System-Characterized Artificial Intelligence Approaches for Cardiac cellular systems and Molecular Signature analysis

The dissertation presents a significant advancement in the field of cardiac cellular systems and molecular signature systems by employing machine learning and generative artificial intelligence techniques. These methodologies are systematically characterized and applied to address critical challenges in these domains. A novel computational model is developed, which combines machine learning tools and multi-physics models. The main objective of this model is to accurately predict complex cellular dynamics, taking into account the intricate interactions within the cardiac cellular system. Furthermore, a comprehensive framework based on generative adversarial networks (GANs) is proposed. This framework is designed to generate synthetic data that faithfully represents an in-vitro cardiac cellular system. The generated data can be used to enhance the understanding and analysis of the system’s behavior. Additionally, a novel AI approach is formulated, which integrates deep learning and GAN techniques for Raman characterization. This approach enables efficient detection of multi-analyte mixtures by leveraging the power of deep learning algorithms and the generation of synthetic data through GANs. Overall, the integration of machine learning, generative artificial intelligence, and multi-physics modeling provides valuable insights and tools for precise prediction and efficient detection in cardiac cellular systems and molecular signature systems

Temperature effect on U(VI) sorption onto Na-bentonite

International audienceU(VI) sorption on a purified Na-bentonite was investigated from 298±2 to 353±2 K by using a batch experimental method as a function of pH, U(VI) concentration, carbonate concentration and solid-to-liquid ratio (m/V). The data at 298±2 K could be well described by a surface complexation model (SCM) with a complex located on layer sites (X2UO2) and three complexes located on edge sites (≡SOUO2+, ≡SO(UO2)3(OH)5, and ≡SO(UO2)3(OH)72-). The intrinsic equilibrium constants (Kint) of the surface reactions at 333±2 K and 353±2 K were obtained by fitting U(VI) sorption curves versus pH on the Na-bentonite. The model enables U(VI) sorption in the presence of carbonate ( =10-3.58 atm) to be described without considering any ternary surface complexes involving carbonate, except for underestimation around pH 7 (6 < pH < 7.5). The standard enthalpy changes ( ) of the surface reactions were evaluated from the Kint values obtained at three temperatures (298±2, 333±2 and 353±2 K) via the van't Hoff equation. The proposed SCM and of the surface reactions enable U(VI) sorption on the Na-bentonite at other temperatures to be predicted

Clinical value of nitric oxide parameters in children with bronchial asthma

Exhaled nitric oxide （eNO）， as a marker of eosinophilic inflammation in the airway， has been widely utilized in the diagnosis and treatment of asthma. The fractional concentration of exhaled NO （FeNO） mainly reflects the degree of airway inflammation. By establishing a pulmonary dynamic model for extended analysis， nitric oxide parameters can be obtained， which contribute to deepen the understanding of the degree of airway inflammation. In this article， recent studies were reviewed to summarize the clinical value of nitric oxide parameters in asthma， aiming to supplement a novel dimension for the evaluation and management of asthma

Adaptive Policy with Wait-kk Model for Simultaneous Translation

Simultaneous machine translation (SiMT) requires a robust read/write policy in conjunction with a high-quality translation model. Traditional methods rely on either a fixed wait-$k$ policy coupled with a standalone wait-$k$ translation model, or an adaptive policy jointly trained with the translation model. In this study, we propose a more flexible approach by decoupling the adaptive policy model from the translation model. Our motivation stems from the observation that a standalone multi-path wait-$k$ model performs competitively with adaptive policies utilized in state-of-the-art SiMT approaches. Specifically, we introduce DaP, a divergence-based adaptive policy, that makes read/write decisions for any translation model based on the potential divergence in translation distributions resulting from future information. DaP extends a frozen wait-$k$ model with lightweight parameters, and is both memory and computation efficient. Experimental results across various benchmarks demonstrate that our approach offers an improved trade-off between translation accuracy and latency, outperforming strong baselines.Comment: Accept to EMNLP 2023 main conference. 17 pages, 12 figures, 5 table

