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Food-derived antioxidant peptides can be explored as natural antioxidants due to their potential health benefits. In this study, antioxidant peptides were isolated and purified from pea protein hydrolysates (PPH). The DPPH and ABTS radical scavenging activities were used as indexes to purify the antioxidant peptides by a series of purification steps including ultrafiltration, ion exchange chromatography, G25 gel filtration chromatography, and reversed-phase chromatography. Three novel antioxidant peptides YLVN, EEHLCFR and TFY were identified, which all exhibited strong antioxidant activity in vitro. EEHLCFR showed stronger DPPH scavenging activity with an IC50 value of 0.027 mg/mL. YLVN showed stronger ABTS scavenging activity with an IC50 value of 0.002 mg/mL and higher ORAC values of 1.120 ± 0.231 μmol TE/μmol, which is even better than that of GSH. Three novel antioxidant peptides significantly elevated LO2 cells viability even at the concentration of 0.025 mg/mL, and cell viability enhanced to 53.42 ± 1.19%, 55.78 ± 1.03%, and 51.09 ± 1.06% respectively, compared to that of H2O2 injury group (48.35 ± 0.96%), and prevented the accumulation of ROS by enhancing the activities of antioxidant enzymes in H2O2-induced oxidative stress LO2 cells. The molecular docking results showed that the potential molecular mechanism of the three novel antioxidant peptides may be in high correlation with the activation of the Keap1-Nrf2 pathway by occupying the Keap1-Nrf2 binding site. These results demonstrate that the three novel antioxidant peptides are potential natural antioxidants that can be devoted to medicine or functional food ingredients.

The ever‐increasing concerns over indoor/outdoor air quality, industrial gas leakage, food freshness, and medical diagnosis require miniaturized gas sensors with excellent sensitivity, selectivity, stability, low power consumption, cost‐effectiveness, and long lifetime. Metal‐organic frameworks (MOFs), featuring structural diversity, large specific surface area, controllable pore size/geometry, and host‐guest interactions, hold great promises for fabricating various MOF‐based devices for diverse applications including gas sensing. Tremendous progress has been made in the past decade on the fabrication of MOF‐based sensors with elevated sensitivity and selectivity toward various analytes due to their preconcentrating and molecule‐sieving effects. Although several reviews have recently summarized different aspects of this field, a comprehensive review focusing on MOF‐based gas sensors is absent. In this review, the latest advance of MOF‐based gas sensors relying on different transduction mechanisms, for example, chemiresistive, capacitive/impedimetric, field‐effect transistor or Kelvin probe‐based, mass‐sensitive, and optical ones are comprehensively summarized. The latest progress for making large‐area MOF films essential to the mass‐production of relevant gas sensors is also included. The structural and compositional features of MOFs are intentionally correlated with the sensing performance. Challenges and opportunities for the further development and practical applications of MOF‐based gas sensors are also given. A comprehensive review on the latest progress of metal‐organic framework (MOF)‐based gas sensors relying on different transduction mechanisms is provided. The sensing performance in terms of sensitivity and selectivity is correlated with the structural and compositional features of MOFs and the transduction mechanisms. Critical future directions toward the further development of MOF‐based gas sensors are indicated.

The traditional pedagogy discipline construction decision support algorithm has the problems of poor discipline construction decision satisfaction, high decision time, and low decision recall rate. Therefore, a decision support algorithm for discipline construction of comparative pedagogy based on evolutionary graph data mining is designed. First, the programme call graph is created based on the programme execution path, and then the graph reduction method is used to decrease the call graph set and create the weighted behavior graph set. The original graph set is then reduced using the subtree reduction procedure. The interference of weights in the graph must be eliminated while mining closed subgraphs using a close graph method. The most frequent subgraph of comparative pedagogy discipline construction is then mined, and an SVM classifier is created to accomplish information mining of comparative pedagogy discipline construction in evolutionary graph data mining. Then, using the complete weighing approach, the attribute classification of comparative pedagogy discipline construction is accomplished, and the decision weight of comparative pedagogy discipline construction is established. Finally, the weight distribution scheme of comparative pedagogy discipline construction is obtained by using European distance, so as to realize the decision support of comparative pedagogy discipline construction. The experimental results show that the decision-making satisfaction of comparative pedagogy discipline construction of this method is 97.32%, the decision-making time is only 0.9 min, and the decision-making recall rate is as high as 98.66%, indicating that the decision-making effect of comparative pedagogy discipline construction of this method is good.

