Multiplexable and Scalable Aqueous Synthesis Platform for Oleate-Based, Bilayer-Coated Gold Nanoparticles.

Despite gold-based nanomaterials having a unique role in nanomedicine, among other fields, synthesis limitations relating to reaction scale-up and control result in prohibitively high gold nanoparticle costs. In this work, a new preparation procedure for lipid bilayer-coated gold nanoparticles in water is presented, using sodium oleate as reductant and capping agent. The seed-free synthesis not only allows for size precision (8-30 nm) but also remarkable particle concentration (10 mm Au). These reaction efficiencies allow for multiplexing and reaction standardization in 96-well plates using conventional thermocyclers, in addition to simple particle purification via microcentrifugation. Such a multiplexing approach also enables detailed spectroscopic investigation of the nonlinear growth process and dynamic sodium oleate/oleic acid self-assembly. In addition to scalability (at gram-level), resulting gold nanoparticles are stable at physiological pH, in common cell culture media, and are autoclavable. To demonstrate the versatility and applicability of the reported method, a robust ligand exchange with thiolated polyethylene glycol analogues is also presented.

Kraft Lignin-Derived Microporous Nitrogen-Doped Carbon Adsorbent for Air and Water Purification.

The study presents a streamlined one-step process for producing highly porous, metal-free, N-doped activated carbon (N-AC) for CO2 capture and herbicide removal from simulated industrially polluted and real environmental systems. N-AC was prepared from kraft lignin─a carbon-rich and abundant byproduct of the pulp industry, using nitric acid as the activator and urea as the N-dopant. The reported carbonization process under a nitrogen atmosphere renders a product with a high yield of 30% even at high temperatures up to 800 °C. N-AC exhibited a substantial high N content (4-5%), the presence of aliphatic and phenolic OH groups, and a notable absence of carboxylic groups, as confirmed by Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, and Boehm's titration. Porosity analysis indicated that micropores constituted the majority of the pore structure, with 86% of pores having diameters less than 0.6 nm. According to BET adsorption analysis, the developed porous structure of N-AC boasted a substantial specific surface area of 1000 m2 g-1. N-AC proved to be a promising adsorbent for air and water purification. Specifically, N-AC exhibited a strong affinity for CO2, with an adsorption capacity of 1.4 mmol g-1 at 0.15 bar and 20 °C, and it demonstrated the highest selectivity over N2 from the simulated flue gas system (27.3 mmol g-1 for 15:85 v/v CO2/N2 at 20 °C) among all previously reported nitrogen-doped AC materials from kraft lignin. Moreover, N-AC displayed excellent reusability and efficient CO2 release, maintaining an adsorption capacity of 3.1 mmol g-1 (at 1 bar and 25 °C) over 10 consecutive adsorption-desorption cycles, confirming N-AC as a useful material for CO2 storage and utilization. The unique cationic nature of N-AC enhanced the adsorption of herbicides in neutral and weakly basic environments, which is relevant for real waters. It exhibited an impressive adsorption capacity for the herbicide 2,4-dichlorophenoxyacetic acid (2,4-D) at 96 ± 6 mg g-1 under pH 6 and 25 °C according to the Langmuir-Freundlich model. Notably, N-AC preserves its high adsorption capacity toward 2,4-D from simulated groundwater and runoff from tomato greenhouse, while performance in real samples from Fyris river in Uppsala, Sweden, causes a decrease of only 4-5%. Owing to the one-step process, high yield, annual abundance of kraft lignin, and use of environmentally friendly activating agents, N-AC has substantial potential for large-scale industrial applications.

Socio-demographic factors associated with COVID-19 vaccine uptake and refusal among Ugandan women.

