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0.432141 | 909be0fcc974416c873bbbc2b357f4a6 | (a) Correlation of fragility with the parameter R for some inorganic glasses (circles, listed in increasing fragility order) and for polystyrene with different molar mass (squares, molecular weight = 550, 990, 2370, and 12,400 in the order of increasing fragility). (b) Correlation of fragility with the parameter δ2(Tg) that characterizes the ratio of integrated intensity of the fast relaxation to the integrated intensity of the boson peak. Data from Ref. [14]. | PMC9407199 | entropy-24-01101-g020.jpg |
0.415907 | 8271701302b749b0975ef9d7ff0898e2 | Correlation between the parameter α taken from Ref. [163] and fragility m (solid circles). X-ray and light scattering data for B2O3 (solid and open triangles, respectively) and CKN (solid and open stars, respectively) and light scattering data for SiO2 (open circle) are added. This figure is from Ref. [14]. | PMC9407199 | entropy-24-01101-g021.jpg |
0.475988 | ae27340f69fc482582364d58c072bf37 | Molecular weight dependence of fragility in various polymers. The shaded area represents the region characteristic of small molecules. The fragility values for PIB are those calculated from dielectric spectroscopy. Reprinted figure with permission from [187]. Copyright (2008) by John Wiley & Sons, Inc. | PMC9407199 | entropy-24-01101-g022.jpg |
0.462896 | 489810e327a74191b270af397a4562e1 | (a) Fragility vs. vl/vt for simple liquids (stars) and some polymers. Polybutadiene (PB, star) and polyisobutylene (PIB, solid triangle) follow the correlation of simple liquids independently of the molecular mass, while polystyrene (PS, open squares) agrees with the correlation for small molecular weights (MW = 550 and 990 g/mol) and strongly deviates with increasing molecular weights (MW = 8000 and 220,000 g/mol). Interestingly, the jump of the specific heat at Tg in PS with different MW is in a good agreement with the correlation (open triangles, right axis). Data from Ref. [14]. (b) Correlation between the nonergodicity parameter α and fragility for different molecular liquids and for PS samples at different molecular weights (solid triangles) [190]. The arrows indicate the direction from shorter chains to longer chains. Crosses represent α calculated from IXS data in other studies. Squares—data for polymethyl methacrylate (PMMA), bisphenol A polycarbonate (BPA-PC), and polyethylene terephthalate (PET) from Ref. [179]. | PMC9407199 | entropy-24-01101-g023.jpg |
0.431227 | 27329d30d3b846d8a83bcbd0c18db7e3 | Schematic presentation of the influence of molecular weight on the temperature dependence of the structural relaxation in polymers. At high temperatures, the behavior is rather MW-independent. An increase in the molecular weight results in a stronger slowdown of the structural dynamics as the temperature is lowered, i.e., the increase of Tg. As a result, the temperature variations of τα appear to be steeper, i.e., more fragile. This slowdown is weaker in flexible polymers and more significant in rigid ones. | PMC9407199 | entropy-24-01101-g024.jpg |
0.436802 | 8e3a93a107ca4cf98024ab9be45e3756 | (a) Schematic presentation of polymers classification based on the relative flexibility of the side groups and backbone. It is observed that polymers tend to be more fragile as the flexibility of the side groups becomes different from that of the backbone (Figure adopted from Ref. [198]). (b) Theoretical prediction for the fragility of polymer melts as a function of the relative backbone and side group rigidity, expressed as the bending energy ratio. Reprinted from [199] with the permission of AIP Publishing. | PMC9407199 | entropy-24-01101-g025.jpg |
0.555633 | 1c586669916f41ea827caf062f3d2aae | Variations of the activation enthalpy ratio with the molecular weight for polystyrene (circles) and polymethylmethacrylate (squares). The data for PS are from Ref. [185] and, for PMMA, are recalculated from Ref. [186]. | PMC9407199 | entropy-24-01101-g026.jpg |
0.488137 | 6ee915cca7ba47b79c0d6efcd2e4b6dc | Molecular weight dependence of fragility for segmental relaxation (red squares) and viscosity (blue triangles) in PS. Solid symbols—data from Ref. [202] and open symbols—data from Ref. [204]. Fitting line according to the model from Ref. [202]. Adapted with permission from [202]. Copyright (2018) American Chemical Society. | PMC9407199 | entropy-24-01101-g027.jpg |
