科研成果

Indoor pollution of manmade semivolatile organic compounds (SVOCs) such as phthalates are a growing threat to human health. Herein we summarize the dust-phase phthalate concentrations in Chinese residences reported from 2011 to 2021 and simulate corresponding airborne concentrations based on equilibrium models. The simulation considers seasonal and regional variations in indoor temperature and PM2.5 concentration, in contrast to the common practice of using constant values. Results show that variations in these two environmental factors lead to up to ten- and six-fold variations in the monthly median gas- and particle-phase concentrations of phthalates, respectively, in residences in individual climate zones. For higher-vapor-pressure species di-n-butyl phthalate and di-isobutyl phthalate, the resultant seasonal and regional variations in aggregate non-diet intake can reach six- and three-fold, respectively. These results have important implications on exposure assessment of SVOCs and epidemiological evaluation of their health effects.

In a reverberant environment, interferences such as reflections and background noise can degrade the perception of the sound source signal. Although the DNN-based methods have made a tremendous breakthrough in addressing this issue, the performance of these models is highly dependent on the completeness of the training dataset, which will limit its generalization under unknown environments. In this article, we propose a physical model-based self-supervised learning (PMSSL) method to realize the DNN model optimization under unknown scenarios. This method incorporates a room reverberation physical model into the sound source enhancement model optimization process, realizing the self-learning of the DNN model under physical constraints. In this process, the time-frequency characteristics of the input signal and the spatial feature of the reverberation environment are utilized for parameter optimization, improving the adaptability of the DNN model under unknown scenarios. Experimental results based on simulated and measured data prove that the proposed method can obtain much more accurate source signal enhancement results compared with the pre-trained models, verifying its effectiveness and adaptability in new environments.

Summary The geographic distribution of plant diversity matches the gradient of habitat heterogeneity from lowlands to mountain regions. However, little is known about how much this relationship is conserved across scales. Using the World Checklist of Vascular Plants and high-resolution biodiversity maps developed by species distribution models, we investigated the associations between species richness and habitat heterogeneity at the scales of Eurasia and the Hengduan Mountains (HDM) in China. Habitat heterogeneity explains seed plant species richness across Eurasia, but the plant species richness of 41/97 HDM families is even higher than expected from fitted statistical relationships. A habitat heterogeneity index combining growing degree days, site water balance, and bedrock type performs better than heterogeneity based on single variables in explaining species richness. In the HDM, the association between heterogeneity and species richness is stronger at larger scales. Our findings suggest that high environmental heterogeneity provides suitable conditions for the diversification of lineages in the HDM. Nevertheless, habitat heterogeneity alone cannot fully explain the distribution of species richness in the HDM, especially in the western HDM, and complementary mechanisms, such as the complex geological history of the region, may have contributed to shaping this exceptional biodiversity hotspot.

The widespread secondary microplastics (MPs) in urban freshwater, originating from plastic wastes, have created a new habitat called plastisphere for microorganisms. The factors influencing the structure and ecological risks of the microbial community within the plastisphere are not yet fully understood. We conducted an in-site incubation experiment in an urban river, using MPs from garbage bags (GB), shopping bags (SB), and plastic bottles (PB). Bacterial communities in water and plastisphere incubated for 2 and 4 weeks were analyzed by 16S high-throughput sequencing. The results showed the bacterial composition of the plastisphere, especially the PB, exhibited enrichment of plastic-degrading and photoautotrophic taxa. Diversity declined in GB and PB but increased in SB plastisphere. Abundance analysis revealed distinct bacterial species that were enriched or depleted in each type of plastisphere. As the succession progressed, the differences in community structure was more pronounced, and the decline in the complexity of bacterial community within each plastisphere suggested increasing specialization. All the plastisphere exhibited elevated pathogenicity at the second or forth week, compared to bacterial communities related to natural particles. These findings highlighted the continually evolving plastisphere in urban rivers was influenced by the plastic substrates, and attention should be paid to fragile plastic wastes due to the rapidly increasing pathogenicity of the bacterial community attached to them.

Ferroelectric diodes can generate a polarization-controlled bidirectional photoresponse to simulate inhibition and promotion behaviors in the artificial neuromorphic system with fast speed, high energy efficiency, and nonvolatility. However, the existing ferroelectric diodes based on ferroelectric oxides suffer from a weak bidirectional photoresponse (below 1 mA/W), difficult miniaturization, and a large response photon energy (over 3 eV). Here, we design a series of van der Waals �−In2Se3/Nb�2 (� = S, Se, and Te) ferroelectric diodes with bidirectional photoresponse by using ab initio quantum transport simulation. These devices show a maximum bidirectional photoresponse of 30 (−19) mA/W and a minimum response photon energy of 1.3 eV at the monolayer thickness. Our work shows advanced optoelectronic applications of the van der Waals ferroelectric diodes in the future artificial neuromorphic system.

Abstract Accurate estimates of aerosol refractive index (RI) are critical for modeling aerosol-radiation interaction, yet this information is limited for ambient organic aerosols, leading to large uncertainties in estimating aerosol radiative effects. We present a semi-empirical model that predicts the real RI n of organic aerosol material from its widely measured oxygen-to-carbon (O:C) and hydrogen-to-carbon (H:C) elemental ratios. The model was based on the theoretical framework of Lorenz-Lorentz equation and trained with n-values at 589 nm () of 160 pure compounds. The predictions can be expanded to predict n-values in a wide spectrum between 300 and 1,200 nm. The model was validated with newly measured and literature datasets of n-values for laboratory secondary organic aerosol (SOA) materials. Uncertainties of predictions for all SOA samples are within 5%. The model suggests that -values of organic aerosols may vary within a relatively small range for typical O:C and H:C values observed in the atmosphere.

As the popularity of hierarchical point forecast reconciliation methods increases, there is a growing interest in probabilistic forecast reconciliation. Many studies have utilized machine learning or deep learning techniques to implement probabilistic forecasting reconciliation and have made notable progress. However, these methods treat the reconciliation step as a fixed and hard post-processing step, leading to a trade-off between accuracy and coherency. In this paper, we propose a new approach for probabilistic forecast reconciliation. Unlike existing approaches, our proposed approach fuses the prediction step and reconciliation step into a deep learning framework, making the reconciliation step more flexible and soft by introducing the Kullback-Leibler divergence regularization term into the loss function. The approach is evaluated using three hierarchical time series datasets, which shows the advantages of our approach over other probabilistic forecast reconciliation methods.

Numerous functional magnetic resonance imaging (fMRI) studies have examined the neural mechanisms of negative emotional words, but scarce evidence is available for the interactions among related brain regions from the functional brain connectivity perspective. Moreover, few studies have addressed the neural networks for negative word processing in bilinguals. To fill this gap, the current study examined the brain networks for processing negative words in the first language (L1) and the second language (L2) with Chinese-English bilinguals. To identify objective indicators associated with negative word processing, we first conducted a coordinate-based meta-analysis on contrasts between negative and neutral words (including 32 contrasts from 1589 participants) using the activation likelihood estimation method. Results showed that the left medial prefrontal cortex (mPFC), the left inferior frontal gyrus (IFG), the left posterior cingulate cortex (PCC), the left amygdala, the left inferior temporal gyrus (ITG), and the left thalamus were involved in processing negative words. Next, these six clusters were used as regions of interest in effective connectivity analyses using extended unified structural equation modeling to pinpoint the brain networks for bilingual negative word processing. Brain network results revealed two pathways for negative word processing in L1: a dorsal pathway consisting of the left IFG, the left mPFC, and the left PCC, and a ventral pathway involving the left amygdala, the left ITG, and the left thalamus. We further investigated the similarity and difference between brain networks for negative word processing in L1 and L2. The findings revealed similarities in the dorsal pathway, as well as differences primarily in the ventral pathway, indicating both neural assimilation and accommodation across processing negative emotion in two languages of bilinguals.