Mechanistic Insight and Translational Possibilities of Obesity-Breast Cancer Connection

Obesity is an independent risk factor for more than thirteen cancer types. In breast cancer context, obesity is strongly associated with higher incidence and poorer prognosis in postmenopausal patients. The management of breast cancer in obese patients is faced with numerous challenges, as dosing, toxicity and drug efficacy. Thus, it is highly demanded to investigate the in-depth mechanisms underlying the obesity-cancer connection and translate these fundamental mechanisms into clinical applications. To this end, we first searched for breast cancer phenotypes driven by the obese environment. In both patients and preclinical models, we identified enhanced stem-like traits in cancer cells exposed to the obese settings and it independently predicted breast cancer risks. Next, as obesity-induced elevated free fatty acid is a central phenotype in obese individuals, we established in vitro cancer cell models by long-term exposure to palmitic acid. Under this condition, cancer cells consistently dedifferentiated towards a cancer stem-like phenotype and displayed enhanced tumor initiation capacity. Mechanistically, we found that the obesity-induced phenotype was governed epigenetically through remodeling the chromatin landscape in cancer cells, specifically through increased chromatin occupancy of the transcription factor CCAAT/enhancer-binding protein beta (C/EBPB). We further identified Lipocalin 2 and Claudin 1 as the key downstream target genes of C/EBPB and functionally demonstrated the critical roles of C/EBPB and its targets in obesity-induced breast cancer initiation phenotypes. The second part of this thesis focuses on the investigation of endotrophin-induced physiological responses in human cells and the clinical translational potential. The cleavage product of collagen VI alpha 3 chain, endotrophin, is upregulated in obese states and has been previously reported as a stimulator for oncogenic transformation of mammary ductal epithelial cells in rodents. In addition, endotrophin presents pro-fibrotic, chemoattractant, pro-angiogenic and epithelial to mesenchymal transition induction properties in murine cells. Here, we demonstrated that the circulating levels of endotrophin is increased in breast cancer patients compared with healthy individuals. Recombinant human endotrophin exerted similar effects on human cell lines as in murine cells, suggesting that endotrophin is a viable target for breast cancer therapy. As such, we developed neutralizing antibodies targeting human endotrophin, and ultimately validated the effectiveness of such antibody in human breast cancer cell lines and human cell line-derived nude mouse models. In both in vitro and in vivo models, the antibody showed potent tumor growth inhibition and anti-cisplatin resistance functions, and thus provided the important stage towards developing targeted therapies for obese breast cancer patients.Doktorgradsavhandlin

A Link-Based Day-to-Day Traffic Assignment Model

Existing day-to-day traffic assignment models are all built upon path flow variables. This paper demonstrates two essential shortcomings of these path-based models. One is that their application requires a given initial path flow pattern, which is typically unidentifiable, i.e., mathematically nonunique and practically unobservable. In particular, we show that, for the path-based models, different initial path flow patterns constituting the same link flow pattern generally gives different day-to-day link flow evolutions. The other shortcoming of the path-based models is the path-overlapping problem. That is, the path-based models ignore the interdependence among paths and thus can give very unreasonable results for networks with paths overlapping with each other. These two path-based problems exist for most (if not all) deterministic day-to-day dynamics whose fixed points are the classic Wardrop user equilibrium. To avoid the two path-based problems, we propose a day-to-day traffic assignment model that directly deals with link flow variables. Our link-based model captures travelers\u27 cost-minimization behavior in their path finding as well as their inertia. The fixed point of our link-based dynamical system is the classic Wardrop user equilibrium

A Generalized Flow Splitting Model for Day-to-day Traffic Assignment

AbstractThe splitting rate model proposed by Smith and Mounce (2011) establishes a traffic evolution process on a link-node network representation, which overcomes the difficulties in applying traditional path-based models and provides the ease of implementing controls at nodes. While their model offers a new method for modeling traffic evolution, it contains an ad-hoc step of flow adjustment to preserve the flow conservation. This flow adjustment step leads to difficulties in analyzing the system properties. This paper proposes a generalized flow splitting model for day-to-day traffic assignment based on the concept of splitting flow at nodes. The proposed model preserves the flow conservation endogenously by introducing the inflow variable into the formulation. The generalized formulation provides the ease to construct a variety of day-to-day traffic assignment models, and serves as a framework for analyzing the models’ properties, such as the invariance property and the preservation of the Lipschitz continuity and strong monotonicity. Specifically, a proportional-adjustment model and a projection-type model are developed based on the proposed generalized formulation. A numerical example demonstrates the ease of implementing the proposed generalized model, as well as its convergence to user equilibrium

Associations Between Genetic Variants in Mrna Splicing-Related Genes and Risk of Lung Cancer: a Pathway-Based Analysis from Published Gwass

mRNA splicing is an important mechanism to regulate mRNA expression. Abnormal regulation of thisprocess may lead to lung cancer. Here, we investigated the associations of 11,966 single-nucleotide polymorphisms (SNPs) in 206 mRNA splicing-related genes with lung cancer risk by using the summarydata from six published genome-wide association studies (GWASs) of Transdisciplinary Research in Cancerof the Lung (TRICL) (12,160 cases and 16,838 controls) and another two lung cancer GWASs of Harvard University (984 cases and 970 controls) and deCODE (1,319 cases and 26,380 controls). We found that a total of 12 significant SNPs with false discovery rate (FDR) ≤0.05 were mapped to one novel gene PRPF6and two previously reported genes (DHX16 and LSM2) that were also confirmed in this study. The sixnovel SNPs in PRPF6 were in high linkage disequilibrium and associated with PRPF6 mRNA expression inlymphoblastoid cells from 373 Europeans in the 1000 Genomes Project. Taken together, our studies shednew light on the role of mRNA splicing genes in the development of lung cancer

