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Due to the great successes of Graph Neural Networks (GNN) in numerous fields, growing research interests have been devoted to applying GNN to molecular learning tasks. The molecule structure can be naturally represented as graphs where atoms and bonds refer to nodes and edges respectively. However, the atoms are not haphazardly stacked together but combined into various spatial geometries. Meanwhile, since chemical reactions mainly occur in substructures such as functional groups, the substructure plays a decisive role in the molecule's properties. Therefore, directly applying GNN to molecular representation learning could ignore the molecular spatial structure and the substructure properties which in turn degrades the performance of downstream tasks. In this paper, we propose Knowledge-Driven Self-Supervised Model for Molecular Representation Learning (KSMRL) to address above problems. The KSMRL consists of two major pathways: (1) the Spatial Information (SI) based pathway which preserves the spatial information of molecular structure, (2) the Subgraph Constraint (SC) based pathway which retains the properties of substructures into the molecular representation. In this manner, both the atomic level and substructure level information can be included in modeling. According to the experimental results on multiple datasets, the proposed KSMRL can generate discriminative molecular representations. In molecular generation tasks, KSMRL combined with Autoregressive Flow (AF) models or Discrete Flow (DF) models outperforms the state-of-the-art baselines over all datasets. In addition, we demonstrate the effectiveness of KSMRL with property optimization experiments. To indicate the ability of predicting specified potential Drug-Target Interactions (DTIs), a case study for discriminating the interactions between molecule generated by KSMRL and targets is also given.

Ni, Co-induced highly distributed NiCoP nanoparticles embedded nitrogen-doped carbon nanotubes (NCNTs) (NiCo/NiCoP-NCNTs) were directly synthesized by a one-step phosphorization and carbonization process. As a bifunctional electrocatalyst for water splitting, NiCo/NiCoP NCNTs show impressive catalytic performance with an overpotential of only 206 mV for the hydrogen evolution reaction and 360 mV for the oxygen evolution reaction in 0.5 mol/L H2SO4and 1 mol/L KOH solutions, respectively. In addition, NiCo/NiCoP NCNTs maintain a stable cell voltage of 1.68 V at 10 mA/cm2with only a 10% decrease in current density over 48 h, showing remarkable stability. The improved catalytic activity can be attributed to the integration of NiCoP nanoparticles and the synergies between NCNTs and NiCo alloy. Additionally, the improved electrocatalytic performance can be attributed to the increased electrochemically active surface area and the reduced electron transfer resistance of the NiCo/NiCoP-NCNTs. Overall, the NiCo/NiCoP-NCNTs demonstrated significant performance for advanced water electrolysis applications.

Hydrogen (H2) stands as a clean energy alternative to fossil fuels, especially within the domain of the hydrogen evolution reaction (HER), offering prospective solutions to mitigate both environmental and energy-related challenges. In this work, we successfully synthesized a sea-urchin-like catalyst, specifically a nickel–cobalt phosphide nanoneedle array on N-doped carbon nanospheres (Ni0.5Co1.5P@NCSs), for efficient HER by a sequential hydrothermal and low-temperature phosphating process. The catalyst exhibits sea-urchin-like structures, offering a specific surface area of 298 m2g–1and consequently furnishing a greater abundance of active sites. Comparing with non-sea-urchin-like Ni0.5Co1.5P@CN catalysts, the Ni0.5Co1.5P@NCSs exhibit an overpotential of 163 mV at 10 mA cm–2, a Tafel slope of 60 mV dec–1, and a maintained current density of approximately 90% during 50 h of continuous electrolysis. Experiments demonstrate that the outstanding electrochemical properties of the Ni0.5Co1.5P@NCSs originate from nitrogen doping of carbon spheres, the distinctive morphology of sea-urchin-like nanoneedle arrays, and simultaneous enhancements in intermediate adsorption energy, charge transfer, and electrolyte diffusion channel shortening. This work emphasizes a preparation strategy for synthesizing an attractive electrocatalyst with a low cost and efficient HER performance.

