Median Correlation Research Articles

Development of methodology to support molecular endotype discovery from synovial fluid of individuals with knee osteoarthritis: The STEpUP OA consortium.

To develop a protocol for largescale analysis of synovial fluid proteins, for the identification of biological networks associated with subtypes of osteoarthritis. Synovial Fluid To detect molecular Endotypes by Unbiased Proteomics in Osteoarthritis (STEpUP OA) is an international consortium utilising clinical data (capturing pain, radiographic severity and demographic features) and knee synovial fluid from 17 participating cohorts. 1746 samples from 1650 individuals comprising OA, joint injury, healthy and inflammatory arthritis controls, divided into discovery (n = 1045) and replication (n = 701) datasets, were analysed by SomaScan Discovery Plex V4.1 (>7000 SOMAmers/proteins). An optimised approach to standardisation was developed. Technical confounders and batch-effects were identified and adjusted for. Poorly performing SOMAmers and samples were excluded. Variance in the data was determined by principal component (PC) analysis. A synovial fluid standardised protocol was optimised that had good reliability (<20% co-efficient of variation for >80% of SOMAmers in pooled samples) and overall good correlation with immunoassay. 1720 samples and >6290 SOMAmers met inclusion criteria. 48% of data variance (PC1) was strongly correlated with individual SOMAmer signal intensities, particularly with low abundance proteins (median correlation coefficient 0.70), and was enriched for nuclear and non-secreted proteins. We concluded that this component was predominantly intracellular proteins, and could be adjusted for using an 'intracellular protein score' (IPS). PC2 (7% variance) was attributable to processing batch and was batch-corrected by ComBat. Lesser effects were attributed to other technical confounders. Data visualisation revealed clustering of injury and OA cases in overlapping but distinguishable areas of high-dimensional proteomic space. We have developed a robust method for analysing synovial fluid protein, creating a molecular and clinical dataset of unprecedented scale to explore potential patient subtypes and the molecular pathogenesis of OA. Such methodology underpins the development of new approaches to tackle this disease which remains a huge societal challenge.

Validation of an App-based Food Frequency Questionnaire: Comparison With 24-hour Diet Recalls Among Women in two Rural Areas of Kenya

A new mobile application with a new semi-quantitative Food Frequency Questionnaire (FFQ) was developed as a quick dietary assessment tool. This study aimed to test the validation of food and nutrient intake measurements from an app-based FFQ, which included 49 dishes and food items, using non-consecutive two-day 24-hour diet recalls as the standard. A total of 179 women aged 19–70 years (mean (SD) = 33.7 (9.9)) from two rural areas of Kenya, Vihiga and Kitui counties, were included. Dietary assessment by the two dietary survey methods was conducted from November to December 2021. The intake of macro- and micronutrients, and food groups between the two methods were compared by Spearman's rank correlation coefficients, cross-classification agreement, and Bland-Altman plots for assessing the validation of the app-based FFQ. The median correlation coefficients between energy-adjusted intakes estimated by the app-based FFQ and the 24-h diet recall were 0.19 (0.10 (protein)–0.26 (fat)) for macronutrients, 0.24 (0.11 (sodium)–0.46 (vitamin B12)) for micronutrients, and 0.26 (0.07 (seasonings)–0.45 (pulses)) for 17-food groups. The median percentage of classification into the same quartiles between the two methods was 31% for macronutrient, 32% for micronutrient and 39% for food group. While 9% for macronutrient, 8% for micronutrient and 10% for the food group were misclassified into the opposite categories. Bland-Altman Plots showed acceptable agreement for macronutrients intake between the two methods. The app-based FFQ was shown to be able to rank or categorize individuals according to their intake of most of the nutrients and food groups we examined. This dietary assessment tool can be used for quantitative dietary data collection. Further research is needed to improve the validation of the app-based FFQ, as it has been shown that intakes of staple foods and sugar are underestimated and others are overestimated, and that estimating intakes of some specific nutrients, such as protein, is challenging. Key words: Diet assessment, food frequency questionnaire, 24-hour diet recall, validation, Kenya

Sentinel‐1 Snow Depth Assimilation to Improve River Discharge Estimates in the Western European Alps

