Shujie Yang

Proteolysis-targeting chimeras (PROTACs) have emerged as a promising molecular approach for degrading undruggable proteins and for overcoming drug resistance in cancer therapy. However, their clinical translation remains limited by challenges such as poor cell membrane permeability, limited intracellular uptake, and potential off-target toxicity. To overcome these barriers, we developed Sonoporation-assisted Precise Intracellular Nanodelivery (SonoPIN), an ultrasound-driven, aptamer-guided microbubble system that enables rapid delivery of therapeutic molecules with cell selectivity. By leveraging aptamer-conjugated microbubbles and ultrasound-induced sonoporation, SonoPIN transiently permeabilizes the membranes of target cells, while leaving nontarget cells undisturbed. Using BRD4, a well-characterized oncogenic transcriptional coactivator and validated PROTAC target critically involved in cancer cell survival, as a model system, we demonstrate that SonoPIN facilitates highly efficient intracellular delivery of fluorescently labeled PROTACs. SonoPIN achieves a sevenfold increase in intracellular fluorescence after 60 s of ultrasound stimulation, resulting in a 70% reduction in BRD4 protein levels specifically in cancer cells. Importantly, BRD4 degradation is undetectable in noncancerous cells. Consequently, approximately 50% of the targeted cancer cells undergo apoptosis while nontarget cells retain more than 99% viability, underscoring the high selectivity of the SonoPIN system. Our study indicates that SonoPIN represents an innovative, noninvasive delivery platform for PROTAC therapeutics, offering a rapid and precise approach for targeted drug delivery in cancer treatment.

Guided elastic waves are a truly cross-disciplinary key enabling technology. For more than five decades, surface acoustic wave (SAW) and bulk acoustic wave devices find widespread applications. Nowadays, different types of guided elastic waves cover the wide spectrum of applications spanning from quantum technologies to the life sciences, from controlling single excitations to macroscopic collective states in condensed matter. Six years after the first 2019 SAW roadmap, we believe it is time to make a step back and take a fresh look at the status of the field and its future challenges. Since the first roadmap in 2019, the spectrum clearly expanded and this new edition presents a current snapshot of the status of this vibrant field and prospects for potential future developments.

biosensors, these systems provide unmatched benefits in sensitivity, speed, portability, and immediate monitoring, thereby transforming diagnostics in human and animal health, environmental sensing, and point-of-care testing. In this review, we provide a comprehensive overview of integrated microfluidics with biosensors, highlighting the synergistic interplay between these two complementary fields and their various biomedical applications. We begin by examining different microfluidic technologies, including 3D dynamic cell culture systems, inertial microfluidic separation, acoustofluidics, dielectrophoresis, optofluidics, and immunoassays. Next, we discuss integrated microfluidic systems that incorporate various biosensor technologies, including electrochemical, electrophysiological, plasmonic, Raman, and quantum sensors. These are designed to detect and analyze DNA, RNA, proteins, exosomes, cells, and small organisms, covering a size range from nanometers to millimeters. Additionally, we discuss the wide range of applications for integrated microfluidic biosensors and examine significant challenges and future opportunities that will influence their ongoing development and practical use. Finally, we highlight successful commercial products developed with integrated microfluidic technologies.

Surface acoustic waves (SAWs) offer great potential for quantum information processing, optomechanics, acoustofludics, and acoustic tweezers. However, existing SAW chips lack the ability to control SAWs in a manner similar to current metamaterials, which can achieve versatile subwavelength-resolution manipulation of bulk acoustic waves. This study presents on-chip phased interdigital metamaterials (PIMs) featuring customized interdigital electrodes whose geometries are encoded with deep-subwavelength-resolution phase profiles, enabling versatile transformation of SAWs and manipulation of fluids and micro/nano-objects. Our on-chip PIMs can transform forward SAWs into waves with desired wavefronts and energy patterns, such as SAWs propagating in a specified direction, a SAW jet with energy confined in a wavelength, and twin jets. They also enable “diode-like” SAW transmission, allowing for routing the information carried by SAWs along a forward pathway while blocking backward communication. Additionally, SAWs generated by PIMs exhibit unique energy patterns, allowing for versatile active control of fluid streaming and micro/nano-object distributions. On-chip phased interdigital metamaterials have electrodes encoded with deep-subwavelength-resolution phase profiles, enabling versatile transformation of surface acoustic waves, diode-like acoustic routing, and manipulation of fluids and particles.

