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With the development of service integration technology, online learning platforms have gathered a large number of learning resources, causing learners to get lost in a variety of course information and it is difficult to obtain learning resources that match their own needs. The proposal of personalized learning gives the problem a direction to solve. However, current personalized learning resource recommendation services facing problems such as excessive candidate resources, sparse history and cold starts. In addition, the learning resources provided also show problems of "difficult or easy, uneven quality". For this article researches the personalized learning recommendation model of learner-learning resource matching. The main content includes three parts: First, build a demand model based on learner registration information, learning behavior and other data. Second, analyze the access behavior of learning resources and assess their quality. Third, calculate the matching degree between learners and learning resources based on the demand model and the quality information of the learning resources, and recommend them.

Background MRI-based radiomics has been used to diagnose breast lesions; however, little research combining quantitative pharmacokinetic parameters of dynamic contrast-enhanced MRI (DCE-MRI) and diffusion kurtosis imaging (DKI) exists. Purpose To develop and validate a multimodal MRI-based radiomics model for the differential diagnosis of benign and malignant breast lesions and analyze the discriminative abilities of different MR sequences. Study type Retrospective. Population In all, 207 female patients with 207 histopathology-confirmed breast lesions (95 benign and 112 malignant) were included in the study. Then 159 patients were assigned to the training group, and 48 patients comprised the validation group. Field strength/sequence T2 -weighted (T2 W), T1 -weighted (T1 W), diffusion-weighted MR imaging (b-values = 0, 500, 800, and 2000 seconds/mm2 ) and quantitative DCE-MRI were performed on a 3.0T MR scanner. Assessment Radiomics features were extracted from T2 WI, T1 WI, DKI, apparent diffusion coefficient (ADC) maps, and DCE pharmacokinetic parameter maps in the training set. Models based on each sequence or combinations of sequences were built using a support vector machine (SVM) classifier and used to differentiate benign and malignant breast lesions in the validation set. Statistical tests Optimal feature selection was performed by Spearman's rank correlation coefficients and the least absolute shrinkage and selection operator algorithm (LASSO). Receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of the radiomics models in the validation set. Results The area under the ROC curve (AUC) of the optimal radiomics model, including T2 WI, DKI, and quantitative DCE-MRI parameter maps was 0.921, with an accuracy of 0.833. The AUCs of the models based on T1 WI, T2 WI, ADC map, DKI, and DCE pharmacokinetic parameter maps were 0.730, 0.791, 0.770, 0.788, and 0.836, respectively. Data conclusion The model based on radiomics features from T2 WI, DKI, and quantitative DCE pharmacokinetic parameter maps has a high discriminatory ability for benign and malignant breast lesions. Level of evidence 3 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:596-607.

A novel method for semantic action recognition through learning a pose lexicon is presented in this paper. A pose lexicon comprises a set of semantic poses, a set of visual poses, and a probabilistic mapping between the visual and semantic poses. This paper assumes that both the visual poses and mapping are hidden and proposes a method to simultaneously learn a visual pose model that estimates the likelihood of an observed video frame being generated from hidden visual poses, and a pose lexicon model establishes the probabilistic mapping between the hidden visual poses and the semantic poses parsed from textual instructions. Specifically, the proposed method consists of two-level hidden Markov models. One level represents the alignment between the visual poses and semantic poses. The other level represents a visual pose sequence, and each visual pose is modeled as a Gaussian mixture. An expectation-maximization algorithm is developed to train a pose lexicon. With the learned lexicon, action classification is formulated as a problem of finding the maximum posterior probability of a given sequence of video frames that follows a given sequence of semantic poses, constrained by the most likely visual pose and the alignment sequences. The proposed method was evaluated on MSRC-12, WorkoutSU-10, WorkoutUOW-18, Combined-15, Combined-17, and Combined-50 action datasets using cross-subject, cross-dataset, zero-shot, and seen/unseen protocols.

In view of the difficulty in detecting and managing illegal buildings, this article uses high-definition cameras mounted on a tower to regularly capture images to construct the “Suspected Illegal Building Information” dataset. Due to the high distance of the tower from the ground, most of the collected data images contian many small targets, and there are many types of target objects to be detected and target size is quite differences, resulting in low detection accuracy. Based on the above problems, this paper makes the following improvements based on the Yolov3 algorithm: (1) using K-menas re-clustering anchor boxes, (2) introducing CIoU optimization loss function, (3) adding 104×104 scale for feature extraction, (4) using Soft-NMS instead traditional NMS algorithm, in addition, the data set is enhanced and optimized. The experimental results show that, compared with the original Yolov3, the improved algorithm has improved the detection accuracy of this experimental data set by 19.1%, which can well meet the needs of the project.

This paper proposes a novel method to utilize three source features for video captioning. It fuses global video features with local object and regional features to model the relationships among objects and their motions and applies object tags instead of visual features to guide the generation of descriptions. Specifically, Multi-feature is firstly extracted by pretrained models and treated as separate inputs alongside video frames. Secondly, an object awareness attention block is designed to fuse the different features information and to learn a joint video representation which has both visual and linguistic semantics. Experiments on MSVD and MSR-VTT datasets have shown the effectiveness of the proposed method, and the ablation studies have verified the contribution of each component.

