A machine learning approach for potential Super-Agers identification using neuronal functional connectivity networks.

INTRODUCTION: Aging is often associated with cognitive decline. Understanding neural factors that distinguish adults in midlife with superior cognitive abilities (Positive-Agers) may offer insight into how the aging brain achieves resilience. The goals of this study are to (1) introduce an optimal labeling mechanism to distinguish between Positive-Agers and Cognitive Decliners, and (2) identify Positive-Agers using neuronal functional connectivity networks data and demographics. METHODS: In this study, principal component analysis initially created latent cognitive trajectories groups. A hybrid algorithm of machine learning and optimization was then designed to predict latent groups using neuronal functional connectivity networks derived from resting state functional magnetic resonance imaging. Specifically, the Optimal Labeling with Bayesian Optimization (OLBO) algorithm used an unsupervised approach, iterating a logistic regression function with Bayesian posterior updating. This study encompassed 6369 adults from the UK Biobank cohort. RESULTS: OLBO outperformed baseline models, achieving an area under the curve of 88% when distinguishing between Positive-Agers and cognitive decliners. DISCUSSION: OLBO may be a novel algorithm that distinguishes cognitive trajectories with a high degree of accuracy in cognitively unimpaired adults. Highlights: Design an algorithm to distinguish between a Positive-Ager and a Cognitive-Decliner.Introduce a mathematical definition for cognitive classes based on cognitive tests.Accurate Positive-Ager identification using rsfMRI and demographic data (AUC = 0.88).Posterior default mode network has the highest impact on Positive-Aging odds ratio.

Mapping the Evolutionary Space of SARS-CoV-2 Variants to Anticipate Emergence of Subvariants Resistant to COVID-19 Therapeutics.

New sublineages of SARS-CoV-2 variants-of-concern (VOCs) continuously emerge with mutations in the spike glycoprotein. In most cases, the sublineage-defining mutations vary between the VOCs. It is unclear whether these differences reflect lineage-specific likelihoods for mutations at each spike position or the stochastic nature of their appearance. Here we show that SARS-CoV-2 lineages have distinct evolutionary spaces (a probabilistic definition of the sequence states that can be occupied by expanding virus subpopulations). This space can be accurately inferred from the patterns of amino acid variability at the whole-protein level. Robust networks of co-variable sites identify the highest-likelihood mutations in new VOC sublineages and predict remarkably well the emergence of subvariants with resistance mutations to COVID-19 therapeutics. Our studies reveal the contribution of low frequency variant patterns at heterologous sites across the protein to accurate prediction of the changes at each position of interest.

Alzheimer's Disease Genetic Influences Impact the Associations between Diet and Resting-State Functional Connectivity: A Study from the UK Biobank.

BACKGROUND: Red wine and dairy products have been staples in human diets for a long period. However, the impact of red wine and dairy intake on brain network activity remains ambiguous and requires further investigation. METHODS: This study investigated the associations between dairy and red wine consumption and seven neural networks' connectivity with functional magnetic resonance imaging (fMRI) data from a sub-cohort of the UK Biobank database. Linear mixed models were employed to regress dairy and red wine consumption against the intrinsic functional connectivity for each neural network. Interactions with Alzheimer's disease (AD) risk factors, including apolipoprotein E4 (APOE4) genotype, TOMM40 genotype, and family history of AD, were also assessed. RESULT: More red wine consumption was associated with enhanced connectivity in the central executive function network and posterior default mode network. Greater milk intake was correlated with more left executive function network connectivity, while higher cheese consumption was linked to reduced posterior default mode network connectivity. For participants without a family history of Alzheimer's disease (AD), increased red wine consumption was positively correlated with enhanced left executive function network connectivity. In contrast, participants with a family history of AD displayed diminished network connectivity in relation to their red wine consumption. The association between cheese consumption and neural network connectivity was influenced by APOE4 status, TOMM40 status, and family history, exhibiting contrasting patterns across different subgroups. CONCLUSION: The findings of this study indicate that family history modifies the relationship between red wine consumption and network strength. The interaction effects between cheese intake and network connectivity may vary depending on the presence of different genetic factors.

Adiposity and insulin resistance moderate the links between neuroelectrophysiology and working and episodic memory functions in young adult males but not females.

