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New advances in top-down construction are developing with the construction of supertall buildings in China. This study presents a finite element analysis for high-rise structural construction with a top-down method (TDM) considering complex environmental conditions. Based on this analysis model, the forces and the deformation of the diaphragm wall, beams, and soldier piles at various stages of construction are computed. Taking a super high-rise building with a 5-story basement in Nanjing as an example, the reliability and accuracy of the model is verified by comparing the measured and simulated results of displacement and stress values at various locations. The research results reveal the relationship between excavation depth, soil settlement and pile displacement, which is convenient for finding the optimal construction critical surface. It lays a foundation for the study of the critical height of the subsequent construction and facilitates the prediction of the weak link in the process. At present, this project is under construction, so this study has reference value for subsequent construction projects. [ABSTRACT FROM AUTHOR]

Existing algorithms for enhancing low-light images predominantly focus on the low-light region, which leads to over-enhancement of the glare region, and the high complexity of the algorithm makes it difficult to apply it to embedded devices. In this paper, a GS-AGC algorithm based on regional luminance perception is proposed. The indirect perception of the human eye's luminance vision was taken into account. All similar luminance pixels that satisfied the luminance region were extracted, and adaptive adjustment processing was performed for the different luminance regions of low-light images. The proposed method was evaluated experimentally on real images, and objective evidence was provided to show that its processing effect surpasses that of other comparable methods. Furthermore, the potential practical value of GS-AGC was highlighted through its effective application in road pedestrian detection and face detection. The algorithm in this paper not only effectively suppressed glare but also achieved the effect of overall image quality enhancement. It can be easily combined with the embedded hardware FPGA for acceleration to improve real-time image processing. [ABSTRACT FROM AUTHOR]

Gene expression profile data have high-dimensionality with a small number of samples. These data characteristics lead to a long training time and low performance in predictive model construction. To address this issue, the paper proposes a feature selection algorithm using non-dominant feature-guide search. The algorithm adopts a filtering framework based on feature sorting and search strategy to overcome the problems of long training time and poor performance. First, the feature pre-selection is completed according to the calculated feature category correlation. Second, a multi-objective optimization feature selection model is constructed. Non-dominant features are defined according to the Pareto dominance theory. Combined with the bidirectional search strategy, the Pareto dominance features under the current category maximum relevance feature are removed one by one. Finally, the optimal feature subset with maximum correlation and minimum redundancy is obtained. Experimental results on six gene expression data sets show that the algorithm is much better than Fisher score, maximum information coefficient, composition of feature relevancy, mini-batch K-means normalized mutual information feature inclusion, and max-Relevance and Min-Redundancy algorithms. Compared to feature selection method based on maximum information coefficient and approximate Markov blanket, the algorithm not only has high computational efficiency but also can obtain better classification capabilities in a smaller dimension. [ABSTRACT FROM AUTHOR]

To evaluate the clinical utility of metagenomic next-generation sequencing (mNGS) for the diagnosis of central nervous system infections (CNSI). Cerebrospinal fluid (CSF) from 54 patients who were high-level clinical suspicion of CNSI was collected and sent for mNGS and conventional tests from January 2019 to March 2022. Twenty out of 54 patients were diagnosed with CNSI and 34 non-CNSI. Among the 34 non-CNSI, one was false positive by mNGS. Among the 20 CNSI, 11 had presumed viral encephalitis and/or meningitis, 5 had presumed bacterial meningitis, 2 had presumed TMB, 1 had Crytococcus meningitis and 1 had neurosyphilis. The sensitivity of viral encephalitis and/or meningitis was 0.73 (8/11); 10 virus were detected; 9/10 was dsDNA; 1/10 was ssRNA. SSRN ranged from 1 to 13. The accuracy rate was 0.4, the accuracy rate was positively correlated with SSRN (r = 0.738, P = 0.015), SSRN ≥ 1, the accuracy rate was 0.4; SSRN ≥ 3, the accuracy rate was 0.66; SSRN ≥ 4, the accuracy rate was 0.75; SSRN ≥ 6, the accuracy rate was 1. The sensitivity of bacterial meningitis was 1. Seven kinds of bacteria were detected, among which 3/7 were gram positive, 3/7 were gram negative, and 1/7 was infected NTM (nontuberculous mycobacteria). The accuracy rate was 0.43 (3/7). The sensitivity of TBM was 0.66 (2/3), the accuracy rate was 1. The sensitivity of Crytococcus meningitis was 1, the accuracy rate was 0.5. PPV (positive predictive value) of mNGS was 0.94, NPV (negative predictive value) of mNGS was 0.89, specificity was 0.97 and sensitivity was 0.8. The AUG for CSF mNGS diagnosis of CNSI was 0.89 (95% CI = 0.78–0.99) Headache, meningeal irritation sign and image of meninges abnormal were correlated with the sensitivity of mNGS (r = 0.451, 0.313, 0.446; p = 0.001, 0.021, 0.001); CSF Glucose and CSF Chloride were negatively correlated with sensitivity of mNGS (r = -0.395, −0.462; p = 0.003, ＜ 0.001). mNGS is a detection means with high sensitivity, wide coverage and strong timeliness, which can help clinicians to identify the pathogen diagnosis quickly, conduct targeted anti-infection treatment early and reduce antibiotic abuse. The pathogen which causing low CSF Glucose, low CSF Chloride or meninges infections was more likely to be detected by mNGS. It may be related to growth and structural characteristics of the pathogen and blood–brain barrier damage. [ABSTRACT FROM AUTHOR]

Magnetic resonance imaging (MRI) has become a widely used medical imaging method. Affected by imaging mechanism, magnetic field inhomogeneity and other factors, MRI images are often interfered by non-Gaussian noise such as Rician noise and non-central chi-square distribution noise. However, the existing MRI denoising methods cannot effectively remove different kinds of noise, and the image is prone to blur and detail loss, even artifacts. Thus, this paper proposes an adaptive denoising algorithm for MRI based on Nonlocal Structural Similarity and Low-Rank Sparse Representation (NSS-LRSR). Different from the existing methods, it is a new paradigm to adaptively filter non-Gaussian noise of MRI and it has a good effect on both spatially stable and spatially varying Rician or non-central chi-square distribution noise. The forward variance stable transformation is used to correct the deviation caused by non-Gaussian noise, and then the non-local information is regrouped. And considering the sparse of similar image blocks, we use improved weighted kernel norm minimization to represent the non-local image blocks based on estimation of noise's standard deviation; thereby the processed image block are aggregated and outputted. Experimental results show that compared with the currently popular algorithms, the proposed NSS-LRSR achieves better results in PSNR and SSIM quantitative indexes. [ABSTRACT FROM AUTHOR]

As a key component of electronic skins, flexible pressure sensors have attracted more and more attention because of the increasingly growing demand. Stability is a key parameter to evaluate pressure sensors, while relatively few reports have focused on it. Here, a paper-based piezoresistive sensor is developed, in which, the airlaid paper based sensing layer is modified with silver nanowires (AgNWs) and poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) and sandwiched in between two convex electrodes. Due to the cross bonding of PEDOT:PSS membrane, the conductive paths of AgNWs networks are strengthened and stabilized, thus the stability of the sensor is found to be significantly improved. Besides, to regulate the compressibility by varying sensing layers, the performance of the proposed sensor can be further improved, and its practical application performances in healthcare pulse monitoring, tiny muscle motion, and voice recognition are demonstrated. The results confirm that PEDOT:PSS has the potential as stabilization media to AgNWs for paper-based flexible wearable electronics. [ABSTRACT FROM AUTHOR]

