High-throughput mechanistic screening of non-equilibrium inhibitors by a fully automated data analysis pipeline in early drug-discovery.

Recent efforts for increasing the success in drug discovery focus on an early, massive, and routine mechanistic and/or kinetic characterization of drug-target engagement as part of a design-make-test-analyze strategy. From an experimental perspective, many mechanistic assays can be translated into a scalable format on automation platforms and thereby enable routine characterization of hundreds or thousands of compounds. However, now the limiting factor to achieve such in-depth characterization at high-throughput becomes the quality-driven data analysis, the sheer scale of which outweighs the time available to the scientific staff of most labs. Therefore, automated analytical workflows are needed to enable such experimental scale-up. We have implemented such a fully automated workflow in Genedata Screener for time-dependent ligand-target binding analysis to characterize non-equilibrium inhibitors. The workflow automates Quality Control (QC) / data modelling and decision-making process in a staged analysis: (1) quality control of raw input data-fluorescence signal-based progress curves - featuring automated rejection of unsuitable measurements; (2) automated model selection - one-step versus two-step binding model - using statistical methods and biological validity rules; (3) result visualization in specific plots and annotated result tables, enabling the scientist to review large result sets efficiently and, at the same time, to rapidly identify and focus on interesting or unusual results; (4) an interactive user interface for immediate adjustment of automated decisions, where necessary. Applying this workflow to first-pass, high-throughput kinetic studies on kinase projects has allowed us to surmount previously rate-limiting manual analysis steps and boost productivity; and is now routinely embedded in a biopharma discovery research process.

Comprehensive review on surfactant adsorption on mineral surfaces in chemical enhanced oil recovery.

With the increasing demand for efficient extraction of residual oil, enhanced oil recovery (EOR) offers prospects for producing more reservoirs' original oil in place. As one of the most promising methods, chemical EOR (cEOR) is the process of injecting chemicals (polymers, alkalis, and surfactants) into reservoirs. However, the main issue that influences the recovery efficiency in surfactant flooding of cEOR is surfactant losses through adsorption to the reservoir rocks. This review focuses on the key issue of surfactant adsorption in cEOR and addresses major concerns regarding surfactant adsorption processes. We first describe the adsorption behavior of surfactants with particular emphasis on adsorption mechanisms, isotherms, kinetics, thermodynamics, and adsorption structures. Factors that affect surfactant adsorption such as surfactant characteristics, solution chemistry, rock mineralogy, and temperature were discussed systematically. To minimize surfactant adsorption, the chemical additives of alkalis, polymers, nanoparticles, co-solvents, and ionic liquids are highlighted as well as implementing with salinity gradient and low salinity water flooding strategies. Finally, current trends and future challenges related to the harsh conditions in surfactant based EOR are outlined. It is expected to provide solid knowledge to understand surfactant adsorption involved in cEOR and contribute to improved flooding strategies with reduced surfactant loss.

Trump Knows Best: Donald Trump's Rejection of Expertise and the 2020 Presidential Election.

Education level-often referred to as the "diploma divide"-has been implicated as one of the key cleavages in the 2016 presidential election. This piece explores this division over time, along with the most common explanations of the education cleavage in 2016, class and racial resentment. Voters' views on the value of expertise are added as a possible explanation of the 2016 electoral divide by education level.

Cultivar Differences and Impact of Plant-Plant Competition on Temporal Patterns of Nitrogen and Biomass Accumulation.

Current niche models cannot explain multi-species plant coexistence in complex ecosystems. One overlooked explanatory factor is within-growing season temporal dynamism of resource capture by plants. However, the timing and rate of resource capture are themselves likely to be mediated by plant-plant competition. This study used Barley (Hordeum sp.) as a model species to examine the impacts of intra-specific competition, specifically inter- and intra-cultivar competition on the temporal dynamics of resource capture. Nitrogen and biomass accumulation of an early and late cultivar grown in isolation, inter- or intra- cultivar competition were investigated using sequential harvests. We did not find changes in the temporal dynamics of biomass accumulation in response to competition. However, peak nitrogen accumulation rate was significantly delayed for the late cultivar by 14.5 days and advanced in the early cultivar by 0.5 days when in intra-cultivar competition; there were no significant changes when in inter-cultivar competition. This may suggest a form of kin recognition as the target plants appeared to identify their neighbors and only responded temporally to intra-cultivar competition. The Relative Intensity Index found competition occurred in both the intra- and inter- cultivar mixtures, but a positive Land Equivalence Ratio value indicated complementarity in the inter-cultivar mixtures compared to intra-cultivar mixtures. The reason for this is unclear but may be due to the timing of the final harvest and may not be representative of the relationship between the competing plants. This study demonstrates neighbor-identity-specific changes in temporal dynamism in nutrient uptake. This contributes to our fundamental understanding of plant nutrient dynamics and plant-plant competition whilst having relevance to sustainable agriculture. Improved understanding of within-growing season temporal dynamism would also improve our understanding of coexistence in complex plant communities.

