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Comorbidities due to inflammatory bowel disease (IBD) and anxiety are commonly acknowledged; however, their underlying basis is unclear. In the current study, we first conducted a clinical retrospective analysis to identify the enhancive incidence rate of IBD before or after the epidemic of Corona Virus Disease 2019 (COVID-19), with higher Generalized Anxiety Disorder-7 (GAD-7), as well as poorer Gastrointestinal Quality of Life Index (GIQLI). Then, the dextran sodium sulfate (DSS) and chronic unpredictable stress (CUS)-induced IBD and anxiety comorbid models were established with the correlational relations between symptoms of IBD and anxiety-related behaviors. We found dysfunctional up-regulation of a new inflammatory factor interleukin (IL)-19 in the colon of DSS/CUS treated mice. Overexpression of IL-19 in colon induced anxious phenotypes, and accelerated the anxious condition and symptoms of colitis in the DSS/CUS model by promoting the expression of inducible nitric oxide synthase (iNOS), IL-1β, and IL-6 pro-inflammatory factors, and activating signal transducer and activator of transcription 3 (STAT3) signaling pathway in the colon. Furthermore, overexpression of IL-19 in the colon also reduced the expression levels of brain-derived neurotrophic factor (BDNF), extracellular signal-regulated kinase (ERK), and cAMP-response element binding protein (CREB) signaling pathways activity in the hippocampus. These results suggest that IL-19 was a pivotal player in DSS/CUS-induced comorbidities of colitis and anxiety with different signaling pathways for the colon and hippocampus, which provides a candidate gene to explore the pathophysiology of comorbidities due to colitis and anxiety.

This dissertation is scientifically divided into two main parts; Part Ⅰ describes the assessment of diverse substrate scaffolds using multicomponent reaction chemistry. The excellent maneuverability and efficiency of multicomponent reactions combined with high-throughput synthesis allow generating big synthesis data. This enables the deeper exploration of the chemical space in a fast, low-cost, and environment-friendly manner. Further coupling with high-throughput screening methods can lead to an acceleration in the early stage of drug discovery. Part Ⅱ demonstrates that bilobed proteins bearing a common primordial structural core diversified into transcription factors, enzymes, and extra-cytoplasmic transport-related proteins. The reason is that evolutionary modifications primarily at its termini enabled distinct structural dynamics. Substrate-binding domain 2, one of those bilobed proteins, undergoes a large conformational change upon ligand binding. The allosteric coupling of substrate-binding domain 2 is specific to L-glutamine, whereas altering structural dynamics can adapt protein for additional ligands. Understanding the molecular determinants of this specificity and/or intramolecular allosteric communication is critical to understanding protein evolution and the development of therapeutics. Drug design benefits not only from generating extensive compounds libraries but also from a better understanding of drug targets, especially very dynamic ones, since the visible pocket from a PDB structure may not exist.

Quantitative myocardial contrast echocardiography (MCE) has been proved to be valuable in detecting myocardial ischemia. During quantitative MCE analysis, myocardial segmentation is a critical step in determining accurate region of interests (ROIs). However, traditional myocardial segmentation mainly relies on manual tracing of myocardial contours, which is time-consuming and laborious. To solve this problem, we propose a fully automatic myocardial segmentation framework that can segment myocardial regions in MCE accurately without human intervention. A total of 100 patients’ MCE sequences were divided into a training set and a test set according to a 7: 3 proportion for analysis. We proposed a bi-directional training schema, which incorporated temporal information of forward and backward direction among frames in MCE sequences to ensure temporal consistency by combining convolutional neural network with recurrent neural network. Experiment results demonstrated that compared with a traditional segmentation model (U-net) and the model considering only forward temporal information (U-net + forward), our framework achieved the highest segmentation precision in Dice coefficient (U-net vs U-net + forward vs our framework: 0.78 ± 0.07 vs 0.79 ± 0.07 vs 0.81 ± 0.07, p

Most existing deep learning-based frameworks for image segmentation assume that a unique ground truth is known and can be used for performance evaluation. This is true for many applications, but not all. Myocardial segmentation of Myocardial Contrast Echocardiography (MCE), a critical task in automatic myocardial perfusion analysis, is an example. Due to the low resolution and serious artifacts in MCE data, annotations from different cardiologists can vary significantly, and it is hard to tell which one is the best. In this case, how can we find a good way to evaluate segmentation performance and how do we train the neural network? In this paper, we address the first problem by proposing a new extended Dice to effectively evaluate the segmentation performance when multiple accepted ground truth is available. Then based on our proposed metric, we solve the second problem by further incorporating the new metric into a loss function that enables neural networks to flexibly learn general features of myocardium. Experiment results on our clinical MCE data set demonstrate that the neural network trained with the proposed loss function outperforms those existing ones that try to obtain a unique ground truth from multiple annotations, both quantitatively and qualitatively. Finally, our grading study shows that using extended Dice as an evaluation metric can better identify segmentation results that need manual correction compared with using Dice., Comment: 12 pages

Most existing deep learning-based frameworks for image segmentation assume that a unique ground truth is known and can be used for performance evaluation. This is true for many applications, but not all. Myocardial segmentation of Myocardial Contrast Echocardiography (MCE), a critical task in automatic myocardial perfusion analysis, is an example. Due to the low resolution and serious artifacts in MCE data, annotations from different cardiologists can vary significantly, and it is hard to tell which one is the best. In this case, how can we find a good way to evaluate segmentation performance and how do we train the neural network? In this paper, we address the first problem by proposing a new extended Dice to effectively evaluate the segmentation performance when multiple accepted ground truth is available. Then based on our proposed metric, we solve the second problem by further incorporating the new metric into a loss function that enables neural networks to flexibly learn general features of myocardium. Experiment results on our clinical MCE data set demonstrate that the neural network trained with the proposed loss function outperforms those existing ones that try to obtain a unique ground truth from multiple annotations, both quantitatively and qualitatively. Finally, our grading study shows that using extended Dice as an evaluation metric can better identify segmentation results that need manual correction compared with using Dice.

In this work, a novel porous coordination polymer (CP) modified by O− groups is synthesized, which exhibits superior adsorption capacity for RE ions (211 mg g-1 for Gd3+, 183 mg g-1 for Pr3+ and 179 mg g-1 for Sm3+). Moreover, RE ions adsorption show a rapid process, especially, at an initial RE ions concentration of 1 ppm, the time to reach equilibrium was as short as 10 min. The high adsorption affinity make the adsorbent not only shows efficient adsorption of lower concentration RE3+ (1 ppm), but also exhibits excellent selectivity in the presence of competitive ions. Besides, the removal rate of RE3+ is more than 99 % in column chromatography separation using the CPs as fillers. The isothermal adsorption results are well described by the Langmuir model, and the adsorption kinetics fits well with pseudo-second-order kinetic model. Zeta potential, FT-IR and XPS demonstrate that electrostatic interactions and coordination reaction between O− groups and RE3+ are mainly adsorption mechanism. As far as we know, ordinary CP material exhibiting fast and efficient adsorption behavior for low concentration REEs is still rarely reported.

Quantum simulation can beat current classical computers with minimally a few tens of qubits and will likely become the first practical use of a quantum computer. One promising application of quantum simulation is to attack challenging quantum chemistry problems. Here we report an experimental demonstration that a small nuclear-magnetic-resonance (NMR) quantum computer is already able to simulate the dynamics of a prototype chemical reaction. The experimental results agree well with classical simulations. We conclude that the quantum simulation of chemical reaction dynamics not computable on current classical computers is feasible in the near future., Comment: 37 pages, 7 figures