Comparison of artificial insemination and natural service cost effectiveness in dairy cattle

Reproductive efficiency in the dairy herd is the most important factor for its economic success and a major concern for dairy farmers when using artificial insemination (AI) or natural service (NS). Our objectives were to estimate, compare and analyse the costs associated with breeding cattle by do-it-yourself (DIY) AI and NS and identify the factors that influence them, under typical dairy farming conditions in Greece. A simulation study was designed based on data from 120 dairy cattle farms that differed in size (range 40 to 285 cows) and milk production level (4000 to 9300 kg per cow per year). Different scenarios were employed to estimate costs associated directly with AI and NS as well as potentially extended calving intervals (ECI) due to AI. Results showed that bull maintenance costs for NS were €1440 to €1670 per year ($1,820 to $2,111). Direct AI costs were higher than those for NS for farms with more than 30 cows and ECI constituted a considerable additional burden. In fact, amongst the factors that affected the amount of milk needed to cover total extra AI costs, number of days open was the dominant one. Semen, feed and heifer prices had a very small effect. When, hypothetically, use of NS bulls results in a calving interval of 12 months, AI daughters with a calving interval of 13.5 months have to produce about 705 kg of additional milk in order to cover the extra cost. Their actual milk production, however, exceeds this limit by more than 25%. When real calving intervals are considered (13.0 v. 13.7 months for NS and AI, respectively) AI daughters turn out to produce more than twice the additional amount of milk needed. It was concluded that even under less than average management conditions, AI is more profitable than the best NS scenario. The efficient communication of this message should be a primary concern of the AI industry.     

海州湾春季短蛸的栖息分布特征及其与环境因子的关系

近年来,我国近海多种重要渔业资源处于不同程度的衰退状态,而短蛸具有生命周期短、生长迅速的特点,在我国近海经济渔获产量中占重要地位。然而,有关短蛸的栖息分布特征及其与环境因子的关系尚缺乏研究,不利于更好地保护和利用其资源。本研究根据2011年和2013—2017年春季海州湾的渔业资源和环境因子调查数据,采用随机森林模型、人工神经网络模型和广义提升回归模型3种机器学习方法分析了短蛸的栖息分布特征及其与环境因子的关系。结果表明: 随机森林模型的拟合效果和预测能力在3种模型中优势较大,选择该模型进行分析表明,底层水温、水深和底层盐度对短蛸的栖息分布有较大影响。短蛸的相对资源密度随底层水温、水深和底层盐度的增加均呈先上升后下降趋势。根据FVCOM模型模拟的环境数据,应用随机森林模型预测了短蛸在海州湾海域的栖息分布,发现短蛸主要分布在34.5°—35.8° N、119.7°—121° E之间的海域。     

Genetics and selective breeding of variation in wing truncation in a flightless aphid control agent

Augmentative biological control by predaceous ladybird beetles can be improved by using flightless morphs, which have longer residence times on the host plants. The two‐spot ladybird beetle, Adalia bipunctata (L.) (Coleoptera: Coccinellidae), is used for the biological control of aphids in greenhouses and on urban trees. Flightlessness due to truncated wings occurs at very low frequency in some natural populations of A. bipunctata. Pure‐breeding strains of this 'wingless' genotype of A. bipunctata can easily be obtained in the laboratory. Such strains have not been commercialized yet due to concerns about their reduced fitness compared to wild‐type strains, which renders mass production more expensive. Wingless strains exhibit, however, wide intra‐population phenotypic variation in the extent of wing truncation which is related to fitness traits. We here use classical quantitative genetic techniques to study the heritability and genetic architecture of variation in wing truncation in a wingless strain of A. bipunctata. Split‐families reared at one of two temperatures revealed strong family‐by‐temperature interaction: heritability was estimated as 0.64 ± 0.09 at 19 °C and 0.29 ± 0.06 at 29 °C. Artificial selection in opposite directions at 21 °C demonstrated that the degree of wing truncation can be altered within a few generations resulting in wingless phenotypes without any wing tissue (realized h² = 0.72), as well as those with minimal truncations (realized h² = 0.61) in two replicates. The latter lines produced more than twice as many individuals. This indicates that selective breeding of wing truncation may be exploited to improve mass rearing of flightless strains of A. bipunctata for commercial biological control. Our work illustrates that cryptic variation can also be a source for the selective breeding of natural enemies.     

