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The OFDM chirp signal is suitable for MIMO radar applications due to its large time-bandwidth product, constant time-domain, and almost constant frequency-domain modulus. Particularly, by introducing the time-frequency structure of the non-linear frequency modulation (NLFM) signal into the design of an OFDM chirp waveform, a new OFDM-NLFM waveform with low peak auto-correlation sidelobe ratio (PASR) and peak cross-correlation ratio (PCCR) is obtained. IN-OFDM is the OFDM-NLFM waveform set currently with the lowest PASR and PCCR. Here we construct the optimization model of the OFDM-NLFM waveform set with the objective function being the maximum of the PASR and PCCR. Further, this paper proposes an OFDM-NLFM waveform set design algorithm inspired by alternating optimization. We implement the proposed algorithm by the alternate execution of two sub-algorithms. First, we keep both the sub-chirp sequence code matrix and sub-chirp rate plus and minus (PM) code matrix unchanged and use the particle swarm optimization (PSO) algorithm to obtain the optimal parameters of the NLFM signal’s time-frequency structure (NLFM parameters). Next, we keep current optimal NLFM parameters unchanged, and optimize the sub-chirp sequence code matrix and sub-chirp rate PM code matrix using the block coordinate descent (BCD) algorithm. The above two sub-algorithms are alternately executed until the objective function converges to the optimal solution. The results show that the PASR and PCCR of the obtained OFDM-NLFM waveform set are about 5 dB lower than that of the IN-OFDM.

The study aimed to investigate the role of androgen receptor (AR)/cell cycle-related kinase (CCRK) signalling pathway in chronic hepatitis B virus (HBV) infection and gender differences, and the contribution of AR regulatory factor signal transducer and activator of transcription 3 (STAT3) in it. AR, CCRK, and phosphorylated STAT3 expressions in liver tissues of chronic HBV-infected patients and non-HBV controls were determined by western blot and compared between genders. The relationships of expression levels with serum HBV DNA levels, liver inflammation activity, and fibrosis score were analysed in chronic HBV-infected patients. The relationships between expression levels of three proteins were also analysed. HBV-infected patients had significantly higher expression levels of AR, CCRK, and p-STAT3

There are few studies for the classification detection and dynamic multitarget detection of the targets in front of vehicles. In order to solve this problem, a dynamic multitarget detection algorithm is proposed. First, a dynamic multitarget detection with displacement at any time is suggested; secondly, a multitarget detection algorithm based on improved You Only Look Once version 3 (YOLOv3) is proposed for the detection of multitarget high probability risk events in front of the vehicle. The YOLOv3 algorithm model is a lightweight backbone network that uses embedded real-time detection technologies. In this paper, we use a lightweight Mobilenetv2 to replace Darknet-53 for feature extraction. Moreover, an optimizer is used for multiobjective feature extraction, group normalization, and multiobjective feature extraction. The results show that in comparison with the original YOLOv3 algorithm, the detection leakage rate of the improved YOLOv3 multitarget detection algorithm is less than 5%, and the amount of model parameters in this paper is reduced by 95% as compared to the traditional data and CPU intertime is reduced to 78%.

Virtual reality technology has brought new vitality to the display of museum exhibits. With the vigorous development of virtual reality technology, the application of virtual reality technology is becoming more and more frequent. Now, the application of virtual reality technology can be seen in many museums. This paper is aimed at using virtual reality technology to study the exhibition space and interaction design of museums. This paper proposes a virtual space display technology based on the octahedral projection method and hemisphere projection method and investigates and analyzes the application of virtual reality technology. Although the realism of the virtual display is not as good as that of the traditional display, it can be improved in terms of technology and other aspects in the future.

Objective. A case-control study was conducted to explore the application and clinical value of machine learning-based cervical cancer (CC) diagnosis and prediction model in adjuvant chemotherapy of CC. Methods. From August 2019 to August 2021, 46 patients with stage IA CC (study group) and 55 patients with high-grade squamous intraepithelial lesions (HSIL) (control group) were retrospectively analyzed. All patients completed routine MRI examinations, the ADC values of diseased CC and normal cervix and cervical tissues in different stages were compared, and the changes of ADC values in CC tissues before and after chemotherapy were analyzed. The training set (IA = 37, HSIL = 44) and test set (IA = 9, HSIL = 11) are set in a ratio of 4 : 1. The preoperative MRI images were collected and uploaded to the radiomics cloud platform after preprocessing, and the cervix was manually delineated layer by layer on OSag-T2WI, OAx-T1WI, and OAx-T2FS, respectively, to obtain a three-dimensional volume of interest (VOI) of the cervix to extract omics features. Variance Threshold analysis, univariate feature selection (SelectKBest), and least absolute shrinkage and selection operator (LASSO) are adopted to reduce the dimension of data and enroll features. The arbitrary forest model was adopted for machine learning, the ROC curve was drawn, and the diagnostic performance of different sequence omics models was analyzed. Results. Compared with ADC of stage A CC and HSIL, the ADC value of CC was remarkably lower than that of normal CC ( P < 0.05 ). The ROC curve analysis of ADC value to differentiate CC and normal cervix indicated that the AUC was 0.838 and the 95% confidence interval was 0.721–0.955. According to the maximum Youden index of 0.848, the optimal critical value of ADC was 1.267 × 10−3 mm2/s and the sensitivity and specificity were 92.21% and 9.48%, respectively. All results are indicated in Table 2. After CC treatment, 12 patients were effective (CR + PR) and 4 patients were ineffective (PD + SD). When the b value was 1000 s/mm2, the ADC value of the effective patients after the second chemotherapy was significantly higher than that of the first chemotherapy and before treatment ( P < 0.05 ). There was no significant difference between the ADC value after the first chemotherapy and before treatment, compared with before treatment ( P > 0.05 ). There was no significant difference in ADC value between the ineffective patients before treatment and after the first and second chemotherapy ( P > 0.05 ). A total of 8 omics features were extracted based on OSag-T2WI, all of which were wavelet features, including 7 texture features and 1 first-order feature. A total of 10 omics features were extracted based on OAx-T1WI, including 6 wavelet first-order features, 2 gradient first-order features, and 2 wavelet texture features. Based on OAx-T2FS, 6 omics features were extracted, including 3 wavelet texture features, 2 original shape features, and 1 logarithmic first-order feature. Based on OSag-T2WI&OAx-T2FS, 9 histological features were extracted, 4 from OSag-T2WI and 5 from OAx-T2FS. The diagnostic performance of the four arbitrary forest models is indicated in Table 1, and the ROC curve is indicated in Figure 6. The diagnostic performance of the omics model based on OSag-T2WI&OAx-T2FS was the best in both the training set and the test set. The AUC of the training set was 0.991 (95% CI (0.94, 1.00)), and the accuracy rate was 0.925. The AUC of the test set was 0.894 (95% CI (0.75, 1.00)), and the accuracy rate was 0.835. On the other hand, the diagnostic efficiency of the group model based on OAx-T1WI was the worst in both the training set and the test set. The AUC of the training set was 0.713 (95% CI (0.52, 0.92)), and the accuracy rate was 0.71. The AUC of test set is 0.513 (95% CI (0.24, 0.77)), and the accuracy rate was 0.56, which has no practical clinical significance. Conclusion. A CC diagnosis and prediction model based on machine learning can better distinguish stage IA CC from HSIL in the absence of clear lesions, which is of great significance for reducing invasive examination before surgery, guiding surgical procedures and adjuvant chemotherapy for CC.

