Search

Understanding the lagged correlations between regional precipitation and large scale climate indicators (LCI) is crucial for water management and drought risk assessment. Copula functions are commonly used to quantify the relationship between precipitation and these factors. However, existing studies often neglect monsoon inland areas and lack quantification of the lagged correlations of precipitation at the monthly scale. To address these limitations, this study constructed a 3-dimensional vine copula-based model to assess the impact of large scale climatic change on regional precipitation. The analysis focused on the Hengshao Drought Corridor (HDC), the driest region in Hunan Province, China. The results indicate that precipitation in this region is influenced primarily by the Pacific Ocean’s circulation pattern, particularly the Niño1 + 2 and Niño3 indices, with a 2 ∼ 3 month lag. The R-Vine Copula provided the best fit when identifying lagged correlations, but due to its fitting center, it did not perform as well as the C-Vine Copula in generating stochastic simulated precipitation. The study revealed that the HDC receives more precipitation when the Niño1 + 2 and Niño3 are asynchronous, but less precipitation when they are synchronous, making the area more susceptible to drought events. These findings have implications for the HDC and other monsoon inland regions worldwide, providing insights to cope with climate extremes and develop appropriate strategies.

Abstract Traditional artificial vision systems built using separate sensing, computing, and storage units have problems with high power consumption and latency caused by frequent data transmission between functional units. An effective approach is to transfer some memory and computing tasks to the sensor, enabling the simultaneous perception‐storage‐processing of light signals. Here, an optical–electrical coordinately modulated memristor is proposed, which controls the conductivity by means of polarization of the 2D ferroelectric Ruddlesden–Popper perovskite film at room temperature. The residual polarization shows no significant decay after 109‐cycle polarization reversals, indicating that the device has high durability. By adjusting the pulse parameters, the device can simulate the bio‐synaptic long/short‐term plasticity, which enables the control of conductivity with a high linearity of ≈0.997. Based on the device, a two‐layer feedforward neural network is built to recognize handwritten digits, and the recognition accuracy is as high as 97.150%. Meanwhile, building optical–electrical reserve pool system can improve 14.550% for face recognition accuracy, further demonstrating its potential for the field of neural morphological visual systems, with high density and low energy loss.

The high-value recycling of discarded phenol-formaldehyde resins (PF) remains an unresolved challenge. Herein, we propose a novel approach leveraging γ-Al2O3 to convert PF into high-value hexamethylbenzene at a low temperature using a one-pot method. This study explores the degradation capability of PF, methylation reaction efficiency, and hydrodeoxygenation capacity among various cost-effective commercial catalysts: γ-Al2O3, ZrO2, and TiO2. It reveals the influence of different reaction times on PF pyrolysis and product distribution, and it was found that high value-added hexamethylbenzene exhibited the highest yield (73.33 wt%) with selectivity (75.83%) using γ-Al2O3 at 350 °C and 2 h of reaction. Experiments using PF models demonstrate the crucial synergy between γ-Al2O3 and C(aryl)-OH in the cleavage of C(aryl)-C(alkyl) bonds and methylation reactions. A pathway for PF C-C/C-O bonds cleavage-methylation tandem reaction is proposed, based on 13C methanol isotope experiments. PF undergoes C(aryl)-C(alkyl) bond cleavage to produce phenolic intermediates, which were then methylated; this is accompanied by the cleavage of C(aryl)-OH and C(aryl)-OCH3, culminating in C-alkylation to form hexamethylbenzene. This research provides new insights into the high-value recycling of PF.

Intratumoral microbiota can regulate the tumor immune microenvironment (TIME) and mediate tumor prognosis by promoting inflammatory response or inhibiting anti-tumor effects. Recent studies have elucidated the potential role of local tumor microbiota in the development and progression of lung adenocarcinoma (LUAD). However, whether intratumoral microbes are involved in the TIME that mediates the prognosis of LUAD remains unknown. Here, we obtained the matched tumor microbiome and host transcriptome and survival data of 478 patients with LUAD in The Cancer Genome Atlas (TCGA). Machine learning models based on immune cell marker genes can predict 1- to 5-year survival with relative accuracy. Patients were stratified into high- and low-survival-risk groups based on immune cell marker genes, with significant differences in intratumoral microbial communities. Specifically, patients in the high-risk group had significantly higher alpha diversity (p < 0.05) and were characterized by an enrichment of lung cancer-related genera such as Streptococcus. However, network analysis highlighted a more active pattern of dominant bacteria and immune cell crosstalk in TIME in the low-risk group compared to the high-risk group. Our study demonstrated that intratumoral microbiota-immune crosstalk was strongly associated with prognosis in LUAD patients, which would provide new targets for the development of precise therapeutic strategies.

Abstract The interplay of water resources with social-economy spheres involves a reciprocal feedback mechanism. With the acceleration of the construction process of modernized water networks in Hunan Province, investigating the adaptation status of the "Water-Social-Economy " composite system (WSE) is crucial for promoting sustainability. This study clarifies the connotation of the adaptability of WSE, and the quantitative analyses were conducted through coupling coordinative degree, harmonious development capacity, and the evolution of development lag types among the 14 cities of Hunan Province from 2005 to 2020. The results show that: (1) The development index of the water resources subsystem (WRS) showed a “downward-fluctuation-upward” trend, while the development index of the social-economy subsystem (SES) showed signs of great improvement, the former didn’t catch up with the latter. (2) The coupling coordination degree of WSE developed well, and reached the coordinative development stage by 2020, but the unbalanced spatial pattern between north to south and east to west still exists and is further intensified. (3) The development ability of WSE improved while the harmony ability reduced, and the development rate of WRS and SES hasn’t achieved dynamic synchronization. Finally, the policies and suggestions to improve the adaptability are put forward, which is of instructive significance for the sustainable development of water suitability.

Lysine plays a crucial role in promoting development, enhancing immune function, and improving the function of central nervous system tissues. The two configurational isomers of amino acids have significantly different effects. Currently, methods for chiral recognition of lysine have been reported; however, previous detection methods have drawbacks such as expensive equipment and complicated detection processes. Fluorescence analysis, on the other hand, boasts high sensitivity, strong selectivity, and simple operation. In this study, we synthesized four novel Binaphthyl-Amine (BINAM)-based fluorescent probes capable of specifically identifying the L-configuration of lysine among the twenty amino acids that constitute human proteins. The enantiomeric fluorescence enhancement ratio (ef or ΔIL/ΔID) reached up to 15.29, demonstrating high enantioselectivity. In addition, we assessed the probe’s recognition capabilities under varying pH levels, reaction times, and metal ion conditions, along with its limit of detection (LOD) and quantum yield. Our results suggest that this probe serves as a highly stable tool for the detection of chiral lysine.

