The present study introduces the concept of physics-informed neural networks to develop a numerical approach for solving singularly perturbed convection-dominated anomalous diffusion problems. A key focus of this research is to analyse the boundary layers that arise in convection-dominated anomalous diffusion problems using an artificial neural network, namely a physics-informed neural network. We explore solutions to convection-dominated anomalous diffusion problems using neural-network corrector functions derived from underlying auxiliary conditions. The thin layers represent regions with rapid variations in the solution, exhibiting intricate behaviour and rendering existing numerical methods less effective. The proposed method is a semi-analytic method that captures the minute behaviour of the solution in the boundary layer and resolves the singularity that naturally arises in fractal boundary-value problems. An in-depth discussion will be provided, supported by numerical examples demonstrating promising, uncompromised results related to linear and nonlinear stationary anomalous diffusion equations.•PINN framework is proposed for singularly perturbed anomalous diffusion problems.•Corrector functions effectively capture thin boundary layers and singular behaviour.Hessian-based analysis is employed to investigate convergence and solution sensitivity.•The proposed method demonstrates strong performance on both linear and nonlinear problems.
The combined effects of Stefan blowing and Arrhenius activation energy bioconvection flow of hybrid nanofluid with microorganisms and thermophoresis create a highly nonlinear transport problem that necessitates precise prediction modeling. The reference dataset is created by using similarity transformations to convert the governing PDEs into a system of nonlinear ODEs, which are then resolved using the HAM (Homotopy Analysis Method). The obtained numerical data are then used to validate, train, and test a Bayesian Regularization neural network (BR-NN) using a feed forward single layer architecture. The ANN's performance is assessed using EH (error histograms), regression analysis, MSE (mean squared error), and function fit curves, which show great agreement with the HAM solutions and high predicted accuracy. This model has uses in numerous advanced industrial and engineering processes where effective heat and mass transport are important. It can be utilized in cooling systems for electronic devices, solar thermal collectors, nuclear reactors, chemical processing equipment, and microfluidic devices, were hybrid nanofluid boost thermal performance. The model's incorporation of surface tension gradient (Marangoni) effects, Stefan blowing, activation energy, and thermophoretic particle deposition makes it appropriate for studying evaporation, coating technologies, drying processes, fuel cells, and biological transport phenomena.
The Autoregressive Integrated Moving Average (ARIMA) model requires appropriate selection of autoregressive (p), differencing (d), and moving average (q) orders for accurate forecasting. Conventional order selection methods, such as grid search and stepwise information-criterion approaches, can be computationally expensive and prone to suboptimal solutions. This study proposes ARIMA–Spider Monkey Optimization (ARIMA–SMO), an automated framework that employs Spider Monkey Optimization to identify optimal ARIMA orders. In the proposed approach, each spider monkey represents a candidate ARIMA (p,d,q) configuration, and model fitness is evaluated using a combination of statistical goodness-of-fit and forecasting accuracy. The local and global leader mechanisms, together with adaptive subgroup restructuring, balance exploration and exploitation of the search space. The method was evaluated using agricultural production time series from India and compared with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Bayesian Optimization (BO)-based ARIMA models. Across all six-production series, ARIMA–SMO consistently achieved the lowest forecasting errors while requiring less computational time than the competing approaches. For the total food grains series, ARIMA–SMO reduced the test RMSE to 35.75 compared with 43.96 (GA), 39.84 (PSO), and 37.12 (BO). The results demonstrate that ARIMA–SMO is an effective and computationally efficient framework for automatic ARIMA order selection and time-series forecasting.
Adherent cell expansion at intermediate scale (<10,000 cm²) typically relies on parallelized T-flasks, roller bottles, multilayer stacks, fixed-bed bioreactors, or microcarriers, each involving trade-offs in handling complexity, shear stress, medium economy, or plastic waste. The CellScrew® addresses these limitations by replacing multiple petroleum-derived vessels with one polylactic acid (PLA) vessel. During rotation, its concentric cylinders arranged in an Archimedean screw generate a fluid film distributing medium and dissolved gases under low-shear while cells remain ttached. This substantially reduces incubator footprint: one shelf accommodates three CellScrew® 6Ks compared with 36 T175s, roughly tripling growth area while lowering material use and handling time.We describe a reproducible protocol for CHO-K1 and HEK-293 expansion in the 850 cm² and 6,000 cm² formats, covering inoculation from T-flask seed trains, in-process supernatant monitoring, and harvest. Quantified attachment, proliferation kinetics, harvest efficiency, and post-thaw recovery showed consistent performance and transferability to other adherent cells.•Expansion standardized in both formats, reaching 200,000 cells/cm² (CHO-K1;850 cm²) and 361,000 cells/cm² (HEK-293; 6,000 cm²).•Mechanical-enzymatic harvest recovers 94 ± 1% of cells at >93% viability.•HEK-293 attachment within first 24 hours from polystyrene without multi-passage adaptation.•In-process metabolite monitoring as an indirect readout of culture density.
