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0.422598 | 4d29ac9da9284a15bbe2b10713df9c5c | Sugar composition (%, w/w) of chicory (a) and coffee (b) high molecular weight material. 1 Fru was estimated as the sum of mannitol and glucitol using its epimerization ratio during the reduction step [30]. Sugar residues: Ara—arabinose, Man—mannose, Gal—galactose, Glc—glucose, Fru—fructose, and UA—uronic acids. | PMC9818759 | foods-12-00134-g001.jpg |
0.394158 | b1221c73b33748119b2672bbd9d368f3 | Phenolic composition (g caffeic acid equivalents (CAE)/100 g of sample) of chicory (purple bars) and coffee (brown bars) high molecular weight material. Lowercase letters refer to significantly different values (p < 0.05) among the samples. | PMC9818759 | foods-12-00134-g002.jpg |
0.426954 | 1478e71d315a4ab58315252ce3c9aec0 | Effect of chicory (purple bars) and coffee (brown bars) high molecular weight material on the growth of S. aureus (a), L. monocytogenes (b), and B. cereus (c) represented as Log CFU/mL (CFU, colony forming units). 1 Control refers to assays with only the bacterial inoculum. Lowercase letters refer to significantly different values (p < 0.05) in relation to each corresponding control. | PMC9818759 | foods-12-00134-g003.jpg |
0.501462 | e68b60be4b964d52be83f774fbdeb311 | Inhibitory activity (%) against α-glucosidase of acarbose and chicory and coffee high molecular weight material (HMWM), at 4 mg/mL. Lowercase letters refer to significantly different values (p < 0.05) among samples. | PMC9818759 | foods-12-00134-g004.jpg |
0.591686 | d37fcef1c1ea4a828dafc939005fb3ee | Example of difference between discriminant (a) modeling and (b) classification methods. In (a) the hyperspace is divided into regions equal to the category number. | PMC9819000 | foods-12-00139-g001.jpg |
0.475506 | f1f42349e0e84c1da1ed3eaa89d2fbcf | Schematic representation of the main steps useful to develop and validate a chemometric approach. | PMC9819000 | foods-12-00139-g002.jpg |
0.425752 | 8d1a7bf8ecb34bb0a1c4d253ac1b5139 | Graphical tools to represent classification model performance. (a) Receiver operating characteristic (ROC) curves; (b) Coomans’ plot. | PMC9819000 | foods-12-00139-g003.jpg |
0.412363 | bd079c1bf08d4cdc910cc0fc64906313 | Comparison of document consistency and health promotion expression, in terms of word count and concept. | PMC9819165 | healthcare-11-00148-g001.jpg |
0.457884 | da278ac5e04a4aa4b0444dcf0836c02c | Diagram of hierarchies in Italian LHAs for health promotion. | PMC9819165 | healthcare-11-00148-g002.jpg |
0.500124 | 033ca139b5884ab28093119a70087b8a | Distribution of LHAs involved in health promotion in Italy. | PMC9819165 | healthcare-11-00148-g003.jpg |
0.439461 | 46b9b5ba2e384950a5bad51fa560eabf | Distribution on the Italian territory, divided by the regions whose LHAs have projects dedicated to the topics indicated. | PMC9819165 | healthcare-11-00148-g004.jpg |
0.475346 | 410ecab7fbe1448c8e51da40c447e5be | The theoretical model. | PMC9819526 | ijerph-20-00707-g001.jpg |
0.455003 | 71aa96498b8a49a581fba95cdeab977d | The relationship between authoritarian leadership and leader effectiveness under the conditions of low and high leader capability. | PMC9819526 | ijerph-20-00707-g002.jpg |
0.439291 | 7cc159453b7f45d4a79b442c3488b69b | sEMG events (expressed by root–mean–square voltage at a specific moment of time): (a) tonic contraction, (b) rhythmic or phasic contraction, (c) light grinding or rubbing of the teeth, (d) severe grinding, (e) mandibular clenching, and (f) pushing the mandible. | PMC9819829 | ijerph-20-00581-g001a.jpg |
0.410797 | 16432610722644819974fe94da6fba5d | Comparative analysis of the summary of relevant activities recorded at the masseter level (sleep time interval—green color, awake time interval—red color, detachment time interval—yellow color) for: (a,c,e,g,i) one participant from the study group and (b,d,f,h,j) one participant from the control group. | PMC9819829 | ijerph-20-00581-g002.jpg |
0.470423 | c042850f602644139fcd9cb722c05c68 | Distribution of clenching, grinding, and other types of events, for the two groups, during nighttime. | PMC9819829 | ijerph-20-00581-g003.jpg |
0.448435 | 4ad6b4c4a81945f79fc7f0ccae2351d0 | Distribution of clenching, grinding, and other types of events, for the two groups, during daytime. | PMC9819829 | ijerph-20-00581-g004.jpg |
0.433341 | 5e0165c91b5240c2a0c69eb2a7a437ce | Evolution of the values of the indices for bruxism and for the activity of the masseter muscle: (a) sleep state and (b) awake state. | PMC9819829 | ijerph-20-00581-g005.jpg |
0.414376 | c14efc1e603c4b169d8002a97bda0164 | Characteristics of cAMP accumulation and Adenylyl cyclase (AC) activity in BV-2 mouse microglial cells. Temporal changes in intracellular cAMP concentration in response to 1 μM PGE1 and another stimulant, (a) 200 mM ethanol, (b) 100 nM S1P, or (c) 1 unit/mL thrombin, were monitored by FRET analysis detailed in the Section 4 Materials and Methods. Fluorescent images were captured every three seconds for three minutes. Normalized nFRET values were plotted over time. The horizontal bar above the graph indicates a 2 min period when indicated drugs were perfused. Data are represented as mean ± SEM (n = 8–20). Statistical analyses of the values from 100 s to 180 s were carried out by two-way repeated measures ANOVA. In all three experiments, values for Basal, PGE1, and PGE1 plus stimulant (ethanol, S1P, or thrombin) were statistically different from each other. AC