Exercise-induced neuroplasticity: a new perspective on rehabilitation for chronic low back pain.

Chronic low back pain patients often experience recurrent episodes due to various peripheral and central factors, leading to physical and mental impairments, affecting their daily life and work, and increasing the healthcare burden. With the continuous advancement of neuropathological research, changes in brain structure and function in chronic low back pain patients have been revealed. Neuroplasticity is an important mechanism of self-regulation in the brain and plays a key role in neural injury repair. Targeting neuroplasticity and regulating the central nervous system to improve functional impairments has become a research focus in rehabilitation medicine. Recent studies have shown that exercise can have beneficial effects on the body, such as improving cognition, combating depression, and enhancing athletic performance. Exercise-induced neuroplasticity may be a potential mechanism through which exercise affects the brain. This article systematically introduces the theory of exercise-induced neuroplasticity, explores the central effects mechanism of exercise on patients with chronic low back pain, and further looks forward to new directions in targeted neuroplasticity-based rehabilitation treatment for chronic low back pain.

A potential research target for cardiac rehabilitation: brain-derived neurotrophic factor.

Cardiovascular diseases pose a major threat to human life, functional activity, and quality of life. Once the disease is present, patients can experience varying degrees of problems or limitations on three levels: physical, psychological, and social. Patients with cardiovascular disease are always at risk for adverse cardiac events, decreased physical activity, psychoemotional disturbances, and limited social participation due to their varying pathologies. Therefore, personalized cardiac rehabilitation is of great significance in improving patients' physical and mental functions, controlling disease progression, and preventing deterioration. There is a consensus on the benefits of cardiac rehabilitation in improving patients' quality of life, enhancing functional activity, and reducing mortality. As an important part of cardiac rehabilitation, Exercise plays an irreplaceable role. Aerobic exercise, resistance training, flexibility training, and other forms of exercise are recommended by many experts. Improvements in exercise tolerance, lipid metabolism, cardiac function, and psychological aspects of the patients were evident with appropriate exercise interventions based on a comprehensive assessment. Further studies have found that brain-derived neurotrophic factor may be an important mediator of exercise's ability to improve cardiovascular health. Brain-derived neurotrophic factor exerts multiple biological effects on the cardiovascular system. This article provides another perspective on the cardiac effects of exercise and further looks at the prospects for the use of brain-derived neurotrophic factor in cardiac rehabilitation. Meanwhile, the new idea that brain-derived neurotrophic factor is a key mediator connecting the brain-cardiac axis is proposed in light of the current research progress, to provide new ideas for clinical rehabilitation and scientific research.

A spatiotemporal atlas of cholestatic injury and repair in mice.

Cholestatic liver injuries, characterized by regional damage around the bile ductular region, lack curative therapies and cause considerable mortality. Here we generated a high-definition spatiotemporal atlas of gene expression during cholestatic injury and repair in mice by integrating spatial enhanced resolution omics sequencing and single-cell transcriptomics. Spatiotemporal analyses revealed a key role of cholangiocyte-driven signaling correlating with the periportal damage-repair response. Cholangiocytes express genes related to recruitment and differentiation of lipid-associated macrophages, which generate feedback signals enhancing ductular reaction. Moreover, cholangiocytes express high TGFß in association with the conversion of liver progenitor-like cells into cholangiocytes during injury and the dampened proliferation of periportal hepatocytes during recovery. Notably, Atoh8 restricts hepatocyte proliferation during 3,5-diethoxycarbonyl-1,4-dihydro-collidin damage and is quickly downregulated after injury withdrawal, allowing hepatocytes to respond to growth signals. Our findings lay a keystone for in-depth studies of cellular dynamics and molecular mechanisms of cholestatic injuries, which may further develop into therapies for cholangiopathies.

A spatiotemporal atlas of mouse liver homeostasis and regeneration.

