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Underwater target detection plays an important role in ocean exploration, to which the improvement of relevant technology is of much practical significance. Although existing target detection algorithms have achieved excellent performance on land, they often fail to achieve satisfactory outcome of detection when in the underwater environment. In this paper, one of the most advanced target detection algorithms, YOLOv5 (You Only Look Once), was first applied in the underwater environment before being improved by combining it with some methods characteristic of the underwater environment. To be specific, the Swin Transformer was treated as the basic backbone network of YOLOv5, which makes the network suitable for those underwater images with blurred targets. It is possible for the network to focus on fusing the relatively important resolution features by improving the method of path aggregation network (PANet) for multi-scale feature fusion. The confidence loss function was improved on the basis of different detection layers, with the network biased to learn high-quality positive anchor boxes and make the network more capable of detecting the target. As suggested by the experimental results, the improved network model is effective in detecting underwater targets, with the mean average precision (mAP) reaching 87.2%, which makes it advantageous over general target detection models and fit for use in the complex underwater environment.

On 6 May 2018, a remarkable subauroral optical event exhibiting three phases of evolution (Strong Thermal Emission Velocity Enhancement [STEVE] → Picket Fence → STEVE) and co‐occurrence with a stable auroral red arc was observed over Alberta, Canada. Using ground‐based instruments (Redline Emission Geospace Observatory and TREx) and satellite measurements (Swarm and DMSP), we characterized the associated ionospheric conditions. The results suggest that a visible STEVE may require density depletion, electron heating, and intense ion drifts. The observed STEVE‐Picket Fence transition indicates that these phenomena may share the same subauroral ion drift channel, yet are modulated by local Ne structure. These findings provide new insights into the physical differences between STEVE and Picket Fence emissions, and offer key observational constraints for understanding magnetosphere‐ionosphere coupling in subauroral regions.

Cisplatin is one of the most effective chemotherapy drugs and is widely used in the treatment of cancer, including hepatocellular carcinoma (HCC) and cervical cancer, but its therapeutic benefit is limited by the development of resistance. Our previous studies demonstrated that BCAT1 promoted cell proliferation and decreased cisplatin sensitivity in HCC cells. However, the exact role and mechanism of how BCAT1 is involved in cisplatin cytotoxicity remain undefined. In this study, we revealed that cisplatin triggered autophagy in cancer cells, with an increase in BCAT1 expression. The cisplatin-induced up-regulation of BCAT1 decreased the cisplatin sensitivity by regulating autophagy through the mTOR signaling pathway. In addition, branched-chain amino acids or leucine treatment inhibited cisplatin- or BCAT1-mediated autophagy and increased cisplatin sensitivity by activating mTOR signaling in cancer cells. Moreover, inhibition of autophagy by chloroquine increased cisplatin sensitivity in vivo. Also, the knockdown of BCAT1 or the administration of leucine activated mTOR signaling, inhibited autophagy, and increased cisplatin sensitivity in cancer cells in vivo. These findings demonstrate a new mechanism, revealing that BCAT1 decreases cisplatin sensitivity in cancer cells by inducing mTOR-mediated autophagy via branched-chain amino acid leucine metabolism, providing an attractive pharmacological target to improve the effectiveness of chemotherapy.