BACKGROUND: This paper analyzes associations of socio-demographic factors with the uptake of COVID-19 vaccines, the refusal to get vaccinated against COVID-19, and various reasons stated for refusing vaccination against COVID-19 among a representative sample of Ugandan women. METHODS: This paper utilizes a representative cross-sectional survey collected among women aged 15-49 years in Uganda between September and November 2021. Regression analyses are used to study the associations of a broad range of socio-demographic characteristics with COVID-19 vaccine uptake, refusal of vaccination, and reasons for refusal among the respondents. RESULTS: 4211 women were included in the analysis. 11% of them were vaccinated against COVID-19, 76% were willing to get vaccinated, 13% were unwilling to get vaccinated. Fear of side effects was the most commonly stated reason for refusing vaccination (69%). Factors significantly and positively associated with being vaccinated against COVID-19 were age, higher education, urban residency, having savings, partial instead of complete income loss during the pandemic, and usage of modern contraceptives. Factors significantly and positively associated with refusing vaccination against COVID-19 were urban residency and current pregnancy, while age, having savings, and using modern contraceptives were factors associated with a lower likelihood of refusing vaccination, albeit with varying statistical significance. Few factors were strongly related to the stated reasons for refusing the vaccines; the fear of side effects significantly increased with age, while having received negative information on the vaccines was significantly less common among women with higher education. CONCLUSIONS: This study documents a low COVID-19 vaccination rate and a high willingness to get vaccinated among Ugandan women. Positive age and education gradients in vaccine uptake point to inequity in access to vaccination, potentially resulting from prioritizations of groups at particularly high risk. Refusal to be vaccinated was relatively low and systematic factors behind vaccine refusal were hardly to be found, even less so for particular reasons given for refusal.

Dynamics and genetic regulation of leaf nutrient concentration in barley based on hyperspectral imaging and machine learning.

Biofortification, the enrichment of nutrients in crop plants, is of increasing importance to improve human health. The wild barley nested association mapping (NAM) population HEB-25 was developed to improve agronomic traits including nutrient concentration. Here, we evaluated the potential of high-throughput hyperspectral imaging in HEB-25 to predict leaf concentration of 15 mineral nutrients, sampled from two field experiments and four developmental stages. Particularly accurate predictions were obtained by partial least squares regression (PLS) modeling of leaf concentrations for N, P and K reaching coefficients of determination of 0.90, 0.75 and 0.89, respectively. We recognized nutrient-specific patterns of variation of leaf nutrient concentration between developmental stages. A number of quantitative trait loci (QTL) associated with the simultaneous expression of leaf nutrients were detected, indicating their potential co-regulation in barley. For example, the wild barley allele of QTL-4H-1 simultaneously increased leaf concentration of N, P, K and Cu. Similar effects of the same QTL were previously reported for nutrient concentrations in grains, supporting a potential parallel regulation of N, P, K and Cu in leaves and grains of HEB-25. Our study provides a new approach for nutrient assessment in large-scale field experiments to ultimately select genes and genotypes supporting plant biofortification.

International travel in times of the COVID-19 pandemic: The case of German school breaks.

The COVID-19 pandemic has triggered severe global restrictions on international travel with the intention of limiting the spread of SARS-CoV-2 across countries. This paper studies the causal effect of the partial relaxation of these travel restrictions in Europe on the COVID-19 incidence in Germany during the summer months of 2020. It exploits the staggered start of the summer school breaks across German states as an exogenous shock to the travel opportunities of the population. While the school breaks also increased mobility within Germany, the event-study type regressions precisely control for domestic mobility and local COVID-19-related restrictions. The intention-to-treat effects of the relaxed travel restrictions show a significant and sizable increase of the COVID-19 incidence in German counties during the later weeks of the school breaks. Part of the increase can be attributed to a mandatory testing regime for travel returnees from high-incidence areas.

Performance and Sustainability Tradeoffs of Oxidized Carbon Nanotubes as a Cathodic Material in Lithium-Oxygen Batteries.

Climate change mitigation efforts will require a portfolio of solutions, including improvements to energy storage technologies in electric vehicles and renewable energy sources, such as the high-energy-density lithium-oxygen battery (LOB). However, if LOB technology will contribute to addressing climate change, improvements to LOB performance must not come at the cost of disproportionate increases in global warming potential (GWP) or cumulative energy demand (CED) over their lifecycle. Here, oxygen-functionalized multi-walled carbon nanotube (O-MWCNT) cathodes were produced and assessed for their initial discharge capacities and cyclability. Contrary to previous findings, the discharge capacity of O-MWCNT cathodes increased with the ratio of carbonyl/carboxyl moieties, outperforming pristine MWCNTs. However, increased oxygen concentrations decreased LOB cyclability, while high-temperature annealing increased both discharge capacity and cyclability. Improved performance resulting from MWCNT post-processing came at the cost of increased GWP and CED, which in some cases was disproportionately higher than the level of improved performance. Based on the findings presented here, there is a need to simultaneously advance research in improving LOB performance while minimizing or mitigating the environmental impacts of LOB production.

