Pairing d-Band Center of Metal Sites with π-Orbital of Alkynes for Efficient Electrocatalytic Alkyne Semi-Hydrogenation.

Electrocatalytic alkyne semi-hydrogenation has attracted ever-growing attention as a promising alternative to traditional thermocatalytic hydrogenation. However, the correlation between the structure of active sites and electrocatalytic performance still remains elusive. Herein, the energy difference (∆ε) between the d-band center of metal sites and π orbital of alkynes as a key descriptor for correlating the intrinsic electrocatalytic activity is reported. With two-dimensional conductive metal organic frameworks as the model electrocatalysts, theoretical and experimental investigations reveal that the decreased ∆ε induces the strengthened d-π orbitals interaction, which thus enhances acetylene π-adsorption and accelerates subsequent hydrogenation kinetics. As a result, Cu3 (HITP)2 featuring the smallest ∆ε (0.10 eV) delivers the highest turnover frequency of 0.36 s-1 , which is about 124 times higher than 2.9 × 10-3  s-1 for Co3 (HITP)2 with the largest ∆ε of 2.71 eV. Meanwhile, Cu3 (HITP)2 presents a high ethylene partial current density of -124 mA cm-2 and a large ethylene Faradaic efficiency of 99.3% at -0.9 V versus RHE. This work will spark the rapid exploration of high-activity alkyne semi-hydrogenation catalysts.

Advances in Selective Electrocatalytic Hydrogenation of Alkynes to Alkenes.

Selective electrochemical hydrogenation of alkynes to alkenes under ambient conditions is a promising alternative to the traditional energy-intensive and high-cost thermocatalytic hydrogenation. However, the systematic summary on the electrocatalysts and electrolyzers remains lacked. Herein, we demonstrate a comprehensive review about recent achievements in the electrocatalysts including noble metal and non-noble-metal materials. Several effective strategies of catalyst design were developed to improve alkyne conversion, and alkene selectivity, for example, accelerating the formation of active hydrogen species, enhancing alkyne adsorption and suppressing the side reactions. Furthermore, the advantages and disadvantages of various electrolyzers are systematically discussed. Accordingly, major challenges and future trends in this field are proposed.

A DDoS Detection Method Based on Feature Engineering and Machine Learning in Software-Defined Networks.

Distributed denial-of-service (DDoS) attacks pose a significant cybersecurity threat to software-defined networks (SDNs). This paper proposes a feature-engineering- and machine-learning-based approach to detect DDoS attacks in SDNs. First, the CSE-CIC-IDS2018 dataset was cleaned and normalized, and the optimal feature subset was found using an improved binary grey wolf optimization algorithm. Next, the optimal feature subset was trained and tested in Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (k-NN), Decision Tree, and XGBoost machine learning algorithms, from which the best classifier was selected for DDoS attack detection and deployed in the SDN controller. The results show that RF performs best when compared across several performance metrics (e.g., accuracy, precision, recall, F1 and AUC values). We also explore the comparison between different models and algorithms. The results show that our proposed method performed the best and can effectively detect and identify DDoS attacks in SDNs, providing a new idea and solution for the security of SDNs.

Cache-Based Privacy Protection Scheme for Continuous Location Query.

Users who initiate continuous location queries are prone to trajectory information leakage, and the obtained query information is not effectively utilized. To address these problems, we propose a continuous location query protection scheme based on caching and an adaptive variable-order Markov model. When a user initiates a query request, we first query the cache information to obtain the required data. When the local cache cannot satisfy the user's demand, we use a variable-order Markov model to predict the user's future query location and generate a k-anonymous set based on the predicted location and cache contribution. We perturb the location set using differential privacy, then send the perturbed location set to the location service provider to obtain the service. We cache the query results returned by the service provider to the local device and update the local cache results according to time. By comparing the experiment with other schemes, the proposed scheme in this paper reduces the number of interactions with location providers, improves the local cache hit rate, and effectively ensures the security of the users' location privacy.

