Achieving Inclusive Healthcare through Integrating Education and Research with AI and Personalized Curricula.

Precision medicine promises significant health benefits but faces challenges such as the need for complex data management and analytics, interdisciplinary collaboration, and education of researchers, healthcare professionals, and participants. Addressing these needs requires the integration of computational experts, engineers, designers, and healthcare professionals to develop user-friendly systems and shared terminologies. The widespread adoption of large language models (LLMs) like GPT-4 and Claude 3 highlights the importance of making complex data accessible to non-specialists. The Stanford Data Ocean (SDO) strives to mitigate these challenges through a scalable, cloud-based platform that supports data management for various data types, advanced research, and personalized learning in precision medicine. SDO provides AI tutors and AI-powered data visualization tools to enhance educational and research outcomes and make data analysis accessible for users from diverse educational backgrounds. By extending engagement and cutting-edge research capabilities globally, SDO particularly benefits economically disadvantaged and historically marginalized communities, fostering interdisciplinary biomedical research and bridging the gap between education and practical application in the biomedical field.

2023 White Paper on Recent Issues in Bioanalysis: Deuterated Drugs; LNP; Tumor/FFPE Biopsy; Targeted Proteomics; Small Molecule Covalent Inhibitors; Chiral Bioanalysis; Remote Regulatory Assessments; Sample Reconciliation/Chain of Custody (PART 1A - Recommendations on Mass Spectrometry, Chromatography, Sample Preparation Latest Developments, Challenges, and Solutions and BMV/Regulated Bioanalysis PART 1B - Regulatory Agencies' Inputs on Regulated Bioanalysis/BMV, Biomarkers/IVD/CDx/BAV, Immunogenicity, Gene & Cell Therapy and Vaccine).

The 17th Workshop on Recent Issues in Bioanalysis (17th WRIB) took place in Orlando, FL, USA on June 19-23, 2023. Over 1000 professionals representing pharma/biotech companies, CROs, and multiple regulatory agencies convened to actively discuss the most current topics of interest in bioanalysis. The 17th WRIB included 3 Main Workshops and 7 Specialized Workshops that together spanned 1 week to allow an exhaustive and thorough coverage of all major issues in bioanalysis of biomarkers, immunogenicity, gene therapy, cell therapy and vaccines.Moreover, in-depth workshops on "EU IVDR 2017/746 Implementation and impact for the Global Biomarker Community: How to Comply with this NEW Regulation" and on "US FDA/OSIS Remote Regulatory Assessments (RRAs)" were the special features of the 17th edition.As in previous years, WRIB continued to gather a wide diversity of international, industry opinion leaders and regulatory authority experts working on both small and large molecules as well as gene, cell therapies and vaccines to facilitate sharing and discussions focused on improving quality, increasing regulatory compliance, and achieving scientific excellence on bioanalytical issues.This 2023 White Paper encompasses recommendations emerging from the extensive discussions held during the workshop and is aimed to provide the bioanalytical community with key information and practical solutions on topics and issues addressed, in an effort to enable advances in scientific excellence, improved quality and better regulatory compliance. Due to its length, the 2023 edition of this comprehensive White Paper has been divided into three parts for editorial reasons.This publication covers the recommendations on Mass Spectrometry Assays, Regulated Bioanalysis/BMV (Part 1A) and Regulatory Inputs (Part 1B). Part 2 (Biomarkers, IVD/CDx, LBA and Cell-Based Assays) and Part 3 (Gene Therapy, Cell therapy, Vaccines and Biotherapeutics Immunogenicity) are published in volume 16 of Bioanalysis, issues 7 and 8 (2024), respectively.

Evolution of diapause in the African turquoise killifish by remodeling the ancient gene regulatory landscape.

Suspended animation states allow organisms to survive extreme environments. The African turquoise killifish has evolved diapause as a form of suspended development to survive a complete drought. However, the mechanisms underlying the evolution of extreme survival states are unknown. To understand diapause evolution, we performed integrative multi-omics (gene expression, chromatin accessibility, and lipidomics) in the embryos of multiple killifish species. We find that diapause evolved by a recent remodeling of regulatory elements at very ancient gene duplicates (paralogs) present in all vertebrates. CRISPR-Cas9-based perturbations identify the transcription factors REST/NRSF and FOXOs as critical for the diapause gene expression program, including genes involved in lipid metabolism. Indeed, diapause shows a distinct lipid profile, with an increase in triglycerides with very-long-chain fatty acids. Our work suggests a mechanism for the evolution of complex adaptations and offers strategies to promote long-term survival by activating suspended animation programs in other species.

Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease.

To understand the dynamic interplay between the human microbiome and host during health and disease, we analyzed the microbial composition, temporal dynamics, and associations with host multi-omics, immune, and clinical markers of microbiomes from four body sites in 86 participants over 6 years. We found that microbiome stability and individuality are body-site specific and heavily influenced by the host. The stool and oral microbiome are more stable than the skin and nasal microbiomes, possibly due to their interaction with the host and environment. We identify individual-specific and commonly shared bacterial taxa, with individualized taxa showing greater stability. Interestingly, microbiome dynamics correlate across body sites, suggesting systemic dynamics influenced by host-microbial-environment interactions. Notably, insulin-resistant individuals show altered microbial stability and associations among microbiome, molecular markers, and clinical features, suggesting their disrupted interaction in metabolic disease. Our study offers comprehensive views of multi-site microbial dynamics and their relationship with host health and disease.

Untargeted metabolomic profiling in children identifies novel pathways in asthma and atopy.

BACKGROUND: Asthma and other atopic disorders can present with varying clinical phenotypes marked by differential metabolomic manifestations and enriched biological pathways. OBJECTIVE: We sought to identify these unique metabolomic profiles in atopy and asthma. METHODS: We analyzed baseline nonfasted plasma samples from a large multisite pediatric population of 470 children aged <13 years from 3 different sites in the United States and France. Atopy positivity (At+) was defined as skin prick test result of ≥3 mm and/or specific IgE ≥ 0.35 IU/mL and/or total IgE ≥ 173 IU/mL. Asthma positivity (As+) was based on physician diagnosis. The cohort was divided into 4 groups of varying combinations of asthma and atopy, and 6 pairwise analyses were conducted to best assess the differential metabolomic profiles between groups. RESULTS: Two hundred ten children were classified as At-As-, 42 as At+As-, 74 as At-As+, and 144 as At+As+. Untargeted global metabolomic profiles were generated through ultra-high-performance liquid chromatography-tandem mass spectroscopy. We applied 2 independent machine learning classifiers and short-listed 362 metabolites as discriminant features. Our analysis showed the most diverse metabolomic profile in the At+As+/At-As- comparison, followed by the At-As+/At-As- comparison, indicating that asthma is the most discriminant condition associated with metabolomic changes. At+As+ metabolomic profiles were characterized by higher levels of bile acids, sphingolipids, and phospholipids, and lower levels of polyamine, tryptophan, and gamma-glutamyl amino acids. CONCLUSION: The At+As+ phenotype displays a distinct metabolomic profile suggesting underlying mechanisms such as modulation of host-pathogen and gut microbiota interactions, epigenetic changes in T-cell differentiation, and lower antioxidant properties of the airway epithelium.

Longitudinal profiling of the microbiome at four body sites reveals core stability and individualized dynamics during health and disease.

To understand dynamic interplay between the human microbiome and host during health and disease, we analyzed the microbial composition, temporal dynamics, and associations with host multi-omics, immune and clinical markers of microbiomes from four body sites in 86 participants over six years. We found that microbiome stability and individuality are body-site-specific and heavily influenced by the host. The stool and oral microbiome were more stable than the skin and nasal microbiomes, possibly due to their interaction with the host and environment. Also, we identified individual-specific and commonly shared bacterial taxa, with individualized taxa showing greater stability. Interestingly, microbiome dynamics correlated across body sites, suggesting systemic coordination influenced by host-microbial-environment interactions. Notably, insulin-resistant individuals showed altered microbial stability and associations between microbiome, molecular markers, and clinical features, suggesting their disrupted interaction in metabolic disease. Our study offers comprehensive views of multi-site microbial dynamics and their relationship with host health and disease. Study Highlights: The stability of the human microbiome varies among individuals and body sites.Highly individualized microbial genera are more stable over time.At each of the four body sites, systematic interactions between the environment, the host and bacteria can be detected.Individuals with insulin resistance have lower microbiome stability, a more diversified skin microbiome, and significantly altered host-microbiome interactions.

