Large-scale Validation Research Articles

Development of Shooting and Dribbling Skill Test Instruments for U-23 Futsal Players in Central Java Province

A futsal basic skills measurement tool with excellent validity and reliability in test shooting and test dribbling was created as a result of this research to be used in the teaching and training of U-23 futsal sports. The research design employed was development research, with procedures for formulating initial product types/models for PORPROV Surakarta futsal players aged 18 to 23 years, with as many as 15 players for small groups and 90 PORPROV players for group trials. This is made up of the six districts of Banyumas Regency, Klaten Regency, Kebumen Regency, and Kendal Regency. The validity, reliability, and norms of the product skills test were then evaluated. Validity and dependability tests for small-scale instruments consisting of the basic skills of the shooting test obtained a value of 0.959 and the shooting retest obtained a value of 0.971 while the reliability test was 0.979. The dribbling validity test for the test was obtained at 0.968 and for the retest was 0.964 with a reliability of 0.893. The results of the large-scale validity of the basic skills of test shooting obtained a value of 0.948 and a retest obtained a value of 0.988 while the reliability test was 0.971. The results of the large-scale validity on the basic skills of the dribbling validity test for the test were obtained at 0.995 and for the retest at 0.995 with a reliability of 0.990, so it can be said that a reliable measuring instrument will still produce the same relative value even though it is carried out at different times. There is no difference between the small group and the large group in the U-23 futsal test shooting and test dribbling basic skill norms so the measuring instrument can be said to be objective.

Efficient assessment of real-world dynamics of circadian rhythms in heart rate and body temperature from wearable data.

Laboratory studies have made unprecedented progress in understanding circadian physiology. Quantifying circadian rhythms outside of laboratory settings is necessary to translate these findings into real-world clinical practice. Wearables have been considered promising way to measure these rhythms. However, their limited validation remains an open problem. One major barrier to implementing large-scale validation studies is the lack of reliable and efficient methods for circadian assessment from wearable data. Here, we propose an approximation-based least-squares method to extract underlying circadian rhythms from wearable measurements. Its computational cost is ∼ 300-fold lower than that of previous work, enabling its implementation in smartphones with low computing power. We test it on two large-scale real-world wearable datasets: [Formula: see text] of body temperature data from cancer patients and ∼ 184 000 days of heart rate and activity data collected from the 'Social Rhythms' mobile application. This shows successful extraction of real-world dynamics of circadian rhythms. We also identify a reasonable harmonic model to analyse wearable data. Lastly, we show our method has broad applicability in circadian studies by embedding it into a Kalman filter that infers the state space of the molecular clocks in tissues. Our approach facilitates the translation of scientific advances in circadian fields into actual improvements in health.

An overview of DNA methylation markers for early detection of gastric cancer: current status, challenges, and prospects.

Background: Gastric cancer (GC) is one of the most common malignancies, with a low 5-year survival rate. However, if diagnosed at an early stage, it can be cured by endoscopic treatment and has a good prognosis. While gastrointestinal X-ray and upper endoscopy are used as national GC screening methods in some GC high-risk countries, such as Japan and Korea, their radiation exposure, invasiveness, and high cost suggest that they are not the optimal tools for early detection of GC in many countries. Therefore, a cost-effective, and highly accurate method for GC early detection is urgently needed in clinical settings. DNA methylation plays a key role in cancer progression and metastasis and has been demonstrated as a promising marker for cancer early detection. Aims and methods: This review provides a comprehensive overview of the current status of DNA methylation markers associated with GC, the assays developed for GC early detection, challenges in methylation marker discovery and application, and the future prospects of utilizing methylation markers for early detection of GC. Through our analysis, we found that the currently reported DNA methylation markers related to GC are mainly in the early discovery stage. Most of them have only been evaluated in tissue samples. The majority of non-invasive assays developed based on blood lack standardized sampling protocols, pre-analytical procedures, and multicenter validation, and they exhibit insufficient sensitivity for early-stage GC detection. Meanwhile, the reported GC DNA methylation markers are generally considered pan-cancer markers. Conclusion: Therefore, future endeavors should focus on identifying additional methylation markers specific to GC and establishing non-invasive diagnostic assays that rely on these markers. These assays should undergo multicenter, large-scale prospective validation in diverse populations.

