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24K Magic puede referirse a:

24K Magic (álbum), un álbum de 2016 de Bruno Mars;
«24K Magic» (canción), la canción que da título al álbum;
24K Magic World Tour, una gira de conciertos.

redpajama_set_name: RedPajamaWikipedia


While I was in the Los Angeles area for the BCS National Championship, I was able to go and see a dear friend of mine who lives in Manhattan Beach. This was the first time I had ever seen the Pacific Ocean and it was much more beautiful than I had expected! Everyone around here always exclaims that "the Pacific has nothing on the Gulf", which I can see where someone saying that would be coming from, however, the Gulf certainly does not have stunning mountain views all around it.
When I said dear friend, I did not go into detail so here's our story. This tiny, beautiful woman, Angie Smith, was once my 7th grade teacher. I know it's hard to believe, because we could pass for sisters, but it is true! She became a huge part of my life over 10 years ago; through living in many different states and now different coasts, we have remained close. The impact teachers can make on their students' lives is so amazing to me and I am so blessed to have walked into her classroom that year. The time we spend together is always precious and now that she has little ones of her own it is even more special to see them grow. I am so thankful to have you as a friend, Angie! After we finished visiting with Angie and her family at their beautiful home, we headed around the corner to the beach! To the Pacific I went!
I loved seeing the lifeguard stands because due to lower tides (I guess), you really don't see these much on the beaches I frequent along the Gulf. And let me be honest, I really loved seeing the lifeguard stands because I felt like I was looking onto a set of "The OC". Am I the ONLY one that still misses that show?
I wish we would have had more time to explore this adorable little town, next time!
Something about salt in the air makes the soul feel better. As much as I love everything about the south, I could definitely get used to this place. I am not sure there's a place in the US that has better weather, at least not one I have visited!
Definitely on the list of places I would go back to and explore more. Still getting used to this whole taking photos thing, but I will say it seems that taking them outdoors is so much easier than taking them in the kitchen! ALWAYS welcoming any and all photography suggestions for beginners!


