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BackgroundThe severe coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has resulted in the most devastating pandemic in modern history. Human immunodeficiency virus (HIV) destroys immune system cells and weakens the body’s ability to resist daily infections and diseases. Furthermore, HIV-infected individuals had double COVID-19 mortality risk and experienced worse COVID-related outcomes. However, the existing research still lacks the understanding of the molecular mechanism underlying crosstalk between COVID-19 and HIV. The aim of our work was to illustrate blood transcriptome crosstalk between COVID-19 and HIV and to provide potential drugs that might be useful for the treatment of HIV-infected COVID-19 patients.MethodsCOVID-19 datasets (GSE171110 and GSE152418) were downloaded from Gene Expression Omnibus (GEO) database, including 54 whole-blood samples and 33 peripheral blood mononuclear cells samples, respectively. HIV dataset (GSE37250) was also obtained from GEO database, containing 537 whole-blood samples. Next, the “Deseq2” package was used to identify differentially expressed genes (DEGs) between COVID-19 datasets (GSE171110 and GSE152418) and the “limma” package was utilized to identify DEGs between HIV dataset (GSE37250). By intersecting these two DEG sets, we generated common DEGs for further analysis, containing Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) functional enrichment analysis, protein-protein interaction (PPI) analysis, transcription factor (TF) candidate identification, microRNAs (miRNAs) candidate identification and drug candidate identification.ResultsIn this study, a total of 3213 DEGs were identified from the merged COVID-19 dataset (GSE171110 and GSE152418), and 1718 DEGs were obtained from GSE37250 dataset. Then, we identified 394 common DEGs from the intersection of the DEGs in COVID-19 and HIV datasets. GO and KEGG enrichment analysis indicated that common DEGs were mainly gathered in chromosome-related and cell cycle-related signal pathways. Top ten hub genes (CCNA2, CCNB1, CDC20, TOP2A, AURKB, PLK1, BUB1B, KIF11, DLGAP5, RRM2) were ranked according to their scores, which were screened out using degree algorithm on the basis of common DEGs. Moreover, top ten drug candidates (LUCANTHONE, Dasatinib, etoposide, Enterolactone, troglitazone, testosterone, estradiol, calcitriol, resveratrol, tetradioxin) ranked by their P values were screened out, which maybe be beneficial for the treatment of HIV-infected COVID-19 patients.ConclusionIn this study, we provide potential molecular targets, signaling pathways, small molecular compounds, and promising biomarkers that contribute to worse COVID-19 prognosis in patients with HIV, which might contribute to precise diagnosis and treatment for HIV-infected COVID-19 patients.

Shape memory polymers (SMPs) are a new member of the smart materials family. SMPs have found wide applications or potential applications in almost all manmade structures and devices. In order to better design smart structures and devices using SMPs, thermomechanical constitutive modeling is essential. In this insight paper, we will focus on presenting several multi-length-scale and multi-physics modeling frameworks, including the thermodynamics consistent model, elasto-viscoplastic model, statistical mechanics model, and phase evaluation law model. The SMPs modeled will include amorphous one-way shape memory polymers, semicrystalline one-way shape memory polymers, semicrystalline two-way shape memory polymers, and functional and mechanical damage effects on SMPs. Finally, we will give some in-depth perspectives on future development in this area of study.

To meet the growing needs of public safety and sustainable development, it is highly desirable to develop flame-retardant polymer materials using a facile and low-cost method. Although conventional solution chemical synthesis has proven to be an efficient way of developing flame retardants, it often requires organic solvents and a complicated separation process. In this review, we summarize the progress made in utilizing simple ball milling (an important type of mechanochemical approach) to fabricate flame retardants and flame-retardant polymer composites. To elaborate, we first present a basic introduction to ball milling, and its crushing, exfoliating, modifying, and reacting actions, as used in the development of high-performance flame retardants. Then, we report the mixing action of ball milling, as used in the preparation of flame-retardant polymer composites, especially in the formation of multifunctional segregated structures. Hopefully, this review will provide a reference for the study of developing flame-retardant polymer materials in a facile and feasible way.

With increasing demand for efficient archive management, robots have been employed in paper-based archive management for large, unmanned archives. However, the reliability requirements of such systems are high due to their unmanned nature. To address this, this study proposes a paper archive access system with adaptive recognition for handling complex archive box access scenarios. The system comprises a vision component that employs the YOLOV5 algorithm to identify feature regions, sort and filter data, and to estimate the target center position, as well as a servo control component. This study proposes a servo-controlled robotic arm system with adaptive recognition for efficient paper-based archive management in unmanned archives. The vision part of the system employs the YOLOV5 algorithm to identify feature regions and to estimate the target center position, while the servo control part uses closed-loop control to adjust posture. The proposed feature region-based sorting and matching algorithm enhances accuracy and reduces the probability of shaking by 1.27% in restricted viewing scenarios. The system is a reliable and cost-effective solution for paper archive access in complex scenarios, and the integration of the proposed system with a lifting device enables the effective storage and retrieval of archive boxes of varying heights. However, further research is necessary to evaluate its scalability and generalizability. The experimental results demonstrate the effectiveness of the proposed adaptive box access system for unmanned archival storage. The system exhibits a higher storage success rate than existing commercial archival management robotic systems. The integration of the proposed system with a lifting device provides a promising solution for efficient archive management in unmanned archival storage. Future research should focus on evaluating the system’s performance and scalability.

Abstract Large-scale integration of wind power generation decreases the equivalent inertia of a power system, and thus makes frequency stability control challenging. However, given the irregular, nonlinear, and non-stationary characteristics of wind power, significant challenges arise in making wind power generation participate in system frequency regulation. Hence, it is important to explore wind power frequency regulation potential and its uncertainty. This paper proposes an innovative uncertainty modeling method based on mixed skew generalized error distribution for wind power frequency regulation potential. The mapping relationship between wind speed and the associated frequency regulation potential is established, and key parameters of the wind turbine model are identified to predict the wind power frequency regulation potential. Furthermore, the prediction error distribution of the frequency regulation potential is obtained from the mixed skew model. Because of the characteristics of error partition, the error distribution model and predicted values at different wind speed sections are summarized to generate the uncertainty interval of wind power frequency regulation potential. Numerical experiments demonstrate that the proposed model outperforms other state-of-the-art contrastive models in terms of the refined degree of fitting error distribution characteristics. The proposed model only requires the wind speed prediction sequence to accurately model the uncertainty interval. This should be of great significance for rationally optimizing system frequency regulation resources and reducing redundant backup.

