Enhancer RNA m6A methylation facilitates transcriptional condensate formation and gene activation.

The mechanistic understanding of nascent RNAs in transcriptional control remains limited. Here, by a high sensitivity method methylation-inscribed nascent transcripts sequencing (MINT-seq), we characterized the landscapes of N6-methyladenosine (m6A) on nascent RNAs. We uncover heavy but selective m6A deposition on nascent RNAs produced by transcription regulatory elements, including promoter upstream antisense RNAs and enhancer RNAs (eRNAs), which positively correlates with their length, inclusion of m6A motif, and RNA abundances. m6A-eRNAs mark highly active enhancers, where they recruit nuclear m6A reader YTHDC1 to phase separate into liquid-like condensates, in a manner dependent on its C terminus intrinsically disordered region and arginine residues. The m6A-eRNA/YTHDC1 condensate co-mixes with and facilitates the formation of BRD4 coactivator condensate. Consequently, YTHDC1 depletion diminished BRD4 condensate and its recruitment to enhancers, resulting in inhibited enhancer and gene activation. We propose that chemical modifications of eRNAs together with reader proteins play broad roles in enhancer activation and gene transcriptional control.

Enhancer release and retargeting activates disease-susceptibility genes.

The functional engagement between an enhancer and its target promoter ensures precise gene transcription1. Understanding the basis of promoter choice by enhancers has important implications for health and disease. Here we report that functional loss of a preferred promoter can release its partner enhancer to loop to and activate an alternative promoter (or alternative promoters) in the neighbourhood. We refer to this target-switching process as 'enhancer release and retargeting'. Genetic deletion, motif perturbation or mutation, and dCas9-mediated CTCF tethering reveal that promoter choice by an enhancer can be determined by the binding of CTCF at promoters, in a cohesin-dependent manner-consistent with a model of 'enhancer scanning' inside the contact domain. Promoter-associated CTCF shows a lower affinity than that at chromatin domain boundaries and often lacks a preferred motif orientation or a partnering CTCF at the cognate enhancer, suggesting properties distinct from boundary CTCF. Analyses of cancer mutations, data from the GTEx project and risk loci from genome-wide association studies, together with a focused CRISPR interference screen, reveal that enhancer release and retargeting represents an overlooked mechanism that underlies the activation of disease-susceptibility genes, as exemplified by a risk locus for Parkinson's disease (NUCKS1-RAB7L1) and three loci associated with cancer (CLPTM1L-TERT, ZCCHC7-PAX5 and PVT1-MYC).

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.

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.

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.

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.

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.

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.

Liquid-Suspended and Liquid-Bridged Liquid Metal Microdroplets.

Liquid metals (LMs) and alloys are attracting increasing attention owing to their combined advantages of high conductivity and fluidity, and have shown promising results in various emerging applications. Patterning technologies using LMs are being actively researched; among them, direct ink writing is considered a potentially viable approach for efficient LM additive manufacturing. However, true LM additive manufacturing with arbitrary printing geometries remains challenging because of the intrinsically low rheological strength of LMs. Herein, colloidal suspensions of LM droplets amenable to additive manufacturing (or "3D printing") are realized using formulations containing minute amounts of liquid capillary bridges. The resulting LM suspensions exhibit exceptionally high rheological strength with yield stress values well above 103  Pa, attributed to inter-droplet capillary attraction mediated by the liquid bridges adsorbed on the oxide skin of the LM droplets. Such liquid-bridged LM suspensions, as extrudable ink-type filaments, are based on uncurable continuous-phase liquid media, have a long pot-life and outstanding shear-thinning properties, and shape retention, demonstrating excellent rheological processability suitable for 3D printing. These findings will enable the emergence of a variety of new advanced applications that necessitate LM patterning into highly complicated multidimensional structures.

The SINEB1 element in the long non-coding RNA Malat1 is necessary for TDP-43 proteostasis.

