Due to the continued evolution of the SARS-CoV-2 pandemic, researchers worldwide are working to mitigate, suppress its spread, and better understand it by deploying digital signal processing (DSP) and machine learning approaches. This study presents an alignment-free approach to classify the SARS-CoV-2 using complementary DNA, which is DNA synthesized from the single-stranded RNA virus. Herein, a total of 1582 samples, with different lengths of genome sequences from different regions, were collected from various data sources and divided into a SARS-CoV-2 and a non-SARS-CoV-2 group. We extracted eight biomarkers based on three-base periodicity, using DSP techniques, and ranked those based on a filter-based feature selection. The ranked biomarkers were fed into k-nearest neighbor, support vector machines, decision trees, and random forest classifiers for the classification of SARS-CoV-2 from other coronaviruses. The training dataset was used to test the performance of the classifiers based on accuracy and F-measure via 10-fold cross-validation. Kappa-scores were estimated to check the influence of unbalanced data. Further, 10 × 10 cross-validation paired t-test was utilized to test the best model with unseen data. Random forest was elected as the best model, differentiating the SARS-CoV-2 coronavirus from other coronaviruses and a control a group with an accuracy of 97.4 %, sensitivity of 96.2 %, and specificity of 98.2 %, when tested with unseen samples. Moreover, the proposed algorithm was computationally efficient, taking only 0.31 s to compute the genome biomarkers, outperforming previous studies.
In Amazonian tropical forests, recent studies have reported increases in aboveground biomass and in primary productivity, as well as shifts in plant species composition favouring fast-growing species over slow-growing ones. This pervasive alteration of mature tropical forests was attributed to global environmental change, such as an increase in atmospheric CO2 concentration, nutrient deposition, temperature, drought frequency, and/or irradiance. We used standardized, repeated measurements of over 2 million trees in ten large (16-52 ha each) forest plots on three continents to evaluate the generality of these findings across tropical forests. Aboveground biomass increased at seven of our ten plots, significantly so at four plots, and showed a large decrease at a single plot. Carbon accumulation pooled across sites was significant (+0.24 MgC ha(-1) y(-1), 95% confidence intervals [0.07, 0.39] MgC ha(-1) y(-1)), but lower than reported previously for Amazonia. At three sites for which we had data for multiple census intervals, we found no concerted increase in biomass gain, in conflict with the increased productivity hypothesis. Over all ten plots, the fastest-growing quartile of species gained biomass (+0.33 [0.09, 0.55] % y(-1)) compared with the tree community as a whole (+0.15 % y(-1)); however, this significant trend was due to a single plot. Biomass of slow-growing species increased significantly when calculated over all plots (+0.21 [0.02, 0.37] % y(-1)), and in half of our plots when calculated individually. Our results do not support the hypothesis that fast-growing species are consistently increasing in dominance in tropical tree communities. Instead, they suggest that our plots may be simultaneously recovering from past disturbances and affected by changes in resource availability. More long-term studies are necessary to clarify the contribution of global change to the functioning of tropical forests.