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Abstract Diabetes retinopathy (DR) is a critical clinical disease with that causes irreversible visual damage in adults, and may even lead to permanent blindness in serious cases. Early identification and treatment of DR is critical. Our aim was to train and externally validate a prediction nomogram for early prediction of DR. 2381 patients with type 2 diabetes mellitus (T2DM) were retrospective study from the First Affiliated Hospital of Xinjiang Medical University in Xinjiang, China, hospitalised between Jan 1, 2019 and Jun 30, 2022. 962 patients with T2DM from the Suzhou BenQ Hospital in Jiangsu, China hospitalised between Jul 1, 2020 to Jun 30, 2022 were considered for external validation. The least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression was performed to identify independent predictors and establish a nomogram to predict the occurrence of DR. The performance of the nomogram was evaluated using a receiver operating characteristic curve (ROC), a calibration curve, and decision curve analysis (DCA). Neutrophil, 25-hydroxyvitamin D3 [25(OH)D3], Duration of T2DM, hemoglobin A1c (HbA1c), and Apolipoprotein A1 (ApoA1) were used to establish a nomogram model for predicting the risk of DR. In the development and external validation groups, the areas under the curve of the nomogram constructed from the above five factors were 0.834 (95%CI 0.820–0.849) and 0.851 (95%CI 0.829–0.874), respectively. The nomogram demonstrated excellent performance in the calibration curve and DCA. This research has developed and externally verified that the nomograph model shows a good predictive ability in assessing DR risk in people with type 2 diabetes. The application of this model will help clinicians to intervene early, thus effectively reducing the incidence rate and mortality of DR in the future, and has far-reaching significance in improving the long-term health prognosis of diabetes patients.

Abstract Contamination of soils by Molybdenum (Mo) has raised increasing concern worldwide. Both fulvic acid (FA) and humic acid (HA) possess numerous positive properties, such as large specific surface areas and microporous structure that facilitates the immobilization of the heavy metal in soils. Despite these characteristics, there have been few studies on the microbiology effects of FA and HA. Therefore, this study aimed to assess the Mo immobilization effects of FA and HA, as well as the associated changes in microbial community in Mo-contaminated soils (with application rates of 0%, 0.5% and 1.0%). The result of the incubation demonstrated a decrease in soil pH (from 8.23 ~ 8.94 to 8.05 ~ 8.77). Importantly, both FA and HA reduced the exchangeable fraction and reducible fraction of Mo in the soil, thereby transforming Mo into a more stable form. Furthermore, the application of FA and HA led to an increase in the relative abundance of Actinobacteriota and Firmicutes, resulting in alterations to the microbial community structure. However, it is worth noting that due to the differing structures and properties of FA and HA, these outcomes were not entirely consistent. In summary, the aging of FA and HA in soil enhanced their capacity to immobilization Mo as a soil amendment. This suggests that they have the potential to serve as effective amendments for the remediation of Mo-contaminated soils.

The severity of soil molybdenum (Mo) pollution is increasing, and effective management of contaminated soil is essential for the sustainable development of soil. To investigate this, a pot experiment was carried out to assess the impact of different rates of humic acid (HA) and fulvic acid (FA) on the mobility of Mo in soil solution and its uptake by alfalfa, wheat and green bristlegrass. The concentration of Mo in Plants and soil was determined using an Atomic Absorption Spectrophotometer. The findings revealed that the application of HA led to an increase in Mo accumulation in the shoot and root of green bristlegrass and wheat, ranging from 10.56 % to 28.73 % and 62.15–115.79 % (shoot), and 17.52–46.53 % and 6.29-81.25 % (root), respectively. Nonetheless, the use of HA resulted in a slight inhibition of plant Mo uptake, leading to reduced Mo accumulation in alfalfa roots compared to the control treatment (from 3284.49 mg/kg to 2140.78–2813.54 mg/kg). On the other hand, the application of FA decreased Mo accumulation in the wheat shoot (from 909.92 mg/kg to 338.54–837.45 mg/kg). Furthermore, the bioavailability of green bristlegrass (with HA) and wheat (with FA) decreased, and the percentage of residual fraction of Mo increased (from 0.39 % to 0.78–0.96 %, from 3.95 % to 3.97∼ 4.34 %). This study aims to elucidate the ternary interaction among Mo, humic substances, and plants (alfalfa, wheat, and green bristlegrass), to enhance both the activation and hyperaccumulation of Mo simultaneously.

