DNA-based Self-Assembly of Chiral Plasmonic Nanostructures with Tailored Optical Response

Surface plasmon resonances generated in metallic nanostructures can be utilized to tailor electromagnetic fields. The precise spatial arrangement of such structures can result in surprising optical properties that are not found in any naturally occurring material. Here, the designed activity emerges from collective effects of singular components equipped with limited individual functionality. Top-down fabrication of plasmonic materials with a predesigned optical response in the visible range by conventional lithographic methods has remained challenging due to their limited resolution, the complexity of scaling, and the difficulty to extend these techniques to three-dimensional architectures. Molecular self-assembly provides an alternative route to create such materials which is not bound by the above limitations. We demonstrate how the DNA origami method can be used to produce plasmonic materials with a tailored optical response at visible wavelengths. Harnessing the assembly power of 3D DNA origami, we arranged metal nanoparticles with a spatial accuracy of 2 nm into nanoscale helices. The helical structures assemble in solution in a massively parallel fashion and with near quantitative yields. As a designed optical response, we generated giant circular dichroism and optical rotary dispersion in the visible range that originates from the collective plasmon-plasmon interactions within the nanohelices. We also show that the optical response can be tuned through the visible spectrum by changing the composition of the metal nanoparticles. The observed effects are independent of the direction of the incident light and can be switched by design between left- and right-handed orientation. Our work demonstrates the production of complex bulk materials from precisely designed nanoscopic assemblies and highlights the potential of DNA self-assembly for the fabrication of plasmonic nanostructures.Comment: 5 pages, 4 figure

Classification tools for carotenoid content estimation in Manihot esculenta via metabolomics and machine learning

Cassava genotypes (Manihot esculenta Crantz) with high pro-vitamin A activity have been identified as a strategy to reduce the prevalence of deficiency of this vitamin. The color variability of cassava roots, which can vary from white to red, is related to the presence of several carotenoid pigments. The present study has shown how CIELAB color measurement on cassava roots tissue can be used as a non-destructive and very fast technique to quantify the levels of carotenoids in cassava root samples, avoiding the use of more expensive analytical techniques for compound quantification, such as UV-visible spectrophotometry and the HPLC. For this, we used machine learning techniques, associating the colorimetric data (CIELAB) with the data obtained by UV-vis and HPLC, to obtain models of prediction of carotenoids for this type of biomass. Best values of R2 (above 90%) were observed for the predictive variable TCC determined by UV-vis spectrophotometry. When we tested the machine learning models using the CIELAB values as inputs, for the total carotenoids contents quantified by HPLC, the Partial Least Squares (PLS), Support Vector Machines, and Elastic Net models presented the best values of R2 (above 40%) and Root-Mean-Square Error (RMSE). For the carotenoid quantification by UV-vis spectrophotometry, R2 (around 60%) and RMSE values (around 6.5) are more satisfactory. Ridge regression and Elastic Network showed the best results. It can be concluded that the use colorimetric technique (CIELAB) associated with UV-vis/HPLC and statistical techniques of prognostic analysis through machine learning can predict the content of total carotenoids in these samples, with good precision and accuracy.CAPES -Coordenação de Aperfeiçoamento de Pessoal de Nível Superior(407323/2013-9)info:eu-repo/semantics/publishedVersio

CoExp: A Web Tool for the Exploitation of Co-expression Networks

Gene co-expression networks are a powerful type of analysis to construct gene groupings based on transcriptomic profiling. Co-expression networks make it possible to discover modules of genes whose mRNA levels are highly correlated across samples. Subsequent annotation of modules often reveals biological functions and/or evidence of cellular specificity for cell types implicated in the tissue being studied. There are multiple ways to perform such analyses with weighted gene co-expression network analysis (WGCNA) amongst one of the most widely used R packages. While managing a few network models can be done manually, it is often more advantageous to study a wider set of models derived from multiple independently generated transcriptomic data sets (e.g., multiple networks built from many transcriptomic sources). However, there is no software tool available that allows this to be easily achieved. Furthermore, the visual nature of co-expression networks in combination with the coding skills required to explore networks, makes the construction of a web-based platform for their management highly desirable. Here, we present the CoExp Web application, a user-friendly online tool that allows the exploitation of the full collection of 109 co-expression networks provided by the CoExpNets suite of R packages. We describe the usage of CoExp, including its contents and the functionality available through the family of CoExpNets packages. All the tools presented, including the web front- and back-ends are available for the research community so any research group can build its own suite of networks and make them accessible through their own CoExp Web application. Therefore, this paper is of interest to both researchers wishing to annotate their genes of interest across different brain network models and specialists interested in the creation of GCNs looking for a tool to appropriately manage, use, publish, and share their networks in a consistent and productive manner

High genetic diversity at the extreme range edge: nucleotide variation at nuclear loci in Scots pine (Pinus sylvestris L.) in Scotland

Nucleotide polymorphism at 12 nuclear loci was studied in Scots pine populations across an environmental gradient in Scotland, to evaluate the impacts of demographic history and selection on genetic diversity. At eight loci, diversity patterns were compared between Scottish and continental European populations. At these loci, a similar level of diversity (θsil=~0.01) was found in Scottish vs mainland European populations, contrary to expectations for recent colonization, however, less rapid decay of linkage disequilibrium was observed in the former (ρ=0.0086±0.0009, ρ=0.0245±0.0022, respectively). Scottish populations also showed a deficit of rare nucleotide variants (multi-locus Tajima's D=0.316 vs D=−0.379) and differed significantly from mainland populations in allelic frequency and/or haplotype structure at several loci. Within Scotland, western populations showed slightly reduced nucleotide diversity (πtot=0.0068) compared with those from the south and east (0.0079 and 0.0083, respectively) and about three times higher recombination to diversity ratio (ρ/θ=0.71 vs 0.15 and 0.18, respectively). By comparison with results from coalescent simulations, the observed allelic frequency spectrum in the western populations was compatible with a relatively recent bottleneck (0.00175 × 4Ne generations) that reduced the population to about 2% of the present size. However, heterogeneity in the allelic frequency distribution among geographical regions in Scotland suggests that subsequent admixture of populations with different demographic histories may also have played a role

