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Unraveling the potential of curcumin-based nanoformulations: Advancing against multidrug resistance in lung cancer
No full textTurmeric, or Curcuma longa L., is treasured for its antiinflammatory, antioxidant, antibacterial, antitoxic, and anticancer properties. Curcumin, the main active compound in turmeric, displays significant therapeutic potential due to its hydrophobic polyphenol structure and ability to exhibit keto-enol tautomerism. However, its low water solubility and bioavailability present challenges in clinical applications. Lung cancer, a prevalent and aggressive form of cancer, is primarily addressed through chemotherapy, radiation therapy, and surgical interventions. Although targeted therapies and immunotherapies have been recently introduced, the survival rate remains low due to toxicity, drug resistance, and late detection. Multidrug resistance (MDR) further complicates treatment by allowing cancer cells to resist the effects of chemotherapeutic agents through various mechanisms, such as overexpression of drug efflux pumps and alterations in cellular signaling pathways. Curcumin has demonstrated potential in overcoming MDR in lung cancer by inducing apoptosis, inhibiting cell invasion, and regulating epigenetic modifications and microRNA expression. Nanoformulations of curcumin improve its solubility and bioavailability, showing effectiveness against cancer cell lines. This chapter delves into innovative therapeutic approaches using curcumin to combat MDR in lung cancer. It essentially focuses on nanoformulation strategies and their potential to alter the tumor microenvironment, ultimately seeking to enhance patient outcomes and survival rates despite the challenges posed by MDR
FedPCL-CDR: A federated prototype-based contrastive learning framework for privacy-preserving cross-domain recommendation.
No full textCross-domain recommendation (CDR) aims to improve recommendation accuracy in sparse domains by transferring knowledge from data-rich domains. However, existing CDR approaches often assume that user-item interaction data across domains is publicly available, neglecting user privacy concerns. Additionally, their performance degrades under sparse overlapping-user conditions because they rely on a large number of fully shared users for knowledge transfer. To address these challenges, we propose a Federated Prototype-based Contrastive Learning (CL) framework for Privacy-Preserving CDR, called FedPCL-CDR. This approach utilizes non-overlapping user information and differential prototypes to improve model performance within a federated learning framework. FedPCL-CDR comprises two key modules: local domain (client) learning and global server aggregation. In the local domain, FedPCL-CDR first clusters all user data and utilizes local differential privacy (LDP) to learn differential prototypes, effectively utilizing non-overlapping user information and protecting user privacy. It then conducts knowledge transfer by employing both local and global prototypes returned from the server in a CL manner. Meanwhile, the global server aggregates differential prototypes sent from local domains to learn both local and global prototypes. Extensive experiments on four CDR tasks across Amazon and Douban datasets demonstrate that FedPCL-CDR surpasses SOTA baselines. Specifically, it outperforms the strongest baseline by an average of 5.76 % in HR@10, 7.36 % in NDCG@10, and 13.53 % in MRR@10 across all tasks. We release our code at https://github.com/Lili1013/FedPCL_CDR
Graph Learning-Empowered Financial Fraud Detection: Progress and Future Directions
Get PDFFinancial fraud detection is an important task in ensuring the integrity and security of financial systems. In recent years, it has been shown that graph learning, which utilizes the relational structure of data, can considerably enhance the detection of fraudulent financial activity by accurately modeling the complex patterns and relationships inherent in financial transactions. In this work, we provide a comprehensive survey on the emerging application of graph learning techniques in detecting and combating financial fraud that can serve as a guidepost for researchers and practitioners interested in leveraging the power of graph learning to create a safer, more secure financial environment. Specifically, we start by introducing the fundamental concepts of graph learning, outlining their unique advantages over traditional machine learning techniques in the context of financial fraud detection. It is worth mentioning that graph learning techniques enable end-to-end training from relational data input to fraud prediction, eliminating the need for additional feature engineering. We then delve into a systematic review of the recent advancements and methodologies in applying graph learning to various financial fraud scenarios, such as credit card fraud, insurance fraud, and money laundering. Furthermore, we provide unique insights regarding several critical challenges, such as data privacy, scalability, and the dynamic nature of financial networks, that are faced when implementing graph learning models in real-world financial ecosystems. We show that practical applications of graph learning still suffer from computational complexity and lack of interpretability, and we offer a forward-looking perspective on potential research directions and improvements that can boost the effectiveness of graph learning applied to financial fraud detection
Telephone numbers extensions
No full textIn this note we bring together in one place the recent extensions of telephone numbers using determinants from certain matrices. A common formula involving Hermite and relevant recurrence relations are given as part of this extension of the connections of these numbers with classical number theory
Location of people
