A ratiometric, fluorometric approach for surface charge mapping of biosilica features.

We describe a surface charge imaging method for heterogeneous biosilicas based on relationships between zeta (ζ) potential, feature size of nanoparticles, and PDMPO fluorescence and apply it to silicified structures from plants and diatoms. The methodology provides the first opportunity to map the surface charge of large heterogeneous biosilica materials and indicates that local surface charge is related to morphology below the diffraction limit (ca. 20-130 nm) with sharper features showing less negative zeta potential equivalent surface charge suggesting that the zeta potential of silica structures can be adjusted by engineering surface morphology. We show that the approach can be used to study living silicified biological tissues without recourse to sectioning and fixation. Further, the approach could be used for the study of other metal oxides possessing hydroxylated moieties. The method has potential to open up opportunities for the engineering of materials with defined charge characteristics for the solution of biomedical engineering problems including materials for tissue replacement.

Platform for Screening Abiotic/Biotic Interactions Using Indicator Displacement Assays.

This paper describes novel adaptations of optically sectioned planar format assays to screen compounds for their affinities to materials surfaces. The novel platform, which we name optically sectioned indicator displacement assays (O-IDA), makes use of displaceable dyes in a format adaptable to high-throughput multiwell plate technologies. We describe two approaches: the first being where the dye exhibits fluorescence in both the surface bound and unbound state and the second, where fluorescence is lost upon displacement of the dye from the surface. Half maximal inhibitory concentration (IC50), binding affinity (Ki), and binding free energy (ΔGads) values can be extracted from the raw data. Representative biomolecules were tested for interactions with silica in an aqueous environment and ZnO(0001)-Zn and (10-10) facets in a nonaqueous environment. We provide the first experimental values for both the binding of small molecules to silica and the facet-dependent ZnO binding affinity of key amino acids associated with ZnO-specific oligopeptides. The specific data will be invaluable to those studying interactions at interfaces both experimentally and computationally. O-IDA provides a general framework for the high-throughput screening of molecule binding to materials surfaces, which has important applications in drug delivery, (bio-) catalysis, biosensing, and biomaterial engineering.

Fluctuation Scaling, Calibration of Dispersion, and Detection of Differences.

Fluctuation scaling describes the relationship between the mean and standard deviation of a set of measurements. An example is Horwitz scaling, which has been reported from interlaboratory studies. Horwitz and similar studies have reported simple exponential and segmented scaling laws with exponents (α) typically between 0.85 (Horwitz) and 1 when not operating near a detection limit. When approaching a detection limit, the exponents change and approach an apparently Gaussian (α = 0) model. This behavior is often presented as a property of interlaboratory studies, which makes controlled replication to understand the behavior costly to perform. To assess the contribution of instrumentation to larger scale fluctuation scaling, we measured the behavior of two inductively coupled plasma atomic emission spectrometry (ICP-AES) systems, in two laboratories measuring thulium using two emission lines. The standard deviation universally increased with the uncalibrated signal, indicating the system was heteroscedastic. The response from all lines and both instruments was consistent with a single exponential dispersion model having parameters α = 1.09 and ß = 0.0035. No evidence of Horwitz scaling was found, and there was no evidence of Poisson noise limiting behavior. The "Gaussian" component was a consequence of background subtraction for all lines and both instruments. The observation of a simple exponential dispersion model in the data allows for the definition of a difference detection limit (DDL) with universal applicability to systems following known dispersion. The DDL is the minimum separation between two points along a dispersion model required to claim they are different according to a particular statistical test. The DDL scales transparently with the mean and works at any location in a response function.

Chemical Measurement and Fluctuation Scaling.

Fluctuation scaling reports on all processes producing a data set. Some fluctuation scaling relationships, such as the Horwitz curve, follow exponential dispersion models which have useful properties. The mean-variance method applied to Poisson distributed data is a special case of these properties allowing the gain of a system to be measured. Here, a general method is described for investigating gain (G), dispersion (ß), and process (α) in any system whose fluctuation scaling follows a simple exponential dispersion model, a segmented exponential dispersion model, or complex scaling following such a model locally. When gain and dispersion cannot be obtained directly, relative parameters, GR and ßR, may be used. The method was demonstrated on data sets conforming to simple, segmented, and complex scaling. These included mass, fluorescence intensity, and absorbance measurements and specifications for classes of calibration weights. Changes in gain, dispersion, and process were observed in the scaling of these data sets in response to instrument parameters, photon fluxes, mathematical processing, and calibration weight class. The process parameter which limits the type of statistical process that can be invoked to explain a data set typically exhibited 0 < α < 1, with α > 4 possible. With two exceptions, calibration weight class definitions only affected ß. Adjusting photomultiplier voltage while measuring fluorescence intensity changed all three parameters (0 < α < 0.8; 0 < ßR < 3; 0 < GR < 4.1). The method provides a framework for calibrating and interpreting uncertainty in chemical measurement allowing robust comparison of specific instruments, conditions, and methods.