AbstractSeasonal snow is an important water source and contributor to river discharge in mountainous regions. Therefore the amount of snow and its distribution are necessary inputs for hydrological modeling. Recent research has shown the potential of the Sentinel‐1 radar satellite to map snow depth (SD) at sub‐kilometer resolution in mountainous regions. In this study we assimilate these new SD retrievals into the Noah‐Multiparameterization land surface model using an ensemble Kalman filter for the western European Alps. The land surface model was coupled to the Hydrological Modeling and Analysis Platform (HyMAP), a global flow routing scheme that provides simulations of routed river discharge. The performance with different precipitation forcing inputs, namely MERRA‐2 (with and without gauge based correction) and ERA5, was compared based on in situ precipitation and SD stations, with ERA5 leading to the best SD performance. The Sentinel‐1 based data assimilation (DA) results show small but systematic improvements for SD estimates, with the mean absolute error reducing from 36.4 cm for the open loop (OL) to 35.6 cm for the DA across all stations and timesteps, improving 318 out of 516 in situ sites. The DA updates in SD also result in enhanced snow water equivalent and discharge simulations. The median temporal correlation between discharge simulations and measurements increases from 0.73 to 0.78 for the DA. This study demonstrates the utility of the Sentinel‐1 SD retrievals to improve not only the representation of snow in mountain ranges, but also the snow melt contribution to river discharge, and hydrological modeling in general.

PHANGS-MeerKAT and MHONGOOSE HI observations of nearby spiral galaxies: Physical drivers of the molecular gas fraction, Rmol

The molecular-to-atomic gas ratio is crucial to our understanding of the evolution of the interstellar medium (ISM) in galaxies. We investigated the balance between the atomic (ΣHI) and molecular gas (ΣH2) surface densities in eight nearby star-forming galaxies using new high-quality observations from MeerKAT and ALMA (for H I and CO, respectively). We defined the molecular gas ratio as Rmol = ΣH2/ΣHI and measured how Rmol depends on local conditions in the galaxy disks using multiwavelength observations. We find that, depending on the galaxy, H I is detected at &gt; 3σ out to 20 − 120 kpc in galactocentric radius (rgal). The typical radius at which ΣHI reaches 1 M⊙ pc−2 is rH I ≈ 22 kpc, which corresponds to 1 − 3 times the optical radius (r25). We note that, Rmol correlates best with the dynamical equilibrium pressure, PDE, among potential drivers studied, with a median correlation coefficient of ⟨ρ⟩ = 0.89. Correlations between Rmol and the star formation rate surface density, total gas surface density, stellar surface density, metallicity, and ΣSFR/PDE (a proxy for the combined effect of the UV radiation field and number density) are present but somewhat weaker. Our results also show a direct correlation between PDE and ΣSFR, supporting self-regulation models. Quantitatively, we measured similar scalings as previous works, and attribute the modest differences that we do find to the effect of varying resolution and sensitivity. At rgal ≳ 0.4r25, atomic gas dominates over molecular gas among our studied galaxies, and at the balance of these two gas phases (Rmol = 1), we find that the baryon mass is dominated by stars, with Σ* &gt; 5 Σgas. Our study constitutes an important step in the statistical investigation of how local galaxy properties (stellar mass, star formation rate, or morphology) impact the conversion from atomic to molecular gas in nearby galaxies.

Assessing on-site plastics fragmentation: Linking macroplastics litter to microplastics pollution in vegetable fields

This study proposes a novel approach to determine whether farmers act as a pollution source of microplastics through macroplastic mismanagement or if they are temporary hosts of in-transit microplastics. Using a case-study approach, it was hypothesized that 90 % of soil microplastics would trace back to macroplastic waste mismanaged by farmers. To determine the amount of soil microplastics, linear transects were established and microplastic litter was counted and classified. Additionally, composite soil samples were used to count and colour-classify microplastics. The Kendall correlation was used to differentiate between the colour ranks of macro- and microplastics to discriminate between plastic on-site fragmentation or exogenous plastic pollution. For this, 37 vegetable farms in central Chile were assessed after the growing season. Farms showed lower microplastic counts than previously reported for similar productive conditions (3.68 ± 3.28 u/kg). A median colour correlation of 0.43 (IQR = 0.18–0.55) was observed between macro- and microplastics. Only in 25 % of the studied fields did on-site plastic fragmentation predominate over exogenous plastic pollution. The applied approach is simple to implement and does not require specialized laboratory equipment. We hope that the proposed methodology will serve in environmental monitoring to evaluate adherence to good agricultural practices and to guide pollution-prevention measures.

