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Marine-derived antioxidant polysaccharides have aroused extensive attention because of their potential nutritional and therapeutic benefits. However, the comprehensive comparison of identified marine-derived antioxidant polysaccharides is still inaccessible, which would facilitate the discovery of more efficient antioxidants from marine organisms. Thus, this review summarizes the sources, chemical composition, structural characteristics, and antioxidant capacity of marine antioxidant polysaccharides, as well as their protective in vivo effects mediated by antioxidative stress reported in the last few years (2013–2019), and especially highlights the dominant role of marine algae as antioxidant polysaccharide source. In addition, the relationships between the chemical composition and structural characteristics of marine antioxidant polysaccharides with their antioxidant capacity were also discussed. The antioxidant activity was found to be determined by multiple factors, including molecular weight, monosaccharide composition, sulfate position and its degree.

Chiral molecules with opposite handedness exhibit distinct physical, chemical, or biological properties. They pose challenges as well as opportunities in understanding the phase behavior of soft matter, designing enantioselective catalysts, and manufacturing single-handed pharmaceuticals. Microscopic particles, arranged in a chiral configuration, could also exhibit unusual optical, electric, or magnetic responses. Here we report a simple method to assemble achiral building blocks, i.e., the asymmetric colloidal dimers, into a family of chiral clusters. Under alternating current electric fields, two to four lying dimers associate closely with a central standing dimer and form both right- and left-handed clusters on a conducting substrate. The cluster configuration is primarily determined by the induced dipolar interactions between constituent dimers. Our theoretical model reveals that in-plane dipolar repulsion between petals in the cluster favors the achiral configuration, whereas out-of-plane attraction between the central dimer and surrounding petals favors a chiral arrangement. It is the competition between these two interactions that dictates the final configuration. The theoretical chirality phase diagram is found to be in excellent agreement with experimental observations. We further demonstrate that the broken symmetry in chiral clusters induces an unbalanced electrohydrodynamic flow surrounding them. As a result, they rotate in opposite directions according to their handedness. Both the assembly and propulsion mechanisms revealed here can be potentially applied to other types of asymmetric particles. Such kinds of chiral colloids will be useful for fabricating metamaterials, making model systems for both chiral molecules and active matter, or building propellers for microscale transport. Significance Although colloids have been used as molecular analogues for understanding how simple building blocks can assemble into functional materials, they are mostly spherical with isotropic properties. We are still far from truly accessing the diversity of structures desired for either fundamental understanding or technological application. Here, we report the electric-field–directed assembly of asymmetric colloids into clusters that exhibit a ubiquitous type of symmetry in nature: the chirality. We further demonstrate that the chirality induces unbalanced hydrodynamic flow, which causes rotational propulsion of chiral clusters that are fully dictated by their handedness. Both the assembly and propulsion mechanisms discovered can be universal and applied to other types of asymmetric particles. They are also useful in modeling active matter and making microengines.

Efficient low-grade heat recovery can help to reduce greenhouse gas emission as over 70% of primary energy input is wasted as heat, but current technologies to fulfill the heat-to-electricity conversion are still far from optimum. Here we report a direct thermal charging cell, using asymmetric electrodes of a graphene oxide/platinum nanoparticles cathode and a polyaniline anode in Fe 2+ /Fe 3+ redox electrolyte via isothermal heating operation. When heated, the cell generates voltage via a temperature-induced pseudocapacitive effect of graphene oxide and a thermogalvanic effect of Fe 2+ /Fe 3+ , and then discharges continuously by oxidizing polyaniline and reducing Fe 3+ under isothermal heating till Fe 3+ depletion. The cell can be self-regenerated when cooled down. Direct thermal charging cells attain a temperature coefficient of 5.0 mV K −1 and heat-to-electricity conversion efficiency of 2.8% at 70 °C (21.4% of Carnot efficiency) and 3.52% at 90 °C (19.7% of Carnot efficiency), outperforming other thermoelectrochemical and thermoelectric systems. Recovery of low-grade heat can aid in reducing greenhouse gas emissions, but heat-to-electricity conversion technologies should be optimized. Here the authors report a direct thermal charging cell that uses asymmetric electrodes and a redox electrolyte to efficiently convert low-grade heat into electricity.

