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Finding birefringent material with giant optical anisotropy is urgent for photonic applications. However, the application of existing birefringent materials is limited by restricted transparency wavelength or optical anisotropy. The process of trial and error is time‐consuming and inefficient in terms of resources, especially when compared to the vast design spaces for materials. The immense chemical space necessitates a high‐throughput approach for giant optical anisotropy materials design. Here, a full‐applied wavelength map of birefringent materials is constructed, from conventional visible expanding to ultraviolet and deep‐ultraviolet region by high‐throughput screening methods. And highlighted 579 materials birefringence exceeds the one of available commercial birefringent materials in corresponding regions. Significantly, 54 materials possess giant birefringence above 0.5 @1064 nm. Via developing a high‐throughput screening technique for microscopic structures with given coordination number, the study further characterizes a comprehensive map of functional modules featuring birefringent activeness. According to the macroscopic and microscopic dual‐functional maps, and a novel crystal Li2(HC3N3S3)∙5H2O with birefringence difference 0.532 @546 nm is designed and synthesized successfully. This study identifies desired materials with high performance in unheeded chemical spaces. [ABSTRACT FROM AUTHOR]

Definition: This Process Synthesis and Intensification (PS+I) framework uses a low-aggregation superstructure to solve the synthesis problem, and its solution is interpreted and translated into a task-integrated intensified process. Then, the process is post-optimized to find a better balance between operation and equipment costs. The solution leads to novel and counterintuitive intensified processes with low cost and energy requirements. [ABSTRACT FROM AUTHOR]

This Process Synthesis and Intensification (PS+I) framework uses a low-aggregation superstructure to solve the synthesis problem, and its solution is interpreted and translated into a task-integrated intensified process. Then, the process is post-optimized to find a better balance between operation and equipment costs. The solution leads to novel and counterintuitive intensified processes with low cost and energy requirements.

In forests, microbial populations in the soil can directly influence the decomposition of carbon from surface plants, promoting carbon storage and stability. However, in sustainable forest management, it is still unclear how soil microorganisms under different plantation types affect organic carbon sequestration and whether the mechanisms of influence are the same. In this research, we focused on four mixed forests and pure Pinus massoniana-planted forest in the state-owned forest farm of Dushan County. Three replicated plots were set up for each model, and soil samples were collected from different layers (0–20 cm, 20–40 cm, and 40–60 cm), totaling 45 samples. We elucidated the effects of soil microorganisms on carbon sequestration under five mixed modification models of P. massoniana and further explored the mechanisms by which microbial functional communities regulate soil carbon sequestration under different mixed models through molecular sequencing and collinear network analysis. Variance analysis indicated that the soil organic carbon (SOC) of the same soil layer varied significantly, and there were also significant differences in the composition of soil bacterial and fungal microbial communities. Moreover, the bacterial community was more sensitive to changes in the vegetation environment, while the fungal community structure was more resistant to changes in the soil environment. Correlation analysis indicated that the diversity and composition of the bacterial community had more positive effects on soil organic carbon than those of the fungal community. Linear fitting and redundancy analysis (RDA) showed that particulate organic carbon (POC) in soil had the strongest correlation with SOC content. Soil microorganisms affected the storage and stability of soil carbon mainly by regulating the conversion of litter (carbon sources) into POC. The soil environment of different mixed models had different effects on soil carbon accumulation. Both correlation and collinearity network analyses indicated that soil microbial functional groups could enhance carbon storage by regulating readily oxidizable carbon (EOC) and POC content in mixed forest plantations. The results of our study provide a sound basis for replanting a reasonable forest model structure to improve forest carbon storage. [ABSTRACT FROM AUTHOR]

Pain is a pervasive symptom in lung cancer patients during the onset of the disease. This study aims to investigate the connectivity disruption patterns of the whole-brain functional network in lung cancer patients with cancer pain (CP+). We constructed individual whole-brain, region of interest (ROI)-level functional connectivity (FC) networks for 50 CP+ patients, 34 lung cancer patients without pain-related complaints (CP−), and 31 matched healthy controls (HC). Then, a ROI-based FC analysis was used to determine the disruptions of FC among the three groups. The relationships between aberrant FCs and clinical parameters were also characterized. The ROI-based FC analysis demonstrated that hypo-connectivity was present both in CP+ and CP− patients compared to HC, which were particularly clustered in the somatomotor and ventral attention, frontoparietal control, and default mode modules. Notably, compared to CP− patients, CP+ patients had hyper-connectivity in several brain regions mainly distributed in the somatomotor and visual modules, suggesting these abnormal FC patterns may be significant for cancer pain. Moreover, CP+ patients also showed increased intramodular and intermodular connectivity strength of the functional network, which could be replicated in cancer stage IV and lung adenocarcinoma. Finally, abnormal FCs within the prefrontal cortex and somatomotor cortex were positively correlated with pain intensity and pain duration, respectively. These findings suggested that lung cancer patients with cancer pain had disrupted connectivity in the intrinsic brain functional network, which may be the underlying neuroimaging mechanisms. [ABSTRACT FROM AUTHOR]

Sorting out the requirements for intelligent functions is the prerequisite and foundation of the top-level design for the development of intelligent ships. In light of the development of inland intelligent ships for 2030, 2035, and 2050, based on the analysis of the division of intelligent ship functional modules by international representative classification societies and relevant research institutions, eight necessary functional modules have been proposed: intelligent navigation, intelligent hull, intelligent engine room, intelligent energy efficiency management, intelligent cargo management, intelligent integration platform, remote control, and autonomous operation. Taking the technical realization of each functional module as the goal, this paper analyzes the status quo and development trend of related intelligent technologies and their feasibility and applicability when applied to each functional module. At the same time, it clarifies the composition of specific functional elements of each functional module, puts forward the stage goals of China's inland intelligent ship development and the specific functional requirements of different modules under each stage, and provides reference for the Chinese government to subsequently formulate the top-level design development planning and implementation path of inland waterway intelligent ships. [ABSTRACT FROM AUTHOR]

