Using unsupervised capsule neural network reveal spatial representations in the human brain.

Humans can extract high-level spatial features from visual signals, but spatial representations in the brain are complex and remain unclear. The unsupervised capsule neural network (U-CapsNet) is sensitive to the spatial location and relationship of the object, contains a special recurrent mechanism and uses a self-supervised generation strategy to represent images, which is similar to the computational principle in the human brain. Therefore, we hypothesized that U-CapsNet can help us understand how the human brain processes spatial information. First, brain activities were studied using functional magnetic resonance imaging during spatial working memory in which participants had to remember the locations of circles for a short time. Then, U-CapsNet served as a computational model of the brain to perform tasks that are identical to those performed by humans. Finally, the representational models were used to compare the U-CapsNet with the brain. The results showed that some human-defined spatial features naturally emerged in the latent space of U-CapsNet. Moreover, representations in U-CapsNet captured the response structure of two types of brain regions during different activity patterns, as well as important factors associated with human behavior. Together, our study not only provides a computationally feasible framework for modeling how the human brain encodes spatial features but also provides insights into the representational format and goals of the human brain.

Neurovascular coupling dysfunction of visual network organization in Parkinson's disease.

Parkinson's disease (PD) has been showed perfusion and neural activity alterations in specific regions, such as the motor and visual networks; however, the clinical significance of coupling changes is still unknown. To identify how neurovascular coupling changes during the pathophysiology of PD, patients and healthy controls underwent multiparametric magnetic resonance imaging to measure neural activity organization of segregation and integration using amplitude of low-frequency fluctuation (ALFF) and functional connectivity strength (FCS), and measure vascular responses using cerebral blood flow (CBF). Neurovascular coupling was calculated as the global CBF-ALFF and CBF-FCS coupling and the regional CBF/ALFF and CBF/FCS ratio. Correlations and dynamic causal modeling was then used to evaluate relationships with disease-alterations to clinical variables and information flow. Neurovascular coupling was impaired in PD with decreased global CBF-ALFF and CBF-FCS coupling, as well as decreased CBF/ALFF in the parieto-occipital cortex (dorsal visual stream) and CBF/FCS in the temporo-occipital cortex (ventral visual stream); these decouplings were associated with motor and non-motor impairments. The distinctive patterns of neurovascular coupling alterations within the dorsal and ventral visual streams of the visual system could potentially provide additional understanding into the pathophysiological mechanisms of PD.

Episodic memory in aspects of brain information transfer by resting-state network topology.

Cognitive functionality emerges due to neural interactions. The interregional signal interactions underlying episodic memory are a complex process. Thus, we need to quantify this process more accurately to understand how brain regions receive information from other regions. Studies suggest that resting-state functional connectivity (FC) conveys cognitive information; additionally, activity flow estimates the contribution of the source region to the activation pattern of the target region, thus decoding the cognitive information transfer. Therefore, we performed a combined analysis of task-evoked activation and resting-state FC voxel-wise by activity flow mapping to estimate the information transfer pattern of episodic memory. We found that the cinguloopercular (CON), frontoparietal (FPN) and default mode networks (DMNs) were the most recruited structures in information transfer. The patterns and functions of information transfer differed between encoding and retrieval. Furthermore, we found that information transfer was a better predictor of memory ability than previous methods. Additional analysis indicated that structural connectivity (SC) had a transportive role in information transfer. Finally, we present the information transfer mechanism of episodic memory from multiple neural perspectives. These findings suggest that information transfer is a better biological indicator that accurately describes signal communication in the brain and strongly influences the function of episodic memory.

Sex Differences in Anatomical Rich-Club and Structural-Functional Coupling in the Human Brain Network.

Structural and functional differences between the brains of female and male adults have been well documented. However, potential sex differences in the patterns of rich-club organization and the coupling between their structural connectivity (SC) and functional connectivity (FC) remain to be determined. In this study, functional magnetic resonance imaging and diffusion tensor imaging techniques were combined to examine sex differences in rich-club organization. Females had a stronger SC-FC coupling than males. Moreover, stronger SC-FC coupling in the females was primarily located in feeder connections and non-rich-club nodes of the left inferior frontal gyrus and inferior parietal lobe and the right superior frontal gyrus and superior parietal gyrus, whereas higher coupling strength in males was primarily located in rich-club connections and rich-club node of the right insula, and non-rich-club nodes of the left hippocampus and the right parahippocampal gyrus. Sex-specific patterns in correlations were also shown between SC-FC coupling and cognitive function, including working memory and reasoning ability. The topological changes in rich-club organization provide novel insight into sex-specific effects on white matter connections that underlie a potential network mechanism of sex-based differences in cognitive function.

Phenanthroline-based microporous organic materials for removal of Cu(II) from aqueous solution and reutilization of spent adsorbent as catalysts.

Microporous organic polymers (MOPs) possessing large specific surface area with high stability are suitable adsorbent to remove contaminants from water, such as organic pollutant and heavy metal contaminants. Herein, a phenanthroline-based microporous organic polymer (Phen-MOP) has been synthesized through the coupling between benzene and 1,10-phenanthroline. The adsorption kinetics and thermodynamics were investigated. This Phen-MOP exhibited good adsorption efficiency for removal of Cu(II) from water with high structural stability and reusability. The maximum removal efficiency could reach to 98.47% at a Cu(II) concentration of 20 mg/L, pH = 7, 25 °C. It was found by investigating the adsorption isotherms that the maximum adsorption capacity Qm was 128.53 mg/g. Interestingly, after the adsorption of Cu(II), the resulting Phen-MOP-Cu can serve as an efficient heterogeneous catalyst for the Ullmann-type reaction. The structure and composition of the Phen-MOP-Cu were characterized by Fourier transform infrared (FT-IR), X-ray diffraction (XRD), thermogravimetric analysis (TGA), Brunauer-Emmett-Teller (BET), scanning electron microscope (SEM), energy-dispersive X-ray spectroscopy (EDX) and X-ray photoelectron spectroscopy (XPS). The results indicated that this catalyst possessed immense specific surface area, large pore volume and high stability. The catalyst was easily recyclable and did not significantly lose catalytic activity after being reused six times.

Ruthenium-catalyzed α-carbonyl sulfoxonium ylide annulations with aryl substituted pyrazoles via C-H/N-H bond functionalizations.

An efficient method for the construction of various pyrazolo[5,1-a]isoquinolines has been achieved via Ru(ii)-catalyzed α-carbonyl sulfoxonium ylide annulations with aryl substituted pyrazoles. This oxidant-free transformation occurred through pyrazole-directed C-H activation, Ru-carbene insertion, and acid-promoted carbonyl condensation processes, enabling dual C-C and C-N bond formation. A broad substrate scope with respect to both coupling components worked efficiently with high yields.

Hemisphere and Gender Differences in the Rich-Club Organization of Structural Networks.

Structural and functional differences in brain hemispheric asymmetry have been well documented between female and male adults. However, potential differences in the connectivity patterns of the rich-club organization of hemispheric structural networks in females and males remain to be determined. In this study, diffusion tensor imaging was used to construct hemispheric structural networks in healthy subjects, and graph theoretical analysis approaches were applied to quantify hemisphere and gender differences in rich-club organization. The results showed that rich-club organization was consistently observed in both hemispheres of female and male adults. Moreover, a reduced level of connectivity was found in the left hemisphere. Notably, rightward asymmetries were mainly observed in feeder and local connections among one hub region and peripheral regions, many of which are implicated in visual processing and spatial attention functions. Additionally, significant gender differences were revealed in the rich-club, feeder, and local connections in rich-club organization. These gender-related hub and peripheral regions are involved in emotional, sensory, and cognitive control functions. The topological changes in rich-club organization provide novel insight into the hemisphere and gender effects on white matter connections and underlie a potential network mechanism of hemisphere- and gender-based differences in visual processing, spatial attention and cognitive control.

Straightforward Synthesis of Bifunctional Phosphorus Phenols via Phosphination of In Situ Generated o-Quinone Methides.

An efficient and practical approach towards bifunctional phosphorus phenols has been developed through a reaction of diphenylphosphine oxide and the o-quinone methides in situ generated from 2-tosylalkyl phenols under basic conditions. This protocol features simple experimental procedures under mild conditions and is easily scaled up. With this method, a variety of diarylmethyl phosphine oxides can be produced with up to 92% yield.

Multi-task Collaborative Pre-training and Adaptive Token Selection: A Unified Framework for Brain Representation Learning.

Structural magnetic resonance imaging (sMRI) reveals the structural organization of the brain. Learning general brain representations from sMRI is an enduring topic in neuroscience. Previous deep learning models neglect that the brain, as the core of cognition, is distinct from other organs whose primary attribute is anatomy. Capturing the high-level representation associated with inter-individual cognitive variability is key to appropriately represent the brain. Given that this cognition-related information is subtle, mixed, and distributed in the brain structure, sMRI-based models need to both capture fine-grained details and understand how they relate to the overall global structure. Additionally, it is also necessary to explicitly express the cognitive information that implicitly embedded in local-global image features. Therefore, we propose MCPATS, a brain representation learning framework that combines Multi-task Collaborative Pre-training (MCP) and Adaptive Token Selection (ATS). First, we develop MCP, including mask-reconstruction to understand global context, distort-restoration to capture fine-grained local details, adversarial learning to integrate features at different granularities, and age-prediction, using age as a surrogate for cognition to explicitly encode cognition-related information from local-global image features. This co-training allows progressive learning of implicit and explicit cognition-related representations. Then, we develop ATS based on mutual attention for downstream use of the learned representation. During fine-tuning, the ATS highlights discriminative features and reduces the impact of irrelevant information. MCPATS was validated on three different public datasets for brain disease diagnosis, outperforming competing methods and achieving accurate diagnosis. Further, we performed detailed analysis to confirm that the MCPATS-learned representation captures cognition-related information.

Using a deep generation network reveals neuroanatomical specificity in hemispheres.

Asymmetry is an important property of brain organization, but its nature is still poorly understood. Capturing the neuroanatomical components specific to each hemisphere facilitates the understanding of the establishment of brain asymmetry. Since deep generative networks (DGNs) have powerful inference and recovery capabilities, we use one hemisphere to predict the opposite hemisphere by training the DGNs, which automatically fit the built-in dependencies between the left and right hemispheres. After training, the reconstructed images approximate the homologous components in the hemisphere. We use the difference between the actual and reconstructed hemispheres to measure hemisphere-specific components due to asymmetric expression of environmental and genetic factors. The results show that our model is biologically plausible and that our proposed metric of hemispheric specialization is reliable, representing a wide range of individual variation. Together, this work provides promising tools for exploring brain asymmetry and new insights into self-supervised DGNs for representing the brain.

Connectional-style-guided contextual representation learning for brain disease diagnosis.

Structural magnetic resonance imaging (sMRI) has shown great clinical value and has been widely used in deep learning (DL) based computer-aided brain disease diagnosis. Previous DL-based approaches focused on local shapes and textures in brain sMRI that may be significant only within a particular domain. The learned representations are likely to contain spurious information and have poor generalization ability in other diseases and datasets. To facilitate capturing meaningful and robust features, it is necessary to first comprehensively understand the intrinsic pattern of the brain that is not restricted within a single data/task domain. Considering that the brain is a complex connectome of interlinked neurons, the connectional properties in the brain have strong biological significance, which is shared across multiple domains and covers most pathological information. In this work, we propose a connectional style contextual representation learning model (CS-CRL) to capture the intrinsic pattern of the brain, used for multiple brain disease diagnosis. Specifically, it has a vision transformer (ViT) encoder and leverages mask reconstruction as the proxy task and Gram matrices to guide the representation of connectional information. It facilitates the capture of global context and the aggregation of features with biological plausibility. The results indicate that CS-CRL achieves superior accuracy in multiple brain disease diagnosis tasks across six datasets and three diseases and outperforms state-of-the-art models. Furthermore, we demonstrate that CS-CRL captures more brain-network-like properties, and better aggregates features, is easier to optimize, and is more robust to noise, which explains its superiority in theory.

Construction of poly(amino acid)s nano-delivery system and sustained release with redox-responsive.

A series of novel poly(amino acid)s materials were designed to prepare drug-loaded nanoparticles by physical encapsulation and chemical bonding. The side chain of the polymer contains a large number of amino groups, which effectively increases the loading rate of doxorubicin (DOX). The structure contains disulfide bonds that showing a strong response to the redox environment, which can achieve targeted drug release in the tumor microenvironment. Nanoparticles mainly present spherical morphology with the suitable size for participating in systemic circulation. cell experiments demonstrate the non-toxicity and good cellular uptake behavior of polymers. In vivo anti-tumor experiments shows nanoparticles could inhibit tumor growth and effectively reduce the side effects of DOX.

Effects of Microstate Dynamic Brain Network Disruption in Different Stages of Schizophrenia.

Schizophrenia is a heterogeneous mental disorder with unknown etiology or pathological characteristics. Microstate analysis of the electroencephalogram (EEG) signal has shown significant potential value for clinical research. Importantly, significant changes in microstate-specific parameters have been extensively reported; however, these studies have ignored the information interactions within the microstate network in different stages of schizophrenia. Based on recent findings, since rich information about the functional organization of the brain can be revealed by functional connectivity dynamics, we use the first-order autoregressive model to construct the functional connectivity of intra- and intermicrostate networks to identify information interactions among microstate networks. We demonstrate that, beyond abnormal parameters, disrupted organization of the microstate networks plays a crucial role in different stages of the disease by 128-channel EEG data collected from individuals with first-episode schizophrenia, ultrahigh-risk, familial high-risk, and healthy controls. According to the characteristics of the microstates of patients at different stages, the parameters of microstate class A are reduced, those of class C are increased, and the transitions from intra- to intermicrostate functional connectivity are gradually disrupted. Furthermore, decreased integration of intermicrostate information might lead to cognitive deficits in individuals with schizophrenia and those in high-risk states. Taken together, these findings illustrate that the dynamic functional connectivity of intra- and intermicrostate networks captures more components of disease pathophysiology. Our work sheds new light on the characterization of dynamic functional brain networks based on EEG signals and provides a new interpretation of aberrant brain function in different stages of schizophrenia from the perspective of microstates.

Sustained-release behavior and the antitumor effect of charge-convertible poly(amino acid)s drug-loaded nanoparticles.

Enhancing tissue permeability and achieving drug aggregation is the key to targeted tumor therapy. A series triblock copolymers of poly(ethylene glycol)-poly(L-lysine)-poly(L-glutamine) were synthesized by ring-opening polymerization, and charge-convertible nano-delivery system was constructed by loading doxorubicin (DOX) with 2-(hexaethylimide) ethanol on side chain. In normal environment (pH = 7.4), the zeta potential of the drug-loaded nanoparticle solution is negative, which is conducive to avoiding the identification and clearance of nanoparticles by the reticulo-endothelial system, while potential-reversal can be achieved in the tumor microenvironment, which effectively promotes cellular uptake. Nanoparticles could effectively reduce the distribution of DOX in normal tissues and achieve targeted aggregation at tumor sites, which can effectively improve the antitumor effect, while would not causing toxicity and damage to normal body.

Metal-organic framework grown in situ on chitosan microspheres as robust host of palladium for heterogeneous catalysis: Suzuki reaction and the p-nitrophenol reduction.

In this study, the metal-organic framework ZIF-8 has been successfully planted on the surface of chitosan microspheres (CS/PDA@ZIF-8) using polydopamine as connecting material for the first time, which avoids the use of expensive, non-renewable, and non-biodegradable polystyrene microspheres commonly used as templates to prepare core-shell structures. Moreover, the metal-organic framework ZIF-8 was prepared specially by three different methods and all characterized by SEM, TEM, and BET, and the ZIF-8 shell prepared at room temperature presents a regular morphology, uniform size, large specific surface area (353.1 m2/g) than the shells prepared by the other methods including. The CS/PDA@ZIF-825@Pd with high catalytic activity and high stability was especially prepared by encapsulating Pd nanoparticles into the pores of CS/PDA@ZIF-825. Notably, the fabricated catalyst performed well in an array of reactions, for example the Kapp value of the p-nitrophenol reduction reaction reached 0.0426 s-1, and the TOF of the Suzuki coupling reaction reached 128 h-1. In addition, the ZIF-67, UiO-66, UiO-66-NH2, HKUST-1, and NH2-MIL-53(Al) were also grown on chitosan microcapsules successively to prepare the core-shell microspheres, which prove the universal applicability of this strategy. And beyond that, the introduction of chitosan microspheres endows the material with biodegradable properties and excellent recycling properties.

Modular-level alterations of single-subject gray matter networks in schizophrenia.

Schizophrenia is often regarded as a psychiatric disorder caused by disrupted connections in the brain. Evidence suggests that the gray matter of schizophrenia patients is damaged in a modular pattern. Recently, abnormal topological organization was observed in the gray matter networks of patients with schizophrenia. However, the modular-level alteration of gray matter networks in schizophrenia remains unclear. In this study, single-subject gray matter networks were constructed for a total of 217 subjects (116 patients with schizophrenia and 101 controls). We analyzed the topological characteristics of the brain network and the strengths of connections between and within modules. Compared with the outcomes in the control group, the global efficiency and participation coefficient values of the single-subject gray matter networks in schizophrenic patients were significantly reduced. The nodal participation coefficient of the regions involving the frontoparietal attention network, default mode network and subcortical network were significantly decreased in subjects with schizophrenia. The intermodule connections between the frontoparietal attention network and visual network and between the default mode network and subcortical network, in the frontoparietal attention network were significantly reduced in the patient group. In the frontoparietal attention network, the intramodule nodal connection strength of the left orbital inferior frontal gyrus and right inferior parietal gyrus was significantly decreased in schizophrenia patients. Reduced intermodule nodal connection strength between the frontoparietal attention network and visual network was associated with the severity of schizophrenia symptoms. These findings suggest that abnormal intramodule and intermodule connections in the structural brain network may a biomarker of schizophrenia symptoms.

Immobilizing of palladium on melamine functionalized magnetic chitosan beads: A versatile catalyst for p-nitrophenol reduction and Suzuki reaction in aqueous medium.

In this study, an environmental-friendly palladium catalyst with high efficiency, magnetic, recoverability, reusability, and excellent stability was prepared and thoroughly characterized by the Fourier transform infrared spectroscopy (FT-IR), Scanning electron microscopy (SEM), X-ray diffraction (XRD), Elemental mapping, Thermogravimetric analysis (TGA) and Energy-dispersive X-ray spectroscopy (EDX). Results demonstrates that melamine provides a coordination point on the surface of chitosan microspheres, which provides a platform for the uniform distribution of palladium (II) and combines with palladium (II) firmly to avoid unnecessary leaching of nanoparticles. Besides, Fe3O4/CS-Me@Pd microcapsules exhibited high catalytic performance in reducing p-NP in water at room temperature (150-300 s). This composite was also effective in the Suzuki-Miyaura coupling reaction under mild conditions with high catalytic performance (TON = 3.8 × 104, TOF = 7.6 × 104). Reproducibility experiments also showed that Fe3O4/CS-Me@Pd microcapsules have high recovery efficiency and can work at least six times during these two catalytic reactions. The hot filtration test indicated that the catalyst has heterogeneous nature.

Rich-Club Analysis in Adults With ADHD Connectomes Reveals an Abnormal Structural Core Network.

Objective: Whether the abnormal connectome of brain's rich-club structure in adults with attention-deficit hyperactivity disorder (ADHD) remains unclear. Method: The current study used diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI) to compare the performance of 42 adults with ADHD and 59 typical development (TD) adults. Results: A reduced density of rich-clubs among structural hub nodes, including the bilateral precuneus, the insula, the caudate nucleus, the left putamen, and the right calcarine, was found in adults with ADHD. Moreover, lower global efficiency was found in adults with ADHD than in TD, which might be caused by a reduced density of rich-club connections in ADHD patients. Conclusion: Given that adults with ADHD have greater coupling strength between structural and functional connectivity than TD adults, connectome abnormalities with a reduced rich-club connectivity density might be accompanied by altered functional brain dynamics in ADHD patients.