Editorial Perspective: Extending IPDMA methodology to drive treatment personalisation in child mental health.

To improve outcomes for youth who do not respond optimally to existing treatments, we need to identify robust predictors, moderators, and mediators that are ideal targets for personalisation in mental health care. We propose a solution to leverage the Individual Patient Data Meta-analysis (IPDMA) approach to allow broader access to individual-level data while maintaining methodological rigour. Such a resource has the potential to answer questions that are unable to be addressed by single studies, reduce researcher burden, and enable the application of newer statistical techniques, all to provide data-driven strategies for clinical decision-making. Using childhood anxiety as the worked example, the editorial perspective outlines the rationale for leveraging IPDMA methodology to build a data repository, the Platform for Anxiety Disorder Data in Youth. We also include recommendations to address the methods and challenges inherent in this endeavour.

Estimation of heritability with genomic information by method R.

Estimating heritabilities with large genomic models by established methods such as restricted maximum likelihood (REML) or Bayesian via Gibbs sampling is computationally expensive. Alternatively, heritability can be estimated indirectly by method R and by maximum predictivity, referred to as MaxPred here, at a much lower computing cost. By method R, the heritability used for predictions with whole and partial data is considered the best estimate when the predictions based on partial data are unbiased relative to those with the complete data. By MaxPred, the heritability estimate is the one that maximizes predictivity. This study compared heritability estimation with genomic information using average information REML (AI-REML), method R and MaxPred. A simulated population was generated with ten generations of 5000 animals each and an effective population size of 80. Each animal had one record for a trait with a heritability of 0.3, a phenotypic variance of 10.0 and was genotyped at 50 k SNP. In method R, the heritability estimate is found when the expectation of a regression coefficient is equal to one. The regression is the EBV of selection candidates calculated with the whole dataset regressed on the EBV of candidates calculated from a partial dataset. In this study, we used the GBLUP framework and therefore, GEBV was calculated. The partial dataset was created by removing the last generation of phenotypes. Predictivity was defined as the correlation between the adjusted phenotypes of the selection candidates and their GEBV calculated from the partial data. We estimated the heritability for populations that included between three and 10 generations. In every scenario, predictivity increased as more data was used and was the highest at the simulated heritability. However, the predictivity for all data subsets and all heritabilities compared did not differ more than 0.01, suggesting MaxPred is not the best indication for heritability estimation. For the whole dataset, the heritability was estimated as 0.30 ± 0.01, 0.26 ± 0.01 and 0.30 ± 0.04 for AI-REML without genomics, AI-REML with genomics and method R with genomics, respectively. Heritability estimation with genomics by method R reduced timing by 83%, implying a reduction in computing time from 9.5 to 1.6 h, on average, compared to AI-REML with genomics. Method R has the potential to estimate heritabilities with large genomic information at a low cost when many generations of animals are present; however, the standard error can be high when only a few iterations are used.

A Method for Reducing Training Time of ML-Based Cascade Scheme for Large-Volume Data Analysis.

We live in the era of large data analysis, where processing vast datasets has become essential for uncovering valuable insights across various domains of our lives. Machine learning (ML) algorithms offer powerful tools for processing and analyzing this abundance of information. However, the considerable time and computational resources needed for training ML models pose significant challenges, especially within cascade schemes, due to the iterative nature of training algorithms, the complexity of feature extraction and transformation processes, and the large sizes of the datasets involved. This paper proposes a modification to the existing ML-based cascade scheme for analyzing large biomedical datasets by incorporating principal component analysis (PCA) at each level of the cascade. We selected the number of principal components to replace the initial inputs so that it ensured 95% variance retention. Furthermore, we enhanced the training and application algorithms and demonstrated the effectiveness of the modified cascade scheme through comparative analysis, which showcased a significant reduction in training time while improving the generalization properties of the method and the accuracy of the large data analysis. The improved enhanced generalization properties of the scheme stemmed from the reduction in nonsignificant independent attributes in the dataset, which further enhanced its performance in intelligent large data analysis.

