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Allergic asthma is a chronic inflammatory airway disease driven by type 2 immune responses, whose pathogenesis correlates with the release of epithelial alarm proteins-thymic stromal lymphopoietin (TSLP), interleukin-25 (IL-25), and interleukin-33 (IL-33)-by airway epithelial cells following barrier injury. This paper systematically reviews the cutting-edge applications of nanoparticles (NPs) in targeting the epithelial alarmin signaling axis and its downstream immune cells, including dendritic cells, macrophages, Th2 cells, and regulatory T cells. By systematically reviewing research progress on nanoparticles in allergic asthma treatment, this review provides crucial theoretical support and technical frameworks for developing precise, efficient, and less-side-effect asthma therapies. It offers forward-looking guidance for advancing asthma treatment from laboratory to clinical translation.

The longstanding cusp–core problem—the discrepancy between the steep central density cusps predicted by cold dark matter (DM) simulations and certain shallow cores observed in dwarf galaxies, in particular the associated diversity of inner profiles—remains hotly debated despite decades of study. Building on a new interpretation of fermionic isothermal halos, we identify a physical mechanism—degeneracy-induced depletion—in which degenerate inner cores of fermionic DM suppress the surrounding density over large scales. This effect persists even in dense baryonic environments. Within the framework of hierarchical structure formation, degeneracies developed in the smallest constituent subhalos induce low-density regions that collectively configure into a King-type core of the host DM halo, with a core density–radius relation consistent with observations. This scenario accounts for the diversity of DM inner profiles through variation in the average degeneracy of constituent subhalos, and suggests a connection between this diversity and the halo formation history. Thus, the cusp–core problem may be reconciled within the standard “cold” DM paradigm without invoking strong baryonic feedback, instead pointing to the fermionic nature of DM.

The mammalian gut harbors a complex and dynamic microbial ecosystem: the microbiota. While emerging studies support that microbiota regulates brain function with a few molecular cues suggested, the overall biochemical landscape of the “microbiota-gut-brain axis” remains largely unclear. Here we use high-coverage metabolomics to comparatively profile feces, blood sera, and cerebral cortical brain tissues of germ-free C57BL/6 mice and their age-matched conventionally raised counterparts. Results revealed for all three matrices metabolomic signatures owing to microbiota, yielding hundreds of identified metabolites including 533 altered for feces, 231 for sera, and 58 for brain with numerous significantly enriched pathways involving aromatic amino acids and neurotransmitters. Multicompartmental comparative analyses single out microbiota-derived metabolites potentially implicated in interorgan transport and the gut-brain axis, as exemplified by indoxyl sulfate and trimethylamine- N -oxide. Gender-specific characteristics of these landscapes are discussed. Our findings may be valuable for future research probing microbial influences on host metabolism and gut-brain communication. The gut microbiota harbours neuroactive potential with links to neurological disorders. Here, the authors apply global metabolomics with an integrated annotation strategy to comparatively profile fecal, blood serum and cerebral cortical brain tissues of eight-week-old germ-free mice vs. age-matched specific-pathogen-free mice, providing a snapshot of the metabolome status linked to the gut-brain axis.

In a prospective clinical study to better understand how biological markers can improve diagnosis of and prognosis for asthmatic and atopic conditions, we contacted over 3500 eligible patients and observed noticeable differences in the range of their likelihood to enroll based on gender (3.8–13.4%), race and ethnicity (4.8–29.8%), and distance to study site (1.1–29.2%). Both the eligible patients and enrolled participants exhibited a more diverse racial and ethnic composition compared to local population demographics. Based on the eligible patients that the study team contacted (“eligible patients”, n = 3648) and those who agreed to enroll (“enrolled participants”, n = 454), we analyzed the gender, age, race and ethnicity composition of the groups, together with their proximity to the study site. Living close to the study site was the largest contributor to a patient's decision to enroll for both adults (odds ratio OR: 2.26, 95% confidence interval CI: 1.64–3.15, p < 0.001) and children (OR: 2.59, 95% CI: 1.67–4.41, p < 0.001). We also observed that patients from White and non-Hispanic racial and ethnic background were more likely to participate in the study among both pediatric (OR: 1.51, CI: 0.92–2.62, p = 0.122) and adult patients (OR: 1.81, CI: 1.18–2.89, p = 0.009). Eligible patients of female gender were also more likely to enroll in both adult (OR: 1.53, CI: 1.16–2.05, p = 0.003) and pediatric groups (OR: 2.14, CI: 1.42–3.22, p < 0.001). Overall, the pediatric patients (18 years old or younger) were much less willing to participate in the clinical biomarker study. Nonetheless, as they age, the enrollment likelihood increased accordingly (5 years OR: 1.71, CI, 1.32–2.21, p < 0.001). The eligible patient population of the study reflected the evolving demographics and different disease prevalence for asthma and other allergic diseases in adult and pediatric groups. These factors in turn influenced the composition of the enrolled participants.

