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Tin and its compounds hold promise for the development of high-capacity anode materials that could replace graphitic carbon used in current lithium-ion batteries. However, the introduced porosity in current electrode designs to buffer the volume changes of active materials during cycling does not afford high volumetric performance. Here, we show a strategy leveraging a sulfur sacrificial agent for controlled utility of void space in a tin oxide/graphene composite anode. In a typical synthesis using the capillary drying of graphene hydrogels, sulfur is employed with hard tin oxide nanoparticles inside the contraction hydrogels. The resultant graphene-caged tin oxide delivers an ultrahigh volumetric capacity of 2123 mAh cm –3 together with good cycling stability. Our results suggest not only a conversion-type composite anode that allows for good electrochemical characteristics, but also a general synthetic means to engineering the packing density of graphene nanosheets for high energy storage capabilities in small volumes. The excessive porous space in carbon anodes for lithium-ion batteries has to be utilized for high volumetric performance. Here the authors show an adaptable sulfur template strategy to yield graphene-caged noncarbon materials with a precisely controlled amount of void, enabling ultrahigh volumetric lithium storage.

Rechargeable sodium-chlorine (Na-Cl 2 ) batteries show high theoretical specific energy density and excellent adaptability for extreme environmental applications. However, the reported cycle life is mostly less than 500 cycles, and the understanding of battery failure mechanisms is quite limited. In this work, we demonstrate that the substantially increased voltage polarization plays a critical role in the battery failure. Typically, the passivation on the porous cathode caused by the deposition of insulated sodium chloride (NaCl) is a crucial factor, significantly influencing the three-phase chlorine (NaCl/Na + , Cl - /Cl 2 ) conversion kinetics. Here, a self-depassivation strategy enabled by iodine anion (I - )-tuned NaCl deposition was implemented to enhance the chlorine reversibility. The nucleation and growth of NaCl crystals are well balanced through strong coordination of the NaI deposition-dissolution process, achieving depassivation on the cathode and improving the reoxidation efficiency of solid NaCl. Consequently, the resultant Na-Cl 2 battery delivers a super-long cycle life up to 2000 cycles. An iodine-induced self-depassivation strategy extends Na-Cl 2 battery life to 2000 cycles by forming high-reactivity NaCl and lowering the chlorine conversion polarization, which successfully solves a key failure mechanism for superior reversibility.

Dynamic functional connectivity (dFC) has been increasingly used to characterize the brain transient temporal functional patterns and their alterations in diseased brains. Meanwhile, naturalistic neuroimaging paradigms have been an emerging approach for cognitive neuroscience with high ecological validity. However, the test–retest reliability of dFC in naturalistic paradigm neuroimaging is largely unknown. To address this issue, we examined the test–retest reliability of dFC in functional magnetic resonance imaging (fMRI) under natural viewing condition. The intraclass correlation coefficients (ICC) of four dFC statistics including standard deviation (Std), coefficient of variation (COV), amplitude of low frequency fluctuation (ALFF), and excursion (Excursion) were used to measure the test–retest reliability. The test–retest reliability of dFC in naturalistic viewing condition was then compared with that under resting state. Our experimental results showed that: (a) Global test–retest reliability of dFC was much lower than that of static functional connectivity (sFC) in both resting‐state and naturalistic viewing conditions; (b) Both global and local (including visual, limbic and default mode networks) test–retest reliability of dFC could be significantly improved in naturalistic viewing condition compared to that in resting state; (c) There existed strong negative correlation between sFC and dFC, weak negative correlation between dFC and dFC‐ICC (i.e., ICC of dFC), as well as weak positive correlation between dFC‐ICC and sFC‐ICC (i.e., ICC of sFC). The present study provides novel evidence for the promotion of naturalistic paradigm fMRI in functional brain network studies. We examined the test–retest reliability of dynamic functional connectivity (dFC) in fMRI under natural viewing condition. Our experimental results showed that both global and local test‐retest reliability of dFC can be significantly improved in naturalistic viewing condition compared to that in resting state.

Although our knowledge of human diseases has increased dramatically, the molecular basis, phenotypic traits, and therapeutic targets of most diseases still remain unclear. An increasing number of studies have observed that similar diseases often are caused by similar molecules, can be diagnosed by similar markers or phenotypes, or can be cured by similar drugs. Thus, the identification of diseases similar to known ones has attracted considerable attention worldwide. To this end, the associations between diseases at the molecular, phenotypic, and taxonomic levels were used to measure the pairwise similarity in diseases. The corresponding performance assessment strategies for these methods involving the terms “category-based,” “simulated-patient-based,” and “benchmark-data-based” were thus further emphasized. Then, frequently used methods were evaluated using a benchmark-data-based strategy. To facilitate the assessment of disease similarity scores, researchers have designed dozens of tools that implement these methods for calculating disease similarity. Currently, disease similarity has been advantageous in predicting noncoding RNA (ncRNA) function and therapeutic drugs for diseases. In this article, we review disease similarity methods, evaluation strategies, tools, and their applications in the biomedical community. We further evaluate the performance of these methods and discuss the current limitations and future trends for calculating disease similarity.

