Rapid Collection Research Articles

Generating trajectory data without behavior modeling: An online virtual reality method in wayfinding performance evaluation for buildings

Wayfinding performance is one of the key issues in pre- or post-occupancy evaluation for large-scale public buildings and such evaluation typically revolves around participants’ wayfinding trajectories. Traditional evaluation methods, including laboratory virtual reality (VR) experiments and computer simulations, have their respective advantages in accuracy or cost-efficiency. However, they either suffer from high costs and low efficiency or sacrifice the diversity of behavioral data for higher efficiency and lower costs. Therefore, this paper proposes a novel method named “sampling as simulation” (SaS) via an online VR wayfinding experiment. This method allows for the rapid collection of behavioral data at a lower cost to generate highly accurate wayfinding trajectory data for evaluation purposes. It utilizes a model-image integrated approach to create realistic online virtual environments and employs a spatiotemporal replay algorithm to generate a crowd of true-trajectory-driven avatars, significantly enhancing the immersion and realism of the online virtual scenes. This method was demonstrated and validated with a wayfinding performance post-occupancy evaluation (POE) project at the International Departure of Satellite Terminal One (S1) in Shanghai Pudong International Airport (PVG). In this example, a total of 13,712 original trajectories were collected within a 72-h time window, serving as the original dataset for evaluation. Based on this dataset, the study effectively simulated wayfinding behavior in three different scenarios and evaluated wayfinding performance. Compared to existing methods, the SaS method not only improves data collection efficiency and reduces costs but also maintains the diversity of behavior by generating transformed trajectories directly from human data without behavior modeling.

Effects of an Increased Financial Incentive on Follow-up in an Online, Automated Smoking Cessation Trial: A randomized Controlled Study Within a Trial.

Poor retention in clinical trials can impact on statistical power, reliability, validity, and generalizability of findings and is a particular challenge in smoking cessation studies. In online trials with automated follow-up mechanisms, poor response also increases the resource need for manual follow-up. This study compared two financial incentives on response rates at 6 months follow up, in an online, automated smoking cessation feasibility trial of a cessation smartphone app (Quit Sense). A study within a trial (SWAT), embedded within a host randomized controlled trial. Host trial participants were randomized 1:1 to receive either a £10 or £20 voucher incentive, for completing the 6-month questionnaire. Stratification for randomization to the SWAT was by minimization to ensure an even split of host trial arm participants and by 6-week response rate. Outcome measures were: Questionnaire completion rate, time to completion, number of completers requiring manual follow-up, and completeness of responses. Two hundred and four participants were randomized to the SWAT. The £20 and £10 incentives did not differ in completion rate at 6 months (79% vs. 74%; p = .362) but did reduce the proportion of participants requiring manual follow-up (46% vs. 62%; p = .018) and the median completion time (7 days vs. 15 days; p = .008). Measure response completeness rates were higher among £20 incentive participants, though differences were small for the host trial's primary smoking outcome. Benefits to using relatively modest increases in incentive for online smoking cessation trials include more rapid completion of follow-up questionnaires and reduced manual follow-up. A modest increase in incentive (from £10 to £20) to promote the completion of follow-up questionnaires in online smoking cessation trials may not increase overall response rates but could lead to more rapid data collection, a reduced need for manual follow-up and reduced missing data among those who initiate completing a questionnaire. Such an improvement may help to reduce bias, increase validity and generalizability, and improve statistical power in smoking cessation trials. Host trial ISRCTN12326962, SWAT repository store ID 164.

A Deep Learning Approach for Surface Crack Classification and Segmentation in Unmanned Aerial Vehicle Assisted Infrastructure Inspections.

Surface crack detection is an integral part of infrastructure health surveys. This work presents a transformative shift towards rapid and reliable data collection capabilities, dramatically reducing the time spent on inspecting infrastructures. Two unmanned aerial vehicles (UAVs) were deployed, enabling the capturing of images simultaneously for efficient coverage of the structure. The suggested drone hardware is especially suitable for the inspection of infrastructure with confined spaces that UAVs with a broader footprint are incapable of accessing due to a lack of safe access or positioning data. The collected image data were analyzed using a binary classification convolutional neural network (CNN), effectively filtering out images containing cracks. A comparison of state-of-the-art CNN architectures against a novel CNN layout "CrackClassCNN" was investigated to obtain the optimal layout for classification. A Segment Anything Model (SAM) was employed to segment defect areas, and its performance was benchmarked against manually annotated images. The suggested "CrackClassCNN" achieved an accuracy rate of 95.02%, and the SAM segmentation process yielded a mean Intersection over Union (IoU) score of 0.778 and an F1 score of 0.735. It was concluded that the selected UAV platform, the communication network, and the suggested processing techniques were highly effective in surface crack detection.

Long-term series wetland classification of Guangdong-Hong Kong-Macao Greater Bay Area based on APSMnet

Wetlands play a crucial role in achieving carbon peak and carbon neutrality goals. Exploring spatiotemporal distribution is one of the fundamental task in wetland research. However, existing large-scale and long-term series wetland mapping methods have challenges related to classification accuracy and obtaining inter-annual wetland samples. Therefore, a rapid sample collection and precise classification method are needed to support wetland resource assessment, conservation, and ecological restoration. In this paper, we propose a novel deep learning method suitable for large-scale and long-term series wetland classification. First, we acquire and preprocess the long-term series remote sensing data on the Google Earth Engine (GEE) platform. Second, the maximum wetland extent is extracted using decision trees and the Otsu algorithm (OTSU). Third, the attention and pyramid structure-based multidimensional feature extraction network (APSMnet) and the inter-annual sample migration algorithm (ISM) are proposed for realizing inter-annual wetland classification and wetland change analysis. Experiments conducted on Landsat 7 data of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 2000 to 2022 demonstrate that the overall accuracy (OA), average accuracy (AA), and kappa coefficient (Kappa) achieved by the proposed wetland classification method are 0.9664, 0.9396, and 0.9570, respectively. Long-term series trend analysis reveals that mangrove/swamp areas experience fluctuations influenced by urbanization and ecological policies, ultimately undergoing restoration and becoming stable. The codes and datasets are made available publicly at https://github.com/louanjun/APSMnet.

A rapid field crop data collection method for complexity cropping patterns using UAV and YOLOv3

A rapid field crop data collection method for complexity cropping patterns using UAV and YOLOv3

Influence of sample preparation methods on FTIR spectra for taxonomic identification of tropical trees in the Atlantic forest

The Atlantic forest is one of the world's major tropical biomes due to its rich biodiversity. Its vast diversity of plant species poses challenges in floristic surveys. Fourier transform infrared spectroscopy (FTIR) enables rapid and residue-free data collection, providing diverse applications in organic sample analysis. FTIR spectra quality depends on the sample preparation methodology. However, no research on FTIR spectroscopy methodology for taxonomy has been conducted with tropical tree species. Hence, this study addresses the sample preparation influence on FTIR spectra for the taxonomic classification of 12 tree species collected in the Serra do Mar State Park (PESM) - Cunha Nucleus – São Paulo State, Brazil. Spectra were obtained from intact fresh (FL), intact dried (DL), and heat-dried ground (GL) leaves. The spectra were evaluated through chemometrics using Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), and Linear Discriminant Analysis (LDA) with validation by LDA-PCA. The results demonstrate that sample preparation directly influences tropical species FTIR spectra categorization capability. The best taxonomic classification result for all techniques, validated by LDA-PCA, was obtained from GL. FTIR spectra evaluation through PCA, HCA, and LDA allow for the observation of phylogenetic relationships among the species. FTIR spectroscopy proves to be a viable technique for taxonomic evaluation of tree species in floristic exploration of tropical biomes which can complement traditional tools used for taxonomic studies.

