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Solar flares are explosions on the Sun. They happen when energy stored in magnetic fields around solar active regions (ARs) is suddenly released. Solar flares and accompanied coronal mass ejections are sources of space weather, which negatively affects a variety of technologies at or near Earth, ranging from blocking high-frequency radio waves used for radio communication to degrading power grid operations. Monitoring and providing early and accurate prediction of solar flares is therefore crucial for preparedness and disaster risk management. In this article, we present a transformer-based framework, named SolarFlareNet, for predicting whether an AR would produce a γ -class flare within the next 24 to 72 h. We consider three γ classes, namely the ≥ M5.0 class, the ≥ M class and the ≥ C class, and build three transformers separately, each corresponding to a γ class. Each transformer is used to make predictions of its corresponding γ -class flares. The crux of our approach is to model data samples in an AR as time series and to use transformers to capture the temporal dynamics of the data samples. Each data sample consists of magnetic parameters taken from Space-weather HMI Active Region Patches (SHARP) and related data products. We survey flare events that occurred from May 2010 to December 2022 using the Geostationary Operational Environmental Satellite X-ray flare catalogs provided by the National Centers for Environmental Information (NCEI), and build a database of flares with identified ARs in the NCEI flare catalogs. This flare database is used to construct labels of the data samples suitable for machine learning. We further extend the deterministic approach to a calibration-based probabilistic forecasting method. The SolarFlareNet system is fully operational and is capable of making near real-time predictions of solar flares on the Web.

Extremely severe and persistent haze occurred in January 2013 over eastern and northern China. The record-breaking high concentrations of fine particulate matter (PM2.5) of more than 700 μg m−3 on hourly average and the persistence of the episodes have raised widespread, considerable public concerns. During that period, 7 of the top 10 polluted cities in China were within the Hebei Province. The three cities in southern Hebei (Shijiazhuang, Xingtai, and Handan) have been listed as the top three polluted cities according to the statistics for the first half of the year 2013. In this study, the Mesoscale Modeling System Generation 5 (MM5) and the Models-3/Community Multiscale Air Quality (CMAQ) modeling system are applied to simulate the 2013 severe winter regional hazes in East Asia and northern China at horizontal grid resolutions of 36 and 12 km, respectively, using the Multi-resolution Emission Inventory for China (MEIC). The source contributions of major source regions and sectors to PM2.5 concentrations in the three most polluted cities in southern Hebei are quantified by aiming at the understanding of the sources of the severe haze pollution in this region, and the results are compared with December 2007, the haziest month in the period 2001–2010. Model evaluation against meteorological and air quality observations indicates an overall acceptable performance and the model tends to underpredict PM2.5 and coarse particulate matter (PM10) concentrations during the extremely polluted episodes. The MEIC inventory is proven to be a good estimation in terms of total emissions of cities but uncertainties exist in the spatial allocations of emissions into fine grid resolutions within cities. The source apportionment shows that emissions from northern Hebei and the Beijing-Tianjin city cluster are two major regional contributors to the pollution in January 2013 in Shijiazhuang, compared with those from Shanxi and northern Hebei for December 2007. For Xingtai and Handan, the emissions from northern Hebei and Henan are important. The industrial and domestic sources are the most significant local contributors, and the domestic and agricultural emissions from Shandong and Henan are non-negligible regional sources, especially for Xingtai and Handan. Even in the top two haziest months (i.e., January 2013 and December 2007), a large fraction of PM2.5 in the three cities may originate from quite different regional sources. These results indicate the importance of establishing a regional joint framework of policymaking and action system to effectively mitigate air pollution in this area, not only over the Beijing-Tianjin-Hebei area, but also surrounding provinces such as Henan, Shandong, and Shanxi.

Symptoms of depression and high risk of sarcopenia are common among the older population; however, the associations between these remain unclear. Thus, the present study identified whether depressive symptoms are associated with older adults’ sarcopenia risks. This nationally representative study in Taiwan investigated the older adult population (≥ 65 years) using a telephone survey conducted between 2019 and 2020. Design: Self-reported data obtained included depressive symptoms (5-item from Center for Epidemiological Studies-Depression scale), sarcopenia risks (SARC-F questionnaire), and individual characteristics. The generalized additive models were used to examine the nonlinear associations between depressive symptoms and the risk of sarcopenia. A total of 1,068 older Taiwanese adults (72.15 ± 5.71 years; 52.7% women) participated in the survey. In the unadjusted model, the results showed a significant nonlinear association between high scores on the CES-D and sarcopenia scores (p <.001). Even after adjusting for covariates (sex, age, residential areas, education, marital status, working status, living status, smoking, drinking, and BMI), it still showed a significant non-linear association (p <.001). The results indicated that depressive symptoms were nonlinearly related to older adults’ sarcopenia risks. Interventions or programs aiming to alleviate depression can be an effective strategy to prevent sarcopenia in the older adult population.

We present FlareDB, a database that provides comprehensive magnetic field information, ultraviolet/extreme ultraviolet (UV/EUV) emissions, and white light continuum images for solar active regions (ARs) associated with 151 significant flares from May 2010 to May 2025. The data, sourced from the Solar Dynamics Observatory (SDO) via the Joint Science Operations Center (JSOC), were processed with SunPy and stored in standardized JSOC FITS format. FlareDB includes all M5.0 and larger flares within 50° of the solar disk center. Key features include (1) Atmospheric Imaging Assembly (AIA) AR patches in Helioprojective Cartesian(HPC) and Lambert Cylindrical Equal-Area (CEA) projections, aligned with corresponding HMI magnetogram patches; (2) quick-look movies with uniform value ranges that ensure consistent visualization, maintain data uniformity, and enhance readiness for machine learning studies; (3) a supplementary web interface that allows the entire dataset of a flare to be downloaded for large flare analysis. One of FlareDB’s primary objectives is to support scientists in predicting and understanding the onset of solar eruptions, including flares and coronal mass ejections. The data set is machine-learning ready for this purpose.

Purpose Hepatic metastasis frequently occurs in patients who have undergone radical pancreatic resection for pancreatic cancer. Besides chemotherapy, various local treatment approaches targeting hepatic lesions have been explored. However, research on radiofrequency ablation (RFA) as a localized therapy for hepatic metastasis is limited. Therefore, we conducted this retrospective study to provide clinical evidence. Methods This is a single-center, retrospective, cohort study. After radical pancreaticoduodenectomy, 32 patients developed metachronous hepatic metastasis with fewer than 3 lesions, the largest of which was less than 3 cm in diameter. These patients underwent combined treatment with chemotherapy and RFA. After 8 weeks of chemotherapy, patients received RFA for hepatic lesions. Additional chemotherapy was administered, and the patients’ tumor status and survival were monitored. The primary endpoint of this study was overall survival (OS). Factors affecting OS were analyzed using the Cox risk model. Results Among the 32 patients, the mean OS was 28.4 months. Univariate and multivariate Cox regression analysis revealed that the time (in months) of liver metastasis (HR = 0.04, 95% CI: 0.01 to 0.19; P < 0.001), the number of liver metastases (HR = 7.08, 95% CI: 1.85 to 27.08, P = 0.004), and PD (progressive disease) response to the second round of chemotherapy (HR = 29.50, 95% CI: 1.46 to 597.27; P = 0.027) were independent predictors of poorer survival. Conclusion Combined therapy with RFA and chemotherapy is safe in patients with hepatic metastasis after radical pancreaticoduodenectomy. Early recurrence (≤12 months), three liver metastatic lesions, and a poor response to the second round of chemotherapy were associated with poor survival.

The TP53 mutation frequency in ovarian serous carcinomas has been reported to range between 50% and 80%, but a stringent analysis of TP53 using purified epithelial samples has not yet been performed to accurately assess the mutation frequency and to correlate it with the histologic grade. The purpose of this study was to assess the TP53 mutational profile in a relatively large series of high-grade (53 primary and 18 recurrent) and 13 low-grade ovarian serous tumors using DNA isolated from affinity-purified tumor cells and to correlate it with in vitro drug resistance. All samples were affinity purified, and the tumor DNA was analyzed for TP53 mutations in exons 4–9. In vitro drug resistance assays to carboplatin, cisplatin, paclitaxel, and taxotere were performed on the same tumor samples and correlated with the TP53 mutation status. TP53 mutations were detected in 57 (80.3%) of 71 high-grade carcinomas and in one (7.8%) of 13 low-grade serous tumors (an invasive low-grade serous carcinoma). The mutations were predominantly missense mutations (59.6%). TP53 mutations were associated with high-grade serous carcinomas and recurrent disease (P < 0.0001). There was no statistically significant correlation between TP53 mutation status and drug resistance assays or clinical stage (P > 0.25). The frequency of TP53 mutations using purified tumor DNA from ovarian serous carcinomas was 80.3%, which is much higher than previously reported. Furthermore, we found that TP53 is not directly involved in the development of drug resistance in high-grade ovarian serous carcinomas.

