A proposed metric set for evaluation of genome assembly quality.

Quality control is essential for genome assemblies; however, a consensus has yet to be reached on what metrics should be adopted for the evaluation of assembly quality. N50 is widely used for contiguity measurement, but its effectiveness is constantly in question. Prevailing metrics for the completeness evaluation focus on gene space, yet challenging areas such as tandem repeats are commonly overlooked. Achieving correctness has become an indispensable dimension for quality control, while prevailing assembly releases lack scores reflecting this aspect. We propose a metric set with a set of statistic indexes for effective, comprehensive evaluation of assemblies and provide a score of a finished assembly for each metric, which can be utilized as a benchmark for achieving high-quality genome assemblies.

RNAdvisor: a comprehensive benchmarking tool for the measure and prediction of RNA structural model quality.

RNA is a complex macromolecule that plays central roles in the cell. While it is well known that its structure is directly related to its functions, understanding and predicting RNA structures is challenging. Assessing the real or predictive quality of a structure is also at stake with the complex 3D possible conformations of RNAs. Metrics have been developed to measure model quality while scoring functions aim at assigning quality to guide the discrimination of structures without a known and solved reference. Throughout the years, many metrics and scoring functions have been developed, and no unique assessment is used nowadays. Each developed assessment method has its specificity and might be complementary to understanding structure quality. Therefore, to evaluate RNA 3D structure predictions, it would be important to calculate different metrics and/or scoring functions. For this purpose, we developed RNAdvisor, a comprehensive automated software that integrates and enhances the accessibility of existing metrics and scoring functions. In this paper, we present our RNAdvisor tool, as well as state-of-the-art existing metrics, scoring functions and a set of benchmarks we conducted for evaluating them. Source code is freely available on the EvryRNA platform: https://evryrna.ibisc.univ-evry.fr.

scDOT: enhancing single-cell RNA-Seq data annotation and uncovering novel cell types through multi-reference integration.

The proliferation of single-cell RNA-seq data has greatly enhanced our ability to comprehend the intricate nature of diverse tissues. However, accurately annotating cell types in such data, especially when handling multiple reference datasets and identifying novel cell types, remains a significant challenge. To address these issues, we introduce Single Cell annotation based on Distance metric learning and Optimal Transport (scDOT), an innovative cell-type annotation method adept at integrating multiple reference datasets and uncovering previously unseen cell types. scDOT introduces two key innovations. First, by incorporating distance metric learning and optimal transport, it presents a novel optimization framework. This framework effectively learns the predictive power of each reference dataset for new query data and simultaneously establishes a probabilistic mapping between cells in the query data and reference-defined cell types. Secondly, scDOT develops an interpretable scoring system based on the acquired probabilistic mapping, enabling the precise identification of previously unseen cell types within the data. To rigorously assess scDOT's capabilities, we systematically evaluate its performance using two diverse collections of benchmark datasets encompassing various tissues, sequencing technologies and diverse cell types. Our experimental results consistently affirm the superior performance of scDOT in cell-type annotation and the identification of previously unseen cell types. These advancements provide researchers with a potent tool for precise cell-type annotation, ultimately enriching our understanding of complex biological tissues.

Implicit learning of convective organization explains precipitation stochasticity.

Accurate prediction of precipitation intensity is crucial for both human and natural systems, especially in a warming climate more prone to extreme precipitation. Yet, climate models fail to accurately predict precipitation intensity, particularly extremes. One missing piece of information in traditional climate model parameterizations is subgrid-scale cloud structure and organization, which affects precipitation intensity and stochasticity at coarse resolution. Here, using global storm-resolving simulations and machine learning, we show that, by implicitly learning subgrid organization, we can accurately predict precipitation variability and stochasticity with a low-dimensional set of latent variables. Using a neural network to parameterize coarse-grained precipitation, we find that the overall behavior of precipitation is reasonably predictable using large-scale quantities only; however, the neural network cannot predict the variability of precipitation (R2 ∼ 0.45) and underestimates precipitation extremes. The performance is significantly improved when the network is informed by our organization metric, correctly predicting precipitation extremes and spatial variability (R2 ∼ 0.9). The organization metric is implicitly learned by training the algorithm on a high-resolution precipitable water field, encoding the degree of subgrid organization. The organization metric shows large hysteresis, emphasizing the role of memory created by subgrid-scale structures. We demonstrate that this organization metric can be predicted as a simple memory process from information available at the previous time steps. These findings stress the role of organization and memory in accurate prediction of precipitation intensity and extremes and the necessity of parameterizing subgrid-scale convective organization in climate models to better project future changes of water cycle and extremes.

