RRID:AB_2154616
DOI: 10.1038/s41419-026-08605-4
Resource: None
Curator: @scibot
SciCrunch record: RRID:AB_2154616
RRID:AB_2154616
DOI: 10.1038/s41419-026-08605-4
Resource: None
Curator: @scibot
SciCrunch record: RRID:AB_2154616
RRID:AB_2737362
DOI: 10.1038/s41419-026-08605-4
Resource: (Proteintech Cat# 15579-1-AP, RRID:AB_2737362)
Curator: @scibot
SciCrunch record: RRID:AB_2737362
RRID:AB_2714188
DOI: 10.1038/s41419-026-08605-4
Resource: (Abcam Cat# ab196652, RRID:AB_2714188)
Curator: @scibot
SciCrunch record: RRID:AB_2714188
RRID:CVCL_1627
DOI: 10.1038/s41419-026-08605-4
Resource: (RRID:CVCL_1627)
Curator: @scibot
SciCrunch record: RRID:CVCL_1627
RRID:AB_10596476
DOI: 10.1038/s41419-026-08605-4
Resource: (Proteintech Cat# 18986-1-AP, RRID:AB_10596476)
Curator: @scibot
SciCrunch record: RRID:AB_10596476
RRID:AB_2224574
DOI: 10.1038/s41419-026-08605-4
Resource: (Proteintech Cat# 10176-2-AP, RRID:AB_2224574)
Curator: @scibot
SciCrunch record: RRID:AB_2224574
RRID:AB_2764312
DOI: 10.1038/s41419-026-08605-4
Resource: (ABclonal Cat# A2352, RRID:AB_2764312)
Curator: @scibot
SciCrunch record: RRID:AB_2764312
BDSC 6815
DOI: 10.1038/s41419-026-08571-x
Resource: RRID:BDSC_6815
Curator: @scibot
SciCrunch record: RRID:BDSC_6815
BDSC 8442
DOI: 10.1038/s41419-026-08571-x
Resource: RRID:BDSC_8442
Curator: @scibot
SciCrunch record: RRID:BDSC_8442
BDSC 50751
DOI: 10.1038/s41419-026-08571-x
Resource: RRID:BDSC_50751
Curator: @scibot
SciCrunch record: RRID:BDSC_50751
BDSC 6409
DOI: 10.1038/s41419-026-08571-x
Resource: RRID:BDSC_6409
Curator: @scibot
SciCrunch record: RRID:BDSC_6409
BDSC 35785
DOI: 10.1038/s41419-026-08571-x
Resource: RRID:BDSC_35785
Curator: @scibot
SciCrunch record: RRID:BDSC_35785
BDSC 34831
DOI: 10.1038/s41419-026-08571-x
Resource: RRID:BDSC_34831
Curator: @scibot
SciCrunch record: RRID:BDSC_34831
RRID:AB_2336790
DOI: 10.1038/s41419-026-08571-x
Resource: (Vector Laboratories Cat# H-1200, RRID:AB_2336790)
Curator: @scibot
SciCrunch record: RRID:AB_2336790
AB_2631370
DOI: 10.1038/s41419-026-08571-x
Resource: (ChromoTek Cat# ytma-20, RRID:AB_2631370)
Curator: @scibot
SciCrunch record: RRID:AB_2631370
RRID:BCBC_4612
DOI: 10.1038/s41419-026-08563-x
Resource: RRID:BCBC_4612
Curator: @scibot
SciCrunch record: RRID:BCBC_4612
RRID:CVCL_1050
DOI: 10.1038/s41419-026-08563-x
Resource: (RRID:CVCL_1050)
Curator: @scibot
SciCrunch record: RRID:CVCL_1050
RRID:CVCL_0045
DOI: 10.1038/s41419-026-08563-x
Resource: (DSMZ Cat# ACC-305, RRID:CVCL_0045)
Curator: @scibot
SciCrunch record: RRID:CVCL_0045
RRID:AB_1853503
DOI: 10.1038/s41419-026-08563-x
Resource: None
Curator: @scibot
SciCrunch record: RRID:AB_1853503
RRID:SCR_008520
DOI: 10.1038/s41419-026-08549-9
Resource: FlowJo (RRID:SCR_008520)
Curator: @scibot
SciCrunch record: RRID:SCR_008520
JAX:001303
DOI: 10.1038/s41419-026-08549-9
Resource: (IMSR Cat# JAX_001303,RRID:IMSR_JAX:001303)
Curator: @scibot
SciCrunch record: RRID:IMSR_JAX:001303
RRID:SCR_002798
DOI: 10.1038/s41419-026-08549-9
Resource: GraphPad Prism (RRID:SCR_002798)
Curator: @scibot
SciCrunch record: RRID:SCR_002798
RRID:IMSR_CRL:194
DOI: 10.1038/s41419-026-08549-9
Resource: RRID:IMSR_CRL:194
Curator: @scibot
SciCrunch record: RRID:IMSR_CRL:194
RRID:CVCL_0063
DOI: 10.1038/s41419-026-08549-9
Resource: (RRID:CVCL_0063)
Curator: @scibot
SciCrunch record: RRID:CVCL_0063
RRID:Addgene_12259
DOI: 10.1038/s41419-026-08549-9
Resource: RRID:Addgene_12259
Curator: @scibot
SciCrunch record: RRID:Addgene_12259
RRID:CVCL_0547
DOI: 10.1038/s41419-026-08549-9
Resource: (BCRC Cat# 60343, RRID:CVCL_0547)
Curator: @scibot
SciCrunch record: RRID:CVCL_0547
RRID:Addgene_8453
DOI: 10.1038/s41419-026-08549-9
Resource: RRID:Addgene_8453
Curator: @scibot
SciCrunch record: RRID:Addgene_8453
RRID:CVCL_0248
DOI: 10.1038/s41419-026-08549-9
Resource: (RRID:CVCL_0248)
Curator: @scibot
SciCrunch record: RRID:CVCL_0248
RRID:SCR_003070
DOI: 10.1038/s41419-026-08549-9
Resource: ImageJ (RRID:SCR_003070)
Curator: @scibot
SciCrunch record: RRID:SCR_003070
RRID:CVCL_0546
DOI: 10.1038/s41419-026-08549-9
Resource: (KCB Cat# KCB 200848YJ, RRID:CVCL_0546)
Curator: @scibot
SciCrunch record: RRID:CVCL_0546
RRID:CVCL_6832
DOI: 10.1038/s41419-026-08457-y
Resource: (RRID:CVCL_6832)
Curator: @scibot
SciCrunch record: RRID:CVCL_6832
RRID:SCR_018361
DOI: 10.1038/s41398-026-04006-5
Resource: Biorender (RRID:SCR_018361)
