SCPLPA: An miRNA-disease association prediction model based on spatial consistency projection and label propagation algorithm.

Identifying the association between miRNA and diseases is helpful for disease prevention, diagnosis and treatment. It is of great significance to use computational methods to predict potential human miRNA disease associations. Considering the shortcomings of existing computational methods, such as low prediction accuracy and weak generalization, we propose a new method called SCPLPA to predict miRNA-disease associations. First, a heterogeneous disease similarity network was constructed using the disease semantic similarity network and the disease Gaussian interaction spectrum kernel similarity network, while a heterogeneous miRNA similarity network was constructed using the miRNA functional similarity network and the miRNA Gaussian interaction spectrum kernel similarity network. Then, the estimated miRNA-disease association scores were evaluated by integrating the outcomes obtained by implementing label propagation algorithms in the heterogeneous disease similarity network and the heterogeneous miRNA similarity network. Finally, the spatial consistency projection algorithm of the network was used to extract miRNA disease association features to predict unverified associations between miRNA and diseases. SCPLPA was compared with four classical methods (MDHGI, NSEMDA, RFMDA and SNMFMDA), and the results of multiple evaluation metrics showed that SCPLPA exhibited the most outstanding predictive performance. Case studies have shown that SCPLPA can effectively identify miRNAs associated with colon neoplasms and kidney neoplasms. In summary, our proposed SCPLPA algorithm is easy to implement and can effectively predict miRNA disease associations, making it a reliable auxiliary tool for biomedical research.

Validated enhancement and temperature modulated absorbance of a WS2 monolayer based on a planar structure.

Transition-metal dichalcogenides (TMDCs), as emerging optoelectronic materials, necessitate the establishment of an experimentally viable system to study their interaction with light. In this study, we propose and analyze a WS2/PMMA/Ag planar Fabry-Perot (F-P) cavity, enabling the direct experimental measurement of WS2 absorbance. By optimizing the structure, the absorbance of A exciton of WS2 up to 0.546 can be experimentally achieved, which matches well with the theoretical calculations. Through temperature and thermal expansion strain induced by temperature, the absorbance of the A exciton can be tuned in situ. Furthermore, temperature-dependent photocurrent measurements confirmed the consistent absorbance of the A exciton under varying temperatures. This WS2/PMMA/Ag planar structure provides a straightforward and practical platform for investigating light interaction in TMDCs, laying a solid foundation for future developments of TMDC-based optoelectronic devices.

Beyond Conventional Charge Density Wave for Strongly Enhanced 2D Superconductivity in 1H-TaS2 Superlattices.

Noncentrosymmetric transition metal dichalcogenide (TMD) monolayers offer a fertile platform for exploring unconventional Ising superconductivity (SC) and charge density waves (CDWs). However, the vulnerability of isolated monolayers to structural disorder and environmental oxidation often degrade their electronic coherence. Herein, an alternative approach is reported for fabricating stable and intrinsic monolayers of 1H-TaS2 sandwiched between SnS blocks in a (SnS)1.15TaS2 van der Waals (vdW) superlattice. The SnS block layers not only decouple individual 1H-TaS2 sublayers to endow them with monolayer-like electronic characteristics, but also protect the 1H-TaS2 layers from electronic degradation. The results reveal the characteristic 3 × 3 CDW order in 1H-TaS2 sublayers associated with electronic rearrangement in the low-lying sulfur p band, which uncovers a previously undiscovered CDW mechanism rather than the conventional Fermi surface-related framework. Additionally, the (SnS)1.15TaS2 superlattice exhibits a strongly enhanced Ising-like SC with a layer-independent Tc of ≈3.0 K, comparable to that of the isolated monolayer 1H-TaS2 sample, presumably attributed to their monolayer-like characteristics and retained Fermi states. These results provide new insights into the long-debated CDW order and enhanced SC of monolayer 1H-TaS2, establishing bulk vdW superlattices as promising platforms for investigating exotic collective quantum phases in the 2D limit.

Engineering Faradaic Electrode Materials for High-Efficiency Water Desalination.