Purpose: To explore interpretable machine learning (ML) methods, with the hope of adding more prognosis value, for predicting survival for patients with Oropharyngeal-Cancer (OPC). Methods: A cohort of 427 OPC patients (Training 341, Test 86) from TCIA database was analyzed. Radiomic features of gross-tumor-volume (GTV) extracted from planning CT using Pyradiomics, and HPV p16 status, etc. patient characteristics were considered as potential predictors. A multi-level dimension reduction algorithm consisting of Least-Absolute-Selection-Operator (Lasso) and Sequential-Floating-Backward-Selection (SFBS) was proposed to effectively remove redundant/irrelevant features. The interpretable model was constructed by quantifying the contribution of each feature to the Extreme-Gradient-Boosting (XGBoost) decision by Shapley-Additive-exPlanations (SHAP) algorithm. Results: The Lasso-SFBS algorithm proposed in this study finally selected 14 features, and our prediction model achieved an area-under-ROC-curve (AUC) of 0.85 on the test dataset based on this feature set. The ranking of the contribution values calculated by SHAP shows that the top predictors that were most correlated with survival were ECOG performance status, wavelet-LLH_firstorder_Mean, chemotherapy, wavelet-LHL_glcm_InverseVariance, tumor size. Those patients who had chemotherapy, with positive HPV p16 status, and lower ECOG performance status, tended to have higher SHAP scores and longer survival; who had an older age at diagnosis, heavy drinking and smoking pack year history, tended to lower SHAP scores and shorter survival. Conclusion: We demonstrated predictive values of combined patient characteristics and imaging features for the overall survival of OPC patients. The multi-level dimension reduction algorithm can reliably identify the most plausible predictors that are mostly associated with overall survival. The interpretable patient-specific survival prediction model, capturing correlations of each predictor and clinical outcome, was developed to facilitate clinical decision-making for personalized treatment. [ABSTRACT FROM AUTHOR]

With the rapid development of computer vision and artificial intelligence technology, visual object detection has made unprecedented progress, and small object detection in complex scenes has attracted more and more attention. To solve the problems of ambiguity, overlap and occlusion in small object detection in complex scenes. In this paper, a multi-scale fusion feature enhanced path aggregation network MSFE-PANet is proposed. By adding attention mechanism and feature fusion, the fusion of strong positioning information of deep feature map and strong semantic information of shallow feature map is enhanced, which helps the network to find interesting areas in complex scenes and improve its sensitivity to small objects. The rejection loss function and network prediction scale are designed to solve the problems of missing detection and false detection of overlapping and blocking small objects in complex backgrounds. The proposed method achieves an accuracy of 40.7% on the VisDrone2021 dataset and 89.7% on the PASCAL VOC dataset. Comparative analysis with mainstream object detection algorithms proves the superiority of this method in detecting small objects in complex scenes. [ABSTRACT FROM AUTHOR]

Lasers have become an important technology for the medical treatment of cancer. However, the high power of visible laser knives makes conventional endoscopic imaging light pollution severe. This phenomenon can cause the physician to be unable to determine the condition of the lesion site. In this article, a colorization method for laser surgery videos is proposed. The method performs real‐time colorization of the laser surgery video under black‐and‐white imaging. The main contribution of this work is to propose a new idea for solving laser contamination in medical surgery videos, along with real‐time generative adversarial networks (RTGANs). The network takes advantage of generative adversarial networks and residual networks for fast colorization of black and white images. To evaluate the method, we conducted experiments using three datasets. The experimental results show that the RTGAN network takes only 0.026 s to colorize a single frame image. Coloring speed up to 67 times faster than advanced methods, and the Peak Signal to Noise Ratio reached a maximum of 35.736. [ABSTRACT FROM AUTHOR]

Aiming at accurate survival prediction of Glioblastoma (GBM) patients following radiation therapy, we developed a subregion-based survival prediction framework via a novel feature construction method on multi-sequence MRIs. The proposed method consists of two main steps: (1) a feature space optimization algorithm to determine the most appropriate matching relation derived between multi-sequence MRIs and tumor subregions, for using multimodal image data more reasonable; (2) a clustering-based feature bundling and construction algorithm to compress the high-dimensional extracted radiomic features and construct a smaller but effective set of features, for accurate prediction model construction. For each tumor subregion, a total of 680 radiomic features were extracted from one MRI sequence using Pyradiomics. Additional 71 geometric features and clinical information were collected resulting in an extreme high-dimensional feature space of 8231 to train and evaluate the survival prediction at 1 year, and the more challenging overall survival prediction. The framework was developed based on 98 GBM patients from the BraTS 2020 dataset under five-fold cross-validation, and tested on an external cohort of 19 GBM patients randomly selected from the same dataset. Finally, we identified the best matching relationship between each subregion and its corresponding MRI sequence, a subset of 235 features (out of 8231 features) were generated by the proposed feature bundling and construction framework. The subregion-based survival prediction framework achieved AUCs of 0.998 and 0.983 on the training and independent test cohort respectively for 1 year survival prediction, compared to AUCs of 0.940 and 0.923 for survival prediction using the 8231 initial extracted features for training and validation cohorts respectively. Finally, we further constructed an effective stacking structure ensemble regressor to predict the overall survival with the C-index of 0.872. The proposed subregion-based survival prediction framework allow us to better stratified patients towards personalized treatment of GBM. [ABSTRACT FROM AUTHOR]

A typical ensemble learning process typically uses a forward integration mechanism to construct the ensemble classifier with a large number of base classifiers. Based on this mechanism, it is difficult to adjust the diversity among base classifiers and optimize the structure inside ensemble since the generation process has a certain amount of randomness, which makes the performance of ensemble classifiers heavily dependent on the human design decisions. To address this issue, we proposed an automatic ensemble classifier construction method based on a dual-layer evolutionary search mechanism, which includes a tree coding-based base classifier population and a binary coding-based ensemble classifier population. Through a collaborative searching process between the two populations, the proposed method can be driven by training data to update the base classifier population and optimize the ensemble classifiers globally. To verify the effectiveness of the dual evolutionary ensemble learning method (DEEL), we tested it on 22 classification tasks from 4 data repositories. The results show that the proposed method can generate a diverse decision tree population on the training data while searching and constructing ensemble classifiers from them. Compared with 9 competitor algorithms, the proposed method achieved the best performance on 17 of 22 test tasks and improved the average accuracies by 0.97–7.65% over the second place. In particular, the generated ensemble classifiers show excellent structure, which involve small number and diverse decision trees. That increases the transparency of ensembles and helps to perform interpretability analysis on them. [ABSTRACT FROM AUTHOR]