Multi-omics microsampling for the profiling of lifestyle-associated changes in health.

Current healthcare practices are reactive and use limited physiological and clinical information, often collected months or years apart. Moreover, the discovery and profiling of blood biomarkers in clinical and research settings are constrained by geographical barriers, the cost and inconvenience of in-clinic venepuncture, low sampling frequency and the low depth of molecular measurements. Here we describe a strategy for the frequent capture and analysis of thousands of metabolites, lipids, cytokines and proteins in 10 µl of blood alongside physiological information from wearable sensors. We show the advantages of such frequent and dense multi-omics microsampling in two applications: the assessment of the reactions to a complex mixture of dietary interventions, to discover individualized inflammatory and metabolic responses; and deep individualized profiling, to reveal large-scale molecular fluctuations as well as thousands of molecular relationships associated with intra-day physiological variations (in heart rate, for example) and with the levels of clinical biomarkers (specifically, glucose and cortisol) and of physical activity. Combining wearables and multi-omics microsampling for frequent and scalable omics may facilitate dynamic health profiling and biomarker discovery.

Dynamic lipidome alterations associated with human health, disease and ageing.

Lipids can be of endogenous or exogenous origin and affect diverse biological functions, including cell membrane maintenance, energy management and cellular signalling. Here, we report >800 lipid species, many of which are associated with health-to-disease transitions in diabetes, ageing and inflammation, as well as cytokine-lipidome networks. We performed comprehensive longitudinal lipidomic profiling and analysed >1,500 plasma samples from 112 participants followed for up to 9 years (average 3.2 years) to define the distinct physiological roles of complex lipid subclasses, including large and small triacylglycerols, ester- and ether-linked phosphatidylethanolamines, lysophosphatidylcholines, lysophosphatidylethanolamines, cholesterol esters and ceramides. Our findings reveal dynamic changes in the plasma lipidome during respiratory viral infection, insulin resistance and ageing, suggesting that lipids may have roles in immune homoeostasis and inflammation regulation. Individuals with insulin resistance exhibit disturbed immune homoeostasis, altered associations between lipids and clinical markers, and accelerated changes in specific lipid subclasses during ageing. Our dataset based on longitudinal deep lipidome profiling offers insights into personalized ageing, metabolic health and inflammation, potentially guiding future monitoring and intervention strategies.

Correction: Chaby et al. Cross-Platform Evaluation of Commercially Targeted and Untargeted Metabolomics Approaches to Optimize the Investigation of Psychiatric Disease. Metabolites 2021, 11, 609.

In the original publication [...].

Robust and High-Throughput Analytical Flow Proteomics Analysis of Cynomolgus Monkey and Human Matrices With Zeno SWATH Data-Independent Acquisition.

Modern mass spectrometers routinely allow deep proteome coverage in a single experiment. These methods are typically operated at nanoflow and microflow regimes, but they often lack throughput and chromatographic robustness, which is critical for large-scale studies. In this context, we have developed, optimized, and benchmarked LC-MS methods combining the robustness and throughput of analytical flow chromatography with the added sensitivity provided by the Zeno trap across a wide range of cynomolgus monkey and human matrices of interest for toxicological studies and clinical biomarker discovery. Sequential Window Acquisition of All Theoretical Fragment Ion Mass Spectra (SWATH) data-independent acquisition (DIA) experiments with Zeno trap activated (Zeno SWATH DIA) provided a clear advantage over conventional SWATH DIA in all sample types tested with improved sensitivity, quantitative robustness, and signal linearity as well as increased protein coverage by up to 9-fold. Using a 10-min gradient chromatography, up to 3300 proteins were identified in tissues at 2 µg peptide load. Importantly, the performance gains with Zeno SWATH translated into better biological pathway representation and improved the ability to identify dysregulated proteins and pathways associated with two metabolic diseases in human plasma. Finally, we demonstrate that this method is highly stable over time with the acquisition of reliable data over the injection of 1000+ samples (14.2 days of uninterrupted acquisition) without the need for human intervention or normalization. Altogether, Zeno SWATH DIA methodology allows fast, sensitive, and robust proteomic workflows using analytical flow and is amenable to large-scale studies.

