Potential Ability Research Articles

Multidentate Chelating Ligands Enable High-Performance Zinc-Bromine Flow Batteries.

Zinc bromine flow battery (ZBFB) is a promising battery technology for stationary energy storage. However, challenges specific to zinc anodes must be resolved, including zinc dendritic growth, hydrogen evolution reaction, and the occurrence of "dead zinc". Traditional additives suppress side reactions and zinc dendrite formation by altering the solvation structure of Zn2+ and adsorbing onto the zinc surface through only a limited number of zincophilic sites, resulting in weak adsorption on zinc metal and potential inability to simultaneously optimize the solvation structure of zinc ions. Obviously, increasing the number of potential zincophilic sites in the additive can significantly enhance the interaction with zinc. Herein, we propose a strong chelate, ethylenediamine tetramethylene phosphonic acid (EDTMPA) as the additive, which boasts six potent zincophilic sites, not only promotes the formation of the water-deficient inner Helmholtz plane but also plays a crucial role in restructuring the solvation environment of Zn2+. As a result, the zinc symmetric flow battery with EDTMPA exhibited exceptional coulombic efficiency of 99.4% over 800 cycles, surpassing the previous studies by a significant margin. Furthermore, the assembled ZBFB has showcased a dendrite-free and enduring cycling over 400 cycles at 80 mA cm-2.

Multidentate Chelating Ligands Enable High‐Performance Zinc‐Bromine Flow Batteries

Zinc bromine flow battery (ZBFB) is a promising battery technology for stationary energy storage. However, challenges specific to zinc anodes must be resolved, including zinc dendritic growth, hydrogen evolution reaction, and the occurrence of "dead zinc". Traditional additives suppress side reactions and zinc dendrite formation by altering the solvation structure of Zn2+ and adsorbing onto the zinc surface through only a limited number of zincophilic sites, resulting in weak adsorption on zinc metal and potential inability to simultaneously optimize the solvation structure of zinc ions. Obviously, increasing the number of potential zincophilic sites in the additive can significantly enhance the interaction with zinc. Herein, we propose a strong chelate, ethylenediamine tetramethylene phosphonic acid (EDTMPA) as the additive, which boasts six potent zincophilic sites, not only promotes the formation of the water‐deficient inner Helmholtz plane but also plays a crucial role in restructuring the solvation environment of Zn2+. As a result, the zinc symmetric flow battery with EDTMPA exhibited exceptional coulombic efficiency of 99.4% over 800 cycles, surpassing the previous studies by a significant margin. Furthermore, the assembled ZBFB has showcased a dendrite‐free and enduring cycling over 400 cycles at 80 mA cm−2.

India’s Security and Climate Policy: Navigating the China Challenge

In a world rich in climate knowledge but short on climate action, it is important to understand the political reasons behind the apparent failure of states to address this planetary crisis. One underexplored dimension of this phenomenon is the impact of broader geopolitics and rivalry, in specific the ability and willingness of potential and actual security and economic rivals to leave their contention at the gates of the United Nations Framework Convention on Climate Change (UNFCCC). This article focuses on India’s multifaceted approach to its ‘China challenge’ with and outside the climate regime. While conventional scholarship focuses on the security and economic dimensions of this rivalry, this study offers a distinct perspective that examines whether this rivalry might lead to a breakdown of cooperation and an increased potential for conflict and weaponisation of seemingly disconnected issue areas. Drawing on a comprehensive triangulation of policy documents, government statements and scholarly literature, this article evaluates the domestic and international factors shaping India’s climate-related China policies, including the countries’ unique economic ambitions, development imperatives, energy needs and global climate commitments. Furthermore, the article examines Sino-Indian interactions in international arenas such as the UNFCCC and Brazil, Russia, India, China, South Africa (BRICS), and how these interactions influence global climate governance.

You're Getting on My Nerves! A board Game to Teach Action Potential Propagation and Cable Properties.

