KDGene: knowledge graph completion for disease gene prediction using interactional tensor decomposition.

The accurate identification of disease-associated genes is crucial for understanding the molecular mechanisms underlying various diseases. Most current methods focus on constructing biological networks and utilizing machine learning, particularly deep learning, to identify disease genes. However, these methods overlook complex relations among entities in biological knowledge graphs. Such information has been successfully applied in other areas of life science research, demonstrating their effectiveness. Knowledge graph embedding methods can learn the semantic information of different relations within the knowledge graphs. Nonetheless, the performance of existing representation learning techniques, when applied to domain-specific biological data, remains suboptimal. To solve these problems, we construct a biological knowledge graph centered on diseases and genes, and develop an end-to-end knowledge graph completion framework for disease gene prediction using interactional tensor decomposition named KDGene. KDGene incorporates an interaction module that bridges entity and relation embeddings within tensor decomposition, aiming to improve the representation of semantically similar concepts in specific domains and enhance the ability to accurately predict disease genes. Experimental results show that KDGene significantly outperforms state-of-the-art algorithms, whether existing disease gene prediction methods or knowledge graph embedding methods for general domains. Moreover, the comprehensive biological analysis of the predicted results further validates KDGene's capability to accurately identify new candidate genes. This work proposes a scalable knowledge graph completion framework to identify disease candidate genes, from which the results are promising to provide valuable references for further wet experiments. Data and source codes are available at https://github.com/2020MEAI/KDGene.

SCARB2 associates with tumor-infiltrating neutrophils and predicts poor prognosis in breast cancer.

BACKGROUND: The tumor microenvironment (TME) plays a crucial role in various aspects of breast cancer development and metastasis. Nevertheless, the expression, prognostic significance, and correlation with clinical features of SCARB2 in breast cancer, as well as the infiltrative characteristics of TME, remain largely unknown. METHODS: We analyzed the differential presentation of SCARB2 mRNA in breast cancer tissues and nontumorous breast tissues and prognosis by The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases. Additionally, the Tumor Immunity Estimation Resource (TIMER) was taken to evaluate the correlation between SCARB2 mRNA presence and tumor-infiltrating immune cells and immune checkpoints in the TME in breast cancer. We performed multiple immunohistochemical staining to verify the SCARB2 protein expression in breast cancer tissues and its relationship to immune cells and checkpoints and clinicopathological features. RESULTS: We identified elevated SCARB2 expression in breast cancer tissues, and high SCARB2 protein presentation was associated with advanced clinical stage and unfavorable prognosis. In addition, enhanced SCARB2 protein presence was closely correlated with up-regulation CD66b+ neutrophils infiltration in tumor tissues (r = 0.210, P < 0.05) and CD68 + CD163+ M2 macrophages in the interstitium (r = 0.233, P < 0.05), as well as the immune checkpoints, including PD-1 (r = 0.314, P < 0.01) protein expression. CONCLUSION: SCARB2 holds promise for predicting the clinical outcome of breast cancer patients and could serve as a potential therapeutic target.

Method for fabricating large-area gratings with a uniform duty cycle without a spatial beam modulator.

A fabrication method for large-area gratings with uniform duty without using a spatial beam modulator is introduced in this study. The inhomogeneity of gratings caused by flaws of the lens and stray light was solved by controlling exposure time within an appropriate range and selecting a suitable beam expansion aperture in the optical path. A model for representing this process was established by analyzing the effects of exposure and development time length, and experimental results exhibited good agreement with the simulation results. Finally, a grating with a period of 550 nm, a uniform duty cycle, and a diameter larger than 30 mm was achieved using a Mach-Zehnder interferometer optical path without a spatial beam modulator. The uniformity of this grating was observed via atomic force microscopy, and the results were highly desirable.

Generations of chaos-modulated pulses based on a gain-switched semiconductor laser subject to delay-synchronized optical feedback for pulsed chaos lidar applications.