Background: Acute sprint exercise is a time-efficient physical activity that improves cardiorespiratory fitness in younger and middle-aged adults. Growing evidence has demonstrated that acute sprint exercise provides equal to or superior health benefits compared with moderate-intensity continuous training, which will dramatically increase aerobic capacity, insulin sensitivity, and muscle capillarization. Although the beneficial effects of acute sprint exercise are well documented, the mechanisms behind how acute sprint exercise prevents disease and benefits health are less understood.Method: We obtained differentially expressed genes in muscle (vastus lateralis) from men and women before and after an acute sprint exercise. Then, we identified hub genes from the protein–protein interaction (PPI) network of differentially expressed genes (DEGs) and key transcription factors in men and women related to acute sprint exercise. Finally, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses are performed on DEGs and sex-biased genes, respectively.Results: First, we identified 127 sexually dimorphic genes in men (90 upregulated and 37 downregulated) and 75 genes in women (90 upregulated and 37 downregulated) in response to acute sprint exercise. Second, CEBPB, SMAD3, and CDKN1A are identified as the top three hub genes related to men-biased genes. Accordingly, the top three hub genes related to women-biased genes are JUN, ACTB, and SMAD7. In addition, CLOCK, ZNF217, and KDM2B are the top three enriched transcriptional factors in men-biased genes, while XLR, SOX2, JUND, and KLF4 are transcription factors enriched most in women-biased genes. Furthermore, based on GO and KEGG enrichment analyses, we identified potential key pathways in regulating the exercise-related response in men and women, respectively.Conclusion: In this study, we found the difference in gene expression and enrichment pathways in muscle in men and women in response to acute sprint exercise. These results will shed new light on the mechanism underlying sex-based differences in skeletal muscle remodeling and metabolism related to acute sprint exercise, which may illustrate the mechanisms behind how acute sprint exercise prevents disease and benefits health.

Airborne E. coli, fecal coliform, and Enterococcus are all related to sewage worker’s syndrome and therefore used as target enteric bioaerosols about researches in wastewater treatment plants (WWTPs). However, most of the studies are often inadequately carried out because they lack systematic studies reports bioaerosols emission characteristics and health risk assessments for these three enteric bacteria during seasonal variation. Therefore, quantitative microbial risk assessment based on Monte Carlo simulation was utilized in this research to assess the seasonal variations of health risks of the three enteric bioaerosols among exposure populations (academic visitors, field engineers, and office staffs) in a WWTP equipped with rotating-disc and microporous aeration modes. The results show that the concentrations of the three airborne bacteria from the rotating-disc aeration mode were 2–7 times higher than the microporous aeration mode. Field engineers had health risks 1.5 times higher than academic visitors due to higher exposure frequency. Health risks of airborne Enterococcus in summer were up to 3 times higher than those in spring and winter. Similarly, health risks associated to E. coli aerosol exposure were 0.3 times higher in summer compared to spring. In contrast, health risks associated with fecal coliform aerosol were between 2 and 19 times lower in summer compared to spring and winter seasons. Data further suggest that wearing of N95 mask could minimize health risks by 1–2 orders of magnitude. This research shed light on seasonal variation of health risks associated with bioaerosol emission from wastewater utilities.

Abstract Our objective was to examine differences in cytokine/chemokine response in chronic hepatitis B(CHB) patients to understand the immune mechanism of HBsAg loss (functional cure) during antiviral therapy. We used an unbiased machine learning strategy to unravel the immune pathways in CHB nucleo(t)side analogue-treated patients who achieved HBsAg loss with peg-interferon-α(peg-IFN-α) add-on or switch treatment in a randomised clinical trial. Cytokines/chemokines from plasma were compared between those with/without HBsAg loss, at baseline, before and after HBsAg loss. Peg-IFN-α treatment resulted in higher levels of IL-27, IL-12p70, IL-18, IL-13, IL-4, IL-22 and GM-CSF prior to HBsAg loss. Probabilistic network analysis of cytokines, chemokines and soluble factors suggested a dynamic dendritic cell driven NK and T cell immune response associated with HBsAg loss. Bayesian network analysis showed a dominant myeloid-driven type 1 inflammatory response with a MIG and I-TAC central module contributing to HBsAg loss in the add-on arm. In the switch arm, HBsAg loss was associated with a T cell activation module exemplified by high levels of CD40L suggesting T cell activation. Our findings show that more than one immune pathway to HBsAg loss was found with peg-IFN-α therapy; by myeloid-driven Type 1 response in one instance, and T cell activation in the other.

This paper examines factors of payment decision as well as the role each factor plays in casual configurations leading to high payment intention under systematic and heuristic information processing routes.

Abstract Background Coronary CT angiography (CCTA) is a complicated CT exam in comparison to other CT protocols. Exam success highly depends on image assessment of experienced radiologist and the procedure is often time-consuming. This study aims to evaluate feasibility of automatic CCTA reconstruction in 0.25 s rotation time, 16 cm coverage CT scanner with best phase selection and AI-assisted motion correction. Methods CCTA exams of 90 patients with heart rates higher than 75 bpm were included in this study. Two image series were reconstructed—one at automatically selected phase and another with additional motion correction. All reconstructions were performed without manual interaction of radiologist. A four-point Likert scale rating system was used to evaluate the image quality of coronary artery segment by two experienced radiologists, according to the 18-segment model. Analysis was done on per-segment basis. Results Total 1194 out of the 1620 segments were identified for quality evaluation in 90 patients. After automatic best phase selection, 1172 segments (98.3%) were rated as having diagnostic image quality (scores 2–4) and the average score is 3.64 ± 0.55. When motion corrections were applied, diagnostic segment number increases to 1192 (99.8%) and the average score is 3.85 ± 0.37. Conclusions With the help of 0.25 s rotation speed, 16-cm z-coverage and AI-assisted motion correction algorithm, CCTA exam reconstruction could be performed with minimum radiologist involvement and still meet image quality requirement.

