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Consumers and marketers often post photos of experiential consumption online. While prior research has studied how human presence in social media images impacts viewers' responses, the findings are mixed. The present research advances the current understanding by incorporating viewers' need for self-identity into their response model. Six studies, including an analysis of field data (14,725 Instagram photos by a top travel influencer) and five controlled experiments, find that the presence (vs. absence) of another human in the photo of an identity-relevant experience (e.g. a vacation, a wedding) can lower viewers' liking and preference for the venue (i.e. the vacation destination, the wedding venue) in the photo. This effect is mediated by viewers' feelings of others' ownership of the venue and moderated by the relevance of the experience to the viewer's self-identity as well as the distinctiveness of the human in the photo. This research is the first to investigate the impact of human presence in shared photos through the lens of psychological ownership and the identity-signaling function of ownership. The findings offer practical insights into when marketers should avoid human presence in advertisements and how to mitigate the negative impact of human presence in online photos. [ABSTRACT FROM AUTHOR]

Brand-related content cocreated by consumers can play a crucial role in brand–consumer interactions and provide brands with valuable insights hidden in vast seas of unstructured data. We propose and evaluate a framework integrating a social network approach and scalable automated content analysis of texts and visuals for studying brand-related communication on social media. To illustrate the proposed approach, we use Twitter content related to two brands: Barclays and Sierra Club. By applying network clustering algorithms we identify different types of organically emerging communities around brands. Cluster-specific diffusion leaders are identified using their in-degree centrality values. To examine the unique characteristics of brand-related content within each cluster, we apply and assess the accuracy of popular off-the-shelf solutions for text and image analysis, also known as application programming interfaces (APIs). Of six sentiment analysis solutions, only one shows acceptable reliability levels. For computer vision APIs, we first identify labels that have unclear or imprecise meaning and calculate accuracy levels, resulting in acceptable accuracy levels for four of the five APIs. We discuss conceptual and practical implications of this integrative approach and of the technological hurdles that these popular automated content analysis applications pose. [ABSTRACT FROM AUTHOR]

Breastfeeding benefits both the mother and infant and is a topic of attention in public health. After childbirth, untreated medical conditions or lack of support lead many mothers to discontinue breastfeeding. For instance, nipple damage and mastitis affect 80% and 20% of US mothers, respectively. Lactation consultants (LCs) help mothers with breastfeeding, providing in-person, remote, and hybrid lactation support. LCs guide, encourage, and find ways for mothers to have a better experience breastfeeding. Current telehealth services help mothers seek LCs for breastfeeding support, where images help them identify and address many issues. Due to the disproportional ratio of LCs and mothers in need, these professionals are often overloaded and burned out.This study aims to investigate the effectiveness of 5 distinct convolutional neural networks in detecting healthy lactating breasts and 6 breastfeeding-related issues by only using red, green, and blue images. Our goal was to assess the applicability of this algorithm as an auxiliary resource for LCs to identify painful breast conditions quickly, better manage their patients through triage, respond promptly to patient needs, and enhance the overall experience and care for breastfeeding mothers.We evaluated the potential for 5 classification models to detect breastfeeding-related conditions using 1078 breast and nipple images gathered from web-based and physical educational resources. We used the convolutional neural networks Resnet50, Visual Geometry Group model with 16 layers (VGG16), InceptionV3, EfficientNetV2, and DenseNet169 to classify the images across 7 classes: healthy, abscess, mastitis, nipple blebs, dermatosis, engorgement, and nipple damage by improper feeding or misuse of breast pumps. We also evaluated the models' ability to distinguish between healthy and unhealthy images. We present an analysis of the classification challenges, identifying image traits that may confound the detection model.The best model achieves an average area under the receiver operating characteristic curve of 0.93 for all conditions after data augmentation for multiclass classification. For binary classification, we achieved, with the best model, an average area under the curve of 0.96 for all conditions after data augmentation. Several factors contributed to the misclassification of images, including similar visual features in the conditions that precede other conditions (such as the mastitis spectrum disorder), partially covered breasts or nipples, and images depicting multiple conditions in the same breast.This vision-based automated detection technique offers an opportunity to enhance postpartum care for mothers and can potentially help alleviate the workload of LCs by expediting decision-making processes.

Despite much progress, image processing remains a significant bottleneck for high-throughput analysis of microscopy data. One popular platform for single-cell time-lapse imaging is the mother machine, which enables long-term tracking of microbial cells under precisely controlled growth conditions. While several mother machine image analysis pipelines have been developed in the past several years, adoption by a non-expert audience remains a challenge. To fill this gap, we implemented our own software, MM3, as a plugin for the multidimensional image viewer napari. napari-MM3 is a complete and modular image analysis pipeline for mother machine data, which takes advantage of the high-level interactivity of napari. Here, we give an overview of napari-MM3 and test it against several well-designed and widely used image analysis pipelines, including BACMMAN and DeLTA. Researchers often analyze mother machine data with custom scripts using varied image analysis methods, but a quantitative comparison of the output of different pipelines has been lacking. To this end, we show that key single-cell physiological parameter correlations and distributions are robust to the choice of analysis method. However, we also find that small changes in thresholding parameters can systematically alter parameters extracted from single-cell imaging experiments. Moreover, we explicitly show that in deep learning-based segmentation, what you put is what you get (WYPIWYG) - that is, pixel-level variation in training data for cell segmentation can propagate to the model output and bias spatial and temporal measurements. Finally, while the primary purpose of this work is to introduce the image analysis software that we have developed over the last decade in our lab, we also provide information for those who want to implement mother machine-based high-throughput imaging and analysis methods in their research.

The magnitude of the dipole effect detected by the cutoff energy measurement was modulated for SrTiO3 (STO)/LaAlO3 (LAO)/SrRuO3 (SRO)/STO (001) subs. epitaxial stacks by ultrathin SrAlOx (SAO) insertion prior to LAO deposition. The difference in frictional forces on the LAO surfaces between the stacks with and without SAO insertion was clearly detected by lateral force microscopy (LFM), which indicated the change in dominating surface atomic layers. From the statistical analysis of LFM images, the SAO insertion was found to improve the lateral uniformity of the stacking sequence of charged monatomic layers in epitaxial LAO along the c-axis [(LaO)+ and (AlO2)−], taking account of the inevitable correlation between the surface-terminating atoms with the stacking sequence of the charged monatomic layers in epitaxial LAO. These results are consistently explainable with the proposed model that the interface dipole effect along the c-axis of the perovskite epitaxial stack is determined by the stacking sequence of charged monatomic layers of LAO. [ABSTRACT FROM AUTHOR]

