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PubMed2025

Proteomics-enabled learning machine algorithms enhance the prediction of cardiovascular diseases in patients with type 2 diabetes mellitus.

BACKGROUND AND AIMS: Estimating the risk of cardiovascular disease (CVD) complications in type 2 diabetes mellitus (T2DM) patients is critical in the medical decision-making process. This study aimed to use a machine learning technique combined with proteomics to develop personalized models for predicting CVD in patients with T2DM. METHODS AND RESULTS: In total, 874 patients with T2DM and 2,920 Olink proteins obtained from the UK Biobank were used in this study. Proteins were screened using Cox regression and LASSO regression. A basic model containing clinical features and a full model combining proteome and clinical features were constructed using the random survival forest algorithm. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the predictive performance of the models and compare them with other CVD predictive models. Compared with the basic model, the full model performed better in predicting CVD, with time-dependent AUCs of 0.81 (3 years), 0.74 (5 years) and 0.74 (10 years) (0.77, 0.69 and 0.67). We calculated the risk scores of the Framingham, ASCVD and Score2-Diabetes models. The results revealed that the prediction performance of the full model was also better than that of the abovementioned models. In terms of differentiation accuracy, the results of the net reclassification improvement index and integrated discrimination improvement index showed that the full model can identify high-risk individuals more accurately (accuracy rate: 79% vs. 69%). CONCLUSIONS: Proteomics can be used to predict cardiovascular complications in diabetic patients. It is also necessary to consider the applicability of the model due to the limitations of the sample size and the constraints of proteomics in clinical applications.

Humans↗

PubMed2026

Exploring the hypothetical role of Bacteroides species in depression progression: insights from metagenomic analysis.

Depression, a psychiatric disorder with significant morbidity and mortality, has a complex etiology. Recent advances in microbiome research have highlighted the potential role of fecal microbiota in depression pathogenesis. This study utilized shotgun metagenomic sequencing to compare the fecal microbiota of 28 depression patients and 26 healthy individuals. Significant differences in fecal microbiota composition were observed between the two groups. We generated 350 non-redundant high-quality metagenome-assembled genomes (MAGs) by binning and conducted comparisons between the depression and control groups. Notably, we found that the MAGs enriched in people with depression mostly belonged to Bacteroides, indicating a close link between Bacteroides abundance and the development of depression, suggesting that Bacteroides might be a potential culprit for depression. In the depression group, we found that the module of nitric oxide synthesis was remarkably enriched, and all Bacteroides MAGs contained genes annotated as nitric oxide synthase, suggesting that increased levels of Bacteroides may contribute to elevated nitric oxide synthesis. A distinct microbial signature consisting of Arthrobacter sp._U41, Bacillus cereus, Campylobacter rectus, and Pasteurella dagmatis accurately discriminates between depressed individuals and healthy controls, achieving an average area under the receiver operating characteristic curve of 0.950. This research sheds light on the potential role of fecal microbiota in depression and highlights specific metabolic pathways and microbial markers for further investigation.IMPORTANCEThis research highlighted significant differences in the composition and function of fecal microbiota between individuals with depression and healthy individuals, particularly the enrichment of Bacteroides metagenome-assembled genomes (MAGs) in depression patients. The upregulation of the nitric oxide synthesis pathway associated with these MAGs belonging to Bacteroides in the gut of depression patients had also been observed. The selected bacterial biomarkers reliably differentiate depression cases from healthy controls with high diagnostic accuracy (mean area under the receiver operating characteristic curve = 0.950). Our results suggest the importance of exploring microbial markers as potential diagnostic and therapeutic targets in managing depression.

Humans↗

PubMed2026

Metabolic-cell-death gene trio predicts survival and cuproptosis sensitivity in colorectal cancer.

