Multiangle social network recommendation algorithms and similarity network evaluation

Multiangle social network recommendation algorithms (MSN) and a new assessmentmethod, called similarity network evaluation (SNE), are both proposed. From the viewpoint of six dimensions, the MSN are classified into six algorithms, including user-based algorithmfromresource point (UBR), user-based algorithmfromtag point (UBT), resource-based algorithm fromtag point (RBT), resource-based algorithm from user point (RBU), tag-based algorithm from resource point (TBR), and tag-based algorithm from user point (TBU). Compared with the traditional recall/precision (RP) method, the SNE is more simple, effective, and visualized. The simulation results show that TBR and UBR are the best algorithms, RBU and TBU are the worst ones, and UBT and RBT are in the medium levels

METABOLIC ENGINEERING OF BACILLUS FOR ENHANCED PRODUCT AND CELLULAR YIELDS

Microbial cultures usually produce a significant amount of acidic byproducts which can represses cell growth and product synthesis. In addition, the production of acids is a waste of carbon source thereby reduces the product yield and productivity. Metabolic engineering provides a powerful approach to optimize the cellular activities and improve product yields by genetically manipulating specific metabolic pathways. Previous work has identified mutation of Pyruvate Kinase (PYK) as an efficient way to reduce acids production; however, complete abolishment of PYK in Bacillus subtilis resulted in dramatically reduced cell growth rate. In this study, an inducible PYK (iPYK) mutant of B. subtilis was constructed and extensively characterized. The results demonstrated that good cell growth rate and low acetate formation can be attained at an appropriate PYK expression level. In addition, mutation at phosphofructokinase (PFK) on the glycolysis pathway also provides an alternative approach to reduce acetate formation.Two outcomes of the pyk mutant of B. subtilis, high phosphoenolpyruvate (PEP) pool and low acetate concentration, prompted us to investigate the deployment of pyk mutation as an efficient way to improve folic acid and recombinant protein production. The high intracellular PEP and glucose-6-phosphate (G6P) concentration in the pyk mutant led to higher folic acid production by providing abundant synthetic precursors. Additional mutations in the folic acid synthesis pathway, along with the pyk mutation, resulted in 8-fold increase in folic acid production. Recombinant protein was improved two-fold by the pyk mutation due to low acetate formation and longer production time in the pyk mutant. In addition, using glycerol instead of glucose as the carbon source reduced acetate production and improved protein production by 60%.The effect of citrate on acetate production in Bacillus thuringiensis (Bt) was investigated and the continuous culture results showed the effectiveness of citrate on reducing acetate formation. These results indicated pyk may be a potential mutation target to reduce acetate formation in Bt

On a State-sponsored Sport System in China

The gold medal success of China in recent Olympic Games can be traced to the advancement of the state-sponsored sport system (SSSS). While the program was developed initially through socialist ideals, it is more than a centralized government system to monopolize resources for glorified sport performance. Participation in competition is an inherent part of the human condition. Success in athletics is associated with national identity and has economic, social, and cultural implications. Because of this, it is essential that the SSSS adjust and improve to keep pace with other facets of China’s quickly changing national reform. In association with emerging economic reform, some sports now receive equal or more funds from private investments compared to government allocation. The state-sponsored sport system must continue to adapt to maintain the Chinese tradition of excellence in competition

Multi-view Contrastive Learning for Entity Typing over Knowledge Graphs

Knowledge graph entity typing (KGET) aims at inferring plausible types of entities in knowledge graphs. Existing approaches to KGET focus on how to better encode the knowledge provided by the neighbors and types of an entity into its representation. However, they ignore the semantic knowledge provided by the way in which types can be clustered together. In this paper, we propose a novel method called Multi-view Contrastive Learning for knowledge graph Entity Typing (MCLET), which effectively encodes the coarse-grained knowledge provided by clusters into entity and type embeddings. MCLET is composed of three modules: i) Multi-view Generation and Encoder module, which encodes structured information from entity-type, entity-cluster and cluster-type views; ii) Cross-view Contrastive Learning module, which encourages different views to collaboratively improve view-specific representations of entities and types; iii) Entity Typing Prediction module, which integrates multi-head attention and a Mixture-of-Experts strategy to infer missing entity types. Extensive experiments show the strong performance of MCLET compared to the state-of-the-artComment: Accepted at EMNLP 2023 Mai

HyperFormer:Enhancing entity and relation interaction for hyper-relational knowledge graph completion

Hyper-relational knowledge graphs (HKGs) extend standard knowledge graphs by associating attribute-value qualifiers to triples, which effectively represent additional fine-grained information about its associated triple. Hyper-relational knowledge graph completion (HKGC) aims at inferring unknown triples while considering its qualifiers. Most existing approaches to HKGC exploit a global-level graph structure to encode hyper-relational knowledge into the graph convolution message passing process. However, the addition of multi-hop information might bring noise into the triple prediction process. To address this problem, we propose HyperFormer, a model that considers local-level sequential information, which encodes the content of the entities, relations and qualifiers of a triple. More precisely, HyperFormer is composed of three different modules: an entity neighbor aggregator module allowing to integrate the information of the neighbors of an entity to capture different perspectives of it; a relation qualifier aggregator module to integrate hyper-relational knowledge into the corresponding relation to refine the representation of relational content; a convolution-based bidirectional interaction module based on a convolutional operation, capturing pairwise bidirectional interactions of entity-relation, entity-qualifier, and relation-qualifier. Furthermore, we introduce a Mixture-of-Experts strategy into the feed-forward layers of HyperFormer to strengthen its representation capabilities while reducing the amount of model parameters and computation. Extensive experiments on three well-known datasets with four different conditions demonstrate HyperFormer's effectiveness

