Search Results (74)

Objective: This study explores the material basis and biological functions of meridian interstitial channels in mini-pigs proximal to the stomach meridian by analyzing differential proteomics between interstitial channels and adjacent non-interstitial channel tissues. Methods: Liquid chromatography–mass spectrometry (LC-MS) under data-dependent acquisition mode was employed to analyze and identify the proteome of subcutaneous connective tissues along the stomach meridian and adjacent tissues. SWATH MS^ALL method and omicsbean online analysis platforms were used for protein quantification and differential proteomic analysis. Differential proteins were subjected to Gene Ontology annotation and KEGG pathway analysis to understand their functions and biological processes. Combining traditional Chinese meridian theory with modern meridian research, proteins most relevant to meridian functions were selected, and their expression levels were assessed using Western blotting. Results: GO annotation and KEGG pathway analysis revealed differences in molecular functions, biological processes, and metabolic pathways among differential proteins. Most downregulated proteins were enzyme functional proteins involved in amino acid metabolism (GOT1), adenosine nucleotide balance conversion (AK1), and calcium ion-binding processes (ANXA6). Most upregulated proteins were structural proteins in the extracellular matrix—collagen proteins (COL3A1, COL6A1, COL6A3, COL6A6, COL12A1, COL14A1) and proteoglycans (DCN, BGN, FMOD)—involved in influencing and regulating collagen fiber generation and arrangement. Intriguingly, almost all differential proteins were associated with gastrointestinal diseases, implying a pathological correlation of differential proteins in the stomach meridian interstitial channel. Conclusions: The stomach meridian interstitial channels in mini-pigs show 72 differentially expressed proteins compared to adjacent tissues. These differences include the upregulation of structural proteins and downregulation of functional proteins, potentially forming the molecular biological basis for the structural and functional specificity of meridians. Full article

The objective of this study was to elucidate the proteomic and transcriptomic alterations within the cartilage in Kashin–Beck disease (KBD) compared to a normal control. We conducted a comparison of the expression profiles of proteins, mRNAs, and lncRNAs via data-independent acquisition (DIA) proteomics and transcriptome sequencing in six KBD individuals and six normal individuals. To facilitate the functional annotation enrichment analysis of the differentially expressed (DE) proteins, DE mRNAs, and DE lncRNAs, we employed bioinformatic analysis utilizing Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Additionally, we conducted integration analysis of multi-omics datasets using mixOmics. We revealed a distinct proteomic signature, highlighting 53 DE proteins, with notable alterations in the pathways related to tryptophan metabolism and microbial metabolism. Additionally, we identified 160 DE mRNAs, with the functional enrichment analysis uncovering pathways related to RNA metabolism and protein splicing. Furthermore, our analysis of the lncRNAs demonstrated biological processes involved in protein metabolism and cellular nitrogen compound metabolic processes. The integrative analysis uncovered significant correlations, including the positive correlation between superoxide dismutase 1 (SOD1) and mitochondrial import receptor subunit TOM6 homolog (TOMM6), and the negative correlation between C-X9-C motif-containing 1 (CMC1) and succinate–CoA ligase [GDP-forming] subunit beta, mitochondrial (SUCLG2). Our results provide novel insights into the molecular mechanisms underlying KBD. Full article

Background: We utilized data-independent acquisition (DIA) to study the poorly understood biology of Mdm2 and MdmX in a p53-null context. Mdm2 and MdmX form an E3-ligase complex that has as its most well-studied function the negative regulation of the tumor suppressor p53; however, it is also known to interact with many other proteins in a p53-independent manner. Methods: In this work, small-molecule and siRNA-based technology were used to modify Mdm2/MdmX activity in a human non-small-cell lung carcinoma cell line lacking p53 expression. Study of the proteome of these cells helped identify biological processes where Mdm2 and MdmX may play roles in a p53-independent manner. Proteins from H1299 cells, treated with the drug MEL23 or siRNA against Mdm2 or MdmX, were analyzed. Results: Protein ontology and function were analyzed, revealing which pathways are affected by modulation of the proteins that form the complex. Insights into how those functions are dependent on the activity of the complex also gained via comparisons among the three groups of samples. Conclusions: We selected a potential target from the DIA analysis and validated it by immunoblotting and qPCR, and this allows us to demonstrate a new interaction partner of the Mdm2-MdmX complex in human cells. Full article

