RRID:AB_11156085
DOI: 10.1101/2025.09.05.674562
Resource: (Abcam Cat# ab133273, RRID:AB_11156085)
Curator: @scibot
SciCrunch record: RRID:AB_11156085
RRID:AB_11156085
DOI: 10.1101/2025.09.05.674562
Resource: (Abcam Cat# ab133273, RRID:AB_11156085)
Curator: @scibot
SciCrunch record: RRID:AB_11156085
RRID:CVCL_0063
DOI: 10.1101/2025.09.05.674562
Resource: (RRID:CVCL_0063)
Curator: @scibot
SciCrunch record: RRID:CVCL_0063
RRID:CVCL_0395
DOI: 10.1101/2025.09.05.674562
Resource: (DSMZ Cat# ACC-256, RRID:CVCL_0395)
Curator: @scibot
SciCrunch record: RRID:CVCL_0395
RRID:CVCL_D603
DOI: 10.1101/2025.09.05.674562
Resource: (ATCC Cat# PTA-5080, RRID:CVCL_D603)
Curator: @scibot
SciCrunch record: RRID:CVCL_D603
RRID:SCR_006431
DOI: 10.1101/2025.05.16.654573
Resource: Parkinson's Progression Markers Initiative (RRID:SCR_006431)
Curator: @scibot
SciCrunch record: RRID:SCR_006431
RRID:AB_528448
DOI: 10.1101/2025.04.29.651334
Resource: (DSHB Cat# 8D12 anti-Repo, RRID:AB_528448)
Curator: @scibot
SciCrunch record: RRID:AB_528448
RRID:AB_528218
DOI: 10.1101/2025.04.29.651334
Resource: (DSHB Cat# Rat-Elav-7E8A10 anti-elav, RRID:AB_528218)
Curator: @scibot
SciCrunch record: RRID:AB_528218
RRID:SCR_025216
DOI: 10.1101/2025.04.12.648511
Resource: University of Pittsburgh Cryo-electron Microscopy Core Facility (RRID:SCR_025216)
Curator: @scibot
SciCrunch record: RRID:SCR_025216
RRID:SCR_002798
DOI: 10.1101/2025.03.20.644303
Resource: GraphPad Prism (RRID:SCR_002798)
Curator: @scibot
SciCrunch record: RRID:SCR_002798
RRID:SCR_014281
DOI: 10.1101/2025.03.20.644303
Resource: StepOne Software (RRID:SCR_014281)
Curator: @scibot
SciCrunch record: RRID:SCR_014281
RRID:SCR_014199
DOI: 10.1101/2025.03.20.644303
Resource: Adobe Photoshop (RRID:SCR_014199)
Curator: @scibot
SciCrunch record: RRID:SCR_014199
RRID:SCR_003070
DOI: 10.1101/2025.03.20.644303
Resource: ImageJ (RRID:SCR_003070)
Curator: @scibot
SciCrunch record: RRID:SCR_003070
RRID:AB_2492288
DOI: 10.1101/2025.03.20.644303
Resource: (Jackson ImmunoResearch Labs Cat# 711-605-152, RRID:AB_2492288)
Curator: @scibot
SciCrunch record: RRID:AB_2492288
RRID:SCR_016137
DOI: 10.1101/2025.03.20.644303
Resource: Microsoft Excel (RRID:SCR_016137)
Curator: @scibot
SciCrunch record: RRID:SCR_016137
RRID:AB_2307443
DOI: 10.1101/2025.03.20.644303
Resource: (Jackson ImmunoResearch Labs Cat# 711-165-152, RRID:AB_2307443)
Curator: @scibot
SciCrunch record: RRID:AB_2307443
RRID:AB_2313584
DOI: 10.1101/2025.03.20.644303
Resource: (Jackson ImmunoResearch Labs Cat# 711-545-152, RRID:AB_2313584)
Curator: @scibot
SciCrunch record: RRID:AB_2313584
RRID:AB_2145527
DOI: 10.1101/2025.03.20.644303
Resource: (Proteintech Cat# 12690-1-AP, RRID:AB_2145527)
Curator: @scibot
SciCrunch record: RRID:AB_2145527
RRID:AB_2811722
DOI: 10.1101/2025.03.20.644303
Resource: (Agilent Cat# GA524, RRID:AB_2811722)
Curator: @scibot
SciCrunch record: RRID:AB_2811722
RRID:AB_839504
DOI: 10.1101/2025.03.20.644303
Resource: (Wako Cat# 019-19741, RRID:AB_839504)
Curator: @scibot
SciCrunch record: RRID:AB_839504
RRID:SCR_000441
DOI: 10.1101/2025.03.20.644303
Resource: EthoVision XT (RRID:SCR_000441)
Curator: @scibot
SciCrunch record: RRID:SCR_000441
RRID:SCR_025700
DOI: 10.1101/2025.03.20.644303
Resource: Behavioral Observation Research Interactive Software (RRID:SCR_025700)
Curator: @scibot
SciCrunch record: RRID:SCR_025700
RRID:SCR_016458
DOI: 10.1101/2024.11.26.625481
Resource: Nornir (RRID:SCR_016458)
Curator: @scibot
SciCrunch record: RRID:SCR_016458
RRID:SCR_017254
DOI: 10.1093/gigascience/giaf115
Resource: IQ-TREE (RRID:SCR_017254)
Curator: @scibot
SciCrunch record: RRID:SCR_017254
RRID:SCR_016888
DOI: 10.1093/gigascience/giaf115
Resource: ropls (RRID:SCR_016888)
Curator: @scibot
SciCrunch record: RRID:SCR_016888
RRID:SCR_017118
DOI: 10.1093/gigascience/giaf115
Resource: OrthoFinder (RRID:SCR_017118)
Curator: @scibot
SciCrunch record: RRID:SCR_017118
RRID:SCR_022865
DOI: 10.1093/gigascience/giaf115
Resource: DIA-NN (RRID:SCR_022865)
Curator: @scibot
SciCrunch record: RRID:SCR_022865
RRID:SCR_015705
DOI: 10.1093/gigascience/giaf115
Resource: GET_HOMOLOGUES (RRID:SCR_015705)
Curator: @scibot
SciCrunch record: RRID:SCR_015705
RRID:SCR_018924
