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

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

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

Mitochondrial dysfunction and endoplasmic reticulum stress induced by activation of PPARα leaded testicular to apoptosis in SD rats explored to di-(2-ethylhexyl) phthalate (DEHP)

Di-2-ethylhexyl phthalate (DEHP), as a common endocrine disrupting chemicals, can induce toxicity to reproductive system. However, the mechanism remains to be explored. In our study, DEHP exposure induced testicular injury in rats. The high throughput transcriptional sequencing was performed to identify differentially expressed genes (DEGs) between the treatment and control groups. KEGG analysis revealed that DEGs were enriched in apoptosis, PPARα, and ER stress pathway. DEHP up-regulated the expression of PPARα, Bax, Bim, caspase-4. GRP78, PERK, p-PERK, eIF2α, p-eIF2α, ATF4 and CHOP. This view has also been confirmed in TM3 and TM4 cells. In vitro, after pre-treatment with GW6471 (an inhibitor of PPARα) or GSK (an inhibitor of PERK), the apoptosis was inhibited and mitochondrial dysfunction was improved. Moreover, the improvement of mitochondrial dysfunction decreased the expression of PERK pathway by using SS-31(a protective agent for mitochondrial function). Interestingly, ER stress promoted the accumulation of ROS by ERO1L (the downstream of CHOP during ER stress), and the ROS further aggravated the ER stress, thus forming a feedback loop during the apoptosis. In this process, a vicious cycle consisting of PERK, eIF2α, ATF4, CHOP, ERO1L, ROS was involved. Taken together, our results suggested that mitochondrial dysfunction and ER stress-ROS feedback loop caused by PPARα activation played a crucial role in DEHP-induced apoptosis. This work provides insight into the mechanism of DEHP-induced reproductive toxicity

A New Potent Inhibitor against α-Glucosidase Based on an In Vitro Enzymatic Synthesis Approach

Inhibiting the activity of intestinal α-glucosidase is considered an effective approach for treating type II diabetes mellitus (T2DM). In this study, we employed an in vitro enzymatic synthesis approach to synthesize four derivatives of natural products (NPs) for the discovery of therapeutic drugs for T2DM. Network pharmacology analysis revealed that the betulinic acid derivative P3 exerted its effects in the treatment of T2DM through multiple targets. Neuroactive ligand–receptor interaction and the calcium signaling pathway were identified as key signaling pathways involved in the therapeutic action of compound P3 in T2DM. The results of molecular docking, molecular dynamics (MD) simulations, and binding free energy calculations indicate that compound P3 exhibits a more stable binding interaction and lower binding energy (−41.237 kcal/mol) with α-glucosidase compared to acarbose. In addition, compound P3 demonstrates excellent characteristics in various pharmacokinetic prediction models. Therefore, P3 holds promise as a lead compound for the development of drugs for T2DM and warrants further exploration. Finally, we performed site-directed mutagenesis to achieve targeted synthesis of betulinic acid derivative. This work demonstrates a practical strategy of discovering novel anti-hyperglycemic drugs from derivatives of NPs synthesized through in vitro enzymatic synthesis technology, providing potential insights into compound P3 as a lead compound for anti-hyperglycemic drug development