Open-source tool for real-time and automated analysis of droplet-based microfluidic

Publisher Copyright: © 2023 The Royal Society of Chemistry.Droplet-based microfluidic technology is a powerful tool for generating large numbers of monodispersed nanoliter-sized droplets for ultra-high throughput screening of molecules or single cells. Yet further progress in the development of methods for the real-time detection and measurement of passing droplets is needed for achieving fully automated systems and ultimately scalability. Existing droplet monitoring technologies are either difficult to implement by non-experts or require complex experimentation setups. Moreover, commercially available monitoring equipment is expensive and therefore limited to a few laboratories worldwide. In this work, we validated for the first time an easy-to-use, open-source Bonsai visual programming language to accurately measure in real-time droplets generated in a microfluidic device. With this method, droplets are found and characterized from bright-field images with high processing speed. We used off-the-shelf components to achieve an optical system that allows sensitive image-based, label-free, and cost-effective monitoring. As a test of its use we present the results, in terms of droplet radius, circulation speed and production frequency, of our method and compared its performance with that of the widely-used ImageJ software. Moreover, we show that similar results are obtained regardless of the degree of expertise. Finally, our goal is to provide a robust, simple to integrate, and user-friendly tool for monitoring droplets, capable of helping researchers to get started in the laboratory immediately, even without programming experience, enabling analysis and reporting of droplet data in real-time and closed-loop experiments.publishersversionepub_ahead_of_prin

A genome survey of Moniliophthora perniciosa gives new insights into Witches' Broom Disease of cacao

<p>Abstract</p> <p>Background</p> <p>The basidiomycete fungus <it>Moniliophthora perniciosa </it>is the causal agent of Witches' Broom Disease (WBD) in cacao (<it>Theobroma cacao</it>). It is a hemibiotrophic pathogen that colonizes the apoplast of cacao's meristematic tissues as a biotrophic pathogen, switching to a saprotrophic lifestyle during later stages of infection. <it>M. perniciosa</it>, together with the related species <it>M. roreri</it>, are pathogens of aerial parts of the plant, an uncommon characteristic in the order Agaricales. A genome survey (1.9× coverage) of <it>M. perniciosa </it>was analyzed to evaluate the overall gene content of this phytopathogen.</p> <p>Results</p> <p>Genes encoding proteins involved in retrotransposition, reactive oxygen species (ROS) resistance, drug efflux transport and cell wall degradation were identified. The great number of genes encoding cytochrome P450 monooxygenases (1.15% of gene models) indicates that <it>M. perniciosa </it>has a great potential for detoxification, production of toxins and hormones; which may confer a high adaptive ability to the fungus. We have also discovered new genes encoding putative secreted polypeptides rich in cysteine, as well as genes related to methylotrophy and plant hormone biosynthesis (gibberellin and auxin). Analysis of gene families indicated that <it>M. perniciosa </it>have similar amounts of carboxylesterases and repertoires of plant cell wall degrading enzymes as other hemibiotrophic fungi. In addition, an approach for normalization of gene family data using incomplete genome data was developed and applied in <it>M. perniciosa </it>genome survey.</p> <p>Conclusion</p> <p>This genome survey gives an overview of the <it>M. perniciosa </it>genome, and reveals that a significant portion is involved in stress adaptation and plant necrosis, two necessary characteristics for a hemibiotrophic fungus to fulfill its infection cycle. Our analysis provides new evidence revealing potential adaptive traits that may play major roles in the mechanisms of pathogenicity in the <it>M. perniciosa</it>/cacao pathosystem.</p

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio