ToF-SIMS analysis of ultrathin films and their fragmentation patterns.

Organic thin films are of great interest due to their intriguing interfacial and functional properties, especially for device applications such as thin-film transistors and sensors. As their thickness approaches single nanometer thickness, characterization and interpretation of the extracted data become increasingly complex. In this study, plasma polymerization is used to construct ultrathin films that range in thickness from 1 to 20 nm, and time-of-flight secondary ion mass spectrometry coupled with principal component analysis is used to investigate the effects of film thickness on the resulting spectra. We demonstrate that for these cross-linked plasma polymers, at these thicknesses, the observed trends are different from those obtained from thicker films with lower degrees of cross-linking: contributions from ambient carbon contamination start to dominate the mass spectrum; cluster-induced nonlinear enhancement in secondary ion yield is no longer observed; extent of fragmentation is higher due to confinement of the primary ion energy; and the size of the primary ion source also affects fragmentation (e.g., Bi1 versus Bi5). These differences illustrate that care must be taken in choosing the correct primary ion source as well as in interpreting the data.

Using Explainable Machine Learning to Interpret the Effects of Policies on Air Pollution: COVID-19 Lockdown in London.

Activity changes during the COVID-19 lockdown present an opportunity to understand the effects that prospective emission control and air quality management policies might have on reducing air pollution. Using a regression discontinuity design for causal analysis, we show that the first UK national lockdown led to unprecedented decreases in road traffic, by up to 65%, yet incommensurate and heterogeneous responses in air pollution in London. At different locations, changes in air pollution attributable to the lockdown ranged from -50% to 0% for nitrogen dioxide (NO2), 0% to +4% for ozone (O3), and -5% to +0% for particulate matter with an aerodynamic diameter less than 10 µm (PM10), and there was no response for PM2.5. Using explainable machine learning to interpret the outputs of a predictive model, we show that the degree to which NO2 pollution was reduced in an area was correlated with spatial features (including road freight traffic and proximity to a major airport and the city center), and that existing inequalities in air pollution exposure were exacerbated: pollution reductions were greater in places with more affluent residents and better access to public transport services.

Preferences for using the London Underground during the COVID-19 pandemic.

The COVID-19 pandemic has drastically impacted people's travel behaviour and introduced uncertainty in the demand for public transport. To investigate user preferences for travel by London Underground during the pandemic, we conducted a stated choice experiment among its pre-pandemic users (N = 961). We analysed the collected data using multinomial and latent class logit models. Our discrete choice analysis provides two sets of results. First, we derive the crowding multiplier estimate of travel time valuation (i.e., the ratio of the value of travel time in uncrowded and crowded situations) for London underground users. The results indicate that travel time valuation of Underground users increases by 73% when it operates at technical capacity. Second, we estimate the sensitivity of the preference for the London Underground relative to the epidemic situation (confirmed new COVID-19 cases) and interventions (vaccination rates and mandatory face masks). The sensitivity analysis suggests that making face masks mandatory is a main driver for recovering the demand for the London underground. The latent class model reveals substantial preference heterogeneity. For instance, while the average effect of mandatory face masks is positive, the preferences of 30% of pre-pandemic users for travel by the Underground are negatively affected. The positive effect of mandatory face masks on the likelihood of taking the Underground is less pronounced among males with age below 40 years, and a monthly income below 10,000 GBP. The estimated preference sensitivities and crowding multipliers are relevant for supply-demand management in transit systems and the calibration of advanced epidemiological models.

Surface analysis tools for characterizing biological materials.

Surfaces represent a unique state of matter that typically have significantly different compositions and structures from the bulk of a material. Since surfaces are the interface between a material and its environment, they play an important role in how a material interacts with its environment. Thus, it is essential to characterize, in as much detail as possible, the surface structure and composition of a material. However, this can be challenging since the surface region typically is only minute portion of the entire material, requiring specialized techniques to selectively probe the surface region. This tutorial will provide a brief review of several techniques used to characterize the surface and interface regions of biological materials. For each technique we provide a description of the key underlying physics and chemistry principles, the information provided, strengths and weaknesses, the types of samples that can be analyzed, and an example application. Given the surface analysis challenges for biological materials, typically there is never just one technique that can provide a complete surface characterization. Thus, a multi-technique approach to biological surface analysis is always required.

Tailor-Made Amino Acids and Fluorinated Motifs as Prominent Traits in Modern Pharmaceuticals.