We generate and analyze chaos-modulated pulses based on a gain-switched semiconductor laser subject to delay-synchronized optical feedback for pulsed chaos lidar applications. Benefited by the aperiodic and uncorrelated chaos waveforms, chaos lidar possesses the advantages of no range ambiguity and immunity to interference and jamming. To improve the detection range while in compliance with the eye-safe regulation, generating chaos-modulated pulses with higher peak power rather than chaos in its CW form is desired. While using an acousto-optic modulator to time-gate the CW chaos into pulses could be lossy and energy inefficient, in this paper, we study the generation of chaos-modulated pulses using a gain-switched laser subject to delay-synchronized optical feedback. Under different feedback strengths and modulation currents of gain-switching, we investigate the quality of the chaos-modulated pulses generated by analyzing their ratio of chaos oscillations, peak sidelobe levels (PSLs), and cross-correlation peaks under different mismatching conditions between the pulse repetition interval (PRI) and the feedback time delay τ. With proper feedback strengths and modulation currents, we find that synchronizing the gain-switching modulation with the delayed feedback (PRI = τ) is essential in generating the chaos-modulated pulses suitable for the pulsed chaos lidar applications. When mismatching occurs, we identify sequences of dynamical periods including stable, periodic, and chaos oscillations evolved within a pulse.

Evolving therapeutics and ensuing cardiotoxicities in triple-negative breast cancer.

Defined as scarce expression of hormone receptors and human epidermal growth factor receptor 2, triple-negative breast cancer (TNBC) is labeled as the most heterogeneous subtype of breast cancer with poorest prognosis. Despite rapid advancements in precise subtyping and tailored therapeutics, the ensuing cancer therapy-related cardiovascular toxicity (CTR-CVT) could exert detrimental impacts to TNBC survivors. Nowadays, this interdisciplinary issue is incrementally concerned by cardiologists, oncologists and other pertinent experts, propelling cardio-oncology as a booming field focusing on the whole-course management of cancer patients with potential cardiovascular threats. Here in this review, we initially profile the evolving molecular subtyping and therapeutic landscape of TNBC. Further, we introduce various monitoring approaches of CTR-CVT. In the main body, we elaborate on typical cardiotoxicities ensuing anti-TNBC treatments in detail, ranging from chemotherapy (especially anthracyclines), surgery, anesthetics, radiotherapy to immunotherapy, with future perspectives on promising directions in the era of artificial intelligence and traditional Chinese medicine.

Crosstalk Between circRNA and Tumor Microenvironment of Hepatocellular Carcinoma: Mechanism, Function and Applications.

Hepatocellular carcinoma (HCC) is one of the most common aggressive tumors in the world. Despite the availability of various treatments, its prognosis remains poor due to the lack of specific diagnostic indicators and the high heterogeneity of HCC cases. CircRNAs are noncoding RNAs with stable and highly specific expression. Extensive research evidence suggests that circRNAs mediate the pathogenesis and progression of HCC through acting as miRNA sponges, protein modulators, and translation templates. Tumor microenvironment (TME) has become a hotspot of immune-related research in recent years due to its effects on metabolism, secretion and immunity of HCC. Accordingly, understanding the role played by circRNAs in TME is important for the study of HCC. This review will discuss the crosstalk between circRNAs and TME in HCC. In addition, we will discuss the current deficiencies and controversies in research on circRNAs and predict future research directions.

Rediscovering hemostasis abnormalities in multiple myeloma: The new era.

Multiple myeloma (MM) is a malignancy arisen from the abnormal proliferation of clonal plasma cells. It has a high risk of developing bleeding and thrombotic complications, which are related to poor prognosis and decreased survival. Multiple factors are involved in the breaking of the hemostasis balance, including disease specific factors, patient-specific factors, and drug factors that change pro-and anticoagulant and fibrinolysis. Recently, with the introduction of new treatments such as monoclonal antibodies, chimeric antigen receptor modified T-cell therapy, antibody-drug conjugates directed against BCMA, programmed death-1 inhibitor, export protein 1 inhibitors, histone deacetylase inhibitors, immunomodulatory drugs, proteasome inhibitors and Bcl-2 inhibitors, the therapy of MM patients has entered into a new era. Furthermore, it arouses a question whether these new treatments would alter the hemostasis balance in MM patients, which highlights the importance of the underlying pathophysiology of hemostasis abnormalities in MM, and on prophylaxis approaches. In this review, we updated the mechanisms of hemostasis abnormalities in MM, the impact of the new drugs on hemostasis balance and reliable therapeutic strategies.