The use of modified biochar for the remediation of heavy metal (HM) has received much attention. However, the immobilization mechanism of biochar to multiple HMs and the interaction of different forms of HMs with microorganisms are still unclear. K

Coronavirus disease (COVID-19) caused by SARS-COV-2 infection has been widely prevalent in many countries and has become a common challenge facing mankind. Traditional Chinese medicine (TCM) has played a prominent role in this pandemic, and especially TCM with the function of "heat-clearing and detoxifying" has shown an excellent role in anti-virus. Fufang Shuanghua oral liquid (FFSH) has been used to treat the corresponding symptoms of influenza such as fever, nasal congestion, runny nose, sore throat, and upper respiratory tract infections in clinic, which are typical symptoms of COVID-19. The content of chlorogenic acid, andrographolide and dehydrated andrographolide as the quality control components of FFSH is not less than 1.0 mg/mL, 60 μg/mL and 60 μg/mL respectively. In this study, UPLC-Q-TOF-MS/MS was employed to describe the chemical profile of FFSH. Virtual screening and fluorescence resonance energy transfer (FRET) were used to screen the effective components of FFSH acting on SARS-CoV-2 main protease (Mpro). As a result, 214 compounds in FFSH were identified or preliminarily characterized by UPLC-Q-TOF-MS/MS, and 61 active ingredients with potential inhibitory effects on Mpro were selected through receptor-based and ligand-based virtual screening. In particular, quercetin, forsythoside A, and linoleic acid showed a good inhibitory effect on Mpro in FRET evaluation with IC50 values of 26.15 μM, 22.26 μM and 47.09 μM respectively, and had a strong binding affinity with the receptor Mpro (6LU7) in molecular docking. CYS145 and HIS41 were the main amino acid residues affected by small molecules in the protein binding domain. In brief, we characterized, for the first time, 214 chemical components in FFSH, and three of them, including quercetin, forsythoside A and linoleic acid, were screened out to exert beneficial anti-COVID-19 effects through CYS145 and HIS41 sites, which may provide a new research strategy for TCM to develop new therapeutic drugs against COVID-19.

Transcription factors (TFs) are proteins specifically involved in gene expression regulation. It is generally accepted in epigenetics that methylated nucleotides could prevent the TFs from binding to DNA fragments. However, recent studies have confirmed that some TFs have capability to interact with methylated DNA fragments to further regulate gene expression. Although biochemical experiments could recognize TFs binding to methylated DNA sequences, these wet experimental methods are time-consuming and expensive. Machine learning methods provide a good choice for quickly identifying these TFs without experimental materials. Thus, this study aims to design a robust predictor to detect methylated DNA-bound TFs. We firstly proposed using tripeptide word vector feature to formulate protein samples. Subsequently, based on recurrent neural network with long short-term memory, a two-step computational model was designed. The first step predictor was utilized to discriminate transcription factors from non-transcription factors. Once proteins were predicted as TFs, the second step predictor was employed to judge whether the TFs can bind to methylated DNA. Through the independent dataset test, the accuracies of the first step and the second step are 86.63% and 73.59%, respectively. In addition, the statistical analysis of the distribution of tripeptides in training samples showed that the position and number of some tripeptides in the sequence could affect the binding of TFs to methylated DNA. Finally, on the basis of our model, a free web server was established based on the proposed model, which can be available at https://bioinfor.nefu.edu.cn/TFPM/.

Soil desiccation is increasingly threatening the growth of vegetation in artificial forests at the margins of arid desert oases, where a variety of annual herbaceous plants coexist. It is important to understand the response of annual desert plants to droughts and mixed growth and the resulting patterns of change in photosynthetic and physiological properties. Our results showed that annual plants were primarily affected by drought stress, and the effect of interspecific competition was significant only under severe drought stress. In the sprouting stage, moderate drought increased seed germination rates, whereas severe drought stress decreased the germination rates. In the growth phase, the aboveground and belowground parts of annual herbaceous plants showed a synergistic response to drought. Under mild and moderate drought stress, annual herbaceous plants promoted photosynthesis by increasing chlorophyll content, thereby promoting plant stem growth. Following moderate and high drought, root vigor increased to maintain basic metabolic activities and annual herbaceous plants used the “shadow and avoid” response by increasing stem and root length to increase competitive ability. Under severe drought stress, planted seedling chlorophyll levels decreased, resulting in a simultaneous reduction in photosynthetic ability. The root growth of annual herbaceous plants depends on their photosynthesis ability but the decrease in biomass led to a decrease in root growth. The mixed habitat reduced the inhibition of seedling stem growth by drought stress and promoted plant growth.

