目次 at IgMin Research

Upon dysfunction of the Endoplasmic Reticulum (ER), eukaryotic cells provoke a gene expression program, namely, the Unfolded Protein Response (UPR), leading to an increase in the size and function of the ER. In the yeast Saccharomyces cerevisiae, the UPR is modulated by the Hac1i protein, which is a transcription factor produced by ER stress. When the UPR is artificially triggered under non-stress conditions by artificial expression of the Hac1i protein, S. cerevisiae cells carry an enforced and enlarged ER, which allows us to obtain commercial...ly valuable materials such as secretory proteins and functional lipids abundantly.Read more

The aim of the study was to determine the incidence of laboratory aspirin resistance; and to study the associations of genetic markers and clinical and laboratory parameters (including parameters of the platelet hemostasis) in patients with non-cardioembolic ischemic stroke using machine learning methods to assess the prognosis of recurrent ischemic strokes. Clinical and laboratory data (including induced platelet aggregation) were analyzed from 296 patients with ischemic stroke who were treated in the stroke center of City Clinical Hospital No.... 1 named after. N.I. Pirogov. The frequencies of polymorphic variants of the ITGB3, GPIba, TBXA2R, ITGA2, PLA2G7, HMOX1, PTGS1, PTGS2, ADRA2A, ABCB1, PEAR1 genes and intergenic region 9p21.3) in patients with non-cardioembolic ischemic stroke, which were identified using hydrogel biochip technology, were determined. Using the developed machine learning model, additional clinical and genetic factors influencing the development of laboratory aspirin resistance and recurrent ischemic stroke were studied. In the future, the identified factors can be used for differentiated prevention of recurrent ischemic strokes.Read more

This study provides a novel way to detect duplicate questions in the Stack Overflow community, posing a daunting problem in natural language processing. Our proposed method leverages the power of deep learning by seamlessly merging Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to capture both local nuances and long-term relationships inherent in textual input. Word embeddings, notably Google’s Word2Vec and GloVe, raise the bar for text representation to new heights. Extensive studies on the Stack Overflow ...dataset demonstrate the usefulness of our approach, generating excellent results. The combination of CNN and LSTM models improves performance while streamlining preprocessing, establishing our technology as a viable piece in the arsenal for duplicate question detection. Aside from Stack Overflow, our technique has promise for various question-and-answer platforms, providing a robust solution for finding similar questions and paving the path for advances in natural language processing.Read more

With the development of science and technology, almost everyone uses 3C (Computer, Communication, and Consumer Electronics) products and can search for information online, play games, chat with others, etc. at any time. Coupled with the impact of the pandemic, school teachers and students cannot go to school and can only use online teaching and distance learning. This requires teachers to quickly learn the relevant knowledge of using information technology for teaching, such as the operation of equipment, online teaching, etc. Establishment of ...meeting rooms. To this day, the Ministry of Education actively promotes digital teaching in primary and secondary schools based on international trends and domestic policy developments. The paper introduced the policy and current state of the application of educational technology in Taiwan.Read more

Sarcasm, a standard social media message, delivers the opposite meaning through irony or teasing. Unfortunately, identifying sarcasm in written text is difficult in natural language processing. The work aims to create an effective sarcasm detection model for social media text data, with possible applications in sentiment analysis, social media analytics, and online reputation management. A hybrid Deep learning strategy is used to construct an effective sarcasm detection model for written content on social media networks. The design emphasizes f...eature extraction, selection, and neural network application. Limited research exists on detecting sarcasm in human speech compared to emotion recognition. The study recommends using Word2Vec or TF-IDF for feature extraction to address memory and temporal constraints. Use feature selection techniques like PCA or LDA to enhance model performance by selecting relevant features. A Capsule Neural Network (CNN) and Long Short-Term Memory (LSTM) collect contextual information and sequential dependencies in textual material. We evaluate Reddit datasets with labelled sarcasm data using metrics like Accuracy. Our hybrid method gets 95.60% accuracy on Reddit.Read more

This paper tackles the vital issue of missing value imputation in data preprocessing, where traditional techniques like zero, mean, and KNN imputation fall short in capturing intricate data relationships. This often results in suboptimal outcomes, and discarding records with missing values leads to significant information loss. Our innovative approach leverages advanced transformer models renowned for handling sequential data. The proposed predictive framework trains a transformer model to predict missing values, yielding a marked improvement i...n imputation accuracy. Comparative analysis against traditional methods—zero, mean, and KNN imputation—consistently favors our transformer model. Importantly, LSTM validation further underscores the superior performance of our approach. In hourly data, our model achieves a remarkable R2 score of 0.96, surpassing KNN imputation by 0.195. For daily data, the R2 score of 0.806 outperforms KNN imputation by 0.015 and exhibits a notable superiority of 0.25 over mean imputation. Additionally, in monthly data, the proposed model’s R2 score of 0.796 excels, showcasing a significant improvement of 0.1 over mean imputation. These compelling results highlight the proposed model’s ability to capture underlying patterns, offering valuable insights for enhancing missing values imputation in data analyses.Read more

This study examined upper limb kinematics under simulated visually impaired conditions. By investigating how upper limb movements adapt in these circumstances, we aimed to gain insights that may prove valuable for both healthy populations and those with clinical conditions. Data, including 3D marker-based motion capture and accelerometer readings, were collected from seven healthy volunteers walking on a treadmill at 1.5 m/s under two conditions: without glasses (NG) and with stroboscopic glasses (G). Each walking condition lasted 2 minutes, an...d two 10-second recordings were analyzed from the midpoint of each session. Contrary to our hypothesis, simulated visual impairment with stroboscopic eyewear at 6Hz did not statistically affect upper limb kinematics, specifically in terms of wrist acceleration and hand distance in the frontal plane. Future research should explore varied visual impairment conditions, extend task durations, and investigate the relationship between subjective gait difficulties and biomechanical data.Read more

For me personally, high technologies in pediatrics are not just new opportunities for solving regular professional problems.

Post-stroke aphasia and hand movement dysfunction are common and disabling conditions. Observations indicate that most patients with post-stroke aphasia also suffer from hand movement dysfunction. Research in human evolution, behavior, and neuroscience has revealed a strong connection between language function and hand-motor function, with the latter playing a critical role in language use. Consequently, there is an urgent need for the development of new, comprehensive, and efficient rehabilitation methods for post-stroke aphasia that is accomp...anied by hand dysfunction. One promising approach involves investigating the shared neural networks between language and hand function as a foundation for novel treatment methods. This article aims to review the current state of clinical research on comprehensive treatments for stroke-induced aphasia and hand dysfunction, as well as to explore their underlying neural mechanisms. The results of this study may provide a valuable reference for the advancement of treatment technologies that effectively address both dysfunctions and enhance clinical outcomes.Read more