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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]

Precise size and shape control in nanocrystal synthesis is essential for utilizing nanocrystals in various industrial applications, such as catalysis, sensing, and energy conversion. However, traditional ensemble measurements often overlook the subtle size and shape distributions of individual nanocrystals, hindering the establishment of robust structure-property relationships. In this study, we uncover intricate shape evolutions and growth mechanisms in Co3O4 nanocrystal synthesis at a subnanometer scale, enabled by deep-learning-assisted statistical characterization. By first controlling synthetic parameters such as cobalt precursor concentration and water amount then using high resolution electron microscopy imaging to identify the geometric features of individual nanocrystals, this study provides insights into the interplay between synthesis conditions and the size-dependent shape evolution in colloidal nanocrystals. Utilizing population-wide imaging data encompassing over 441,067 nanocrystals, we analyze their characteristics and elucidate previously unobserved size-resolved shape evolution. This high-throughput statistical analysis is essential for representing the entire population accurately and enables the study of the size dependency of growth regimes in shaping nanocrystals. Our findings provide experimental quantification of the growth regime transition based on the size of the crystals, specifically (i) for faceting and (ii) from thermodynamic to kinetic, as evidenced by transitions from convex to concave polyhedral crystals. Additionally, we introduce the concept of an onset radius, which describes the critical size thresholds at which these transitions occur. This discovery has implications beyond achieving nanocrystals with desired morphology; it enables finely tuned correlation between geometry and material properties, advancing the field of colloidal nanocrystal synthesis and its applications.

Particle tracking (PT) is a popular technique in microscopy, microfluidics and colloidal transport studies, where image analysis is used to reconstruct trajectories from bright spots in a video. The performance of many PT algorithms has been rigorously tested for directed and Brownian motion in open media. However, PT is frequently used to track particles in porous media where complex geometries and viscous flows generate particles with high velocity variability over time. Here, we present an evaluation of four PT algorithms for a simulated dispersion of particles in porous media across a range of particle speeds and densities. Of special note, we introduce a new velocity-based PT linking algorithm (V-TrackMat) that achieves high accuracy relative to the other PT algorithms. Our findings underscore that traditional statistics, which revolve around detection and linking proficiency, fall short in providing a holistic comparison of PT codes because they tend to underpenalize aggressive linking techniques. We further elucidate that all codes analyzed show a decrease in performance due to high speeds, particle densities, and trajectory noise. However, linking algorithms designed to harness velocity data show superior performance, especially in the case of high-speed advective motion. Lastly, we emphasize how PT error can influence transport analysis.

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.

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]

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.

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.

Interest in virus-based therapeutics for the treatment of genetic and oncolytic diseases has created a demand for high-yield, low-cost virus-manufacturing processes. However, traditional analytical methods of assessing infectious virus titer require multiple processing steps and manual counting, limiting sample throughput, and increasing human error. This bottleneck severely limits the development of new manufacturing unit operations to drive down costs. In this work, we utilize an Incucyte Live-Cell Analysis System to develop a high-throughput infectious titer assay for adenovirus expressing a GFP-transgene. Although previous studies have demonstrated live-cell imaging's potential for use with other viruses, they provide little guidance regarding the selection of the viewing and analysis parameters. To fill this gap, we develop an algorithmic approach to identify the optimum viewing and analysis parameters and create a statistical workflow for quantifying infectious adenovirus in a sample dilution series in a standard 24-well microplate. The developed assay is comparable to Hexon staining, the gold-standard for adenovirus infectious titer, with a Pearson correlation coefficient of 0.9. Finally, the developed algorithmic approach and statistical workflow were applied to create an assay for adenovirus titer using a 96-well microplate, allowing five times more samples to be quantified compared to the standard 24-well plate. While this assay uses a GFP-insert that precludes its use in a clinical environment, the key learnings surrounding the careful use of viewing and analysis parameters, and the statistical workflow are widely applicable to implementing life-cell imaging for dilution-series-based assays. Moreover, this method directly enables the fast and accurate evaluation of virus samples in a preclinical environment. • A preclinical live-cell imaging assay for infectious titer was validated against gold standard methods. • Titer estimates are significantly impacted by viewing and imaging analysis parameters. • Scale-up an automated workflow technique from 24-well microplates to 96-well microplates. [ABSTRACT FROM AUTHOR]

Saliency maps are input-resolution matrices used for visualizing local interpretations of image classifiers. Their pixel values reflect the importance of corresponding image locations for the model's decision. Despite numerous proposals on how to obtain such maps, their evaluation remains an open question. This paper presents a carefully designed experimental procedure along with a set of quantitative interpretation evaluation metrics that rely solely on the original model behavior. Previously noticed evaluation biases have been attenuated by separating locations with high and low values, considering the full saliency map resolution, and using classifiers with diverse accuracies and all the classes in the dataset. We used the proposed evaluation metrics to compare and analyze seven well-known interpretation methods. Our experiments confirm the importance of object background as well as negative saliency map pixels, and we show that the scale of their impact on the model is comparable to that of positive ones. We also demonstrate that a good class score interpretation does not necessarily imply a good probability interpretation. DeepLIFT and LRP- ϵ methods proved most successful altogether, while Grad-CAM and Ablation-CAM performed very poorly, even in the detection of positive relevance. The retention of positive values alone in the latter two methods was responsible for the inaccurate detection of irrelevant locations as well. [ABSTRACT FROM AUTHOR]