Background: Ammonia (NH₃), a harmful gas, reduces livestock productivity, threatens their health, and causes economic losses. Luteolin (Lut), an anti-inflammatory flavonoid, may counteract these effects. Methods: Our study explored luteolin’s protective mechanisms on chicken splenic lymphocytes under ammonia stress using a simulation model and four-dimensional fast data-independent acquisition (4D-FastDIA) proteomics. We identified 316 proteins, with 69 related to ammonia’s negative effects and 247 to Lut’s protection. Thirty differentially expressed proteins (DEPs) were common to both groups, with 27 showing counter-regulation with Lut. Results: Gene Ontology (GO) analysis showed DEPs enriched in molecular responses to interferons and the negative regulation of immune responses, mainly located extracellularly. Molecular function analysis revealed DEPs in antigen binding and synthase activity. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis linked DEPs to pathways like estrogen signaling, NOD-like receptor signaling, cytokine–cytokine receptor interaction, and JAK-STAT signaling. The quantitative real-time polymerase chain reaction (qRT-PCR) results indicated that the mRNA levels of Interferon Alpha and Beta Receptor subunit 2 (IFNAR2) and Signal Transducer and Activator of Transcription 1 (STAT1) were trending downward. This observation was in strong agreement with the downregulation noted in the proteomics analysis. Conclusions: Lut’s protective role against ammonia’s adverse effects on chicken splenic lymphocytes is linked to the modulation of key signaling pathways, offering insights for further research on treating ammonia exposure with Lut. Full article

As the core equipment in ship power systems, the accurate and real-time diagnosis of ship diesel engine faults directly affects navigation safety and operation efficiency. Existing methods (e.g., expert systems, traditional machine learning) can hardly cope with the complex failure modes and dynamic operation environment due to the problems of relying on artificial features and insufficient generalization ability. In this paper, we propose a BiLSTM-CRF-based knowledge graph construction method for ship diesel engine faults, aiming at integrating multi-source heterogeneous data through deep learning and knowledge graph technology, and mining the deep semantic associations among fault phenomena, causes, and solutions. The research framework covers data acquisition, ontology modeling, and knowledge extraction and storage, and the BiLSTM-CRF model is used to fuse bi-directional contextual features with label transfer probability to achieve high-precision entity recognition and relationship extraction. Finally, a scalable knowledge graph is constructed by Neo4j. Experiments show that the model significantly outperforms baseline methods such as HMM, CRF, and BiLSTM, and the graph visualization clearly presents the fault causality network, which supports knowledge reasoning and decision optimization. For example, “high exhaust temperature” can be related to potential causes such as “turbine failure” and “poor cooling”, and recommended measures can be taken. This method not only improves fault diagnosis accuracy and efficiency but also provides a novel method for intelligent ship health management. Full article

Although HBIM (Historical Building Information Modeling) excels in geometric data acquisition and modeling within Scan-to-BIM (Building Information Modeling) workflows, its application in digital documentation faces persistent challenges, such as balancing precision and efficiency, ambiguous information structures, and the absence of standardized protocols. To address these issues, this study refines key steps from the systematic Scan-to-BIM process and proposes a documentation-oriented HBIM workflow. The workflow is designed to tackle data complexity and semantic alignment challenges through detailed strategic planning, standard data collection, efficient geometric modeling, and ontology-based information integration. Validated in the Valentino Castle’s north wing digital archiving project, the proposed framework emphasizes archival management and standardization, reducing reliance on high-precision point cloud data and complex geometric modeling. Instead, it adopts low-precision geometric models as information storage containers, employing standardized information structures to manage and transfer heterogeneous data. Key contributions include the following: (1) establishing a requirements-driven and model-level-based framework for standardized project management; (2) introducing a component alignment concept to harmonize IFC (Industry Foundation Classes) standards and traditional terminologies; and (3) developing a four-level information structure to enhance HBIM parameter and database management. The workflow significantly reduces data acquisition and modeling time while offering a replicable methodology for heritage documentation, promoting cross-disciplinary collaboration and standardization in digital preservation practices. Full article