Abstract Background Ovarian cancer (OC) has the highest mortality rate in gynecologic tumors. Despite decades of continuous efforts, the survival rate of patients has not improved significantly, mostly due to drug resistance. Exosomes are hot topics in recent years. Cells can affect the biological behaviors of other cells by transferring exosomes. So far, numerous researchers have found that tumor cells can secrete exosomes which play a important role in the development of tumors. Solid tumors can promote angiogenesis. When drug resistance occurs, it seems that more blood vessels form. We suppose that exosomes derived from chemoresistant OC cells can also promote angiogenesis. Results We investigate whether exosomes secreted by chemoresistant SKOV3-DDP cells (SKOV3-DDP-exo) and sensitive SKOV3 cells (SKOV3-exo) influence angiogenesis. After exosomes were extracted, exosomes were co-cultured with HUVECs. We found that SKOV3-DDP-exo and SKOV3-exo are absorbed by endothelial cells and promote the proliferation, migration, invasion and tube formation of endothelial cells. Moreover, SKOV3-DDP-exo is more powerful in angiogenesis, suggesting that parts of the components of SKOV3-DDP-exo are significantly radical. We also found that miR-130a was highly expressed in drug-resistant OC cells. Also, we found that miR-130a in SKOV3-DDP-exo is higher than SKOV3-exo. Therefore, we suggest that miR-130a in exosomes is the main cause of chemoresistant OC cells promoting angiogenesis.

The magneto-refractive properties of Er/Yb co-doped silica fiber (EYDF) are important in magnetic field sensing. Here, we proposed a theoretical calculation model of the EYDF using density functional theory (DFT) and demonstrated it by an interferometric digital hologram magneto-refractive measurement system. The calculation results show that the fiber material, doped with Er/Yb atoms, has a large net spin and asymmetric spin distribution, which enables the elements to play an important role in the magnetic characteristics of the material. The experimental results show that the refractive index of the fiber decreases linearly with the increase of the magnetic field. The sensitivity of EYDF is 3.8279 × 10-5 RIU/Gs and is two orders of magnitude higher than that of single-mode fiber which is confirmed by the theoretical calculation. It demonstrated that the EYDF has advantages and potential of high sensitivity, miniaturization, and easy integration for magnetic field detection in harsh environments.

A fascinating photonic platform with a small device scale, fast operating speed, as well as low energy consumption is two-dimensional (2D) materials, thanks to their in-plane crystalline structures and out-of-plane quantum confinement. The key to further advancement in this research field is the ability to modify the optical properties of the 2D materials. The modifications typically come from the materials themselves, for example, altering their chemical compositions. This article reviews a comparably less explored but promising means, through engineering the photonic surroundings. Rather than modifying materials themselves, this means manipulates the dielectric and metallic environments, both uniform and nanostructured, that directly interact with the materials. For 2D materials that are only one or a few atoms thick, the interaction with the environment can be remarkably efficient. This review summarizes the three degrees of freedom of this interaction: weak coupling, strong coupling, and multifunctionality. In addition, it reviews a relatively timing concept of engineering that directly applied to the 2D materials by patterning. Benefiting from the burgeoning development of nanophotonics, the engineering of photonic environments provides a versatile and creative methodology of reshaping light–matter interaction in 2D materials.

Background: Breast cancer remains one of most lethal illnesses and the most common malignancies among women, making it important to discover novel biomarkers and therapeutic targets for the disease. Immunotherapy has become a promising therapeutic tool for breast cancer. The role of TRIM8 in breast cancer has rarely been reported.Method: Here we identified TRIM8 expression and its potential function on survival in patients with breast cancer using TCGA (The cancer genome atlas), GEO (Gene expression omnibus) database and METABRIC (Molecular Taxonomy of Breast Cancer International Consortium). Then, TIMER and TISIDB databases were used to investigate the correlations between TRIM8 mRNA levels and immune characteristics. Using stepwise cox regression, we established an immune prognostic signature based on five differentially expression immune-related genes (DE-IRGs). Finally, a nomogram, accompanied by a calibration curve was proposed to predict 1-, 3-, and 5-year survival for breast cancer patients.Results: We found that TRIM8 expression was dramatically lower in breast cancer tissues in comparison with normal tissues. Lower TRIM8 expression was related with worse prognosis in breast cancer. TIMER and TISIDB analysis showed that there were strong correlations between TRIM8 expression and immune characteristics. The receiver operating characteristic (ROC) curve confirmed the good performance in survival prediction and showed good accuracy of the immune prognostic signature. We demonstrated the model usefulness of predictions by nomogram and calibration curves. Our findings indicated that TRIM8 might be a potential link between progression and prognosis survival of breast cancer.Conclusion: This is a comprehensive study to reveal that tripartite motif 8 (TRIM8) may serve as a potential prognostic biomarker associating with immune characteristics and provide a novel therapeutic target for the treatment of breast cancer.

