AI Scientific Illustration Gallery

Generate a research material using GEO satellite data.

A green composite photothermal oil-absorbing material made of polyaniline and cuttlebone is irradiated by the sun on an oil-spill-affected sea surface. The material heats up, melts the solid oil, and then adsorbs it.

Based on the core content of your article (a novel fluorinated surfactant system based on β-cyclodextrin-polyethylene glycol conjugates for stabilizing IL/scCO₂ microemulsions), here's a professional, clear, and visually appealing graphical abstract design concept. --- ### I. Core Design Concept Use a **"left-to-right" visual narrative flow** to summarize the entire study: **Molecular Design → Interfacial Assembly → Functional Application**. Ensure the graphic is clearly discernible in a single-column width. ### II. Suggested Composition (Three-Part Layout) **Part 1 (Left): Molecular Structure and Design** * **Visual Elements**: 1. Draw a simplified model of **β-cyclodextrin (β-CD)** (like a truncated cone or cylinder, using lines to represent glucose units). 2. Within the cavity of β-CD, embed a simplified model of the **imidazolium cation ([Bmim]⁺)**, highlighting the butyl chain extending into the cavity, using **dashed lines** or a **glowing effect** to illustrate the **host-guest inclusion interaction**. 3. From one end of β-CD, extend a **wavy line** representing the **PEG chain**, and label it "PEG" or "CO₂-philic" at the end. 4. Draw a simplified structural formula of the **BCA molecule** next to it, and use an **arrow** pointing to β-CD to indicate that it is included as a "guest." 5. Near the PEG chain or on BCA, schematically add the functional group symbols for **thiol (-SH)** and **acrylate (C=C)**, laying the groundwork for dynamic crosslinking. * **Caption/Title**: **Molecular Design: Host-Guest Surfactant Conjugate** **Part 2 (Middle): Interfacial Assembly and Reinforcement** * **Visual Elements**: 1. Draw a clear **interface**, with the upper part using a light blue background and CO₂ molecule models (•) to represent the **scCO₂ phase**, and the lower part using a light green or yellow background to represent the **ionic liquid (IL) phase**. 2. At the interface, arrange multiple **β-CD-PEG molecules from Part 1**, with their β-CD heads immersed in the IL phase (including [Bmim]⁺) and their PEG tails extending into the scCO₂ phase. 3. Crucially: between these arranged molecules, draw a **network structure of covalent bonds** (which can be achieved by connecting thiol and acrylate sites with short chains), forming a **"crosslinked network"** covering the interface. 4. Label the network with ***G′ > G″*** or **Elastic Film**

Lats1-AKO and Lats1^f/f^ mice were fed a high-fat diet for 12 weeks, followed by local injection of AAV-BST2 into subcutaneous adipose tissue. The high-fat diet was continued until week 24. Please draw a schematic diagram of the experimental design.

PPT Layout Suggestions (Horizontal Design Recommended): You can build the PPT according to the following steps: Step 1: Establish a Timeline Draw a horizontal line with an arrow. Label the time points below the line: Day -5, Day 0, Day 14. Step 2: Add Intervention Boxes [Day -5 to Day 0]: Draw a rectangular box above the line. Text: Abx Pre-treatment (5 days) Icon: Draw a medicine bottle or capsule (search for "Medicine" in PPT's built-in icons). [Day 1 to Day 14]: Draw two rectangular boxes side by side above the line (or one large box with two lines of text). Text 1: FMT (Daily gavage) Text 2: 5-FU Modeling (i.p. injection) Icon: Draw a syringe. Step 3: Add Endpoint Assessments [After Day 14]: Draw an endpoint box at the end of the line. Text: Behavioral Tests (e.g., MWM, NOR) & Sacrifice. Icon: Draw a maze or brain icon.

Recommended colors for plotting.

The mechanism of a green composite photothermal oil-absorbing material made of polyaniline and cuttlebone is described. Upon solar irradiation on an oil spill at sea, the material heats up, melting the solid oil, which is then adsorbed. The microstructure of the cuttlebone exhibits a regular square pore structure.

