<div>Abstract<p>Despite widespread utilization of immunotherapy, treating immune-cold tumors has proved to be a challenge. Here, we report that expression of the immune checkpoint molecule B7-H4 is prevalent among immune-cold triple-negative breast cancers (TNBC), where its expression inversely correlates with that of PD-L1. Glycosylation of B7-H4 interferes with its interaction/ubiquitination by AMFR, resulting in B7-H4 stabilization. B7-H4 expression inhibits doxorubicin-induced cell death through the suppression of eIF2α phosphorylation required for calreticulin exposure vis-à-vis the cancer cells. NGI-1, which inhibits B7-H4 glycosylation causing its ubiquitination and subsequent degradation, improves the immunogenic properties of cancer cells treated with doxorubicin, enhancing their phagocytosis by dendritic cells and their capacity to elicit CD8<sup>+</sup> IFNγ-producing T-cell responses. In preclinical models of TNBC, a triple combination of NGI-1, camsirubicin (a noncardiotoxic doxorubicin analogue) and PD-L1 blockade was effective in reducing tumor growth. Collectively, our findings uncover a strategy for targeting the immunosuppressive molecule B7-H4.</p>Significance:<p>This work unravels the regulation of B7-H4 stability by ubiquitination and glycosylation, which affects tumor immunogenicity, particularly regarding immune-cold breast cancers. The inhibition of B7-H4 glycosylation can be favorably combined with immunogenic chemotherapy and PD-L1 blockade to achieve superior immuno-infiltration of cold tumors, as well as improved tumor growth control.</p><p><i>See related commentary by Pearce and Läubli, p. 1789</i>.</p><p><i>This article is highlighted in the In This Issue feature, p. 1775</i></p></div>
In view of the shortcomings of traditional camera and sensor type monitoring, such as blind spots, limited recognition distance and sensitive scene limitations, this paper proposes a human activity detection and monitoring method based on channel-state-information (CSI). The CSI information of the WiFi signal in the monitored area. Next, use the Butterworth low-pass filter to detect and remove abnormal data. And then use the principal component analysis (PCA) to extract the features of the human body posture, gait information, and number of people model; Learn to build a number recognition model for CSI data; because everyone is different, gait information can be used as an ID for human identification to identify different identities, and the human gait information based on Dynamic Time Warping (DTW) can be Effective identification, so as to play the effect of regional environmental monitoring. In the experiment, this method can achieve 92% capture performance for human gesture recognition, more than 93% error in indoor area recognition is less than 1, and the correct rate of gait recognition is up to 95.2%.
Objective To investigate the risk factor for pancreatic cancer and establish a risk model for Han population. Methods Our population-based case-control study was carried out in Beijing from January 2002 to April 2004. One hundred and ninteen patients with pancreatic cancer and 238 healthy controls completed the questionnaire which was used for the risk factor analysis. Logistic regression analysis was used to calculate odds ratios (ORs), 95% confidence intervals (CIs) and β, which were further used to establish the risk model.Results According to the study, people who have smoked more than 17 pack-years had a higher risk to develop pancreatic cancer compared to non-smokers or light smokers (no more than 17 pack-years) (OR 1.98; 95%CI 1.11～3.49,). More importantly, heavy smokers in men had increased risk for developing pancreatic cancer (OR 2.11; 95%CI 1.18～3.78) than women. Heavy alcohol drinkers (20 cup-years) were found to have increased risk for pancreatic cancer (OR 3.681; 95%CI 1.604～8.443). Daily diet with high meat intake was also linked to pancreatic cancer. About 18.49% of the pancreatic cancer patients had diabetes mellitus compared to the control group of 5.77% (P=0.0003). Typical symptoms of pancreatic cancer were anorexia, upper abdominal pain, bloating, jaundice and weight loss. The high risk score of the two groups were 80.6 (95% CI 74.9～86.3) and 7.4(95% CI 6.0～8.7) (P0.001),respectively. A score above 45 were set as the cut-off value of high risk screening of pancreatic cancer.Conclusion High-dose smoking, high-dose drinking, high meat diet and diabetes were major risk factors for pancreatic cancer in Han population. This high risk model is an easy method for primary screening of pancreatic cancer. It may be helpful for detecting early pancreatic cancers, but further validation is needed.