activity of BV-2 mouse microglial cells. (d) AC activity of BV-2 cells in response to 1 min drug stimulation was measured using a cAMP accumulation assay. Cells were incubated with 500 μM IBMX for 10 min and stimulated with 1 μM PGE1 or PGE1 plus another stimulant: 200 mM ethanol, 100 nM S1P, or 1 unit/mL thrombin for 1 min. Data are represented as mean ± SEM in triplicate. * indicates values are significantly larger than the value for cells treated with PGE1 alone at p < 0.05 using one-way ANOVA. (e) The effects of ethanol on PGE1-stimulated AC activity were examined. Cells were incubated with 500 μM IBMX for 10 min and stimulated with 1 μM PGE1 with varying concentrations of ethanol (0, 25, 50, 75, 100, 150, or 200 mM). * Indicates value is significantly larger than PGE1 alone at p < 0.05 using one-way ANOVA. | PMC9820266 | ijms-24-00347-g001.jpg |
0.553481 | c8df868608ec421baad56b8121e5b70f | Preparation of NGS samples. | PMC9820266 | ijms-24-00347-g002.jpg |
0.394816 | 9202c178cd824dadb5ce2060c4a0eb6b | AC activity of AC7 knockout (KO) clones of BV-2 mouse microglial cells. (a) Cells were stimulated with 1 μM PGE1 for 1 min after 10 min of incubation with 500 μM IBMX. Data are represented as mean ± SEM in triplicate. The value for the wild-type clone (WT) does not differ from the value of the parental BV-2 cells. * indicates the values for AC7 KO clones No. 1 to No. 9 are significantly smaller than the value of the parental BV-2 cells at p < 0.05 using one-way ANOVA. (b) Cells of the WT clone and AC7 KO clones No. 2 and No. 4 were incubated with 500 μM IBMX for 10 min and stimulated with 1 μM PGE1 or PGE1 plus another stimulant: 100 nM S1P or 200 mM ethanol for 1 min. Data are represented as mean ± SEM in triplicate. * indicates the values of clones No. 2 and No. 4 are significantly smaller than the value of the WT clone at p < 0.05 using one-way ANOVA. # indicates that, for the WT clone, values for PGE1 plus S1P and PGE1 plus ethanol are significantly larger than the value for PGE1 alone at p < 0.05 using one-way ANOVA. There are no significant differences among the three treatment groups in the AC7 KO clone No. 2 and No. 4. | PMC9820266 | ijms-24-00347-g003.jpg |
0.42854 | 77eaa112ed7f4a038619fd0af1ea1aba | Effects of AC7 KO on phagocytosis, bacteria-killing, and oxidative burst of BV-2 cells. Assays are detailed in the Section 4 Materials and Methods. (a) Phagocytosis assay with parental cells, the WT clone, and two AC7 KO clones were carried out in the presence of no drugs (Basal), 10 μM PGE1, 10 μM PGE1 plus 50 mM ethanol, or 50 mM ethanol. Data are presented as mean fluorescent intensity (MFI) and are represented as mean ± SEM in triplicate. * indicates the value is significantly smaller than the corresponding basal value. # indicates the value is significantly smaller than the basal value of parental cells at p < 0.05 using one-way ANOVA. (b) Bacteria-killing activity of the parental cells and one of the AC7 clones was measured by counting live bacteria before (T0) and after 90 min incubation (T90). One set of samples included 50 mM ethanol during the incubation (T90 EtOH). Data are represented as mean ± SEM in triplicate. * indicates the value is significantly smaller than the corresponding T0 value at p < 0.05 using one-way ANOVA. (c) Oxidative burst was measured under the basal condition, 10 μM PGE1, 10 μM PGE1 plus 50 mM ethanol, or 50 mM ethanol. Data are presented as MFI and are represented as mean ± SEM in triplicate. * indicates the value is significantly smaller than the corresponding basal value. | PMC9820266 | ijms-24-00347-g004.jpg |
0.463582 | 49dbb69c32cf46d88c4831f5bf103fe6 | Activation of BV-2 AC7 KO cells. (a) The quantity of iNOS mRNA was measured by qRTPCR after activation with no drugs (Basal), 3 ng/mL lipopolysaccharide (LPS) plus 40 U/mL interferon (IFN)-γ or 3 ng/mL LPS plus 40 U/mL IFN-γ plus 50 mM ethanol as indicated. The expression level of mRNA was normalized to β-actin mRNA in the same sample. Data are represented as mean ± SEM in triplicate. The basal value for KO No. 4 is 4.8 × 10−5 ± 4.0 × 10−6. * indicates the value is significantly larger than the corresponding LPS + IFN value at p < 0.05 using one-way ANOVA. (b) NO production was assessed by measuring nitrite in the culture medium as described in the Section 4 Materials and Methods. Cells were incubated with 3 ng/mL LPS and indicated concentrations of IFN-γ with or without 50 mM ethanol. Data are represented as mean ± SEM in triplicate. * indicates that in the parental cells, IFN-γ concentration-dependently increased NO production and ethanol significantly decrease NO production at p < 0.05 using two-way ANOVA. For AC7 KO clone No. 4, IFN-γ or ethanol did not have any significant effects on NO production. (c) The quantity of Arg1 mRNA was measured by qRTPCR under basal conditions or activated with 20 U/mL interleukin 4 (IL-4) or 20 U/mL IL-4 plus 50 mM ethanol. The expression level of mRNA was normalized to β-actin mRNA in the same sample. Data are represented as mean ± SEM in triplicate. The basal value for KO No. 4 is 3.9 × 10−6 ± 6.0 × 10−7 (d) The quantity of interleukin 10 (IL-10) mRNA was measured by qRTPCR under basal conditions or activated with 100 ng/mL LPS plus 10 μM PGE1 or 100 ng/mL LPS plus 10 μM PGE1 plus 50 mM ethanol. The mRNA expression level was normalized to β-actin mRNA in the same sample. Data are represented as mean ± SEM in triplicate. The three values of AC7 KO cells are not significantly different from one another. | PMC9820266 | ijms-24-00347-g005.jpg |