Effective identification of debris-covered glaciers in Western China using multiple machine-learning algorithms

Debris-covered glaciers (DCGs) represent a substantial portion of global glaciers, but their automatic identification remains a challenge due to environmental complexity and glacier type variability. This study focuses on 30 glacial areas across Western China, representing continental, sub-continental, and maritime glaciers. Using four machine-learning algorithms—Random Forests (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Support Vector Machine (SVM)—we integrated multiple environmental variables, including surface reflectance, normalized indices, surface temperature (ST), and topography, to classify DCGs through a three-process approach. Process 1 distinguished overall ice/snow cover from other land features, while Processes 2 and 3, both building on results of Process 1, further separated ice from snow cover and differentiated debris from other land features, respectively. Results showed that RF generally outperformed the other algorithms, with median Matthews correlation coefficient (MCC) values exceeding 0.999 in Process 1 and reaching 0.612 and 0.784 in Processes 2 and 3, respectively, and the average MCC value for DCGs identification reached 0.819. Regional modeling significantly improved the accuracy of DCG identification in Processes 2 and 3, particularly in continental glacier regions, with a notable increase in MCC values. Additionally, the independent binary-class models outperformed the integrated multi-class model in Processes 2 and 3, further validating the necessity of process-specific modeling. Comparison with the GGI18 glacier inventory showed a high level of consistency with the DCGs identified by the optimal algorithms, with an R2 value of 0.971. Despite uncertainties in maritime glacier regions, the proposed method demonstrates robustness and high spatiotemporal generalization. This approach, combining machine learning and environmental variables, offers a reliable tool for automated DCG identification and can significantly aid future glacier inventory compilation and monitoring efforts.

Physical mechanism-based simulation methods for soil and ground heat flux from observations at multi-sites

It is important to obtain the soil heat flux at a specific depth (Gz) and at the ground’s surface (G0) to understand the surface energy balance and climate change. However, previous evaluations of Gz and G0 simulations based on multiple sites have been relatively insufficient. We evaluated the accuracy of using physical mechanism-based gradient and calorimetry methods to simulate Gz and changes in heat storage (ΔS) based on observations collected at 28 sites. Three methods for simulating G0 were also evaluated using triple collocation (TC) and extended triple collocation (ETC) metrics. The ability of the gradient method to simulate Gz was strong, particularly for the lower soil layer, with a median Nash-Sutcliffe efficiency (NSE), correlation coefficient (CC), and index of agreement for the evaluated sites of 0.76, 0.86, and 0.91, respectively. The accuracy of the calorimetry method for simulating ΔS was generally lower than that of the gradient method for determining Gz. Overall, the combination of gradient analysis and calorimetry showed the best performance in simulating G0. The TC evaluation showed that the combination of gradient and calorimetry had a strong ability to simulate G0 in most land cover types (root mean square error (RMSE) <4 W/m2) but this ability was lost in the snow cover type (RMSE > 10 W/m2). When evaluated by ETC, the three methods had strong effects only on alpine meadow, floodland, and shrub types (CC > 0.70). Appropriate evaluation indices were recommended based on the application of the model.

Privately evaluating sensitive population record linkage without ground truth data

AbstractData containing personal information allow detailed studies in the health and social sciences, such as population-related analysis. However, such studies often require the linking of two or more databases because information about a person can be scattered across multiple data sources. To address this issue of data being scattered, researchers have been working on linking records across multiple data sources to identify records that refer to the same person, or the same group of individuals (known as group linkage) using quasi-identifiers such as names and addresses which can be missing, out of date or contain errors or variations, making record linkage a very challenging task. Record linkage applications often also lack ground truth data in the form of matching and non-matching record pairs, which challenges the assessment of the quality of linkage algorithms. Furthermore, when linkage is conducted on sensitive data, for example personal health records, due to privacy concerns ground truth can generally not be obtained using methods such as crowd sourcing. This study therefore aims to develop methods to assess the linkage quality of sensitive data by using publicly available data sets, such as census or voter data, in a privacy-preserving manner, with a focus on the group linkage problem. Assuming that distinct groups, such as siblings in a family, are identifiable in both the sensitive and public data sets, we develop a novel method to estimate linkage quality using public data by encoding information that is commonly available in both sensitive and public data sets into a common representation using Bloom filters. Comparing these Bloom filters then allows the estimation of linkage quality. An evaluation using a real sensitive birth data set and a public census data set from Scotland shows the effectiveness of our proposed method for quality estimation, which achieves a median correlation of 98% with linkage quality calculated based on ground truth data.

