Meteorological and human mobility data on predicting COVID-19 cases by a novel hybrid decomposition method with anomaly detection analysis: A case study in the capitals of Brazil.

In 2020, Brazil was the leading country in COVID-19 cases in Latin America, and capital cities were the most severely affected by the outbreak. Climates vary in Brazil due to the territorial extension of the country, its relief, geography, and other factors. Since the most common COVID-19 symptoms are related to the respiratory system, many researchers have studied the correlation between the number of COVID-19 cases with meteorological variables like temperature, humidity, rainfall, etc. Also, due to its high transmission rate, some researchers have analyzed the impact of human mobility on the dynamics of COVID-19 transmission. There is a dearth of literature that considers these two variables when predicting the spread of COVID-19 cases. In this paper, we analyzed the correlation between the number of COVID-19 cases and human mobility, and meteorological data in Brazilian capitals. We found that the correlation between such variables depends on the regions where the cities are located. We employed the variables with a significant correlation with COVID-19 cases to predict the number of COVID-19 infections in all Brazilian capitals and proposed a prediction method combining the Ensemble Empirical Mode Decomposition (EEMD) method with the Autoregressive Integrated Moving Average Exogenous inputs (ARIMAX) method, which we called EEMD-ARIMAX. After analyzing the results poor predictions were further investigated using a signal processing-based anomaly detection method. Computational tests showed that EEMD-ARIMAX achieved a forecast 26.73% better than ARIMAX. Moreover, an improvement of 30.69% in the average root mean squared error (RMSE) was noticed when applying the EEMD-ARIMAX method to the data normalized after the anomaly detection.

Community-based network analyses reveal emerging connectivity patterns of protein-protein interactions in murine melanoma secretome.

Protein-protein interaction networks (PPINs) are static representations of protein connections in which topological features such as subgraphs (communities) may contain proteins functionally related, revealing an additional layer of interactome complexity. We created two PPINs from the secretomes of a paired set of murine melanocytes (a normal melanocyte and its transformed phenotype). Community structures, identified by a graph clustering algorithm, resulted in the identification of subgraphs in both networks. Interestingly, the underlying structure of such communities revealed shared and exclusive proteins (core and exclusive nodes, respectively), in addition to proteins that changed their location within each community (rewired nodes). Functional enrichment analysis of core nodes revealed conserved biological functions in both networks whereas exclusive and rewired nodes in the tumoral phenotype network were enriched in cancer-related processes, including TGFß signaling. We found a remarkable shift in the tumoral interactome, resulting in an emerging pattern which was driven by the presence of exclusive nodes and may represent functional network motifs. Our findings suggest that the rearrangement in the tumoral interactome may be correlated with the malignant transformation of melanocytes associated with substrate adhesion impediment. The interactions found in core and new/rewired nodes might potentially be targeted for therapeutic intervention in melanoma treatment. SIGNIFICANCE: Malignant transformation is a result of synergistic action of multiple molecular factors in which genetic alterations as well as protein expression play paramount roles. During oncogenesis, cellular crosstalk through the secretion of soluble mediators modulates the phenotype of transformed cells which ultimately enables them to successfully disrupt important signaling pathways, including those related to cell growth and proliferation. Therefore, in this work we profiled the secretomes of a paired set of normal and transformed phenotypes of a murine melanocyte. After assembling the two interactomes, clusters of functionally related proteins (network communities) were observed as well as emerging patterns of network rewiring which may represent an interactome signature of transformed cells. In summary, the significance of this study relies on the understanding of the repertoire of 'normal' and 'tumoral' secretomes and, more importantly, the set of interacting proteins (the interactome) in both of these conditions, which may reveal key components that might be potentially targeted for therapeutic intervention.

Community-based network analyses reveal emerging connectivity patterns of protein-protein interactions in murine melanoma secretome

Protein-protein interaction networks (PPINs) are static representations of protein connections in which topological features such as subgraphs (communities) may contain proteins functionally related, revealing an additional layer of interactome complexity. We created two PPINs from the secretomes of a paired set of murine melanocytes (a normal melanocyte and its transformed phenotype). Community structures, identified by a graph clustering algorithm, resulted in the identification of subgraphs in both networks. Interestingly, the underlying structure of such communities revealed shared and exclusive proteins (core and exclusive nodes, respectively), in addition to proteins that changed their location within each community (rewired nodes). Functional enrichment analysis of core nodes revealed conserved biological functions in both networks whereas exclusive and rewired nodes in the tumoral phenotype network were enriched in cancer-related processes, including TGFβ signaling. We found a remarkable shift in the tumoral interactome, resulting in an emerging pattern which was driven by the presence of exclusive nodes and may represent functional network motifs. Our findings suggest that the rearrangement in the tumoral interactome may be correlated with the malignant transformation of melanocytes associated with substrate adhesion impediment. The interactions found in core and new/rewired nodes might potentially be targeted for therapeutic intervention in melanoma treatment. Significance: Malignant transformation is a result of synergistic action of multiple molecular factors in which genetic alterations as well as protein expression play paramount roles. During oncogenesis, cellular crosstalk through the secretion of soluble mediators modulates the phenotype of transformed cells which ultimately enables them to successfully disrupt important signaling pathways, including those related to cell growth and proliferation. Therefore, in this work we profiled the secretomes of a paired set of normal and transformed phenotypes of a murine melanocyte. After assembling the two interactomes, clusters of functionally related proteins (network communities) were observed as well as emerging patterns of network rewiring which may represent an interactome signature of transformed cells. In summary, the significance of this study relies on the understanding of the repertoire of ‘normal’ and ‘tumoral’ secretomes and, more importantly, the set of interacting proteins (the interactome) in both of these conditions, which may reveal key components that might be potentially targeted for therapeutic intervention.