0.418846 | 7e33a613dc914c31a4c4276fae91d9e1 | Surrogate Reporter. The figure shows how the reporter generates mRFP-eGFP fusion protein because of a gene-editing event. PCMV: cytomegalovirus promoter; mRFP: coding sequence of monomeric red fluorescent protein; AC7: AC7 gene derived target sequence; Stop: in-frame stop codon; eGFP: coding sequence of enhanced green fluorescent protein; 2A: self-cleaving peptide; H-2KK: mouse MHC class I alloantigen; DSB: double-strand break; NHEJ: non-homologous end-joining. | PMC9820266 | ijms-24-00347-g006.jpg |
0.504754 | 5207d16a27da43dfb37dcca42bd40c2a | Pedigree of the family SD5. The affected family member is shown with closed and crossed-out symbols, and the unaffected parents with open symbols. Proband is marked by the black arrow. Fragments of the Sanger electropherograms of the TPM1 gene are shown near the symbol of a family member. The letters in the upper row do not designate amino acids but nucleotides (A—adenine, green peaks; C—cytosine, blue peaks; G—guanine, black peaks; and T—thymine, red peaks). The heterozygous mutation c.656A>T (p.D219V) is marked by the red arrow. | PMC9820293 | ijms-24-00018-g001.jpg |
0.565866 | 7cb4710da0bb4334924ba29aa5570691 | Temperature dependences of the excess heat capacity (Cp) monitored by DSC and deconvolution analysis of the heat sorption curves for WT Tpm (A) and Tpm with mutation D219V (B). Solid lines represent the experimental curves after the subtraction of instrumental and chemical baselines, and dotted red lines represent the individual thermal transitions (calorimetric domains) obtained from fitting the non-two-state model [25] to the data. | PMC9820293 | ijms-24-00018-g002.jpg |
0.408204 | af8e36ce9d9f42318ca098999126ed6f | The effect of the D219V Tpm mutation on the Tpm affinity for F-actin was estimated by the co-sedimentation assay (A), and the thermal stability of the Tpm-actin complex was determined by light scattering (B). | PMC9820293 | ijms-24-00018-g003.jpg |
0.471949 | 3f452342ad474ad9b04d8931ed13e37f | The effects of the D219V Tpm mutation on the dependence of the sliding velocity of the F-actin–Tpm filament on the concentration of ventricular (A) and atrial (B) myosin in the in vitro motility assay. Each data point represents the Mean ± SD from three experiments. The data are fitted with the Hill equation. The parameters of the Hill equation are presented in Table 2. | PMC9820293 | ijms-24-00018-g004.jpg |
0.451957 | 6409adcb04444713987cc3fa5712b4b4 | The effects of the D219V Tpm mutation on the Ca2+-dependent sliding velocity of regulated thin filaments moving over ventricular (A) and atrial (B) myosin in the in vitro motility assay. Each data point represents the mean ± SD from three experiments. The data are fitted with the Hill equation, and the parameters of the pCa-velocity relationships are presented in Table 3. | PMC9820293 | ijms-24-00018-g005.jpg |
0.489279 | 71c71581bb1e4e959d7535eef6cb5a90 | The dependence of the maximal sliding velocity of thin filaments with WT and D219V Tpm at saturated Ca2+ concentration on LV (A) and LA (B) myosin concentrations added to the flow cell. C50 for LV myosin with WT Tpm was 30.1 ± 0.9 µg/mL and 25.9 ± 3.1 µg/mL with D219V Tpm. The C50 for LA myosin with WT Tpm was 45.3 ± 1.5 µg/mL and 47.2 ± 1.1 µg/mL with D219V Tpm. | PMC9820293 | ijms-24-00018-g006.jpg |
0.428649 | 4193eab231c949549b3d42f18a605b09 | Location of the D219 Tpm residues in the structure of the regulatory unit of the thin filament of cardiac muscle ([31]; PDB code 6KN7). Actin atoms are shown by space-filling (CPK) representation (cyan); the Tpm molecules are shown by red ribbons; Tn-Cs are magenta ribbons; Tn-Is are orange ribbons; and Tn-Ts are blue ribbons. An h-bond between the D219 residue of Tpm and the K326 residue of actin is shown in the inset. Neighbor E218 residue of Tpm and the K328 residue of actin are also shown in CPK. | PMC9820293 | ijms-24-00018-g007.jpg |
0.450539 | 0ba1d91056a2463d891afe4e5d17d74b | The time course of the existence of h-bonds between E218 and D (or V)219 Tpm residues and K326 and K328 residues of neighboring actin monomers during a 200 ns-long MD trajectory with WT Tpm (A,C) and D219V Tpm (B,D); vertical axis: one means at least one h-bond between the residues, zero otherwise; (A–D) correspond to two different Tpm strands. The h-bond between E218 of Tpm and K326 of actin is red; the h-bond between E218 of Tpm and K328 of actin is blue; and the h-bond between D (or V)219 Tpm residue and K326 of actin is green. | PMC9820293 | ijms-24-00018-g008.jpg |
0.458364 | c32b42344460451fa0a117e231a639b1 | In early preterm gestation patients, SLC38A4 protein levels were significantly reduced in IUGR complicated placentas. In IUGR and PE + IUGR placentas, compared to preterm controls, mRNA expressions (assessed via qPCR) of SLC38A1 (A), SLC38A2 (B), and SLC38A4 (C) were not different. Western blots (D) showed that, at the protein level, SLC38A1 (E) was unchanged between groups. SLC38A4 (F) was significantly decreased in both IUGR and PE + IUGR cohorts (n = 11–19 samples/cohort). Results are displayed as medians. ** p < 0.01, *** p < 0.001 (Kruskal Wallis non-parametric test for qPCR results and Western blot analysis). Blue dots (male pregnancies), purple dots (female pregnancies). | PMC9820794 | ijms-24-00403-g001.jpg |
