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.

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.

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.

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.

Design optimization and implementation of a Fourier transform spectrometer with rotating motion for 0.1†cm-1 resolution spectroscopy.

In this paper, we present the design optimization and implementation of a high-resolution near-infrared Fourier transform spectrometer (FTS) based on a rotating motion. The FTS system incorporates a rotating mirror-pair for scanning the optical path length (OPL). The design optimization process is performed to maximize the scanning range to obtain a resolution of 0.1 cm-1 while taking into account constraints on the volume of the system and the availability of commercial optics. By using a pattern search algorithm, we optimized the geometrical parameters of the rotating part, and found the best solution to satisfy the constraints. A data processing method is implemented to correct the nonlinear OPL scanning using a He-Ne laser. The performance of the implemented FTS is verified through spectral analysis within the spectral range of 1550 ± 25 nm. This spectral band corresponds to the wavelength range of the amplified spontaneous emission (ASE) obtained from an Er-doped fiber amplifier used in this study. Additionally, gas spectroscopy conducted using the FTS system successfully detects and analyzes the distinct absorption lines of hydrogen cyanide in 16.5 cm gas cell. The detection sensitivity of a single measurement is evaluated based on the noise equivalent absorption coefficient of 1.45 × 10-5 cm-1 Hz-1/2 calculated from 5-sec measurement time, 2000 spectral elements, and 208 signal-to-noise ratio with 0.2 scan/sec.

Novel Latent Heat Storage Systems Based on Liquid Metal Matrices with Suspended Phase Change Material Microparticles.

Phase change materials (PCMs) are considered useful tools for efficient thermal management and thermal energy utilization in various application fields. In this study, a colloidal PCM-in-liquid metal (LM) system is demonstrated as a novel platform composite with excellent latent heat storage capability, high thermal and electrical conductivities, and unique viscoelastic properties. In the proposed formulation, eutectic Ga-In is utilized as a high-thermal-conductivity and high-fluidity liquid matrix in which paraffinic PCM microparticles with various solid-liquid phase transition temperatures are suspended as fillers. Good compatibility between the fillers and matrix is achieved by the nanosized inorganic oxides (titania) adsorbed at the filler-matrix interface; thus, the composite is produced via simple vortex mixing without tedious pre- or post-processing. The composite shows unique trade-off effects among various properties, i.e., elastic modulus, yield stress, density, thermal conductivity, and melting or crystallization enthalpy, which can be easily controlled by varying the contents of the suspended fillers. A Joule heating device incorporating the composite exhibits improved electrothermal performance owing to the synergy between the high electrical conductivity and latent heat storage capability of the composite. The proposed platform may be exploited for the rational design and facile manufacture of high-performance form-factor-free latent heat storage systems for various potential applications such as battery thermal management and flexible heaters.

Phase-Locked Synthetic Wavelength Interferometer Using a Femtosecond Laser for Absolute Distance Measurement without Cyclic Error.

We present a phase-locked synthetic wavelength interferometer that enables a complete elimination of cyclic errors in absolute distance measurements. With this method, the phase difference between the reference and measurement paths is fed back into a phase lock-in system, which is then used to control the synthetic wavelength and set the phase difference to zero using an external cavity acousto-optic modulator. We validated the cyclic error removal of the proposed phase-locked method by comparing it with the conventional phase-measuring method of the synthetic wavelength interferometer. By analyzing the locked error signal, we achieved a precision of 0.6 mrad in phase without any observed cyclic errors.

Ultra-thin light-weight laser-induced-graphene (LIG) diffractive optics.

The realization of hybrid optics could be one of the best ways to fulfill the technological requirements of compact, light-weight, and multi-functional optical systems for modern industries. Planar diffractive lens (PDL) such as diffractive lenses, photonsieves, and metasurfaces can be patterned on ultra-thin flexible and stretchable substrates and be conformally attached on top of arbitrarily shaped surfaces. In this review, we introduce recent research works addressed to the design and manufacturing of ultra-thin graphene optics, which will open new markets in compact and light-weight optics for next-generation endoscopic brain imaging, space internet, real-time surface profilometry, and multi-functional mobile phones. To provide higher design flexibility, lower process complexity, and chemical-free process with reasonable investment cost, direct laser writing (DLW) of laser-induced-graphene (LIG) is actively being applied to the patterning of PDL. For realizing the best optical performances in DLW, photon-material interactions have been studied in detail with respect to different laser parameters; the resulting optical characteristics have been evaluated in terms of amplitude and phase. A series of exemplary laser-written 1D and 2D PDL structures have been actively demonstrated with different base materials, and then, the cases are being expanded to plasmonic and holographic structures. The combination of these ultra-thin and light-weight PDL with conventional bulk refractive or reflective optical elements could bring together the advantages of each optical element. By integrating these suggestions, we suggest a way to realize the hybrid PDL to be used in the future micro-electronics surface inspection, biomedical, outer space, and extended reality (XR) industries.

