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Abstract A probabilistic limit equilibrium framework combining empirical load transfer factor and anisotropy of soil cohesion is developed to conduct pile‐reinforced slope reliability analysis. The anisotropy of soil cohesion is determined conditioned on that the thrust force direction is parallel to the major principal direction and it is easily combined with load transfer factor, which are related with soil parameters, and pile parameters. The proposed method is illustrated against a homogeneous soil slope. The sensitivity studies of pile parameters on factor of safety (FS; calculated at respective means of soil parameters) and β demonstrated that the anisotropy of soil cohesion tends to pose significant effect on reliability index β than on FS. The effect of anisotropy of soil cohesion on FS is found to be slightly different under different pile locations, whereas its effect on β is observed to be least if piles are drilled at the middle part of slope and more significant effect is observed when piles are drilled at the lower and upper part of slope. The plots from the sensitivity studies provide an alternative tool for pile designs aiming at the target reliability index β. The proposed method contributes to the pile‐reinforced slope stability within limit equilibrium framework.

Introduction: Ulva lactuca polysaccharide (ULP) is green algae extract with numerous biological activities, including anticoagulant, anti-inflammatory, and antiviral effects. However, the inhibitory ability of ULP in the development of hepatocellular carcinoma warrants further studies. Objectives: To elucidate the anti-tumor mechanism of ULP action and evaluate its regulatory effect on gut microbiota and metabolism in H22 hepatocellular carcinoma tumor-bearing mice. Methods: An H22 tumor-bearing mouse model was established by subcutaneously injecting H22 hepatoma cells. The gut microbiota composition in cecal feces was assessed and subjected to untargeted metabolomic sequencing. The antitumor activity of ULP was verified further by western blot, RT-qPCR, and reactive oxygen species (ROS) assays. Results: Administration of ULP alleviated tumor growth by modulating the compositions of the gut microbial communities (Tenericutes, Agathobacter, Ruminiclostridium, Parabacteroides, Lactobacillus, and Holdemania) and metabolites (docosahexaenoic acid, uric acid, N-Oleoyl Dopamine, and L-Kynurenine). Mechanistically, ULP promoted ROS production by inhibiting the protein levels of JNK, c-JUN, PI3K, Akt, and Bcl-6, thereby delaying the growth of HepG2 cells. Conclusion: ULP attenuates tumor growth in H22 tumor-bearing mice by modulating gut microbial composition and metabolism. ULP inhibits tumor growth mainly by promoting ROS generation.

The prediction of drug-target protein interaction (DTI) is a crucial task in the development of new drugs in modern medicine. Accurately identifying DTI through computer simulations can significantly reduce development time and costs. In recent years, many sequence-based DTI prediction methods have been proposed, and introducing attention mechanisms has improved their forecasting performance. However, these methods have some shortcomings. For example, inappropriate dataset partitioning during data preprocessing can lead to overly optimistic prediction results. Additionally, only single non-covalent intermolecular interactions are considered in the DTI simulation, ignoring the complex interactions between their internal atoms and amino acids. In this paper, we propose a network model called Mutual-DTI that predicts DTI based on the interaction properties of sequences and a Transformer model. We use multi-head attention to extract the long-distance interdependent features of the sequence and introduce a module to extract the sequence's mutual interaction features in mining complex reaction processes of atoms and amino acids. We evaluate the experiments on two benchmark datasets, and the results show that Mutual-DTI outperforms the latest baseline significantly. In addition, we conduct ablation experiments on a label-inversion dataset that is split more rigorously. The results show that there is a significant improvement in the evaluation metrics after introducing the extracted sequence interaction feature module. This suggests that Mutual-DTI may contribute to modern medical drug development research. The experimental results show the effectiveness of our approach. The code for Mutual-DTI can be downloaded from https://github.com/a610lab/Mutual-DTI.

