361 research outputs found

    DataSheet_1_An exosome-derived lncRNA signature identified by machine learning associated with prognosis and biomarkers for immunotherapy in ovarian cancer.pdf

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    BackgroundOvarian cancer (OC) has the highest mortality rate among gynecological malignancies. Current treatment options are limited and ineffective, prompting the discovery of reliable biomarkers. Exosome lncRNAs, carrying genetic information, are promising new markers. Previous studies only focused on exosome-related genes and employed the Lasso algorithm to construct prediction models, which are not robust.Methods420 OC patients from the TCGA datasets were divided into training and validation datasets. The GSE102037 dataset was used for external validation. LncRNAs associated with exosome-related genes were selected using Pearson analysis. Univariate COX regression analysis was used to filter prognosis-related lncRNAs. The overlapping lncRNAs were identified as candidate lncRNAs for machine learning. Based on 10 machine learning algorithms and 117 algorithm combinations, the optimal predictor combinations were selected according to the C index. The exosome-related LncRNA Signature (ERLS) model was constructed using multivariate COX regression. Based on the median risk score of the training datasets, the patients were divided into high- and low-risk groups. Kaplan-Meier survival analysis, the time-dependent ROC, immune cell infiltration, immunotherapy response, and immune checkpoints were analyzed.Results64 lncRNAs were subjected to a machine-learning process. Based on the stepCox (forward) combined Ridge algorithm, 20 lncRNA were selected to construct the ERLS model. Kaplan-Meier survival analysis showed that the high-risk group had a lower survival rate. The area under the curve (AUC) in predicting OS at 1, 3, and 5 years were 0.758, 0.816, and 0.827 in the entire TCGA cohort. xCell and ssGSEA analysis showed that the low-risk group had higher immune cell infiltration, which may contribute to the activation of cytolytic activity, inflammation promotion, and T-cell co-stimulation pathways. The low-risk group had higher expression levels of PDL1, CTLA4, and higher TMB. The ERLS model can predict response to anti-PD1 and anti-CTLA4 therapy. Patients with low expression of PDL1 or high expression of CTLA4 and low ERLS exhibited significantly better survival prospects, whereas patients with high ERLS and low levels of PDL1 or CTLA4 exhibited the poorest outcomes.ConclusionOur study constructed an ERLS model that can predict prognostic risk and immunotherapy response, optimizing clinical management for OC patients.</p

    DataSheet_2_An exosome-derived lncRNA signature identified by machine learning associated with prognosis and biomarkers for immunotherapy in ovarian cancer.xlsx

    No full text
    BackgroundOvarian cancer (OC) has the highest mortality rate among gynecological malignancies. Current treatment options are limited and ineffective, prompting the discovery of reliable biomarkers. Exosome lncRNAs, carrying genetic information, are promising new markers. Previous studies only focused on exosome-related genes and employed the Lasso algorithm to construct prediction models, which are not robust.Methods420 OC patients from the TCGA datasets were divided into training and validation datasets. The GSE102037 dataset was used for external validation. LncRNAs associated with exosome-related genes were selected using Pearson analysis. Univariate COX regression analysis was used to filter prognosis-related lncRNAs. The overlapping lncRNAs were identified as candidate lncRNAs for machine learning. Based on 10 machine learning algorithms and 117 algorithm combinations, the optimal predictor combinations were selected according to the C index. The exosome-related LncRNA Signature (ERLS) model was constructed using multivariate COX regression. Based on the median risk score of the training datasets, the patients were divided into high- and low-risk groups. Kaplan-Meier survival analysis, the time-dependent ROC, immune cell infiltration, immunotherapy response, and immune checkpoints were analyzed.Results64 lncRNAs were subjected to a machine-learning process. Based on the stepCox (forward) combined Ridge algorithm, 20 lncRNA were selected to construct the ERLS model. Kaplan-Meier survival analysis showed that the high-risk group had a lower survival rate. The area under the curve (AUC) in predicting OS at 1, 3, and 5 years were 0.758, 0.816, and 0.827 in the entire TCGA cohort. xCell and ssGSEA analysis showed that the low-risk group had higher immune cell infiltration, which may contribute to the activation of cytolytic activity, inflammation promotion, and T-cell co-stimulation pathways. The low-risk group had higher expression levels of PDL1, CTLA4, and higher TMB. The ERLS model can predict response to anti-PD1 and anti-CTLA4 therapy. Patients with low expression of PDL1 or high expression of CTLA4 and low ERLS exhibited significantly better survival prospects, whereas patients with high ERLS and low levels of PDL1 or CTLA4 exhibited the poorest outcomes.ConclusionOur study constructed an ERLS model that can predict prognostic risk and immunotherapy response, optimizing clinical management for OC patients.</p

