One-Step Hydrothermal Synthesis of NVO Cathodes with Varied Lattice NH4 + Content: Effect on Structural Evolution and Electrochemical Performance.

Aqueous zinc ion batteries have been extensively researched due to their distinctive advantages such as low cost and high safety. Vanadium oxides are important cathode materials, however, poor cycle life caused by vanadium dissolution limits their application. Recent studies show that the lattice NH4 + in vanadium oxides can act as a pillar to enhance structural stability and play a crucial role in improving its cycling stability. Nevertheless, there is still a lack of research on the effect of the lattice NH4 + content on structural evolution and electrochemical performance. Herein, we synthesize vanadium oxides with different contents of lattice NH4 + by a one-step hydrothermal reaction. The vanadium oxides with lattice NH4 + exhibit high initial capacity, as well as good cycling stability and rate performance compared to bare vanadium oxide. Combined with electrochemical analyses, ex-situ structural characterizations, and in-situ X-ray diffraction tests, we reveal that the lattice NH4 + content plays a positive role in vanadium oxides' structural stability and cation diffusion kinetics. This work presents a direction for designing high-performance vanadium cathodes for aqueous zinc ion batteries.

Inferring Historical Introgression with Deep Learning.

Resolving phylogenetic relationships among taxa remains a challenge in the era of big data due to the presence of genetic admixture in a wide range of organisms. Rapidly developing sequencing technologies and statistical tests enable evolutionary relationships to be disentangled at a genome-wide level, yet many of these tests are computationally intensive and rely on phased genotypes, large sample sizes, restricted phylogenetic topologies, or hypothesis testing. To overcome these difficulties, we developed a deep learning-based approach, named ERICA, for inferring genome-wide evolutionary relationships and local introgressed regions from sequence data. ERICA accepts sequence alignments of both population genomic data and multiple genome assemblies, and efficiently identifies discordant genealogy patterns and exchanged regions across genomes when compared with other methods. We further tested ERICA using real population genomic data from Heliconius butterflies that have undergone adaptive radiation and frequent hybridization. Finally, we applied ERICA to characterize hybridization and introgression in wild and cultivated rice, revealing the important role of introgression in rice domestication and adaptation. Taken together, our findings demonstrate that ERICA provides an effective method for teasing apart evolutionary relationships using whole genome data, which can ultimately facilitate evolutionary studies on hybridization and introgression.

Association between real-world home blood pressure measurement patterns and blood pressure variability among older individuals with hypertension: A community-based blood pressure variability study.

Home blood pressure (BP) monitoring is a useful tool for hypertension management. BP variability (BPV) has been associated with an increased risk of cardiovascular events. However, little is known about the correlation between BPV and different measurement patterns of long-term home BP monitoring. This longitudinal cohort study aimed to assess the associations between dynamic BP measurement patterns and BPV. A total of 1128 participants (mean age, 77.4 ± 9.3 years; male, 51%) with 23 269 behavior measuring units were included. We used sliding window sampling to classify the home BP data with a regular 6-month interval into units in a sliding manner until the data are not continuous. Three measurement patterns (stable frequent [SF], stable infrequent [SI], and unstable [US]) were assessed based on the home BP data obtained within the first 3 months of the study, and the data in the subsequent 3 months were used to assess the BPV of that unit. We used linear mixed-effects model to assess the association between BP measurement patterns and BPV with adjustment for possible confounding factors including average BP. Average real variability and coefficient variability were used as measures of the BPV. No significant differences were observed in average BP between the SF, SI, and US patterns. However, BPV in the SF group was significantly lower than that in the US and SI groups (all p-values < .05). The BPV in SI and US groups was not significantly different. A stable and frequent BP measuring pattern was independently associated with a lower BPV.