Single-cell transcriptomics across 2,534 microbial species reveals functional heterogeneity in the rumen microbiome.

Deciphering the activity of individual microbes within complex communities and environments remains a challenge. Here we describe the development of microbiome single-cell transcriptomics using droplet-based single-cell RNA sequencing and pangenome-based computational analysis to characterize the functional heterogeneity of the rumen microbiome. We generated a microbial genome database (the Bovine Gastro Microbial Genome Map) as a functional reference map for the construction of a single-cell transcriptomic atlas of the rumen microbiome. The atlas includes 174,531 microbial cells and 2,534 species, of which 172 are core active species grouped into 12 functional clusters. We detected single-cell-level functional roles, including a key role for Basfia succiniciproducens in the carbohydrate metabolic niche of the rumen microbiome. Furthermore, we explored functional heterogeneity and reveal metabolic niche trajectories driven by biofilm formation pathway genes within B. succiniciproducens. Our results provide a resource for studying the rumen microbiome and illustrate the diverse functions of individual microbial cells that drive their ecological niche stability or adaptation within the ecosystem.

An automated hybrid approach via deep learning and radiomics focused on the midbrain and substantia nigra to detect early-stage Parkinson's disease.

Objectives: The altered neuromelanin in substantia nigra pars compacta (SNpc) is a valuable biomarker in the detection of early-stage Parkinson's disease (EPD). Diagnosis via visual inspection or single radiomics based method is challenging. Thus, we proposed a novel hybrid model that integrates radiomics and deep learning methodologies to automatically detect EPD based on neuromelanin-sensitive MRI, namely short-echo-time Magnitude (setMag) reconstructed from quantitative susceptibility mapping (QSM). Methods: In our study, we collected QSM images including 73 EPD patients and 65 healthy controls, which were stratified into training-validation and independent test sets with an 8:2 ratio. Twenty-four participants from another center were included as the external validation set. Our framework began with the detection of the brainstem utilizing YOLO-v5. Subsequently, a modified LeNet was applied to obtain deep learning features. Meanwhile, 1781 radiomics features were extracted, and 10 features were retained after filtering. Finally, the classified models based on radiomics features, deep learning features, and the hybrid of both were established through machine learning algorithms, respectively. The performance was mainly evaluated using accuracy, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). The saliency map was used to visualize the model. Results: The hybrid feature-based support vector machine (SVM) model showed the best performance, achieving ACC of 96.3 and 95.8% in the independent test set and external validation set, respectively. The model established by hybrid features outperformed the one radiomics feature-based (NRI: 0.245, IDI: 0.112). Furthermore, the saliency map showed that the bilateral "swallow tail" sign region was significant for classification. Conclusion: The integration of deep learning and radiomic features presents a potent strategy for the computer-aided diagnosis of EPD. This study not only validates the accuracy of our proposed model but also underscores its interpretability, evidenced by differential significance across various anatomical sites.

Manipulating Local Chemistry and Coherent Structures for High-Rate and Long-Life Sodium-Ion Battery Cathodes.

Layered sodium transition-metal (TM) oxides generally suffer from severe capacity decay and poor rate performance during cycling, especially at a high state of charge (SoC). Herein, an insight into failure mechanisms within high-voltage layered cathodes is unveiled, while a two-in-one tactic of charge localization and coherent structures is devised to improve structural integrity and Na+ transport kinetics, elucidated by density functional theory calculations. Elevated Jahn-Teller [Mn3+O6] concentration on the particle surface during sodiation, coupled with intense interlayer repulsion and adverse oxygen instability, leads to irreversible damage to the near-surface structure, as demonstrated by X-ray absorption spectroscopy and in situ characterization techniques. It is further validated that the structural skeleton is substantially strengthened through the electronic structure modulation surrounding oxygen. Furthermore, optimized Na+ diffusion is effectively attainable via regulating intergrown structures, successfully achieved by the Zn2+ inducer. Greatly, good redox reversibility with an initial Coulombic efficiency of 92.6%, impressive rate capability (86.5 mAh g-1 with 70.4% retention at 10C), and enhanced cycling stability (71.6% retention after 300 cycles at 5C) are exhibited in the P2/O3 biphasic cathode. It is believed that a profound comprehension of layered oxides will herald fresh perspectives to develop high-voltage cathode materials for sodium-ion batteries.

A conserved N-terminal motif of CUL3 contributes to assembly and E3 ligase activity of CRL3KLHL22.

The CUL3-RING E3 ubiquitin ligases (CRL3s) play an essential role in response to extracellular nutrition and stress stimuli. The ubiquitin ligase function of CRL3s is activated through dimerization. However, how and why such a dimeric assembly is required for its ligase activity remains elusive. Here, we report the cryo-EM structure of the dimeric CRL3KLHL22 complex and reveal a conserved N-terminal motif in CUL3 that contributes to the dimerization assembly and the E3 ligase activity of CRL3KLHL22. We show that deletion of the CUL3 N-terminal motif impairs dimeric assembly and the E3 ligase activity of both CRL3KLHL22 and several other CRL3s. In addition, we found that the dynamics of dimeric assembly of CRL3KLHL22 generates a variable ubiquitination zone, potentially facilitating substrate recognition and ubiquitination. These findings demonstrate that a CUL3 N-terminal motif participates in the assembly process and provide insights into the assembly and activation of CRL3s.

Integrated transcriptomic and lipidomic analysis provides key insights into lipid content changes during pecan (Carya illinoensis) fruit development.

Pecans [Carya illinoinensis (Wangenh.) K. Koch] are highly valued for their abundance of quality healthy lipids, positively impacting human health and making themselves a preferred choice for nutritionally rich foods. However, a comprehensive understanding of the high-resolution characteristics of pecan fruit lipid composition and its dynamic changes, as well as the transfer between embryo and pericarp during development, remains incomplete. In this study, through integrated multi-omics analysis, we observed significant spatiotemporal heterogeneity in lipid changes between the pericarp and embryo. It showed smaller fluctuations and more stable lipid levels in the pericarp while exhibiting a dynamic pattern of initially increasing and then decreasing lipid content in the embryo. In this study, a total of 52 differentially expressed genes were identified, related to fatty acid synthesis and metabolism pathways in the two tissues, with changes in oleic acid and linoleic acid composition being the primary features of the embryo. This research lays the foundation for further understanding the differential regulation mechanisms of lipid metabolism between embryo and pericarp. Overall, this study filled the knowledge gap regarding dynamic changes in pericarp lipid metabolites, provided crucial insights into the lipid metabolism network during pecan fruit development, and established a scientific basis for the genetic improvement of pecan crops.

Reconciling the Cooperative-Competitive Patterns among Tumor and Immune Cells for Triple-Negative Breast Cancer Treatment Using Multimodule Nanocomplexes.

Targeting the competitive-cooperative relationships among tumor cells and various immune cells can efficiently reverse the immune-dysfunction microenvironment to boost the immunotherapies for the triple-negative breast cancer treatment. Hence, a bacterial outer membrane vesicle-based nanocomplex is designed for specifically targeting malignant cells and immune cells to reconcile the relationships based on metabolic-immune crosstalk. By uniquely utilizing the property of charge-reversal polymers to realize function separation, the nanocomplexes could synergistically regulate tumor cells and immune cells. This approach could reshape the immunosuppressive competition-cooperation pattern into one that is immune-responsive, showcasing significant potential for inducing tumor remission in TNBC models.

Raman Flow Cytometry and Its Biomedical Applications.

Raman flow cytometry (RFC) uniquely integrates the "label-free" capability of Raman spectroscopy with the "high-throughput" attribute of traditional flow cytometry (FCM), offering exceptional performance in cell characterization and sorting. Unlike conventional FCM, RFC stands out for its elimination of the dependency on fluorescent labels, thereby reducing interference with the natural state of cells. Furthermore, it significantly enhances the detection information, providing a more comprehensive chemical fingerprint of cells. This review thoroughly discusses the fundamental principles and technological advantages of RFC and elaborates on its various applications in the biomedical field, from identifying and characterizing cancer cells for in vivo cancer detection and surveillance to sorting stem cells, paving the way for cell therapy, and identifying metabolic products of microbial cells, enabling the differentiation of microbial subgroups. Moreover, we delve into the current challenges and future directions regarding the improvement in sensitivity and throughput. This holds significant implications for the field of cell analysis, especially for the advancement of metabolomics.