0.4574 | dd367a0968424abaa26cd8c48e1cd18c | Temperature-dependent structural relaxation times τ(T) in LDA water. τ(T) increases about an order of magnitude when going from H2O (open symbols) to D2O (closed symbols). For liquids, this constitutes an unusually large isotope effect. The experimentally determined fragilities for LDA water are mH2O_LDA ≈ mD2O_LDA = 14 ± 1. The lines present the expected temperature dependence of τ(T) estimated from Equation (51) using the total MSD of LDA water (solid lines) and the MSD with zero-point vibrations excluded (dashed lines). The fragilities, estimated from the MSD data with zero-point vibrations taken into account, are: mH2O ≈ 14.5 and mD2O ≈ 19, similar to the experimentally determined values. When zero-point contributions to the MSD are excluded, the predicted fragility becomes mH2O ≈ 37 and mD2O ≈ 35. The calculations using LDA’s total MSD reproduce the temperature dependence of τ(T) well and thus emphasize the importance of quantum fluctuations in the dynamics of water at low temperatures. Data from Ref. [207]. | PMC9407199 | entropy-24-01101-g028.jpg |
0.45196 | c6bd425733ba496cae18924d84ebd5f0 | Comparison of low- (solid triangles) and high-temperature data for the structural relaxation time in water. Open squares—dielectric spectroscopy data in water [213] and solid circles—shifted viscosity data [214]. The dashed line presents an approximation of the low-temperature behavior by an Arrhenius dependence, and the solid blue line is an approximation of the high-temperature behavior using the Vogel–Fulcher–Tammann function. The solid red line presents a hypothetical transition line between low-temperature (quantum) and high-temperature (over barriers) regimes [208]. | PMC9407199 | entropy-24-01101-g029.jpg |
0.495626 | 896552ab1f624ae4913f3609408b59b4 | Marketable fruit from “G32” (weak flavor) and “J120” (strong flavor) collections and free glutamate contents. (A) Marketable fruit of “G32”, scale bar = 2.1 cm; (B) marketable fruit of “J120”, scale bar = 2.5 cm; (C) free glutamate content of “G32” and “J120” (** p < 0.01). | PMC9407550 | foods-11-02450-g001.jpg |
0.459854 | 3d5595c20cb1432c97ded5b3551af2bc | Differential fruit chemotype between “G32” and “J120”. (A,B): principal components analysis of identified metabolites from “G32” and “J120” in negative ionization (NEG) and positive ionization (POS) modes, respectively. A mixture of equal volumes of “G32” and “J120” samples was used for quality control (QC); (C,D): cluster analysis of metabolites from “G32” and “J120” fruit in NEG and POS modes, respectively. Colors indicate the level of accumulation of metabolites from low (green) to high (red). | PMC9407550 | foods-11-02450-g002.jpg |
0.393402 | c3eaea8b9ff6480391c55bc5434e9345 | Differentially accumulating metabolites between “G32” and “J120”. (A,B): identified differential metabolites in “J120” compared to “G32” in negative ionization (NEG) and positive ionization (POS) modes, respectively. (C,D): pie chart of the differential metabolites identified between “G32” and “J120”. | PMC9407550 | foods-11-02450-g003.jpg |
0.358538 | f985a114fb854e138949f6594d8c2e98 | KEGG classification of differential metabolites between “G32” and “J120”. (A,B): KEGG classification of metabolites differentially accumulated in “J120” and “G32” in NEG and POS modes, respectively. | PMC9407550 | foods-11-02450-g004.jpg |
0.33267 | 51731e3711cb42dcbfc7fb03f927f33b | Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis of differentially accumulated metabolites in “G32” and “J120”. | PMC9407550 | foods-11-02450-g005.jpg |
0.476058 | 72bcfbee6bdd4b50b259559557788f40 | Frequency distribution of taste scores for 346 MAGIC population elite lines of marketable bottle gourd fruit. The DNA of 28 elite lines with extreme phenotypes (high and low taste scores) was selected to prepare strong- and weak-flavor bulks. | PMC9407550 | foods-11-02450-g006.jpg |
0.450591 | 3f16f3bbce974cde8eb4729bad32d53f | Identification of fruit-flavor-related QTLs in strong- and weak-flavor bulks (A–C). Single-nucleotide polymorphism (SNP) index plot of strong-flavor bulk, weak-flavor bulk, and ΔSNP-index plot from QTL-seq analysis, respectively. The X-axis represents the position of eleven chromosomes and the Y-axis represents SNP-index values calculated based on 1-Mb intervals with a 10-kb sliding window. | PMC9407550 | foods-11-02450-g007.jpg |
0.389343 | 667890badaa649849f3b33e92a65a73e | Schematic representation of the model to predict the primary emission of chemicals associated with the use of a liquid product. | PMC9407831 | ijerph-19-10122-g001.jpg |
0.429447 | c5b9814e13b142a7a7278b7416e98210 | Indoor gas-phase concentrations of (a) acetic acid and (b) decamethylcyclopentasiloxane (D5). | PMC9407831 | ijerph-19-10122-g002.jpg |
0.456844 | 35d00964c6d54b17b93c55d643242c7d | Influence of diffusion coefficient (D) on the indoor gas-phase concentration of acetic acid: (a) concentration profile and (b) fraction of mass emitted from the product liquid to air. | PMC9407831 | ijerph-19-10122-g003.jpg |
