OSCR

Selective vulnerability of cerebral vasculature to <i>NOTCH3</i> variants in small vessel disease and rescue by phosphodiesterase-5 inhibitor.

Code ↔ Paper

2 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 2 matches
  1. [1] § MATERIALS AND METHODS › F-actin staining and quantification ↔ BlueAreaCenter2dis_2.py, lines 23–73 · score 0.66 · gradient magnitude, Gaussian, Sobel, Canny, filter, resize
  2. [2] § MATERIALS AND METHODS › F-actin staining and quantification ↔ Spatial dispersion.py, lines 6–71 · score 0.50 · spatial dispersion, probability, etp, dsp

Paper

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The authors' code

Python · 233 lines · 10 KB · MIT · 1 match

  1. import math
  3. import cv2
  4. import imutils
  5. import numpy as np
  6. import csv
  8. # 读取图像
  9. # image = cv2.imread('D:/PycharmProjects/segment-anything-main/finish/is/isogenic control_24.png')
  11. # image = cv2.imread('./finish/Arg153Cys-3/Arg153Cys/stack-30.png')
  12. # output_path = './finish/Arg153Cys-3/Arg-red/stack-30.png'
  13. # lunkuo_path = './finish/Arg153Cys-3/Arg-red/lunkuo/stack-30.png'
  14. # dis_a ngle_csv = './finish/Arg153Cys-3/data/Arg/patient_30_dis.csv'
  17. # image = cv2.imread('./finish/Arg153Cys-3/isogenic/isogenic control_14.png')
  18. # output_path = './finish/Arg153Cys-3/iso-red/stack-14.png'
  19. # lunkuo_path = './finish/Arg153Cys-3/iso-red/lunkuo/stack-14.png'
  20. # dis_angle_csv = './finish/Arg153Cys-3/data/iso/iso_14_dis.csv'
  23. for p in range(1, 43):
  25. # image = cv2.imread("./finish/Arg153Cys-3/Arg153Cys/stack-" + str(p) + ".png")
  26. # output_path = "./finish/Arg153Cys-3/11.23/Arg/Arg-red/stack-" + str(p) + ".png"
  27. # lunkuo_path = "./finish/Arg153Cys-3/11.23/Arg/Arg-red/lunkuo/stack-" + str(p) + ".png"
  28. # dis_csv = "./finish/Arg153Cys-3/11.23/Arg/patient_" + str(p) + "_dis.csv"
  29. # angle_csv = "./finish/Arg153Cys-3/11.23/Arg/patient_" + str(p) + "_angle.csv"
  31. if p < 10:
  32. image = cv2.imread("./R153C/R153C_SNAP(Treatment 2)/0" + str(p) + "_R153C_SNAP.png")
  33. # print("./R153C/C224Y(patient)/0" + str(p) + "_C224Y.png")
  34. output_path = "./R153C/result/R153C_SNAP(Treatment 2)/image/0" + str(p) + "_R153C_SNAP.png"
  35. lunkuo_path = "./R153C/result/R153C_SNAP(Treatment 2)/image/outline/0" + str(p) + "_R153C_SNAP.png"
  36. dis_csv = "./R153C/result/R153C_SNAP(Treatment 2)/distance/0" + str(p) + "_R153C_SNAP.csv"
  37. angle_csv = "./R153C/result/R153C_SNAP(Treatment 2)/angle/0" + str(p) + "_R153C_SNAP.csv"
  38. else:
  39. image = cv2.imread("./R153C/R153C_SNAP(Treatment 2)/" + str(p) + "_R153C_SNAP.png")
  40. # print("./C224Y/C224Y(patient)/0" + str(p) + "_C224Y.png")
  41. output_path = "./R153C/result/R153C_SNAP(Treatment 2)/image/" + str(p) + "_R153C_SNAP.png"
  42. lunkuo_path = "./R153C/result/R153C_SNAP(Treatment 2)/image/outline/" + str(p) + "_R153C_SNAP.png"
  43. dis_csv = "./R153C/result/R153C_SNAP(Treatment 2)/distance/" + str(p) + "_R153C_SNAP.csv"