Curator: @scibot
SciCrunch record: RRID:SCR_018361
RRID:AB_2106903
DOI: 10.1038/s41398-026-03953-3
Resource: (Cell Signaling Technology Cat# 2251, RRID:AB_2106903)
Curator: @scibot
SciCrunch record: RRID:AB_2106903
RRID:AB_2313606
DOI: 10.1038/s41398-026-03953-3
Resource: (Vector Laboratories Cat# BA-1000, RRID:AB_2313606)
Curator: @scibot
SciCrunch record: RRID:AB_2313606
Addgene_12259
DOI: 10.1038/s41388-026-03744-6
Resource: RRID:Addgene_12259
Curator: @scibot
SciCrunch record: RRID:Addgene_12259
Addgene_65656
DOI: 10.1038/s41388-026-03744-6
Resource: RRID:Addgene_65656
Curator: @scibot
SciCrunch record: RRID:Addgene_65656
Addgene_62962
DOI: 10.1038/s41388-026-03744-6
Resource: None
Curator: @scibot
SciCrunch record: RRID:Addgene_62962
Addgene_10878
DOI: 10.1038/s41388-026-03744-6
Resource: RRID:Addgene_10878
Curator: @scibot
SciCrunch record: RRID:Addgene_10878
Addgene_12260
DOI: 10.1038/s41388-026-03744-6
Resource: RRID:Addgene_12260
Curator: @scibot
SciCrunch record: RRID:Addgene_12260
RRID:CVCL_5765
DOI: 10.1038/s41388-026-03744-6
Resource: (RRID:CVCL_5765)
Curator: @scibot
SciCrunch record: RRID:CVCL_5765
RRID:CVCL_0027
DOI: 10.1038/s41388-026-03744-6
Resource: (TKG Cat# TKG 0205, RRID:CVCL_0027)
Curator: @scibot
SciCrunch record: RRID:CVCL_0027
RRID:CVCL_0063
DOI: 10.1038/s41388-026-03744-6
Resource: (CCLV Cat# CCLV-RIE 1018, RRID:CVCL_0063)
Curator: @scibot
SciCrunch record: RRID:CVCL_0063
RRID:SCR_025469
DOI: 10.1021/acsmacrolett.6c00098
Resource: University of Virginia School of Medicine Biomolecular Magnetic Resonance Core Facility (RRID:SCR_025469)
Curator: @scibot
SciCrunch record: RRID:SCR_025469
RRID:SCR_025127
DOI: 10.1021/acs.langmuir.5c06157
Resource: University of Pittsburgh Dietrich School Materials Characterization Laboratory Core Facility (RRID:SCR_025127)
Curator: @scibot
SciCrunch record: RRID:SCR_025127
RRID:SCR_027240
DOI: 10.1021/acs.joc.5c03172
Resource: None
Curator: @scibot
SciCrunch record: RRID:SCR_027240
RRID:SCR_027247
DOI: 10.1021/acs.joc.5c03172
Resource: None
Curator: @scibot
SciCrunch record: RRID:SCR_027247
RRID:SCR_023370
DOI: 10.1021/acs.jnatprod.6c00169
Resource: University of Arizona Analytical and Biological Mass Spectrometry Core Facility (RRID:SCR_023370)
Curator: @scibot
SciCrunch record: RRID:SCR_023370
RRID:SCR_017945
DOI: 10.1021/acs.jnatprod.5c01544
Resource: Northwestern University Proteomics Core Facility (RRID:SCR_017945)
Curator: @scibot
SciCrunch record: RRID:SCR_017945
RRID:AB_10563452
DOI: 10.1021/acs.chemrestox.5c00500
Resource: (Thermo Fisher Scientific Cat# M32407, RRID:AB_10563452)
Curator: @scibot
SciCrunch record: RRID:AB_10563452
RRID:AB_2867130
DOI: 10.1021/acs.chemrestox.5c00500
Resource: None
Curator: @scibot
SciCrunch record: RRID:AB_2867130
RRID:AB_2090334
DOI: 10.1021/acs.chemrestox.5c00500
Resource: None
Curator: @scibot
SciCrunch record: RRID:AB_2090334
RRID:AB_2914548
DOI: 10.1021/acs.chemrestox.5c00500
Resource: None
Curator: @scibot
SciCrunch record: RRID:AB_2914548
RRID:AB_2552661
DOI: 10.1021/acs.chemrestox.5c00500
Resource: None
Curator: @scibot
SciCrunch record: RRID:AB_2552661
RRID:AB_2789644
DOI: 10.1021/acs.chemrestox.5c00500
Resource: None
Curator: @scibot
SciCrunch record: RRID:AB_2789644
RRID:AB_2552458
DOI: 10.1021/acs.chemrestox.5c00500
Resource: None
Curator: @scibot
SciCrunch record: RRID:AB_2552458
RRID:AB_228378
DOI: 10.1021/acs.chemrestox.5c00500
Resource: (Thermo Fisher Scientific Cat# 31464, RRID:AB_228378)
Curator: @scibot
SciCrunch record: RRID:AB_228378
RRID:AB_628122
DOI: 10.1016/j.cub.2024.04.021
Resource: (Santa Cruz Biotechnology Cat# sc-508, RRID:AB_628122)
Curator: @scibot
SciCrunch record: RRID:AB_628122
RRID:AB_390918
DOI: 10.1016/j.cub.2024.04.021
Resource: (Roche Cat# 11867423001, RRID:AB_390918)
Curator: @scibot
SciCrunch record: RRID:AB_390918
RRID:SCR_009961
DOI: 10.1016/j.actbio.2026.02.003
Resource: OHSU Advanced Light Microscopy Core Facility (RRID:SCR_009961)
Curator: @scibot
SciCrunch record: RRID:SCR_009961
RRID:CVCL_0481
DOI: 10.1007/s12031-026-02512-1
Resource: (ATCC Cat# CRL-1721, RRID:CVCL_0481)
Curator: @scibot
SciCrunch record: RRID:CVCL_0481
RRID:SCR_019037
DOI: 10.1007/s11357-026-02219-6
Resource: Bio Rad ChemiDoc MP Imaging System (RRID:SCR_019037)
Curator: @scibot
SciCrunch record: RRID:SCR_019037
RRID:AB_10986489
DOI: 10.1007/s11060-026-05539-x
