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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.

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.

Cobalt removal is considered a common method to improve the performance of polycrystalline diamond compact (PDC). To effectively reduce the pollution during the process of cobalt removal, a neutral electrolyte was used in this work to remove cobalt from PDC by electrolysis with ultrasonication. The electrode process and mass transfer model of cobalt removal in a NaCl–KCl solution were established by performing electrochemical impedance spectroscopy (EIS) measurements. Different electrode potentials and electrolytic durations were set to investigate the limits of cobalt removal in the neutral electrolyte. Cobalt removal was assessed by conducting potentiodynamic polarisation, scanning electron microscopy (SEM), and energy dispersive spectroscopy (EDS) measurements. The ImageJ2x image analysis software was used to study the collected spectra. The experimental results indicated that cobalt was successfully removed in the neutral electrolyte and that ultrasonication enhanced its removal. The optimal electrode potential for cobalt removal in the NaCl–KCl solution was less than 500 mV or more than 2000 mV. As the electrolysis time increased, the porosity of the PDC surface first increased to 9.4 % and then decreased to 7.2 %, and the depth of cobalt removal was up to 224 μm. Over time, the rate of cobalt removal decreased, suggesting that the efficacy of ultrasonication in promoting the cobalt removal process gradually weakened as electrolysis products continued to accumulate. [ABSTRACT FROM AUTHOR]

Semantic segmentation is a fundamental step in image understanding, playing a crucial role in the fields of automatic driving, medical image analysis, defect detection, etc. Despite significant progress in deep learning-based image segmentation, challenges in terms of accuracy and efficiency still exist, especially for small-scale objects. In this paper, we present a novel data augmentation method for small-scale objects in images, aiming to address the issue of class imbalance. Specifically, we extract small-scale objects from one image and then copy-scale-and-paste them to other images. Additionally, a novel multi-scale feature fusion module is proposed to effectively combine features from both deep and shallow neural network layers. Subsequently, the data augmentation method and multi-scale feature fusion module are utilized in the proposed Fast-SegNet architecture for semantic segmentation. Extensive experiments demonstrate that Fast-SegNet could improve segmentation performance, especially for small-scale objects with an acceptable computational cost. State-of-the-art performance has been achieved on CamVid, CityScapes, and MOST (Micro-optical sectioning tomography) datasets with respect to the tradeoff between accuracy and speed. Specifically, the CamVid dataset yields mean IoU (Intersection over Union) values of 45.7% and 38.6% for small-scale objects as Pedestrian and Bicyclist, respectively. The CityScapes dataset demonstrates mean IoU of 43.43% and 43.56% for small-scale objects as Traffic Light and Rider, respectively. The MOST dataset results in a segmentation mean IoU of 88.2% for vessels in the mouse brain. In conclusion, our approach achieves better results in terms of accuracy and efficiency on three datasets. Codes are available at https://github.com/apple1986/Fast-SegNet. [ABSTRACT FROM AUTHOR]

Novel Stitched Foam‐filled Honeycomb Sandwich (SFHS) panels have been fabricated using vacuum‐assisted resin transfer molding to address the weak interfaces between the face sheets and the core in the Foam‐filled Honeycomb Sandwich (FHS) panel. The SFHS panels have shown better load‐bearing capacity and performance characteristics compared to FHS panel after Low‐Velocity Impact (LVI) tests. After the LVI test, MATLAB image processing was used to analyze the impact damage areas and failure mechanisms. In addition, Machine Learning regression algorithms were employed to predict the optimal amount of energy absorbed during low‐velocity impact testing of fabricated panels with a maximum impactor drop height of 700 mm. The results indicated that nylon yarn stitching significantly improved energy absorption and interfacial behavior compared to unstitched honeycomb panels. This research also revealed that SFHS1 panels with adjacent honeycomb cell stitching are more impact resistant, provide increased load carrying capacity, and are cost‐effective. These panels can be utilized by modern engineers to increase economy, durability, and functionality in industrial, automotive, and construction applications. Highlights: Stitched Foam Filled Honeycomb Sandwich (SFHS) panels, manufactured via resin transfer molding, and eliminates weak interfaces between the face sheets and core.SFHS panels outperformed unstitched panels in load‐bearing and energy absorption during low‐velocity impact, as confirmed by MATLAB image analysis showing reduced damage and failure.Machine learning algorithms particularly polynomial regression model predicted maximum absorption energy precisely with 99.9% accuracy, close to experimental results.SFHS panels can be used in automotive and industrial applications due to their through‐thickness stitching. [ABSTRACT FROM AUTHOR]

The increasing incidence of skin cancer necessitates advancements in early detection methods, where deep learning can be beneficial. This study introduces SkinNet-14, a novel deep learning model designed to classify skin cancer types using low-resolution dermoscopy images. Unlike existing models that require high-resolution images and extensive training times, SkinNet-14 leverages a modified compact convolutional transformer (CCT) architecture to effectively process 32 × 32 pixel images, significantly reducing the computational load and training duration. The framework employs several image preprocessing and augmentation strategies to enhance input image quality and balance the dataset to address class imbalances in medical datasets. The model was tested on three distinct datasets—HAM10000, ISIC and PAD—demonstrating high performance with accuracies of 97.85%, 96.00% and 98.14%, respectively, while significantly reducing the training time to 2–8 s per epoch. Compared to traditional transfer learning models, SkinNet-14 not only improves accuracy but also ensures stability even with smaller training sets. This research addresses a critical gap in automated skin cancer detection, specifically in contexts with limited resources, and highlights the capabilities of transformer-based models that are efficient in medical image analysis. [ABSTRACT FROM AUTHOR]