0.487232 | 7ed0026e634a457f945679a603e97b86 | Influence of octanol/gas and material/gas partition coefficients (Koa and Ksa) on the chemical indoor gas-phase concentration: (a) concentration profile and (b) fraction of mass emitted from the product liquid to air. | PMC9407831 | ijerph-19-10122-g004.jpg |
0.507463 | 8f427caa4a27416487222b3a208cafbb | Influence of the airflow rate (Q) on the chemical indoor gas-phase concentration. | PMC9407831 | ijerph-19-10122-g005.jpg |
0.381795 | 1372684fde034b88b72f682a3af218ee | The PRISMA flowchart for the studies included in systematic review and meta-analysis. | PMC9407961 | ijerph-19-10346-g001.jpg |
0.452805 | 1536dbb1b0954b8f846bc3b351b88c20 | Funnel plot of publication bias. The closed dots indicate the observed studies, and the open dots indicate the missing studies imputed by trim-and-fill method (based on the estimator L0). | PMC9407961 | ijerph-19-10346-g002.jpg |
0.362729 | 20dfd59fcaa44bbdb1728ce1dfc4f3b0 | Chromosome 11p13 deletions from 6 patients with WAGR syndrome identified from whole exome sequencing data. | PMC9408430 | genes-13-01431-g001.jpg |
0.607477 | 0a48cfe21cc54965927faec6084ec7fb | Anterior segment photographs of 6 patients with WAGR syndrome. (A). Anterior segment of case 1 at her first visit. (B). Anterior segment of case 1 one year later. (C). Anterior segment of case 2 showing keratolenticular adhesion, corneal neovascularization, aniridia, and cataract. (D). Anterior segment of case 3 showing keratolenticular adhesion, corneal neovascularization, aniridia, and the thin lens with cataract. (E). Anterior segment of case 4 showing keratolenticular adhesion, corneal neovascularization, aniridia, and the cataract. (F). Anterior segment of case 5 showing nuclear cataracts and aniridia. (G). Anterior segment of case 6 showing subcapsular cataract and aniridia. (White arrow: keratolenticular adhesion; red arrow: corneal neovascularization.). | PMC9408430 | genes-13-01431-g002.jpg |
0.462039 | 3643358ae39445dcb14bee0bb49fb8cf | Ultrasound biomicroscopy of 3 patients with WAGR syndrome. (A). Anterior segment of case 1 showing the thin lens of the right eye and aniridia of both eyes. (B). Anterior segment of case 2 showing keratolenticular adhesion, aniridia, and thin lens. (C). Anterior segment of case 3 showing keratolenticular adhesion, aniridia, and thin lens. (White arrow: keratolenticular adhesion). | PMC9408430 | genes-13-01431-g003.jpg |
0.392417 | dc768c0597de493690a3d066725fa484 | Ocular features of the patients with WAGR syndrome. (A). Fundus photograph of case 1 showing increased cup-to-disc ratio in both eyes. (B). OCT images of case 1 showing macular hypoplasia in both eyes. (C). B-scan images of case 5 showing increased cup-to-disc ratio in both eyes. (D). Fundus photograph of case 6 showing macular hypoplasia of both eyes. (E). OCT images of case 6 showing macular hypoplasia in both eyes. | PMC9408430 | genes-13-01431-g004.jpg |
0.461137 | e0fc74b75a0b4dac86d23ce93093cfcf | Contact angle measurement of coated specimens (N = 20) (p < 0.05). | PMC9408939 | ijms-23-09291-g001.jpg |
0.388784 | 2a4e28099a68434ca34d80e1ce1c4439 | Adhesion strength analysis by Burger’s test (ASTM D–3359) of MBG–coated Ti (a) before, (b) after test; MBG–Ag1–coated Ti (c) before, (d) after test; MBG–Ag5–coated Ti (e) before, (f) after test; and MBG–Ag10–coated Ti (g) before, (h) after test (N = 3). | PMC9408939 | ijms-23-09291-g002.jpg |
0.485388 | b92fc02786bc4b1da9433b59aade5163 | (1) XRD patterns of (a) SLA Ti, (b) Oxide Ti, (c) MBG–coated Ti, (d) MBG–Ag1–coated Ti, (e) MBG–Ag5–coated Ti, and (f) MBG–Ag10–coated Ti. (2) XRD patterns of (a) MBG–coated Ti, (b) MBG–Ag1–coated Ti, (c) MBG–Ag5–coated Ti, and (d) MBG–Ag10–coated Ti after immersion in the SBF solution for 24 h. | PMC9408939 | ijms-23-09291-g003.jpg |
0.491826 | b0afca1777ea4400acff511f2cc952d6 | FTIR patterns of (a) SLA Ti, (b) Oxide Ti, (c) MBG–coated Ti, (d) MBG–Ag1–coated Ti, (e) MBG–Ag5–coated Ti, and (f) MBG–Ag10–coated Ti. | PMC9408939 | ijms-23-09291-g004.jpg |
0.48009 | bf10e82aa5cf451989d2f853aed83ed2 | SEM images of control groups (a) SLA Ti, and (b) Oxide Ti. | PMC9408939 | ijms-23-09291-g005.jpg |
0.456424 | 1bacc7dea9ff475aa9df01bb255e8ea4 | SEM images of (a) MBG–coated Ti, (b) MBG–Ag1–coated Ti, (c) MBG–Ag5–coated Ti, and (d) MBG–Ag10–coated Ti (N = 15). | PMC9408939 | ijms-23-09291-g006.jpg |
0.448609 | 275bec4aa6094c29875a39865ef3894b | (1) SEM images and EDS results of (a) MBG–coated Ti, (b) MBG–Ag1–coated Ti, (c) MBG–Ag5–coated Ti, and (d) MBG–Ag10–coated Ti. (2) SEM images and EDS results of (a) MBG–coated Ti, (b) MBG–Ag1–coated Ti, (c) MBG–Ag5–coated Ti, and (d) MBG–Ag10–coated Ti after immersion in SBF for 24 h. | PMC9408939 | ijms-23-09291-g007a.jpg |
