Time Series Analysis of Landsat Data for Investigating the Relationship between Land Surface Temperature and Forest Changes in Paphos Forest, Cyprus

This study aims to investigate how alternations of the land surface temperature (LST) affects the normalized difference vegetation index (NDVI) in Paphos forest, Cyprus, using Landsat-5 and Landsat-8 imagery for the time periods 1993–2000 and 2013–2018, respectively. A total of 262 Landsat images were processed to compute the mean monthly NDVI and LST values and create a time series. Using the Cook’s distance, the effect of missing values in the analysis of the time series were examined. Results from the cross-correlation and cross-variograms, decomposition model, and the BFAST algorithm were compared to produce reliable conclusions on forest changes and satellite, meteorological, and environmental data were combined to interpret the changes that occurred inside the forest. The decomposition analysis showed a decrease of 2.7% in the LST for the period 1993–2000 and an increase of 4.6% in the LST during the period 2013–2018. The NDVI trend is negatively correlated to the LST trend for both time periods. An increase in the LST trend was identified in November 1998 as well as in the NDVI trend in October 1994 and May 2014 that was caused by favorable climatic conditions. An increase in the NDVI trend from May 2014 to December 2015 may be related to reduced pityocampa attacks. An abrupt decrease was detected in December 2015 that was probably caused by the locust invasion that occurred in the island earlier that year. A positive correlation appears for LST and NDVI variables for time lags 4, 5, 6, 7, and 8 months. Overall, it was shown that LST and NDVI analysis is very promising for identifying potential forest decline

Time Series Analysis of Landsat Data for Investigating the Relationship between Land Surface Temperature and Forest Changes in Paphos Forest, Cyprus

This study aims to investigate how alternations of the land surface temperature (LST) affects the normalized difference vegetation index (NDVI) in Paphos forest, Cyprus, using Landsat‐5 and Landsat‐8 imagery for the time periods 1993–2000 and 2013–2018, respectively. A total of 262 Landsat images were processed to compute the mean monthly NDVI and LST values and create a time series. Using the Cook’s distance, the effect of missing values in the analysis of the time series were examined. Results from the cross‐correlation and cross‐variograms, decomposition model, and the BFAST algorithm were compared to produce reliable conclusions on forest changes and satellite, meteorological, and environmental data were combined to interpret the changes that occurred inside the forest. The decomposition analysis showed a decrease of 2.7% in the LST for the period 1993–2000 and an increase of 4.6% in the LST during the period 2013–2018. The NDVI trend is negatively correlated to the LST trend for both time periods. An increase in the LST trend was identified in November 1998 as well as in the NDVI trend in October 1994 and May 2014 that was caused by favorable climatic conditions. An increase in the NDVI trend from May 2014 to December 2015 may be related to reduced pityocampa attacks. An abrupt decrease was detected in December 2015 that was probably caused by the locust invasion that occurred in the island earlier that year. A positive correlation appears for LST and NDVI variables for time lags 4, 5, 6, 7, and 8 months. Overall, it was shown that LST and NDVI analysis is very promising for identifying potential forest decline

Molecular basis of mood and cognitive adverse events elucidated via a combination of pharmacovigilance data mining and functional enrichment analysis

Drug-induced Mood- and Cognition-related adverse events (MCAEs) are often only detected during the clinical trial phases of drug development, or even after marketing, thus posing a major safety concern and a challenge for both pharmaceutical companies and clinicians. To fill some gaps in the understanding and elucidate potential biological mechanisms of action frequently associated with MCAEs, we present a unique workflow linking observational population data with the available knowledge at molecular, cellular, and psychopharmacology levels. It is based on statistical analysis of pharmacovigilance reports and subsequent signaling pathway analyses, followed by evidence-based expert manual curation of the outcomes. Our analysis: (a) ranked pharmaceuticals with high occurrence of such adverse events (AEs), based on disproportionality analysis of the FDA Adverse Event Reporting System (FAERS) database, and (b) identified 120 associated genes and common pathway nodes possibly underlying MCAEs. Nearly two-thirds of the identified genes were related to immune modulation, which supports the critical involvement of immune cells and their responses in the regulation of the central nervous system function. This finding also means that pharmaceuticals with a negligible central nervous system exposure may induce MCAEs through dysregulation of the peripheral immune system. Knowledge gained through this workflow unravels putative hallmark biological targets and mediators of drug-induced mood and cognitive disorders that need to be further assessed and validated in experimental models. Thereafter, they can be used to substantially improve in silico/in vitro/in vivo tools for predicting these adversities at a preclinical stage