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0.46134 | 122d64b6f21443c992af2310cba68cba | STROBE flow diagram.COVID-19, Coronavirus Disease 2019; ICU, Intensive Care Unit; NMBAs, neuromuscular blocking agents; ARDS, acute respiratory distress syndrome; MMSE, mini-mental state examination. | PMC9510617 | fmed-09-994900-g0001.jpg |
0.394858 | 667050677048479589f6b62b70d35f95 | Distributions of VFDs between patients treated (PT) or not (NO-PT) by physiotherapists at 28 days. Blue bars denote patients who died (VFD = 0), dark gray bars denote patients alive with VFD = 0 and light gray bars identify patients alive with VFDs > 0. | PMC9510617 | fmed-09-994900-g0002.jpg |
0.452582 | 69acebb1ff994c0898f0b53a839d7605 | Flowchart of study design. | PMC9510988 | fneur-13-981249-g0001.jpg |
0.437546 | 7fc1d401fe3a425298097fb61de63b95 | Changes between STWT states and their impact on self-rated health and subjective well-being. Data set: NEPS SC4, SUF 12.0.0. Results of Table 2. Regression coefficients and 95% confidence intervals of six linear fixed-effect analyses with cluster-robust standard errors. n = number of individuals. Py = person-years. Time-varying controls: Age dummies, region of education or work, and household composition. The effect of transitional events on health and well-being were investigated in different estimation samples (S1-S6) that include the person-years of the reference state and the person-years of the state that was entered afterwards | PMC9511745 | 12889_2022_14227_Fig1_HTML.jpg |
0.474238 | b61788ad946b4c19ab46814c29c63456 | Trajectories of self-rated health and subjective well-being by state reached after school-leave. Data set: NEPS SC4, SUF 12.0.0. Adjusted predictions at the mean (APMs) and 95% confidence intervals of ten linear fixed-effect regressions with cluster-robust standard errors. n = number of individuals. Time-varying controls: Region and household composition. Red horizontal line represents predicted averages of health and well-being during school. To test how different states entered after school-leave affected trajectories of health and well-being, fixed-effects impact functions were estimated stratified by state reached in the year “0”. For transitions to “inactivity”, no subplot is shown. For the sample of university students, no estimate was calculable for year 6 or higher due to low case number | PMC9511745 | 12889_2022_14227_Fig2_HTML.jpg |
0.478593 | 09e60609deb147a4b521aba819cae445 | Baseline analysis. Correlations between BMD and HT, WT, BMI, HG-ave, and HG-max at BMD in women aged 50 to 54 years. (A) Correlations are shown by sorting by LS in 2006 (n = 98), (B) LS in 2002 (n = 137), (C) FN in 2006, and (D) FN in 2002. BMD, bone mineral density; r, Pearson correlation coefficient. *P < 0.05, **P < 0.01, ***P < 0.001. | PMC9512233 | meno-29-1176-g001.jpg |
0.478547 | 0ee525c61a014c948610dc1e3df1368f | Follow-up analysis of LS BMD. (A) Analysis of 84 patients who had BMD measurements in 2006 and 2016. Left: Women 50 to 54 years old in 2006 had a significantly lower LS BMD in 2016. The line of osteoporosis (<0.722 g/cm2) is shown. Middle (Left): LS BMD in 2006 in cases without osteoporosis (>0.722) and cases with osteoporosis (<0.722) in 2016. The cutoff line for the diagnosis of osteoporosis (<0.759 g/cm2) is shown. Middle (Right): LS BMD in 2006 in cases without osteoporosis (>0.759) and cases with osteoporosis (<0.759) in 2016. Right (Left): in the ROC curve, the cutoff (arrow) of LS BMD in 2006, when the Youden Index was maximum, was less than 0.834 g/cm2. Right (Right): in the ROC curve, the cutoff (arrow) of LS BMD in 2006, when the Youden Index was maximum, was less than 0.867 g/cm2. (B) Analysis of 83 patients who had BMD measurements in 2002 and 2011. Left: LS BMD of women 50 to 54 years old in 2002 was significantly lower in 2011. Middle (Left): cutoff less than 0.834, determined by the change in 2006 to 2016, was applied to 2002. Middle (Right): cutoff less than 0.867, determined by the change in 2006 to 2016, was applied to 2002. BMD, bone mineral density; ROC, receiver operating characteristic. ***P < 0.001. | PMC9512233 | meno-29-1176-g002.jpg |
0.441216 | ff091ed23efa4b658ac1cc021a683737 | Follow-up analysis of FN BMD. (A) Left: women who were 50 to 54 years old in 2006 had a significantly lower FN BMD in 2016. This indicates a line of osteoporosis (<0.561 g/cm2). Middle (Left): FN values in 2006 in cases who were not osteoporotic (FN > 0.561) and those who were osteoporotic (FN < 0.561) in 2016. This indicates a line of osteoporosis (<0.536 g/cm2). Middle (Right): FN values in 2006 in cases who were not osteoporotic (FN > 0.536) and those who were osteoporotic (FN < 0.536) in 2016. Right (Left): in the ROC curve, the cutoff (arrow) of FN in 2006, when the Youden Index was maximum, was less than 0.702 g/cm2. Right (Right): in the ROC curve, the cutoff (arrow) of FN in 2006, when the Youden Index was maximum, was less than 0.676 g/cm2. (B) Left: FN BMD of women 50 to 54 years old in 2002 was significantly lower than that in 2011. Middle (Left): cutoff less than 0.702, determined by the change in 2006 to 2016, was fitted to 2002. Middle (Right): cutoff less than 0.676, determined by the change in 2006 to 2016, was fitted to 2002. BMD, bone mineral density; ROC, receiver operating characteristic. ***P < 0.001. | PMC9512233 | meno-29-1176-g003.jpg |
0.378306 | 2431d804d1b6403b8867f333728700f2 | Follow-up analysis. Correlation between the amount or rate of change in BMD in women aged 50 to 54 years after 9 or 10 years and HT, WT, BMI, HG-ave, and HG-max at BMD measurement in women aged 50 to 54 years. (A) Sorted by the amount of change in LS from 2006 to 2016 (n = 84). (B) Sorted by the amount of change in LS from 2002 to 2011 (n = 83). (C) Sorted by the amount of change in FN from 2006 to 2016. (D) Sorted by the amount of change in FN from 2002 to 2011. Correlation with the amount or rate of change showed that women with heavier WT and larger BMI had a larger decrease in FN BMD. r, Pearson correlation coefficient. BMD, bone mineral density. *P < 0.05, **P < 0.01, ***P < 0.001. | PMC9512233 | meno-29-1176-g004.jpg |
0.45421 | fe87d9cce4c74c41be603c09b9707c81 | Preoperative radiological images show a meningoencephalocele in the lateral sphenoid sinus and a bony defect in the left middle skull base.