ABSTRACT Immersive media applications often create an immersive experience for users through head‐mounted displays. However, the computing power and storage capacity of terminal devices are limited, and the local computing architecture cannot meet the high resolution and low latency requirements of panoramic video frames. As a new computing paradigm, cloud, edge and end collaborative computing architecture selectively schedules computing tasks to cloud servers and edge servers with higher computing power, which can effectively improve computing efficiency. However, for dependent computational tasks, the scheduling of each task needs to consider its previous tasks, network state, and computational resources of different servers. Therefore, how to make computational offloading decisions and resource allocation for dependent tasks is a key issue for collaborative computing architectures. This paper investigates and analyzes the immersive media scenarios and the basic computation offloading strategies, and construct a dependent task model graph and optimization problem model. Based on threshold strategy, greedy strategy of heuristic algorithm and deep reinforcement learning model, a scheduling strategy under collaborative computing architecture is designed to maximize the reward related to delay and cost. Finally, the basic performance of the computational task scheduling strategy based on deep reinforcement learning and greedy policy is verified through simulation experiments. The experimental results show that the algorithm reduces the latency by more than 1.8 ms and increases the timely completion rate by more than relative to several basic scheduling schemes, which can effectively improve the service quality and user experience.

Mechanoimmunology explores how mechanical forces orchestrate immune responses, offering insights into immune cell functions and the mechanisms underlying mechanotransduction. A critical challenge in this field is the absence of reliable platforms that apply precise, consistent mechanical stimuli to individual cells while enabling reproducible immune activation. Here, we present a nanoscale acoustic oscillator for mechanoimmunology applications: NAOMI. NAOMI features micropatterned pillars that support uniform cell monolayer formation with an integrated acoustic transducer that delivers highly controlled 3D nanoscale oscillations (±1-nanometer deviation) for up to 72 hours. Unlike conventional passive platforms relying on static stiffness or surface topography, NAOMI enables dynamic, programmable stimulation with high precision and reproducibility. Validation studies demonstrate that NAOMI notably enhances mechanical stress intensity and cell displacement, driving robust M1 polarization in macrophages. NAOMI provides a practical and versatile platform for studying mechanoimmunology, offering high precision, stability, and tunability. Its capabilities also position it well to support future research and drive innovative discoveries in the field.

With the proliferation of IoT devices, botnet-based distributed denial-of-service (DDoS) attacks have become increasingly severe, overwhelming system resources with large-scale malicious traffic and posing a serious threat to online service availability. Multi-agent Moving Target Defense (MTD) systems enhance attack resilience by dynamically reconfiguring proxy server clusters. However, identifying stealthy attackers within these systems remains a major challenge, particularly intelligent adversaries who can mimic legitimate user behavior or those who exploit proxy address leaks to facilitate indirect attacks. Existing traffic-based detection methods struggle to effectively identify such highly evasive threats due to their limited accuracy. Therefore, improving user behavior evaluation in MTD systems to efficiently detect and mitigate stealthy attackers is a critical research problem. To enhance detection accuracy, this paper proposes a DDoS attacker detection model for multi-agent MTD systems based on spatiotemporal feature fusion, leveraging both temporal and spatial dimensions to improve resistance against evasion strategies. In the spatial domain, we integrate user characteristics with proxy-side metrics, expanding detection scope beyond usercentric evaluations. In the temporal domain, we track anomalous patterns across multiple defense cycles, capturing behavioral inconsistencies over successive proxy reassignments. By jointly analyzing spatiotemporal features and iteratively refining trust scores, our model effectively identifies persistent malicious attackers, significantly improving detection capability against stealthy threats. Additionally, we design an optimized decisionmaking mechanism that accounts for the complexity and diversity of attack strategies, enhancing adaptability and generalization through refined feature selection and classification strategies. Experimental results demonstrate that our approach outperforms conventional detection methods in key metrics such as attack response time, false positive rate, and false negative rate.

The complex interaction of spin, valley and lattice degrees of freedom allows natural materials to create exotic topological phenomena. The interplay between topological wave materials and hydrodynamics could offer promising opportunities for visualizing topological physics and manipulating bioparticle unconventionally. Here we present topological acoustofluidic chips to illustrate the complex interaction between elastic valley spin and nonlinear fluid dynamics. We created valley streaming vortices and chiral swirling patterns for backward-immune particle transport. Using tracer particles, we observed arrays of clockwise and anticlockwise valley vortices due to an increase in elastic spin density. Moreover, we discovered exotic topological pressure wells in fluids, creating nanoscale trapping fields for manipulating DNA molecules. We also found a 93.2% modulation in the bandwidth of edge states, dependent on the orientation of the substrate's crystallographic structure. Our study sets the stage for uncovering topological acoustofluidic phenomena and visualizing elastic valley spin, revealing the potential for topological-material applications in life sciences.