Does experimental evolution produce better biological control agents? A critical review of the evidence

Biological control of crop pests is considered a good alternative or complement to the use of pesticides. However, legislation restricts the importation of natural enemies of pests. A potential way to circumvent this limitation is by using experimental evolution and/or artificial selection to improve native biological control agents. Here, we review studies that have used these methodologies and evaluate their success. Experimental evolution or artificial selection has been used on a wide range of traits, with most focusing on improving the performance of natural enemies in ecologically relevant environments, such as in the presence of pesticides or at different temperatures. Although most studies were poorly replicated, the selected traits generally improved following the selection process. However, correlated responses (often in the form of trade‐offs) with other traits of interest were common. We suggest that the selection procedure can be improved by increasing replication and performing experimental evolution under more semi‐natural environments, to ensure that the most useful traits are being selected.     

Vascular tissue engineering: Fabrication and characterization of acetylsalicylic acid-loaded electrospun scaffolds coated with amniotic membrane lysate

As the incidence of small-diameter vascular graft (SDVG) occlusion is considerably high, a great amount of research is focused on constructing a more biocompatible graft. The absence of a biocompatible surface in the lumen of the engineered grafts that can support confluent lining with endothelial cells (ECs) can cause thrombosis and graft failure. Blood clot formation is mainly because of the lack of an integrated endothelium. The most effective approach to combat this problem would be using natural extracellular matrix constituents as a mimic of endothelial basement membrane along with applying anticoagulant agents to provide local antithrombotic effects. In this study, we fabricated aligned and random electrospun poly-L-lactic acid (PLLA) scaffolds containing acetylsalicylic acid (ASA) as the anticoagulation agent and surface coated them with amniotic membrane (AM) lysate. Vascular scaffolds were structurally and mechanically characterized and assessed for cyto- and hemocompatibility and their ability to support endothelial differentiation was examined. All the scaffolds showed appropriate tensile strength as expected for vascular grafts. Lack of cytotoxicity, cellular attachment, growth, and infiltration were proved using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay and scanning electron microscopy. The blood compatibilities of different scaffolds examined by in vitro hemolysis and blood coagulation assays elucidated the excellent hemocompatibility of our novel AM-coated ASA-loaded nanofibers. Drug-loaded scaffolds showed a sustained release profile of ASA in 7 days. AM-coated electrospun PLLA fibers showed enhanced cytocompatibility for human umbilical vein ECs, making a confluent endothelial-like lining. In addition, AM lysate-coated ASA-PLLA-aligned scaffold proved to support endothelial differentiation of Wharton's jelly-derived mesenchymal stem cells. Our results together indicated that AM lysate-coated ASA releasing scaffolds have promising potentials for development of a biocompatible SDVG.     

Synthetic biology and metabolic engineering of actinomycetes for natural product discovery

Actinomycetes are one of the most valuable sources of natural products with industrial and medicinal importance. After more than half a century of exploitation, it has become increasingly challenging to find novel natural products with useful properties as the same known compounds are often repeatedly re-discovered when using traditional approaches. Modern genome mining approaches have led to the discovery of new biosynthetic gene clusters, thus indicating that actinomycetes still harbor a huge unexploited potential to produce novel natural products. In recent years, innovative synthetic biology and metabolic engineering tools have greatly accelerated the discovery of new natural products and the engineering of actinomycetes. In the first part of this review, we outline the successful application of metabolic engineering to optimize natural product production, focusing on the use of multi-omics data, genome-scale metabolic models, rational approaches to balance precursor pools, and the engineering of regulatory genes and regulatory elements. In the second part, we summarize the recent advances of synthetic biology for actinomycetal metabolic engineering including cluster assembly, cloning and expression, CRISPR/Cas9 technologies, and chassis strain development for natural product overproduction and discovery. Finally, we describe new advances in reprogramming biosynthetic pathways through polyketide synthase and non-ribosomal peptide synthetase engineering. These new developments are expected to revitalize discovery and development of new natural products with medicinal and other industrial applications.     

Use of artificial neural networks for analysis of the factors affecting particle size in mebudipine nanoemulsion

In this study, a nanoemulsion containing mebudipine [composed of ethyl oleate (oil phase), Tween 80 (T80), Span 80 (S80) (surfactants), polyethylene glycol 400, ethanol (cosurfactants), and deionized water] was prepared with the aim of improving its bioavailability for an effective antihypertensive therapy. Particle size of the formulation was measured by dynamic light scattering. Then, artificial neural networks were used in identifying factors that influence the particle size of the nanoemulsion. Three variables, namely, amount of surfactant system (T80?+?S80), amount of polyethylene glycol, and amount of ethanol as cosurfactants, were considered as input values and the particle size was used as output. The developed model showed that all the three inputs had some degrees of effect on particles size: increasing the value of each input decreased the size. Furthermore, amount of surfactant was found to be the dominant factor in controlling the final particle size of nanoemulsion.

Communicated by Ramaswamy H. Sarma