Background Efficient and effective genome scaffolding tools are still in high demand for generating reference-quality assemblies. While long read data itself is unlikely to create a chromosome-scale assembly for most eukaryotic species, the inexpensive Hi-C sequencing technology, capable of capturing the chromosomal profile of a genome, is now widely used to complete the task. However, the existing Hi-C based scaffolding tools either require a priori chromosome number as input, or lack the ability to build highly continuous scaffolds. Results We design and develop a novel Hi-C based scaffolding tool, pin_hic, which takes advantage of contact information from Hi-C reads to construct a scaffolding graph iteratively based on N-best neighbors of contigs. Subsequent to scaffolding, it identifies potential misjoins and breaks them to keep the scaffolding accuracy. Through our tests on three long read based de novo assemblies from three different species, we demonstrate that pin_hic is more efficient than current standard state-of-art tools, and it can generate much more continuous scaffolds, while achieving a higher or comparable accuracy. Conclusions Pin_hic is an efficient Hi-C based scaffolding tool, which can be useful for building chromosome-scale assemblies. As many sequencing projects have been launched in the recent years, we believe pin_hic has potential to be applied in these projects and makes a meaningful contribution.

Background: DNA methylation is an important epigenetic modification, which plays an important role in regulating gene expression at the transcriptional level. In tumor research, it has been found that the change of DNA methylation leads to the abnormality of gene structure and function, which can provide early warning for tumorigenesis. Our study aims to explore the relationship between the occurrence and development of tumor and the level of DNA methylation. Moreover, this study will provide a set of prognostic biomarkers, which can more accurately predict the survival and health of patients after treatment.Methods: Datasets of bladder cancer patients and control samples were collected from TCGA database, differential analysis was employed to obtain genes with differential DNA methylation levels between tumor samples and normal samples. Then the protein-protein interaction network was constructed, and the potential tumor markers were further obtained by extracting Hub genes from subnet. Cox proportional hazard regression model and survival analysis were used to construct the prognostic model and screen out the prognostic markers of bladder cancer, so as to provide reference for tumor prognosis monitoring and improvement of treatment plan.Results: In this study, we found that DNA methylation was indeed related with the occurrence of bladder cancer. Genes with differential DNA methylation could serve as potential biomarkers for bladder cancer. Through univariate and multivariate Cox proportional hazard regression analysis, we concluded that FASLG and PRKCZ can be used as prognostic biomarkers for bladder cancer. Patients can be classified into high or low risk group by using this two-gene prognostic model. By detecting the methylation status of these genes, we can evaluate the survival of patients.Conclusion: The analysis in our study indicates that the methylation status of tumor-related genes can be used as prognostic biomarkers of bladder cancer.

Currently, the risk factors for subsequent fracture following vertebral augmentation remain incomplete and controversial. To provide clinicians with accurate information for developing a preventive strategy, we carried out a comprehensive evaluation of previously controversial and unexplored risk factors.We retrospectively reviewed patients with osteoporotic vertebral compression fracture in lumbar spine who received vertebral augmentation between January 2019 and December 2020. Based on whether refracture occurred, patients were assigned to refracture and non-refracture group. The clinical characteristics, imaging parameters (severity of vertebral compression, spinal sagittal alignment, degeneration of paraspinal muscles), and surgical indicators (cement distribution and leakage, correction of spinal sagittal alignment) were collected and analyzed.There were 128 patients and 16 patients in non-refracture and refracture group. The incidence of previous fracture, multiple fractures, and cement leakage were notably higher, relative cross-sectional area of psoas (r-CSAThe occurrence of subsequent fracture might be the consequence of multiple factors. Previous fracture, cement leakage, post-LL, and correction of BA were identified as independent risk factors. Furthermore, the correction of BA should not exceed 3.45°, especially in patients with risk factors.

Motivation Identification of Drug–Target Interactions (DTIs) is an essential step in drug discovery and repositioning. DTI prediction based on biological experiments is time-consuming and expensive. In recent years, graph learning-based methods have aroused widespread interest and shown certain advantages on this task, where the DTI prediction is often modeled as a binary classification problem of the nodes composed of drug and protein pairs (DPPs). Nevertheless, in many real applications, labeled data are very limited and expensive to obtain. With only a few thousand labeled data, models could hardly recognize comprehensive patterns of DPP node representations, and are unable to capture enough commonsense knowledge, which is required in DTI prediction. Supervised contrastive learning gives an aligned representation of DPP node representations with the same class label. In embedding space, DPP node representations with the same label are pulled together, and those with different labels are pushed apart. Results We propose an end-to-end supervised graph co-contrastive learning model for DTI prediction directly from heterogeneous networks. By contrasting the topology structures and semantic features of the drug–protein-pair network, as well as the new selection strategy of positive and negative samples, SGCL-DTI generates a contrastive loss to guide the model optimization in a supervised manner. Comprehensive experiments on three public datasets demonstrate that our model outperforms the SOTA methods significantly on the task of DTI prediction, especially in the case of cold start. Furthermore, SGCL-DTI provides a new research perspective of contrastive learning for DTI prediction. Availability and implementation The research shows that this method has certain applicability in the discovery of drugs, the identification of drug–target pairs and so on.