ETHNOPHARMACOLOGICAL RELEVANCE Cornus officinalis (CO) has been widely used as Chinese herbal medicine and has a good clinical efficacy in liver disease. In particular, it has a significant therapeutic effect on metabolic liver disease. However, systematic pharmacological studies on its hepatoprotective effect on non-alcoholic fatty liver disease (NAFLD) are lacking. AIM OF THE STUDY We investigated the impact of Cornus officinalis extract (COE) on two mouse models of NAFLD, screened the potential mechanisms of action by using metabolomics assays, and explored the protective effects on hepatocyte proliferation by regulating glutamate metabolism and tricarboxylic acid (TCA) cycle. METHODS The main components of COE were identified by high performance liquid chromatograph (HPLC). Male C57BL/6J mice were subjected to construct carbon tetrachloride (CCl4) or methionine choline deficient (MCD) induced NAFLD mice. Liver function and lipid biochemical indicators were detected using commercial assay kits. Masson staining, Western blot, and immunohistochemistry analyses were used for assessing hepatic injury and fibrosis. LC-MS non-targeted analysis was performed using the 1290 Ultra-High Performance Liquid Chromatograph System and the 6540 Q-TOF Mass Spectrometry. Palmitic acid (PA) induced L-02 cell model was established. The mediators in glutamate metabolism and TCA cycle were assessed by assay kits. RESULTS In vivo experiments validated that COE significantly improved liver function in NAFLD mice, reduced lipid accumulation, and alleviated pathological damage and liver fibrosis. The non-targeted metabolomics analysis combined with Ingenuity Pathway Analysis (IPA) located glutamate metabolism and the downstream TCA cycle as potential mechanisms of COE, which was further confirmed in NAFLD model mice and PA-induced L-02 cells. Finally, we confirmed that COE could promote mitochondrial energy supply by remodeling the homeostasis of the TCA cycle, thereby enhancing hepatocyte proliferation. CONCLUSIONS This study demonstrated that COE could significantly improve CCl4 or MCD-induced NAFLD by promoting hepatocyte proliferation. These results highlighted the potential of COE as leads for the development of innovative treatments for NAFLD.

Mechano-chemo cancer treatment is an emerging therapeutic strategy that enhances chemotherapy efficacy by combining chemical agents with mechanical forces to improve drug uptake and overcome resistance. However, current approaches for delivering mechanical forces, including magnetic stress, hydrodynamic shear, and ultrasonic cavitation, suffer from limited tunability, poor spatial precision, and off-target effects, restricting their clinical potential. Here, we introduce ChemoTAP (chemotherapy-enhanced tunable acoustofluidic permeabilization), an acoustofluidic system that utilizes standing surface acoustic waves (SAWs) to achieve highly localized, tunable mechanical stimulation, enhancing tumor cell permeability and improving chemotherapeutic efficiency. By fine-tuning SAW parameters, ChemoTAP transiently modulates membrane permeability by activating mechanosensitive ion channels, leading to cytoskeletal remodeling and a 2.73-fold increase in intracellular calcium ion flux in HeLa cells. This SAW-induced mechanotransduction response synergistically enhances the cytotoxic effects of cisplatin, increasing tumor cell apoptosis by 1.78-fold through mitochondrial membrane depolarization, reactive oxygen species generation, and endoplasmic reticulum stress pathways. Unlike conventional ultrasound-based cavitation methods, ChemoTAP enables precise, non-invasive mechanical stimulation without requiring microbubbles, offering a controllable and scalable alternative for mechano-chemo cancer treatment. ChemoTAP establishes a foundation for further studies in mechanotherapy treatment pathways and promotes the broader integration of acoustics in oncology.

In this paper, we investigated a food chain model driven by the Ornstein-Uhlenbeck process, incorporating the Holling type Ⅱ functional response, nonlinear prey refuge, and the Allee effect in the top predator. First, the biological significance of the Ornstein-Uhlenbeck process was illustrated, and its rationality was explained. Subsequently, the existence and uniqueness of the global solution of the model were established, and its ultimate boundedness was analyzed. Then, by constructing a Lyapunov function and applying Itô's formula, the existence of the stationary distribution of the model was demonstrated. Furthermore, the conditions for the system extinction were provided. Finally, numerical simulations were conducted to verify the theoretical results and confirm the validity of the conclusions.

