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The unique expression patterns of circRNAs linked to the advancement and prognosis of cancer underscore their considerable potential as valuable biomarkers. Repurposing existing drugs for new indications can significantly reduce the cost of cancer treatment. Computational prediction of circRNA-cancer and drug-cancer relationships is crucial for precise cancer therapy. However, prior computational methods fail to analyze the interaction between circRNAs, drugs, and cancer at the systematic level. It is essential to propose a method that uncover more valuable information for achieving cancer-centered multi-association prediction. In this paper, we present a novel computational method, AutoEdge-CCP, to unveil cancer-associated circRNAs and drugs. We abstract the complex relationships between circRNAs, drugs, and cancer into a multi-source heterogeneous network. In this network, each molecule is represented by two types information, one is the intrinsic attribute information of molecular features, and the other is the link information explicitly modeled by autoGNN, which searches information from both intra-layer and inter-layer of message passing neural network. The significant performance on multi-scenario applications and case studies establishes AutoEdge-CCP as a potent and promising association prediction tool. Author summary: CircRNAs serve as crucial biomarkers and drug targets in cancer therapy. Predicting cancer-associated circRNAs and drugs contributes to uncover intricate molecular mechanisms driving tumorigenesis, thus offering novel insights into cancer diagnosis, treatment, and research. However, prevailing predictive methods often neglect the comprehensive interactions within circRNAs, drugs, and cancer, leading to an incomplete understanding of their complex interplay. In response, we introduce AutoEdge-CCP, a framework that models circRNA-cancer-drug interactions within a multi-source heterogeneous network. Each molecule combines intrinsic attribute information describing molecular features with interaction information derived through autoGNN, revealing pivotal circRNAs and drugs associated with cancer. Experimental results across multi-scenario attest to AutoEdge-CCP's superior performance compared to competing methods, particularly in predicting novel circRNAs and drugs associated with cancer. Additionally, visualization of edge embeddings and case studies provide interpretable insights into the prediction outcomes. [ABSTRACT FROM AUTHOR]

The gene regulatory structure of cells involves not only the regulatory relationship between two genes, but also the cooperative associations of multiple genes. However, most gene regulatory network inference methods for single cell only focus on and infer the regulatory relationships of pairs of genes, ignoring the global regulatory structure which is crucial to identify the regulations in the complex biological systems. Here, we proposed a graph-based Deep learning model for Regulatory networks Inference among Genes (DeepRIG) from single-cell RNA-seq data. To learn the global regulatory structure, DeepRIG builds a prior regulatory graph by transforming the gene expression of data into the co-expression mode. Then it utilizes a graph autoencoder model to embed the global regulatory information contained in the graph into gene latent embeddings and to reconstruct the gene regulatory network. Extensive benchmarking results demonstrate that DeepRIG can accurately reconstruct the gene regulatory networks and outperform existing methods on multiple simulated networks and real-cell regulatory networks. Additionally, we applied DeepRIG to the samples of human peripheral blood mononuclear cells and triple-negative breast cancer, and presented that DeepRIG can provide accurate cell-type-specific gene regulatory networks inference and identify novel regulators of progression and inhibition. Author summary: Although many methods have been proposed to infer the gene regulatory network of a single cell, they only focus on the regulatory relationships of pairs of genes and ignore the global regulatory structure. Here, we present a deep learning-based model to learn the global regulatory structure and reconstruct the gene regulatory networks from single-cell RNA sequencing data with a graph view. We utilize the weighted gene co-expression analysis to build a prior regulatory graph of gene and a graph autoencoder to deconstruct the latent regulatory structure among genes. We performed extensive experiments on varieties of single-cell RNA sequencing datasets and compared our method with 9 stat-of-the-art gene regulatory network inference method. The results show that our method can significantly improve the accuracy of gene regulatory network inference and can be applied to identify key regulators in a wide range of scenarios. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to evaluate the pre- and postoperative changes in the recently urodynamic and quality of life (QoL) in nonmenopausal women diagnosed with cervical cancer and treated with radical hysterectomy (RH). Patients and methods: Twenty-eight nonmenopausal women (28–49 years) with cervical carcinoma (FIGO stage Ia2-IIa) underwent a radical hysterectomy. Urodynamic studies were performed 1 week before (U0) and 3–6 months (U1) after surgery. A self-administered condition-specific QoL questionnaire (PFDI-20, PFIQ-7) was applied at U0 and U1. Results: Data from the urodynamics analysis performed at U1 showed that the average first sensation volume (119.39 ± 12.28 ml vs 150.43 ± 31.45 ml, P < 0.001), the residual urine volume (6.39 ± 10.44 ml vs. 42.32 ± 33.72 ml, P < 0.001), and the time of urination (46.10 ± 16.65 s vs. 74.31 ± 23.94 s, P < 0.001) were increased, while the bladder volume at a strong desire to void (448.89 ± 86.62 ml vs. 322.82 ± 50.89 ml, P < 0.001), the bladder compliance (82.63 ± 58.06 ml/cmH2O vs. 37.45 ± 28.66 ml/cmH2O, P < 0.001), the average flow rate (Qave) (23.86 ± 4.25 ml/s vs. 12.57 ± 2.37 ml/s, P < 0.001), the maximum natural flow rate (Qmax) (25.42 ± 6.46 ml/s vs. 14.43 ± 5.32 ml/s, P < 0.001), and the pressure at a peak flow rate (PdetQmax) (36.53 ± 11.20 cmH2O vs. 31.43 ± 10.56 cmH2O, P < 0.05) were decreased. At the same time, functional pelvic problems derived from prolapse (PFDI-20 scores) and their impact on the patients’ Qol (PFIQ-7 score) were significantly improved at 3–6 months postoperation. Conclusion: Radical hysterectomy results in urodynamic changes, and 3–6 months postoperation may be an important period for changes in bladder dysfunction after RH. Urodynamic and QoL analyses may provide methods for assessing symptoms. [ABSTRACT FROM AUTHOR]