BackgroundAutophagy plays an important role in triple-negative breast cancer (TNBC). However, the prognostic value of autophagy-related genes (ARGs) in TNBC remains unknown. In this study, we established a survival model to evaluate the prognosis of TNBC patients using ARGs signature.MethodsA total of 222 autophagy-related genes were downloaded from The Human Autophagy Database. The RNA-sequencing data and corresponding clinical data of TNBC were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed autophagy-related genes (DE-ARGs) between normal samples and TNBC samples were determined by the DESeq2 package. Then, univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were performed. According to the LASSO regression results based on univariate Cox, we identified a prognostic signature for overall survival (OS), which was further validated by using the Gene Expression Omnibus (GEO) cohort. We also found an independent prognostic marker that can predict the clinicopathological features of TNBC. Furthermore, a nomogram was drawn to predict the survival probability of TNBC patients, which could help in clinical decision for TNBC treatment. Finally, we validated the requirement of an ARG in our model for TNBC cell survival and metastasis.ResultsThere are 43 DE-ARGs identified between normal and tumor samples. A risk model for OS using CDKN1A, CTSD, CTSL, EIF4EBP1, TMEM74, and VAMP3 was established based on univariate Cox regression and LASSO regression analysis. Overall survival of TNBC patients was significantly shorter in the high-risk group than in the low-risk group for both the training and validation cohorts. Using the Kaplan–Meier curves and receiver operating characteristic (ROC) curves, we demonstrated the accuracy of the prognostic model. Multivariate Cox regression analysis was used to verify risk score as an independent predictor. Subsequently, a nomogram was proposed to predict 1-, 3-, and 5-year survival for TNBC patients. The calibration curves showed great accuracy of the model for survival prediction. Finally, we found that depletion of EIF4EBP1, one of the ARGs in our model, significantly reduced cell proliferation and metastasis of TNBC cells.ConclusionBased on six ARGs (CDKN1A, CTSD, CTSL, EIF4EBP1, TMEM74, and VAMP3), we developed a risk prediction model that can help clinical doctors effectively predict the survival status of TNBC patients. Our data suggested that EIF4EBP1 might promote the proliferation and migration in TNBC cell lines. These findings provided a novel insight into the vital role of the autophagy-related genes in TNBC and may provide new therapeutic targets for TNBC.

Abstract The sulfur cathode of lithium‐sulfur (Li‐S) batteries suffers from inherent problems of insufficient mechanical strength and the dissolution of sulfur and polysulfides. Inspired by the extraordinarily resilient and strong binding force of the Great Wall binder, that is, the sticky rice mortar, we extracted highly branched amylopectin (HBA), the effective ingredient, as a low‐cost, nontoxic and environmentally benign aqueous binder for the sulfur cathode. The HBA‐based cells outperform the Li‐S batteries based on the traditional polyvinyldene diflouride (PVDF) binder and a lowly branched polysaccharide binder. The improved electrochemical performance in the HBA‐based cell could be attributed to two mechanisms. First, the branched structure of the HBA provides enhanced mechanical and adhesive properties, which allow for a robust electronic and ionic conductive framework to be maintained throughout the cathode after extended cycling. Second, the HBA shows enhanced polysulfide retention due to the polymer's abundant lone‐pair rich hydroxyl groups and the formation of C─S bonds between the HBA and polysulfides prohibits the shuttle effect of polysulfides. The improved mechanical properties and polysulfide retention function of the HBA binder facilitate the HBA‐based Li‐S battery to deliver a long cycle life of 500 cycles at 2 C while only displaying a capacity fading of 0.104% per cycle.

OBJECTIVES Chromium is usually found as Cr(Ⅲ) and Cr(Ⅵ) in environmental water samples. The different valences of chromium produce different physiological function, thus it is necessary to analyze the different valences of chromium accurately. At present, the contents of Cr(Ⅲ) and Cr(Ⅵ) are mostly determined after separation or the content of Cr(Ⅲ) or Cr(Ⅵ) is measured first, then the total content of Cr is determined after oxidation or reduction. The content of another valence of chromium is then calculated by the subtraction method. The available method needs a complex procedure. Moreover, the valence of Cr is easily modified during the sample treatment, resulting in large error and low precision. OBJECTIVES To find a simple and accurate method for determination of Cr(Ⅲ) and Cr(Ⅵ). METHODS The first derivative spectrophotometric method was used for simultaneously determining of Cr(Ⅲ) and Cr(Ⅵ), which eliminated the interference of Cr(Ⅲ) on Cr(Ⅵ). RESULTS Chromogenic reagent EDTA-2Na was added to the mixed water sample in a 70℃water-bath at pH 3-3.5 for 15min, and the absorbance was measured. When the derivative interval factor was 10nm, the first derivative value of absorption for Cr(Ⅵ) was the maximum at 330nm wavelength and the value for Cr(Ⅲ)-EDTA was zero. The concentration of Cr(Ⅵ) can be obtained by the first derivative spectrophotometric method, whereas Cr(Ⅲ) can be determined directly at the maximum wavelength of 543nm. Under the optimal conditions, the concentration range was 0-100mg/L for Cr(Ⅵ) and 0-120mg/L for Cr(Ⅲ). The equation of linear regression for Cr(Ⅲ) was A=0.0036ρ-0.0002 (r2=0.9999), for Cr(Ⅵ) was D=0.00072ρ-0.00013 (r2=0.9996). and the limit of detection was 0.005mg/L for Cr(Ⅵ) and 0.006mg/L for Cr(Ⅲ). The recoveries for Cr(Ⅲ) and Cr(Ⅵ) were 97.8%-102.6%. Conclusion The method meets the requirements for analyzing waste water.

Abstract Background MicroRNAs (miRNAs) are a kind of small noncoding RNA molecules that are direct posttranscriptional regulations of mRNA targets. Studies have indicated that miRNAs play key roles in complex diseases by taking part in many biological processes, such as cell growth, cell death and so on. Therefore, in order to improve the effectiveness of disease diagnosis and treatment, it is appealing to develop advanced computational methods for predicting the essentiality of miRNAs. Result In this study, we propose a method (PESM) to predict the miRNA essentiality based on gradient boosting machines and miRNA sequences. First, PESM extracts the sequence and structural features of miRNAs. Then it uses gradient boosting machines to predict the essentiality of miRNAs. We conduct the 5-fold cross-validation to assess the prediction performance of our method. The area under the receiver operating characteristic curve (AUC), F-measure and accuracy (ACC) are used as the metrics to evaluate the prediction performance. We also compare PESM with other three competing methods which include miES, Gaussian Naive Bayes and Support Vector Machine. Conclusion The results of experiments show that PESM achieves the better prediction performance (AUC: 0.9117, F-measure: 0.8572, ACC: 0.8516) than other three computing methods. In addition, the relative importance of all features also further shows that newly added features can be helpful to improve the prediction performance of methods.