An Integrated Data Acquisition Approach for the Structural Health Monitoring and Real-Time Earthquake Response Assessment of a Retrofitted Adobe Church in Peru.

The structural health monitoring (SHM) of buildings provides relevant data for the evaluation of the structural behavior over time, the efficiency of maintenance, strengthening, and post-earthquake conditions. This paper presents the design and implementation of a continuous SHM system based on dynamic properties, base accelerations, crack widths, out-of-plane rotations, and environmental data for the retrofitted church of Kuñotambo, a 17th century adobe structure, located in the Peruvian Andes. The system produces continuous hourly records. The organization, data collection, and processing of the SHM system follows different approaches and stages, concluding with the assessment of the structural and environmental conditions over time compared to predefined thresholds. The SHM system was implemented in May 2022 and is part of the Seismic Retrofitting Project of the Getty Conservation Institute. The initial results from the first twelve months of monitoring revealed seasonal fluctuations in crack widths, out-of-plane rotations, and natural frequencies, influenced by hygrothermal cycles, and an apparent positive trend, but more data are needed to justify the nature of these actions. This study emphasizes the necessity for extended data collection to establish robust correlations and refine monitoring strategies, aiming to enhance the longevity and safety of historic adobe structures under seismic risk.

Childhood attention-deficit hyperactivity disorder: socioeconomic inequalities in symptoms, impact, diagnosis and medication.

BACKGROUND: Children from disadvantaged backgrounds are at greater risk of attention-deficit hyperactivity disorder (ADHD)-related symptoms, being diagnosed with ADHD, and being prescribed ADHD medications. We aimed to examine how inequalities manifest across the 'patient journey', from perceptions of impacts of ADHD symptoms on daily life, to the propensity to seek and receive a diagnosis and treatment. METHODS: We investigated four 'stages': (1) symptoms, (2) caregiver perception of impact, (3) diagnosis and (4) medication, in two data sets: UK Millennium Cohort Study (MCS, analytic n ~ 9,000), with relevant (parent-reported) information on all four stages (until 14 years); and a population-wide 'administrative cohort', which includes symptoms (child health checks) and prescriptions (dispensing records), born in Scotland, 2010-2012 (analytic n ~ 100,000), until ~6 years. We described inequalities according to maternal occupational status, with percentages and relative indices of inequality (RII). RESULTS: The prevalence of ADHD symptoms and medication receipt was considerably higher in the least compared to the most advantaged children in the administrative cohort (RIIs of 5.9 [5.5-6.4] and 8.1 [4.2-15.6]) and the MCS (3.08 [2.68-3.55], 3.75 [2.21-6.36]). MCS analyses highlighted complexities between these two stages, however, those from least advantaged backgrounds, with ADHD symptoms, were the least likely to perceive impacts on daily life (15.7% vs. average 19.5%) and to progress from diagnosis to medication (44.1% vs. average 72.5%). CONCLUSIONS: Despite large inequalities in ADHD symptoms and medication, parents from the least advantaged backgrounds were less likely to report impacts of ADHD symptoms on daily life, and their children were less likely to have received medication postdiagnosis, highlighting how patient journeys differed according to socioeconomic circumstances.

Debate: Response to "Should academics collaborate with digital companies to improve young people's mental health".

Much of the debate about academic collaboration with digital companies (see Livingstone, Orben & Odgers, 2023) has surrounded commercial use of data and children's mental health. The debate has also spilled into the educational value of technologies and academic collaboration with companies to improve their learning design. Given the close relationship between learning and mental health, the evaluation of digital companies' impact should focus on both their emotional and educational effects. The collaborative models used by educational researchers provide a source of inspiration for transparent evaluations and evidence-based recommendations for holistic interventions that target children's learning and mental health.

Debate: We need data infrastructure as well as data sharing - conflicts of interest in video game research.

