Saltwater Intrusion in the Changjiang Estuary

Saltwater intrusion in the Changjiang Estuary and the impacts of river discharge, tide, wind, sea level rise, river basin, and major estuary projects on saltwater intrusion are studied in this chapter. There is a net landward flow in the NB (North Branch) when river discharge is low during spring tide, resulting in a type of saltwater intrusion known as the SSO (saltwater-spilling-over from the NB into the SB (South Branch)), which is the most striking characteristic of saltwater intrusion in the estuary. A three-dimension numerical model with HSIMT-TVD advection scheme was developed to study the hydrodynamic processes and saltwater intrusion in the Changjiang Estuary. Saltwater intrusion in the estuary is controlled mainly by river discharge and tide, but is also influenced by wind, sea level rise, river basin, and estuary projects. Saltwater intrusion is enhanced when river discharge decreases. There is more time for the reservoir to take freshwater from the river when river discharge is larger. The fortnightly spring tide generates greater saltwater intrusion than the neap tide. The saltwater intrusion in the SP (South Passage) is stronger than that in the NP (North Passage), and the intrusion in the NP is stronger than that in the NC (North Channel). The northerly wind produces southward currents along the Subei coast as well as the landward Ekman transport, which enhances the saltwater intrusion in the NC and NB and weakens the saltwater intrusion in the NP and SP. Saltwater intrusion becomes stronger as the sea level rises and is much stronger when river discharge is much small. The DWP (Deep Waterway Project) alleviates the saltwater intrusion in the NC and the lower reaches of the NP and enhances the saltwater intrusion in the SP and in the upper reaches of the NP. The Three Gorges Dam (TGD) increases river discharge in winter, which weakens saltwater intrusion, and is favorable for reducing the burden of freshwater supplement in the highly populated estuarine region. The Water Diversion South to the North Project (WDP) decreases river discharge, enhances saltwater intrusion, and is unfavorable for freshwater supply in the estuary

Li-rich and super Li-rich giants produced by element diffusion

Context. About 0.2-2% of giant stars are Li-rich, whose lithium abundance (A(Li)) is higher than 1.5 dex. Among them, near 6% are super Li-rich with A(Li) exceeding 3.2 dex. Meanwhile, the formation mechanism of these Li-rich and super Li-rich giants is still under debate. Aims. Considering the compact He core of red giants, attention is paid to the effect of element diffusion on A(Li). In particular, when the He core flash occurs, the element diffusion makes the thermohaline mixing zone extend inward and connect to the inner convection region of stars. Then, a large amount of 7Be produced by the He flash can be transferred to stellar surface, finally turning into 7Li. Thus, the goal of this work is to propose the mechanism of A(Li) enrichment and achieve the consistency between the theoretical and observation data. Methods. Using the Modules for Experiments in Stellar Astrophysics (MESA), we simulate the evolution of low-mass stars, with considering the effects of element diffusion on the Li abundances. The timescale ratio of Li-rich giants to normal giants is estimated by population synthesis method. Then we get the theoretical value of A(Li) and make a comparison with observations. Results. Considering the influence of element diffusion in the model results in the increase of lithium abundance up to about 1.8dex, which can reveal Li-rich giants. Simultaneously, introducing high constant diffusive mixing coefficients (Dmix) with the values from 10e11 to 10e15in the model allows A(Li) to increase from 2.4 to 4.5dex, which can explain the most of Li-rich and super Li-rich giant stars. The population synthesis method reveals that the amount of Li-rich giants among giants is about 0.2-2%, which is consistent with observation estimated levels

T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representations

In this work, we investigate a simple and must-known conditional generative framework based on Vector Quantised-Variational AutoEncoder (VQ-VAE) and Generative Pre-trained Transformer (GPT) for human motion generation from textural descriptions. We show that a simple CNN-based VQ-VAE with commonly used training recipes (EMA and Code Reset) allows us to obtain high-quality discrete representations. For GPT, we incorporate a simple corruption strategy during the training to alleviate training-testing discrepancy. Despite its simplicity, our T2M-GPT shows better performance than competitive approaches, including recent diffusion-based approaches. For example, on HumanML3D, which is currently the largest dataset, we achieve comparable performance on the consistency between text and generated motion (R-Precision), but with FID 0.116 largely outperforming MotionDiffuse of 0.630. Additionally, we conduct analyses on HumanML3D and observe that the dataset size is a limitation of our approach. Our work suggests that VQ-VAE still remains a competitive approach for human motion generation.Comment: Accepted to CVPR 2023. Project page: https://mael-zys.github.io/T2M-GPT

Magnetic Activities of M-type Stars Based on LAMOST DR5 and Kepler and K2 Missions

