Field and laboratory experiments are designed to measure Sargassum biomass per area (density), surface reflectance, nutrient contents, and pigment concentrations. An alternative floating algae index‐biomass density model is established to link the spectral reflectance to Sargassum biomass density, with a relative uncertainty of ~12%. Monthly mean integrated Sargassum biomass in the Caribbean Sea and central West Atlantic reached at least 4.4 million tons in July 2015. The average %C, %N, and %P per dry weight are 27.16, 1.06, and 0.10, respectively. The mean chlorophyll‐a (Chl‐a) concentration is ~0.05% of the dry weight. With these parameters, the amounts of nutrients and pigments can be estimated directly from remotely sensed Sargassum biomass. During bloom seasons, Sargassum carbon can account for ~18% of the total particulate organic carbon in the upper water column. This study provides the first quantitative assessment of the overall Sargassum biomass, nutrients, and pigment abundance from remote sensing observations, thus helping to quantify their ecological roles and facilitate management decisions.

Deep learning-based super-resolution (SR) methods have been widely used in natural images; however, their applications in satellite-derived sea surface temperature (SST) have not yet been fully discussed. Hence, it is necessary to analyze the validity of deep learning-based SR methods in SST reconstruction. In this study, an SR model, including multiscale feature extraction and multireceptive field mapping, was first proposed. Then, the proposed model and four other existing SR models were applied to SST reconstruction and analyzed. First, compared with the bicubic interpolation method, the SR models can improve the reconstruction accuracy. Compared with four other SR models, the proposed model can achieve the lowest mean squared error (MAE) in the East China Sea (ECS), in the northwest Pacific (NWP) and in the west Atlantic (WA), the second-lowest MAE in the southeast Pacific (SEP); the lowest root mean squared error (RMSE) in ECS and WA, the second-lowest RMSE in NWP and SEP. Additionally, ODRE model can acquire the highest or the second-highest peak single-to-noise ratio (PSNR) and structural similarity index (SSIM) in ECS, NWP and SEP. Moreover, the number of missing pixels and SST variety are two essential factors in the SR performance. The proposed multiscale feature extraction process can enhance the SR performance, especially for small regions and stable SST regions. Finally, while a deeper network can be helpful in achieving SR performance, the approach of simply adding more dilation convolutions may not enhance the reconstruction accuracy.

HMGB1 that belongs to the High Mobility Group-box superfamily, is a nonhistone chromatin associated transcription factor. It is present in the nucleus of eukaryotes and can be actively secreted or passively released by kinds of cells. HMGB1 is important for maintaining DNA structure by binding to DNA and histones, protecting it from damage. It also regulates the interaction between histones and DNA, affecting chromatin packaging, and can influence gene expression by promoting nucleosome sliding. And as a DAMP, HMGB1 binding to RAGE and TLRs activates NF-κB, which triggers the expression of downstream genes like IL-18, IL-1β, and TNF-α. HMGB1 is known to be involved in numerous physiological and pathological processes. Recent studies have demonstrated the significance of HMGB1 as DAMPs in the female reproductive system. These findings have shed light on the potential role of HMGB1 in the pathogenesis of diseases in female reproductive system and the possibilities of HMGB1-targeted therapies for treating them. Such therapies can help reduce inflammation and metabolic dysfunction and alleviate the symptoms of reproductive system diseases. Overall, the identification of HMGB1 as a key player in disease of the female reproductive system represents a significant breakthrough in our understanding of these conditions and presents exciting opportunities for the development of novel therapies.