The accurate detection of burn wounds is a key research direction in the field of burn medicine, as diagnostic results directly influence the risk of wound infection and the formation of hypertrophic scars. Currently, burn diagnosis is primarily dependent on the clinical judgment of physicians, but its accuracy is typically only between 65% and 70%. Therefore, a non-invasive, efficient method for burn severity assessment is urgently needed. Hyperspectral imaging (HSI), as a non-invasive and contactless spectral detection technique, has been shown to precisely monitor structural changes in burn-affected skin tissue and holds significant potential for burn depth diagnosis. However, research on the application of burn severity detection remains relatively limited, which restricts its widespread use in clinical settings. A burn severity detection classification network (MBNet) based on the Mamba model is proposed in this paper. Through a bidirectional scanning strategy, MBNet effectively captures the long-term dependencies of spectral features, accurately establishes the relationships between bands, and efficiently distinguishes subtle spectral differences under different burn conditions. MBNet provides a reliable and efficient method for clinical burn severity assessment. A comparison of MBNet with seven typical machine learning algorithms on a custom dataset demonstrates that MBNet significantly outperforms these methods in terms of accuracy.

•Spatial scales of pain-preferential activity pattern were investigated by taking advantage of machine learning techniques.•Pain-preferential information was identified at both fine-grained and coarse-grained spatial scales.•Fine-grained local pain-distinguishing information was widely distributed within the brain.•Spatial distribution of pain-distinguishing information showed large inter-subject variability that was associated with personal pain behavior. How pain emerges from human brain remains an unresolved question in pain neuroscience. Neuroimaging studies have suggested that all brain areas activated by painful stimuli were also activated by tactile stimuli, and vice versa. Nonetheless, pain-preferential spatial patterns of voxel-level activation in the brain have been observed when distinguishing painful and tactile brain activations using multivariate pattern analysis (MVPA). According to two hypotheses, the neural activity pattern preferentially encoding pain could exist at a global, coarse-grained, regional level, corresponding to the “pain connectome” hypothesis proposing that pain-preferential information may be encoded by the synchronized activity across multiple distant brain regions, and/or exist at a local, fine-grained, voxel level, corresponding to the “intermingled specialized/preferential neurons” hypothesis proposing that neurons responding specially or preferentially to pain could be present and intermingled with non-pain neurons within a voxel. Here, we systematically investigated the spatial scales of pain-distinguishing information in the human brain measured by fMRI using machine learning techniques, and found that pain-distinguishing information could be detected at both coarse-grained spatial scales across widely distributed brain regions and fine-grained spatial scales within many local areas. Importantly, the spatial distribution of pain-distinguishing information in the brain varies across individuals and such inter-individual variations may be related to a person's trait about pain perception, particularly the pain vigilance and awareness. These results provide new insights into the longstanding question of how pain is represented in the human brain and help the identification of characteristic neuroimaging measurements of pain.

Gene delivery as a promising and valid tool has been used for treating many serious diseases that conventional drug therapies cannot cure. Due to the advancement of physical technology and nanotechnology, advanced physical gene delivery methods such as electroporation, magnetoporation, sonoporation and optoporation have been extensively developed and are receiving increasing attention, which have the advantages of briefness and nontoxicity. This review introduces the technique detail of membrane perforation, with a brief discussion for future development, with special emphasis on nanoparticles mediated optoporation that have developed as an new alternative transfection technique in the last two decades. In particular, the advanced physical approaches development and new technology are highlighted, which intends to stimulate rapid advancement of perforation techniques, develop new delivery strategies and accelerate application of these techniques in clinic.

A bstract The effect of black holes on entanglement harvesting has been of considerable interest over the past decade. Research involving stationary Unruh-DeWitt (UDW) detectors near a (2+1)-dimensional Bañados-Teitelboim-Zanelli (BTZ) black hole has uncovered phenomena such as entanglement shadows, entanglement amplification through black hole rotation, and differences between bipartite and tripartite entanglement. For a (1+1)-dimensional Schwarzschild black hole, it has been shown that two infalling UDW detectors can harvest entanglement from the scalar quantum vacuum even when separated by an event horizon. In this paper, we calculate the mutual information between two UDW detectors coupled to a massless quantum scalar field, with the detectors starting at rest and falling radially into a non-rotating (2+1)-dimensional BTZ black hole. The trajectory of the detectors includes regions where both detectors are switched on outside of the horizon; where one detector is switched on inside of the horizon while the other switches on outside; and where both detectors switch on inside of the horizon. We investigate different black hole masses, detector energy gaps, widths and temporal separations of the detector switching functions, and field boundary conditions. We find that black holes — even the simplest kind having constant curvature — significantly affect the correlation properties of quantum fields in the vacuum state. These correlations, both outside and inside the horizon, can be mapped out by infalling detectors.

