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Measuring permeability in a high school physics course has long been a hard task. However, with the advent of using smartphones in the classroom, it is not only possible but even easily done. This paper offers detailed instructions on how to measure permeability using a smartphone’s magnetometer, starting with experimentally discovering the equation for measuring the magnetic field of a coil with a negligible length. All discussed activities and experiments can be implemented with a focus on inquiry.

Background While others have reported severe acute respiratory syndrome-related coronavirus 2(SARS-CoV-2) seroprevalence studies in health care workers (HCWs), we leverage the use of a highly sensitive coronavirus antigen microarray to identify a group of seropositive health care workers who were missed by daily symptom screening that was instituted prior to any epidemiologically significant local outbreak. Given that most health care facilities rely on daily symptom screening as the primary method to identify SARS-CoV-2 among health care workers, here, we aim to determine how demographic, occupational, and clinical variables influence SARS-CoV-2 seropositivity among health care workers. Methods We designed a cross-sectional survey of HCWs for SARS-CoV-2 seropositivity conducted from May 15th to June 30th 2020 at a 418-bed academic hospital in Orange County, California. From an eligible population of 5,349 HCWs, study participants were recruited in two ways: an open cohort, and a targeted cohort. The open cohort was open to anyone, whereas the targeted cohort that recruited HCWs previously screened for COVID-19 or work in high-risk units. A total of 1,557 HCWs completed the survey and provided specimens, including 1,044 in the open cohort and 513 in the targeted cohort. Demographic, occupational, and clinical variables were surveyed electronically. SARS-CoV-2 seropositivity was assessed using a coronavirus antigen microarray (CoVAM), which measures antibodies against eleven viral antigens to identify prior infection with 98% specificity and 93% sensitivity. Results Among tested HCWs (n = 1,557), SARS-CoV-2 seropositivity was 10.8%, and risk factors included male gender (OR 1.48, 95% CI 1.05–2.06), exposure to COVID-19 outside of work (2.29, 1.14–4.29), working in food or environmental services (4.85, 1.51–14.85), and working in COVID-19 units (ICU: 2.28, 1.29–3.96; ward: 1.59, 1.01–2.48). Amongst 1,103 HCWs not previously screened, seropositivity was 8.0%, and additional risk factors included younger age (1.57, 1.00-2.45) and working in administration (2.69, 1.10–7.10). Conclusion SARS-CoV-2 seropositivity is significantly higher than reported case counts even among HCWs who are meticulously screened. Seropositive HCWs missed by screening were more likely to be younger, work outside direct patient care, or have exposure outside of work.

Recent advances in deep learning enable using chemical structures and phenotypic profiles to accurately predict assay results for compounds virtually, reducing the time and cost of screens in the drug discovery process. The relative strength of high-throughput data sources - chemical structures, images (Cell Painting), and gene expression profiles (L1000) - has been unknown. Here we compare their ability to predict the activity of compounds structurally different from those used in training, using a sparse dataset of 16,979 chemicals tested in 376 assays for a total of 542,648 readouts. Deep learning-based feature extraction from chemical structures provided a remarkable ability to predict assay activity for structures dissimilar to those used for training. Image-based profiling performed even better, but requires wet lab experimentation. It outperformed gene expression profiling, and at lower cost. Furthermore, the three profiling modalities are complementary, and together can predict a wide range of diverse bioactivity, including cell-based and biochemical assays. Our study shows that, for many assays, predicting compound activity from phenotypic profiles and chemical structures is an accurate and efficient way to identify potential treatments in the early stages of the drug discovery process.

Supplementary Data from Lipid Metabolic Reprogramming Extends beyond Histologic Tumor Demarcations in Operable Human Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) is among the deadliest malignancies and potentially curable only with radical surgical resection at early stages. The tumor microenvironment has been shown to be central to the development and progression of PDAC. A better understanding of how early human PDAC metabolically communicates with its environment and differs from healthy pancreas could help improve PDAC diagnosis and treatment. Here we performed deep proteomic analyses from diagnostic specimens of operable, treatment-naïve PDAC patients (n = 14), isolating four tissue compartments by laser-capture microdissection: PDAC lesions, tumor-adjacent but morphologically benign exocrine glands, and connective tissues neighboring each of these compartments. Protein and pathway levels were compared between compartments and with control pancreatic proteomes. Selected targets were studied immunohistochemically in the 14 patients and in additional tumor microarrays, and lipid deposition was assessed by nonlinear label-free imaging (n = 16). Widespread downregulation of pancreatic secretory functions was observed, which was paralleled by high cholesterol biosynthetic activity without prominent lipid storage in the neoplastic cells. Stromal compartments harbored ample blood apolipoproteins, indicating abundant microvasculature at the time of tumor removal. The features best differentiating the tumor-adjacent exocrine tissue from healthy control pancreas were defined by upregulation of proteins related to lipid transport. Importantly, histologically benign exocrine regions harbored the most significant prognostic pathways, with proteins involved in lipid transport and metabolism, such as neutral cholesteryl ester hydrolase 1, associating with shorter survival. In conclusion, this study reveals prognostic molecular changes in the exocrine tissue neighboring pancreatic cancer and identifies enhanced lipid transport and metabolism as its defining features.Significance:In clinically operable pancreatic cancer, regions distant from malignant cells already display proteomic changes related to lipid transport and metabolism that affect prognosis and may be pharmacologically targeted.

Solid organ transplant (SOT) recipients represent a vulnerable patient population and are of high risk for airborne viral infections, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV2). Treatment of COVID-19 is still challenging, as no proven therapeutic regimen is available for immunocompromised patients. Our aim was to evaluate the efficacy and safety of remdesivir (RDV) therapy in infected hospitalized SOT patients. All transplanted recipients (N = 25; lung: 19; kidney: 3, liver: 2, heart: 1) who needed hospital care were reviewed in the time period between September 2020 and May 2021 out of the 945 patients treated at the Department. Case control matched patients receiving RDV (all in need of supplementary oxygen) and standard of care (SOC) were included as controls. Among the 25 SOT patients (female:male = 11:14; average age = 53.2 ± 12.7 years), 15 received RDV medication (RDV-TX), and in 10 cases SOC treatment was used (SOC-TX). Significantly worse clinical score was noted in RDV patients compared with RDV-TX; however, transfer to a higher intensity care unit as well as 60-day survival of RDV-TX patients were significantly worse. All SOT fatalities within 60 days of follow-up were lung transplant recipients (6 out of 19 lung transplant patients). No adverse events were noted related to RDV therapy. In SOT patients, especially lung transplant recipients, with severe COVID-19 needing supplementary oxygen, RDV treatment was safe; however, outcome was significantly worse as compared with nontransplanted individuals with initially worse clinical parameters.

