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Aims: The aim of this study was to explore the clinical characteristics and risk factors for hypersensitivity reactions induced by antituberculosis drugs. Methods: A retrospective analysis was conducted on the medical records of patients with active tuberculosis (TB) treated in the TB ward at West China Hospital, Sichuan University, from November 2010 to April 2020. Results: Out of 7106 patients with active tuberculosis, 205 experienced hypersensitivity reactions to antituberculosis drugs; the incidence of hypersensitivity was 2.9%. The predominant clinical manifestation was a rash, observed in 57.1% (117/205) of these cases. Additionally, 19.0% (39/205) of patients presented with concurrent liver injury. The laboratory parameters white blood cell count, total lymphocyte count, monocyte count, eosinophil count, basophil count, alanine aminotransferase, aspartate aminotransferase and alkaline phosphatase were significantly elevated in patients with hypersensitivity compared to those without. In 38 patients who tested positive for oral antituberculosis drug provocation, 14 (36.8%) were allergic to more than two antituberculosis drugs. Significant risk factors included being female (odds ratio [OR] = 1.387, 95% confidence intervals [CI]: 1.016–1.894), under 65 years of age (OR = 1.826, 95% CI: 1.145–2.913), existing liver disease (OR = 2.464, 95% CI: 1.822–3.333) and a history of allergic diseases (OR = 6.633, 95% CI: 2.681–16.406) and were significantly correlated with hypersensitivity to antituberculosis drugs. Conclusions: Hypersensitivity reactions to antituberculosis drugs primarily affect the skin, with significant associations observed with liver injury. Females, individuals younger than 65 years, those with pre‐existing liver disease and patients with a history of allergic diseases are at elevated risk for hypersensitivity. [ABSTRACT FROM AUTHOR]

The exploration of multi‐layer coupling mechanisms between earthquakes and the ionosphere is crucial for utilizing ionospheric precursors in earthquake prediction. A significant research task involves continuously tracking the spatio‐temporal changes in ionospheric parameters, acquiring comprehensive seismic anomaly information, and capturing "deterministic" precursor anomalies. Based on data from the China Seismo‐Electromagnetic Satellite (CSES), we enhance the Pattern Informatics (PI) Method and propose an Improved Pattern Informatics (IPI) Method. The IPI method enables the calculation of the spatio‐temporal dynamics of electron density anomalies detected by the CSES satellite. The seismic signals in the electron density during earthquake on 2021 at Maduo are investigated in this work. The results show that: (a) Compared to original electron density images, the IPI method‐derived models extract distinct electron density anomaly signals, regardless of the data whether are collected during descending (daytime) or ascending (nighttime) orbits, or across different time scales of change window. (b) The electron density anomalies appear about 40 days prior to the Maduo Mw7.3 earthquake. The evolution of these anomalies follows a pattern of appearance, persistence, disappearance, re‐emergence, and final disappearance. Moreover, the evolution trends of the IPI hotspot images at daytime and nighttime are similar. These results suggest that the IPI method can capture the spatio‐temporal trends of ionospheric parameters and effectively extract electronic precursors related to strong earthquakes. Key Points: Proposed an Improved Pattern Informatics Method for processing electron density data and capturing anomalous spatio‐temporal patternConducted a feasibility study of the method using the 2021 Maduo Mw7.3 Earthquake as a case studyObserved a recurring emergence and dissipation in the electron density prior to the Maduo Mw7.3 Earthquake [ABSTRACT FROM AUTHOR]

The Pattern Informatics Method (PI) was initially developed for medium-to-long-term earthquake prediction by analyzing changes in seismic activity. It has since been refined and extended to identify ionospheric anomalies associated with earthquakes. Notable advancements include the development of modified and improved methods, which have demonstrated their capability to detect significant short-term and ionospheric anomalies preceding earthquake events. In this study, the IPI method was applied to infrared satellite observation data for the first time, and a new algorithm for extracting short-term and imminent anomalies from infrared earthquakes was explored based on the IPI method, from which we obtained the MIPI (Modified Improved Pattern Informatics Method). Using 1° × 1° nighttime Outgoing Longwave Radiation (OLR) data from NOAA_18 satellites of the National Oceanic and Atmospheric Administration's Climate Prediction Center (NOAA-CPC) of the United States, the evolution of OLR anomalies before the Ridgecrest Ms 6.9 earthquake in the United States on 6 July 2019 as recorded by the China Earthquake Networks Center (CENC) and the Maduo Ms 7.4 earthquake in China on 21 May 2021 as recorded by the China Earthquake Networks Center (CENC) were studied. In order to make the IPI method suitable for the calculation of OLR data, two modifications were made to the IPI algorithm: (1) the quartile method was applied for automatically determining the abnormal changes in the OLR observation data and they were used as the input data instead of ionospheric data; (2) the standard deviation of the multi-year OLR residual data of each grid was used instead of the maximum anomaly index used in the original method to re-assign and obtain the relative anomaly index, and finally the anomaly evolution time series diagram was drawn. The results show the following: (1) The MIPI method can effectively extract short-term and imminent OLR anomalies prior to earthquakes. (2) Short-term and imminent OLR anomalies appeared about two weeks before each earthquake and lasted until the earthquake occurrence, disappearing after the earthquake. During this process, the anomalies exhibited a certain evolutionary trend. (3) The short-term and imminent OLR anomalies prior to each earthquake were distributed near the epicenter or near the seismogenic fault, about 200 KM away from the epicenters. The above results are similar to the spatiotemporal evolution characteristics of seismic infrared short-term anomalies previously studied, which indicates that the MIPI method can effectively extract seismic infrared anomalies and might provide a practical method for the extraction of seismic infrared short-term and imminent anomalies. [ABSTRACT FROM AUTHOR]

