ALBERT

Description: Potamogeton crispus (curlyleaf pondweed) and Myriophyllum spicatum (Eurasian watermilfoil) are widely thought to competitively displace native macrophytes in North America. However, their perceived competitive superiority has not been comprehensively evaluated. Coexistence theory suggests that invader displacement of native species through competitive exclusion is most likely where high niche overlap results in competition for limiting resources. Thus, evaluation of niche similarity can serve as a starting point for predicting the likelihood of invaders having direct competitive impacts on resident species. Across two environmental gradients structuring macrophyte communities—water depth and light availability—both P. crispus and M. spicatum are thought to occupy broad niches. For a third dimension, phenology, the annual growth cycle of M. spicatum is typical of other species, whereas the winter-ephemeral phenology of P. crispus may impart greater niche differentiation and thus lower risk of native species being competitively excluded. Using an unprecedented dataset comprising 3404 plant surveys from Minnesota collected using a common protocol, we modeled niches of 34 species using a probabilistic niche framework. Across each niche dimension, P. crispus had lower overlap with native species than did M. spicatum; this was driven in particular by its distinct phenology. These results suggest that patterns of dominance seen in P. crispus and M. spicatum have likely arisen through different mechanisms, and that direct competition with native species is less likely for P. crispus than M. spicatum. This research highlights the utility of fine-scale, abundance-based niche models for predicting invader impacts.

Description: Para-, or 4-nitrophenol, and related nitroaromatics are broadly used compounds in industrial processes and as a result are among the most common anthropogenic pollutants in aqueous industrial effluent; this requires development of practical remediation strategies. Their catalytic reduction to the less toxic and synthetically desirable aminophenols is one strategy. However, to date, the majority of work focuses on catalysts based on precisely tailored, and often noble metal-based nanoparticles. The cost of such systems hampers practical, larger scale application. We report a facile route to bulk cobalt oxide-based materials, via a combined mechanochemical and calcination approach. Vibratory ball milling of CoCl2(H2O)6 with KOH, and subsequent calcination afforded three cobalt oxide-based materials with different combinations of CoO(OH), Co(OH)2, and Co3O4 with different crystallite domains/sizes and surface areas; Co@100, Co@350 and Co@600 (Co@###; # = calcination temp). All three prove active for the catalytic reduction of 4-nitrophenol and related aminonitrophenols. In the case of 4-nitrophenol, Co@350 proved to be the most active catalyst, therein its retention of activity over prolonged exposure to air, moisture, and reducing environments, and applicability in flow processes is demonstrated.

Description: With expanding kidney transplantation programs, remaining hemodialysis patients are more likely to have a high comorbidity burden and may therefore be more prone to lose muscle mass. Our aim was to analyze risk factors for muscle loss in hemodialysis patients with high comorbidity. Fifty-four chronic hemodialysis patients (Charlson Comorbidity Index 9.0 ± 3.4) were followed for 20 weeks using 4-weekly measurements of lean tissue mass, intracellular water, and body cell mass (proxies for muscle mass), handgrip strength (HGS), and biochemical parameters. Mixed models were used to analyze covariate effects on LTM. LTM (−6.4 kg, interquartile range [IQR] −8.1 to −4.8), HGS (−1.9 kg, IQR −3.1 to −0.7), intracellular water (−2.11 L, IQR −2.9 to −1.4) and body cell mass (−4.30 kg, IQR −5.9 to −2.9) decreased in all patients. Conversely, adipose tissue mass increased (4.5 kg, IQR 2.7 to 6.2), resulting in no significant change in body weight (−0.5 kg, IQR −1.0 to 0.1). Independent risk factors for LTM loss over time were male sex (−0.26 kg/week, 95% CI −0.33 to −0.19), C-reactive protein above median (−0.1 kg/week, 95% CI −0.2 to −0.001), and baseline lean tissue index ≥10th percentile (−1.6 kg/week, 95% CI −2.1 to −1.0). Age, dialysis vintage, serum albumin, comorbidity index, and diabetes did not significantly affect LTM loss over time. In this cohort with high comorbidity, we found universal and prominent muscle loss, which was further accelerated by male sex and inflammation. Stable body weight may mask muscle loss because of concurrent fat gain. Our data emphasize the need to assess body composition in all hemodialysis patients and call for studies to analyze whether intervention with nutrition or exercise may curtail muscle loss in the most vulnerable hemodialysis patients.

Description: The coronavirus disease (COVID-19) pandemic has increased unemployment and food insecurity in the United States (US). Prior to the pandemic, college students exhibited higher rates of food insecurity than nonstudent households. The objectives of this study were to assess the prevalence and determinants of food insecurity among college students during the COVID-19 pandemic. We administered an online survey to 651 students on three diverse campuses at a state-funded university in Texas, US, in May 2020. Food security was assessed using a multistep approach that included the 2-item Food Sufficiency Screener and 6-Item USDA Food Security Survey Module (FSSM). Overall, 34.5% of respondents were classified as food insecure within the last 30 days. The strongest predictors of food insecurity were change in current living arrangement (OR = 2.70, 95% CI: 2.47, 2.95), being furloughed (OR = 3.22, 95% CI: 2.86, 3.64), laid off (OR = 4.07, 95% CI: 3.55, 4.66), or losing part-time work (OR = 5.73, 95% CI: 5.09, 6.46) due to the COVID-19 pandemic. These findings highlight the high prevalence of food insecurity among college students during the COVID-19 pandemic, with students who experienced housing insecurity and/or loss of income due to the pandemic being impacted the most.

