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Profiles the civil rights leader, discussing his career as a pastor, his fight for African American rights, and his legacy.

Multi-organ biological aging clocks across different organ systems have been shown to predict human disease and mortality. Here, we extend this multi-organ framework to plasma metabolomics, developing five organ-specific metabolome-based biological age gaps (MetBAGs) using 107 plasma non-derivatized metabolites from 274,247 UK Biobank participants. Our age prediction models achieve a mean absolute error of approximately 6 years (0.25< r  < 0.42). Crucially, including composite metabolites (e.g. sums or ratios of raw metabolites) results in poor generalizability to independent test data due to multicollinearity. Genome-wide associations identify 405 MetBAG-locus pairs (P < 5 × 10 −8 /5). Using SBayesS, we estimate the SNP-based heritability (0.09< h S N P 2  < 0.18), negative selection signatures (−0.93 <  S  < −0.76), and polygenicity (0.001< Pi  < 0.003) for the 5 MetBAGs. Genetic correlation and Mendelian randomization analyses reveal potential causal links between the 5 MetBAGs and cardiometabolic conditions (e.g., metabolic disorders and hypertension). Integrating multi-organ and multi-omics features improves disease category and mortality predictions. The 5 MetBAGs extend existing biological aging clocks to study human aging and disease across multiple biological scales. All results are publicly available at https://labs-laboratory.com/medicine/ . Aging affects multiple organs and tracking these changes could improve our understanding of disease risk. Here, the authors show that metabolomics-based organ-specific aging clocks can predict future risk of cardiometabolic disease and mortality.

\"Alexander Hamilton was one of America's founders. He was the first secretary of the treasury and George Washington's right-hand man. But he also made some dangerous enemies during his short yet dramatic life \"-- Provided by publisher.

Electrochemical energy storage is an enabling technology for personal and industrial electronics, adoption of intermittent renewable energy, and the electrification of transportation. From a fundamental solid-state chemistry perspective, and in the context of batteries, it is interesting to explore new mixed ionic–electronic conductors that can withstand large changes in composition and electronic configuration over ∼1000 charge– discharge cycles to function as electrode materials and to explore new pure ion conductors with extremely low electronic conductivities that could function as solid electrolytes or interfacial coatings. Understanding the mechanisms that facilitate ion and/or electron transport or induce material degradation are the keys to discovering and engineering the next generation of battery materials. Relatively few unique crystal structures underpin most battery materials. We are particularly interested in novel complex oxides that might offer new insights into structure–property relationships or even new performance characteristics. This talk will focus on recent examples from our lab: (i) defects, electrochemistry, and metal–metal bonding in NaNb7O18 and NaNb13O33 framework structures (Wadsley–Roth derivatives) with tunnel-blocking defects, and (ii) new lithium-rich “layered” structures, Li3MO4 (M = Nb, Ta) synthesized via instantaneous ion exchange in a molten salt flux. Both families of materials are characterized with an ‘NMR crystallography’ approach that combines X-ray and neutron diffraction with DFT-supported solid-state NMR spectroscopy.

Beginning readers will learn about the milestones in George Washington's life.

Background Recent research1,2 has generated increasing interest in modeling human aging and disease within a multi‐organ framework. Plasma proteomics3 has emerged as a widely used approach for predicting individual chronological age, resulting in the proteome‐based biological age gap (ProtBAG). Here, we used CSF proteomics from the ADNI study to derive 11 organ‐specific ProtBAGs using 2 machine learning (ML) methods. Method CSF proteomics was generated using the SomaScan 7k platform in ADNI, which included 7,008 protein levels from 736 participants (mean age: 73.3 ± 7.4 years; 57% women). Missing proteomics values were imputed using AutoComplete4. Organ‐enriched proteins for 11 organ systems were determined by at least four‐fold higher mRNA levels in the tissue of interest than in other organ tissues. These organ‐enriched proteins were then fit to a linear support vector regression (SVR) and LASSO regression model. Nested random holdout cross‐validation (50 repetitions) was implemented; mean absolute error (MAE) and Pearson's r were used to evaluate model performance. Result For proteomics imputation, we chose the imputed results with the copy‐mask amount of 0.3 (2% missing rate in data), which led to the best model performance (r2=0.54). Overall, LASSO and Linear SVR achieved comparable MAE values with only slight differences between the two models. The brain and hepatic showed the lowest MAE values (Linear SVR: 4.56 and 4.52 for the brain and hepatic ProtBAGs; LASSO 4.55 and 4.52 for the brain and hepatic ProtBAGs) (Figure 1). Across the 11 organ systems, MAE values from our analyses, ranging from 4.5 to 6, were in line with previous literature using brain imaging2. In addition, the brain and hepatic showed the highest Pearson's r values (Linear SVR: 0.62 and 0.64 for the brain and hepatic ProtBAGs; LASSO 0.63 and 0.65 for the brain and hepatic ProtBAGs) (Figure 2). Other organ systems, such as the endocrine, female reproductive system, and male reproductive system, showed relatively inferior model performance. Conclusion This study leverages CSF proteomics data from ADNI to accurately develop 11 organ‐specific ProtBAGs, enriching the organ aging clock framework established in previous literature using plasma proteomics. Future research will investigate the relationship between these ProtBAGs, cognition, and AD progression.