Purpose: Surgical workflow recognition has emerged as an important part of computer-assisted intervention systems for the modern operating room, which also is a very challenging problem. Although the CNN-based approach achieves excellent performance, it does not learn global and long-range semantic information interactions well due to the inductive bias inherent in convolution. Methods: In this paper, we propose a temporal-based Swin Transformer network (TSTNet) for the surgical video workflow recognition task. TSTNet contains two main parts: the Swin Transformer and the LSTM. The Swin Transformer incorporates the attention mechanism to encode remote dependencies and learn highly expressive representations. The LSTM is capable of learning long-range dependencies and is used to extract temporal information. The TSTNet organically combines the two components to extract spatiotemporal features that contain more contextual information. In particular, based on a full understanding of the natural features of the surgical video, we propose a priori revision algorithm (PRA) using a priori information about the sequence of the surgical phase. This strategy optimizes the output of TSTNet and further improves the recognition performance. Results: We conduct extensive experiments using the Cholec80 dataset to validate the effectiveness of the TSTNet-PRA method. Our method achieves excellent performance on the Cholec80 dataset, which accuracy is up to 92.8% and greatly exceeds the state-of-the-art methods. Conclusion: By modelling remote temporal information and multi-scale visual information, we propose the TSTNet-PRA method. It was evaluated on a large public dataset, showing a high recognition capability superior to other spatiotemporal networks. [ABSTRACT FROM AUTHOR]

Simple Summary: This study recruited 3272 non-small cell lung cancer (NSCLC) cases to analyze the predictive abilities of serum tumor markers (CEA, SCC-Ag, CYFRA 21-1, NSE, ProGRP, TPSA and CA199) for metastasis and clinical stage, and found that tumor marker levels may be indicative of tumor metastasis (intrapulmonary, lymphatic and distant metastasis) and stage. Increased CEA and CA199 provided an accurate prediction of intrapulmonary and distant metastasis. Increased CEA, CYFRA 21-1 and CA199 provided an accurate prediction of lymphatic metastasis and higher tumor stage. Combined detection of serum tumor markers can indicate tumor metastasis and stage in NSCLC patients. Objective: This study aimed to explore the roles of serum tumor markers for metastasis and stage of non-small cell lung cancer (NSCLC). Methods: This study recruited 3272 NSCLC patients admitted to the Tianjin Union Medical Center and the Tianjin Medical University Cancer Institute and Hospital. The predictive abilities of some serum tumor markers (carcinoembryonic antigen (CEA), squamous cell carcinoma antigen (SCC-Ag), cytokeratin-19 fragment (CYFRA 21-1), neuron-specific enolase (NSE), pro-gastrin-releasing peptide (ProGRP), total prostate-specific antigen (TPSA) and carbohydrate antigen 199 (CA199)) for NSCLC metastasis (intrapulmonary, lymphatic and distant metastasis) and clinical stage were analyzed. Results: Tumor markers exhibited different numerical and proportional distributions in NSCLC patients. Elevated CEA, CYFRA 21-1 and CA199 levels were indicative of tumor metastasis and stage. Increased CEA and CA199 provided an accurate prediction of intrapulmonary and distant metastasis with the area under the receiver operator characteristic curve (AUC) of 0.69 both (p < 0.001); Increased CEA, CYFRA 21-1 and CA199 provided an accurate prediction of lymphatic metastasis with the AUC of 0.62 (p < 0.001). Conclusion: Combined detection of serum tumor markers can indicate tumor metastasis and stage in NSCLC patients. [ABSTRACT FROM AUTHOR]

Change detection determines and evaluates changes by comparing bi-temporal images, which is a challenging task in the remote-sensing field. To better exploit the high-level features, deep-learning-based change-detection methods have attracted researchers' attention. Most deep-learning-based methods only explore the spatial–spectral features simultaneously. However, we assume the key spatial-change areas should be more important, and attention should be paid to the specific bands which can best reflect the changes. To achieve this goal, we propose the spatial–spectral joint attention network (SJAN). Compared with traditional methods, SJAN introduces the spatial–spectral attention mechanism to better explore the key changed areas and the key separable bands. To be more specific, a novel spatial-attention module is designed to extract the spatially key regions first. Secondly, the spectral-attention module is developed to adaptively focus on the separable bands of land-cover materials. Finally, a novel objective function is proposed to help the model to measure the similarity of learned spatial–spectral features from both spectrum amplitude and angle perspectives. The proposed SJAN is validated on three benchmark datasets. Comprehensive experiments have been conducted to demonstrate the effectiveness of the proposed SJAN. [ABSTRACT FROM AUTHOR]

Regional development brings significant changes in industrial structure and water consumption. Researching the trend in water consumption by changes in industrial structure can promote water conservation. The grey niche model describes the industrial changes in China and analyzes the water consumption of different leading industries. Using data from 2014 to 2019, and taking the economy as the influencing reason and the industrial niche as the weight, water consumption was predicted. The average percentage errors of the prediction results were all less than 0.1%. While improving the forecasting accuracy, the water consumption forecasting has been strengthened. The calculation results show that regional industry is undergoing transformation, and tertiary industry is rising in the national economy. The successful implementation of industrial water-saving measures has kept the water consumption of industrially developed cities stable but the rapid development of tertiary industries will increase water consumption. Incorporating changes in industrial structure into water use analysis allows the Chinese government to draft water conservation policies for various industries. [ABSTRACT FROM AUTHOR]

Excessive application of ammonium‐based N fertilizers promote nitrification, which is mainly responsible for intensive acidification in cropland soils. Thus, there is an urgent need for the development of control measures to manage soil acidification induced by N fertilization. In this study, we investigated the potential of enriching organic C bioavailability to mitigate acidification induced by N fertilization through microbial N immobilization using an indoor incubation experiment. Results showed that the sole application of ammonium sulfate [(NH4)2SO4] or urea to a cropland soil promoted nitrification of fertilizer N, and subsequently soil pH decreased by 0.48–1.00 units after 45‐d incubation. The combined application of glucose with N fertilizers induced microbial immobilization of almost all fertilizer N within 1 d and gradually induced mineralization of soil organic N after 1 wk of incubation. More than 65% of immobilized total extractable N was returned to soil solution and mainly existed as NH4+–N and extractable organic N, with the pH decrement being <0.10 units after 45‐d incubation. These results indicated that (a) enriching organic C bioavailability in cropland soils intensified microbial N immobilization, (b) intensification of microbial N immobilization induced a buffering effect to regulate soil inorganic N supply, and (c) microbial N immobilization had great potential to inhibit soil acidification and retard aluminum mobilization. The results obtained in this study provide a theoretical strategy to control soil acidification and increase N use efficiency in croplands. Core Ideas: Glucose stimulated microbial N immobilization for temporary storing fertilizer N in croplands.Glucose inhibited soil nitrification and acidification through microbial N immobilization.Intensifying microbial N immobilization had a buffering effect to regulate soil inorganic N supply.Improving organic C availability has potential to mitigate soil acidification due to N fertilization. [ABSTRACT FROM AUTHOR]