Abstract Hyperlipidemia poses a serious threat to human health and evaluating the ability of natural active substances to regulate disorders of lipid metabolism is the focus of food functionality research in recent years. Bioactive peptides are distinguished by their broad range of sources, high nutritional content, ease of absorption and use by the body, and ease of determining their sequences. Bioactive peptides have a wide range of potential applications in the area of medicines and food. The regulation of lipid metabolism disorder caused by bioactive peptides from different sources provides a reference for the development and research of bioactive peptides for lipid reduction. Graphical Abstract

Abstract Enteromorpha prolifera is a nutrient-rich green alga and abound in the Yellow Sea and the Bohai Sea of China. In this study, E. prolifera was anaerobically digested for biogas production. The variations of chemical compositions and microbial community structure as well as the physical structure of E. prolifera in anaerobic digestion process were investigated. This is the first report of multiple ways to deeply analysis the process of E. prolifera anaerobic digestion. Results from the present work showed that the biogas obtained from E. prolifera anaerobic digestion could achieve 409.7 mL•g− 1 TS with an average methane concentration of 53.2%, and the VFAs content in substrate played a vital role for driving the biogas production of flora. Moreover, S1 of Thermotogaceae and Cenarchaeum, the dominant bacteria and archaea in digestion flora, respectively, played important roles in degrading E. prolifera, acidizing slurry, and providing methanogenic substrate for methanogens. Graphical Abstract

Ganoderma lucidum triterpenoids (GP) have been reported to help prevent and improve hyperlipidemia. Modulation of the gut microbiota was proposed as underlying factor as well as a novel measure to prevent and treat hyperlipidemia. The effects of GP on high-fat diet (HFD)-induced hyperlipidemia and gut microbiota modulation were determined in rats. Ultra-performance liquid chromatography tandem quadrupole time-of-flight mass spectrometry (UPLC-QTOF MS-MS) indicated that GP were enriched with ganoderic acids G, B, H, A, and F. After feeding with GP supplementation, serum lipid levels including total triglyceride, total cholesterol, and low-density-lipoprotein cholesterol were significantly decreased in hyperlipidemic rats. Furthermore, administration of GP also has reversed the HFD-induced gut microbiota dysbiosis, including a significant increase in Alloprevotella and reduced proportion of Blautia. The result above suggests that GP would be developed as a functional food to ameliorate lipid metabolic disorders and hyperlipidemia.

Abstract Unlike traditional video recommendations, micro-video inherits the characteristics of social platforms, such as social relation. A large amount of micro-videos showing explosive growth is badly affecting the user’s choice. In this paper, we propose a multi-source multi-net micro-video recommendation model that recommends micro-videos fitting users’ best interests. Different from existing works, as micro-video inherits the characteristics of social platforms, we simultaneously incorporate multi-source content data of items and multi-networks of users to learn user and item representations for recommendation. This information can be complementary to each other in a way that multi-modality data can bridge the semantic gap among items, while multi-type user networks, such as following and reposting, are able to propagate the preferences among users. Furthermore, to discover the hidden categories of micro-videos that properly match users’ interests, we interactively learn the user–item representations and perform the hidden item category clustering. The resulted categorical representations are interacted with user representations to model user preferences at different levels of hierarchies. Finally, multi-source content item data, multi-type user networks and hidden item categories are jointly modelled in a unified recommender, and the parameters of the model are collaboratively learned to boost the recommendation performance. Experiments on a real dataset demonstrate the effectiveness of the proposed model and its advantage over the state-of-the-art baselines.

Community question answering (CQA) services have emerged as a type of popular social platforms. In the social network, experts provide knowledgeable answers to the questions in their domain of expertise, while celebrities publish influential opinions toward the topics led by some questions. Given the large amount of knowledge organized in the format of question–answers, an interesting research problem is to recommend questions to users so as to maximize their engagements with the platform. However, recommending questions in CQA services is a non-trivial task. Data sources in the CQA services are of different types. It is challenging to incorporate heterogeneous information for the recommendation task. Furthermore, data sparsity is an inherent problem in such platforms. In this paper, we propose a model that is able to jointly model both implicit and explicit information for question recommendation. The model integrates multiple data sources and addresses the problem of data heterogeneity. In the proposed model, we dynamically discover latent user groups and incorporate those hierarchical information to bridge the semantic gaps among users in the shared latent space. We evaluate the proposed model on two real-world datasets, and demonstrate that our model outperforms the state-of-the-art alternatives by a large margin. We also investigate different structures of the proposed model to study the effects of different data sources.