Facial attribute editing is a popular direction in face generation, which aims to modify facial attributes in the face image and remain unedited attributes unchanged. However, generative models are prone to affect the unedited attributes when editing multiple facial attributes. Currently, the concatenation of the prior knowledge with hidden features is still data-driven work. Due to the feature coupling in data-driven models, high-entanglement implicit semantics are generated, which is incomprehensible for human beings. Besides, multi-attribute boundaries of the implicit semantics are ambiguous, which is complicated to effectively control the editing process. In this paper, we propose a knowledge-guided explicit feature disentanglement network that is compatible with human cognition, leveraging a classification method with the prior knowledge to encode features. Specifically, we select 13 facial attribute labels for a comprehensive and explicit presentation of this task and design a knowledge-guided feature disentanglement module to transform the implicit feature representations into explicit feature semantics. We also construct a semantic space that can independently manipulate facial attributes. In addition, our proposed model can be combined with existing facial attribute editing models to obtain multiple variant models. Our proposed model is fully validated by various experiments and the variant model has achieved better performance than the benchmark model in facial attribute editing.

Variational optimization (VO) pansharpening method proposes a model for optimizing the energy function by delineating the acquisition process of remote sensing images. However, traditional VO methods face challenges in solving the operators. Currently, some model-driven methods alleviate this problem by employing deep unfolding techniques. However, most model-driven approaches do not take into account the unknown and variable degradation process inherent in remote sensing images when solving the optimization model. They typically utilize a fixed Gaussian downsampling operator or a two-layer convolutional neural network module to directly simulate the degradation process during model unfolding, which leads to models that lack sufficient generalization. Therefore, this article proposes a pansharpening method based on deep nonlocal unfolding with degradation kernel estimation. Specifically, we expand the iterative process of solving the energy function into three modules: degradation kernel estimator, image generator, and three-branch prior network. First, the degradation kernel estimator is employed to fit the real degradation process of the image and optimize the generation of an adaptive degradation kernel. Simultaneously, we incorporate local priors, nonlocal spatial priors, and nonlocal spectral priors into the three-branch prior network to adaptively capture local and nonlocal prior features. These prior features are then fused with the generated image through residual connections to approximate the real remote sensing image. Experimental results on datasets from two different types of satellites demonstrate the superiority of our approach.

Benchmark datasets play an important role in evaluating Natural Language Understanding (NLU) models. However, shortcuts—unwanted biases in the benchmark datasets—can damage the effectiveness of benchmark datasets in revealing models’ real capabilities. Since shortcuts vary in coverage, productivity, and semantic meaning, it is challenging for NLU experts to systematically understand and avoid them when creating benchmark datasets. In this paper, we develop a visual analytics system, ShortcutLens, to help NLU experts explore shortcuts in NLU benchmark datasets. The system allows users to conduct multi-level exploration of shortcuts. Specifically, Statistics View helps users grasp the statistics such as coverage and productivity of shortcuts in the benchmark dataset. Template View employs hierarchical and interpretable templates to summarize different types of shortcuts. Instance View allows users to check the corresponding instances covered by the shortcuts. We conduct case studies and expert interviews to evaluate the effectiveness and usability of the system. The results demonstrate that ShortcutLens supports users in gaining a better understanding of benchmark dataset issues through shortcuts, inspiring them to create challenging and pertinent benchmark datasets.

Some natural languages belong to the same family or share similar syntactic and/or semantic regularities. This property persuades researchers to share computational models across languages and benefit from high-quality models to boost existing low-performance counterparts. In this article, we follow a similar idea, whereby we develop statistical and neural machine translation (MT) engines that are trained on one language pair but are used to translate another language. First we train a reliable model for a high-resource language, and then we exploit cross-lingual similarities and adapt the model to work for a close language with almost zero resources. We chose Turkish (Tr) and Azeri or Azerbaijani (Az) as the proposed pair in our experiments. Azeri suffers from lack of resources as there is almost no bilingual corpus for this language. Via our techniques, we are able to train an engine for the Az → English (En) direction, which is able to outperform all other existing models.

Aligning users across networks using graph representation learning has been found effective where the alignment is accomplished in a low-dimensional embedding space. Yet, highly precise alignment remains challenging, especially for nodes with long-range connectivity to labeled anchors. To alleviate this limitation, we propose WL-Align which employs a regularized representation learning framework to learn distinctive node representations. It extends the Weisfeiler-Lehman Isormorphism Test and learns the alignment in alternating phases of “across-network Weisfeiler-Lehman relabeling” and “proximity-preserving representation learning”. The across-network Weisfeiler-Lehman relabeling is achieved through iterating the anchor-based label propagation and a similarity-based hashing to exploit the known anchors’ connectivity to different nodes in an efficient and robust manner. The representation learning module preserves the second-order proximity within individual networks and is regularized by the across-network Weisfeiler-Lehman hash labels. Extensive experiments on real-world and synthetic datasets have demonstrated that our proposed WL-Align outperforms the state-of-the-art methods, achieving significant performance improvements in the “exact matching” scenario.