Copyright of Architectural Journal / Jian Zhu Xue Bao is the property of Architectural Journal Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Scientific modeling is a syllogistic system of definitive premise, sound inference and consistent explanation to understand, define, quantify, visualize or simulate feature of the target. Single-model is defined to an informative representation for identifying a property of a target object/phenomenon, and meta-model integrates the relevant single-models to explain phenomenological realities. Human recognition-behavioral adaptation is an information-metabolism system to maintain homeostasis of human-self, and that has been investigated in neurological, psychiatric and psychological aspects. I analyzed human recognition-behavioral adaptation-system via scientific modeling. Neurological meta-model of human recognition-behavioral adaptation system was synthesized as complex-network of the functional neuronal modules, and the meta-model was integrated to Mentality-model in the psychiatric aspect, and to Personality-model in the psychological aspect. The integrated meta-models successfully explained phenomenological realities in the aspects. From the above, I comprehended that the meta-model of human recognition-behavioral adaptation-system has been developed to Biopsychosocial model integrating the biological, psychological and socio-environmental factors. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, two laboratory-scale SBBR reactors were established in a plateau habitat. Using high flux sequencing, the SBBR process was compared by natural sediment and autotrophic sludge to characterize the functional modules and functional genes of carbon, nitrogen, and phosphorus metabolism under different working conditions and to analyze the reaction mechanism. The results showed that all the functional modules of carbon metabolism and nitrogen metabolism were found in the SBBR process, except for methane metabolism, which occurred at 25 °C in tank 2, the functional modules related to methane metabolism are enhanced at all working conditions. Except for methane metabolism, all functional genes in tank 2 are inhibited by different working conditions, whereas tank 1 shows a slight enhancement. The different working conditions in nitrogen metabolism demonstrate inhibition of functional modules and functional genes in both tanks. Oxidative phosphorylation was missing five functional modules, except for M00153, where only two genes, K00424 and K22501, are missing, all of the required genes are missing in the other four functional modules. Overall the different conditions demonstrated some inhibition in both reaction tanks of the SBBR process. It is preferable to use self-cultivated sludge for membrane acclimation when operating the SBBR process in a plateau habitat. The findings of this study can be used to further research microbial carbon, nitrogen, and phosphorus metabolism mechanisms in SBBR processes in plateau habitats. [ABSTRACT FROM AUTHOR]

In forests, microbial populations in the soil can directly influence the decomposition of carbon from surface plants, promoting carbon storage and stability. However, in sustainable forest management, it is still unclear how soil microorganisms under different plantation types affect organic carbon sequestration and whether the mechanisms of influence are the same. In this research, we focused on four mixed forests and pure Pinus massoniana-planted forest in the state-owned forest farm of Dushan County. Three replicated plots were set up for each model, and soil samples were collected from different layers (0–20 cm, 20–40 cm, and 40–60 cm), totaling 45 samples. We elucidated the effects of soil microorganisms on carbon sequestration under five mixed modification models of P. massoniana and further explored the mechanisms by which microbial functional communities regulate soil carbon sequestration under different mixed models through molecular sequencing and collinear network analysis. Variance analysis indicated that the soil organic carbon (SOC) of the same soil layer varied significantly, and there were also significant differences in the composition of soil bacterial and fungal microbial communities. Moreover, the bacterial community was more sensitive to changes in the vegetation environment, while the fungal community structure was more resistant to changes in the soil environment. Correlation analysis indicated that the diversity and composition of the bacterial community had more positive effects on soil organic carbon than those of the fungal community. Linear fitting and redundancy analysis (RDA) showed that particulate organic carbon (POC) in soil had the strongest correlation with SOC content. Soil microorganisms affected the storage and stability of soil carbon mainly by regulating the conversion of litter (carbon sources) into POC. The soil environment of different mixed models had different effects on soil carbon accumulation. Both correlation and collinearity network analyses indicated that soil microbial functional groups could enhance carbon storage by regulating readily oxidizable carbon (EOC) and POC content in mixed forest plantations. The results of our study provide a sound basis for replanting a reasonable forest model structure to improve forest carbon storage.

Background: A feature of higher plants is the modular principle of body organisation. One of these conservative morphological modules that regulate plant growth, histogenesis and organogenesis is meristems—structures that contain pools of stem cells and are generally organised according to a common principle. Basic content: The development of meristems is under the regulation of molecular modules that contain conservative interacting components and modulate the expression of target genes depending on the developmental context. In this review, we focus on two molecular modules that act in different types of meristems. The WOX-CLAVATA module, which includes the peptide ligand, its receptor and the target transcription factor, is responsible for the formation and control of the activity of all meristem types studied, but it has its own peculiarities in different meristems. Another regulatory module is the so-called florigen-activated complex, which is responsible for the phase transition in the shoot vegetative meristem (e.g., from the vegetative shoot apical meristem to the inflorescence meristem). Conclusions: The review considers the composition and functions of these two functional modules in different developmental programmes, as well as their appearance, evolution and use in plant breeding. [ABSTRACT FROM AUTHOR]

Sorting out the requirements for intelligent functions is the prerequisite and foundation of the top-level design for the development of intelligent ships. In light of the development of inland intelligent ships for 2030, 2035, and 2050, based on the analysis of the division of intelligent ship functional modules by international representative classification societies and relevant research institutions, eight necessary functional modules have been proposed: intelligent navigation, intelligent hull, intelligent engine room, intelligent energy efficiency management, intelligent cargo management, intelligent integration platform, remote control, and autonomous operation. Taking the technical realization of each functional module as the goal, this paper analyzes the status quo and development trend of related intelligent technologies and their feasibility and applicability when applied to each functional module. At the same time, it clarifies the composition of specific functional elements of each functional module, puts forward the stage goals of China’s inland intelligent ship development and the specific functional requirements of different modules under each stage, and provides reference for the Chinese government to subsequently formulate the top-level design development planning and implementation path of inland waterway intelligent ships.

The analysis of time-varying activity and connectivity patterns (i.e., the chronnectome) using resting-state magnetic resonance imaging has become an important part of ongoing neuroscience discussions. The majority of previous work has focused on variations of temporal coupling among fixed spatial nodes or transition of the dominant activity/connectivity pattern over time. Here, we introduce an approach to capture spatial dynamics within functional domains (FDs), as well as temporal dynamics within and between FDs. The approach models the brain as a hierarchical functional architecture with different levels of granularity, where lower levels have higher functional homogeneity and less dynamic behavior and higher levels have less homogeneity and more dynamic behavior. First, a high-order spatial independent component analysis is used to approximate functional units. A functional unit is a pattern of regions with very similar functional activity over time. Next, functional units are used to construct FDs. Finally, functional modules (FMs) are calculated from FDs, providing an overall view of brain dynamics. Results highlight the spatial fluidity within FDs, including a broad spectrum of changes in regional associations, from strong coupling to complete decoupling. Moreover, FMs capture the dynamic interplay between FDs. Patients with schizophrenia show transient reductions in functional activity and state connectivity across several FDs, particularly the subcortical domain. Activity and connectivity differences convey unique information in many cases (e.g., the default mode) highlighting their complementarity information. The proposed hierarchical model to capture FD spatiotemporal variations provides new insight into the macroscale chronnectome and identifies changes hidden from existing approaches.

The problem of computerized calculation of the optimal required diet for an individual who belongs to a certain category of the population and receives various physical loads is being considered. The concept of rational nutrition and the conditions for its observance have been clarified. A general classification of population categories was conducted in relation to the food caloric content daily needs. The existing approaches to calculating the caloric content of food using computer programs are analyzed. Their shortcomings are pointed out. In addition to the calorie content of foods, the supply of vitamins to the body and the individual preferences of the user are taken into account. A project that considers the specific nutrition needs of a person is proposed. A functional structure of the program is defined. An algorithm that calculates the calorie content of a finished dish is described. The choice of the software development environment is rationalized. [ABSTRACT FROM AUTHOR]

Background: A feature of higher plants is the modular principle of body organisation. One of these conservative morphological modules that regulate plant growth, histogenesis and organogenesis is meristems—structures that contain pools of stem cells and are generally organised according to a common principle. Basic content: The development of meristems is under the regulation of molecular modules that contain conservative interacting components and modulate the expression of target genes depending on the developmental context. In this review, we focus on two molecular modules that act in different types of meristems. The WOX-CLAVATA module, which includes the peptide ligand, its receptor and the target transcription factor, is responsible for the formation and control of the activity of all meristem types studied, but it has its own peculiarities in different meristems. Another regulatory module is the so-called florigen-activated complex, which is responsible for the phase transition in the shoot vegetative meristem (e.g., from the vegetative shoot apical meristem to the inflorescence meristem). Conclusions: The review considers the composition and functions of these two functional modules in different developmental programmes, as well as their appearance, evolution and use in plant breeding.

Recently, the population aging has drawn extensive attention in China. The establishment of senior service centers in communities helps older people to age well in a familiar living environment. In this study, we found, through on-site mapping, non-participant observation, and questionnaire interviews, that senior service centers in old communities generally suffer from a disconnect between function and demand, insufficient usable area, and large differences in space utilization efficiency. We propose 3 types of functional configuration models for senior service centers to fulfill the needs of different types of older people. Further, we suggest that the potential space in old communities be fully explored to expand the usable area of the senior service center. Through space-sharing, time - sharing and crowdsharing, linking the senior service center with other service facilities in the community to improve the efficiency of the use of the facilities. [ABSTRACT FROM AUTHOR]

Stem secondary xylem produced by cambial division and differentiation is the main source of tree biomass. The secondary xylem formation involves a complex transcriptional regulatory network, however, the underlying mechanism is still being explored. Here, we report that PagHAM4a and PagHAM4b are positive regulators of cambial differentiation into secondary xylem in hybrid poplar (Populus alba × Populus glandulosa clone 84K). Overexpression of PagHAM4a and PagHAM4b enhanced cambial activity and increased the number of secondary xylem cells in the stems of poplar. By contrast, single or double mutations of PagHAM4a and PagHAM4b by CRISPR-Cas9 decreased cambial activity, leading to a significant reduction of secondary xylem. Neither overexpression nor mutation of the two genes affected the size of vessels and fibers in xylem. Both PagHAM4a- and PagHAM4b-regulated gene networks were mainly centered in the stage when cambium had just initiated secondary growth, but the molecular networks regulated by the two genes were distinct. Further analysis revealed that PagSCL21 and PagTCP20 are direct targets of PagHAM4a and PagHAM4b, respectively, and their overexpression also promoted cambial differentiation into secondary xylem. Taken together, we identified two novel key regulatory modules in poplar, PagHAM4a-PagSCL21 and PagHAM4b-PagTCP20, which provide new insights into the mechanism of secondary xylem formation in trees., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Society for Experimental Biology.)

Cow milk consumption (CMC) and alterations of gut bacterial composition are proposed to be closely related to human health and disease. Our research aims to investigate the changes in human gut microbial composition in Chinese peri-/postmenopausal women with different CMC habits. A total of 517 subjects were recruited and questionnaires about their CMC status were collected; 394 subjects were included in the final analyses. Fecal samples were used for studying gut bacterial composition. All the subjects were divided into a control group (n = 248) and a CMC group (n = 146) according to their CMC status. Non-parametric tests and LEfSe at different taxonomic levels were used to reveal differentially abundant taxa and functional categories across different CMC groups. Relative abundance (RA) of one phylum (p_Actinobacteria), three genera (g_Bifidobacterium, g_Anaerostipes, and g_Bacteroides), and 28 species diversified significantly across groups. Specifically, taxa g_Anaerostipes (p < 0.01), g_Bacteroides (p < 0.05), s_Anaerostipes_hadrus (p < 0.01), and s_Bifidobacterium_pseudocatenulatum (p < 0.01) were positively correlated with CMC levels, but p_Actinobacteria (p < 0.01) and g_Bifidobacterium (p < 0.01) were negatively associated with CMC levels. KEGG module analysis revealed 48 gut microbiome functional modules significantly (p < 0.05) associated with CMC, including Vibrio cholerae pathogenicity signature, cholera toxins (p = 9.52e-04), and cephamycin C biosynthesis module (p = 0.0057), among others. In conclusion, CMC was associated with changes in gut microbiome patterns including beta diversity and richness of some gut microbiota. The alterations of certain bacteria including g_Anaerostipes and s_Bifidobacterium_pseudocatenulatum in the CMC group should be important for human health. This study further supports the biological value of habitual cow milk consumption. [ABSTRACT FROM AUTHOR]

Cancer is a lethal disease that drew the entire world over the past decades. Currently, numerous researches focused on these cancer treatments. Most familiar among them is the targeted therapy; a customized treatment type depends on the cancer drug targets. Further, the selection of targets is a quite sensitive task. The computational approaches are lagging in this field. This paper is intended to propose an optimized multi-functional score–based co-clustering with MapReduce (MR-CoCopt) approach for drug target module mining with optimal functional score set selection. This approach uses biological functional measures for co-clustering, MapReduce framework for handling redundant modules and complex protein interaction network (PIN), and non-swarm intelligence algorithm-bladderworts suction for optimal functional score set selection. It extracts the cancer-specific drug target modules in protein interaction networks. The protein complex coverage of the results is compared with the existing approach. The biological significance of the results is analyzed for the presence of cancer drug targets and drug target characteristics. From these results, novel cancer drug target modules are suggested for the targeted therapy and the active pharmaceutical drugs for these modules are also highlighted. [ABSTRACT FROM AUTHOR]

Plant tissues are distinguished by their gene expression patterns, which can help identify tissue-specific highly expressed genes and their differential functional modules. For this purpose, large-scale soybean transcriptome samples were collected and processed starting from raw sequencing reads in a uniform analysis pipeline. To address the gene expression heterogeneity in different tissues, we utilized an adversarial deconfounding autoencoder (AD-AE) model to map gene expressions into a latent space and adapted a standard unsupervised autoencoder (AE) model to help effectively extract meaningful biological signals from the noisy data. As a result, four groups of 1,743, 914, 2,107, and 1,451 genes were found highly expressed specifically in leaf, root, seed and nodule tissues, respectively. To obtain key transcription factors (TFs), hub genes and their functional modules in each tissue, we constructed tissue-specific gene regulatory networks (GRNs), and differential correlation networks by using corrected and compressed gene expression data. We validated our results from the literature and gene enrichment analysis, which confirmed many identified tissue-specific genes. Our study represents the largest gene expression analysis in soybean tissues to date. It provides valuable targets for tissue-specific research and helps uncover broader biological patterns. Code is publicly available with open source at https://github.com/LingtaoSu/SoyMeta. [ABSTRACT FROM AUTHOR]

Abstract Background Pathway analysis is widely applied in transcriptome analysis. Given certain transcriptomic changes, current pathway analysis tools tend to search for the most impacted pathways, which provides insight into underlying biological mechanisms. Further refining of the enriched pathways and extracting functional modules by “crosstalk” analysis have been proposed. However, the upstream/downstream relationships between the modules, which may provide extra biological insights such as the coordination of different functional modules and the signal transduction flow have been ignored. Results To quantitatively analyse the upstream/downstream relationships between functional modules, we developed a novel GEne Set Topological Impact Analysis (GESTIA), which could be used to assemble the enriched pathways and functional modules into a super-module with a topological structure. We showed the advantages of this analysis in the exploration of extra biological insight in addition to the individual enriched pathways and functional modules. Conclusions GESTIA can be applied to a broad range of pathway/module analysis result. We hope that GESTIA may help researchers to get one additional step closer to understanding the molecular mechanism from the pathway/module analysis results.

Abstract Background Sepsis is a fatal disease referring to the presence of a known or strongly suspected infection coupled with systemic and uncontrolled immune activation causing multiple organ failure. However, current knowledge of the role of lncRNAs in sepsis is still extremely limited. Methods We performed an in silico investigation of the gene coexpression pattern for the patients response to all-cause sepsis in consecutive intensive care unit (ICU) admissions. Sepsis coexpression gene modules were identified using WGCNA and enrichment analysis. lncRNAs were determined as sepsis biomarkers based on the interactions among lncRNAs and the identified modules. Results Twenty-three sepsis modules, including both differentially expressed modules and prognostic modules, were identified from the whole blood RNA expression profiling of sepsis patients. Five lncRNAs, FENDRR, MALAT1, TUG1, CRNDE, and ANCR, were detected as sepsis regulators based on the interactions among lncRNAs and the identified coexpression modules. Furthermore, we found that CRNDE and MALAT1 may act as miRNA sponges of sepsis related miRNAs to regulate the expression of sepsis modules. Ultimately, FENDRR, MALAT1, TUG1, and CRNDE were reannotated using three independent lncRNA expression datasets and validated as differentially expressed lncRNAs. Conclusion The procedure facilitates the identification of prognostic biomarkers and novel therapeutic strategies of sepsis. Our findings highlight the importance of transcriptome modularity and regulatory lncRNAs in the progress of sepsis.

ABSTRACT Viruses are the most abundant living entities in marine ecosystems, playing critical roles in altering the structure and function of microbial communities and driving ocean biogeochemistry. Phages that infect Roseobacter clade-affiliated (RCA) cluster strains are an important component of marine viral communities. Here, we characterize the genome sequences of two new RCA phages, CRP-9 and CRP-13, which infect RCA strain FZCC0023. Genomic analysis reveals that CRP-9 and CRP-13 represent a novel evolutionary lineage of marine phages. They both have a DNA replication module most similar to those in Cobavirus group phages. In contrast, their morphogenesis and packaging modules are distinct from those in cobaviruses but homologous to those in HMO-2011-type phages. The genomic architecture of CRP-9 and CRP-13 suggests a genomic recombination event between distinct phage groups. Metagenomic data sets were examined for metagenome-assembled viral genomes (MAVGs) with similar recombinant genome architectures. Fifteen CRP-9-type MAVGs were identified from marine viromes. Additionally, 158 MAVGs were identified containing HMO-2011-type morphogenesis and packaging modules with other types of DNA replication genes, providing more evidence that recombination between different phage groups is a major driver of phage evolution. Altogether, this study significantly expands the understanding of diversity and evolution of marine roseophages. Meanwhile, the analysis of these novel RCA phages and MAVGs highlights the critical role of recombination in shaping phage diversity. These phage sequences are valuable resources for inferring the evolutionary connection of distinct phage groups. IMPORTANCE Diversity and evolution of phages that infect the relatively slow-growing but dominant Roseobacter lineages are largely unknown. In this study, RCA phages CRP-9 and CRP-13 have been isolated on a Roseobacter RCA strain and shown to have a unique genomic architecture, which appears to be the result of a recombination event. CRP-9 and CRP-13 have a DNA replication module most similar to those in Cobavirus group phages and morphogenesis and packaging modules most similar to those in HMO-2011-type phages. HMO-2011-type morphogenesis and packaging modules are found in combination with distinct types of DNA replication genes, suggesting compatibility with various DNA replication modules. Altogether, this study contributes toward a better understanding of marine viral diversity and evolution.

ABSTRACT Highly neurotoxic methylmercury (MeHg) accumulates in marine organisms, thereby negatively affecting human and environmental health. Recent studies have revealed that oceanic prokaryotes harboring the hgcAB gene pair are involved in Hg methylation. Presently, little is known about the distribution and phylogeny of these genes in distinct oceanic regions of the western North Pacific. In this study, we used metagenomics to survey the distribution of hgcAB genes in the seawater columns of the subarctic Oyashio region and the subtropical Kuroshio region. The hgcAB genes were detected in the MeHg-rich offshore mesopelagic layers of both the Oyashio region, which is a highly productive area in the western North Pacific, and the Kuroshio region, which has low productivity. Comparative analysis revealed that hgcAB genes belonging to the Nitrospina-like lineage were dominant in the MeHg-rich mesopelagic layers of both regions. These results indicate that Nitrospina-like bacteria are the dominant Hg methylators in the mesopelagic layers throughout the western North Pacific. IMPORTANCE MeHg is highly neurotoxic and accumulates in marine organisms. Thus, understanding MeHg production in seawater is critical for environmental and human health. Recent studies have shown that microorganisms harboring mercury-methylating genes (hgcA and hgcB) are involved in MeHg production in several marine environments. Knowing the distribution and phylogeny of hgcAB genes in seawater columns can facilitate assessment of microbial MeHg production in the ocean. We report that hgcAB genes affiliated with the microaerophilic Nitrospina lineage were detected in the MeHg-rich mesopelagic layers of two hydrologically distinct oceanic regions of the western North Pacific. This finding facilitates understanding of the microbial Hg methylation and accumulation in seawater columns of the western North Pacific.

Abstract Understanding and exploring the functional modules (FMs) consisting of local atomic groups can promote the development of the materials with functional performances. Oxygen‐containing tetrahedral modules are popular in deep‐ultraviolet (DUV) optical materials, but their weak optical anisotropy is adverse to birefringence. Here, the fluorooxosulfate group is proved as a new birefringence‐enhanced FM for the first time. The birefringence of fluorooxosulfates can be 4.8–15.5 times that of sulfates with the same metal cations while maintaining a DUV band gap. The polarizing microscope measurement confirms the birefringence enhancement by using the millimeter crystals experimentally. The theoretical studies from micro and macro levels further reveal a novel universal strategy that the fluorine induced anisotropic electronic distribution in fluorooxo–tetrahedral group is responsible for the enhancement of birefringence. This study will guide the future discovery of DUV optical materials with enlarged birefringence.

Understanding and exploring the functional modules (FMs) consisting of local atomic groups can promote the development of the materials with functional performances. Oxygen‐containing tetrahedral modules are popular in deep‐ultraviolet (DUV) optical materials, but their weak optical anisotropy is adverse to birefringence. Here, the fluorooxosulfate group is proved as a new birefringence‐enhanced FM for the first time. The birefringence of fluorooxosulfates can be 4.8–15.5 times that of sulfates with the same metal cations while maintaining a DUV band gap. The polarizing microscope measurement confirms the birefringence enhancement by using the millimeter crystals experimentally. The theoretical studies from micro and macro levels further reveal a novel universal strategy that the fluorine induced anisotropic electronic distribution in fluorooxo–tetrahedral group is responsible for the enhancement of birefringence. This study will guide the future discovery of DUV optical materials with enlarged birefringence. [ABSTRACT FROM AUTHOR]

In order to achieve a high degree of visual realism in surgery simulation, we present a virtual surgery system framework, which is based on point primitives for the virtual surgery scene rendering and the biomechanical calculation of the soft tissue. To embody the superiority of this framework, two virtual surgery systems based on point primitives we developed are exhibited in this paper. Six critical functional modules were selected as representative of basic and advanced virtual surgery skill. These modules were: 1) point-based texture mapping; 2) deformation simulation; 3) cutting simulation; 4) tearing simulation; 5) dynamic texture mapping; and 6) 3-D display. These modules were elaborated by including working principle, execution process, and the performance of the algorithm. The experimental results have shown that point primitives-based virtual surgery systems obtained higher performance in terms of computational efficiency and rendering effect than traditional meshes-based virtual surgery system.

Abstract Background Clustering molecular network is a typical method in system biology, which is effective in predicting protein complexes or functional modules. However, few studies have realized that biological molecules are spatial-temporally regulated to form a dynamic cellular network and only a subset of interactions take place at the same location in cells. Results In this study, considering the subcellular localization of proteins, we first construct a co-localization human protein interaction network (PIN) and systematically investigate the relationship between subcellular localization and biological functions. After that, we propose a Locational and Topological Overlap Model (LTOM) to preprocess the co-localization PIN to identify functional modules. LTOM requires the topological overlaps, the common partners shared by two proteins, to be annotated in the same localization as the two proteins. We observed the model has better correspondence with the reference protein complexes and shows more relevance to cancers based on both human and yeast datasets and two clustering algorithms, ClusterONE and MCL. Conclusion Taking into consideration of protein localization and topological overlap can improve the performance of module detection from protein interaction networks.

Abstract Background Molecular profiles change in response to perturbations. These changes are coordinated into functional modules via regulatory interactions. The genes and their products within a functional module are expected to be differentially expressed in a manner coherent with their regulatory network. This perspective presents a promising approach to increase precision in detecting differential signals as well as for describing differential regulatory signals within the framework of a priori knowledge about the underlying network, and so from a mechanistic point of view. Results We present Coherent Network Expression (CoNE), an effective procedure for identifying differentially activated functional modules in molecular interaction networks. Differential gene expression is chosen as example, and differential signals coherent with the regulatory nature of the network are identified. We apply our procedure to systematically simulated data, comparing its performance to alternative methods. We then take the example case of a transcription regulatory network in the context of particle-induced pulmonary inflammation, recapitulating and proposing additional candidates to previously obtained results. CoNE is conveniently implemented in an R-package along with simulation utilities. Conclusion Combining coherent interactions with error control on differential gene expression results in uniformly greater specificity in inference than error control alone, ensuring that captured functional modules constitute real findings.