BACKGROUND: Metabolic cell death (MCD) modulates colorectal cancer (CRC) progression, yet its prognostic value remains unexplored. We aimed to build an MCD-centred gene signature for outcome prediction and precision therapy. METHODS: Transcriptomes of 1,174 CRC patients were integrated. Weighted gene co-expression network analysis, differential expressions and least absolute shrinkage and selection operator (LASSO) + random survival forest were successively applied to derive a three-gene (CDKN2A/MPC1/AHCY) risk model. Functional, immune-infiltration, drug-sensitivity and genomic analyses were performed, followed by validation in fresh clinical specimens and cell lines. RESULTS: Integrative metabolic-death transcriptomics identified CDKN2A, MPC1 and AHCY as the hub drivers of CRC. Their three-gene signature robustly stratified patients into high- and low-risk subsets [3-year area under the curve (AUC) 0.83-0.85, P<0.001]. High-risk tumors were enriched for extracellular matrix (ECM)-receptor-interaction pathways, displayed abundant myeloid-derived suppressor cell (MDSC) infiltration and were more vulnerable to AZD8186, AZ960 and JAK inhibitors. Guided by these in-silico findings, we functionally confirmed that CDKN2A silencing markedly repressed proliferation, invasion and migration of SW480/HCT116 cells and potentiated cuproptosis via up-regulation of lipoylated DLAT/DLST and CTR1. CONCLUSIONS: We report the first MCD-derived prognostic platform for CRC that simultaneously predicts survival and therapeutic response. Targeting CDKN2A-enhanced cuproptosis represents a promising metabolic-precision strategy for high-risk patients.

Colorectal cancer (CRC)↗

PubMed2026

Discovery and validation of a multi-protein panel for predicting non-fatal major adverse cardiovascular events in diabetic kidney disease.

OBJECTIVE: To identify plasma protein biomarkers associated with incident non-fatal major adverse cardiovascular events (MACE) in diabetic kidney disease (DKD) patients. RESEARCH DESIGN AND METHODS: We analyzed 317 DKD patients from the UK Biobank. Plasma proteomics and clinical data (demographics, metabolism, renal function) were integrated. In an exploratory discovery phase, three sequential Cox regression models (crude, socio-demographic-adjusted, socio-demographic-metabolic adjusted) screened non-fatal MACE-associated proteins. To prevent information leakage, the cohort was then randomly split into training (70%) and testing (30%) sets; machine-learning feature selection, hyperparameter optimization, and final model development were performed exclusively within the training set. The associated proteins were input into the four-step machine-learning pipeline (LASSO-Cox, random survival forest, Boruta, XGBoost-Cox). Predictive performance was validated using Kaplan-Meier survival analyses, longitudinal trajectory modeling, and ROC benchmarking. An interactive web application was deployed for clinical implementation. RESULTS: Of 1,463 plasma proteins, 561 were associated with non-fatal MACE across Cox models, with 14 overlapping proteins. Nine core proteins (ANG, IL1R1, CXCL14, ESAM, PTGDS, HAVCR1, FGFR2, IGSF8, CCL3) were validated: ANG showed the strongest non-fatal MACE association (HR = 3.88, 95%CI 2.33-6.48, p<0.001), and all high-expression groups had elevated non-fatal MACE risk. GO/KEGG enrichment highlighted inflammatory-immune pathways like positive regulation of MAPK cascade, Cytokine-cytokine receptor interaction and PI3K-Akt signaling pathway as key mechanisms. The model integrating proteins, demographic factors, and clinical variables achieved the highest predictive performance across non-fatal MACE (AUC = 0.768), myocardial infarction (MI) (0.808), and stroke (0.816) outcomes, with superior stability in cross-validation. CoxBoost + Elastic Net framework was selected as the optimal framework via benchmarking of 101 algorithms. The model demonstrated favorable calibration in high-risk patients and yielded positive net clinical benefit across decision thresholds of 5% to 45%. The web tool (https://jiangli2941.github.io/MACE-prediction-v2/) enables input of 28 variables, outputs non-fatal MACE risk status, risk probability, and highlights abnormal indicators. CONCLUSION: Plasma proteomics combined with machine learning identifies robust non-fatal MACE predictors in DKD.

Humans↗

PubMed2026

A pan-cancer multi-omic SuperLearner for regulated cell death survival topologies.