Recent studies suggested that cancer was a risk factor for coronavirus disease 2019 (COVID-19). Toll-like receptor 7 (TLR7), a severe acute respiratory syndrome 2 (SARS-CoV-2) virus's nucleic acid sensor, was discovered to be aberrantly expressed in many types of cancers. However, its expression pattern across cancers and association with COVID-19 has not been systematically studied. In this study, we proposed a computational framework to comprehensively study the roles of TLR7 in COVID-19 and pan-cancers at genetic, gene expression, protein, epigenetic, and single-cell levels. We found TLR7 mRNA expression was significantly up-regulated in 6 cancer types and down-regulated in 6 cancer types, further validated in the HPA database at the protein level. The genes significantly co-expressed with TLR7 were mainly enriched in the toll-like receptor signaling pathway, endolysosome, and signaling pattern recognition receptor activity. In addition, the abnormal TLR7 expression was associated with Mismatch repair (MMR), microsatellite instability (MSI), tumor mutational burden (TMB) in various cancers. Mined by the ESTIMATE algorithm, the expression of TLR7 was also closely linked to various immune infiltration patterns in pan-cancer, and TLR7 was mainly enriched in macrophages, as revealed by single-cell RNA sequencing. Finally, TLR7 expressions were very sensitive to a few targeted drugs, such as Alectinib and Imiquimod. In conclusion, TLR7 might be essential in the pathogenesis of COVID-19 and cancers.

Human-virus protein-protein interactions (PPIs) play critical roles in viral infection. For example, the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) binds primarily to human angiotensin-converting enzyme 2 (ACE2) protein to infect human cells. Thus, identifying and blocking these PPIs contribute to controlling and preventing viruses. However, wet-lab experiment-based identification of human-virus PPIs is usually expensive, labor-intensive, and time-consuming, which presents the need for computational methods. Many machine-learning methods have been proposed recently and achieved good results in predicting human-virus PPIs. However, most methods are based on protein sequence features and apply manually extracted features, such as statistical characteristics, phylogenetic profiles, and physicochemical properties. In this work, we present an embedding-based neural framework with convolutional neural network (CNN) and bi-directional long short-term memory unit (Bi-LSTM) architecture, named Emvirus, to predict human-virus PPIs (including human–SARS-CoV-2 PPIs). In addition, we conduct cross-viral experiments to explore the generalization ability of Emvirus. Compared to other feature extraction methods, Emvirus achieves better prediction accuracy.

The dynamic characteristics of running away pump turbines (PTs) with a large head variable amplitude have not been understood thus far, primarily because of two difficulties in simulation and analysis. The first is how to provide accurate time-varying boundary conditions for transient simulation of the turbine runaway process (TRP). The other is how to determine the specific appearance time of each frequency component of the complex pressure fluctuations. This study presented a one- and three-dimensional (1D-3D) coupled approach considering waterway dynamics to provide accurate unsteady boundary conditions for the transient flow simulation of a PT with a large head variable amplitude. The short-time Fourier transformation (STFT) approach was adopted to analyse the time-frequency characteristics of the transient pressures and impeller forces. The study found that the fluctuations of pressures and impeller forces during the TRP of the PT with a large head variable amplitude contained two exclusive fluctuation frequency components. The former was approximately three times the rated rotational frequency of the impeller. The later was a series of integer fold transient rotational frequencies of the impeller, which was irrelevant to the rotor-stator interactions. The findings have important value for controlling the pressure fluctuations during the TRP of PTs.

Summary: Senescent cells are a major contributor to age-dependent cardiovascular tissue dysfunction, but knowledge of their in vivo cell markers and tissue context is lacking. To reveal tissue-relevant senescence biology, we integrate the transcriptomes of 10 experimental senescence cell models with a 224 multi-tissue gene co-expression network based on RNA-seq data of seven tissues biopsies from ∼600 coronary artery disease (CAD) patients. We identify 56 senescence-associated modules, many enriched in CAD GWAS genes and correlated with cardiometabolic traits—which supports universality of senescence gene programs across tissues and in CAD. Cross-tissue network analyses reveal 86 candidate senescence-associated secretory phenotype (SASP) factors, including COL6A3. Experimental knockdown of COL6A3 induces transcriptional changes that overlap the majority of the experimental senescence models, with cell-cycle arrest linked to modulation of DREAM complex-targeted genes. We provide a transcriptomic resource for cellular senescence and identify candidate biomarkers, SASP factors, and potential drivers of senescence in human tissues.

Identifying the potential associations between microbes and diseases is the first step for revealing the pathological mechanisms of microbe-associated diseases. However, traditional culture-based microbial experiments are expensive and time-consuming. Thus, it is critical to prioritize disease-associated microbes by computational methods for further experimental validation. In this study, we proposed a novel method called MNNMDA, to predict microbe-disease associations (MDAs) by applying a Matrix Nuclear Norm method into known microbe and disease data. Specifically, we first calculated Gaussian interaction profile kernel similarity and functional similarity for diseases and microbes. Then we constructed a heterogeneous information network by combining the integrated disease similarity network, the integrated microbe similarity network and the known microbe-disease bipartite network. Finally, we formulated the microbe-disease association prediction problem as a low-rank matrix completion problem, which was solved by minimizing the nuclear norm of a matrix with a few regularization terms. We tested the performances of MNNMDA in three datasets including HMDAD, Disbiome, and Combined Data with small, medium and large sizes respectively. We also compared MNNMDA with 5 state-of-the-art methods including KATZHMDA, LRLSHMDA, NTSHMDA, GATMDA, and KGNMDA, respectively. MNNMDA achieved area under the ROC curves (AUROC) of 0.9536 and 0.9364 respectively on HDMAD and Disbiome, better than the AUCs of compared methods under the 5-fold cross-validation for all microbe-disease associations. It also obtained a relatively good performance with AUROC 0.8858 in the combined data. In addition, MNNMDA was also better than other methods in area under precision and recall curve (AUPR) under the 5-fold cross-validation for all associations, and in both AUROC and AUPR under the 5-fold cross-validation for diseases and the 5-fold cross-validation for microbes. Finally, the case studies on colon cancer and inflammatory bowel disease (IBD) also validated the effectiveness of MNNMDA. In conclusion, MNNMDA is an effective method in predicting microbe-disease associations. Availability: The codes and data for this paper are freely available at Github https://github.com/Haiyan-Liu666/MNNMDA.