In the contemporary digital landscape, Distributed Denial of Service (DDoS) attacks pose a significant threat to the availability and integrity of online services. As cloud services and network infrastructures increasingly adopt distributed architectures, the challenge of defending against these attacks has become more complex. This paper introduces ClusterX, a novel two-tiered scheduling framework designed to enhance DDoS defense and service quality in distributed clusters. ClusterX incorporates Moving Target Defense (MTD) principles with a hierarchical, collaborative approach, featuring Program Chairs and Area Chairs that dynamically manage user-proxy mappings to counteract the fluid nature of DDoS attacks. The architecture of ClusterX is underpinned by a proactive transfer mechanism, which leverages user-service activity profiles to intelligently redistribute user traffic across clusters. This mechanism ensures that no single cluster becomes a bottleneck, maintaining low latency and high service quality even under high traffic conditions. Program Chairs oversee the global traffic distribution, while Area Chairs execute localized scheduling decisions, working in tandem to prevent traffic congestion and enhance the resilience of the network. Through extensive simulations and real-world experiments, we demonstrate the effectiveness of ClusterX in detecting and mitigating DDoS attacks, while maintaining high service availability. The results highlight the superiority of our approach in adapting to the dynamic nature of modern cyber threats and the benefits of a coordinated defense strategy in distributed cloud environments. ClusterX represents a significant advancement in the field of network security, offering a robust and adaptive solution to one of the most pressing challenges in cybersecurity today.

Abstract Achieving high‐performance spectral imaging over large fields of view (FOV) has remained challenging, particularly with conventional bulky optical systems. Here, a compact transversely dispersive metasurface array is introduced, designed for wide FOV spectral imaging. The system achieves efficient aperture encoding via distinct phase gradients tailored to different incident angles. A genetic algorithm optimizes the spectral reconstruction process, allowing for high‐accuracy imaging with a single snapshot. The system demonstrates 120° FOV spectral imaging across 11 channels in the 400–700 nm visible range, using a 91 × 91 metasurface array with 81.5% efficiency in light focusing and first‐order diffraction. This approach represents an advancement over traditional methods, offering broad potential in material classification, chemical analysis, remote sensing, and defect detection.

Controlled intracellular delivery of biomolecular cargo is critical for developing targeted therapeutics and cell reprogramming. Conventional delivery approaches (e.g., endocytosis of nano-vectors, microinjection, and electroporation) usually require time-consuming uptake processes, labor-intensive operations, and/or costly specialized equipment. Here, we present an acoustofluidics-based intracellular delivery approach capable of effectively delivering various functional nanomaterials to multiple cell types (e.g., adherent and suspension cancer cells). By tuning the standing acoustic waves in a glass capillary, our approach can push cells in flow to the capillary wall and enhance membrane permeability by increasing membrane stress to deform cells via acoustic radiation forces. Moreover, by coating the capillary with cargo-encapsulated nanoparticles, our approach can achieve controllable cell-nanoparticle contact and facilitate nanomaterial delivery beyond Brownian movement. Based on these mechanisms, we have successfully delivered nanoparticles loaded with small molecules or protein-based cargo to U937 and HeLa cells. Our results demonstrate enhanced delivery efficiency compared to attempts made without the use of acoustofluidics. Moreover, compared to conventional sonoporation methods, our approach does not require special contrast agents with microbubbles. This acoustofluidics-based approach creates exciting opportunities to achieve controllable intracellular delivery of various biomolecular cargoes to diverse cell types for potential therapeutic applications and biophysical studies.

Advanced biofabrication techniques can create tissue-like constructs that can be applied for reconstructive surgery or as in vitro three-dimensional (3D) models for disease modeling and drug screening. While various biofabrication techniques have recently been widely reviewed in the literature, acoustics-based technologies still need to be explored. The rapidly increasing number of publications in the past two decades exploring the application of acoustic technologies highlights the tremendous potential of these technologies. In this review, we contend that acoustics-based methods can address many limitations inherent in other biofabrication techniques due to their unique advantages: noncontact manipulation, biocompatibility, deep tissue penetrability, versatility, precision in-scaffold control, high-throughput capabilities, and the ability to assemble multilayered structures. We discuss the mechanisms by which acoustics directly dictate cell assembly across various biostructures and examine how the advent of novel acoustic technologies, along with their integration with traditional methods, offers innovative solutions for enhancing the functionality of organoids. Acoustic technologies are poised to address fundamental challenges in biofabrication and tissue engineering and show promise for advancing the field in the coming years.

