Students\u27 Perceptions of Multimedia Classrooms at East Tennessee State University.

The purpose of this study was to investigate students\u27 perceptions of multimedia classrooms at East Tennessee State University regarding technologies in multimedia classrooms, students\u27 learning achievements, instructors\u27 instructional methods, and students\u27 learning styles. Two surveys in multimedia classrooms and traditional classrooms were designed to measure and compare students\u27 perceptions of multimedia classrooms. The VARK (Fleming, 2002b) learning style survey was used to calculate the students\u27 learning styles. The research was conducted during spring semester, 2002. Participants in this study included 187 students in multimedia classrooms and 110 students in traditional classrooms at East Tennessee State University. The majority of students were from the School of Business and the College of Applied Science and Technology. The results of data analysis showed that there were no significant differences in students\u27 perceptions of multimedia classrooms regarding technologies, learning achievements, and learning styles. However, there were significant differences in students\u27 perceptions of multimedia classrooms regarding instructors\u27 instructional methods. Students in multimedia classrooms had better perceptions of instructors\u27 instructional methods than students in traditional classrooms. Furthermore, the majority of students in multimedia classrooms and traditional classrooms had positive attitudes towards multimedia classrooms. Several recommendations for future research, VARK learning styles, and administrators and policy makers at East Tennessee State University resulted from this study. A future study with a larger and more diverse population using both quantitative and qualitative methodology is recommended to further explore the effectiveness of multimedia classrooms in higher education. Reinforcement of training, technical support, and classroom maintenance are recommended to administrators and policy makers at East Tennessee State University in order to use multimedia classrooms more effectively

Ameliorative effects of parecoxib in combination with ultrasound-guided paravertebral block (UGPB) on stress and inflammatory responses following thoracoscopic surgery

Purpose: To investigate the ameliorative effects of parecoxib combined with ultrasound-guided paravertebral block (UGPB) on stress and inflammatory responses after thoracoscopic surgery.Methods: Forty thoracoscopic surgery patients were randomized into placebo (control) and parecoxib groups. Parecoxib was administered pre-operation, 24 h and 48 h after operation. Arterial blood was collected, and endotoxin (ET), thromboxane A2 (TXA2), interleukin 6 (IL-6) and tumor necrosis factor alpha (TNF-α) levels were measured. Opioid dosage, infusion volume, blood loss, operation time, visual analogue scale (VAS) score at 24 h and 48 h, and hospitalization period were recorded.Results: No significant differences were observed in age, sex, height, body weight, opioid dosage, surgery time, blood loss, or infusion volume between groups. VAS scores in the parecoxib group were significantly lower than the control group after 24 and 48 h. The hospitalization period of the parecoxib group was significantly shorter than the control group. Plasma levels of ET, TXA2, IL-6 and TNF-α in the parecoxib group were lower than the control group after 24 h; however, there was no significant difference after 48 h.Conclusion: Parecoxib, combined with UGPB, effectively relieves thoracoscopic pain, stress, and inflammatory responses of patients after thoracoscopic surgery. This treatment would improve the postoperative quality of life of lung cancer patients.Keywords: Parecoxib, Paravertebral block, Stress response, Inflammatory respons

Gene-to-metabolite network for biosynthesis of lignans in MeJA-elicited Isatis indigotica hairy root cultures.

Root and leaf tissue of Isatis indigotica shows notable anti-viral efficacy, and are widely used as "Banlangen" and "Daqingye" in traditional Chinese medicine. The plants' pharmacological activity is attributed to phenylpropanoids, especially a group of lignan metabolites. However, the biosynthesis of lignans in I. indigotica remains opaque. This study describes the discovery and analysis of biosynthetic genes and AP2/ERF-type transcription factors involved in lignan biosynthesis in I. indigotica. MeJA treatment revealed differential expression of three genes involved in phenylpropanoid backbone biosynthesis (IiPAL, IiC4H, Ii4CL), five genes involved in lignan biosynthesis (IiCAD, IiC3H, IiCCR, IiDIR, and IiPLR), and 112 putative AP2/ERF transcription factors. In addition, four intermediates of lariciresinol biosynthesis were found to be induced. Based on these results, a canonical correlation analysis using Pearson's correlation coefficient was performed to construct gene-to-metabolite networks and identify putative key genes and rate-limiting reactions in lignan biosynthesis. Over-expression of IiC3H, identified as a key pathway gene, was used for metabolic engineering of I. indigotica hairy roots, and resulted in an increase in lariciresinol production. These findings illustrate the utility of canonical correlation analysis for the discovery and metabolic engineering of key metabolic genes in plants

Characterization of anti-leukemia components from Indigo naturalis using comprehensive two-dimensional K562/cell membrane chromatography and in silico target identification.