To assess the diagnostic accuracy of histogram analysis of diffusion kurtosis imaging (DKI) in breast lesions.Our institutional review board approved this retrospective study. Seventy-two breast lesions (30 benign and 42 malignant) in 71 patients were histopathologically confirmed. All breast lesions were evaluated by 3.0-T diffusion-weighted imaging (DWI) with 4 b-values of 0, 500, 800, and 2000s/mmFor the malignant breast lesions, the mean, median, and 10th/25th/75th percentile values of ADC and D were significantly lower, while the skewness of ADC and D were significantly higher in comparison of the benign lesions(all p 0.05). The malignant lesions had significantly higher mean, median, and 10th/25th/75th/90th percentile K values than did the benign lesions (all p 0.05). Within each set of parameters, the 10th percentile ADC (Az = 0.752) and D, (Az = 0.834) coupled with the 75th percentile K (Az = 0.904) were the best metrics for differentiating benign from malignant breast lesions. After comparing the parameters in pairs, the Az for the 75th percentile K was significantly higher than that for the 10th percentile ADC (p = 0.0321) in differentiating benign from malignant breast lesions. When comparing the combination of the 75th percentile K and the 10th percentile D (Az = 0.937) with the 10th percentile D, 75th percentile K and the mean K, a significantly higher Az was observed for the combination than that for the 10th percentile D and the mean K (p = 0.0097 and p = 0.0431, respectively). The diagnostic sensitivity and specificity of the combination of the 75th percentile K and the 10th percentile D were 85.71% and 93.33%, respectively.Histogram analysis of DKI can accurately reflect the histologic characteristics and heterogeneity and is a reliable method for diagnosing breast lesions.

Antitumor function of the immune system has been harnessed to eradicate tumor cells as cancer therapy. Therapeutic cancer vaccines aim to help immune cells recognize tumor cells, which are difficult to target owing to immune escape. Many attempts at vaccine designs have been conducted throughout the last decades. In addition, as the advanced understanding of immunosuppressive mechanisms mediated by tumor cells, combining cancer vaccines with other immune therapies seems to be more efficient for cancer treatment. Acute myeloid leukemia (AML) is the most common acute leukemia in adults with poor prognosis. Evidence has shown T-cell-mediated immune responses in AML, which encourages the utility of immune therapies in AML. This review discusses cancer vaccines in AML from vaccine design as well as recent progress in vaccination combination with other immune therapies.Lay abstract Acute myeloid leukemia (AML) is the most common acute leukemia in adults with poor prognosis. Evidence has shown that the immune system can recognize and eradicate AML cells. Immunotherapy, which aims at enhancing this antitumor function, emerges as a powerful cancer therapy. Cancer vaccine, one of the immunotherapies, helps the immune system recognize tumor cells. The treatment strategy has been explored in AML patients throughout the last decades. This review was a brief introduction of the development and design principle of cancer vaccine in AML. Moreover, we also demonstrated recent progress in vaccination combination with other immune therapies.

Background To investigate the trends and correlation between antibacterial consumption and carbapenem resistance in Gram-negative bacteria from 2012 to 2019 in a tertiary-care teaching hospital in southern China. Methods This retrospective study included data from hospital-wide inpatients collected between January 2012 and December 2019. Data on antibacterial consumption were expressed as defined daily doses (DDDs)/1000 patient-days. Antibacterials were classified according to the Anatomical Therapeutic Chemical (ATC) classification system. The trends in antimicrobial usage and resistance were analyzed by linear regression, while Pearson correlation analysis was used for assessing correlations. Results An increasing trend in the annual consumption of tetracyclines, β-lactam/β-lactamase inhibitor (BL/BLI) combinations, and carbapenems was observed (P Acinetobacter baumannii (A. baumannii) significantly increased (P A. baumannii (P Escherichia coli (E. coli; P A. baumannii to carbapenems was positively correlated with cephalosporin/β-lactamase inhibitor (C/BLI) combinations (P P Burkholderia cepacia (B. cepacia) to carbapenems (P P P Enterobacter cloacae (E. cloacae) to carbapenems. Conclusion These results revealed significant correlations between consumption of antibiotics and carbapenem resistance rates in Gram-negative bacteria. Implementing proper management strategies and reducing the unreasonable use of antibacterial drugs may be an effective measure to reduce the spread of carbapenem-resistant Gram-negative bacteria (CRGN), which should be confirmed by further studies.

Tumor vaccines aim to expand tumor-specific T cells and reactivate existing tumor-specific T cells that are in a dormant or unresponsive state. As such, there is growing interest in improving the durable anti-tumor activity of tumor vaccines. Failure of vaccine-activated T cells to protect against tumors is thought to be the result of the immune escape mechanisms of tumor cells and the intricate immunosuppressive tumor microenvironment. In this review, we discuss how tumor cells and the tumor microenvironment influence the effects of tumor infiltrating lymphocytes and summarize how to improve the efficacy of tumor vaccines by improving the design of current tumor vaccines and combining tumor vaccines with other therapies, such as metabolic therapy, immune checkpoint blockade immunotherapy and epigenetic therapy.

The computing-based cloud storage model realizes the security and reliable storage of massive image data. Nowadays, how to quickly obtain high-quality image data is the focus of attention in the research field. This paper discusses the cloud storage model, proposing a combined data caching strategy based on HBase and Redis technology. Besides, the model improves the technical defects of Memcached, setting key indexes which is stored in Hbase, having achieved the mapping between index and DataNode. The results of experiment show that the proposed optimization model can improve the ability of rapid acquisition and analysis of image data and achieve a higher retrieval efficiency about image data.

Classification problem is one of the essential tasks in data mining. Traditional classification strategies are predominantly via cost-insensitive equilibrium data. They tend to be concentrated on the overall accuracy of a model, and such classifiers are improper for unbalanced sample data. Hence, optimizing unbalanced samples to improve classifier performance is an issue worthy of discussion. Based on the information-rich minority samples that are difficult to learn, Majority Weighted Minority Oversampling Technique (MWMOTE) uses the clustering method to generate synthetic samples from the weighted information samples. However, the accuracy of the clustering should be optimized. To this end, a method called NC_Link_MWMOTE is presented for efficiently handling imbalanced learning problems. We propose a solution by using NC_Link-based hierarchical clustering method to synthesize different samples from a small number of samples, thus optimizing the clustering effect. NC_Link_MWMOTE was evaluated on six different levels of equilibrium data sets. The simulation results show that our method is effective and outperforms competitive baseline method in terms of various assessment metrics, such as Fl-score and Area Under Curve (AUC).

Siglec-15 (S15) is another important mechanism of tumor immune escape besides the PD-L1/PD-1 pathway and represents a new kind of immune checkpoint inhibitor. However, the associations of tumor Siglec-15 expression with clinicopathological characteristics and outcomes of non-small cell lung cancer (NSCLC), and tumor-infiltrating lymphocytes (TILs) in a tumor microenvironment (TME) have so far been unclear. A total of 324 NSCLC surgical samples on tumor microarray were used in this study for investigating the association of S15 expression with clinicopathological characteristics and overall survival (OS) as well as correlation with TILs using multiplex immunofluorescence staining and PD-L1. Results showed that the expression of S15 in adenocarcinoma was significantly higher than that in squamous cell carcinoma. S15 expression was positively correlated with CD8+ T cell density in the stroma. The expression rate of PD-L1 in lung squamous cell carcinoma was higher than that in lung adenocarcinoma. S15 expression was not associated with the prognosis of early NSCLC. The pathological mechanism of the co-expression of S15 and PD-L1 in resectable NSCLC remains to be further studied.

Foreigners make various grammatical errors when learning Chinese due to the negative transfer of their mother tongue, learning strategies, etc. At present, the research on grammatical errors mainly focuses on a certain word or a certain kind of errors, resulting in a lack of comprehensive understanding. In this paper, a statistical analysis on large-scale data sets of grammatical errors made by second language learners is conducted, including words with grammatical errors and their quantities. The statistical analysis gives people a more comprehensive understanding of grammatical errors and have certain guiding significance for teaching Chinese as a second language (TCSL). Because of the large proportion of grammatical errors of “的[de](of)”, the usages of “的[de](of)” are integrated into automatic recognition of Chinese grammatical errors. Experimental results show that the performance is overall improved.

Lijuan Zhou,1,* Yanqiu Gao,2 Wei Cao,1 Jia Liu,1 Hongya Guan,1 Hua Zhang,2 Yun Shi,3 Wenying Lv,4 Long Cheng5,* 1Translational Medicine Center, 2Department of Respiratory Medicine, Affiliated Zhengzhou Central Hospital of Zhengzhou University, Zhengzhou, 3Department of Gynecology, Dongzhimen Hospital, Beijing University of Chinese Medicine, 4Chaoyangmen Community Health Service Center, 5Key Laboratory of Bioactive Substances and Resources Utilization of Chinese Herbal Medicine, Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China *These authors contributed equally to this work Objectives: Teicoplanin, an antibiotic, has poor clinical efficacy when using the current drug label’s recommended regimen, which is approved by the China Food and Drug Administration. This study explores the appropriate loading and maintenance doses of teicoplanin and evaluates the therapeutic target of teicoplanin trough concentration (minimum concentration [Cmin]).Subjects and methods: All patients treated with teicoplanin from February 2015 to August 2016 at Zhengzhou Central Hospital were screened for enrollment. A total of 113 subjects were included and then divided into four groups: A (received three to six doses at a loading dose of 400mg at 12-hour intervals, followed by maintenance dosing of 400 mg/day), B (received three doses at a loading dose of 400 mg at 12-hour intervals, followed by maintenance dosing of 400mg/day), C (received two doses at a loading dose of 400 mg at 12-hour intervals, followed by maintenance dosing of 200 mg/day), and D (received one to three doses at a loading dose of 400 mg at 12-hour intervals, followed by maintenance dosing of 200 mg/day). Cmin values of teicoplanin were detected with high-performance liquid chromatography on day 4, 30 minutes before maintenance-dose administration. Teicoplanin Cmin, efficacy, and safety were compared among the four groups.Results: Mean Cmin differed significantly among the four groups (A, 18.11±6.37 mg/L; B, 15.91±4.94 mg/L; C, 17.06±5.66 mg/L; D, 11.97±3.76 mg/L) (P

In the field of software engineering, software defect prediction is the hotspot of the researches which can effectively guarantee the quality during software development. However, the problem of class imbalanced datasets will affect the accuracy of overall classification of software defect prediction, which is the key issue to be solved urgently today. In order to better solve this problem, this paper proposes a model named ASRA which combines attribute selection, sampling technologies and ensemble algorithm. The model adopts the Chi square test of attribute selection and then utilizes the combined sampling technique which includes SMOTE over-sampling and under-sampling to remove the redundant attributes and make the datasets balance. Afterwards, the model ASRA is eventually established by ensemble algorithm named Adaboost with basic classifier J48 decision tree. The data used in the experiments comes from UCI datasets. It can draw the conclusion that the effect of software defect prediction classification which using this model is improved and better than before by comparing the precision P, F-measure and AUC values from the results of the experiments.

This paper proposes a semantic representation, pose lexicon , for action recognition. The lexicon is composed of a set of semantic poses, a set of visual poses and a probabilistic mapping between the visual and semantic poses. Specially, an action can be represented by a sequence of semantic poses extracted from an associated textual instruction. Visual frames of the action are considered to be generated from a sequence of hidden visual poses. To learn the lexicon, a visual pose model is learned from training samples by a Gaussian Mixture model to characterize the likelihood of an observed visual frame being generated by a visual pose. A pose lexicon model is also learned by an extended hidden Markov alignment model to encode the probabilistic mapping between hidden visual poses and semantic poses sequences. With the lexicon, action classification is formulated as a problem of finding the maximum posterior probability of a given sequence of visual frames that fits to a given sequence of semantic poses through the most likely visual pose and alignment sequences. The efficacy of the proposed method was evaluated on MSRC-12, WorkoutSU-10, WorkoutUOW-18, Combined-15 and Combined-17 action datasets using cross-subject, cross-dataset and zero-shot protocols.

