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PubMed2026

Amplicon and metagenomic sequencing reveal thifluzamide drive rhizosphere microbial structural shifts and functional adaption.

Thifluzamide (TF) is a widely used phenyl urea fungicide in rice production; however, its impacts on the structural composition and functional dynamics of the rhizosphere microbiome remain poorly understood. Here, we systematically investigated the effects of TF on the structure, interactions, and functional potential of the rice (Oryza sativa L.) rhizosphere microbiome using integrated amplicon sequencing and metagenomic approaches. TF application significantly altered both bacterial and fungal community composition, bacterial diversity was markedly reduced, whereas fungal diversity increased. With bacterial diversity markedly reduced while fungal diversity increased. Beta-diversity analyses revealed strong treatment-driven community separation, indicating pronounced TF-induced microbial restructuring. Co-occurrence network analysis demonstrated reduced complexity and connectivity in bacterial networks but increased negative co-occurrence patterns within fungal communities, suggesting contrasting stability responses between microbial kingdoms. Metagenomic profiling further revealed substantial functional shifts, including the differential enrichment of KEGG and COG pathways associated with xenobiotic metabolism. Notably, while total ARG abundance remained stable, TF exposure altered the resistome profile by selectively enriching specific classes of antibiotic resistance genes (ARGs), biocide resistance genes (BRGs), and mobile genetic elements (MGEs). Strong positive correlations between MGEs and ARGs highlighted an elevated potential for horizontal gene transfer. Metagenome-assembled genome (MAG) analysis identified specific TF-enriched bacterial taxa, including Methylophilus, Sulfurospirillum, and Azospirillum, which harbored genes involved in pesticide degradation and xenobiotic transformation. Collectively, these findings demonstrate that TF profoundly reshapes the rice rhizosphere microbiome by altering microbial diversity, interaction networks, resistance gene profiles, and functional capacities. This study provides genomic insights into fungicide-microbiome interactions, underscoring the potential ecological implications associated with TF application, while identifying candidate microbial taxa that may contribute to pesticide degradation and rhizosphere microecology resilience.

Rhizosphere↗
PubMed2026

Role of IFIT1 and IFIT3 in systemic lupus erythematosus: modeling a diagnosis and exploring immune regulation.

Systemic lupus erythematosus (SLE) is a complex autoimmune disorder characterized by multi-organ involvement and a protracted clinical course. Current diagnostic strategies, which rely heavily on clinical symptoms and serology, are often insufficient for early detection. Therefore, highly accurate diagnostic biomarkers are urgently needed to facilitate early intervention and optimize personalized treatment strategies. D atasets GSE61635 and GSE135779 were integrated to identify differentially expressed genes. Weighted gene co-expression network analysis (WGCNA) was performed to isolate the module with the strongest clinical relevance. Mendelian randomization and single‑cell RNA‑seq were used to identify key disease‑relevant genes. A diagnostic model was then constructed, and gene set variation analysis (GSVA), along with gene set enrichment analysis (GSEA), was conducted to elucidate the underlying molecular pathways. IFIT1 and IFIT3 were identified as 2 core genes highly expressed in monocytes and T cells of SLE patients. Functional enrichment analysis revealed that these genes were enriched in immune-related pathways, metabolic pathways related to inflammation and genomic stability. The diagnostic model showed good accuracy, with an area under the curve (AUC) of 0.974 on the training set and 0.912 on the validation set. IFIT1 and IFIT3 represent promising biomarkers for diagnosing SLE and appear to mediate key immune and metabolic disturbances. Furthermore, the developed model serves as an accurate and reliable instrument for early diagnosis and personalized therapy. Large-scale clinical studies are warranted to further validate these findings and evaluate their clinical application.

Humans↗
PubMed1985

Elementary complex number analysis of lung models.

Formulae from the complex number method of network analysis are used by respiratory physiologists with increasing frequency. As yet, however, no elementary derivation of these has been found in physiological journals, the reader usually being referred to electronic texts: the derivations in these texts are not directed to respiratory problems and are generally more involved than required for respiratory purposes. The present presentation of the complex number analysis method is elementary, the necessary grounding in the field of complex numbers being given in an Appendix. The analysis is directed first to a one-compartment lung, then to a simple two-compartment lung (effectively the same method applying also to simple multiple-compartment lungs), and finally an indication is given of the analysis of more complex models that include air-compression effects. By gaining an understanding of the underlying principles the respiratory physiologist will improve the extent and depth of application of this powerful method of analysis. In particular, he will be in a position to analyse the body plethysmographic measurement of the non-uniform lung.