The identification and region extraction of brain tumors is an essential aspect of clinical image analysis and the diagnosis of brain-related illnesses. The precise and accurate identification of tumors from MRI images is particularly significant in the effective formulating of treatments such as surgery, radiation therapy, and drug therapy. The challenge of segmentation stems from the variability in the size, location, and appearance of tumors, making it a complex task. Various segmentation and classification techniques have been created and designed for brain tumor diagnosis; however, these traditional techniques are time-consuming and subjective and require expertise in image processing. In recent times, deep learning-based approaches have shown promising results in brain tumor segmentation. This research aims to develop a brain tumor segmentation and classification model that enables medical professionals to locate and measure tumors accurately and develop effective treatment and rehabilitation strategies. The process involves segmenting the tumor and further classifying it into its two major types. The parameter estimation from the segmented output provides an insight that is pivotal in the evaluation of MRI brain tumors. With further research and development, deep learning-based segmentation and classification could become an important tool for accurate detection and evaluation of brain tumors. The development of deep learning-based segmentation and classification methods can greatly benefit the medical community, and according to the finding from the experiment, it is shown that the proposed framework excels in brain tumor segmentation and classification with an accuracy of 99.3%. [ABSTRACT FROM AUTHOR]

Recognizing and analyzing medical images is crucial for disease early detection and treatment planning with appropriate treatment options based on the patient's individual needs and disease history. Deep learning technologies are widely used in the field of healthcare because they can analyze images rapidly and precisely. However, because each object on the image has the potential to hold illness information in medical images, it is critical to analyze the images with minimal information loss. In this context, Capsule Network (CapsNet) architecture is an important approach that aims to reduce information loss by storing the location and properties of objects in images as capsules. However, because CapsNet maintains information on each object in the image, the existence of several objects in complicated images can impair CapsNet's performance. This work proposes a new model called HMedCaps to improve the performance of CapsNet. In the proposed model, it is aimed to develop a deeper and hybrid structure by using Residual Block and FractalNet module together in the feature extraction layer. While it is aimed to obtain rich feature maps by increasing the number of features extracted by deepening the network, it is aimed to prevent the vanishing gradient problem that may occur in the network with increasing depth with these modules with skip connections. Furthermore, a new squash function is proposed to make distinctive capsules more prominent by customizing capsule activation. The CIFAR10 dataset of complex images, RFMiD dataset of retinal images, and Blood Cell Count Dataset dataset of blood cell images were used to evaluate the study. When the proposed model was compared with the basic CapsNet and studies in the literature, it was observed that the performance in complex images was improved and more accurate classification results were obtained in the field of medical image analysis. The proposed hybrid HMedCaps architecture has the potential to make more accurate diagnoses in the field of medical image analysis. [ABSTRACT FROM AUTHOR]

Virtual microscopy is becoming increasingly common in both medical education and routine clinical practice. Virtual microscopy software is typically designed either for (1) training students in anatomy, histology, and histopathology, or (2) quantitative analysis—but not both simultaneously. QuPath is one of the most widely used software applications for histopathology image analysis in research and provides a comprehensive set of computational tools to evaluate histology slides. We have enhanced QuPath by developing a new extension, QuPath Edu, which adapts the software to function as an intuitive microanatomy learning environment. Additionally, we have created an entirely new, complementary software platform called OpenMicroanatomy, which provides an alternative way to access QuPath Edu teaching content through a web interface. These tools have been used in teaching of first year medical and dentistry students at the University of Oulu Medical Faculty, and we conducted a user survey for the Class of 2023 to assess the usability and student experience. In general, the introduced annotation and quiz features were appreciated by the students and the system usability of OpenMicroanatomy was considered excellent (SUS score 84.8). Together, these freely available tools enable teachers to develop and deploy innovative training material for anatomy, histopathology, quantitative analysis, and artificial intelligence in a wide range of contexts. This unique combination can provide the next generation of students with essential multidisciplinary skills. [ABSTRACT FROM AUTHOR]

Background: Metastatic prostate cancer is a highly heterogeneous and dynamic disease and practicable tools for patient stratification and resistance monitoring are urgently needed. Liquid biopsy analysis of circulating tumor cells (CTCs) and circulating tumor DNA are promising, however, comprehensive testing is essential due to diverse mechanisms of resistance. Previously, we demonstrated the utility of mRNA-based in situ padlock probe hybridization for characterizing CTCs. Methods: We have developed a novel combinatorial dual-color (CoDuCo) assay for in situ mRNA detection, with enhanced multiplexing capacity, enabling the simultaneous analysis of up to 15 distinct markers. This approach was applied to CTCs, corresponding tumor tissue, cancer cell lines, and peripheral blood mononuclear cells for single-cell and spatial gene expression analysis. Using supervised machine learning, we trained a random forest classifier to identify CTCs. Image analysis and visualization of results was performed using open-source Python libraries, CellProfiler, and TissUUmaps. Results: Our study presents data from multiple prostate cancer patients, demonstrating the CoDuCo assay's ability to visualize diverse resistance mechanisms, such as neuroendocrine differentiation markers (SYP, CHGA, NCAM1) and AR-V7 expression. In addition, druggable targets and predictive markers (PSMA, DLL3, SLFN11) were detected in CTCs and formalin-fixed, paraffin-embedded tissue. The machine learning-based CTC classification achieved high performance, with a recall of 0.76 and a specificity of 0.99. Conclusions: The combination of high multiplex capacity and microscopy-based single-cell analysis is a unique and powerful feature of the CoDuCo in situ assay. This synergy enables the simultaneous identification and characterization of CTCs with epithelial, epithelial-mesenchymal, and neuroendocrine phenotypes, the detection of CTC clusters, the visualization of CTC heterogeneity, as well as the spatial investigation of tumor tissue. This assay holds significant potential as a tool for monitoring dynamic molecular changes associated with drug response and resistance in prostate cancer. [ABSTRACT FROM AUTHOR]

