No abstract

Higher-energy collisional dissociation (HCD) is a well-established fragmentation technique in liquid chromatography tandem mass spectrometry (LC-MS/MS) and is used to study protein post translational modifications (PTMs) during peptide mapping. However, labile PTMs like glycosylation, glycation, sulfonylation, or phosphorylation tend to fragment earlier than peptide backbones under HCD. This leads to complicated MS/MS spectra, compromising data quality and downstream data interpretation. Electron-transfer/higher-energy collision dissociation (EThcD) has been used to analyze PTMs, but important components might be missed because of the increased duty cycle. To address this issue, modification-specific fragment ions formed in HCD experiments could be utilized to trigger EThcD analysis only for modified peptides. The trigger for EThcD was established by applying HCD with a high normalized collision energy, generating multiple informative Amadori derived lysine signature ions from a glycated peptide. These signature ions were then applied to trigger targeted EThcD for lysine glycation identification. This improved approach can further expand the characterization efforts of highly labile PTMs in therapeutic proteins.

With the emergence and development of new software architectures such as microservices, how to effectively handle the service load and ensure the service capability of the system has become an urgent problem to be solved. Load balancing technology needs to achieve high availability of microservices without affecting the delayed response of requests. According to different principles of adoption, mainstream load balancing technologies have emerged, such as polling methods, hash algorithms, and artificial intelligence technologies. This article categorizes and summarizes load balancing technologies for microservice architecture, and elaborates the methods and characteristics of current mainstream load balancing technologies. Based on the comparative analysis of existing technologies, this paper summarizes and points out the future development direction of load balancing technology.

To explore the emergency care method for patients suffering from acute cerebral infarction with hypertension and diabetes and its clinical effects. A total of 80 patients were selected, and were divided into the observation group and control group. The patients in the control group were given the routine emergency care, while the patients in the observation group were give the comprehensive emergency care, and the effects of clinical care of the patients in the two groups were compared. After receiving the comprehensive emergency care, the mortality rate, disability rate, recurrence rate, rescuing time, emergency triage time and NIHSS score of patients in observation group were lower than those of control group, and the differences between the two groups were of statistical significance (P < 0.05). Giving patients suffering from acute cerebral infarction with hypertension and diabetes the comprehensive emergency care could not only save the lives of patients effectively and rapidly, reducing the mortality rate, disability rate and recurrence rate, but also effectively shorten the rescue time and clinic time of the patients, reduce the degree of neurologic impairment caused to the patients. Therefore, comprehensive emergency care is worth promoting in clinical practice.

Thanks to the development of geographic information technology, geospatial representation learning based on POIs (Point-of-Interest) has gained widespread attention in the past few years. POI is an important indicator to reflect urban socioeconomic activities, widely used to extract geospatial information. However, previous studies often focus on a specific area, such as a city or a district, and are designed only for particular tasks, such as land-use classification. On the other hand, large-scale pre-trained models (PTMs) have recently achieved impressive success and become a milestone in artificial intelligence (AI). Against this background, this study proposes the first large-scale pre-training geospatial representation learning model called GeoBERT. First, we collect about 17 million POIs in 30 cities across China to construct pre-training corpora, with 313 POI types as the tokens and the level-7 Geohash grids as the basic units. Second, we pre-train GeoEBRT to learn grid embedding in self-supervised learning by masking the POI type and then predicting. Third, under the paradigm of “pre-training + fine-tuning”, we design five practical downstream tasks. Experiments show that, with just one additional output layer fine-tuning, GeoBERT outperforms previous NLP methods (Word2vec, GloVe) used in geospatial representation learning by 9.21% on average in F1-score for classification tasks, such as store site recommendation and working/living area prediction. For regression tasks, such as POI number prediction, house price prediction, and passenger flow prediction, GeoBERT demonstrates greater performance improvements. The experiment results prove that pre-training on large-scale POI data can significantly improve the ability to extract geospatial information. In the discussion section, we provide a detailed analysis of what GeoBERT has learned from the perspective of attention mechanisms.