Facilitating goal-oriented behaviour in the Stroop task: when executive control is influenced by automatic processing.

A portion of Stroop interference is thought to arise from a failure to maintain goal-oriented behaviour (or goal neglect). The aim of the present study was to investigate whether goal- relevant primes could enhance goal maintenance and reduce the Stroop interference effect. Here it is shown that primes related to the goal of responding quickly in the Stroop task (e.g. fast, quick, hurry) substantially reduced Stroop interference by reducing reaction times to incongruent trials but increasing reaction times to congruent and neutral trials. No effects of the primes were observed on errors. The effects on incongruent, congruent and neutral trials are explained in terms of the influence of the primes on goal maintenance. The results show that goal priming can facilitate goal-oriented behaviour and indicate that automatic processing can modulate executive control

Using Watershed Pour-Point Elevations to Evaluate the Base of Fresh Groundwater in the Cumberland Plateau of Eastern Kentucky

Horizontal drilling with hydraulic fracturing at shallow depths (less than 2,200 ft) in the Devonian Berea Sandstone oil and gas play, along with the potential for high-volume hydraulic fracturing in the nascent Cambrian Rogersville Shale gas play, have generated a renewed interest in protecting groundwater quality in eastern Kentucky. A critical component of protection is an accurate understanding of the distribution of fresh water in the subsurface. The “Fresh-Saline Water Interface Map of Kentucky” by H.T. Hopkins, published by the U.S. Geological Survey and Kentucky Geological Survey in 1966, has been a critical reference for assessing the maximum depth of fresh groundwater and is an important guidance document for well operators and regulatory agencies. To create the map, Hopkins assumed that total depth of domestic water wells equaled the base of fresh groundwater (total dissolved solids less than 1,000 ppm). Most domestic wells fail to penetrate the deepest fresh groundwater, however, and consequently, Hopkins’s map likely underestimates the depth of the fresh-saline water interface. Our study also used total depths of wells to map the base of fresh groundwater, but increased the data density by adding data from domestic water wells drilled after 1966. In the 14-county study area, the number of wells increased from 50 used by Hopkins to 4,824 in this study. Total well depths were contour mapped using Petra software. Despite the increased data density, the inclusion of a greater number of shallow wells produced contour patterns that impeded resolution of deep fresh groundwater distribution (i.e., noise). To limit the influence of shallow wells, we eliminated wells with total depths above the elevations of watershed pour points in each watershed defined by 14- and 11-digit hydrologic unit codes. This excluded wells that did not penetrate the deepest fresh groundwater in low-order watersheds. We then created maps based on all wells with total depths below the elevations of their respective pour points in 14- and 11-digit hydrologic units (n = 3,203 and 854, respectively), as well as maps based on the single deepest well in the 14- and 11-digit hydrologic units (n = 1,420 and 74, respectively). The pour-point method improved the resolution of deep fresh groundwater distribution, and the map using the single deepest well depth in each 11-digit hydrologic unit provided the clearest illustration of deep fresh groundwater distribution. Throughout most of the study area, the estimated depth of fresh groundwater derived from the 11-digit hydrologic unit deepest-well map is, on average, 147 ft deeper than the interface shown on the Hopkins map; in eastern Lawrence County, the difference exceeds 500 ft. Even though our study resulted in an improved estimate of maximum fresh groundwater depth, uncertainties remain in the data and methods. To reflect this uncertainty, the term “deepest observed fresh water” should be used as an alternative to “fresh-saline water interface.

Evaluation of Geologic CO\u3csub\u3e2\u3c/sub\u3e Sequestration Potential and CO\u3csub\u3e2\u3c/sub\u3e Enhanced Oil Recovery in Kentucky

Kentucky gets approximately 95 percent of its electricity from coal-fired power plants, which produce significant amounts of carbon dioxide (CO2). In 2005, Kentucky coal-fired plants vented 102.8 million short tons of CO2 into the atmosphere. The economic vitality of the state will be affected by its ability to develop and apply a portfolio of technologies that will mitigate input of CO2 into the atmosphere. One technology that has the potential to assist in this challenge is geologic carbon storage, which captures CO2 at point sources and injects it into deep rock strata that can store it for tens of thousands of years and longer. Previous studies suggest that Kentucky has the capacity to store up to 1 billion tons of CO2 in underground strata. By necessity, the capacity calculations are high-level estimates, and consequently, actual capacity remains unproved and even speculative. In addition, other factors such as infrastructure, engineering, and economic and regulatory policy will affect the viability of geologic carbon storage in the state. This report is divided into five chapters, each addressing specific technical aspects pertinent to geologic carbon storage, which is the overarching theme. Chapter 1 is an introduction and overview of geologic carbon storage and the data needed to evaluate its potential. Chapter 2 is a geologic evaluation of the potential to use CO2 for enhanced oil recovery. Chapter 3 is an evaluation of subsurface formation-water geochemistry and implications for CO2 sequestration. Chapter 4 is an evaluation of CO2 storage potential with an emphasis along some of the state\u27s major river corridors. Chapter 5 is a geologic evaluation of CO2 storage potential for nominated coal-to-liquids (gasification) sites