High-Thermal-Conductivity and High-Fluidity Heat Transfer Emulsion with 89 wt % Suspended Liquid Metal Microdroplets.

Colloidal suspensions of thermally conductive particles in a carrier fluid are considered promising heat transfer fluids for various thermal energy transfer applications, such as transportation, plants, electronics, and renewable energy systems. The thermal conductivity (k) of the particle-suspended fluids can be improved substantially by increasing the concentration of conductive particles above a "thermal percolation threshold," which is limited because of the vitrification of the resulting fluid at the high particle loadings. In this study, eutectic Ga-In liquid metal (LM) was employed as a soft high-k filler dispersed as microdroplets at high loadings in paraffin oil (as a carrier fluid) to produce an emulsion-type heat transfer fluid with the combined advantages of high thermal conductivity and high fluidity. Two types of the LM-in-oil emulsions, which were produced via the probe-sonication and rotor-stator homogenization (RSH) methods, demonstrated significant improvements in k, i.e., Δk ∼409 and ∼261%, respectively, at the maximum investigated LM loading of 50 vol % (∼89 wt %), attributed to the enhanced heat transport via high-k LM fillers above the percolation threshold. Despite the high filler loading, the RSH-produced emulsion retained remarkably high fluidity, with a relatively low viscosity increase and no yield stress, demonstrating its potential as a circulatable heat transfer fluid.

Cognitive Video Surveillance Management in Hierarchical Edge Computing System with Long Short-Term Memory Model.

Nowadays, deep learning (DL)-based video surveillance services are widely used in smart cities because of their ability to accurately identify and track objects, such as vehicles and pedestrians, in real time. This allows a more efficient traffic management and improved public safety. However, DL-based video surveillance services that require object movement and motion tracking (e.g., for detecting abnormal object behaviors) can consume a substantial amount of computing and memory capacity, such as (i) GPU computing resources for model inference and (ii) GPU memory resources for model loading. This paper presents a novel cognitive video surveillance management with long short-term memory (LSTM) model, denoted as the CogVSM framework. We consider DL-based video surveillance services in a hierarchical edge computing system. The proposed CogVSM forecasts object appearance patterns and smooths out the forecast results needed for an adaptive model release. Here, we aim to reduce standby GPU memory by model release while avoiding unnecessary model reloads for a sudden object appearance. CogVSM hinges on an LSTM-based deep learning architecture explicitly designed for future object appearance pattern prediction by training previous time-series patterns to achieve these objectives. By referring to the result of the LSTM-based prediction, the proposed framework controls the threshold time value in a dynamic manner by using an exponential weighted moving average (EWMA) technique. Comparative evaluations on both simulated and real-world measurement data on the commercial edge devices prove that the LSTM-based model in the CogVSM can achieve a high predictive accuracy, i.e., a root-mean-square error metric of 0.795. In addition, the suggested framework utilizes up to 32.1% less GPU memory than the baseline and 8.9% less than previous work.

SARS-CoV-2 restructures host chromatin architecture.

Some viruses restructure host chromatin, influencing gene expression, with implications for disease outcome. Whether this occurs for SARS-CoV-2, the virus causing COVID-19, is largely unknown. Here we characterized the 3D genome and epigenome of human cells after SARS-CoV-2 infection, finding widespread host chromatin restructuring that features widespread compartment A weakening, A-B mixing, reduced intra-TAD contacts and decreased H3K27ac euchromatin modification levels. Such changes were not found following common-cold-virus HCoV-OC43 infection. Intriguingly, the cohesin complex was notably depleted from intra-TAD regions, indicating that SARS-CoV-2 disrupts cohesin loop extrusion. These altered 3D genome/epigenome structures correlated with transcriptional suppression of interferon response genes by the virus, while increased H3K4me3 was found in the promoters of pro-inflammatory genes highly induced during severe COVID-19. These findings show that SARS-CoV-2 acutely rewires host chromatin, facilitating future studies of the long-term epigenomic impacts of its infection.

Deep-Reinforcement-Learning-Based IoT Sensor Data Cleaning Framework for Enhanced Data Analytics.

