Journal of Biophotonics

Fluorescent voltage-sensitive dyes (VSDs) enable non-invasive, high-throughput optical measurement of membrane potential in living cells, but the analytical reliability of such measurements depends critically on whether the dye and associated imaging conditions perturb the system under study. Here, we systematically characterise the photophysical performance and cell-perturbing effects of FluoVolt, a widely adopted VSD, across cancer cell lines (GIN31 glioblastoma and SK-MEL-30 melanoma) and primary human macrophages. Photobleaching kinetics were strongly cell-type-dependent, with SK-MEL-30 cells exhibiting complete fluorescence loss within 400 seconds under standard widefield conditions. FluoVolt staining combined with laser excitation caused an approximately 2.5-fold increase in cell detachment relative to unstained controls, and dual-wavelength excitation (488 + 405 nm) reduced GIN31 cell viability by approximately 17.5%. Critically, morphological changes, a transition from elongated to amoeboid-like phenotypes, were detected under staining conditions alone, prior to any laser exposure, indicating baseline dye-induced perturbation independent of phototoxicity. Halving dye concentration and loading time significantly attenuated these effects while preserving measurable fluorescence signal. These findings identify FluoVolt staining and excitation as previously uncharacterised sources of systematic measurement artefact and provide practical, actionable guidance for protocol design, control selection, and data interpretation in optical membrane potential imaging.

AO_SCPLOWBSTRACTC_SCPLOWEarly tumor detection is critical for improving patient survival and recovery. Clinically, tissue palpation is routinely used to identify regions of abnormal stiffness, a hallmark of many pathological conditions. However, palpation is restricted to anatomically accessible sites and remains highly operator dependent. Here, we introduce a method for real-time quantitative stiffness mapping using an unmodified commercial endoscope, with the goal of enhancing diagnostic capabilities and restoring mechanical feedback during endoscopic procedures. Our approach combines shear wave elastography with speckle imaging and an innovative synchronization strategy that enables the measurement of shear wave propagation using an unmodified commercial endoscope. The resulting wave fields are analyzed with the noise-correlation-inspired (NCI) method[1], providing pixel-wise estimates of shear wave velocity and, consequently, quantitative maps of local tissue stiffness. The method demonstrated robust performance in both benchtop and endoscopic configurations. Validation was achieved on polymer phantoms as well as on ex vivo and in vivo biological tissues, highlighting its potential for minimally invasive biomechanical imaging and real-time tissue characterization.

Alterations of fluorescence properties in retinal pigment epithelium (RPE) cells caused by diseases such as age-related macular degeneration (AMD) highlight the need for detailed analysis of the fluorescent RPE granules at the individual level. Precise segmentation and classification of these granules remain challenging due to their limited visual separability. In this study, we present Classi4RPE, a computational algorithm designed to accurately segment RPE granules and classify them into three categories -- lipofuscin (L), melanolipofuscin (ML), and melanin (M) -- based on fluorescence lifetime imaging data, which provide distinctive contrast. The method is implemented in a custom Python framework and employs seeded watershed segmentation to isolate individual granules. Lipofuscin granules are identified as hyperfluorescent structures with longer lifetimes, while granules with shorter lifetimes are further analyzed based on their spatial lifetime distribution from the center to edge, enabling discrimination of ML from other melanin-rich granules. Our approach achieves high performance, with mean sensitivities of 0.99 for L granules and 0.90 for ML granules, and corresponding specificities of 0.93 and 0.98, respectively, compared to manually annotated ground truth. These results demonstrate the potential of Classi4RPE to surpass human visual limitations and provide a robust tool for quantitative RPE analysis.

The ultrastructure of endothelial cells (ECs) "in situ" is of great interest due to their involvement in many physiological processes. In some organs, these cells form transcellular pores or fenestrae, allowing for the rapid exchange of molecules between blood and interstitium. Despite their importance, no optical images of these dynamic morphological structures have yet been acquired in situ. Major obstacles to their in-situ imaging are the lack of specifical labels for fenestrae and their size well below the optical diffraction limit. Here, we report how we have overcome these challenges and managed to visualize the EC ultrastructure in situ in 25 {micro}m thick liver sections. To enable this, a lipophilic, fluorescent membrane dye was infused into the portal vein of murine livers to stain the sinusoidal ECs before the organ was harvested. Tissue sections were subsequently imaged using a novel, super-resolution optical-sectioning structured illumination microscope (OS-SIM), providing approx. 170 nm spatial resolution with significantly faster image acquisition compared to confocal microscopy.