Marine heatwaves (MHWs) and cold‐spells (MCSs) are extreme sea surface temperature events with significant impacts on marine ecosystems. However, the connection between these events and mixed layer depth (MLD) variations, as well as how their intensity relates to MLD changes, remains unclear. Integrating OISST V2.1 data with Argo profiles, this analysis finds that during MHWs, MLD decreases by 8.10% globally, while during MCSs, it increases by 8.13%. In 5° × 5° bins, 80.46% of ocean regions show MLD shallowing during MHWs, while 67.69% show deepening during MCSs. A significant global correlation between the intensity of MHWs/MCSs and MLD changes, with coefficients of −0.85 and −0.86, respectively. MHWs are more common in mesoscale anticyclonic eddies (AEs) (19.45%) than in cyclonic eddies (CEs) (10.11%). For MCSs, the pattern reverses, with 8.57% in AEs and 20.82% in CEs. Restratification and mesoscale eddies are two important factors driving MLD changes during these events. Plain Language Summary Marine heatwaves (MHWs) involve prolonged periods of sea surface temperatures (SSTs) above the 90th percentile of the climatological threshold, while marine cold‐spells (MCSs) involve SSTs below the 10th percentile. MHWs and MCSs both significantly impact marine ecosystems, particularly fragile coral reef ecosystems. A substantial amount of literature currently examines the characteristics of MHWs/MCSs, such as their frequency, duration, and cumulative days. However, the relationship between MHWs/MCSs and internal oceanic factors like mixed layer depth (MLD) variation is not fully understood. By combining OISST V2.1 data with Argo profiles, this study finds that MHWs are linked to significant MLD shallowing compared to background values. There is also a strong, statistically significant correlation between MHW intensity and the degree of MLD shallowing, at the 99% confidence level. In contrast, during MCSs, the MLD typically deepens relative to the climatological background. However, the degree of this deepening varies regionally with MCS intensity. Interestingly, MLD shallowing is observed during MCSs when the intensity exceeds −2.4°C. Further analysis indicates that mesoscale eddies and restratification are two mechanisms driving the variation in MLD during MHWs and MCSs. Key Points During marine heatwaves (MHWs), the mixed layer depth (MLD) shallows by 8.10% on average globally, while during MCSs, it deepens by 8.13% A significant correlation between the intensity of MHWs/MCSs and the relative change ratio of MLD Mesoscale eddies occurring alongside MHWs/MCSs can modify the usual trend of MLD shallowing during MHWs and deepening during MCSs

The adult brain contains dozens of different types of interneurons that control and refine neuronal circuits. Mi et al. used single-cell transcriptomics to investigate when these subtypes emerge during interneuron development in the mouse. Transcriptomes of embryonic interneurons showed similarities to adult classes of differentiated interneurons, thus dividing the immature embryonic interneurons themselves into classes. Nearly a dozen classes of embryonic neurons could be identified soon after their last mitosis by transcriptomic similarity with known classes of adult cortical interneurons. Thus, the fate of embryonic interneurons can be read in their transcriptomes well before the neurons migrate and reach their final sites of differentiation and circuit integration. Science , this issue p. 81 Single-cell transcriptomics reveals embryonic correlates of adult interneuron classes. GABAergic interneurons (GABA, γ-aminobutyric acid) regulate neural-circuit activity in the mammalian cerebral cortex. These cortical interneurons are structurally and functionally diverse. Here, we use single-cell transcriptomics to study the origins of this diversity in the mouse. We identify distinct types of progenitor cells and newborn neurons in the ganglionic eminences, the embryonic proliferative regions that give rise to cortical interneurons. These embryonic precursors show temporally and spatially restricted transcriptional patterns that lead to different classes of interneurons in the adult cerebral cortex. Our findings suggest that shortly after the interneurons become postmitotic, their diversity is already patent in their diverse transcriptional programs, which subsequently guide further differentiation in the developing cortex.

