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0.427057 | 0b02b93845794bcc9ef08e23cf5de4ab | Schematic diagram of the various PCM arrays in the mask in the presence of SRAFs. The line number of the main feature varies from 57 in the pitch line to 1 in the isolate line. The main features are represented by the violet lines and the SRAFs are the yellow lines. d1 and d2 is the distances of the lines at the edge of main feature to the nearest SRAFs and the next nearest SRAFs, respectively. | PMC10059855 | nanomaterials-13-01050-g001.jpg |
0.412227 | d205e2277fe2475a963845d72e814ff8 | The experimental results of focused-energy matrix analysis after the developing process. Each shot on the wafer has a unique energy dose and focus position (E/F) which increases from left to right by 2 mJ/cm2 in energy dose and from up to down by 20 nm in focus position. The dose (E) is in the range of 20 mJ/cm2 to 40 mJ/cm2 and the focus (F) is in the range of −80 nm to 80 nm. The SEM images are acquired on the center of pitch line arrays of these shots. The E/F used in the shots are labeled on parts of the images. The CDs of the center lines in each shot are labeled under the corresponding images in nm. For the patterns with pronounced defects, the reliable CD measurement is not available, therefore N.A. is used as the label. The selected E/F parameters for the research in this work are marked with blue squares. | PMC10059855 | nanomaterials-13-01050-g002.jpg |
0.416337 | 2b68558b43b64d4bbea18e878bfe1386 | The lines containing only photoresist represent the pattern of the photomask. The lithography is performed without adding SRAF in the mask. The array types are labeled on the top of figure and the selected E/F parameters of 26/0, 34/0, and 38/0 are label on the pitch line image of the top, middle, and bottom panel, respectively. The CDs of lines at the center and edge of array are labeled under the images. The CDs of collapsed lines are labeled as N.A. Single CD value is labeled under each image of the twin line and isolate line arrays. | PMC10059855 | nanomaterials-13-01050-g003.jpg |
0.456677 | 35bb2d1fd51048b7a41c0039d2725b78 | The CD of the line at the edges of various of patterns as a function of d1 under an energy dose of (a) 26 mJ/cm2, (b) 34 mJ/cm2, and (c) 38 mJ/cm2. The focus position is kept at 0 nm. The SRAF is classified by ∆d = d1 − d2, which is 0 or 10 nm. | PMC10059855 | nanomaterials-13-01050-g004.jpg |
0.471953 | 9788fb68c3964afca04e4577c153526d | The SEM images focusing on the lines at the edge of various arrays after the developing process. The lithography is performed with additional SRAFs on the edge of the main feature in the mask. The array types are labeled on the top of figure and the selected E/F parameters of 26/0, 34/0, and 38/0 are labeled on the pitch line image of the top, middle, and bottom panels, respectively. The CDs of lines at the edge of the array are provided under the images. | PMC10059855 | nanomaterials-13-01050-g005.jpg |
0.51402 | 3819bdd3eadb4279a651eb35ea44e8a8 | The SEM images focusing on the TiN/GSTC stack lines at the edge of various of arrays after the SRAF-lithography and subsequent plasma etching. The array types are labeled at the top of figure and the selected E/F parameters of 26/0, 34/0, and 38/0 are labeled on the pitch line image of the top, middle, and bottom panels, respectively. The white stripes at the edge are due to the over etch of SiO2 caused by the micro-loading effect. | PMC10059855 | nanomaterials-13-01050-g006.jpg |
0.469809 | 3c0a76247c7346f1990d53a8ef6977d7 | (a) The top view SEM image of the pitch line array composed of TiN/GSTC film stack after the SRAF-lithography and subsequent plasma etching. The E/F parameter is 34/0. (b) The cross-sectional TEM image of the pitch line array. The observed region is marked by the dashed line in green in (a). The magnified cross-sectional TEM image of the (c) edge and (d) center region of the pitch line array. The C and Pt films are deposited on the top of the stacked structure during the preparation of the TEM samples using the focused ion beam system. | PMC10059855 | nanomaterials-13-01050-g007.jpg |
0.443223 | b3a1c60a46fb4820a2b63ff54f7079ac | The STEM and corresponding elemental mapping analysis of the cross-sectional TiN/GSTC film stack after the SRAF-lithography and subsequent plasma etching: (a) STEM image and EDS map for (b) Ti, (c) N, (d) Ge, (e) Sb, and (f) Te. The scale bar is 200 nm. The C and Pt films are deposited on the top of the stacked structure during the preparation of the TEM samples using the focused ion beam system. | PMC10059855 | nanomaterials-13-01050-g008.jpg |
0.409496 | f386d155fd3f4c0e93537cfe5d12764b | Flow of patients through the study. | PMC10059856 | jcm-12-02176-g001.jpg |
0.435346 | 53a88b919faf402fabcd7f6813cea22d | Surgery time in minutes. | PMC10059856 | jcm-12-02176-g002.jpg |
0.421565 | 3139da5ac570489f9951eba1092485ef | Hospitalization days. | PMC10059856 | jcm-12-02176-g003.jpg |
0.403365 | 87248c0ecc0d44c59ab599767290d6fc | Intraoperative blood loss in milliliters. | PMC10059856 | jcm-12-02176-g004.jpg |
0.381748 | ef0c73379c9747beb61b37ebe0ed887b | Postoperative complication after Clavien–Dindo classification. | PMC10059856 | jcm-12-02176-g005.jpg |
0.42647 | 0f5109dd21b742e19b2c52e4bb6b9c72 | Overall survival in patients with KMT2Ar or mutated NPM1.OS, overall survival. | PMC10060155 | 41586_2023_5812_Fig10_ESM.jpg |
0.509189 | 73b90cb4494d42368fbd533c88ce8e66 | Kaplan-Meier curve of duration of response in patients with complete remission or complete remission with partial haematologic recovery with censoring at time of the allogeneic stem cell transplant.DOR, duration of response; NR, not reached. | PMC10060155 | 41586_2023_5812_Fig11_ESM.jpg |
0.411565 | 5d785078110e4715a67a9a6afa3ff72b | Mutation analysis and response.This cohort included 37 patients evaluable for this analysis. CR, complete remission; CRh, complete remission with partial haematologic recovery; CRp, complete remission with incomplete platelet recovery; MLFS, morphologic leukaemia-free state; NR, not reached; PD, progressive dVAF, variant allele frequency. | PMC10060155 | 41586_2023_5812_Fig12_ESM.jpg |
0.513689 | d7d27f4151d54b72bdab875c5b0bac1a | Transcriptional changes following treatment with the menin inhibitor revumenib in patients with relapsed or refractory acute leukaemia with KMT2Ar or mutated NPM1.RNA-seq before and after treatment with revumenib, showing downregulation of critical leukaemogenic target genes MEIS1, HOXA9 and PBX3 and increase in expression of genes associated with differentiation (CD11b, CD14), with transcriptional suppression of FLT3, a putative transcriptional target of MEIS1. The change in bone marrow blast percentage following treatment is shown. Box plots represent median gene expression or median bone marrow blast percentage, and the 95% CI along with percentage change in gene expression following treatment. Responders are shown in red, nonresponders in black. Results were obtained using a paired t-test with a two-sided P value. Adjustments were not made for multiple comparisons. This analysis included a cohort of 21 evaluable patients. Revumenib was administered in continious 28-day cycles. C2D1, day 1 of treatment cycle 2. | PMC10060155 | 41586_2023_5812_Fig1_HTML.jpg |
0.426658 | 734a085acb594ad6a507e6b31534e903 | Characterization of remissions with the menin inhibitor revumenib in susceptible relapsed or refractory acute leukaemia subtypes.a, Time to response, duration of treatment (censored at time of HSCT) and patient status by the cutoff date. *Other reasons for treatment discontinuation included no response, relapse, death and donor lymphocyte infusion. b, Kaplan–Meier curve of duration of response (DOR) in patients with CR or CR/CRh without censoring at the time of an allogeneic stem cell transplant performed in 12 of 18 evaluable patients. | PMC10060155 | 41586_2023_5812_Fig2_HTML.jpg |
