Dynamic Signal Research Articles

Additive manufacturing of personalized scaffolds for vascular cell studies in large arteries: A case study on carotid arteries in sickle cell disease patients

Patient-specific models have increasingly gained significance in medical and research domains. In the context of hemodynamic studies, computational fluid dynamics emerges as a highly innovative and promising approach. We propose to augment these computational studies with cell-based experiments in individualized artery geometries using personalized scaffolds and vascular cell experiments. Previous research has demonstrated that the development of Sickle Cell Disease (SCD)-Related Vasculopathy is dependent on personal geometries and flow characteristics of the carotid artery. This fact leaves conventional animal experiments unsuitable for gaining patient-specific insights into cellular signaling, as they cannot replicate the personalized geometry. These personalized dynamics of cellular signaling may further impact disease progression, yet remains unclear. This paper presents a six-step methodology for creating personalized large artery scaffolds, focusing on high-precision models that yield biologically interpretable patient-specific results. The methodology outlines the creation of personalized large artery models via Additive Manufacturing suitably for supporting cell culture and other cellular experiments. Additionally, it discusses how different Computer-Aided-Design (CAD) construction modes can be used to obtain high-precision personalized models, while simplifying model reconfigurations and facilitating adjustments to general designs such as system connections to bioreactors, fluidic systems and visualization tools. A proposal for quality control measures to ensure geometric congruence for biological relevance of the results is added. This innovative, interdisciplinary approach appears promising for gaining patient-specific insights into pathophysiology, highlighting the importance of personalized medicine for understanding complex diseases.

Wastewater monitoring of human and avian influenza A viruses in Northern Ireland: a genomic surveillance study

Influenza Aviruses (IAVs) are significant pathogens of humans and other animals. Although endemic in humans and birds, novel IAV strains can emerge, jump species, and cause epidemics, like the latest variant of H5N1. Wastewater-based epidemiology (WBE) has been shown capable of detecting human IAVs. We aimed to assess whether whole-genome sequencing (WGS) of IAVs from wastewater is possible and can be used to discriminate between circulating strains of human and any non-human IAVs, such as those of avian origin. Using a pan-IAV RT-quantitative PCR assay, six wastewater treatment works (WWTWs) across Northern Ireland were screened from Aug 1to Dec 5, 2022. Ananopore WGS approach was used to sequence RT-qPCR-positive samples. Phylogenetic analysis of sequences relative to currently circulating human and non-human IAVs was performed. Forcomparative purposes, clinical data (PCR test results) were supplied by The Regional Virus Laboratory, Belfast Health and Social Care Trust (Belfast, Northern Ireland, UK). We detected a dynamic IAV signal in wastewater from Sept 5, 2022, onwards across Northern Ireland, which did not show a clear positive relationship with the clinical data obtained for the region. Meta (mixed strain) whole-genome sequences were generated from wastewater samples displaying homology to only human and avian IAVstrains. The relative proportion of IAV reads of human versus avian origin differed across time and sample site. Adiversity in subtypes and lineages was detected (eg, H1N1, H3N2, and several avian). Avian segment 8related to those found in recent H5N1 clade 2.3.4.4b was identified. WBE affords a means to monitor circulating human and avian IAV strains and provide crucial genetic information. As such, WBE can provide rapid, cost-effective, year-round One Health surveillance to help control IAV epidemic and pandemic-related threats. However, optimisation of WBE protocols are necessary to ensure observed wastewater signals not only correlate with clinical case data, but yield information on the wider environmental pan-influenz-ome. Department of Health for Northern Ireland.

Single feature to achieve gas recognition: Humidity interference suppression strategy based on temperature modulation and principal component linear discriminant analysis

Metal oxide semiconductor (MOS) sensors have been broadly employed for gas detection. However, the distinctive chemical detection principle of MOS sensors renders them susceptible to interference by humidity. This paper proposes a novel method for suppressing humidity interference. This method possesses advantages, including rapid recognition, low power consumption, and the capability to achieve recognition using a single feature. Temperature modulation technology was used to record the sensor's resistance variation, and the response features of the obtained dynamic response signal are affected by both humidity and the measured gas. The suppression of humidity interference is achieved through the investigation of the response features of humidity and the measured gas. The response features of humidity and gas are amplified by column normalization. Principal Component Linear Discriminant Analysis (PC-LDA) is utilized to acquire humidity and gas information within data. Using ethanol as the primary experimental subject, suppression of humidity interference is achieved through a single feature (PC-LD2). Quantitative recognition of relative humidity (RH) can be achieved using PC-LD1. Multiple types of waveform and sensor were used to demonstrate the generalization of this method.