Harnessing 12-lead ECG and MRI data to personalise repolarisation profiles in cardiac digital twin models for enhanced virtual drug testing

Cardiac digital twins are computational tools capturing key functional and anatomical characteristics of patient hearts for investigating disease phenotypes and predicting responses to therapy. When paired with large-scale computational resources and large clinical datasets, digital twin technology can enable virtual clinical trials on virtual cohorts to fast-track therapy development. Here, we present an open-source automated pipeline for personalising ventricular electrophysiological function based on routinely acquired magnetic resonance imaging (MRI) data and the standard 12-lead electrocardiogram (ECG).Using MRI-based anatomical models, a sequential Monte-Carlo approximate Bayesian computational inference method is extended to infer electrical activation and repolarisation characteristics from the ECG. Fast simulations are conducted with a reaction-Eikonal model, including the Purkinje network and biophysically-detailed subcellular ionic current dynamics for repolarisation. For each patient, parameter uncertainty is represented by inferring an envelope of plausible ventricular models rather than a single one, which means that parameter uncertainty can be propagated to therapy evaluation. Furthermore, we have developed techniques for translating from reaction-Eikonal to monodomain simulations, which allows more realistic simulations of cardiac electrophysiology. The pipeline is demonstrated in three healthy subjects, where our inferred pseudo-diffusion reaction-Eikonal models reproduced the patient's ECG with a median Pearson's correlation coefficient of 0.9, and then translated to monodomain simulations with a median correlation coefficient of 0.84 across all subjects. We then demonstrate our digital twins for virtual evaluation of Dofetilide with uncertainty quantification. These evaluations using our cardiac digital twins reproduced dose-dependent QTc and T peak to T end prolongations that are in keeping with large population drug response data.The methodologies for cardiac digital twinning presented here are a step towards personalised virtual therapy testing and can be scaled to generate virtual populations for clinical trials to fast-track therapy evaluation. The tools developed for this paper are open-source, documented, and made publicly available.

DNA methylation-array interlaboratory comparison trial demonstrates highly reproducible paediatric CNS tumour classification across 13 international centres.

DNA methylation profiling, recently endorsed by the World Health Organisation (WHO) as a pivotal diagnostic tool for brain tumours, most commonly relies on bead arrays. Despite its widespread use, limited data exist on the technical reproducibility and potential cross-institutional differences. The LOGGIC Core BioClinical Data Bank registry conducted a prospective laboratory comparison trial with 12 international laboratories to enhance diagnostic accuracy for paediatric low-grade gliomas, focusing on technical aspects of DNA methylation data generation and profile interpretation under clinical real-time conditions. Four representative low-grade gliomas of distinct histologies were centrally selected, and DNA extraction was performed. Participating laboratories received a DNA aliquot and performed the DNA methylation-based classification and result interpretation without knowledge of tumour histology. Additionally, participants were required to interpret the copy number profile derived from DNA methylation data and conduct DNA sequencing of the BRAF hotspot p.V600 due to its relevance for low-grade gliomas. Results had to be returned within 30 days. High technical reproducibility was observed, with a median pairwise correlation of 0.99 (range 0.94-0.99) between coordinating laboratory and participants. DNA methylation-based tumour classification and copy number profile interpretation were consistent across all centres, and BRAF mutation status was accurately reported for all cases. Eleven out of 12 centres successfully reported their analysis within the 30-day timeframe. Our study demonstrates remarkable concordance in DNA methylation profiling and profile interpretation across 12 international centres. These findings underscore the potential contribution of DNA methylation analysis to the harmonisation of brain tumour diagnostics.

Impact of Emerging Deep Learning-Based MR Image Reconstruction Algorithms on Abdominal MRI Radiomic Features.

This study aims to evaluate, on one MRI vendor's platform, the impact of deep learning (DL)-based reconstruction techniques on MRI radiomic features compared to conventional image reconstruction techniques. Under IRB approval and informed consent, we prospectively collected undersampled coronal T2-weighted MR images of the abdomen (1.5 T; Philips Healthcare) from 17 pediatric and adult subjects and reconstructed them using a conventional image reconstruction technique (compressed sensitivity encoding [C-SENSE]) and two DL-based reconstruction techniques (SmartSpeed [Philips Healthcare, US FDA cleared] and SmartSpeed with Super Resolution [SmartSpeed-SuperRes, not US FDA cleared to date]). Eight regions of interest (ROIs) across organs/tissues (liver, spleen, kidney, pancreas, fat, and muscle) were manually placed. Eighty-six MRI radiomic features were then extracted. Pearson's correlation coefficients (PCCs) and intraclass correlation coefficients (ICCs) were calculated between (A) C-SENSE versus SmartSpeed, and (B) C-SENSE versus SmartSpeed-SuperRes. To quantify the impact from the perspective of the whole MR image, cross-ROI mean PCCs and ICCs were calculated for individual radiomic features. The impact of image reconstruction on individual radiomic features in different organs/tissues was evaluated using ANOVA analyses. According to cross-ROI mean PCCs, 50 out of 86 radiomic features were highly correlated (PCC, ≥0.8) between SmartSpeed and C-SENSE, whereas only 15 radiomic features were highly correlated between SmartSpeed-SuperRes and C-SENSE reconstructions. According to cross-ROI mean ICCs, 58 out of 86 radiomic features had high agreements (ICC ≥0.75) between SmartSpeed and C-SENSE, whereas only 9 radiomic features had high agreements between SmartSpeed-SuperRes and C-SENSE reconstructions. For SmartSpeed reconstruction, the psoas muscle ROI appeared to be impacted most with the lowest median (IQR) correlation of 0.57 (0.25). The circular liver ROI was impacted most by SmartSpeed-SuperRes (PCC, 0.60 [0.22]). ANOVA analyses suggest that the impact of DL reconstruction algorithms on radiomic features varies significantly among different organs/tissues (P < 0.001). MRI radiomic features are significantly altered by DL-based reconstruction compared to a conventional reconstruction technique. The impact of DL reconstruction algorithms on radiomic features varies significantly between different organs/tissues.

Evaluation of global seamless soil moisture products over China: A perspective of soil moisture sensitivity to precipitation

Accurate and timely information about soil moisture (SM) is not only critical to the study of climate change, but is also significant for agricultural production, drought monitoring, and hydrometeorological predictions. With the development of satellite remote sensing techniques, land surface models, and data assimilation techniques, a number of global seamless soil moisture products have been released with highly variable accuracy. A comprehensive evaluation of these products is necessary to select a suitable SM product for use. In this study, we provided such an evaluation of four global seamless SM products (SMAP-L4, ERA5-Land, GLDAS-Noah, and GLEAM) over China based on the stations from the automatic soil moisture observation (ASMO) system. Compared to previous studies that focused on a direct comparison between SM products and in situ SM observations, we specifically evaluated these products from the perspective of SM sensitivity to precipitation. This is because in situ precipitation observations are more spatially representative than in situ SM observations. Precipitation is a regional event, therefore in situ precipitation observations can be better scale-matched with coarse resolution SM products compared to in situ SM observations. Further, in addition to surface soil moisture (SSM), root zone soil moisture (RZSM) was evaluated in this study. The combination of all statistics illustrates that SMAP-L4 and ERA5-Land generally performed better than GLDAS-Noah and GLEAM. Regardless of the SSM or RZSM estimates, SMAP-L4 had the lowest median bias and RMSE of the four products, and ERA5-Land had the lowest median correlation coefficient (R) value. The lowest median ubRMSE was obtained by SMAP-L4 in SSM evaluation and ERA5-Land performed better than other RZSM products with the lowest median ubRMSE. From the perspective of SM sensitivity to precipitation, SMAP-L4 can capture daily precipitation dynamics most effectively, closely followed by ERA5-Land. This partially explains the superiority of SMAP-L4 and ERA5-Land over GLDAS-Noah and GLEAM for SM monitoring. The results of the metrics under different precipitation conditions varied considerably among the products. But while considering of ubRMSE alone, all products performed better under arid and humid conditions than to semi-arid and semi-humid conditions. This may be due to the fact that SM products had a limited ability to capture the SM dynamics under moderate precipitation. These findings may provide new insights into the selection of appropriate SM products and SM estimation in the future.

Assessing predictions on fitness effects of missense variants in HMBS in CAGI6.