Figure Design Proposal: Integrated Flowchart Figure Title: Integrated Computational and Experimental Strategy for Identifying Upstream Transcriptional Regulators of Fgf4 Design Concept: Integrates a "left-to-right process narrative" with a "top-to-bottom results presentation," merging strategy and results. Employs professional colors, high information density, and clear visual guidance. Detailed Explanation and Enhancement Points for Each Module: Left Side - Strategy Flowchart Area Design: Use grayscale or low-saturation color blocks and arrows to clearly define steps, reflecting the methodology. Add small icons (such as a DNA double helix or magnifying glass) next to the "MEME Prediction" and "TOMTOM Comparison" steps to enhance recognition. Key Points: Indicate key parameters, such as sequence range, MEME's E-value threshold, and the name of the database used. Middle - Results Visualization Area Top (MEME Result Display): Sequence Logos: Display the predicted Top 3 motifs side-by-side in high-definition, colorful sequence logo format. This is the core result of the MEME analysis and must be visually appealing. Annotation: Clearly label the MEME E-value and width below each logo. Bottom (TOMTOM Results and Filtering): Bar Chart: Plot the top 3-5 candidate transcription factors with the highest significance after TOMTOM comparison as a horizontal bar chart, using their matching -log10(p-value) as the metric. Design: Use professional color schemes (such as viridis or Set2 color palettes). Arrange bars in descending order from left to right by value, and directly label the factor name (e.g., KLF5) and specific p-value at the end of the bar. Filtering Path: To the right of the bar chart, use a funnel icon or filtering icon to point to the final 1-2 "core candidate factors," highlighting them with different colors or star markers. Right Side - Experimental Validation Bridge Area Design: Delineate with dashed boxes or light-colored backgrounds to indicate this is the next step guided by prediction. Content: Briefly illustrate subsequent key validation experiments with icons and text, such as ChIP-qPCR (magnetic beads and DNA icons) and reporter gene assays (luciferase icon), with arrows pointing to "validated regulators." Function: This module greatly enhances the scientific integrity and depth of the figure, indicating that the research goes beyond computational prediction and completes closed-loop validation.

A schematic diagram illustrating the synthesis of a polyaniline/cuttlebone composite material is presented. The specific experimental steps are as follows: (1) Pretreatment of Raw Materials: The raw cuttlebone material is initially immersed in a 25% ethanol solution to remove impurities. Subsequently, the material is rinsed multiple times with distilled water to eliminate any residual ethanol. The purified cuttlebone material is then dried in a 40°C constant temperature drying oven for 24 hours to obtain a dry and pure material, preparing it for subsequent composite formation. Afterwards, the cuttlebone is immersed in a 3.2g/L dopamine solution in 10 mM Tris buffer within a suction flask, vacuumed, and soaked for 6 hours. (2) Synthesis of Polyaniline/Cuttlebone Composite: 150 mL of deionized water, 2 mL of aniline, and 12 g of boric acid are sequentially added to a 250 mL round-bottom flask and stirred for 1 hour to obtain solution A. Next, 5 g of ammonium persulfate is dissolved in 10 mL of deionized water to obtain solution B. Finally, 5 g of the purified cuttlebone and solution B are added to solution A, vacuumed for 0.5 hours, and allowed to stand for 12 hours. After standing, the sample solution is filtered using a vacuum pump and washed multiple times with deionized water and ethanol to remove unreacted aniline salts and other impurities. The resulting solid product is then placed in a petri dish to air dry naturally until completely dry, yielding the composite material. (3) PDMS/PANI Modification: A specific amount of polydimethylsiloxane prepolymer and curing agent are added to ethyl acetate at a weight ratio of 10:1 and stirred thoroughly to form a homogeneous solution (2 mg/mL). The PANI-modified cuttlebone is then immersed in the solution for 10 minutes. Finally, the modified sponge is cured at 100 degrees Celsius for 1 hour. The experimental flowchart should be concise.

A schematic diagram illustrating the synthesis of a polyaniline/cuttlebone composite material. The specific experimental steps are as follows: (1) Pretreatment of raw materials, including impurity removal and polydopamine coating: First, the dried cuttlebone material is immersed in a 25% ethanol solution to remove non-target substances from the raw material. Then, the immersed material is repeatedly rinsed with distilled water to ensure the removal of any residual ethanol solution. Finally, the purified cuttlebone material is dried in a 40 °C constant temperature drying oven for 24 h to obtain dry and pure cuttlebone material, preparing it for subsequent composite formation. Subsequently, the cuttlebone is immersed in a 3.2 g/L dopamine solution in 10 mM Tris buffer within a suction flask, vacuumed, and soaked for 6 hours. (2) Synthesis of the Polyaniline/Cuttlebone Composite: Add 150 mL of deionized water, 2 mL of aniline, and 12 g of boric acid sequentially to a 250 mL round-bottom flask, and stir for 1 h to obtain solution A. Next, dissolve 5 g of ammonium persulfate in 10 mL of deionized water to obtain solution B. Finally, weigh 5 g of the purified cuttlebone and add it along with solution B to solution A. Apply vacuum for 0.5 h, then allow to stand for 12 h. After standing, filter the sample solution using a vacuum pump and wash it repeatedly with deionized water and ethanol solution to remove unreacted aniline salts and other impurities. After washing, place the resulting solid product in a Petri dish and allow it to air dry naturally. Once the solid is dry, the composite material is obtained. (3) PDMS/PANI Modification: A certain amount of polydimethylsiloxane prepolymer and curing agent are added to ethyl acetate at a weight ratio of 10:1 and stirred thoroughly to form a homogeneous solution (2 mg/mL). Then, the PANI-modified cuttlebone is immersed in the above solution for 10 min. Finally, the modified sponge is cured at 100 degrees Celsius for 1 h.

Schematic diagram of a moisture-wicking fabric, showing rapid spreading of moisture through grooves on the fibers and channels between the fibers, diffusing outwards. The image should be simple and elegant, with minimal color difference and text.

Three-stage progressive research: from basic mechanism analysis → novel molecule design → formulation development and application verification, with each stage building upon the previous, ultimately achieving efficient skin vitrification cryopreservation. Stage 1: Analysis of the Structure-Activity Relationship and Regulatory Mechanism of Vitrification Agents Core Objective: To elucidate the "structure-property" relationship and molecular synergistic mechanism of vitrification agents. Research Content and Methods: Basic Characterization of Vitrification Performance: Determine the critical vitrification concentration and analyze vitrification transition characteristics using differential scanning calorimetry. Molecular Mechanism Simulation: Computer simulation (molecular structure optimization, energy minimization, electrostatic potential distribution, interaction energy calculation, hydration and water molecule residence time analysis). Stage Output: Model of the structure-activity relationship and synergistic regulatory mechanism of vitrification agents. [Suggested Illustration]: Molecular structure model + schematic diagram of energy/hydration interaction Stage 2: Design and Synthesis of Novel Vitrification Molecules Based on Structure-Activity Relationship Core Objective: To establish a novel vitrification molecule design strategy and obtain high-performance candidate molecules. Research Content and Methods: Molecular Design and Synthesis: Design and chemically synthesize novel vitrification molecules based on the structure-activity relationship from Stage 1. Structure and Performance Verification: Characterize the molecular structure using infrared spectroscopy, nuclear magnetic resonance (hydrogen/carbon NMR), and high-resolution mass spectrometry; test its vitrification performance (critical cooling/heating rate) and ice crystal inhibition ability (ice nucleation/growth, recrystallization inhibition). Stage Output: Candidate molecules with excellent vitrification and ice crystal inhibition properties. [Suggested Illustration]: Molecular design flowchart + structural characterization spectra + microscopic images of ice crystal inhibition Stage 3: Development of Efficient Cryoprotectant Formulations and Verification of Skin Cryopreservation Effect Core Objective: To optimize cryoprotectant formulations, establish a skin vitrification cryopreservation protocol, and verify its effectiveness. Research Content and Methods: Formulation and Process Optimization: Optimize cryoprotectant formulations based on the candidate molecules from Stage 2; test the permeability of protective agents and develop loading/unloading protocols. Skin Cryopreservation and Evaluation: Design a skin vitrification cryopreservation procedure and evaluate the cryopreservation effect through cell viability tests, histological staining, and mechanical property analysis. Stage Output: Efficient vitrification cryoprotectant formulation and optimized skin cryopreservation protocol. [Suggested Illustration]: Schematic diagram of formulation optimization + skin tissue sections + mechanical property curves Progressive Relationship Stage 1 provides the "structure-property" design basis for Stage 2, Stage 2 provides the core functional molecules for Stage 3, and Stage 3 verifies the application effect of the entire process, forming a "mechanism-design-application" closed loop. Illustration Style: Clear flowchart, with three stages distinguished by color, arrows indicating progressive logic, and key nodes accompanied by simplified schematic diagrams.