Industry data sharing has the potential to revolutionise evidence on video gaming and mental health, as well as a host of other critical topics. However, collaborative data sharing agreements between academics and industry partners may also afford industry enormous power in steering the development of this evidence base. In this paper, we outline how nonfinancial conflicts of interest may emerge when industry share data with academics. We then go on to describe ways in which such conflicts may affect the quality of the evidence base. Finally, we suggest strategies for mitigating this impact and preserving research independence. We focus on the development of data infrastructure: technological, social, and educational architecture that facilitates unfettered and free access to the kinds of high-quality data that industry hold, but without industry involvement.

Artificial intelligence enables reliable and standardized measurements of implant alignment in long leg radiographs with total knee arthroplasties.

PURPOSE: The purpose of this study was to evaluate the reliability of a newly developed AI-algorithm for the evaluation of long leg radiographs (LLR) after total knee arthroplasties (TKA). METHODS: In the validation cohort 200 calibrated LLRs of eight different common unconstrained and constrained knee systems were analysed. Accuracy and reproducibility of the AI-algorithm were compared to manual reads regarding the hip-knee-ankle (HKA) as well as femoral (FCA) and tibial component (TCA) angles. In the evaluation cohort all institutional LLRs with TKAs in 2018 (n = 1312) were evaluated to assess the algorithms' ability of handling large data sets. Intraclass correlation (ICC) coefficient and mean absolute deviation (sMAD) were calculated to assess conformity between the AI software and manual reads. RESULTS: Validation cohort: The AI-software was reproducible on 96% and reliable on 92.1% of LLRs with an output and showed excellent reliability in all measured angles (ICC > 0.97) compared to manual measurements. Excellent results were found for primary unconstrained TKAs. In constrained TKAs landmark setting on the femoral and tibial component failed in 12.5% of LLRs (n = 9). Evaluation cohort: Mean measurements for all postoperative TKAs (n = 1240) were 0.2° varus ± 2.5° (HKA), 89.3° ± 1.9° (FCA), and 89.1° ± 1.6° (TCA). Mean measurements on preoperative revision TKAs (n = 74) were 1.6 varus ± 6.4° (HKA), 90.5° ± 3.1° (FCA), and 88.9° ± 4.1° (TCA). CONCLUSIONS: AI-powered applications are reliable for automated analysis of lower limb alignment on LLRs with TKAs. They are capable of handling large data sets and could, therefore, lead to more standardized and efficient postoperative quality controls. LEVEL OF EVIDENCE: Diagnostic Level III.

Detecting Trivariate Associations in High-Dimensional Datasets.

Detecting correlations in high-dimensional datasets plays an important role in data mining and knowledge discovery. While recent works achieve promising results, detecting multivariable correlations especially trivariate associations still remains a challenge. For example, maximal information coefficient (MIC) introduces generality and equitability to detect bivariate correlations but fails to detect multivariable correlation. To solve the problem mentioned above, we proposed quadratic optimized trivariate information coefficient (QOTIC). Specifically, QOTIC equitably measures dependence among three variables. Our contributions are three-fold: (1) we present a novel quadratic optimization procedure to approach the correlation with high accuracy; (2) QOTIC exceeds existing methods in generality and equitability as QOTIC has general test functions and is applicable in detecting multivariable correlation in datasets of various sample sizes and noise levels; (3) QOTIC achieved both higher accuracy and higher time-efficiency than previous methods. Extensive experiments demonstrate the excellent performance of QOTIC.

PIQMEE: Bayesian Phylodynamic Method for Analysis of Large Data Sets with Duplicate Sequences.