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text:
APOE4 impairs myelination via cholesterol dysregulation in oligodendrocytes
Joel W. Blanchard1,2 na1 nAff11,
Leyla Anne Akay ORCID: orcid.org/0000-0002-5944-24661,2,3 na1,
Jose Davila-Velderrain ORCID: orcid.org/0000-0003-0271-62673 na1 nAff12,
Djuna von Maydell1,2,3 na1,
Hansruedi Mathys1,2 nAff13,
Shawn M. Davidson ORCID: orcid.org/0000-0002-3475-03824,
Audrey Effenberger ORCID: orcid.org/0000-0003-2405-21661,2,
Chih-Yu Chen5,
Kristal Maner-Smith5,
Ihab Hajjar6,
Eric A. Ortlund ORCID: orcid.org/0000-0001-8855-30295,7,
Michael Bula1,2,
Emre Agbas1,2,
Ayesha Ng1,2,
Xueqiao Jiang1,2,
Martin Kahn1,2,
Cristina Blanco-Duque1,2,
Nicolas Lavoie1,2,
Liwang Liu1,2,
Ricardo Reyes8,
Yuan-Ta Lin1,2,
Tak Ko1,
Lea R'Bibo8,
William T. Ralvenius1,2,
David A. Bennett9,
Hugh P. Cam1,2,
Manolis Kellis ORCID: orcid.org/0000-0001-7113-96303,10 &
Li-Huei Tsai ORCID: orcid.org/0000-0003-1262-05921,2,10
Nature volume 611, pages 769–779 (2022)Cite this article
635 Altmetric
Myelin biology and repair
Oligodendrocyte
APOE4 is the strongest genetic risk factor for Alzheimer's disease1,2,3. However, the effects of APOE4 on the human brain are not fully understood, limiting opportunities to develop targeted therapeutics for individuals carrying APOE4 and other risk factors for Alzheimer's disease4,5,6,7,8. Here, to gain more comprehensive insights into the impact of APOE4 on the human brain, we performed single-cell transcriptomics profiling of post-mortem human brains from APOE4 carriers compared with non-carriers. This revealed that APOE4 is associated with widespread gene expression changes across all cell types of the human brain. Consistent with the biological function of APOE2,3,4,5,6, APOE4 significantly altered signalling pathways associated with cholesterol homeostasis and transport. Confirming these findings with histological and lipidomic analysis of the post-mortem human brain, induced pluripotent stem-cell-derived cells and targeted-replacement mice, we show that cholesterol is aberrantly deposited in oligodendrocytes—myelinating cells that are responsible for insulating and promoting the electrical activity of neurons. We show that altered cholesterol localization in the APOE4 brain coincides with reduced myelination. Pharmacologically facilitating cholesterol transport increases axonal myelination and improves learning and memory in APOE4 mice. We provide a single-cell atlas describing the transcriptional effects of APOE4 on the aging human brain and establish a functional link between APOE4, cholesterol, myelination and memory, offering therapeutic opportunities for Alzheimer's disease.
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Fig. 1: Cell-type-specific APOE4-associated pathway alterations.
Fig. 2: APOE4 alters cholesterol homeostasis and localization in human post-mortem oligodendrocytes.
Fig. 3: APOE4 alters cholesterol homeostasis and localization in iPS-cell-derived oligodendrocytes.
Fig. 4: APOE4 leads to impaired myelination in mice and humans.
Fig. 5: iPS-cell-derived APOE4 oligodendroglia exhibit myelination deficits in neuronal co-cultures.
Fig. 6: Cyclodextrin improves myelination and learning and memory in old APOE4 mice.
The snRNA-seq data are available at the Synapse AD Knowledge Portal (syn38120890), including corresponding ROSMAP metadata. The data are available under the controlled use conditions set by human privacy regulations. To access the data, a data use agreement is needed. This registration is in place solely to ensure anonymity of the ROSMAP study participants. A data use agreement can be agreed with either RUSH university Medical centre (RUMC) or with SAGE, which maintains Synapse, and can be downloaded from their websites. See our code repository for a detailed list of data availability and download links.
Codes, along with detailed instructions on how to reproduce the analyses presented herein, are available on GitHub (https://github.com/djunamay/apoe4myelin).
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We thank the individuals who donated post-mortem brain samples, and their families, for enabling this research; Y. Zhou, E. McNamara and T. Garvey for administrative support and animal care; and J. M. Bonner for input on the lipidomic analyses. We acknowledge support from the Robert A. and Renee E. Belfer Family, The JPB Foundation, The Carol and Gene Ludwig Family Foundation, the Cure Alzheimer's Fund and the National Institutes of Health (RF1 AG062377, RF1 AG054012-01, U54HG008097 and 747UG3NS115064). J.W.B. was supported by the NIH grant UG3-NS115064, R01NS114239-01A1, Cure Alzheimer's Fund; L.A.A. was supported by NIH grant RF1-AG0540124 and the MIT BCS Henry E. Singleton Graduate Student Fellowship; D.v.M. was supported by the MIT BCS Broshy Graduate Student Fellowship and the MIT BCS Halis Graduate Student Fellowship; X.J. was supported by NIH grant U01-NS110453; Y.-T.L. was supported by NIH grant R01-AG058002; W.T.R. was supported by an Alzheimer's Association Research Fellowship; H.P.C. was supported by NIH grants RF1-AG054012 and RF1-AG062377. ROSMAP is supported by NIA grants P30AG20262, R01AG15819, R01AG17917, U01AG46152, U01AG 61356 and P30AG72975. RF1AG057470, RF1AG051633, K24AG062786 from NIA to I.H. ROSMAP resources can be requested at https://www.radc.rush.edu. Graphic illustrations were generated using BioRender under agreements VI24HP0GQM and NE24HPHF0X.
Joel W. Blanchard
Present address: Nash Family Department of Neuroscience, Black Family Stem Cell Institute, Ronald M. Loeb Center for Alzheimer's Disease, Friedman Brain Institute, Icahn School of Medicine at Mt Sinai, New York, NY, USA
Jose Davila-Velderrain
Present address: Human Technopole, Milan, Italy
Hansruedi Mathys
Present address: Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA, USA
These authors contributed equally: Joel W. Blanchard, Leyla Anne Akay, Jose Davila-Velderrain, Djuna von Maydell
Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
Joel W. Blanchard, Leyla Anne Akay, Djuna von Maydell, Hansruedi Mathys, Audrey Effenberger, Michael Bula, Emre Agbas, Ayesha Ng, Xueqiao Jiang, Martin Kahn, Cristina Blanco-Duque, Nicolas Lavoie, Liwang Liu, Yuan-Ta Lin, Tak Ko, William T. Ralvenius, Hugh P. Cam & Li-Huei Tsai
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
Joel W. Blanchard, Leyla Anne Akay, Djuna von Maydell, Hansruedi Mathys, Audrey Effenberger, Michael Bula, Emre Agbas, Ayesha Ng, Xueqiao Jiang, Martin Kahn, Cristina Blanco-Duque, Nicolas Lavoie, Liwang Liu, Yuan-Ta Lin, William T. Ralvenius, Hugh P. Cam & Li-Huei Tsai
MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA
Leyla Anne Akay, Jose Davila-Velderrain, Djuna von Maydell & Manolis Kellis
Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
Shawn M. Davidson
Department of Medicine, Emory Integrated Metabolomics and Lipidomics Core, Emory University School of Medicine, Atlanta, GA, USA
Chih-Yu Chen, Kristal Maner-Smith & Eric A. Ortlund
Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
Ihab Hajjar
Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
Eric A. Ortlund
Nash Family Department of Neuroscience, Black Family Stem Cell Institute, Ronald M. Loeb Center for Alzheimer's Disease, Friedman Brain Institute, Icahn School of Medicine at Mt Sinai, New York, NY, USA
Ricardo Reyes & Lea R'Bibo
Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
David A. Bennett
Broad Institute of Harvard and MIT, Cambridge, MA, USA
Manolis Kellis & Li-Huei Tsai
Leyla Anne Akay
Djuna von Maydell
Audrey Effenberger
Chih-Yu Chen
Kristal Maner-Smith
Michael Bula
Emre Agbas
Ayesha Ng
Xueqiao Jiang
Martin Kahn
Cristina Blanco-Duque
Nicolas Lavoie
Liwang Liu
Yuan-Ta Lin
Tak Ko
Lea R'Bibo
William T. Ralvenius
Hugh P. Cam
Manolis Kellis
Li-Huei Tsai
J.D.-V. and D.v.M. performed computational analyses of all functional genomic data. J.W.B. and L.A.A. designed and performed the experiments and data analysis. H.M., A.N. and X.J. generated the single-cell data. D.A.B. provided biospecimens and data and reviewed the manuscript. S.M.D. performed lipidomic data generation and analysis. C.-Y.C., K.M.-S., I.H., W.T.R. and E.A.O. generated and analysed human lipidomic data. E.A., M.B. and A.E. assisted with tissue culture. Y.T.-L. and T.K. generated the iPS cell lines used in the study. L.L., M. Kahn, C.B.-D., R.R., L.R'B. and N.L. assisted with experiments and data analysis. The study was conceived and designed by J.W.B., L.A.A., J.D.-V., D.v.M., L.-H.T. and M. Kellis. All of the authors contributed to writing the paper and making the figures.
Correspondence to Manolis Kellis or Li-Huei Tsai.
The authors J.W.B., L.A.A., J.D.V., D.v.M., A.E., M. Kellis and L.-H.T. have filed a patent application (PCT/US2022/020271) based on the findings.
Nature thanks Gonclao Castelo-Branco, Jeremy Miller and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Extended Data Fig. 1 Subject-level metadata and single-cell annotation quality control.
a, Study cohort description by APOE genotype groups: APOE3/3-carriers (grey), APOE3/4-carriers (pink), APOE4/4-carriers (red); balanced according to positive (glow) or negative pathological diagnosis (no glow) Cartoons generated with BioRender. APOE4/4 group comprised 3 male and 5 female subjects, all with an AD diagnosis. AD status is defined based on pathological (high amyloid and tau pathology) and cognitive diagnosis of Alzheimer's dementia (final consensus cognitive diagnosis, cogdx=4). b, Distribution of pathology variables, PMI, and age at death in cohort. Unadjusted wilcoxon test p-values shown, two-sided (N = 6 per group, except for APOE4/4, where N = 8). Boxplots indicate median, 25th and 75th percentiles. c, Experimental and computational workflow of the single-cell analysis involves single nuclei isolation and sequencing, followed by computational analysis for sub-clustering and cell type annotation. Cartoons generated with BioRender d, Two-dimensional representation of all high-quality cells included in downstream analysis labelled by major cell types. e, Cell-type-specific marker gene expression projected onto two-dimensional representation of cell space. Coloured bars indicate the cell type for which a given gene was considered a marker. See panel i for legend. f, Expanded view of immune and vascular cell types within cell space. g, Enrichment of markers from two independent datasets (columns) within genes with preferential gene expression across annotated cell groups (rows). h, Distribution of inter-subject correlation values by cell type. Boxplots indicate median, 25th and 75th percentiles. i, Pairwise correlations of cell-type-specific individual-level transcriptomic profiles (average expression values across cells of a given type, N = 32 profiles per cell type). j, Median number of cells per subject by cell type. k, Fraction of subjects lacking cells of a given type. l, Individual distributions across cell types. m, Individual cell-type fractions organized by pathological diagnosis and APOE genotype.
Extended Data Fig. 2 APOE-associated and lipid pathway changes in APOE4 and AD.
a, Curation process for APOE-associated pathway database. Brain cell type expression was defined as nonzero detection of a gene in >10% of cells of that cell type. b, Transcriptional activity scores of APOE-associated pathways that show cell-type-specific patterns. c, Individual-level average expression of cholesterol biosynthesis genes from Fig. 2a in oligodendrocytes. d, Overrepresentation of lipid-related pathways (database from Fig. 1c) within genes differentially expressed in APOE4 relative to APOE3 in human post-mortem oligodendrocytes as estimated by a negative binomial mixed model (NBMM). e, Altered lipid-associated pathways (see methods) in APOE3/3 vs APOE3/4-carriers, in no AD background (control) (nominal p-value < 0.05, linear model, unadjusted, N = 6 per group). Pathways of special interest are highlighted in bold. (-) indicates negative regulation. f–k, Pathway activity scores for 'Cholesterol biosynthesis III (via desmosterol) stratified by APOE genotype and/or AD pathology (p-values, linear model, unadjusted, n = 6 per group). Boxplots indicate median, 25th and 75th percentiles.
Extended Data Fig. 3 Large-scale lipidomic analysis of human prefrontal cortex.
a, Overview of ROSMAP cohorts on which lipidomic analysis was performed. Cartoons generated with BioRender b, Cohort clinicopathological characteristics. NFT = neurofibrillary tangles (p values calculated with Wilcoxon rank-sum test, two-sided). c, A single cholesterol ester (ChE) species passed quality control (see Methods) and showed significant (p<0.05, Wilcoxon rank-sum test, two-sided) differential concentration when comparing APOE3/3 vs APOE3/4 and APOE4/4-carriers stratified by pathology. ChE(18:1) indicates a ChE with carboxylate position 18 and one unsaturated fatty acid bond. d, Distribution of the concentration of ChE species from c) (p-value = 0.046, Wilcoxon rank-sum test, two-sided). Boxplots indicate median, 25th and 75th percentiles.
Extended Data Fig. 4 Lipid droplets in human and mouse brain.
