Construction of a prognostic assessment model for colon cancer patients based on immune-related genes and exploration of related immune characteristics

Objectives: To establish a novel risk score model that could predict the survival and immune response of patients with colon cancer.Methods: We used The Cancer Genome Atlas (TCGA) database to get mRNA expression profile data, corresponding clinical information and somatic mutation data of patients with colon cancer. Limma R software package and univariate Cox regression were performed to screen out immune-related prognostic genes. GO (Gene ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) were used for gene function enrichment analysis. The risk scoring model was established by Lasso regression and multivariate Cox regression. CIBERSORT was conducted to estimate 22 types of tumor-infiltrating immune cells and immune cell functions in tumors. Correlation analysis was used to demonstrate the relationship between the risk score and immune escape potential.Results: 679 immune-related genes were selected from 7846 differentially expressed genes (DEGs). GO and KEGG analysis found that immune-related DEGs were mainly enriched in immune response, complement activation, cytokine-cytokine receptor interaction and so on. Finally, we established a 3 immune-related genes risk scoring model, which was the accurate independent predictor of overall survival (OS) in colon cancer. Correlation analysis indicated that there were significant differences in T cell exclusion potential in low-risk and high-risk groups.Conclusion: The immune-related gene risk scoring model could contribute to predicting the clinical outcome of patients with colon cancer

SSD7-FFAM: A Real-Time Object Detection Network Friendly to Embedded Devices from Scratch

The high requirements for computing and memory are the biggest challenges in deploying existing object detection networks to embedded devices. Living lightweight object detectors directly use lightweight neural network architectures such as MobileNet or ShuffleNet pre-trained on large-scale classification datasets, which results in poor network structure flexibility and is not suitable for some specific scenarios. In this paper, we propose a lightweight object detection network Single-Shot MultiBox Detector (SSD)7-Feature Fusion and Attention Mechanism (FFAM), which saves storage space and reduces the amount of calculation by reducing the number of convolutional layers. We offer a novel Feature Fusion and Attention Mechanism (FFAM) method to improve detection accuracy. Firstly, the FFAM method fuses high-level semantic information-rich feature maps with low-level feature maps to improve small objects’ detection accuracy. The lightweight attention mechanism cascaded by channels and spatial attention modules is employed to enhance the target’s contextual information and guide the network to focus on its easy-to-recognize features. The SSD7-FFAM achieves 83.7% mean Average Precision (mAP), 1.66 MB parameters, and 0.033 s average running time on the NWPU VHR-10 dataset. The results indicate that the proposed SSD7-FFAM is more suitable for deployment to embedded devices for real-time object detection

SSD7-FFAM: A Real-Time Object Detection Network Friendly to Embedded Devices from Scratch

The high requirements for computing and memory are the biggest challenges in deploying existing object detection networks to embedded devices. Living lightweight object detectors directly use lightweight neural network architectures such as MobileNet or ShuffleNet pre-trained on large-scale classification datasets, which results in poor network structure flexibility and is not suitable for some specific scenarios. In this paper, we propose a lightweight object detection network Single-Shot MultiBox Detector (SSD)7-Feature Fusion and Attention Mechanism (FFAM), which saves storage space and reduces the amount of calculation by reducing the number of convolutional layers. We offer a novel Feature Fusion and Attention Mechanism (FFAM) method to improve detection accuracy. Firstly, the FFAM method fuses high-level semantic information-rich feature maps with low-level feature maps to improve small objects’ detection accuracy. The lightweight attention mechanism cascaded by channels and spatial attention modules is employed to enhance the target’s contextual information and guide the network to focus on its easy-to-recognize features. The SSD7-FFAM achieves 83.7% mean Average Precision (mAP), 1.66 MB parameters, and 0.033 s average running time on the NWPU VHR-10 dataset. The results indicate that the proposed SSD7-FFAM is more suitable for deployment to embedded devices for real-time object detection

Herdsmen’s Adaptation to Climate Changes and Subsequent Impacts in the Ecologically Fragile Zone, China

The change of land surface can exert significant influence on the future climate change. This study analyzed the effects of herdsmen’s adaptation to climate changes on the livestock breeding, income, and land surface dynamics with a land surface parameterization scheme. The empirical analysis was first carried out on the impacts of the adaptation measures of herdsmen on their income in the context of the climate change with the positive mathematical programming (PMP) model on the basis of the household survey data in the Three-River Source Region, an ecologically fragile area in Qinghai Province, China. Then, the land surface parameterization process is analyzed based on the agent-based model (ABM), which involves the herdsmen’s adaptation measures on climate change, and it also provides reference for the land surface change projection. The result shows that the climate change adaptation measures will have a positive effect on the increasing of the amount of herdsman’s livestock and income as well as future land surface dynamics. Some suggestions on the land use management were finally proposed, which can provide significant reference information for the land use planning

Experimental and Numerical Study of an Innovative Infill Web-Strips Steel Plate Shear Wall with Rigid Beam-to-Column Connections

