Computer vision models enable mixed linear modeling to predict arbuscular mycorrhizal fungal colonization using fungal morphology.

The presence of Arbuscular Mycorrhizal Fungi (AMF) in vascular land plant roots is one of the most ancient of symbioses supporting nitrogen and phosphorus exchange for photosynthetically derived carbon. Here we provide a multi-scale modeling approach to predict AMF colonization of a worldwide crop from a Recombinant Inbred Line (RIL) population derived from Sorghum bicolor and S. propinquum. The high-throughput phenotyping methods of fungal structures here rely on a Mask Region-based Convolutional Neural Network (Mask R-CNN) in computer vision for pixel-wise fungal structure segmentations and mixed linear models to explore the relations of AMF colonization, root niche, and fungal structure allocation. Models proposed capture over 95% of the variation in AMF colonization as a function of root niche and relative abundance of fungal structures in each plant. Arbuscule allocation is a significant predictor of AMF colonization among sibling plants. Arbuscules and extraradical hyphae implicated in nutrient exchange predict highest AMF colonization in the top root section. Our work demonstrates that deep learning can be used by the community for the high-throughput phenotyping of AMF in plant roots. Mixed linear modeling provides a framework for testing hypotheses about AMF colonization phenotypes as a function of root niche and fungal structure allocations.

Prenatal exposure to bisphenols affects pregnancy outcomes and offspring development in rats.

The objective of this study was to evaluate the effects of gestational exposure to low doses of bisphenol A (BPA), bisphenol S (BPS), and bisphenol F (BPF) on pregnancy outcomes and offspring development. Pregnant Sprague-Dawley rats were orally dosed with vehicle, 5 µg/kg body weight (BW)/day of BPA, BPS and BPF, or 1 µg/kg BW/day of BPF on gestational days 6-21. Pregnancy and gestational outcomes, including number of abortions and stillbirths, were monitored. Male and female offspring were subjected to morphometry at birth, followed by pre- and post-weaning body weights, post-weaning food and water intakes, and adult organ weights. Ovarian follicular counts were also obtained from adult female offspring. We observed spontaneous abortions in over 80% of dams exposed to 5 µg/kg of BPF. BPA exposure increased Graafian follicles in female offspring, while BPS and BPF exposure decreased the number of corpora lutea, suggesting reduced ovulation rates. Moreover, BPA exposure increased male kidney and prostate gland weights, BPF decreased epididymal adipose tissue weights, and BPS had modest effects on male abdominal adipose tissue weights. Prenatal BPS exposure reduced anogenital distance (AGD) in male offspring, suggesting possible feminization, whereas both BPS and BPA induced oxidative stress in the testes. These results indicate that prenatal exposure to BPF affects pregnancy outcomes, BPS alters male AGD, and all three bisphenols alter certain organ weights in male offspring and ovarian function in female offspring. Altogether, it appears that prenatal exposure to BPA or its analogues can induce reproductive toxicity even at low doses.

Identification of predictive MRI and functional biomarkers in a pediatric piglet traumatic brain injury model.

Traumatic brain injury (TBI) at a young age can lead to the development of long-term functional impairments. Severity of injury is well demonstrated to have a strong influence on the extent of functional impairments; however, identification of specific magnetic resonance imaging (MRI) biomarkers that are most reflective of injury severity and functional prognosis remain elusive. Therefore, the objective of this study was to utilize advanced statistical approaches to identify clinically relevant MRI biomarkers and predict functional outcomes using MRI metrics in a translational large animal piglet TBI model. TBI was induced via controlled cortical impact and multiparametric MRI was performed at 24 hours and 12 weeks post-TBI using T1-weighted, T2-weighted, T2-weighted fluid attenuated inversion recovery, diffusion-weighted imaging, and diffusion tensor imaging. Changes in spatiotemporal gait parameters were also assessed using an automated gait mat at 24 hours and 12 weeks post-TBI. Principal component analysis was performed to determine the MRI metrics and spatiotemporal gait parameters that explain the largest sources of variation within the datasets. We found that linear combinations of lesion size and midline shift acquired using T2-weighted imaging explained most of the variability of the data at both 24 hours and 12 weeks post-TBI. In addition, linear combinations of velocity, cadence, and stride length were found to explain most of the gait data variability at 24 hours and 12 weeks post-TBI. Linear regression analysis was performed to determine if MRI metrics are predictive of changes in gait. We found that both lesion size and midline shift are significantly correlated with decreases in stride and step length. These results from this study provide an important first step at identifying relevant MRI and functional biomarkers that are predictive of functional outcomes in a clinically relevant piglet TBI model. This study was approved by the University of Georgia Institutional Animal Care and Use Committee (AUP: A2015 11-001) on December 22, 2015.

Independent and combined effects of Bisphenol A and Diethylhexyl Phthalate on gestational outcomes and offspring development in Sprague-Dawley rats.