HeteEdgeWalk: A Heterogeneous Edge Memory Random Walk for Heterogeneous Information Network Embedding.

Most Heterogeneous Information Network (HIN) embedding methods use meta-paths to guide random walks to sample from HIN and perform representation learning in order to overcome the bias of traditional random walks that are more biased towards high-order nodes. Their performance depends on the suitability of the generated meta-paths for the current HIN. The definition of meta-paths requires domain expertise, which makes the results overly dependent on the meta-paths. Moreover, it is difficult to represent the structure of complex HIN with a single meta-path. In a meta-path guided random walk, some of the heterogeneous structures (e.g., node type(s)) are not among the node types specified by the meta-path, making this heterogeneous information ignored. In this paper, HeteEdgeWalk, a solution method that does not involve meta-paths, is proposed. We design a dynamically adjusted bidirectional edge-sampling walk strategy. Specifically, edge sampling and the storage of recently selected edge types are used to better sample the network structure in a more balanced and comprehensive way. Finally, node classification and clustering experiments are performed on four real HINs with in-depth analysis. The results show a maximum performance improvement of 2% in node classification and at least 0.6% in clustering compared to baselines. This demonstrates the superiority of the method to effectively capture semantic information from HINs.

Functional Aqueous Zinc-Acetylene Batteries for Electricity Generation and Electrochemical Acetylene Reduction to Ethylene.

The available processes for removing acetylene impurities from crude ethylene are tremendously energy-intensive. Herein, we demonstrate a novel aqueous Zn-C2 H2 battery, which not only switches energy-consuming acetylene removal to electricity generation, but also reduces acetylene to ethylene through a unique discharge mechanism: C2 H2 +Zn+H2 O→C2 H4 +ZnO. Under a pure acetylene stream, this Zn-C2 H2 battery exhibits an open circuit potential of 1.14 V and a peak power density of 2.2 mW cm-2 , which exceed those of reported Zn-CO2 batteries. Even for simulated crude ethylene, the Zn-C2 H2 battery manifests an acetylene conversion of 99.97 % and continuously produces polymer-grade ethylene with only ≈3 ppm acetylene during a long-term discharge operation. Such a functional battery is universally appliable for reducing other alkynes and generating electricity. Therefore, this work provides an effective strategy for green ethylene purification and the design of functional batteries.

[Transcriptomics of therapeutic effect of Huangqi Liuyi decoction in treating type 2 diabetes].

In this study, Illumina sequencing platform was applied in sequencing rat pancreas, counting expression of target points, analyzing expression differences among blank group, model group and Huangqi Liuyi decoction group and exploring the therapeutic effect and mechanism of Huangqi Liuyi decoction on type 2 diabetes mellitus. According to the result, 24.25% of these genes belonged to the unknown functional class, which was the largest classification unit according to the classification analysis of genes by eggNOG. The rest were classified as energy conversion, amino acid transport and metabolism, nucleotide transport and metabolism, carbohydrate transport and metabolism, coenzyme transport and metabolism, and lipid transport and metabolism, etc.Huangqi Liuyi decoction may play a therapeutic role in the treatment of type 2 diabetes mellitus through four metabolic pathways, namely environmental information processing, cellular process, organismal system and human diseases according to KEGG enrichment analysis. This study shows that, Huangqi Liuyi decoction can significantly improve the fasting blood glucose and glycosylated hemoglobin in type 2 diabetic rats.

[Model Predicts of Astragalus membranceus Transcriptional Gene].

