Detecting Weak Signals of the Future: A System Implementation Based on Text Mining and Natural Language Processing

[EN] Organizations, companies and start-ups need to cope with constant changes on the market which are difficult to predict. Therefore, the development of new systems to detect significant future changes is vital to make correct decisions in an organization and to discover new opportunities. A system based on business intelligence techniques is proposed to detect weak signals, that are related to future transcendental changes. While most known solutions are based on the use of structured data, the proposed system quantitatively detects these signals using heterogeneous and unstructured information from scientific, journalistic and social sources, applying text mining to analyze the documents and natural language processing to extract accurate results. The main contributions are that the system has been designed for any field, using different input datasets of documents, and with an automatic classification of categories for the detected keywords. In this research paper, results from the future of remote sensors are presented. Remote sensing services are providing new applications in observation and analysis of information remotely. This market is projected to witness a significant growth due to the increasing demand for services in commercial and defense industries. The system has obtained promising results, evaluated with two different methodologies, to help experts in the decision-making process and to discover new trends and opportunities.This research is partially supported by EIT Climate-KIC of the European Institute of Technology (project EIT Climate-KIC Accelerator-TC_3.1.5_190607_P066-1A) and InnoCENS from Erasmus + (573965-EPP-1-2016-1-SE-EPPKA2-CBHE-JP).Griol-Barres, I.; Milla, S.; Cebrián Ferriols, AJ.; Fan, H.; Millet Roig, J. (2020). Detecting Weak Signals of the Future: A System Implementation Based on Text Mining and Natural Language Processing. 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Temporal and Spatial Variations in the Leaf Area Index and Its Response to Topography in the Three-River Source Region, China from 2000 to 2017

The Three-River Source Region (TRSR) is an important area for the ecological security of China. Vegetation growth has been affected by the climate change, topography, and human activities in this area. However, few studies have focused on analyzing time series tendencies of vegetation change in various terrain conditions. To address this issue in the TRSR, this study explored vegetation stability, tendency, and sustainability with multiple methods (e.g., coefficient of variation, Theil-Sen median trend analysis, Mann-Kendall test, and Hurst index) based on the 2000–2017 Global LAnd Surface Satellite Leaf Area Index (GLASS LAI) product. The differentiation patterns of LAI variations and multiyear mean LAI value under different topographic factors were also investigated in combination with digital elevation model (DEM). The results showed that (1) the mean LAI value in the study area increased, with a linear tendency of 0.013·10 a−1; (2) LAI values decreased from southeast to northwest in terms of spatial distribution and the CV indicated LAI variations were relatively stable; (3) the trend analysis revealed that the improved area of LAI accounted for 62.72% which was larger than the degraded area (37.28%), and hurst index revealed a weak anti-sustaining effect of the current tendencies; and (4) the increasing trend was found in multiyear mean LAI value as relief amplitude and slope increased, while LAI stability improved with increasing slope. They exhibited a clear regular pattern. Moreover, significant improvement in LAI generally occurred in low-altitude and flat areas. Finally, the overall improvement and sustainability of LAI improved when moving from sunny aspects to shady aspects, but the LAI stability decreased. Note that vegetation degradation was observed in some high slope areas and was further aggravated. This study is beneficial for revealing the spatial and temporal changes of LAI and their changing rules as a function of different topographic factors in the TRSR. Meanwhile, the results of this study provide theoretical support for sustainable development of this area

Temporal and Spatial Variations in the Leaf Area Index and Its Response to Topography in the Three-River Source Region, China from 2000 to 2017

The Three-River Source Region (TRSR) is an important area for the ecological security of China. Vegetation growth has been affected by the climate change, topography, and human activities in this area. However, few studies have focused on analyzing time series tendencies of vegetation change in various terrain conditions. To address this issue in the TRSR, this study explored vegetation stability, tendency, and sustainability with multiple methods (e.g., coefficient of variation, Theil-Sen median trend analysis, Mann-Kendall test, and Hurst index) based on the 2000–2017 Global LAnd Surface Satellite Leaf Area Index (GLASS LAI) product. The differentiation patterns of LAI variations and multiyear mean LAI value under different topographic factors were also investigated in combination with digital elevation model (DEM). The results showed that (1) the mean LAI value in the study area increased, with a linear tendency of 0.013·10 a−1; (2) LAI values decreased from southeast to northwest in terms of spatial distribution and the CV indicated LAI variations were relatively stable; (3) the trend analysis revealed that the improved area of LAI accounted for 62.72% which was larger than the degraded area (37.28%), and hurst index revealed a weak anti-sustaining effect of the current tendencies; and (4) the increasing trend was found in multiyear mean LAI value as relief amplitude and slope increased, while LAI stability improved with increasing slope. They exhibited a clear regular pattern. Moreover, significant improvement in LAI generally occurred in low-altitude and flat areas. Finally, the overall improvement and sustainability of LAI improved when moving from sunny aspects to shady aspects, but the LAI stability decreased. Note that vegetation degradation was observed in some high slope areas and was further aggravated. This study is beneficial for revealing the spatial and temporal changes of LAI and their changing rules as a function of different topographic factors in the TRSR. Meanwhile, the results of this study provide theoretical support for sustainable development of this area