0.427212 | bcd37962d1fb4abc83695553028b20ec | Revumenib suppresses both KMT2Ar and NPM1-mutant AML in preclinical models of leukaemia.NSG mice were engrafted with the KMT2Ar cell line MOLM-13 (a; n = 10) or patient derived xenografts (PDX) harbouring either a KMT2Ar (d; n = 5) or NPM1 mutation (g; n = 5). Mice were treated for 28 days with revumenib, which was formulated in chow, across a dose range of 0.025% to 0.2% (a) or at 0.1% fixed dose (d, g). Leukaemic burden (CD45+) was assessed at end of treatment (b; 0.025%: n = 3; 0.05-0.2%: n = 5, data represent mean ± SEM) or throughout the study (d, g; data represent mean ± SEM). For MOLM-13 engrafted mice, revumenib showed clear dose-dependent exposure (c; n = 3 with 3 individual measurements per timepoint and dose, data represent mean ± SD), which translated into dose-responsive effect on survival benefit (a) and leukaemic burden at end of treatment (b). Similarly, revumenib treatment of the PDX models led to significant suppression of leukaemic burden and significant survival benefit in each (d, g). P-values were determined by log-rank (Mantel–Cox) tests. Adjustments were not made for multiple comparisons. Revumenib treatment also led to broad changes in the transcriptional program (f, i; n = 3 per treatment group; GEO accession number for RNAseq data, GSE216730) with GSEA results consistent with previously reported signatures (e, h; n = 3 per treatment group). GSEA, gene set enrichment analysis; NES, normalized enrichment score; NSG, NOD scid gamma; PDX, patient-derived xenograft; SD, standard deviation; SEM, standard error of the mean. | PMC10060155 | 41586_2023_5812_Fig3_ESM.jpg |
0.437312 | d8f23099e30b4869a8451a55f8d72296 | CONSORT diagram and patient disposition on trial.The CONSORT diagram shows the number of patients from Arms A and B who discontinued treatment and lists the reasons for treatment and study discontinuation. | PMC10060155 | 41586_2023_5812_Fig4_ESM.jpg |
0.35108 | ccb037db9a1a4b3aa7e007fa5b7b5e7f | Dose escalation schema.The revumenib dose was adjusted based on the body surface area (BSA) for patients weighing less than 40 kg as indicated for each corresponding dose level shown in parentheses. CYP3A4i, cytochrome P450 3A4 inhibitor; q12h, every 12 h; R/R, relapsed or refractory. | PMC10060155 | 41586_2023_5812_Fig5_ESM.jpg |
0.519534 | 5d642a8ae72e4da88c1e9f6129d0f5b7 | Morphologic evidence of myeloid differentiation.Photomicrographs of bone marrow biopsies demonstrating morphologic evidence of myeloid differentiation in a patient who achieved complete remission; magnification is 40x. | PMC10060155 | 41586_2023_5812_Fig6_ESM.jpg |
0.456371 | 51061a43171d443abe55c09552c0c8f5 | Changes in peripheral blood during differentiation syndrome are associated with the menin inhibitor revumenib.Example from a 71-year-old patient with KMT2Ar AML relapsed after an allogeneic stem cell transplant, who received revumenib at 339 mg PO q12h (Arm A), and achieved CRh, MRD negative remission. Differentiation syndrome manifested as chest pain with a small pericardial effusion, and a possible prodrome of neck pain likely related to expansion of cervical nodes, all resolved promptly with initiation of steroids followed by tapering doses. Hydroxyurea was used to control leukocytosis. AML, acute myeloid leukaemia; ANC, absolute neutrophil count; CRh, complete remission with partial haematologic recovery; MRD, minimal or measurable residual disease; PB, peripheral blood; PO, by mouth; q12h, every 12 h; WBC, white blood cell. | PMC10060155 | 41586_2023_5812_Fig7_ESM.jpg |
0.461256 | 0913b69de5674b1a89faee8c05b06a53 | Dose proportional exposure was achieved across both arms.The half-life of revumenib in Arm A (without strong CYP3A4 inhibitors) was approximately 3 h at the cycle 1 day 8 assessment of the 276-mg q12h dose level and was approximately 8 h at the same assessment in Arm B (with a strong CYP3A4 inhibitor) at the 163-mg q12h dose level. Data represent mean ± SD. Data cutoff date for pharmacokinetic analysis was July 11, 2022. CYP3A4, cytochrome P450 3A4; q12h, every 12 h; SD, standard deviation. | PMC10060155 | 41586_2023_5812_Fig8_ESM.jpg |
0.421412 | 71d19fe753b943bfb5aa63edd96d6829 | Response in extra-medullary disease.A. PET scan at baseline and after 2 cycles of treatment. B. Computed tomographic scans of target lesions and spleen from a 19-year-old with relapsed KMT2Ar AML, previously treated with three prior lines of therapy including 2 allogeneic stem cell transplants and local radiation to the spleen and abdominal nodes, received revumenib at 276 mg PO q12h (Arm A), achieved CRh, MRD negative remission with resolution of extramedullary disease in abdominal nodes and spleen. AML, acute myeloid leukaemia; AP, anterior-posterior; CRh, complete remission with partial haematologic recovery; D, day; FDG, fluorodeoxyglucose; MRD, minimal or measurable residual disease; PET, positron emission tomography; PO, by mouth; q12h, every 12 h; SUV, standardized uptake value. | PMC10060155 | 41586_2023_5812_Fig9_ESM.jpg |
0.538727 | dc0cc2ae869f45469d15e0ad7445820c | Examples of intracellular sensing using ATRI. (A) General
structure
of ATRI-based sensors. (B) Bisarylbutadiyne sensor for hydrogen sulfide 1.23 (C) Reversible sensor for
the quantification of pH 3.26 (D) Detection of intracellular esterase activity using sensor 4. | PMC10061367 | ac2c05708_0002.jpg |
0.43186 | 6eea0ea9785f40d0aaa3ed3d2b6ba2ce | Development of a bisarylbutadiyne esterase
sensor. (A) Deprotonation
of the difluorophenol scaffold 5 at physiological pH.
(B) Esterase-sensitive compounds 4 and 6 synthesized as part of this work. (C) Overlaid Raman alkyne peaks
of difluorophenol 5 (blue), AM ester 4 (orange),
and acetate 6 (green) [100 μM, PBS/DMSO (pH 7.4,
8:2 v/v), 532 nm, 1 × 20 s exposure, 50× lens. Spectra were
acquired after 1 h of incubation at 37 °C]. Peak centers were
determined using a non-linear Gauss fitting function (Orgin2021).
(D) LoD study of esters 4 and 6 using PLE
(100 μM, PBS/DMSO (8:2 v/v), 532 nm, 1 × 20 s exposure,
50× lens. Spectra were acquired after 1 h of incubation at 37
°C). | PMC10061367 | ac2c05708_0003.jpg |
0.474451 | 7e80d838f1b5491ea23b0f86fb81d92f | Assessment of 4 as an intracellular esterase sensor.
(A) Overlaid alkyne peaks of the average spectra of difluorophenol 5 in live HepG2 cells (blue), AM ester 4 in live
HepG2 cells (red), and 4 in fixed HepG2 cells (orange).
[532 nm, 1 × 0.5 s exposure, 60× lens, 1 μm step size.
Maps were acquired after treatment with 5 or 4 (10 μM) in media for 30 min. To fix, cells were pre-treated
with PFA (4% v/v) and Triton X-100 (0.05% v/v) in PBS for 2 h prior
to addition of 5 or 4]. (B) Ratio of peak
intensities at 2212 and 2226 cm–1 taken from the
average spectra of maps of difluorophenol 5 in live HepG2
cells and AM ester 4 in live HepG2 cells or fixed HepG2
cells. [532 nm, 1 × 0.5 s exposure, 60× lens, 5 μm
step size. Maps were acquired after treatment with 5 or 4 (10 μM) in media for 30 min. To fix, cells were pre-treated
with PFA (4% v/v) and Triton X-100 (0.05% v/v) in PBS for 2 h prior
to addition of 5 or 4]. ****T test p ≤ 1 × 10–4. (C) Pseudo-Raman spectra generated from SRS spectral sweeps (2248–2185
cm–1, 14 images) of 4 in live HepG2
cells and in fixed HepG2 cells. All images were acquired at 512 ×
512 pixels and a 9–48 μs pixel dwell time. Images were
acquired after treatment with 4 (10 μM) in media
for 30 min. To fix, cells were pre-treated with PFA (4% v/v) and Triton
X-100 (0.05% v/v) for 2 h prior to addition of 4. (D)
Tandem SRS–fluorescence imaging of live HepG2 cells treated
with a solution of 4 (10 μM) and appropriate working
concentrations of organelle stains (MitoTracker red 250 nM; LysoTracker
green 62.5 nM; ER-Tracker green 1 μM) in media. Fluorescence
images were acquired initially (MitoTracker red λex = 633 nm, λem = 640–750 nm; LysoTracker
green λex = 488 nm, λem = 495–600
nm; ER-Tracker green λex = 488 nm, λem = 495–600 nm) before SRS images at 2923 cm–1 (CH3, protein) and 2218 cm–1 (alkyne).