Airway Resistance↗
PubMed2025

Genome-Wide Identification of the TIFY Family in Cannabis sativa L. and Its Potential Functional Analysis in Response to Alkaline Stress and in Cannabinoid Metabolism.

TIFY transcription factors play crucial regulatory roles in secondary metabolism and stress response. However, the expression patterns of the Cannabis sativa L. TIFY gene family under alkali stress, their involvement in cannabinoid metabolism, and their underlying genetic evolutionary mechanisms remain largely unexplored. In this study, we used bioinformatics approaches to conduct genome-wide identification and functional characterization of the C. sativa TIFY gene family. Fourteen TIFY genes were identified and mapped onto seven chromosomes. These genes were classified into four subfamilies: TIFY, JAZ, ZML, and PPD, with the JAZ subfamily further subdivided into five distinct branches. Collinearity analysis suggested that gene duplication events contributed to the expansion of the TIFY gene family in C. sativa. Weighted gene coexpression network analysis (WGCNA) revealed that CsJAZ2, CsJAZ3, and CsJAZ6 participated in the cannabinoid regulatory network. Cis-element analysis indicated that the promoter regions of TIFY genes were enriched in hormone- and stress-responsive elements. Furthermore, transcriptome and RT-qPCR analyses were conducted to examine gene expression patterns under alkaline stress (the RNA employed in RT-qPCR was extracted from the apical leaves of samples subjected to short-duration alkaline stress treatment). The results showed that CsJAZ5 and CsJAZ6 were downregulated, whereas CsPPD1, CsTIFY1, and CsZML1 were upregulated in response to alkali stress. In summary, CsJAZ5, CsPPD1, and CsTIFY1 may serve as candidate genes for the development of alkali-tolerant cultivars, while CsJAZ2 and CsJAZ3 may be valuable targets for enhancing cannabinoid production. This study provides important molecular insights and a theoretical basis for future research on the evolutionary dynamics and functional roles of TIFY transcription factors, particularly in stress adaptation and cannabinoid metabolism.

Cannabis↗
PubMed2024

Networks of human milk microbiota are associated with host genomics, childhood asthma, and allergic sensitization.

The human milk microbiota (HMM) is thought to influence the long-term health of offspring. However, its role in asthma and atopy and the impact of host genomics on HMM composition remain unclear. Through the CHILD Cohort Study, we followed 885 pregnant mothers and their offspring from birth to 5 years and determined that HMM was associated with maternal genomics and prevalence of childhood asthma and allergic sensitization (atopy) among human milk-fed infants. Network analysis identified modules of correlated microbes in human milk that were associated with subsequent asthma and atopy in preschool-aged children. Moreover, reduced alpha-diversity and increased Lawsonella abundance in HMM were associated with increased prevalence of childhood atopy. Genome-wide association studies (GWASs) identified maternal genetic loci (e.g., ADAMTS8, NPR1, and COTL1) associated with HMM implicated with asthma and atopy, notably Lawsonella and alpha-diversity. Thus, our study elucidates the role of host genomics on the HMM and its potential impact on childhood asthma and atopy.

Humans↗
PubMed2006

A methodology for the structural and functional analysis of signaling and regulatory networks.

BACKGROUND: Structural analysis of cellular interaction networks contributes to a deeper understanding of network-wide interdependencies, causal relationships, and basic functional capabilities. While the structural analysis of metabolic networks is a well-established field, similar methodologies have been scarcely developed and applied to signaling and regulatory networks. RESULTS: We propose formalisms and methods, relying on adapted and partially newly introduced approaches, which facilitate a structural analysis of signaling and regulatory networks with focus on functional aspects. We use two different formalisms to represent and analyze interaction networks: interaction graphs and (logical) interaction hypergraphs. We show that, in interaction graphs, the determination of feedback cycles and of all the signaling paths between any pair of species is equivalent to the computation of elementary modes known from metabolic networks. Knowledge on the set of signaling paths and feedback loops facilitates the computation of intervention strategies and the classification of compounds into activators, inhibitors, ambivalent factors, and non-affecting factors with respect to a certain species. In some cases, qualitative effects induced by perturbations can be unambiguously predicted from the network scheme. Interaction graphs however, are not able to capture AND relationships which do frequently occur in interaction networks. The consequent logical concatenation of all the arcs pointing into a species leads to Boolean networks. For a Boolean representation of cellular interaction networks we propose a formalism based on logical (or signed) interaction hypergraphs, which facilitates in particular a logical steady state analysis (LSSA). LSSA enables studies on the logical processing of signals and the identification of optimal intervention points (targets) in cellular networks. LSSA also reveals network regions whose parametrization and initial states are crucial for the dynamic behavior. We have implemented these methods in our software tool CellNetAnalyzer (successor of FluxAnalyzer) and illustrate their applicability using a logical model of T-Cell receptor signaling providing non-intuitive results regarding feedback loops, essential elements, and (logical) signal processing upon different stimuli. CONCLUSION: The methods and formalisms we propose herein are another step towards the comprehensive functional analysis of cellular interaction networks. Their potential, shown on a realistic T-cell signaling model, makes them a promising tool.

Animals↗
PubMed2026

Differential Proteomic Profiling of Responders and Non-responders to Direct-Acting Antivirals Treatment in Chronic Hepatitis C Virus Infection.

Hepatitis C Virus (HCV), particularly genotype 3 (GT-3), is highly prevalent in India and is associated with faster progression to cirrhosis, hepatocellular carcinoma, and higher treatment failure rates. Although Direct-Acting Antivirals (DAAs) have revolutionized HCV therapy, 5-10% of patients fail to achieve sustained virological response (SVR). This proteomic study aimed to identify changes in the proteomic profile before and after treatment of both responders and non-responders to HCV treatment. Paired plasma samples from HCV GT-3 infected patients were collected before and 12 weeks after initiating DAAs treatment, along with healthy controls. Quantitative proteomic analysis was performed on the paired samples. Differentially expressed proteins (DEPs) were identified and subjected to functional analysis including gene set enrichment analysis (GSEA) and protein-protein interaction (PPI) network analysis. GSEA revealed enrichment in extracellular matrix organization and innate immune pathways. Expression patterns of candidate proteins selected based on fold change and false discovery rate (FDR) criteria were further evaluated in an independent cohort. Western blot confirmed key expression trends of candidate proteins. Proteins linked to extracellular matrix remodeling and angiogenesis showed differential expression patterns. Successful validation of these candidate proteins in large independent cohorts holds potential to predict therapeutic outcomes.

Humans↗
PubMed2001

Genetic evidence for Near-Eastern origins of European cattle.

The limited ranges of the wild progenitors of many of the primary European domestic species point to their origins further east in Anatolia or the fertile crescent. The wild ox (Bos primigenius), however, ranged widely and it is unknown whether it was domesticated within Europe as one feature of a local contribution to the farming economy. Here we examine mitochondrial DNA control-region sequence variation from 392 extant animals sampled from Europe, Africa and the Near East, and compare this with data from four extinct British wild oxen. The ancient sequences cluster tightly in a phylogenetic analysis and are clearly distinct from modern cattle. Network analysis of modern Bos taurus identifies four star-like clusters of haplotypes, with intra-cluster diversities that approximate to that expected from the time depth of domestic history. Notably, one of these clusters predominates in Europe and is one of three encountered at substantial frequency in the Near East. In contrast, African diversity is almost exclusively composed of a separate haplogroup, which is encountered only rarely elsewhere. These data provide strong support for a derived Near-Eastern origin for European cattle.

Africa↗
PubMed1998

Using neural networks as an aid in the determination of disease status: comparison of clinical diagnosis to neural-network predictions in a pedigree with autosomal dominant limb-girdle muscular dystrophy.

Studies of the genetics of certain inherited diseases require expertise in the determination of disease status even for single-locus traits. For example, in the diagnosis of autosomal dominant limb-girdle muscular dystrophy (LGMD1A), it is not always possible to make a clear-cut determination of disease, because of variability in the diagnostic criteria, age at onset, and differential presentation of disease. Mapping such diseases is greatly simplified if the data present a homogeneous genetic trait and if disease status can be reliably determined. Here, we present an approach to determination of disease status, using methods of artificial neural-network analysis. The method entails "training" an artificial neural network, with input facts (based on diagnostic criteria) and related results (based on disease diagnosis). The network contains weight factors connecting input "neurons" to output "neurons," and these connections are adjusted until the network can reliably produce the appropriate outputs for the given input facts. The trained network can be "tested" with a second set of facts, in which the outcomes are known but not provided to the network, to see how well the training has worked. The method was applied to members of a pedigree with LGMD1A, now mapped to chromosome 5q. We used diagnostic criteria and disease status to train a neural network to classify individuals as "affected" or "not affected." The trained network reproduced the disease diagnosis of all individuals of known phenotype, with 98% reliability. This approach defined an appropriate choice of clinical factors for determination of disease status. Additionally, it provided insight into disease classification of those considered to have an "unknown" phenotype on the basis of standard clinical diagnostic methods.