Continuous wave diffuse correlation spectroscopy (cw-DCS) is a noninvasive optical technique to monitor the tissues blood flow changes. This technique measures the tissue blood flow index (BFI) by evaluating the decay rate of the autocorrelation function. The derived BFI is proportional to mean squared displacements of the red blood cells considered as the fast-dynamic scatterer component of tissue in time. However, biological tissue contains static scatterer component and slow-dynamic scatterer component which affect the decay rate of autocorrelation function and as a result the derived BFI. In this study, we assessed the fractional contribution of static, slow-dynamic and fast-dynamic scatterer components of a medium in the flow index derived by cw-DCS. The measurements performed on Agar-based phantom with tube showed that presence of static scatterer component and slow-dynamic scatterer component led to substantial underestimation ({approx} 123%) of the flow index derived by Siegert relation, compared to effective diffusion coefficient of fast-dynamic scatterers components derived by modified Siegert relation and bi-exponential model. The less underestimation was observed for the corresponding parameters obtained from the liquid phantom measurements ({approx} 25%) as well as during the forearm occlusion test and respiratory challenges ({approx} 16% - 26%).

ObjectiveRobotic-assisted needle insertions lack haptic feedback, a key sensory cue for detecting tissue transitions and regulating puncture force. Modeling this feedback requires an understanding of soft-tissue biomechanics during insertion. Vibroacoustic signals generated by needle-tissue interactions may provide an additional sensing modality, but their interpretation requires validation against anatomical ground truth. MethodsA multimodal framework was developed to correlate vibroacoustic signals with high-resolution post-puncture microCT ({micro}CT) imaging in Manduca sexta, an insect model containing interconnected soft-tissue layers. A custom clip-on prototype recorded vibroacoustic signals during manual needle insertions. Three trajectory-marking strategies were evaluated to determine 3D coordinates of soft-tissue layer crossings and to assess correlations between acoustic events and anatomical transitions. Distances between layer crossings and needle displacement were used for spatiotemporal alignment of vibroacoustic and {micro}CT data. ResultsA {micro}CT-compatible nylon string preserved puncture trajectories without artifacts and enabled high-resolution 3D reconstruction of anatomy and needle paths. Fusion of vibroacoustic and imaging data allowed identification of acoustic events associated with tissue entry, exit, and transitions. ConclusionBy integrating high-resolution {micro}CT imaging with vibroacoustic sensing, this study establishes a biologically grounded framework for validating the relationship between vibroacoustic signals and anatomical tissue transitions during needle insertion, providing a basis for future quantitative analyses. SignificanceThis work provides initial evidence for correlating vibroacoustic signals recorded during needle insertion with corresponding {micro}CT-identified tissue barriers. Because vibroacoustics offers substantially higher temporal and spatial resolution than most imaging modalities, it has the potential to improve tissue sensing and procedural accuracy in future needle-based interventions.

We present a non-invasive approach for continuous monitoring of lactate dynamics in-vivo using near-infrared (NIR) spectroscopy. Lactate-related spectral features were measured non-invasively within the overtone region (1600-1850 nm). Several anatomical measurement sites were evaluated, and the middle phalanx of the dorsal finger emerged as the most promising location due to its superior spectral quality and stable tissue perfusion, becoming the exclusive site for all further experiments. Across multiple exercise sessions, predictive models achieved high within-day accuracy (R2[≥] 0.8), while cross-day performance was affected by spectral drift and physiological variability. A dynamic offset-correction procedure effectively mitigated these baseline shifts, enabling stable prediction accuracy across days, weeks, and subjects. These findings demonstrate the feasibility of NIR-based lactate estimation and highlight the importance of adaptive correction strategies for reliable long-term, non-invasive monitoring.

Optical coherence tomography (OCT) is widely used for retinal disease assessment, but automated quantitative analysis remains challenging because of anatomical variability and noisy imaging conditions. This study presents an interpretable OCT classification framework based on four anatomically guided retinal layers, combining preprocessing, adaptive segmentation, targeted feature engineering, and supervised classification to identify Normal, CNV, DME, and Drusen cases. Layer-specific descriptors included statistical, derivative, fluid-related, and GLCM texture markers. Feature correlation and ranking analyses showed that the proposed descriptors were highly complementary, that the most informative features were concentrated in layers 2 and 4, and that layers 1 and 3 contributed supportive structural information. Among the evaluated classifiers, the neural network performed best, achieving an accuracy of 98.17%, sensitivity of 97.88%, specificity of 99.38%, and AUC of 0.9985. Computational analysis showed efficient training and inference, with a total training time of 2216.3 s, prediction speed of approximately 160000 observations per second, and a compact model size of about 11 kB. These results demonstrated that anatomically guided feature extraction can provide accurate, efficient, and interpretable OCT disease classification, offering a practical alternative to less transparent end-to-end deep learning approaches.

Uniform, reproducible blood smears are critical for reliable hematological evaluation. Manual smear preparation, however, is user-dependent and introduces variability that limits quantitative microscopy. Here we developed BUsmear, a low-cost, 3D-printed, motorized blood smear device that prepares two smears simultaneously from a printed stage and a micro-motor drive with tunable linear velocity, controlled through a joystick-operated driver module. By spreading two slides in parallel with fully repeatable slide-to-slide motion, the device doubles throughput while eliminating operator-dependent motion artifacts, and can be fabricated on any benchtop 3D printer in under one day. To validate smear quality, we analyzed blood films from three donors together with a manual smear prepared by an expert from the blood of one of the same donors, giving a matched device-versus-manual pair. Automated Cellpose segmentation of 14,046 red blood cells across 12 bright-field fields showed that cell diameter was preserved and closely matched the expert smear (6.7-7.8 um across groups, within the 6.2-8.2 um human reference range; 6.7 vs 6.9 um in the matched pair), and that all films formed non-aggregated monolayers (Clark-Evans index of aggregation 1.04-1.24). Critically, red blood cells in the device films were markedly more circular than in the expert manual smear (mean eccentricity 0.405 vs 0.502; 0.443 vs 0.502 in the matched pair), with complete separation between the two methods at the level of whole fields of view. Because eccentricity reports smear-induced cell distortion, this indicates that a constant, mechanically controlled spreading velocity preserves red blood cell morphology better than skilled manual technique. BUsmear offers an accessible route to standardized smear geometry for quantitative analysis, including AI-based morphometry, and its low cost may be particularly advantageous in low-income countries with a high prevalence of malaria.

Spatial frequency domain imaging (SFDI) has demonstrated the ability to provide early, quantitative assessment of burn wound severity. Previous studies using a research-grade SFDI platform showed that high-dimensional datasets incorporating multiple spatial frequencies and wavelengths can predict healing outcomes in controlled porcine models of graded burns. To assess the impact of reduced measurement dimensionality on diagnostic performance, we compared classifications derived from a research-grade SFDI system (Reflect RS) with those obtained using a simplified commercial SFDI platform (Clarifi RS). Both systems were used to image graded burns 24 hours after injury. Pixel-level classifiers were trained using regions defined by 28-day healing outcomes, and models based on the full Reflect dataset were compared with classifiers generated from reduced Reflect feature sets and datasets designed to mimic Clarifi acquisition features. Performance was evaluated using leave-one-subject-out validation. The full Reflect dataset achieved the highest classification performance, with mean F1 scores approaching 0.88. Although reducing the number of measured features decreased classification accuracy, simplified models and Clarifi-based datasets maintained F1 scores greater than 0.8 for binary classification. These findings indicate that dimensionality reduction produces a measurable but manageable loss in performance and support the development of clinically practical, application-specific SFDI systems for burn assessment.