Validating the Multi-Mode Model’s Ability to Reproduce Diverse Tokamak Scenarios

A large-scale validation exercise was conducted to assess the multi-mode model (MMM) anomalous transport model in the integrated modeling code TRANSP. The validation included 6 EAST discharges, 17 KSTAR discharges, 72 JET ITER-like wall D-D discharges, and 4 DIII-D fusion plasma discharges. Using the MMM, the study computed anomalous thermal, particle, impurity, and momentum transport within TRANSP. Simulations for EAST, KSTAR, and JET focused on electron and ion temperatures and safety factor profiles, while DIII-D simulations also considered electron density, toroidal rotation frequency, and flow shear. The predicted profiles were compared to experimental data at the diagnostic time, quantifying the comparison using root-mean-square (RMS) deviation and relative offsets. The study found an average RMS deviation of 9.3% for predicted electron temperature and 10.5% for ion temperature, falling within the experimental measurement error range 20%. The MMM model demonstrated computational efficiency and the ability to accurately reproduce a wide range of discharges, including various scenarios and plasma parameters, such as plasma density, gyroradius, collisionality, beta, safety factor and heating method variations.

Forecasting small-scale dynamics of fluid turbulence using deep neural networks

Turbulence in fluid flows is characterized by a wide range of interacting scales. Since the scale range increases as some power of the flow Reynolds number, a faithful simulation of the entire scale range is prohibitively expensive at high Reynolds numbers. The most expensive aspect concerns the small-scale motions; thus, major emphasis is placed on understanding and modeling them, taking advantage of their putative universality. In this work, using physics-informed deep learning methods, we present a modeling framework to capture and predict the small-scale dynamics of turbulence, via the velocity gradient tensor. The model is based on obtaining functional closures for the pressure Hessian and viscous Laplacian contributions as functions of velocity gradient tensor. This task is accomplished using deep neural networks that are consistent with physical constraints and explicitly incorporate Reynolds number dependence to account for small-scale intermittency. We then utilize a massive direct numerical simulation database, spanning two orders of magnitude in the large-scale Reynolds number, for training and validation. The model learns from low to moderate Reynolds numbers and successfully predicts velocity gradient statistics at both seen and higher (unseen) Reynolds numbers. The success of our present approach demonstrates the viability of deep learning over traditional modeling approaches in capturing and predicting small-scale features of turbulence.

The influence of loss to follow-up in autism screening research: Taking stock and moving forward.

How best to improve the early detection of autism spectrum disorder (ASD) is the subject of significant controversy. Some argue that universal ASD screeners are highly accurate, whereas others argue that evidence for this claim is insufficient. Relatedly, there is no clear consensus as to the optimal role of screening for making referral decisions for evaluation and treatment. Published screening research can meaningfully inform these questions-but only through careful consideration of children who do not complete diagnostic follow-up. We developed two simulation models that re-analyze the results of a large-scale validation study of the M-CHAT-R/F by Robins etal. (2014, Pediatrics, 133, 37). Model #1 re-analyzes screener accuracy across six scenarios, each reflecting different assumptions regarding loss to follow-up. Model #2 builds on this by closely examining differential attrition at each point of the multi-step detection process. Estimates of sensitivity ranged from 40% to 94% across scenarios, demonstrating that estimates of accuracy depend on assumptions regarding the diagnostic status of children who were lost to follow-up. Across a range of plausible assumptions, data also suggest that children with undiagnosed ASD may be more likely to complete follow-up than children without ASD, highlighting the role of clinicians and caregivers in the detection process. Using simulation modeling as a quantitative method to examine potential bias in screening studies, analyses suggest that ASD screening tools may be less accurate than is often reported. Models also demonstrate the critical importance of every step in a detection process-including steps that determine whether children should complete an additional evaluation. We conclude that parent and clinician decision-making regarding follow-up may contribute more to detection than is widely assumed.