Electrophysiology is one of the most intimidating topics within the foundational neuroscience curriculum to most undergraduate students. Keeping student attention and engagement during these lectures is equally challenging for educators. Game-based learning is used in many disciplines and levels of education and allows students to apply what they have learned and build community within the classroom. You're Getting on my Nerves was created to help students apply their knowledge of cable properties and practice vocabulary terms with their peers. This board game was originally created using inexpensive products but is also now available for purchase, allowing educators the flexibility to use the game within their budget and available timeframe. Additionally, it can be scaled from introductory to advanced levels and act as a relaxed and entertaining study tool. Students learn what changes in the cell can increase or decrease the action potential's ability to propagate down the axon and begin to describe different cable properties. Each player receives a card to keep track of the amplitude of their action potential. The goal is to move their game piece from the axon hillock to the axon terminal without decaying their action potential to 0. Players draw game cards that instruct them on where to move along the gameboard. The gameboard has color-coded spaces with changes in the axon. Students begin to quickly learn which changes in the cell allow their game piece to propagate forward as they compete with their peers to reach the axon terminal.

Modulatory effects of cancer stem cell-derived extracellular vesicles on the tumor immune microenvironment.

Cancer stem cells (CSCs), accounting for only a minor cell proportion (< 1%) within tumors, have profound implications in tumor initiation, metastasis, recurrence, and treatment resistance due to their inherent ability of self-renewal, multi-lineage differentiation, and tumor-initiating potential. In recent years, accumulating studies indicate that CSCs and tumor immune microenvironment act reciprocally in driving tumor progression and diminishing the efficacy of cancer therapies. Extracellular vesicles (EVs), pivotal mediators of intercellular communications, build indispensable biological connections between CSCs and immune cells. By transferring bioactive molecules, including proteins, nucleic acids, and lipids, EVs can exert mutual influence on both CSCs and immune cells. This interaction plays a significant role in reshaping the tumor immune microenvironment, creating conditions favorable for the sustenance and propagation of CSCs. Deciphering the intricate interplay between CSCs and immune cells would provide valuable insights into the mechanisms of CSCs being more susceptible to immune escape. This review will highlight the EV-mediated communications between CSCs and each immune cell lineage in the tumor microenvironment and explore potential therapeutic opportunities.

GMILT: A Novel Transformer Network That Can Noninvasively Predict EGFR Mutation Status.

Noninvasively and accurately predicting the epidermal growth factor receptor (EGFR) mutation status is a clinically vital problem. Moreover, further identifying the most suspicious area related to the EGFR mutation status can guide the biopsy to avoid false negatives. Deep learning methods based on computed tomography (CT) images may improve the noninvasive prediction of EGFR mutation status and potentially help clinicians guide biopsies by visual methods. Inspired by the potential inherent links between EGFR mutation status and invasiveness information, we hypothesized that the predictive performance of a deep learning network can be improved through extra utilization of the invasiveness information. Here, we created a novel explainable transformer network for EGFR classification named gated multiple instance learning transformer (GMILT) by integrating multi-instance learning and discriminative weakly supervised feature learning. Pathological invasiveness information was first introduced into the multitask model as embeddings. GMILT was trained and validated on a total of 512 patients with adenocarcinoma and tested on three datasets (the internal test dataset, the external test dataset, and The Cancer Imaging Archive (TCIA) public dataset). The performance (area under the curve (AUC) =0.772 on the internal test dataset) of GMILT exceeded that of previously published methods and radiomics-based methods (i.e., random forest and support vector machine) and attained a preferable generalization ability (AUC =0.856 in the TCIA test dataset and AUC =0.756 in the external dataset). A diameter-based subgroup analysis further verified the efficiency of our model (most of the AUCs exceeded 0.772) to noninvasively predict EGFR mutation status from computed tomography (CT) images. In addition, because our method also identified the "core area" of the most suspicious area related to the EGFR mutation status, it has the potential ability to guide biopsies.

How Does Neoliberalism Influence the Growing Trend of Higher Education?

With the election of Thatcher's Conservative government, the U.K. embarked on a reform of higher education that introduced the core idea of neoliberalism. The ensuing knowledge economy, university rankings, and the prevalence of accountability are all changes brought about by neoliberalism in higher education in the U.K. This study seeks to argue that while neoliberalism has brought some benefits to U.K. higher education, there are additional problems that cannot be ignored. These include the potential inability of graduates from disadvantaged backgrounds to access the career opportunities they desire, the claim of disempowerment by academics, and the disadvantaged status of humanities.