All images were acquired at 512 × 512 pixels and a 9–48
μs pixel dwell time. False colors and scale bars representing
10 μm were applied in ImageJ. Merged images of 4 and the organelle stains were generated in ImageJ and the Pearson’s
R values were calculated using the Coloc2 tool. | PMC10061367 | ac2c05708_0004.jpg |
0.386847 | 6b330361042a43a7b5b33dd8760c8820 | Ratiometric and phasor
analysis of 4 as an intracellular
esterase sensor. (A) Ratiometric study of 4 in live and
fixed HepG2 cells treated with cell viability stains ethidium homodimer
(EthD-1) and calcein AM [to fix, cells were pre-treated with PFA (4%
v/v) and Triton X-100 (0.05% v/v) in PBS for 2 h prior to addition
of 4 and cell viability stains]. Images were acquired
after treatment with 4 (10 μM), EthD-1 (4 μM),
and calcein AM (2 μM) in media for 30 min. Fluorescence images
were acquired initially (EthD-1 λex = 514 nm, λem = 540–650 nm; calcein AM λex = 488
nm, λem = 493–526 nm) before SRS images at
2923 cm–1 (CH3, protein) and SRS spectral
sweeps (2253–2181 cm–1, 18 images). Images
at 2232 and 2219 cm–1 were taken from the corresponding
images of the SRS spectral sweeps. All images were acquired at 512
× 512 pixels, 9–48 μs pixel dwell time. False colors
and scale bars representing 10 μm were applied in ImageJ. Ratio
bars show the Fire LUT scaled between values of 0 and 2. (B) Ratio
of the intensities at 2219 and 2232 cm–1 in live
and fixed HepG2 cells. Pseudo-Raman spectra were generated from >3
cells in each spectral sweep (2253–2181 cm–1, 18 images), and the intensities at 2219 and 2232 cm–1 were extracted. ****T test p ≤
1 × 10–4. (C) Spectral phasor analysis of the
SRS spectral sweeps (2253–2181 cm–1, 18 images)
of live and fixed HepG2 cells treated with 4 as seen
in (A). SRS spectral sweeps were background-subtracted on ImageJ,
and phasor plots were generated using an ImageJ plugin. The corresponding
images of live and fixed cells were then generated from appropriate
ROIs on the spectral phasor plot. (D) Overlaid pseudo-Raman spectra
of the live and fixed HepG2 cells taken from the spectral phasor output
maps. | PMC10061367 | ac2c05708_0005.jpg |
0.432916 | 8f60634c338447beaa76bd68401864af | Localized UV irradiation experiment and subsequent phasor analysis.
(A) Study of 4 in live and UV-irradiated cells. Following
UV irradiation, images were acquired after treatment with 4 (10 μM) in media for 30 min. Images at 2232 and 2219 cm–1 were taken from the corresponding images of SRS spectral
sweeps (2253–2181 cm–1, 18 images). All images
were acquired at 512 × 512 pixels, 9–48 μs pixel
dwell time. False colors and scale bars representing 10 μm were
applied in ImageJ. (B) Ratio of the intensities at 2219 and 2232 cm–1 in live and UV-irradiated HepG2 cells. Pseudo-Raman
spectra were generated from >3 cells in each of the live and UV-irradiated
areas of the spectral sweep (2253–2181 cm–1, 18 images), and the intensities at 2219 and 2232 cm–1 were extracted. ****T test p ≤
1 × 10–4. (C) Spectral phasor analysis of the
SRS spectral sweep (2253–2181 cm–1, 18 images)
of live and UV-irradiated HepG2 cells as seen in (A). The SRS spectral
sweep was background-subtracted on ImageJ, and a phasor plot was generated
using an ImageJ plugin. The corresponding images of live and UV-irradiated
cells were then generated from appropriate ROIs on the spectral phasor
plot (using Figure 4C as the reference). | PMC10061367 | ac2c05708_0006.jpg |
0.401679 | ee0b1ebce3f64a3fb45191c29ffd01bf | Chromosomal microarray analysis (CMA) for Patient 1. A Whole genome view of CMA data, showing weighted log2 ratio (top panel) and allele difference (bottom panel). These data show a copy number loss on chromosome 4, with a corresponding decrease in allele difference, and a copy number gain on chromosome 10, with a corresponding increase in allele difference. B View of CMA data for the chromosome 4q region, highlighting log2 ration -0.5, decrease in allele difference (two tracks, normal pattern is 3 tracks) and smooth signal of 1 all representing a 19.4 Mb monoallelic terminal loss (red bar). C View of CMA data for the chromosome 10p region, highlighting log2 ration 0.5, increase in allele difference (four tracks tracks) and smooth signal of 3 all representing a 5.2 Mb terminal gain (blue bar). Screenshots were taken from the Chromosome Analysis Suite (ChAS) software from Affymetrix, Inc | PMC10061865 | 12920_2023_1491_Fig1_HTML.jpg |
0.441031 | 551304612ea448a28efff46d148d9a13 | Chromosome analysis and fluorescence in situ hybridization (FISH) for Patient 1 and 2. A G-band chromosome analysis for Patient 1 showed one normal copy of chromosome 4, a derivative chromosome 4, and two normal copies of chromosome 10. Standard ideograms are shown next to the normal chromosomes, as well as a hypothetical ideogram for the derivative chromosome 4. The red and blue bars highlight the regions that were found by CMA to be lost/gained. B Metaphase FISH for Patient 1, focusing on the derivative chromosome 4. This chromosome showed a 4p-specific signal but was missing a 4q-specific signal (left). In a separate hybridization, this chromosome was found to hybridize with a 10p-specific FISH probe (right). C Chromosome analysis for Patient 2 showed two normal copies of chromosome 4, one normal copy of chromosome 10, and a derivative chromosome 10. Standard ideograms are shown next to the normal chromosomes, as well as a hypothetical ideogram for the derivative chromosome 10. D Metaphase FISH for Patient 2, focusing on the derivative chromosome 10. This chromosome showed a 10q-specific signal but was missing a 10p-specific signal (left). In a separate hybridization, this chromosome was also found to hybridize with a 4q-specific FISH probe (right). E Chromosome 4 and 10 karyotype of the siblings’ mother | PMC10061865 | 12920_2023_1491_Fig2_HTML.jpg |
0.478813 | bc2bab7e82904528a7fd36a6c530cc38 | Diagram depicting meiosis of a balanced translocation carrier resulting in reciprocal unbalanced translocations in the offspring. The balanced translocation chromosomes are paired, forming quadrivalent, then segregated during the meiosis I, into two different unbalanced translocations arrangements | PMC10061865 | 12920_2023_1491_Fig3_HTML.jpg |
0.43487 | e15aff10b002463592276cb23a9f6f59 | Population-level model. (a) Cross-immunity, σij, for variants i and j, with lighter colours corresponding to greater cross-immunity. (b) Illustration of the normalized transmission rate for each variant, showing a fitness valley. (c) Model schematic. | PMC10061940 | eoac037_fig1.jpg |
0.428372 | 2b71ca5bc15c42789686fdde164edabf | Antigenic evolution with or without immunocompromised individuals and epistasis. (a) No epistasis in an entirely immunocompetent population (p = 0, ξ = 0). (b) Strong epistasis in an entirely immunocompetent population (p = 0, ξ = 0.8). (c) No epistasis and a small immunocompromised subpopulation (p = 0.05, ξ = 0). (d) Strong epistasis and a small immunocompromised subpopulation (p = 0.05, ξ = 0.8). (e) No epistasis and a small immunocompromised subpopulation with faster within-host evolution in immunocompromised individuals (p = 0.05, ξ = 0.8, μC = 5 μH). (f) Strong epistasis and a small immunocompromised subpopulation with faster within-host evolution in immunocompromised individuals (p = 0.05, ξ = 0.8, μC = 5 μH). All other parameter values given in Supplementary Table 3. Dynamics are shown for a single simulation. | PMC10061940 | eoac037_fig2.jpg |
0.491964 | cfee83f4e7e143a2a2da45ade6437e61 | Sensitivity analysis. Top row: maximum distance between observed variants (darker shading indicates larger jumps in antigenic space); bottom row: total number of variants observed. (a) Varying the strength of cross-immunity (η) and epistasis (ξ) when 5% of the population is immunocompromised (p = 0.05). (b) Varying the percentage of the population that is immunocompromised and the relative recovery periods (with η = 5 and ξ = 0.8). All other parameters as in Supplementary Table 3. All datapoints are averaged over 10 simulations. | PMC10061940 | eoac037_fig3.jpg |
0.457729 | 6344823514164b6dba720dabbd0cf4c4 | Phylogenetic tree for SARS-CoV-2 variants shortly after the emergence of Omicron. Three variants of concern (Alpha, Delta and Omicron) are highlighted to illustrate that there had been high mutation supply for the Delta variant. Data downloaded from Nextstrain (nextstrain.org) on 08/02/2022 [18, 45] and provided by the Global Initiative for Sharing All Influenza Data (GISAID, gisaid.org) [46–48]. Data plotted using the ggtree software package in R [49–51]. | PMC10061940 | eoac037_fig4.jpg |
0.389039 | 2d86f0365f724f59b6394783c7732b86 | A well-defined slightly heterogeneous enhancing solid mass containing scattered calcifications of approximate dimensions 70x55x40 mm in the LUQ, which was in front of the left kidney and inferior to the spleen and pancreas without a definite claw sign with any adjacent solid organs was found. The arrow shows the tumor feeding vessels from segment III of the left liver lobe passing into the upper part of the above mass without peripheral tissue. | PMC10061980 | 12887_2023_3954_Figa_HTML.jpg |
0.495537 | dfe4cee0a578429d906b2917e55f3832 | Epithelial type hepatoblastoma with post-chemotherapy changes (calcification, fibrosis and hemosiderin laden macrophages). | PMC10061980 | 12887_2023_3954_Figb_HTML.jpg |
0.425547 | a5ed465e6bc6409680bafa2bb765c526 | CT imaging after preoperative chemotherapy showing a significant reduction in the size of the tumor. | PMC10061980 | 12887_2023_3954_Figc_HTML.jpg |
0.47028 | 1e23668d617a4158bf5dc439caf06a70 | Flow chart of ML procedure. | PMC10063679 | 41598_2023_32570_Fig1_HTML.jpg |