Detection of Cannabinoids in Oral Fluid Specimens as the Preferred Biological Matrix for a Point-of-Care Biosensor Diagnostic Device.

An increasing number of countries have started to decriminalize or legalize the consumption of cannabis for recreational and medical purposes. The active ingredients in cannabis, termed cannabinoids, affect multiple functions in the human body, including coordination, motor skills, memory, response time to external stimuli, and even judgment. Cannabinoids are a unique class of terpeno-phenolic compounds, with 120 molecules discovered so far. There are certain situations when people under the influence of cannabis may be a risk to themselves or the public safety. Over the past two decades, there has been a growing research interest in detecting cannabinoids from various biological matrices. There is a need to develop a rapid, accurate, and reliable method of detecting cannabinoids in oral fluid as it can reveal the recent intake in comparison with urine specimens, which only show a history of consumption. Significant improvements are continuously made in the analytical formats of various technologies, mainly concerning improving their sensitivity, miniaturization, and making them more user-friendly. Additionally, sample collection and pretreatment have been extensively studied, and specific devices for collecting oral fluid specimens have been perfected to allow rapid and effective sample collection. This review presents the recent findings regarding the use of oral fluid specimens as the preferred biological matrix for cannabinoid detection in a point-of-care biosensor diagnostic device. A critical review is presented, discussing the findings from a collection of review and research articles, as well as publicly available data from companies that manufacture oral fluid screening devices. Firstly, the various conventional methods used to detect cannabinoids in biological matrices are presented. Secondly, the detection of cannabinoids using point-of-care biosensors is discussed, emphasizing oral fluid specimens. This review presents the current pressing technological challenges and highlights the gaps where new technological solutions can be implemented.

Behavioral Insights from Vaccine Adoption in Nigeria: Cross-Sectional Survey Findings.

To generate behavioral insights for the development of effective vaccination interventions, we need approaches that combine rapid and inexpensive survey data collection with instruments based on easy-to-use behavior models. This study demonstrates how an inexpensive digital survey helped identify the drivers of COVID-19 vaccination in Nigeria. This study aims to illustrate how behavioral insights can be generated through inexpensive digital surveys. We designed and conducted a cross-sectional survey with multistage sampling. Data were collected from Nigerians (aged ≥18 years) from 120 strata based on age, sex, state, and urban or rural location. Respondents were recruited via advertisements on Meta platforms (Facebook and Instagram) using the Virtual Lab open-source tool. We used a Meta Messenger chatbot for data collection; participants were compensated with 400 naira (US $0.87 cents). Data collection took 2 weeks. In total, 957 respondents completed the survey, at an advertising cost of US $1.55 per respondent. An 18-item instrument measuring core motivators, ability barriers, sociodemographic characteristics, and respondents' vaccination status was pretested before data collection. We ran separate logistic regression models to examine the relationships between vaccine uptake and core motivators, ability barriers, and sociodemographic variables. A final model that predicted vaccine uptake included all 3 sets of variables. About 56% (n=540) of respondents reported that they had received at least 1 COVID-19 vaccination. Three core motivators were positively associated with vaccine uptake: the belief that the COVID-19 vaccine promised a better life (adjusted odds ratio [aOR] 3.51, 95% CI 2.23-5.52), the belief that the vaccine would allow respondents to do more things they enjoyed (aOR 1.97, 95% CI 1.33-2.93), and respondents' perception that their friends and family members accepted their decision to get vaccinated (aOR 1.62, 95% CI 1.06-2.48). Two ability barriers were negatively associated with vaccine uptake: cost- or income-related concerns lowered the odds of being vaccinated (aOR 0.35, 95% CI 0.24-0.50) and the lack of availability of vaccines at places respondents routinely visited also lowered their odds of being vaccinated (aOR 0.29, 95% CI 0.21-0.40). After adjusting for other variables, the perceived fear of getting COVID-19 and the hardship associated with the disease were no longer associated with vaccine uptake. These findings suggest that hope is more important for Nigerians than fear when it comes to vaccine adoption, enjoying life is more important than worrying about getting the disease, and approval from friends and family is more powerful than their disapproval. These findings suggest that emphasizing the benefits of leading a fuller life after being vaccinated is more likely to succeed than increasing Nigerians' fear of COVID-19. This study identifies a very different set of factors associated with COVID-19 vaccine adoption than previous Nigerian studies.

Modified Procedure for Simple and Rapid Collection of Zebrafish Fluid Samples for Biochemical Analyses.

This study addresses the challenge of collecting blood samples from zebrafish for biochemical analysis. Traditional methods are cumbersome due to low blood flow and rapid coagulation. Based on a previously published technique, we simplified the process by applying an anticoagulant solution directly to the incision site. The modified protocol involves immersing the fish in an ice bath, making a cross-sectional incision, and immediately applying anticoagulant solution. Centrifugation of the specimens provides a streamlined and efficient approach to zebrafish fluid sample collection, compatible with classic biochemical marker analyses.

Exploring the geological structure of a large-scale landslide using semi-airborne TEM in the Karst area of southwest China

Abstract The karst strata in the southwestern mountainous areas of China are extensively developed, creating a fragile geological environment. Landslide geological disasters occur frequently in these areas due to high annual rainfall, concentrated time periods, and frequent human engineering activities. Conventional ground geophysical methods face challenges in complex terrains, making it difficult to quickly and reliably obtain underground structures in landslide-prone areas. The semi-airborne transient electromagnetic method (SATEM) combines ground-based transmission and airborne reception of electromagnetic responses. This method, characterized by large emission magnetic moment and rapid data collection in the air, offers advantages in detecting deep geological structures in complex terrain areas. This article presents the application of a newly developed loop source SATEM system, integrated with multi-rotor unmanned aerial vehicles (UAVs), to conduct deep geological structure detection experiments on a large-scale landslide. The inversion results clearly depict the spatial distribution of the Feixianguan Formation and Longtan Formation, as well as vertical fractures developed in the strata due to underground coal mining. The detection test demonstrates the applicability of the SATEM method in detecting underground structures in Karst Plateau special landform areas, providing a new detection approach for similar regions.

The portable hand streamer — Rapid seismic imaging for shallow targets

In this paper, we introduce a portable hand streamer seismic system. The system is appropriate for characterizing the mechanical properties of geologic and engineered materials in the upper 5 to 10 m depth and for reflection imaging from tens to hundreds of meters depth. Unlike other seismic land streamer systems that are typically pulled behind a vehicle with a long string of geophones, our recording system and electric seismic source are contained within an electric utility cart to allow ease of mobility for a single operator. Our 48-channel contact-coupled streamer is tethered to the cart to enable low-effort data collection. Similar to a ground-penetrating radar system, this approach allows rapid data collection on roads, sidewalks, undeveloped paths, and native materials. Signal processing through standard surface- and body-wave approaches follows data collection. We present three case studies where we (1) map Quaternary and older strata to identify active faulting, (2) map shallow bedrock, and (3) identify the footprint of previous subsurface engineered structures. We suggest that this low-cost tool and approach can be used for decameter-scale subsurface site characterization in two or three dimensions. Future advances involve the integration of an autonomous vehicle with a fully programmable seismic source and wholly automated signal processing to enable real-time analysis of shallow seismic data.