Large-Scale Experimental Validation of Optimized Cast Steel Replaceable Modular Yielding Links for Eccentrically Braced Frames

Large-Scale Experimental Validation of Optimized Cast Steel Replaceable Modular Yielding Links for Eccentrically Braced Frames

Advances in the application of computational pathology in diagnosis, immunomicroenvironment recognition, and immunotherapy evaluation of breast cancer: a narrative review.

Breast cancer (BC) is a prevalent and highly lethal malignancy affecting women worldwide. Immunotherapy has emerged as a promising therapeutic strategy for BC, offering potential improvements in patient survival. Neoadjuvant therapy (NAT) has also gained significant clinical traction. With the advancement of computer technology, Artificial Intelligence (AI) has been increasingly applied in pathology research, expanding and redefining the scope of the field. This narrative review aims to provide a comprehensive overview of the current literature on the application of computational pathology in BC, specifically focusing on diagnosis, immune microenvironment recognition, and the evaluation of immunotherapy and NAT response. A thorough examination of relevant literature was conducted, focusing on studies investigating the role of computational pathology in BC diagnosis, immune microenvironment recognition, and immunotherapy and NAT assessment. The application of computational pathology has shown significant potential in BC management. AI-based techniques enable improved diagnosis and classification of BC subtypes, enhance the identification and characterization of the immune microenvironment, and facilitate the evaluation of immunotherapy and NAT response. However, challenges related to data quality, standardization, and algorithm development still need to be addressed. The integration of computational pathology and AI has transformative implications for BC patient care. By leveraging AI-based technologies, clinicians can make more informed decisions in diagnosis, treatment planning, and therapeutic response assessment. Future research should focus on refining AI algorithms, addressing technical challenges, and conducting large-scale clinical validation studies to facilitate the translation of computational pathology into routine clinical practice for BC patients.

Consensus classification of pediatric hepatocellular tumors: A report from the Children's Hepatic tumors International Collaboration (CHIC).

Liver tumors are rare in children with histologic heterogeneity that makes diagnosis challenging. Systematic histopathological review, performed as part of collaborative therapeutic protocols, identified relevant histologic subtypes that are important to distinguish. The Children's Hepatic tumors International Collaboration (CHIC) was established to study pediatric liver tumors on a global scale and led to establishment of a provisional consensus classification for use in international clinical trials. The current study is the validation of this initial classification and first large-scale application by international expert reviewers. The CHIC initiative includes data from 1605 children treated on eight multicenter hepatoblastoma (HB) trials. Review of 605 available tumors was performed by seven expert pathologists from three consortia (US, EU, Japan). Cases with discordant diagnoses were collectively reviewed to reach a final consensus diagnosis. Of 599 cases with sufficient material for review, 570 (95.2%) were classified as HB by all consortia, and 29 (4.8%) as non-HB, which included "hepatocellular neoplasm, NOS" and malignant rhabdoid tumors. 453 of 570 HBs were classified as epithelial by final consensus. Some patterns (i.e., small cell undifferentiated, macrotrabecular, cholangioblastic) were selectively identified by reviewers from different consortia. All consortia identified a similar number of mixed epithelial-mesenchymal HB. This study represents the first large-scale application and validation of the pediatric malignant hepatocellular tumors consensus classification. It is a valuable resource to train future generations of investigators on accurate diagnosis of these rare tumors and provides a framework for further international collaborative studies and refinement of the current classification of pediatric liver tumors.

Large-scale validation study of an improved semiautonomous urine cytology assessment tool: AutoParis-X.