Resource: None
Curator: @scibot
SciCrunch record: RRID:AB_10986489
RRID:SCR_003070
DOI: 10.1007/s11060-026-05539-x
Resource: ImageJ (RRID:SCR_003070)
Curator: @scibot
SciCrunch record: RRID:SCR_003070
RRID:CVCL_1167
DOI: 10.1007/s11060-026-05539-x
Resource: (NCBI_Iran Cat# C519, RRID:CVCL_1167)
Curator: @scibot
SciCrunch record: RRID:CVCL_1167
RRID:AB_2943245
DOI: 10.1007/s11060-026-05539-x
Resource: None
Curator: @scibot
SciCrunch record: RRID:AB_2943245
RRID:SCR_002798
DOI: 10.1007/s11060-026-05539-x
Resource: GraphPad Prism (RRID:SCR_002798)
Curator: @scibot
SciCrunch record: RRID:SCR_002798
RRID:AB_2892682
Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 192, in init t = row['document']['title'] TypeError: string indices must be integers
RRID:AB_3718609
Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 192, in init t = row['document']['title'] TypeError: string indices must be integers
RRID:AB_955447
Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 192, in init t = row['document']['title'] TypeError: string indices must be integers
RRID:AB_955417
Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 192, in init t = row['document']['title'] TypeError: string indices must be integers
RRID:AB_2714032
Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 192, in init t = row['document']['title'] TypeError: string indices must be integers
RRID:AB_2076150
Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 192, in init t = row['document']['title'] TypeError: string indices must be integers
RRID:AB_10644283
Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 192, in init t = row['document']['title'] TypeError: string indices must be integers
RRID:AB_3678465
Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 192, in init t = row['document']['title'] TypeError: string indices must be integers
RRID:AB_2753196
Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 192, in init t = row['document']['title'] TypeError: string indices must be integers
RRID:AB_2107448
Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 192, in init t = row['document']['title'] TypeError: string indices must be integers
RRID:AB_302459
Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 192, in init t = row['document']['title'] TypeError: string indices must be integers
RRID:AB_2756528
Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 192, in init t = row['document']['title'] TypeError: string indices must be integers
RRID:AB_2894870
Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 192, in init t = row['document']['title'] TypeError: string indices must be integers
RRID:AB_10597232
Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 192, in init t = row['document']['title'] TypeError: string indices must be integers
RRID:AB_2289842
Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 192, in init t = row['document']['title'] TypeError: string indices must be integers
RRID:AB_3083804
Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 192, in init t = row['document']['title'] TypeError: string indices must be integers
RRID:AB_2210545
Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 192, in init t = row['document']['title'] TypeError: string indices must be integers
RRID:AB_2801561
Traceback (most recent call last): File "/home/ubuntu/dashboard/py/create_release_tables.py", line 54, in format_anno_for_release parsedanno = HypothesisAnnotation(anno) File "/home/ubuntu/dashboard/py/hypothesis.py", line 192, in init t = row['document']['title'] TypeError: string indices must be integers
RRID:AB_2655049
DOI: 10.1007/s00262-025-04285-9
Resource: (Miltenyi Biotec Cat# 130-110-518, RRID:AB_2655049)
Curator: @scibot
SciCrunch record: RRID:AB_2655049
RRID:AB_2725963
DOI: 10.1007/s00262-025-04285-9
Resource: (Miltenyi Biotec Cat# 130-113-135, RRID:AB_2725963)
Curator: @scibot
SciCrunch record: RRID:AB_2725963
RRID:AB_2733101
DOI: 10.1007/s00262-025-04285-9
Resource: None
Curator: @scibot
SciCrunch record: RRID:AB_2733101
RRID:Addgene_15483
DOI: 10.1007/s00204-026-04356-5
Resource: RRID:Addgene_15483
Curator: @scibot
SciCrunch record: RRID:Addgene_15483
RRID:CVCL_0286
DOI: 10.1002/prp2.70224
Resource: (ATCC Cat# CRL-1446, RRID:CVCL_0286)
Curator: @scibot
SciCrunch record: RRID:CVCL_0286
RRID:CVCL_0063
DOI: 10.1002/advs.202519393
Resource: (RRID:CVCL_0063)
