SEnSCA: Identifying possible ligand-receptor interactions and its application in cell-cell communication inference.

Multicellular organisms have dense affinity with the coordination of cellular activities, which severely depend on communication across diverse cell types. Cell-cell communication (CCC) is often mediated via ligand-receptor interactions (LRIs). Existing CCC inference methods are limited to known LRIs. To address this problem, we developed a comprehensive CCC analysis tool SEnSCA by integrating single cell RNA sequencing and proteome data. SEnSCA mainly contains potential LRI acquisition and CCC strength evaluation. For acquiring potential LRIs, it first extracts LRI features and reduces the feature dimension, subsequently constructs negative LRI samples through K-means clustering, finally acquires potential LRIs based on Stacking ensemble comprising support vector machine, 1D-convolutional neural networks and multi-head attention mechanism. During CCC strength evaluation, SEnSCA conducts LRI filtering and then infers CCC by combining the three-point estimation approach and single cell RNA sequencing data. SEnSCA computed better precision, recall, accuracy, F1 score, AUC and AUPR under most of conditions when predicting possible LRIs. To better illustrate the inferred CCC network, SEnSCA provided three visualization options: heatmap, bubble diagram and network diagram. Its application on human melanoma tissue demonstrated its reliability in CCC detection. In summary, SEnSCA offers a useful CCC inference tool and is freely available at https://github.com/plhhnu/SEnSCA.

GEnDDn: An lncRNA-Disease Association Identification Framework Based on Dual-Net Neural Architecture and Deep Neural Network.

Accumulating studies have demonstrated close relationships between long non-coding RNAs (lncRNAs) and diseases. Identification of new lncRNA-disease associations (LDAs) enables us to better understand disease mechanisms and further provides promising insights into cancer targeted therapy and anti-cancer drug design. Here, we present an LDA prediction framework called GEnDDn based on deep learning. GEnDDn mainly comprises two steps: First, features of both lncRNAs and diseases are extracted by combining similarity computation, non-negative matrix factorization, and graph attention auto-encoder, respectively. And each lncRNA-disease pair (LDP) is depicted as a vector based on concatenation operation on the extracted features. Subsequently, unknown LDPs are classified by aggregating dual-net neural architecture and deep neural network. Using six different evaluation metrics, we found that GEnDDn surpassed four competing LDA identification methods (SDLDA, LDNFSGB, IPCARF, LDASR) on the lncRNADisease and MNDR databases under fivefold cross-validation experiments on lncRNAs, diseases, LDPs, and independent lncRNAs and independent diseases, respectively. Ablation experiments further validated the powerful LDA prediction performance of GEnDDn. Furthermore, we utilized GEnDDn to find underlying lncRNAs for lung cancer and breast cancer. The results elucidated that there may be dense linkages between IFNG-AS1 and lung cancer as well as between HIF1A-AS1 and breast cancer. The results require further biomedical experimental verification. GEnDDn is publicly available at https://github.com/plhhnu/GEnDDn.

Porcine nasal septum cartilage-derived decellularized matrix promotes chondrogenic differentiation of human umbilical mesenchymal stem cells without exogenous growth factors.

BACKGROUND: In the domain of plastic surgery, nasal cartilage regeneration is of significant importance. The extracellular matrix (ECM) from porcine nasal septum cartilage has shown potential for promoting human cartilage regeneration. Nonetheless, the specific biological inductive factors and their pathways in cartilage tissue engineering remain undefined. METHODS: The decellularized matrix derived from porcine nasal septum cartilage (PN-DCM) was prepared using a grinding method. Human umbilical cord mesenchymal stem cells (HuMSCs) were cultured on these PN-DCM scaffolds for 4 weeks without exogenous growth factors to evaluate their chondroinductive potential. Subsequently, proteomic analysis was employed to identify potential biological inductive factors within the PN-DCM scaffolds. RESULTS: Compared to the TGF-ß3-cultured pellet model serving as a positive control, the PN-DCM scaffolds promoted significant deposition of a Safranin-O positive matrix and Type II collagen by HuMSCs. Gene expression profiling revealed upregulation of ACAN, COL2A1, and SOX9. Proteomic analysis identified potential chondroinductive factors in the PN-DCM scaffolds, including CYTL1, CTGF, MGP, ITGB1, BMP7, and GDF5, which influence HuMSC differentiation. CONCLUSION: Our findings have demonstrated that the PN-DCM scaffolds promoted HuMSC differentiation towards a nasal chondrocyte phenotype without the supplementation of exogenous growth factors. This outcome is associated with the chondroinductive factors present within the PN-DCM scaffolds.

