Impact of 1.5†T Magnetic Field on Treatment Plan Quality in MR-Guided Radiotherapy: Typical Phantom Test Cases.

PURPOSE: This study aims to investigate the influence of the magnetic field on treatment plan quality using typical phantom test cases, which encompass a circle target test case, AAPM TG119 test cases (prostate, head-and-neck, C-shape, multi-target test cases), and a lung test case. MATERIALS AND METHODS: For the typical phantom test cases, two plans were formulated. The first plan underwent optimization in the presence of a 1.5 Tesla magnetic field (1.5 T plan). The second plan was re-optimized without a magnetic field (0 T plan), utilizing the same optimization conditions as the first plan. The two plans were compared based on various parameters, including con-formity index (CI), homogeneity index (HI), fit index (FI) and dose coverage of the planning target volume (PTV), dose delivered to organs at risk (OARs) and normal tissue (NT), monitor unit (MU). A plan-quality metric (PQM) scoring procedure was employed. For the 1.5 T plans, dose verifications were performed using an MR-compatible ArcCHECK phantom. RESULTS: A smaller dose influence of the magnetic field was found for the circle target, prostate, head-and-neck, and C-shape test cases, compared with the multi-target and lung test cases. In the multi-target test case, the significant dose influence was on the inferior PTV, followed by the superior PTV. There was a relatively large dose influence on the PTV and OARs for lung test case. No statistically significant differences in PQM and MUs were observed. For the 1.5 T plans, gamma passing rates were all higher than 95% with criteria of 2 mm/3% and 2 mm/2%. CONCLUSION: The presence of a 1.5 T magnetic field had a relatively large impact on dose parameters in the multi-target and lung test cases compared with other test cases. However, there were no significant influences on the plan-quality metric, MU and dose accuracy for all test cases.

The effects of plyometric training on physical fitness and skill-related performance in female basketball players: a systematic review and meta-analysis.

Objective: This study aims to analyze the effects of plyometric training (PT) on physical fitness and skill-related performance in female basketball players. Method: Five databases, including Web of Science, Scopus, PubMed, EBSCOhost, and Google Scholar, were used to select articles published up to 20 December 2023, using a combination of keywords related to PT and female basketball players. The risk of bias and the certainty of evidence in included articles were assessed using the Cochrane risk of bias (RoB2) tool and "The Grading of Recommendations Assessment, Development, and Evaluation" (GRADE). Results: Ten studies were included for the systematic review, and eight for the meta-analysis, totalling 246 female basketball players aged 14.5-22.5 years. Most of these players were highly trained. Most of the included studies exhibited concerns regarding the risk of bias. The PT programs lasted 4-8 weeks, conducted 2-3 sessions per week, with sessions lasting 20-90 min and including 29-190 jumps. In the systematic review, most studies showed that PT significantly improved performance in countermovement jump (CMJ), squat jump (SJ), Sargent jump, standing long jump, lateral hop, medicine ball throw, t-Test, Illinois agility, lane agility drill, linear 20-m sprint, stable and dynamic leg balance, dribbling, passing, shooting, and various basketball-specific tests, as well as increased muscle volume and thigh cross-sectional area. However, some studies showed PT to induce no significant changes in performance during CMJ, t-Test, Illinois agility, knee extensor/flexor strength, linear sprint, and single leg balance tests. In the meta-analysis, CMJ height (ES = 0.37; p = 0.036), vertical jump (VJ) peak power (ES = 0.57; p = 0.015), VJ peak velocity (ES = 0.26; p = 0.004), and t-Test performance time (ES = 0.32; p = 0.004) were significantly improved with small effects following PT. Conclusion: The effect of PT on performance in female basketball players was mixed. Most studies indicated that PT could improve various measures of physical fitness and skill-related performance, but performance remained unchanged in some tests. More studies with established tests are needed to investigate the effect of PT on female basketball players in the future. Systematic Review Registration: https://inplasy.com/, Identifier INPLASY2023120078.

Statistical comparison for assessing agreement between two mass spectrometric methods for the analysis of polychlorinated dibenzo-p-dioxins and furans (PCDDs/Fs) in contaminated soils.