INTRODUCTION: Regulated cell death (RCD) pathways influence tumor progression and immune modulation. We previously constructed a signature database mapping 25 RCD forms across seven multi-omic layers and 33 tumor types (CancerRCDShiny). Despite their ability to identify risk populations, translating these signatures into personalized clinical workflows requires a shift from cohort stratification to individualized risk mapping by modeling patient risk (survival topologies) to capture the non-linear dynamics of RCD signatures. METHODS: We engineered a pan-cancer multi-omic SuperLearner pipeline across 33 cancer types. Phase I performed zero-leakage harmonization and groupwise imputation to prevent cross-cohort amalgamation. Phase II deployed Elastic Net-regularized Cox regression as a CANARY diagnostic to map proportional hazards failures. Strata with a 35% missingness barrier entered Phase III, deploying a Quadripartite ensemble: Random Survival Forests, XGBoost, Survival-Boruta, and Multi-Task Logistic Regression, fused within an Elastic Net Multi-View Meta-Learner (MVL), with post-hoc TreeSHAP and LIME interpretability. RESULTS: The CANARY diagnostic demonstrated the structural invalidity of pan-cancer geometric proportional hazards. Across 96 admissible strata, Phase III executed algorithmic displacement: continuous multi-omic topologies suppressed static genomic mutations and copy number variations (85.7% vs. 0.0% apex retention). The MVL stabilized predictions against extreme variance; LIME surrogate validations (R 2 < 0.10) confirmed the systematic failure of linear interpretative proxies. N-dimensional TreeSHAP interaction mapping exposed synergistic and antagonistic rescue trajectories defining individualized Survival Topologies, which were invisible to additive models. The architecture was deployed as CancerRCDPredictor, a digital molecular tumor board with integrated LLM capabilities. The MVL SuperLearner achieved a median C-index of 0.749 (IQR: 0.722-0.836) across 96 modelable strata, with 95% bootstrap confidence intervals confirming precision (median width: 0.052) and permutation significance in 93.8% of strata (p < 0.001). External CPTAC validation across ten cancer types demonstrated significant cross-cohort generalizability in clear cell renal carcinoma (KIRC; C-index 0.675, p = 0.017) and modest performance across the remaining adequately powered cancers (median 0.582), underscoring the need for larger multi-institutional validation cohorts. CONCLUSION: This pan-cancer multi-omic SuperLearner bypasses linear topological failures, advancing beyond generalized stratification to establish a deterministically mapped architecture for predicting RCD-related survival topologies. Through the CancerRCDPredictor interface, multi-omic insights translate into individualized survival topology exploration, providing a foundation for future precision oncology validation.

SuperLearner↗

PubMed1999

Apolipoprotein B signal peptide polymorphism distribution among south Amerindian populations.

We report the distribution of the APOB signal peptide polymorphism in 5 native populations of South America: 2 samples of Mataco and 1 sample each of Pilagá and Toba from the Argentinian Chaco and 1 sample of Ache from the Paraguay forest. A randomly selected subsample of a previously studied sample from the Cayapa of Ecuador (Scacchi et al. 1997) was reanalyzed to investigate probable differences attributable to sampling, laboratory techniques, or interobserver error. The polymorphism observed in the signal peptide region of the APOB gene among native populations of South America exhibits the same range of variation found among geographic continental populations, confirming the high genetic heterogeneity of South Amerindians. Extremes in the allele prevalences were found among the Mataco and Ache, populations not far apart geographically. The small differences in genotype and allele frequencies between the subsample of the Cayapa analyzed here and the original Cayapa sample and between the 2 Mataco samples were not statistically significant and most likely were due to sampling error.

Alleles↗

PubMed2006

Rotation forest: A new classifier ensemble method.

We propose a method for generating classifier ensembles based on feature extraction. To create the training data for a base classifier, the feature set is randomly split into K subsets (K is a parameter of the algorithm) and Principal Component Analysis (PCA) is applied to each subset. All principal components are retained in order to preserve the variability information in the data. Thus, K axis rotations take place to form the new features for a base classifier. The idea of the rotation approach is to encourage simultaneously individual accuracy and diversity within the ensemble. Diversity is promoted through the feature extraction for each base classifier. Decision trees were chosen here because they are sensitive to rotation of the feature axes, hence the name "forest." Accuracy is sought by keeping all principal components and also using the whole data set to train each base classifier. Using WEKA, we examined the Rotation Forest ensemble on a random selection of 33 benchmark data sets from the UCI repository and compared it with Bagging, AdaBoost, and Random Forest. The results were favorable to Rotation Forest and prompted an investigation into diversity-accuracy landscape of the ensemble models. Diversity-error diagrams revealed that Rotation Forest ensembles construct individual classifiers which are more accurate than these in AdaBoost and Random Forest, and more diverse than these in Bagging, sometimes more accurate as well.

Algorithms↗

PubMed1986

The effect of nitrogen fertilization on forest blueberries.

In a randomized prospective trial with hens it is shown that artificially fertilized forest blueberries are toxic. In a high dose (1.7 1/hens) the birds died within 72 hours (p less than 0.01). In a lower dose (0.33 1/hens) all survived but a lifelong change in the egg production occurred implying defects of the shells (p less than 0.0000001). It seems that fertilizers change the microflora in the ground, which releases soluble aluminiumnitrate which is absorbed in the berries. Aluminium poisoning has been found to have clinical significance in dialysis patients and probably also in senile dementia. In smaller doses it can be difficult to establish this relation as the symptoms can have a latency period of more than 10 years.