BAF-Net: bidirectional attention-aware fluid pyramid feature integrated multimodal fusion network for diagnosis and prognosis.

Objective. To go beyond the deficiencies of the three conventional multimodal fusion strategies (i.e. input-, feature- and output-level fusion), we propose a bidirectional attention-aware fluid pyramid feature integrated fusion network (BAF-Net) with cross-modal interactions for multimodal medical image diagnosis and prognosis.Approach. BAF-Net is composed of two identical branches to preserve the unimodal features and one bidirectional attention-aware distillation stream to progressively assimilate cross-modal complements and to learn supplementary features in both bottom-up and top-down processes. Fluid pyramid connections were adopted to integrate the hierarchical features at different levels of the network, and channel-wise attention modules were exploited to mitigate cross-modal cross-level incompatibility. Furthermore, depth-wise separable convolution was introduced to fuse the cross-modal cross-level features to alleviate the increase in parameters to a great extent. The generalization abilities of BAF-Net were evaluated in terms of two clinical tasks: (1) an in-house PET-CT dataset with 174 patients for differentiation between lung cancer and pulmonary tuberculosis. (2) A public multicenter PET-CT head and neck cancer dataset with 800 patients from nine centers for overall survival prediction.Main results. On the LC-PTB dataset, improved performance was found in BAF-Net (AUC = 0.7342) compared with input-level fusion model (AUC = 0.6825;p< 0.05), feature-level fusion model (AUC = 0.6968;p= 0.0547), output-level fusion model (AUC = 0.7011;p< 0.05). On the H&N cancer dataset, BAF-Net (C-index = 0.7241) outperformed the input-, feature-, and output-level fusion model, with 2.95%, 3.77%, and 1.52% increments of C-index (p= 0.3336, 0.0479 and 0.2911, respectively). The ablation experiments demonstrated the effectiveness of all the designed modules regarding all the evaluated metrics in both datasets.Significance. Extensive experiments on two datasets demonstrated better performance and robustness of BAF-Net than three conventional fusion strategies and PET or CT unimodal network in terms of diagnosis and prognosis.

BINDTI: A bi-directional Intention network for drug-target interaction identification based on attention mechanisms.

The identification of drug-target interactions (DTIs) is an essential step in drug discovery. In vitro experimental methods are expensive, laborious, and time-consuming. Deep learning has witnessed promising progress in DTI prediction. However, how to precisely represent drug and protein features is a major challenge for DTI prediction. Here, we developed an end-to-end DTI identification framework called BINDTI based on bi-directional Intention network. First, drug features are encoded with graph convolutional networks based on its 2D molecular graph obtained by its SMILES string. Next, protein features are encoded based on its amino acid sequence through a mixed model called ACmix, which integrates self-attention mechanism and convolution. Third, drug and target features are fused through bi-directional Intention network, which combines Intention and multi-head attention. Finally, unknown drug-target (DT) pairs are classified through multilayer perceptron based on the fused DT features. The results demonstrate that BINDTI greatly outperformed four baseline methods (i.e., CPI-GNN, TransfomerCPI, MolTrans, and IIFDTI) on the BindingDB, BioSNAP, DrugBank, and Human datasets. More importantly, it was more appropriate to predict new DTIs than the four baseline methods on imbalanced datasets. Ablation experimental results elucidated that both bi-directional Intention and ACmix could greatly advance DTI prediction. The fused feature visualization and case studies manifested that the predicted results by BINDTI were basically consistent with the true ones. We anticipate that the proposed BINDTI framework can find new low-cost drug candidates, improve drugs' virtual screening, and further facilitate drug repositioning as well as drug discovery. BINDTI is publicly available at https://github.com/plhhnu/BINDTI.

Finding potential lncRNA-disease associations using a boosting-based ensemble learning model.

