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- Alexandridis, Nikolaos, et al.
(författare)
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Models of natural pest control : Towards predictions across agricultural landscapes
- 2021
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Ingår i: Biological Control. - : Elsevier BV. - 1049-9644. ; 163
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Forskningsöversikt (refereegranskat)abstract
- Natural control of invertebrate crop pests has the potential to complement or replace conventional insecticide-based practices, but its mainstream application is hampered by predictive unreliability across agroecosystems. Inconsistent responses of natural pest control to changes in landscape characteristics have been attributed to ecological complexity and system-specific conditions. Here, we review agroecological models and their potential to provide predictions of natural pest control across agricultural landscapes. Existing models have used a multitude of techniques to represent specific crop-pest-enemy systems at various spatiotemporal scales, but less wealthy regions of the world are underrepresented. A realistic representation of natural pest control across systems appears to be hindered by a practical trade-off between generality and realism. Nonetheless, observations of context-sensitive, trait-mediated responses of natural pest control to land-use gradients indicate the potential of ecological models that explicitly represent the underlying mechanisms. We conclude that modelling natural pest control across agroecosystems should exploit existing mechanistic techniques towards a framework of contextually bound generalizations. Observed similarities in causal relationships can inform the functional grouping of diverse agroecosystems worldwide and the development of the respective models based on general, but context-sensitive, ecological mechanisms. The combined use of qualitative and quantitative techniques should allow the flexible integration of empirical evidence and ecological theory for robust predictions of natural pest control across a wide range of agroecological contexts and levels of knowledge availability. We highlight challenges and promising directions towards developing such a general modelling framework.
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| 2. |
- Mitchell, Megan, I, et al.
(författare)
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Extracellular Vesicle Capture by AnTibody of CHoice and Enzymatic Release (EV-CATCHER) : A customizable purification assay designed for small-RNA biomarker identification and evaluation of circulating small-EVs
- 2021
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Ingår i: Journal of Extracellular Vesicles. - : John Wiley & Sons. - 2001-3078. ; 10:8
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Tidskriftsartikel (refereegranskat)abstract
- Circulating nucleic acids, encapsulated within small extracellular vesicles (EVs), provide a remote cellular snapshot of biomarkers derived from diseased tissues, however selective isolation is critical. Current laboratory-based purification techniques rely on the physical properties of small-EVs rather than their inherited cellular fingerprints. We established a highly-selective purification assay, termed EV-CATCHER, initially designed for high-throughput analysis of low-abundance small-RNA cargos by next-generation sequencing. We demonstrated its selectivity by specifically isolating and sequencing small-RNAs from mouse small-EVs spiked into human plasma. Western blotting, nanoparticle tracking, and transmission electron microscopy were used to validate and quantify the capture and release of intact small-EVs. As proof-of-principle for sensitive detection of circulating miRNAs, we compared small-RNA sequencing data from a subset of small-EVs serum-purified with EV-CATCHER to data from whole serum, using samples from a small cohort of recently hospitalized Covid-19 patients. We identified and validated, only in small-EVs, hsa-miR-146a and hsa-miR-126-3p to be significantly downregulated with disease severity. Separately, using convalescent sera from recovered Covid-19 patients with high anti-spike IgG titers, we confirmed the neutralizing properties, against SARS-CoV-2 in vitro, of a subset of small-EVs serum-purified by EV-CATCHER, as initially observed with ultracentrifuged small-EVs. Altogether our data highlight the sensitivity and versatility of EV-CATCHER.
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