High-throughput combination assay for studying biofilm formation of uropathogenic Escherichia coli.

Uropathogenic Escherichia coli, the most common cause for urinary tract infections, forms biofilm enhancing its antibiotic resistance. To assess the effects of compounds on biofilm formation of uropathogenic Escherichia coli UMN026 strain, a high-throughput combination assay using resazurin followed by crystal violet staining was optimized for 384-well microplate. Optimized assay parameters included, for example, resazurin and crystal violet concentrations, and incubation time for readouts. For the assay validation, quality parameters Z' factor, coefficient of variation, signal-to-noise, and signal-to-background were calculated. Microplate uniformity, signal variability, edge well effects, and fold shift were also assessed. Finally, a screening with known antibacterial compounds was conducted to evaluate the assay performance. The best conditions found were achieved by using 12 µg/mL resazurin for 150 min and 0.023% crystal violet. This assay was able to detect compounds displaying antibiofilm activity against UMN026 strain at sub-inhibitory concentrations, in terms of metabolic activity and/or biomass.

A new and effective method to induce infection of Phyllachora maydis into corn for tar spot studies in controlled environments.

BACKGROUND: Tar spot of corn is a significant and spreading disease in the continental U.S. and Canada caused by the obligate biotrophic fungus Phyllachora maydis. As of 2023, tar spot had been reported in 18 U.S. states and one Canadian Province. The symptoms of tar spot include chlorotic flecking followed by the formation of black stromata where conidia and ascospores are produced. Advancements in research and management for tar spot have been limited by a need for a reliable method to inoculate plants to enable the study of the disease. The goal of this study was to develop a reliable method to induce tar spot in controlled conditions. RESULTS: We induced infection of corn by P. maydis in 100% of inoculated plants with a new inoculation method. This method includes the use of vacuum-collection tools to extract ascospores from field-infected corn leaves, application of spores to leaves, and induction of the disease in the dark at high humidity and moderate temperatures. Infection and disease development were consistently achieved in four independent experiments on different corn hybrids and under different environmental conditions in a greenhouse and growth chamber. Disease induction was impacted by the source and storage conditions of spores, as tar spot was not induced with ascospores from leaves stored dry at 25 ºC for 5 months but was induced using ascospores from infected leaves stored at -20 ºC for 5 months. The time from inoculation to stromata formation was 10 to 12 days and ascospores were present 19 days after inoculation throughout our experiments. In addition to providing techniques that enable in-vitro experimentation, our research also provides fundamental insights into the conditions that favor tar spot epidemics. CONCLUSIONS: We developed a method to reliably inoculate corn with P. maydis. The method was validated by multiple independent experiments in which infection was induced in 100% of the plants, demonstrating its consistency in controlled conditions. This new method facilitates research on tar spot and provides opportunities to study the biology of P. maydis, the epidemiology of tar spot, and for identifying host resistance.

Assessment of symptom induction via artificial inoculation of the obligate biotrophic fungus Phyllachora maydis (Maubl.) on corn leaves.

OBJECTIVE: Tar spot is a foliar disease of corn caused by Phyllachora maydis, which produces signs in the form of stromata that bear conidia and ascospores. Phyllachora maydis cannot be cultured in media; therefore, the inoculum source for studying tar spot comprises leaves with stromata collected from naturally infected plants. Currently, there is no effective protocol to induce infection under controlled conditions. In this study, an inoculation method was assessed under greenhouse and growth chamber conditions to test whether stromata of P. maydis could be induced on corn leaves. RESULTS: Experiments resulted in incubation periods ranging between 18 and 20 days and stromata development at the beginning of corn growth stage VT-R1 (silk). The induced stromata of P. maydis were confirmed by microscopy, PCR, or both. From thirteen experiments conducted, four (31%) resulted in the successful production of stromata. Statistical analyses indicate that if an experiment is conducted, there are equal chances of obtaining successful or unsuccessful infections. The information from this study will be valuable for developing more reliable P. maydis inoculation methods in the future.

Pump-probe phase spectroscopy with submilliradian sensitivity and nanosecond time delay using Michelson interferometers.