Water desalination technologies play a key role in addressing the global water scarcity crisis and ensuring a sustainable supply of freshwater. In contrast to conventional capacitive deionization, which suffers from limitations such as low desalination capacity, carbon anode oxidation, and co-ion expulsion effects of carbon materials, the emerging faradaic electrochemical deionization (FDI) presents a promising avenue for enhancing water desalination performance. These electrode materials employed faradaic charge-transfer processes for ion removal, achieving higher desalination capacity and energy-efficient desalination for high salinity streams. The past decade has witnessed a surge in the advancement of faradaic electrode materials and considerable efforts have been made to explore optimization strategies for improving their desalination performance. This review summarizes the recent progress on the optimization strategies and underlying mechanisms of faradaic electrode materials in pursuit of high-efficiency water desalination, including phase, doping and vacancy engineering, nanocarbon incorporation, heterostructures construction, interlayer spacing engineering, and morphology engineering. The key points of each strategy in design principle, modification method, structural analysis, and optimization mechanism of faradaic materials are discussed in detail. Finally, this work highlights the remaining challenges of faradaic electrode materials and present perspectives for future research.

PB2 residue 473 contributes to the mammalian virulence of H7N9 avian influenza virus by modulating viral polymerase activity via ANP32A.

Since the first human infection reported in 2013, H7N9 avian influenza virus (AIV) has been regarded as a serious threat to human health. In this study, we sought to identify the virulence determinant of the H7N9 virus in mammalian hosts. By comparing the virulence of the SH/4664 H7N9 virus, a non-virulent H9N2 virus, and various H7N9-H9N2 hybrid viruses in infected mice, we first pinpointed PB2 as the primary viral factor accounting for the difference between H7N9 and H9N2 in mammalian virulence. We further analyzed the in vivo effects of individually mutating H7N9 PB2 residues different from the closely related H9N2 virus and consequently found residue 473, alongside the well-known residue 627, to be critical for the virulence of the H7N9 virus in mice and the activity of its reconstituted viral polymerase in mammalian cells. The importance of PB2-473 was further strengthened by studying reverse H7N9 substitutions in the H9N2 background. Finally, we surprisingly found that species-specific usage of ANP32A, a family member of host factors connecting with the PB2-627 polymorphism, mediates the contribution of PB2 473 residue to the mammalian adaption of AIV polymerase, as the attenuating effect of PB2 M473T on the viral polymerase activity and viral growth of the H7N9 virus could be efficiently complemented by co-expression of chicken ANP32A but not mouse ANP32A and ANP32B. Together, our studies uncovered the PB2 473 residue as a novel viral host range determinant of AIVs via species-specific co-opting of the ANP32 host factor to support viral polymerase activity.IMPORTANCEThe H7N9 avian influenza virus has been considered to have the potential to cause the next pandemic since the first case of human infection reported in 2013. In this study, we identified PB2 residue 473 as a new determinant of mouse virulence and mammalian adaptation of the viral polymerase of the H7N9 virus and its non-pathogenic H9N2 counterparts. We further demonstrated that the variation in PB2-473 is functionally linked to differential co-opting of the host ANP32A protein in supporting viral polymerase activity, which is analogous to the well-known PB2-627 polymorphism, albeit the two PB2 positions are spatially distant. By providing new mechanistic insight into the PB2-mediated host range determination of influenza A viruses, our study implicated the potential existence of multiple PB2-ANP32 interfaces that could be targets for developing new antivirals against the H7N9 virus as well as other mammalian-adapted influenza viruses.

Identifying potential ligand-receptor interactions based on gradient boosted neural network and interpretable boosting machine for intercellular communication analysis.

Cell-cell communication is essential to many key biological processes. Intercellular communication is generally mediated by ligand-receptor interactions (LRIs). Thus, building a comprehensive and high-quality LRI resource can significantly improve intercellular communication analysis. Meantime, due to lack of a "gold standard" dataset, it remains a challenge to evaluate LRI-mediated intercellular communication results. Here, we introduce CellGiQ, a high-confident LRI prediction framework for intercellular communication analysis. Highly confident LRIs are first inferred by LRI feature extraction with BioTriangle, LRI selection using LightGBM, and LRI classification based on ensemble of gradient boosted neural network and interpretable boosting machine. Subsequently, known and identified high-confident LRIs are filtered by combining single-cell RNA sequencing (scRNA-seq) data and further applied to intercellular communication inference through a quartile scoring strategy. To validation the predictions, CellGiQ exploited several evaluation strategies: using AUC and AUPR, it surpassed six competing LRI prediction models on four LRI datasets; through Venn diagrams and molecular docking, its predicted LRIs were validated by five other popular intercellular communication inference methods; based on the overlapping LRIs, it computed high Jaccard index with six other state-of-the-art intercellular communication prediction tools within human HNSCC tissues; by comparing with classical models and literature retrieve, its inferred HNSCC-related intercellular communication results was further validated. The novelty of this study is to identify high-confident LRIs based on machine learning as well as design several LRI validation ways, providing reference for computational LRI prediction. CellGiQ provides an open-source and useful tool to decompose LRI-mediated intercellular communication at single cell resolution. CellGiQ is freely available at https://github.com/plhhnu/CellGiQ.