ABSTRACT Differences in tissue microbiota-host interaction between lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) about recurrence and metastasis have not been well studied. In this study, we performed bioinformatics analyses to identify the genes and tissue microbes significantly associated with recurrence or metastasis. All lung cancer patients were divided into the recurrence or metastasis (RM) group and the nonrecurrence and nonmetastasis (non-RM) group according to whether or not they had recurred or metastasized within 3 years after the initial surgery. Results showed that there were significant differences between LUAD and LUSC in gene expression and microbial abundance associated with recurrence and metastasis. Compared with non-RM, the bacterial community of RM had a lower richness in LUSC. In LUSC, host genes significantly correlated with tissue microbe, whereas host-tissue microbe interaction in LUAD was rare. Then, we established a novel multimodal machine learning model based on genes and microbes to predict the recurrence and metastasis risk of a LUSC patient, which achieves an area under the curve (AUC) of 0.81. In addition, the predicted risk score was significantly associated with the patient’s survival. IMPORTANCE Our study elucidates significant differences in RM-associated host-microbe interactions between LUAD and LUSC. Besides, the microbes in tumor tissue could be used to predict the RM risk of LUSC, and the predicted risk score is associated with patients’ survival.

The water resources in Lianshui Basin is facing a prominent contradiction between production and ecology, as well as fierce competition among various regions. However, existing researches on spatial equilibrium typically tend to address only one of the dimensions among water resources system and other systems or among different regions, which can not meet the requirements for optimal allocation of water resources aiming for spatial equilibrium under the high-quality development strategy. The definition and quantitative evaluation method of spatial equilibrium remain unclear. In order to respond to the policy of spatial equilibrium and overcome the dilemma of water resources development and utilization under the new normal, the connotation of spatial equilibrium at two levels of system and region was presented, a quantitative dynamic evaluation method for the spatial equilibrium degree in system-region two stages was proposed in this study. A comprehensive evaluation indicators system was built for the water resources composite system composed of three subsystems: water resources, social economy and ecological environment, and a dynamic quantization model of coupling coordination degree based on the comprehensive evaluation indices of three subsystems was established, the temporal and spatial dynamic evolution process of the coupling coordination degree of water resources composite system was analyzed. At the same time, Theil index was used to research the dynamic evolution characteristics of the regional difference in coupling coordination degree in Lianshui Basin. This paper also identified the main source of difference based on the decomposition of Theil index, and the degree and trend of spatial equilibrium of water resources in Lianshui Basin were comprehensively analyzed. The results showed that the coupling coordination degree of the composite system of each district in Lianshui Basin was continuously improving, but there was no significant trend in the difference of coupling coordination degree among regions, which means the degree of spatial equilibrium in the “system stage” has notably improved, but the degree of spatial equilibrium in the “region stage” had no significant and stable increase. In addition, the difference within the region was always the main reason for the deficiency of spatial equilibrium.

BackgroundAging-related cognitive decline is associated with brain structural changes and synaptic loss. However, the molecular mechanisms of cognitive decline during normal aging remain elusive.ResultsUsing the GTEx transcriptomic data from 13 brain regions, we identified aging-associated molecular alterations and cell-type compositions in males and females. We further constructed gene co-expression networks and identified aging-associated modules and key regulators shared by both sexes or specific to males or females. A few brain regions such as the hippocampus and the hypothalamus show specific vulnerability in males, while the cerebellar hemisphere and the anterior cingulate cortex regions manifest greater vulnerability in females than in males. Immune response genes are positively correlated with age, whereas those involved in neurogenesis are negatively correlated with age. Aging-associated genes identified in the hippocampus and the frontal cortex are significantly enriched for gene signatures implicated in Alzheimer’s disease (AD) pathogenesis. In the hippocampus, a male-specific co-expression module is driven by key synaptic signaling regulators including VSNL1, INA, CHN1 and KCNH1; while in the cortex, a female-specific module is associated with neuron projection morphogenesis, which is driven by key regulators including SRPK2, REPS2 and FXYD1. In the cerebellar hemisphere, a myelination-associated module shared by males and females is driven by key regulators such as MOG, ENPP2, MYRF, ANLN, MAG and PLP1, which have been implicated in the development of AD and other neurodegenerative diseases.ConclusionsThis integrative network biology study systematically identifies molecular signatures and networks underlying brain regional vulnerability to aging in males and females. The findings pave the way for understanding the molecular mechanisms of gender differences in developing neurodegenerative diseases such as AD.

Immersion cooling is widely used for thermal management of servers. The two-phase immersion cooling, which transfers heat by boiling, possesses efficient temperature control ability under intensive heat generation. In the process of temperature control through boiling, the generation and transportation of bubbles play a crucial role in calculating the heat-transfer capacity. Therefore, it holds immense significance to obtain a profound understanding of the mechanisms underlying bubble formation and detachment. Currently, numerous mechanistic explanations and empirical correlations have been proposed to elucidate the various parameters of bubbles during the boiling process. These findings were considered to be valuable references when selecting appropriate boiling media and designing efficient heating surfaces. To comprehensively present the progress of bubble formation and heat transfer in the boiling system, the forces exerted on the bubbles are highlighted in this article. A meticulous review of bubble-force analysis and correlation formulae pertaining to various relevant parameters (e.g., nucleation sites density, bubble growth rate, bubble growth period, and detachment frequency) was conducted. This review article was also expected to provide a novel foundation for further exploration of enhanced boiling heat transfer.

Solving Mathematics Word Problem (MWP) is a basic ability of humanity, which can be mastered by most students at a young age. The existing artificial intelligence system is not good enough in numerical questions, like MWPs. The hard part of this problem is translating natural language sentences in MWP into mathematical expressions or equations. In recent researches, the Transformer network, which proved a great success in machine translation, is applied to automatic mathematic word problem-solving. While previous works have only shown the ability of Transformer model in MWP, how multiple factors such as encoding, decoding, and pre-training affect the performance of Transformer model has not received enough attention. The study is the first to examine the role of these factors experimentally. This paper proposes several methods to improve Transformer network performance in MWPs under the basis of previous studies, achieves higher accuracy compared to the previous state of the art. Pre-training on target tasks dataset improves the translation quality of the Transformer model greatly. Different token encoding and search algorithms also benefit prediction accuracy at the expense of more training and testing time.