Traditional stator-permanent magnet flux-reversal motors have the problems of large cogging torque, limited improvement of power density, and low fault-tolerant performance. Based on the traditional flux-reversal motor, this paper proposes a design scheme of a flux-reversal motor with a stator-segmented double-winding and double-sequence permanent magnet structure. The motor adopts a stator-segmented modular design, each independent stator segment is connected by permanent magnets, and a bipolar permanent magnet array is arranged on the teeth of the stator segment. Meanwhile, a double-winding system composed of independent power windings and fault-tolerant windings is configured to realize the dual characteristics of high power density and high reliability. A two-dimensional finite element model is established to simulate and analyze the motor, which verifies the feasibility of the motor structure design. The simulation results show that the motor has improved operation stability, better fault-tolerant performance, and theoretically lower maintenance cost, as well as being especially suitable for the petroleum and chemical industries, electric vehicles, aerospace, and other application fields with high requirements for motor reliability and power density.

The purpose of Fuzzy Comprehensive CS-SVR Model (FCCS-SVR) is to evaluate and monitor the health status of a radar equipment and then keep its safe operation. Due to reasons such as few samples, slow changes and the nonlinear structure of data of fault monitoring signal, the health status evaluation of a radar system is quite difficult. By establishing the evaluation index system of a radar, the combination of AHP method and Entropy weight method is studied in this paper. In order to evaluate the value of health status, several optimization algorithms including PSO, GA, BA and CS are used for optimizing the parameters of SVR model. Meanwhile, in order to avoid the problem that the system is at the edge of the state, a radar health assessment method based on the combination of Fuzzy Comprehensive Evaluation and Cuckoo Search-Support Vector Regression (CS-SVR), which is named as Fuzzy Comprehensive CS-SVR (FCCS-SVR), is further proposed. The result of case analysis reflects that the state evaluation of the radar system is realized. The system performance analysis shows that the use of FCCS-SVR evaluation method provides a high recognition rate and can accurately assess the health status of the radar system.

We evaluated the effect of moderate-intensity exercise intervention in children and summarized the optimal exercise intervention program. Five significant databases, namely, Web of Science, PubMed, and China National Knowledge Infrastructure, were searched, and the literature was screened strictly according to the inclusion and exclusion criteria and analyzed using Stata 15.1 software. There were 25 studies from 22 articles, with a total of 2118 subjects included in the results. According to the meta-analysis, exercise intervention effectively improved children's working memory [SMD = -1.05, 95% CI (-1.26, -0.84)] and cognitive flexibility [SMD = -0.86, 95% CI (-1.04, -0.69)], with a minor improvement in inhibitory control [SMD = -0.55, 95% CI (-0.68, -0.42)]. a) Improvements in children's working memory and cognitive flexibility by moderate-intensity exercise interventions reached large effect sizes, and improvements in inhibitory control obtained moderate effect sizes. b) Better improvement in working memory for children aged 10 to 12 years than for children aged 6 to 9 years and better cognitive flexibility for children aged 6 to 9 years than for children aged 10 to 12 years. c) Exercise intervention programs lasting 8 to 12 weeks, 3 to 4 times/week, and 30 min/time are most effective in improving executive function in children.

The purpose of infrared and visible image fusion is to combine the advantages of both and generate a fused image that contains target information and has rich details and contrast. However, existing fusion algorithms often overlook the importance of incorporating both local and global feature extraction, leading to missing key information in the fused image. To address these challenges, this paper proposes a dual-branch fusion network combining convolutional neural network (CNN) and Transformer, which enhances the feature extraction capability and motivates the fused image to contain more information. Firstly, a local feature extraction module with CNN as the core is constructed. Specifically, the residual gradient module is used to enhance the ability of the network to extract texture information. Also, jump links and coordinate attention are used in order to relate shallow features to deeper ones. In addition, a global feature extraction module based on Transformer is constructed. Through the powerful ability of Transformer, the global context information of the image can be captured and the global features are fully extracted. The effectiveness of the proposed method in this paper is verified on different experimental datasets, and it is better than most of the current advanced fusion algorithms.