Artificial intelligence (AI) systems surpass certain human intelligence abilities in a statistical sense as a whole, but are not yet the true realization of these human intelligence abilities and behaviors. There are differences, and even contradictions, between the cognition and behavior of AI systems and humans. With the goal of achieving general AI, this study contains a review of the role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on the three-layer framework proposed by David Marr, and the limitations of the current development of AI are explored and analyzed. The differences and inconsistencies between the cognition mechanisms of the human brain and the computation mechanisms of AI systems are analyzed. They are found to be the cause of the differences and contradictions between the cognition and behavior of AI systems and humans. Additionally, eight important research directions and their scientific issues that need to focus on brain-inspired AI research are proposed: highly imitated bionic information processing, a large-scale deep learning model that balances structure and function, multi-granularity joint problem solving bidirectionally driven by data and knowledge, AI models that simulate specific brain structures, a collaborative processing mechanism with the physical separation of perceptual processing and interpretive analysis, embodied intelligence that integrates the brain cognitive mechanism and AI computation mechanisms, intelligence simulation from individual intelligence to group intelligence (social intelligence), and AI-assisted brain cognitive intelligence.

In the clades of animals that diverged from the bony fish, a group of Mas-related G-protein-coupled receptors (MRGPRs) evolved that have an active role in itch and allergic signals1,2. As an MRGPR, MRGPRX2 is known to sense basic secretagogues (agents that promote secretion) and is involved in itch signals and eliciting pseudoallergic reactions3–6. MRGPRX2 has been targeted by drug development efforts to prevent the side effects induced by certain drugs or to treat allergic diseases. Here we report a set of cryo-electron microscopy structures of the MRGPRX2–Gi1trimer in complex with polycationic compound 48/80 or with inflammatory peptides. The structures of the MRGPRX2–Gi1complex exhibited shallow, solvent-exposed ligand-binding pockets. We identified key common structural features of MRGPRX2 and describe a consensus motif for peptidic allergens. Beneath the ligand-binding pocket, the unusual kink formation at transmembrane domain 6 (TM6) and the replacement of the general toggle switch from Trp6.48to Gly6.48(superscript annotations as per Ballesteros–Weinstein nomenclature) suggest a distinct activation process. We characterized the interfaces of MRGPRX2 and the Gitrimer, and mapped the residues associated with key single-nucleotide polymorphisms on both the ligand and G-protein interfaces of MRGPRX2. Collectively, our results provide a structural basis for the sensing of cationic allergens by MRGPRX2, potentially facilitating the rational design of therapies to prevent unwanted pseudoallergic reactions.

Vertical distribution of phytoplankton is crucial for assessing the trophic status and primary production in inland waters. However, there is sparse information about phytoplankton vertical distribution due to the lack of sufficient measurements. Here, we report, to the best of our knowledge, the first Mie–fluorescence–Raman lidar (MFRL) measurements of continuous chlorophyll a(Chl-a) profiles as well as their parametrization in inland water. The lidar-measured Chl-aduring several experiments showed good agreement with the in situ data. A case study verified that MFRL had the potential to profile the Chl-aconcentration. The results revealed that the maintenance of subsurface chlorophyll maxima (SCM) was influenced by light and nutrient inputs. Furthermore, inspired by the observations from MFRL, an SCM model built upon surface Chl-aconcentration and euphotic layer depth was proposed with root mean square relative difference of 16.5% compared to MFRL observations, providing the possibility to map 3D Chl-adistribution in aquatic ecosystems by integrated active–passive remote sensing technology. Profiling and modeling Chl-aconcentration with MFRL are expected to be of paramount importance for monitoring inland water ecosystems and environments.