0.4168 | 9ea3ce144f4147dfb7b6a4aec77b264b | SHAP values for the presence of weight gain at discharge classification. | PMC10063679 | 41598_2023_32570_Fig2_HTML.jpg |
0.412625 | 4304f99ed6d347fbb703dd31ba3fd6be | SHAP values for discharge weight prediction. | PMC10063679 | 41598_2023_32570_Fig3_HTML.jpg |
0.376669 | 6dcd528798174adfa4b28a6119f83305 | Receiver operating characteristic (ROC) curve and Precision-recall (PR) curve for weight gain at discharge classification based on NNST and NNST-plus model. | PMC10063679 | 41598_2023_32570_Fig4_HTML.jpg |
0.463224 | 9c6b2bbf8b044fd69d3c7142c5dc84fc | Common symptoms, transmission, precautions, and treatment suggested for novel SARS-CoV-2 variants | PMC10063927 | 13577_2023_903_Fig1_HTML.jpg |
0.445429 | 2629d8e0feab491981b905de4af0b9d0 | Mechanisms by which the mutations in newer SARS-CoV-2 variants favor them | PMC10063927 | 13577_2023_903_Fig2_HTML.jpg |
0.405945 | 618a0c6be50941d6b2982f54da9678fd | Effect of initial pH on lignin degradation in biomass by microbial consortia | PMC10064694 | 13068_2023_2306_Fig1_HTML.jpg |
0.402315 | e3da4366cbe94f688666a1bd24ef5aa2 | Lignin degradation in biomass under different treatment conditions by microbial consortia (Degradation temperature: 30 °C; Rotation speed: 180 r/min; pH: 4) | PMC10064694 | 13068_2023_2306_Fig2_HTML.jpg |
0.440904 | 02163277350e4adba5182c7a56e6b4a8 | Saccharification efficiency of biomass under different treatment conditions | PMC10064694 | 13068_2023_2306_Fig3_HTML.jpg |
0.399659 | 1ddb6a71b1774816a72636d67af2137c | SEM morphology of biomass under different treatment conditions. (A: Eucalyptus root; B: Eucalyptus root after steam explosion treatment; C: Eucalyptus root after steam explosion- J-6 combined treatment; D: Eucalyptus root after steam explosion- J-1 combined treatment; E: bagasse; F: bagasse after steam explosion treatment; G: bagasse after steam explosion-J-6 combined treatment; H: bagasse after steam explosion-J-1 combined treatment; I: corn straw; J: corn straw after steam explosion treatment; K: corn straw after steam explosion- J-6 combined treatment; L: corn straw after steam explosion- J-1 combined treatment) | PMC10064694 | 13068_2023_2306_Fig4_HTML.jpg |
0.43127 | 0c734ad9b2974f839a824a7b4dd64f73 | Bacterial community composition of different biomass degradation systems | PMC10064694 | 13068_2023_2306_Fig5_HTML.jpg |
0.432208 | 8e53b31d657249cab6058af8e03d1be9 | Bacterial community diversity of different biomass degradation systems | PMC10064694 | 13068_2023_2306_Fig6_HTML.jpg |
0.462846 | 90ac55ff33bc45fdba79e965d1ff7258 | Slow-wave sleep in adult SST cre × Ai32 mice exposed to ethanol at P7 was impaired compared to saline controls. (A) Representative cortical LFP recording with raw LFP shown in bottom trace and time-frequency pseudocolor spectrograph shown in center. Top trace shows r.m.s. delta amplitude and 1 standard deviation above mean delta amplitude (red line) extracted from a 24 h recording. Periods of delta amplitude above the red line were classified as slow-wave sleep (horizontal blue markers) and coincided with behavioral inactivity as previously described (Wilson et al., 2016; Lewin et al., 2018). Slow-wave changes in ethanol mice compared to saline (n = 8/group) included (B) shorter SWS bout durations and (C) increased sleep-wake transitions, which together constitute sleep fragmentation. (D) In addition, delta amplitude oscillations were modified to display significantly fewer high amplitude waves. Horizontal lines highlight regions of significant post hoc comparisons between treatment groups (p < 0.05). (E) Stereological cell counts were used to evaluate the number of somatostatin cells in neocortex, in sections double labeled with ant-GFP and anti-somatostatin antibodies (n = 5 mice/group). In 85% of the SST-Cre cells, SST-immunolabeling (arrowheads) was found, but it was not confirmed in the remaining cells (arrow). We did not find SST-immunolabeled cells that lacked SST-Cre. Scale bar = 10 um. (F,G) P7 Ethanol exposure significantly reduced SST cell count as assessed in both GFP cells and SST immunolabeled cells. (H) Separation of stereological cell counts by anterior-posterior location indicated that the ethanol-induced reduction of SST cells is similar throughout the neocortex. Measured neuron densities were separated by their rostral-caudal section number. In brains that had more than 9 sections, the final small end sections were combined. This line graph provides a qualitative overview of local differences, as the stereological strategy was designed to sample the whole cortex, and data from each section includes an average of only 24 sampling sites from each brain. (I) The location of the neocortex (white lines) that was sampled for stereological estimates of SST neuron number and density is shown on a reconstruction of one brain made from block-face images taken during sectioning. Every 12th 50 uM thick coronal section through the neocortex was sampled. Asterisks signify significant difference between ethanol and saline conditions in all panels. | PMC10067632 | fnins-17-1127711-g001.jpg |
0.455263 | f8947661c3a44485937da19c07eca7ac | (A) Adult SSTcreXAi32 mice exposed to EtOH (n = 9) or saline (n = 5) at P7, received optical stimulation of SST neurons in prefrontal cortex. (B) Histological section showing typical prefrontal cortex optotrode location (including surface damage during histology). Associated atlas image below. White and Blue markers denote electrode (gray) attached to optical fiber (blue). PrL, prelimbic cortex; FrA, frontal association cortex, OFC, orbitofrontal cortex. Blue shows DAPI staining and green shows SST-GFP expression. (C) Histological and atlas section showing visual cortex electrode location (including surface damage during histology). RS, retrosplenial cortex, V1, primary visual cortex, V2, secondary visual cortex. (D) Prefrontal cortex optical stimulation evoked a robust, positive wave lasting 200–400 ms, which corresponds to a 2–5 Hz oscillation (delta frequency band) in P7 saline-treated adult mice and which traveled the anterior-posterior extent of the neocortex, producing a similar, though smaller wave in the visual cortex. The same stimulation in P7 ethanol-treated adult mice evoked an early field potential without the later evoked slow-wave. No evoked slow-wave was observed in the visual cortex of P7 ethanol-treated mice. Shown are means (solid line, n > 5 mice) and SEM. Blue mark indicates 473 nm, 50 ms flash in prefrontal cortex. Repeated measures ANOVA detects a significant difference between evoked waveforms in saline and ethanol treated mice at time points marked by green horizontal line. In PFC, the difference between saline and ethanol responses was primarily >200 ms post flash. | PMC10067632 | fnins-17-1127711-g002.jpg |
0.469008 | 5519e3d7793a4115908a84865e853b78 | PFC unit recordings from adult SSTcreXAi32 mice exposed to P7 saline or ethanol (n = 4 saline mice, n = 47 units; 4 EtOH mice, n = 46 units). (A–D) Rasterplots and peri-stimulus time histogram examples of diverse unit responses to 50 ms, 473 nm light (vertical blue mark) in PFC of saline-treated mice. (A) A putative SST neuron in an ethanol exposed mouse excited during the light followed by suppression (relative to pre-stimulus activity). Horizontal line represents mean spontaneous activity with red shading representing ± 2 S.D. (B) A putative SST neuron in a saline-exposed mouse excited during the light and showing an excitatory rebound 300–400 ms later. (C) An ethanol-exposed non-SST neuron (i.e., no excitation to light) showing a delayed excitatory response 400–500 ms post flash. (D) A saline-exposed non-SST neuron displaying suppression 100–200 ms post-flash and an excitatory rebound at >400 ms. (E) Proportion of all units showing early (<200 ms) suppression and late (>200 ms) excitation in adults exposed to P7 saline or ethanol. There was a significant decrease in the probability of showing a late excitation response in the ethanol-treated mice (asterisk, p < 0.05). (F) Proportion of optogenetically identified putative SST neurons showing early (<200 ms) suppression and late (>200 ms) excitation in adults exposed to P7 saline or ethanol. There was a significant decrease in the probability of showing a late excitation response in the ethanol-treated mice (asterisk, p < 0.05) compared to saline controls. | PMC10067632 | fnins-17-1127711-g003.jpg |
0.507863 | 97c7f292f88940f4aff5047567f31d23 | Using adult SSTcreXAi32 mice exposed to EtOH (n = 6) or saline at P7 (n = 6) (A), closed loop optical stimulation of cortical SST GABAergic neurons in frontal cortex (B) with 50 ms flashes triggered on spontaneously occurring slow-waves, induced sustained slow wave activity in saline but not EtOH mice. Blue marks signify timing of light flashes. (C) A 50 ms delay in light activation reinforced slow-wave activity compared to no light, while a 0 ms delay was much less effective. Gray and blue marks signify timing of light flashes starting either 0 or 50 ms, respectively, post slow-wave sleep peak. (D) Over a prolonged period (30–60 min) of such stimulation, delta band oscillation amplitude was significantly enhanced in saline controls stimulated at the 50 ms delay but not at the 0 ms delay. Asterisks = sig. diff between delays. (E) In P7 EtOH treated adult mice, neither stimulation protocol was effective at enhancing delta oscillation amplitude. (F,G) Replotting the data shows the robust enhancement of delta amplitude in saline treated mice, but not in P7 EtOH treated mice, suggesting that stimulation of spared SST cortical neurons cannot compensate to improve sleep-related slow-wave amplitude. Asterisks = significant post-hoc test differences between groups. | PMC10067632 | fnins-17-1127711-g004.jpg |