Using two Michelson interferometers, we describe an experimental scheme for sensitive pump-probe spectral interferometry measurements at long time delays. It has practical advantages over the Sagnac interferometer method typically used when long-time delays are required. First, with the Sagnac interferometer, achieving many nanosecond delays requires expanding the size of the interferometer so that the reference pulse arrives before the probe pulse. Because the two pulses still pass through the same region of the sample, long-lived effects can still affect the measurement. In our scheme, the probe and reference pulses are spatially separated at the sample, alleviating the need for a large interferometer. Second, in our scheme, a fixed delay between probe and reference pulses is straightforward to produce and is continuously adjustable while maintaining alignment. Two applications are demonstrated. First, transient phase spectra are presented in a thin tetracene film with up to 5 ns probe delay. Second, impulsive stimulated Raman measurements are presented in Bi4Ge3O12. The signal-to-noise using the double Michelson technique is comparable to previously described methods with the added advantage of arbitrarily long pump-probe time delays.

Monitoring tar spot disease in corn at different canopy and temporal levels using aerial multispectral imaging and machine learning.

Introduction: Tar spot is a high-profile disease, causing various degrees of yield losses on corn (Zea mays L.) in several countries throughout the Americas. Disease symptoms usually appear at the lower canopy in corn fields with a history of tar spot infection, making it difficult to monitor the disease with unmanned aircraft systems (UAS) because of occlusion. Methods: UAS-based multispectral imaging and machine learning were used to monitor tar spot at different canopy and temporal levels and extract epidemiological parameters from multiple treatments. Disease severity was assessed visually at three canopy levels within micro-plots, while aerial images were gathered by UASs equipped with multispectral cameras. Both disease severity and multispectral images were collected from five to eleven time points each year for two years. Image-based features, such as single-band reflectance, vegetation indices (VIs), and their statistics, were extracted from ortho-mosaic images and used as inputs for machine learning to develop disease quantification models. Results and discussion: The developed models showed encouraging performance in estimating disease severity at different canopy levels in both years (coefficient of determination up to 0.93 and Lin's concordance correlation coefficient up to 0.97). Epidemiological parameters, including initial disease severity or y0 and area under the disease progress curve, were modeled using data derived from multispectral imaging. In addition, results illustrated that digital phenotyping technologies could be used to monitor the onset of tar spot when disease severity is relatively low (< 1%) and evaluate the efficacy of disease management tactics under micro-plot conditions. Further studies are required to apply and validate our methods to large corn fields.

Contour-Based Detection and Quantification of Tar Spot Stromata Using Red-Green-Blue (RGB) Imagery.

Quantifying symptoms of tar spot of corn has been conducted through visual-based estimations of the proportion of leaf area covered by the pathogenic structures generated by Phyllachora maydis (stromata). However, this traditional approach is costly in terms of time and labor, as well as prone to human subjectivity. An objective and accurate method, which is also time and labor-efficient, is of an urgent need for tar spot surveillance and high-throughput disease phenotyping. Here, we present the use of contour-based detection of fungal stromata to quantify disease intensity using Red-Green-Blue (RGB) images of tar spot-infected corn leaves. Image blocks (n = 1,130) generated by uniform partitioning the RGB images of leaves, were analyzed for their number of stromata by two independent, experienced human raters using ImageJ (visual estimates) and the experimental stromata contour detection algorithm (SCDA; digital measurements). Stromata count for each image block was then categorized into five classes and tested for the agreement of human raters and SCDA using Cohen's weighted kappa coefficient (κ). Adequate agreements of stromata counts were observed for each of the human raters to SCDA (κ = 0.83) and between the two human raters (κ = 0.95). Moreover, the SCDA was able to recognize "true stromata," but to a lesser extent than human raters (average median recall = 90.5%, precision = 89.7%, and Dice = 88.3%). Furthermore, we tracked tar spot development throughout six time points using SCDA and we obtained high agreement between area under the disease progress curve (AUDPC) shared by visual disease severity and SCDA. Our results indicate the potential utility of SCDA in quantifying stromata using RGB images, complementing the traditional human, visual-based disease severity estimations, and serve as a foundation in building an accurate, high-throughput pipeline for the scoring of tar spot symptoms.

Wheat Spike Blast Image Classification Using Deep Convolutional Neural Networks.