Adopting a computational approach for the assessment of urine cytology specimens has the potential to improve the efficiency, accuracy, and reliability of bladder cancer screening, which has heretofore relied on semisubjective manual assessment methods. As rigorous, quantitative criteria and guidelines have been introduced for improving screening practices (e.g., The Paris System for Reporting Urinary Cytology), algorithms to emulate semiautonomous diagnostic decision-making have lagged behind, in part because of the complex and nuanced nature of urine cytology reporting. In this study, the authors report on the development and large-scale validation ofa deep-learning tool, AutoParis-X, whichcan facilitate rapid, semiautonomous examination of urine cytology specimens. The results of this large-scale, retrospective validation study indicate that AutoParis-X can accurately determine urothelial cell atypia and aggregate a wide variety of cell-related and cluster-related information across a slide to yield an atypia burden score, which correlates closely with overall specimen atypia and is predictive of Paris system diagnostic categories. Importantly, this approach accounts for challenges associated with the assessment of overlapping cell cluster borders, which improve the ability to predict specimen atypia and accurately estimate the nuclear-to-cytoplasm ratio for cells in these clusters. The authors developed a publicly available, open-source, interactive web application that features a simple, easy-to-use display for examining urine cytology whole-slide images and determining the level of atypia in specific cells, flagging the most abnormal cells for pathologist review. The accuracy of AutoParis-X (and other semiautomated digital pathology systems) indicates that these technologies are approaching clinical readiness and necessitates full evaluation of these algorithms in head-to-head clinical trials.

Real-Time Real-World Digital Monitoring of Adolescent Suicide Risk During the Six Months Following Emergency Department Discharge: Protocol for an Intensive Longitudinal Study.

Suicide is the second leading cause of death in adolescents, and self-harm is one of the strongest predictors of death by suicide. The rates of adolescents presenting to emergency departments (EDs) for suicidal thoughts and behaviors (STBs) have increased. Still, existing follow-up after ED discharge is inadequate, leaving a high-risk period for reattempts and suicide. There is a need for innovative evaluation of imminent suicide risk factors in these patients, focusing on continuous real-time evaluations with low assessment burden and minimal reliance on patient disclosure of suicidal intent. This study examines prospective longitudinal associations between observed real-time mobile passive sensing, including communication and activity patterns, and clinical and self-reported assessments of STB over 6 months. This study will include 90 adolescents recruited on their first outpatient clinic visit following their discharge from the ED due to a recent STB. Participants will complete brief weekly assessments and be monitored continuously for their mobile app usage, including mobility, activity, and communication patterns, over 6 months using the iFeel research app. Participants will complete 4 in-person visits for clinical assessment at baseline and at the 1-, 3-, and 6-month follow-ups. The digital data will be processed, involving feature extraction, scaling, selection, and dimensionality reduction. Passive monitoring data will be analyzed using both classical machine learning models and deep learning models to identify proximal associations between real-time observed communication, activity patterns, and STB. The data will be split into a training and validation data set, and predictions will be matched against the clinical evaluations and self-reported STB events (ie, labels). To use both labeled and unlabeled digital data (ie, passively collected), we will use semisupervised methods in conjunction with a novel method that is based on anomaly detection notions. Participant recruitment and follow-up started in February 2021 and are expected to be completed by 2024. We expect to find prospective proximal associations between mobile sensor communication, activity data, and STB outcomes. We will test predictive models for suicidal behaviors among high-risk adolescents. Developing digital markers of STB in a real-world sample of high-risk adolescents presenting to ED can inform different interventions and provide an objective means to assess the risk of suicidal behaviors. The results of this study will be the first step toward large-scale validation that may lead to suicide risk measures that aid psychiatric follow-up, decision-making, and targeted treatments. This novel assessment could facilitate timely identification and intervention to save young people's lives. DERR1-10.2196/46464.

Pipe Viscometer for Continuous Viscosity and Density Measurement of Oil Well Barrier Materials