SignificanceDiffuse Correlation Spectroscopy (DCS) is an established technique for non-invasive monitoring of Cerebral Blood Flow (CBF), but existing applications primarily measure CBF changes averaged over cortical tissue volumes. A method capable of targeting blood flow within a particular intracranial artery would expand the utility of DCS for vessel-specific cerebral perfusion monitoring and continuous bedside assessment. AimWe aim to investigate the feasibility of targeted, non-invasive monitoring of blood flow in the Anterior Cerebral Artery (ACA) using DCS through optimization of probe geometry and placement. ApproachA custom-built DCS system operating at 785 nm was used to probe ACA from the glabellar region. Source-detector (SD) separation, probe orientation, and probe location were systematically optimized using lower-limb motor tasks and a mental arithmetic task. The optimized configuration was evaluated using an ACA-mimicking multilayer phantom and validated in forty healthy volunteers during lower-limb activation tasks and postural changes. ResultsAn SD separation of 17 mm, vertical probe orientation, and glabellar placement provided the highest sensitivity to ACA-related blood flow changes. Phantom experiments demonstrated sensitivity to flow changes in a vessel located 45 mm beneath the scalp and showed that the frontal sinus, cerebrospinal fluid, and the absence of cortical tissue beneath the glabella along the longitudinal fissure together provide the best optical window for probing deep ACA flow. Using the optimized probe configuration, significant increases in relative CBF were observed during standing leg marching (66.4 {+/-} 38.6%) and supine leg crunches (39.4 {+/-} 32.2%) (p < 0.01), with consistent responses during supine-to-stand postural transitions. ConclusionThe proposed DCS approach enables targeted measurement of blood flow within the ACA territory. This technique provides a framework for continuous, non-invasive monitoring of ACA perfusion and has potential applications in cerebrovascular monitoring and stroke care.

Microscopes are essential tools for discoveries on a scale invisible to the unaided human eye. The development of immuno-fluorescence followed by molecular biology techniques and fluorescent fusion proteins have revolutionised the use of optical microscopy in bioscience. The quality of the data produced is dependent upon the sample, its preparation and the instrument used. However, instruments can degrade over time without easily visible changes to the produced images and, in turn, negatively impacts results. By testing instruments and doing comparisons between results over time and between different instruments, problems can be highlighted and corrective action can be taken. Using small fluorescent beads the point spread function (PSF) of the microscope can be recorded and the image resolution measured. Beads were prepared in a concentration matched to the field of view size and dried onto coverslips and mounted on slides. The beads were then imaged as 3D Z-stacks of sufficient size to fully enclose the PSF of the system. This data was uploaded to OMERO and processed using OMERO-metrics, an OMERO plugin developed for this purpose. This paper summarizes the development of workflows and protocols to enable this process, presents the results obtained and demonstrates the detection of significant instrument issues.

Background. Scanning electron microscopy (SEM) is widely used to determine fibrin fiber structural properties such as fiber diameter and fiber length. However, conventional SEM preparation protocols are time-consuming and typically require conductive sputter coating. The coating process introduces an additional layer onto the sample surface and may influence measurements of nanoscale fiber structure. Furthermore, preparation of purified fibrinogen clots often follows protocols originally developed for plasma clots, resulting in unnecessary processing steps. Objective. To evaluate indium tin oxide (ITO) as a flat, conductive substrate for SEM imaging of fibrin fibers, investigate the effects of sputter coating on measured fiber diameter, and develop a simplified SEM preparation protocol for purified fibrinogen clots. Methods. Platelet-poor plasma clots and purified fibrinogen clots were formed on ITO substrates and imaged by SEM following 0 s, 45 s, or 90 s sputter coating. Fibrin fiber diameters were quantified and compared across coating conditions. For purified fibrinogen clots, an ITO-based simplified preparation protocol, in which clots were formed and imaged directly on the conductive ITO surface, was compared with a previously developed, standardized SEM protocol, in which clots were formed in microtube lids and subsequently transferred onto carbon tape for imaging. Results. Fiber diameter measurements were affected by sputter coating duration, with increasing coating time resulting in larger apparent fiber diameters. Plasma and purified fibrinogen clots exhibited distinct fiber diameter distributions and coating responses. For purified fibrinogen clots, the simplified ITO-based protocol produced fiber diameter measurements that were not significantly different from those obtained using the standardized lid-to-carbon-tape workflow when identical coating times were applied. Conclusions. ITO provides a practical conductive substrate for SEM imaging of fibrin fibers and enables substantial simplification of purified fibrinogen clot preparation. When coating conditions are matched, the simplified ITO-based protocol yields fiber diameter measurements comparable to those obtained using the previously standardized lid-to-carbon-tape workflow. These findings support the use of ITO as an alternative conductive imaging substrate and provide a simplified workflow for SEM analysis of purified fibrinogen clots. By reducing washing and transfer steps, this workflow may also provide a useful platform for future controlled studies of fibrin interactions with added proteins or other associated components.