By integrating biological and bioinformatics data, Turek et al. (2023) [[21]] performed a genome assembly on the B10v3 cucumber genome and presented a well-organized and annotated reference genome of cucumber, contributing to better understanding of the cucumber genome. Using genome-wide classification and functional annotation, Cenci et al. (2022) [[18]] effectively characterized a large gene family across multiple species and established a classification framework for gene functional transfer, enabling a better understanding of the evolutionary history of one gene. In addition, several papers explored functional genomics of plant genomes, such as gene functional annotation and the development of functional genomics tools. Comparative genomics and functional genomics are two basic branches of plant genomics. [Extracted from the article]

Emerging evidence indicates that circular RNAs (circRNAs) can provide new insights and potential therapeutic targets for disease diagnosis and treatment. However, traditional biological experiments are expensive and time-consuming. Recently, deep learning with a more powerful ability for representation learning enables it to be a promising technology for predicting disease-associated circRNAs. In this review, we mainly introduce the most popular databases related to circRNA, and summarize three types of deep learning-based circRNA-disease associations prediction methods: feature-generation-based, type-discrimination and hybrid-based methods. We further evaluate seven representative models on benchmark with ground truth for both balance and imbalance classification tasks. In addition, we discuss the advantages and limitations of each type of method and highlight suggested applications for future research. [ABSTRACT FROM AUTHOR]

To address the challenges posed by the excessive exploitation of natural river sand, manufactured sand (MS) was used as a substitute material for natural sand to produce manufactured sand-based ultra-high performance concrete (UHPMC). This study aims to investigate the effects of MS replacement ratio, stone powder content, steel fiber content, and steel fiber shape on the fracture characteristics and damage evolution of UHPMC under tensile load through tensile tests combined with acoustic emission (AE) technology. The research results indicated that the stress-strain behavior of UHPMC could be divided into low strain hardening, low strain softening, and high strain softening. The tensile strength and energy absorption of the UHPMC were less correlated with MS replacement ratio. The tensile strength showed a trend of first increasing and then decreasing with the increase of stone powder content, and UHPMC with a 4% stone powder content presented the better tensile strength. The tensile damage process of UHPMC was mainly concentrated in the range of tensile strain from 0.0002 to 0.0025. The microstructure analysis of the UHPMC matrix revealed that an optimal amount of stone powder enhances the bond property between MS and mortar, thereby improving tensile performance. AE monitoring indicated that AE amplitude and AE energy provide sensitive identification capabilities for classifying the elastic state, strain hardening, and strain softening stages of UHPMC. The findings of this study contribute to understanding the fracture characteristics and damage evolution of UHPMC, promoting the engineering application of MS in ultra-high-performance concrete. • Fracture characteristics and damage evolution of UHPMC are evaluated by direct tensile tests and AE monitoring. • The developed UHPMC is categorized into low strain hardening, low strain softening, and high strain softening. • A stone powder content of 8% to 12% contributes to enhancing the compactness of the ITZ and achieving strain hardening. • AE amplitude demonstrates a satisfactory capability to identify the different damage stages of UHPMC under tension. [ABSTRACT FROM AUTHOR]