Use of a Modified Threshold Function in Fuzzy Cognitive Maps for Improved Failure Mode Identification

Fuzzy cognitive maps (FCMs) provide a rapid and efficient approach for system modeling and simulation. The literature demonstrates numerous successful applications of FCMs in identifying failure modes. The standard process of failure mode identification using FCMs involves monitoring crucial concept/node values for excesses. Threshold functions are used to limit the value of nodes within a pre-specified range, which is usually [0, 1] or [-1, + 1]. However, traditional FCMs using the tanh threshold function possess two crucial drawbacks for this particular.Purpose(i) a tendency to reduce the values of state vector components, and (ii) the potential inability to reach a limit state with clearly identifiable failure states. The reason for this is the inherent mathematical nature of the tanh function in being asymptotic to the horizontal line demarcating the edge of the specified range. To overcome these limitations, this paper introduces a novel modified tanh threshold function that effectively addresses both issues.

Assisted suicide in persons with mental disorders: a review of clinical-ethical arguments and recommendations.

Persons with mental disorders have the same right to self-determination as patients with somatic diseases, also regarding death and dying. However, there are several challenges that render persons with mental disorders especially vulnerable to inappropriate conduct of assisted suicide: their wish to die may be a symptom of their mental disease and not an autonomous choice, decision-making competence may be compromised by their illness and more difficult to assess, the severity of suffering may be more difficult to evaluate from an external perspective, the wish to die may be more variable over time and the prognostic uncertainty in mental illness makes it more difficult to determine whether the severe suffering is, in fact, treatment-resistant. After reviewing the clinical and ethical background of assisted suicide in persons with mental disorders, we assess each of these challenges to a medically and ethically justified practice of assisted suicide in mentally ill persons, based on relevant clinical and ethical literature. We conclude that the only ethically valid argument to exclude persons with mental disorders from suicide assistance is their potential inability to make a free, autonomous decision. However, the mentioned challenges should be taken into account in evaluating a person's request for assisted suicide and for promoting her well-informed and deliberated decision-making. In addition to assessing the person's decision-making capacity, the evaluation process should be guided by the goal to empower the person to make an autonomous choice between the available options. We conclude the paper with perspectives for a clinically and ethically justified practice of evaluating requests for assisted suicide in persons with mental disorders.

Enhancing sustainability integration in Sustainable Enterprise Resource Planning (S-ERP) system: Application of Transaction Cost Theory and case study analysis

Environmental stewardship and sustainability have become critical priorities in the contemporary business environment. Corporations are integrating sustainable practices at the business process level via Sustainable Enterprise Resource Planning (S-ERP). However, a recognised shortfall of S-ERP systems lies in their potential inability to integrate sustainability metrics across all business functions holistically. To navigate this limitation, our study introduces the application of Transaction Cost Theory (TCT). By treating business processes as input-output systems, we apply this theory to quantify sustainability likelihood and overall losses. This novel approach bridges the gap, allowing for the comprehensive integration of sustainability metrics across all processes. The essence of our methodology is to leverage static input data collected by S-ERP regarding environmental impact and to extrapolate this data to provide a broader understanding of potential losses and gains. We've tested and validated this approach through two comprehensive case studies; one is about sustainable product design and development, and the other is about the sustainability evaluation of modular versus conventional construction methods. The results inform the formulation of robust sustainability policies at the business process level for systems akin to S-ERP, paving the way for more sustainable business practices.

Active and Transfer Learning of High-Dimensional Neural Network Potentials for Transition Metals.

Classical molecular dynamics (MD) simulations represent a very popular and powerful tool for materials modeling and design. The predictive power of MD hinges on the ability of the interatomic potential to capture the underlying physics and chemistry. There have been decades of seminal work on developing interatomic potentials, albeit with a focus predominantly on capturing the properties of bulk materials. Such physics-based models, while extensively deployed for predicting the dynamics and properties of nanoscale systems over the past two decades, tend to perform poorly in predicting nanoscale potential energy surfaces (PESs) when compared to high-fidelity first-principles calculations. These limitations stem from the lack of flexibility in such models, which rely on a predefined functional form. Machine learning (ML) models and approaches have emerged as a viable alternative to capture the diverse size-dependent cluster geometries, nanoscale dynamics, and the complex nanoscale PESs, without sacrificing the bulk properties. Here, we introduce an ML workflow that combines transfer and active learning strategies to develop high-dimensional neural networks (NNs) for capturing the cluster and bulk properties for several different transition metals with applications in catalysis, microelectronics, and energy storage, to name a few. Our NN first learns the bulk PES from the high-quality physics-based models in literature and subsequently augments this learning via retraining with a higher-fidelity first-principles training data set to concurrently capture both the nanoscale and bulk PES. Our workflow departs from status-quo in its ability to learn from a sparsely sampled data set that nonetheless covers a diverse range of cluster configurations from near-equilibrium to highly nonequilibrium as well as learning strategies that iteratively improve the fingerprinting depending on model fidelity. All the developed models are rigorously tested against an extensive first-principles data set of energies and forces of cluster configurations as well as several properties of bulk configurations for 10 different transition metals. Our approach is material agnostic and provides a methodology to transfer and build upon the learnings from decades of seminal work in molecular simulations on to a new generation of ML-trained potentials to accelerate materials discovery and design.