A knowledge distillation-based multi-scale relation-prototypical network for cross-domain few-shot defect classification

Surface defect classification plays a very important role in industrial production and mechanical manufacturing. However, there are currently some challenges hindering its use. The first is the similarity of different defect samples makes classification a difficult task. Second, the lack of defect samples leads to poor accuracies when using deep learning methods. In this paper, we first design a novel backbone network, ResMSNet, which draws on the idea of multi-scale feature extraction for small discriminative regions in defect samples. Then, we introduce few-shot learning for defect classification and propose a Relation-Prototypical network (RPNet), which combines the characteristics of ProtoNet and RelationNet and provides classification by linking the prototypes distances and the nonlinear relation scores. Next, we consider a more realistic scenario where the base dataset for training the model and target defect dataset for applying the model are usually obtained from domains with large differences, called cross-domain few-shot learning. Hence, we further improve RPNet to KD-RPNet inspired by knowledge distillation methods. Through extensive comparative experiments and ablation experiments, we demonstrate that either our ResMSNet or RPNet proves its effectiveness and KD-RPNet outperforms other state-of-the-art approaches for few-shot defect classification.</p

Study of a high-precision complex 3D geological modelling method based on a fine KNN and kriging coupling algorithm: a case study for Jiangsu, China

A high-precision, complex, three-dimensional (3D) geological model can directly express the attributes of stratum thickness, geological structure, lithology and spatial form, which can provide a reliable basis for the development and utilization of underground space and planning decisions. However, it is difficult to perform accurate modelling due to the lack of basic data. As such, this paper proposes coupling a machine learning algorithm (K-nearest neighbour (KNN)) with the kriging algorithm to construct the topological relationship between the Delaunay triangle and the Thiessen polygon in order to perform the simulation and prediction of virtual drilling. Based on KNN, support vector machine (SVM) and neural network algorithms as well as the virtual borehole encryption data, data standardization processing and analysis are carried out. Through model verification, algorithm optimization is realized, and the optimal modelling method is explored. The results show that the fine KNN algorithm improved by Bayesian optimization can effectively improve the modelling accuracy through 0.1-m encryption, standardization processing and 5-fold cross-validation. Stratum modelling combined with the fine KNN and kriging algorithms can obtain a more accurate modelling without adding virtual boreholes. The improved levels of upper and lower hybrid modelling with an appropriate number of profile boreholes can also effectively optimize model accuracy. Both modelling accuracy and efficiency can be significantly improved by using Delaunay triangles and Thiessen polygons with virtual boreholes. Stratum modelling can effectively express the geological pinch-out in areas with adequate degrees of stratification, and hybrid modelling performs well in irregular geological bodies such as karsts and lenses

Simulation and Analysis of the Thermal-Mechanical Response of an Energy Pile

AbstractAn energy pile undertakes the functions of supporting the superstructure and controlling the indoor temperature of the building, and the thermal-mechanical coupling response of an energy pile makes its load transfer mechanism different from that of conventional engineering piles. Moreover, the thermal-mechanical coupling responses of the energy piles in summer and winter conditions are also different and need to be explored separately. Based on a ground source heat pump pile foundation workshop project in Kunshan city, Jiangsu Province, a multiphysics simulation study was carried out. The simulation results of the outlet water temperature and pile settlement are consistent with the real-world measurements, which verifies the reliability of the numerical simulation. The responses of the temperature distribution, axial stress, lateral shear stress, and settlement of the energy pile in summer and winter were analyzed, and the response laws of the energy pile in different seasons were obtained. Compared with the pure conventional load state, under the effect of thermal-mechanical coupling in winter conditions, the maximum compressive stress of the pile body is reduced by about 11.5%, but the settlement of the pile top increases by about 47.66%. Therefore, the winter conditions should be used as the design energy for the normal use of the pile. The control condition of the limit state: compared with the pure conventional load state, the maximum compressive stress of the pile increases by about 12% and the settlement of the pile top decreases by about 7.23% under the thermal-mechanical coupling effect of the summer condition. Therefore, the summer condition is the pile control conditions for the limit state of the body’s carrying capacity

SAMAug: Point Prompt Augmentation for Segment Anything Model

This paper introduces SAMAug, a novel visual point augmentation method for the Segment Anything Model (SAM) that enhances interactive image segmentation performance. SAMAug generates augmented point prompts to provide more information about the user's intention to SAM. Starting with an initial point prompt, SAM produces an initial mask, which is then fed into our proposed SAMAug to generate augmented point prompts. By incorporating these extra points, SAM can generate augmented segmentation masks based on both the augmented point prompts and the initial prompt, resulting in improved segmentation performance. We conducted evaluations using four different point augmentation strategies: random sampling, sampling based on maximum difference entropy, maximum distance, and saliency. Experiment results on the COCO, Fundus, COVID QUEx, and ISIC2018 datasets show that SAMAug can boost SAM's segmentation results, especially using the maximum distance and saliency. SAMAug demonstrates the potential of visual prompt augmentation for computer vision. Codes of SAMAug are available at github.com/yhydhx/SAMAu