Manufactured sand (MS) was used to partially replace the traditional quartz sand (QS) to prepare a novel ultra-high-performance manufactured sand concrete (UHPMC). To better understand the fracture damage mechanism of UHPMC, small-scale sample tensile tests were conducted to investigate the tensile behavior of three types of UHPMC (low strain hardening, low strain softening and high strain softening). Acoustic emission (AE) and digital image correlation (DIC) are employed simultaneously to monitor and assess the damage characteristics of UHPMC. The results indicate that the classical AE characteristic parameters (ringing counts, duration and b -value) provide a satisfactory result in identifying the feature stages of low strain hardening and high strain softening. Based on the Gaussian mixture model (GMM) and support vector machine (SVM), an automatic probability classification algorithm has been developed that achieves the quantitative categorization of three UHPMC cracking types (shear or tensile cracks). Among them, tensile damage is dominant during the entire loading process, whereas shear damage, including aggregate dislocation and fiber debonding and pullout, accumulates during the hardening and softening stages. DIC visualization and AE source localization complement each other in identifying crack distribution, contributing to further revealing the tensile damage mechanism of UHPMC. The results obtained in this study can provide data and theoretical support for fracture damage characterization and health monitoring of ultra-high-performance concrete materials under tensile conditions. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to explore whether estrogen-related receptors α (ERRα) can prognosticate the occurrence and development of ovarian endometriosis (EMs) as a non-invasive biomarker.Methods: The ectopic and its' correspond eutopic endometria from 47 patients with ovarian EMs and the control normal endometria from 32 cases were collected and detected the mRNA expression of ERRα by RT-qPCR. The serum protein of ERRα were tested by ELISA. The menstrual cycle, ovarian cyst sizes, relative clinical tumour markers, such as CA125, CA19-9 and HE-4, and other demographic data were also involved into analysis in these patients by SPSS 22.0 (IBM, Chicago, IL, USA).Results: The ERRα mRNA expression in ectopic endometria were significantly lower than it in eutopic endometria (P < 0.05) and the normal endometria (P < 0.05). Similarly, the serum ERRα levels in patients with EMs were significantly lower than it in control group (P < 0.01). Moreover, the serum ERRα levels were decreasing with the increasing of the pathological stages and ovarian cyst sizes. While, the serum CA125, CA19-9, CA125/ERRα ratio and CA19-9/ERRα ratio in the study group were significantly higher than those in the control group (all P < 0.01).Conclusion: The expression of ERRα is correlated with the development of ovarian EMs pathological stages and ovarian cyst sizes and can be used as a novel and non-invasive biomarker for evaluating the progression of ovarian EMs. [ABSTRACT FROM AUTHOR]

Virus-encoded circular RNA (circRNA) participates in the immune response to viral infection, affects the human immune system, and can be used as a target for precision therapy and tumor biomarker. The coronaviruses SARS-CoV-1 and SARS-CoV-2 (SARS-CoV-1/2) that have emerged in recent years are highly contagious and have high mortality rates. In coronaviruses, little is known about the circRNA encoded by the SARS-CoV-1/2. Therefore, this study explores whether SARS-CoV-1/2 encodes circRNA and characteristics and functions of circRNA. Based on RNA-seq data of SARS-CoV-1 and SARS-CoV-2 infections, we used circRNA identification tools (circRNA_finder, find_circ and CIRI2) to identify circRNAs. The number of circRNAs encoded by SARS-CoV-1 and SARS-CoV-2 was identified as 151 and 470, respectively. It can be found that SARS-CoV-2 shows more prominent circRNA encoding ability than SARS-CoV-1. Expression analysis showed that only a few circRNAs encoded by SARS-CoV-1/2 showed high expression levels, and the positive strand produced more abundant circRNAs. Then, based on the identified SARS-CoV-1/2-encoded circRNAs, we performed circRNA identification and characterization using the previously developed CirRNAPL. Finally, target gene prediction and functional enrichment analysis were performed. It was found that viral circRNA is closely related to cancer and has a potential role in regulating host cell functions. This study studied the characteristics and functions of viral circRNA encoded by coronavirus SARS-CoV-1/2, providing a valuable resource for further research on the function and molecular mechanism of coronavirus circRNA. [ABSTRACT FROM AUTHOR]