Structural analysis of modern pharmaceutical practices allows for the identification of two rapidly growing trends: the introduction of tailor-made amino acids and the exploitation of fluorinated motifs. Curiously, the former represents one of the most ubiquitous classes of naturally occurring compounds, whereas the latter is the most xenobiotic and comprised virtually entirely of man-made derivatives. Herein, 39 selected compounds, featuring both of these traits in the same molecule, are profiled. The total synthesis, source of the corresponding amino acids and fluorinated residues, and medicinal chemistry aspects and biological properties of the molecules are discussed.

Exploiting the Semidestructive Nature of Gas Cluster Ion Beam Time-of-Flight Secondary Ion Mass Spectrometry Imaging for Simultaneous Localization and Confident Lipid Annotations.

Lipids have been recognized as key players in cell signaling and disease. Information on their location and distribution within a biological system, under varying conditions, is necessary to understand the contributions of different lipid species to an altered phenotype. Imaging mass spectrometry techniques, such as time-of-flight secondary ion mass spectrometry (ToF-SIMS) and matrix-assisted laser desorption/ionization (MALDI), are capable of revealing global lipid distributions in tissues in an untargeted fashion. However, to confidently identify the species present in a sample, orthogonal analyses like tandem MS (MS/MS) are often required. This can be accomplished by bulk sample analysis with liquid chromatography (LC)-MS/MS, which can provide confident lipid identifications, at the expense of losing location-specific information. Here, using planarian flatworms as a model system, we demonstrate that imaging gas cluster ion beam (GCIB)-ToF-SIMS has the unique capability to simultaneously detect, identify, and image lipid species with subcellular resolution in tissue sections. The parallel detection of both, intact lipids and their respective fragments, allows for unique identification of some species without the need of performing an additional orthogonal MS/MS analysis. This was accomplished by correlating intact lipid and associated fragment SIMS images. The lipid assignments, respective fragment identities, and locations gathered from ToF-SIMS data were confirmed via LC-MS/MS on lipid extracts and ultrahigh mass resolution MALDI-MS imaging. Together, these data show that the semidestructive nature of ToF-SIMS can be utilized advantageously to enable both confident molecular annotations and to determine the locations of species within a biological sample.

An unsupervised MVA method to compare specific regions in human breast tumor tissue samples using ToF-SIMS.

Imaging time-of-flight secondary ion mass spectrometry (ToF-SIMS) and principal component analysis (PCA) were used to investigate two sets of pre- and post-chemotherapy human breast tumor tissue sections to characterize lipids associated with tumor metabolic flexibility and response to treatment. The micron spatial resolution imaging capability of ToF-SIMS provides a powerful approach to attain spatially-resolved molecular and cellular data from cancerous tissues not available with conventional imaging techniques. Three ca. 1 mm(2) areas per tissue section were analyzed by stitching together 200 µm × 200 µm raster area scans. A method to isolate and analyze specific tissue regions of interest by utilizing PCA of ToF-SIMS images is presented, which allowed separation of cellularized areas from stromal areas. These PCA-generated regions of interest were then used as masks to reconstruct representative spectra from specifically stromal or cellular regions. The advantage of this unsupervised selection method is a reduction in scatter in the spectral PCA results when compared to analyzing all tissue areas or analyzing areas highlighted by a pathologist. Utilizing this method, stromal and cellular regions of breast tissue biopsies taken pre- versus post-chemotherapy demonstrate chemical separation using negatively-charged ion species. In this sample set, the cellular regions were predominantly all cancer cells. Fatty acids (i.e. palmitic, oleic, and stearic), monoacylglycerols, diacylglycerols and vitamin E profiles were distinctively different between the pre- and post-therapy tissues. These results validate a new unsupervised method to isolate and interpret biochemically distinct regions in cancer tissues using imaging ToF-SIMS data. In addition, the method developed here can provide a framework to compare a variety of tissue samples using imaging ToF-SIMS, especially where there is section-to-section variability that makes it difficult to use a serial hematoxylin and eosin (H&E) stained section to direct the SIMS analysis.

Reconstructing accurate ToF-SIMS depth profiles for organic materials with differential sputter rates.