HER2-positive breast cancer is a highly heterogeneous tumor, and about 30% of patients still suffer from recurrence and metastasis after trastuzumab targeted therapy. Predicting individual prognosis is of great significance for the further development of precise therapy. With the continuous development of computer technology, more and more attention has been paid to computer-aided diagnosis and prognosis prediction based on Hematoxylin and Eosin (H&E) pathological images, which are available for all breast cancer patients undergone surgical treatment. In this study, we first enrolled 127 HER2-positive breast cancer patients with known recurrence and metastasis status from Cancer Hospital of the Chinese Academy of Medical Sciences. We then proposed a novel multimodal deep learning method integrating whole slide H&E images (WSIs) and clinical information to accurately assess the risk of relapse and metastasis in patients with HER2-positive breast cancer. Specifically, we obtained the whole H&E staining images from the surgical specimens of breast cancer patients, and these images were adjusted to size 512 × 512 pixels. The deep convolutional neural network (CNN) was applied to these images to retrieve image features, which were combined with the clinical data. Based on the combined features. After that, a novel multimodal model was constructed for predicting the prognosis of each patient. The model achieved an area under curve (AUC) of 0.76 in the two-fold cross-validation (CV). To further evaluate the performance of our model, we downloaded the data of all 123 HER2-positive breast cancer patients with available H&E image and known recurrence and metastasis status in The Cancer Genome Atlas (TCGA), which was severed as an independent testing data. Despite the huge differences in race and experimental strategies, our model achieved an AUC of 0.72 in the TCGA samples. As a conclusion, H&E images, in conjunction with clinical information and advanced deep learning models, could be used to evaluate the risk of relapse and metastasis in patients with HER2-positive breast cancer.

This research investigated the value co-creation behaviors in livestreaming platforms and the internal mechanism of perceived value on consumer value co-creation behavior on the short-video platform TikTok. This research selected the Tiktok platform as the research object, and uses structural equation model to analyze consumer data. The results indicated that consumer-perceived value mediates the relationship between consumer engagement and citizenship behaviors. In addition, short-video platforms exhibit significant community attributes, and interactive behavior forms the primary part of consumer engagement that enhances the perceived value. Consumers join short-video platforms to look for communities that interest them. Consumers’ responses generate the perceived value. Thus, it enhances consumers’ intentions to continue using a particular service, which then increases the likelihood of citizenship behavior. This study also found that consumers’ creative behavior in short-video platforms embodies social and functional values. This suggested that consumers of livestreaming and short-video platforms such as TikTok tend to seek social recognition by sharing their opinions or daily lives. The examination of the mediating role of perceived value on the relationship between consumer engagement behavior and citizenship behavior revealed that perceived value significantly mediated the relationship between consumer engagement behavior and citizenship behavior. This provided evidence of the fact that consumers usually actively participate on the platform by uploading content and sharing their creations, with the motivation to generate significant social impact and gain the recognition of others. Firstly, TikTok’s consumers deeply engage on platforms in which their cognitive messages are integrated into the platform through interacting with others, browsing, and creating short videos. Secondly, TikTok provides a channel with high level of interactivity that facilitates social interaction among strangers. Video uploaders and fans develop positive interactive relationships. Thirdly, TikTok viewers may become video providers at some point; the act of co-creation creates economic value for the platform and generates emotional, functional, and social values for its consumers.

The study of disease-gene associations is an important topic in the field of computational biology. The accumulation of massive amounts of biomedical data provides new possibilities for exploring potential relations between diseases and genes through computational strategy, but how to extract valuable information from the data to predict pathogenic genes accurately and rapidly is currently a challenging and meaningful task. Therefore, we present a novel computational method called PGAGP for inferring potential pathogenic genes based on an adaptive network embedding algorithm. The PGAGP algorithm is to first extract initial features of nodes from a heterogeneous network of diseases and genes efficiently and effectively by Gaussian random projection and then optimize the features of nodes by an adaptive refining process. These low-dimensional features are used to improve the disease-gene heterogenous network, and we apply network propagation to the improved heterogenous network to predict pathogenic genes more effectively. By a series of experiments, we study the effect of PGAGP’s parameters and integrated strategies on predictive performance and confirm that PGAGP is better than the state-of-the-art algorithms. Case studies show that many of the predicted candidate genes for specific diseases have been implied to be related to these diseases by literature verification and enrichment analysis, which further verifies the effectiveness of PGAGP. Overall, this work provides a useful solution for mining disease-gene heterogeneous network to predict pathogenic genes more effectively.

Abstract DNA‐based biological ingredient identification and downstream pharmacology network analysis are commonly used in research for Traditional Chinese Medicine preparations (TCM formulas). Advancements in bioinformatics tools and the accumulation of related data have become driving forces for progress in this field. However, a lack of a platform integrating biological ingredient identification and downstream pharmacology network analysis hinders the deep understanding of TCM. In this study, we developed the TCM‐Suite platform composed of two sub‐databases, Holmes‐Suite and Watson‐Suite, for TCM biological ingredient identification and network pharmacology investigation, respectively, both are among the most complete: In the Holmes‐Suite, we collected and processed six types of marker gene sequences, accounting for 1,251,548 marker gene sequences. In the Watson‐Suite, we curated and integrated a massive number of entries from more than 10 public databases. Importantly, we developed a comprehensive pipeline to integrate TCM biological ingredient identification and downstream network pharmacology research, allowing users to simultaneously identify components of a TCM formula and analyze its potential pharmacology mechanism. Furthermore, we designed search engines and a user‐friendly platform to better search and visualize these rich resources. TCM‐Suite is a comprehensive and holistic platform for TCM‐based drug discovery and repurposing. TCM‐Suite website: http://TCM-Suite.AImicrobiome.cn.

Based on the data of Shanghai and Shenzhen A-share listed companies from 2015 to 2019, this paper studies the influence of air quality on the R&D investment of listed companies from the perspectives of investor sentiment and government concern. It is found that, on the whole, air quality has a significant inhibitory effect on R&D investment. Air quality significantly promotes investor sentiment, which serves as a path to further restrain the R&D investment of listed companies. Air pollution is an effective concern for the government and serves as a veil between air quality and R&D investment. Furthermore, this paper analyzes the heterogeneity of enterprises from the aspects of regional technology complexity, property right nature, whether it is a polluting enterprise or not, and whether it is a key regulated enterprise or not, and comes to relevant conclusions. This paper expands the research on air quality and enterprise R&D investment decision-making, which helps to clarify and improve the transmission mechanism and implementation effect of environmental protection policies.