DOI: 10.1093/gigascience/giaf115
Resource: Computational Analysis of gene Family Evolution (RRID:SCR_018924)
Curator: @scibot
SciCrunch record: RRID:SCR_018924
RRID:SCR_017226
DOI: 10.1093/gigascience/giaf115
Resource: Juicer (RRID:SCR_017226)
Curator: @scibot
SciCrunch record: RRID:SCR_017226
RRID:SCR_015008
DOI: 10.1093/gigascience/giaf115
Resource: BUSCO (RRID:SCR_015008)
Curator: @scibot
SciCrunch record: RRID:SCR_015008
RRID:SCR_017016
DOI: 10.1093/gigascience/giaf115
Resource: Flye (RRID:SCR_017016)
Curator: @scibot
SciCrunch record: RRID:SCR_017016
RRID:SCR_005491
DOI: 10.1093/gigascience/giaf115
Resource: Jellyfish (RRID:SCR_005491)
Curator: @scibot
SciCrunch record: RRID:SCR_005491
RRID:SCR_002942
DOI: 10.1093/gigascience/giaf115
Resource: SMRT-Analysis (RRID:SCR_002942)
Curator: @scibot
SciCrunch record: RRID:SCR_002942
RRID:SCR_017227
DOI: 10.1093/gigascience/giaf115
Resource: 3D de novo assembly (RRID:SCR_017227)
Curator: @scibot
SciCrunch record: RRID:SCR_017227
RRID:SCR_005309
DOI: 10.1093/gigascience/giaf115
Resource: MAKER (RRID:SCR_005309)
Curator: @scibot
SciCrunch record: RRID:SCR_005309
RRID:SCR_012954
DOI: 10.1093/gigascience/giaf115
Resource: RepeatMasker (RRID:SCR_012954)
Curator: @scibot
SciCrunch record: RRID:SCR_012954
RRID:SCR_010279
DOI: 10.1038/s44321-025-00305-4
Resource: Adobe Illustrator (RRID:SCR_010279)
Curator: @scibot
SciCrunch record: RRID:SCR_010279
RRID:SCR_002285
DOI: 10.1038/s44321-025-00305-4
Resource: Fiji (RRID:SCR_002285)
Curator: @scibot
SciCrunch record: RRID:SCR_002285
RRID:SCR_002798
DOI: 10.1038/s44321-025-00305-4
Resource: GraphPad Prism (RRID:SCR_002798)
Curator: @scibot
SciCrunch record: RRID:SCR_002798
RRID:Addgene_52659
DOI: 10.1038/s42003-025-08854-7
Resource: RRID:Addgene_52659
Curator: @scibot
SciCrunch record: RRID:Addgene_52659
RRID:SCR_023422
DOI: 10.1038/s41598-025-19453-1
Resource: University of Iowa Institute of Human Genetics Genomics Division Core Facility (RRID:SCR_023422)
Curator: @scibot
SciCrunch record: RRID:SCR_023422
plasmid_121159
DOI: 10.1038/s41598-025-19280-4
Resource: RRID:Addgene_121159
Curator: @scibot
SciCrunch record: RRID:Addgene_121159
RRID:Addgene_54206
DOI: 10.1038/s41598-025-19280-4
Resource: None
Curator: @scibot
SciCrunch record: RRID:Addgene_54206
RRID:Addgene_42307
DOI: 10.1038/s41598-025-19280-4
Resource: RRID:Addgene_42307
Curator: @scibot
SciCrunch record: RRID:Addgene_42307
RRID:Addgene_57164
DOI: 10.1038/s41598-025-19280-4
Resource: None
Curator: @scibot
SciCrunch record: RRID:Addgene_57164
MMRRC_034848
DOI: 10.1038/s41594-025-01657-8
Resource: (MMRRC Cat# 034848-JAX,RRID:MMRRC_034848-JAX)
Curator: @scibot
SciCrunch record: RRID:MMRRC_034848-JAX
RRID:CVCL_A426
DOI: 10.1038/s41467-025-64080-z
Resource: (RRID:CVCL_A426)
Curator: @scibot
SciCrunch record: RRID:CVCL_A426
RRID:CVCL_Y019
DOI: 10.1038/s41467-025-64080-z
Resource: (RRID:CVCL_Y019)
Curator: @scibot
SciCrunch record: RRID:CVCL_Y019
RRID:AB_477163
DOI: 10.1038/s41467-025-64080-z
Resource: (Sigma-Aldrich Cat# L9393, RRID:AB_477163)
Curator: @scibot
SciCrunch record: RRID:AB_477163
RRID:AB_621848
DOI: 10.1038/s41467-025-64080-z
Resource: (LI-COR Biosciences Cat# 926-32213, RRID:AB_621848)
Curator: @scibot
SciCrunch record: RRID:AB_621848
RRID:AB_621847
DOI: 10.1038/s41467-025-64080-z
Resource: (LI-COR Biosciences Cat# 926-32212, RRID:AB_621847)
Curator: @scibot
SciCrunch record: RRID:AB_621847
RRID:AB_10891298
DOI: 10.1038/s41467-025-64080-z
Resource: (R and D Systems Cat# AF6868, RRID:AB_10891298)
Curator: @scibot
SciCrunch record: RRID:AB_10891298
RRID:AB_2814919
DOI: 10.1038/s41467-025-64080-z
Resource: (LI-COR Biosciences Cat# 926-32280, RRID:AB_2814919)
Curator: @scibot
SciCrunch record: RRID:AB_2814919
RRID:AB_2617216
DOI: 10.1038/s41467-025-64080-z
Resource: (DSHB Cat# IIH6 C4, RRID:AB_2617216)
Curator: @scibot
SciCrunch record: RRID:AB_2617216
RRID:Addgene_119947
DOI: 10.1038/s41467-025-64026-5
Resource: RRID:Addgene_119947
Curator: @scibot
SciCrunch record: RRID:Addgene_119947
RRID:CVCL_0152
DOI: 10.1038/s41420-025-02717-0
Resource: (NCBI_Iran Cat# C558, RRID:CVCL_0152)
Curator: @scibot
SciCrunch record: RRID:CVCL_0152
RRID:Addgene_110060
DOI: 10.1038/s41420-025-02717-0
