1,020 research outputs found
Fig. 5 in Phragmalin and mexicanolide limonoids with reversal of multidrug resistance from the seeds of Chukrasia tabularis A. Juss
No full textFig. 5. Key HMBC () and ROESY(Published as part of Heng, Li, Zhao, Mengling, Xu, Rong, Tao, Rong, Wang, Chengcheng, Zhang, Lina, Bu, Yunge, Luo, Jun & Li, Yi, 2021, Phragmalin and mexicanolide limonoids with reversal of multidrug resistance from the seeds of Chukrasia tabularis A. Juss, pp. 1-7 in Phytochemistry (112606) 182 on page 5, DOI: 10.1016/j.phytochem.2020.112606, http://zenodo.org/record/829144
sj-docx-1-tam-10.1177_17588359211038477 – Supplemental material for TGFBR2 mutation predicts resistance to immune checkpoint inhibitors in patients with non-small cell lung cancer
No full textSupplemental material, sj-docx-1-tam-10.1177_17588359211038477 for TGFBR2 mutation predicts resistance to immune checkpoint inhibitors in patients with non-small cell lung cancer by Teng Li, Han Wang, Jiachen Xu, Chengcheng Li, Yudong Zhang, Guoqiang Wang, Yutao Liu, Shangli Cai, Wenfeng Fang, Junling Li and Zhijie Wang in Therapeutic Advances in Medical Oncology</p
sj-pdf-1-tct-10.1177_15330338231152350 - Supplemental material for A Randomized Phase III Study of Anlotinib Versus Bevacizumab in Combination With CAPEOX as First-Line Therapy for <i>RAS/BRAF</i> Wild-Type Metastatic Colorectal Cancer: A Clinical Trial Protocol
No full textSupplemental material, sj-pdf-1-tct-10.1177_15330338231152350 for A Randomized Phase III Study of Anlotinib Versus Bevacizumab in Combination With CAPEOX as First-Line Therapy for RAS/BRAF Wild-Type Metastatic Colorectal Cancer: A Clinical Trial Protocol by Jinjie He, Yue Liu, Chengcheng Liu, Hanguang Hu, Lifeng Sun, Dong Xu, Jun Li, Junye Wang, Xiaobing Chen, Rongbo Lin, Yi Jiang, Yanqiao Zhang, Weisheng Zhang, Ying Cheng, Xiaohong Wu, Mingzhi Fang, Enxiao Li, Ye Xu, Ye Chen, Jiayi Li, Yanyan Cui, Zhanyu Pan, Songnan Zhang, Ying Yuan and Kefeng Ding in Technology in Cancer Research & Treatment</p
Highly transparent PVA/nanolignin composite films with excellent UV shielding, antibacterial and antioxidant performance
No full textIn the present work, polyvinyl alcohol (PVA) nanocomposite films containing various contents of lignin nanoparticles (LNP) (1, 2 and 3 wt%) were formulated by a simple solvent cast method and cross-linked by adding glutaraldehyde (GA) and citric acid (CA). Herein, we investigated the effects of crosslinker types and LNP loading on optical, thermal, mechanical, antioxidant and antibacterial behaviour of PVA nanocomposite films for active food packaging. The results of UV irradiation shielding effect showed that the addition of 3 wt% of LNP reduced significantly the UV-B/UV-C transmittance to 0 for both P-GA-3LNP (PVA film with 3 wt% of LNP cross-linked by GA) and P-CA-3LNP (PVA film with 3 wt% of LNP cross-linked by CA) films. Results of thermogravimetric analysis (TGA) showed that thermal stability for all PVA nanocomposites was improved, ascribing it to the strong interactions between LNP and PVA in presence of GA and CA. In the meanwhile, plasticization was also observed for both GA and CA crosslinked PVA films, with tensile strength increased from 26.0 MPa (PVA) to 38.1 MPa (P-GA-3LNP) and 32.7 MPa (P-CA-3LNP) and constant values of elongation at break (240, 229 and 237%) for these three films. Additionally, P-GA-3LNP film showed more evident antibacterial and antioxidant ability than P-CA-3LNP, with evidence of better maintained freshness of shrimps under UV irradiation fluorescence measurements
Temperature-Sensitive Nanocarbon Hydrogel for Photothermal Therapy of Tumors
No full textWanlin Tan,1,2 Chen Sijie,1,2 Yan Xu,1,2 Mingyu Chen,1,2 Haiqin Liao,1,2 Chengcheng Niu1,2 1Department of Ultrasound Diagnosis, the Second Xiangya Hospital, Central South University, Changsha, Hunan, People’s Republic of China; 2Research Center of Ultrasonography, the Second Xiangya Hospital, Central South University, Changsha, Hunan, People’s Republic of ChinaCorrespondence: Chengcheng Niu, Email [email protected]: Intelligent hydrogels continue to encounter formidable obstacles in the field of cancer treatment. A wide variety of hydrogel materials have been designed for diverse purposes, but materials with satisfactory therapeutic effects are still urgently needed.Methods: Here, we prepared an injectable hydrogel by means of physical crosslinking. Carbon nanoparticle suspension injection (CNSI), a sentinel lymph node imaging agent that has been widely used in the clinic, with sodium β-glycerophosphate (β-GP) were added to a temperature-sensitive chitosan (CS) hydrogel (CS/GP@CN) as an agent for photothermal therapy (PTT). After evaluating the rheological, morphological, and structural properties of the hydrogel, we used 4T1 mouse breast cancer cells and B16 melanoma cells to assess its in vitro properties. Then, we intratumorally injected the hydrogel into BALB/c tumor-bearing mice to assess the in vivo PTT effect, antitumor immune response and the number of lung metastases.Results: Surprisingly, this nanocarbon hydrogel called CS/GP@CN hydrogel not only had good biocompatibility and a great PTT effect under 808nm laser irradiation but also facilitated the maturation of dendritic cells to stimulate the antitumor immune response and had an extraordinary antimetastatic effect in the lungs.Discussion: Overall, this innovative temperature-sensitive nanocarbon hydrogel, which exists in a liquid state at room temperature and transforms to a gel at 37 °C, is an outstanding local delivery platform with tremendous PTT potential and broad clinical application prospects.Keywords: temperature-sensitive chitosan, carbon nanoparticle suspension, photothermal therapy, hydrogels, antitumor immune respons
Statistical Estimation and Inference for Large-Scale Categorical Data
No full textCategorical data become increasingly ubiquitous in the modern big data era.
