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Background/Objectives: Induced pluripotent stem cells (iPSCs) reprogrammed from somatic cells into cells with most of the ESC (embryonic stem cell) characteristics show promise toward solving ethical problems currently facing stem cell research and eventually yield clinical grade pluripotent stem cells for therapies and regenerative medicine. In recent years, an increasing body of research suggests that the chemical induction of pluripotency (CIP) method can yield iPSCs in vitro, yet its application in avian species remains unreported. Methods: Herein, we successfully obtained stably growing chicken embryonic fibroblasts (CEFs) using the tissue block adherence method and employed 12 small-molecule compounds to induce chicken iPSC formation. Results: The final optimized iPSC induction system was bFGF (10 ng/mL), CHIR99021 (3 μM), RepSox (5 μM), DZNep (0.05 μM), BrdU (10 μM), BMP4 (10 ng/mL), vitamin C (50 μg/mL), EPZ-5676 (5 μM), and VPA (0.1 mM). Optimization of the induction system revealed that the highest number of clones was induced with 8 × 104 cells per well and at 1.5 times the original concentration. Upon characterization, these clones exhibited iPSC characteristics, leading to the development of a stable compound combination for iPSC generation in chickens. Concurrently, employing a deletion strategy to investigate the functionality of small-molecule compounds during induction, we identified CHIR99021 and BrdU as critical factors for inducing chicken iPSC formation. Conclusions: In conclusion, this study provides a reference method for utilizing small-molecule combinations in avian species to reprogram cells and establish a network of cell fate determination mechanisms. [ABSTRACT FROM AUTHOR]

The determination of sex in mammals is established and controlled by various complex mechanisms. In contrast, sex control in poultry remains an unresolved issue. In this study, RNA-sequencing was conducted for male gonads and ovarian tissues in chicken embryos of up to 18.5 days to identify metabolic factors influencing male and female sex differentiation, as well as gonadal development. Our results reveal that PKM2, a critical glycolysis-related protein, plays a significant role in chicken sex differentiation via PPARG, a crucial hormone gene. We propose that our discoveries bolster the notion that glycolysis and oxidative phosphorylation function as antecedent contributors to sexual phenotypic development and preservation. [ABSTRACT FROM AUTHOR]

The identification of accurate gene insertion sites on chicken sex chromosomes is crucial for advancing sex control breeding materials. In this study, the intergenic region NC_006127.4 on the chicken Z chromosome and the non-repetitive sequence EE0.6 on the W chromosome were selected as potential gene insertion sites. Gene knockout vectors targeting these sites were constructed and transfected into DF-1 cells. T7E1 enzyme cleavage and luciferase reporter enzyme analyses revealed knockout efficiencies of 80.00% (16/20), 75.00% (15/20), and 75.00% (15/20) for the three sgRNAs targeting the EE0.6 site. For the three sgRNAs targeting the NC_006127.4 site, knockout efficiencies were 70.00% (14/20), 60.00% (12/20), and 45.00% (9/20). Gel electrophoresis and high-throughput sequencing were performed to detect potential off-target effects, showing no significant off-target effects for the knockout vectors at the two sites. EdU and CCK-8 proliferation assays revealed no significant difference in cell proliferation activity between the knockout and control groups. These results demonstrate that the EE0.6 and NC_006127.4 sites can serve as gene insertion sites on chicken sex chromosomes for gene editing without affecting normal cell proliferation. [ABSTRACT FROM AUTHOR]

As an RNA binding protein (RBP), DDX5 is widely involved in the regulation of various biological activities. While recent studies have confirmed that DDX5 can act as a transcriptional cofactor that is involved in the formation of gametes, few studies have investigated whether DDX5 can be used as a transcription factor to regulate the formation of primordial germ cells (PGCs). In this study, we found that DDX5 was significantly up-regulated during chicken PGC formation. Under different PGC induction models, the overexpression of DDX5 not only up-regulates PGC markers but also significantly improves the formation efficiency of primordial germ cell-like cells (PGCLC). Conversely, the inhibition of DDX5 expression can significantly inhibit both the expression of PGC markers and PGCLC formation efficiency. The effect of DDX5 on PGC formation in vivo was consistent with that seen in vitro. Interestingly, DDX5 not only participates in the formation of PGCs but also positively regulates their migration and proliferation. In the process of studying the mechanism by which DDX5 regulates PGC formation, we found that DDX5 acts as a transcription factor to bind to the promoter region of BMP4—a key gene for PGC formation—and activates the expression of BMP4. In summary, we confirm that DDX5 can act as a positive transcription factor to regulate the formation of PGCs in chickens. The obtained results not only enhance our understanding of the way in which DDX5 regulates the development of germ cells but also provide a new target for systematically optimizing the culture and induction system of PGCs in chickens in vitro. [ABSTRACT FROM AUTHOR]

The perception systems of autonomous vehicles face significant challenges under adverse conditions, with issues such as obscured objects and false detections due to environmental noise. Traditional approaches, which typically focus on noise removal, often fall short in such scenarios. Addressing the lack of diverse adverse weather data in existing automotive datasets, we propose a novel data augmentation method that integrates realistically simulated adverse weather effects into clear condition datasets. This method not only addresses the scarcity of data but also effectively bridges domain gaps between different driving environments. Our approach centers on a conditional generative model that uses segmentation maps as a guiding mechanism to ensure the authentic generation of adverse effects, which greatly enhances the robustness of perception and object detection systems in autonomous vehicles, operating under varied and challenging conditions. Besides the capability of accurately and naturally recreating over 90% of the adverse effects, we demonstrate that this model significantly improves the performance and accuracy of deep learning algorithms for autonomous driving, particularly in adverse weather scenarios. In the experiments employing our augmented approach, we achieved a 2.46% raise in the 3D average precision, a marked enhancement in detection accuracy and system reliability, substantiating the model's efficacy with quantifiable improvements in 3D object detection compared to models without augmentation. This work not only serves as an enhancement of autonomous vehicle perception systems under adverse conditions but also marked an advancement in deep learning models in adverse condition research. [ABSTRACT FROM AUTHOR]

Transformer-based models have gained popularity in the field of natural language processing (NLP) and are extensively utilized in computer vision tasks and multi-modal models such as GPT4. This paper presents a novel method to enhance the explainability of transformer-based image classification models. Our method aims to improve trust in classification results and empower users to gain a deeper understanding of the model for downstream tasks by providing visualizations of class-specific maps. We introduce two modules: the "Relationship Weighted Out" and the "Cut" modules. The "Relationship Weighted Out" module focuses on extracting class-specific information from intermediate layers, enabling us to highlight relevant features. Additionally, the "Cut" module performs fine-grained feature decomposition, taking into account factors such as position, texture, and color. By integrating these modules, we generate dense class-specific visual explainability maps. We validate our method with extensive qualitative and quantitative experiments on the ImageNet dataset. Furthermore, we conduct a large number of experiments on the LRN dataset, which is specifically designed for automatic driving danger alerts, to evaluate the explainability of our method in scenarios with complex backgrounds. The results demonstrate a significant improvement over previous methods. Moreover, we conduct ablation experiments to validate the effectiveness of each module. Through these experiments, we are able to confirm the respective contributions of each module, thus solidifying the overall effectiveness of our proposed approach. [ABSTRACT FROM AUTHOR]