Background/Objectives: Dental caries remains a prevalent chronic disease globally, driven by complex interactions between the host, diet, and microbial communities. This study employs a metatranscriptomic RNA-Seq analysis to explore the functional dynamics of the dentin microbiome in both healthy and carious teeth. By examining the transcriptional activity of bacterial communities, we aimed to identify key microbial species and molecular functions associated with caries progression. Methods: Samples from six patients (three healthy and three decayed teeth) were analyzed using the Illumina NovaSeq 2000 platform, with data processed through the SAMSA2 pipeline for taxonomic and functional annotation. Results: The differential expression analysis revealed significant upregulation of Streptococcus and Lactobacillus species, including S. mutans, S. sobrinus, and L. salivarius, in carious samples, highlighting their roles in acid production and carbohydrate metabolism. Additionally, Mycobacterium species, known for their biofilm-forming capabilities and acid tolerance, were upregulated in decayed teeth. The Gene Ontology (GO) enrichment analysis identified unique molecular functions and biological processes in carious teeth, such as carbohydrate metabolism, oxidative stress response, and bacterial cell wall biogenesis, which are critical for microbial survival in acidic environments. In contrast, healthy teeth exhibited functions related to homeostasis and nutrient acquisition, reflecting a balanced microbial community. Conclusions: The study underscores the polymicrobial nature of dental caries, with multiple bacterial species contributing to disease progression through diverse metabolic and stress-response mechanisms. These findings provide deeper insights into the ecological shifts within the oral microbiome during caries development, emphasizing the importance of a functional metatranscriptomic analysis in understanding the pathogenesis of dental caries. Full article

The deconstruction of a building marks the end of its life cycle. At the same time, this phase offers many opportunities for the acquisition and reuse of used building materials. During the deconstruction phase, planning and execution are carried out using a variety of different software applications. Despite the use of digital 3D models according to the Building Information Modeling (BIM) method, there is no interoperability along the value chain in the deconstruction of a building. To counteract the lack of interoperability in the deconstruction phase of buildings, the article develops an ontology for the formalization of knowledge within the deconstruction phase and the usability of heterogeneous data sets in connection with the 3D BIM model. After assessing the status quo on the topic of BIM in deconstruction and ontologies in building construction, the systematic development of the deconstruction ontology (DCO) and its validation on a demonstrator is conducted. The result is the presentation of the basic usability of ontologies in conjunction with graphical databases and digital 3D models in the deconstruction phase. This ensures interoperability along the value chain and counteracts data loss. Full article

White lupin (Lupinus albus) has become a model plant for understanding plant adaptations to phosphorus (P) and iron (Fe) deficiency, two major limiting factors for plant productivity. In response to both nutrient deficiencies, white lupin forms cluster roots, bottle-brush-like root structures that aid in P and Fe acquisition from soil. While the cluster root function is well-studied, not much is known about the signaling pathways involved in sensing and responding to a P and Fe deficiency. Sucrose has been identified as a long-distance signal sent in increased concentrations from shoot to root in response to both a P and Fe deficiency. Thus, sucrose plays a dual role both as a signal and as a major source of energy for the root. To unravel the responses to sucrose as a signal, we performed an Illumina paired-end cDNA sequencing of white lupin roots treated with sucrose for 20, 40 or 80 min, compared to untreated controls (0 min). We identified 634 up-regulated and 956 down-regulated genes in response to sucrose. Twenty minutes of sucrose treatment showed the most responses, with the ethylene-activated signaling pathway as the most enriched Gene Ontology (GO) category. The number of up-regulated genes decreased at 40 min and 80 min, and protein dephosphorylation became the most enriched category. Taken together, our findings indicate active responses to sucrose as a signal at 20 min after a sucrose addition, but fewer responses and a potential resetting of signal transduction pathways by the dephosphorylation of proteins at 40 and 80 min. Full article