A gas chromatography coupled to high-resolution mass spectrometry (GC-HR/MS) has been used as the standard method for the quantification of polychlorinated dibenzo-p-dioxins and furans (PCDDs/Fs), which are regulated at screening and action levels in the environment. However, several alternative methods have been attempted due to the disadvantage of its high cost. Although a gas chromatography with triple quadrupole mass spectrometry (GC-QqQ-MS/MS) has been used in a wide variety of sample matrices, showing that they are interchangeable, there has been a lack of comprehensive studies on statistical agreement with GC-HR/MS. In this study, a pairwise comparison of the total concentrations of PCDDs/Fs in 90 soil field samples obtained by two mass spectrometric methods was performed using the Passing-Bablok (P&B) regression and Bland-Altman (B&A) analysis for the method comparison. According to the result of the B&A analysis, the concentration range of PCDDs/Fs was between 98.2 and 1760 pg/g showed good agreement between two methods at the 95 % confidence level (CL). Although there was a large discrepancy between the two methods in the low concentrations (<16.5 pg/g of PCDDs/Fs), this result was similar to the P&B regression analysis. As the verification results by B&A and P&B regression analysis, the interchangeable concentration range between the two methods was confirmed to be adequate for the monitoring of PCDDs/Fs regulating levels in soils.

Influence of Admixture Source on Fresh Properties of Self-Consolidating Concrete.

The study presented herein was intended to (1) compare the optimum (minimum) dosage requirements of four different sources of polycarboxylate-based high-range water-reducing admixtures (HRWRAs) and viscosity-modifying admixtures (VMAs) in attaining slump flows of 508 mm, 635 mm, and 711 mm, and a visual stability index (VSI) of 0 (highly stable concrete) or 1 (stable concrete), and (2) assess the flowability/viscosity, stability, passing ability, and filling ability of the resulting self-consolidating concretes. The test results showed that the optimum dosage requirements to obtain a uniform slump flow and visual stability index varied among the four selected admixture sources. The required dosage amount for HRWRAs was highest for the polycarboxylate-ester (PCE) type and lowest for the polycarboxylate-acid (PCA) type. Acceptable flowability plastic viscosity dynamic and static stability, passing ability, and filling ability of self-consolidating concrete can be achieved with the proper dosing of the four studied admixture sources.

Robust Regression Techniques for Multiple Method Comparison and Transformation.

A generalization of Passing-Bablok regression is proposed for comparing multiple measurement methods simultaneously. Possible applications include assay migration studies or interlaboratory trials. When comparing only two methods, the method boils down to the usual Passing-Bablok estimator. It is close in spirit to reduced major axis regression, which is, however, not robust. To obtain a robust estimator, the major axis is replaced by the (hyper-)spherical median axis. This technique has been applied to compare SARS-CoV-2 serological tests, bilirubin in neonates, and an in vitro diagnostic test using different instruments, sample preparations, and reagent lots. In addition, plots similar to the well-known Bland-Altman plots have been developed to represent the variance structure.

SGK-Net: A Novel Navigation Scene Graph Generation Network.

Scene graphs can enhance the understanding capability of intelligent ships in navigation scenes. However, the complex entity relationships and the presence of significant noise in contextual information within navigation scenes pose challenges for navigation scene graph generation (NSGG). To address these issues, this paper proposes a novel NSGG network named SGK-Net. This network comprises three innovative modules. The Semantic-Guided Multimodal Fusion (SGMF) module utilizes prior information on relationship semantics to fuse multimodal information and construct relationship features, thereby elucidating the relationships between entities and reducing semantic ambiguity caused by complex relationships. The Graph Structure Learning-based Structure Evolution (GSLSE) module, based on graph structure learning, reduces redundancy in relationship features and optimizes the computational complexity in subsequent contextual message passing. The Key Entity Message Passing (KEMP) module takes full advantage of contextual information to refine relationship features, thereby reducing noise interference from non-key nodes. Furthermore, this paper constructs the first Ship Navigation Scene Graph Simulation dataset, named SNSG-Sim, which provides a foundational dataset for the research on ship navigation SGG. Experimental results on the SNSG-sim dataset demonstrate that our method achieves an improvement of 8.31% (R@50) in the PredCls task and 7.94% (R@50) in the SGCls task compared to the baseline method, validating the effectiveness of our method in navigation scene graph generation.