Traditional Chinese Medicine (TCM) has been developed for thousands of years and has formed an integrated theoretical system based on a large amount of clinical practice. However, essential ingredients in TCM herbs have not been fully identified, and their precise mechanisms and targets are not elucidated. In this study, a new strategy combining comprehensive two-dimensional K562/cell membrane chromatographic system and in silico target identification was established to characterize active components from Indigo naturalis, a famous TCM herb that has been widely used for the treatment of leukemia in China, and their targets. Three active components, indirubin, tryptanthrin and isorhamnetin, were successfully characterized and their anti-leukemia effects were validated by cell viability and cell apoptosis assays. Isorhamnetin, with undefined cancer related targets, was selected for in silico target identification. Proto-oncogene tyrosine-protein kinase (Src) was identified as its membrane target and the dissociation constant (Kd) between Src and isorhamnetin was 3.81 μM. Furthermore, anti-leukemia effects of isorhamnetin were mediated by Src through inducing G2/M cell cycle arrest. The results demonstrated that the integrated strategy could efficiently characterize active components in TCM and their targets, which may bring a new light for a better understanding of the complex mechanism of herbal medicines

Exploring Decision-based Black-box Attacks on Face Forgery Detection

Face forgery generation technologies generate vivid faces, which have raised public concerns about security and privacy. Many intelligent systems, such as electronic payment and identity verification, rely on face forgery detection. Although face forgery detection has successfully distinguished fake faces, recent studies have demonstrated that face forgery detectors are very vulnerable to adversarial examples. Meanwhile, existing attacks rely on network architectures or training datasets instead of the predicted labels, which leads to a gap in attacking deployed applications. To narrow this gap, we first explore the decision-based attacks on face forgery detection. However, applying existing decision-based attacks directly suffers from perturbation initialization failure and low image quality. First, we propose cross-task perturbation to handle initialization failures by utilizing the high correlation of face features on different tasks. Then, inspired by using frequency cues by face forgery detection, we propose the frequency decision-based attack. We add perturbations in the frequency domain and then constrain the visual quality in the spatial domain. Finally, extensive experiments demonstrate that our method achieves state-of-the-art attack performance on FaceForensics++, CelebDF, and industrial APIs, with high query efficiency and guaranteed image quality. Further, the fake faces by our method can pass face forgery detection and face recognition, which exposes the security problems of face forgery detectors

Characterization of a Ag+-Selective Electrode Based on Naphthalimide Derivative as Ionophore

A naphthalimide derivative has been explored as neutral ionophore for Ag+-selective electrode. Potentiometric response revealed that electrode based on the proposed ionophore with 2-nitrophenyl octyl ether as solvent in a poly (vinyl chloride) membrane matrix shows a measuring range of 1.0×10-6-1.0×10-2 M with a slope of 50.4±0.3 mV/decade. This electrode has high selectivity to Ag+ with respect to alkaline, alkaline earth and other heavy metal ions

CARE: A Large Scale CT Image Dataset and Clinical Applicable Benchmark Model for Rectal Cancer Segmentation

Rectal cancer segmentation of CT image plays a crucial role in timely clinical diagnosis, radiotherapy treatment, and follow-up. Although current segmentation methods have shown promise in delineating cancerous tissues, they still encounter challenges in achieving high segmentation precision. These obstacles arise from the intricate anatomical structures of the rectum and the difficulties in performing differential diagnosis of rectal cancer. Additionally, a major obstacle is the lack of a large-scale, finely annotated CT image dataset for rectal cancer segmentation. To address these issues, this work introduces a novel large scale rectal cancer CT image dataset CARE with pixel-level annotations for both normal and cancerous rectum, which serves as a valuable resource for algorithm research and clinical application development. Moreover, we propose a novel medical cancer lesion segmentation benchmark model named U-SAM. The model is specifically designed to tackle the challenges posed by the intricate anatomical structures of abdominal organs by incorporating prompt information. U-SAM contains three key components: promptable information (e.g., points) to aid in target area localization, a convolution module for capturing low-level lesion details, and skip-connections to preserve and recover spatial information during the encoding-decoding process. To evaluate the effectiveness of U-SAM, we systematically compare its performance with several popular segmentation methods on the CARE dataset. The generalization of the model is further verified on the WORD dataset. Extensive experiments demonstrate that the proposed U-SAM outperforms state-of-the-art methods on these two datasets. These experiments can serve as the baseline for future research and clinical application development.Comment: 8 page

RPG-Palm: Realistic Pseudo-data Generation for Palmprint Recognition

Palmprint recently shows great potential in recognition applications as it is a privacy-friendly and stable biometric. However, the lack of large-scale public palmprint datasets limits further research and development of palmprint recognition. In this paper, we propose a novel realistic pseudo-palmprint generation (RPG) model to synthesize palmprints with massive identities. We first introduce a conditional modulation generator to improve the intra-class diversity. Then an identity-aware loss is proposed to ensure identity consistency against unpaired training. We further improve the B\'ezier palm creases generation strategy to guarantee identity independence. Extensive experimental results demonstrate that synthetic pretraining significantly boosts the recognition model performance. For example, our model improves the state-of-the-art B\'ezierPalm by more than $5\%$ and $14\%$ in terms of TAR@FAR=1e-6 under the $1:1$ and $1:3$ Open-set protocol. When accessing only $10\%$ of the real training data, our method still outperforms ArcFace with $100\%$ real training data, indicating that we are closer to real-data-free palmprint recognition.Comment: 12 pages,8 figure