Bisphenol A (BPA) and Diethylhexyl Phthalate (DEHP) are well-studied endocrine disrupting chemicals (EDCs), however, the effects of mixtures of these EDCs are not. To assess the consequences of prenatal exposure to a mixture of these EDCs, dams were orally administered either saline (control), BPA (5 µg/kg BW/day), high dose DEHP (HD-D; 7.5 mg/kg BW/day), or a combination of BPA with HD-D in experiment 1; saline, BPA (5 µg/kg BW/day), low-dose DEHP (LD-D; 5 µg/kg BW/day) or a combination of BPA with LD-D in experiment 2. Gestational weights, number of abortions, litter size and weights, number of live births and stillbirths were recorded. Morphometric measures were obtained at birth and body weight, food and water intake were monitored weekly from postnatal weeks 3-12. Offspring were sacrificed at 16-24 weeks of age and organ weights were measured. The abortion rate of dams exposed to HD-D and the mixtures, BPA + LD-D and BPA + HD-D were higher at 9, 14 and 27% respectively. Prenatal exposure to BPA or HD-D significantly decreased relative thymus weights in male but not female offspring. Apoptotic cells were detected in thymus sections of both male and female offspring prenatally exposed to DEHP. Relative heart weights increased in BPA + HD-D exposed male offspring compared to the other groups. The results indicate that a mixture of BPA and DEHP, produced a pronounced effect on pregnancy outcomes. Male offspring appear to be more susceptible to the programming effects of these EDCs or their mixture suggesting a need to reconsider the possible additive, antagonistic or synergistic effects of EDC mixtures.

Using Differential Evolution to Design Optimal Experiments.

Differential Evolution (DE) has become one of the leading metaheuristics in the class of Evolutionary Algorithms, which consists of methods that operate off of survival-of-the-fittest principles. This general purpose optimization algorithm is viewed as an improvement over Genetic Algorithms, which are widely used to find solutions to chemometric problems. Using straightforward vector operations and random draws, DE can provide fast, efficient optimization of any real, vector-valued function. This article reviews the basic algorithm and a few of its modifications with various enhancements. We provide guidance for practitioners, discuss implementation issues and give illustrative applications of DE with the corresponding R codes to find different types of optimal designs for various statistical models in chemometrics that involve the Arrhenius equation, reaction rates, concentration measures and chemical mixtures.

Microencapsulation of retinyl palmitate by melt dispersion for cosmetic application.

Retinyl palmitate was encapsulated in wax matrix by melt dispersion for the purpose of economic and sustainable cosmeceutical formulation with minimum use of synthetic chemicals. We evaluated the effect of different process variables of microencapsulation by melt dispersion. In this study, a three level definitive screening design was applied, where the microcapsule properties were analysed through statistical analysis to understand the effect of four process variables: type of wax, theoretical loading capacity, surface concentration and stirring speed. Microparticles were characterised for size using image analysis; loading capacity and encapsulation efficiency using ultraviolet-visible spectroscopy; antioxidant activity through DPPH (2,2-diphenyl-1-picrylhydrazyl) assay. Melt dispersion method was effective to produce microcapsules with a spherical shape and mean size as small as 28 µm. The encapsulation efficiency ranged 60-80%. Theoretical loading capacity (p-value = 0.00232, significance level, α = 1%) and surfactant% (p = 0.0573, α = 10%) were found to be the most significant factors to control the actual loading capacity and size of microcapsules.

Nitric oxide-releasing antibacterial albumin plastic for biomedical applications.

Designing innovative materials for biomedical applications is desired to prevent surface fouling and risk of associated infections arising in the surgical care patient. In the present study, albumin plastic was fabricated and nitric oxide (NO) donor, S-nitroso-N-acetylpenicillamine (SNAP), was incorporated through a solvent swelling process. The albumin-SNAP plastic was evaluated in terms of mechanical and thermal properties, and bacterial adhesion to the plastic surface. Thermal and viscoelastic analyses showed no significant difference between albumin-SNAP plastics and pure, water-plasticized albumin samples. Bacteria adhesion tests revealed that albumin-SNAP plastic can significantly reduce the surface-bound viable gram-positive Staphylococcus aureus and gram-negative Pseudomonas aeruginosa bacterial cells by 98.7 and 98.5%, respectively, when compared with the traditional polyvinyl chloride medical grade tubing material. The results from this study demonstrate NO-releasing albumin plastic's potential as a material for biomedical device applications. © 2018 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 106A: 1535-1542, 2018.

Multi-objective optimal experimental designs for event-related fMRI studies.

In this article, we propose an efficient approach to find optimal experimental designs for event-related functional magnetic resonance imaging (ER-fMRI). We consider multiple objectives, including estimating the hemodynamic response function (HRF), detecting activation, circumventing psychological confounds and fulfilling customized requirements. Taking into account these goals, we formulate a family of multi-objective design criteria and develop a genetic-algorithm-based technique to search for optimal designs. Our proposed technique incorporates existing knowledge about the performance of fMRI designs, and its usefulness is shown through simulations. Although our approach also works for other linear combinations of parameters, we primarily focus on the case when the interest lies either in the individual stimulus effects or in pairwise contrasts between stimulus types. Under either of these popular cases, our algorithm outperforms the previous approaches. We also find designs yielding higher estimation efficiencies than m-sequences. When the underlying model is with white noise and a constant nuisance parameter, the stimulus frequencies of the designs we obtained are in good agreement with the optimal stimulus frequencies derived by Liu and Frank, 2004, NeuroImage 21: 387-400. In addition, our approach is built upon a rigorous model formulation.

Identifying promising compounds in drug discovery: genetic algorithms and some new statistical techniques.

Throughout the drug discovery process, discovery teams are compelled to use statistics for making decisions using data from a variety of inputs. For instance, teams are asked to prioritize compounds for subsequent stages of the drug discovery process, given results from multiple screens. To assist in the prioritization process, we propose a desirability function to account for a priori scientific knowledge; compounds can then be prioritized based on their desirability scores. In addition to identifying existing desirable compounds, teams often use prior knowledge to suggest new, potentially promising compounds to be created in the laboratory. Because the chemistry space to search can be dauntingly large, we propose the sequential elimination of level combinations (SELC) method for identifying new optimal compounds. We illustrate this method on a combinatorial chemistry example.