Gene expression is the basis for cells to achieve various functions, while DNA methylation constitutes a critical epigenetic mechanism governing gene expression regulation. Here we propose DeepMethyGene, an adaptive recursive convolutional neural network model based on ResNet that predicts gene expression using DNA methylation information. Our model transforms methylation Beta values to M values for Gaussian distributed data optimization, dynamically adjusts the output channels according to input dimension, and implements residual blocks to mitigate the problem of gradient vanishing when training very deep networks. Benchmarking against the state-of-the-art geneEXPLORE model (R 2  = 0.449), DeepMethyGene (R 2  = 0.640) demonstrated superior predictive performance. Further analysis revealed that the number of methylation sites and the average distance between these sites and gene transcription start sites (TSS) significantly affected the prediction accuracy. By exploring the complex relationship between methylation and gene expression, this study provides theoretical support for disease progression prediction and clinical intervention. Relevant data and code are available at https://github.com/yaoyao-11/DeepMethyGene .

Depression, a widespread and highly heritable mental health condition, profoundly affects millions of individuals worldwide. Neuroimaging studies have consistently revealed volumetric abnormalities in subcortical structures associated with depression. However, the genetic underpinnings shared between depression and subcortical volumes remain inadequately understood. Here, we investigate the extent of polygenic overlap using the bivariate causal mixture model (MiXeR), leveraging summary statistics from the largest genome-wide association studies for depression ( N  = 674,452) and 14 subcortical volumetric phenotypes ( N  = 33,224). Additionally, we identify shared genomic loci through conditional/conjunctional FDR analyses. MiXeR shows that subcortical volumetric traits share a substantial proportion of genetic variants with depression, with 44 distinct shared loci identified by subsequent conjunctional FDR analysis. These shared loci are predominantly located in intronic regions (58.7%) and non-coding RNA intronic regions (25.4%). The 269 protein-coding genes mapped by these shared loci exhibit specific developmental trajectories, with the expression level of 55 genes linked to both depression and subcortical volumes, and 30 genes linked to cognitive abilities and behavioral symptoms. These findings highlight a shared genetic architecture between depression and subcortical volumetric phenotypes, enriching our understanding of the neurobiological underpinnings of depression. Depression affects millions of people worldwide. Here, the authors show a substantial polygenic overlap between depression and brain subcortical volumes, identifying 44 shared loci.

Salt-affected soil is a prominent ecological and environmental problem in dry farming areas throughout the world. China has nearly 9.9 million km 2 of salt-affected land. The identification, monitoring, and utilization of soil salinization have become important research topics for promoting sustainable progress. In this paper, using field-measured spectral data and soil salinity parameter data, through analysis and transformation of spectral data, five machine learning models, namely, random forest regression (RFR), support vector regression (SVR), gradient-boosted regression tree (GBRT), multilayer perceptron regression (MLPR), and least angle regression (Lars) are compared. The following performance measures of each model were evaluated: the collinear problems, handling data noise, stability, and the accuracy. In terms of these four aspects, the performance of each model on estimating soil salinity is evaluated. The results demonstrate that among the five models, RFR has the best performance in dealing with collinearity, RFR and MLPR have the best performance in dealing with data noise, and the SVR model is the most stable. The Lars model has the highest accuracy, with a determination coefficient ( R 2 ) of 0.87, ratio of performance to deviation (RPD) of 2.67, root mean square error (RMSE) of 0.18, and mean absolute percentage error (MAPE) of 0.11. Then, the comprehensive comparison and analysis of the five models are carried out, and it is found that the comprehensive performance of RFR model is the best; hence, this method is most suitable for estimating soil salinity using hyperspectral data. This study can provide a reference for the selection of regression methods in subsequent studies on estimating soil salinity using hyperspectral data.