Search

The rapid spread of SARS-CoV-2 has placed a significant burden on public health systems to provide swift and accurate diagnostic testing highlighting the critical need for innovative testing approaches for future pandemics. In this study, we present a novel sample pooling procedure based on compressed sensing theory to accurately identify virally infected patients at high prevalence rates utilizing an innovative viral RNA extraction process to minimize sample dilution. At prevalence rates ranging from 0-14.3%, the number of tests required to identify the infection status of all patients was reduced by 69.26% as compared to conventional testing in primary human SARS-CoV-2 nasopharyngeal swabs and a coronavirus model system. Our method provided quantification of individual sample viral load within a pool as well as a binary positive-negative result. Additionally, our modified pooling and RNA extraction process minimized sample dilution which remained constant as pool sizes increased. Compressed sensing can be adapted to a wide variety of diagnostic testing applications to increase throughput for routine laboratory testing as well as a means to increase testing capacity to combat future pandemics.

Background: Accumulating evidence shows that pyroptosis plays a crucial role in hepatocellular carcinoma (HCC). However, the relationship between pyroptosis-related long non-coding RNAs (lncRNAs) and HCC tumor characteristics remains enigmatic. We aimed to explore the predictive effect of pyroptosis-related lncRNAs (PRLs) in the prognosis of HCC.Methods: We comprehensively analyzed the role of the PRLs in the tumor microenvironment and HCC prognosis by integrating genomic data from patients of HCC. Consensus clustering analysis of PRLs was applied to identify HCC subtypes. A prognostic model was then established with a training cohort from The Cancer Genome Atlas (TCGA) using univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Further, we evaluated the accuracy of this predictive model using a validation set. We predicted IC50s of commonly used chemotherapeutic and targeted drugs through the R package pRRophetic.Results: Based on pyroptosis-related lncRNAs, a prognostic risk signature composed of seven PRLs (MKLN1AS, AL031985.3, SNHG4, GHRLOS, AC005479.2, AC099850.4, and AC026412.3) was established. For long-term prognosis of HCC patients, our model shows excellent accuracy to forecast overall survival of HCC individuals both in training set and testing set. We found a significant correlation between clinical features and the risk score. Patients in the high-risk group had tumor characteristics associated with progression such as aggressive pathological grade and stage. Besides that, gene set enrichment analysis (GSEA) showed that cell cycle and focal adhesion were significantly enriched in the high-risk group.Conclusion: The association of the risk model constituted by these seven pyroptosis-related lncRNAs with clinical prognosis, tumor microenvironment, chemotherapy and small molecule drugs was evaluated. Our study provides strong evidence for individualized prediction of prognosis, shedding light on immunotherapy in HCC patients.

Abstract The null space condition for ℓ 1 minimization in compressed sensing is a necessary and sufficient condition on the sensing matrices under which a sparse signal can be uniquely recovered from the observation data via ℓ 1 minimization. However, verifying the null space condition is known to be computationally challenging. Most of the existing methods can provide only upper and lower bounds on the proportion parameter that characterizes the null space condition. In this paper, we propose new polynomial-time algorithms to establish upper bounds of the proportion parameter. We leverage on these techniques to find upper bounds and further develop a new procedure—tree search algorithm—that is able to precisely and quickly verify the null space condition. Numerical experiments show that the execution speed and accuracy of the results obtained from our methods far exceed those of the previous methods which rely on linear programming (LP) relaxation and semidefinite programming (SDP).

