Decomposition and Decoupling Analysis of Carbon Emissions in Xinjiang Energy Base, China

China faces a difficult choice of maintaining socioeconomic development and carbon emissions mitigation. Analyzing the decoupling relationship between economic development and carbon emissions and its driving factors from a regional perspective is the key for the Chinese government to achieve the 2030 emission reduction target. This study adopted the logarithmic mean Divisia index (LMDI) method and Tapio index, decomposed the driving forces of the decoupling, and measured the sector’s decoupling states from carbon emissions in Xinjiang province, China. The results found that: (1) Xinjiang’s carbon emissions increased from 93.34 Mt in 2000 to 468.12 Mt in 2017. Energy-intensive industries were the key body of carbon emissions in Xinjiang. (2) The economic activity effect played the decisive factor to carbon emissions increase, which account for 93.58%, 81.51%, and 58.62% in Xinjiang during 2000–2005, 2005–2010, and 2010–2017, respectively. The energy intensity effect proved the dominant influence for carbon emissions mitigation, which accounted for −22.39% of carbon emissions increase during 2000–2010. (3) Weak decoupling (WD), expansive coupling (EC), expansive negative decoupling (END) and strong negative decoupling (SND) were identified in Xinjiang during 2001 to 2017. Gross domestic product (GDP) per capita elasticity has a major inhibitory effect on the carbon emissions decoupling. Energy intensity elasticity played a major driver to the decoupling in Xinjiang. Most industries have not reached the decoupling state in Xinjiang. Fuel processing, power generation, chemicals, non-ferrous, iron and steel industries mainly shown states of END and EC. On this basis, it is suggested that local governments should adjust the industrial structure, optimize energy consumption structure, and promote energy conservation and emission reduction to tap the potential of carbon emissions mitigation in key sectors

Latent Network Construction for Univariate Time Series Based on Variational Auto-Encode

Time series analysis has been an important branch of information processing, and the conversion of time series into complex networks provides a new means to understand and analyze time series. In this work, using Variational Auto-Encode (VAE), we explored the construction of latent networks for univariate time series. We first trained the VAE to obtain the space of latent probability distributions of the time series and then decomposed the multivariate Gaussian distribution into multiple univariate Gaussian distributions. By measuring the distance between univariate Gaussian distributions on a statistical manifold, the latent network construction was finally achieved. The experimental results show that the latent network can effectively retain the original information of the time series and provide a new data structure for the downstream tasks

Interpretation for Individual Brain Age Prediction Based on Gray Matter Volume

The relationship between age and the central nervous system (CNS) in humans has been a classical issue that has aroused extensive attention. Especially for individuals, it is of far greater importance to clarify the mechanisms between CNS and age. The primary goal of existing methods is to use MR images to derive high-accuracy predictions for age or degenerative diseases. However, the associated mechanisms between the images and the age have rarely been investigated. In this paper, we address the correlation between gray matter volume (GMV) and age, both in terms of gray matter themselves and their interaction network, using interpretable machine learning models for individuals. Our goal is not only to predict age accurately but more importantly, to explore the relationship between GMV and age. In addition to targeting each individual, we also investigate the dynamic properties of gray matter and their interaction network with individual age. The results show that the mean absolute error (MAE) of age prediction is 7.95 years. More notably, specific locations of gray matter and their interactions play different roles in age, and these roles change dynamically with age. The proposed method is a data-driven approach, which provides a new way to study aging mechanisms and even to diagnose degenerative brain diseases

Prognostic and Diagnostic Significance of SDPR-Cavin-2 in Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) is a malignant tumor worldwide. Due to the lack of early prediction marker, numerous patients were diagnosed in their late stage. The family of cavins plays important roles in caveolae formation and cellular processes. Cavin-2, one of the members of cavins, has been reported as a suppresser in cancers. In this study, we have investigated its expression pattern and clinical significance in HCC. Methods: RT‑qPCR was performed to detect the expression of cavin-2. Results: Cavin-2 was down-regulated in HCC and associated with tumor differentiation (r=-0.275, P=0.013) and tumor-node-metastasis (TNM) stage (r=-0.216, P=0.035). The Overall survival analysis showed that patients with lower cavin-2 expression had a relatively poor prognosis. Meanwhile, the multivariate analysis revealed that cavin-2 was an independent prognostic factor. The receiver operating characteristic curve analyses indicated that plasma cavin-2 presented a high accuracy (AUC=0.727, 0.865, 0.901) for diagnosing HCC cases from controls, hepatitis B and cirrhosis patients, respectively. Meanwhile, plasma cavin-2 showed a high sensitivity (88.4%, 89.9%) for detecting HCC with the serum α‑fetoprotein (AFP) levels below 200 ng/ml from those hepatitis B and cirrhosis cases. Conclusion: Our data suggested that cavin-2 might be considered as a potential prognostic and diagnostic indicator in HCC