Wheat blast is a threat to global wheat production, and limited blast-resistant cultivars are available. The current estimations of wheat spike blast severity rely on human assessments, but this technique could have limitations. Reliable visual disease estimations paired with Red Green Blue (RGB) images of wheat spike blast can be used to train deep convolutional neural networks (CNN) for disease severity (DS) classification. Inter-rater agreement analysis was used to measure the reliability of who collected and classified data obtained under controlled conditions. We then trained CNN models to classify wheat spike blast severity. Inter-rater agreement analysis showed high accuracy and low bias before model training. Results showed that the CNN models trained provide a promising approach to classify images in the three wheat blast severity categories. However, the models trained on non-matured and matured spikes images showing the highest precision, recall, and F1 score when classifying the images. The high classification accuracy could serve as a basis to facilitate wheat spike blast phenotyping in the future.

Evaluation and validation of Biolog OmniLog® system for antibacterial activity assays.

Minimal inhibitory concentration of antimicrobials, determined by the broth microdilution method, requires visual assessment or absorbance measurement using a spectrophotometer. Both procedures are usually performed manually, requiring the presence of an operator to assess the plates at specific time point. To increase the throughput of antimicrobial susceptibility testing, and concurrently convert into an automatic assay, the Biolog OmniLog® system was validated for a new, label-free application using standard 96-well microplates. OmniLog was evaluated for its signal strength to ensure that the signal intensity, detected and measured by the system's camera, was satisfactory. Variability due to the plate location inside the OmniLog incubator, as well as variation between wells, was investigated. Then the system was validated by determining the minimal inhibitory concentration of ciprofloxacin, piperacillin and linezolid against a selected Gram-negative and Gram-positive strains. No significant difference was observed in relation to position of the plates within the system. Plate edge effects were noticeable, thus the edge wells were not included in further experiments. Minimal inhibitory concentration results were comparable to those obtained by conventional protocol as well as to values defined by the Clinical Laboratory Standards Institute or published in the literature.

Inactivation of the gene encoding the cationic antimicrobial peptide resistance factor MprF increases biofilm formation but reduces invasiveness of Listeria monocytogenes.

AIMS: To understand the genetics involved in surface attachment and biofilm formation of Listeria monocytogenes. METHODS AND RESULTS: An in vitro screen of a Himar1 transposon library of L. monocytogenes strain 15G01 identified three transposants that produced significantly different biofilm levels when compared to the wild-type strain; two mutants exhibited enhanced biofilm formation and one produced less biofilm biomass than the wild-type. The mutant 15G01 mprF::Himar1, which had a transposon insertion in the mprF gene, was selected for further analysis. The mutant produced a more densely populated biofilm on solid surfaces such as stainless steel and polystyrene, as determined using scanning electron and light microscopy. The 15G01 mprF::Himar1 mutant remained viable in biofilms, but showed an increase in sensitivity to the cationic antimicrobial gallidermin. The mutant also displayed reduced invasiveness in CaCo-2 intestinal cells, suggesting virulence properties are compromised by the inactivation of mprF. CONCLUSIONS: Biofilm formation and gallidermin resistance of L. monocytogenes is influenced by mprF, but this trait is associated with a compromise in invasiveness. SIGNIFICANCE AND IMPACT OF THE STUDY: The presence of pathogenic microorganisms in the food processing environment can cause a significant problem, especially when these microorganisms are established as biofilms. This study shows that the inactivation of the mprF gene results in enhanced biofilm formation and abiotic surface attachment of L. monocytogenes.

Tar Spot: An Understudied Disease Threatening Corn Production in the Americas.

Tar spot of corn has been a major foliar disease in several Latin American countries since 1904. In 2015, tar spot was first documented in the United States and has led to significant yield losses of approximately 4.5 million t. Tar spot is caused by an obligate pathogen, Phyllachora maydis, and thus requires a living host to grow and reproduce. Due to its obligate nature, biological and epidemiological studies are limited and impact of disease in corn production has been understudied. Here we present the current literature and gaps in knowledge of tar spot of corn in the Americas, its etiology, distribution, impact and known management strategies as a resource for understanding the pathosystem. This will in tern guide current and future research and aid in the development of effective management strategies for this disease.

Epidemiological Criteria to Support Breeding Tactics Against the Emerging, High-Consequence Wheat Blast Disease.