To properly process and reconstruct 3D ToF-SIMS data from systems such as multi-component polymers, drug delivery scaffolds, cells and tissues, it is important to understand the sputtering behavior of the sample. Modern cluster sources enable efficient and stable sputtering of many organics materials. However, not all materials sputter at the same rate and few studies have explored how different sputter rates may distort reconstructed depth profiles of multicomponent materials. In this study spun-cast bilayer polymer films of polystyrene and PMMA are used as model systems to optimize methods for the reconstruction of depth profiles in systems exhibiting different sputter rates between components. Transforming the bilayer depth profile from sputter time to depth using a single sputter rate fails to account for sputter rate variations during the profile. This leads to inaccurate apparent layer thicknesses and interfacial positions, as well as the appearance of continued sputtering into the substrate. Applying measured single component sputter rates to the bilayer films with a step change in sputter rate at the interfaces yields more accurate film thickness and interface positions. The transformation can be further improved by applying a linear sputter rate transition across the interface, thus modeling the sputter rate changes seen in polymer blends. This more closely reflects the expected sputtering behavior. This study highlights the need for both accurate evaluation of component sputter rates and the careful conversion of sputter time to depth, if accurate 3D reconstructions of complex multi-component organic and biological samples are to be achieved. The effects of errors in sputter rate determination are also explored.

Electron-transfer studies of a peroxide dianion.

A peroxide dianion (O2(2-)) can be isolated within the cavity of hexacarboxamide cryptand, [(O2)⊂mBDCA-5t-H6](2-), stabilized by hydrogen bonding but otherwise free of proton or metal-ion association. This feature has allowed the electron-transfer (ET) kinetics of isolated peroxide to be examined chemically and electrochemically. The ET of [(O2)⊂mBDCA-5t-H6](2-) with a series of seven quinones, with reduction potentials spanning 1 V, has been examined by stopped-flow spectroscopy. The kinetics of the homogeneous ET reaction has been correlated to heterogeneous ET kinetics as measured electrochemically to provide a unified description of ET between the Butler-Volmer and Marcus models. The chemical and electrochemical oxidation kinetics together indicate that the oxidative ET of O2(2-) occurs by an outer-sphere mechanism that exhibits significant nonadiabatic character, suggesting that the highest occupied molecular orbital of O2(2-) within the cryptand is sterically shielded from the oxidizing species. An understanding of the ET chemistry of a free peroxide dianion will be useful in studies of metal-air batteries and the use of [(O2)⊂mBDCA-5t-H6](2-) as a chemical reagent.

Quantification of artistic style through sparse coding analysis in the drawings of Pieter Bruegel the Elder.

Recently, statistical techniques have been used to assist art historians in the analysis of works of art. We present a novel technique for the quantification of artistic style that utilizes a sparse coding model. Originally developed in vision research, sparse coding models can be trained to represent any image space by maximizing the kurtosis of a representation of an arbitrarily selected image from that space. We apply such an analysis to successfully distinguish a set of authentic drawings by Pieter Bruegel the Elder from another set of well-known Bruegel imitations. We show that our approach, which involves a direct comparison based on a single relevant statistic, offers a natural and potentially more germane alternative to wavelet-based classification techniques that rely on more complicated statistical frameworks. Specifically, we show that our model provides a method capable of discriminating between authentic and imitation Bruegel drawings that numerically outperforms well-known existing approaches. Finally, we discuss the applications and constraints of our technique.

Integrating holism and reductionism in the science of art perception.

The contextualist claim that universalism is irrelevant to the proper study of art can be evaluated by examining an analogous question in neuroscience. Taking the reductionist-holist debate in visual neuroscience as a model, we see that the analog of orthodox contextualism is untenable, whereas integrated approaches have proven highly effective. Given the connection between art and vision, unified approaches are likewise more germane to the scientific study of art.

Back to the basics of time-of-flight secondary ion mass spectrometry of bio-related samples. I. Instrumentation and data collection.

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is used widely throughout industrial and academic research due to the high information content of the chemically specific data it produces. Modern ToF-SIMS instruments can generate high mass resolution data that can be displayed as spectra and images (2D and 3D). This enables determining the distribution of molecules across and into a surface and provides access to information not obtainable from other methods. With this detailed chemical information comes a steep learning curve in how to properly acquire and interpret the data. This Tutorial is aimed at helping ToF-SIMS users to plan for and collect ToF-SIMS data. The second Tutorial in this series will cover how to process, display, and interpret ToF-SIMS data.

Back to the basics of time-of-flight secondary ion mass spectrometry data analysis of bio-related samples. II. Data processing and display.

This is the second half of a two-part Tutorial on the basics of the time-of-flight secondary ion mass spectrometry (ToF-SIMS) analysis of bio-related samples. Part I of this Tutorial series covers planning for a ToF-SIMS experiment, preparing and shipping samples, and collecting ToF-SIMS data. This Tutorial aims at helping the ToF-SIMS user to process, display, and interpret ToF-SIMS data. ToF-SIMS provides detailed chemical information about surfaces but comes with a steep learning. The purpose of this Tutorial is to provide the reader with a solid foundation in the ToF-SIMS data analysis.

