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Recent years have witnessed incredible growth in RNA therapeutics, which has benefited significantly from decades of research on lipid nanoparticles, specifically its key component—the ionizable lipid. This comment discusses the major ionizable lipid types, and provides perspectives for future development. RNA therapeutics have benefited significantly from decades of research on lipid nanoparticles, specifically its key component—the ionizable lipid. This comment discusses the major ionizable lipid types, and provides perspectives for future development.

Solvent effects influence all stages of the chemical processes, modulating the stability of intermediates and transition states, as well as altering reaction rates and product ratios. However, accurately modelling these effects remains challenging. Here, we present a general strategy for generating reactive machine learning potentials to model chemical processes in solution. Our approach combines active learning with descriptor-based selectors and automation, enabling the construction of data-efficient training sets that span the relevant chemical and conformational space. We apply this strategy to investigate a Diels-Alder reaction in water and methanol. The generated machine learning potentials enable us to obtain reaction rates that are in agreement with experimental data and analyse the influence of these solvents on the reaction mechanism. Our strategy offers an efficient approach to the routine modelling of chemical reactions in solution, opening up avenues for studying complex chemical processes in an efficient manner. Modelling reactions in solution is challenging. Machine learning potentials offer promising alternatives but need large datasets. Here the authors report an automated active learning approach using descriptor-based selectors to model Diels-Alder reactions.

The discovery of the spin Hall effect 1 enabled the efficient generation and manipulation of the spin current. More recently, the magnetic spin Hall effect 2 , 3 was observed in non-collinear antiferromagnets, where the spin conservation is broken due to the non-collinear spin configuration. This provides a unique opportunity to control the spin current and relevant device performance with controllable magnetization. Here, we report a magnetic spin Hall effect in a collinear antiferromagnet, Mn 2 Au. The spin currents are generated at two spin sublattices with broken spatial symmetry, and the antiparallel antiferromagnetic moments play an important role. Therefore, we term this effect the ‘antiferromagnetic spin Hall effect’. The out-of-plane spins from the antiferromagnetic spin Hall effect are favourable for the efficient switching of perpendicular magnetized devices, which is required for high-density applications. The antiferromagnetic spin Hall effect adds another twist to the atomic-level control of spin currents via the antiferromagnetic spin structure. A magnetic spin Hall effect is reported in the collinear antiferromagnet Mn 2 Au.

Emerging evidence has demonstrated that circular RNAs (circRNAs) play critical roles in the development and progression of human cancer. However, the biological functions and underlying mechanisms of circRNAs in triple-negative breast cancer (TNBC) remain to be investigated. In our present study, we found that the novel circRNA circHIF1A was significantly overexpressed in breast cancer tissues and that it was associated with metastasis, poor prognosis, and the TNBC subtype. Gain- and loss-of-function experiments were conducted to investigate the biological roles of circHIF1A in TNBC. Overexpression of circHIF1A significantly promoted TNBC growth and metastasis in vitro and in vivo, while knockdown of circHIF1A exerted the opposite effects. Mechanistically, circHIF1A modulated the expression and translocation of NFIB through posttranscriptional and posttranslational modifications, resulting in the activation of the AKT/STAT3 signaling pathway and inhibition of P21. The RNA binding protein FUS could regulate the biogenesis of circHIF1A by interacting with the flanking intron, and FUS was transcriptionally regulated by NFIB, thus forming the circHIF1A/NFIB/FUS positive feedback loop. Moreover, circHIF1A could be packaged into exosomes and was upregulated in the plasma of breast cancer patients. Our findings indicated that circHIF1A played a critical role in the growth and metastasis of TNBC via a positive feedback loop and that circHIF1A could be a promising biomarker for breast cancer diagnosis and a potential therapeutic target for TNBC treatment.

From around the end of the First World War to the mid-1960s, as the voices of the exploited and oppressed black groups were drowned out by white supremacist ideas, black people generally suffered from racial discrimination, and the stereotypes brought about by social essentialism impact of impressions. The construction of identities of marginalized black groups becomes a matter of concern. For Toni Morrison’s novel “Sula”, the existing research has obtained the image analysis of the characters in “Sula”, the symbolic meaning in the novel, the construction of character identity, and the embodiment of traditional culture in “Sula”. However, few studies have combined essentialism and identity construction and Sula’s and Shadracket’s analyses. Therefore, this thesis explores the embodiment of essentialism in “Sula”, as well as Sula and Shadrack’s resistance to essentialism and self-identity construction and combines theoretical analysis and textual analysis. Sula used her unique and heterogeneous behavior to break through the shackles of social essentialism on black women. In contrast to most black women, she constructed her self-identity in a different way from most black women, which can be better understood using Plato’s “cave theory”. Shadrack created “World Suicide Day” to resist the uncertainty of death and the prejudice brought by social essentialism and used Sula as a “mirror” to re-construct his identity, which can be used in Lacan’s “mirror stage” theory to explain.

