Extraction and In Vitro Skincare Effect Assessment of Polysaccharides Extract from the Roots of Abelmoschus manihot (L.).

Obtaining high-added value compounds from agricultural waste receives increasing attention, as it can both improve resource utilization efficiency and reduce waste generation. In this study, polysaccharides are extracted from the discarded roots of Abelmoschus manihot (L.) by the high-efficiency ultrasound-assisted extraction (UAE). The optimized condition was determined as solid-liquid ratio SL ratio = 1:20, temperature T = 30 °C and time T = 40 min, achieving an extraction yield of 13.41%. Composition analysis revealed that glucose (Glc, 44.65%), rhamnose (Rha, 26.30%), galacturonic acid (GalA, 12.50%) and galactose (Gal, 9.86%) are the major monosaccharides of the extract. The extract showed a low degree of esterification (DE) value of 40.95%, and its Fourier-transform infrared (FT-IR) spectrum exhibited several characteristic peaks of polysaccharides. Inspired by the wide cosmetic applications of polysaccharides, the skincare effect of the extract was evaluated via the moisture retention, total phenolic content (TPC) quantification, 2,2-Diphenyl-1-picrylhydrazyl (DPPH)-free radical scavenging activity, anti-hyaluronidase and anti-elastase activity experiments. The extract solutions demonstrated a 48 h moisture retention rate of 10.75%, which is superior to that of commercially available moisturizer hyaluronic acid (HA). Moreover, both the TPC value of 16.16 mg GAE/g (dw) and DPPH-free radical scavenging activity of 89.20% at the concentration of 2 mg/mL indicated the strong anti-oxidant properties of the extract. Furthermore, the anti-hyaluronidase activity and moderate anti-elastase activity were determined as 72.16% and 42.02%, respectively. In general, in vitro skincare effect experiments suggest moisturizing, anti-oxidant, anti-radical and anti-aging activities of the A. manihot root extract, indicating its potential applications in the cosmetic industry.

Can three-dimensional nitrate structure be reconstructed from surface information with artificial intelligence? - A proof-of-concept study.

Nitrate is one of the essential variables in the ocean that is a primary control of the upper ocean pelagic ecosystem. Its three-dimensional (3D) structure is vital for understanding the dynamic and ecosystem. Although several gridded nitrate products exist, the possibility of reconstructing the 3D structure of nitrate from surface data has never been exploited. In this study, we employed two advanced artificial intelligence (AI) networks, U-net and Earthformer, to reconstruct nitrate concentration in the Indian Ocean from surface data. Simulation from an ecosystem model was utilized as the labeling data to train and test the AI networks, with wind vectors, wind stress, sea surface temperature, sea surface chlorophyll-a, solar radiation, and precipitation as the input. We compared the performance of two networks and different pre-processing methods. With the input features decomposed into climatology and anomaly components, the Earthformer achieved optimal reconstruction results with a lower normalized mean square error (NRMSE = 0.1591), spatially and temporally, outperforming U-net (NRMSE = 0.2007) and the climatology prediction (NRMSE = 0.2089). Furthermore, Earthformer was more capable of identifying interannual nitrate anomalies. With a network interpretation technique, we quantified the spatio-temporal importance of every input feature in the best case (Earthformer with decomposed inputs). The influence of different input features on nitrate concentration in the adjacent Java Sea exhibited seasonal variation, stronger than the interannual one. The feature importance highlighted the role of dynamic factors, particularly the wind, matching our understanding of the dynamic controls of the ecosystem. Our reconstruction and network interpretation technique can be extended to other ecosystem variables, providing new possibilities in studies of marine environment and ecology from an AI perspective.

The coastal ocean response to the global warming acceleration and hiatus.

Coastlines are fundamental to humans for habitation, commerce, and natural resources. Many coastal ecosystem disasters, caused by extreme sea surface temperature (SST), were reported when the global climate shifted from global warming to global surface warming hiatus after 1998. The task of understanding the coastal SST variations within the global context is an urgent matter. Our study on the global coastal SST from 1982 to 2013 revealed a significant cooling trend in the low and mid latitudes (31.4% of the global coastlines) after 1998, while 17.9% of the global coastlines changed from a cooling trend to a warming trend concurrently. The trend reversals in the Northern Pacific and Atlantic coincided with the phase shift of Pacific Decadal Oscillation and North Atlantic Oscillation, respectively. These coastal SST changes are larger than the changes of the global mean and open ocean, resulting in a fast increase of extremely hot/cold days, and thus extremely hot/cold events. Meanwhile, a continuous increase of SST was detected for a considerable portion of coastlines (46.7%) with a strengthened warming along the coastlines in the high northern latitudes. This suggests the warming still continued and strengthened in some regions after 1998, but with a weaker pattern in the low and mid latitudes.

Allocation of marine environmental carrying capacity in the Xiamen Bay.

Three optimization methods are employed to allocate Marine Environmental Carrying Capacity (MECC) in the Xiamen Bay. The hydrodynamic and pollutant fields are first simulated by the Princeton Ocean Model. Taking phosphorus as an index of the water quality, the response fields are then calculated. These response fields represent the relationship between the concentration of the sea zone and the pollution sources. Finally, MECC is optimized and distributed in the Xiamen Bay by three optimization methods. The results show classical linear optimization can only maximize the satisfaction level for one of the stake holders', e.g., dischargers or environmental protection bureau, satisfaction level. However, the fuzzy and grey fuzzy optimizations can provide a compromise, and therefore a fairer result, by incorporating the conflicting goals of all of the different stakeholders. Compared with fuzzy optimization, the grey fuzzy optimization provides a more flexible choice for the decision-makers.