Rheological, thermal, and in vitro starch digestibility properties of oat starch-lipid complexes.

The influence of oat lipids on the structural, thermal, rheological, and in vitro digestibility properties of oat starch under heat processing conditions was investigated. X-ray diffraction, fourier infrared spectroscopy, and differential scanning calorimetry revealed the formation of a V-shaped crystal structure between starch and lipid, resulting in enhanced orderliness and enthalpy. Oat lipids decreased the final viscosity and gel strength of oat starch while weakening the trend towards gel network formation. Additionally, oat lipids exhibited enhanced resistance to starch hydrolase, leading to elevated contents of slowly digestible starch and resistant starch. Consequently, this leads to an augmentation in the rate constants for the rapid digestion fraction (k1) and the slow digestion fraction (k2). When the lipid content reached 7.50 %, a significant increase of 42.20 % was observed in the maximum digestibility of slow digestion fraction (C∞2), while a notable decrease of 44.06 % was noted in the maximum digestibility of rapid digestion fraction (C∞1). The correlation analysis revealed that lipid content, final viscosity, and enthalpy exerted significant influences on in vitro starch digestion. These results demonstrate the substantial impact of lipid content on oat starch structure, subsequently affecting its thermal, rheological, and digestive properties.

Donor engineering of a benzothiadiazole-based D-A-D-type molecular semiconductor for perovskite solar cells: a theoretical study.

Due to the easy formation of compact molecular packing arrangements and the favorable photophysical and electrochemical properties, donor-acceptor-donor (D-A-D)-type small molecule hole-transporting materials (HTMs) have been widely synthesized and researched to improve the efficiency and stability of perovskite solar cells (PSCs). The main approach in recent experiments has been to seek good acceptors, whereas the influence of the electron-donating units has been less reported. In this work, six new benzothiadiazole-based D-A-D-type HTMs are tailored by employing the ethyl-substituted phenoxazine (POZ), phenothiazine (PTZ) and carbazole (CZ) as the donors. To obtain an elementary understanding of new HTMs, the electronic, optical, hole-transporting and interfacial properties are simulated with quantum chemistry methods. The results indicate that all tailored HTMs exhibit suitable energy alignment compared with the band structures of the perovskite, and the continuous highest occupied molecular orbital (HOMO) levels will be helpful for interfacial energy regulation. In comparison with the YN1, the maximum absorption wavelengths of the newly designed HTMs are red-shifted due to the decreased excitation energies from the ground-state to the first singlet excited-state. Importantly, the hole mobilities of all designed HTMs are distinctly higher than the referenced YN1, which is contributed by the better planarity of the molecular skeleton and the easier orbital overlapping between adjacent molecules. The interfacial simulations manifest that the FAPbI3/SM37 system displays a more stable adsorption configuration and greater charge redistributions at the interface compared to YN1, which further promotes the separation of photogenerated electron-hole pairs. Moreover, larger Stokes shifts and better solubility are also acquired for the new HTMs. In summary, our calculations not only propose several potential highly efficient HTMs, but also provide useful insights at the atomic level for the experimental synthesis of new D-A-D-type HTMs.

Effect of milling and defatting treatment on texture and digestion properties of oat rice.

Oat rice with great sensory acceptance was developed based on the combination method of milling and defatting (petroleum ether) treatment. In this study, the effect of milling and defatting treatment on the texture and digestion properties of oat rice was investigated. Results showed that milling and defatting treatment enhanced stickiness, enthalpy, and starch digestibility. The pasting temperature and hardness of oat rice were reduced. The lipid content of oat rice was significantly reduced by milling and defatting treatment, leading to a decrease in the formation of starch-lipid complex. Fourier transform infrared spectroscopy and X-ray diffraction analyses revealed that the application of milling and defatting treatments led to a reduction in the content of starch-lipid complexes in oats during the cooking process. Milling and defatting significantly enhanced both the rapid and slow digestion rates of oat rice. Specifically, the rapid digestion rate was found to be 2.5 times higher than the slow digestion rate. The nutritive components of oat rice were properly preserved, and the viscosity and elasticity of oat rice reached the maximum when milling for 40 s and defatting. This study provides a theoretical basis for oat products.

The role of digital finance for the growth of renewable energy: evidence from China.

Developing renewable energy (RE) is the inevitable choice for China to achieve its climate goals. However, financing RE investments remains challenging. Meanwhile, China's digital finance (DF) is profoundly influencing the trajectory of the energy transition. This study empirically investigates the role of DF on the growth of RE, what aspects of DF matter, and its geographical attenuation process, taking both spatial and temporal dimensions into consideration. The empirical results show that DF and its coverage breadth and usage depth can facilitate RE development in both local and neighboring regions, with a comparatively limited effect of digitalization level. The impact of DF on the growth of RE is heterogeneous and has been declining over time. Specifically, this effect is observable only in the eastern regions. The spillover effects of DF on RE development vary in different spatial thresholds, which has clear boundary effects and geographical decay characteristics.

Metabolomics analysis of quality components metabolism during the growth process of pepino (Solanum muricatum) fruit.

Pepino (Solanum muricatum), a horticultural crop that has experienced significant growth in the highlands of China over the past two decades, is widely embraced by consumers due to its distinctive taste and nutritional advantages. This study focused on the cultivar 'Qingcanxiang' of pepino grown on the Qinghai-Tibetan Plateau was analyzed using UPLC-QTOF-MS and RNA-seq transcriptome sequencing. Fruit samples were collected at three distinct stages of development, and the results of the metabolomics and transcriptomics were compared and correlated. The study's findings indicate that the 'Qingcanxiang' fruit contained a total of 187 metabolites, comprising 12 distinct categories of compounds, including amino acids and their derivatives, organic acids, sugars and alcohols, phenols and phenolic acids. Of these metabolites, 94 were identified as differential. Significant variations in nutrient composition were observed across the three growth stages of the fruit. Specifically, the stage spanning from the growth to the maturation was identified as the critical stages for nutrient accumulation and flavor development. Transcriptome sequencing analysis revealed a set of highly associated genes between aspartate and quinic acid, namely SIR2, IRAK4, RP-L29, and CCNH. These genes are potentially involved in the regulation of both amino acid and phenolic acid synthesis. Through the application of metabolomics and transcriptomics, this investigation elucidates the alterations in metabolites and the underlying molecular regulatory mechanisms of pepino fruits during three growth stages. The findings furnish a theoretical foundation for the evaluation of nutritional quality and the enhancement of breeding strategies for pepino.

Effects of milling on texture and in vitro starch digestibility of oat rice.

Compared with other oat products, consumers in China prefer oat rice and porridge made from naked oat. However, this oat product usually has poor sensory acceptance, which is directly related to the texture properties. This study aimed to use the milling method to improve the oat rice texture. The nutrient component, microstructure, pasting, and thermal properties of oat treated with different degrees of milling (0 s, 20 s, 40 s, 60 s, and 80 s) were researched. The results showed that milling would damage the bran layer of oat rice, increasing starch, ß-glucan, total leached solids content, and the gelatinization enthalpy (ΔH). Meanwhile, oil, protein content, the pasting viscosity, and the pasting temperature were decreased. Milling made oat rice sticky and soft, and the bound water and non-flowing water migrated like flowing water. The cross-section of oat rice showed that milling damaged the surface of oat rice, which was beneficial to water entry and starch dissolution, and increased the viscosity of oat rice. When the milling time was 40 s and 60 s, the appearance, aroma, taste, texture, and overall acceptability of oat porridge were better. Moreover, rapid digestion fraction (k1) and slow digestion fraction (k2) are the lowest and have the effect of low blood glucose rise rate.

Assessment of suitable cultivation region for Pepino (Solanum muricatum) under different climatic conditions using the MaxEnt model and adaptability in the Qinghai-Tibet plateau.