SARS-CoV, MERS-CoV, SARS-CoV-2, Dengue, Zika, bats, camels, pigs, civets, monkeys, Aedes mosquitoes, humans

Image depicting the disruption of the HBV viral envelope lipid layer, resulting in increased exposure of surface antigens and a greater number of antibody binding sites.

**Title:** Dissecting Host Protein Interactions Mediating LNP Lysosomal Escape Using Proximity Labeling Technology **Key Scientific Questions:** 1. How do the lipid components of LNPs specifically influence their lysosomal escape efficiency? Are there key lipid structural features that determine escape efficiency? 2. Which host cell proteins functionally interact with LNPs at the critical spatiotemporal nodes of lysosomal escape? How do these interacting proteins mediate or hinder the escape process? **Research Methods:** First, a multi-component lipid library will be constructed and screened in LLC-luc cells using the Siluc reporter system to identify "high-escape" and "low-escape" LNPs with significantly different lysosomal escape efficiencies. The subcellular localization of LNPs will be dynamically tracked using confocal microscopy to precisely determine their escape time window. Subsequently, at the critical time point of escape, Ce6 photoactivated proximity labeling technology will be used to spatiotemporally label host proteins proximal to LNPs, and interacting protein groups will be identified by mass spectrometry analysis. Candidate regulatory proteins will be screened by comparing the differential interacting proteins of high and low escape LNPs. Finally, CRISPR-Cas9 gene knockout technology will be used to verify the functional role of key proteins in LNP lysosomal escape. **Expected Conclusions:** This study is expected to establish a direct correlation map between LNP lipid composition and lysosomal escape efficiency, revealing key lipid chemical features that affect escape efficiency. For the first time, the dynamic interaction network between LNPs and host proteins will be captured at the precise spatiotemporal node of lysosomal escape, and several key host factors (such as specific membrane fusion proteins, lipid transfer proteins, or ion channels) that mediate or hinder the escape process will be identified. These findings will elucidate the molecular mechanism of LNP lysosomal escape, provide a theoretical basis and new engineering targets for the rational design of efficient delivery systems, and promote the development of nucleic acid drug delivery technology.

Microalgae-Derived Biomaterials Applications: This illustration visually represents the diverse applications of biomaterials derived from microalgae. The central image depicts microalgae, branching out to showcase its use in drug delivery (e.g., a localized drug release system), wound healing (e.g., a hydrogel patch applied to a wound), and tissue regeneration (e.g., a scaffold promoting new tissue growth).

Low concentrations of compound Af can stimulate tumor-associated macrophages (TAMs) to adopt an M1-like anti-tumor phenotype. Furthermore, the 'find me' and 'eat me' signals released by Af-induced glioblastoma multiforme (GBM) cells can further enhance the killing of GBM cells by M1-like TAMs. Af can exert toxicity on GBM cells through this TAM-dependent effect. High concentrations of Af disable TAMs and disrupt the interaction between GBM cells and TAMs, such as the positive feedback loop mediated by IL-6/STAT3, thereby eliminating its pro-GBM functions. By targeting GBM with Af-loaded platelets for Af delivery, it can synergize with chemotherapy and immunotherapy to produce anti-GBM efficacy. By loading Af together with the sonosensitizer fluorescein (Flu) onto platelets and then combining these dual-loaded platelets with directional ultrasound irradiation of GBM, Af and Flu can be targeted and actively delivered to GBM through 'ultrasound-controlled release', thereby enhancing the activity of Flu-mediated GBM sonodynamic therapy (GBM-SDT).