Time-varying stimuli that prolong IKK activation promote nuclear remodeling and mechanistic switching of NF-κB dynamics.

Temporal properties of molecules within signaling networks, such as sub-cellular changes in protein abundance, encode information that mediate cellular responses to stimuli. How dynamic signals relay and process information is a critical gap in understanding cellular behaviors. In this work, we investigate transmission of information about changing extracellular cytokine concentrations from receptor-level supramolecular assemblies of IκB kinases (IKK) downstream to the nuclear factor κB (NF-κB) transcription factor (TF). In a custom robot-controlled microfluidic cell culture, we simultaneously measure input-output (I/O) encoding of IKK-NF-κB in dual fluorescent-reporter cells. When compared with single cytokine pulses, dose-conserving pulse trains prolong IKK assemblies and lead to disproportionately enhanced retention of nuclear NF-κB. Using particle swarm optimization, we demonstrate that a mechanistic model does not recapitulate this emergent property. By contrast, invoking mechanisms for NF-κB-dependent chromatin remodeling to the model recapitulates experiments, showing how temporal dosing that prolongs IKK assemblies facilitates switching to permissive chromatin that sequesters nuclear NF-κB. Remarkably, using simulations to resolve single-cell receptor data accurately predicts same-cell NF-κB time courses for more than 80% of our single cell trajectories. Our data and simulations therefore suggest that cell-to-cell heterogeneity in cytokine responses are predominantly due to mechanisms at the level receptor-associated protein complexes.

Neuropeptide signaling network of Caenorhabditis elegans: from structure to behavior.

Neuropeptides are abundant signaling molecules that control neuronal activity and behavior in all animals. Owing in part to its well-defined and compact nervous system, Caenorhabditis elegans has been one of the primary model organisms used to investigate how neuropeptide signaling networks are organized and how these neurochemicals regulate behavior. We here review recent work that has expanded our understanding of the neuropeptidergic signaling network in C. elegans by mapping the evolutionary conservation, the molecular expression, the receptor-ligand interactions, and the system-wide organization of neuropeptide pathways in the C. elegans nervous system. We also describe general insights into neuropeptidergic circuit motifs and the spatiotemporal range of peptidergic transmission that have emerged from in vivo studies on neuropeptide signaling. With efforts ongoing to chart peptide signaling networks in other organisms, the C. elegans neuropeptidergic connectome can serve as a prototype to further understand the organization and the signaling dynamics of these networks at organismal level.

Multi-Channel Signals in Dynamic Force-Clamp Mode of Microcantilever Sensors for Detecting Cellular Peripheral Brush.

The development of numerous diseases, such as renal cyst, cancer, and viral infection, is closely associated with the pathological changes and defects in the cellular peripheral brush. Therefore, it is necessary to develop a potential new method to detect lesions of cellular peripheral brush. Here, a piecewise linear viscoelastic constitutive model of cell is established considering the joint contribution of the peripheral brush and intra-cellular structure. By combining the Laplace transformation and its inverse transformation, and the differential method in the temporal domain and differential quadrature method (DQM) in the spatial domain, the signal interpretation models for quasi-static and dynamic signals of microcantilever are solved. The influence mechanisms of the peripheral brush on the viscoelastic properties of cells and quasi-static/dynamic signals of microcantilever are clarified. The results not only reveal that the peripheral brush has significant effects on the complex modulus of the cell and multi-channel signals of the microcantilever, but also suggest that an alternative mapping method by collecting multi-channel signals including quasi-static and higher frequency signals with more brush indexes could be potentially used to identify cancerous cells.

Self-Healing and Shear-Stiffening Electrodes for Wearable Biopotential Sensing and Gesture Recognition.