Introduction: Long non-coding RNAs (lncRNAs) have been in the clinical use as potential prognostic biomarkers of various types of cancer. Identifying associations between lncRNAs and diseases helps capture the potential biomarkers and design efficient therapeutic options for diseases. Wet experiments for identifying these associations are costly and laborious. Methods: We developed LDA-SABC, a novel boosting-based framework for lncRNA-disease association (LDA) prediction. LDA-SABC extracts LDA features based on singular value decomposition (SVD) and classifies lncRNA-disease pairs (LDPs) by incorporating LightGBM and AdaBoost into the convolutional neural network. Results: The LDA-SABC performance was evaluated under five-fold cross validations (CVs) on lncRNAs, diseases, and LDPs. It obviously outperformed four other classical LDA inference methods (SDLDA, LDNFSGB, LDASR, and IPCAF) through precision, recall, accuracy, F1 score, AUC, and AUPR. Based on the accurate LDA prediction performance of LDA-SABC, we used it to find potential lncRNA biomarkers for lung cancer. The results elucidated that 7SK and HULC could have a relationship with non-small-cell lung cancer (NSCLC) and lung adenocarcinoma (LUAD), respectively. Conclusion: We hope that our proposed LDA-SABC method can help improve the LDA identification.

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.

Balancing low-carbon and eco-friendly development: coordinated development strategy for land use carbon emission efficiency and land ecological security.

Correctly identifying and handling the relationship between land use carbon emission efficiency (LUCEE) and land ecological security (LES) are important to promote carbon neutrality in the overall layout of ecological civilization construction. This study takes 30 provinces in China as the research unit and measures the level of LUCEE and LES in each province in the period from 2011 to 2020 via a super-efficient slack-based measure model considering undesirable output. The coupling coordination degree (CCD) of LUCEE and LES is calculated, and its spatiotemporal evolution pattern is explored by kernel density estimation and standard deviational ellipse (SDE). The Dagum Gini coefficient is used to study spatial regional differences and the sources of differences. Results show that (1) China's LUCEE exhibited a downward and then an upward trend, as well as a spatial pattern of "high in the west and low in the east" with obvious regional differences. The LES experienced a positive transformation of "less secure → basically secure → more secure" nationwide, with no apparent regional differences. (2) The kernel density curves showed a continuous increase in CCD in general, while interprovincial differences increased, then decreased, and shifted from multipolar to bipolar differentiation. (3) The migration of SDE centers in CCD demonstrated a path of "southeast → southwest → northeast," and the ellipticity increased from 0.167 to 0.173, showing a trend of concentrated distribution. (4) The overall Gini coefficient of the national CCD indicated a decreasing trend, but imbalances remained, with the largest annual average value in the western region (0.120) and the smallest in the northeast (0.044). The main source of regional disparity was the intensity of transvariation. Accordingly, this study proposes targeted regional development strategies to promote low-carbon sustainable land use and improve the ability of land ecosystems to prevent security risks.

LDA-VGHB: identifying potential lncRNA-disease associations with singular value decomposition, variational graph auto-encoder and heterogeneous Newton boosting machine.

Long noncoding RNAs (lncRNAs) participate in various biological processes and have close linkages with diseases. In vivo and in vitro experiments have validated many associations between lncRNAs and diseases. However, biological experiments are time-consuming and expensive. Here, we introduce LDA-VGHB, an lncRNA-disease association (LDA) identification framework, by incorporating feature extraction based on singular value decomposition and variational graph autoencoder and LDA classification based on heterogeneous Newton boosting machine. LDA-VGHB was compared with four classical LDA prediction methods (i.e. SDLDA, LDNFSGB, IPCARF and LDASR) and four popular boosting models (XGBoost, AdaBoost, CatBoost and LightGBM) under 5-fold cross-validations on lncRNAs, diseases, lncRNA-disease pairs and independent lncRNAs and independent diseases, respectively. It greatly outperformed the other methods with its prominent performance under four different cross-validations on the lncRNADisease and MNDR databases. We further investigated potential lncRNAs for lung cancer, breast cancer, colorectal cancer and kidney neoplasms and inferred the top 20 lncRNAs associated with them among all their unobserved lncRNAs. The results showed that most of the predicted top 20 lncRNAs have been verified by biomedical experiments provided by the Lnc2Cancer 3.0, lncRNADisease v2.0 and RNADisease databases as well as publications. We found that HAR1A, KCNQ1DN, ZFAT-AS1 and HAR1B could associate with lung cancer, breast cancer, colorectal cancer and kidney neoplasms, respectively. The results need further biological experimental validation. We foresee that LDA-VGHB was capable of identifying possible lncRNAs for complex diseases. LDA-VGHB is publicly available at https://github.com/plhhnu/LDA-VGHB.

