Function Prediction Research Articles

Unveiling the bacterial diversity and potential of the Avicennia marina ecosystem for enhancing plant resilience to saline conditions

BackgroundAvicennia marina ecosystems are critical for coastal protection, water quality enhancement, and biodiversity support. These unique ecosystems thrive in extreme saline conditions and host a diverse microbiome that significantly contributes to plant resilience and growth. Global food security is increasingly threatened by crop yield losses due to abiotic stresses, including saline soils. Traditional plant breeding for salt tolerance is both costly and time-consuming. This study explores the potential of bacteria from A. marina to enhance plant growth under saline conditions, emphasizing their ecological significance.ResultsWe analyzed the microbiome of A. marina from the Red Sea coast using high-throughput Illumina sequencing and culture-dependent methods across various compartments (bulk soil, rhizosphere, rhizoplane, roots, and leaves). Our findings revealed distinct compartment-specific microbial communities, with Proteobacteria being the dominant phylum. Functional predictions indicated diverse microbial roles in metal uptake and plant growth promotion (PGP). Remarkably, our culture-dependent methods allowed us to recover 56% of the bacterial diversity present in the microbiome, resulting in the isolation and characterization of 256 bacterial strains. These isolates were screened for PGP traits, including salt and heat tolerance, siderophore production, and pectinase activity. Out of the 77 bacterial isolates tested, 11 demonstrated a significant ability to enhance Arabidopsis growth under salt stress.ConclusionsOur study highlights the ecological significance of mangrove microbiomes and the potential of culture collections in offering innovative solutions for ecological restoration and crop production in saline conditions. The unique collection of mangrove bacteria, particularly from the rhizosphere and endophytes, showcases significant PGP traits and stress tolerance capabilities. These findings emphasize the importance of functional traits, such as salt tolerance, in the recruitment of endophytic bacteria by plants over taxonomic affiliation. The identified bacterial strains hold potential not only for developing biofertilizers to improve crop productivity but also for ecological restoration projects aimed at rehabilitating saline-degraded lands, thereby contributing to overall ecosystem health and sustainability.

Salinity affects the lipid metabolism and intestinal microbial composition of swimming crab Portunus trituberculatus

The swimming crab (Portunus trituberculatus) is an aquaculture species with high commercial value. However, there is limited information available on the effects of salinity on the lipid metabolism and gut microbiota of P. trituberculatus. This study investigated the hepatopancreatic index, hepatopancreatic lipid metabolism, physiological and biochemical indicators and gut microbiota of P. trituberculatus cultured at salinity 28 ‰, 25 ‰, 22 ‰ and 19 ‰ for 8 weeks. The results showed that the highest expression level of SREBP1, FAS, LPR1, ACOX and FABP was found in 19 ‰ group compared to the 28 ‰ group, along with the enzyme activities of CPT1 and FAS. On the contrary, the expression level of DGAT1 and LPR2 in the 28 ‰ group was significantly higher than that of 19 ‰ group, along with the enzyme activities of LPS. The results of fatty acid content showed that only polyunsaturated fatty acids showed significant changes in response to salinity, such as C18:2n6, C20:2n6 and C20:3n3. Moreover, the intestine microbial α-diversity index of crab in the 28 ‰ treatment was higher than that of other groups. The dominant phylum in gut samples was Proteobacteria, followed by Firmicutes and Bacteroidota. Moreover, Mycoplasma, Candidatus Endoecteinascidia and Shewanella showed a significant decline in the 28 ‰ group compared to the 19 ‰ group. Bacterial functional pathway prediction has also shown that the KEGG pathways involved in ovarian steroidogenesis, NF-kappa B signaling pathway and regulation of lipolysis in adipocytes increased in 28 ‰ group compared with 19 ‰ group. In conclusion, hepatopancreatic fatty acid metabolism was more active in the 19 ‰ group, whereas the 28 ‰ group not only promoted triglyceride synthesis in the hepatopancreas, but also facilitated ovarian development of P. trituberculatus by influencing the gut microbial composition.