Purpose Methods Results Conclusions To quantify ellipsoid zone (EZ) loss during anti‐VEGF therapy for neovascular age‐related macular degeneration (nAMD) and correlate these findings with nAMD disease activity using artificial intelligence‐based algorithms.Spectral domain optical coherence tomography (Spectralis, Heidelberg Engineering) images from nAMD treatment‐naïve patients from the Fight Retinal Blindness! (FRB!) Registry from Zürich, Switzerland were processed at baseline and over 3 years of follow‐up. An approved deep learning algorithm (Fluid Monitor, RetInSight) was used to automatically quantify intraretinal fluid (IRF), subretinal fluid (SRF) and pigment epithelial detachment (PED). An ensemble U‐net deep learning algorithm was used to automated quantify EZ integrity based on EZ layer thickness. The impact of fluid volumes on EZ thickness and late‐stages outcomes were calculated using Wilcoxon rank‐sum tests, a linear mixed model and a longitudinal panel regression model.Two hundred and eleven eyes from 158 patients were included. The mean ± SD EZ loss area in the central 6 mm was 1.81 ± 2.68 mm2 at baseline and reached 6.21 ± 6.15 mm2 at month 36. Higher fluid volumes (top 25%) of IRF and PED in the central 1 and 6 mm of the macula were significantly associated with more advanced EZ thinning and loss compared to the low fluid volume subgroup. The high SRF subgroup in the linear regression model showed no statistically significant association with EZ integrity in the central macula; however, the longitudinal analysis revealed an increased EZ thickness with no additional loss.Intraretinal fluid and PED volumes and their resolution pattern have an impact on alteration of the underlying EZ layer. AI‐supported quantifications are helpful in quantifying early signs of macular atrophy and providing individual risk profiles as a basis for tailored therapies for optimized visual outcomes. [ABSTRACT FROM AUTHOR]

We describe an improvement to the photoacoustic method for determining diffusion coefficients of dyes in soft matter, such as agar hydrogels. The proposed method is based on the properties of thermal wave formed due to a heat transfer at the interface of the two media: the gel and polyethylene film. It is inherently simple, requires a small number of reagents and relatively fast, since samples are small. To assess the obtained results, we independently determined diffusion coefficients of the dyes using an optical image analysis method and found both sets of results to be in a good agreement. [ABSTRACT FROM AUTHOR]

Acute radiation dermatitis (ARD) is the most common side effect reported by patients undergoing radiation therapy (RT). Currently, the assessment of the severity of the reaction is based on the visual assessment of the skin, which is a subjective method, depending on many factors. The main aim of this study was to investigate the usefulness of hyperspectral imaging (HSI) in the assessment of ARD and find physiological factors that could be correlated with ARD. In this clinical pilot trial, weekly acquisitions of hyperspectral camera images of irradiated skin were performed for 5 weeks of RT and at the posttreatment follow-up visit which took place 30 to 40 days after the last fraction of RT. At the same time, the severity of radiodermatitis was assessed based on the criteria of the National Cancer Institute Common Terminology Criteria for Adverse Events (CTCAE). The content and concentration of chromophores in irradiated skin were quantitatively determined using a hyperspectral camera. The use of HSI supported by image analysis and processing methods allowed for the determination of the content and distribution of hemoglobin and melanin in the irradiated skin. It was found that the hemoglobin concentration is correlated with the subjective assessment made according to the CTCAE protocol. HSI is a sensitive and specific method of analyzing the concentration of chromophores in the skin, including hemoglobin. A clear correlation was found between hemoglobin concentration and CTCAE v.5 scale because of which HSI can be considered as an objective method of skin assessment during RT. [ABSTRACT FROM AUTHOR]

Persistent homology (PH) is a powerful mathematical method to automatically extract relevant insights from images, such as those obtained by high-resolution imaging devices like electron microscopes or new-generation telescopes. However, the application of this method comes at a very high computational cost that is bound to explode more because new imaging devices generate an ever-growing amount of data. In this paper, we present PixHomology, a novel algorithm for efficiently computing zero-dimensional PH on 2D images, optimizing memory and processing time. By leveraging the Apache Spark framework, we also present a distributed version of our algorithm with several optimized variants, able to concurrently process large batches of astronomical images. Finally, we present the results of an experimental analysis showing that our algorithm and its distributed version are efficient in terms of required memory, execution time, and scalability, consistently outperforming existing state-of-the-art PH computation tools when used to process large datasets. [ABSTRACT FROM AUTHOR]

Background: Understanding the environmental impacts on root growth and root health is essential for effective agricultural and environmental management. Hyperspectral imaging (HSI) technology provides a non-destructive method for detailed analysis and monitoring of plant tissues and organ development, but unfortunately examples for its application to root systems and the root-soil interface are very scarce. There is also a notable lack of standardized guidelines for image acquisition and data analysis pipelines. Methods: This study investigated HSI techniques for analyzing rhizobox-grown root systems across various imaging configurations, from the macro- to micro-scale, using the imec VNIR SNAPSCAN camera. Focusing on three graminoid species with different root architectures allowed us to evaluate the influence of key image acquisition parameters and data processing techniques on the differentiation of root, soil, and root-soil interface/rhizosheath spectral signatures. We compared two image classification methods, Spectral Angle Mapper (SAM) and K-Means clustering, and two machine learning approaches, Random Forest (RF) and Support Vector Machine (SVM), to assess their efficiency in automating root system image classification. Results: Our study demonstrated that training a RF model using SAM classifications, coupled with wavelength reduction using the second derivative spectra with Savitzky-Golay (SG) smoothing, provided reliable classification between root, soil, and the root-soil interface, achieving 88–91% accuracy across all configurations and scales. Although the root-soil interface was not clearly resolved, it helped to improve the distinction between root and soil classes. This approach effectively highlighted spectral differences resulting from the different configurations, image acquisition settings, and among the three species. Utilizing this classification method can facilitate the monitoring of root biomass and future work investigating root adaptations to harsh environmental conditions. Conclusions: Our study addressed the key challenges in HSI acquisition and data processing for root system analysis and lays the groundwork for further exploration of VNIR HSI application across various scales of root system studies. This work provides a full data analysis pipeline that can be utilized as an online Python-based tool for the semi-automated analysis of root-soil HSI data. [ABSTRACT FROM AUTHOR]

