Detecting single molecules inside a carbon nanotube to control molecular sequences using inertia trapping phenomenon

Here we show the detection of single gas molecules inside a carbon nanotube based on the change in resonance frequency and amplitude associated with the inertia trapping phenomenon. As its direct implication, a method for controlling the sequence of small molecule is then proposed to realize the concept of manoeuvring of matter atom by atom in one dimension. The detection as well as the implication is demonstrated numerically with the molecular dynamics method. It is theoretically assessed that it is possible for a physical model to be fabricated in the very near future

Real-World Image Super Resolution via Unsupervised Bi-directional Cycle Domain Transfer Learning based Generative Adversarial Network

Deep Convolutional Neural Networks (DCNNs) have exhibited impressive performance on image super-resolution tasks. However, these deep learning-based super-resolution methods perform poorly in real-world super-resolution tasks, where the paired high-resolution and low-resolution images are unavailable and the low-resolution images are degraded by complicated and unknown kernels. To break these limitations, we propose the Unsupervised Bi-directional Cycle Domain Transfer Learning-based Generative Adversarial Network (UBCDTL-GAN), which consists of an Unsupervised Bi-directional Cycle Domain Transfer Network (UBCDTN) and the Semantic Encoder guided Super Resolution Network (SESRN). First, the UBCDTN is able to produce an approximated real-like LR image through transferring the LR image from an artificially degraded domain to the real-world LR image domain. Second, the SESRN has the ability to super-resolve the approximated real-like LR image to a photo-realistic HR image. Extensive experiments on unpaired real-world image benchmark datasets demonstrate that the proposed method achieves superior performance compared to state-of-the-art methods.Comment: 12 pages, 5 figures,3 tables. This work is submitted to IEEE Transactions on Systems, Man, and Cybernetics: Systems (2022). It's under review by IEEE Transactions on Systems, Man, and Cybernetics: Systems for no

Evolution and vulnerability analysis of the global trade pattern in the lithium industry chain

[Objective] This study aims to simulate the vulnerability of the lithium industry trade network in the event of interruption risks. The goal is to effectively identify key nodes and potential risks in the network, providing decision support for optimizing trade patterns and avoiding interruption risks. [Methods] Analyzing the evolution of the lithium industry trade pattern based on trade flow methods, intentional attack simulations were conducted to assess the vulnerability of the lithium industry trade network after trade interruptions occurred in the top 10% of nodes by PageRank centrality. [Results] The research reveals: (1) The global trade pattern of the lithium industry chain is undergoing profound restructuring and transformation, with China’s position highlighted in the global trade network. (2) Invulnerability in the upstream network of the lithium industry chain has improved during the sample period, while the risk resistance capabilities of the midstream and downstream networks are relatively stable. (3) The vulnerability ranking of the lithium industry chain is downstream < midstream < upstream. When trade interruptions occur in the top 10% of global key nodes, the overall performance of the upstream, midstream, and downstream trade networks decreases by an average of 60%, 35%, and 23.5%, respectively. [Conclusion] To maintain the security and stability of China’s and the global lithium industry, the following measures should be implemented: enhance and refine safety risk warning and emergency support mechanisms within the lithium industry chain; establish a cooperative, win-win framework among key stakeholders in the lithium industry chain to bolster positive response capabilities across the industry, supply chain, and value chain; and improve domestic self-sufficiency and global allocation capabilities for lithium resources

Responses of economic and anatomical leaf traits to soil fertility factors in eight coexisting broadleaf species in temperate forests

The multidimensionality of leaf traits allows plants to have diverse survival strategies to adapt to complex living environments. Whether the anatomical traits of leaves are associated with leaf economic traits and which group of traits are more strongly correlated with soil fertility factors remains unclear. We measured four leaf economic traits, four anatomical traits, and five soil fertility factors of eight coexisting broadleaf species distributed in mixed broadleaved-Korean pine (Pinus koraiensis) forests located in Northeast China. Results show a strong interdependence between economic and anatomical traits (p < 0.05). The range of variation between economic and anatomical traits were almost equal, but the causes of variation were different. Specific leaf area was positively correlated with the abaxial epidermis, negatively correlated with the ratio of spongy tissue to leaf thickness (ST/LT), and not correlated with adaxial epidermis. Leaf dry matter content was negatively correlated with the abaxial epidermis and adaxial epidermis, positively correlated with ST/LT. Specific leaf area, palisade tissue, and ST/LT showed stronger correlation with soil fertility factors than other traits. Soil fertility factors dominating trait variation were dependent upon the trait. Our results suggest anatomical traits can be considered in economic trait dimension. The coupled relationship between anatomical and economic traits is potentially a cost-effective adaptation strategy for species to improve efficiency in resource utilization. Our results provide evidence for the complex soil-trait relationship and suggest that future studies should emphasize the role of anatomic traits in predicting soil fertility changes

Floatcascade learning for fast imbalanced web mining

This paper is concerned with the problem of Imbalanced Classification (IC) in web mining, which often arises on the web due to the 'Matthew Effect'. As web IC applications usually need to provide online service for user and deal with large volume of data, classification speed emerges as an important issue to be addressed. In face detection, Asymmetric Cascade is used to speed up imbalanced classification by building a cascade structure of simple classifiers, but it often causes a loss of classification accuracy due to the iterative feature addition in its learning procedure. In this paper, we adopt the idea of cascade classifier in imbalanced web mining for fast classification and propose a novel asymmetric cascade learning method called FloatCascade to improve the accuracy. To the end, FloatCascade selects fewer yet more effective features at each stage of the cascade classifier. In addition, a decision-tree scheme is adopted to enhance feature diversity and discrimination capability for FloatCascade learning. We evaluate FloatCascade through two typical IC applications in web mining: web page categorization and citation matching. Experimental results demonstrate the effectiveness and efficiency of FloatCascade comparing to the state-of-the-art IC methods like Asymmetric Cascade, Asymmetric AdaBoost and Weighted SVM.EI

Microbial Electricity Generation Enhances Decabromodiphenyl Ether (BDE-209) Degradation

We thank Hao Yu and Ye Deng at the University of Oklahoma for assistance with GeoChip hybridization and data pre-processing. We also thank Professor Bixian Mai and Dr. Leheng Yu in Guangzhou Institute of Geochemistry, CAS, for their helps in PBDE congener analyses.Conceived and designed the experiments: MYX JG GPS. Performed the experiments: YGY MYX. Analyzed the data: MYX YGY. Contributed reagents/materials/analysis tools: ZLH JZZ. Wrote the paper: MYX YGY ZLH.Due to environmental persistence and biotoxicity of polybrominated diphenyl ethers (PBDEs), it is urgent to develop potential technologies to remediate PBDEs. Introducing electrodes for microbial electricity generation to stimulate the anaerobic degradation of organic pollutants is highly promising for bioremediation. However, it is still not clear whether the degradation of PBDEs could be promoted by this strategy. In this study, we hypothesized that the degradation of PBDEs (e.g., BDE-209) would be enhanced under microbial electricity generation condition. The functional compositions and structures of microbial communities in closed-circuit microbial fuel cell (c-MFC) and open-circuit microbial fuel cell (o-MFC) systems for BDE-209 degradation were detected by a comprehensive functional gene array, GeoChip 4.0, and linked with PBDE degradations. The results indicated that distinctly different microbial community structures were formed between c-MFCs and o-MFCs, and that lower concentrations of BDE-209 and the resulting lower brominated PBDE products were detected in c-MFCs after 70-day performance. The diversity and abundance of a variety of functional genes in c-MFCs were significantly higher than those in o-MFCs. Most genes involved in chlorinated solvent reductive dechlorination, hydroxylation, methoxylation and aromatic hydrocarbon degradation were highly enriched in c-MFCs and significantly positively correlated with the removal of PBDEs. Various other microbial functional genes for carbon, nitrogen, phosphorus and sulfur cycling, as well as energy transformation process, were also significantly increased in c-MFCs. Together, these results suggest that PBDE degradation could be enhanced by introducing the electrodes for microbial electricity generation and by specifically stimulating microbial functional genes.Yeshttp://www.plosone.org/static/editorial#pee

Multiwalled carbon nanotubes co-delivering sorafenib and epidermal growth factor receptor siRNA enhanced tumor-suppressing effect on liver cancer.

OBJECTIVE: This study aimed to investigate the effects of multiwalled carbon nanotubes (MWNTs) co-delivering sorafenib (Sor) and epidermal growth factor receptor (EGFR) siRNA (MWNT/Sor/siRNA) on tumor growth in liver cancer (LC). RESULTS: MWNT/Sor/siRNA was proved to possess increased Sor release, high siRNA stability, and enhanced cellular uptake. In addition, MWNT treatment has few effects on cell proliferation and apoptosis in HepG2 cells; however, MWNT/Sor/siRNA treatment significantly inhibited clone number and induced cell apoptosis, which shows a more favorable antitumor effect than MWNT/Sor and free Sor and free siRNA in HepG2 cells. Moreover MWNT/Sor/siRNA treatment has the most significant antitumor effect CONCLUSIONS: MWNT/Sor/siRNA exhibited a superior antitumor effect METHODS: The MWNT/Sor and MWNT/Sor/siRNA were prepared, and then the morphologies of MWNT/Sor/siRNA were analyzed

Liquid biopsy-based single-cell metabolic phenotyping of lung cancer patients for informative diagnostics.

Accurate prediction of chemo- or targeted therapy responses for patients with similar driver oncogenes through a simple and least-invasive assay represents an unmet need in the clinical diagnosis of non-small cell lung cancer. Using a single-cell on-chip metabolic cytometry and fluorescent metabolic probes, we show metabolic phenotyping on the rare disseminated tumor cells in pleural effusions across a panel of 32 lung adenocarcinoma patients. Our results reveal extensive metabolic heterogeneity of tumor cells that differentially engage in glycolysis and mitochondrial oxidation. The cell number ratio of the two metabolic phenotypes is found to be predictive for patient therapy response, physiological performance, and survival. Transcriptome analysis reveals that the glycolytic phenotype is associated with mesenchymal-like cell state with elevated expression of the resistant-leading receptor tyrosine kinase AXL and immune checkpoint ligands. Drug targeting AXL induces a significant cell killing in the glycolytic cells without affecting the cells with active mitochondrial oxidation

Surface Immobilization of Redox-Labile Fluorescent Probes: Enabling Single-Cell Co-Profiling of Aerobic Glycolysis and Oncogenic Protein Signaling Activities.

An analytical method is described for profiling lactate production in single cells via the use of coupled enzyme reactions on surface-grafted resazurin molecules. The immobilization of the redox-labile probes was achieved through chemical modifications on resazurin, followed by bio-orthogonal click reactions. The lactate detection was demonstrated to be sensitive and specific. The method was incorporated into a single-cell barcode chip for simultaneous quantification of aerobic glycolysis activities and oncogenic signaling phosphoproteins in cancer. The interplay between glycolysis and oncogenic signaling activities was interrogated on a glioblastoma cell line. Results revealed a drug-induced oncogenic signaling reliance accompanying shifted metabolic paradigms. A drug combination that exploits this induced reliance exhibited synergistic effects in growth inhibition