Endocytosis of GluA2-containing alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs) in CA1 of the hippocampus regulates forgetting; deficits in forgetting nociceptive memory can induce serious stress disorders. As a transporter of GluA2-containing AMPAR, the functions of glutamate receptor interacting protein 1 (GRIP1) in forgetting and possible stress responses remain unclear. Lentivirus-mediated interference of GRIP1 expression or function in the dorsal CA1 of the hippocampus in mice indicated that GRIP1 overexpression enhanced spatial memory, impaired forgetting in a Barnes maze, and induced anxiety-like behavior in the open field and elevated plus-maze test. In contrast, GRIP1 knockdown impaired learning capacity. Furthermore, inhibition of the PDZ2 and PDZ4/5 domains of GRIP1 and GluA2-dn enhanced learning capacity, whereas GluA2-dn impaired spatial memory; inhibition of the PDZ2 and PDZ4/5 domains of GRIP1 also decreased forgetting capacity to some extent. Importantly, inhibition of both the PDZ2 and PDZ4/5 domains of GRIP1 induced anxiety-like behavior but not GluA2-dn. Furthermore, optogenetic control of both GluA1 and GluA2 insertion into the postsynaptic membrane impaired memory and induced anxiety-like behavior. In vitro experiments showed that GRIP1-ov and -dn, inhibition of PDZ2 and PDZ4/5 domains of GRIP1, and GluA2-dn decreased glycine-induced GluA1 and GluA2 exocytosis; meanwhile, GRIP1-ov and -dn, and interference of PDZ2 and PDZ4/5 domains of GRIP1 blocked AMPA- and NMDA-induced GluA1 and GluA2 endocytosis. Overall, these results suggest that GRIP1 drives AMPA receptor endocytosis and exocytosis bidirectionally; furthermore, GRIP1-induced stabilization of anchoring postsynaptic GluA1 and GluA2 impairs forgetting and induces anxiety-like behavior. GRIP1 may provide a potential therapeutic target in posttraumatic syndromes and anxiety disorders.

The outbreak of acute respiratory disease in 2019, namely Coronavirus Disease-2019 (COVID-19), has become an unprecedented healthcare crisis. To mitigate the pandemic, there are a lot of collective and multidisciplinary efforts in facilitating the rapid discovery of protein inhibitors or drugs against COVID-19. Although many computational methods to predict protein inhibitors have been developed [ 1– 5], few systematic reviews on these methods have been published. Here, we provide a comprehensive overview of the existing methods to discover potential inhibitors of COVID-19 virus, so-called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). First, we briefly categorize and describe computational approaches by the basic algorithms involved in. Then we review the related biological datasets used in such predictions. Furthermore, we emphatically discuss current knowledge on SARS-CoV-2 inhibitors with the latest findings and development of computational methods in uncovering protein inhibitors against COVID-19.

Survival analysis is a primary problem before clinical treatments to cancer patients after their operations. In order to make this kind of analysis simple, many corresponding tools have been proposed. Though these tools are easy to use, there exist still two fatal flaws. One is that sample grouping is commonly empirical and wrongly based on original gene expressions or survival time. The other is that their feature selection methods mostly depend univariate semi-supervised regression or the multivariate one without considering the small sample size compared with the high dimension.In order to solve the two problems, we design an automatic feature selection web tool which can also satisfy interactive sample grouping.An automatic feature selection is performed on user-defined data or TCGA data. users can also perform manual feature selection. Then, hierarchical clustering is used and an automatic re-clustering strategy is proposed after interactive risk score split. Kaplan-Meier survival curve and log-rank test are utilized as the measurement.Experimental results on 53 datasets from TCGA demonstrate the effectiveness of our method. The tree view, heat map and scatter map can intuitively display the result of the selected genes to the doctors for further research.This method is suitable for survival analysis of high-dimensional small sample data sets. At the same time, it also provides a platform for researchers to analyze custom data. It solves the problems of the existing web tools and provides an effective feature selection method for survival analysis.The full code package is freely available and can be downloaded at https://github.com/Yuan-23/IOFS-SA-ecp-data-main, and the online version at https://bioinfor.nefu.edu.cn/IOFS-SA/ is ready for use freely.

Recent advances in spatial transcriptomics (ST) have brought unprecedented opportunities to understand tissue organization and function in spatial context. However, it is still challenging to precisely dissect spatial domains with similar gene expression and histology in situ. Here, we present DeepST, an accurate and universal deep learning framework to identify spatial domains, which performs better than the existing state-of-the-art methods on benchmarking datasets of the human dorsolateral prefrontal cortex. Further testing on a breast cancer ST dataset, we showed that DeepST can dissect spatial domains in cancer tissue at a finer scale. Moreover, DeepST can achieve not only effective batch integration of ST data generated from multiple batches or different technologies, but also expandable capabilities for processing other spatial omics data. Together, our results demonstrate that DeepST has the exceptional capacity for identifying spatial domains, making it a desirable tool to gain novel insights from ST studies.

We systematically studied the magneto-resistance of strontium (Sr) doped topological insulator (TI) Bi[Formula: see text]Se[Formula: see text] films. For Bi[Formula: see text]Se[Formula: see text] films with relatively large classic magneto-resistance, we found that slightly Sr doping can completely suppress the classic magneto-resistance and make the weak antilocalization effect dominate in the weak-magnetic-field region. Consequently, the value of the magneto-resistance is enhanced by more than 2.7 times at 0.2 T in slightly doped samples. In contrast, in the strong-magnetic-field region, the magneto-resistance is strongly reduced by doping but exhibits linear magneto-resistance behavior. Our results suggest that the linear magneto-resistance behavior originates from the mobility fluctuation induced by disorder described by the Parish–Littlewood model. Our findings not only gain insights into the doping effect on the topological insulator but also provide an effective way to manipulate the magneto-transport properties of TI for potential applications in future.