Next-generation sequencing of pathogen quasispecies within a host yields data sets of tens to hundreds of unique sequences. However, the full data set often contains thousands of sequences, because many of those unique sequences have multiple identical copies. Data sets of this size represent a computational challenge for currently available Bayesian phylogenetic and phylodynamic methods. Through simulations, we explore how large data sets with duplicate sequences affect the speed and accuracy of phylogenetic and phylodynamic analysis within BEAST 2. We show that using unique sequences only leads to biases, and using a random subset of sequences yields imprecise parameter estimates. To overcome these shortcomings, we introduce PIQMEE, a BEAST 2 add-on that produces reliable parameter estimates from full data sets with increased computational efficiency as compared with the currently available methods within BEAST 2. The principle behind PIQMEE is to resolve the tree structure of the unique sequences only, while simultaneously estimating the branching times of the duplicate sequences. Distinguishing between unique and duplicate sequences allows our method to perform well even for very large data sets. Although the classic method converges poorly for data sets of 6,000 sequences when allowed to run for 7 days, our method converges in slightly more than 1 day. In fact, PIQMEE can handle data sets of around 21,000 sequences with 20 unique sequences in 14 days. Finally, we apply the method to a real, within-host HIV sequencing data set with several thousand sequences per patient.

Associations of perceived adverse lifetime experiences with brain structure in UK Biobank participants.

BACKGROUND: Adversity experiences (AEs) are major risk factors for psychiatric illness, and ample evidence suggests that adversity-related changes in brain structure enhance this vulnerability. To achieve greater understanding of the underlying biological pathways, increased convergence among findings is needed. Suggested future directions may benefit from the use of large population samples which may contribute to achieving this goal. We addressed mechanistic pathways by investigating the associations between multiple brain phenotypes and retrospectively reported AEs in early life (child adversity) and adulthood (partner abuse) in a large population sample, using a cross-sectional approach. METHODS: The UK Biobank resource was used to access imaging-derived phenotypes (IDPs) from 6,751 participants (aged: M = 62.1, SD = 7.2, range = 45-80), together with selected reports of childhood AEs and adult partner abuse. Principal component analysis was used to reduce the dimensionality of the data prior to multivariate tests. RESULTS: The data showed that participants who reported experiences of childhood emotional abuse ('felt hated by family member as a child') had smaller cerebellar and ventral striatum volumes. This result was also depicted in a random subset of participants; however, we note small effect sizes ( ηp2  < .01), suggestive of modest biological changes. CONCLUSIONS: Using a large population cohort, this study demonstrates the value of big datasets in the study of adversity and using automatically preprocessed neuroimaging phenotypes. While retrospective and cross-sectional characteristics limit interpretation, this study demonstrates that self-perceived adversity reports, however nonspecific, may still expose neural consequences, identifiable with increased statistical power.

Interactive Analysis for Large Volume Data from Fluorescence Microscopy at Cellular Precision.

The main objective for understanding fluorescence microscopy data is to investigate and evaluate the fluorescent signal intensity distributions as well as their spatial relationships across multiple channels. The quantitative analysis of 3D fluorescence microscopy data needs interactive tools for researchers to select and focus on relevant biological structures. We developed an interactive tool based on volume visualization techniques and GPU computing for streamlining rapid data analysis. Our main contribution is the implementation of common data quantification functions on streamed volumes, providing interactive analyses on large data without lengthy preprocessing. Data segmentation and quantification are coupled with brushing and executed at an interactive speed. A large volume is partitioned into data bricks, and only user-selected structures are analyzed to constrain the computational load. We designed a framework to assemble a sequence of GPU programs to handle brick borders and stitch analysis results. Our tool was developed in collaboration with domain experts and has been used to identify cell types. We demonstrate a workflow to analyze cells in vestibular epithelia of transgenic mice.

Roles of membrane transporters: connecting the dots from sequence to phenotype.