In recent years, there has been a surge in new chaos-based cryptographic algorithms, many of which claim to have unusually large keyspaces. Although cryptographic primitives such as symmetric-key ciphers should have a secret keyspace large enough to resist brute force attacks, simply increasing the size of a secret key may not lead to improved security margins. An n-bit key may not necessarily have a keyspace of 2 n - 1 due to the key scheduling algorithm or how the key is used. In this paper, we cryptanalyse several chaos-based algorithms from the perspective of their key schedules. Our numerical analysis is based on the known-plaintext attack model, Kerckhoff's principle and considers the number representations used for real number computation. Our analysis reveals that the actual security margins for these ciphers are significantly lower, some by a factor of over 2 100 than what was claimed. We then provide accurate keyspace estimates for these ciphers. Finally, we highlight alternative solutions for how secret keys can be used in the context of chaos-based cryptography and propose a simple key schedule as a proof of concept. Despite its simplicity, the proposed key schedule not only ensures that the keyspace matches the key length but also passes both the NIST and ENT statistical test suites, making it a viable option for generating secure cryptographic keys. Our work contributes towards addressing one of the fundamental problems in chaos-based cryptography that limits its real-world impact and reputation within the cryptographic community. [ABSTRACT FROM AUTHOR]

Recent progress in Deep Learning (DL) has shown potential in intelligent healthcare applications, enhancing patients' quality of life. However, improving DL precision requires a larger and diverse dataset, leading to privacy and confidentiality challenges when consolidating data at a centralized server. To address this, we propose a skin cancer detection method prioritizing patient information and privacy. "Skin-net," a novel Convolutional Neural Network (CNN) model, integrates progressively private Federated Learning (FL) for accurate classification of complex skin lesion images. FL ensures data confidentiality during model training. Skin-net achieves promising results, with 98.3%± accuracy, 98.8%± sensitivity, and 97.9%± specificity, while preserving data privacy. It offers an effective pathway for skin cancer analysis and image augmentation, mitigating privacy concerns in medical image analysis. [ABSTRACT FROM AUTHOR]

In large-scale astronomical surveys, traditional supernova detection pipelines rely on complex and relatively inefficient image differencing techniques. This paper proposes an end-to-end deep-learning supernova detection network, the Real-Time SuperNova DEtection TRansformer (RT-SNDETR). This network partially replaces traditional pipelines by integrating image differencing, source detection, and Real-bogus classification, achieving a speed 51.49 times that of the fastest image differencing method, SFFT. Additionally, it remains competitive with methods like YOLO v8, offering a well-balanced trade-off between speed and accuracy. Experimental results highlight RT-SNDETR's superior performance, with an average precision(AP) of 96.30 per cent on synthetic samples and 76.60 per cent on real supernova samples. It significantly outperforms other detection networks, including RT-DETR (+5.6 per cent AP on synthetic/+5.1 per cent AP on real samples) and Cascade R-CNN (+8.9 per cent AP on synthetic/ +28.6 per cent AP on real samples). The incorporation of CycleGAN-based data generation methods plays a significant role in enhancing RT-SNDETR's performance. These methods simulate realistic PSF variations, enabling the object detection network to learn more robust features and improving its generalization to real supernovae data. Additionally, by integrating unsupervised domain adaptation techniques, RT-SNDETR achieves an AP of 81.70 per cent on real SDSS supernova survey samples. This study demonstrates RT-SNDETR's potential to significantly enhance both the speed and accuracy of supernova detection, making it a highly effective solution for large-scale astronomical surveys. [ABSTRACT FROM AUTHOR]

With the advancement of information and communication technology, the integration of smart systems into discrete sequential processes has been realized in manufacturing systems. Specifically, to optimize the efficiency of operations and production, accurate and rapid detection of faults is crucial. Numerous studies have attempted to identify faults in various electromechanical systems; however, the performance and effectiveness often depend on the quality of the collected signals and the system operation methodology. For instance, the measurements of various types of analog sensor signals—such as voltage, current, 3-axis acceleration, and temperature—vary according to the installation locations and positions within a system. Moreover, although an automated manufacturing system typically operates under predefined control sequences, identifying common signal patterns for every healthy or faulty state is challenging. Consequently, we focused on detecting faults using only image data captured from on-site camera in automated sequential manufacturing processes, without relying on additional manufacturing information. We proposed a method based on convolutional long short-term memory (LSTM) networks for the next visual status prediction and an unsupervised statistical process control chart to classify the current status as normal or faulty. Following the detection of a fault, we conducted unsupervised fault type clustering to provide valuable insights for maintenance efforts. The validation and verification demonstrated accurate fault detection (94.2%) and effective fault type clustering in two distinct manufacturing processes of a USB packaging system. Since no information other than the original process images captured by the on-site camera were used for fault detection and fault type classification, it can be assumed that the proposed methodology can be easily applied to other or more complex manufacturing processes. [ABSTRACT FROM AUTHOR]

In 2002, Israel started the construction of a 708-kilometer wall to control the Palestinian territories. The Wall offered a massive canvas, which the Palestinians used to develop graffiti and murals. The artwork and graffiti are location-specific, ephemeral, transitory, and unstable. This paper aims to document, archive, and analyze discourse and themes emerging from the artwork that developed on the Wall between 2018 and 2022. The analysis of these images has revealed that, while the purpose of building the Wall was to curb and stifle their resistance, Palestinians turned it into a platform where they were able to express their commitment to national resistance, freedom, justice, love, poetry, business, and even personalization of their own stories. The Wall has also attracted international solidarity and sparked a new protest movement called "struggle tourism", focusing on the Palestinian presence in the region's physical, moral, and political landscape. [ABSTRACT FROM AUTHOR]

Nighttime driving safety is a key focus in transportation research due to accidents caused by drivers' inability to clearly see road obstacles, leading to delayed or incorrect decisions. To address this, the use of mechanoluminescent materials on surfaces like roads and buildings offers a potential solution for better object contour detection in poor visibility. This study investigates the use of SrAl 2 O 4 as a phosphor matrix, exploring its luminescence characteristics and the effects of doping with Eu2+ and Dy3+. The optimal doping ratios were determined to be 2 % Eu and 1 % Dy by trap modulation, producing phosphors with significant mechanical luminescence and a prolonged afterglow. Spectroscopic analysis and image assessment demonstrated a visible afterglow lasting up to 60 min, along with impressive mechanical luminescence performance. By combining the developed SrAl 2 O 4 :Eu2+, Dy3+ phosphor-based coating with polydimethylsiloxane, a real-time surface stress sensing system was devised utilizing digital camera and other optical sensors. This advancement facilitates the visualization of stresses in complex or confined environments, potentially improving nighttime driving safety through enhanced object contour detection. [ABSTRACT FROM AUTHOR]

ABSTRACT Immunohistochemistry (IHC) is a widely used technique in diagnostic pathology and biomedical research, but there is still a need to shorten the operation process and reduce the cost of antibodies. This study aims to assess a novel IHC technique that incorporates mechanical microvibration (MMV) to expedite the process, reduce antibody consumption, and enhance staining quality. MMV was generated using coin vibration motors attached to glass slides mounted with consecutive tissue sections. Multiple antibodies targeting various antigens were used to stain cancerous and normal tissues, with and without microvibration. Various parameters were tested, including incubation durations, temperatures, and antibody dilutions. The novel method showed the potential to achieve comparable or superior outcomes in significantly less time, utilizing over 10 times less antibody than controls. MMV improved specific staining quality, yielding stronger, and better‐defined positive reactions. This was validated through a multicenter double‐blind assessment and quantitative image analysis. The possible mechanisms were also investigated. MMV shortens immunohistochemical staining duration, reduces antibody usage, and enhances staining specificity, likely by accelerating antibody movement and diffusion. These improvements translate to time and cost savings, offering clinical and financial value for diagnostic pathology and biomedical research. [ABSTRACT FROM AUTHOR]

Lithium‐ion batteries (LIBs) are increasingly encouraged to enhance their environmental friendliness and safety while maintaining optimal energy density and cost‐effectiveness. Although various electrolytes using greener and safer glyme solvents have been reported, the low charge voltage (usually lower than 4.0 V vs Li/Li+) restricts the energy density of LIBs. Herein, tetraglyme, a less‐toxic, non‐volatile, and non‐flammable ether solvent, is exploited to build safer and greener LIBs. It is demonstrated that ether electrolytes, at a standard salt concentration (1 m), can be reversibly cycled to 4.5 V vs Li/Li+. Anchored with Boron‐rich cathode‐electrolyte interphase (CEI) and mitigated current collector corrosion, the LiNi0.8Mn0.1Co0.1O2 (NMC811) cathode delivers competitive cyclability versus commercial carbonate electrolytes when charged to 4.5 V. Synchrotron spectroscopic and imaging analyses show that the tetraglyme electrolyte can sufficiently suppress the overcharge behavior associated with the high‐voltage electrolyte decomposition, which is advantageous over previously reported glyme electrolytes. The new electrolyte also enables minimal transition metal dissolution and deposition. NMC811||hard carbon full cell delivers excellent cycling stability at C/3 with a high average Coulombic efficiency of 99.77%. This work reports an oxidation‐resilient tetraglyme electrolyte with record‐high 4.5 V stability and enlightens further applications of glyme solvents for sustainable LIBs by designing Boron‐rich interphases. [ABSTRACT FROM AUTHOR]

Automatic pavement disease detection aims to address the inefficiency in practical detection. However, traditional methods heavily rely on low-level image analysis, handcrafted features, and classical classifiers, leading to limited effectiveness and poor generalization in complex scenarios. Although significant progress has been made with deep learning methods, challenges persist in handling high-resolution images and diverse disease types. Therefore, this paper proposes a novel approach based on the lightweight Transformer Patch Labeling Network (LTPLN) to enhance the efficiency of automatic pavement disease detection and overcome the limitations of existing methods. Firstly, the input images undergo histogram equalization preprocessing to enhance image quality. Subsequently, the images are evenly partitioned into small patch blocks, serving as inputs to the enhanced Transformer model. This enhancement strategy involves integrating feature map labels at each layer of the model to reduce computational complexity and enhance model lightweightness. Furthermore, a depthwise separable convolution module is introduced into the Transformer architecture to introduce convolutional bias and reduce the model's dependence on large amounts of data. Finally, an iterative training process utilizing the label distillation strategy based on expectation maximization is employed to update the labels of patch blocks and roughly locate the positions of pavement diseases under weak supervision. Experimental results demonstrate that compared to the baseline model, the proposed enhanced model achieves a reduction of 2.5G Flops computational complexity and a 16% speed improvement on a private pavement disease dataset, with only a 1.2 percentage point decrease in AUC accuracy. Moreover, compared to other mainstream image classification models, this model exhibits more balanced performance on a public dataset, with improved accuracy and speed that better align with the practical requirements of pavement inspection. These findings highlight the significant performance advantages of the LTPLN model in automatic pavement disease detection tasks, making it more efficiently applicable in real-world scenarios. [ABSTRACT FROM AUTHOR]