HyperFormer: Enhancing Entity and Relation Interaction for Hyper-Relational Knowledge Graph Completion

Hyper-relational knowledge graphs (HKGs) extend standard knowledge graphs by associating attribute-value qualifiers to triples, which effectively represent additional fine-grained information about its associated triple. Hyper-relational knowledge graph completion (HKGC) aims at inferring unknown triples while considering its qualifiers. Most existing approaches to HKGC exploit a global-level graph structure to encode hyper-relational knowledge into the graph convolution message passing process. However, the addition of multi-hop information might bring noise into the triple prediction process. To address this problem, we propose HyperFormer, a model that considers local-level sequential information, which encodes the content of the entities, relations and qualifiers of a triple. More precisely, HyperFormer is composed of three different modules: an entity neighbor aggregator module allowing to integrate the information of the neighbors of an entity to capture different perspectives of it; a relation qualifier aggregator module to integrate hyper-relational knowledge into the corresponding relation to refine the representation of relational content; a convolution-based bidirectional interaction module based on a convolutional operation, capturing pairwise bidirectional interactions of entity-relation, entity-qualifier, and relation-qualifier. realize the depth perception of the content related to the current statement. Furthermore, we introduce a Mixture-of-Experts strategy into the feed-forward layers of HyperFormer to strengthen its representation capabilities while reducing the amount of model parameters and computation. Extensive experiments on three well-known datasets with four different conditions demonstrate HyperFormer's effectiveness. Datasets and code are available at https://github.com/zhiweihu1103/HKGC-HyperFormer.Comment: Accepted at CIKM'2

Case report: Surgical treatment of McCune-Albright syndrome with hyperthyroidism and retrosternal goiter: A case report and literature review

IntroductionMcCune-Albright syndrome (MAS) is a low-incidence syndrome consisting of the clinical triad of fibrous structural dysplasia of bone, endocrine disease, and skin pigmentation. Thyroid dysfunction is the second most common endocrine dysregulation in MAS. However, there are no treatment guidelines for MAS complicated with hyperthyroidism. Notably, no case of MAS complicated with retrosternal goiter and hyperthyroidism has been reported to our knowledge.Case presentationWe report a 27-year-old man with MAS who developed the typical triad of bone fibrous dysplasia, skin pigmentation and hyperthyroidism, complaining of recent fast-growing neck mass and difficulty in breathing. Hyperthyrodism was under control by Thiamazole, and computed tomography showed an enlarged thyroid extending retrosternally. We performed a total thyroidectomy on the patient. At the 1-year follow-up, the patient's dyspnea, hyperthyroidism, and bone pain were all significantly alleviated.ReviewWe searched the literature for previous case reports concerning MAS patients complicated with thyroid dysregulation. A total of 17 articles and 22 patients were identified to form our database. Among them, 9 studies clearly mentioned surgical intervention in 11 patients, and prognoses were also reported. Surgery was the most common intervention chosen and indicated a satisfactory prognosis.ConclusionWe report a rare case of MAS patient complicated with retrosternal goiter and hyperthyroidism. Our review provides an overview of MAS cases requiring interventions on thyroid function, and total thyroidectomy should be a proper treatment for these patients

Multi-level semantic information guided image generation for few-shot steel surface defect classification

Surface defect classification is one of key points in the field of steel manufacturing. It remains challenging primarily due to the rare occurrence of defect samples and the similarity between different defects. In this paper, a multi-level semantic method based on residual adversarial learning with Wasserstein divergence is proposed to realize sample augmentation and automatic classification of various defects simultaneously. Firstly, the residual module is introduced into model structure of adversarial learning to optimize the network structure and effectively improve the quality of samples generated by model. By substituting original classification layer with multiple convolution layers in the network framework, the feature extraction capability of model is further strengthened, enhancing the classification performance of model. Secondly, in order to better capture different semantic information, we design a multi-level semantic extractor to extract rich and diverse semantic features from real-world images to efficiently guide sample generation. In addition, the Wasserstein divergence is introduced into the loss function to effectively solve the problem of unstable network training. Finally, high-quality defect samples can be generated through adversarial learning, effectively expanding the limited training samples for defect classification. The experimental results substantiate that our proposed method can not only generate high-quality defect samples, but also accurately achieve the classification of defect detection samples

Tunable photochemical deposition of silver nanostructures on layered ferroelectric CuInP2_2S6

2D layered ferroelectric materials such as CuInP$_2$S6 (CIPS) are promising candidates for novel and high-performance photocatalysts, owning to their ultrathin layer thickness, strong interlayer coupling, and intrinsic spontaneous polarization, while how to control the photocatalytic activity in layered CIPS remains unexplored. In this work, we report for the first time the photocatalytic activity of ferroelectric CIPS for the chemical deposition of silver nanostructures (AgNSs). The results show that the shape and spatial distribution of AgNSs on CIPS are tunable by controlling layer thickness, environmental temperature, and light wavelength. The ferroelectric polarization in CIPS plays a critical role in tunable AgNS photodeposition, as evidenced by layer thickness and temperature dependence experiments. We further reveal that AgNS photodeposition process starts from the active site creation, selective nanoparticle nucleation/aggregation, to the continuous film formation. Moreover, AgNS/CIPS heterostructures prepared by photodeposition exhibit excellent resistance switching behavior and good surface enhancement Raman Scattering activity. Our findings provide new insight into the photocatalytic activity of layered ferroelectrics and offer a new material platform for advanced functional device applications in smart memristors and enhanced chemical sensors.Comment: 18 pages, 5 figure