Using chemical and DNA marker analysis to authenticate a high-value food, manuka honey.

Ensuring the authenticity of food is a rapidly emerging issue, especially in regard to high-value products that are marketed through increasingly complex global food chains. With the ever-increasing potential for mislabeling, fraud and adulteration, governments are increasingly having to invest in, and assure, the authenticity of foods in international trade. This is particularly the case for manuka honey, an iconic New Zealand food product. We show how the authenticity of a specific type of honey can be determined using a combination of chemicals derived from nectar and DNA derived from pollen. We employ an inter-disciplinary approach to evaluate a selection of authenticity markers, followed by classification modelling to produce criteria that consistently identify manuka honey from New Zealand. The outcome of our work provides robust identification criteria that can be applied in a regulatory setting to authenticate a high-value natural food. Our approach can transfer to other foods where assurance of authenticity must take into account a high level of natural variability.

A view not to be missed: Salient scene content interferes with cognitive restoration.

Attention Restoration Theory (ART) states that built scenes place greater load on attentional resources than natural scenes. This is explained in terms of "hard" and "soft" fascination of built and natural scenes. Given a lack of direct empirical evidence for this assumption we propose that perceptual saliency of scene content can function as an empirically derived indicator of fascination. Saliency levels were established by measuring speed of scene category detection using a Go/No-Go detection paradigm. Experiment 1 shows that built scenes are more salient than natural scenes. Experiment 2 replicates these findings using greyscale images, ruling out a colour-based response strategy, and additionally shows that built objects in natural scenes affect saliency to a greater extent than the reverse. Experiment 3 demonstrates that the saliency of scene content is directly linked to cognitive restoration using an established restoration paradigm. Overall, these findings demonstrate an important link between the saliency of scene content and related cognitive restoration.

Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field.

Designing tight-binding ligands is a primary objective of small-molecule drug discovery. Over the past few decades, free-energy calculations have benefited from improved force fields and sampling algorithms, as well as the advent of low-cost parallel computing. However, it has proven to be challenging to reliably achieve the level of accuracy that would be needed to guide lead optimization (∼5× in binding affinity) for a wide range of ligands and protein targets. Not surprisingly, widespread commercial application of free-energy simulations has been limited due to the lack of large-scale validation coupled with the technical challenges traditionally associated with running these types of calculations. Here, we report an approach that achieves an unprecedented level of accuracy across a broad range of target classes and ligands, with retrospective results encompassing 200 ligands and a wide variety of chemical perturbations, many of which involve significant changes in ligand chemical structures. In addition, we have applied the method in prospective drug discovery projects and found a significant improvement in the quality of the compounds synthesized that have been predicted to be potent. Compounds predicted to be potent by this approach have a substantial reduction in false positives relative to compounds synthesized on the basis of other computational or medicinal chemistry approaches. Furthermore, the results are consistent with those obtained from our retrospective studies, demonstrating the robustness and broad range of applicability of this approach, which can be used to drive decisions in lead optimization.

Green space and stress: evidence from cortisol measures in deprived urban communities.

Contact with green space in the environment has been associated with mental health benefits, but the mechanism underpinning this association is not clear. This study extends an earlier exploratory study showing that more green space in deprived urban neighbourhoods in Scotland is linked to lower levels of perceived stress and improved physiological stress as measured by diurnal patterns of cortisol secretion. Salivary cortisol concentrations were measured at 3, 6 and 9 h post awakening over two consecutive weekdays, together with measures of perceived stress. Participants (n = 106) were men and women not in work aged between 35-55 years, resident in socially disadvantaged districts from the same Scottish, UK, urban context as the earlier study. Results from linear regression analyses showed a significant and negative relationship between higher green space levels and stress levels, indicating living in areas with a higher percentage of green space is associated with lower stress, confirming the earlier study findings. This study further extends the findings by showing significant gender differences in stress patterns by levels of green space, with women in lower green space areas showing higher levels of stress. A significant interaction effect between gender and percentage green space on mean cortisol concentrations showed a positive effect of higher green space in relation to cortisol measures in women, but not in men. Higher levels of neighbourhood green space were associated with healthier mean cortisol levels in women whilst also attenuating higher cortisol levels in men. We conclude that higher levels of green space in residential neighbourhoods, for this deprived urban population of middle-aged men and women not in work, are linked with lower perceived stress and a steeper (healthier) diurnal cortisol decline. However, overall patterns and levels of cortisol secretion in men and women were differentially related to neighbourhood green space and warrant further investigation.