Multi-granularity prior networks for uncertainty-informed patient-specific quality assurance.

Deep Learning Automated Patient-Specific Quality Assurance (PSQA) aims to reduce clinical resource requirements. It is vital to ensure the safety and effectiveness of radiation therapy by predicting the dose difference metric (Gamma passing rate) and its distribution. However, current research overlooks uncertainty quantification in model predictions, limiting their trustworthiness in real clinical environments. This paper proposes a Multi-granularity Uncertainty Quantification (MGUQ) framework. A Bayesian framework that quantifies uncertainties at multiple granularities for multi-task PSQA, specifically Gamma Passing Rate (GPR) prediction and Dose Difference Prediction (DDP), integrates visualization-based interactive components. Using Bayesian theory, we derive a comprehensive multi-granularity loss function that comprises granularity-specific loss and coherence loss components. Additionally, we proposed Multi-granularity Prior Networks, a dual-stream network architecture, to infer the distributions of DDP (modeled as t-distributions) and GPR (modeled as Gaussian distributions) under specific statistical assumptions. Comprehensive evaluations are conducted on a dataset from ''Peeking Union Medical College Hospital'', and results show that our proposed method achieves a minimum MAE loss of 0.864 with a 2%/3 mm criterion and realizes the uncertainty visualization of dose difference. Further, it also achieves 100% Clinical Accuracy (CA) with a workload of 67.2%. Experiments demonstrate that the proposed framework can enhance the trustworthiness of deep learning applications in PSQA.

GEMF: a novel geometry-enhanced mid-fusion network for PLA prediction.

Accurate prediction of protein-ligand binding affinity (PLA) is important for drug discovery. Recent advances in applying graph neural networks have shown great potential for PLA prediction. However, existing methods usually neglect the geometric information (i.e. bond angles), leading to difficulties in accurately distinguishing different molecular structures. In addition, these methods also pose limitations in representing the binding process of protein-ligand complexes. To address these issues, we propose a novel geometry-enhanced mid-fusion network, named GEMF, to learn comprehensive molecular geometry and interaction patterns. Specifically, the GEMF consists of a graph embedding layer, a message passing phase, and a multi-scale fusion module. GEMF can effectively represent protein-ligand complexes as graphs, with graph embeddings based on physicochemical and geometric properties. Moreover, our dual-stream message passing framework models both covalent and non-covalent interactions. In particular, the edge-update mechanism, which is based on line graphs, can fuse both distance and angle information in the covalent branch. In addition, the communication branch consisting of multiple heterogeneous interaction modules is developed to learn intricate interaction patterns. Finally, we fuse the multi-scale features from the covalent, non-covalent, and heterogeneous interaction branches. The extensive experimental results on several benchmarks demonstrate the superiority of GEMF compared with other state-of-the-art methods.

Delta4-based Dosimetric Error Detection in Volumetric-modulated Arc Therapy: Clinical Significance and Implications.

Background: Volumetric-modulated arc therapy (VMAT) is an efficient method of administering intensity-modulated radiotherapy beams. The Delta4 device was employed to examine patient data. Aims and Objectives: The utility of the Delta4 device in identifying errors for patient-specific quality assurance of VMAT plans was studied in this research. Materials and Methods: Intentional errors were purposely created in the collimator rotation, gantry rotation, multileaf collimator (MLC) position displacement, and increase in the number of monitor units (MU). Results: The results show that when the characteristics of the treatment plans were changed, the gamma passing rate (GPR) decreased. The largest percentage of erroneous detection was seen in the increasing number of MU, with a GPR ranging from 41 to 92. Gamma analysis was used to compare the dose distributions of the original and intentional error designs using the 2%/2 mm criteria. The percentage of dose errors (DEs) in the dose-volume histogram (DVH) was also analyzed, and the statistical association was assessed using logistic regression. A modest association (Pearson's R-values: 0.12-0.67) was seen between the DE and GPR in all intentional plans. The findings indicated a moderate association between DVH and GPR. The data reveal that Delta4 is effective in detecting mistakes in treatment regimens for head-and-neck cancer as well as lung cancer. Conclusion: The study results also imply that Delta4 can detect errors in VMAT plans, depending on the details of the defects and the treatment plans employed.