The mechanism by which mammalian liver cell responses are coordinated during tissue homeostasis and perturbation is poorly understood, representing a major obstacle in our understanding of many diseases. This knowledge gap is caused by the difficulty involved with studying multiple cell types in different states and locations, particularly when these are transient. We have combined Stereo-seq (spatiotemporal enhanced resolution omics-sequencing) with single-cell transcriptomic profiling of 473,290 cells to generate a high-definition spatiotemporal atlas of mouse liver homeostasis and regeneration at the whole-lobe scale. Our integrative study dissects in detail the molecular gradients controlling liver cell function, systematically defining how gene networks are dynamically modulated through intercellular communication to promote regeneration. Among other important regulators, we identified the transcriptional cofactor TBL1XR1 as a rheostat linking inflammation to Wnt/ß-catenin signaling for facilitating hepatocyte proliferation. Our data and analytical pipelines lay the foundation for future high-definition tissue-scale atlases of organ physiology and malfunction.

A scATAC-seq atlas of chromatin accessibility in axolotl brain regions.

Axolotl (Ambystoma mexicanum) is an excellent model for investigating regeneration, the interaction between regenerative and developmental processes, comparative genomics, and evolution. The brain, which serves as the material basis of consciousness, learning, memory, and behavior, is the most complex and advanced organ in axolotl. The modulation of transcription factors is a crucial aspect in determining the function of diverse regions within the brain. There is, however, no comprehensive understanding of the gene regulatory network of axolotl brain regions. Here, we utilized single-cell ATAC sequencing to generate the chromatin accessibility landscapes of 81,199 cells from the olfactory bulb, telencephalon, diencephalon and mesencephalon, hypothalamus and pituitary, and the rhombencephalon. Based on these data, we identified key transcription factors specific to distinct cell types and compared cell type functions across brain regions. Our results provide a foundation for comprehensive analysis of gene regulatory programs, which are valuable for future studies of axolotl brain development, regeneration, and evolution, as well as on the mechanisms underlying cell-type diversity in vertebrate brains.

Preparation and Characterization of Bio-Based PLA/PEG/g-C3N4 Low-Temperature Composite Phase Change Energy Storage Materials.

As energy and environmental issues become more prominent, people must find sustainable, green development paths. Bio-based polymeric phase change energy storage materials provide solutions to cope with these problems. Therefore, in this paper, a fully degradable polyethylene glycol (PEG20000)/polylactic acid (PLA)/g-C3N4 composite phase change energy storage material (CPCM) was obtained by confinement. The CPCM was characterized by FTIR and SEM for compatibility, XRD and nanoindentation for mechanical properties and DSC, LFA, and TG for thermal properties. The results showed that the CPCM was physical co-mingling; when PLA: PEG: g-C3N4 was 6:3:1, the consistency was good. PEG destroys the crystallization of PLA and causes the hardness to decrease. When PLA: PEG: g-C3N4 was 6: 3: 1, it had a maximum hardness of 0.137 GPa. The CPCM had a high latent enthalpy, and endothermic and exothermic enthalpies of 106.1 kJ/kg and 80.05 kJ/kg for the PLA: PEG: g-C3N4 of 3: 6: 1. The CPCM showed an increased thermal conductivity compared to PLA, reaching 0.30 W/(m·K),0.32 W/(m·K) when PLA: PEG: g-C3N4 was 6: 3: 1 and when PLA: PEG: g-C3N4 was 3: 6: 1, respectively. Additionally, the CPCM was stable within 250 °C, indicating a wide appliable temperature range. The CPCM can be applied to solar thermal power generation, transportation, and building construction.

Single-cell chromatin accessibility profiling of cell-state-specific gene regulatory programs during mouse organogenesis.

In mammals, early organogenesis begins soon after gastrulation, accompanied by specification of various type of progenitor/precusor cells. In order to reveal dynamic chromatin landscape of precursor cells and decipher the underlying molecular mechanism driving early mouse organogenesis, we performed single-cell ATAC-seq of E8.5-E10.5 mouse embryos. We profiled a total of 101,599 single cells and identified 41 specific cell types at these stages. Besides, by performing integrated analysis of scATAC-seq and public scRNA-seq data, we identified the critical cis-regulatory elements and key transcription factors which drving development of spinal cord and somitogenesis. Furthermore, we intersected accessible peaks with human diseases/traits-related loci and found potential clinical associated single nucleotide variants (SNPs). Overall, our work provides a fundamental source for understanding cell fate determination and revealing the underlying mechanism during postimplantation embryonic development, and expand our knowledge of pathology for human developmental malformations.

A novel computational method enables RNA editome profiling during human hematopoiesis from scRNA-seq data.

RNA editing is a post-transcriptional modification with a cell-specific manner and important biological implications. Although single-cell RNA-seq (scRNA-seq) is an effective method for studying cellular heterogeneity, it is difficult to detect and study RNA editing events from scRNA-seq data because of the low sequencing coverage. To overcome this, we develop a computational method to systematically identify RNA editing sites of cell types from scRNA-seq data. To demonstrate its effectiveness, we apply it to scRNA-seq data of human hematopoietic stem/progenitor cells (HSPCs) with an annotated lineage differentiation relationship according to previous research and study the impacts of RNA editing on hematopoiesis. The dynamic editing patterns reveal the relevance of RNA editing on different HSPCs. For example, four microRNA (miRNA) target sites on 3' UTR of EIF2AK2 are edited across all HSPC populations, which may abolish the miRNA-mediated inhibition of EIF2AK2. Elevated EIF2AK2 may thus activate the integrated stress response (ISR) pathway to initiate global translational attenuation as a protective mechanism to maintain cellular homeostasis during HSPCs' differentiation. Besides, our findings also indicate that RNA editing plays an essential role in the coordination of lineage commitment and self-renewal of hematopoietic stem cells (HSCs). Taken together, we demonstrate the capacity of scRNA-seq data to exploit RNA editing events of cell types, and find that RNA editing may exert multiple modules of regulation in hematopoietic processes.

Few-Shot Partial Multi-View Learning.

It is often the case that data are with multiple views in real-world applications. Fully exploring the information of each view is significant for making data more representative. However, due to various limitations and failures in data collection and pre-processing, it is inevitable for real data to suffer from view missing and data scarcity. The coexistence of these two issues makes it more challenging to achieve the pattern classification task. Currently, to our best knowledge, few appropriate methods can well-handle these two issues simultaneously. Aiming to draw more attention from the community to this challenge, we propose a new task in this paper, called few-shot partial multi-view learning, which focuses on overcoming the negative impact of the view-missing issue in the low-data regime. The challenges of this task are twofold: (i) it is difficult to overcome the impact of data scarcity under the interference of missing views; (ii) the limited number of data exacerbates information scarcity, thus making it harder to address the view-missing issue in turn. To address these challenges, we propose a new unified Gaussian dense-anchoring method. The unified dense anchors are learned for the limited partial multi-view data, thereby anchoring them into a unified dense representation space where the influence of data scarcity and view missing can be alleviated. We conduct extensive experiments to evaluate our method. The results on Cub-googlenet-doc2vec, Handwritten, Caltech102, Scene15, Animal, ORL, tieredImagenet, and Birds-200-2011 datasets validate its effectiveness. The codes will be released at https://github.com/zhouyuan888888/UGDA.

Cell atlas of CCl 4-induced progressive liver fibrosis reveals stage-specific responses.

Chronic liver injury leads to progressive liver fibrosis and ultimately cirrhosis, a major cause of morbidity and mortality worldwide. However, there are currently no effective anti-fibrotic therapies available, especially for late-stage patients, which is partly attributed to the major knowledge gap regarding liver cell heterogeneity and cell-specific responses in different fibrosis stages. To reveal the multicellular networks regulating mammalian liver fibrosis from mild to severe phenotypes, we generated a single-nucleus transcriptomic atlas encompassing 49 919 nuclei corresponding to all main liver cell types at different stages of murine carbon tetrachloride (CCl 4)-induced progressive liver fibrosis. Integrative analysis distinguished the sequential responses to injury of hepatocytes, hepatic stellate cells and endothelial cells. Moreover, we reconstructed cell-cell interactions and gene regulatory networks implicated in these processes. These integrative analyses uncovered previously overlooked aspects of hepatocyte proliferation exhaustion and disrupted pericentral metabolic functions, dysfunction for clearance by apoptosis of activated hepatic stellate cells, accumulation of pro-fibrotic signals, and the switch from an anti-angiogenic to a pro-angiogenic program during CCl 4-induced progressive liver fibrosis. Our dataset thus constitutes a useful resource for understanding the molecular basis of progressive liver fibrosis using a relevant animal model.