A: MR angiography images show an arteriovenous malformation in the right parietal lobe. B: CT image reveals a bony defect in the left middle skull base and a continuous structure that protrudes into the left lateral sphenoid sinus. C: Three-dimensional bone computed tomography image reveals a large bony defect outside the foramen rotundum (black arrow). D: Coronal CT image with multiple arachnoid pits in the greater wing of the sphenoid bone (white arrowheads). E: Axial magnetic resonance imaging (MRI) reveals a trabecular structure with a low signal on a T1-weighted image (not shown) and a high signal on a T2-weighted image. F: Sagittal constructive interference in the steady-state sequence shows the empty sella. G–I: Retrospective MRI on T2-weighted images shows how the mass gradually increased (G: 12 years before surgery, H: 9 years before surgery, I: 2 years before surgery).
| PMC9512490 | 2188-4226-9-0281-g001.jpg |
0.438155 | eb462eda19b54bdea44efec130acc024 | Intraoperative images.
A: Multiple arachnoid pits (white arrowheads) in the middle fossa exposed after a left frontotemporal craniotomy. B: Partially removed brain tissue covered with dura. Asterisk (*) indicates the V2 root through the medial bone defect. C: Double asterisks (**) indicate the large bony defect and arachnoid pit. D: 70-degree endonasal endoscopic view. Brain tissue covered with mucosa was revealed in the sphenoid sinus. E: The dural defect was patched with temporal fascia after resection of the meningoencephalocele. F: The large bony defect and multiple bone pits in the middle fossa were repaired with the pedunculated temporalis fascia and a bone fragment made of the inner plate of the frontotemporal bone.
| PMC9512490 | 2188-4226-9-0281-g002.jpg |
0.447603 | 34df38d4169a466087fc5f87e2bfad72 | Histological findings of the resected specimen.
A: The mass consists of brain tissue with a cyst partially covered with epithelium and a thick connective tissue membrane. H&E. B–D: Higher magnification of the area indicated by an arrow in A. Glial tissue immunolabeled with glial fibrillary acidic protein (GFAP) is intermixed with fibrocollagenous tissue, whose surface was stained green with Elastica-Goldner staining (El-Gold). B, H&E; C, GFAP; D, El-Gold. E–G: Higher magnification images taken from the consecutive section of A. The area indicated by the dotted square shows an irregular surface of gliotic cortical tissue covered with ciliated columnar epithelium lined with a dense fibrocollagenous band and sparse fibrous tissue. Inset: Sparse fibrous tissue indicated by a square in E includes epithelial membrane antigen (EMA)-labeled cells that are meningothelial in nature. E, H&E; F, GFAP; G, El-Gold; inset in E, EMA. H: A higher magnification image taken from the area indicated by H in A shows cortical tissue with fibrillary gliosis and a dysmorphic large neuron (arrow) and scattered Rosenthal fibers (arrowheads). H&E. I: A higher magnification image taken from the area indicated by I in A shows uneven neuronal distribution with a dysmorphic neuron (arrow) and closely adjacent small neurons (arrowheads). Inset: A binuclear dysmorphic neuron. Klüver-Barrera staining. J: A higher magnification image taken from the area indicated by the square in A exhibits abnormal clustered arterioles and venules with thickened fibrous adventitia. El-Gold. Bar = 800 μm for A, 30 μm for B, and inset in E, 50 μm for C, D, and H, 100 μm in E–G, 75 μm for I, 115 μm for J, and 10 μm for inset in I.
| PMC9512490 | 2188-4226-9-0281-g003.jpg |
0.467992 | 4c64a9bfc2694c4889eae336aa7417bb | An integrated theoretical framework of the predictive factors associated with TNB mental health disparities.Minority stressors for transgender and nonbinary (TNB) people within a socioecological framework, with internal (proximal) stressors occurring at the individual level, and external (distal) stressors occurring at the interpersonal as well as structural and systemic levels. The association of minority stressors for TNB people with adverse mental health outcomes is explained in part by general psychological mediation processes. This framework integrates minority-stress theory29 and psychological mediation theory60 within a socioecological framework51 to explain contextual factors that drive documented mental health disparities within TNB populations. Mental and behavioural health disparity outcomes are on the right. A series of concentric yellow circles depicts the varying levels at which stigma-related stressors occur, from individual and internal, to interpersonal and external, to structural and systemic, as predictors on the left. PTSD, post-traumatic stress disorder. | PMC9513020 | 44159_2022_109_Fig1_HTML.jpg |
0.447895 | 6ea25ea2e4da4647966b0eb9ad78046e | Protective and health promotive factors associated with well-being and decreased adverse mental health.Protective and health promotive factors (internal and external resources) are placed within the integrated theoretical framework of Fig. 1. Internal and external resources are recursively related and increase well-being and reduce adverse mental health. Coping strategies are moderators of the relations of minority stressors for transgender and nonbinary (TNB) people with adverse mental health. Protective and health promotive factors counteract minority stressors for TNB people, comprising population-specific factors associated with mental health and well-being in TNB populations. | PMC9513020 | 44159_2022_109_Fig2_HTML.jpg |
0.45699 | 159fef63c1d54e509a72c46172973911 | The flowchart of including and dividing patients. | PMC9513445 | fpubh-10-954816-g0001.jpg |
