Physiologically based pharmacokinetic modeling in obesity: applications and challenges.

INTRODUCTION: Rising global obesity rates pose a threat to people's health. Obesity causes a series of pathophysiologic changes, making the response of patients with obesity to drugs different from that of nonobese, thus affecting the treatment efficacy and even leading to adverse events. Therefore, understanding obesity's effects on pharmacokinetics is essential for the rational use of drugs in patients with obesity. AREAS COVERED: Articles related to physiologically based pharmacokinetic (PBPK) modeling in patients with obesity from inception to October 2023 were searched in PubMed, Embase, Web of Science and the Cochrane Library. This review outlines PBPK modeling applications in exploring factors influencing obesity's effects on pharmacokinetics, guiding clinical drug development and evaluating and optimizing clinical use of drugs in patients with obesity. EXPERT OPINION: Obesity-induced pathophysiologic alterations impact drug pharmacokinetics and drug-drug interactions (DDIs), altering drug exposure. However, there is a lack of universal body size indices or quantitative pharmacology models to predict the optimal for the patients with obesity. Therefore, dosage regimens for patients with obesity must consider individual physiological and biochemical information, and clinically individualize therapeutic drug monitoring for highly variable drugs to ensure effective drug dosing and avoid adverse effects.

MPEK: a multitask deep learning framework based on pretrained language models for enzymatic reaction kinetic parameters prediction.

Enzymatic reaction kinetics are central in analyzing enzymatic reaction mechanisms and target-enzyme optimization, and thus in biomanufacturing and other industries. The enzyme turnover number (kcat) and Michaelis constant (Km), key kinetic parameters for measuring enzyme catalytic efficiency, are crucial for analyzing enzymatic reaction mechanisms and the directed evolution of target enzymes. Experimental determination of kcat and Km is costly in terms of time, labor, and cost. To consider the intrinsic connection between kcat and Km and further improve the prediction performance, we propose a universal pretrained multitask deep learning model, MPEK, to predict these parameters simultaneously while considering pH, temperature, and organismal information. Through testing on the same kcat and Km test datasets, MPEK demonstrated superior prediction performance over the previous models. Specifically, MPEK achieved the Pearson coefficient of 0.808 for predicting kcat, improving ca. 14.6% and 7.6% compared to the DLKcat and UniKP models, and it achieved the Pearson coefficient of 0.777 for predicting Km, improving ca. 34.9% and 53.3% compared to the Kroll_model and UniKP models. More importantly, MPEK was able to reveal enzyme promiscuity and was sensitive to slight changes in the mutant enzyme sequence. In addition, in three case studies, it was shown that MPEK has the potential for assisted enzyme mining and directed evolution. To facilitate in silico evaluation of enzyme catalytic efficiency, we have established a web server implementing this model, which can be accessed at http://mathtc.nscc-tj.cn/mpek.

Nanocluster-agminated amorphous cobalt nanofilms for highly selective electroreduction of nitrate to ammonia.

Developing highly-efficient electrocatalysts for the nitrate reduction reaction (NITRR) is a persistent challenge. Here, we present the successful synthesis of 14 amorphous/low crystallinity metal nanofilms on three-dimensional carbon fibers (M-NFs/CP), including Al, Ti, Mn, Fe, Co, Ni, Cu, Zn, Ag, In, Sn, Pb, Au, or Bi, using rapid thermal evaporation. Among these samples, our study identifies the amorphous Co nanofilm with fine agglomerated Co clusters as the optimal electrocatalyst for NITRR in a neutral medium. The resulting Co-NFs/CP exhibits a remarkable Faradaic efficiency (FENH3) of 91.15 % at - 0.9 V vs RHE, surpassing commercial Co foil (39 %) and Co powder (20 %), despite sharing the same metal composition. Furthermore, during the electrochemical NITRR, the key intermediates on the surface of the Co-NFs/CP catalyst were detected by in situ Fourier-transform infrared (FTIR) spectroscopy, and the possible reaction ways were probed by Density functional theory (DFT) calculations. Theoretical calculations illustrate that the abundant low-coordinate Co atoms of Co-NFs/CP could enhances the adsorption of *NO3 intermediates compared to crystalline Co. Additionally, the amorphous Co structure lowers the energy barrier for the rate-determining step (*NH2→*NH3). This work opens a new avenue for the controllable synthesis of amorphous/low crystallinity metal nano-catalysts for various electrocatalysis reaction applications.