Considerable interest has been shown in the coexistence between airborne radar and commercial communication systems in recent years. In particular, the integrated radar and communication system (IRCS) is promising for the airborne platforms like Unmanned Air Vehicles (UAVs). However, due to fast varying channels caused by high mobility, it is a great challenge for the fusion center to collect detection information within a given delay threshold through the air-to-ground (A2G) communication. Based on slowly varying components of the channel, i.e, target spectrum, power spectral densities of the signal dependent clutters, path loss and shadow fading, this paper considers the problem of power minimization for an IRCS and a base station (BS) coexisting in the same frequency band. The latency bound, latency violation probability (LVP), and channel capacity for the A2G communication to the fusion center are considered based on the effective capacity (EC) theory. The detection performance for radar and the rate requirement of BS user are also taken into account. The power allocation problem is non-convex and formulated to a monotonic optimization problem. Afterward, an efficient heuristic scheduling algorithm with acceptable computational complexity is proposed to solve the formulated problem. Then, the robust power allocation with channel estimation error is considered. Simulation results demonstrate the effectiveness of the proposed heuristic algorithm from the perspectives of the total transmit power, EC, and LVP of the IRCS.

Abstract Background Viral infectious diseases are the serious threat for human health. The receptor-binding is the first step for the viral infection of hosts. To more effectively treat human viral infectious diseases, the hidden virus-receptor interactions must be discovered. However, current computational methods for predicting virus-receptor interactions are limited. Result In this study, we propose a new computational method (IILLS) to predict virus-receptor interactions based on Initial Interaction scores method via the neighbors and the Laplacian regularized Least Square algorithm. IILLS integrates the known virus-receptor interactions and amino acid sequences of receptors. The similarity of viruses is calculated by the Gaussian Interaction Profile (GIP) kernel. On the other hand, we also compute the receptor GIP similarity and the receptor sequence similarity. Then the sequence similarity is used as the final similarity of receptors according to the prediction results. The 10-fold cross validation (10CV) and leave one out cross validation (LOOCV) are used to assess the prediction performance of our method. We also compare our method with other three competing methods (BRWH, LapRLS, CMF). Conlusion The experiment results show that IILLS achieves the AUC values of 0.8675 and 0.9061 with the 10-fold cross validation and leave-one-out cross validation (LOOCV), respectively, which illustrates that IILLS is superior to the competing methods. In addition, the case studies also further indicate that the IILLS method is effective for the virus-receptor interaction prediction.

Abstract Background A drug-drug interaction (DDI) is defined as a drug effect modified by another drug, which is very common in treating complex diseases such as cancer. Many studies have evidenced that some DDIs could be an increase or a decrease of the drug effect. However, the adverse DDIs maybe result in severe morbidity and even morality of patients, which also cause some drugs to withdraw from the market. As the multi-drug treatment becomes more and more common, identifying the potential DDIs has become the key issue in drug development and disease treatment. However, traditional biological experimental methods, including in vitro and vivo, are very time-consuming and expensive to validate new DDIs. With the development of high-throughput sequencing technology, many pharmaceutical studies and various bioinformatics data provide unprecedented opportunities to study DDIs. Result In this study, we propose a method to predict new DDIs, namely DDIGIP, which is based on Gaussian Interaction Profile (GIP) kernel on the drug-drug interaction profiles and the Regularized Least Squares (RLS) classifier. In addition, we also use the k-nearest neighbors (KNN) to calculate the initial relational score in the presence of new drugs via the chemical, biological, phenotypic data of drugs. We compare the prediction performance of DDIGIP with other competing methods via the 5-fold cross validation, 10-cross validation and de novo drug validation. Conlusion In 5-fold cross validation and 10-cross validation, DDRGIP method achieves the area under the ROC curve (AUC) of 0.9600 and 0.9636 which are better than state-of-the-art method (L1 Classifier ensemble method) of 0.9570 and 0.9599. Furthermore, for new drugs, the AUC value of DDIGIP in de novo drug validation reaches 0.9262 which also outperforms the other state-of-the-art method (Weighted average ensemble method) of 0.9073. Case studies and these results demonstrate that DDRGIP is an effective method to predict DDIs while being beneficial to drug development and disease treatment.

Purpose: This study proposes an S-TextBLCNN model for the efficacy of traditional Chinese medicine (TCM) formula classification. This model uses deep learning to analyze the relationship between herb efficacy and formula efficacy, which is helpful in further exploring the internal rules of formula combination.Methods: First, for the TCM herbs extracted from Chinese Pharmacopoeia, natural language processing (NLP) is used to learn and realize the quantitative expression of different TCM herbs. Three features of herb name, herb properties, and herb efficacy are selected to encode herbs and to construct formula-vector and herb-vector. Then, based on 2,664 formulae for stroke collected in TCM literature and 19 formula efficacy categories extracted from Yifang Jijie, an improved deep learning model TextBLCNN consists of a bidirectional long short-term memory (Bi-LSTM) neural network and a convolutional neural network (CNN) is proposed. Based on 19 formula efficacy categories, binary classifiers are established to classify the TCM formulae. Finally, aiming at the imbalance problem of formula data, the over-sampling method SMOTE is used to solve it and the S-TextBLCNN model is proposed.Results: The formula-vector composed of herb efficacy has the best effect on the classification model, so it can be inferred that there is a strong relationship between herb efficacy and formula efficacy. The TextBLCNN model has an accuracy of 0.858 and an F1-score of 0.762, both higher than the logistic regression (acc = 0.561, F1-score = 0.567), SVM (acc = 0.703, F1-score = 0.591), LSTM (acc = 0.723, F1-score = 0.621), and TextCNN (acc = 0.745, F1-score = 0.644) models. In addition, the over-sampling method SMOTE is used in our model to tackle data imbalance, and the F1-score is greatly improved by an average of 47.1% in 19 models.Conclusion: The combination of formula feature representation and the S-TextBLCNN model improve the accuracy in formula efficacy classification. It provides a new research idea for the study of TCM formula compatibility.