0.48114 | 286bc9be9f1f419aa605ecaf32c91504 | XPS patterns of (a) MBG–Ag1–coated Ti, (b) MBG–Ag5–coated Ti, and (c) MBG–Ag10–coated Ti. | PMC9408939 | ijms-23-09291-g008.jpg |
0.440134 | f6104ce775a449f09e9e355b92e85697 | The inhibition zones of (A) MBG–coated Ti, (B) MBG–Ag1-coated Ti, (C) MBG–Ag5–coated Ti, and (D) MBG–Ag10–coated Ti against 107 CFU/mL Aggregatibacter actinomycetemcomitans. | PMC9408939 | ijms-23-09291-g009.jpg |
0.402795 | bc08df4c370b42e9ae438b3ae64e10c7 | The inhibition zones of (A) MBG–coated Ti, (B) MBG–Ag1–coated Ti, (C) MBG–Ag5––coated Ti, and (D) MBG–Ag10–coated Ti against 107 CFU/mL Streptococcus mutans. | PMC9408939 | ijms-23-09291-g010.jpg |
0.460614 | 8e9ac44d3f2342e687444d8ec7466edc | Bacterial diversity analysis of yak rumen samples for different dietary concentrate ratios. Alpha diversity between different groups’ (A) Chao 1 value and (B) Shannon index; (C) beta diversity principal coordinate analysis (PCoA). Differences based on concentrate to forage ratio of different diets were used. | PMC9410728 | fmicb-13-964564-g001.jpg |
0.483285 | 92c42a65fd6e46f59b2215d718abdbda | Bacterial composition of rumen samples from yaks with different dietary concentrate to forage ratio. Bacterial composition at the phylum (A) and genus (B) levels; (C) significantly different bacterial phylum and genus between groups, where different letters indicate significant differences between groups. | PMC9410728 | fmicb-13-964564-g002.jpg |
0.450551 | dc380d58fd7f4467adb9bd6ec4f9edae | Corresponding validation plots and orthogonal projections to latent structures-discriminant analysis (OPLS-DA) score plots derived from gas chromatography time-of-flight mass spectrometer (GC-TOF/MS) metabolite profiles of yak rumen samples with different dietary concentrate ratios. Corresponding validation plots and OPLS-DA score plots for (A,B) group C50 vs. group C65 (C,D) group C50 vs. group C80, and (E,F) group C65 vs. group C80. C50, diets with 50% concentrate; C65, diets with 65% concentrate; C80, diets with 80% concentrate. | PMC9410728 | fmicb-13-964564-g003.jpg |
0.423842 | 42e73fee5c874cb398199247b5f2d846 | Pathway analysis of differential metabolites in rumen samples from yaks with different dietary concentrate ratios using the Bos taurus Kyoto Encyclopedia of Genes and Genomes (KEGG) database of MetaboAnalyst 4.0. Larger circles indicate richer pathways and darker colors indicate higher pathway impact values. The closer the color is to red, the smaller the p-value. | PMC9410728 | fmicb-13-964564-g004.jpg |
0.532544 | 8bf4fec82e1b4ba89e4036619c17e8fc | Correlation matrix between genus level and rumen differential metabolites. Each row in the figure represents a metabolite, each column represents a genus, and each lattice represents the Pearson correlation coefficient between metabolite and genus levels. Red color indicates positive correlation, while blue color indicates negative correlation. *p < 0.05; **p < 0.01. | PMC9410728 | fmicb-13-964564-g005.jpg |
0.41273 | 9aafde1b53914235b1930abbb0203f53 | Results of the comparison of the perceived degree of stress, measured by the Visual Analogue Scale, between working men (M, blue bars) and women (W, red bars), with preschoolers (KID) and without (NOKID). | PMC9411539 | 41598_2022_18744_Fig1_HTML.jpg |
0.401415 | a500a37c65b340f1860e1d1929f33e0a | Characterization of salicylic acid nanoparticles using Transmission electron microscopy (TEM), Zeta potential, and Zeta sizer. (a) Transmission electron microscopy (TEM) of SA-NPs, (b) zeta potential of SA-NPs, and (c) average size of SA-NPs. | PMC9412284 | molecules-27-05112-g001.jpg |
0.568535 | 65bbc94e20ba42619afa277ac841aa02 | Expression profiling of LEA gene expression in Catharanthus roseus leaves of plants treated with SANPs and bulk SA at 0.05 and 0.1 mM, using qRT-PCR analysis. The actin gene (MG813871.1) was used as an internal reference gene for data normalization. Error bars represent standard errors. | PMC9412284 | molecules-27-05112-g002.jpg |
0.536595 | dcb96e4e92b742208fb6abfc624b0fda | The effect of SANPs and bulk SA at 0.05 and 0.1 mM as a foliar application on the expression profiling of drought-tolerance related genes in Catharanthus roseus leaves of treated plants using qRT-PCR analysis. (A) WRKY1 gene; (B) WRKY2 gene; (C) WRKY40 gene. Data were normalized using the Actin gene (MG813871.1) as an internal reference gene. Error bars represent standard errors. | PMC9412284 | molecules-27-05112-g003.jpg |
0.561567 | 298a93cae9f8488e9aee9c6ee3481e93 | Expression profiling of MYC2 gene expression in Catharanthus roseus leaves of plants treated with SANPs and bulk SA at 0.05 and 0.1 mM, using qRT-PCR analysis. The actin gene (MG813871.1) was used as an internal reference gene for data normalization. Error bars represent standard errors. | PMC9412284 | molecules-27-05112-g004.jpg |