A schematic diagram illustrating the molecular mechanism by which the extracellular matrix protein Fibronectin (FN) promotes viral infection. FN interacts with the grass carp reovirus (GCRV) outer capsid protein VP7 and the host membrane receptor protein ITGB1, activating the NF-κB signaling pathway and cytoskeletal protein rearrangement, inducing the formation of "pseudopodia" on the cell surface, and promoting viral infection. The pro-viral mechanism of FN is evolutionarily conserved and also applies to other aquatic viruses such as SVCV and KHV, as well as the mammalian virus VSV.

As an expert in scientific illustration, create a scientific journal abstract figure in the style of top journals such as *Nature* or *Nature Materials*. The image should be connected by a "technical evolution arrow" running from the bottom left to the top right. The overall background should be a clean, light gray gradient. The style should be high-precision 3D scientific visualization, with accurate atomic/molecular structures, realistic material textures, and a minimalist yet technological feel. Use a coordinated color scheme: warm orange/red tones for the problem panel, blue/cyan tones for the mechanism panel, and green/purple tones for the solution panel. * **Top Left: MEMS Device Friction and Wear Failure:** A detailed 3D cross-sectional view of a silicon-based MEMS device, with a magnified view of the contact interface between two movable micro-components (micro-beams). Show "friction" and "wear" effects at the contact interface. * **Bottom Left: Hydration Lubrication Mechanism:** The upper part shows a silicon substrate surface grafted with dense, mushroom-shaped zwitterionic polymer brushes (poly(sulfobetaine methacrylate), PSBMA). The ends of the polymer brushes have positive and negative charge centers (represented by "+" and "-" spheres). Around the polymer brushes, draw a thin layer of blue translucent water molecules (H2O) in a ball-and-stick model, forming a thin "hydration layer". The hydration layer deforms under the pressure of the micro-beam, and water molecules are squeezed out at the compressed location. * **Right: Ionic Liquid Enhanced Lubrication Mechanism:** The upper part also shows a substrate grafted with polymer brushes. Show three types of brushes side by side: zwitterionic brushes (PSBMA), cationic brushes (poly(2-(methacryloyloxy)ethyltrimethylammonium chloride), PMETAC), and anionic brushes (poly(3-sulfopropyl methacrylate potassium salt), PSPMA). Distinguish each brush with slightly different colors and charge labels. Around the brushes, positively charged imidazolium cations ([BMIM]+) and negatively charged hexafluorophosphate anions ([PF6]-) are strongly attracted and enriched by the oppositely charged polymer brush sites. These ionic liquid molecules are closely and orderly arranged, forming a thick, dense, and colorful "ionic liquid lubrication layer" that completely fills the contact gap. The ionic liquid layer deforms slightly under the pressure of the micro-beam, and the ionic liquid layer remains continuous at the compressed location. * **Bottom (Experimental Validation):** Place a simple line graph with "Load" or "Time" on the X-axis and "Coefficient of Friction" on the Y-axis. Show two lines: a high, fluctuating line (labeled "Water Lubrication") and a significantly lower and flatter line (labeled "Ionic Liquid Lubrication"). * **Icons and Legends:** Add a simple legend at the bottom to explain the meaning of molecules, charges, and data plots.

APPROVED Conceptual schematic illustrating the workflow for identifying and validating potential therapeutic targets of Polyphyllin III in breast cancer. The schematic, designed in a Nature-style format with a clean white background and horizontal left-to-right flow, begins with public breast cancer cohorts and clinical datasets, such as TCGA and METABRIC, represented by database icons and patient silhouettes. A central module depicts the bioinformatic integration of subtype-stratified gene expression analysis, focusing on potential targets including HER2, STAT3, Bcl-2 family proteins, and HIF-1α, across Luminal A, Luminal B, HER2-enriched, and TNBC subtypes. Conceptual association links connect target expression levels with clinical endpoints, specifically overall survival and recurrence-free survival, using abstract survival curve icons. A parallel branch illustrates a comparison between paclitaxel-sensitive and paclitaxel-resistant cohorts, highlighting differential target expression and its association with therapy response. The schematic emphasizes the clinical relevance of these findings.