Abstract Background To explore the relationship between changes in salivary cytokine levels and the occurrence of white spot lesions in adolescents receiving clear aligner orthodontic treatment and investigate the predictive value of various factors for lesion occurrence. Methods We retrospectively analyzed sixthy eight adolescent in the permanent dentition period, who received clear aligner orthodontics in our hospital were randomly divided into two groups according to the occurrence or aggravation of white spot lesions after treatment. The general condition of the oral cavity was analyzed, saliva was collected, and inflammation-related cytokines with varying transcription levels between groups were screened by transcriptome analysis. The expression levels of inflammatory cytokines in the saliva of the patients in the two groups were measured, and the risk factors for white spot lesions were screened by correlation analysis and binary logistic regression analysis. The value of the independent and combined application of risk factors for predicting the occurrence of white spot lesions in adolescent patients after invisible orthodontic treatment was analyzed by receiver operating characteristic (ROC) curve analysis. Results Transcriptome and GO and KEGG pathway analyses showed that there were differences in the transcription levels of inflammatory cytokines such as CXCL1, CXCL2, CXCL8, CCL3, CCL4, IL-1β and IL-2 between groups. The levels of CXCL8, CCL3, CCL4, IL-1β and IL-2 in the saliva of patients with white spot lesions were significantly higher in patients after invisible orthodontic treatment (P

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects over 50 million elderly individuals worldwide. Although the pathogenesis of AD is not fully understood, based on current research, researchers are able to identify potential biomarker genes and proteins that may serve as effective targets against AD. This article aims to present a comprehensive overview of recent advances in AD biomarker identification, with highlights on the use of various algorithms, the exploration of relevant biological processes, and the investigation of shared biomarkers with co-occurring diseases. Additionally, this article includes a statistical analysis of key genes reported in the research literature, and identifies the intersection with AD-related gene sets from databases such as AlzGen, GeneCard, and DisGeNet. For these gene sets, besides enrichment analysis, protein–protein interaction (PPI) networks utilized to identify central genes among the overlapping genes. Enrichment analysis, protein interaction network analysis, and tissue-specific connectedness analysis based on GTEx database performed on multiple groups of overlapping genes. Our work has laid the foundation for a better understanding of the molecular mechanisms of AD and more accurate identification of key AD markers.

Abstract Carbon dioxide (CO2) fixation into value-added chemicals has attracted growing attention and one promising atom-efficient pathway is via the cycloaddition with three member-ring compounds like epoxides. Herein, we demonstrated that encapsulation of linear poly(ionic liquid)s (PILs) on ordered mesoporous carbon materials provides a facile and feasible approach towards environmental-friendly heterogeneous catalysts with high performance in CO2 cycloaddition with epoxides under mild conditions. A series of novel linear phenolic hydroxyl group functional imidazolium-based PILs synthesized from hydroxymethylation reaction between 4-(imidazol-1-yl)phenol-1-butyl-imidazolium iodide and formaldehyde was loaded on ordered mesoporous carbon FDU-15–600 derived from mesoporous phenolic resin. By virtue of controlling the initial polymerization temperature, the molecular weight of PILs was facilely modulated, reaching strong host–guest interaction during the PIL immobilization. Highly stable immobilized PIL species with spatial satisfaction of ionic moieties and surface groups were thus realized to enable a synergic CO2 conversion via cycloaddition with epoxides. The optimal catalyst exhibited high yield and stable recyclability by using atmospheric CO2 under metal-additive-solvent-free conditions and the activity surprisingly exceeded the corresponding homogeneous parent IL and PIL. Excellent substrate compatibility was found by extending the transformation of more than ten epoxides including the inert ones such as disubstituted cyclohexene oxide. The significantly enhanced activity is attributed to the synergistic effect of the surface hydrogen groups and ionic moieties to accelerate the rate-determining ring-opening process.