0.42128 | fd805d118a18441485e967833704bbe0 | The effect of SANPs and bulk SA at 0.05 and 0.1 mM as a foliar application on the expression profiling of drought-tolerance related genes in Catharanthus roseus leaves of treated plants using qRT-PCR analysis. (A) MPK1 gene; (B) MPK6 gene. Data were normalized using the Actin gene (MG813871.1) as an internal reference gene. Error bars represent standard errors. | PMC9412284 | molecules-27-05112-g005.jpg |
0.399979 | 0d77540cd8394010894b43d4af32067b | Schematic diagram of drought stress, exogenous application with SA and SANPs, and genetic response for Catharanthus roseus plants. | PMC9412284 | molecules-27-05112-g006.jpg |
0.571558 | a8990c6a0f8243a084d97d8b5768fb8e | FT-IR spectrum ASE + SO and ASE + BNO. | PMC9412625 | molecules-27-05187-g001.jpg |
0.458174 | 7f01d312d17342fa99444f910f3c8f54 | Visual observation of the formulations. | PMC9412625 | molecules-27-05187-g002.jpg |
0.437836 | dc893c69c8df4fd580fa1f9e82544c4a | (A) Particle size, PDI, and zeta potential of the SONLC and ASE + SONLC stored at temperatures of 4 °C and 32 °C. (B) Particle size, PDI, and zeta potential of the BNONLC and ASE + BNONLC stored at temperatures of 4 °C and 32 °C. | PMC9412625 | molecules-27-05187-g003.jpg |
0.485678 | dc418652c3f641ada06fcca70782388a | Effect of myristic acid esters on Size (A), PDI (B), and zeta potential (C). | PMC9412625 | molecules-27-05187-g004.jpg |
0.439662 | 1d04e6c752fd46b597c0f6c2e147a898 | Particle size of SONLC and ASE + SONLC (A); PDI (B) and Zeta potential (C) of BNONLC and ASE + BNONLC stored at temperatures of 4 °C and 32 °C. | PMC9412625 | molecules-27-05187-g005.jpg |
0.484885 | 58cff4e6183d4d61be810783fd6e26e9 | UV-VIS spectra of nanoemulsions formulations and NLCs containing annatto seed oily extract in chloroform at room temperature. | PMC9412625 | molecules-27-05187-g006.jpg |
0.407443 | 0652ec75dac649e79cbdc1aed21d9887 | Measles breakthrough cases in countries with endemic or sporadic measles transmission. In populations where the majority of individuals are naïve, MV can circulate endemically and most of the infected individuals will be unvaccinated subjects (top). On the other hand, in populations with high vaccination coverages (and lower MV circulation), the number of vaccination failure cases among susceptible individuals will be higher and so will be the proportion of breakthrough cases (bottom). | PMC9413104 | microorganisms-10-01567-g001.jpg |
0.442985 | 674d85299b2840968423a2ff69dba634 | Possible vaccine outcomes in relation to type G immunoglobulins (IgG) levels. Immunization with a first vaccine dose causes the development of an immune response and the production of long-lasting levels of IgG (continuous lines) while a second vaccine dose causes a boost of IgG levels (dotted lines) that increases protection duration. Successfully immunized individuals maintain, already after the first dose, or develop after the second dose, an immunity that protects them from future infections and IgG levels are above the protective level (grey lines). Secondary vaccine failure occurs when the level of IgG drops below the protective level (although IgG can still be detected in these individuals), while non-responders are those subjects that never develop protective immunity (unmeasurable IgG levels). | PMC9413104 | microorganisms-10-01567-g002.jpg |
0.523428 | d53505ff671b4ad99377a13a634b53c6 | Graphical representation of molecular and serological profiles that can be detected during diagnostic tests in different cases after MV infection compared to non-infected subjects. A primary infection usually causes the development of both IgM, which disappear in later stages of the infection, and long-lasting IgG. When a case meets the clinical definition of measles the presence of IgM and/or the detection of the virus indicates the occurrence of an active acute infection (right panel), while their absence implies a different infection causing the symptoms (middle panel). During an acute infection, a non-immunized subject will not yet present measurable IgG while, in a post-acute infection (beyond 10 days), low-avidity IgG (indicating a recent infection) will be measurable in these subjects (while IgM and virus levels drop) (red boxes in the right panel). During reinfection, IgG would not be measurable in non-responders while high-avidity IgG (indicating a past infection) can be detected in secondary vaccination failure (yellow boxes in the right panel). During a post-acute reinfection non-responders can present low-avidity IgG. A recovered or successfully vaccinated individual will present no acute infection markers (IgM and virus) while possessing high-avidity IgG (left panel). | PMC9413104 | microorganisms-10-01567-g003.jpg |
