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0.406628 | a92605eccf6a44129509939cbac6ef28 | After grinding: (a) maximum shear Srz is located on Fiber ID 11; (b) path of maximum shear Srz runs through local membrane thickness; (c) shear stress Srz plotted for all nodes on the interface of Fiber ID 11, along with average experimental IFSS for HS40/F3G carbon/epoxy interface. | PMC10305614 | polymers-15-02596-g013.jpg |
0.385377 | 22a3052abc9e4b14a3b1a736bcb32c93 | Before push-out: shear stress Srz at the interfaces of all push-out fibers. Areas where Srz>IFSS are colored gray. | PMC10305614 | polymers-15-02596-g014.jpg |
0.473823 | f767150cbcae4b1586eeb2b2459d6712 | Before push-out: shear stress Srz in the matrix in cylindrical coordinate system at fiber ID 11 axis. Areas where Srz>IFSS are colored gray. | PMC10305614 | polymers-15-02596-g015.jpg |
0.484898 | 7e7e4e765e00404894e38306ea51708d | Mesh parameters of the FE Model. | PMC10305614 | polymers-15-02596-g0A1.jpg |
0.449771 | fe9597671e834ccab6ae3ccaab9198ab | Different model sizes for convergence study. Smaller model is the “central part” of a larger model. | PMC10305614 | polymers-15-02596-g0A2.jpg |
0.454419 | fb7f21c4711b4cd8834751e4e5fecae4 | Convergence study results: (a) inner mesh density; (b) outer mesh density; (c) through-thickness mesh; (d) model size. | PMC10305614 | polymers-15-02596-g0A3.jpg |
0.423936 | 1aea25721055461abc4762785991456b | Computational time for the convergence studies: (a) inner mesh density; (b) outer mesh density; (c) through-thickness mesh; (d) model size. | PMC10305614 | polymers-15-02596-g0A4a.jpg |
0.428138 | b8c32ee1ea5046dc919a2b641bf80086 | (a) Probed volume: the matrix volume under the probed area. (b) Average residual stress in probed volume at different model sizes. (c) The whole matrix volume in FE model. (d) Average residual stress in the whole matrix volume at different model sizes. | PMC10305614 | polymers-15-02596-g0A5a.jpg |
0.404125 | 5d7e69adfde5462aab20ca55701942db | After curing: on an in-plane cut through the middle of membrane thickness, von Mises stress in matrix is shown at fiber push-out area for different model sizes. | PMC10305614 | polymers-15-02596-g0A6.jpg |
0.444086 | 20b96f2a6de14a468ece0af5b121c6bc | Schematic diagram of the experimental setup for the considered nanopositioning system. | PMC10305727 | micromachines-14-01208-g001.jpg |
0.437355 | 5fb3ce7b3cd54133a2eeaad24d96d7fc | Comparison between the response of the identified model and the response of the experiment. | PMC10305727 | micromachines-14-01208-g002.jpg |
0.408839 | 2a1f2d290e0d4c2ca16323ff62a389e6 | The error between the actual output and the simulated output. | PMC10305727 | micromachines-14-01208-g003.jpg |
0.516616 | 9b2ed1a853a9498bac097112735a1b78 | Frequency response of the identified linear dynamic model. | PMC10305727 | micromachines-14-01208-g004.jpg |
0.462833 | 953246db6df6475f9183f3b027f5525c | Relay hysteresis operator. | PMC10305727 | micromachines-14-01208-g005.jpg |
0.435661 | 8c6279c33bc24001941cc39d26d5deda | Preisach model. | PMC10305727 | micromachines-14-01208-g006.jpg |
0.480079 | c1c3209a07f34542aa8b6420554d8e67 | Stop hysteresis operator. | PMC10305727 | micromachines-14-01208-g007.jpg |
0.477537 | 829dda14cb5d40199a5f8995549a01a3 | Block diagram of the modified Preisach model. | PMC10305727 | micromachines-14-01208-g008.jpg |
0.455454 | e29e669a534047768017d24e5b38edf4 | The excitation signals of the PEA in the training phase and the corresponding output signals of the sensor: (a) first training signal; (b) second training signal; (c) third training signal. | PMC10305727 | micromachines-14-01208-g009.jpg |
0.427273 | dec9fdd021ac474f9b94a7b483c66c64 | Comparison between the actual hysteresis loops and the predicted hysteresis loops for the considered actuator: (a) data A; (b) data B. | PMC10305727 | micromachines-14-01208-g010.jpg |
0.546106 | f71bdf1023ec4b8aad5bbdc492cc6a71 | The instantaneous prediction errors obtained from the difference between the actual and predicted responses for test data; A and B. | PMC10305727 | micromachines-14-01208-g011.jpg |
0.578127 | 2b50aa4d91214db2bb23b54491e696e2 | Block diagram of the proposed nanopositioning control scheme. | PMC10305727 | micromachines-14-01208-g012.jpg |
0.451371 | 3c87028c5ea24cd4a5c7c1c16008fafd | Block diagram for the proposed 2-DOF H∞ controller design. | PMC10305727 | micromachines-14-01208-g013.jpg |
0.443296 | 4ea88a3ff3764801a018487b2783c8f0 | Generalized block diagram with a 2-DOF H∞ controller design. | PMC10305727 | micromachines-14-01208-g014.jpg |
0.490589 | e138237853b04ab1a3f7edfd00c877e1 | Comparison of the desired response of the sensitivity functions with the response of the inverse weighting functions: (a) output sensitivity function response SO and (SOGK1−1) with 1/We; (b) K2SO with 1/Wu. | PMC10305727 | micromachines-14-01208-g015.jpg |
0.537533 | 3981eceaef0f4a4c93151e0642ffe69d | Complementary sensitivity function. | PMC10305727 | micromachines-14-01208-g016.jpg |
0.452263 | d8cabe8816eb4193a553184d98605a09 | Tracking results obtained by the 2-DOF H∞ controller without hysteresis compensation for (a) reference signal A; (b) reference signal B. | PMC10305727 | micromachines-14-01208-g017.jpg |
0.43584 | 135a8e0d400c451c95f0503615993324 | Tracking results obtained by only the inverse LSSVM model-based controller for (a) reference signal A; (b) reference signal B. | PMC10305727 | micromachines-14-01208-g018.jpg |
0.429784 | 006a12908ec74568935b67bb16e77ac1 | Tracking results obtained by the proposed control scheme for (a) reference signal A; (b) reference signal B. | PMC10305727 | micromachines-14-01208-g019.jpg |
0.48559 | 4f5f3ffb53a940358061316f5a256be2 | The input–output relationship obtained by the proposed control for (a) reference signal A; (b) reference signal B. | PMC10305727 | micromachines-14-01208-g020.jpg |
0.401029 | bf21a97b349e4e1290137d10733de534 | Comparison of the proposed control scheme with the PID–Preisach control scheme in terms of error levels in dataset B. | PMC10305727 | micromachines-14-01208-g021.jpg |
0.426942 | 203a2971eda84e349f1096eb874151a9 | (a) Clinical photograph of enlarged cervical lymph node with a sinus tract. (b) Granulomatous lymphadenitis showing a epithelioid cell cluster with characteristic elongated nuclei and lymphocytes and plasma cells forming a granuloma. Inset shows Langerhans multinucleated giant cell (Giemsa 100x) (c) Hodgkins Lymphoma showing polymorphous population of lymphoid cells, Reed-Sternberg cells (arrow), few neutrophils and histiocytes (Giemsa 40x) (d) Metastasis to lymph node showing polymorphous population of lymphoid cells and few large pleomorphic cells with scant cytoplasm in hemorrhagic background (Giemsa 40x) | PMC10305904 | JCytol-40-75-g001.jpg |
0.460747 | 56c8f26fb2d34effb3b572b8c7b6a931 | Cytomorphological diagnosis of thyroid gland lesions | PMC10305904 | JCytol-40-75-g002.jpg |
0.525261 | 8cbba948378644df970daacaeef4a12e | (a) Follicular neoplasm showing follicular epithelial cells having mild anisonucleosis in microfollicles and also scattered singly (Giemsa 40x) (b) Papillary carcinoma of thyroid showing pleomorphic cells in papillary fronds with few showing intranuclear inclusions (arrow) (Giemsa 40x) | PMC10305904 | JCytol-40-75-g003.jpg |
0.440562 | 2972c6105d7f44d083c06662417736a6 | (a) CT brain showing the soft tissue density area of the scalp in left frontal region causing erosion of both inner and outer table of the frontal bone (white arrow) (b) Photomicrograph shows round to oval cells with longitudinal nuclear groove, eosinophils, neutrophils, and lymphocytes (Giemsa 100x) (c) Langerhans cell histiocytosis showing sheets of round to oval histiocytes with longitudinal nuclear groove (H and E, 100x) | PMC10305904 | JCytol-40-75-g004.jpg |
0.53595 | 40e93254d083435fa04f1cc5a4efeba6 | The dual-system agent having a coupled successor-representation(SR)-based system and individual-representation(IR)-based system, adopted from the previous study [35].(A) Schematic illustrations of SR (left) and IR (right). In SR, action A1 is represented by a set of "discounted cumulative occupancies" of its successor actions (including A1 itself), i.e., (temporally discounted) cumulative frequencies with which each successor action is taken, starting from A1, under a given policy in the environment. The widths of the arrows indicate the probabilities of state transitions (to state S1 or S2) and action selections (A2 or A3 at S1 and A4 or A5 at S2), and the darkness of each circle indicates the occupancy of each action. Value of A1 is given by the dot product of the vector of occupancy of each action (x1
