Due to network operation and maintenance relying heavily on network traffic monitoring, traffic matrix analysis has been one of the most crucial issues for network management related tasks. However, it is challenging to reliably obtain the precise measurement in computer networks because of the high measurement cost, and the unavoidable transmission loss. Although some methods proposed in recent years allowed estimating network traffic from partial flow-level or link-level measurements, they often perform poorly for traffic matrix estimation nowadays. Despite strong assumptions like low-rank structure and the prior distribution, existing techniques are usually task-specific and tend to be significantly worse as modern network communication is extremely complicated and dynamic. To address the dilemma, this paper proposed a diffusion-based traffic matrix analysis framework named Diffusion-TM, which leverages problem-agnostic diffusion to notably elevate the estimation performance in both traffic distribution and accuracy. The novel framework not only takes advantage of the powerful generative ability of diffusion models to produce realistic network traffic, but also leverages the denoising process to unbiasedly estimate all end-to-end traffic in a plug-and-play manner under theoretical guarantee. Moreover, taking into account that compiling an intact traffic dataset is usually infeasible, we also propose a two-stage training scheme to make our framework be insensitive to missing values in the dataset. With extensive experiments with real-world datasets, we illustrate the effectiveness of Diffusion-TM on several tasks. Moreover, the results also demonstrate that our method can obtain promising results even with $5\%$ known values left in the datasets.
Transcriptome foundation models TFMs hold great promises of deciphering the transcriptomic language that dictate diverse cell functions by self-supervised learning on large-scale single-cell gene expression data, and ultimately unraveling the complex mechanisms of human diseases. However, current TFMs treat cells as independent samples and ignore the taxonomic relationships between cell types, which are available in cell ontology graphs. We argue that effectively leveraging this ontology information during the TFM pre-training can improve learning biologically meaningful gene co-expression patterns while preserving TFM as a general purpose foundation model for downstream zero-shot and fine-tuning tasks. To this end, we present \textbf{s}ingle \textbf{c}ell, \textbf{Cell}-\textbf{o}ntology guided TFM scCello. We introduce cell-type coherence loss and ontology alignment loss, which are minimized along with the masked gene expression prediction loss during the pre-training. The novel loss component guide scCello to learn the cell-type-specific representation and the structural relation between cell types from the cell ontology graph, respectively. We pre-trained scCello on 22 million cells from CellxGene database leveraging their cell-type labels mapped to the cell ontology graph from Open Biological and Biomedical Ontology Foundry. Our TFM demonstrates competitive generalization and transferability performance over the existing TFMs on biologically important tasks including identifying novel cell types of unseen cells, prediction of cell-type-specific marker genes, and cancer drug responses.
Fatty acid synthase (FASN) promotes tumor progression in multiple cancers. In this study, we comprehensively examined the expression, prognostic significance, and promoter methylation of FASN, and its correlation with immune cell infiltration in pan-cancer. Our results demonstrated that elevated FASN expression was significantly associated with an unfavorable prognosis in many cancer types. Furthermore, FASN promoter DNA methylation can be used as a tumor prognosis marker. Importantly, high levels of FASN were significantly negatively correlated with tumor immune infiltration in 35 different cancers. Additionally, FASN was significantly associated with tumor mutational burden (TMB) and microsatellite instability (MSI) in multiple malignancies, suggesting that it may be essential for tumor immunity. We also investigated the effects of FASN expression on immunotherapy efficacy and prognosis. In up to 15 tumors, it was significantly negatively correlated with immunotherapy-related genes, such as PD-1, PD-L1, and CTLA-4. Moreover, we found that tumors with high FASN expression may be more sensitive to immunotherapy and have a good prognosis with PD-L1 treatment. Finally, we confirmed the tumor-suppressive effect of mir-195-5p through FASN. Altogether, our results suggested that FASN may serve as a novel prognostic indicator and immunotherapeutic target in various malignancies.
The development process of gastrointestinal anastomosis is from complex to simple, from two layers to one layer, from extramucosal anastomosis to seromuscular anastomosis. With the rapid development of anastomosis instruments, the anastomosis process becomes more and more convenient. However, relevant studies have shown that related complications such as anastomotic leakage still occur. This study sought to investigate the feasibility and safety of seromuscular layer sutures in the reinforcement of esophagojejunostomy after open radical total gastrectomy.This study retrospectively analyzed patients who underwent Roux-en-Y esophagojejunostomy after open radical total gastrectomy at The Third Department of Surgery, The Fourth Hospital of Hebei Medical University from April 2019 to May 2020. The inclusion criteria of patients were between 18 and 80 years old; pathology confirmed gastric adenocarcinoma; preoperative imaging showed no distant metastasis and did not receive neoadjuvant therapy; no complex infectious diseases; no blood transfusion was performed before operation. A total of 192 patients were included according to the inclusion criteria. They were divided into the following 2 groups based on whether seromuscular layer suturing of the anastomosis was performed: (I) group A (the simple anastomosis group, n=76); (II) and group B (the seromuscular layer suture group, n=116). The baseline data, surgical data and postoperative complications were compared between the two groups.All patients underwent esophagojejunostomy Roux-en-Y anastomosis after open radical total gastrectomy, and no perioperative deaths occurred. There was no significant difference in baseline data between the two groups. Group B had an earlier time to liquid diet than group A (4.23±0.76 vs. 4.57±0.58 days, P<0.001). The incidence of postoperative anastomotic leakage in group B (1.72%) was lower than that in group A (9.21%), and the difference was statistically significant (P=0.03). The incidence of pleural effusion was lower in group B (15.52%) than group A (32.89%), and the difference was statistically significant (P=0.005).Compared to the simple anastomosis, seromuscular layer sutures after esophagojejunostomy may decrease the rates of postoperative anastomotic leakage and pleural effusion. This suture method is feasible and may provide a new option to increase surgical safety.
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