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Advanced oxidation processes (AOPs) demonstrate great micropollutant degradation efficiency. In this study, CuFe2O4 was successfully used to activate peracetic acid (PAA) to remove Rhodamine B. Acetyl(per)oxyl radicals were the dominant species in this novel system. The addition of 2,4-hexadiene (2,4-HD) and Methanol (MeOH) significantly inhibited the degradation efficiency of Rhodamine B. The ≡Cu2+/≡Cu+ redox cycle dominated PAA activation, thereby producing organic radicals (R-O˙) including CH3C(O)O˙ and CH3C(O)OO˙, which accounted for the degradation of Rhodamine B. Increasing either the concentration of CuFe2O4 (0–100 mg/L) or PAA (10–100 mg/L) promoted the removal efficiency of this potent system. In addition, weakly acid to weakly alkali pH conditions (6–8) were suitable for pollutant removal. The addition of Humid acid (HA), HCO3−, and a small amount of Cl− (10–100 mmol·L−1) slightly inhibited the degradation of Rhodamine B. However, degradation was accelerated by the inclusion of high concentrations (200 mmol·L−1) of Cl−. After four iterations of catalyst recycling, the degradation efficiency remained stable and no additional functional group characteristic peaks were observed. Taking into consideration the reaction conditions, interfering substances, system stability, and pollutant-removal efficiency, the CuFe2O4/PAA system demonstrated great potential for the degradation of Rhodamine B.

Let A be a unital ∗-algebra with the unit I over the complex field C and let η≠0,±1 be a complex number. For any A,B∈A, A⋄ηB=AB+ηBA* is referred to as the η-Jordan ∗-product. Suppose that n≥3 is a fixed positive integer. In this study, it is shown that if a map φ:A→A satisfies φ(A1⋄ηA2⋄η⋯⋄ηAn)=∑k=1nA1⋄η⋯⋄ηAk−1⋄ηφ(Ak)⋄ηAk+1⋄η⋯⋄ηAn for all A1,A2⋯An−3∈I,iI and An−2,An−1,An∈A, then φ is an additive ∗-derivation and φ(ηA)=ηφ(A) for all A∈A, where i is the imaginary unit. In application, characterizations of prime ∗-algebras, von Neumann algebras with no central summands of type I1 and factor von Neumann algebras are obtained.

The accurate probabilistic forecasting of ultra-short-term power generation from distributed photovoltaic (DPV) systems is of great significance for optimizing electricity markets and managing energy on the user side. Existing methods regarding cluster information sharing tend to easily trigger issues of data privacy leakage during information sharing, or they suffer from insufficient information sharing while protecting data privacy, leading to suboptimal forecasting performance. To address these issues, this paper proposes a privacy-preserving deep federated learning method for the probabilistic forecasting of ultra-short-term power generation from DPV systems. Firstly, a collaborative feature federated learning framework is established. For the central server, information sharing among clients is realized through the interaction of global models and features while avoiding the direct interaction of raw data to ensure the security of client data privacy. For local clients, a Transformer autoencoder is used as the forecasting model to extract local temporal features, which are combined with global features to form spatiotemporal correlation features, thereby deeply exploring the spatiotemporal correlations between different power stations and improving the accuracy of forecasting. Subsequently, a joint probability distribution model of forecasting values and errors is constructed, and the distribution patterns of errors are finely studied based on the dependencies between data to enhance the accuracy of probabilistic forecasting. Finally, the effectiveness of the proposed method was validated through real datasets.

