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Phenotypic and mechanistic elucidation for the role of adipocyte in breast cancer development
No full textThe unique microenvironment of breast cancer consists of various types of adipocytes and plays a critical role in tumorigenesis and metastasis. But the effects of different types of adipocytes on breast cancer behavior are undetermined due to their different features, rendering the addition of adipocytes during post-operation lipofilling controversial. Additionally, the underlying mechanisms are not fully elucidated. This study aims to understand the role of adipocytes in breast cancer invasiveness at cellular and molecular levels.
In this study, various types of adipocytes including preadipocytes, adipocyte-like adipocytes (AL), brown adipocytes (BAT), white adipocytes (WAT), and adipose-derived stem cell (ASC) were co-cultured with breast cancer cells in direct and indirect manners. The invasiveness of breast cancer cells was assessed regarding their proliferation, migration, and invasion. The alteration of co-cultured cells as well as the underlying mechanisms at morphological, organellar and molecular levels at serial time points were examined. Furthermore, the functional role of adipocytes on breast cancer behavior was explored in vitro and in a xenograft breast tumor mouse model. Tumor growth, proliferation rate and overall survival of mice were compared among groups.
The in vitro study suggested that luminal A breast cancer cell line (MCF7) co-cultured with all types of adipocytes showed a higher cancer invasiveness than MCF7 alone, among which MCF7-BAT co-cultivation was the highest, especially in direct co-cultivation. Furthermore, the tumorigenic potential of the co-cultivated cells was observed in the xenograft tumor mouse model, where the mixture of BAT and MCF7 showed increased tumor growth and decreased animal survival, but adipose-derive stem cell (ASC) showed no significant hazardous effects on both MCF7 and triple-negative breast cancer (MDA-MB-231) growth. Morphologically, MCF7-adipocyte cell-cell fusion was observed in all types of co-cultivation, and the fused cell proportion was significantly higher in MCF7-BAT than in other co-cultures. Mechanistically, the transcriptional levels of empirical cell fusion, adipogenesis, mitochondrial dynamics related molecules were substantially higher in MCF7-BAT fusion hybrid cells, particularly IRX3. In addition, IRX3 knock down significantly ameliorated the cancer invasiveness via modulated mitochondrial functions.
In conclusion, breast cancer microenvironment was significantly educated by various types of adipocytes, which exerted heterogeneous effects on breast cancer behavior. Among all types of adipocytes, brown adipocytes could facilitate breast cancer invasiveness by spontaneously forming fusion hybrid cells with breast cancer cells. Upregulated adipogenesis was the critical molecular mechanism that mediated cancer behavior of fusion hybrid cells through mitochondrial functional regulation. The altered molecular, organellar, and cellular mechanisms potentialized the development of new targeted therapy and cell therapy for breast cancer adjuvant therapy, as well as new surrogate biomarkers for precisely predicting long-term outcomes.published_or_final_versionSurgeryDoctoralDoctor of Philosoph
Forensic and pattern analysis on the bitcoin blockchain
No full textCryptocurrency-related crimes are on the rise and have a wide-ranging impact across various areas. To effectively combat and prevent these illicit activities, cryptocurrency forensics (crypto forensics) is essential. At its core, this field relies on the investigation and analysis of blockchain data. However, the inherent pseudonymity and dynamics of Bitcoin introduce significant complexities to these investigations.
The collection and validation of Bitcoin addresses are indispensable processes in blockchain forensic analysis, crucial for identifying suspicious transactions, tracing fund flows, and conducting de-anonymization investigations. Address clustering, which groups addresses likely controlled by the same entity, serves as a foundational technique. The accuracy of clustering outcomes significantly impacts the reliability of crypto forensic findings. While heuristic-based address clustering is commonly adopted, its effectiveness faces limitations primarily due to the absence of ground truth data. This lack introduces fundamental uncertainty into clustering results, hindering the validation of forensic conclusions. This uncertainty is compounded by the increasing adoption of privacy-enhancing technologies, which complicate address relationships and create additional hurdles for investigators. Moreover, other blockchain dynamic factors, such as introducing new network features, further challenge the accuracy of clustering, collectively making reliable forensic analysis increasingly complex.
This study undertakes multiple approaches to address these limitations. In the first part, confronting the challenge of unavailable ground truth labels, we develop a simulation model to assess the potential error rates of two widely used clustering heuristics: the multi-input and one-time change address heuristics. The second part provides an in-depth behavioral analysis of peeling chains, a common structure utilized by entities such as exchanges and mixers. This analysis enhances our understanding of the operational characteristics of transaction data associated with privacy-enhancing practices. Building on works and insights from these first two parts, the third part introduces an enhanced simulation platform that more accurately replicates real-world Bitcoin transaction structures. Additionally, we propose and evaluate a novel heuristic algorithm specifically designed to improve the classification of one-time change addresses. This refined simulator provides a robust environment for assessing address clustering methods based on transaction details. The new heuristic aims to reduce misclassifications and achieve better clustering results.