Mössbauer spectroscopy provides significant insights into the electronic structure and environment of the metal centers. Herein, we investigate the electronic structures of a set of nonheme diiron complexes by evaluating two key parameters pertaining to Mössbauer spectroscopy, namely, the isomer shift (δ) and quadrupole splitting (|ΔEQ|), using different levels of density functional theory (DFT). The diiron systems investigated here span diverse oxidation states, bridging motifs, and spin coupling patterns, which present a challenging case for theoretical predictions. We demonstrate that the combination of B97-D3/def2-TZVP is an efficient approach in modeling both the δ and |ΔEQ| values with high accuracy for the representative nonheme diiron complexes. We also show that δ is accurately predicted irrespective of the choice of approximate density functional while the |ΔEQ| is sensitive to the level of theory employed. Further investigation shows that the present methodology assessed using synthetic nonheme diiron complexes could be extended to nonheme diiron enzyme active sites, featuring both ferromagnetic and antiferromagnetic coupling between the iron centers.

Laeviganoids A–T (1–20), 20 new ent-clerodane-type diterpenoids featuring a 2-furanone (1–3) or a furan (4–20) ring, as well as six analogues (21–26), were isolated from the roots of Croton laevigatus. Their structures were determined by spectroscopic data analysis, experimental electronic circular dichroism measurements, and X-ray crystallographic studies. Compounds 4–6, 16, 21–24, and 26could influence the anti-inflammatory protumoral phenotype of macrophages. Among these compounds, 21and 26are the most potent, as evidenced by consistently downregulating the classic anti-inflammatory cytokine IL-10 and upregulating the classic pro-inflammatory cytokine TNF-α on the secretion level in RAW 264.7 cells.

A novel trifluoromethylation of (hetero)arenes and polarized alkenes using simple trifluoroacetic anhydride (TFAA) as the low price CF3source under photoredox catalysis was developed without using additives such as bases, hyperstoichiometric oxidant, or auxiliaries. The reaction showed excellent tolerance, including to some important natural products and prodrugs, even on the gram scale and extended to ketones. This simple protocol provides a practical utilization of TFAA. Several perfluoroalkylations and trifluoromethylation/cyclizations were successfully achieved under identical conditions.

Background and Objectives An overdose of acetaminophen (APAP) usually leads to acute liver injury, and oxidative stress is one of the fundamental mechanisms used to characterize it. Procyanidins (PCs) can reduce the oxidative stress in the liver of mice. This study aimed to investigate the potential protective role of PCs against APAP-induced acute liver injury.Materials and Methods Experiments were performed on male Kunming mice in six groups: phosphate-buffered saline, PCs, APAP, and PCs pretreated with 10, 50, and 100 mg/kg. The mice were peritoneally injected with PCs 30 min before the administration of APAP. First, survival rates of mice were scored every 12 hr for three days in succession. Furthermore, serum levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (T-Bil), total cholesterol (TC), triglyceride (TG), tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), and interleukin-1 (IL-6) were determined. Additionally, histological analysis and hepatic oxidative stress including the levels of superoxide dismutase (SOD), malondialdehyde (MDA), and glutathione (GSH) were assessed. Finally, the protein expression of nuclear factor erythroid 2-related factor 2 (Nrf2) and SOD-1 was detected by Western blotting.Results The data indicated that PCs improved survival rates of APAP-induced liver injury in mice models. Moreover, PCs could reduce the elevated serum levels of ALT, AST, T-Bil, TC, TG, TNF-α, IL-1β, and IL-6 due to APAP exposure with a dose-dependent manner. Besides, PCs pretreatment attenuated hepatic histopathological damage and oxidative stress which manifested the increases of SOD and GSH, whereas the decrease of MDA. Furthermore, PCs enhanced the protein expression of Nrf2 and SOD-1 in the PCs pretreatment groups compared with the APAP group.Conclusion PCs ameliorated APAP-induced acute liver injury, and Nrf2 signaling pathway modulating antioxidative stress might be involved in it.

circRNA LRP6 participates in high-glucose-regulated cellular behaviours, while its role in gestational diabetes mellitus (GDM) is unclear. Our preliminary sequencing analysis revealed the altered expression of LRP6, suggesting its potential involvement in GDM and possible clinical value. This study explored the involvement of LRP6 in GDM.

circRNA LRP6 participates in high-glucose-regulated cellular behaviours, while its role in gestational diabetes mellitus (GDM) is unclear. Our preliminary sequencing analysis revealed the altered expression of LRP6, suggesting its potential involvement in GDM and possible clinical value. This study explored the involvement of LRP6 in GDM.

