The synthesis of spongistatin side -chain analogs: The total synthesis of spongistatin 2 and 23-epi-spongistatin 2

The design and synthesis of a series of spongistatin side-chain analogs and the total synthesis of (+)- spongistatin 2 and (−)-23-epi-spongistatin 2 are described herein. Chapter One highlights the isolation, biological activities and structural determination of the spongipyrans, and then summarizes the only total synthesis of spongistatins 1 and 2 reported to date. Chapter Two describes the design and synthesis of a series of spongistatin side-chain analogs, three of those analogs display significant in vitro activity against a variety of human cancer cell lines. Chapter Three outlines our first generation synthetic approach towards the total synthesis of the spongistatins, including the synthesis of the C(1)–C(17) AB fragment 122. Unfortunately, the coupling between the AB dithiane (+)- 122 and the CD iodide (−)-123 proved unsuccessful under a variety of conditions. Chapter Four describes our second generation approach to the total synthesis of spongistatin 2, including the synthesis of the C(1)–C(12) AB fragment (−)- 188 and C(13)–C(28) CD fragment (−)- 187. In addition, the powerful sulfone coupling followed by Julia exo-methylenation protocol was successfully applied to the union of the C(1)–C(12) AB fragment (−)-188 and the C(13)–C(28) CD fragment (−)-187. Although further elaboration of (−)- 200 did not lead to the desired ABCD aldehyde due to unsuccessful dithiane deprotection, this approach laid the foundation for our third generation approach which is described in Chapter Five. The only difference between our second and third generation approaches was that we substituted the C(17) dithiane for a protected alcohol to generate the revised C(13)–C(28) CD fragment (+)-208. Coupling between the CD sulfone (+)-208 and the AB iodide (−)-188 and further elaboration to the 23-epi- ABCD aldehyde (+)-235a was achieved in 13 steps and 41% overall yield. It was later found that CD spiroketal was epimerized during AB spiroketal formation. The critical Wittig coupling between the EF phosphonium salt (+)-189 and the ABCD aldehyde (+)-235a was then achieved via the Kishi titration protocol. Selective deprotection of the TMS ethers and the TIPS ester with KF in methanol afforded seco acid (+)-237. Macrolactonization via the Yamaguchi protocol proceeded smoothly to furnish exclusively the desired regioisomer (+)-238 in excellent yield (85%). To our great disappointment, global deprotection of macrolactone (+)-238 under various conditions was not successful. We believed that either the C(9) or C(38) TBS ether was resistant to removal. Chapter Six summarizes our successful fourth generation approach to the total synthesis of spongistatin 2. In order to circumvent the global deprotection hurdle, we decided to exchange the C(9) and C(38) TBS ethers for more labile TES ethers. Synthesis of the 23-epi-ABCD aldehyde (+)-241a is thus described. The crucial Wittig coupling between the EF phosphonium salt (+)-242 and the 23-epi-ABCD aldehyde (+)-241a was achieved via the Kishi titration protocol in 34% yield. Selective deprotection of the TMS ethers and the TIPS ester with KF in methanol afforded the seco acid (+)-255 in 75% yield. Macrolactonization proceeded smoothly via the Yamaguchi protocol to furnish macrolactone (+)- 256 in 81% yield. Finally, global deprotection with HF in MeCN-H2O at 0°C completed the total synthesis of both (+)-spongistatin 2 (18% yield) and (−)-23-epi-spongistatin 2 (48% yield). These results demonstrated that (−)-23-epi-spongistatin 2 could be converted into (+)-spongistatin 2 under acidic conditions. The total syntheses of (+)-spongistatin 2 and (−)-23-epispongistatin 2 have been achieved in 41 steps (the longest linear sequence)

Wetbulb Globe Temperature

<p>This dataset contains simplified WetBulb Globe Temperature (WBGT) at hourly frequency spanning from 1979 to 2022. The variables utilized for WBGT calculation include dry-bulb temperature, humidity, and surface pressure obtained from the ERA5 reanalysis. First, an isobaric wet-bulb temperature (Tw) is computed using these variables. Then the simplified WBGT is determined through the formula WBGT*= 0.7Tw+0.3Td. More details are described in "Dawei Li*, Jiacan Yuan*, and Robert E. Kopp (2020): Escalating global exposure to compound heat-humidity extremes with warming. <em>Environmental Research Letters</em>. DOI:10.1088/1748-9326/ab7d04". Please cite this article when using this dataset.</p> <p>WBGT-ERA5-v2.0 is an updated version for WBGT-ERA5-v1.1. In the version of WBGT-ERA5-v1.1, an assumption was made that RH = q/qs, where saturation specific humidity (qs) was considered equal to the saturation mixing ratio rs. In the version of WBGT-ERA5-v1.2, we calculate RH and qs exactly following their original definitions: RH = e/es (where e is vapor pressure and es is saturated vapor pressure), and qs = rs/(1+rs). The updates will slightly improve the precision of the wet-bulb temperature estimation under high-temperature condition  </p>This project is supported by National Natural Science Foundation of China (Grant No. 42175066), Shanghai Municipal Natural Science Fund (20ZR1407400) and Shanghai Pujiang Program (Grant No. 20PJ1401600)

Prediction of Sea Surface Temperature in the East China Sea Based on LSTM Neural Network