Construction and analysis of competitive endogenous RNA networks and prognostic models associated with ovarian cancer based on the exoRBase database.

OBJECTIVE: To construct a competitive endogenous RNA (ceRNA) regulatory network in blood exosomes of patients with ovarian cancer (OC) using bioinformatics and explore its pathogenesis. METHODS: The exoRbase2.0 database was used to download blood exosome gene sequencing data from patients OC and normal controls and the expression profiles of exosomal mRNA, long non-coding RNA (lncRNA), and circular RNA (circRNA) were detected independently using R language for differential expression analysis. TargetScan and miRanda databases were combined for the prediction and differential expression of mRNA-binding microRNAs (miRNA). The miRcode and starBase databases were used to predict miRNAs that bind to differentially expressed lncRNAs and circRNAs repectively. The relevant mRNA, circRNA, lncRNA and their corresponding miRNA prediction data were imported into Cytoscape software for visualization of the ceRNA network. The R language and KEGG Orthology-based Annotation System (KOBAS) were used to execute and illustrate the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Hub genes were identified using The CytoHubba plugin. RESULTS: Thirty-one differentially expressed mRNAs, 17 differentially expressed lncRNAs, and 24 differentially expressed circRNAs were screened. Cytoscape software was used to construct the ceRNA network with nine mRNA nodes, two lncRNA nodes, eight circRNA nodes, and 51 miRNA nodes. Both GO and KEGG were focused on the Spliceosome pathway, indicating that spliceosomes are closely linked with the development of OC, while heterogenous nuclear ribonucleoprotein K and RNA binding motif protein X-linked genes were the top 10 score Hub genes screened by Cytoscape software, including two lncRNAs, four mRNAs, and four circRNAs. In patients with OC, the expression of eukaryotic translation initiation factor 4 gamma 2 (EIF4G2), SERPINE 1 mRNA binding protein 1 (SERBP1), ribosomal protein L15 (RPL15) and human leukocyte antigen complex P5 (HCP5) was significantly higher whereas that of testis expressed transcript, Y-linked 15 and DEAD-box helicase 3 Y-linked genes was lower compared to normal controls Immunocorrelation scores revealed that SERBP1 was significantly and negatively correlated with endothelial cells and CD4+ T cells and positively correlated with natural killer (NK) cells and macrophages, respectively; RPL15 was significantly positively correlated with macrophages and endothelial cells and negatively correlated with CD8+ T cells and uncharacterized cells, respectively. EIF4G2 was significantly and negatively correlated with endothelial cells and CD4+ T cells, and positively correlated with uncharacterized cells, respectively. Based on the survival data and the significant correlation characteristics derived from the multifactorial Cox analysis (P < 0.05), the survival prediction curves demonstrated that the prognostic factors associated with 3-year survival in patients with OC were The prognostic factors associated with survival were Macrophage, RPL15. CONCLUSION: This study successfully constructs a ceRNA regulatory network in blood exosomes of OV patients, which provides the specific targets for diagnosis and treatment of OC.

Scalable Face Image Coding via StyleGAN Prior: Toward Compression for Human-Machine Collaborative Vision.

The accelerated proliferation of visual content and the rapid development of machine vision technologies bring significant challenges in delivering visual data on a gigantic scale, which shall be effectively represented to satisfy both human and machine requirements. In this work, we investigate how hierarchical representations derived from the advanced generative prior facilitate constructing an efficient scalable coding paradigm for human-machine collaborative vision. Our key insight is that by exploiting the StyleGAN prior, we can learn three-layered representations encoding hierarchical semantics, which are elaborately designed into the basic, middle, and enhanced layers, supporting machine intelligence and human visual perception in a progressive fashion. With the aim of achieving efficient compression, we propose the layer-wise scalable entropy transformer to reduce the redundancy between layers. Based on the multi-task scalable rate-distortion objective, the proposed scheme is jointly optimized to achieve optimal machine analysis performance, human perception experience, and compression ratio. We validate the proposed paradigm's feasibility in face image compression. Extensive qualitative and quantitative experimental results demonstrate the superiority of the proposed paradigm over the latest compression standard Versatile Video Coding (VVC) in terms of both machine analysis as well as human perception at extremely low bitrates (< 0.01 bpp), offering new insights for human-machine collaborative compression.