Photoelectron angular distributions (PADs) produced from the photoionization of chiral molecules using elliptically polarized light exhibit a forward/backward asymmetry with respect to the optical propagation direction. By recording these distributions using the velocity-map imaging (VMI) technique, the resulting photoelectron elliptical dichroism (PEELD) has previously been demonstrated as a promising spectroscopic tool for studying chiral molecules in the gas phase. The use of elliptically polarized laser pulses, however, produces PADs (and consequently, PEELD distributions) that do not exhibit cylindrical symmetry about the propagation axis. This leads to significant limitations and challenges when employing conventional VMI acquisition and data processing strategies. Using novel photoelectron image analysis methods based around Hankel transform reconstruction tomography and machine learning, however, we have quantified—for the first time—significant symmetry-breaking contributions to PEELD signals that are of a comparable magnitude to the symmetric terms in the multiphoton ionization of (1R,4R)-(+)- and (1S,4S)-(−)-camphor. This contradicts any assumptions that symmetry-breaking can be ignored when reconstructing VMI data. Furthermore, these same symmetry-breaking terms are expected to appear in any experiment where circular and linear laser fields are used together. This ionization scheme is particularly relevant for investigating dynamics in chiral molecules, but it is not limited to them. Developing a full understanding of these terms and the role they play in the photoionization of chiral molecules is of clear importance if the potential of PEELD and related effects for future practical applications is to be fully realized. [ABSTRACT FROM AUTHOR]

The erosion of high-end fashion brands by fast-fashion copycats (e.g., Zara, H&M) has stirred controversies and unceasing legal attempts to copyright fashion designs. Despite the purported negative impact of copycats, the effect of fashion copycats on high-end brands remains empirically unclear. Research on this topic has been impeded by the absence of a modeling framework to quantify fashion and by the lack of consumer-level data on fashion choices. The authors collect data on the posting behaviors of consumers on a fashion-specific social media platform and develop a dynamic structural model with deep learning image analytics to characterize consumers' choices of brands and styles. Results suggest that fast-fashion copycats can both harm high-end brands (a cannibalization effect) and help them (a market expansion effect). The authors also identify both static and dynamic mechanisms that contribute to the market expansion effect: The affordability of mixing copycats with high-end brands boosts the number of high-end items featured in posts by financially constrained consumers (a static mechanism). In addition, good styles from copycats enable users to build their popularity on social media over time, which may increase the users' valuation of high-end brands and reduce the users' future costs via sponsorship opportunities (dynamic mechanisms). The results could inform policy makers about the potential consequences of prohibiting fashion copycats. [ABSTRACT FROM AUTHOR]

Structural studies aiming to visualize the interaction of Tau with microtubules (MTs) face several challenges, the main concerning the fact that Tau has multiple MT-interacting regions. In particular, the four (or three) pseudo-repeats of Tau bind to identical elements along the MT lattice but do it through non-identical residues. In addition, any given Tau molecule can use all its repeats or just one for its engagement with MTs. Finally, the binding of one Tau is not necessarily in register with respect to the next one. The mismatch in the MT and Tau repeats, therefore, challenges conventional modes of image analysis when visualizing these samples using cryo-electron microscopy. This commentary is dedicated to those challenges and ways to circumvent them while aiming for an atomic description of the Tau-tubulin interaction.

Microscopic image analysis is an important task from the diagnostic point of view because microscopic investigation is often required to diagnose the root cause of some diseases. Some inherent characteristics of the microscopic images often create some problems for the automated analysis algorithms and therefore, a lot of developments are to be performed to enrich the automated and computer-aided diagnostic systems. The proposed work proposes a novel microscopic image segmentation approach that is based on affinity propagation-based clustering. The main objective of this work is to provide an elegant solution for computer-aided diagnostic systems by introducing an efficient segmentation approach because segmentation plays a crucial role in many biomedical image analysis frameworks. A balanced semi-supervised framework is proposed that can work with a lesser number of annotations. The proposed approach uses incremental and decremental learning besides affinity propagation clustering. It allows a balance between new learning and forgetting already learned information. The proposed affinity calculation method is used for clustering purposes. The proposed approach can be helpful for real-life microscopic image interpretation purposes and can act as a third eye for physicians. The proposed approach can be helpful where properly annotated ground truth segmented data are not available. Some real-life microscopic data are considered to perform the experiments and the obtained results are quite encouraging and prove the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The surveillance and management of cargo fleets is a crucial objective of intelligent transportation systems. Load, especially overload, has a destructive effect on roads and bridges, and monitoring it can increase the life of road surface and its structure. For low-end hardware with lack of CPU power and no GPU support, this paper presents a rapid method to detect whether heavy vehicles have loads or not; then it proposes a fast method for classifying load types to distinguish soil and construction waste from other miscellaneous loads for heavy weight vehicles. This paper applies a method for classifying cargo types using image processing and texture image classification. This method extracts features for statistical analysis of texture images based on gray-level co-occurrence matrices and local binary patterns. The classification is carried out by support vector machine, k-nearest neighborhood, K-mean, artificial neural networks and random forest classifiers. A large number of positive and negative patterns have been used to train these classifiers. We compare the performance of proposed extracted features and classifiers. The simulation results demonstrate that soil and construction waste can be identified from other miscellaneous loads effective in real-time implementation. [ABSTRACT FROM AUTHOR]

This paper proposes a method for an automatic detection of 3D-display-friendly scenes from video sequences. Manual selection of such scenes by a human user would be extremely time consuming and would require additional evaluation of the result on 3D display. The input videos can be intentionally captured or taken from other sources, such as films. First, the input video is analyzed and the camera trajectory is estimated. The optimal frame sequence that follows defined rules, based on optical attributes of the display, is then extracted. This ensures the best visual quality and viewing comfort. The following identification of a correct focusing distance is an important step to produce a sharp and artifact-free result on a 3D display. Two novel and equally efficient focus metrics for 3D displays are proposed and evaluated. Further scene enhancements are proposed to correct the unsuitably captured video. Multiple image analysis approaches used in the proposal are compared in terms of both quality and time performance. The proposal is experimentally evaluated on a state-of-the-art 3D display by Looking Glass Factory and is suitable even for other multi-view devices. The problem of optimal scene detection, which includes the input frames extraction, resampling, and focusing, was not addressed in any previous research. Separate stages of the proposal were compared with existing methods, but the results show that the proposed scheme is optimal and cannot be replaced by other state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Accurate segmentation of lesion areas in medical image analysis can assist clinicians develop more personalized treatment tools, improving treatment efficacy and survival rates. Furthermore, the single encoding structure within the U-shaped architecture limits the network's capacity to aggregate semantic information of various scales. In this paper, we propose a residual T-shaped network (RTNet) for medical image segmentation. The RTNet incorporates Multi-Level Feature Enhanced Residual Block (MFER Block) and attention gates into a T-shaped structure, making our network capture richer contextual information at multiple scales. First, by utilizing two sets of encoders with different structures, our network can extract image features at different scales. Secondly, the MFER Block is employed to combine and learn the feature representations from the encoders, facilitating more effective feature fusion. Finally, the attention gates fuse those feature maps obtained from each stage via skip connections, then they are concatenated by the decoder to generate the final feature map for segmentation. Experimental results show that, on the BUSI and ISIC2017 datasets, the IoU of our RTNet reached 67.39% and 84.39%, respectively; and the DSC score achieved 79.95% and 91.44%, respectively. Our network outperforms state-of-the-art models for medical image segmentation. [ABSTRACT FROM AUTHOR]

