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As an ideal model organism, Caenorhabditis elegans has been increasingly used in the evaluation of the nutritional function of food. Compared with other models such as cells and mice, C. elegans have the advantages of small size, short lifespan, high genetic homology with humans, easy observation of research results, and large batch culture in the laboratory. Therefore, the article summarizes the research methods, potential mechanisms and research progress of C. elegans in food nutritional function such as anti-obesity, antioxidant and anti-aging. In order to provide a reference for new methods in the evaluation of food nutrition function.

Numerical weather prediction of sea surface temperature (SST) is crucial for regional operational forecasts. Deep learning offers an alternative approach to traditional numerical general circulation models for numerical weather prediction. In our previous work, we developed a sophisticated deep learning model known as the Attention-based Context Fusion Network (ACFN). This model integrates an attention mechanism with a convolutional neural network framework. In this study, we applied the ACFN model to the South China Sea to evaluate its performance in predicting SST. The results indicate that for a 1-day lead time, the ACFN model achieves a Mean Absolute Error of 0.215 °C and a coefficient of determination ( R 2 ) of 0.972. In addition, in situ buoy data were utilized to validate the forecast results. The Mean Absolute Error for forecasts using these data increased to 0.500 °C for a 1-day lead time, with a corresponding R 2 of 0.590. Comparative analyses show that the ACFN model surpasses traditional models such as ConvLSTM and PredRNN in terms of accuracy and reliability. [ABSTRACT FROM AUTHOR]

The first operational version of the coupled Hurricane Analysis and Forecast System (HAFSv1) launched in 2023 consists of the HYbrid Coordinate Ocean Model (HYCOM) and finite-volume cubed-sphere (FV3) dynamic atmosphere model. This system is a product of efforts involving improvements and updates over a 4-year period (2019-2022) through extensive collaborations between the Environmental Modeling Center at the US National Centers for Environmental Prediction (NCEP) and NOAA Atlantic Oceanography and Meteorology Laboratory. To provide two sets of numerical guidance, the initial operational capability of HAFSv1 was configured to two systems -HFSA and HFSB. In this study, we present in-depth analysis of the forecast skills of the upper ocean that was co-evolved by the HFSA and HFSB. We chose hurricane Laura (2020) as an example to demonstrate the interactions between the storm and oceanic mesoscale features. Comparisons performed with the available in situ observations from gliders as well as Argos and National Data Buoy Center moorings show that the HYCOM simulations have better agreement for weak winds than high winds (greater than Category 2). The skill metrics indicate that the model sea-surface temperature (SST) and mixed layer depth (MLD) have a relatively low correlation. The SST, MLD, mixed layer temperature (MLT), and ocean heat content (OHC) are negatively biased. For high winds, SST and MLT are more negative, while MLD is closer to the observations with improvements of about 8%-19%. The OHC discrepancy is proportional to predicted wind intensity. Contrarily, the mixed layer salinity (MLS) uncertainties are smaller and positive for higher winds, probably owing to the higher MLD. The less-negative bias of MLD for high winds implies that the wind-force mixing is less effective owing to the higher MLD and high buoyancy stability (approx. 1.5-1.7 times) than the observations. The heat budget analysis suggests that the maximum heat loss by hurricane Laura was 0(< 3°C per day). The main contributor here is advection, followed by entrainment, which act against or with each other depending on the storm quadrant. We also found relatively large unaccountable heat residuals for the in-storm period, and the residuals notably led the heat tendency, meaning that further improvements of the subscale simulations are warranted. In summary, HYCOM simulations showed no systematic differences forced by either HFSA or HFSB. [ABSTRACT FROM AUTHOR]

In this study, we explore the interannual variability of Sea Surface Salinity (SSS) in the Dotson–Getz Trough located in West Antarctica, focusing on the month of February. Utilizing the oceanic analysis product EN4, we first validate the EN4 SSS with data from a singular ship-based survey, and then delve into potential factors that may influence SSS, with a particular emphasis on surface freshwater flux, sea ice concentration (SIC), and also the surface stress curl, which will induce upwelling via Ekman transport to affect the SSS. Our findings primarily indicate a link between SSS and sea ice concentration, showcasing a negative correlation where the peak (average) coefficient is around −0.6 (−0.4), further affirming the substantial interannual variability of SSS in this region. [ABSTRACT FROM AUTHOR]

We present a review of in situ observations regarding the interactions between tropical cyclones and the ocean in the western North Pacific for the year 2023. A total of at least 13 tropical cyclones occurred during this period. According to the Japan Meteorological Agency, Typhoon Mawar recorded the yearly minimum pressure at 900 hPar. On average, each tropical cyclone captured 7.4 surface drifters and 25.2 Argo floats when the search radius is 300 km. During Guchol, the maximum in situ Lagrangian current reached 1.23 m/s, with sustained wind speeds of the tropical cyclone up to 31.7 m/s and a relative position of 174 km. Additionally, several Argo floats were active during tropical cyclones, with maximum sea surface temperature cooling reaching 0.66 °C. This annual review provides a comprehensive summary of the current state of in situ observations regarding tropical cyclone–ocean interaction. These findings serve as valuable references for both scientific research and operational forecasting. [ABSTRACT FROM AUTHOR]