Recent research has generated increasing interest in modeling human aging and disease within a multi-organ framework. Plasma proteomics has emerged as a widely used approach for predicting individual chronological age, resulting in the proteome-based biological age gap (ProtBAG). Here, we used CSF proteomics from the ADNI study to derive 11 organ-specific ProtBAGs using 2 machine learning (ML) methods. CSF proteomics was generated using the SomaScan 7k platform in ADNI, which included 7,008 protein levels from 736 participants (mean age: 73.3 ± 7.4 years; 57% women). Missing proteomics values were imputed using AutoComplete . Organ-enriched proteins for 11 organ systems were determined by at least four-fold higher mRNA levels in the tissue of interest than in other organ tissues. These organ-enriched proteins were then fit to a linear support vector regression (SVR) and LASSO regression model. Nested random holdout cross-validation (50 repetitions) was implemented; mean absolute error (MAE) and Pearson's r were used to evaluate model performance. For proteomics imputation, we chose the imputed results with the copy-mask amount of 0.3 (2% missing rate in data), which led to the best model performance (r =0.54). Overall, LASSO and Linear SVR achieved comparable MAE values with only slight differences between the two models. The brain and hepatic showed the lowest MAE values (Linear SVR: 4.56 and 4.52 for the brain and hepatic ProtBAGs; LASSO 4.55 and 4.52 for the brain and hepatic ProtBAGs) (Figure 1). Across the 11 organ systems, MAE values from our analyses, ranging from 4.5 to 6, were in line with previous literature using brain imaging . In addition, the brain and hepatic showed the highest Pearson's r values (Linear SVR: 0.62 and 0.64 for the brain and hepatic ProtBAGs; LASSO 0.63 and 0.65 for the brain and hepatic ProtBAGs) (Figure 2). Other organ systems, such as the endocrine, female reproductive system, and male reproductive system, showed relatively inferior model performance. This study leverages CSF proteomics data from ADNI to accurately develop 11 organ-specific ProtBAGs, enriching the organ aging clock framework established in previous literature using plasma proteomics. Future research will investigate the relationship between these ProtBAGs, cognition, and AD progression.

Recent research has generated increasing interest in modeling human aging and disease within a multi-organ framework. Plasma proteomics has emerged as a widely used approach for predicting individual chronological age, resulting in the proteome-based biological age gap (ProtBAG). Here, we used CSF proteomics from the ADNI study to derive 11 organ-specific ProtBAGs using 2 machine learning (ML) methods. CSF proteomics was generated using the SomaScan 7k platform in ADNI, which included 7,008 protein levels from 736 participants (mean age: 73.3 ± 7.4 years; 57% women). Missing proteomics values were imputed using AutoComplete . Organ-enriched proteins for 11 organ systems were determined by at least four-fold higher mRNA levels in the tissue of interest than in other organ tissues. These organ-enriched proteins were then fit to a linear support vector regression (SVR) and LASSO regression model. Nested random holdout cross-validation (50 repetitions) was implemented; mean absolute error (MAE) and Pearson's r were used to evaluate model performance. For proteomics imputation, we chose the imputed results with the copy-mask amount of 0.3 (2% missing rate in data), which led to the best model performance (r =0.54). Overall, LASSO and Linear SVR achieved comparable MAE values with only slight differences between the two models. The brain and hepatic showed the lowest MAE values (Linear SVR: 4.56 and 4.52 for the brain and hepatic ProtBAGs; LASSO 4.55 and 4.52 for the brain and hepatic ProtBAGs) (Figure 1). Across the 11 organ systems, MAE values from our analyses, ranging from 4.5 to 6, were in line with previous literature using brain imaging . In addition, the brain and hepatic showed the highest Pearson's r values (Linear SVR: 0.62 and 0.64 for the brain and hepatic ProtBAGs; LASSO 0.63 and 0.65 for the brain and hepatic ProtBAGs) (Figure 2). Other organ systems, such as the endocrine, female reproductive system, and male reproductive system, showed relatively inferior model performance. This study leverages CSF proteomics data from ADNI to accurately develop 11 organ-specific ProtBAGs, enriching the organ aging clock framework established in previous literature using plasma proteomics. Future research will investigate the relationship between these ProtBAGs, cognition, and AD progression.