Steel plate shear walls (SPSWs) offer good energy dissipation capability when subjected to seismic forces as a robust lateral load resisting structure. This research investigated the cyclic behaviors of innovative infill web-strips (IWS-SPSW) and conventional unstiffened steel plate shear (USPSW) experimentally and numerically. As a result, two specimens of a 1:3 scale three-story single-bay IWS-SPSW and USPSW were fabricated and tested under cyclic lateral loading. Rigid moment-resistant connections were used for the steel plate shear wall beam-column connection. The steel shear walls with infill web strips showed high ductility and less shear load-bearing than the USPSW. The hysteresis results showed that the IWS-SPSW had high energy dissipation with no severe beam-columns damages. On the other hand, the USPSW displayed severe post-buckling, infill panel cracks, and first-floor column damages. Moreover, the IWS-SPSW shear strength did not fall in the test specimen beyond 2.5% average story drift, where the structure exhibited great seismic behavior. FE models were created and validated with experimental data. It has been proven that the infill web-strips can affect an SPSW system’s high performance and overall energy dissipation. From a parametric study, the material features of the infill web-strips, such as steel strength and thickness, can enhance the system’s impact even more

Identification of inflammatory factor-related genes associated with the prognosis and immune cell infiltration in colorectal cancer patients

This study aims to identify the inflammatory factor-related genes which help to predict the prognosis of patients with colorectal cancer. GSEA (Gene Set Enrichment Analysis) was used to acquire inflammation-related genes and the corresponding expression information was collected from TCGA database to determine the DEGs (differentially-expressed genes) in CRC patients. We conducted enrichment analysis and PPI (protein–protein interaction) of these DEGs. Besides, key genes that are both differentially-expressed and prognosis-related were screened out, which were used to establish the prognostic model. We obtained 79 DEGs and 19 prognostic genes, 10 prognostic-related differential genes were eventually screened. These genes were used to construct the prognostic model. We also identified that the immune infiltration score of macrophages between different risk groups was significantly different and similar distinction was witnessed in immune function score of APC (antigen-presenting cell) co-stimulation and type I IFN (interferon) response

A High-Speed Low-Cost VLSI System Capable of On-Chip Online Learning for Dynamic Vision Sensor Data Classification

This paper proposes a high-speed low-cost VLSI system capable of on-chip online learning for classifying address-event representation (AER) streams from dynamic vision sensor (DVS) retina chips. The proposed system executes a lightweight statistic algorithm based on simple binary features extracted from AER streams and a Random Ferns classifier to classify these features. The proposed system’s characteristics of multi-level pipelines and parallel processing circuits achieves a high throughput up to 1 spike event per clock cycle for AER data processing. Thanks to the nature of the lightweight algorithm, our hardware system is realized in a low-cost memory-centric paradigm. In addition, the system is capable of on-chip online learning to flexibly adapt to different in-situ application scenarios. The extra overheads for on-chip learning in terms of time and resource consumption are quite low, as the training procedure of the Random Ferns is quite simple, requiring few auxiliary learning circuits. An FPGA prototype of the proposed VLSI system was implemented with 9.5~96.7% memory consumption and <11% computational and logic resources on a Xilinx Zynq-7045 chip platform. It was running at a clock frequency of 100 MHz and achieved a peak processing throughput up to 100 Meps (Mega events per second), with an estimated power consumption of 690 mW leading to a high energy efficiency of 145 Meps/W or 145 event/μJ. We tested the prototype system on MNIST-DVS, Poker-DVS, and Posture-DVS datasets, and obtained classification accuracies of 77.9%, 99.4% and 99.3%, respectively. Compared to prior works, our VLSI system achieves higher processing speeds, higher computing efficiency, comparable accuracy, and lower resource costs

Farrerol Ameliorates TNBS-Induced Colonic Inflammation by Inhibiting ERK1/2, JNK1/2, and NF-κB Signaling Pathway

Farrerol, a type of 2, 3-dihydro-flavonoid, is obtained from Rhododendron. Previous studies have shown that Farrerol performs multiple biological activities, such as anti-inflammatory, antibacterial, and antioxidant activity. In this study, we aim to investigate the effect of Farrerol on colonic inflammation and explore its potential mechanisms. We found that the effect of Farrerol was evaluated via the 2,4,6-trinitrobenzene sulfonic acid (TNBS)-induced colitis model in mice and found that Farrerol has a protective effect on TNBS-induced colitis. Farrerol administration significantly improved the weight change, clinical scores, colon length, and intestinal epithelium barrier damage and markedly decreased the inflammatory cytokines production in TNBS-induced mice. The protective effect of Farrerol was also observed in LPS-induced RAW264.7 cells. We found that Farrerol observably reduced the production of inflammatory mediators including IL-1β, IL-6, TNF-α, COX-2, and iNOS in LPS-induced RAW264.7 cells via suppressing AKT, ERK1/2, JNK1/2, and NF-κB p65 phosphorylation. In conclusion, the study found that Farrerol has a beneficial effect on TNBS-induced colitis and might be a natural therapeutic agent for IBD treatment