Aluminum↗

PubMed2001

[Coarse woody debris and its function in forest ecosystem].

Coarse woody debris (CWD) is an important structural and functional element in forest ecosystem. In this paper, the definition, dynamics, and role of CWD in a forest ecosystem were overviewed. CWD include all woody debris above and under the ground with a diameters > or = 2.5 cm, and mainly comes from the dead trees resulted from competition and disturbances. The biomass of CWD changes like "U" form along with the development of a forest community. The decay rate of CWD is determined by the compound course of the respiration and natural fragmentation. Insects and microorganisms play an important role in the course. CWD basically has two functions in an ecosystem. One is as the nutrition pool for some living creatures, and the other is that the large CWD can form some special habitats for animals and plants. Conclusively these two functions are helpful to reserve the biodiversity and the integrity of ecological process in a natural ecosystem. Gaps are formed while dead trees falling down, and all gaps in a forest succeed one another randomly in the development of a forest ecosystem, which pushes the inner circulation of the forest. Because of the vital roles of CWD in a forest ecosystem, much more attention should be paid in the management of forest ecosystem.

Ecosystem↗

PubMed2002

Transport of fallout radiocesium in the soil by bioturbation: a random walk model and application to a forest soil with a high abundance of earthworms.

It is well known that bioturbation can contribute significantly to the vertical transport of fallout radionuclides in grassland soils. To examine this effect also for a forest soil, activity-depth profiles of Chernobyl-derived 134Cs from a limed plot (soil, hapludalf under spruce) with a high abundance of earthworms (Lumbricus rubellus) in the Olu horizon (thickness=3.5 cm) were evaluated and compared with the corresponding depth profiles from an adjacent control plot. For this purpose, a random-walk based transport model was developed, which considers (i) the presence of an initial activity-depth distribution, (ii) the deposition history of radiocesium at the soil surface, (iii) individual diffusion/dispersion coefficients and convection rates for the different soil horizons, and (iv) mixing by bioturbation within one soil horizon. With this model, the observed 134Cs-depth distribution at the control site (no bioturbation) and at the limed site could be simulated quite satisfactorily. It is shown that the observed, substantial long-term enrichment of 134Cs in the bioturbation horizon can be modeled by an exceptionally effective diffusion process, combined with a partial reflection of the randomly moving particles at the two borders of the bioturbation zone. The present model predicts significantly longer residence times of radiocesium in the organic soil layer of the forest soil than obtained from a first-order compartment model, which does not consider bioturbation explicitly.

Animals↗

PubMed2003

Genetic diversity of forest arabica coffee (Coffea arabica L.) in Ethiopia as revealed by random amplified polymorphic DNA (RAPD) analysis.

Genetic diversity within the forest Coffea arabica L. gene pool in Ethiopia has not been extensively examined with molecular markers. In the present study, a total of 75 polymorphic RAPD bands generated by twelve random primers were used to assess genetic diversity among 144 genotypes representing 16 C. arabica populations. The number of polymorphic bands detected with each primer ranged from 2 to 9 with a mean of 6.25 bands per primer. Banding patterns ranged in percentage polymorphism from 37% to 73% with an overall mean of 56% for the populations analyzed. The amount of genetic variation among populations estimated by Shannon-Weaver diversity index was (H = 0.30). The within population and between populations differentiation values were 0.65 and 0.35, respectively. Genetic differentiations within and between zones of sample collection sites were 0.80 and 0.20, respectively. Within population average similarities estimated by simple matching coefficients ranged from 0.72 to 0.85, with an overall average of 0.78. In the cluster analysis that used individual samples as operational taxonomic units, most of the representatives of the same population failed to cluster before they joined members of other populations. Nevertheless, most of the populations were clustered on the basis of their geographic closeness and an east west differentiation was observed at approximately 75% similarity. The results obtained provide information on how to select sites for in situ conservation of C. arabica germplasm.

Coffea↗

PubMed1998

Characterization and detection of Anopheles vestitipennis and Anopheles punctimacula (Diptera: Culicidae) larval habitats in Belize with field survey and SPOT satellite imagery.