Adolescent↗
PubMed2007

Genetic network and pathway analysis of differentially expressed proteins during critical cellular events in fracture repair.

Bone repair consists of inflammation, intramembranous ossification, chondrogenesis, endochondral ossification, and remodeling. To better understand the translational regulation of these distinct but interrelated cellular events, we used the second generation of BD Clontechtrade mark Antibody Microarray to dissect and functionally characterize proteins differentially expressed between intact and fractured rat femur at each of these cellular events. Genetic network analysis showed that proteins differentially expressed within a given cellular event tend to be physically or functionally correlated. Seventeen such interacting networks were established over five cellular events that were most frequently associated with cell cycle, cell death, cell-to-cell signaling and interaction, and cell growth and proliferation. Eighteen molecular pathways were significantly enriched during the bone repair process, of which ERK/MAPK, NF-kB, PDGF, and T-cell receptor signaling pathways were significant during three or more cellular events. The analyses revealed dynamic temporal expression patterns and cellular-event-specific functions. The inflammation event on Day 1 was characteristic of the cell cycle-related molecular changes. The relative quiet stage of intramembranous ossification on Day 4 and the molecularly most active stage of chondrogenesis on Day 7 were featured by coordinated cell death and cell-proliferation signals. Endochondral ossification on Day 14 experienced a clear transition from the molecular/cellular function to the physiological system development/function. The osteoclast-mediated remodeling on Day 28 was highlighted by the integrin signaling pathway. The distinct changes in protein expression during these cellular events provide a molecular basis for developing cellular event-targeted therapeutic strategy to accelerate bone healing.

Animals↗
PubMed2005

Beyond strategy: exploring the brokerage role of facilities manager in hospitals.

PURPOSE: Seeks to explore the brokerage role of facilities manager in hospitals, based on the premise that facilities management (FM) is largely concerned with "strategic brokerage". Strategic brokerage is the term coined by Akhaghi to explain the integration of a wide range of support services to ensure the effective operation of the core business of an organization. DESIGN/METHODOLOGY/APPROACH: The research was conducted in the health service sector using a single case study approach to examine the brokerage potential for FM in a hospital in the Sydney Metropolitan area. A social network analysis technique was used to identify and analyse the communication networks of players in a hospital environment. Two general questions guided the analysis. First, what is the brokerage potential within the FM process? Second, where are the opportunities for brokerage? FINDINGS: The results indicate that identifying relationship linkages between different functional units can create potential brokerage opportunities. ORIGINALITY/VALUE: The proposition is made that viewing FM from a brokerage perspective can add value to the delivery of health-care services.

Cooperative Behavior↗
PubMed1999

Using sensitivity analysis for efficient quantification of a belief network.

Sensitivity analysis is a method to investigate the effects of varying a model's parameters on its predictions. It was recently suggested as a suitable means to facilitate quantifying the joint probability distribution of a Bayesian belief network. This article presents practical experience with performing sensitivity analyses on a belief network in the field of medical prognosis and treatment planning. Three network quantifications with different levels of informedness were constructed. Two poorly-informed quantifications were improved by replacing the most influential parameters with the corresponding parameter estimates from the well-informed network quantification; these influential parameters were found by performing one-way sensitivity analyses. Subsequently, the results of the replacements were investigated by comparing network predictions. It was found that it may be sufficient to gather a limited number of highly-informed network parameters to obtain a satisfying network quantification. It is therefore concluded that sensitivity analysis can be used to improve the efficiency of quantifying a belief network.

Algorithms↗
PubMed2004

Non-linear survival analysis using neural networks.

We describe models for survival analysis which are based on a multi-layer perceptron, a type of neural network. These relax the assumptions of the traditional regression models, while including them as particular cases. They allow non-linear predictors to be fitted implicitly and the effect of the covariates to vary over time. The flexibility is included in the model only when it is beneficial, as judged by cross-validation. Such models can be used to guide a search for extra regressors, by comparing their predictive accuracy with that of linear models. Most also allow the estimation of the hazard function, of which a great variety can be modelled. In this paper we describe seven different neural network survival models and illustrate their use by comparing their performance in predicting the time to relapse for breast cancer patients.