x2
x3
x4
x5) and a weight vector (w1
w2
w3
w4
w5), which is updated by reward prediction errors (RPEs). By contrast, in IR, action A1 is represented just by itself, separately from other actions. Value of A1, Q(A1), is directly updated by RPEs. (B) Different cortical regions/populations having SR or IR may unevenly target/activate the direct and indirect pathways of basal ganglia (BG), which have been suggested to be crucial for learning from positive and negative feedbacks, respectively. The line widths of the arrows indicate the suggested preferences of projections/activations described in the Introduction and [35]. (C) The dual-system agent incorporating the potentially uneven projections/activations from the cortical regions/populations having SR or IR to the BG pathways. Each of the SR-based system and the IR-based system develops the system-specific value of each action, and the average of the two system-specific values, named the integrated action value, is used for soft-max action selection and calculation of SARSA-type TD-RPEs. The TD-RPEs are used for updating the system-specific values (or more specifically, the IR-based system-specific values and the weights for the SR-based system-specific values), with the learning rates for the SR- and IR-based systems in the cases of positive (non-negative) and negative TD-RPEs, denoted as αSR+, αSR−, αIR+, and αIR−, can take different values. | PMC10306209 | pcbi.1011206.g001.jpg |
0.54889 | 68de8e1c034843438332d1e602844733 | Environmental model describing possible development of obsession-compulsion cycle, adopted from the previous study [10].There are two states: the relief state and the obsession state. At the relief state, the agent can take the "abnormal reaction" (to an intrusive thought, i.e., spontaneously arising anxiety e.g., about door lock), which induces a transition to the obsession state, or the "other" action (e.g., just ignore or forget about the intrusive thought), with which the agent stays at the relief state. At the obsession state, the agent can take the "compulsion" action (e.g., confirms door lock), which requires a small cost (0.01) but induces a transition back to the relief state with a high probability (50%), or the "other" action, which requires no cost but induces a transition back to the relief state only with a small probability (10%). Every stay at the obsession state imposes punishment (negative reward −1). | PMC10306209 | pcbi.1011206.g002.jpg |
0.469307 | cec3dd4965a843e0be821a5c9e984e81 | Behavior of different types of the dual-system agents.(A) Behavior of the agent that effectively had IR-based system only ((αSR+, αSR−, αIR+, αIR−) = (0, 0, 0.1, 0.1)). Top: An example of the time evolution of the difference in the action values of the "abnormal reaction" and the "other" at the relief state (Qabnormal − Qother@relief). Middle: The moving average (over the past 100 time steps) of the proportion that the agent was at the obsession state in the same example simulation. Bottom: The average of the moving-average proportion of the obsession state across 100 simulations (black line), presented with ±SD (gray thin lines). (B) Behavior of the agent that effectively had SR-based system only ((αSR+, αSR−, αIR+, αIR−) = (0.1, 0.1, 0, 0)). (C) Behavior of the agent having IR- and SR-based systems, both of which learned equally from positive and negative RPEs ((αSR+, αSR−, αIR+, αIR−) = (0.05, 0.05, 0.05, 0.05)). (D) Behavior of the agent having appetitive SR- and aversive IR-based systems ((αSR+, αSR−, αIR+, αIR−) = (0.09, 0.01, 0.01, 0.09)). (E) Proportion of the obsession state during time-steps 4901~5000, averaged across 100 simulations, in various cases with different learning rates. The horizontal and vertical axes indicate the learning rates of the SR-based system from positive and negative RPEs (i.e., αSR+ and αSR−), respectively, while αSR+ + αIR+ and αSR− + αIR− (i.e., total learning rates from positive and negative RPEs, respectively) were kept constant at 0.1. | PMC10306209 | pcbi.1011206.g003.jpg |
0.452797 | dac7073711d145fc8eebb32717487040 | Behavior of the original and modified dual-system agents in the long run.(A) Behavior of the original appetitive SR + aversive IR agent for 50000 time steps. The learning rates were set as (αSR+, αSR−, αIR+, αIR−) = (0.09, 0.01, 0.01, 0.09) (same for (A,B, D-F)). Arrangements are the same as those in Fig 3A–3D. (B) Integrated action values, system-specific values/weights, and SR matrix of the original appetitive SR + aversive IR agent. Top: Integrated value of each action averaged across 100 simulations (brown: "other" at the relief state, purple: "abnormal reaction", black: "other" at the obsession state, red: "compulsion"; same below). Middle: Weights for SR-based system-specific values (top four lines) and IR-based system-specific values (bottom four lines) averaged across 100 simulations. Bottom: SR matrix at time step 5000 (left) and 50000 (right) averaged across 100 simulations. Each row indicates the SR of each action shown in the left, with the darkness of the squares indicating the discounted cumulative occupancies of the actions shown in the bottom. (C) Behavior of the appetitive IR ((αIR+, αIR−) = (0.09, 0.01)) + aversive IR ((αIR+, αIR−) = (0.01, 0.09)) agent. The moving average of the proportion that the agent was at the obsession state (top) and the system-specific values of the compulsion (bottom) in a single simulation are shown. (D,E) Results for the appetitive SR + aversive IR agent with a modification, in which the rate of the update of SR matrix decreased over time according to 0.01/(1 + time-step/1000). (F,G) Results for the appetitive SR + aversive IR agent with another modification, in which the IR-based system-specific values and the weights for the SR-based system-specific values decayed at a constant rate (0.001 at each time step) while the rate of the update of SR matrix was fixed at 0.01 as in the original agent. (H) Average proportion of the obsession state during time-steps 49901~50000 for the modified agent with the decay of values/weights, in various cases with different learning rates. Notations are the same as those in Fig 3E. (I) The average of the moving-average proportion of the obsession state across 100 simulations (black line), presented with ±SD (gray thin lines), of the appetitive SR + aversive IR agent with the decay of values/weights, with the inverse temperature increased tenfold (top) or fifty-fold (bottom: −SD is mostly invisible). | PMC10306209 | pcbi.1011206.g004.jpg |
0.433454 | a7d7b617fd404e2f95f5270fe86d354b | Behavior of the modified agent with the decay of values/weights in the modified environmental model, in which there were multiple "other" actions. (A) Modified environmental model assuming multiple "other" actions.(B) Behavior of the appetitive SR + aversive IR agent ((αSR+, αSR−, αIR+, αIR−) = (0.09, 0.01, 0.01, 0.09)) with the decay of values/weights in the environment with two "other" actions at each state. Top: The average of the moving-average proportion of the obsession state across 100 simulations (black line), presented with ±SD (gray thin lines). Middle: Integrated action value averaged across 100 simulations (brown: average of "other" actions at the relief state, purple: "abnormal reaction", black: average of "other" actions at the obsession state, red: "compulsion"). Bottom: SR matrices averaged across 100 simulations, in which each row indicates the SR of "abnormal reaction" and "compulsion" and the mean SR of "other" actions at each state shown in the left (i.e., averaged across "other" actions at the same states), with the darkness of the squares indicating the discounted cumulative occupancies of "abnormal reaction" and "compulsion" and the summed discounted cumulative occupancies of "other" actions at each state shown in the bottom (i.e., summed across "other" actions at the same states). (C) Average proportion of the obsession state during time-steps 49901~50000 for the agent with the decay of values/weights, in various cases with different learning rates, in the environment with two "other" actions at each state. The color bar is the same as the one in Fig 4H. (D,E) Results for the cases where there were eight "other" actions at each state in the environmental model. (F) Behavior (average ±SD of the moving-average proportion of the obsession state) of the appetitive SR + aversive IR agent with the decay of values/weights in the environment where there were eight "other" actions at the relief state and one "other" action at the obsession state (top) or one "other" action at the relief state and eight "other" actions at the obsession state (bottom). | PMC10306209 | pcbi.1011206.g005.jpg |
0.421996 | 65e467f6f2654f969a5caae3846f0d61 | Behavior of the dual-system agents in the two-stage decision task, as compared to the agent with balanced or imbalanced memory trace.(A,B) Comparison between the SR-only agent ((αSR+, αSR−, αIR+, αIR−) = (0.3, 0.3, 0, 0)) (A) and the appetitive SR + aversive IR agent ((αSR+, αSR−, αIR+, αIR−) = (0.27, 0.03, 0.03, 0.27)) (B). Left panels: Distributions of the estimated parameter representing the degree of model-based control (w) across 97 simulations (out of 100 simulations: see the Materials and Methods for details). Right panels: Proportion of stay choice (i.e., choosing the same option as the one at the previous trial) at the first stage depending on whether reward was obtained or not and whether common or rare transition was occurred at the previous trial. The bars indicate the average across 1000 simulations, and the error-bars indicate ±SEM. (C,D) Comparison between the RL model developed in the original two-stage task study [8] with balanced eligibility trace (λ = 0.4) (C) and a modified model with imbalanced eligibility trace ((λ1, λ2) = (0.4, 0) for positive and negative TD-RPE, respectively) (D). The degree of model-based control (w) was set to 0.5 in the simulations of both models. Left panels: Distributions of the estimated w across 100 simulations. Right panels: Mean (±SEM) proportion of stay choice at the first stage across 1000 simulations. (E) Results for the neutral SR + IR agent ((αSR+, αSR−, αIR+, αIR−) = (0.15, 0.15, 0.15, 0.15)). Configurations are the same those in (A-D), with the left panel showing the distribution of w across 97 out of 100 simulations. | PMC10306209 | pcbi.1011206.g006.jpg |
0.421125 | 75b28c4f5a58430bbf6927d68bac1ca8 | Behavior of the dual-system agents in the delayed feedback task examined in [10].(A) Total obtained outcome (feedback) from stimuli causing immediate (left) or delayed feedback (right) in each session, averaged across 1000 simulations, for the appetitive SR + aversive IR agent (red) and the SR-only agent (blue). The error-bars indicate ±SEM. (B) Total obtained outcome (feedback) in sessions 5 and 6, averaged across 1000 simulations, from stimuli with immediate or delayed feedback for the two types of agents. The error-bars indicate ±SEM. (C) Results of fitting of the choices of the two types of agents (47 and 45 out of 50 simulations for each, separately conducted from those shown in (B)) by the separate eligibility trace actor-critic model considered in [10]. The horizontal and vertical axes indicate the estimated parameters ν+ and ν− (decay time scale of the trace for positive and negative RPEs), respectively. | PMC10306209 | pcbi.1011206.g007.jpg |
0.464345 | a81fa98c60494bb79974758a402e0cc6 | Schematic diagram of the proposed opponent SR + IR learning in multiple parallel cortico-basal ganglia (BG)-dopamine (DA) circuits.It is hypothesized that there exist preferential connections from the cortical populations having SR and IR to the striatal direct and indirect pathway neurons expressing D1 and D2 receptors, respectively. Such preferential connections cause greater learning of SR- and IR-based systems from positive and negative DA signals, respectively. It implements the appetitive SR + aversive IR agent in the canonical cortico-BG-DA circuit for reward reinforcement learning (RL), in which DA represents reward prediction error (RPE) (left part of this figure). In contrast, in the recently revealed cortico-BG-DA circuit for threat/aversiveness RL, in which DA represents threat/aversiveness PE, the same greater learning of SR/IR-based systems from positive/negative DA signals implements the aversive SR + appetitive IR agent (right part of this figure). | PMC10306209 | pcbi.1011206.g008.jpg |
0.462041 | 1cb74104bbf842f69aa85ff7d3dfbcee | Behavior in the two-stage decision task: additional simulation result and results obtained by analysis of publicly available experimental data.(A) Behavior of the aversive SR + appetitive IR agent ((αSR+, αSR−, αIR+, αIR−) = (0.03, 0.27, 0.27, 0.03)) in the punishment version of the two-stage task. Top: Distribution of the estimated parameter representing the degree of model-based control (w) (across 98 out of 100 simulations). Bottom: Mean (±SEM) proportion of stay choice at the first stage across 1000 simulations. (B) Mean (±SEM) proportion of stay choice at the first stage of the participants with high scores of OCI-R (≥ 40) in Experiment 1 (23 out of 548 participants) (top panel) and Experiment 2 (58 out of 1413 participants) (bottom panel) of [9], obtained by analysis of the data at https://osf.io/usdgt/. Cases where the "reward" at the previous trial was negative in the data file were omitted from the analysis. | PMC10306209 | pcbi.1011206.g009.jpg |
0.445592 | cac3e7ff53354db387c0b84d54d2662f | Behavior of the dual-system agents in the alternative environmental model.(A) Diagram of action-state transitions. At the relief state, the agent can have "normal (thought)" or "intrusive (thought)". Having "intrusive" causes transition to the obsession state, with punishment. At the obsession state, the agent can take "compulsion", which causes a stay at the obsession state with punishment, or "depart", which causes transition to the relief state. (B) Examples of the moving-average proportion of the obsession state (averaged over 100 time-steps, plotted every 100 time-steps) in the cases of the different types of agents (from top to bottom: aversive SR + appetitive IR (αSR+, αSR−, αIR+, αIR−) = (0.01, 0.09, 0.09, 0.01), appetitive SR + aversive IR (0.09, 0.01, 0.01, 0.09), neutral SR + neutral IR (0.05, 0.05, 0.05, 0.05), SR-only (0.1, 0.1, 0, 0) and IR-only (0, 0, 0.1, 0.1)). (C) Percentage of the period of repetitive obsession-compulsions, in which the moving-average proportion of the obsession state was ≥ 0.5, during time-steps 0~50000 in various cases with different learning rates, averaged across 100 simulations for each case. The horizontal and vertical axes indicate αSR+ and αSR−, respectively, while αSR+ + αIR+ and αSR− + αIR− were kept constant at 0.1 (in the same manner as in Figs 3E and 4H). (D-F) Percentage of the period of repetitive obsession-compulsions in the cases where the size of punishment upon staying at the obsession state (originally 0.2 in (C)) was changed to 0.1, 0.3, 0.4, or 0.5 (D), the inverse temperature (originally 10 in (C)) was changed to 5 or 20 (E), or the time discount factor (originally 0.8 in (C)) was changed to 0.7 or 0.9 (F). | PMC10306209 | pcbi.1011206.g010.jpg |
0.478338 | 67517455898048ab983f838bc60913c9 | Assessment of stromal scar depth on corneal OCT images. | PMC10306333 | cornea-42-1052-g001.jpg |
0.400563 | b393a2382e414bc88ec9258b7a45b0c2 | Corneal thickness and position of the stromal scar as predictive factors for successful BB formation. Diagram representing the corneal thickness at the thinnest point (A), the depth of the stromal scar (B), the ratio of the depth of the stromal scar over the thinnest corneal thickness (C), and the receiving operating characteristic (ROC) curves related to B (D) and C (E). Median and interquartile range are represented. AUROC: area under receiver operating characteristics curve; BB+: successful big-bubble formation; BB-: failed big-bubble formation; ROC: receiver operating characteristics; Sc: stromal scar; TP: thinnest point. Mann–Whitney test; **: P <0.01; ****: P <0.0001. | PMC10306333 | cornea-42-1052-g002.jpg |
0.42364 | 85908c48dfc74149adab9ffa4bfec83c | Functional connectivity density (FCD) linked with vicarious traumatization.A Brain images showing that vicarious traumatization is negatively linked to FCD in the right ITG after adjusting for sex, age and head motion (color key indicates the strength of negative correlation). B Scatter plot depicting the correlation between vicarious traumatization and FCD in the right ITG. C Plot showing the similarity of co-activation pattern of right ITG to large-scale functional networks. L left, R right, ITG inferior temporal gyrus, DMN default mode network, CEN central executive network, DAN dorsal attention network, VAN ventral attention network, SMN somatomotor network, VN visual network, AFN affective network. | PMC10307857 | 41398_2023_2525_Fig1_HTML.jpg |
0.44401 | 7c34fc93eb124a02b271c612c8d309c7 | Resting-state functional connectivity (RSFC) linked with vicarious traumatization.A Brain regions whose functional connectivity strengths with the right ITG (seed) are linked to vicarious traumatization: left medial prefrontal cortex, left orbitofrontal cortex, right superior frontal gyrus, right inferior parietal lobule and bilateral precuneus. B Scatter plot showing the correlation between vicarious traumatization and the overall mean functional connectivity strength of these brain regions with right ITG. C Similarity of co-activation pattern of brain regions linked with right ITG to large-scale functional networks. L left, R right, ITG inferior temporal gyrus, DMN default mode network, CEN central executive network, DAN dorsal attention network, VAN ventral attention network, SMN somatomotor network, VN visual network, AFN affective network. | PMC10307857 | 41398_2023_2525_Fig2_HTML.jpg |
0.421541 | 594e319de45f46a5936a41acb83d0d70 | Mediator role of psychological resilience in the association of FCD and RSFC with vicarious traumatization.Psychological resilience mediates the linkage of (A) right ITG FCD and (B) right ITG-DMN connectivity to vicarious traumatization. Sex, age and head motion are controlled for and standardized estimates are indicated in the path diagram (***p < 0.001; **p < 0.01; *p < 0.05). ITG inferior temporal gyrus, DMN default mode network, X independent variable, M mediator variable, Y dependent variable, CI confidence interval, T1 October 2019–January 2020, T2 February–April 2020. | PMC10307857 | 41398_2023_2525_Fig3_HTML.jpg |
0.39186 | 5b108833bb21455aa7a44bb7256c54af | Technical description of ROILoc. ROILoc aims at locating and extracting any region of interest on a given MRI. | PMC10308024 | fninf-17-1130845-g0001.jpg |
0.493561 | bfb9f0b1df9144418e27c6946f5ce3b5 | Complete overview of HSF. A (T1w or T1w) MRI passes through ROILoc to extract the left and right hippocampi. Each subvolume is then randomly augmented to obtain 21 different versions of the same hippocampus. Each segmentation goes through five independent deep learning models, and the final segmentation is a voxel-wise plurality vote across all segmentations. A voxel-wise aleatoric uncertainty map is computed for further post hoc analysis. | PMC10308024 | fninf-17-1130845-g0002.jpg |
0.378315 | 1e8e9ff1a1a745d3842db35e32cf35db | Segmentation example from a random subject. The dentate gyrus is in red, CA1/2/3 are in green, yellow, and purple, and the subiculum is in blue. | PMC10308024 | fninf-17-1130845-g0003.jpg |
0.391366 | 6fdfb44abeec431aad959a8b4681e8ac | Cumming estimation plots comparing HSF (T2w) against ASHS (T1w and T2w), HIPS (T1w and T2w), and HippUnfold (HU) (T1w and T2w). The first row illustrates three performance metrics—the dice coefficient (higher is better), the Hausdorff distance (lower is better), and the volumetric similarity (higher is better). The vertical bars in this row represent the mean ±std for each metric group. The dashed line in this row represents the inter-rater reliability for manual segmentation obtained in the earlier study of Bouyeure et al. (2021). As this earlier study only computed the inter-rater comparison as the dice coefficient, it is not available for the other two metrics. The second row depicts the mean effect size (Cohen's d) with a black dot to facilitate statistical comparison between the groups. The black bars in this row represent 95% CIs for variability estimations. The 95% CIs are obtained through non-parametric bootstrap resampling to generate distributions of all possible effect sizes. | PMC10308024 | fninf-17-1130845-g0004.jpg |
0.411589 | dc965eccb3cf48b4806eaeecbd939f0b | Lifespan dynamics of hippocampal subfields. Trend lines (surrounded by standard errors) are defined as natural cubic splines with a number of degrees of freedom minimizing an Akaike Information Criterion. Vertical dashed lines indicate inflection points. | PMC10308024 | fninf-17-1130845-g0005.jpg |
0.487406 | 5bbf37bc0d204325acc5d30fc62ff166 | Normalized anteroposterior composition of subfields, going from 0% of the hippocampus (head) to 100% (tail). Vertical black lines are approximate delimiters of the head, body, and tail of the hippocampus. | PMC10308024 | fninf-17-1130845-g0006.jpg |
0.400259 | 0a5b66fd48154f2daddc7aeac2ce42cd | The M5 mutant phenocopies loopless Ndc80 in vivo and in vitro
Schematic of a cold‐shock assay following an electroporation experiment. Immunofluorescence images showing the attachment status of kinetochores to microtubules in cells electroporated with recombinant Ndc80‐wt, Ndc80‐∆L, or Ndc80‐M5 complexes. The number of cells with multipolar spindles and the total number of analyzed cells are shown. Some signal from the tubulin channel is visible in the CENP‐C channel. Scale bar: 5 μm.Total Internal Reflection Fluorescence (TIRF) microscopy was used to investigate Ndc80Alexa488 complexes (0.6 nM) added to trimeric Ndc80TMR (10 pM) on fluorescent taxol‐stabilized microtubules that were attached to a passivated glass surface. Typical kymographs showing virtually motionless Ndc80 trimers (magenta) and transiently binding Ndc80 monomers (yellow). Wild‐type (wt) monomers associate with wt trimers (left), but not with M5 trimers (right). Scale bars: vertical (100 s), horizontal (5 μm).Quantification of the intensity of the monomeric Ndc80 associating with microtubule‐bound trimeric Ndc80. A threshold for binding was set at an intensity equivalent to one Alexa488 copy. Intensities well above 1 (yellow) could thus reflects multiple monomers binding simultaneously.Fraction of time there was at least one monomer (added to solution at a concentration of 0.6 nM) present at the microtubule‐bound trimer (10 pM), tested in various combinations of wild‐type (wt), loopless (ΔL) and M5 monomers or trimers. All analyzed traces of Ndc80 trimers are shown (n = 42, 63, 33, 30, 34 for conditions 1–5). Horizontal lines show median values and statistical significance was determined using a two‐tailed Mann–Whitney test. P‐values: 1 (wt‐trimer + wt‐monomer) vs. 2 (M5‐trimer + wt‐monomer): 7·10−7 (***); 2 vs. 3: 0.17 (n.s.); 1 vs. 3: 1·10−6 (***); 1 vs. 4: 3·10−15 (***); 3 vs. 4: 1·10−8 (***); 1 vs. 5: 1·10−13 (***); 4 vs. 5 0.23 (n.s.).
Source data are available online for this figure.
| PMC10308368 | EMBJ-42-e112504-g001.jpg |
0.433215 | e46f989bc2474776aa17224c8d39b13f | Structural analysis and loop‐dependent clustering of Ndc80 on microtubules
ACartoon of a chromosome pair during mitosis with sister kinetochores that are attached (green) and unattached (red) to microtubules. Unattached kinetochores trigger a spindle assembly checkpoint (SAC) signal. One outer kinetochore contains a lawn of Ndc80 complexes, resulting in many Ndc80 complexes binding to a single microtubule.BPrediction of the full‐length Ndc80 structure with residues that comprise the tail, the hinge, the loop, and the tetramerization domain indicated. The box shows the loop region at a 6× magnification. See Fig EV1 for more information.C, DLow‐angle Pt/C shadowing of Mis12:Ndc80 (panel C) and Mis12:Ndc80Δloop (Δ431–463) (panel D) complexes. The Mis12 complex appears as a 20 nm rod‐like extension and marks the SPC24:SPC25 side of the Ndc80 complex.ESize exclusion chromatography coupled with multi‐angle light scattering (SEC‐MALS) profiles of fluorescently labeled Ndc80 and Ndc80Δloop. Calculated (and theoretical) masses are indicated. See Appendix Fig S1 for more information.FTotal Internal Reflection Fluorescence (TIRF) microscopy was used to investigate Ndc80Alexa488 complexes on taxol‐stabilized microtubules that were attached to a passivated glass surface. Kymographs show Ndc80 complexes at a concentration of 0.2 nM with (FL, blue) or without (ΔL, orange) the loop. Scale bars: vertical (5 μm), horizontal (5 s).GQuantification of Ndc80 residence times for data as in panel (F). Solid line represents a single exponential fit.HOne‐dimensional diffusion of Ndc80 complexes (with n indicated) on microtubules. Traces were split into segments of 0.5 s and averaged. Mean values (circles) and SEM (shaded area) are shown.IDistribution of the initial brightness of Ndc80 complexes on stabilized microtubules.JTypical fields of view showing decoration of taxol‐stabilized microtubules (cyan) incubated with full‐length or loopless Ndc80 (yellow) at the indicated concentration. Images show an average projection of 200 frames. The contrast between individual fluorescent channels (inverted grayscale) was fixed. Auto‐contrast was used for the composite images to highlight the differences in the uniformity of Ndc80 decoration.KDistribution of the brightness of Ndc80 complexes at indicated concentrations on taxol‐stabilized microtubules.
Source data are available online for this figure.
| PMC10308368 | EMBJ-42-e112504-g002.jpg |
0.422605 | d51c450197cc41a0a7d49ef50cdeeebb | Mutation of critical residues in the loop impairs chromosome congression
Schematic of an electroporation experiment.Immunoblot of NDC80 levels following depletion of the Ndc80 complex by RNAi.Quantification of the time that cells spent in mitosis following various treatments. Each dot represents a cell and the red lines indicate median values. Nocodazole was added 17 h after electroporation and 1 h before microscopy. A minimum of 30 cells were analyzed for each condition.Immunofluorescence microscopy of mitotic cells stained for DNA (DAPI), kinetochores (CREST), and Ndc80 complexes. The Ndc80 antibody (9G3) detects endogenous Ndc80 (column 1) and electroporated recombinant Ndc80 (columns 3, 4, 5). Representative cells with a metaphase plate or uncongressed chromosomes are shown. Scale bar: 5 μm.Overview of mutations in the Ndc80 loop region and the effects on the time spent in mitosis following the experimental setup outlined in panel (A). Colors indicate whether cells divided normally, sometimes with delayed chromosome congression (green), or showed long arrests followed by a catastrophic division (orange). Each dot represents a cell and the red lines indicate median values. Mutation NDC80G434A‐Y435A did not support the formation of stable and soluble Ndc80 complexes. A minimum of 30 cells were analyzed for each condition.
Source data are available online for this figure.
| PMC10308368 | EMBJ-42-e112504-g003.jpg |
0.433215 | e757613367684667898bd7050142d978 | Structural in silico analysis of the human Ndc80 complex
Boundaries and Predicted Aligned Error (PAE) scores of the three Ndc80 segments that were predicted by AF2 multimer. These fragments were used to generate a composite prediction of the full‐length Ndc80 complex. More information can be found in the Materials and Methods section.The prediction of the full‐length Ndc80 complexes with colors representing the different subunits (as in Fig 1B) and the local prediction confidence intervals.
| PMC10308368 | EMBJ-42-e112504-g004.jpg |
0.465775 | a42fa41cc664481cb400daa3efd7948b | Loop‐proximal Ndc80‐Ndc80 crosslinking rescues increased diffusivity of loopless Ndc80 trimers
Representation of the peptides used to raise AB‐849 and AB‐850 and immunoblots showing their recognition of wild‐type, M5 and loopless Ndc80 complexes. 9G3 is a commercially available monoclonal antibody raised against NDC8056–642, later shown to recognize NDC80200–215. Asterisk shows the non‐specific recognition of another protein, presumably NUF2.Diffusion of full‐length and loopless Ndc80 trimers in absence and presence of AB‐849. The primary rabbit polyclonal AB‐849 was detected using a Alexa650‐labeled anti‐rabbit secondary IgG antibody. Scale bars: vertical (100 s), horizontal (5 μm).One‐dimensional diffusion of full‐length (blue), loopless (orange), and M5 (black) Ndc80 trimers in presence and absence of AB‐849 and AB‐850 as described in panel (B) (see Appendix Fig S4 for more information). Traces of Ndc80 trimers (with n indicated in the legend for panel D) on microtubules were analyzed. Traces were split into segments of 60 s and averaged (see Materials and Methods). Mean (circles) and SEM (shaded areas) values are shown. We note that the omission of reducing agents, a precondition to use the antibody as a crosslinker, slightly decreased the overall diffusion of Ndc80‐modules on microtubules (compare with Fig 2E).Summary showing diffusion coefficients (μm2/min) that follow from the data shown in panel (C). Mean values, SEM, and number of diffusion traces (n) are indicated. Statistically significant differences were determined using a two‐tailed t‐test. P‐values: no AB, FL vs. ΔL: 0.0180 (*); ΔL, no AB vs. AB‐849: 0.0108 (*); M5, no AB vs. AB‐849: 0.0458 (*); ΔL, AB849 vs. AB‐850: 0.0156 (*); AB‐850, ΔL vs. FL: 0.0461 (*); AB‐850, ΔL vs. M5: 0.0494 (*).
Source data are available online for this figure.
| PMC10308368 | EMBJ-42-e112504-g005.jpg |
0.422744 | e1794b38455c42468a92fba0566ccdd0 | Synergistic contributions of the Ndc80 loop and tail to kinetochore‐microtubule binding and SAC silencingExperimental workflow to investigate the time that cells, electroporated with recombinant Ndc80 complexes, spent in mitosis in the presence and absence of mitotic kinases inhibitors. Every dot represents a cell and red lines indicate median values. At least 30 cells were analyzed for each condition.Source data are available online for this figure.
| PMC10308368 | EMBJ-42-e112504-g006.jpg |
0.468886 | 37d4b0ae83f044b2be9ca9a64b823829 | NDC80 D436 and E438 promote Ndc80 clustering on microtubules and chromosome congression
Effects of M13 and M14 on the time spent in mitosis following the experimental setup outlined in Fig 4. All other datapoints are also shown in Fig 4E. Colors indicate whether cells divided normally, sometimes with delayed chromosome congression (green), or showed long arrests followed by a catastrophic division (orange). Each dot represents a cell and the red lines indicate median values. A minimum of 30 cells were analyzed for each condition.In an assay as presented in Fig 1, non‐uniform Ndc80 distributions result in a high standard deviation (SD) of Ndc80 fluorescence along a microtubule, indicating clustering. See Appendix Fig S5 for more information.A model for the synergistic contributions of the Ndc80 loop and the Ndc80 tail to robust kinetochore‐microtubule attachments.A schematic representation of loop‐mediated interactions that stabilize Ndc80‐Ndc80 interactions when Ndc80 complexes align following their anchoring on multivalent receptors at both ends.
Source data are available online for this figure.
| PMC10308368 | EMBJ-42-e112504-g007.jpg |
0.484309 | a0ee82fe5a2d4e88b9ce0148d5ddc449 | Alignments, phylogenetic tree, and structural conservation of the Ndc80 kink and loop
Sequence alignment of the loop region of the NDC80 subunit in various species. Residue numbers correspond to the human NDC80.Unrooted phylogenetic tree that was generated with complete NDC80 sequences. Sequences in panel (A) were arranged according to this tree. Species belonging to the Chordata phylum (light green) and the Fungi kingdom (purple) are indicated. Black dots mark species for which we predicted the structure.Predicted structures of the NDC80:NUF2 region spanning the hinge and loop regions. Shown in similar orientations following structural alignment to the human fragment NDC80376–516:NUF2269–356.
Source data are available online for this figure.
| PMC10308368 | EMBJ-42-e112504-g008.jpg |
0.448565 | b3bfeddfa92d42cea66b71c97dfaf89a | The loop stabilizes end‐on Ndc80‐microtubule interactions under force
Schematic of the optical trap experiment and a typical force trace. A glass bead coated with full‐length or loopless Ndc80 trimers is held in an optical trap near a microtubule end. The displacement of the bead (left Y axis) and the corresponding force (right Y axis) are shown along and across the microtubules axis (black and gray, respectively). Typical stages of an experiment (steps 1–8) are described.Correlations of stall duration and stall force. Each datapoint represents a single stall event. Filled squares: stalls resulting in microtubule rescue. Open squares: stalls resulting in bead detachment from the microtubule.The frequency of rescue events after a stall is plotted for full‐length (n = 46) and loopless (n = 44) Ndc80 after binning based on stall duration.
Source data are available online for this figure.
| PMC10308368 | EMBJ-42-e112504-g010.jpg |
0.480123 | 6ee25f219ab7438da4e9bffbe7b3dd52 | The loop reduces diffusion of Ndc80 trimers without affecting their end‐tracking
APreparation of TMR‐labeled, streptavidin‐mediated Ndc80 trimers. See Appendix Fig S1 for more information.B–DTMR‐labeled Ndc80 trimers with (FL, blue) or without (ΔL, orange) the loop were added to taxol‐stabilized microtubules to measure their residence time (panel C) and one‐dimensional diffusion (panel D). Ndc80 trimers with and without the loop are shown. Scale bars: vertical (1,000 s), horizontal (5 μm).EOne‐dimensional diffusion of Ndc80 trimers (with n indicated) on microtubules. Traces were split into segments of 60 s and averaged. Mean values (circles) and SEM (shaded area) are shown.FKymographs of full‐length and loopless Ndc80 trimers (10 pM) that reside on dynamically growing and shortening microtubules. Trimers remain bound to the ends of shortening microtubules. Scale bars: vertical (100 s), horizontal (5 μm).GDistribution of initial brightness of end‐tracking Ndc80 trimers with full‐length (blue) or loopless (orange) Ndc80.HFraction of Ndc80 trimers that switches from lateral microtubule binding to tracking shortening ends. Data from four repeats (total 140 events) for full‐length Ndc80 trimers (blue), and two repeats (total 158 events) for loopless Ndc80 trimers (orange). Horizontal lines indicate average values.IEnd‐tracking speed of full‐length (blue) and loopless (orange) Ndc80 trimers that follow shortening microtubule ends. Compared to Ndc80‐free shortening ends in the same field of views (gray). Horizontal lines indicate median values.
Source data are available online for this figure.
| PMC10308368 | EMBJ-42-e112504-g011.jpg |
0.406303 | 8865cd56e8d041d5b270fb678cfc945a | Characterization of AB‐849 and AB‐850 in vitro
AB‐849 and AB‐850 recognize NDC80 in a HeLa cell lysate (lys) using immunoblotting. Recombinant Ndc80 complex (p) was used as a reference (see Fig 6A) Antibody dilutions are indicated and short (top panel) and long (bottom panel) exposures are shown.Experimental workflow to electroporate Ndc80 complexes with AB‐849 or AB‐850 into cells.The localization of AB‐849, AB‐850, NDC80, CENP‐C, and DNA was analyzed using immunofluorescence microscopy. Representative cells are shown. Scale bar: 10 μm.Overview of mutants M15–M20, mutated in the epitope region of AB‐849 to further test putative effects of the AB on the SAC.Time spent in mitosis in the presence or absence of nocodazole. Cells were electroporated with various Ndc80 constructs, when indicated following mixture with AB‐849 or AB‐850. Every dot represents a cell and red lines indicate median values. Effects of AB‐849 on the time spent in mitosis were not recapitulated by any of the mutants M15–M20.
Source data are available online for this figure.
| PMC10308368 | EMBJ-42-e112504-g012.jpg |
0.508996 | c074f1c5bb374ef189c54fd0736b3ad9 | Electroporation efficiency and a comparison of loop mutants
AImmunofluorescence quantification of Ndc80, KNL1, and NSL1 at kinetochores following electroporation of different Ndc80 constructs. The number of kinetochores analyzed for NDC80: wt ‐ 758, ∆L ‐ 2,053, M5 ‐ 1,612, KNL1: wt ‐ 697, ∆L ‐ 635, M5 ‐ 602, and NSL1: wt ‐ 647, ∆L ‐ 644, M5 ‐ 611. Red lines indicate median value with interquartile range.BSchematic representation of the predicted structure of the NDC80:NUF2 loop region with annotations to illustrate residues with side‐chains that pack a hydrophobic core and mutants that interfere with chromosome congression as illustrated in Fig 4E and Wimbish et al (2020).CImmunofluorescence quantification of various kinetochore proteins in cells that were treated as described in panel (A), but with nocodazole added 15 h after electroporation and 3 h before fixation. The number of kinetochores analyzed for CENP‐T: wt ‐ 799, ∆L ‐ 750, M5 ‐ 855, BUB1: wt ‐ 2,226, ∆L ‐ 1,993, M5 ‐ 1,598, and BUBR1: wt ‐ 2,315, ∆L ‐ 2,112, M5 ‐ 1,549. Red lines indicate median value with interquartile range.DA new anti‐SKA antibody, generated against the full‐length recombinant SKA complex, mainly recognizes SKA3 in a HeLa cell lysate using immunoblotting. Asterisks indicate non‐specific bands that are not sensitive to depletion of the SKA complex by RNAi.E–GThe antibody also recognizes SKA by immunofluorescence. SKA levels are higher in MG132 arrested cells than in STLC or nocodazole arrested cells. Scale bar: 5 μm.H, ISKA levels recruited to kinetochores in nocodazole‐exposed cells with various Ndc80 complexes. Scale bar: 5 μm.
Source data are available online for this figure.
| PMC10308368 | EMBJ-42-e112504-g013.jpg |
0.439387 | 3379b27aa1c643f6b3884140153d24ab | Loop‐dependent binding between Ndc80 monomers and trimers on microtubules
Supplementary information for Fig 5A–D. High‐speed recordings to quantify residence time of wild‐type and loopless Ndc80 monomers. The top two kymographs show a microtubule with monomers binding and unbinding. The lower two kymographs show the binding and unbinding of Ndc80 monomers to a microtubule with a Ndc80 trimer. Since trimers are practically motionless on this timescale, only their initial location was recorded and indicated. Corresponding mean residence times and SEM. are shown. The number of analyzed events is indicated. Scale bar: 5 μm.Distribution of residence times of wild‐type and loopless Ndc80 monomers associating with microtubule‐bound Ndc80 trimers. A single‐exponential fit described the residence time of Ndc80Δloop, likely corresponding to Ndc80Δloop:microtubule off‐rates. Residence time of full‐length Ndc80 could be fitted with two exponents, likely corresponding to Ndc80:microtubule and Ndc80:trimer:microtubule off‐rates.Typical kymographs showing Ndc80 trimers (magenta) and transiently binding Ndc80 monomers (yellow). Scale bars: vertical (100 s), horizontal (5 μm).
Source data are available online for this figure.
| PMC10308368 | EMBJ-42-e112504-g015.jpg |
0.417358 | 9fecafa4a9964acf9411007f706db477 | Flow chart. | PMC10310516 | sfad032fig1.jpg |
0.454061 | 9632d366249f45cbadfd17b2961e0400 | Prevalence of pruritus. | PMC10310516 | sfad032fig2.jpg |
0.483835 | 8c2ef063357f44c0be95945c1481d7fe | Mean values of dimensions of 5-D Itch scale in patients with pruritus moderate to very severe (N = 306) during the last 2 weeks. | PMC10310516 | sfad032fig3.jpg |
0.453607 | b3ffff0d4bec4e5e96c20a72d558257b | Localizations of pruritus according to 5-D Itch scale in patients with WI-NRS ≥4. Diameter of each circle is proportional to the frequency of pruritus in that localization. Localizations on the limbs are symmetrical. | PMC10310516 | sfad032fig4.jpg |
0.423375 | 8147f0796b054797bc2162db1080db73 | Quality of life according to pruritus intensity. Mean (95% CI) (A) EQ-5D index score and (B) EQ-5D VAS according to pruritus severity, and (C) SF-12 PCS and (D) SF-12 MCS. | PMC10310516 | sfad032fig5.jpg |
0.473904 | 35a49b59ac1a4a1a9f95c66a51df00b8 | Patient flowchart. | PMC10310532 | fneur-14-1205487-g0001.jpg |
0.414259 | c79578afc0fb4379ba60c5432eae391e | Cumulative unpreventable 30-day readmission-free survival stratified by a stroke nurse navigator implementation period. Log-rank P = 0.029; adjusted HR 0.48, 95% CI (0.23–0.99). | PMC10310532 | fneur-14-1205487-g0002.jpg |
0.454277 | 7b4d87fcb12647b89f1dd89f6212ed10 | Metabolic network of steroid hormones. | PMC10310992 | fendo-14-1196935-g001.jpg |
0.43367 | 3d5d6956c694413fb52e61d68f880e9e | Typical chromatograms of multiple reaction monitoring for target steroid hormones. (A) Corticosteroids, progestins and androgens. (B) Estrogens and the metabolites. | PMC10310992 | fendo-14-1196935-g002.jpg |
0.544345 | fcf8d01be14842a1bf7016472e1ff49a | Changes of steroid metabolism in GDM women. (A) Phase diagrams showed the changes in different types of serum steroid hormones in GDM women compared with the health controls. (B) Volcano plot of serum steroid hormones in GDM women compared with the health controls. FC, fold change. | PMC10310992 | fendo-14-1196935-g003.jpg |
0.42324 | e40b98f38f4142dea10b4f83e400f227 | The ROC analyses of significantly changed indicators. (A) ROC curves of the combination model and the screened individual elements. (B–F) ROC curves of the steroid indicators with statistically significant change in GDM. B: Progestins, androgens and parent estrogens; C: Estrogens in 2- or 4- pathway; D: Estrogens in 16- pathway; E: Calculated pathway flux; F: Calculated pathway ratio. | PMC10310992 | fendo-14-1196935-g004.jpg |
0.421572 | 65dfb36ab9034884a48358c874501419 | Designs
of nucleophilic aromatic ring-opening polymerization (SNArROP). (a) Established cyclic monomer scaffolds for preparing
chemically recyclable polymers. (b) Classical SNAr condensation
for the preparation of polyphenylene sulfide (PPS). (c) Dynamic self-correcting
SNAr condensation for the synthesis of porous polymer networks.
(d) New concept for SNArROP for the synthesis of chemically
recyclable polythioethers. The combination of dynamic SNAr chemistry and an appropriate ring size for ROP enable the reversibility
of this SNArROP strategy. | PMC10311534 | ja3c03455_0002.jpg |
0.398401 | f8f4d6f725bd4a5b8f7342286584d93e | Identifying the appropriate
electron withdrawing group and ring
size for the BT monomers. (a) Exchange reactions showing
rapid equilibrium established with p-NO2 substituted phenyl sulfide substrate at room temperature but no
conversion from the −CN, and −CHO substrates. (b) Calculated
ΔH for SNArROP with different ring
sizes from 5 to 8, suggesting enthalpically favorable polymerization
of 7- and 8-membered ring substrates. | PMC10311534 | ja3c03455_0003.jpg |
0.504892 | 0636be0a9c094915b1267c0e492ece5b | Synthesis and characterization of the BT monomers.
Synthesis of the 8-membered ring substrate BT1 through
an efficient [3,3]-sigmatropic rearrangement and its convenient transformation
to BT2-BT7. (i) NaBH4, MeOH,
0 °C. (ii) NaH, MeI, THF. (iii) NaH, C6H13I, THF. (iv) NaH, BnBr, DMF. (v) NaH, allyl bromide, DMF. (vi) Ac2O, DMAP, pyridine. (vii) tBuOK,
PPh3PMeBr, THF. The X-ray of BT1 and BT2 are shown as the insert. | PMC10311534 | ja3c03455_0004.jpg |
0.436484 | 1e49cfbce8404e4e9d9028e0f7c2d47c | SNArROP of BT monomersand characterization
of PBTs. (a) Results of BT polymerizations
by BTPP-based catalytic systems. (b) SEC curves for PBT2 produced at different [BT2]0/[BTPP]0/[C12H25SH]0 ratios. (c) Mn–conversion correlation (blue) and D̵–conversion correlation (red) of SNArROP of BT2. | PMC10311534 | ja3c03455_0005.jpg |
0.441521 | 62c99149203f4be69f46b0091aefc158 | Mechanical properties of PBT2. (a) DMA storage modulus
and tan δ profiles of PBT2 with different molecular
weights. (b) Tensile stress–strain curves of PBT2 with different molecular weights. | PMC10311534 | ja3c03455_0006.jpg |
0.410156 | 7b532072088b43d4802cc6c6eef888a7 | Chemical recyclability of PBT2. (a) SEC curves for
depolymerization of PBT2 (46.5 kDa) with substoichiometric
DBU and C12H25SH (0.55 equiv relative to repeat
units). (b) Overlays of 1H NMR spectra of initial and recycled BT2 and PBT2. | PMC10311534 | ja3c03455_0007.jpg |
0.474005 | 37924afc1c30407b864cc728907fe830 | Overall schema of challenges in seeking help for women’s sexual concerns | PMC10311729 | 12913_2023_9719_Fig1_HTML.jpg |
0.40351 | 630023c608a241859631fb0168cf826a | (a) N170 face perception paradigm. Only the data from the face and car trials were used in the present analyses. (b) Grand average ERP waveforms from the PO8 electrode site for the face and car trials. (c) Grand average face-minus-car difference wave at PO8, along with the simulated N170 difference wave (Gaussian function, mean = 129 ms, SD = 14 ms, peak amplitude = −4.6 μV). (d) Artificial waveform overlaid with the low-pass filtered version of that waveform for several different filter cutoffs. (e) Artificial waveform overlaid with the high-pass filtered version of that waveform for several different filter cutoffs. The number next to each high-pass filtered waveform is the artifactual peak percentage (APP). Note that the simulated waveforms were preceded and followed by 1000 ms of zero values to avoid edge artifacts. All the filters used here were noncausal Butterworth filters with a slope of 12 dB/octave, and cutoff frequencies indicate the half-amplitude point. The figure was adopted from Kappenman et al. (2021). | PMC10312706 | nihpp-2023.06.13.544794v1-f0001.jpg |
0.453774 | f39e30aed79e43c09902686db3feee9c | N170 data quality metrics for four different scoring methods and several combinations of high-pass filter cutoffs (0, 0.01, 0.05, 0.1, 0.5, 1, and 2 Hz) and low-pass filter cutoffs (5, 10, 20, 30, 40, 80, and 115 Hz). The signal was defined as the score (e.g., peak amplitude) obtained from the grand average ERP difference wave (faces minus cars). The noise was defined as the root mean square (RMS) of the single-participant standardized measurement error (SME) for that score. The signal-to-noise ratio (SNR) was computed as the signal divided by the noise. SNR is unitless. For latency scores, the signal is not consistently reduced by filtering, so only the RMS(SME) value is provided for the peak latency and 50% area latency scores. | PMC10312706 | nihpp-2023.06.13.544794v1-f0002.jpg |
0.443526 | e9bae2bcd30741e38f8cc723d7568c15 | (a) MMN passive auditory oddball task. (b) Grand average ERP waveforms at FCz electrode site for deviant and standard trials. (c) Grand average deviant-minus-standard wave at FCz along with its simulated MMN difference wave (Gaussian function, mean = 190 ms, SD = 33 ms, peak amplitude = −2.82 μV). (d) Artificial waveform overlaid with the low-pass filtered version of that waveform for several different filter cutoffs. (e) Artificial waveform overlaid with the high-pass filtered version of that waveform for several different filter cutoffs. The number next to each high-pass filtered waveform is the artifactual peak percentage (APP). Note that the simulated waveforms were preceded and followed by 1000 ms of zero values to avoid edge artifacts. All the filters used here were noncausal Butterworth filters with a slope of 12 dB/octave, and cutoff frequencies indicate the half-amplitude poin | PMC10312706 | nihpp-2023.06.13.544794v1-f0003.jpg |
0.415696 | a8323083d5404e78b37ec8ce5a1353ac | MMN data quality metrics for four different scoring methods and several combinations of high-pass filter cutoffs (0, 0.01, 0.05, 0.1, 0.5, 1, and 2 Hz) and low-pass filter cutoffs (5, 10, 20, 30, 40, 80, and 115 Hz). The signal was defined as the score (e.g., peak amplitude) obtained from the grand average ERP difference wave (deviants minus standards). The noise was defined as the root mean square (RMS) of the single-participant standardized measurement error (SME) for that score. The signal-to-noise ratio (SNR) was computed as the signal divided by the noise. SNR is unitless. For latency scores, the signal is not consistently reduced by filtering, so only the RMS(SME) value is provided for the peak latency and 50% area latency scores. | PMC10312706 | nihpp-2023.06.13.544794v1-f0004.jpg |
0.430232 | 538a5cd525274a20a3c64774d8a12cdc | (a) N2pc simple visual search task. (b) Grand average waves from the PO7/PO8 electrode sites for contralateral and ipsilateral conditions. (c) Grand average contralateral-minus-ipsilateral difference wave at PO7/PO8, along with the simulated N2pc difference wave (Gaussian function, mean = 257 ms, SD = 28 ms, peak amplitude = −1.62 μV). (d) Artificial waveform overlaid with the low-pass filtered version of that waveform for several different filter cutoffs. (e) Artificial waveform overlaid with the high-pass filtered version of that waveform for several different filter cutoffs. The number next to each high-pass filtered waveform is the artifactual peak percentage (APP). Note that the simulated waveforms were preceded and followed by 1000 ms of zero values to avoid edge artifacts. All the filters used here were noncausal Butterworth filters with a slope of 12 dB/octave, and cutoff frequencies indicate the half-amplitude point. | PMC10312706 | nihpp-2023.06.13.544794v1-f0005.jpg |
0.414742 | c13d1634d7084bb4ad29cd7bbaacbaf5 | N2pc data quality metrics for four different scoring methods and several combinations of high-pass filter cutoffs (0, 0.01, 0.05, 0.1, 0.5, 1, and 2 Hz) and low-pass filter cutoffs (5, 10, 20, 30, 40, 80, and 115 Hz). The signal was defined as the score (e.g., peak amplitude) obtained from the grand average ERP difference wave (contralateral minus ipsilateral). The noise was defined as the root mean square (RMS) of the single-participant standardized measurement error (SME) for that score. The signal-to-noise ratio (SNR) was computed as the signal divided by the noise. SNR is unitless. For latency scores, the signal is not consistently reduced by filtering, so only the RMS(SME) value is provided for the peak latency and 50% area latency scores. | PMC10312706 | nihpp-2023.06.13.544794v1-f0006.jpg |
0.449628 | 94adfa41e0c14b568c576dfa4a822560 | (a) P3 active visual oddball paradigm. (b) Grand average ERP waveforms from the Pz electrode site for the frequent and rare trials. (c) Grand average rare-minus-frequent difference wave at Pz, along with the simulated P3 difference wave (Ex-Gaussian function, mean = 310 ms, SD = 58 ms, λ = 2000 ms, peak amplitude = 8.6 μV). (d) Artificial waveform overlaid with the low-pass filtered version of that waveform for several different filter cutoffs. (e) Artificial waveform overlaid with the high-pass filtered version of that waveform for several different filter cutoffs. The number next to each high-pass filtered waveform is the artifactual peak percentage (APP). Note that the artificial waveforms were preceded and followed by 1000 ms of zero values to avoid edge artifacts. All the filters used here were noncausal Butterworth filters with a slope of 12 dB/octave, and cutoff frequencies indicate the half-amplitude point. | PMC10312706 | nihpp-2023.06.13.544794v1-f0007.jpg |
0.41063 | 0936092ba4a14bf4aa1f7ef70006d05d | P3 data quality metrics for four different scoring methods and several combinations of high-pass filter cutoffs (0, 0.01, 0.05, 0.1, 0.5, 1, and 2 Hz) and low-pass filter cutoffs (5, 10, 20, 30, 40, 80, and 115 Hz). The signal was defined as the score (e.g., peak amplitude) obtained from the grand average ERP difference wave (frequent minus rare). The noise was defined as the root mean square (RMS) of the single-participant standardized measurement error (SME) for that score. The signal-to-noise ratio (SNR) was computed as the signal divided by the noise. SNR is unitless. For latency scores, the signal is not consistently reduced by filtering, so only the RMS(SME) value is provided for the peak latency and 50% area latency scores. | PMC10312706 | nihpp-2023.06.13.544794v1-f0008.jpg |
0.403991 | d276cd1f6271454b88b2ffe60a3aba56 | (a) N400 word pair judgment paradigm. (b) Grand average ERP waveforms at CPz electrode site for unrelated and related trials. (c) Grand average unrelated-minus-related difference wave along with its simulated N400 difference wave (Ex-Gaussian function, mean = 280 ms, SD = 65 ms, λ = 1400 ms, peak amplitude = −9.65 μVV) (d) Artificial waveform overlaid with the low-pass filtered version of that waveform for several different filter cutoffs. (e) Artificial waveform overlaid with the high-pass filtered version of that waveform for several different filter cutoffs. The number next to each high-pass filtered waveform is the artifactual peak percentage (APP). Note that the artificial waveforms were preceded and followed by 1000 ms of zero values to avoid edge artifacts. All the filters used here were noncausal Butterworth filters with a slope of 12 dB/octave, and cutoff frequencies indicate the half-amplitude point. | PMC10312706 | nihpp-2023.06.13.544794v1-f0009.jpg |
0.412976 | 6ccabbc5d89f40cab64b7374a6ad7d5c | N400 data quality metrics for four different scoring methods and several combinations of high-pass filter cutoffs (0, 0.01, 0.05, 0.1, 0.5, 1, and 2 Hz) and low-pass filter cutoffs (5, 10, 20, 30, 40, 80, and 115 Hz). The signal was defined as the score (e.g., peak amplitude) obtained from the grand average ERP difference wave (unrelated minus related). The noise was defined as the root mean square (RMS) of the single-participant standardized measurement error (SME) for that score. The signal-to-noise ratio (SNR) was computed as the signal divided by the noise. SNR is unitless. For latency scores, the signal is not consistently reduced by filtering, so only the RMS(SME) value is provided for the peak latency and 50% area latency scores. | PMC10312706 | nihpp-2023.06.13.544794v1-f0010.jpg |
0.438421 | 10f25faae17c4d1fa37e8f11aa9795cf | (a) LRP flankers task. (b) Grand average ERP waveforms from the C3/C4 electrode sites for the contralateral and ipsilateral trials. (c) Grand average contralateral-minus-ipsilateral difference wave at C3/C4, along with the simulated LRP difference wave (Gaussian function, mean = −47 ms, SD = 36 ms, peak amplitude = −3.2 μV). (d) Artificial waveform overlaid with the low-pass filtered version of that waveform for several different filter cutoffs. (e) Artificial waveform overlaid with the high-pass filtered version of that waveform for several different filter cutoffs. The number next to each high-pass filtered waveform is the artifactual peak percentage (APP). Note that the artificial waveforms were preceded and followed by 1000 ms of zero values to avoid edge artifacts. All the filters used here were noncausal Butterworth filters with a slope of 12 dB/octave, and cutoff frequencies indicate the half-amplitude point. | PMC10312706 | nihpp-2023.06.13.544794v1-f0011.jpg |
0.399041 | c77efb251a1b4667a6f498114dadb173 | LRP data quality metrics for four different scoring methods and several combinations of high-pass filter cutoffs (0, 0.01, 0.05, 0.1, 0.5, 1, and 2 Hz) and low-pass filter cutoffs (5, 10, 20, 30, 40, 80, and 115 Hz). The signal was defined as the score (e.g., peak amplitude) obtained from the grand average ERP difference wave (contralateral minus ipsilateral). The noise was defined as the root mean square (RMS) of the single-participant standardized measurement error (SME) for that score. The signal-to-noise ratio (SNR) was computed as the signal divided by the noise. SNR is unitless. For latency scores, the signal is not consistently reduced by filtering, so only the RMS(SME) value is provided for the peak latency and 50% area latency scores. | PMC10312706 | nihpp-2023.06.13.544794v1-f0012.jpg |
0.471508 | 9513766f23cb45f5a893b22f5bbf00d5 | (a) ERN flankers task. (b) Grand average ERP waveforms from the FCz electrode site for correct and incorrect responses. (c) Grand average incorrect-minus-correct difference wave at FCz, along with its simulated ERP difference wave (Ex-Gaussian function, mean = 76 ms, SD = 26 ms, λ = −300 ms, peak amplitude = −12.5 μV). (d) Artificial waveform overlaid with the low-pass filtered version of that waveform for several different filter cutoffs. (e) Artificial waveform overlaid with the high-pass filtered version of that waveform for several different filter cutoffs. The number next to each high-pass filtered waveform is the artifactual peak percentage (APP). Note that the artificial waveforms were preceded and followed by 1000 ms of zero values to avoid edge artifacts. All the filters used here were noncausal Butterworth filters with a slope of 12 dB/octave, and cutoff frequencies indicate the half-amplitude point. | PMC10312706 | nihpp-2023.06.13.544794v1-f0013.jpg |
0.466189 | 3fe1fd11a52e46c78b4120bc264e4c24 | ERN data quality metrics for four different scoring methods and several combinations of high-pass filter cutoffs (0, 0.01, 0.05, 0.1, 0.5, 1, and 2 Hz) and low-pass filter cutoffs (5, 10, 20, 30, 40, 80, and 115 Hz). The signal was defined as the score (e.g., peak amplitude) obtained from the grand average ERP difference wave (correct minus incorrect). The noise was defined as the root mean square (RMS) of the single-participant standardized measurement error (SME) for that score. The signal-to-noise ratio (SNR) was computed as the signal divided by the noise. SNR is unitless. For latency scores, the signal is not consistently reduced by filtering, so only the RMS(SME) value is provided for the peak latency and 50% area latency scores. | PMC10312706 | nihpp-2023.06.13.544794v1-f0014.jpg |
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