The virtual prototyping dynamic model of a certain gun recoil system is constructed by using LMS Virtual.Lab. The PRO/E model is introduced and simplified, and then the gun top carriage entity model is set up by adding certain constraints. Then, the backlash and counter-recoil functions are formed by means of FORTRAN and Virtual.Lab user-defined subroutines TSDA. Finally, virtual prototyping is tested by full-charge fire at 0o, the simulated displacement and velocity results arre compared with the experimented data by using the maximum method, the error is limited in the scope of engineering application and the simulated results is useful.
Abstract Background Protein phosphorylation networks play an important role in cell signaling. In these networks, phosphorylation of a protein kinase usually leads to its activation, which in turn will phosphorylate its downstream target proteins. A phosphorylation network is essentially a causal network, which can be learned by causal inference algorithms. Prior efforts have applied such algorithms to data measuring protein phosphorylation levels, assuming that the phosphorylation levels represent protein activity states. However, the phosphorylation status of a kinase does not always reflect its activity state, because interventions such as inhibitors or mutations can directly affect its activity state without changing its phosphorylation status. Thus, when cellular systems are subjected to extensive perturbations, the statistical relationships between phosphorylation states of proteins may be disrupted, making it difficult to reconstruct the true protein phosphorylation network. Here, we describe a novel framework to address this challenge. Results We have developed a causal discovery framework that explicitly represents the activity state of each protein kinase as an unmeasured variable and developed a novel algorithm called “InferA” to infer the protein activity states, which allows us to incorporate the protein phosphorylation level, pharmacological interventions and prior knowledge. We applied our framework to simulated datasets and to a real-world dataset. The simulation experiments demonstrated that explicit representation of activity states of protein kinases allows one to effectively represent the impact of interventions and thus enabled our framework to accurately recover the ground-truth causal network. Results from the real-world dataset showed that the explicit representation of protein activity states allowed an effective and data-driven integration of the prior knowledge by InferA, which further leads to the recovery of a phosphorylation network that is more consistent with experiment results. Conclusions Explicit representation of the protein activity states by our novel framework significantly enhances causal discovery of protein phosphorylation networks.
ABSTRACT Lu, G. and Liu, D., 2019. Design of obstacle avoidance algorithm for submarine intelligent robot. In: Hoang, A.T. and Aqeel Ashraf, M. (eds.), Research, Monitoring, and Engineering of Coas...
Abstract Tumor microenvironment (TME) plays an important role in determining the oncogenic behavior of tumors and their response to immune therapies. Profiling the cellular states of cells within a TME and further studying their interactions would shed light on complex mechanisms that define different TMEs and allow us to understand different immune evasion mechanisms. Currently, only a small fraction of head and neck tumors respond to contemporary immunotherapy, indicating that distinct immune environments (thereby distinct immune evasion mechanisms) exist among tumors. Thus, investigating different immune environments is of significant clinical importance. We developed a novel computational framework to study the cellular states of single cells and interactions among them. We have collected single-cell RNA sequence (scRNAseq) data of over 100,000 cells from 18 head and neck squamous carcinoma (HNSC) tumors, including tumor cells, stromal cells, and immune-related (CD45+) cells. Based on the assumption that the cellular state of a single cell can be represented by the activation status of biologic processes in the cell, which in turn can be represented by the expression status of gene modules regulated by the processes, we applied the nested hierarchical Dirichlet processes (NHDP) model to identify the gene modules potentially regulated by different biologic processes. Gene modules identified by NHDP model are shown to reflect activation status of biologic processes, such as activation or exhaustion of T cell, which enabled us to group cells according to cellular states. Discovery of gene modules from single cells enabled us to deconvolute bulk transcriptomic data from TCGA to infer the presence and abundance of cells with different cellular states. Based on such information, we discovered 4 subtypes of immune environments, and patients belonging to different