Breast Neoplasms↗
PubMed1990

Social network characteristics and the duration of primary relationships after entry into long-term care.

This research extends the study of social network analysis into the context of long-term care. Network density, reciprocity, and intensity were hypothesized to explain duration of the ties between frail elderly persons and their networks after they enter a residential care home (RCH), which is a type of long-term care facility. Using longitudinal data from interviews with 81 new, elderly RCH residents and 75 of their closest others, multiple regression analyses showed that the density of frail elderly people's networks has the strongest effect on tie duration. Secondary direct effects were also shown for reciprocity, mental status, being state-financed, White, and having returned home. The intensity of elderly people's ties does not explain tie duration.

Aged↗
PubMed2002

The Biological System of the Elements (BSE)--a brief introduction into historical and applied aspects with special reference on "ecotoxicological identity cards" for different element species (e.g. As and Sn).

There are different methods to estimate and predict effects of chemical elements and corresponding speciation forms in biochemistry and toxicology, including statements on essentiality and antagonisms. Two approaches are given here: (1) "identity cards" describing biologically fundamental aspects of element chemistry and (2) qualitative discussions which assume the existence of (indirect ways into) chemical autocatalysis to be essential for maintaining life and permitting reproduction. The latter method, developed by the present authors, draws upon Stoichiometric Network Analysis, a safe procedure for complexity reduction in feedback networks) and provides estimates of concentration regimes for different elements suitable for survival and reproduction. The biochemical hierarchy level considered here is that of (metallo-)proteins. Thermodynamic toxicity aspects are given in correlations with DMSO solvent affinities and thiocyanate bonding modes. Effects of antagonists and of ion substitution within metalloenzymes or of metabolic simplification can be dealt with, likewise increased sensitivities within symbiotic relationships and within carcinomas are explained which are relevant for environmental monitoring and tumour therapy, respectively.

Animals↗
PubMed2003

Antibody-based technologies for target discovery.

Antibodies have proven to be exquisite investigation tools in the field of life sciences. They also constitute one of the oldest and most successful biological products for diagnostics and therapeutics. This review investigates the current use of antibodies in target discovery. To address this topic in a larger context, established and emerging technologies that are expected to contribute to target discovery will first be examined. These technologies include: mass spectroscopic analysis of proteins, protein-protein interaction and other network analysis approaches, as well as protein and antibody arrays. The potential of antibody engineering and the ANTIBIOMIX technology will then be discussed; antibody therapeutics, however, will not be examined.

Animals↗
PubMed1990

Analysis of a double-layered support system.

This analysis focuses on the support functions of a social network consisting of the families, educational staff, and peers of 33 adolescent females. These women were lower-class school dropouts who joined the Israeli Army and--toward the end of their service--participated in a six-month intensive program of educational upgrading. The program was administered by the Israeli Army and operated by female soldiers close in age, but not in social background and education, to the program's participants. Data were collected by a semi-structured interview and analyzed in terms of the emotional, cognitive, and behavioral support functions of the social network. Analysis shows that (1) whereas the educational staff and the peers provide the young women with all three types of support, their parents' is limited to some aspects of emotional support, and is conditional (to success); (2) the educational staff also induced the peers to act as supporters; and (3) as a result, the participants had access to a "double layer" of emotional, cognitive, and behavioral support, and benefited from being both recipients and providers of support, in the coping-enhancing conditions of sociocultural and situational similarity.

Adolescent↗
PubMed1978

The effects of undernutrition on Purkinje cell dendritic growth in the rat.

The effects of undernutrition on the developing cerebellum were studied in 30-day-old rats undernourished from birth by restricting access to the lactating mother. These animals showed a significant reduction in cerebellar weight when compared with well-nourished controls. Quantitative studies of the cerebellar vermis revealed a 34.2% reduction in total area, with the density of both granule cells and Purkinje cells increased. Network analysis of Golgi-Cox preparations indicated a significant increase in the density of dendritic fields of Purkinje cells, although there was a 37% decrease in overall network size, due to reduction in the total number of dendritic segments, and a reduction in the length of distal segments. Topological analysis indicated that the network had developed by terminal branching, as in normal animals, but with some deviation from the usual purely random branching pattern. All the observed modifications may be accounted for in terms of alterations in protein synthesis and DNA synthesis occurring in undernourished animals. This leads to alterations in the extent of the interneuronal matrix, a reduction in the number of granule cells and direct effects on Purkinje cell metabolism, all of which influence dendritic development, although the relative importance of each of these factors awaits precise definition.

Animals↗