Pepino (Solanum muricatum), a member of the Solanaceae family originating from South America, is cultivated globally. However, the cultivation range and suitable habitat of Pepino have not been extensively studied, which hampers the further development of its cultivation industry. Therefore, we aimed at enrich and expand the planting scope of Pepino. Currently, the main cultivation areas of Pepino in China are the Yunnan-Guizhou Plateau and the Loess Plateau, where the altitude is above 1000 m. In this study, ArcGIS combined with the MaxEnt model was used for prediction, whose area under curve value was 0.949. The main climatic factors affecting the distribution of Pepino are temperature seasonality, annual means temperature, mean temperature of the coldest quarter, elevation, isothermality, and the climate factors, and their cumulative contribution rate of 87.6%. Pepino's main potential distribution areas are located in Yunnan-Guizhou Plateau, Yunnan Province, Hexi Corridor of Loess Plateau, and low altitude areas of Qinghai-Tibet Plateau. The main distribution ranges from 1000 to 2000 m above sea level, and the total suitable area accounts for 20.09% of China's total land area. The prediction results reveal an expanded potential area for Pepino, with no significant migration in the central region of the main potential distribution area by 2050 and 2070. No studies have been conducted on the open-area cultivation of Pepino in northern China. Our findings revealed that the yield and quality in the four experimental sites and final actual cultivation conditions were consistent with the predicted results of MaxEnt. The yiel d per plant in Xunhua and Minhe was significantly different from that in Xining, which was low, and that in Minhe was the highest. Overall, the fruit quality in the Xining region was the lowest among the three regions, which was related to the climatic differences in each region. These results align with the predicted outcomes, indicating that Xining is the least suitable area. Further, these data verify the accuracy of the prediction results. The climate data of the four regions were analyzed simultaneously to elucidate the influence of different climate conditions on the growth of Pepino. Our findings are of considerable significance for introducing characteristic horticultural crops in the Qinghai-Tibet Plateau and using the MaxEnt model to predict the cultivation range of crops.

Mechanistic insights into the changes of enzyme activity in food processing under microwave irradiation.

Microwave (MW) and enzyme catalysis are two emerging processing tools in the field of food industry. Recently, MW has been widely utilized as a novel type of green and safe heating energy. However, the effect of MW irradiation on enzyme activity is not described clearly. The intrinsic mechanisms behind enzyme activation and inactivation remain obscure. To apply better MW to the field of enzyme catalysis, it is essential to gain insights into the mechanism of MW action on enzyme activity. This review summarizes the changes in various enzyme activity during food processing, especially under MW irradiation. The intrinsic mechanism of thermal and nonthermal effects of MW irradiation was analyzed from the perspective of enzyme reaction kinetics and spatial structure. MW irradiation temperature is a vital parameter affecting the catalytic activity of enzymes. Activation of the enzyme activity is achieved even at high MW power when the enzyme is operating at its optimum temperature. However, when the temperature exceeds the optimum temperature, the enzyme activity is inhibited. In addition to MW dielectric heating effect, nonthermal MW effects also alter the microenvironment of reactive system. Taken together, enzyme activity is influenced by both thermal and nonthermal MW effects.

DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation.

Recently, one critical issue looms large in the field of recommender systems - there are no effective benchmarks for rigorous evaluation - which consequently leads to unreproducible evaluation and unfair comparison. We, therefore, conduct studies from the perspectives of practical theory and experiments, aiming at benchmarking recommendation for rigorous evaluation. Regarding the theoretical study, a series of hyper-factors affecting recommendation performance throughout the whole evaluation chain are systematically summarized and analyzed via an exhaustive review on 141 papers published at eight top-tier conferences within 2017-2020. We then classify them into model-independent and model-dependent hyper-factors, and different modes of rigorous evaluation are defined and discussed in-depth accordingly. For the experimental study, we release DaisyRec 2.0 library by integrating these hyper-factors to perform rigorous evaluation, whereby a holistic empirical study is conducted to unveil the impacts of different hyper-factors on recommendation performance. Supported by the theoretical and experimental studies, we finally create benchmarks for rigorous evaluation by proposing standardized procedures and providing performance of ten state-of-the-arts across six evaluation metrics on six datasets as a reference for later study. Overall, our work sheds light on the issues in recommendation evaluation, provides potential solutions for rigorous evaluation, and lays foundation for further investigation.

Identification of the key metabolites and related genes network modules highly associated with the nutrients and taste components among different Pepino (Solanum muricatum) cultivars.

There is considerable knowledge about plant compounds that produce flavor, scent, and aroma. Aside from the similarities, however, groups of plant-produced nutrients and taste components have little in common with each other. Network analysis holds promise for metabolic gene discovery, which is especially important in plant systems where metabolic networks are not yet fully resolved. To bridge this gap, we propose a joint model of gene regulation and metabolic reactions in two different pepino varieties. Differential metabolomics analysis is carried out for detection of eventual interaction of compound. We adopted a multi-omics approach to profile the transcriptome and metabolome analyze differences in phenolic acids, flavonoids, organic acids, lipids, alkaloids, and sugars between LOF and SRF. The two most predominant classes of metabolites are phenolic acids and lipids in pepino. Overall results show enrichment in most DEGs was carbohydrate and biosynthesis of secondary metabolites pathway. Results of DEMs predominantly comprised N-p-coumaroyl agmatine and tryptamine, and significant differences were observed in their expression between LOF and SRF. Integrated DEMs and DEGs specific networks were constructed by combining two types of networks: transcriptional regulatory networks composed of interactions between DEMs and the regulated genes, and pepino metabolite-metabolite interaction networks. Newly discovered features, such as DEGs (USPA, UBE2 and DELLA) involved in the production of secondary metabolites are found in coregulated gene clusters. Moreover, lipid metabolites were most involved in DEMs correlations by OPLS-DA while identifying a significant number of DEGs co-regulated by SENP1, HMGCS et al. These results further that the metabolite discrepancies result from characterized the nutrients and taste components between two pepino genotype. Among the possible causes of the differences between species in pepino metabolite concentrations is co-regulated by these DEGs, continue to suggest that novel features of metabolite biosynthetic pathway remain to be uncovered. Finally, the integrated metabolome and transcriptome analyses have revealed that many important metabolic pathways are regulated at the transcriptional level. The metabolites content differences observed among varieties of the same species mainly originates from different regulated genes and enzymes expression. Overall, this study provides new insights into the underlying causes of differences in the plant metabolites and suggests that genetic data can be used to improve its nutrients and taste components.

Adversary Agnostic Robust Deep Reinforcement Learning.

Deep reinforcement learning (DRL) policies have been shown to be deceived by perturbations (e.g., random noise or intensional adversarial attacks) on state observations that appear at test time but are unknown during training. To increase the robustness of DRL policies, previous approaches assume that explicit adversarial information can be added into the training process, to achieve generalization ability on these perturbed observations as well. However, such approaches not only make robustness improvement more expensive but may also leave a model prone to other kinds of attacks in the wild. In contrast, we propose an adversary agnostic robust DRL paradigm that does not require learning from predefined adversaries. To this end, we first theoretically show that robustness could indeed be achieved independently of the adversaries based on a policy distillation (PD) setting. Motivated by this finding, we propose a new PD loss with two terms: 1) a prescription gap maximization (PGM) loss aiming to simultaneously maximize the likelihood of the action selected by the teacher policy and the entropy over the remaining actions and 2) a corresponding Jacobian regularization (JR) loss that minimizes the magnitude of gradients with respect to the input state. The theoretical analysis substantiates that our distillation loss guarantees to increase the prescription gap and hence improves the adversarial robustness. Furthermore, experiments on five Atari games firmly verify the superiority of our approach compared to the state-of-the-art baselines.

Conversation-Based Adaptive Relational Translation Method for Next POI Recommendation With Uncertain Check-Ins.

The uncertain check-ins bring challenges for current static next point-of-interest (POI) recommendation methods. Fortunately, the conversation-based recommendation has been shown the merit of integrating immediate user preference for more accurate recommendations. We, therefore, propose a conversation-based adaptive relational translation (CART) approach for the next POI recommendation over uncertain check-ins. It is equipped with recommender and conversation modules to interactively acquire users' immediate preferences and make dynamic recommendations. Specifically, the recommender built upon the adaptive relational translation method performs location prediction via modeling both users' historical sequential behaviors and the immediate preference received from conversations; the conversation module aims to achieve successful recommendations in fewer conversation turns by learning a conversational strategy, whereby the recommender can be updated via the user response. Extensive experiments on four real-world datasets show the superiority of our proposed CART over the state of the arts.

Buried Interface Regulation by Bio-Functional Molecules for Efficient and Stable Planar Perovskite Solar Cells.

Among the factors that lead to the reduction of the efficiency of perovskite solar cells (PSCs) the difficulty involved in realizing a high-quality film and the efficient charge transfer that takes place at the interface between electron-transport layer (ETL) and perovskite is worth mentioning. Here, a strategy for planar-type devices by natural bio-functional interfaces that uses a buried electron-transport layer made of cobalamin complexed tin oxide (SnO2 @B12 ) is demonstrated. Having systematically investigated the effects of SnO2 @B12 interfacial layer in perovskite solar cells, it can be concluded that cobalamin can chemically link the SnO2 layer and the perovskite layer, resulting in improved perovskite film quality and interfacial defect passivation. Utilizing SnO2 @B12 improves the efficiency of planar-type PSCs by 20.60 %. Furthermore, after 250 h of exposure to an ambient atmosphere, unsealed PSCs containing SnO2 @B12 degrade by 10 %. This research provides a viable method for developing bio-functional molecules that will increase the effectiveness and durability of planar-perovskite solar cells.

Diversity of bacterial community in Jerusalem artichoke (Helianthus tuberosus L.) during storage is associated with the genotype and carbohydrates.

Jerusalem artichoke (JA) is a fructan-accumulating crop that has gained popularity in recent years. The objective of the present study was to determine the dynamics of the JA-microbiome during storage. The microbial population on the surface of the JA tuber was determined by next-generation sequencing of 16S rRNA amplicons. Subsequently, the changes in carbohydrate and degree of polymerization of fructan in tubers during storage were measured. Among different genotypes of JA varieties, intergeneric differences were observed in the diversity and abundance of bacterial communities distributed on the surface of tubers. Additionally, bacterial diversity was significantly higher in storage-tolerant varieties relative to the storage-intolerant varieties. Redundancy analysis (RDA) and the correlation matrix indicated a relationship between changes in the carbohydrates and microbial community succession during tuber storage. The tuber decay rate correlated positively with the degree of polymerization of fructan. Moreover, Dysgonomonas and Acinetobacter in perishable varieties correlated significantly with the decay rate. Therefore, the bacteria associated with the decay rate may be involved in the degradation of the degree of polymerization of fructan. Furthermore, Serratia showed a significant positive correlation with inulin during storage but a negative correlation with the decay rate, suggesting its antagonistic role against pathogenic bacteria on the surface of JA tubers. However, the above correlation was not observed in the storage-tolerant varieties. Functional annotation analysis revealed that storage-tolerant JA varieties maintain tuber quality through enrichment of biocontrol bacteria, including Flavobacterium, Sphingobacterium, and Staphylococcus to resist pathogens. These results suggested that crop genotype and the structural composition of carbohydrates may result in differential selective enrichment effects of microbial communities on the surface of JA varieties. In this study, the relationship between microbial community succession and changes in tuber carbohydrates during JA storage was revealed for the first time through the combination of high-throughput sequencing, high-performance liquid chromatography (HPLC), and high-performance ion-exchange chromatography (HPIC). Overall, the findings of this study are expected to provide new insights into the dynamics of microbial-crop interactions during storage.

Pepino (Solanum muricatum) Metabolic Profiles and Soil Nutrient Association Analysis in Three Growing Sites on the Loess Plateau of Northwestern China.

Different soil nutrients affect the accumulation characteristics of plant metabolites. To investigate the differences among the metabolites of pepino grown in greenhouses on the Loess Plateau in northwest China, we investigated the main soil nutrients and their correlation with metabolites. A total of 269 pepino metabolites were identified using UPLC-QTOF-MS to detect metabolites in fruits from three major pepino growing regions and analyze their differential distribution characteristics. A total of 99 of these substances differed among pepino fruits from the three areas, and the main classes of the differential metabolites were, in order of number: amino acids and derivatives, nucleotides and derivatives, organic acids, alkaloids, vitamins, saccharides and alcohols, phenolic acids, lipids and others. An environmental factor analysis identified soil nutrients as the most significant differentiator. Five soil nutrient indicators: TN (total nitrogen), TP (total phosphorus), AP (available phosphorus), AK (available potassium), and OM (organic matter), exhibited significant differences in three growing sites. Metabolite and soil nutrient association analysis using redundancy analysis (RDA) and the Mantel test indicated that TN and OM contributed to the accumulation of amino acids and derivatives, nucleotides and derivatives, and alkaloids while inhibiting organic acids, vitamins coagulation biosynthesis. Moreover, AP and TP were associated with the highest accumulation of saccharides and, alcohols, phenolic acids. Consequently, differences in soil nutrients were reflected in pepino metabolite variability. This study clarified the metabolite variability and the relationship between pepino and soil nutrients in the main planting areas of northwest China. It provides a theoretical basis for the subsequent development of Pepino's nutritional value and cultivation management.

Who Wants to Shop With You: Joint Product-Participant Recommendation for Group-Buying Service.

Recent years have witnessed the great success of group buying (GB) in social e-commerce, opening up a new way of online shopping. In this business model, a user can launch a GB as an initiator to share her interested product with social friends. The GB is clinched once enough friends join in as participants to copurchase the shared product. As such, a successful GB depends on not only whether the initiator can find her interested product but also whether the friends are willing to join in as participants. Most existing recommenders are incompetent in such complex scenario, as they merely seek to help users find their preferred products and cannot help identify potential participants to join in a GB. To this end, we propose a novel joint product-participant recommendation (J2PRec) framework, which recommends both candidate products and participants for maximizing the success rate of a GB. Specifically, J2PRec first designs a relational graph embedding module, which effectively encodes the various relations in GB for learning enhanced user and product embeddings. It then jointly learns the product and participant recommendation tasks under a probabilistic framework to maximize the GB likelihood, i.e., boost the success rate of a GB. Extensive experiments on three real-world datasets demonstrate the superiority of J2PRec for GB recommendation.

Tectorigenin ameliorated high-fat diet-induced nonalcoholic fatty liver disease through anti-inflammation and modulating gut microbiota in mice.

Nonalcoholic fatty liver disease (NAFLD) is a complex pathogenesis of liver disease combined with liver inflammation and gut microbiota dysbiosis. Tectorigenin (Tg) is derived from many plants with excellent anti-inflammation activity. However, the beneficial effect of Tg on NAFLD associated with gut microbiota remained unclear. This study aimed to investigate the underlying beneficial effect of Tg on NAFLD in high-fat diet (HFD)-fed mice. Results showed that Tg alleviated lipid profiles and liver steatosis, and reduced serum lipopolysaccharide (LPS) and total bile acid (TBA) levels. Besides, RT-qPCR and Western blot suggested that Tg alleviated hepatic lipid accumulation through inhibiting the lipogenesis and promoting the lipolysis, prevented gut-derived LPS-induced liver inflammatory via restoring intestinal barrier and restraining pro-inflammatory cytokines release, meanwhile, promoted the BA circulation via activating BA receptor and promoting BA synthesis. Moreover, Tg reverted the HFD-induced gut microbial dysbiosis by promoting the growth of beneficial Akkermansia, and inhibiting the proportions of harmful microbes, including Blautia, Lachnoclostridium, Lachnospiraceae_UCG-006, Roseburia, Romboutsia and Faecalibaculum, which were highly correlated with NAFLD-related parameters in serum and liver. Thus, Tg could attenuate NAFLD through mediating the liver-gut axis, and it could be used as a dietary supplement for NAFLD treatment via its anti-inflammatory and prebiotic effects.

A Flexible Method to Fabricate Exsolution-Based Nanoparticle-Decorated Materials in Seconds.