Background Migraine is a complex neurological disorder with poorly understood molecular mechanisms. Despite advances in genetic and omics research, the shared mechanisms between central and peripheral nervous systems in migraine pathogenesis remain unclear. Methods We employed a multi-omics approach, integrating human trigeminal ganglion (TG) single-nucleus RNA sequencing (snRNA-seq) data and expression quantitative trait loci (eQTL) data from eight major cortical cell types. Mendelian randomization (MR) analysis was used to prioritize susceptibility genes, followed by functional enrichment, molecular network mapping, and computational drug screening. Key findings were experimentally validated in the primary sensory cortex hindlimb area brain region and TG, given their established roles in pain processing. Results We identified 586 migraine-associated genes in TG and 1,108 in the cortex, with 109 overlapping genes. These overlapping genes converge on pathways including autophagy and neuroinflammation, suggesting shared mechanisms of central and peripheral nervous systems. Five hub genes - HSP90AB1, EGFR, ERBB3, MET and ATG7 - were implicated in both TG and cortical tissues. Experimental validation identified five hub genes strongly linked to migraine, with ATG7 emerging as a key candidate. Immunofluorescence co-localization revealed ATG7’s prominent expression in both cortical astrocytes and neurons, suggesting its dual role in glial and neuronal pathways underlying migraine pathophysiology. Western blot analysis revealed that in the S1HL brain region of migraine model mice, the protein level of LC3-II showed an increasing trend, while the expressions of both LC3-I and p62 exhibited decreasing trends compared to the control group. Furthermore, both the LC3-II/LC3-I ratio and the LC3-II/p62 ratio were significantly elevated in the model group, suggesting that the upregulation of ATG7 promotes the activation of autophagic flux in the migraine model, with the autophagic flux remaining unobstructed. Conclusions Our study provides novel insights into migraine’s central and peripheral mechanisms, highlighting cell-type-specific genetic contributions and potential therapeutic targets. The integrative framework combining snRNA-seq, eQTL, GWAS, and MR enhances the understanding of migraine biology and accelerates drug discovery, offering a pathway toward more effective treatments.

Background There is an association between migraine and dementia, however, their causal relationship remains unclear. This study employed bidirectional two-sample Mendelian randomization (MR) to investigate the potential causal relationship between migraine and dementia and its subtypes: Alzheimer’s disease (AD), vascular dementia (VaD), frontotemporal dementia (FTD), and dementia with Lewy bodies (DLB). Methods Summary-level statistics data were obtained from publicly available genome-wide association studies (GWAS) for both migraine and five types of dementia. Single nucleotide polymorphisms (SNPs) associated with migraine and each dementia subtype were selected. MR analysis was conducted using inverse variance weighting (IVW) and weighted median (WM) methods. Sensitivity analyses included Cochran’s Q test, MR pleiotropy residual sum and outlier (MR-PRESSO) analysis, the intercept of MR-Egger, and leave-one-out analysis. Results Migraine showed a significant causal relationship with AD and VaD, whereas no causal relationship was observed with all-cause dementia, FTD, or DLB. Migraine may be a potential risk factor for AD (odds ratio [OR]: 1.09; 95% confidence interval [CI]: 0.02–0.14; P  = 0.007), while VaD may be a potential risk factor for migraine (OR: 1.04; 95% CI: 0.02–0.06; P  = 7.760E-5). Sensitivity analyses demonstrated the robustness of our findings. Conclusion Our study suggest that migraine may have potential causal relationships with AD and VaD. Migraine may be a risk factor for AD, and VaD may be a risk factor for migraine. Our study contributes to unraveling the comprehensive genetic associations between migraine and various types of dementia, and our findings will enhance the academic understanding of the comorbidity between migraine and dementia.

Surface defect detection plays a crucial role in industrial manufacturing, as it directly impacts whether a product meets the required standards. However, due to the scarcity of certain defect samples, traditional defect detection models often struggle to maintain satisfactory performance under data-limited scenarios. Although recent few-shot segmentation methods have made progress, they still face challenges in terms of segmentation accuracy and robustness. To address these limitations, this paper proposes a novel few-shot segmentation framework named TMCNet, which integrates triplet-based feature enhancement and multi-scale cascaded decoding. First, to enhance the representational power of target defect features in the support set, we introduce a triplet-based feature enhancement module (TBFE) that captures defects from three complementary perspectives: salient characteristics, local fine-grained details, and global contextual dependencies. This enables the construction of more discriminative and comprehensive defect representations. Second, to address the limited receptive field commonly encountered in the few-shot pipeline, we propose a multi-receptive field enhancement module (MRFE) that incorporates multi-scale dilated convolutions and a channel-spatial hybrid attention mechanism, thereby enriching semantic feature expression. Third, to better handle large variations in defect scale, we design a multi-scale cascaded decoder (MSCD) that progressively refines the segmentation output by fusing multi-scale predictions, leading to more precise and reliable defect delineation. Extensive comparative experiments on two industrial few-shot defect segmentation datasets—FSSD-12 and Surface Defects-4i—demonstrate the superior performance of TMCNet. Under the 1-shot setting, our method achieves mIoU scores of 67.8% and 42.3%, respectively, significantly outperforming existing state-of-the-art approaches. Moreover, a series of ablation studies validate the effectiveness and individual contributions of each proposed module, further confirming the design rationale and technical soundness of the overall framework.

Osteoarthritis (OA) is a prevalent chronic disease, characterized by progressive joint degeneration and primarily affects older adults. OA leads to reduced functional abilities, a lower quality of life, and an increased mortality rate. Currently, effective treatment options for OA are lacking. Non-coding RNAs (ncRNAs) are functional RNAs transcribed from DNA but not translated into proteins. Among ncRNAs, small interfering RNAs (siRNAs), transfer RNAs (tRNAs), and ribosomal RNAs (rRNAs) have become significant in the field, which is intricately linked to the progression of OA and perform significant regulatory functions in transcription, post-transcription, and post-translation, making them potential biological targets for the prevention, diagnosis, and treatment of OA. This review summarizes the general functions of siRNAs, tRNAs, and rRNAs and their application in OA. The primary focus has been on regulating cartilage degradation. Other participations include regulating synovium, protecting anterior cruciate ligament cells, and diagnosis. No clinical trials were found as challenges such as effective delivery systems, immune responses, long-term effects, and interactions between therapies need to be demonstrated first.

• A rice mutant, Oryza sativa short postembryonic roots 1 (Osspr1), has been characterized. It has short postembryonic roots, including adventitious and lateral roots, and a lower iron content in its leaves. • OsSPR1 was identified by map-based cloning. It encodes a novel mitochondrial protein with the Armadillo-like repeat domain. • Osspr1 mutants exhibited decreased root cell elongation. The iron content of the mutant shoots was significantly altered compared with that of wild-type shoots. A similar pattern of alteration of manganese and zinc concentrations in shoots was also observed. Complementation of the mutant confirmed that OsSPR1 is involved in post-embryonic root elongation and iron homeostasis in rice. OsSPR1 was found to be ubiquitously expressed in various tissues throughout the plant. The transcript abundance of various genes involved in iron uptake and signaling via both strategies I and II was similar in roots of wild-type and mutant plants, but was higher in the leaves of mutant plants. • Thus, a novel mitochondrial protein that is involved in root elongation and plays a role in metal ion homeostasis has been identified.

Background Lipoprotein lipase (LPL) deficiency is an autosomal recessive genetic disorder characterized by extreme hypertriglyceridemia, with no cure presently available. The purpose of this study was to test the possibility of using cell therapy to alleviate LPL deficiency. Methods The LPL coding sequence was cloned into the MSCV retrovirus vector, after which MSCV-hLPL and MSCV (empty construct without LPL coding sequence) virion suspensions were made using the calcium chloride method. A muscle cell line (C2C12), kidney cell line (HEK293T) and pre-adipocyte cell line (3 T3-L1) were transfected with the virus in order to express recombinant LPL in vitro. Finally, each transfected cell line was injected subcutaneously into nude mice to identify the cell type which could secret recombinant LPL in vivo. Control cells were transfected with the MSCV empty vector. LPL activity was analyzed using a radioimmunoassay. Results After virus infection, the LPL activity at the cell surface of each cell type was significantly higher than in the control cells, which indicates that all three cell types can be used to generate functional LPL. The transfected cells were injected subcutaneously into nude mice, and the LPL activity of the nearby muscle tissue at the injection site in mice injected with 3 T3-L1 cells was more than 5 times higher at the injection sites than at non-injected control sites. The other two types of cells did not show this trend. Conclusion The subcutaneous injection of adipocytes overexpressing LPL can improve the LPL activity of the adjacent tissue of nude mice. This is a ground-breaking preliminary study for the treatment of LPL deficiency, and lays a good foundation for using cell therapy to correct LPL deficiency.