Comprehensive Assessment of Performances of Long Time-Series LAI, FVC and GPP Products over Mountainous Areas: A Case Study in the Three-River Source Region, China

Vegetation biophysical products offer unique opportunities to examine long-term vegetation dynamics and land surface phenology (LSP). It is important to understand the time-series performances of various global biophysical products for global change research. However, few endeavors have been dedicated to assessing the performances of long-term change characteristics or LSP extraction derived from different satellite products, especially in mountainous areas with highly fragmented and rugged surfaces. In this paper, we assessed the time-series characteristics and LSP detections of Global LAnd Surface Satellite (GLASS) leaf area index (LAI), fractional vegetation cover (FVC), and gross primary production (GPP) products across the Three-River Source Region (TRSR). The performances of products’ temporal agreements and their statistical relationship as a function of topographic indices and heterogeneous pixels, respectively, were investigated through intercomparison among three products during the period 2000 to 2018. The results show that the phenological differences between FVC and two other products are beyond 10 days over more than 35% of the pixels in TRSR. The long-term trend of FVC diverges significantly from GPP and LAI for 13.96% of the total pixels, and the percentages of mismatched pixels between FVC and two other products are 33.24% in the correlation comparison. Moreover, good agreements are observed between GPP and LAI, both in terms of LSP and interannual variations. Finally, the LSP and long-term dynamics of the three products exhibit poor performances on heterogeneous surfaces and complex topographic areas, which reflects the potential impacts of environmental factors and algorithmic imperfections on the quality and performances of different products. Our study highlights the spatiotemporal disparities in detections of surface vegetation activity in mountainous areas by using different biophysical products. Future global change studies may require multiple high-quality satellite products with long-term stability as data support

Table2_Identification and validation of ferroptosis-related lncRNA signatures as a novel prognostic model for glioma.XLSX

Background: Ferroptosis is a newly discovered form of regulated cell death with distinct properties and recognizing functions involved in physical conditions or various diseases, including cancers. However, the relationship between gliomas and ferroptosis-related lncRNAs (FRLs) remains unclear.Methods: We collected a total of 1850 samples from The Cancer Genome Atlas (TCGA) and Genotype Tissue Expression (GTEX) databases, including 698 tumor and 1,152 normal samples. A list of ferroptosis-related genes was downloaded from the Ferrdb website. Differentially expressed FRLs (DEFRLS) were analyzed using the “limma” package in R software. Subsequently, prognosis-related FRLs were obtained by univariate Cox analysis. Finally, a prognostic model based on the 3 FRLs was constructed using Cox regression analysis with the least absolute shrinkage and selection operator (LASSO) algorithm. The prognostic power of the model was assessed using receiver operating characteristic (ROC) curve analysis and Kaplan-Meier (K-M) survival curve analysis. In addition, we further explored the relationship of the immune landscape and somatic mutations to prognostic model characteristics. Finally, we validated the function of LINC01426 in vitro.Results: We successfully constructed a 3-FRLs signature and classified glioma patients into high-risk and low-risk groups based on the risk score calculated from this signature. Compared with traditional clinicopathological features [age, sex, grade, isocitrate dehydrogenase (IDH) status], the prognostic accuracy of this model is more stable and stronger. Additionally, the model had stable predictive power for overall survival over a 5-year period. In addition, we found significant differences between the two groups in cellular immunity, the numbers of many immune cells, including NK cells, CD4+, CD8+ T-cells, and macrophages, and the expression of many immune-related genes. Finally, the two groups were also significantly different at the level of somatic mutations, especially in glioma prognosis-related genes such as IDH1 and ATRX, with lower mutation rates in the high-risk group leading to poorer prognosis. Finally, we found that the ferroptosis process of glioma cells was inhibited after knocking down the expression of LINC01426.Conclusion: The proposed 3-FRL signature is a promising biomarker for predicting prognostic features in glioma patients.</p