Cotton-Net: efficient and accurate rapid detection of impurity content in machine-picked seed cotton using near-infrared spectroscopy

Widespread adoption of machine-picked cotton in China, the impurity content of seed cotton has increased significantly. This impurity content holds direct implications for the valuation of seed cotton and exerts a consequential influence on the ensuing quality of processed lint and textiles. Presently, the primary approach for assessing impurity content in seed cotton primarily depends on semi-automated testing instruments, exhibiting suboptimal detection efficiency and not well-suited for the impurity detection requirements during the purchase of seed cotton. To address this challenge, this study introduces a seed cotton near-infrared spectral (NIRS) data acquisition system, facilitating the rapid collection of seed cotton spectral data. Three pretreatment algorithms, namely SG (Savitzky-Golay convolutional smoothing), SNV (Standard Normal Variate Transformation), and Normalization, were applied to preprocess the seed cotton spectral data. Cotton-Net, a one-dimensional convolutional neural network aligned with the distinctive characteristics of the seed cotton spectral data, was developed in order to improve the prediction accuracy of seed cotton impurity content. Ablation experiments were performed, utilizing SELU, ReLU, and Sigmoid functions as activation functions. The experimental outcomes revealed that after normalization, employing SELU as the activation function led to the optimal performance of Cotton-Net, displaying a correlation coefficient of 0.9063 and an RMSE (Root Mean Square Error) of 0.0546. In the context of machine learning modeling, the LSSVM model, developed after Normalization and Random Frog algorithm processing, demonstrated superior performance, achieving a correlation coefficient of 0.8662 and an RMSE of 0.0622. In comparison, the correlation coefficient of Cotton-Net increased by 4.01%. This approach holds significant potential to underpin the subsequent development of rapid detection instruments targeting seed cotton impurities.

Thermally Activated Delayed Fluorescence (TADF)-active Coinage-metal Sulfide Clusters for High-resolution X-ray Imaging.

The study of facile-synthesis and low-cost X-ray scintillators with high light yield, low detection limit and high X-ray imaging resolution plays a vital role in medical and industrial imaging fields. However, the optimal balance between X-ray absorption, decay lifetime and excitonic utilization efficiency of scintillators to achieve high-resolution imaging is extremely difficult due to the inherent contradiction. Here two thermally activated delayed fluorescence (TADF)-actived coinage-metal clusters M6 S6 L6 (M=Ag or Cu) were synthesized by simple solvothermal reaction, where the cooperation of heavy atom-rich character and TADF mechanism supports strong X-ray absorption and rapid luminescent collection of excitons. Excitingly, Ag6 S6 L6 (SC-Ag) displays a high photoluminescence quantum yield of 91.6 % and scintillating light yield of 17420 photons MeV-1 , as well as a low detection limit of 208.65 nGy s-1 that is 26 times lower than the medical standard (5.5 μGy s-1 ). More importantly, a high X-ray imaging resolution of 16 lp/mm based on SC-Ag screen is demonstrated. Besides, rigid core skeleton reinforced by metallophilicity endows clusters M6 S6 L6 strong resistance to humidity and radiation. This work provides a new view for the design of efficient scintillators and opens the research door for silver clusters in scintillation application.

Thermally Activated Delayed Fluorescence (TADF)‐active Coinage‐metal Sulfide Clusters for High‐resolution X‐ray Imaging

AbstractThe study of facile‐synthesis and low‐cost X‐ray scintillators with high light yield, low detection limit and high X‐ray imaging resolution plays a vital role in medical and industrial imaging fields. However, the optimal balance between X‐ray absorption, decay lifetime and excitonic utilization efficiency of scintillators to achieve high‐resolution imaging is extremely difficult due to the inherent contradiction. Here two thermally activated delayed fluorescence (TADF)‐actived coinage‐metal clusters M6S6L6 (M=Ag or Cu) were synthesized by simple solvothermal reaction, where the cooperation of heavy atom‐rich character and TADF mechanism supports strong X‐ray absorption and rapid luminescent collection of excitons. Excitingly, Ag6S6L6 (SC‐Ag) displays a high photoluminescence quantum yield of 91.6 % and scintillating light yield of 17420 photons MeV−1, as well as a low detection limit of 208.65 nGy s−1 that is 26 times lower than the medical standard (5.5 μGy s−1). More importantly, a high X‐ray imaging resolution of 16 lp/mm based on SC‐Ag screen is demonstrated. Besides, rigid core skeleton reinforced by metallophilicity endows clusters M6S6L6 strong resistance to humidity and radiation. This work provides a new view for the design of efficient scintillators and opens the research door for silver clusters in scintillation application.