ABSTRACT Objectives Methods Results Conclusion Critical/urgent X‐ray findings are not always communicated in an appropriate time frame to ED physicians. The practice of radiographers alerting referrers to clinically significant image findings (verbally, via image flags or written comment) is noted internationally but risk assessment data is unavailable in the literature. A hybrid radiographer comment and alert model was piloted in New South Wales and a risk–benefit assessment conducted for timely and safe communication of abnormal X‐ray appearances to ED physicians.Radiographer comments (n = 1102) were provided to five New South Wales EDs by 69 radiographers for a period of 3–12 months. Site auditors classified comments as true positive (TP), false positive (FP) or indeterminate (ID) with respect to the radiology report. FP comments were investigated with ED referrers and a low‐medium‐high‐risk assessment was provided by two independent reviewers.A total of 42 FP (3.9%; 95% confidence interval [CI] 2.9–5.3) comments were analysed for any adverse outcomes. Risk assessments demonstrated 37 low, 5 low‐moderate and no high‐risk cases. A total of 282 direct or potential patient benefits were identified (26.4%; 95% CI 23.8–29.1). A total of 42 radiology report discrepancies were incidentally found: (3.9%; 95% CI 2.9–5.3). Audit results demonstrated areas where the radiographer comment could mitigate risk.The provision of radiographer alerts with a written comment for ED was found to be low risk to patients in the pilot study. Radiographers communicating directly with the emergency team when abnormal image appearances are detected can reduce diagnostic error and improve patient safety and health outcomes. [ABSTRACT FROM AUTHOR]

ABSTRACT The Kronecker product enlightens a sophisticated and esthetically pleasing algebraic structure that is a fundamental framework for diverse applications, such as signal processing, image analysis, and quantum computing. This study, proposing an iterative methodology grounded in Wirtinger calculus and polar decomposition, delves into the nearest Kronecker product problem under orthonormal constraints. The iterative approach ensures global convergence toward a local solution from any initial value. Numerical experiments are given to show the efficiency and effectiveness of our approach. Mainly, when the target matrix in the objective function is decomposable, the recovery of the original structure is achievable. [ABSTRACT FROM AUTHOR]

In recent years, due to the wide application of multi-sensor vision systems, multimodal image acquisition technology has continued to develop. Monomodal images cannot meet the needs of image analysis, which requires image fusion and stitching to process images for better image analysis. Image registration is an important prerequisite of image fusion and stitching. Most of the existing multimodal image registration methods are only suitable for two modalities, and cannot uniformly register multimodal image data. Therefore, this paper proposes a multimodal remote sensing image registration method based on adaptive multi-scale PIIFD (AM-PIIFD). This method extracts KAZE features in the scale space constructed by nonlinear diffusion filtering. It can effectively preserve the edge feature information while filtering out the noise. Then, the proposed AM-PIIFD feature descriptor is used to describe the multi-scale features. Finally, according to the consistency of the feature main orientation, most of the mismatches are removed, and the image alignment transformation is realized. The qualitative and quantitative comparisons with the other three advanced methods show that our method can achieve good performance in multimodal remote sensing image registration. [ABSTRACT FROM AUTHOR]

In real-life scenarios, information about the number of clusters is unknown. Due to this, clustering algorithms are unable to generate the valuable partitions. Beside this, the appropriate and optimal number of features is also required to produce the good quality clusters. The selection of optimal number of clusters and feature is a challenging task in the clustering. To resolve these problems, an automatic multi-objective-based clustering approach called HMOSHSSA is proposed in this paper. In HMOSHSSA, the spotted hyena and salp swarm algorithms are hybridized to obtain a better trade-off between these algorithms' intensification and diversification capabilities. Two novel concepts for encoding and threshold setting are incorporated in the HMOSHSSA. The encoding scheme is used to choose the optimal number of clusters and features during the optimization process. The variance of dataset is used for setting the threshold values for both clusters and features. A novel fitness function is proposed to improve the optimization process. The suggested algorithm's performance is evaluated using eight well-known real-world datasets. The statistical significance of HMOSHSSA is measured through t-tests. Results reveal that the proposed approach is able to detect the optimal number of clusters and features from a given dataset without user intervention. This approach is also deployed for solving microarray data analysis and image segmentation problems. HMOSHSSA outperformed the other considered algorithms in terms of performance measures. [ABSTRACT FROM AUTHOR]

ABSTRACT Landslides represent a growing global geological hazard, further intensified by climate‐induced changes. Remote sensing data, through its capacity for repetitive collection and change detection techniques, that compare and quantify the spatio‐temporal alterations over time, plays a critical role in landslide detection. Considering the February 2023 São Sebastião event and Sentinel‐2 imagery, we assessed diverse unsupervised change detection techniques, encompassing both traditional and recent machine learning‐based approaches. Notably, the Floating References (FR) and Homogeneous Blocks Single‐class Classification (HBSC) methods outperform classic approaches and deliver the most accurate results with F1‐Score and kappa coefficient exceeding 0.96 and 0.92, respectively. These outcomes demonstrate the efficacy of machine learning in automating landslide delineation and underscore the necessity of meticulous data and parameter selection in achieving high‐accuracy automatic landslide mapping. Lastly, this study fills a significant gap in the existing literature by evaluating unsupervised change detection methods for landslide mapping within the Brazilian context. [ABSTRACT FROM AUTHOR]

