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AbstractWith the rapid development of quantum computers, researchers have shown quantum advantages in physics-oriented problems. Quantum algorithms tackling computational biology problems are still lacking. In this paper, we demonstrate the quantum advantage in analyzing CITE-seq data. CITE-seq, a single-cell technology, enables researchers to simultaneously measure expressions of RNA and surface protein detected by antibody-derived tags (ADTs) in the same cells. CITE-seq data hold tremendous potential for identifying the ADTs associated with targeted genes and identifying cell types effectively. However, both tasks are challenging since the best subset of ADTs needs to be identified from enormous candidate subsets. To surmount the challenge, we develop a quantum algorithm named bisection Grover’s search (BGS) for the best subset selection of ADT markers in CITE-seq data. BGS takes advantage of quantum parallelism by integrating binary search and Grover’s algorithm to enable fast computation. Theoretical results are provided to show the privilege of BGS in the estimation error and computational complexity. The empirical performance of the BGS algorithm is demonstrated on both the IBM quantum computer and simulator. [ABSTRACT FROM AUTHOR]

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Dye wastewater is a kind of refractory organic wastewater. Fenton coupled with micro-nano bubbles (MNBs+FT) was used for the degradation of Congo red (CR), aiming at simplifying the organic pollutants degradation process and reducing the cost of the process. The optimum condition of Fenton alone, the outlet pressure of the cavitation process and different combinations on the degradation of CR dye wastewater were discussed in this study. The results showed that the degradation of CR (100 mg/L) could reach 94.4% by using the MNBs+FT at the pH of 7, which was 72% higher than that using Fenton oxidation alone and 79% higher than that using MNBs alone. Based on the same degradation efficiency, the traditional Fenton process alone required 8 times the dose of oxidants of these combination systems, and the synergy coefficient of MNBs+FT was up to 2.44. ESR analysis indicated that ·OH was the predominant active species during the degradation of CR and MNBs+FT improved the utilization efficiency of H2O2 and produced more ·OH. Besides, the MNBs+FT could extend the pH range of the high-efficiency oxidation reaction, and it could also keep a high degradation rate under neutral conditions, which eliminated the process of adjusting the pH and reduced the anti-corrosion requirements of the equipment. According to the economic analysis results, the total cost of treatment for the MNBs/FT was about 13% of the cost of only the Fenton process. This study provides a reference for the application of MNBs+FT systems in full-scale dye wastewater treatment. [ABSTRACT FROM AUTHOR]

The onset and progression of pulmonary arterial hypertension (PAH), a malignant disease, are associated with environmental and epigenetic factors. Recent advancements in transcriptomics and proteomics technology have provided new insights into PAH and identified novel gene targets involved in the development of the disease. Transcriptomic analysis has led to the discovery of possible novel pathways, such as miR-483 targeting several PAH-related genes and a mechanistic link between the increase in HERV-K mRNA and protein. Proteomic analysis has revealed crucial details, including the loss of SIRT3 activity and the significance of the CLIC4/Arf6 pathway in PAH pathogenesis. Gene profiles and protein interaction networks of PAH have been analyzed, clarifying the roles of differentially expressed genes or proteins in the occurrence and development of PAH. This article discusses these recent advances. [ABSTRACT FROM AUTHOR]

Subsampling methods aim to select a subsample as a surrogate for the observed sample. Such methods have been used pervasively in large-scale data analytics, active learning, and privacy-preserving analysis in recent decades. Instead of model-based methods, in this article, we study model-free subsampling methods, which aim to identify a subsample, that is, not confined by model assumptions. Existing model-free subsampling methods are usually built upon clustering techniques or kernel tricks. Most of these methods suffer from either a large computational burden or a theoretical weakness. In particular, the theoretical weakness is that the empirical distribution of the selected subsample may not necessarily converge to the population distribution. Such computational and theoretical limitations hinder the broad applicability of model-free subsampling methods in practice. We propose a novel model-free subsampling method by using optimal transport techniques. Moreover, we develop an efficient subsampling algorithm, that is, adaptive to the unknown probability density function. Theoretically, we show the selected subsample can be used for efficient density estimation by deriving the convergence rate for the proposed subsample kernel density estimator. We also provide the optimal bandwidth for the proposed estimator. Numerical studies on synthetic and real-world datasets demonstrate the performance of the proposed method is superior. [ABSTRACT FROM AUTHOR]

Regression models with a functional response and functional covariate have received significant attention recently. While various nonparametric and semiparametric models have been developed, there is an urgent need for model selection and diagnostic methods. In this article, we develop a unified framework for estimation and model selection in nonparametric function-on-function regression. We propose a general nonparametric functional regression model with the model space constructed through smoothing spline analysis of variance (SS ANOVA). The proposed model reduces to some of the existing models when selected components in the SS ANOVA decomposition are eliminated. We propose new estimation procedures under either L1 or L2 penalty and show that the combination of the SS ANOVA decomposition and L1 penalty provides powerful tools for model selection and diagnostics. We establish consistency and convergence rates for estimates of the regression function and each component in its decomposition under both the L1 and L2 penalties. Simulation studies and real examples show that the proposed methods perform well. Technical details and additional simulation results are available in online . [ABSTRACT FROM AUTHOR]