Background: Circular RNAs (circRNAs) can regulate microRNA activity and are related to various diseases, such as cancer. Functional research on circRNAs is the focus of scientific research. Accurate identification of circRNAs is important for gaining insight into their functions. Although several circRNA prediction models have been developed, their prediction accuracy is still unsatisfactory. Therefore, providing a more accurate computational framework to predict circRNAs and analyse their looping characteristics is crucial for systematic annotation. Results: We developed a novel framework, CircDC, for classifying circRNAs from other lncRNAs. CircDC uses four different feature encoding schemes and adopts a multilayer convolutional neural network and bidirectional long short-term memory network to learn high-order feature representation and make circRNA predictions. The results demonstrate that the proposed CircDC model is more accurate than existing models. In addition, an interpretable analysis of the features affecting the model is performed, and the computational framework is applied to the extended application of circRNA identification. Conclusions: CircDC is suitable for the prediction of circRNA. The identification of circRNA helps to understand and delve into the related biological processes and functions. Feature importance analysis increases model interpretability and uncovers significant biological properties. The relevant code and data in this article can be accessed for free at https://github.com/nmt315320/CircDC.git. [ABSTRACT FROM AUTHOR]

Abstract Currently, 18-crown-6-like graphene nanopore has emerged and received much theoretical and experimental attention. Inspired by the multilayer oxygen-containing structure of the K+ channel (KcsA), we adopted molecular dynamics to investigate the selectivity of K+/Na+ for a series of multilayer 18-crown-6-like graphene nanopores. The results demonstrated that the nanopores of the multilayer graphene could facilitate the selectivity of K+/Na+ compared to the monolayer graphene. A proper combination of layer number and interlayer spacing could achieve a high selectivity. The spatial distribution of fluid molecules and ionic hydration microstructures indicated that the easier dehydration of K+ could lead to the more uniformity of K+ pathways in the central region of the nanopore and in turn be helpful in increasing the selectivity of K+. Meanwhile, the increased unsuitability for the pore wall oxygen atoms to compensate the partial dehydrated Na+ could enhance the resistance of Na+. These findings provide useful insights to the further design of 18-crown-6-like and other crown-ether-like graphene nanopore-based nanodevices as sensors and ion separators. Graphical abstract Image 1 [ABSTRACT FROM AUTHOR]

This study aimed to explore the changes of the vaginal microbiota and enzymes in the women with high-risk human papillomavirus (HR-HPV) infection and cervical lesions. A total of 448 participants were carried out HPV genotyping, cytology tests, and microecology tests, and 28 participants were treated as sub-samples, in which vaginal samples were characterized by sequencing the bacterial 16S V4 ribosomal RNA (rRNA) gene region. The study found the prevalence of HR-HPV was higher in patients with BV (P = 0.036). The HR-HPV infection rate was 72.73% in G. vaginalis women, which was significantly higher than that of women with lactobacillus as the dominant microbiota (44.72%) (P = 0.04). The positive rate of sialidase (SNA) was higher in women with HR-HPV infection (P = 0.004) and women diagnosed with cervical intraepithelial neoplasia (CIN) (P = 0.041). In HPV (+) women, the α-diversity was significantly higher than that in HPV (−) women. The 16S rRNA gene-based amplicon sequencing results showed that Lactobacillus was the dominant bacteria in the normal vaginal microbiota. However, the proportion of Gardnerella and Prevotella were markedly increased in HPV (+) patients. Gardnerella and Prevotella are the most high-risk combination for the development of HPV (+) women. The SNA secreted by Gardnerella and Prevotella may play a significant role in HPV infection progress to cervical lesions. [ABSTRACT FROM AUTHOR]

Background: The natural history of human papillomavirus (HPV) is influenced by vaginal microenvironment disorders, such as bacterial vaginosis (BV). The objective of this study was to assess the epidemiology of HPV combined with BV prevalence among Chinese women aged 20-35 years.Methods: A total of 2000 sexually active women aged 20-35 years voluntarily enrolled in this study and underwent a ThinPrep cytologic test and PCR-reverse dot blot human papillomavirus genotyping (PCR-RDB HPV test). BV was diagnosed if clue cells were observed (20% more than epithelial cells).Results: The overall HPV infection rate in this population was 16.2% (324/2000). Compared with HPV-negative individuals, BV prevalence was higher in the High-risk human papillomavirus (HR-HPV) (5.9% vs. 3.1%, P < 0.001). BV and HPV-51, -52 infection were more commonly associated with each other. In patients with cervical lesions (≥ CIN 1), the BV prevalence rate was higher than in patients with negative for intraepithelial lesion or malignancy (NILM) (11.9% vs. 3.8%, P = 0.002).Conclusion: BV was found to be related to HPV-51, -52 infections and cervical lesions. To better manage HPV infected population, more attention should be paid to the prevention and proper treatment of BV. [ABSTRACT FROM AUTHOR]