Multiomic signals associated with maternal epidemiological factors contributing to preterm birth in low- and middle-income countries.

Preterm birth (PTB) is the leading cause of death in children under five, yet comprehensive studies are hindered by its multiple complex etiologies. Epidemiological associations between PTB and maternal characteristics have been previously described. This work used multiomic profiling and multivariate modeling to investigate the biological signatures of these characteristics. Maternal covariates were collected during pregnancy from 13,841 pregnant women across five sites. Plasma samples from 231 participants were analyzed to generate proteomic, metabolomic, and lipidomic datasets. Machine learning models showed robust performance for the prediction of PTB (AUROC = 0.70), time-to-delivery (r = 0.65), maternal age (r = 0.59), gravidity (r = 0.56), and BMI (r = 0.81). Time-to-delivery biological correlates included fetal-associated proteins (e.g., ALPP, AFP, and PGF) and immune proteins (e.g., PD-L1, CCL28, and LIFR). Maternal age negatively correlated with collagen COL9A1, gravidity with endothelial NOS and inflammatory chemokine CXCL13, and BMI with leptin and structural protein FABP4. These results provide an integrated view of epidemiological factors associated with PTB and identify biological signatures of clinical covariates affecting this disease.

A systems approach points to a therapeutic role for retinoids in asparaginase-associated pancreatitis.

Among drug-induced adverse events, pancreatitis is life-threatening and results in substantial morbidity. A prototype example is the pancreatitis caused by asparaginase, a crucial drug used to treat acute lymphoblastic leukemia (ALL). Here, we used a systems approach to identify the factors affecting asparaginase-associated pancreatitis (AAP). Connectivity Map analysis of the transcriptomic data showed that asparaginase-induced gene signatures were potentially reversed by retinoids (vitamin A and its analogs). Analysis of a large electronic health record database (TriNetX) and the U.S. Federal Drug Administration Adverse Events Reporting System demonstrated a reduction in AAP risk with concomitant exposure to vitamin A. Furthermore, we performed a global metabolomic screening of plasma samples from 24 individuals with ALL who developed pancreatitis (cases) and 26 individuals with ALL who did not develop pancreatitis (controls), before and after a single exposure to asparaginase. Screening from this discovery cohort revealed that plasma carotenoids were lower in the cases than in controls. This finding was validated in a larger external cohort. A 30-day dietary recall showed that the cases received less dietary vitamin A than the controls did. In mice, asparaginase administration alone was sufficient to reduce circulating and hepatic retinol. Based on these data, we propose that circulating retinoids protect against pancreatic inflammation and that asparaginase reduces circulating retinoids. Moreover, we show that AAP is more likely to develop with reduced dietary vitamin A intake. The systems approach taken for AAP provides an impetus to examine the role of dietary vitamin A supplementation in preventing or treating AAP.

Challenging obesity and sex based differences in resting energy expenditure using allometric modeling, a sub-study of the DIETFITS clinical trial.

BACKGROUND & AIMS: Resting energy expenditure (REE) is a major component of energy balance. While REE is usually indexed to total body weight (BW), this may introduce biases when assessing REE in obesity or during weight loss intervention. The main objective of the study was to quantify the bias introduced by ratiometric scaling of REE using BW both at baseline and following weight loss intervention. DESIGN: Participants in the DIETFITS Study (Diet Intervention Examining The Factors Interacting with Treatment Success) who completed indirect calorimetry and dual-energy X-ray absorptiometry (DXA) were included in the study. Data were available in 438 participants at baseline, 340 at 6 months and 323 at 12 months. We used multiplicative allometric modeling based on lean body mass (LBM) and fat mass (FM) to derive body size independent scaling of REE. Longitudinal changes in indexed REE were then assessed following weight loss intervention. RESULTS: A multiplicative model including LBM, FM, age, Black race and the double product (DP) of systolic blood pressure and heart rate explained 79% of variance in REE. REE indexed to [LBM0.66 × FM0.066] was body size and sex independent (p = 0.91 and p = 0.73, respectively) in contrast to BW based indexing which showed a significant inverse relationship to BW (r = -0.47 for female and r = -0.44 for male, both p < 0.001). When indexed to BW, significant baseline differences in REE were observed between male and female (p < 0.001) and between individuals who are overweight and obese (p < 0.001) while no significant differences were observed when indexed to REE/[LBM0.66 × FM0.066], p > 0.05). Percentage predicted REE adjusted for LBM, FM and DP remained stable following weight loss intervention (p = 0.614). CONCLUSION: Allometric scaling of REE based on LBM and FM removes body composition-associated biases and should be considered in obesity and weight-based intervention studies.