Resource: RRID:Addgene_110060
Curator: @scibot
SciCrunch record: RRID:Addgene_110060
RRID:CVCL_UL49
DOI: 10.1038/s41420-025-02717-0
Resource: (RRID:CVCL_UL49)
Curator: @scibot
SciCrunch record: RRID:CVCL_UL49
RRID:Addgene_12260
DOI: 10.1038/s41420-025-02717-0
Resource: RRID:Addgene_12260
Curator: @scibot
SciCrunch record: RRID:Addgene_12260
RRID:Addgene_62988
DOI: 10.1038/s41420-025-02717-0
Resource: RRID:Addgene_62988
Curator: @scibot
SciCrunch record: RRID:Addgene_62988
RRID:Addgene_12259
DOI: 10.1038/s41420-025-02717-0
Resource: RRID:Addgene_12259
Curator: @scibot
SciCrunch record: RRID:Addgene_12259
RRID:Addgene_174592
DOI: 10.1038/s41420-025-02717-0
Resource: None
Curator: @scibot
SciCrunch record: RRID:Addgene_174592
RRID:CVCL_0023
DOI: 10.1038/s41420-025-02717-0
Resource: (CCLV Cat# CCLV-RIE 1035, RRID:CVCL_0023)
Curator: @scibot
SciCrunch record: RRID:CVCL_0023
RRID:AB_2050336
DOI: 10.1038/s41420-025-02717-0
Resource: (Abcam Cat# ab91526, RRID:AB_2050336)
Curator: @scibot
SciCrunch record: RRID:AB_2050336
RRID:AB_2943221
DOI: 10.1038/s41420-025-02717-0
Resource: (Cell Signaling Technology Cat# 19581, RRID:AB_2943221)
Curator: @scibot
SciCrunch record: RRID:AB_2943221
RRID:AB_2728748
DOI: 10.1038/s41420-025-02717-0
Resource: (Cell Signaling Technology Cat# 14429, RRID:AB_2728748)
Curator: @scibot
SciCrunch record: RRID:AB_2728748
RRID:AB_1904022
DOI: 10.1038/s41420-025-02717-0
Resource: (Cell Signaling Technology Cat# 4409, RRID:AB_1904022)
Curator: @scibot
SciCrunch record: RRID:AB_1904022
RRID:AB_2180216
DOI: 10.1038/s41420-025-02717-0
Resource: (Cell Signaling Technology Cat# 3965, RRID:AB_2180216)
Curator: @scibot
SciCrunch record: RRID:AB_2180216
RRID:AB_2137707
DOI: 10.1038/s41420-025-02717-0
Resource: (Cell Signaling Technology Cat# 3868, RRID:AB_2137707)
Curator: @scibot
SciCrunch record: RRID:AB_2137707
RRID:AB_836889
DOI: 10.1038/s41420-025-02717-0
Resource: (Cell Signaling Technology Cat# 4523, RRID:AB_836889)
Curator: @scibot
SciCrunch record: RRID:AB_836889
RRID:AB_1904025
DOI: 10.1038/s41420-025-02717-0
Resource: (Cell Signaling Technology Cat# 4412, RRID:AB_1904025)
Curator: @scibot
SciCrunch record: RRID:AB_1904025
RRID:AB_2291471
DOI: 10.1038/s41420-025-02717-0
Resource: (Cell Signaling Technology Cat# 3195, RRID:AB_2291471)
Curator: @scibot
SciCrunch record: RRID:AB_2291471
RRID:AB_2728768
DOI: 10.1038/s41420-025-02717-0
Resource: (Cell Signaling Technology Cat# 13901, RRID:AB_2728768)
Curator: @scibot
SciCrunch record: RRID:AB_2728768
RRID:AB_331292
DOI: 10.1038/s41420-025-02717-0
Resource: (Cell Signaling Technology Cat# 3936, RRID:AB_331292)
Curator: @scibot
SciCrunch record: RRID:AB_331292
RRID:AB_330924
DOI: 10.1038/s41420-025-02717-0
Resource: (Cell Signaling Technology Cat# 7076, RRID:AB_330924)
Curator: @scibot
SciCrunch record: RRID:AB_330924
RRID:AB_915950
DOI: 10.1038/s41420-025-02717-0
Resource: (Cell Signaling Technology Cat# 2775, RRID:AB_915950)
Curator: @scibot
SciCrunch record: RRID:AB_915950
RRID:AB_2099233
DOI: 10.1038/s41420-025-02717-0
Resource: (Cell Signaling Technology Cat# 7074, RRID:AB_2099233)
Curator: @scibot
SciCrunch record: RRID:AB_2099233
RRID:AB_2687616
DOI: 10.1038/s41420-025-02717-0
Resource: (Cell Signaling Technology Cat# 13116, RRID:AB_2687616)
Curator: @scibot
SciCrunch record: RRID:AB_2687616
RRID:AB_10695459
DOI: 10.1038/s41420-025-02717-0
Resource: (Cell Signaling Technology Cat# 5741, RRID:AB_10695459)
Curator: @scibot
SciCrunch record: RRID:AB_10695459
RRID:AB_2572291
DOI: 10.1038/s41420-025-02717-0
Resource: (Cell Signaling Technology Cat# 14793, RRID:AB_2572291)
Curator: @scibot
SciCrunch record: RRID:AB_2572291
RRID:AB_2798928
DOI: 10.1038/s41420-025-02717-0
Resource: (Cell Signaling Technology Cat# 26632, RRID:AB_2798928)
Curator: @scibot
SciCrunch record: RRID:AB_2798928
RRID:AB_2798306
DOI: 10.1038/s41420-025-02717-0
Resource: (Cell Signaling Technology Cat# 13733, RRID:AB_2798306)
Curator: @scibot
SciCrunch record: RRID:AB_2798306
RRID:AB_10950495
DOI: 10.1038/s41420-025-02717-0
Resource: (Cell Signaling Technology Cat# 8146, RRID:AB_10950495)
Curator: @scibot
SciCrunch record: RRID:AB_10950495
RRID:AB_561053
DOI: 10.1038/s41420-025-02717-0