Quantifying cells within intact three-dimensional biological specimens remains a major challenge, as standard optical and histological techniques are inherently two-dimensional, destructive, or constrained by light scattering. Optical clearing can extend imaging depth but is time-consuming, disruptive to tissue integrity, and often incompatible with downstream analyses, limiting its practical use for routine three-dimensional quantification. X-ray computed tomography can overcome these limitations, yet conventional micro-CT lacks the soft-tissue contrast required for cellular-scale analysis. Here, we introduce an integrated imaging framework in which propagation-based phase-contrast X-ray CT is combined with volumetric nuclear segmentation to enable three-dimensional cell quantification in unstained volumetric tissue. We imaged ex vivo human liver tissue and segmented nuclei throughout the reconstructed volume, extracting quantitative nuclear metrics and spatial organisation metrics, including equivalent diameter, minor-to-major axis ratio and nearest-neighbour distance. We assessed measurement consistency across two non-overlapping volumes of interest and benchmark slice-resolved nuclear metrics against haematoxylin and eosin histology. The resulting high-contrast volumetric datasets preserve tissue context, allowing quantitative measurements to be interpreted alongside surrounding architecture and microstructure. Together, these results show that laboratory phase-contrast X-ray CT supports nucleibased volumetric cell quantification in intact unstained tissue and provides a framework for context-preserving quantitative analysis in three dimensions.

Blood clot formation and thrombolysis are dynamic biological processes that play central roles in hemostasis, thrombosis, and thrombolytic therapy. Monitoring clot evolution is challenging, as existing approaches often rely on specialized hardware or complex acquisition protocols. This study presents dense speed-of-sound shift imaging (DSI), a noninvasive ultrasound framework for spatiotemporal monitoring of coagulation and lysis from ultrasound image sequences acquired with a single imaging transducer. DSI estimates interframe displacements using dense optical flow and reconstructs slowness-shift maps by solving a regularized inverse problem, from which relative speed-of-sound (SoS) shifts are derived. Using this approach, we quantified spatially localized acoustic signatures of material solidification, clot formation, and enzymatic clot dissolution across systems of increasing biological complexity, including thermally gelling gelatin, fibrin clots, and porcine and human whole blood. DSI detected composition-dependent clot properties, with fibrinogen primarily affecting SoS shift magnitude and thrombin primarily affecting clotting kinetics. In both porcine and human whole blood, DSI tracked the full transition from rapid clot formation to tPA-mediated thrombolysis, revealing markedly slower lysis kinetics and species-dependent differences in clotting amplitude and stabilization time. Together, these results establish DSI as a simple, ultrasound-based platform for quantitative monitoring of coagulation, thrombolysis, and related biological material transitions.

Temperature is a fundamental parameter governing all molecular processes, including those that define life. Fluorescence microscopy is a powerful tool to observe molecular processes in living systems in real time. Precise control and measurement of temperature during fluorescence microscopy is therefore essential. We present here a robust temperature measurement based on the excited-state lifetime of the widely available and relatively inexpensive fluorescent dye pentamethine cyanine (Cy5). The excited-state lifetime of Cy5 shows a monotonic decline in the measurement range of 0 {degrees}C - 80 {degrees}C. The measured dependency is linear until 39 {degrees}C and monoexponential above. The dependance of excited-state lifetime upon temperature is used to measure temperature up to a precision of 0.5 {degrees}C or less, a temporal resolution down to <1 millisecond and to resolve temperature gradients with spatial resolutions that are only diffraction-limited. The far-red excitation and emission of Cy5 leaves bandwidth to simultaneously measure at least 3 additional spectral channels in standard fluorescent microscopes simultaneously. We demonstrate determination of temperature during 4-color live-cell fluorescence microscopy for a temperature-controlled experiment. We also show its applicability in measuring temperature gradients and laser-induced sample heating such as during STED nanoscopy.