Prevalence and risk factors for abnormal glucose metabolism in first-episode and drug-naïve major depressive disorder outpatients with comorbid anxiety: A cross-sectional study from a large sample of the Chinese Han population.

BACKGROUND: Both abnormal glucose metabolism and anxiety have been reported to be common in major depressive disorder (MDD). However, few studies have explored glucose disturbances in first-episode and drug-naive (FEDN) MDD patients with anxiety. The purpose of this study was to examine the prevalence and risk factors of glucose disturbance in FEND MDD patients comorbid with anxiety. METHODS: 1718 FEDN MDD patients were included in this study. The positive subscale of the Positive and Negative Syndrome Scale (PANSS), Hamilton Anxiety Rating Scale (HAMA), and Hamilton Depression Rating Scale (HAMD) were used to measure psychotic, anxiety and depressive symptoms respectively. Sociodemographic and biochemical indicators were also collected. RESULTS: The prevalence of glucose disorders in MDD patients combined with anxiety was 15.7 %, significantly higher than in MDD patients without anxiety symptoms (7.1 %). Glucose disturbances were associated with HAMD score, HAMA score, thyroid-stimulating hormone (TSH), thyroid peroxidase antibody (TPOAb), anti-thyroglobulin (TGAb), total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein (LDL-C), fasting blood glucose (FBG), systolic blood pressure (SBP), diastolic blood pressure (DBP), suicide attempts, and psychotic symptoms. Further logistic regression showed that illness duration, TSH, TGAb, and TPOAb levels were correlates of glucose disturbances in MDD patients with anxiety. LIMITATIONS: No causal relationship could be drawn due to the cross-sectional design. CONCLUSIONS: Our findings suggest that TSH, TGAb and TPOAb may be promising biomarkers of glucose disturbances in MDD comorbid with anxiety, suggesting the importance of regular assessment of thyroid function parameters for abnormal glucose metabolism prevention.

Integrating evolutionarily conserved mechanism of response to radiation for exploring novel Caenorhabditis elegans radiation-responsive genes for estimation of radiation dose associated with spaceflight.

During space exploration, space radiation is widely recognized as an inescapable perilous stressor, owing to its capacity to induce genomic DNA damage and escalate the likelihood of detrimental health outcomes. Rapid and reliable estimation of space radiation dose holds paramount significance in accurately assessing the health risks associated with spaceflight. However, the identification of space radiation-responsive genes, with their potential to serve as early indicators for diagnosing radiation dose associated with spaceflight, continues to pose a significant challenge. In this study, based on the evolutionarily conserved mechanism of radiation response, an in silico analysis method of homologous comparison was performed to identify the Caenorhabditis elegans orthologues of human radiation-responsive genes with possible roles in the major processes of response to radiation, and thereby to explore the potential C. elegans radiation-responsive genes for evaluating the levels of space radiation exposure. The results showed that there were 60 known C. elegans radiation-responsive genes and 211 C. elegans orthologues of human radiation-responsive genes implicated in the major processes of response to radiation. Through an investigation of all available transcriptomic datasets obtained from space-flown C. elegans, it was observed that the expression levels of the majority of these putative C. elegans radiation-responsive genes identified in this study were notably changed across various spaceflight conditions. Furthermore, this study indicated that within the identified genes, 19 known C. elegans radiation-responsive genes and 40 newly identified C. elegans orthologues of human radiation-responsive genes exhibited a remarkable positive correlation with the duration of spaceflight. Moreover, a noteworthy presence of substantial multi-collinearity among the majority of these identified genes was observed. This observation lends support to the possibility of treating each identified gene as an independent indicator of radiation dose in space. Ultimately, a subset of 15 potential radiation-responsive genes was identified, presenting the most promising indicators for estimation of radiation dose associated with spaceflight in C. elegans.