Any questions please contact: lixuebo.china@gmail.com How to cite. Li, X., Huang, Y., Wang, G., and Zheng, X.: High-frequency observation during sand and dust storms at the Qingtu Lake Observatory, Earth Syst. Sci. Data, 13, 5819–5830, https://doi.org/10.5194/essd-13-5819-2021, 2021. These datasets include the flow and dust information in an entire sand and dust storm. The velocity components and temperature components were collected from the Campbell CSAT-3B anemometer sonic (sampling frequency: 50Hz). The dust information (PM10) was collected from TSI DUSTTRAK 8530 (sampling frequency: 1Hz). The collected time for sonic was from04:00 am 03/28/2016 to 04:00 am 03/29/2016, UTC+08:00 (in total: 24 hours). The collected time for dust monitor was from 08:00 am 03/28/2016 to 20:00 am 03/28/2016, UTC+08:00 (in total: 12 hours). "ux", "uy" and "uz" correspond to the anemometer axes x, y and z with 11 heights in column and 24 hours in row. (x corresponds to the north-west direction) "tem" indicates the temperature collected from the sonics with 11 heights in column and 24 hours in row. "pm10" represent the PM10 collected from the dust monitor with 11 heights in column and 12 hours in row. Load data from files Data were saved in two versions: (1) MATLAB Files; (2)HDF5 Files. Here, we introduced the way to load from '.mat' and '.h5'. 1), load data from the '.mat' file: load('ESSD_sds_all_data.mat', 'ux','uy','uz','tem','pm10') 2), load data from the '.h5' file: h5read('ESSD_sds_all_data.h5','/ux'); h5read('ESSD_sds_all_data.h5','/uy').... Note: The size of 'ux','uy','uz','tem' are '4320000*11', where 4320000=24hours*3600seconds*50Hz, 11 indicates the measured heights from 0.9m to 30m; The size of 'pm10' are '43200*11', where 43200 =12hours*3600seconds*1Hz, 11 indicates the measured heights from 0.9m to 30m; &nbsp

Right ventricular free wall longitudinal strain (RVFWLS) carries important diagnostic and prognostic significance in a variety of clinical settings, but its measurement is time consuming and operator dependent, limiting its value in routine clinical practice. Automated RVFWLS measurements can overcome these limitations. The purpose of this study was to determine the feasibility, reproducibility, and prognostic implications of automated RVFWLS compared with manual assessment of RVFWLS.A total of 206 patients with a wide range of right ventricular (RV) function were retrospectively selected for this study. Fully automated (Auto-Strain), semiautomated (automated with editing), and manual (standard manual assessment) RVFWLS were measured using two-dimensional speckle-tracking echocardiography in the same RV-focused apical four-chamber view. RV ejection fraction (RVEF) was measured using three-dimensional echocardiography. Abnormal RV systolic function was defined as RVEF45%. Agreement for RVFWLS measurements among the three different methods was based on a combination of Pearson correlation, Bland-Altman analyses, and coefficients of variation (CVs).Automated RVFWLS measurements were feasible in 204 subjects (99%). Automated (8 ± 1 sec/patient) and semiautomated (50 ± 10 sec/patient) assessments had shorter analysis times compared with manual measurement (103 ± 25 sec/patient, P .001 for both). Semiautomated RVFWLS had a stronger correlation with manual RVFWLS than automated RVFWLS (r = 0.850 vs r = 0.708, P .001). Semiautomated RVFWLS had smaller biases, narrower limits of agreement (LOA), and lower CVs against manual assessment in comparison with the fully automated method in the whole study population (bias of 0.62 and 1.15, LOA of 6.54 and 10.50, and CVs of 9.3% and 16.9%, respectively), in the subgroup with RVEF45% (bias of 0.81 and 1.43, LOA of 6.32 and 10.42, and CVs of 10.2% and 18.6%, respectively), and in those with RVEF ≥ 45% (bias of 0.34 and 0.73, LOA of 6.86 and 10.63, and CVs of 8.0% and 14.5%, respectively). Seventy-six patients experienced adverse cardiovascular events during a median follow-up period of 26 months. Fully automated, semiautomated, and manual RVFWLS were associated with poor outcomes. The prognostic implications of fully automated RVFWLS were inferior to those of semiautomated and manual RVFWLS. Three RVFWLS measurements were highly reproducible.Both fully automated and semiautomated RVFWLS provide rapid and reproducible assessments of RV function and carry important prognostic implication. Moreover, the semiautomated approach performs better than the fully automated method. Therefore, semiautomated RVFWLS can provide a better balance between feasibility and clinical significance and displays potential for clinical application.

Energy poverty has negative impacts on the residents’ life from various aspects. A comprehensive understanding of these impacts is the top priority in energy poverty governance. Previous qualitative studies have shown that energy poverty has the potential to negatively impact the individual development of residents through multiple pathways. However, few scholars have explored this issue from a quantitative perspective. To fill the gaps in existing research, this study aims to examine the impact of energy poverty on individual development and explore the serial mediating effects of learning behavior and health condition in the relationship. A total of 2289 valid samples are obtained from the dataset of Chinese General Social Survey (CGSS). SPSS 26.0 and PROCESS 3.5 are used to conduct serial mediating effects analysis. The results show that energy poverty can significantly negatively impact the individual development of residents. Learning behavior and health condition are found to independently or serially mediate the relationship between energy poverty and individual development. Health condition has the stronger mediating effect, whereas the mediating effect of learning behavior is weaker. This study may contribute to a better understanding of the consequences of energy poverty in government and academia.