With the increasing interest in the application of large language models (LLMs) in the medical field, the feasibility of its potential use as a standardized patient in medical assessment is rarely evaluated. Specifically, we delved into the potential of using ChatGPT, a representative LLM, in transforming medical education by serving as a cost-effective alternative to standardized patients, specifically for history-taking tasks. The study aims to explore ChatGPT's viability and performance as a standardized patient, using prompt engineering to refine its accuracy and use in medical assessments. A 2-phase experiment was conducted. The first phase assessed feasibility by simulating conversations about inflammatory bowel disease (IBD) across 3 quality groups (good, medium, and bad). Responses were categorized based on their relevance and accuracy. Each group consisted of 30 runs, with responses scored to determine whether they were related to the inquiries. For the second phase, we evaluated ChatGPT's performance against specific criteria, focusing on its anthropomorphism, clinical accuracy, and adaptability. Adjustments were made to prompts based on ChatGPT's response shortcomings, with a comparative analysis of ChatGPT's performance between original and revised prompts. A total of 300 runs were conducted and compared against standard reference scores. Finally, the generalizability of the revised prompt was tested using other scripts for another 60 runs, together with the exploration of the impact of the used language on the performance of the chatbot. The feasibility test confirmed ChatGPT's ability to simulate a standardized patient effectively, differentiating among poor, medium, and good medical inquiries with varying degrees of accuracy. Score differences between the poor (74.7, SD 5.44) and medium (82.67, SD 5.30) inquiry groups (P<.001), between the poor and good (85, SD 3.27) inquiry groups (P<.001) were significant at a significance level (α) of .05, while the score differences between the medium and good inquiry groups were not statistically significant (P=.16). The revised prompt significantly improved ChatGPT's realism, clinical accuracy, and adaptability, leading to a marked reduction in scoring discrepancies. The score accuracy of ChatGPT improved 4.926 times compared to unrevised prompts. The score difference percentage drops from 29.83% to 6.06%, with a drop in SD from 0.55 to 0.068. The performance of the chatbot on a separate script is acceptable with an average score difference percentage of 3.21%. Moreover, the performance differences between test groups using various language combinations were found to be insignificant. ChatGPT, as a representative LLM, is a viable tool for simulating standardized patients in medical assessments, with the potential to enhance medical training. By incorporating proper prompts, ChatGPT's scoring accuracy and response realism significantly improved, approaching the feasibility of actual clinical use. Also, the influence of the adopted language is nonsignificant on the outcome of the chatbot.

This study presents a full‐period imaging of the thermospheric composition response to the 14 October 2023 annular solar eclipse, combining GOLD (Global‐scale Observations of the Limb and Disk) far‐ultraviolet observations with solar‐irradiance‐driven WACCM‐X (Whole Atmosphere Community Climate Model with thermosphere–ionosphere extension) modeling. GOLD detected up to 80K cooling and >30% airglow reductions in the umbra, with ΣO/N2 increasing ∼20%. WACCM‐X reproduced overall trends but overestimated the ΣO/N2 increase by ∼5%, underestimated the cooling by ∼60K, and showed slower recovery. The discrepancies between the WACCM‐X simulations and the GOLD observations are further corroborated by supplementary TIMED (Thermosphere, Ionosphere, Mesosphere Energetics and Dynamics) observations. The model‐data comparison reveals that eclipse‐induced thermospheric changes arise from coupled photochemical, dynamical, and radiative processes, providing new insight into the atmospheric response to abrupt solar forcing and refining theoretical models.

While the isolated effects of solar flares on low‐latitude ionospheric electrodynamics have been well documented, the coupled system response of the equatorial electrojet (EEJ), auroral electrojet (AEJ), field‐aligned currents (FACs), and asymmetric ring current (ASY‐H) remains poorly understood. This study statistically analyzes 1,657 X/M‐class flares (2001–2017) to quantify rapid electrodynamic changes across current systems. Our results indicate (a) flare intensity‐dependent enhancements in eastward EEJ, suppressed equatorial ionospheric vertical drift (Vz), and increased ASY‐H; (b) negligible flare influence on AEJ; and (c) R2 FACs intensification in the dusk sector, linking ionospheric dynamics to asymmetric ring current perturbations. These observations reveal transient electrodynamic coupling within the geospace associated with flares, independent of solar wind forcing, advancing understanding of flare‐driven ionosphere‐magnetosphere interactions.

The accumulation of senescent cells is a major contributor to aging and various age-related diseases, making developing senolytic compounds that are capable of clearing these cells an important area of research. However, progress has been hampered by the limited number of known senolytics and the incomplete understanding of their mechanisms. This study presents a powerful senolytic predictor built using phenotypic data and machine learning techniques to identify compounds with potential senolytic activity. A comprehensive training dataset consisting of 111 positive and 3951 negative compounds was curated from the literature. The dataset was used to train machine learning models, incorporating traditional molecular fingerprints, molecular descriptors, and MoLFormer molecular embeddings. By applying MoLFormer-based oversampling and testing different algorithms, it was found that the Support Vector Machine (SVM) and Multilayer Perceptron (MLP) models with MoLFormer embeddings exhibited the best performance, achieving Area Under the Curve (AUC) scores of 0.998 and 0.997, and F1 scores of 0.948 and 0.941, respectively. This senolytic predictor was then used to perform virtual screening of compounds from the DrugBank and TCMbank databases. In the DrugBank database, 98 structurally novel candidate compounds with potential senolytic activity were identified. For TCMbank, 714 potential senolytic compounds were predicted and 81 medicinal herbs with possible senolytic properties were identified. Moreover, pathway enrichment analysis revealed key targets and potential mechanisms underlying senolytic activity. In an experimental screening of predicted compounds, panaxatriol was found to exhibit senolytic activity on the etoposide-induced senescence of the IMR-90 cell line. Additionally, voclosporin was found to extend the lifespan of C. elegans more effectively than metformin, demonstrating the value of our model for drug repurposing. This study not only provides an efficient framework for discovering novel senolytic agents, but also highlights the predicted novel senolytic compounds and herbs as valuable starting points for future research into senolytic drug development.