Summary The barrier material is a crucial component for wells, as it provides mechanical support to the casing and prevents the uncontrolled flow of formation fluids, ensuring zonal isolation. One of the essential prerequisites for the success of cementing an oil and gas well is the efficient removal of in-situ fluids and their adequate replacement by the barrier material. The quality of the mud displacement is affected by both the density and the viscosity hierarchy among subsequent fluids. Consequently, accurate and reliable measurement of fluid properties can help ensure consistent large-scale mixing of cementing fluids and verification that the properties of the mixed fluid are according to plan. In this paper, we investigate the implementation of a pipe viscometer for future automated measurements of density and viscosity of materials for zonal isolation and perform a sequential validation of the viscometer that starts with small-scale batch mixing and characterization of particle-free calibration liquids, followed by conventional Class G cement and selected new barrier materials. Finally, a larger-scale validation of the pipe viscometer was performed by integrating it into a yard-scale batch mixer for in-line characterization of expanding Class G oilwell cement mixing. In all cases, flow curves derived from pipe viscosity measurements were compared with offline measurements using a rheometer and a conventional oilfield viscometer. After a series of measurements and comparisons, the investigated in-line measurement system proved adequate for viscosity estimation. The flow curve of the barrier materials showed results similar to measurements using a conventional viscometer, validating the proposed test configuration to continuously measure the rheological behavior of the barrier material. The pipe viscometer flow curves are generally found to be in good quantitative agreement with independent viscometer characterization of the fluids, although some of the pipe viscometer measurements likely exhibited entrance length effects. Future improvements to the pipe viscometer design involve the assessment of even longer pipe sections to allow full flow development at the highest shear rate range and possibly different pipe diameters to improve the measurement resolution of low-shear rate viscosity.

A comparative study of gastric adenocarcinoma HER2 IHC phenotype and mass spectrometry-based quantification.

Gastric cancer is a highly heterogeneous malignant tumor of the digestive system. Anti-HER2 treatment can inhibit downstream signaling pathways and improve clinical treatment and outcomes in patients with HER2 protein overexpression. Currently, two standard methods for evaluating HER2 expression status are immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH). However, these low-throughput assays often produce discordant or equivocal results. In this study, we presented a new HER2 protein detection method based on mass spectrometry selected reaction monitoring (MS-SRM) and validated the method. We conducted a retrospective study on 118 formalin-fixed paraffin-embedded (FFPE) tissues from patients with advanced gastric adenocarcinoma in northern China, and we compared the MS-SRM results with those from IHC and correlated them with FISH. We established and validated the upper and lower detection limits (300-700 amol/μg) for abnormal HER2 protein expression in advanced gastric cancer. We also found that, among samples with mixed Lauren subtypes, those with a high level of HER2 expression had typical intestinal type features in pathology. This study demonstrated that the MS-SRM method can overcome the limitations and deficiencies of IHC, directly quantify the expression of HER2 protein in tumor cells and be used as a supplement to IHC. It has the potential to be used as a companion diagnosis for new drugs used to treat advanced gastric cancer. Large-scale clinical validation is required.

Longitudinal monitoring of Apparent Diffusion Coefficient (ADC) in patients with prostate cancer undergoing MR-guided radiotherapy on an MR-Linac at 1.5 T: a prospective feasibility study.