0.430155 | 67ac56fc1c8b4ba080599dcb86ebfeed | Network architectures of deep learning algorithms using both sides of mastoid anterior–posterior (AP) view with symmetry evaluation function and using single side. (a) A network architecture that predicts mastoiditis from both sides using symmetry evaluation. This architecture consists of a main network and an auxiliary path. The main network receives images of both ears as input, and three categories of mastoiditis are predicted through a convolutional neural network (CNN), feature map, and mastoiditis classifier for each ear. Each mastoiditis classifier predicts three mastoid categories as a probability vector. The symmetry evaluation layer in the auxiliary path receives both feature maps corresponding to the three mastoiditis categories and calculates the absolute value per pixel between both feature maps. The symmetry classifier predicts the difference between mastoiditis categories on both sides as three-valued probability vector. The symmetry loss is calculated from the symmetry classifier, and this loss is added to each mastoiditis loss calculated from the mastoiditis classifiers to obtain the final losses on both sides. (b) A network architecture predicting mastoiditis on only one side. This architecture receives only the image of one ear as input and predicts mastoiditis with similar structure to the main network in (a). | PMC10067950 | 41598_2023_32147_Fig1_HTML.jpg |
0.430766 | 9df441b987d94e26b8ccdc8557b20a2e | Confusion matrices for three mastoiditis categories with gold standard test set. (a) Confusion matrix of the algorithm using both sides of mastoid anterior–posterior (AP) view with symmetry evaluation function. (b) Confusion matrix of the algorithm using single side without symmetry evaluation. (c,d) Confusion matrices of two head and neck radiologists. The algorithm with symmetry evaluation shows higher accuracy for normal categories than the algorithm using single side and similar accuracy for mild and severe categories. | PMC10067950 | 41598_2023_32147_Fig2_HTML.jpg |
0.415548 | e0cabbeebec747c28f7ef14423bde41c | Confusion matrices for three mastoiditis categories predicted by deep learning algorithms with external validation sets. (a,b) Confusion matrices with temporal external test set. (a) Confusion matrix of the algorithm using both sides of mastoid anterior–posterior (AP) view with symmetry evaluation function. (b) Confusion matrix of the algorithm using single side without symmetry evaluation. (c,d) Confusion matrices with geographic external test set. (c) Confusion matrix of the algorithm using both sides of mastoid AP view with symmetry evaluation function. (d) Confusion matrix of the algorithm using single side without symmetry evaluation. The algorithm with symmetry evaluation has higher accuracy for normal categories than the algorithm using single side. | PMC10067950 | 41598_2023_32147_Fig3_HTML.jpg |
0.466783 | d3a7f26d63194858b2ba272e27c869d3 | Class activation mappings from deep learning algorithm using both sides of mastoid anterior–posterior (AP) view with symmetry evaluation function. Each figure consists of 12 subfigures. The first and second rows in each image are the original mastoid AP view images and class activation mappings for normal, mild, and severe categories, respectively. The third row consists of an image showing the difference between the right and left ears and class activation mappings for symmetry grades 0, 1, and 2. Probabilities of mastoiditis category or symmetry grade are annotated on the corresponding class activation mapping. (a) Class activation mappings of bilateral normal mastoid air cells. (b,c) Class activation mappings of bilateral symmetric mild (b) and severe (c) mastoiditis cases. (d) Class activation mappings of a case with right side severe mastoiditis and left side normal mastoid air cells (symmetry grade 2) (e,f) Class activation mappings of symmetry grade 1 cases (e) a case with right side normal mastoid air cells and left side mild mastoiditis and (f) a case with right side mild mastoiditis and left side severe mastoiditis. | PMC10067950 | 41598_2023_32147_Fig4_HTML.jpg |
0.447841 | 671a533d8f5f4df2bf333bcb79e7d755 | Monkeys navigated to a remembered goal by integrating optic flow.a Monkeys used a joystick with two degrees-of-freedom to navigate to a cued target (yellow disc) using optic flow cues generated by ground plane elements (brown triangles) in a virtual environment. b Left: The time course of sensory input variables—linear (top) and angular (bottom) velocities—during one example trial. Middle: Overhead view of the spatial position of the monkey during the trial. Black open circle denotes the monkey’s response (stopping location). As there are no visual landmarks, the position becomes latent to the monkey as soon as the target is turned off. Right: Monkey’s hand velocity along two leading principal components of hand position, while maneuvering the joystick. Yellow shaded regions correspond to the time period (~300 ms) when the target was visible on the screen. Time is also coded by color. c Top: Overhead view of the spatial distribution of target positions across trials. Bottom: Movement trajectories of one monkey during a representative subset of trials. Blue dot denotes starting location. d Example trials showing “incorrect” (left) and “correct” (right) responses of a monkey. e Left: Comparison of the radial distance of the response against radial distance of the target across a subset of trials from three different monkeys. Right: Angular eccentricity of the response versus target angle. Black diagonal lines have unity slope. The starting position was taken as the origin. f Left: Cumulative distribution of stopping distance (from the target center) across trials of the three monkeys. Dashed curves show the corresponding null distribution calculated by shuffling response and target locations. Gray region highlights the range of stopping distances that guaranteed reward. The cumulative probability of an arbitrary stopping distance can also be interpreted as the hit rate (fraction of correct trials) if that stopping distance was taken to be the edge of the reward zone. With this interpretation, we can construct ROC curves by plotting the true hit rates against shuffled hit rates across the range of stopping distances. Right: ROC curves from the three monkeys, averaged across sessions. Data from individual recording sessions are overlaid in thin lines. Inset—Histograms of the area under the corresponding ROC curves (AUC). g In a random subset (10%) of the trials, the target remained visible throughout such that the world state was fully observable to the monkeys. Histograms of the AUCs for trials in which the world state was latent (gray shaded) or fully observable (black open). Trials were pooled across monkeys. Inset—Mean AUCs of individual monkeys under the two conditions (L latent, O fully observable). a–c reprinted from Lakshminarasimhan et al.16, Copyright (2020), with permission from Elsevier. Source data are provided as a Source data file. | PMC10067966 | 41467_2023_37400_Fig1_HTML.jpg |
0.408425 | eabf63a7836c4144a23add676d23754b | Latent states dictate population dynamics.a Anatomical location of the multi-electrode arrays (Red—monkey B, Green—monkey Q, Blue—monkey S) superimposed on the 3D reconstructed brain of monkey S (IPS—intraparietal sulcus, STS—superior temporal sulcus, LF—lateral fissure). b Spike rasters of four example neurons from one of the recording sessions. Red bar denotes the 300-ms period during which the target appeared on the screen, and rasters are shown until the end of the stopping period while monkeys waited for feedback. c Peak-normalized response of neurons calculated by averaging across the set of trials with nearby (left panels) or distant (right panels) targets. Neurons are sorted according to the timing of their peak response observed in the set of trials with nearby (top panels) or distant (bottom panels) targets. Spike times were rescaled based on the trial duration before trial-averaging and the resulting response profile of each neuron was subsequently normalized by the peak activity observed in the condition used for sorting. Neurons from all three monkeys are combined before sorting (see Fig. S2a, b for individual monkeys). d Similar to a, but with trials grouped by target angle. e Left: Comparison of the pattern similarity of the population dynamics between trials within the same (abscissa) or different (ordinate) groups, shown separately for each monkey. Pattern similarity was defined as the correlation coefficient between the firing rate maps taken from either the same trial group (odd vs even trials) or different trial groups (nearby vs distant targets). Right: Time course of the pattern similarity, computed as the correlation between population activity vectors (columns of the rate maps) taken from the same trial group (odd vs even trials) or different trial groups (nearby vs distant targets). f Similar to c, but with trials grouped by target angle. In e, f, n = 112, n = 68, n = 64 neurons in monkey B, S and Q, respectively. Error bars denote ±1 SEM. Source data are provided as a Source data file. | PMC10067966 | 41467_2023_37400_Fig2_HTML.jpg |