This paper presents the development and application of a deep learning-based method for inverting CO2 atmospheric plumes from power plants using satellite imagery of the CO2 total column mixing ratios (XCO2). We present an end-to-end CNN approach, processing the satellite XCO2 images to derive estimates of the power plant emissions, that is resilient to missing data in the images due to clouds or to the partial view of the plume due to the limited extent of the satellite swath. The CNN is trained and validated exclusively on CO2 simulations from 8 power plants in Germany in 2015. The evaluation on this synthetic dataset shows an excellent CNN performance with relative errors close to 20 %, which is only significantly affected by substantial cloud cover. The method is then applied to 39 images of the XCO2 plumes from 9 power plants, acquired by the Orbiting Carbon Observatory-3 Snapshot Area Maps (OCO3-SAMs), and the predictions are compared to average annual reported emissions. The results are very promising, showing a relative difference of the predictions to reported emissions only slightly higher than the relative error diagnosed from the experiments with synthetic images. Furthermore, the analysis of the area of the images in which the CNN-based inversion extract the information for the quantification of the emissions, based on integrated gradient techniques, demonstrates that the CNN effectively identifies the location of the plumes in the OCO-3 SAM images. This study demonstrates the feasibility of applying neural networks that have been trained on synthetic datasets for the inversion of atmospheric plumes in real satellite imagery of XCO2, and provides the tools for future applications. [ABSTRACT FROM AUTHOR]

Counterfeit goods are pervasive, being found in products as diverse as textiles and optical media to pharmaceuticals and sensitive electronics. Here, an anti‐counterfeit platform is reported in which plasmonic nanoparticles (NPs) are used to create unique image tags that can be authenticated quickly and reliably. Specifically, plasmonic NPs are assembled into periodic arrays of NP clusters by template‐assisted self‐assembly (TASA), where the light scattering responses from the arrays are analyzed by dark‐field optical microscopy. Tag design proved modular as plasmonic NPs with different optical responses can be selected and paired with Templates with different features (e.g., well size, well shape, and number and arrangement of wells in an array), giving access to a variety of color responses and unique images. These images can be differentiated from one another and authenticated by image analysis. Authentication methods based on shallow and deep neural networks are compared, where deep neural networks authenticated TASA tags with higher accuracy. Given the ease of tag fabrication and rapid image analysis, these platforms are ideal for on‐the‐fly tagging and supply‐chain authentication of critical goods. [ABSTRACT FROM AUTHOR]

Complex organ structures are formed with high reproducibility. To achieve such intricate morphologies, the responsible epithelium undergoes multiple simultaneous shape changes, such as elongation and folding. However, these changes have typically been assessed separately. In this study, we revealed how distinct shape changes are controlled during internal organ morphogenesis. The Drosophila embryonic hindgut undergoes left-right asymmetric rotation and anteroposterior elongation in a tissue-autonomous manner driven by cell sliding and convergent extension, respectively, in the hindgut epithelia. However, the regulation of these processes remains unclear. Through genetic analysis and live imaging, we demonstrated that cell sliding and convergent extension are independently regulated by Myosin1D and E-cadherin, and Par-3, respectively, whereas both require MyosinII activity. Using a mathematical model, we demonstrated that independently regulated cellular dynamics can simultaneously cause shape changes in a single mechanical system using anisotropic edge contraction. Our findings indicate that distinct cellular dynamics sharing a common apparatus can be independently and simultaneously controlled to form complex organ shapes. This suggests that such a mechanism may be a general strategy during complex tissue morphogenesis. Author summary: Complex organ structures are formed with high reproducibility. To achieve such intricate morphologies, the tissue undergoes multiple shape changes, such as elongation and folding, which are driven by distinct cellular behaviors. However, these changes have been assessed separately and the regulatory mechanisms are not fully understood. In this study, we explored the regulation of multiple tissue deformations using the Drosophila embryonic hindgut as a model system. The hindgut simultaneously undergoes left-right asymmetric rotation and anterior-posterior elongation, driven by cell sliding and intercalation, respectively. Through genetic analysis, live imaging, and mathematical modeling, we reveal that distinct genetic pathways regulate these processes, despite both utilizing common cell boundary contraction. Our findings indicate that distinct cellular dynamics sharing a common apparatus can be independently and simultaneously controlled to form complex organ shapes. These mechanisms might be widely applicable in the morphogenesis of complex organs. [ABSTRACT FROM AUTHOR]

Summary Roots are important in agricultural and natural systems for determining plant productivity and soil carbon inputs. Sometimes, the amount of roots in a sample is too much to fit into a single scanned image, so the sample is divided among several scans, and there is no standard method to aggregate the data. Here, we describe and validate two methods for standardizing measurements across multiple scans: image concatenation and statistical aggregation. We developed a Python script that identifies which images belong to the same sample and returns a single, larger concatenated image. These concatenated images and the original images were processed with RhizoVision Explorer, a free and open‐source software. An R script was developed, which identifies rows of data belonging to the same sample and applies correct statistical methods to return a single data row for each sample. These two methods were compared using example images from switchgrass, poplar, and various tree and ericaceous shrub species from a northern peatland and the Arctic. Most root measurements were nearly identical between the two methods except median diameter, which cannot be accurately computed by statistical aggregation. We believe the availability of these methods will be useful to the root biology community. [ABSTRACT FROM AUTHOR]