The rehabilitation of wind turbine foundations after damage is increasingly common. However, limited research exists on the deformation of wind turbine foundations after rehabilitation. Artificial intelligence methods can be used to analyze future deformation state and predict post-rehabilitation deformation of foundations. This paper focuses on analyzing the stability of damaged wind turbine foundations after rehabilitation, as well as establishing and evaluating machine learning models. Specifically, Decision Tree (DT), Extreme Gradient Boosting (XGB), Support Vector Regression (SVR), and Long Short-Term Memory Network (LSTM) models are utilized to predict the vertical displacement of the rehabilitated foundation. Hence, the stability of the rehabilitated foundation is discussed in correlation with the measured wind speed, based on the foundation vertical displacement data. During the development of the machine learning model, the most suitable combination of hyperparameters is determined. The prediction performance of the SVR and LSTM models, which exhibit good performance, is compared to further evaluate their effectiveness. Furthermore, the models are analyzed and validated. The results indicate that the vertical displacements of the rehabilitated foundations gradually get close to a state of steady fluctuation over time. The SVR model is identified as the most effective in predicting the vertical displacements of wind turbine foundations after rehabilitation. This study aims to analyze and predict the vertical displacement of wind turbine foundations after rehabilitation based on extensive field monitoring data and powerful machine learning models.

Abstract Xinjiang is an important power production base in China, and its electric energy production needs not only meet the demand of Xinjiang's electricity consumption, but also make up for the shortage of electricity in at least 19 provinces or cities in China. Therefore, it is of great significance to know ahead of time the electric energy production of Xinjiang in the future. In such terms, accurate electric energy production forecasts are imperative for decision makers to develop an optimal strategy that includes not only risk reduction, but also the betterment of the economy and society as a whole. According to the characteristics of the historical data of monthly electricity generation in Xinjiang from January 2001 to August 2020 , the suitable and widely used SARIMA (Seasonal autoregressive integrated moving mean model) method and Holt-winter method were used to construct the monthly electric energy production in Xinjiang for the first time. The results of our analysis showed that the established SARIMA((1,2,3,4,6,7,11),2,1)(1,0,1)12 model had higher prediction accuracy than that of the established Holt-Winters' multiplicative model. We predicted the monthly electric energy production from August 2021 to August 2022 by the SARIMA((1,2,3,4,6,7,11),2,1)(1,0,1)12 model, and errors are very small compared to the actual values, indicating that our model has a very good prediction performance. Therefore, based on our study, we provided a simple and easy scientific tool for the future power output prediction in Xinjiang. Our research methods and research ideas can also provide scientific reference for the prediction of electric energy production elsewhere.

ABSTRACT: Objectives: Bacterial pneumonia is a common serious infectious disease with high morbidity and mortality. Prokineticin 2 (PK2) has recently been identified as a novel immunomodulator in a variety of diseases; however, its role in bacterial pneumonia remains unclear. Methods: The levels of PK2 were measured and analyzed in patients with pneumonia and healthy controls. The effects of PK2 on the host response to pneumonia were evaluated by in vivo animal experiments and in vitro cell experiments. Results: PK2 levels dramatically decreased in patients with pneumonia compared with healthy controls, and PK2 levels were lower in patients with severe pneumonia than in pneumonia. In a mouse model of bacterial pneumonia, transtracheal administration of recombinant PK2 significantly alleviated lung injury and improved the survival, which was associated with increased host's bacterial clearance capacity, as manifested by decreased pulmonary bacterial loads. PK2 enhanced the chemotaxis, phagocytosis, and killing ability of macrophages, whereas the protective efficacy of PK2 was abolished after macrophage depletion. Conclusion: Impaired alveolar macrophage function caused by decreased PK2 is a new endogenous cause of the occurrence and development of bacterial pneumonia. The administration of recombinant PK2 may be a potential adjuvant therapy for bacterial pneumonia.

Abstract Background The cultivated Camellia sasanqua forms a divergent double flower pattern, and the stamen petaloid is a vital factor in the phenomenon. However, the regulation mechanism remains largely unclear. Results Here, a comprehensive comparative transcriptome analysis of the wild-type, “semi-double”, “peony double”, and “rose double” was performed. The cluster analysis of global gene expression level showed petal and stamen difficulty separable in double flower. The crucial pathway and genes related to double flower patterns regulation were identified by pairwise comparisons and weighted gene coexpression network (WGCNA). Divergent genes expression, such as AUX1 and AHP, are involved in plant hormone signaling and photosynthesis, and secondary metabolites play an important role. Notably, the diversity of a petal-specific model exhibits a similar molecular signature to the stamen, containing extensin protein and PSBO1, supporting the stamen petaloid point. Moreover, the expansion of class A gene activity influenced the double flower formation, showing that the key function of gene expression was probably demolished. Conclusions Overall, this work confirmed the ABCE model and provided new insights for elucidating the molecular signature of double formation.

White spot lesions (WSLs) have long been a noteworthy complication during orthodontic treatment. Recently, an increasing number of orthodontists have found that adolescents undergoing orthodontic treatment with clear aligners are at a higher risk of developing WSLs. The oral microbiota and metabolites are considered the etiologic and regulatory factors of WSLs, but the specific impact of clear aligners on the oral microbiota and metabolites is unknown. This study investigated the differences in the salivary microbiome and metabolome between adolescents with and without WSLs treated with clear aligners. Fifty-five adolescents (aged 11-18) with Invisalign appliances, 27 with and 28 without WSLs, were included. Saliva samples were analyzed using 16S rRNA gene sequencing and ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS); the data were further integrated for Spearman correlation analysis. The relative abundances of 14 taxa, including Actinobacteria, Actinomycetales, Rothia, Micrococcaceae, Subdoligranulum, Capnocytophaga, Azospira, Olsenella, Lachnoanaerobaculum, and Abiotrophia, were significantly higher in the WSL group than in the control group. Metabolomic analysis identified 27 potential biomarkers, and most were amino acids, including proline and glycine. The metabolites were implicated in 6 metabolic pathways, including alanine, aspartate and glutamate metabolism; glycine, serine and threonine metabolism; and aminoacyl-tRNA biosynthesis. There was a correlation between the salivary microbial and metabolomic datasets, reflecting the impact of clear aligners on the metabolic activity of the oral flora. A concordant increase in the levels of Lachnoanaerobaculum, Rothia, Subdoligranulum and some amino acids had predictive value for WSL development. In summary, when adolescents undergo long-term clear aligner therapy with poor oral hygiene habits, clear aligners can disrupt the balance of the oral microecosystem and lead to oral microbiota dysbiosis, thereby increasing the risk of developing WSLs. Our findings might contribute to the understanding of the pathogenesis of WSLs and provide candidate biomarkers for the diagnosis and treatment of WSLs associated with clear aligners.

Abstract Quantum entanglement is a key resource in quantum technology, and its quantification is a vital task in the current noisy intermediate-scale quantum (NISQ) era. This paper combines hybrid quantum-classical computation and quasi-probability decomposition to propose two variational quantum algorithms, called variational entanglement detection (VED) and variational logarithmic negativity estimation (VLNE), for detecting and quantifying entanglement on near-term quantum devices, respectively. VED makes use of the positive map criterion and works as follows. Firstly, it decomposes a positive map into a combination of quantum operations implementable on near-term quantum devices. It then variationally estimates the minimal eigenvalue of the final state, obtained by executing these implementable operations on the target state and averaging the output states. Deterministic and probabilistic methods are proposed to compute the average. At last, it asserts that the target state is entangled if the optimized minimal eigenvalue is negative. VLNE builds upon a linear decomposition of the transpose map into Pauli terms and the recently proposed trace distance estimation algorithm. It variationally estimates the well-known logarithmic negativity entanglement measure and could be applied to quantify entanglement on near-term quantum devices. Experimental and numerical results on the Bell state, isotropic states, and Breuer states show the validity of the proposed entanglement detection and quantification methods.

Introduction: Clear aligner treatment (CAT) has become popular over recent years because it is both comfortable and aesthetically acceptable. However, most of patients undergoing orthodontic treatment request dental bleaching. A safe and controlled bleaching treatment at the same time as the clear aligner treatment can save time and improve patient satisfaction with the outcome of the treatment.Aim: This study was aimed to develop a thermoforming film loaded with hydrogen peroxide as a clear aligner and detect its efficiency on teeth blenching and its influence on shear bonding strength for attachment.Methods: The thermoforming film loaded with sodium alginate-dopamine/Mesoporous silica nanoparticles compound gel was immersed in 6 wt% hydrogen peroxide solution and the hydrogen peroxide was loaded into mesoporous silica nanoparticle channels by capillary action. Then, a thermoforming film loaded with sustained-release hydrogen peroxide gel was made. Six dentition models were prepared with 90 isolated human premolars and divided into the experiment group, the condition control group and the blank control group, respectively. Then, the experiment group wore the clear aligner made by the thermoforming film loaded with hydrogen peroxide for 40 days; the conditional control group wore the clear aligner made by the ordinary thermoforming film for 40 days; and the blank control group wore no clear aligner. The aligners were updated every 10 days and the color of teeth was measured every 10 days. Tooth color should be determined by specific parameters (L, a* and b*). What’s more, in order to determine the influence of the thermoforming film loaded with sustained-release hydrogen peroxide gel on shear bonding strength for attachment. The shear bonding strength of attachment of isolated premolars were measured.Results: Isolated premolars treated by bleaching experiments showed an increase in L value (ΔL = 7.76 ± 0.64) and a decrease in both a* (Δa = −0.82 ± 0.12) and b* (Δb = −3.10 ± 0.21) values. However, the isolated premolars in conditional control group and blank control group exhibited that an decrease in L value (ΔLCCG = −0.91 ± 0.24; ΔLBCG = −0.86 ± 0.15)and a increase in both a* (ΔaCCG = 0.19 ± 0.05; ΔaBCG = 0.18 ± 0.04) and b* (ΔbCCG = 0.43 ± 0.11; ΔbBCG = 0.31 ± 0.10) value. While the shear bonding strength for attachment after bleaching was 22.78 ± 2.28 MPa, which had no significant change compared with the shear bonding strength for attachment without bleaching experiment (22.21 ± 2.77 MPa) (p > 0.05). Conclusion: A thermoforming film featuring the sustained release of hydrogen peroxide had a good bleaching effect on isolated teeth and had no significant influence on the shear bonding strength for attachment.