Classification of imbalanced data is an important challenge in current research. Sampling is an important way to solve the problem of imbalanced data classification, but some traditional sampling algorithms are susceptible to outliers. Therefore, an iF-ADASYN sampling algorithm is proposed in this paper. First, based on the ADASYN algorithm, we introduce the isolation Forest algorithm to overcome its vulnerability to outliers. Then, a calculation method of anomaly index which can delete outliers accurately of minority data is presented. The experimental results of four UCI public imbalanced datasets show that the algorithm can effectively improve the accuracy of the minority class, and increase the stability. In the real thrombus dataset, the AUC value of the iF-ADASYN algorithm is more significant than that of SMOTE and ADASYN algorithms, and the recognition rate of patients with thrombosis increased by 20%. The iF-ADASYN algorithm obtains better resistance to outliers than the original ADASYN algorithm. Meanwhile, it improves the accuracy of minority class decision boundary region division. [ABSTRACT FROM AUTHOR]

As an anti-inflammatory alkaloid, nicotine plays dual roles in treating diseases. Here we reviewed the anti-inflammatory and pro-inflammatory effects of nicotine on inflammatory diseases, including inflammatory bowel disease, arthritis, multiple sclerosis, sepsis, endotoxemia, myocarditis, oral/skin/muscle inflammation, etc. , mainly concerning the administration methods, different models, therapeutic concentration and duration, and relevant organs and tissues. According to the data analysis from recent studies in the past 20 years, nicotine exerts much more anti-inflammatory effects than pro-inflammatory ones, especially in ulcerative colitis, arthritis, sepsis, and endotoxemia. On the other hand, in oral inflammation, nicotine promotes and aggravates some diseases such as periodontitis and gingivitis, especially when there are harmful microorganisms in the oral cavity. We also carefully analyzed the nicotine dosage to determine its safe and effective range. Furthermore, we summarized the molecular mechanism of nicotine in these inflammatory diseases through regulating immune cells, immune factors, and the vagus and acetylcholinergic anti-inflammatory pathways. By balancing the "beneficial" and "harmful" effects of nicotine, it is meaningful to explore the effective medical value of nicotine and open up new horizons for remedying acute and chronic inflammation in humans. [ABSTRACT FROM AUTHOR]

Reductions in soil productivity and soil water retention capacity, and water scarcity during crop growth, may occur due to long-term suboptimal tillage and fertilization practices. Therefore, the application of appropriate tillage (subsoiling) and fertilization (organic fertilizer) practices is important for improving soil structure, water conservation and soil productivity. We hypothesize that subsoiling tillage combined with organic fertilizer has a better effect than subsoiling or organic fertilizer alone. A field experiment in Henan, China, has been conducted since 2011 to explore the effects of subsoiling and organic fertilizer, in combination, on winter wheat (Triticum aestivum L.) farming. We studied the effects of conventional tillage (CT), subsoiling (S), organic fertilizer (OF), and organic fertilizer combined with subsoiling (S+OF) treatments on dry matter accumulation (DM), water consumption (ET), water use efficiency (WUE) at different growth stages, yield, and water production efficiency (WPE) of winter wheat over 3 years (2016–2017, 2017–2018, 2018–2019). We also analyzed the soil structure, soil organic carbon, soil microbial biomass carbon and nitrogen, and soil enzymes in 2019. The results indicate that compared with CT, the S, OF and S+OF treatments increased the proportion of >0.25 mm aggregates, and S+OF especially led to increased soil organic carbon, soil microbial biomass carbon and nitrogen, soil enzyme activity (sucrase, cellulose, and urease). S+OF treatment was most effective in reducing ET, and increasing DM and WUE during the entire growth period of wheat. S+OF treatment also increased the total dry matter accumulation (Total DM) and total water use efficiency (total WUE) by 18.6–32.0% and 36.6–42.7%, respectively, during these 3 years. Wheat yield and WPE under S+OF treatment increased by 11.6–28.6% and 26.8–43.6%, respectively, in these 3 years. Therefore, S+OF in combination was found to be superior to S or OF alone, which in turn yielded better results than the CT. [ABSTRACT FROM AUTHOR]

Bacterial wilt (BW) caused by Ralstonia solanacearum (R. solanacearum), is a vascular disease affecting diverse solanaceous crops and causing tremendous damage to crop production. However, our knowledge of the mechanism underlying its resistance or susceptibility is very limited. In this study, we characterized the physiological differences and compared the defense-related transcriptomes of two tobacco varieties, 4411-3 (highly resistant, HR) and K326 (moderately resistant, MR), after R. solanacearum infection at 0, 10, and 17 days after inoculation (dpi). A total of 3967 differentially expressed genes (DEGs) were identified between the HR and MR genotypes under mock condition at three time points, including1395 up-regulated genes in the HR genotype and 2640 up-regulated genes in the MR genotype. Also, 6,233 and 21,541 DEGs were induced in the HR and MR genotypes after R. solanacearum infection, respectively. Furthermore, GO and KEGG analyses revealed that DEGs in the HR genotype were related to the cell wall, starch and sucrose metabolism, glutathione metabolism, ABC transporters, endocytosis, glycerolipid metabolism, and glycerophospholipid metabolism. The defense-related genes generally showed genotype-specific regulation and expression differences after R. solanacearum infection. In addition, genes related to auxin and ABA were dramatically up-regulated in the HR genotype. The contents of auxin and ABA in the MR genotype were significantly higher than those in the HR genotype after R. solanacearum infection, providing insight into the defense mechanisms of tobacco. Altogether, these results clarify the physiological and transcriptional regulation of R. solanacearum resistance infection in tobacco, and improve our understanding of the molecular mechanism underlying the plant-pathogen interaction. [ABSTRACT FROM AUTHOR]

Purpose: To develop a novel method based on feature selection, combining convolutional neural network (CNN) and ensemble learning (EL), to achieve high accuracy and efficiency of glioma detection and segmentation using multiparametric MRIs. Methods: We proposed an evolutionary feature selection‐based hybrid approach for glioma detection and segmentation on 4 MR sequences (T2‐FLAIR, T1, T1Gd, and T2). First, we trained a lightweight CNN to detect glioma and mask the suspected region to process large batch of MRI images. Second, we employed a differential evolution algorithm to search a feature space, which composed of 416‐dimensions radiomics features extracted from four sequences of MRIs and 128‐dimensions high‐order features extracted by the CNN, to generate an optimal feature combination for pixel classification. Finally, we trained an EL classifier using the optimal feature combination to segment whole tumor (WT) and its subregions including non‐enhancing tumor (NET), peritumoral edema (ED), and enhancing tumor (ET) in the suspected region. Experiments were carried out on 300 glioma patients from the BraTS2019 dataset using fivefold cross‐validation, and the model was independently validated using the rest 35 patients from the same database. Results: The approach achieved a detection accuracy of 98.8% using four MRIs. The Dice coefficients (and standard deviations) were 0.852 ± 0.057, 0.844 ± 0.046, and 0.799 ± 0.053 for segmentation of WT (NET+ET+ED), tumor core (NET+ET), and ET, respectively. The sensitivities and specificities were 0.873 ± 0.074, 0.863 ± 0.072, and 0.852 ± 0.082; the specificities were 0.994 ± 0.005, 0.994 ± 0.005, and 0.995 ± 0.004 for the WT, tumor core, and ET, respectively. The performances and calculation times were compared with the state‐of‐the‐art approaches, our approach yielded a better overall performance with average processing time of 139.5 s per set of four sequence MRIs. Conclusions: We demonstrated a robust and computational cost‐effective hybrid segmentation approach for glioma and its subregions on multi‐sequence MR images. The proposed approach can be used for automated target delineation for glioma patients. [ABSTRACT FROM AUTHOR]