The Internet of things (IoT) combines different sources of collected data which are processed and analyzed to support smart city applications. Machine learning and deep learning algorithms play a vital role in edge intelligence by minimizing the amount of irrelevant data collected from multiple sources to facilitate these smart city applications. However, the data collected by IoT sensors can often be noisy, redundant, and even empty, which can negatively impact the performance of these algorithms. To address this issue, it is essential to develop effective methods for detecting and eliminating irrelevant data to improve the performance of intelligent IoT applications. One approach to achieving this goal is using data cleaning techniques, which can help identify and remove noisy, redundant, or empty data from the collected sensor data. This paper proposes a deep reinforcement learning (deep RL) framework for IoT sensor data cleaning. The proposed system utilizes a deep Q-network (DQN) agent to classify sensor data into three categories: empty, garbage, and normal. The DQN agent receives input from three received signal strength (RSS) values, indicating the current and two previous sensor data points, and receives reward feedback based on its predicted actions. Our experiments demonstrate that the proposed system outperforms a common time-series-based fully connected neural network (FCDQN) solution, with an accuracy of around 96% after the exploration mode. The use of deep RL for IoT sensor data cleaning is significant because it has the potential to improve the performance of intelligent IoT applications by eliminating irrelevant and harmful data.

CCN3/NOV promotes metastasis and tumor progression via GPNMB-induced EGFR activation in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer. TNBC patients typically exhibit unfavorable outcomes due to its rapid growth and metastatic potential. Here, we found overexpression of CCN3 in TNBC patients. We identified that CCN3 knockdown diminished cancer stem cell formation, metastasis, and tumor growth in vitro and in vivo. Mechanistically, ablation of CCN3 reduced activity of the EGFR/MAPK pathway. Transcriptome profiling revealed that CCN3 induces glycoprotein nonmetastatic melanoma protein B (GPNMB) expression, which in turn activates the EGFR pathway. An interrogation of the TCGA dataset further supported the transcriptional regulation of GPNMB by CCN3. Finally, we showed that CCN3 activates Wnt signaling through a ligand-dependent or -independent mechanism, which increases microphthalmia-associated transcription factor (MITF) protein, a transcription factor inducing GPNMB expression. Together, our findings demonstrate the oncogenic role of CCN3 in TNBC, and we propose CCN3 as a putative therapeutic target for TNBC.

Acute depletion of human core nucleoporin reveals direct roles in transcription control but dispensability for 3D genome organization.

The nuclear pore complex (NPC) comprises more than 30 nucleoporins (NUPs) and is a hallmark of eukaryotes. NUPs have been suggested to be important in regulating gene transcription and 3D genome organization. However, evidence in support of their direct roles remains limited. Here, by Cut&Run, we find that core NUPs display broad but also cell-type-specific association with active promoters and enhancers in human cells. Auxin-mediated rapid depletion of two NUPs demonstrates that NUP93, but not NUP35, directly and specifically controls gene transcription. NUP93 directly activates genes with high levels of RNA polymerase II loading and transcriptional elongation by facilitating full BRD4 recruitment to their active enhancers. dCas9-based tethering confirms a direct and causal role of NUP93 in gene transcriptional activation. Unexpectedly, in situ Hi-C and H3K27ac or H3K4me1 HiChIP results upon acute NUP93 depletion show negligible changesS2211-1247(22)01437-1 of 3D genome organization ranging from A/B compartments and topologically associating domains (TADs) to enhancer-promoter contacts.

Direct-Writable and Thermally One-Step Curable "Water-Stained" Epoxy Composite Inks.

In this study, a simple method for preparing direct-writable and thermally one-step curable epoxy composite inks was proposed. Specifically, colloidal inks containing a mixture of ordinary epoxy resin and anhydride-type hardener with the suspended alumina microplates, as exemplary fillers, are "stained" with small amounts of water. This increases the elasticity of the ink via the interparticle capillary attraction and promotes curing of the epoxy matrix in low-temperature ranges, causing the three-dimensional (3D) printed ink to avoid structural disruption during one-step thermal curing without the tedious pre-curing step. The proposed mechanisms for the shape retention of thermally cured water-stained inks were discussed with thorough analyses using shear rheometry, DSC, FTIR, and SEM. Results of the computer-vision numerical analysis of the SEM images reveal that the particles in water-stained inks are oriented more in the vertical direction than those in water-free samples, corroborating the proposed mechanisms. The suggested concept is extremely simple and does not require any additional cost to the one required for the preparation of the common epoxy-filler composites, which is thus expected to be well-exploited in various applications where 3D printing of epoxy-based formulations is necessary.

3D-printable, lightweight, and electrically conductive metal inks based on evaporable emulsion templates jammed with natural rheology modifiers.

Conductive metal inks with 3D-printable rheological properties have gained considerable attention, owing to their potential for manufacturing 3D electronics. Typically, such inks are formulated with high volume fractions of metal particles to achieve both rheological and electrical percolation. However, this leads to a high product cost and weight, making this approach potentially undesirable for practical application. In this study, naturally occurring ingredients, i.e., bee pollen microparticles (BPMPs) and citric acids (CAs), are used to produce a jammed hexane-in-aqueous suspension-type emulsion with controllable viscoelasticity as a template for conductive metal particles. Correspondingly, it is possible to develop 3D-printable, lightweight, and conductive inks. The BPMPs and CAs, as rheology modifiers, facilitate the 3D printability of the ink. After drying, the ink forms 3D networks without macroscopic discontinuities. Hexanes co-dispersed with BPMPs and CAs in the aqueous continuous phase improve the ink rheological processability and create internal macropores within the 3D-printed structure upon evaporation under ambient conditions, decreasing the product density. A conductive copper ink based on the emulsion template shows excellent 3D printability and electrical percolation at low metal loadings (<10 vol%); moreover, the printed ink with the optimized formulation has a remarkably low density (<2 g âˆ™ cm-3).

