CYR61 confers chemoresistance by upregulating survivin expression in triple-negative breast cancer.

Cysteine-rich angiogenic inducer 61 (CYR61) is a protein from the CCN family of matricellular proteins that play diverse regulatory roles in the extracellular matrix. CYR61 is involved in cell adhesion, migration, proliferation, differentiation, apoptosis, and senescence. Here, we show that CYR61 induces chemoresistance in triple-negative breast cancer (TNBC). We observed that CYR61 is overexpressed in TNBC patients, and CYR61 expression correlates negatively with the survival of patients who receive chemotherapy. CYR61 knockdown reduced cell migration, sphere formation and the cancer stem cell (CSC) population and increased the chemosensitivity of TNBC cells. Mechanistically, CYR61 activated Wnt/ß-catenin signaling and increased survivin expression, which are associated with chemoresistance, the epithelial-mesenchymal transition, and CSC-like phenotypes. Altogether, our study demonstrates a novel function of CYR61 in chemotherapy resistance in breast cancer.

Adaptive Dataset Management Scheme for Lightweight Federated Learning in Mobile Edge Computing.

Federated learning (FL) in mobile edge computing has emerged as a promising machine-learning paradigm in the Internet of Things, enabling distributed training without exposing private data. It allows multiple mobile devices (MDs) to collaboratively create a global model. FL not only addresses the issue of private data exposure but also alleviates the burden on a centralized server, which is common in conventional centralized learning. However, a critical issue in FL is the imposed computing for local training on multiple MDs, which often have limited computing capabilities. This limitation poses a challenge for MDs to actively contribute to the training process. To tackle this problem, this paper proposes an adaptive dataset management (ADM) scheme, aiming to reduce the burden of local training on MDs. Through an empirical study on the influence of dataset size on accuracy improvement over communication rounds, we confirm that the amount of dataset has a reduced impact on accuracy gain. Based on this finding, we introduce a discount factor that represents the reduced impact of the size of the dataset on the accuracy gain over communication rounds. To address the ADM problem, which involves determining how much the dataset should be reduced over classes while considering both the proposed discounting factor and Kullback-Leibler divergence (KLD), a theoretical framework is presented. The ADM problem is a non-convex optimization problem. To solve it, we propose a greedy-based heuristic algorithm that determines a suboptimal solution with low complexity. Simulation results demonstrate that our proposed scheme effectively alleviates the training burden on MDs while maintaining acceptable training accuracy.

Dynamic Bandwidth Slicing in Passive Optical Networks to Empower Federated Learning.

Federated Learning (FL) is a decentralized machine learning method in which individual devices compute local models based on their data. In FL, devices periodically share newly trained updates with the central server, rather than submitting their raw data. The key characteristics of FL, including on-device training and aggregation, make it interesting for many communication domains. Moreover, the potential of new systems facilitating FL in sixth generation (6G) enabled Passive Optical Networks (PON), presents a promising opportunity for integration within this domain. This article focuses on the interaction between FL and PON, exploring approaches for effective bandwidth management, particularly in addressing the complexity introduced by FL traffic. In the PON standard, advanced bandwidth management is proposed by allocating multiple upstream grants utilizing the Dynamic Bandwidth Allocation (DBA) algorithm to be allocated for an Optical Network Unit (ONU). However, there is a lack of research on studying the utilization of multiple grant allocation. In this paper, we address this limitation by introducing a novel DBA approach that efficiently allocates PON bandwidth for FL traffic generation and demonstrates how multiple grants can benefit from the enhanced capacity of implementing PON in carrying out FL flows. Simulations conducted in this study show that the proposed solution outperforms state-of-the-art solutions in several network performance metrics, particularly in reducing upstream delay. This improvement holds great promise for enabling real-time data-intensive services that will be key components of 6G environments. Furthermore, our discussion outlines the potential for the integration of FL and PON as an operational reality capable of supporting 6G networking.

Deep Reinforcement Learning-Empowered Cost-Effective Federated Video Surveillance Management Framework.