In the image analysis of the hydrophobic flocculation process of coal particles, the recognition accuracy of floc characteristic parameters is affected by bubbles, floc superposition, and floc morphology. An image processing method was studied and designed based on the PyCharm Community platform for clarity recognition and floc segmentation of coal particle floc characteristics. Firstly, the critical value of image clarity was determined to be 1.31 by using the Laplace convolution accounting method after the average gray level correction, and the fuzzy flocs whose clarity was less than the critical value were eliminated. Then, by comparing the segmentation effect of the threshold, edge, and region segmentation algorithm on the flocs, the histogram bimodal method was determined to be the best image processing segmentation method for fine coal floc collection. The fractal theory was used to verify the effect of image processing. The results showed that the surface fractal dimension of the floc image after deblurring and segmentation was maintained near 2.05, indicating that the processed image can obtain more stable and reliable floc characteristic parameters. [ABSTRACT FROM AUTHOR]

Retinal fundus image classification is pivotal for the early detection of various eye disorders. Leveraging advancements in deep learning, this research introduces a robust framework for retinal fundus image classification. In this research paper, we introduce a novel methodology that integrates the Active Gradient Deep Convolutional Neural Network (AG-DCNN) architecture with the Red Spider Optimization (RSO) algorithm. Extensive evaluations conducted on well-established retinal image datasets such as DRIVE, STARE, CHASE DB1, HRF, DRISHTI-GS, and RFMiD illustrate substantial enhancements in classification accuracy, sensitivity, and specificity when compared to conventional approaches. The AG-DCNN with RSO exhibits superior performance and remarkable generalization abilities across diverse datasets. This novel approach not only enhances the latest advances in retinal image classification but also holds promise for early disease diagnosis and improved patient care. Through extensive examinations, our proposed method consistently outperforms established techniques, establishing itself as the benchmark in retinal fundus image classification. The integration of AG-DCNN and RSO showcases its efficacy and potential impact in advancing the field of medical image analysis. [ABSTRACT FROM AUTHOR]

BACKGROUND RESULTS CONCLUSION Determining the freshness of chilled pork is of paramount importance to consumers worldwide. Established freshness indicators such as total viable count, total volatile basic nitrogen and pH are destructive and time‐consuming. Color change in chilled pork is also associated with freshness. However, traditional detection methods using handheld colorimeters are expensive, inconvenient and prone to limitations in accuracy. Substantial progress has been made in methods for pork preservation and freshness evaluation. However, traditional methods often necessitate expensive equipment or specialized expertise, restricting their accessibility to general consumers and small‐scale traders. Therefore, developing a user‐friendly, rapid and economical method is of particular importance.This study conducted image analysis of photographs captured by smartphone cameras of chilled pork stored at 4 °C for 7 days. The analysis tracked color changes, which were then used to develop predictive models for freshness indicators. Compared to handheld colorimeters, smartphone image analysis demonstrated superior stability and accuracy in color data acquisition. Machine learning regression models, particularly the random forest and decision tree models, achieved prediction accuracies of more than 80% and 90%, respectively.Our study provides a feasible and practical non‐destructive approach to determining the freshness of chilled pork. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Objectives: This article focuses on the detection of cells in low-contrast brightfield microscopy images; in our case, it is chronic lymphocytic leukaemia cells. The automatic detection of cells from brightfield time-lapse microscopic images brings new opportunities in cell morphology and migration studies; to achieve the desired results, it is advisable to use state-of-the-art image segmentation methods that not only detect the cell but also detect its boundaries with the highest possible accuracy, thus defining its shape and dimensions. Methods: We compared eight state-of-the-art neural network architectures with different backbone encoders for image data segmentation, namely U-net, U-net++, the Pyramid Attention Network, the Multi-Attention Network, LinkNet, the Feature Pyramid Network, DeepLabV3, and DeepLabV3+. The training process involved training each of these networks for 1000 epochs using the PyTorch and PyTorch Lightning libraries. For instance segmentation, the watershed algorithm and three-class image semantic segmentation were used. We also used StarDist, a deep learning-based tool for object detection with star-convex shapes. Results: The optimal combination for semantic segmentation was the U-net++ architecture with a ResNeSt-269 background with a data set intersection over a union score of 0.8902. For the cell characteristics examined (area, circularity, solidity, perimeter, radius, and shape index), the difference in mean value using different chronic lymphocytic leukaemia cell segmentation approaches appeared to be statistically significant (Mann–Whitney U test, P <.0001). Conclusion: We found that overall, the algorithms demonstrate equal agreement with ground truth, but with the comparison, it can be seen that the different approaches prefer different morphological features of the cells. Consequently, choosing the most suitable method for instance-based cell segmentation depends on the particular application, namely, the specific cellular traits being investigated. [ABSTRACT FROM AUTHOR]

The control of bacterial growth is key to the prevention and treatment of tuberculosis (TB). Granulomas represent independent foci of the host immune response that present heterogeneous capacity for control of bacterial growth. At the whole tissue level, B cells and CD4 or CD8 T cells have an established role in immune protection against TB. Immune cells interact within each granuloma response, but the impact of granuloma immune composition on bacterial replication remains unknown. Here we investigate the associations between immune cell composition, including B cell, CD4, and CD8 T cells, and the state of replicating Mycobacterium tuberculosis (Mtb) within the granuloma. A measure of ribosomal RNA synthesis, the RS ratio®, represents a proxy measure of Mtb replication at the whole tissue level. We adapted the RS ratio through use of in situ hybridization, to identify replicating and non-replicating Mtb within each designated granuloma. We applied a regression model to characterize the associations between immune cell populations and the state of Mtb replication within each respective granuloma. In the evaluation of nearly 200 granulomas, we identified heterogeneity in both immune cell composition and proportion of replicating bacteria. We found clear evidence of directional associations between immune cell composition and replicating Mtb. Controlling for vaccination status and endpoint post-infection, granulomas with lower CD4 or higher CD8 cell counts are associated with a higher percent of replicating Mtb. Conversely, changes in B cell proportions were associated with little change in Mtb replication. This study establishes heterogeneity across granulomas, demonstrating that certain immune cell types are differentially associated with control of Mtb replication. These data suggest that evaluation at the granuloma level may be imperative to identifying correlates of immune protection. [ABSTRACT FROM AUTHOR]