Electrochemical Knoevenagel condensation has emerged as an attractive alternative to conventional condensation methods because it avoids the use of stoichiometric oxidants and enables reactions under mild conditions. However, a general electrochemical methodology for the condensation of indolin-2-one derivatives has not been established. Herein, we describe a practical electrochemical method for the Knoevenagel condensation of indolin-2-one derivatives with aromatic aldehydes in an ethanol-water medium using a simple undivided cell. The reaction conditions were systematically optimized by evaluating the supporting electrolyte, solvent composition, and electrolysis parameters. The developed method proceeds at room temperature within a short reaction time and affords the desired products in good to excellent yields. The robustness and general applicability of the methodology were validated across a diverse range of aromatic and heteroaromatic aldehydes as well as indolin-2-one derivatives. The use of a green solvent system, operational simplicity, and broad substrate compatibility make this methodology readily applicable to the synthesis of 3-alkenylindolin-2-one derivatives for synthetic and medicinal chemistry studies.A practical electrochemical methodology for the Knoevenagel condensation of indolin-2-one derivatives in an ethanol-water medium.Validated across structurally diverse substrates with short reaction times under mild, room-temperature conditions using a simple undivided electrochemical cell.
Approximate nearest neighbor (ANN) search is an integral part of contemporary vector databases; however, the performance of this algorithm highly depends on the specifics of data, recall requirements, storage capabilities, and query workload. All these factors complicate ANN even further in multi-vector databases, where every record has several vectors and queries may utilize arbitrary combinations of vector fields. This paper provides an independent implementation and experimental analysis of MINT (Multi-Vector Search Index Tuning) framework, originally introduced in [14], for workload-aware ANN index tuning in multi-vector databases. The implementation follows the core workflow of MINT that includes workload-induced candidate index generation, cost and recall estimation of generated indexes through sampling, extended-k query planning, and configuration search under constraints of required recall and storage capabilities. The system was implemented in Python with usage of NumPy, hnswlib, scikit-learn, SciPy, and h5py, constructing HNSW indexes only after selecting a proper configuration. The experiments were conducted for a multi-vector database, composed of six columns based on GloVe, SIFT, Deep1M, and Music100 collections. The present study is focused on reproducibility at the implementation level, analysis of the tuning process and its assumptions, and comparison of workload aware configurations with baselines. The quantitative results obtained for the benchmark pipeline need to be considered separately from those of the optimization pipeline since the two execution pipelines have different experimental setups that include sampling fraction, recall threshold, top-k value, workload setup, and other restrictions; hence, benchmarking results cannot be viewed as the outputs of the optimization pipeline.
This study develops a one-dimensional numerical scheme based on the finite element pair P1NC−P1for simulating wave attenuation over submerged break- waters, with a case study at Uluwatu Beach, South Bali. The formulation extends our previous scheme by incorporating radiation boundary conditions (RBC) and employing a Crank–Nicolson time integration scheme, which is unconditionally stable, second-order accurate, and capable of conserving mass and energy. The model is validated against benchmark cases, including the well-known standing waves in a closed basin, a bottom step-up and an idealized submerged breakwater, showing excellent agreement with analytical and another numerical scheme. The effect of bottom step-up and submerged breakwater will produce a reflected and transmitted wave, whose both masses is closely related to the change in the bathymetry. Furthermore, the total mass between the reflected and the transmitted waves is always constant and equals to the mass of incident waves. Application to the site-specific bathymetry of Uluwatu Beach, derived from GEBCO data, indicates that an artificial submerged breakwater 10 m wide and 10 m high, positioned 0.5 km offshore, can reduce shoreline wave amplitudes by approximately 35% against no-breakwater scenario. The results demonstrate that the proposed method is both accurate and well suited for the design of coastal protection structures in wave-exposed environments.● Developed a new finite element method for simulating wave propagation and attenuation under complex coastal bathymetry.● Demonstrated accurate and physically consistent prediction of reflected and transmitted waves over submerged coastal structures.● Applied the proposed model to Uluwatu Beach, Bali, showing that submerged breakwaters can significantly reduce shoreline wave amplitudes.
