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Laser-heated electrospray ionization with mass spectrometry enables melting temperature measurements of aggregation-prone proteins from which thermochemical and mechanistic information about protein unfolding and ligand loss is deduced.

 

There is a general lack of consensus on the best practices for filtering of single‐nucleotide polymorphisms (SNPs) and whether it is better to use SNPs or include flanking regions (full “locus”) in phylogenomic analyses and subsequent comparative methods. Using genotyping‐by‐sequencing data from 22Glycinespecies, we assessed the effects of SNP vs. locus usage and SNP retention stringency. We compared branch length, node support, and divergence time estimation across 16 datasets with varying amounts of missing data and total size. Our results revealed five aspects of phylogenomic data usage that may be generally applicable: (1) tree topology is largely congruent across analyses; (2) filtering strictly for SNP retention (e.g., 90–100%) reduces support and can alter some inferred relationships; (3) absolute branch lengths vary by two orders of magnitude between SNP and locus datasets; (4) data type and branch length variation have little effect on divergence time estimation; and (5) phylograms alter the estimation of ancestral states and rates of morphological evolution. Using SNP or locus datasets does not alter phylogenetic inference significantly, unless researchers want or need to use absolute branch lengths. We recommend against using excessive filtering thresholds for SNP retention to reduce the risk of producing inconsistent topologies and generating low support. 

Conjugating biomolecules, such as antibodies, to bioconjugate moieties on lipid surfaces is a powerful tool for engineering the surface of diverse biomaterials, including cells and nanoparticles. We developed supported lipid bilayers (SLBs) presenting well-defined spatial distributions of functional moieties as models for precisely engineered functional biomolecular-lipid surfaces. We used quartz crystal microbalance with dissipation (QCM-D) and atomic force microscopy (AFM) to determine how vesicles containing a mixture of 1,2-dipalmitoyl-sn-glycero-3-phosphatidylcholine (DPPC) and 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-[azido(polyethylene glycol)-2000] (DSPE-PEG-N3) form SLBs as a function of the lipid phase transition temperature (Tm). Above the DPPC Tm, DPPC/DSPE-PEG-N3 vesicles form SLBs with functional azide moieties on SiO2 substrates via vesicle fusion. Below this Tm, DPPC/DSPE-PEG-N3 vesicles attach to SiO2 intact. Intact DPPC/DSPE-PEG-N3 vesicles on the SiO2 surfaces fuse and rupture to form SLBs when temperature is brought above the DPPC Tm. AFM studies show uniform and complete DPPC/DSPE-PEG-N3 SLB coverage of SiO2 surfaces for different DSPE-PEG-N3 concentrations. As the DSPE-PEG-N3 concentration increases from 0.01 to 6 mol%, the intermolecular spacing of DSPE-PEG-N3 in the SLBs decreases from 4.6 to 1.0 nm. The PEG moiety undergoes a mushroom to brush transition as DSPE-PEG-N3 concentration varies from 0.1 to 2.0 mol%. Via copper-free click reaction, IgG was conjugated to SLB surfaces with 4.6 nm or 1.3 nm inter-DSPE-PEG-N3 spacing. QCM-D and AFM data show; 1) uniform and complete IgG layers of similar mass and thickness on the two types of SLB; 2) a higher-viscosity/less rigid IgG layer on the SLB with 4.6 nm inter-DSPE-PEG-N3 spacing. Our studies provide a blueprint for SLBs modeling spatial control of functional macromolecules on lipid surfaces, including surfaces of lipid nanoparticles and cells. 

In recent decades, nucleic acid self-assemblies have emerged as popular nanomaterials due to their programmable and robust assembly, prescribed geometry, and versatile functionality. However, it remains a challenge to purify large quantities of DNA nanostructures or DNA-templated nanocomplexes for various applications. Commonly used purification methods are either limited by a small scale or incompatible with functionalized structures. To address this unmet need, we present a robust and scalable method of purifying DNA nanostructures by Sepharose resin-based size exclusion. The resin column can be manually packed in-house with reusability. The separation is driven by a low-pressure gravity flow in which large DNA nanostructures are eluted first followed by smaller impurities of ssDNA and proteins. We demonstrated the efficiency of the method for purifying DNA origami assemblies and protein-immobilized DNA nanostructures. Compared to routine agarose gel electrophoresis that yields 1 μg or less of purified products, this method can purify ∼100–1000 μg of DNA nanostructures in less than 30 min, with the overall collection yield of 50–70% of crude preparation mixture. The purified nanocomplexes showed more precise activity in evaluating enzyme functions and antibody-triggered activation of complement protein reactions. 

Brain responses in visual cortex are typically modeled as a positively and negatively weighted sum of all features within a deep neural network (DNN) layer. However, this linear fit can dramatically alter a given feature space, making it unclear whether brain prediction levels stem more from the DNN itself, or from the flexibility of the encoding model. As such, studies of alignment may benefit from a paradigm shift toward more constrained and theoretically driven mapping methods. As a proof of concept, here we present a case study of face and scene selectivity, showing that typical encoding analyses do not differentiate between aligned and misaligned tuning bases in model-to-brain predictivity. We introduce a new alignment complexity measure -- tuning reorientation -- which favors DNNs that achieve high brain alignment via minimal distortion of the original feature space. We show that this measure helps arbitrate between models that are superficially equal in their predictivity, but which differ in alignment complexity. Our experiments broadly signal the benefit of sparse, positive-weighted encoding procedures, which directly enforce an analogy between the tuning directions of model and brain feature spaces. 

Complex patterns of genome evolution associated with the end-Cretaceous [Cretaceous-Paleogene (K–Pg)] mass extinction limit our understanding of the early evolutionary history of modern birds. Here, we analyzed patterns of avian molecular evolution and identified distinct macroevolutionary regimes across exons, introns, untranslated regions, and mitochondrial genomes. Bird clades originating near the K–Pg boundary exhibited numerous shifts in the mode of molecular evolution, suggesting a burst of genomic heterogeneity at this point in Earth’s history. These inferred shifts in substitution patterns were closely related to evolutionary shifts in developmental mode, adult body mass, and patterns of metabolic scaling. Our results suggest that the end-Cretaceous mass extinction triggered integrated patterns of evolution across avian genomes, physiology, and life history near the dawn of the modern bird radiation.

 

According to the efficient coding hypothesis, neural populations encode information optimally when representations are high-dimensional and uncorrelated. However, such codes may carry a cost in terms of generalization and robustness. Past empirical studies of early visual cortex (V1) in rodents have suggested that this tradeoff indeed constrains sensory representations. However, it remains unclear whether these insights generalize across the hierarchy of the human visual system, and particularly to object representations in high-level occipitotemporal cortex (OTC). To gain new empirical clarity, here we develop a family of object recognition models with parametrically varying dropout proportion , which induces systematically varying dimensionality of internal responses (while controlling all other inductive biases). We find that increasing dropout produces an increasingly smooth, low-dimensional representational space. Optimal robustness to lesioning is observed at around 70% dropout, after which both accuracy and robustness decline. Representational comparison to large-scale 7T fMRI data from occipitotemporal cortex in the Natural Scenes Dataset reveals that this optimal degree of dropout is also associated with maximal emergent neural predictivity. Finally, using new techniques for achieving denoised estimates of the eigenspectrum of human fMRI responses, we compare the rate of eigenspectrum decay between model and brain feature spaces. We observe that the match between model and brain representations is associated with a common balance between efficiency and robustness in the representational space. These results suggest that varying dropout may reveal an optimal point of balance between the efficiency of high-dimensional codes and the robustness of low dimensional codes in hierarchical vision systems.