Plant disease epidemiology can make a significant contribution for cultivar selection by elucidating the principles of an epidemic under different levels of resistance. For emerging diseases as wheat blast (WB), epidemiological parameters can provide support for better selection of genetic resources. Field experiments were conducted at two locations in Bolivia in 2018-2019 to characterize the temporal dynamics of the disease on 10 cultivars with different levels of reaction to WB. Logistic models best (R2 = 0.70-0.96) fit the disease progress curve in all cultivars followed by Gompertz (R2 = 0.64-0.94), providing additional evidence of a polycyclic disease. Total area under disease progress curve (tAUDPC), final disease severity (Ymax), and logistic apparent infection rates (rL*) were shown to be appropriate epidemiological parameters for describing resistance and cultivar selection. Cultivars that showed a high spike AUDPC (sAUDPC) showed a high leaf AUDPC (lAUDPC). tAUPDC, Ymax, and rL* were positively correlated among them (P < 0.01) and all were negatively correlated with grain weight (P < 0.01). Based on the epidemiological parameters used, cultivars that showed resistance to WB were Urubó, San Pablo, and AN-120, which were previously reported to have effective resistance against the disease under field conditions. The information generated could help breeding programs to make technical decisions about relevant epidemiological parameters to consider prior to cultivar release.

Temporal Dynamics of Wheat Blast Epidemics and Disease Measurements Using Multispectral Imagery.

Wheat blast is a devastating disease caused by the Triticum pathotype of Magnaporthe oryzae. M. oryzae Triticum is capable of infecting leaves and spikes of wheat. Although symptoms of wheat spike blast (WSB) are quite distinct in the field, symptoms on leaves (WLB) are rarely reported because they are usually inconspicuos. Two field experiments were conducted in Bolivia to characterize the change in WLB and WSB intensity over time and determine whether multispectral imagery can be used to accurately assess WSB. Disease progress curves (DPCs) were plotted from WLB and WSB data, and regression models were fitted to describe the nature of WSB epidemics. WLB incidence and severity changed over time; however, the mean WLB severity was inconspicuous before wheat began spike emergence. Overall, both Gompertz and logistic models helped to describe WSB intensity DPCs fitting classic sigmoidal shape curves. Lin's concordance correlation coefficients were estimated to measure agreement between visual estimates and digital measurements of WSB intensity and to estimate accuracy and precision. Our findings suggest that the change of wheat blast intensity in a susceptible host population over time does not follow a pattern of a monocyclic epidemic. We have also demonstrated that WSB severity can be quantified using a digital approach based on nongreen pixels. Quantification was precise (0.96 < r> 0.83) and accurate (0.92 < ρ > 0.69) at moderately low to high visual WSB severity levels. Additional sensor-based methods must be explored to determine their potential for detection of WLB and WSB at earlier stages.

Regenerative potential, metabolic profile, and genetic stability of Brachypodium distachyon embryogenic calli as affected by successive subcultures.

Brachypodium distachyon, a model species for forage grasses and cereal crops, has been used in studies seeking improved biomass production and increased crop yield for biofuel production purposes. Somatic embryogenesis (SE) is the morphogenetic pathway that supports in vitro regeneration of such species. However, there are gaps in terms of studies on the metabolic profile and genetic stability along successive subcultures. The physiological variables and the metabolic profile of embryogenic callus (EC) and embryogenic structures (ES) from successive subcultures (30, 60, 90, 120, 150, 180, 210, 240, and 360-day-old subcultures) were analyzed. Canonical discriminant analysis separated EC into three groups: 60, 90, and 120 to 240 days. EC with 60 and 90 days showed the highest regenerative potential. EC grown for 90 days and submitted to SE induction in 2 mg L-1 of kinetin-supplemented medium was the highest ES producer. The metabolite profiles of non-embryogenic callus (NEC), EC, and ES submitted to principal component analysis (PCA) separated into two groups: 30 to 240- and 360-day-old calli. The most abundant metabolites for these groups were malonic acid, tryptophan, asparagine, and erythrose. PCA of ES also separated ages into groups and ranked 60- and 90-day-old calli as the best for use due to their high levels of various metabolites. The key metabolites that distinguished the ES groups were galactinol, oxaloacetate, tryptophan, and valine. In addition, significant secondary metabolites (e.g., caffeoylquinic, cinnamic, and ferulic acids) were important in the EC phase. Ferulic, cinnamic, and phenylacetic acids marked the decreases in the regenerative capacity of ES in B. distachyon. Decreased accumulations of the amino acids aspartic acid, asparagine, tryptophan, and glycine characterized NEC, suggesting that these metabolites are indispensable for the embryogenic competence in B. distachyon. The genetic stability of the regenerated plants was evaluated by flow cytometry, showing that ploidy instability in regenerated plants from B. distachyon calli is not correlated with callus age. Taken together, our data indicated that the loss of regenerative capacity in B. distachyon EC occurs after 120 days of subcultures, demonstrating that the use of EC can be extended to 90 days.