Objective: Astragali Radix, one of the most widely used traditional Chinese medicines, has been extracted a variety of efficiently ingredients. However, the formation mechanism of the secondary metabolites is still unclear and the genome of Astragali Radix has not been sequenced yet. This study aimed at predicting gene model according to transcriptome data, which can be helpful to the use of Astragalus membranceus in the medicine field. Methods: Based on the transcriptome data of Astragalus membranceus and the existing whole genome sequence of its closely related species, the unigene of transcriptome of Astragalus membranceus was predicted and analyzed. Results: Astragalus membranceus had close genetic relationship with Glycine max,Medicago truncatula and Lotus japonicus. Astragalus membranceus shared 2 039 sequences with Medicago truncatula, and their base similarity was 90. 14%. There were 1 948 sequences matched up with Glycine max, and their base similarity was 90. 40%. In the aligement of Astragalus membranceus and Lotus corniculatus, the matched sequences dropped to 1 003,and the base similarity dropped to 89. 77%. Conclusion: The matched gene with three species discributed in chromosome genomes and extrachromosomal genome.

[Study on quality evaluation of sequence and SSR information in transcriptome of Astragalus membranacus].

In this study, 454/Roche GS FLX sequencing technology was used to obtain the data of the Astragalus membranaceus. Four hundred and fifty-four Sequencing System Software was applied to carry out the transcription of the group from scratch. Using MISA tools, 9 893 unigenes were selected for the sequence of the genome of A. membranaceus, and the information of SSR locus was analyzed. According to the result, the average length of reads was 413 bp, about 86% of the reads was involved in the splicing, the length of the N50 was 1 205 bp, the number of unigenes was measured by the whole transcript. 1 729 SSR loci in the A. membranaceus transcriptome were searched, the occurrence frequency of SSR was 9.24%, the frequency of SSR in the whole transcriptome was 13.42%, the average length of SSR was 7.97 kb. One hundred and twenty-seven kinds of core repeat sequences were found, the dominant type was TG/AC type of dinucleotide, it appeared to account for 4.25% of the total SSR locus. The results of the sequence of the transcription of the A. membranaceus transcriptome revealed the overall expression, and a large number of unigenessequence was obtained, and the SSR locus in the genome of the A. membranaceus is high, and the type is diverse, and the polymorphism of the gene is high.

[Study on accumulation of polysaccharides and alcohol-soluble extracts contents of Dendrobium officinale leaves].

This paper revealed the accumulation regularity of polysaccharides and alcohol-soluble extracts contents of Dendrobium officinale leaves, which have provided basis for the development and utilization of the leaves. The polysaccharides and alcohol-soluble extracts contents of three D. officinale strains leaves collected in different growing periods were determined by phenol-sulfric acid method and hot-dip method respectively. The results showed that the content of polysaccharides in leaves was 4.45% -12.17%, and was about a quarter in stems. The alcohol-soluble extracts content in leaves was 7.45% - 29.34%, and was 1.5 times that of stems. The quality variation of polysaccharides in leaves was closely related to the phenophase. The leaves with lower level of metabolism in three stages: winter, early germination stage and deciduous period, which led to lower content of polysaccharides. The leaves at the vigorous growth stage with higher content of polysaccharides. The alcohol-soluble extracts were closely associated to the formation and germination of buds. The content of alcohol-soluble extracts peaked before sprout, and promoted the growth of new shoots.

[Effect of strains and parts on amino acids of Dendrobium officinale].

The aim of the paper is to reveals the variations of Dendrobium officinale amino acids in different strains and parts for breeding excellent varieties, and providing scientific basis for the expanding of medicinal or edible parts. The contents of 17 amino acids in 11 strains of D. officinale were determined by hydrochloric acid hydrolysis method. The total amino acids content of leaves was from 6.76 to 7.97 g per 100 g, and the stems was from 1.61 to 2.44 g per 100 g. As the content of amino acids in leaves was significantly higher than that of stems, and the composition was close to the ideal protein standard proposed by FAO/WHO. The leaves of D. officinale had the good prospect for the development of functional foods. The 9 x 66 strain which with high yield and polysaccharide content had the highest amino acids content both in stems and leaves, indicated crossbreeding could improve the quality of varieties. Compared the amino acids content of D. officinale in two main harvest periods, the harvest time has a significant impact on amino acids content of D. officinale. This study demonstrates that the harvesting time of D. officinale stems is suitable for leaves as well, which is the period before bolssom.

