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The developmental and behavioral effects of prenatal exposure to cocaine and/or ethanol were examined in rats. Pregnant rats received ethanol (E; 2 g/kg, b.i.d.) orally, cocaine (C; 6 mg/kg/day, IV), or both (C/E) on gestational days 8-20. Controls consisted of pair-fed (PF) and untreated (UNT) groups. Offspring were weighed and examined for developmental markers beginning postnatal day one (PD1). On PD21 pups were individually observed in an open-field following either an injection of cocaine (10 mg/kg, IP), an injection of saline, or no treatment. Drug-treated and PF dams ate less food and gained less weight than the UNT dams. C and E litters had slightly increased mortality rates. Pups from both the C and E groups appeared less sensitive to the locomotor stimulant effect of cocaine. Pups from the E group engaged in significantly less spontaneous stereotypic locomotion than UNT and PF pups, while male pups from the C group exhibited a decrease in spontaneous exploratory behavior. Thus, prenatal exposure to C or E altered spontaneous and/or cocaine-induced behavior in weanling-aged rats, while the C/E combination did not augment either effect.

Most of the world’s mountain glaciers have been retreating for more than a century in response to climate change. Glacier retreat is evident on all continents, and the rate of retreat has accelerated during recent decades. Accurate, spatially explicit information on the position of glacier margins over time is useful for analyzing patterns of glacier retreat and measuring reductions in glacier surface area. This information is also essential for evaluating how mountain ecosystems are evolving due to climate warming and the attendant glacier retreat. Here, we present a non-comprehensive spatially explicit dataset showing multiple positions of glacier fronts since the Little Ice Age (LIA) maxima, including many data from the pre-satellite era. The dataset is based on multiple historical archival records including topographical maps; repeated photographs, paintings, and aerial or satellite images with a supplement of geochronology; and own field data. We provide ESRI shapefiles showing 728 past positions of 94 glacier fronts from all continents, except Antarctica, covering the period between the Little Ice Age maxima and the present. On average, the time series span the past 190 years. From 2 to 46 past positions per glacier are depicted (on average: 7.8).

A natural holm oak forest was selectively thinned to test thinning as a tool to reduce tree mortality, increase productivity, and reverse the recent regression of the dominant species (Quercus ilex) induced by climate change. Thinning increased aboveground productivity and reduced stem mortality in this Mediterranean forest during four years just after thinning, contributing to the maintenance of forest functioning under changing climatic conditions. Q. ilex was the only species positively affected by the thinning: stem growth increased for all stem sizes, and mortality was significantly lower in thinned plots. On the contrary, mortality rates of Phillyrea latifolia and Arbutus unedo were not significantly lower. Stem growth increased for P. latifolia only in the smallest stem-size class. Our results highlight the suitability of selective thinning for improving the forest productivity and ensuring the conservation of Mediterranean coppices. Other benefits of selective thinning, such as a decrease in the risk of fire dispersion and an increase in the water supply for human populations, are also discussed.

Abstract The effect of modulated air jets, introduced through the combustor shell, on the temperature distribution and nitric oxide emissions is investigated. Temperature and emissions measurements have been made at a number of forcing frequencies in the range of 100–850 Hz, blowing ratios in the range of 4–10 and equivalence ratios between 0.6 and 1.0. Open-loop flame response to forcing has also been acquired by recording pressure spectra. Results show that substantial reductions in nitric oxide emissions index (15–30%) can be obtained over a wide range of flow conditions with side-air jet forcing. In addition, forcing also alters the time averaged temperature field, with higher mean temperatures close to the dump plane, due to enhanced fuel-air mixing. The higher temperatures and volumetric heat release obtained with forcing can enable more compact combustor designs. The lower emissions are potentially linked to greater unsteadiness with forcing.

Abstract In developed countries, buildings are involved in almost 50% of total energy use and 30% of global green-house gas emissions. Buildings' operational energy is highly dependent on various building physical, operational, and functional characteristics, as well as meteorological and temporal properties. Besides physics-based building energy modeling, machine learning techniques can provide faster and higher accuracy estimates, given buildings' historic energy consumption data. Looking beyond individual building levels, forecasting buildings’ energy performance helps city and community managers have a better understanding of their future energy needs, and plan for satisfying them more efficiently. Focusing on an urban-scale, this study systematically reviews 70 journal articles, published in the field of building energy performance forecasting between 2015 and 2018. The recent literature have been categorized according to five criteria: 1. Learning Method, 2. Building Type, 3. Energy Type, 4. Input Data, and 5. Time-scale. The scarcity of building energy performance forecasting studies in urban-scale versus individual level is considerable. There is no study incorporating building functionality in terms of space functionality share percentages, nor assessing the effects of climate change on urban buildings energy performance using machine learning approaches and future weather scenarios. There is no optimal criteria combination for achieving the most accurate machine learning-based forecast, as there is no universal measure able to provide such global comparison. Accuracy levels are highly correlated with the characteristics of forecasting problems. The goal is to provide a comprehensive status of machine learning applications in urban building energy performance forecasting, during 2015–2018.

Abstract The growth and development of maize (Zea mays L.) largely depends on its nutrient uptake through the root. Hence, studying its growth, response, and associated metabolic reprogramming to stress conditions is becoming an important research direction. A genome-scale metabolic model (GSM) for the maize root was developed to study its metabolic reprogramming under nitrogen stress conditions. The model was reconstructed based on the available information from KEGG, UniProt, and MaizeCyc. Transcriptomics data derived from the roots of hydroponically grown maize plants were used to incorporate regulatory constraints in the model and simulate nitrogen-non-limiting (N+) and nitrogen-deficient (N−) condition. Model-predicted flux-sum variability analysis achieved 70% accuracy compared with the experimental change of metabolite levels. In addition to predicting important metabolic reprogramming in central carbon, fatty acid, amino acid, and other secondary metabolism, maize root GSM predicted several metabolites (l-methionine, l-asparagine, l-lysine, cholesterol, and l-pipecolate) playing a regulatory role in the root biomass growth. Furthermore, this study revealed eight phosphatidylcholine and phosphatidylglycerol metabolites which, even though not coupled with biomass production, played a key role in the increased biomass production under N-deficient conditions. Overall, the omics-integrated GSM provides a promising tool to facilitate stress condition analysis for maize root and engineer better stress-tolerant maize genotypes.

• Premise of the study: Climate change is associated with phenological shifts in an increasing number of organisms worldwide. However, accurate estimates of these shifts are dependent on long‐term data sets that include phenological observations from before annual average temperatures began to rise.• Methods: We compared the first flowering times of native prairie plants between 2007 and 2010 with historical data recorded by O. A. Stevens from 1910 to 1961. By merging climate variable data from the same time period, it also was possible to correlate first flowering dates with associated climate variables.• Key results: Over the past 100 years, spring temperatures in the Red River Valley near Fargo, North Dakota, USA, have increased, and growing seasons have lengthened significantly. Seventy‐five percent of the 178 species observed by Stevens had flowering times that were sensitive to at least one variable related to temperature or precipitation. Over the past 4 yr, 5% to 17% of the species observed have significantly shifted their first flowering time either earlier or later relative to the previous century.• Conclusions: The results of this study indicate that as spring temperatures in the northern Great Plains have increased and the growing season has lengthened, some spring flowering species have advanced their first flowering time, some fall species have delayed their first flowering, and some species have not changed. Given the importance of flowering timing for reproductive success, the changing climate in the Great Plains is expected to have long‐term ecological and evolutionary consequences for native plant species.