MicroRNA (miR)-126 is frequently downregulated in malignancies, including breast cancer (BC). Despite its tumor-suppressive role, the mechanisms underlying miR-126 deregulation in BC remain elusive. Through silencing experiments, we identified Early B Cell Factor 1 (EBF1), ETS Proto-Oncogene 2 (ETS2), and Krüppel-Like Factor 2 (KLF2) as pivotal regulators of miR-126 expression. These transcription factors were found to be downregulated in BC due to epigenetic silencing or a “poised but not transcribed” promoter state, impairing miR-126 expression. Gene Ontology analysis of differentially expressed miR-126 target genes in the Cancer Genome Atlas: Breast Invasive Carcinoma (TCGA-BRCA) cohort revealed their involvement in cancer-related pathways, primarily signal transduction, chromatin remodeling/transcription, and differentiation/development. Furthermore, we defined interconnections among transcription factors, miR-126, and target genes, identifying a potential feed-forward loop (FFL) crucial in maintaining cellular identity and preventing the acquisition of stemness properties associated with cancer progression. Our findings propose that the dysregulation of the EBF1/ETS2/KLF2/miR-126 axis disrupts this FFL, promoting oncogenic transformation and progression in BC. This study provides new insights into the molecular mechanisms of miR-126 downregulation in BC and highlights potential targets for therapeutic intervention. Further research is warranted to clarify the role of this FFL in BC, and to identify novel therapeutic strategies aimed at modulating this network as a whole, rather than targeting individual signals, for cancer management. Full article

As the complexity of mission planning increases, relying on the subjective experience of planners is no longer sufficient to meet the needs of modern mission planning. Knowledge mapping, as a structured knowledge management technique, provides an effective solution for systematically integrating knowledge in the task-planning domain. The mission-planning business model is able to systematically capture and portray domain knowledge in mission planning through a formal representation of mission planning processes, rules, and constraints. Thus, it becomes an important source of knowledge for mission-planning knowledge mapping. This paper proposes a business-model-driven knowledge graph construction method for mission planning. First, under the support of conceptual business knowledge, the multidimensional task-planning ontology network expression method is utilized to construct the task-planning ontology network, and then the data-based business knowledge is structured to transform it into business data mapping to complete the acquisition of business knowledge. Then, the task-planning ontology network is constructed using the multidimensional task-planning ontology network representation method under the support of conceptual knowledge. Subsequently, a domain knowledge categorization algorithm based on Ullman subgraph matching is used to realize the matching mapping between the ontology network and business data mapping to complete the categorization of task-planning domain knowledge. Finally, the generated task-planning domain knowledge graph is stored in the Neo4j graph database. In order to ensure the completeness of the knowledge graph, an adaptive adjustment method based on its actual effectiveness is conceived, which is able to detect and adjust the completeness of the knowledge graph. The effectiveness of the proposed methodology is validated by constructing a space-station mission-planning knowledge graph driven by a space-station mission-planning business model. Full article

Hemorrhagic disease caused by grass carp reovirus (GCRV) infection is a major problem affecting the grass carp aquaculture industry. Therefore, inhibiting the spread of GCRV infection is of great economic significance. Herein, we sequenced five tissues (gill, liver, intestine, kidney, and muscle) from grass carp before and after GCRV infection using data-independent acquisition proteomic and untargeted metabolomic technologies, and quantitatively identified 10,808 proteins and 4040 metabolites. Then, we analyzed the differentially expressed proteins (DEPs) and metabolites (DEMs) before and after GCRV infection in the five tissues. Gene ontology analysis revealed that the five tissue DEPs were enriched in metabolic, including carbohydrate and lipid metabolic processes. Chemical taxonomy analysis showed that the categories of DEMs mainly included carbohydrates and lipids, such as fatty acids, glycerophospholipids, steroids, and their derivatives. Both the proteomic and the metabolomic data showed that GCRV affected the carbohydrate and lipid metabolism in the host. Shared pathway analysis was performed at both the protein and metabolic levels, showing significant enrichment of the glycolysis and pentose phosphate pathways (p < 0.001). Further analysis of glycolysis and pentose phosphate pathway inhibitors revealed that these two pathways are important for GCRV replication. As the kidney was the most affected among the five tissues, we analyzed the butanoate metabolism in the kidney, which revealed that most of the differentially expressed proteins and differently expressed metabolites in the butanoate metabolism were related to the TCA cycle. Further investigation showed that fumaric acid, an intermediate product in the TCA cycle, significantly inhibited GCRV replication in the CIK cells (p < 0.001), and that this inhibitory effect may be related to its induction of interferon system activation. The addition of fumaric acid to feed increased the survival rate of juvenile grass carp by 19.60% during GCRV infection, and protected the tissues of those infected with GCRV, making it a potential anti-GCRV feed additive. Our results provide new perspectives on GCRV pathogenesis and antiviral strategies for grass carp. Full article