  44. angle_csv = "./R153C/result/R153C_SNAP(Treatment 2)/angle/" + str(p) + "_R153C_SNAP.csv"
  47. # 图像预处理
  48. image = imutils.resize(image, height=512)
  50. # 计算图像的梯度
  51. gradient_x = cv2.Sobel(image, cv2.CV_64F, 1, 0, ksize=3)
  52. gradient_y = cv2.Sobel(image, cv2.CV_64F, 0, 1, ksize=3)
  53. gradient_magnitude = np.sqrt(gradient_x**2 + gradient_y**2)
  54. # 计算梯度的均值和标准差
  55. mean_gradient = np.mean(gradient_magnitude)
  56. stddev_gradient = np.std(gradient_magnitude)
  57. # 根据梯度的统计信息自适应计算阈值
  58. threshold = mean_gradient + 2 * stddev_gradient
  60. gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
  61. # gray = cv2.GaussianBlur(gray, (5, 5), 0)
  63. gray = cv2.bilateralFilter(gray, 5, sigmaColor=100, sigmaSpace=100)
  65. print('thereshold:', threshold)
  66. binary = cv2.Canny(gray, threshold1=(threshold / 2.5), threshold2=int(threshold))
  67. # binary = cv2.Canny(gray, threshold1=30, threshold2=120)
  68. # 轮廓检索
  69. green_contours, hierarchy = cv2.findContours(binary,
  70. cv2.RETR_EXTERNAL,
  71. cv2.CHAIN_APPROX_SIMPLE)
  72. green_contours_len = len(green_contours)
  73. print("green_contours-len:", green_contours_len)
  75. # cv2.imshow('binary', binary)
  76. cv2.imwrite(lunkuo_path, binary)
  78. # 转换颜色空间（BGR到HSV）
  79. hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
  81. # 定义蓝色的HSV范围
  82. lower_blue = np.array([75, 50, 50])
  83. upper_blue = np.array([130, 255, 255])
  85. # 创建蓝色掩码
  86. blue_mask = cv2.inRange(hsv_image, lower_blue, upper_blue)
  88. # 寻找蓝色区域的轮廓
  89. blue_contours, _ = cv2.findContours(blue_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
  91. average_distances = [] # 存放所有中心点到每一个绿色轮廓的平均值
  92. angle_list = [] # 存储所有绿色轮廓的角度
  93. all_distances = []
  94. temp_n = 1 # 全局变量用于限制计算angle_list的个数
  95. # 遍历蓝色区域的轮廓
  96. for blue_contour in blue_contours:
  97. # 计算每个蓝色区域的面积
  98. blue_area = cv2.contourArea(blue_contour)
  99. # 设置一个阈值，仅绘制较大的蓝色区域
  100. if blue_area > 400: # 更改阈值以匹配您的需求
  101. # 计算中心坐标
  102. M = cv2.moments(blue_contour)
  103. if M["m00"] != 0: # 零阶矩“m00”的含义比较直观，它表示一个轮廓的面积
  104. cX = int(M["m10"] / M["m00"])
  105. cY = int(M["m01"] / M["m00"])
  107. # 在图像上绘制中心坐标
  108. cv2.circle(image, (cX, cY), 5, (0, 0, 255), -1) # 绘制中心坐标
  109. cv2.drawContours(image, [blue_contour], -1, (0, 0, 255), 2) # 绘制蓝色区域
  111. distances = [] # 存储到所有绿色线条的距离
  112. n = 0
  113. for i in range(len(green_contours)):
  114. # 筛掉面积过小的轮廓
  115. # area = cv2.contourArea(green_contours[i])
  116. green_area = cv2.arcLength(green_contours[i], False)
  117. if green_contours_len < 200:
  118. if green_area < 100:
  119. n += 0
  120. continue
  121. n += 1
  122. # rect = cv2.minAreaRect(green_contours[i]) # 返回一个Box2D结构，其中包括以下详细信息–（中心(x,y)，（宽度、高度）、旋转角度）
  123. (x, y), (w, h), angle = cv2.minAreaRect(green_contours[i])
  124. # print('angle:', angle)
  125. # # 计算矩形框的四个顶点坐标
  126. # box = cv2.boxPoints(rect)
  127. # box = np.int0(box)
  128. if angle != 90:
  129. if h > w:
  130. angle = angle + 90
  131. else:
  132. if h > w:
  133. angle = 0
  134. if temp_n == 1: # 只在第一次计入角度