Enhanced thermophilic hydrogen production from co-substrate of pretreated waste activated sludge and food waste: Analysis from microbial growth and metabolism

Response surface methodology was employed to establish a thermophilic hydrogen production process from co-substrate of food waste (FW) and waste activated sludge (WAS), resulting in a maximum hydrogen production of 2602.68 ± 54.88 mL/L, which was 5.4 and 21.9 times of FW and WAS, respectively. The co-substrate facilitated the butyrate pathway of hydrogen production and decreased lactate accumulation, and significantly increased the activity of butyric kinase and hydrogenase (p < 0.05). Meanwhile, it could promote microbial growth by creating a more suitable redox environment. Microbial community analysis showed that Thermoanaerobacterium (hydrogen production genus) was specifically enriched and dominant in co-substrate (82.3%). Further functional prediction analysis showed that the co-substrate effectively promoted carbohydrate metabolism. Furthermore, pretreatment improved sludge biodegradability. This study establishes a feasible hydrogen production process, profoundly revealed the mechanism of enhanced anaerobic fermentation from the perspective of microbial growth and metabolism, which lays solid foundation on the hydrogen production from waste biomass.

Post-earthquake functionality and resilience prediction of bridge networks based on data-driven machine learning method

Earthquake-induced bridge damage can disrupt transportation networks, potentially hindering emergency response and post-disaster recovery efforts, and posing public safety risks in affected areas. Rapid and accurate assessment of post-earthquake resilience of bridge networks is crucial for evaluating urban seismic performance. Traditional resilience assessment methods, constrained by complex traffic distribution processes, struggle to quickly evaluate the traffic performance of bridge networks during the post-earthquake recovery period. This paper presents a two-layer stacking ensemble model for predicting the functionality and resilience of bridge networks. The first layer integrates advantages of four base learners, including random forest (RF), artificial neural network (ANN), convolutional neural network (CNN), and extreme gradient boosting (XGBoost). The second layer completes regression of functionality based on a support vector machine (SVM). Bayesian optimization and 5-fold cross-validation are employed for hyperparameter tuning of the ensemble model. Finally, the proposed model is validated using the Sioux-Falls bridge network. Results demonstrate that the developed model provides rapid predictions of post-earthquake network functionality and resilience. Additionally, this model can guide post-earthquake repair decisions and assist in optimizing the allocation of regional repair resources.

Brain-phenotype predictions of language and executive function can survive across diverse real-world data: Dataset shifts in developmental populations

Predictive modeling potentially increases the reproducibility and generalizability of neuroimaging brain-phenotype associations. Yet, the evaluation of a model in another dataset is underutilized. Among studies that undertake external validation, there is a notable lack of attention to generalization across dataset-specific idiosyncrasies (i.e., dataset shifts). Research settings, by design, remove the between-site variations that real-world and, eventually, clinical applications demand. Here, we rigorously test the ability of a range of predictive models to generalize across three diverse, unharmonized developmental samples: the Philadelphia Neurodevelopmental Cohort (n=1291), the Healthy Brain Network (n=1110), and the Human Connectome Project in Development (n=428). These datasets have high inter-dataset heterogeneity, encompassing substantial variations in age distribution, sex, racial and ethnic minority representation, recruitment geography, clinical symptom burdens, fMRI tasks, sequences, and behavioral measures. Through advanced methodological approaches, we demonstrate that reproducible and generalizable brain-behavior associations can be realized across diverse dataset features. Results indicate the potential of functional connectome-based predictive models to be robust despite substantial inter-dataset variability. Notably, for the HCPD and HBN datasets, the best predictions were not from training and testing in the same dataset (i.e., cross-validation) but across datasets. This result suggests that training on diverse data may improve prediction in specific cases. Overall, this work provides a critical foundation for future work evaluating the generalizability of brain-phenotype associations in real-world scenarios and clinical settings.

A nested case-control study of circular ribonucleic acid expression profiles in the peripheral blood of pregnant women with pre-eclampsia.

