Unveiling the role of IGF1R in autism spectrum disorder: a multi-omics approach to decipher common pathogenic mechanisms in the IGF signaling pathway

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition marked by impairments in social interaction, communication, and repetitive behaviors. Emerging evidence suggests that the insulin-like growth factor (IGF) signaling pathway plays a critical role in ASD pathogenesis; however, the precise pathogenic mechanisms remain elusive. This study utilizes multi-omics approaches to investigate the pathogenic mechanisms of ASD susceptibility genes within the IGF pathway. Whole-exome sequencing (WES) revealed a significant enrichment of rare variants in key IGF signaling components, particularly the IGF receptor 1 (IGF1R), in a cohort of Chinese Han individuals diagnosed with ASD, as well as in ASD patients from the SFARI SPARK WES database. Subsequent single-cell RNA sequencing (scRNA-seq) of cortical tissues from children with ASD demonstrated elevated expression of IGF receptors in parvalbumin (PV) interneurons, suggesting a substantial impact on their development. Notably, IGF1R appears to mediate the effects of IGF2R on these neurons. Additionally, transcriptomic analysis of brain organoids derived from ASD patients indicated a significant association between IGF1R and ASD. Protein-protein interaction (PPI) and gene regulatory network (GRN) analyses further identified ASD susceptibility genes that interact with and regulate IGF1R expression. In conclusion, IGF1R emerges as a central node within the IGF signaling pathway, representing a potential common pathogenic mechanism and therapeutic target for ASD. These findings highlight the need for further investigation into the modulation of this pathway as a strategy for ASD intervention

Single-nucleus RNA-sequencing anslysis of PFC from human.

Brain   aging is accompanied by a decline and loss of cognitive and learning memory,   and is associated with many neurodegenerative diseases, thus posing a   significant threat to global health. There are no systematic studies on the   single-cell level of human brain aging to explain cell-specific   transcriptomic changes. Here, by integrating two independent single-nucleus   transcriptome sequencing (snRNA-seq) data of human prefrontal cortex tissue,   we found that the prefrontal cortex has highly cellular heterogeneity during   aging. The findings showed significantly reduced excitatory neurons during   aging in healthy individuals. However, the number of microglia and   oligodendrocytes were significantly increased. We compared levels of gene   expression in cells across all stage by cell type, and identified 93 unique   differentially expressed genes (DEGs) that implicated all major cell types.   All or most of DEGs in oligodendrocyte, OPC, microglia, excitatory, and   inhibitory neurons were downregulated, whereas most DEGs in astrocytes, and   endotheliocyte were upregulated.    </p

<b>Single-Cell Transcriptome Unveils Unique Transcriptomic Signatures of Human Organ-Specific Endothelial Cells</b>

Single-Cell Transcriptome Unveils Unique Transcriptomic Signatures of Human Organ-Specific Endothelial Cells</p

Aircraft Target Detection in Low Signal-to-Noise Ratio Visible Remote Sensing Images

With the increasing demand for the wide-area refined detection of aircraft targets, remote sensing cameras have adopted an ultra-large area-array detector as a new imaging mode to obtain broad width remote sensing images (RSIs) with higher resolution. However, this imaging technology introduces new special image degradation characteristics, especially the weak target energy and the low signal-to-noise ratio (SNR) of the image, which seriously affect the target detection capability. To address the aforementioned issues, we propose an aircraft detection method for RSIs with low SNR, termed L-SNR-YOLO. In particular, the backbone is built blending a swin-transformer and convolutional neural network (CNN), which obtains multiscale global and local RSI information to enhance the algorithm&rsquo;s robustness. Moreover, we design an effective feature enhancement (EFE) block integrating the concept of nonlocal means filtering to make the aircraft features significant. In addition, we utilize a novel loss function to optimize the detection accuracy. The experimental results demonstrate that our L-SNR-YOLO achieves better detection performance in RSIs than several existing advanced methods

Complementary-View Multiple Human Tracking

The global trajectories of targets on ground can be well captured from a top view in a high altitude, e.g., by a drone-mounted camera, while their local detailed appearances can be better recorded from horizontal views, e.g., by a helmet camera worn by a person. This paper studies a new problem of multiple human tracking from a pair of top- and horizontal-view videos taken at the same time. Our goal is to track the humans in both views and identify the same person across the two complementary views frame by frame, which is very challenging due to very large field of view difference. In this paper, we model the data similarity in each view using appearance and motion reasoning and across views using appearance and spatial reasoning. Combing them, we formulate the proposed multiple human tracking as a joint optimization problem, which can be solved by constrained integer programming. We collect a new dataset consisting of top- and horizontal-view video pairs for performance evaluation and the experimental results show the effectiveness of the proposed method

Improved Deformable Convolution Method for Aircraft Object Detection in Flight Based on Feature Separation in Remote Sensing Images

Aircraft object detection in remote sensing images is a challenging task, especially for small objects, complex backgrounds, and aircraft objects in flight. Due to the lack of contextual relationship between the aircraft object and the surrounding background during flight and the small number of pixels in the object itself, the use of regular rectangular convolution for feature extraction results in many background pixels being sampled. This article proposes YOLO-FRS for aircraft object in flight detection, which uses a new module based on deformable convolution (DCN), the feature response separation deformable convolution (FRS-DCN) module. The FRS-DCN module adds semantic segmentation supervision on the basis of stacked DCNs, so that the background and object in the input of DCNs in this module are separated as much as possible. We design a soft label annotation and loss calculation method for semantic segmentation supervision. In addition, we propose a flight-state aircraft object dataset that includes multiple backgrounds and cloud interference. YOLO-FRS was tested on the proposed dataset, and the results showed that the FRS-DCN module improved the performance of aircraft object detection. Compared with multiple mainstream deep convolution models, the YOLO-FRS model also exhibits competitive performance