Table4_Identification and validation of ferroptosis-related lncRNA signatures as a novel prognostic model for glioma.XLSX

Background: Ferroptosis is a newly discovered form of regulated cell death with distinct properties and recognizing functions involved in physical conditions or various diseases, including cancers. However, the relationship between gliomas and ferroptosis-related lncRNAs (FRLs) remains unclear.Methods: We collected a total of 1850 samples from The Cancer Genome Atlas (TCGA) and Genotype Tissue Expression (GTEX) databases, including 698 tumor and 1,152 normal samples. A list of ferroptosis-related genes was downloaded from the Ferrdb website. Differentially expressed FRLs (DEFRLS) were analyzed using the “limma” package in R software. Subsequently, prognosis-related FRLs were obtained by univariate Cox analysis. Finally, a prognostic model based on the 3 FRLs was constructed using Cox regression analysis with the least absolute shrinkage and selection operator (LASSO) algorithm. The prognostic power of the model was assessed using receiver operating characteristic (ROC) curve analysis and Kaplan-Meier (K-M) survival curve analysis. In addition, we further explored the relationship of the immune landscape and somatic mutations to prognostic model characteristics. Finally, we validated the function of LINC01426 in vitro.Results: We successfully constructed a 3-FRLs signature and classified glioma patients into high-risk and low-risk groups based on the risk score calculated from this signature. Compared with traditional clinicopathological features [age, sex, grade, isocitrate dehydrogenase (IDH) status], the prognostic accuracy of this model is more stable and stronger. Additionally, the model had stable predictive power for overall survival over a 5-year period. In addition, we found significant differences between the two groups in cellular immunity, the numbers of many immune cells, including NK cells, CD4+, CD8+ T-cells, and macrophages, and the expression of many immune-related genes. Finally, the two groups were also significantly different at the level of somatic mutations, especially in glioma prognosis-related genes such as IDH1 and ATRX, with lower mutation rates in the high-risk group leading to poorer prognosis. Finally, we found that the ferroptosis process of glioma cells was inhibited after knocking down the expression of LINC01426.Conclusion: The proposed 3-FRL signature is a promising biomarker for predicting prognostic features in glioma patients.</p

Table3_Identification and validation of ferroptosis-related lncRNA signatures as a novel prognostic model for glioma.DOC

Background: Ferroptosis is a newly discovered form of regulated cell death with distinct properties and recognizing functions involved in physical conditions or various diseases, including cancers. However, the relationship between gliomas and ferroptosis-related lncRNAs (FRLs) remains unclear.Methods: We collected a total of 1850 samples from The Cancer Genome Atlas (TCGA) and Genotype Tissue Expression (GTEX) databases, including 698 tumor and 1,152 normal samples. A list of ferroptosis-related genes was downloaded from the Ferrdb website. Differentially expressed FRLs (DEFRLS) were analyzed using the “limma” package in R software. Subsequently, prognosis-related FRLs were obtained by univariate Cox analysis. Finally, a prognostic model based on the 3 FRLs was constructed using Cox regression analysis with the least absolute shrinkage and selection operator (LASSO) algorithm. The prognostic power of the model was assessed using receiver operating characteristic (ROC) curve analysis and Kaplan-Meier (K-M) survival curve analysis. In addition, we further explored the relationship of the immune landscape and somatic mutations to prognostic model characteristics. Finally, we validated the function of LINC01426 in vitro.Results: We successfully constructed a 3-FRLs signature and classified glioma patients into high-risk and low-risk groups based on the risk score calculated from this signature. Compared with traditional clinicopathological features [age, sex, grade, isocitrate dehydrogenase (IDH) status], the prognostic accuracy of this model is more stable and stronger. Additionally, the model had stable predictive power for overall survival over a 5-year period. In addition, we found significant differences between the two groups in cellular immunity, the numbers of many immune cells, including NK cells, CD4+, CD8+ T-cells, and macrophages, and the expression of many immune-related genes. Finally, the two groups were also significantly different at the level of somatic mutations, especially in glioma prognosis-related genes such as IDH1 and ATRX, with lower mutation rates in the high-risk group leading to poorer prognosis. Finally, we found that the ferroptosis process of glioma cells was inhibited after knocking down the expression of LINC01426.Conclusion: The proposed 3-FRL signature is a promising biomarker for predicting prognostic features in glioma patients.</p

