DESIGN AND OPTIMIZATION OF THE VARIABLE-DENSITY LATTICE STRUCTURE BASED ON LOAD PATHS

Lattice structure is more and more widely used in engineering by replacing solid structure. But its mechanical performances are constrained by the external shape if the unit cells are directly filled in the design domain, and the traditional topology optimization methods are difficult to give the explicitly mechanical guidance for the distribution of internal unit cells. In this paper, a novel design and optimization method of variable-density lattice structure is proposed in order to simultaneously optimize the external shape and the internal unit cells. First of all, the envelope model of any given structure should be established, and the load paths need to be visualized by the theory of load path. Then, the design criteria of external shape are established based on the principle of smoother load paths in the structure. An index of load flow capacity is defined to indicate the load paths density and to control the density distribution of unit cells, and a detailed optimization strategy is given. Finally, three examples of a cantilever plate, an L-shaped bracket and a classical three-point bending beam are used to verify the method. The results show that the models designed by the proposed method have better mechanical performances, lower material usage and less printing time

Decoding tumor heterogeneity in uveal melanoma: basement membrane genes as novel biomarkers and therapeutic targets revealed by multi-omics approaches for cancer immunotherapy

Background: Uveal melanoma (UVM) is a primary intraocular malignancy that poses a significant threat to patients’ visual function and life. The basement membrane (BM) is critical for establishing and maintaining cell polarity, adult function, embryonic and organ morphogenesis, and many other biological processes. Some basement membrane protein genes have been proven to be prognostic biomarkers for various cancers. This research aimed to develop a novel risk assessment system based on BMRGs that would serve as a theoretical foundation for tailored and accurate treatment.Methods: We used gene expression profiles and clinical data from the TCGA-UVM cohort of 80 UVM patients as a training set. 56 UVM patients from the combined cohort of GSE84976 and GSE22138 were employed as an external validation dataset. Prognostic characteristics of basement membrane protein-related genes (BMRGs) were characterized by Lasso, stepwise multifactorial Cox. Multivariate analysis revealed BMRGs to be independent predictors of UVM. The TISCH database probes the crosstalk of BMEGs in the tumor microenvironment at the single-cell level. Finally, we investigated the function of ITGA5 in UVM using multiple experimental techniques, including CCK8, transwell, wound healing assay, and colony formation assay.Results: There are three genes in the prognostic risk model (ADAMTS10, ADAMTS14, and ITGA5). After validation, we determined that the model is quite reliable and accurately forecasts the prognosis of UVM patients. Immunotherapy is more likely to be beneficial for UVM patients in the high-risk group, whereas the survival advantage may be greater for UVM patients in the low-risk group. Knockdown of ITGA5 expression was shown to inhibit the proliferation, migration, and invasive ability of UVM cells in vitro experiments.Conclusion: The 3-BMRGs feature model we constructed has excellent predictive performance which plays a key role in the prognosis, informing the individualized treatment of UVM patients. It also provides a new perspective for assessing pre-immune efficacy

Long-Term Trends and Variability of Hydroclimate Variables and Their Linkages with Climate Indices in the Songhua River

The long-term trends and variability of hydroclimate variables are critical for water resource management, as well as adaptation to climate change. Three popular methods were used in this study to explore the trends and variability of hydroclimate variables during last 122 years in the Songhua River (SHR), one of most important river systems in China. Results show the followings: (1) There was an obvious pattern of decadal oscillations, with three positive and three negative precipitation and streamflow anomalies. The lengths of these phases vary from 11 to 36 years. (2) Annual temperature demonstrated a statistically significant increasing trend in the last 122 years, and the trend magnitude was 0.30 °C/10 years in the last 50–60 years, being larger than that of the global surface temperature. It has increased much faster since 1970. (3) Monthly precipitation in the winter season in recent years was almost the same as that in earlier periods, but a significantly increasing monthly streamflow was observed due to snowmelt under a warming climate. (4) A statistically significant correlation between hydroclimate variables and climate indices can be determined. These results could be used to make better water resource management decisions in the SHR, especially under future climate change scenarios

Improved deep residual shrinkage network for a multi-cylinder heavy-duty engine fault detection with single channel surface vibration