Decomposing effective radiative forcing due to aerosol cloud interactions by global cloud regimes

Quantifying effective radiative forcing due to aerosol-cloud interactions (EERFACI) remains a largely uncertain process, and the magnitude remains unconstrained in general circulation models. Previous studies focus on the magnitude of ERFACI arising from all cloud types, or examine it in the framework of dynamical regimes. Aerosol forcing due to aerosol-cloud interactions in the HadGEM3-GA7.1 global climate model is decomposed into several global observational cloud regimes. Regimes are assigned to model gridboxes and forcing due to aerosol-cloud interactions is calculated on a regime-by-regime basis with a 20-year averaging period. Patterns of regime occurrence are in good agreement with satellite observations. ERFACI is then further decomposed into three terms, representing radiative changes within a given regime, transitions between different cloud regimes, and nonlinear effects. The total global mean ERFACI is urn:x-wiley:00948276:media:grl62928:grl62928-math-0003 Wm−2. When decomposed, simulated ERFACI is greatest in the thick stratocumulus regime (−0.51 Wm−2)

CloudTracks: A Dataset for Localizing Ship Tracks in Satellite Images of Clouds

Clouds play a significant role in global temperature regulation through their effect on planetary albedo. Anthropogenic emissions of aerosols can alter the albedo of clouds, but the extent of this effect, and its consequent impact on temperature change, remains uncertain. Human-induced clouds caused by ship aerosol emissions, commonly referred to as ship tracks, provide visible manifestations of this effect distinct from adjacent cloud regions and therefore serve as a useful sandbox to study human-induced clouds. However, the lack of large-scale ship track data makes it difficult to deduce their general effects on cloud formation. Towards developing automated approaches to localize ship tracks at scale, we present CloudTracks, a dataset containing 3,560 satellite images labeled with more than 12,000 ship track instance annotations. We train semantic segmentation and instance segmentation model baselines on our dataset and find that our best model substantially outperforms previous state-of-the-art for ship track localization (61.29 vs. 48.65 IoU). We also find that the best instance segmentation model is able to identify the number of ship tracks in each image more accurately than the previous state-of-the-art (1.64 vs. 4.99 MAE). However, we identify cases where the best model struggles to accurately localize and count ship tracks, so we believe CloudTracks will stimulate novel machine learning approaches to better detect elongated and overlapping features in satellite images. We release our dataset openly at {zenodo.org/records/10042922}.Comment: 11 pages, 5 figures, submitted to Journal of Machine Learning Researc

The impact of polio eradication on routine immunization and primary health care: A mixed-methods study

Background: After 2 decades of focused efforts to eradicate polio, the impact of eradication activities on health systems continues to be controversial. This study evaluated the impact of polio eradication activities on routine immunization (RI) and primary healthcare (PHC).Methods: Quantitative analysis assessed the effects of polio eradication campaigns on RI and maternal healthcare coverage. A systematic qualitative analysis in 7 countries in South Asia and sub-Saharan Africa assessed impacts of polio eradication activities on key health system functions, using data from interviews, participant observation, and document review.Results: Our quantitative analysis did not find compelling evidence of widespread and significant effects of polio eradication campaigns, either positive or negative, on measures of RI and maternal healthcare. Our qualitative analysis revealed context-specific positive impacts of polio eradication activities in many of our case studies, particularly disease surveillance and cold chain strengthening. These impacts were dependent on the initiative of policy makers. Negative impacts, including service interruption and public dissatisfaction, were observed primarily in districts with many campaigns per year.Conclusions: Polio eradication activities can provide support for RI and PHC, but many opportunities to do so remain missed. Increased commitment to scaling up best practices could lead to significant positive impacts

First light and reionization epoch simulations (FLARES) V : the redshift frontier

JWST is set to transform many areas of astronomy, one of the most exciting is the expansion of the redshift frontier to z > 10. In its first year, alone JWST should discover hundreds of galaxies, dwarfing the handful currently known. To prepare for these powerful observational constraints, we use the First Light And Reionization Epoch simulations (flares) to predict the physical and observational properties of the z > 10 population of galaxies accessible to JWST. This is the first time such predictions have been made using a hydrodynamical model validated at low redshift. Our predictions at z = 10 are broadly in agreement with current observational constraints on the far-UV luminosity function and UV continuum slope beta, though the observational uncertainties are large. We note tension with recent constraints z similar to 13 from Harikane et al. () - compared to these constraints, flares predicts objects with the same space density should have an order-of-magnitude lower luminosity, though this is mitigated slightly if dust attenuation is negligible in these systems. Our predictions suggest that in JWST's first cycle alone, around 600 galaxies should be identified at z > 10, with the first small samples available at z > 13.Peer reviewe

