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Abstract Background Extended depth of focus (EDOF) and multifocal (Multi) intraocular lenses (IOL) can provide a fixed distance of near vision, which may result in some discomfort for patients who prefer different near distances. The aim of this study was to compare the vision, comfortable near distance (CND) and visual comfort in patients who underwent implantation of EDOF, Multi, and monofocal (Mono) IOLs. Methods A total of 100 eyes were implanted with Tecnis ZXR00, ZMB00 or ZCB00 IOLs. Uncorrected distance, intermediate, and near visual acuity (UDVA, UIVA, and UNVA, respectively), corrected distance visual acuity (CDVA), the fluctuations of CND, the ability to see at comfortable or standard near distance and visual comfort were evaluated at 3-month postoperative. Results At 3 months postoperative, the EDOF and Multi groups showed non-inferiority compared to the Mono group in the UDVA (P > 0.05) and CDVA (P > 0.05) but superiority in the UNVA (P

Abstract Background Conducting traditional wet experiments to guide drug development is an expensive, time-consuming and risky process. Analyzing drug function and repositioning plays a key role in identifying new therapeutic potential of approved drugs and discovering therapeutic approaches for untreated diseases. Exploring drug-disease associations has far-reaching implications for identifying disease pathogenesis and treatment. However, reliable detection of drug-disease relationships via traditional methods is costly and slow. Therefore, investigations into computational methods for predicting drug-disease associations are currently needed. Results This paper presents a novel drug-disease association prediction method, RAFGAE. First, RAFGAE integrates known associations between diseases and drugs into a bipartite network. Second, RAFGAE designs the Re_GAT framework, which includes multilayer graph attention networks (GATs) and two residual networks. The multilayer GATs are utilized for learning the node embeddings, which is achieved by aggregating information from multihop neighbors. The two residual networks are used to alleviate the deep network oversmoothing problem, and an attention mechanism is introduced to combine the node embeddings from different attention layers. Third, two graph autoencoders (GAEs) with collaborative training are constructed to simulate label propagation to predict potential associations. On this basis, free multiscale adversarial training (FMAT) is introduced. FMAT enhances node feature quality through small gradient adversarial perturbation iterations, improving the prediction performance. Finally, tenfold cross-validations on two benchmark datasets show that RAFGAE outperforms current methods. In addition, case studies have confirmed that RAFGAE can detect novel drug-disease associations. Conclusions The comprehensive experimental results validate the utility and accuracy of RAFGAE. We believe that this method may serve as an excellent predictor for identifying unobserved disease-drug associations.

Geometric deep learning has demonstrated a great potential in non-Euclidean data analysis. The incorporation of geometric insights into learning architecture is vital to its success. Here we propose a curvature-enhanced graph convolutional network (CGCN) for biomolecular interaction prediction. Our CGCN employs Ollivier-Ricci curvature (ORC) to characterize network local geometric properties and enhance the learning capability of GCNs. More specifically, ORCs are evaluated based on the local topology from node neighborhoods, and further incorporated into the weight function for the feature aggregation in message-passing procedure. Our CGCN model is extensively validated on fourteen real-world bimolecular interaction networks and analyzed in details using a series of well-designed simulated data. It has been found that our CGCN can achieve the state-of-the-art results. It outperforms all existing models, as far as we know, in thirteen out of the fourteen real-world datasets and ranks as the second in the rest one. The results from the simulated data show that our CGCN model is superior to the traditional GCN models regardless of the positive-to-negative-curvature ratios, network densities, and network sizes (when larger than 500).

Highlights A series of layered oxide cathode materials were synthesized by high-temperature shock strategy for the first time. The approaching ultimate solid reaction rate of the layered nickel-rich layered oxide LiNixCoyMnzO2 was investigated for the first time. Ultrafast average reaction rate of phase transition from Ni0.6Co0.2Mn0.2(OH)2 to Li-containing oxides is 66.7 (% s-1), that is, taking only 1.5 s.

The creep behavior of crushed sugarcane end-leaf (SEL) was studied, with consideration of different loading force, moisture content, and feeding amount. Statistical analysis software was used to develop and fit the regression data to a strain change law as a function of time. The four-element Burgers model was used. A further goal was to analyze the effect of different test conditions on the fitted creep characteristic parameters. The instantaneous elasticity coefficient E0 was found to increase when the loading force increased; the value of delayed elasticity coefficient E1 increased and the value of cohesion coefficient η0 decreased when the feeding amount increased; the value of delayed elasticity coefficient E1 and cohesion coefficient η0 decreased when the moisture content was increased. Therefore, the loading force, moisture content, and feeding amount of crushed SEL all affected the creep capacity of crushed SEL in compression. The results can provide substantial theoretical reference for the silage production of crushed SEL.

Gentiana szechenyii Kanitz. and Gentiana veitchiorum Hemsl. are two wild medicinal plants widely used in Tibetan medicine. In recent decades, their wild populations have declined rapidly due to persistent over-harvesting of their flowers. Because their flowers are small and dense, it is difficult to rely on manual counting under wild conditions. The combination of deep learning and unmanned aerial vehicle remote sensing (UAVRS) is a new method for plant surveys. We trained 12 of the most advanced or widely used YOLO models on a custom dataset to achieve quantitative detection of both plant flowers in UAVRS images. The accuracy, precision and recall of G. szechenyii Flower (GSF) detection can reach 97.00%, 90.40% and 95.40%, respectively (based on YOLOv7). Similarly, those of G. veitchiorum Flower (GVF) detection can reach 93.40%, 91.30% and 90.60%, respectively (based on YOLOv5n), and the highest mAP can reach 94.90% (based on YOLOv5m). Based on the detection results, it is calculated that the total yield of dried GSF and GVF that can be harvested as medicinal materials in study area ranges from 1.88 to 2.10 g·m−2 and 4.42–4.62 g·m−2, respectively. The results show that deep learning and UAVRS can be used for quantitative detection of GSF and GVF, which is helpful for further research and protection of these two plants.

Objective To examine the current prevalence and cost of paediatric off-label drug prescriptions in Gansu, China, and the potential influencing factors.Design The prevalence of off-label prescriptions in paediatrics was evaluated according to the National Medical Products Administration drug instructions in the China Pharmaceutical Reference (China Pharmaceutical Reference, MCDEX) database. The evidence of the prescription was determined by existing clinical practice guidelines and the Thomson Grade in the Micromedex 2021 compendium. We used logistic regression to investigate the characteristics that influence paediatric off-label drug use after single-factor regression analysis.Setting A multicentre cross-sectional study of outpatient paediatric prescriptions in 196 secondary and tertiary hospitals in Gansu Province, China, in March and September 2020.Results We retrieved 104 029 paediatric prescriptions, of which 39 480 (38.0%) contained off-label use. The most common diseases treated by off-label drugs were respiratory system diseases (n=15 831, 40.1%). A quarter of off-label prescriptions had adequate evidence basis (n=10 130, 25.6%). Unapproved indications were the most common type of off-label drug use (n=25 891, 65.6%). A total of 1177 different drugs were prescribed off-label, with multienzyme tablets being the most common drug (n=1790, 3.5%). The total cost of the prescribed off-label drugs was ¥106 116/day. Off-label prescriptions were less frequent in tertiary than in secondary hospitals. Topical preparations were more commonly prescribed off-label than other types of drugs. Senior-level clinicians prescribed drugs off-label more often than intermediate and junior clinicians.Conclusion Off-label drug use is widespread in paediatric practice in China. Three-quarters of the prescriptions may potentially include inappropriate medication use, resulting in a daily economic burden of about ¥81 000 in 2020 in Gansu Province with 25 million inhabitants. The management of off-label drug use in paediatrics in China needs improvement.