This paper presents an evaluation of predictions submitted for the "HMBS" challenge, a component of the sixth round of the Critical Assessment of Genome Interpretation held in 2021. The challenge required participants to predict the effects of missense variants of the human HMBS gene on yeast growth. The HMBS enzyme, critical for the biosynthesis of heme in eukaryotic cells, is highly conserved among eukaryotes. Despite the application of a variety of algorithms and methods, the performance of predictors was relatively similar, with Kendall's tau correlation coefficients between predictions and experimental scores around 0.3 for a majority of submissions. Notably, the median correlation (≥ 0.34) observed among these predictors, especially the top predictions from different groups, was greater than the correlation observed between their predictions and the actual experimental results. Most predictors were moderately successful in distinguishing between deleterious and benign variants, as evidenced by an area under the receiver operating characteristic (ROC) curve (AUC) of approximately 0.7 respectively. Compared with the recent two rounds of CAGI competitions, we noticed more predictors outperformed the baseline predictor, which is solely based on the amino acid frequencies. Nevertheless, the overall accuracy of predictions is still far short of positive control, which is derived from experimental scores, indicating the necessity for considerable improvements in the field. The most inaccurately predicted variants in this round were associated with the insertion loop, which is absent in many orthologs, suggesting the predictors still heavily rely on the information from multiple sequence alignment.

Seasonal-scale intercomparison of SMAP and fused SMOS-SMAP soil moisture products

Two L-band passive microwave satellite sensors, onboard the Soil Moisture and Ocean Salinity (SMOS) launched in 2009 and Soil Moisture Active Passive (SMAP) launched in 2015, are specifically designed for surface soil moisture (SM) monitoring. The first global continuous fused L-band satellite SM product based on SMOS and SMAP observations (SMOS-SMAP-INRAE-BORDEAUX, the so-called Fused-IB) was recently released to the public. Currently, the performance of Fused-IB has only been evaluated collectively over the entire data records in the study period, without specific evaluation for individual seasons. To fill this gap, this study intercompared the Fused-IB and the enhanced SMAP-L3 version 6 (SMAP-E) SM products against in situ SM data from the International Soil Moisture Network (ISMN) from 2016 to 2020 regarding the whole period and different seasons. We aim to investigate the performance of these two products at different time scales and to explore the potential eco-hydrological factors (i.e., precipitation and vegetation) driving their seasonal variations. Results show that both SM products are in good agreement with the in situ measurements, demonstrating high median correlation (R) and low ubRMSD (median R = 0.70 and ubRMSD = 0.058 m3/m3 for Fused-IB vs. R = 0.68 and ubRMSD = 0.059 m3/m3 for SMAP-E) during 2016–2020. For most land use and land cover (LULC) types, Fused-IB outperformed SMAP-E with higher accuracy and lower errors, particularly in forests, partly due to the advantage of the robust SMAP-IB (SMAP-INRAE-BORDEAUX) algorithm used to generate Fused-IB in forests, which avoids the pronounced saturation effects of vegetation optical depth caused by relying on optical information. Besides, both products had superior performances across most LULC types in summer (JJA) and autumn (SON), yet increased uncertainties were observed in forests, grasslands, and croplands during spring (MAM) and winter (DJF). These uncertainties could be mainly attributed to the effects of vegetation growth in forests, grasslands and croplands, and the interception of water from rainfall events in grasslands. The results of this study can serve as a reference for algorithm developers to enhance the accuracy of SM and thus promote hydro-meteorological applications that benefit from L-band radiometer soil moisture products.

Accuracy of Automatic Processing of Speech-Language Pathologist and Child Talk During School-Based Therapy Sessions.