Nucleus accumbens-1 (Nac1 or NAC-1) belongs to the BTB/POZ (Pox virus and Zinc finger/Bric-a-brac Tramtrack Broad complex) transcription factor family and is a novel protein that potentially participates in self-renewal and pluripotency in embryonic stem cells. In human cancer, NAC-1 is upregulated in several types of neoplasms, but particularly in recurrent chemoresistant ovarian carcinomas, suggesting a biological role for NAC-1 in the development of drug resistance in ovarian cancer. We have assessed this possibility and shown a correlation between NAC-1 expression and ex vivo paclitaxel resistance in ovarian serous carcinoma tissues and cell lines. We found that expression of Gadd45-γ-interacting protein 1 (Gadd45gip1), a downstream target negatively regulated by NAC-1, was reduced in paclitaxel-resistant cells. Ectopic expression of NAC-1 or knockdown of Gadd45gip1 conferred paclitaxel resistance, whereas NAC-1 knockdown or ectopic expression of Gadd45gip1 increased paclitaxel sensitivity. Furthermore, silencing NAC-1 expression or disrupting NAC-1 homodimerization by a dominant negative NAC-1 protein that contained only the BTB/POZ domain induced the expression of Gadd45γ, which interacted with Gadd45gip1. Reducing Gadd45γ expression by small hairpin RNAs partially enhanced paclitaxel resistance. Thus, this study provides new evidence that NAC-1 upregulation and homodimerization contribute to tumor recurrence by equipping ovarian cancer cells with the paclitaxel-resistant phenotype through negative regulation of the Gadd45 pathway.

To address the measurement instability of reflective ultrasonic anemometers in complex wind fields, this study systematically investigates the mechanisms by which shadow effects caused by transducers and reflector support pillars affect measurement accuracy under varying wind speeds and directions. By integrating particle image velocimetry (PIV) experiments with computational fluid dynamics (CFD) simulations, 1:1 and 1:2 scale models are employed to reveal the flow field characteristics and error mechanisms. The results indicate that at a wind direction of 0°, wall-following vortices and turbulent wakes generated by transducer structures cause systematic wind speed deviations along the measurement paths. At a 45° wind direction, flow disturbances around the support pillars become the dominant source of shadow effects. The 1:1 scale model exhibits insufficient decay of large-scale, low-frequency turbulent energy, resulting in the accumulation of turbulent kinetic energy and significant wind speed errors at 0°. In contrast, the 1:2 scale model enables efficient energy transfer through high-frequency, small-scale vortices, enhances vortex intensity uniformity, and achieves improved spatial homogeneity in cross-wind measurement errors. These findings provide an important theoretical foundation for improving the high-precision measurement performance of reflective ultrasonic anemometers in complex wind environments.

RNA junctions are important structural elements of RNA molecules. They are formed when three or more helices come together in three-dimensional space. Recent studies have focused on the annotation and prediction of coaxial helical stacking (CHS) motifs within junctions. Here we exploit such predictions to develop an efficient alignment tool to handle RNA secondary structures with CHS motifs. Specifically, we build upon our Junction-Explorer software for predicting coaxial stacking and RNAJAG for modelling junction topologies as tree graphs to incorporate constrained tree matching and dynamic programming algorithms into a new method, called CHSalign, for aligning the secondary structures of RNA molecules containing CHS motifs. Thus, CHSalign is intended to be an efficient alignment tool for RNAs containing similar junctions. Experimental results based on thousands of alignments demonstrate that CHSalign can align two RNA secondary structures containing CHS motifs more accurately than other RNA secondary structure alignment tools. CHSalign yields a high score when aligning two RNA secondary structures with similar CHS motifs or helical arrangement patterns, and a low score otherwise. This new method has been implemented in a web server, and the program is also made freely available, at http://bioinformatics.njit.edu/CHSalign/.