With the development of my country's industrial level, the prefabricated building (PB) model has gradually become the mainstream. In order to more scientifically study the development policy of the prefabricated building industry, a system dynamics model with the boundary of resource input, technology research and development, production capacity and market demand system is constructed from the perspective of industry chain development, based on the influence of external subjects of the industry chain on the development of the industry chain. Afterwards, dynamic simulations of the development trend of prefabricated buildings under different policy scenarios are carried out. The results show that the increase in capital investment in the industrial chain and the improvement of the prefabricated assembly rate of government-invested construction projects are strong short-term incentive policies, while corporate tax incentives and prefabricated building purchase loan incentives are long-term incentive policies. Combined with the simulation results, the policy formulation recommendations based on the development trend of the industry chain are put forward.

Leakage current is one of the important parameters reflecting the operation status of distribution network-type surge arresters. At this stage, the polymagnetic current sensor has the advantages of miniaturization and high accuracy for leakage current measurement, but the complexity of electromagnetic interference in the field easily introduces more noise interference signals, which limits the performance of the polymagnetic current sensor for field application. To this end, this paper proposes a leakage current measurement method for distribution network-type surge arresters based on EEMD-SVD and low-rank RBF neural network methods. Firstly, the measured leakage current is decomposed by Ensemble Empirical Mode Decomposition (EEMD) to decompose the modal components containing eigenfrequencies, and then Singular Value Decomposition (SVD) is used to extract the non-eigenfrequency signals. Finally, a low-rank Radical Basis Function (RBF) neural network is used to approximate the leakage current signal after de-interference and combined with the Gaussian window function to remove the white noise interference. The experimental results show that the signal-to-noise ratio of the polymagnetic current sensor is improved by about 20dB and the maximum average absolute error is only 2.62%, which can truly reflect the leakage current of the network-type lightning arrester.

Abstract Background Long noncoding RNAs (lncRNAs) are integral to a plethora of critical cellular biological processes, including the regulation of gene expression, cell differentiation, and the development of tumors and cancers. Predicting the relationships between lncRNAs and diseases can contribute to a better understanding of the pathogenic mechanisms of disease and provide strong support for the development of advanced treatment methods. Results Therefore, we present an innovative Node-Adaptive Graph Transformer model for predicting unknown LncRNA-Disease Associations, named NAGTLDA. First, we utilize the node-adaptive feature smoothing (NAFS) method to learn the local feature information of nodes and encode the structural information of the fusion similarity network of diseases and lncRNAs using Structural Deep Network Embedding (SDNE). Next, the Transformer module is used to capture potential association information between the network nodes. Finally, we employ a Transformer module with two multi-headed attention layers for learning global-level embedding fusion. Network structure coding is added as the structural inductive bias of the network to compensate for the missing message-passing mechanism in Transformer. NAGTLDA achieved an average AUC of 0.9531 and AUPR of 0.9537 significantly higher than state-of-the-art methods in 5-fold cross validation. We perform case studies on 4 diseases; 55 out of 60 associations between lncRNAs and diseases have been validated in the literatures. The results demonstrate the enormous potential of the graph Transformer structure to incorporate graph structural information for uncovering lncRNA-disease unknown correlations. Conclusions Our proposed NAGTLDA model can serve as a highly efficient computational method for predicting biological information associations.

Abstract Background circRNAs play an important role in drug resistance and cancer development. Recently, many studies have shown that the expressions of circRNAs in human cells can affect the sensitivity of cells to therapeutic drugs, thus significantly influencing the therapeutic effects of these drugs. Traditional biomedical experiments required to verify this sensitivity relationship are not only time-consuming but also expensive. Hence, the development of an efficient computational approach that can accurately predict the novel associations between drug sensitivities and circRNAs is a crucial and pressing need. Methods In this research, we present a novel computational framework called MNCLCDA, which aims to predict the potential associations between drug sensitivities and circRNAs to assist with medical research. First, MNCLCDA quantifies the similarity between the given drug and circRNA using drug structure information, circRNA gene sequence information, and GIP kernel information. Due to the existence of noise in similarity information, we employ a preprocessing approach based on random walk with restart for similarity networks to efficiently capture the useful features of circRNAs and drugs. Second, we use a mixed neighbourhood graph convolutional network to obtain the neighbourhood information of nodes. Then, a graph-based contrastive learning method is used to enhance the robustness of the model, and finally, a double Laplace-regularized least-squares method is used to predict potential circRNA-drug associations through the kernel matrices in the circRNA and drug spaces. Results Numerous experimental results show that MNCLCDA outperforms six other advanced methods. In addition, the excellent performance of our proposed model in case studies illustrates that MNCLCDA also has the ability to predict the associations between drug sensitivity and circRNA in practical situations. Conclusions After a large number of experiments, it is illustrated that MNCLCDA is an efficient tool for predicting the potential associations between drug sensitivities and circRNAs, thereby can provide some guidance for clinical trials.

Abstract Background Grassroots medical institutions are the primary “battlefield” of hypertension management based on hierarchical diagnosis and treatment policies in China. There is a considerable difference in the insured population and reimbursement ratio between different medical insurance programs. The management of hypertension directly affects the development trend of cardiovascular and cerebrovascular diseases. Methods To understand the difference between different medical insurance programs regarding the management of older hypertensive patients, all outpatients aged 60 and above with hypertension in the basic medical insurance database of Beijing, China, from April 1, 2019, to January 31, 2020, were included. Medical behaviours included patients’ choice of hospital level, type of hospital, number of hospitals visited, grassroots medical institutions and cross-district visits. Medication adherence was evaluated by calculating the medication possession ratio of antihypertensive medications. First, we adopted a statistical description for medical behaviours and medication adherence. Then, multivariate logistic regression was used to analyse the influencing factors of medication adherence. Results This study included 1.29 million patients with Urban Employee Basic Medical Insurance (UEBMI) and 0.31 million patients with Urban‒Rural Resident Basic Medical Insurance (URRBMI). The proportions of patients with UEBMI who chose tertiary hospitals, comprehensive hospitals, grassroots medical institutions and cross-district visits were 25.84%, 56.09%, 57.34% and 39.32%, respectively, while those of patients with URRBMI were 11.14%, 60.59%, 81.28% and 6.07%, respectively. The medication adherence rates of men and women taking one medication were 61.04% and 55.86%, respectively. UEBMI patients who took their medication accounted for 62.36%, while only 40.27% of URRBMI patients adhered to their medication. The percentages of young-old, old-old and oldest-old patients who took their antihypertensive medications were 58.05%, 59.09% and 56.78%, respectively. The adherence to taking ≥ 2 medications (35.47%) was lower than that to taking one medication (58.33%). The medication adherence rates of patients with UEBMI and URRBMI for taking ≥ 2 medications were 37.21% and 27.45%, respectively. Conclusions Patients with UEBMI were more inclined to choose tertiary hospitals and cross-district visits than patients with URRBMI. The adherence of patients with UEBMI was better than that of patients with URRBMI in China.

Abstract Itinerant kagome lattice magnets exhibit many novel correlated and topological quantum electronic states with broken time-reversal symmetry. Superconductivity, however, has not been observed in this class of materials, presenting a roadblock in a promising path toward topological superconductivity. Here, we report that novel superconductivity can emerge at the interface of kagome Chern magnet TbMn6Sn6 and metal heterostructures when elemental metallic thin films are deposited on either the top (001) surface or the side surfaces. Superconductivity is also successfully induced and systematically studied by using various types of metallic tips on different TbMn6Sn6 surfaces in point-contact measurements. The anisotropy of the superconducting upper critical field suggests that the emergent superconductivity is quasi-two-dimensional. Remarkably, the interface superconductor couples to the magnetic order of the kagome metal and exhibits a hysteretic magnetoresistance in the superconducting states. Taking into account the spin-orbit coupling, the observed interface superconductivity can be a surprising and more realistic realization of the p-wave topological superconductors theoretically proposed for two-dimensional semiconductors proximity-coupled to s-wave superconductors and insulating ferromagnets. Our findings of robust superconductivity in topological-Chern-magnet/metal heterostructures offer a new direction for investigating spin-triplet pairing and topological superconductivity.