a, Co-localization of immunohistochemistry against lipid-droplet associated protein perilipin-1 (PLIN1) with Bodipy-cholesterol staining in the human prefrontal cortex from APOE3/3 and APOE3/4 individuals (n = 3 imaged per genotype). Cell nuclei stained with Hoechst dye. b, Transmission electron microscopy (TEM) on corpus callosum from six-month-old APOE3-TR and APOE4-TR mice (males, n = 3 per genotype). The number of lipid droplets was quantified in four 1 μm2 areas per image from three images per mouse. Right panel, dots represent mean value per mouse and bar represents mean value for group, p value was calculated using unpaired, two-tailed student's t-test. c, Representative images of Bodipy-cholesterol staining, with markers for microglia (IBA1), astrocytes (GFAP) and oligodendrocytes (OLIG2) in the prefrontal cortex of APOE4-carriers (n = four individuals). Dotted outline in the OLIG2 panel depicts the 2 μm radius around the nucleus that was quantified for the presence of Bodipy-cholesterol. Scale bar 10 μm. Bodipy-cholesterol staining was quantified for cell type based on localization with cell-type-specific markers. Bars depict means from different biological replicates. P values calculated using unpaired, two-tailed student's t-test. The dotted outline in the OLIG2 panel depicts the 2 μm radius around the nucleus that was quantified for the presence of Bodipy-cholesterol. d, Co-localization of perilipin-1 (PLIN1) immunoreactivity around OLIG2-positive nuclei in prefrontal cortex from an APOE3/4 individual. e, Quantification of the number of perilipin-1 punctae around representative OLIG2-positive nuclei in APOE3/3 and APOE3/4 individuals (n=4 per genotype). The number of Perilipin-1 punctae was determined using Imaris software. P-value was calculated using unpaired, two-tailed student's t-test.
Extended Data Fig. 5 Comparison of post-mortem oligodendrocytes and iPSC-derived oligodendroglia.
a, Comparison of the relative expression of myelination genes (MYRF, MOG, PLP1, PLLP, MAG, OPALIN) in iPSC-derived brain cell types and aggregated cell type gene expression profiles from post-mortem human brain single-nucleus data. b, Staining of MBP, MYRF, and MOG in iPSC-derived oligodendroglia. Scale bar 10 μm. c, Representative images of cultures of iPSC-derived oligodendroglia stained for PLP1 and MBP. Dots represent the percentage of nuclei positive for each marker across independent wells subjected to the same conditions (n = 5 biological replicates). Bars represent mean across all wells, error bars represent standard deviation. d, Principal component analysis of relative gene expression for post-mortem human brain cells and iPSC-derived counterparts. e, Transcriptional similarity between post-mortem human cell types and iPSC-derived oligodendroglia, p values calculated with Wilcoxon test, two-sided. Distributions represent distances between each post-mortem cell type (x-axis) and iPSC oligodendroglia in scaled gene space (N = 32 per post-mortem cell type, N = 6-8 for iPSC cell types). f–g, Gene set activity scores by GSVA on scaled expression values using genes shown in a or h, p value calculated by Wilcoxon test, two-sided. Boxplots indicate median, 25th and 75th percentiles. h, Relative expression of cholesterol-associated genes across iPSC-derived oligodendroglia, post-mortem human brain oligodendrocytes, and additional brain cell types. i, Gene expression distribution in iPSC-derived oligodendroglia. j, APOE immunoreactivity in APOE3/3 (n = 4 biological replicates) and APOE4/4 (n = 5 biological replicates) iPSC-derived oligodendroglia. Dots in right panel plots represent mean from independent images, p values were calculated with an unpaired, two-tailed student's t-test. k, Distribution of gene nonzero detection rates in human post-mortem oligodendrocytes (snRNAseq). The nonzero APOE detection-rate is circa 4%, corresponding to circa 53rd percentile of genes. l, Distribution of expression levels for APOE-non-expressing (n = 53,095) and APOE-expressing (n = 1,882) post-mortem oligodendrocytes (snRNAseq). Groups were identified by K-means clustering on the non-zero detection rate.
Extended Data Fig. 6 Lipidomics on iPSC-derived oligodendroglia.
a, Volcano plots depicting differentially (adjusted p value < 0.05) detected lipid species in APOE4/4 oligodendroglia, compared to isogenic APOE3/3 controls. Each detected lipid species is organized according to lipid class, with cholesteryl esters having the highest number (15) of differentially expressed species.
Extended Data Fig. 7 Cell stress in APOE4 oligodendrocytes.
a, mRNA expression levels of SOAT1 (ACAT1) and CYP46A1 from bulk sequencing of isogenic iPSC-derived APOE3/3 and APOE4/4 oligodendroglia (n = 3 biological replicates per genotype, adjusted p values shown, computed by linear model). b, Perilipin-1 (PLIN1) immunoreactivity in APOE4/4 iPSC-derived oligodendroglia, and PLIN1 co-localization with Bodipy-cholesterol staining in APOE4/4 iPSC-derived oligodendroglia. c, Representative Bodipy staining for lipid droplets (n = 6 biological replicates). Scale bar represents 10 μm. Lipid droplets were quantified in two different isogenic sets of APOE3/3 and APOE4/4 oligodendroglia, that were generated from different individuals. Data points represent biological replicates and bars show means. p values were calculated using an unpaired, two-tailed student's t-test. d, Percent of Bodipy-cholesterol staining in lysosome. Quantification was performed using ImageJ software with the same threshold setting for all images and conditions. Data points represent the mean of four images from one biological replicate. Bars represent means from four biological replicates. p value was calculated using unpaired, two-tailed student's t-test. e, (left) Box plot of ATF-6 mediated unfolded protein response gene set activity (computed by GSVA) in human post-mortem oligodendrocytes. unadjusted p-value = 0.016, linear model. Boxplots indicate median, 25th and 75th percentiles. (right) Pathway gene members that are differentially expressed (FDR < 0.05, negative binomial mixed model) f, Representative images of immunohistochemistry against ATF-6 protein in APOE3/3 and APOE4/4 iPSC-derived oligodendrocytes, and quantification of number of cells with nuclear ATF-6 immunoreactivity (n = 5 biological replicates). Dots represent technical replicates, and bars represent mean per genotype. p value was calculated using an unpaired, two-tailed student's t-test. g, Fold change for unfolded protein response genes in APOE4/4 vs APOE3/3 oligodendroglia (N = 3 per genotype) from panel e (adjusted p-value < 0.05, negative binomial distribution).
Extended Data Fig. 8 Myelin expression in humans and mice.
a, Myelin-associated gene expression changes in APOE3/3 vs APOE3/4 post-mortem oligodendrocytes for individuals with and without AD pathology (logFC<0 indicates down in APOE3/4; Wilcoxon test by wilcoxauc(), adjusted p value < 0.05). b, Immunohistochemistry for Myelin Basic Protein (MBP) in APOE3/3 and APOE3/4 individuals with AD diagnosis (n = 3 per genotype), and quantification. Mean fluorescence intensity quantified using FIJI ImageJ software. Dots represent mean value calculated from four images from one individual, and bars represent mean value from three separate individuals for each genotype. p value was calculated using an unpaired, two-tailed student's t-test. c, Immunohistochemistry for myelin basic protein (MBP) in hippocampal slices of APOE3/3-TR (n = 7 mice) and APOE4/4-TR (n = 7 mice) at nine months of age, quantified according to mean fluorescence intensity. Quantification was performed using ImageJ. Bars represent mean intensity from all mice for each genotype, and error bars represent standard deviation. p values were calculated using unpaired, two-tailed student's t-test. Scale bar represent 200 μm. d, Western Blot for Myelin Basic Protein (MBP) in APOE3/3-TR (n = 4) and APOE4/4-TR (n = 4) mouse cortex at six months of age. Each lane is a brain lysate prepared from a different mouse. Total area and intensity of bands normalized to GAPDH was quantified via ImageJ using mean intensity for each band. Bars represent means. p value was calculated using an unpaired, two-tailed student's t-test. e, TEM on corpus callosum from APOE3/3-TR (n = 3) and APOE4/4-TR (n = 3) knock-in mice at 2 months of age. G-ratio was quantified using ImageJ software as stated in Main Fig. 4d). Scale bar represents 500 μm. Data points representing g-ratios for axons from each genotype, p value was calculated using an unpaired Wilcoxon test.
Extended Data Fig. 9 Myelin expression in iPSC-derived oligodendrocyte cultures.
a, Schematic of iPSC-derived NGN2-induced neuron (iNeuron) and oligodendrocyte encapsulation in Matrigel. Cartoons generated with BioRender b, Representative image of Neurofilament (red), Myelin Basic Protein (green), and Hoechst (blue) immunoreactivity in iPSC-derived neuron and oligodendrocyte co-culture, after two weeks encapsulated. Scale bar represents 50 μm. c, Representative images of (left) Neurofilament (red), MBP (green), and Hoechst (blue), and (right) Neurofilament (red), O4 (green), and Hoechst (blue) immunoreactivity in iPSC-derived neuron and oligodendrocyte co-culture, after six weeks encapsulated. Scale bar represents 50 μm. d, iPSC-derived oligodendroglia were labelled with GFP to visualize cellular localization after six weeks of co-culture with NGN2-induced neurons. Scale bar represents 50 μm. e, Representative image of Neurofilament (red), MBP (green), and Hoechst (blue), and Neurofilament (red), and Hoechst (blue) immunoreactivity in iPSC-derived oligodendroglia and NGN2-induced neuron after six weeks of co-culture. Scale bar represents 50 μm. f, TEM on myelinated axon from iPSC-derived neuron and oligodendrocyte co-culture, suggesting presence of myelin rings. g, Representative images showing Neurofilament (red), MBP (green) immunoreactivity in APOE3/3 parental and APOE4/4 isogenic co-cultures after three weeks encapsulated. Scale bar represents 10 μm. h, Axonal area per co-culture, comparing APOE3/3 (parental), APOE4/4 (isogenic) and APOE-/- (isogenic) co-cultures (n = 3 biological replicates). Axonal area was calculated by measuring the area immunoreactive to neurofilament, and normalized to APOE3/3. Bars represent means, error bars represent standard deviation, and p values were calculated using one-way ANOVA with Bonferroni correction. i, MBP density between APOE3/3 (parental) and APOE4/4 (isogenic) mono-cultures (n = 6 biological replicates) P value represents unpaired, two-tailed student's t-test. j, Representative images showing Neurofilament (red), and MBP (green) immunoreactivity in APOE3/3 (parental), APOE4/4 (isogenic), APOE4/4 (isogenic) with recombinant human APOE3 protein, and APOE knock-out (isogenic) co-culture conditions (n = 3 biological replicates). The area of neuronal axon (immunoreactive against Neurofilament) positive for MBP was calculated using Imaris, and each experimental condition was compared to the APOE3/3 control condition. P values were calculated using one-way ANOVA with Bonferroni correction.
Extended Data Fig. 10 (2-Hydroxypropyl)-beta-cyclodextrin treatment in APOE4 knock-in mice.
a, Bodipy (neutral lipid) staining in control and (2-Hydroxypropyl)-beta-cyclodextrin (cyclodextrin) treated iPSC-derived APOE4/4 oligodendrocytes (n = 6 biological replicates). The number of lipid droplets was normalized by the total number of cell nuclei for each image. Bars represent the mean number of droplets per cell for each condition, error bars represent standard deviation. P value was calculated using unpaired, two-tailed student's t-test. b, MBP and Bodipy-cholesterol staining in control (n = 5 mice) and cyclodextrin-treated (n = 4 mice) APOE4/4-TR mouse brain. Scale bar represents 50 μm. Quantification of cholesterol-myelin colocalization, and MBP signal, in cyclodextrin-treated APOE4/4-TR mice. MBP-cholesterol colocalization was quantified using Imaris. MBP staining was quantified using Image J. Bars represent mean, error bars represent standard deviation, and p-value was calculated using an unpaired two-tailed student's t-test. c, Representative activity traces for open field assay on APOE4/4-TR control- (n = 14 mice) and cyclodextrin-treated (n = 15 mice) used for Novel Object Recognition task. Distance moved (centimeters) and duration in the centre (seconds) were measured and quantified using Noldus EthoVision software, with the same parameters for each animal. Dots represent individual mice, error bars are standard error of the mean, and p values were calculated using an unpaired, two-tailed student's t-test. d, Representative activity traces for open field assay on APOE4/4-TR control- (n = 10 mice) and cyclodextrin- treated (n = 9 mice) used for the Puzzle Box task. Distance moved (centimeters) and duration in the centre (seconds) were measured and quantified using Noldus EthoVision software, with the same parameters for each animal. Dots represent individual mice, error bars represent standard error of the mean, and p values were calculated using an unpaired, two-tailed student's t-test.
Table of contents for Supplementary Tables 1–16 and Supplementary Videos 1 and 2.
Reporting Summary
Supplementary Table 1
Supplementary Table 10
Supplementary Video 1
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Blanchard, J.W., Akay, L.A., Davila-Velderrain, J. et al. APOE4 impairs myelination via cholesterol dysregulation in oligodendrocytes. Nature 611, 769–779 (2022). https://doi.org/10.1038/s41586-022-05439-w
Issue Date: 24 November 2022
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redpajama_set_name: RedPajamaCommonCrawl


