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Item type:Publication, Guidelines for correlation coefficient threshold settings in metabolite correlation networks exemplified on a potato association panel(BioMed Central Ltd, 2021-12-01)Correlation network analysis has become an integral tool to study metabolite datasets. Networks are constructed by omitting correlations between metabolites based on two thresholds—namely the r and the associated p-values. While p-value threshold settings follow the rules of multiple hypotheses testing correction, guidelines for r-value threshold settings have not been defined. Results: Here, we introduce a method that allows determining the r-value threshold based on an iterative approach, where different networks are constructed and their network topology is monitored. Once the network topology changes significantly, the threshold is set to the corresponding correlation coefficient value. The approach was exemplified on: (i) a metabolite and morphological trait dataset from a potato association panel, which was grown under normal irrigation and water recovery conditions; and validated (ii) on a metabolite dataset of hearts of fed and fasted mice. For the potato normal irrigation correlation network a threshold of Pearson’s |r|≥ 0.23 was suggested, while for the water recovery correlation network a threshold of Pearson’s |r|≥ 0.41 was estimated. For both mice networks the threshold was calculated with Pearson’s |r|≥ 0.84. Conclusions: Our analysis corrected the previously stated Pearson’s correlation coefficient threshold from 0.4 to 0.41 in the water recovery network and from 0.4 to 0.23 for the normal irrigation network. Furthermore, the proposed method suggested a correlation threshold of 0.84 for both mice networks rather than a threshold of 0.7 as applied earlier. We demonstrate that the proposed approach is a valuable tool for constructing biological meaningful networks. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Applying grey clustering method and Pearson correlation to assess water quality(IJETAE Publication House, 2022-08-01)The Mantaro River travels through Junín, Ayacucho, and Huancavelica departments,in Peru, and receives not only domestic discharges from the population but also tailings fields and dumps containing lead, silver, copper, and zinc from mining companies. To be able to assess the water quality, the grey clustering method was applied, using Center-point Triangular Whitenization Weight Functions (CTWF) for this purpose. As well, Pearson correlation between parameters was used to understand the dynamics of pollution. In the watershed of the Mantaro river the results of the Monitoring of the Surface Water published in 2018 by the Mantaro Water Administrative Authority were compared to the Peruvian Environmental Quality Standards (ECA) Category 1, subcategory A parameters. The results obtained suggest that the main driver of pollution in the Mantaro river is not mining, but domestic waste in landfills and probably agriculture. Finally, this study shows responsible environmental management by mining companies, besides, could be helpful for regional and local authorities of Peru in making decisions to improve the management of the Mantaro river watershed and make the population aware of the sustainable use of water. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Assessing the Impact of Immigration on Peruvian Society: A Quantitative Approach to the Analysis of Economical, Social, Educational and Governmental Indicators (2000–2022)(Multidisciplinary Digital Publishing Institute (MDPI), 2026-07-10)In this study, we conduct an exploratory and econometric assessment of the multidimensional impact of recent immigration, predominantly Venezuelan, on Peru (2000–2022). Utilizing 4565 time-series indicators, we apply a two-stage methodology: Pearson correlations to identify baseline exploratory associations, followed by Ordinary Least Squares (OLS) with temporal controls and First Differences models to isolate genuine structural effects from time-trend artifacts and spurious correlations. Econometric validation refines oversimplified public narratives. Socially, immigration robustly correlates with increased food insecurity, localized detainments, and a reduced youth demographic, while aggregate crime complaints are identified as a time-trend artifact. Economically, migration structurally stimulates income for the poorest 40% and broadens financial inclusion, despite negatively impacting aggregate macroeconomic consumption. Educationally, the influx of skilled migrants robustly drives scientific publications and increases average formal education years, though it introduces challenges like delayed primary school attendance and a negative shift in educational gender parity. Finally, perceived impacts on central government expenditures and consumption tax revenues are econometrically isolated as either time-trend artifacts or spurious correlations rather than direct migratory consequences. This approach separates true structural responses from historical inertia, providing a balanced, quantitative perspective on migration in Peru.