Purpose: To develop a novel method based on feature selection, combining convolutional neural network (CNN) and ensemble learning (EL), to achieve high accuracy and efficiency of glioma detection and segmentation using multiparametric MRIs. Methods: We proposed an evolutionary feature selection‐based hybrid approach for glioma detection and segmentation on 4 MR sequences (T2‐FLAIR, T1, T1Gd, and T2). First, we trained a lightweight CNN to detect glioma and mask the suspected region to process large batch of MRI images. Second, we employed a differential evolution algorithm to search a feature space, which composed of 416‐dimension radiomic features extracted from four sequences of MRIs and 128‐dimension high‐order features extracted by the CNN, to generate an optimal feature combination for pixel classification. Finally, we trained an EL classifier using the optimal feature combination to segment whole tumor (WT) and its subregions including nonenhancing tumor (NET), peritumoral edema (ED), and enhancing tumor (ET) in the suspected region. Experiments were carried out on 300 glioma patients from the BraTS2019 dataset using fivefold cross validation, the model was independently validated using the rest 35 patients from the same database. Results: The approach achieved a detection accuracy of 98.8% using four MRIs. The Dice coefficients (and standard deviations) were 0.852 ± 0.057, 0.844 ± 0.046, and 0.799 ± 0.053 for segmentation of WT (NET+ET+ED), tumor core (NET+ET), and ET, respectively. The sensitivities and specificities were 0.873 ± 0.074, 0.863 ± 0.072, and 0.852 ± 0.082; the specificities were 0.994 ± 0.005, 0.994 ± 0.005, and 0.995 ± 0.004 for the WT, tumor core, and ET, respectively. The performances and calculation times were compared with the state‐of‐the‐art approaches, our approach yielded a better overall performance with average processing time of 139.5 s per set of four sequence MRIs. Conclusions: We demonstrated a robust and computational cost‐effective hybrid segmentation approach for glioma and its subregions on multi‐sequence MR images. The proposed approach can be used for automated target delineation for glioma patients. [ABSTRACT FROM AUTHOR]

Automatic differentiation of benign and malignant breast lesions on multiple DCE-MRI series is a challenging task. The performance of the Convolutional Neural Networks (CNNs) based methods is severely affected when the number of DCE-MRI series is inadequate or inconsistent. This paper is motivated by the need of capturing spatial-temporal features from consistent DCE-MRI series for most CNN-based classification methods, and aims at designing an interpolation network that can enlarge the DCE-MRI series. Therefore, our method achieves the objective of breast lesion classification for inconsistent DCE-MRI series with a two-stage method, i.e., DCE-MRI interpolation and classification. Inspired by the learning-based data augmentation, we propose a variable-length multiple DCE-MRI series interpolation method using learned transformations to enlarge DCE-MRI series. Specifically, the forward and backward contrast transformations are learned to estimate the kinetic and spatial variation between different DCE-MRI series. Then, an adaptive warping method is proposed to generate multiple interpolated DCE-MRI series. Finally, the spatial-temporal features are extracted by a new two-stream network from the interpolated DCE-MRI and they are further used to classify breast lesions. We justify the proposed method through extensive experiments using 1223 DCE-MRI slices. Comparing to other methods, it achieves better results on both single series interpolation and multiple series interpolation. The interpolated DCE-MRI greatly improves the classification accuracy nearly by 5% and the best accuracy is 81.9%. [ABSTRACT FROM AUTHOR]

Long‐term conventional tillage leads to soil compaction and formation of a plough layer, which affects soil physical properties, transport of water, and growth of crop roots toward deeper soil, resulting in soil degradation and a decline in crop productivity. Long‐term sub‐soiling tillage has proven to be an effective measure for remediating the plough layer and improving the structure and physical properties of the soil. In this study, we experimentally investigated the effects of long‐term sub‐soiling tillage in an arid region of Henan Province, China, along a deep soil (0–100 cm) profile over 8 years of sub‐soiling tillage at a depth of 30 cm and compared the results against conventional tillage at a depth of 15 cm. We measured soil pore distributions of macropores (> 1 mm in diameter), mesopores (0.16–1.0 mm), and total pores (> 0.16 mm) measured by X‐ray computed tomography (CT), soil total porosity (ϕ) and > 0.16 mm equivalent porosity measured by conventional methods, soil bulk density (ρs), soil organic carbon content (SOC), the proportion of macroaggregates (> 0.25 mm) (PMA), soil field moisture capacity (fc), available moisture content, and saturated hydraulic conductivity (Ksat). The results indicate that long‐term sub‐soiling tillage increased soil pore numbers at 0–35 cm depth (macropores, mesopores, and total pores), improved pore shape, and significantly increased porosity at 0–20 cm depth (macropores, mesopores, and total pores; p < 0.05) compared to conventional tillage. In addition, ϕ and > 0.16 mm equivalent porosity were increased by 10.4 and 87.1% at depths of 0–60 cm under sub‐soiling tillage, respectively. SOC (0–55 cm depth), fc (0–45 cm depth), available moisture content (0–40 cm depth), Ksat (0–40 cm depth), and PMA (0–50 cm depth) were increased by 16.7, 14.3, 23.8, 471.5, and 98.3%, respectively, and ρs (0–60 cm depth) was reduced by 8.6%. Observed correlations between SOC, soil pore parameters, and soil physical properties suggest that soil pore parameters and soil physical properties can be improved by increasing SOC. These correlations are stronger under sub‐soiling than that under conventional tillage. Therefore, sub‐soiling is an effective technique for improving soil pore characteristics and physical properties while preventing soil degradation. [ABSTRACT FROM AUTHOR]

A complex network is characterized by community structure, so it is of great theoretical and practical significance to discover hidden functions by detecting the community structure in complex networks. In this paper, a multiobjective brain storm optimization based on novelty search (MOBSO-NS) community detection method is proposed to solve the current issue of premature convergence caused by the loss of diversity in complex network community detection based on multiobjective optimization algorithm and improve the accuracy of community discovery. The proposed method designs a novel search strategy where novelty individuals are first constructed to improve the global search ability, thus avoiding falling into local optimal solutions; then, the objective space is divided into 3 clusters: elite cluster, ordinary cluster, and novel cluster, which are mapped to the decision space, and finally, the populations are disrupted and merged. In addition, the introduction of a restarting strategy is introduced to avoid stagnation by premature convergence. Experimental results show that the algorithm with good global searchability can find the Pareto optimal network community structure set with uniform distribution and high convergence and excavate the network community with higher quality. [ABSTRACT FROM AUTHOR]