Organic second-order nonlinear optical materials are of great significance in the field of integrated photonics, optical data storage and optical information processing. In this work, two iodide salts with benzo[e]indol cations as electron-withdrawing groups were designed and synthesized, which are (E)-2-(4-hydroxy-3-methoxystyryl)-1,1,3-trimethyl-1H-benzo[e]indol-3-ium iodide (1-TB) and (E)-2-(3,4-dihydroxystyryl)-1,1,3-trimethyl-1H-benzo[e]indol-3-ium iodide (2-TB). Under the irradiation of 1064 nm laser, the second harmonic generations of the 1-TB and 2-TB powders are 760 and 580 times that of urea, respectively. Among them, crystals of 1-TB are successfully grown by a slow cooling method without using seed crystals. Bulk crystal with a size of 8.0 × 3.0 × 1.0 mm3 and many submillimeter single crystals with excellent optical quality are obtained.

An activity recognition system essentially processes raw sensor data and maps them into latent activity classes. Most of the previous systems are built with supervised learning techniques and pre-defined data sources, and result in static models. However, in realistic and dynamic environments, original data sources may fail and new data sources become available, a robust activity recognition system should be able to perform evolution automatically with dynamic sensor availability in dynamic environments. In this paper, we propose methods that automatically incorporate dynamically available data sources to adapt and refine the recognition system at run-time. The system is built upon ensemble classifiers which can automatically choose the features with the most discriminative power. Extensive experimental results with publicly available datasets demonstrate the effectiveness of our methods.

Background: Self-related information is difficult to ignore and forget, which brings valuable implications for educational practice. Self-referential encoding techniques involve integrating self-referencing cues during the processing of learning material. However, the evidence base and effective implementation boundaries for these techniques in teaching and learning remain uncertain due to research variability. Aims: The present meta-analysis aims to quantitatively synthesize the results from studies applying self-referential encoding techniques in education. Methods: The analysis was based on data from 20 independent samples, including 1082 students from 13 primary studies identified through a systematic literature search. Results: Results from random effect models show that incorporating self-referential encoding techniques improved learning (g = 0.40, 95% CI [0.18, 0.62]). Subgroup analysis showed that the valence of learning material serves as a significant boundary condition for this strategy. The students' cohorts, types of learning materials, and research context did not moderate the effect sizes. Conclusions: Our results suggest that incorporating self-referential encoding techniques on negative materials shows an aversive effect. Overall, there is a universal benefit to using self-referential encoding techniques as an appropriate design guideline in educational contexts. Implications for teaching practice and future directions are discussed. Further studies are needed to investigate the effectiveness in more diverse educational and teaching situations.

In clinical practice, repairing osteochondral defects (OCDs) is challenging because of the complex cartilage/subchondral bone structure and intricate immunological microenvironment. Here, we identify the crucial role of adaptive immunity dysfunction by revealing that an increase of T helper 17 (Th17) cells exacerbated osteochondral tissue degradation via its pro-inflammatory cytokine interleukin-17 (IL-17) in the early-stage OCDs. Next, we leveraged this adaptive immunity mechanism and combined it with regenerative signals to develop a multifunctional hydrogel system capable of simultaneously tackling immune dysfunction and regenerative deficiency. Rapid IL-4 release from the methacrylated hyaluronic acid (HAMA) hydrogel exerts a potent immunomodulatory effect by inhibiting the differentiation and function of Th17 cells. Moreover, transforming growth factor-beta1 anchored on methacrylated hyaluronic acid and heparin (HAMA@HepMA) microparticles provides sustained regenerative signals, which synergistically transform the pro-inflammatory microenvironment into a pro-regenerative niche for enhanced OCDs healing. Our study suggests that targeting specific immune pathways can significantly enhance the efficacy of regenerative strategies, paving the way for innovative treatments in orthopedic medicine.