Smoothing splines have been used pervasively in nonparametric regressions. However, the computational burden of smoothing splines is significant when the sample size n is large. When the number of predictors d ≥ 2 , the computational cost for smoothing splines is at the order of O (n 3) using the standard approach. Many methods have been developed to approximate smoothing spline estimators by using q basis functions instead of n ones, resulting in a computational cost of the order O (n q 2) . These methods are called the basis selection methods. Despite algorithmic benefits, most of the basis selection methods require the assumption that the sample is uniformly distributed on a hypercube. These methods may have deteriorating performance when such an assumption is not met. To overcome the obstacle, we develop an efficient algorithm that is adaptive to the unknown probability density function of the predictors. Theoretically, we show the proposed estimator has the same convergence rate as the full-basis estimator when q is roughly at the order of O [ n 2 d / { (p r + 1) (d + 2) } ] , where p ∈ [ 1 , 2 ] and r ≈ 4 are some constants depend on the type of the spline. Numerical studies on various synthetic datasets demonstrate the superior performance of the proposed estimator in comparison with mainstream competitors. Supplementary files for this article are available online. [ABSTRACT FROM AUTHOR]

We aimed to investigate the expression levels of GABA and GABAA receptor α1 and α2 subunits in patients with major depressive disorder (MDD) during onset and remission. 48 patients with MDD during onset and 45 patients with MDD during remission who were treated in our university were selected. Moreover, the control group included 46 healthy volunteers recruited in the community. The depression and anxiety symptoms were assessed by Hamilton Depression (HAMD) Scale and Hamilton Anxiety (HAMA) Scale. ELISA was used to determine the serum GABA levels. The mRNA expression of GABAA receptor α1 and α2 subunits in peripheral blood were detected by RT-PCR. The expression levels of serum GABA and of GABAA receptor α1 and α2 subunits in MDD depression attack group were notably decreased in comparison with those in MDD remission group and control group ((4.10 ± 0.73) v.s. (5.91 ± 1.25) and (5.83 ± 1.17) umol/L, F = 5.61, p < 0.001; (0.53 ± 0.32) v.s. (0.91 ± 0.18) and (0.93 ± 0.21), F = 8.37, p < 0.001; (1.45 ± 0.86) v.s. (2.33 ± 1.49) and (2.28 ± 1.50), F = 8.23, p < 0.001). However, there were no marked difference in the levels of these three indices between the MDD remission group and the control group (p > 0.05). Serum GABA levels were negatively correlated with HAMA total score (r = −0.34, p = 0.02), HAMD total score (r = −0.46, p = 0.01) and depression core symptom score (r = −0.32, p = 0.03). During the onset of MDD, there may be GABA neuronal dysfunction and abnormal expression of GABAA receptor subunits, and those changes showed a state change, which gradually returned to normal during remission. [ABSTRACT FROM AUTHOR]

Acute myeloid leukemia (AML) was confirmed to be associated with hematopoietic insufficiency, as well as abnormal proliferation, differentiation or survival of myeloid progenitors. Multiple studies reported that microRNA-204 (miR-204) and Hepatocyte growth factor (HGF) played important roles in types of cancers. However, the potential molecular regulatory mechanism between miR-204 and HGF in AML remains to be further defined. Real-time PCR (RT-PCR) was adopted to detect the expression of miR-204 and HG. Relative protein levels were detected by western blot assay. The viability, cell cycle, apoptosis, migration, and invasion were analyzed by MTT, flow cytometry, and transwell assays. Moreover, the target relationship between miR-204 and HGF was predicted by MiRcode website and confirmed by luciferase reporter, RNA pull-down, and western blot assays. Our data suggested that miR-204 was downregulated in AML serum samples and cells. MiR-204 overexpression repressed cell proliferation, migration, invasion, and induced cell apoptosis in AML cells. HGF was upregulated in AML samples and cells, and HGF knockdown inhibited the malignancy of AML cells. In addition, HGF was directly targeted by miR-204. HGF overexpression reversed the effects of miR-204 mimic on AML cell proliferation, apoptosis, migration, and invasion. Besides, miR-204 regulated the c-Met signaling by targeting HGF, thereby regulating the downstream protein levels related to cell proliferation, apoptosis, migration, and invasion in AML cells. In conclusion, miR-204 could regulate AML progression through regulating the HGF/c-Met pathway. [ABSTRACT FROM AUTHOR]

AbstractMuscle foods, valued for their significant nutrient content such as high-quality protein, vitamins, and minerals, are vulnerable to adulteration and fraud, stemming from dishonest vendor practices and insufficient market oversight. Traditional analytical methods, often limited to laboratory-scale., may not effectively detect adulteration and fraud in complex applications. Raman spectroscopy (RS), encompassing techniques like Surface-enhanced RS (SERS), Dispersive RS (DRS), Fourier transform RS (FTRS), Resonance Raman spectroscopy (RRS), and Spatially offset RS (SORS) combined with chemometrics, presents a potent approach for both qualitative and quantitative analysis of muscle food adulteration. This technology is characterized by its efficiency, rapidity, and noninvasive nature. This paper systematically summarizes and comparatively analyzes RS technology principles, emphasizing its practicality and efficacy in detecting muscle food adulteration and fraud when combined with chemometrics. The paper also discusses the existing challenges and future prospects in this field, providing essential insights for reviews and scientific research in related fields. [ABSTRACT FROM AUTHOR]