Deep-water strata preserved in the distal northern Indian margin record the evolution of the Neotethys Ocean before and during the India-Asia initial collision. These strata are however difficult to date precisely as radiolarian are the only fossils present. We here revise the Cretaceous to Paleocene stratigraphy of deep-water sediments deposited in the distal part of the Indian passive margin, located just south of the Yarlung–Tsangpo suture zone near Saga. Four radiolarian assemblages are illustrated and correlated to the Cecrops septemporatus zone to Aurisaturnalis carinatus zone (late Valanginian to Barremian), the Turbocapsula costata zone (Aptian), the Spoletoensis zone (Albian), and the RP6a subzone (lower Selandian). Four units deposited from the Early Cretaceous to the Paleocene are thus identified (bottom to top): Rilang, Duobeng, Chuangde, and Sangdanlin formations. Such a revised stratigraphic scheme allows correlation with the sedimentary successions exposed in the Gyangze and Zhongba areas. Stratigraphic correlation proves that Indian-derived sandstones below the India to Asia provenance reversal (IAPR) were deposited on the Indian passive margin rather than on Neotethys oceanic crust. Sand injection complex preserved in the lower part of the deep-water sediments is related to extensional tectonics associated with Lower Cretaceous volcanism documented all along the northern margin of India. This study provides a much improved framework to interpret the geological evolution of the deep-water edge of the northern Indian margin during progressive closure of the Neotethys Ocean culminated with the onset of the India-Asia collision.

Odorants are detected as smell in the nasal epithelium of mammals by two G-protein-coupled receptor families, the odorant receptors and the trace amine-associated receptors1,2(TAARs). TAARs emerged following the divergence of jawed and jawless fish, and comprise a large monophyletic family of receptors that recognize volatile amine odorants to elicit both intraspecific and interspecific innate behaviours such as attraction and aversion3–5. Here we report cryo-electron microscopy structures of mouse TAAR9 (mTAAR9) and mTAAR9–Gsor mTAAR9–Golftrimers in complex with β-phenylethylamine, N,N-dimethylcyclohexylamine or spermidine. The mTAAR9 structures contain a deep and tight ligand-binding pocket decorated with a conserved D3.32W6.48Y7.43motif, which is essential for amine odorant recognition. In the mTAAR9 structure, a unique disulfide bond connecting the N terminus to ECL2 is required for agonist-induced receptor activation. We identify key structural motifs of TAAR family members for detecting monoamines and polyamines and the shared sequence of different TAAR members that are responsible for recognition of the same odour chemical. We elucidate the molecular basis of mTAAR9 coupling to Gsand Golfby structural characterization and mutational analysis. Collectively, our results provide a structural basis for odorant detection, receptor activation and Golfcoupling of an amine olfactory receptor.

Social network alignment aims at aligning person identities across social networks. Embedding based models have been shown effective for the alignment where the structural proximity preserving objective is typically adopted for the model training. With the observation that “overly-close” user embeddings are unavoidable for such models causing alignment inaccuracy, we propose a novel learning framework which tries to enforce the resulting embeddings to be more widely apart among the users via the introduction of carefully implanted pseudo anchors. We further proposed a meta-learning algorithm to guide the updating of the pseudo anchor embeddings during the learning process. The proposed intervention via the use of pseudo anchors and meta-learning allows the learning framework to be applicable to a wide spectrum of network alignment methods. We have incorporated the proposed learning framework into several state-of-the-art models. Our experimental results demonstrate its efficacy where the methods with the pseudo anchors implanted can outperform their counterparts without pseudo anchors by a fairly large margin, especially when there only exist very few labeled anchors.

The chromatin-modifying enzyme ATAD2 confers oncogenic competence and proliferative advantage in malignances. We previously identified ATAD2 as a marker and driver of cell proliferation in ovarian cancer (OC); however, the mechanisms whereby ATAD2 is regulated and involved in cell proliferation are still unclear. Here, we disclose that ATAD2 displays a classical G2/M gene signature, functioning to facilitate mitotic progression. ATAD2 ablation caused mitotic arrest and decreased the ability of OC cells to pass through nocodazole-arrested mitosis. ChIP-seq data analyses demonstrated that DREAM and MYBL2-MuvB (MMB), two switchable MuvB-based complexes, bind the CHR elements in the ATAD2 promoter, representing a typical feature and principle mechanism of the periodic regulation of G2/M genes. As a downstream target of MYBL2, ATAD2 deletion significantly impaired MYBL2-driven cell proliferation. Intriguingly, ATAD2 silencing also fed back to destabilize the MYBL2 protein. The significant coexpression of MYBL2 and ATAD2 at both the bulk tissue and single-cell levels highlights the existence of the MYBL2-ATAD2 signaling in OC patients. This signaling is activated during tumorigenesis and correlated with TP53mutation, and its hyperactivation was found especially in high-grade serous and drug-resistant OCs. Disrupting this signaling by CRISPR/Cas9-mediated ATAD2 ablation inhibited the in vivo growth of OC in a subcutaneous tumor xenograft mouse model, while pharmacologically targeting this signaling with an ATAD2 inhibitor demonstrated high therapeutic efficacy in both drug-sensitive and drug-resistant OC cells. Collectively, we identified a novel MYBL2-ATAD2 proliferative signaling axis and highlighted its potential application in developing new therapeutic strategies, especially for high-grade serous and drug-resistant OCs.

DT and AAS are two important technical means to connect the manufacturing physical world and information space. Based on the technical vision of realizing D&A (DT&AAS) automatic configuration, standard implementation and trusted interoperability process, this paper takes AAS as the work center, and integrates and designs a blockchain-driven D&A interoperability implementation architecture in the I4.0 workshop. (1) D&A interoperability dimension and function design adopts the unified standard framework RAMI4.0; (2) D&A information architecture design introduces the open-source BC the Tangle cross-chain interoperability framework; (3) D&A data interaction design based on Automation ML server framework; (4) The I4.0 workshop implementation framework takes AAS as the work center. Based on the above conceptual framework, an experimental case is designed and developed.

Background: Baicalein (BA) is a flavonoid extract from the root of Scutellaria baicalensis Georgiwith excellent biological activities, such as antioxidant and anti-inflammatory activities. However, its poor water solubility limits its further development. Objective: This study aims to prepare BA-loaded Solutol HS15 (HS15-BA) micelles, evaluate the bioavailability, and explore protective effects on carbon tetrachloride (CCl4) induced acute liver injury. Methods: The thin-film dispersion method was used to prepare HS15-BA micelles. The physicochemical, in vitrorelease, pharmacokinetics, and hepatoprotective effects of HS15-BA micelles were studied. Results: The optimal formulation showed a spherical shape by characterization of the transmission electron microscope (TEM) with an average small size (12.50 nm). The pharmacokinetic results illustrated that HS15-BA increased the oral bioavailability of BA. The in vivoresults showed that HS15-BA micelles significantly inhibited the activity of the CCl4-induced liver injury marker enzymes aspartate transaminase (AST) and alanine transaminase (ALT). Also, CCl4 induced oxidative damage to liver tissue, leading to increased L-glutathione (GSH) and superoxide dismutase (SOD) activity and decreased malondialdehyde (MDA) activity, while HS15-BA significantly reversed the above changes. Moreover, BA also had a hepatoprotective effect through anti-inflammatory activity; the results of ELISA and RT-PCR revealed that HS15-BA pretreatment significantly inhibited the increase in the expression of inflammatory factors induced by CCl4. Conclusion: In summary, our study confirmed that HS15-BA micelles enhanced the bioavailability of BA, and showed hepatoprotective effects through antioxidant and anti-inflammatory activities. HS15 could be considered a promising oral delivery carrier in treating liver disease.

Alkane monooxygenase (AlkB) is a widely occurring integral membrane metalloenzyme that catalyzes the initial step in the functionalization of recalcitrant alkanes with high terminal selectivity. AlkB enables diverse microorganisms to use alkanes as their sole carbon and energy source. Here we present the 48.6-kDa cryo‐electron microscopy structure of a natural fusion from Fontimonas thermophilabetween AlkB and its electron donor AlkG at 2.76 Å resolution. The AlkB portion contains six transmembrane helices with an alkane entry tunnel within its transmembrane domain. A dodecane substrate is oriented by hydrophobic tunnel-lining residues to present a terminal C–H bond toward a diiron active site. AlkG, an [Fe–4S] rubredoxin, docks via electrostatic interactions and sequentially transfers electrons to the diiron center. The archetypal structural complex presented reveals the basis for terminal C–H selectivity and functionalization within this broadly distributed evolutionary class of enzymes.

This study evaluated the effects of different sources of dietary carbohydrates on the growth, antioxidant capacity, and digestive enzyme activity, and carbohydrate metabolic enzyme activity of Litopenaeus vannamei were evaluated. The experimental diets, including glucose, sucrose, potato starch, corn starch, and wheat starch, were fed to L. vannamei for 35 days. We found that shrimp fed on corn starch and wheat starch grew better, whereas shrimp fed on wheat starch showed the highest digestive enzyme activity. In addition, the starch group showed higher total superoxide dismutase (T-SOD) activity and lower malondialdehyde (MDA) content. The glucose, sucrose, starch, and corn starch fed shrimp showed significantly higher levels of hepatopancreatic hexokinase (HK), whereas those fed on glucose and potato starch also showed significantly higher levels of hepatopancreatic phosphofructokinase (PFK) activity. The activities of phosphoenolpyruvate carboxykinase (PEPCK) and glucose 6-phosphatase (G6Pase) were higher in shrimp fed on glucose, whereas higher fructose 1,6-bisphosphatase (FBPase) activity was observed in shrimp fed on wheat starch, glucose, and sucrose. The insulin content of shrimp in the glucose group and potato starch group was higher, whereas that of the glucose group was higher than that of the potato starch group. In brief, the addition of wheat starch to the diet plays a positive role in the growth, glycolysis, gluconeogenesis, and antioxidant activity of shrimp, and is a promising choice for juvenile shrimp culture. [ABSTRACT FROM AUTHOR]

As the melt front gradually moves away from the heat source during the heat transfer process, phase change materials (PCMs) inevitably cause the heat transfer efficiency to decrease with heat transfer time, especially for a shaped PCM (SPCM). In this study, it is proposed to compress the SPCM phase-changed region by pressure to reduce the thermal resistance between the heat source and the phase transition interface, thus slowing down the rate of heat transfer efficiency decrease. We established a one-dimensional semi-infinite compression heat transfer theoretical model and prepared a thermoelastic SPCM (TESPCM) for compression heat transfer experiments, which verified the reliability of the theoretical model. Experimental demonstrate that the pressure enhancement contributes to the heat transfer efficiency and becomes more pronounced with heat transfer time. The pressure-enhanced heat transfer was analyzed by the theoretical model with the following conclusions: pressure enhancement not only improves the latent heat utilization, heat transfer cut-off time, and maximum tolerable heat flux of TESPCM but also increases the specific energy and specific power of TESPCM. The effect of pressure on heat transfer efficiency tends to accelerate with increasing compression. Furthermore, the improvement of heat transfer by pressure enhancement becomes more significant with the increase of thermal conductivity, but the effect of latent heat on temperature control decreases with the increase of compression.

Spicatulides A–G (1–7), seven new phenolic–monoterpenoid hybrid molecules, along with two known compounds, 8and 9, were isolated and identified from Chloranthus spicatus. Compound 1represents an unprecedented skeleton featuring an aryl-fused 2-oxabicyclo[4.3.1]decane moiety, and compound 2is the first example of a denudaquinol–normonoterpenoid adduct. Their structures with absolute configurations were elucidated on the basis of spectroscopic data analyses and TDDFT-ECD calculations. Compounds 3, 5, 6, and 9exhibited the activity of reducing lipogenesis in HepG2 cells in a dose-dependent manner.

