Exposure to different surface-modified polystyrene nanoparticles caused anxiety, depression, and social deficit in mice via damaging mitochondria in neurons.

Nanoplastics (NPs) are unavoidable hazardous materials that result from the human production and use of plastics. While there is evidence that NPs can bioaccumulate in the brain, no enough research regarding the pathways by which NPs reach the brain was conducted, and it is also urgently needed to evaluate the health threat to the nervous system. Here, we observed accumulation of polystyrene nanoplastics (PS-NPs) with different surface modifications (PS, PS-COOH, and PS-NH2) in mouse brains. Further studies showed that PS-NPs disrupted the tight junctions between endothelial cells and transport into endothelial cells via the endocytosis and macropinocytosis pathways. Additionally, NPs exposure induced a series of alternations in behavioral tests, including anxiety- and depression-like changes and impaired social interaction performance. Further results identified that NPs could be internalized into neurons and localized in the mitochondria, bringing about mitochondrial dysfunction and a concurrent decline of ATP production, which might be associated with abnormal animal behaviors. The findings provide novel insights into the neurotoxicity of NPs and provide a basis for the formulation of policy on plastic production and usage by relevant government agencies.

Establishing reaction networks in the 16-electron sulfur reduction reaction.

The sulfur reduction reaction (SRR) plays a central role in high-capacity lithium sulfur (Li-S) batteries. The SRR involves an intricate, 16-electron conversion process featuring multiple lithium polysulfide intermediates and reaction branches1-3. Establishing the complex reaction network is essential for rational tailoring of the SRR for improved Li-S batteries, but represents a daunting challenge4-6. Herein we systematically investigate the electrocatalytic SRR to decipher its network using the nitrogen, sulfur, dual-doped holey graphene framework as a model electrode to understand the role of electrocatalysts in acceleration of conversion kinetics. Combining cyclic voltammetry, in situ Raman spectroscopy and density functional theory calculations, we identify and directly profile the key intermediates (S8, Li2S8, Li2S6, Li2S4 and Li2S) at varying potentials and elucidate their conversion pathways. Li2S4 and Li2S6 were predominantly observed, in which Li2S4 represents the key electrochemical intermediate dictating the overall SRR kinetics. Li2S6, generated (consumed) through a comproportionation (disproportionation) reaction, does not directly participate in electrochemical reactions but significantly contributes to the polysulfide shuttling process. We found that the nitrogen, sulfur dual-doped holey graphene framework catalyst could help accelerate polysulfide conversion kinetics, leading to faster depletion of soluble lithium polysulfides at higher potential and hence mitigating the polysulfide shuttling effect and boosting output potential. These results highlight the electrocatalytic approach as a promising strategy for tackling the fundamental challenges regarding Li-S batteries.

Introducing the embedded random phase approximation: H2 dissociative adsorption on Cu(111) as an exemplar.

The random phase approximation (RPA) as a means of treating electron correlation recently has been shown to outperform standard density functional theory (DFT) approximations in a variety of cases. However, the computational cost of the RPA is substantially more than DFT, especially when aiming to study extended surfaces. Properly accounting for sufficient surface ensemble size, Brillouin zone sampling, and vacuum separation of periodic images in standard periodic-planewave-based DFT code raises the cost to achieve converged results. Here, we show that sub-system embedding schemes enable use of the RPA for modeling heterogeneous reactions at reduced computational cost. We explore two different embedded RPA (emb-RPA) approaches, periodic emb-RPA and cluster emb-RPA. We use the (experimentally and theoretically) well-studied H2 dissociative adsorption on Cu(111) as our exemplar, and first perform full periodic RPA calculations as a benchmark. The full RPA results match well the semi-empirical barrier fit to experimental observables and others derived from high-level computations, e.g., from recent embedded n-electron valence second order perturbation theory [Zhao et al., J. Chem. Theory Comput. 16(11), 7078-7088 (2020)] and quantum Monte Carlo [Doblhoff-Dier et al., J. Chem. Theory Comput. 13(7), 3208-3219 (2017)] simulations. Among the two emb-RPA approaches tested, the cluster emb-RPA accurately reproduces the energy profile (maximum error of 50 meV along the reaction pathway) while reducing the computational cost by approximately two orders of magnitude. We therefore expect that the embedded cluster approach will enable wider RPA implementation in heterogeneous catalysis.