A novel model for relation prediction in knowledge graphs exploiting semantic and structural feature integration.

Relation prediction is a critical task in knowledge graph completion and associated downstream tasks that rely on knowledge representation. Previous studies indicate that both structural features and semantic information are meaningful for predicting missing relations in knowledge graphs. This has led to the development of two types of methods: structure-based methods and semantics-based methods. Since these two approaches represent two distinct learning paradigms, it is difficult to fully utilize both sets of features within a single learning model, especially deep features. As a result, existing studies usually focus on only one type of feature. This leads to an insufficient representation of knowledge in current methods and makes them prone to overlooking certain patterns when predicting missing relations. In this study, we introduce a novel model, RP-ISS, which combines deep semantic and structural features for relation prediction. The RP-ISS model utilizes a two-part architecture, with the first component being a RoBERTa module that is responsible for extracting semantic features from entity nodes. The second part of the system employs an edge-based relational message-passing network designed to capture and interpret structural information within the data. To alleviate the computational burden of the message-passing network on the RoBERTa module during the sampling process, RP-ISS introduces a node embedding memory bank, which updates asynchronously to circumvent excessive computation. The model was assessed on three publicly accessible datasets (WN18RR, WN18, and FB15k-237), and the results revealed that RP-ISS surpasses all baseline methods across all evaluation metrics. Moreover, RP-ISS showcases robust performance in graph inductive learning.

An improved method of local coloring in vertical-slot fishways to enhance the fish migration effect of grass carp.

Vertical-slot fishway (VSF) has been used in many water conservancy projects to restore the river connectivity. A high-quality fishway project should facilitate fish to discovering the exit and passing through, avoiding to long stay in the fishway and delay the migration. Current research on fishway engineering has not yielded an expected passing ratio of fish migration, and it is therefore of great significance to further study the assisting effect of VSF in fish migration. To begin with, we preliminarily determined the attractive and repelling colors of grass carps based on their swimming behavior in a static water pool configured with local colors. Combined with the migration route of the grass carp in a VSF pool without local coloring, four local coloring cases were designed. Based on the camera results of the four experimental local coloring cases, a comparative analysis was conducted with the blank control group frame by frame. This was followed by the statistics of the number of successfully migrated grass carps and their total completion time. On that basis, the assisting effect of VSF in fish migration under the four cases was evaluated in terms of the reduction rate of migration route length, the reduction rate of completion time, and the improvement rate of passing ratio. The research outcomes indicated that green and blue act as attractive colors while yellow and red serve as repelling colors for grass carp. Adding colors to the training wall and dividing wall in the VSF pool, the migration route of grass carp was appropriately adjusted, alongside a shortened completion time and an improved passing ratio. Of the four local coloring cases, the recommended case showed a significant effect on migration route, with more concentrated moving trajectories and shortened route length. Typically, the migration route length decreased by 26%, and the frequency of fish long staying at the junction between the training wall and dividing wall was markedly reduced, as well as the frequency of fish swimming along the water flow from upstream to downstream. The completion time was shortened by 26%, and the passing ratio was enhanced by 44%. The approach of combining local coloring with fish behavior and fishway hydraulics in the pool surpassed the method that optimizes the fishway design only from the fishway hydraulics. The improved method greatly shortened the migration route length, reduced the completion time, and significantly improved the passing ratio of fish passage objects in the VSF. The present research mainly focuses on using model experiments to evaluate the local coloring cases. In the future studies, we will configure local colors to the sidewalls of on-site fishways using environmentally friendly paint or colored organic glass panels. With the monitoring results of the completion time and passing ratio of fish passage objects, the recommended case can be further verified and optimized, thereby providing a more reasonable and feasible local coloring case for assisting fish migration in the VSF project.