INTRODUCTION: Obesity and insulin resistance negatively influence neural activity and cognitive function, but electrophysiological mechanisms underlying these interrelationships remain unclear. This study investigated whether adiposity and insulin resistance moderated neural activity and underlying cognitive functions in young adults. METHODS: Real-time electroencephalography (EEG) was recorded in 38 lean (n = 12) and obese (n = 26) young adults with (n = 15) and without (n = 23) insulin resistance (18-38 years, 55.3% female) as participants completed three neurocognitive tasks in working memory (Operation Span), inhibitory control (Stroop), and episodic memory (Visual Association Test). Body fat percentage was quantified by a dual-energy X-ray absorptiometry scan (DEXA/DXA). Fasting serum insulin and glucose were quantified to calculate Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) values, for which a higher value indicates more insulin resistance. Hierarchical moderated regression analysis tested these interrelationships. RESULTS: In males, greater frontal negative slow wave (fNSW) and positive slow wave (PSW) amplitudes were linked to higher working memory accuracy in participants with low, but not high, body fat percentage and HOMA-IR levels. In contrast, body fat percentage and HOMA-IR did not moderate these associations in females. Furthermore, body fat percentage and HOMA-IR values moderated the relationship between greater fNSW amplitudes and better episodic memory accuracy in males, but not females. Finally, body fat percentage and insulin resistance did not moderate the link between neural activity and inhibitory control for either sex. CONCLUSION: Young adult males, but not females, with higher body adiposity and insulin resistance showed reduced neural activity and worse underlying working and episodic memory functions.

A classification algorithm based on dynamic ensemble selection to predict mutational patterns of the envelope protein in HIV-infected patients.

BACKGROUND: Therapeutics against the envelope (Env) proteins of human immunodeficiency virus type 1 (HIV-1) effectively reduce viral loads in patients. However, due to mutations, new therapy-resistant Env variants frequently emerge. The sites of mutations on Env that appear in each patient are considered random and unpredictable. Here we developed an algorithm to estimate for each patient the mutational state of each position based on the mutational state of adjacent positions on the three-dimensional structure of the protein. METHODS: We developed a dynamic ensemble selection algorithm designated k-best classifiers. It identifies the best classifiers within the neighborhood of a new observation and applies them to predict the variability state of each observation. To evaluate the algorithm, we applied amino acid sequences of Envs from 300 HIV-1-infected individuals (at least six sequences per patient). For each patient, amino acid variability values at all Env positions were mapped onto the three-dimensional structure of the protein. Then, the variability state of each position was estimated by the variability at adjacent positions of the protein. RESULTS: The proposed algorithm showed higher performance than the base learner and a panel of classification algorithms. The mutational state of positions in the high-mannose patch and CD4-binding site of Env, which are targeted by multiple therapeutics, was predicted well. Importantly, the algorithm outperformed other classification techniques for predicting the variability state at multi-position footprints of therapeutics on Env. CONCLUSIONS: The proposed algorithm applies a dynamic classifier-scoring approach that increases its performance relative to other classification methods. Better understanding of the spatiotemporal patterns of variability across Env may lead to new treatment strategies that are tailored to the unique mutational patterns of each patient. More generally, we propose the algorithm as a new high-performance dynamic ensemble selection technique.

Exploring the secrets of super-aging: a UK Biobank study on brain health and cognitive function.

Communities across the globe are faced with a rapidly aging society, where age is the main risk factor for cognitive decline and development of Alzheimer's and related diseases. Despite extensive research, there have been no successful treatments yet. A rare group of individuals called "super-agers" have been noted to thrive with their exceptional ability to maintain a healthy brain and normal cognitive function even in old age. Studying their traits, lifestyles, and environments may provide valuable insight. This study used a data-driven approach to identify potential super-agers among 7121 UK Biobank participants and found that these individuals have the highest total brain volume, best cognitive performance, and lowest functional connectivity. The researchers suggest a novel hypothesis that these super-agers possess enhanced neural processing efficiency that increases with age and introduce a definition of the "neural efficiency index." Furthermore, several other types of aging were identified and significant structural-functional differences were observed between them, highlighting the benefit of research efforts in personalized medicine and precision nutrition.