The health monitoring and fault diagnosis of heavy-duty engines are increasingly important for energy storage ecosystem. During operation, vibration characters corresponding to the specific fault need to be extracted from the overall system vibration. Faulty characteristics emanating from one single cylinder are also mixed with those from other cylinders. Besides, the change of working condition brings strong nonlinearities in surface vibration. To solve these problems, an improved deep residual shrinkage network (IDRSN) is developed for detecting diverse engine faults at various degrees using single channel surface vibration signal. Within IDRSN, a wide convolution kernel is utilized in first convolution layer to capture the long-term fault-related impacts and eliminate the short-time random impact. The residual network module is adopted to enhance the focus the relevant components of vibration signals. Mini-batch training strategy is used to improve the model stability. Meanwhile, Gradient-weighted class activation map is adopted to assess the consistency between the learned knowledge and the fault-related information. The IDRSN is implemented to diagnosing a diesel engine under various faults, faulty degrees and operating speeds. Comparisons with existing models are analyzed in terms of hyper-parameters, training samples, noise resistance, and visualization. Results demonstrate the proposed IDRSN's superior performance on fault diagnosis accuracy, stability, anti-noise performance, and anti-interference performance. An average accuracy rate of 98.38 % was achieved by the proposed IDRSN, in comparison to 96.64 % and 93.56 % achieved by the DRSN and the wide-kernel deep convolutional neural network respectively. These results highlight the proposed IDRSN's superiority in diagnosing multiple faults under various working conditions, offering a low-cost, highly effective, and applicable approach for complex fault diagnosis tasks

HDAC is indispensable for IFN-γ-induced B7-H1 expression in gastric cancer

Abstract Background B7 homolog 1 (B7-H1) overexpression on tumor cells is an important mechanism of immune evasion in gastric cancer (GC). Elucidation of the regulation of B7-H1 expression is urgently required to guide B7-H1-targeted cancer therapy. Interferon gamma (IFN-γ) is thought to be the main driving force behind B7-H1 expression, and epigenetic factors including histone acetylation are recently linked to the process. Here, we investigated the potential role of histone deacetylase (HDAC) in IFN-γ-induced B7-H1 expression in GC. The effect of Vorinostat (SAHA), a small molecular inhibitor of HDAC, on tumor growth and B7-H1 expression in a mouse GC model was also evaluated. Results RNA-seq data from The Cancer Genome Atlas revealed that expression of B7-H1, HDAC1–3, 6–8, and 10 and SIRT1, 3, 5, and 6 was higher, and expression of HDAC5 and SIRT4 was lower in GC compared to that in normal gastric tissues; that HDAC3 and HDAC1 expression level significantly correlated with B7-H1 in GC with a respective r value of 0.42 (p < 0.001) and 0.21 (p < 0.001). HDAC inhibitor (Trichostatin A, SAHA, and sodium butyrate) pretreatment suppressed IFN-γ-induced B7-H1 expression on HGC-27 cells. HDAC1 and HDAC3 gene knockdown had the same effect. SAHA pretreatment or HDAC knockdown resulted in impaired IFN-γ signaling, demonstrated by the reduction of JAK2, p-JAK1, p-JAK2, and p-STAT1 expression and inefficient STAT1 nuclear translocation. Furthermore, SAHA pretreatment compromised IFN-γ-induced upregulation of histone H3 lysine 9 acetylation level in B7-H1 gene promoter. In the grafted mouse GC model, SAHA treatment suppressed tumor growth, inhibited B7-H1 expression, and elevated the percentage of tumor-infiltrating CD8+ T cells. Conclusion HDAC is indispensable for IFN-γ-induced B7-H1 in GC. The study suggests the possibility of targeting B7-H1 using small molecular HDAC inhibitors for cancer treatment

Table1_Decoding tumor heterogeneity in uveal melanoma: basement membrane genes as novel biomarkers and therapeutic targets revealed by multi-omics approaches for cancer immunotherapy.xls

Background: Uveal melanoma (UVM) is a primary intraocular malignancy that poses a significant threat to patients’ visual function and life. The basement membrane (BM) is critical for establishing and maintaining cell polarity, adult function, embryonic and organ morphogenesis, and many other biological processes. Some basement membrane protein genes have been proven to be prognostic biomarkers for various cancers. This research aimed to develop a novel risk assessment system based on BMRGs that would serve as a theoretical foundation for tailored and accurate treatment.Methods: We used gene expression profiles and clinical data from the TCGA-UVM cohort of 80 UVM patients as a training set. 56 UVM patients from the combined cohort of GSE84976 and GSE22138 were employed as an external validation dataset. Prognostic characteristics of basement membrane protein-related genes (BMRGs) were characterized by Lasso, stepwise multifactorial Cox. Multivariate analysis revealed BMRGs to be independent predictors of UVM. The TISCH database probes the crosstalk of BMEGs in the tumor microenvironment at the single-cell level. Finally, we investigated the function of ITGA5 in UVM using multiple experimental techniques, including CCK8, transwell, wound healing assay, and colony formation assay.Results: There are three genes in the prognostic risk model (ADAMTS10, ADAMTS14, and ITGA5). After validation, we determined that the model is quite reliable and accurately forecasts the prognosis of UVM patients. Immunotherapy is more likely to be beneficial for UVM patients in the high-risk group, whereas the survival advantage may be greater for UVM patients in the low-risk group. Knockdown of ITGA5 expression was shown to inhibit the proliferation, migration, and invasive ability of UVM cells in vitro experiments.Conclusion: The 3-BMRGs feature model we constructed has excellent predictive performance which plays a key role in the prognosis, informing the individualized treatment of UVM patients. It also provides a new perspective for assessing pre-immune efficacy.</p