Quantitative Evaluation Research Articles

The Quantitative Evaluation of the Cell Structure Uniformity of Microcellular TPU with Low Porosity via a Digital Image Processing Method

The cell structure uniformity of microcellular polymers significantly impacts material performance, especially for low-porosity microcellular TPU used in chip polishing. The distribution of the cell structure of polishing pads directly affects the removal rate and process repeatability. Despite its importance, no quantitative method for evaluating cell structure uniformity has been reported in the literature. In this study, a digital image processing method that involves morphological operations of scanning electron microscopy (SEM) images, binarization, and cell localization, and the statistical evaluation of cell structure parameters was established to evaluate cell structure uniformity. A quantitative metric, the cell structure uniformity index (CUI), was calculated based on cell structure indices, incorporating the cell size index (Ud), the cell number index (Un), and the cell local spacing index (Ur). By establishing an ideal model and analyzing representative SEM images, the effectiveness and efficiency of the method for evaluating cell structure uniformity of microcellular TPU were successfully validated. The results demonstrated that low-porosity TPU foams exhibited relatively low cell structure uniformity compared to the ideal model. The heterogeneous nucleation process in TPU caused non-uniform cell structures due to the temporal and spatial non-homogeneities during the early cell nucleation process. As the cells grew, they merged and reduced the distance between them, resulting in improved cell structure uniformity.

Research on the evaluation method for safety cognitive ability of workers in high-risk construction positions

PurposeSafety cognitive ability is a key factor influencing unsafe behavior. However, the existing achievements have not yet involved the division of the hierarchical relationship of factors influencing safety cognition and lack a quantitative evaluation system of safety cognitive ability. The purpose of this paper is to find out the deficiencies in the safety cognition of workers in high-risk construction positions and to provide practical suggestions for improving their safety cognitive ability and reducing unsafe behavior.Design/methodology/approachBased on the iceberg model, the factors influencing the safety cognitive ability of workers in high-risk construction positions and their hierarchical relationship were determined, and an evaluation index system consisting of four primary indicators and 20 secondary indicators was constructed. The game theory algorithm was used to optimize the subjective and objective weights of the indicators calculated by the sequential analysis method (G1) and the entropy weighting method (EWM) to obtain the optimal combination weight value. The Matlab software was used for cloud mapping and similarity calculation to determine the safety cognitive ability level of the object to be evaluated.FindingsThe research results indicate that the comprehensive level of safety cognitive ability of scaffolders in this construction project is at “Level III”, the fundamental factors and compliance factors are at “Level IV”, the auxiliary factors and driving factors are at “Level III”. This conclusion aligns with the situation learned from the previous field investigation, which validates the feasibility and scientificity of the proposed evaluation method.Research limitations/implicationsConsidering that the safety cognitive ability of construction workers is constantly changing, this study has not yet delved into the specific impacts of various influencing factors on the level of safety cognitive ability. Future research can utilize simulation software, such as MATLAB and Vensim, to construct dynamic simulation models that accurately simulate the changing rules of construction workers’ safety cognitive ability under the influence of different factors.Practical implicationsThis research broadens the application scope of the iceberg model, enriches the analysis model of the factors influencing the safety cognitive ability of workers in high-risk construction positions and provides a novel perspective for similar research. The safety cognitive ability evaluation method proposed in this paper can not only accurately evaluate the safety cognitive ability level of workers in high-risk positions such as scaffolders but also provide practical suggestions for improving the safety cognitive ability of workers, which is of great significance to improve the safety management level and reduce unsafe behavior in the construction field.Originality/valueThis research fills the research gap of workers in high-risk construction positions and the quantification of safety cognitive ability. The iceberg model is used to realize the hierarchical division of the factors influencing safety cognitive ability. Additionally, an evaluation method for the safety cognitive ability of workers in high-risk construction positions based on the game theory combination weighting method and cloud model is proposed, which realizes the quantitative evaluation of safety cognitive ability.

Mitigating social bias in sentiment classification via ethnicity-aware algorithmic design

Sentiment analysis tools are frequently employed to analyze large amounts of natural language data gathered from social networks and generate valuable insights on public opinion. Research has discovered that these tools tend to be biased against some demographic groups, based on social attributes such as gender, age, and ethnicity. Sentiment classification works dealt with this issue by means of data balancing and algorithmic approaches. However, one crucial limitation of existing methods is the inability to tackle social bias while maintaining satisfactory model performance. In this paper, we aim to fill this gap by proposing a sentiment classification method that entails ethnicity-aware algorithmic design. Specifically, our method involves balanced training and a custom ethnicity-aware loss function that leverages ethnicity group information to foster a fair model optimization process. The proposed loss incentivizes the model to iteratively improve accuracy for currently underperforming demographic or social groups, therefore simultaneously decreasing social bias and boosting overall performance. Our extensive qualitative and quantitative experimental evaluation involving a large corpus of user reviews demonstrated the effectiveness of the proposed method, also when compared to popular baselines for sentiment classification.

Detection of carotid plaques on panoramic radiographs using deep learning

ObjectivesPanoramic radiographs (PRs) can reveal an incidental finding of atherosclerosis, or carotid artery calcification (CAC), in 3-15% of examined patients. However, limited training in identification of such calcifications among dental professionals results in missed diagnoses. This study aimed to detect CAC on PRs using an artificial intelligence (AI) model based on a vision transformer. Methods6,404 PRs were obtained from one hospital and screened for the presence of CAC based on electronic medical records. CAC was manually annotated with bounding boxes by an oral radiologist and reviewed and revised by three experienced clinicians to achieve consensus. An AI approach based on Faster R-CNN and Swin Transformer was trained and evaluated based on 185 PRs with CAC and 185 PRs without CAC. Reported and replicated diagnostic performances of published AI approaches based on convolutional neural networks (CNNs) were used for comparison. Quantitative evaluation of the performance of the models included precision, F1-score, recall, area-under-the-curve (AUC), and average precision (AP). ResultsThe proposed method based on Faster R-CNN and Swin Transformer achieved a precision of 0.895, recall of 0.881, F1-score of 0.888, AUC of 0.950, and AP of 0.942, surpassing models based on a CNN. ConclusionsThe detection performance of this newly developed and validated model was improved compared to previously reported models. Clinical significanceIntegrating AI models into dental imaging to assist dental professionals in the detection of CAC on PRs has the potential to significantly enhance the early detection of carotid artery atherosclerosis and its clinical management.

Metal artifact reduction method based on single spectral CT (MARSS).

For patients with metal implants, computed tomography (CT) imaging results suffer from metal artifacts, which seriously affect image evaluation and even lead to misdiagnosis. Because spectral CT technology has the advantage of quantitative imaging, basis material decomposition, and so on, the current metal artifact reduction methods are utilizing spectral information to reduce metal artifacts with good results. However, they usually require projection data from multiple spectra or energy-windows, which is difficult to realize in conventional CT. To satisfy the status quo, the aim of this work is to propose a metal artifact reduction (MAR) method based on single spectral CT (MARSS). By incorporating prior information, the average density of some base materials, and a constrained image reconstruction model is established. It forces the solution spaces of the materials to be discrete and finite, making the model easier tosolve. The MARSS method uses the idea of discrete tomography to establish a constrained reconstruction model. By incorporating priori knowledge, the constraint forces the solution spaces of some materials to be discrete, which can effectively downsize the solution space and reduce the ill-posedness of this problem. Then, an iteration algorithm is developed to solve this model. This algorithm iterates alternately between reconstruction and discretization. It ensures that the solution spaces are discrete while the polychromatic projection of the reconstructed image converges to that of the scannedobject. The MRASS method significantly reduces artifacts and restores structures near metal to a large extent. Unlike the comparison MAR methods, it effectively prevents the introduction of new artifacts and distortion of thestructure. The MARSS method can achieve MAR based on single spectral CT. Subjective and quantitative evaluation of the results show that the method significantly improves image quality compared to competingmethods.