Development of a Novel In Silico Classification Model to Assess Reactive Metabolite Formation in the Cysteine Trapping Assay and Investigation of Important Substructures.

Predicting whether a compound can cause drug-induced liver injury (DILI) is difficult due to the complexity of drug mechanism. The cysteine trapping assay is a method for detecting reactive metabolites that bind to microsomes covalently. However, it is cumbersome to use 35S isotope-labeled cysteine for this assay. Therefore, we constructed an in silico classification model for predicting a positive/negative outcome in the cysteine trapping assay. We collected 475 compounds (436 in-house compounds and 39 publicly available drugs) based on experimental data performed in this study, and the composition of the results showed 248 positives and 227 negatives. Using a Message Passing Neural Network (MPNN) and Random Forest (RF) with extended connectivity fingerprint (ECFP) 4, we built machine learning models to predict the covalent binding risk of compounds. In the time-split dataset, AUC-ROC of MPNN and RF were 0.625 and 0.559 in the hold-out test, restrictively. This result suggests that the MPNN model has a higher predictivity than RF in the time-split dataset. Hence, we conclude that the in silico MPNN classification model for the cysteine trapping assay has a better predictive power. Furthermore, most of the substructures that contributed positively to the cysteine trapping assay were consistent with previous results.

Quantified difference of the collapsed cone convolution (CCC) and Monte Carlo (MC) algorithms based on DVH and gamma analysis for cervical cancer radiation therapy.

OBJECTIVE: To quantify the difference between the (collapsed cone convolution) CCC algorithm and the (Monte Carlo) MC algorithm and remind that the planners should pay attention to some possible uncertainties of the two algorithms when employing the two algorithms. METHODS: Thirty patients' cervical cancer VMAT plans were designed with a Pinnacle TPS (Philips) and divided equally into two groups: the simple group (SG, target volume was only the PTV) and the complex group (CG, target volume included the PTV and PGTV). The plans from the Pinnacle TPS were transferred to the Monaco TPS (Elekta). The plans' parameters all remained unchanged, and the dose was recalculated. Gamma passing rates (GPRs) obtained from dose distribution from Pinnacle TPS compared with that from Monaco TPS with SNC software based on three triaxial planes (transverse, sagittal and coronal). GPRs and DVH were used to quantify the difference between the CCC algorithm in pinnacle TPS and the MC algorithm in Monaco TPS. RESULTS: Among the statistical dose indexes in DVHs from the Pinnacle and Monaco TPSs, there were 7(7/15) dose indexes difference with statistically significant differences in the SG, and 10(10/18) dose indexes difference with statistically significant differences in the CG. With 3%/3 mm criterion, the most (5/6) GPRs were greater than 95% from the SG and CG. But with 2%/2 mm criterion, the most (5/6) GPRs were less than 90% from the two groups. In addition, we found that GPRs were also related to the selected triaxial planes and the complexity of the plan (GPRs varied with the SG and CG). CONCLUSIONS: Obvious difference between the CCC and MC algorithms from Pinnacle and Monaco TPS. DVH maybe better than 2D gamma analysis on quantifying difference of the CCC and MC algorithms. Some attention should be paid to the uncertainty of the TPS algorithm, especially when the indicator on the DVH is at the critical point of the threshold value, because the algorithm used may overestimate or underestimate the DVH indicator.

Invariant point message passing for protein side chain packing.

Protein side chain packing (PSCP) is a fundamental problem in the field of protein engineering, as high-confidence and low-energy conformations of amino acid side chains are crucial for understanding (and designing) protein folding, protein-protein interactions, and protein-ligand interactions. Traditional PSCP methods (such as the Rosetta Packer) often rely on a library of discrete side chain conformations, or rotamers, and a forcefield to guide the structure to low-energy conformations. Recently, deep learning (DL) based methods (such as DLPacker, AttnPacker, and DiffPack) have demonstrated state-of-the-art predictions and speed in the PSCP task. Building off the success of geometric graph neural networks for protein modeling, we present the Protein Invariant Point Packer (PIPPack) which effectively processes local structural and sequence information to produce realistic, idealized side chain coordinates using χ $$ \chi $$ -angle distribution predictions and geometry-aware invariant point message passing (IPMP). On a test set of ∼1400 high-quality protein chains, PIPPack is highly competitive with other state-of-the-art PSCP methods in rotamer recovery and per-residue RMSD but is significantly faster.