Brain tumors are diseases characterized by abnormal cell growth within or around brain tissues, including various types such as benign and malignant tumors. However, there is currently a lack of early detection and precise localization of brain tumors in MRI images, posing challenges to diagnosis and treatment. In this context, achieving accurate target detection of brain tumors in MRI images becomes particularly important as it can improve the timeliness of diagnosis and the effectiveness of treatment. To address this challenge, we propose a novel approach-the YOLO-NeuroBoost model. This model combines the improved YOLOv8 algorithm with several innovative techniques, including dynamic convolution KernelWarehouse, attention mechanism CBAM (Convolutional Block Attention Module), and Inner-GIoU loss function. Our experimental results demonstrate that our method achieves mAP scores of 99.48 and 97.71 on the Br35H dataset and the open-source Roboflow dataset, respectively, indicating the high accuracy and efficiency of this method in detecting brain tumors inMRI images. This research holds significant importance for improving early diagnosis and treatment of brain tumors and provides new possibilities for the development of the medical image analysis field. [ABSTRACT FROM AUTHOR]

This study shows how bacterial viruses (bacteriophages, phages) interact with calcium carbonate during precipitation from aqueous solution. Using electron microscopy, epifluorescence microscopy, X-ray diffraction, and image analysis, we demonstrate that bacteriophages can strongly influence the formation of the vaterite phase. Importantly, bacteriophages may selectively bind both amorphous calcium carbonate (ACC) and vaterite, and indirectly affect the formation of structural defects in calcite crystallites. Consequently, the surface properties of calcium carbonate phases precipitating in the presence of viruses may exhibit different characteristics. These findings may have significant implications in determining the role of bacterial viruses in modern microbially-rich carbonate sedimentary environments, as well as in biomedical technologies. Finally, the phage-vaterite system, as a biocompatible material, may serve as a basis for the development of promising drug delivery carriers. [ABSTRACT FROM AUTHOR]

Imaging the internal architecture of fast-vibrating structures at micrometer scale and kilohertz frequencies poses great challenges for numerous applications, including the study of biological oscillators, mechanical testing of materials, and process engineering. Over the past decade, X-ray microtomography with retrospective gating has shown very promising advances in meeting these challenges. However, breakthroughs are still expected in acquisition and reconstruction procedures to keep improving the spatiotemporal resolution, and study the mechanics of fast-vibrating multiscale structures. Thereby, this works aims to improve this imaging technique by minimising streaking and motion blur artefacts through the optimisation of experimental parameters. For that purpose, we have coupled a numerical approach relying on tomography simulation with vibrating particles with known and ideal 3D geometry (micro-spheres or fibres) with experimental campaigns. These were carried out on soft composites, imaged in synchrotron X-ray beamlines while oscillating up to 400 Hz, thanks to a custom-developed vibromechanical device. This approach yields homogeneous angular sampling of projections and gives reliable predictions of image quality degradation due to motion blur. By overcoming several technical and scientific barriers limiting the feasibility and reproducibility of such investigations, we provide guidelines to enhance gated-CT 4D imaging for the analysis of heterogeneous, high-frequency oscillating materials. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions Adult‐type diffuse gliomas are among the central nervous system's most aggressive malignant primary neoplasms. Despite advancements in systemic therapies and technological improvements in radiation oncology treatment delivery, the survival outcome for these patients remains poor. Fast and accurate assessment of tumor response to oncologic treatments is crucial, as it can enable the early detection of recurrent or refractory gliomas, thereby allowing timely intervention with life‐prolonging salvage therapies.Radiomics is a developing field with great potential to improve medical image interpretation. This study aims to apply a radiomics‐based predictive model for classifying response to radiotherapy within the first 3 months post‐treatment.Ninety‐five patients were selected from the Burdenko Glioblastoma Progression Dataset. Tumor regions were delineated in the axial plane on contrast‐enhanced T1(CE T1W) and T2 fluid‐attenuated inversion recovery (T2_FLAIR) magnetic resonance imaging (MRI). Hand‐crafted radiomic (HCR) features, including first‐ and second‐order features, were extracted using PyRadiomics (3.7.6) in Python (3.10). Then, recursive feature elimination with a random forest (RF) classifier was applied for feature dimensionality reduction. RF and support vector machine (SVM) classifiers were built to predict treatment outcomes using the selected features. Leave‐one‐out cross‐validation was employed to tune hyperparameters and evaluate the models.For each segmented target, 186 HCR features were extracted from the MRI sequence. Using the top‐ranked radiomic features from a combination of CE T1W and T2_FLAIR, an optimized classifier achieved the highest averaged area under the curve (AUC) of 0.829 ± 0.075 using the RF classifier. The HCR features of CE T1W produced the worst outcomes among all models (0.603 ± 0.024 and 0.615 ± 0.075 for RF and SVM classifiers, respectively).We developed and evaluated a radiomics‐based predictive model for early tumor response to radiotherapy, demonstrating excellent performance supported by high AUC values. This model, harnessing radiomic features from multi‐modal MRI, showed superior predictive performance compared to single‐modal MRI approaches. These results underscore the potential of radiomics in clinical decision support for this disease process. [ABSTRACT FROM AUTHOR]

SeS2 positive electrodes are promising components for the development of high-energy, non-aqueous lithium sulfur batteries. However, the (electro)chemical and structural evolution of this class of positive electrodes is not yet fully understood. Here, we use operando physicochemical measurements to elucidate the dissolution and deposition processes in the SeS2 positive electrodes during lithium sulfur cell charge and discharge. Our analysis of real-time imaging reveals the pivotal role of Se in the SeS2 nucleation process, while S enables selective depositions. During the initial discharge, SeS2 converts into Se and S separately, with the dissolved Se acting as nucleation sites due to their lower nucleation potential. The Se effectively catalyzes the growth of S particles, resulting in improved lithium sulfur battery performance compared to cells using positive electrodes containing only Se or S as active materials. By adjusting the Se-to-S ratio, we demonstrate that a low concentration of Se enables uniform catalytic sites, promotes the homogeneous distribution of S and favours improved lithium sulfur battery performance. SexSy is a promising positive electrode material for non-aqueous Li||chalcogen batteries. However, the behaviour of S and Se in the electrode is unclear. Here, the authors investigate the physicochemical phenomena of SexSy and the catalytic role of Se during battery testing. [ABSTRACT FROM AUTHOR]