The nature of the silicaphilic fluorescence of PDMPO.

PDMPO (2-(4-pyridyl)-5-((4-(2-dimethylaminoethylaminocarbamoyl)methoxy)phenyl)oxazole), has unique silica specific fluorescence and is used in biology to understand biosilicification. This 'silicaphilic' fluorescence is not well understood nor is the response to local environmental variables like solvent and pH. We investigated PDMPO in a range of environments: using UV-vis and fluorescence spectroscopy supported by computational data, (SPARC, molecular dynamics simulations, density functional theory calculations), dynamic light scattering and zeta potential measurements to understand the PDMPO-silica interaction. From absorption data, PDMPO exhibited a pKa of 4.20 for PDMPOH2(2+) to PDMPOH(+). Fluorescence emission measurements revealed large shifts in excited state pKa* values with different behaviour when bound to silica (pKa* of 10.4). PDMPO bound to silica particles is located in the Stern layer with the dye exhibiting pH dependent depolarising motion. In aqueous solution, PDMPO showed strong chromaticity with correlation between the maximum emission wavelength for PDMPOH(+)* and dielectric constant (4.8-80). Additional chromatic effects were attributed to changes in solvent accessible surface area. Chromatic effects were also observed for silica bound dye which allow its use as a direct probe of bulk pH over a range far in excess of what is possible for the dye alone (3-5.2). The unique combination of chromaticity and excited state dynamics allows PDMPO to monitor pH from 3 to 13 while also reporting on surface environment opening a new frontier in the quantitative understanding of (bio)silicification.

When one plus one does not equal two: fluorescence anisotropy in aggregates and multiply labeled proteins.

The behavior of fluorescence anisotropy and polarization in systems with multiple dyes is well known. Homo-FRET and its consequent energy migration cause the fluorescence anisotropy to decrease as the number of like fluorophores within energy transfer distance increases. This behavior is well understood when all subunits within a cluster are saturated with fluorophores. However, incomplete labeling as might occur from a mixture of endogenous and labeled monomer units, incomplete saturation of binding sites, or photobleaching produces stochastic mixtures. Models in widespread and longstanding use that describe these mixtures apply an assumption of equal fluorescence efficiency for all sites first stated by Weber and Daniel in 1966. The assumption states that fluorophores have the same brightness when free in solution as they do in close proximity to each other in a cluster. The assumption simplifies descriptions of anisotropy trends as the fractional labeling of the cluster changes. However, fluorophores in close proximity often exhibit nonadditivity due to such things as self-quenching behavior or exciplex formation. Therefore, the anisotropy of stochastic mixtures of fluorophore clusters of a particular size will depend on the behavior of those fluorophores in clusters. We present analytical expressions for fractionally labeled clusters exhibiting a range of behaviors, and experimental results from two systems: an assembled tetrameric cluster of fluorescent proteins and stochastically labeled bovine serum albumin containing up to 24 fluorophores. The experimental results indicate that clustered species do not follow the assumption of equal fluorescence efficiency in the systems studied with clustered fluorophores showing reduced fluorescence intensity. Application of the assumption of equal fluorescence efficiency will underpredict anisotropy and consequently underestimate cluster size in these two cases. The theoretical results indicate that careful selection of the fractional labeling in strongly quenched systems will enhance opportunities to determine cluster sizes, making accessible larger clusters than are currently considered possible.

Analysis of layered assays and volume microarrays in stratified media.