OGNNMDA: a computational model for microbe-drug association prediction based on ordered message-passing graph neural networks.

In recent years, many excellent computational models have emerged in microbe-drug association prediction, but their performance still has room for improvement. This paper proposed the OGNNMDA framework, which applied an ordered message-passing mechanism to distinguish the different neighbor information in each message propagation layer, and it achieved a better embedding ability through deeper network layers. Firstly, the method calculates four similarity matrices based on microbe functional similarity, drug chemical structure similarity, and their respective Gaussian interaction profile kernel similarity. After integrating these similarity matrices, it concatenates the integrated similarity matrix with the known association matrix to obtain the microbe-drug heterogeneous matrix. Secondly, it uses a multi-layer ordered message-passing graph neural network encoder to encode the heterogeneous network and the known association information adjacency matrix, thereby obtaining the final embedding features of the microbe-drugs. Finally, it inputs the embedding features into the bilinear decoder to get the final prediction results. The OGNNMDA method performed comparative experiments, ablation experiments, and case studies on the aBiofilm, MDAD and DrugVirus datasets using 5-fold cross-validation. The experimental results showed that OGNNMDA showed the strongest prediction performance on aBiofilm and MDAD and obtained sub-optimal results on DrugVirus. In addition, the case studies on well-known drugs and microbes also support the effectiveness of the OGNNMDA method. Source codes and data are available at: https://github.com/yyzg/OGNNMDA.

Passing path predicts shooting outcome in football.

What determines the outcome of a shot (scored or unscored) in football (soccer)? Numerous studies have investigated various aspects of this question, including the skills and physical/mental state of the shooter or goalkeeper, the positional information of shots, as well as the attacking styles and defensive formations of the opposing team. However, a critical question has received limited attention: How does the passing path affect the outcome of a shot? In other words, does the path of the ball before shooting significantly influence the result when the same player takes two shots from the same location? This study aims to fill the gap in the literature by conducting qualitative studies using a dataset comprising 34,938 shots, along with corresponding passing paths from top-tier football leagues and international competitions such as the World Cup. Eighteen path features were extracted and applied to three different machine-learning models. The results indicate that the passing path, whether with or without the positional information of shots, can indeed predict shooting outcomes and reveal influential path features. Moreover, it suggests that taking quick actions to move the ball across areas with a high probability of scoring a goal can significantly increases the chance of a successful shot. Interestingly, certain path features that are commonly considered important for team performance, such as the distribution of passes among players and the overall path length, were found to be less significant for shooting outcomes. These findings enhance our understanding of the effective ball-passing and provide valuable insights into the critical factors for achieving successful shots in football games.

BiMPADR: A Deep Learning Framework for Predicting Adverse Drug Reactions in New Drugs.

Detecting the unintended adverse reactions of drugs (ADRs) is a crucial concern in pharmacological research. The experimental validation of drug-ADR associations often entails expensive and time-consuming investigations. Thus, a computational model to predict ADRs from known associations is essential for enhanced efficiency and cost-effectiveness. Here, we propose BiMPADR, a novel model that integrates drug gene expression into adverse reaction features using a message passing neural network on a bipartite graph of drugs and adverse reactions, leveraging publicly available data. By combining the computed adverse reaction features with the structural fingerprints of drugs, we predict the association between drugs and adverse reactions. Our models obtained high AUC (area under the receiver operating characteristic curve) values ranging from 0.861 to 0.907 in an external drug validation dataset under differential experiment conditions. The case study on multiple BET inhibitors also demonstrated the high accuracy of our predictions, and our model's exploration of potential adverse reactions for HWD-870 has contributed to its research and development for market approval. In summary, our method would provide a promising tool for ADR prediction and drug safety assessment in drug discovery and development.

