Poly(triazole) Networks via Photo-initiated Click Reactions: Copper-catalyzed Azide-Alkyne Cycloaddition Polymerization and Thiol-Norbornene Polymerization

The focus of this thesis is to develop poly(triazole) glassy networks through photoinitiated click reactions utilizing either copper-catalyzed azide-alkyne cycloaddition (CuAAC) polymerization or radical-mediated thiol-norbornene polymerization and to investigate the structure-property relationships of the poly(triazole) networks and their potential applications in dental restorative material or 3D printing material. Thanks to the near quantitative yields, rapid photocuring kinetics, both the photo-CuAAC polymerization and the thiol-ene photopolymerization are powerful tools for converting liquid resins into vitrified/solidified polymeric networks in a spatially and temporally controlled manner. Firstly, ether-based CuAAC formulation was investigated as ester-free dental restorative resin, and the photopolymerized CuAAC network exhibited comparable or superior mechanical properties with reduced polymerization-induced shrinkage stress compared with conventional BisGMA/TEGDMA (70/30) resin. In addition, the ether-based CuAAC network displayed much improved water stabilities in comparison to the previously developed urethane-based CuAAC network, forwarding its development as dental restorative materials. Secondly, thiol-norbornene polymerization was investigated as an alternative approach in forming poly(triazole) networks by employing triazole-embedded norbornene monomers. The structure-property relationships were examined of the photopolymerized triazole/thiol-norbornene networks. Not only the value of triazoles in forming tough glassy networks was demonstrated through side-by-side comparison with structurally similar urethane/thiol-norbornene network, previously unseen retained-ductile behaviors were also observed with two of the triazole/thiol-norbornene networks. Furthermore, taking advantage of the rapid photocuring kinetics of the thiol-norbornene polymerization, one of the triazole/thiol-norbornene resins was implemented in stereolithography (SLA) 3D printing to fabricate conventionally unmoldable objects and challenging structures in high precision, expanding the application of the poly(triazole) glassy networks with high ductility and high tensile toughness. Lastly, secondary chemistry (cyanate ester) was introduced into the triazole/thiol-norbornene network to form an interpenetrating network or a hybrid network with high glass-transition temperatures and high strength. Overall, this dissertation mainly aims at exploring photo-initiated click reactions in forming poly(triazole) networks with high mechanical performances for demanding applications

How to Ask Better Questions? A Large-Scale Multi-Domain Dataset for Rewriting Ill-Formed Questions

We present a large-scale dataset for the task of rewriting an ill-formed natural language question to a well-formed one. Our multi-domain question rewriting MQR dataset is constructed from human contributed Stack Exchange question edit histories. The dataset contains 427,719 question pairs which come from 303 domains. We provide human annotations for a subset of the dataset as a quality estimate. When moving from ill-formed to well-formed questions, the question quality improves by an average of 45 points across three aspects. We train sequence-to-sequence neural models on the constructed dataset and obtain an improvement of 13.2% in BLEU-4 over baseline methods built from other data resources. We release the MQR dataset to encourage research on the problem of question rewriting.Comment: AAAI 202

Nutritional, physical, and flavour properties of cooked marinated meat products

Protein, fat, shear force, color, and chewiness were the primary factors to evaluate the characteristics of cooked marinated meat products. Overall contents of protein and fat of cooked marinated meat products in black pigs (144.0 g/100 g) were higher than that of white pigs (131.3 g/100 g). The tenderness of cooked marinated meat products of black pigs was higher than white pigs. Total content and number of flavour components of cooked marinated meat products from black pigs were higher than those of white pigs. The flavour contents were 11 163.33 and 3 478.79 μg/g of cooked marinated meat products in black and white pigs, respectively. 2-Pentyl-furan, nonanal, octanal, hexanal, eucalyptol, hexanoic acid, 1-octen-3-ol, 2-octen-1-ol were the most prevalent odor-active compounds of cooked marinated meat products. The cooked marinated meat products from black pigs were associated with aldehydes, ketones, alcohol, esters, acid, pyrrole, and hydroxylamine. The cooked marinated meat products from white pigs were mainly associated with alcohol, ester, acid, phenol, furfural and alkane. Theoretical basis and guidance from these findings support the use of modern meat science and technology for enhancing the quality of traditional Chinese meat products in large-scale production. This would facilitate the global trade of these products. Meanwhile, this study, for the first time, provided some profound insights into the industrial production and flavour control of traditional cooked marinated meat products