Ring resonator (RR) devices are closed-loop waveguides where waves circulate only at the resonant frequencies. They have been used in sensor technology and optical tweezers, but controlling micron-scale particles with optical RR tweezers is challenging due to insufficient force, short working distances, and photodamage. To overcome these obstacles, an acoustofluidic RR-based tweezing method is developed to manipulate micro-sized particles that can enhance particle trapping through the resonance interaction of acoustic waves with high Q factor (>3000), more than 20 times greater than traditional acoustic transducers. Particles can be precisely manipulated within the RR by adjusting the signal phase, with trapping amplified by enlarging the connected waveguide. Rapid particle mixing is achieved when particles are placed between the waveguide and RR. The signal path is strengthened by strategically positioning the RR in a two-dimensional plane. Acoustofluidic RR tweezers have immense potential for advancing applications in biosensing, mechanobiology, lab-on-a-chip, and cell-cell communication research.

The isolation of viruses from complex biological samples is essential for creating sensitive bioassays that assess the efficacy and safety of viral therapeutics and vaccines, which have played a critical role during the COVID-19 pandemic. However, existing methods of viral isolation are time-consuming and labor-intensive due to the multiple processing steps required, resulting in low yields. Here, we introduce the rapid, efficient, and high-resolution acoustofluidic isolation of viruses from complex biological samples via Bessel beam excitation separation technology (BEST). BEST isolates viruses by utilizing the nondiffractive and self-healing properties of 2D, in-plane acoustic Bessel beams to continuously separate cell-free viruses from biofluids, with high throughput and high viral RNA yield. By tuning the acoustic parameters, the cutoff size of isolated viruses can be easily adjusted to perform dynamic, size-selective virus isolation while simultaneously trapping larger particles and separating smaller particles and contaminants from the sample, achieving high-precision isolation of the target virus. BEST was used to isolate severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from human saliva samples and Moloney Murine Leukemia Virus from cell culture media, demonstrating its potential use in both practical diagnostic applications and fundamental virology research. With high separation resolution, high yield, and high purity, BEST is a powerful tool for rapidly and efficiently isolating viruses. It has the potential to play an important role in the development of next-generation viral diagnostics, therapeutics, and vaccines.

Droplet microfluidics has emerged as a valuable technology for a multitude of chemical and biomedical applications, offering the capability to create independent microenvironments for high-throughput assays. Central to numerous droplet microfluidic applications is the picoinjection of materials into individual droplets, yet existing picoinjection methods often exhibit high power requirements, lack biocompatibility, and/or suffer from limited controllability. Here, we present an acoustofluidic picoinjector that generates acoustic pressure at the droplet interface to enable on-demand, energy-efficient, and biocompatible injection at high precision. We validate our platform by performing acid-base titrations by iteratively injecting picoliter volume reagents into droplets to induce pH transitions detectable by color change in solution. Additionally, we demonstrate the versatility of the acoustofluidic picoinjector in the synthesis of metallic nanoparticles, yielding highly monodisperse and reproducible particle morphologies compared to conventional bulk-phase techniques. By facilitating controlled delivery of reagents or biological samples with unparalleled accuracy, acoustofluidic picoinjection broadens the utility of droplet microfluidics for a myriad of applications in chemical and biological research.

Separating plasma from whole blood is an important sample processing technique required for fundamental biomedical research, medical diagnostics, and therapeutic applications. Traditional protocols for plasma isolation require multiple centrifugation steps or multiunit microfluidic processing to sequentially remove large red blood cells (RBCs) and white blood cells (WBCs), followed by the removal of small platelets. Here, we present an acoustofluidic platform capable of efficiently removing RBCs, WBCs, and platelets from whole blood in a single step. By leveraging differences in the acoustic impedances of fluids, our device generates significantly greater forces on suspended particles than conventional microfluidic approaches, enabling the removal of both large blood cells and smaller platelets in a single unit. As a result, undiluted human whole blood can be processed by our device to remove both blood cells and platelets (>90%) at low voltages (25 Vpp). The ability to successfully remove blood cells and platelets from plasma without altering the properties of the proteins and antibodies present creates numerous potential applications for our platform in biomedical research, as well as plasma-based diagnostics and therapeutics. Furthermore, the microfluidic nature of our device offers advantages such as portability, cost efficiency, and the ability to process small-volume samples.

Surface acoustic wave (SAW)-enabled acoustofluidic technologies have recently atttracted increasing attention for applications in biology, chemistry, biophysics, and medicine. Most SAW acoustofluidic devices generate acoustic energy which is then transmitted into custom microfabricated polymer-based channels. There are limited studies on delivering this acoustic energy into convenient commercially-available glass tubes for manipulating particles and fluids. Herein, we have constructed a capillary-based SAW acoustofluidic device for multifunctional fluidic and particle manipulation. This device integrates a converging interdigitated transducer to generate focused SAWs on a piezoelectric chip, as well as a glass capillary that transports particles and fluids. To understand the actuation mechanisms underlying this device, we performed finite element simulations by considering piezoelectric, solid mechanic, and pressure acoustic physics. This experimental study shows that the capillary-based SAW acoustofluidic device can perform multiple functions including enriching particles, patterning particles, transporting particles and fluids, as well as generating droplets with controlled sizes. Given the usefulness of these functions, we expect that this acoustofluidic device can be useful in applications such as pharmaceutical manufacturing, biofabrication, and bioanalysis.