0.419968 | 33e7ca161dd44f0fb875dde1b78d3253 | In late gestation patients, the expression of transporters were indifferent between IUGR complicated placentas and preterm controls. The mRNA levels (assessed via qPCR) of the transporters (A–C) and protein levels (assessed via Western blots) (D) when quantified (E,F) did not reveal changes to the expression of system A amino acid transporters. (n = 8–25 samples/cohort). Results are displayed as medians (Kruskal Wallis non-parametric test). Blue dots (male pregnancies), purple dots (female pregnancies). | PMC9820794 | ijms-24-00403-g002.jpg |
0.438985 | d8a30a1529404dfa98c85d9e182a06ee | Glucose deprivation significantly upregulated transporter expression in vitro trophoblast cultures. Compared to normal media controls, glucose deprived cultures significantly upregulated SLC38A1 (A), SLC38A2 (B), and SLC38A4 (C) expression (n = 6/condition). Additionally, in western blots (D), the expressions of SLC38A1 (E) and SLC38A4 (F) were not significantly altered (n = 6/condition). Results are displayed as medians. * p < 0.05, ** p < 0.01, **** p < 0.0001 (repeated measures are for one-way ANOVA for qPCR analysis and for Western blot analysis). | PMC9820794 | ijms-24-00403-g003.jpg |
0.490289 | 558c8b46ba4d4f488f74df10b1e7f6cf | IUGR relevant hypoxic stress conditions induced SLC38A4 expression at the gene level, however, protein expression remains unaffected in vitro trophoblast cultures. Compared to normoxic controls, hypoxic trophoblasts showed insignificant changes to the expressions of SLC38A1 (A) and SLC38A2 (B). SLC38A4 expression was significantly upregulated under hypoxic conditions (C) (n = 6/condition) (assessed via qPCR). Western blots (D) showed patient derived variations in response to hypoxia, which did not consistently alter the expressions of SLC38A1 (E) or SLC38A4 (F) (n = 7/condition). Results are displayed as medians * p < 0.05 (the paired t-test was used for qPCR analysis, and the Wilcoxon nonparametric test was used for Western blot analysis). | PMC9820794 | ijms-24-00403-g004.jpg |
0.475144 | e32ea83e145449e596c20342c2b8cfe2 | Graphical 3D models to demonstrate surgical over-contouring during augmentation of the alveolar process. (a) Lateral view of the mandible with a single-tooth gap on the position of the second right incisor; the bone defect was compensated without exceeding the extent of the adjacent bone level. (b) Lateral view of the mandible with a single-tooth gap on the position of the second right incisor; the bone defect was over-compensated by an over-augmentation of the alveolar process beyond the extent of the adjacent bone level. This is defined here as “over-contouring”. (c) Isometric view of the mandible with a single-tooth gap on the position of the second right incisor; the difference between appropriate augmentation (left half) and over-contouring (right half) is clearly visible. (d) Lateral view of the mandible with a single-tooth gap on the position of the second right incisor; the difference between appropriate augmentation (left half) and over-contouring (right half) is easily recognizable. | PMC9820942 | jcm-12-00006-g001a.jpg |
0.494864 | fdbdc0a2a1b84f2a9f6b95a7d452ffcf | Clinical example of over-contouring. (a) Class III defect with pronounced vestibular deficit and minor vertical deficit. (b) Post-OP: Attachment of a cortico-cancellous allogeneic bone block. Vestibular over-contouring with the cortical plate and vertical over-contouring of the block can be seen. The block protrudes over the limbus alveolaris, i.e., over the bone border of the adjacent teeth. (c) After 5 months: the over-contoured portion of the cortical portion of the block was not resorbed, but part of the cancellous portion of the block was palatally resorbed away. (d) The over-contoured block penetrates the mucosa. The cortical portion is revealed. | PMC9820942 | jcm-12-00006-g002a.jpg |
0.464115 | 2c87831663e04e8e8cca12c5bf575233 | Clinical example of no over-contouring. (a) Class III defect with pronounced vestibular deficit and minor vertical deficit. (b) Attachment osteoplasty with a cancellous allogeneic bone block. The block fits into the contour of the surrounding alveolar process (within the envelope). The lining with the bovine granules is visible (vestibular opaque line). Cranially, the bone block ends at the bone border of the adjacent teeth. | PMC9820942 | jcm-12-00006-g003a.jpg |
0.454585 | e34e7d273cde4878aec58d5af2ff1187 | Receiver operating characteristic curve illustrating the diagnostic ability of the height of the vertical alveolar ridge augmentation in predicting complications. Solid blue: ROC curve; black diagonal line: chance level; vertical red line (J): maximum value of Youden’s index for this ROC curve. | PMC9820942 | jcm-12-00006-g004.jpg |
0.453673 | 03c3107f31e645b89c5e420319bbf64c | Spineplots on the occurrence of a complication in presence of over-contouring, depending on the type of the bone block (autogenous vs. allogeneic). (a) In 100% of complications with autogenous bone blocks, over-contouring was present. (b) However, when there was no over-contouring when using autogenous bone blocks, there were no complications at all. (c) In 42% of complications with allogeneic bone blocks, over-contouring was present. (d) However, when there was no over-contouring when using allogeneic bone blocks, complications occurred in only 6% of cases. | PMC9820942 | jcm-12-00006-g005.jpg |
0.420727 | e02924ff4d284b48872a0558418ca0ea | Logistic regression analysis for the probability of a complication given the height of the vertical augmentation and the presence of over-contouring. The light blue curve represents over-contouring, while the black curve represents a perfectly matching augmentation. (a) Logistic regression in the overall group. (b) Logistic regression restricted to the study group using autogenous bone blocks. It can be seen that with increasing vertical augmentation, the risk of a complication does not increase at all as long as the augmentation remains below the level of the surrounding, natural alveolar ridge. (c) Logistic regression restricted to the study group using allogeneic bone blocks. | PMC9820942 | jcm-12-00006-g006a.jpg |
0.447797 | bd4af50893a74475b67c140a50cc4f8c | The oligoacene dimers considered in this work. Singlet and triplet exciton states have been determined at the eclipsed configuration and along the interchromophore longitudinal (x axis) translation coordinate. | PMC9822017 | molecules-28-00119-g001.jpg |
0.459941 | 513a304e1f3348e6b18d70d4b4bc3810 | Frontier molecular orbital levels and shapes of (left) naphthalene and (right) anthracene, from ωB97X-D/6-31G* calculations (HOMO and LUMO abbreviated as H and L). | PMC9822017 | molecules-28-00119-g002.jpg |
0.455448 | 167d5969d0924fbda8817d5af8836b5a | Definition of SA diabatic states (FE, CR, each symmetry with a specific color code used throughout this work: yellow for Ag, orange for Au, blue for Bg and green for Bu) defined as linear combinations of diabatic states (black, LE, CT) and employed to analyze the nature and the effect of interactions on the adiabatic exciton states of oligoacene dimers. | PMC9822017 | molecules-28-00119-g003.jpg |
0.432035 | cec42152f6b5453d8341db39392ffcdb | Lowest energy singlet exciton states of (left) naphthalene and (right) anthracene: (a,b) adiabatic excitation energy profiles of the low-lying exciton states depicted with different color codes for different symmetries: yellow for Ag, orange for Au, blue for Bg and green for Bu. The lowest energy exciton states not included in the diabatization procedure are also shown (dark blue, Bg symmetry, dark red, Au symmetry), (c,d) CT character of the lowest four exciton states. | PMC9822017 | molecules-28-00119-g004.jpg |
0.395096 | 2808c2325bb645508f7a929c79bf884f | Wavefunction composition of the adiabatic lowest exciton states of (left) naphthalene and (right) anthracene in terms of the SA diabatic states defined in Figure 3. (a,b) 1Ag state and (c,d) 1Bg state. Red and brown lines represent the contributions to the total wavefunction of CT states, while green and blue lines represent the weight of LE states. | PMC9822017 | molecules-28-00119-g005.jpg |
0.424423 | 70f36928e0bc44cd9455162cf9c4df9d | Analysis of the excitation energy profiles of singlet exciton states (Ag symmetry) for (left) naphthalene and (right) anthracene dimer (TDA-ωB97X-D/6-31G*) in terms of SA diabatic states (green for FE states, red for CR states) and their interactions. (a,b) Computed adiabatic and SA diabatic excitation energy profiles. (c,d) Magnitude and modulation along the longitudinal translation coordinate of the (grey) De(2)−Dh(2), De(4)−Dh(4) interactions, coupling FE and CR states, and of the (dark-turquoise) H(2,4)−Ve(2,4) interactions mixing FE(2) and FE(4) states. | PMC9822017 | molecules-28-00119-g006.jpg |
0.497216 | b3e44d25073a40188eaa3dcc4f8caa75 | Analysis of the excitation energy profiles of singlet exciton states (Bg symmetry) for (left) naphthalene and (right) anthracene dimer (TDA-ωB97X-D/6-31G*) in terms of SA diabatic states (green for FE states, red for CR states) and their interactions. (a,b) Computed adiabatic and SA diabatic excitation energy profiles. (c,d) Magnitude and modulation along the longitudinal translation coordinate of the (grey) De(1)−Dh(1), De(3)−Dh(3) interactions, coupling FE and CR states, and of the (dark-turquoise) H(1,3)−Ve(1,3) interactions mixing FE(1) and FE(3) states. | PMC9822017 | molecules-28-00119-g007.jpg |
0.474319 | 22345bfabc204295ac0e44e350a0c123 | Lowest energy triplet exciton states of (left) naphthalene and (right) anthracene: (a,b) adiabatic excitation energy profiles of the low-lying exciton states depicted with different color codes for different symmetries: yellow for Ag, orange for Au, blue for Bg and green for Bu. The lowest energy exciton states not included in the diabatization procedure are also shown (dark blue, Bg symmetry, dark red, Au symmetry); (c,d) CT character of the lowest four exciton states. | PMC9822017 | molecules-28-00119-g008.jpg |
0.454162 | 3b7a575c1bff424a81f9182bf25476fe | Analysis of the excitation energy profiles of triplet exciton states (Bg symmetry) for (left) naphthalene and (right) anthracene dimer (TDA-ωB97X-D/6-31G*) in terms of SA diabatic states (green for FE states, red for CR states) and their interactions. (a,b) Computed adiabatic and SA diabatic excitation energy profiles. (c,d) Magnitude and modulation along the longitudinal translation coordinate of the (grey) De(1)−Dh(1), De(3)−Dh(3) interactions, coupling FE and CR states, and of the (dark-turquoise) H(1,3)−Ve(1,3) interactions mixing FE(1) and FE(3) states. | PMC9822017 | molecules-28-00119-g009.jpg |
0.496327 | caafe6e8bd0743fb87a13e58241206a2 | Analysis of the excitation energy profiles of triplet exciton states (Au symmetry) for (left) naphthalene and (right) anthracene dimer (TDA-ωB97X-D/6-31G*) in terms of SA diabatic states (green for FE states, red for CR states) and their interactions. (a,b) Computed adiabatic and SA diabatic excitation energy profiles. (c,d) Magnitude and modulation along the longitudinal translation coordinate of the (grey) De(1)+Dh(1), De(3)+Dh(3) interactions, coupling FE and CR states, and of the (dark-turquoise) H(1,3)+Ve(1,3) interactions mixing FE(1) and FE(3) states. | PMC9822017 | molecules-28-00119-g010.jpg |
