Search Results (102,976)

In the lifetime anthology of Robert Frost’s poetry, one poem consistently stands out as the most beloved and recognizable of his works. To the average reader, for over a hundred years “The Road Not Taken” has engendered images of individuality and the need to avoid following the crowd; this despite clear evidence within the verse that contradicts that reading. Most Frost scholars would agree the poem is the most misunderstood poem in Frost’s collection, and the academy has presented several intelligent and deeply introspective alternatives. However, none of these have garnered enough of a consensus to displace the initial misunderstanding. Through an interdisciplinary approach that makes use of the added epistemic approaches of historical research and the psychology of regret, this paper will uncover a hidden creation story for “The Road Not Taken,” and through a fulsome review of the poem’s origination, reveal a more basic axiom as to the purpose behind Frost’s two roads. Full article

In active distribution networks (ADNs), the extensive deployment of distributed generations (DGs) heightens system nonlinearity and non-stationarity, which can weaken fault characteristics and reduce fault detection accuracy. To improve fault detection accuracy in distribution networks, a method combining improved multivariate variational mode decomposition (IMVMD) and YOLOv10 network for active distribution network fault detection is proposed. Firstly, an MVMD method optimized by the northern goshawk optimization (NGO) algorithm named IMVMD is introduced to adaptively decompose zero-sequence currents at both ends of line sources and loads into intrinsic mode functions (IMFs). Secondly, considering the spatio-temporal correlation between line sources and loads, a dynamic time warping (DTW) algorithm is utilized to determine the optimal alignment path time series for corresponding IMFs at both ends. Then, the Markov transition field (MTF) transforms the 1D time series into 2D spatio-temporal images, and the MTF images of all lines are concatenated to obtain a comprehensive spatio-temporal feature map of the distribution network. Finally, using the spatio-temporal feature map as input, the lightweight YOLOv10 network autonomously extracts fault features to achieve precise fault-line selection. Experimental results demonstrate the robustness of the proposed method, achieving a fault detection accuracy of 99.88%, which can ensure accurate fault-line selection under complex scenarios involving simultaneous phase-to-ground faults at two points. Full article

Magnetism is an important property of doped two-dimensional nanostructures. By introducing dopant atoms or molecules, the electronic structure and magnetic behavior of the two-dimensional nanostructures can be altered. However, the complexity of the doping process requires different strategies for the preparation and testing of various types, layers, and scales of doped two-dimensional materials using traditional techniques. This process is resource-intensive, inefficient, and can pose safety risks when dealing with chemically unstable materials. Deep learning-based methods offer an effective solution to overcome these challenges and improve production efficiency. In this study, a deep learning-based method is proposed for predicting the magnetism of doped two-dimensional nanostructures. An image dataset was constructed for deep learning using a publicly available database of doped two-dimensional nanostructures. The ResNet model was enhanced by incorporating the Swin Transformer module, resulting in the Swin–ResNet network architecture. A comparative analysis was conducted with various deep learning models, including Resnet, Res2net, ResneXt, and Swin Transformer, to evaluate the performance of the optimized model in predicting the magnetism of doped two-dimensional nanostructures. The optimized model demonstrated significant improvements in magnetism prediction, with a best accuracy of 0.9. Full article

Background/Objectives: High breast density is a risk factor for breast cancer and can reduce the sensitivity of mammography. Given the influence of breast density on patient risk stratification and screening accuracy, it is crucial to monitor the prevalence of extremely dense breasts within local populations. Moreover, there is a lack of comprehensive understanding regarding breast density prevalence in Switzerland. Therefore, this study aimed to determine the prevalence of breast density in a selected Swiss population. Methods: To overcome the potential variability in breast density classifications by human readers, this study utilized commercially available deep convolutional neural network breast classification software. A retrospective analysis of mammographic images of women aged 40 years and older was performed. Results: A total of 4698 mammograms from women (58 ± 11 years) were included in this study. The highest prevalence of breast density was in category C (heterogeneously dense), which was observed in 41.5% of the cases. This was followed by category B (scattered areas of fibroglandular tissue), which accounted for 22.5%. Conclusion: Notably, extremely dense breasts (category D) were significantly more common in younger women, with a prevalence of 34%. However, this rate dropped sharply to less than 10% in women over 55 years of age. Full article

DNA topoisomerase II (TOP2) is an enzyme that resolves DNA topological problems arising in various nuclear processes, such as transcription. Aclarubicin, a member of the anthracyclines, is known to prevent the association of TOP2 with DNA, inhibiting the early step of TOP2 catalytic reactions. During our research on the subnuclear distribution of human TOP2B, we found that aclarubicin affects the mobility of TOP2B in the nucleus. FRAP analysis demonstrated that aclarubicin decreased the nuclear mobility of EGFP-tagged TOP2B in a concentration-dependent manner. Aclarubicin exerted its inhibitory effects independently of TOP2B enzymatic activities: TOP2B mutants defective for either ATPase or topoisomerase activity also exhibited reduced nuclear mobility in the presence of aclarubicin. Immunofluorescence analysis showed that aclarubicin antagonized the induction of DNA damage by etoposide. Although the prevention of the TOP2-DNA association is generally considered a primary action of aclarubicin in TOP2 inhibition, our findings highlight a previously unanticipated effect of aclarubicin on TOP2B in the cellular environment. Full article

Introduction: Predicting stroke outcomes in acute ischemic stroke (AIS) can be challenging, especially for patients with large vessel occlusion (LVO). Available tools such as infarct volume and the National Institute of Health Stroke Scale (NIHSS) have shown limited accuracy in predicting outcomes for this specific patient population. The present study aimed to confirm whether sudden metabolic changes due to blood-brain barrier (BBB) disruption during LVO reflect differences in circulating metabolites and RNA between small and large core strokes. The second objective was to evaluate whether integrating molecular markers with existing neurological and imaging tools can enhance outcome predictions in LVO strokes. Methods: The infarction volume in patients was measured using magnetic resonance diffusion-weighted images, and the 90-day stroke outcome was defined by a modified Rankin Scale (mRS). Differential expression patterns of miRNAs were identified by RNA sequencing of serum-driven exosomes. Nuclear magnetic resonance (NMR) spectroscopy was used to identify metabolites associated with AIS with small and large infarctions. Results: We identified 41 miRNAs and 11 metabolites to be significantly associated with infarct volume in a multivariate regression analysis after adjusting for the confounders. Eight miRNAs and ketone bodies correlated significantly with infarct volume, NIHSS (severity), and mRS (outcome). Through integrative analysis of clinical, radiological, and omics data using machine learning, our study identified 11 top features for predicting stroke outcomes with an accuracy of 0.81 and AUC of 0.91. Conclusions: Our study provides a future framework for advancing stroke therapeutics by incorporating molecular markers into the existing neurological and imaging tools to improve predictive efficacy and enhance patient outcomes. Full article

Directed Energy Deposition (DED) processes necessitate a consistent material flow to the melt pool, typically achieved through pneumatic conveying of metal powder via thin pipes. This study aims to record and analyze the multiphase fluid–solid flow. An experimental setup utilizing a high-speed camera and specialized optics was constructed, and the flow through thin transparent pipes was recorded. The resulting information was analyzed and compared with coupled Computational Fluid Dynamics-Discrete Element Modeling (CFD-DEM) simulations, with special attention to the solids flow fluctuations. The proposed methodology shows a significant improvement in accuracy and reliability over existing approaches, particularly in capturing flow rate fluctuations and particle velocity distributions in small-scale systems. Moreover, it allows for accurately analyzing Particle Size Distribution (PSD) in the same setup. This paper details the experimental design, video analysis using particle tracking, and a novel method for deriving volumetric concentrations and flow rate from flat images. The findings confirm the accuracy of the CFD-DEM simulations and provide insights into the dynamics of pneumatic conveying and individual particle movement, with the potential to improve DED efficiency by reducing variability in material deposition rates. Full article

This microreview, conducted by interdisciplinary teams, examines complex heritage material systems, such as medieval colors and modern and contemporary artworks. Our multi-analytical approach, a significant aspect of our research, is a means to this end. The conservation of works of art is our shared goal, as it ensures their accessibility and the transfer of cultural heritage to future generations. We seek to interpret the damage, usefulness, and innovation of the experimental design in this context. As Jan Wouters rightly points out, “The terminology used nowadays to describe the potential damage to objects caused by analysis should be refined beyond the destructiveness/non-invasiveness polarization. A terminology should include at least degree level intervention (low, medium, high), usefulness, and innovation”. Complementing micro- or sub-micro-sampling with the appropriate analytical methods is crucial, as exemplified in medieval, modern, and contemporary collections studies. Finally, a novel perspective for exploring the information contained in the multiscale heterogeneity of organic historical materials is envisaged, and it includes UV/Visible photoluminescence spectral imaging using a low-intensity ultraviolet synchrotron beam. Full article

LAH, an acetogenin from the Annonaceae family, has demonstrated antitumor activity in several cancer cell lines and in vivo models, where it reduced the tumor size and induced programmed cell death. We focused on the effects of LAH on mitochondrial dynamics, mTOR signaling, autophagy, and apoptosis in colorectal cancer (CRC) cells to explore its anticancer potential. Methods: CRC cells were treated with LAH, and its effects on mitochondrial respiration and glycolysis were measured using Seahorse XF technology. The changes in mitochondrial dynamics were observed through fluorescent imaging, while Western blot analysis was used to examine key autophagy and apoptosis markers. Results: LAH significantly inhibited mitochondrial complex I activity, inducing ATP depletion and a compensatory increase in glycolysis. This disruption caused mitochondrial fragmentation, a trigger for autophagy, as shown by increased LC3-II expression and mTOR suppression. Apoptosis was also confirmed through the cleavage of caspase-3, contributing to reduced cancer cell viability. Conclusions: LAH’s anticancer effects in CRC cells are driven by its disruption of mitochondrial function, triggering both autophagy and apoptosis. These findings highlight its potential as a therapeutic compound for further exploration in cancer treatment. Full article

Accurate monitoring of estuarine turbidity patterns is important for maintaining aquatic ecological balance and devising informed estuarine management strategies. This study aimed to enhance the prediction of estuarine turbidity patterns by enhancing the performance of the multilayer perceptron (MLP) network through the introduction of stochastic gradient descent (SGD) and momentum gradient descent (MGD). To achieve this, Sentinel-2 multispectral imagery was used as the base on which spectral radiance properties of estuarine waters were analyzed against field-measured turbidity data. In this case, blue, green, red, red edge, near-infrared and shortwave spectral bands were selected for empirical relationship establishment and model development. Inverse distance weighting (IDW) spatial interpolation was employed to produce raster-based turbidity data of the study area based on field-measured data. The IDW image was subsequently binarized using the bi-level thresholding technique to produce a Boolean image. Prior to empirical model development, the selected spectral bands were calibrated to turbidity using multilayer perceptron neural network trained with the sigmoid activation function with stochastic gradient descent (SGD) optimizer and then with sigmoid activation function with momentum gradient descent optimizer. The Boolean image produced from IDW interpolation was used as the base on which the sigmoid activation function calibrated image pixels to turbidity. Empirical models were developed using selected uncalibrated and calibrated spectral bands. The results from all the selected models generally revealed a stronger relationship of the red spectral channel with measured turbidity than with other selected spectral bands. Among these models, the MLP trained with MGD produced a coefficient of determination (r²) value of 0.92 on the red spectral band, followed by the MLP with MGD on the green spectral band and SGD on the red spectral band, with r² values of 0.75 and 0.72, respectively. The relative error of mean (REM) and r² results revealed accurate turbidity prediction by the sigmoid with MGD compared to other models. Overall, this study demonstrated the prospect of deploying ensemble techniques on Sentinel-2 multispectral bands in spatially constructing missing estuarine turbidity data. Full article

Compressed Sensing SAR Imaging is based on an accurate observation matrix. As the observed scene enlarges, the resource consumption of the method increases exponentially. In this paper, we propose a weighted ℓ_2/3-norm regularization SAR imaging method based on approximate observation. Initially, to address the issues brought by the precise observation model, we employ an approximate observation operator based on the Chirp Scaling Algorithm as a substitute. Existing approximate observation models typically utilize ℓ_q(q = 1, 1/2)-norm regularization for sparse constraints in imaging. However, these models are not sufficiently effective in terms of sparsity and imaging detail. Finally, to overcome the aforementioned issues, we apply ℓ_2/3 regularization, which aligns with the natural image gradient distribution, and further constrain it using a weighted matrix. This method enhances the sparsity of the algorithm and balances the detail insufficiency caused by the penalty term. Experimental results demonstrate the excellent performance of the proposed method. Full article