    Estimation of intrinsic parameters in crowdsourcing problems

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    The term "crowdsourcing" was first coined by Jeff Howe in 2006 to refer to the idea of outsourcing a task to the public. More generally, crowdsourcing means employing the services of a large number of individuals, either paid or unpaid, to acquire information or input into a work or project, often via the internet. In contrast to outsourcing, crowdsourcing frequently involves a broader, less-specific population. Many online services such as Netflix and Amazon, which have strong and efficient recommendation systems, are all using crowdsourcing algorithms to utilize the user provided data to understand the quality of their items. As of 2021, crowdsourcing generally entails leveraging the internet to attract and divide work among people in order to get a cumulative output. Nowadays, a wide range of research projects and applications utilize crowdsourcing and enjoy the benefits including low cost, high speed, high quality, and high flexibility. The widely used Wikipedia is the most successful product that is developed by the use of crowdsourcing techniques. In this thesis, one typical crowdsourcing problem is introduced, and the goal of the problem is to find the unknown intrinsic parameters that stand for the quality of different items. By modeling the problem through a random matrix of observations/ratings, several different algorithms are presented, all of which give the estimations of the unknown vector parameters. Finally, comparisons are made under different metrics, and the conclusion is derived.U of I OnlyAuthor requested U of Illinois access only (OA after 2yrs) in Vireo ETD syste

    Deep learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge

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    A key factor for assessing the state of the heart after myocardial infarction (MI) is to measure whether the myocardium segment is viable after reperfusion or revascularization therapy. Delayed enhancement-MRI or DE-MRI, which is performed 10 min after injection of the contrast agent, provides high contrast between viable and nonviable myocardium and is therefore a method of choice to evaluate the extent of MI. To automatically assess myocardial status, the results of the EMIDEC challenge that focused on this task are presented in this paper. The challenge's main objectives were twofold. First, to evaluate if deep learning methods can distinguish between non-infarct and pathological exams, i.e. exams with or without hyperenhanced area. Second, to automatically calculate the extent of myocardial infarction. The publicly available database consists of 150 exams divided into 50 cases without any hyperenhanced area after injection of a contrast agent and 100 cases with myocardial infarction (and then with a hyperenhanced area on DE-MRI), whatever their inclusion in the cardiac emergency department. Along with MRI, clinical characteristics are also provided. The obtained results issued from several works show that the automatic classification of an exam is a reachable task (the best method providing an accuracy of 0.92), and the automatic segmentation of the myocardium is possible. However, the segmentation of the diseased area needs to be improved, mainly due to the small size of these areas and the lack of contrast with the surrounding structures.info:eu-repo/semantics/publishedVersio

    DataSheet_3_An exosome-derived lncRNA signature identified by machine learning associated with prognosis and biomarkers for immunotherapy in ovarian cancer.docx

    No full text
    BackgroundOvarian cancer (OC) has the highest mortality rate among gynecological malignancies. Current treatment options are limited and ineffective, prompting the discovery of reliable biomarkers. Exosome lncRNAs, carrying genetic information, are promising new markers. Previous studies only focused on exosome-related genes and employed the Lasso algorithm to construct prediction models, which are not robust.Methods420 OC patients from the TCGA datasets were divided into training and validation datasets. The GSE102037 dataset was used for external validation. LncRNAs associated with exosome-related genes were selected using Pearson analysis. Univariate COX regression analysis was used to filter prognosis-related lncRNAs. The overlapping lncRNAs were identified as candidate lncRNAs for machine learning. Based on 10 machine learning algorithms and 117 algorithm combinations, the optimal predictor combinations were selected according to the C index. The exosome-related LncRNA Signature (ERLS) model was constructed using multivariate COX regression. Based on the median risk score of the training datasets, the patients were divided into high- and low-risk groups. Kaplan-Meier survival analysis, the time-dependent ROC, immune cell infiltration, immunotherapy response, and immune checkpoints were analyzed.Results64 lncRNAs were subjected to a machine-learning process. Based on the stepCox (forward) combined Ridge algorithm, 20 lncRNA were selected to construct the ERLS model. Kaplan-Meier survival analysis showed that the high-risk group had a lower survival rate. The area under the curve (AUC) in predicting OS at 1, 3, and 5 years were 0.758, 0.816, and 0.827 in the entire TCGA cohort. xCell and ssGSEA analysis showed that the low-risk group had higher immune cell infiltration, which may contribute to the activation of cytolytic activity, inflammation promotion, and T-cell co-stimulation pathways. The low-risk group had higher expression levels of PDL1, CTLA4, and higher TMB. The ERLS model can predict response to anti-PD1 and anti-CTLA4 therapy. Patients with low expression of PDL1 or high expression of CTLA4 and low ERLS exhibited significantly better survival prospects, whereas patients with high ERLS and low levels of PDL1 or CTLA4 exhibited the poorest outcomes.ConclusionOur study constructed an ERLS model that can predict prognostic risk and immunotherapy response, optimizing clinical management for OC patients.</p