0.421813 | 38c96c364a5146bf980613d2917365c6 | Lowest energy singlet and triplet exciton-state analysis via electron-hole correlation plots [71] for naphthalene and anthracene dimers in their eclipsed configuration. The grey scale used is shown on the right panel. From TDA-ωB97X-D/6-31G* calculations. The magnitude of the CT contribution is shown on the bottom part of each panel. | PMC9822017 | molecules-28-00119-g011.jpg |
0.489224 | d35c2a7e08d5452dba45d0a05377b88d | Comparison between vertical absorption spectra predicted for (a) naphthalene and (b) anthracene dimer, from TDA-ωB97X-D/6-31G* calculations. Modulation along the longitudinal shift from 0 Å to 8 Å. Different colors are used to plot spectra computed for different displacements. Black bar: absorption peak of the Bb state of the monomer, calculated at TDA-ωB97X-D/6-31G* level. | PMC9822017 | molecules-28-00119-g012.jpg |
0.432776 | e2d3b48aed2445c7924bdb739a6a6ab4 | (a) Chemical equilibria controlling crystal growth and termination; deprotonation of linker (i) and additives (ii), complexation (iii) between metal ions and linkers = growth phase, (iv) competition between linker and additive for coordination sites on metal ions = termination phase. (b) Relationship between nanocrystal size, local metal-linker ratios, amount of modulator added and diffusion and complexation rates can be schematically represented in a seesaw model, with Regime I on the left and Regime II on the right side. | PMC9822160 | molecules-28-00253-g001.jpg |
0.480897 | 98b375910f734b4899f86e52ee297792 | SEM micrographs of bioNICS1 with increasing amount of dichloroacetic acid as an additive. (a) 1.5 mol; (b) 2 mol; (c) 3 mol; (d) 4 mol; (e) 6 mol; (f) pristine bioNICS-1 with no additive. | PMC9822160 | molecules-28-00253-g002.jpg |
0.433298 | b551f1af43604600aa51a8bfc5d7d452 | Calculated size of crystal domains plotted against molar addition of used additive; (a) and (b) acetic acid; (c) and (d) dichloroacetic acid; (e) and (f) formic acid; and (g) and (h) propionic acid. Colored ribbon represents a region of 50–100 nm sizes. Graphs on the right side of figure show lower molar additions of acids to emphasize seesaw trend. Error bars are omitted for clarity. Graphs with error bars are found in Supplementary Figure S10. | PMC9822160 | molecules-28-00253-g003.jpg |
0.454405 | bfdebf6c8e7345a798282cb00e7ddc79 | BET surface area of selected materials (a) conventional and (b) microwave heating arranged according to acid used. Colors of the squares are correspondent to the isotherms and pore size distribution presented in Supplementary. Dotted lines represent the BET value of pristine materials. | PMC9822160 | molecules-28-00253-g004.jpg |
0.449161 | 45b0bb7441044d09aa974afae239d21f | Comparison of thermogravimetric curves of selected, activated bioNICS1 materials crystalized under microwave heating for 2 h (a). Grey rectangle is magnified and presented in (b), where the line and arrow are set to the 191 °C marking the start of degradation of the linker. | PMC9822160 | molecules-28-00253-g005.jpg |
0.492091 | 4886be7f5311466ea0acae729d0ec102 | DRIFTS spectra of the specified samples purged with 10 % NH3 in Ar measured from room temperature (red lines) to 150 °C (green lines) shown in three wavenumber regions. Black lines represent the baseline of the activated materials. Represented samples are bioNICS1-MW-2h (A), bioNICS1-FA-18-2h (B) and bioNICS1-PA-6-2h (C). | PMC9822160 | molecules-28-00253-g006.jpg |
0.516935 | f78a6fb4c99747e093bb9e11b4e9cefc | Scheme of the proposed acid binding sites for ammonia in (a) bioNICS1-FA-18-2h and (b) bioNICS1-PA-6-2h. | PMC9822160 | molecules-28-00253-g007.jpg |
0.469112 | 16db4c5191bc490ca956d066504758b6 | Ammonia sorption kinetics profile of selected materials ((A) bioNICS1-MW-2h; (B) bioNICS1-FA-18-2h; (C) bioNICS1-PA-6-2h) and corresponding inserts (below) showing physiosorbed and chemisorbed fractions of adsorbed ammonia, together with remaining NH3 derivative species. Ammonia dosing pressure—blue line, heating temperature—red line. | PMC9822160 | molecules-28-00253-g008.jpg |
0.531616 | 3be04a1c78404e17a012f434b5bb9837 | The process framework of the method and its position in the SLAM system. The method will be inserted into the SLAM system where it was before converting the 2D LiDAR data to the point cloud data. | PMC9823419 | sensors-23-00018-g001.jpg |
0.457787 | 06f2ac9a18644a54868f792b2a1de6a3 | The process of keyframe extraction. Scans that meet the similarity threshold will be treated as keyframes and added to the window to update the window. | PMC9823419 | sensors-23-00018-g002.jpg |
0.453272 | 67012855a871400693d4c19079b12cab | Region segmentation method. The LiDAR is located in the center of all regions, each ring has an equal area, and a ring represents a laser points region. There are three regions in the figure. | PMC9823419 | sensors-23-00018-g003.jpg |
0.430922 | aec950c6b63442dbaaaeb6384b6ed3bd | Clustering of laser points. The angle difference between points i and i+1 in the same region is greater than the θthreshold, and they are divided into different clusters. | PMC9823419 | sensors-23-00018-g004.jpg |