2.2 Elucidate the key molecular mechanisms of polyphyllin III's action on different subtypes of breast cancer cells. ① Based on previous sensitivity difference results, select four cell lines: MCF-7 (Luminal A), BT-474 (Luminal B), SK-BR-3 (HER2-enriched), and MDA-MB-231 (TNBC). Use RNA-seq technology to systematically compare the transcriptome changes of each subtype before and after polyphyllin III treatment. Focus on the enrichment of apoptosis, oxidative stress, and subtype-specific signaling pathways (such as ER, HER2, STAT3), and screen for key differentially expressed genes. Combine Western blotting and qRT-PCR to verify the expression changes of candidate targets (such as Bcl-2 family members, NOXA, PUMA, EGFR, p-STAT3, etc.), and identify the core effector molecules with the most significant response in each subtype. ② Conduct functional intervention experiments targeting the dominant mechanisms of each subtype: In TNBC, set up a control group, a polyphyllin III group, an NAC (ROS scavenger) group, and a polyphyllin III + NAC group to detect apoptosis (Cleaved Caspase-3/PARP), ROS levels, mitochondrial membrane potential, and JNK/p38 phosphorylation status. In HER2⁺ cells, set up a control group, a polyphyllin III group, and a HER2 overexpression/knockdown group to evaluate HER2-AKT/ERK pathway activity and cell survival rate. In Luminal A cells, combine ER inhibitors (fulvestrant) or ERα siRNA to observe whether drug resistance is reversed. At the same time, evaluate functional phenotypic changes through Annexin V/PI flow cytometry, cell cycle analysis, and scratch/Transwell experiments. ③ Construct stable knockout or overexpression cell lines of key node genes (such as HIF-1α, STAT3, or HER2) to verify their necessity in polyphyllin III-induced cell death, signal inhibition, and phenotypic changes, and detect related protein ubiquitination or subcellular localization changes, thereby establishing the core molecular mechanism axis driving drug efficacy differences in each subtype.

The specific measures for the research on multi-modal data intelligent analysis to empower the reform and practice of university teaching mode are as follows: To ensure the realization of the research objectives, this project will focus on three core levels: "data foundation construction, analysis method research, and teaching practice closed-loop", which respectively correspond to solving the black box problem of teaching evaluation, the dormant problem of teaching data, and the open-loop problem of teaching optimization. The overall research framework is shown in Figure 1, and the specific measures for step-by-step implementation include: (1) Constructing a unified and standardized multi-modal teaching data base. First, we will focus on opening up and managing the data scattered in smart classrooms. The core task is to formulate and implement the "Teaching Multi-modal Data Governance and Privacy Security Specification" to systematically clean, desensitize, and spatio-temporally align the original data such as classroom videos, audios, courseware, and interactive texts. On this basis, relying on data lake warehouse technology, we will build a standardized and safely shareable teaching theme database. This database not only realizes the centralized storage and efficient management of data, but also ensures that all data applications are carried out within the compliance framework through strict data security protocols, providing a solid and reliable data foundation for subsequent intelligent analysis. (2) Developing intelligent analysis tools that deeply integrate with educational theories. The focus of this stage is to transform cutting-edge information technology into analytical tools with educational explanatory power. We will systematically introduce models in the fields of computer vision and natural language processing, and deeply adapt and innovatively apply them to educational scenarios. The specifics include: ① Dynamic analysis of teaching behavior: Going beyond simple "head-up rate" statistics, using pose recognition technology to analyze the dynamic changes of student group behavior patterns (such as listening, writing, and collaboration) under specific teaching events (such as group discussions and teacher questions), and visualize the teacher's classroom movement trajectory and interaction range. ② Classroom cognitive level assessment: Applying natural language processing technology to deeply analyze the transcribed teacher-student dialogue text to realize automated identification of the cognitive level of questions and construction of the logical structure map of classroom discussions, so as to quantitatively evaluate the depth and quality of thinking in classroom dialogues. The final result will be reflected in a set of interactive visualization dashboards embedded in the teaching process, providing teachers with intuitive and easy-to-understand "classroom teaching analysis reports" to help them reflect on their teaching. (3) Carry out data-based teaching practice closed-loop iteration and effect verification. In order to promote the effective transformation of analytical results into teaching productivity, we will form a "research-practice community" with front-line teachers and carry out empirical research using action research methods. By selecting typical courses in engineering majors, we will work with cooperative teachers to jointly establish an iterative closed loop of "data feedback-teaching intervention-effect evaluation". We will regularly provide teachers with data analysis reports and organize joint seminars to jointly interpret data, diagnose teaching problems, and design and implement precise teaching intervention strategies (such as optimizing question design and adjusting interaction methods). By systematically comparing the process data (behavioral and cognitive indicators), outcome data (academic performance), and subjective feedback (teacher-student surveys and reflections) before and after the intervention, we will comprehensively verify the actual effect of data analysis-driven teaching improvement, and continuously optimize the analysis model and method in the iteration. Through the above measures

