Modeling Multi-Targets Sentiment Classification via Graph Convolutional Networks and Auxiliary Relation

Existing solutions do not work well when multi-targets coexist in a sentence. The reason is that the existing solution is usually to separate multiple targets and process them separately. If the original sentence has N target, the original sentence will be repeated for N times, and only one target will be processed each time. To some extent, this approach degenerates the fine-grained sentiment classification task into the sentencelevel sentiment classification task, and the research method of processing the target separately ignores the internal relation and interaction between the targets. Based on the above considerations, we proposes to use Graph Convolutional Network (GCN) to model and process multi-targets appearing in sentences at the same time based on the positional relationship, and then to construct a graph of the sentiment relationship between targets based on the difference of the sentiment polarity between target words. In addition to the standard target-dependent sentiment classification task, an auxiliary node relation classification task is constructed. Experiments demonstrate that our model achieves new comparable performance on the benchmark datasets: SemEval-2014 Task 4, i.e., reviews for restaurants and laptops. Furthermore, the method of dividing the target words into isolated individuals has disadvantages, and the multi-task learning model is beneficial to enhance the feature extraction ability and expression ability of the model

Pharmacokinetics and bioequivalence of sunitinib and Sutent® in Chinese healthy subjects: an open-label, randomized, crossover study

Purpose: The purpose of this study was to examine the pharmacokinetics (PK), bioequivalence and safety of generic sunitinib and its original product Sutent® in healthy Chinese subjects through a phase-I clinical trial.Methods: The study selected two groups of 24 healthy Chinese subjects in a 1:1 ratio through random allocation. Each participant received either 12.5 mg of sunitinib or Sutent® per cycle. A total of 15 different time points were employed for blood sample collection during each cycle. Furthermore, a comprehensive assessment of the drugs’ safety was consistently maintained throughout the trial.Results: The average adjusted geometric mean ratios (GMR) (90% CI) for the primary PK parameters Cmax, AUC0-t and AUC0-∞ were 97.04% (93.06%–101.19%), 98.45% (93.27%–103.91%) and 98.22% (93.15%–103.56%), respectively. The adjusted GMRs for essential pharmacokinetic (PK) parameters all met the requirements for bioequivalence, with values within the acceptable range of 80%–125%. In addition, the two drugs showed comparable results for the other PK parameters. These results indicate that the two drugs were bioequivalent. Furthermore, both drugs showed well safety.Conclusion: The research results proved that the PK and safety profiles of sunitinib in healthy Chinese subjects were comparable to those of Sutent®. These results advocate the clinical application of generic sunitinib as a potential alternative to original product Sutent® in the treatment of certain medical conditions

Immunization against inhibin DNA vaccine as an alternative therapeutic for improving follicle development and reproductive performance in beef cattle

The objective of the present study was to investigate the potential role of immunization against INH on follicular development, serum reproductive hormone (FSH, E2, and P4) concentrations, and reproductive performance in beef cattle. A total of 196 non-lactating female beef cattle (4-5 years old) with identical calving records (3 records) were immunized with 0.5, 1.0, 1.5, or 2.0 mg [(T1, n = 58), (T2, n = 46), (T3, n = 42) and (T4, n = 36), respectively] of the pcISI plasmid. The control (C) group (n = 14) was immunized with 1.0 mL 0.9% saline. At 21d after primary immunization, all beef cattle were boosted with half of the primary immunization dose. On day 10 after primary immunization, the beef cattle immunized with INH DNA vaccine evidently induced anti-INH antibody except for the T1 group. The T3 group had the greatest P/N value peak among all the groups. The anti-INH antibody positive rates in T2, T3 and T4 groups were significantly higher than that in C and T1 groups. RIA results indicated that serum FSH concentration in T2 group increased markedly on day 45 after booster immunization; the E2 amount in T3 group was significantly increased on day 10 after primary immunization, and the levels of E2 also improved in T2 and T3 groups after booster immunization; the P4 concentration in T2 group was significantly improved on day 21 after primary immunization. Ultrasonography results revealed that the follicles with different diameter sizes were increased, meanwhile, the diameter and growth speed of ovulatory follicle were significantly increased. Furthermore, the rates of estrous, ovulation, conception, and twinning rate were also significantly enhanced. These findings clearly illustrated that INH DNA vaccine was capable of promoting the follicle development, thereby improving the behavioral of estrous and ovulation, eventually leading to an augment in the conception rates and twinning rate of beef cattle

Discovery of potential biomarkers for osteoporosis using LC/GC−MS metabolomic methods