Overall, this research presents a simulation framework to quantify the uncertainties in heuristic clustering results. This facilitates a clearer assessment of the reliability and limitations of address clustering algorithms, thus strengthening the basis for the admissibility of clustering findings as forensic evidence. The proposed heuristic more effectively captures relevant transaction patterns, helping to alleviate the uncertainties introduced by privacy techniques in forensic analysis. Additionally, all three parts of this study contribute a comprehensive analysis of Bitcoin blockchain data from different periods, examining aspects such as transaction types, address reuse, and structural details. The identified characteristics and observed trends serve as a basis for refining forensic tools and methodologies.published_or_final_versionComputer ScienceDoctoralDoctor of Philosoph
Prospective associations between muscle strength and genetic susceptibility to type 2 diabetes with incident type 2 diabetes: a UK Biobank study
No full textBackground: This study explored whether the prospective associations between muscle strength and incident type 2 diabetes (T2D) differ by varying levels of genetic susceptibility to T2D. Methods: This study included 141,848 white British individuals from the UK Biobank. Muscle strength was expressed as the relative value of grip strength (measured by a hand dynamometer) divided by fat-free mass (measured via bioelectrical impedance analysis). Three categories of muscle strength (low, medium and high) were generated based on the sex- and age-specific tertiles. Genetic risk of T2D was estimated using a weighted polygenic risk score based on 138 independent single-nucleotide polymorphisms for T2D. During a median 7.4-year follow-up, 4,743 incident T2D cases were accrued. Cox regression with age as the underlying timescale was fit. Results: High muscle strength was associated with a 44% lower hazard of T2D (HR:0.56, 95%CI:0.52–0.60), compared with low muscle strength, after adjustment for genetic risk of T2D. The inverse association between muscle strength and incident T2D was weaker in individuals with high genetic susceptibility. There was evidence of interaction between muscle strength and genetic susceptibility to T2D (p-additive = 0.010, p-multiplicative = 0.046). The estimated 8-year absolute risk of T2D was lower for high genetic risk—high muscle strength (2.47%), compared with low (2.89%) or medium (4.00%) genetic risk combined with low muscle strength. Conclusions: Higher muscle strength was associated with lower relative risk of developing T2D, irrespective of genetic susceptibility to T2D, while such association was weaker in the high genetic risk group. Individuals at high genetic risk of T2D but with high muscle strength may have a lower 8-year absolute risk of developing T2D, compared with those at low or medium genetic risk but with low muscle strength. Our findings inform future clinical trials to prevent or delay the onset of T2D by implementing muscle-strengthening interventions among individuals of varying levels of genetic susceptibility to T2D, including those with high genetic risk
Validation of the Iwate scoring system for the stratification of laparoscopic liver resections: An international multicenter study
No full textIntroductionThe Iwate difficulty scoring system (DSS) is one of the most widely validated DSS for laparoscopic liver resection (LLR). However, these studies only validated the 4 difficulty levels and did not validate the 12-point difficulty index of the system. To address current limitations in the studies validating the Iwate difficulty scoring system (DSS), we performed an international multicenter study to validate the Iwate DSS across both its four difficulty levels and 12-point difficulty index.MethodsA retrospective cohort study of 22,252 patients undergoing LLR across 64 centers worldwide between 2005 and 2021 was performed. Baseline characteristics and perioperative outcomes were analyzed across the four difficulty levels and 12-point difficulty index of the Iwate DSS.ResultsA total of 14,759 patients met the inclusion criteria. The main indications for LLR were hepatocellular carcinoma/intrahepatic cholangiocarcinoma (52.8 %), and metastatic tumors liver (26.5 %). In terms of underlying liver pathology, 5127 patients (34.8 %) had liver cirrhosis, and 1214 patients (8.3 %) had portal hypertension. Intraoperative outcomes (operation time, blood loss, blood transfusion, use of Pringles maneuver and open conversion) and postoperative outcomes (length of stay, morbidity, major complications, and 90-day mortality) significantly increased with stepwise increases across the four difficulty levels (P P P ConclusionThe Iwate DSS 12-point difficulty index and four difficulty levels correlated well with LLR difficulty as determined by key surrogate perioperative measures.</p
Stoichiometric Selective Carbonylation of Methane to Acetic Acid by Chemical Looping
Get PDFThe conversion of methane to valuable products is one of the main challenges of modern chemistry. Acetic acid (AcOH) is a key chemical reagent in industry, produced nowadays by the carbonylation of methanol over homogeneous Rh and Ir catalysts. Here, we propose a stepwise chemical looping approach for the highly selective stoichiometric synthesis of AcOH by carbonylation of methane with CO using single-site Pt over isolated phosphotungstic anions on a titania support (Pt-HPW-TiO2). The reaction proceeds by methane activation, which coincides with the reduction of initially oxidized Pt species in the presence of CO at 423 K and results in surface acetates attached to TiO2. Subsequent hydrolysis by water at ambient temperature results in the synthesis of AcOH in a stoichiometric amount corresponding to 1.5 Pt. Spent Pt-HPW-TiO2 is restored to the initial state by subsequent calcination in air. This approach provides an opportunity for the selective synthesis of AcOH (>99% in liquid phase) from methane, carbon monoxide, and air. A high concentration of AcOH (1.1 wt %) in an aqueous solution can be obtained at a high conversion of methane (4.5%).published_or_final_versio
The Equity Implications of School Principal Evaluation for Student Achievement: A Critical Quantitative Policy Analysis