Perennial ryegrass (Lolium perenne L.) was planted in uranium-contaminated soil mixture (river sand and vermiculite mixed with equal volume ratio) supplemented with different amounts of citric acid (0, 1, 5, and 10 mmol/kg) and divided into 4 treatments (Con+0, Con+1, Con+5, and Con+10) to investigate the effects of citric acid concentrations on the remediation efficiency and enhanced mechanism of perennial ryegrass in the lab. The uranium content in the plant tissues showed that the roots were the predominant tissue for uranium accumulation, and the subcellular distributions of uranium in the root cells was in the order: cell wall fraction > cytosol fraction > organelle fraction. However, in the shoot cells the order was cell wall fraction > organelle fraction > cytosol fraction. In this study, the optimal concentration of citric acid added was 5 mmol/kg, and the removal efficiency of U in the shoots and roots increased by 47.37% and 30.10% respectively. The treatment with 5 mmol/kg citric acid had the highest contents of photosynthetic pigment and soluble protein, the highest activity of antioxidant enzymes, and the lowest content of MDA (malondialdehyde) and relative electrical conductivity. Moreover, the damage to the cell ultrastructure of perennial ryegrass observed by TEM (transmission electron microscope) was significantly alleviated when 5 mmol/kg citric acid was added. All results indicate that perennial ryegrass can accumulate uranium with elevated uranium tolerance and enrichment ability when 5 mmol/kg citric acid is added under uranium stress. These results suggest that citric acid has significant effects on improving the uranium phytoremediation potential of perennial ryegrass.

Background: Anovulation is one of the main causes of female infertility, This study will evaluate the effectiveness and safety of Bushen Culuan Decoction for anovulatory infertility caused by six diseases, including anovulatory abnormal uterine bleeding, polycystic ovarian syndrome, hyperprolactinemia, luteinized unruptured follicle syndrome, corpus luteum insufficiency and premature ovarian insufficiency.Methods: This is a randomized, double-blinded, double-dummy, parallel, positively controlled, adaptive, multicenter clinical trial. All participants will be randomly allocated by a central randomization system to the treatment group or the control group in a 1:1 ratio. The treatment group will undergo a 14day-treatment with Bushen Culuan Decoction 13 g three times a day and a 5-day- treatment with clomiphene citrate placebo tablets 50 mg once a day starting on day 5 of every menstrual period. The control group will undergo a 14-day treatment with Bushen Culuan Decoction placebo 13 g three times a day and a 5-day treatment with clomiphene citrate tablets 50 mg once a day from day 5 in every menstrual period. The whole treatment will last through 3 menstrual periods or 6 menstrual periods, depending on whether ovulation is regained in the first 3 menstrual periods. All statistical analyses will be performed in SPSS 21.0 (SPSS, Chicago, Illinois, USA), and a p value Discussion: This study has been approved by the Medical Ethics Committee of Xiyuan Hospital China Academy of Chinese Medical Sciences (No. 2017XLA037-2). The results of this study will be offered for publication in peer-reviewed journals. Trial registration: Clinicaltrials.gov, NCT03709849. Registered on 19 November 2018.

By combining angle-resolved photoemission spectroscopy, scanning tunneling microscopy, atomic force microscope based piezoresponse force microscopy and first-principles calculations, we have studied the low-energy band structure, atomic structure, and charge polarization on the surface of a topological semimetal candidate TaNiTe_{5}. Dirac-like surface states were observed on the (010) surface by angle-resolved photoemission spectroscopy, consistent with the first-principles calculations. On the other hand, piezoresponse force microscopy reveals a switchable ferroelectriclike polarization on the same surface. We propose that the noncentrosymmetric surface relaxation observed by scanning tunneling microscopy could be the origin of the observed ferroelectriclike state in this novel material. Our findings provide a new platform with the coexistence of a ferroelectriclike surface charge distribution and novel surface states.

Characteristics of particle clustering in a particle-laden turbulent boundary layer at a moderate frictional Reynolds number (-scaling in the log layer. Furthermore, the fluid-phase sweep and ejection events inside particle clusters are observed to be weaker than the cases outside cluster-occupied regions, and the local rate of strain inside cluster-occupied regions is relatively higher than in all unconditional statistics. Finally, conditional statistics reveal that those sand grains with relatively small slip velocities are prone to be captured by low-momentum turbulent motions to form particle clusters.

Thermoelectric generator (TEG) system as a burgeoning method for light-duty automotive waste heat recovery has drawn the researcher’s attention for the last decade with the advantages of no moving parts and high reliability [1]. However, the low heat to electricity conversion efficiency is the key drawback that prevents the TEG from wildly using in practice. Furthermore, the efficiency of heat to electricity conversion for thermoelectric (TE) material is in the sharp of parabola, which means TE materials have a peak point of conversion efficiency and a best range for working temperatures. As a result, during most of the time in vehicle driving, the TEMs will not perform at their best range for working temperature and affect the efficiency of TEG system subsequently. Although there are plenty of researches focusing on the improvement of efficiency for the (TE) material, there are rare researches regarding the heat transfer enhancement on the system level, especially for the transient performance improvement for TEG.

Drug–target interaction (DTI) is an important step in drug discovery. Although there are many methods for predicting drug targets, these methods have limitations in using discrete or manual feature representations. In recent years, deep learning methods have been used to predict DTIs to improve these defects. However, most of the existing deep learning methods lack the fusion of topological structure and semantic information in DPP representation learning process. Besides, when learning the DPP node representation in the DPP network, the different influences between neighboring nodes are ignored. In this paper, a new model DTI-MGNN based on multi-channel graph convolutional network and graph attention is proposed for DTI prediction. We use two independent graph attention networks to learn the different interactions between nodes for the topology graph and feature graph with different strengths. At the same time, we use a graph convolutional network with shared weight matrices to learn the common information of the two graphs. The DTI-MGNN model combines topological structure and semantic features to improve the representation learning ability of DPPs, and obtain the state-of-the-art results on public datasets. Specifically, DTI-MGNN has achieved a high accuracy in identifying DTIs (the area under the receiver operating characteristic curve is 0.9665).