Data aggregation algorithm for wireless sensor networks with different initial energy of nodes.

Data aggregation plays a critical role in sensor networks for efficient data collection. However, the assumption of uniform initial energy levels among sensors in existing algorithms is unrealistic in practical production applications. This discrepancy in initial energy levels significantly impacts data aggregation in sensor networks. To address this issue, we propose Data Aggregation with Different Initial Energy (DADIE), a novel algorithm that aims to enhance energy-saving, privacy-preserving efficiency, and reduce node death rates in sensor networks with varying initial energy nodes. DADIE considers the transmission distance between nodes and their initial energy levels when forming the network topology, while also limiting the number of child nodes. Furthermore, DADIE reconstructs the aggregation tree before each round of data transmission. This allows nodes closer to the receiving end with higher initial energy to undertake more data aggregation and transmission tasks while limiting energy consumption. As a result, DADIE effectively reduces the node death rate and improves the efficiency of data transmission throughout the network. To enhance network security, DADIE establishes secure transmission channels between transmission nodes prior to data transmission, and it employs slice-and-mix technology within the network. Our experimental simulations demonstrate that the proposed DADIE algorithm effectively resolves the data aggregation challenges in sensor networks with varying initial energy nodes. It achieves 5-20% lower communication overhead and energy consumption, 10-20% higher security, and 10-30% lower node mortality than existing algorithms.

Deprotonated 2-thiolimidazole serves as a metal-free electrocatalyst for selective acetylene hydrogenation.

Metal-free catalysts offer a desirable alternative to traditional metal-based electrocatalysts. However, metal-free catalysts, featuring defined active sites, rarely show activities as promising as metal-based materials. Here we report 2-thiolimidazole as an efficient metal-free catalyst for selective electrocatalytic hydrogenation of acetylene into ethylene. Under alkaline conditions, the sulfhydryl and imino groups of 2-thiolimidazole are spontaneously deprotonated into dianions. Deprotonation thus enriches the negative charges of pyridinic N sites in 2-thiolimidazole to enhance the adsorption of electrophilic acetylene through the σ-configuration. Ethylene partial current densities show a volcano relationship with the negative charges of the pyridinic N sites in various imidazole derivatives. Consequently, the deprotonated 2-thiolimidazole exhibits an ethylene partial current density and faradaic efficiency competitive with metal-based catalysts like Cu and Pd. This work highlights the tunability and promising potential of metal-free molecules in electrocatalysis.

FGF19 improves sevoflurane-induced cognitive dysfunction in rats through the PGC-1α/BDNF/FNDC5 pathway.

Postoperative cognitive dysfunction (POCD) is a serious central nervous system complication characterized by impaired memory, reduced information processing ability, and anxiety. Recently, the role of FGF19 in neurological diseases has been reported. However, the effect and mechanisms of FGF19 in improving symptoms of POCD remain unknown. This study aimed to identify the role and exploring the underlying mechanisms of FGF19 in POCD. Here, rats were separated into four different groups, including control, sevoflurane (sev), sev + AAV-empty, and sev + AAV-FGF19 group. Then, the Morris water maze (MWM) test was applied to identify the effect of FGF19 on POCD rats. The result proved that FGF19 improved sevoflurane induced cognitive dysfunction in rats. Subsequently, the expressions of TNF-α, IL-6, IL-1ß, and IL-10 were detected to verify the anti-neuroinflammatory effects of FGF19 in POCD rats. Furthermore, DHE fluorescent staining assay showed that FGF19 could inhibit sevoflurane-induced oxidative stress in POCD rats. Besides, NISSL staining and TUNEL assay were applied to reveal that FGF19 could alleviate hippocampal neuron injury induced by sevoflurane in rats. Moreover, mechanistic studies confirmed that FGF19 improved symptoms of POCD by mediated PGC-1α/BDNF/FNDC5 pathway. Together, these results suggested that FGF19 improves sevoflurane-induced POCD in rats through the PGC-1α/BDNF/FNDC5 pathway.