In recent years, more and more single-cell technologies have been developed. A vast amount of single-cell omics data has been generated by large projects, such as the Human Cell Atlas, the Mouse Cell Atlas, the Mouse RNA Atlas, the Mouse ATAC Atlas, and the Plant Cell Atlas. Based on these single-cell big data, thousands of bioinformatics algorithms for quality control, clustering, cell-type annotation, developmental inference, cell-cell transition, cell-cell interaction, and spatial analysis are developed. With powerful experimental single-cell technology and state-of-the-art big data analysis methods based on artificial intelligence, the molecular landscape at the single-cell level can be revealed. With spatial transcriptomics and single-cell multi-omics, even the spatial dynamic multi-level regulatory mechanisms can be deciphered. Such single-cell technologies have many successful applications in oncology, assisted reproduction, embryonic development, and plant breeding. We not only review the experimental and bioinformatics methods for single-cell research, but also discuss their applications in various fields and forecast the future directions for single-cell technologies. We believe that spatial transcriptomics and single-cell multi-omics will become the next booming business for mechanism research and commercial industry. Public summary: • Single-cell sequencing can reveal molecular characteristics at the cell level • Spatial omics can reconstruct the organization of cells and their interactions • Single-cell multi-omics will picture the cell in a comprehensive way

BackgroundLocal recurrence and distant metastasis are the main causes of death in patients with lung cancer. Multiple studies have described the recurrence or metastasis of lung cancer at the genetic level. However, association between the microbiome of lung cancer tissue and recurrence or metastasis remains to be discovered. Here, we aimed to identify the bacterial biomarkers capable of distinguishing patients with lung cancer from recurrence or metastasis, and how it related to the severity of patients with lung cancer.MethodsWe applied microbiome pipeline to bacterial communities of 134 non-recurrence and non-metastasis (non-RM) and 174 recurrence or metastasis (RM) samples downloaded from The Cancer Genome Atlas (TCGA). Co-occurrence network was built to explore the bacterial interactions in lung cancer tissue of RM and non-RM. Finally, the Kaplan–Meier survival analysis was used to evaluate the association between bacterial biomarkers and patient survival.ResultsCompared with non-RM, the bacterial community of RM had lower richness and higher Bray–Curtis dissimilarity index. Interestingly, the co-occurrence network of non-RM was more complex than RM. The top 500 genera in relative abundance obtained an area under the curve (AUC) of 0.72 when discriminating between RM and non-RM. There were significant differences in the relative abundances of Acidovorax, Clostridioides, Succinimonas, and Shewanella, and so on between RM and non-RM. These biomarkers played a role in predicting the survival of lung cancer patients and were significantly associated with lung cancer stage.ConclusionThis study provides the first evidence for the prediction of lung cancer recurrence or metastasis by bacteria in lung cancer tissue. Our results highlights that bacterial biomarkers that distinguish RM and non-RM are also associated with patient survival and disease severity.

With the rise of live streaming commerce, the relationship between consumers and content creators on the short-video platforms has become closer, forming a peculiar culture and language in each consumer community, which promotes the short-video platforms to become a natural breeding ground for forming consumer communities. While such communities give birth to its own language and culture from the interaction between content creators and consumers, this kind of co-creation can not only enhance the consumers’ trust to improve commodity premium space, but also strengthen the ties within the community and spread the information outside the communities, and consequently, expand community scale. Based on the view of the value co-creation from the language and culture among content creators and consumers in the communities, this study starts from the point of product type, employs consumers’ Willingness to pay premium (WoPP) as a proxy variable of brand advocacy in the co-creation of cultural and language values in consumer communities, and conducts three single-factor experiments between two groups. By analyzing the experimental results, this study identified the influence under the potential relationship mechanism, social comparison, and found another variable that can moderate the relationship, consumer trust, portrays the relationship between the product types of the live streaming commerce and the consumers’ WoPP, and explores the mediating effect of social comparison and the moderate effect of consumer trust effect. This paper also analyzes and discusses the WoPP caused by the co-creation of cultural and language values co-created by creators and consumer communities.

COVID-19 has caused enormous challenges to global economy and public health. The identification of patients with the COVID-19 infection by CT scan images helps prevent its pandemic. Manual screening COVID-19-related CT images spends a lot of time and resources. Artificial intelligence techniques including deep learning can effectively aid doctors and medical workers to screen the COVID-19 patients. In this study, we developed an ensemble deep learning framework, DeepDSR, by combining DenseNet, Swin transformer, and RegNet for COVID-19 image identification. First, we integrate three available COVID-19-related CT image datasets to one larger dataset. Second, we pretrain weights of DenseNet, Swin Transformer, and RegNet on the ImageNet dataset based on transformer learning. Third, we continue to train DenseNet, Swin Transformer, and RegNet on the integrated larger image dataset. Finally, the classification results are obtained by integrating results from the above three models and the soft voting approach. The proposed DeepDSR model is compared to three state-of-the-art deep learning models (EfficientNetV2, ResNet, and Vision transformer) and three individual models (DenseNet, Swin transformer, and RegNet) for binary classification and three-classification problems. The results show that DeepDSR computes the best precision of 0.9833, recall of 0.9895, accuracy of 0.9894, F1-score of 0.9864, AUC of 0.9991 and AUPR of 0.9986 under binary classification problem, and significantly outperforms other methods. Furthermore, DeepDSR obtains the best precision of 0.9740, recall of 0.9653, accuracy of 0.9737, and F1-score of 0.9695 under three-classification problem, further suggesting its powerful image identification ability. We anticipate that the proposed DeepDSR framework contributes to the diagnosis of COVID-19.

Microsatellite instability (MSI), an important biomarker for immunotherapy and the diagnosis of Lynch syndrome, refers to the change of microsatellite (MS) sequence length caused by insertion or deletion during DNA replication. However, traditional wet-lab experiment-based MSI detection is time-consuming and relies on experimental conditions. In addition, a comprehensive study on the associations between MSI status and various molecules like mRNA and miRNA has not been performed. In this study, we first studied the association between MSI status and several molecules including mRNA, miRNA, lncRNA, DNA methylation, and copy number variation (CNV) using colorectal cancer data from The Cancer Genome Atlas (TCGA). Then, we developed a novel deep learning framework to predict MSI status based solely on hematoxylin and eosin (H&E) staining images, and combined the H&E image with the above-mentioned molecules by multimodal compact bilinear pooling. Our results showed that there were significant differences in mRNA, miRNA, and lncRNA between the high microsatellite instability (MSI-H) patient group and the low microsatellite instability or microsatellite stability (MSI-L/MSS) patient group. By using the H&E image alone, one can predict MSI status with an acceptable prediction area under the curve (AUC) of 0.809 in 5-fold cross-validation. The fusion models integrating H&E image with a single type of molecule have higher prediction accuracies than that using H&E image alone, with the highest AUC of 0.952 achieved when combining H&E image with DNA methylation data. However, prediction accuracy will decrease when combining H&E image with all types of molecular data. In conclusion, combining H&E image with deep learning can predict the MSI status of colorectal cancer, the accuracy of which can further be improved by integrating appropriate molecular data. This study may have clinical significance in practice.

Abstract The 2019 coronavirus disease (COVID-19) outbreak caused by the SARS-CoV-2 virus is an ongoing global health emergency. However, the virus’ pathogenesis remains unclear, and there is no cure for the disease. We investigated the dynamic changes of blood immune response in patients with COVID-19 at different stages by using 5’ gene expression, T cell receptor (TCR), and B cell receptors (BCR) V(D)J transcriptome analysis at a single-cell resolution. We obtained single-cell mRNA sequencing (scRNA-seq) data of 341,420 peripheral blood mononuclear cells (PBMCs) and 185,430 clonotypic T cells and 28,802 clonotypic B cells from 25 samples of 16 patients with COVID-19 for dynamic studies. In addition, we used three control samples. We found expansion of dendritic cells (DCs), CD14+ monocytes, and megakaryocytes progenitor cells (MP)/platelets and a reduction of naïve CD4+ T lymphocytes in patients with COVID-19, along with a significant decrease of CD8+ T lymphocytes, and natural killer cells (NKs) in patients in critical condition. The type I interferon (IFN-I), mitogen-activated protein kinase (MAPK), and ferroptosis pathways were activated while the disease was active, and recovered gradually after patient conditions improved. Consistent with this finding, the mRNA level of IFN-I signal-induced gene IFI27 was significantly increased in patients with COVID-19 compared with that of the controls in a validation cohort that included 38 patients and 35 controls. The concentration of interferon-α (IFN-α) in the serum of patients with COVID-19 increased significantly compared with that of the controls in an additional cohort of 215 patients with COVID-19 and 106 controls, further suggesting the important role of the IFN-I pathway in the immune response of COVID-19. TCR and BCR sequences analyses indicated that patients with COVID-19 developed specific immune responses against SARS-CoV-2 antigens. Our study reveals a dynamic landscape of human blood immune responses to SARS-CoV-2 infection, providing clues for therapeutic potentials in treating COVID-19.

Abstract The outbreak of a novel febrile respiratory disease called COVID-19, caused by a newfound coronavirus SARS-CoV-2, has brought a worldwide attention. Prioritizing approved drugs is critical for quick clinical trials against COVID-19. In this study, we first manually curated three Virus-Drug Association (VDA) datasets. By incorporating VDAs with the similarity between drugs and that between viruses, we constructed a heterogeneous Virus-Drug network. A novel Random Walk with Restart method (VDA-RWR) was then developed to identify possible VDAs related to SARS-CoV-2. We compared VDA-RWR with three state-of-the-art association prediction models based on fivefold cross-validations (CVs) on viruses, drugs and virus-drug associations on three datasets. VDA-RWR obtained the best AUCs for the three fivefold CVs, significantly outperforming other methods. We found two small molecules coming together on the three datasets, that is, remdesivir and ribavirin. These two chemical agents have higher molecular binding energies of − 7.0 kcal/mol and − 6.59 kcal/mol with the domain bound structure of the human receptor angiotensin converting enzyme 2 (ACE2) and the SARS-CoV-2 spike protein, respectively. Interestingly, for the first time, experimental results suggested that navitoclax could be potentially applied to stop SARS-CoV-2 and remains to further validation.

The increasing interest in two-dimensional materials with unique crystal structures and novel band characteristics has provided numerous new strategies and paradigms in the field of photodetection. However, as the demand for wide-spectrum detection increases, the size of integrated systems and the limitations of mission modules pose significant challenges to existing devices. In this paper, we present a van der Waals heterostructure photodetector based on Ta2NiSe5/WSe2, leveraging the inherent characteristics of heterostructures. Our results demonstrate that this detector exhibits excellent broad-spectrum detection ability from the visible to the infrared bands at room temperature, achieving an extremely high on/off ratio, without the need for an external bias voltage. Furthermore, compared to a pure material detector, it exhibits a fast response and low dark currents (~3.6 pA), with rise and fall times of 278 μs and 283 μs for the response rate, respectively. Our findings provide a promising method for wide-spectrum detection and enrich the diversity of room-temperature photoelectric detection.

As the carriers of massive data, data centers are constantly needed to process and calculate all kinds of information from various fields and have become an important infrastructure for the convenience of human life. Data centers are booming around the world, accompanied by the problems of high power consumption and poor heat dissipation. One of the most effective solutions to these problems is to adapt a two-phase liquid immersion cooling technology, which is a more energy-saving and efficient method than the traditional cooling methods; the reason for this is mainly that in two-phase liquid immersion cooling technology, the heat transfer caused by the phase change of liquid coolants (electronic fluoride liquids) helps to cool and improve the temperature uniformity of electronic components. However, the requirements for the electronic fluoride liquids used in two-phase liquid immersion cooling systems are strict. The thermophysical properties (saturated vapor pressure, density, surface tension, viscosity, thermal conductivity and latent heat of vaporization, etc.) of the liquid coolants play a very key role in the heat dissipation capacity of two-phase liquid immersion cooling systems. However, it is not always easy to obtain new electronic fluoride liquids under many actual conditions and reasonable prediction models of their thermophysical properties could contribute to the preliminary screening of the coolants. Thus, the prediction models of their key thermophysical properties (saturated vapor pressure, saturation density, surface tension, viscosity and thermal conductivity) are reviewed, and the accuracy and practicality of these prediction models in predicting the thermophysical properties of electronic fluoride liquids (FC-72, HFE-7100 and Novec 649) are evaluated. This work will provide a valuable reference for actual engineering applications.

One advantage of single-cell RNA sequencing is its ability in revealing cell heterogeneity by cell clustering. However, cell clustering based on single-cell RNA sequencing is challenging due to the high transcript amplification noise, sparsity and outlier cell populations. In this study, we propose a novel sparse subspace clustering method called Structured Sparse Subspace Clustering and Completion for single-cell RNA sequencing analysis by assuming the cells related together are in the same subspace, and so the relationships among cells can be described within a subspace instead of between cell pairs. The proposed optimization model is solved by the Linearized Alternating Direction Method of Multipliers, in which data completion and spectral clustering are combined as a whole by mutual constraint. It is worth noting that random walk is used in the model to make the coefficient matrix more diagonal in the optimum iterative procedure, and the effect is significant. Our model is applied and compared with 5 state-of-the-art clustering methods on 6 public single cell datasets and a simulated data set with cell numbers varying from 56 to over 3000. As a result, our model outperforms the other clustering methods in clustering accuracy as evaluated by Adjusted Rand Index, Normalized Mutual Information, Homogeneity and Completeness, especially compared with the other improved sparse subspace clustering methods.

The high dimension, high redundancy and class imbalance of cancer multiple omics data are the main challenges for cancer diagnosis. Existing studies have neglected the role of functional proteomics in the occurrence and development of cancer. In this study, a novel hybrid feature selection and ensemble learning framework, referred to as the three-stage feature selection and twice-competitional ensemble learning method (TSFS-TCEM), is proposed for cancer diagnosis. Firstly, we combine the transcriptome and functional proteomics data to construct a multi-omics data on breast cancer, which is the first time to apply these combined biological data for diagnosing breast cancer. Secondly, the proposed method introduces multiple models during the feature selection and diagnostic model construction. The three-stage feature selections integrate the features from different types of data and the twice-competitional ensemble learning framework resolves the data imbalance problem suffer from a single classifier. The TSFS-TCEM achieves a diagnostic accuracy of 99.64%, outperforming all compared methods. In addition, the 5-fold cross-validation sensitivity, specificity and F-Measure of the method are above 99.63%.

Some cholesterol-lowering drugs can increase the risk of type 2 diabetes, but the mechanism behind this is not fully understood. Here the authors show that there is a single genetic regulatory module that influences both cholesterol levels and glucose levels, providing a link between cholesterol levels and diabetes.

As one of the most common cancers of the digestive system, colon cancer is a predominant cause of cancer-related deaths worldwide. To investigate prognostic genes in the tumor microenvironment of colon cancer, we collected 461 colon adenocarcinoma (COAD) and 172 rectal adenocarcinoma (READ) samples from The Cancer Genome Atlas (TCGA) database, and calculated the stromal and immune scores of each sample. We demonstrated that stromal and immune scores were significantly associated with colon cancer stages. By analyzing differentially expressed genes (DEGs) between two stromal and immune score groups, we identified 952 common DEGs. The significantly enriched Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms for these DEGs were associated with T-cell activation, immune receptor activity, and cytokine–cytokine receptor interaction. Through univariate Cox regression analysis, we identified 22 prognostic genes. Furthermore, nine key prognostic genes, namely, HOXC8, SRPX, CCL22, CD72, IGLON5, SERPING1, PCOLCE2, FABP4, and ARL4C, were identified using the LASSO Cox regression analysis. The risk score of each sample was calculated using the gene expression of the nine genes. Patients with high-risk scores had a poorer prognosis than those with low-risk scores. The prognostic model established with the nine-gene signature was able to effectively predict the outcome of colon cancer patients. Our findings may help in the clinical decisions and improve the prognosis for colon cancer.

Since the outbreak of SARS-CoV-2 in 2019, the Chinese horseshoe bats were considered as a potential original host of SARS-CoV-2. In addition, cats, tigers, lions, mints, and ferrets were naturally or experimentally infected with SARS-CoV-2. For the surveillance and control of this highly infectious disease, it is critical to trace susceptible animals and predict the consequence of potential mutations at the binding region of viral spike protein and host ACE2 protein. This study proposed a novel bioinformatics framework to systematically trace susceptible animals to SARS-CoV-2 and predict the binding affinity between susceptible animals’ mutated/un-mutated ACE2 receptors. As a result, we identified a few animals posing a potential risk of infection with SARS-CoV-2 using the docking analysis of ACE2 protein and viral spike protein. The binding affinity of some of these species is weaker than that of humans but more potent than that of Chinese horseshoe bats. We also found that a few point mutations in human ACE2 protein or viral spike protein could significantly enhance their binding affinity, posing an enormous potential threat to public health. The ancestors of the Omicron may evolve rapidly through the accumulation of mutations in infecting the host and jumped into human beings. These findings indicate that if the epidemic expands, there may be a human-animal-human transmission route, which will increase the difficulty of disease prevention and control.

In this study, we proposed an ensemble learning method, simultaneously integrating a low-rank matrix completion model and a ridge regression model to predict anticancer drug response on cancer cell lines. The model was applied to two benchmark datasets, including the Cancer Cell Line Encyclopedia (CCLE) and the Genomics of Drug Sensitivity in Cancer (GDSC). As previous studies suggest, the dual-layer integrated cell line-drug network model was one of the best models by far and outperformed most state-of-the-art models. Thus, we performed a head-to-head comparison between the dual-layer integrated cell line-drug network model and our model by a 10-fold crossvalidation study. For the CCLE dataset, our model has a higher Pearson correlation coefficient between predicted and observed drug responses than that of the dual-layer integrated cell line-drug network model in 18 out of 23 drugs. For the GDSC dataset, our model is better in 26 out of 28 drugs in the phosphatidylinositol 3-kinase (PI3K) pathway and 26 out of 30 drugs in the extracellular signal-regulated kinase (ERK) signaling pathway, respectively. Based on the prediction results, we carried out two types of case studies, which further verified the effectiveness of the proposed model on the drug-response prediction. In addition, our model is more biologically interpretable than the compared method, since it explicitly outputs the genes involved in the prediction, which are enriched in functions, like transcription, Src homology 2/3 (SH2/3) domain, cell cycle, ATP binding, and zinc finger.

For resectable cancer patients, a method that could precisely predict the risk of postoperative recurrence would be crucial for guiding adjuvant treatment. Since T cell receptor (TCR) repertoires had been shown to be closely related to the dynamics of cancers, here we enrolled a cohort of patients to evaluate the potential of TCR repertoires in predicting the prognosis of resectable non-small cell lung cancers. Specifically, TCRβ repertoires were analyzed in surgical tumor tissues and matched adjacent non-tumor tissues from 39 patients enrolled with resectable non-small cell lung cancer, through target enrichment and high-throughput sequencing. As a result, there are significant differences between the TCR repertories of tumor samples and those of matched adjacent non-tumor samples as evaluated by criteria like the number of clonotypes. In addition, TCR repertoires were significantly associated with a few clinical features, as well as somatic mutations. Finally, certain TCRβ variable-joining (V-J) pairings were featured to build a logistic regression model in predicting postoperative recurrence of resectable non-small cell lung cancers with a testing area under the receiver operating characteristic curve (AUC) of around 0.9. Thus, we hypothesize that TCR repertoires could be potentially used to predict prognosis after curative surgery for non-small cell lung cancer patients.