BACKGROUND: Plant membrane transporters are involved in diverse cellular processes underpinning plant physiology, such as nutrient acquisition, hormone movement, resource allocation, exclusion or sequestration of various solutes from cells and tissues, and environmental and developmental signalling. A comprehensive characterization of transporter function is therefore key to understanding and improving plant performance. SCOPE AND CONCLUSIONS: In this review, we focus on the complexities involved in characterizing transporter function and the impact that this has on current genomic annotations. Specific examples are provided that demonstrate why sequence homology alone cannot be relied upon to annotate and classify transporter function, and to show how even single amino acid residue variations can influence transporter activity and specificity. Misleading nomenclature of transporters is often a source of confusion in transporter characterization, especially for people new to or outside the field. Here, to aid researchers dealing with interpretation of large data sets that include transporter proteins, we provide examples of transporters that have been assigned names that misrepresent their cellular functions. Finally, we discuss the challenges in connecting transporter function at the molecular level with physiological data, and propose a solution through the creation of new databases. Further fundamental in-depth research on specific transport (and other) proteins is still required; without it, significant deficiencies in large-scale data sets and systems biology approaches will persist. Reliable characterization of transporter function requires integration of data at multiple levels, from amino acid residue sequence annotation to more in-depth biochemical, structural and physiological studies.

A Longitudinal Population Analysis of Cumulative Risks of Circumcision.

BACKGROUND: Circumcision is widely accepted for newborns in the United States. However, circumcision carries a risk of complications, the rates of which are not well described in the contemporary era. METHODS: We performed a longitudinal population analysis of the California Office of Statewide Health Planning and Development database between 2005 and 2010. Using International Classification of Procedures, Ninth Revision, Clinical Modification and Current Procedural Terminology codes, we calculated early and late complication rates by Kaplan-Meier survival estimates. Late complications were defined as those that occurred between 30 d and 5 y after circumcision. Descriptive analysis of complications was obtained by analysis of variance, chi-square test, or log-rank test. On adjusted analysis, a Cox proportional hazard model was performed to determine the risk of early and late complications, adjusting for patient demographics. RESULTS: A total of 24,432 circumcised children under age 5 y were analyzed. Overall, cumulative complication rates over 5 y were 1.5% in neonates, 0.5% of which were early, and 2.9% in non-neonates, 2.2% of which were early. On adjusted analysis, non-neonates had a higher risk of early complications (OR 18.5). In both neonates and non-neonates, the majority of patients with late complications underwent circumcision revision. CONCLUSIONS: Circumcision has a complication rate higher than previously recognized. Most patients with late complications after circumcision received an operative circumcision revision. Clinicians should weigh the surgical risks against the reported medical benefits of circumcision when counseling parents about circumcision.

The Affordable Care Act Attenuates Financial Strain According to Poverty Level.

We use data from the 2011-2016 National Health Interview Survey to examine how the Patient Protection and Affordable Care Act (ACA) has influenced disparities in health care-related financial strain, access to care, and utilization of services by categories of the Federal Poverty Level (FPL). We use multivariable regression analyses to determine the ACA's effects on these outcome measures, as well as to determine how changes in these measures varied across different FPL levels. We find that the national implementation of the ACA's insurance expansion provisions in 2014 was associated with improvements in health care-related financial strain, access, and utilization. Relative to adults earning more than 400% of the FPL, the largest effects were observed among those earning between 0% to 124% and 125% to 199% of the FPL after the implementation of the ACA. Both groups experienced reductions in disparities in financial strain and uninsurance relative to the highest FPL group. Overall, the ACA has attenuated health care-related financial strain and improved access to and the utilization of health services for low- and middle-income adults who have traditionally not met income eligibility requirements for public insurance programs. Policy changes that would replace the ACA with less generous age-based tax subsidies and reductions in Medicaid funding could reverse these gains.

Treatments of Missing Values in Large National Data Affect Conclusions: The Impact of Multiple Imputation on Arthroplasty Research.