Curator: @scibot
SciCrunch record: RRID:CVCL_0063
RRID:CVCL_0286
DOI: 10.1002/advs.202519132
Resource: (ATCC Cat# CRL-1446, RRID:CVCL_0286)
Curator: @scibot
SciCrunch record: RRID:CVCL_0286
RRID:CVCL_0031
DOI: 10.1002/advs.202519132
Resource: (NCI-DTP Cat# MCF7, RRID:CVCL_0031)
Curator: @scibot
SciCrunch record: RRID:CVCL_0031
RRID:CVCL_0062
DOI: 10.1002/advs.202519132
Resource: (RRID:CVCL_0062)
Curator: @scibot
SciCrunch record: RRID:CVCL_0062
RRID:CVCL_0598
DOI: 10.1002/advs.202518339
Resource: (ATCC Cat# CRL-10317, RRID:CVCL_0598)
Curator: @scibot
SciCrunch record: RRID:CVCL_0598
RRID:CVCL_0045
DOI: 10.1002/advs.202518339
Resource: (DSMZ Cat# ACC-305, RRID:CVCL_0045)
Curator: @scibot
SciCrunch record: RRID:CVCL_0045
RRID:CVLC_0033
DOI: 10.1002/advs.202518339
Resource: None
Curator: @dhovakimyan1
SciCrunch record: RRID:CVCL_0033
RRID:CVCL_0493
DOI: 10.1002/advs.202517577
Resource: (ATCC Cat# TIB-71, RRID:CVCL_0493)
Curator: @scibot
SciCrunch record: RRID:CVCL_0493
CVCL_0123
DOI: 10.1002/advs.202517577
Resource: (ATCC Cat# CL-173, RRID:CVCL_0123)
Curator: @scibot
SciCrunch record: RRID:CVCL_0123
RRID:CVCL_6898
DOI: 10.1002/advs.202517528
Resource: (RRID:CVCL_6898)
Curator: @scibot
SciCrunch record: RRID:CVCL_6898
RRID:CVCL_7254
DOI: 10.1002/advs.202517048
Resource: (KCLB Cat# 80009, RRID:CVCL_7254)
Curator: @scibot
SciCrunch record: RRID:CVCL_7254
RRID:CVCL_B288
DOI: 10.1002/advs.202517048
Resource: (RRID:CVCL_B288)
Curator: @scibot
SciCrunch record: RRID:CVCL_B288
RRID:CVCL_0327
DOI: 10.1002/advs.202516670
Resource: (ECACC Cat# 92110305, RRID:CVCL_0327)
Curator: @scibot
SciCrunch record: RRID:CVCL_0327
CVCL_0027
DOI: 10.1002/advs.202516670
Resource: (TKG Cat# TKG 0205, RRID:CVCL_0027)
Curator: @scibot
SciCrunch record: RRID:CVCL_0027
RRID:CVCL_0470
DOI: 10.1002/advs.202516670
Resource: (TKG Cat# TKG 0509, RRID:CVCL_0470)
Curator: @scibot
SciCrunch record: RRID:CVCL_0470
RRID:CVCL_0063
DOI: 10.1002/advs.202516670
Resource: (RRID:CVCL_0063)
Curator: @scibot
SciCrunch record: RRID:CVCL_0063
RRID:SCR_017344
DOI: 10.1002/advs.202514783
Resource: Cell Ranger (RRID:SCR_017344)
Curator: @scibot
SciCrunch record: RRID:SCR_017344
RRID:SCR_002285
DOI: 10.1002/advs.202514783
Resource: Fiji (RRID:SCR_002285)
Curator: @scibot
SciCrunch record: RRID:SCR_002285
RRID:SCR_010279
DOI: 10.1002/advs.202514783
Resource: Adobe Illustrator (RRID:SCR_010279)
Curator: @scibot
SciCrunch record: RRID:SCR_010279
RRID:SCR_018685
DOI: 10.1002/advs.202514783
Resource: Monocle3 (RRID:SCR_018685)
Curator: @scibot
SciCrunch record: RRID:SCR_018685
RRID:CVCL_B5IZ
DOI: 10.1002/advs.202514783
Resource: None
Curator: @scibot
SciCrunch record: RRID:CVCL_B5IZ
RRID:SCR_002798
DOI: 10.1002/advs.202514783
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DOI: 10.1002/advs.202514783
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DOI: 10.1002/advs.202512891
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DOI: 10.1002/advs.202511449
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RRID:CVCL_1168
DOI: 10.1002/advs.202511449
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plasmid_63934
DOI: 10.1002/advs.202510999
Resource: RRID:Addgene_63934
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plasmid_29644
DOI: 10.1002/advs.202510999
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RRID:CVCL_0004
DOI: 10.1002/advs.202510999
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RRID:CVCL_0002
DOI: 10.1002/advs.202510999
Resource: (RRID:CVCL_0002)
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RRID:CVCL_0063
DOI: 10.1002/advs.202510999
Resource: (RRID:CVCL_0063)
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plasmid_48138
DOI: 10.1002/advs.202510999
Resource: RRID:Addgene_48138
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RRID:CVCL_0425
DOI: 10.1002/advs.202510999
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Curator: @scibot
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plasmid_74224
DOI: 10.1002/advs.202510999
Resource: RRID:Addgene_74224
Curator: @scibot
SciCrunch record: RRID:Addgene_74224
RRID:CVCL_0063
DOI: 10.1002/advs.202510075
Resource: (RRID:CVCL_0063)
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RRID:CVCL_0367
DOI: 10.1002/advs.202510075
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RRID:CVCL_9773
DOI: 10.1002/advs.202510075
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DOI: 10.1002/advs.202510075
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Plasmid_154786
DOI: 10.1002/advs.202510075
Resource: RRID:Addgene_154786
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RRID:CVCL_1783
DOI: 10.1002/1878-0261.70148
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RRID:CVCL_JQ59
DOI: 10.1002/1878-0261.70148
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RRID:CVCL_1670
DOI: 10.1002/1878-0261.70148
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RRID:CVCL_0042
DOI: 10.1002/1873-3468.70334
Resource: (RRID:CVCL_0042)
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SciCrunch record: RRID:CVCL_0042
RRID:IMSR_JAX
DOI: 10.70322/jrbtm.2026.10002
Resource: None
Curator: @AleksanderDrozdz
SciCrunch record: RRID:IMSR_JAX:005104
The Jackson Laboratory_034848
DOI: 10.64898/2026.02.27.708522
Resource: None
Curator: @AleksanderDrozdz
SciCrunch record: RRID:MMRRC_034848-JAX
为评估在实际废水处理中的潜在应用,考察了 CUF/体系的抗干扰能力,这源于各种无机阴离子和有机污染物的竞争反应与清除效应。在 CUF/PMS 体系中分别添加典型无机阴离子(HCO₃⁻、HPO₄²⁻和 SO₄²⁻)及有机污染物(腐殖酸),结果如图 8c 所示。HCO₃⁻和 HPO₄²⁻对 CUF/PMS 体系的催化性能表现出积极影响,因其水解过程会干预氢离子(H⁺)与氢氧根离子(OH⁻)的比例(式 S28-S32)[46],而 SO₄²⁻对 CUF 的催化反应影响较弱。 此外,在腐殖酸存在下,磺胺甲噁唑的降解效率受到明显抑制,仅有 88.8%的磺胺甲噁唑能被去除,这归因于腐殖酸分子在活化过硫酸盐生成活性氧物种过程中产生的竞争效应。
实际用于处理废水的各种影响 X. He, K.E. O'Shea Selective oxidation of H(1)-antihistamines by unactivated peroxymonosulfate (PMS): Influence of inorganic anions and organic compounds Water Res., 186 (2020), Article 116401,
此外,高浓度氢离子(H⁺)与磺胺甲恶唑的竞争会消耗活性氧物种(ROS)(式 S25-S26)[44],导致降解效率较低。当 pH 值从 9 增加到 11 时,溶液中反应活性高于 HSO₅⁻的 SO₅²⁻比例逐渐增加,这有利于活性氧物种的产生[45]。 此外,碱活化过一硫酸盐可归因于提高了磺胺甲噁唑的降解效率
进一步探究PH的影响,电子互斥,碱活化PMS 44.X. Huang, W. Ren, X. Liu, C. Lin, M. He, W. Ouyang CuMgFe-LDO as superior peroxymonosulfate activator for imidacloprid removal: Performance, mechanism and effect of pH Chem. Eng. J., 441 (2022), Article 136135, 10.1016/j.cej.2022.136135 45.L. Hu, G. Zhang, M. Liu, Q. Wang, P. Wang Enhanced degradation of Bisphenol A (BPA) by peroxymonosulfate with Co3O4-Bi2O3 catalyst activation: Effects of pH, inorganic anions, and water matrix Chem. Eng. J., 338 (2018), pp. 300-310,
此外,通过三种典型的等温模型(Langmuir、Freundlich 和 Temkin 模型)分析了吸附剂(CUH 和 CUF)与吸附质(磺胺甲恶唑)之间的吸附相互作用,结果如图 4b-d 和表 S4 所示。CUH 和 CUF 的实验数据与 Freundlich 模型拟合度最高(R 2 最接近),表明多分子层吸附占主导作用。Freundlich 指数(n = 异质性因子)的值可用于解释三种吸附行为:当 1/n < 1、=1 和>1 时,分别对应物理过程、线性过程和化学过程[34]。CUH 和 CUF 的 n 值分别为 0.894 和 0.868,表明催化剂表面存在物理吸附。
freundlish吸附线和其他等温吸附线判断吸附类型
当浓度从 0.5 mM 提升至 1.0 mM 时,磺胺甲噁唑的降解效率从 59.1%上升至 90.9%,同时 k 值也增加了 3.4 倍,这表明产生了更多活性氧物种以氧化磺胺甲噁唑。随着初始过一硫酸盐浓度从 1.0 mM 进一步增加至 2.0 mM,观察到磺胺甲噁唑降解效率仅有微小提升,同时 k 值略有增加,这归因于过量过一硫酸盐浓度下活性氧物种的自淬灭效应
PMS建议投加量,以及测量反应速率常数K的重要性,自淬灭效应 J. Yan, H. Liu, C. Dou, Y. Wu, W. Dong Quantitative probing of reactive oxygen species and their selective degradation on contaminants in peroxymonosulfate-based process enhanced by picolinic acid
CUF 的初始投加量与磺胺甲噁唑降解效率呈正相关:在催化剂投加量分别为 50、100、150 和 200 mg/L 时,CUF/PMS 体系对磺胺甲噁唑的去除率分别达到 81.2%、90.9%、92.8%和 95.7%。同时,由于更多活性位点与 PMS 作用产生大量活性氧物种,催化反应常数(k 值)提升了 2.3 倍。综合考虑降解效率的微弱提升与金属资源消耗,最终确定 CUF 的最佳催化剂投加量为 100 mg/L。
催化剂投加量参考值
通常,当反应温度升高时,分子尺寸较大的磺胺甲噁唑分子更容易获得更大的能量,以克服各种阻力(扩散阻力和障碍),进而与活性位点接触。
温度升高SAs容易获得更大能量
The effect of solution pH on the adsorption performance of the prepared nanoparticles was also investigated because the surface potential of adsorbents and adsorbates could be decided by their surface group under different solution pH. Owing to the sulfamethoxazole with two dissociation constants of pKa1 = 1.7 and pKa2 = 5.6, the sulfamethoxazole displayed cations forms (SMX+) at pH < 1.7, neutral ions form at 1.7 < pH < 5.6 (SMX±), and anions forms pH > 5.6 (SMX-), which would promote or inhibit adsorption performance of adsorbents
PH影响 Y. Ma, L. Yang, L. Wu, P. Li, X. Qi, L. He, S. Cui, Y. Ding, Z. Zhang Carbon nanotube supported sludge biochar as an efficient adsorbent for low concentrations of sulfamethoxazole removal
Sarah Jeong. How to Make a Bot That Isn't Racist. Vice, March 2016. URL:
This source caught my attention because the title already suggests that bias in bots is a real design problem, not just a technical mistake. It connects to the chapter by reminding us that even simple program structures can still produce harmful outcomes if the data, rules, or assumptions behind them are biased.