Resource: (Cell Signaling Technology Cat# 2118, RRID:AB_561053)
Curator: @scibot
SciCrunch record: RRID:AB_561053
RRID:MMRRC_034848-JAX
DOI: 10.1038/s41398-025-03583-1
Resource: (MMRRC Cat# 034848-JAX,RRID:MMRRC_034848-JAX)
Curator: @scibot
SciCrunch record: RRID:MMRRC_034848-JAX
BL60355
DOI: 10.1016/j.jmoldx.2021.04.007
Resource: RRID:BDSC_60355
Curator: @scibot
SciCrunch record: RRID:BDSC_60355
BL51635
DOI: 10.1016/j.jmoldx.2021.04.007
Resource: RRID:BDSC_51635
Curator: @scibot
SciCrunch record: RRID:BDSC_51635
BL50897
DOI: 10.1016/j.jmoldx.2021.04.007
Resource: RRID:BDSC_50897
Curator: @scibot
SciCrunch record: RRID:BDSC_50897
BL36304
DOI: 10.1016/j.jmoldx.2021.04.007
Resource: RRID:BDSC_36304
Curator: @scibot
SciCrunch record: RRID:BDSC_36304
AB_2315866
DOI: 10.1016/j.jmoldx.2021.04.007
Resource: (UC Davis/NIH NeuroMab Facility Cat# 75-358, RRID:AB_2315866)
Curator: @scibot
SciCrunch record: RRID:AB_2315866
BL36303
DOI: 10.1016/j.jmoldx.2021.04.007
Resource: RRID:BDSC_36303
Curator: @scibot
SciCrunch record: RRID:BDSC_36303
RRID:IMSR_JAX:022739
DOI: 10.1016/j.isci.2025.113556
Resource: (IMSR Cat# JAX_022739,RRID:IMSR_JAX:022739)
Curator: @scibot
SciCrunch record: RRID:IMSR_JAX:022739
RRID:AB_2876479
DOI: 10.1016/j.isci.2025.113556
Resource: (BioLegend Cat# 137645, RRID:AB_2876479)
Curator: @scibot
SciCrunch record: RRID:AB_2876479
RRID:AB_2561650
DOI: 10.1016/j.isci.2025.113556
Resource: (BioLegend Cat# 331918, RRID:AB_2561650)
Curator: @scibot
SciCrunch record: RRID:AB_2561650
RRID:AB_2632619
DOI: 10.1016/j.isci.2025.113556
Resource: (BioLegend Cat# 982604, RRID:AB_2632619)
Curator: @scibot
SciCrunch record: RRID:AB_2632619
RRID:AB_312751
DOI: 10.1016/j.isci.2025.113556
Resource: (BioLegend Cat# 100712, RRID:AB_312751)
Curator: @scibot
SciCrunch record: RRID:AB_312751
RRID:AB_1186099
DOI: 10.1016/j.isci.2025.113556
Resource: (BioLegend Cat# 127608, RRID:AB_1186099)
Curator: @scibot
SciCrunch record: RRID:AB_1186099
RRID:AB_493698
DOI: 10.1016/j.isci.2025.113556
Resource: (BioLegend Cat# 100429, RRID:AB_493698)
Curator: @scibot
SciCrunch record: RRID:AB_493698
RRID:AB_2564590
DOI: 10.1016/j.isci.2025.113556
Resource: (BioLegend Cat# 103149, RRID:AB_2564590)
Curator: @scibot
SciCrunch record: RRID:AB_2564590
RRID:AB_2783135
DOI: 10.1016/j.isci.2025.113556
Resource: (BioLegend Cat# 156503, RRID:AB_2783135)
Curator: @scibot
SciCrunch record: RRID:AB_2783135
RRID:AB_2565848
DOI: 10.1016/j.isci.2025.113556
Resource: (BioLegend Cat# 317343, RRID:AB_2565848)
Curator: @scibot
SciCrunch record: RRID:AB_2565848
RRID:AB_1279055
DOI: 10.1016/j.isci.2025.113556
Resource: (BioLegend Cat# 328620, RRID:AB_1279055)
Curator: @scibot
SciCrunch record: RRID:AB_1279055
RRID:AB_604100
DOI: 10.1016/j.isci.2025.113556
Resource: (BioLegend Cat# 318304, RRID:AB_604100)
Curator: @scibot
SciCrunch record: RRID:AB_604100
RRID:AB_2340580
DOI: 10.1016/j.isci.2025.113556
Resource: (Jackson ImmunoResearch Labs Cat# 709-606-098, RRID:AB_2340580)
Curator: @scibot
SciCrunch record: RRID:AB_2340580
RRID:SCR_016216
DOI: 10.1016/j.dcn.2025.101621
Resource: FMRIPREP (RRID:SCR_016216)
Curator: @scibot
SciCrunch record: RRID:SCR_016216
Addgene_100043
DOI: 10.1016/j.celrep.2025.116187
Resource: RRID:Addgene_100043
Curator: @scibot
SciCrunch record: RRID:Addgene_100043
Addgene_162379
DOI: 10.1016/j.celrep.2025.116187
Resource: RRID:Addgene_162379
Curator: @scibot
SciCrunch record: RRID:Addgene_162379
RRID:SCR_022888
DOI: 10.1007/s12104-025-10247-0
Resource: None
Curator: @scibot
SciCrunch record: RRID:SCR_022888
RRID:IMSR_JAX:008231
DOI: 10.1002/jcsm.70090
Resource: (IMSR Cat# JAX_008231,RRID:IMSR_JAX:008231)
Curator: @scibot
SciCrunch record: RRID:IMSR_JAX:008231
RRID:AB_2142367
DOI: 10.1002/gcc.70085
Resource: (Agilent Cat# M7240, RRID:AB_2142367)
Curator: @scibot
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33:55 "They torture Dominika so that she confesses she is a traitor. But you can't kill the Freedom(TM) in her! Did Russia buy out the Guantanamo franchise? For tortures by hard rock? I like that USA in their propaganda assigns to its enemies something that USA has been guilty of themselves. Soviets suddenly drop nuclear bombs on a peaceful city, use chemical weaponry, and suddenly, hard rock tortures."<br /> haha, yeah, every single time... almost as if psychological projection was some natural instinct...<br /> see also: chauvinism, group narcissm, collectivism, hivemind, meme: "Our Blessed Homeland versus Their Barbarous Wastes"