In response to the challenges of accurate identification and localization of garbage in intricate urban street environments, this paper proposes EcoDetect-YOLO, a garbage exposure detection algorithm based on the YOLOv5s framework, utilizing an intricate environment waste exposure detection dataset constructed in this study. Initially, a convolutional block attention module (CBAM) is integrated between the second level of the feature pyramid etwork (P2) and the third level of the feature pyramid network (P3) layers to optimize the extraction of relevant garbage features while mitigating background noise. Subsequently, a P2 small-target detection head enhances the model’s efficacy in identifying small garbage targets. Lastly, a bidirectional feature pyramid network (BiFPN) is introduced to strengthen the model’s capability for deep feature fusion. Experimental results demonstrate EcoDetect-YOLO’s adaptability to urban environments and its superior small-target detection capabilities, effectively recognizing nine types of garbage, such as paper and plastic trash. Compared to the baseline YOLOv5s model, EcoDetect-YOLO achieved a 4.7% increase in mAP0.5, reaching 58.1%, with a compact model size of 15.7 MB and an FPS of 39.36. Notably, even in the presence of strong noise, the model maintained a mAP0.5 exceeding 50%, underscoring its robustness. In summary, EcoDetect-YOLO, as proposed in this paper, boasts high precision, efficiency, and compactness, rendering it suitable for deployment on mobile devices for real-time detection and management of urban garbage exposure, thereby advancing urban automation governance and digital economic development.

In our previous study, a new fermented food (PWF) created by utilizing pineapple by-products and whey proteins as a matrix via co-fermentation with lactic acid bacteria and yeast was developed, and, in the current study, we examined the impact of a pineapple–whey protein fermentation product on a cefixime-induced dysbiosis model in mice using 16S sequencing and untargeted metabolomics techniques. The results indicated that the pineapple–whey protein fermentation product played a positive role in restoring the intestinal flora. In this study, cefixime reduced the overall abundance of intestinal flora and decreased the relative abundance of probiotics in the gut, while also inhibiting amino acid metabolism. The addition of PWF normalized the intestinal flora to a steady state, significantly increasing the populations of Weissella, Lactococcus, Faecalibaculum, and Bacteroides acidophilus, while decreasing the numbers of Akkermansia and Escherichia-Shigella. Additionally, PWF modulated microbial metabolites, such as L-glutamate and L-threonine, and upregulated amino-acid-related metabolic pathways, including those involving glycine, serine, and threonine. In conclusion, PWF can alleviate intestinal flora dysbiosis and metabolic disturbances induced by antibiotic interventions. It is suggested that PWF could be a potential dietary strategy for patients with antibiotic-associated diarrhea.

In response to the lack of accurate and reliable parameters in the discrete element simulation analysis of the sugarcane leaf crushing and return device, in this work, the actual and simulated errors of two stacking angles α and β of sugarcane leaves were used as indicators to calibrate the discrete element parameters. The second-order regression models between the important parameters and the indicators were obtained by Plackett-Burman test, steepest climb test, and Box-Behnken optimization test, and the analysis of variance and interaction factors were performed. The response surface method and particle swarm optimization algorithm were used to find the best significance parameters, and the best combination of significance parameters was obtained: the static friction coefficient between sugarcane leaves was 0.306, the rolling friction coefficient between sugarcane leaves was 0.198, and the recovery coefficient of sugarcane leaf-plate collision was 0.102. The relative errors of the simulation results and the physical test stacking angle α and stacking angle β were 0.609% and 1.643%, respectively. The calibration parameters can provide a theoretical reference for the design and research of sugarcane leaf crushing and returning machines, as well as the calibration of discrete element model parameters for leaf crops with high water content.

To improve the accuracy of discrete element simulation parameters of sugarcane tail-leaf (STL) feed during dust removal and crushing, this study used a combination of physical tests and EDEM software simulations to calibrate the discrete element simulation parameters of crumbs and dust in the feed. Taking the experimental physical stacking angle (SA) as the response value, the second-order regression models of SA and significant factors were established by Plackett-Burman test, steepest climb test, and Box-Behnken test. Variance analysis and interaction effect analysis were conducted. Taking the accumulation angle of 41.27° obtained by physical experiments as the target value, the significant parameters were optimized. The optimal combination of the following parameters was obtained: tail stem-dust static friction coefficient (SFC) of 0.46, tail leaf-dust coefficient of sliding friction (COSF) of 0.205, JKR surface energy of 0.26, and dust-steel collision recovery coefficient (CRC) of 0.338. Through software simulation verification, the average value was 40.81°, and the relative error of the SA with the physical experiment was 1.13%. The results showed that the calibrated parameters are real and reliable, which can provide a theoretical reference for the design optimization of the straw crushing device, feed processing device, and other related components.

Summary: Molecular representation learning plays an important role in molecular property prediction. Existing molecular property prediction models rely on the de facto standard of covalent-bond-based molecular graphs for representing molecular topology at the atomic level and totally ignore the non-covalent interactions within the molecule. In this study, we propose a molecular geometric deep learning model to predict the properties of molecules that aims to comprehensively consider the information of covalent and non-covalent interactions of molecules. The essential idea is to incorporate a more general molecular representation into geometric deep learning (GDL) models. We systematically test molecular GDL (Mol-GDL) on fourteen commonly used benchmark datasets. The results show that Mol-GDL can achieve a better performance than state-of-the-art (SOTA) methods. Extensive tests have demonstrated the important role of non-covalent interactions in molecular property prediction and the effectiveness of Mol-GDL models. Motivation: Geometric deep learning (GDL) has demonstrated huge power and enormous potential in molecular data analysis. However, a great challenge still remains for highly efficient molecular representations. Currently, covalent-bond-based molecular graphs are the de facto standard for representing molecular topology at the atomic level. Here, we demonstrate that molecular graphs constructed only from non-covalent bonds can achieve similar or even better results than covalent-bond-based models in molecular property prediction. This demonstrates the great potential of novel molecular representations beyond the de facto standard of covalent-bond-based molecular graphs. Based on the finding, we propose molecular geometric deep learning (Mol-GDL). In our Mol-GDL, molecular topology is modeled as a series of molecular graphs, each focusing on a different scale of atomic interactions. In this way, both covalent interactions and non-covalent interactions are incorporated into the molecular representation on an equal footing.

Abstract Background The identification and enumeration of medicinal plants at high elevations is an important part of accurate yield calculations. However, the current assessment of medicinal plant reserves continues to rely on field sampling surveys, which are cumbersome and time-consuming. Recently, unmanned aerial vehicle (UAV) remote sensing and deep learning (DL) have provided ultrahigh-resolution imagery and high-accuracy object recognition techniques, respectively, providing an excellent opportunity to improve the current manual surveying of plants. However, accurate segmentation of individual plants from drone images remains a significant challenge due to the large variation in size, geometry, and distribution of medicinal plants. Results In this study, we proposed a new pipeline for wild medicinal plant detection and yield assessment based on UAV and DL that was specifically designed for detecting wild medicinal plants in an orthomosaic. We used a drone to collect panoramic images of Lamioplomis rotata Kudo (LR) in high-altitude areas. Then, we annotated and cropped these images into equally sized sub-images and used a DL model Mask R-CNN for object detection and segmentation of LR. Finally, on the basis of the segmentation results, we accurately counted the number and yield of LRs. The results showed that the Mask R-CNN model based on the ResNet-101 backbone network was superior to ResNet-50 in all evaluation indicators. The average identification precision of LR by Mask R-CNN based on the ResNet-101 backbone network was 89.34%, while that of ResNet-50 was 88.32%. The cross-validation results showed that the average accuracy of ResNet-101 was 78.73%, while that of ResNet-50 was 71.25%. According to the orthomosaic, the average number and yield of LR in the two sample sites were 19,376 plants and 57.93 kg and 19,129 plants and 73.5 kg respectively. Conclusions The combination of DL and UAV remote sensing reveals significant promise in medicinal plant detection, counting, and yield prediction, which will benefit the monitoring of their populations for conservation assessment and management, among other applications.

