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Upscaling flux tower measurements based on machine learning (ML) algorithms is an essential approach for large‐scale net ecosystem CO2 exchange (NEE) estimation, but existing ML upscaling methods face some challenges, particularly in capturing NEE interannual variations (IAVs) that may relate to lagged effects. With the capacity to characterize temporal memory effects, the Long Short‐Term Memory (LSTM) networks are expected to help solve this problem. Here we explored the potential of LSTM for predicting NEE across various ecosystems using flux tower data over 82 sites in North America. The LSTM model with differentiated plant function types (PFTs) demonstrates the capability to explain 79.19% (R2 = 0.79) of the monthly variations in NEE within the testing set, with RMSE and Mean Absolute Error values of 0.89 and 0.57 g C m−2 d−1 respectively (r = 0.89, p < 0.001). Moreover, the LSTM model performed robustly in predicting cross‐site variability, with 67.19% of the sites that can be predicted by both LSTM models with and without distinguished PFTs showing improved predictive ability. Most importantly, the IAV of predicted NEE highly correlated with that in flux observations (r = 0.81, p < 0.001), clearly outperforming that by the random forest model (r = −0.21, p = 0.011). Among all nine PFTs, solar‐induced chlorophyll fluorescence, downward shortwave radiation, and leaf area index are the most important variables for explaining NEE variations, collectively accounting for approximately 54.01% in total. This study highlights the great potential of LSTM for improving carbon flux upscaling with multi‐source remote sensing data. Plain Language Summary: Net ecosystem exchange (NEE) of CO2 is a crucial process that regulates carbon exchange between terrestrial ecosystems and the atmosphere. Currently, the growing availability of flux tower measurements and the rapid development of artificial intelligence technology, have made the machine learning‐based data‐driven flux upscaling approach a popular way for estimating carbon fluxes over large scales. Several upscaling NEE data sets have been derived with different machine learning methods; however, the lack of representing memory effects of climate and environmental factors in the modeling remains an important source of uncertainty for NEE estimates. To address this issue, we constructed site‐level Long Short‐Term Memory (LSTM) training models by plant function types in North America to improve the simulation of monthly scale NEE and its interannual variations. The established LSTM model improves the prediction of the temporal variability of NEE, showing great potential for improving carbon flux upscaling. Key Points: The Long Short‐Term Memory (LSTM) model with differentiated plant function types (PFTs) demonstrates the capability to explain 79.19% of the monthly variations in net ecosystem exchange (NEE)The LSTM model exhibited clear advantages over the RF model in capturing the interannual variations of NEEThe relative importance of feature variables for predicting monthly NEE dynamics across different PFTs in North America was quantified [ABSTRACT FROM AUTHOR]

Tenosynovial giant cell tumor (TGCT) is a rare and locally aggressive benign tumor arising from the synovium of joints, bursae, and tendon sheaths. It is classified into localized (L-TGCT) and diffuse (D-TGCT) forms based on the extent of involvement. Surgical resection is the primary treatment, though achieving a definitive cure remains challenging due to the high recurrence rates, especially in D-TGCT. Systemic therapies targeting the CSF1-CSF1R axis have emerged as promising treatment options. CSF1R tyrosine kinase inhibitors (TKIs) such as imatinib, nilotinib, pexidartinib, and vimseltinib, alongside anti-CSF1R antibodies like emactuzumab, cabiralizumab, and lacnotuzumab, have shown encouraging results in managing TGCT, particularly when surgery is not feasible or poses significant morbidity. Other potential therapies, including local treatments and anti-inflammatory drugs, are being explored for TGCT management. This review provides an overview of systemic treatment options for D-TGCT, highlighting emerging therapeutic modalities and their potential implications. Effective management is crucial due to TGCT's significant morbidity despite its non-life-threatening nature, necessitating novel approaches to improve patient prognosis and quality of life. [ABSTRACT FROM AUTHOR]

Currently, ascorbic acid (AA) is widely used as a skin whitening material, but, AA, an unstable hydrophilic molecule, cannot penetrate the skin easily, due to the hydrophobic character of the stratum corneum. Therefore, we conjugated AA with hydrated zinc oxide—an inorganic matrix with positive surface charge, to improve the stability of AA. The metal-conjugated-ascorbic acid (ZnAA) was then combined with yeast vacuole through the vacuolar membrane proteins that relate to metal transportation to create an enhanced vacuole that contained ZnAA. The characteristics of vacuole with ZnAA (ZnAA_Vac) were next examined by various tests that included X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT–IR), Field emission scanning electron microscopy (FE–SEM), and energy-dispersive X-ray (EDX) analysis. Furthermore, the ability of ZnAA_Vac to degrade melanin was confirmed in both melanoma cell line B16F10, and the artificial human skin MelanoDerm. The results showed that ZnAA_Vac possessed a higher depigmenting effect than the wild-type vacuole or ascorbic acid by reducing 75% of melanin color. Interestingly, ZnAA_Vac was found to be harmless, and did not cause any cytotoxicity to the cells. Overall, ZnAA_Vac is expected to provide a robust, harmless, and effective whitening agent for the skin. [ABSTRACT FROM AUTHOR]