Stress-induced degradation of inulin in Cichorium intybus: Exploring the transcriptional regulation of Fructan 1-exohydrolases

The preservation of the polyfructan inulin, a valuable commercial product obtained from chicory during the harvest season is severely hampered by its degradation due to low temperatures in late autumn. The degradation process is primarily attributed to the activity of fructan exohydrolases (1-FEH1, 1-FEH2a, and 1-FEH2b). However, the upregulation mechanism of 1-FEH genes in response to cold has remained poorly understood. This thesis endeavors to explore the roles of different transcription factors responsible for regulating the expression of 1-FEH genes and seeks to uncover the underlying transcription regulatory network allowing 1-FEH genes to respond to environmental cues. Thus, the overall aim of this thesis is to shed light on the complex transcriptional regulation of 1-FEH genes and thereby to provide direction for future chicory breeding efforts. The primary objective of the initial part of this thesis was to investigate the variation in expression levels of the three isoforms of 1-FEHs in mature taproots and young seedlings subjected to a detailed time course of cold treatment. The qRT-PCR results indicate that 1-FEH1 exhibited a prolonged and consistent cold-induced up-regulation whereas 1-FEH2a and 1-FEH2b were rapidly but only transiently up-regulated within the initial 24 hours following exposure to low temperatures; interestingly, expression of 1-FEH genes was also affected by heat stress and water deficiency. A co-expression analysis conducted in this study identified a set of cold-inducible transcription factors, namely CiNAC5, CiDREB1A/C/D, and CiDREB2A. Further investigations using the dual luciferase assay, promoter deletion analysis, electrophoretic mobility shift assay (EMSA) and yeast-two-hybrid revealed that i) CiNAC5 specifically activates the promoter region (-353 to ATG) of p1-FEH1; ii) CiDREB2A was identified as a key regulator of 1-FEH2b, which responds to a range of stress conditions by binding to the DRE cis-element located on the 1-FEH2b promoter; iii) CiDREB2B has been found to bind to CiMYB5 (yeast-two-hybrid), resulting in a synergistic upregulation of 1-FEH2b (dual luciferase assay ) and offering an explanation for the strong up-regulation of 1-FEH2b under heat stress; iv) the identification of a single nucleotide polymorphism (SNP) located on the DRE cis-element within the promoter region of p1-FEH2a was observed, which resulted in the inability of the promoter to be recognized and activated by CiDREB1 and CiDREB2. Furthermore, this thesis established a CRISPR RNP delivery system and a chicory protoplast regeneration method, which will facilitate future research on the individual functions of 1-FEH1 and 1-FEH2. By identifying the transcription factors involved in regulating 1-FEH genes, this thesis offers valuable insights into the mechanisms underlying the regulation of 1-FEHs and may have significant implications for the commercial production of inulin in chicory

Parametric Array Loudspeakers and Applications in Active Noise Control

University of Technology Sydney. Faculty of Engineering and Information Technology.Parametric array loudspeakers (PALs) are known for their capability of generating highly directional audio sound waves. Owing to this feature, they are used as secondary sources in active noise control (ANC) systems to mitigate the unwanted noise in the target regions whilst at the same time minimizing spillover effects on other areas. The primary aim of this thesis is to investigate the feasibility of using multiple PALs in an ANC system to create a large quiet zone. To achieve this, a partial wave expansion model is proposed first based on the quasilinear solution of both Westervelt and Kuznetsov equations to predict the audio sound generated by a PAL in a free field. The model is then extended to accommodate reflection, transmission, and scattering phenomena, which are common in real applications and can have significant effects on the noise reduction performance of ANC systems. The proposed model is validated by experiments conducted in anechoic rooms, and the validated model incorporated with the multi-channel ANC theory is then used to investigate the quiet zone size controlled by multiple PALs. It is found the existing prediction models for PALs are either inaccurate or time-consuming, while the proposed model is more than 100 times faster in both near and far fields without any loss of accuracy. It therefore enables reliable and fast simulations for multi-channel ANC systems, which require heavy computations due to large numbers of PALs. A key finding is that the directivity of the audio sound generated by a PAL is severely deteriorated if sound waves are reflected from a non-rigid surface, truncated by a thin partition, or scattered by a sphere (simulating a human head). This implies the sharp directivity for PALs is not guaranteed as expected when they are used in complex acoustic environments. Finally, both simulations and experiments showed that multiple PALs can create a large quiet zone of comparable size when compared to traditional omnidirectional loudspeakers. However, the spillover effects of using PALs on the sound field outside the quiet zone are much smaller, which demonstrates PALs provide a promising alternative as secondary sources in multi-channel ANC systems

Earth-Rock Dams’ Breach Modelling

Simulation of dam breach process has significant influence on the evaluation of consequence of dam breach flood. In this study, research progresses on the numerical modeling of earth-rock dams’ breach process are summarized, especially the latest research results of the author’s research team in recent years. However, there still has a considerable gap in the versatility of computer software and visualization technology of dam breaching process. It is suggested that more efforts should be made in the future to study the detailed physically based numerical model for core dam and concrete face rockfill dam; further, more attention should be paid to the application of visualization technology in dam breach process simulation. Finally, the universal and friendly visualization computer software that can accurately simulate the dam failure process and flood routing for earth-rock dams is sorely needed