Description: The importance of monitoring, preserving, and, where needed, improving the quality of water resources in the open ocean, coastal regions, estuaries, and inland water bodies cannot be overstated [...]

Description: This article begins with a brief outline of recent advances in the application of computer modeling to sustainability research, identifying important gaps in coverage and associated limits in methodological capability, particularly in regard to taking account of the tangled human factors that are often impediments to a sustainable future. It then describes some of the ways in which a new transdisciplinary approach within “human simulation” can contribute to the further development of sustainability modeling, more effectively addressing such human factors through its emphasis on stakeholder, policy professional, and subject matter expert participation, and its focus on constructing more realistic cognitive architectures and artificial societies. Finally, the article offers philosophical reflections on some of the ontological, epistemological, and ethical issues raised at the intersection of sustainability research and social simulation, considered in light of the importance of human factors, including values and worldviews, in the modeling process. Based on this philosophical analysis, we encourage more explicit conversations about the value of naturalism and secularism in finding and facilitating effective and ethical strategies for sustainable development.

Description: The COVID-19 pandemic restrictions sent college students online and off campus, potentially reducing access to healthy food. The objective of this cross-sectional, internet-based study was to use qualitative and quantitative survey methods to evaluate whether COVID-19 pandemic restrictions in Texas, USA affected college students’ ability to buy food, how/what they shopped for, how they prepared food, what they ate, how they felt about eating, and overall dietary quality (assessed using Healthy Eating Index [HEI] scores). Survey responses from 502 students (87.5% female; 59.6% nonwhite, mean age 27.5 ± 0.4 years, 〉50% graduate students) were analyzed. The qualitative analysis of open-ended questions revealed 110 codes, 17 subthemes, and six themes. Almost all students experienced changes in at least one area, the most common being changes in shopping habits. Participants with low or very low food security had lower HEI scores compared to food secure students (p = 0.047). Black students were more likely to report changes in their ability to buy food (p = 0.035). The COVID-19 restrictions varied in their impact on students’ ability to access sufficient healthy food, with some students severely affected. Thus, universities should establish procedures for responding to emergencies, including identifying at-risk students and mobilizing emergency funds and/or food assistance.

Description: Palmer amaranth (Amaranthus palmeri S. Wats.) is a highly competitive weed that can be difficult to manage in many cropping systems. Research to date has not quantified the growth and development of A. palmeri in a manner that allows direct comparisons across cropping systems. Research was conducted to compare the growth, development, and seed production of A. palmeri when competing with corn (Zea mays L.), cotton (Gossypium hirsutum L.), peanut (Arachis hypogaea L.), and soybean [Glycine max (L.) Merr.] when emerging with crops or emerging three weeks after crops emerge. Regardless of when A. palmeri emerged, seed production was greatest and similar in cotton and peanut and exceeded that of corn and soybean; seed production in soybean exceeded that of corn. However, seed production was approximately 10-fold greater when A. palmeri emerged with crops compared with emergence three weeks later. These results illustrate the importance of controlling weeds during the first three weeks of the season relative to contributions of A. palmeri to the weed seed bank and is the first report comparing seed production in presence of these crops in a manner allowing a statistical comparison of seed production and highlighting the importance of crop sequence for seed bank management.

Description: Field studies were conducted in 2016 and 2017 to determine if multispectral imagery collected from an unmanned aerial vehicle (UAV) equipped with a five-band sensor could successfully identify Palmer amaranth (Amaranthus palmeri) infestations of various densities growing among soybeans (Glycine max [L.] Merr.). The multispectral sensor captures imagery from five wavebands: 475 (blue), 560 (green), 668 (red), 840 (near infrared [NIR]), and 717 nm (red-edge). Image analysis was performed to examine the spectral properties of discrete Palmer amaranth and soybean plants at various weed densities using these wavebands. Additionally, imagery was subjected to supervised classification to evaluate the usefulness of classification as a tool to differentiate the two species in a field setting. Date was a significant factor influencing the spectral reflectance values of the Palmer amaranth densities. The effects of altitude on reflectance were less clear and were dependent on band and density being evaluated. The near infrared (NIR) waveband offered the best resolution in separating Palmer amaranth densities. Spectral separability in the other wavebands was less defined, although low weed densities were consistently able to be discriminated from high densities. Palmer amaranth and soybean were found to be spectrally distinct regardless of imaging date, weed density, or waveband. Soybean exhibited overall lower reflectance intensity than Palmer amaranth across all wavebands. The reflectance of both species within blue, green, red, and red-edge wavebands declined as the season progressed, while reflectance in NIR increased. Near infrared and red-edge wavebands were shown to be the most useful for species discrimination and maintained their utility at most weed densities. Palmer amaranth weed densities were found to be spectrally distinct from one another in all wavebands, with greatest distinction when using the red, NIR and red-edge wavebands. Supervised classification in a two-class system was consistently able to discriminate between Palmer amaranth and soybean with at least 80% overall accuracy. The incorporation of a weed density component into these classifications introduced an error of 65% or greater into these classifications. Reducing the number of classes in a supervised classification system could improve the accuracy of discriminating between Palmer amaranth and soybean.