Streptavidin-biotin, avidin-biotin interactions are classical models for protein-ligand binding, yet the energetic changes accompanying biotin binding remain poorly resolved. Using fluorescent dyes as energy sensors, we show that biotin binding produces two distinct regimes occurring in parallel as concentration of biotin increases cooperativity and conformational rearrangements, wherein cooperativity is observed via exchange broadening of fluorescence linewidth and conformational rearrangements exclusively observed in emission energy. Where the first biotin binding creates the highest contribution to the emission energy. Further analysis of tetramer-tetramer only interactions revealed extremely long ranged intermolecular interactions extending to hundreds of nm. The intermolecular interactions become negligible only at concentrations of approximately 10 nM for both streptavidin and avidin. Affinity values estimated for these diluted samples were below 1 nM.

Chemical imaging is a powerful branch of modern microscopy encumbered by a lack of flexible, high-throughput analysis tools. Bespoke analytical pipelines typically perform ratiometric analysis on two layers in a multispectral image to describe the relative composition of molecules in a sample. This strategy has been implemented across fields, spanning histopathology, cell biology, environmental science, and materials science. The commercialization of chemical imaging microscopes has facilitated the collection of large multispectral datasets, necessitating accessible ways to process them. This paper describes Multispectral Analysis Graphical User Interface (msaGUI), a desktop graphical user interface to analyze individual and batch datasets of multispectral images. Data is loaded as CSV, TSV, or TIFFs and processed through a user-defined sequence of modular image operations that can be flexibly combined, e.g. to reduce spectral crosstalk or background noise. After analysis, data is visualized as exportable images, histograms, and statistics. To yield publication-quality figures, outputted images are fully customizable. Written in Python with open-source libraries, the msaGUI program is packaged into an executable for Windows and Mac for a fully no-code application. Other operating systems are supported via the Python source code. In summary, msaGUI provides a rapid and user-friendly solution for analyzing and visualizing multispectral data.

This study aims to improve the detection performance of age-related macular degeneration (AMD) in low-quality retinal images. BackgroundAMD is a leading cause of vision loss among older adults globally, and accurate detection is crucial for clinical management. However, low-quality optical coherence tomography (OCT) images significantly compromise diagnostic accuracy. ObjectiveTo enhance AMD detection in low-quality images using noise-augmented data augmentation and an improved YOLO deep learning model. MethodsPublic datasets from UCSD and Duke University were utilized; the training dataset comprised 24,980 OCT images (high-quality and noise-augmented low-quality), while the testing dataset included 1,000 images (584 AMD, 416 normal). The model is based on the YOLOv8n framework, integrated with Squeeze-and-Excitation blocks (SEblock) and Adaptive Sparse Self-Attention (ASSA), with an additional 160x160 detection layer for detecting small lesions. Evaluation metrics included accuracy, sensitivity, specificity, and F2-score. ResultsThe proposed model achieved an accuracy of 99.02%, sensitivity of 98.17%, specificity of 100%, and an F2-score of 98.50% on the Duke dataset. Detection rates were significantly improved compared to traditional methods, particularly in low-quality images, with a detection rate of 89.60%, markedly superior to original YOLOv8n (55.10%) and classical models like ResNet50. ConclusionThe enhanced model, employing noise-augmented training data and improved attention mechanisms, demonstrates excellent AMD detection capabilities in low-quality OCT images, showing broad potential for clinical applications.

The hindlimb ischaemia (HLI) mouse model is a widely used preclinical model of chronic limb-threatening ischaemia (CLTI). While CLTI involves complex interactions between impaired perfusion, inflammation and muscle wasting, the standard imaging approach, laser Doppler imaging (LDI), only assesses perfusion. MRI is used clinically to assess neural tracts, inflammation, and perfusion in the brain. We therefore evaluated whether a multimodal MRI approach could longitudinally monitor recovery in the HLI mouse model. Mice underwent MRI three days pre-HLI surgery, and on Days +3 and +7 post-surgery, with histology on Day +7. The MRI detected significant increases in muscle volume and inflammation after HLI surgery, with significant decreases in perfusion, vascular length, and muscle fibre integrity. Overall, MRI can monitor inflammation, muscle fibre integrity, and vascular recovery post-HLI and should be applied in future studies to identify mechanisms of therapeutic recovery in a sequential in vivo analysis without requiring animal sacrifice. O_FIG O_LINKSMALLFIG WIDTH=186 HEIGHT=200 SRC="FIGDIR/small/739107v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@79843dorg.highwire.dtl.DTLVardef@1d8e90borg.highwire.dtl.DTLVardef@1abd740org.highwire.dtl.DTLVardef@c0d296_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG