The absorption and uptake of recombinant human follicle-stimulating hormone through vaginal subcutaneous injections - a pharmacokinetic study

<p>Abstract</p> <p>Background</p> <p>Follicle stimulating hormone (FSH) has been routinely used for ovulation induction. Because of rapid clearance of the hormone, FSH is commonly administered by daily intramuscular or subcutaneous injections in in-vitro fertilization (IVF). To reduce the number of visits to the clinic, an intermittent vaginal injection of rhFSH every 3 days employing the concepts of mesotherapy and uterine first-pass effect was invented and has successfully been applied in women receiving IVF treatment. This study was designed to monitor the pharmacokinetic pattern of rhFSH administered vaginally.</p> <p>Methods</p> <p>Twelve healthy women with regular ovulatory cycles were recruited. All volunteers received gonadotrophin-releasing hormone agonist to suppress pituitary function and were assigned to receive single dose recombinant human FSH (rhFSH, Puregon 300) either using conventional abdominal subcutaneous injection or vaginal subcutaneous injection in a randomized cross-over study. Serum samples were collected at pre- scheduled time intervals after injections of rhFSH to determine immunoreactive FSH levels. Pharmacokinetic parameters characterizing rate [maximal plasma concentrations (Cmax) and time of maximal plasma concentrations (tmax)] and extent [area under the plasma concentration-time curve (AUC) and clearance] of absorption of rhFSH were compared.</p> <p>Results</p> <p>Vaginal injection of rhFSH was well tolerated and no drug-related adverse reaction was noted. Our analysis revealed that tmax was significantly earlier (mean 6.67 versus 13.33 hours) and Cmax was significantly higher (mean 17.77 versus 13.96 IU/L) in vaginal versus abdominal injections. The AUC_0-∞ was 1640 versus 1134 IU·hour/L in vaginal and abdominal injections, respectively. Smaller plasma elimination rate constant (0.011 versus 0.016 hour-1), longer mean residence time (106.58 versus 70.47 hours), and slower total body clearance (292.2 versus 400.1 mL/hour) were also found in vaginal injection.</p> <p>Conclusion</p> <p>The vaginal injection mode elicited a rapid and highly extended absorption of rhFSH injected compared to conventional abdominal injection. These data indicate that the rate and extent of FSH absorption from the injection site can vary depending on the route of the FSH administration.</p

Research on the Motivation and Attitude of College students' Physical Education in Taiwan

College students' physical education plays an important role in physical activity and cultivates the concept of independent health management. At present, what kind of learning attitude do Taiwan college students face in physical education? What motivation does the student influence the attitude of the physical education? What is the relevance? All of the above are the purpose of this study. The research method adopts the questionnaire survey method, and the survey data adopts descriptive statistical analysis, independent sample t test, single factor variance analysis, LSD post hoc comparison method, and typical correlation analysis. Research results: 1. The different background variables of Taiwanese college students are that the main motivation factor of physical education is to obtain good health fitness for "physical health". 2. Taiwanese college students have different background variables. They all think that the "cognitive learning" of physical education is the main factor of attitude, that is, the knowledge about health care and sports skills. 3. There is a positive correlation between learning motivation and learning attitude (ρ=.90). Learning motivation is one of the important factors affecting learning attitude. Research conclusions: 1. The factors of Taiwanese male and female college students' motivation for learning in physical education are mainly based on "physical health". 2. Freshmen have higher motivations and learning attitudes in physical education than second-grade to fourth-grade. 3. Taiwan female college students average 1 or 2 times per week, male college students have the most athletes 2 to 3 times per week, more than 90% of college students like sports. 4. There is a positive correlation between learning motivation and learning attitude, indicating that the stronger the attribute of learning motivation "physical health", the higher the student's learning attitude. 5. Satisfying students' motivation for learning helps students to learn positively. 6. Another important task of the college physical education class is to prepare students for future lifelong sports

A Comprehensive Review of Machine Learning Advances on Data Change: A Cross-Field Perspective

Recent artificial intelligence (AI) technologies show remarkable evolution in various academic fields and industries. However, in the real world, dynamic data lead to principal challenges for deploying AI models. An unexpected data change brings about severe performance degradation in AI models. We identify two major related research fields, domain shift and concept drift according to the setting of the data change. Although these two popular research fields aim to solve distribution shift and non-stationary data stream problems, the underlying properties remain similar which also encourages similar technical approaches. In this review, we regroup domain shift and concept drift into a single research problem, namely the data change problem, with a systematic overview of state-of-the-art methods in the two research fields. We propose a three-phase problem categorization scheme to link the key ideas in the two technical fields. We thus provide a novel scope for researchers to explore contemporary technical strategies, learn industrial applications, and identify future directions for addressing data change challenges

Obstacle-Resistant Deployment Algorithms for Wireless Sensor Networks

[[abstract]]Node deployment is an important issue in wireless sensor networks (WSNs). Sensor nodes should be efficiently deployed in a predetermined region in a low-cost and high-coverage-quality manner. Random deployment is the simplest way to deploy sensor nodes but may cause unbalanced deployment and, therefore, increase hardware costs and create coverage holes. This paper presents the efficient obstacle-resistant robot deployment (ORRD) algorithm, which involves the design of a node placement policy, a serpentine movement policy, obstacle-handling rules, and boundary rules. By applying the proposed ORRD, the robot rapidly deploys a near-minimal number of sensor nodes to achieve full sensing coverage, even though there exist unpredicted obstacles with regular or irregular shapes. Performance results reveal that ORRD outperforms the existing robot deployment mechanism in terms of power conservation and obstacle resistance and, therefore, achieves better deployment performance.[[incitationindex]]SC

Phase-controlled vibrational laser percussion drilling

In this study, a phase-controlled vibration was applied to a laser percussion drilling process to improve the depth of penetration. To investigate the effects of phase-controlled vibration on the depth of penetration, a novel method that controls the phase offset between the accelerating motion and the emission of the laser beam was developed. The method is based on coaxial sensing of the working surface using a photodiode, coupled with microcontroller control of the drilling laser operation. Through real-time optical signal acquisition and analysis of laser machining processes, correlations between the accelerating motion and the emission of the laser beam were simultaneously obtained. All of the processing work was performed in air at standard atmospheric conditions, and gas assist was not used. This study showed that the application of phase-controlled vibration improved the depth of penetration in laser percussion machining and can contribute to the development of precision drilling in the industry

Knowledge-Enriched Visual Storytelling

Stories are diverse and highly personalized, resulting in a large possible output space for story generation. Existing end-to-end approaches produce monotonous stories because they are limited to the vocabulary and knowledge in a single training dataset. This paper introduces KG-Story, a three-stage framework that allows the story generation model to take advantage of external Knowledge Graphs to produce interesting stories. KG-Story distills a set of representative words from the input prompts, enriches the word set by using external knowledge graphs, and finally generates stories based on the enriched word set. This distill-enrich-generate framework allows the use of external resources not only for the enrichment phase, but also for the distillation and generation phases. In this paper, we show the superiority of KG-Story for visual storytelling, where the input prompt is a sequence of five photos and the output is a short story. Per the human ranking evaluation, stories generated by KG-Story are on average ranked better than that of the state-of-the-art systems. Our code and output stories are available at https://github.com/zychen423/KE-VIST.Comment: AAAI 202

Face recognition using nonparametric-weighted Fisherfaces

This study presents an appearance-based face recognition scheme called the nonparametric-weighted Fisherfaces (NW-Fisherfaces). Pixels in a facial image are considered as coordinates in a high-dimensional space and are transformed into a face subspace for analysis by using nonparametric-weighted feature extraction (NWFE). According to previous studies of hyperspectral image classification, NWFE is a powerful tool for extracting hyperspectral image features. The Fisherfaces method maximizes the ratio of between-class scatter to that of within-class scatter. In this study, the proposed NW-Fisherfaces weighted the between-class scatter to emphasize the boundary structure of the transformed face subspace and, therefore, enhances the separability for different persons' face. The proposed NW-Fisherfaces was compared with Orthogonal Laplacianfaces, Eigenfaces, Fisherfaces, direct linear discriminant analysis, and null space linear discriminant analysis methods for tests on five facial databases. Experimental results showed that the proposed approach outperforms other feature extraction methods for most databases. © 2012 Li et al

QuantTune: Optimizing Model Quantization with Adaptive Outlier-Driven Fine Tuning

Transformer-based models have gained widespread popularity in both the computer vision (CV) and natural language processing (NLP) fields. However, significant challenges arise during post-training linear quantization, leading to noticeable reductions in inference accuracy. Our study focuses on uncovering the underlying causes of these accuracy drops and proposing a quantization-friendly fine-tuning method, \textbf{QuantTune}. Firstly, our analysis revealed that, on average, 65\% of quantization errors result from the precision loss incurred by the dynamic range amplification effect of outliers across the target Transformer-based models. Secondly, \textbf{QuantTune} adjusts weights based on the deviation of outlier activations and effectively constrains the dynamic ranges of the problematic activations. As a result, it successfully mitigates the negative impact of outliers on the inference accuracy of quantized models. Lastly, \textbf{QuantTune} can be seamlessly integrated into the back-propagation pass in the fine-tuning process without requiring extra complexity in inference software and hardware design. Our approach showcases significant improvements in post-training quantization across a range of Transformer-based models, including ViT, Bert-base, and OPT. QuantTune reduces accuracy drops by 12.09\% at 8-bit quantization and 33.8\% at 7-bit compared to top calibration methods, outperforming state-of-the-art solutions by over 18.84\% across ViT models