Social-LCA providing new insights and learnings for social responsibility—case pulp and paper industry

Abstract Purpose This paper presents the lessons learnt from testing the ORIENTING S-LCA methodology for a case product of Stora Enso, a globally operating forest industry company. The main purpose of the study was to explore what kind of additional insights and benefits the S-LCA method could provide compared to other existing social responsibility methods and approaches used by companies. Methods The paper briefly describes the applied ORIENTING S-LCA method and its application in the case study, focusing on main learnings gained and development needs identified. The S-LCA methodology proposed in ORIENTING builds upon the UNEP Guidelines for S-LCA (UNEP 2020) and the Handbook for Product Social Impact Assessment (Goedkoop et al. 2020). S-LCA methodology relies on the reference scale approach (RSA) and combines qualitative and quantitative evaluations for assessing social performance and social risk along the product’s life cycle. Social performance assessment approach was applied for company’s own processes covering several life cycle stages and the risk assessment for assessing the supply chain. Results and discussion Based on the case company’s experience, social assessments are currently conducted mainly on company or facility level. Moving towards product-based assessments could help gaining a more transparent view of products’ value chains and bring new possibilities for example to supplier specific sustainability audits. With the product-based S-LCA, the company could more easily identify, e.g., in which life cycle stage they would have most gaps on information and on which social matters, and take different stakeholders’ needs better into consideration in different life cycle stages. Areas that require further development concern especially data availability, assessment efficacy, and formulation of social topics. Conclusions The study demonstrates that the S-LCA methodology offers several benefits when addressing social sustainability issues in the pulp and paper industry. Comprehensive view over different social topics, life cycle perspective, and stakeholder identification are key elements of S-LCA that may support identifying relevant sustainability topics for the purposes of corporate sustainability reporting. Additionally, they can support defining appropriate mitigation actions that will be required for complying with the requirements related to corporate sustainability due diligence.

Multiscale modeling of metal-hydride interphases—quantification of decoupled chemo-mechanical energies

The quantification of interphase properties between metals and their corresponding hydrides is crucial for modeling the thermodynamics and kinetics of the hydrogenation processes in solid-state hydrogen storage materials. In particular, interphase boundary energies assume a pivotal role in determining the kinetics of nucleation, growth, and coarsening of hydrides, alongside accompanying morphological evolution during hydrogenation. The total interphase energy arises from both chemical bonding and mechanical strains in these solid-state systems. Since these contributions are usually coupled, it is challenging to distinguish via conventional computational approaches. Here, a comprehensive atomistic modeling methodology is developed to decouple chemical and mechanical energy contributions using first-principles calculations, of which feasibility is demonstrated by quantifying chemical and elastic strain energies of key interfaces within the FeTi metal-hydride system. Derived materials parameters are then employed for mesoscopic micromechanical analysis, predicting crystallographic orientations in line with experimental observations. The multiscale approach outlined verifies the importance of the chemo-mechanical interplay in the morphological evolution of growing hydride phases, and can be generalized to investigate other systems. In addition, it can streamline the design of atomistic models for the quantitative evaluation of interphase properties between dissimilar phases and allow for efficient predictions of their preferred phase boundary orientations.

Frequency Domain-Based Super Resolution Using Two-Dimensional Structure Consistency for Ultra-High-Resolution Display

Recent advancements in the field of super resolution (SR) have seen the adoption of generative adversarial networks (GANs) for realistic images. In this case, when performing with low-resolution (LR) images, several challenges arise due to the loss of high-frequency details from high-resolution (HR) images, potentially leading to unwanted distortions in the generated SR images. Our paper presents a novel solution by using two-dimensional structure consistency (TSC) for image analysis. The TSC serves as a mask, enabling adaptive analysis based on the unique frequency characteristics of different image regions. Furthermore, a mutual loss mechanism, which dynamically adjusts the training process based on the results filtered by the TSC-based mask, is introduced. Additionally, the TSC loss is proposed to enhance our model capacity to generate precise TSC in high-frequency regions. As a result, our method effectively reduces distortions in high-frequency areas while preserving clarity in regions containing low-frequency components. Our method outperforms other SR techniques, demonstrating superior results in both qualitative and quantitative evaluations. Quantitative measurements, including PSNR, SSIM, and the perceptual metric LPIPS, show comparable PSNR and SSIM values, while the perceptual SR quality is notably improved according to the LPIPS metric.