0.397742 | 6b500855b03f434a8ecba15f5de0ab82 | Distribution of the ASFVs from the territory of the RF, EU and Asia based on the sub-division of sequences using the CVR locus. Sequences represent ASFV samples obtained between 2007 and 2020, from either DPs (circles) or WBs (squares). The sub-division of CVR sequences into six groups is graphically presented as indicated by the key provided in the figure. | PMC9413668 | pathogens-11-00919-g001.jpg |
0.408586 | 86c2edbac0fa406b83d09e2999d25a66 | Maximum-likelihood phylogenetic tree based on the 281 bp partial sequence of the B602L gene (CVR) of the ASFV genome. Included in this analysis are the 55 sequences generated in this study from isolates within the RF, as well as sequences obtained from Genbank. Solid black circles are used to identify isolates from this study that belonged to groups other than group 1 (Georgia 2007/1); isolates that showed 100% identity to Georgia 2007/1 are not shown. | PMC9413668 | pathogens-11-00919-g002.jpg |
0.408683 | 4f60a59df683423e9b3eda0a4c6def41 | Mean use report of number per species in plant species frequency categories. | PMC9413674 | plants-11-02093-g001.jpg |
0.444214 | 52487ab265584ef88940be40d9de0e67 | Map of the study area. Locations where data were collected are marked with a red circle. | PMC9413674 | plants-11-02093-g002.jpg |
0.457372 | 42e89b58cd74415693e1de05da615008 | (A) Solitary, irregular 3.6×3.8 cm plaque over the dorsal aspect of the left proximal forearm in an 87-year-old man. The tissue cultured Cladophialophora bantiana (C. bantiana), a neurotropic fungus known for causing brain abscesses in immunocompetent and immunocompromised patients. (B) Complete healing of the left proximal forearm site 1 month after complete excision of the lesion with direct closure. | PMC9413773 | ActaDV-101-7-138-g001.jpg |
0.526005 | c8b7994b116b41d7ace8676eef627f9f | Haematoxylin and eosin stain (×40 magnification) of a biopsy of the patient’s left proximal forearm demonstrating pigmented (dematiaceous) hyphae and yeast-like structures in an inflammatory background, including multinucleate histiocytes. The biopsy was in keeping with phaeohyphomycosis. | PMC9413773 | ActaDV-101-7-138-g002.jpg |
0.520912 | 04b373b8b5e9457da84e4a07a1bf1cdc | Anti-inflammatory activity of the essential oil of Z. acanthopodium fruits from Myanmar (EOZM). (a) The cytotoxicity of EOZM in RAW 264.7 cells. Cell viability were by MTS assay. (b) The inhibitin of EOZM on LPS-induced NO production in RAW 264.7 cells. Results were presented as mean ± standard deviation (SD) of three independent tests, **** p < 0.0001. | PMC9413833 | molecules-27-05243-g001.jpg |
0.534168 | 70a8cc5b54b7463c89c840ff5edb0ae1 | Anti-inflammatory activity of the essential oil of Z. acanthopodium fruits from Myanmar (EOZC). (a) The cytotoxicity of EOZC in RAW 264.7 cells. Cell viability were by MTS assay. (b) The inhibitin of EOZC on LPS-induced NO produc-tion in RAW 264.7 cells. Results were presented as mean ± standard deviation (SD) of three independent tests, *** p = 0.002, **** p < 0.0001. | PMC9413833 | molecules-27-05243-g002.jpg |
0.470869 | cf54e43988234332bb5f19dc1aed4d04 | Larvicidal activity of essential oils from Z. acanthopodium fruits. | PMC9413833 | molecules-27-05243-g003.jpg |
0.462562 | fd8d495f1a0a4970ad00e77c624815dd | The wheel-legged robot BHR-W. | PMC9414717 | micromachines-13-01252-g001.jpg |
0.462725 | c0fd1ad277d34207af0662882504a244 | Cloud maps of the range of movement of the robot’s limbs. (a) The cloud map of arms; (b) The cloud map of legs. | PMC9414717 | micromachines-13-01252-g002.jpg |
0.496652 | a7440c5af46844a3a93034bc6931438e | Three-dimensional (3D) structural model of the robot limbs. (a) Integrated structure of the robot leg and motors; (b) Structure of the robot arm. | PMC9414717 | micromachines-13-01252-g003.jpg |
0.399925 | dc6651ff9b9944c99e7fa69e8d20956b | Kinematic analysis of the relationship between elbow joint angle and torque. | PMC9414717 | micromachines-13-01252-g004.jpg |
0.502801 | 9f7d7ca408154beba6c18eca18188ae6 | Generalized coordinates of the wheel-legged robot model: (a) The inverted pendulum model on wheels; (b) The simplified model of body locomotion. | PMC9414717 | micromachines-13-01252-g005.jpg |
0.443516 | b02f72bcb5da49ca9eba6d10ab989bc2 | Overview of the controller architecture including two-wheeled balance controller, crawling on four limbs controller and mode transition controller. α=[α1α2α3]T is the angle of each joint of the arm. | PMC9414717 | micromachines-13-01252-g006.jpg |