TripletCell: a deep metric learning framework for accurate annotation of cell types at the single-cell level.

Single-cell RNA sequencing (scRNA-seq) has significantly accelerated the experimental characterization of distinct cell lineages and types in complex tissues and organisms. Cell-type annotation is of great importance in most of the scRNA-seq analysis pipelines. However, manual cell-type annotation heavily relies on the quality of scRNA-seq data and marker genes, and therefore can be laborious and time-consuming. Furthermore, the heterogeneity of scRNA-seq datasets poses another challenge for accurate cell-type annotation, such as the batch effect induced by different scRNA-seq protocols and samples. To overcome these limitations, here we propose a novel pipeline, termed TripletCell, for cross-species, cross-protocol and cross-sample cell-type annotation. We developed a cell embedding and dimension-reduction module for the feature extraction (FE) in TripletCell, namely TripletCell-FE, to leverage the deep metric learning-based algorithm for the relationships between the reference gene expression matrix and the query cells. Our experimental studies on 21 datasets (covering nine scRNA-seq protocols, two species and three tissues) demonstrate that TripletCell outperformed state-of-the-art approaches for cell-type annotation. More importantly, regardless of protocols or species, TripletCell can deliver outstanding and robust performance in annotating different types of cells. TripletCell is freely available at https://github.com/liuyan3056/TripletCell. We believe that TripletCell is a reliable computational tool for accurately annotating various cell types using scRNA-seq data and will be instrumental in assisting the generation of novel biological hypotheses in cell biology.

Potential of dissimilarity measure-based computation of protein thermal stability data for determining protein interactions.

Determining the interacting proteins in multiprotein complexes can be technically challenging. An emerging biochemical approach to this end is based on the 'thermal proximity co-aggregation' (TPCA) phenomenon. Accordingly, when two or more proteins interact to form a complex, they tend to co-aggregate when subjected to heat-induced denaturation and thus exhibit similar melting curves. Here, we explore the potential of leveraging TPCA for determining protein interactions. We demonstrate that dissimilarity measure-based information retrieval applied to melting curves tends to rank a protein-of-interest's interactors higher than its non-interactors, as shown in the context of pull-down assay results. Consequently, such rankings can reduce the number of confirmatory biochemical experiments needed to find bona fide protein-protein interactions. In general, rankings based on dissimilarity measures generated through metric learning further reduce the required number of experiments compared to those based on standard dissimilarity measures such as Euclidean distance. When a protein mixture's melting curves are obtained in two conditions, we propose a scoring function that uses melting curve data to inform how likely a protein pair is to interact in one condition but not another. We show that ranking protein pairs by their scores is an effective approach for determining condition-specific protein-protein interactions. By contrast, clustering melting curve data generally does not inform about the interacting proteins in multiprotein complexes. In conclusion, we report improved methods for dissimilarity measure-based computation of melting curves data that can greatly enhance the determination of interacting proteins in multiprotein complexes.

Genotype imputation accuracy and the quality metrics of the minor ancestry in multi-ancestry reference panels.

Large-scale imputation reference panels are currently available and have contributed to efficient genome-wide association studies through genotype imputation. However, whether large-size multi-ancestry or small-size population-specific reference panels are the optimal choices for under-represented populations continues to be debated. We imputed genotypes of East Asian (180k Japanese) subjects using the Trans-Omics for Precision Medicine reference panel and found that the standard imputation quality metric (Rsq) overestimated dosage r2 (squared correlation between imputed dosage and true genotype) particularly in marginal-quality bins. Variance component analysis of Rsq revealed that the increased imputed-genotype certainty (dosages closer to 0, 1 or 2) caused upward bias, indicating some systemic bias in the imputation. Through systematic simulations using different template switching rates (θ value) in the hidden Markov model, we revealed that the lower θ value increased the imputed-genotype certainty and Rsq; however, dosage r2 was insensitive to the θ value, thereby causing a deviation. In simulated reference panels with different sizes and ancestral diversities, the θ value estimates from Minimac decreased with the size of a single ancestry and increased with the ancestral diversity. Thus, Rsq could be deviated from dosage r2 for a subpopulation in the multi-ancestry panel, and the deviation represents different imputed-dosage distributions. Finally, despite the impact of the θ value, distant ancestries in the reference panel contributed only a few additional variants passing a predefined Rsq threshold. We conclude that the θ value substantially impacts the imputed dosage and the imputation quality metric value.

