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A naive Bayes classifier for identifying Class II YSOs has been constructed and applied to a region of the Northern Galactic Plane containing 8 million sources with good quality Gaia EDR3 parallaxes. The classifier uses the five features: Gaia G-band variability, WISE mid-infrared excess, UKIDSS and 2MASS near-infrared excess, IGAPS Hα excess, and overluminosity with respect to the main sequence. A list of candidate Class II YSOs is obtained by choosing a posterior threshold appropriate to the task at hand, balancing the competing demands of completeness and purity. At a threshold posterior greater than 0.5, our classifier identifies 6504 candidate Class II YSOs. At this threshold, we find a false positive rate around 0.02 per cent and a true positive rate of approximately 87 per cent for identifying Class II YSOs. The ROC curve rises rapidly to almost one with an area under the curve around 0.998 or better, indicating the classifier is efficient at identifying candidate Class II YSOs. Our map of these candidates shows what are potentially three previously undiscovered clusters or associations. When comparing our results to published catalogues from other young star classifiers, we find between one quarter and three quarters of high probability candidates are unique to each classifier, telling us no single classifier is finding all young stars.

 

The understanding and rational design of heterogeneous catalysts for complex reactions, such as CO₂reduction, requires knowledge of elementary steps and chemical species prevalent on the catalyst surface under operating conditions. Using in situ nanoscale surface-enhanced Raman scattering, we probe the surface of a Ag nanoparticle during plasmon-excitation-driven CO₂reduction in water. Enabled by the high spatiotemporal resolution and surface sensitivity of our method, we detect a rich array of C₁–C₄species formed on the photocatalytically active surface. The abundance of multi-carbon compounds, such as butanol, suggests the favorability of kinetically challenging C–C coupling on the photoexcited Ag surface. Another advance of this work is the use of isotope labeling in nanoscale probing, which allows confirmation that detected species are the intermediates and products of the catalytic reaction rather than spurious contaminants. The surface chemical knowledge made accessible by our approach will inform the modeling and engineering of catalysts.

 

Understanding the structural mechanism by which proteins and peptides aggregate is crucial, given the role of fibrillar aggregates in debilitating amyloid diseases and bioinspired materials. Yet, this is a major challenge as the assembly involves multiple heterogeneous and transient intermediates. Here, we analyze the co-aggregation of Aβ 40 and Aβ 16–22 , two widely studied peptide fragments of Aβ 42 implicated in Alzheimer’s disease. We demonstrate that Aβ 16–22 increases the aggregation rate of Aβ 40 through a surface-catalyzed secondary nucleation mechanism. Discontinuous molecular dynamics simulations allowed aggregation to be tracked from the initial random coil monomer to the catalysis of nucleation on the fibril surface. Together, the results provide insight into how dynamic interactions between Aβ 40 monomers/oligomers on the surface of preformed Aβ 16–22 fibrils nucleate Aβ 40 amyloid assembly. This new understanding may facilitate development of surfaces designed to enhance or suppress secondary nucleation and hence to control the rates and products of fibril assembly. 

The aggregation of monomeric amyloid β protein (Aβ) peptide into oligomers and amyloid fibrils in the mammalian brain is associated with Alzheimer’s disease. Insight into the thermodynamic stability of the Aβ peptide in different polymeric states is fundamental to defining and predicting the aggregation process. Experimental determination of Aβ thermodynamic behavior is challenging due to the transient nature of Aβ oligomers and the low peptide solubility. Furthermore, quantitative calculation of a thermodynamic phase diagram for a specific peptide requires extremely long computational times. Here, using a coarse-grained protein model, molecular dynamics (MD) simulations are performed to determine an equilibrium concentration and temperature phase diagram for the amyloidogenic peptide fragment Aβ_16–22. Our results reveal that the only thermodynamically stable phases are the solution phase and the macroscopic fibrillar phase, and that there also exists a hierarchy of metastable phases. The boundary line between the solution phase and fibril phase is found by calculating the temperature-dependent solubility of a macroscopic Aβ_16–22fibril consisting of an infinite number of β-sheet layers. This in silico determination of an equilibrium (solubility) phase diagram for a real amyloid-forming peptide, Aβ_16–22, over the temperature range of 277–330 K agrees well with fibrillation experiments and transmission electron microscopy (TEM) measurements of the fibril morphologies formed. This in silico approach of predicting peptide solubility is also potentially useful for optimizing biopharmaceutical production and manufacturing nanofiber scaffolds for tissue engineering.