Background/Aims: As a type of malignant tumor commonly found in the bile duct, cholangiocarcinoma (CCA) has a poor prognosis. Long non-coding RNA (lncRNA) has recently drawn increasing attention because it functions as a competing endogenous RNA (ceRNA) to hinder miRNA functions that participate in posttranscriptional regulatory networks in tumors. Therefore, to investigate the mechanisms of CCA carcinogenesis and to enhance treatment efficiency, the expression profiles, including lncRNA, miRNA, and mRNA data, were comprehensively integrated and analyzed in this study.Methods: A comprehensive comparison was performed on the RNA-sequencing and miRNA profiles data of 36 CCA samples and 9 normal samples from The Cancer Genome Atlas (TCGA) database. Then, a dysregulated lncRNA-related ceRNA network was established by using four public databases.Results: In summary, 1,410 lncRNAs, 64 miRNAs, and 3,494 mRNAs appeared as genes that were aberrantly expressed in CCA. Then, a dysregulated ceRNA network related to the lncRNAs was constructed. The network included 116 lncRNAs, 13 miRNAs and 60 mRNAs specific to CCA. The survival analysis showed that, among them, 26 lncRNAs, 3 miRNAs, and 13 mRNAs were prognostic biomarkers for patients with CCA. Finally, three mRNAs were selected for validation of their expression levels in the Gene Expression Omnibus (GEO) database. The results indicated that the expression of those genes was highly consistent between the TCGA and GEO databases.Conclusions: The findings in this study provide a better understanding of the ceRNA network involved in CCA biology and lay a solid foundation for improving CCA diagnosis and prognosis.

In recent years, with the rapid economic development and the deepening of global economic integration, import and export trade has developed rapidly. With the development of international trade, international logistics has emerged. Modern logistics is a necessity for import and export trade, and the development of logistics has a direct influence on the development of import and export. In order to adapt to the rapid development of import and export trade, the modern logistics industry is developing in the direction of quality, diversity and efficiency. As the manufacturing capital, Dongguan has great potential for trade development. Relying on the superior geographical environment and the original industrial system, and further integrating resources on this basis, Dongguan vigorously develops its modern logistics industry and promotes a high degree of connection between international trade and characteristic industries, promoting the development of Dongguan’s import and export trade. Taking Dongguan as the research object and combining the data of Dongguan’s import and export trade and modern logistics industries, this paper established a quadratic curve fitting model to analyze the relationship between the total transportation volume and the total import and export volume, conducted elasticity and marginal analysis based on the empirical results, and put forward feasible suggestions for the development of Dongguan’s logistics and import and export trade.

We demonstrate the low temperature, large area growth of ultrathin carbon films by chemical vapor deposition under atmospheric pressure on various substrates. In particularly, uniform and continuous carbon films with the thickness of 2-5 nm were successfully grown at a temperature as low as 500 oC on copper foils, as well as glass substrates coated with a 100 nm thick copper layer. The characterizations revealed that the low-temperature-grown carbon films consist on few short, curved graphene layers and thin amorphous carbon films. Particularly, the low-temperature grown samples exhibited over 90% transmittance at a wavelength range of 400-750 nm and comparable sheet resistance in contrast with the 1000oC-grown one. This low-temperature growth method may offer a facile way to directly prepare visible ultrathin carbon films on various substrate surfaces that are compatible with temperatures (500-600oC) used in several device processing technologies.

In this work we directly synthesized molybdenum disulfide (MoS2) nanosheets on carbon nanotube film (MoS2@CNT) via a two-step chemical vapor deposition method (CVD). By etching the obtained MoS2@CNT into 10% wt HNO3, the morphology of MoS2 decorated on CNT bundles was modulated, resulting in more catalytic active MoS2 edges being exposed for significantly enhanced electrochemical performance. Our results revealed that an 8 h acid etching sample exhibited the best performance for the oxygen evolution reaction, i.e., the current density reached 10 mA/cm2 under 375 mV over-potential, and the tafel slope was as low as 94 mV/dec. The enhanced behavior was mainly originated from the more catalytic sites in MoS2 induced by the acid etching treatment and the higher conductivity from the supporting CNT films. Our study provides a new route to produce two-dimensional layers on CNT films with tunable morphology, and thus may open a window for exploring its promising applications in the fields of catalytic-, electronic-, and electrochemical-related fields.