Stem cell exosomes in the pathophysiology of cardiovascular diseases

This study focused on the current state of the therapeutic potential of extracellular vesicles, which depends on the methods of their isolation and composition and the characteristics of the vesicular and nonvesicular components. Myocardial damage, particularly as a result of acute myocardial infarction, leads to irreversible death of cardiomyocytes and sarcomeres and ultimately to heart failure. The adult heart has limited regenerative capacity; therefore, stimulation of endogenous repair and regenerative potentials using cell therapy has potential. Moreover, the benefit from the injection of stem cells and progenitor cells into the damaged myocardium is mediated by the factors they secrete. In particular, exosomes, nanosized secreted extracellular vesicles of endosomal origin, have become key signaling organelles in intercellular communication and are currently considered key regenerative components of the secretome of stem and progenitor cells. Exosomes released from cardiac embryonic and mesenchymal stem cells, resident stem and progenitor cells (including a specific subgroup of cardiosphere cells), induced pluripotent stem cells, and cardiomyocytes isolated from these cells have cardioprotective, immunomodulatory, and reparative abilities. The use of exosomes in the targeted transport of drugs in lipid-like nanocontainers and extracellular vesicles is another promising area. Because artificial drug carriers, including liposomes and lipid-based nanoparticles, are limited by potential toxicity, immunogenicity, and inability to target specific organs, exosomes hold good promise as potential drug carriers. Compounds can be transported both inside exosomes and on their surface. Secreted extracellular vesicles, particularly exosomes, can be considered a key functional component of the secretome of stem cells and cardiogenic progenitor cells (mesenchymal stem cells, endogenous cardiac progenitor cells, cardiospheres, bone marrow embryonic stem cells, and bone marrow induced pluripotent stem cells). They have demonstrated therapeutic efficacy in preclinical models in the study of cardiovascular pathology.

Deciphering core microbiota in rhizosphere soil and roots of healthy and Rhizoctonia solani-infected potato plants from various locations.

Black scurf caused by Rhizoctonia solani severely affects potato production. Through amplification of V3-V4 and ITS1-5f variable regions of 16S and internal transcribed spacer (ITS) rRNA, the study was based on the location (Kunming, Qujing, and Zhaotong), plant components (rhizosphere soil and roots), and sample types (healthy and diseased) to assess the diversity of bacterial and fungal communities. We found plant components significantly influence microbial diversity, with rhizosphere soil being more diverse than roots, and the microbial community in the root is mainly derived from the rhizosphere soil. Moreover, the rhizosphere soil and roots of healthy potato plants exhibit greater microbial diversity compared to those of potato plants infected by Rhizoctonia solani. Bacterial phyla Actinobacteriota and Acidobacteriota were enriched in rhizosphere soil compared to that of roots, whereas Proteobacteria and Cyanobacteria showed the opposite trend. Fungal phylum Ascomycota was found in low relative abundance in rhizosphere soil than in roots, whereas Basidiomycota showed the opposite trend. Bacterial genera including Streptomyces, Lysobacter, Bacillus, Pseudomonas, Ensifer, Enterobacter, and the Rhizobium group (Allorhizobium, Neorhizobium, Pararhizobium, Rhizobium), along with fungal genera such as Aspergillus, Penicillium, Purpureocillium, and Gibberella moniliformis, have the potential ability of plant growth promotion and disease resistance. However, most fungal species and some bacterial species are pathogenic to potato and could provide a conducive environment for black scurf infection. Interaction within the bacterial network increased in healthy plants, contrasting with the trend in the fungal network. Our findings indicate that R. solani significantly alters potato plant microbial diversity, underscoring the complexity and potential interactions between bacterial and fungal communities for promoting potato plant health and resistance against black scurf.

Simple fabrication of electrochemical sensor based on integration of dual signal amplification by the supporting electrode and modified nanochannel array for direct and sensitive detection of vitamin B2.

Development of simple and reliable sensor for detecting vitamin content is of great significance for guiding human nutrition metabolism, overseeing the quality of food or drugs, and advancing the treatment of related diseases. In this work, a simple electrochemical sensor was conveniently fabricated by modification a carbon electrode with vertically-ordered mesoporous silica film (VMSF), enabling highly sensitive electrochemical detection of vitamin B2 (VB2) based on the dual enrichment of the analyte by the supporting electrode and nanochannels. The widely used glassy carbon electrode (GCE), was preactivated using simple electrochemical polarization, The resulting preactivated GCE (p-GCE) exhibited improved potential resolution ability and enhanced peak current of VB2. Stable modification of VMSF on p-GCE (VMSF/p-GCE) was achieved without introducing another binding layer. The dual enrichment effect of the supporting p-GCE and nanochannels facilitated sensitive detection of VB2, with a linear concentration ranged from 20 nM to 7 μM and from 7 μM to 20 μM. The limit of detection (LOD), determined based on a signal-to-noise ratio of three (S/N = 3), was found to be 11 nM. The modification of ultra-small nanochannels of VMSF endowed VMSF/p-GCE with excellent anti-interference and anti-fouling performance, along with high stability. The constructed sensor demonstrated the capability to achieve direct electrochemical detection of VB2 in turbid samples including milk and leachate of compound vitamin B tablet without the need for complex sample pretreatment. The fabricated electrochemical can be easily regenerated and has high reusability. The advantages of simple preparation, high detection performance, and good regeneration endow the constructed electrochemical sensor with great potential for direct detection of small molecule in complex samples.

Assembly of Titanium‐oxo Clusters from Embonic Acid‐Modified {Ti2} Molecular Building Blocks with Efficient Sulfur‐Catalyzed Oxidation Activities

AbstractTo design and synthesize high‐nucleated titanium‐oxo clusters rationally based on molecular building blocks with potential coordination ability is still a great challenge. In this work, a family of titanium‐oxo clusters, including Ti2, Ti6, Ti12, and Ti20 clusters, have been assembled from large‐size ligand embonic acid‐modified {Ti2} molecular building blocks under solvothermal conditions. By modulating auxiliary linkers and solvent types, the {Ti2} building blocks exhibit varying degrees of polymerization to form high‐nuclear titanium‐oxo clusters. Notably, Ti20 with high nucleation, as the decamer containing {Ti2} building blocks, can act as a stable and heterogeneous catalyst to efficiently catalyze the oxidation of sulfides to sulfones and sulfoxides when using H2O2 as the green oxidant. This study provides a new approach for the design and synthesis of highly nucleated and homometallic TOCs based on {Ti2} units.

Oleic Acid Could Act as a Channel Blocker in the Inhibition of nAChR: Insights from Molecular Dynamics Simulations.

The activation of the muscular nicotinic acetylcholine receptor (nAChR) produces the opening of the channel, with the consequent increase in the permeability of cations, triggering an excitatory signal. Free fatty acids (FFA) are known to modulate the activity of the receptor as noncompetitive antagonists, acting at the membrane-AChR interface. We present molecular dynamics simulations of a model of nAChR in a desensitized closed state embedded in a lipid bilayer in which distinct membrane phospholipids were replaced by two different monounsaturated FFA that differ in the position of a double bond. This allowed us to detect and describe that the cis-18:1ω-9 FFA were located at the interface between the transmembrane segments of α2 and γ subunits diffused into the channel lumen with the consequent potential ability to block the channel to the passage of ions.