Growing evidence shows that microbes in human body and body surface play critical roles in the development of many human diseases. Predicting the underlying associations between diseases and microbes is essential for deeply understanding the pathogenesis of diseases. However, biological experiments to find the relationship between microbes and diseases is usually laborious and time-consuming, which presents the need for effective computational tools. In this study, we propose a computational model of node-information-based Link Propagation for Human Microbe-Disease Association prediction (LPHMDA) to prioritize disease-related microbes. LPHMDA and 3 popular methods including KATZHMDA, PBHMDA, and LRLSHMDA were implemented and compared on the Human Microbe-Disease Association Database (HMDAD) based on cross-validation. As a result, LPHMDA achieved an AUC of 0.9135 in leave-one-out cross-validation (LOOCV), outperforming those of the 3 compared methods. In addition, the performances of LPHDMA on the 3-fold CV, 5-fold CV and 10-fold CV were also better than those of the other 3 canonical methods, further demonstrating its superiority. Finally, we took colorectal carcinoma, asthma and obesity as case studies. Interestingly, 9, 9 and 8 of the top 10 novel microbes predicted by LPHMDA to be associated with the 3 diseases respectively could be confirmed by literatures, providing potential disease-associated microbes for further experimental validation. In summary, LPHMDA is an effective method for prioritizing disease-associated microbes.

Identifying disease-related microRNAs (miRNAs) is crucial to understanding the etiology and pathogenesis of many diseases. However, existing computational methods are facing a few dilemmas such as lacking “negative samples” (i.e. confirmed unrelated miRNA-disease pairs). In this study, we proposed LRMCMDA, a low-rank matrix completion-based method to predict miRNA-disease associations. LRMCMDA firstly constructs a bipartite miRNA-disease graph from known associations and defines its R-projected miRNA graph, in which two miRNAs are connected if they are adjacent to the same disease in the bipartite graph. Similarly, we can define its D-projected disease graph. It then infers negative samples by assuming that connecting an unrelated miRNA-disease pair in the bipartite graph will change its R-projected miRNA graph and D-projected disease graph. Providing with both known miRNA-disease associations and negative samples, LRMCMDA infers associations between all miRNAs and diseases using a low-rank matrix completion model, in which miRNA similarity and disease similarity are incorporated into regularization terms. The assumption is that similar miRNAs will associate with similar diseases and vice versa. We compared LRMCMDA with a few state-of-the-art algorithms on several established miRNA-disease databases. LRMCMDA achieves an AUC of 0.8882 on the 5-fold cross-validation, significantly outperforming canonical methods when predicting miRNA-disease associations, and associating miRNAs with isolated diseases. The experimental results demonstrate that LRMCMDA effectively infers novel miRNA-disease associations. In addition, the case studies on cancers have further proven that LRMCMDA is useful in identifying potential cancer-associated miRNAs for experimental validation.

Copy number variations (CNVs) play an important role in many types of cancer. With the rapid development of next generation sequencing (NGS) techniques, many methods for detecting CNVs of a single sample have emerged: (ⅰ) require genome-wide data of both case and control samples, (ⅱ) depend on sequencing depth and GC content correction algorithm, (ⅲ) rely on statistical models built on CNV positive and negative sample datasets. These make them costly in the data analysis and ineffective in the targeted sequencing data. In this study, we developed a novel alignment-free method called DL-CNV to call CNV from the target sequencing data of a single sample. Specifically, we collected two sets of samples. The first set consists of 1301 samples, in which 272 have CNVs in ERBB2 and the second set is composed of 1148 samples with 63 samples containing CNVs in MET. Finally, we found that a testing AUC of 0.9454 for ERBB2 and 0.9220 for MET. Furthermore, we hope to make the CNV detection could be more accurate with clinical pgold standardq (e.g. FISH) information and provide a new research direction, which can be used as the supplement to the existing NGS methods.

In recent years, more and more studies have shown that microRNAs (miRNAs) play a key role in many important biological processes. Dysregulation of miRNAs can lead to a variety of diseases like cancers, thus predicting potential miRNA-disease associations is important for understanding drug development and disease pathogenesis, diagnosis and treatment. It is known that experimental methods to validate miRNA-disease associations typically involve miRNA knockout or knockdown, which is time and labor-intensive. As a result, computational models have been developed to predict unknown miRNA-disease associations from available information related to miRNAs, diseases, genes, and so on. However, their performances are yet to be improved. Noticing that appropriately combining multiple data-source is usually helpful for improving prediction accuracy, we have developed IMDAILM: Inferring miRNA-Disease Association by integrating lncRNA and miRNA data, a low-rank matrix completion model integrating miRNA, long noncoding RNA (lncRNA) and disease information to predict miRNA-disease associations. Specifically, the miRNA-disease association network and the lncRNA-disease association network are fused to form a new heterogeneous network consisting of 3 types of nodes representing miRNAs, lncRNAs and diseases. In addition, a negative sample inference method was proposed to infer unrelated miRNA-disease pairs. Based on both heterogeneous network and negative samples, a low-rank matrix completion model is proposed and solved. In practice, IMDAILM achieved an area under the curve (AUC) of 0.8884 for predicting miRNAs associated with diseases under the 5-fold cross-validation (CV), outperforming a few recent methods. IMDAILM also yielded an AUC of 0.8870 for predicting both lncRNAs and miRNAs associated with diseases. In addition, the 5-fold CV results indicate that IMDAILM is also superior to other methods in predicting miRNAs associated with isolated diseases. Finally, we confirmed a few novel predicted miRNAs associated with specific diseases like lung cancers by literature mining. In summary, the integration of lncRNA information into a matrix completion framework contributes to the prediction of miRNA-disease associations.

Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences. Public summary: • “Can machines think?” The goal of artificial intelligence (AI) is to enable machines to mimic human thoughts and behaviors, including learning, reasoning, predicting, and so on. • “Can AI do fundamental research?” AI coupled with machine learning techniques is impacting a wide range of fundamental sciences, including mathematics, medical science, physics, etc. • “How does AI accelerate fundamental research?” New research and applications are emerging rapidly with the support by AI infrastructure, including data storage, computing power, AI algorithms, and frameworks.