  135. angle_list.append(angle)
  136. dist = cv2.pointPolygonTest(green_contours[i], (cX, cY), True)
  137. distances.append(abs(dist))
  138. # # 绘制轮廓
  139. cv2.drawContours(image, [green_contours[i]], -1, (0, 0, 255), 1)
  140. else:
  141. if green_area < 200:
  142. n += 0
  143. continue
  144. n += 1
  145. # rect = cv2.minAreaRect(green_contours[i])
  146. (x, y), (w, h), angle = cv2.minAreaRect(green_contours[i])
  147. # print('angle:', angle)
  148. # # 计算矩形框的四个顶点坐标
  149. # box = cv2.boxPoints(rect)
  150. # box = np.int0(box)
  151. # # 计算矩形框的四个顶点坐标
  152. # box = cv2.boxPoints(rect)
  153. # box = np.int0(box)
  154. # box0 = box[0]
  155. # box1 = box[1]
  156. if angle != 90:
  157. if h > w:
  158. angle = angle + 90
  159. else:
  160. if h > w:
  161. angle = 0
  162. if temp_n == 1: # 只在第一次计入角度
  163. angle_list.append(angle)
  164. # # 计算矩形框的四个顶点坐标
  165. # box = cv2.boxPoints(rect)
  166. # box = np.int0(box)
  167. dist = cv2.pointPolygonTest(green_contours[i], (cX, cY), True)
  168. distances.append(abs(dist))
  169. # # 绘制轮廓
  170. cv2.drawContours(image, [green_contours[i]], -1, (0, 0, 255), 1)
  171. temp_n = 0
  172. all_distances.append(distances) # 所有距离
  173. print('dist:', distances)
  174. print('dist-len:', len(distances))
  175. temp = sum(distances)/len(distances) # 一个中心点到所有绿色轮廓距离的平均值
  176. print('eachline-average-len:', temp)
  177. average_distances.append(temp)
  179. # # 将列表转换为 NumPy 数组
  180. # array = np.array(all_distances)
  181. # # 计算每一列的平均值
  182. # column_means = np.mean(array, axis=0)
  183. # print('column_means:', column_means)
  184. # print(len(column_means))
  185. #
  186. # dis_angle = []
  187. # dis_angle.append(column_means.tolist())
  188. # dis_angle.append(angle_list)
  189. # print('dis_angle:', dis_angle)
  190. #
  191. # # 距离写入csv
  192. # with open(dis_angle_csv, mode='a', newline='') as file:
  193. # writer = csv.writer(file)
  194. # # 使用zip函数将数据按列写入CSV文件
  195. # for row in zip(*dis_angle):
  196. # writer.writerow(row)
  199. # 距离写入csv
  200. with open(dis_csv, mode='a', newline='') as file:
  201. writer = csv.writer(file)
  202. # 使用zip函数将数据按列写入CSV文件
  203. for row in zip(*all_distances):
  204. writer.writerow(row)
  206. # 角度写入csv
  207. with open(angle_csv, mode='a', newline='') as file1:
  208. writer = csv.writer(file1)
  210. # 遍历一维列表，并将每个元素写入CSV文件的指定列的不同行
  211. for item in angle_list:
  212. # 创建一个包含空字符串的列表，用于表示CSV文件的一行
  213. row = ["" for _ in range(len(angle_list))]
  214. # 在指定列中插入要写入的值
  215. row[0] = item
  216. writer.writerow(row)
  219. # 显示带有标记的图像
  220. print('average_distances:', average_distances)
  221. all_averge_distances = sum(average_distances)/len(average_distances)
  222. print("n=", n)
  223. print('angle_list_len:', len(angle_list))
  224. print('angle_list:', angle_list)
  225. print('all-averge-distances:', all_averge_distances)
  226. cv2.imwrite(output_path, image)
  227. # cv2.imshow('Marked Image', image)
  228. # cv2.waitKey(0)
  229. # cv2.destroyAllWindows()

BlueAreaCenter2dis_2.py at commit 6a9db28, under MIT · at the source

Overview

Authors: Xiangjun Zhao1, Chaowen Yu1,2, Antony Adamson3, Aite Zhao4, Huiyu Zhou5, Pankaj Sharma6, Tao Wang1,7,8
ORCID iDs: Xiangjun Zhao, Antony Adamson, Huiyu Zhou, Tao Wang
  1. Division of Evolution, Infection and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine, and Health, The University of Manchester, Manchester, UK
  2. Children’s Hospital of Chongqing Medical University, Chongqing 400014, P.R. China
  3. Genome Editing Unit, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
  4. College of Computer Science and Technology, Qingdao University, Qingdao, P.R. China
  5. School of Computing and Mathematical Sciences, University of Leicester, Leicester, UK
  6. Institute of Cardiovascular Research, Royal Holloway, University of London, Ashford and St Peter’s NHS Foundation Trust, and Imperial College Healthcare NHS Trust, London, UK
  7. Manchester Centre for Genomic Medicine, Manchester University NHS Foundation Trust, Manchester, UK
  8. Geoffrey Jefferson Brain Research Centre, Manchester Academic Health Science Centre, Northern Care Alliance and University of Manchester, Manchester, UK
Institutions: University of Manchester (United Kingdom); Children's Hospital of Chongqing Medical University (China); Qingdao University (China); University of Leicester (United Kingdom); Imperial College Healthcare NHS Trust (United Kingdom); Royal Holloway University of London (United Kingdom); Manchester Academic Health Science Centre (United Kingdom); Manchester University NHS Foundation Trust (United Kingdom)
Journal: Science advances, volume 12, issue 14, article eaeb1134
Dates: received 1 August 2025; accepted 4 March 2026; published online 3 April 2026; in print April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1126/sciadv.aeb1134 · PMID 41931622 · PMCID PMC13048245 · OpenAlex W7148880438
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), stroke (population), cellular / molecular (subfield)
Methods: Preprocessing, Statistics, Smoothing, state filtering, decompositions
MeSH: CADASIL*, Phosphodiesterase 5 Inhibitors*, Receptor, Notch3*, Humans, Induced Pluripotent Stem Cells, Muscle, Smooth, Vascular, Mutation, Myocytes, Smooth Muscle, Signal Transduction (* major topic)
Topic: Cerebrovascular and genetic disorders (Neurology, Medicine), according to OpenAlex
Funding: British Heart Foundation (PG/12/31/2952); China Scholarship Council (201908500055)
Citations: cited by 2 papers (Europe PMC); 75 references in the paper
Research resources: Mironov in the FBMH EM Core Facility RRID:SCR_021147

Abstract

NOTCH3 variants cause CADASIL (cerebral autosomal dominant arteriopathy and subcortical infarcts and leukoencephalopathy), the most common monogenetic form of small vessel disease (SVD) and vascular dementia (VaD). The molecular mechanisms driving CADASIL pathogenesis remain poorly understood, and no specific treatments are currently available. NOTCH3 is mainly expressed in vascular smooth muscle cells (VSMCs) that arise from different embryonic origins. Using human induced pluripotent stem cell (iPSC) models, we generated origin-specific VSMCs and found that cerebral, but not peripheral, VSMC mimics are selectively vulnerable to NOTCH3 variants. CADASIL iPSC–derived brain-specific VSMCs acquired a synthetic phenotype, accompanied with extensive extracellular matrix accumulation and impaired cell adhesion leading to anoikis. Furthermore, an endothelial-independent nitric oxide signaling was substantially impaired in CADASIL iPSC–derived VSMCs. Phosphodiesterase-5 inhibition successfully reversed the functional abnormality and survival of mutant VSMCs. Our findings uncovered mechanistic insights and suggest a viable therapeutic strategy for NOTCH3-associated SVD/VaD, reinforcing the value of patient-specific iPSCs for disease modeling and potential drug discovery.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

zhaoaite/ActinDetection

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 6a9db28608a0c96d4bf720998b81af74dc0fb0cf, 13 December 2025
Languages: Python (3)
Size: 8 files, 3 scripts
Software Heritage: not archived
Found in: “Data, code, and materials availability:”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (2 files), pandas (2 files), OpenCV (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
5 files
At the source: github.com/zhaoaite/ActinDetection

doi:10.5061/dryad.2280gb66p

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: “Data, code, and materials availability:”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
At the source: doi.org/10.5061/dryad.2280gb66p

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 3 scripts, each with its path and the digest of its content;
  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data, code, and materials availability

All data and code needed to evaluate and reproduce the results in the paper are present in the paper and/or the Supplementary Materials. Materials can be made available upon request to the corresponding author T.W. () and following execution of a Material Transfer Agreement (MTA). This study generated new materials, i.e., the isoCtrl iPSC lines created by CRISPR-Cas9 gene editing. The code for quantification of F-actin cytoskeleton organization is available on Dryad and can be accessed via this unique DOI link: https://doi.org/10.5061/dryad.2280gb66p. The code is also available on Github at https://github.com/zhaoaite/ActinDetection. The RNA-seq data have been deposited at Array Express: www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-16303.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 9 MeSH terms, 2 funders, 72 references, 1 RRID.

