Search Results (634)

Background/Objectives: Image-guided adaptive brachytherapy is a core component of definitive treatment for locally advanced cervical cancer (LACC). However, implantation remains challenging in bulky, asymmetric, or anatomically complex tumors, where standard applicator geometry or purely straight interstitial trajectories may be insufficient for individualized target coverage. This study evaluated the real-world implementation of a patient-specific 3D-printed template-guided adaptive brachytherapy workflow for complex LACC. Methods: We retrospectively reviewed 120 consecutive patients with FIGO 2018 stage IB3–IVA cervical cancer treated with definitive chemoradiotherapy followed by high-dose-rate image-guided brachytherapy between March 2023 and March 2025. All patients were treated using a patient-specific 3D-printed template-guided hybrid intracavitary/interstitial workflow integrating CT/MRI-based target assessment, individualized catheter trajectory planning, template fabrication, implantation verification, and adaptive treatment planning. Straight-channel or curved-channel guidance was selected according to residual tumor geometry and pelvic anatomy, with flexible plastic interstitial catheters used for curved or anatomically constrained trajectories. Procedural deliverability, dosimetry, toxicity, early clinical outcomes, and exploratory dose–outcome patterns were analyzed. Results: The median HR-CTV volume was 55.9 cm³, and the median HR-CTV D90 was 92.9 Gy EQD2. Median organ-at-risk D2cc values remained within contemporary institutional and guideline-consistent constraints. A total of 555 template-guided HDR brachytherapy fractions were delivered. The median applicator-and-catheter placement time was 4.21 min per fraction, with a median of 7.25 implanted channels. Minor and major insertion-related bleeding occurred in 10.8% and 1.7% of patients, respectively. At a median follow-up of 20.1 months, estimated 3-year overall survival, progression-free survival, local recurrence-free survival, regional recurrence-free survival, and distant metastasis-free survival were 77.9%, 76.8%, 94.3%, 98.0%, and 86.2%, respectively. Late grade ≥ 3 gastrointestinal and genitourinary toxicities occurred in 2.5% and 1.7% of patients, respectively, with no grade 4–5 events. Exploratory dose–outcome analyses suggested hypothesis-generating dose–outcome patterns, but these findings were not intended to define or validate a clinical dose threshold. Conclusions: This real-world implementation study supports the feasibility of patient-specific 3D-printed template-guided adaptive brachytherapy for complex LACC. By translating CT/MRI-based individualized trajectory planning into template-guided intracavitary/interstitial catheter placement, this workflow achieved guideline-consistent target coverage, acceptable organ-at-risk doses, efficient procedural delivery, and low severe toxicity within the available follow-up. Dose–outcome findings remain exploratory and require validation in more mature cohorts. Full article

Multi-exposure is an efficient way to capture real-world high-dynamic-range (HDR) scenes. However, HDR imaging suffers from severe ghosting artifacts in dynamic scenes due to the temporal gap between sequential exposures. In this article, we categorize the literature on two important topics on HDR imaging: multi-exposure fusion (MEF) and ghost removal. Conventional filter-based and data-driven methods are studied in pixel space and feature space. For popular deep learning-based approaches, we provide a granular taxonomy based on their alignment and fusion domains: pixel-space methods, which typically employ explicit motion compensation such as optical flow or spatial transformers, and feature-space methods, which leverage implicit alignment through deformable convolutions, attention mechanisms, or latent representation merging. Representative works are compared across different supervision settings, and key design principles are summarized. In addition, this survey summarizes commonly used datasets and evaluation metrics, discussing their applicability under diverse output forms. Finally, major bottlenecks and promising directions for future research are outlined. Full article

High dynamic range (HDR) radiance-field reconstruction from single-exposure low dynamic range (LDR) images is limited by the information loss in saturated regions, while event cameras provide complementary measurements whose dynamic range far exceeds that of conventional sensors. In practical hybrid sensor systems, however, the RGB camera usually has a higher spatial resolution than the event sensor, which makes existing event-aided HDR reconstruction methods difficult to apply directly. A straightforward solution is to super-resolve the event stream before reconstruction, but this 2D preprocessing introduces a global color cast and view-inconsistent high-frequency artifacts once the super-resolved events supervise a 3D radiance field. We propose a framework for HDR radiance-field reconstruction from resolution-mismatched event–image inputs. The framework incorporates a pretrained 2D event super-resolution prior and corrects its transfer to 3D reconstruction through a color correction module, which anchors the rendered radiance to the chrominance of the LDR images, and a dual-resolution event-stream constraint, which supervises the synthesized events at both the super-resolved and the native resolution. Experiments on the EvHDR-NeRF benchmark show that the proposed method achieves higher six-scene mean HDR fidelity than the strongest baseline while reducing the global color cast and alleviating multi-view artifacts. Full article