Inhibition of foodborne bacteria by antibacterial coatings printed onto food packaging films.

Films containing antibacterial compounds could be used for packaging perishable foods such as fresh fish and meat for sea freighting over long distances. However, existing commercialised options (films with nanosilver zeolites or wasabi extract) are only permitted for food contact in certain regions and films containing alternative antibacterial ingredients are required e.g. for exports to Europe. Certain non-volatile phenolic plant extracts have shown promising antibacterial activity against a wide range of foodborne bacteria in in vitro assays and when integrated in coatings for perishable foods such as fish and meat. Extracts rich in gallotannins tend to show stronger antibacterial effects than other phenols such as flavonoids. Such extracts could be coated onto commercial barrier films by means of flexographic printing-a more industrially feasible option than rod coating or solvent casting typically used in antibacterial coating research. The goal of the present work was to investigate the antibacterial effect of printed latex coatings containing extracts rich in gallotannins and other types of phenolic compounds against 16 common spoilage and pathogenic bacteria of fish and meat. The largest zones of inhibition in disk diffusion assays were obtained with plastic films with coatings containing tannic acid alone, followed by tannic acid with phenolic-rich extracts of feijoa skin or mango seed. Significant inhibition was seen for all bacteria. This study shows that coatings with gallotannins as the main active ingredient can be printed onto commercial barrier films to control the bacteria that limit the shelf-life of fresh fish and meat.

Regularized quantile regression for SNP marker estimation of pig growth curves.

BACKGROUND: Genomic growth curves are generally defined only in terms of population mean; an alternative approach that has not yet been exploited in genomic analyses of growth curves is the Quantile Regression (QR). This methodology allows for the estimation of marker effects at different levels of the variable of interest. We aimed to propose and evaluate a regularized quantile regression for SNP marker effect estimation of pig growth curves, as well as to identify the chromosome regions of the most relevant markers and to estimate the genetic individual weight trajectory over time (genomic growth curve) under different quantiles (levels). RESULTS: The regularized quantile regression (RQR) enabled the discovery, at different levels of interest (quantiles), of the most relevant markers allowing for the identification of QTL regions. We found the same relevant markers simultaneously affecting different growth curve parameters (mature weight and maturity rate): two (ALGA0096701 and ALGA0029483) for RQR(0.2), one (ALGA0096701) for RQR(0.5), and one (ALGA0003761) for RQR(0.8). Three average genomic growth curves were obtained and the behavior was explained by the curve in quantile 0.2, which differed from the others. CONCLUSIONS: RQR allowed for the construction of genomic growth curves, which is the key to identifying and selecting the most desirable animals for breeding purposes. Furthermore, the proposed model enabled us to find, at different levels of interest (quantiles), the most relevant markers for each trait (growth curve parameter estimates) and their respective chromosomal positions (identification of new QTL regions for growth curves in pigs). These markers can be exploited under the context of marker assisted selection while aiming to change the shape of pig growth curves.

Artificial neural networks as auxiliary tools for the improvement of bean plant architecture.