Environmental impacts and nitrogen-carbon-energy nexus of vegetable production in subtropical plateau lake basins

Vegetables are important economic crops globally, and their production has approximately doubled over the past 20 years. Globally, vegetables account for 13% of the harvested area but consume 25% of the fertilizer, leading to serious environmental impacts. However, the quantitative evaluation of vegetable production systems in subtropical plateau lake basins and the establishment of optimal management practices to further reduce environmental risks are still lacking. Using the life cycle assessment method, this study quantified the global warming, eutrophication, acidification, and energy depletion potential of vegetable production in a subtropical plateau lake basin in China based on data from 183 farmer surveys. Our results indicated that vegetable production in the study area, the Erhai Lake Basin, was high but came at a high environmental cost, mainly due to low fertilizer efficiency and high nutrient loss. Root vegetables have relatively high environmental costs due to the significant environmental impacts of fertilizer production, transportation, and application. A comprehensive analysis showed that the vegetable production in this region exhibited low economic and net ecosystem economic benefits, with ranges of 7.88–8.91 × 103 and 7.35–8.69 × 103 $ ha−1, respectively. Scenario analysis showed that adopting strategies that comprehensively consider soil, crop, and nutrient conditions for vegetable production can reduce environmental costs (with reductions in global warming potential (GWP), eutrophication potential (EP), acidification potential (AP), and energy depletion potential (EDP) by 10.6–28.2%, 65.1–73.5%, 64.5–71.9%, 47.8–70.4%, respectively) compared with the current practices of farmers. This study highlighted the importance of optimizing nutrient management in vegetable production based on farmers’ practices, which can achieve more yield with less environmental impacts and thereby avoid the “trade-off” effect between productivity and environmental sustainability.

Multimass Analysis of Adeno-Associated Virus Vectors by Orbitrap-Based Charge Detection Mass Spectrometry.

Adeno-associated virus (AAV) vectors have attracted significant attention as the main platform for gene therapy. To ensure the safety and efficacy of AAV vectors when used as gene therapy drugs, it is essential to assess their critical quality attributes (CQAs). These CQAs include the genome packaging status, the size of the genome encapsidated within the AAV capsid, and the stoichiometry of viral proteins (VPs) that constitute the AAV capsids. Analytical methods have been established for evaluating CQAs, such as analytical ultracentrifugation, capillary gel electrophoresis with laser-induced fluorescence detection, and capillary gel electrophoresis using sodium dodecyl sulfate with UV detection. Here, we present a multimass analysis of AAV vectors using orbitrap-based charge detection mass spectrometry (CDMS), a single-ion mass spectrometry. Orbitrap-based CDMS facilitates the quantitative evaluation of the genome packaging status based on the mass distribution of empty and full particles. Additionally, we established a novel method to analyze the encapsidated genome directly without pretreatment, such as protein digestion or heat treatment, and to estimate the stoichiometric variation of VP for the capsid based on the mass distribution constituted by the single peak corresponding to AAV particles. Orbitrap-based CDMS is a distinctive method that allows multiple mass characterizations of AAV vectors with a small sample volume of 20 μL for 1013 cp/mL in a short time (30 min), and it holds the potential to become a new standard method in the assessment of CQAs for AAV vectors.

Process evaluation findings from Strong Hearts, Healthy Communities 2.0: a cardiovascular disease prevention intervention for rural women

BackgroundStrong Hearts, Healthy Communities 2.0 (SHHC-2.0) was a 24-week cardiovascular disease prevention program that was effective in improving physical activity and nutrition behaviors and clinical outcomes among women in 11 rural New York, USA towns. This study evaluated the delivery of SHHC-2.0 to prepare the intervention for further dissemination.MethodsThis process evaluation was guided by the Medical Research Council recommendations and engaged program leaders and participants (i.e., women over age 40) using quantitative and qualitative methods. The quantitative evaluation included examination of enrollment and retention data, a participant survey, and a fidelity checklist completed after classes. Descriptive and comparative statistics were used to assess implementation measures: program reach, participant attendance, dose delivered, program length, perceived effectiveness, fidelity, and participant satisfaction. The qualitative evaluation included focus groups (n = 13) and interviews (n = 4) using semi-structured guides; audio was recorded and transcripts were deductively coded and analyzed using directed content analysis and iterative categorization approaches. Comparisons across towns and between intervention and waitlist control groups were explored.ResultsAverage reach within towns was 7.5% of the eligible population (range 0.7-15.7%). Average attendance was 59.8% of sessions (range 42.0-77.4%). Average dose delivered by leaders was 86.4% of curriculum components (range 73.5-95.2%). Average session length was 51.8 ± 4.8 min across 48 sessions. Leaders’ perceived effectiveness rating averaged 4.1 ± 0.3 out of 5. Fidelity to curricular components was 81.8% (range 67.4-93.2%). Participants reported being “more than satisfied” with the overall program (88.8%) and the health benefits they obtained (72.9%). Qualitative analysis revealed that participants: (1) gained new knowledge and enjoyable experiences; (2) perceived improvements in their physical activity, nutrition, and/or health; (3) continued to face some barriers to physical activity and healthy eating, with those relating to social support being reduced; and (4) rated leaders and the group structure highly, with mixed opinions on the research elements.ConclusionsSHHC-2.0 had broad reach, was largely delivered as intended, and participants expressed high levels of satisfaction with the program and its health benefits. Our findings expand on best practices for implementing cardiovascular disease prevention programs in rural communities.Clinical trials Registrationwww.clinicaltrials.gov #NCT03059472.