Introduction Most orthopedic surgeons believe patients should resume physical activity (PA) after recovering from hip pathologies; however, guidance regarding PA type, intensity, duration, or long-term restrictions is lacking. Most children do not achieve the recommended 60 minutes of moderate-to-vigorous PA (MVPA). Children recovering from hip pathologies may be at heightened risk of insufficient PA due to prolonged restrictions. Objective This scoping review will map evidence on PA, sedentary behavior, and movement-related outcomes in children and adolescents with hip pathologies. Inclusion criteria Eligible studies include participants aged 5-17 years diagnosed with Legg-Calvé-Perthes disease, slipped capital femoral epiphysis, developmental dysplasia of the hip, musculoskeletal hip infection, transient synovitis, traumatic hip dislocation, or hip osteochondroma. Case studies, observational studies, randomized controlled trials, reviews, and meta-analyses published in English reporting PA, sedentary behavior, or movement-related outcomes will be included. Studies focused on neuromotor or syndromic conditions, or other significant comorbidities, will be excluded. Articles published between 1974 to August 20, 2025 will be considered. Methods Following Joanna Briggs Institute (JBI) methodology and PRISMA-ScR, we will search five databases from inception, supplemented by grey literature. Two reviewers will screen and extract data. Findings will be summarized descriptively and thematically. Review registration Open Science Framework [osf.io/5f7zv]
Ploidy verification in salmonid production is often constrained by the need to analyze fresh samples. Here, we present an optimized protocol for early-stage ploidy determination in Atlantic salmon (Salmo salar) fry using 70 % ethanol fixation at −20 °C and flow cytometry. A staining solution containing Propidium Iodide (PI) and RNase A was employed to ensure stoichiometric DNA quantification, combined with internal biological standardization to minimize instrumental variation. The protocol achieved a 92.7 % analytical measurability rate and a mean coefficient of variation (CV) of 3.10 %, comparable to values reported for fresh blood samples. The normalized fluorescence ratio discriminated all valid samples, yielding values of 1.00 ± 0.05 for diploids (2n) and 1.38 ± 0.04 for triploids (3n) produced by thermal shock applied at 15 and 30 min post-fertilization. Cross-validation by nuclear morphometry confirmed a significant increase in the major nuclear diameter of putative triploids (7.69 ± 1.04 µm) relative to diploids (6.63 ± 0.83 µm). However, an area under the curve (AUC) of 0.76 indicated lower diagnostic performance than flow cytometry. This protocol provides a practical method for ploidy assessment in Atlantic salmon fry, compatible with field conditions at the sample preservation stage
This method presents DiColorimetry, an author-developed Android application for smartphone-assisted colorimetric assessment of total phenolic content (TPC), radical scavenging activity (RSA) and general colour characteristics in vegetable oils. Smartphone-based imaging itself is not presented as a new analytical principle; the methodological contribution is the integration of previously separate image acquisition, ROI-based colour extraction, assay-specific signal processing, calibration or reference handling, calculation and data export within a single mobile workflow.• The application integrates assay-specific TPC and RSA analysis with a standalone colour-data extraction module, including region-of-interest selection and CSV export.• Analytical performance was evaluated using 40 commercial vegetable oil samples from ten botanical categories and compared with UV/VIS spectrophotometry under controlled imaging conditions using a Samsung Galaxy A33 5G smartphone.• DiColorimetry showed close agreement with the reference method: mean signal-acquisition relative standard deviations were below 1%, and all paired TPC and RSA results showed absolute relative deviations below 5%.The standalone module provides RGB, HSV, CIELAB, and CIELCh values but requires a user-established calibration model for quantitative application. DiColorimetry is intended as an integrated assay-specific smartphone readout and screening tool rather than as a new smartphone-imaging principle or a universal replacement for calibrated analytical instrumentation.