Macrophage-orchestrated, low-grade chronic inflammation plays a pivotal role in obesity and atherogenesis. However, the underlying regulatory mechanisms remain incompletely understood. Here, we identify major vault protein (MVP), the main component of unique cellular ribonucleoprotein particles, as a suppressor for NF-κB signaling in macrophages. Both global and myeloid-specific MVP gene knockout aggravates high-fat diet induced obesity, insulin resistance, hepatic steatosis and atherosclerosis in mice. The exacerbated metabolic disorders caused by MVP deficiency are accompanied with increased macrophage infiltration and heightened inflammatory responses in the microenvironments. In vitro studies reveal that MVP interacts with TRAF6 preventing its recruitment to IRAK1 and subsequent oligomerization and ubiquitination. Overexpression of MVP and its α-helical domain inhibits the activity of TRAF6 and suppresses macrophage inflammation. Our results demonstrate that macrophage MVP constitutes a key constraint of NF-κB signaling thereby suppressing metabolic diseases. Metabolic diseases are associated with chronic, low-grade inflammation. Here the authors show that major vault protein (MVP) suppresses NF-κB signalling in macrophages via an IRAK1–TRAF6 axis and that loss of MVP in myeloid cells exacerbates the inflammatory response in mice fed a high fat diet.

Long noncoding RNAs (lncRNAs) play crucial roles in tumor progression and are aberrantly expressed in various cancers. However, the functional roles of lncRNAs in breast cancer remain largely unknown. Based on public databases and integrating bioinformatics analyses, the overexpression of lncRNA BCRT1 in breast cancer tissues was detected and further validated in a cohort of breast cancer tissues. The effects of lncRNA BCRT1 on proliferation, migration, invasion and macrophage polarization were determined by in vitro and in vivo experiments. Luciferase reporter assay and RNA immunoprecipitation (RIP) were carried out to reveal the interaction between lncRNA BCRT1, miR-1303, and PTBP3. Chromatin immunoprecipitation (ChIP) and RT-PCR were used to evaluate the regulatory effect of hypoxia-inducible factor-1α (HIF-1α) on lncRNA BCRT1. LncRNA BCRT1 was significantly upregulated in breast cancer tissues, which was correlated with poor prognosis in breast cancer patients. LncRNA BCRT1 knockdown remarkably suppressed tumor growth and metastasis in vitro and in vivo. Mechanistically, lncRNA BCRT1 could competitively bind with miR-1303 to prevent the degradation of its target gene PTBP3, which acts as a tumor-promoter in breast cancer. LncRNA BCRT1 overexpression could promote M2 polarization of macrophages, mediated by exosomes, which further accelerated breast cancer progression. Furthermore, lncRNA BCRT1 was upregulated in response to hypoxia, which was attributed to the binding of HIF-1α to HREs in the lncRNA BCRT1 promoter. Collectively, these results reveal a novel HIF-1α/lncRNA BCRT1/miR-1303/PTBP3 pathway for breast cancer progression and suggest that lncRNA BCRT1 might be a potential biomarker and therapeutic target for breast cancer.

Object detection in Unmanned Aerial Vehicle (UAV) imagery remains challenging due to the prevalence of small targets, complex backgrounds, and the stringent requirement for real-time processing on computationally constrained platforms. Existing methods often struggle to balance detection accuracy, particularly for small objects, with operational efficiency. To address these challenges, this paper proposes YOLO-CAM, an enhanced object detector based on YOLOv5n. First, a novel Combined Attention Mechanism (CAM) is integrated to synergistically recalibrate features across both channel and spatial dimensions, enhancing the network’s focus on small targets while suppressing background clutter. Second, the detection head is strategically optimized by introducing a dedicated high-resolution head for tiny targets and removing a redundant head, thereby expanding the detectable size spectrum down to small pixels with reduced parameters. Finally, the CIoU loss is replaced with the inner-Focal-EIoU loss to improve bounding box regression accuracy, especially for low-quality examples and small objects. Extensive experiments on the challenging VisDrone2019 benchmark demonstrate the effectiveness of our method. YOLO-CAM achieves a mean Average Precision (mAP0.5) of 31.0%, which represents a significant 7.5% improvement over the baseline YOLOv5n, while maintaining a real-time inference speed of 128 frames per second. Comparative studies show that our approach achieves a superior balance between accuracy and efficiency compared to other state-of-the-art detectors. The results indicate that the proposed YOLO-CAM establishes a new way for accuracy–efficiency trade-offs in UAV-based detection. Due to its lightweight design and high performance, it is particularly suitable for deployment on resource-limited UAV platforms for applications requiring reliable real-time small object detection.

How do neurons encode long-term memories? Bilateral imaging of neuronal activity in the mouse hippocampus reveals that, from one day to the next, ~40% of neurons change their responsiveness to cues, but thereafter only 1% of cells change per day. Despite these changes, neuronal responses are resilient to a lack of exposure to a previously completed task or to hippocampus lesions. Unlike individual neurons, the responses of which change after a few days, groups of neurons with inter- and intrahemispheric synchronous activity show stable responses for several weeks. The likelihood that a neuron maintains its responsiveness across days is proportional to the number of neurons with which its activity is synchronous. Information stored in individual neurons is relatively labile, but it can be reliably stored in networks of synchronously active neurons.

Underwater Wireless Optical Communication (UWOC) plays a crucial role in marine exploration and observation due to its high speed and low latency characteristics, while research on underwater time and frequency transfer (UTFT) is relatively lacking. The complicated underwater environments, absorption and scattering effects severely degrade signal stability and signal-to-noise-ratio (SNR). In response to this issue, a photon packet transmission model is established based on the Monte Carlo simulation (MCS). The effects of different parameters, including water conditions, divergence angles, receiving apertures, are systematically analyzed, with key indicators such as phase noise and Allan deviation, identified as performance measures. An experimental platform is also built using kaolin turbidity to obtain experimental results corresponding to different frequencies and turbidity levels, which are then compared with simulation results. The high consistency between simulation and experimental results verifies the reliability of the proposed model. This research provides a feasible method for performance prediction and tolerance design of UTFT networks.