Efficient isolation and analysis of exosomal biomarkers hold transformative potential in biomedical applications. However, current methods are prone to contamination and require costly consumables, expensive equipment, and skilled personnel. Here, we introduce an innovative spaceship-like disc that allows Acoustic Separation and Concentration of Exosomes and Nucleotide Detection: ASCENDx. We created ASCENDx to use acoustically driven disc rotation on a spinning droplet to generate swift separation and concentration of exosomes from patient plasma samples. Integrated plasmonic nanostars on the ASCENDx disc enable label-free detection of enriched exosomes via surface-enhanced Raman scattering. Direct detection of circulating exosomal microRNA biomarkers from patient plasma samples by the ASCENDx platform facilitated a diagnostic assay for colorectal cancer with 95.8% sensitivity and 100% specificity. ASCENDx overcomes existing limitations in exosome-based molecular diagnostics and holds a powerful position for future biomedical research, precision medicine, and point-of-care medical diagnostics.

The addition of surface acoustic wave (SAW) technologies to microfluidics has greatly advanced lab-on-a-chip applications due to their unique and powerful attributes, including high-precision manipulation, versatility, integrability, biocompatibility, contactless nature, and rapid actuation. However, the development of SAW microfluidic devices is limited by complex and time-consuming micro/nanofabrication techniques and access to cleanroom facilities for multistep photolithography and vacuum-based processing. To simplify the fabrication of SAW microfluidic devices with customizable dimensions and functions, we utilized the additive manufacturing technique of aerosol jet printing. We successfully fabricated customized SAW microfluidic devices of varying materials, including silver nanowires, graphene, and poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS). To characterize and compare the acoustic actuation performance of these aerosol jet printed SAW microfluidic devices with their cleanroom-fabricated counterparts, the wave displacements and resonant frequencies of the different fabricated devices were directly measured through scanning laser Doppler vibrometry. Finally, to exhibit the capability of the aerosol jet printed devices for lab-on-a-chip applications, we successfully conducted acoustic streaming and particle concentration experiments. Overall, we demonstrated a novel solution-based, direct-write, single-step, cleanroom-free additive manufacturing technique to rapidly develop SAW microfluidic devices that shows viability for applications in the fields of biology, chemistry, engineering, and medicine.

The study of molecular mechanisms at the single-cell level holds immense potential for enhancing immunotherapy and understanding neuroinflammation and neurodegenerative diseases by identifying previously concealed pathways within a diverse range of paired cells. However, existing single-cell pairing platforms have limitations in low pairing efficiency, complex manual operation procedures, and single-use functionality. Here, we report a multiparametric cellular immunity analysis by a modular acoustofluidic platform: CIAMAP. This platform enables users to efficiently sort and collect effector-target (i.e., NK92-K562) cell pairs and monitor the real-time dynamics of immunological response formation. Furthermore, we conducted transcriptional and protein expression analyses to evaluate the pathways that mediate effector cytotoxicity toward target cells, as well as the synergistic effect of doxorubicin on the cellular immune response. Our CIAMAP can provide promising building blocks for high-throughput quantitative single-cell level coculture to understand intercellular communication while also empowering immunotherapy by precision analysis of immunological synapses.

While mesenchymal stem cells (MSCs) have gained enormous attention due to their unique properties of self-renewal, colony formation, and differentiation potential, the MSC secretome has become attractive due to its roles in immunomodulation, anti-inflammatory activity, angiogenesis, and anti-apoptosis. However, the precise stimulation and efficient production of the MSC secretome for therapeutic applications are challenging problems to solve. Here, we report on Acoustofluidic Interfaces for the Mechanobiological Secretome of MSCs: AIMS. We create an acoustofluidic mechanobiological environment to form reproducible three-dimensional MSC aggregates, which produce the MSC secretome with high efficiency. We confirm the increased MSC secretome is due to improved cell-cell interactions using AIMS: the key mediator N-cadherin was up-regulated while functional blocking of N-cadherin resulted in no enhancement of the secretome. After being primed by IFN-γ, the secretome profile of the MSC aggregates contains more anti-inflammatory cytokines and can be used to inhibit the pro-inflammatory response of M1 phenotype macrophages, suppress T cell activation, and support B cell functions. As such, the MSC secretome can be modified for personalized secretome-based therapies. AIMS acts as a powerful tool for improving the MSC secretome and precisely tuning the secretory profile to develop new treatments in translational medicine.