Small extracellular vesicle CA1 as a promising diagnostic biomarker for nasopharyngeal carcinoma.

Nasopharyngeal carcinoma (NPC), a malignant cancer originating from the epithelial cells of the nasopharynx, presents diagnostic challenges with current methods such as plasma Epstein-Barr virus (EBV) DNA testing showing limited efficacy. This study focused on identifying small extracellular vesicle (sEV) proteins as potential noninvasive biomarkers to enhance NPC diagnostic accuracy. We isolated sEVs from plasma and utilized 4D label-free proteomics to identify differentially expressed proteins (DEPs) among healthy controls (NC = 10), early-stage NPC (E-NPC = 10), and late-stage NPC (L-NPC = 10). Eighteen sEV proteins were identified as potential biomarkers. Subsequently, parallel reaction monitoring (PRM) proteomic analysis preliminarily confirmed sEV carbonic anhydrase 1 (CA1) as a highly promising biomarker for NPC, particularly in early-stage diagnosis (NC = 15; E-NPC = 10; L-NPC = 15). To facilitate this, we developed an automated, high-throughput and highly sensitive CA1 immune-chemiluminescence chip technology characterized by a broad linear detection range and robust controls. Further validation in an independent retrospective cohort (NC = 89; E-NPC = 39; L-NPC = 172) using this technology confirmed sEV CA1 as a reliable diagnostic biomarker for NPC (AUC = 0.9809) and E-NPC (AUC = 0.9893), independent of EBV-DNA testing. Notably, sEV CA1 exhibited superior diagnostic performance compared to EBV-DNA, with a significant incremental net reclassification improvement of 27.61 % for NPC and 72.11 % for E-NPC detection. Thus, this study identifies sEV CA1 as an innovative diagnostic biomarker for NPC and E-NPC independent of EBV-DNA. Additionally, it establishes an immune-chemiluminescence chip technology for the detection of sEV CA1 protein, paving the way for further validation and clinical application.

Accurate Density Prediction of Sesquiterpenoid HEDFs and the Multiproperty Computing Server SesquiterPre.

Accurate and rapid evaluation of density is crucial for evaluating the packing and combustion characteristics of high-energy-density fuels (HEDFs). This parameter is pivotal in the selection of high-performance HEDFs. Our study leveraged a polycyclic compound density data set and quantum chemical (QC) descriptors to establish a correlation with the target properties using the XGBoost algorithm. We utilized a recursive feature elimination method to simplify the model and developed a concise and interpretable density prediction model incorporating only six QC descriptors. The model demonstrated robust performance, achieving coefficients of determination (R 2) of 0.967 and 0.971 for internal and external test sets, respectively, and root-mean-square errors (RMSE) of 0.031 and 0.027 g/cm3, respectively. Compared to the other two mainstream methods, the marginal discrepancy between the predicted and actual molecular densities underscores the model's superior predictive ability and more usefulness for energy density calculation. Furthermore, we developed a web server (SesquiterPre, https://sespre.cmdrg.com/#/) that can simultaneously calculate the density, enthalpy of combustion, and energy density of sesquiterpenoid HEDFs, which greatly facilitates the use of researchers and is of great significance for accelerating the design and screening of novel sesquiterpenoid HEDFs.

QuanDB: a quantum chemical property database towards enhancing 3D molecular representation learning.