In this dissertation, we propose novel statistical learning and inference methods for large-scale categorical data, focusing on latent variable models and their applications to psychometrics. In psychometric assessments, the subjects' underlying aptitude often cannot be fully captured by raw scores due to differing item difficulties. Latent variable models, are popularly used to capture this unobserved proficiency. This dissertation studies two types of latent variable models with categorical responses. The first type assumes multiple discrete latent traits, commonly known as cognitive diagnosis models (CDMs), a special family of discrete latent variable models. The second type assumes a continuous latent score, commonly known as the item response theory (IRT) models. Although both have been widely applied in large-scale assessments, many challenges still exist for efficient learning and statistical inference. This dissertation studies four important problems that arise in these contexts.
The first part develops novel algorithms to estimate large latent Q-matrix in CDMs. Q-matrix plays an important role in CDMs; it specifies the inter-dependence between items and subjects' latent attributes. Accurate knowledge of Q-matrix is critical for cognitive diagnoses, item categorization and assessment design.
However, in practice, many assessments either do not have accurate Q-matrix specification or even do not provide Q-matrix. Furthermore, existing methods are not scalable with the size of Q-matrix, despite the prevalence of large Q-matrix.
We propose a penalized likelihood approach, with computational complexity growing linearly with Q sizes, to learn large Q-matrix from observational data.
The estimation consistency and the robustness of the proposed method across various CDMs are also established.
The second part develops learning and inference methods for a unidimensional IRT model, the Rasch model, under the missing data setting. Data missingness is prevalent in large-scale assessments; examples include SAT and GRE where subjects' responses are combined from multiple tests administered year-round from a large item pool. Direct inference to compare subjects’ latent scores under the missing data setting remains open and challenging in the literature. In this part, we obtain point estimators for the latent scores and derive their asymptotic distribution under a flexible missing-entry design in double asymptotic settings.
We show our estimator is statistically efficient and optimal, which is amongst the first results in the binary matrix completion literature.
The third part concerns measurement biases in IRT models. Novel estimation and inference procedures are developed for biases brought by measurement non-invariant items under the differential item functioning (DIF) framework. Existing methods either require knowing anchor items, i.e. DIF-free items or adopt regularization to ensure model identifiability where easy inference is not permitted. We propose a novel minimal L1 condition for simultaneous DIF detection and model identification. It does not require any knowledge of anchor items and permits easy inference for both binary and multiple groups settings.
The fourth part considers privacy issues for releasing tabular (categorical) data to the public. In the differential privacy (DP) framework, we recommend an optimal mechanism, where data utility is maximized under a privacy constraint. Common users' practices, including merging related cells or integrating multiple data sources, are considered. Valid inference procedures are developed for the associated privacy-protected data.PhDStatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/174638/1/lccvic_1.pd
Preparation and study of polystyrene/organic montmorillonite nanocomposite as lubricant additive of drilling fluid
No full textMultispectral visual detection method for conveyor belt longitudinal tear
No full textAs an important part of modern coal mine production, conveyor belts are widely used in the coal collection and transportation. In order to ensure the safe operation of the coal mine conveyor belt and solve the drawbacks of the existing conveyor belt longitudinal tear detection technology, a multispectral visual detection method for conveyor belt longitudinal tear is proposed in this paper. The experimental results show that the multispectral visual detection method not only can identify the conveyor belt longitudinal tear, but also accurately classifies and identify other states of the conveyor belt. The accuracy of multispectral visual detection method is over 90.06%, and the precision of longitudinal tearing recognition is over 92.04%. The proposed method is verified to meet the requirements of reliability and real-time in the industrial field.Accepted Author ManuscriptTransport Engineering and Logistic
Design Concept of an Automated Irrigation System for Simulating Saltwater Intrusion in a Mesocosm Experiment
No full textOblique incidence reflectance difference (OIRD) study on the conductive interface formation in LAO/STO structure
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