•Both direct and indirect energy-related CO2 emissions are considered.•The driving factors are analysed at both the national and provincial levels.•Economic output was the dominant positive driving ...factor.•While energy intensity was the dominant negative driving factor.•Driving factors in China show significant spatial characteristics.
To grasp the characteristics of CO2 emissions across provinces in China and to determine changes in the centre of gravity of CO2 emissions over the 2000–2014 period, a gravity model is first used to examine the spatial distribution and centre of gravity of energy-related CO2 emissions. Then, to explore the main factors driving CO2 emission changes and to uncover feasible ways to reduce CO2 emissions, this paper decomposes changes in energy-related CO2 emissions into a population effect (ΔCP), an economic output effect (ΔCQ), an industrial structure effect (ΔCS), an energy intensity effect (ΔCI), an energy structure effect (ΔCM) and a carbon dioxide emission coefficient effect (ΔCU) at both the national and provincial levels based on the Log-Mean Divisia Index (LMDI) method. The results indicate that (1) energy-related CO2 emissions rose by approximately 5.46 billion tonnes during the 2000–2014 period, with secondary industry accounting for approximately 80% of total CO2 emissions. (2) Economic output (Q) was the dominant positive driving factor, and energy intensity (I) was the dominant negative driving factor. The population changes had a weak positive effect on CO2 emissions, but the industrial structure effect and energy structure effect varied considerably over the years without showing clear trends. (3) Over multiple spatial scales, the contribution ratios of the factors varied significantly across provinces; in general, the positive driving effects outweighed the negative inhibiting effects. Based on these empirical findings, policy recommendations to further reduce CO2 emissions are provided. The Chinese central and local governments should make full use of the important inhibiting factors, i.e., energy intensity and energy structure, and strive for breakthroughs in secondary sector.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
The existing deep networks have shown excellent performance in remote sensing scene classification (RSSC), which generally requires a large amount of class-balanced training samples. However, deep ...networks will result in underfitting with imbalanced training samples since they can easily bias toward the majority classes. To address these problems, a multigranularity decoupling network (MGDNet) is proposed for remote sensing image scene classification. To begin with, we design a multigranularity complementary feature representation (MGCFR) method to extract fine-grained features from remote sensing images, which utilizes region-level supervision to guide the attention of the decoupling network. Second, a class-imbalanced pseudolabel selection (CIPS) approach is proposed to evaluate the credibility of unlabeled samples. Finally, the diversity component feature (DCF) loss function is developed to force the local features to be more discriminative. Our model performs satisfactorily on three public datasets: UC Merced (UCM), NWPU-RESISC45, and Aerial Image Dataset (AID). Experimental results show that the proposed model yields superior performance compared with other state-of-the-art methods.
Network slicing has emerged as a promising networking paradigm to provide resources tailored for Industry 4.0 and diverse services in 5G networks. However, the increased network complexity poses a ...huge challenge in network management due to virtualized infrastructure and stringent quality-of-service requirements. Digital twin (DT) technology paves a way for achieving cost-efficient and performance-optimal management, through creating a virtual representation of slicing-enabled networks digitally to simulate its behaviors and predict the time-varying performance. In this article, a scalable DT of network slicing is developed, aiming to capture the intertwined relationships among slices and monitor the end-to-end (E2E) metrics of slices under diverse network environments. The proposed DT exploits the novel graph neural network model that can learn insights directly from slicing-enabled networks represented by non-Euclidean graph structures. Experimental results show that the DT can accurately mirror the network behaviour and predict E2E latency under various topologies and unseen environments.
The endoplasmic reticulum (ER) is an essential organelle in eukaryotic cells for the storage and regulated release of calcium and as the entrance to the secretory pathway. Protein misfolding in the ...ER causes accumulation of misfolded proteins (ER stress) and activation of the unfolded protein response (UPR), which has evolved to maintain a productive ER protein-folding environment. Both ER stress and UPR activation are documented in many different human cancers. In this Review, we summarize the impact of ER stress and UPR activation on every aspect of cancer and discuss outstanding questions for which answers will pave the way for therapeutics.
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DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Abstract
Background
Colorectal cancer (CRC) is the third most common cancer and leading cause of cancer-related deaths worldwide. Aberrant cellular metabolism is a hallmark of cancer cells, and ...disturbed metabolism showed clinical significance in CRC. The membrane-associated RING-CH 8 (MARCH8) protein, the first MARCH E3 ligase, plays an oncogenic role and serves as a prognostic marker in multiple cancers, however, the role of MARCH8 in CRC is unclear. In the present study, we aimed to investigate the biomarkers and their underlying mechanism for CRC.
Method
In this study, we first examined the function of MARCH8 in CRC by analysing public database. Besides, we performing gene silencing studies and generating cellular overexpression and xenograft models. Then its protein substrate was identified and validated. In addition, the expression of MARCH8 was investigated in tissue samples from CRC patients, and the molecular basis for decreased expression was analysed.