Previous studies have shown that the three-dimensional (3D) geometric and electronic structure of molecules play a crucial role in determining their key properties and intermolecular interactions. Therefore, it is necessary to establish a quantum chemical (QC) property database containing the most stable 3D geometric conformations and electronic structures of molecules. In this study, a high-quality QC property database, called QuanDB, was developed, which included structurally diverse molecular entities and featured a user-friendly interface. Currently, QuanDB contains 154,610 compounds sourced from public databases and scientific literature, with 10,125 scaffolds. The elemental composition comprises nine elements: H, C, O, N, P, S, F, Cl, and Br. For each molecule, QuanDB provides 53 global and 5 local QC properties and the most stable 3D conformation. These properties are divided into three categories: geometric structure, electronic structure, and thermodynamics. Geometric structure optimization and single point energy calculation at the theoretical level of B3LYP-D3(BJ)/6-311G(d)/SMD/water and B3LYP-D3(BJ)/def2-TZVP/SMD/water, respectively, were applied to ensure highly accurate calculations of QC properties, with the computational cost exceeding 107 core-hours. QuanDB provides high-value geometric and electronic structure information for use in molecular representation models, which are critical for machine-learning-based molecular design, thereby contributing to a comprehensive description of the chemical compound space. As a new high-quality dataset for QC properties, QuanDB is expected to become a benchmark tool for the training and optimization of machine learning models, thus further advancing the development of novel drugs and materials. QuanDB is freely available, without registration, at https://quandb.cmdrg.com/ .

Long-term prognosis of patients with gallbladder carcinoma after curative-intent resection based on changes in the ratio of carbohydrate antigen 19-9 to total bilirubin (CA19-9/TB): a multicenter retrospective cohort study.

BACKGROUND: The prognostic value of carbohydrate antigen 19-9 (CA19-9) is known to be affected by elevated bilirubin levels in patients with gallbladder carcinoma (GBC). The clinical significance of changes in the ratio of CA19-9 levels to total bilirubin (TB) levels in patients with GBC after curative-intent resection remains unknown. The aim of this study was to determine the prognostic value of changes in preoperative and postoperative CA19-9/TB ratio in these patients. METHODS: Prospectively collected data on consecutive patients who underwent curative-intent resection for GBC between January 2015 and December 2020 stored in a multicenter database from 10 hospitals were analyzed in this retrospective cohort study. Based on the adjusted CA19-9 defined as the ratio of CA19-9 to TB, and using 2×10 3  U/µmol as the upper normal value, patients were divided into a normal group (with normal preoperative and postoperative adjusted CA19-9), a normalization group (with abnormal preoperative but normal postoperative adjusted CA19-9), and a non-normalization group (with abnormal postoperative adjusted CA19-9). The primary outcomes were overall survival (OS) and recurrence-free survival (RFS). The log-rank test was used to compare OS and RFS among the groups. The Cox regression model was used to determine factors independently associated with OS and RFS. RESULTS: The normal group ( n =179 patients) and the normalization group ( n =73 patients) had better OS and RFS than the non-normalization group ( n =65 patients) (the 3-year OS rates 72.0%, 58.4% and 24.2%, respectively; the RFS rates 54.5%, 25.5% and 11.8%, respectively; both P <0.001). There were no significant differences between the normal and the normalization groups in OS and RFS (OS, P =0.255; RFS, P =0.130). Cox regression analysis confirmed that the non-normalization group was independently associated with worse OS and RFS. Subgroup analysis revealed that the non-normalization group of patients who received adjuvant therapy had significantly improved OS and RFS as compared to those who did not receive adjuvant therapy (OS, P =0.025; RFS, P =0.003). CONCLUSIONS: Patients with GBC who underwent curative-intent surgical resection with postoperative abnormal levels of adjusted CA19-9 (the CA19-9/TB ratio) were associated with poorer long-term survival outcomes. Adjuvant therapy after surgery improved the long-term outcomes of these patients.

Water masses influence the variation of microbial communities in the Yangtze River Estuary and its adjacent waters.

The Yangtze River estuary (YRE) are strongly influenced by the Kuroshio and terrigenous input from rivers, leading to the formation of distinct water masses, however, there remains a limited understanding of the full extent of this influence. Here the variation of water masses and bacterial communities of 58 seawater samples from the YRE and its adjacent waters were investigated. Our findings suggested that there were 5 water masses in the studied area: Black stream (BS), coastal water in the East China Sea (CW), nearshore mixed water (NM), mixed water in the middle and deep layers of the East China Sea (MM), and deep water blocks in the middle of the East China Sea (DM). The CW mass harbors the highest alpha diversity across all layers, whereas the NM mass exhibits higher diversity in the surface layer but lower in the middle layers. Proteobacteria was the most abundant taxa in all water masses, apart from that, in the surface layer masses, Cyanobacterium, Bacteroidota, and Actinobacteriota were the highest proportion in CW, while Bacteroidota and Actinobacteriota were the highest proportion in NM and BS; in the middle layer, Bacteroidota and Actinobacteriota were dominant phylum in CW and BS masses, but Cyanobacterium was main phylum in NM mass; in the bottom layer, Bacteroidota and Actinobacteriota were the dominant phylum in CW, while Marininimicrobia was the dominated phylum in DM and MM masses. Network analysis suggests water masses have obvious influence on community topological characteristics, moreover, community assembly across masses also differ greatly. Taken together, these results emphasized the significant impact of water masses on the bacterial composition, topological characteristics and assembly process, which may provide a theoretical foundation for predicting alterations in microbial communities within estuarine ecosystems under the influence of water masses.

Medication adherence framework: A population-based pharmacokinetic approach and its application in antimalarial treatment assessments.

We reported here on the development of a pharmacometric framework to assess patient adherence, by using two population-based approaches - the percentile and the Bayesian method. Three different dosing strategies were investigated in patients prescribed a total of three doses; (1) non-observed therapy, (2) directly observed administration of the first dose, and (3) directly observed administration of the first two doses. The percentile approach used population-based simulations to derive optimal concentration percentile cutoff values from the distribution of simulated drug concentrations at a specific time. This was done for each adherence scenario and compared to full adherence. The Bayesian approach calculated the posterior probability of each adherence scenario at a given drug concentration. The predictive performance (i.e., Youden index, receiver operating characteristic [ROC] curve) of both approaches were highly influenced by sample collection time (early was better) and interindividual variability (smaller was better). The complexity of the structural model and the half-life had a minimal impact on the predictive performance of these methods. The impact of the assay limitation (LOQ) on the predictive performance was relatively small if the fraction of LOQ data was less than 20%. Overall, the percentile method performed similar or better for adherence predictions compared to the Bayesian approach, with the latter showing slightly better results when investigating the adherence to the last dose only. The percentile approach showed acceptable adherence predictions (area under ROC curve > 0.74) when sampling the antimalarial drugs piperaquine at day 7 postdose and lumefantrine at day 3 postdose (i.e., 12 h after the last dose). This could be a highly useful approach when evaluating programmatic implementations of preventive and curative antimalarial treatment programs in endemic areas.

A Phase 1, Randomized, Double-Blind, Placebo-Controlled, Single Ascending Dose Study to Evaluate the Pharmacokinetics, Immunogenicity, Safety, and Tolerability After Subcutaneous Administration of Tozorakimab in Healthy Chinese Participants.

Tozorakimab is a high-affinity human immunoglobulin G1 monoclonal antibody that neutralizes interleukin (IL)-33, an IL-1 family cytokine. This phase 1, single-center, randomized, double-blind, placebo-controlled, single ascending dose study (NCT05070312) evaluated tozorakimab in a healthy Chinese population. Outcomes included the characterization of the pharmacokinetic (PK) profile and immunogenicity of tozorakimab. Safety outcomes included treatment-emergent adverse events (TEAEs) and clinical laboratory, electrocardiogram, and vital sign parameters. Healthy, non-smoking, male, and female Chinese participants aged 18-45 years with a body mass index 19-24 kg/m2 were enrolled. In total, 36 participants across 2 cohorts of 18 participants were randomized 2:1 to receive a single subcutaneous dose of tozorakimab (300 mg [2 mL] or 600 mg [4 mL]) or matching placebo (2 or 4 mL). Tozorakimab showed dose-dependent serum PK concentrations with an approximate monophasic distribution in serum over time and a maximum observed peak concentration of 20.1 and 33.7 µg/mL in the 300- and 600-mg cohorts, respectively. No treatment-emergent anti-drug antibodies for tozorakimab were observed in any of the participants. There were no clinically relevant trends in the occurrence of TEAEs across the treatment groups. There were no clinically relevant trends over time in clinical laboratory (hematology, clinical chemistry, and urinalysis), electrocardiogram, or vital sign parameters in any treatment group. Overall, tozorakimab demonstrated dose-dependent systemic exposure in healthy Chinese participants and was well tolerated, with no safety concerns identified in this study.

Realizing ampere-level CO2 electrolysis at low voltage over a woven network of few-atom-layer ultralong silverene nanobelts with ultrahigh aspect ratio by pairing with formaldehyde oxidation.

The development of advanced multi-functional electrocatalysts and their industrial operation on paired electrocatalysis systems presents a promising avenue for the gradual penetration of renewable energy into practical production. Herein, a self-supported conductive network of silverene nanobelts (Ag-ene NBs) was delicately assembled (Ag-NB-NWs), in which ultralong and few-atom-layer Ag-ene NBs with a high edge-to-facet ratio were interconnected, serving as "superreactors" for electron transfer and mass transport during the reaction. Such superstructures as electrocatalysts delivered an unparalleled performance toward the CO2-to-CO conversion with exclusively high faradaic efficiency (FE) and partial current densities of up to 1 A cm-2. Remarkably, the membrane electrode assembly (MEA) cell with Ag-NB-NWs as the cathode was capable of ultrastable and continuous operation for over 240 h at 0.4 A with ∼100% selectivity. More importantly, by further using Ag-NB-NWs as a bifunctional electrocatalyst, a record-low voltage overall CO2 electrolysis system coupling cathodic CO2 reduction with anodic formaldehyde oxidation in MEA cell was performed to achieve concurrent feed gas generation and formate production, substantially improving electrochemical techno-economic feasibility.

Distinct Assembly Processes Structure Planktonic Bacterial Communities Among Near- and Offshore Ecosystems in the Yangtze River Estuary.

The estuarine system functions as natural filters due to its ability to facilitate material transformation, planktonic bacteria play a crucial role in the cycling of complex nutrients and pollutants within estuaries, and understanding the community composition and assembly therein is crucial for comprehending bacterial ecology within estuaries. Despite extensive investigations into the composition and community assembly of two bacterial fractions (free-living, FLB; particle-attached, PAB), the process by which bacterioplankton communities in these two habitats assemble in the nearshore and offshore zones of estuarine ecosystems remains poorly understood. In this study, we conducted sampling in the Yangtze River Estuary (YRE) to investigate potential variations in the composition and community assembly of FLB and PAB in nearshore and offshore regions. We collected 90 samples of surface, middle, and bottom water from 16 sampling stations and performed 16S rRNA gene amplicon analysis along with environmental factor measurements. The results unveiled that the nearshore communities demonstrated significantly greater species richness and Chao1 indices compared to the offshore communities. In contrast, the nearshore communities had lower values of Shannon and Simpson indices. When compared to the FLB, the PAB exhibit a higher level of biodiversity and abundance. However, no distinct alpha and beta diversity differences were observed between the bottom, middle, and surface water layers. The community assembly analysis indicated that nearshore communities are predominantly shaped by deterministic processes, particularly due to heterogeneous selection of PAB; In contrast, offshore communities are governed more by stochastic processes, largely due to homogenizing dispersal of FLB. Consequently, the findings of this study demonstrate that nearshore and PAB communities exhibit higher levels of species diversity, while stochastic and deterministic processes exert distinct influences on communities among near- and offshore regions. This study further sheds new light on our understanding of the mechanisms governing bacterial communities in estuarine ecosystems.

Systematic tracking of nitrogen sources in complex river catchments: Machine learning approach based on microbial metagenomics.

Tracking nitrogen pollution sources is crucial for the effective management of water quality; however, it is a challenging task due to the complex contaminative scenarios in the freshwater systems. The contaminative pattern variations can induce quick responses of aquatic microorganisms, making them sensitive indicators of pollution origins. In this study, the soil and water assessment tool, accompanied by a detailed pollution source database, was used to detect the main nitrogen pollution sources in each sub-basin of the Liuyang River watershed. Thus, each sub-basin was assigned to a known class according to SWAT outputs, including point source pollution-dominated area, crop cultivation pollution-dominated area, and the septic tank pollution-dominated area. Based on these outputs, the random forest (RF) model was developed to predict the main pollution sources from different river ecosystems using a series of input variable groups (e.g., natural macroscopic characteristics, river physicochemical properties, 16S rRNA microbial taxonomic composition, microbial metagenomic data containing taxonomic and functional information, and their combination). The accuracy and the Kappa coefficient were used as the performance metrics for the RF model. Compared with the prediction performance among all the input variable groups, the prediction performance of the RF model was significantly improved using metagenomic indices as inputs. Among the metagenomic data-based models, the combination of the taxonomic information with functional information of all the species achieved the highest accuracy (0.84) and increased median Kappa coefficient (0.70). Feature importance analysis was used to identify key features that could serve as indicators for sudden pollution accidents and contribute to the overall function of the river system. The bacteria Rhabdochromatium marinum, Frankia, Actinomycetia, and Competibacteraceae were the most important species, whose mean decrease Gini indices were 0.0023, 0.0021, 0.0019, and 0.0018, respectively, although their relative abundances ranged only from 0.0004 to 0.1 %. Among the top 30 important variables, functional variables constituted more than half, demonstrating the remarkable variation in the microbial functions among sites with distinct pollution sources and the key role of functionality in predicting pollution sources. Many functional indicators related to the metabolism of Mycobacterium tuberculosis, such as K24693, K25621, K16048, and K14952, emerged as significant important factors in distinguishing nitrogen pollution origins. With the shortage of pollution source data in developing regions, this suggested approach offers an economical, quick, and accurate solution to locate the origins of water nitrogen pollution using the metagenomic data of microbial communities.

Quantitative Predictive Studies of Multiple Biological Activities of TRPV1 Modulators.

TRPV1 channel agonists and antagonists, which have powerful analgesic effects without the addictive qualities associated with traditional analgesics, have become a focus area for the development of novel analgesics. In this study, quantitative structure-activity relationship (QSAR) models for three bioactive endpoints (Ki, IC50, and EC50) were successfully constructed using four machine learning algorithms: SVM, Bagging, GBDT, and XGBoost. These models were based on 2922 TRPV1 modulators and incorporated four types of molecular descriptors: Daylight, E-state, ECFP4, and MACCS. After the rigorous five-fold cross-validation and external test set validation, the optimal models for the three endpoints were obtained. For the Ki endpoint, the Bagging-ECFP4 model had a Q2 value of 0.778 and an R2 value of 0.780. For the IC50 endpoint, the XGBoost-ECFP4 model had a Q2 value of 0.806 and an R2 value of 0.784. For the EC50 endpoint, the SVM-Daylight model had a Q2 value of 0.784 and an R2 value of 0.809. These results demonstrate that the constructed models exhibit good predictive performance. In addition, based on the model feature importance analysis, the influence between substructure and biological activity was also explored, which can provide important theoretical guidance for the efficient virtual screening and structural optimization of novel TRPV1 analgesics. And subsequent studies on novel TRPV1 modulators will be based on the feature substructures of the three endpoints.

SNR-enhanced signal delivery scheme with delta-sigma modulation in a four-mode fiber system.

This Letter proposes a scheme for optimizing the signal-to-noise ratio (SNR) of signal to improve the system performance by a 1 bit delta-sigma modulation (DSM) in a four-mode MDM system for mobile fronthaul. A 1 bit digitalized signal with an SNR of 60 dB from transmitter digital signal processing (Tx DSP) can be achieved. Based on this system, an experimental demonstration of the ultrahigh-order 1048576-QAM signal transmission over a 50 km strong-coupling few-mode fiber (FMF) is successfully realized. With DSP, the bit error rate (BER) of the received 1048576-QAM signals over four modes transmission is below the 20% soft-decision forward error correction (20% SD-FEC) threshold of 2.4 × 10-2. To the best of our knowledge, this is the first time that the combination of DSM technology and strong-coupling MDM system is achieved and that the highest-modulation order with DSM reported in MDM system is reached. This experimental demonstration of the proposed novel scheme in MDM system can provide an effective solution for ultra-large-capacity mobile fronthaul in the future.

Intelligent Protein Design and Molecular Characterization Techniques: A Comprehensive Review.

In recent years, the widespread application of artificial intelligence algorithms in protein structure, function prediction, and de novo protein design has significantly accelerated the process of intelligent protein design and led to many noteworthy achievements. This advancement in protein intelligent design holds great potential to accelerate the development of new drugs, enhance the efficiency of biocatalysts, and even create entirely new biomaterials. Protein characterization is the key to the performance of intelligent protein design. However, there is no consensus on the most suitable characterization method for intelligent protein design tasks. This review describes the methods, characteristics, and representative applications of traditional descriptors, sequence-based and structure-based protein characterization. It discusses their advantages, disadvantages, and scope of application. It is hoped that this could help researchers to better understand the limitations and application scenarios of these methods, and provide valuable references for choosing appropriate protein characterization techniques for related research in the field, so as to better carry out protein research.

Strongly Coupled Ag/Sn-SnO2 Nanosheets Toward CO2 Electroreduction to Pure HCOOH Solutions at Ampere-Level Current.

Electrocatalytic reduction of CO2 converts intermittent renewable electricity into value-added liquid products with an enticing prospect, but its practical application is hampered due to the lack of high-performance electrocatalysts. Herein, we elaborately design and develop strongly coupled nanosheets composed of Ag nanoparticles and Sn-SnO2 grains, designated as Ag/Sn-SnO2 nanosheets (NSs), which possess optimized electronic structure, high electrical conductivity, and more accessible sites. As a result, such a catalyst exhibits unprecedented catalytic performance toward CO2-to-formate conversion with near-unity faradaic efficiency (≥ 90%), ultrahigh partial current density (2,000 mA cm-2), and superior long-term stability (200 mA cm-2, 200 h), surpassing the reported catalysts of CO2 electroreduction to formate. Additionally, in situ attenuated total reflection-infrared spectra combined with theoretical calculations revealed that electron-enriched Sn sites on Ag/Sn-SnO2 NSs not only promote the formation of *OCHO and alleviate the energy barriers of *OCHO to *HCOOH, but also impede the desorption of H*. Notably, the Ag/Sn-SnO2 NSs as the cathode in a membrane electrode assembly with porous solid electrolyte layer reactor can continuously produce ~ 0.12 M pure HCOOH solution at 100 mA cm-2 over 200 h. This work may inspire further development of advanced electrocatalysts and innovative device systems for promoting practical application of producing liquid fuels from CO2.

SmRAV1, an AP2 and B3 Transcription Factor, Positively Regulates Eggplant's Response to Salt Stress.

Salt stress is a lethal abiotic stress threatening global food security on a consistent basis. In this study, we identified an AP2 and B3 domain-containing transcription factor (TF) named SmRAV1, and its expression levels were significantly up-regulated by NaCl, abscisic acid (ABA), and hydrogen peroxide (H2O2) treatment. High expression of SmRAV1 was observed in the roots and sepal of mature plants. The transient expression assay in Nicotiana benthamiana leaves revealed that SmRAV1 was localized in the nucleus. Silencing of SmRAV1 via virus-induced gene silencing (VIGS) decreased the tolerance of eggplant to salt stress. Significant down-regulation of salt stress marker genes, including SmGSTU10 and SmNCED1, was observed. Additionally, increased H2O2 content and decreased catalase (CAT) enzyme activity were recorded in the SmRAV1-silenced plants compared to the TRV:00 plants. Our findings elucidate the functions of SmRAV1 and provide opportunities for generating salt-tolerant lines of eggplant.

Controlling the Fluorescence Behavior of Hydrophobic Pigments by Supramolecular Self-Assembling on Organic Layered Silicate Minerals.

This research focused on the supramolecular self-assembly of organic fluorescent molecules on organically modified layered silicate minerals to design and prepare layered nanocomposites with excellent fluorescence properties. Aromatic hydrocarbons are hydrophobic and poorly loaded on the hydrophilic surface of layered silicate minerals, but they are easily captured by an organically modified mineral surface. Montmorillonite (MMT) and saponite (SAP), typical 2:1 type layered silicate minerals with different octahedral cations, were modified with the cationic surfactant octadecyl trimethylammonium chloride (OTAC) and loaded with pyrene (an aromatic hydrocarbon dye) with different molar ratios to the cationic surfactant by supramolecular self-assembling to construct fluorescent nanocomposites. The effect of pyrene concentration and the octahedral cation of the 2:1 type layered silicate minerals on photoluminescence properties was investigated. The fluorescence spectra of the nanocomposites prepared under low pyrene concentrations showed two bands at around 400 and 470 nm, corresponding to the monomer and excimer emissions; the band intensity of the excimer shoots up with the increase of pyrene concentration, reflecting different contributions from monomer and dimer species and the formation of radical aggregates. The excellent heat resistance of the layered silicate structure can effectively protect pyrene molecules from external environmental influences.