No full textThis study investigated causal links between states’ principal evaluation policies and academic achievement among Black and Hispanic students, focusing on two policy levers: (a) required inclusion of student growth data (output-based) and (b) mandated observations or site visits (input-based). We used repeated cross-sectional data on policy levers collected at eight time points during 2005 to 2019 and a counterfactual estimation framework for causal inferences. States enacting the output-based policy lever did not typically have higher reading or math achievement scores on the National Assessment of Educational Progress among Black and Hispanic eighth-grade students than states without these policies. In contrast, the input-based policy lever had a significant positive effect on eighth-grade reading achievement scores, particularly among Black students. The importance of focusing on principal behaviors rather than student achievement is discussed for enhancing school accountability with racial equity.</p
Structure-to-process modeling drives experimentally validated unified dual-phase steel
No full textUnified dual-phase (UniDP) steels enable tailored performance from a single composition, revolutionizing sustainable material systems by addressing recyclability and weldability challenges. Traditional design frameworks, constrained by forward "process-structure" models and costly uncertainty quantification, falter under sparse data and complex microstructures. Here, a microstructure-centric inverse design strategy is proposed that replaces uncertainty quantification with direct "structure-to-composition/process modeling", leveraging real microstructural features to map composition and processing parameters. Specifically, our approach integrates a variational autoencoder to encode authentic microstructural features into a latent space and a multilayer perceptron to predict composition, processing routes, and properties. Combined with specific latent space sampling, the framework achieves high-efficacy design exploration. The experimental success of UniDP steels stands as a cornerstone of this work: the designed alloy consistently achieves the target properties in all three performance tiers, at a lower cost than other commercial alloys. Latent space analysis further validated the model's ability to interpolate seamlessly between microstructures and encode multi-scale property relationships, confirming its robustness for real-world applications. By experimentally demonstrating the viability of microstructure-driven inverse design, this work not only resolves longstanding barriers in complex alloy systems but also establishes a replicable, uncertainty quantification-free framework for sustainable material innovation
Elastomer-constrained flat tube actuators (EFTAs)
No full textEthylene–vinyl acetate (EVA) flat tube has been widely used in designing soft wearable robots because of low-cost material, easy fabrication, and large deformation. However, only a bending-type flat tube actuator was proposed in previous studies, and more deformation modes are required to satisfy diverse robot design requirements. Herein, we propose elastomer-constrained flat tube actuators (EFTAs) with five actuation modes, extending, bending, helical, twisting, and contracting motions. Flat tubes are folded into various patterns, which are secured by silicone rubbers with different hardness. The silicone rubber fixes the arrangement of flat tubes for programmable deformation while providing resilience force. The bending angle, output force, load capability, and dynamic response tests of the EFTA-B are conducted. In addition, the deformation capability and force output of the other four actuators are also introduced in this study. We also compare our design with conventional fiber-reinforced actuators with five basic motions. EFTAs present comparable deformation performances and can be fabricated in easy processes. </p
Job preferences of Chinese PhD students in competitive labor markets
No full textThe job choices of PhD students have become an increasingly important topic, but there is a lack of research focusing on understanding the decision-making processes of PhD students when presented with various job options. Most of the studies have relied on the revealed preferences of PhD students than on their stated preferences but the latter is a more accurate form of eliciting decision-making processes. This study uses data collected from mainland Chinese PhD students based at Hong Kong universities through a custom-designed questionnaire to assess their job decision-making of various job scenarios relative to the competitive labor market of mainland China. Guided by Random Utility Theory, a Discrete Choice Experiment (DCE) method is employed, which is a novel approach within the field of higher education and effective in detecting job preferences. The findings show that salary, job security and sector of employment are the main attributes favored by PhD students when it comes to job preferences. Among these, salary stands out as the most important and overriding attribute. For the most part, no trade-offs between salary and job security were identified, and PhD students desire both, with a particular emphasis on a higher salary, as much as possible. These findings suggest that salary along with job security, are prime factors in job choices due to the need to survive in highly competitive job markets. This is further confirmed by the negligible preference for the higher education sector compared with the government sector, the latter perceived in China as a bastion of job security and stability (and prestige), and the preference for jobs in other sectors of activity if key job attributes are guaranteed
Who Holds Sovereign Debt and Why It Matters
No full textThis paper studies whether investor composition affects the sovereign debt market. We construct a data set of sovereign debt holdings by foreign and domestic bank, nonbank private and official investors for 101 countries across three decades. Compared with other investors, private nonbank investors absorb a disproportionate share of the debt supply, and their demand for emerging market debt is most price responsive. A counterfactual analysis of emerging market sovereigns shows a 10% increase in debt leads to a 5.8% yield increase but an outsized 8.4% increase without nonbank investors. We conclude that sovereigns are vulnerable to the loss of nonbanks.</p