0.400803 | 86a88293380e42b1a7a435033fb74613 | Nomogram to predict 3, 5, and 8-year overall survival in elderly patients with solitary bone plasmacytoma. | PMC9513445 | fpubh-10-954816-g0002.jpg |
0.437178 | 8138d934ff8340579b379767afc9eba5 | The AUC of nomogram of 3-, 5-, and 8-year in the training set (A) and validation set (B). AUC, Receiver operating curve. | PMC9513445 | fpubh-10-954816-g0003.jpg |
0.477813 | cc2dab38e89d42839f21e9e6e2eacac7 | The calibration curves for predictions of overall survival in the training set (A–C) and validation set (D–F) at 3, 5, and 8-year. | PMC9513445 | fpubh-10-954816-g0004.jpg |
0.439666 | ea6f9b26c52f4f8793e55d0bb69ad310 | Kaplan-Meier survival curves for the training set (A) and validation set (B). | PMC9513445 | fpubh-10-954816-g0005.jpg |
0.403161 | 3f505951d0b14e3e9b426cc39b572bf7 | Map of Nyakach Sub-County in Kisumu County showing the study eco-epidemiological zones. Lakeshore zone (highlighted in blue), hillside zone (highlighted in brown), and highland plateau zone (purple highlighted) | PMC9516797 | 13071_2022_5447_Fig1_HTML.jpg |
0.419618 | c4d23b02c7874e3ea552c50f434b665c | Larval habitat distribution cluster heat map. A Larval habitat distribution by seasonality. B Larval habitat distribution across topography. The hierarchical clustering dendrogram pattern represents the relationship between larval habitat type and landscape zones or seasons based on average linkage and Euclidean distance. The use of the same color indicates the availability of larval habitat types across landscape zones or seasons | PMC9516797 | 13071_2022_5447_Fig2_HTML.jpg |
0.438909 | 96ba78c2bcb143e0ac9276dcb2d2953a | Distribution of larval habitats, households with the adult vector survey, and parasitological survey households. A Overview of the study area, including all three eco-epidemiological zones. B, C, and D Distributions of three surveys in lakeshore zone, hillside zone, and highland plateau zone, respectively | PMC9516797 | 13071_2022_5447_Fig3_HTML.jpg |
0.502154 | f921a00200cc4163800b6bd87ade83c9 | Proximal location of the cuff. | PMC9517126 | ijerph-19-11242-g001.jpg |
0.449134 | f93028db161b4e749e0fbd67ebeaec8c | Distal location of the cuff. | PMC9517126 | ijerph-19-11242-g002.jpg |
0.459131 | 7f40d2181df94a4ba219e1b7650551b5 | The location of the three study sites. | PMC9517536 | ijerph-19-11606-g001.jpg |
0.389164 | 52486cfbfb2242e9830585fabcf9ce0b | The frequencies of answers for distance (top), duration (middle), and frequency (bottom) across study locations. MH, Rai Mae Hia; BH, Buak Haad Park; 3K, Three Kings Monument. | PMC9517536 | ijerph-19-11606-g002a.jpg |
0.445314 | 2e9c2011ed8c4494a6d1daea766373d7 | Charts displaying the relationship between age and duration and WHR. The line at 0.8 represented a low-risk WHR, as recommended by WHO [10]. | PMC9517536 | ijerph-19-11606-g003.jpg |
0.481854 | f10d7dfa0d5746e485a572b4e80870c8 | Passive hip flexion with the knee extended test (hamstrings). | PMC9517817 | ijerph-19-10747-g001.jpg |
0.46546 | 1bc90b597c924d1b9ee5c18a187bc547 | Passive hip flexion with the knee flexed test (gluteus maximus). | PMC9517817 | ijerph-19-10747-g002.jpg |
0.484359 | b3cf2721530e464f9edbc2de820313ed | Passive hip extension with the knee relaxed test (iliopsoas). | PMC9517817 | ijerph-19-10747-g003.jpg |
0.446521 | 1da5aa28f1504b56a3ad6b4eec178b63 | Passive hip adduction with the 90° hip flexion test (piriformis); * [59,60]. | PMC9517817 | ijerph-19-10747-g004.jpg |
0.429734 | 58b43bf9ce584af3bfedd5a5da20967d | Passive hip abduction with the knee extended test (adductors). | PMC9517817 | ijerph-19-10747-g005.jpg |
0.433911 | c97d3dd1103e471da6f7705da122c7fe | Passive hip abduction with the 90° hip flexion test (adductors monoarticular). | PMC9517817 | ijerph-19-10747-g006.jpg |
0.436379 | edeada17d6a94e5a8f7dbcd1f075fd89 | Passive internal hip rotation test (external rotators). | PMC9517817 | ijerph-19-10747-g007.jpg |
0.429829 | 7b28d148138e43bcb0f80b3929e01818 | Passive external hip rotation test (internal rotators). | PMC9517817 | ijerph-19-10747-g008.jpg |
0.414946 | feceafc5ef294541b9ac52a3e8908c63 | Passive knee flexion test (quadriceps). | PMC9517817 | ijerph-19-10747-g009.jpg |
0.477515 | 636db0fe28b94b189f7109bec0cd8055 | Ankle dorsiflexion with the knee extended test (gastrocnemius). | PMC9517817 | ijerph-19-10747-g010.jpg |
0.470139 | b94e0937a31b45cabf57d91eda7f0508 | Ankle dorsiflexion with the knee flexed test (soleus). | PMC9517817 | ijerph-19-10747-g011.jpg |
0.432013 | 7452167aee4d4f889d95f575ad4b9dd2 | Development history of the pollution fee system in China. | PMC9518126 | ijerph-19-10660-g001.jpg |
0.435669 | 315ca3e9555c44a2a97d44888c187ffb | Approach to enterprise emission reduction. | PMC9518126 | ijerph-19-10660-g002.jpg |
0.440435 | f91477ee96374653a5ecd3dfdb6af696 | Spatial distribution of the pollution fee reform areas in China during 2006–2013. | PMC9518126 | ijerph-19-10660-g003.jpg |
0.527318 | 090c7f82eaa647b8a9b97b2609aee1fd | Results of the parallel trend test. | PMC9518126 | ijerph-19-10660-g004.jpg |
0.4089 | a59d790e467448e58a3ecd314be62d21 | Before matching and after matching. | PMC9518126 | ijerph-19-10660-g005.jpg |
0.544999 | f88bb86559274380a3f64ebcd6c81a57 | Results of the Placebo test. | PMC9518126 | ijerph-19-10660-g006.jpg |
0.41512 | b20542eec6984e1d894dadbbf824f19a | Validation of RiboTag mice for BEC-specific translatome analysis.(A)
Tie2-Cre; Rpl22HA/HA (Ribo-Tag) mice, which are designed for expressing HA-tagged ribosomal protein in BECs. (B) Immunohistochemistry for Ai9 (Cre reporter, Red) and CD31 (endothelial cell marker, Green) in adult Tie2-Cre; Ai9; Rpl22HA/HA mice. The regions in the dotted boxes in the left images are shown on the right at higher resolution. Scale bars, 1 mm (200 μm in the insets). (C) Immunohistochemical staining for Gfap and Ai9 (left column) and for Pdgfrb and Ai9 (right column) in the cortex of adult Tie2-Cre; Ai9; Rpl22HA/HA mice. Gfap+ astrocytes and Pdgfr+ pericytes did not overlap with Cre recombinase, as represented by the Ai9 reporter gene. (D) Immunoprecipitation of the HA-tagged ribosomal protein RPL22HA, which is a component of actively translating polyribosomes. The expression of HA-tagged RPL22A was investigated by immunoprecipitation with mouse IgG (-) or anti-HA (+) or with anti-Flag (+) and subsequent Western blotting with anti-HA. | PMC9518886 | pone.0275036.g001.jpg |
0.414904 | f38aa62a54a24f25afb1f4231ae678a5 | Comparative analysis of the conventional RiboTag method and our new RiboTag protocol.(A) Workflow diagram and validation of the RiboTag method. (B) Images of vessels isolated with a cell strainer (40 μm) before the immunoprecipitation step. (C, D) RT–PCR to verify the BEC specificity of mRNA pools isolated by the standard RiboTag protocol (C) and by our optimized RiboTag protocol (D). Brain endothelial cell markers: Tie2, Mfsd2a, Claudin-5, Occludin, VCAM-1, P-gp, neuronal cell marker: Syt1, astrocyte marker: Gfap, and pericyte marker: Pdgfrβ. The amount of mRNA isolated from the whole cortex (7.3 ± 0.668 ng) (E) and the visual cortex (1.05 ± 0.125 ng) (F). Mean ± SEM; n = 6 for the whole cortex, n = 8 for the visual cortex. | PMC9518886 | pone.0275036.g002.jpg |
0.39906 | 44d2440bc3684ecba89e52441b4ebe8f | Generation of cDNA libraries for NGS from the low mRNA yield derived from BECs.(A) The process used to generate a cDNA library for RNA-seq from the small amount of pooled mRNA originating from BECs in the cortex of Tie2-Cre; Ai9; Rpl22HA/HA mice. TapeStation gel image (B) and electropherogram (C) showing PCR amplification of the cDNA library from the cortex of Tie2-Cre; Ai9; Rpl22HA/HA mice. (D) The specificity of the cDNA library from the cortex of Tie2-Cre; Ai9; Rpl22HA/HA mice was verified by using RT–qPCR. Mean ± SEM; *P<0.05, ***P<0.001, ****P<0.0001 by the Kruskal–Wallis test, n = 10. After fragmentation into 300 bp pieces and labeling with dual barcode oligos, the quality of the cDNA was validated by using TapeStation gel images (E) and electropherograms (F). | PMC9518886 | pone.0275036.g003.jpg |
0.392457 | 5015b12dbbe24a669c386bd927194f0a | Seasonal changes in Lake Redon, Pyrenees. (A) Illustration of the snow and ice cover melting period. Photographs: Marc Sala-Faig. (B) Seasonal and depth variation of chlorophyll-a and (C) oxygen. Black circles indicate sampling points and dashed lines the isotherms (°C). The line above each graph indicates the snow and ice-cover thickness in arbitrary units. | PMC9519062 | fmicb-13-935378-g001.jpg |
0.477492 | 3d2e36b20a354e3bb1178196db027ced | Seasonal fluctuations of bacterioplankton number of 16S rRNA gene copies (A) and OTU richness (B) at different depths. Thick black lines at the top of each graph indicate periods of ice cover. | PMC9519062 | fmicb-13-935378-g002.jpg |
0.431645 | f05300d2fa534fa98772934408068623 | Seasonal difference in bacterioplankton community composition. (A) PCA analyses using Hellinger distance of the bacterioplankton OTUs. Numbers refer to OTUs listed in Supplementary Table S4. There is a seasonal trajectory for the upper layers (2, 10, 20-m depth), whereas variation is lower in the deeper layers (35, 60-m depth). (B) Temporal depth distribution of the bacterioplankton groups was obtained using k-means clustering with Hellinger distance. Dashed lines denote isotherms. Circles indicate sampling points, and the color corresponds to each of the six clusters: UI (under-ice), TH (thaw and hypolimnion), ES (early stratification), EP (epilimnion), OE (overturn and early under-ice), and DL (deep layers). The line above the graph indicates the snow and ice-cover thickness in arbitrary units. (C) The best number of k-means groups was assessed by maximizing the total indicative value (IndVal) of the significant OTUs at each partition. | PMC9519062 | fmicb-13-935378-g003.jpg |
0.427448 | 8e3e15097b5a457789c2efd81e3b6240 | Characteristics of the bacterial seasonal clusters’ environment. (A) Bacterioplankton clusters in a discriminant environmental space. Clusters: UI (under-ice), TH (thaw and hypolimnion), ES (early stratification), EP (epilimnion), OE (overturn and early under-ice), and DL (deep layer). The symbol size of each cluster is proportional to the number of samples included. (B) Heat map indicating the average environmental conditions for each bacterioplankton seasonal cluster. The average cluster values clusters indicated within the boxes, and colors indicate the relative rank among clusters, from the highest (red) to the lowest (pale yellow). The variables are sorted according to their loading in the first axis of the discriminant analysis (A), with the lowest p-values at both extremes and the less relevant variables in the middle. The asterisk (*) indicates no significance in the discriminant analysis. | PMC9519062 | fmicb-13-935378-g004.jpg |
0.39728 | cd3bdf69974d4c6d930157744a9e72c0 | Indicator taxa of seasonal clusters. OTUs are ranked according to their significant indicator values. Colors indicate the taxonomic class. The number of indicator OTUs and classes (parenthesis) are indicated below the x-axis. | PMC9519062 | fmicb-13-935378-g005.jpg |
0.422464 | e2971f8e12d248d28f82a2108738c182 | Patterns of occurrence and seasonality in the bacterioplankton community. (A) OTUs ranked by occurrence in the samples. Colors specify cluster indicators: UI (under-ice), TH (thaw and hypolimnion), ES (early stratification), EP (epilimnion), OE (overturn and early under-ice), and DL (deep layer). (B) Comparison of the OTU indicator values with the OTU’s mean and maximum abundance ratio in the samples. A larger ratio indicates flatter seasonal OTU profiles, that is, lower occasional blooming. The symbol size is proportional to OTU occurrence. The inserted plots show examples of the OTU abundance’s time series at each sampling depth (line colors as in Figure 2). Arrows indicate the corresponding OTU in the larger plot. | PMC9519062 | fmicb-13-935378-g006.jpg |
0.463306 | f4f613d733c2432bb3b1e07ceea83caf | Overview of the synthetic lethality (SL) prediction model. A. Identification of essential and mutant SL genes using text-mining and high-throughput screening (HTS) data (COSMIC and CCLE). B. Matching and prediction of essential-mutant SL gene pairs for cancer-specific types. C. Enrichment and filtering of SL gene pairs by gene co-expression and co-occurrences. D. Selection of candidate (CD82-KRAS) SL gene pairs using RAS-mutant HTS and PubChem bioassay data. | PMC9519430 | gr1.jpg |
0.445844 | 675e06d264564d8ba2234c2d52123f5f | Workflow of enrichment and filtering of synthetic lethality (SL) gene pairs. To extract and rank more reliable SL gene pairs, we used three criteria: (i) Essential gene candidate score (Sg), gene co-expression, and co-occurrences to enrich and filter SL gene pairs. In total, 586 essential genes were identified based on the threshold of the (ii) essential gene candidate score (Sg) > 10. Subsequently, we enriched and set Spearman’s correlation (iv) p-value < 0.05 according to gene co-expression obtained from (iii) CCLE gene expression data. Next, we filtered each SL gene pair by co-occurrences (v) in the literature. The threshold of (vi) co-occurrences of two genes in one article was > 0 or ≥ 0. Sg, essential gene score. | PMC9519430 | gr2.jpg |
0.372467 | 651adea7de0448698745a196fe1312e4 | Dependency parse tree of an example sentence and trigger term extraction. A. The dependency parse tree for “significance of the member of TM4SF (MRP-1/CD9, KAI1/CD82, and CD151) in human colon cancer.” “KAI1/CD82” is an essential gene in “colon cancer,” and “significance” is the common ancestor in the dependency parse tree. B. The top seven trigger terms in colon cancer. The nmod, dep, and conj. are Universal Stanford Dependencies representing grammatical relations between words. Abbreviations. nomd, nominal modifier; the nmod relation is used for nominal dependents of another noun or noun phrase and functionally corresponds to an attribute or genitive complement. Dep, unspecified dependency; a dependency can be labeled as dep when it is impossible to determine a more precise relation. Conj, conjunct; a conjunct is a relation between two elements connected by a coordinating conjunction, such as and, or, etc. The links for the main organizing principles of the Universal Dependencies taxonomy were as follows: https://universaldependencies.org/u/dep/all.html (accessed on 2 Feb 2022). St, trigger term score. | PMC9519430 | gr3.jpg |
0.520231 | 6498123f06b34e5ebde446af71fb9494 | Identification of KRAS-mutant synthetic lethality (SL) gene pairs. The synthetic lethal relationships of these genes were identified using RAS-mutant high-throughput screening (HTS) data and text-mining analysis. Predicted SL gene pairs were compared with HTS data using a Venn diagram with the threshold of gene co-expression and co-occurrences in colon cancer (A and B). The left circle (red) denotes the number of predicted SL gene pairs; the right circle (blue) represents the number of SL gene pairs recorded in the screening data. A. Stricter thresholds were set for gene co-expression (Spearman correlation p-value < 0.05) and co-occurrence (number of co-occurrences of two genes in the literature ≥ 0). B. Stricter thresholds were set for gene co-expression (Spearman correlation p-value < 0.05) and co-occurrence (number of co-occurrences of two genes in the literature > 0). C. Two potential SL gene pairs in colon cancer, including CD82 (essential)-KRAS (mutant) and CD82 (essential)-NRAS (mutant) gene pairs, were identified. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) | PMC9519430 | gr4.jpg |
0.490107 | 3e88374ead0d41b6acc61fd9c79d732d | PubChem bioassay of the CD82-KRAS synthetic lethality (SL) gene pair in colon cancer. A. A PubChem database search revealed that a chemical compound screen assay (fluorometric microculture cytotoxicity assay (FMCA)) demonstrated that digitonin was used to precipitate tetraspanins (a protein complex including CD82) and significantly suppressed the growth of a KRAS/BRAF-mutant colon cancer cell line (https://pubchem.ncbi.nlm.nih.gov/bioassay/471512). B. The half-maximal inhibitory concentration (IC50) values for three cancer cell lines are presented. | PMC9519430 | gr5.jpg |
0.526271 | 50b8658f19ba44afb03248a0bf3f7bde | Effects of siRNA-mediated CD82 depletion on cell viability of KRAS-mutant cancer cell lines. siRNA-mediated knockdown of CD82 resulted in the downregulation of p-MEK and decreased cell viability following chemotherapy. A. Western blot (WB) analysis of DLD-1 W/M (KRAS mutant) and DLD-1 W/− (KRAS wild-type). CD82 expression was higher in DLD-1 W/M cells than in DLD-1 W/− cells. The colon cancer cell lines were transiently transfected with control siRNA or CD82 siRNA. The WB results of siRNA-CD82 in DLD-1 W/− and W/M cells. B. Results of the MTT assay after CD82 knockdown and treatment with 5-FU for 72. | PMC9519430 | gr6.jpg |
0.477423 | 64867d886acb49fa9df6df7d5f1909fe | Map of Ondo State showing the study area. Source: Ondo State web portal, (2019). | PMC9519504 | gr1.jpg |
0.374045 | f9a6229edbcb4aca858bd6b4489e01ee | Frequency of usage of CRIN approved insecticides. | PMC9519504 | gr2.jpg |
0.446069 | 19089c3514b243d989714e731c43d638 | Error bar of usage of CRIN approved insecticides. | PMC9519504 | gr3.jpg |
0.411185 | b4a14622d106433f90e825c73a78ded2 | Protein presence in pathway models. (A) 46% of the measured proteins in the dataset were found in at least one pathway in the combined pathway collection. (B) The distribution of the number of pathways in which a protein participates shows that most proteins are only present in very few pathways. | PMC9519890 | fimmu-13-963357-g001.jpg |
0.469675 | b9746b75009544e2b15bfd9301297c33 | Changes in pathway activities in COVID-19 patients. (A) The box plot shows the number of less and more active pathways in each tissue. (B–H) For each tissue, the effect size of all significant pathways from the Wilcoxon test are shown. The cutoff of >0.3 for effect size is indicated with a dashed line. Only the 69 pathways, which show significant changes in protein abundance in at least one tissue are included on the x-axis. | PMC9519890 | fimmu-13-963357-g002.jpg |
0.404869 | 7fd1deda2bfe4481ba9963afe2b35b3a | Protein-pathway network of 69 changed pathways in COVID-19 patients. Pathways are shown as rectangles and the proteins present in a pathway are connected to the pathway node and shaped as circles. The pathway nodes visualize the heatmap of pathway activity change in COVID-19 patients from thyroid, lung, heart, liver, spleen, kidney and testis in that order. Blue indicates a lower pathway activity in COVID-19 patients (protein abundance distribution shifted to the left) and red indicates an increased activity (protein abundance distribution shifted to the right). Most of the pathways are highly connected with many proteins in common. | PMC9519890 | fimmu-13-963357-g003.jpg |
0.448936 | da536211ff5549cebd9e398f3ba38352 | Heatmap of pathway activities per tissue (p-value < 0.05, Wilcoxon test). Pathway activities are highly tissue specific and only few pathways show significant changes in multiple tissue. All significant changed pathway are shown and the color gradient indicates the effect size. Blue: the pathway is less active in COVID-19 patients, red: the pathway is more active in COVID-19 patients. Grey: unmeasured or non-significant pathways (p-value >= 0.05). | PMC9519890 | fimmu-13-963357-g004.jpg |
0.36718 | b8fd273c50c547c5aec260985a989dad | COVID-19 protein expression visualized on the Kinin Kallikrein pathway (WP5089). The pathway has activities changed in most tissues in COVID-19 patients. (A) Protein abundance is visualized on protein nodes in the pathway. Top row is data from COVID-19 patients and bottom row is data from non-COVID-19 patients. The higher the abundance the darker the value. The data nodes are split in seven columns for the seven different tissues. (B) The pathway figure shows the differential expression of the proteins (log2 fold change) between COVID-19 and non-COVID-19 patients. All proteins are downregulated or not changed in all tissues. The data nodes are split in seven columns for the seven different tissues. Left to right: thyroid, lung, heart, liver, spleen, kidney, testis. | PMC9519890 | fimmu-13-963357-g005.jpg |
0.410544 | 8a4f617044d7423c8e0c407e729c6334 | COVID-19 protein expression visualized in the cholesterol biosynthesis pathway (WP197) in the testis. All proteins in the pathway are less abundant in COVID-19 patients. | PMC9519890 | fimmu-13-963357-g006.jpg |
0.607243 | 9ffcd1965fc647578f0ff0093466f89e | The multidisciplinary team of the ETHeart program with medical professionals from Berlin (green scrubs) and engineers (dark turtlenecks) from Zurich. Illustration created by Payko. | PMC9520014 | gr1.jpg |
0.461639 | 680ccf5a1d154d6eb8bc3fd779f831c3 | Imaginary, yet typical end-stage heart failure patient relying on an implanted Left Ventricular Assist Device (LVAD) to maintain the necessary blood flow through the body. Illustration created by Payko | PMC9520014 | gr2.jpg |
0.400934 | d09e79da15f24a7caef81882de676776 | Flowchart of study selection. | PMC9520262 | fmed-09-956188-g001.jpg |
0.399378 | 45659b152f36482294543a6c731155bf | Risk of bias graph. | PMC9520262 | fmed-09-956188-g002.jpg |
0.450221 | 9955afd953b04219880efd34ac1b61f4 | Network meta-analysis plot for the assessment of high intensity laser therapy (HILT) and other physical therapy modalities (nodes are weighted in accordance with the number of trials including the respective treatments. The larger the size of node and the thicker the lines are, the more studies are involved). Treatment relative ranking [the PrBest means the estimated probability that the treatment is the best one. The lower the value of Mean Rank is, the higher the efficacy of the treatment may be. The ranking probability plot for the assessment of improved visual analog scale (VAS) pain at the end of the physical therapy modalities is shown]. | PMC9520262 | fmed-09-956188-g003.jpg |
0.487418 | 6dab9c3274e24b45a9c241ea4f628eb0 | Forest plot of the visual analog scale (VAS)-pain in high intensity laser therapy (HILT) vs. Low level laser therapy. | PMC9520262 | fmed-09-956188-g004.jpg |
0.523549 | 7137425557d2409e81e1e32b2daeccc8 | Forest plot of the visual analog scale (VAS)-pain in high intensity laser therapy (HILT) vs. Placebo laser (plus exercise). | PMC9520262 | fmed-09-956188-g005.jpg |
0.447911 | f11a1bb60c90413db6ee7ddfbdb29570 | Network meta-analysis plot for the assessment of high intensity laser therapy (HILT) and other physical therapy modalities (nodes are weighted in accordance with the number of trials including the respective treatments. The larger the size of node and the thicker the lines are, the more studies are involved). Treatment relative ranking (the PrBest means the estimated probability that the treatment is the best one. The lower the value of Mean Rank is, the higher the efficacy of the treatment may be. The ranking probability plot for the assessment of improved WOMAC total at the end of the physical therapy modalities is shown). | PMC9520262 | fmed-09-956188-g006.jpg |
0.450329 | 946f0f80d8804a26a6cfbf83039d1c55 | Forest plot of the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC)-total in high intensity laser therapy (HILT) vs. Low level laser therapy. | PMC9520262 | fmed-09-956188-g007.jpg |
0.410171 | cef701e779cb4cdbb82ca5e0f2a1e71f | Forest plot of the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC)-pain (A), WOMAC-stiffness (B), WOMAC-function (C), and WOMAC-total (D) in high intensity laser therapy (HILT) vs. Placebo laser (plus exercise). | PMC9520262 | fmed-09-956188-g008.jpg |
0.418586 | f2377a52dc3d4c8fac1f72f83cc7be4a | PSY1 expression increased carotenoid accumulation in ‘Royal Gala’ apple fruit. (A) Fruit of WT and PSY transgenic lines (OE-1, OE-5, and OE-7) at different developmental stages. (B) Bar graphs of carotenoid content measured by HPLC as beta-carotene equivalents (left column) and chlorophyll (right column) in fruit skin (top panel) and fruit flesh (bottom panel) in wild type and transgenic lines OE-1, OE-5, and OE-7. The error bars represent the standard errors of the mean of three biological replicates, with each replicate a pool of 5–7 fruit. Bar graph with asterisk show significant difference from WT at the same fruit stage using Dunnett’s test (*P < 0.05; **P < 0.01). | PMC9520574 | fpls-13-967143-g001.jpg |
0.427883 | 73a92d1134554026aca336d366665d19 | Visualization of chlorophyll and carotenoid autofluorescence in wild type (WT) and PSY ‘Royal Gala’ fruit. Fresh fruit tissues, at 120, 135, and 150 D, were analyzed by confocal microscopy to show plastids containing chlorophyll (red), carotenoid (green) and both pigments (yellow). Graph represents fluorescence emission spectra of WT and PSY transgenic tissue. Fluorescence emission between 500 and 600 nm represents carotenoids and 650–750, chlorophyll pigments. | PMC9520574 | fpls-13-967143-g002.jpg |
0.446885 | c52b8fed33cb4b91b62ab4b4ccc3f9ea | Comparison of carotenoid gene expression in wild-type (WT) and PSY ‘Royal Gala’ apple lines, OE-5 and OE-7, as determined by qPCR, relative to expression of housekeeping genes (MdActin and MdEF1A) in fruit skin (A) and fruit flesh (B). Bars represent means and SE of four biological replicates. PSY, phytoene synthase; PDS, phytoene desaturase; ZDS, zeta carotene desaturase; CRTISO, carotene isomerase; LCB, lycopene beta-cyclase; BCH, beta-carotene hydroxylase. Bar graph with asterisk shows significant difference from WT at the same fruit stage using Dunnett’s test (*P < 0.05; **P < 0.01). | PMC9520574 | fpls-13-967143-g003.jpg |
0.485557 | 1d58a1dded2b4ef0ad9d1c0b52bdea87 | Differentially expressed genes in PSY transgenic apple fruit. Venn diagram of number of differentially expressed genes (log2 fold change ≥ 2, P < 0.05) between WT and OE-7 fruit at four developmental stages (90, 120, 135, 150 D). Upregulated (A), downregulated (B) in PSY fruit flesh. | PMC9520574 | fpls-13-967143-g004.jpg |
0.39677 | 7230e64a1fd140c798d0a092ed45cf13 | Analysis of carotenoid-associated transcription factor genes. (A) Heat map of differentially expressed transcription factor genes (Supplementary Table S4) in apple fruit flesh during development. The average gene expression of biological replicates at each of the four stages: 90, 120, 135, and 150 D, from WT and PSY transgenic line OE-7, were clustered using Euclidean distance relationships. Green to red color gradient indicates low to high relative gene expression. (B) Correlation matrix of gene transcripts and carotenoid metabolites (β-carotene, BCAR; total carotenoid content, TOTCAR) in transgenic PSY fruit. Values represent correlation coefficients with color gradient from green to red indicating weak to strong correlations at P < 0.05. | PMC9520574 | fpls-13-967143-g005.jpg |
0.367173 | 6dc2d5ac9ec0485482ef2239ac38b343 | PSY1 increased fruit carotenoid content in apple in the absence of light. (A) Representative bagged and non-bagged fruit of WT and PSY line OE-3 at 150 D. (B) Graphs showing carotenoid content in fruit skin (left) and flesh (right) of different PSY lines (OE-2, OE-3, OE-4) and WT. Error bars represent standard error of total carotenoid contents of three biological replicates. Bar graph with asterisk show significant difference from the non-bagged fruit of the same line using Dunnett’s test (*P < 0.05; **P < 0.01). (C) Stained fruit sections of WT and OE-3 displaying plastids (arrowed) in the cells. | PMC9520574 | fpls-13-967143-g006.jpg |