Correct specification of a conditional quantile model implies that a particular conditional moment is equal to zero. We nonparametrically estimate the conditional moment function via series regression and test whether it is identically zero using uniform functional inference. Our approach is theoretically justified via a strong Gaussian approximation for statistics of growing dimensions in a general time series setting. We propose a novel bootstrap method in this nonstandard context and show that it significantly outperforms the benchmark asymptotic approximation in finite samples, especially for tail quantiles such as Value‐at‐Risk (VaR). We use the proposed new test to study the VaR and CoVaR (Adrian and Brunnermeier (2016)) of a collection of US financial institutions.

Distribution modeling methods are used to provide occurrence probability surfaces for modeled targets. While most often used for modeling species, distribution modeling methods can also be applied to vegetation types. However, surfaces provided by distribution modeling need to be transformed into classified wall-to-wall maps of vegetation types to be useful for practical purposes, such as nature management and environmental planning. The paper compares the performance of three methods for assembling predictions for multiple vegetation types, modeled individually, into a wall-to-wall map. The authors used grid-cell based probability surfaces from distribution models of 31 vegetation types to test the three assembly methods. The first, a probability-based method, selected for each grid cell the vegetation type with the highest predicted probability of occurrence in that cell. The second, a performance-based method, assigned the vegetation types, ordered from high to low model performance, to a fraction of the grid cells given by the vegetation type’s prevalence in the study area. The third, a prevalence-based method, differed from the performance-based method by assigning vegetation types in the order from low to high prevalence. Thus the assembly methods worked in two principally different ways: the probability-based method assigned vegetation types to grid cells in a cell-by-cell manner, and both the performance-based method and prevalence-based method assigned them in a type-by-type manner. All methods were evaluated by use of reference data collected in the field, more or less independently of the data used to parameterize the vegetation-type models. Quantity, allocation, and total disagreement, as well as proportional dissimilarity metrics, were used for evaluation of assembly methods. Overlay analysis showed 38.1% agreement between all three assembly methods. The probability-based method had the lowest total disagreement with, and proportional dissimilarity from, the reference datasets, but the differences between the three methods were small. The three assembly methods differed strongly with respect to the distribution of the total disagreement on its quantity and allocation components: the cell-by-cell assignment method strongly favored allocation disagreement and the type-by-type methods strongly favored quantity disagreement. The probability-based method best reproduced the general pattern of variation across the study area, but at the cost of many rare vegetation types, which were left out of the assembled map. By contrast, the prevalence-based and performance-based methods represented vegetation types in accordance with nationwide area statistics. The results show that maps of vegetation types with wall-to-wall coverage can be assembled from individual distribution models with a quality acceptable for indicative purposes, but all the three tested methods currently also have shortcomings. The results also indicate specific points in the methodology for map assembly that may be improved. area frame survey, assembly strategies, distribution modeling, spatial probabilities, vegetation mapping, vegetation types

Recently we have concluded that image-based features derived from the microenvironment have an enormous impact on successfully determining the class of an object1. Here we demonstrate that deep learning-based phenotypic analysis of cells with a properly chosen microenvironment-size provides results comparable to our earlier neighbourhood-based methods that utilise hand-crafted image features. We hypothesised that treating cells with equal weight, regardless of their position within the cellular microenvironment, is suboptimal, and direct neighbours have a larger impact on the phenotype of the cell-of-interest than cells in its larger proximity. Hence we present a novel approach that (1) considers the fully featured view of the cell-of-interest, (2) includes the neighbourhood and (3) gives lesser weight to cells that are far from the cell. To achieve this, we present a transformation similar to those characteristic for fisheye cameras. Such a transformation satisfies all the above defined criteria, with a fast rate of transform for any images. Using the proposed transformation with proper settings we could significantly increase the accuracy of single-cell phenotyping, both in case of cell culture and tissue-based microscopy images. The range of potential applications of the proposed method goes beyond microscopy, as we present improved results on the iWildCam 2020 dataset containing images of wild animals.

Targeted spatial transcriptomics methods capture the topology of cell types and states in tissues at single cell- and subcellular resolution by measuring the expression of a predefined set of genes. The selection of an optimal set of probed genes is crucial for capturing and interpreting the spatial signals present in a tissue. However, current selections often rely on marker genes, precluding them from detecting continuous spatial signals or novel states. We present Spapros, an end-to-end probe set selection pipeline that optimizes both probe set specificity for cell type identification and within-cell-type expression variation to resolve spatially distinct populations while taking into account prior knowledge, as well as probe design and expression constraints. To facilitate data analysis and interpretation, Spapros also provides rules for cell type identification. We evaluated Spapros by selecting probes on 6 different data sets and built an evaluation pipeline with 12 quality metrics to find that Spapros outperforms other selection approaches in both cell type recovery and recovering expression variation beyond cell types. Furthermore, we used Spapros to design a SCRINSHOT experiment of adult lung tissue to demonstrate how probes selected with Spapros identify cell types of interest and detect spatial variation even within cell types. Spapros enables optimal probe set selection, probe set evaluation, and probe design, as a freely available Python package.

Measuring the phenotypic effect of treatments on cells through imaging assays is an efficient and powerful way of studying cell biology, and requires computational methods for transforming images into quantitative data that highlight phenotypic outcomes. Here, we present an optimized strategy for learning representations of treatment effects from high-throughput imaging data, which follows a causal framework for interpreting results and guiding performance improvements. We use weakly supervised learning (WSL) for modeling associations between images and treatments, and show that it encodes both confounding factors and phenotypic features in the learned representation. To facilitate their separation, we constructed a large training dataset with Cell Painting images from five different studies to maximize experimental diversity, following insights from our causal analysis. Training a WSL model with this dataset successfully improves downstream performance, and produces a reusable convolutional network for image-based profiling, which we call Cell Painting CNN-1. We conducted a comprehensive evaluation of our strategy on three publicly available Cell Painting datasets, discovering that representations obtained by the Cell Painting CNN-1 can improve performance in downstream analysis for biological matching up to 30% with respect to classical features, while also being more computationally efficient.

Comet assay provides an easy solution to estimate DNA damage in single cells through microscopy assessment. It is widely used in the analysis of genotoxic damages induced by radiotherapy or chemotherapeutic agents. DNA damage is quantified at the single-cell level by computing the displacement between the genetic material within the nucleus, typically called “comet head”, and the genetic material in the surrounding part of the cell, considered as the “comet tail”. Today, the number of works based on Comet Assay analyses is really impressive. In this work, besides revising the solutions available to obtain reproducible and reliable quantitative data, we developed an easy-to-use tool named CometAnalyser. It is designed for the analysis of both fluorescent and silver-stained wide-field microscopy images and allows to automatically segment and classify the comets, besides extracting Tail Moment and several other intensity/morphological features for performing statistical analysis. CometAnalyser is an open-source deep-learning tool. It works with Windows, Macintosh, and UNIX-based systems. Source code, standalone versions, user manual, sample images, video tutorial and further documentation are freely available at: https://sourceforge.net/p/cometanalyser.HIGHLIGHTSComet assay provides an easy solution to estimate DNA damage in single cells.Today, an impressive number of works are based on Comet Assay analyses, especially in the field of cancer research.Comet assay was originally performed as a qualitative analysis.None of the free tools today available work on both fluorescent- and silver-stained images.We developed CometAnalyser, an open-source deep-learning tool designed for easy segmentation and classification of comets in fluorescent- and silver-stained images.

Aim: Obstructive sleep apnea (OSA) activates the complement system; however, the levels of membrane attack complex (MAC) are unaltered suggesting regulatory mechanisms. Our aim was to investigate complement factor H (CFH) and clusterin, two important complement regulators in OSA. Materials & methods: We analyzed clusterin and CFH levels in plasma of 86 patients with OSA and 33 control subjects. Results: There was no difference in CFH levels between patients (1099.4/784.6–1570.5/μg/ml) and controls (1051.4/652.0–1615.1/μg/ml, p = 0.72). Clusterin levels were higher in patients with OSA (309.7/217.2–763.2/μg/ml vs 276.1/131.0–424.3/μg/ml, p = 0.048) with a trend for a positive correlation with disease severity (p = 0.073). Conclusion: Increase in clusterin levels may be protective in OSA by blocking the MAC formation.

In battery electric vehicles, permanent magnet synchronous IPM motors are gaining more and more ground due to their high power density and high-efficiency operation. Many research examine their specific characteristics. In order to reach a desired total torque, low torque ripple, high efficiency, many pre-plannings have to be executed. The modern age engineering industry can rely much on complex simulation software, such as MotorAnalysis – PM. In this paper, an initial IPM motor design with delta magnet arrangement was created for vehicle application. This study had a strong focus on finding correlation between rotor layout arrangement and crucial motor operationial attributes, such as: torque components, torque ripple, cogging torque and efficiency. Time stepping magnetostatics FE and time stepping transient FE simulations were used. Each arrangement changement held its own simulation file, thus the effect of each change could have been separately examined. Arrangements, where the distance between magnets is smaller, resulted in greater torque and efficiency. Usage of enlarged magnets had the same results. Size should be increased and distance should be decreased with care to avoid a growth in torque ripple.

Organ- and body-scale cell atlases have the potential to transform our understanding of human biology. To capture the variability present in the population, these atlases must include diverse demographics such as age and ethnicity from both healthy and diseased individuals. The growth in both size and number of single-cell datasets, combined with recent advances in computational techniques, for the first time makes it possible to generate such comprehensive large-scale atlases through integration of multiple datasets. Here, we present the integrated Human Lung Cell Atlas (HLCA) combining 46 datasets of the human respiratory system into a single atlas spanning over 2.2 million cells from 444 individuals across health and disease. The HLCA contains a consensus re-annotation of published and newly generated datasets, resolving under- or misannotation of 59% of cells in the original datasets. The HLCA enables recovery of rare cell types, provides consensus marker genes for each cell type, and uncovers gene modules associated with demographic covariates and anatomical location within the respiratory system. To facilitate the use of the HLCA as a reference for single-cell lung research and allow rapid analysis of new data, we provide an interactive web portal to project datasets onto the HLCA. Finally, we demonstrate the value of the HLCA reference for interpreting disease-associated changes. Thus, the HLCA outlines a roadmap for the development and use of organ-scale cell atlases within the Human Cell Atlas.

Traumatic brain injury (TBI) was shown to lead to the development of cerebral microbleeds (CMBs), which are associated with long term cognitive decline and gait disturbances in patients. The elderly is one of the most vulnerable parts of the population to suffer TBI. Importantly, ageing is known to exacerbate microvascular fragility and to promote the formation of CMBs. In this overview, the effect of ageing is discussed on the development and characteristics of TBI-related CMBs, with special emphasis on CMBs associated with mild TBI. Four cases of TBI-related CMBs are described to illustrate the concept that ageing exacerbates the deleterious microvascular effects of TBI and that similar brain trauma may induce more CMBs in old patients than in young ones. Recommendations are made for future prospective studies to establish the mechanistic effects of ageing on the formation of CMBs after TBI, and to determine long-term consequences of CMBs on clinically relevant outcome measures including cognitive performance, gait and balance function.

Aim: Distribution modelling is a useful approach to obtain knowledge about the spatial distribution of biodiversity, required for, for example, red-list assessments. While distribution modelling methods have been applied mostly to single species, modelling of communities and ecosystems (EDM; ecosystem-level distribution modelling) produces results that are more directly relevant for management and decision-making. Although the choice of predictors is a pivotal part of the modelling process, few studies have compared the suitability of different sets of predictors for EDM. In this study, we compare the performance of 50 single environmental variables with that of 11 composite landscape gradients (CLGs) for prediction of ecosystem types. The CLGs represent gradients in landscape element composition derived from multivariate analyses, for example “inner-outer coast” and “land use intensity.” Location: Norway. Methods: We used data from field-based ecosystem-type mapping of nine ecosystem types, and environmental variables with a resolution of 100 × 100 m. We built nine models for each ecosystem type with variables from different predictor sets. Logistic regression with forward selection of variables was used for EDM. Models were evaluated with independently collected data. Results: Most ecosystem types could be predicted reliably, although model performance differed among ecosystem types. We identified significant differences in predictive power and model parsimony across models built from different predictor sets. Climatic variables alone performed poorly, indicating that the current climate alone is not sufficient to predict the current distribution of ecosystems. Used alone, the CLGs resulted in parsimonious models with relatively high predictive power. Used together with other variables, they consistently improved the models. Main conclusions: Our study highlights the importance of variable selection in EDM. We argue that the use of composite variables as proxies for complex environmental gradients has the potential to improve predictions from EDMs and thus to inform conservation planning as well as improve the precision and credibility of red lists and global change assessments.conservation planning, distribution modelling, ecosystem classification, ecosystem types, IUCN Red List of Ecosystems, landscape gradients, spatial prediction, species response curves

Massive multiple-input multiple-output (MIMO) is a key technology in modern cellular wireless communication systems to attain a very high system throughput in a dynamic multi-user environment. Massive MIMO relies on deploying base stations equipped with a large number of antenna elements. One possible way to deploy base stations equipped with hundreds or thousands of antennas is creating extremely large aperture arrays. In this paper, we investigate channel modeling aspects of massive MIMO systems with large aperture arrays, in which many users are located in the near-field of the aperture. Oneand two-dimensional antenna geometries, different propagation models, and antenna element patterns are compared in terms of inter-user correlation, condition number of the multi-user channel matrix, and spectral efficiency to identify key design parameters and essential modeling assumptions. As our analysis reveals by choosing spectral-efficiency as a design objective, the size of the aperture is the critical design parameter.

Pancreatic ductal adenocarcinoma (PDAC) is among the deadliest malignancies and potentially curable only with radical surgical resection at early stages. The tumor microenvironment has been shown to be central to the development and progression of PDAC. A better understanding of how early human PDAC metabolically communicates with its environment and differs from healthy pancreas could help improve PDAC diagnosis and treatment. Here we performed deep proteomic analyses from diagnostic specimens of operable, treatment-naïve PDAC patients (n = 14), isolating four tissue compartments by laser-capture microdissection: PDAC lesions, tumor-adjacent but morphologically benign exocrine glands, and connective tissues neighboring each of these compartments. Protein and pathway levels were compared between compartments and with control pancreatic proteomes. Selected targets were studied immunohistochemically in the 14 patients and in additional tumor microarrays, and lipid deposition was assessed by nonlinear label-free imaging (n = 16). Widespread downregulation of pancreatic secretory functions was observed, which was paralleled by high cholesterol biosynthetic activity without prominent lipid storage in the neoplastic cells. Stromal compartments harbored ample blood apolipoproteins, indicating abundant microvasculature at the time of tumor removal. The features best differentiating the tumor-adjacent exocrine tissue from healthy control pancreas were defined by upregulation of proteins related to lipid transport. Importantly, histologically benign exocrine regions harbored the most significant prognostic pathways, with proteins involved in lipid transport and metabolism, such as neutral cholesteryl ester hydrolase 1, associating with shorter survival. In conclusion, this study reveals prognostic molecular changes in the exocrine tissue neighboring pancreatic cancer and identifies enhanced lipid transport and metabolism as its defining features. Significance: In clinically operable pancreatic cancer, regions distant from malignant cells already display proteomic changes related to lipid transport and metabolism that affect prognosis and may be pharmacologically targeted.

Here, we describe a scalable and automated, high-content microscopy -based mini-immunofluorescence assay (mini-IFA) for serological testing i.e., detection of antibodies. Unlike conventional IFA, which often relies on the use of cells infected with the target pathogen, our assay employs transfected cells expressing individual viral antigens. The assay builds on a custom neural network-based image analysis pipeline for the automated and multiplexed detection of immunoglobulins (IgG, IgA, and IgM) in patient samples. As a proof-of-concept, we employed high-throughput equipment to set up the assay for measuring antibody response against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection with spike (S), membrane (M), and nucleo (N) proteins, and the receptor-binding domain (R) as the antigens. We compared the automated mini-IFA results from hundreds of patient samples to the visual observations of human experts and to the results obtained with conventional ELISA. The comparisons demonstrated a high correlation to both, suggesting high sensitivity and specificity of the mini-IFA. By testing pre-pandemic samples and those collected from patients with RT-PCR confirmed SARS-CoV-2 infection, we found mini-IFA to be most suitable for IgG and IgA detection. The results demonstrated N and S proteins as the ideal antigens, and the use of these antigens can serve to distinguish between vaccinated and infected individuals. The assay principle described enables detection of antibodies against practically any pathogen, and none of the assay steps require high biosafety level environment. The simultaneous detection of multiple Ig classes allows for distinguishing between recent and past infection.Public abstractThe manuscript describes a miniaturized immunofluorescence assay (mini-IFA) for measuring antibody response in patient blood samples. The automated method builds on machine-learning -guided image analysis with SARS-CoV-2 as the model pathogen. The method enables simultaneous measurement of IgM, IgA, and IgG responses against different virus antigens in a high throughput manner. The assay relies on antigens expressed through transfection and allows for differentiation between vaccine-induced and infection-induced antibody responses. The transfection-based antigen expression enables performing the assay at a low biosafety level laboratory and allows fast adaptation of the assay to emerging pathogens. Our results provide proof-of-concept for the approach, demonstrating fast and accurate measurement of antibody responses in a clinical and research set-up.

Summary Systematic insight into cellular dysfunction can improve understanding of disease etiology, risk assessment, and patient stratification. We present a multiparametric high-content imaging platform enabling quantification of low-density lipoprotein (LDL) uptake and lipid storage in cytoplasmic droplets of primary leukocyte subpopulations. We validate this platform with samples from 65 individuals with variable blood LDL-cholesterol (LDL-c) levels, including familial hypercholesterolemia (FH) and non-FH subjects. We integrate lipid storage data into another readout parameter, lipid mobilization, measuring the efficiency with which cells deplete lipid reservoirs. Lipid mobilization correlates positively with LDL uptake and negatively with hypercholesterolemia and age, improving differentiation of individuals with normal and elevated LDL-c. Moreover, combination of cell-based readouts with a polygenic risk score for LDL-c explains hypercholesterolemia better than the genetic risk score alone. This platform provides functional insights into cellular lipid trafficking and has broad possible applications in dissecting the cellular basis of metabolic disorders.

Introduction Several Aristolochia species were used as medicinal herb across Europe and in recent years, their antimicrobial activity has also been investigated. Materials and methods In this study, A. clematitis was selected to evaluate the aristolochic acids I and II (AA I and AA II) concentrations and the antimicrobial activity of methanol, hexane, butanol, and ethyl acetate extracts of the root, stem, leaf, root, and fruit. AA I and AA II contents were measured by a validated high-performance liquid chromatography–ultraviolet method. Results Each fraction of the plant contained AA I and AA II and the root was found to have the highest contents of AA I (1.09%) and AA II (0.7454%). The minimum inhibitory concentrations of all extracts were determined by standard microdilution method. The fruit’s extracts showed the most efficient antimicrobial effect against both methicillin sensitive and resistant Staphylococcus aureus strains. Conclusion Correlation between the AA I and AA II concentrations and the antimicrobial effect was not found.

Incorporating information about the surroundings can have a significant impact on successfully determining the class of an object. This is of particular interest when determining the phenotypes of cells, for example, in the context of high-throughput screens. We hypothesized that an ideal approach would consider the fully featured view of the cell of interest, include its neighboring microenvironment, and give lesser weight to cells that are far from the cell of interest. To satisfy these criteria, we present an approach with a transformation similar to those characteristic of fisheye cameras. Using this transformation with proper settings, we could significantly increase the accuracy of single-cell phenotyping, both in the case of cell culture and tissue -based microscopy images, and we present improved results on a dataset containing images of wild animals.

Recent studies provide conflicting evidence on the persistence of SARS-CoV-2 immunity induced by mRNA vaccines. Here, we aim to quantify the persistence of humoral immunity following vaccination using a coronavirus antigen microarray that includes 10 SARS-CoV-2 antigens. In a prospective longitudinal cohort of 240 healthcare workers, composite SARS-CoV-2 IgG antibody levels did not wane significantly over a 6-month study period. In the subset of the study population previously exposed to SARS-CoV-2 based on seropositivity for nucleocapsid antibodies, higher composite anti-spike IgG levels were measured before the vaccine but no significant difference from unexposed individuals was observed at 6 months. Age, vaccine type, or worker role did not significantly impact composite IgG levels, although non-significant trends towards lower antibody levels in older participants and higher antibody levels with Moderna vaccine were observed at 6 months. A small subset of our cohort were classified as having waning antibody titers at 6 months, and these individuals were less likely to work in patient care roles and more likely to have prior exposure to SARS-CoV-2.

Proteins are prone to aggregate when they are expressed above their solubility limits, a phenomenon termed supersaturation. Aggregation may occur as proteins emerge from the ribosome or after they fold and accumulate in the cell, but the relative importance of these two routes remain poorly known. Here, we systematically probed the solubility limits of each Escherichia coli protein upon overexpression using an image-based screen coupled with machine learning. The analysis suggests that competition between folding and aggregation from the unfolded state governs the two aggregation routes. Remarkably, the majority (70%) of insoluble proteins have low supersaturation risks in their unfolded states and rather aggregate after folding. Furthermore, a substantial fraction (∼35%) of the proteome remain soluble at concentrations much higher than those found naturally, indicating a large margin of safety to tolerate gene expression changes. We show that high disorder content and low surface stickiness are major determinants of high solubility and are favored in abundant bacterial proteins. Overall, our proteome-wide study provides empirical insights into the molecular determinants of protein aggregation routes in a bacterial cell.

Uremic cardiomyopathy is characterized by diastolic dysfunction (DD), left ventricular hypertrophy (LVH), and fibrosis. Angiotensin-II plays a major role in the development of uremic cardiomyopathy via nitro-oxidative and inflammatory mechanisms. In heart failure, the beta-3 adrenergic receptor (β3-AR) is up-regulated and coupled to endothelial nitric oxide synthase (eNOS)-mediated pathways, exerting antiremodeling effects. We aimed to compare the antiremodeling effects of the angiotensin-II receptor blocker losartan and the β3-AR agonist mirabegron in uremic cardiomyopathy. Chronic kidney disease (CKD) was induced by 5/6th nephrectomy in male Wistar rats. Five weeks later, rats were randomized into four groups: (1) sham-operated, (2) CKD, (3) losartan-treated (10 mg/kg/day) CKD, and (4) mirabegron-treated (10 mg/kg/day) CKD groups. At week 13, echocardiographic, histologic, laboratory, qRT-PCR, and Western blot measurements proved the development of uremic cardiomyopathy with DD, LVH, fibrosis, inflammation, and reduced eNOS levels, which were significantly ameliorated by losartan. However, mirabegron showed a tendency to decrease DD and fibrosis; but eNOS expression remained reduced. In uremic cardiomyopathy, β3-AR, sarcoplasmic reticulum ATPase (SERCA), and phospholamban levels did not change irrespective of treatments. Mirabegron reduced the angiotensin-II receptor 1 expression in uremic cardiomyopathy that might explain its mild antiremodeling effects despite the unchanged expression of the β3-AR.

Graphical abstract, Today, we are fully immersed into the era of 3D biology. It has been extensively demonstrated that 3D models: (a) better mimic the physiology of human tissues; (b) can effectively replace animal models; (c) often provide more reliable results than 2D ones. Accordingly, anti-cancer drug screenings and toxicology studies based on multicellular 3D biological models, the so-called “-oids” (e.g. spheroids, tumoroids, organoids), are blooming in the literature. However, the complex nature of these systems limit the manual quantitative analyses of single cells’ behaviour in the culture. Accordingly, the demand for advanced software tools that are able to perform phenotypic analysis is fundamental. In this work, we describe the freely accessible tools that are currently available for biologists and researchers interested in analysing the effects of drugs/treatments on 3D multicellular -oids at a single-cell resolution level. In addition, using publicly available nuclear stained datasets we quantitatively compare the segmentation performance of 9 specific tools.

Single nucleus segmentation is a frequent challenge of microscopy image processing, since it is the first step of many quantitative data analysis pipelines. The quality of tracking single cells, extracting features or classifying cellular phenotypes strongly depends on segmentation accuracy. Worldwide competitions have been held, aiming to improve segmentation, and recent years have definitely brought significant improvements: large annotated datasets are now freely available, several 2D segmentation strategies have been extended to 3D, and deep learning approaches have increased accuracy. However, even today, no generally accepted solution and benchmarking platform exist. We review the most recent single-cell segmentation tools, and provide an interactive method browser to select the most appropriate solution.