Lung cancer has become one of the most dangerous cancers to human health and the mortality rate is the highest among all the causes of cancer death. Non-small cell lung cancer (NSCLC) accounts for about 80%-85% of lung cancer. Chemotherapy is the main treatment for advanced NSCLC, but the 5-year survival rate is low. Epidermal growth factor receptor (EGFR) mutations are the most common driver mutations in lung cancer, but EGFR exon 20 insertions (EGFR ex20ins) mutation belongs to one of the rare mutations, accounting for about 4%-10% of overall EGFR mutations, thus around 1.8% of advanced NSCLC patients. In recent years, targeted therapies represented by EGFR tyrosine kinase inhibitors (TKIs) have become an important treatment option for patients with advanced NSCLC, however, NSCLC patients with EGFR ex20ins mutation are not sensitive to most of EGFR-TKIs treatments. Currently, some of the targeted drugs for EGFR ex20ins mutation have achieved significant efficacy, while some of them are still under clinical investigation. In this article, we will describe various treatment methods for EGFR ex20ins mutation and their efficacy.

The Coronavirus (COVID-19) outbreak of December 2019 has become a serious threat to people around the world, creating a health crisis that infected millions of lives, as well as destroying the global economy. Early detection and diagnosis are essential to prevent further transmission. The detection of COVID-19 computed tomography images is one of the important approaches to rapid diagnosis. Many different branches of deep learning methods have played an important role in this area, including transfer learning, contrastive learning, ensemble strategy, etc. However, these works require a large number of samples of expensive manual labels, so in order to save costs, scholars adopted semi-supervised learning that applies only a few labels to classify COVID-19 CT images. Nevertheless, the existing semi-supervised methods focus primarily on class imbalance and pseudo-label filtering rather than on pseudo-label generation. Accordingly, in this paper, we organized a semi-supervised classification framework based on data augmentation to classify the CT images of COVID-19. We revised the classic teacher-student framework and introduced the popular data augmentation method Mixup, which widened the distribution of high confidence to improve the accuracy of selected pseudo-labels and ultimately obtain a model with better performance. For the COVID-CT dataset, our method makes precision, F1 score, accuracy and specificity 21.04%, 12.95%, 17.13% and 38.29% higher than average values for other methods respectively, For the SARS-COV-2 dataset, these increases were 8.40%, 7.59%, 9.35% and 12.80% respectively. For the Harvard Dataverse dataset, growth was 17.64%, 18.89%, 19.81% and 20.20% respectively. The codes are available at https://github.com/YutingBai99/COVID-19-SSL.

Abstract Background Propensity score analysis is increasingly used to control for confounding factors in observational studies. Unfortunately, unavoidable missing values make estimating propensity scores extremely challenging. We propose a new method for estimating propensity scores in data with missing values. Materials and methods Both simulated and real-world datasets are used in our experiments. The simulated datasets were constructed under 2 scenarios, the presence (T = 1) and the absence (T = 0) of the true effect. The real-world dataset comes from LaLonde’s employment training program. We construct missing data with varying degrees of missing rates under three missing mechanisms: MAR, MCAR, and MNAR. Then we compare MTNN with 2 other traditional methods in different scenarios. The experiments in each scenario were repeated 20,000 times. Our code is publicly available at https://github.com/ljwa2323/MTNN . Results Under the three missing mechanisms of MAR, MCAR and MNAR, the RMSE between the effect and the true effect estimated by our proposed method is the smallest in simulations and in real-world data. Furthermore, the standard deviation of the effect estimated by our method is the smallest. In situations where the missing rate is low, the estimation of our method is more accurate. Conclusions MTNN can perform propensity score estimation and missing value filling at the same time through shared hidden layers and joint learning, which solves the dilemma of traditional methods and is very suitable for estimating true effects in samples with missing values. The method is expected to be broadly generalized and applied to real-world observational studies.

Abstract African swine fever virus (ASFV) is an important pathogen causing acute infectious disease in domestic pigs and wild boars that seriously endangers the global swine industry. As ASFV is structurally complex and encodes a large number of functional proteins, no effective vaccine has been developed to date. Thus, dissecting the mechanisms of immune escape induced by ASFV proteins is crucial. A previous study showed that the ASFV-encoded protein is an important factor in host immunity. In this study, we identified a negative regulator, MGF505-3R, that significantly downregulated cGAS/STING- and poly (dG:dC)-mediated IFN-β and interferon stimulation response element (ISRE) reporter activity and suppressed IFNB1 and IFIT2 mRNA levels. In addition, TBK1, IRF3 and IκBα phosphorylation levels were also inhibited. Mechanistically, MGF505-3R interacted with cGAS/TBK1/IRF3 and targeted TBK1 for degradation, thereby disrupting the cGAS-STING-mediated IFN-β signaling pathway, which appears to be highly correlated with autophagy. Knockdown MGF505-3R expression enhanced IFN-β and IL-1β production. Taken together, our study revealed a negative regulatory mechanism involving the MGF505-3R-cGAS-STING axis and provided insights into an evasion strategy employed by ASFV that involves autophagy and innate signaling pathways.

Abstract Background There is a growing body of evidence from biological experiments suggesting that microRNAs (miRNAs) play a significant regulatory role in both diverse cellular activities and pathological processes. Exploring miRNA-disease associations not only can decipher pathogenic mechanisms but also provide treatment solutions for diseases. As it is inefficient to identify undiscovered relationships between diseases and miRNAs using biotechnology, an explosion of computational methods have been advanced. However, the prediction accuracy of existing models is hampered by the sparsity of known association network and single-category feature, which is hard to model the complicated relationships between diseases and miRNAs. Results In this study, we advance a new computational framework (GATMDA) to discover unknown miRNA-disease associations based on graph attention network with multi-source information, which effectively fuses linear and non-linear features. In our method, the linear features of diseases and miRNAs are constructed by disease-lncRNA correlation profiles and miRNA-lncRNA correlation profiles, respectively. Then, the graph attention network is employed to extract the non-linear features of diseases and miRNAs by aggregating information of each neighbor with different weights. Finally, the random forest algorithm is applied to infer the disease-miRNA correlation pairs through fusing linear and non-linear features of diseases and miRNAs. As a result, GATMDA achieves impressive performance: an average AUC of 0.9566 with five-fold cross validation, which is superior to other previous models. In addition, case studies conducted on breast cancer, colon cancer and lymphoma indicate that 50, 50 and 48 out of the top fifty prioritized candidates are verified by biological experiments. Conclusions The extensive experimental results justify the accuracy and utility of GATMDA and we could anticipate that it may regard as a utility tool for identifying unobserved disease-miRNA relationships.

