Diagnosis and management of vaginal leiomyoma: A case report and literature review

Objectives: Leiomyomas are benign mesenchymal tumors that consist of smooth muscle cells and varying amounts of fibrous stroma. Uterine leiomyomas are the most common, affecting 20% to 30% of reproductive-age women, but vaginal leiomyomas are rare. Treatments gradually diversify with increased awareness of vaginal leiomyoma, but transvaginal fibroid resection remains the commonly used scheme. Case report: Herein, we present the case of a 50-year-old asymptomatic woman who had a mass in the left anterior wall of the vagina discovered by gynecological examination and ultrasound. We used oxytocin diluent injection during surgery to create a water pad in the tissue space and then performed a transvaginal myomectomy. There was little or negligible intraoperative bleeding and no peripheral tissue injury, early or late postoperative complications, incision dehiscence, and no surgical site infection. Conclusions: Transvaginal ultrasonography is the preferred examination for vaginal leiomyomas, and transvaginal myomectomy is the classic treatment method. The formation of a water pad with oxytocin dilution can effectively reduce intraoperative bleeding and shorten surgery time

Experimental observation of highly anisotropic elastic properties of two-dimensional black arsenic

Anisotropic two-dimensional layered materials with low-symmetric lattices have attracted increasing attention due to their unique orientation-dependent mechanical properties. Black arsenic (b-As), with the puckered structure, exhibits extreme in-plane anisotropy in optical, electrical and thermal properties. However, experimental research on mechanical properties of b-As is very rare, although theoretical calculations predicted the exotic elastic properties of b-As, such as anisotropic Young's modulus and negative Poisson's ratio. Herein, experimental observations on highly anisotropic elastic properties of b-As were demonstrated using our developed in situ tensile straining setup based on the effective microelectromechanical system. The cyclic and repeatable load-displacement curves proved that Young's modulus along zigzag direction was ~1.6 times greater than that along armchair direction, while the anisotropic ratio of ultimate strain reached ~2.5, attributed to hinge structure in armchair direction. This study could provide significant insights to design novel anisotropic materials and explore their potential applications in nanomechanics and nanodevices.Comment: 19 pages, 5 figure

Highly Anisotropic Elastic Properties of Suspended Black Arsenic Nanoribbons

Anisotropy, as an exotic degree of freedom, enables us to discover the emergent two-dimensional (2D) layered nanomaterials with low in-plane symmetry and to explore their outstanding properties and promising applications. 2D black arsenic (b-As) with puckered structure has garnered increasing attention these years owing to its extreme anisotropy with respect to the electrical, thermal, and optical properties. However, the investigation on mechanical properties of 2D b-As is still lacking, despite much effort on theoretical simulations. Herein, we report the highly anisotropic elastic properties of suspended b-As nanoribbons via atomic force microscope-based nanoindentation. It was found that the extracted Young's modulus of b-As nanoribbons exhibits remarkable anisotropy, which approximates to 72.2 +- 5.4 and 44.3 +- 1.4 GPa along zigzag and armchair directions, respectively. The anisotropic ratio reaches up to ~ 1.6. We expect that these results could lay a solid foundation for the potential applications of 2D anisotropic nanomaterials in the next-generation nanomechanics and optoelectronics.Comment: 17 pages, 5 figure

Preparation and physicochemical properties: a new extruded rice using cassava starch and broken rice flour

With the increasing demand for nutrition and health, many researchers are trying to develop a rice product with lower aging rate and convenient nutrient fortification. Being composed of high amylopectin content, cassava starch (CS) shows a lower retrogradation tendency compared to rice starch. So, it has a broad application prospect to partially replace rice starch with CS in rice by extrusion technology. In this study, a new extruded rice (ER) was prepared by broken rice flour and CS using single-screw extruder through “improved extrusion cooking technology,” and the maximum addition level of CS in ER was 30%. Color parameters and texture profile analysis showed that ER was a little darker in appearance with lower hardness, adhesiveness and chewiness. Rapid visco analysis demonstrated that the viscosity of ER paste appeared earlier during the initial heating phase and displayed a lower retrogradation trend than normal rice in the cooling process. The gelatinization temperature and gelatinization enthalpy decreased with the increasing CS in ER, while the degree of gelatinization increased to 76.36% when the content of CS was 30% after extrusion. The X-ray diffraction patterns of control was typical A-type structure, while ER changed to V-type structure with a lower degree of crystallinity. The microstructure observation showed that ER exhibited a looser and more porous structure with increasing the content of CS, which facilitated easier cooking and nutritional enhancement

Sensory Features in Affective Analysis: A Study Based on Neural Network Models

This study proposes an ensemble model to incorporate sensory features of lexical items in English from external resources into neural affective analysis frameworks. This allows the models to take the combined effects of bi-directional feeling between the sensory lexicon and the writer to infer human affective knowledge. We evaluate our model on two affective analysis tasks. The ensemble model exhibits the best accuracy and the results with 1% F1-score improvement over the baseline LSTM model in the sentiment analysis task. The performance shows that perceptual information can contribute to the performance of sentiment classification tasks significantly. This study also provides a support for the linguistic finding that correlations exist between sensory features and sentiments in the language