"Macrobot": An Automated Segmentation-Based System for Powdery Mildew Disease Quantification.

Managing plant diseases is increasingly difficult due to reasons such as intensifying the field production, climatic change-driven expansion of pests, redraw and loss of effectiveness of pesticides, rapid breakdown of the disease resistance in the field, and other factors. The substantial progress in genomics of both plants and pathogens, achieved in the last decades, has the potential to counteract this negative trend, however, only when the genomic data is supported by relevant phenotypic data that allows linking the genomic information to specific traits. We have developed a set of methods and equipment and combined them into a "Macrophenomics facility." The pipeline has been optimized for the quantification of powdery mildew infection symptoms on wheat and barley, but it can be adapted to other diseases and host plants. The Macrophenomics pipeline scores the visible powdery mildew disease symptoms, typically 5-7 days after inoculation (dai), in a highly automated manner. The system can precisely and reproducibly quantify the percentage of the infected leaf area with a theoretical throughput of up to 10000 individual samples per day, making it appropriate for phenotyping of large germplasm collections and crossing populations.

Evaluating the suitability of hyper- and multispectral imaging to detect foliar symptoms of the grapevine trunk disease Esca in vineyards.

BACKGROUND: Grapevine trunk diseases (GTDs) such as Esca are among the most devastating threats to viticulture. Due to the lack of efficient preventive and curative treatments, Esca causes severe economic losses worldwide. Since symptoms do not develop consecutively, the true incidence of the disease in a vineyard is difficult to assess. Therefore, an annual monitoring is required. In this context, automatic detection of symptoms could be a great relief for winegrowers. Spectral sensors have proven to be successful in disease detection, allowing a non-destructive, objective, and fast data acquisition. The aim of this study is to evaluate the feasibility of the in-field detection of foliar Esca symptoms over three consecutive years using ground-based hyperspectral and airborne multispectral imaging. RESULTS: Hyperspectral disease detection models have been successfully developed using either original field data or manually annotated data. In a next step, these models were applied on plant scale. While the model using annotated data performed better during development, the model using original data showed higher classification accuracies when applied in practical work. Moreover, the transferability of disease detection models to unknown data was tested. Although the visible and near-infrared (VNIR) range showed promising results, the transfer of such models is challenging. Initial results indicate that external symptoms could be detected pre-symptomatically, but this needs further evaluation. Furthermore, an application specific multispectral approach was simulated by identifying the most important wavelengths for the differentiation tasks, which was then compared to real multispectral data. Even though the ground-based multispectral disease detection was successful, airborne detection remains difficult. CONCLUSIONS: In this study, ground-based hyperspectral and airborne multispectral approaches for the detection of foliar Esca symptoms are presented. Both sensor systems seem to be suitable for the in-field detection of the disease, even though airborne data acquisition has to be further optimized. Our disease detection approaches could facilitate monitoring plant phenotypes in a vineyard.

Common Pitfalls in the Interpretation of COVID-19 Data and Statistics.

Policymakers, experts and the general public heavily rely on the data that are being reported in the context of the coronavirus pandemic. Daily data releases on confirmed COVID-19 cases and deaths provide information on the course of the pandemic.

Optimizing the procedure of grain nutrient predictions in barley via hyperspectral imaging.

Hyperspectral imaging enables researchers and plant breeders to analyze various traits of interest like nutritional value in high throughput. In order to achieve this, the optimal design of a reliable calibration model, linking the measured spectra with the investigated traits, is necessary. In the present study we investigated the impact of different regression models, calibration set sizes and calibration set compositions on prediction performance. For this purpose, we analyzed concentrations of six globally relevant grain nutrients of the wild barley population HEB-YIELD as case study. The data comprised 1,593 plots, grown in 2015 and 2016 at the locations Dundee and Halle, which have been entirely analyzed through traditional laboratory methods and hyperspectral imaging. The results indicated that a linear regression model based on partial least squares outperformed neural networks in this particular data modelling task. There existed a positive relationship between the number of samples in a calibration model and prediction performance, with a local optimum at a calibration set size of ~40% of the total data. The inclusion of samples from several years and locations could clearly improve the predictions of the investigated nutrient traits at small calibration set sizes. It should be stated that the expansion of calibration models with additional samples is only useful as long as they are able to increase trait variability. Models obtained in a certain environment were only to a limited extent transferable to other environments. They should therefore be successively upgraded with new calibration data to enable a reliable prediction of the desired traits. The presented results will assist the design and conceptualization of future hyperspectral imaging projects in order to achieve reliable predictions. It will in general help to establish practical applications of hyperspectral imaging systems, for instance in plant breeding concepts.