An evaluation framework for operational interventions on urban mass public transport during a pandemic.

Decision making in a rapidly changing context, such as the development and progression of a pandemic, requires a dynamic assessment of multiple variable and competing factors. Seemingly beneficial courses of action can rapidly fail to deliver a positive outcome as the context changes. In this paper, we present a flexible data-driven agent-based simulation framework that considers multiple outcome criteria to increase opportunities for safe mobility and economic interactions on urban transit networks while reducing the potential for Covid-19 contagion in a dynamic setting. Using a case study of the Victoria line on the London Underground, we model a number of operational interventions with varied demand levels and social distancing constraints including: alterations to train headways, dwell times, signalling schemes, and train paths. Our model demonstrates that substantial performance gains ranging from 12.3-195.7% can be achieved in metro service provision when comparing the best performing operational scheme and headway with those realised on the Victoria line during the pandemic.

The Relationship Between Stress and Sleep Sufficiency in the Context of Varied Workplace Social Support.

OBJECTIVE: Sufficient sleep is essential for well-being. We examined the relationship between work-related social support, work stress, and sleep sufficiency, predicting that workers with higher social support would report higher sleep sufficiency across varying levels of work stress. METHODS: The data set analyzed in the present study included 2213 workers from approximately 200 small (<500 employees) businesses in high, medium, and low hazard industries across Colorado. RESULTS: Perceived social support variables moderated the relationship between work stress and sleep sufficiency such that employees reporting higher levels of social support reported higher sleep sufficiency when work stress was low or moderate but not high. CONCLUSIONS: Although preventing work stress is optimal, in cases where employers cannot apply primary interventions to prevent stress (eg, eliminating/reducing night shifts), employers should attempt to increase social support or other more relevant resources for employees.

ToF-SIMS depth profiling of cells: z-correction, 3D imaging, and sputter rate of individual NIH/3T3 fibroblasts.

Proper display of three-dimensional time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging data of complex, nonflat samples requires a correction of the data in the z-direction. Inaccuracies in displaying three-dimensional ToF-SIMS data arise from projecting data from a nonflat surface onto a 2D image plane, as well as possible variations in the sputter rate of the sample being probed. The current study builds on previous studies by creating software written in Matlab, the ZCorrectorGUI (available at http://mvsa.nb.uw.edu/), to apply the z-correction to entire 3D data sets. Three-dimensional image data sets were acquired from NIH/3T3 fibroblasts by collecting ToF-SIMS images, using a dual beam approach (25 keV Bi(3)(+) for analysis cycles and 20 keV C(60)(2+) for sputter cycles). The entire data cube was then corrected by using the new ZCorrectorGUI software, producing accurate chemical information from single cells in 3D. For the first time, a three-dimensional corrected view of a lipid-rich subcellular region, possibly the nuclear membrane, is presented. Additionally, the key assumption of a constant sputter rate throughout the data acquisition was tested by using ToF-SIMS and atomic force microscopy (AFM) analysis of the same cells. For the dried NIH/3T3 fibroblasts examined in this study, the sputter rate was found to not change appreciably in x, y, or z, and the cellular material was sputtered at a rate of approximately 10 nm per 1.25 × 10(13) ions C(60)(2+)/cm(2).

Identifying individual cell types in heterogeneous cultures using secondary ion mass spectrometry imaging with C60 etching and multivariate analysis.

Tissue engineering approaches fabricate and subsequently implant cell-seeded and unseeded scaffold biomaterials. Once in the body, these biomaterials are repopulated with somatic cells of various phenotypes whose identification upon explantation can be expensive and time-consuming. We show that imaging time-of-flight secondary ion mass spectrometry (TOF-SIMS) can be used to distinguish mammalian cell types in heterogeneous cultures. Primary rat esophageal epithelial cells (REEC) were cultured with NIH 3T3 mouse fibroblasts on tissue culture polystyrene and freeze-dried before TOF-SIMS imaging. Results show that a short etching sequence with C(60)(+) ions can be used to clean the sample surface and improve the TOF-SIMS image quality. Principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) were used to identify peaks whose contributions to the total variance in the multivariate model were due to either the two cell types or the substrate. Using PLS-DA, unknown regions of cellularity that were otherwise unidentifiable by SIMS could be classified. From the loadings in the PLS-DA model, peaks were selected that were indicative of the two cell types and TOF-SIMS images were created and overlaid that showed the ability of this method to distinguish features visually.