Background Recent research1,2 has generated increasing interest in modeling human aging and disease within a multi‐organ framework. Plasma proteomics3 has emerged as a widely used approach for predicting individual chronological age, resulting in the proteome‐based biological age gap (ProtBAG). Here, we used CSF proteomics from the ADNI study to derive 11 organ‐specific ProtBAGs using 2 machine learning (ML) methods. Method CSF proteomics was generated using the SomaScan 7k platform in ADNI, which included 7,008 protein levels from 736 participants (mean age: 73.3 ± 7.4 years; 57% women). Missing proteomics values were imputed using AutoComplete4. Organ‐enriched proteins for 11 organ systems were determined by at least four‐fold higher mRNA levels in the tissue of interest than in other organ tissues. These organ‐enriched proteins were then fit to a linear support vector regression (SVR) and LASSO regression model. Nested random holdout cross‐validation (50 repetitions) was implemented; mean absolute error (MAE) and Pearson’s r were used to evaluate model performance. Result For proteomics imputation, we chose the imputed results with the copy‐mask amount of 0.3 (2% missing rate in data), which led to the best model performance (r 2=0.54). Overall, LASSO and Linear SVR achieved comparable MAE values with only slight differences between the two models. The brain and hepatic showed the lowest MAE values (Linear SVR: 4.56 and 4.52 for the brain and hepatic ProtBAGs; LASSO 4.55 and 4.52 for the brain and hepatic ProtBAGs) (Figure 1). Across the 11 organ systems, MAE values from our analyses, ranging from 4.5 to 6, were in line with previous literature using brain imaging2. In addition, the brain and hepatic showed the highest Pearson’s r values (Linear SVR: 0.62 and 0.64 for the brain and hepatic ProtBAGs; LASSO 0.63 and 0.65 for the brain and hepatic ProtBAGs) (Figure 2). Other organ systems, such as the endocrine, female reproductive system, and male reproductive system, showed relatively inferior model performance. Conclusion This study leverages CSF proteomics data from ADNI to accurately develop 11 organ‐specific ProtBAGs, enriching the organ aging clock framework established in previous literature using plasma proteomics. Future research will investigate the relationship between these ProtBAGs, cognition, and AD progression.

Background We investigated sex differences in a machine learning‐derived imaging signature of AD brain atrophy (i.e., SPARE‐AD5), in relation to age, genetic factors (APOE ε4 allele), and multi‐organ biological age gap (BAG2,3). Methods Data from the iSTAGING and MULTI consortia included 53,622 participants without diagnosed cognitive impairment (mean age: 61.8 ± 12.6 years; 54% women). The SPARE‐AD model uses a support vector machine with a linear kernel to distinguish between cognitively normal individuals and those with AD5. Generalized linear models assessed sex differences and nine BAG associations with SPARE‐AD, adjusting for age, sex, APOE ε4, and interactions, and analysis of covariance (ANCOVA) with Tukey's test to assess differences in SPARE‐AD scores between APOE ε4 allele carrier groups. Results Overall, SPARE‐AD increased with age (β = 0.018, p < 2e‐16). Women had higher SPARE‐AD scores than men (β = ‐0.393, p < 2e‐16). Women had higher SPARE‐AD scores at younger ages but lower values at older ages (β = 0.006, p < 2e‐16 for the age‐sex interaction term) when compared to males (Figure 1a). Furthermore, SPARE‐AD was positively associated with the number of APOE ε4 alleles (β = 0.018, p = 1.06e‐6). Non‐carriers and heterozygous carriers of the APOE ε4 allele exhibited lower SPARE‐AD scores compared to homozygous carriers in analyses of both combined sexes and in men alone; this pattern was not observed in women (Figure 1b‐d). Among the nine BAGs, the brain BAG was most strongly associated with SPARE‐AD in both sexes combined (β = 0.018, p = 1.09e‐302) (Figure 2a) and separately (women: β = 0.017, p = 5.08e‐128; men: β = 0.019, p = 4.24e‐175) (Figure 2b‐c). Other significant BAG associations were observed in men and not in women, including musculoskeletal (β = 0.004, p = 0.02), immune (β = 0.004, p = 0.02), and metabolic BAGs (β = 0.005, p = 0.02) (Figure 2b‐d). Conclusion SPARE‐AD scores increased with age and were higher in women at younger ages but lower than men at older ages, with a significant age*sex interaction, and were positively associated with the number of the APOE ε4 allele, particularly in men.