The Yamada-Ota thermal conductivity model, along with an intelligent neuro-computing paradigm, is commonly used to study the thermophysical behavior of Sn-W/C₃H₈O₂ nanomaterials during thermophoresis and Soret-Dufour effects. The concept has numerous applications, including electronic device cooling, solar energy systems, thermal energy storage, chemical reactors, biomedical heat transfer, and nanofluid-based heat exchangers. It accurately predicts effective thermal conductivity, heat and mass transfer, and nanoparticle transport, while the intelligent predictive-computing approach improves prediction accuracy, lowers computational costs, and helps to optimize advanced thermal management systems and industrial processes. This report uses Intelligent Bayesian optimization trained by back propagating neural networks to examine the mass and heat transport parameters of a nanofluid made of Sn-W/C₃H₈O₂ nanoparticles dispersed in water. This study inspects the impacts of Soret and Dufour effects on Sn-W/C₃H₈O₂ hybrid nanomaterial under thermophoresis effects using Yamada-Ota model. The resulting higher-order nonlinear ODEs are numerically solved using MATLAB's bvp4c technique. The thermal field rises as increase the values of nanoparticles volume fraction.
Admission to an adult intensive care unit (ICU) is a profoundly disruptive experience for both patients and their families. Family-Centred Care (FCC) is the predominant framework for addressing their needs, yet the secondary evidence base is heterogeneous and the specific nursing contribution remains insufficiently theorised. This protocol describes a scoping review designed to systematically map the secondary evidence on FCC in adult ICUs, with particular attention to the nursing role, the nurse–family relationship, and the instruments used to measure FCC-related constructs. Conducted in accordance with the JBI methodology and reported following the PRISMA extension for Scoping Reviews (PRISMA-ScR), the review will search six databases (PubMed, CINAHL, Embase, Cochrane, Web of Science, and Scopus) alongside grey literature, with no date restrictions and eligible sources in English and Italian. Only formal secondary sources (i.e., systematic, scoping, integrative, and narrative reviews, clinical practice guidelines, consensus statements, and position papers) will be included. Two independent reviewers will screen records and extract data using a pre-tested charting tool. The resulting higher-order map of conceptual frameworks, interventions, implementation barriers and facilitators, measurement instruments, and outcomes will clarify the state of existing syntheses and orient future critical care nursing research, practice, and implementation.• Provides a structured, reproducible methodological framework (JBI methodology, PRISMA-ScR reporting) to systematically map the extensive and heterogeneous secondary evidence on Family-Centred Care (FCC) in adult ICUs.• Explicitly targets the underexplored nursing role, mapping how the nurse–family relational dynamic is conceptualised and implemented across existing reviews and guidelines.• Offers a comprehensive, pre-tested, multi-matrix data extraction tool tailored to synthesise diverse evidence types, including interventions, implementation barriers and facilitators, psychometric instruments, and outcomes.
Quantitative evaluation of reinforcement dispersion in composite microstructures is still often based on qualitative interpretation of scanning electron microscopy (SEM) images, particularly in agro-waste-reinforced MMC studies. This method presents a statistical approach for describing particle distribution using Lorenz curves and the Gini coefficient derived from segmented microstructural data. Particle areas obtained from image analysis are used to construct cumulative distributions. This enables quantitative assessment of inequality in the projected-area contribution of particles used to support reinforcement-dispersion evaluation. The approach provides a reproducible framework for comparing projected particle-area contribution patterns across samples without relying on subjective visual interpretation. The method is applicable to particulate-reinforced systems where feature size distributions can be extracted from micrographs.The study:• Converts segmented microstructural features into cumulative distributions for dispersion analysis.• Uses Lorenz curves to represent distribution patterns of reinforcement phases.• Applies the Gini coefficient as a scalar metric for comparing dispersion across samples.
This study inspects the flow dynamics of Stefan blowing effects and heat radiation on chemical reactive flow of hybrid nanofluid using a Riga plate in the existence of activation energy. To model and address the intricate nonlinear issue, artificial neural networks (ANNs) that have been trained using the Bayesian regularization back-propagation strategy (ANN-BRS) are utilized. Gyrotactic microbes are integrated with nanoparticles in various applications, including microfluidic platforms, bio-microsystems on chip-scale devices, enzyme-based biosensors, bacteria-driven micromixers, microbial fuel cells, and other micro-engineered systems, to enhance thermal efficacy. This method can also help environmental engineering by improving wastewater treatment procedures by allowing microbes to more effectively degrade pollutants. It raises the production of biofuel in the realm of renewable energy by advancing the creation of more effective photo bioreactors. Material scientists to produce regulated nanostructured materials with consistent compositions and thermal characteristics may use this idea. A higher Stefan blowing factor leads to a higher flow profile. The concentration field decreases as the values of the chemical reaction parameter increases.