HealthWorks: results of a multi-component group-randomized worksite environmental intervention trial for weight gain prevention.

BACKGROUND: U.S. adults are at unprecedented risk of becoming overweight or obese, and most scientists believe the primary cause is an obesogenic environment. Worksites provide an opportunity to shape the environments of adults to reduce obesity risk. The goal of this group-randomized trial was to implement a four-component environmental intervention at the worksite level to positively influence weight gain among employees over a two-year period. Environmental components focused on food availability and price, physical activity promotion, scale access, and media enhancements. METHODS: Six worksites in a U.S. metropolitan area were recruited and randomized in pairs at the worksite level to either a two-year intervention or a no-contact control. Evaluations at baseline and two years included: 1) measured height and weight; 2) online surveys of individual dietary intake and physical activity behaviors; and 3) detailed worksite environment assessment. RESULTS: Mean participant age was 42.9 years (range 18-75), 62.6% were women, 68.5% were married or cohabiting, 88.6% were white, 2.1% Hispanic. Mean baseline BMI was 28.5 kg/m(2) (range 16.9-61.2 kg/m(2)). A majority of intervention components were successfully implemented. However, there were no differences between sites in the key outcome of weight change over the two-year study period (p = .36). CONCLUSIONS: Body mass was not significantly affected by environmental changes implemented for the trial. Results raise questions about whether environmental change at worksites is sufficient for population weight gain prevention. TRIAL REGISTRATION: ClinicalTrials.gov: NCT00708461.

Modelling the propagation of infectious disease via transportation networks.

The dynamics of human mobility have been known to play a critical role in the spread of infectious diseases like COVID-19. In this paper, we present a simple compact way to model the transmission of infectious disease through transportation networks using widely available aggregate mobility data in the form of a zone-level origin-destination (OD) travel flow matrix. A key feature of our model is that it not only captures the propagation of infection via direct connections between zones (first-order effects) as in most existing studies but also transmission effects that are due to subsequent interactions in the remainder of the system (higher-order effects). We demonstrate the importance of capturing higher-order effects in a simulation study. We then apply our model to study the first wave of COVID-19 infections in (i) Italy, and, (ii) the New York Tri-State area. We use daily data on mobility between Italian provinces (province-level OD data) and between Tri-State Area counties (county-level OD data), and daily reported caseloads at the same geographical levels. Our empirical results indicate substantial predictive power, particularly during the early stages of the outbreak. Our model forecasts at least 85% of the spatial variation in observed weekly COVID-19 cases. Most importantly, our model delivers crucial metrics to identify target areas for intervention.

Road traffic casualties in Great Britain at daylight savings time transitions: a causal regression discontinuity design analysis.

OBJECTIVE: To determine whether daylight savings time (DST) transitions have an effect on road traffic casualties in Great Britain using causal regression discontinuity design (RDD) analysis. We undertake aggregate and disaggregate spatial and temporal analyses to test the commonly referenced sleep and light hypotheses. DESIGN: The study takes the form of a natural experiment in which the DST transitions are interventions to be evaluated. Two outcomes are tested: (1) the total number of casualties of all severities and (2) the number of fatalities. DATA: Data were obtained from the UK Department for Transport STATS19 database. Over a period of 14 years between 2005 and 2018, 311 766 total casualties and 5429 fatalities occurred 3 weeks on either side of the Spring DST transition and 367 291 total casualties and 6650 fatalities occurred 3 weeks on either side of the Autumn DST transition. PRIMARY OUTCOME MEASURE: An RDD method was applied. The presence of a causal effect was determined via the degree of statistical significance and the magnitude of the average treatment effect. RESULTS: All significant average treatment effects are negative (54 significant models out of 287 estimated), indicating that there are fewer casualties following the transitions. Overall, bootstrapped summary statistics indicate a reduction of 0.75 in the number of fatalities (95% CI -1.61 to -0.04) and a reduction of 4.73 in the number of total casualties (95% CI -6.08 to -3.27) on average per year at both the Spring and Autumn DST transitions combined. CONCLUSIONS: The results indicate minor reductions in the number of fatalities following the DST transitions, and thus, our analysis does not support the most recent UK parliamentary estimate that there would be 30 fewer fatalities in Great Britain if DST was to be abolished. Furthermore, the results do not provide conclusive support for either the sleep or light hypotheses.