Long non-coding RNA DLGAP1 antisense RNA 1 accelerates glioma progression via the microRNA-628-5p/DEAD-box helicase 59 pathway

Objectives: Abnormal expression of long non-coding RNAs (lncRNAs) plays a prominent role in glioma progression. However, the biological function and mechanism of lncRNA DLGAP1 antisense RNA 1 (DLGAP1-AS1) in gliomas are still unknown. Methods: The authors assessed DLGAP1-AS1 and miR-628-5p expression in glioma tissues and cell lines using quantitative real-time polymerase chain reaction (qRT-PCR) and evaluated their effects on glioma cell proliferation, migration, invasion, and epithelial-mesenchymal transition (EMT) using the cell counting kit-8 (CCK-8) assay, 5-Ethynyl-2′-deoxyuridine (EdU) assay, Transwell assay, and western blot, respectively. The expression of DEAD-box helicase 59 (DDX59) was quantified using western blotting, and a dual-luciferase reporter gene assay was performed to detect the interaction between DLGAP1-AS1 and miR-628-5p. Results: The authors observed increased DLGAP1-AS1 expression in glioma tissues and cell lines with higher WHO grades and shorter survival time. DLGAP1-AS1 promoted the proliferation, migration, invasion, and EMT of glioma cells, while miR-628-5p counteracted these effects. The authors identified DLGAP1-AS1 as a molecular sponge of miR-628-5p in glioma cells as the biological functions of DLGAP1-AS1 are partially mediated via miR-628-5p. In addition, DLGAP1-AS1 upregulated DDX59 expression by inhibiting miR-628-5p expression. Conclusion: The DLGAP1-AS1/miR-628-5p/DDX59 axis regulates glioma progression

Poly[[hepta-μ2-aqua-bis­(μ2-pyrazine-2-carboxyl­ato)dibarium] bis­(pyrazine-2-carboxyl­ate)]

In the layered title coordination polymer, {[Ba2(C5H3N2O2)2(H2O)7](C5H3N2O2)2}n, the coordination geometries around the two independent BaII ions can be described as bicapped square-anti­prismatic [BaNO9] arrangements. A two-dimensional polymeric framework with (6,3) topology can be observed in the ac plane, the nodes being provided by BaII ions and the connectors being N and O atoms belonging to pyrazine-2-carboxyl­ate ligands and O atoms of bridging water mol­ecules. Non-coordinating pyrazine-2-carboxyl­ate ions are located between the polymeric layers in the crystal and are interconnected through extensive O—H⋯N,O hydrogen bonding

Protective Effects of Peroxisome Proliferator-Activated Receptor-α Agonist, Wy14643, on Hypoxia/Reoxygenation Injury in Primary Rat Hepatocytes

This study investigates the effects and possible mechanism of an agonist of PPARα, Wy14643, on primary hepatocytes subjected to H/R injury in rats. H/R induced a significant increase ALT, AST, MDA in the culture medium and ROS in the hepatocytes. These effects were reversed by pretreatment with Wy14643 in the dose-dependent manner. The activity of SOD and the level of GSH in the hepatocytes were decreased after H/R, which were increased by Wy14643 pretreatment. Moreover, the mRNA expressions of PPARα significantly increased in H/R+Wy14643 groups when compared with that in H/R group. A PPARα agonist, Wy14643, exerts significant protective effect against H/R injury in primary hepatocytes via PPARα activation and attenuating oxidative stress

The variability of optical \feii emission in PG QSO 1700+518

It is found that \feii emission contributes significantly to the optical and ultraviolet spectra of most active galactic nuclei. The origin of the optical/UV \feii emission is still a question open to debate. The variability of \feii would give clues to this origin. Using 7.5 yr spectroscopic monitoring data of one Palomer-Green (PG) quasi-stellar object (QSO), PG 1700+518, with strong optical \feii emission, we obtain the light curves of the continuum \lv, \feii, the broad component of \hb, and the narrow component of \hb by the spectral decomposition. Through the interpolation cross-correlation method, we calculate the time lags for light curves of \feii, the total \hb, the broad component of \hb, and the narrow component of \hb with respect to the continuum light curve. We find that the \feii time lag in PG1700+518 is $209^{+100}_{-147}$ days, and the \hb time lag cannot be determined. Assuming that \feii and \hb emission regions follow the virial relation between the time lag and the FWHM for the \hb and \feii emission lines, we can derive that the \hb time lag is $148^{+72}_{-104}$ days. The \hb time lag calculated from the empirical luminosity--size relation is 222 days, which is consistent with our measured \feii time lag. Considering the optical \feii contribution, PG 1700+518 shares the same characteristic on the spectral slope variability as other 15 PG QSOs in our previous work, i.e., harder spectrum during brighter phase.Comment: 6 apges, ApJ, in pres

Multimodal Federated Learning with Missing Modality via Prototype Mask and Contrast

In real-world scenarios, multimodal federated learning often faces the practical challenge of intricate modality missing, which poses constraints on building federated frameworks and significantly degrades model inference accuracy. Existing solutions for addressing missing modalities generally involve developing modality-specific encoders on clients and training modality fusion modules on servers. However, these methods are primarily constrained to specific scenarios with either unimodal clients or complete multimodal clients, struggling to generalize effectively in the intricate modality missing scenarios. In this paper, we introduce a prototype library into the FedAvg-based Federated Learning framework, thereby empowering the framework with the capability to alleviate the global model performance degradation resulting from modality missing during both training and testing. The proposed method utilizes prototypes as masks representing missing modalities to formulate a task-calibrated training loss and a model-agnostic uni-modality inference strategy. In addition, a proximal term based on prototypes is constructed to enhance local training. Experimental results demonstrate the state-of-the-art performance of our approach. Compared to the baselines, our method improved inference accuracy by 3.7\% with 50\% modality missing during training and by 23.8\% during uni-modality inference. Code is available at https://github.com/BaoGuangYin/PmcmFL.Comment: 23 page