Neoteric Semiembedded ß-Tricalcium Phosphate Promotes Osteogenic Differentiation of Mesenchymal Stem Cells under Cyclic Stretch.

ß-Tricalcium phosphate (ß-TCP) is a bioactive material for bone regeneration, but its brittleness limits its use as a standalone scaffold. Therefore, continuous efforts are necessary to effectively integrate ß-TCP into polymers, facilitating a sturdy ion exchange for cell regulation. Herein, a novel semiembedded technique was utilized to anchor ß-TCP nanoparticles onto the surface of the elastic polymer, followed by hydrophilic modification with the polymerization of dopamine. Cell adhesion and osteogenic differentiation of mesenchymal stem cells (MSCs) under static and dynamic uniaxial cyclic stretching conditions were investigated. The results showed that the new strategy was effective in promoting cell adhesion, proliferation, and osteogenic induction by the sustained release of Ca2+ in the vicinity and creating a reasonable roughness. Specifically, released Ca2+ from ß-TCP could activate the calcium signaling pathway, which further upregulated calmodulin and calcium/calmodulin-dependent protein kinase II genes in MSCs. Meanwhile, the roughness of the membrane and the uniaxial cyclic stretching activated the PIEZO1 signaling pathway. Chemical and mechanical stimulation promotes osteogenic differentiation and increases the expression of related genes 2-8-fold. These findings demonstrated that the neoteric semiembedded structure was a promising strategy in controlling both chemical and mechanical factors of biomaterials for cell regulation.

Multi-omics analysis reveals the unique landscape of DLD in the breast cancer tumor microenvironment and its implications for immune-related prognosis.

Background: Cuproptosis, i.e., copper-induced programmed cell death, has potential implications in cancer therapy. However, the impact of the cuproptosis-related gene (CRG) dihydrolipoyl dehydrogenase (DLD) on breast cancer (BC) prognosis remains underexplored. Methods: We employed real-time quantitative PCR and multiplexed immunostaining techniques to quantify DLD expression in both BC and the adjacent non-cancerous tissues. Immunofluorescence analysis was employed to assess the influence of DLD on immune cells and immunological checkpoints in the BC microenvironment. DLD knockdown experiments were conducted in BC cell lines MDA-MB-468 and SK-BR-3, with knockdown efficiency validated via western blot. Subsequently, we performed the cell counting kit-8 (CCK-8) assay, clone formation assay, Transwell migration assay, and invasion assay. To construct a prognostic model, we employed a Lasso-Cox regression analysis of immune-related genes associated with DLD. Additionally, we established a competing endogenous RNA network based on CRGs to evaluate potential regulatory pathways. Results: Compared to the adjacent tissues, BC tissues exhibited markedly elevated DLD expression levels. In vitro experiments demonstrated that DLD knockdown effectively inhibited BC cell migration, invasion, and proliferation. DLD exhibited positive correlations with CD68+ macrophages and PD-L1 in the tumor, as well as with macrophages and CD4+ T cells in the stroma. Tumor regions with high DLD expression were enriched in PD-L1 and macrophages, while stromal regions with high DLD expression contained CD4+ T cells and macrophages. The AUC values for 1-, 3-, and 5-year overall survival in TCGA-BRCA training set were 0.67, 0.66, and 0.66, respectively. A nomogram with a C-index of 0.715 indicated that risk score, tumor stage, and age could serve as independent prognostic factors for BC. Conclusion: Our findings underscore the significant predictive significance of DLD in BC and its influence on the tumor microenvironment. DLD represents a promising diagnostic and prognostic marker for BC, offering novel avenues for the identification of therapeutic targets and the enhancement of immunotherapy in BC.