0.49295 | 18932dfb70fd4229a1c14e36ce38bc38 | Merge laser-point cloud blocks. Calculate using points i and i+1, d is the distance between the two points. Blocks 1 and 2 satisfy both the angle threshold and distance threshold. | PMC9823419 | sensors-23-00018-g005.jpg |
0.425258 | c0cd4d68d837426d94ab3922fb164776 | Laser-point cloud blocks cannot be merged. Calculate using points i and i+1, d is the distance between the two points. (a) The angle threshold is met, but the distance threshold is not. (b) The angle threshold is not met. | PMC9823419 | sensors-23-00018-g006.jpg |
0.427405 | 4f82386aff6045c78500118bb5dc5011 | Three-dimensional simulation environment and mobile robot. Red rectangular blocks represent obstacles, and blue cylinder represents mobile robots. | PMC9823419 | sensors-23-00018-g007.jpg |
0.474305 | 42d985baefb84a47ad59f6c39a49a926 | A frame of LiDAR scanning in the simulation environment. The points in the figure represent the point cloud of obstacles. (i) Indicates an abnormal laser point; (ii) refers to a robot. The side length of each grid is 1 m. | PMC9823419 | sensors-23-00018-g008.jpg |
0.557502 | 9f4f6db0b5f24115a9a08555873f3c73 | Clustering noise reduction results. The scan is clustered into 4 clusters, and the outlier is removed. Different lines represent different clusters. The color of lines has no special meaning, just to distinguish different clusters. Visualization using the MarkerArray plugin of RVIZ. | PMC9823419 | sensors-23-00018-g009.jpg |
0.481518 | eeed954d7ce5460cabcd5dfcafeea784 | In the Cartographer algorithm, the time consumed comparison between the keyframe extraction method, our method, and the scan-to-submap method in processing a frame of data. | PMC9823419 | sensors-23-00018-g010.jpg |
0.483126 | 214b2248985e4659afee5acbbff2a3f6 | Columnar error diagram of two usage modes. (a,b) represent the influence of different methods on the trajectory accuracy. Figure (a) shows that our method can improve the trajectory accuracy to a certain extent. Figure (b) shows that the keyframe extraction method does not reduce the trajectory accuracy. | PMC9823419 | sensors-23-00018-g011.jpg |
0.48042 | cdfdf2dff0d44b359a90edc488101db7 | Comparison of the average time required by the proposed method and DBSCAN method in processing a frame of data. | PMC9823419 | sensors-23-00018-g012.jpg |
0.478759 | b7ec22dd754244bab47ace384d97878c | Components of the operational context. | PMC9823603 | sensors-23-00548-g001.jpg |
0.472505 | e10af467795042eabeed2a9d11d33acf | Minimizing the number of active nodes—Illustrative example. A reallocation in order to keep the number of active nodes at a minimum. | PMC9823603 | sensors-23-00548-g002.jpg |
0.480226 | 6c21bd1de59445b5ac97944bf656aeaf | Schematic view of a Neural Network. Deep Neural Networks (DNNs) are just Neural Networks with multiple hidden layers. | PMC9823603 | sensors-23-00548-g003.jpg |
0.522367 | 5569acb0c381453da2c7ab4024d7cac4 | Schematic view of a neuron. Elements from the array returned by the previous layer are multiplied by a scalar (the parameters θ) and then a non-linear activation function is applied. | PMC9823603 | sensors-23-00548-g004.jpg |
0.470034 | cf1a12de0f554c2c896720a00aa2ce75 | The agent–environment interaction in an RL decision process. | PMC9823603 | sensors-23-00548-g005.jpg |
0.454932 | 64c303c7c1bb4f899b8e7ce4a4400e72 | Input–output representation example for a set of five tasks and node capacity C = 5, for the case where the NF allocation heuristic is followed. The set of available tasks is a sequence containing the cost of each task. Concerning the output of the DNN, the sequence A is the order in which the tasks are allocated following the NF rule. This is a simple example, but maximizing the occupancy ratio is an NP-optimization problem, which becomes challenging to solve for large sets of tasks. | PMC9823603 | sensors-23-00548-g006.jpg |
0.455953 | dacc0084cf384b7597a147c97eb05ffd | DRL-based actor-critic policy gradient. We use a DRL-based actor-critic policy gradient method. This means that two DNNs are trained. The first DNN parameterizes the policy used by the agent (a.k.a. actor) to map states to probabilities of taking each action. The second DNN parameterizes the critic, which maps states to the reward that it is expected to receive from the environment following the current policy. | PMC9823603 | sensors-23-00548-g007.jpg |
0.442323 | ef6169785635411aa7da7b396cedddcf | Neural network architecture. The encoder reads the input sequence S=[c1,⋯,cL] of task costs and ultimately produces an allocation order sequence A, which can be defined as numbers “pointing” at positions in the input sequence S. | PMC9823603 | sensors-23-00548-g008.jpg |
0.445523 | 71f762f56fd8408c94ad8357b45b2052 | Benchmark heuristics. Visual example of how a given sequence of tasks would be allocated following each of the three benchmark heuristics. | PMC9823603 | sensors-23-00548-g009.jpg |
0.564695 | b098ec895d754c68be9543efc38bfb2c | Training history. Problem 2: batch average occupancy ratio (%) after each training step, compared to the average occupation ratio obtained using the NF, FF and FFD heuristics. | PMC9823603 | sensors-23-00548-g010.jpg |
0.43184 | 09da9fa503a54a5ca0c37c402eb00f9d | Problem 4 results by size of the input set of tasks. Average occupancy ratio obtained in Problem 4 when applying the trained DNN plus an NF heuristic and when applying the three benchmark heuristics. | PMC9823603 | sensors-23-00548-g011.jpg |
