Endometrial nitric oxide production and nitric oxide synthases in the equine endometrium: Relationship with microvascular density during the estrous cycle

Nitric oxide (NO) plays an important role in angiogenesis and in the regulation of the blood flow. This study was carried out to investigate (i) the effects of endogenous estrogens and progestins and exogenous progesterone (P₄) (5 ng/ml or 1 μg/ml) or estradiol 17β (E₂β) (50 pg/ml or 1 μg/ml) on in vitro endometrial NO synthesis; (ii) the presence of different isoforms of NO synthase; (iii) and their relationship to microvascular density in the equine endometrium during the estrous cycle. NOS expression was also evaluated in the myometrium. Expression of endothelial and inducible forms of NOS in the uterus was assessed by Western blot and immunocytochemistry. Vascular density in endometrial tissue was determined on histologic sections. In the luteal phase, compared to the follicular phase, endometrial NO production increased without exogenous hormones and with exogenous E₂β (1 μg/ml). Although immunocytochemistry revealed iNOS and eNOS expression in the endometrium, no positive signal for iNOS was detected by Western blot. Endothelial NOS was observed in endometrial glands, endothelial cells, fibroblasts, blood and lymphatic vessels. Endometrial eNOS expression was the highest in the follicular and mid-luteal phases while it was found to be the lowest in the early luteal phase. In the follicular phase, hyperplasia of endometrial tissue with respect to myometrium was detected. No difference in vascular density was present between phases. All together, NO may play some roles in both proliferative and secretory phases of endometrial development in the mare.     

Target and non-target site mechanisms of fungicide resistance and their implications for the management of crop pathogens

Fungicides are indispensable for high-quality crops, but the rapid emergence and evolution of fungicide resistance have become the most important issues in modern agriculture. Hence, the sustainability and profitability of agricultural production have been challenged due to the limited number of fungicide chemical classes. Resistance to site-specific fungicides has principally been linked to target and non-target site mechanisms. These mechanisms change the structure or expression level, affecting fungicide efficacy and resulting in different and varying resistance levels. This review provides background information about fungicide resistance mechanisms and their implications for developing anti-resistance strategies in plant pathogens. Here, our purpose was to review changes at the target and non-target sites of quinone outside inhibitor (QoI) fungicides, methyl-benzimidazole carbamate (MBC) fungicides, demethylation inhibitor (DMI) fungicides, and succinate dehydrogenase inhibitor (SDHI) fungicides and to evaluate if they may also be associated with a fitness cost on crop pathogen populations. The current knowledge suggests that understanding fungicide resistance mechanisms can facilitate resistance monitoring and assist in developing anti-resistance strategies and new fungicide molecules to help solve this issue. © 2023 Society of Chemical Industry.     

Genomic prediction for meat and carcass traits in Nellore cattle using a Markov blanket algorithm

This study was carried out to evaluate the advantage of preselecting SNP markers using Markov blanket algorithm regarding the accuracy of genomic prediction for carcass and meat quality traits in Nellore cattle. This study considered 3675, 3680, 3660 and 524 records of rib eye area (REA), back fat thickness (BF), rump fat (RF), and Warner–Bratzler shear force (WBSF), respectively, from the Nellore Brazil Breeding Program. The animals have been genotyped using low-density SNP panel (30 k), and subsequently imputed for arrays with 777 k SNPs. Four Bayesian specifications of genomic regression models, namely Bayes A, Bayes B, Bayes Cπ and Bayesian Ridge Regression methods were compared in terms of prediction accuracy using a five folds cross-validation. Prediction accuracy for REA, BF and RF was all similar using the Bayesian Alphabet models, ranging from 0.75 to 0.95. For WBSF, the predictive ability was higher using Bayes B (0.47) than other methods (0.39 to 0.42). Although the prediction accuracies using Markov blanket of SNP markers were lower than those using all SNPs, for WBSF the relative gain was lower than 13%. With a subset of informative SNPs markers, identified using Markov blanket, probably, is possible to capture a large proportion of the genetic variance for WBSF. The development of low-density and customized arrays using Markov blanket might be cost-effective to perform a genomic selection for this trait, increasing the number of evaluated animals, improving the management decisions based on genomic information and applying genomic selection on a large scale.     

Outbreak of shoot blight and dieback of Eucalyptus spp., caused by Pseudoplagiostoma eucalypti in Brazil

An outbreak of a new and severe disease was observed in Eucalyptus plantations of Bahia state, Brazil. An Ascomycota fungus has been frequently associated with the main symptoms of the disease namely leaf spot, branch cankers, shoot blight, defoliation, and dieback. Based on morphological characteristics, phylogenetic analysis (ITS and TEF-1α genes), and pathogenicity test on Eucalyptus plants, Pseudoplagiostoma eucalypti was identified as the causal agent of the disease. Although P. eucalytpi has been known from in Brazil since 1998, this is the first report of it causing severe disease and die-back on Eucalyptus spp. and we also record new symptoms associated with the pathogen.     