Membrane fusion-mediated delivery of small-molecule HER2 tyrosine kinase inhibitor for effective tumor chemosensitization.

Although nanocarriers have been widely applied in the delivery of anticancer drugs, many commercialized anticancer nanodrug systems still suffer from the problem of being easily trapped by lysosomes, which severely limits the drug delivery efficiency of a nanodrug system. Meanwhile, in drug-resistant tumors, the efflux of anticancer therapeutic drugs via the drug efflux transporters on the plasma membrane of cancer cells can significantly decrease the intracellular drug concentration and lead to the failure of the drug treatment. Here, we developed a small-molecule tyrosine kinase inhibitor (TKI)- and doxorubicin (Dox, a common anticancer drug)-loaded membrane fusion liposome (MFL) (termed Dox@Lapa-MFL) to achieve tumor cell membrane fusion-mediated drug delivery and enhanced chemotherapy of drug-resistant tumor. MFL could deliver drugs in a membrane fusion manner, circumventing the capture by lysosomes. Lapatinib, as the TKI doped in the MFL, could inhibit the efflux of Dox by ATP-binding cassette transporters (ABC transporters), further promoting the intracellular Dox accumulation. As a result, Dox achieved effective killing of drug-resistant tumors under the dual effect of MFL and lapatinib. To the best of our knowledge, it is the first example that employs membrane fusion-mediated TKI delivery for achieving tumor chemosensitization with good biosafety. This work presents an efficient and easily achievable strategy for treating drug-resistant tumors, which may hold promise for clinical applications.

MS²-GNN: Exploring GNN-Based Multimodal Fusion Network for Depression Detection.

Major depressive disorder (MDD) is one of the most common and severe mental illnesses, posing a huge burden on society and families. Recently, some multimodal methods have been proposed to learn a multimodal embedding for MDD detection and achieved promising performance. However, these methods ignore the heterogeneity/homogeneity among various modalities. Besides, earlier attempts ignore interclass separability and intraclass compactness. Inspired by the above observations, we propose a graph neural network (GNN)-based multimodal fusion strategy named modal-shared modal-specific GNN, which investigates the heterogeneity/homogeneity among various psychophysiological modalities as well as explores the potential relationship between subjects. Specifically, we develop a modal-shared and modal-specific GNN architecture to extract the inter/intramodal characteristics. Furthermore, a reconstruction network is employed to ensure fidelity within the individual modality. Moreover, we impose an attention mechanism on various embeddings to obtain a multimodal compact representation for the subsequent MDD detection task. We conduct extensive experiments on two public depression datasets and the favorable results demonstrate the effectiveness of the proposed algorithm.

Ru Single Atoms on One-Dimensional CF@g-C3N4 Hierarchy as Highly Stable Catalysts for Aqueous Levulinic Acid Hydrogenation.

Herein, we report a stable catalyst with Ru single atoms anchored on a one-dimensional carbon fiber@graphitic carbon nitride hierarchy, by assembling wet wipes composed of fiber-derived carbon fiber (CF), melamine-derived graphitic carbon nitride (g-C3N4) and RuCl3 before NaBH4 reduction. The atomically dispersed Ru species (3.0 wt%) are tightly attached via N-coordination provided by exterior g-C3N4 nanosheets, and further stabilized by the interior mesoporous CF. The obtained CF@g-C3N4-Ru SAs catalyst can be cycled six times without notable leaching of Ru or loss of GVL yield in the acidic media. This catalyst is more stable than Ru nanoparticles supported on CF@g-C3N4, as well as Ru single atoms anchored on CF and g-C3N4, and proves to be one of the most efficient metal catalysts for aqueous LA hydrogenation to γ-valerolactone (GVL). The isolated Ru atoms by strong N-coordination, and their enhanced electron/mass transfer afforded by the one-dimensional hierarchy, can be responsible for the excellent durability of CF@g-C3N4-Ru SAs under harsh reaction conditions.

Chromosomal-level genome of velvet bean (Mucuna pruriens) provides resources for L-DOPA synthetic research and development.