Currently, with the intensification of global education competition, how to cultivate innovative and versatile talents has become the focus of education today. Informal learning spaces (ILS) have received widespread attention due to their benefits for interdisciplinary interaction. Many forms of ILS have been built, and coffee shops are one of them. However, there is no uniform standard for the design of cafes as ILS, and there are many problems with the spatial quality. This study took a university cafe as a case study and conducted research on the environmental quality and influencing factors from the perspective of environmental behavior. In terms of research methods, this study adopted a combination of ultra-wideband positioning, questionnaire surveys, and environmental data measurements. This research method could obtain the complete relationship between user behavior, time, and location, which improved the shortcomings of traditional research methods of fragmented and accidental data acquisition, and helped to obtain more accurate research results. The study identified the impact proportions of four criteria layer elements and 26 scheme layer factors, which was also the main contribution of this study. In terms of four criteria layer elements, the physical elements had the highest proportion, accounting for 32.46%. The ontology elements ranked second, accounting for 27.07%. The atmosphere elements ranked third, accounting for 24.19%. Finally, facility elements accounted for 16.29%. The top three scheme layer factors were equipment and facilities, noise, and illumination, with weights of 11.18%, 10.71%, and 8.35%, respectively. The study summarized the development directions of cafes as ILS, such as considering the learning needs of different groups, installing sound-absorbing and soundproof devices, adding grilles to external windows, and improving glare problems. Full article

Colostrum intake is a crucial determinant of survival in newborn rabbits. Neonates rely entirely on passive immunity transfer from their mothers while suckling colostrum. The goal of this study was to explore the protein differences of rabbit milk during different lactation periods. Our findings showed that the daily milk yield exhibited an increasing trend from the 2nd to the 21st day of lactation. A data-independent acquisition proteomics approach identified a total of 2011 proteins. Significantly, different abundances were found for 525 proteins in the colostrum and the mature milk samples. Eleven differentially abundant proteins (DAPs) were examined using parallel reaction monitoring, which verified the reliability of the proteomic data. Gene Ontology analysis revealed that these DAPs were primarily associated with glycosyltransferase activity, macromolecule transmembrane transporter activity, and regulation of acute inflammatory response. The dominant metabolic pathways of the DAPs involve the complement and coagulation cascades. A protein–protein interaction analysis identified apolipoprotein B, apolipoprotein A1, triose phosphate isomerase 1, and albumin as the hub proteins responsible for distinguishing differences between biological properties in rabbit colostrum and mature milk. These findings enhance our comprehension of the rabbit milk proteome, particularly in expanding our knowledge regarding the requirements of neonatal rabbits. Full article

The museum system is exposed to a high risk of seismic hazards. However, it is difficult to carry out seismic hazard prevention to protect cultural relics in collections due to the lack of real data and diverse types of seismic hazards. To address this problem, we developed a deep-learning-based multi-source feature-fusion method to assess the data on seismic damage caused by collected cultural relics. Firstly, a multi-source data-processing strategy was developed according to the needs of seismic impact analysis of the cultural relics in the collection, and a seismic event-ontology model of cultural relics was constructed. Additionally, a seismic damage data-classification acquisition method and empirical calculation model were designed. Secondly, we proposed a deep learning-based multi-source feature-fusion matching method for cultural relics. By constructing a damage state assessment model of cultural relics using superpixel map convolutional fusion and an automatic data-matching model, the quality and processing efficiency of seismic damage data of the cultural relics in the collection were improved. Finally, we formed a dataset oriented to the seismic damage risk analysis of the cultural relics in the collection. The experimental results show that the accuracy of this method reaches 93.6%, and the accuracy of cultural relics label matching is as high as 82.6% compared with many kinds of earthquake damage state assessment models. This method can provide more accurate and efficient data support, along with a scientific basis for subsequent research on the impact analysis of seismic damage to cultural relics in collections. Full article