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.

Circ_0026579 knockdown ameliorates lipopolysaccharide-induced human lung fibroblast cell injury by regulating CXCR1 via miR-370-3p.

Pneumonia is an inflammatory disease in lower respiratory tracts and its development involves the regulation of RNAs. Circular RNAs are a class of RNA subgroups that can mediate the progression of pneumonia. However, the molecular mechanism of circ_0026579 in regulating pneumonia occurrence remains unclear. The study is designed to reveal the role of circ_0026579 in lipopolysaccharide (LPS)-induced human lung fibroblast cell injury and the underlying mechanism. The expression levels of circ_0026579, miR-370-3p and C-X-C motif chemokine receptor 1 (CXCR1) were detected by quantitative real-time polymerase chain reaction or by western blotting. The production of tumour necrosis factor-α, interleukin (IL)-1ß and IL-6 was assessed by enzyme-linked immunosorbent assays. Malondialdehyde and superoxide dismutase levels were analysed using commercial kits. Cell viability, proliferation and apoptosis were analysed by cell counting kit-8 assay, 5-Ethynyl-2'-deoxyuridine assay and flow cytometry analysis, respectively. The binding relationship between miR-370-3p and circ_0026579 or CXCR1 was identified by dual-luciferase reporter assay, RNA immunoprecipitation assay and RNA pull-down assay. Circ_0026579 and CXCR1 expression were significantly upregulated, whereas miR-370-3p was downregulated in the serum of pneumonia patients. LPS treatment induced inflammatory response, oxidative stress and cell apoptosis and inhibited cell proliferation in MRC-5 cells; however, these effects were reversed after circ_0026579 depletion. In terms of the mechanism, circ_0026579 acted as a miR-370-3p sponge, and miR-370-3p combined with CXCR1. Additionally, circ_0026579 depletion ameliorated LPS-induced MRC-5 cell disorder by increasing miR-370-3p expression. CXCR1 overexpression also relieved the miR-370-3p-mediated effects in LPS-treated MRC-5 cells. Further, circ_0026579 induced CXCR1 expression by interacting with miR-370-3p. Circ_0026579 absence ameliorated MRC-5 cell dysfunction induced by LPS through the regulation of the miR-370-3p/CXCR1 axis.

Optimal batch determination for improved harmonization and prognostication of multi-center PET/CT radiomics feature in head and neck cancer.

Objective. To determine the optimal approach for identifying and mitigating batch effects in PET/CT radiomics features, and further improve the prognosis of patients with head and neck cancer (HNC), this study investigated the performance of three batch harmonization methods.Approach. Unsupervised harmonization identified the batch labels by K-means clustering. Supervised harmonization regarding the image acquisition factors (center, manufacturer, scanner, filter kernel) as known/given batch labels, and Combat harmonization was then implemented separately and sequentially based on the batch labels, i.e. harmonizing features among batches determined by each factor individually or harmonizing features among batches determined by multiple factors successively. Extensive experiments were conducted to predict overall survival (OS) on public PET/CT datasets that contain 800 patients from 9 centers.Main results. In the external validation cohort, results show that compared to original models without harmonization, Combat harmonization would be beneficial in OS prediction with C-index of 0.687-0.740 versus 0.684-0.767. Supervised harmonization slightly outperformed unsupervised harmonization in all models (C-index: 0.692-0.767 versus 0.684-0.750). Separate harmonization outperformed sequential harmonization in CT_m+clinic and CT_cm+clinic models with C-index of 0.752 and 0.722, respectively, while sequential harmonization involved clinical features in PET_rs+clinic model further improving the performance and achieving the highest C-index of 0.767.Significance. Optimal batch determination especially sequential harmonization for Combat holds the potential to improve the prognostic power of radiomics model in multi-center HNC dataset with PET/CT imaging.