Association of subclinical hypothyroidism with metabolic syndrome and its components among outpatients with first-episode drug-naïve major depressive disorder: a large-scale cross-sectional study.

Both metabolic syndrome (MetS) and subclinical hypothyroidism (SCH) are prevalent in major depressive disorder (MDD) patients. However, their relationship in this population remains unknown. The study assessed the association between SCH and MetS in 1706 first-episode drug-naïve (FEDN) MDD patients. We also compared the relationship between MetS and clinical symptoms in patients with and without comorbid SCH. The Positive and Negative Syndrome Scale positive subscale, the Hamilton Anxiety Rating Scale, and the Hamilton Depression Rating Scale were used to detect clinical symptoms. Serum levels of free triiodothyronine, free thyroxine, thyroid stimulating hormone (TSH), anti-thyroglobulin, thyroid peroxidases antibody, cholesterol, triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and fasting glucose were measured. The Area Under the Curve (AUC) was used to test the performance of serum TSH in identifying MetS patients. The prevalence of MetS and SCH was 34.5% (n = 585) and 61% (n = 1034), respectively. The presence of SCH increased the risk of MetS, hyperglycemia, hypertension, obesity, and low HDL-C by 4.91, 3.51, 3.54, 2.02, and 2.34 times, respectively. Serum TSH had a nice ability to distinguish MetS patients from non-MetS patients (AUC value = 0.77). MetS and its components exhibited a positive association with clinical profiles only in SCH patients, but not in non-SCH patients. Taken together, our study suggested SCH was closely related to MetS and might play a vital role in the relationship between MetS and clinical symptoms. Regular thyroid function checks might help early detect MetS.

Efficacy and safety of zuranolone for the treatment of depression: A systematic review and meta-analysis.

Major depressive disorder (MDD) and postpartum depression (PPD) are common and burdensome conditions. This study aims to evaluate the efficacy and safety of zuranolone, a neuroactive steroid γ-aminobutyric acid type A receptors-positive allosteric modulator, in treating MDD and PPD. A comprehensive literature search was conducted until September 2023, identifying seven randomized controlled trials (RCTs). The results demonstrated that zuranolone significantly decreased Hamilton Rating Scale for Depression (HAM-D) scores in patients with PPD or MDD at day 15 (concluding the 14-day course) and day 42-45 (4 weeks after treatment cessation) compared with the placebo, albeit exhibiting a diminishing trend. Moreover, a higher percentage of patients with PPD or MDD achieved HAM-D response and remission with zuranolone treatment compared with placebo at day 15. However, zuranolone did not significantly increase the proportion of MDD patients achieving HAM-D remission at 42/43 days. Adverse events (AEs) such as somnolence, dizziness, and sedation were linked to zuranolone, with a higher but not statistically significant rate of discontinuation due to AEs in the zuranolone group. Overall, our findings support the rapid antidepressant effects of zuranolone in MDD and PPD, along with a relatively favorable safety and tolerability. Large-scale longitudinal RCTs are needed to evaluate the long-term efficacy of zuranolone.

Bio-Master: Design and Validation of a High-Throughput Biochemical Profiling Platform for Crop Canopies.

