Molecular Players in Preserving Excitatory-Inhibitory Balance in the Brain

Information processing in the brain relies on a functional balance between excitation and inhibition, the disruption of which leads to network destabilization and many neurodevelopmental disorders, such as autism spectrum disorders. One of the homeostatic mechanisms that maintains the excitatory and inhibitory balance is called synaptic scaling: Neurons dynamically modulate postsynaptic receptor abundance through activity-dependent gene transcription and protein synthesis. In the first part of my thesis work, I discuss our findings that a chromatin reader protein L3mbtl1 is involved in synaptic scaling. We observed that knockout and knockdown of L3mbtl1 cause a lack of synaptic downscaling of glutamate receptors in hippocampal primary neurons and organotypic slice cultures. Genome-wide mapping of L3mbtl1 protein occupancies on chromatin identified Ctnnb1 and Gabra2 as downstream target genes of L3mbtl1-mediated transcriptional regulation. Importantly, partial knockdown of Ctnnb1 by itself prevents synaptic downscaling. Another aspect of maintaining E/I balance centers on GABAergic inhibitory neurons. In the next part of my thesis work, we address the role of the scaffold protein Shank1 in excitatory synapses onto inhibitory interneurons. We showed that parvalbumin-expressing interneurons lacking Shank1 display reduced excitatory synaptic inputs and decreased levels of inhibitory outputs to pyramidal neurons. As a consequence, pyramidal neurons in Shank1 mutant mice exhibit increased E/I ratio. This is accompanied by a reduced expression of an inhibitory synapse scaffolding protein gephyrin. These results provide novel insights into the roles of chromatin reader molecules and synaptic scaffold molecules in synaptic functions and neuronal homeostasis

Early neuronal accumulation of DNA double strand breaks in Alzheimer's disease.

The maintenance of genomic integrity is essential for normal cellular functions. However, it is difficult to maintain over a lifetime in postmitotic cells such as neurons, in which DNA damage increases with age and is exacerbated by multiple neurological disorders, including Alzheimer's disease (AD). Here we used immunohistochemical staining to detect DNA double strand breaks (DSBs), the most severe form of DNA damage, in postmortem brain tissues from patients with mild cognitive impairment (MCI) or AD and from cognitively unimpaired controls. Immunostaining for γH2AX-a post-translational histone modification that is widely used as a marker of DSBs-revealed increased proportions of γH2AX-labeled neurons and astrocytes in the hippocampus and frontal cortex of MCI and AD patients, as compared to age-matched controls. In contrast to the focal pattern associated with DSBs, some neurons and glia in humans and mice showed diffuse pan-nuclear patterns of γH2AX immunoreactivity. In mouse brains and primary neuronal cultures, such pan-nuclear γH2AX labeling could be elicited by increasing neuronal activity. To assess whether pan-nuclear γH2AX represents DSBs, we used a recently developed technology, DNA damage in situ ligation followed by proximity ligation assay, to detect close associations between γH2AX sites and free DSB ends. This assay revealed no evidence of DSBs in neurons or astrocytes with prominent pan-nuclear γH2AX labeling. These findings suggest that focal, but not pan-nuclear, increases in γH2AX immunoreactivity are associated with DSBs in brain tissue and that these distinct patterns of γH2AX formation may have different causes and consequences. We conclude that AD is associated with an accumulation of DSBs in vulnerable neuronal and glial cell populations from early stages onward. Because of the severe adverse effects this type of DNA damage can have on gene expression, chromatin stability and cellular functions, DSBs could be an important causal driver of neurodegeneration and cognitive decline in this disease

Elevation Estimation-Driven Building 3D Reconstruction from Single-View Remote Sensing Imagery

Building 3D reconstruction from remote sensing images has a wide range of applications in smart cities, photogrammetry and other fields. Methods for automatic 3D urban building modeling typically employ multi-view images as input to algorithms to recover point clouds and 3D models of buildings. However, such models rely heavily on multi-view images of buildings, which are time-intensive and limit the applicability and practicality of the models. To solve these issues, we focus on designing an efficient DSM estimation-driven reconstruction framework (Building3D), which aims to reconstruct 3D building models from the input single-view remote sensing image. First, we propose a Semantic Flow Field-guided DSM Estimation (SFFDE) network, which utilizes the proposed concept of elevation semantic flow to achieve the registration of local and global features. Specifically, in order to make the network semantics globally aware, we propose an Elevation Semantic Globalization (ESG) module to realize the semantic globalization of instances. Further, in order to alleviate the semantic span of global features and original local features, we propose a Local-to-Global Elevation Semantic Registration (L2G-ESR) module based on elevation semantic flow. Our Building3D is rooted in the SFFDE network for building elevation prediction, synchronized with a building extraction network for building masks, and then sequentially performs point cloud reconstruction, surface reconstruction (or CityGML model reconstruction). On this basis, our Building3D can optionally generate CityGML models or surface mesh models of the buildings. Extensive experiments on ISPRS Vaihingen and DFC2019 datasets on the DSM estimation task show that our SFFDE significantly improves upon state-of-the-arts. Furthermore, our Building3D achieves impressive results in the 3D point cloud and 3D model reconstruction process

A lightweight network for improving wheat ears detection and counting based on YOLOv5s

IntroductionRecognizing wheat ears plays a crucial role in predicting wheat yield. Employing deep learning methods for wheat ears identification is the mainstream method in current research and applications. However, such methods still face challenges, such as high computational parameter volume, large model weights, and slow processing speeds, making it difficult to apply them for real-time identification tasks on limited hardware resources in the wheat field. Therefore, exploring lightweight wheat ears detection methods for real-time recognition holds significant importance.MethodsThis study proposes a lightweight method for detecting and counting wheat ears based on YOLOv5s. It utilizes the ShuffleNetV2 lightweight convolutional neural network to optimize the YOLOv5s model by reducing the number of parameters and simplifying the complexity of the calculation processes. In addition, a lightweight upsampling operator content-aware reassembly of features is introduced in the feature pyramid structure to eliminate the impact of the lightweight process on the model detection performance. This approach aims to improve the spatial resolution of the feature images, enhance the effectiveness of the perceptual field, and reduce information loss. Finally, by introducing the dynamic target detection head, the shape of the detection head and the feature extraction strategy can be dynamically adjusted, and the detection accuracy can be improved when encountering wheat ears with large-scale changes, diverse shapes, or significant orientation variations.Results and discussionThis study uses the global wheat head detection dataset and incorporates the local experimental dataset to improve the robustness and generalization of the proposed model. The weight, FLOPs and mAP of this model are 2.9 MB, 2.5 * 109 and 94.8%, respectively. The linear fitting determination coefficients R2 for the model test result and actual value of global wheat head detection dataset and local experimental Site are 0.94 and 0.97, respectively. The improved lightweight model can better meet the requirements of precision wheat ears counting and play an important role in embedded systems, mobile devices, or other hardware systems with limited computing resources