A common problem in confocal microscopy is the decrease in intensity of excitation light and emission signal from fluorophores as they travel through 3D specimens, resulting in decreased signal detected as a function of depth. Here, we report a visualization program compatible with widely used fluorophores in cell biology to facilitate image interpretation of differential protein disposition in 3D specimens. Glioblastoma cell clusters were fluorescently labeled for mitochondrial complex I (COXI), P2X7 receptor (P2X7R), β-Actin, Ki-67, and DAPI. Each cell cluster was imaged using a laser scanning confocal microscope. We observed up to ~70% loss in fluorescence signal across the depth in Z-stacks. This progressive underrepresentation of fluorescence intensity as the focal plane deepens hinders an accurate representation of signal location within a 3D structure. To address these challenges, we developed ProDiVis: a program that adjusts apparent fluorescent signals by normalizing one fluorescent signal to a reference signal at each focal plane. ProDiVis serves as a free and accessible, unbiased visualization tool to use in conjunction with fluorescence microscopy images and imaging software. [ABSTRACT FROM AUTHOR]

Resin canals produce and transport oleoresins that are important for tree defenses within the Pinaceae family. Rapid measurement techniques are needed to better understand how resin canal characteristics vary due to genetic and environmental effects. Here we describe a semi-automated microscopy imaging system that was built for quantifying longitudinal resin canals. Tree increment cores from 210 loblolly pine (Pinus taeda L.) trees were prepared into radial strips and the transverse surface of the samples polished with 400 and then 600 grit sandpaper. Each sample was imaged along its entire length (pith to bark) with the images collected from a monochrome camera connected to a Plan Fluorite 4× objective lens. The samples were imaged on the transverse surface via transmitted 850 nm near-infrared light directed at the radial surfaces of the samples. A total of 24 153 images were collected and then processed offline in Python using the Open Computer Vision Library (OpenCV) using a series of algorithms including contrast correction, noise removal, thresholding, contour identification, erosion, and dilation. A total of 24 491 resin canals were identified and their size quantified. The resin canals were assigned into annual rings and positioned within the earlywood or latewood of a ring using cross-correlation whereby a pseudo-density value was derived from the images and matched with density values measured by X-ray densitometry. Of the total resin canals identified, 51.5% were in the earlywood and 48.5% in the latewood, with the majority being detected in the earlywood in the first six years and the latewood in years 7 and above. This study represents the most information collected on the resin canals of loblolly pine. The detailed description of the hardware and image analysis methods should serve as a useful guide to others interested in imaging resin canals as well as other anatomical features. [ABSTRACT FROM AUTHOR]

An article with three goals, namely, to (1) provide the set of ideas and information needed to understand, at a basic level, the application of convolutional neural networks (CNNs) to analyze images in biology; (2) trace a path to adopting and adapting, at code level, the applications of machine learning (ML) that are freely available and potentially applicable in biology research; (3) by using as examples the networks described in the recent article by Ríos et al. [ABSTRACT FROM AUTHOR]

Patterns of lineal descent play a critical role in the development of metazoan embryos. In eutelic organisms that generate a fixed number of somatic cells, invariance in the topology of their cell lineage provides a powerful opportunity to interrogate developmental events with empirical repeatability across individuals. Studies of embryonic development using the nematode Caenorhabditis elegans have been drivers of discovery. These studies have depended heavily on high-throughput lineage tracing enabled by 4D fluorescence microscopy and robust computer vision pipelines. For a range of applications, computer-aided yet manual lineage tracing using 4D label-free microscopy remains an essential tool. Deep learning approaches to cell detection and tracking in fluorescence microscopy have advanced significantly in recent years, yet solutions for automating cell detection and tracking in 3D label-free imaging of dense tissues and embryos remain inaccessible. Here, we describe embGAN, a deep learning pipeline that addresses the challenge of automated cell detection and tracking in label-free 3D time-lapse imaging. embGAN requires no manual data annotation for training, learns robust detections that exhibits a high degree of scale invariance, and generalizes well to images acquired in multiple labs on multiple instruments. We characterize embGAN's performance using lineage tracing in the C. elegans embryo as a benchmark. embGAN achieves near–state-of-the-art performance in cell detection and tracking, enabling high-throughput studies of cell lineage without the need for fluorescent reporters or transgenics. [ABSTRACT FROM AUTHOR]

Radiomics is an emerging field of medical imaging that aims at improving the accuracy of diagnosis, prognosis, treatment planning and monitoring non-invasively through the automated or semi-automated quantitative analysis of high-dimensional image features. Specifically in the field of nuclear medicine, radiomics utilizes imaging methods such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) to evaluate biomarkers related to metabolism, blood flow, cellular activity and some biological pathways. Lung cancer ranks among the leading causes of cancer-related deaths globally, and radiomics analysis has shown great potential in guiding individualized therapy, assessing treatment response, and predicting clinical outcomes. In this review, we summarize the current state-of-the-art radiomics progress in lung cancer, highlighting the potential benefits and existing limitations of this approach. The radiomics workflow was introduced first including image acquisition, segmentation, feature extraction, and model building. Then the published literatures were described about radiomics-based prediction models for lung cancer diagnosis, differentiation, prognosis and efficacy evaluation. Finally, we discuss current challenges and provide insights into future directions and potential opportunities for integrating radiomics into routine clinical practice. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing interest in synthetic data for several computer vision applications, such as automotive, detection and tracking, surveillance, medical image analysis and robotics. Early use of synthetic data was aimed at performing controlled experiments under the analysis by synthesis approach. Currently, synthetic data are mainly used for training computer vision models, especially deep learning ones, to address well-known issues of real data, such as manual annotation effort, data imbalance and bias, and privacy-related restrictions. In this work, we survey the use of synthetic training data focusing on applications related to video surveillance, whose relevance has rapidly increased in the past few years due to their connection to security: crowd counting, object and pedestrian detection and tracking, behaviour analysis, person re-identification and face recognition. Synthetic training data are even more interesting in this kind of application, to address further, specific issues arising, e.g., from typically unconstrained image or video acquisition conditions and cross-scene application scenarios. We categorise and discuss the existing methods for creating synthetic data, analyse the synthetic data sets proposed in the literature for each of the considered applications, and provide an overview of their effectiveness as training data. We finally discuss whether and to what extent the existing synthetic data sets mitigate the issues of real data, highlight existing open issues, and suggest future research directions in this field. [ABSTRACT FROM AUTHOR]

Lateral flow assay (LFA) is a handful diagnostic technology that can identify severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and other common respiratory viruses in one strip, which can be tested at the point-of-care without the need for equipment or skilled personnel outside the laboratory. Although its simplicity and practicality make it an appealing solution, it remains a grand challenge to substantially enhance the colorimetric LFA sensitivity. In this work, we present a straightforward approach to enhance the sensitivity of LFA by imposing the flow constraints in nitrocellulose (NC) membranes via a number of vertical femtosecond laser micromachined microchannels which is important for prolonged specific binding interactions. Porous NC membrane surfaces were structured with different widths and densities µ-channels employing a second harmonic of the Yb:KGW femtosecond laser and sample XYZ translation over a microscope objective-focused laser beam. The influence of the microchannel parameters on the vertical wicking speed was evaluated from the video recordings. The obtained results indicated that µ-channel length, width, and density in NC membranes controllably increased the immunological reaction time between the analyte and the labeled antibody by 950%. Image analysis of the colorimetric indicators confirmed that the flow rate delaying strategy enhanced the signal sensitives by 40% compared with pristine NC LFA. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI)‐based tools have shown promise in histopathology image analysis in improving the accuracy of oral squamous cell carcinoma (OSCC) detection with intent to reduce human error. Objectives: This systematic review and meta‐analysis evaluated deep learning (DL) models for OSCC detection on histopathology images by assessing common diagnostic performance evaluation metrics for AI‐based medical image analysis studies. Methods: Diagnostic accuracy studies that used DL models for the analysis of histopathological images of OSCC compared to the reference standard were analyzed. Six databases (PubMed, Google Scholar, Scopus, Embase, ArXiv, and IEEE) were screened for publications without any time limitation. The QUADAS‐2 tool was utilized to assess quality. The meta‐analyses included only studies that reported true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) in their test sets. Results: Of 1267 screened studies, 17 studies met the final inclusion criteria. DL methods such as image classification (n = 11) and segmentation (n = 3) were used, and some studies used combined methods (n = 3). On QUADAS‐2 assessment, only three studies had a low risk of bias across all applicability domains. For segmentation studies, 0.97 was reported for accuracy, 0.97 for sensitivity, 0.98 for specificity, and 0.92 for Dice. For classification studies, accuracy was reported as 0.99, sensitivity 0.99, specificity 1.0, Dice 0.95, F1 score 0.98, and AUC 0.99. Meta‐analysis showed pooled estimates of 0.98 sensitivity and 0.93 specificity. Conclusion: Application of AI‐based classification and segmentation methods on image analysis represents a fundamental shift in digital pathology. DL approaches demonstrated significantly high accuracy for OSCC detection on histopathology, comparable to that of human experts in some studies. Although AI‐based models cannot replace a well‐trained pathologist, they can assist through improving the objectivity and repeatability of the diagnosis while reducing variability and human error as a consequence of pathologist burnout. [ABSTRACT FROM AUTHOR]

To improve the prognosis of patients suffering from pulmonary diseases, such as lung cancer, early diagnosis and treatment are crucial. The analysis of CT images is invaluable for diagnosis, whereas high quality segmentation of the airway tree are required for intervention planning and live guidance during bronchoscopy. Recently, the Multi-domain Airway Tree Modeling (ATM'22) challenge released a large dataset, both enabling training of deep-learning based models and bringing substantial improvement of the state-of-the-art for the airway segmentation task. The ATM'22 dataset includes a large group of COVID'19 patients and a range of other lung diseases, however, relatively few patients with severe pathologies affecting the airway tree anatomy was found. In this study, we introduce a new public benchmark dataset (AeroPath), consisting of 27 CT images from patients with pathologies ranging from emphysema to large tumors, with corresponding trachea and bronchi annotations. Second, we present a multiscale fusion design for automatic airway segmentation. Models were trained on the ATM'22 dataset, tested on the AeroPath dataset, and further evaluated against competitive open-source methods. The same performance metrics as used in the ATM'22 challenge were used to benchmark the different considered approaches. Lastly, an open web application is developed, to easily test the proposed model on new data. The results demonstrated that our proposed architecture predicted topologically correct segmentations for all the patients included in the AeroPath dataset. The proposed method is robust and able to handle various anomalies, down to at least the fifth airway generation. In addition, the AeroPath dataset, featuring patients with challenging pathologies, will contribute to development of new state-of-the-art methods. The AeroPath dataset and the web application are made openly available. [ABSTRACT FROM AUTHOR]