Mucuna pruriens, commonly called velvet bean, is the main natural source of levodopa (L-DOPA), which has been marketed as a psychoactive drug for the clinical management of Parkinson's disease and dopamine-responsive dystonia. Although velvet bean is a very important plant species for food and pharmaceutical manufacturing, the lack of genetic and genomic information about this species severely hinders further molecular research thereon and biotechnological development. Here, we reported the first velvet bean genome, with a size of 500.49 Mb and 11 chromosomes encoding 28,010 proteins. Genomic comparison among legume species indicated that velvet bean speciated ∼29 Ma from soybean clade, without specific genome duplication. Importantly, we identified 21 polyphenol oxidase coding genes that catalyse l-tyrosine to L-DOPA in velvet bean, and two subfamilies showing tandem expansion on Chr3 and Chr7 after speciation. Interestingly, disease-resistant and anti-pathogen gene families were found contracted in velvet bean, which might be related to the expansion of polyphenol oxidase. Our study generated a high-quality genomic reference for velvet bean, an economically important agricultural and medicinal plant, and the newly reported L-DOPA biosynthetic genes could provide indispensable information for the biotechnological and sustainable development of an environment-friendly L-DOPA biosynthesis processing method.

The single-cell stereo-seq reveals region-specific cell subtypes and transcriptome profiling in Arabidopsis leaves.

Understanding the complex functions of plant leaves requires a thorough characterization of discrete cell features. Although single-cell gene expression profiling technologies have been developed, their application in characterizing cell subtypes has not been achieved yet. Here, we present scStereo-seq (single-cell spatial enhanced resolution omics sequencing) that enabled us to show the bona fide single-cell spatial transcriptome profiles of Arabidopsis leaves. Subtle but significant transcriptomic differences between upper and lower epidermal cells have been successfully distinguished. Furthermore, we discovered cell-type-specific gene expression gradients from the main vein to the leaf edge, which led to the finding of distinct spatial developmental trajectories of vascular cells and guard cells. Our study showcases the importance of physical locations of individual cells for exerting complex biological functions in plants and demonstrates that scStereo-seq is a powerful tool to integrate single-cell location and transcriptome information for plant biology study.

Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays.

Spatially resolved transcriptomic technologies are promising tools to study complex biological processes such as mammalian embryogenesis. However, the imbalance between resolution, gene capture, and field of view of current methodologies precludes their systematic application to analyze relatively large and three-dimensional mid- and late-gestation embryos. Here, we combined DNA nanoball (DNB)-patterned arrays and in situ RNA capture to create spatial enhanced resolution omics-sequencing (Stereo-seq). We applied Stereo-seq to generate the mouse organogenesis spatiotemporal transcriptomic atlas (MOSTA), which maps with single-cell resolution and high sensitivity the kinetics and directionality of transcriptional variation during mouse organogenesis. We used this information to gain insight into the molecular basis of spatial cell heterogeneity and cell fate specification in developing tissues such as the dorsal midbrain. Our panoramic atlas will facilitate in-depth investigation of longstanding questions concerning normal and abnormal mammalian development.

Hierarchical Prototype Refinement With Progressive Inter-Categorical Discrimination Maximization for Few-Shot Learning.

Metric-based few-shot learning categorizes unseen query instances by measuring their distance to the categories appearing in the given support set. To facilitate distance measurement, prototypes are used to approximate the representations of categories. However, we find prototypical representations are generally not discriminative enough to represent the discrepancy of inter-categorical distribution of queries, thereby limiting the classification accuracy. To overcome this issue, we propose a new Progressive Hierarchical-Refinement (PHR) method, which effectively refines the discrimination of prototypes by conducting the Progressive Discrimination Maximization strategy based on the hierarchical feature representations. Specifically, we first encode supports and queries into the representation space of spatial level, global level, and semantic level. Then, the refining coefficients are constructed by exploring the metric information contained in these hierarchical embedding spaces simultaneously. Under the guidance of the refining coefficients, the meta-refining loss progressively maximizes the discrimination degree of inter-categorical prototypical representations. In addition, the refining vectors are adopted to further enhance the representations of prototypes. In this way, the metric-based classification can be more accurate. Our PHR method shows the competitive performance on the miniImagenet, CIFAR-FS, FC100, and CUB datasets. Moreover, PHR presents good compatibility. It can be incorporated with other few-shot learning models, making them more accurate.