Remote sensing estimates of global sea surface nitrate: Methodology and validation.

Information about sea surface nitrate (SSN) concentrations is crucial for estimating oceanic new productivity and for carbon cycle studies. Due to the absence of optical properties in SSN and the intricate relationships with environmental factors affecting spatiotemporal dynamics, developing a more representative and widely applicable remote sensing inversion algorithm for SSN is challenging. Most methods for the remote estimation of SSN are based on data-driven neural networks or deep learning and lack mechanistic descriptions. Since fitting functions between the SSN and sea surface temperature (SST), mixed layer depth (MLD), and chlorophyll (Chl) content have been established for the open ocean, it is important to include the remote sensing indicator photosynthetically active radiation (PAR), which is critical in nitrate biogeochemical processes. In this study, we employed an algorithm for estimating the monthly average SSN on a global 1° by 1° resolution grid; this algorithm relies on the empirical relationship between the World Ocean Atlas 2018 (WOA18) monthly interpolated climatology of nitrate in each 1°â€¯× 1° grid and the estimated monthly SST and PAR datasets from Moderate Resolution Imaging Spectroradiometer (MODIS) and MLD from the Hybrid Coordinate Ocean Model (HYCOM). These results indicated that PAR potentially affects SSN. Furthermore, validation of the SSN model with measured nitrate data from different months and locations for the years 2018-2023 yielded a high prediction accuracy (N = 12,846, R2 = 0.93, root mean square difference (RMSE) = 3.12 µmol/L, and mean absolute error (MAE) = 2.22 µmol/L). Further independent validation and sensitivity tests demonstrated the validity of the algorithm for retrieving SSN.

Coevolutionary Neural Solution for Nonconvex Optimization With Noise Tolerance.

The existing solutions for nonconvex optimization problems show satisfactory performance in noise-free scenarios. However, they are prone to yield inaccurate results in the presence of noise in real-world problems, which may lead to failures in optimizing nonconvex problems. To this end, in this article, we propose a coevolutionary neural solution (CNS) by combining a simplified neurodynamics (SND) model with the particle swarm optimization (PSO) algorithm. Specifically, the proposed SND model does not leverage the time-derivative information, exhibiting greater stability compared to existing models. Furthermore, due to the noise tolerance capacity and rapid convergence property exhibited by the SND model, the CNS can rapidly achieve the optimal solution even in the presence of various perturbations. Theoretical analyses ensure that the proposed CNS is globally convergent with robustness and probability. In addition, the effectiveness of the CNS is compared with those of the existing solutions by a class of illustrative examples. We further apply the proposed solution to design a finite impulse response (FIR) filter and a pressure vessel to demonstrate its performance.

Discovery of chalcone-modified estradiol analogs as antitumour agents that Inhibit tumour angiogenesis and epithelial to mesenchymal transition.

Angiogenesis plays an essential role in tumourigenesis and tumour progression, and anti-angiogenesis therapies have shown promising antitumour effects in solid tumours. 2-Methoxyestradiol (2ME2), an endogenous metabolite of estradiol, has been regarded as a potential antitumour agent mainly targeting angiogenesis. Here we synthesized a novel series of chalcones based on 2-methoxyestradiol and evaluated their potential activities against tumours. Compound 11e was demonstrated to have potent antiangiogenic activity. Further studies showed that 11e suppressed tumour growth in human breast cancer (MCF-7) xenograft models without obvious side effects. Evaluation of the mechanism revealed that 11e targeted the epithelial to mesenchymal transition (EMT) process in MCF-7 cells and inhibited HUVEC migration and then contributed to hindrance of angiogenesis. Thus, 11e may be a promising antitumour agent with excellent efficacy and low toxicity.