Computer-Aided Diagnosis of Duchenne Muscular Dystrophy Based on Texture Pattern Recognition on Ultrasound Images Using Unsupervised Clustering Algorithms and Deep Learning.

OBJECTIVE: The feasibility of using deep learning in ultrasound imaging to predict the ambulatory status of patients with Duchenne muscular dystrophy (DMD) was previously explored for the first time. The present study further used clustering algorithms for the texture reconstruction of ultrasound images of DMD data sets and analyzed the difference in echo intensity between disease stages. METHODS: k-means (Kms) and fuzzy c-means (FCM) clustering algorithms were used to reconstruct the DMD data-set textures. Each image was reconstructed using seven texture-feature categories, six of which were used as the primary analysis items. The task of automatically identifying the ambulatory function and DMD severity was performed by establishing a machine-learning model. RESULTS: The experimental results indicated that the Gaussian Naïve Bayes and k-nearest neighbors classification models achieved an accuracy of 86.78% in ambulatory function classification. The decision-tree model achieved an identification accuracy of 83.80% in severity classification. A deep convolutional neural network model was established as the main structure of the deep-learning model while automatic auxiliary interpretation tasks of ambulatory function and severity were performed, and data augmentation was used to improve the recognition performance of the trained model. Both the visual geometry group (VGG)-16 and VGG-19 models achieved 98.53% accuracy in ambulatory-function classification. The VGG-19 model achieved 92.64% accuracy in severity classification. CONCLUSION: Regarding the overall results, the Kms and FCM clustering algorithms were used in this study to reconstruct the characteristic texture of the gastrocnemius muscle group in DMD, which was indeed helpful in quantitatively analyzing the deterioration of the gastrocnemius muscle group in patients with DMD at different stages. Subsequent combination of machine-learning and deep-learning technologies can automatically and accurately assist in identifying DMD symptoms and tracking DMD deterioration for long-term observation.

Identification of ferroptosis-related key genes associated with immune infiltration in sepsis by bioinformatics analysis and in vivo validation.

OBJECTIVES: Sepsis is a life-threatening infectious disease in which an immune inflammatory response is triggered. The potential effect of ferroptosis-related genes (FRGs) in inflammation of sepsis remained unclear. We focused on identifying and validating core FRGs and their association with immune infiltration in blood from currently all patients with sepsis. METHODS: All current raw data of septic blood were obtained from Gene Expression Omnibus. After removing the batch effect merging into a complete dataset and obtaining Diferentially expressed genes (DEGs). Common cross-talk genes were identified from DEGs and FRGs. WGCNA, GO, KEGG, PPI, GESA, ROC curves, and LASSO regression analysis were performed to indentify and validate key genes based on external septic datasets. Infiltrated immune cells in 2 hub genes (MAPK14 and ACSL4) were conducted using CIBERSORT algorithm and Spearman correlation analysis. Further, the expressions of 2 core FRGs were verified in the LPS-induced ALI and cardiac injury sepsis mice. RESULTS: MAPK14 and ACSL4 were identified, mostly enriched in T cell infiltration through NOD-like receptor signaling pathway according to the high or low 2 hub genes expression. The upregulated 2 ferroptosis-related genes were validated in LPS-induced ALI and cardiac injury mice, accompanied by upregulation of the NLRP3 pathway. CONCLUSION: MAPK14 and ACSL4 could become robustly reliable and promising biomarkers for sepsis by regulating ferroptosis through the NLRP3 pathway, which is mainly associated with T-cell infiltration.

Synergistically optimized electron and phonon transport in high-performance copper sulfides thermoelectric materials via one-pot modulation.

Optimizing thermoelectric conversion efficiency requires the compromise of electrical and thermal properties of materials, which are hard to simultaneously improve due to the strong coupling of carrier and phonon transport. Herein, a one-pot approach realizing simultaneous second phase and Cu vacancies modulation is proposed, which is effective in synergistically optimizing thermoelectric performance in copper sulfides. Multiple lattice defects, including nanoprecipitates, dislocations, and nanopores are produced by adding a refined ratio of Sn and Se. Phonon transport is significantly suppressed by multiple mechanisms. An ultralow lattice thermal conductivity is therefore obtained. Furthermore, extra Se is added in the copper sulfide for optimizing electrical transport properties by inducing generating Cu vacancies. Ultimately, an excellent figure of merit of ~1.6 at 873 K is realized in the Cu1.992SSe0.016(Cu2SnSe4)0.004 bulk sample. The simple strategy of inducing compositional and structural modulation for improving thermoelectric parameters promotes low-cost high-performance copper sulfides as alternatives in thermoelectric applications.

Machine learning- and WGCNA-mediated double analysis based on genes associated with disulfidptosis, cuproptosis and ferroptosis for the construction and validation of the prognostic model for breast cancer.

BACKGROUND: Disulfidptosis, a recently discovered cellular death mechanism, has not been extensively studied in relation to breast cancer (BC). Specifically, no previous research has integrated disulfidptosis-related genes (DRGs), cuproptosis-related genes (CRGs), and ferroptosis-related genes (FRGs) to construct a prognostic signature for BC. METHODS: DRGs, CRGs and FRGs with prognostic potential were identified through Cox regression analysis. A predictive model was constructed by intersecting the core genes obtained from unsupervised cluster analysis and weighted correlation network analysis (WGCNA). Differences in chemotherapy drug sensitivity, immune checkpoint levels were analyzed according to different risk score groups. The expression of the core disulfidptosis gene, SLC7A11, was analyzed using immunofluorescence. RESULTS: Single-cell RNA sequencing analysis revealed differential expression of DRGs in the BC tumor microenvironment. We developed a prognostic model, consisting of six genes, based on machine learning which included unsupervised cluster analysis and Lasso-Cox analysis. An internal training set and a validation set, both derived from the Cancer Genome Atlas-Breast Cancer (TCGA-BRCA) database, GSE20685 and GSE42568 as external validation sets all verified the model's validity. The low-risk group exhibited increased sensitivity to paclitaxel. Additionally, the high-risk group demonstrated significantly higher expression of tumor mutation burden and microsatellite instability compared to the low-risk group. A nomogram confirmed that the risk score can be an independent risk factor for BC. Notably, our findings highlighted the impact of SLC7A11 on the BC tumor microenvironment. Immunofluorescence analysis revealed significantly higher expression of SLC7A11 in BC tissues compared to paracancerous tissues. CONCLUSION: Multiplex analysis based on DRGs, CRGs and FRGs correlated strongly with BC, providing new insights for developing clinical prognostic tools and designing immunotherapy regimens for BC patients.

Developing and validating a survival prediction model based on blood exosomal ceRNA network in patients with PAAD.

BACKGROUND: Among the most lethal cancers, pancreatic adenocarcinoma (PAAD) is an essential component of digestive system malignancies that still lacks effective diagnosis and treatment methods. As exosomes and competing endogenous RNA (ceRNA) regulatory networks in tumors go deeper, we expect to construct a ceRNA regulatory network derived from blood exosomes of PAAD patients by bioinformatics methods and develop a survival prediction model based on it. METHODS: Blood exosome sequencing data of PAAD patients and normal controls were downloaded from the exoRbase database, and the expression profiles of exosomal mRNA, lncRNA, and circRNA were differentially analyzed by R. The related mRNA, circRNA, lncRNA, and their corresponding miRNA prediction data were imported into Cytoscape software to visualize the ceRNA network. Then, we conducted GO and KEGG enrichment analysis of mRNA in the ceRNA network. Genes that express differently in pancreatic cancer tissues compared with normal tissues and associate with survival (P < 0.05) were determined as Hub genes by GEPIA. We identified optimal prognosis-related differentially expressed mRNAs (DEmRNAs) and generated a risk score model by performing univariate and multivariate Cox regression analyses. RESULTS: 205 DEmRNAs, 118 differentially expressed lncRNAs (DElncRNAs), and 98 differentially expressed circRNAs (DEcircRNAs) were screened out. We constructed the ceRNA network, and a total of 26 mRNA nodes, 7 lncRNA nodes, 6 circRNA nodes, and 16 miRNA nodes were identified. KEGG enrichment analysis showed that the DEmRNAs in the regulatory network were mainly enriched in Human papillomavirus infection, PI3K-Akt signaling pathway, Osteoclast differentiation, and ECM-receptor interaction. Next, six hub genes (S100A14, KRT8, KRT19, MAL2, MYO5B, PSCA) were determined through GEPIA. They all showed significantly increased expression in cancer tissues compared with control groups, and their high expression pointed to adverse survival. Two optimal prognostic-related DEmRNAs, MYO5B (HR = 1.41, P < 0.05) and PSCA (HR = 1.10, P < 0.05) were included to construct the survival prediction model. CONCLUSION: In this study, we successfully constructed a ceRNA regulatory network in blood exosomes from PAAD patients and developed a two-gene survival prediction model that provided new targets which shall aid in diagnosing and treating PAAD.

