Progression of swine fecal microbiota during early stages of life and its association with performance: a longitudinal study.

BACKGROUND: It is vital to understand healthy gut microbiota composition throughout early life stages when environments are changing, and immunity is developing. There are limited large-scale longitudinal studies classifying healthy succession of swine microbiota. The objectives of this study were to (a) determine the microbiota composition of fecal samples collected from piglets within a few days after birth until one-week post-weaning, and (b) investigate the associations of early fecal microbiota with pig growth performance in nursery and later growing stages. Fecal samples were collected from nine cohorts of 40 pigs (n = 360) from distinct farrowing sources in Ontario and Quebec, Canada at four timepoints from birth to one-week post-weaning, with pig body weight was recorded at each fecal sampling. RESULTS: Microbiota was dominated by the phyla Firmicutes, Bacteroides and Proteobacteria. There were notable differences in genera abundance between pigs from different provinces and farming systems. Over the early life stage, the genera Bacteroides, Escherichia/Shigella, and Clostridium cluster XIVa were abundant preweaning, while Prevotella dominated post-weaning. Hierarchical clustering identified three major stages of microbiota development, each associated with distinct composition. Stage one occurs from birth to 7 days, stage two from 7 days after birth until weaning, and stage three from weaning to one-week post-weaning. Three enterotypes were identified in stage two that showed differences in growth before weaning, and in the grower production stage. Piglets with a microbiota enterotype characterized by higher abundance of Prevotella and unclassified Ruminococcaceae had lower growth performance in the pre-weaning stage, and the growing stage. CONCLUSION: These findings help identify the timing of microbiota shifts across early swine life which may be the optimal time for external intervention to shift the microbiota to a beneficial state. The project findings should help decrease antimicrobial use, increase animal welfare, and have positive economic impacts.

Integrating population dynamics models and distance sampling data: a spatial hierarchical state-space approach.

Stochastic versions of Gompertz, Ricker, and various other dynamics models play a fundamental role in quantifying strength of density dependence and studying long-term dynamics of wildlife populations. These models are frequently estimated using time series of abundance estimates that are inevitably subject to observation error and missing data. This issue can be addressed with a state-space modeling framework that jointly estimates the observed data model and the underlying stochastic population dynamics (SPD) model. In cases where abundance data are from multiple locations with a smaller spatial resolution (e.g., from mark-recapture and distance sampling studies), models are conventionally fitted to spatially pooled estimates of yearly abundances. Here, we demonstrate that a spatial version of SPD models can be directly estimated from short time series of spatially referenced distance sampling data in a unified hierarchical state-space modeling framework that also allows for spatial variance (covariance) in population growth. We also show that a full range of likelihood based inference, including estimability diagnostics and model selection, is feasible in this class of models using a data cloning algorithm. We further show through simulation experiments that the hierarchical state-space framework introduced herein efficiently captures the underlying dynamical parameters and spatial abundance distribution. We apply our methodology by analyzing a time series of line-transect distance sampling data for fin whales (Balaenoptera physalus) off the U.S. west coast. Although there were only seven surveys conducted during the study time frame, 1991-2014, our analysis detected presence of strong density regulation and provided reliable estimates of fin whale densities. In summary, we show that the integrative framework developed herein allows ecologists to better infer key population characteristics such as presence of density regulation and spatial variability in a population's intrinsic growth potential.

Stable variable ranking and selection in regularized logistic regression for severely imbalanced big binary data.

We develop a novel covariate ranking and selection algorithm for regularized ordinary logistic regression (OLR) models in the presence of severe class-imbalance in high dimensional datasets with correlated signal and noise covariates. Class-imbalance is resolved using response-based subsampling which we also employ to achieve stability in variable selection by creating an ensemble of regularized OLR models fitted to subsampled (and balanced) datasets. The regularization methods considered in our study include Lasso, adaptive Lasso (adaLasso) and ridge regression. Our methodology is versatile in the sense that it works effectively for regularization techniques involving both hard- (e.g. Lasso) and soft-shrinkage (e.g. ridge) of the regression coefficients. We assess selection performance by conducting a detailed simulation experiment involving varying moderate-to-severe class-imbalance ratios and highly correlated continuous and discrete signal and noise covariates. Simulation results show that our algorithm is robust against severe class-imbalance under the presence of highly correlated covariates, and consistently achieves stable and accurate variable selection with very low false discovery rate. We illustrate our methodology using a case study involving a severely imbalanced high-dimensional wildland fire occurrence dataset comprising 13 million instances. The case study and simulation results demonstrate that our framework provides a robust approach to variable selection in severely imbalanced big binary data.

Simultaneous multi-crop land suitability prediction from remote sensing data using semi-supervised learning.

Land suitability models for Canada are currently based on single-crop inventories and expert opinion. We present a data-driven multi-layer perceptron that simultaneously predicts the land suitability of several crops in Canada, including barley, peas, spring wheat, canola, oats, and soy. Available crop yields from 2013-2020 are downscaled to the farm level by masking the district level crop yield data to focus only on areas where crops are cultivated and leveraging soil-climate-landscape variables obtained from Google Earth Engine for crop yield prediction. This new semi-supervised learning approach can accommodate data from different spatial resolutions and enables training with unlabelled data. The incorporation of a crop indicator function further allows for the training of a multi-crop model that can capture the interdependences and correlations between various crops, thereby leading to more accurate predictions. Through k-fold cross-validation, we show that compared to the single crop models, our multi-crop model could produce up to a 2.82 fold reduction in mean absolute error for any particular crop. We found that barley, oats, and mixed grains were more tolerant to soil-climate-landscape variations and could be grown in many regions of Canada, while non-grain crops were more sensitive to environmental factors. Predicted crop suitability was associated with a region's growing season length, which supports climate change projections that regions of northern Canada will become more suitable for agricultural use. The proposed multi-crop model could facilitate assessment of the suitability of northern lands for crop cultivation and be incorporated into cost-benefit analyses.

