Characterization of endophytic bacteriome diversity and associated beneficial bacteria inhabiting a macrophyte Eichhornia crassipes

IntroductionThe endosphere of a plant is an interface containing a thriving community of endobacteria that can affect plant growth and potential for bioremediation. Eichhornia crassipes is an aquatic macrophyte, adapted to estuarine and freshwater ecosystems, which harbors a diverse bacterial community. Despite this, we currently lack a predictive understanding of how E. crassipes taxonomically structure the endobacterial community assemblies across distinct habitats (root, stem, and leaf).MethodsIn the present study, we assessed the endophytic bacteriome from different compartments using 16S rRNA gene sequencing analysis and verified the in vitro plant beneficial potential of isolated bacterial endophytes of E. crassipes.Results and discussionPlant compartments displayed a significant impact on the endobacterial community structures. Stem and leaf tissues were more selective, and the community exhibited a lower richness and diversity than root tissue. The taxonomic analysis of operational taxonomic units (OTUs) showed that the major phyla belonged to Proteobacteria and Actinobacteriota (> 80% in total). The most abundant genera in the sampled endosphere was Delftia in both stem and leaf samples. Members of the family Rhizobiaceae, such as in both stem and leaf samples. Members of the family Rhizobiaceae, such as Allorhizobium- Neorhizobium-Pararhizobium-Rhizobium were mainly associated with leaf tissue, whereas the genera Nannocystis and Nitrospira from the families Nannocystaceae and Nitrospiraceae, respectively, were statistically significantly associated with root tissue. Piscinibacter and Steroidobacter were putative keystone taxa of stem tissue. Most of the endophytic bacteria isolated from E. crassipes showed in vitro plant beneficial effects known to stimulate plant growth and induce plant resistance to stresses. This study provides new insights into the distribution and interaction of endobacteria across different compartments of E. crassipes Future study of endobacterial communities, using both culture-dependent and -independent techniques, will explore the mechanisms underlying the wide-spread adaptability of E. crassipesto various ecosystems and contribute to the development of efficient bacterial consortia for bioremediation and plant growth promotion

Spammer detection using multi-classifier information fusion based on evidential reasoning rule

Abstract Spammer detection is essentially a process of judging the authenticity of users, and thus can be regarded as a classification problem. In order to improve the classification performance, multi-classifier information fusion is usually used to realize the automatic detection of spammers by utilizing the information from multiple classifiers. However, the existing fusion strategies do not reasonably take the uncertainty from the results of different classifiers (views) into account, and the relative importance and reliability of each classifier are not strictly distinguished. Therefore, in order to detect spammers effectively, this paper develops a novel multi-classifier information fusion model based on the evidential reasoning (ER) rule. Firstly, according to the user's characterization strategy, the base classifiers are constructed through the profile-based, content-based and behavior-based. Then, the idea of multi-classifier fusion is combined with the ER rule, and the results of base classifiers are aggregated by considering their weights and reliabilities. Extensive experimental results on the real-world dataset verify the effectiveness of the proposed model

Production performance analysis of sheep MSTN gene C2361T locus

The myostatin (MSTN) gene exhibits significant nucleotide sequence variations in sheep, impacting growth characteristics and muscular traits of the body. However, its influence on specific growth traits in some sheep remains to be further elucidated. This study utilized single nucleotide polymorphism sequence analysis to investigate the role of the MSTN gene in meat production performance across four sheep breeds: Charolais sheep, Australian White sheep, crossbreeds of Australian White and Small-tailed Han, and crossbreeds of Charolais and Small-tailed Han. At a SNP locus of the MSTN gene, the C2361T site was identified, with three genotypes detected: CC, CT, and TT, among which CC predominated. Gene substitution effect analysis revealed that replacing C with T could elevate the phenotypic value. Comparative analysis of data from different genotypes within the same breed highlighted the superiority of CC and TT genotypes in phenotypic values, underscoring the significance of specific genotypes in influencing key traits. Contrasting the performance of different genotypes across breeds, Charolais sheep and Charolais Han hybrids demonstrated superiority across multiple indicators, offering valuable insights for breeding new sheep varieties. Analysis of gender effects on growth characteristics indicated that ewes exhibited significantly wider chest, waist, and hip widths compared to rams, while rams displayed better skeletal growth and muscle development. Additionally, the MSTN gene also exerted certain effects on lamb growth characteristics, with the CC genotype closely associated with weight. These findings not only contribute crucial insights for sheep breeding but also pave the way for future research exploring the interaction of this gene with others

Partially reduced Ni0.8Co0.15Al0.05LiO2-δ for low-temperature SOFC cathode

Nowadays, Ni0.8Co0.15Al0.05LiO2-δ (NCAL) has been increasingly applied into the solid oxide fuel cell (SOFC) field as a promising electrode material. Here, the performances of NCAL cathode were investigated for low-temperature SOFCs (LT-SOFCs) on Ce0.8Sm0.2O2-δ (SDC) electrolyte. After on-line reduction of NCAL for 30 min, the partially reduced NCAL, i.e., NCAL(r), was employed as the new cathode and its performances were also investigated. The area specific resistances of NCAL and NCAL(r) cathodes on SDC electrolyte are 7.076 and 1.214 Ω cm2 at 550 °C, respectively. Moreover, NCAL(r) exhibits the activation energy of 0.46 eV for oxygen reduction reaction (ORR), which is much lower than that of NCAL (0.88 eV). The fuel cell consisted of NCAL electrodes and SDC electrolyte shows an open circuit voltage (OCV) of 0.95 V and power output of 436 mW cm−2 at 550 °C. After cathode on-line optimization, the cell\u27s OCV and power output are significantly increased to 1.01 V and 648 mW cm−2, which mainly attributed to the accelerated ORR and decreased electrode polarization resistance. These results demonstrate that NCAL(r) is a promising cathode material for LT-SOFCs

Development and validation of an integrated system for lung cancer screening and post-screening pulmonary nodules management: a proof-of-concept study (ASCEND-LUNG)Research in context

Summary: Background: In order to address the low compliance and dissatisfied specificity of low-dose computed tomography (LDCT), efficient and non-invasive approaches are needed to complement its limitations for lung cancer screening and management. The ASCEND-LUNG study is a prospective two-stage case–control study designed to evaluate the performance of a liquid biopsy-based comprehensive lung cancer screening and post-screening pulmonary nodules management system. Methods: We aimed to develop a comprehensive lung cancer system called Peking University Lung Cancer Screening and Management System (PKU-LCSMS) which comprises a lung cancer screening model to identify specific populations requiring LDCT and an artificial intelligence-aided (AI-aided) pulmonary nodules diagnostic model to classify pulmonary nodules following LDCT. A dataset of 465 participants (216 cancer, 47 benign, 202 non-cancer control) were used for the two models’ development phase. For the lung cancer screening model development, cancer participants were randomly split at a ratio of 1:1 into the train and validation cohorts, and then non-cancer controls were age-matched to the cancer cases in a 1:1 ratio. Similarly, for the AI-aided pulmonary nodules model, cancer and benign participants were also randomly divided at a ratio of 2:1 into the train and validation cohorts. Subsequently, during the model validation phase, sensitivity and specificity were validated using an independent validation cohort consisting of 291 participants (140 cancer, 25 benign, 126 non-cancer control). Prospectively collected blood samples were analyzed for multi-omics including cell-free DNA (cfDNA) methylation, mutation, and serum protein. Computerized tomography (CT) images data was also obtained. Paired tissue samples were additionally analyzed for DNA methylation, DNA mutation, and messenger RNA (mRNA) expression to further explore the potential biological mechanisms. This study is registered with ClinicalTrials.gov, NCT04817046. Findings: Baseline blood samples were evaluated for the whole screening and diagnostic process. The cfDNA methylation-based lung cancer screening model exhibited the highest area under the curve (AUC) of 0.910 (95% CI, 0.869–0.950), followed by the protein model (0.891 [95% CI, 0.845–0.938]) and lastly the mutation model (0.577 [95% CI, 0.482–0.672]). Further, the final screening model, which incorporated cfDNA methylation and protein features, achieved an AUC of 0.963 (95% CI, 0.942–0.984). In the independent validation cohort, the multi-omics screening model showed a sensitivity of 99.2% (95% CI, 0.957–1.000) at a specificity of 56.3% (95% CI, 0.472–0.652). For the AI-aided pulmonary nodules diagnostic model, which incorporated cfDNA methylation and CT images features, it yielded a sensitivity of 81.1% (95% CI, 0.732–0.875), a specificity of 76.0% (95% CI, 0.549–0.906) in the independent validation cohort. Furthermore, four differentially methylated regions (DMRs) were shared in the lung cancer screening model and the AI-aided pulmonary nodules diagnostic model. Interpretation: We developed and validated a liquid biopsy-based comprehensive lung cancer screening and management system called PKU-LCSMS which combined a blood multi-omics based lung cancer screening model incorporating cfDNA methylation and protein features and an AI-aided pulmonary nodules diagnostic model integrating CT images and cfDNA methylation features in sequence to streamline the entire process of lung cancer screening and post-screening pulmonary nodules management. It might provide a promising applicable solution for lung cancer screening and management. Funding: This work was supported by Science, Science, Technology & Innovation Project of Xiongan New Area, Beijing Natural Science Foundation, CAMS Innovation Fund for Medical Sciences (CIFMS), Clinical Medicine Plus X-Young Scholars Project of Peking University, the Fundamental Research Funds for the Central Universities, Research Unit of Intelligence Diagnosis and Treatment in Early Non-small Cell Lung Cancer, Chinese Academy of Medical Sciences, National Natural Science Foundation of China, Peking University People's Hospital Research and Development Funds, National Key Research and Development Program of China, and the fundamental research funds for the central universities

From Signal to Knowledge: The Diagnostic Value of Raw Data in the Artificial Intelligence Prediction of Human Data for the First Time

Encouraging and astonishing developments have recently been achieved in image-based diagnostic technology. Modern medical care and imaging technology are becoming increasingly inseparable. However, the current diagnosis pattern of signal to image to knowledge inevitably leads to information distortion and noise introduction in the procedure of image reconstruction (from signal to image). Artificial intelligence (AI) technologies that can mine knowledge from vast amounts of data offer opportunities to disrupt established workflows. In this prospective study, for the first time, we develop an AI-based signal-to-knowledge diagnostic scheme for lung nodule classification directly from the computed tomography (CT) raw data (the signal). We find that the raw data achieves almost comparable performance with CT, indicating that it is possible to diagnose diseases without reconstructing images. Moreover, the incorporation of raw data through three common convolutional network structures greatly improves the performance of the CT models in all cohorts (with a gain ranging from 0.01 to 0.12), demonstrating that raw data contains diagnostic information that CT does not possess. Our results break new ground and demonstrate the potential for direct signal-to-knowledge domain analysis