text:
Q: Do inference used tensorflow-gpu1.15 with a trained model, Get "ValueError: underlying buffer has been detached" I haved trained a segmentation model, and then  i would like to use this trained model to do   inference at some test images. the following code is used.
import io
import os
import sys

import numpy as np
import tensorflow as tf
from PIL import Image
from PIL import ImageFile
from utils import load_config

ImageFile.LOAD_TRUNCATED_IMAGES = True
sys.stdout = io.TextIOWrapper(sys.stdout.detach(), encoding='utf-8')
sys.stderr = io.TextIOWrapper(sys.stderr.detach(), encoding='utf-8')

def resize_image(image, new_size, resize_method):
    if resize_method == 'square':
        return image.resize((new_size, new_size), Image.ANTIALIAS)
    elif resize_method == 'inscale':
        orig_width, orig_height = image.size
        orig_max_size = orig_width if (orig_height < orig_width) else orig_height
        # resize
        scale_factor = new_size / orig_max_size
        new_width = int(orig_width * scale_factor)
        new_height = int(orig_height * scale_factor)
        image = image.resize([new_width, new_height], Image.ANTIALIAS)
        return image

def pad(image, new_size):
    new_height, new_width, _ = image.shape

    h_need_pad = new_size - new_height
    h_top = int(h_need_pad / 2)
    h_bottom = h_need_pad - h_top