Changing traditional microarray methods by using both sides of a substrate or stacking microarrays combined with optical sectioning enables the detection of more than one assay along the z-axis. Here we demonstrate two sided substrates, multilayer arrays with up to 5 substrates, and 2- and 3-dimensional antigen microarrays. By replacing standard substrates with multiple 30 µm layers of glass or mica, high density multilayer and 3-dimensional volume arrays were created within a stratified medium. Although a decrease in fluorescence intensity with increasing number of substrate layers was observed together with a concomitant broadening of the axial resolution, quantitative results were obtained from this stratified system using calibrated intensities. Two- and three-dimensional antigen microarrays were generated via microcontact printing and detected as indirect immunoassays with quantum dot conjugated antibodies. Volume arrays were analysed by confocal laser scanning microscopy producing clear patterns, even when the assays were overlapped spatially.

Macromolecular binding and kinetic analysis with optically sectioned planar format assays.

Real-time analysis of macromolecular interactions and competitive binding of ligands to receptors on surfaces are not typically performed using fluorescence intensity methods due to background interference from solution fluorescence. Separation-free optically sectioned planar format assays (OSPFAs) with confocal detection remove this problem. We report OSPFAs for indicator displacement and kinetic assessment of binding. A commercial androgen receptor binding domain indicator displacement assay adapted as an OSPFA yielded an IC(50) of 6.5 nM for testosterone with Z' = 0.77. These measured IC(50) and Z' values are in ranges suitable for drug screening applications with Z' > 0.5 indicating good to excellent screenability. An OSPFA was applied to study the rate of antibody binding to a sandwich immunoassay on a planar surface. Langmuir fits provided forward rate constants in the range 2 × 10(3) M(-1) s(-1) to 6 × 10(4) M(-1) s(-1) and reverse constants 1 × 10(-4) s(-1) to 4 × 10(-3) s(-1) which cover a useful range for characterising probe-target interactions. This work demonstrates the suitability of OSPFAs for investigating kinetics and binding interactions using fluorescence under wash-free conditions. OSPFAs are an alternative to other separation-free methods such as acoustic, surface plasmon resonance, ellipsometry, fluorescence polarisation and other related methods. OSPFAs should make practical dynamic binding studies for small and large molecules including cases where the reaction under investigation results in no appreciable mass change on a surface.

Retracted article: Quantum dots high fluorescent signal amplification immunoassay using branched DNA and peptide nucleic acid conjugated antibody.

I, Yuan-Cheng Cao, hereby wholly retract this Analyst paper for correction. This article was submitted for publication without the knowledge and approval of the co-authors listed. Signed: Yuan-Cheng Cao, Newcastle University, UK, December 2011. This retraction is endorsed by May Copsey, Editor. Retraction published 16th December 2011.

Population density and spreading of COVID-19 in England and Wales.

We investigated daily COVID-19 cases and deaths in the 337 lower tier local authority regions in England and Wales to better understand how the disease propagated over a 15-month period. Population density scaling models revealed residual variance and skewness to be sensitive indicators of the dynamics of propagation. Lockdowns and schools reopening coincided with increased variance indicative of conditions with local impact and country scale heterogeneity. University reopening and December holidays reduced variance indicative of country scale homogenisation which reached a minimum in mid-January 2021. Homogeneous propagation was associated with better correspondence with normally distributed residuals while heterogeneous propagation was more consistent with skewed models. Skewness varied from strongly negative to strongly positive revealing an unappreciated feature of community propagation. Hot spots and super-spreading events are well understood descriptors of regional disease dynamics that would be expected to be associated with positively skewed distributions. Positively skewed behaviour was observed; however, negative skewness indicative of "cold-spots" and "super-isolation" dominated for approximately 8 months during the period of study. In contrast, death metrics showed near constant behaviour in scaling, variance, and skewness metrics over the full period with rural regions preferentially affected, an observation consistent with regional age demographics in England and Wales. Regional positions relative to density scaling laws were remarkably persistent after the first 5-9 days of the available data set. The determinants of this persistent behaviour probably precede the pandemic and remain unchanged.

Universality of political corruption networks.

Corruption crimes demand highly coordinated actions among criminal agents to succeed. But research dedicated to corruption networks is still in its infancy and indeed little is known about the properties of these networks. Here we present a comprehensive investigation of corruption networks related to political scandals in Spain and Brazil over nearly three decades. We show that corruption networks of both countries share universal structural and dynamical properties, including similar degree distributions, clustering and assortativity coefficients, modular structure, and a growth process that is marked by the coalescence of network components due to a few recidivist criminals. We propose a simple model that not only reproduces these empirical properties but reveals also that corruption networks operate near a critical recidivism rate below which the network is entirely fragmented and above which it is overly connected. Our research thus indicates that actions focused on decreasing corruption recidivism may substantially mitigate this type of organized crime.

