Brain functional network (BFN) analysis is becoming a crucial way to explore the inherent organized pattern of the brain and reveal potential biomarkers for diagnosing neurological or psychological disorders. In so doing, a well-estimated BFN is of great concern. In practice, however, noises or artifacts involved in the observed data (i.e., fMRI time series in this paper) generally lead to a poor estimation of BFN, and thus a complex preprocessing pipeline is often used to improve the quality of the data prior to BFN estimation. One of the popular preprocessing steps is data-scrubbing that aims at removing “bad” volumes from the fMRI time series according to the amplitude of the head motion. Despite its helpfulness in general, this traditional scrubbing scheme cannot guarantee that the removed volumes are necessarily unhelpful, since such a step is fully independent to the subsequent BFN estimation task. Moreover, the removal of volumes would reduce the statistical power, and different numbers of volumes are generally scrubbed for different subjects, resulting in an inconsistency or bias in the estimated BFNs. To address these issues, we develop a new learning framework that conducts BFN estimation and data-scrubbing simultaneously by an alternating optimization algorithm. The newly developed algorithm adaptively weights volumes (instead of removing them directly) for the task of BFN estimation. As a result, the proposed method can not only reduce the difficulty of threshold selection involved in the traditional scrubbing scheme, but also provide a more flexible framework that scrubs the data in the subsequent FBN estimation model. Finally, we validate the proposed method by identifying subjects with mild cognitive impairment (MCI) from normal controls based on the estimated BFNs, achieving an 80.22% classification accuracy, which significantly improves the baseline methods.
Chinese government proposed the “Double Carbon Target” (DCT) in 2020 to deal with the increasing global warming crisis. In this regard, the study identifies temporal and spatial evolution characteristics of environmental efficiency through the DEA-SBM model and further explores the impact of DCT on the environmental efficiency of coal cities using scenario analysis method. Empirical results show that: 1) Both economic efficiency and environmental efficiency of China’s coal cities are first rising and then falling during the period 2003–2022, and the gap between coal cities and non-coal cities was very small before 2011, but it begins to be enlarged after 2011. The main reason is environmental regulation has exerted a significant impact on coal cities; 2) the difference in environmental efficiency among coal cities is huge due to their policies for supporting renewable energy. Some cities have broken carbon lock-in by the favorite policy for renewable energy, while others have been trapped into path dependence on the coal-related industry; 3) generally, the more amount of emission reduction required, the lower the environmental efficiency of coal cities in the carbon neutralization scenario. Furthermore, some cities rich of renewable energy resources, such as Erdos, and Xuzhou, still have better environmental performance under different carbon neutralization scenarios, while others will encounter many transformation barriers and even may cause a social crisis. Therefore, it is suggested that some coal cities in northwest China can vigorously develop solar energy to improve environmental efficiency.
Trophoblast cells synthesize and secrete prostaglandins (PGs), which are essential for ruminants in early gestation to recognize pregnancy. Hormones in the intrauterine environment play an important role in regulating PGs synthesis during implantation, but the underlying mechanism remains unclear. In this study, co-treatment of sheep trophoblast cells (STCs) with progesterone (P4), estradiol (E2), and interferon-tau (IFN-τ) increased the ratio of prostaglandin E2 (PGE2) to prostaglandin F2α (PGF2α) and upregulated peroxisome proliferator-activated receptor γ (PPARγ) expression, while inhibiting the mechanistic target of rapamycin (mTOR) pathway and activating cellular autophagy. Under hormone treatment, inhibition of PPARγ activity decreased the ratio of PGE2/PGF2α and cellular activity, while activating expression of the mTOR downstream marker—the phosphorylation of p70S6K (p-p70S6K). We also found that the PPARγ/mTOR pathway played an important role in regulating trophoblast cell function. Inhibition of the mTOR pathway by rapamycin increased the ratio of PGE2/PGF2α and decreased the expression of apoptosis-related proteins after inhibiting PPARγ activity. In conclusion, our findings provide new insights into the molecular mechanism of prostaglandin regulation of trophoblast cells in sheep during early pregnancy, indicating that the PPARγ/mTOR pathway plays an important role in PGs secretion and cell viability.
We report here the identification and characterization of a protein, ERIS, an endoplasmic reticulum (ER) IFN stimulator, which is a strong type I IFN stimulator and plays a pivotal role in response to both non-self-cytosolic RNA and dsDNA. ERIS (also known as STING or MITA) resided exclusively on ER membrane. The ER retention/retrieval sequence RIR was found to be critical to retain the protein on ER membrane and to maintain its integrity. ERIS was dimerized on innate immune challenges. Coumermycin-induced ERIS dimerization led to strong and fast IFN induction, suggesting that dimerization of ERIS was critical for self-activation and subsequent downstream signaling.
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