Model-based evaluation of spatiotemporal data reduction methods with unknown ground truth through optimal visualization and interpretability metrics.

Optimizing and benchmarking data reduction methods for dynamic or spatial visualization and interpretation (DSVI) face challenges due to many factors, including data complexity, lack of ground truth, time-dependent metrics, dimensionality bias and different visual mappings of the same data. Current studies often focus on independent static visualization or interpretability metrics that require ground truth. To overcome this limitation, we propose the MIBCOVIS framework, a comprehensive and interpretable benchmarking and computational approach. MIBCOVIS enhances the visualization and interpretability of high-dimensional data without relying on ground truth by integrating five robust metrics, including a novel time-ordered Markov-based structural metric, into a semi-supervised hierarchical Bayesian model. The framework assesses method accuracy and considers interaction effects among metric features. We apply MIBCOVIS using linear and nonlinear dimensionality reduction methods to evaluate optimal DSVI for four distinct dynamic and spatial biological processes captured by three single-cell data modalities: CyTOF, scRNA-seq and CODEX. These data vary in complexity based on feature dimensionality, unknown cell types and dynamic or spatial differences. Unlike traditional single-summary score approaches, MIBCOVIS compares accuracy distributions across methods. Our findings underscore the joint evaluation of visualization and interpretability, rather than relying on separate metrics. We reveal that prioritizing average performance can obscure method feature performance. Additionally, we explore the impact of data complexity on visualization and interpretability. Specifically, we provide optimal parameters and features and recommend methods, like the optimized variational contractive autoencoder, for targeted DSVI for various data complexities. MIBCOVIS shows promise for evaluating dynamic single-cell atlases and spatiotemporal data reduction models.

Phylogenetic Biodiversity Metrics Should Account for Both Accumulation and Attrition of Evolutionary Heritage.

Phylogenetic metrics are essential tools used in the study of ecology, evolution and conservation. Phylogenetic diversity (PD) in particular is one of the most prominent measures of biodiversity and is based on the idea that biological features accumulate along the edges of phylogenetic trees that are summed. We argue that PD and many other phylogenetic biodiversity metrics fail to capture an essential process that we term attrition. Attrition is the gradual loss of features through causes other than extinction. Here we introduce "EvoHeritage", a generalization of PD that is founded on the joint processes of accumulation and attrition of features. We argue that while PD measures evolutionary history, EvoHeritage is required to capture a more pertinent subset of evolutionary history including only components that have survived attrition. We show that EvoHeritage is not the same as PD on a tree with scaled edges; instead, accumulation and attrition interact in a more complex non-monophyletic way that cannot be captured by edge lengths alone. This leads us to speculate that the one-dimensional edge lengths of classic trees may be insufficiently flexible to capture the nuances of evolutionary processes. We derive a measure of EvoHeritage and show that it elegantly reproduces species richness and PD at opposite ends of a continuum based on the intensity of attrition. We demonstrate the utility of EvoHeritage in ecology as a predictor of community productivity compared with species richness and PD. We also show how EvoHeritage can quantify living fossils and resolve their associated controversy. We suggest how the existing calculus of PD-based metrics and other phylogenetic biodiversity metrics can and should be recast in terms of EvoHeritage accumulation and attrition.

The non-Riemannian nature of perceptual color space.

The scientific community generally agrees on the theory, introduced by Riemann and furthered by Helmholtz and Schrödinger, that perceived color space is not Euclidean but rather, a three-dimensional Riemannian space. We show that the principle of diminishing returns applies to human color perception. This means that large color differences cannot be derived by adding a series of small steps, and therefore, perceptual color space cannot be described by a Riemannian geometry. This finding is inconsistent with the current approaches to modeling perceptual color space. Therefore, the assumed shape of color space requires a paradigm shift. Consequences of this apply to color metrics that are currently used in image and video processing, color mapping, and the paint and textile industries. These metrics are valid only for small differences. Rethinking them outside of a Riemannian setting could provide a path to extending them to large differences. This finding further hints at the existence of a second-order Weber­Fechner law describing perceived differences.