Protected area networks and savannah bird biodiversity in the face of climate change and land degradation.

The extent to which climate change might diminish the efficacy of protected areas is one of the most pressing conservation questions. Many projections suggest that climate-driven species distribution shifts will leave protected areas impoverished and species inadequately protected while other evidence suggests that intact ecosystems within protected areas will be resilient to change. Here, we tackle this problem empirically. We show how recent changes in distribution of 139 Tanzanian savannah bird species are linked to climate change, protected area status and land degradation. We provide the first evidence of climate-driven range shifts for an African bird community. Our results suggest that the continued maintenance of existing protected areas is an appropriate conservation response to the challenge of climate and environmental change.

Distributed lag models for hydrological data.

The distributed lag model (DLM), used most prominently in air pollution studies, finds application wherever the effect of a covariate is delayed and distributed through time. We specify modified formulations of DLMs to provide computationally attractive, flexible varying-coefficient models that are applicable in any setting in which lagged covariates are regressed on a time-dependent response. We investigate the application of such models to rainfall and river flow and in particular their role in understanding the impact of hidden variables at work in river systems. We apply two models to data from a Scottish mountain river, and we fit to some simulated data to check the efficacy of our model approach. During heavy rainfall conditions, changes in the influence of rainfall on flow arise through a complex interaction between antecedent ground wetness and a time-delay in rainfall. The models identify subtle changes in responsiveness to rainfall, particularly in the location of peak influence in the lag structure.

Do multiple herbivores maintain chemical diversity of Scots pine monoterpenes?

A central issue in our understanding of the evolution of the diversity of plant secondary metabolites (PSMs) is whether or not compounds are functional, conferring an advantage to the plant, or non-functional. We examine the hypothesis that the diversity of monoterpene PSMs within a plant species (Scots pine Pinus sylvestris) may be explained by different compounds acting as defences against high-impact herbivores operating at different life stages. We also hypothesize that pairwise coevolution, with uncorrelated interactions, is more likely to result in greater PSM diversity, than diffuse coevolution. We tested whether up to 13 different monoterpenes in Scots pine were inhibitory to herbivory by slugs (Arion ater), bank voles (Clethrionomys glareolus), red deer (Cervus elaphus) and capercaillie (Tetrao urogallus), each of which attack trees at a different life stage. Plants containing more α-pinene were avoided by both slugs and capercaillie, which may act as reinforcing selective agents for this dominant defensive compound. Herbivory by red deer and capercaillie were, respectively, weakly negatively associated with δ(3)-carene, and strongly negatively correlated with the minor compound ß-ocimene. Three of the four herbivores are probably contributory selective agents on some of the terpenes, and thus maintain some, but by no means all, of the phytochemical diversity in the species. The correlated defensive function of α-pinene against slugs and capercaillie is consistent with diffuse coevolutionary processes.

Regression analysis of spatial data.

Many of the most interesting questions ecologists ask lead to analyses of spatial data. Yet, perhaps confused by the large number of statistical models and fitting methods available, many ecologists seem to believe this is best left to specialists. Here, we describe the issues that need consideration when analysing spatial data and illustrate these using simulation studies. Our comparative analysis involves using methods including generalized least squares, spatial filters, wavelet revised models, conditional autoregressive models and generalized additive mixed models to estimate regression coefficients from synthetic but realistic data sets, including some which violate standard regression assumptions. We assess the performance of each method using two measures and using statistical error rates for model selection. Methods that performed well included generalized least squares family of models and a Bayesian implementation of the conditional auto-regressive model. Ordinary least squares also performed adequately in the absence of model selection, but had poorly controlled Type I error rates and so did not show the improvements in performance under model selection when using the above methods. Removing large-scale spatial trends in the response led to poor performance. These are empirical results; hence extrapolation of these findings to other situations should be performed cautiously. Nevertheless, our simulation-based approach provides much stronger evidence for comparative analysis than assessments based on single or small numbers of data sets, and should be considered a necessary foundation for statements of this type in future.