This study examines the accuracy of Interaction Detection in Early Childhood Settings (IDEAS), a program that automatically transcribes audio files and estimates linguistic units relevant to speech-language therapy, including part-of-speech units that represent features of language complexity, such as adjectives and coordinating conjunctions. Forty-five video-recorded speech-language therapy sessions involving 27 speech-language pathologists (SLPs) and 56 children were used. The F measure determines the accuracy of IDEAS diarization (i.e., speech segmentation and speaker classification). Two additional evaluation metrics, namely, median absolute relative error and correlation, indicate the accuracy of IDEAS for the estimation of linguistic units as compared with two conditions, namely, Oracle (manual diarization) and Voice Type Classifier (existing diarizer with acceptable accuracy). The high F measure for SLP talk data suggests high accuracy of IDEAS diarization for SLP talk but less so for child talk. These differences are reflected in the accuracy of IDEAS linguistic unit estimates. IDEAS median absolute relative error and correlation values for nine of the 10 SLP linguistic unit estimates meet the accuracy criteria, but none of the child linguistic unit estimates meet these criteria. The type of linguistic units also affects IDEAS accuracy. IDEAS was tailored to educational settings to automatically convert audio recordings into text and to provide linguistic unit estimates in speech-language therapy sessions and classroom settings. Although not perfect, IDEAS is reliable in automatically capturing and returning linguistic units, especially in SLP talk, that are relevant in research and practice. The tool offers a way to automatically measure SLP talk in clinical settings, which will support research seeking to understand how SLP talk influences children's language growth.

Waveform modelling of hydroacoustic teleseismic earthquake records from autonomous Mermaid floats

SUMMARY We present a computational technique to model hydroacoustic waveforms from teleseismic earthquakes recorded by mid-column Mermaid floats deployed in the Pacific, taking into consideration bathymetric effects that modify seismo-acoustic conversions at the ocean bottom and acoustic wave propagation in the ocean layer, including reverberations. Our approach couples axisymmetric spectral-element simulations performed for moment-tensor earthquakes in a 1-D solid Earth to a 2-D Cartesian fluid–solid coupled spectral-element simulation that captures the conversion from displacement to acoustic pressure at an ocean-bottom interface with accurate bathymetry. We applied our workflow to 1129 seismograms for 682 earthquakes from 16 Mermaids (short for Mobile Earthquake Recording in Marine Areas by Independent Divers) owned by Princeton University that were deployed in the Southern Pacific as part of the South Pacific Plume Imaging and Modeling (SPPIM) project. We compare the modelled synthetic waveforms to the observed records in individually selected frequency bands aimed at reducing local noise levels while maximizing earthquake-generated signal content. The modelled waveforms match the observations very well, with a median correlation coefficient of 0.72, and some as high as 0.95. We compare our correlation-based traveltime measurements to measurements made on the same data set determined by automated arrival-time picking and ray- traced traveltime predictions, with the aim of opening up the use of Mermaid records for global seismic tomography via full-waveform inversion.

2-Dimensional Echocardiographic Global Longitudinal Strain With Artificial Intelligence Using Open Data From a UK-Wide Collaborative

BackgroundGlobal longitudinal strain (GLS) is reported to be more reproducible and prognostic than ejection fraction. Automated, transparent methods may increase trust and uptake. ObjectivesThe authors developed open machine-learning–based GLS methodology and validate it using multiexpert consensus from the Unity UK Echocardiography AI Collaborative. MethodsWe trained a multi-image neural network (Unity-GLS) to identify annulus, apex, and endocardial curve on 6,819 apical 4-, 2-, and 3-chamber images. The external validation dataset comprised those 3 views from 100 echocardiograms. End-systolic and -diastolic frames were each labelled by 11 experts to form consensus tracings and points. They also ordered the echocardiograms by visual grading of longitudinal function. One expert calculated global strain using 2 proprietary packages. ResultsThe median GLS, averaged across the 11 individual experts, was −16.1 (IQR: −19.3 to −12.5). Using each case’s expert consensus measurement as the reference standard, individual expert measurements had a median absolute error of 2.00 GLS units. In comparison, the errors of the machine methods were: Unity-GLS 1.3, proprietary A 2.5, proprietary B 2.2. The correlations with the expert consensus values were for individual experts 0.85, Unity-GLS 0.91, proprietary A 0.73, proprietary B 0.79. Using the multiexpert visual ranking as the reference, individual expert strain measurements found a median rank correlation of 0.72, Unity-GLS 0.77, proprietary A 0.70, and proprietary B 0.74. ConclusionsOur open-source approach to calculating GLS agrees with experts’ consensus as strongly as the individual expert measurements and proprietary machine solutions. The training data, code, and trained networks are freely available online.

Enhanced Diagnostic Precision: Assessing Tumor Differentiation in Head and Neck Squamous Cell Carcinoma Using Multi-Slice Spiral CT Texture Analysis.