Purpose: The automated clipping of surgical nasal endoscopic video is a challenging task because there are many hard frames that have indiscriminative visual features which lead to misclassification. Prior works mainly aim to classify these hard frames along with other frames, and it would seriously affect the performance of classification. Methods: We propose a hard frame detection method using a convolutional LSTM network (called HFD-ConvLSTM) to remove invalid video frames automatically. Firstly, a new separator based on the coarse-grained classifier is defined to remove the invalid frames. Meanwhile, the hard frames are detected via measuring the blurring score of a video frame. Then, the squeeze-and-excitation is used to select the informative spatial–temporal features of endoscopic videos and further classify the video frames with a fine-grained ConvLSTM learning from the reconstructed training set with hard frames. Results: We justify the proposed solution through extensive experiments using 12 surgical videos (duration:8501 s). The experiments are performed on both hard frame detection and video frame classification. Nearly 88.3% fuzzy frames can be detected and the classification accuracy is boosted to 95.2%. HFD-ConvLSTM achieves superior performance compared to other methods. Conclusion: HFD-ConvLSTM provides a new paradigm for video clipping by breaking the complex clipping problem into smaller, more easily managed 2-classification problems. Our investigation reveals that the hard framed detection based on blurring score calculation is effective for nasal endoscopic video clipping. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the adsorption capacity of aluminum-modified biochars for arsenate (As(V)) and the effect of the biochars on As(V) adsorption by acidic Ultisols. Materials and methods: Rice straw was collected from Yingtan, Jiangxi Province. Rice straw and the biochar derived from rice straw were both treated with aluminum (Al) to prepare two kinds of Al-modified biochars. An Ultisol was collected from Jinxian, Jiangxi Province, and an indoor incubation was used to prepare biochar-ameliorated soil. Zeta potential and Fourier transform infrared spectroscopy (FTIR) were used to characterize the effects of Al modification on surface charge and functional groups of the biochars. The batch method was used to investigate the adsorption and desorption of As(V) by the biochars and the soil. Arsenic in equilibrium solution was determined using hydride generation–atomic fluorescence spectrometry. Results and discussion: The yield of biochar derived from Al-treated rice straw was significantly greater than that of the biochar from untreated rice straw. The Al content of Al-treated biochar from rice straw was higher than that of biochar derived from Al-treated rice straw. Modification did not change the morphology of Al oxide on biochar. Zeta potential of the two modified biochars changed to a positive value compared with that of unmodified biochar, and more change in zeta potential was observed for the Al-treated biochar from rice straw than that of biochar derived from Al-treated rice straw. After As(V) was adsorbed by the two modified biochars, new absorption peaks were found in the FTIR spectra from the biochars at 892 cm−1 (biochar from Al-treated rice straw) and 884 cm−1 (Al-treated biochar from rice straw). The peaks suggested that a chemical bond was formed between As(V) and Al on biochars during As(V) adsorption. The two Al-modified biochars adsorbed much more As(V) than the unmodified biochar. The desorption of adsorbed As(V) in the modified biochars was very low, which indicated that specific adsorption was the dominant mechanism for As(V) adsorption. The addition of modified biochar promoted the adsorption of As(V) by the Ultisol. Moreover, Al-treated biochar from rice straw enhanced As(V) adsorption by the soil more than the biochar derived from Al-treated rice straw. Conclusions: Compared with the effect of adding unmodified biochar, adding either of the two Al-modified biochars significantly enhanced the adsorption of As(V) by the Ultisol. Therefore, Al-modified biochar can be used to immobilize As(V) in arsenic-contaminated soils, especially in acidic soils. [ABSTRACT FROM AUTHOR]

Pollen development is critical to the reproductive success of flowering plants, but how it is regulated is not well understood. Here, we isolated two allelic male-sterile mutants of OsMYB80 and investigated how OsMYB80 regulates male fertility in rice. OsMYB80 was barely expressed in tissues other than anthers, where it initiated the expression during meiosis, reached the peak at the tetrad-releasing stage and then quickly declined afterward. The osmyb80 mutants exhibited premature tapetum cell death, lack of Ubisch bodies, no exine and microspore degeneration. To understand how OsMYB80 regulates anther development, RNA-seq analysis was conducted to identify genes differentially regulated by OsMYB80 in rice anthers. In addition, DNA affinity purification sequencing (DAP-seq) analysis was performed to identify DNA fragments interacting with OsMYB80 in vitro. Overlap of the genes identified by RNA-seq and DAP-seq revealed 188 genes that were differentially regulated by OsMYB80 and also carried an OsMYB80-interacting DNA element in the promoter. Ten of these promoter elements were randomly selected for gel shift assay and yeast one-hybrid assay, and all showed OsMYB80 binding. The 10 promoters also showed OsMYB80-dependent induction when co-expressed in rice protoplast. Functional annotation of the 188 genes suggested that OsMYB80 regulates male fertility by directly targeting multiple biological processes. The identification of these genes significantly enriched the gene networks governing anther development and provided much new information for the understanding of pollen development and male fertility. [ABSTRACT FROM AUTHOR]

Purpose: To examine the effect of soil acidification on growth and nitrogen (N) uptake by maize in Ultisols. Materials and methods: A clay Ultisol derived from Quaternary red earth and a sandy Ultisol derived from tertiary red sandstone were used in this study. A pot experiment was conducted with maize growing in the two Ultisols acidified to different pH values. Urea with 15N abundance of 10.11% was used to investigate the distribution of N fertilizer between soil and plant. Total N content and 15N abundance in plant and soil samples were determined by elemental analysis-isotope mass spectrometry. Results and discussion: Critical soil pHs of 4.8 and 5.0 were observed for maize growing in the clay and sandy Ultisols, respectively. Below the critical soil pH, increasing soil pH significantly increased maize height and the yield of maize shoots and roots (both P < 0.05), but changes in soil pH showed no significant effect on maize growth above the critical soil pH in both Ultisols. Maize growing in the sandy Ultisol was more sensitive to changes in soil pH than in the clay Ultisol. Increase in the pH in both Ultisols also increased N accumulation in maize, the N derived from fertilizer in maize, physiological N use efficiency, and N use efficiency (NUE) by maize. Changes in soil pH had a greater effect on these parameters below the critical soil pH, compared to above. The change in soil pH had a greater effect on N accumulation in maize, the N derived from fertilizer in maize, and NUE in the sandy Ultisol than in the clay Ultisol. The NUE increased by 24.4% at pH 6.0, compared with pH 4.0 in the clay Ultisol, while the NUE at pH 5.0 was 4.8 times that at pH 4.0 in the sandy Ultisol. The increase in soil pH increased the ratio of N accumulation in maize/soil residue N and decreased the potential loss of fertilizer N from both Ultisols. Conclusions: Soil acidification inhibited maize growth, reduced N uptake by maize, and thus, decreased NUE. To maintain soil pH of acidic soils above the critical values for crops is of practical importance for sustainable food production in acidic soils. [ABSTRACT FROM AUTHOR]