Accurate assessment of crop biochemical profiles plays a crucial role in diagnosing their physiological status. The conventional destructive methods, although reliable, demand extensive laboratory work for measuring various traits. On the other hand, nondestructive techniques, while efficient and adaptable, often suffer from reduced precision due to the intricate interplay of the field environment and canopy structure. Striking a delicate balance between efficiency and accuracy, we have developed the Bio-Master phenotyping system. This system is capable of simultaneously measuring four vital biochemical components of the canopy profile: dry matter, water, chlorophyll, and nitrogen content. Bio-Master initiates the process by addressing structural influences, through segmenting the fresh plant and then further chopping the segment into uniform small pieces. Subsequently, the system quantifies hyperspectral reflectance and fresh weight over the sample within a controlled dark chamber, utilizing an independent light source. The final step involves employing an embedded estimation model to provide synchronous estimates for the four biochemical components of the measured sample. In this study, we established a comprehensive training dataset encompassing a wide range of rice varieties, nitrogen levels, and growth stages. Gaussian process regression model was used to estimate biochemical contents utilizing reflectance data obtained by Bio-Master. Leave-one-out validation revealed the model's capacity to accurately estimate these contents at both leaf and plant scales. With Bio-Master, measuring a single rice plant takes approximately only 5 min, yielding around 10 values for each of the four biochemical components across the vertical profile. Furthermore, the Bio-Master system allows for immediate measurements near the field, mitigating potential alterations in plant status during transportation and processing. As a result, our measurements are more likely to faithfully represent in situ values. To summarize, the Bio-Master phenotyping system offers an efficient tool for comprehensive crop biochemical profiling. It harnesses the benefits of remote sensing techniques, providing significantly greater efficiency than conventional destructive methods while maintaining superior accuracy when compared to nondestructive approaches.

326K at E Protein Is Critical for Mammalian Adaption of TMUV.

Outbreaks of Tembusu virus (TMUV) infection have caused huge economic losses to the poultry industry in China since 2010. However, the potential threat of TMUV to mammals has not been well studied. In this study, a TMUV HB strain isolated from diseased ducks showed high virulence in BALB/c mice inoculated intranasally compared with the reference duck TMUV strain. Further studies revealed that the olfactory epithelium is one pathway for the TMUV HB strain to invade the central nervous system of mice. Genetic analysis revealed that the TMUV HB virus contains two unique residues in E and NS3 proteins (326K and 519T) compared with duck TMUV reference strains. K326E substitution weakens the neuroinvasiveness and neurovirulence of TMUV HB in mice. Remarkably, the TMUV HB strain induced significantly higher levels of IL-1ß, IL-6, IL-8, and interferon (IFN)-α/ß than mutant virus with K326E substitution in the brain tissue of the infected mice, which suggested that TMUV HB caused more severe inflammation in the mouse brains. Moreover, application of IFN-ß to infected mouse brain exacerbated the disease, indicating that overstimulated IFN response in the brain is harmful to mice upon TMUV infection. Further studies showed that TMUV HB upregulated RIG-I and IRF7 more significantly than mutant virus containing the K326E mutation in mouse brain, which suggested that HB stimulated the IFN response through the RIG-I-IRF7 pathway. Our findings provide insights into the pathogenesis and potential risk of TMUV to mammals.

Recent Research Progress in Fluorescent Probes for Detection of Amyloid-ß In Vivo.

Alzheimer's disease (AD) is a neurodegenerative disease. Due to its complex pathological mechanism, its etiology is not yet clear. As one of the main pathological markers of AD, amyloid-ß (Aß) plays an important role in the development of AD. The deposition of Aß is not only related to the degeneration of neurons, but also can activate a series of pathological events, including the activation of astrocytes and microglia, the breakdown of the blood-brain barrier, and the change in microcirculation, which is the main cause of brain lesions and death in AD patients. Therefore, the development of efficient and reliable Aß-specific probes is crucial for the early diagnosis and treatment of AD. This paper focuses on reviewing the application of small-molecule fluorescent probes in Aß imaging in vivo in recent years. These probes efficiently map the presence of Aß in vivo, providing a pathway for the early diagnosis of AD and providing enlightenment for the design of Aß-specific probes in the future.

CellDialog: A Computational Framework for Ligand-receptor-mediated Cell-cell Communication Analysis III.

Intercellular communication significantly influences tumor progression, metastasis, and therapy resistance. An intercellular communication inference method includes two main procedures: ligand-receptor interaction (LRI) curation and LRI-mediated intercellular communication strength measurement. The construction of a comprehensive, high-confident and well-organized LRI database contributes to intercellular communication inference. Here, we developed a computational framework named CellDialog to reconstruct an intercellular connectivity network based on the combined expression of ligands and receptors involved in sender and receiver cells. CellDialog first captures high-confident LRIs through LRI feature extraction, feature selection, and classification. Furthermore, CellDialog uses a three-point estimation approach to measure the LRI-mediated intercellular communication strength by combining LRI filtering and single-cell RNA sequencing data. A comparison analysis of CellDialog and the other tools was conducted, and it was found that CellDialog can efficiently decode intercellular communications. Additionally, CellDialog offers a heatmap view and network view for intercellular communication visualization. In summary, CellDialog provides a tool that allows researchers to analyze intercellular signal transduction. It is freely available at https://github.com/plhhnu/CellDialog.

Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network.

Background: Microbes have dense linkages with human diseases. Balanced microorganisms protect human body against physiological disorders while unbalanced ones may cause diseases. Thus, identification of potential associations between microbes and diseases can contribute to the diagnosis and therapy of various complex diseases. Biological experiments for microbe-disease association (MDA) prediction are expensive, time-consuming, and labor-intensive. Methods: We developed a computational MDA prediction method called GPUDMDA by combining graph attention autoencoder, positive-unlabeled learning, and deep neural network. First, GPUDMDA computes disease similarity and microbe similarity matrices by integrating their functional similarity and Gaussian association profile kernel similarity, respectively. Next, it learns the feature representation of each microbe-disease pair using graph attention autoencoder based on the obtained disease similarity and microbe similarity matrices. Third, it selects a few reliable negative MDAs based on positive-unlabeled learning. Finally, it takes the learned MDA features and the selected negative MDAs as inputs and designed a deep neural network to predict potential MDAs. Results: GPUDMDA was compared with four state-of-the-art MDA identification models (i.e., MNNMDA, GATMDA, LRLSHMDA, and NTSHMDA) on the HMDAD and Disbiome databases under five-fold cross validations on microbes, diseases, and microbe-disease pairs. Under the three five-fold cross validations, GPUDMDA computed the best AUCs of 0.7121, 0.9454, and 0.9501 on the HMDAD database and 0.8372, 0.8908, and 0.8948 on the Disbiome database, respectively, outperforming the other four MDA prediction methods. Asthma is the most common chronic respiratory condition and affects ~339 million people worldwide. Inflammatory bowel disease is a class of globally chronic intestinal disease widely existed in the gut and gastrointestinal tract and extraintestinal organs of patients. Particularly, inflammatory bowel disease severely affects the growth and development of children. We used the proposed GPUDMDA method and found that Enterobacter hormaechei had potential associations with both asthma and inflammatory bowel disease and need further biological experimental validation. Conclusion: The proposed GPUDMDA demonstrated the powerful MDA prediction ability. We anticipate that GPUDMDA helps screen the therapeutic clues for microbe-related diseases.

A new dual functional H2S donor for fluorescence imaging and anti-inflammatory application.

This study explored FL-H2S, a novel fluorescein-based H2S donor, as an anti-inflammatory agent. The results demonstrated the efficient release of H2S by FL-H2S, along with its biocompatibility, real-time intracellular H2S release and imaging capability. In vivo experiments using a rat model confirmed the anti-inflammatory effects of FL-H2S, evidenced by reduced foot swelling. We also successfully elucidated the anti-inflammatory mechanism through ELISA and WB analysis.

Effects of Multiple High-Dose Methamphetamine Administration on Enteric Dopaminergic Neurons and Intestinal Motility in the Rat Model.

Several studies have identified the effects of methamphetamine (MA) on central dopaminergic neurons, but its effects on enteric dopaminergic neurons (EDNs) are unclear. The aim of this study was to investigate the effects of MA on EDNs and intestinal motility. Male Sprague-Dawley rats were randomly divided into MA group and saline group. The MA group received the multiple high-dose MA treatment paradigm, while the controls received the same saline treatment. After enteric motility was assessed, different intestinal segments (i.e., duodenum, jejunum, ileum, and colon) were taken for histopathological, molecular biological, and immunological analysis. The EDNs were assessed by measuring the expression of two dopaminergic neuronal markers, dopamine transporter (DAT) and tyrosine hydroxylase (TH), at the transcriptional and protein levels. We also used c-Fos protein, a marker of neural activity, to detect the activation of EDNs. MA resulted in a significant reduction in TH and DAT mRNA expression as well as in the number of EDNs in the duodenum and jejunum (p < 0.05). MA caused a dramatic increase in c-Fos expression of EDNs in the ileum (p < 0.001). The positional variability of MA effects on EDNs paralleled the positional variability of its effect on intestinal motility, as evidenced by the marked inhibitory effect of MA on small intestinal motility (p < 0.0001). This study found significant effects of MA on EDNs with locational variability, which might be relevant to locational variability in the potential effects of MA on intestinal functions, such as motility.