Acidification increases abundances of Vibrionales and Planctomycetia associated to a seaweed-grazer system: potential consequences for disease and prey digestion efficiency

Ocean acidification significantly affects marine organisms in several ways, with complex interactions. Seaweeds might benefit from rising CO2 through increased photosynthesis and carbon acquisition, with subsequent higher growth rates. However, changes in seaweed chemistry due to increased CO2 may change the nutritional quality of tissue for grazers. In addition, organisms live in close association with a diverse microbiota, which can also be influenced by environmental changes, with feedback effects. As gut microbiomes are often linked to diet, changes in seaweed characteristics and associated microbiome can affect the gut microbiome of the grazer, with possible fitness consequences. In this study, we experimentally investigated the effects of acidification on the microbiome of the invasive brown seaweed Sargassum muticum and a native isopod consumer Synisoma nadejda. Both were exposed to ambient CO2 conditions (380 ppm, pH 8.16) and an acidification treatment (1,000 ppm, pH 7.86) for three weeks. Microbiome diversity and composition were determined using high-throughput sequencing of the variable regions V5-7 of 16S rRNA. We anticipated that as a result of acidification, the seaweed-associated bacterial community would change, leading to further changes in the gut microbiome of grazers. However, no significant effects of elevated CO2 on the overall bacterial community structure and composition were revealed in the seaweed. In contrast, significant changes were observed in the bacterial community of the grazer gut. Although the bacterial community of S. muticum as whole did not change, Oceanospirillales and Vibrionales (mainly Pseudoalteromonas) significantly increased their abundance in acidified conditions. The former, which uses organic matter compounds as its main source, may have opportunistically taken advantage of the possible increase of the C/N ratio in the seaweed under acidified conditions. Pseudoalteromonas, commonly associated to diseased seaweeds, suggesting that acidification may facilitate opportunistic/pathogenic bacteria. In the gut of S. nadejda, the bacterial genus Planctomycetia increased abundance under elevated CO2. This shift might be associated to changes in food (S. muticum) quality under acidification. Planctomycetia are slow-acting decomposers of algal polymers that could be providing the isopod with an elevated algal digestion and availability of inorganic compounds to compensate the shifted C/N ratio under acidification in their food. In conclusion, our results indicate that even after only three weeks of acidified conditions, bacterial communities associated to ungrazed seaweed and to an isopod grazer show specific, differential shifts in associated bacterial community. These have potential consequences for seaweed health (as shown in corals) and isopod food digestion. The observed changes in the gut microbiome of the grazer seem to reflect changes in the seaweed chemistry rather than its microbial composition.Erasmus Mundus Doctoral Programme MARES on Marine Ecosystem Health Conservation [MARES_13_08]; FCT (Foundation for Science and Technology, Portugal) [SFRH/BPD/63703/2009, SFRH/BPD/107878/2015, SFRH/BPD/116774/2016]; EU SEAS-ERA project INVASIVES [SEAS-ERA/0001/2012]; [CCMAR/Multi/04326/2013

An open-source solution for advanced imaging flow cytometry data analysis using machine learning

Imaging flow cytometry (IFC) enables the high throughput collection of morphological and spatial information from hundreds of thousands of single cells. This high content, information rich image data can in theory resolve important biological differences among complex, often heterogeneous biological samples. However, data analysis is often performed in a highly manual and subjective manner using very limited image analysis techniques in combination with conventional flow cytometry gating strategies. This approach is not scalable to the hundreds of available image-based features per cell and thus makes use of only a fraction of the spatial and morphometric information. As a result, the quality, reproducibility and rigour of results are limited by the skill, experience and ingenuity of the data analyst. Here, we describe a pipeline using open-source software that leverages the rich information in digital imagery using machine learning algorithms. Compensated and corrected raw image files (.rif) data files from an imaging flow cytometer (the proprietary .cif file format) are imported into the open-source software CellProfiler, where an image processing pipeline identifies cells and subcellular compartments allowing hundreds of morphological features to be measured. This high-dimensional data can then be analysed using cutting-edge machine learning and clustering approaches using “user-friendly” platforms such as CellProfiler Analyst. Researchers can train an automated cell classifier to recognize different cell types, cell cycle phases, drug treatment/control conditions, etc., using supervised machine learning. This workflow should enable the scientific community to leverage the full analytical power of IFC-derived data set. It will help to reveal otherwise unappreciated populations of cells based on features that may be hidden to the human eye that include subtle measured differences in label free detection channels such as bright-field and dark-field imagery