A New Evaluation Metric for Quantitative Accuracy of LC-MS/MS-Based Proteomics with Data-Independent Acquisition.

Data-independent acquisition (DIA) has improved the identification and quantitation coverage of peptides and proteins in liquid chromatography-tandem mass spectrometry-based proteomics. However, different DIA data-processing tools can produce very different identification and quantitation results for the same data set. Currently, benchmarking studies of DIA tools are predominantly focused on comparing the identification results, while the quantitative accuracy of DIA measurements is acknowledged to be important but insufficiently investigated, and the absence of suitable metrics for comparing quantitative accuracy is one of the reasons. A new metric is proposed for the evaluation of quantitative accuracy to avoid the influence of differences in false discovery rate control stringency. The part of the quantitation results with high reliability was acquired from each DIA tool first, and the quantitative accuracy was evaluated by comparing quantification error rates at the same number of accurate ratios. From the results of four benchmark data sets, the proposed metric was shown to be more sensitive to discriminating the quantitative performance of DIA tools. Moreover, the DIA tools with advantages in quantitative accuracy were consistently revealed by this metric. The proposed metric can also help researchers in optimizing algorithms of the same DIA tool and sample preprocessing methods to enhance quantitative accuracy.

BioEncoder: A metric learning toolkit for comparative organismal biology.

In the realm of biological image analysis, deep learning (DL) has become a core toolkit, for example for segmentation and classification. However, conventional DL methods are challenged by large biodiversity datasets characterized by unbalanced classes and hard-to-distinguish phenotypic differences between them. Here we present BioEncoder, a user-friendly toolkit for metric learning, which overcomes these challenges by focussing on learning relationships between individual data points rather than on the separability of classes. BioEncoder is released as a Python package, created for ease of use and flexibility across diverse datasets. It features taxon-agnostic data loaders, custom augmentation options, and simple hyperparameter adjustments through text-based configuration files. The toolkit's significance lies in its potential to unlock new research avenues in biological image analysis while democratizing access to advanced deep metric learning techniques. BioEncoder focuses on the urgent need for toolkits bridging the gap between complex DL pipelines and practical applications in biological research.

Metric error monitoring as a component of metacognitive processing.

Metacognitive processing constitutes one of the contemporary target domains in consciousness research. Error monitoring (the ability to correctly report one's own errors without feedback) is considered one of the functional outcomes of metacognitive processing. Error monitoring is traditionally investigated as part of categorical decisions where choice accuracy is a binary construct (choice is either correct or incorrect). However, recent studies revealed that this ability is characterized by metric features (i.e., direction and magnitude) in temporal, spatial, and numerical domains. Here, we discuss methodological approaches to investigating metric error monitoring in both humans and non-human animals and review their findings. The potential neural substrates of metric error monitoring measures are also discussed. This new scope of metacognitive processing can help improve our current understanding of conscious processing from a new perspective. Thus, by summarizing and discussing the perspectives, findings, and common applications in the metric error monitoring literature, this paper aims to provide a guideline for future research.

Metric learning for comparing genomic data with triplet network.

Many biological applications are essentially pairwise comparison problems, such as evolutionary relationships on genomic sequences, contigs binning on metagenomic data, cell type identification on gene expression profiles of single-cells, etc. To make pair-wise comparison, it is necessary to adopt suitable dissimilarity metric. However, not all the metrics can be fully adapted to all possible biological applications. It is necessary to employ metric learning based on data adaptive to the application of interest. Therefore, in this study, we proposed MEtric Learning with Triplet network (MELT), which learns a nonlinear mapping from original space to the embedding space in order to keep similar data closer and dissimilar data far apart. MELT is a weakly supervised and data-driven comparison framework that offers more adaptive and accurate dissimilarity learned in the absence of the label information when the supervised methods are not applicable. We applied MELT in three typical applications of genomic data comparison, including hierarchical genomic sequences, longitudinal microbiome samples and longitudinal single-cell gene expression profiles, which have no distinctive grouping information. In the experiments, MELT demonstrated its empirical utility in comparison to many widely used dissimilarity metrics. And MELT is expected to accommodate a more extensive set of applications in large-scale genomic comparisons. MELT is available at https://github.com/Ying-Lab/MELT.

A Multi-Institutional Natural Language Processing Pipeline to Extract Performance Status From Electronic Health Records.

PURPOSE: Performance status (PS), an essential indicator of patients' functional abilities, is often documented in clinical notes of patients with cancer. The use of natural language processing (NLP) in extracting PS from electronic medical records (EMRs) has shown promise in enhancing clinical decision-making, patient monitoring, and research studies. We designed and validated a multi-institute NLP pipeline to automatically extract performance status from free-text patient notes. PATIENTS AND METHODS: We collected data from 19,481 patients in Harris Health System (HHS) and 333,862 patients from veteran affair's corporate data warehouse (VA-CDW) and randomly selected 400 patients from each data source to train and validate (50%) and test (50%) the proposed pipeline. We designed an NLP pipeline using an expert-derived rule-based approach in conjunction with extensive post-processing to solidify its proficiency. To demonstrate the pipeline's application, we tested the compliance of PS documentation suggested by the American Society of Clinical Oncology (ASCO) Quality Metric and investigated the potential disparity in PS reporting for stage IV non-small cell lung cancer (NSCLC). We used a logistic regression test, considering patients in terms of race/ethnicity, conversing language, marital status, and gender. RESULTS: The test results on the HHS cohort showed 92% accuracy, and on VA data demonstrated 98.5% accuracy. For stage IV NSCLC patients, the proposed pipeline achieved an accuracy of 98.5%. Furthermore, our analysis revealed a documentation rate of over 85% for PS among NSCLC patients, surpassing the ASCO Quality Metrics. No disparities were observed in the documentation of PS. CONCLUSION: Our proposed NLP pipeline shows promising results in extracting PS from free-text notes from various health institutions. It may be used in longitudinal cancer data registries.

Evaluation and comparison of covariate balance metrics in studies with time-dependent confounding.

Marginal structural models have been increasingly used by analysts in recent years to account for confounding bias in studies with time-varying treatments. The parameters of these models are often estimated using inverse probability of treatment weighting. To ensure that the estimated weights adequately control confounding, it is possible to check for residual imbalance between treatment groups in the weighted data. Several balance metrics have been developed and compared in the cross-sectional case but have not yet been evaluated and compared in longitudinal studies with time-varying treatment. We have first extended the definition of several balance metrics to the case of a time-varying treatment, with or without censoring. We then compared the performance of these balance metrics in a simulation study by assessing the strength of the association between their estimated level of imbalance and bias. We found that the Mahalanobis balance performed best. Finally, the method was illustrated for estimating the cumulative effect of statins exposure over one year on the risk of cardiovascular disease or death in people aged 65 and over in population-wide administrative data. This illustration confirms the feasibility of employing our proposed metrics in large databases with multiple time-points.

Validation and recalibration of sex estimation methods using pubic nonmetric traits for the Chilean population.

Chile had a violent military coup (1973-1990) that resulted in 3,000 victims declared detained, missing or killed; many are still missing and unidentified. Currently, the Human Rights Unit of the Forensic Medical Service in Chile applies globally recognised forensic anthropological approaches, but many of these methods have not been validated in a Chilean sample. As current research has demonstrated population-specificity with extant methods, the present study aims to validate sex estimation methods in a Chilean population and thereafter establish population-specific equations. A sample of 265 os coxae of known age and sex of adult Chileans from the Santiago Subactual Osteology Collection were analysed. Visual assessment and scoring of the pelvic traits were performed in accordance with the Phenice (1969) and Klales et al. (2012) methods. The accuracy of Phenice (1969) in the Chilean sample was 96.98%, with a sex bias of 7.68%. Klales et al. (2012) achieved 87.17% accuracy with a sex bias of -15.39%. Although both methods showed acceptable classification accuracy, the associated sex bias values are unacceptable in forensic practice. Therefore, six univariate and eight multivariate predictive models were formulated for the Chilean population. The most accurate univariate model was the ventral arc at 96.6%, with a sex bias of 5.2%. Classification accuracy using all traits was 97.0%, with a sex bias of 7.7%. This study provides Chilean practitioners a population-specific morphoscopic standard with associated classification probabilities acceptable to accomplish legal admissibility requirements in human rights and criminal cases specific to the second half of the 20th century.

Ultrasound-Derived Fat Fraction for Hepatic Steatosis Assessment: Prospective Study of Agreement With MRI PDFF and Sources of Variability in a Heterogeneous Population.