Supplemental digital content is available in the text.BACKGROUND:Patients with hypertension are at a high risk for stroke, but a healthy lifestyle can greatly reduce the risk of stroke. However, there has been no research on the change in prestroke health behaviors in Chinese patients with hypertensive stroke over a decade. OBJECTIVES:The aims of this study were to determine whether prestroke health behaviors of patients with hypertensive stroke changed over a decade and to explore the predictors of prestroke health behaviors over a decade. METHODS:This study used data from 2 cross-sectional studies conducted in the neurology departments of 3 hospitals in Guangzhou, China. In total, 110 hypertensive stroke patients were recruited in stage I (2008-2009), and 119 hypertensive stroke patients were recruited in stage II (2018-2019). Patients' stroke knowledge was measured by the Stroke Knowledge Questionnaire. Patients' prestroke health behavior was measured by the Health Behavior Scale for Stroke Patients. RESULTS:The total score of prestroke health behaviors significantly increased over the decade (P< .001), but the scores of the subcategories of low-fat diet, low-sugar diet, and blood pressure checkups decreased over the decade (P< .05). Stroke knowledge was a significant predictor of prestroke health behaviors in stage I (P< .05). Besides stroke knowledge, sex and age were significant predictors of prestroke health behaviors in stage II (P< .05). CONCLUSIONS:Prestroke health behaviors of hypertensive stroke patients significantly improved over the decade. Moreover, prestroke health behaviors were significantly influenced by stroke-related knowledge over the decade. Healthcare providers should focus in particular on assisting patients who are male, young, and middle-aged, and lack stroke-related knowledge to improve their prestroke health behaviors, especially in terms of adherence to a low-fat/low-sugar diet and regular blood pressure checks.

Female-to-male sex reversals (pseudomales) are common in lower vertebrates and have been found in natural populations, which is a concern under rapid changes in environmental conditions. Pseudomales can exhibit altered spermatogenesis. However, the regulatory mechanisms underlying pseudomale spermatogenesis remain unclear. Here, we characterized spermatogenesis in Chinese tongue sole (Cynoglossus semilaevis), a species with genetic and environmental sex determination, based on a high-resolution single-cell RNA-seq atlas of cells derived from the testes of genotypic males and pseudomales. We identified five germ cell types and six somatic cell types and obtained a single-cell atlas of dynamic changes in gene expression during spermatogenesis in Chinese tongue sole, including alterations in pseudomales. We detected decreased levels of Ca2+signaling pathway-related genes in spermatogonia, insufficient meiotic initiation in spermatocytes, and a malfunction of somatic niche cells in pseudomales. However, a cluster of CaSRgenes and MAPK signaling factors were upregulated in undifferentiated spermatogonia of pseudomales. Additionally, we revealed that Z chromosome-specific genes, such as piwil2, dhx37, and ehmt1, were important for spermatogenesis. These results improve our understanding of reproduction after female-to-male sex-reversal and provide new insights into the adaptability of reproductive strategies in lower vertebrates.

With the rapid development of artificial intelligence (AI), there is a trend in moving AI applications, such as neural machine translation (NMT), from cloud to mobile devices. Constrained by limited hardware resources and battery, the performance of on-device NMT systems is far from satisfactory. Inspired by conditional computation, we propose to improve the performance of on-device NMT systems with dynamic multi-branch layers. Specifically, we design a layer-wise dynamic multi-branch network with only one branch activated during training and inference. As not all branches are activated during training, we propose shared-private reparameterization to ensure sufficient training for each branch. At almost the same computational cost, our method achieves improvements of up to 1.7 BLEU points on the WMT14 English-German translation task and 1.8 BLEU points on the WMT20 Chinese-English translation task over the Transformer model, respectively. Compared with a strong baseline that also uses multiple branches, the proposed method is up to 1.5 times faster with the same number of parameters.

Aiming at the problem of complex and low efficiency of intelligent patrol paths in 220/500 kV bionic substations, this paper proposes a method for collaborative inspection of multiple patrol robots. Firstly, the deficiencies of the Biological Inspired Neural Network algorithm(BINN) in complex paths and large corner scenarios are analyzed, and an improved method for solving the neuron activity value near the boundary and an improved method for the neuron activity value near the obstacle are proposed, in order to solve the phenomenon that the decrease of neurons that can transmit activity values in the neighborhood leads to generally low neuron activity values, which leads to unreasonable path planning; Secondly, establish a substation area segmentation model based on the variable tangent method, divide the substation area into several sub-intervals without obstacles, and use the priority heuristic algorithm to select the robot travel path, so as to realize the multi-robot joint inspection task ; Finally, the method proposed in this paper reduces the path length, number of turns and rotation angle by 11.38%, 38,89% and 20.51, respectively, compared with the BINN algorithm, and by 5.75%, 18.18%, and 16.68 compared with the A* algorithm, which verifies the feasibility and effectiveness of the proposed method.