Recently, multi-modal vision-language foundation models have gained significant attention in the medical field. While these models offer great opportunities, they still face crucial challenges, such as the requirement for fine-grained knowledge understanding in computer-aided diagnosis and the capability of utilizing very limited or even no task-specific labeled data in real-world clinical applications. In this study, we present MaCo, a masked contrastive chest X-ray foundation model that tackles these challenges. MaCo explores masked contrastive learning to simultaneously achieve fine-grained image understanding and zero-shot learning for a variety of medical imaging tasks. It designs a correlation weighting mechanism to adjust the correlation between masked chest X-ray image patches and their corresponding reports, thereby enhancing the model's representation learning capabilities. To evaluate the performance of MaCo, we conducted extensive experiments using 6 well-known open-source X-ray datasets. The experimental results demonstrate the superiority of MaCo over 10 state-of-the-art approaches across tasks such as classification, segmentation, detection, and phrase grounding. These findings highlight the significant potential of MaCo in advancing a wide range of medical image analysis tasks. Multi-modal foundation models are increasingly important in medical applications. Here, authors show a masked contrastive chest X-ray model that achieves fine-grained image understanding and zero-shot capabilities, outperforming existing methods [ABSTRACT FROM AUTHOR]

Background: To analyse and compare the grading of diabetic retinopathy (DR) severity level using standard 35° ETDRS 7-fields photography and CLARUS™ 500 ultra-widefield imaging system. Methods: A cross-sectional analysis of retinal images of patients with type 2 diabetes (n = 160 eyes) was performed for this study. All patients underwent 7-fields colour fundus photography (CFP) at 35° on a standard Topcon TRC-50DX® camera, and ultra-widefield (UWF) imaging at 200° on a CLARUS™ 500 (ZEISS, Dublin, CA, USA) by an automatic montage of two 133° images (nasal and temporal). 35° 7-fields photographs were graded by two graders, according to the Early Treatment Diabetic Retinopathy Study (ETDRS). For CLARUS UWF images, a prototype 7-fields grid was applied using the CLARUS review software, and the same ETDRS grading procedures were performed inside that area only. Grading of DR severity level was compared between these two methods to evaluate the agreement between both imaging techniques. Results: Images of 160 eyes from 83 diabetic patients were considered for analysis. According to the 35° ETDRS 7-fields images, 22 eyes were evaluated as DR severity level 10–20, 64 eyes were evaluated as DR level 35, 41 eyes level 43, 21 eyes level 47, 7 eyes level 53, and 5 eyes level 61. The same DR severity level was achieved with CLARUS 500 UWF images in 92 eyes (57%), showing a perfect agreement (k > 0.80) with the 7-fields 35° technique. Fifty-seven eyes (36%) showed a higher DR level with CLARUS UWF images, mostly due to a better visualization of haemorrhages and a higher detection rate of intraretinal microvascular abnormalities (IRMA). Only 11 eyes (7%) showed a lower severity level with the CLARUS UWF system, due to the presence of artifacts or media opacities that precluded the correct evaluation of DR lesions. Conclusions: UWF CLARUS 500 device showed nearly perfect agreement with standard 35° 7-fields images in all ETDRS severity levels. Moreover, CLARUS images showed an increased ability to detect haemorrhages and IRMA helping with finer evaluation of lesions, thus demonstrating that a UWF photograph can be used to grade ETDRS severity level with a better visualization than the standard 7-fields images. Trial registration: Approved by the AIBILI - Association for Innovation and Biomedical Research on Light and Image Ethics Committee for Health with number CEC/009/17- EYEMARKER. [ABSTRACT FROM AUTHOR]

Plumage pigmentation and ornamentation can indicate male phenotypic quality in many avian species and represent traits that are often selected for by females. Melanins are some of the most widespread classes of pigments in the animal kingdom and form the many shades of black and brown feather colors across raptor taxa. Eumelanin is responsible for the darker shades of black and brown, whereas pheomelanin is responsible for the lighter brown shades and rusty red coloration that are found in many buteonine raptors, including Swainson's Hawks (Buteo swainsoni). Plumage polymorphism has been examined in various raptor species along with morphometric differences as a basis for sexual dimorphism; however, the potential for sexual dimorphism in the form of pheomelanin-pigmented plumage has rarely been discussed. We examined this phenomenon in Swainson's Hawks by digitally sampling breast feathers from adult museum specimens, and we utilized an imaging toolbox for multispectral image analysis in the open-source ImageJ software to compare red pixel values between sexes. We found a significant difference in normalized red pixel value means between male and female birds, with higher redness values strongly correlating with male individuals, regardless of morph type. This indicates a relationship between pheomelanin pigmentation and sex, illustrating a basis for sexual dimorphism within the species, as well as the potential for sexual selection of redder males. [ABSTRACT FROM AUTHOR]

Objectives To evaluate the accuracy of ChatGPT and Bard in answering pathology examination questions requiring image interpretation. Methods The study evaluated ChatGPT-4 and Bard's performance using 86 multiple-choice questions, with 17 (19.8%) focusing on general pathology and 69 (80.2%) on systemic pathology. Of these, 62 (72.1%) included microscopic images, and 57 (66.3%) were first-order questions focusing on diagnosing the disease. The authors presented these artificial intelligence (AI) tools with questions, both with and without clinical contexts, and assessed their answers against a reference standard set by pathologists. Results ChatGPT-4 achieved a 100% (n = 86) accuracy rate in questions with clinical context, surpassing Bard's 87.2% (n = 75). Without context, the accuracy of both AI tools declined significantly, with ChatGPT-4 at 52.3% (n = 45) and Bard at 38.4% (n = 33). ChatGPT-4 consistently outperformed Bard across various categories, particularly in systemic pathology and first-order questions. A notable issue identified was Bard's tendency to "hallucinate" or provide plausible but incorrect answers, especially without clinical context. Conclusions This study demonstrated the potential of ChatGPT and Bard in pathology education, stressing the importance of clinical context for accurate AI interpretations of pathology images. It underlined the need for careful AI integration in medical education. [ABSTRACT FROM AUTHOR]

Objectives ChatGPT (OpenAI, San Francisco, CA) has shown impressive results across various medical examinations, but its performance in kidney pathology is not yet established. This study evaluated proficiencies of GPT-4 Vision (GPT-4V), an updated version of the platform with the ability to analyze images, on kidney pathology questions and compared its responses with those of nephrology trainees. Methods Thirty-nine questions (19 text-based questions and 20 with various kidney biopsy images) designed specifically for the training of nephrology fellows were employed. Results GPT-4V displayed comparable accuracy rates in the first and second runs (67% and 72%, respectively, P  =  .50). The aggregated accuracy, however—particularly, the consistent accuracy—of GPT-4V was lower than that of trainees (72% and 67% vs 79%). Both GPT-4V and trainees displayed comparable accuracy in responding to image-based and text-only questions (55% vs 79% and 81% vs 78%, P  =  .11 and P  =  .67, respectively). The consistent accuracy in image-based, directly asked questions for GPT-4V was 29%, much lower than its 88% consistency on text-only, directly asked questions (P  =  .02). In contrast, trainees maintained similar accuracy in directly asked image-based and text-based questions (80% vs 77%, P  =  .65). Although the aggregated accuracy for correctly interpreting images was 69%, the consistent accuracy across both runs was only 39%. The accuracy of GPT-4V in answering questions with correct image interpretation was significantly higher than for questions with incorrect image interpretation (100% vs 0% and 100% vs 33% for the first and second runs, P  =  .001 and P  =  .02, respectively). Conclusions The performance of GPT-4V in handling kidney pathology questions, especially those including images, is limited. There is a notable need for enhancement in GPT-4V proficiency in interpreting images. [ABSTRACT FROM AUTHOR]