SUMMARY: Autophagy is a self‐degradative process that is crucial for maintaining cellular homeostasis by removing damaged cytoplasmic components and recycling nutrients. Such an evolutionary conserved proteolysis process is regulated by the autophagy‐related (Atg) proteins. The incomplete understanding of plant autophagy proteome and the importance of a proteome‐wide understanding of the autophagy pathway prompted us to predict Atg proteins and regulators in Arabidopsis. Here, we developed a systems‐level algorithm to identify autophagy‐related modules (ARMs) based on protein subcellular localization, protein–protein interactions, and known Atg proteins. This generates a detailed landscape of the autophagic modules in Arabidopsis. We found that the newly identified genes in each ARM tend to be upregulated and coexpressed during the senescence stage of Arabidopsis. We also demonstrated that the Golgi apparatus ARM, ARM13, functions in the autophagy process by module clustering and functional analysis. To verify the in silico analysis, the Atg candidates in ARM13 that are functionally similar to the core Atg proteins were selected for experimental validation. Interestingly, two of the previously uncharacterized proteins identified from the ARM analysis, AGD1 and Sec14, exhibited bona fide association with the autophagy protein complex in plant cells, which provides evidence for a cross‐talk between intracellular pathways and autophagy. Thus, the computational framework has facilitated the identification and characterization of plant‐specific autophagy‐related proteins and novel autophagy proteins/regulators in higher eukaryotes. Significance Statement: We used a network‐based algorithm to identify autophagy protein modules in Arabidopsis by integrating the text‐mined autophagy proteins and the protein–protein interactions. According to functional importance, four proteins in a module were detected as the candidates in the regulation of autophagy. Two of them are known autophagy proteins and the other two candidates, AGD1 and Sec14, were experimentally validated. [ABSTRACT FROM AUTHOR]

Moonlighting proteins provide more options for cells to execute multiple functions without increasing the genome and transcriptome complexity. Although there have long been calls for computational methods for the prediction of moonlighting proteins, no method has been designed for determining moonlighting long noncoding ribonucleicacidz (RNAs) (mlncRNAs). Previously, we developed an algorithm MoonFinder for the identification of mlncRNAs at the genome level based on the functional annotation and interactome data of lncRNAs and proteins. Here, we update MoonFinder to MoonFinder v2.0 by providing an extensive framework for the detection of protein modules and the establishment of RNA–module associations in human. A novel measure, moonlighting coefficient, was also proposed to assess the confidence of an ncRNA acting in a moonlighting manner. Moreover, we explored the expression characteristics of mlncRNAs in sepsis, in which we found that mlncRNAs tend to be upregulated and differentially expressed. Interestingly, the mlncRNAs are mutually exclusive in terms of coexpression when compared to the other lncRNAs. Overall, MoonFinder v2.0 is dedicated to the prediction of human mlncRNAs and thus bears great promise to serve as a valuable R package for worldwide research communities (https://cran.r-project.org/web/packages/MoonFinder/index.html). Also, our analyses provide the first attempt to characterize mlncRNA expression and coexpression properties in adult sepsis patients, which will facilitate the understanding of the interaction and expression patterns of mlncRNAs. [ABSTRACT FROM AUTHOR]

Inferring potential associations between microRNAs (miRNAs) and human diseases can help people understand the pathogenesis of complex human diseases. Several computational approaches have been presented to discover novel miRNA-disease associations based on a top-ranked association model. However, some top-ranked miRNAs are not easily used to reveal the association between miRNAs and diseases. This study aims to infer miRNA-disease relationship by identifying a functional module. We first construct a miRNA functional similarity network derived from a disease similarity network and a known miRNA-disease relationship network. We then present an improved K-means (i.e., IK-means) algorithm to detect miRNA functional modules and used 243 diseases to validate the performance of our proposed method. Experimental results indicate that the performance of IK-means is better compared with classical K-means algorithms. Case studies on some functional modules further demonstrate the applicability of IK-means in the identification of new miRNA-disease associations. [ABSTRACT FROM AUTHOR]

Gastrodia elata, also named Tianma, is a valuable traditional Chinese herbal medicine. It has numerous important pharmacological roles such as in sedation and lowering blood pressure and as anticonvulsant and anti-aging, and it also has effects on the immune and cardiovascular systems. The whole genome sequencing of G. elata has been completed in recent years, which provides a strong support for the construction of the G. elata gene functional analysis platform. Therefore, in our research, we collected and processed 39 transcriptome data of G. elata and constructed the G. elata gene co-expression networks, then we identified functional modules by the weighted correlation network analysis (WGCNA) package. Furthermore, gene families of G. elata were identified by tools including HMMER, iTAK, PfamScan, and InParanoid. Finally, we constructed a gene functional analysis platform for G. elata1. In our platform, we introduced functional analysis tools such as BLAST, gene set enrichment analysis (GSEA), and cis-elements (motif) enrichment analysis tool. In addition, we analyzed the co-expression relationship of genes which might participate in the biosynthesis of gastrodin and predicted 19 mannose-binding lectin antifungal proteins of G. elata. We also introduced the usage of the G. elata gene function analysis platform (GelFAP) by analyzing CYP51G1 and GFAP4 genes. Our platform GelFAP may help researchers to explore the gene function of G. elata and make novel discoveries about key genes involved in the biological processes of gastrodin.

In natural environments, the production of neurotoxic and bioaccumulative methylmercury (MeHg) is mediated by microorganisms carrying the genes hgcA and hgcB. However, the contribution of these microorganisms to mercury (Hg) methylation or MeHg accumulation in the ocean is poorly understood. Here we determined the total Hg (THg) and MeHg concentrations in seawater samples and conducted a metagenomic survey of the hgcAB genes and functional modules involved in metabolic pathways in the East China Sea (ECS). In the metagenomic analyses, we used paired-end reads and assembled contigs for hgcAB enumeration and phylogenetic analyses in the seawater column. To evaluate the relative abundance of hgcAB in the metagenomic data, we estimated the abundance of recA (single-copy gene of bacteria) as well and then compared them. Moreover, the profiles of prokaryotic community composition were analyzed by 16S rRNA gene (V4 region) deep-sequencing. In the mesopelagic layers, the hgcA sequences were detected, and there was a positive correlation between hgcA abundance relative to the recA and MeHg concentrations. Thus, the quantification of the hgcA sequences could provide valuable information to evaluate the potential environments of microbial MeHg accumulation in the seawater column. A phylogenetic analysis using the assembled contigs revealed that all of the hgcA sequences in the mesopelagic layers were affiliated with Nitrospina-like sequences. The 16S rRNA gene analysis revealed that Nitrospinae were abundant in the mesopelagic layers. Although the lineages of Deltaproteobacteria, Firmicutes, and Spirochaetes were detected in the seawater column, their hgcAB sequences were not detected in our metagenomes, despite the fact that they are closely related to previously identified Hg methylators. The metabolic pathway analysis revealed that the modules related to sulfur and methane metabolism were prominent in the mesopelagic layers. However, no hgcA sequences affiliated with sulfate-reducing bacteria (SRB) or methanogens were detected in these layers, suggesting that these bacteria could not be strongly involved in the Hg accumulation in the seawater column. Our results indicate that Nitrospina-like bacteria with hgcAB genes could play a critical role in microbial Hg accumulation in the oxygenated mesopelagic layers of the ECS.

Background: Pathway analysis is widely applied in transcriptome analysis. Given certain transcriptomic changes, current pathway analysis tools tend to search for the most impacted pathways, which provides insight into underlying biological mechanisms. Further refining of the enriched pathways and extracting functional modules by “crosstalk” analysis have been proposed. However, the upstream/downstream relationships between the modules, which may provide extra biological insights such as the coordination of different functional modules and the signal transduction flow have been ignored. Results: To quantitatively analyse the upstream/downstream relationships between functional modules, we developed a novel GEne Set Topological Impact Analysis (GESTIA), which could be used to assemble the enriched pathways and functional modules into a super-module with a topological structure. We showed the advantages of this analysis in the exploration of extra biological insight in addition to the individual enriched pathways and functional modules. Conclusions: GESTIA can be applied to a broad range of pathway/module analysis result. We hope that GESTIA may help researchers to get one additional step closer to understanding the molecular mechanism from the pathway/module analysis results. [ABSTRACT FROM AUTHOR]

Gastrodia elata , also named Tianma, is a valuable traditional Chinese herbal medicine. It has numerous important pharmacological roles such as in sedation and lowering blood pressure and as anticonvulsant and anti-aging, and it also has effects on the immune and cardiovascular systems. The whole genome sequencing of G. elata has been completed in recent years, which provides a strong support for the construction of the G. elata gene functional analysis platform. Therefore, in our research, we collected and processed 39 transcriptome data of G. elata and constructed the G. elata gene co-expression networks, then we identified functional modules by the weighted correlation network analysis (WGCNA) package. Furthermore, gene families of G. elata were identified by tools including HMMER, iTAK, PfamScan, and InParanoid. Finally, we constructed a gene functional analysis platform for G. elata 1. In our platform, we introduced functional analysis tools such as BLAST, gene set enrichment analysis (GSEA), and cis- elements (motif) enrichment analysis tool. In addition, we analyzed the co-expression relationship of genes which might participate in the biosynthesis of gastrodin and predicted 19 mannose-binding lectin antifungal proteins of G. elata. We also introduced the usage of the G. elata gene function analysis platform (GelFAP) by analyzing CYP51G1 and GFAP4 genes. Our platform GelFAP may help researchers to explore the gene function of G. elata and make novel discoveries about key genes involved in the biological processes of gastrodin. [ABSTRACT FROM AUTHOR]

In natural environments, the production of neurotoxic and bioaccumulative methylmercury (MeHg) is mediated by microorganisms carrying the genes hgcA and hgcB. However, the contribution of these microorganisms to mercury (Hg) methylation or MeHg accumulation in the ocean is poorly understood. Here we determined the total Hg (THg) and MeHg concentrations in seawater samples and conducted a metagenomic survey of the hgcAB genes and functional modules involved in metabolic pathways in the East China Sea (ECS). In the metagenomic analyses, we used paired-end reads and assembled contigs for hgcAB enumeration and phylogenetic analyses in the seawater column. To evaluate the relative abundance of hgcAB in the metagenomic data, we estimated the abundance of recA (single-copy gene of bacteria) as well and then compared them. Moreover, the profiles of prokaryotic community composition were analyzed by 16S rRNA gene (V4 region) deep-sequencing. In the mesopelagic layers, the hgcA sequences were detected, and there was a positive correlation between hgcA abundance relative to the recA and MeHg concentrations. Thus, the quantification of the hgcA sequences could provide valuable information to evaluate the potential environments of microbial MeHg accumulation in the seawater column. A phylogenetic analysis using the assembled contigs revealed that all of the hgcA sequences in the mesopelagic layers were affiliated with Nitrospina -like sequences. The 16S rRNA gene analysis revealed that Nitrospinae were abundant in the mesopelagic layers. Although the lineages of Deltaproteobacteria, Firmicutes, and Spirochaetes were detected in the seawater column, their hgcAB sequences were not detected in our metagenomes, despite the fact that they are closely related to previously identified Hg methylators. The metabolic pathway analysis revealed that the modules related to sulfur and methane metabolism were prominent in the mesopelagic layers. However, no hgcA sequences affiliated with sulfate-reducing bacteria (SRB) or methanogens were detected in these layers, suggesting that these bacteria could not be strongly involved in the Hg accumulation in the seawater column. Our results indicate that Nitrospina -like bacteria with hgcAB genes could play a critical role in microbial Hg accumulation in the oxygenated mesopelagic layers of the ECS. [ABSTRACT FROM AUTHOR]

Background: Sepsis is a fatal disease referring to the presence of a known or strongly suspected infection coupled with systemic and uncontrolled immune activation causing multiple organ failure. However, current knowledge of the role of lncRNAs in sepsis is still extremely limited.Methods: We performed an in silico investigation of the gene coexpression pattern for the patients response to all-cause sepsis in consecutive intensive care unit (ICU) admissions. Sepsis coexpression gene modules were identified using WGCNA and enrichment analysis. lncRNAs were determined as sepsis biomarkers based on the interactions among lncRNAs and the identified modules.Results: Twenty-three sepsis modules, including both differentially expressed modules and prognostic modules, were identified from the whole blood RNA expression profiling of sepsis patients. Five lncRNAs, FENDRR, MALAT1, TUG1, CRNDE, and ANCR, were detected as sepsis regulators based on the interactions among lncRNAs and the identified coexpression modules. Furthermore, we found that CRNDE and MALAT1 may act as miRNA sponges of sepsis related miRNAs to regulate the expression of sepsis modules. Ultimately, FENDRR, MALAT1, TUG1, and CRNDE were reannotated using three independent lncRNA expression datasets and validated as differentially expressed lncRNAs.Conclusion: The procedure facilitates the identification of prognostic biomarkers and novel therapeutic strategies of sepsis. Our findings highlight the importance of transcriptome modularity and regulatory lncRNAs in the progress of sepsis. [ABSTRACT FROM AUTHOR]

Summary: Wood (secondary xylem) formation in tree species is dependent on auxin‐mediated vascular cambium activity in stems. However, the complex regulatory networks underlying xylem formation remain elusive.Xylem development in Populus was characterized based on microscopic observations of stem sections in transgenic plants. Transcriptomic, quantitative real‐time PCR, chromatin immunoprecipitation PCR, and electrophoretic mobility shift assay analyses were conducted to identify target genes involved in xylem development. Yeast two‐hybrid, pull‐down, bimolecular fluorescence complementation, and co‐immunoprecipitation assays were used to validate protein–protein interactions.PaC3H17 and its target PaMYB199 were found to be predominantly expressed in the vascular cambium and developing secondary xylem in Populus stems and play opposite roles in controlling cambial cell proliferation and secondary cell wall thickening through an overlapping pathway. Further, PaC3H17 interacts with PaMYB199 to form a complex, attenuating PaMYB199‐driven suppression of its xylem targets. Exogenous auxin application enhances the dual control of the PaC3H17‐PaMYB199 module during cambium division, thereby promoting secondary cell wall deposition.Dual regulation of xylem formation by an auxin‐mediated PaC3H17‐PaMYB199 module represents a novel regulatory mechanism in Populus, increasing our understanding of the regulatory networks involved in wood formation. [ABSTRACT FROM AUTHOR]

Coronary artery disease (CAD) is a complex human disease, involving multiple genes and their nonlinear interactions, which often act in a modular fashion. Genome-wide single nucleotide polymorphism (SNP) profiling provides an effective technique to unravel these underlying genetic interplays or their functional involvements for CAD. This study aimed to identify the susceptible pathways and modules for CAD based on SNP omics. First, the Wellcome Trust Case Control Consortium (WTCCC) SNP datasets of CAD and control samples were used to assess the joint effect of multiple genetic variants at the pathway level, using logistic kernel machine regression model. Then, an expanded genetic network was constructed by integrating statistical gene–gene interactions involved in these susceptible pathways with their protein–protein interaction (PPI) knowledge. Finally, risk functional modules were identified by decomposition of the network. Of 276 KEGG pathways analyzed, 6 pathways were found to have a significant effect on CAD. Other than glycerolipid metabolism, glycosaminoglycan biosynthesis, and cardiac muscle contraction pathways, three pathways related to other diseases were also revealed, including Alzheimer’s disease, non-alcoholic fatty liver disease, and Huntington’s disease. A genetic epistatic network of 95 genes was further constructed using the abovementioned integrative approach. Of 10 functional modules derived from the network, 6 have been annotated to phospholipase C activity and cell adhesion molecule binding, which also have known functional involvement in Alzheimer’s disease. These findings indicate an overlap of the underlying molecular mechanisms between CAD and Alzheimer’s disease, thus providing new insights into the molecular basis for CAD and its molecular relationships with other diseases.

As an economically important crop, apple is one of the most cultivated fruit trees in temperate regions worldwide. Recently, a large number of high-quality transcriptomic and epigenomic datasets for apple were made available to the public, which could be helpful in inferring gene regulatory relationships and thus predicting gene function at the genome level. Through integration of the available apple genomic, transcriptomic, and epigenomic datasets, we constructed co-expression networks, identified functional modules, and predicted chromatin states. A total of 112 RNA-seq datasets were integrated to construct a global network and a conditional network (tissue-preferential network). Furthermore, a total of 1,076 functional modules with closely related gene sets were identified to assess the modularity of biological networks and further subjected to functional enrichment analysis. The results showed that the function of many modules was related to development, secondary metabolism, hormone response, and transcriptional regulation. Transcriptional regulation is closely related to epigenetic marks on chromatin. A total of 20 epigenomic datasets, which included ChIP-seq, DNase-seq, and DNA methylation analysis datasets, were integrated and used to classify chromatin states. Based on the ChromHMM algorithm, the genome was divided into 620,122 fragments, which were classified into 24 states according to the combination of epigenetic marks and enriched-feature regions. Finally, through the collaborative analysis of different omics datasets, the online database AppleMDO (http://bioinformatics.cau.edu.cn/AppleMDO/) was established for cross-referencing and the exploration of possible novel functions of apple genes. In addition, gene annotation information and functional support toolkits were also provided. Our database might be convenient for researchers to develop insights into the function of genes related to important agronomic traits and might serve as a reference for other fruit trees.

Catharanthus roseus is a medicinal plant, which can produce monoterpene indole alkaloid (MIA) metabolites with biological activity and is rich in vinblastine and vincristine. With release of the scaffolded genome sequence of C. roseus, it is necessary to annotate gene functions on the whole-genome level. Recently, 53 RNA-seq datasets are available in public with different tissues (flower, root, leaf, seedling, and shoot) and different treatments (MeJA, PnWB infection and yeast elicitor). We used in-house data process pipeline with the combination of PCC and MR algorithms to construct a co-expression network exploring multi-dimensional gene expression (global, tissue preferential, and treat response) through multi-layered approaches. In the meanwhile, we added miRNA-target pairs, predicted PPI pairs into the network and provided several tools such as gene set enrichment analysis, functional module enrichment analysis, and motif analysis for functional prediction of the co-expression genes. Finally, we have constructed an online croFGD database (http://bioinformatics.cau.edu.cn/croFGD/). We hope croFGD can help the communities to study the C. roseus functional genomics and make novel discoveries about key genes involved in some important biological processes.

Enzyme-catalyzed micro/nanomotors (MNMs) exhibit tremendous potential for biological isolation and sensing, because of their biocompatibility, versatility, and ready access to biofuel. However, flow field generated by enzyme-catalyzed reactions might significantly hinder performance of surface-linked functional moieties, e.g., the binding interaction between MNMs and target cargos. Herein, we develop enzymatic micromotors with spatially selective distribution of urease to enable the independent operation of various modules and facilitate the capture and sensing of exosomes. When urease is modified into the motors' cavity, the flow field from enzyme catalysis has little effect on the exterior surface of the motors. The active motion and encapsulating urease internally result in enhancement of ∼35% and 18% in binding efficiency of target cargos, e.g., exosomes as an example here, compared to their static counterparts and moving micromotors with urease modified externally, respectively. Once exosomes are trapped, they can be transferred to a clean environment by the motors for Raman signal detection and/or identification using the surface Raman enhancement scattering (SERS) effect of coated gold nanoshell. The biocatalytic micromotors, achieving spatial separation between driving module and function module, offer considerable promise for future design of multifunctional MNMs in biomedicine and diagnostics.