Antenatal sildenafil treatment attenuates pulmonary hypertension in experimental congenital diaphragmatic hernia.

BACKGROUND: Lung hypoplasia and persistent pulmonary hypertension of the newborn limit survival in congenital diaphragmatic hernia (CDH). Unlike other diseases resulting in persistent pulmonary hypertension of the newborn, infants with CDH are refractory to inhaled nitric oxide (NO). Nitric oxide mediates pulmonary vasodilatation at birth in part via cyclic GMP production. Phosphodiesterase type 5 (PDE5) limits the effects of NO by inactivation of cyclic GMP. Because of the limited success in postnatal management of CDH, we hypothesized that antenatal PDE5 inhibition would attenuate pulmonary artery remodeling in experimental nitrofen-induced CDH. METHODS AND RESULTS: Nitrofen administered at embryonic day 9.5 to pregnant rats resulted in a 60% incidence of CDH in the offspring and recapitulated features seen in human CDH, including structural abnormalities (lung hypoplasia, decreased pulmonary vascular density, pulmonary artery remodeling, right ventricular hypertrophy), and functional abnormalities (decreased pulmonary artery relaxation in response to the NO donor 2-(N,N-diethylamino)-diazenolate-2-oxide). Antenatal sildenafil administered to the pregnant rat from embryonic day 11.5 to embryonic day 20.5 crossed the placenta, increased fetal lung cyclic GMP and decreased active PDE5 expression. Antenatal sildenafil improved lung structure, increased pulmonary vessel density, reduced right ventricular hypertrophy, and improved postnatal NO donor 2-(N,N-diethylamino)-diazenolate-2-oxide-induced pulmonary artery relaxation. This was associated with increased lung endothelial NO synthase and vascular endothelial growth factor protein expression. Antenatal sildenafil had no adverse effect on retinal structure/function and brain development. CONCLUSIONS: Antenatal sildenafil improves pathological features of persistent pulmonary hypertension of the newborn in experimental CDH and does not alter the development of other PDE5-expressing organs. Given the high mortality/morbidity of CDH, the potential benefit of prenatal PDE5 inhibition in improving the outcome for infants with CDH warrants further studies.

Compositional analysis of the tonsil microbiota in relationship to Streptococcus suis disease in nursery pigs in Ontario.

BACKGROUND: The tonsil of the soft palate in pigs is the colonization site of both commensal and pathogenic microbial agents. Streptococcus suis infections are a significant economic problem in the swine industry. The development of S. suis disease remains poorly understood. The purpose of this study was to identify whether the tonsillar microbiota profile in nursery pigs is altered with S. suis disease. Here, the dynamics of the tonsillar microbiota from 20 healthy pigs and 43 diseased pigs with S. suis clinical signs was characterized. RESULTS: Based on the presence or absence of S. suis in the systemic sites, diseased pigs were classified into confirmed (n = 20) or probable (n = 23) group, respectively. Microbiota composition was assessed using the V3-V4 hypervariable region of the 16S rRNA, and results were analyzed to identify the diversity of the tonsillar microbiota. The taxonomic composition of the tonsil microbiota proved to be highly diverse between individuals, and the results showed statistically significant microbial community structure among the diagnosis groups. The confirmed group had the lowest observed species richness while the probable group had higher phylogenetics diversity level compared to the healthy group. Un-weighted Unifrac also demonstrated that the probable group had a higher beta diversity than both the healthy and the confirmed group. A Dirichlet-multinomial mixture (DMM) model-based clustering method partitioned the tonsil microbiota into two distinct community types that did not correspond with disease status. However, there was an association between Streptococcus suis serotype 2 and DMM community type 1 (p = 0.03). ANCOM-BC identified 24 Streptococcus amplicon sequence variants (ASVs) that were differentially abundant between the DMM community types. CONCLUSIONS: This study provides a comprehensive analysis of the structure and membership of the tonsil microbiota in nursery pigs and uncovers differences and similarities across varying S. suis disease status. While the overall abundance of Streptococcus was not different among the diagnosis groups, the unique profile of DMM community type 1 and the observed correlation with S. suis serotype 2 could provide insight into potential tonsillar microbiota involvement in S. suis disease.

Impact of Swine and Cattle Manure Treatment on the Microbial Composition and Resistome of Soil and Drainage Water.

Evaluating potential environmental and clinical impacts of industrial antibiotic use is critical in mitigating the spread of antimicrobial resistance. Using soil columns to simulate field application of swine or cattle manure and subsequent rain events, and a targeted qPCR-based approach, we tracked resistance genes from source manures and identified important differences in antimicrobial resistance gene transport and enrichment over time in the soil and water of artificially drained cropland. The source manures had distinct microbial community and resistance gene profiles, and these differences were also reflected in the soil columns after manure application. Antibiotic resistance genes (ARGs) were only significantly enriched in effluent samples following the first rain event (day 11) for both soil types compared to the control columns, illustrating the high background level of resistance present in the control soils chosen. For swine, the genes tetQ, tet(36), tet44, tetM, sul2 and ant(6)-ib persisted in the soil columns, whereas tetO, strB and sul1 persisted in effluent samples. Conversely, for cattle manure sul2 and strB persisted in both soil and effluent. The distinct temporal dynamics of ARG distribution between soil and effluent water for each manure type can be used to inform potential mitigation strategies in the future.