Abstract Background Climate change caused by environmental pollution is the most important one of many environmental health hazards currently faced by human beings. In particular, the extreme temperature is an important risk factor for death from respiratory and circulatory diseases. This study aims to explore the meteorological-health effect and find out the vulnerable individuals of extreme temperature events in a less developed city in western China. Method We collected the meteorological data and data of death caused by respiratory and circulatory diseases in Mianyang City from 2013 to 2019. The nonlinear distributed lag model and the generalized additive models were combined to study the influence of daily average temperature (DAT) on mortality from respiratory and circulatory diseases in different genders, ages. Results The exposure-response curves between DAT and mortality from respiratory and circulatory diseases presented a nonlinear characteristic of the “V” type. Cumulative Relative Risk of 30 days (CRR 30 ) of deaths from respiratory diseases with 4.48 (2.98, 6.73) was higher than that from circulatory diseases with 2.77 (1.96, 3.92) at extremely low temperature, while there was no obvious difference at extremely high temperature. The health effects of low temperatures on the respiratory system of people of all ages and genders were persistent, while that of high temperatures were acute and short-term. The circulatory systems of people aged

Abstract Background Angle kappa plays a vital role in the implantation of multifocal intraocular lens (MIOL). Large angle kappa is related to a higher risk of postoperative photic phenomena. This study aims to compare preoperative angle kappa in the eyes of cataract patients obtained from the Pentacam Scheimpflug system (Pentacam), optical low-coherence reflectometry (Lenstar), and ray-tracing aberrometry (iTrace). Methods One hundred thirteen eyes of 113 patients with cataracts were included. Each eye was examined 3 times using all devices to obtain angle kappa and pupil diameter. When considering dependent eyes for one individual, angle kappa in both right eyes and left eyes should be analysed separately. The repeatability and reproducibility were evaluated using the within-subject standard deviation (Sw), repeatability (2.77 Sw), and intraclass correlation coefficient (ICC). The difference, correlation, and agreement between devices were evaluated by paired t-tests, Pearson tests, and Bland-Altman analysis, respectively. Results Intraoperator repeatability and interoperator and intersession reproducibility of angle kappa showed an Sw of less than 0.05 mm, a 2.77 Sw of 0.14 mm or less, and an ICC of more than 0.96. Angle kappa was not significantly different between Pentacam and Lenstar (P > 0.05), while angle kappa was significantly different between Pentacam and iTrace and between Lenstar and iTrace (P < 0.05). There was a strong correlation between Pentacam and Lenstar for angle kappa (r =0.907 to 0.918) and a weak or moderate correlation between Pentacam and iTrace and between Lenstar and iTrace (r =0.292 to 0.618). There were narrow 95% limits of agreement (LoA) between Pentacam and Lenstar for angle kappa and wide 95% LoA between Pentacam and iTrace and between Lenstar and iTrace. No significant differences in pupil diameter were found between Pentacam and Lenstar in either eye (P > 0.05). Positive angle kappa (nasal light reflex) was found in most cataract patients (79.25% to 84.91%) through 3 different devices in both eyes. Conclusions The 3 devices provided high intraoperator repeatability and interoperator and intersession reproducibility for angle kappa measurements. The measurement of preoperative angle kappa in the eyes of patients with cataracts by Pentacam and Lenstar has good agreement.

Abstract Background To assess postoperative changes in angle alpha, and to evaluate the postoperative visual quality of patients with different angle alpha values after implantation of extended depth of focus (EDOF) intraocular lenses (IOLs). Methods Seventy-nine eyes of 79 patients who had phacoemulsification with EDOF IOLs implantation were enrolled. A cut-off value of 0.3 mm, 0.4 mm, and 0.5 mm in preoperative angle alpha was chosen to divide eyes into groups. Distance, intermediate, and near visual acuities, modulation transfer function (MTF), and aberrations were recorded during a 6-month follow-up. A patient questionnaire was completed. Results There were no significant differences in angle alpha postoperatively compared to preoperatively. No significant differences were found in visual acuity and MTF between all groups. With 5 mm pupil diameter, there were significant differences of higher-order aberrations and spherical aberration in ocular aberration and internal aberration between angle alpha

The search for low-altitude targets becomes a more challenging task for airborne phased array radar, due to the limited system resources and serious ground clutter. In this article, an optimal search strategy using the digital elevation model (DEM) is proposed to allow for a fast and high-probability searches for low-altitude targets. First, a clutter-free detection region is formed by introducing the DEM. Compared with the traditional methods, the detection probability of the low-altitude targets can be improved. Second, the search problem is transformed into a constrained multiobjective optimization problem. The proposed strategy can simultaneously minimize the search time and maximize the target detection probability. Finally, two intelligent evolutionary algorithms are compared to verify the proposed strategy, and the proposed strategy is also testified by utilizing real DEM data of Dujiangyan, Sichuan, China. Simulated results indicate that the proposed optimal search framework improves the detection probability and reduces time cost for low-altitude targets.

Plant AT-rich sequences and zinc-binding proteins (PLATZ) play crucial roles in response to environmental stresses. Nevertheless, PLATZ gene family has not been systemically studied in Rosaceae species, such as in apple, pear, peach, or strawberry. In this study, a total of 134 PLATZ proteins were identified from nine Rosaceae genomes and were classified into seven phylogenetic groups. Subsequently, the chromosomal localization, duplication, and collinearity relationship for apple PLATZ genes were investigated, and segmental duplication is a major driving-force in the expansion of PLATZ in Malus. Expression profiles analysis showed that PLATZs had distinct expression patterns in different tissues, and multiple genes were significantly changed after drought and ABA treatments. Furthermore, the co-expression network combined with RNA-seq data showed that PLATZ might be involved in drought stress by regulating ABA signaling pathway. In summary, this study is the first in-depth and systematic identification of PLATZ gene family in Rosaceae species, especially for apple, and provided specific PLATZ gene resource for further functional research in response to abiotic stress.

The pore structure of the bed has an important influence on the heat transfer and permeability characteristics in the iron ore sintering process. In this study, liquid nitrogen was applied to quench the high-temperature zone sinter to reproduce the original porous structure to the maximum extent. The effect of quick lime on pore characteristics of the high-temperature zone in the iron ore sinter was studied. XCT technology was applied to scan the sinter, and the model of solid phase, pore, and closed pore was reconstructed. The equivalent spherical diameter of the pore and its distribution frequency, volume fraction, sphericity, and surface porosity were statistically analyzed. The results indicate that the addition of quick lime increases the permeability of the green bed and shortens the sintering time. Meanwhile, it promotes the flow of the molten liquid phase, resulting in the bubble aggregation more fully. With the increase of the addition of quick lime, the porosity of the sinter decreases gradually. Finally, the pore skeleton model was established, and the pore skeleton parameters were statistically analyzed. With the increase of quick lime, the total pore volume and total pore length, the number of pore segments, branching nodes and terminal nodes all decrease.

Single-pixel holography generates holographic images with a single-pixel detector making this relatively inexpensive. Here the authors report a high-throughput single-pixel compressive holography method for imaging biological tissue which can either provide a large field of view or high resolution.

Iron (Fe) plays an important role in cellular respiration and catalytic reactions of metalloproteins in plants and animals. Plants maintain iron homeostasis through absorption, translocation, storage, and compartmentalization of iron via a cooperative regulative network. Here, we showed different physiological characteristics in the leaves and roots of Malus baccata under Fe sufficiency and Fe deficiency conditions and propose that MbHY5 (elongated hypocotyl 5), an important transcription factor for its function in photomorphogenesis, participated in Fe deficiency response in both the leaves and roots of M. baccata. The gene co-expression network showed that MbHY5 was involved in the regulation of chlorophyll synthesis and Fe transport pathway under Fe-limiting conditions. Specifically, we found that Fe deficiency induced the expression of MbYSL7 in root, which was positively regulated by MbHY5. Overexpressing or silencing MbYSL7 influenced the expression of MbHY5 in M. baccata.

The need for high-precision microflow control is increasingly evident across various fields. For instance, microsatellites employed in gravitational wave detection require flow supply systems with a high accuracy of up to 0.1 nL/s to achieve on-orbit attitude control and orbit control. However, conventional flow sensors are unable to provide the necessary precision in the nanoliter per second range, and thus, alternative methods are required. In this study, we propose the use of image processing technology for rapid microflow calibration. Our method involves capturing images of the droplets at the outlet of the flow supply system to rapidly obtain the flow rate, and we used the gravimetric method to verify the accuracy of our approach. We conducted several microflow calibration experiments within the 1.5 nL/s range and demonstrated that image processing technology can achieve the desired accuracy of 0.1 nL/s while saving more than two-thirds of the time required to obtain the flow rate within an acceptable margin of error compared to the gravimetric method. Our study presents an efficient and innovative approach to addressing the challenges of measuring microflows with high precision, particularly in the nanoliter per second range, and has the potential for widespread applications in various fields.

Background To develop a highly discriminative machine learning model for the prediction of intensive care unit admission (>24h) using the easily available preoperative information from electronic health records. An accurate prediction model for ICU admission after surgery is of great importance for surgical risk assessment and appropriate utilization of ICU resources. Method Data were collected retrospectively from a large hospital, comprising 135,442 adult patients who underwent surgery except for cardiac surgery between 1 January 2014, and 31 July 2018 in China. Multiple existing predictive machine learning algorithms were explored to construct the prediction model, including logistic regression, random forest, adaptive boosting, and gradient boosting machine. Four secondary analyses were conducted to improve the interpretability of the results. Results A total of 2702 (2.0%) patients were admitted to the intensive care unit postoperatively. The gradient boosting machine model attained the highest area under the receiver operating characteristic curve of 0.90. The machine learning models predicted intensive care unit admission better than the American Society of Anesthesiologists Physical Status (area under the receiver operating characteristic curve: 0.68). The gradient boosting machine recognized several features as highly significant predictors for postoperatively intensive care unit admission. By applying subgroup analysis and secondary analysis, we found that patients with operations on the digestive, respiratory, and vascular systems had higher probabilities for intensive care unit admission. Conclusion Compared with conventional American Society of Anesthesiologists Physical Status and logistic regression model, the gradient boosting machine could improve the performance in the prediction of intensive care unit admission. Machine learning models could be used to improve the discrimination and identify the need for intensive care unit admission after surgery in elective noncardiac surgical patients, which could help manage the surgical risk.

Sentiment analysis refers to the process of automatically identifying the emotions expressed by people. Its accuracy is highly dependent on the amount of training data. However, it takes time and cost for humans to collect a large number of data. Many research works used generative models to generate a large amount of data based on a small amount of data for sentiment analysis. However, training on long texts and inaccurate sentiment information that might be generated are two severe challenges. It is difficult to improve the sentiment analysis accuracy effectively. In this paper, we propose a novel data augmentation framework based on Sequence generative adversarial networks (SeqGAN) to improve the sentiment analysis accuracy when the dataset already has a certain amount of data and contains long texts. Penalty-based SeqGAN is used to generate high-quality and diversified text data. Long short-term memory (LSTM) networks with attention mechanisms are used to conduct sentence compression for the training data of SeqGAN. A sentiment dictionary is used to retain the sentiment words for compressed data. We also propose a data screening method to obtain more accurate data from the generated data. The results of the usability, novelty, and diversity of the generated data show that the proposed sentence compression method can help SeqGAN learn more information from the long text data. The data generated by the proposed framework improve the classification accuracy of four classifiers applied on two distinct text datasets.

Alzheimer’s disease (AD) is the most common form of dementia. Currently, only symptomatic management is available, and early diagnosis and intervention are crucial for AD treatment. As a recent deep learning strategy, generative adversarial networks (GANs) are expected to benefit AD diagnosis, but their performance remains to be verified. This study provided a systematic review on the application of the GAN-based deep learning method in the diagnosis of AD and conducted a meta-analysis to evaluate its diagnostic performance. A search of the following electronic databases was performed by two researchers independently in August 2021: MEDLINE (PubMed), Cochrane Library, EMBASE, and Web of Science. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was applied to assess the quality of the included studies. The accuracy of the model applied in the diagnosis of AD was determined by calculating odds ratios (ORs) with 95% confidence intervals (CIs). A bivariate random-effects model was used to calculate the pooled sensitivity and specificity with their 95% CIs. Fourteen studies were included, 11 of which were included in the meta-analysis. The overall quality of the included studies was high according to the QUADAS-2 assessment. For the AD vs. cognitively normal (CN) classification, the GAN-based deep learning method exhibited better performance than the non-GAN method, with significantly higher accuracy (OR 1.425, 95% CI: 1.150–1.766, P = 0.001), pooled sensitivity (0.88 vs. 0.83), pooled specificity (0.93 vs. 0.89), and area under the curve (AUC) of the summary receiver operating characteristic curve (SROC) (0.96 vs. 0.93). For the progressing MCI (pMCI) vs. stable MCI (sMCI) classification, the GAN method exhibited no significant increase in the accuracy (OR 1.149, 95% CI: 0.878–1.505, P = 0.310) or the pooled sensitivity (0.66 vs. 0.66). The pooled specificity and AUC of the SROC in the GAN group were slightly higher than those in the non-GAN group (0.81 vs. 0.78 and 0.81 vs. 0.80, respectively). The present results suggested that the GAN-based deep learning method performed well in the task of AD vs. CN classification. However, the diagnostic performance of GAN in the task of pMCI vs. sMCI classification needs to be improved.Systematic Review Registration: [PROSPERO], Identifier: [CRD42021275294].

Cumulative research studies have verified that multiple circRNAs are closely associated with the pathogenic mechanism and cellular level. Exploring human circRNA–disease relationships is significant to decipher pathogenic mechanisms and provide treatment plans. At present, several computational models are designed to infer potential relationships between diseases and circRNAs. However, the majority of existing approaches could not effectively utilize the multisource data and achieve poor performance in sparse networks. In this study, we develop an advanced method, GATGCN, using graph attention network (GAT) and graph convolutional network (GCN) to detect potential circRNA–disease relationships. First, several sources of biomedical information are fused via the centered kernel alignment model (CKA), which calculates the corresponding weight of different kernels. Second, we adopt the graph attention network to learn latent representation of diseases and circRNAs. Third, the graph convolutional network is deployed to effectively extract features of associations by aggregating feature vectors of neighbors. Meanwhile, GATGCN achieves the prominent AUC of 0.951 under leave-one-out cross-validation and AUC of 0.932 under 5-fold cross-validation. Furthermore, case studies on lung cancer, diabetes retinopathy, and prostate cancer verify the reliability of GATGCN for detecting latent circRNA–disease pairs.