STGNNks: Identifying cell types in spatial transcriptomics data based on graph neural network, denoising auto-encoder, and k-sums clustering.

BACKGROUND: Spatial transcriptomics technologies fully utilize spatial location information, tissue morphological features, and transcriptional profiles. Integrating these data can greatly advance our understanding about cell biology in the morphological background. METHODS: We developed an innovative spatial clustering method called STGNNks by combining graph neural network, denoising auto-encoder, and k-sums clustering. First, spatial resolved transcriptomics data are preprocessed and a hybrid adjacency matrix is constructed. Next, gene expressions and spatial context are integrated to learn spots' embedding features by a deep graph infomax-based graph convolutional network. Third, the learned features are mapped to a low-dimensional space through a zero-inflated negative binomial (ZINB)-based denoising auto-encoder. Fourth, a k-sums clustering algorithm is developed to identify spatial domains by combining k-means clustering and the ratio-cut clustering algorithms. Finally, it implements spatial trajectory inference, spatially variable gene identification, and differentially expressed gene detection based on the pseudo-space-time method on six 10x Genomics Visium datasets. RESULTS: We compared our proposed STGNNks method with five other spatial clustering methods, CCST, Seurat, stLearn, Scanpy and SEDR. For the first time, four internal indicators in the area of machine learning, that is, silhouette coefficient, the Davies-Bouldin index, the Caliniski-Harabasz index, and the S_Dbw index, were used to measure the clustering performance of STGNNks with CCST, Seurat, stLearn, Scanpy and SEDR on five spatial transcriptomics datasets without labels (i.e., Adult Mouse Brain (FFPE), Adult Mouse Kidney (FFPE), Human Breast Cancer (Block A Section 2), Human Breast Cancer (FFPE), and Human Lymph Node). And two external indicators including adjusted Rand index (ARI) and normalized mutual information (NMI) were applied to evaluate the performance of the above six methods on Human Breast Cancer (Block A Section 1) with real labels. The comparison experiments elucidated that STGNNks obtained the smallest Davies-Bouldin and S_Dbw values and the largest Silhouette Coefficient, Caliniski-Harabasz, ARI and NMI, significantly outperforming the above five spatial transcriptomics analysis algorithms. Furthermore, we detected the top six spatially variable genes and the top five differentially expressed genes in each cluster on the above five unlabeled datasets. And the pseudo-space-time tree plot with hierarchical layout demonstrated a flow of Human Breast Cancer (Block A Section 1) progress in three clades branching from three invasive ductal carcinoma regions to multiple ductal carcinoma in situ sub-clusters. CONCLUSION: We anticipate that STGNNks can efficiently improve spatial transcriptomics data analysis and further boost the diagnosis and therapy of related diseases. The codes are publicly available at https://github.com/plhhnu/STGNNks.

Identifying potential drug-target interactions based on ensemble deep learning.

Introduction: Drug-target interaction prediction is one important step in drug research and development. Experimental methods are time consuming and laborious. Methods: In this study, we developed a novel DTI prediction method called EnGDD by combining initial feature acquisition, dimensional reduction, and DTI classification based on Gradient boosting neural network, Deep neural network, and Deep Forest. Results: EnGDD was compared with seven stat-of-the-art DTI prediction methods (BLM-NII, NRLMF, WNNGIP, NEDTP, DTi2Vec, RoFDT, and MolTrans) on the nuclear receptor, GPCR, ion channel, and enzyme datasets under cross validations on drugs, targets, and drug-target pairs, respectively. EnGDD computed the best recall, accuracy, F1-score, AUC, and AUPR under the majority of conditions, demonstrating its powerful DTI identification performance. EnGDD predicted that D00182 and hsa2099, D07871 and hsa1813, DB00599 and hsa2562, D00002 and hsa10935 have a higher interaction probabilities among unknown drug-target pairs and may be potential DTIs on the four datasets, respectively. In particular, D00002 (Nadide) was identified to interact with hsa10935 (Mitochondrial peroxiredoxin3) whose up-regulation might be used to treat neurodegenerative diseases. Finally, EnGDD was used to find possible drug targets for Parkinson's disease and Alzheimer's disease after confirming its DTI identification performance. The results show that D01277, D04641, and D08969 may be applied to the treatment of Parkinson's disease through targeting hsa1813 (dopamine receptor D2) and D02173, D02558, and D03822 may be the clues of treatment for patients with Alzheimer's disease through targeting hsa5743 (prostaglandinendoperoxide synthase 2). The above prediction results need further biomedical validation. Discussion: We anticipate that our proposed EnGDD model can help discover potential therapeutic clues for various diseases including neurodegenerative diseases.