This study was to investigate the longitudinal change of post-operative anxiety and depression, their related risk factors and prognostic value in colorectal cancer (CRC) patients after resection.Totally, 302 CRC patients who underwent resection were consecutively recruited. Their anxiety and depression were assessed by hospital anxiety and depression scale (HADS) at Month 0 (M0) and then every 3 months till Month 36 (M36).Within 36-month follow-up period, HADS-A score (from 8.3 ± 3.3 at M0 to 8.8 ± 3.4 at M36, P = 0.179) exhibited an upward trend with time but without statistical significance; while anxiety rate (from 46.4% at M0 to 52.6% at M36, P = 0.019) was increased steadily with time longitudinally. Meanwhile, both HADS-D score (from 7.4±3.0 at M0 to 9.2±3.5 at M36, P 0.001) and depression rate (from 33.8% at M0 to 57.9% at M36, P 0.001) were elevated greatly with time longitudinally. Furthermore, multivariate logistic regression revealed that female and tumor size (≥5 cm) were common independent risk factors for baseline/1-year/2-year/3-year anxiety (all P 0.05); meanwhile, female, marry status (single/divorced/widowed vs. married) and advanced TNM stage were common independent risk factors for baseline/1-year/2-year/3-year depression (all P 0.05). As for new-onset anxiety and depression, no independent factor associated with new-onset anxiety was observed; meanwhile, female and TNM stage were independent risk factors for new-onset depression (both P 0.05). Additionally, baseline/1-year anxiety and baseline/1-year/2-year/3-year depression were associated with lower accumulating OS (all P 0.05).Post-operative anxiety and depression are highly prevalent and continuously progress, which also correlate with worse survival prognosis in CRC patients.

Corona current is the key parameter to characterize the corona discharge. Researches on the spectrum characteristics of corona current and the relationships between corona effects are of great significance on optimization design of Ultra High-Voltage Direct Current (UHVDC) transmission line. Basing on the corona current experimental data measured on an actual UHVDC test line, the spectrum characteristics and variation regularity of corona current within the spectrum band of 0.15–4 MHz were studied, and the correlation between energy spectrum of corona current and Radio Interference (RI) under the same conditions was also analyzed in this paper. It was found that variation regularity of logarithmic energy spectrum of corona current was remarkably similar to that of RI within 0.15–4 MHz. Basing on the conclusions above, a formula for estimating the logarithmic energy spectrum of corona current with the maximum nominal surface electric field strength of line was proposed, and the key parameters in the formula were established for a particular typed conductor which was 6-bundled. Those results not only can improve the theoretical understanding of the relationships between corona current and RI, but also provide a new method for estimating the spectrum characteristics of corona current.

Software defect prediction is one of the important ways to guarantee the quality of software systems. Combining various algorithms in machine learning to predict software defects has become a hot topic in the current study. The paper uses the datasets of MDP as the experimental research objects and takes ensemble learning as research focus to construct software defect prediction model. With experimenting five different types of ensemble algorithms and analyzing the features and procedures, this paper discusses the best ensemble algorithm which is Random Forest through experimental comparison. Then we utilize the SMOTE over-sampling and Resample methods to improve the quality of datasets to build a complete new software defect prediction model. Therefore, the results show that the model can improve defect classification performance effectively.

Objective The clinicopathological significance of Mucin5AC (MUC5AC) in lung adenocarcinoma with mucin production is still unclear. This study aimed to explore MUC5AC expression in lung adenocarcinoma with mucin production and its correlation with histological subtypes, common driver mutations and its impact on prognosis. Methods MUC5AC and thyroid transcription factor 1 immunohistochemistry was performed on surgical samples from 90 patients with lung adenocarcinoma with mucin production. Common driver mutations including EGFR and KRAS mutations and ALK rearrangement were detected by established methods. Results MUC5AC was significantly associated with lymphovascular invasion (P = 0.023) and tumors with intra-cytoplasmic mucin (P Conclusions The clinicopathological features of non-pure mucinous subtype of lung adenocarcinoma with mucin production were distinct and should be distinguished from pure mucinous subtype. MUC5AC was associated with poor prognosis and could be a potential therapeutic target for this distinct type of lung adenocarcinoma that has few effective treatments.

Soybean (Glycine max L.) is an economically important crop providing a great source for vegetable oil and protein. Yield losses of soybean under current climate change keep increasing, despite the progressive increase in yield through breeding and management practices since the 1960s. Conventional breeding facilitated the development of high-quality soybeans with enhanced tolerance to severe environmental fluctuations such as drought, flooding, heat, and salinity. However, conventional approaches are laborious, time consuming, and looks inefficient to fulfill the increasing demands of the growing world population. The advances in marker-assisted and genomics-assisted breeding, sequencing technologies, and bioinformatics tools have enabled the soybean improvement at a faster pace. The rapidly accumulating genomic resources have enabled the development of molecular markers associated with many important quantitative trait loci, provided a clear picture of genomic variations in soybean germplasm, and identified key genes for genetic engineering. This knowledge is being utilized to facilitate the development of climate-smart soybeans. In this chapter, we discuss and summarize the advances in soybean improvement through conventional and genomics-assisted breeding, genetic engineering approaches, and available bioinformatics tools for soybean. This chapter also highlights soybean genetic resources, diversity analysis, association mapping, as well as recent strategies such as gene editing and nanotechnology application in soybean breeding programs. This information could facilitate the incorporation of climatic-smart traits in breeding for more stable soybean production with the changing climate.