To examine the expression of circular ribonucleic acid (circRNA) in the peripheral blood of pregnant women with preeclampsia (PE) prior to the onset of the disease. A prospective nested case-control study included pregnant women who delivered at Fujian Maternal and Child Health Hospitals between November 2021 and October 2022. The study included three women diagnosed with PE (the PE group) and three healthy pregnant women (the control group). The expression of circRNAs in the peripheral blood before clinical diagnosis at 20 weeks gestation was analyzed by microarray technology, and the functional analysis and regulatory network prediction were done. There were 162 circRNAs with differentially expressed multiplicity of > 2.0 in the PE group compared to the control group. Out of them, 30 circRNAs were up-regulated and 132 were down-regulated. Gene ontology (GO) analysis revealed that the host genes of the differentially expressed circRNAs mainly participated in protein localization to plasma membrane, insemination, etc. Most enriched terms were related to cytoplasmic dynein complex, microtubule cytoskeleton, etc. In terms of molecular function, the enriched terms were related to dynein light intermediate chain binding, polyubiquitin modification-dependent protein binding, etc. Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analysis revealed that differentially expressed circRNAs were involved in vasopressin-regulated water reabsorption, cyclic guanosine monophosphate-protein kinase G (cGMP-PKG), calcium- and other signaling pathways. CircRNAs are involved in a variety of biological processes and regulatory network related to the pathogenesis of PE.

Bacillus quorum quenching shapes the citrus mycobiome through interkingdom signaling

Microbiomes are sustained through infinite yet mutually interacting microbial communities, with bacteria and fungi serving as the major constituents. In recent times, microbial interventions have become popular for microbiome manipulation to achieve sustainable goals. Whether and how the introduced biocontrol agent drives fungal microbial assemblages (mycobiome) and the role of interkingdom signaling in shaping the microbiome structure and function remain poorly understood. Here, we implemented wild-type (WT) Bacillus subtilis L1–21 and its quorum quenching (QQ) mutants (L1–21Δytnp, L1–21Δyxel) individually and as consortia to explore the enrichment patterns of key mycobiome members in Huanglongbing (HLB) infected citrus compartments including leaf endosphere, root endosphere, and rhizosphere soil. The application of WT and its QQ mutants produced differential mycobiome enrichment across citrus compartments. Our findings reveal that application of WT B. subtilis enriched beneficial fungi such as Trichoderma (15.82 %) in leaf endosphere. In contrast, pathogenic fungi Fusarium (47.5 %) and Gibberella (0.47 %) involved in citrus root decline were adundant in the L1–21Δytnp treated root endosphere while Nigrospora (11 %) was predominant in L1–21Δyxel treated leaf endosphere, affirming the role of bacterial quorum sensing (QS) molecules in shaping the fungal community composition. In general, based on the fungal functional prediction, fungal pathogens were highly abundant in mutant-treated plants, particularly in leaf endosphere (L1–21Δytnp: 25 %; L1–21Δyxel: 36.35 %) compared to WT (20.93 %). Additionally, some fungal members exhibited strong compartment specificity and both mutants induced distinct mycobiome shifts in rhizosphere soil, leaf, and root endopshere. In conclusion, B. subtilis QQ modifies bacterial QS networks facilitating beneficial fungi to establish, while loss of QQ leads to enrichment of pathogenic fungal groups. Our study provides a direct link of perception and regulation of mycobiome through bacterial-based QS and QQ system, and its association with disease outcomes.

The Historical Evolution and Significance of Multiple Sequence Alignment in Molecular Structure and Function Prediction

Multiple sequence alignment (MSA) has evolved into a fundamental tool in the biological sciences, playing a pivotal role in predicting molecular structures and functions. With broad applications in protein and nucleic acid modeling, MSAs continue to underpin advancements across a range of disciplines. MSAs are not only foundational for traditional sequence comparison techniques but also increasingly important in the context of artificial intelligence (AI)-driven advancements. Recent breakthroughs in AI, particularly in protein and nucleic acid structure prediction, rely heavily on the accuracy and efficiency of MSAs to enhance remote homology detection and guide spatial restraints. This review traces the historical evolution of MSA, highlighting its significance in molecular structure and function prediction. We cover the methodologies used for protein monomers, protein complexes, and RNA, while also exploring emerging AI-based alternatives, such as protein language models, as complementary or replacement approaches to traditional MSAs in application tasks. By discussing the strengths, limitations, and applications of these methods, this review aims to provide researchers with valuable insights into MSA’s evolving role, equipping them to make informed decisions in structural prediction research.

Efficacy of composite bacterial deodorant constructed with Camellia sinensis and its in-situ deodorization mechanism on pig manure

Here, we constructed a novel bacterial deodorant (BD) composed of Delftia tsuruhatensis, Paracoccus denitrificans, Pediococcus acidilactici, and Bacillus velezensis. The BD alone removed 64.84 % of NH3, 100 % of H2S, and 63.68 % of comprehensive odor (OU) during a five-day fermentation of pig manure. The effect was enhanced by introducing Camellia sinensis in the composite bacterial deodorant (CBD) treatment, with the removal efficiency (RE) of NH3 and OU being 88.68 % and 88.14 %, respectively. In prolonged trials, maximum RE of NH3, H2S and OU RE reached 90.16 %, 92.32 % and 100 % in CBD group. Bacterial composition of manure revealed that the abundance of odor-producing microbes (Kurthia, Solibacillus, Proteiniphilum and Acholeplasma) and potential pathogens decreased after CBD application. Functional prediction and correlation analyses indicated that the process of nitrification, denitrification and S/N assimilation were facilitated, while S/N mineralization and methanogenesis processes might be inhibited. This deodorant promoted the conversion of malodorous substances into non-odorous forms, establishing an efficient odor removal system in hoggery. Therefore, the bacterial deodorant compounded with C. sinensis has been shown to be an effective method for deodorizing pig farms. This approach will advance the livestock industry toward greener practices and environmental protection, contributing positively to the development of a sustainable agro-ecosystem.

Resting-state brain functional connectivity in patients with chronic intractable pain who respond to spinal cord stimulation therapy

BackgroundSpinal cord stimulation (SCS) is widely accepted as a useful treatment for patients with intractable chronic pain. However, its effectiveness varies between individuals. Therefore, a tool for evaluating its effectiveness in advance is eagerly awaited. We examined whether resting-state functional magnetic resonance imaging as a diagnostic and prognostic tool can predict responsiveness to SCS. MethodsTwenty-nine patients with intractable chronic pain participated in this study. Participants were divided into responder and non-responder groups based on a pain relief rate after SCS trials. All participants underwent resting-state functional magnetic resonance imaging scans before the SCS trials. We searched for functional connectivity that differed significantly in strength between the two groups and was correlated with pain relief rate. We conducted receiver operating characteristic (ROC) analysis and a one-sample proportion test to determine the cut-off value and evaluate the predictive power of the functional connectivity-based prediction model. ResultsIn total, 14 and 15 participants were assigned to the responder and non-responder groups, respectively. Functional connectivity between the middle anterior cingulate cortex and precuneus/posterior cingulate cortex showed significant between-group differences and a significant negative correlation with the pain relief rate. Moreover, this functional connectivity could accurately predict SCS responsiveness greater than chance (sensitivity: 71%; specificity: 87%; area under the curve: 0.814; P<0.001). ConclusionsFor patients with intractable chronic pain, functional connectivity between the middle anterior cingulate cortex and precuneus/posterior cingulate cortex is a promising candidate biomarker to estimate responsiveness to spinal cord stimulation before treatment.