Transposable elements (TEs) comprise a large proportion of long non-coding RNAs (lncRNAs). Here, we employed CRISPR to delete a short interspersed nuclear element (SINE) in Malat1, a cancer-associated lncRNA, to investigate its significance in cellular physiology. We show that Malat1 with a SINE deletion forms diffuse nuclear speckles and is frequently translocated to the cytoplasm. SINE-deleted cells exhibit an activated unfolded protein response and PKR and markedly increased DNA damage and apoptosis caused by dysregulation of TDP-43 localization and formation of cytotoxic inclusions. TDP-43 binds stronger to Malat1 without the SINE and is likely 'hijacked' by cytoplasmic Malat1 to the cytoplasm, resulting in the depletion of nuclear TDP-43 and redistribution of TDP-43 binding to repetitive element transcripts and mRNAs encoding mitotic and nuclear-cytoplasmic regulators. The SINE promotes Malat1 nuclear retention by facilitating Malat1 binding to HNRNPK, a protein that drives RNA nuclear retention, potentially through direct interactions of the SINE with KHDRBS1 and TRA2A, which bind to HNRNPK. Losing these RNA-protein interactions due to the SINE deletion likely creates more available TDP-43 binding sites on Malat1 and subsequent TDP-43 aggregation. These results highlight the significance of lncRNA TEs in TDP-43 proteostasis with potential implications in both cancer and neurodegenerative diseases.

Sex-specific neuroprotection by inhibition of the Y-chromosome gene, SRY, in experimental Parkinson's disease.

Parkinson's disease (PD) is a debilitating neurodegenerative disorder caused by the loss of midbrain dopamine (DA) neurons. While the cause of DA cell loss in PD is unknown, male sex is a strong risk factor. Aside from the protective actions of sex hormones in females, emerging evidence suggests that sex-chromosome genes contribute to the male bias in PD. We previously showed that the Y-chromosome gene, SRY, directly regulates adult brain function in males independent of gonadal hormone influence. SRY protein colocalizes with DA neurons in the male substantia nigra, where it regulates DA biosynthesis and voluntary movement. Here we demonstrate that nigral SRY expression is highly and persistently up-regulated in animal and human cell culture models of PD. Remarkably, lowering nigral SRY expression with antisense oligonucleotides in male rats diminished motor deficits and nigral DA cell loss in 6-hydroxydopamine (6-OHDA)-induced and rotenone-induced rat models of PD. The protective effect of the SRY antisense oligonucleotides was associated with male-specific attenuation of DNA damage, mitochondrial degradation, and neuroinflammation in the toxin-induced rat models of PD. Moreover, reducing nigral SRY expression diminished or removed the male bias in nigrostriatal degeneration, mitochondrial degradation, DNA damage, and neuroinflammation in the 6-OHDA rat model of PD, suggesting that SRY directly contributes to the sex differences in PD. These findings demonstrate that SRY directs a previously unrecognized male-specific mechanism of DA cell death and suggests that suppressing nigral Sry synthesis represents a sex-specific strategy to slow or prevent DA cell loss in PD.

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.

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.

Electrostatic Stabilization of Nano Liquid Metals in Doped Nonpolar Liquids.

Liquid metals and alloys are attracting renewed attention owing to their potential for application in various advanced technologies. Eutectic gallium-indium (EGaIn) has been focused on in particular because of its integrated advantages of high conductivity, low melting point, and low toxicity. In this study, the colloidal behavior of nano-dispersed EGaIn in nonpolar oils is investigated. Although the nonpolar oil continuous phase is commonly considered to be free of electric charges, electrostatic repulsion appears to be crucial in the colloidal stabilization of the nano-dispersed EGaIn phases, the modulation of which is possible by doping the oil phases with different types of oil-soluble surfactants. The qualitative correlation between the observed colloidal stabilities and the "zero field" particle mobilities inferred from the field-dependent electrophoretic mobilities indicates that the electric charging of EGaIn particles in surfactant-doped nonpolar oils is a static phenomenon that is maintained in equilibrium, rather than a solely field-induced process. A systematic investigation of the charging properties of these unique biphasic particles, consisting of the liquid Ga-In bulk and the solid Ga2 O3 surface that formed spontaneously, reveals the complicated system-dependent nature of the charging mechanisms mediated by ionic and nonionic surfactants in nonpolar media.