Quality healthcare requires two critical components: patients' best interests and best decisions to achieve that goal. The first goal is the lodestar, unchanged and unchanging over time. The second component is a more dynamic and rapidly changing paradigm in healthcare. Clinical decision-making has transitioned from an opinion-based paradigm to an evidence-based and data-driven process. A realization that technology and artificial intelligence can bring value adds a third component to the decision process. And the fertility sector is not exempt. The debate about AI is front and centre in reproductive technologies. Launching the transition from a conventional provider-driven decision paradigm to a software-enhanced system requires a roadmap to enable effective and safe implementation. A key nodal point in the ascending arc of AI in the fertility sector is how and when to bring these innovations into the ART routine to improve workflow, outcomes, and bottom-line performance. The evolution of AI in other segments of clinical care would suggest that caution is needed as widespread adoption is urged from several fronts. But the lure and magnitude for the change that these tech tools hold for fertility care remain deeply engaging. Exploring factors that could enhance thoughtful implementation and progress towards a tipping point (or perhaps not) should be at the forefront of any 'next steps' strategy. The objective of this Opinion is to discuss four critical areas (among many) considered essential to successful uptake of any new technology. These four areas include value proposition, innovative disruption, clinical agency, and responsible computing. [ABSTRACT FROM AUTHOR]

The article discusses the standardized nuclear medicine residency training program in China. The program, known as Standardized Training for Resident Doctors (STRD), was implemented in 2014 and follows a "5 + 3" model, with five years of undergraduate medical education followed by three years of residency training in the chosen specialty. The Nuclear Medicine Residency Training Program (NMRTP) in China aims to provide essential knowledge and skills in nuclear medicine over a three-year period. The program includes training in various departments related to nuclear medicine, such as oncology and cardiology, and emphasizes both theoretical and clinical knowledge. The article suggests that more specialized training and additional subspecialty training may be needed in the future to meet the growing demand for nuclear medicine professionals. [Extracted from the article]

3D cell culture has emerged as a promising approach to replicate the complex behaviors of cells within living organisms. This study aims to analyze spatiotemporal behavior of the morphological characteristics of cell structure at multiscale in 3D scaffold-free spheroids using chondrogenic progenitor ATDC5 cells. Over a 14-day culture period, it exhibited cell hypertrophy in the spheroids regarding cellular and nuclear size as well as changes in morphology. Moreover, biological analysis indicated a signification up-regulation of normal chondrocyte as well as hypertrophic chondrocyte markers, suggesting early hypertrophic chondrocyte differentiation. Cell nuclei underwent changes in volume, sphericity, and distribution in spheroid over time, indicating alterations in chromatin organization. The ratio of chromatin condensation volume to cell nuclear volume decreased as the cell nuclei enlarged, potentially signifying changes in chromatin state during hypertrophic chondrocyte differentiation. Our image analysis techniques in this present study enabled detailed morphological measurement of cell structure at multi-scale, which can be applied to various 3D culture models for in-depth investigation. [ABSTRACT FROM AUTHOR]

This study aimed to compare the performance of elastomeric contact and non-contact object surfaces in CNN-based classification for pattern recognition. The study used capsule images of the rear surface of the fired cartridge cases, which have distinct traces depending on the gun they were fired from. The images were captured from the cartridge cases in three different categories: non-contact white light illumination, non-contact RGB-White light illumination, and elastomeric contact RGB-White light illumination. Elastomeric contact images were obtained from an elastomer with glass-integrated silicone rubber under RGB-White light. A general examination of the images from these three categories revealed that the elastomeric contact images displayed the lines and depth details on the capsule surface much better without surface glare compared to the other two image formats. Non-contact images under RGB-White light lacked a depth factor, but the surface lines were more visible, while under white light the surface was affected by glare and the surface lines were less pronounced. When these three categories of images were trained in CNN, it was observed that elastomeric contact images reconstructed the capsule surface and characterized the details on the surface much better, and as a result, they achieved higher accuracy in image classification. [ABSTRACT FROM AUTHOR]

Objective: The purposes of this study were to investigate the relationship between carcass unit price per 1 kg (UP) and multiple muscles and intermuscular fat (IF)/subcutaneous fat of beef carcasses using image analysis of cross-section images for Wagyu beef cattle in Japan, and to estimate their genetic parameters. Methods: The carcasses used in this study were 1,807 Japanese Black (Wagyu) cattle (1,216 steers and 591 heifers). An analysis of variance was conducted with UP as the dependent variable and market date, age in months, sex, and image analysis traits (IAT) as fixed effects, and standard partial regression coefficients were calculated for each IAT on UP. Also, the heritability of each IAT that affected UP and genetic correlation among IAT vs carcass grading traits were estimated. Results: Not only IAT related to carcass grading traits, M. trapezius dorsi, M. latissimus dorsi, and IF traits were significant differences in UP (p<0.05). The heritability of IAT associated with UP was estimated at 0.38 to 0.85. The genetic correlations between the area and thickness of M. trapezius dorsi and M. latissimus dorsi vs rib eye area (REA) were estimated to be moderately positive (0.53 to 0.66), while the genetic correlations between the IF area percentage vs carcass weight, REA, and yield score were estimated to be negative (–0.40, –0.56, and –0.34). Conclusion: UP was influenced by various traits, including M. trapezius dorsi, M. latissimus dorsi, and IF traits, as well as image analysis associated with carcass grading traits. Since these IAT associated with UP had hereditary and desirable genetic correlations with carcass grading traits, these traits were also important for genetic improvement. [ABSTRACT FROM AUTHOR]

