No abstract

No abstract

Normative modeling is a statistical approach to quantify the degree to which a particular individual-level measure deviates from the pattern observed in a normative reference population. When applied to human brain morphometric measures it has the potential to inform about the significance of normative deviations for health and disease. Normative models can be implemented using a variety of algorithms that have not been systematically appraised. To address this gap, eight algorithms were compared in terms of performance and computational efficiency using brain regional morphometric data from 37,407 healthy individuals (53.33% female; aged 3 to 90 years) collated from 86 international MRI datasets. Performance was assessed with the mean absolute error (MAE) and computational efficiency was inferred from central processing unit (CPU) time. The algorithms evaluated were Ordinary Least Squares Regression (OLSR), Bayesian Linear Regression (BLR), Generalized Additive Models for Location, Scale, and Shape (GAMLSS), Parametric Lambda, Mu, Sigma (LMS), Multivariable Fractional Polynomial Regression (MFPR), Warped Bayesian Linear Regression (WBLG), and Hierarchical Bayesian Regression (HBR). Model optimization involved testing 9 covariate combinations pertaining to acquisition features, parcellation software versions, and global neuroimaging measures (i.e., total intracranial volume, mean cortical thickness, and mean cortical surface area). Statistical comparisons across models at PFDR<0.05 indicated that MFPR-derived sex and region-specific models with nonlinear polynomials for age and linear effect of global measures had superior predictive accuracy; for models of regional subcortical volume the MAE range was 70-80 mm3 and the corresponding range for regional cortical thickness and regional cortical surface area was 0.09-0.26 mm; and 24-660 mm2 respectively. The MFPR-derived models were also computationally more efficient with a CPU time below one second compared to a range of 2 seconds to 60 minutes for the other algorithms. MFPR-derived models were robust across distinct age groups when sample sizes exceeded 3,000. These results support CentileBrain (https://centilebrain.org/) an empirically benchmarked framework for normative modeling that is freely available to the scientific community.

Ion mobility-mass spectrometry (IM-MS) is a powerful separation technique providing an additional dimension of separation to support the enhanced separation and characterization of complex components from the tissue metabolome and medicinal herbs. The integration of machine learning (ML) with IM-MS can overcome the barrier to the lack of reference standards, promoting the creation of a large number of proprietary collision cross section (CCS) databases, which help to achieve the rapid, comprehensive, and accurate characterization of the contained chemical components. In this review, advances in CCS prediction using ML in the past 2 decades are summarized. The advantages of ion mobility-mass spectrometers and the commercially available ion mobility technologies with different principles (e.g., time dispersive, confinement and selective release, and space dispersive) are introduced and compared. The general procedures involved in CCS prediction based on ML (acquisition and optimization of the independent and dependent variables, model construction and evaluation, etc.) are highlighted. In addition, quantum chemistry, molecular dynamics, and CCS theoretical calculations are also described. Finally, the applications of CCS prediction in metabolomics, natural products, foods, and the other research fields are reflected.

Lotus (Nelumbo nucifera), an ancient aquatic plant, possesses a unique pharmacological activity that is primarily contributed by benzylisoquinoline alkaloids (BIAs). However, only few genes and enzymes involved in BIA biosynthesis in N. nucifera have been isolated and characterized. In the present study, we identified the regiopromiscuity of the O-methyltransferase designated as NnOMT6 isolated from N. nucifera; NnOMT6 was found to catalyze the methylation of monobenzylisoquinoline 6-O/7-O, aporphine skeleton 6-O, phenylpropanoid 3-O, and protoberberine 2-O. We further probed the key residues for affecting NnOMT6 activity via molecular docking and molecular dynamics simulation. Verification using site-directed mutagenesis revealed that the residues D316, N130, L135, N176A, D269, and E328 were critical for BIA O-methyltransferase activities; furthermore, N323A, a mutant of NnOMT6, demonstrated a substantial increase in catalytic efficiency for BIAs and a broader acceptor scope compared with wild-type NnOMT6. To the best of our knowledge, the present is the first to report the O-methyltransferase activity of aporphine skeleton without benzyl moiety substitutions in N. nucifera. The study findings provided biocatalysts for the semisynthesis of related medical compounds and gave insights into protein engineering to strengthen the O-methyltransferase activity in plants.