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A bstract This paper investigates non-thermal leptogenesis from inflaton decays in the minimal extension of the canonical type-I seesaw model, where a complex singlet scalar ϕ is introduced to generate the Majorana masses of right-handed neutrinos (RHNs) and to play the role of inflaton. First, we systematically study non-thermal leptogenesis with the least model dependence. We give a general classification of the parameter space and find four characteristic limits by carefully examining the interplay between inflaton decay into RHNs and the decay of RHNs into the standard-model particles. Three of the four limits are truly non-thermal, with a final efficiency larger than that of thermal leptogenesis. Two analytic estimates for these three limits are provided with working conditions to examine the validity. In particular, we find that the strongly non-thermal RHNs scenario occupies a large parameter space, including the oscillation-preferred K range, and works well for a relatively-low reheating temperature T RH ≥ 10 3 GeV, extending the lower bound on the RHN mass to 2 × 10 7 GeV. The lepton flavor effects are discussed. Second, we demonstrate that such a unified picture for inflation, neutrino masses, and baryon number asymmetry can be realized by either a Coleman-Weinberg potential (for the real part of ϕ ) or a natural inflation potential (for the imaginary part of ϕ ). The allowed parameter ranges for successful inflation and non-thermal leptogenesis are much more constrained than those without inflationary observations. We find that non-thermal leptogenesis from inflaton decay offers a testable framework for the early Universe. It can be further tested with upcoming cosmological and neutrino data. The model-independent investigation of non-thermal leptogenesis should be useful in exploring this direction.

A bstract Connecting inflation with neutrino physics through non-thermal leptogenesis via direct inflaton-right-handed neutrino (RHN) coupling naturally incorporates neutrino reheating, leaving no ambiguity regarding the early history of the universe. In ref. [1], we demonstrate that non-thermal leptogenesis from inflaton decay expands the viable parameter space compared to thermal leptogenesis and provides a natural link to inflation. In this work, we refine our previous findings by closely examining the dynamics of neutrino reheating. We first calculate the duration of neutrino reheating on a general basis, then analyze inflationary observables consistent with neutrino reheating across four models, establishing a direct connection between baryon asymmetry and the spectral index. This approach places these two important observables on the same plane and yields specific predictions that help break the degeneracy among inflationary models. The well-motivated and economical framework offers a simple, natural, and testable description of the early universe.

This study is concerned with the restoration of painted pottery images from the Yangshao period. The objective is to enhance the efficiency and accuracy of the restoration process for complex pottery patterns. Conventional restoration techniques encounter difficulties in accurately and efficiently reconstructing intricate designs. To address this issue, the study proposes an AI-assisted restoration workflow that combines Stable Diffusion models (SD) with Low-Rank Adaptation (LoRA) technology. By training a LoRA model on a dataset of typical Yangshao painted pottery patterns and integrating image inpainting techniques, the accuracy and efficiency of the restoration process are enhanced. The results demonstrate that this method provides an effective restoration tool while maintaining consistency with the original artistic style, supporting the digital preservation of cultural heritage. This approach also offers archaeologists flexible restoration options, promoting the broader application and preservation of cultural heritage.

The spread of Christianity to China initiated a process of indigenization, particularly evident in Christian art. This study explores the indigenization of early 20th-century Chinese Christian paintings through literature reviews, case studies, and comparative research. The analysis covers four forms of primary research. First, it explores the indigenization of Christian concepts, tracing their development from the introduction of Nestorian Christianity in the Tang dynasty through the establishment of Fu Jen Catholic University in the Republican era. Matteo Ricci’s implementation of the “Ricci Rule” during the late Ming dynasty, subsequently expanded by Celso Costantini, played a crucial role in the indigenous adaptation of Christian painting in China. The second facet focuses on the Beijing Catholic School of Painting, led by Chen Yuandu, a group that innovated Chinese Christian art by integrating local artistic expressions with traditional depictions of saints, assimilating symbols from Chinese literati painting, and preserving time-honored Chinese painting techniques. The third facet examines the strategy behind Christian painting methods. Fourth, this study discusses how the Fu Jen School faced varied reception and evaluations from domestic and international audiences under the complex social currents of the Republic of China and how the artists reflected the national spirit and artistic responsibility in their narrative paintings. Fundamentally, the practice of Christian painting at the early 20th-century Catholic School is not only an innovative artistic endeavor but also a significant case of cultural exchange between East and West and religious localization.

Microglia, a kind of highly dynamic central neural system immunity cells, transform phenotype based on diverse stimuli, playing vital roles in adult neurogenesis and synaptic connection for affecting AD progression. Particularly, microglia are mainly composed of anti-inflammatory types in mild AD patients, which offer immune surveillance to clear away amyloid beta plaques and facilitate adult neurogenesis. However, these microglia transform into pro-inflammatory alike phenotype to accelerate the deterioration of the disease if exposed to chronic but slow-level plaques stimulation. Thus, how to control microgial prototype becomes a promising therapeutic direction. It has been found that adult-born neurons activation mediated by SuM stimulation in early AD mice could promote microglia phagocytosis, improving cognitive functions. In this paper, I focus on whether microglia promote AHN by increasing BDNF release after SuM-ABN activation and whether chronic ABN-stimulated microglia could transform or maintain anti-inflammatory phenotype to prevent improper synaptic cut, leading to cognitive enhancement.

With the rapid development of digital information technology, artificial intelligence, computer technology, and biotechnology, science fiction movies have become an important medium for exploring the concepts of “posthumanism” and “cyborg”. In science fiction movies, cyborgs are endowed with multiple genders and social identities, especially in the presentation of female cyborgs, which reveal challenges and reflections on traditional gender binary oppositions. Based on feminist theory, deconstructing the construction and gendered path of female cyborgs in science fiction films can explore the duality of female cyborgs’ bodies in traditional gender power structures. Research has found that as the pre-factor of Seberg’s identity construction, the participation of technology has also been put into the gender discourse system. Despite the potential liberating power of cyborgs as a form of existence that transcends gender, the gender identity of female cyborgs is still subject to traditional patriarchal norms in a commercialized and male-dominated society, providing the possibility of re-examining the relationship between gender and body.

The applications of synthetic biology have expanded rapidly in the past decades, thanks to advances in DNA synthesis, gene sequencing and lower costs, as well as rapid advances in genomics and data science. Synthetic biology can be used to design new biological systems, or redesign existing systems to implement properties and new functions that humans need, for applications as diverse as disease diagnosis, manufacturing, agriculture and medicine. In the pharmaceutical sector, the process of traditional drug discovery is time-consuming, expensive and challenging, with a low success rate. High expectations have been placed on how to apply synthetic biology in drug development to improve the efficiency and success rate of drug development. In this review, the discovery of new natural products, verification of targets, large-scale drug production and the specific application of synthetic biology in the field of drug research and development are discussed, and the application prospects and existing problems of synthetic biology technology in drug research and opening are prospected.

The horse industry constitutes a vital economic sector in Xinjiang, China. This study quantitatively assesses the sector’s sustainable development through eco-efficiency analysis across northern Xinjiang counties from 2001 to 2021. The research employs four analytical methods: the S-SBM model for efficiency measurement, kernel density estimation for distribution analysis, Moran’s index for spatial autocorrelation examination, and convergence tests for long-term trend assessment. Results demonstrate a consistent decline in eco-efficiency, decreasing from 0.821 in 2001 to 0.444 in 2021, with an average value of 0.557. Significant regional disparities emerge, with efficiency scores ranging from 0.499 to 1.285 across different prefectures. Spatial analysis reveals pronounced clustering effects, particularly in Yili Prefecture. Convergence tests indicate the presence of β-convergence but the absence of σ-convergence, suggesting narrowing efficiency gaps over time despite persistent regional inequalities. These empirical findings provide substantive evidence for policymakers seeking to enhance Xinjiang’s equine economy sustainability and resource efficiency. The study contributes to the limited literature on ecological efficiency measurement in animal husbandry sectors.

Type 2 diabetes (T2D) is a disease characterized by heterogeneously progressive loss of islet β cell insulin secretion usually occurring after the presence of insulin resistance (IR) and it is one component of metabolic syndrome (MS), and we named it metabolic dysfunction syndrome (MDS). The pathogenesis of T2D is not fully understood, with IR and β cell dysfunction playing central roles in its pathophysiology. Dyslipidemia, hyperglycemia, along with other metabolic disorders, results in IR and/or islet β cell dysfunction via some shared pathways, such as inflammation, endoplasmic reticulum stress (ERS), oxidative stress, and ectopic lipid deposition. There is currently no cure for T2D, but it can be prevented or in remission by lifestyle intervention and/or some medication. If prevention fails, holistic and personalized management should be taken as soon as possible through timely detection and diagnosis, considering target organ protection, comorbidities, treatment goals, and other factors in reality. T2D is often accompanied by other components of MDS, such as preobesity/obesity, metabolic dysfunction associated steatotic liver disease, dyslipidemia, which usually occurs before it, and they are considered as the upstream diseases of T2D. It is more appropriate to call “diabetic complications” as “MDS-related target organ damage (TOD)”, since their development involves not only hyperglycemia but also other metabolic disorders of MDS, promoting an up-to-date management philosophy. In this review, we aim to summarize the underlying mechanism, screening, diagnosis, prevention, and treatment of T2D, especially regarding the personalized selection of hypoglycemic agents and holistic management based on the concept of “MDS-related TOD”.

Tissue development and disease lead to changes in cellular organization, nuclear morphology, and gene expression, which can be jointly measured by spatial transcriptomic technologies. However, methods for jointly analyzing the different spatial data modalities in 3D are still lacking. We present a computational framework to integrate Spatial Transcriptomic data using over-parameterized graph-based Autoencoders with Chromatin Imaging data (STACI) to identify molecular and functional alterations in tissues. STACI incorporates multiple modalities in a single representation for downstream tasks, enables the prediction of spatial transcriptomic data from nuclear images in unseen tissue sections, and provides built-in batch correction of gene expression and tissue morphology through over-parameterization. We apply STACI to analyze the spatio-temporal progression of Alzheimer’s disease and identify the associated nuclear morphometric and coupled gene expression features. Collectively, we demonstrate the importance of characterizing disease progression by integrating multiple data modalities and its potential for the discovery of disease biomarkers. Methods for jointly analysing the different spatial data modalities in 3D are lacking. Here the authors report the computational framework STACI (Spatial Transcriptomic data using over-parameterized graph-based Autoencoders with Chromatin Imaging data) which they apply to an Alzheimer’s disease mouse model.