The endomembrane system is essential for healthy cell function, with the various compartments carrying out a large number of specific biochemical reactions. To date, almost all of our understanding of the endomembrane system has come from the study of cultured cells growing as monolayers. However, monolayer-grown cells only poorly represent the environment encountered by cells in the human body. As a first step to address this disparity, we have developed a platform that allows us to investigate and quantify changes to the endomembrane system in three-dimensional (3D) cell models, in an automated and highly systematic manner. HeLa Kyoto cells were grown on custom-designed micropatterned 96-well plates to facilitate spheroid assembly in the form of highly uniform arrays. Fully automated high-content confocal imaging and analysis were then carried out, allowing us to measure various spheroid-, cellular- and subcellular-level parameters relating to size and morphology. Using two drugs known to perturb endomembrane function, we demonstrate that cell-based assays can be carried out in these spheroids, and that changes to the Golgi apparatus and endosomes can be quantified from individual cells within the spheroids. We also show that image texture measurements are useful tools to discriminate cellular phenotypes. The automated platform that we show here has the potential to be scaled up, thereby allowing large-scale robust screening to be carried out in 3D cell models. [ABSTRACT FROM AUTHOR]

Explainable Artificial Intelligence (XAI) is an emerging machine learning field that has been successful in medical image analysis. Interpretable approaches are able to "unbox" the black-box decisions made by AI systems, aiding medical doctors to justify their diagnostics better. In this paper, we analyze the performance of three different XAI strategies for medical image analysis in ophthalmology. We consider a multimodal deep learning model that combines optical coherence tomography (OCT) and infrared reflectance (IR) imaging for the diagnosis of age-related macular degeneration (AMD). The classification model is able to achieve an accuracy of 0.94, performing better than other unimodal alternatives. We analyze the XAI methods in terms of their ability to identify retinal damage and ease of interpretation, concluding that grad-CAM and guided grad-CAM can be combined to have both a coarse visual justification and a fine-grained analysis of the retinal layers. We provide important insights and recommendations for practitioners on how to design automated and explainable screening tests based on the combination of two image sources. [ABSTRACT FROM AUTHOR]

ABSTRACT Background & Aims Methods Results Conclusions The clinicopathologic and imaging features of hepatocellular adenomas (HCAs) in Asian population remain unclear. We aimed to analyse clinicopathologic and imaging features of HCAs in Korea and propose an imaging‐based method to differentiate β‐catenin mutated HCA (βHCA) from other subtypes.This retrospective multicenter study included pathologically confirmed HCAs from three tertiary institutions in Korea between 2010 and 2023. HCA subtypes were determined according to the current World Health Organization classification using immunohistochemical staining. Two abdominal radiologists reviewed computed tomography and magnetic resonance imaging scans. The clinical characteristics and imaging features of HCA subtypes were compared. A scoring system for βHCA was developed and validated using development (January 2010–April 2021) and validation (May 2021–March 2023) cohorts.121 patients (47 men; mean age, 39.0 years ±13.5) with 138 HCAs were included. HCAs displayed characteristic clinicopathologic features, including a high proportion of male (38.8%) and obese patients (35.5%), with the inflammatory subtype being the most common (38.4%) and a low percentage of patients using oral contraceptive (5.0%). Each HCA subtype demonstrated distinct clinical and imaging features. The scoring system incorporating tumour heterogeneity and iso to high hepatobiliary phase signal intensity on MRI for differentiating βHCA exhibited high performance in both the development (AUC 0.92, 95% CI: 0.87–0.97) and the validation cohort (AUC 0.91, 95% CI: 0.77–1.00).This comprehensive analysis of clinicopathologic and imaging features of HCAs in Korea contributes to the characterisation of HCAs across different geographical regions. The imaging‐based scoring system effectively differentiates βHCA. [ABSTRACT FROM AUTHOR]

The article contains the procedure of image acquisition, including sampling of analyzed material as well as technical solutions of hardware and preprocessing used in research. A dataset of digital images containing identified objects were obtained with help of automated mechanical system for controlling the microscope table and used to train the YOLO models. The performance of YOLOv4 as well as YOLOv8 deep learning networks was compared on the basis of automatic image analysis. YOLO constitutes a one-stage object detection model, aiming to examine the analyzed image only once. By utilizing a single neural network, the image is divided into a grid of cells, and predictions are made for bounding boxes, as well as object class probabilities for each box. This approach allows real-time detection with minimal accuracy loss. The study involved ciliated protozoa Vorticella as a test object. These organisms are found both in natural water bodies and in treatment plants that employ the activated sludge method. As a result of its distinct appearance, high abundance and sedentary lifestyle, Vorticella are good subjects for detection tasks. To ensure that the training dataset is accurate, the images were manually labeled. The performance of the models was evaluated using such metrics as accuracy, precision, and recall. The final results show the differences in metrics characterizing the obtained outputs and progress in the software over subsequent versions of the YOLO algorithm. [ABSTRACT FROM AUTHOR]

Gastric cancer is one of the most common cancers and is one of the leading causes of cancer-related deaths in worldwide. Early diagnosis and treatment are essential for a positive outcome. The integration of artificial intelligence in the pathology field is increasingly widespread, including histopathological images analysis. In recent years, the application of digital pathology technology emerged as a potential solution to enhance the understanding and management of gastric cancer. Through sophisticated image analysis algorithms, artificial intelligence technologies facilitate the accuracy and sensitivity of gastric cancer diagnosis and treatment and personalized therapeutic strategies. This review aims to evaluate the current landscape and future potential of artificial intelligence in transforming gastric cancer pathology, so as to provide ideas for future research. [ABSTRACT FROM AUTHOR]

We elucidate a previously unknown synthesis pathway that leads to polymeric nanospheres, orientation-controlled microgels, or microspheroids via single-step polymerization of divinylbenzene (DVB) using initiated chemical vapor deposition (iCVD) in liquid crystals (LC). iCVD supplies vapor-phase reactants continuously, avoiding the critical limitation of reactant-induced disruption of LC structure that has plagued past LC-templated polymerization processes. LC is leveraged as a real-time display of the polymerization conditions and particle emergence, captured using an in situ long-focal range microscope. Detailed image analysis unravels key LC-guided mechanisms during polymerization. pDVB forms nanospheres due to poor solubilization by nematic LC. The nanospheres partition to the LC-solid interface and further assemble into microgel clusters whose orientation is guided by the LC molecular alignment. On further polymerization, microgel clusters transition to microspheroids that resemble liquid drops. We identify key energetic factors that guide trajectories along the synthesis pathway, providing the fundamental basis of a framework for engineering particle synthesis with shape control. [ABSTRACT FROM AUTHOR]

Large amounts of data can be processed and analyzed by methods based on Artificial Intelligence (AI). Thus, many processes in the lab can become faster and more efficient. However, one should be aware of the limits of such computer‐based applications. We demonstrate how an AI‐based program can first be trained with real images to be able to recognize and analyze particles on electron microscopic images on its own with high precision. [ABSTRACT FROM AUTHOR]

S2‐glass fiber reinforced plastics (S2‐GFRP) and basalt fiber reinforced plastics (BFRP) have emerged as crucial materials due to their exceptional mechanical properties, and milling of composite materials plays an important role in achieving desired properties. However, they have proven challenges due to relative inhomogeneity compared with metals, resulting unpredictability in quality of milling operations. The objective of this work is to investigate the effect of cutting parameters, tool geometry and tool surface materials on the surface quality of composites using burrs as a metric. S2‐GFRP and BFRP composites were produced by the vacuum infusion method. Helical and straight flute end mills were manufactured from high‐speed steel (HSS) and carbide rounds, and half of them were coated with titanium nitride using reactive magnetron sputtering technique. Taguchi L18 orthogonal array is used to determine the effect of tool material, tool angle, coating, cutting direction, spindle speed, and feed rate on the machining quality of S2‐GFRPs and BFRPs with respect to burr formations. Milling experiments were conducted under dry conditions and then the burrs were imaged to calculate the total area and length. Statistical analysis was also performed to optimize the machining parameters and tool type for ensuring the structural integrity and performance of the final composite parts. The results showed that the selection of tool material has the most significant impact on the burr area and length of the machined surface. The novel image analysis allows to analyze the extent of the burr size with a desirable operation speed for industrial applications. Highlights: Aerospace grade S2‐Glass (S2‐GFRP) and basalt fiber reinforced plastics (BFRP) were manufactured.Carbide and HSS end mills were fabricated and coated with titanium nitride protective layer.FRPs were machined at various process parameters designed by Taguchi method.Distinctive image processing was firstly used to compute milling induced Burr area and length.Statistical analysis was performed to quantify the contribution of parameters and optimize milling. [ABSTRACT FROM AUTHOR]

Visual observing muscle tissue regeneration is used to measure experimental effect size in biological research to discover the mechanism of muscle strength decline due to illness or aging. Quantitative computer imaging analysis for support evaluating the recovery phase has not been established because of the localized nature of recovery and the difficulty in selecting image features for cells in regeneration. We constructed MyoRegenTrack for segmenting cells and classifying their regeneration phase in hematoxylin–eosin (HE) stained images. A straightforward approach to classification is supervised learning. However, obtaining detailed annotations for each fiber in a whole slide image is impractical in terms of cost and accuracy. Thus, we propose to learn individual recovery phase classification utilizing the proportions of cell class depending on the days after muscle injection to induce regeneration. We extract implicit multidimensional features from the HE-stained tissue images and train a classifier using weakly supervised learning, guided by their class proportion for elapsed time on recovery. We confirmed the effectiveness of MyoRegenTrack by comparing its results with expert annotations. A comparative study of the recovery relation between two different muscle injections shows that the analysis result using MyoRegenTrack is consistent with findings from previous studies. [ABSTRACT FROM AUTHOR]

Traditional mosquito identification methods, relied on microscopic observation and morphological characteristics, often require significant expertise and experience, which can limit their effectiveness. This study introduces a self-supervised learning-based image classification model using the Bootstrap Your Own Latent (BYOL) algorithm, designed to enhance mosquito species identification efficiently. The BYOL algorithm offers a key advantage by eliminating the need for labeled data during pretraining, as it autonomously learns important features. During fine-tuning, the model requires only a small fraction of labeled data to achieve accurate results. Our approach demonstrates impressive performance, achieving over 96.77% accuracy in mosquito image analysis, with minimized both false positives and false negatives. Additionally, the model's overall accuracy, measured by the area under the ROC curve, surpasses 99.55%, highlighting its robustness and reliability. A notable finding is that fine-tuning with just 10% of labeled data produces results comparable to using the full dataset. This is particularly valuable for resource-limited settings with limited access to advanced equipment and expertise. Our model provides a practical solution for mosquito identification, overcoming the challenges of traditional microscopic methods, such as the time-consuming process and reliance on specialized knowledge in healthcare services. Overall, this model supports personnel in resource-constrained environments by facilitating mosquito vector density analysis and paving the way for future mosquito species identification methodologies. [ABSTRACT FROM AUTHOR]

Defect-engineered and even amorphous two-dimensional (2D) materials have recently gained interest due to properties that differ from their pristine counterparts. Since these properties are highly sensitive to the exact atomic structure, it is crucial to be able to characterize them at atomic resolution over large areas. This is only possible when the imaging process is automated to reduce the time spent on manual imaging, which at the same time reduces the observer bias in selecting the imaged areas. Since the necessary datasets include at least hundreds if not thousands of images, the analysis process similarly needs to be automated. Here, we introduce disorder into graphene and monolayer hexagonal boron nitride (hBN) using low-energy argon ion irradiation, and characterize the resulting disordered structures using automated scanning transmission electron microscopy annular dark field imaging combined with convolutional neural network-based analysis techniques. We show that disorder manifests in these materials in a markedly different way, where graphene accommodates vacancy-type defects by transforming hexagonal carbon rings into other polygonal shapes, whereas in hBN the disorder is observed simply as vacant lattice sites with very little rearrangement of the remaining atoms. Correspondingly, in the case of graphene, the highest introduced disorder leads to an amorphous membrane, whereas in hBN, the highly defective lattice contains a large number of vacancies and small pores with no indication of amorphisation. Overall, our study demonstrates that combining automated imaging and image analysis is a powerful way to characterize the structure of disordered and amorphous 2D materials, while also illustrating some of the remaining shortcomings with this methodology. [ABSTRACT FROM AUTHOR]

