High-Throughput Cryo-EM Enabled by User-Free Preprocessing Routines

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Computational Modeling of Protein Stability: Quantitative Analysis Reveals Solutions to Pervasive Problems

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Age‐related deterioration of motor function in male and female 5xFAD mice from 3 to 16 months of age

Alzheimer's disease (AD) is a neurodegenerative disorder that leads to age‐related cognitive and sensori‐motor dysfunction. There is an increased understanding that motor dysfunction contributes to overall AD severity, and a need to ameliorate these impairments. The 5xFAD mouse develops the neuropathology, cognitive and motor impairments observed in AD, and thus may be a valuable animal model to study motor deficits in AD. Therefore, we assessed age‐related changes in motor ability of male and female 5xFAD mice from 3 to 16 months of age, using a battery of behavioral tests. At 9‐10 months, 5xFAD mice showed reduced body weight, reduced rearing in the open‐field and impaired performance on the rotarod compared to wild‐type controls. At 12‐13 months, 5xFAD mice showed reduced locomotor activity on the open‐field, and impaired balance on the balance beam. At 15‐16 months, impairments were also seen in grip strength. Although sex differences were observed at specific ages, the development of motor dysfunction was similar in male and female mice. Given the 5xFAD mouse is commonly on a C57BL/6 × SJL hybrid background, a subset of mice may be homozygous recessive for the Dysf ^im mutant allele, which leads to muscular weakness in SJL mice and may exacerbate motor dysfunction. We found small effects of Dysf ^im on motor function, suggesting that Dysf ^im contributes little to motor dysfunction in 5xFAD mice. We conclude that the 5xFAD mouse may be a useful model to study mechanisms that produce motor dysfunction in AD, and to assess the efficacy of therapeutics on ameliorating motor impairment.     

Complex patterns of dopamine‐related gene expression in the ventral tegmental area of male zebra finches relate to dyadic interactions with long‐term female partners

Dopaminergic projections from the ventral tegmental area (VTA) to multiple efferent targets are implicated in pair bonding, yet the role of the VTA in the maintenance of long‐term pair bonds is not well characterized. Complex interactions between numerous neuromodulators modify activity in the VTA, suggesting that individual differences in patterns of gene expression in this region may explain individual differences in long‐term social interactions in bonded pairs. To test this hypothesis we used RNA‐seq to measure expression of over 8000 annotated genes in male zebra finches in established male‐female pairs. Weighted gene co‐expression network analysis identified a gene module that contained numerous dopamine‐related genes with TH found to be the most connected gene of the module. Genes in this module related to male agonistic behaviors as well as bonding‐related behaviors produced by female partners. Unsupervised learning approaches identified two groups of males that differed with respect to expression of numerous genes. Enrichment analyses showed that many dopamine‐related genes and modulators differed between these groups, including dopamine receptors, synthetic and degradative enzymes, the avian dopamine transporter and several GABA‐ and glutamate‐related genes. Many of the bonding‐related behaviors closely associated with VTA gene expression in the two male groups were produced by the male's partner, rather than the male himself. Collectively, results highlight numerous candidate genes in the VTA that can be explored in future studies and raise the possibility that the molecular/genetic organization of the VTA may be strongly shaped by a social partner and/or the strength of the pair bond.     

An extracellular vesicle epitope profile is associated with acute myocardial infarction

The current standard biomarker for myocardial infarction (MI) is high‐sensitive troponin. Although powerful in clinical setting, search for new markers is warranted as early diagnosis of MI is associated with improved outcomes. Extracellular vesicles (EVs) attracted considerable interest as new blood biomarkers. A training cohort used for diagnostic modelling included 30 patients with STEMI, 38 with stable angina (SA) and 30 matched‐controls. Extracellular vesicle concentration was assessed by nanoparticle tracking analysis. Extracellular vesicle surface‐epitopes were measured by flow cytometry. Diagnostic models were developed using machine learning algorithms and validated on an independent cohort of 80 patients. Serum EV concentration from STEMI patients was increased as compared to controls and SA. EV levels of CD62P, CD42a, CD41b, CD31 and CD40 increased in STEMI, and to a lesser extent in SA patients. An aggregate marker including EV concentration and CD62P/CD42a levels achieved non‐inferiority to troponin, discriminating STEMI from controls (AUC = 0.969). A random forest model based on EV biomarkers discriminated the two groups with 100% accuracy. EV markers and RF model confirmed high diagnostic performance at validation. In conclusion, patients with acute MI or SA exhibit characteristic EV biomarker profiles. EV biomarkers hold great potential as early markers for the management of patients with MI.     

Accelerating Atomic Catalyst Discovery by Theoretical Calculations‐Machine Learning Strategy

Atomic catalysts (AC) are emerging as a highly attractive research topic, especially in sustainable energy fields. Lack of a full picture of the hydrogen evolution reaction (HER) impedes the future development of potential electrocatalysts. In this work, the systematic investigation of the HER process in graphdyine (GDY) based AC is presented in terms of the adsorption energies, adsorption trend, electronic structures, reaction pathway, and active sites. This comprehensive work innovatively reveals GDY based AC for HER covering all the transition metals (TM) and lanthanide (Ln) metals, enabling the screening of potential catalysts. The density functional theory (DFT) calculations carefully explore the HER performance beyond the comparison of sole H adsorption. Therefore, the screened catalysts candidates not only match with experimental results but also provide significant references for novel catalysts. Moreover, the machine learning (ML) technique bag‐tree approach is innovatively utilized based on the fuzzy model for data separation and converse prediction of the HER performance, which indicates a similar result to the theoretical calculations. From two independent theoretical perspectives (DFT and ML), this work proposes pivotal guidelines for experimental catalyst design and synthesis. The proposed advanced research strategy shows great potential as a general approach in other energy‐related areas.