Objective: To assess intrarater reliability of ultrasound‐determined measurements of skeletal muscle characteristics across different measurement outcomes, imaging techniques, and age groups. Methods: 2D ultrasound images (B‐mode) of the quadriceps were obtained from young (26 ± 4 year, n = 8 M, 8 F) and older (70 ± 7 year, n = 7 M, 5 F) adults on two occasions, separated by 6 ± 3 days. With participants in both standing and supine postures, images were collected from five anatomical sites along the anterior (two sites) and lateral (three sites) compartments of the thigh corresponding to 56%, 39%, and 22% (lateral only) of femur length. Images were analysed for muscle thickness, pennation angle, and echogenicity. Intraclass correlation coefficients (ICC) were used to assess reliability. Results: Muscle thickness values were higher (p < 0.05) on images collected in the stand versus supine posture only for muscles of the anterior compartment, independent of age. Echogenicity values were higher (p < 0.05) in the vastus intermedius on images collected in the supine versus stand posture only in older adults. Pennation angle values were not impacted by imaging posture (p > 0.05). ICC values for thickness, echogenicity, and pennation angle were generally higher for analyses conducted on images collected in the supine versus stand posture. Imaging posture generated a greater difference in ICC values in the lateral versus anterior muscles and in older versus younger participants. Conclusion: Our findings suggest that participant posture during imaging impacts the absolute values and intrarater reliability of ultrasound‐determined muscle characteristics in a muscle‐specific fashion, and this effect is greater in older compared to younger individuals. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HI) analysis approaches have recently become increasingly complex and sophisticated. Recently, the combination of spectral-spatial information and superpixel techniques have addressed some hyperspectral data issues, such as the higher spatial variability of spectral signatures and dimensionality of the data. However, most existing superpixel approaches do not account for specific HI characteristics resulting from its high spectral dimension. In this work, we propose a multiscale superpixel method that is computationally efficient for processing hyperspectral data. The Simple Linear Iterative Clustering (SLIC) oversegmentation algorithm, on which the technique is based, has been extended hierarchically. Using a novel robust homogeneity testing, the proposed hierarchical approach leads to superpixels of variable sizes but with higher spectral homogeneity when compared to the classical SLIC segmentation. For validation, the proposed homogeneity-based hierarchical method was applied as a preprocessing step in the spectral unmixing and classification tasks carried out using, respectively, the Multiscale sparse Unmixing Algorithm (MUA) and the CNN-Enhanced Graph Convolutional Network (CEGCN) methods. Simulation results with both synthetic and real data show that the technique is competitive with state-of-the-art solutions. [ABSTRACT FROM AUTHOR]

The Remote Sensing Image Change Captioning (RSICC) has recently emerged in the field of remote sensing image interpretation; it aims to automatically predict natural language captions of significant semantic changes in bi-temporal remote sensing images. Recent studies of RSICC have improved the accuracy of change captions of bi-temporal remote sensing images to a large extent. Nevertheless, there still remain challenges in multi-scale perception of ground objects and feature enhancement of bi-temporal remote sensing images. To address these challenges and further improve the accuracy of RSICC, a novel deep learning–based end-to-end scale-wised feature enhancement network (SFEN) is proposed in this paper. SFEN integrates four efficient blocks: 1) the siamese backbone network (SBN) to extract initial features of bi-temporal remote sensing images, 2) the siamese receptive field fusion (SRFF) block to explicitly capture multi-scale semantic information of ground objects in bi-temporal feature maps, 3) the siamese global feature enhancement (SGFE) block to adaptively enhance key information and filtering redundant features of bi-temporal feature maps in both channel and spatial dimensions, 4) the change caption decoder (CCD) to map bi-temporal feature maps into natural language. The SFEN aims to precisely capture significant semantic information of ground objects in bi-temporal remote sensing images and predict accurate change captions. Experimental results on LEVIR-CC dataset demonstrate our SFEN outperforms recent state-of-the-art (SOTA) approach in RSICC by 5.2% on CIDEr-D and achieves a new SOTA. [ABSTRACT FROM AUTHOR]

Waterbody extraction from satellite imagery plays a crucial role in various environmental monitoring and management applications. Accurate identification and delineation of water bodies are essential for assessing water resources, monitoring changes in aquatic ecosystems, and supporting decision-making processes. This review presents a comprehensive analysis of different methods used for waterbody extraction from satellite images, highlighting their strengths, limitations, and recent advancements. This review begins by discussing traditional methods, such as thresholding-based methods, machine learning methods, and object-based image analysis, which have been widely employed in the past. Consequently, the focus shifts towards, how deep learning models, such as convolutional neural networks (CNNs) have been applied to improve waterbody extraction accuracy and address challenges posed by spectral variations, cloud cover, and sensor limitations. Overall, this review serves as a valuable resource for researchers, practitioners, and decision-makers involved in water resource management and environmental monitoring. [ABSTRACT FROM AUTHOR]

Phenol red (PR) is a commonly used compound in culture media as a pH indicator. However, it is unknown whether this compound can interfere with the pharmacological induction of ferroptosis. Here, using high-content live-cell imaging death analysis, we determined that the presence of PR in the culture medium preconditioned normal and tumor cells to ferroptosis induced by system x c − inhibition mediated by imidazole ketone erastin (IKE) or GPX4 blockade in response to RSL-3, but had no significant effects against treatment with the endoperoxide FINO 2. Mechanistically, we revealed that PR decreases the levels of the antiferroptotic genes Slc7a11, Slc3a2 , and Gpx4 , while promoting the overexpression de Acls4 , a key inducer of ferroptosis. Additionally, through superresolution analysis, we determined that the presence of PR mislocalizes the system x c − from the plasma membrane. Thus, our results show that the presence of PR in the culture medium can be a problematic artifact for the accurate interpretation of cell sensitivity to IKE or RSL-3-mediated ferroptosis induction. [Display omitted] • Phenol red determines sensitivity to ferroptosis inducers in normal and tumor cells. • Phenol red promotes ferroptosis induction by treatment with IKE and RSL-3. • Phenol red promotes overexpression of ACSL4. [ABSTRACT FROM AUTHOR]

Folklore reflects the concept of fear, with which genres such as incantations and prayers are directly associated. These texts establish adialogue with danger, either with commands or supplications, and they indicate what kind of dangers are imminent. By combining psychoanalytical and folklore theories, the paper offers the analysis of the conceptual image of fear and of the defensive function of prayers and incantations within the material of Slovenian incantation and prayer collections: what kind of threats one wants to chase away by incantations or/and magical signs and objects; and what kind of form and use contain the preventive incantations themselves. [ABSTRACT FROM AUTHOR]

Calcium carbide residue (CCR) has been identified as an effective replacement for traditional lime for the stabilization of expansive soils. The shrinkage of expansive clay severely threatens the foundation layers’ long-term stability. The efficacy of CCR stabilization in reducing compressibility and enhancing the strength and stiffness of clay has been widely reported; however, the calcium-based additive stabilized soil tends to lose its engineered integrity under field-exposed seasonal moisture variation, specifically, shrinkage induced by prolonged drying. In this study, the shrinkage characteristic of CCR-treated clay (6%, 9%, and 12%) slurry was investigated by digital image analysis and mercury displacement method to quantify volumetric shrinkage strain. The results were compared with those of 4% and 6% lime-treated clay, which revealed the higher efficacy of CCR in reducing shrinkage strain by 47–57%. A characteristic shrinkage curve that depicted the significant variation of shrinkage phases in CCR-treated clay samples is developed. The dominance of the proportional shrinkage phase is observed in both untreated and treated clay samples. The shrinkage caused by evaporation, hydration, and decalcification is postulated in treated clays. This study is expected to contribute to the understanding of shrinkage mechanisms of CCR-treated clay and assist in the future design of stabilized embankment sections or subgrade for unpaved roads. [ABSTRACT FROM AUTHOR]