Thermal exposure of metallic alloys can promote the precipitation of deleterious secondary particles that degrade mechanical and corrosion performance, making their quantitative characterisation from SEM/BSE micrographs an important step in microstructural assessment. Quantifying precipitate regions in SEM/BSE micrographs of thermally aged steels can be difficult when image contrast, particle size, and particle connectivity change across conditions. This method describes a reproducible workflow for generating reference bright-particle masks, training a lightweight U-Net segmentation model, and converting predicted masks into conventional microstructural descriptors. The workflow combines contrast-enhanced preprocessing, condition-aware reference mask generation, image-level data splitting, overlapping patch extraction, supervised U-Net training, fixed-threshold inference, and connected-component analysis. It is intended for SEM/BSE datasets where bright regions are used as image-based indicators of Cr/Mo-rich deleterious particles, while recognising that crystallographic phase identification requires complementary evidence. In the present application, the reported performance metrics quantify agreement with threshold-derived reference masks on held-out micrographs; they should not be interpreted as validation against independent phase-resolved ground truth. The demonstrated applicability and reported performance are limited to 2205 duplex stainless steel aged at 850 °C and imaged at 5000× using the acquisition protocol described here. Transfer to other materials or imaging domains requires separate validation and may require image recalibration, model fine-tuning, transfer learning, or complete retraining.•The workflow provides a reproducible route from raw SEM/BSE images to binary masks and particle-level descriptors.•Image-level splitting and fixed inference settings reduce data leakage and post-hoc condition tuning.•The method preserves compatibility with conventional area fraction, particle count, particle size, and morphology measurements
Stroke is a major cause of long-term disability globally, with upper limb sensorimotor deficits being among the most debilitating consequences. Accurate prediction of recovery trajectories can help clinicians individualize rehabilitation planning and optimize resource allocation, particularly in low- and middle-income countries (LMICs), where access to high-cost neurophysiological or imaging tools is limited. This protocol describes a prospective cohort study designed to develop and validate both conventional statistical and machine learning–based models for predicting upper limb sensorimotor recovery and quality of life at three and six months post-stroke. One hundred first-ever stroke survivors will be recruited and assessed using a standardized measurement tool consisting of demographic details and motor, sensory, and quality-of-life domains and complemented by 3D kinematic and video-based movement analysis. Statistical and machine learning algorithms (multivariable regression, logistic regression, random forest, support vector machine, and deep neural networks) will be trained and validated to forecast outcomes. This protocol aims to establish an accessible and scalable prediction framework to guide post-stroke rehabilitation strategies.•Development and validation of a multimodal, data-driven model for upper limb sensorimotor recovery prediction.•Multidomain and 3D kinematic variables are combined for comprehensive prognostication.•This provides a scalable, low-cost approach suitable for diverse rehabilitation settings.
Intravenous smart pump (IVSP) usability evaluations are often conducted in single-site laboratory or simulation settings, which can limit access to experienced frontline nurses. This methods article describes a field-deployable, conference-based approach for evaluating IVSP usability with practicing critical-care nurses. The method brought a standardized simulation environment to a national critical-care nursing conference while preserving experimental control through consistent pump stations, scripted clinical scenarios, counterbalanced device order, and common data-collection procedures. The approach integrates direct observation of programming performance with wearable eye tracking, physiologic monitoring, subjective workload ratings, healthcare-specific usability assessment, think-aloud comments, and device preference ranking. The method is intended for researchers, health systems, manufacturers, and nurse-engineer teams seeking a scalable way to evaluate bedside technologies with intended users.• Adapts traditional laboratory-based usability testing into a portable, conference-based method.• Combines standardized clinical tasks with multimodal measures of visual attention, workload, usability, and performance.• Provides a reproducible framework for evaluating medical-device usability with frontline clinicians.