AdaMM: Adaptive Object Movement and Motion Tracking in Hierarchical Edge Computing System.

This paper presents a novel adaptive object movement and motion tracking (AdaMM) framework in a hierarchical edge computing system for achieving GPU memory footprint reduction of deep learning (DL)-based video surveillance services. DL-based object movement and motion tracking requires a significant amount of resources, such as (1) GPU processing power for the inference phase and (2) GPU memory for model loading. Despite the absence of an object in the video, if the DL model is loaded, the GPU memory must be kept allocated for the loaded model. Moreover, in several cases, video surveillance tries to capture events that rarely occur (e.g., abnormal object behaviors); therefore, such standby GPU memory might be easily wasted. To alleviate this problem, the proposed AdaMM framework categorizes the tasks used for the object movement and motion tracking procedure in an increasing order of the required processing and memory resources as task (1) frame difference calculation, task (2) object detection, and task (3) object motion and movement tracking. The proposed framework aims to adaptively release the unnecessary standby object motion and movement tracking model to save GPU memory by utilizing light tasks, such as frame difference calculation and object detection in a hierarchical manner. Consequently, object movement and motion tracking are adaptively triggered if the object is detected within the specified threshold time; otherwise, the GPU memory for the model of task (3) can be released. Moreover, object detection is also adaptively performed if the frame difference over time is greater than the specified threshold. We implemented the proposed AdaMM framework using commercial edge devices by considering a three-tier system, such as the 1st edge node for both tasks (1) and (2), the 2nd edge node for task (3), and the cloud for sending a push alarm. A measurement-based experiment reveals that the proposed framework achieves a maximum GPU memory reduction of 76.8% compared to the baseline system, while requiring a 2680 ms delay for loading the model for object movement and motion tracking.

Reversible Polymorphic Transition and Hysteresis-Driven Phase Selectivity in Single-Crystalline C8-BTBT Rods.

Organic semiconductors (OSCs) are highly susceptible to the formation of metastable polymorphs that are often transformed by external stimuli. However, thermally reversible transformations in OSCs with stability have not been achieved due to weak van der Waals forces, and poor phase homogeneity and crystallinity. Here, a polymorph of a single crystalline 2,7-dioctyl[1] benzothieno[3,2-b][1]benzothio-phene rod on a low molecular weight poly(methyl methacrylate) (≈120k) that limits crystal coarsening during solvent vapor annealing is fabricated. Molecules in the polymorph lie down slightly toward the substrate compared to the equilibrium state, inducing an order of greater resistivity. During thermal cycling, the polymorph exhibits a reversible change in resistivity by 5.5 orders with hysteresis; this transition is stable toward bias and thermal cycling. Remarkably, varying cycling temperatures leads to diverse resistivities near room temperature, important for nonvolatile multivalue memories. These trends persist in the carrier mobility and on/off ratio of the polymorph field-effect transistor. A combination of in situ grazing incident wide angle X-ray scattering analyses, visualization for electronic and structural analysis simulations, and density functional theory calculations reveals that molecular tilt governs the charge transport characteristics; the polymorph transforms as molecules tilt, and thereby, only a homogeneous single-crystalline phase appears at each temperature.

A Systematic Investigation on the Properties of Silica Nanoparticles "Multipoint"-Grafted with Poly(2-acrylamido-2-methylpropanesulfonate-co-acrylic Acid) in Extreme Salinity Brines and Brine-Oil Interfaces.

Nanoparticles (NPs) may have great potential for various subsurface applications, including oil and gas recovery, reservoir imaging, and environmental remediation. One of the important challenges for these downhole applications is to achieve colloidal stability in subsurface media at high salinity and high temperature. It has been previously shown that several functional NPs "multipoint"-grafted with anionic poly(2-acrylamido-2-methyl-1-propanesulfonate-co-acrylic acid; AMPS-co-AA) exhibited remarkable colloidal stabilities in specific environments mimicking the harsh subsurface aquatic media, such as the American Petroleum Institute (API) brine. However, many important properties of such particles, other than the colloidal stabilities, must be studied in a more systematic fashion for a wide range of salt concentrations (Cs). Herein, we investigate various properties of the silica (SiO2) NPs multipoint-grafted with poly(AMPS-co-AA), SiO2-g-poly(AMPS-co-AA), in NaCl and CaCl2 solutions across a range of salinities. The brush behavior of the grafted random copolymers was investigated in both salt solutions from salt-free conditions up to extreme salinities. The particles displayed brine-oil interfacial activity with increasing Cs, stabilizing oil-in-brine emulsions as Pickering emulsifiers. A high internal phase emulsion (HIPE) with an internal oil phase of up to 80 vol % could be formed in CaCl2 solutions at high Cs, which exhibited gel-like behaviors.

Yield Stress Enhancement of a Ternary Colloidal Suspension via the Addition of Minute Amounts of Sodium Alginate to the Interparticle Capillary Bridges.

Capillary suspensions are ternary solid-liquid-liquid systems produced via the addition of a small amount of secondary fluid to the bulk fluid that contained the dispersed solid particles. The secondary fluid could exert strong capillary forces between the particles and dramatically change the rheological properties of the suspension. So far, research has focused on capillary suspensions that consist of additive-free fluids, whereas capillary suspensions with additives, particularly those of large molecular weight that are highly relevant for industrial purposes, have been relatively less studied. In this study, we performed a systematic analysis of the properties of capillary suspensions that consist of paraffin oil (bulk phase), water (secondary phase), and α-Al2O3 microparticles (particle phase), in which the aqueous secondary phase contained an important eco-friendly polymeric binder, sodium alginate (SA). It was determined that the yield stress of the suspension increased significantly with the increase in the SA content in the aqueous secondary phase, which was attributed to the synergistic effect of the capillary force and hydrogen bonding force that may be related to the increase in the number of capillary bridges. The amounts of SA used to induce a significant change in the yield stress in this study were very small (<0.02% of the total sample volume). The addition of Ca2+ ions to the SA-containing secondary phase further increased the yield stress with possible gelation of the SA chains-in the presence of excess Ca2+ ions, however, the yield stress decreased because of the microscopic phase separation that occurred in the aqueous secondary phase. The microstructures of the sintered porous materials that were produced by using capillary suspensions as precursors were qualitatively well correlated to the rheological behavior of the precursor suspensions, suggesting a new method for the subtle control of the microstructures of porous materials using the addition of minute amounts of polymeric additives.

Enhancer RNAs in cancer: regulation, mechanisms and therapeutic potential.

Enhancers are distal genomic elements critical for gene regulation and cell identify control during development and diseases. Many human cancers were found to associate with enhancer malfunction, due to genetic and epigenetic alterations, which in some cases directly drive tumour growth. Conventionally, enhancers are known to provide DNA binding motifs to recruit transcription factors (TFs) and to control target genes. However, recent progress found that most, if not all, active enhancers pervasively transcribe noncoding RNAs that are referred to as enhancer RNAs (eRNAs). Increasing evidence points to functional roles of at least a subset of eRNAs in gene regulation in both normal and cancer cells, adding new insights into the action mechanisms of enhancers. eRNA expression was observed to be widespread but also specific to tumour types and individual patients, serving as opportunities to exploit them as potential diagnosis markers or therapeutic targets. In this review, we discuss the brief history of eRNA research, their functional mechanisms and importance in cancer gene regulation, as well as their therapeutic and diagnostic values in cancer. We propose that further studies of eRNAs in cancer will offer a promising 'eRNA targeted therapy' for human cancer intervention.