Publisher Copyright: Copyright © 2021 Horváth et al. Oral epithelial cells monitor microbiome composition and initiate immune response upon dysbiosis, as in the case of Candida imbalances. Candida species, such as C. albicans and C. parapsilosis, are the most prevalent yeasts in the oral cavity. Comparison of healthy oral epithelial cell responses revealed that while C. albicans infection robustly activated inflammation cascades, C. parapsilosis primarily activated various inflammation-independent pathways. In posttranscriptional regulatory processes, several miRNAs were altered by both species. For C. parapsilosis, the dose of yeast cells directly correlated with changes in transcriptomic responses with higher fungal burdens inducing significantly different and broader changes. MicroRNAs (miRNAs) associated with carbohydrate metabolism-, hypoxia-, and vascular development-related responses dominated with C. parapsilosis infection, whereas C. albicans altered miRNAs linked to inflammatory responses. Subsequent analyses of hypoxia-inducible factor 1a (HIF1-a) and hepatic stellate cell (HSC) activation pathways predicted target genes through which miRNA-dependent regulation of yeast-specific functions may occur, which also supported the observed species-specific responses. Our findings suggest that C. parapsilosis is recognized as a commensal at low doses by the oral epithelium; however, increased fungal burden activates different pathways, some of which overlap with the inflammatory processes robustly induced by C. albicans. IMPORTANCE A relatively new topic within the field of immunology involves the role of miRNAs in innate as well as adaptive immune response regulation. In recent years, posttranscriptional regulation of host-pathogenic fungal interactions through miRNAs was also suggested. Our study reveals that the distinct nature of human oral epithelial cell responses toward C. parapsilosis and C. albicans is possibly due to species-specific fine-tuning of host miRNA regulatory processes. The findings of this study also shed new light on the nature of early host cell transcriptional responses to the presence of C. parapsilosis and highlight the species’ potential inflammation-independent host activation processes. These findings contribute to our better understanding of how miRNA deregulation at the oral immunological barrier, in non-canonical immune cells, may discriminate between fungal species, particularly Candida species with high or low pathogenic potential.

The reduced derivative of α-conotoxin MI, a 14 amino acid peptide is characterized by NMR-pH titrations and molecular dynamics simulations to determine the protonation constants of the nine basic moieties, including four cysteine thiolates, and the charge-dependent structural properties. The peptide conformation at various protonation states was determined. The results show that the disulfide motifs in the native globular α-conotoxin MI occur between those cysteine moieties that exhibit the most similar thiolate basicities. Since the basicity of thiolates correlates to its redox potential, this phenomenon can be explained by the higher reactivity of the two thiolates with higher basicities. The folding of the oxidized peptide is further facilitated by the loop-like structure of the reduced form, which brings the thiolate groups into sufficient proximity. The 9 group-specific protonation constants and the related, charge-dependent, species-specific peptide structures are presented.

Hypocotyl length determination is a widely used method to phenotype young seedlings. The measurement itself has been developed from using rulers and millimeter papers to the assessment of digitized images, yet it remained a labour-intensive, monotonous and time consuming procedure. To make high-throughput plant phenotyping possible, we developed a deep learning-based approach to simplify and accelerate this method. Our pipeline does not require a specialized imaging system but works well with low quality images, produced with a simple flatbed scanner or a smartphone camera. Moreover, it is easily adaptable for a diverse range of datasets, not restricted to Arabidopsis thaliana. Furthermore, we show that the accuracy of the method reaches human performance. We not only provide the full code at https://github.com/biomag-lab/hypocotyl-UNet, but also give detailed instructions on how the algorithm can be trained with custom data, tailoring it for the requirements and imaging setup of the user.One-sentence summaryA deep learning-based algorithm, providing an adaptable tool for determining hypocotyl or coleoptile length of different plant species.

AIM: Many countries lack informative, high‐resolution, wall‐to‐wall vegetation or land cover maps. Such maps are useful for land use and nature management, and for input to regional climate and hydrological models. Land cover maps based on remote sensing data typically lack the required ecological information, whereas traditional field‐based mapping is too expensive to be carried out over large areas. In this study, we therefore explore the extent to which distribution modelling (DM) methods are useful for predicting the current distribution of vegetation types (VT) on a national scale. LOCATION: Mainland Norway, covering ca. 324,000 km². METHODS: We used presence/absence data for 31 different VTs, mapped wall‐to‐wall in an area frame survey with 1081 rectangular plots of 0.9 km². Distribution models for each VT were obtained by logistic generalised linear modelling, using stepwise forward selection with an F‐ratio test. A total of 116 explanatory variables, recorded in 100 m × 100 m grid cells, were used. The 31 models were evaluated by applying the AUC criterion to an independent evaluation dataset. RESULTS: Twenty‐one of the 31 models had AUC values higher than 0.8. The highest AUC value (0.989) was obtained for Poor/rich broadleaf deciduous forest, whereas the lowest AUC (0.671) was obtained for Lichen and heather spruce forest. Overall, we found that rare VTs are predicted better than common ones, and coastal VTs are predicted better than inland ones. CONCLUSIONS: Our study establishes DM as a viable tool for spatial prediction of aggregated species‐based entities such as VTs on a regional scale and at a fine (100 m) spatial resolution, provided relevant predictor variables are available. We discuss the potential uses of distribution models in utilizing large‐scale international vegetation surveys. We also argue that predictions from such models may improve parameterisation of vegetation distribution in earth system models.

Abstract Cyclodextrins are very important excipients in the pharmaceutical industry. Given the multitude of native and semisynthetic cyclodextrin derivatives, there is a need for a rapid and reliable method for the selection of the optimal cyclodextrins for further pharmaceutical testing. During our research, circular dichroism (CD) spectroscopy has been successfully used to describe the qualitative and quantitative complexation of model compounds with different cyclodextrins. For the appearance of a circular dichroism signal, either a chiral or a chirally perturbed chromophore is required. Achiral or racemic compounds do not have corresponding circular dichroism spectra and neither do chiral cyclodextrins due to the absence of a chromophore group. During complexation of a chromophoric guest molecule, its absorption transition becomes chirally perturbed in the proximity of a cyclodextrin molecule and an induced circular dichroism (ICD) signal appears. This phenomenon gives an inherent selectivity to the method. The sign and intensity of the induced circular dichroism signal in case of different cyclodextrins provides information about the approximate structure of the complex as well as their stability relative to each other. In this study, we report a straightforward induced circular dichroism -based approach for the rapid preselection of the optimal cyclodextrin. The distinctive features of the method were demonstrated using five azole-type antifungal drug molecules (fluconazole, miconazole, clotrimazole, bifonazole and tioconazole) along with native α-, β-, and γ-cyclodextrins, as well as dimethyl-, trimethyl-, carboxymethyl-, hydroxypropyl- and sulfobuthylether-β-cyclodextrins. In addition, with the aid of this method, 27 stability constants were determined, amongst which 16 have been unavailable in the literature previously. Graphic abstract

A new series (6) of C5-curcuminoid derivatives (2E,6E-2,6-dibenzylidene-4-hydroxycyclohexanones) is described here with their evaluation for in vitro antiproliferative activities. Evaluation of 31 compounds against human A2780 (ovarian), C33A (cervix) and MDA-MB-231 (breast) cancer cell lines was performed to obtain structure activity relation data. The best performer was (2E,6E)-2,6-bis(3′-nitrobenzylidene)-4-hydroxycyclohexanone (6h) with IC50 values of 0.68 μM (A2780), 0.69 μM (C33A) and 0.92 μM (MDA-MB-231) compared to cisplatin with 1.30 μM, 3.69 μM and 19.13 μM, respectively. According to calculated physicochemical properties some members in series 6, namely (2E,6E)-2,6-bis[(4′-pyridinyl)methylene]-4-hydroxycyclohexanone (6p) [IC50 = 0.76 μM (A2780), 2.69 μM (C33A), 1.28 μM (MDA-MB-231)] seem to have improved bioavailability compared to curcumin. Selected members of series 6 were involved in circular dichroism spectroscopic measurements in order to determine their interaction with natural DNA. Based on these data, we conclude that these derivatives do not bind to DNA in vitro. A proposal is summarized based on mass spectrometric assessment for fingerprint analysis in biological research of such C5-curcuminoids.

Questions: Substantial variation between observers has been found when comparing parallel land-cover maps, but how can we know which map is better? What magnitude of error and inter-observer variation is expected when assigning land-cover types and is this affected by the hierarchical level of the type system, observer characteristics, and ecosystem properties? Study area: Hvaler, south-east Norway. Methods: Eleven observers assigned mapping units to 120 stratified random points. At each observation point, the observers first assigned a mapping unit to the point independently. The group then decided on a ‘true’ reference mapping unit for that point. The reference was used to estimate total error. ‘Ecological distance’ to the reference was calculated to grade the errors. Results: Individual observers frequently assigned different mapping units to the same point. Deviating assignments were often ecologically close to the reference. Total error, as percentage of assignments that deviated from the reference, was 35.0% and 16.4% for low and high hierarchical levels of the land-cover-type system, respectively. The corresponding figures for inter-observer variation were 42.8% and 19.4%, respectively. Observer bias was found. Particularly high error rates were found for land-cover types characterised by human disturbance. Conclusions: Access to a ‘true’ mapping unit for each observation point enabled estimation of error in addition to the inter-observer variation typically estimated by the standard pairwise comparisons method for maps and observers. Three major sources of error in the assignment of land-cover types were observed: dependence on system complexity represented by the hierarchical level of the land-cover-type system, dependence on the experience and personal characteristics of the observers, and dependence on properties of the mapped ecosystem. The results support the necessity of focusing on quality in land-cover mapping, among commissioners, practitioners and other end users. Taxonomic reference: Lid & Lid (2005) for vascular plants. Syntaxonomic reference: Halvorsen et al. (2015) for land-cover types. Abbreviations: ED = Ecological distance; GLM = Generalised linear model; LCE = Local complex environmental variable; NiN = Nature in Norway; TPI = Topographic position index.

This paper examines the relationships of construals of the properties of psychological distance dimensions with well-being. Construal-Level-Theory (CLT) has identified space, time, social distance, and hypotheticality as psychological distance dimensions. Close objects are construed, or mentally represented, in terms of low-level features. These are concrete, specific, unstructured, and contextualized representations. Distant objects are construed in terms of high-level features. These are abstract, global, coherent, and decontextualized representations. Additionally, the properties of construals, like values, give them meaning and importance. These dimensions, properties, and construals have been shown to guide evaluations, decisions, predictions, and other behaviors. Little research, however, has applied them to issues of mental health and psychological well-being. This paper examines identity, security, value, and control as important properties of psychological distance dimensions. The review demonstrates that, in many circumstances, when these properties of distance dimensions are construed at high-levels, they are associated with psychological well-being and behavioral adjustment.

A traumatic brain injury (TBI) induces the formation of cerebral microbleeds (CMBs), which are associated with cognitive impairments, psychiatric disorders, and gait dysfunctions in patients. Elderly people frequently suffer TBIs, especially mild brain trauma (mTBI). Interestingly, aging is also an independent risk factor for the development of CMBs. However, how TBI and aging may interact to promote the development of CMBs is not well established. In order to test the hypothesis that an mTBI exacerbates the development of CMBs in the elderly, we compared the number and cerebral distribution of CMBs and assessed them by analysing susceptibility weighted (SW) MRI in young (25 ± 10 years old, n = 18) and elder (72 ± 7 years old, n = 17) patients after an mTBI and in age-matched healthy subjects (young: 25 ± 6 years old, n = 20; aged: 68 ± 5 years old, n = 23). We found significantly more CMBs in elder patients after an mTBI compared with young patients; however, we did not observe a significant difference in the number of cerebral microhemorrhages between aged and aged patients with mTBI. The majority of CMBs were found supratentorially (lobar and basal ganglion). The lobar distribution of supratentorial CMBs showed that aging enhances the formation of parietal and occipital CMBs after mTBIs. This suggests that aging and mTBIs do not synergize in the induction of the development of CMBs, and that the different distribution of mTBI-induced CMBs in aged patients may lead to specific age-related clinical characteristics of mTBIs.

