A tricarboxylic acid cycle-based machine learning model to select effective drug targets for the treatment of esophageal squamous cell carcinoma

Background: The tricarboxylic acid cycle (TCA cycle) is an important metabolic pathway and closely related to tumor development. However, its role in the development of esophageal squamous cell carcinoma (ESCC) has not been fully investigated.Methods: The RNA expression profiles of ESCC samples were retrieved from the TCGA database, and the GSE53624 dataset was additionally downloaded from the GEO database as the validation cohort. Furthermore, the single cell sequencing dataset GSE160269 was downloaded. TCA cycle-related genes were obtained from the MSigDB database. A risk score model for ESCC based on the key genes of the TCA cycle was built, and its predictive performance was evaluated. The association of the model with immune infiltration and chemoresistance were analyzed using the TIMER database, the R package “oncoPredict” score, TIDE score and so on. Finally, the role of the key gene CTTN was validated through gene knockdown and functional assays.Results: A total of 38 clusters of 8 cell types were identified using the single-cell sequencing data. The cells were divided into two groups according to the TCA cycle score, and 617 genes were identified that were most likely to influence the TCA cycle. By intersecting 976 key genes of the TCA cycle with the results of WGCNA, 57 genes significantly associated with the TCA cycle were further identified, of which 8 were screened through Cox regression and Lasso regression to construct the risk score model. The risk score was a good predictor of prognosis across subgroups of age, N, M classification and TNM stage. Furthermore, BI-2536, camptothecin and NU7441 were identified as possible drug candidates in the high-risk group. The high-risk score was associated with decreased immune infiltration in ESCC, and the low-risk group had better immunogenicity. In addition, we also evaluated the relationship between risk scores and immunotherapy response rates. Functional assays showed that CTTN may affect the proliferation and invasion of ESCC cells through the EMT pathway.Conclusion: We constructed a predictive model for ESCC based on TCA cycle-associated genes, which achieved good prognostic stratification. The model are likely associated with the regulation of tumor immunity in ESCC

MINER: Improving Out-of-Vocabulary Named Entity Recognition from an Information Theoretic Perspective

NER model has achieved promising performance on standard NER benchmarks. However, recent studies show that previous approaches may over-rely on entity mention information, resulting in poor performance on out-of-vocabulary (OOV) entity recognition. In this work, we propose MINER, a novel NER learning framework, to remedy this issue from an information-theoretic perspective. The proposed approach contains two mutual information-based training objectives: i) generalizing information maximization, which enhances representation via deep understanding of context and entity surface forms; ii) superfluous information minimization, which discourages representation from rote memorizing entity names or exploiting biased cues in data. Experiments on various settings and datasets demonstrate that it achieves better performance in predicting OOV entities

CBLab: Supporting the Training of Large-scale Traffic Control Policies with Scalable Traffic Simulation

Traffic simulation provides interactive data for the optimization of traffic control policies. However, existing traffic simulators are limited by their lack of scalability and shortage in input data, which prevents them from generating interactive data from traffic simulation in the scenarios of real large-scale city road networks. In this paper, we present \textbf{C}ity \textbf{B}rain \textbf{Lab}, a toolkit for scalable traffic simulation. CBLab consists of three components: CBEngine, CBData, and CBScenario. CBEngine is a highly efficient simulator supporting large-scale traffic simulation. CBData includes a traffic dataset with road network data of 100 cities all around the world. We also develop a pipeline to conduct a one-click transformation from raw road networks to input data of our traffic simulation. Combining CBEngine and CBData allows researchers to run scalable traffic simulations in the road network of real large-scale cities. Based on that, CBScenario implements an interactive environment and a benchmark for two scenarios of traffic control policies respectively, with which traffic control policies adaptable for large-scale urban traffic can be trained and tuned. To the best of our knowledge, CBLab is the first infrastructure supporting traffic control policy optimization in large-scale urban scenarios. CBLab has supported the City Brain Challenge @ KDD CUP 2021. The project is available on GitHub:~\url{https://github.com/CityBrainLab/CityBrainLab.git}.Comment: Accepted by KDD2023 (Applied Data Science Track

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements