Electrokinetic transport of CTAB induces multiphasic behavior during capillary adsorption and desorption.

Cationic surfactant coatings (e.g., CTAB) are commonly used in CE to control EOF and thereby improve separation efficiencies. However, our understanding of surfactant adsorption and desorption dynamics under EOF conditions is limited. Here, we apply automated zeta potential analysis to study the adsorption and desorption kinetics of CTAB in a capillary under different transport conditions: diameter, length, voltage alternation pattern and frequency, and applied pressure. In contrast to other studies, we observe slower kinetics at distinct capillary wall zeta potential ranges. Within these ranges, which we call "stagnant regimes," the EOF mobility significantly counteracts the electrophoretic (EP) mobility of CTA+ and hinders the net transport. By constructing a numerical model to compare with our experiments and recasting our experimental data in terms of the net CTA+ transport volume normalized by surface area, we reveal that the EP mobility of CTA+ and the capillary surface-area-to-volume ratio dictate the zeta potential range and the duration of the stagnant regime and thereby govern the overall reaction kinetics. Our results indicate that further transport-oriented studies can significantly aid in the understanding and design of electrokinetic systems utilizing CTAB and other charged surfactants.

Nanofluidic diodes based on asymmetric bio-inspired surface coatings in straight glass nanochannels.

In this study, we present nanofluidic diodes fabricated from straight glass nanochannels and functionalized using bio-inspired polydopamine (PDA) and poly-L-lysine (PLL) coatings. The resulting PDA coatings are shown to be asymmetric due to a combination of transport considerations which can be leveraged to provide a measure of control over the effective channel geometry. By subsequently introducing a layer of amine-bearing PLL chains covalently bound to the PDA, we enhance heterogeneities in the charge and ion distributions within the channel and enable significant current rectification between forward-bias and reverse-bias modes; our PDA-PLL-coated channels yielded a rectification ratio greater than 1000 in a 100 nm channel filled with 0.01× phosphate-buffered saline solution (PBS). We further demonstrated that at higher ionic strength conditions, reducing the solution pH increased the number of protonated amines within the PLL layer, amplifying the charge disparities along the channel and leading to greater rectification. As nanofluidic diodes with bipolar surface charge distributions tend to provide superior performance compared to those with a single wall charge polarity, we imposed a more bipolar charge distribution in our devices by partially coating our PDA-PLL-coated channels with negatively charged polyacrylic acid (PAA). These enhanced bipolar channels exhibited greater current rectification than the PDA-PLL-coated channels, reaching rectification ratios in excess of 100 even in more physiologically-relevant 1× PBS solutions. Our fabrication approach and the results herein provide a promising platform from which the scientific community can build upon in the relentless endeavor for improved sensitivity in biosensors and other analytical devices.

Real-Time Zeta Potential Analysis of Microchannel Surfaces during Aminosilane Deposition and Exposure Using Current Monitoring.

Surface coatings are extensively used in capillary electrophoresis to increase separation efficiency and resolution. The stability of these coatings across a wide pH range is desirable to achieve repeatable migration times; therefore, a comprehensive understanding of coating degradation timescales is needed. We present a novel platform for automated zeta potential analysis based upon current monitoring that delivers improved time resolution over the existing methods. Using our platform, we measure the zeta potential continuously during aminosilane coating reactions and infer changes in the surface composition. We found that the change in the zeta potential after coating depended on the monomer type and solvent, while its stability was influenced by the coating solvent and exposure pH. Our versatile platform provides an elegant approach for evaluating the molecular composition, reactivity, and stability of surfaces in real time.

Asymmetric Weights and Retrieval Practice in an Autoassociative Neural Network Model of Paired-Associate Learning.

Rizzuto and Kahana (2001) applied an autoassociative Hopfield network to a paired-associate word learning experiment in which (1) participants studied word pairs (e.g., ABSENCE-HOLLOW), (2) were tested in one direction (ABSENCE-?) on a first test, and (3) were tested in the same direction again or in the reverse direction (?-HOLLOW) on a second test. The model contained a correlation parameter to capture the dependence between forward versus backward learning between the two words of a word pair, revealing correlation values close to 1.0 for all participants, consistent with neural network models that use the same weight for communication in both directions between nodes. We addressed several limitations of the model simulations and proposed two new models incorporating retrieval practice learning (e.g., the effect of the first test on the second) that fit the accuracy data more effectively, revealing substantially lower correlation values (average of .45 across participants, with zero correlation for some participants). In addition, we analyzed recall latencies, finding that second test recall was faster in the same direction after a correct first test. Only a model with stochastic retrieval practice learning predicted this effect. In conclusion, recall accuracy and recall latency suggest asymmetric learning, particularly in light of retrieval practice effects.

Frontal cortex tracks surprise separately for different sensory modalities but engages a common inhibitory control mechanism.

The brain constantly generates predictions about the environment to guide action. Unexpected events lead to surprise and can necessitate the modification of ongoing behavior. Surprise can occur for any sensory domain, but it is not clear how these separate surprise signals are integrated to affect motor output. By applying a trial-to-trial Bayesian surprise model to human electroencephalography data recorded during a cross-modal oddball task, we tested whether there are separate predictive models for different sensory modalities (visual, auditory), or whether expectations are integrated across modalities such that surprise in one modality decreases surprise for a subsequent unexpected event in the other modality. We found that while surprise was represented in a common frontal signature across sensory modalities (the fronto-central P3 event-related potential), the single-trial amplitudes of this signature more closely conformed to a model with separate surprise terms for each sensory domain. We then investigated whether surprise-related fronto-central P3 activity indexes the rapid inhibitory control of ongoing behavior after surprise, as suggested by recent theories. Confirming this prediction, the fronto-central P3 amplitude after both auditory and visual unexpected events was highly correlated with the fronto-central P3 found after stop-signals (measured in a separate stop-signal task). Moreover, surprise-related and stopping-related activity loaded onto the same component in a cross-task independent components analysis. Together, these findings suggest that medial frontal cortex maintains separate predictive models for different sensory domains, but engages a common mechanism for inhibitory control of behavior regardless of the source of surprise.

Testing the primary and convergent retrieval model of recall: Recall practice produces faster recall success but also faster recall failure.

The primary and convergent retrieval (PCR) model assumes that the act of successful recall not only boosts associations between the item and retrieval cues but additionally strengthens associations within the item (i.e., between the features of an item), speeding the rate of information retrieval from memory. The latter effect is termed intra-item learning and is a unique benefit of recall practice (i.e., the "testing effect"). Prior work confirmed the prediction that recall practice produces faster subsequent recall than restudy practice even if accuracy is higher following restudy. The current study replicated this result, but also examined the downside of recall practice: that after a failure to recall during practice, participants will be faster in their failure to recall on a subsequent recall test. This prediction was confirmed in a multisession cued recall experiment that collected accuracy and recall latency measurements for no practice, recall practice, or restudy, with an immediate or delayed final test. The linear ballistic accumulator model was fit to latency distributions, and model comparison determined that these effects reflect differences in drift rates, as predicted by the PCR model.

The elimination of positive priming with increasing prime duration reflects a transition from perceptual fluency to disfluency rather than bias against primed words.

With immediate repetition priming of forced choice perceptual identification, short prime durations produce positive priming (i.e., priming the target leads to higher accuracy, while priming the foil leads to lower accuracy). Many theories explain positive priming following short duration primes as reflecting increased perceptual fluency for the primed target (i.e., decreased identification latency). However, most studies only examine either accuracy or response times, rather than considering the joint constraints of response times and accuracy to properly address the role of decision biases and response caution. This is a critical oversight because several theories propose that the transition to negative priming following a long duration prime reflects a decision strategy to compensate for the effect of increased perceptual fluency. In contrast, the nROUSE model of Huber and O'Reilly (2003) explains this transition as reflecting perceptual habituation, and thus a change to perceptual disfluency. We confirmed this prediction by applying a sequential sampling model (the diffusion race model) to accuracy and response time distributions from a new single item same-different version of the priming task. In this way, we measured strategic biases and perceptual fluency in each condition for each subject. The nROUSE model was only applied to accuracy from the original forced-choice version of the priming task. This application of nROUSE produced separate predictions for each subject regarding the degree of fluency and disfluency in each condition, and these predictions were confirmed by the drift rate parameters (i.e., fluency) from the response time model in contrast to the threshold parameters (i.e., bias).

Comparing different kinds of words and word-word relations to test an habituation model of priming.

Huber and O'Reilly (2003) proposed that neural habituation exists to solve a temporal parsing problem, minimizing blending between one word and the next when words are visually presented in rapid succession. They developed a neural dynamics habituation model, explaining the finding that short duration primes produce positive priming whereas long duration primes produce negative repetition priming. The model contains three layers of processing, including a visual input layer, an orthographic layer, and a lexical-semantic layer. The predicted effect of prime duration depends both on this assumed representational hierarchy and the assumption that synaptic depression underlies habituation. The current study tested these assumptions by comparing different kinds of words (e.g., words versus non-words) and different kinds of word-word relations (e.g., associative versus repetition). For each experiment, the predictions of the original model were compared to an alternative model with different representational assumptions. Experiment 1 confirmed the prediction that non-words and inverted words require longer prime durations to eliminate positive repetition priming (i.e., a slower transition from positive to negative priming). Experiment 2 confirmed the prediction that associative priming increases and then decreases with increasing prime duration, but remains positive even with long duration primes. Experiment 3 replicated the effects of repetition and associative priming using a within-subjects design and combined these effects by examining target words that were expected to repeat (e.g., viewing the target word 'BACK' after the prime phrase 'back to'). These results support the originally assumed representational hierarchy and more generally the role of habituation in temporal parsing and priming.

Using continual flash suppression to investigate cognitive aftereffects.

In a naming task with visually presented words, Prioli and Kahan (2015) reported that negatively valenced words were identified more slowly than neutral words in a condition with continual flash suppression (CFS), which involves showing the dominant eye changing Mondrian patterns, delaying awareness for the word shown to the other eye. However, when these same words were shown to both eyes (i.e., no CFS), negatively valenced words were identified more quickly. The authors hypothesized that the negative word deficit with CFS reflects greater habituation (i.e., a cognitive aftereffect) that accrues for negative words before the observer becomes aware of the word. However, aftereffects typically occur in response to a target stimulus that follows an adapting stimulus, rather than in response to a single stimulus that is initially processed without awareness. Thus, it is not immediately obvious that the explanation provided by Prioli and Kahan is adequate. Here I report a simulation using a model that was previously applied to cognitive aftereffects, demonstrating that their account can explain this crossover interaction. These results suggest that CFS may be a useful technique for studying cognitive aftereffects without concern for conscious decision strategies.

The push-pull of serial dependence effects: Attraction to the prior response and repulsion from the prior stimulus.

In the "serial dependence" effect, responses to visual stimuli appear biased toward the last trial's stimulus. However, several kinds of serial dependence exist, with some reflecting prior stimuli and others reflecting prior responses. One-factor analyses consider the prior stimulus alone or the prior response alone and can consider both variables only via separate analyses. We demonstrate that one-factor analyses are potentially misleading and can reach conclusions that are opposite from the truth if both dependencies exist. To address this limitation, we developed two-factor analyses (model comparison with hierarchical Bayesian modeling and an empirical "quadrant analysis"), which consider trial-by-trial combinations of prior response and prior stimulus. Two-factor analyses can tease apart the two dependencies if applied to a sufficiently large dataset. We applied these analyses to a new study and to four previously published studies. When applying a model that included the possibility of both dependencies, there was no evidence of attraction to the prior stimulus in any dataset, but there was evidence of attraction to the prior response in all datasets. Two of the datasets contained sufficient constraint to determine that both dependencies were needed to explain the results. For these datasets, the dependency on the prior stimulus was repulsive rather than attractive. Our results are consistent with the claim that both dependencies exist in most serial dependence studies (the two-dependence model was not ruled out for any dataset) and, furthermore, that the two dependencies work against each other.

"Leap before you look": Conditions that suppress explicit, knowledge-based learning during visuomotor adaptation.

When learning a novel visuomotor mapping (e.g., mirror writing), accuracy can improve quickly through explicit, knowledge-based learning (e.g., aim left to go right), but after practice, implicit or procedural learning takes over, producing fast, natural movements. This procedural learning occurs automatically, whereas it has recently been found that knowledge-based learning can be suppressed by the gradual introduction of the novel mapping when participants must make fast movements and visuomotor perturbations are small (e.g., 30° rotations). We explored the range of task instructions, perturbation parameters, and feedback that preclude or encourage this suppression. Using a reaching task with a rotation between screen position and movement direction, we found that knowledge-based learning could be suppressed even for an extreme 90° rotation, but only if it was introduced gradually and only under instructions to move quickly. If the rotation was introduced abruptly or if instructions emphasized accuracy over speed, knowledge-based learning occurred. A second experiment indicated that knowledge-based learning always occurred in the absence of continuous motion feedback, evidenced by the time course of learning, the aftereffects of learning when the rotation was abruptly removed, and the outcome of formal model comparison between a dual-state (procedural and knowledge-based) versus a single-state (procedural only) learning model of the data. A third experiment replicated the findings and verified that the knowledge-based component of the dual-state model corresponded to explicit aiming, whereas the procedural component was slow to unlearn. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Playing "Duck Duck Goose" with neurons: change detection through connectivity reduction.

Reduced connectivity between sending and receiving neurons (i.e., synaptic depression) may facilitate change detection by reducing responses for recently viewed objects so new objects can be highlighted. In the experiment reported here, we investigated high-level change detection following semantic satiation, which is the loss of meaning following repetition of a word. A computer simulation of a word-reading neural network with synaptic depression identified key predictions of connectivity reduction. A dynamic-causal-modeling analysis of magnetoencephalography (MEG) responses collected during a category-matching task identified connectivity reduction between a cortical region related to orthography and a cortical region related to semantics as the cause of the reduced MEG response to a repeated word. As predicted, prior repetitions of a category-matching word presented immediately after the repeated word enhanced semantic novelty, as measured with the M400 component. These results demonstrate that a combination of neural-network modeling and connectivity analyses can reveal the manner in which connectivity fluctuations underlie cognitive functions.

Gestalt formation promotes awareness of suppressed visual stimuli during binocular rivalry.

Continuous flash suppression leverages binocular rivalry to render observers unaware of a static image for several seconds. To achieve this effect, rapidly flashing noise masks are presented to the dominant eye while a static stimulus is presented to the non-dominant eye. Eventually "breakthrough" occurs, wherein awareness shifts to the static image shown to the non-dominant eye. We tested the hypothesis that Gestalt formation can promote breakthrough. In two experiments, we presented pacman-shaped objects that might or might not align to form illusory Kanizsa objects. To measure the inception of breakthrough, observers were instructed to press a key at the moment of partial breakthrough. After pressing the key, which stopped the trial, observers reported how many pacmen were seen and where they were located. Supporting the Gestalt hypothesis, breakthrough was faster when the pacmen were aligned and observers more often reported pairs of pacmen if they were aligned. To address whether these effects reflected illusory shape perception, a computational model was applied to the pacman report distributions and breakthrough times for an experiment with four pacmen. A full account of the data required an increased joint probability of reporting all four pacmen, suggesting an influence of a perceived illusory cross.

The road not taken: creative solutions require avoidance of high-frequency responses.

To investigate individual differences in creativity as measured with a complex problem-solving task, we developed a computational model of the remote associates test (RAT). For 50 years, the RAT has been used to measure creativity. Each RAT question presents three cue words that are linked by a fourth word, which is the correct answer. We hypothesized that individuals perform poorly on the RAT when they are biased to consider high-frequency candidate answers. To assess this hypothesis, we tested individuals with 48 RAT questions and required speeded responding to encourage guessing. Results supported our hypothesis. We generated a norm-based model of the RAT using a high-dimensional semantic space, and this model accurately identified correct answers. A frequency-biased model that included different levels of bias for high-frequency candidate answers explained variance for both correct and incorrect responses. Providing new insight into the nature of creativity, the model explains why some RAT questions are more difficult than others, and why some people perform better than others on the RAT.

An interference account of cue-independent forgetting in the no-think paradigm.

Memory suppression is investigated with the no-think paradigm, which produces forgetting following repeated practice of not thinking about a memory [Anderson MC, Green C (2001) Nature 410:366-369]. Because the forgotten item is not retrieved even when tested with an independent, semantically related cue, it has been assumed that this forgetting is due to an inhibition process. However, this conclusion is based on a single stage to recall, whereas global memory models, which produce forgetting through a process of interference, include both a sampling and a recovery stage to recall. By assuming that interference exists during recovery, these models can explain cue-independent forgetting. We tested several predictions of this interference explanation of cue-independent forgetting by modifying the think/no-think paradigm. We added a condition where participants quickly pressed enter rather than not thinking. We also manipulated initial memory strength and tested recognition memory. Most importantly, learning to quickly press enter produced as much cue-independent forgetting as no-think instructions. Demonstrating the adequacy of two-stage recall, a simple computational model (SAM-RI) simultaneously captured the original cue, independent cue, and recognition results.

Isolating highly enriched populations of circulating epithelial cells and other rare cells from blood using a magnetic sweeper device.

The enumeration of rare circulating epithelial cells (CEpCs) in the peripheral blood of metastatic cancer patients has shown promise for improved cancer prognosis. Moving beyond enumeration, molecular analysis of CEpCs may provide candidate surrogate endpoints to diagnose, treat, and monitor malignancy directly from the blood samples. Thorough molecular analysis of CEpCs requires the development of new sample preparation methods that yield easily accessible and purified CEpCs for downstream biochemical assays. Here, we describe a new immunomagnetic cell separator, the MagSweeper, which gently enriches target cells and eliminates cells that are not bound to magnetic particles. The isolated cells are easily accessible and can be extracted individually based on their physical characteristics to deplete any cells nonspecifically bound to beads. We have shown that our device can process 9 mL of blood per hour and captures >50% of CEpCs as measured in spiking experiments. We have shown that the separation process does not perturb the gene expression of rare cells. To determine the efficiency of our platform in isolating CEpCs from patients, we have isolated CEpCs from all 47 tubes of 9-mL blood samples collected from 17 women with metastatic breast cancer. In contrast, we could not find any circulating epithelial cells in samples from 5 healthy donors. The isolated CEpCs are all stored individually for further molecular analysis.

A visual sense of number emerges from divisive normalization in a simple center-surround convolutional network.

Many species of animals exhibit an intuitive sense of number, suggesting a fundamental neural mechanism for representing numerosity in a visual scene. Recent empirical studies demonstrate that early feedforward visual responses are sensitive to numerosity of a dot array but substantially less so to continuous dimensions orthogonal to numerosity, such as size and spacing of the dots. However, the mechanisms that extract numerosity are unknown. Here, we identified the core neurocomputational principles underlying these effects: (1) center-surround contrast filters; (2) at different spatial scales; with (3) divisive normalization across network units. In an untrained computational model, these principles eliminated sensitivity to size and spacing, making numerosity the main determinant of the neuronal response magnitude. Moreover, a model implementation of these principles explained both well-known and relatively novel illusions of numerosity perception across space and time. This supports the conclusion that the neural structures and feedforward processes that encode numerosity naturally produce visual illusions of numerosity. Taken together, these results identify a set of neurocomputational properties that gives rise to the ubiquity of the number sense in the animal kingdom.

A modeling framework for determining modulation of neural-level tuning from non-invasive human fMRI data.

Many neuroscience theories assume that tuning modulation of individual neurons underlies changes in human cognition. However, non-invasive fMRI lacks sufficient resolution to visualize this modulation. To address this limitation, we developed an analysis framework called Inferring Neural Tuning Modulation (INTM) for "peering inside" voxels. Precise specification of neural tuning from the BOLD signal is not possible. Instead, INTM compares theoretical alternatives for the form of neural tuning modulation that might underlie changes in BOLD across experimental conditions. The most likely form is identified via formal model comparison, with assumed parametric Normal tuning functions, followed by a non-parametric check of conclusions. We validated the framework by successfully identifying a well-established form of modulation: visual contrast-induced multiplicative gain for orientation tuned neurons. INTM can be applied to any experimental paradigm testing several points along a continuous feature dimension (e.g., direction of motion, isoluminant hue) across two conditions (e.g., with/without attention, before/after learning).

A 'compensatory selection' effect with standardized tests: Lack of correlation between test scores and success is evidence that test scores are predictive of success.

We introduce the statistical concept of 'compensatory selection', which arises when selecting a subset of applicants based on multiple predictors, such as when standardized test scores are used in combination with other predictors required in a school application (e.g., previous grades, references letters, and personal statements). Post-hoc analyses often fail to find a positive correlation between test scores and subsequent success, and this failure is sometimes taken as evidence against the predictive validity of the standardized test. The present analysis reveals that the failure to find a negative correlation indicates that the standardized test is in fact a valid predictor of success. This is due to compensation between predictors during selection: Some students are admitted despite a low test score because their application is exceptional in other respects, while other students are admitted primarily based on a high test score despite weakness in the rest of their application. This compensatory selection process introduces a negative correlation between test scores and other predictors among those admitted (a 'collider bias' or 'Berkson's paradox' effect). If test scores are valid predictors of success, this negative correlation between the predictors counteracts the positive correlation between test scores and success that would have been observed if all applicants were admitted. If test scores are not predictive of success, but were nevertheless used in a compensatory selection process, there would be a spurious negative correlation between test scores and success (i.e., an admitted student with a weak application except for a high test score would be unlikely to succeed). The selection effect that is described here is fundamentally different from the well-known 'restricted range' problem and can powerfully alter results even in situations that accept most applicants.

Electrooxidation of Phenol on Polyelectrolyte Modified Carbon Electrodes for Use in Insulin Pump Infusion Sets.

BACKGROUND: Many type 1 diabetes patients using continuous subcutaneous insulin infusion (CSII) suffer from the phenomenon of unexplained hypoglycemia or "site loss." Site loss is hypothesized to be caused by toxic excipients, for example, phenolic compounds within insulin formulations that are used as preservatives and stabilizers. Here, we develop a bioinspired polyelectrolyte-modified carbon electrode for effective electrooxidative removal of phenol from insulin and eventual incorporations into an infusion set of a CSII device. METHODS: We modified a carbon screen printed electrode (SPE) with poly-L-lysine (PLL) to avoid passivation due to polyphenol deposition while still removing phenolic compounds from insulin injections. We characterized these electrodes using scanning electron microscopy (SEM) and electrochemical impedance spectroscopy (EIS) and compared their data with data from bare SPEs. Furthermore, we performed electrochemical measurements to determine the extent of passivation, and high-performance liquid chromatography (HPLC) measurements to confirm both the removal of phenol and the integrity of insulin after phenol removal. RESULTS: Voltammetry measurements show that electrode passivation due to polyphenol deposition is reduced by a factor of 2X. HPLC measurements confirm a 10x greater removal of phenol by our modified electrodes relative to bare electrodes. CONCLUSION: Using bioinspired polyelectrolytes to modify a carbon electrode surface aids in the electrooxidation of phenolic compounds from insulin and is a step toward integration within an infusion set for mitigating site loss.