0.447881 | 99c7d5d2e9aa4fbba2c04081924e4c0b | Neurons represent sensory variables, latent world states, and motor variables.a Causal structure of the task, illustrating the recurrent nature of the interaction between sensory inputs, latent states, and motor outputs. b Schematic of the generalized additive model (GAM) used to fit spike trains of single neurons. c Activity of simultaneously recorded neurons during a random thirty second epoch during the experiment (left) and the corresponding prediction reconstructed using the model (right). Neurons are arranged according to their contribution to the leading principal component (PC) (bottom—lowest; top—highest) for visualization. d Proportion of neurons tuned to different variables. Error bars denote ± 1 standard error of binomial proportions (n = 244). e Cumulative distribution of the contribution of different predictors, calculated as the reduction in variance explained by the model after removing those predictors. Black shows the distribution of the variance explained by the full model. f Top: Example tuning functions showing sensitivity of neurons to different task variables and other explanatory variables. Shaded regions denote ± 1 SEM across validation sets (n = 10). Bottom: Peak-normalized tuning functions of all significantly tuned neurons, sorted according to the peak feature. Across the population, tuning to individual task variables had peak responses that tiled all values of the feature space. In contrast, most neurons produced stereotyped responses to Local field potential (LFP) phase. Source data are provided as a Source data file. | PMC10067966 | 41467_2023_37400_Fig3_HTML.jpg |
0.44997 | 169aab7122f44eabb57005084ea0a130 | Coupling encapsulates population structure.a Comparison of the log likelihoods of the model constructed using only external inputs as predictors against those of a model that also included effects of spike-history (brown), against a model with both spike-history and inter-neuronal coupling (maroon). Each circle denotes an individual neuron. Inset shows the cumulative distribution of the contribution of spike history and neuron-neuron coupling, calculated as the reduction in variance explained by the model after removing those filters. Black curve shows the distribution of the variance explained by the full model. b Top left: Spike history filter of an example neuron. Bottom left: Model with spike history filter accurately captures the autocorrelation function of the neuron. Top right: Bidirectional coupling filters between an example pair of neurons. Bottom right: Coupling filters capture the cross-correlation between the pair, whereas spike-history filters alone do not. c Top: Structure of the peak-to-trough amplitude of cross-correlation between the activity of all pairs of simultaneously recorded neurons from an example monkey. Neurons are ordered according to the weight of their contribution to the first principal component of the population activity. Coupled model (bottom), but not the uncoupled model (middle), captures the structure of cross-correlation of the full population. d Left: The strength of the coupling filters between all pairs of neurons in the population shown in c. Strength of the filter was computed by taking the total area under the filter. A strength greater than one corresponds to excitatory coupling whereas less than one corresponds to inhibitory coupling. The diagonal elements correspond to the strength of the spike-history filter (self-coupling) as a special case. Right: Details of the coupling (off-diagonal) and spike-history filters of a subset of the neural population, highlighting the diversity in the filter profiles across neuronal pairs. e Top: Frequency distribution over the coupling strengths between all pairs of neurons, pooled across monkeys. A vast majority of the neurons were weakly coupled (note the log scale of the frequency axis). Bottom: Each coupling filter was expressed as a weighted sum of seven exponential basis functions with different decay constants ([6, 12, 24, 48, 96, 192, 384] milliseconds). Data points show the average magnitude of weighting of the different basis functions across all coupling filters, pooled across monkeys. Black line denotes the best fit power-law relationship. Error bars denote ± 1 SEM (n = 244). f Strength of coupling decreased as a function of distance between the electrodes from which the neurons were recorded. Points above and below the black line correspond to excitatory and inhibitory couplings respectively. Source data are provided as a Source data file. | PMC10067966 | 41467_2023_37400_Fig4_HTML.jpg |
0.431606 | bed665e57e3840449d778e692972a86a | Population activity predicts behavior on individual trials.a We trained a linear decoder using ordinary least-squares to decode task variables from population activity (Methods). Columns correspond to six representative trials and rows correspond to different variables (linear velocity, angular velocity, target distance, target angle, and hand velocity along the two leading principal components of hand position). Gray traces correspond to ground truth. b Decoder performance for each variable, quantified as the correlation between observed and decoded estimates shown separately for individual monkeys. c Average performance of the decoder for distance-to-target plotted against the mean behavioral accuracy (fraction of correct trials) of different monkeys. d Left: Error in decoding distance to target is correlated with the behavioral error (difference between target distance and stopping distance) across trials. Middle: The cumulative distribution of decoding error shown separately for trials in which the monkey undershoots (purple) or overshoots (gold). Right: ROC curves constructed by plotting the cumulative probabilities for the two sets of trials against each other, and the associated classification accuracy quantified as area under the curve (AUC, inset). e Left: Across trials (gray dots), the time-averaged error in decoding target distance is correlated with the time-averaged error in decoding linear velocity. Ellipses map out contours corresponding to 1 SD (assuming Gaussianity) for individual monkeys. Middle: Similar to left panel, but for the angular domain. Right: Linear (open symbols) and angular (closed symbols) correlation coefficients for individual monkeys computed from data, plotted against correlations computed using surrogate data. For each pair of decoders, surrogate correlations were computed by taking pairs of random projections of the original data along directions that overlap by the same angle as the pair of decoders. Error bars and shaded regions in c–e denote ± 1 SEM estimated by bootstrapping (n = 1000 trials). Source data are provided as a Source data file. | PMC10067966 | 41467_2023_37400_Fig5_HTML.jpg |
0.463119 | cb16063260324eb4be1d635f03dcf30d | Behavioral manipulations trigger changes in coupling and latent representation.a In separate experimental sessions, we manipulated sensory input (by altering the density of optic flow), motor output (by altering the sensitivity of the joystick), or latent state (by perturbing the monkey off his intended trajectory while traveling) (“Methods”). b These manipulations produced modest behavioral effects as shown by the ROC curves for a subset of experimental sessions, quantified as area under the curve (AUC, inset). c Coupling filters (shown for a representative subset of all directed pairs of five neurons) fit to data from baseline trials (gray) and manipulation trials (colored). d Top left: Cumulative distribution across all pairs of neurons of the correlation coefficient between coupling filters fit to baseline trials and manipulated trials. Black curve shows the baseline distribution computed using odd and even baseline trials. Gray curves show the null distribution constructed by shuffling the neuronal pairs. Shaded regions denote ±1 SEM estimated by bootstrapping (n = 25,805/20,798/22,506 neuron pairs for sensory/latent/motor manipulation). Top right: Cumulative distribution of the correlation coefficient between tuning function to sensory variables (the two sensory variables - linear and angular velocity - were concatenated) fit to baseline trials and manipulated trials across all neurons. Bottom left: Similar to top right, but computed using tuning to latent variables. Bottom right: Similar to top right, but computed using motor tuning. e Contribution of coupling to neuronal response, quantified as the improvement in the model log likelihood (LL) over uncoupled model, was comparable during baseline and manipulated trials. f Baseline-corrected stability to manipulated task (stability index) computed using cumulative distributions in d (“Methods”) for coupling filters, latent tuning, sensory tuning, and motor tuning under all three manipulations. Both sensory and motor tunings were robust to manipulations, whereas coupling and latent tunings were not. Source data are provided as a Source data file. | PMC10067966 | 41467_2023_37400_Fig6_HTML.jpg |
