Tuning the Chiral Growth of Plasmonic Bipyramids via the Wavelength and Polarization of Light.

Circularly polarized light (CPL) is a versatile tool to prepare chiral nanostructures, but the mechanism for inducing enantioselectivity is not well understood. This work shows that the energy and polarization of visible photons can initiate photodeposition at different sites on plasmonic nanocrystals. Here, CPL on achiral gold bipyramids (AuBPs) creates hot holes that oxidatively deposit PbO2 asymmetrically. We show for the first time that the location of PbO2 photodeposition and hence optical dissymmetry depends on the CPL wavelength. Specifically, 488 and 532 nm CPL induce PbO2 growth in the middle of AuBPs, whereas 660 nm CPL induces PbO2 growth at the tips. Our observations show that wavelength-dependent plasmonic field distributions are more important than surface lightning rod effects in localizing plasmon-mediated photochemistry. The largest optical dissymmetry occurs at excitation wavelengths between the transverse and longitudinal resonances of the AuBPs because higher-order modes are required to induce chiral electric fields.

A review of global wilderness area identification since the 21st century.

Wilderness areas are natural landscape elements that are relatively undisrupted by human activity and play a critical role in maintaining ecological equilibrium, preserving naturalness, and ensuring ecosystem resilience. Since 2000, monitoring of global wilderness areas has increased owing to the availability of spatial map data and remote sensing imagery related to human activity and/or human footprint. Progress has been made in the remote sensing of wilderness areas by relying on available historical literature (e.g., published papers, books, and reports). However, to our knowledge, a synthesis of wilderness area research from a remote sensing perspective has not yet been performed. In this preliminary review, we discuss the concept of wilderness in different historical eras and systematically summarize dynamic wilderness monitoring at local, national, and global scales, available remotely sensed indicators, disparities and commonalities in identification methods, and mapping uncertainties. Finally, since this field remains in its initial stage owing to a lack of unified standards and vertical/horizontal comparisons, we present insights into future research directions, particularly with regard to remote sensing. The findings of this review may help to improve the overall understanding of current wilderness patterns (i.e., increases/decreases) and the mechanisms by which they change, as well as provide guidance for global nature conservation programs.

Evaluating annual soil carbon emissions under biochar-added farmland subjecting from freeze-thaw cycle.

Straw biochar is a commonly recognized agricultural amendment that can improve soil quality and reduce carbon emissions while sequestering soil carbon. However, the mechanisms underlying biochar's effects on annual soil carbon emissions in seasonally frozen soil areas and intrinsic drivers have not been clarified. Here, a 2-y field experiment was conducted to investigate the effects of different biochar dosages (0, 15, and 30, t ha-1; B0 (CK), B15, and B30, respectively) on carbon emissions (CO2 and CH4) microbial colony count, and soil-environment factors. The study period was the full annual cycle, including the freeze-thaw period (FTP) and the crop growth period (CP). Structural equation modeling (SEM) was developed to reveal the key drivers and potential mechanisms of biochar on carbon emissions. Biochar application reduced soil carbon emissions, with the reduction rate positively related to the biochar application rate (B30 best). During FTP, the reduction rate was 11.5% for CO2 and 48.2% for CH4. During CP, the reduction rate was 17.9% for CO2 and 34.5% for CH4. Overall, compared with CK, B30 treatment had a significant effect on reducing total soil carbon emissions (P < 0.05), with an average decrease of 16.7% during the two-year test period. The study also showed that for soils with continuous annual cycles (FTP and CP), carbon emissions were best observed from 10:00-13:00. After two years of freeze-thaw cycling, biochar continued to improve soil physical and chemical properties, thereby increasing soil microbial colony count. Compared with B0, the B30 treatment significantly increased the total colony count by 74.3% and 263.8% during FTP and CP (P < 0.05). Structural equation modeling (SEM) indicated that, with or without biochar application, the soil physicochemical properties directly or indirectly affected soil CO2 and CH4 emission fluxes through microbial colony count. The total effects of biochar application on CO2 emission fluxes were 0.50 (P < 0.05) and 0.64 (P < 0.01), respectively, but there was no significant effect on CH4 emission fluxes (P > 0.05). Among them, soil water content (SWC), soil temperature (ST) and soil organic carbon (SOC) were the main environmental determinants of CO2 emission fluxes during the FTP and CP. The total effects were 0.57, 0.65, and 0.53, respectively. For CH4, SWC, soil salinity (SS) and actinomycete colony count were the main environmental factors affecting its emission. The total effects were 0.50, 0.45, 0.44, respectively. For freeze-thaw alternating soils, the application of biochar is a feasible option for addressing climate change through soil carbon sequestration and greenhouse gas emissions mitigation. Soil water-heat-salt-fertilization and microbial communities are important for soil carbon emissions as the reaction matrix and main participants of soil carbon and nitrogen biochemical transformation.

Assessing the reproducibility and validity of a food frequency questionnaire for pregnant women from the Chinese Miao ethnic group.

Background: Currently, no food frequency questionnaire is available to be administered exclusively to ethnic minorities in China. This study aimed to evaluate the reproducibility and validity of a culturally tailored semi-quantitative food frequency questionnaire (FFQ) designed for pregnant women belonging to the Miao ethnic group in China. Methods: A total of 74 questions in the FFQ were administered to collect dietary information from Miao women in China during their pregnancy. This study included 153 and 127 pregnant women, respectively, for testing the validity and reproducibility of the results. Baseline FFQ data (FFQ1) were collected initially, followed by the administration of a repeated FFQ 4-6 weeks later (FFQ2). Two 24-h recalls (24HR) were used as references to compare food groups and nutrient intake. Pearson/Spearman's coefficients were used to measure the validity and reproducibility of the FFQ. Quartile cross-classification, weighted kappa coefficients, and Bland-Altman plots were employed to assess the agreement. Results: Most food groups and nutrient intake estimated by the FFQ were higher than those estimated by the 24HR. Food groups and nutrients' correlations for FFQ vs. 24HR after being energy-adjusted and de-attenuated, respectively, were 0.10 (vegetables) to 0.45 (grains/tubers) and 0.15 (iron) to 0.52 (riboflavin). Comparatively, correlation coefficients for FFQ1 vs. FFQ2 ranged from 0.41 (fruit) to 0.71 (vegetables) and from 0.45 (energy) to 0.64 (calcium). The percentage of pregnant women classified in the same or adjacent quartiles ranged from 64.08% (vegetables) to 95.29% (sour soup) and from 68.88% (vitamin E) to 78.81% (energy). Weighted kappa coefficients exceeded 0.2 for food groups and most nutrients, and Bland-Altman plots demonstrated acceptable agreement between the two tools. Conclusions: This study provides novel information on the validation of FFQ. It demonstrates that the FFQ exhibits ideal reproducibility and acceptable validity in estimating and ranking the intake of food groups and most nutrients among pregnant women belonging to the Chinese Miao ethnic group.

A Fusion Learning Model Based on Deep Learning for Single-Cell RNA Sequencing Data Clustering.

Single-cell RNA sequencing (scRNA-seq) technology provides a means for studying biology from a cellular perspective. The fundamental goal of scRNA-seq data analysis is to discriminate single-cell types using unsupervised clustering. Few single-cell clustering algorithms have taken into account both deep and surface information, despite the recent slew of suggestions. Consequently, this article constructs a fusion learning framework based on deep learning, namely scGASI. For learning a clustering similarity matrix, scGASI integrates data affinity recovery and deep feature embedding in a unified scheme based on various top feature sets. Next, scGASI learns the low-dimensional latent representation underlying the data using a graph autoencoder to mine the hidden information residing in the data. To efficiently merge the surface information from raw area and the deeper potential information from underlying area, we then construct a fusion learning model based on self-expression. scGASI uses this fusion learning model to learn the similarity matrix of an individual feature set as well as the clustering similarity matrix of all feature sets. Lastly, gene marker identification, visualization, and clustering are accomplished using the clustering similarity matrix. Extensive verification on actual data sets demonstrates that scGASI outperforms many widely used clustering techniques in terms of clustering accuracy.

Spin effect on redox acceleration and regioselectivity in Fe-catalyzed alkyne hydrosilylation.

Iron catalysts are ideal transition metal catalysts because of the Earths abundant, cheap, biocompatible features of iron salts. Iron catalysts often have unique open-shell structures that easily undergo spin crossover in chemical transformations, a feature rarely found in noble metal catalysts. Unfortunately, little is known currently about how the open-shell structure and spin crossover affect the reactivity and selectivity of iron catalysts, which makes the development of iron catalysts a low efficient trial-and-error program. In this paper, a combination of experiments and theoretical calculations revealed that the iron-catalyzed hydrosilylation of alkynes is typical spin-crossover catalysis. Deep insight into the electronic structures of a set of well-defined open-shell active formal Fe(0) catalysts revealed that the spin-delocalization between the iron center and the 1,10-phenanthroline ligand effectively regulates the iron center's spin and oxidation state to meet the opposite electrostatic requirements of oxidative addition and reductive elimination, respectively, and the spin crossover is essential for this electron transfer process. The triplet transition state was essential for achieving high regioselectivity through tuning the nonbonding interactions. These findings provide an important reference for understanding the effect of catalyst spin state on reaction. It is inspiring for the development of iron catalysts and other Earth-abundant metal catalysts, especially from the point of view of ligand development.

Causal relationship between inflammatory factors and cerebral small vessel disease: Univariate, multivariate, and summary-data-based mendelian randomization analysis.

OBJECTIVE: To explore the impact of inflammatory factors on the incidence of cerebral small vessel disease (CSVD), we performed a mendelian randomization (MR) study to analyze the causal relationship between multiple inflammatory factors and CSVD imaging markers and utilized summary-data-based mendelian randomization (SMR) analysis to infer whether the impact of instrumental variables (IVs) on disease is mediated by gene expression or DNA methylation. METHODS: Using public databases such as UKB and IEU, and original genome-wide association studies, we obtained IVs related to exposure (inflammatory factors) and outcome (CSVD imaging markers). We performed the inverse variance weighted, weighted median, and MR-Egger methods to assess causal effects between exposure and outcome in univariate MR analysis. To evaluate their heterogeneity, a series of sensitivity analyses were conducted, including the Cochrane Q test, MR-Egger intercept test, MR-Presso, and leave-one-out analysis. We also applied mediation and multivariate MR analysis to explore the interactions between positive exposures on the same outcome. Additionally, we conducted the SMR, which utilizes instruments within or near relevant genes in blood or brain tissues, to elucidate the causal associations with CSVD markers. RESULTS: ABO Univariate MR of multiple cohorts revealed that the risk of small vessel stroke (SVS) increases with elevated levels of TNF-related apoptosis-inducing ligand (TRAIL, OR, 1.23, 95% CI, 1.08-1.39) and interleukin-1 receptor-like 2, (IL-1RL2, OR, 1.29, 95% CI, 1.04-1.61). IL-18 was a potential risk factor for extensive basal ganglia perivascular space burden (BGPVS, OR, 1.02, 95% CI, 1.00-1.05). Moreover, the risk of extensive white matter perivascular space burden (WMPVS) decreased with rising levels of E-selectin (OR, .98, 95% CI, .97-1.00), IL-1RL2 (OR, .97, 95% CI, .95-1.00), IL-3 receptor subunit alpha (IL-3Ra, OR, .98, 95% CI, .97-1.00), and IL-5 receptor subunit alpha (IL-5Ra, OR, .98, 95% CI, .97-1.00). Mediation and multivariate MR analysis indicated that E-selectin and IL-3Ra might interact during the pathogenesis of WMPVS. SMR estimates showed that TRAIL-related IVs rs5030044 and rs2304456 increased the risk of SVS by increasing the expression of gene Kininogen-1 (KNG1) in the cerebral cortex, particularly in the frontal cortex (ßsmr = .10, Psmr = .003, FDR = .04). Instruments (rs507666 and rs2519093) related to E-selectin and IL-3Ra could increase the risk of WMPVS by enhancing DNA methylation of the gene ABO in blood tissue (ßsmr = .01-.02, Psmr = .001, FDR = .01-.03). CONCLUSION: According to MR and SMR analysis, higher levels of TRAIL increased the risk of SVS by upregulating gene expression of KNG1 in brain cortex tissues. In addition, protective effects of E-selectin and IL-3a levels on WMPVS were regulated by increased DNA methylation of gene ABO in blood tissue.