The Irpinia Fault, also known as the Monte Marzano Fault System, located in the Southern Apennines (Italy), is one of the most seismically active structures in the Mediterranean. It is the source of the 1980, Ms 6.9, multi-segment rupture earthquake that caused significant damage and nearly 3,000 casualties. Paleoseismological surveys indicate that this structure has generated at least four Mw ~ 7 surface-rupturing earthquakes in the past 2 ka. This paper presents a comprehensive, high-resolution geophysical investigation focused on the southernmost fault segment of the Monte Marzano Fault System, i.e., the Pantano-Ripa Rossa Fault, outcropping within the Pantano di San Gregorio Magno intramontane basin. The project, named TEst Site IRpinia fAult (TESIRA), was supported by the University of Napoli Federico II to study the near-surface structure of this intra-basin fault splay that repeatedly ruptured co-seismically in the past thousands of years. Our imaging approach included 2D and 3D electrical and seismic surveys, gravimetry, 3D FullWaver electrical tomography, drone-borne GPR and magnetic surveys, and CO2 soil flux assessment across the surface rupture. This multidisciplinary investigation improved our understanding of the basin shallow structure, providing an image of a rather complex subsurface fault and basin geometry. Seismic data suggest that fault activity at the Pantano segment of MMFS is characterized by a near-surface cumulative displacement greater than previous estimations, calling into question earlier assumptions about the timing of its activation. Despite some challenges with our drone-mounted survey equipment, the integrated dataset provides a comprehensive and reliable image of the subsurface structure. This work demonstrates the utility of developing an integrated approach at high-resolution geophysical imaging and interpretation of fault zones with weak morphological expressions. [ABSTRACT FROM AUTHOR]

Multiple researchers have effectively utilized InSAR for early identification research in southwest China. However, the intricate geological structure in this region includes deeply buried and unpredictable anti-dip stratiform rock slopes, posing an additional challenge for InSAR remote sensing. This study establishes a coupling relationship between slope topple deformation patterns and surface macro-deformation characteristics through a centrifuge model test, analyzing slope bending and topple deformation stages. The InSAR interpretation of prototype slopes is employed to retrospectively infer the slope stage and visualize the slope deformation process. This research provides technical assistance for identifying and preventing slope failures. The result demonstrates that: (1) The failure process of anti-dip stratiform rocky slope involves four major stages. Failures often begin at the slope's base and move to the middle and top regions, eventually resulting in entire slope failure when the stepped fracture surface forms. (2) Experimental image recognition analysis was conducted to monitor the rate changes in the deep and surface layers, establishing a corresponding coupling relationship between them. Observations reveal that significant deformation areas exhibit a bottom-to-top development pattern. (3) A comparison of the InSAR interpretation results for the Zhayong deformation with the test conclusions reveals that the landslide was in the progressive deformation stage. In summary, this study provides valuable technical support for utilizing InSAR technology to identify and prevent slope failures in complex geological conditions. [ABSTRACT FROM AUTHOR]

Scanning Electron Microscopy with Energy Dispersive Spectroscopy (SEM‐EDS) is an indispensable and versatile technique that provides detailed 2D spatial insights into the microstructure of heterogenous cementitious systems. To foster clear and systematic understanding of SEM‐EDS analysis in advancing research on cementitious materials, the state‐of‐the‐art principles, analytical approaches, and applications of SEM‐EDS analysis in cementitious systems are reviewed. This review aims to assist researchers in selecting the most appropriate strategy for SEM‐EDS analysis to quantify phase assemblage, elucidate environmental interactions, and investigate microstructure evolution in cementitious systems. The fundamental concepts related to equipment, signal generation, acquisition of diverse EDS data are first presented. Subsequently, various analysis approaches, including point analysis, grid analysis, and mapping analysis are discussed. This review then emphasizes the practical significance and potential value of SEM‐EDS analysis in addressing phase quantification challenges pertaining to cementitious systems. It is posited that the SEM‐EDS analysis holds the promise of becoming the characterization backbone for quantitative research on cementitious systems. [ABSTRACT FROM AUTHOR]

Background Objectives Methods Results Conclusions Reduced cerebrospinal fluid (CSF) clearance may play a vital role in the pathogenesis of normal pressure hydrocephalus (NPH), but the radiologic marker is yet to be elucidated.This open‐label study presents two novel neuroimaging biomarkers based on enlarged perivascular spaces (ePVS) of the sub‐insular territory: the Hedgehog and Hedgehog‐Halo (H‐H) sign, designed to predict gait symptom severity and tap response in NPH.We retrospectively reviewed 203 patients with possible NPH with baseline magnetic resonance imaging and gait analyses before and after lumbar puncture (LP). The Hedgehog/H‐H sign was scored using T2‐weighted images. The clinical severity at baseline and post‐tap gait improvement was compared in patients with and without Hedgehog/H‐H sign. The association between Hedgehog/H‐H sign and post‐tap gait outcomes was assessed using multivariate regression. The diagnostic performance of Hedgehog/H‐H sign was compared with conventional radiological markers.Patients with H‐H showed higher global disability and more severe gait impairment than those without any signs. Following LP, patients with Hedgehog/H‐H sign significantly improved in various gait parameters, unlike those with neither sign. Additionally, sub‐insular ePVS was significantly associated with post‐tap gait improvement after adjusting covariates. Finally, the Hedgehog/H‐H sign showed a higher performance than conventional markers in predicting post‐tap gait response.The Hedgehog/H‐H sign is a useful neuroimaging biomarker related to the severity and tap response in NPH. This biomarker can be readily applied in clinical practice before undergoing LP, independent of conventional radiological signs. Further research is warranted to determine applicability in predicting post‐shunt gait response. [ABSTRACT FROM AUTHOR]