Synthèse sur la situation des enfants sans abri logés dans les écoles en France
Résumé
Le sans-abrisme infantile connaît une augmentation alarmante en France, avec une hausse de 133 % depuis 2020, exacerbée par l'inflation et la crise du logement.
Face à ce que le reportage décrit comme les "carences de l'État", des collectifs citoyens, notamment "Jamais sans toi" à Lyon, organisent l'occupation d'établissements scolaires pour offrir un abri nocturne à des familles à la rue.
Ce document de synthèse se penche sur ce phénomène à travers le témoignage d'une famille d'origine angolaise – une mère et ses enfants – hébergée dans une école lyonnaise.
Leur parcours met en lumière la précarité extrême, le traumatisme d'une tentative d'expulsion avortée, et l'impact psychologique profond sur les enfants.
La situation révèle une tension critique entre la solidarité citoyenne, incarnée par les enseignants et les parents d'élèves, et l'inaction des pouvoirs publics, qui non seulement échouent à proposer des solutions de logement pérennes, mais exercent également une pression administrative sur les acteurs de cette solidarité.
Le reportage met en évidence une crise sociale majeure : l'explosion du nombre d'enfants sans domicile fixe en France.
• Expansion et Causes :
◦ Le sans-abrisme infantile a augmenté de 133 % depuis 2020.
◦ Les facteurs identifiés sont l'inflation, la multiplication des expulsions locatives et la pénurie de logements sociaux.
◦ Les solutions d'urgence, conçues pour être temporaires, "s'éternisent".
En 2023, les familles logées dans des écoles y sont restées en moyenne plus de six mois.
• L'Occupation des Écoles comme Palliatif :
◦ Face à cette situation, des collectifs citoyens comme "Jamais sans toi" à Lyon organisent l'occupation d'écoles pour héberger des familles. ◦ Ampleur du phénomène à Lyon :
▪ Actuellement, 17 écoles de la métropole lyonnaise accueillent 25 familles.
▪ Depuis 2014, une soixantaine d'établissements ont servi de refuge à plus de 1000 enfants.
◦ Ce mouvement n'est pas limité à Lyon ; des initiatives similaires existent à Strasbourg, Rennes et Paris.
◦ Ce soutien repose sur la "générosité citoyenne" (parents d'élèves, professeurs, habitants) qui compense les défaillances de l'État.
Le reportage se concentre sur le témoignage poignant de Lucy (16 ans), Lina (12 ans) et leur mère, qui illustre la réalité humaine derrière les statistiques.
• De l'Angola à la Précarité en France :
◦ Arrivée en France lorsque Lucy avait 10 ans et Lina 5 ou 6 ans.
◦ Premières expériences d'hébergement précaire : le 115 à Dijon dans une chambre partagée, puis un foyer à Digoin.
◦ La journée, la famille devait quitter le 115 et trouver refuge dans des associations (Secours Populaire, églises) pour manger.
◦ Lina décrit sa déception face à la réalité française, loin de l'image idéalisée des dessins animés :
« Un pays super bien, que tout se passait bien, qu'on avait une vie normale ».
◦ Elle a également été victime de moqueries et de racisme à l'école en raison de sa langue et de ses cheveux.
• Le Traumatisme de l'Expulsion Manquée (OQTF) :
◦ Il y a deux ans, la famille a fait l'objet d'une Obligation de Quitter le Territoire Français (OQTF).
◦ La police est intervenue en pleine nuit dans leur appartement. Lucy, alors âgée de 14 ans, décrit une scène de panique et de violence :
ses parents criant, son père menotté, et les enfants enfermés dans une chambre avec des policiers.
◦ La famille a été conduite à Paris après 5 heures de route et placée dans un centre de détention pendant 4 heures.
◦ À l'aéroport, leur vol pour l'Angola a été annulé. Les autorités les ont alors "abandonnés à l'aéroport", leur ordonnant simplement "de plus retourner où [ils] étaient".
• La Rupture Familiale et l'Errance :
◦ Après cet épisode, la famille est revenue à Lyon.
Le mariage des parents n'étant pas reconnu en France, leur séparation a suivi. La mère s'est retrouvée seule avec ses enfants.
◦ Ils ont enchaîné les solutions d'hébergement temporaires :
un camping à Trévoux, un appartement à Bellecour, puis une association qui les a logés avec d'autres femmes, avant de trouver refuge dans l'école.
L'école, bien qu'offrant un toit, impose des conditions de vie extrêmement contraignantes et précaires.
Aspect
Description
Logement
La famille dort sur des matelas gonflables dans une salle de classe. Les vêtements sont stockés dans les armoires de la classe et des valises.
Routine
Lever obligatoire entre 6h30 et 6h50.
La famille doit quitter les lieux avant 8h30 et ne peut revenir qu'après 18h00, une fois tous les élèves partis.
Discrétion
La nuit, il est interdit d'allumer les lumières pour ne pas attirer l'attention.
La famille utilise les lampes de poche des téléphones pour s'éclairer.
Insecurité
Des jeunes jouant dans la cour sont déjà montés et ont fouillé dans leurs affaires, profitant d'une porte laissée ouverte.
Perturbations
La vie de la famille est rythmée par la sonnerie de l'école, qui retentit "toutes les heures".
Lutte de la mère
Elle cherche activement du travail (nettoyage, restauration) et des formations gratuites, mais sa situation rend les démarches très difficiles.
La précarité et l'instabilité ont des conséquences profondes sur le bien-être et le développement des enfants.
• Le Poids du Secret et de la Honte :
◦ Lucy cache sa situation à la plupart de ses amies par peur du jugement :
« J'angoisse un peu, sachant que beaucoup de jeunes de mon âge [...] se permettent de juger tout simplement. »
◦ Elle exprime un profond désir de normalité : « Des fois, je me dis que j'aimerais juste avoir une vie normale comme plein d'ados de mon âge. »
◦ Lina exprime également la peur d'être mise à l'écart par ses camarades parce qu'elle vit dans une école.
• Aspirations et Résilience :
◦ Malgré les épreuves, Lucy est une bonne élève et aspire à devenir avocate.
Son ambition est directement liée à son vécu : « J'ai envie d'être avocate, de défendre les gens parce que je me dis que tout le monde a le droit à une deuxième chance. »
◦ Face à la détresse, elle a développé une stratégie de contrôle émotionnel : « Quand c'est dur, bah je prends sur moi et puis je me dis ça va aller. »
◦ Sa plus grande peur reste matérielle et existentielle : « J'ai peur de me retrouver à la rue. Ça me fait peur. »
Le reportage oppose la solidarité active du terrain à la réponse passive, voire répressive, des institutions.
• Le Soutien du Corps Enseignant :
◦ Une enseignante de l'école s'est fortement impliquée, dormant sur place la première nuit pour rassurer l'équipe périscolaire.
◦ Elle a accueilli la famille chez elle pendant les vacances de Noël, une période particulièrement symbolique car la famille avait passé le Noël précédent dehors.
◦ Une cagnotte organisée par ses collègues a permis d'offrir des cadeaux et un repas de fête à la famille.
• La Pression de la Hiérarchie :
◦ Suite à l'occupation, l'enseignante et ses collègues ont été convoquées par l'inspectrice d'académie.
◦ La rencontre est décrite comme "un bon remontage de bretelle", où elles se sont fait "engueuler".
L'inspectrice les a qualifiées d' "inconscientes", leur faisant porter "toute la responsabilité" sans reconnaître la vulnérabilité de la famille.
• L'Absence de Solutions Pérennes :
◦ Près d'un an après le début de l'occupation, "il n'y a aucune proposition de la mairie, de la métropole, aucune perspective, rien."
◦ L'occupation de l'école a donc dû se poursuivre au-delà de l'année scolaire, mais avec des règles plus strictes :
la famille n'a plus le droit d'être dans le bâtiment pendant les heures de classe.
Phelan (1995) de-scribed this kind of negative visibility as a trap because of what it might meanfor groups of people or individuals.
Phelan pointed out that being seen does not mean being understood or respected. The visibility of Indigenous people is often distorted by mainstream culture into stereotypes. This "visibility trap" is display, but authentic expression and voice are lost.
Roots are in capitals,
Roots are in capitals, and are not words in use at all, but serve as an elucidation of the words grouped together and a connection between them.
J.R.R. Tolkien's note in the Qenya Lexicon[1]
saaron knew
Sauron new
knew that he had been wrong - not everyone would want to use the ring for their own power and Glory
yes Frodo succumbed at the very very end but - he and Sam made it that far and - fate or Providence or the intervention of Uru? himself did the rest
some people are capable of selfless and purely good acts
it wasn't just just Sauron who fell - it was his entire worldview
hope and love and care and friendship - can triumph over evil - however powerful it may seem at the time
The Lord of the Rings from Sauron's perspective
In Deep Geek 907K subscribers
iberation from mundane,menial tasks in these circumstances is tantamount to liberation from the ability tomake a living – and, more to the point, the ability to make a living as a musician.
AI will take job opportunities from musicians, who already struggle to make a living.
Third, commercial applications using machine learning to generate cheap musicshould be a cause for concern, even if the only kind of music presently at risk is thehistorically stigmatised genre of production music.
I agree, even though commercial music isn't the same as music created by artists, outsourcing it to AI removes career outlets for musicians.
As a consequence, what may seem like empty marketing hype at pre-sent may end up shaping the agenda for future work in this domain, encouragingcertain pursuits while suppressing others.
How much money is made in the AI music industry? I will research this next.
According to this line of argument,delegating to machines mundane and menial forms of creative work – like turningfinancial reports into news stories (Martin 2019) – might free up creative energies thatcould be more fruitfully directed elsewhere. Extended to production music, concedingthis domain to machines would presumably liberate musicians to pursue more aes-thetically rewarding activities
This is an argument that is still being discussed today, it does seem that AII is replacing creative pursuits rather than mundane tasks, leaving humans with no outlet left to be human.
machine learning by certain of these firms hardly represents the most innovativeapplications of such technologies
This article is 5 years old. I am reading it from today's lens, knowing that AI music technology has advanced far beyond where it was then, making these issues even more prevalent now.
this power would be redistributed to musicianswho, by and large, do not work directly for the companies in question, but whosemusic does.