Machine learning partners in criminal networks.

Recent research has shown that criminal networks have complex organizational structures, but whether this can be used to predict static and dynamic properties of criminal networks remains little explored. Here, by combining graph representation learning and machine learning methods, we show that structural properties of political corruption, police intelligence, and money laundering networks can be used to recover missing criminal partnerships, distinguish among different types of criminal and legal associations, as well as predict the total amount of money exchanged among criminal agents, all with outstanding accuracy. We also show that our approach can anticipate future criminal associations during the dynamic growth of corruption networks with significant accuracy. Thus, similar to evidence found at crime scenes, we conclude that structural patterns of criminal networks carry crucial information about illegal activities, which allows machine learning methods to predict missing information and even anticipate future criminal behavior.

Statistical models for identifying frequent hitters in high throughput screening.

High throughput screening (HTS) interrogates compound libraries to find those that are "active" in an assay. To better understand compound behavior in HTS, we assessed an existing binomial survivor function (BSF) model of "frequent hitters" using 872 publicly available HTS data sets. We found large numbers of "infrequent hitters" using this model leading us to reject the BSF for identifying "frequent hitters." As alternatives, we investigated generalized logistic, gamma, and negative binomial distributions as models for compound behavior. The gamma model reduced the proportion of both frequent and infrequent hitters relative to the BSF. Within this data set, conclusions about individual compound behavior were limited by the number of times individual compounds were tested (1-1613 times) and disproportionate testing of some compounds. Specifically, most tests (78%) were on a 309,847-compound subset (17.6% of compounds) each tested ≥ 300 times. We concluded that the disproportionate retesting of some compounds represents compound repurposing at scale rather than drug discovery. The approach to drug discovery represented by these 872 data sets characterizes the assays well by challenging them with many compounds while each compound is characterized poorly with a single assay. Aggregating the testing information from each compound across the multiple screens yielded a continuum with no clear boundary between normal and frequent hitting compounds.

Rural-urban scaling of age, mortality, crime and property reveals a loss of expected self-similar behaviour.

The urban scaling hypothesis has improved our understanding of cities; however, rural areas have been neglected. We investigated rural-urban population density scaling in England and Wales using 67 indicators of crime, mortality, property, and age. Most indicators exhibited segmented scaling about a median critical density of 27 people per hectare. Above the critical density, urban regions preferentially attract young adults (25-40 years) and lose older people (> 45 years). Density scale adjusted metrics (DSAMs) were analysed using hierarchical clustering, networks, and self-organizing maps (SOMs) revealing regional differences and an inverse relationship between excess value of property transactions and a range of preventable mortality (e.g. diabetes, suicide, lung cancer). The most striking finding is that age demographics break the expected self-similarity underlying the urban scaling hypothesis. Urban dynamism is fuelled by preferential attraction of young adults and not a fundamental property of total urban population.

City size and the spreading of COVID-19 in Brazil.

The current outbreak of the coronavirus disease 2019 (COVID-19) is an unprecedented example of how fast an infectious disease can spread around the globe (especially in urban areas) and the enormous impact it causes on public health and socio-economic activities. Despite the recent surge of investigations about different aspects of the COVID-19 pandemic, we still know little about the effects of city size on the propagation of this disease in urban areas. Here we investigate how the number of cases and deaths by COVID-19 scale with the population of Brazilian cities. Our results indicate small towns are proportionally more affected by COVID-19 during the initial spread of the disease, such that the cumulative numbers of cases and deaths per capita initially decrease with population size. However, during the long-term course of the pandemic, this urban advantage vanishes and large cities start to exhibit higher incidence of cases and deaths, such that every 1% rise in population is associated with a 0.14% increase in the number of fatalities per capita after about four months since the first two daily deaths. We argue that these patterns may be related to the existence of proportionally more health infrastructure in the largest cities and a lower proportion of older adults in large urban areas. We also find the initial growth rate of cases and deaths to be higher in large cities; however, these growth rates tend to decrease in large cities and to increase in small ones over time.

The Distribution of Standard Deviations Applied to High Throughput Screening.