Sea surface temperature (SST) is an important physical factor in the interaction between the ocean and the atmosphere. Accurate monitoring and prediction of the temporal and spatial distribution of SST are of great significance in dealing with climate change, disaster prevention, disaster reduction, and marine ecological protection. This study establishes a prediction model of sea surface temperature for the next five days in the East China Sea using long-term and short-term memory neural networks (LSTM). It investigates the influence of different parameters on prediction accuracy. The sensitivity experiment results show that, based on the same training data, the length of the input data of the LSTM model can improve the model’s prediction performance to a certain extent. However, no obvious positive correlation is observed between the increase in the input data length and the improvement of the model’s prediction accuracy. On the contrary, the LSTM model’s performance decreases with the prediction length increase. Furthermore, the single-point prediction results of the LSTM model for the estuary of the Yangtze River, Kuroshio, and the Pacific Ocean are accurate. In particular, the prediction results of the point in the Pacific Ocean are the most accurate at the selected four points, with an RMSE of 0.0698 °C and an R2 of 99.95%. At the same time, the model in the Pacific region is migrated to the East China Sea. The model was found to have good mobility and can well represent the long-term and seasonal trends of SST in the East China Sea

Significant Wave Height Prediction in the South China Sea Based on the ConvLSTM Algorithm

Deep learning methods have excellent prospects for application in wave forecasting research. This study employed the convolutional LSTM (ConvLSTM) algorithm to predict the South China Sea (SCS) significant wave height (SWH). Three prediction models were established to investigate the influences of setting different parameters and using multiple training data on the forecasting effects. Compared with the SWH data from the China–France Ocean Satellite (CFOSAT), the SWH of WAVEWATCH III (WWIII) from the pacific islands ocean observing system are accurate enough to be used as training data for the ConvLSTM-based SWH prediction model. Model A was preliminarily established by only using the SWH from WWIII as the training data, and 20 sensitivity experiments were carried out to investigate the influences of different parameter settings on the forecasting effect of Model A. The experimental results showed that Model A has the best forecasting effect when using three years of training data and three hourly input data. With the same parameter settings as the best prediction performance Model A, Model B and C were also established by using more different training data. Model B used the wind shear velocity and SWH as training and input data. When making a 24-h SWH forecast, compared with Model A, the root mean square error (RMSE) of Model B is decreased by 17.6%, the correlation coefficient (CC) is increased by 2.90%, and the mean absolute percentage error (MAPE) is reduced by 12.2%. Model C used the SWH, wind shear velocity, wind and wave direction as training and input data. When making a 24-h SWH forecast, compared with Model A, the RMSE of Model C decreased by 19.0%, the CC increased by 2.65%, and the MAPE decreased by 14.8%. As the performance of the ConvLSTM-based prediction model mainly rely on the SWH training data. All the ConvLSTM-based prediction models show a greater RMSE in the nearshore area than that in the deep area of SCS and also show a greater RMSE during the period of typhoon transit than that without typhoon. Considering the wind shear velocity, wind, and wave direction also used as training data will improve the performance of SWH prediction

Study on Process Optimization and Immunomodulatory Effect of Dendrobium Huoshanense Tea

To explore the synergisic effect of DH (Dendrobium huoshanense) and KBT (Keemun black tea) on preventing immunity reduction. The solid-liquid ratio, extraction time and extraction times of DH and KBT were studied by single factor test and orthogonal test with the extraction rate of Dendrobium polysaccharide and black tea polyphenols as indexes, the extraction techniques of DH and Keemun black tea were optimized respectively. The optimal ratio of KBT and DH was determined by toxicity test and phagocytosis test on RAW264.7 cells. The results showed that the optimum extraction conditions of DH polysaccharide were as follows: The ratio of material to liquid was 1:80 (g/mL), the extraction time was 80 min, and the extraction times were 4 times. The optimum extraction conditions of KBT polyphenols were as follows: The ratio of material to liquid was 1:80 (g/mL), the extraction time was 25 min, and the extraction times were 5 times. The ratio of KBT to DH was determined by observing the effect of different proportions of the mixture on macrophages. The results of the in vitro experiment showed that the extract mixture of 75% KBT and 25% DH had the best immune-enhancing effect. The results of the in vivo test showed that the high-dose group of KTDH could significantly reduce the changes of levels of cytokines IL-2 and IL-6 due to immunosuppression in mice (P<0.01). To sum up, DH and KBT can synergistically enhance immunity and prevent immune decline, which can provide new ideas for the development and application of DH and KBT

The impact of changes in dietary knowledge on adult overweight and obesity in China

<div><p>Overweight and obesity are rapidly growing threats in China. Improvement in dietary knowledge can potentially prevent overweight and obesity, conditions which are receiving substantial attention from international organizations and governments. The purpose of this study was to investigate the impact of changes in dietary knowledge on adult overweight and obesity, using a balanced panel data consisting of 10,401 samples from the 2006, 2009, and 2011 iterations of the China Health and Nutrition Survey. Results indicate that overweight and obesity are becoming increasingly problematic in China, and the level of dietary knowledge among Chinese adults needs improvement. Moreover, the empirical results indicate that changes in dietary knowledge among adults has no significant influence on adult overweight and obesity, a likely result of lacking systematic dietary knowledge and having inadequate guidance on overweight/obesity-related behaviors.</p></div

Dietary knowledge questions in the China Health and Nutrition Survey and corresponding correct answers.

<p>Dietary knowledge questions in the China Health and Nutrition Survey and corresponding correct answers.</p