High dynamic range (HDR) images require protection of both confidentiality and integrity, but conventional LDR-oriented security methods are difficult to apply directly because HDR images differ substantially in file structure, numerical representation, and compression mechanisms. This paper focuses on secure protection of PIZ-compressed OpenEXR HDR images, where preserving compression syntax and decoder compatibility is essential. In our previous work, we proposed a commutative joint encryption and authentication scheme for uncompressed OpenEXR HDR images (This paper is an extended version of our previously published work in Multimedia Tools and Applications, entitled “Commutative Encryption and Authentication for OpenEXR High Dynamic Range Images” by Chen and Yan. Compared with the previous work, this extended version specifically addresses the challenge of integrating PIZ compression encoding into the joint encryption and authentication process for OpenEXR HDR images, which was not considered in our earlier study). However, that scheme did not address OpenEXR images encoded with PIZ compression. To overcome this limitation, this paper presents a compression-unimpaired joint encryption and authentication scheme that operates directly on Huffman-coded values in the chunk data of PIZ-compressed OpenEXR images. Specifically, we partition the histogram of values output according to the corresponding Huffman code into two segments: an encrypted part for confidentiality and an embedded part for authentication. To the best of our knowledge, this study is the first attempt to investigate commutative encryption and authentication directly in the PIZ-compressed OpenEXR HDR image domain. Experimental results on seven OpenEXR HDR images show that the decrypted and authenticated images preserve high visual quality, with PSNR values exceeding 75 dB in most cases. The protected files remain decodable by OpenEXR-compatible software, and the file size is preserved because the proposed operations replace valid Huffman codes with codes of identical length. These results indicate that the proposed scheme can jointly support confidentiality, authentication, format compliance, and compression preservation. Full article

The BRCA1 protein serves an essential function in maintaining genomic integrity, to the extent that up to 80% of women carrying a pathogenic BRCA1 variant develop breast cancer (BC). Most of these carriers would benefit from prophylactic care, but genetic screens that uncover variants of uncertain significance (VUSs) do not provide insight on disease risk or clinical decision-making. In accordance with guidelines established by The American College of Molecular Genetics (ACMG) and Association for Molecular Pathology (AMP), this study produced computational and functional evidence to inform the reclassification of BRCA1 VUSs as pathogenic or benign, with a specific focus on the abundant subset of missense variants within the RING domain. A six-feature linear support vector machine (LSVM) specifically trained on BRCA1 RING variants performed well (84% accurate in predicting in vitro binding loss) and provided supporting classification evidence for 322 VUS. A mammalian cell co-immunoprecipitation (co-IP) assay that quantified the binding between variant BRCA RING constructs and endogenous BARD1 provided corroborating strong evidence for nine VUSs and correlated with a homology-directed repair (HDR) assay by Starita et al. (p = 0.04). The combined evidence warrants the reclassification of three VUSs as likely benign (N16S, A17D, and E100D) and one as likely pathogenic (H41P), and underscores the promise of domain-specific approaches for missense VUS reclassification. Full article

The Changbai Mountain area, the largest Cenozoic intraplate volcanic field in eastern China, features abundant high-temperature hot springs and high geothermal potential. However, the genesis and aggregation patterns of its geothermal systems remain poorly understood. This study recalculates crustal and residual deep/mantle heat- flow components along a representative profile and synthesizes published geological, geophysical, geochemical, and geothermal evidence to characterize the main geothermal system elements, including caprock, reservoirs, water source, and migration pathways. Controlling factors are examined from three dimensions: deep dynamics, magmatic heat source, and fault characteristics. Results reveal a “Cold crust–Hot mantle” thermal structure. The heat-flow calculation indicates that crustal radiogenic heat contributes approximately 40% of the surface heat flow, implying a dominant deep heat contribution. The available evidence suggests the presence of potential hydrothermal reservoirs in carbonate and clastic rocks, possible HDR targets in deeper metamorphic rocks, and locally effective basaltic sealing units. Fault systems and meteoric recharge likely control fluid circulation. Geothermal systems are controlled by mantle upwelling and lithospheric thinning due to western Pacific Plate subduction, multi-source heat coupling, effective caprock sealing, and fault-controlled water–heat conduction. These results provide a conceptual framework for future geothermal exploration and testing. This study elucidates the aggregation patterns and genetic mechanisms, providing a theoretical basis for exploration and development. Full article

