
網膜視細胞において、転写因子NRLはRNA結合タンパク質やRループと相互作用し、遺伝子発現を制御している。


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RNA-binding proteins (RBPs) perform diverse functions including the regulation of chromatin dynamics and the coupling of transcription with RNA processing. However, our understanding of their actions in mammalian neurons remains limited. Using affinity purification, yeast-two-hybrid and proximity ligation assays, we identified interactions of multiple RBPs with NRL, a Maf-family bZIP transcription factor critical for retinal rod photoreceptor development and function. In addition to splicing, many NRL-interacting RBPs are associated with R-loops, which form during transcription and increase during photoreceptor maturation. Focusing on DHX9 RNA helicase, we demonstrate that its expression is modulated by NRL and that the NRL-DHX9 interaction is positively influenced by R-loops. ssDRIP-Seq analysis reveals both stranded and unstranded R-loops at distinct genomic elements, characterized by active and inactive epigenetic signatures and enriched at neuronal genes. NRL binds to both types of R-loops, suggesting an epigenetically independent function. Our findings suggest additional functions of NRL during transcription and highlight complex interactions among transcription factors, RBPs, and R-loops in regulating photoreceptor gene expression in the mammalian retina.

### Competing Interest Statement

The authors have declared no competing interest.

Maf-family bZIP transcription factor NRL interacts with RNA-binding proteins and R-loops in retinal photoreceptors

網膜視細胞において-mafファミリーbzip転写因子nrlはrna結合タンパク質およびrループと相互作用する


網膜視細胞において、MafファミリーbZIP転写因子NRLはRNA結合タンパク質およびRループと相互作用する


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EyeDiff，一種基於穩定擴散模型的文字到圖像生成模型，能根據文字提示生成多模態眼科圖像，有效解決罕見眼疾數據不足的問題，並提升常見和罕見眼疾的診斷準確率。


eyediff-基於文字到圖像的擴散模型-提升罕見眼疾診斷效率


EyeDiff：基於文字到圖像的擴散模型，提升罕見眼疾診斷效率



EyeDiff는 텍스트-이미지 변환 모델을 통해 다양한 안과 이미지를 생성하여 희귀 안과 질환 진단을 위한 데이터 불균형 문제를 해결하고 진단 정확도를 향상시킵니다.


eyediff-텍스트-이미지-변환-모델을-이용한-안과-질환-진단-개선-희귀-질환-진단-향상에-주목


EyeDiff: 텍스트-이미지 변환 모델을 이용한 안과 질환 진단 개선 - 희귀 질환 진단 향상에 주목



EyeDiffと呼ばれる新しいテキストから画像への拡散モデルは、自然言語のプロンプトから多様な眼科画像を生成することで、希少な眼疾患の診断精度を大幅に向上させることができます。


テキストから画像への拡散モデルeyediff-希少な眼疾患の診断を向上


テキストから画像への拡散モデルEyeDiff：希少な眼疾患の診断を向上



EyeDiff, a novel text-to-image diffusion model, effectively generates realistic synthetic multimodal ophthalmic images from textual descriptions, thereby addressing data scarcity and imbalance in eye disease datasets, and ultimately improving the accuracy of AI models in diagnosing both common and rare eye diseases.


eyediff-a-text-to-image-diffusion-model-for-generating-synthetic-multimodal-ophthalmic-images-to-improve-rare-and-common-eye-disease-diagnosis-


EyeDiff: A Text-to-Image Diffusion Model for Generating Synthetic Multimodal Ophthalmic Images to Improve Rare and Common Eye Disease Diagnosis



在白化病小鼠中，錯誤連接到視丘錯誤區域的視網膜神經節細胞軸突會形成突觸分離的子迴路，這表明活動依賴性突觸重塑在視覺迴路組織中的作用。


A Hebbian model of circuit remodeling predicts that two sets of inputs with sufficiently distinct activity patterns will synaptically capture separate sets of target cells. Mice in which a subset of retinal ganglion cells (RGCs) target the wrong region of the dorsal lateral geniculate nucleus (dLGN) provide the conditions for testing this prediction. In albino mice, mistargeted RGC axons form an island of terminals that is distinct from the surrounding neuropil. Blocking retinal activity during development prevents the formation of this island. However, the synaptic connectivity of the island was unknown. Here, we combine light and electron microscopy to determine if this activity-dependent island of axon terminals represent a synaptically segregated subcircuit. We reconstructed the microcircuitry of the boundary between the island and non-island RGCs and found a remarkably strong segregation within retinogeniculate connectivity. We conclude that, when sets of retinal input are established in the wrong part of the dLGN, the developing circuitry responds by forming a synaptically isolated subcircuit from the otherwise fully connected network. The fact that there is a developmental starting condition that can induce a synaptically segregated microcircuit has important implications for our understanding of the organization of visual circuits and for our understanding of the implementation of activity dependent development.

### Competing Interest Statement

The authors have declared no competing interest.

Mistargeted retinal axons form synaptically segregated subcircuits in the visual thalamus of albino mice

白化病小鼠視丘中錯誤連接的視網膜軸突形成突觸分離的子迴路


백색증 쥐에서 발생하는 망막 축삭의 표적화 오류는 시각 시상에서 시냅스적으로 분리된 하위 회로를 형성하며, 이는 활동 의존적 시냅스 리모델링에 의해 발생한다.


백색증-쥐의-시각-시상에서-잘못-표적화된-망막-축삭은-시냅스적으로-분리된-하위-회로를-형성한다


백색증 쥐의 시각 시상에서 잘못 표적화된 망막 축삭은 시냅스적으로 분리된 하위 회로를 형성한다



アルビノマウスにおける網膜軸索の誤標的化は、視床においてシナプス的に分離したサブサーキットの形成につながり、活動依存的な神経回路の発達におけるシナプス可塑性の重要性を示唆している。


アルビノマウスの視床において-誤って標的化された網膜軸索はシナプス的に分離したサブサーキットを形成する


アルビノマウスの視床において、誤って標的化された網膜軸索はシナプス的に分離したサブサーキットを形成する



SLOctolyzer는 SLO 이미지에서 망막 혈관을 자동으로 분할하고 임상적으로 의미 있는 파라미터를 추출하는 오픈소스 도구로, OCT 이미지 분석과 안과 질환 연구에 유용하다.


sloctolyzer-slo-이미지에서-망막-혈관-분할-및-특징-추출을-위한-완전-자동-분석-도구


SLOctolyzer: SLO 이미지에서 망막 혈관 분할 및 특징 추출을 위한 완전 자동 분석 도구



SLOctolyzerは、網膜血管のセグメンテーションと測定のための初のオープンソースツールキットであり、生SLO画像を再現性と臨床的に意味のある網膜血管パラメータに変換します。


sloctolyzer-走査型レーザー検眼鏡画像におけるセグメンテーションと特徴抽出のための全自動分析ツールキット


SLOctolyzer：走査型レーザー検眼鏡画像におけるセグメンテーションと特徴抽出のための全自動分析ツールキット
