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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.

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. [ABSTRACT FROM AUTHOR]

In this paper, we consider using total variation (TV) minimization to recover signals whose gradients have a sparse support, from a small number of measurements. We establish a proof for the performance guarantee of TV minimization in recovering one-dimensional signal with sparse gradient support. This answers the open question of proving the fidelity of TV minimization in such a setting. We have shown that, when the number of Gaussian measurements M ≳√NK logN, the TV minimization guarantees the exact recovery of any signal of size N with at most K non-zero gradients with high probability; when M ≲√NK, the TV minimization cannot find the original signal with a moderate probability. Last but not least, when M grows linearly with the signal dimension, we will also show that the recoverable sparsity K grows linearly with the signal dimension as well. [ABSTRACT FROM AUTHOR]

Purpose: The authors present a novel paddle-based rotating-shield brachytherapy (P-RSBT) method, whose radiation-attenuating shields are formed with a multileaf collimator (MLC), consisting of retractable paddles, to achieve intensity modulation in high-dose-rate brachytherapy. Methods: Five cervical cancer patients using an intrauterine tandem applicator were considered to assess the potential benefit of the P-RSBT method. The P-RSBT source used was a 50 kV electronic brachytherapy source (Xoft Axxent™). The paddles can be retracted independently to form multiple emission windows around the source for radiation delivery. The MLC was assumed to be rotatable. P-RSBT treatment plans were generated using the asymmetric dose–volume optimization with smoothness control method [Liu et al., Med. Phys. 41(11), 111709 (11pp.) (2014)] with a delivery time constraint, different paddle sizes, and different rotation strides. The number of treatment fractions (fx) was assumed to be five. As brachytherapy is delivered as a boost for cervical cancer, the dose distribution for each case includes the dose from external beam radiotherapy as well, which is 45 Gy in 25 fx. The high-risk clinical target volume (HR-CTV) doses were escalated until the minimum dose to the hottest 2 cm³ (D2cm³) of either the rectum, sigmoid colon, or bladder reached their tolerance doses of 75, 75, and 90 Gy3, respectively, expressed as equivalent doses in 2 Gy fractions (EQD2 with α/β = 3 Gy). Results: P-RSBT outperformed the two other RSBT delivery techniques, single-shield RSBT (S-RSBT) and dynamic-shield RSBT (D-RSBT), with a properly selected paddle size. If the paddle size was angled at 60°, the average D 90 increases for the delivery plans by P-RSBT on the five cases, compared to S-RSBT, were 2.2, 8.3, 12.6, 11.9, and 9.1 Gy10, respectively, with delivery times of 10, 15, 20, 25, and 30 min/fx. The increases in HR-CTV D 90, compared to D-RSBT, were 16.6, 12.9, 7.2, 3.7, and 1.7 Gy10, respectively. P-RSBT HR-CTV D 90-values were insensitive to the paddle size for paddles angled at less than 60°. Increasing the paddle angle from 5° to 60° resulted in only a 0.6 Gy10 decrease in HR-CTV D 90 on average for five cases when the delivery times were set to 15 min/fx. The HR-CTV D 90 decreased to 2.5 and 11.9 Gy10 with paddle angles of 90° and 120°, respectively. Conclusions: P-RSBT produces treatment plans that are dosimetrically and temporally superior to those of S-RSBT and D-RSBT, although P-RSBT systems may be more mechanically challenging to develop than S-RSBT or D-RSBT. A P-RSBT implementation with 4–6 shield paddles would be sufficient to outperform S-RSBT and D-RSBT if delivery times are constrained to less than 15 min/fx. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of a generalized version of the coupon collector problem, in which the collector receives d coupons each run and chooses the least-collected coupon so far. In the asymptotic case when the number of coupons n goes to infinity, we show that, on average, (n log n)/d + (n/d)(m - 1) log log n + 0 (mn) runs are needed to collect m sets of coupons. An exact algorithm is also developed for any finite case to compute the exact mean number of runs. Numerical examples are provided to verify our theoretical predictions. [ABSTRACT FROM AUTHOR]

Minimizing the rank of a matrix subject to constraints is a challenging problem that arises in many applications in machine learning, control theory, and discrete geometry. This class of optimization problems, known as rank minimization, is NP-hard, and for most practical problems there are no efficient algorithms that yield exact solutions. A popular heuristic replaces the rank function with the nuclear norm-equal to the sum of the singular values-of the decision variable and has been shown to provide the optimal low rank solution in a variety of scenarios. In this paper, we assess the practical performance of this heuristic for finding the minimum rank matrix subject to linear equality constraints. We characterize properties of the null space of the linear operator defining the constraint set that are necessary and sufficient for the heuristic to succeed. We then analyze linear constraints sampled uniformly at random, and obtain dimension-free bounds under which our null space properties hold almost surely as the matrix dimensions tend to infinity. Finally, we provide empirical evidence that these probabilistic bounds provide accurate predictions of the heuristic's performance in non-asymptotic scenarios. [ABSTRACT FROM AUTHOR]

Expander graphs have been recently proposed to construct efficient compressed sensing algorithms. In particular, it has been shown that any n-dimensional vector that is k-sparse can be fully recovered using O(k log n) measurements and only O(k log n) simple recovery iterations. In this paper, we improve upon this result by considering expander graphs with expansion coefficient beyond ¾ and show that, with the same number of measurements, only O(k) recovery iterations are required, which is a significant improvement when n is large. In fact, full recovery can be accomplished by at most 2k very simple iterations. The number of iterations can he reduced arbitrarily close to k, and the recovery algorithm can be implemented very efficiently using a simple priority queue with total recovery time O(n log(n/k))). We also show that by tolerating a small penalty on the number of measurements, and not on the number of recovery iterations, one can use the efficient construction of a family of expander graphs to come up with explicit measurement matrices for this method. We compare our result with other recently developed expander-graph-based methods and argue that it compares favorably both in terms of the number of required measurements and in terms of the time complexity and the simplicity of recovery. Finally, we will show how our analysis extends to give a robust algorithm that finds the position and sign of the k significant elements of an almost k-sparse signal and then, using very simple optimization techniques, finds a k-sparse signal which is close to the best k-term approximation of the original signal. [ABSTRACT FROM AUTHOR]

The optimal design of waste treatment processes always involves several objectives to be considered like cost and Environmental Impact (EI). Hence a multi-objective optimisation framework is required whose solution is not a single value but a Pareto set, which includes the alternatives representing potential compromise solutions among the objectives. Further, uncertainties are inherent in EI assessment, these uncertainties need to be propagated and analysed. In this paper, a multi-objective optimisation algorithm called MOEGA is developed. This new and efficient algorithm identifies more trade- off solutions (with and without uncertainties in EI) than before. [ABSTRACT FROM AUTHOR]