SAELGMDA: Identifying human microbe-disease associations based on sparse autoencoder and LightGBM.

Introduction: Identification of complex associations between diseases and microbes is important to understand the pathogenesis of diseases and design therapeutic strategies. Biomedical experiment-based Microbe-Disease Association (MDA) detection methods are expensive, time-consuming, and laborious. Methods: Here, we developed a computational method called SAELGMDA for potential MDA prediction. First, microbe similarity and disease similarity are computed by integrating their functional similarity and Gaussian interaction profile kernel similarity. Second, one microbe-disease pair is presented as a feature vector by combining the microbe and disease similarity matrices. Next, the obtained feature vectors are mapped to a low-dimensional space based on a Sparse AutoEncoder. Finally, unknown microbe-disease pairs are classified based on Light Gradient boosting machine. Results: The proposed SAELGMDA method was compared with four state-of-the-art MDA methods (MNNMDA, GATMDA, NTSHMDA, and LRLSHMDA) under five-fold cross validations on diseases, microbes, and microbe-disease pairs on the HMDAD and Disbiome databases. The results show that SAELGMDA computed the best accuracy, Matthews correlation coefficient, AUC, and AUPR under the majority of conditions, outperforming the other four MDA prediction models. In particular, SAELGMDA obtained the best AUCs of 0.8358 and 0.9301 under cross validation on diseases, 0.9838 and 0.9293 under cross validation on microbes, and 0.9857 and 0.9358 under cross validation on microbe-disease pairs on the HMDAD and Disbiome databases. Colorectal cancer, inflammatory bowel disease, and lung cancer are diseases that severely threat human health. We used the proposed SAELGMDA method to find possible microbes for the three diseases. The results demonstrate that there are potential associations between Clostridium coccoides and colorectal cancer and one between Sphingomonadaceae and inflammatory bowel disease. In addition, Veillonella may associate with autism. The inferred MDAs need further validation. Conclusion: We anticipate that the proposed SAELGMDA method contributes to the identification of new MDAs.

Deciphering ligand-receptor-mediated intercellular communication based on ensemble deep learning and the joint scoring strategy from single-cell transcriptomic data.

BACKGROUND: Cell-cell communication in a tumor microenvironment is vital to tumorigenesis, tumor progression and therapy. Intercellular communication inference helps understand molecular mechanisms of tumor growth, progression and metastasis. METHODS: Focusing on ligand-receptor co-expressions, in this study, we developed an ensemble deep learning framework, CellComNet, to decipher ligand-receptor-mediated cell-cell communication from single-cell transcriptomic data. First, credible LRIs are captured by integrating data arrangement, feature extraction, dimension reduction, and LRI classification based on an ensemble of heterogeneous Newton boosting machine and deep neural network. Next, known and identified LRIs are screened based on single-cell RNA sequencing (scRNA-seq) data in certain tissues. Finally, cell-cell communication is inferred by incorporating scRNA-seq data, the screened LRIs, a joint scoring strategy that combines expression thresholding and expression product of ligands and receptors. RESULTS: The proposed CellComNet framework was compared with four competing protein-protein interaction prediction models (PIPR, XGBoost, DNNXGB, and OR-RCNN) and obtained the best AUCs and AUPRs on four LRI datasets, elucidating the optimal LRI classification ability. CellComNet was further applied to analyze intercellular communication in human melanoma and head and neck squamous cell carcinoma (HNSCC) tissues. The results demonstrate that cancer-associated fibroblasts highly communicate with melanoma cells and endothelial cells strong communicate with HNSCC cells. CONCLUSIONS: The proposed CellComNet framework efficiently identified credible LRIs and significantly improved cell-cell communication inference performance. We anticipate that CellComNet can contribute to anticancer drug design and tumor-targeted therapy.

Sustainable development of urban agglomeration based on material metabolism: a case study on Fujian Delta, China.