Highly selective electrocatalytic alkynol semi-hydrogenation for continuous production of alkenols.

Alkynols semi-hydrogenation is a critical industrial process as the product, alkenols, have extensive applications in chemistry and life sciences. However, this class of reactions is plagued by the use of high-pressure hydrogen, Pd-based catalysts, and low efficiency of the contemporary thermocatalytic process. Here, we report an electrocatalytic approach for selectively hydrogenating alkynols to alkenols under ambient conditions. For representative 2-methyl-3-butene-2-ol, Cu nanoarrays derived electrochemically from CuO, achieve a high partial current density of 750 mA cm-2 and specific selectivity of 97% at -0.88 V vs. reversible hydrogen electrode in alkaline solution. Even in a large two-electrode flow electrolyser, the Cu nanoarrays deliver a single-pass alkynol conversion of 93% with continuous production of 2-methyl-3-butene-2-ol at a rate of ~169 g gCu-1 h-1. Theoretical and in situ electrochemical infrared investigations reveal that the semi-hydrogenation performance is enhanced by exothermic alkynol adsorption and alkenol desorption on the Cu surfaces. Furthermore, this electrocatalytic semi-hydrogenation strategy is shown to be applicable to a variety of alkynol substrates.

Synthetic carbon nanomaterials for electrochemical energy conversion.

Carbon nanomaterials have attracted widespread attention in electrochemical energy conversion due to their large surface area, excellent electrical/thermal conductivity and good chemical stability. However, the structure-activity relationship of carbon nanomaterials remains unclear. This review is thus on the synthesis methods of carbon nanomaterials including two-dimensional graphene, graphene nanoribbons, nanographene, heteroatom doped porous carbon and graphdiyne as electrocatalysts for the hydrogen evolution reaction, oxygen evolution reaction, and oxygen reduction reaction in fuel cells, electrolyzers and CO2 reduction. The correlation between the electronic/chemical properties and electrochemical performance of synthetic carbon nanostructures will be profoundly discussed. Additionally, the emerging challenges and some perspectives on the development of synthetic carbon nanomaterials for electrochemical energy conversion are discussed.

Effects of Various Processing Methods on the Metabolic Profile and Antioxidant Activity of Dendrobium catenatum Lindley Leaves.

The metabolite profiles and antioxidant activity of Dendrobium catenatum Lindley leaf, a new functional ingredient for food product development, were evaluated in samples that had been prepared using various methods, including freeze-drying, hot-air drying, rolling before drying, steaming before drying, steaming and rolling before drying, and drying at 100, 80, and 60 °C. The concentrations of polysaccharides and flavonoids, as well as the antioxidant capacity of each sample, were determined. Furthermore, two nucleosides, four amino acids, one monoaromatic compound, and eight flavonoids were identified in dried leaves using high-performance liquid chromatography-diode array detector-electrospray ionization-multistage mass spectrometry (HPLC-DAD-ESI-MSn) and ultraviolet (UV) spectral analyses. The content of polar compounds such as cytidylic acid, arginine, tyrosine, and hydroxybenzoic acid hexose increased dramatically during hot-air-drying and rolling-before-drying treatments, while flavonol C-glycosides remained stable throughout the various treatments and drying temperatures. Rolling before drying at 100 °C was identified as the most suitable process when manufacturing tea products from D. catenatum leaves. This process resulted in a high-antioxidant-activity and visually appealing tea. This report details a potential strategy that should be applied in the manufacturing processes of high-quality products from D. catenatum leaves.

Regulating Water Reduction Kinetics on MoP Electrocatalysts Through Se Doping for Accelerated Alkaline Hydrogen Production.