While most current image forgery localization (IFL) deep learning models focus primarily on the foreground of tampered images, they often neglect the essential complementary background semantic information. This oversight tends to create significant gaps in these models’ ability to thoroughly interpret and understand a tampered image, thereby limiting their effectiveness in extracting critical tampering traces. Given the above, this paper presents a novel contrastive learning and edge-reconstruction-driven complementary learning network (CECL-Net) for image forgery localization. CECL-Net enhances the understanding of tampered images by employing a complementary learning strategy that leverages foreground and background features, where a unique edge extractor (EE) generates precise edge artifacts, and edge-guided feature reconstruction (EGFR) utilizes the edge artifacts to reconstruct a fully complementary set of foreground and background features. To carry out the complementary learning process more efficiently, we also introduce a pixel-wise contrastive supervision (PCS) method that attracts consistent regions in features while repelling different regions. Moreover, we propose a dense fusion (DF) strategy that utilizes multi-scale and mutual attention mechanisms to extract more discriminative features and improve the representational power of CECL-Net. Experiments conducted on two benchmark datasets, one Artificial Intelligence (AI)-manipulated dataset and two real challenge datasets, indicate that our CECL-Net outperforms seven state-of-the-art models on three evaluation metrics. Full article

Islet transplantation represents a promising therapeutic approach for diabetes management, yet the isolation and evaluation of pancreatic islets remain challenging. This study focuses on the isolation of islets from rabbit pancreases, followed by a comprehensive assessment of their viability and functionality. We developed a novel method for isolating islet cells from the pancreas of adult rabbits. We successfully isolated viable islets, which were subsequently evaluated through a combination of viability assays, an insulin enzyme-linked immunosorbent assay (ELISA), and fluorescence lifetime imaging microscopy (FLIM). The viability assays indicated a high percentage of intact islets post-isolation, while the insulin ELISA demonstrated robust insulin secretion in response to glucose stimulation. FLIM provided insights into the metabolic state of the islets, revealing distinct fluorescence lifetime signatures correlating with functional viability. Our findings underscore the potential of rabbit islets as a model for studying islet biology and diabetes therapy, highlighting the efficacy of combining traditional assays with advanced imaging techniques for comprehensive functional assessments. This research contributes to the optimization of islet isolation protocols and enhances our understanding of islet functional activity dynamics in preclinical settings. Full article

This research paper explores the use of machine learning to relate images of flame structure and luminosity to measured NOx emissions. Images of reactions produced by 16 aero-engine derived injectors for a ground-based turbine operated on a range of fuel compositions, air pressure drops, preheat temperatures and adiabatic flame temperatures were captured and postprocessed. The experimental investigations were conducted under atmospheric conditions, capturing CO, NO and NOx emissions data and OH* chemiluminescence images from 27 test conditions. The injector geometry and test conditions were based on a statistically designed test plan. These results were first analyzed using the traditional analysis approach of analysis of variance (ANOVA). The statistically based test plan yielded 432 data points, leading to a correlation for NOx emissions as a function of injector geometry, test conditions and imaging responses, with 70.2% accuracy. As an alternative approach to predicting emissions using imaging diagnostics as well as injector geometry and test conditions, a random forest machine learning algorithm was also applied to the data and was able to achieve an accuracy of 82.6%. This study offers insights into the factors influencing emissions in ground-based turbines while emphasizing the potential of machine learning algorithms in constructing predictive models for complex systems. Full article

Discogenic low back pain (LBP) is a significant clinical condition arising from degeneration of the intervertebral disc, a common yet complex cause of chronic pain, defined by fissuring in the annulus fibrosus resulting in vascularization of growing granulation tissue and growth of nociceptive nerve fibers along the laceration area. This paper delves into the anatomical and pathophysiological underpinnings of discogenic LBP, emphasizing the role of intervertebral disc degeneration in the onset of pain. The pathogenesis is multifactorial, involving processes like mitochondrial dysfunction, accumulation of advanced glycation end products, and pyroptosis, all contributing to disc degeneration and subsequent pain. Despite its prevalence, diagnosing discogenic LBP is challenging due to the overlapping symptoms with other forms of LBP and the absence of definitive diagnostic criteria. Current diagnostic approaches include clinical evaluations, imaging techniques, and the exploration of potential biomarkers. Treatment strategies range from conservative management, such as physical therapy and pharmacological interventions, to more invasive procedures such as spinal injections and surgery. Emerging therapies targeting molecular pathways involved in disc degeneration are under investigation and hold potential for future clinical application. This paper highlights the necessity of a multidisciplinary approach combining clinical, imaging, and molecular data to enhance the accuracy of diagnosis and the effectiveness of treatment for discogenic LBP, ultimately aiming to improve patient outcomes. Full article