Decorating metallic nanoparticles on the surface of oxide support is a promising approach to tailor the catalytic activity of perovskite. Here, for the first time using thermal shock to rapidly fabricate nanoparticle-decorated materials (NDMs) is proposed. Low-cost and size-tailorable carbon paper is used as the heating source during the thermal shock. It is reported that by thermal shock technique, only ≈13 s including heating and treating time is needed to fabricate the exsolution-based NDMs (the fastest method to date). Benefitted by the sufficiently provided driving force and the short treating time, as compared to the product prepared by the conventionally furnace-based method, higher particle density and smaller particle size of the exsolved catalysts are acquired for the thermal shock fabricated NDM, giving rise to a fascinating improvement (12-fold) of the electrochemical performance. This work develops a new technique to rapidly fabricate NDMs in an economic and high-throughput manner, profoundly improving the flexibility of the application of exsolution-based materials in electrochemical devices.

Manipulating Electrocatalytic Activity of Perovskite Oxide Through Electrochemical Treatment.

Perovskite oxides are widely used in electrochemical cells, profiting from their excellent accommodation of different elements and structure stability. Here, it is reported that when rapidly exceeding the electrochemical stability window of a perovskite oxide through electrochemical treatment, nanoparticles can dynamically exsolve from the perovskite lattice, yielding a nanoparticle decorated material (NDM) with fascinating particle population and distribution. It is reported that as compared to the NDM produced by chemical gas reduction, electrochemical treatment fabricated NDM shows much better electrochemical performance. At 900 °C, a peak power density (PPD) of 896 mW cm-2 (more than tenfold enhancement) is obtained for a yttrium stabilized zirconia (YSZ) electrolyte-supported symmetrical cell with La0.43 Ca0.37 Ti0.8 Co0.1 Fe0.1 O3- δ (LCTCF) electrode after electrochemical treatment for several minutes, while it only reaches to 210 mW cm-2 after chemical gas treatment for tens of hours using humidified hydrogen as fuel. The study establishes a new fairyland for tuning the performance of-but not limited to-the electrochemical cells.

An Exploration of Pepino (Solanum muricatum) Flavor Compounds Using Machine Learning Combined with Metabolomics and Sensory Evaluation.

Flavor is one of the most important characteristics that directly determines the popularity of a food. Moreover, the flavor of fruits is determined by the interaction of multiple metabolic components. Pepino, an emerging horticultural crop, is popular for its unique melon-like flavor. We analyzed metabolomics data from three different pepino growing regions in Haidong, Wuwei, and Jiuquan and counted the status of sweetness, acidity, flavor, and overall liking ratings of pepino fruit in these three regions by sensory panels. The metabolomics and flavor ratings were also integrated and analyzed using statistical and machine learning models, which in turn predicted the sensory panel ratings of consumers based on the chemical composition of the fruit. The results showed that pepino fruit produced in the Jiuquan region received the highest ratings in sweetness, flavor intensity, and liking, and the results with the highest contribution based on sensory evaluation showed that nucleotides and derivatives, phenolic acids, amino acids and derivatives, saccharides, and alcohols were rated in sweetness (74.40%), acidity (51.57%), flavor (56.41%), and likability (33.73%) dominated. We employed 14 machine learning strategies trained on the discovery samples to accurately predict the outcome of sweetness, sourness, flavor, and liking in the replication samples. The Radial Sigma SVM model predicted with better accuracy than the other machine learning models. Then we used the machine learning models to determine which metabolites influenced both pepino flavor and consumer preference. A total of 27 metabolites most important for pepino flavor attributes to distinguish pepino originating from three regions were screened. Substances such as N-acetylhistamine, arginine, and caffeic acid can enhance pepino's flavor intensity, and metabolites such as glycerol 3-phosphate, aconitic acid, and sucrose all acted as important variables in explaining the liking preference. While glycolic acid and orthophosphate inhibit sweetness and enhance sourness, sucrose has the opposite effect. Machine learning can identify the types of metabolites that influence fruit flavor by linking metabolomics of fruit with sensory evaluation among consumers, which conduces breeders to incorporate fruit flavor as a trait earlier in the breeding process, making it possible to select and release fruit with more flavor.