No full textThis is a definition of location of people in the Thematic Encyclopedia of Regional Science. This thematic Encyclopedia explores the multifaceted world of regional science, presenting a systematic and coherent overview of its central topics. It highlights the interdisciplinary nature of the field, examining the wide range of concepts, theories, methods and models that shape spatial-oriented approaches to the social sciences. Contributions from expert scholars delve into key aspects of regional science, from urban poverty and natural resource management to smart cities and AI. Highly accessible entries cover the definition, history, theoretical background, and applications of each topic, as well as avenues for future research
Attention Driven YOLOv5 Network for Enhanced Landslide Detection Using Satellite Imagery of Complex Terrain
Get PDFLandslide hazard detection is a prevalent problem in remote sensing studies, particularly with the technological advancement of computer vision. With the continuous and exceptional growth of the computational environment, the manual and partially automated procedure of landslide detection from remotely sensed images has shifted toward automatic methods with deep learning. Furthermore, attention models, driven by human visual procedures, have become vital in natural hazard-related studies. Hence, this paper proposes an enhanced YOLOv5 (You Only Look Once version 5) network for improved satellite-based landslide detection, embedded with two popular attention modules: CBAM (Convolutional Block Attention Module) and ECA (Efficient Channel Attention). These attention mechanisms are incorporated into the backbone and neck of the YOLOv5 architecture, distinctly, and evaluated across three YOLOv5 variants: nano (n), small (s), and medium (m). The experiments use open-source satellite images from three distinct regions with complex terrain. The standard metrics, including F-score, precision, recall, and mean average precision (mAP), are computed for quantitative assessment. The YOLOv5n + CBAM demonstrates the most optimal results with an F-score of 77.2%, confirming its effectiveness. The suggested attention-driven architecture augments detection accuracy, supporting post-landslide event assessment and recovery
Synergizing Machine Learning and Physical Models for Enhanced Gas Production Forecasting: A Comparative Study of Short- and Long-Term Feasibility
Get PDFAdvanced strategies for production forecasting, operational optimization, and decision-making enhancement have been employed through reservoir management and machine learning (ML) techniques. A hybrid model is established to predict future gas output in a gas reservoir through historical production data, including reservoir pressure, cumulative gas production, and cumulative water production for 67 months. The procedure starts with data preprocessing and applies seasonal exponential smoothing (SES) to capture seasonality and trends in production data, while an Artificial Neural Network (ANN) captures complicated spatiotemporal connections. The history replication in the models is quantified for accuracy through metric keys such as mean absolute error (MAE), root mean square error (RMSE), and R-squared. The future forecast is compared with an outcome of a previous physical model that integrates wells and reservoir properties to simulate gas production using regressions and forecasts based on empirical and theoretical relationships. Regression analysis ensures alignment between historical data and model predictions, forming a baseline for hybrid model performance evaluation. The results reveal the complementary attributes of these methodologies, providing insights into integrating data-driven and physics-based approaches for optimal reservoir management. The hybrid model captured the production rate conservatively with an extra margin of three years in favor of the physical model
Longitudinal effects of sex differences and apolipoprotein E genotype on white matter engagement among elderly.
Get PDFThe apolipoprotein E (APOE) ɛ4 allele is the primary genetic risk factor that influences lipid metabolism and contributes to distinctive Alzheimer's disease pathologies, including increased hippocampal atrophy and accelerated cognitive decline. Synaptic dysfunction can occur in APOE4 carriers even before the appearance of any clinical symptoms. Recent evidence has suggested that this genetic risk factor impacts males and females differently. The sex-specific vulnerability for females to cognitive decline, particularly memory, intensifies post-menopause and emphasizes the need for further investigation. White matter abnormalities, APOE4 allele and disruptions in default mode network connectivity serve as early indicators that are crucial for better understanding Alzheimer's disease progression. This study aims to explore relationships between biological sex, APOE4, default mode network-white matter activity and memory function as measured by the Selective Reminding Test. Participants were categorized by risk level on their APOE4 status. Using longitudinal data from the Harvard Aging Brain Study, we examined sex differences in default mode network-white matter engagement among older individuals with and without the APOE4 allele. Our findings demonstrated a significant reduction in default mode network-white matter activity in the right posterior corona radiata in the high-risk group compared to the low-risk group. High-risk females showed reduction in default mode network-white matter activity in the right superior longitudinal fasciculus, which positively correlated with free recall performance, compared to their low-risk counterparts. Unlike females, males showed no significant changes between the low- and high-risk groups. These results underscore the effectiveness of white matter engagement mapping in differentiating longitudinal changes in memory function related to the genetic risk factor APOE4 and biological sex
Assessing the role of Australia's Pharmaceutical Benefits Scheme as a tool for addressing inequality in access to medications and allocation of public funds for pregnant women.