Chemoprevention of prostate cancer in men with high-grade prostatic intraepithelial neoplasia (HGPIN): a systematic review and adjusted indirect treatment comparison

Background: High-grade prostatic intraepithelial neoplasia (HGPIN) is the precursor or premalignant form of prostate cancer. At least 30% patients with a confirmed HGPIN will develop prostate cancer within 1 year after repeated biopsy. HGPIN patients are the appropriate at-risk population for chemoprevention strategies investigation against prostate cancer. However the commonly used chemoprevention agents that targeted on hormonal imbalance or lifestyle-related factors showed varied results in HGPIN patients. Methods: Literature searches were conducted in PubMed, EMBASE and Cochrane library according to Cochrane guidelines before January 31st, 2017. Direct meta-analysis were performed to summarize the efficacy of candidate chemopreventative agents Dutasteride, Flutamide, Toremifene, Selenium, Green tea components, Lycopene and natural food products combination. Adjusted indirect meta-analyses were employed to compare the relative efficacy of these candidate chemoprevention agents head-to-head. Results: The overall incidence of prostate cancer in HGPIN was slightly decreased by chemoprevention agents (25.7% vs 31.5%, RR = 0.92, 95% CI: 0.83-1.03, P = 0.183), with minor heterogeneity (I2 = 22.3%, χ2 = 15.08, P = 0.237), but without statistical significance. Subgroup analysis showed that green tea catechins significantly decreased prostate cancer in HGPIN patients (7.60% vs 23.1%, RR = 0.39, 95% CI: 0.16-10.97, P P = 0.044), with moderate heterogeneity (I2 = 47.9%, χ2 = 1.92, P = 0.166). The adjusted indirect meta-analysis favored green tea catechins over other chemoprevention agents, and significantly when compared to natural food products combination (RR = 0.355, 95% CI: 0.134-0.934). Conclusion: The overall efficacy of chemoprevention agents in HGPIN patients is limited. But Green tea catechins showed the superiority to decrease prostate cancer in HGPIN patients

The Role of Autophagy and NLRP3 Inflammasome in Liver Fibrosis

Liver fibrosis is an intrinsic repair process of chronic injury with excessive deposition of extracellular matrix. As an early stage of various liver diseases, liver fibrosis is a reversible pathological process. Therefore, if not being controlled in time, liver fibrosis will evolve into cirrhosis, liver failure, and liver cancer. It has been demonstrated that hepatic stellate cells (HSCs) play a crucial role in the formation of liver fibrosis. In particular, the activation of HSCs is a key step for liver fibrosis. Recent researches have suggested that autophagy and inflammasome have biological effect on HSC activation. Herein, we review current studies about the impact of autophagy and NOD-like receptors containing pyrin domain 3 (NLRP3) inflammasome on liver fibrosis and the underlying mechanisms

Higher-Order Conditional Random Fields-Based 3D Semantic Labeling of Airborne Laser-Scanning Point Clouds