As highly concentrated urbanized areas, urban agglomerations bear increasing resource depletion and environmental pressures, which threaten the regional sustainable development. Resource and environmental problems arising from the process of urbanization can be attributed to the dislocation or maladjustment of material metabolism in time or space. Conducting research on material metabolism at the level of urban agglomerations is helpful in finding the root causes of environmental problems to provide support for the reduction of regional resource consumption and pollution emissions. The material metabolism characteristics of the urban agglomeration and internal cities of the Fujian Delta Urban Agglomeration (FDUA) in China are evaluated using the material flow analysis. The following results are observed. (1) The economic development of the FDUA is still at risk of resource consumption, and a large proportion of hidden flow (HF > 80%) drags down the overall metabolic efficiency and sustainable development. (2) The discharge of various pollutants in the FDUA generally shows a downward trend. Improving metabolic efficiency, delayed MCI growth, and improved overall regional environmental quality are observed. (3) Cities that have relatively scarce land resources but are economically developed, such as Xiamen, still bear a relatively heavy ecological burden (ECdmc > 1). (4) Regional collaboration is conducive to the sustainable development of multiple regions. On the one hand, the results of this study provide decision-making basis for the sustainable development of the national ecological civilization demonstration area. On the other hand, this work guides the establishment of a comprehensive industrial linkage and cooperation mechanism for the same type of small- and medium-sized urban agglomerations.

CellEnBoost: A Boosting-Based Ligand-Receptor Interaction Identification Model for Cell-to-Cell Communication Inference.

Cell-to-cell communication (CCC) plays important roles in multicellular organisms. The identification of communication between cancer cells themselves and one between cancer cells and normal cells in tumor microenvironment helps understand cancer genesis, development and metastasis. CCC is usually mediated by Ligand-Receptor Interactions (LRIs). In this manuscript, we developed a Boosting-based LRI identification model (CellEnBoost) for CCC inference. First, potential LRIs are predicted by data collection, feature extraction, dimensional reduction, and classification based on an ensemble of Light gradient boosting machine and AdaBoost combining convolutional neural network. Next, the predicted LRIs and known LRIs are filtered. Third, the filtered LRIs are applied to CCC elucidation by combining CCC strength measurement and single-cell RNA sequencing data. Finally, CCC inference results are visualized using heatmap view, Circos plot view, and network view. The experimental results show that CellEnBoost obtained the best AUCs and AUPRs on the collected four LRI datasets. Case study in human head and neck squamous cell carcinoma (HNSCC) tissues demonstrates that fibroblasts were more likely to communicate with HNSCC cells, which is in accord with the results from iTALK. We anticipate that this work can contribute to the diagnosis and treatment of cancers.

Effects of non-invasive brain stimulation (NIBS) for executive function on subjects with ADHD: a protocol for a systematic review and meta-analysis.

INTRODUCTION: Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder with a high risk of multiple mental health and social difficulties. Executive function domains are associated with distinct ADHD symptom burdens. Non-invasive brain stimulation (NIBS) mainly includes repetitive transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS), which is a promising technique, but its impact on the executive function of ADHD is uncertain. Therefore, the aim of this systematic review and meta-analysis is to derive solid and updated estimates on the effect of NIBS on executive function in children/adults with ADHD. METHODS AND ANALYSIS: A systematic search will be performed through EMBASE, MEDLINE, PsycINFO and Web of Science databases from inception until 22 August 2022. Handsearching of grey literature and the reference lists of selected articles will also be conducted. Empirical studies assessing the effect of NIBS (TMS or tDCS) on executive function in children or adults with ADHD will be included. Two investigators will independently perform literature identification, data extraction and risk of bias assessment. Relevant data will be pooled by a fixed-effects or random-effects model according to I2 statistic. Sensitivity analysis will be performed to test the robustness of the pooled estimates. Subgroup analyses will be conducted to investigate the potential heterogeneity. This protocol will generate a systematic review and meta-analysis that comprehensively synthesises the evidence on the NIBS treatment of executive function deficit of ADHD.Ethics approval is not required as this is a protocol for a systematic review of published literature. The results will be submitted to a peer-reviewed journal or a conference. PROSPERO REGISTRATION NUMBER: CRD42022356476.