Electronic nose (E-nose) combined with gas chromatography–mass spectrometry (GC-MS) was used to analyze the volatile components of silver wormwood from different habitats, and the antibacterial activity of essential oils was also studied, to provide a scientific basis for quality control of silver wormwood and rational utilization of their essential oils. In this study, the total content of essential oils in silver wormwood was determined by steam distillation; the volatile components were conducted in an overall analysis by E-nose combined with chemometrics; the volatile components were analyzed and identified by GC-MS; and two G-negative bacteria and one Gram-positive bacteria were used as test bacteria to determine the antibacterial activity of the essential oils from silver wormwood. The results showed that principal component analysis (PCA) and linear discriminant analysis (LDA) of E-nose could distinguish the essential oils of silver wormwood from different habitats, and the odor difference of essential oils was obvious. A total of 87 volatile components were identified by GC-MS, and there were significant differences in components and contents in silver wormwood from different habitats; PCA and hierarchical cluster analysis (HCA) could effectively distinguish silver wormwood from different habitats. The essential oils from silver wormwood from different habitats all had a certain inhibitory effect on Bacillus subtilis, Staphylococcus aureus, and Escherichia coli. Therefore, the combination of E-nose and GC-MS could quickly distinguish silver wormwood from different habitats and provide a reference for quality control, drug selection, and comprehensive utilization of silver wormwood.

Anomaly detection on dynamic graphs refers to detecting entities whose behaviors obviously deviate from the norms observed within graphs and their temporal information. This field has drawn increasing attention due to its application in finance, network security, social networks, and more. However, existing methods face two challenges: dynamic structure constructing challenge - difficulties in capturing graph structure with complex time information and negative sampling challenge - unable to construct excellent negative samples for unsupervised learning. To address these challenges, we propose Unsupervised Generative Anomaly Detection on Dynamic Graphs (GADY). To tackle the first challenge, we propose a continuous dynamic graph model to capture the fine-grained information, which breaks the limit of existing discrete methods. Specifically, we employ a message-passing framework combined with positional features to get edge embeddings, which are decoded to identify anomalies. For the second challenge, we pioneer the use of Generative Adversarial Networks to generate negative interactions. Moreover, we design a loss function to alter the training goal of the generator while ensuring the diversity and quality of generated samples. Extensive experiments demonstrate that our proposed GADY significantly outperforms the previous state-of-the-art method on three real-world datasets. Supplementary experiments further validate the effectiveness of our model design and the necessity of each module.

With the continuous improvement of computing power and deep learning algorithms in recent years, the foundation model has grown in popularity. Because of its powerful capabilities and excellent performance, this technology is being adopted and applied by an increasing number of industries. In the intelligent transportation industry, artificial intelligence faces the following typical challenges: few shots, poor generalization, and a lack of multi-modal techniques. Foundation model technology can significantly alleviate the aforementioned issues. To address these, we designed the 1st Foundation Model Challenge, with the goal of increasing the popularity of foundation model technology in traffic scenarios and promoting the rapid development of the intelligent transportation industry. The challenge is divided into two tracks: all-in-one and cross-modal image retrieval. Furthermore, we provide a new baseline and benchmark for the two tracks, called Open-TransMind. According to our knowledge, Open-TransMind is the first open-source transportation foundation model with multitask and multi-modal capabilities. Simultaneously, Open-TransMind can achieve state-of-the-art performance on detection, classification, and segmentation datasets of traffic scenarios. Our source code is available at https://github.com/Traffic-X/Open-TransMind.

In industrial applications, it is necessary to select different types of networks according to different communication requirements. To meet this requirement, a converged network of wired and wireless networks is frequently employed. Notably, fulfilling the end-to-end transmission requirements of converged networks is challenging. As a solution, converged-network scheduling methods have proved valuable. In this paper, a network scheduling method for the convergence of industrial wireless networks and time-sensitive networks is proposed. Additionally, the proposed method is tested and verified. The results show that the end-to-end average transmission delay is reduced and the jitter is acceptable.