Hybrid MRI linear accelerators (MR-Linac) might enable individualized online adaptation of radiotherapy using quantitative MRI sequences as diffusion-weighted imaging (DWI). The purpose of this study was to investigate the dynamics of lesion apparent diffusion coefficient (ADC) in patients with prostate cancer undergoing MR-guided radiation therapy (MRgRT) on a 1.5T MR-Linac. The ADC values at a diagnostic 3T MRI scanner were used as the reference standard. In this prospective single-center study, patients with biopsy-confirmed prostate cancer who underwent both an MRI exam at a 3T scanner (MRI3T) and an exam at a 1.5T MR-Linac (MRL) at baseline and during radiotherapy were included. Lesion ADC values were measured by a radiologist and a radiation oncologist on the slice with the largest lesion. ADC values were compared before vs. during radiotherapy (during the second week) on both systems via paired t-tests. Furthermore, Pearson correlation coefficient and inter-reader agreement were computed. A total of nine male patients aged 67 ± 6 years [range 60 - 67 years] were included. In seven patients, the cancerous lesion was in the peripheral zone, and in two patients the lesion was in the transition zone. Inter-reader reliability regarding lesion ADC measurement was excellent with an intraclass correlation coefficient of (ICC) > 0.90 both at baseline and during radiotherapy. Thus, the results of the first reader will be reported. In both systems, there was a statistically significant elevation of lesion ADC during radiotherapy (mean MRL-ADC at baseline was 0.97 ± 0.18 × 10-3 mm2/s vs. mean MRL-ADC during radiotherapy 1.38 ± 0.3 × 10-3 mm2/s, yielding a mean lesion ADC elevation of 0.41 ± 0.20 × 10-3 mm2/s, p < 0.001). Mean MRI3T-ADC at baseline was 0.78 ± 0.165 × 10-3 mm2/s vs. mean MRI3T-ADC during radiotherapy 0.99 ± 0.175 × 10-3 mm2/s, yielding a mean lesion ADC elevation of 0.21 ± 0.96 × 10-3 mm2/s p < 0.001). The absolute ADC values from MRL were consistently significantly higher than those from MRI3T at baseline and during radiotherapy (p < = 0.001). However, there was a strong positive correlation between MRL-ADC and MRI3T-ADC at baseline (r = 0.798, p = 0.01) and during radiotherapy (r = 0.863, p = 0.003). Lesion ADC as measured on MRL increased significantly during radiotherapy and ADC measurements of lesions on both systems showed similar dynamics. This indicates that lesion ADC as measured on the MRL may be used as a biomarker for evaluation of treatment response. In contrast, absolute ADC values as calculated by the algorithm of the manufacturer of the MRL showed systematic deviations from values obtained on a diagnostic 3T MRI system. These preliminary findings are promising but need large-scale validation. Once validated, lesion ADC on MRL might be used for real-time assessment of tumor response in patients with prostate cancer undergoing MR-guided radiation therapy.

Nivolumab as second-line treatment of patients with metastatic renal cell carcinoma with high or normal fibrinogen levels: A prospective, cohort study.

e16510 Background: In a number of coagulation-related studies, elevated fibrinogen levels were found in patients with renal cell carcinoma (RCC) and correlated with more advanced disease. The impact of hyperfibrinogenemia on the clinical outcomes of immunotherapy remains uncertain. Methods: In this prospective cohort study, fibrinogen levels were measured one week prior to the second-line nivolumab therapy (240 mg or 480 mg every 2 or 4 weeks) and then monthly until disease progression or unacceptable toxicity. High fibrinogen level was defined as ≤5 g/L. Key eligibility criteria included metastatic clear cell RCC resistant to the first-line targeted therapy, ≤3 MSKCC risk factors, ECOG PS 0-2, platelet count &lt;400×10(9)/L, no heart diseases, no anticoagulation therapy, and age 40-65 years. The primary endpoint was overall survival (OS) in cohorts with high (H) and normal (N) fibrinogen levels. Secondary endpoints included progression-free survival (PFS), objective response rate (ORR), and fibrinogen levels during therapy. Results: Between November 2018 and January 2021, 82 patients were enrolled, of whom 41 and 41, respectively, were recruited in H and N cohorts. The groups were well balanced at baseline. The median follow-up time was 32.4 months. Compared to patients in N cohort, patients in H cohort had shorter OS (P=0.001), shorter PFS (P&lt;0.01), and lower ORR (P=0.012). In H cohort, a below-median fibrinogen level was associated with longer observed OS (median, 23.7 vs. 19.4 months; P=0.03). In N cohort, 6 (15%) patients with stable disease had an increase in fibrinogen levels during nivolumab therapy. Patient characteristics and clinical outcomes are summarized. Conclusions: High fibrinogen levels affect the clinical outcomes in patients with metastatic renal cell carcinoma treated with second-line nivolumab. These findings require large-scale prospective validation. Clinical trial information: KCRB0181209 . [Table: see text]

Determining MSI status by a one-step PCR melting curve analysis of Chinese-specific mononucleotide repeats.