The survivin protein contributes to the development and progression of tumors. Protein expression and mRNA levels correlate with clinicopathological parameters and survival of cancer patients. Our purpose was to evaluate whether circulating survivin levels have any diagnostic or predictive value in lung cancer. 118 patients with advanced stage lung cancer participated in our study. 53 suffered from adenocarcinoma (ADC), 33 from squamous cell carcinoma (SqCC), and 32 from small cell lung cancer (SCLC). We also enrolled 21 control subjects. Blood samples were collected before and after two cycles of chemotherapy. We measured survivin concentrations with ELISA. Non-parametric tests were used for analysis. We did not find significant difference in survivin levels between patients and control subjects (17.19/0–829.74/vs. 49.13/0–165.92/pg/ml; p = 0.07). We found lower survivin concentrations in patients with SqCC (0/0–171.24/pg/ml) than in those with ADC (24.94/0–626.46 pg/ml) and SCLC (45.51/0–829.74/pg/ml) (ADC vs. SqCC p < 0.0001, ADC vs. SCLC p = 0.0405, SqCC vs. SCLC p < 0.0001). Survivin levels were higher in stage IV patients than in patients without distant metastases (p = 0.0061), and concentrations were progressively higher with increasing number of metastatic organ sites (p = 0.04). We observed a decrease in survivin levels in ADC patients after platinum plus pemetrexed chemotherapy (26.22/0–626.46/pg/ml before vs. 0/0–114.36/pg/ml after; p = 0.01). Neither progression-free nor overall survival correlated with survivin levels at baseline. Our data imply that survivin may be involved in the development of metastases and it might be used as a biomarker of disease progression. However, circulating survivin concentrations do not predict survival of patients with lung cancer.

SummarySystematic insight into cellular dysfunctions can improve understanding of disease etiology, risk assessment and patient stratification. We present a multiparametric high-content imaging platform enabling quantification of low-density lipoprotein (LDL) uptake and lipid storage in cytoplasmic droplets of primary leukocyte subpopulations. We validated this platform with samples from 65 individuals with variable blood LDL-cholesterol (LDL-c) levels, including familial hypercholesterolemia (FH) and non-FH subjects. We integrated lipid storage data into a novel readout, lipid mobilization, measuring the efficiency with which cells deplete lipid reservoirs. Lipid mobilization correlated positively with LDL uptake and negatively with hypercholesterolemia and age, improving differentiation of individuals with normal and elevated LDL-c. Moreover, combination of cell-based readouts with a polygenic risk score for LDL-c explained hypercholesterolemia better than the genetic risk score alone. This platform provides functional insights into cellular lipid trafficking from a few ml’s of blood and is applicable to dissect metabolic disorders, such as hypercholesterolemia.MotivationWe have limited information on how cellular lipid uptake and processing differ between individuals and influence the development of metabolic diseases, such as hypercholesterolemia. Available assays are labor intensive, require skilled personnel and are difficult to scale to higher throughput, making it challenging to obtain systematic functional cell-based data from individuals. To overcome this problem, we established a scalable automated analysis pipeline enabling reliable quantification of multiple cellular readouts, including lipid uptake, storage and mobilization, from different white blood cell populations. This approach provides new personalized insights into the cellular basis of hypercholesterolemia and obesity.Graphical Abstract

Oral squamous cell carcinoma (OSCC) is a serious health issue worldwide. OSCC is highly associated with oral candidiasis, although it is unclear whether the fungus promotes the genesis and progression of OSCC or cancer facilitates the growth of the fungus. Therefore, we investigated whether Candida could directly influence OSCC development and progression. Our in vitro results suggest that the presence of live C. albicans, but not C. parapsilosis, enhances the progression of OSCC by stimulating the production of matrix metalloproteinases, oncometabolites, pro-tumor signaling routes, and overexpression of prognostic marker genes associated with metastatic events. We also found that oral candidiasis triggered by C. albicans enhanced the progression of OSCC in vivo through the induction of inflammation and overexpression of metastatic genes and markers of epithelial-mesenchymal transition. Taken together, these results suggest that C. albicans actively participates in the complex process of OSCC progression.

Machine learning (ML) provides a powerful set of tools that can improve the accuracy of distribution models by automatically representing underlying ecological relationships empirically captured from large sets of data. However, more recent methodological advances in ML that have been less frequently applied, e.g. in the field of deep learning, may yield additional potential for Distribution Modeling. In this project, we use two ML algorithms, Random Forest (RF) and multi-layer feed-forward artificial neural networks (ANN), to predict the occurrences of Vegetation Types (VT) across Norway. Accurate predictions may support environmental management or the validation of earth system models. The VT data (derived from the AR18x18 data set; n=31 classes) covers the entire spatial scope in 0.9 ha plots on a systematically sampled 18 km grid (n = 1,081 plots, n = 22,154 observations). It was obtained through a field-based survey by a group of trained experts between 2004 and 2014. We use the cloud-based platform "Google Earth Engine" to generate a set of remotely sensed predictor variables based on SENTINEL-2 satellite imagery (i.e. surface reflectance from 12 spectral bands and six vegetation indices). These are then combined with ancillary environmental rasters used previously to model Norwegian VT distribution, e.g. representing climate, land cover, or geological properties (n=55, before one-hot encoding). Preliminary results suggest that in both modeling approaches, the generated SENTINEL-2 variables, particularly the Normalized Difference Vegetation Index (NDVI), have the highest predictive power as measured by permutation importance. The mean overall accuracy using 5-fold cross-validation shows only minor differences between the two methods (approx. 0.45 for ANN vs. 0.44 for RF; the respective F1-scores are 0.35 for ANN and 0.34 for RF; most frequent class baseline accuracy = 0.136). The modeling challenges we currently face include a class imbalance in the VT data set, reconciling the different spatial resolutions of the environmental predictors, and discrepancies in the timing of data acquisition. The next steps in the project will be to incorporate spatial cross-validation into the workflow and to analyze the differences between the ML methods in detail (e.g. regarding the ability to model rare VTs or differences in variable importance). Moreover, we will evaluate the possibility to include additional satellite data sources. This work is a contribution to the Strategic Research Initiative ‘Land Atmosphere Interaction in Cold Environments’ (LATICE) of the University of Oslo.

The C-3 phenolic hydroxy group containing morphine derivatives (morphine, oxymorphone, naloxone, naltrexone) are excellent candidates for the synthesis of 3-O-functionalized molecules. Achieving free carboxylic group containing derivatives gives the opportunity for further modification and conjugation that could be used for immunization and immunoassays. For this purpose ethyl bromo- and chloroacetate can be used as O-alkylating agents. Hydrolyzing the products affords the appropriate free carboxylic group containing 3-O-carboxyalkyl derivatives. As these molecules contain an acidic and a basic functional group the protonation macro- and microconstants were determined too, using pH-potentiometry and NMR-pH titration, beside fully characterizing their structure using IR, CD, NMR and HR-MS measurements.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.