Visual news can be a broad topic and encapsulate a myriad of forms, conventions, and representational content. The present study delves deeply into the image-based side of news to explore how visual news is produced, framed, and presented at four print and digital news outlets in urban and regional parts of Australia and China. We first conducted a qualitative denotative, stylistic-semiotic, and ideological framing analysis of a total of 1,408 images, published as part of 674 articles. Next, we deepened our understanding of the image analysis results through semi-structured interviews with 14 editorial staff in the visuals departments at these four outlets. Using framing theory and the hierarchy of influences model as theoretical lenses, this study uncovers how economic, social, and political factors affect the types of visual stories that journalists in these countries cover and sheds light on how those stories are presented. Specifically, our results indicate that outlets in Australia illustrate their news more than comparable outlets in China and that the types of visuals used also differed considerably. Online templates and editor directives influenced visual news in Australia to a greater degree while comparable Chinese news outlets paid more attention to audience expectations and political considerations. [ABSTRACT FROM AUTHOR]

Introduction The integration of Point of Care Ultrasound (POCUS) into the care of trauma patients, specifically the E-FAST, has improved the accuracy of initial diagnoses and improved time to surgical intervention in critically ill patients. Physician assistants (PAs) are critically important members of any military trauma resuscitation team and are often team leaders in a pre-hospital setting. They may receive training in ultrasound but there are little data to support their use or evaluate their effectiveness in using POCUS. We designed a study to evaluate the image quality of an E-FAST Exam performed by Emergency Medicine Physician Assistant (EMPA) Fellows and Emergency Medicine (EM) Interns following identical training. Our hypothesis is that image quality obtained by EMPAs will be non-inferior to those images obtained by EM Interns. Materials and Methods This is a prospective single-blinded study comparing the image quality of E-FAST exams performed by first year EM interns and first year EMPA fellows. All participants completed standard POCUS training prior to enrollment in the study. A total of 8 EMPAs and 8 EM first year residents completed 10 recorded E-FAST exams to be used as study images. Participants also viewed a 15-question slide show containing images of positive (6) and negative (9) E-FAST exams and recorded their interpretations. Images were reviewed by expert reviewers who were blinded to which images were collected by which group. An image quality score was recorded for each view as well as an overall image quality score. Image quality was rated on a 1 to 5 image quality scale. Results For overall image quality, the mean score for EMPAs was 3.6 ± 0.5 and for EM residents was 3.2 ± 0.5 with statistical significance favoring better image quality from the EMPAs. The time to completion for the EFAST exam for EMPAs was 4.8 ± 1.3 minutes and for interns it was 3.4 ± 1.4 minutes (P value = 0.02). There was no difference in image interpretation quiz scores between the groups (mean score 92% among interns and 95% among PAs). Conclusions POCUS is an imaging modality which is very portable and relatively inexpensive which makes it ideal for military medicine. PAs are essential members of military trauma teams, and often run an initial trauma resuscitation. Being able to correctly identify patients who have free fluid early in the course of treatment allows for more correct evacuation criteria to ensure the sickest patients get to care the fastest. Although there are limited data to support POCUS use by non-physicians, our data support a growing body of evidence that it is not the profession or baseline medical education that determines an individual's ability to use and incorporate ultrasound into bedside and clinical practice. Our study shows that with training and experience PAs or other members of the military health care team can use the EFAST to better care for trauma patients. [ABSTRACT FROM AUTHOR]

There are several in vitro systems that enable evaluation of the absorption direction, but there are few quantitative systems that enable easy evaluation of the excretion direction. Enteroids, organoids derived from intestine, have been frozen and passaged for various research. But it is not clear how the freezing and passaging affect the expression and function of transporters. We investigated the effects of passage and cryopreservation of enteroids. We focused on P-gp (P-glycoprotein) and compared the transfer rates of rhodamine 123 (Rh123) into the lumen of enteroids with and without a P-gp inhibitor. mRNA expression levels did not change significantly before and after passage and cryopreservation. Accumulation of Rh123 in the lumen of enteroids was observed. With some P-gp inhibitors, excretion of Rh123 into the lumen of enteroids was inhibited and the nonexcreted Rh123 accumulated in enteroids epithelial cells. The transfer rate of Rh123 into the lumen of enteroids with a P-gp inhibitor was significantly decreased compared to that of without a P-gp inhibitor. Before and after passage and cryopreservation, the transfer rate was almost the same as that of primary cultured enteroids. We succeeded in easily evaluating whether a component is a substrate of P-gp using enteroids. [Display omitted] • mRNA expression level of efflux intestinal transporter was not changed before and after passage and number of culture days. • This method allows semi-quantitative evaluation of efflux transporters function regardless of the shapes of enteroids. • Our method enables to easily evaluate whether a component is a substrate of P-gp using enteroids. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the repeatability of retinal and choroidal optical coherence tomography angiography (OCT‐A) indices among healthy children and compare it to healthy young adults. Methods: This prospective study captured 3 mm × 3 mm and 6 mm × 6 mm macular OCT‐A scans including superficial and deep retinal layers, choriocapillaris and deep choroid over two visits, 1 week apart at approximately the same time of day, for 22 healthy adults (18–30 years) and 21 children (6–15 years). Magnification and projection‐artefact corrected indices extracted using a custom image analysis program and individual biometry were compared between visits using Bland–Altman analysis and intraclass correlation (ICC). Retinal indices included foveal avascular zone metrics, perfusion and vessel density and choroidal indices included choriocapillaris flow deficit metrics and deep choroid perfusion density, in the foveal, parafoveal and perifoveal regions. Repeatability between adults and children was compared with F‐test. Results: Bland–Altman analysis showed that the mean differences between repeated OCT‐A indices were not significantly different from zero for either of the zones, layers and scan sizes in the two age groups (p > 0.05) except for foveal vessel density and foveal avascular zone perimeter (p = 0.04 for both) of 6‐mm‐deep retinal layer scans. The ICC ranged between 0.67 and 0.99. Significantly higher variability between visits (p < 0.05) in the indices was noted among adults than children, especially for choroidal indices of larger scan size. Conclusion: The retinal and choroidal OCT‐A indices in the foveal, parafoveal and perifoveal zones were repeatable in healthy children except for the foveal vessel density and foveal avascular zone perimeter of the 6‐mm‐deep retinal layer, which exhibited statistically borderline differences between visits. The adult group showed more variability between visits compared to children, especially in the larger scan size for choroidal OCT‐A indices. [ABSTRACT FROM AUTHOR]