Cuttlebone-inspired honeycomb structure realizing good out-of-plane compressive performances validated by DLP additive manufacturing

Inspired by cuttlebone microstructure, this study proposes a novel bionic square-tube cuttlebone-inspired honeycomb (S-CIH) structure prepared by digital light processing (DLP) method. Fifteen configurations of honeycombs of same mass including S-CIH, regular honeycomb (S-RH) and sinusoidal honeycomb (S-SH) structures are evaluated and compared in terms of the deformation behavior and compressive performances under out-of-plane compression, especially energy-absorbing capacity. The results indicate that the S-CIH structure achieves the improved energy absorption 8.859 J/g, over 40 % and 60 % than that of S-RH and S-SH structure with same relative density and mass, respectively. The mechanism analysis of the asymmetric corrugated walls disposed in cross and parallel in S-CIH, reveals that the magnitude of amplitude determines the buckling mode thus folding mode of walls, and the magnitude of amplitude gradient determines whether the whole honeycomb structure can achieve a progressive failure. Optimal structural ratio can enable S-CIH delay the complete failure and improve the long-term failure-resistance. This indicates that S-CIH design has the potential ability to strengthen and toughen other brittle matrix materials, and the cuttlebone-like architecture provides a new biological design of mechanically efficient meta-honeycomb beyond conventional honeycomb structure.

Brigatinib, a newly discovered AXL inhibitor, suppresses AXL-mediated acquired resistance to osimertinib in EGFR-mutated non-small cell lung cancer.

In addition to the classical resistance mechanisms, receptor tyrosine-protein kinase AXL is a main mechanism of resistance to third-generation epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI) osimertinib in EGFR-mutated non-small cell lung cancer (NSCLC). Developing an effective AXL inhibitor is important to sensitize osimertinib in clinical application. In this study we assessed the efficacy of brigatinib, a second-generation of anaplastic lymphoma kinase (ALK)-TKI, as a novel AXL inhibitor, in overcoming acquired resistance to osimertinib induced by AXL activation. We established an AXL-overexpression NSCLC cell line and conducted high-throughput screening of a small molecule chemical library containing 510 anti-tumor drugs. We found that brigatinib potently inhibited AXL expression, and that brigatinib (0.5 μM) significantly enhanced the anti-tumor efficacy ofosimertinib (1 μM) in AXL-mediated osimertinib-resistant NSCLC cell lines in vitro. We demonstrated that brigatinib had a potential ability to bind AXL kinase protein and further inhibit its downstream pathways in NSCLC cell lines. Furthermore, we revealed that brigatinib might decrease AXL expression through increasing K48-linked ubiquitination of AXL and promoting AXL degradation in HCC827OR cells and PC-9OR cells. In AXL-high expression osimertinib-resistant PC-9OR and HCC827OR cells derived xenograft mouse models, administration of osimertinib (10 mg·kg-1·d-1) alone for 3 weeks had no effect, and administration of brigatinib (25 mg·kg-1·d-1) alone caused a minor inhibition on the tumor growth; whereas combination of osimertinib and brigatinib caused marked tumor shrinkages. We concluded that brigatinib may be a promising clinical strategy for enhancing osimertinib efficacy in AXL-mediated osimertinib-resistant NSCLC patients.

Transport platform stabilization mechanism using controlled suspension

In this paper the authors present the development of a novel transport mechanism designed to perform gravity survey work on difficult terrain. Thus, in the first part of the paper the authors present review and analysis of existing patents and commercial developments of transport platforms capable of stabilizing loads while travelling in urban environments with potholes and steps. In the second part we present technical solution of a steerable suspension with a torsion bar as an elastic element based on the elimination of the drawbacks of the existing developments. The core feature of this development is the potential ability to adapt the suspension to different types of surfaces by changing the elastic characteristics of the torsion bar. We also propose an alternative to the generally accepted kinematic scheme by using a conical gearbox, which allows to achieve a tight arrangement of suspension mechanisms within the dimensions of the transport platform. In addition, authors propose the stabilization mechanism, that allows to change the clearance of the transport platform and provides stabilization of the gravity exploration research equipment, characterized by sensitivity to deviations from the vertical.

Early warning indicators via latent stochastic dynamical systems.

Detecting early warning indicators for abrupt dynamical transitions in complex systems or high-dimensional observation data are essential in many real-world applications, such as brain diseases, natural disasters, and engineering reliability. To this end, we develop a novel approach: the directed anisotropic diffusion map that captures the latent evolutionary dynamics in the low-dimensional manifold. Then three effective warning signals (Onsager-Machlup indicator, sample entropy indicator, and transition probability indicator) are derived through the latent coordinates and the latent stochastic dynamical systems. To validate our framework, we apply this methodology to authentic electroencephalogram data. We find that our early warning indicators are capable of detecting the tipping point during state transition. This framework not only bridges the latent dynamics with real-world data but also shows the potential ability for automatic labeling on complex high-dimensional time series.