We performed a statistical study of magnetic activities of M-type stars by combining the spectra of LAMOST DR5 with light curves from the Kepler and K2 missions. We mainly want to study the relationship between chromospheric activity and flares, and their relations of magnetic activity and rotation period. We have obtained the maximum catalog of 516,688 M-type stellar spectra of 480,912 M stars from LAMOST DR5 and calculated their equivalent widths of chromospheric activity indicators (Hα, Hβ, Hγ, Hδ, Ca II H&K, and He I D3). Using the Hα indicator, 40,464 spectra of 38,417 M stars show chromospheric activity, and 1791 of these 5499 M-type stars with repeated observations have Hα variability. We used an automatic detection plus visual inspection method to detect 17,432 flares on 8964 M-type stars from the catalog by cross-matching LAMOST DR5 and the Kepler and K2 databases. We used the Lomb–Scargle method to calculate their rotation periods. We find that the flare frequency is consistent with the ratio of activities of these chromospheric activity indicators as a function of spectral type in M0–M3. We find the equivalent widths of Hα and Ca II H have a significant statistical correlation with the flare amplitude in M-type stars. We confirm that the stellar flare is affected by both the stellar magnetic activity and the rotation period. Finally, using the Hα equivalent width equal to 0.75 Å and using the rotation period equal to 10 days as the threshold for the M-type stellar flare time frequency are almost equivalent

LATS kinase-mediated CTCF phosphorylation and selective loss of genomic binding.

Chromatin topological organization is instrumental in gene transcription. Gene-enhancer interactions are accommodated in the same CTCF-mediated insulated neighborhoods. However, it remains poorly understood whether and how the 3D genome architecture is dynamically restructured by external signals. Here, we report that LATS kinases phosphorylated CTCF in the zinc finger (ZF) linkers and disabled its DNA-binding activity. Cellular stress induced LATS nuclear translocation and CTCF ZF linker phosphorylation, and altered the landscape of CTCF genomic binding partly by dissociating it selectively from a small subset of its genomic binding sites. These sites were highly enriched for the boundaries of chromatin domains containing LATS signaling target genes. The stress-induced CTCF phosphorylation and locus-specific dissociation from DNA were LATS-dependent. Loss of CTCF binding disrupted local chromatin domains and down-regulated genes located within them. The study suggests that external signals may rapidly modulate the 3D genome by affecting CTCF genomic binding through ZF linker phosphorylation

Temporal change in multimorbidity prevalence, clustering patterns, and the association with mortality: findings from the China Kadoorie Biobank study in Jiangsu Province

Objectives: The characteristics of multimorbidity in the Chinese population are currently unclear. We aimed to determine the temporal change in multimorbidity prevalence, clustering patterns, and the association of multimorbidity with mortality from all causes and four major chronic diseases. Methods: This study analyzed data from the China Kadoorie Biobank study performed in Wuzhong District, Jiangsu Province. A total of 53,269 participants aged 30–79 years were recruited between 2004 and 2008. New diagnoses of 15 chronic diseases and death events were collected during the mean follow-up of 10.9 years. Yule's Q cluster analysis method was used to determine the clustering patterns of multimorbidity. A Cox proportional hazards model was used to estimate the associations of multimorbidity with mortalities. Results: The overall multimorbidity prevalence rate was 21.1% at baseline and 27.7% at the end of follow-up. Multimorbidity increased more rapidly during the follow-up in individuals who had a higher risk at baseline. Three main multimorbidity patterns were identified: (i) cardiometabolic multimorbidity (diabetes, coronary heart disease, stroke, and hypertension), (ii) respiratory multimorbidity (tuberculosis, asthma, and chronic obstructive pulmonary disease), and (iii) mental, kidney and arthritis multimorbidity (neurasthenia, psychiatric disorders, chronic kidney disease, and rheumatoid arthritis). There were 3,433 deaths during the follow-up. The mortality risk increased by 24% with each additional disease [hazard ratio (HR) = 1.24, 95% confidence interval (CI) = 1.20–1.29]. Compared with those without multimorbidity at baseline, both cardiometabolic multimorbidity and respiratory multimorbidity were associated with increased mortality from all causes and four major chronic diseases. Cardiometabolic multimorbidity was additionally associated with mortality from cardiovascular diseases and diabetes, with HRs of 2.64 (95% CI = 2.19–3.19) and 28.19 (95% CI = 14.85–53.51), respectively. Respiratory multimorbidity was associated with respiratory disease mortality, with an HR of 9.76 (95% CI = 6.22–15.31). Conclusion: The prevalence of multimorbidity has increased substantially over the past decade. This study has revealed that cardiometabolic multimorbidity and respiratory multimorbidity have significantly increased mortality rates. These findings indicate the need to consider high-risk populations and to provide local evidence for intervention strategies and health management in economically developed regions