Representational Geometries Reveal Differential Effects of Response Correlations on Population Codes in Neurophysiology and Functional Magnetic Resonance Imaging.

Two sensory neurons usually display trial-by-trial spike-count correlations given the repeated representations of a stimulus. The effects of such response correlations on population-level sensory coding have been the focal contention in computational neuroscience over the past few years. In the meantime, multivariate pattern analysis (MVPA) has become the leading analysis approach in functional magnetic resonance imaging (fMRI), but the effects of response correlations among voxel populations remain underexplored. Here, instead of conventional MVPA analysis, we calculate linear Fisher information of population responses in human visual cortex (five males, one female) and hypothetically remove response correlations between voxels. We found that voxelwise response correlations generally enhance stimulus information, a result standing in stark contrast to the detrimental effects of response correlations reported in empirical neurophysiological studies. By voxel-encoding modeling, we further show that these two seemingly opposite effects actually can coexist within the primate visual system. Furthermore, we use principal component analysis to decompose stimulus information in population responses onto different principal dimensions in a high-dimensional representational space. Interestingly, response correlations simultaneously reduce and enhance information on higher- and lower-variance principal dimensions, respectively. The relative strength of the two antagonistic effects within the same computational framework produces the apparent discrepancy in the effects of response correlations in neuronal and voxel populations. Our results suggest that multivariate fMRI data contain rich statistical structures that are directly related to sensory information representation, and the general computational framework to analyze neuronal and voxel population responses can be applied in many types of neural measurements.SIGNIFICANCE STATEMENT Despite the vast research interest in the effect of spike-count noise correlations on population codes in neurophysiology, it remains unclear how the response correlations between voxels influence MVPA in human imaging. We used an information-theoretic approach and showed that unlike the detrimental effects of response correlations reported in neurophysiology, voxelwise response correlations generally improve sensory coding. We conducted a series of in-depth analyses and demonstrated that neuronal and voxel response correlations can coexist within the visual system and share some common computational mechanisms. These results shed new light on how the population codes of sensory information can be evaluated via different neural measurements.

Effects of Rhizopus-arrhizus-31-Assisted Pretreatment on the Extraction and Bioactivity of Total Flavonoids from Hibiscus manihot L.

The Hibiscus manihot L. (HML) Medic, an edible hibiscus of the Malvaceae family, is abundant with flavonoids. The study investigated how Rhizopus-arrhizus-31-assisted pretreatment affects the extraction and bioactivity of flavonoids from HML. The fiber structure of the fermented flavonoid sample (RFF) appears looser, more porous, and more disordered than the unfermented flavonoid sample (RUF). RFF demonstrates milder conditions and yields higher extraction rates. According to the Box-Behnken response surface optimization experiment, the optimal conditions for RFF include a material-liquid ratio of 1:41 g/mL, a 2 h extraction time, a 57% ethanol concentration, and an extraction temperature of 800 °C, resulting in a 3.69% extraction yield, which is 39.25% higher than that of RUF. Additionally, RFF exhibits greater activity than RUF in the radical-scavenging system. The IC50 values for DPPH, OH, and ABTS radicals are 83.43 µg/mL and 82.62 µg/mL, 208.38 µg/mL and 175.99 µg/mL, and 108.59 µg/mL and 75.39 µg/mL for RUF and RFF, respectively. UPLC-QTOF-MS analysis of the active components in the HML flavonoid sample revealed significant differences in the chromatograms of RUF and RFF, indicating that biofermentation led to substantial changes in composition and content from HML.

Effect of pH on the emulsifying performance of protein-polysaccharide complexes.

BACKGROUND: Protein-polysaccharide complexes have been successfully used for emulsion stabilization. However, it is unclear how the complex's surface charge influences aggregation stability and coalescence stability of emulsions, and whether a low charged interfacial film can still maintain the coalescence stability of oil droplets. In the present study, the effects of pH (around the pI of protein) on the aggregation and coalescence stability of emulsions were investigated. RESULTS: Whey protein isolate (WPI) and peach gum polysaccharides (PGP) complexes (WPI-PGP complexes) were synthesized at pH 3, 4 and 5. Their sizes were 598, 274 and 183 nm, respectively, and their ζ-potentials were +2.9, -8.6 and -22.8 mV, respectively. Interface rheological experiments showed that WPI-PGP complex at pH 3 had the lowest interfacial tension, and formed the softest film compared to the complexes at pH 4 and 5. Microfluidic experiments showed that all WPI-PGP complexes were able to stabilize droplets against coalescence within short timescales (milliseconds). At pH 3, no coalescence was observed even under conditions where the continuous phase flow influenced the shape of oil droplets (from spheres to ellipsoids). At pH 4 and 5, the model emulsions were stable over 16 days of storage, extensive aggregation and creaming occurred at pH 3 after 8 days. Importantly, no coalescence took place. CONCLUSION: The present study confirmed that the aggregation stability of the emulsions was mainly determined by the surface charge of the complex, whereas the coalescence stability of emulsions is expectedly determined by steric repulsion, providing new insights into how to prepare stable food emulsions. © 2024 Society of Chemical Industry.

Assessment of vibration modulated regional cerebral blood flow with MRI.

Human brain experiences vibration of certain magnitude and frequency during various physical activities such as vehicle transportation and machine operation, which may cause traumatic brain injury or other brain diseases. However, the mechanisms of brain pathogenesis due to vibration are not fully elucidated due to the lack of techniques to study brain functions while applying vibration to the brain at a specific magnitude and frequency. Here, this study reported a custom-built head-worn electromagnetic actuator that applied vibration to the brain in vivo at an accurate frequency inside a magnetic resonance imaging scanner while cerebral blood flow (CBF) was acquired. Using this technique, CBF values from 45 healthy volunteers were quantitatively measured immediately following vibration at 20, 30, 40 Hz, respectively. Results showed increasingly reduced CBF with increasing frequency at multiple regions of the brain, while the size of the regions expanded. Importantly, the vibration-induced CBF reduction regions largely fell inside the brain's default mode network (DMN), with about 58 or 46% overlap at 30 or 40 Hz, respectively. These findings demonstrate that vibration as a mechanical stimulus can change strain conditions, which may induce CBF reduction in the brain with regional differences in a frequency-dependent manner. Furthermore, the overlap between vibration-induced CBF reduction regions and DMN suggested a potential relationship between external mechanical stimuli and cognitive functions.

A temporal decomposition method for identifying venous effects in task-based fMRI.

The spatial resolution of functional magnetic resonance imaging (fMRI) is fundamentally limited by effects from large draining veins. Here we describe an analysis method that provides data-driven estimates of these effects in task-based fMRI. The method involves fitting a one-dimensional manifold that characterizes variation in response timecourses observed in a given dataset, and then using identified early and late timecourses as basis functions for decomposing responses into components related to the microvasculature (capillaries and small venules) and the macrovasculature (large veins), respectively. We show the removal of late components substantially reduces the superficial cortical depth bias of fMRI responses and helps eliminate artifacts in cortical activity maps. This method provides insight into the origins of the fMRI signal and can be used to improve the spatial accuracy of fMRI.

A drug screening to identify novel combinatorial strategies for boosting cancer immunotherapy efficacy.

BACKGROUND: Chimeric antigen receptor (CAR) T cells and immune checkpoint blockades (ICBs) have made remarkable breakthroughs in cancer treatment, but the efficacy is still limited for solid tumors due to tumor antigen heterogeneity and the tumor immune microenvironment. The restrained treatment efficacy prompted us to seek new potential therapeutic methods. METHODS: In this study, we conducted a small molecule compound library screen in a human BC cell line to identify whether certain drugs contribute to CAR T cell killing. Signaling pathways of tumor cells and T cells affected by the screened drugs were predicted via RNA sequencing. Among them, the antitumor activities of JK184 in combination with CAR T cells or ICBs were evaluated in vitro and in vivo. RESULTS: We selected three small molecule drugs from a compound library, among which JK184 directly induces tumor cell apoptosis by inhibiting the Hedgehog signaling pathway, modulates B7-H3 CAR T cells to an effector memory phenotype, and promotes B7-H3 CAR T cells cytokine secretion in vitro. In addition, our data suggested that JK184 exerts antitumor activities and strongly synergizes with B7-H3 CAR T cells or ICBs in vivo. Mechanistically, JK184 enhances B7-H3 CAR T cells infiltrating in xenograft mouse models. Moreover, JK184 combined with ICB markedly reshaped the tumor immune microenvironment by increasing effector T cells infiltration and inflammation cytokine secretion, inhibiting the recruitment of MDSCs and the transition of M2-type macrophages in an immunocompetent mouse model. CONCLUSION: These data show that JK184 may be a potential adjutant in combination with CAR T cells or ICB therapy.

Poster Session: Investigating temporal evolutions of perceptual choice within biological and artificial neural networks.

Perceptual decisions involve a process that evolves over time until it reaches a decision boundary. It's important to understand how this process unfolds. Recent psychophysical data indicates that the visual system extracts motion axis information faster than motion direction information (Kwon et al., 2015, J Vision). To understand the underlying mechanisms, we developed a biophysically realistic cortical network model of decision making. We generalized the two-variable reduced spiking neural network (Wong et al., 2006, J Neuroscience) to four-variable. The network input is based on motion energy (Adelson et al., 1985, Josa a) and the temporal profile of surround influence (Tadin et al., 2006, J Neuroscience). The model reproduces the prior experimental findings, showing the motion axis extraction before direction extraction. It reveals a stronger axis-wise inhibitory connection between the selective neural populations than the direction-wise inhibitory connection. We further designed a recurrent deep neural network to validate the neural population connectivity pattern. Our model provides a quantitative explanation for the temporal evolution of motion direction judgments. The results show that the spatiotemporal filtering for visual motion integration, the center-surround antagonism, and stronger axis-wise inhibitory connection between the selective neural populations can explain how the visual system can extract motion axis orientation before detecting motion direction.

Ultra-high field (10.5T) diffusion-weighted MRI of the macaque brain.

Diffu0sion-weighted magnetic resonance imaging (dMRI) is a non-invasive imaging technique that provides information about the barriers to the diffusion of water molecules in tissue. In the brain, this information can be used in several important ways, including to examine tissue abnormalities associated with brain disorders and to infer anatomical connectivity and the organization of white matter bundles through the use of tractography algorithms. However, dMRI also presents certain challenges. For example, historically, the biological validation of tractography models has shown only moderate correlations with anatomical connectivity as determined through invasive tract-tracing studies. Some of the factors contributing to such issues are low spatial resolution, low signal-to-noise ratios, and long scan times required for high-quality data, along with modeling challenges like complex fiber crossing patterns. Leveraging the capabilities provided by an ultra-high field scanner combined with denoising, we have acquired whole-brain, 0.58 mm isotropic resolution dMRI with a 2D-single shot echo planar imaging sequence on a 10.5 Tesla scanner in anesthetized macaques. These data produced high-quality tractograms and maps of scalar diffusion metrics in white matter. This work demonstrates the feasibility and motivation for in-vivo dMRI studies seeking to benefit from ultra-high fields.

Atypically larger variability of resource allocation accounts for visual working memory deficits in schizophrenia.

Working memory (WM) deficits have been widely documented in schizophrenia (SZ), and almost all existing studies attributed the deficits to decreased capacity as compared to healthy control (HC) subjects. Recent developments in WM research suggest that other components, such as precision, also mediate behavioral performance. It remains unclear how different WM components jointly contribute to deficits in schizophrenia. We measured the performance of 60 SZ (31 females) and 61 HC (29 females) in a classical delay-estimation visual working memory (VWM) task and evaluated several influential computational models proposed in basic science of VWM to disentangle the effect of various memory components. We show that the model assuming variable precision (VP) across items and trials is the best model to explain the performance of both groups. According to the VP model, SZ exhibited abnormally larger variability of allocating memory resources rather than resources or capacity per se. Finally, individual differences in the resource allocation variability predicted variation of symptom severity in SZ, highlighting its functional relevance to schizophrenic pathology. This finding was further verified using distinct visual features and subject cohorts. These results provide an alternative view instead of the widely accepted decreased-capacity theory and highlight the key role of elevated resource allocation variability in generating atypical VWM behavior in schizophrenia. Our findings also shed new light on the utility of Bayesian observer models to characterize mechanisms of mental deficits in clinical neuroscience.

Ultra-high-resolution fMRI of Human Ventral Temporal Cortex Reveals Differential Representation of Categories and Domains.

Human ventral temporal cortex (VTC) is critical for visual recognition. It is thought that this ability is supported by large-scale patterns of activity across VTC that contain information about visual categories. However, it is unknown how category representations in VTC are organized at the submillimeter scale and across cortical depths. To fill this gap in knowledge, we measured BOLD responses in medial and lateral VTC to images spanning 10 categories from five domains (written characters, bodies, faces, places, and objects) at an ultra-high spatial resolution of 0.8 mm using 7 Tesla fMRI in both male and female participants. Representations in lateral VTC were organized most strongly at the general level of domains (e.g., places), whereas medial VTC was also organized at the level of specific categories (e.g., corridors and houses within the domain of places). In both lateral and medial VTC, domain-level and category-level structure decreased with cortical depth, and downsampling our data to standard resolution (2.4 mm) did not reverse differences in representations between lateral and medial VTC. The functional diversity of representations across VTC partitions may allow downstream regions to read out information in a flexible manner according to task demands. These results bridge an important gap between electrophysiological recordings in single neurons at the micron scale in nonhuman primates and standard-resolution fMRI in humans by elucidating distributed responses at the submillimeter scale with ultra-high-resolution fMRI in humans.SIGNIFICANCE STATEMENT Visual recognition is a fundamental ability supported by human ventral temporal cortex (VTC). However, the nature of fine-scale, submillimeter distributed representations in VTC is unknown. Using ultra-high-resolution fMRI of human VTC, we found differential distributed visual representations across lateral and medial VTC. Domain representations (e.g., faces, bodies, places, characters) were most salient in lateral VTC, whereas category representations (e.g., corridors/houses within the domain of places) were equally salient in medial VTC. These results bridge an important gap between electrophysiological recordings in single neurons at a micron scale and fMRI measurements at a millimeter scale.

Understanding multivariate brain activity: Evaluating the effect of voxelwise noise correlations on population codes in functional magnetic resonance imaging.

Previous studies in neurophysiology have shown that neurons exhibit trial-by-trial correlated activity and that such noise correlations (NCs) greatly impact the accuracy of population codes. Meanwhile, multivariate pattern analysis (MVPA) has become a mainstream approach in functional magnetic resonance imaging (fMRI), but it remains unclear how NCs between voxels influence MVPA performance. Here, we tackle this issue by combining voxel-encoding modeling and MVPA. We focus on a well-established form of NC, tuning-compatible noise correlation (TCNC), whose sign and magnitude are systematically related to the tuning similarity between two units. We show that this form of voxelwise NCs can improve MVPA performance if NCs are sufficiently strong. We also confirm these results using standard information-theoretic analyses in computational neuroscience. In the same theoretical framework, we further demonstrate that the effects of noise correlations at both the neuronal level and the voxel level may manifest differently in typical fMRI data, and their effects are modulated by tuning heterogeneity. Our results provide a theoretical foundation to understand the effect of correlated activity on population codes in macroscopic fMRI data. Our results also suggest that future fMRI research could benefit from a closer examination of the correlational structure of multivariate responses, which is not directly revealed by conventional MVPA approaches.

Analysis and prospect of clinical psychology based on topic models: hot research topics and scientific trends in the latest decades.

The popularity of research topics in clinical psychology has always been changing over time. In this study, we use Latent Dirichlet Allocation (LDA), a well-established statistical modeling approach in machine learning, to extract hot research topics in published review articles in clinical psychology. In Study 1, we use LDA to extract existing topics between 1981 to 2018 from the review articles published on three premium journals in clinical psychology. Results provide stable information about all topics and their proportions. In Study 2, we use a dynamic variant of LDA to identify the development of hot topics from 2007 to 2018. Results show that meta-analysis, psychotherapy, professional development, and depression constantly stay as hot topics all over the 12 years. We also find that behavior intervention has a clear rising trend since 2007. Our results provide a comprehensive summary of the popularity of research topics in clinical psychology in the last couple of years, and the results here can help clinical researchers form a structured view of past research and plan future research directions.

Flexible top-down modulation in human ventral temporal cortex.

Visual neuroscientists have long characterized attention as inducing a scaling or additive effect on fixed parametric functions describing neural responses (e.g., contrast response functions). Here, we instead propose that top-down effects are more complex and manifest in ways that depend not only on attention but also other cognitive processes involved in executing a task. To substantiate this theory, we analyze fMRI responses in human ventral temporal cortex (VTC) in a study where stimulus eccentricity and cognitive task are varied. We find that as stimuli are presented farther into the periphery, bottom-up stimulus-driven responses decline but top-down attentional enhancement increases substantially. This disproportionate enhancement of weak responses cannot be easily explained by conventional models of attention. Furthermore, we find that attentional effects depend on the specific cognitive task performed by the subject, indicating the influence of additional cognitive processes other than attention (e.g., decision-making). The effects we observe replicate in an independent experiment from the same study, and also generalize to a separate study involving different stimulus manipulations (contrast and phase coherence). Our results suggest that a quantitative understanding of top-down modulation requires more nuanced characterization of the multiple cognitive factors involved in completing a perceptual task.

Nanomaterial-based biosensors for sensing key foodborne pathogens: Advances from recent decades.

Foodborne pathogen contamination has become a severe threat to human health. Traditional methods for foodborne pathogen detection have several disadvantages, including long detection time, low sensitivity, and low selectivity. The emergence of multiple excellent nanomaterials enables the construction of novel biosensors for foodborne pathogen detection. Based on the outstanding properties of nanomaterials, the novel biosensors possess the advantages of sensitivity, specificity, rapidity, accuracy, and simplicity. The present review comprehensively summarizes the advanced biosensors, including electrochemical, colorimetric, fluorescent, and surface enhanced Raman scattering biosensors for sensing key foodborne pathogens in recent decades. Furthermore, several issues are identified for further exploration, and possible directions for the development of biosensors are discussed.

A critical assessment of data quality and venous effects in sub-millimeter fMRI.

Advances in hardware, pulse sequences, and reconstruction techniques have made it possible to perform functional magnetic resonance imaging (fMRI) at sub-millimeter resolution while maintaining high spatial coverage and acceptable signal-to-noise ratio. Here, we examine whether sub-millimeter fMRI can be used as a routine method for obtaining accurate measurements of fine-scale local neural activity. We conducted fMRI in human visual cortex during a simple event-related visual experiment (7 T, gradient-echo EPI, 0.8-mm isotropic voxels, 2.2-s sampling rate, 84 slices), and developed analysis and visualization tools to assess the quality of the data. Our results fall along three lines of inquiry. First, we find that the acquired fMRI images, combined with appropriate surface-based processing, provide reliable and accurate measurements of fine-scale blood oxygenation level dependent (BOLD) activity patterns. Second, we show that the highly folded structure of cortex causes substantial biases on spatial resolution and data visualization. Third, we examine the well-recognized issue of venous contributions to fMRI signals. In a systematic assessment of large sections of cortex measured at a fine scale, we show that time-averaged T2*-weighted EPI intensity is a simple, robust marker of venous effects. These venous effects are unevenly distributed across cortex, are more pronounced in gyri and outer cortical depths, and are, to a certain degree, in consistent locations across subjects relative to cortical folding. Furthermore, we show that these venous effects are strongly correlated with BOLD responses evoked by the experiment. We conclude that sub-millimeter fMRI can provide robust information about fine-scale BOLD activity patterns, but special care must be exercised in visualizing and interpreting these patterns, especially with regards to the confounding influence of the brain's vasculature. To help translate these methodological findings to neuroscience research, we provide practical suggestions for both high-resolution and standard-resolution fMRI studies.

Multiplexed, high-throughput measurements of cell contraction and endothelial barrier function.

Vascular leakage, protein exudation, and edema formation are events commonly triggered by inflammation and facilitated by gaps that form between adjacent endothelial cells (ECs) of the vasculature. In such paracellular gap formation, the role of EC contraction is widely implicated, and even therapeutically targeted. However, related measurement approaches remain slow, tedious, and complex to perform. Here, we have developed a multiplexed, high-throughput screen to simultaneously quantify paracellular gaps, EC contractile forces, and to visualize F-actin stress fibers, and VE-cadherin. As proof-of-principle, we examined barrier-protective mechanisms of the Rho-associated kinase inhibitor, Y-27632, and the canonical agonist of the Tie2 receptor, Angiopoietin-1 (Angpt-1). Y-27632 reduced EC contraction and actin stress fiber formation, whereas Angpt-1 did not. Yet both agents reduced thrombin-, LPS-, and TNFα-induced paracellular gap formation. This unexpected result suggests that Angpt-1 can achieve barrier defense without reducing EC contraction, a mechanism that has not been previously described. This insight was enabled by the multiplex nature of the force-based platform. The high-throughput format we describe should accelerate both mechanistic studies and the screening of pharmacological modulators of endothelial barrier function.

p120 inhibits LPS/TNFα-induced endothelial Ang2 synthesis and release in an NF-κB independent fashion.

Adherens junction protein p120 is thought to be crucial for maintaining vascular integrity, which is important in many pathologies and diseases including atherosclerosis, vascular malformations, hemorrhagic stroke, sepsis and others. However, the mechanisms responsible for this is not completely understood. In this study, using an unbiased proteomics approach, followed by other experimental techniques, we identified that in HUVECs p120 overexpression inhibits LPS/TNFα-induced angiopoietin-2 (Ang2) expression, a key switch of endothelial destabilization. Interestingly, p120 overexpression did not inhibit LPS/TNFα-induced expression of adhesion molecules/cytokines including VCAM-1, ICAM-1, E-selectin, MCP-1, IL-8 and IL-6 in our experimental system. Furthermore, this p120-mediated repression of Ang2 is in an NF-κB independent manner, possibly via transcription factor Ets1. Our results demonstrate that p120 influences vascular integrity by secreted signals, providing new insights into the mechanisms of p120-mediated vascular stability.

VE-cadherin regulates migration inhibitory factor synthesis and release.

OBJECTIVE: Vascular endothelial (VE)-cadherin-mediated adherens junction is critical to maintain endothelial integrity. Besides its role of homophilic intercellular adhesion, VE-cadherin also has a role of outside-in signaling with functional consequences for vascular physiology. However, the nature of these signals remains not completely understood. MATERIALS AND METHODS: Human umbilical vein endothelial cells (HUVECs) were used in cell culture experiments. Confluent HUVECs were treated with VE-cadherin function-blocking antibodies BV9 (50 µg/ml) or IgG control. Antibody array was used to screen for cytokine/chemokine in supernatant. For VE-cadherin knockdown, siRNA transfection was used. ELISA, Western blot, and qRT-PCR were used to confirm the expression of screened cytokine/chemokine. To explore the possible mechanisms, Scr phosphorylation was detected and Scr inhibitor PP2 (1 µM) was used. To investigate in vivo relevance of the findings, BV9 and the indicated neutralizing antibodies were injected into mice and then lung vascular leak and inflammation were examined by Evans blue assay and lung tissue H&E, respectively. RESULTS: Using a non-biased, high-throughout human cytokine/chemokine antibody array, we first found that disruption of VE-cadherin-mediated adhesion by function-blocking antibody BV9 triggered the release of migration inhibitory factor (MIF). This VE-cadherin-mediated release of MIF further confirmed by ELISA with both VE-cadherin blocking antibody and siRNA technique was due to enhanced expression of MIF mRNA, which was mediated by Src kinase activation. In addition, in vivo lung vascular leak induced by VE-cadherin function-blocking antibody was partly alleviated by neutralizing MIF. CONCLUSIONS: VE-cadherin regulates MIF synthesis and release via Src kinase. Our data provide additional evidence to the concept that VE-cadherin transfers intracellular signals to coordinate the state of cell-cell adhesion with gene expression.

Comet-like Heterodimers "Gold Nanoflower @Graphene Quantum Dots" Probe with FRET "Off" to DNA Circuit Signal "On" for Sensing and Imaging MicroRNA In Vitro and In Vivo.

Cardiovascular diseases have recently become the number one cause of death worldwide and the risk of getting cardiovascular diseases is doubled as the age increases. MicroRNA-34a (miRNA-34a) as an important potential sensor of aging and cellular senescence could be used in early diagnostics. Herein, a new ultrasensitive platform on the basis of the fluorescence resonance energy transfer (FRET) "off" to DNA circuit signal "on" principle was established, termed comet-like heterodimers gold nanoflower (AuNF) @ graphene quantum dots (GQDs) probe. We discussed that the distance of 4 nm between AuNF and GQDs would increase fluorescence quenching efficiency, and light up sensitivity after the probe combined with a target miRNA initiating DNA circuit strategy. The target miRNA-34a can be quantified down to 0.1 fM, which is about 2 orders of magnitude lower than the existing sensing protocols. Furthermore, we constructed the aging myocardial cell and animal model, and the nanoprobe presented low cytotoxicity and satisfied signal imaging in vitro and in vivo. Significantly, this platform herein is envisioned to provide a reliable guidance for early diagnosing cardiovascular diseases and proposing therapeutic protocols.

Action video game play facilitates the development of better perceptual templates.

The field of perceptual learning has identified changes in perceptual templates as a powerful mechanism mediating the learning of statistical regularities in our environment. By measuring threshold-vs.-contrast curves using an orientation identification task under varying levels of external noise, the perceptual template model (PTM) allows one to disentangle various sources of signal-to-noise changes that can alter performance. We use the PTM approach to elucidate the mechanism that underlies the wide range of improvements noted after action video game play. We show that action video game players make use of improved perceptual templates compared with nonvideo game players, and we confirm a causal role for action video game play in inducing such improvements through a 50-h training study. Then, by adapting a recent neural model to this task, we demonstrate how such improved perceptual templates can arise from reweighting the connectivity between visual areas. Finally, we establish that action gamers do not enter the perceptual task with improved perceptual templates. Instead, although performance in action gamers is initially indistinguishable from that of nongamers, action gamers more rapidly learn the proper template as they experience the task. Taken together, our results establish for the first time to our knowledge the development of enhanced perceptual templates following action game play. Because such an improvement can facilitate the inference of the proper generative model for the task at hand, unlike perceptual learning that is quite specific, it thus elucidates a general learning mechanism that can account for the various behavioral benefits noted after action game play.