Genetic dissection of grain elements predicted by hyperspectral imaging associated with yield-related traits in a wild barley NAM population.

Enhancing the accumulation of essential mineral elements in cereal grains is of prime importance for combating human malnutrition. Biofortification by breeding holds great potential for improving nutrient accumulation in grains. However, conventional breeding approaches require element analysis of many grain samples, which causes high costs. Here we applied hyperspectral imaging to estimate the concentration of 15 grain elements (C, B, Ca, Cd, Cu, Fe, K, Mg, Mn, Mo, N, Na, P, S, Zn) in high-throughput in the wild barley nested association mapping (NAM) population HEB-25, comprising 1,420 BC1S3 lines derived from crossing 25 wild barley accessions with the cultivar 'Barke'. Nutrient concentrations varied largely with a multitude of lines having higher micronutrient concentration than 'Barke'. In a genome-wide association study (GWAS), we located 75 quantitative trait locus (QTL) hotspots, whereof many could be explained by major genes such as NO APICAL MERISTEM-1 (NAM-1) and PHOTOPERIOD 1 (Ppd-H1). The GWAS approach revealed exotic alleles that were able to increase grain element concentrations. Remarkably, a QTL linked to GIBBERELLIN 20 OXIDASE 2 (HvGA20ox2) significantly increased several grain elements without yield loss. We conclude that introgressing promising exotic alleles into elite breeding material can assist in improving the nutritional value of barley grains.

Phenoliner: A New Field Phenotyping Platform for Grapevine Research.

In grapevine research the acquisition of phenotypic data is largely restricted to the field due to its perennial nature and size. The methodologies used to assess morphological traits and phenology are mainly limited to visual scoring. Some measurements for biotic and abiotic stress, as well as for quality assessments, are done by invasive measures. The new evolving sensor technologies provide the opportunity to perform non-destructive evaluations of phenotypic traits using different field phenotyping platforms. One of the biggest technical challenges for field phenotyping of grapevines are the varying light conditions and the background. In the present study the Phenoliner is presented, which represents a novel type of a robust field phenotyping platform. The vehicle is based on a grape harvester following the concept of a moveable tunnel. The tunnel it is equipped with different sensor systems (RGB and NIR camera system, hyperspectral camera, RTK-GPS, orientation sensor) and an artificial broadband light source. It is independent from external light conditions and in combination with artificial background, the Phenoliner enables standardised acquisition of high-quality, geo-referenced sensor data.

Non-invasive Presymptomatic Detection of Cercospora beticola Infection and Identification of Early Metabolic Responses in Sugar Beet.

Cercospora beticola is an economically significant fungal pathogen of sugar beet, and is the causative pathogen of Cercospora leaf spot. Selected host genotypes with contrasting degree of susceptibility to the disease have been exploited to characterize the patterns of metabolite responses to fungal infection, and to devise a pre-symptomatic, non-invasive method of detecting the presence of the pathogen. Sugar beet genotypes were analyzed for metabolite profiles and hyperspectral signatures. Correlation of data matrices from both approaches facilitated identification of candidates for metabolic markers. Hyperspectral imaging was highly predictive with a classification accuracy of 98.5-99.9% in detecting C. beticola. Metabolite analysis revealed metabolites altered by the host as part of a successful defense response: these were L-DOPA, 12-hydroxyjasmonic acid 12-O-ß-D-glucoside, pantothenic acid, and 5-O-feruloylquinic acid. The accumulation of glucosylvitexin in the resistant cultivar suggests it acts as a constitutively produced protectant. The study establishes a proof-of-concept for an unbiased, presymptomatic and non-invasive detection system for the presence of C. beticola. The test needs to be validated with a larger set of genotypes, to be scalable to the level of a crop improvement program, aiming to speed up the selection for resistant cultivars of sugar beet. Untargeted metabolic profiling is a valuable tool to identify metabolites which correlate with hyperspectral data.

A shift toward smaller cell size via manipulation of cell cycle gene expression acts to smoothen Arabidopsis leaf shape.

Understanding the relationship of the size and shape of an organism to the size, shape, and number of its constituent cells is a basic problem in biology; however, numerous studies indicate that the relationship is complex and often nonintuitive. To investigate this problem, we used a system for the inducible expression of genes involved in the G1/S transition of the plant cell cycle and analyzed the outcome on leaf shape. By combining a careful developmental staging with a quantitative analysis of the temporal and spatial response of cell division pattern and leaf shape to these manipulations, we found that changes in cell division frequency occurred much later than the observed changes in leaf shape. These data indicate that altered cell division frequency cannot be causally involved in the observed change of shape. Rather, a shift to a smaller cell size as a result of the genetic manipulations performed correlated with the formation of a smoother leaf perimeter, i.e. appeared to be the primary cellular driver influencing form. These data are discussed in the context of the relationship of cell division, growth, and leaf size and shape.

Modelling Visual Search with the Selective Attention for Identification Model (VS-SAIM): A Novel Explanation for Visual Search Asymmetries.

In earlier work, we developed the Selective Attention for Identification Model (SAIM [16]). SAIM models the human ability to perform translation-invariant object identification in multiple object scenes. SAIM suggests that central for this ability is an interaction between parallel competitive processes in a selection stage and a object identification stage. In this paper, we applied the model to visual search experiments involving simple lines and letters. We presented successful simulation results for asymmetric and symmetric searches and for the influence of background line orientations. Search asymmetry refers to changes in search performance when the roles of target item and non-target item (distractor) are swapped. In line with other models of visual search, the results suggest that a large part of the empirical evidence can be explained by competitive processes in the brain, which are modulated by the similarity between target and distractor. The simulations also suggest that another important factor is the feature properties of distractors. Finally, the simulations indicate that search asymmetries can be the outcome of interactions between top-down (knowledge about search items) and bottom-up (feature of search items) processing. This interaction in VS-SAIM is dominated by a novel mechanism, the knowledge-based on-centre-off-surround receptive field. This receptive field is reminiscent of the classical receptive fields but the exact shape is modulated by both, top-down and bottom-up processes. The paper discusses supporting evidence for the existence of this novel concept.

LEAFPROCESSOR: a new leaf phenotyping tool using contour bending energy and shape cluster analysis.

*Significant progress has been made in the identification of the genetic factors controlling leaf shape. However, no integrated solution for the quantification and categorization of leaf form has been developed. In particular, the analysis of local changes in margin growth, which define many of the differences in shape, remains problematical. *Here, we report on a software package (LEAFPROCESSOR) which provides a semi-automatic and landmark-free method for the analysis of a range of leaf-shape parameters, combining both single metrics and principal component analysis. In particular, we explore the use of bending energy as a tool for the analysis of global and local leaf perimeter deformation. *As a test case for the implementation of the LEAFPROCESSOR program, we show that this integrated analysis leads to deeper insights into the morphogenic changes underpinning a series of previously identified Arabidopsis leaf-shape mutants. Our analysis reveals that many of these mutants which, at first sight, show similar leaf morphology, can be distinguished via our shape analysis. *The LEAFPROCESSOR program provides a novel integrated tool for the analysis of leaf shape.

Phased control of expansin activity during leaf development identifies a sensitivity window for expansin-mediated induction of leaf growth.

Expansins are cell wall proteins associated with the process of plant growth. However, investigations in which expansin gene expression has been manipulated throughout the plant have often led to inconclusive results. In this article, we report on a series of experiments in which overexpression of expansin was targeted to specific phases of leaf growth using an inducible promoter system. The data indicate that there is a restricted window of sensitivity when increased expansin gene expression leads to increased endogenous expansin activity and an increase in leaf growth. This phase of maximum expansin efficacy corresponds to the mid phase of leaf growth. We propose that the effectiveness of expansin action depends on the presence of other modulating factors in the leaf and we suggest that it is the control of expression of these factors (in conjunction with expansin gene expression) that defines the extent of leaf growth. These data help to explain some of the previously observed variation in growth response following manipulation of expansin gene expression and highlight a potential linkage of the expression of modifiers of expansin activity with the process of exit from cell division.