Preventing water inrush hazards in coal mines by coal gangue backfilling in gobs: influences of the particle size and stress on seepage characteristics.

The backfilling mining method that fills gobs with coal gangue can prevent water inrush hazards, protect groundwater resources, and protect the ecological environment of the mining area. However, initial conditions including the particle size distribution of gangue and the stress environment may affect the seepage characteristics of gangue backfill and inrush prevention ability. Taking the particle size and stress as main controlling factors, the seepage tests were designed for gangue to evaluate influences of the particle size and stress on the void ratio, permeability, and non-Darcian flow factor of gangue. In the meantime, the four stages in dynamic changes of seepage channels were studied and the impervious envelope lines of gangue backfill materials were provided. The results show that the larger the particle sizes, the stronger the crushing resistance of particles; under high stress (> 6.67 MPa), seepage channels in small gangue particles (< 5 mm) change in a more complex manner, and the non-Darcian flow phenomena become more significant. The particle size and stress exert significant influences on the seepage characteristics. Therefore, when reducing water inrush hazards by gangue backfilling in gobs, the particle size distribution should be optimized by combining the stress and water pressure conditions. Seepage channels in gangue backfill materials vary with changes in the particle size and stress. Their variation can be divided into four stages: shrinkage of seepage channels, reconstruction of seepage channels, dynamic equilibrium between slight expansion and shrinkage, and persistence of the impervious effect. After the first and second stages have been fully developed, the preliminary impervious conditions are met; after full development of the fourth stage, the gangue backfill materials reach an impervious state.

Predicting potential lncRNA biomarkers for lung cancer and neuroblastoma based on an ensemble of a deep neural network and LightGBM.

Introduction: Lung cancer is one of the most frequent neoplasms worldwide with approximately 2.2 million new cases and 1.8 million deaths each year. The expression levels of programmed death ligand-1 (PDL1) demonstrate a complex association with lung cancer. Neuroblastoma is a high-risk malignant tumor and is mainly involved in childhood patients. Identification of new biomarkers for these two diseases can significantly promote their diagnosis and therapy. However, in vivo experiments to discover potential biomarkers are costly and laborious. Consequently, artificial intelligence technologies, especially machine learning methods, provide a powerful avenue to find new biomarkers for various diseases. Methods: We developed a machine learning-based method named LDAenDL to detect potential long noncoding RNA (lncRNA) biomarkers for lung cancer and neuroblastoma using an ensemble of a deep neural network and LightGBM. LDAenDL first computes the Gaussian kernel similarity and functional similarity of lncRNAs and the Gaussian kernel similarity and semantic similarity of diseases to obtain their similar networks. Next, LDAenDL combines a graph convolutional network, graph attention network, and convolutional neural network to learn the biological features of the lncRNAs and diseases based on their similarity networks. Third, these features are concatenated and fed to an ensemble model composed of a deep neural network and LightGBM to find new lncRNA-disease associations (LDAs). Finally, the proposed LDAenDL method is applied to identify possible lncRNA biomarkers associated with lung cancer and neuroblastoma. Results: The experimental results show that LDAenDL computed the best AUCs of 0.8701, 107 0.8953, and 0.9110 under cross-validation on lncRNAs, diseases, and lncRNA-disease pairs on Dataset 1, respectively, and 0.9490, 0.9157, and 0.9708 on Dataset 2, respectively. Furthermore, AUPRs of 0.8903, 0.9061, and 0.9166 under three cross-validations were obtained on Dataset 1, and 0.9582, 0.9122, and 0.9743 on Dataset 2. The results demonstrate that LDAenDL significantly outperformed the other four classical LDA prediction methods (i.e., SDLDA, LDNFSGB, IPCAF, and LDASR). Case studies demonstrate that CCDC26 and IFNG-AS1 may be new biomarkers of lung cancer, SNHG3 may associate with PDL1 for lung cancer, and HOTAIR and BDNF-AS may be potential biomarkers of neuroblastoma. Conclusion: We hope that the proposed LDAenDL method can help the development of targeted therapies for these two diseases.