This paper presents a novel framework to achieve 3D semantic labeling of objects (e.g., trees, buildings, and vehicles) from airborne laser-scanning point clouds. To this end, we propose a framework which consists of hierarchical clustering and higher-order conditional random fields (CRF) labeling. In the hierarchical clustering, the raw point clouds are over-segmented into a set of fine-grained clusters by integrating the point density clustering and the classic K-means clustering algorithm, followed by the proposed probability density clustering algorithm. Through this process, we not only obtain a more uniform size and more homogeneous clusters with semantic consistency, but the topological relationships of the cluster’s neighborhood are implicitly maintained by turning the problem of topology maintenance into a clustering problem based on the proposed probability density clustering algorithm. Subsequently, the fine-grained clusters and their topological context are fed into the CRF labeling step, from which the fine-grained cluster’s semantic labels are learned and determined by solving a multi-label energy minimization formulation, which simultaneously considers the unary, pairwise, and higher-order potentials. Our experiments of classifying urban and residential scenes demonstrate that the proposed approach reaches 88.5% and 86.1% of “m F 1 ” estimated by averaging all classes of the F 1 -scores. We prove that the proposed method outperforms five other state-of-the-art methods. In addition, we demonstrate the effectiveness of the proposed energy terms by using an “ablation study” strategy

Autophagy-dependent lysosomal calcium overload and the ATP5B-regulated lysosomes-mitochondria calcium transmission induce liver insulin resistance under perfluorooctane sulfonate exposure

Perfluorooctane sulfonate (PFOS), an officially listed persistent organic pollutant, is a widely distributed perfluoroalkyl substance. Epidemiological studies have shown that PFOS is intimately linked to the occurrence of insulin resistance (IR). However, the detailed mechanism remains obscure. In previous studies, we found that mitochondrial calcium overload was concerned with hepatic IR induced by PFOS. In this study, we found that PFOS exposure noticeably raised lysosomal calcium in L-02 hepatocytes from 0.5 h. In the PFOS-cultured L-02 cells, inhibiting autophagy alleviated lysosomal calcium overload. Inhibition of mitochondrial calcium uptake aggravated the accumulation of lysosomal calcium, while inhibition of lysosomal calcium outflowing reversed PFOS-induced mitochondrial calcium overload and IR. Transient receptor potential mucolipin 1 (TRPML1), the calcium output channel of lysosomes, interacted with voltage-dependent anion channel 1 (VDAC1), the calcium intake channel of mitochondria, in the PFOS-cultured cells. Moreover, we found that ATP synthase F1 subunit beta (ATP5B) interacted with TRPML1 and VDAC1 in the L-02 cells and the liver of mice under PFOS exposure. Inhibiting ATP5B expression or restraining the ATP5B on the plasma membrane reduced the interplay between TRPML1 and VDAC1, reversed the mitochondrial calcium overload and deteriorated the lysosomal calcium accumulation in the PFOS-cultured cells. Our research unveils the molecular regulation of the calcium crosstalk between lysosomes and mitochondria, and explains PFOS-induced IR in the context of activated autophagy

Development and validation of a deep learning model for predicting postoperative survival of patients with gastric cancer

Abstract Background Deep learning (DL), a specialized form of machine learning (ML), is valuable for forecasting survival in various diseases. Its clinical applicability in real-world patients with gastric cancer (GC) has yet to be extensively validated. Methods A combined cohort of 11,414 GC patients from the Surveillance, Epidemiology and End Results (SEER) database and 2,846 patients from a Chinese dataset were utilized. The internal validation of different algorithms, including DL model, traditional ML models, and American Joint Committee on Cancer (AJCC) stage model, was conducted by training and testing sets on the SEER database, followed by external validation on the Chinese dataset. The performance of the algorithms was assessed using the area under the receiver operating characteristic curve, decision curve, and calibration curve. Results DL model demonstrated superior performance in terms of the area under the curve (AUC) at 1, 3, and, 5 years post-surgery across both datasets, surpassing other ML models and AJCC stage model, with AUCs of 0.77, 0.80, and 0.82 in the SEER dataset and 0.77, 0.76, and 0.75 in the Chinese dataset, respectively. Furthermore, decision curve analysis revealed that the DL model yielded greater net gains at 3 years than other ML models and AJCC stage model, and calibration plots at 3 years indicated a favorable level of consistency between the ML and actual observations during external validation. Conclusions DL-based model was established to accurately predict the survival rate of postoperative patients with GC