A hybrid Autoformer framework for electricity demand forecasting

Electricity demand forecasting is of great significance to the electricity system and residents’ life, but it is difficult to forecast the electricity demand series because of the influence of cyclical factors. Electricity demand forecasting also faces the problem of small data amounts. Therefore, we need to design a model that is less affected by data volume and can cope with complex electricity demand series. Based on the Autoformer model, this paper establishes a novel forecasting framework with excellent performance. In the part of data preprocessing, multiple linear regression with 10 variables and Bootstrap processing are added. In the part of the model, the Auto Correlation mechanism is modified to better extract the historical and nonlinear characteristics of electricity demand series from different time spans. Using this framework, we further analyze the impact of working days and seasonal changes on the electricity demand in Taixing City and New South Wales. In addition, we propose a new electricity demand forecasting method, which can adjust the original sequence according to the actual situation. The experimental results show that this method can achieve good precision in demand forecasting. Taking Taixing of China and New South Wales of Australia as examples, the forecasting performance with the proposed framework is better than that of Autoformer, Reformer, Informer, and other mainstream models. The forecasting indexes with our proposed framework of the test set are MAE: 35.05, RMSE: 47.28, MAPE: 1.63 in Taixing and MAE: 193.17, RMSE: 239.96, MAPE: 2.43 in NS

Two-element interferometer for millimeter-wave solar flare observations

In this paper, we present the design and implementation of a two-element interferometer working in the millimeter wave band (39.5 GHz - 40 GHz) for observing solar radio emissions through nulling interference. The system is composed of two 50 cm aperture Cassegrain antennas mounted on a common equatorial mount, with a separation of 230 wavelengths. The cross-correlation of the received signals effectively cancels the quiet solar component of the large flux density (~3000 sfu) that reduces the detection limit due to atmospheric fluctuations. The system performance is obtained as follows: the noise factor of the AFE in the observation band is less than 2.1 dB, system sensitivity is approximately 12.4 K (~34 sfu) with an integration time constant of 0.1 ms (default), the frequency resolution is 153 kHz, and the dynamic range is larger than 30 dB. Through actual testing, the nulling interferometer observes a quiet sun with a low level of output fluctuations (of up to 50 sfu) and has a significantly lower radiation flux variability (of up to 190 sfu) than an equivalent single-antenna system, even under thick cloud cover. As a result, this new design can effectively improve observation sensitivity by reducing the impact of atmospheric and system fluctuations during observation

Effect of Inoculated Fermentation with Different Aspergillus Strains on the Formation of the Umami Taste of Liuyang Douchi

In order to investigate the effect of inoculated fermentation with different Aspergillus strains on the umami taste of Liuyang Douchi, the differences in the umami taste and protein degradation of Liuyang Douchi fermented naturally (NF) or by four dominant Aspergillus flavus strains isolated from Douchi (A. flavus 7214, A. flavus 7622, A. flavus 6112, and A. flavus 5322) alone and their mixture in an equal proportion (M4) were compared. The results showed that the major protease produced by each of the four strains was serine proteases, and A. flavus 5322 had the strongest protease activity, with significantly higher activity of acidic protease than the other three strains. The contents of amino acid nitrogen and water-soluble protein in Aspergillus fermented Douchi were higher than those in NF Douchi after the end of pile-fermentation stage. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) analysis showed that the degradation trends of six proteins in Douchi were consistent, all of which exhibited rapid degradation during the pile-fermentation process. Additionally, the results of sensory evaluation and electronic tongue analysis showed that Douchi fermented by A. flavus 5322 showed the strongest umami taste, which significantly increased when compared with NF. Using ultra-high performance liquid chromatography-electrospray/quadrupole time-of-flight tandem mass spectrometry (UPLC-ESI-Q-TOF-MS/MS) combined with correlation and peptidomics analysis, a total of 43 potential umami peptides were identified. The total peak area of potential umami peptides in Douchi fermented by A. flavus 6112 was the highest, followed by A. flavus 5322 with little difference, significantly greater than that in NF Douchi. In summary, inoculated fermentation, especially with A. flavus 5322, could promote protein degradation and the release of potential umami peptides, thereby enhancing the umami taste of Liuyang Douchi. This study will provide theoretical guidance for improving the umami taste of Liuyang Douchi and development of industrial production strains

Igwas: Image-Based Genome-Wide Association of Self-Supervised Deep Phenotyping of Retina Fundus Images

Existing imaging genetics studies have been mostly limited in scope by using imaging-derived phenotypes defined by human experts. Here, leveraging new breakthroughs in self-supervised deep representation learning, we propose a new approach, image-based genome-wide association study (iGWAS), for identifying genetic factors associated with phenotypes discovered from medical images using contrastive learning. Using retinal fundus photos, our model extracts a 128-dimensional vector representing features of the retina as phenotypes. After training the model on 40,000 images from the EyePACS dataset, we generated phenotypes from 130,329 images of 65,629 British White participants in the UK Biobank. We conducted GWAS on these phenotypes and identified 14 loci with genome-wide significance (