Comprehensive assessment of imaging quality of artificial intelligence-assisted compressed sensing-based MR images in routine clinical settings

BackgroundConventional MR acceleration techniques, such as compressed sensing, parallel imaging, and half Fourier often face limitations, including noise amplification, reduced signal-to-noise ratio (SNR) and increased susceptibility to artifacts, which can compromise image quality, especially in high-speed acquisitions. Artificial intelligence (AI)-assisted compressed sensing (ACS) has emerged as a novel approach that combines the conventional techniques with advanced AI algorithms. The objective of this study was to examine the imaging quality of the ACS approach by qualitative and quantitative analysis for brain, spine, kidney, liver, and knee MR imaging, as well as compare the performance of this method with conventional (non-ACS) MR imaging.MethodsThis study included 50 subjects. Three radiologists independently assessed the quality of MR images based on artefacts, image sharpness, overall image quality and diagnostic efficacy. SNR, contrast-to-noise ratio (CNR), edge content (EC), enhancement measure (EME), scanning time were used for quantitative evaluation. The Cohen’s kappa correlation coefficient (k) was employed to measure radiologists’ inter-observer agreement, and the Mann Whitney U-test used for comparison between non-ACS and ACS.ResultsThe qualitative analysis of three radiologists demonstrated that ACS images showed superior clinical information than non-ACS images with a mean k of ~ 0.70. The images acquired with ACS approach showed statistically higher values (p < 0.05) for SNR, CNR, EC, and EME compared to the non-ACS images. Furthermore, the study’s findings indicated that ACS-enabled images reduced scan time by more than 50% while maintaining high imaging quality.ConclusionIntegrating ACS technology into routine clinical settings has the potential to speed up image acquisition, improve image quality, and enhance diagnostic procedures and patient throughput.

Assessment of functional performance in self-rectifying passive crossbar arrays utilizing sneak path current

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Mass spectrometry proteomic profiling of postmortem human muscle degradation for PMI estimation

The discovery of new methods for determining the post-mortem interval is of significant forensic interest. Mass spectrometry has enhanced the accuracy of assessing post-mortem protein decay, with skeletal muscle being the most studied substrate due to its intrinsic properties of postmortem decay. In this pilot study, human skeletal muscle tissue (iliopsoas) was harvested and allowed to decay under controlled temperature and humidity conditions at predetermined intervals.The samples were analyzed using mass spectrometry proteomics for both qualitative and quantitative evaluation of proteins and peptides. Candidate proteins were validated through immunoblotting.The results were significant, identifying several proteins that could aid in estimating the post-mortem interval. Notably, PLIN4, MYOZ2, SYNPO2, and BAG3 were validated by immunoblotting over a broader range of experimental points and temperatures. Furthermore, human results were compared with animal muscle samples from a previous study, revealing similarities in decomposition kinetics.This analysis of human samples marks a step forward in the potential forensic application of proteomic evaluation by mass spectrometry.

An adaptive meta-imitation learning-based recommendation environment simulator: A case study on ship-cargo matching

High-quality shipping is one of the effective ways for sustainable cities in inland river basins to improve transportation efficiency and reduce energy consumption. Currently, the biggest challenge faced by shipping is the high empty-ship rate, which makes it impossible to directly apply machine learning methods due to the cold-start problem. Although some researchers have tried to utilize deep reinforcement learning(DRL)-based recommendation that do not rely on manually labeled data to alleviate the cold-start problem, progress has been slow due to the lack of available training environment. Therefore, this paper introduces an adaptive meta-imitation learning-based recommendation environment simulator, termed AMIL-Simulator. Specifically, we construct a conditionally guided diffusion model to simulate shipowner behavior in a dynamically changing environment. Moreover, we propose a shipowner reward model based on adaptive meta-imitation learning, enabling the learning of shipowner rewards across multiple tasks, even when confronted with limited samples and imbalanced categories. By conducting extensive quantitative experimental evaluations and shipowner-cargo matching studies, the results demonstrate the effectiveness of AMIL-Simulator, particularly in smaller-scale and cold-start environments.

A quantitative evaluation method of laser treatment efficacy for pigmentary dermatosis based on image segmentation technology

A quantitative evaluation method of laser treatment efficacy for pigmentary dermatosis based on image segmentation technology

A Pneumatic Soft Exoskeleton System Based on Segmented Composite Proprioceptive Bending Actuators for Hand Rehabilitation.

Soft pneumatic actuators/robotics have received significant interest in the medical and health fields, due to their intrinsic elasticity and simple control strategies for enabling desired interactions. However, current soft hand pneumatic exoskeletons often exhibit uniform deformation, mismatch the profile of the interacting objects, and seldom quantify the assistive effects during activities of daily life (ADL), such as extension angle and predicted joint stiffness. The lack of quantification poses challenges to the effective and sustainable advancement of rehabilitation technology. This paper introduces the design, modeling, and testing of pneumatic bioinspired segmented composite proprioceptive bending actuators (SCPBAs) for hand rehabilitation in ADL tasks. Inspired by human finger anatomy, the actuator's soft-joint-rigid-bone segmented structure provides a superior fit compared to continuous structures in traditional fiber-reinforced actuators (FRAs). A quasi-static model is established to predict the bending angles based on geometric parameters. Quantitative evaluations of predicted joint stiffness and extension angle utilizing proprioceptive bending are performed. Additionally, a soft under-actuated hand exoskeleton equipped with SCPBAs demonstrates their potential in ADL rehabilitation scenarios.

The Performance of the Optical Flow Field based Dense Image Matching for UAV Imagery

Abstract. With the rapid development of sensing platforms, unmanned aerial vehicle (UAV)-based mapping has become increasingly popular because of its economic efficiency and flexibility, especially for providing 3D information to support urban growth monitoring and change detection to meet sustainable development goals (SDGs). This paper presents an improved optical flow field-based dense matching algorithm (OFFDM) for low-altitude UAV images based on the Ph.D. thesis of Yuan (Yuan, 2018). First, high-precision seed points were used to compute the optical flow field within stereo pairs, effectively minimizing redundant calculations during the fine-matching phase. Second, a fine-matching approach, integrating multiple constraints, was applied to refine the coarse matching results based on the optical flow field. Extensive dense matching experiments on UAV low-altitude aerial imagery assessed the performance of OFFDIM across four dimensions: 3D point cloud visualization, matching success rate, precision, and reliability. Extensive experiments on low-altitude UAV imagery, characterized by a resolution of 7cm per pixel over a 10,608×8,608 pixel dimension and a 60% forward overlap, evaluate the OFFDM's efficacy. The quantitative evaluation revealed that the proposed method achieved an accuracy of ±0.7 pixels in image coordinates and ±20 cm on the ground, with a matching success rate exceeding 97%. The processing time was approximately 272 seconds for handling one single stereo pair. When compared to the widely adopted PMVS algorithm, known for its effectiveness in dense matching for UAV images, the proposed method demonstrated higher completeness and improved matching efficiency by more than five times. These results demonstrated that the proposed approach is more suitable for dense matching on UAV imagery-based high-precision 3D spatial data extraction, supporting global mapping tasks more effectively.

Interpretable Dynamic Directed Graph Convolutional Network for Multi-Relational Prediction of Missense Mutation and Drug Response.