Please create an editable theoretical model diagram. The overall layout and flow should be a horizontal flow from left to right, clearly demonstrating the causal logic chain from "Input" to "Environment" to "Outcomes." Divide the canvas horizontally into three main areas: "Input," "Environment," and "Outcomes," corresponding to the control variables, core intervention processes, and learning development effects in the theoretical framework, respectively. Specific elements and drawing steps: Draw the "Input" section: On the far left of the canvas, draw a rectangular box titled "Input: Control Variables." The box can be divided into two to three sub-items, such as "Family Background (e.g., parental education level, socioeconomic status)," "Demographic Characteristics (e.g., gender, discipline)," and "Prior Academic Foundation (High School)." These elements are arranged in parallel, indicating that they are antecedent variables that need to be controlled in the research model. Draw the "Environment" section: In the center of the canvas, first draw a prominent shape (e.g., rounded rectangle or diamond) representing the "National Scholarship Award Event" as the core intervention starting point in the environment. From this shape, draw a large dashed rectangular box to the lower right, titled "Dynamic Changes in Capital Structure (Core Mediating Mechanism)." Within this dashed box, draw four parallel rightward-pointing arrows representing "Cultural Capital Appreciation (Internalized/Objectified/Institutionalized Forms)," "Social Capital Expansion (Networks/Resources)," "Economic Capital Increment," and "Symbolic Capital Endowment (Honor/Label)." These four rectangles should be distinguished using slightly different colors or shades to indicate that they are parallel and interconnected processes. In the "Environment" section, draw an arrow from the "National Scholarship Award Event" pointing downwards to this "Dynamic Changes in Capital Structure" dashed box, indicating that the event triggers the subsequent capital transformation process. Draw the "Outcomes" section: On the far right of the canvas, draw a large rectangular box titled "Outcomes: Learning Development Effects." Within the box, arrange three sub-boxes in parallel from top to bottom, representing "Cognitive Development (e.g., knowledge exploration, ability development, clarity of career goals)," "Affective Development (e.g., self-efficacy, social responsibility, psychological resilience)," and "Behavioral Development (e.g., academic achievement, output of results, self-directed learning)." These three sub-boxes should use a coordinated color scheme to reflect that they belong to the same dimension of effect but are at different levels. Connect the core paths: From the "Dynamic Changes in Capital Structure" dashed box in the "Environment" section, draw three arrows (or use one main arrow and then branch it) pointing to the three effect sub-boxes in the "Outcomes" section, clearly indicating that changes in the three major capitals are the direct mediating mechanism leading to the development of cognitive, affective, and behavioral dimensions.

A graphical abstract illustrating BPS-induced neurotoxicity in mice via the gut-brain axis.