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Highly efficient and stable oxygen reduction reaction (ORR) electrocatalysts are remarkably important but challenging for advancing the large-scale commercialization of practical proton exchange membrane fuel cells (PEMFCs). In this work, we report that the introduction of interstitial hydrogen atoms into PtPd nanotubes can significantly promote ORR performance without scarifying the durability. The enhanced mass activity was 8.8 times higher than that of commercial Pt/C. The accelerated durability test showed negligible activity attenuation after 30,000 cycles. Additionally, H 2 /O 2 fuel cell tests further verified the excellent activity of PtPd-H nanotubes with a maximum power density of 1.32 W·cm −2 , superior to that of commercial Pt/C (1.16 W·cm −2 ). Density functional theory calculations demonstrated the incorporation of hydrogen atoms gives rise to the broadening of Pt d-band and the downshift of d-band center, which consequently leads to the weaker intermediates binding and enhanced ORR activity.

Various supported Ir/TiO2 catalysts were prepared using different Ir precursors (i.e., H2IrCl6, (NH4)2IrCl6 and Ir(acac)3) and tested for vapor phase selective hydrogenation of crotonaldehyde. The choice of Ir precursor significantly altered the Ir-TiOx interaction in the catalyst, which thus had essential influences on the geometric and electronic properties of the Ir species, reducibility, and surface acidity, and, consequently, their reaction behaviors. The Ir/TiO2-N catalyst using (NH4)2IrCl6 as the precursor gave the highest initial reaction rates and turnover frequencies of crotyl alcohol formation. Such high performance was ascribed to the high Ir dispersion and high surface concentration of Ir0 species, as well as a higher surface acidity, in the Ir/TiO2-N catalyst compared to its counterparts, indicating the synergistic roles of the Ir-TiOx interface in the reaction, as the interfacial sites were responsible for the adsorption/activation of H2 and the C=O bond in the crotonaldehyde molecule.

With the digital transformation of power systems, higher demands are being placed on smart grids for the timely and precise acquisition of the status of transmission and transformation equipment during operational and maintenance processes. When a transformer is energized under no-load conditions, an excitation inrush phenomenon occurs in the windings, posing a hazard to the stable operation of the power system. A machine learning approach is proposed in this paper for predicting the internal magnetic field of transformers under excitation inrush condition, enabling the monitoring of transformer operation status. Experimental results indicate that the mean absolute percentage error (MAPE) for predicting the transformer’s magnetic field using the deep neural network (DNN) model is 4.02%. The average time to obtain a single magnetic field data prediction is 0.41 s, which is 46.68 times faster than traditional finite element analysis (FEA) method, validating the effectiveness of machine learning for magnetic field prediction.

Single-cell technologies enable the dynamic analyses of cell fate mapping. However, capturing the gene regulatory relationships and identifying the driver factors that control cell fate decisions are still challenging. We present CEFCON, a network-based framework that first uses a graph neural network with attention mechanism to infer a cell-lineage-specific gene regulatory network (GRN) from single-cell RNA-sequencing data, and then models cell fate dynamics through network control theory to identify driver regulators and the associated gene modules, revealing their critical biological processes related to cell states. Extensive benchmarking tests consistently demonstrated the superiority of CEFCON in GRN construction, driver regulator identification, and gene module identification over baseline methods. When applied to the mouse hematopoietic stem cell differentiation data, CEFCON successfully identified driver regulators for three developmental lineages, which offered useful insights into their differentiation from a network control perspective. Overall, CEFCON provides a valuable tool for studying the underlying mechanisms of cell fate decisions from single-cell RNA-seq data. Deciphering the roles of gene regulation in cell fate decisions is crucial. Here, authors present CEFCON, a network-based framework that reveals cell-lineage-specific gene regulatory networks and identifies driver regulators controlling cell fate decisions from single-cell transcriptomics data.

Rapid and accurate identification and tracking of lightning clusters from massive lightning detection data are crucial for real-time thunderstorm nowcasting and climatological analyses of thunderstorm activity. Although density-based clustering algorithms can identify clusters of arbitrary shapes at fine scales, their performance is often hindered by large data volumes and significant variations in lightning density. To address these challenges, we propose a multi-scale spatiotemporal lightning clustering framework, termed CC3D-CSCAP. It consists of two main components. First, the 3-D connected component algorithm (CC3D) performs coarse-scale segmentation by dividing the lightning dataset into spatiotemporally disconnected subsets using 26-connectivity. Then, the cylinder-based scan clustering algorithm with adaptive parameters (CSCAP) is applied to each subset for fine-scale identification of lightning clusters. Since the lightning subset may still contain multiple thunderstorms with varying lightning densities, CSCAP adaptively determines clustering parameters based on the statistical characteristics (time difference and spatial distance) of subset. Compared with fixed-parameter methods, CC3D-CSCAP identifies more clusters (771,033) while retaining a high percentage of usable lightning strokes (98.988%). The clustering results align well with the theoretical criteria for optimal clustering and are promising for global applications in lightning data analysis, nowcasting, and climatological studies of convective systems.