One of the most common events to program for is around time: We can also tell programs to wait for a period of time, or start at a given time.
This part made me think about how simple scheduling can make a bot feel much more active and intentional, even when it is doing a very basic task. A bot that posts at regular times may look more “human” or organized, which also raises ethical questions about transparency and whether other users should know they are interacting with automation.
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Of the several properties used for identifying minerals, it is good to consider which will be most useful for identifying them in small grains surrounded by other minerals.
This type of challenge of identifying minerals in such a messy environment full of other surrounding minerals seems pretty challenging. However it seems there are tests to ease this challenge and there consideration in finding the best method for it.
有个相当到位的说法是,集体是没有良心的;但是,由良心之辈组成的集体是具有良心的集体。
人之初,性本善。性相近,习相远。 苟不教,性乃迁。教之道,贵以专。
The most common mineral precipitated by organisms is calcite, or calcium carbonate (CaCO3). Calcite is often precipitated by organisms as a polymorph called aragonite. Polymorphs are crystals with the same chemical formula but different crystal structures. Marine invertebrates such as corals and clams precipitate aragonite or calcite for their shells and structures. Upon death, their hard parts accumulate on the ocean floor as sediments and eventually may become the sedimentary rock limestone.
This example of corals and clams turning calcite or agaronite that has precipitated, into a form of protection, then becoming the sedimentary rock limestone upon death shows how living organisms use these elements and eventually become apart of a geological formation.
Heat is energy that causes atoms in substances to vibrate. Temperature is a measure of the intensity of the vibration. If the vibrations are violent enough, chemical bonds are broken and the crystals melt releasing the ions into the melt. Magma is molten rock with freely moving ions. When magma is emplaced at depth or extruded onto the surface (then called lava), it starts to cool and mineral crystals can form.
Heat breaks bonds and melts minerals. The cooling lets ions slow down and re‑form those bonds, creating new crystals.
CC BY-SA 3.0], via Wikimedia Commons Precipitation is the reverse process, in which ions in solution come together to form solid minerals.
atoms or molecules that have already been separated and dispersed yet this is the moment when ions bond again.
page from Highlig
这是什么
Typically, substances like coal, pearl, opal, or obsidian that do not fit the definition of a mineral are called mineraloids.
I assumed some of these were minerals before reading but I see they are classified as mineraloids since they don't have the feature of crystalline and aren't made from geologic process.
Citizens of these countries are willing to make personal sacrifices (e.g., less convenience, longer wait times, fewer diagnostic and treatment options, more minimalistic guidelines for chronic disease prevention and management) so that their health care systems are more equitable and provide universal coverage.
Compromise is needed such as less convience , longer wait times , fewer diagnostic and treatment options
In fact, the scope of services provided by family physicians has eroded in the era of Medicare-sponsored payment reforms.16 Medicare also does not support direct primary care models or other innovative ways for family physicians to deliver and be paid for their services, instead of being tied to the specialist-dominated Current Procedural Terminology (CPT) coding system.17
The scope of coverage would be lower with univeral care.
In 2016, the United Kingdom's National Health Service (NHS) refused to pay for lumacaftor/ivacaftor (Orkambi), an expensive cystic fibrosis drug.10 After two years of negotiations, a deal was reached allowing the manufacturer to supply the drug and its future versions. Although the price has not been disclosed, the NHS is thought to have agreed to about £10,000 to £20,000 ($12,000 to $25,000) per year per patient.11 In contrast, the same drug costs more than $258,000 per year per patient in the United States.12
Health care itself is expensive and thus governments might not always be able to pay it.