Owing to its low cost, high conductivity, and chemical stability, Molybdenum phosphide (MoP) has great potential for electrochemically catalyzing the hydrogen evolution reaction (HER). Unfortunately, the development of high-activity MoP still remains a grand challenge in alkali-electrolyzers due to its sluggish water reduction kinetics. Here, we demonstrate a novel strategy for regulating the HER kinetics of the MoP nanowire cathode through partially substituting P atoms with Se dopants. In alkaline solutions, the Se-doped MoP (Se-MoP) nanowire cathode exhibits excellent HER performance with a greatly-decreased overpotential of ∼61 mV at 10 mA cm-2 and a Tafel slope of ∼63 mV dec-1, outperforming currently reported MoP-based electrocatalysts. Experimental and theoretical investigations reveal that Se doping not only facilitates the water dissociation on MoP, but also optimize the hydrogen adsorption free energy, eventually speeding up the sluggish alkaline HER kinetics. Therefore, this work paves a new path for designing MoP-based electrocatalyst with high HER performance in alkaline electrolyzers.

Efficient electrocatalytic acetylene semihydrogenation by electron-rich metal sites in N-heterocyclic carbene metal complexes.

Electrocatalytic acetylene semihydrogenation is a promising alternative to thermocatalytic acetylene hydrogenation due to its environmental benignity and economic efficiency, but its performance is far below that of the thermocatalytic reaction because of strong competition from side reactions, including hydrogen evolution, overhydrogenation and carbon-carbon coupling reactions. We develop N-heterocyclic carbene-metal complexes, with electron-rich metal centers owing to the strongly σ-donating N-heterocyclic carbene ligands, as electrocatalysts for selective acetylene semihydrogenation. Experimental and theoretical investigations reveal that the copper sites in N-heterocyclic carbene-copper facilitate the absorption of electrophilic acetylene and the desorption of nucleophilic ethylene, ultimately suppressing the side reactions during electrocatalytic acetylene semihydrogenation, and exhibit superior semihydrogenation performance, with faradaic efficiencies of ≥98 % under pure acetylene flow. Even in a crude ethylene feed containing 1 % acetylene (1 × 104 ppm), N-heterocyclic carbene-copper affords a specific selectivity of >99 % during a 100-h stability test, continuous ethylene production with only ~30 ppm acetylene, a large space velocity of up to 9.6 × 105 mL·gcat-1·h-1, and a turnover frequency of 2.1 × 10-2 s-1, dramatically outperforming currently reported thermocatalysts.

Single-Crystalline Mo-Nanowire-Mediated Directional Growth of High-Index-Faceted MoNi Electrocatalyst for Ultralong-Term Alkaline Hydrogen Evolution.

As appealing alternatives to noble-metal-based electrocatalysts for catalyzing hydrogen evolution reaction (HER) in alkali electrolyzers, earth-abundant MoNi-based catalysts have attracted intensive attention. Unfortunately, the exploration of MoNi-based electrocatalysts with superior intrinsic activity and ultralong-term stability still remains a grand challenge. Here, ultralong high-index faceted Mo@MoNi core-shell nanowires were topochemically synthesized through the thermal reduction of Mo@NiMoO4 core-shell nanowires, where single-crystalline Mo support facilitates the topological transformation of NiMoO4 into high-index faceted MoNi. When the as-achieved Mo@MoNi core-shell nanowire film serve as a free-standing cathode in alkaline solutions, it exhibit a remarkably decreased HER overpotential of 18 mV at 10 mA cm-2 and a Tafel slope of ∼33 mV dec-1, which are much lower than those for the state-of-the-art earth-abundant electrocatalysts and even commercial Pt/C. Experimental and theoretical investigations reveal that the exposed high-index (331) facets of MoNi can considerably reduce the energy barriers of initial water dissociation and subsequent hydrogen combination steps, which synergistically accelerates the sluggish alkaline HER kinetics. Significantly, after a 70-day HER operation, the overpotential of Mo@MoNi electrocatalysts at 10 mA cm-2 decreases by only 4 mV. Therefore, this work sheds a bright light on the rational design of high-performance HER electrocatalysts and their practical utilization for alkaline electrolyzers.