Doxorubicin (Dox) is the standard treatment approach for osteosarcoma (OS), while acquired drug resistance seriously attenuates its treatment efficiency. The present study aimed to investigate the potential roles of metabolic reprogramming and the related regulatory mechanism in Dox-resistant OS cells. The results showed that the ATP levels, lactate generation, glucose consumption and oxygen consumption rate were significantly increased in Dox-resistant OS cells compared with parental cells. Furthermore, the results revealed that the increased expression of estrogen-related receptor alpha (ERRα) was involved in metabolic reprogramming in chemotherapy resistant OS cells, since targeted inhibition of ERRα restored the shifting of metabolic profiles. Mechanistic analysis indicated that the mRNA stability, rather than ERRα transcription was markedly increased in chemoresistant OS cells. Therefore, it was hypothesized that the 3ʹ-untranslated region of ERRα mRNA was methylated by N 6 -methyladenine, which could further recruit insulin-like growth factor 2 mRNA binding protein 1 (IGF2BP1) to suppress mRNA decay and increase mRNA stability. IGF2BP1 knockdown downregulated ERRα and reversed the metabolic alteration of resistant OS cells. Additionally, the oncogenic effect of the IGF2BP1/ERRα axis on Dox-resistant OS cells was verified by in vitro and in vivo experiments. Clinical analysis also revealed that the expression levels of IGF2BP1 and ERRα were associated with the clinical progression of OS. Collectively, the current study suggested that the IGF2BP1/ERRα axis could regulate metabolic reprogramming to contribute to the chemoresistance of OS cells.

Parkinson’s disease (PD), ranking as the second most prevalent neurodegenerative disorder globally, presents a pressing need for innovative animal models to deepen our understanding of its pathophysiology and explore potential therapeutic interventions. The development of such animal models plays a pivotal role in unraveling the complexities of PD and investigating promising treatment avenues. In this study, we employed transcriptome sequencing on BmN cells treated with 1 μg/ml rotenone, aiming to elucidate the underlying toxicological mechanisms. The investigation brought to light a significant reduction in mitochondrial membrane potential induced by rotenone, subsequently triggering mitophagy. Notably, the PTEN induced putative kinase 1 (PINK1)/Parkin pathway emerged as a key player in the cascade leading to rotenone-induced mitophagy. Furthermore, our exploration extended to silkworms exposed to 50 μg/ml rotenone, revealing distinctive motor dysfunction as well as inhibition of Tyrosine hydroxylase (TH) gene expression. These observed effects not only contribute valuable insights into the impact and intricate mechanisms of rotenone exposure on mitophagy but also provide robust scientific evidence supporting the utilization of rotenone in establishing a PD model in the silkworm. This comprehensive investigation not only enriches our understanding of the toxicological pathways triggered by rotenone but also highlights the potential of silkworms as a valuable model organism for PD research.

Background Cementoblasts on the tooth-root surface are responsible for cementum formation (cementogenesis) and sensitive to Porphyromonas gingivalis stimulation. We have previously proved transcription factor CXXC-type zinc finger protein 5 (CXXC5) participates in cementogenesis. Here, we aimed to elucidate the mechanism in which CXXC5 regulates P. gingivalis- inhibited cementogenesis from the perspective of mitochondrial biogenesis. Methods In vivo, periapical lesions were induced in mouse mandibular first molars by pulp exposure, and P. gingivalis was applied into the root canals. In vitro, a cementoblast cell line (OCCM-30) was induced cementogenesis and submitted for RNA sequencing. These cells were co-cultured with P. gingivalis and examined for osteogenic ability and mitochondrial biogenesis. Cells with stable CXXC5 overexpression were constructed by lentivirus transduction, and PGC-1α (central inducer of mitochondrial biogenesis) was down-regulated by siRNA transfection. Results Periapical lesions were enlarged, and PGC-1α expression was reduced by P. gingivalis treatment. Upon apical inflammation, Cxxc5 expression decreased with Il-6 upregulation. RNA sequencing showed enhanced expression of osteogenic markers, Cxxc5 , and mitochondrial biogenesis markers during cementogenesis. P. gingivalis suppressed osteogenic capacities, mitochondrial biogenesis markers, mitochondrial (mt)DNA copy number, and cellular ATP content of cementoblasts, whereas CXXC5 overexpression rescued these effects. PGC-1α knockdown dramatically impaired cementoblast differentiation, confirming the role of mitochondrial biogenesis on cementogenesis. Conclusions CXXC5 is a P. gingivalis -sensitive transcription factor that positively regulates cementogenesis by influencing PGC-1α-dependent mitochondrial biogenesis. A5vAuiTS6Tkq1RfziX_nr_ Video Abstract