Surveys of larval habitats of Anopheles vestitipennis and Anopheles punctimacula were conducted in Belize, Central America. Habitat analysis and classification resulted in delineation of eight habitat types defined by dominant life forms and hydrology. Percent cover of tall dense macrophytes, shrubs, open water, and pH were significantly different between sites with and without An. vestitipennis. For An. punctimacula, percent cover of tall dense macrophytes, trees, detritus, open water, and water depth were significantly different between larvae positive and negative sites. The discriminant function for An. vestitipennis correctly predicted the presence of larvae in 65% of sites and correctly predicted the absence of larvae in 88% of sites. The discriminant function for An. punctimacula correctly predicted 81% of sites for the presence of larvae and 45% for the absence of larvae. Canonical discriminant analysis of the three groups of habitats (An. vestitipennis positive; An. punctimacula positive; all negative) confirmed that while larval habitats of An. punctimacula are clustered in the tree dominated area, larval habitats of An. vestitipennis were found in both tree dominated and tall dense macrophyte dominated environments. The forest larval habitats of An. vestitipennis and An. punctimacula seem to be randomly distributed among different forest types. Both species tend to occur in denser forests with more detritus, shallower water, and slightly higher pH. Classification of dry season (February) SPOT multispectral satellite imagery produced 10 land cover types with the swamp forest and tall dense marsh classes being of particular interest. The accuracy assessment showed that commission errors for the tall, dense marsh and swamp forest appeared to be minor; but omission errors were significant, especially for the swamp forest (perhaps because no swamp forests are flooded in February). This means that where the classification indicates there are An. vestitipennis breeding sites, they probably do exist; but breeding sites in many locations are not identified and could be more abundant than indicated.

Animals↗

PubMed2006

Neither host-specific nor random: vascular epiphytes on three tree species in a Panamanian lowland forest.

BACKGROUND AND AIMS: A possible role of host tree identity in the structuring of vascular epiphyte communities has attracted scientific attention for decades. Specifically, it has been suggested that each host tree species has a specific subset of the local species pool according to its own set of properties, e.g. physicochemical characteristics of the bark, tree architecture, or leaf phenology patterns. METHODS: A novel, quantitative approach to this question is presented, taking advantage of a complete census of the vascular epiphyte community in 0.4 ha of undisturbed lowland forest in Panama. For three locally common host-tree species (Socratea exorrhiza, Marila laxiflora, Perebea xanthochyma) null models were created of the expected epiphyte assemblages assuming that epiphyte colonization reflected random distribution of epiphytes in the forest. KEY RESULTS: In all three tree species, abundances of the majority of epiphyte species (69-81 %) were indistinguishable from random, while the remaining species were about equally over- or under-represented compared with their occurrence in the entire forest plot. Permutations based on the number of colonized trees (reflecting observed spatial patchiness) yielded similar results. Finally, a third analysis (canonical correspondence analysis) also confirmed host-specific differences in epiphyte assemblages. In spite of pronounced preferences of some epiphytes for particular host trees, no epiphyte species was restricted to a single host. CONCLUSIONS: The epiphytes on a given tree species are not simply a random sample of the local species pool, but there are no indications of host specificity either.

Araceae↗

PubMed2003

The effect of forest clear-cutting on habitat use in Sichuan snub-nosed monkey (Rhinopithecus roxellana) in Shennongjia Nature Reserve, China.

The habitat use of two groups of Sichuan snub-nosed monkey ( Rhinopithecus roxellana) was observed, using the focal group method, for 7 months in four seasons from June 2000 to October 2001. The habitats were classed into primary forest and three successional habitats: after clear-cutting: grassland, shrub forest and young forest. The results showed that the large group of monkeys had larger range areas than the small group in the same season. Both groups had larger range sizes in summer or autumn than in winter or spring. They spent most of their time using primary forest and young forest, rarely used shrub forest and did not use grassland. In each season, they used the habitats non-randomly and preferred primary forest. The preference order of habitats for both groups every season was the same: primary forest > young forest > shrub forest >/= grassland. The results suggested that primary forest was high-quality habitat and should be conserved as a top priority. Clear-cutting would cause habitat loss and habitat fragmentation, and should therefore be prohibited. High-quality habitat for the monkeys is difficult to restore from clear-cutting.

Animal Migration↗

PubMed2006

A spatially explicit decision support model for restoration of forest bird habitat.