Cite

This paper

Zhao, X., Yu, C., Adamson, A., Zhao, A., Zhou, H., Sharma, P., & Wang, T. (2026). Selective vulnerability of cerebral vasculature to &lt;i&gt;NOTCH3&lt;/i&gt; variants in small vessel disease and rescue by phosphodiesterase-5 inhibitor. Science advances, 12(14), eaeb1134. https://doi.org/10.1126/sciadv.aeb1134

BibTeX

@article{zhao2026selective,
author = {Zhao, Xiangjun and Yu, Chaowen and Adamson, Antony and Zhao, Aite and Zhou, Huiyu and Sharma, Pankaj and Wang, Tao},
title = {{Selective vulnerability of cerebral vasculature to \&lt;i\&gt;NOTCH3\&lt;/i\&gt; variants in small vessel disease and rescue by phosphodiesterase-5 inhibitor}},
journal = {Science advances},
year = {2026},
month = apr,
volume = {12},
number = {14},
pages = {eaeb1134},
publisher = {American Association for the Advancement of Science},
issn = {2375-2548},
doi = {10.1126/sciadv.aeb1134},
url = {https://doi.org/10.1126/sciadv.aeb1134},
pmid = {41931622},
pmcid = {PMC13048245}
}

RIS

TY - JOUR
AU - Zhao, Xiangjun
AU - Yu, Chaowen
AU - Adamson, Antony
AU - Zhao, Aite
AU - Zhou, Huiyu
AU - Sharma, Pankaj
AU - Wang, Tao
TI - Selective vulnerability of cerebral vasculature to &lt;i&gt;NOTCH3&lt;/i&gt; variants in small vessel disease and rescue by phosphodiesterase-5 inhibitor
T2 - Science advances
J2 - Sci Adv
PY - 2026
DA - 2026/04/03
VL - 12
IS - 14
SP - eaeb1134
SN - 2375-2548
PB - American Association for the Advancement of Science
DO - 10.1126/sciadv.aeb1134
UR - https://doi.org/10.1126/sciadv.aeb1134
LA - en
ER -

CSL-JSON

{
"id": "10.1126/sciadv.aeb1134",
"type": "article-journal",
"title": "Selective vulnerability of cerebral vasculature to &lt;i&gt;NOTCH3&lt;/i&gt; variants in small vessel disease and rescue by phosphodiesterase-5 inhibitor",
"container-title": "Science advances",
"author": [
{
"family": "Zhao",
"given": "Xiangjun"
},
{
"family": "Yu",
"given": "Chaowen"
},
{
"family": "Adamson",
"given": "Antony"
},
{
"family": "Zhao",
"given": "Aite"
},
{
"family": "Zhou",
"given": "Huiyu"
},
{
"family": "Sharma",
"given": "Pankaj"
},
{
"family": "Wang",
"given": "Tao"
}
],
"container-title-short": "Sci Adv",
"volume": "12",
"issue": "14",
"page": "eaeb1134",
"DOI": "10.1126/sciadv.aeb1134",
"PMID": "41931622",
"PMCID": "PMC13048245",
"ISSN": "2375-2548",
"publisher": "American Association for the Advancement of Science",
"URL": "https://doi.org/10.1126/sciadv.aeb1134",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
3
]
]
}
}

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[8] doi:10.1021/acs.jmedchem.6c00231 [code]
Applying Deep-Learning-Driven &lt;i&gt;De Novo&lt;/i&gt; Design to Hit Identification: A Case Study on A&lt;sub&gt;2A&lt;/sub&gt; Adenosine Receptor Antagonists.
Journal: Journal of medicinal chemistry
In common: pandas, NumPy, cellular / molecular, 1 reference
[9] doi:10.1002/cns.71114 [code]
Dynamic Functional Connectivity Changes in Cortical and Cortico-Striatal Strokes in Mice.
Journal: CNS neuroscience & therapeutics
In common: OpenCV, pandas, NumPy, stroke
[10] doi:10.1186/s12951-026-04551-7
The role of AI-assisted drug repurposing in neurological disorders: a systematic review of validation strategies, challenges and opportunities.
Journal: Journal of nanobiotechnology
In common: 2 references

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