e15532 Background: Microsatellite instability (MSI) is a hypermutated phenotype primarily caused by the deficiency of DNA mismatch repair activity. Conventional MSI detection platforms are time consuming and need extra normal tissue. Due to the short DNA sequences of microsatellite loci in MSI samples, the method can be simplified to a one-step approach to differentiate MSI status by observing the melting curve. Herein a novel feasible MSI status detection method based on one step-PCR in single tumor tissue was developed, and validated in colorectal cancer samples. Methods: Specific microsatellite sites shorter than 12 bp in length (feasibility of fluorescence probes) were selected from public databases for analysis. In tumor tissues from 98 CRC patients with known MSI status, microsatellite sites with the highest receiver operating characteristic (ROC)-area under the ROC curve (AUC) were eventually selected for the final PCR panel design. The cut-off value of this assay was defined by the number of melting peaks in a specific Tm value range. An independent set of 199 tumor tissues from CRC patients with known MSI by Sanger sequencing was recruited to validate the performance of this novel PCR MSI kit. Results: The ROC-AUCs of the pre-selected eight mononucleotide repeats were 0.82, 0.93, 0.79, 0.78, 0.98, 0.95, 0.92, and 0.79. When 0 or 1 marker had a melting peak within the specified Tm value range, the sample was identified as "MSS/MSI-low (MSI-L)"; when 2 or more markers had melting peaks within the specified Tm value range, the sample was determined to be "MSI-high (MSI-H)". In the clinical sample set, compared with the results given by conventional MSI detection method, the sensitivity of the 8-gene panel for MSI-H was 99.4% (166/167), the specificity of the 8-gene panel was 100%, and the overall concordance reached to 99.5%. Conclusions: The novel PCR-MSI detection method using 8-gene panel developed in this study was feasible and accessible in clinical practice, and a large-scale clinical validation is in progress. [Table: see text]

A Large-Scale Application of the Stochastic Approach for Estimating the Shear Strength of Natural Rock Discontinuities

Reliable shear strength determination of large in situ discontinuities is still a challenge faced by the rock mechanics field. This is principally due to the limited availability of surface roughness and morphology information of in situ discontinuities and the unresolved management of the ‘scale effect’ phenomenon. Recently, a stochastic approach for predicting the shear strength of large-scale discontinuities was established, encompassing random field theory, a semi-analytical shear strength model, and a stochastic analysis framework. A key aspect of the new approach is the application at field scale, thereby minimising or bypassing the scale effect. The approach has been validated at laboratory scale and an initial large-scale deterministic-based validation showed promising results. However, to date, no large-scale experimental-based validation has been undertaken. This paper presents the first rigorous application of the employed semi-analytical shear strength model and the stochastic approach on a 2 m-by-2 m discontinuity surface, with comparison of prediction to experimental shear strength data. The shear strength model was found to generally produce peak and residual predictions within a ± 10% relative error range, with good agreement between predicted and observed damage areas. It was observed that, applying the stochastic approach to seed traces with gradient statistics equivalent to that of the surface, produced predictions that closely resemble the experimental results. Whereas, predicting shear strength from different seed traces results in more variability of predictions, with many falling within ± 20% of the experimental data. The predictions of residual shear strength tended to be more accurate than peak shear strength.

The use of a new ECG patch for exercise and prolonged monitoring: usability, evaluation of signal quality and utility to detect arrhythmias

