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Background and purpose: Colorectal carcinoma (CRC) is a common malignant tumor with incidence and mortality rates second only to gastric cancer and esophageal cancer among digestive system malignancies. Increasing evidence suggests that immune cells play a significant role in the occurrence and development of CRC. The aim of this study was to construct a prognostic model related to immune cell-associated genes to predict the prognosis of CRC patients and enable precise management. Methods: Single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (RNA-seq) data, along with clinical information for colorectal cancer, were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Differential genes of immune cell subtypes were extracted, and genes related to prognosis were screened in TCGA data using Cox regression and LASSO regression, with external validation using GSE39582 and GSE41258. The prognostic model was used to analyze chemotherapy drug sensitivity, assess immunotherapy efficacy, analyze pathways related to risk scores, and perform clinical correlation analysis. Finally, the expression levels of model genes were validated in 10 fresh frozen CRC tissues with post-operative pathological examination and cell lines using real-time fluorescence quantitative polymerase chain reaction (RTFQ-PCR) and immunohistochemistry. All samples were approved by the Fudan University Shanghai Cancer Center Ethics Committee (No. 050432-4-2108*). Results: We defined 16 cell clusters based on the scRNA-seq dataset (GSE161277) and labeled these clusters as different cell types using the R package celldex. Differential analysis of immune cell subtypes yielded 374 differentially expressed genes. Using univariate Cox and LASSO analyses, we constructed a 9-gene risk prognostic model in CRC. This risk model exhibited reliable prognostic prediction performance and played an important role in predicting anti-tumor drug sensitivity, immunotherapy efficacy, potential molecular mechanisms, and clinical characteristics. Patients with high-risk scores had a lower probability of benefiting from immunotherapy. Conclusion: We constructed a 9-gene risk prognostic model based on the heterogeneity of immune cells in the CRC tumor microenvironment, which predicted the survival and treatment outcomes of CRC patients.

To solve the problem of low track prediction accuracy caused by the limitations of deep learning and the cumulative error generated by recursive prediction strategies, a short-term prediction algorithm for air target tracks based on residual correction CNN-BiLSTM was proposed. Firstly, a convolution module was introduced to extract potentially associated spatial location features from the track data, and a bidirectional long and short time memory network was used to extract temporal features from the track data, achieving real-time one-step prediction and multi-step advance prediction of air targets. Then, the integrated moving average autoregression was introduced as a residual correction model to model the residual generated by real-time one-step prediction, and the residual value of the hybrid neural network model for multi-step advanced prediction is calculated. Finally, the output results of the hybrid neural network model and the residual correction model are fused to obtain the final trajectory prediction value. Experiment results proved that the algorithm can significantly improve the accuracy of short-term prediction of airborne target tracks.

The online judge (OJ) is a widely used system for programming education,learning and contests.Users often get lost in searching for tasks of interest in the massive database.How to recommend suitable programming tasks to the users and plan the learning path is a significant research topicin the development of online programming evaluation system.Existing traditional recommendation methods have the limitation of making a trade-off between interpretability and effectiveness.This paper proposes a task-recommending model for the OJ platform -hybrid programming task recommendation model based on knowledge graph and collaborative filtering for online judge (HKGCF).The HKGCF model can help users improve their learning effect by recommending questions that match their current knowledge levels and skills.The model is designed based on a hybrid strategy that integrates the knowledge graph representation learning with an improved collaborative filtering algorithm.The model is implemented and integrated into the OJ platform of Beihang University,and meet the specific interaction formats of the OJ platform.We conducted two experiments,an online and an offline test,to validate the proposed model and its implementations.The results show that the proposed model outperforms the representative conventional recommendation algorithm interms of interpretability and accuracy

Sharding is a technology that solves the problem of blockchain capacity expansion.However,sharding may make it ea-sier for malicious nodes to be concentrated in a single shard,thus hindering the safe operation of the entire system.This paper proposes a reputation-based sharding consensus protocol(RBSCP),which establishes a reputation mechanism to measure node behavior and encourage nodes to follow the protocol.The reputation level-based sharding method reduces the difference in the reputation level distribution in different shards,so as to prevent malicious nodes from concentrating on a single shard to do evil.A double-chain model combining verification chain and record chain is proposed.Through the differentiated storage of transactions,the storage capacity of the blockchain is expanded while the security of the blockchain is improved.By associating the vo-ting shares with the node reputation and differentiating the node commitments,a reputation-based fast Byzantine fault tolerance(RFBFT) algorithm is proposed,which enables honest nodes to reach consensus faster and reduces the impact of malicious nodes.Security analysis shows that RBSCP can guarantee the rationality of node distribution in shards and the security of consensus process,and prevent double spend attack and nothing at stake attack.Experimental results show that RBSCP can achieve low sharding latency,low consensus latency and high throughput under the premise of ensuring security.

Objective: To understand the epidemiological features and changing patterns of intestinal parasitic diseases in Jiangxi Province from 1989 to 2014, so as to provide scientific evidence for formulating the preventive and control strategy for intestinal parasitic diseases., Methods: The data regarding the status of intestinal parasitic diseases in Jiangxi Province were captured from the 1989 national survey on the distribution of human parasites in China and the 2014 national survey on major human parasitic diseases in China, and the status of human intestinal parasite infections was compared between the two surveys in Jiangxi Province., Results: The prevalence rate of human intestinal parasite infections was 79.59% and 9.64% in Jiangxi Province in 1989 and 2014, with a decline rate of 87.89% during the 25-year period ( χ 2 = 30 870.5, P < 0.01). There were 24 and 20 species of human intestinal parasites detected in Jiangxi Province in 1989 and 2014, respectively, with totally 26 species detected during the two surveys. In Jiangxi Province, the 3 most highly prevalent human intestinal parasites included Ascaris lumbricoides (71.06%), hookworm (17.61%) and Enterobius vermicularis (17.59%) in 1989, and E. vermicularis (13.73%), hookworm (4.66%), whipworm (0.95%) in 2014. A higher rate of human intestinal parasite infections was found in females than in males in both surveys in Jiangxi Province ( P < 0.01), and the prevalence of human intestinal parasite infections reduced by 82.50% to 95.31% in different age groups in Jiangsu Province during the 25-year period., Conclusions: The prevalence of human intestinal parasite infections shows a remarkable decline tendency in Jiangxi Province, and soil-transmitted nematodes remain the main species of human intestinal parasites in Jiangxi Province.