subtypes exhibited significantly different survival outcomes. Furthermore, we applied NHDP to tumor and stromal cells and discovered gene modules expressed in nonimmune cells. The expression status of these gene modules is highly predictive of the expression status of the gene modules from immune cells in TCGA HNSC tumors. That is, this approach enables to study the interactions among different types of cells in a TME. Finally, based on express status of gene modules from tumor and stromal cells, we can accurately predict the immune subtype of a tumor. In summary, we developed a novel computational framework to investigate distinct immune environments, and such information can potentially be used to predict tumor response to contemporary immune therapies and develop novel immune therapy targets. Citation Format: Xueer Chen, Lujia Chen, Cornelius Kurten, Lazar Vujanovic, Aditi Kulkarni, Robert Ferris, Xinghua Lu. Discovering distinct immune environments defined by the states of single cells within tumor microenvironment [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Optimizing Survival and Quality of Life through Basic, Clinical, and Translational Research; 2019 Apr 29-30; Austin, TX. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(12_Suppl_2):Abstract nr B33.
Most 'transcriptomic' data from microarrays are generated from small sample sizes compared to the large number of measured biomarkers, making it very difficult to build accurate and generalizable disease state classification models. Integrating information from different, but related, 'transcriptomic' data may help build better classification models. However, most proposed methods for integrative analysis of 'transcriptomic' data cannot incorporate domain knowledge, which can improve model performance. To this end, we have developed a methodology that leverages transfer rule learning and functional modules, which we call TRL-FM, to capture and abstract domain knowledge in the form of classification rules to facilitate integrative modeling of multiple gene expression data. TRL-FM is an extension of the transfer rule learner (TRL) that we developed previously. The goal of this study was to test our hypothesis that "an integrative model obtained via the TRL-FM approach outperforms traditional models based on single gene expression data sources". To evaluate the feasibility of the TRL-FM framework, we compared the area under the ROC curve (AUC) of models developed with TRL-FM and other traditional methods, using 21 microarray datasets generated from three studies on brain cancer, prostate cancer, and lung disease, respectively. The results show that TRL-FM statistically significantly outperforms TRL as well as traditional models based on single source data. In addition, TRL-FM performed better than other integrative models driven by meta-analysis and cross-platform data merging. The capability of utilizing transferred abstract knowledge derived from source data using feature mapping enables the TRL-FM framework to mimic the human process of learning and adaptation when performing related tasks. The novel TRL-FM methodology for integrative modeling for multiple 'transcriptomic' datasets is able to intelligently incorporate domain knowledge that traditional methods might disregard, to boost predictive power and generalization performance. In this study, TRL-FM's abstraction of knowledge is achieved in the form of functional modules, but the overall framework is generalizable in that different approaches of acquiring abstract knowledge can be integrated into this framework.
Abstract Motivation: The Gene Ontology (GO) is a controlled vocabulary designed to represent the biological concepts pertaining to gene products. This study investigates the methods for identifying informative subsets of GO terms in an automatic and objective fashion. This task in turn requires addressing the following issues: how to represent the semantic context of GO terms, what metrics are suitable for measuring the semantic differences between terms, how to identify an informative subset that retains as much as possible of the original semantic information of GO. Results: We represented the semantic context of a GO term using the word-usage-profile associated with the term, which enables one to measure the semantic differences between terms based on the differences in their semantic contexts. We further employed the information bottleneck methods to automatically identify subsets of GO terms that retain as much as possible of the semantic information in an annotation database. The automatically retrieved informative subsets align well with an expert-picked GO slim subset, cover important concepts and proteins, and enhance literature-based GO annotation. Availability: http://carcweb.musc.edu/TextminingProjects/ Contact: xinghua@pitt.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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