Precise identification of target sites of RNA-binding proteins (RBP) is important to understand their biochemical and cellular functions. A large amount of experimental data is generated by in vivo and in vitro approaches. The binding preferences determined from these platforms share similar patterns but there are discernable differences between these datasets. Computational methods trained on one dataset do not always work well on another dataset. To address this problem which resembles the classic “domain shift” in deep learning, we adopted the adversarial domain adaptation (ADDA) technique and developed a framework (RBP-ADDA) that can extract RBP binding preferences from an integration of in vivo and vitro datasets. Compared with conventional methods, ADDA has the advantage of working with two input datasets, as it trains the initial neural network for each dataset individually, projects the two datasets onto a feature space, and uses an adversarial framework to derive an optimal network that achieves an optimal discriminative predictive power. In the first step, for each RBP, we include only the in vitro data to pre-train a source network and a task predictor. Next, for the same RBP, we initiate the target network by using the source network and use adversarial domain adaptation to update the target network using both in vitro and in vivo data. These two steps help leverage the in vitro data to improve the prediction on in vivo data, which is typically challenging with a lower signal-to-noise ratio. Finally, to further take the advantage of the fused source and target data, we fine-tune the task predictor using both data. We showed that RBP-ADDA achieved better performance in modeling in vivo RBP binding data than other existing methods as judged by Pearson correlations. It also improved predictive performance on in vitro datasets. We further applied augmentation operations on RBPs with less in vivo data to expand the input data and showed that it can improve prediction performances. Lastly, we explored the predictive interpretability of RBP-ADDA, where we quantified the contribution of the input features by Integrated Gradients and identified nucleotide positions that are important for RBP recognition. Author summary RNA binding proteins (RBPs) regulate every aspect of RNA biology, including splicing, translation, transportation, and degradation. To fully understand the function of RBPs and the mechanism of RBP-RNA recognition, an accurate knowledge of the RBP-RNA binding preferences is essential. There are in vitro and in vivo experimental approaches such as RNAcompete or eCLIP that can determine RBP-RNA binding preferences in a high-through manner. However, because of the intrinsic differences between in vitro and in vivo experimental conditions, the binding preferences determined from in vitro and in vivo do not always agree with each other. To solve this problem and best utilize both types of data, we have adopted the adversarial domain adaptation (ADDA) technique into the analysis of RNA binding proteins and developed a framework (RBP-ADDA) that can extract RBP binding preferences from an integration of in vivo and vitro datasets. We showed that RBP-ADDA outperforms other contemporary methods in predicting RBA binding preferences on both in vivo and in vitro data. To the best of our knowledge, this is the first time that adversarial domain adaptation has been applied to the computational study of gene regulations.

Previous research focused on the qualitative discussion of the correlation between surgical time and mortality in acute pancreatitis (AP). Recommendations for surgical timing of necrotizing pancreatitis are delayed as far as possible, without recommendations for individuals. The aim of this article is to predict timing of surgical intervention in necrotizing pancreatitis with recurrent neural network (RNN). Time series data in AP were retrospectively extracted from a hospital in China (n = 15,813) to develop model, and the Cerner Health Facts database in the United States (n = 142,650) was used to externally validate. The developed model, time-aware Phased-Decay long short-term memory (LSTM), was used to predict timing of surgical intervention and critical clinical features in necrotizing pancreatitis based on laboratory tests, compared to other machine learning models. Area under the ROC curve (AUC) of RNN-based models was more than 0.70. The AUC of time-aware Phased-Decay LSTM (0.75) with more explanatory was similar to that (0.76) of Phased-Decay LSTM to predict surgical timing. The developed model visualized the specific surgical process and laboratory indicators changes for the patients with AP from the onset to discharge. Different clinical features had different contribution modes with time to the specific surgical event in AP. Heart function contributed the most to the prediction of surgical intervention at the onset and in the first week. Our developed model could monitor the specific surgical process from the onset of AP to discharge and extract the contribution modes of clinical features with time in necrotizing pancreatitis.

Motivated by the importance of fault-tolerant control for robot movement, a fault-tolerant motion planning scheme is proposed to eliminate the joint velocity jump of the redundant manipulator at the time of failure, and enhance the motion stability of the manipulator fault-tolerant operation. The joint velocity jump is eliminated by replacing the degradation scheme at the moment of joint failure, and a neural dynamic method is introduced to eliminate the position error of the end-effector of the manipulator, and the analysis method based on pseudoinverse and inverse-free is used to plan the motion of the manipulator in real time. The simulation results of the redundant manipulator based on the planar four-link indicate that the fault-tolerant scheme can not only realize the manipulator fault-tolerant control without joint velocity jump, but also effectively guarantee the manipulator operation precision.

Purpose. To identify the ability of releasing and uptaking exosomes in rat lens and characterize the exosomal microRNA profile of lens-derived exosomes. Methods. The rat lenses were cultured ex vivo and the medium was collected. The exosomes were isolated from medium and measured in size and concentration by nanoflow cytometry (nFCM) and transmission electron microscopy (TEM) and verified with CD63 and TSG101 by Western blot. The miRNAs in exosomes released from lens epithelial cells (LECs) were sequenced. The plasma exosomes labeled by PKH26 were used to verify the exosomes uptake LECs, and their colocalized fluorescence was imaged by confocal microscopy. Results. LECs released numerous exosomes into the medium through the capsule, which contained abundant miRNAs. The most abundant miRNAs included miR-184, let-7c-5p, let-7a-5p, let-7b-5p, let-7f-5p, miR-125a-5p, miR-204-5p, miR-125b-5p, miR-1b, and miR-23a-3p. The LECs but not the lens fibre cells showed exosome uptake. The LECs uptake more PKH26-labeled exosomes at day 7 than day 3 and day 14. Conclusions. Our results suggested that LECs can release and uptake exosomes through the capsule. Exosomes may be an important way for the lens to communicate among LECs, aqueous humour, vitreous body, and other ocular tissues.

Value-added utilization of pineapple waste is very import for the food industry and environmental protection. In this study, whey protein (2.6%, w/w) was added to pineapple waste in order to make up for the protein deficiency of the raw material and give the final products better flavor characteristics. Autochthonous Lactococcus lactis LA5 and Hanseniaspora opuntiae SA2 were used for the co-inoculation of pineapple by-products; during fermentation, the metabolite profiling and microbial community dynamics were investigated. Results showed that the contents of organic acids, total FAAs, total phenolic compounds and flavonoids significantly increased with fermentation, and 152 kinds of peptides were identified in the final products. Relevant analyses demonstrated that dominant strains including Lactococcus lactis, Hanseniaspora and Saccharomyces not only significantly promoted the accumulation of organic acids, total phenols and other active substances, but also inhibited the growth of pathogenic bacteria and further influenced the fermentation process of pineapple waste.

In recent years, sparse direction-of-arrival (DOA) estimation for multiple-input multiple-output (MIMO) radar has attracted extensive attention and been extensively studied, especially the method based on the classic least absolute shrinkage and selection operator (LASSO) estimator. The alternating-direction method of multipliers (ADMM) is an effective method for solving this problem at the cost of introducing an additional user parameter. To avoid introducing an additional user parameter, this paper adopts an equivalent transformation in the form of the generalized SParse Iterative Covariance-based Estimation (qSPICE) cost function to obtain a mean squared minimized form of the cost function. Then, the problem is transformed into a sparse optimization problem in the form of a weighted LASSO. Next, this unconstrained optimization problem is decomposed into three subproblems, which are solved separately to reduce the dimension of each problem and thus reduce the overall computational complexity based on ADMM. Simulation results and measured data indicate that the proposed method significantly outperforms the traditional super-resolution DOA estimation method and ADMM-LASSO method and slightly outperforms qSPICE in terms of resolution and sidelobe suppression capability. In addition, the proposed method has a much lower computational complexity and substantially fewer iterations than qSPICE.