Video surveillance systems are integral to bolstering safety and security across multiple settings. With the advent of deep learning (DL), a specialization within machine learning (ML), these systems have been significantly augmented to facilitate DL-based video surveillance services with notable precision. Nevertheless, DL-based video surveillance services, which necessitate the tracking of object movement and motion tracking (e.g., to identify unusual object behaviors), can demand a significant portion of computational and memory resources. This includes utilizing GPU computing power for model inference and allocating GPU memory for model loading. To tackle the computational demands inherent in DL-based video surveillance, this study introduces a novel video surveillance management system designed to optimize operational efficiency. At its core, the system is built on a two-tiered edge computing architecture (i.e., client and server through socket transmission). In this architecture, the primary edge (i.e., client side) handles the initial processing tasks, such as object detection, and is connected via a Universal Serial Bus (USB) cable to the Closed-Circuit Television (CCTV) camera, directly at the source of the video feed. This immediate processing reduces the latency of data transfer by detecting objects in real time. Meanwhile, the secondary edge (i.e., server side) plays a vital role by hosting a dynamically controlling threshold module targeted at releasing DL-based models, reducing needless GPU usage. This module is a novel addition that dynamically adjusts the threshold time value required to release DL models. By dynamically optimizing this threshold, the system can effectively manage GPU usage, ensuring resources are allocated efficiently. Moreover, we utilize federated learning (FL) to streamline the training of a Long Short-Term Memory (LSTM) network for predicting imminent object appearances by amalgamating data from diverse camera sources while ensuring data privacy and optimized resource allocation. Furthermore, in contrast to the static threshold values or moving average techniques used in previous approaches for the controlling threshold module, we employ a Deep Q-Network (DQN) methodology to manage threshold values dynamically. This approach efficiently balances the trade-off between GPU memory conservation and the reloading latency of the DL model, which is enabled by incorporating LSTM-derived predictions as inputs to determine the optimal timing for releasing the DL model. The results highlight the potential of our approach to significantly improve the efficiency and effective usage of computational resources in video surveillance systems, opening the door to enhanced security in various domains.

TBHQ Alleviates Particulate Matter-Induced Pyroptosis in Human Nasal Epithelial Cells.

Pyroptosis represents a type of cell death mechanism notable for its cell membrane disruption and the subsequent release of proinflammatory cytokines. The Nod-like receptor family pyrin domain containing inflammasome 3 (NLRP3) plays a critical role in the pyroptosis mechanism associated with various diseases resulting from particulate matter (PM) exposure. Tert-butylhydroquinone (tBHQ) is a synthetic antioxidant commonly used in a variety of foods and products. The aim of this study is to examine the potential of tBHQ as a therapeutic agent for managing sinonasal diseases induced by PM exposure. The occurrence of NLRP3 inflammasome-dependent pyroptosis in RPMI 2650 cells treated with PM < 4 µm in size was confirmed using Western blot analysis and enzyme-linked immunosorbent assay results for the pyroptosis metabolites IL-1ß and IL-18. In addition, the inhibitory effect of tBHQ on PM-induced pyroptosis was confirmed using Western blot and immunofluorescence techniques. The inhibition of tBHQ-mediated pyroptosis was abolished upon nuclear factor erythroid 2-related factor 2 (Nrf2) knockdown, indicating its involvement in the antioxidant mechanism. tBHQ showed potential as a therapeutic agent for sinonasal diseases induced by PM because NLRP3 inflammasome activation was effectively suppressed via the Nrf2 pathway.

Enhanced intratumoral delivery of immunomodulator MPLA via hyperbranched polyglycerol-coated biodegradable nanoparticles.

Immunomodulatory agents (IMA) have significant potential to enhance cancer treatment but have demonstrated limited efficacy beyond the preclinical setting due to poor pharmacokinetics and toxicity associated with systemic administration. On the other hand, when locally delivered, IMAs require repeated administration to optimize immune stimulation. To overcome these challenges, we encapsulated the toll-like receptor (TLR)4 agonist monophosphoryl lipid A (MPLA) within hyperbranched polyglycerol (HPG)-coated biodegradable nanoparticles (NP) engineered for gradual drug release from the nanoparticle core, resulting in a more persistent stimulation of anti-tumor immune responses while minimizing systemic side effects. In a model of malignant melanoma, we demonstrate that HPG-NP encapsulation significantly improves the antitumor efficacy of MPLA by enhancing its ability to remodel the tumor microenvironment (TME). Compared to free MPLA, HPG-NP-MPLA significantly increased the natural killer cell and cytotoxic T cell mediated antitumor immune response and tuned the tumor draining lymph nodes towards a T helper (Th)1 response. Furthermore, when combined with local delivery of a chemotherapeutic agent, HPG-NP-MPLA induces the conversion of an immunosuppressive TME to immunogenic TME and significantly improves survival.