    Ultrafast charge migration in ionized iodo-alkyne chain I(CC)nH+

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    We report ultrafast charge migration in ionized iodo-alkyne chain I(CC)nH+ for n = 1, 2, …, 5. The dynamics of electron density become more complicated with the increasing length of the molecular chain. However, the essential properties of charge migration in I(CC)nH+ can be clearly interpreted in terms of the electron flux. By systematic investigations of the dynamics of electron density, hole density, and the electron flux for different molecules, the size dependence of charge migration in I(CC)nH+ is discussed

    Comparing the Level of Carbon Sequestration Capability of Different Soft Landscape in UBC

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    In response to growing concerns about carbon emission and climate change, recent studies have investigated in carbon storage, carbon neutralization and carbon sequestration. In this study, I expanded on this body of work by investigating the carbon sequestration rates of soft landscapes in the University of British Columbia Vancouver campus and compare their carbon sequestration capacity. The significance of carbon sequestration rates in soft landscapes is discussed in the context of urban planning and the role of vegetation in mitigating climate change. LiDAR data and aerial photos are used to estimate above-ground carbon sequestration, and GIS and R are used for data analysis. The research objectives are to compare the attributes of different soft landscapes, estimate their carbon sequestration rates, identify which soft landscapes have the highest carbon sequestration capacity, and discuss the limitations of the study and possible improvements for future research. The proposed methods include data pre-processing, developing a canopy height model, and estimating carbon sequestration capacity for each soft landscape area. The study aims to provide valuable insights for optimizing urban soft landscape services to increase carbon storage in cities, and to explore the potential for incorporating soft landscapes as a sustainable urban infrastructure element for carbon sequestration. Moreover, the findings of this study may inform decisions regarding the implementation of sustainable landscape design practices that can be applied to new and existing urban green spaces, with the goal of maximizing the potential of soft landscapes to provide ecosystem services that benefit human well-being and the environment. Disclaimer: “UBC SEEDS provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Coordinator about the current status of the subject matter of a project/report.”Forestry, Faculty ofForest and Conservation Sciences, Department ofUnreviewedGraduat

    Bridge the Gap Between CV and NLP! An Optimization-based Textual Adversarial Attack Framework

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    Despite recent success on various tasks, deep learning techniques still perform poorly on adversarial examples with small perturbations. While optimization-based methods for adversarial attacks are well-explored in the field of computer vision, it is impractical to directly apply them in natural language processing due to the discrete nature of the text. To address the problem, we propose a unified framework to extend the existing optimization-based adversarial attack methods in the vision domain to craft textual adversarial samples. In this framework, continuously optimized perturbations are added to the embedding layer and amplified in the forward propagation process. Then the final perturbed latent representations are decoded with a masked language model head to obtain potential adversarial samples. In this paper, we instantiate our framework with an attack algorithm named Textual Projected Gradient Descent (T-PGD). We find our algorithm effective even using proxy gradient information. Therefore, we perform the more challenging transfer black-box attack and conduct comprehensive experiments to evaluate our attack algorithm with several models on three benchmark datasets. Experimental results demonstrate that our method achieves an overall better performance and produces more fluent and grammatical adversarial samples compared to strong baseline methods. All the code and data will be made public.Comment: Codes are available at: https://github.com/Phantivia/T-PG

    Assessing 30-Year Land Use and Land Cover Change and the Driving Forces in Qianjiang, China, Using Multitemporal Remote Sensing Images

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    Assessing Land Use and Land Cover Change (LULCC) related with aquaculture areas is vital for evaluating the impacts of aquaculture ponds on the environment and developing a sustainable aquaculture production system. Most studies analyze changes in aquaculture land in coastal areas, and little research focuses on the inland area, where the conversions between agriculture and aquaculture land is primarily driven by socioeconomic factors. This study assessed LULCC related to aquaculture areas in Qianjiang City, China, from 1990 to 2022, using multitemporal Landsat images and a combination of decision tree classifier and visual interpretation. The LULCC was analyzed by the transition matrix. Results showed that the main LULC type was farmland, which accounted for more than 70% of the study area from 1990 to 2022. The built-up and aquaculture land showed an increasing trend year by year. In contrast, there was a gradual decline in forest/grass land from 1990 to 2016, and then its area increased slightly from 2016 to 2022 due to the policy of returning farmland to forest. Water areas were mainly composed of rivers and ponds, with subtle changes during the study period. The main driving forces of LULCC in Qianjiang City were economic and policy factors, with rapid GDP growth and government policies being the dominant factors
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