0.47349 | 11960d6fa9d9418bbbefd9507264447c | The model of limbs crawling motion: (a) The variable height crawling model; (b) The crawling turning model. | PMC9414717 | micromachines-13-01252-g007.jpg |
0.444895 | eea9a785dcc94084a1dfcbb4f7afd4cd | The model of kneeling down. | PMC9414717 | micromachines-13-01252-g008.jpg |
0.443523 | 7d75954fd9e9490094e3825e289a8d21 | An under-actuated second-order inverted pendulum model: (a) The kneeling posture; (b) The leaning back posture; (c) The squatting posture. | PMC9414717 | micromachines-13-01252-g009.jpg |
0.484802 | a32656577ca94f3ca674d09d98b8d1b4 | The transition process from crawling to kneeling (a–e). | PMC9414717 | micromachines-13-01252-g010.jpg |
0.457333 | 7ea3eb29ff1c43818a07cff585039aa9 | Upright balanced moving experiment on the outdoor road: (a–c) Forward experiment; (d–f) Turning experiment. | PMC9414717 | micromachines-13-01252-g011.jpg |
0.413635 | edcbd3259dac40cf854545a0fb152cb4 | Date graph of the upright balanced moving experiment on the outdoor road: v is the forward velocity of the robot; θ is the angle between the CoM and the z-axis. | PMC9414717 | micromachines-13-01252-g012.jpg |
0.441372 | ea6db5d7da864fbe9f659dfb8b5833e2 | The robot crosses a channel with a size of 30 cm × 80 cm × 60 cm and performs a turn (a–h). | PMC9414717 | micromachines-13-01252-g013.jpg |
0.43079 | 56f1337c73e943a28ef93ed9f7e56b82 | The transition experiment of the robot from the upright balanced moving mode to kneeling on the ground: (a) Balanced standing; (b–e) From balanced standing to kneeling. | PMC9414717 | micromachines-13-01252-g014.jpg |
0.424198 | 44fd016a15d14bed940e4aa3e62a4acc | The position and velocity data of the CoM during the kneeling down experiment (a–d). | PMC9414717 | micromachines-13-01252-g015.jpg |
0.410515 | 70b6e45a9d094c4d9f3ad251181b2c4a | The transition of robot posture from crawling on all limbs to kneeling: (a) Crawling on limbs; (b–e) From crawling to kneeling. | PMC9414717 | micromachines-13-01252-g016.jpg |
0.429554 | ce92840c3e86456cae2fd8df890bd3a1 | Mode transition experiment from kneeling to balanced standing (a–f ). | PMC9414717 | micromachines-13-01252-g017.jpg |
0.550011 | 9519e495311348c08f7057a7d62a328a | The position and velocity data of the CoM during the modal transition from kneeling to balanced standing (a,b). PCoMx is the projection of the distance of the CoM relative to the coordinate system ∑w on the x-axis and vx is the velocity of the CoM in the x-axis direction. | PMC9414717 | micromachines-13-01252-g018.jpg |
0.436655 | 710d3ac1532e49d9b8a5bbeac967f046 | The experiment of the robot standing up (a–e). | PMC9414717 | micromachines-13-01252-g019.jpg |
0.423055 | d688efe9318344d8b4206cf183559b6f | The position and velocity data of the CoM relative to the coordinate system ∑w during standing up (a–c). | PMC9414717 | micromachines-13-01252-g020.jpg |
0.445815 | 0bffed2957c9488391b5c2e48d33c2c6 | Snapshot (3D holotomography) of SW1573 cells untreated (control) and treated with PTX, CLC and VBL at 0, 10 and 20 h after exposure. Yellow arrows: subpopulation of cells showing a rounded morphology. Scale bar = 20 µm. | PMC9415461 | molecules-27-05261-g001.jpg |
0.409166 | b0895d00c7c24a42a969d5dca2b9be22 | Snapshot of SW1573 cells treated with microtubule targeting drugs PTX, CLC and VBL. (a) Displaced mitotic spindle from the center of the cell (yellow arrows) during division, with loss of cell polarity; (b) Cells trying to undergo mitosis: Unresolved cytokinesis resulted in a new, polynucleated entity (blue arrow); (c,e) Mitotic cells showing disruption of cell structure, typical of microtubule destabilization; (d,f) Necrotic cells after exposure to drugs. Red arrows point to membrane breakdown, sign of necrotic cells. Scale bar = 20 µm. | PMC9415461 | molecules-27-05261-g002.jpg |
0.428472 | 9da03abe567d43089fcd8e6186f6ec8e | Time series of the 11 phenotypic-related parameters (mean and standard deviation): (A) Cell area (0–1000 µm2); (A%) Cell area (0.4–2%); (P) Cell perimeter (50–400 µm); (FF) Cell form factor (0–0.8); (EX) Cell extent (0.4–0.8); (C) Cell compactness (0–15); (EC) Cell eccentricity (0.2–1); (RI) Mean RI (1.34–1.37); (DMD) Average dry mass density (0.05–0.2 pg/µm3); (DM) Dry mass (80–250 pg); and (G) Cell granularity (5–15). For larger individual graphs please refer to Table S1 (Supplementary Materials). For each phenotypic parameter graph, dark lines represent mean values and shaded areas denote standard deviation. | PMC9415461 | molecules-27-05261-g003.jpg |