We report Al0.8Ga0.2As recessed-window single-photon avalanche photodiodes with high internal single-photon detection efficiency and low dark count probability. External quantum efficiency was increased by a factor of 2 at \lambda = 405 nm. Annealing in arsine with hydrogen carrier gas reduced the dark count probability by a factor of 100, to \sim 10^{-6} /gate with a \sim 5$ ns gate, at room temperature. The activation energies of primary carrier traps, which give rise to afterpulsing, are extracted in a temperature range from 150 to 200 K. [ABSTRACT FROM PUBLISHER]

We propose a scheme to generate photon–phonon entanglement and spin squeezing in a hybrid system which is composed of the nitrogen–vacancy (NV) centers, nanomechanical resonator (NMR), and optical cavity. The triplet ground states of the NV centers which have been embedded in the middle of the clamped–clamped NMR are dressed by two strong microwave fields. Kerr nonlinearity is established by an applied microwave field and the vibrational modes of NMR coupling to the dressed-state transitions. The quantum correlations of the applied microwave field, vibrational mode, and NV centers are realized based on the parametric down-conversion-like and beam-splitter-like interactions in the hybrid system, which generate the photon–phonon entanglement and spin squeezing. In this nonlinear processes, the dynamically mediated strain of NMR, as a nonlinear switch, plays the central role in the establishment of such quantum correlations. Our scheme can be applied to quantum nondemolition measurement, quantum sensing, and quantum metrology. • Photon–phonon entanglement and spin squeezing are established based on parametric down-conversion-like and beam-splitter-like interactions. • In the existed schemes based on NV centers, the triplet ground states were just dressed partially, and simplified to the two-level system. In our scheme, we study in the dressed picture and found quantum correlations. • Our scheme can be applied to quantum nondemolition measurement, quantum sensing, and quantum metrology. [ABSTRACT FROM AUTHOR]

Gestational trophoblastic disease (GTD) is a group of interrelated but distinct diseases and has a serious impact on the reproductive health of women. To analyze the expression of Nanog in GTD and to evaluate its potential to predict the development of gestational trophoblastic neoplasia (GTN). The study included 41 normal first-trimester placentas matched by gestational age to 53 regressed-hydatidiform-moles (rHMs), 56 malignant-HMs (mHMs) and 17 choriocarcinomas (CCAs) and evaluated the Nanog expression by immunohistochemistry. The chi-square test, ANOVA, Fisher's exact test and logistic regression were performed to assess the Nanog expression and clinical prognostic factors in GTD. Compared to normal placenta levels, the Nanog expression was increased in GTD samples (p <.05). In HMs, Nanog expression was positively correlated with serum β-hCG levels,uterine size and theca-lutein cysts (p <.05). Compared with the low-risk metastatic group (Federation of Gynecology and Obstetrics (FIGO) score ≤ 6), the high-risk metastatic group (FIGO score >7) had higher Nanog expression (p =.030). Moreover, logistic regression analysis showed that the positive expression of Nanog had the highest risk of developing into GTN (OR = 4.764, p <.001). Nanog is an independent predictor of clinical outcomes. It can also be a reliable predictor for GTN development from GTD. [ABSTRACT FROM AUTHOR]

Residual Ca2+ decreases the efficiency and increases the power consumption of the chlor-alkali industry. However, Ca2+ and Na+ sieving is challenging due to the similar ionic radii of these cations. Inspired by the presence of carbonyl oxygens in key selective filters of biological Ca2+ and Na+ channels, we used molecular dynamics to investigate the effects of carbonyl oxygen atoms in modified graphene nanopores of various sizes (characteristic diameters: 0.57–1.50 nm) on Ca2+/Na+ sieving. The results demonstrated that selectivity is closely associated with the different roles of the carbonyl oxygen atoms. In small nanopores, Ca2+ sheds increased numbers of water molecules due to the predominant steric effect of carbonyl oxygen atoms. Thus, Ca2+ must overcome a higher energy barrier than Na+. This requirement prevents the passage of Ca2+. In large nanopores, carbonyl oxygen atoms do preferentially substitute water molecules outside the first hydration shell of Ca2+ compared with those outside the first hydration shell of Na+, thereby hindering Na+ departure from the nanopore. These findings provide useful guidance for the further development of Ca2+ separation materials as sensors and ion separators. Image 1 [ABSTRACT FROM AUTHOR]

An antireflection coating was created for InP-based pin photodiodes using natural lithography with 100 nm-diameter SiO2 spheres. The surface showed a normal-incidence reflection of <5% for wavelengths from 900 to 2500 nm. Photodiodes with surface texturing showed an enhancement in quantum efficiency with no dark current degradation. [ABSTRACT FROM AUTHOR]