    w_need_pad = new_size - new_width
    w_left = int(w_need_pad / 2)
    w_right = w_need_pad - w_left

    pad_image = np.pad(image,
                       ((h_top, h_bottom), (w_left, w_right), (0, 0)),
                       mode='constant',
                       constant_values=0)
    pad_image = pad_image.astype(np.float32)
    return pad_image

def preprocess_image(image_path, config):
    origin_ref_image = Image.open(image_path, 'r').convert('RGB')
    w, h = origin_ref_image.size
    image = resize_image(origin_ref_image, config.train_size , config.resize_method)
    image = np.array(image, dtype=np.float32)
    if config.resize_method == 'inscale':
        image = pad(image, config.train_size)
    image = (image - config.mean_rgb) / config.var
    image = np.expand_dims(image, axis=0)
    return image, w, h

def crop_image(image, new_size, orig_width, orig_height):
    orig_max_size = orig_width if (orig_height < orig_width) else orig_height
    scale_factor = new_size / orig_max_size
    new_width = int(orig_width * scale_factor)
    new_height = int(orig_height * scale_factor)

    h_need_pad = new_size - new_height
    h_top = int(h_need_pad / 2)
    h_bottom = h_top + new_height

    w_need_pad = new_size - new_width
    w_left = int(w_need_pad / 2)
    w_right = w_left + new_width

    return image[h_top:h_bottom, w_left:w_right]

def main_fast(image_folder, pb_file_path, config, score_folder, with_softmax):
    graph_def = tf.GraphDef()
    with open(pb_file_path, "rb") as f:
        graph_def.ParseFromString(f.read())
        _ = tf.import_graph_def(graph_def, name="")

    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        #input = sess.graph.get_tensor_by_name("input:0")
        #raw_output = sess.graph.get_tensor_by_name("softmax/Reshape_1:0")
        input = sess.graph.get_tensor_by_name("input_node:0")
        raw_output = sess.graph.get_tensor_by_name("output_node:0")
        if not with_softmax:
            raw_output = tf.contrib.slim.softmax(raw_output)
        background_score, foreground_score = tf.split(raw_output, num_or_size_splits=2, axis=3)
        foreground_score = tf.squeeze(foreground_score)

        images_names = os.listdir(os.path.join(image_folder))
        images_names.sort()
        # result directory
        if not os.path.exists(score_folder):
            os.makedirs(score_folder)

        for i in range(0, len(images_names)):
            if i%100==0:
                print(f'Start process image: {i}')
            cur_frame_path = os.path.join(image_folder, images_names[i])
            cur_image, origin_width, origin_height = preprocess_image(cur_frame_path, config)
            fore_score = sess.run(foreground_score, feed_dict={input: cur_image})

            # process prediction
            fore_score = fore_score * 255
            fore_score = np.array(fore_score, dtype=np.uint8)
            fore_score = crop_image(fore_score, config.train_size, origin_width, origin_height)

            score_image = Image.fromarray(fore_score, mode='L')
            score_image = score_image.resize([origin_width, origin_height], resample=Image.BILINEAR)
            save_path = os.path.join(score_folder, images_names[i][:-4] + '-mask.png')
            score_image.save(save_path)

if __name__ == '__main__':
    config = load_config.load_yaml(yaml_name='config_files/apu_384.yml', quantize=False)

    pb_file_path = 'xxx.pb'
    with_softmax = False

    image_folder= '../images'
    save_dir = 'results'

    main_fast(image_folder, pb_file_path, config, save_dir, with_softmax)

but i meet following error, bellowing is error info.I'm confused, because i have trained model successfully. but error occured when do inference.
2022-04-07 08:22:39.354461: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3699850000 Hz
2022-04-07 08:22:39.354980: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7837e80 initialized for p
latform Host (this does not guarantee that XLA will be used). Devices:
2022-04-07 08:22:39.354992: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version

--- Logging error ---
Traceback (most recent call last):
  File "/root/.pyenv/versions/3.6.8/lib/python3.6/site-packages/tensorflow_core/python/util/module_wrapper.py", line
167, in __getattribute__
    return attr_map[name]
KeyError: 'GraphDef'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/root/.pyenv/versions/3.6.8/lib/python3.6/logging/__init__.py", line 996, in emit
    stream.write(msg)
ValueError: underlying buffer has been detached
Call stack:
  File "test.py", line 132, in <module>
    main_fast(image_folder, pb_file_path, config, save_dir, with_softmax)
  File "test.py", line 83, in main_fast
    graph_def = tf.GraphDef()
  File "/root/.pyenv/versions/3.6.8/lib/python3.6/site-packages/tensorflow_core/python/util/module_wrapper.py", line
187, in __getattribute__
    self._tfmw_add_deprecation_warning(name, attr)):
  File "/root/.pyenv/versions/3.6.8/lib/python3.6/site-packages/tensorflow_core/python/util/module_wrapper.py", line
139, in _tfmw_add_deprecation_warning
    _call_location(), full_name, rename)
  File "/root/.pyenv/versions/3.6.8/lib/python3.6/site-packages/tensorflow_core/python/platform/tf_logging.py", line
    get_logger().warning(msg, *args, **kwargs)
Message: 'From %s: The name %s is deprecated. Please use %s instead.\n'
Arguments: ('test.py:83', 'tf.GraphDef', 'tf.compat.v1.GraphDef')
--- Logging error ---
Traceback (most recent call last):
  File "/root/.pyenv/versions/3.6.8/lib/python3.6/site-packages/tensorflow_core/python/util/module_wrapper.py", line
    return attr_map[name]
KeyError: 'Session'
During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/root/.pyenv/versions/3.6.8/lib/python3.6/logging/__init__.py", line 996, in emit
    stream.write(msg)

ValueError: underlying buffer has been detached
Call stack:
  File "test.py", line 132, in <module>
    main_fast(image_folder, pb_file_path, config, save_dir, with_softmax)
  File "test.py", line 88, in main_fast
    with tf.Session() as sess:
  File "/root/.pyenv/versions/3.6.8/lib/python3.6/site-packages/tensorflow_core/python/util/module_wrapper.py", line
    self._tfmw_add_deprecation_warning(name, attr)):
  File "/root/.pyenv/versions/3.6.8/lib/python3.6/site-packages/tensorflow_core/python/util/module_wrapper.py", line
    _call_location(), full_name, rename)
  File "/root/.pyenv/versions/3.6.8/lib/python3.6/site-packages/tensorflow_core/python/platform/tf_logging.py", line
    get_logger().warning(msg, *args, **kwargs)
Message: 'From %s: The name %s is deprecated. Please use %s instead.\n'
Arguments: ('test.py:88', 'tf.Session', 'tf.compat.v1.Session')
start process image:1

following is all installed pakages in my python environments
root@1aa0f8999969:/portrait_segmentation1# pip list
Package              Version
-------------------- -----------
absl-py              0.9.0
astor                0.8.1
astroid              2.4.1
attrs                19.3.0
backcall             0.1.0
bleach               3.1.5
contextlib2          0.6.0.post1
cycler               0.10.0
Cython               0.29.17
decorator            4.4.2
defusedxml           0.6.0
easydict             1.9
entrypoints          0.3
future               0.18.2
gast                 0.2.2
google-pasta         0.2.0
grpcio               1.29.0
h5py                 2.10.0
importlib-metadata   1.6.0
ipykernel            5.2.1
ipython              7.14.0
ipython-genutils     0.2.0
ipywidgets           7.5.1
isort                4.3.21
jedi                 0.17.0
Jinja2               2.11.2
joblib               0.15.1
jsonschema           3.2.0
jupyter              1.0.0
jupyter-client       6.1.3
jupyter-console      6.1.0
jupyter-core         4.6.3
Keras-Applications   1.0.8
Keras-Preprocessing  1.1.2
kiwisolver           1.2.0
lazy-object-proxy    1.4.3
lxml                 4.2.4
Markdown             3.2.2
MarkupSafe           1.1.1
matplotlib           3.2.1
mccabe               0.6.1
mistune              0.8.4
mock                 4.0.2
nbconvert            5.6.1
nbformat             5.0.6
nest                 1.3.0
nets                 0.0.2
notebook             6.0.3
numpy                1.18.4
opencv-python        3.4.6.27
opt-einsum           3.2.1
packaging            20.3
pandas               1.0.3
pandocfilters        1.4.2
parso                0.7.0
pexpect              4.8.0
pickleshare          0.7.5
Pillow               7.1.2
pip                  20.1.1
prometheus-client    0.7.1
prompt-toolkit       3.0.5
protobuf             3.12.0
ptyprocess           0.6.0
pycocotools-fix      2.0.0.1
Pygments             2.6.1
pylint               2.5.2
pyparsing            2.4.7
PyQt5                5.10.1
pyrsistent           0.16.0
python-dateutil      2.8.1
pytz                 2020.1
PyYAML               6.0
pyzmq                19.0.1
qtconsole            4.7.4
QtPy                 1.9.0
scikit-learn         0.23.1
scipy                1.4.1
Send2Trash           1.5.0
setuptools           46.4.0
sip                  4.19.8
six                  1.14.0
sklearn              0.0
tensorboard          1.15.0
tensorflow-estimator 1.15.1
tensorflow-gpu       1.15.0rc3
termcolor            1.1.0
terminado            0.8.3
testpath             0.4.4
tf-slim              1.1.0
threadpoolctl        2.0.0
toml                 0.10.1
torch                1.5.0
tornado              6.0.4
traitlets            4.3.3
typed-ast            1.4.1
utils                1.0.1
wcwidth              0.1.9
webencodings         0.5.1
Werkzeug             1.0.1
wheel                0.34.2
widgetsnbextension   3.5.1
wrapt                1.12.1
zipp                 3.1.0

I wonder where the problem is, hope anyone can help me.

redpajama_set_name: RedPajamaStackExchange


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There is a two-night minimum stay at this glamping property and a refundable damage deposit of $200 is required.
Pets are welcome to stay within this glamping accommodation however, there is an additional fee of $25.