0.447324 | 99bb7c978092402f98c1a0629e4f667b | Problem 5 results by size of the input set of tasks. | PMC9823603 | sensors-23-00548-g012.jpg |
0.498665 | aaf13b6872ce4cd7b73cfedb7a12d6ef | Treatment procedures. (Adobe Illustrator 2022). | PMC9825020 | anntransplant-28-e938467-g001.jpg |
0.442863 | 5ce5301af7044296bc838fdeffc0bc06 | Kaplan-Meier analysis of survival in PCNSL patients. (A) PFS and OS for all PCNSL patients. (B) PFS and OS for patients who received ASCT. (C) PFS for treatment with ASCT compared to without ASCT (P=0.001). (D) OS for treatment with ASCT compared to without ASCT (P=0.52). (R software, version 4.2.0). | PMC9825020 | anntransplant-28-e938467-g002.jpg |
0.432144 | 6538898b49aa4b6692821dc0932b6baa | Comparison of survival between groups ASCT as consolidation and ASCT as salvage treatment by the log-rank test. (A) Comparison of progression-free survival (P=0.21). (B) Comparison of overall survival (P=0.069). (R software, version 4.2.0). | PMC9825020 | anntransplant-28-e938467-g003.jpg |
0.465517 | 3f33b34694a34245b35dadc1855d72fb | Comparison of survival between groups treatment with consolidation and without consolidation after complete remission by the log-rank test. (A) Comparison of PFS (P=0.0093). (B) Comparison of OS (P=0.09). (R software, version 4.2.0). | PMC9825020 | anntransplant-28-e938467-g004.jpg |
0.427251 | cc68cdd1b6374497aa3f3e2e0810965f | The PerkinElmer FL6500 equipped with a pulsed xenon lamp | PMC9825072 | 10895_2022_3136_Fig1_HTML.jpg |
0.536992 | 4b8b79f8f6a2427aaa737a2259e89942 | Synchronous fluorescence spectra of a DNA, b DNA-based vaccine, c RNA and d RNA-based vaccine solutions diluted in normal saline | PMC9825072 | 10895_2022_3136_Fig2_HTML.jpg |
0.572524 | 5fc07f9765f84444816532e1af9ed15e | Cumulative variance of PC scores against the number of PCs | PMC9825072 | 10895_2022_3136_Fig3_HTML.jpg |
0.396583 | e637cbd972e841d0a6e7c41d0326a8f9 | PCA scores plots of the synchronous fluorescence spectra of DNA-based vaccines (blue) and RNA-based vaccines (red) | PMC9825072 | 10895_2022_3136_Fig4_HTML.jpg |
0.612341 | 4e6db53618dd4302b119f7b8a99317e6 | PC1 loading plots of the PCA model applied to the DNA- and RNA-based vaccines’ synchronous fluorescence spectra contributing to 90.8% of the variance among the data | PMC9825072 | 10895_2022_3136_Fig5_HTML.jpg |
0.462803 | 263fc1c030054ee5b411d757ae9d1a9d | PCA-GMM clustering of the synchronous fluorescence spectra of DNA-based vaccines (blue) and RNA-based vaccines (red) | PMC9825072 | 10895_2022_3136_Fig6_HTML.jpg |
0.460427 | f47d6420cebe4c37af0d4ab3569b48fb | U-Matrix for the synchronous fluorescence spectra of DNA-based and RNA-based vaccines | PMC9825072 | 10895_2022_3136_Fig7_HTML.jpg |
0.470004 | 2c68506ef18c40e2858427356c580852 | Label-Matrix for the synchronous fluorescence spectra of DNA-based and RNA-based vaccines | PMC9825072 | 10895_2022_3136_Fig8_HTML.jpg |
0.481278 | 41d1cae8677b45bf9c279e7a3e10f0d4 | An overview of AMR++ with resistance-conferring variants confirmation. After running the variant confirmation pipeline for each alignment to an N-type, S-type, H-type, I-type or F-type ARG, the remaining part of the AMR++ pipeline is run on the confirmed read alignments, and the read alignments that do not need confirmation. Pipeline A and pipeline B are shown in the Supplement. | PMC9825433 | gkac1047fig1.jpg |
0.434057 | 8538e3f64f3647ebb71783dac8a3a2fc | New CTD Tetramer tool generates CGPD-tetramers that can help fill in knowledge gaps and construct potential molecular mechanistic pathways. (A) A CGPD-tetramer is a computationally generated information block composed of four units: an initiating chemical (C), an interacting gene (G), a modulated phenotype (P), and a disease (D) outcome. To generate a tetramer, five direct dyad evidence statements are integrated from CTD: C–G interaction, C–P interaction, C–D association, G–D association, and an imported G–P annotation, since GO biological process terms are the equivalent vocabulary for phenotypes in CTD (19). A tetramer will be generated only if all five direct dyad evidence statements currently exist in CTD. This computational process generates a selective, but supported, set of tetramers and, importantly, does not require a priori knowledge by the user. (B) The CTD Tetramer tool (http://ctdbase.org/tools/tetramerQuery.go) can be queried for any phenotype and/or environmental disease-of-interest to automatically generate all possible tetramers. (C) For Alzheimer disease, the tool generates 7289 tetramers, composed of 91 chemicals, 95 genes, and 703 phenotypes. This output can be manually sorted, surveyed and filtered to focus on any subset of chemicals-of-interest (here, air pollutants and metals) as well as phenotype clusters (e.g. response to metal, cell signaling, mitochondrial-related, neuron-related, and cardiovascular-related), resulting in a sub-set of 601 tetramers, composed of 11 chemicals, 62 genes and 88 unique phenotypes. (D) Users can manually assemble the tetramers by hand by linking them together using the shared genes (green boxes/text/arrows) that connect different phenotype clusters (purple boxes) to build a complex, interrelated map. This manual process, outlined in (21), fills knowledge gaps with potential molecular mechanistic steps (e.g. intermediate genes and phenotypes) that link air pollution/metal exposure to Alzheimer disease, producing a testable framework for experimental verification. | PMC9825590 | gkac833fig1.jpg |
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