Orange peel magnetic activated carbon for removal of acid orange 7 dye from water

Magnetic activated carbon resources with a remarkably high specific surface area have been successfully synthesized using orange peels as the precursor and ZnCl2 as the activating agent. The impregnation ratio was set at 0.5, while the pyrolysis temperature spanned from 700 to 900 °C. This comprehensive study delved into the influence of activation temperatures on the resultant pore morphology and specific surface area. Optimal conditions were discerned, leading to a magnetic activated carbon material exhibiting an impressive specific surface area at 700 °C. The Brunauer–Emmett–Teller surface area reached 155.09 m2/g, accompanied by a total pore volume of 0.1768 cm3/g, and a mean pore diameter of 4.5604 nm. The material displayed noteworthy properties, with saturation magnetization (Ms) reaching 17.28 emu/g, remanence (Mr) at 0.29 emu/g, and coercivity (Hc) of 13.71 G. Additionally, the composite demonstrated super-paramagnetic behaviour at room temperature, facilitating its rapid collection within 5 s through an external magnetic field. Factors such as absorbent dose, initial concentration of the adsorbate, contact time, and pH were systematically examined. The adsorption behaviour for acid orange 7 (AO7) was found to adhere to the Temkin isotherm models (R2 = 0.997). The Langmuir isotherm model suggested a monolayer adsorption, and the calculated maximum monolayer capacity (Qm) was 357.14 mg/g, derived from the linear solvation of the Langmuir model using 0.75 g/L as an adsorbent dose and 150–500 mg/L as AO7 dye concentrations. The pseudo-second order model proved to be the best fit for the experimental data of AO7 dye adsorption, with a high coefficient of determination (R2) ranging from 0.999 to 1.000, outperforming other kinetic models.

Observing ER Dynamics over Long Timescales Using Light Sheet Fluorescence Microscopy.

The recent significant progress in developmental bio-imaging of live multicellular organisms has been greatly facilitated by the development of light sheet fluorescence microscopy (LSFM). Both commercial and custom LSFM systems offer the best means for long-term rapid data collection over a wide field of view at single-cell resolution. This is thanks to the low light exposure required for imaging and consequent limited photodamage to the biological sample, and the development of custom holders and mounting techniques that allow for specimens to be imaged in near-normal physiological conditions. This method has been successfully applied to plant cell biology and is currently seen as one of the most efficient techniques for 3D time-lapse imaging for quantitative studies. LSFM allows one to capture and quantify dynamic processes across various levels, from plant subcellular compartments to whole cells, tissues, and entire plant organs. Here we present a method to carry out LSFM on Arabidopsis leaves expressing fluorescent markers targeted to the ER. We will focus on a protocol to mount the sample, test the phototoxicity of the LSFM system, set up a LSFM experiment, and monitor the dynamics of the ER during heat shock.

Rapid miRNA detection in skin interstitial fluid using a hydrogel microneedle patch integrated with DNA probes and graphene oxide.

MicroRNA (miRNA) is a type of short, non-coding nucleic acid molecule that plays essential roles in diagnosing and prognosing various types of cancer. MiRNA is abundantly present in skin interstitial fluid (ISF), providing real-time and localized physiological information. Hydrogel microneedle (HMN) patches enable miRNA collection in a fast, pain-free, minimally invasive, and user-friendly manner. In this study, we introduced a fluorescence-based HMN assay, namely the HMN-miR sensor, composed of methacrylated hyaluronic acid (MeHA) and a graphene oxide-probe DNA (GO.pDNA) conjugate for miR21 and miR210 detection. The HMN-miR sensor demonstrates excellent skin penetration efficiency, rapid ISF collection capability, and sufficient miRNA detection and sequence identification specificity. The HMN-miR sensor facilitates a new assay that, with further optimization, could be applied in future clinical settings. Its simple fabrication process and excellent biocompatibility give it significant potential for various clinical uses, such as personalized cancer treatment and monitoring the healing progress of burn wounds.