A Sustainable Business Model for a Neutral Host Supporting 5G and beyond (5GB) Ultra-Dense Networks: Challenges, Directions, and Architecture.

With the deployment of the fifth generation (5G) mobile network systems and the envisioned heterogeneous ultra-dense networks (UDNs), both small cell (SmC) and distributed antenna system (DAS) technologies are required by mobile network operators (MNOs) and venue owners to support multiple spectrum bands, multiple radio access technologies (RATs), multiple optical central offices (COs), and multiple MNOs. As a result, the neutral host business model representing a third party responsible for managing the network enterprise on behalf of multiple MNOs has emerged as a potential solution, mainly influenced by the desire to provide a high user experience without significantly increasing the total cost of ownership (TCO). However, designing a sustainable business model for a neutral host is a nontrivial task, especially when considered in the context of 5G and beyond (5GB) UDNs. In this paper, under an integrated optical wireless network infrastructure, we review how SmC and DAS technologies are evolving towards the adoption of the neutral host business model and identify key challenges and requirements for 5GB support. Thus, we explore recent candidate advancements in heterogeneous network integration technologies for the realization of an efficient 5GB neutral host business model design capable of accommodating both SmC and DAS. Furthermore, we propose a novel design architecture that relies on virtual radio access network (vRAN) to enable real-time dynamic resource allocation and radio over Ethernet (RoE) for flexible and reconfigurable fronthaul. The results from our simulations using MATLAB over two real-life deployment scenarios validate the feasibility of utilizing switched RoE considering end-to-end delay requirements of 5GB under different switching schemes, as long as the queuing delay is kept to a minimum. Finally, the results show that incorporating RoE and vRAN technologies into the neutral host design results in substantial TCO reduction by about 81% in an indoor scenario and 73% in an outdoor scenario.

NANOG prion-like assembly mediates DNA bridging to facilitate chromatin reorganization and activation of pluripotency.

Human NANOG expression resets stem cells to ground-state pluripotency. Here we identify the unique features of human NANOG that relate to its dose-sensitive function as a master transcription factor. NANOG is largely disordered, with a C-terminal prion-like domain that phase-transitions to gel-like condensates. Full-length NANOG readily forms higher-order oligomers at low nanomolar concentrations, orders of magnitude lower than typical amyloids. Using single-molecule Förster resonance energy transfer and fluorescence cross-correlation techniques, we show that NANOG oligomerization is essential for bridging DNA elements in vitro. Using chromatin immunoprecipitation sequencing and Hi-C 3.0 in cells, we validate that NANOG prion-like domain assembly is essential for specific DNA recognition and distant chromatin interactions. Our results provide a physical basis for the indispensable role of NANOG in shaping the pluripotent genome. NANOG's unique ability to form prion-like assemblies could provide a cooperative and concerted DNA bridging mechanism that is essential for chromatin reorganization and dose-sensitive activation of ground-state pluripotency.

EggBlock: Design and Implementation of Solar Energy Generation and Trading Platform in Edge-Based IoT Systems with Blockchain.

In this paper, to balance power supplement from the solar energy's intermittent and unpredictable generation, we design a solar energy generation and trading platform (EggBlock) using Internet of Things (IoT) systems and blockchain technique. Without a centralized broker, the proposed EggBlock platform can promote energy trading between users equipped with solar panels, and balance demand and generation. By applying the second price sealed-bid auction, which is one of the suitable pricing mechanisms in the blockchain technique, it is possible to derive truthful bidding of market participants according to their utility function and induce the proceed transaction. Furthermore, for efficient generation of solar energy, EggBlock proposes a Q-learning-based dynamic panel control mechanism. Specifically, we set the instantaneous direction of the solar panel and the amount of power generation as the state and reward, respectively. The angle of the panel to be moved becomes an action at the next time step. Then, we continuously update the Q-table using transfer learning, which can cope with recent changes in the surrounding environment or weather. We implement the proposed EggBlock platform using Ethereum's smart contract for reliable transactions. At the end of the paper, measurement-based experiments show that the proposed EggBlock achieves reliable and transparent energy trading on the blockchain and converges to the optimal direction with short iterations. Finally, the results of the study show that an average energy generation gain of 35% is obtained.