Plasma metabolic profiling implicates dysregulated lipid metabolism and glycolytic shift in hyperinflammatory ARDS.

Using latent class analysis (LCA) of clinical and protein biomarkers, researchers have identified two phenotypes of the acute respiratory distress syndrome (ARDS) with divergent clinical trajectories and treatment responses. We investigated whether plasma metabolites differed among patients with LCA-derived hyperinflammatory and hypoinflammatory ARDS, and we tested the prognostic utility of adding metabolic clusters to LCA phenotypes. We analyzed data from 93 patients with ARDS and sepsis enrolled in a multicenter prospective cohort of critically ill patients, comparing 970 metabolites between the two LCA-derived phenotypes. In all, 188 metabolites were differentially abundant between the two LCA-derived phenotypes. After adjusting for age, sex, confounding medications, and comorbid liver and kidney disease, 82 metabolites remained significantly different. Patients with hyperinflammatory ARDS had reduced circulating lipids but high levels of pyruvate, lactate, and malate. Metabolic cluster and LCA-derived phenotypes were each significantly and independently associated with survival. Patients with hyperinflammatory ARDS may be experiencing a glycolytic shift leading to dysregulated lipid metabolism. Metabolic profiling offers prognostic information beyond what is captured by LCA phenotypes alone. Deeper biological profiling may identify key differences in pathogenesis among patients with ARDS and may lead to novel targeted therapies.

Early prediction and longitudinal modeling of preeclampsia from multiomics.

Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear. We developed machine-learning models for early prediction of preeclampsia (first 16 weeks of pregnancy) and over gestation by analyzing six omics datasets from a longitudinal cohort of pregnant women. For early pregnancy, a prediction model using nine urine metabolites had the highest accuracy and was validated on an independent cohort (area under the receiver-operating characteristic curve [AUC] = 0.88, 95% confidence interval [CI] [0.76, 0.99] cross-validated; AUC = 0.83, 95% CI [0.62,1] validated). Univariate analysis demonstrated statistical significance of identified metabolites. An integrated multiomics model further improved accuracy (AUC = 0.94). Several biological pathways were identified including tryptophan, caffeine, and arachidonic acid metabolisms. Integration with immune cytometry data suggested novel associations between immune and proteomic dynamics. While further validation in a larger population is necessary, these encouraging results can serve as a basis for a simple, early diagnostic test for preeclampsia.

Endogenous Retroviral Elements Generate Pathologic Neutrophils in Pulmonary Arterial Hypertension.

Rationale: The role of neutrophils and their extracellular vesicles (EVs) in the pathogenesis of pulmonary arterial hypertension is unclear. Objectives: To relate functional abnormalities in pulmonary arterial hypertension neutrophils and their EVs to mechanisms uncovered by proteomic and transcriptomic profiling. Methods: Production of elastase, release of extracellular traps, adhesion, and migration were assessed in neutrophils from patients with pulmonary arterial hypertension and control subjects. Proteomic analyses were applied to explain functional perturbations, and transcriptomic data were used to find underlying mechanisms. CD66b-specific neutrophil EVs were isolated from plasma of patients with pulmonary arterial hypertension, and we determined whether they produce pulmonary hypertension in mice. Measurements and Main Results: Neutrophils from patients with pulmonary arterial hypertension produce and release increased neutrophil elastase, associated with enhanced extracellular traps. They exhibit reduced migration and increased adhesion attributed to elevated ß1-integrin and vinculin identified by proteomic analysis and previously linked to an antiviral response. This was substantiated by a transcriptomic IFN signature that we related to an increase in human endogenous retrovirus K envelope protein. Transfection of human endogenous retrovirus K envelope in a neutrophil cell line (HL-60) increases neutrophil elastase and IFN genes, whereas vinculin is increased by human endogenous retrovirus K deoxyuridine triphosphate diphosphatase that is elevated in patient plasma. Neutrophil EVs from patient plasma contain increased neutrophil elastase and human endogenous retrovirus K envelope and induce pulmonary hypertension in mice, mitigated by elafin, an elastase inhibitor. Conclusions: Elevated human endogenous retroviral elements and elastase link a neutrophil innate immune response to pulmonary arterial hypertension.