Synergistic toxicity of cadmium and triadimefon on the microbiota and health of Rana dybowskii tadpoles

The skin and gut microbiota are crucial to amphibians. Triadimefon (TF), a widely used triazole fungicide, controls crop diseases and regulates growth, with uncertain effects on amphibian microbiota. Contamination, typically involving mixed chemicals at low concentrations, including cadmium (Cd) and TF, may detrimentally affect amphibian growth, survival, and microbiota health in both the skin and gut, but few research has examined these consequences. This research examines the impact of Cd and TF on Rana dybowskii tadpoles, focusing on survival, body mass, and microbiome changes over 28 days across four groups: control, Cd, TF, and Cd + TF groups. Results showed significant reductions in survival and body mass in Cd and TF-treated groups, with the combination group being the most affected. Microbiota analysis revealed significant dysbiosis in both gut and skin microbiomes under pollutant stress, with a marked microbiota and a shift in dominant microbial communities. Function prediction analysis based on the microbiome composition highlighted significant differences across various biological pathways, including metabolism, immune system, environmental adaptation, and disease resistance. These alterations suggest that pollutant exposure compromises the tadpoles' ability to maintain homeostasis and resist pathogens. In conclusion, this study reveals the detrimental effects of Cd and TF on the survival, growth, and microbiomes of R. dybowskii tadpoles, indicating significant environmental and health risks.

Bacterial and fungal diversity, community composition, functional groups, and co-occurrence network succession in dryland and paddy soils along a 3000-year chronosequence

There is a limited understanding of soil microbial community successional trajectories during dryland and paddy soil long-term management after wetland reclamation. We examined the effects of long-term reclamation on the microbial diversity, community composition, functional groups, and co-occurrence network in dryland and paddy soils using a 3000-year soil chronosequence established in the floodplain of the middle and lower Yangtze River, employing the “space-for-time” method and 16S rRNA and ITS amplicon sequencing. Over time, reclaimed land experienced varying losses in microbial richness and diversity, with paddy soils exhibiting higher alpha diversity than drylands due to greater resources and ecological niches. Proteobacteria dominated drylands, Chloroflexi prevailed in paddy soils, and fungal communities in both land-use types were primarily Ascomycota and Basidiomycota. Functional predictions showed higher chemoheterotrophy and aerobic_chemoheterotrophy in dryland soils and more animal_parasites_or_symbionts and dung_saprotrophs in paddy soils. During the 2000–3000 year reclamation, the abundance of pathogens increased, posing risks to agricultural production. Co-occurrence network analysis indicated that, compared to paddy soils, dryland soils may form a more complex and stable microbial cooperative network. Soil organic carbon and pH influenced microbial communities in drylands, while bulk density and cation exchange capacity were crucial in paddy soils. Partial least squares path modeling indicated that reclamation duration impacts microbial network complexity and diversity, with soil properties regulating paddy soil microbial networks. These findings broaden our understanding of microbial community succession in dryland and paddy soils after long-term wetland reclamation, supporting soil health and sustainable agriculture.

The expression profile analysis and functional prediction of lncRNAs in peripheral blood mononuclear cells in maintenance hemodialysis patients developing heart failure

Heart failure (HF) is the leading cause of death in patients with maintenance hemodialysis (MHD). Biomarkers has an important guiding role in the early diagnosis, risk stratification, and prognostic assessment of HF. Increasing studies have indicated that long non-coding RNAs (lncRNAs) have played an indispensable role in the regulatory network of HF. This study was aiming to explore the expression profiles of lncRNAs in patients treated with MHD developing heart failure. Peripheral blood mononuclear cells were isolated from 4 hemodialysis patients with reduced ejection fraction (HFrEF) and 4 hemodialysis patients with preserved ejection fraction (HFpEF), respectively. The expression profile analysis of lncRNAs was performed by using Illumina Novaseq 6000 sequencer. Quantitative real time polymerase chain reaction (qRT-PCR) was used to verify the expression of representative differentially expressed lncRNAs. Based on lncRNA-miRNA-mRNA-KEGG network analysis, the potential role of candidate lncRNAs and their association with the severity of HF were further evaluated. In total, 1,429 differentially expressed lncRNAs were found between patients with HFrEF and patients with HFpEF, of which 613 were up-regulated and 816 were down-regulated (P < 0.05). Five candidate lncRNAs were screened out by a series of bioinformatic analyses. After being compared with miRBase, ENST00000561762, one of the 5 candidates, was considered the most likely lncRNA to be serving as a precursor for miRNA. Nine predicted target genes were found by further lncRNA-miRNA-mRNA-KEGG network analysis, and among which ITGB5 was enriched in the actin dynamics signaling pathway. In another cohort of hemodialysis patients, the expression of lncRNA ENST00000561762 was verified by qRT-PCR. Further analysis revealed that there was a strong correlation between left ventricular ejection fraction and ENST00000561762, proBNP, and 6-minute walk distance, respectively. LncRNAs expression profile was remarkably different in hemodialysis patients with HFrEF compared to those with HFpEF. Among which, lncRNA ENST00000561762 was considered as a promising biomarker for patients with HFrEF as it was predicted to be a miRNA precursor to regulate the actin dynamics signaling pathway.

Advancements in phasor-based FLIM: multi-component analysis and lifetime probes in biological imaging.

Fluorescence lifetime imaging microscopy (FLIM) is a reliable method that achieves imaging by detecting fluorescence lifetimes within samples. Owing to its unique temporal characteristic, it can complement fluorescence intensity measurement. Technological and methodological advancements in FLIM have broadened its applications across various domains. The processing of fluorescence lifetime data is crucial for enhancing the speed and accuracy of imaging. Thus, various lifetime fitting algorithms have been developed to improve the imaging speed. The phasor analysis (PA) method is an approach for processing fluorescence lifetime data, capable of directly converting lifetime signals into visual graphics without fitting, which outperforms traditional approaches in speed. Furthermore, lifetime probes with distinct lifetimes are readily implemented for visualization and cluster analysis combined with PA, facilitating the prediction of specific biological states or functions. This review examines various lifetime probes employed in phasor-based FLIM and discusses their roles in the PA method. The methods for multi-component PA within complex biological environments were also described. Additionally, we focused on the advantages of the phasor vector rule and the unmixing of multi-component analysis based on PA. The integration of lifetime probes with phasor-based FLIM facilitates rapid and intuitive detection methods for analyzing complex biological environments.

Identification of Novel Proteins Mediating Causal Association Between Smoking and Essential Hypertension: A Mendelian Randomization Study.

Smoking is a factor for hypertension. We aim to reveal novel plasma proteins mediating the relationship of smoking with hypertension and identify potential drug targets for hypertension on the basis of Mendelian randomization design. Data for smoking were selected from the largest genome-wide association study meta-analysis performed by the Genome-Wide Association Study and Sequencing Consortium of Alcohol and Nicotine Use. Data for plasma proteins were selected from the deCODE Health study and the UK Biobank Pharma Proteomics Project. Data for hypertension were extracted from the FinnGen Study. Moreover, proteome-wide Mendelian randomization and colocalization analyses, 2-step Mendelian randomization, and gene function and network prediction, as well as druggability assessment were performed. We finally identified 8 proteins (ANXA4 [annexin A4], DLK1 [protein delta homolog 1], KLB [β-klotho], MMP8 [matrix metallopeptidase 8], PLAT [tissue-type plasminogen activator], POSTN [periostin], SAT2 [thialysine N-ε-acetyltransferase], and IFNLR1 [interferon λ receptor 1]) mediating association of smoking with hypertension. PLAT and IFNLR1 were identified to be involved in the complement and coagulation cascades and the Janus kinase/signal transducer and activator of transcription signaling pathway. ANXA4, KLB, MMP8, PLAT, and IFNLR1 had druggability. Moreover, IFNLR1 had strong evidence of genetic colocalization, because the posterior probability for H4 of IFNLR1 was 91.3%. This study identified the 8 proteins that mediate causal association between smoking and essential hypertension. Interferon λ receptor agonist targeting IFNLR1 may open a new avenue for treating hypertension. Our discoveries provide new insights into protein pathogenesis of hypertension and to better guide hypertension prevention and treatment among smokers.