A Systematic review of the factors that affect soccer players' short-passing ability-based on the Loughborough Soccer Passing Test.

OBJECTIVE: This study synthesizes evidence from the Loughborough Passing Test to evaluate the short-passing ability of soccer players and summarizes the reported variables that affect this ability to provide support for the development and improvement of short-passing abilities in soccer players. METHODS: In this systematic review using the PRISMA guidelines, a comprehensive search was conducted in Web of Science, PubMed, and EBSCOhost from inception to July 2023 to identify relevant articles from the accessible literature. Only studies that used the Loughborough test to assess athletes' short-passing ability were included. The quality of the included studies was independently assessed by two reviewers using the PEDro scale, and two authors independently completed the data extraction. RESULTS: Based on the type of intervention or influencing factor, ten studies investigated training, nine studies investigated fatigue, nine studies investigated supplement intake, and five studies investigated other factors. CONCLUSION: Evidence indicates that fitness training, small-sided games training, and warm-up training have positive effects on athletes' short-passing ability, high-intensity special-position training and water intake have no discernible impact, mental and muscular exhaustion have a significantly negative effect, and the effect of nutritional ergogenic aid intake is not yet clear. Future research should examine more elements that can affect soccer players' short-passing ability. TRIAL REGISTRATION: https://inplasy.com/ ., identifier: INPLASY20237.

Neural Network Model Based on Branch Architecture for the Quality Assurance of Volumetric Modulated Arc Therapy.

Radiation therapy relies on quality assurance (QA) to verify dose delivery accuracy. However, current QA methods suffer from operation lag as well as inaccurate performance. Hence, to address these shortcomings, this paper proposes a QA neural network model based on branch architecture, which is based on the analysis of the category features of the QA complexity metrics. The designed branch network focuses on category features, which effectively improves the feature extraction capability for complexity metrics. The branch features extracted by the model are fused to predict the GPR for more accurate QA. The performance of the proposed method was validated on the collected dataset. The experiments show that the prediction performance of the model outperforms other QA methods; the average prediction errors for the test set are 2.12% (2%/2 mm), 1.69% (3%/2 mm), and 1.30% (3%/3 mm). Moreover, the results indicate that two-thirds of the validation samples' model predictions perform better than the clinical evaluation results, suggesting that the proposed model can assist physicists in the clinic.

Development of a Simulation-Based Mastery Learning Curriculum for Late Goals of Care Discussions.

INTRODUCTION: Fellows in critical care medicine (CCM) routinely help patients and families navigate complex decisions near the end of life. These "late goals of care" (LGOC) discussions require rigorous skills training and impact patient care. Innovation is needed to ensure that fellow training in leading these discussions is centered on reproducible competency-based standards. The aims of this study were to (1) describe the development of a simulation-based mastery learning (SBML) curriculum for LGOC discussions and (2) set a defensible minimum passing standard (MPS) to ensure uniform skill acquisition among learners. INNOVATION: We developed an SBML curriculum for CCM fellows structured around REMAP, a mnemonic outlining foundational components of effective communication around serious illness. A multidisciplinary expert panel iteratively created an LGOC discussion assessment tool. Pilot testing was completed to refine the checklist, set the MPS, and assess skill acquisition. OUTCOMES: The LGOC discussion assessment tool included an 18-item checklist and 6 scaled items. The tool produced reliable data (k ≥ 0.7 and ICC of ≥ 0.7). Using the Mastery Angoff method, the panel set the MPS at 87%. Ten CCM fellows participated in the pilot study. Performance on the checklist significantly improved from a median score of 52% (IQR 44%-72%) at pretest to 96% (IQR 82%-97%) at post-test (P = 0.005). The number of learners who met the MPS similarly improved from 10% during pre-testing to 70% during post-testing (P = 0.02). COMMENT: We describe the development of a LGOC SBML curriculum for CCM fellows which includes a robust communication skills assessment and the delineation of a defensible MPS.