Summary Objectives This study aimed to investigate the effect of reminiscence therapy-involved care (RTC) program on anxiety, depression, quality of life and survival in colorectal cancer (CRC) patients. Methods There were 210 post-resection CRC patients recruited and randomly received RTC (N = 105) or control care (CC) (N = 105) for 12 months. Their anxiety, depression and quality of life were assessed using Hospital Anxiety and Depression Scale (HADS) and European Organization for Research and Treatment of Cancer quality of life Questionnaire-Core 30 (QLQ-C30) at baseline (Month (M) 0), M3, M6 and M12. Patients were further followed up to 36 months, followed by the overall survival (OS) calculation. Results HADS-anxiety score was decreased at M6/M12 and anxiety patients’ percentage was reduced at M12 in RTC group compared with CC group; HADS-depression score was lower at M6/M12, while depression patients’ percentage was similar at each time point in RTC group compared with CC group; QLQ-C30 global health status score and QLQ-C30 functions score were increased at M6/M12, while QLQ-C30 symptoms score was similar at each time point in RTC group compared with CC group. Further sub-group analysis displayed that: in patients with age ≥65 years, patients with pathological grade G2 and patients with TNM stage Ⅱ-Ⅲ, RTC showed more remarkable effect. Additionally, OS showed a higher trend in RTC group compared with CC group, but without statistical difference. Conclusion RTC contributes to anxiety and depression alleviations as well as the quality of life improvement in CRC patients.

Ursolic acid (UA) is a natural pentacyclic triterpenoid compound existing in various traditional Chinese medicinal herbs, and it possesses diverse pharmacological actions and some undesirable adverse effects, even toxicological activities. Due to UA's low solubility and poor bioavailability, and its interaction with gut microbiota after oral administration, the pharmacokinetics of UA remain elusive, leading to obscurity in the pharmacokinetics-pharmacodynamics (PK-PD) profile and relationship for UA. Based on literatures from PubMed, Google Scholar, ResearchGate, Web of Science and Wiley Online Library, with keywords of "pharmacology", "toxicology", "pharmacokinetics", "PK-PD" and "ursolic acid", herein we systematically review the pharmacology and toxicity of UA, and rethink on its pharmacokinetics on the basis of PK-PD model, and seek to delineate the underlying mechanisms for the characteristics of pharmacology and toxicology of UA, and for the pharmacokinetic features of UA particularly from the organ tropism and the interactions between UA and gut microbiota, and lay a solid foundation for development of UA-derived therapeutic agents in clinical settings.

Background: High quality nursing care (HQNC) has been reported to effectively prevent psychological disorders and improve the quality of life (QoL) in patients with hepatocellular carcinoma (HCC) during the treatment. However, the exact effect of HQNC remains controversial. This systematic review will be aimed to assess the effectiveness of HQNC on psychological disorders and QoL in patients with HCC. Methods: Eligible prospective controlled clinical trials were searched from Google Scholar, Medline, Excerpt Medica Database (Embase), PubMed, Web of Science (WOS), Cochrane Library, China Scientific Journal Database (CSJD), China National Knowledge Infrastructure (CNKI), Chinese BioMedical Database (CBM) and Wanfang Database. Papers in English or Chinese published from January 2000 to July 2020 will be included without any restrictions. The clinical outcomes including psychological outcomes, QoL, and adverse events of HQNC in patients with HCC were systematically evaluated. Study selection and data extraction will be performed independently by two reviewers. Stata 14.0 and Review Manager 5.3 were used for data analysis. Methodological quality for each eligible study will be assessed by using Cochrane risk of bias tool. Subgroup and meta-regression analysis will be carried out depending on the availability of sufficient data. Results: The results of this systematic review will be published in a peer-reviewed journal. Conclusion: The results of this study may provide helpful evidence of HQNC on psychological effects and QoL in patients with HCC. INPLASY registration number: INPLASY202070096.

Currently, Deep Learning and Convolutional Neural Network (CNN) have been widely used in many fields and have generated very high value in these fields, especially in the field of image recognition. But there are some deficiencies in certain issues of image recognition. For example, CNN’s recognizing performance is not good at different angles of objects and overlapping objects. Also, CNN is sometimes very sensitive to slight perturbations, modifying one pixel of a recognized image may cause recognition errors. For these problems, the capsule network (CapsNet) proposed by Geoffrey Hinton can solve the problems of traditional convolutional networks. Shortly after CapsNet was proposed, the model structure was relatively simple, and many aspects could be explored for improvement. This paper will optimize CapsNet from two aspects: “optimization of routing mechanism” and “increase Dropout operation.” And carry out experiments and results analysis on these optimizations.

Although primary mucin-producing adenocarcinoma of the lung is uncommon, each subtype has distinct clinical, pathological, molecular, and prognostic characteristics. This study aimed to determine correlations between clinical and pathological features and genetic phenotypes with the prognosis. We immunohistochemically examined the protein levels of thyroid transcription factor 1 (TTF-1), Napsin A, and anaplastic lymphoma kinase (ALK) and genetically examined epidermal growth factor receptor (EGFR) and KRAS mutations in these mucin-producing tumors. A total of 75 cases of mucin-producing adenocarcinoma of the lung were examined. ALK protein positivity was 33.3 % (25/75), and primarily occurred in solid predominant with mucin production subtype (SA). KRAS mutations occurred in 22.7 % (17/75) of patients, predominantly in invasive mucinous adenocarcinoma (IMA). Positive TTF-1 and Napsin A expression was more common in SA, while they were both negative in IMA. The 1-, 3-, and 5-year progression-free survival rates of mucin-producing lung adenocarcinoma were 85, 64, and 38 %, respectively; the overall survival rates were 90, 67, and 50 %, respectively. Larger tumors, advanced stage, and lymph node metastasis were associated with poor prognosis. Mucinous minimally invasive adenocarcinoma (m-MIA) had the best prognosis, followed by IMA, SA, and acinar or papillary predominant adenocarcinoma with mucin production (A/P). KRAS mutations were an independent positive prognostic factor for postoperative progress.