The corpus callosum, historically considered primarily for homotopic connections, supports many heterotopic connections, indicating complex interhemispheric connectivity. Understanding this complexity is crucial yet challenging due to diverse cell-specific wiring patterns. Here, we utilized public AAV bulk tracing and single-neuron tracing data to delineate the anatomical connection patterns of mouse brains and conducted wide-field calcium imaging to assess functional connectivity across various brain states in male mice. The single-neuron data uncovered complex and dense interconnected patterns, particularly for interhemispheric-heterotopic connections. We proposed a metric “heterogeneity” to quantify the complexity of the connection patterns. Computational modeling of these patterns suggested that the heterogeneity of upstream projections impacted downstream homotopic functional connectivity. Furthermore, higher heterogeneity observed in interhemispheric-heterotopic projections would cause lower strength but higher stability in functional connectivity than their intrahemispheric counterparts. These findings were corroborated by our wide-field functional imaging data, underscoring the important role of heterotopic-projection heterogeneity in interhemispheric communication. How interhemispheric connections are organized and how interhemispheric communication are regulated are not fully understood. Here authors delineate the diverse single-neuron projection patterns of interhemispheric connections in mice and uncover their influence on functional dynamics, highlighting the importance of heterotopic projections in interhemispheric communication.

Ideal silicon negative electrodes for high-energy lithium-ion batteries are expected to feature high capacity, minimal expansion, long lifespan, and fast charging. Yet, engineered silicon materials face a fundamental paradox associated with particle deformation and charge transfer, which hinders the industrial use of advanced silicon electrode materials. Here we show a sieving-pore design for carbon supports that overcomes these mechano-kinetic limitations to enable stable, fast (de)alloying chemistries of silicon negative electrodes. Such a sieving-pore structure features an inner nanopore body with reserved voids to accommodate high-mass-content silicon deformation and an outer sub-nanopore entrance to induce both pre-desolvation and fast intrapore transport of ions during cycling. Importantly, the sieving effect yields inorganic-rich solid electrolyte interphases to mechanically confine the in-pore silicon, producing a stress-voltage coupling effect that mitigates the formation of detrimental crystalline Li 15 Si 4 . As a result, this design enables low electrode expansion (58% at the specific capacity of 1773 mAh g − 1 and areal capacity of 4 mAh cm − 2 ), high initial/cyclic Coulombic efficiency (93.6%/99.9%), and minimal capacity decay (0.015% per cycle). A practical pouch cell with such a sieving-pore silicon negative electrode delivers 80% capacity retention over 1700 cycles at 2 A as well as a 10-min fast charging capability. Silicon electrodes promise high energy for lithium-ion batteries but face swelling and durability issues. Here, the authors develop a sieving-pore design that enables stable, fast-charging silicon electrodes with long cycle life, low expansion, and industrial-scale potential.

•We used naturalistic paradigm fMRI to investigate the effective connectivities between the amygdala and insula and the modulatory effect of the input arousal on the effective connections.•Amygdala was the site of driving arousal input.•Arousal had a modulatory effect on the reciprocal connections between amygdala and insula. Emotional arousal is a complex state recruiting distributed cortical and subcortical structures, in which the amygdala and insula play an important role. Although previous neuroimaging studies have showed that the amygdala and insula manifest reciprocal connectivity, the effective connectivities and modulatory patterns on the amygdala-insula interactions underpinning arousal are still largely unknown. One of the reasons may be attributed to static and discrete laboratory brain imaging paradigms used in most existing studies. In this study, by integrating naturalistic-paradigm (i.e., movie watching) functional magnetic resonance imaging (fMRI) with a computational affective model that predicts dynamic arousal for the movie stimuli, we investigated the effective amygdala-insula interactions and the modulatory effect of the input arousal on the effective connections. Specifically, the predicted dynamic arousal of the movie served as regressors in general linear model (GLM) analysis and brain activations were identified accordingly. The regions of interest (i.e., the bilateral amygdala and insula) were localized according to the GLM activation map. The effective connectivity and modulatory effect were then inferred by using dynamic causal modeling (DCM). Our experimental results demonstrated that amygdala was the site of driving arousal input and arousal had a modulatory effect on the reciprocal connections between amygdala and insula. Our study provides novel evidence to the underlying neural mechanisms of arousal in a dynamical naturalistic setting.

Automatic and robust object detection in remote sensing images is of vital significance in real-world applications such as land resource management and disaster rescue. However, poor performance arises when the state-of-the-art natural image detection algorithms are directly applied to remote sensing images, which largely results from the variations in object scale, aspect ratio, indistinguishable object appearances, and complex background scenario. In this paper, we propose a novel Feature Enhancement Network (FENet) for object detection in optical remote sensing images, which consists of a Dual Attention Feature Enhancement (DAFE) module and a Context Feature Enhancement (CFE) module. Specifically, the DAFE module is introduced to highlight the network to focus on the distinctive features of the objects of interest and suppress useless ones by jointly recalibrating the spatial and channel feature responses. The CFE module is designed to capture global context cues and selectively strengthen class-aware features by leveraging image-level contextual information that indicates the presence or absence of the object classes. To this end, we employ a context encoding loss to regularize the model training which promotes the object detector to understand the scene better and narrows the probable object categories in prediction. We achieve our proposed FENet by unifying DAFE and CFE into the framework of Faster R-CNN. In the experiments, we evaluate our proposed method on two large-scale remote sensing image object detection datasets including DIOR and DOTA and demonstrate its effectiveness compared with the baseline methods.

•Gyral peaks, the highest foci on gyri, is under the regulation of genetic factors.•Genetic influence on gyral peaks is heterogeneous across cortical regions.•Genetic influence on gyral peaks is relatively hemispheric symmetry.•Heritability estimates are linearly decreasing with the gyrification timeline.•Earlier formed cortical folds are under stronger genetic influences than later ones.•The pits and peaks coupled by their time of appearance are also positively correlated in respect of their heritability estimates. Genetic mechanisms have been hypothesized to be a major determinant in the formation of cortical folding. Although there is an increasing number of studies examining the heritability of cortical folding, most of them focus on sulcal pits rather than gyral peaks. Gyral peaks, which reflect the highest local foci on gyri and are consistent across individuals, remain unstudied in terms of heritability. To address this knowledge gap, we used high-resolution data from the Human Connectome Project (HCP) to perform classical twin analysis and estimate the heritability of gyral peaks across various brain regions. Our results showed that the heritability of gyral peaks was heterogeneous across different cortical regions, but relatively symmetric between hemispheres. We also found that pits and peaks are different in a variety of anatomic and functional measures. Further, we explored the relationship between the levels of heritability and the formation of cortical folding by utilizing the evolutionary timeline of gyrification. Our findings indicate that the heritability estimates of both gyral peaks and sulcal pits decrease linearly with the evolution timeline of gyrification. This suggests that the cortical folds which formed earlier during gyrification are subject to stronger genetic influences than the later ones. Moreover, the pits and peaks coupled by their time of appearance are also positively correlated in respect of their heritability estimates. These results fill the knowledge gap regarding genetic influences on gyral peaks and significantly advance our understanding of how genetic factors shape the formation of cortical folding. The comparison between peaks and pits suggests that peaks are not a simple morphological mirror of pits but could help complete the understanding of folding patterns.

Highlights Assembly strategies that reinforce the roles of carbon architectures as active materials, electrochemical reaction frameworks, and current collectors in high-energy and high-power lithium-ion batteries are summarized. To enhance structural stability and volumetric performance, the rational design of carbon architectures for high-capacity noncarbons in terms of the interface, network skeleton, void space, and densification, is discussed in detail. Designing carbon cages that protect the electroactive noncarbon is highlighted as a promising strategy that solves the challenges associated with future high-capacity noncarbon anode construction. Lithium-ion batteries (LIBs), which are high-energy-density and low-safety-risk secondary batteries, are underpinned to the rise in electrochemical energy storage devices that satisfy the urgent demands of the global energy storage market. With the aim of achieving high energy density and fast-charging performance, the exploitation of simple and low-cost approaches for the production of high capacity, high density, high mass loading, and kinetically ion-accessible electrodes that maximize charge storage and transport in LIBs, is a critical need. Toward the construction of high-performance electrodes, carbons are promisingly used in the enhanced roles of active materials, electrochemical reaction frameworks for high-capacity noncarbons, and lightweight current collectors. Here, we review recent advances in the carbon engineering of electrodes for excellent electrochemical performance and structural stability, which is enabled by assembled carbon architectures that guarantee sufficient charge delivery and volume fluctuation buffering inside the electrode during cycling. Some specific feasible assembly methods, synergism between structural design components of carbon assemblies, and electrochemical performance enhancement are highlighted. The precise design of carbon cages by the assembly of graphene units is potentially useful for the controlled preparation of high-capacity carbon-caged noncarbon anodes with volumetric capacities over 2100 mAh cm −3 . Finally, insights are given on the prospects and challenges for designing carbon architectures for practical LIBs that simultaneously provide high energy densities (both gravimetric and volumetric) and high rate performance.