The manipulation of microstructure from the growth stage is desired in perovskite materials to improve the environmental stability and enhance the perovskite‐based device performance. Herein, the traditional challenges of self‐assembly of multifunctional perovskite nano/microwires (MWs) are overcome by developing a new nanocrystal strategy to intelligently build self‐inlaid jagged CsPbBr3 MWs for photodetectors (PDs) with a combination of improved light‐harvesting capability, heat dissipation, and humidity stability. With a collection of compelling features, the as‐constructed PDs deliver an ultralow dark current of 10−12 A, excellent response speed of 26 ms, superior detectivity (D*) of 1.9 × 1012 Jones, and peak external quantum efficiency of 939%. Furthermore, attributing to the interlocking–protecting effect of the self‐inlaid monocrystalline CsPbBr3 nanostructure, the CsPbBr3 MW PDs display improved stability against heat and humidity with promoted heat dissipation ability at 60 °C and 97% photocurrent retention under 80% humidity treatment for 200 min, respectively. This work represents a significant advancement in developing high‐performance and environmentally stable PDs.

PurposeSleep duration is thought to play a key role in the development of metabolic syndrome. However, the results have been inconsistent.MethodsWe conducted a systematic review and meta-analysis of cohort studies and searched publications in PubMed, Embase, Cochrane Central Register of Controlled Trials, and Clinicaltrials.gov. The summary relative risks (RRs) were estimated using a random model. The sensitivity analysis was performed by sequentially excluding each study to test the robustness of the pooled estimates.FindingWe included 13 studies involving 300,202 patients in which short sleep and long sleep significantly increased the risk of metabolic syndrome 15% (RR = 1.15, 95%CI = 1.09-1.22, p < 0.001) and 19% (RR = 1.19, 95%CI = 1.05-1.35, p < 0.001). Moreover, the relationship between sleep duration and metabolic syndrome risk presented a U-shaped curve. Short and long sleep increased the risk of obesity by 14% (RR = 1.14, 95%CI = 1.07-1.22, p

Summary: The endoplasmic reticulum (ER)-resident transmembrane protein kinase/RNase Ire1 is a conserved sensor of the cellular unfolded protein response and has been implicated in lipid homeostasis, including lipid synthesis and transport, across species. Here we report a novel catabolic role of Ire1 in regulating lipid mobilization in Drosophila. We found that Ire1 is activated by nutrient deprivation, and, importantly, fat body-specific Ire1 deficiency leads to increased lipid mobilization and sensitizes flies to starvation, whereas fat body Ire1 overexpression results in the opposite phenotypes. Genetic interaction and biochemical analyses revealed that Ire1 regulates lipid mobilization by promoting Xbp1s-associated FoxO degradation and suppressing FoxO-dependent lipolytic programs. Our results demonstrate that Ire1 is a catabolic sensor and acts through the Xbp1s-FoxO axis to hamper the lipolytic response during chronic food deprivation. These findings offer new insights into the conserved Ire1 regulation of lipid homeostasis.

In response to the key engineering problems of photovoltaic grid-connected inverter cluster resonance suppression affected by grid-connected inverter impedance, in this paper, a control strategy based on a disturbance observer is proposed to dynamically compensate for the damping coefficient of the controlled system and improve the robustness of the system. First, an engineering mathematical model of a 200 MW photovoltaic inverter cluster is established, and the mechanism of the active damping of the cluster inverter influenced by the disturbance is analyzed. Secondly, the capacitor current feedback is utilized to constitute the virtual damping, and the inverter output impedance is reshaped to suppress the resonance peak. Then, a Kalman filter is used to improve the traditional disturbance observer to accurately detect disturbance of the system during the dynamic process of the cluster inverter so as to better adapt to changes in grid impedance and dynamically compensate for the virtual damping of the cluster resonant system. Finally, the proposed control strategy is verified with respect to a practical PV power station. The experimental results demonstrate the feasibility of the proposed control method.

The lack of high-quality, highly specialized labeled images, and the expensive annotation cost are always critical issues in the image segmentation field. However, most of the present methods, such as deep learning, generally require plenty of train cost and high-quality datasets. Therefore, an optimizable image segmentation method (OISM) based on the simple linear iterative cluster (SLIC), feature migration model, and random forest (RF) classifier, is proposed for solving the small sample image segmentation problem. In the approach, the SLIC is used for extracting the image boundary by clustering, the Unet feature migration model is used to obtain multidimensional superpixels features, and the RF classifier is used for predicting and updating the image segmentation results. It is demonstrated that the proposed OISM has acceptable accuracy, and it retains better target boundary than improved Unet model. Furthermore, the OISM shows the potential for dealing with the fatigue image identification of turbine blades, which can also be a promising method for the effective image segmentation to reveal the microscopic damages and crack propagations of high-performance structures for aeroengine components.

Construction waste reduction (CWR) is an important approach to tackling the environmental problems resulting from increasing construction and demolition activities. Existing studies have explored various factors influencing construction stakeholders’ behavior or behavior intention regarding CWR mainly in terms of individual traits and environmental issues. With the advancement of information society, message framing has been explored as an emerging low-cost nudge approach for promoting pro-environment behavior or behavior intention in various research fields. Yet, few studies have investigated the influence of message framing on project managers’ behavior intentions regarding CWR. This study explores the relationship between project managers’ behavior intentions regarding CWR and message framing of CWR in terms of economic benefit vs. environmental benefit and small scale vs. large scale. A questionnaire experiment with 120 randomly selected project managers was conducted in Hangzhou. Two-way ANOVA and linear regression were performed to test the hypotheses. The results show that environmental benefit information has a higher impact on project managers’ behavior intentions regarding CWR than economic benefit information, while scale framing has an insignificant influence. The findings provide an alternative approach to increasing project managers’ awareness of CWR and further improve construction waste management.

In this paper, we regard the Baidu index as an indicator of investors' attention to China's epidemic stocks. We believe that when seeking information to guide investment decisions, investor sentiment is usually affected by the information provided by the Baidu search engine, which may cause stock prices to fluctuate. Therefore, we constructed a GARCH extended model including the Baidu index to predict the return of epidemic stocks and compared it with the benchmark model. The empirical research in this paper finds that the forecast model including the Baidu index is significantly better than the benchmark model. This has important reference value both for investors in predicting stock trends and for the government's formulation of policies to prevent excessive stock market volatility.