The achievement of flexible skin electrodes for dynamic monitoring of biopotential is one of the challenging issues in flexible electronics due to the interference of large acceleration and heavy sweat that influence the stability of skin-electrode interfaces. This work presents materials and techniques to achieve self-healing and shear-stiffening electrodes and an associated flexible system that can be used for multichannel biopotential measurement on the skin. The electrode that is based on a composite of silver (Ag) flakes, Ag nanowires, and polyborosiloxane offers an electrical conductivity of 9.71 × 104 S/m and a rheological characteristic that ensures stable and fully conformal contact with skin and easy removal under different shear rates. The electrode can maintain its conductivity even after being stretched by more than 60% and becomes self-healed after mechanical damage. The combination of the electrodes with a screen-printed multichannel flexible sensor allows stable monitoring of both static and dynamic electromyography signals, leading to the acquisition of high-quality multilead biopotential signals that can be readily extracted to yield gesture recognition results with over 97.42% accuracy. The conductive self-healing materials and flexible sensors may be utilized in various daily biopotential sensing applications, allowing highly stable dynamic measurement to facilitate artificial intelligence-enabled health condition diagnosis and human-computer interface.

Numerical investigation of new rete ridge formation in a multi-layer model of skin subjected to tissue expansion

The skin is a multilayered organ with microstructural and antomical heterogeneities that contribute to its unique mechanophysiology. Between the epidermis layer at the top and the dermis layer below, the basal keratinocytes form an interface with sinusoidal-like geometry termed rete ridges. In previous computational work we showed that the rete ridges contribute to lower delamination risk by increasing surface area and reducing the stress jump across the interface. Experimentally, we and others have shown that upon repeated tissue expansion and growth, physiological rete ridge frequency is preserved. Here we implement a 2D multilayered skin model where each layer is able to grow in response to applied loading toward recovering the layer-specific homeostatic stretch. Our simulations support the hypothesis that mechanics of growing tissue can explain secondary buckling and new rete ridge formation in tissue expansion. The process is robust with respect to parameters such as homeostatic stretch, layer thicknesses, and shear moduli of the different layers. Thicker epidermis suppresses higher frequency features, and so does a stiffer epidermis with respect to the basal layer. Interestingly, new rete ridge valleys are formed at locations that were originally peaks of the sine wave, whereas original valleys remain valleys. This pattern might have a connection to the localization of stem cell and transient amplifying cells in the epidermis. This study does not discard the role of cell–cell signaling dynamics, but rather emphasizes the possibility of achieving robust geometric patterns with simple rules of growing tissue, even in the absence of complex regulatory networks.

Deconvolving organogenesis in space and time via spatial transcriptomics in thick tissues.

Organ development is guided by a space-time landscape that constraints cell behavior. This landscape is challenging to characterize for the hair follicle - the most abundant mini organ - due to its complex microscopic structure and asynchronous development. We developed 3DEEP, a tissue clearing and spatial transcriptomic strategy for characterizing tissue blocks up to 400 µm in thickness. We captured 371 hair follicles at different stages of organogenesis in 1 mm 3 of skin of a 12-hour-old mouse with 6 million transcripts from 81 genes. From this single time point, we deconvoluted follicles by age based on whole-organ molecular pseudotimes to animate a stop-motion 3D atlas of follicle development along its trajectory. We defined molecular stages for hair follicle organogenesis and characterized the order of emergence for its structures, differential signaling dynamics at its top and bottom, morphogen shifts preceding and accompanying structural changes, and series of structural changes leading to the formation of its canal and opening. We further found that hair follicle stem cells and their niche are established and stratified early in organogenesis, before the formation of the hair bulb. Overall, this work demonstrates the power of increased depth of spatial transcriptomics to provide a four-dimensional analysis of organogenesis.

Novel phosphatidylinositol flippases contribute to phosphoinositide homeostasis in the plasma membrane.

Phosphatidylinositol is a precursor of various phosphoinositides, which play crucial roles in intracellular signaling and membrane dynamics and have impact on diverse aspects of cell physiology. Phosphoinositide synthesis and turnover occur in the cytoplasmic leaflet of the organellar and plasma membranes. P4-ATPases (lipid flippases) are responsible for translocating membrane lipids from the exoplasmic (luminal) to the cytoplasmic leaflet, thereby regulating membrane asymmetry. However, the mechanism underlying phosphatidylinositol translocation across cellular membranes remains elusive. Here, we discovered that the phosphatidylcholine flippases ATP8B1, ATP8B2, and ATP10A can also translocate phosphatidylinositol at the plasma membrane. To explore the function of these phosphatidylinositol flippases, we used cells depleted of CDC50A, a protein necessary for P4-ATPase function and ATP8B1 and ATP8B2, which express in HeLa cells. Upon activation of the Gq-coupled receptor, depletion of phosphatidylinositol 4,5-bisphosphate [PtdIns(4,5)P2] was accelerated in CDC50A knockout (KO) and ATP8B1/8B2 double KO cells compared with control cells, suggesting a decrease in PtdIns(4,5)P2 levels within the plasma membrane of the KO cells upon stimulation. These findings highlight the important role of P4-ATPases in maintaining phosphoinositide homeostasis and suggest a mechanism for asymmetry of phosphatidylinositol in the cytoplasmic leaflet of the plasma membrane.