Get PDFINTRODUCTION: Medication use during pregnancy is common, and socioeconomic disparities in access may contribute to maternal and fetal health inequalities. This study examines socioeconomic disparities in access to and expenditure on medications dispensed through Australia's Pharmaceutical Benefits Scheme (PBS), evaluating its role in promoting equal access to medications for pregnant women. METHODS: We analysed the Maternity1000 linked administrative dataset, which includes data on 57 443 women who gave birth in Queensland, Australia, between 1 July 2017 and 30 June 2018. Socioeconomic quintiles were assigned using the Index of Relative Socioeconomic Disadvantage. Medication prevalence rates, usage proportions and costs (2022/2023 Australian dollar) were calculated, followed by concentration curves and indices to assess inequality. RESULTS: Medication prevalence was higher among more disadvantaged women (Q1 (most disadvantaged): 67% vs Q5 (least disadvantaged): 60%), who were also dispensed a higher average number of medications per pregnancy (Q1: 2.8 (95% CI 2.7 to 2.9) vs Q5: 2.4 (95% CI 2.3 to 2.5)). However, the total medication cost (patient contribution amount plus public subsidy) was, on average, lower for these women (Q1: 52 (95% CI 50 to 54)), indicating potential disparities in access to newer, higher cost treatments. The unadjusted concentration index suggested mild pro-poor inequality in access (CI=-0.031; p<0.001), which was attenuated and statistically insignificant after adjusting for maternal demographic and clinical characteristics (CINA=-0.007; p=0.089). Government expenditure on medications showed no significant socioeconomic inequality (unadjusted CI=0.001; p=0.965). CONCLUSION: The PBS facilitates equitable access to publicly funded medications for pregnant women. However, the uniform distribution of public funds across socioeconomic groups suggests possible limitations in progressivity, as public subsidies are not disproportionately benefiting the most disadvantaged women overall. This may reflect missed opportunities to distribute public funds more effectively and efficiently, particularly if disadvantaged women are under-represented in access to newer, higher cost therapies, and warrants ongoing evaluation
A 150,000-year lacustrine record of the Indo-Australian monsoon from northern Australia
Get PDFNearly two thirds of the world's population depend on monsoon rainfall, with monsoon failure and extreme precipitation affecting societies for millennia. Monsoon hydroclimate is predicted to change as the climate warms, albeit with uncertain regional trajectories. Multiple glacial-interglacial terrestrial records of east Asian monsoon variability exist, but there are no terrestrial records of equivalent length from the coupled Indo-Australian monsoon at its southern limit — Australia. We present a continuous 150,000-year lacustrine record of monsoon dynamics from the core monsoon region of northern Australia based on the proportion of dryland tree pollen in the total dryland pollen spectra and the hydrogen isotope composition of long chain n-alkanes. We show that rainfall at the site depends strongly on sea level, which changes proximity of the coast to the site by 320 km over the last glacial-interglacial cycle. Long-term trends in rainfall are broadly anti-phased with the east Asian monsoon modulated by coastal proximity. The record also contains multiple, short intervals (∼2 to < 10,000 years) of large changes in tree cover (from 5 to 95 % tree pollen over 3000 years in one instance). Changes in tree cover are frequently but not always, accompanied by synchronous large changes in the other hydroclimate proxies. While these wetter periods cannot be easily ascribed to orbitally induced changes in insolation or coastal proximity, they are correlated with most Heinrich events. This relationship implies that strong asymmetry in inter-hemispheric monsoon rainfall might be one outcome of the current weakening in the strength of the Atlantic meridional overturning circulation, through a reduction in oceanic heat transfer from the Southern to the Northern Hemisphere
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