Influence of Temperature Effect on the Static and Dynamic Performance of Gas-Lubricated Microbearings.

Gas-lubricated microbearings are widely applied in multiple fields due to their advantages of high-speed, low friction level and other features. The operating environment of microbearings is complex, and the difference of temperature has an important influence on their comprehensive performance. In this investigation, FEM (finite element method) is employed to investigate the static, dynamic and limit characteristics of microbearings lubricated by different kinds of gas at different temperatures. The results show that the rise of temperature leads to the decline of equivalent viscosity of gas, which weakens the load capacity of microbearings, and furthermore, affects the operating stability of microbearings. The dynamic performances of microbearings at different temperatures are very different, and the two dynamic limit characteristics are more sensitive to temperature when it changes.

First-principles study of diffusion of oxygen vacancies and interstitials in ZnO.

A comprehensive investigation of oxygen vacancy and interstitial diffusion in ZnO has been performed using ab initio total energy calculations with both the local density approximation (LDA) and the generalized gradient approximation (GGA). Based on our calculation results, oxygen octahedral interstitials are fast diffusers, contributing to annealing processes, as well as being responsible for the self-diffusion of oxygen for n-type ZnO, and oxygen vacancies are responsible for the self-diffusion of oxygen for p-type ZnO.

Effects of Re, W and Co on dislocation nucleation at the crack tip in the γ-phase of Ni-based single-crystal superalloys by atomistic simulation.

The effects of Re, W and Co on dislocation nucleation at the crack tip in Ni have been studied by the molecular dynamics method. The results show that the activation energy of dislocation nucleation is lowered by the addition of Re, W and Co; moreover, the activation energy decreases when the alloying element increases from 1 at.% to 2 at.%. The energy landscapes of the atoms are studied to elucidate these effects. Quantification analyses of the bonding strength between Ni and X (X = Re, W or Co) reveal that strong bonding between Ni and X (X = Re, W or Co) in the dislocation nucleation process can suppress the cleavage process and enhance the ability of dislocation nucleation. The surface energy and unstable stacking fault energy are also calculated to understand the alloying effects on the dislocation nucleation process. The results imply that interaction between alloying elements and Ni atoms plays a role in promoting the dislocation nucleation process at the crack tip. The ability of Re, W and Co in improving the ductility of the Ni crack system is in the order W > Re > Co. The results could provide useful information in the design of Ni-based superalloys.

First-principles study of the interactions of Ti and Zr with the tips of open-ended single-wall carbon nanotubes.

Density functional theory is used to study the interactions of Ti and Zr with the tips of open-ended single-wall carbon nanotubes. It is found that Ti or Zr atoms can saturate the dangling bonds of a tip to make it closed. Zr displays much stronger interaction with the contacted carbon atom than Ti. The Fermi energies of the hybrid systems increase dramatically, and the peak values of the density of states near the Fermi levels increase significantly. The field emission properties are discussed qualitatively.