Background/Objectives: Single-stranded oligonucleotides (ssODNs) are used as donor templates for therapeutic gene editing by CRISPR-Cas9 cleavage and homology-directed repair (HDR). Although ssODN sequence fidelity is critical to the safety and efficacy of editing, standard quality control methods cannot resolve individual nucleotide errors. Methods: We performed deep sequencing of ssODNs from three manufacturers and amplicons from edited hematopoietic stem/progenitor cells. Results: We find that synthesis errors are present in all ssODNs tested at rates that vary more than two-fold among manufacturers, at positions that are dependent on sequence context. These synthesis errors are propagated into the genome by HDR at frequencies proportional to their abundance in the ssODN. In our sickle cell mutation correction protocol, the most prevalent SNEs are predicted to produce benign β-globin variants, while the less frequent frameshift deletions are predicted to generate β-thalassemia-like alleles. Conclusions: Current quality control standards are insufficient to detect these errors, and deep sequencing of ssODNs should be incorporated into regulatory submissions for clinical gene editing programs. Full article

Background/Objectives: Caffeine is a widely used ergogenic aid; however, evidence regarding its effects on neuromuscular and metabolic responses during resistance training remains limited. Therefore, this study aimed to analyze the effects of caffeine supplementation on blood glucose concentration and neuromuscular activation, assessed through electromyographic activity, during a muscular endurance test. Methods: Eleven resistance-trained men (25.7 ± 5.9 years) participated in this randomized, double-blind, crossover trial. Six laboratory visits were conducted, including one-repetition maximum (1RM) and maximal voluntary isometric contraction (MVIC) testing in the bench press exercise. From the second visit onward (baseline; BL), participants completed a 24 h dietary recall (24hDR), pre- and post-exercise blood glucose assessments, and a muscular endurance test performed at 80% of 1RM until concentric failure, while electromyographic activity was recorded from the clavicular and sternal portions of the pectoralis major, triceps brachii, and anterior deltoid. From the third visit onward, participants received one of the following randomized interventions 60 min before testing: low-dose caffeine (LC = 3 mg·kg⁻¹), high-dose caffeine (HC = 6 mg·kg⁻¹), low-dose placebo (LP = 230 mg), or high-dose placebo (HP = 460 mg). Results: There was an increase in the number of repetitions in all conditions vs. BL, with no significant differences (p > 0.05). Electromyographic activity did not differ between conditions (p > 0.05). Blood glucose decreased post-test in BL and placebo conditions (p < 0.05) but remained stable with caffeine (p > 0.05). Conclusions: Low and high doses of caffeine did not significantly affect muscular endurance performance or neuromuscular activation. However, caffeine supplementation appeared to attenuate the post-exercise decline in blood glucose concentration following exercise performed to concentric failure. Full article

Background/Objectives: Clinical studies of prostate cancer treated with radically hypofractionated high-dose-rate brachytherapy (HDR-BT) have reported a significant loss of tumor control that contradicts the standard linear-quadratic (LQ) and low-$\alpha /\beta$-ratio paradigm for prostate cancer. In a previous study by our group, we showed that the linear–quadratic–linear (LQL) model could describe this response, but the underlying biological drivers remained unclear. In this follow-up study, we further investigate whether the interplay between hypoxia and reoxygenation kinetics can explain the poor response to extreme hypofractionation. Methods: We analyzed a large dataset of 3239 patients (44 schedules) using a three-compartment reoxygenation model (the MSK model) that simulates the dynamics of oxic, intermediate, and hypoxic cell populations. Results: The results show that the MSK model achieves an excellent fit to the clinical data ($p>0.99$) while maintaining a biologically plausible low $\alpha /\beta$ ratio (≤8 Gy). The reoxygenation model provided a performance comparable to the LQL model for low-risk prostate cancer and slightly inferior for intermediate-risk. Conclusions: This suggests that the observed reduction in tumor control may not necessarily be a failure of the LQ formalism but, rather, a consequence of oxygen dynamics associated with ultra-hypofractionated schedules. Nonetheless, neither this nor our previous work can provide insight into the driving mechanism and should only be interpreted as showing that both hypotheses are compatible with the clinical data. Full article