An in silico method for predicting Ames activities of primary aromatic amines by calculating the stabilities of nitrenium ions.

In this paper, we describe an in silico first principal approach to predict the mutagenic potential of primary aromatic amines. This approach is based on the so-called "nitrenium hypothesis", which was developed by Ford et al. in the early 1990s. This hypothesis asserts that the mutagenic effect for this class of molecules is mediated through the transient formation of a nitrenium ion and that the stability of this cation is correlated with the mutagenic potential. Here we use quantum mechanical calculations at different levels of theory (semiempirical AM1, ab initio HF/3-21G, HF/6-311G(d,p), and DFT/B3LYP/6-311G(d,p)) to compute the stability of nitrenium ions. When applied to a test set of 257 primary aromatic amines, we show that this method can correctly differentiate between Ames active and inactive compounds, and furthermore that it is able to rationalize and predict SAR trends within structurally related chemical series. For this test set, the AM1 nitrenium stability calculations are found to provide a good balance between speed and accuracy, resulting in an overall accuracy of 85%, and sensitivity and specificity of 91% and 72%, respectively. The nitrenium-based predictions are also compared to the commercial software packages DEREK, MULTICASE, and the MOE-Toxicophore descriptor. One advantage of the approach presented here is that the calculation of relative stabilities results in a continuous spectrum of activities and not a simple yes/no answer. This allows us to observe and rationalize subtle trends due to the different electrostatic properties of the organic molecules. Our results strongly indicate that nitrenium ion stability calculations should be used as a complementary approach to assist the medicinal chemist in prioritizing and selecting nonmutagenic primary aromatic amines during preclinical drug discovery programs.

Discovery of novel inhibitors of Trypanosoma cruzi trans-sialidase from in silico screening.

trans-Sialidase from Trypanosoma cruzi (TcTS) has emerged as a potential drug target for treatment of Chagas disease. Here, we report the results of virtual screening for the discovery of novel TcTS inhibitors, which targeted both the sialic acid and sialic acid acceptor sites of this enzyme. A library prepared from the Evotec database of commercially available compounds was screened using the molecular docking program GOLD, following the application of drug-likeness filters. Twenty-three compounds selected from the top-scoring ligands were purchased and assayed using a fluorimetric assay. Novel inhibitor scaffolds, with IC(50) values in the submillimolar range were discovered. The 3-benzothiazol-2-yl-4-phenyl-but-3-enoic acid scaffold was studied in more detail, and TcTS inhibition was confirmed by an alternative sialic acid transfer assay. Attempts to obtain crystal structures of these compounds with TcTS proved unsuccessful but provided evidence of ligand binding at the active site.

Arylsulfonamide CB2 receptor agonists: SAR and optimization of CB2 selectivity.

A high-throughput screening campaign resulted in the discovery of a highly potent dual cannabinoid receptor 1 (CB1) and 2 (CB2) agonist. Following a thorough SAR exploration, a series of selective CB2 full agonists were identified.

Maternal smoking during pregnancy specifically reduces human fetal desert hedgehog gene expression during testis development.

CONTEXT: Maternal cigarette smoking during gestation increases cryptorchidism and hypospadias and reduces testis size and fertility in sons by unknown mechanisms. OBJECTIVE: The objective of the study was to determine whether maternal smoking is linked with changes in male human fetal endocrinology, testis gene expression, and liver concentrations of cigarette smoke chemicals. DESIGN: This was an observational study of the male fetus, comparing pregnancies during which the mothers either did or did not smoke. SETTING: The study was conducted at the universities of Aberdeen, Glasgow, and Nottingham and Macaulay Institute (Aberdeen). PATIENTS/PARTICIPANTS: Testes, blood, and livers were collected from 69 morphologically normal human male fetuses of women undergoing elective termination of normal second-trimester pregnancies. MAIN OUTCOME MEASURES: Testosterone, human chorionic gonadotropin, LH, and cotinine; expression of 30 reproductive/developmental genes; liver concentrations of 16 polycyclic aromatic hydrocarbons; and Leydig, Sertoli. and germ cell numbers were determined. RESULTS: There were no significant differences in fetal size, testis weight, cell numbers, seminiferous tubule diameter, or circulating LH and testosterone. Fetuses from smoking mothers had smoking range cotinine levels and liver concentrations of polycyclic aromatic hydrocarbons that were significant predictors of maternal smoking (P < 0.001). Only the Sertoli cell-specific gene, desert hedgehog (DHH), was significantly altered by maternal smoking (reduced 1.8-fold, P = 0.013). CONCLUSIONS: The consequences of reduced DHH signaling in men and mice are consistent with epidemiology for effects of gestational maternal smoking on sons. Given the absence of other observed effects of maternal smoking, we concluded that reduced DHH is part of a mechanism linking maternal gestational smoking with impaired reproductive development in male offspring.