Engineered chimeric receptors for dissecting interferon signaling.

Though interferons (IFNs) were once heralded as panaceas to numerous diseases, how cells decode varying IFN stimuli and subsequently produce (in)appropriate signaling remain unclear. Our labs recently engineered novel erythropoietin receptor-IFN chimeric receptors, and we highlight their utility in two cases uncovering differential genetic determinants of type I (IFN-α/β) and type III (IFN-λ) IFN signaling. These and other types of synthetic (cytokine) receptors could be expanded to real-time signaling dynamics and in vivo studies.

Time-Responsive Visualization of Cryogenic Detection Based on the Dynamic Optical Signals of CaZnOS.

Cryogenic detection technology is essential to ensure safety and effectiveness in fields such as medical refrigeration, cold chain transport, and cryogenic bioengineering. In this paper, a time-responsive visual cryogenic detection strategy is developed based on the storage properties of CaZnOS: Pb2+, Pr3+ phosphors with shallow traps. Since the carrier release rate from the trap center receives the influence of ambient temperature and storage time, the storage time of the temperature-sensitive product can be determined by the different optical signals of CaZnOS: Pb2+, Pr3+ phosphors obtained under 980 nm laser irradiation. In addition, CaZnOS: Pb2+, Pr3+ phosphors with multimode luminescence enable time-responsive visual detection of ambient temperature under extreme conditions. This work not only demonstrates the potential of CaZnOS: Pb2+, Pr3+ phosphors for visual detection of temperature and time but also paves the way for the development of various applications relying on cryogenic monitoring.

Behavioral biometric optical tactile sensor for instantaneous decoupling of dynamic touch signals in real time

Decoupling dynamic touch signals in the optical tactile sensors is highly desired for behavioral tactile applications yet challenging because typical optical sensors mostly measure only static normal force and use imprecise multi-image averaging for dynamic force sensing. Here, we report a highly sensitive upconversion nanocrystals-based behavioral biometric optical tactile sensor that instantaneously and quantitatively decomposes dynamic touch signals into individual components of vertical normal and lateral shear force from a single image in real-time. By mimicking the sensory architecture of human skin, the unique luminescence signal obtained is axisymmetric for static normal forces and non-axisymmetric for dynamic shear forces. Our sensor demonstrates high spatio-temporal screening of small objects and recognizes fingerprints for authentication with high spatial-temporal resolution. Using a dynamic force discrimination machine learning framework, we realized a Braille-to-Speech translation system and a next-generation dynamic biometric recognition system for handwriting.

Effects of zinc in podocytes and cortical collecting duct in vitro and Dahl salt-sensitive rats in vivo

Zinc is one of the essential divalent cations in the human body and a fundamental microelement involved in the regulation of many cellular and subcellular functions. Experimental studies reported that zinc deficiency is associated with renal damage and could increase blood pressure. It was proposed that zinc dietary supplementation plays a renoprotective role. Our study aimed to investigate the effects of zinc on intracellular signaling in renal cells and explore the correlation between dietary zinc and the progression of salt-induced hypertension. The impact of extracellular zinc concentrations on two different kidney epithelial cell types - podocytes and principal cells of the cortical collecting duct (CCD), was tested. In podocytes, a rise in extracellular zinc promotes TRPC6 channel-mediated calcium entry but not altered intracellular zinc levels. However, we observe the opposite effect in CCD cells with no alteration in calcium levels and steady-state elevation in intracellular zinc. Moreover, prolonged extracellular zinc exposure leads to cytotoxic insults in CCD cells but not in podocytes, characterized by increased cell death and disrupted cytoskeletal organization. Next, we tested if dietary zinc plays a role in the development of hypertension in Dahl salt-sensitive rats. Neither zinc-rich nor deficient diets impact the regular development of salt-sensitive hypertension. These results suggest specialized roles for zinc in renal function, implicating its involvement in proliferation and apoptosis in CCD cells and calcium signaling and cytoskeletal dynamics modulation in podocytes. Further research is required to elucidate the detailed mechanisms of zinc action and its implications in renal health and disease.