Tumor heterogeneity presents a significant challenge in predicting drug responses, especially as missense mutations within the same gene can lead to varied outcomes such as drug resistance, enhanced sensitivity, or therapeutic ineffectiveness. These complex relationships highlight the need for advanced analytical approaches in oncology. Due to their powerful ability to handle heterogeneous data, graph convolutional networks (GCNs) represent a promising approach for predicting drug responses. However, simple bipartite graphs cannot accurately capture the complex relationships involved in missense mutation and drug response. Furthermore, Deep learning models for drug response are often considered "black boxes", and their interpretability remains a widely discussed issue. To address these challenges, we propose an Interpretable Dynamic Directed Graph Convolutional Network (IDDGCN) framework, which incorporates four key features: (1) the use of directed graphs to differentiate between sensitivity and resistance relationships, (2) the dynamic updating of node weights based on node-specific interactions, (3) the exploration of associations between different mutations within the same gene and drug response, and (4) the enhancement of interpretability models through the integration of a weighted mechanism that accounts for the biological significance, alongside a ground truth construction method to evaluate prediction transparency. The experimental results demonstrate that IDDGCN outperforms existing state-of-the-art models, exhibiting excellent predictive power. Both qualitative and quantitative evaluations of its interpretability further highlight its ability to explain predictions, offering a fresh perspective for precision oncology and targeted drug development.

A Comprehensive and Repeatable Contrast-Enhanced Ultrasound Quantification Approach for Clinical Evaluations of Tumor Blood Flow.

The aim of this study is to define a comprehensive and repeatable contrast-enhanced ultrasound (CEUS) imaging protocol and analysis method to quantitatively assess lesional blood flow. Easily repeatable CEUS evaluations are essential for longitudinal treatment monitoring. The quantification method described here aims to provide a structure for future clinical studies. This retrospective analysis study included liver CEUS studies in 80 patients, 40 of which contained lesions (primarily hepatocellular carcinoma, n = 28). Each patient was given at least 2 injections of a microbubble contrast agent, and 60-second continuous loops were acquired for each injection to enable evaluation of repeatability. For each bolus injection, 1.2 mL of contrast was delivered, whereas continuous, stationary scanning was performed. Automated respiratory gating and motion compensation algorithms dealt with breathing motion. Similar in size regions of interest were drawn around the lesion and liver parenchyma, and time-intensity curves (TICs) with linearized image data were generated. Four bolus transit parameters, rise time (RT), mean transit time (MTT), peak intensity (PI), and area under the curve (AUC), were extracted either directly from the actual TIC data or from a lognormal distribution curve fitted to the TIC. Interinjection repeatability for each parameter was evaluated with coefficient of variation. A 95% confidence interval was calculated for all fitted lognormal distribution curve coefficient of determination (R2) values, which serves as a data quality metric. One-sample t tests were performed between values obtained from injection pairs and between the fitted lognormal distribution curve and direct extraction from the TIC calculation methods to establish there were no significant differences between injections and measurement precision, respectively. Average interinjection coefficient of variation with both the fitted curve and direct calculation of RT and MTT was less than 21%, whereas PI and AUC were less than 40% for lesion and parenchyma regions of interest. The 95% confidence interval for the R2 value of all fitted lognormal curves was [0.95, 0.96]. The 1-sample t test for interinjection value difference showed no significant differences, indicating there was no relationship between the order of the repeated bolus injections and the resulting parameters. The 1-sample t test between the values from the fitted lognormal distribution curve and the direct extraction from the TIC calculation found no statistically significant differences (α = 0.05) for all perfusion-related parameters except lesion and parenchyma PI and lesion MTT. The scanning protocol and analysis method outlined and validated in this study provide easily repeatable quantitative evaluations of lesional blood flow with bolus transit parameters in CEUS data that were not available before. With vital features such as probe stabilization ideally performed with an articulated arm and an automated respiratory gating algorithm, we were able to achieve interinjection repeatability of blood flow parameters that are comparable or surpass levels currently established for clinical 2D CEUS scans. Similar values and interinjection repeatability were achieved between calculations from a fitted curve or directly from the data. This demonstrated not only the strength of the protocol to generate TICs with minimal noise, but also suggests that curve fitting might be avoided for a more standardized approach. Utilizing the imaging protocol and analysis method defined in this study, we aim for this methodology to potentially assist clinicians to assess true perfusion changes for treatment monitoring with CEUS in longitudinal studies.