0.431691 | 768ea79cf3ef4d8ab60ef270cb04f667 | A recurrent neural network model operating in closed loop recapitulates experimental findings.a Schematic of the recurrent neural network architecture. The network comprised 100 fully-connected neurons receiving a transient signal (target location) on two input channels, and two motor output channels that control linear and angular acceleration which then determined the signal received by two sensory input channels (velocity). Analogous to the function of the virtual-reality setup in our experiments that converts the joystick output into visual input, the “World” block integrates the motor output to generate subsequent velocity input. So this architecture mimics the interaction between the monkey and the virtual reality. b The recurrent weights were trained using a standard supervised learning algorithm (Backpropagation-through-time (BPTT), “Methods”) to generate appropriate outputs to random target locations. Left: The network’s trajectory in response to 16 different targets are shown (red—target, black—starting location). Right: Learning was stopped when the network performance matched the average monkey as measured by the respective ROC curves. c Neurons in the network exhibited sequential dynamics in a target location dependent manner (compare with Fig. 2). d We fit a generalized additive model (GAM) to the model neurons. Left: Model neurons were tuned to different task variables including latents (despite not explicitly training to learn them) (compare with Fig. 3). Right: Coupling was weak and concentrated around zero (top) but nonetheless affected the goodness of fit (bottom-right) (compare with Fig. 4). Model neurons exhibited mixed selectivity to a degree that was comparable to PPC neurons (bottom-left, “Methods”). Error bars denote ± 1 SEM (n = 244/n = 100 for data/model). e We trained linear readouts on the model population response to decode task variables. Left: Across trials (dots), the error in decoding target distance was correlated with the performance (quantified by stopping distance) of the network (compare with Fig. 5d). Right: Errors in decoding target distance and angles were correlated with errors in decoding linear (gray) and angular velocity (brown), respectively (compare with Fig. 5e). f Simulated manipulations. We added different amounts of noise to the sensory input channels (sensory manipulation), multiplied the transformation implemented by the “World” block by a non-unity gain factor (motor manipulation), or added a randomly timed Gaussian pulse to the sensory input channels to displace the model off its intended trajectory (latent manipulation). Top: The network readily adapted to sensory manipulation without additional training, but motor and latent manipulations required a small amount of additional training of recurrent weights to elicit comparable behavioral performance. Bottom: Sensory and motor tunings were robust to all three manipulations largely because the input and output weights did not change during the additional training. However, similar to PPC neurons (compare with Fig. 6f), the coupling and latent tunings were affected because the additional training modified the recurrent weights and thus also the latent state representation. Source data are provided as a Source data file. | PMC10067966 | 41467_2023_37400_Fig7_HTML.jpg |
0.389664 | 957516e238814b90bf5777cfbd26de7b | Key steps of BALF processing workflow for quantitative LC–MS/MS analysis. The workflow includes value-added collection of endogenous peptides for analysis (if desired), along with immunoaffinity-based depletion of the 14 most abundant plasma proteins, as well as protein trapping for concentration, clean-up, and tryptic digestion. The resulting peptides are compatible with isobaric peptide labeling or label-free analysis by LC–MS/MS | PMC10068177 | 12014_2023_9404_Fig1_HTML.jpg |
0.494235 | b19f5a5dcc574bdcac5234182b7980cf | Venn diagram of proteins identified from LC–MS/MS analysis of six representative BALF samples processed via our workflow. | PMC10068177 | 12014_2023_9404_Fig2_HTML.jpg |
0.384437 | bb2e736f7b5340e79312a7e90144271d | Magnetic resonance imaging findings of the brain. Axial diffusion-weighted imaging (a-1 - a-2) and fluid-attenuated inversion recovery imaging (b-1 - b-2) obtained at admission reveal multiple hyperintense lesions bilaterally in the periventricular white matter, centrum semiovale, and corpus callosum. T1-weighted imaging (c-1 - c-2) acquired after injection of gadolinium contrast medium shows no enhancement of the lesions. | PMC10070256 | SNI-14-89-g001.jpg |
0.454669 | e3fba89d51964ed5b262ef1c93441777 | Axial computed tomography (CT) of the chest (a-1 - a-3) shows faint diffuse ground glass opacities in the upper and middle lung fields bilaterally. 18 F-fluorodeoxyglucose (FDG)-positron emission tomography (PET) (b-1 - b-3) reveals slightly diffuse FDG uptake in the upper and middle lung fields bilaterally without uptake by lymph nodes. CT of the abdomen (c) demonstrates diffuse enlargement of both kidneys without lymph node swelling. FDG-PET (d) shows remarkably high FDG uptake in both kidneys. | PMC10070256 | SNI-14-89-g002.jpg |
0.413321 | d808c0c428864902a433be0816a02a97 | Histopathology of the resected lesion by random skin biopsy. Pathologic specimen (a and b) shows occlusion of small vessels by neoplastic cells with prominent nucleoli within subcutaneous adipose tissue. Tumor cells show positive staining for CD20 (c) and negative staining for CD3 (d). Magnification: (a) ×200; (b-d) ×400. Scale bars, 100 μm. | PMC10070256 | SNI-14-89-g003.jpg |
0.390862 | c2787197ea8a44c4baf6de7fdd3f1613 | Brain magnetic resonance imaging (MRI) findings at 1 month after admission. The multiple high-intensity lesions apparent on MRI obtained at admission have decreased on diffusion-weighted imaging (a-1 - a-2), and no new lesions are identified on fluid-attenuated inversion recovery imaging (b-1 - b-2) and gadolinium-enhanced T1-weighted imaging (c-1 - c-2). | PMC10070256 | SNI-14-89-g004.jpg |
0.478555 | 7e14a48e07a54c28be7459d501770cad | Whole body computed tomography (a-1 - a-3 and c) and 18F-fluorodeoxyglucose (FDG)-positron emission tomography (b-1 - b-3 and d) obtained after three cycles of chemotherapy show complete disappearance of the abnormal signs and FDG accumulation identified on admission, indicating achievement of complete remission. | PMC10070256 | SNI-14-89-g005.jpg |
0.423415 | 33e663d478314c808f37932bcc82be98 | Brucella abortus subclade C1, 46 strains from Kazakhstan. Maximum parsimony tree based on core genome SNPs. 1,146 SNPs were called by mapping on genome accession GCA_000740155 (B. abortus clade B strain Tulya). The size of the resulting tree is 1,151 SNPs (homoplasia 0.44%). Thirty-three whole-genome SNP (wgSNP) genotypes are resolved. Branch lengths of two SNPs and more are indicated by black numbers. Strains are labeled in gray with collection year and strain Ids and colored according to region of origin as indicated. The MLVA11 genotype is indicated for new lineages distinct from GT72 (GT338 to GT341). The blue star indicates the root of the phylogeny (branching point toward B. melitensis type strain 16 M used as outgroup). From the blue star, early splits define six branches, labeled A to F. Blue branches A to D are defined by a few ancient strains isolated between 1947 and 1970. Blue branch F is defined by one recent strain, KAZ021 isolated in 2015. Red branch E with 34 strains (24 wgSNP genotypes) is remarkable by its diversity (24 wgSNP genotypes) and high number of associated strains (34 out of 46). It contains all but one of the recent strains (isolated in 2007–2015) together with five ancient strains. The E branch is structured into four subbranches labeled I to IV in red. Strains closely related or coincident in terms of wgSNP genotype define eight clusters labeled WGS_1 to WGS_8. | PMC10073595 | fmicb-14-1106994-g001.jpg |
0.428089 | a104b58b962045d2b34e3df2936a3bf0 | Geographic origin of the B. abortus clade C1 strains from Kazakhstan. The size of the labels reflect the number of strains (from 1 to 10) in the corresponding location. The color code shown in the central inset reflects the phylogenetic position (branch assignment) indicated in Figures 1, 3. | PMC10073595 | fmicb-14-1106994-g002.jpg |
0.398175 | 9bc76baf6f6841068c78f58b06d39a7f | Brucella abortus subclade C1, position of the Kazakhstan strains within the global subclade C1 phylogeny. Maximum parsimony tree based on core genome SNPs. A total of 123 strains was used, including 77 selected strains of worldwide origins in addition to the 46 Kazakhstan strains. 5,446 SNPs were called by mapping on genome accession GCA_000740155 (B. abortus strain Tulya). The size of the resulting tree is 5,487 SNPs (homoplasia 0.75%). Strains are colored according to country of origin as indicated. The blue star indicates the root of the phylogeny (branching point toward B. abortus clade B strain Tulya used as outgroup). Representative estimated divergence dates are indicated (CE, common era). | PMC10073595 | fmicb-14-1106994-g003.jpg |
0.498346 | 3a75556f1f254f7cb0797646bc111288 | Zoom on B. abortus subclade C1, position of the Kazakhstan strains within the global subclade C1 phylogeny. Close-up on Figure 3. Strains are colored according to country of origin as indicated and labeled with region of origin when known. The blue star indicates the root of the phylogeny (branching point toward B. abortus clade B strain Tulya used as outgroup). Branch names A to F defined in Figure 1 are shown. Representative estimated divergence dates are indicated (CE, common era). | PMC10073595 | fmicb-14-1106994-g004.jpg |
0.39801 | accfed105f84466a929991c4eefff4d6 | Global B. abortus phylogeny. Maximum parsimony tree based on core genome SNPs. A total of 236 representative strains was used and 16,987 SNPs were called by mapping on genome accession GCA_000740155 (B. abortus strain Tulya). The size of the resulting tree is 17,178 SNPs (homoplasia 1.12%). Nodes are colored according to geographic origin as indicated. The blue star indicates the root of the phylogeny (branching point toward the B. melitensis type strain 16 M). The largest branch lengths and the scale are shown. Representative estimated divergence dates are indicated (CE, common era; BCE, before common era). | PMC10073595 | fmicb-14-1106994-g005.jpg |
0.458677 | b9dc6bea1cc64f759bf158c8152bc395 | PRISMA flow diagram. | PMC10074763 | 10.1177_13634615211067360-fig1.jpg |
0.615448 | 19b9dd08578445e7930817f6665adf1c | Proposed thematic relationships and their effects on the mental
healthcare of migrants. | PMC10074763 | 10.1177_13634615211067360-fig2.jpg |
0.426943 | 8b1342adf744484a9cd9f55d3ba4f1a4 |
Flowchart depicting patient recruitment for the analysis transcatheter aortic valve replacement-transcatheter aortic valve replacement. TAVR: Transcatheter aortic valve replacement.