Analysis of factors affecting the effectiveness of oil spill clean-up: A bayesian entwork approach

Ship-related marine oil spills pose a significant threat to the environment, and while it may not be possible to prevent such incidents entirely, effective clean-up efforts can minimize their impact on the environment. The success of these clean-up efforts is influenced by various factors, including accident-related factors such as the type of accident, location, and environmental weather conditions, as well as emergency response-related factors such as available resources and response actions. To improve targeted and effective responses to oil spills resulting from ship accidents and enhance oil spill emergency response methods, it is essential to understand the factors that affect their effectiveness. In this study, a data-driven Bayesian network (TAN) analysis approach was used with data from the U.S. Coast Guard (USCG) to identify the key accident-related factors that impact oil spill clean-up performance. The analysis found that the amount of discharge, severity, and the location of the accident are the most critical factors affecting the clean-up ratio. These findings are significant for emergency management and planning oil spill clean-up efforts.Postprint (published version

Road Traffic Law Adaptive Decision-making for Self-Driving Vehicles

Self-driving vehicles have their own intelligence to drive on open roads. However, vehicle managers, e.g., government or industrial companies, still need a way to tell these self-driving vehicles what behaviors are encouraged or forbidden. Unlike human drivers, current self-driving vehicles cannot understand the traffic laws, thus rely on the programmers manually writing the corresponding principles into the driving systems. It would be less efficient and hard to adapt some temporary traffic laws, especially when the vehicles use data-driven decision-making algorithms. Besides, current self-driving vehicle systems rarely take traffic law modification into consideration. This work aims to design a road traffic law adaptive decision-making method. The decision-making algorithm is designed based on reinforcement learning, in which the traffic rules are usually implicitly coded in deep neural networks. The main idea is to supply the adaptability to traffic laws of self-driving vehicles by a law-adaptive backup policy. In this work, the natural language-based traffic laws are first translated into a logical expression by the Linear Temporal Logic method. Then, the system will try to monitor in advance whether the self-driving vehicle may break the traffic laws by designing a long-term RL action space. Finally, a sample-based planning method will re-plan the trajectory when the vehicle may break the traffic rules. The method is validated in a Beijing Winter Olympic Lane scenario and an overtaking case, built in CARLA simulator. The results show that by adopting this method, the self-driving vehicles can comply with new issued or updated traffic laws effectively. This method helps self-driving vehicles governed by digital traffic laws, which is necessary for the wide adoption of autonomous driving

Effect of Low-Density Lipoprotein Cholesterol Goal Achievement on Vascular Physiology Evaluated by Quantitative Flow Ratio in Patients Who Underwent Percutaneous Coronary Intervention

Purpose: The change in coronary physiology from lipid-lowering therapy (LLT) lacks an appropriate method of examination. Quantitative flow ratio (QFR) is a novel angiography-based approach allowing rapid assessment of coronary physiology. This study sought to determine the impact of low-density lipoprotein cholesterol (LDL-C) goal achievement on coronary physiology through QFR.Methods: Cases involving percutaneous coronary intervention (PCI) and 1-year angiographic follow-up were screened and assessed by QFR analysis. Patients were divided into two groups according to the LDL-C level at the 1-year follow-up: (1) goal-achievement group (LDL-C < 1.8 mmol/L or reduction of ≥50%, n = 146, lesion = 165) and (2) non-achievement group (n = 286, lesion = 331). All QFR data and major adverse cardiovascular and cerebrovascular events (MACCEs) at 1 year were compared between groups.Results: No differences between the groups in quantitative coronary angiography (QCA) data or QFR post-PCI were found. At the 1-year follow-up, lower percentage diameter stenosis (DS%) and percentage area stenosis (AS%) were recorded in the goal-achievement group (27.89 ± 10.16 vs. 30.93 ± 12.03, p = 0.010, 36.57 ± 16.12 vs. 41.68 ± 17.39, p = 0.003, respectively). Additionally, a better change in QFR was found in the goal-achievement group (0.003 ± 0.068 vs. −0.018 ± 0.086, p = 0.007), with a lower incidence of physiological restenosis and MACCEs (2.1 vs. 8.4%, p = 0.018, 5.4 vs. 12.6%, p = 0.021, respectively).Conclusion: Evaluated by QFR, patients who achieved the LDL-C goal appear to have a better coronary physiological benefit. This group of patients also has a better clinical outcome

Hybrid quantum-classical convolutional neural network for phytoplankton classification

The taxonomic composition and abundance of phytoplankton have a direct impact on marine ecosystem dynamics and global environment change. Phytoplankton classification is crucial for phytoplankton analysis, but it is challenging due to their large quantity and small size. Machine learning is the primary method for automatically performing phytoplankton image classification. As large-scale research on marine phytoplankton generates overwhelming amounts of data, more powerful computational resources are required for the success of machine learning methods. Recently, quantum machine learning has emerged as a potential solution for large-scale data processing by harnessing the exponentially computational power of quantum computers. Here, for the first time, we demonstrate the feasibility of using quantum deep neural networks for phytoplankton classification. Hybrid quantum-classical convolutional and residual neural networks are developed based on the classical architectures. These models strike a balance between the limited function of current quantum devices and the large size of phytoplankton images, making it possible to perform phytoplankton classification on near-term quantum computers. Our quantum models demonstrate superior performance compared to their classical counterparts, exhibiting faster convergence, higher classification accuracy and lower accuracy fluctuation. The present quantum models are versatile and can be applied to various tasks of image classification in the field of marine science

PALMD regulates aortic valve calcification via altered glycolysis and NF-κB-mediated inflammation

Recent genome-wide association and transcriptome-wide association studies have identified an association between the PALMD locus, encoding palmdelphin, a protein involved in myoblast differentiation, and calcific aortic valve disease (CAVD). Nevertheless, the function and underlying mechanisms of PALMD in CAVD remain unclear. We herein investigated whether and how PALMD affects the pathogenesis of CAVD using clinical samples from CAVD patients and a human valve interstitial cell (hVIC) in vitro calcification model. We showed that PALMD was upregulated in calcified regions of human aortic valves and calcified hVICs. Furthermore, silencing of PALMD reduced hVIC in vitro calcification, osteogenic differentiation, and apoptosis, whereas overexpression of PALMD had the opposite effect. RNA-Seq of PALMD-depleted hVICs revealed that silencing of PALMD reduced glycolysis and nuclear factor-κB (NF-κB)–mediated inflammation in hVICs and attenuated tumor necrosis factor α–induced monocyte adhesion to hVICs. Having established the role of PALMD in hVIC glycolysis, we examined whether glycolysis itself could regulate hVIC osteogenic differentiation and inflammation. Intriguingly, the inhibition of PFKFB3-mediated glycolysis significantly attenuated osteogenic differentiation and inflammation of hVICs. However, silencing of PFKFB3 inhibited PALMD-induced hVIC inflammation, but not osteogenic differentiation. Finally, we showed that the overexpression of PALMD enhanced hVIC osteogenic differentiation and inflammation, as opposed to glycolysis, through the activation of NF-κB. The present study demonstrates that the genome-wide association– and transcriptome-wide association–identified CAVD risk gene PALMD may promote CAVD development through regulation of glycolysis and NF-κB–mediated inflammation. We propose that targeting PALMD-mediated glycolysis may represent a novel therapeutic strategy for treating CAVD

A study on the correlation between hemoglobin concentration and the storage quality of suspended red blood cells prepared from the whole blood of Tibetan male residents

BackgroundPrevious studies reported that the blood of Tibetans living at different altitudes may vary slightly. There is evidence that the harsh environmental conditions at high altitudes, such as low pressure and hypoxia, may affect the morphology and hemorheology of red blood cells (RBCs). Hypoxia would increase the levels of hemoglobin ([Hb]) and hematocrit (Hct), potentially increasing blood hyperviscosity and compromising blood collection and transfusions. Therefore, it is critical to investigate the in vitro storage quality of Tibetan RBCs.ObjectivesIn this study, the in vitro quality of suspended RBCs (SRBCs) prepared from whole blood (WB) of Tibetan residents with varying Hb concentrations ([Hb]) was measured during storage, and the relationship between the major factors in RBC storage and [Hb] was studied.Materials and methodsThe WB of Tibetan men was divided into three groups based on [Hb] levels (group A: 120 < Hb ≤ 185 g/L; group B: 185 < Hb ≤ 210 g/L; group C: Hb > 210 g/L). The SRBCs prepared from WB were examined aseptically on days 1, 14, 21, and 35 after storage.Results[Hb] was not correlated with mean corpuscular volume (MCV), adenosine triphosphate (ATP), pH, P50, and hemolysis. There was a moderate or strong negative association between platelets (PLT) and [Hb] from days 1 to 35, and the PLT number of group C was lower than group A during storage. Group C had the highest change rates of electrolytes, glucose, and lactate, and there were moderate or strong positive correlations between lactate and [Hb] (r = 0.3772, p = 0.0045), glucose and [Hb] (r = 0.5845, p < 0.0001), Na+ and [Hb] (r = 0.3966, p = 0.0027), and K+ and [Hb] (r = 0.4885, p = 0.0002). Group B had the highest change rates of 2,3-DPG on day 35, and there was a negative correlation between 2,3-DPG and [Hb] (r = −0.4933, p = 0.0001).ConclusionsThese new data on the [Hb] could have implications for researchers wishing to study the storage quality of Tibetan SRBCs, particularly in the context of erythrocyte metabolism, and we propose finding a new, suitable alternative solution for plateau SRBCs, particularly the blood with [Hb] greater than 185 g/L. Our results could have important implications for researchers wishing to study the potential framework of high-altitude-induced SRBC storage lesions