Under Medicare for All, urban hospitals would close unless payments increased or regulatory burdens decreased, thereby reducing operating costs. The impact on rural hospitals would likely be mixed; those that already receive most of their income from public insurance plans and have high rates of uninsured patients could come out ahead. However, rural hospital sustainability would further depend on changes to existing alternative payment models, such as critical access definitions.
Under medicare rural hospitals would have to close because of cost issue.
There is no dispute that administrative costs for Medicare (about 2% of overall costs1) are less than those for private health insurance, for which the Affordable Care Act sets the allowable overhead at 20% of premiums.2 However, expansion of Medicare or private insurance would not address the deeper problems in our health care system.
Health care is complex and making it universal does not address the problem of healthcare itself.
The necessary political compromises and private concessions to the doctors (reimbursements of their customary, reasonable, and prevailing fees), to the hospitals (cost plus reimbursement), and to the Republicans created a 3-part plan, including the Democratic proposal for comprehensive health insurance (“Part A”), the revised Republican program of government subsidized voluntary physician insurance (“Part B”), and Medicaid. Finally, in 1965, Johnson signed it into law as part of his Great Society Legislation, capping 20 years of congressional debate.
Another important checkpoint
For may of the same reasons they failed before: interest group influence (code words for class), ideological differences, anti-communism, anti-socialism, fragmentation of public policy, the entrepreneurial character of American medicine, a tradition of American voluntarism, removing the middle class from the coalition of advocates for change through the alternative of Blue Cross private insurance plans, and the association of public programs with charity, dependence, personal failure and the almshouses of years gone by.
important cue on so much failures.
In 1917, the US entered WWI and anti-German fever rose. The government-commissioned articles denouncing “German socialist insurance” and opponents of health insurance assailed it as a “Prussian menace” inconsistent with American values. Other efforts during this time in California, namely the California Social Insurance Commission, recommended health insurance, proposed enabling legislation i n 1917, and then held a referendum. New York, Ohio, Pennsylvania, and Illinois also had some efforts aimed at health insurance. But in the Red Scare, immediately after the war, when the government attempted to root out the last vestiges of radicalism, opponents of compulsory health insurance associated it with Bolshevism and buried it in an avalanche of anti-Communist rhetoric.
Anticommunist rhetoric stopped another attempt of reform.
In 1906, the American Association of Labor Legislation (AALL) finally led the campaign for health insurance. They were a typical progressive group whose mandate was not to abolish capitalism but rather to reform it. In 1912, they created a committee on social welfare which held its first national conference in 1913. Despite its broad mandate, the committee decided to concentrate on health insurance, drafting a model bill in 1915.
AAL tried for health insurance coverage.
The campaign for some form of universal government-funded health care has stretched for nearly a century in the US On several occasions, advocates believed they were on the verge of success; yet each time they faced defeat
Looking into efforts around us when universal healthcare efforts were attempted but failed.
Many non-silicate minerals are economically important and provide metallic resources such as copper, lead, and iron.
Non-silicate minerals also include salt, fertilizers. There are five types of common non silicate groups which have many different uses the properties provide.
Geologists identify minerals by their physical properties.
Geologists identify minerals by testing their physical properties. These properties are listed as luster and color, streak, hardness ,crystal habit, and cleavage , fracture.
Minerals are categorized based on their composition and structure.
Silicate minerals are also known as rock forming minerals. They are called rock forming minerals because they are built around the silicon-oxygen tetrahedron. It’s a pyramid shape with four sides.
Precipitation is the reverse process, in which ions in solution come together to form solid minerals.
Temperature and pressure are important factors in the creation of a mineral. When water evaporates it leaves behind minerals .
Rocks are composed of minerals that have a specific chemical composition.
A rock is composed of three types of minerals, igneous, sedimentary, and metamorphic. There are four substances that aren’t considered a mineral, as they are called mineraloids.
Polymers are chains, sheets, or three-dimensional structures, and are formed by multiple tetrahedra covalently bonded via their corner oxygen atoms. Pyroxenes are commonly found in mafic igneous rocks such as peridotite, basalt, and gabbro, as well as metamorphic rocks like eclogite and blue-schist.
The passage explains that pyroxenes form when silica tetrahedra link into single‑chain polymers, and because of their Fe‑Mg‑rich chemistry and structural stability, they appear widely in mafic igneous rocks and high‑pressure metamorphic rocks.
he chemical formula is (Fe,Mg)2SiO4. As previously described, the comma between iron (Fe) and magnesium (Mg) indicates these two elements occur in a solid solution. Not to be confused with a liquid solution, a solid solution occurs when two or more elements have similar properties and can freely substitute for each other in the same location in the crystal structure.
The text is explaining that olivine’s formula shows Fe and Mg can swap places in the crystal because they are similar ions, creating a solid solution series rather than a single fixed mineral.
Minerals are categorized based on their composition and structure. Silicate minerals are built around a molecular ion called the silicon-oxygen tetrahedron. A tetrahedron has a pyramid-like shape with four sides and four corners. Silicate minerals form the largest group of minerals on Earth, comprising the vast majority of the Earth’s mantle and crust. Of the nearly four thousand known minerals on Earth, most are rare. There are only a few that make up most of the rocks likely to be encountered by surface dwelling creatures like us. These are generally called the rock-forming minerals.