The present study explored the role of LAMP3 and related molecular mechanisms in the efficacy of radiation exposure in laryngeal squamous cell carcinoma (LSCC). A lentivirus vector containing the LAMP3 gene was transfected into HEp-2 cells to construct siRNA-LAMP3 and complementation (siLAMP3+LAMP3) groups. Treatment with 4 Gy or 8 Gy radiation was administered to evaluate the role of LAMP3 in radiation therapy. Apoptosis was detected by Annexin V/propidium iodide double staining. Cell migration and invasion were measured in vitro using Transwell and Matrigel assays. Downstream genes regulated by LAMP3 were analyzed using RNA sequencing. Furthermore, a patient-derived xenograft (PDX) model of LSCC was established to verify the efficacy of radiation exposure and the associated signaling pathways downstream of LAMP3. The efficacy of radiation showed that cell proliferation was significantly inhibited by siRNA-LAMP3 knockdown. Increased apoptosis was also observed. Notably, the inhibitory effect was attenuated and apoptosis rates were decreased after LAMP3 complementation. In vitro cellular assays showed that migration and invasion were significantly suppressed by siRNA-LAMP3 knockdown and increased after LAMP3 complementation. Analysis of the efficacy of radiation exposure in the PDX model showed that LAMP3-specific knockdown inhibited tumor growth and that tumor growth was further reduced by the combined radiotherapy treatment. According to transcriptome analysis, the extracellular matrix-receptor interaction pathway is regulated by LAMP3, and further analysis revealed significant differences in key-associated molecules, including laminin subunit gamma-2 (LAMC2) and tenascin-C (TNC). Validation of the in vivo PDX model using qPCR and Western blot analyses supported the abovementioned results. The present findings suggest that reduced LAMP3 expression enhances the efficacy of radiation exposure in LSCC by regulating the LAMP3/LAMC2/TNC signaling pathway. Impact statement It is important to establish effective early diagnostic indicators and reliable treatment strategies for laryngeal squamous cell carcinoma (LSCC). We previously found that expression of LAMP3 was significantly higher in cancerous tissues compared to adjacent normal surgical margin tissues. The present study explored the role of LAMP3 and the related molecular mechanisms in the efficacy of radiation exposure in LSCC. In vitro Transwell and Matrigel assays were performed, and a patient-derived xenograft (PDX) model of LSCC was established. Associated signaling pathways downstream of LAMP3 were analyzed using RNA sequencing. Signaling pathways regulated by LAMP3 were clearly identified by combining the PDX model with transcriptome analysis. Reduced LAMP3 expression enhanced the efficacy of radiation exposure in LSCC. Thus, by utilizing this molecule as a marker, specific groups of patients may be screened for targeted therapy in the future.

Primary cardiac tumors are very rare and generally benign. The most common type, cardiac rhabdomyoma, comprises 45% to 75% of primary cardiac tumors. Cardiac rhabdomyoma is a rare benign tumor that commonly presents with tuberous sclerosis. We present a case of an infant with multifocal cardiac rhabdomyomas with an atrial septal defect and tricuspid insufficiency and no sign of tuberous sclerosis. She was successfully treated with an operation, the treatment plan included mass resection, tricuspid annuloplasty, and closure of the patent foramen ovale. The right atrial lesion was resected entirely, while the lobulated lesion in the right ventricle was resected as two pieces. There was no evidence of recurrence 1 year after the surgery.

The detection of indoor location based on magnetic fields collected by embedded sensors in smartphones has been progressed rapidly. Most current approaches rely on the particle filter (PF) scheme which combines the pedestrian dead reckoning (PDR) technique with patterns of previously recorded magnetic field intensity. The key challenges include inherent blindness and particle degradation problems. Here, a fusion algorithm combining the extended Kalman filter (EKF) and the PF scheme is proposed to address these issues. EKF is first used to reduce the possible location regions by fusing the PDR and magnetic field intensity results. The particle generation, update, and resampling processes are conducted afterward, and the final position is calculated by using the weighted mean of particles. As such, the blindness and particle degradation problems are alleviated by using particles in the reduced location regions at each processing step. Experiments show a localization accuracy of 1-2m when the user walks smoothly, which is better than those of traditional PF schemes, especially in cases under heavy magnetic distortions by using a reduced number of particles.

Energy consumption from municipal wastewater treatment plants (WWTPs) has brought escalating dual pressure of high expenditure and greenhouse gas emissions to environmental engineers and policy makers. In order to identify the underlying factors affecting energy usage, a comprehensive nationwide investigation program was done to analyze 1184 urban WWTPs in China over a period of 30 years (1984–2013). This investigation included a consolidated evaluation of key design parameters (scale, treatment process and effluent standard), different treatment units and geographical distribution of WWTPs in terms of energy consumption. Comparisons were also made between the statistical- and the baseline-value of unit energy consumption. These findings demonstrated the importance of the integrated effects of key design parameters on WWTPs energy consumption, and specific reference benchmark, recommendations, strategies and future development were provided accordingly. The present work can hopefully assist in understanding of how and to what extent key factors are incorporated into design guidelines, and also help decisions makers and engineers construct and upgrade WWTPs in the best energy saving way.

