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Although populist figures are often thought to thrive during crises that allow them to ‘perform’ decisive leadership, the US experience under Donald Trump during the COVID-19 crisis demonstrates that the opposite may sometimes occur. Despite its scientific and medical prowess, the US suffered more coronavirus cases and deaths than any other country in the world during the first year of the pandemic, and this abysmal performance was largely attributable to a failure of government. Fixated on the pandemic's economic effects and its potential political fallout, the Trump administration's framing of the crisis tried to minimize the public health emergency, externalize blame through a focus on the Chinese scapegoat and accuse the media and Democrats of hyping the pandemic to undermine Trump's presidency. In responding to the pandemic, Trump and his allies cast doubt on scientific and medical expertise that called for more aggressive testing, mask wearing and social-distancing measures. Trump delegated responsibility for crisis management to subnational governments and the private sector, and he politicized their efforts to regulate social behaviour in the public interest, intensifying partisan polarization.

The error-related negativity (ERN) is a negative deflection in the electroencephalography (EEG) waveform at frontal-central scalp sites that occurs after error commission. The relationship between the ERN and broader patterns of brain activity measured across the entire scalp that support error processing during early childhood is unclear. We examined the relationship between the ERN and EEG microstates – whole-brain patterns of dynamically evolving scalp potential topographies that reflect periods of synchronized neural activity – during both a go/no-go task and resting-state in 90, 4-8-year-old children. The mean amplitude of the ERN was quantified during the − 64 to 108 millisecond (ms) period of time relative to error commission, which was determined by data-driven microstate segmentation of error-related activity. We found that greater magnitude of the ERN associated with greater global explained variance (GEV; i.e., the percentage of total variance in the data explained by a given microstate) of an error-related microstate observed during the same − 64 to 108 ms period (i.e., error-related microstate 3), and to greater parent-report-measured anxiety risk. During resting-state, six data-driven microstates were identified. Both greater magnitude of the ERN and greater GEV values of error-related microstate 3 associated with greater GEV values of resting-state microstate 4, which showed a frontal-central scalp topography. Source localization results revealed overlap between the underlying neural generators of error-related microstate 3 and resting-state microstate 4 and canonical brain networks (e.g., ventral attention) known to support the higher-order cognitive processes involved in error processing. Taken together, our results clarify how individual differences in error-related and intrinsic brain activity are related and enhance our understanding of developing brain network function and organization supporting error processing during early childhood.

High-throughput screening (HTS) is a widespread method in early drug discovery for identifying promising chemical matter that modulates a target or phenotype of interest. Because HTS campaigns involve screening millions of compounds, it is often desirable to initiate screening with a subset of the full collection. Subsequently, virtual screening methods prioritize likely active compounds in the remaining collection in an iterative process. With this approach, orthogonal virtual screening methods are often applied, necessitating the prioritization of hits from different approaches. Here, we introduce a novel method of fusing these prioritizations and benchmark it prospectively on 17 screening campaigns using virtual screening methods in three descriptor spaces. We found that the fusion approach retrieves 15% to 65% more active chemical series than any single machine-learning method and that appropriately weighting contributions of similarity and machine-learning scoring techniques can increase enrichment by 1% to 19%. We also use fusion scoring to evaluate the tradeoff between screening more chemical matter initially in lieu of replicate samples to prevent false-positives and find that the former option leads to the retrieval of more active chemical series. These results represent guidelines that can increase the rate of identification of promising active compounds in future iterative screens.

Digital elevation models (DEMs) generated from unmanned aerial vehicle (UAV) photogrammetry offer opportunities for on-demand DEM production in environmental modelling and flood risk prediction applications. The DEM and orthoimage accuracies that can be achieved using lightweight UAV on-board sensors only, are compared with cases where progressively higher numbers of Global Navigation Satellite System (GNSS) referenced ground targets are utilized. Unacceptably large 95% planimetric orthoimage errors of 5.22 m (root mean square error (RMSE) 3.27 m), and DEM 95% elevation errors of 5.03 m (RMSE 2.2 m) are observed when using the on-board positioning and orientation sensors only. Introducing GNSS ground control points (GCPs) in increasing numbers progressively and substantially improves data accuracy. Remarkably small xy orthoimage errors of 0.076 m (RMS) and DEM elevation errors of 0.08 m (RMS) are achieved using 1 GCP for every 2 ha of ground area and utilizing more GCPs produced more or less identical results. These accuracies compare very favourably with the best commercial airborne survey DEMs, suggesting strong potential for the application of lightweight UAV photogrammetric DEMs in local environmental modelling and flood risk prediction applications. The potential of these DEMS for flood prediction is subsequently assessed and demonstrated by comparison with published flood risk maps and flood depth data, and by cross-comparing the outputs of the UAV DEM flood model predictions.

The Farm Security Administration (FSA) was the New Deal’s most sustained and successful effort to address chronic rural poverty in the United States, particularly through its program of rural rehabilitation. But at the time and since the effectiveness of rural rehabilitation has been overshadowed by the political fight over the FSA’s survival and the more ambitious (and controversial) resettlement program. Examined in terms of the practical goals of reducing rural poverty, rather than as a gauge for political and ideological trends, the FSA had decidedly modest results. Some of this can be attributed to the limitations and restrictions imposed by hostile political forces and a stingy Congress, but as this essay shows, much of the fault lay within the FSA, as it struggled to define goals and carry out anti-poverty programs for some of the poorest people in America.

"How do we reduce the cost of producing a barrel of oil and improve well productivity?" This is probably the most frequently asked question by operators. According to a US Energy Information Administration study, average drilling and completion costs in 2015 in five major unconventional plays in the US dropped 25 to 30% since 2012. Despite an overall price drop in drilling and completion, which accounts for up to 65% of capital expenditure, unconventional oil and natural gas extraction remains non profitable for many operators at current oil prices. Increasing well productivity often requires well intervention with expensive logging tools and high-tech equipment. The use of these technologies is cost prohibitive for some unconventional plays during a market downturn, and incurs the additional risk of the loss of tools downhole. Drill cuttings analysis during mud logging, a well-known formation evaluation tool in conventional drilling, is often overlooked or ignored in unconventional well evaluation because of the limited data from conventional techniques. However, with the development of advanced drill-cuttings analysis such as scanning electron microscope-based automated mineralogical techniques can now provide data-rich information such as brittleness, rock elastic properties and rock typing that can improve completion design and post-stimulation production. One of the main advantages of drill cuttings is that they are readily available and advanced analysis is relatively inexpensive and low-risk. This paper reviews the use of advanced drill cutting analysis (ACA) for hydraulic fracture treatment design optimization in well 1-H in the Wolfcamp formation. The results from the review show the correlation between some formation properties obtained from the ACA and fracture treatment parameters such as treating pressure and pump rate. The post-fracture oil production of well 1-H is compared to the offset geometrically completed well. Limitations of ACA and other factors that can affect the post-stimulation production are also discussed.

This study examines the impact of migration on women's positions in Chinese rural households. A number of studies have found that rural Chinese migrant women experience more autonomy and freedom in urban areas than they would at home. But do these experiences carry over into marriage when they return to rural areas? Using a survey of more than 3,000 married, rural women in Anhui and Sichuan provinces and controlling for potential endogeneity of migration and return, this paper explores four main categories of women's status: women's views on male/female relationships, women's roles in household decision making, women's relationships with their husbands, and women's views concerning parents and children. It concludes that for women from Anhui and Sichuan, migration has some statistically significant lasting effects on a woman's position in the household, though the effects are not always positive, nor are they universal.