central bank implements dynamic issuance by monitoring cash leakage in real-time, so as to maintain the stability of the amount of money circulating on the market, thereby boosting the currency turnover rate and financial vitality. To demonstrate the efficacy of the DIM, we employ the Agent-Based Modeling (ABM) tool to develop a macroeconomic simulation model for qualitative analysis that includes four entities: Central Bank, households, firms, and commercial banks. The multi-cycle operation process of the model includes a variety of economic indicators demonstrating that DIM has the potential to boost economic vitality and social production efficiency without exerting an adverse effect on citizens' incomes, commodity prices, or the stability of the macroeconomic system. Finally, the function principle and potential risks of DIM are explained from a systems perspective, which offers a novel perspective for the functional design of CBDC and highlights that the hierarchical structure is a meaningful domain as the developmental direction. [ABSTRACT FROM AUTHOR]

Central Bank Digital Currency (CBDC) pledges to realize a vast array of new functionalities, such as frictionless consumer payment and money-transfer systems, as well as precise supervision of money circulation, thereby enabling a number of new financial instruments and monetary policy levers. This study proposes, from a system feedback loop and cybernetics perspective, a Dynamic Issuance Mechanism (DIM) for CBDC that can theoretically enhance the vitality of economic operations. In accordance with this mechanism, the central bank implements dynamic issuance by monitoring cash leakage in real-time, so as to maintain the stability of the amount of money circulating on the market, thereby boosting the currency turnover rate and financial vitality. To demonstrate the efficacy of the DIM, we employ the Agent-Based Modeling (ABM) tool to develop a macroeconomic simulation model for qualitative analysis that includes four entities: Central Bank, households, firms, and commercial banks. The multi-cycle operation process of the model includes a variety of economic indicators demonstrating that DIM has the potential to boost economic vitality and social production efficiency without exerting an adverse effect on citizens' incomes, commodity prices, or the stability of the macroeconomic system. Finally, the function principle and potential risks of DIM are explained from a systems perspective, which offers a novel perspective for the functional design of CBDC and highlights that the hierarchical structure is a meaningful domain as the developmental direction. [ABSTRACT FROM AUTHOR]