The article discusses the use of deep learning in image segmentation for cancer management, emphasizing the importance of medical imaging in cancer diagnosis and treatment. It highlights the role of quantitative imaging in characterizing tumors and monitoring treatment response. The text explores the application of deep learning models like U-Nets and CNNs in medical imaging, specifically for image segmentation tasks. Two studies are referenced, showcasing the effectiveness of AI models in automating image segmentation processes and improving accuracy in cancer management. [Extracted from the article]

Droplet digital PCR (ddPCR) is recognized as a high-precision method for nucleic acid quantification, extensively utilized in biomedical research and clinical diagnostics. This technique employs microfluidic technology to partition the nucleic acid-containing reaction mixture into discrete droplets for amplification, achieving absolute quantification by identifying and enumerating the number of fluorescent droplets. The accuracy of droplet quantification is pivotal to the success of the assay. However, current image-processing tools are operationally complex, and commercial instruments are costly. Moreover, the designed algorithms exhibit a need for enhanced accuracy and are often restricted to use by trained personnel with specific microscopy equipment. In response to these challenges, we introduce an automated device (A-MMD), capable of detecting fluorescent droplets in ddPCR images captured by multiple microscopes. The device integrates three distinct algorithms tailored for the image processing of Laser Scanning Confocal Microscopy (LSCM), inverted microscopy, and self-assembled microscopy. Experimental validation using λ DNA demonstrated a 100.00% identification rate for positive droplets across all three image types, and the average identification rates for total droplets being 99.27% for LSCM, 98.96% for inverted microscopy, and 99.08% for self-assembled microscopy. Furthermore, the A-MMD is equipped with a user-friendly interface (UI) that streamlines the operational process, enabling non-specialists to efficiently perform droplet detection tasks. Our device not only has good environmental adaptability and identification accuracy, but also significantly reduces costs and operational complexity. It offers an economical, efficient, and user-friendly solution for ddPCR image analysis, thereby further propelling the advancement and application of nucleic acid detection technology. [ABSTRACT FROM AUTHOR]

Multi-modal image analysis using deep learning (DL) lays the foundation for neoadjuvant treatment (NAT) response monitoring. However, existing methods prioritize extracting multi-modal features to enhance predictive performance, with limited consideration on real-world clinical applicability, particularly in longitudinal NAT scenarios with multi-modal data. Here, we propose the Multi-modal Response Prediction (MRP) system, designed to mimic real-world physician assessments of NAT responses in breast cancer. To enhance feasibility, MRP integrates cross-modal knowledge mining and temporal information embedding strategy to handle missing modalities and remain less affected by different NAT settings. We validated MRP through multi-center studies and multinational reader studies. MRP exhibited comparable robustness to breast radiologists, outperforming humans in predicting pathological complete response in the Pre-NAT phase (ΔAUROC 14% and 10% on in-house and external datasets, respectively). Furthermore, we assessed MRP's clinical utility impact on treatment decision-making. MRP may have profound implications for enrolment into NAT trials and determining surgery extensiveness. Deep learning for medical image analysis is a promising new avenue to predict treatment response, however the clinical application of these methods has been so far limited. Here, the authors propose a model to predict chemotherapy response in breast cancer in real world clinical settings. [ABSTRACT FROM AUTHOR]

In plant biology research, X-ray computed tomography (CT) has been demonstrated to be useful for studying the complex tissues of trees qualitatively and quantitatively. The cambium plays a very important role in plant growth and development. However, this tissue is relatively poorly studied due to its complex nature and its localization between layers of xylem and phloem cells. Antecedent environmental conditions may have a substantial impact on plant responses to drought and recovery dynamics. Therefore, this study aimed to investigate the impact of previous drought exposure on the 3D microstructure of two commercial Eucalyptus clones by monitoring the development of the cambium, in the same plant and same area of cambial tissue, during a 4-week trial consisting of well-watered, mild drought, severe drought, and rewatering phases. To track microstructural changes, the plants were imaged weekly with non-destructive X-ray micro-computed tomography (μCT). On the last day of the experiment, X-ray nano-CT was also performed. Our results indicate that trees subjected to earlier well-watered conditions were more affected by the severe drought, compared to those previously exposed to mild drought conditions. Thus, previous exposure to water deficit may provide the benefit of increased drought tolerance during future water deficit. Previous mild drought exposure can facilitate morphological adjustments, which potentially enhances drought tolerance during subsequent drought events. [ABSTRACT FROM AUTHOR]

Psoriasis, being a chronic, inflammatory, lifelong skin disorder, has become a major threat to the human population. The precise and effective diagnosis of psoriasis continues to be difficult for clinicians due to its varied nature. In northern India, the prevalence of psoriasis among adult population ranges from 0.44 to 2.8%. Chronic plaque psoriasis accounts for over 90% of cases. This study utilized a dataset of 325 raw images collected from a reputable local hospital using a digital camera under uniform lighting conditions. These images were processed to generate 496 image patches (both diseased and normal), which were then normalized and resized for model training. An automated psoriasis image recognition framework was developed using four state-of-the-art deep transfer learning models: VGG16, VGG19, MobileNetV1, and ResNet-50. The convolutional layers adopted various edge, shape, and color filters to generate the feature map for psoriasis detection. Each pre-trained model was adapted with two dense layers, one dropout layer, and one output layer to classify input images. Among these models, MobileNetV1 achieved the best performance, with 94.84% sensitivity, 89.37% specificity, and 97.24% overall accuracy. Hyper-parameter tuning was performed using grid search to optimize learning rates, batch sizes, and dropout rates. The AdaGrad (Adaptive gradient)) optimizer was chosen for its adaptive learning rate capabilities, facilitating quicker convergence in model performance. Consequently, the methodology's performance improved to 94.25% sensitivity, 96.42% specificity, and 99.13% overall accuracy. The model's performance was also compared with non-machine learning-based diagnostic methods, yielding a Dice coefficient of 0.98. However, the model's effectiveness is dependent upon high-quality input images, as poor image conditions may affect accuracy, and it may not generalize well across diverse demographics or psoriasis variations, highlighting the need for varied training datasets for robustness. [ABSTRACT FROM AUTHOR]