In the AI music business, music is a profit; musicians contribute to the data, and can get compensation for their efforts through redistribution.
One possi-bility would be the creation of some kind of ownership fund, either targeting individ-ual firms or the music technology sector more broadly. In line with other workerownership funds proposed over the years (Guinan 2019; Gowan 2019), shares mightbe issued to a body representing those musicians whose creative output is exploitednot just by music AI companies but other music tech firms as well. The main appealof such funds is that they redistribute not just wealth, but economic power, includingthe power to determine how and where to invest resources.
Allocating a portion of profits for public use redistributes wealth, which is also good for the economy.
The proceeds would be directed to the Trust Fund, which then distributedthe monies raised to pay for free concerts across North America. Not only did this pro-vide underemployed musicians living outside major urban areas with paid work,redressing geographic disparities in cultural participation, but it also diminished some-what the winner-take-all tendencies that technologies of mass reproduction exacer-bate
This solved the royalty split issue of who should get credit; the solution was to redistribute the money to the public by helping musicians in need.
The same principle holds for machine learning techniques, despite the distance sepa-rating them from the Markov processes employed by Olson and Belar. What ties themtogether is a reliance on what Adrian Mackenzie refers to as ‘probabilization’, as‘formalisms derived from statistics’
AI music generation works the same way as randomly generated melodies; it randomly generates a song based on its training data, which can result in segments that emulate existing songs.
While certain trigrams are more probable and others less so, it’snot the case that an improbable sequence (like E4-D4-C#5) somehow counts for less,or that the single song where it appears contributes less than others. The song’s con-tribution isn’t the pattern, but its impact on the overall distribution of probabilities.
There is a difference between blatantly stealing a melody versus a statistically likely repetition. If you're only looking at three notes of a melody, you will find many songs with those same three notes in the same sequence.
If it is difficult to isolate the contribution made by anysingle input, this is because no input contributes in isolation.
The music industry thrives on recycled ideas; a new idea fuels a new genre, and samples are passed around dozens of times.
Again,within current copyright regimes this test applies only at the level of individual works.A prominent case in point is Robin Thicke and Pharrell Williams’ 2013 song ‘BlurredLines’. Following a lengthy lawsuit, in 2015 a jury found the two musicians guilty ofhaving infringed upon Marvin Gaye’s 1977 hit ‘Got to Give It Up’.
There is a line of what can and can't be flagged as a copyright infringement; an identical chord progression can't be sued for, but a sample without permission can.
like the shared conven-tions governing a genre – to produce a technical resource
Meaning some aspects of genres are not able to be copyrighted, things such as common genre drum beats, and chord progressions cannot be copyrighted.
determining thecontribution their works made to its training, and apportioning royalties accordingly.Such difficulties would appear to rule out, either in principle or in practice, anyclaim that authors of training data might have on works generated by a machinelearner trained on their music.
It would be very hard to keep track of the amount of stake both people and machines had in the creation of something to determine royalty splits.
Artist Rights Watch, for one, has called for musicians to invoke the marketingrestriction clause in recording and publishing contracts to refuse their music’s use ‘forAI purposes of any kind’
An answer to "Is any music safe from data harvesting?"
whose system istrained on a large number of musical ‘stems’ that an in-house composer in theiremploy creates for hire. Barring that, companies can assert some sort of exemption.
This is a fairer way to do this, as now the data isn't from somewhere they don't have permission to use.
Crucially, developers of commercial systems, unlike academicresearchers, aren’t obliged to reveal the sources of their training data
This is likely because they have access to data that they dont have direct permission to use.
Thatthese companies have title to the algorithms they developed isn’t in dispute; what is in dispute,however, is whether the works their systems produce belongs to them, some other party, ornobody at all.
Should AI products be stripped of all ownership and become public property?
A variety of legal doctrines have been mobi-lised in support of each of these candidates. Some have appealed to utilitarian theoryto buoy the claims of programmers and/or owners of AI systems, arguing that grant-ing them rights to AI-generated works will encourage the continued growth of the AIsector
How will this be resolved?
Yet AIs, unlike humans, are insensible to such rewards,whether monetary or symbolic. Insofar as ‘machines need no incentive to work’,
This is an important detail when answering the question of how much each part influences the creation. AIs aren't like humans; they don't need rest.
Granting authors a temporary monopoly over their creations is regarded as animportant spur to creation, one that ideally harmonises individual and general interest:artists are rewarded for their investments of time, effort, and resources
In AI music creation, the question that needs to be answered is what is the power balance, how much of the creation process is influenced by a users prompt, the data from artists, the process the machine went through to create the song, and the programmers who made the AI music generator.?
What is more, there’s little appetite within legal circles for reforming statutesto grant machines rights on the works they produce.
Is it ethical to pay a machine?
in UK lawmachines aren’t eligible for copyright assignment, with rights defaulting to their creatorsor owners.
Is this still true in 2025?
machine turned out, their recombination in different configurations
At what point does a song generated from existing material become "original" enough to evade copyright?
Another, less visible place wherethe same transition can be seen is in traditional production music companies, whichhave also adopted a platform model. But in contrast to these and other, more familiardigital platforms (like Facebook or Amazon), the platformization of commercial musicAI doesn’t involve one group of users being connected to another, but instead agroup of users being connected to an AI system.