Prompting Explicit and Implicit Knowledge for Multi-hop Question Answering Based on Human Reading Process

Pre-trained language models (PLMs) leverage chains-of-thought (CoT) to simulate human reasoning and inference processes, achieving proficient performance in multi-hop QA. However, a gap persists between PLMs’ reasoning abilities and those of humans when tackling complex problems. Psychological studies suggest a vital connection between explicit information in input passages and human prior knowledge during reading. Nevertheless, current research has given less attention to linking input passages and PLMs’ pre-training-based knowledge derived from human reading processes. In this study, we introduce a prompting explicit and implicit knowledge (PEI) framework, which uses prompts to connect explicit and implicit knowledge, aligning with human reading process for multi-hop QA. We consider the input passages as explicit knowledge, using it to elicit implicit knowledge through unified prompt reasoning. Additionally, our model incorporates type-specific reasoning via prompts, a form of implicit knowledge. Experimental results show that PEI performs comparably to the state-of-the-art on HotpotQA. Ablation studies confirm the effectiveness of our model in bridging and incorporating explicit and implicit knowledge

SensoryT5: Infusing Sensorimotor Norms into T5 for Enhanced Fine-grained Emotion Classification

In traditional research approaches, sensory perception and emotion classification have traditionally been considered separate domains. Yet, the significant influence of sensory experiences on emotional responses is undeniable. The natural language processing (NLP) community has often missed the opportunity to merge sensory knowledge with emotion classification. To address this gap, we propose SensoryT5, a neurocognitive approach that integrates sensory information into the T5 (Text-to-Text Transfer Transformer) model, designed specifically for fine-grained emotion classification. This methodology incorporates sensory cues into the T5’s attention mechanism, enabling a harmonious balance between contextual understanding and sensory awareness. The resulting model amplifies the richness of emotional representations. In rigorous tests across various detailed emotion classification datasets, SensoryT5 showcases improved performance, surpassing both the foundational T5 model and current state-of-the-art works. Notably, SensoryT5’s success signifies a pivotal change in the NLP domain, highlighting the potential influence of neurocognitive data in refining machine learning models’ emotional sensitivity

Leveraging Sensory Knowledge into Text-to-Text Transfer Transformer for Enhanced Emotion Analysis

This study proposes an innovative model (i.e., SensoryT5), which integrates sensory knowledge into the T5 (Text-to-Text Transfer Transformer) framework for emotion classification tasks. By embedding sensory knowledge within the T5 model's attention mechanism, SensoryT5 not only enhances the model's contextual understanding but also elevates its sensitivity to the nuanced interplay between sensory information and emotional states. Experiments on four emotion classification datasets, three sarcasm classification datasets one subjectivity analysis dataset, and one opinion classification dataset (ranging from binary to 32-class tasks) demonstrate that our model outperforms state-of-the-art baseline models (including the baseline T5 model) significantly. Specifically, SensoryT5 achieves a maximal improvement of 3.0% in both the accuracy and the F1 score for emotion classification. In sarcasm classification tasks, the model surpasses the baseline models by the maximal increase of 1.2% in accuracy and 1.1% in the F1 score. Furthermore, SensoryT5 continues to demonstrate its superior performances for both subjectivity analysis and opinion classification, with increases in ACC and the F1 score by 0.6% for the subjectivity analysis task and increases in ACC by 0.4% and the F1 score by 0.6% for the opinion classification task, when compared to the second-best models.} These improvements underscore the significant potential of leveraging cognitive resources to deepen NLP models' comprehension of emotional nuances and suggest an interdisciplinary research between the areas of NLP and neuro-cognitive science

Study on the correlation between pre-treatment Glasgow score and blood inflammatory markers and prognosis of nasopharyngeal carcinoma patients

Objective To investigate the correlation between Glasgow score and blood inflammatory markers before treatment with the efficacy of nasopharyngeal carcinoma. Methods Cases from the two clinical centers were divided into training set and validation set， and the clinical characteristics of the two groups were compared to be balanced. To search the independent prognostic risk factors of nasopharyngeal carcinoma and then the prognostic index of each patient was calculated， and the patients were divided into high-risk， intermediate-risk and low-risk groups. Further validation in the validation set. Results Cox multivariate analysis of the training set showed that age >50， T3-T4， N2-N3， GPS score of 1-2， NLR>2.5， and LMR≤2.35 before treatment were poor prognostic factors affecting the 5-year disease-specific survival rate of patients with nasopharyngeal carcinoma. Conclusion The combination of GPS， NLR， LMR and age， TNM staging may provide a new way for the prognosis evaluation of patients with nasopharyngeal carcinoma before treatment