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Here we report the Simons Genome Diversity Project data set: high quality genomes from 300 individuals from 142 diverse populations. These genomes include at least 5.8 million base pairs that are not present in the human reference genome. Our analysis reveals key features of the landscape of human genome variation, including that the rate of accumulation of mutations has accelerated by about 5% in non-Africans compared to Africans since divergence. We show that the ancestors of some pairs of present-day human populations were substantially separated by 100,000 years ago, well before the archaeologically attested onset of behavioural modernity. We also demonstrate that indigenous Australians, New Guineans and Andamanese do not derive substantial ancestry from an early dispersal of modern humans; instead, their modern human ancestry is consistent with coming from the same source as that of other non-Africans.

Fluorescence sensing is crucial to studying biological processes and diagnosing diseases, especially in the second near‐infrared (NIR‐II) window with reduced background signals. However, it's still a great challenge to construct "off‐on" sensors when the sensing wavelength extends into the NIR‐II region to obtain higher imaging contrast, mainly due to the difficult synthesis of spectral overlapped quencher. Here, we present a new fluorescence quenching strategy, which utilizes steric hindrance quencher (SHQ) to tune the molecular packing state of fluorophores and suppress the emission signal. Density functional theory (DFT) calculations further reveal that large SHQs can competitively pack with fluorophores and prevent their self‐aggregation. Based on this quenching mechanism, a novel activatable "off‐on" sensing method is achieved via bio‐analyte responsive invalidation of SHQ, namely the Steric Hindrance Invalidation geNerated Emission (SHINE) strategy. As a proof of concept, the ClO−‐sensitive SHQ lead to the bright NIR‐II signal release in epileptic mouse hippocampus under the skull and high photon scattering brain tissue, providing the real‐time visualization of ClO− generation process in living epileptic mice. [ABSTRACT FROM AUTHOR]

Diabetic nephropathy (DN) is a primary diabetic complication ascribed to the pathological changes in renal microvessels. This study investigated the nuclear factor erythroid 2‐related factor 2 (Nrf2)/Kelch ECH associating protein (Keap1)/antioxidant response element (ARE) signaling pathway impact of chitooligosaccharides (COS) with a certain degree of polymerization (DP) on DN mouse models and high glucose‐damaged human kidney 2 (HK‐2) cells. The findings indicated that COS effectively reduced the renal function indexes (uric acid [UA], urinary albumin excretion rate [UAER], urine albumin‐to‐creatinine ratio [UACR], blood urea nitrogen [BUN], and creatinine [Cre]) of DN mice. It increased (p <.05) the superoxide dismutase (SOD), glutathione peroxidase (GSH‐Px), and catalase (CAT) antioxidant enzyme activity in the serum and kidneys, and decreased (p <.05) the malondialdehyde (MDA) content. The mechanistic investigation showed that COS significantly increased (p <.05) Nrf2 and downstream target gene (GCLM, GCLC, HO‐1, and NQO‐1) expression, and substantially decreased (p <.05) Keap1 expression. The protein level was consistent with the messenger RNA (mRNA) level in in vitro and in vivo models. The docking data indicated that COS and Keap1 protein binding included six hydrogen bond formation processes (Gly364, Arg415, Arg483, His436, Ser431, and Arg380). The COS intervention mechanism may be related to the Nrf2/Keap1/ARE antioxidant pathway. Therefore, it provides a scientific basis for COS application in developing special medical food for DN patients. [ABSTRACT FROM AUTHOR]

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]