Toward more accurate surface properties of ceria using many-body perturbation theory.

Despite the wide applications, the ab initio modeling of the ceria based catalyst is challenging. The partial occupation in the 4f orbitals creates a fundamental challenge for commonly used density functional theory (DFT) methods, including semilocal functionals with Hubbard U correction to force localization and hybrid functionals. In this work, we benchmark the random phase approximation (RPA) for ceria surface properties, including surface energy and hydrogenation energy, compared to the results utilizing the DFT + U approach or hybrid functionals. We show that, for the latter approaches, different surface properties require opposite directions of parameter tuning. This forms a dilemma for the parameter based DFT methods, as the improvement of a certain property by tuning parameters will inevitably lead to the worsening of other properties. Our results suggest that the parameter-free many-body perturbation theory methods exemplified by RPA are a promising strategy to escape the dilemma and provide highly accurate descriptions, which will allow us to better understand the catalytic reactions in ceria related systems.

Changing on the Concentrations of Neonicotinoids in Rice and Drinking Water through Heat Treatment Process.

Neonicotinoids (NEOs) have become the most widely used insecticides in the world since the mid-1990s. According to Chinese dietary habits, rice and water are usually heated before being consumed, but the information about the alteration through the heat treatment process is very limited. In this study, NEOs in rice samples were extracted by acetonitrile (ACN) and in tap water, samples were extracted through an HLB cartridge, then, a high-performance liquid chromatography system and a triple quadrupole mass spectrometry (HPLC-MS/MS) were applied for target chemical analysis. The parents of NEOs (p-NEOs) accounted for >99% of the total NEOs mass (∑NEOs) in both uncooked (median: 66.8 ng/g) and cooked (median: 41.4 ng/g) rice samples from Guangdong Province, China, while the metabolites of NEOs (m-NEOs) involved in this study accounted for less than 1%. We aimed to reveal the concentration changes of NEOs through heat treatment process, thus, several groups of rice and water samples from Guangdong were cooked and boiled, respectively. Significant (p < 0.05) reductions in acetamiprid, imidacloprid (IMI), thiacloprid, and thiamethoxam (THM) have been observed after the heat treatment of the rice samples. In water samples, the concentrations of THM and dinotefuran decreased significantly (p < 0.05) after the heat treatment. These results indicate the degradation of p-NEOs and m-NEOs during the heat treatment process. However, the concentrations of IMI increased significantly in tap water samples (p < 0.05) after heat treatment process, which might be caused by the potential IMI precursors in those industrial pesticide products. The concentrations of NEOs in rice and water can be shifted by the heat treatment process, so this process should be considered in relevant human exposure studies.

Restructuring and Activation of Cu(111) under Electrocatalytic Reduction Conditions.

The dynamic restructuring of Cu surfaces in electroreduction conditions is of fundamental interest in electrocatalysis. We decode the structural dynamics of a Cu(111) electrode under reduction conditions by joint first-principles calculations and operando electrochemical scanning tunneling microscopy (ECSTM) experiments. Combining global optimization and grand canonical density functional theory, we unravel the potential- and pH-dependent restructuring of Cu(111) in acidic electrolyte. At reductive potential, Cu(111) is covered by a high density of H atoms and, below a threshold potential, Cu adatoms are formed on the surface in a (4×4) superstructure, a restructuring unfavorable in vacuum. The strong H adsorption is the driving force for the restructuring, itself induced by the electrode potential. On the restructured surface, barriers for hydrogen evolution reaction steps are low. Restructuring in electroreduction conditions creates highly active Cu adatom sites not present on Cu(111).

Efficacy and Safety of Mesenchymal Stromal Cells Therapy for COVID-19 Infection: A Systematic Review and Meta-analysis.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is an infectious respiratory disease prevalent worldwide with a high mortality rate, and there is currently no specific medicine to treat patients. OBJECTIVE: We aimed to assess the safety and efficacy of stem cell therapy for COVID-19 by providing references for subsequent clinical treatments and trials. METHOD: We systematically searched PubMed, Embase, Cochrane, and Web of Science, using the following keywords: "stem cell" or "stromal cell" and "COVID-19." Controlled clinical trials published in English until 24th August 2021 were included. We followed the PRISMA guidelines and used Cochrane Collaboration's tool for assessing the risk of bias. We analysed the data using a fixed-effect model. RESULTS: We identified 1779 studies, out of which eight were eligible and included in this study. Eight relevant studies consisted of 156 patients treated with stem cells and 144 controls (300 individuals in total). There were no SAEs associated with stem cell therapy in all six studies, and no significant differences in AEs (p = 0.09, I2 = 40%, OR = 0.53, 95% CI: 0.26 to 1.09) between the experimental group and control group were observed. Moreover, the meta-analysis found that stem cell therapy effectively reduced the high mortality rate of COVID-19 (14/156 vs. 43/144; p<0.0001, I2 = 0%, OR=0.18, 95% CI: 0.08 to 0.41). CONCLUSION: This study suggests that MSCs therapy for COVID-19 has shown some promising results in safety and efficacy. It effectively reduces the high mortality rate of COVID-19 and does not increase the incidence of adverse events.

Coaxial interferometry for camera-based ultrasound-modulated optical tomography with paired illumination.

Ultrasound-modulated optical tomography (UOT), which combines the advantages of both light and ultrasound, is a promising imaging modality for deep-tissue high-resolution imaging. Among existing implementations, camera-based UOT gains huge advances in modulation depth through parallel detection. However, limited by the long exposure time and the slow framerate of modern cameras, the measurement of UOT signals always requires holographic methods with additional reference beams. This requirement increases system complexity and is susceptible to environmental disturbances. To overcome this challenge, we develop coaxial interferometry for camera-based UOT in this work. Such a coaxial scheme is enabled by employing paired illumination with slightly different optical frequencies. To measure the UOT signal, the conventional phase-stepping method in holography can be directly transplanted into coaxial interferometry. Specifically, we performed both numerical investigations and experimental validations for camera-based UOT under the proposed coaxial scheme. One-dimensional imaging for an absorptive target buried inside a scattering medium was demonstrated. With coaxial interferometry, this work presents an effective way to reduce system complexity and cope with environmental disturbances for camera-based UOT.

First Evidence of Prenatal Exposure to Emerging Poly- and Perfluoroalkyl Substances Associated with E-Waste Dismantling: Chemical Structure-Based Placental Transfer and Health Risks.

Limited information is available about prenatal exposure to per- and polyfluoroalkyl substances (PFAS) in electronic waste (e-waste) recycling sites. In this study, we determined 21 emerging PFAS and 13 legacy PFAS in 94 paired maternal and cord serum samples collected from an e-waste dismantling site in Southern China. We found 6:2 fluorotelomer sulfonate (6:2 FTSA), 6:2 chlorinated polyfluorinated ether sulfonate (6:2 Cl-PFESA), and perfluorooctanephosphonate (PFOPA) as the major emerging PFAS, regardless of matrices, at median concentrations of 2.40, 1.78, and 0.69 ng/mL, respectively, in maternal serum samples, and 2.30, 0.73, and 0.72 ng/mL, respectively, in cord serum samples. Our results provide evidence that e-waste dismantling activities contribute to human exposure to 6:2 FTSA, 6:2 Cl-PFESA, and PFOPA. The trans-placental transfer efficiencies of emerging PFAS (0.42-0.94) were higher than that of perfluorooctanesulfonic acid (0.37) and were structure-dependent. The substitution of fluorine with chlorine or hydrogen and/or hydrophilic functional groups may alter trans-placental transfer efficiencies. Multiple linear regression analysis indicated significant associations between maternal serum concentrations of emerging PFAS and maternal clinical parameters, especially liver function and erythrocyte-related biomarkers. This study provides new insights into prenatal exposure to multiple PFAS in e-waste dismantling areas and the prevalence of emerging PFAS in people living near the sites.

Hydrogen-Induced Restructuring of a Cu(100) Electrode in Electroreduction Conditions.

The rearrangement of Cu surfaces under electrochemical conditions is known to play a key role in the surface activation for major electrocatalytic reactions. Despite the extensive experimental insights into such rearrangements, from surface-sensitive spectroscopy and microscopy, the spatial and temporal resolution of these methods is insufficient to provide an atomistic picture of the electrochemical interface. Theoretical characterization has also been challenged by the diversity of restructuring configurations, surface stoichiometry, adsorbate configurations, and the effect of the electrode potential. Here, atomistic insight into the restructuring of the electrochemical interface is gained from first principles. Cu(100) restructuring under varying applied potentials and adsorbate coverages is studied by grand canonical density functional theory and global optimization techniques, as well as ab initio molecular dynamics and mechanistic calculations. We show that electroreduction conditions cause the formation of a shifted-row reconstruction on Cu(100), induced by hydrogen adsorption. The reconstruction is initiated at 1/6 ML H coverage, when the Cu-H bonding sufficiently weakens the Cu-Cu bonds between the top- and sublayer, and further stabilized at 1/3 ML when H adsorbates fill all the created 3-fold hollow sites. The simulated scanning tunneling microscopy (STM) images of the calculated reconstructed interfaces agree with experimental in situ STM. However, compared to the thermodynamic prediction, the onsets of reconstruction events in the experiment occur at more negative applied voltages. This is attributed to kinetic effects in restructuring, which we describe via different statistical models, to produce the potential- and pH-dependent surface stability diagram. This manuscript provides rich atomistic insight into surface restructuring in electroreduction conditions, which is required for the understanding and design of Cu-based materials for electrocatalytic processes. It also offers the methodology to study the problem of in situ electrode reconstruction.

Improving the Accuracy of Modelling CO2 Electroreduction on Copper Using Many-Body Perturbation Theory.

Copper (Cu) remains the most important metal catalyst for the carbon dioxide reduction reaction (CO2 RR) into C2 products. Due to limited evidence from in situ experiments, mechanistic studies are often performed in the framework of density functional theory (DFT), using functionals at the generalized gradient approximation (GGA) level, which have fundamental difficulties to correctly describe CO adsorption and surface stability. We employ the adiabatic connection fluctuation dissipation theorem within the random phase approximation (RPA), in combination with the linearized Poisson-Boltzmann equation to describe solvation effects, to investigate the mechanism of CO2 RR on the Cu(100) facet. Qualitatively different from the DFT-GGA results, RPA results propose the formation of *OCCHO as the potential determining step towards C2 products. The results suggest that it is important to use more accurate methods like RPA when modeling reactions involving multiple CO-related species like CO2 RR.

Modeling Electrochemical Processes with Grand Canonical Treatment of Many-Body Perturbation Theory.

Electrocatalysis plays a key role in sustainable energy conversion and storage. It is critical to model the grand canonical treatment of electrons, which accounts for the electrochemical potential explicitly, at the atomic scale and understand these reactions at electrified interfaces. However, such a grand canonical treatment for electrocatalytic modeling is in practice restricted to a treatment of electronic structure with density functional theory, and more accurate methods are in many cases desirable. Here, we develop an original workflow combining the grand canonical treatment of electrons with many-body perturbation theory in its random phase approximation (RPA). Using the potential dependent adsorption of carbon monoxide on the copper (100) facet, we show that the grand canonical RPA energetics provide the correct on-top Cu geometry for CO at reducing potential. The match with experimental results is significantly improved compared to the functionals at the generalized gradient approximation level, which is the most commonly used approximation for electrochemical applications. We expect this development to pave the way to further electrochemical applications using RPA.

Revisiting the link between magnetic properties and chemisorption at graphene nanoribbon zigzag edge.

Graphene has received tremendous interest in both chemical and physical fields. Among different edges of the graphene system, the zigzag edge terminated graphene nanoribbons (ZGNRs) show unique magnetic properties in the antiferromagnetic (AFM) state. However, to date, the understanding of ZGNR chemical properties is mainly based on the partial radical concept, and in previous studies, the energy differences between the ferromagnetic (FM) and AFM states are smaller than experimental evidence. Here, we report that the strongly constrained and appropriately normed functional gives a significantly larger energy difference, which matches the experimental observation. Furthermore, utilizing the energetics in the large difference case, we propose a conceptual supplement to the previous partial radical concept: the overall stabilization of the AFM state compared to the nonmagnetic (NM) state consists of two parts that affect the adsorption energy conversely. The NM-FM energy differences will strengthen the adsorption, being in line with the previous partial radical concept. The FM-AFM energy differences will instead weaken the adsorption. We perform calculations of H, OH, and LiS radical adsorption energies on ZGNRs to show that this weakening effect is numerically non-negligible: at least a ∼0.2 eV difference in the adsorption energies is found. We expect that this refinement of the partial radical concept can provide a more comprehensive understanding of the chemical properties of ZGNRs. The differences in adsorption energies for the H, OH, and LiS radicals found here lead to significant changes in the predicted reactivity of the ZGNR models.

Diffusion Barriers for Carbon Monoxide on the Cu(001) Surface Using Many-Body Perturbation Theory and Various Density Functionals.

First-principles calculations play a key role in understanding the interactions of molecules with transition-metal surfaces and the energy profiles for catalytic reactions. However, many of the commonly used density functionals are not able to correctly predict the surface energy as well as the adsorption site preference for a key molecule such as CO, and it is not clear to what extent this shortcoming influences the prediction of reaction or diffusion pathways. Here, we report calculations of carbon monoxide diffusion on the Cu(001) surface along the [100] and [110] pathways, as well as the surface energy of Cu(001), and CO-adsorption energy and compare the performance of the Perdew-Burke-Ernzerhof (PBE), PBE + D2, PBE + D3, RPBE, Bayesian error estimation functional with van der Waals correlation (BEEF-vdW), HSE06 density functionals, and the random phase approximation (RPA), a post-Hartree-Fock method based on many-body perturbation theory. We critically evaluate the performance of these methods and find that RPA appears to be the only method giving correct site preference, overall barrier, adsorption enthalpy, and surface energy. For all of the other methods, at least one of these properties is not correctly captured. These results imply that many density functional theory (DFT)-based methods lead to qualitative and quantitative errors in describing CO interaction with transition-metal surfaces, which significantly impacts the description of diffusion pathways. It is well conceivable that similar effects exist when surface reactions of CO-related species are considered. We expect that the methodology presented here will be used to get more detailed insights into reaction pathways for CO conversion on transition-metal surfaces in general and Cu in particular, which will allow us to better understand the catalytic and electrocatalytic reactions involving CO-related species.

CALLR: a semi-supervised cell-type annotation method for single-cell RNA sequencing data.

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) technology has been widely applied to capture the heterogeneity of different cell types within complex tissues. An essential step in scRNA-seq data analysis is the annotation of cell types. Traditional cell-type annotation is mainly clustering the cells first, and then using the aggregated cluster-level expression profiles and the marker genes to label each cluster. Such methods are greatly dependent on the clustering results, which are insufficient for accurate annotation. RESULTS: In this article, we propose a semi-supervised learning method for cell-type annotation called CALLR. It combines unsupervised learning represented by the graph Laplacian matrix constructed from all the cells and supervised learning using sparse logistic regression. By alternately updating the cell clusters and annotation labels, high annotation accuracy can be achieved. The model is formulated as an optimization problem, and a computationally efficient algorithm is developed to solve it. Experiments on 10 real datasets show that CALLR outperforms the compared (semi-)supervised learning methods, and the popular clustering methods. AVAILABILITY AND IMPLEMENTATION: The implementation of CALLR is available at https://github.com/MathSZhang/CALLR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.