Results
Systematic analysis reveals that MARCH8 is a beneficial prognostic marker in CRC. In CRC, MARCH8 exhibited tumor-suppressive activity both in vivo and in vitro. Furthermore, we found that MARCH8 is negatively correlated with hexokinase 2 (HK2) protein in CRC patients. MARCH8 regulates glycolysis and promotes ubiquitination-mediated proteasome degradation to reduces HK2 protein levels. Then HK2 inhibitor partially rescues the effect of MARCH8 knockdown in CRC
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Poised chromatin and elevated miR-32 repressed MARCH8 expression.
Conclusion
In summary, we propose that in CRC, poised chromatin and miR-32 decrease the expression of MARCH8, further bind and add ubiquitin, induce HK2 degradation, and finally repress glycolysis to promote tumor suppressors in CRC.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Understanding and exploring the transport behaviors of ions and molecules in the nano and sub-nano confinement has great meaning in the fields of nanofluidics and basic transport physics. With the ...rapid progress in nanofabrication technology and effective characterization protocols, more and more anomalous transport behaviors have been observed and the ions/molecules inside small confinement can behave dramatically differently from bulk systems and present new mechanisms. In this Mini Review, we summarize the recent advances in the anomalous ionic/molecular transport behaviors in nano and sub-nano confinement. Our discussion includes the ionic/molecular transport of various confinement with different surface properties, static structures, and dynamic structures. Furthermore, we provide a brief overview of the latest applications of nanofluidics in membrane separation and energy conversion.
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IJS, KILJ, NUK, PNG, UL, UM
Summary
Removal of astringency by endogenously formed acetaldehyde, achieved by postharvest anaerobic treatment, is of critical importance for many types of persimmon fruit. Although an anaerobic ...environment accelerates de‐astringency, it also has the deleterious effect of promoting excessive softening, reducing shelf life and marketability. Some hypoxia‐responsive ethylene response factors (ERFs) participate in anaerobic de‐astringency, but their role in accelerated softening was unclear. Undesirable rapid softening induced by high CO2 (95%) was ameliorated by adding the ethylene inhibitor 1‐MCP (1 μL/L), resulting in reduced astringency while maintaining firmness, suggesting that CO2‐induced softening involves ethylene signalling. Among the hypoxia‐responsive genes, expression of eight involved in fruit cell wall metabolism (Dkβ‐gal1/4, DkEGase1, DkPE1/2, DkPG1, DkXTH9/10) and three ethylene response factor genes (DkERF8/16/19) showed significant correlations with postdeastringency fruit softening. Dual‐luciferase assay indicated that DkERF8/16/19 could trans‐activate the DkXTH9 promoter and this interaction was abolished by a mutation introduced into the C‐repeat/dehydration‐responsive element of the DkXTH9 promoter, supporting the conclusion that these DkERFs bind directly to the DkXTH9 promoter and regulate this gene, which encodes an important cell wall metabolism enzyme. Some hypoxia‐responsive ERF genes are involved in deastringency and softening, and this linkage was uncoupled by 1‐MCP. Fruit of the Japanese cultivar ‘Tonewase’ provide a model for altered anaerobic response, as they lost astringency yet maintained firmness after CO2 treatment without 1‐MCP and changes in cell wall enzymes and ERFs did not occur.
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BFBNIB, DOBA, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UILJ, UKNU, UL, UM, UPUK
Deep learning has achieved excellent performance in remote-sensing image scene classification, since a large number of datasets with annotations can be applied for training. However, in actual ...applications, there is just a few annotated samples and a large number of unannotated samples in remote-sensing images, which leads to overfitting of the deep model and affects the performance of scene classification. In order to address these problems, a semi-supervised representation consistency Siamese network (SS-RCSN) is proposed for remote-sensing image scene classification. First, considering intraclass diversity and interclass similarity of remote-sensing images, Involution-generative adversarial network (GAN) is utilized to extract the discriminative features from remote-sensing images via unsupervised learning. Then, Siamese network with a representation consistency loss is proposed for semi-supervised classification, which aims to reduce the differences of labeled and unlabeled data. Experimental results on UC Merced dataset, RESICS-45 dataset, aerial image dataset (AID), and RS dataset demonstrate that our method yields superior classification performance compared with other semi-supervised learning (SSL) methods.
The reflection of rightward moving shocks (RMSs) belonging to the first and second families, over an initially steady oblique shock wave (SOSW) produced by a wedge, is studied in this paper. Various ...possible combinations of primary reflection (reflection at the intersection point of the RMS and the SOSW) and secondary reflection (reflection, on the wedge, of reflected shock waves of the primary reflection) are identified and the transition conditions are studied. For an RMS of the first family, the shock reflection problem can be shown to be equivalent to a shock interference problem. If the wedge angle is large, then the problem is equivalent to a shock interaction problem with two incident shock waves of the same family so that we have type VI, type V and type IV shock interferences. Interestingly, when the wedge angle is small enough, deflection angle reversal is observed for the SOSW so that the right part of the SOSW can no longer be regarded as one incident shock wave. It is now the left part of the SOSW that becomes one incident shock wave. As a result, for a small wedge angle, type I or type II shock interference is observed. If the RMS belongs to the second family, then the primary reflection may have regular and Mach reflections, and one reflected shock of this primary reflection reflects over the wall as another pseudo-steady shock reflection, while the other reflected shock wave may be smooth or have a kink or a triple point, as in single, transitional and double Mach reflection of pseudo-steady shock reflection.
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