Learning effective molecular feature representation to facilitate molecular property prediction is of great significance for drug discovery. Recently, there has been a surge of interest in pre-training graph neural networks (GNNs) via self-supervised learning techniques to overcome the challenge of data scarcity in molecular property prediction. However, current self-supervised learning-based methods suffer from two main obstacles: the lack of a well-defined self-supervised learning strategy and the limited capacity of GNNs. Here, we propose Knowledge-guided Pre-training of Graph Transformer (KPGT), a self-supervised learning framework to alleviate the aforementioned issues and provide generalizable and robust molecular representations. The KPGT framework integrates a graph transformer specifically designed for molecular graphs and a knowledge-guided pre-training strategy, to fully capture both structural and semantic knowledge of molecules. Through extensive computational tests on 63 datasets, KPGT exhibits superior performance in predicting molecular properties across various domains. Moreover, the practical applicability of KPGT in drug discovery has been validated by identifying potential inhibitors of two antitumor targets: hematopoietic progenitor kinase 1 (HPK1) and fibroblast growth factor receptor 1 (FGFR1). Overall, KPGT can provide a powerful and useful tool for advancing the artificial intelligence (AI)-aided drug discovery process. Accurate property prediction relies on effective molecular representation. Here, the authors introduce KPGT, a knowledge-guided self-supervised framework that improves molecular representation, leading to superior predictions of molecular properties and advancing AI-driven drug discovery.

Temperature is one of the most important environmental stimuli to record and amplify. While traditional thermoelectric materials are attractive for temperature/heat flow sensing applications, their sensitivity is limited by their low Seebeck coefficient (∼100 μV K −1 ). Here we take advantage of the large ionic thermoelectric Seebeck coefficient found in polymer electrolytes (∼10,000 μV K −1 ) to introduce the concept of ionic thermoelectric gating a low-voltage organic transistor. The temperature sensing amplification of such ionic thermoelectric-gated devices is thousands of times superior to that of a single thermoelectric leg in traditional thermopiles. This suggests that ionic thermoelectric sensors offer a way to go beyond the limitations of traditional thermopiles and pyroelectric detectors. These findings pave the way for new infrared-gated electronic circuits with potential applications in photonics, thermography and electronic-skins. The design of electronic skin for medical imaging or robotics applications calls for high capability of temperature sensing. Here, Zhao et al . integrate ionic thermoelectric gating to an organic thin-field transistor to detect temperature at sensitivity comparable to that of pyroelectric materials.

Transition metal-based homogeneous photocatalysts offer a wealth of opportunities for organic synthesis. The most versatile ruthenium(II) and iridium(III) polypyridyl complexes, however, are among the rarest metal complexes. Moreover, immobilizing these precious catalysts for recycling is challenging as their opacity may obstruct light transmission. Recovery of homogeneous catalysts by conventional polymeric membranes is promising but limited, as the modulation of their pore structure and tolerance of polar organic solvents are challenging. Here, we report the effective recovery of homogeneous photocatalysts using covalent organic framework (COF) membranes. An array of COF membranes with tunable pore sizes and superior organic solvent resistance were prepared. Ruthenium and iridium photoredox catalysts were recycled for 10 cycles in various types of photochemical reactions, constantly achieving high catalytical performance, high recovery rates, and high permeance. We successfully recovered the photocatalysts at gram-scale. Furthermore, we demonstrated a cascade isolation of an iridium photocatalyst and purification of a small organic molecule product with COF membranes possessing different pore sizes. Our results indicate an intriguing potential to shift the paradigm of the pharmaceutical and fine chemical synthesis campaign. Transition metal-based homogenous photocatalysts are important in organic synthesis, but the metals used can be rare and immobilization of the catalysts for recycling is challenging. Here, the authors report the recovery of such catalysts using covalent organic framework membranes with tuneable pore sizes.

Phytoplankton blooms in coastal oceans can be beneficial to coastal fisheries production and ecosystem function, but can also cause major environmental problems 1 , 2 —yet detailed characterizations of bloom incidence and distribution are not available worldwide. Here we map daily marine coastal algal blooms between 2003 and 2020 using global satellite observations at 1-km spatial resolution. We found that algal blooms occurred in 126 out of the 153 coastal countries examined. Globally, the spatial extent (+13.2%) and frequency (+59.2%) of blooms increased significantly ( P  < 0.05) over the study period, whereas blooms weakened in tropical and subtropical areas of the Northern Hemisphere. We documented the relationship between the bloom trends and ocean circulation, and identified the stimulatory effects of recent increases in sea surface temperature. Our compilation of daily mapped coastal phytoplankton blooms provides the basis for global assessments of bloom risks and benefits, and for the formulation or evaluation of management or policy actions. Satellite observations reveal global increases in the extent and frequency of phytoplankton blooms between 2003 and 2020 and provide insights into the relationship between blooms, ocean circulation and sea surface temperature.

Identifying a set of influential spreaders in complex networks plays a crucial role in effective information spreading. A simple strategy is to choose top- r ranked nodes as spreaders according to influence ranking method such as PageRank, ClusterRank and k -shell decomposition. Besides, some heuristic methods such as hill-climbing, SPIN, degree discount and independent set based are also proposed. However, these approaches suffer from a possibility that some spreaders are so close together that they overlap sphere of influence or time consuming. In this report, we present a simply yet effectively iterative method named VoteRank to identify a set of decentralized spreaders with the best spreading ability. In this approach, all nodes vote in a spreader in each turn, and the voting ability of neighbors of elected spreader will be decreased in subsequent turn. Experimental results on four real networks show that under Susceptible-Infected-Recovered (SIR) and Susceptible-Infected (SI) models, VoteRank outperforms the traditional benchmark methods on both spreading rate and final affected scale. What’s more, VoteRank has superior computational efficiency.

Fuel cell vehicles, the only all-electric technology with a demonstrated >300 miles per fill travel range, use Pt as the electrode catalyst. The high price of Pt creates a major cost barrier for large-scale implementation of polymer electrolyte membrane fuel cells. Nonprecious metal catalysts (NPMCs) represent attractive low-cost alternatives. However, a significantly lower turnover frequency at the individual catalytic site renders the traditional carbon-supported NPMCs inadequate in reaching the desired performance afforded by Pt. Unconventional catalyst design aiming at maximizing the active site density at much improved mass and charge transports is essential for the next-generation NPMC. We report here a method of preparing highly efficient, nanofibrous NPMC for cathodic oxygen reduction reaction by electrospinning a polymer solution containing ferrous organometallics and zeolitic imidazolate framework followed by thermal activation. The catalyst offers a carbon nanonetwork architecture made of microporous nanofibers decorated by uniformly distributed high-density active sites. In a single-cell test, the membrane electrode containing such a catalyst delivered unprecedented volumetric activities of 3.3 A·cm⁻³ at 0.9 V or 450 A·cm⁻³ extrapolated at 0.8 V, representing the highest reported value in the literature. Improved fuel cell durability was also observed.

It is highly desirable to reduce the membrane thickness in order to maximize the throughput and break the trade-off limitation for membrane-based gas separation. Two-dimensional membranes composed of atomic-thick graphene or graphene oxide nanosheets have gas transport pathways that are at least three orders of magnitude higher than the membrane thickness, leading to reduced gas permeation flux and impaired separation throughput. Here we present nm-thick molecular sieving membranes composed of porous two-dimensional metal-organic nanosheets. These membranes possess pore openings parallel to gas concentration gradient allowing high gas permeation flux and high selectivity, which are proven by both experiment and molecular dynamics simulation. Furthermore, the gas transport pathways of these membranes exhibit a reversed thermo-switchable feature, which is attributed to the molecular flexibility of the building metal-organic nanosheets. Reducing membrane thickness to nanometre scale should increase the throughput of gas separation sieves. Here, the authors report a sieving membrane composed of two-dimensional metal-organic framework nanosheets, exhibiting both high permeation flux and thermally switchable behaviour.