Background c-ros oncogene 1 (ROS1) rearrangement presents one of the newest molecular targets in non-small cell lung cancer (NSCLC). ROS1 rearrangement is predominantly found in adenocarcinoma cases and is exclusive to other oncogenes, such as epidermal growth factor receptor (EGFR), Kirsten rat sarcoma viral oncogene homolog (KRAS), and anaplastic lymphoma kinase (ALK). The aim of this study was to investigate the clinicopathological characteristics and outcomes of ROS1-rearranged patients with lung adenocarcinoma without EGFR and KRAS mutations and ALK rearrangements. Methods Wild-type EGFR/KRAS/ALK patients with lung adenocarcinoma were selected from Beijing Chest Hospital. Specimens were conducted in tissue microarrays. ROS1 rearrangement was screened using fluorescence in situ hybridization. Results Our study included 127 patients with lung adenocarcinoma without EGFR and KRAS mutations and ALK rearrangements. ROS1 rearrangement was detected in five (3.9%) of the 127 patients. Compared with ROS1-negative patients, the positive rate of ROS1 in female patients was significantly higher than in male patients (9.8% vs. 0.0%, P = 0.009). There were no differences in age, smoking status, stage or histological subtype between ROS1-positive and ROS1-negative patients. No significant difference in survival was detected between the ROS1-positive and ROS1-negative patients. Conclusions ROS1 rearrangement is a rare subset of lung adenocarcinoma. In 127 patients with lung adenocarcinoma, 3.9% of ROS1-positive patients with wild-type EGFR/KRAS/ALK were found.

Primary pulmonary mucinous adenocarcinoma (PPMA) is one of the important subtypes of lung adenocarcinoma. Detection of anaplastic lymphoma receptor tyrosine kinase (ALK) rearrangements and of KRAS and epidermal growth factor receptor (EGFR) mutations will help in diagnosing and predicting treatment outcome. The aim of this study was to investigate the clinicopathological significance of ALK rearrangements, KRAS and EGFR mutations in PPMA. ALK expression was detected immunohistochemically. KRAS and EGFR mutations were determined by the amplification refractory mutation system. Seventy-three patients of PPMA were enrolled. ALK rearrangements were detected in 34.2 % of patients and were more frequent in upper/middle lobe, stage III-IV, lymphatic permeation-positive patients and non-smokers. ALK rearrangements were significantly increased in the solid tumor predominant with mucin production subtype, and in special tissue structures, including signet ring cells, cribriform, and micropapillary patterns. KRAS mutations were observed in 23.3 % of patients and were more prevalent in invasive mucinous adenocarcinoma and lower lobe tumors. Only one case of ALK rearrangements harbored KRAS mutation, and no cases manifested with the coexistence of ALK rearrangements and EGFR mutations. KRAS and EGFR co-mutation was detected in one case. PPMA patients with ALK rearrangements or KRAS mutation represent a unique subtype in NSCLC. The results provide basis data for target therapy screening of PPMA patients.

Climate change has increased the occurrence of extreme weather patterns globally, causing significant reductions in crop production, and hence threatening food security. In order to meet the food demand of the growing world population, a faster rate of genetic gains leading to productivity enhancement for major crops is required. Grain legumes are an essential commodity in optimal human diets and animal feed because of their unique nutritional composition. Currently, limited water is a major constraint in grain legume production. Root system architecture (RSA) is an important developmental and agronomic trait, which plays vital roles in plant adaptation and productivity under water-limited environments. A deep and proliferative root system helps extract sufficient water and nutrients under these stress conditions. The integrated genetics and genomics approach to dissect molecular processes from genome to phenome is key to achieve increased water capture and use efficiency through developing better root systems. Success in crop improvement under drought depends on discovery and utilization of genetic variations existing in the germplasm. In this review, we summarize current progress in the genetic diversity in major legume crops, quantitative trait loci (QTLs) associated with RSA, and the importance and applications of recent discoveries associated with the beneficial root traits towards better RSA for enhanced drought tolerance and yield.

In recent years, clustering has become a hotspot in the field of data mining, as one of the key technologies of getting data distribution and observing the characteristics of class. However, some clustering algorithms depend on the selection of initial clustering centers, and the clustering results easily fall into local optimal. To solve the above problem, the paper integrates differential evolution algorithm and adaptive opposition-based learning. The algorithm makes use of reverse factor to guide algorithm search space approaching to the global optimal solution in each generation. In this paper, the improved algorithm is combined with classical K-means algorithm. According to the result of the three sets of data from UCI data verification, it demonstrates that the improved clustering algorithm can not only cluster better and converge faster, but also effectively suppress the occurrence of prematurity.

Detailed imaging and analysis of skin structures are becoming increasingly important in modern healthcare and clinic diagnosis. Nanometer resolution imaging techniques such as SEM and AFM can cause harmful damage to the sample and cannot measure the whole skin structure from the very surface through epidermis, dermis to subcutaneous. Conventional optical microscopy has the highest imaging efficiency, flexibility in onsite applications and lowest cost in manufacturing and usage, but its image resolution is too low to be accepted for biomedical analysis. Infrared parameter indirect microscopic imaging (PIMI) uses an infrared laser as the light source due to its high transmission in skins. The polarization of optical wave through the skin sample was modulated while the variation of the optical field was observed at the imaging plane. The intensity variation curve of each pixel was fitted to extract the near field polarization parameters to form indirect images. During the through-skin light modulation and image retrieving process, the curve fitting removes the blurring scattering from neighboring pixels and keeps only the field variations related to local skin structures. By using the infrared PIMI, we can break the diffraction limit, bring the wide field optical image resolution to sub-200nm, in the meantime of taking advantage of high transmission of infrared waves in skin structures.