With the continuous improvement of computing power and deep learning algorithms in recent years, the foundation model has grown in popularity. Because of its powerful capabilities and excellent performance, this technology is being adopted and applied by an increasing number of industries. In the intelligent transportation industry, artificial intelligence faces the following typical challenges: few shots, poor generalization, and a lack of multi-modal techniques. Foundation model technology can significantly alleviate the aforementioned issues. To address these, we designed the 1st Foundation Model Challenge, with the goal of increasing the popularity of foundation model technology in traffic scenarios and promoting the rapid development of the intelligent transportation industry. The challenge is divided into two tracks: all-in-one and cross-modal image retrieval. Furthermore, we provide a new baseline and benchmark for the two tracks, called Open-TransMind. According to our knowledge, Open-TransMind is the first open-source transportation foundation model with multi-task and multi-modal capabilities. Simultaneously, Open-TransMind can achieve state-of-the-art performance on detection, classification, and segmentation datasets of traffic scenarios. Our source code is available at https://github.com/Traffic-X/Open-TransMind.

All-inorganic perovskite solar cells are attractive photovoltaic devices because of their excellent optoelectronic performance and thermal stability. Unfortunately, the currently used efficient inorganic perovskite materials can spontaneously transform into undesirable phases without light-absorption properties. Studies have been carried out to stabilize all-inorganic perovskite by mixing low-dimensional perovskite. Compared with organic two-dimensional (2D) perovskite, inorganic 2D Cs2PbI2Cl2 shows superior thermal stability. Our group has successfully fabricated 2D/3D mixed-dimensional Cs2PbI2Cl2/CsPbI2.5Br0.5 films with increasing phase stability. The high boiling point of dimethyl sulfoxide (DMSO) makes it a preferred solvent in the preparation of Cs2PbI2Cl2/CsPbI2.5Br0.5 inorganic perovskite. When the perovskite films are prepared by the one-step solution method, it is difficult to evaporate the residual solvent molecules from the prefabricated films, resulting in films with rough surface morphology and high defect density. This study used the rapid precipitation method to control the formation of perovskite by treating it with methanol/isopropanol (MT/IPA) mixed solvent to produce densely packed, smooth, and high-crystallized perovskite films. The bulk defects and the carrier transport barrier of the interface were effectively reduced, which decreased the recombination of the carriers in the device. As a result, this effectively improved photoelectric performance. Through treatment with MT/IPA, the photoelectric conversion efficiency (PCE) of solar cells prepared in the N2 atmosphere increased from 13.44% to 14.10%, and the PCE of the device prepared in the air increased from 3.52% to 8.91%.

The intrinsic biophysical properties of cells, such as mechanical, acoustic, and electrical properties, are valuable indicators of a cell's function and state. However, traditional single-cell biophysical characterization methods are hindered by limited measurable properties, time-consuming procedures, and complex system setups. This study presents acousto-dielectric tweezers that leverage the balance between controllable acoustophoretic and dielectrophoretic forces applied on cells through surface acoustic waves and alternating current electric fields, respectively. Particularly, the balanced acoustophoretic and dielectrophoretic forces can trap cells at equilibrium positions independent of the cell size to differentiate between various cell-intrinsic mechanical, acoustic, and electrical properties. Experimental results show our mechanism has the potential for applications in single-cell analysis, size-insensitive cell separation, and cell phenotyping, which are all primarily based on cells' intrinsic biophysical properties. Our results also show the measured equilibrium position of a cell can inversely determine multiple biophysical properties, including membrane capacitance, cytoplasm conductivity, and acoustic contrast factor. With these features, our acousto-dielectric tweezing mechanism is a valuable addition to the resources available for biophysical property-based biological and medical research.

In industrial application, it is necessary to select different kind of networks according to different kind of communication requirement. The converged network of wired and wireless networks is able to meet this need. It is a challenge to meet the end-to-end transmission requirements of converged networks. Therefore, the converged network scheduling mechanism is important. In this paper, a network scheduling method for convergence of industrial wireless network and TSN is proposed. Then, a test and verification for the method proposed is implemented. The results show that the end-to-end average transmission delay is reduced and the jitter is acceptable.

In recent years, an increasing number of real-time applications have become more sensitive to the freshness of information, which requires that packets reach the receiver as promptly as possible. As a measure of information freshness, it is of great interest to measure the age of information (AoI) on multi-source networks. In this paper, we propose a new queueing system: the systems with N sources, Single buffer, Non-source-aware, and LCFS-S (NSLS-Q system). To simplify the study, we first studied the queueing system for Two sources, Single buffer, Non-source-aware, and LCFS-S (TSLS-Q system). We then generalize the conclusions to the NSLS-Q system. We model the queueing system using a stochastic hybrid system (SHS) to solve for the age of information in the queueing system. In this, Markov chains are used to represent the state transitions. We then compared the system with other queueing systems through numerical results. The results show that the queue model performs better in terms of AoI compared to the traditional queue model.