PurposeFor early diagnosis of osteoporosis (OP), plasma metabolomics of OP was studied by untargeted LC/GC−MS in a Chinese elderly population to find possible diagnostic biomarkers.MethodsA total of 379 Chinese community-dwelling older adults aged ≥65 years were recruited for this study. The BMD of the calcaneus was measured using quantitative ultrasound (QUS), and a T value ≤-2.5 was defined as OP. Twenty-nine men and 47 women with OP were screened, and 29 men and 36 women were matched according to age and BMI as normal controls using propensity matching. Plasma from these participants was first analyzed by untargeted LC/GC−MS, followed by FC and P values to screen for differential metabolites and heatmaps and box plots to differentiate metabolites between groups. Finally, metabolic pathway enrichment analysis of differential metabolites was performed based on KEGG, and pathways with P ≤ 0.05 were selected as enrichment pathways.ResultsWe screened metabolites with FC>1.2 or FC<1/1.2 and P<0.05 and found 33 differential metabolites in elderly men and 30 differential metabolites in elderly women that could be potential biomarkers for OP. 2-Aminomuconic acid semialdehyde (AUC=0.72, 95% CI 0.582-0.857, P=0.004) is highly likely to be a biomarker for screening OP in older men. Tetradecanedioic acid (AUC=0.70, 95% CI 0.575-0.818, P=0.004) is highly likely to be a biomarker for screening OP in older women.ConclusionThese findings can be applied to clinical work through further validation studies. This study also shows that metabolomic analysis has great potential for application in the early diagnosis and recurrence monitoring of OP in elderly individuals

AlxCoCrCuFeNi (X = 0, 0.8) high entropy alloys fabricated by laser powder bed fusion

ABSTRACTThe AlxCoCrCuFeNi (x = 0, 0.8) high entropy alloys (HEAs) were prepared by laser powder bed fusion (LPBF), and the effects of volumetric energy density (VED) and heat treatment on microstructures and mechanical properties of HEAs were investigated. The results show that the CoCrCuFeNi HEA has a single FCC phase structure and exhibits good ductility. Its yield strength is relatively low, being 520 MPa. After the alloy composition was adjusted from CoCrCuFeNi to Al0.8CoCrCuFeNi, the HEA changed from FCC single-phase to FCC + BCC dual-phase structure and the hardness and compressive properties were significantly improved. Among them, the Al0.8CoCrCuFeNi HEA prepared with the VED of 167 J/mm3 has the best mechanical properties, with a compressive yield strength of 652 MPa and good plasticity. After annealing at 600°C and 900°C for 1 h, the mechanical properties of HEAs are significantly improved. In particular, the microhardness and compressive yield strength of the Al0.8CoCrCuFeNi HEA annealed at 600°C for 1 h are 378 HV and 810 MPa, respectively. The coordinated deformation between FCC and BCC domains, back stress strengthening, solid solution strengthening and precipitation strengthening after heat treatment make the Al0.8CoCrCuFeNi HEA achieve a balance of high strength and good ductility

Multidimensional Self-Attention for Aspect Term Extraction and Biomedical Named Entity Recognition

Wide attention has been paid to named entity recognition (NER) in specific fields. Among the representative tasks are the aspect term extraction (ATE) in user online comments and the biomedical named entity recognition (BioNER) in medical documents. Existing methods only perform well in a particular field, and it is difficult to maintain an advantage in other fields. In this article, we propose a supervised learning method that can be used for much special domain NER tasks. The model consists of two parts, a multidimensional self-attention (MDSA) network and a CNN-based model. The multidimensional self-attention mechanism can calculate the importance of the context to the current word, select the relevance according to the importance, and complete the update of the word vector. This update mechanism allows the subsequent CNN model to have variable-length memory of sentence context. We conduct experiments on benchmark datasets of ATE and BioNER tasks. The results show that our model surpasses most baseline methods

Aspect-level sentiment classification with fused local and global context

Abstract Sentiment analysis aims to determine the sentiment orientation of a text piece (sentence or document), but many practical applications require more in-depth analysis, which makes finer-grained sentiment classification the ideal solution. Aspect-level Sentiment Classification (ALSC) is a task that identifies the emotional polarity for aspect terms in a sentence. As the mainstream Transformer framework in sentiment classification, BERT-based models apply self-attention mechanism that extracts global semantic information for a given aspect, while a certain proportion of local information is missing in the process. Although recent ALSC models have achieved good performance, they suffer from robustness issues. In addition, uneven distribution of samples greatly hurts model performance. To address these issues, we present the PConvBERT (Prompt-ConvBERT) and PConvRoBERTa (Prompt-ConvRoBERTa) models, in which local context features learned by a Local Semantic Feature Extractor (LSFE) are fused with the BERT/RoBERTa global features. To deal with the robustness problem of many deep learning models, adversarial training is applied to increase model stability. Additionally, Focal Loss is applied to alleviate the impact of unbalanced sample distribution. To fully explore the ability of the pre-training model itself, we also propose natural language prompt approaches that better solve the ALSC problem. We utilize masked vector outputs of templates for sentiment classification. Extensive experiments on public datasets demonstrate the effectiveness of our model

Parameters Sensitivity Analysis of Position-Based Impedance Control for Bionic Legged Robots’ HDU

For the hydraulic drive unit (HDU) on the joints of bionic legged robots, this paper proposes the position-based impedance control method. Then, the impedance control performance is tested by a HDU performance test platform. Further, the method of first-order sensitivity matrix is proposed to analyze the dynamic sensitivity of four main control parameters under four working conditions. To research the parameter sensitivity quantificationally, two sensitivity indexes are defined, and the sensitivity analysis results are verified by experiments. The results of the experiments show that, when combined with corresponding optimization strategies, the dynamic compliance composition theory and the results from sensitivity analysis can compensate for the control parameters and optimize the control performance in different working conditions