ABSTRACT Image based screening is now routinely available for early detection of cancer and other diseases. Quantitative analysis for effects of risk factors on digital images is important to extract biological insights for modifiable factors in prevention studies and understand pathways for targets in preventive drugs. However, current approaches are restricted to summary measures within the image with the assumption that all relevant features needed to characterize an image can be identified and appropriately quantified. Motivated by data challenges in breast cancer, we propose a nonparametric statistical framework for risk factor screening that uses the whole mammogram image as outcome. The proposed permutation test allows assessment of whether a set of scalar risk factors is associated with the whole image in the presence of correlated residuals across the spatial domain. We provide extensive simulation studies and illustrate an application to the Joanne Knight Breast Health Cohort using the mammogram imaging data. [ABSTRACT FROM AUTHOR]

Animals combine colour change and behavioural choices to enhance concealment and adapt to changes in habitat in time and space. However, non‐native and invasive habitat‐forming plants and seaweeds can change the landscape, challenging animals to remain camouflaged, especially when the colour of the new habitat differs from the native backgrounds.The chameleon prawn (Hippolyte varians) exhibits remarkable colour variation and effective camouflage against different native seaweeds in shallow tidepools. Individuals optimize crypsis by choosing colour‐matching seaweeds and changing colour over time. In some locations, the prawn's native habitats are now replaced by non‐native seaweeds of different coloration and structure, making it important to understand whether substrate choice and colour change facilitates the occupation of these seaweeds and enables prawns to camouflage against new backgrounds.Using image analysis and visual modelling of a fish predator, we assessed the colour variation and camouflage of chameleon prawns occupying the non‐native seaweeds brown wireweed (Sargassum muticum) and pink harpoon weed (Asparagopsis armata) in southwest UK. We performed laboratory trials to examine whether prawns maintain their preference for colour‐matching native substrates when given a choice between them and non‐native seaweeds, and if they can change their coloration to improve camouflage against non‐native substrates.Prawns exhibit phenotypic diversity and camouflage that varied with the non‐native seaweed species, with low colour variation and effective camouflage on pink harpoon weed, but high colour diversity and reduced concealment against brown wireweed. Prawns choose non‐native seaweeds when the alternative native substrate provides mismatching coloration, but they did not exhibit any preference between colour‐matching native and non‐native seaweeds. Once in non‐native habitats, prawns change their appearance over a few days to match the background, sometimes faster than when changing on native seaweeds of contrasting coloration.Although human activities considerably modify the coloration of marine landscapes by increasing the establishment of non‐native seaweeds, the impacts of those changes appear less severe for colour‐changing species, such as chameleon prawns, and will depend on the similarity between the colour of the new substrates and the original native backgrounds, and how animals use behaviour and physiology to match new habitats. [ABSTRACT FROM AUTHOR]

Purpose: Total-body dynamic positron emission tomography (PET) imaging with total-body coverage and ultrahigh sensitivity has played an important role in accurate tracer kinetic analyses in physiology, biochemistry, and pharmacology. However, dynamic PET scans typically entail prolonged durations ( 60 minutes), potentially causing patient discomfort and resulting in artifacts in the final images. Therefore, we propose a dynamic frame prediction method for total-body PET imaging via deep learning technology to reduce the required scanning time. Methods: On the basis of total-body dynamic PET data acquired from 13 subjects who received [68Ga]Ga-FAPI-04 (68Ga-FAPI) and 24 subjects who received [68Ga]Ga-PSMA-11 (68Ga-PSMA), we propose a bidirectional dynamic frame prediction network that uses the initial and final 10 min of PET imaging data (frames 1–6 and frames 25–30, respectively) as inputs. The peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) were employed as evaluation metrics for an image quality assessment. Moreover, we calculated parametric images (68Ga-FAPI: , 68Ga-PSMA: ) based on the supplemented sequence data to observe the quantitative accuracy of our approach. Regions of interest (ROIs) and statistical analyses were utilized to evaluate the performance of the model. Results: Both the visual and quantitative results illustrate the effectiveness of our approach. The generated dynamic PET images yielded PSNRs of 36.056 ± 0.709 dB for the 68Ga-PSMA group and 33.779 ± 0.760 dB for the 68Ga-FAPI group. Additionally, the SSIM reached 0.935 ± 0.006 for the 68Ga-FAPI group and 0.922 ± 0.009 for the 68Ga-PSMA group. By conducting a quantitative analysis on the parametric images, we obtained PSNRs of 36.155 ± 4.813 dB (68Ga-PSMA, ) and 43.150 ± 4.102 dB (68Ga-FAPI, ). The obtained SSIM values were 0.932 ± 0.041 (68Ga-PSMA) and 0.980 ± 0.011 (68Ga-FAPI). The ROI analysis conducted on our generated dynamic PET sequences also revealed that our method can accurately predict temporal voxel intensity changes, maintaining overall visual consistency with the ground truth. Conclusion: In this work, we propose a bidirectional dynamic frame prediction network for total-body 68Ga-PSMA and 68Ga-FAPI PET imaging with a reduced scan duration. Visual and quantitative analyses demonstrated that our approach performed well when it was used to predict one-hour dynamic PET images. https://github.com/OPMZZZ/BDF-NET. [ABSTRACT FROM AUTHOR]