Bone age assessment (BAA) is an essential topic in the clinical practice of evaluating the biological maturity of children. Because the manual method is time-consuming and prone to observer variability, it is attractive to develop computer-aided and automated methods for BAA. In this paper, we present a fully automatic BAA method. To eliminate noise in a raw X-ray image, we start with using U-Net to precisely segment hand mask image from a raw X-ray image. Even though U-Net can perform the segmentation with high precision, it needs a bigger annotated dataset. To alleviate the annotation burden, we propose to use deep active learning (AL) to select unlabeled data samples with sufficient information intentionally. These samples are given to Oracle for annotation. After that, they are then used for subsequential training. In the beginning, only 300 data are manually annotated and then the improved U-Net within the AL framework can robustly segment all the 12611 images in RSNA dataset. The AL segmentation model achieved a Dice score at 0.95 in the annotated testing set. To optimize the learning process, we employ six off-the-shell deep Convolutional Neural Networks (CNNs) with pretrained weights on ImageNet. We use them to extract features of preprocessed hand images with a transfer learning technique. In the end, a variety of ensemble regression algorithms are applied to perform BAA. Besides, we choose a specific CNN to extract features and explain why we select that CNN. Experimental results show that the proposed approach achieved discrepancy between manual and predicted bone age of about 6.96 and 7.35 months for male and female cohorts, respectively, on the RSNA dataset. These accuracies are comparable to state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Modulation of dystrophin pre-mRNA splicing is an attractive strategy to ameliorate the severe phenotype of Duchenne muscular dystrophy (DMD), although this requires a better understanding of the mechanism of splicing regulation. Aberrant splicing caused by gene mutations provides a good model to study splicing regulatory cis-elements and binding proteins. In this study, we identified skipping of in-frame exon 25 induced by a nonsense mutation (NM_004006.2:c.3340A > T;p.Lys1114*) in the DMD gene. Site-directed mutagenesis study in minigenes suggested that c.3340A > T converts an exonic splicing enhancer sequence (ESE) to a silencer element (ESS). Indeed, RNA pull-down and functional study provided evidence that c.3340A > T abolishes the binding of the splicing enhancer protein Tra2β and promotes interactions with the repressor proteins hnRNP A1, hnRNP A2, and hnRNP H. By carefully analyzing the sequence motif encompassing the mutation site, we concluded that the skipping of exon 25 was due to disruption of a Tra2β-dependent ESE and the creation of a new ESS associated with hnRNP A1 and hnRNP A2, which in turn increased the recruitment of hnRNP H to a nearby binding site. Finally, we demonstrated that c.3340A > T impairs the splicing of upstream intron 24 in a splicing minigene assay. In addition, we showed that the correct splicing of exon 25 is finely regulated by multiple splicing regulators that function in opposite directions by binding to closely located ESE and ESS. Our results clarify the detailed molecular mechanism of exon skipping induced by the nonsense mutation c.3340A > T and also provide information on exon 25 splicing. [ABSTRACT FROM AUTHOR]

The main purpose of this study conducted from August 2010 was to find biomass and productivity of epilithic algae and their relations to environmental factors and try to explore the restrictive factors affecting the growth of algae in the Gufu River, the one of the branches of Xiangxi River located in the Three Gorges Reservoir of the Yangtze River, Hubei Province, Central China. An improved method of in situ primary productivity measurement was utilized to estimate the primary production of the epilithic algae. It was shown that in rivers, lakes, and reservoirs, algae are the main primary producers and have a central role in the ecosystem. Chlorophyll a concentration and ash-free dry mass (AFDM) were estimated for epilithic algae of the Gufu River basin in Three Gorges Reservoir area. Environmental factors in the Gufu River ecosystem highlighted differences in periphyton chlorophyll a ranging from 1.49 mg m (origin) to 69.58 mg m (terminal point). The minimum and maximum gross primary productivity of epilithic algae were 96.12 and 1439.89 mg C m day, respectively. The mean net primary productivity was 290.24 mg C m day. The mean autotrophic index (AFDM:chlorophyll a) was 407.40. The net primary productivity, community respiration ratio (P/R ratio) ranged from 0.98 to 9.25 with a mean of 2.76, showed that autotrophic productivity was dominant in the river. Relationship between physicochemical characteristics and biomass was discussed through cluster and stepwise regression analysis which indicated that altitude, total nitrogen (TN), NO -N, and NH -N were significant environmental factors affecting the biomass of epilithic algae. However, a negative logarithmic relationship between altitude and the chlorophyll a of epilithic algae was high. The results also highlighted the importance of epilithic algae in maintaining the Gufu River basin ecosystems health. [ABSTRACT FROM AUTHOR]

In this paper, the flowability, compressive strength, hydration heat, porosity and calcium hydroxide content of cement-silica fume-fly ash-based ultra-high-strength concrete (UHSC) incorporating either nano-silica (SiO2) or nano-calcium carbonate (CaCO3) were investigated. Test results showed that the flowability of UHSC was reduced by incorporating nano-silica and nano-calcium carbonate. After incorporating nano-silica, the hydration heat flow of UHSC increased, and the hydration heat of UHSC increased at first but decreased later. For UHSC with nano-calcium carbonate, the hydration heat flow increased and the hydration heat decreased. With the increase of both nano-silica and nano-calcium carbonate content, the compressive strength of UHSC increased at first and then decreased. The porosity decreased at first and increased later with the increase of nano-silica and nano-calcium carbonate content. As regards the calcium hydroxide content, this decreased with the increase of nano-silica content, but increased with the increase of nano-calcium carbonate content. [ABSTRACT FROM AUTHOR]

Main conclusion: ZmCCD7/ZpCCD7 encodes a carotenoid cleavage dioxygenase that may mediate strigolactone biosynthesis highly responsive to phosphorus deficiency and undergoes negative selection over domestication from Zea ssp. parviglumis to Zea mays. Carotenoid cleavage dioxygenase 7 (CCD7) functions to suppress shoot branching by controlling strigolactone biosynthesis. However, little is known about CCD7 and its functions in maize and its ancestor ( Zea ssp. parviglumis) with numerous shoot branches. We found that ZmCCD7 and ZpCCD7 had the same coding sequence, indicating negative selection of the CCD7 gene over domestication from Zea ssp. parviglumis to Zea mays. CCD7 expression was highly responsive to phosphorus deficiency in both species, especially in the meristematic zone and the pericycle of the elongation zone of maize roots. Notably, the crown root had the strongest ZmCCD7 expression in the meristematic zone under phosphorus limitation. Transient expression of GFP tagged ZmCCD7/ZpCCD7 in maize protoplasts indicated their localization in the plastid. Further, ZmCCD7/ZpCCD7 efficiently catalyzed metabolism of six different linear and cyclic carotenoids in E. coli, and generated β-ionone by cleaving β-carotene at the 9,10 (9′,10′) position. Together with suppression of shoot branching in the max3 mutant by transformation of ZmCCD7/ ZpCCD7, our work suggested that ZmCCD7/ ZpCCD7 encodes a carotenoid cleavage dioxygenase mediating strigolactone biosynthesis in maize and its ancestor. [ABSTRACT FROM AUTHOR]