ОСОБЛИВОСТІ УПРАВЛІННЯ БІЗНЕС-ПРОЦЕСАМИ НА ОСНОВІ РЕФЛЕКСИВНОГО ПІДХОДУ: ТЕОРЕТИКО-МЕТОДОЛОГІЧНІ АСПЕКТИ

In the context of the war in Ukraine and the rapidly changing business environment, the implementation of innovative approaches to business process management has become increasingly relevant. The reflexive approach ensures continuous analysis and optimisation of business processes, enhancing the competitiveness and adaptability of enterprises amidst uncertainty and risks. Thе article examines the theoretical and methodological aspects of managing business processes based on the reflexive approach during the war in Ukraine. The main principles and stages of reflexive management, its impact on the efficiency of business processes, and the organisational structure of enterprises are analysed. The article aims to develop a theoretical and methodological foundation for implementing the reflexive approach to business process management in wartime. It is established that the reflexive approach to business process management during wartime ensures improved process efficiency, cost reduction, and enhanced quality of products and services. Research aims to expand scientific knowledge on new management methods that can enhance the effectiveness of business processes in challenging environments. The development of new management approaches and tools tailored to wartime conditions will enable Ukrainian enterprises to operate effectively, maintain competitiveness, and increase resilience even amidst uncertainty. The research results demonstrate that applying the reflexive approach enables enterprises to adapt to external changes, respond promptly to wartime challenges, and improve their competitiveness. The use of modern reflexive approach tools in business process management in Ukraine is substantiated, allowing for the rapid collection and analysis of data, process modelling and optimisation, effective knowledge and collaboration management, decision support, and performance evaluation, thus maintaining business competitiveness in challenging conditions. The article provides practical recommendations for implementing the reflexive approach in business process management and identifies prospects for further research in this direction.

High-throughput optimisations for 3D chemical imaging of pharmaceutical solid oral dosage forms.

Chemical imaging of pharmaceutical solid oral dosage forms is a key technique for quality assurance and issue diagnosis. This technique can be further augmented using 3D chemical imaging via serial sections and image stacking. However, the additional collection time this entails means that 3D imaging is utilised for a very niche set of applications. Previous attempts have been made to optimize the process but have often compromised the quality of the resulting chemical images to achieve the gains in process time. In this study, several optimisation strategies are employed to increase the efficiency of 3D chemical image collection without sacrificing the quality of the final chemical images. The use of automated microscope macros and a kinematic mounting system allowed for rapid sample processing and efficient utilisation of equipment time. The automated macros allow the Raman microscope to collect mapping data continuously from multiple samples without the need for operator intervention steps. The kinematic mounting system allows rapid and accurate sample transfer and positioning between instruments. These optimisations resulted in a three times speed increase in collection time while keeping the same signal-to-noise ratio of the resulting chemical images. These optimisations will allow the rapid collection of statistically robust 3D chemical image data within a set time frame that is more amenable to an industrial workflow.

Methodology to enable high-throughput imaging of Arabidopsis seedlings on cover glass-bottom multiwell plates.

High-throughput imaging enables rapid collection of large datasets and is used widely in many systems. However, this is not often used in plant-based systems due to issues related to the need to mount tissues and autofluorescence of plant metabolites. We therefore developed methodology enabling high-throughput imaging of Arabidopsis roots. In this system, growth media supplemented with India Ink (to block autofluorescence from cotyledons) is poured directly into multi-well coverglass-bottom plates and seedlings grown such that the roots grow down with the gravity vector and along the coverglass, effectively mounting themselves for imaging. This method enables high-throughput imaging of Arabidopsis roots.