In this study, comparative effects of foliar application of ceria nanoparticles (NPs) and Ce3+ ions on common bean plants were investigated. Soil grown bean seedlings were exposed to ceria NPs and Ce3+ ions at 0, 40, 80, and 160 mg Ce·L−1 every other day at the vegetative growth stage for 17 d. The plants were harvested 47 d after the last treatment. Performed analyses involved growth, physiological and biochemical parameters of the plants and nutritional quality of the pods. Ceria NPs at 40 mg Ce·L−1 increased dry weight of the plants by 51.8% over the control. Neither ceria NPs nor Ce3+ ions significantly affected other vegetative growth parameters. Pod yields and nutrient contents except for several mineral elements were also not significantly different among groups. Compared to control, pods from ceria NPs at 80 mg Ce·L−1 had significantly less S and Mn. At 40 and 80 mg Ce·L−1, ceria NPs reduced pod Mo by 27% and 21%, while Ce3+ ions elevated Mo contents by 20% and 18%, respectively, compared with control. Ce3+ ions at 80 and 160 mg Ce·L−1 significantly increased pod Zn by 25% and 120%, respectively, compared with control. At the end of the experiment, Ce3+ ions at 40, 80, and 160 mg Ce·L−1 increased contents of malondialdehyde (MDA) by 46%, 65%, and 82% respectively as compared with control. While ceria NPs led to a significant increase of MDA level only at the highest concentration. X-ray absorption near edge structure (XANES) analysis of the leaf samples revealed that both ceria NPs and Ce3+ ions kept their original chemical species after foliar applications, suggesting the observed effects of ceria NPs and Ce3+ ions on the plants were probably due to their nano-specific properties and ionic properties respectively.

A highly efficient and regioselective annulation of benzodiynes promoted by triflic acid has been developed. This protocol provides a step and atom-economic access to a series of naphthalenyl triflates. Furthermore, direct synthetic applications of this reaction are also elaborated through cross-coupling reactions to generate synthetically useful multisubstituted naphthalenes.

Electrochromic devices have many important commercial applications ranging from electronic paper like displays, antiglare rear-view mirrors in cars, to energy-saving smart windows in buildings. Monovalent ions such as H+, Li+, and Na+ are widely used as insertion ions in electrochromic devices, but they have limitations such as instability, high cost, and hard handling. The utilization of multivalent ions as insertion ions has been overlooked probably because of the strong electrostatic interactions between ions and intercalation framework as well as the resulted difficulties of intercalation.The bivalent cation, Mg2+, has been proved to be efficient electrochromic insertion ions for the electrochromic processes of the nonstoichiometric W18O49 nanowires. Using Mg2+ based aqueous electrolyte, the electrochromic W18O49 nanowires shows a short coloration/bleaching time of 2.7/3.6 s, and a high tinting productiveness of 116 cm2/c, together with excellent cyclic constancy. The utilization of bivalent Mg2+ as insertion ions will benefit electrochromic devices with low cost, high stability and simple operation.

Compounds that exhibit the unique behavior of negative thermal expansion (NTE)-the physical property of contraction of the lattice parameters on warming-can be applied widely in modern technologies. Consequently, the search for and design of an NTE material with operational and controllable qualities at room temperature are important topics in both physics and materials science. In this work, we demonstrate a new route to achieve magnetic manipulation of a giant NTE in (Mn0.95Ni0.05)CoGe via strong magnetostructural (MS) coupling around room temperature (∼275 to ∼345 K). The MS coupling is realized through the weak bonding between the nonmagnetic CoGe-network and the magnetic Mn-sublattice. Application of a magnetic field changes the NTE in (Mn0.95Ni0.05)CoGe significantly: in particular, a change of Δ L/ L along the a axis of absolute value 15290(60) × 10-6-equivalent to a -31% reduction in NTE-is obtained at 295 K in response to a magnetic field of 8 T.

It is currently a very active research area to develop multifunctional nanocomposites (NCs) which integrate the novel properties from various nanomaterials for multimodal imaging and simultaneous therapy. These theranostic nanoplatforms can provide complementary information from each imaging modality for accurate diagnosis and, at the same time, afford an imaging-guided focused tumor therapy. Among them, core/shell Fe₃O₄@Au NCs have attracted wide attention due to their unique advantages in magnetic targeting, multimodal imaging and photothermal therapy. This study developed a layer-by-layer assembling approach to synthesize Fe₃O₄@Au NCs with high photothermal conversion efficiency. The as-synthesized NCs showed significant photothermal ablation capability to HeLa cells in vitro under near infrared laser irradiation. To ensure the safety for medical applications, the bio-effects of Fe₃O₄@Au NCs on RAW264.7 cells were carefully assessed, in terms of cell viability, oxidative stress and apoptosis. We have demonstrated that Fe₃O₄@Au NCs had good biocompatibility in RAW264.7 cells and no significant cytotoxicity was found. Therefore, the Fe₃O₄@Au NCs synthesized in this study have great potential as an ideal candidate for CT/MR imaging and photothermal therapy.

To reduce the volatilization of the volatile-containing slags and obtain accurate measurement results of slag performance, the volatility degree and deviation mechanism must be determined. Non-isothermal thermogravimetric analysis at different heating rates was used to establish the volatilization kinetic model, and it reveals the volatilization mechanism of CaF2–Na2O–CaO–SiO2–Al2O3–MgO–B2O3 synthetic sodium-containing fluoride mould flux. The results demonstrated that the evaporation of NaF and SiF4 was the decisive factor that led to the change in composition and deviation of properties of the tested slags. The most probable kinetic mechanism function for the evaporation of volatile component from sodium-containing fluoride mould flux could be expressed by $$g(\alpha ) = [ - \ln (1 - \alpha )]^{2/3}$$ , with an apparent activation energy of 164.866 kJ mol−1 and pre-exponential factor of 2.13 × 10−4 s−1, where α is the conversion rate at any time step in the volatilization process. The reaction mechanism was random nucleation followed by growth, which was the limiting factor for the volatilization of synthetic sodium-containing fluoride mould flux. The method of increasing heating rate and adding protection gas in the measurement system will help to obtain more accurate results of slag performance.