Is any music safe from data harvesting? There appears to be no safeguard against having your music being used in training data.
he fact that onedoesn’t pay with money doesn’t mean one isn’t paying in some other way, usingsome other currency. As with so many other digital services, payment is still beingmade: it is simply that it is being made in the form of personal data
That is how these types of services stay afloat, by profiting off your data, and the data it is trained on
do not sell products to clients, but services. A case inpoint is Mubert, a company that bridges the consumer and business-to-business mar-kets. For brands, content producers, and/or brick-and-mortar businesses, Mubert offersa range of subscription plans. For a flat monthly fee, one can generate as muchbespoke music as one needs or desires ‘for free’
This means the business avoids copyright responsibility, as the user is the one who actually generates the music.
25,000 MIDI files on its site,in such genres as klezmer, tango, and the blues, while bitmidi.com boasts roughly113,000 MIDI files, from an equally diverse range of genres and styles.
So, MIDI websites have significantly affected the music industry, which doesn't exactly answer the research question, but is adjacent. Also I just checked out BitMIdi, it was really weird, it has a ton of instrumenetal versions of songs, basically the elevator music versions of songs. It's really wierd, its versions of songs with chip tune drums, midi saxohpone, and midi strings.
Weav Run, whose appadjusts tracks according to the cadence of one’s stride whilst walking or running, withnot just the tempo of a track changing in real time, but also its texture, timbre, andarrangement (Weav Music 2019). A third example is AI Music, whose founder describesits applications as a means of ‘shape-shifting’ music so that it can adjust to differentlistening situations
This is a really cool idea.
suchmusic is not intended for direct consumption by end users, but is marketed instead toother cultural producers, typically for use in mixed media products like games, adver-tisements, or online web content.
Does this mean royalty-free music, or non-copyrighted music?
the ‘MusicComposing Machine’ developed at RCA in the 1950s
Wow, i had no idea something like this existed that long ago, I want learn more about how it actually functioned.
one that transposes tracks from one musical genre to another
How does this work?
Since 2015 there has been a marked growth in the number of startups and technol-ogy companies seeking to commercialise music produced using artificial intelligence.
I had no idea that generative AI was around 10 years ago; I thought there was only narrow AI with tools such as Siri and Grammarly. This opens my eyes to the hidden landscape of AI in the past decades. The truth is, AI has been around for many decades, and looks very different now than it did before.
the article sketches a couple ofalternative models (levy-based trust funds, ownership funds) thatcould provide a more equitable institutional
the goal of the research in this article is to find a more erthical split in profits among AI models, the user, and the artists that are part of training data.
the music that constitutes the trainingset necessary for machine learners to learn. Given the massivedatasets mobilised to train machine learners, existing copyrightregimes prove inadequate in the face of the questions of distribu-tive justice
This means that AI models are trained on music they don't have ownership over, producing music that people profit off of, created from material that was protected under copyright.
The oracle prices are used to compute funding rates. They are also a component in the mark price which is used for margining, liquidations, and triggering TP/SL orders.
oracle price 只是 mark_price
的一个 component,还有什么其他因素呢
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The girl looked at the ground the table legs rested on.
Her body language says it all.
“Well,” the man said, “if you don’t want to you don’t have to. I wouldn’t have you do it if you didn’t want to. But I know it’s perfectly simple.” “And you really want to?
She's obviously struggling with this decision she is unsure of what will happen.
It’s really an awfully simple operation, Jig,” the man said. “It’s not really an operation at all.
That's something a person would say who obviously isn't the one going through a procedure not sure if that's comforting or more infuriating.
“I feel fine,” she said. “There’s nothing wrong with me. I feel fine.”
That's a lie.
“And if I do it you’ll be happy and things will be like they were and you’ll love me?”
It seems like they were having a good time drinking beers. I wonder what he did or said to make her think he no longer loved her.
Where every developer is the tech lead of an AI dev team
Whitney Phillips. Internet Troll Sub-Culture's Savage Spoofing of Mainstream Media [Excerpt]. Scientific American, May 2015. URL: https://www.scientificamerican.com/article/internet-troll-sub-culture-s-savage-spoofing-of-mainstream-media-excerpt/ (visited on 2023-12-05).
The Jenkem prank shows that trolls know exactly how to weaponize sensationalism and exploit weaknesses in journalistic culture to make a point, if journalists treated troll-generated stories with more skepticism instead of just chasing clicks, would trolls lose most of their power?
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I found myself intrigued that the film did not end with a happy ending nor a very dark ending either. There was not exactly any clear "good guy" in this film for the acceptation of Brutus who called a "noble man" multiple times in the film. There are many aspects of this film that wrestle with notions of loyalty, honor, and patriotism.
Critiques are two-way. It is not just one person providing critical feedback, but rather the designer articulating the rationale for their decisions (why they made the choices that they did) and the critic responding to those judgements. The critic might also provide their own counter-judgements to understand the designer’s rationale further.
I have to agree with professor Ko that good critique is a conversation over a single opinion. I've been in situations such as group project in my other INFO classes where people can simply say that that looks good or "I think you should fix that small..." without having proper reasoning. ALso the article mention the hamburger rule as it's not just about being nice, but about giving feedback that will help people or project grow. This reminded me that giving critical feedback is important and that also helping the other grow, Personally, I am very much open to critical feedback as long as the reasoning is good!
Ask anyone who has dealt with persistent harassment online, especially women: [trolls stopping because they are ignored] is not usually what happens. Instead, the harasser keeps pushing and pushing to get the reaction they want with even more tenacity and intensity. It’s the same pattern on display in the litany of abusers and stalkers, both online and off, who escalate to more dangerous and threatening beha
I agree with the idea that just “not feeding the trolls” doesn’t always work. Sometimes ignoring them gives them more space to keep spreading hate, especially when the target is already being attacked or harassed. I think the article makes a good point that it’s unfair to put all the responsibility on the person being targeted.
The authors highlight that the relationship between family income and children’s academic achievement has strengthened over recent decades. This growing “income achievement gap” replaces the older concern about the “race gap.” It shows that economic inequality is now the main barrier to educational equity.