Classification using a scale of visual notes is a strategy used to select erect bean plants in order to improve bean plant architectures. Use of morphological traits associated with the phenotypic expression of bean architecture in classification procedures may enhance selection. The objective of this study was to evaluate the potential of artificial neural networks (ANNs) as auxiliary tools in the improvement of bean plant architecture. Data from 19 lines were evaluated for 22 traits, in 2007 and 2009 winter crops. Hypocotyl diameter and plant height were selected for analysis through ANNs. For classification purposes, these lines were separated into two groups, determined by the plant architecture notes. The predictive ability of ANNs was evaluated according to two scenarios to predict the plant architecture - training with 2007 data and validating in 2009 data (scenario 1), and vice versa (scenario 2). For this, ANNs were trained and validated using data from replicates of the evaluated lines for hypocotyl diameter individually, or together with the mean height of plants in the plot. In each scenario, the use of data from replicates or line means was evaluated for prediction through previously trained and validated ANNs. In both scenarios, ANNs based on hypocotyl diameter and mean height of plants were superior, since the error rates obtained were lower than those obtained using hypocotyl diameter only. Lower apparent error rates were verified in both scenarios for prediction when data on the means of the evaluated traits were submitted to better trained and validated ANNs.

Genetic diversity among cotton cultivars in two environments in the State of Mato Grosso.

Using commercial cultivars to compose crossing blocks in cotton is a promising strategy, because these materials have desirable agronomic and technological characteristics. The objective of this study was to evaluate the genetic diversity among 16 cotton cultivars cultivated in two environments in the State of Mato Grosso, the largest national producer, using agronomical and technological traits. There was significant effect to cultivars for all traits, while genotype x environment interaction was significant only for average boll weight, short fiber index, and maturity of fibers. Therefore, because of the presence of genotype x environment interaction for three traits, we chose to study genetic diversity among cotton cultivars separately in each environment and investigate the interaction impact on the diversity among genotype pairs. Based on agronomical and technological performance and genetic diversity among cultivars in both environments, the most promising cross involves FM 910 and LD CV 02. We also observed that lint percentage and average boll weight presented a higher discrimination capacity in both environments.

Regularized quantile regression applied to genome-enabled prediction of quantitative traits.

Genomic selection (GS) is a variant of marker-assisted selection, in which genetic markers covering the whole genome predict individual genetic merits for breeding. GS increases the accuracy of breeding values (BV) prediction. Although a variety of statistical models have been proposed to estimate BV in GS, few methodologies have examined statistical challenges based on non-normal phenotypic distributions, e.g., skewed distributions. Traditional GS models estimate changes in the phenotype distribution mean, i.e., the function is defined for the expected value of trait-conditional on markers, E(Y|X). We proposed an approach based on regularized quantile regression (RQR) for GS to improve the estimation of marker effects and the consequent genomic estimated BV (GEBV). The RQR model is based on conditional quantiles, Qτ(Y|X), enabling models that fit all portions of a trait probability distribution. This allows RQR to choose one quantile function that "best" represents the relationship between the dependent and independent variables. Data were simulated for 1000 individuals. The genome included 1500 markers; most had a small effect and only a few markers with a sizable effect were simulated. We evaluated three scenarios according to symmetrical, positively, and negatively skewed distributions. Analyses were performed using Bayesian LASSO (BLASSO) and RQR considering three quantiles (0.25, 0.50, and 0.75). The use of RQR to estimate GEBV was efficient; the RQR method achieved better results than BLASSO, at least for one quantile model fit for all evaluated scenarios. The gains in relation to BLASSO were 86.28 and 55.70% for positively and negatively skewed distributions, respectively.

Comparison between different selection indices in energy cane breeding.

Sugarcane breeding programs have been adapting to a new market demand: aside from high sucrose yield per hectare, the sector needs new cultivars with higher fiber percentages. The selection of sugarcane clones based on phenotype alone is a complex task. The selected clones should display high performance in a series of yield- and quality-related traits. Selection indices can provide information about which clones can best combine the traits of agronomic interest. In this study, different selection indices were evaluated in a population of 220 clones. The following traits were evaluated: weight of 10 stalks with straw, weight of 10 stalks with no straw, tons of cane per hectare with straw, tons of cane per hectare with no straw, sucrose content, fiber percentage, and tons of fiber per hectare. The selection indices of Smith (1936) and Hazel (1943) and Mulamba and Mock (1978), the base index (Williams, 1962), and the index of Pesek and Baker (1969) were used. The selection index of Mulamba and Mock (1978) without economic weight estimates, the index of Mulamba and Mock with economic weights based on heritability, and the index of Pesek and Baker (1969) with the desired gains based on genetic standard deviations were efficient for the selection of energy cane clones with good fiber yield, sucrose content, and tons of cane per hectare.