0.441985 | 0654aa5e92974726a6c3748ffabfa059 | FFT analysis of the time series of the 11 phenotypic related parameters. Absolute values of (a) maximal amplitude and (b) phase. (A) Cell area (µm2); (A%) Cell area (%); (P) Cell perimeter (µm); (FF) Cell form factor; (EX) Cell extent; (C) Cell compactness; (EC) Cell eccentricity; (RI) Mean RI; (DMD) Average dry mass density (pg/µm3); (DM) Dry mass (pg); and (G) Cell granularity. Column bar groups correspond, from left to right, to control cells, PTX, CLC and VBL treatment. | PMC9415461 | molecules-27-05261-g004.jpg |
0.437434 | 0c0bc40453e44701ae5ae0e1c4454c69 | Correlation matrix (heat-map) of phenotypic parameters for (a) untreated SW1573 cells, and SW1573 cells treated with (b) PTX; (c) CLC; and (d) VBL. Legend: (A) Cell area (µm2); (A%) Cell area (%); (P) Cell perimeter (µm); (FF) Cell form factor; (EX) Cell extent; (C) Cell compactness; (EC) Cell eccentricity; (RI) Mean RI; (DMD) Average dry mass density (pg/µm3); (DM) Dry mass (pg); and (G) Cell granularity. | PMC9415461 | molecules-27-05261-g005.jpg |
0.519073 | 9ca065ea64f441778a6ea4f7dc43051a | Correlation matrix (heat-map) of phenotypic parameters (Table 2) together with the cluster analysis grouping the tested conditions. Red: positive correlation, White: no correlation, Blue: negative correlation. | PMC9415461 | molecules-27-05261-g006.jpg |
0.410112 | fcaa7b7886a14ecd84eb53ad164a6033 | A Neighbor-Joining (NJ) phylogenetic tree (1000 bootstrap replicates) constructed using Kimura two-parameter correction methods of MEGA X, from the alignment of the HPV16 E6 sequence from sub-lineages A1, A2, A3, A4, B1, B2, B3, B4, C1, C3, C4, D1, D2, D3, D4 and HPV16 E6 sequences from the present study. | PMC9415711 | viruses-14-01724-g001.jpg |
0.426805 | 70438ea8258d4fd8965438d887b6dbd0 | Use of CRISPR-Cas9 in Host Factor Screening, Validation, and Therapeutic Applications. CRISPR-Cas9 serves as a valuable tool in host factor discovery, validation, and therapeutic intervention. As a tool for in vitro studies, CRISPR-Cas9 enables researchers to conduct high-throughput genetic screens of thousands of potential host factors in cells lines and primary cells. After potential host factors are identified through genetic screens, CRISPR-Cas9 can be used in follow-up studies to validate and determine the mechanism of these host factors. CRISPR-Cas9 is also being investigated as a therapeutic strategy against infection. | PMC9415735 | pathogens-11-00891-g001.jpg |
0.38923 | a73c834f2ddf4f50a34963afb4eebebe | Pooled versus Arrayed CRISPR-Cas9 Screens in Host Factor Discovery. Genetic screens aimed at host factor discovery can be conducted in a pooled or an arrayed format. (a) Pooled libraries of sgRNAs are delivered to cells, resulting in a population of cells with different genetic perturbations. Cells are then selected based on successful genetic editing. Following viral infection, cells undergo next-generation sequencing to identify screen hits. (b) In arrayed screens, CRISPR-Cas9 reagents are synthesized to target a single gene in each well of multi-well plates. Following infection, the phenotype of interest for the screen can be directly observed in each well. | PMC9415735 | pathogens-11-00891-g002.jpg |
0.401803 | 54e7fc8d47194543a59a4331292bd83a | Induction of CCR5-Δ32 into CD4+ T cells using CRISPR Cas9. HIV infects cells through engagement of its main receptor, CD4, and a co-receptor, such as CCR5. A minor allele of CCR5 containing a 32 base pair exonic deletion results in the expression of a truncated protein that is unable to engage HIV Env and mediate viral entry. CRISPR-Cas9 gene editing has enabled the engineering of CD4+ T cells and stem cells that carry this mutation for use in curative cell-based therapies. | PMC9415735 | pathogens-11-00891-g003.jpg |
0.462761 | 85b380689698418dbbcd62623a44809d | HIV-1 host factors influence the viral life cycle. (A) Through genetic perturbation or chemical inhibition, researchers can decrease the expression or activity of a given HIV dependency factor to reduce HIV infection and replication. Likewise, through the use of gene activation or small molecule agonists, researchers can increase protein expression or activity of restriction factors to reduce HIV infection. The font size of host “dependency” and “restriction” factors demonstrate how the expression level of these proteins affect viral infection levels. (B) Throughout the course of the viral life cycle, HIV encounters several host proteins, some shown here. Certain proteins, which are recruited to assist in the various stages of infection, are known as dependency factors (in blue) while others are known to restrict HIV infection (in red). | PMC9415735 | pathogens-11-00891-g004.jpg |
0.415242 | d9ac44e4a5ad400aaaf0d9edda647f9d | Gallic acid standard curve for the total phenolic acids content determination. | PMC9415796 | molecules-27-05227-g001.jpg |
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