The red-emitting Ca9Eu(PO4)7 nanophosphors have been successfully synthesized by a facile hydrothermal method. X-ray diffraction (XRD), photoluminescence (PL) spectroscopy, GSAS structural refinement and scanning electron microscopy (SEM) were used to characterize the materials. Rietveld refinement of powder X-ray diffraction patterns of Ca9Eu(PO4)7 demonstrated that the structure of Ca9Eu(PO4)7 consisted of [PO4] tetrahedra. Ca/Eu atoms filled the space between the tetrahedral groups with Eu3+ ions distributed randomly on three individual Ca crystallographic sites. The major red emission peak located at 616 nm under 397 nm excitation attributed to the electric dipole transition 5D0→7F2 of Eu3+ was a parity forbidden f–f configuration transition, which would only occur when Eu3+ occupied lattice sites without inversion symmetry. Introducing Gd, La, and Y ions into samples made luminescence properties of Ca9Eu1−xLnx(PO4)7 (Ln = Gd, La, Y) phosphors decrease. pH value plays a key role in the formation of Ca9Eu(PO4)7 nanophosphor morphology. With increasing pH in the initial reaction solution, the morphology of samples changed from rod to sphere and their aspect ratio also decreased. Different morphologies have different photoluminescence properties, and samples prepared at pH 7 manifested better photoluminescence properties relative to other samples. This novel red-emitting phosphor could be applied in white light-emitting diodes. [ABSTRACT FROM AUTHOR]

Main conclusion: ZD958 was the most low-N-efficient line among five maize and two teosinte lines. Zea parviglumis and Zea diploperennis were insensitive to N limitation. Maize and teosinte genetically and evolutionarily diverged in gene regulation. GDH2, ASN2, and T4 were consistently down-regulated across seven lines. Maternal asymmetric inheritance and heterosis vigor made ZD958 low-N-efficient. Nitrogen (N) deficiency remains a serious limiting factor for maize production in many developing countries. It is particularly important to better understand how hybrid maize responds to N limitation. ZD958, a dominant high-yield hybrid in North China, was comparatively analyzed with four other maize and two teosinte lines at physiological and transcriptional levels. ZD958 was the most low-N-efficient line among five maize and two teosinte lines due to its largest biomass accumulation at a lowest N concentration under N limitation; while Zea parviglumis and Zea diploperennis had large root systems and were insensitive to N limitation. In anti-parallel with down-regulation of N metabolic genes in the ZD958 root, carbon allocation towards the root was enhanced for the significant increase in the root length. Variations in expression patterns of ten genes mediating N uptake, transport, and metabolism indicated large genetic and evolutionary divergence among seven lines under N limitation. Notably, GDH2, ASN2, and VAAT5 were consistently down-regulated under N limitation across these maize and teosinte lines, suggesting essential evolutionary conservation of gene regulation in response to N limitation and providing molecular markers for N nutritional diagnosis. Asymmetric inheritance, mostly from its maternal donor Z58, and heterosis vigor made ZD958 low-N-efficient at the seedling stage. The superior traits in crown roots in ZD958 may be derived from its paternal donor Chang7-2. Thus, Z58, Chang7-2, and two wild maize lines ( Z. parviglumis and Z. diploperennis) provide valuable germplasms for N-efficient and large-root maize breeding. [ABSTRACT FROM AUTHOR]

A group G is said to be a B(n,k) group if for any n-element subset A of G, ∣A2∣≤k. In this paper, characterizations of B(5,16) groups and B(5,17) groups are given. [ABSTRACT FROM PUBLISHER]

High throughput pre-treatment imaging features may predict radiation treatment outcome and guide individualized treatment in radiotherapy (RT). Given relatively small patient sample (as compared with high dimensional imaging features), identifying potential prognostic imaging biomarkers is typically challenging. We aimed to develop robust machine learning methods for patient survival prediction using pre-treatment quantitative CT image features for a subgroup of head-and-neck cancer patients. Three neural network models, including back propagation (BP), Genetic Algorithm-Back Propagation (GA-BP), and Probabilistic Genetic Algorithm-Back Propagation (PGA-BP) neural networks were trained to simulate association between patient survival and radiomics data in radiotherapy. To evaluate the models, a subgroup of 59 head-and-neck patients with primary cancers in oral tongue area were utilized. Quantitative image features were extracted from planning CT images, a novel t-Distributed Stochastic Neighbor Embedding (t-SNE) method was used to remove irrelevant and redundant image features before fed into the network models. 80% patients were used to train the models, and remaining 20% were used for evaluation. Of the three supervised machine-learning methods studied, PGA-BP yielded the best predictive performance. The reported actual patient survival interval of 30.5 ± 21.3 months, the predicted survival times were 47.3 ± 38.8, 38.5 ± 13.5 and 29.9 ± 15.3 months using the traditional PCA. Combining with the novel t-SNE dimensionality reduction algorithm, the predicted survival intervals are 35.8 ± 15.2, 32.3 ± 13.1 and 31.6 ± 15.8 months for the BP, GA-BP and PGA-BP neural network models, respectively. The work demonstrated that the proposed probabilistic genetic algorithm optimized neural network models, integrating with the t-SNE dimensionality reduction algorithm, achieved accurate prediction of patient survival. The proposed PGA-BP neural network, integrating with an advanced dimensionality reduction algorithm (t-SNE), improved patient survival prediction accuracy using pre-treatment quantitative CT image features of head-and-neck cancer patients. [ABSTRACT FROM AUTHOR]

Lithium–sulfur batteries (LSBs) are regarded as one of the most promising energy-recycling storage systems due to their high energy density (up to 2600 Wh kg−1), high theoretical specific capacity (as much as 1672 mAh g−1), environmental friendliness, and low cost. Originating from the complicated redox of lithium polysulfide intermediates, Li–S batteries suffer from several problems, restricting their application and commercialization. Such problems include the shuttle effect of polysulfides (Li2Sx (2 < x ≤ 8)), low electronic conductivity of S/Li2S/Li2S2, and large volumetric expansion of S upon lithiation. In this study, a lotus root-like nitrogen-doped carbon nanofiber (NCNF) structure, assembled with vanadium nitride (VN) catalysts, was fabricated as a 3D freestanding current collector for high performance LSBs. The lotus root-like NCNF structure, which had a multichannel porous nanostructure, was able to provide excellent (ionically/electronically) conductive networks, which promoted ion transport and physical confinement of lithium polysulfides. Further, the structure provided good electrolyte penetration, thereby enhancing the interface contact with active S. VN, with its narrow resolved band gap, showed high electrical conductivity, high catalytic effect and polar chemical adsorption of lithium polysulfides, which is ideal for accelerating the reversible redox kinetics of intermediate polysulfides to improve the utilization of S. Tests showed that the VN-decorated multichannel porous carbon nanofiber structure retained a high specific capacity of 1325 mAh g−1 after 100 cycles at 0.1 C, with a low capacity decay of 0.05% per cycle, and demonstrated excellent rate capability. [ABSTRACT FROM AUTHOR]

In this work, black phosphorus quantum dots (BPQDs) were decorated on hexagonal ZnIn2S4 flower-like microspheres to form zero-dimensional/two-dimensional (0D/2D) structures. Interface interactions between the BPQDs and ZnIn2S4 resulted in optimum effective charge transfer, thereby improving the photocatalytic performance of the material. Thus, the 0.2% BPQD–ZnIn2S4 sample showed 30% higher H2 evolution rates compared to pure ZnIn2S4. This study provides a simple route for the synthesis of photocatalysts. The results obtained herein can pave the way for designing effective catalysts for solar-to-chemical energy conversion and feasible approaches to obtain cheap, clean, and efficient photocatalysts. [ABSTRACT FROM AUTHOR]