COVID-19 forecasting and intervention planning using gated recurrent unit and evolutionary algorithm.

The rapid spread of COVID-19, caused by the SARS-CoV-2 virus, has had and continues to pose a significant threat to global health. We propose a predictive model based on the gated recurrent unit (GRU) that investigates the influence of non-pharmaceutical interventions (NPIs) on the progression of COVID-19. The proposed model is validated by case studies for multiple states in the United States. It should be noted that the proposed model can be generalized to other regions of interest. The results show that the predictive model can achieve accurate forecasts across the US. The forecast is then utilized to identify the optimal mitigation policies. The goal is to identify the best stringency level for each policy that can minimize the total number of new COVID-19 cases while minimizing the mitigation costs. A meta-heuristics method, named multi-population evolutionary algorithm with differential evolution (MPEA-DE), has been developed to identify optimal mitigation strategies that minimize COVID-19 infection cases while reducing economic and other negative implications. We compared the optimal mitigation strategies identified by the MPEA-DE model with three baseline search strategies. The results show that MPEA-DE performs better than other baseline models based on prescription dominance.

Mutability Patterns Across the Spike Glycoprotein Reveal the Diverging and Lineage-specific Evolutionary Space of SARS-CoV-2.

Mutations in the spike glycoprotein of SARS-CoV-2 allow the virus to probe the sequence space in search of higher-fitness states. New sublineages of SARS-CoV-2 variants-of-concern (VOCs) continuously emerge with such mutations. Interestingly, the sites of mutation in these sublineages vary between the VOCs. Whether such differences reflect the random nature of mutation appearance or distinct evolutionary spaces of spike in the VOCs is unclear. Here we show that each position of spike has a lineage-specific likelihood for mutations to appear and dominate descendent sublineages. This likelihood can be accurately estimated from the lineage-specific mutational profile of spike at a protein-wide level. The mutability environment of each position, including adjacent sites on the protein structure and neighboring sites on the network of comutability, accurately forecast changes in descendent sublineages. Mapping of imminent changes within the VOCs can contribute to the design of immunogens and therapeutics that address future forms of SARS-CoV-2.

Limited Variation between SARS-CoV-2-Infected Individuals in Domain Specificity and Relative Potency of the Antibody Response against the Spike Glycoprotein.

The spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is arranged as a trimer on the virus surface, composed of three S1 and three S2 subunits. Infected and vaccinated individuals generate antibodies against spike, which can neutralize the virus. Most antibodies target the receptor-binding domain (RBD) and N-terminal domain (NTD) of S1; however, antibodies against other regions of spike have also been isolated. The interhost variability in domain specificity and relative neutralization efficacy of the antibodies is still poorly characterized. To this end, we tested serum and plasma samples collected from 85 coronavirus disease 2019 (COVID-19) convalescent subjects. Samples were analyzed using seven immunoassays that employ different domains, subunits, and oligomeric forms of spike to capture the antibodies. Samples were also tested for their neutralization of pseudovirus containing SARS-CoV-2 spike and of replication-competent SARS-CoV-2. While the total amount of anti-spike antibodies produced varied among convalescent subjects, we observed an unexpectedly fixed ratio of RBD- to NTD-targeting antibodies. The relative potency of the response (defined as the measured neutralization efficacy relative to the total level of spike-targeting antibodies) also exhibited limited variation between subjects and was not associated with the overall amount of antispike antibodies produced. These studies suggest that host-to-host variation in the polyclonal response elicited against SARS-CoV-2 spike in early pandemic subjects is primarily limited to the quantity of antibodies generated rather than their domain specificity or relative neutralization potency. IMPORTANCE Infection by SARS-CoV-2 elicits antibodies against various domains of the spike protein, including the RBD and NTD of subunit S1 and against subunit S2. The antibody responses of different infected individuals exhibit different efficacies to inactivate (neutralize) the virus. Here, we show that the observed variation in the neutralizing activity of the antibody responses in COVID-19 convalescent subjects is caused by differences in the amounts of antibodies rather than their recognition properties or the potency of their antiviral activity. These findings suggest that COVID-19 vaccine strategies that focus on enhancing the overall level of the antibodies will likely elicit a more uniformly efficacious protective response.