High-precision dynamic gesture recognition based on microfiber sensor and EMT-Net

Dynamic gesture recognition, which utilizes flexible wearable sensors and deep learning, is invaluable for human–computer interaction. Nevertheless, the primary challenges persist are the rapid detection of intricate gestures and the accurate recognition of dynamic signals. In this study, we suggest utilizing a microfiber sensor to identify the variations in wrist skin and detect dynamic gesture. In order to tackle the issue of insufficient feature extraction in the detected signals, resulting in reduced accuracy in recognition, we introduce a network dubbed EMT-Net (improve multi-head attention transformer network). This network utilizes a transformer encoder to capture and represent the characteristics of dynamic gesture signal and uses a CNN to classify the encoded features. To ensure that the model comprehensively captures the temporal and statistical characteristics of the signals, we enhance the multi-head attention mechanism by restricting certain attention heads to concentrate solely on the statistical features of the signals while allowing others to focus on the temporal features and global dependencies. Furthermore, because of the varying discriminatory abilities of different characteristics, we have developed an attention module to redistribution the attention weights on statistical features. The experimental results demonstrate that the microfiber sensor effectively recognizes ten distinct forms of dynamic gesture signals. Simultaneously, EMT-Net attains proficient identification with an accuracy of 98.80%, precision of 98.81%, recall of 98.80%, and an F1 score of 98.80%. The application value of this dynamic gesture recognition technology, which utilizes microfiber sensors and EMT-Net, is significant. The forthcoming alterations in human–computer interaction, virtual reality, and various other domains are anticipated.

Congeneric and Robust Adhesive Epidermal Patch for Anti‐Interference Physiological Signal Recognition

AbstractEpidermal patches utilized for the transduction of biopotentials and biomechanical signals are pivotal in wearable health monitoring. However, the shortcomings, such as inferior conformal ability, deficient adhesion, and motion artifacts, severely impede the bioelectrodes from perceiving stable and superior‐quality physiological signals. Herein, a polymer epidermal patch possessing a spontaneous Janus structure is facilely prepared through itaconic acylhydrazine (IAH) induced gradient polymerization. The solubility discrepancy of the monomers in IAH authorized the Janus structure with distinct adhesion properties on each side. Moreover, the hydrogen bond network constructed by IAH confers the polymer with a high degree of skin compliance, enabling dynamic and stable mechanical properties to withstand complex monitoring environments. By integrating skin‐like softness (Young's modulus ≈0.16 MPa), robust adhesion (35 kPa), and high signal‐to‐noise ratio (32 dB), this epidermal patch displays exceptional elasticity within the physiological activity spectrum, provides swift electrical and mechanical self‐recovery capabilities, and resists interference in dynamic signal monitoring (deformation, compression, humidity, etc.). By demonstrating multifaceted applications for Electrocardiogram recording under diverse disturbances, the epidermal patch profiles a promising noninvasive, enduring wearable bioelectronic interface with immunity to interference.

Proteomic Profile of Circulating Extracellular Vesicles in the Brain after Δ9-Tetrahydrocannabinol Inhalation.

Given the increasing use of cannabis in the US, there is an urgent need to better understand the drug's effects on central signaling mechanisms. Extracellular vesicles (EVs) have been identified as intercellular signaling mediators that contain a variety of cargo, including proteins. Here, we examined whether the main psychoactive component in cannabis, Δ9-tetrahydrocannabinol (THC), alters EV protein signaling dynamics in the brain. We first conducted in vitro studies, which found that THC activates signaling in choroid plexus epithelial cells, resulting in transcriptional upregulation of the cannabinoid 1 receptor and immediate early gene c-fos, in addition to the release of EVs containing RNA cargo. Next, male and female rats were examined for the effects of either acute or chronic exposure to aerosolized ('vaped') THC on circulating brain EVs. Cerebrospinal fluid was extracted from the brain, and EVs were isolated and processed with label-free quantitative proteomic analyses via high-resolution tandem mass spectrometry. Interestingly, circulating EV-localized proteins were differentially expressed based on acute or chronic THC exposure in a sex-specific manner. Taken together, these findings reveal that THC acts in the brain to modulate circulating EV signaling, thereby providing a novel understanding of how exogenous factors can regulate intercellular communication in the brain.