0.398084 | 1fa6f133eb8b4cceb183b798ed1c5fb7 | Differentially expressed genes (DEGs) in bagged apple fruit. (A) Go Plot R of DEGs in bagged fruit of WT and OE-3 fruit. The scatter plots show log2 fold change (logFC) for each gene under the gene ontology (GO) number. Red dots indicate upregulated genes and blue dots show downregulated genes. The z-score bars are a measure of the extent to which each identified process is upregulated or downregulated. (B) Heat map of differentially expressed transcription factor genes in bagged fruit. Clustering done using the average expression values of three biological replicates of bagged (BG) and non-bagged (NBG) fruit of WT and OE-3 transgenic line. Green to red color gradients indicate low to high relative gene expression. The highlighted TFs are common to the bagging and fruit development (Figure 5A) data sets. | PMC9520574 | fpls-13-967143-g007.jpg |
0.379658 | c70b4bfcc5754e31bfe8f1270675453a | Correlations between carotenoid content and TF gene expression as determined by qRT-PCR in (A) fruit skin and (B) fruit flesh of WT and PSY fruit. R2 values indicate coefficient of determination. | PMC9520574 | fpls-13-967143-g008.jpg |
0.394613 | 2765398c0bb940508c45ca627856016f | Experimental design. | PMC9520596 | fnins-16-939915-g001.jpg |
0.449616 | d64e94db42a043a98ca8ff2e44fe7ea2 | Effects of ApoE on cognitive function and hippocampal synaptic ultrastructure in aging mice. (A) Y-maze test, n = 20. (B) Navigation test, n = 20. (C) Platform crossing, n = 20. (D) Target quadrant time, n = 20. (E) MWM representative figures. (F) Ultrastructure of hippocampal synapses. (G) Synaptic active zone length, n = 4 (Five synapses were randomly selected from each mouse). (H) Synaptic cleft width, n = 4 (Five synapses were randomly selected from each mouse). (I) Thickness of PSD, n = 4 (Five synapses were randomly selected from each mouse). (J) Synaptic interface curvature, n = 4 (Five synapses were randomly selected from each mouse). **p < 0.01 vs. Control group, ##p < 0.01, #p < 0.05 vs. Model group. | PMC9520596 | fnins-16-939915-g002.jpg |
0.450969 | 86e270e0a9d14bf9be083f22c2a253b9 | ApoE Deletion aggravates the dysregulation of SYP and PSD-95 expression in the hippocampus of aging mice. (A) Immunohistochemistry staining for SYP in different hippocampal regions. (B) Quantification of SYP intensity, n = 8. (C) Immunohistochemistry staining for PSD-95 in different hippocampal regions. (D) Quantification of PSD-95 intensity, n = 8. **p < 0.01 vs. Control group, #p < 0.05 vs. Model group. | PMC9520596 | fnins-16-939915-g003.jpg |
0.43338 | eb11c6dea94e4e9fba16492dce842bf1 | Deletion of ApoE affects the gut microbial composition of aging mice. (A) Chao 1 index, n = 8. (B) Observed species index, n = 8. (C) Shannon index, n = 8. (D) PCoA analysis. (E) Relative abundance of gut microbiota (phylum level). (F) Relative abundance of gut microbiota (genus level). *P < 0.05 vs. Control group, ##p < 0.01 vs. Model group. | PMC9520596 | fnins-16-939915-g004.jpg |
0.472164 | 611cce715e3e4e18bcf9d03a08661471 | LEfSe and PICRUSt2 analysis. (A) Cladogram of LEfSe analysis. (B) LDA of LEfSe analysis. (C) PICRUSt2 analysis. | PMC9520596 | fnins-16-939915-g005.jpg |
0.442395 | 97ffddc8f35c4edf94ad81e471b27b2a | Deletion of ApoE alters the metabolic profile of the hippocampus of aging mice. (A) PLS-DA of LC-MS about model group vs. control group. (B) PLS-DA of LC-MS about ApoE group vs. model group. (C) PLS-DA of GC-MS about model group vs. control group. (D) PLS-DA of GC-MS about ApoE group vs. model group. (E) Differentially abundant metabolites about model group vs. control group. (F) Differentially abundant metabolites about ApoE group vs. model group. (G) Analysis of metabolic pathway enrichment. | PMC9520596 | fnins-16-939915-g006.jpg |
0.484829 | 6ed194bba7d94a458a0e4037479fc199 | Deletion of ApoE alters blood lipids and oxidative stress levels in rapidly aging mice. (A) TC, n = 8. (B) TG, n = 8. (C) LDL, n = 8. (D) SOD in serum, n = 8. (E) GSH-Px in serum, n = 8. (F) MDA in serum, n = 8. (G) SOD in brain tissue, n = 8. (H) GSH-Px in brain tissue, n = 8. (I) MDA in brain tissue, n = 8. **p < 0.01 vs. Control group. #p < 0.05, ##p < 0.01 vs. Model group. | PMC9520596 | fnins-16-939915-g007.jpg |
0.419612 | 2977f2f621414ea88a5d9a3baa134099 | Subnational Government Responsiveness to Community Advocates’ Campaigns (as of June 30, 2019) | PMC9520791 | 12939_2022_1717_Fig1_HTML.jpg |
0.473767 | 5aa41ea129e84a4ab1f59560fa87bbe8 | Molecular
interactions of HSA with GAL. (A) Cartoon structural
representation, (B) interacting residues, and (C) potential surface
cavity representation of HSA residues interacting with GAL. | PMC9521020 | ao2c04004_0002.jpg |
0.45319 | 8e80a6553cee4cd890a9b39c58191d89 | (A) RMSD fluctuations
of the protein backbone, bound (red) and
free (black) HSA during 250 ns production. (B) RMSD of ligand during
the production runs. | PMC9521020 | ao2c04004_0003.jpg |
0.412211 | 41f490f6ebbb4261ae90fa9a81fe79b8 | (A) Rg for GAL-bound HSA plotted
as
a function of snapshots. (B) SASA plotted as a function of snapshots. | PMC9521020 | ao2c04004_0004.jpg |
0.420931 | 5f4c40e2c3eb49108bcaf6a5c4a50c18 | (A) HSA intramolecular
hydrogen bonds (H bonds) monitored during
250 ns production runs (GAL-bound HSA). (B) Intermolecular hydrogen
bond analysis of HSA–GAL complex. | PMC9521020 | ao2c04004_0005.jpg |
0.443595 | da1d828f5ab5441f8997c7b9ad8e9da7 | GAL binding affinity
estimated via LIE methodology (electrostatics
plotted in red and net van der Waals plotted in black). | PMC9521020 | ao2c04004_0006.jpg |
0.401996 | 51810fe9a5b3404c8669b61239b0926b | Fluorescence-based binding.
(A) Intrinsic fluorescence quenching
of HSA with increasing GAL concentration. (B) MSV plot of HSA–GAL
system. | PMC9521020 | ao2c04004_0007.jpg |
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