Fully inkjet-printed Ag2Se flexible thermoelectric devices for sustainable power generation.

Flexible thermoelectric devices show great promise as sustainable power units for the exponentially increasing self-powered wearable electronics and ultra-widely distributed wireless sensor networks. While exciting proof-of-concept demonstrations have been reported, their large-scale implementation is impeded by unsatisfactory device performance and costly device fabrication techniques. Here, we develop Ag2Se-based thermoelectric films and flexible devices via inkjet printing. Large-area patterned arrays with microscale resolution are obtained in a dimensionally controlled manner by manipulating ink formulations and tuning printing parameters. Printed Ag2Se-based films exhibit (00 l)-textured feature, and an exceptional power factor (1097 µWm-1K-2 at 377 K) is obtained by engineering the film composition and microstructure. Benefiting from high-resolution device integration, fully inkjet-printed Ag2Se-based flexible devices achieve a record-high normalized power (2 µWK-2cm-2) and superior flexibility. Diverse application scenarios are offered by inkjet-printed devices, such as continuous power generation by harvesting thermal energy from the environment or human bodies. Our strategy demonstrates the potential to revolutionize the design and manufacture of multi-scale and complex flexible thermoelectric devices while reducing costs, enabling them to be integrated into emerging electronic systems as sustainable power sources.

Angiosperm-wide analysis of fruit and ovary evolution aided by a new nuclear phylogeny supports association of the same ovary type with both dry and fleshy fruits.

Fruit functions in seed protection and dispersal and belongs to many dry and fleshy types, yet their evolutionary pattern remains unclear in part due to uncertainties in the phylogenetic relationships among several orders and families. Thus we used nuclear genes of 502 angiosperm species representing 231 families to reconstruct a well supported phylogeny, with resolved relationships for orders and families with previously uncertain placements. Using this phylogeny as a framework, molecular dating supports a Triassic origin of the crown angiosperms, followed by the emergence of most orders in the Jurassic and Cretaceous and their rise to ecological dominance during the Cretaceous Terrestrial Revolution. The robust phylogeny allowed an examination of the evolutionary pattern of fruit and ovary types, revealing a trend of parallel carpel fusions during early diversifications in eudicots, monocots, and magnoliids. Moreover, taxa in the same order or family with the same ovary type can develop either dry or fleshy fruits with strong correlations between specific types of dry and fleshy fruits; such associations of ovary, dry and fleshy fruits define several ovary-fruit "modules" each found in multiple families. One of the frequent modules has an ovary containing multiple ovules, capsules and berries, and another with an ovary having one or two ovules, achenes (or other single-seeded dry fruits) and drupes. This new perspective of relationships among fruit types highlights the closeness of specific dry and fleshy fruit types, such as capsule and berry, that develop from the same ovary type and belong to the same module relative to dry and fleshy fruits of other modules (such as achenes and drupes). Further analyses of gene families containing known genes for ovary and fruit development identified phylogenetic nodes with multiple gene duplications, supporting a possible role of whole-genome duplications, in combination with climate changes and animal behaviors, in angiosperm fruit and ovary diversification.

Automatic segmentation of hepatocellular carcinoma on dynamic contrast-enhanced MRI based on deep learning.

Objective. Precise hepatocellular carcinoma (HCC) detection is crucial for clinical management. While studies focus on computed tomography-based automatic algorithms, there is a rareness of research on automatic detection based on dynamic contrast enhanced (DCE) magnetic resonance imaging. This study is to develop an automatic detection and segmentation deep learning model for HCC using DCE.Approach: DCE images acquired from 2016 to 2021 were retrospectively collected. Then, 382 patients (301 male; 81 female) with 466 lesions pathologically confirmed were included and divided into an 80% training-validation set and a 20% independent test set. For external validation, 51 patients (42 male; 9 female) in another hospital from 2018 to 2021 were included. The U-net architecture was modified to accommodate multi-phasic DCE input. The model was trained with the training-validation set using five-fold cross-validation, and furtherly evaluated with the independent test set using comprehensive metrics for segmentation and detection performance. The proposed automatic segmentation model consisted of five main steps: phase registration, automatic liver region extraction using a pre-trained model, automatic HCC lesion segmentation using the multi-phasic deep learning model, ensemble of five-fold predictions, and post-processing using connected component analysis to enhance the performance to refine predictions and eliminate false positives.Main results. The proposed model achieved a mean dice similarity coefficient (DSC) of 0.81 ± 0.11, a sensitivity of 94.41 ± 15.50%, a precision of 94.19 ± 17.32%, and 0.14 ± 0.48 false positive lesions per patient in the independent test set. The model detected 88% (80/91) HCC lesions in the condition of DSC > 0.5, and the DSC per tumor was 0.80 ± 0.13. In the external set, the model detected 92% (58/62) lesions with 0.12 ± 0.33 false positives per patient, and the DSC per tumor was 0.75 ± 0.10.Significance.This study developed an automatic detection and segmentation deep learning model for HCC using DCE, which yielded promising post-processed results in accurately identifying and delineating HCC lesions.

Structural and functional insights into the lipid regulation of human anion exchanger 2.

Anion exchanger 2 (AE2) is an electroneutral Na+-independent Cl-/HCO3- exchanger belongs to the SLC4 transporter family. The widely expressed AE2 participates in a variety of physiological processes, including transepithelial acid-base secretion and osteoclastogenesis. Both the transmembrane domains (TMDs) and the N-terminal cytoplasmic domain (NTD) are involved in regulation of AE2 activity. However, the regulatory mechanism remains unclear. Here, we report a 3.2 Å cryo-EM structure of the AE2 TMDs in complex with PIP2 and a 3.3 Å full-length mutant AE2 structure in the resting state without PIP2. We demonstrate that PIP2 at the TMD dimer interface is involved in the substrate exchange process. Mutation in the PIP2 binding site leads to the displacement of TM7 and further stabilizes the interaction between the TMD and the NTD. Reduced substrate transport activity and conformation similar to AE2 in acidic pH indicating the central contribution of PIP2 to the function of AE2.

Modeling critical thermoelectric transports driven by band broadening and phonon softening.

Critical phenomena are one of the most captivating areas of modern physics, whereas the relevant experimental and theoretical studies are still very challenging. Particularly, the underlying mechanism behind the anomalous thermoelectric properties during critical phase transitions remains elusive, i.e., the current theoretical models for critical electrical transports are either qualitative or solely focused on a specific transport parameter. Herein, we develop a quantitative theory to model the electrical transports during critical phase transitions by incorporating both the band broadening effect and carrier-soft TO phonon interactions. It is found that the band-broadening effect contributes an additional term to Seebeck coefficient, while the carrier-soft TO phonon interactions greatly affects both electrical resistivity and Seebeck coefficient. The universality and validity of our model are well confirmed by experimental data. Furthermore, the features of critical phase transitions are effectively tuned. For example, alloying S in Cu2Se can reduce the phase transition temperature but increase the phase transition parameter b. The maximum thermoelectric figure of merit zT is pushed to a high value of 1.3 at the critical point (377 K), which is at least twice as large as those of normal static phases. This work not only provides a clear picture of the critical electrical transports but also presents new guidelines for future studies in this exciting area.

Altered whole-brain functional network in patients with frontal low-grade gliomas: a resting-state functional MRI study.

PURPOSE: Gliomas are the most common primary brain tumor. Currently, topological alterations of whole-brain functional network caused by gliomas are not fully understood. The work here clarified the topological reorganization of the functional network in patients with unilateral frontal low-grade gliomas (LGGs). METHODS: A total of 45 patients with left frontal LGGs, 19 with right frontal LGGs, and 25 healthy controls (HCs) were enrolled. All the resting-state functional MRI (rs-fMRI) images of the subjects were preprocessed to construct the functional network matrix, which was used for graph theoretical analysis. A two-sample t-test was conducted to clarify the differences in global and nodal network metrics between patients and HCs. A network-based statistic approach was used to identify the altered specific pairs of regions in which functional connectivity in patients with LGGs. RESULTS: The local efficiency, clustering coefficient, characteristic path length, and normalized characteristic path length of patients with unilateral frontal LGGs were significantly lower than HCs, while there were no significant differences of global efficiency and small-worldness between patients and HCs. Compared with the HCs, betweenness centrality, degree centrality, and nodal efficiency of several brain nodes were changed significantly in patients. Around the tumor and its adjacent areas, the inter- and intra-hemispheric connections were significantly decreased in patients with left frontal LGGs. CONCLUSION: The patients with unilateral frontal LGGs have altered global and nodal network metrics and decreased inter- and intra-hemispheric connectivity. These topological alterations may be involved in functional impairment and compensation of patients.

Intra-articular injection of platelet-rich plasma vs hyaluronic acid as an adjunct to TMJ arthrocentesis: A systematic review and meta-analysis.

OBJECTIVE: We aimed to find out if there is any difference in outcomes with the use of platelet-rich plasma (PRP) or hyaluronic acid (HA) intra-articular injections after temporomandibular joint arthrocentesis. METHODS: A systematic search of the electronic databases of PubMed, Embase, and Scopus was undertaken up to 5th May 2023. Randomized controlled trials (RCTs) comparing PRP with HA after TMJ arthrocentesis were included. RESULTS: Seven RCTs were eligible. Pooled analysis failed to demonstrate any significant difference in MMO between PRP and HA groups at 1 month (MD: 0.21 95 % CI: -1.29, 1.70), 3 months (MD: 0.92 95 % CI: -2.96, 4.80), and 6 months (MD: -0.05 95 % CI: -2.08, 1.97). The inter-study heterogeneity was high with I2 values of 85 %, 98 %, and 81 % respectively. Similarly, there was no statistically significant difference in pain scores between the PRP and HA groups at 1 month (MD: 0.42 95 % CI: -2.25, 3.10), 3 months (MD: 0.90 95 % CI: -1.60, 3.41), and 6 months (MD: 0.06 95 % CI: -0.92, 1.04) with inter-study heterogeneity of 99 %, 99 %, and 92 % respectively. CONCLUSION: Intra-articular use of PRP or HA after TMJ arthrocentesis may lead to comparable clinical outcomes. The current evidence is low-quality and fraught with high heterogeneity.

The differentiation courses of the Tfh cells: a new perspective on autoimmune disease pathogenesis and treatment.

The follicular helper T cells are derived from CD4+T cells, promoting the formation of germinal centers and assisting B cells to produce antibodies. This review describes the differentiation process of Tfh cells from the perspectives of the initiation, maturation, migration, efficacy, and subset classification of Tfh cells, and correlates it with autoimmune disease, to provide information for researchers to fully understand Tfh cells and provide further research ideas to manage immune-related diseases.

Amorphous porous organic polymers containing main group elements.

Amorphous porous organic polymers (aPOPs) are a type of highly crosslinked polymers. These polymers are generally constructed from rigid organic building blocks, which have become an important subclass of POPs with diverse applications. In the early stage of development, a wide range of carbon-based building blocks and network forming chemistry afforded a large library of aPOPs with rich structures and properties. Recently, implanting main group elements with diverse geometric structures and electronic configurations into aPOPs has proven to be a useful tool to fine-tune the structures and properties of these polymers. Herein, we outline the recent advances in the field of main group (MG)-aPOPs where main-group elements either played unique roles in tuning the structures and properties of MG-aPOPs, or offered new strategies in the synthesis of MG-aPOPs. Furthermore, this Review discusses various challenges remaining in the field from the perspectives of synthetic strategies and characterization techniques, and presents some specific studies that may potentially address the challenges.

Mesoporous Nickel Sulfide Microsphere Encapsulated in Nitrogen, Sulfur Dual-Doped Carbon with Large Subsurface Region for Enhanced Sodium Storage.

Nickel sulfides are promising anode candidates in sodium ion batteries (SIBs) due to high capacity and abundant reserves. However, their applications are restricted by poor cycling stability and slow reaction kinetics. Thus, mesoporous nickel sulfide microsphere encapsulated in nitrogen, sulfur dual-doped carbon (MNS@NSC) is prepared. The packaged structure and carbon matrix restrain the volume variation together, the N, S dual-doping improves the electronic conductivity and offers extra active sites for sodium storage. Ex-situ X-ray diffraction  appeals copper collector adsorbs polysulfide to inhibit the polysulfide accumulation and enhance conductivity. Moreover, the large subsurface attributed to C-S-S-C bonding further boosts pseudocapacitive capacity, conducive to charge transfer. As a result, MNS@NSC delivers a high reversible capacity of 640.2 mAh g-1 after 100 cycles at 0.1 A g-1 , an excellent rate capability (569.8 mAh g-1 at 5 A g-1 ), and a remained capacity of 513.8 mAh g-1 after undergoing 10000 circulations at 10 A g-1 . The MNS@NSC|| Na3 V2 (PO4 )3 full cell shows a cycling performance of specific capacity of 230.8 mAh g-1 after 100 cycles at 1 A g-1 . This work puts forward a valid strategy of combing structural design and heteroatom doping to synthesize high-performance nickel sulfide materials in SIBs.

The Role of Exosomes from Mesenchymal Stem Cells in Spinal Cord Injury: A Systematic Review.

Spinal cord injury (SCI) is a serious nervous system disease that usually leads to the impairment of the motor, sensory, and autonomic nervous functions of the spinal cord, and it places a heavy burden on families and healthcare systems every year. Due to the complex pathophysiological mechanism of SCI and the poor ability of neurons to regenerate, the current treatment scheme has very limited effects on the recovery of spinal cord function. In addition, due to their unique advantages, exosomes can be used as carriers for cargo transport. In recent years, some studies have confirmed that treatment with mesenchymal stem cells (MSCs) can promote the recovery of SCI nerve function. The therapeutic effect of MSCs is mainly related to exosomes secreted by MSCs, and exosomes may have great potential in SCI therapy. In this review, we summarized the repair mechanism of mesenchymal stem cells-derived exosomes (MSCs-Exos) in SCI treatment and discussed the microRNAs related to SCI treatment based on MSCs-Exos and their mechanism of action, which is helpful to further understand the role of exosomes in SCI.

BTSC-TNAS: A neural architecture search-based transformer for brain tumor segmentation and classification.

Glioblastoma (GBM), isolated brain metastasis (SBM), and primary central nervous system lymphoma (PCNSL) possess a high level of similarity in histomorphology and clinical manifestations on multimodal MRI. Such similarities have led to challenges in the clinical diagnosis of these three malignant tumors. However, many existing models solely focus on either the task of segmentation or classification, which limits the application of computer-aided diagnosis in clinical diagnosis and treatment. To solve this problem, we propose a multi-task learning transformer with neural architecture search (NAS) for brain tumor segmentation and classification (BTSC-TNAS). In the segmentation stage, we use a nested transformer U-shape network (NTU-NAS) with NAS to directly predict brain tumor masks from multi-modal MRI images. In the tumor classification stage, we use the multiscale features obtained from the encoder of NTU-NAS as the input features of the classification network (MSC-NET), which are integrated and corrected by the classification feature correction enhancement (CFCE) block to improve the accuracy of classification. The proposed BTSC-TNAS achieves an average Dice coefficient of 80.86% and 87.12% for the segmentation of tumor region and the maximum abnormal region in clinical data respectively. The model achieves a classification accuracy of 0.941. The experiments performed on the BraTS 2019 dataset show that the proposed BTSC-TNAS has excellent generalizability and can provide support for some challenging tasks in the diagnosis and treatment of brain tumors.

Structural insights into the conformational changes of BTR1/SLC4A11 in complex with PIP2.

BTR1 (SLC4A11) is a NH3 stimulated H+ (OH-) transporter belonging to the SLC4 family. Dysfunction of BTR1 leads to diseases such as congenital hereditary endothelial dystrophy (CHED) and Fuchs endothelial corneal dystrophy (FECD). However, the mechanistic basis of BTR1 activation by alkaline pH, transport activity regulation and pathogenic mutations remains elusive. Here, we present cryo-EM structures of human BTR1 in the outward-facing state in complex with its activating ligands PIP2 and the inward-facing state with the pathogenic R125H mutation. We reveal that PIP2 binds at the interface between the transmembrane domain and the N-terminal cytosolic domain of BTR1. Disruption of either the PIP2 binding site or protonation of PIP2 phosphate groups by acidic pH can transform BTR1 into an inward-facing conformation. Our results provide insights into the mechanisms of how the transport activity and conformation changes of BTR1 are regulated by PIP2 binding and interaction of TMD and NTD.