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – EU funding. Main funding source(s): ITEA. Background Accurate detection of myocardial ischemia and arrhythmia’s during free-living exercise could play a pivotal role in optimizing screening and monitoring of individuals with underlying cardiovascular disease. Although remote electrocardiogram (ECG) solutions are emerging rapidly, these applications are generally not designed nor validated for continuous use during vigorous exercise. Purpose In this proof-of-concept study, we evaluated the usability, signal quality and arrhythmia detection of a new self-adhesive dry electrode ECG patch, the Nighthawk Vital Signs patch, during exercise and prolonged continuous ECG monitoring in individuals with chronic coronary syndrome (CCS). Methods Six consecutive CCS patients scheduled for an exercise stress test as part of routine cardiac follow-up were recruited. The traditional 12-lead exercise ECG was combined with monitoring with the 1-lead patch. Following the exercise test, the subjects continuously wore the sensor for five days. Intraclass correlation coefficients (ICC) and Wilcoxon signed-rank tests were used to assess the utility to detect arrhythmias with the patch. Signal quality for ECG recording was evaluated by using a signal quality indicator (SQI) based on the average QRS complex. Connection time and signal-to-noise ratio (SNR) were additionally calculated for prolonged ECG monitoring. Comfort and usability of the patch were evaluated by an online questionnaire. Results Six male patients with CCS (mean age 69.8 ± 6.2 years) completed the protocol. The mean time the patch was worn, was 118.3 hours (SD 5.6). The level of agreement between the patch and the 12-lead ECG was excellent for amount of premature atrial contractions (PAC’s) and premature ventricular contractions (PVC’s) during exercise stage, recovery stage and total test duration (ICC = 0.998, 0.998, 1.000, 1.000, 0.998, 1.000). There were no significant differences in the total amount of PAC’s and PVC’s detected with both methods during the exercise test (p = 0.785 and 0.180, respectively). One episode of atrial fibrillation was detected identically on both recorded ECG’s. Signal quality based on average QRS SQI during exercise did decrease slightly during (sub)maximal effort. Total connection time during recording was above 88% in all subjects and the SNR was above the target limit of 18dB. There were no reports on skin irritation, erythema or pain. Conclusion The Nighthawk Vital Signs ECG patch based on self-adhesive dry electrodes technology can be used for accurate arrhythmia detection during vigorous exercise. The sensor is also usable for prolonged ECG monitoring in free-living conditions and can therefore be of potential use in cardiac rehabilitation and telemonitoring for prevention of exercise-related cardiovascular events. However, more research is needed in order to analyze the signal quality over time together with a larger-scale validation study focusing on both arrhythmia and ischemia detection.

Reducing Boolean networks with backward equivalence

BackgroundBoolean Networks (BNs) are a popular dynamical model in biology where the state of each component is represented by a variable taking binary values that express, for instance, activation/deactivation or high/low concentrations. Unfortunately, these models suffer from the state space explosion, i.e., there are exponentially many states in the number of BN variables, which hampers their analysis.ResultsWe present Boolean Backward Equivalence (BBE), a novel reduction technique for BNs which collapses system variables that, if initialized with same value, maintain matching values in all states. A large-scale validation on 86 models from two online model repositories reveals that BBE is effective, since it is able to reduce more than 90% of the models. Furthermore, on such models we also show that BBE brings notable analysis speed-ups, both in terms of state space generation and steady-state analysis. In several cases, BBE allowed the analysis of models that were originally intractable due to the complexity. On two selected case studies, we show how one can tune the reduction power of BBE using model-specific information to preserve all dynamics of interest, and selectively exclude behavior that does not have biological relevance.ConclusionsBBE complements existing reduction methods, preserving properties that other reduction methods fail to reproduce, and vice versa. BBE drops all and only the dynamics, including attractors, originating from states where BBE-equivalent variables have been initialized with different activation values The remaining part of the dynamics is preserved exactly, including the length of the preserved attractors, and their reachability from given initial conditions, without adding any spurious behaviours. Given that BBE is a model-to-model reduction technique, it can be combined with further reduction methods for BNs.

Validation studies of Japanese administrative health care data: A scoping review.

Large-scale administrative health care databases are increasingly being utilized for research. However, there has not been much literature that validated administrative data in Japan; a previous review identified six validation studies published between 2011 and 2017. We conducted a literature review of studies that assessed the validity of Japanese administrative health care data. We searched for studies published by March 2022 that compared individual-level administrative data with a reference standard from another data source, as well as studies that validated administrative data using other data within the same database. The eligible studies were also summarized based on characteristics which included data types, settings, reference standard used, numbers of patients, and conditions validated. There were 36 eligible studies, including 29 that used external reference standard and seven that validated administrative data using other data within the same database. Chart review was the reference standard in 21 studies (range of the numbers of patients, 72-1674; 11 studies conducted in single institutions and nine studies in 2-5 institutions). Five studies used a disease registry as the reference standard. Diagnoses of cardiovascular diseases, cancer, and diabetes were frequently evaluated. Validation studies are being conducted at an increasing rate in Japan, although most of them are small scale. Further large-scale comprehensive validation studies are necessary to effectively utilize the databases for research.