Abstract Background An increasing number of biological and clinical evidences have indicated that the microorganisms significantly get involved in the pathological mechanism of extensive varieties of complex human diseases. Inferring potential related microbes for diseases can not only promote disease prevention, diagnosis and treatment, but also provide valuable information for drug development. Considering that experimental methods are expensive and time-consuming, developing computational methods is an alternative choice. However, most of existing methods are biased towards well-characterized diseases and microbes. Furthermore, existing computational methods are limited in predicting potential microbes for new diseases. Results Here, we developed a novel computational model to predict potential human microbe-disease associations (MDAs) based on Weighted Meta-Graph (WMGHMDA). We first constructed a heterogeneous information network (HIN) by combining the integrated microbe similarity network, the integrated disease similarity network and the known microbe-disease bipartite network. And then, we implemented iteratively pre-designed Weighted Meta-Graph search algorithm on the HIN to uncover possible microbe-disease pairs by cumulating the contribution values of weighted meta-graphs to the pairs as their probability scores. Depending on contribution potential, we described the contribution degree of different types of meta-graphs to a microbe-disease pair with bias rating. Meta-graph with higher bias rating will be assigned greater weight value when calculating probability scores. Conclusions The experimental results showed that WMGHMDA outperformed some state-of-the-art methods with average AUCs of 0.9288, 0.9068 ±0.0031 in global leave-one-out cross validation (LOOCV) and 5-fold cross validation (5-fold CV), respectively. In the case studies, 9, 19, 37 and 10, 20, 45 out of top-10, 20, 50 candidate microbes were manually verified by previous reports for asthma and inflammatory bowel disease (IBD), respectively. Furthermore, three common human diseases (Crohn’s disease, Liver cirrhosis, Type 1 diabetes) were adopted to demonstrate that WMGHMDA could be efficiently applied to make predictions for new diseases. In summary, WMGHMDA has a high potential in predicting microbe-disease associations.

Objective: Previous studies discussing phenotypic and temporal heterogeneity of knee osteoarthritis (KOA) separately have fatal limitations that either clustering patients with similar severity or assuming all knees have a single common progression pattern, which are unreliable. This study tried to uncover more reliable information on phenotypic and temporal heterogeneity of KOA.Design: Data were from Osteoarthritis Initiative database. Six hundred and seventy-eight unilateral knees that have greater Kellgren and Lawrence (KL) grade than the contralateral knees at baseline and in all follow-up 48 months were included. Measurements of biomarkers at baseline were chosen. Subtype and Stage Inference model (SuStaIn) was applied as a subtype-progression model to identify subtypes, subtype biomarker progress sequences and stages of KOA.Results: This study identified three subtypes which account for 15, 61, and 24% of knees, respectively. Each subtype has distinct subtype biomarker progress sequence. For knees with KL grade 0/1, 2, 3, and 4, they have different distributions on stage and 26, 53, 89, and 95% of them are strongly assigned to subtypes. When assessing whether a knee has KL (grade ≥ 2), subtypes and stages from subtypes-progression model (SuStaIn) are significantly better fitting than those from subtypes-only (mixture of Gaussians) (likelihood ratio = 105.59, p = 2.2 × 10−16) or stages-only (SuStaIn where setting c = 1) (likelihood ratio = 58.04, p = 2.57 × 10−14) model. Stages in subtypes-progression model has greater β than stages-only model. Subtypes from subtypes-progression model have no statistical significance.Conclusions: For subtypes-progression model, stages contain more complete temporal information and subtypes are closer to real OA subtypes.

Researchers have realized that microRNAs (miRNAs) play significant roles in the pathogenesis of various diseases. Although many computational models have been proposed to predict the associations between miRNAs and diseases, prediction performance could still be improved. In this paper, we propose a novel self-weighted, multi-kernel, multi-label learning (SwMKML) method to predict disease-related miRNAs. SwMKML adaptively learns two optimal kernel matrices for both miRNAs and diseases from multiple kernels constructed from known miRNA-disease associations. Moreover, the miRNA-disease associations predicted from both spaces are updated simultaneously based on a multi-label framework. Compared with four state-of-the-art computational models, SwMKML achieved best results of 95.5%, 93.1%, and 84.1% in global leave-one-out cross-validation, 5-fold cross-validation, and overall prediction accuracy, respectively. A case study conducted on head and neck neoplasms further identified two potential prognostic biomarkers, hsa-mir-125b-1 and hsa-mir-125b-2, for the disease. SwMKML is freely available at Github, and we anticipate that it may become an effective tool for potential miRNA-disease association prediction. Keywords: graph-based learning, multi-kernel learning, miRNA-disease association prediction

Abstract Background Non-coding RNAs (ncRNAs) are emerging as key regulators and play critical roles in a wide range of tumorigenesis. Recent studies have suggested that long non-coding RNAs (lncRNAs) could interact with microRNAs (miRNAs) and indirectly regulate miRNA targets through competing interactions. Therefore, uncovering the competing endogenous RNA (ceRNA) regulatory mechanism of lncRNAs, miRNAs and mRNAs in post-transcriptional level will aid in deciphering the underlying pathogenesis of human polygenic diseases and may unveil new diagnostic and therapeutic opportunities. However, the functional roles of vast majority of cancer specific ncRNAs and their combinational regulation patterns are still insufficiently understood. Results Here we develop an integrative framework called CeModule to discover lncRNA, miRNA and mRNA-associated regulatory modules. We fully utilize the matched expression profiles of lncRNAs, miRNAs and mRNAs and establish a model based on joint orthogonality non-negative matrix factorization for identifying modules. Meanwhile, we impose the experimentally verified miRNA-lncRNA interactions, the validated miRNA-mRNA interactions and the weighted gene-gene network into this framework to improve the module accuracy through the network-based penalties. The sparse regularizations are also used to help this model obtain modular sparse solutions. Finally, an iterative multiplicative updating algorithm is adopted to solve the optimization problem. Conclusions We applied CeModule to two cancer datasets including ovarian cancer (OV) and uterine corpus endometrial carcinoma (UCEC) obtained from TCGA. The modular analysis indicated that the identified modules involving lncRNAs, miRNAs and mRNAs are significantly associated and functionally enriched in cancer-related biological processes and pathways, which may provide new insights into the complex regulatory mechanism of human diseases at the system level.

In order to enhance the anti-jamming capability of aeronautic swarm tactical network in the complicated electromagnetic environment, we address the problem of bandit-based cognitive anti-jamming strategy for enabling reliable information transmission. We first present an adversarial multiuser multi-armed bandit model for the aeronautic swarm network employing airborne cognitive radios with the same-frequency simultaneous transmit and receive feature. Then, we utilize the improved energy detection method to perform jamming sensing and derive the closed expression of false alarm probability, false detection probability, and the optimal decision threshold in the case of single and multi-jammer. Finally, using the jamming sensing output to calculate reward and with the objective of maximizing the throughput of each airborne radio, a decentralized selfish doubling trick kl-UCB++ anti-jamming strategy is developed to allocate an optimal configuration of transmitting power and spectrum channel to each radio. This anytime bandit strategy is simultaneously minimaxed optimal and asymptotically optimal. The simulation results validate that the aggregate average throughput, cumulative regret obtained with the proposed anti-jamming strategy outperform the well-known UCB, kl-UCB++ bandit algorithm.

A growing body of research has uncovered the role of long noncoding RNAs (lncRNAs) in multiple biological processes and tumorigenesis. Predicting novel interactions between diseases and lncRNAs could help decipher disease pathology and discover new drugs. However, because of a lack of data, inferring disease-lncRNA associations accurately and efficiently remains a challenge. In this paper, we present a novel network consistency projection for LncRNA-disease association prediction (NCPLDA) model by integrating the lncRNA-disease association probability matrix with the integrated disease similarity and lncRNA similarity. The lncRNA-disease association probability matrix is calculated based on known lncRNA-disease associations and disease semantic similarity. The integrated disease similarity and lncRNA similarity are computed based on disease semantic similarity, lncRNA functional similarity and Gaussian interaction profile kernel similarity. In leave-one-out cross validation experiments, NCPLDA achieved outstanding AUCs of 0.8900, 0.8996, and 0.9012 for three datasets. Furthermore, prostate cancer and ovarian cancer case studies demonstrated that the NCPLDA can effectively infer undiscovered lncRNAs.