Key drivers of heavy metal bioavailability in river sediments and microbial community responses under long-term high-concentration pollution

Given the urgent need for effective environmental management of metal-polluted ecosystems, understanding the drivers of heavy metal bioavailability and microbial adaptation is crucial. The Dongdagou River, a major pollution source to the upper Yellow River, presents significant risks to regional water quality and biodiversity. This study investigates heavy metal bioavailability and its drivers, alongside microbial community responses, in 39 surface sediment samples from the river. The results revealed severe contamination, particularly with cadmium (Cd). Statistical analysis revealed that effective sulfur (ES) plays a crucial role in driving bioavailability. High-throughput sequencing indicated that bacterial communities were primarily dominated by Proteobacteria, with increased microbial diversity observed downstream. Functional predictions highlighted the prevalence of chemoheterotrophy and nitrogen cycling processes, alongside a significant presence of metal-resistance genes and enzymes, such as Cu-Zn superoxide dismutase and metal-efflux transporters. These adaptations imply that microbial communities are developing mechanisms of resilience in response to prolonged heavy metal exposure. These findings offer valuable insights for formulating targeted remediation strategies in environments affected by heavy metal pollution.

Effects of Spent Substrate of Oyster Mushroom (Pleurotus ostreatus) on Feed Utilization and Liver Serum Indices of Hu Sheep from the Perspective of Duodenal Microorganisms

This study aimed to evaluate the effects of Pleurotus ostreatus spent mushroom substrate (P.SMS), which is characterized by high production but low utilization, on feed utilization and liver serum indices from the perspective of duodenal microorganisms. Forty-five 3-month-old Hu sheep were randomly assigned to five groups and fed diets in which whole-plant corn silage (WPCS) was substituted with P.SMS at levels of 0% (Con), 5% (PSMS5), 10% (PSMS10), 15% (PSMS15), or 20% (PSMS20). The results indicated that the addition of P.SMS complexly influenced the apparent digestibility of dry matter, organic matter, and crude protein, with PSMS10 showing the highest digestibility of these nutrients. P.SMS inclusion significantly affected serum alanine aminotransferase levels, with PSMS5 showing higher levels than both the Con and PSMS20 groups (p &lt; 0.05). Importantly, the inclusion of P.SMS did not affect the richness and diversity of duodenal microorganisms. Significant differences in the phyla Verrucomicrobiota and Spirochaetota were observed between the Con and PSMS20 groups. The observed trend towards an increase in the genus Trichoderma (p = 0.057) suggests that P.SMS is susceptible to contamination by this genus, which in turn affects the structure of the intestinal flora. Furthermore, functional gene predictions indicated differences in amino acid metabolism among the groups (p &lt; 0.05). In conclusion, feeding with 10% P.SMS resulted in the highest digestibility without adversely affecting the structure of the duodenal community or liver function.

Seasonal Changes and Age-Related Effects on the Intestinal Microbiota of Captive Chinese Monals (Lophophorus lhuysii)

The Chinese monal (Lophophorus lhuysii) is a large-sized and vulnerable (VU in IUCN) bird from southwestern China. This study applied 16S rRNA high-throughput sequencing to comprehensively examine the gut microbiota of captive Chinese monals (located in Baoxing, Sichuan, China) across varying seasons and life stages. Dominant bacterial phyla identified included Proteobacteria, Firmicutes, Bacteroidetes, and Actinobacteria. Significant seasonal and age-associated shifts were observed within specific bacterial groups, particularly marked by seasonal fluctuations in beta diversity. Moreover, linear discriminant analysis effect size (LEfSe) and functional predictions highlighted distinct winter signatures, indicating possible functional shifts in energy metabolism and disease resistance. In mid-aged adults, an expansion of Gamma-Proteobacteria suggested an elevated susceptibility of the gut microbiota of Chinese monals to chronic disorders and microbial imbalance. Putative pathogenic bacteria exhibited increased abundance in spring and summer, likely driven by temperature, host physiological cycles, interspecies interactions, and competition. These findings imply that the diversity, and structure of the gut microbiota in captive Chinese monals are strongly influenced by seasonal and age-related factors. The insights provided here are essential for improving breeding strategies and preventing gastrointestinal diseases in captivity.

Identification and cognitive function prediction of Alzheimer's disease based on multivariate pattern analysis of hippocampal volumes.

Alzheimer's disease (AD) is strongly associated with slowly progressive hippocampal atrophy. Elucidating the relationships between local morphometric changes and disease status for early diagnosis could be aided by machine learning algorithms trained on neuroimaging datasets. This study intended to propose machine learning models for the accurate identification and cognitive function prediction across the AD severity spectrum based on structural magnetic resonance imaging (sMRI) of the bilateral hippocampi. The high-resolution sMRI data of 120 AD dementia patients, 232 amnestic mild cognitive impairment (aMCI) patients, and 206 healthy controls (HCs) were included from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The classification capacity and cognitive predict ability of hippocampal volume was evaluated by multiple pattern analysis using the support vector machine (SVM) and relevance vector regression (RVR) application of the Pattern Recognition for Neuroimaging Toolbox, separately. For validation, the analyses were performed using a biomarker-based regrouping method and another independent local dataset. The SVM application produced a total accuracy of 94.17%, 80.85%, and 70.74% and area under receiver operating characteristic curves of 0.97, 0.87, and 0.72 between HC versus AD dementia, HC versus aMCI, and aMCI versus AD dementia classification, respectively. The RVR application significantly predicted the baseline and mean cognitive function at three years of follow-up. Qualitatively consistent results were obtained using different regrouping method and the local dataset. The machine learning methods based on the bilateral hippocampi distinguished across the AD severity spectrum and predicted the baseline and the longitudinal cognitive function with greater accuracy.

Structural Characteristics and Driving Factors of Rhizosphere Microbial Communities in the Rhizosphere of Six Stipa Species Across the Ningxia Steppe

The relation and interaction of rhizosphere microbial communities with local environmental factors and root traits is currently a vibrant research hotspot. Yet little is known about how the morphological and functional properties of roots in steppe plants affect microbial community structure. Hence, this study investigated the rhizosphere soil of six Stipa species across the Ningxia steppe in China to examine how the composition of their microbial communities responds to both root traits as well as surrounding environmental factors. Our results reveal significant differences (p &lt; 0.001) in the composition of rhizosphere microbial communities among different Stipa species. The dominant bacterial and fungal phyla are Proteobacteria and Ascomycota, respectively; further, Mortierellomycota plays a key role in the fungal community and is closely associated with other fungal taxa. According to the functional gene predictions for bacteria and fungi, the rhizosphere microbes associated with Stipa species are mainly related to organic matter metabolism and nitrogen cycling. We find that soil physicochemical properties (SOC, TN, TP, AP, SWC, FL, SL) and root traits (RTD, Rtn) are pivotal factors which directly influence the structure of microbial communities dwelling in the rhizosphere of Stipa species. The dominant phyla of fungi and bacteria can respond to those properties in two contrasting ways. One group, consisting of bacteria such as Acidobacteria and fungi like Mortierellomycota, has a relative abundance that is positively correlated with soil nutrients (SOC, TN, AN, TP), whereas the second group, which includes bacteria such as Bacteroidetes and fungi like Ascomycota, is characterized by a negative correlation. More importantly, our results show that root traits significantly impact (p &lt; 0.001) fungal diversity, whereby the morphological and functional properties indirectly affect the composition of bacterial and fungal communities by modulating soil properties. Altogether, the findings suggest that the morphological and functional properties of Stipa roots play a prominent role in shaping the community structure of rhizosphere microbes in steppe, providing a theoretical basis for exploring changes in these communities across space and time, as well as offering insights for grassland conservation and sustainable management.