Direct carbon dioxide (CO2) conversion into valuable chemicals like dimethyl carbonate (DMC) is an atom efficient avenue for CO2 utilization but greatly challenges the catalyst designation because the requirement of multiple active sites. Herein, regulation of the basicity of porous poly(ionic liquid)s (PPILs) was reached by post-treating epoxy-functional precursor and utilizing 1,5,7-triazodicyclic [4.4.0] Dec-5-ene (TBD) to convert epoxy functional ionic moieties into the multifunctional sites with nucleophilic-leaving capable anions, hydroxyl group and TBD derived basic sites. The constructed catalyst was highly active in the one-pot and two-step DMC synthesis by coupling the CO2 cycloaddition with epoxide and successive transesterification. A high yield up to 93% was observed by using atmospheric CO2 under the metal-solvent-additive free condition. Stable reusability and extendibility by using multiple epoxides further reveal the efficiency and potentiality of the present catalyst in CO2 fixation.

Abstract Distributed quantum information processing is essential for building quantum networks and enabling more extensive quantum computations. In this regime, several spatially separated parties share a multipartite quantum system, and the most natural set of operations is Local Operations and Classical Communication (LOCC). As a pivotal part in quantum information theory and practice, LOCC has led to many vital protocols such as quantum teleportation. However, designing practical LOCC protocols is challenging due to LOCC’s intractable structure and limitations set by near-term quantum devices. Here we introduce LOCCNet, a machine learning framework facilitating protocol design and optimization for distributed quantum information processing tasks. As applications, we explore various quantum information tasks such as entanglement distillation, quantum state discrimination, and quantum channel simulation. We discover protocols with evident improvements, in particular, for entanglement distillation with quantum states of interest in quantum information. Our approach opens up new opportunities for exploring entanglement and its applications with machine learning, which will potentially sharpen our understanding of the power and limitations of LOCC. An implementation of LOCCNet is available in Paddle Quantum, a quantum machine learning Python package based on PaddlePaddle deep learning platform.

BackgroundNecrotizing enterocolitis (NEC) is the leading cause of neonatal gastrointestinal-related death, while the etiology and pathogenesis are poorly understood.MethodsThe levels of CCL3 in intestinal tissue from modeling mice and patients were measured and analyzed. HE staining, TUNEL, Annexin and FCM were used to assess pathological changes and apoptosis in intestinal tissue and epithelial cells. CCL3, CCR4, cytokines, tight junction protein ZO-1, apoptosis-related genes and ERK1/2-NF-κB signaling pathway were detected by ELISA, Q-PCR, Western blotting and immunofluorescence.ResultsCCL3 levels in the intestinal tissue significantly elevated in patients with NEC and mouse models. Blockade of CCL3 significantly alleviated NEC-related intestinal tissue damage, while administration of recombinant CCL3 aggravated intestinal injury by exacerbating intestinal epithelial cell apoptosis in NEC mice. Importantly, CCR4 blockade reversed CCL3-mediated damage to intestinal tissue and intestinal epithelial cell apoptosis both in vivo and in vitro. Further mechanistic studies showed that CCL3 regulated apoptosis-related BAX/BCL-2 expression through the activation of the ERK1/2 and NF-κB pathways, which could be reversed by anti-CCR4 treatment. Furthermore, ERK1/2 inhibition reduced CCL3-mediated phosphorylation of NF-κB in IEC-6 cells, while inhibition of NF-κB had no obvious effect on ERK1/2 phosphorylation. As expected, inhibition of NF-κB regulated BAX/BCL-2 expression and alleviated CCL3-induced epithelial cell apoptosis. These results indicate that high expression of CCL3 in NEC lesions promotes intestinal epithelial apoptosis through the CCL3-CCR4-ERK1/2-NFκB-BAX/BCL2 signalling axis, thereby exacerbating NEC-related intestinal injury.ConclusionsOur study represents an important conceptual advance that CCL3 may be one of the key culprits of intestinal tissue damage in NEC patients, and blocking either CCL3, CCR4, or NF-κB may represent a novel effective immunotherapy for NEC.

