Global transcription profiling reveals differential responses to chronic nitrogen stress and putative nitrogen regulatory components in Arabidopsis

<p>Abstract</p> <p>Background</p> <p>A large quantity of nitrogen (N) fertilizer is used for crop production to achieve high yields at a significant economic and environmental cost. Efforts have been directed to understanding the molecular basis of plant responses to N and identifying N-responsive genes in order to manipulate their expression, thus enabling plants to use N more efficiently. No studies have yet delineated these responses at the transcriptional level when plants are grown under chronic N stress and the understanding of regulatory elements involved in N response is very limited.</p> <p>Results</p> <p>To further our understanding of the response of plants to varying N levels, a growth system was developed where N was the growth-limiting factor. An Arabidopsis whole genome microarray was used to evaluate global gene expression under different N conditions. Differentially expressed genes under mild or severe chronic N stress were identified. Mild N stress triggered only a small set of genes significantly different at the transcriptional level, which are largely involved in various stress responses. Plant responses were much more pronounced under severe N stress, involving a large number of genes in many different biological processes. Differentially expressed genes were also identified in response to short- and long-term N availability increases. Putative N regulatory elements were determined along with several previously known motifs involved in the responses to N and carbon availability as well as plant stress.</p> <p>Conclusion</p> <p>Differentially expressed genes identified provide additional insights into the coordination of the complex N responses of plants and the components of the N response mechanism. Putative N regulatory elements were identified to reveal possible new components of the regulatory network for plant N responses. A better understanding of the complex regulatory network for plant N responses will help lead to strategies to improve N use efficiency.</p

Linking urbanization and the environment: conceptual and empirical advances

Urbanization is one of the biggest social transformations of modern time, driving and driven by multiple social, economic, and environmental processes. The impacts of urbanization on the environment are profound, multifaceted and are manifested at the local, regional, and global scale. This article reviews recent advances in conceptual and empirical knowledge linking urbanization and the environment, focusing on six core aspects: air pollution, ecosystems, land use, biogeochemical cycles and water pollution, solid waste management, and the climate. We identify several emerging trends and remaining questions in urban environmental research, including (a) increasing evidence on the amplified or accelerated environmental impacts of urbanization; (b) varying distribution patterns of impacts along geographical and other socio-economic gradients; (c) shifting focus from understanding and quantifying the impacts of urbanization toward understanding the processes and underlying mechanisms; (d) increasing focus on understanding complex interactions and interlinkages among different environmental, social, economic, and cultural processes; and (e) conceptual advances that call for articulating and using a systems approach in cities. In terms of governing the urban environment, there is an increasing focus on public participation and coproduction of knowledge with stakeholders. Cities are actively experimenting toward sustainability under a plethora of guiding concepts that manifests their aspirational goals, with varying levels of implementation and effectiveness

Backreaction in Axion Monodromy, 4-forms and the Swampland

Axion monodromy models can always be described in terms of an axion coupled to 3-form gauge fields with non-canonical kinetic terms. The presence of the saxions parametrising the kinetic metrics of the 3-form fields leads to backreaction effects in the inflationary dynamics. We review the case in which saxions backreact on the K\"ahler metric of the inflaton leading to a logarithmic scaling of the proper field distance at large field. This behaviour is universal in Type II string flux compactifications and consistent with a refinement of the Swampland Conjecture. The critical point at which this behaviour appears depends on the mass hierarchy between the inflaton and the saxions. However, in tractable compactifications, such a hierarchy cannot be realised without leaving the regime of validity of the effective theory, disfavouring transplanckian excursions in string theory.Comment: Proceedings prepared for the "Workshop on Geometry and Physics", November 2016, Ringberg Castl

Driving Big Data : A First Look at Driving Behavior via a Large-Scale Private Car Dataset

The increasing number of privately owned vehicles in large metropolitan cities has contributed to traffic congestion, increased energy waste, raised CO2 emissions, and impacted our living conditions negatively. Analysis of data representing citizens' driving behavior can provide insights to reverse these conditions. This article presents a large-scale driving status and trajectory dataset consisting of 426,992,602 records collected from 68,069 vehicles over a month. From the dataset, we analyze the driving behavior and produce random distributions of trip duration and millage to characterize car trips. We have found that a private car has more than 17% probability to make four trips per day, and a trip has more than 25% probability to last 20-30 minutes and 33% probability to travel 10 Kilometers during the trip. The collective distributions of trip mileage and duration follow Weibull distribution, whereas the hourly trips follow the well known diurnal pattern and so the hourly fuel efficiency. Based on these findings, we have developed an application which recommends the drivers to find the nearby gas stations and possible favorite places from past trips. We further highlight that our dataset can be applied for developing dynamic Green maps for fuel-efficient routing, modeling efficient Vehicle-to-Vehicle (V2V) communications, verifying existing V2V protocols, and understanding user behavior in driving their private cars.Peer reviewe

Detect Depression from Social Networks with Sentiment Knowledge Sharing

Social network plays an important role in propagating people's viewpoints, emotions, thoughts, and fears. Notably, following lockdown periods during the COVID-19 pandemic, the issue of depression has garnered increasing attention, with a significant portion of individuals resorting to social networks as an outlet for expressing emotions. Using deep learning techniques to discern potential signs of depression from social network messages facilitates the early identification of mental health conditions. Current efforts in detecting depression through social networks typically rely solely on analyzing the textual content, overlooking other potential information. In this work, we conduct a thorough investigation that unveils a strong correlation between depression and negative emotional states. The integration of such associations as external knowledge can provide valuable insights for detecting depression. Accordingly, we propose a multi-task training framework, DeSK, which utilizes shared sentiment knowledge to enhance the efficacy of depression detection. Experiments conducted on both Chinese and English datasets demonstrate the cross-lingual effectiveness of DeSK

Activation of Serotonin 2C Receptors in Dopamine Neurons Inhibits Binge-like Eating in Mice

Acknowledgments and Disclosures This work was supported by the National Institutes of Health (Grant Nos. R01DK093587 and R01DK101379 [to YX], R01DK092605 to [QT], R01DK078056 [to MM]), the Klarman Family Foundation (to YX), the Naman Family Fund for Basic Research (to YX), Curtis Hankamer Basic Research Fund (to YX), American Diabetes Association (Grant Nos. 7-13-JF-61 [to QW] and 1-15-BS-184 [to QT]), American Heart Association postdoctoral fellowship (to PX), Wellcome Trust (Grant No. WT098012 [to LKH]), and Biotechnology and Biological Sciences Research Council (Grant No. BB/K001418/1 [to LKH]). The anxiety tests (e.g., open-field test, light-dark test, elevated plus maze test) were performed in the Mouse Neurobehavior Core, Baylor College of Medicine, which was supported by National Institutes of Health Grant No. P30HD024064. PX and YH were involved in experimental design and most of the procedures, data acquisition and analyses, and writing the manuscript. XC assisted in the electrophysiological recordings; LV-T assisted in the histology study; XY, KS, CW, YY, AH, LZ, and GS assisted in surgical procedures and production of study mice. MGM, QW, QT, and LKH were involved in study design and writing the manuscript. YX is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. The authors report no biomedical financial interests or potential conflicts of interest.Peer reviewedPublisher PD