P65 signaling dynamics drive the developmental progression of hematopoietic stem and progenitor cells through cell cycle regulation

Most gene functions have been discovered through phenotypic observations under loss of function experiments that lack temporal control. However, cell signaling relies on limited transcriptional effectors, having to be re-used temporally and spatially within the organism. Despite that, the dynamic nature of signaling pathways have been overlooked due to the difficulty on their assessment, resulting in important bottlenecks. Here, we have utilized the rapid and synchronized developmental transitions occurring within the zebrafish embryo, in conjunction with custom NF-kB reporter embryos driving destabilized fluorophores that report signaling dynamics in real time. We reveal that NF-kB signaling works as a clock that controls the developmental progression of hematopoietic stem and progenitor cells (HSPCs) by two p65 activity waves that inhibit cell cycle. Temporal disruption of each wave results in contrasting phenotypic outcomes: loss of HSPCs due to impaired specification versus proliferative expansion and failure to delaminate from their niche. We also show functional conservation during human hematopoietic development using iPSC models. Our work identifies p65 as a previously unrecognized contributor to cell cycle regulation, revealing why and when pro-inflammatory signaling is required during HSPC development. It highlights the importance of considering and leveraging cell signaling as a temporally dynamic entity.

Light-gated integrator for highlighting kinase activity in living cells

Protein kinases are key signaling nodes that regulate fundamental biological and disease processes. Illuminating kinase signaling from multiple angles can provide deeper insights into disease mechanisms and improve therapeutic targeting. While fluorescent biosensors are powerful tools for visualizing live-cell kinase activity dynamics in real time, new molecular tools are needed that enable recording of transient signaling activities for post hoc analysis and targeted manipulation. Here, we develop a light-gated kinase activity coupled transcriptional integrator (KINACT) that converts dynamic kinase signals into “permanent” fluorescent marks. KINACT enables robust monitoring of kinase activity across scales, accurately recording subcellular PKA activity, highlighting PKA activity distribution in 3D cultures, and identifying PKA activators and inhibitors in high-throughput screens. We further leverage the ability of KINACT to drive signaling effector expression to allow feedback manipulation of the balance of GαsR201C-induced PKA and ERK activation and dissect the mechanisms of oncogenic G protein signaling.

Sustainable Operation and Management of a Dynamic Supply Chain under the Framework of a Community with a Shared Future for Mankind

The values of a community with a shared future for mankind include the views of common interests, sustainable development, and global governance. This article will fully consider introducing the value concept of a community with a shared future into the operation and management of dynamic supply chains. Based on the optimal information fusion mechanism of artificial intelligence, this article aims to examine the operation and management of dynamic supply chains within the framework of a community with a shared future for mankind. The core idea is to consider the common interests among enterprises, establish a global collaborative operation concept for upstream, midstream, and downstream enterprises, and achieve the goal of sustainable development. Firstly, a type of composite dynamic supply chain model is considered, in which the total inventory of each node in the supply chain is further subdivided into raw material inventory and finished product inventory. At the same time, we have considered factors such as the signing of procurement contracts between core enterprises and upstream enterprises, as well as the signing of supply contracts between core enterprises and downstream enterprises. Secondly, the static and dynamic monitoring information of the enterprise has been established. We use steady-state Kalman filtering theory to obtain dynamic reference signals for upstream enterprises, core enterprises, and downstream enterprises. Based on the optimal information fusion processing mechanism of artificial intelligence, the coefficient weighting method is used to obtain the optimal fusion signals of upstream enterprises, core enterprises, and downstream enterprises. Once again, through high-quality switching strategies, enterprises can achieve in-order switching, improve production efficiency, reduce downtime, enhance their competitiveness and responsiveness, and transform the dynamic supply chain, including order switching, into a discrete-time linear switching system for processing. Fourthly, sufficient conditions, robustness analysis results, and inventory control criteria for the solvability of dynamic supply chain H∞ with order switching are provided. Finally, data analysis is conducted using historical order information from three fruit companies to verify the validity and feasibility of the conclusions in this article and to improve the performance of the dynamic supply chain system. The research findings of this article enrich the exploration of the operation and management of dynamic supply chains and the construction of a community with a shared future for mankind.