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This study explores the potential of machine learning algorithms for earthquake prediction, utilizing fluid chemical anomaly data from hot springs. Six hot springs, located within an active fault zone along the southeastern coast of China, were carefully chosen as hydrochemical monitoring sites for an extended period of two and a half years. Using this data, a prediction model integrating six algorithms was developed to forecast M ≥ 5 earthquakes in Taiwan. The model's performance was validated against recorded earthquake events, and the factors influencing its predictive capability were analyzed. Our comprehensive analysis conclusively demonstrates the superiority of machine learning algorithms over traditional statistical methods for earthquake prediction. Additionally, including sampling time in the data sets significantly improves the model's predictive performance. However, it is important to note that the model's predictive performance varies across different hot spring and indicators type, highlighting the importance of identifying optimal indicators for specific scenarios. The model parameters, including the anomaly detection rate (P) and earthquake response time threshold (M), significantly impact the model's predictive capabilities. Therefore, adjustments are needed to optimize the model's performance for practical use. Despite limitations such as the inability to differentiate pre‐earthquake anomalies from post‐earthquake anomalies and pinpoint the precise location of earthquakes, this study successfully showcases the potential of machine learning algorithms in earthquake prediction, paving the way for further research and improved prediction methods. Plain Language Summary: This study explores the potential of utilizing machine learning algorithms for earthquake prediction based on hydrothermal fluid chemical anomaly data. An earthquake prediction model integrating six machine learning algorithms was developed using continuous hot spring hydrochemical monitoring data. The model's prediction performance was validated using actual earthquake event data, and various factors influencing its predictive capability were analyzed. The results demonstrate that the machine learning algorithm's prediction performance exceeds that of traditional methods, and the selection of hot springs, indicator types, and model parameters significantly impacts the model's prediction performance. This study successfully showcased the potential of machine learning algorithms in earthquake prediction, while also indicating new avenues for further research and improvement of prediction methods. Key Points: An earthquake prediction model based on machine learning using hydrochemical data from hot springs is proposed with sufficient accuracyThe earthquake prediction accuracy of the local outlier factor algorithm outperformed the traditional statistical methodThe predictive performance of the model varies across different hot springs, indicators type, and model parameters [ABSTRACT FROM AUTHOR]

In this study, 117,718 ionospheric perturbations, with a space size (t) of 20–300 s but no amplitude (A) limit, were automatically globally searched via software utilizing ion density data measured by the DEMETER satellite for over 6 years. The influence of geomagnetic storms on the ionosphere was first examined. The results demonstrated that storms can globally enhance positive ionospheric irregularities but rarely induce plasma variations of more than 100%. The probability of PERs with a space size falling in 200–300 s (1400–2100 km if a satellite velocity of 7 km/s is considered) occurring in a geomagnetically perturbed period shows more significance than that in a quiet period. Second, statistical work was performed on ion PERs to check their dependence on local time, and it was shown that 24.8% of the perturbations appeared during the daytime (10:30 LT) and 75.2% appeared during the nighttime (22:30 LT). Ionospheric fluctuations with an absolute amplitude of A < 10% tend to be background variations, and the percentages of positive perturbations with a small A < 20% occur at an amount of 64% during the daytime and 26.8% during the nighttime, but this number is reversed for mid–large-amplitude PERs. Large positive PERs with A > 100% mostly occurred at night and negative ones with A < −100% occurred entirely at night. There was a demarcation point in the space size of t = 120 s, and the occurrence probabilities of day PERs were always higher than that of nighttime ones before this point, while this trend was contrary after this point. Finally, distributions of PERs according to different ranges of amplitude and space scale were characterized by typical seasonal variations either in the daytime or nighttime. EIA only exists in the dayside equinox and winter, occupying two low-latitude crests with a lower Np in both hemispheres. Large WSAs appear within all periods, except for dayside summer, and are full of PERs with an enhanced amplitude, especially on winter nights. The WN-like structure is obvious during all seasons, showing large-scale space. On the other hand, several magnetically anomalous zones of planetary-scale non-dipole fields, such as the SAMA, Northern Africa anomaly, and so on, were also successfully detected by extreme negative ion perturbations during this time. [ABSTRACT FROM AUTHOR]

The rapid development of Earth observation technology has promoted the continuous accumulation of images in the field of remote sensing. However, a large number of remote sensing images still lack manual annotations of objects, which makes the strongly supervised deep learning object detection method not widely used, as it lacks generalization ability for unseen object categories. Considering the above problems, this study proposes a few-shot remote sensing image object detection method that integrates context dependencies and global features. The method can be used to fine-tune the model with a small number of sample annotations based on the model trained in the base class, as a way to enhance the detection capability of new object classes. The method proposed in this study consists of three main modules, namely, the meta-feature extractor (ME), reweighting module (RM), and feature fusion module (FFM). These three modules are respectively used to enhance the context dependencies of the query set features, improve the global features of the support set that contains annotations, and finally fuse the query set features and support set features. The baseline of the meta-feature extractor of the entire framework is based on the optimized YOLOv5 framework. The reweighting module of the support set feature extraction is based on a simple convolutional neural network (CNN) framework, and the foreground feature enhancement of the support sets was made in the preprocessing stage. This study achieved beneficial results in the two benchmark datasets NWPU VHR-10 and DIOR. Compared with the comparison methods, the proposed method achieved the best performance in the object detection of the base class and the novel class. [ABSTRACT FROM AUTHOR]