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.

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.

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.

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.

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.

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.

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.

Social distancing in public transport: mobilising new technologies for demand management under the Covid-19 crisis.

Dense urban areas are especially hardly hit by the Covid-19 crisis due to the limited availability of public transport, one of the most efficient means of mass mobility. In light of the Covid-19 pandemic, public transport operators are experiencing steep declines in demand and fare revenues due to the perceived risk of infection within vehicles and other facilities. The purpose of this paper is to explore the possibilities of implementing social distancing in public transport in line with epidemiological advice. Social distancing requires effective demand management to keep vehicle occupancy rates under a predefined threshold, both spatially and temporally. We review the literature of five demand management methods enabled by new information and ticketing technologies: (i) inflow control with queueing, (ii) time and space dependent pricing, (iii) capacity reservation with advance booking, (iv) slot auctioning, and (v) tradeable travel permit schemes. Thus the paper collects the relevant literature into a single point of reference, and provides interpretation from the viewpoint of practical applicability during and after the pandemic.

Surfactants influence polymer nanoparticle fate within the brain.

Drug delivery to the brain is limited by poor penetration of pharmaceutical agents across the blood-brain barrier (BBB), within the brain parenchyma, and into specific cells of interest. Nanotechnology can overcome these barriers, but its ability to do so is dependent on nanoparticle physicochemical properties including surface chemistry. Surface chemistry can be determined by a number of factors, including by the presence of stabilizing surfactant molecules introduced during the formulation process. Nanoparticles coated with poloxamer 188 (F68), poloxamer 407 (F127), and polysorbate 80 (P80) have demonstrated uptake in BBB endothelial cells and enhanced accumulation within the brain. However, the impact of surfactants on nanoparticle fate, and specifically on brain extracellular diffusion or intracellular targeting, must be better understood to design nanotherapeutics to efficiently overcome drug delivery barriers in the brain. Here, we evaluated the effect of the biocompatible and commonly used surfactants cholic acid (CHA), F68, F127, P80, and poly (vinyl alcohol) (PVA) on poly (lactic-co-glycolic acid)-poly (ethylene glycol) (PLGA-PEG) nanoparticle transport to and within the brain. The inclusion of these surfactant molecules decreases diffusive ability through brain tissue, reflecting the surfactant's role in encouraging cellular interaction at short length and time scales. After in vivo administration, PLGA-PEG/P80 nanoparticles demonstrated enhanced penetration across the BBB and subsequent internalization within neurons and microglia. Surfactants incorporated into the formulation of PLGA-PEG nanoparticles therefore represent an important design parameter for controlling nanoparticle fate within the brain.

Quantifying responses to changes in the jurisdiction of a congestion charge: A study of the London western extension.

This paper quantifies behavioural responses to changes in the jurisdiction of a congestion charge, with a successive focus on (i) an extension and (ii) a reduction in the size of the charging zone. We exploit the unanticipated nature of both the implementation and removal of London's Western Expansion Zone (WEZ) as quasi-natural experiments to test whether individual responses to policies are asymmetric. We use the UK Department of Transport Annual Average Daily Flow (AADF) data, which records traffic flows for seven transport modes (including cars, buses, bicycles, heavy and light goods vehicles). Using a difference-in-differences approach, we find that the introduction of the WEZ led to a 4.9% decline in road traffic flows in the new congestion charge area. These results are robust to different model specifications. HGVs traffic did not significantly change post-WEZ, which indicates that their road demand is price inelastic. The removal of the WEZ led to no significant variations in traffic. This result indicates asymmetry in behaviour with persistent changes in post-intervention traffic demand levels.

Evaluating the speed camera sites selection criteria in the UK.

INTRODUCTION: Speed cameras have been implemented to improve road safety over recent decades in the UK. Although the safety impacts of the speed camera have been estimated thoroughly, the criteria for selecting camera sites have rarely been studied. This paper evaluates the current speed camera sites selection criteria in the UK based on safety performance. METHOD: A total of 332 speed cameras and 2,513 control sites with road traffic accident data are observed from 2002 to 2010. Propensity score matching method and empirical Bayes method are employed and compared to estimate the safety effects of speed cameras under different scenarios. RESULTS: First, the main characteristics of speed cameras meeting and not meeting the selection criteria are identified. The results indicate that the proximity to school zones and residential neighborhoods, as well as population density, are the main considerations when selecting speed camera sites. Then the official criteria used for selecting camera sites are evaluated, including site length (a stretch of road that has a fixed speed camera or has had one in the past), previous accident history, and risk value (a numerical scale of the risk level). The results suggest that a site length of 500 m should be used to achieve the optimum safety effects of speed cameras. Furthermore, speed cameras are most effective in reducing crashes when the requirement of minimum number of historical killed and seriously injured collisions (KSIs) is met. In terms of the risk value, it is found that the speed cameras can obtain optimal effectiveness with a risk value greater than or equal to 30, rather than the recommended risk value of 22.

Exploring Social Ecological Determinants of Physical Activity Among Adult Survivors of Childhood Cancer.

Purpose: Adult survivors of childhood cancer (ASCCs) are at high risk for cardiovascular disease from chemotherapy- and radiation therapy-related cardiotoxicity. Physical activity (PA) can reduce this risk, but the majority of ASCCs do not engage in sufficient PA. The purpose of this study was to identify barriers, facilitators, and resources for PA among ASCCs using the ecological model of physical activity (EMPA) as a theoretical framework. Methods: A concept elicitation survey was distributed independently to ASCCs (diagnosed with cancer before the age of 18, and currently 18-39 years old) and parents/legal guardians of an ASCC. The survey consisted of open-ended questions asking about barriers, facilitators, and resources for PA. Content analysis of open-ended questions categorized responses into levels of the EMPA and identified key themes. Results: Seventeen ASCCs and eight parents of ASCCs completed the survey. The majority of barriers, facilitators, and resources reported were at the individual and microsystem level of the EMPA. Six themes emerged, suggesting that ASCC's PA was related to proximity/access, social support, equipment, time/schedule, finances, and health-related barriers. Conclusion: This is the first study to examine barriers, facilitators, and resources of PA among ASCCs using the EMPA. Findings from this study provide a multilevel perspective on the influences of PA among ASCCs, and can be used for future, in-depth qualitative studies and quantitative survey development, and as a foundational step toward supportive efforts in increasing PA among ASCCs.

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.

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.

Are multiple speed cameras more effective than a single one? Causal analysis of the safety impacts of multiple speed cameras.

Most previous studies investigate the safety effects of a single speed camera, ignoring the potential impacts from adjacent speed cameras. The mutual influence between two or even more adjacent speed cameras is a relevant attribute worth taking into account when evaluating the safety impacts of speed cameras. This paper investigates the safety effects of two or more speed cameras observed within a specific radius which are defined as multiple speed cameras. A total of 464 speed cameras at treated sites and 3119 control sites are observed and related to road traffic accident data from 1999 to 2007. The effects of multiple speed cameras are evaluated using pairwise comparisons between treatment units with different doses based on the propensity score methods. The spatial effect of multiple speed cameras is investigated by testing various radii. There are two major findings in this study. First, sites with multiple speed cameras perform better in reducing the absolute number of road accidents than those with a single camera. Second, speed camera sites are found to be most effective with a radius of 200 m. For a radius of 200 m and 300 m, the reduction in the personal injury collisions by multiple speed cameras are 21.4 % and 13.2 % more than a single camera. Our results also suggest that multiple speed cameras are effective within a small radius (200 m and 300 m).

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.

Do speed cameras reduce road traffic collisions?

This paper quantifies the effect of speed cameras on road traffic collisions using an approximate Bayesian doubly-robust (DR) causal inference estimation method. Previous empirical work on this topic, which shows a diverse range of estimated effects, is based largely on outcome regression (OR) models using the Empirical Bayes approach or on simple before and after comparisons. Issues of causality and confounding have received little formal attention. A causal DR approach combines propensity score (PS) and OR models to give an average treatment effect (ATE) estimator that is consistent and asymptotically normal under correct specification of either of the two component models. We develop this approach within a novel approximate Bayesian framework to derive posterior predictive distributions for the ATE of speed cameras on road traffic collisions. Our results for England indicate significant reductions in the number of collisions at speed cameras sites (mean ATE = -15%). Our proposed method offers a promising approach for evaluation of transport safety interventions.

Highly-reactive haloester surface initiators for ARGET ATRP readily prepared by radio frequency glow discharge plasma.

New surface initiators for ARGET ATRP (activators regenerated by electron transfer atomic transfer radical polymerization) have been prepared by the plasma deposition of haloester monomers. Specifically, methyl 3-bromopropionate (M3BP), methyl 2-chloropropionate, and ethyl 2-fluoropropionate (E2FP) were plasma deposited onto glass discs using RF glow discharge plasma. This technique creates surface coatings that are resistant to delamination and rich in halogen species making them good candidates for surface initiators for ARGET ATRP. Of all the plasma polymerized surface coatings, M3BP showed the highest halogen content and was able to grow 2-hydroxyethyl methacrylate (HEMA) polymer brushes on its surface via ARGET ATRP in as little as 15 min as confirmed by XPS. Surprisingly, E2FP, a fluoroester, was also able to grow HEMA polymer brushes despite fluorine being a poor leaving group for ARGET ATRP. The versatility of RF glow discharge plasma offers a clear advantage over other techniques previously used to immobilize ARGET ATRP surface initiators.