Summary: Nano-wrinkled polymeric membranes have received tremendous attention for their broad applications in tunable optics, biomedicine, surface wettability, and flexible electronics. However, achieveing a low-cost and scalable synthesis routine remains highly desirable. Here, we invent a simple, bottom-up, polymer engineering strategy by introducing water molecules into the polymerization process. Water evaporation induces intermolecular forces that build a crosslinking reaction gradient from the material surface to its interior, which buckles the surface-forming wrinkles. The shape, geometry, size, orientation, and arrangement of the wrinkles on the polymer surface can be designed accordingly. Moreover, the as-fabricated nano-wrinkled, polymeric membranes display outstanding performance in energy and sensing applications, boosting output current and sensing sensitivity by 611% and 164%, respectively, compared to their flat counterparts. This work may contribute to a low-cost, scalable, and environmentally friendly strategy to engineer polymer surfaces with controllable wrinkles, showing great potential for the development of various soft-matter technologies.

Background: This article studies the relationship between the COVID-19 epidemic, public sentiment, and the volatility of infectious disease equities from the perspective of the United States. We use weekly data from January 3, 2020 to March 7, 2021. This provides a sufficient dataset for empirical analysis. Granger causality test results prove the two-way relationship between the fluctuation of infectious disease equities and confirmed cases. In addition, confirmed cases will cause the public to search for COVID-19 tests, and COVID-19 tests will also cause fluctuations in infectious disease equities, but there is no reverse correlation. The results of this research are useful to investors and policy makers. Investors can use the number of confirmed cases to predict the volatility of infectious disease equities. Similarly, policy makers can use the intervention of retrieved information to stabilize public sentiment and equity market fluctuations, and integrate a variety of information to make more scientific judgments on the trends of the epidemic.

Sustainable strategies like Corporate Social Responsibility (CSR) have become an essential requirement for the energy sector to be practiced across their business activities. Many studies addressed CSR-related strategies but did not examine their critical success factors (CSFs) on regional bases. Therefore, the world's largest energy consumer, China, has been chosen to analyze the critical success factors of corporate social responsibility specific to energy sector. In this study, literature is reviewed for the collection of CSFs, validated and evaluated by experts and case industrial managers situated in China. For the analysis and visualization of influential CSFs to promote CSR, ‘Decision Making Trial and Evaluation Laboratory’ (DEMATEL) method was used in this study. The paper highlights that energy companies of China should focus on the most influential success factors for CSR implementation by categorizing CSFs into cause and effect group. The authors' best knowledge is the first study that examined the CSFs related to the energy sector on a regional basis. This study provides initial insights into the strategic process of CSR implementation. This work proposes a decision framework to assist managers of the Chinese energy sector to further extend strong roots in the CSR implementation.

Abstract Background Recent studies reported that blood-based microRNAs (miRNAs) could detect cancers and predict prognosis have opened a new field of utilizing circulating miRNAs as cancer biomarkers. In this pilot study, we conducted for the first time, to our knowledge, the evaluation of the applicability of salivary miRNAs as novel biomarkers for nasopharyngeal carcinoma (NPC) detection. Methods Microarray miRNA expression profiling was performed on saliva samples from 22 newly diagnosed NPC patients and 25 healthy controls, and 12 significantly down-regulated miRNAs were selected for quantitative real-time-PCR (qRT-PCR) validation and further analysis. Their target genes enriched by gene ontology and pathway analysis were used to construct regulatory and interaction networks. The receiver operating characteristic analyses (ROC) and logistic regression were calculated to assess discriminatory accuracy. Results Twelve dysregulated miRNAs screened by microarray that showed the same expression patterns with qRT-PCR analysis. Through bioinformatics analysis, the most prominent hub gene probably regulated by the 12 down-regulated miRNAs is found to be TP53. The ROC including the 12 miRNAs separated NPC patients from healthy controls with very high accuracy (areas under the receiver operating characteristic curve [AUC] = 0.999, sensitivity = 100.00%, specificity = 96.00%). Furthermore, if only six significantly dysregulated miRNAs were selected for the ROC analysis, the accuracy is still impressive (AUC = 0.941, sensitivity = 95.45%, specificity = 80.00%). Conclusions This study highlights the potential for salivary miRNAs as biomarkers for the detection of NPC. Meanwhile, differentially expressed miRNAs in saliva might play critical roles in NPC by regulating their target genes, which associated with some significant pathways, such as p53 signaling pathway.

We conducted molecular dynamics (MD) simulations of tension and compression along the direction and MD simulations of compression along the and directions on nanolaminated graphene/Cu (NGCu) composites to investigate the effects of the incorporated graphene and the deformation mechanisms related to the loading direction. The deformation behavior and the defect structures were found to be strongly dependent on the loading conditions. An asymmetric tension-compression deformation behavior was thus found in graphene/Cu nanolaminates under the loading, which was dominated by stacking faults and deformation twins formed by dislocation slide under tension and compression, respectively. High density and ordered nanotwins were formed at the graphene/Cu interfaces. Two different formation mechanisms of the twins were found under the compression, and the nucleated twins were easy to be thickened with the assistance of the graphene wrinkles. Multiple twins were formed under the compression by the dislocation cross-slip. This study provides a way to introduce graphene reinforcement and twin boundary to Cu matrix composites and design nanotwinned graphene/Cu composites with excellent mechanical performance. Keywords: Deformation Mechanisms, Molecular Dynamics, Graphene/Cu Composites, Interface, Twin

Silicon is a promising anode material for rechargeable Li-ion battery (LIB) due to its high energy density and relatively low operating voltage. However, silicon based electrodes suffer from rapid capacity degradation during electrochemical cycling. The capacity decay is predominantly caused by (i) cracking due to large volume variations during lithium insertion/extraction and (ii) surface degradation due to excessive solid electrolyte interface (SEI) formation. In this work, we demonstrate that coating of a-Si thin film with a Li-active, nanoporous SiOx layer can result in exceptional electrochemical performance in Li-ion battery. The SiOx layer provides improved cracking resistance to the thin film and prevent the active material loss due to excessive SEI formation, benefiting the electrode cycling stability. Half-cell experiments using this anode material show an initial reversible capacity of 2173 mAh g−1 with an excellent coulombic efficiency of 90.9%. Furthermore, the electrode shows remarkable capacity retention of ∼97% after 100 cycles at C/2 charging rate. The proposed anode architecture is free from Li-inactive binders and conductive additives and provides mechanical stability during the charge/discharge process. Keywords: Amorphous silicon, Thin film, Solid electrolyte interphase, Silicon oxide, Anode, Li-ion battery

An effective integrated design optimization method is developed to reduce the maximum von Mises stress around vent holes of a high-pressure turbine sealing disk. It mainly includes four different shape designs (circular, elliptical, race-track, and four-arc) for holes, an updated self-developed modelling and meshing tool, an APDL-based strength analysis, and a self-proposed efficient switching delayed particle swarm optimization (SDPSO) algorithm. The main idea of SDPSO is: (1) by evaluating an evolutionary factor and utilizing a probability transition matrix, a non-homogeneous Markov chain is determined and auto-updated in each generation; (2) the evolutionary factor and the Markov chain are used to adaptively select the inertia weight, acceleration coefficients, and delayed information to adjust the particle’s velocity. The performance of SDPSO is evaluated through two benchmark optimization problems with constraints. The results show that SDPSO is superior to two well-known PSO algorithms in optimization capability, numerical robustness, and convergence speed. Furthermore, SDPSO is used for the stress optimization of vent holes with four different shapes. The results show that: (1) SDPSO is suitable and valuable for practical engineering optimization problems with constraints; (2) the developed integrated design optimization method is effective and advanced for reducing the maximum von Mises stress around the vent holes; and (3) the four-arc hole has more tremendous advantages in reducing the maximum von Mises stress, followed by the elliptical hole, the race-track hole, and the circular hole.

Abstract Background Many evidences have demonstrated that circRNAs (circular RNA) play important roles in controlling gene expression of human, mouse and nematode. More importantly, circRNAs are also involved in many diseases through fine tuning of post-transcriptional gene expression by sequestering the miRNAs which associate with diseases. Therefore, identifying the circRNA-disease associations is very appealing to comprehensively understand the mechanism, treatment and diagnose of diseases, yet challenging. As the complex mechanism between circRNAs and diseases, wet-lab experiments are expensive and time-consuming to discover novel circRNA-disease associations. Therefore, it is of dire need to employ the computational methods to discover novel circRNA-disease associations. Result In this study, we develop a method (DWNN-RLS) to predict circRNA-disease associations based on Regularized Least Squares of Kronecker product kernel. The similarity of circRNAs is computed from the Gaussian Interaction Profile(GIP) based on known circRNA-disease associations. In addition, the similarity of diseases is integrated by the mean of GIP similarity and sematic similarity which is computed by the direct acyclic graph (DAG) representation of diseases. The kernels of circRNA-disease pairs are constructed from the Kronecker product of the kernels of circRNAs and diseases. DWNN (decreasing weight k-nearest neighbor) method is adopted to calculate the initial relational score for new circRNAs and diseases. The Kronecker product kernel based regularised least squares approach is used to predict new circRNA-disease associations. We adopt 5-fold cross validation (5CV), 10-fold cross validation (10CV) and leave one out cross validation (LOOCV) to assess the prediction performance of our method, and compare it with other six competing methods (RLS-avg, RLS-Kron, NetLapRLS, KATZ, NBI, WP). Conlusion The experiment results show that DWNN-RLS reaches the AUC values of 0.8854, 0.9205 and 0.9701 in 5CV, 10CV and LOOCV, respectively, which illustrates that DWNN-RLS is superior to the competing methods RLS-avg, RLS-Kron, NetLapRLS, KATZ, NBI, WP. In addition, case studies also show that DWNN-RLS is an effective method to predict new circRNA-disease associations.

Here, a new three-dimensional graphene-carbon nanotube (3-D GR-CNT) structure was proposed, and the compressive mechanical properties and deformation behavior of the 3-D GR-CNT structure were evaluated using molecular dynamics (MD) simulations. It was found that the 3-D GR-CNT structure had outstanding mechanical properties, especially the ultrahigh Young's modulus, which was up to 1018 GPa and as high as that of GR. The 3-D GR-CNT structure did not have plastic deformation during the compression. The effects of GR length and CNT diameter were evaluated, and it was demonstrated that when the ratio of CNT diameter to GR length was about 0.6, the ultimate stress of the 3-D GR-CNT structure was the highest. Owing to the low density of the 3-D GR-CNT structure, the structure had outstanding specific strength. At a small compressive deformation, GRs produced buckling deformation with wrinkles to resist compression. After reaching the critical buckling stress, CNTs began to produce wrinkles; and after reaching the failure stress, the destruction started from the junctions. In addition, compared with the sp3 carbon atoms, the sp2 carbon atoms were more suitable for the junctions, as more energy could be absorbed by the sp2 carbon atoms in the junctions. Keywords: Three-dimensional graphene-carbon nanotube structures, Mechanical properties, Deformation behavior, Molecular dynamics

Two-dimensional nanomaterials are able to sustain ultra-large elastic strains, which in turn hold potential to alter the many functional properties. However, to achieve such large elastic strains in macro-forms suitable for applications has been a challenge. This paper reports an innovative approach to overcome this challenge by using a martensitic transforming substrate to induce ultra-large elastic lattice strains in metallic thin films deposited on it, as demonstrated in a Nb film-on-NiTi substrate system. This design is based on a novel concept of “lattice strain matching” between the uniform elastic lattice strain of the Nb film and the uniform crystallographic lattice strain of the martensitic transformation of the NiTi substrate. By this principle, the Nb film was able to exhibit reversible elastic lattice strains between −3.66% in compression and+3.74% in tension, for a total elastic strain span of +7.40% (the maximum in one loading deformation) by mechanical deformation of the substrate. These elastic lattice strains are 10–20 times of what are possible for bulk Nb or metallic thin films on conventional substrates. The findings of this work offer a unique opportunity to use ultra-large elastic strains as a means to engineer and improve functional properties of thin film materials.