Potential Anti-Obesity,Anti-Steatosis,and Anti-Inflammatory Properties of Extracts from the Microalgae Chlorella vulgaris and Chlorococcum amblystomatis under Different Growth Conditions

Microalgae are known as a producer of proteins and lipids, but also of valuable compounds for human health benefits (e.g., polyunsaturated fatty acids (PUFAs); minerals, vitamins, or other compounds). The overall objective of this research was to prospect novel products, such as nutraceuticals from microalgae, for application in human health, particularly for metabolic diseases. Chlorella vulgaris and Chlorococcum amblystomatis were grown autotrophically, and C. vulgaris was additionally grown heterotrophically. Microalgae biomass was extracted using organic solvents (dichloromethane, ethanol, ethanol with ultrasound-assisted extraction). Those extracts were evaluated for their bioactivities, toxicity, and metabolite profile. Some of the extracts reduced the neutral lipid content using the zebrafish larvae fat metabolism assay, reduced lipid accumulation in fatty-acid-overloaded HepG2 liver cells, or decreased the LPS-induced inflammation reaction in RAW264.7 macrophages. Toxicity was not observed in the MTT assay in vitro or by the appearance of lethality or malformations in zebrafish larvae in vivo. Differences in metabolite profiles of microalgae extracts obtained by UPLC-LC-MS/MS and GNPS analyses revealed unique compounds in the active extracts, whose majority did not have a match in mass spectrometry databases and could be potentially novel compounds. In conclusion, microalgae extracts demonstrated anti-obesity, anti-steatosis, and anti-inflammatory activities and could be valuable resources for developing future nutraceuticals. In particular, the ultrasound-assisted ethanolic extract of the heterotrophic C. vulgaris significantly enhanced the anti-obesity activity and demonstrated that the alteration of culture conditions is a valuable approach to increase the production of high-value compounds.     

Ultrasound-Assisted Extraction for the Recovery of Carotenoids from Guava’s Pulp and Waste Powders

Plant Foods for Human Nutrition - In this work, lycopene- and β-carotene-rich extracts were obtained from guava’s pulp and waste powders using maceration (ME), ultrasonic bath (BUAE,...     

Comparison between vegetation indices for detecting spatial and temporal variabilities in soybean crop using canopy sensors

Precision Agriculture - Crop monitoring through remote sensing techniques enable greater knowledge of average variability in crop growth. Canopy sensors help provide information on the variability...     

Prediction of Genetic Gain in Sweet Corn using Selection Indexes

Journal of Crop Science and Biotechnology - The cultivation of sweet corn is expanding in Brazil, but there are serious constraints about the availability of commercial cultivars. The selection of...     

Meta-analysis of fungicide efficacy on soybean target spot and cost–benefit assessment

Target spot of soybean has spread in Brazil, the southeastern United States and Argentina in the last decade. A collaborative network of field Uniform Fungicide Trials (UFT) in Brazil was created in 2011 to study the target spot control efficacy of fungicides, including azoxystrobin + benzovindiflupyr (AZ_BF), carbendazim (CZM), fluxapyroxad + pyraclostrobin (FLUX_PYRA), epoxiconazole + FLUX_PYRA (EPO_FLUX_PYRA), mancozeb (MZB) and prothioconazole + trifloxystrobin (PROT_TRIF). Network meta-analysis was used to conduct a quantitative synthesis of UFT data collected from 2012 to 2016 and to evaluate the effects of disease pressure (DP, low ≤ 35% target spot severity in the nontreated control < high) and year of experiment on the overall mean efficacy and yield response to each of the tested fungicides. Based on mean percentage control of target spot severity, the tested fungicides fall into three efficacy groups (EG): high EG, FLUX_PYRA (76.2% control relative to the nontreated control) and EPO_FLUX_PYRA (75.7% control); intermediate EG, PROT_TRIF (66.5% control) and low EG, MZB (49.6% control), AZ_BF (46.7% control) and CZM (32.4% control). DP had a significant effect on yield response. At DP_Low, the highest response was due to PROT_TRIF (+342 kg ha⁻¹, +12.8%) and EPO_FLUX_PYRA (+295.5 kg ha⁻¹, +11.2%), whereas at DP_High, EPO_FLUX_PYRA and FLUX_PYRA outperformed the other treatments, with yield responses of 503 kg ha⁻¹ (+20.2%) and 469 kg ha⁻¹ (+19.1%), respectively. The probability of a positive return on fungicide investment ranged from 0.26 to 0.56 at DP_Low and from 0.34 to 0.66 at DP_High.