Search Results (3)

Codon usage bias refers to the preferential use of synonymous codons, a widespread phenomenon found in bacteria, plants, and animals. Codon bias varies among species, families, and groups within kingdoms and between genes within an organism. Codon usage bias (CUB) analysis sheds light on the evolutionary dynamics of various species and optimizes targeted gene expression in heterologous host plants. As a significant order of gymnosperms, species within Gnetales possess extremely high ecological and pharmaceutical values. However, comprehensive analyses of CUB within the chloroplast genomes of Gnetales species remain unexplored. A systematic analysis was conducted to elucidate the codon usage patterns in 13 diverse Gnetales species based on the chloroplast genomes. Our results revealed that chloroplast coding sequences (cp CDSs) in 13 Gnetales species display a marked preference for AT bases and A/T-ending codons. A total of 20 predominantly high-frequency codons and between 2 and 7 optimal codons were identified across these species. The findings from the ENC-plot, PR2-plot, and neutrality analyses suggested that both mutation pressure and natural selection exert influence on the codon bias in these 13 Gnetales species, with natural selection emerging as the predominant influence. Correspondence analysis (COA) demonstrated variation in the codon usage patterns among the Gnetales species and indicated mutation pressure is another factor that could impact CUB. Additionally, our research identified a positive correlation between the measure of idiosyncratic codon usage level of conservatism (MILC) and synonymous codon usage order (SCUO) values, indicative of CUB’s potential influence on gene expression. The comparative analysis concerning codon usage frequencies among the 13 Gnetales species and 4 model organisms revealed that Saccharomyces cerevisiae and Nicotiana tabacum were the optimal exogenous expression hosts. Furthermore, the cluster and phylogenetic analyses illustrated distinct patterns of differentiation, implying that codons, even with weak or neutral preferences, could affect the evolutionary trajectories of these species. Our results reveal the characteristics of codon usage patterns and contribute to an enhanced comprehension of evolutionary mechanisms in Gnetales species. Full article

In the last few decades, the efforts of the scientific community to search earthquake signatures in the atmospheric, ionospheric and magnetospheric media have grown rapidly. The increasing amount of good quality data from both ground stations and satellites has allowed for the detections of anomalies with high statistical significance such as ionospheric plasma density perturbations and/or atmospheric temperature and pressure changes. However, the identification of a causal link between the observed anomalies and their possible seismic trigger has so far been prevented by difficulties in the identification of confounders (such as solar and atmospheric activity) and the lack of a global analytical lithospheric–atmospheric–magnetospheric model able to explain (and possibly forecast) any anomalous signal. In order to overcome these problems, we have performed a multi-instrument analysis of a low-latitude seismic event by using high-quality data from both ground bases and satellites and preserving their statistical significance. An earthquake (Mw = 7.2) occurred in the Caribbean region on 14 August 2021 under both solar quiet and fair weather conditions, thus proving an optimal case study to reconstruct the link between the lithosphere, atmosphere, ionosphere, and magnetosphere. The good match between the observations and novel magnetospheric–ionospheric–lithospheric coupling (M.I.L.C.) modeling of the event confirmed that the fault break generated an atmospheric gravity wave that was able to mechanically perturb the ionospheric plasma density, in turn triggering a variation in the magnetospheric field line resonance frequency. Full article

Floods, storms and hurricanes are devastating for human life and agricultural cropland. Near-real-time (NRT) discharge estimation is crucial to avoid the damages from flood disasters. The key input for the discharge estimation is precipitation. Directly using the ground stations to measure precipitation is not efficient, especially during a severe rainstorm, because precipitation varies even in the same region. This uncertainty might result in much less robust flood discharge estimation and forecasting models. The use of satellite precipitation products (SPPs) provides a larger area of coverage of rainstorms and a higher frequency of precipitation data compared to using the ground stations. In this paper, based on SPPs, a new NRT flood forecasting approach is proposed to reduce the time of the emergency response to flood disasters to minimize disaster damage. The proposed method allows us to forecast floods using a discharge hydrograph and to use the results to map flood extent by introducing SPPs into the rainfall–runoff model. In this study, we first evaluated the capacity of SPPs to estimate flood discharge and their accuracy in flood extent mapping. Two high temporal resolution SPPs were compared, integrated multi-satellite retrievals for global precipitation measurement (IMERG) and tropical rainfall measurement mission multi-satellite precipitation analysis (TMPA). The two products are evaluated over the Ottawa watershed in Canada during the period from 10 April 2017 to 10 May 2017. With TMPA, the results showed that the difference between the observed and modeled discharges was significant with a Nash–Sutcliffe efficiency (NSE) of −0.9241 and an adapted NSE (ANSE) of −1.0048 under high flow conditions. The TMPA-based model did not reproduce the shape of the observed hydrographs. However, with IMERG, the difference between the observed and modeled discharges was improved with an NSE equal to 0.80387 and an ANSE of 0.82874. Also, the IMERG-based model could reproduce the shape of the observed hydrographs, mainly under high flow conditions. Since IMERG products provide better accuracy, they were used for flood extent mapping in this study. Flood mapping results showed that the error was mostly within one pixel compared with the observed flood benchmark data of the Ottawa River acquired by RadarSat-2 during the flood event. The newly developed flood forecasting approach based on SPPs offers a solution for flood disaster management for poorly or totally ungauged watersheds regarding precipitation measurement. These findings could be referred to by others for NRT flood forecasting research and applications. Full article