| PMC10074998 | WJC-15-95-g001.jpg |
0.435228 | 701a9e18aaa241d19d7e8b1ec0738ec7 | Variables with the highest importance in a gradient boosting model to predict the need for a permanent pacemaker at 30 d. | PMC10074998 | WJC-15-95-g002.jpg |
0.547068 | aee132d95c694264ade6068e733f8744 |
Receiver operator curves of the gradient boosting model and permanent pacemaker risk score model to predict the need for a permanent pacemaker at 30 d. GBM: Gradient boosting model; PPM: Permanent pacemaker model.
| PMC10074998 | WJC-15-95-g003.jpg |
0.445687 | be866d071dd54b42bed5b56c25114c9b | Variables with the highest importance in the gradient boosting model to predict the need for a permanent pacemaker at 1 year. | PMC10074998 | WJC-15-95-g004.jpg |
0.427297 | de6853d748d9415493fbc575fe08f4b9 |
Receiver operator curves curves of gradient boosting model and permanent pacemaker risk score model predicting the need for a permanent pacemaker at 1 year. GBM: Gradient boosting model; PPM: Permanent pacemaker model.
| PMC10074998 | WJC-15-95-g005.jpg |
0.448861 | f19ec9bea72b4ee79439f57cee4681a7 | Patient selection flow diagram. | PMC10075306 | fsurg-10-1121357-g001.jpg |
0.481594 | 80e0a7d0b9ec4c468c809159af9c290a | Kaplan–Meier plots for PFS and OS according to ACCI score and pT4 subgroup. | PMC10075306 | fsurg-10-1121357-g002.jpg |
0.535541 | 67aac7854adc4cd7bfb977f6cb505912 | Effects of different treatments on ROS content in VD cell model. The experiment was repeated three times, and the data was shown as mean ± standard deviation. (n = 3 for every group, *P < 0.05, **P < 0.01, ****P < 0.001 by one-way ANOVA). | PMC10076271 | 41598_2022_21298_Fig10_HTML.jpg |
0.464025 | df58e1dd1e9f4adaa8c6c274f937056c | Schematic diagram of the antioxidative effect of imperatorin by regulating the Nrf2 signaling pathway. | PMC10076271 | 41598_2022_21298_Fig11_HTML.jpg |
0.537467 | 5d38ae7d520e42ecad8805f6c0a899d9 | Immunofluorescence staining of rat hippocampal neurons (×200). The cell bodies and processes of hippocampal neurons stained with MAP2 antibody were orange-red, all nuclei stained with DAPI were blue, Merge was a dual fluorescence image after the integration of orange-red MAP2 and blue DAPI images (×200). | PMC10076271 | 41598_2022_21298_Fig1_HTML.jpg |
0.503066 | 3244a621ced94cecbff1517032b52ef0 | (A) Cell morphology of hippocampal neuronal cells treated with different concentrations of CoCl2 for 24 h (× 200) (a, b, c, d, e, f respectively represent 50 μmol/l, 100 μmol/l, 150 μmol/l, 200 μmol/l, 300 μmol/l, 400 μmol/l CoCl2 group). (B) Cell viability of hippocampal neuronal cells treated with different concentrations of CoCl2 for 24 h (n = 6 for every group, *P < 0.05, ***P < 0.001 vs. normal group by one-way ANOVA). | PMC10076271 | 41598_2022_21298_Fig2_HTML.jpg |
0.49701 | b586b5aee6274a799f87bfa90d43f795 | The effects of IMP on the cell viability of CoCl2-induced hypoxia hippocampal neuronal cells. (A) Cell viability of normal hippocampal neuronal cells treated with different concentrations of IMP for 24 h. (B) Cell viability of CoCl2-induced hypoxia hippocampal neuronal cells treated with different concentrations of IMP for 24 h. (C) The interventional time of the viability of CoCl2-induced hypoxia hippocampal neuronal cells. (n = 6 for every group, *P < 0.05, **P < 0.01, ****P < 0.0001, vs. CoCl2-induced control group by one-way ANOVA). | PMC10076271 | 41598_2022_21298_Fig3_HTML.jpg |
0.476589 | ff898095048046d591ade67738679a9d | The effect of IMP on the apoptosis rate of CoCl2-induced hypoxia hippocampal neuronal cells. In the figure, the upper left quadrant is dead cells (Q1), the upper right quadrant is late apoptotic cells (Q2), the lower right quadrant is early apoptotic cells (Q3), and the lower left quadrant is living cells (Q4). The total number of apoptotic cells is the sum of Q2 + Q3. (A) Normal group, (B) CoCl2 treatment group, (C) CoCl2 + 5.0 μmol/l IMP, (D) CoCl2 + 7.5 μmol/l IMP, (E) CoCl2 + 10.0 μmol/l IMP. (n = 6 for every group,, **P < 0.01, ***P < 0.01, ****P < 0.0001). | PMC10076271 | 41598_2022_21298_Fig4_HTML.jpg |
0.467604 | cc265fb2eac0419597b0597cfdd23155 | The effect of IMP on the mitochondrial membrane potential of CoCl2-induced hypoxia in hippocampal neuronal cells. The Q2 quadrant in the figure is the polymer of JC-10, which is expressed as red fluorescence. When the mitochondrial membrane potential drops, the Q3 quadrant of JC-10 decomposed into monomers and expressed as green fluorescence. (A) Normal group, (B) CoCl2 treatment group, (C) CoCl2 + 5.0 μmol/l IMP, (D) CoCl2 + 7.5 μmol/l IMP, (E) CoCl2 + 10.0 μmol/l IMP. (n = 6 for every group; ns, there was no statistical difference; ****P < 0.0001). | PMC10076271 | 41598_2022_21298_Fig5_HTML.jpg |
0.445637 | c50fcbb0eeb1417c90ff3be5c7adc447 | The effect of IMP on the nuclear translocation of Nrf2 protein in CoCl2-induced hypoxia in hippocampal neuronal cells (× 400). Red fluorescence is positive staining for Nrf2 protein, indicating the expression of Nrf2 protein, and blue fluorescence is DAPI staining, indicating cell nucleus. Emerge image is a fusion image of Nrf2 protein and cell nucleus for positioning. The Zoom image is a magnified image randomly selected from the emerge image to observe the Nrf2 positioning. (A) Normal group, (B) CoCl2 treatment group, (C) CoCl2 + 5.0 μmol/l IMP, (D) CoCl2 + 7.5 μmol/l IMP, (E) CoCl2 + 10.0 μmol/l IMP. | PMC10076271 | 41598_2022_21298_Fig6_HTML.jpg |
0.421062 | 4bd9b883ffe54dc9a70884d5d6164ec2 | The effects of IMP on the expression of Nrf2, NQO-1, HO-1 mRNA after 24 h intervention in hippocampal neuronal cells. (A) Nrf2 mRNA, (B) NQO-1 mRNA, (C) HO-1 mRNA. (n = 6 for every group, ns, there was no statistical difference, **P < 0.01, ***P < 0.001, ****P < 0.0001). | PMC10076271 | 41598_2022_21298_Fig7_HTML.jpg |
0.502137 | 14c41db9751a474a965e0cdec5df17dd | The effects of IMP on the expression of Nrf2, NQO-1 and HO-1 protein after 24 h intervention in hippocampal neuronal cells. (A): the western blot images of Nrf2, NQO-1, HO-1, and β-actin expression, (B): the relative expression of Nrf2, (C): the relative expression of NQO-1, (D): the relative expression of HO-1. Dividing lines were used to make explicit for the grouping of blots cropped from diferent parts of the same gel or from diferent gels. The experiment was repeated three times, and the data was shown as mean ± standard deviation. (n = 3 for every group, **P < 0.01, ***P < 0.001, ****P < 0.0001). | PMC10076271 | 41598_2022_21298_Fig8_HTML.jpg |
0.496766 | 22d6a175373e464cb8a139ae755154f1 | IMP exerts a protective effect on the VD cell model prepared by CoCl2-induced hypoxic hippocampal neuronal cells by regulating the Nrf2 signaling pathway. (A): the relative expression of Nrf2 after transfection of the overexpression vector. (B): the relative expression of Nrf2 after siRNA-1, siRNA-2, siRNA-3 transfection. (C): relative expression of total Nrf2 in VD cell model by different treatments. (D): relative expression of nuclear Nrf2 in VD cell model with different treatments. Dividing lines were used to make explicit for the grouping of blots cropped from diferent parts of the same gel or from diferent gels. The experiment was repeated three times, and the data was shown as mean ± standard deviation. (n = 3 for every group, *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001 by one-way ANOVA). | PMC10076271 | 41598_2022_21298_Fig9_HTML.jpg |
0.433443 | 4bf132687d9445a8b447e363fea93b7a | Flowchart of the study population selection process. IPT isolated premature thelarche, CPP central precocious puberty. | PMC10076281 | 41598_2023_32768_Fig1_HTML.jpg |
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