Developing reliable dry-format colorimetric LAMP assays remains challenging because most existing systems depend on pH-sensitive dyes e.g. Hydroxy Naphthol Blue and Phenol Red. These dyes lose performance after lyophilization or when exposed to extraction-free sample buffers, as they require low-buffer conditions to generate accurate color transitions. Lyophilization disrupts pH stability, and common lysis additives further interfere with signal clarity. Consequently, creating a robust, field-ready colorimetric LAMP platform has been constrained. Here, we developed a Mn-PAPS Colorimetric LAMP assay, a metal ion–based colorimetric system that is inherently pH-independent and fully compatible with freeze-drying. Using Escherichia coli as a model organism, this study demonstrates the feasibility, and diagnostic performance of the lyophilized colorimetric LAMP platform. The Mn-PAPS Colorimetric LAMP platform demonstrated sensitivity in a single-digit CFU range and maintained clear visual contrast with a high analytical accuracy across 69 spiked and unspiked reference samples. Incorporation of daughter primers reduced incubation time from 120 to 45 minutes. Two lyophilized formats demonstrated room-temperature stability, supporting field deployment.• Resolves the long-standing limitation of pH-sensitive colorimetric LAMP through a metal-ion dye system.• Demonstrates single-digit CFU sensitivity and high analytical accuracy using a micropipette-free workflow.• Provides stable performance under ambient conditions through lyophilization-compatible reagent formats.
Reduced-form credit-portfolio models monitor risk through a single aggregate default rate, the exposure-weighted mean of origin-specific default rates across rating grades, sectors, or regions. That aggregate is only the level coordinate of the default-rate configuration: an origin-asymmetric shock that raises one origin’s default rate and lowers another’s by an offsetting amount leaves the aggregate unchanged while the composition of default reorganizes beneath it. This article describes a method to monitor that missing coordinate. From a decomposed migration matrix we form a normalized divergence statistic—a scalar for two origins and a vector for many—that is invariant to the common level and responds to composition rotations; we chart it with a scalar control limit and a Hotelling T2 control chart on the general-K vector, bound the forecast bias a stale matrix imparts to probability of default, expected credit loss, and economic capital, and sign the rotation through a structural Merton model. We set out the two distinct central limit theorems the diagnostic rests on—one cross-sectional, governing the control limits, one temporal, governing the structural regime—and show that under a regime-switching Merton specification the reverse-hazard rate is an inverse Mills ratio, so that the direction of the rotation is itself regime-dependent. The method runs on the segment- or rating-decomposed default data institutions already report.• A normalized divergence statistic isolates the composition of default that the aggregate rate ignores.• Scalar and Hotelling T2 control charts detect, time, and attribute origin-asymmetric default rotations.• A closed-form reverse-hazard rate signs the rotation and makes its direction regime-dependent.
Tooth-on-Chip platforms recapitulate dental epithelial–mesenchymal (DE-DM) interactions, offering physiologically relevant in vitro models for tooth regeneration. However, broader adoption requires chip-scale analytical methods capable of resolving cell-type-specific transcriptional programs and characterizing mineral formation. Here, we present a multi-modal workflow adapting five established approaches to fibrin hydrogel-based Tooth-on-Chip constructs. Whole-construct RNA extraction yielded high-integrity RNA suitable for bulk RNA sequencing, enabling pooled transcriptional comparisons between dental epithelium and mesenchyme. Magnetic-activated cell sorting achieved efficient recovery and enrichment from chip-relevant cell inputs, while translating this approach to intact constructs revealed a platform-level incompatibility between fibrin dissolution and downstream sorting. Fixation strategies preserved tissue morphology for spatial transcriptomics but came at the cost of RNA quality, and Raman spectroscopy combined with transmission electron microscopy enabled ultrastructural assessment of calcium phosphate deposition, though confirming mature hydroxyapatite required further validation. Together, this workflow establishes a practical framework for benchmarking analytical readouts in fibrin-based Tooth-on-Chip and related organ-on-chip co-culture systems.Established whole-construct RNA extraction for bulk transcriptional profiling of DE-DM co-culturesAdapted cell sorting and dissociation strategies for cell-type-specific enrichment, identifying key compatibility constraintsApplied spatial transcriptomics preparation and spectroscopic/ultrastructural imaging to characterize tissue architecture and calcium phosphate deposition