Endogenous gene tagging using CRISPR has changed the understanding of the role played by different proteins due to the ability to track and study proteins in their natural state. With CRISPR-based gene tagging, it is possible to insert fluorescent, luminescent, epitope, affinity, and proximity labels into the target protein at its endogenous genomic location without affecting its physiological expression and dynamics. Here, we discuss the DNA-repair mechanisms employed in endogenous gene tagging, including homology-dependent repair, NHEJ-based integration, and alternative approaches that can be used with challenging cell types. Key aspects of efficient CRISPR tagging experiments are also described. Additionally, we review recent advances in the increasing array of protein tag technologies, including fluorescent proteins, split-reporter technologies, NanoLuc/HiBiT, peptide epitopes, and proximity biotinylation enzymes. Lastly, we review the scalability of endogenous tagging approaches using multiplex editing, atlas-scale proteome tagging, iPSC-based disease modeling, and drug discovery platforms for assessing target engagement, protein degradation, phenotype screening, and mechanism of action of compounds. Although difficult in primary and pluripotent cells, new methods based on avoiding double-strand breaks, such as prime editing, PASTE, and CRISPR associated transposases, will drive the future expansion of endogenous tagging approaches. Such developments firmly set up CRISPR gene tagging as a fundamental technology in quantitative cell biology and translational pharmacology. Full article

High dynamic range (HDR) image reconstruction from a single low dynamic range (LDR) input remains an important problem for computational photography, particularly when practical deployment on consumer-grade hardware is considered. With the increasing availability of hardware supporting HDR, public demand for capturing and viewing HDR images has grown significantly. Recent research has explored deep learning-based approaches to reconstruct HDR images from low dynamic range (LDR) inputs by extracting regional pixel features or leveraging the camera response function (CRF) for model training. Many of these approaches employ Convolutional Neural Network (CNN) architectures and utilize skip connections to preserve learned information. Nevertheless, the configuration-level effects of data augmentation in HDR reconstruction remain insufficiently discussed. Existing CNN-based approaches, such as HDRCNN, HDRUNet, and ExpandNet, have demonstrated promising reconstruction ability, but they may involve a heavy backbone architecture, a long training time, or a limited discussion of how preprocessing configurations affect reconstruction performance. This study presents an engineering-oriented HDR reconstruction framework derived from HDRCNN, focusing on practical efficiency, structural fidelity, and training feasibility. The proposed framework introduces three modifications: (1) a configuration-level comparison of composite data augmentation settings, including unsharp masking, denoising, Gaussian blur, and brightness–contrast adjustment; (2) the replacement of the original VGG16 backbone with a ResNet50-based encoder enhanced with attention blocks and squeeze-and-excitation (SE) blocks for improved multi-scale feature extraction and channel-wise recalibration; and (3) the integration of mixed-precision training with cosine annealing learning-rate scheduling to reduce computational cost. Experimental results on the SI-HDR dataset show that the best composite augmentation configuration improves PSNR from 19.05 dB to 22.10 dB and SSIM from 0.6444 to 0.7714 without increasing the training time. Compared with the original VGG16-based HDRCNN setting, the ResNet50-based model reduces training time while improving SSIM from 0.2705 to 0.8512. Under the adopted comparison protocol, the proposed model achieves the shortest training time and slightly higher PSNR than HDRUNet, while HDRUNet retains a higher SSIM. This indicates a trade-off among pixel-wise fidelity, structural similarity, and computational efficiency. The current evaluation is limited by a small test setting, composite rather than operation-level augmentation analysis, and the use of PSNR and SSIM only; therefore, future work should include full benchmark evaluation, additional perceptual/HDR-specific metrics, and controlled component-level ablation studies. Full article

Background: Accurate dietary assessment is essential for estimating habitual and acute exposures. This study compared a 7-day food diary (7dFD) and repeated 24-h dietary recall (2×24hDR) for habitual and acute intake. Habitual fish and n-3 PUFA estimates were evaluated against whole-blood EPA and DHA, and acute intake of fish, rye bread, and coffee was explored using operational indicators. Methods: In a within-subject comparative design, 120 Danish adults aged 18–60 years completed both methods. Fish and n-3 fatty acid intakes were compared with whole-blood EPA and DHA. Usual fish intake from 2×24hDR was estimated using the Multiple Source Method (MSM) with Food Propensity Questionnaire (FPQ) data. Acute intake was assessed using single-day, consumption-day, single-meal, maximum-day, and meal-weighted estimates. Results: EPA, DHA, total fish, and fatty fish intakes estimated from the 7dFD and MSM-adjusted 2×24hDR correlated with biomarkers r = 0.23–0.46. Supplement inclusion improved EPA and DHA correlations (r = 0.26–0.59). The unadjusted 2×24hDR identified fewer fish consumers than the 7dFD (63% vs. 88%), whereas FPQ-based modelling identified 97%. Acute intake estimates varied by method and definition: the 2×24hDR produced higher short-term and upper- percentile estimates, while the 7dFD produced more stable distributions. Limitations include modest sample size, upper-percentile uncertainty, and non-equivalent supplement assessment. Conclusions: In this adult convenience sample, the 7dFD provided more stable habitual intake estimates, whereas the 2×24hDR produced higher short-term and upper-percentile estimates under the applied operational acute-intake definitions. These findings are context-dependent and should not be taken as evidence that either method is generally superior. Full article

High dynamic range (HDR) imaging has received sustained interest for its ability to capture a wider scene radiance range than conventional imaging by fusing multi-exposure images. However, increasing the number of exposures aggravates ghosting artifacts during fusion, prompting modern devices to use only one or two shots. This leads to the fact that a single exposure is unable to simultaneously preserve details in both dark and bright regions, and even dual-exposure settings are insufficient to capture the full scene radiance. Under limited exposure conditions, distinct challenges arise for both physics-driven and data-driven models, with the former struggling to model unobserved irradiance distributions and the latter having difficulty capturing diverse exposure variations, leading to unrealistic brightness artifacts in highlights and shadows. To address this problem, we propose a mixed causal–acausal assisted compensation model that integrates physics-driven and data-driven modules to generate interpolated and extrapolated pseudo-exposure images for recovering missing brightness information. The proposed model decomposes pseudo-exposure image generation into a hybrid representation consisting of a causal part reflecting radiance variation and an acausal part capturing the residual scene structure. The causal representation is derived by estimating the physics-driven intensity mapping function from adjacent exposure images while the acausal one is obtained through a data-driven attention-augmented hybrid network. Both theoretical analysis and experimental results demonstrate that the pseudo-exposure images perform well in both objective and subjective evaluations. In addition, it is validated that incorporating interpolated or extrapolated images into raw images can indeed mitigate brightness artifacts in dual-exposure fusion as well as in single-exposure enhancement. Full article

High dynamic range (HDR) imaging offers an enhanced visual experience by capturing a wider range of real-world luminance levels in digital images. Driven by the increasing demand for high-quality visuals, HDR monitor technology has seen significant advancements. As such monitors become commonplace in both consumer and professional settings, efficient methods are needed for both converting standard dynamic range (SDR) content to HDR—known as reverse tone mapping—and optimizing natural HDR lighting content for display on HDR monitors. A reverse tone mapping procedure aims to produce natural lighting levels, but even on high-end HDR monitors, such images still require adjustment to avoid hard clipping. This paper presents a solution that jointly does both steps: (1) reverse tone mapping to a display-aware HDR representation, and (2) direct generation of an image tailored for a chosen monitor brightness value. We propose a novel neural network architecture conditioned on the target peak brightness via a lightweight multi-layer perceptron (MLP) module injected at the bottleneck, which predicts a bracketed stack of LDR exposures serving as the method’s HDR representation. In this manner, the ill-posed tone mapping problem is guided by auxiliary information about display characteristics, improving visual quality. Experiments throughout the full consumer HDR range (100–4000 nits) show consistent improvements over the display-agnostic baseline in peak luminance utilization, local contrast, color and perceptual quality. Full article

Recent advances in deep convolutional neural networks (D-CNNs) have improved automated welding defect inspection. This study presents an explainable comparative framework for multi-class classification of defects in Aluminium 5083 TIG weld joints using High Dynamic Range (HDR) image data, integrating a transfer-learning model, stratified five-fold cross-validation, computational-time analysis, and Grad-CAM-based visual interpretation. Five transfer-learning-based D-CNN architectures such as VGG16, VGG19, Inception V3, MobileNet, and DenseNet were trained, validated, and tested under a common evaluation protocol to assess their suitability for welding defect classification. The dataset was organised into classes such as good weld, contamination, lack of fusion, lack of penetration, and misalignment. Model performance was compared using multiple evaluation metrics. Stratified five-fold cross-validation was also performed to assess model stability. Alongside the cross-validation, training/inference times were also recorded to evaluate computational feasibility. Grad-CAM was used as an explainable artificial intelligence (XAI) technique in order to provide visual interpretation of weld regions. Among evaluated models, DenseNet achieved the best overall performance, with a classification accuracy of 98%, and showed the least confusion across defect classes. The Grad-CAM visualisations showed that the model focused on defect-relevant weld regions, demonstrating that transfer-learning D-CNNs with XAI can support TIG welding defect classification and effective visual quality assessment. Full article