BACKGROUND. Metabolic dysfunction-associated steatotic liver disease is a growing global public health concern. Quantitative ultrasound measurements, such as ultrasound-derived fat fraction (UDFF), could provide noninvasive, cost-effective, and portable steatosis evaluation. OBJECTIVE. The purpose of this article was to evaluate utility of UDFF for steatosis assessment using proton density fat fraction (PDFF) as reference in patients undergoing liver MRI for heterogeneous indications and to assess UDFF variability. METHODS. This prospective study included a primary analysis of 187 patients (mean age, 53.8 years; 112 men, 75 women) who underwent 3-T liver MRI for any clinical indication from December 2020 to July 2021. Patients underwent investigational PDFF measurement, including determination of PDFFwhole-liver (mean PDFF of entire liver), and PDFFvoxel (PDFF in single voxel within right lobe, measured by MR spectroscopy), as well as investigational ultrasound with UDFF calculation (mean of five inter-costal measurements) within 1 hour after MRI. In a subanalysis, 21 of these patients underwent additional UDFF measurements 1, 3, and 5 hours after meal consumption. The study also included repeatability and reproducibility analysis of 30 patients (mean age, 26.3 years; 10 men, 20 women) who underwent clinical abdominal ultrasound between November 2022 and January 2023; in these patients, three operators sequentially performed UDFF measurements. RESULTS. In primary analysis, UDFF and PDFFwhole-liver measurements showed intra-class correlation coefficient (ICC) of 0.79. In Bland-Altman analysis, UDFF and PDFFvoxel measurements showed mean difference of 1.5% (95% CI, 0.6-2.4%), with 95% limits of agreement from -11.0% to 14.0%. UDFF measurements exhibited AUC for detecting PDFFvoxel at historic thresholds of 6.5% and greater, 17.4% and greater, and 22.1% and greater of 0.90, 0.95, and 0.95, respectively. In subanalysis, mean UDFF was not significantly different across time points with respect to meal consumption (p = .21). In repeatability and reproducibility analysis, ICC for intraoperator repeatability ranged from 0.98 to 0.99 and for interoperator reproducibility from 0.90 to 0.96. Visual assessment of patient-level data plots indicated increasing variability of mean UDFF measurements across operators and of intercostal measurements within individual patients with increasing steatosis. CONCLUSION. UDFF showed robust agreement with PDFF, diagnostic performance for steatosis grades, and intraoperator repeatability and interoperator reproducibility. Nonetheless, UDFF exhibited bias toward slightly larger values versus PDFF; intraoperator and interoperator variation increased with increasing steatosis. CLINICAL IMPACT. UDFF shows promise for steatosis assessment across diverse populations, although continued optimization remains warranted.

MHQ: constructing an aggregate metric of population mental wellbeing.

BACKGROUND: According to the World Health Organization (WHO), mental health is 'a state of wellbeing in which the individual realizes his or her own abilities, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to his or her community'. Any population metric of mental health and wellbeing should therefore not only reflect the presence or absence of mental challenges but also a person's broad mental capacity and functioning across a range of cognitive, social, emotional and physical dimensions. However, while existing metrics of mental health typically emphasize ill health, existing metrics of wellbeing typically focus on happiness or life satisfaction, indirectly infer wellbeing from a selection of social and economic factors, or do not reflect a read out of the full spectrum of mental functioning that impacts people's everyday life and that spans the continuum from distress and the inability to function, through to the ability to function to one's full potential. METHODS: We present the Mental Health Quotient, or MHQ, a population metric of mental wellbeing that comprehensively captures mental functioning, and examine how it relates to functional productivity. We describe the 47-item assessment and the life impact rating scale on which the MHQ metric is based, as well as the rationale behind each step of the nonlinear algorithm used to construct the MHQ metric. RESULTS: We demonstrate a linear relationship between the MHQ metric and productive life function where movement on the scale from any point or in any direction relates to an equivalent shift in productive ability at the population level, a relationship that is not borne out using simple sum scores. We further show that this relationship is the same across all age groups. Finally, we demonstrate the potential for the types of insights arising from the MHQ metric, offering examples from the Global Mind Project, an initiative that aims to track and understand our evolving mental wellbeing, and since 2020 has collected responses from over 1 million individuals across 140 + countries. CONCLUSION: The MHQ is a metric of mental wellbeing that aligns with the WHO definition and is amenable to large scale population monitoring.

Six Sigma - is it time to re-evaluate its value in laboratory medicine?

The Sigma metric is widely used in laboratory medicine.