This study was conducted to assess heavy metals in the overlying water and sediments of Luhun Reservoir, Henan Province, China, which is positioned downstream from a molybdenum (Mo) mining area. The pollution indexes indicated that deposition of all metals may have been affected by the mining area. The single element pollution factor (Pi) of Mo was the highest among all heavy metals, with a mean value of 2.05. However, the sediments were subject to long-term accumulation of metals, particularly Mo, Cd, Pb, and Zn, which originated from anthropogenic sources. The mean individual element potential ecological risk index values for Cd were above 385, while the mean value comprehensive potential ecological risk index was 465, which indicates a high ecological risk. Moreover, the enriched heavy metals had different spatial distributions in the Luhun Reservoir sediments. Finally, Pearson correlation analysis indicated that the Pb was mainly affected by different anthropogenic sources and had no relationship with other metals, which suggests that the influence of mining area on heavy metal concentrations in the reservoir is difficult to disentangle.

Objective. Prokineticin 2 (PK2) has been shown to regulate food intake, fat production, and the inflammation process, which play vital roles in the pathogenesis of obesity. The first aim of this study was to investigate serum PK2 levels in children with obesity and normal-weight children. The second aim was to compare the levels of PK2 between children with obesity, with and without nonalcoholic fatty liver disease (NAFLD). Methods. Seventy normal-weight children and 91 children with obesity (22 with NAFLD) were recruited. Circulating PK2, IL-6, and TNF-α were measured by enzyme-linked immunosorbent assays. Anthropometric and biochemical measurements related to adiposity, lipid profile, and insulin resistance were examined for all participants. Results. Serum PK2 was significantly higher in children with obesity than in the normal-weight controls. Circulating PK2 levels were not different between the patients with and without NAFLD. Circulating PK2 was positively correlated with BMI, BMI z-score, insulin, glucose, HOMA-IR, total cholesterol, low-density lipoprotein cholesterol, alanine aminotransferase, and gamma-glutamyl transpeptidase. Binary logistic regression revealed that the odds ratios for obesity were significantly elevated with increasing PK2. Conclusions. PK2 was strongly associated with obesity, and it may also be related to metabolic disorders and insulin resistance. This trial is registered with ChiCTR2000038838.

Singular value decomposition is central to many problems in engineering and scientific fields. Several quantum algorithms have been proposed to determine the singular values and their associated singular vectors of a given matrix. Although these algorithms are promising, the required quantum subroutines and resources are too costly on near-term quantum devices. In this work, we propose a variational quantum algorithm for singular value decomposition (VQSVD). By exploiting the variational principles for singular values and the Ky Fan Theorem, we design a novel loss function such that two quantum neural networks (or parameterized quantum circuits) could be trained to learn the singular vectors and output the corresponding singular values. Furthermore, we conduct numerical simulations of VQSVD for random matrices as well as its applications in image compression of handwritten digits. Finally, we discuss the applications of our algorithm in recommendation systems and polar decomposition. Our work explores new avenues for quantum information processing beyond the conventional protocols that only works for Hermitian data, and reveals the capability of matrix decomposition on near-term quantum devices.

Most ghost imaging reconstruction algorithms require a large measurement time to retrieve the object information clearly. But not all groups of data play a positive role in reconstructing the object image. Abandoning some redundant data can not only enhance the quality of reconstruction images but also speed up the computation process. Here, we propose a method to screen the data using two threshold values set by a proportional parameter during the sampling process. Experimental results show that the reserved data after screening can be used in several reconstruction algorithms, and the reconstruction quality is enhanced or at least remains at the same level. Meanwhile, the computing time costs are greatly reduced, and so is the data storage.