The costs of preventing and treating chagas disease in Colombia

Background: The objective of this study is to report the costs of Chagas disease in Colombia, in terms of vector disease control programmes and the costs of providing care to chronic Chagas disease patients with cardiomyopathy. Methods: Data were collected from Colombia in 2004. A retrospective review of costs for vector control programmes carried out in rural areas included 3,084 houses surveyed for infestation with triatomine bugs and 3,305 houses sprayed with insecticide. A total of 63 patient records from 3 different hospitals were selected for a retrospective review of resource use. Consensus methodology with local experts was used to estimate care seeking behaviour and to complement observed data on utilisation. Findings: The mean cost per house per entomological survey was $4.4 (in US$ of 2004), whereas the mean cost of spraying a house with insecticide was $27. The main cost driver of spraying was the price of the insecticide, which varied greatly. Treatment of a chronic Chagas disease patient costs between$46.4 and $7,981 per year in Colombia, depending on severity and the level of care used. Combining cost and utilisation estimates the expected cost of treatment per patient-year is$1,028, whereas lifetime costs averaged $11,619 per patient. Chronic Chagas disease patients have limited access to healthcare, with an estimated 22% of patients never seeking care. Conclusion: Chagas disease is a preventable condition that affects mostly poor populations living in rural areas. The mean costs of surveying houses for infestation and spraying infested houses were low in comparison to other studies and in line with treatment costs. Care seeking behaviour and the type of insurance affiliation seem to play a role in the facilities and type of care that patients use, thus raising concerns about equitable access to care. Preventing Chagas disease in Colombia would be cost-effective and could contribute to prevent inequalities in health and healthcare.Wellcome Trus

Diabetes-related excess mortality in Mexico: a comparative analysis of National Death Registries between 2017-2019 and 2020

OBJECTIVE: To estimate diabetes-related mortality in Mexico in 2020 compared with 2017-2019 after the onset of the coronavirus disease 2019 (COVID-19) pandemic. RESEARCH DESIGN AND METHODS: This retrospective, state-level study used national death registries of Mexican adults aged ≥20 years for the 2017-2020 period. Diabetes-related death was defined using ICD-10 codes listing diabetes as the primary cause of death, excluding certificates with COVID-19 as the primary cause of death. Spatial and negative binomial regression models were used to characterize the geographic distribution and sociodemographic and epidemiologic correlates of diabetes-related excess mortality, estimated as increases in diabetes-related mortality in 2020 compared with average 2017-2019 rates. RESULTS: We identified 148,437 diabetes-related deaths in 2020 (177 per 100,000 inhabitants) vs. an average of 101,496 deaths in 2017-2019 (125 per 100,000 inhabitants). In-hospital diabetes-related deaths decreased by 17.8% in 2020 versus 2017-2019, whereas out-of-hospital deaths increased by 89.4%. Most deaths were attributable to type 2 diabetes (130 per 100,000 inhabitants). Compared with 2018-2019 data, hyperglycemic hyperosmolar state and diabetic ketoacidosis were the two contributing causes with the highest increase in mortality (128% and 116% increase, respectively). Diabetes-related excess mortality clustered in southern Mexico and was highest in states with higher social lag, rates of COVID-19 hospitalization, and prevalence of HbA1c ≥7.5%. CONCLUSIONS: Diabetes-related deaths increased among Mexican adults by 41.6% in 2020 after the onset of the COVID-19 pandemic, occurred disproportionately outside the hospital, and were largely attributable to type 2 diabetes and hyperglycemic emergencies. Disruptions in diabetes care and strained hospital capacity may have contributed to diabetes-related excess mortality in Mexico during 2020

Methodological considerations in the analysis of fecal glucocorticoid metabolites in tufted capuchins (Cebus apella)

Analysis of fecal glucocorticoid (GC) metabolites has recently become the standard method to monitor adrenocortical activity in primates noninvasively. However, given variation in the production, metabolism, and excretion of GCs across species and even between sexes, there are no standard methods that are universally applicable. In particular, it is important to validate assays intended to measure GC production, test extraction and storage procedures, and consider the time course of GC metabolite excretion relative to the production and circulation of the native hormones. This study examines these four methodological aspects of fecal GC metabolite analysis in tufted capuchins (Cebus apella). Specifically, we conducted an adrenocorticotrophic hormone (ACTH) challenge on one male and one female capuchin to test the validity of four GC enzyme immunoassays (EIAs) and document the time course characterizing GC me- tabolite excretion in this species. In addition, we compare a common field-friendly technique for extracting fecal GC metabolites to an established laboratory extraction methodology and test for effects of storing “field extracts” for up to 1 yr. Results suggest that a corticosterone EIA is most sensitive to changes in GC production, provides reliable measures when extracted according to the field method, and measures GC metabolites which remain highly stable after even 12 mo of storage. Further, the time course of GC metabolite excretion is shorter than that described yet for any primate taxa. These results provide guidelines for studies of GCs in tufted capuchins, and underscore the importance of validating methods for fecal hormone analysis for each species of interest