Clinical trials have shown that epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) did not improve the survival of patients with EGFR-mutated non-small cell lung cancer (NSCLC) because of the high crossover of treatments. Realistically, the role of EGFR-TKIs in NSCLC with mutated EGFR is not well known. We retrospectively analysed data from patients with recurrent or metastatic NSCLC. Clinical prognostic factors were identified by Cox proportional hazards modelling. Among 503 patients, the median overall survival (OS) for all of patients was 11.7 months. Cox analysis showed that PS 0–1, recurrent disease, EGFR mutations, or EGFR-TKI treatment were associated with improved OS. In patients with EGFR-activating mutations, Cox analysis showed that patients with adenocarcinoma, recurrent disease, or EGFR-TKI treatment had significantly longer survival. Patients with EGFR-activating mutations who received EGFR-TKI therapy had a median OS of 24.3 months, which was significantly longer than those who had not received EGFR-TKI therapy (10.8 months). Patients with wild-type EGFR had a median OS of 9.7 months and Cox analysis showed that PS score and disease type were independent predictors. EGFR-TKI therapy is an independently prognostic factor for NSCLC with mutated EGFR. A more effective therapy is needed for patients with wild-type EGFR.

Iridescent films composed of the chiral nematic liquid crystal phase of cellulose nanocrystals (CNC) have attracted significant interest due to their fascinating optical properties. However, the current fabrication method, i.e., solution casting with a subsequent evaporation process, has significant limitations and therefore hinders the application of CNC iridescent films. In the present study, we demonstrate, for the first time, that vacuum-assisted self-assembly (VASA) can be used to fabricate highly oriented, large area, smooth, and structurally homogeneous CNC iridescent films. It was found that a long ultrasonic pretreatment is necessary for obtaining CNC iridescent films via VASA. Furthermore, it was also found that the iridescent color of the CNC films can be tuned by the sonication time, suspension volume, and degree of vacuum. By combining CD spectroscopy, SEM, and WAXD techniques, the internal structure of CNC iridescent films prepared by VASA has been investigated in detail. Moreover, the origin of the ultrasonic pretreatment effect on the self-assembly behavior of CNCs is also discussed.

This article presents a method to predict and assess the interaction between a human body and a spring mattress. A three-layer artificial neural network model was developed to simulate and predict an indentation curve of human spine, characterized with the depth of lumbar lordosis and four inclination angles: cervicothoracic, thoracolumbar, lumbosacral and the back-hip (β). By comparing the spinal indentation curves described by the optimal evaluation parameters (depth of lumbar lordosis, cervicothoracic, thoracolumbar and lumbosacral), a better design of five-zone spring mattresses was obtained for individuals to have an effective support to the main part of the body. Using such approach, an operating process was further introduced, in which appropriate stiffness proportions were proposed to design mattress for the normal body types of Chinese young women. Finally, case studies were undertaken, which show that the method developed is feasible and practical.

EGFR and KRAS genes and ALK arrangements are three genetic drivers of lung adenocarcinoma. The aim of this study was to investigate the clinicopathologic characteristics of the ALK rearrangements in patients with primary lung adenocarcinoma and with identified EGFR or KRAS status.Patients with primary lung adenocarcinoma who had enough tissue for study were enrolled. EGFR and KRAS status were identified by DNA sequencing. ALK rearrangements were detected in a tissue microarray by using fluorescent in situ hybridization.Of 332 patients with primary lung adenocarcinoma, the frequency of the EGFR or the KRAS mutations, and the ALK rearrangements were 44.9% (149/332), 7.2% (24/332), and 9.6% (32/332), respectively. Only one (1/332, 0.3%) patient had the coexistence of the EGFR mutation (L858R at exon 21) and the ALK rearrangement. Compared with ALK-negative patients, ALK-positive patients were significantly younger (P0.001). The incidence of the ALK rearrangements was much higher in EGFR wild-type patients than in those with EGFR mutations (P0.001). There was no difference in the gender, smoking, disease stage, or histologic subtypes between the ALK-positive patients and the ALK-negative patients.The ALK rearrangements are independently associated with younger age and the EGFR wild type. The EGFR mutations and the ALK rearrangements are rarely coexistent. The ALK rearrangements and the KRAS mutations are mutually exclusive.

Acanthus ilicifolius alkaloid A (4-hydroxy-2(3H)benzoxazolone, HBOA) is a naturally occurring compound that has been separated from Acanthus ilicifolius. Previous studies have reported the beneficial effects of HBOA on HSC-T6 cells. This study was undertaken in order to synthesize HBOA and two of its derivatives, specifically, 4-acetoxy-2(3H)-benzoxazolone (AcO-BOA) and 3-acetyl-4-acetoxy-2-benzoxazolone (TC-3), and to investigate the hepatoprotective potentials of these three compounds on CCl4-induced liver injury in mice. HBOA was prepared from 2-nitroresorcinol by a 'one pot' reduction and subsequent cyclization with urea. The acyl derivatives, AcO-BOA and TC-3, were prepared from HBOA using a substitution reaction. The compounds were synthesized with good yields (63.08–68.22%). An acute liver injury model was established by administering CCl4 intraperitoneally to Kunming mice. The mice were then intragastrically administered bifendate (150 mg/kg) or the synthesized compounds at three different doses (200, 100 and 50 mg/kg). The treatment with CCl4 was observed to increase the levels of aminotransferase (ALT), aspartate aminotransferase (AST), lactic dehydrogenase (LDH) and malondialdehyde (MDA) and decrease the levels of superoxide dismutase (SOD), catalase (CAT), glutathione (GSH) and glutathione peroxidase (Gpx) in the liver tissues of the mice. Furthermore, treatment with CCl4 elevated the expression level of the proinflammatory mediator TNF-α. However, HBOA and its derivatives attenuated the changes induced by CCl4. Furthermore, CCl4-induced histopathological changes were reduced by treatment with these compounds. These results suggest that HBOA and its acyl derivatives are able to significantly alleviate the hepatotoxicity induced by CCl4 in mice.

This paper presents a novel method for learning a pose lexicon comprising semantic poses defined by textual instructions and their associated visual poses defined by visual features. The proposed method simultaneously takes two input streams, semantic poses and visual pose candidates, and statistically learns a mapping between them to construct the lexicon. With the learned lexicon, action recognition can be cast as the problem of finding the maximum translation probability of a sequence of semantic poses given a stream of visual pose candidates. Experiments evaluating pre-trained and zero-shot action recognition conducted on MSRC-12 gesture and WorkoutSu-10 exercise datasets were used to verify the efficacy of the proposed method., Accepted by the 2016 IEEE International Conference on Multimedia and Expo (ICME 2016). 6 pages paper and 4 pages supplementary material

Because of the growing mass of data and the requirements of data mining's individuation, the traditional centralized data mining method can't adapt to this kind of demand. Cloud computing provided a cheap solution for massive data storage, analysis and handling. In order to achieve the purpose of parallel data mining in cloud environment, an improved algorithm based on the traditional Naive Bayes has been proposed in this paper. First, proposing the designing ideas of the improved algorithm in MapReduce programming model. Then using the actual data to test the algorithm. The experimental result validated that the new algorithm has higher performance and better scalability.

For Wireless Sensor Networks (WSNs) with a small quantity of sensors and very low SNR, distributed detection and decision fusion rules based on multi-bit knowledge of local sensors are proposed. At local sensors, observations are quantized to multi-bit local decisions. Three quantification algorithms are investigated, which are based on weight, statistics and redundancy, respectively. Corresponding suboptimal fusion rules at the fusion center are also discussed by approximating the optimal likelihood ratio test. System level detection performance measures, namely probabilities of detection and false alarm, are derived analytically by employing probability theory. Finally, Monte Carlo methods are employed to study the performance of proposed decision fusion rules with parameters such as Rayleigh fading channel and Gaussian noise. Numerical results show that, under non-ideal channel, commonly used schemes based on weight cannot improve the system performance even with a large number and high SNR. Fortunately, schemes based on statistics and redundancy can enhance the system capability when the node is deficient and SNR is low. Furthermore, schemes based on statistics have the best stability among the three schemes, and schemes based on redundancy have the best performance among the three when quantization degree is high.

The design, fabrication and performance of a vacuum system for the BEPCII storage ring are described in this paper. The arc beam pipes and their antechambers are made by machining upper and lower chamber halves, then welding them at the periphery to form a complete chamber. The photon absorbers and the pumping stations are located in the antechambers. The special vacuum components, such as photon absorbers, RF shielding bellows, and the in-vacuum wiggler, have been manufactured according to the characteristics of the BEPCII storage ring and shown to perform well. The vacuum systems of the BEPCII storage ring have been assembled and run over a two years period, and they had accumulated 1000 A h of beam dose in the colliding mode as of the end of July 2009. On average, the dynamic pressure rise of the BEPCII storage ring is less than 3 x 10(-7) Pa/A. The effect of a cloud of photoelectrons around the positron beam was not found in the high beam current, because the beam pipe with an antechamber in the positron ring was coated by a TiN layer of about 100 nm in thickness. Ion currents of a few specially positioned ion pumps were slight high in the high beam current, which can be explained as an effect of electron multiplying. (C) 2010 Elsevier B.V. All rights reserved.

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Background: Exercise is important for fitness and recovery of older adults after hospitalization for treatment of cardiovascular disease. Home-based, nurse-led exercise programs may be beneficial. Objective: The aim of this study was to test the effects of a low-intensity, home-based exercise protocol led by an advanced practice nurse on health-related quality of life (HRQOL), physical fitness, and left ventricular ejection fraction (LVEF) in older adults after hospital discharge with a cardiovascular disease diagnosis. Methods: The study was randomized and single blinded. Seventy-seven older adults (≥75 years old, mean = 80.68 years old) were included; 32 subjects in the intervention and 29 in the control group completed the study. The low-intensity, home-based exercise protocol is composed of 14-type joint exercises and walking for 12 weeks. The main outcome measures were assessments on the Medical Outcomes Study Short-Form-36, the Senior Fitness Test, and LVEF at baseline and 12 weeks after hospital discharge. Results: After 12 weeks, the intervention group showed significant improvements in HRQOL (physical functioning, role-physical, bodily pain, and vitality; p < .05) as well as on the Senior Fitness Test (chair stands, arm curls, Timed Up and Go, and 6-minute walk distance; p < .05); there was no significant improvement in LVEF (p = .56). Conclusions: The low-intensity, home-based exercise led by an advanced practice nurse was effective in improving HRQOL and physical fitness. Adherence was high, and there were no adverse events related to exercise.

This paper presents a method for extracting discriminative key poses for skeleton-based action recognition. Poses are represented by normalized joint locations, velocities and accelerations of skeleton joints. An extended label consistent K-SVD (ELC-KSVD) algorithm is proposed for learning the common and action-specific dictionaries. Discriminative key poses are represented by the atoms of the action-specific dictionaries. With the specific dictionaries, sparse codes are obtained for representing action instances through max pooling and temporal pyramid. A SVM classifier is trained for action recognition. The proposed method was evaluated on the MSRC-12 gesture and MSR-Action 3D datasets. Experimental results have shown that the proposed method is effective in extracting discriminative key poses.

In some GPS failure conditions, positioning for mobile target is difficult. This paper proposed a new method based on INS/UWB for attitude angle and position synchronous tracking of indoor carrier. Firstly, error model of INS/UWB integrated system is built, including error equation of INS and UWB. And combined filtering model of INS/UWB is researched. Simulation results show that the two subsystems are complementary. Secondly, integrated navigation data fusion strategy of INS/UWB based on Kalman filtering theory is proposed. Simulation results show that FAKF method is better than the conventional Kalman filtering. Finally, an indoor experiment platform is established to verify the integrated navigation theory of INS/UWB, which is geared to the needs of coal mine working environment. Static and dynamic positioning results show that the INS/UWB integrated navigation system is stable and real-time, positioning precision meets the requirements of working condition and is better than any independent subsystem.