Table1_Identification and validation of ferroptosis-related lncRNA signatures as a novel prognostic model for glioma.XLSX

Background: Ferroptosis is a newly discovered form of regulated cell death with distinct properties and recognizing functions involved in physical conditions or various diseases, including cancers. However, the relationship between gliomas and ferroptosis-related lncRNAs (FRLs) remains unclear.Methods: We collected a total of 1850 samples from The Cancer Genome Atlas (TCGA) and Genotype Tissue Expression (GTEX) databases, including 698 tumor and 1,152 normal samples. A list of ferroptosis-related genes was downloaded from the Ferrdb website. Differentially expressed FRLs (DEFRLS) were analyzed using the “limma” package in R software. Subsequently, prognosis-related FRLs were obtained by univariate Cox analysis. Finally, a prognostic model based on the 3 FRLs was constructed using Cox regression analysis with the least absolute shrinkage and selection operator (LASSO) algorithm. The prognostic power of the model was assessed using receiver operating characteristic (ROC) curve analysis and Kaplan-Meier (K-M) survival curve analysis. In addition, we further explored the relationship of the immune landscape and somatic mutations to prognostic model characteristics. Finally, we validated the function of LINC01426 in vitro.Results: We successfully constructed a 3-FRLs signature and classified glioma patients into high-risk and low-risk groups based on the risk score calculated from this signature. Compared with traditional clinicopathological features [age, sex, grade, isocitrate dehydrogenase (IDH) status], the prognostic accuracy of this model is more stable and stronger. Additionally, the model had stable predictive power for overall survival over a 5-year period. In addition, we found significant differences between the two groups in cellular immunity, the numbers of many immune cells, including NK cells, CD4+, CD8+ T-cells, and macrophages, and the expression of many immune-related genes. Finally, the two groups were also significantly different at the level of somatic mutations, especially in glioma prognosis-related genes such as IDH1 and ATRX, with lower mutation rates in the high-risk group leading to poorer prognosis. Finally, we found that the ferroptosis process of glioma cells was inhibited after knocking down the expression of LINC01426.Conclusion: The proposed 3-FRL signature is a promising biomarker for predicting prognostic features in glioma patients.</p

DataSheet1_Identification and validation of ferroptosis-related lncRNA signatures as a novel prognostic model for glioma.CSV

Background: Ferroptosis is a newly discovered form of regulated cell death with distinct properties and recognizing functions involved in physical conditions or various diseases, including cancers. However, the relationship between gliomas and ferroptosis-related lncRNAs (FRLs) remains unclear.Methods: We collected a total of 1850 samples from The Cancer Genome Atlas (TCGA) and Genotype Tissue Expression (GTEX) databases, including 698 tumor and 1,152 normal samples. A list of ferroptosis-related genes was downloaded from the Ferrdb website. Differentially expressed FRLs (DEFRLS) were analyzed using the “limma” package in R software. Subsequently, prognosis-related FRLs were obtained by univariate Cox analysis. Finally, a prognostic model based on the 3 FRLs was constructed using Cox regression analysis with the least absolute shrinkage and selection operator (LASSO) algorithm. The prognostic power of the model was assessed using receiver operating characteristic (ROC) curve analysis and Kaplan-Meier (K-M) survival curve analysis. In addition, we further explored the relationship of the immune landscape and somatic mutations to prognostic model characteristics. Finally, we validated the function of LINC01426 in vitro.Results: We successfully constructed a 3-FRLs signature and classified glioma patients into high-risk and low-risk groups based on the risk score calculated from this signature. Compared with traditional clinicopathological features [age, sex, grade, isocitrate dehydrogenase (IDH) status], the prognostic accuracy of this model is more stable and stronger. Additionally, the model had stable predictive power for overall survival over a 5-year period. In addition, we found significant differences between the two groups in cellular immunity, the numbers of many immune cells, including NK cells, CD4+, CD8+ T-cells, and macrophages, and the expression of many immune-related genes. Finally, the two groups were also significantly different at the level of somatic mutations, especially in glioma prognosis-related genes such as IDH1 and ATRX, with lower mutation rates in the high-risk group leading to poorer prognosis. Finally, we found that the ferroptosis process of glioma cells was inhibited after knocking down the expression of LINC01426.Conclusion: The proposed 3-FRL signature is a promising biomarker for predicting prognostic features in glioma patients.</p