This study explores the efficacy of texture analysis by using preoperative multi-slice spiral computed tomography (MSCT) to non-invasively determine the grade of cellular differentiation in head and neck squamous cell carcinoma (HNSCC). In a retrospective study, MSCT scans of patients with HNSCC were analyzed and classified based on its histological grade as moderately differentiated, well-differentiated, or poorly differentiated. The location of the tumor was categorized as either in the bone or in soft tissues. Segmentation of the lesion areas was conducted, followed by texture analysis. Eleven GLCM parameters across five different distances were calculated. Median values and correlations of texture parameters were examined in relation to tumor differentiation grade by using Spearman's correlation coefficient and Kruskal-Wallis and Dunn tests. Forty-six patients were included, predominantly female (87%), with a mean age of 66.7 years. Texture analysis revealed significant parameter correlations with histopathological grades of tumor differentiation. The study identified no significant age correlation with tumor differentiation, which underscores the potential of texture analysis as an age-independent biomarker. The strong correlations between texture parameters and histopathological grades support the integration of this technique into the clinical decision-making process.

Variant patterns of electrical activation and recovery in normal human hearts revealed by noninvasive electrocardiographic imaging.

Although electrical activity of the normal human heart is well characterized by the electrocardiogram, detailed insights into within-subject and between-subject variations of ventricular activation and recovery by noninvasive electroanatomic mapping are lacking. We characterized human epicardial activation and recovery within and between normal subjects using non-invasive electrocardiographic imaging (ECGI) as a basis to better understand pathology. Epicardial activation and recovery were assessed by ECGI in 22 normal subjects, 4 subjects with bundle branch block (BBB) and 4 with long-QT syndrome (LQTS). We compared characteristics between the ventricles [left ventricle (LV) and right ventricle (RV)], sexes, and age groups (<50/≥50years). Pearson's correlation coefficient (CC) was used for within-subject and between-subject comparisons. Age of normal subjects averaged 49 ± 14 years, 6/22 were male, and no structural/electrical heart disease was present. The average activation time was longer in LV than in RV, but not different by sex or age. Electrical recovery was similar for the ventricles, but started earlier and was on average shorter in males. Median CCs of between-subject comparisons of the ECG signals, activation, and recovery patterns were 0.61, 0.32, and 0.19, respectively. Within-subject beat-to-beat comparisons yielded higher CCs (0.98, 0.89, and 0.82, respectively). Activation and/or recovery patterns of patients with BBB or LQTS contrasted significantly with those found in the normal population. Activation and recovery patterns vary profoundly between normal subjects, but are stable individually beat to beat, with a male preponderance to shorter recovery. Individual characterization by ECGI at baseline serves as reference to better understand the emergence, progression, and treatment of electrical heart disease.

Prospective Randomized Trial Comparing 2 Devices for Deep Inspiration Breath Hold Management in Breast Radiation Therapy: Results of the BRAVEHeart Trial

Aims: The BRAVEHeart trial prospectively randomized left-sided breast cancer patients to one of two deep inspiration breath hold (DIBH) biofeedback devices: a novel chest surface tracking system and an abdominal block tracking system. The primary hypothesis was that the accuracy of chest tracking would be higher than that of abdominal tracking as the chest is a more direct surrogate of the breast target.Methods: Left-sided breast cancer patients were treated in DIBH with IMRT delivery. Patients were randomized to either the novel chest surface system or abdominal block system for active management of breath hold with visual feedback. On both trial arms the unallocated system monitored passively.239,296 cine EPID images were analyzed retrospectively to extract the chest wall position. Treatment accuracy was quantified as the deviation of the internal chest wall during treatment relative to the planned position from the digitally reconstructed radiograph. The correlation between motion of the external surrogate and internal chest wall was calculated per breath hold. Ease of use was assessed with questionnaires for both radiation therapists and patients and appointment length recorded.Results: Data from 26 participants were available for analysis. No difference was found in delivered treatment accuracy between arms. Across all patients and fractions, the median correlation between internal chest wall movement and external surrogate was 0.69 for the chest surface and 0.17 for the abdominal block. Patients found it easy to follow visual feedback from both systems. No difference was found in appointment length between arms.Conclusions: No statistical evidence was found for superior treatment accuracy, satisfaction or appointment length for the novel chest surface tracking device compared to the abdominal block system. During DIBH, the median per-breath hold correlation of internal chest wall movement to the motion of the chest surface was higher than the median correlation of the abdominal block to the chest surface.