Seasonally dependent future changes in the US Midwest hydroclimate and extremes

This study investigates the responses of the hydroclimate and extremes in the US Midwest to global warming, based on ensemble projections of the Coupled Model Intercomparison Project Phase 6 and the Multi-model Initial-condition Large-ensemble Simulations. The precipitation response features a seasonally dependent change with increased precipitation in April-May but reduced precipitation in July-August. The late-spring wetting is attributed to the enhanced low-level moisture-transporting southerlies, which are induced by regional sea level pressure anomalies linked to the poleward shift of the North America westerly jet (NAWJ). The late-summer drying is attributed to the weakened storm track, which is also linked to the poleward NAWJ shift. The seasonally dependent future changes of the Midwest precipitation are analogous to its climatological seasonal progression, which increases over late spring as the NAWJ approaches the Midwest and decreases over late summer as the NAWJ migrates away. In response to the mean precipitation changes, extremely wet late springs (April-May precipitation above the 99th percentile of the historical period) and extremely dry late summers (below the 1st percentile) will occur much more frequently, implying increased late-spring floods and late-summer droughts. Future warming in the Midwest is amplified in late summer due to the reduced precipitation. With amplified background warming and increased occurrence, future late-summer droughts will be more devastating. Our results highlight that, under a time-invariant poleward jet shift, opposite precipitation changes arise before and after the peak rainy month, leading to substantial increases in the subseasonal extremes. The severity of such climate impacts is obscured in projections of the rainy season mean.

Prevalence [P] of molecular biomarkers [MB] expression [exp] in patients [pts] with metastatic adenocarcinoma lung [MAC]: Illinois CancerCare [ILCC] analysis.

e21004 Background: Treatment of MAC is guided by MB. These include Programmed Death Ligand 1 (PDL1), Epidermal Growth Factor Receptor (EGFR), Anaplastic Lymphoma Kinase (ALK) & ROS1. There is little information available on P of MB in US Midwestern population, a largely homogenous Caucasian group. We examined the expression (exp) of the MB in pts with MAC who presented at ILCC in 2018, one of the largest practices in US Midwest. Methods: We conducted a retrospective analysis of all MAC pts who presented at ILCC in 2018. We analyzed the exp of PDL1, EGFR, ALK, and ROS1 in these pts. PDL1 status was defined as high if PDL1 was ≥50%, low if 1- < 50% and -ve if 0- < 1%. High and low groups [all pts with > 1%] were designated +ve. Results: In 2018 there were 125 pts seen at ILCC with MAC. As of 6/2019, 57/125 [46%] pts were alive. Pts: median age 69 yrs [45-89], median PS 1 [0-3], males 62%, current/former smokers 90%, Caucasians 95%. PDL1 results were available in 104/125 pts [83%], EGFR in 95 (76%), ALK in 97 (78%), and ROS1 in 95 (76%). Previously reported papers have described PDL1+ to be as high as 58% in MAC. However, in our study only 53/104 (51%) pts were PDL1+. Though, 26/104 (25%) did have high PDL1 exp [consistent with other reports]. 27/104 (26%) had low PDL1 exp and 51/104 (49%) pts were PDL1-ve. EGFR mutation [M] was found in 11/95 (12%), ALK M was found in 5/97 (5%) pts & ROS1 M was found in 2/95 (2%), the results of these three mutations are consistent with other groups. Conclusions: Our results indicate that exp of PDL1+ pts at ILCC is lower than reported by others, yet the proportion of those with ≥50% PDL1 in our group is similar to others. Exp of EGFR, ALK, and ROS1 in our pts is similar to other studies.[Table: see text]

Subfield crop yields and temporal stability in thousands of US Midwest fields

Understanding subfield crop yields and temporal stability is critical to better manage crops. Several algorithms have proposed to study within-field temporal variability but they were mostly limited to few fields. In this study, a large dataset composed of 5520 yield maps from 768 fields provided by farmers was used to investigate the influence of subfield yield distribution skewness on temporal variability. The data are used to test two intuitive algorithms for mapping stability: one based on standard deviation and the second based on pixel ranking and percentiles. The analysis of yield monitor data indicates that yield distribution is asymmetric, and it tends to be negatively skewed (p < 0.05) for all of the four crops analyzed, meaning that low yielding areas are lower in frequency but cover a larger range of low values. The mean yield difference between the pixels classified as high-and-stable and the pixels classified as low-and-stable was 1.04 Mg ha−1 for maize, 0.39 Mg ha−1 for cotton, 0.34 Mg ha−1 for soybean, and 0.59 Mg ha−1 for wheat. The yield of the unstable zones was similar to the pixels classified as low-and-stable by the standard deviation algorithm, whereas the two-way outlier algorithm did not exhibit this bias. Furthermore, the increase in the number years of yield maps available induced a modest but significant increase in the certainty of stability classifications, and the proportion of unstable pixels increased with the precipitation heterogeneity between the years comprising the yield maps.