SERVICES & DELIVERABLES / A CLOSER LOOK
From understanding papers and literature to expressing mechanisms, data, and motion. Explore reading notes, scientific visuals, and the materials and methods behind each handoff.
SELECTED VISUAL STUDIES
From tissue environments to cellular mechanisms, from source tables to research figures. Detail, depth, and a considered reading order give the work a clear form.
Browse the collection · Open a complete figure
Establish the tissue context before moving into detail.
Connect the tissue environment, cellular interface, and porous carrier in one composition, keeping the wider story and fine detail in view.
Cargo begins outside the membrane; establish inside and outside.
Follow extracellular cargo, membrane invagination, and an enclosed vesicle. Structure, sequence, and annotations form separate visual layers.
Open pores and fiber orientation establish the material structure.
Build hierarchy with a porous fiber network, adherent cells, and an enlarged interface. Material, space, and annotation explain the subject together.
Log-scaled concentrations and four-parameter curves reveal the response range.
Bring concentrations, individual observations, four-parameter curves, and residuals into one coherent figure, all connected to the source data.
Show effect size alongside BH-adjusted statistical evidence.
Volcano, expression heatmap, and PCA views communicate changes, patterns, and sample relationships. Specialist analysis starts with a feasibility review.
Original scientific visuals and synthetic-data figures illustrate the approach; they are not client projects or experimental results.
Discuss a similar visualization01 / UNDERSTANDING THE RESEARCH
Paper reading, literature themes, figure analysis, and reading notes. Start with your question, separating the authors’ conclusions, supporting evidence, and unresolved points.
Jumper et al. / NATURE / 2021
How can protein structure be predicted from amino-acid sequence? Identify the task before tracing the validation.
SOURCE LOCATION / Abstract · Fig. 1Structured paper reading, methods and figure reasoning, comparisons across papers, journal-club notes, and question lists. Specialist areas and systematic reviews require separate assessment.
Papers or accessible source links, a reference list, your questions, and whether the work supports a journal club, project discussion, or later visualization.
Structured notes, evidence locations, comparison tables, and open questions. References and analytical inferences are distinguished and traceable.
How can protein structure be predicted from amino-acid sequence? Identify the task before tracing the validation.
Abstract · Fig. 1Predictions are evaluated in CASP14 and on later structures. pLDDT estimates local prediction reliability.
Fig. 1–2 · ResultsShallow alignments and dependence on other-chain contacts affect predictions. Structure accuracy alone does not validate function or drug efficacy.
Fig. 5 · MSA depth and cross-chain contactsPublic-paper reading notes, not client work. Source: Jumper et al., Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021). The distinction between structure accuracy and functional or drug validation is a reading-boundary note; this work does not evaluate efficacy or reproduce the experiments.
02 / SCIENTIFIC ILLUSTRATION
Give research a clear visual hierarchy across molecular, cellular, pharmacological, immunological, and disease-mechanism topics. Relationships must follow the evidence you provide.
Conceptual mechanism · No audio · Not a specific pathway
Recognition, binding, and signalling unfold in order. Spatial layers, annotations, and pacing organize the reading for research presentations and mechanism communication.
Confirm the mechanism and storyboard before developing still and motion layers. MP4, key frames, and agreed source files can form the handoff; editability is specified separately.
A generic ligand–receptor concept, not a specific protein or experimentally established pathway. The ligand remains extracellular; intracellular signalling is represented by a visual cue.
Original AI-assisted conceptual artwork, composed and annotated as independent cell and lipid-carrier subjects. No experimentally established interaction is implied. Forms and scale are simplified; project-specific mechanisms need evidence-based review. The base artwork is raster; text and vector layers are delivered as agreed.
Mechanism diagrams, graphical abstracts, conceptual cellular and drug-carrier artwork, multi-panel figure assembly, and consistent styling across existing figures.
The research objective, relationships and supporting references, sketches or existing figures, and the intended publication or presentation format.
High-resolution artwork and composed figures, with separate text, vector objects, and source files as agreed. Embedded raster artwork is not an editable 3D model; editability is defined before work begins.
Cell cutaways, intracellular structures, annotations, and figure layouts. Cell types and structural details are reviewed against project-specific evidence.
Original AI-assisted conceptual cell illustration. Forms and proportions are simplified artistically; it does not identify a cell type or establish a mechanism. Project-specific figures require literature-based and researcher review.
Spatial relationships among the carrier, membrane, and payload. Formulation, scale, and interactions must come from the actual research.
Begin with the membrane and the spaces it separates.
Conceptual structural focus · No specified formulation, scale, or measured result
Original AI-assisted lipid-nanocarrier concept. It represents no specific formulation, drug, scale, or measured result. Fine connections are artistically simplified, not a molecularly precise model. This artwork is a raster asset.
03 / RESEARCH DATA
From data cleaning and group checks to figure assembly, keep the files useful beyond the first export. Statistical methods depend on study design, replicate type, and the data.
Experimental-table cleanup, sample and group checks, missing-value records, descriptive plots, consistent legends and units, figure assembly, and processing documentation.
Original data and its dictionary, sample IDs, groups, replicate types, units, study design, and any existing analysis method.
Tidy tables, figure files, and calculation notes. Reproducible scripts are provided where feasible and agreed; filtering and exclusion decisions are documented.
| Group | Observations | Mean | Sample SD |
|---|---|---|---|
| A | 6 | 23.50 | 3.83 |
| B | 6 | 31.50 | 3.27 |
| C | 6 | 29.67 | 3.98 |
This figure uses synthetic data to explain the handoff format, not real experiments or client projects. Each group has six observations; means and sample SDs are calculated from the same CSV. Replicate types are unspecified; no significance testing or scientific inference is performed.
ADDITIONAL SUPPORT / IMAGE QUANTIFICATION
Follow the relationship between images, ROI review, and normalized outputs. The WB workflow has been tested internally; challenging material still needs manual review. Other image tasks are assessed first.
Confirm groups, target/control pairing, and whether the images are suitable for quantification.
| Sample | Corrected target | Corrected control | Target / control | Relative to A mean |
|---|---|---|---|---|
| A1 | 12,000 | 20,000 | 0.6000 | 0.9908 |
| A2 | 13,800 | 22,000 | 0.6273 | 1.0358 |
| A3 | 11,200 | 19,000 | 0.5895 | 0.9734 |
| B1 | 19,800 | 21,000 | 0.9429 | 1.5569 |
| B2 | 18,600 | 20,000 | 0.9300 | 1.5357 |
| B3 | 21,600 | 23,000 | 0.9391 | 1.5508 |
Calculation: each ROI is assigned an area of 100 pixels and a mean background of 30 arbitrary units per pixel, giving an integrated background of 3,000. The corrected target/control ratio is divided by the mean ratio for group A. Each point is a simulated sample; replicate types are unspecified and no inferential statistics are applied. The illustrated bands are not used in the calculation.
A CONVERSATION IS A GOOD START
Tell us what you are working on, and what you want to make of it.