BACKGROUND: Despite the advantages of large, national datasets, one continuing concern is missing data values. Complete case analysis, where only cases with complete data are analyzed, is commonly used rather than more statistically rigorous approaches such as multiple imputation. This study characterizes the potential selection bias introduced using complete case analysis and compares the results of common regressions using both techniques following unicompartmental knee arthroplasty. METHODS: Patients undergoing unicompartmental knee arthroplasty were extracted from the 2005 to 2015 National Surgical Quality Improvement Program. As examples, the demographics of patients with and without missing preoperative albumin and hematocrit values were compared. Missing data were then treated with both complete case analysis and multiple imputation (an approach that reproduces the variation and associations that would have been present in a full dataset) and the conclusions of common regressions for adverse outcomes were compared. RESULTS: A total of 6117 patients were included, of which 56.7% were missing at least one value. Younger, female, and healthier patients were more likely to have missing preoperative albumin and hematocrit values. The use of complete case analysis removed 3467 patients from the study in comparison with multiple imputation which included all 6117 patients. The 2 methods of handling missing values led to differing associations of low preoperative laboratory values with commonly studied adverse outcomes. CONCLUSION: The use of complete case analysis can introduce selection bias and may lead to different conclusions in comparison with the statistically rigorous multiple imputation approach. Joint surgeons should consider the methods of handling missing values when interpreting arthroplasty research.

Multi-Sensor Data Integration Using Deep Learning for Characterization of Defects in Steel Elements.

Nowadays, there is a strong demand for inspection systems integrating both high sensitivity under various testing conditions and advanced processing allowing automatic identification of the examined object state and detection of threats. This paper presents the possibility of utilization of a magnetic multi-sensor matrix transducer for characterization of defected areas in steel elements and a deep learning based algorithm for integration of data and final identification of the object state. The transducer allows sensing of a magnetic vector in a single location in different directions. Thus, it enables detecting and characterizing any material changes that affect magnetic properties regardless of their orientation in reference to the scanning direction. To assess the general application capability of the system, steel elements with rectangular-shaped artificial defects were used. First, a database was constructed considering numerical and measurements results. A finite element method was used to run a simulation process and provide transducer signal patterns for different defect arrangements. Next, the algorithm integrating responses of the transducer collected in a single position was applied, and a convolutional neural network was used for implementation of the material state evaluation model. Then, validation of the obtained model was carried out. In this paper, the procedure for updating the evaluated local state, referring to the neighboring area results, is presented. Finally, the results and future perspective are discussed.

[The Problems of Analysis of Medical Statistics Data].

The article presents the results of usability of medical statistics in complex automated analysis. It is demonstrated that processing of data of large volume is necessary both for stage of analysis and procedures of its preliminary processing. The article summarizes and classifies problems limiting quality of complex analysis of data of medical statistics. The algorithm is proposed including sequentially applied procedures supporting correct preparation of indices for further analysis. The algorithm was applied to 1.5 million of records of medical statistics collected in the Medial Informational Analytical Center of the Health Care Department of the Primorskiy Krai in 2004-2014.

Statins may facilitate Parkinson's disease: Insight gained from a large, national claims database.

OBJECTIVE: Using a large U.S. claims database (MarketScan), we investigated the controversy surrounding the role of statins in Parkinson's disease (PD). METHODS: We performed a retrospective case-control analysis. First, we identified 2322 incident PD cases having a minimum of 2.5 years of continuous enrollment prior to earliest diagnosis code or prescription of antiparkinson medication. A total of 2322 controls were then matched individually by age, gender, and a follow-up window to explore the relationship of statin use with incident PD. RESULTS: Statin usage was significantly associated with PD risk, with the strongest associations being for lipophilic (odds ratio = 1.58, P < .0001) versus hydrophilic (odds ratio = 1.19, P = .25) statins, statins plus nonstatins (odds ratio = 1.95, P < .0001), and for the initial period after starting statins (<1 year odds ratio = 1.82, 1-2.5 years odds ratio = 1.75, and ≥2.5 years odds ratio = 1.37; Ptrend < .0001). CONCLUSION: The use of statin (especially lipophilics) was associated with higher risk of PD, and the stronger association in initial use suggests a facilitating effect. © 2017 International Parkinson and Movement Disorder Society.