An exercise-inducible metabolite that suppresses feeding and obesity.

Exercise confers protection against obesity, type 2 diabetes and other cardiometabolic diseases1-5. However, the molecular and cellular mechanisms that mediate the metabolic benefits of physical activity remain unclear6. Here we show that exercise stimulates the production of N-lactoyl-phenylalanine (Lac-Phe), a blood-borne signalling metabolite that suppresses feeding and obesity. The biosynthesis of Lac-Phe from lactate and phenylalanine occurs in CNDP2+ cells, including macrophages, monocytes and other immune and epithelial cells localized to diverse organs. In diet-induced obese mice, pharmacological-mediated increases in Lac-Phe reduces food intake without affecting movement or energy expenditure. Chronic administration of Lac-Phe decreases adiposity and body weight and improves glucose homeostasis. Conversely, genetic ablation of Lac-Phe biosynthesis in mice increases food intake and obesity following exercise training. Last, large activity-inducible increases in circulating Lac-Phe are also observed in humans and racehorses, establishing this metabolite as a molecular effector associated with physical activity across multiple activity modalities and mammalian species. These data define a conserved exercise-inducible metabolite that controls food intake and influences systemic energy balance.

Robust identification of temporal biomarkers in longitudinal omics studies.

MOTIVATION: Longitudinal studies increasingly collect rich 'omics' data sampled frequently over time and across large cohorts to capture dynamic health fluctuations and disease transitions. However, the generation of longitudinal omics data has preceded the development of analysis tools that can efficiently extract insights from such data. In particular, there is a need for statistical frameworks that can identify not only which omics features are differentially regulated between groups but also over what time intervals. Additionally, longitudinal omics data may have inconsistencies, including non-uniform sampling intervals, missing data points, subject dropout and differing numbers of samples per subject. RESULTS: In this work, we developed OmicsLonDA, a statistical method that provides robust identification of time intervals of temporal omics biomarkers. OmicsLonDA is based on a semi-parametric approach, in which we use smoothing splines to model longitudinal data and infer significant time intervals of omics features based on an empirical distribution constructed through a permutation procedure. We benchmarked OmicsLonDA on five simulated datasets with diverse temporal patterns, and the method showed specificity greater than 0.99 and sensitivity greater than 0.87. Applying OmicsLonDA to the iPOP cohort revealed temporal patterns of genes, proteins, metabolites and microbes that are differentially regulated in male versus female subjects following a respiratory infection. In addition, we applied OmicsLonDA to a longitudinal multi-omics dataset of pregnant women with and without preeclampsia, and OmicsLonDA identified potential lipid markers that are temporally significantly different between the two groups. AVAILABILITY AND IMPLEMENTATION: We provide an open-source R package (https://bioconductor.org/packages/OmicsLonDA), to enable widespread use. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Prediction of gestational age using urinary metabolites in term and preterm pregnancies.

Assessment of gestational age (GA) is key to provide optimal care during pregnancy. However, its accurate determination remains challenging in low- and middle-income countries, where access to obstetric ultrasound is limited. Hence, there is an urgent need to develop clinical approaches that allow accurate and inexpensive estimations of GA. We investigated the ability of urinary metabolites to predict GA at time of collection in a diverse multi-site cohort of healthy and pathological pregnancies (n = 99) using a broad-spectrum liquid chromatography coupled with mass spectrometry (LC-MS) platform. Our approach detected a myriad of steroid hormones and their derivatives including estrogens, progesterones, corticosteroids, and androgens which were associated with pregnancy progression. We developed a restricted model that predicted GA with high accuracy using three metabolites (rho = 0.87, RMSE = 1.58 weeks) that was validated in an independent cohort (n = 20). The predictions were more robust in pregnancies that went to term in comparison to pregnancies that ended prematurely. Overall, we demonstrated the feasibility of implementing urine metabolomics analysis in large-scale multi-site studies and report a predictive model of GA with a potential clinical value.