The historical area of bottomland hardwood forest in the Mississippi Alluvial Valley has been reduced by >75%. Agricultural production was the primary motivator for deforestation; hence, clearing deliberately targeted higher and drier sites. Remaining forests are highly fragmented and hydrologically altered, with larger forest fragments subject to greater inundation, which has negatively affected many forest bird populations. We developed a spatially explicit decision support model, based on a Partners in Flight plan for forest bird conservation, that prioritizes forest restoration to reduce forest fragmentation and increase the area of forest core (interior forest >1 km from "hostile" edge). Our primary objective was to increase the number of forest patches that harbor >2000 ha of forest core, but we also sought to increase the number and area of forest cores >5000 ha. Concurrently, we targeted restoration within local (320 km2) landscapes to achieve > or =60% forest cover Finally, we emphasized restoration of higher-elevation bottomland hardwood forests in areas where restoration would not increase forest fragmentation. Reforestation of 10% of restorable land in the Mississippi Alluvial Valley (approximately 880,000 ha) targeted at priorities established by this decision support model resulted in approximately 824,000 ha of new forest core. This is more than 32 times the amount of core forest added through reforestation of randomly located fields (approximately 25,000 ha). The total area of forest core (1.6 million ha) that resulted from targeted restoration exceeded habitat objectives identified in the Partners in Flight Bird Conservation Plan and approached the area of forest core present in the 1950s.

Animals↗

PubMed2004

Association genetics of complex traits in conifers.

Association studies are becoming the experimental approach of choice to dissect complex traits in many organisms from humans to model plant systems. The candidate gene based-association approach has several important advantages for complex trait dissection in many coniferous forest tree species, including random mating and unstructured populations, adequate levels of nucleotide diversity, rapid decay of linkage disequilibrium, and precise evaluation of phenotype from clonal or progeny testing. Allele discovery using association approaches should lead to more-efficient methods of marker-assisted breeding and a deeper understanding of genetic adaptation in forest trees.

Chromosome Mapping↗

PubMed2001

[Forest soil seed bank and natural regeneration].

The characteristics of temporal dynamics and spatial distribution of forest soil seed bank and their effects on natural regeneration were reviewed in this paper. The density of the seed bank of forest soil, which is affected by the types and ages of forest, is lower than that of cultivated land and grassplot, and can vary widely. The extent to which the composition of forest soil seed bank is influenced by its counterpart plantation depends on the stages of succession. The dynamics of forest soil seed bank is the combined effect of periodical and random factors. It is suggested that forest soil seed bank is the material base of forest regeneration, and the configuration and function of forest soil seed bank would influence the ability and the direction of natural forest regeneration. According to the characteristics of forest soil seed bank and obstructive factors influencing regeneration, some artificial measures can be adopted to accelerate natural regeneration. Studies on the forest soil seed bank should focus on the application of basic theories of disturbed ecology, and regard the reconstruction of degenerative ecosystem and the succession of populations in the ecotone as the main contents to study. As for the research method, it is advisable to employ long-term and located observations.

Regeneration↗

PubMed2006

Population structure, persistence, and seasonality of autochthonous Escherichia coli in temperate, coastal forest soil from a Great Lakes watershed.

The common occurrence of Escherichia coli in temperate soils has previously been reported, however, there are few studies to date to characterize its source, distribution, persistent capability and genetic diversity. In this study, undisturbed, forest soils within six randomly selected 0.5 m2 exclosure plots (covered by netting of 2.3 mm2 mesh size) were monitored from March to October 2003 for E. coli in order to describe its numerical and population characteristics. Culturable E. coli occurred in 88% of the samples collected, with overall mean counts of 16 MPN g(-1), ranging from < 1 to 1657 (n = 66). Escherichia coli counts did not correlate with substrate moisture content, air, or soil temperatures, suggesting that seasonality were not a strong factor in population density control. Mean E. coli counts in soil samples (n = 60) were significantly higher inside than immediately outside the exclosures; E. coli distribution within the exclosures was patchy. Repetitive extragenic palindromic polymerase chain reaction (Rep-PCR) demonstrated genetic heterogeneity of E. coli within and among exclosure sites, and the soil strains were genetically distinct from animal (E. coli) strains tested (i.e. gulls, terns, deer and most geese). These results suggest that E. coli can occur and persist for extended periods in undisturbed temperate forest soils independent of recent allochthonous input and season, and that the soil E. coli populations formed a cohesive phylogenetic group in comparison to the set of fecal strains with which they were compared. Thus, in assessing E. coli sources within a stream, it is important to differentiate background soil loadings from inputs derived from animal and human fecal contamination.

Animals↗