The silicon oxygen tetrahedron as the structural key to silicates, then zooms out to explain why silicates are overwhelmingly the most important minerals on Earth
The science is in. AI chatbots are changing our minds, and not for the better. The good news is that you can take advantage of the AI revolution in many ways, while also protecting your own mind from being influenced by the AI hive mind.
As with most things in life, moderation and understanding is key to utilizing a tool such as AI. We must protect our own individuality from AI, but that doesn't mean we should never use it.
“You are my intellectual sparring partner. Your job is to disagree with me constructively, not to agree. For every idea I present: 1) identify and challenge hidden assumptions; 2) build a strong counter-argument; 3) stress-test my logic for flaws, logical gaps, or weaknesses; 4) offer alternative perspectives to mine; and 5) prioritize truth over consensus.”
Annotating this to keep the prompt handy. I am going to test this to see how the opinion is swayed.
When chatbots write for you, they also think for you.
We must always write without using AI. If we write using it, it is plainly removing our thoughts and thinking for us.
Accept the fact that your intelligence, education and awareness of the issue does not make you immune to the influence of AI tools.
We must maintain awareness that AI will have an affect on us. We are well past the point of it not having an influence on our intelligence.
AI chatbots can give users the feeling of a shared reality
This is already a huge issue when searching for something online. If someone finds even one website or person who supports their idea, they feel acknowledged and understood. AI can give us this false sense of validation by always agreeing with the user.
Applied to the LLM era, the major chatbots function both as cognitive tools and also as thought partners that co-construct reality with users. They sustain, elaborate on and magnify our beliefs. And when they hallucinate, they can cause us to hallucinate, too.
I feel that AI should be used as a tool rather than a fully functioning brain. There is too much trust placed in AI. People don't seem to understand that it co-constructs reality from our input. When it has a hiccup, it will cause us to have a hiccup as well.
And it’s a recurring cycle. As homogenized writing proliferates, those generic texts get sucked into the training data, creating a feedback loop of ever-increasing blandness, a genericization of the world’s knowledge and perspective. As chatbots get blander, we get blander. And as we get blander, the chatbots get even blander.
Maybe by using AI more and more, we can effectively dumb it down enough to where we no longer trust or believe what it is telling us? They do learn from our input, so if we are inputting bland ideas, it will get more bland.
The researchers gamed autocomplete suggestions to either favor or oppose the death penalty, felon voting rights, fracking, or genetically modified organisms. Then they measured how much participants in the study would be swayed in their opinions by the suggestions. What they found is that biased autocomplete changed opinions more than just reading the biased point of view. Apparently, the interactive, co-writing nature of AI autocomplete suggestions plays a crucial role in persuasion.
AI is already shaping the way we think and form opinions. It has tremendous power to influence the entire human race.
Also: A strong majority of participants did not believe the AI autocomplete was biased and did not believe they were influenced in their thinking. Even more interestingly, some participants were warned that the autocomplete was biased and it still changed their opinions.
People did not believe the AI was biased and furthermore did not believe they were being swayed or influenced.
Hundreds of millions of people now use the same small handful of AI models to write emails, draft reports, brainstorm ideas and polish their writing. Because those models were trained on massive datasets that overrepresent English, Western viewpoints and the perspectives of educated, high-income, liberal males, that tends to be the tone and style of writing, regardless of whether the user fits into that mold.
AI is actively erasing our ability to personally express ourselves and have our own thoughts. There are only a small handful of AI models we can use, and if we are all using them to "improve" our writings, then we are all being heard in the same way.
the emerging field of social-emotional learnin
In 2017, this seems like a bold claim, but yes, I guess in the grand scheme of things, the last 50 years is a recent time frame relative to the education of people since the beginning of time.
DS research methodology
Cool idea
62 females, 90 males; mean age ± S.D. =21.1 ± 7.5 years
This doesn't make sense to me. 21.1 +/- 7.5 would mean that one standard deviation covers 14-28 year olds. That seems like a very young age to be one standard, meaning there are also younger?
isualizing models in compelling wayscan make analytics data straightforward for non-specialists to observe and understand.
Key point from our interview with an evaluator in another class.
Blackboard(http://www.blackboard.com/) can record logs for user activity in courses
We use Connexus
analyze log traces in an online learning sessionto make inferences about students’motivational orientations.
Again, as a school counselor, I often check log traces in our online learning platform. Last week, a student completed a 17-chapter health class in 8 hours.
Huang et al. In Press; Lai et al. 2016; Liu et al. 2015
I know it is common, but I always note when authors cite themselves.
self-efficacy
Associated with my DRP
United States
Again, as a school counselor, this is a vast oversimplification, as there is no direct mandate for us; however, ASCA, the overarching governing body, has incredibly clear goals and methodologies for this.
Furthermore, Moffitt et al. (2011) find that the emotional skill of self-control inchildhood is associated with better physical health, less substance dependence, betterpersonal finances, and fewer instances of criminal offending in adulthood.
As flawed as the marshmallow test was, this has been evident for a very long time in a myriad of studies.
(http://www.casel.org/
As a high school counselor, this is a good curriculum
Wikipedia
Interestingly, Wikipedia is a data conglomerate.
createopportunities
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shocked not only artificial intelligenceexperts, who thought such an event was 10 to15 years away, but also educators,
At the rate of scientific advancement, why was this surprising to some? Also, as an educator, technologies always create new jobs; it has been like this since the dawn of time. The first tractor, conveyor belts, phones, computers, the list goes on, and so do the jobs.