Serum Midkine for AFP-negative hepatocellular carcinoma diagnosis: a systematic review and meta-analysis

Abstract Introduction To date, alpha-feto protein (AFP) remains the most widely used serum biomarker for hepatocellular carcinoma (HCC) diagnosis and prognosis. However, its role has become controversial as close to 30% of early stage HCC patients are AFP negative. Different studies on the diagnostic performance of novel AFP-negative HCC biomarkers have shown inconsistent results of sensitivity, specificity, and area under the receiver operating curve (AUROC). Here, we conducted a systematic review and meta-analysis to resolve this inconsistency. Methods Relevant studies were systematically searched from PubMed, Embase, Cochrane library, Scopus, and the China National Knowledge Infrastructure (chkd-cnki) databases up to the 20th October 2022. The Newcastle–Ottawa Scale was used to assess the methodological quality of included studies. Sensitivity, specificity, and area under the curve were pooled using the random effect model. Results Five studies, with a total of 286 patients, were included. Serum Midkine was assessed using enzyme-linked immunosorbent assay (ELISA) in all the studies, at diagnostic thresholds varying from 0.387 to 5.1 ng/ml. The summary estimates for serum Midkine were 76% (95% CI 70–81%) sensitivity, 85% (95% CI 82–87%) specificity, and 91% area under the receiver operating characteristic curve (AUC), while the pooled diagnostic odds ratio (DOR) was 27.64 (95% CI 4.95–154.17). Conclusion Based on these findings, serum Midkine is a very promising diagnostic biomarker for AFP-negative HCC and should be validated further in large cohort studies

Long noncoding RNA ROR promotes breast cancer by regulating the TGF-β pathway

Abstract Background Breast cancer is the leading cause of oncological mortality among women. Efficient detection of cancer cells in an early stage and potent therapeutic agents targeting metastatic tumors are highly needed to improve survival rates. Emerging evidence indicates that lncRNAs (long noncoding RNAs) are critical regulators of fundamental cellular processes in a variety of tumors including breast cancer. The functional details of these regulatory elements, however, remain largely unexplored. Methods In this study, lncRNA ROR (linc-ROR) was examined by real-time PCR in different breast cancer cell lines and breast tumor tissues/non-tumor tissues were collected from both breast cancer patients and healthy controls. Linc-ROR was knockdown in breast cancer cell lines and the effects on cell proliferation, migration and invasion were tested both in vitro and in vivo tumor model. Effects of linc-ROR knockdown on TGF-β signaling pathway were investigated by Western blot. Results Our studies have suggested that linc-ROR, a critical factor for embryonic stem cell maintenance, probably acts as an oncogenic factor in breast cancer cells, causing poor prognostic outcomes. Overexpression of linc-ROR seems to be responsible for promoting proliferation and invasion of cancer cells as well as tumor growth in nude mice. The regulatory action of linc-ROR can affect the activity of the TGF-β signaling pathway, which has been proven critical for mammary development and breast cancer. Conclusions The results have highlighted the potential importance of linc-ROR in the progression of advanced breast cancer, and thus will stimulate efforts in the development of novel diagnostic and therapeutic strategies

Identification of Circulating Long Noncoding RNA Linc00152 as a Novel Biomarker for Diagnosis and Monitoring of Non-Small-Cell Lung Cancer

Objective. Long noncoding RNAs (lncRNAs) have been reported to play vital roles in non-small-cell lung cancer (NSCLC). Recently, long noncoding RNA Linc00152 has been reported to play important roles in various cancers. In this study, our aim was to investigate its expression pattern and clinical significance and further evaluate its diagnostic value for NSCLC. Methods. The levels of Linc00152 were detected in NSCLC tissues and plasma samples by quantitative real-time PCR (qRT-PCR). Receiver operating characteristic (ROC) curves were depicted to evaluate the diagnostic value. Results. We found that Linc00152 levels were upregulated in both NSCLC tissues and plasma samples. Plasma Linc00152 levels were significantly lower in postoperative samples than in preoperative samples. Besides, high Linc00152 expression was significantly correlated with tumor size (r=0.293, P=0.005) and tumor stage (r=0.324, P=0.011). The ROC curves indicated that plasma Linc00152 has high diagnostic accuracy for NSCLC, and the area under curve (AUC) for NSCLC versus healthy was 0.816 (95% CI: 0.757–0.875). Moreover, we found that the combination of Linc00152 and CEA could provide a more powerful diagnosis efficiency than Linc00152 or CEA alone (AUC = 0.881, 95% CI: 0.836–0.926). Conclusions. Plasma Linc00152 could serve as a promising biomarker for diagnosing and monitoring NSCLC