Solar eclipses generate significant wave activity in the Earth's upper atmosphere. The source region of eclipse‐induced Traveling Atmospheric Disturbances (TADs) in the upper thermosphere—particularly the relative contributions of gravity waves from the thermosphere itself versus the lower atmosphere—remains unknown. Using the Whole Atmosphere Community Climate Model with thermosphere–ionosphere extension (WACCM‐X), we investigate TADs triggered by the 26 December 2019 annular solar eclipse. Simulations demonstrate that eclipse‐shadow passage launches thermospheric wave disturbances as TADs, with primary excitation occurring above 80 km altitude rather than in the lower atmosphere. These TADs propagate at speeds of ∼550 m/s, with trajectories dictated by the eclipse path. Thermospheric heating rates, temperature, and neutral wind analyses along propagation paths reveal that 80%–90% of the disturbance amplitudes originate in the upper atmosphere, while the lower atmosphere contributes 10%–20% of the disturbance amplitudes.

In this paper, we propose the usage of an ensemble learning approach for predicting total electron content (TEC). The training data set spans from 2007 to 2016, while the testing data set is set to the year 2017. The model inputs in our study included Solar radio flux (F107), Solar Wind plasma speed, By, Bz, Dst, Ap, AE, day of year, universal time, 30‐day and 90‐day TEC averages. Specifically, eXtreme Gradient Boosting (XGBoost), Gradient Boosting Decision Tree, and Decision Tree were utilized for 1‐hr TEC prediction at high‐ (80°W, 80°N), mid‐ (80°W, 40°N), and low‐ latitudes (80°W, 10°N). Results indicate that all three models performed well in predicting TEC, with a mean error of only approximately 0.6 TECU at high‐ and mid‐ latitudes and 1.13 TECU at low latitudes. At the same time, we compared the model with 1‐day Beijing University of Aeronautics and Astronautics model during the period of magnetic storm from 25 August 2018 to 27 August 2018 and a quiet period from 13 December 2018 to 15 December 2018. In the magnetic storm period, Our model showed an average reduction of 1.83 TECU compared to BUAA model. During the quiet period, XGBoost exhibit an average error that is 1.14 TECU lower than that of BUAA model. Moreover, TEC prediction over the region between the 20°N–45°N and 70°E−120°E during geomagnetic storm has an error of 2.74 TECU, showing the stability and superiority of XGBoost. Overall, the ensemble learning approach exhibits its advantage in predicting TEC.

Drug development faces significant financial and time challenges, highlighting the need for more efficient strategies. This study evaluated the druggability of the entire human proteome using Fpocket. We identified 15,043 druggable pockets in 20,255 predicted protein structures, significantly expanding the estimated druggable proteome from 3000 to over 11,000 proteins. Notably, many druggable pockets were found in less studied proteins, suggesting untapped therapeutic opportunities. The results of a pairwise pocket similarity analysis identified 220,312 similar pocket pairs, with 3241 pairs across different protein families, indicating shared drug-binding potential. In addition, 62,077 significant matches were found between druggable pockets and 1872 known drug pockets, highlighting candidates for drug repositioning. We repositioned progesterone to ADGRD1 for pemphigus and breast cancer, as well as estradiol to ANO2 for shingles and medulloblastoma, which were validated via molecular docking. Off-target effects were analyzed to assess the safety of drugs such as axitinib, linking newly identified targets with known side effects. For axitinib, 127 new targets were identified, and 46 out of 48 documented side effects were linked to these targets. These findings demonstrate the utility of pocket similarity in drug repositioning, target expansion, and improved drug safety evaluation, offering new avenues for the discovery of new indications and side effects of existing drugs.