Scenario-visualization for the assessment of perceived green space qualities at the urban-rural fringe.

The provision of green space is increasingly being perceived as an important factor for quality of life. However, green spaces often face high developmental pressure. The main objective of this study is to investigate a prospective approach to green space planning by combining three-dimensional (3D) visualization of green space scenarios and survey techniques to facilitate improved participation of the public. Aside from the 'Status quo', scenarios 'Agriculture', 'Recreation', 'Nature conservation' and 'Wind turbines' are visualized in three dimensions. In order to test responses, a survey was conducted both in print format and on the Internet. Overall, 49 different visualizations that belong to one of the scenarios were available in the survey and were rated according to the perceived esthetic, recreational and ecological values. The highest rated scenes include vegetation elements such as meadows with orchards, single trees, shrubs or forest. The least attractive scenes are those where buildings are highly dominant or where there are no vegetation elements. Based on the ratings for the individual images and on the corresponding scenarios, our study shows that there is high potential for improving the existing landscape. All suggested changes are either rated about equal to or considerably higher than the status quo, with the scenario 'Nature conservation' receiving the highest scores.

Development of a spectral clustering method for the analysis of molecular data sets.

A spectral clustering method is presented and applied to two-dimensional molecular structures, where it has been found particularly useful in the analysis of screening data. The method provides a means to quantify (1) the degree of intermolecular similarity within a cluster and (2) the contribution that the features of a molecule make to a cluster. In an application of the spectral clustering method to an example data set of 125 COX-2 inhibitors, these two criteria were used to place the molecules into clusters of chemically related two-dimensional structures.

Possible human impacts on adaptive radiation: beak size bimodality in Darwin's finches.

Adaptive radiation is facilitated by a rugged adaptive landscape, where fitness peaks correspond to trait values that enhance the use of distinct resources. Different species are thought to occupy the different peaks, with hybrids falling into low-fitness valleys between them. We hypothesize that human activities can smooth adaptive landscapes, increase hybrid fitness and hamper evolutionary diversification. We investigated this possibility by analysing beak size data for 1755 Geospiza fortis measured between 1964 and 2005 on the island of Santa Cruz, Galápagos. Some populations of this species can display a resource-based bimodality in beak size, which mirrors the greater beak size differences among species. We first show that an historically bimodal population at one site, Academy Bay, has lost this property in concert with a marked increase in local human population density. We next show that a nearby site with lower human impacts, El Garrapatero, currently manifests strong bimodality. This comparison suggests that bimodality can persist when human densities are low (Academy Bay in the past, El Garrapatero in the present), but not when they are high (Academy Bay in the present). Human activities may negatively impact diversification in 'young' adaptive radiations, perhaps by altering adaptive landscapes.

Quantifying species composition in root mixtures using two methods: near-infrared reflectance spectroscopy and plant wax markers.

Understanding of plant interactions is greatly limited by our ability to identify and quantify roots belonging to different species. We proposed and compared two methods for estimating the root biomass proportion of each species in artificial mixtures: near-infrared reflectance spectroscopy (NIRS) and plant wax markers. Two sets of artificial root mixtures composed of two or three herbaceous species were prepared. The proportion of root material of each species in mixtures was estimated from NIRS spectral data (i) and the concentration patterns of n-alkanes (ii), n-alcohols (iii), and n-alkanes +n-alcohols combined (iv). For each data set, calibration equations were developed using multivariate statistical models. The botanical composition of root mixtures was predicted well for all the species considered. The accuracy varied slightly among methods: alkanes < alcohols = alkanes + alcohols < NIRS. Correlation coefficients between predicted and actual root proportions ranged from 0.89 to 0.99 for alkanes + alcohols predictions and from 0.97 to 0.99 for NIRS predictions. These two methods provide promising potential for understanding allocation patterns and competitive interactions.