High throughput screening (HTS) assesses compound libraries for "activity" using target assays. A subset of HTS data contains a large number of sample measurements replicated a small number of times providing an opportunity to introduce the distribution of standard deviations (DSD). Applying the DSD to some HTS data sets revealed signs of bias in some of the data and discovered a sub-population of compounds exhibiting high variability which may be difficult to screen. In the data examined, 21% of 1189 such compounds were pan-assay interference compounds. This proportion reached 57% for the most closely related compounds within the sub-population. Using the DSD, large HTS data sets can be modelled in many cases as two distributions: a large group of nearly normally distributed "inactive" compounds and a residual distribution of "active" compounds. The latter were not normally distributed, overlapped inactive distributions - on both sides -, and were larger than typically assumed. As such, a large number of compounds are being misclassified as "inactive" or are invisible to current methods which could become the next generation of drugs. Although applied here to HTS, it is applicable to data sets with a large number of samples measured a small number of times.

Unveiling relationships between crime and property in England and Wales via density scale-adjusted metrics and network tools.

Scale-adjusted metrics (SAMs) are a significant achievement of the urban scaling hypothesis. SAMs remove the inherent biases of per capita measures computed in the absence of isometric allometries. However, this approach is limited to urban areas, while a large portion of the world's population still lives outside cities and rural areas dominate land use worldwide. Here, we extend the concept of SAMs to population density scale-adjusted metrics (DSAMs) to reveal relationships among different types of crime and property metrics. Our approach allows all human environments to be considered, avoids problems in the definition of urban areas, and accounts for the heterogeneity of population distributions within urban regions. By combining DSAMs, cross-correlation, and complex network analysis, we find that crime and property types have intricate and hierarchically organized relationships leading to some striking conclusions. Drugs and burglary had uncorrelated DSAMs and, to the extent property transaction values are indicators of affluence, twelve out of fourteen crime metrics showed no evidence of specifically targeting affluence. Burglary and robbery were the most connected in our network analysis and the modular structures suggest an alternative to "zero-tolerance" policies by unveiling the crime and/or property types most likely to affect each other.

Spectrally resolved frequency domain analysis of multi-fluorophore systems undergoing energy transfer.

Complex systems of fluorophores undergoing energy transfer can exhibit a variety of anomalous lifetime behavior when probed with frequency domain methods. When presented in traditional apparent lifetime format the data from such systems can exhibit "nodal" behavior in which the computed lifetime approaches +/-infinity. The location of the nodes is system and frequency dependent. In addition, simpler systems, not undergoing energy transfer, show ill behavior in the region of zero lifetime (tau(m)) and long lifetime (tau(pi)) due to noise in typical measurements. Here, we systematically investigate systems of multiple fluorophores with and without energy transfer to provide insight into frequency domain investigations of complex systems of fluorophores. The results of simulations are compared to data collected from a multi-fluorophore system designed to exhibit fluorescence resonance energy transfer (FRET) using imaging spectroscopic fluorescence lifetime imaging microscopy (ISFLIM). The results are applicable to both cuvette and imaging arrangements.

Correction: Rural to Urban Population Density Scaling of Crime and Property Transactions in English and Welsh Parliamentary Constituencies.

[This corrects the article DOI: 10.1371/journal.pone.0149546.].

Rural to Urban Population Density Scaling of Crime and Property Transactions in English and Welsh Parliamentary Constituencies.

Urban population scaling of resource use, creativity metrics, and human behaviors has been widely studied. These studies have not looked in detail at the full range of human environments which represent a continuum from the most rural to heavily urban. We examined monthly police crime reports and property transaction values across all 573 Parliamentary Constituencies in England and Wales, finding that scaling models based on population density provided a far superior framework to traditional population scaling. We found four types of scaling: i) non-urban scaling in which a single power law explained the relationship between the metrics and population density from the most rural to heavily urban environments, ii) accelerated scaling in which high population density was associated with an increase in the power-law exponent, iii) inhibited scaling where the urban environment resulted in a reduction in the power-law exponent but remained positive, and iv) collapsed scaling where transition to the high density environment resulted in a negative scaling exponent. Urban scaling transitions, when observed, took place universally between 10 and 70 people per hectare. This study significantly refines our understanding of urban scaling, making clear that some of what has been previously ascribed to urban environments may simply be the high density portion of non-urban scaling. It also makes clear that some metrics undergo specific transitions in urban environments and these transitions can include negative scaling exponents indicative of collapse. This study gives promise of far more sophisticated scale adjusted metrics and indicates that studies of urban scaling represent a high density subsection of overall scaling relationships which continue into rural environments.