The often-perceived limitations of image analysis have for many years impeded the widespread application of such systems as first line characterisation tools. Image analysis has, however, undergone a notable resurgence in the pharmaceutical industry fuelled by developments system capabilities and the desire of scientists to characterize the morphological nature of their particles more adequately. The importance of particle shape as well as size is now widely acknowledged. With the increasing use of modelling and simulations, and ongoing developments though the integration of machine learning and artificial intelligence, the utility of image analysis is increasing significantly driven by the richness of the data obtained. Such datasets provide means to circumvent the requirement to rely on less informative descriptors and enable the move towards the use of whole distributions. Combining the improved particle size and shape measurement and description with advances in modelling and simulations is enabling improved means to elucidate the link between particle and bulk powder properties. In addition to improved capabilities to describe input materials, approaches to characterize single components within multicomponent systems are providing scientists means to understand how their material may change during manufacture thus providing a means to link the behaviour of final dosage forms with the particle properties at the point of action. The aim is to provide an overview of image analysis and update readers with innovations and capabilities to other methods in the small molecule arena. We will also describe the use of AI for the improved analysis using image analysis. [Display omitted] [ABSTRACT FROM AUTHOR]

Purpose: Spatial intratumoral heterogeneity poses a significant challenge for accurate response assessment in glioblastoma. Multimodal imaging coupled with advanced image analysis has the potential to unravel this response heterogeneity. Methods: Based on automated tumor segmentation and longitudinal registration with follow-up imaging, we categorized contrast-enhancing voxels of 61 patients with suspected recurrence of glioblastoma into either true tumor progression (TP) or pseudoprogression (PsP). To allow the unbiased analysis of semantically related image regions, adjacent voxels with similar values of cerebral blood volume (CBV), FET-PET, and contrast-enhanced T1w were automatically grouped into supervoxels. We then extracted first-order statistics as well as texture features from each supervoxel. With these features, a Random Forest classifier was trained and validated employing a 10-fold cross-validation scheme. For model evaluation, the area under the receiver operating curve, as well as classification performance metrics were calculated. Results: Our image analysis pipeline enabled reliable spatial assessment of tumor response. The predictive model reached an accuracy of 80.0% and a macro-weighted AUC of 0.875, which takes class imbalance into account, in the hold-out samples from cross-validation on supervoxel level. Analysis of feature importances confirmed the significant role of FET-PET-derived features. Accordingly, TP- and PsP-labeled supervoxels differed significantly in their 10th and 90th percentile, as well as the median of tumor-to-background normalized FET-PET. However, CBV- and T1c-related features also relevantly contributed to the model's performance. Conclusion: Disentangling the intratumoral heterogeneity in glioblastoma holds immense promise for advancing precise local response evaluation and thereby also informing more personalized and localized treatment strategies in the future. [ABSTRACT FROM AUTHOR]

Background: Previous studies of emotion recognition abilities of people with eating disorders used accuracy to identify performance deficits for these individuals. The current study examined eating disorder symptom severity as a function of emotion categorization abilities, using a visual cognition paradigm that offers insights into how emotional faces may be categorized, as opposed to just how well these faces are categorized. Methods: Undergraduate students (N = 87, 50 women, 34 men, 3 non-binary) completed the Bubbles task and a standard emotion categorization task, as well as a set of questionnaires assessing their eating disorder symptomology and comorbid disorders. We examined the relationship between visual information use (assessed via Bubbles) and eating disorder symptomology (EDDS) while controlling for anxiety (STAI), depression (BDI-II), alexithymia (TAS), and emotion regulation difficulties (DERS-sf). Results: Overall visual information use (i.e. how well participants used facial features important for accurate emotion categorization) was not significantly related to eating disorder symptoms, despite producing interpretable patterns for each emotion category. Emotion categorization accuracy was also not related to eating disorder symptoms. Conclusions: Results from this study must be interpreted with caution, given the non-clinical sample. Future research may benefit from comparing visual information use in patients with an eating disorder and healthy controls, as well as employing designs focused on specific emotion categories, such as anger. Plain English summary: Men and women with severe eating disorder symptoms may find it harder to identify and describe emotions than people with less severe eating disorder symptoms. However, previous work makes it difficult to determine why emotion recognition deficits exist, and what underlying abilities or strategies are actually different due to a deficit. In addition to a typical emotion recognition task (emotion categorization), this study used the Bubbles task, which allowed us to determine which parts of an image are important for emotion recognition, and whether participants used these parts during the task. In 87 undergraduate students (47 female; 49 with clinically-significant eating disorder symptoms), there was no significant relationship between task performance and eating disorder symptom severity, before and after controlling for the relationship with other comorbid disorders. Our results imply that emotion recognition deficits are unlikely to be an important mechanism underlying eating disorder pathology in participants with a range of eating disorders symptoms. [ABSTRACT FROM AUTHOR]

Seagrasses are in danger due to anthropogenic activities causing a reduction in seagrass area and global damage. It is crucial to map seagrass distribution, as there is limited information on its existence. Sentinel-2A satellite provides high-resolution multispectral data with a 10-m resolution. The study aimed to evaluate the ability of Sentinel-2A imagery from the Google Earth Engine (GEE) platform to classify with the random forest (RF) algorithm in mapping seagrass in shallow water and various other objects in the study area. The results showed the area’s presence of seagrass, coral, sand, sand seagrass, and rubble. Also, the photo transect method was used for collecting field data. The random forest algorithm had an accuracy of 76% in classifying each of the five classes. The combination of Sentinel-2A imagery and random forest algorithms can provide insight into the status and distribution of seagrass. [ABSTRACT FROM AUTHOR]