Semi-supervised Text Regression with Conditional Generative Adversarial Networks

Enormous online textual information provides intriguing opportunities for understandings of social and economic semantics. In this paper, we propose a novel text regression model based on a conditional generative adversarial network (GAN), with an attempt to associate textual data and social outcomes in a semi-supervised manner. Besides promising potential of predicting capabilities, our superiorities are twofold: (i) the model works with unbalanced datasets of limited labelled data, which align with real-world scenarios; and (ii) predictions are obtained by an end-to-end framework, without explicitly selecting high-level representations. Finally we point out related datasets for experiments and future research directions

Wheat stripe rust grading by deep learning with attention mechanism and images from mobile devices

Wheat stripe rust is one of the main wheat diseases worldwide, which has significantly adverse effects on wheat yield and quality, posing serious threats on food security. Disease severity grading plays a paramount role in stripe rust disease management including breeding disease-resistant wheat varieties. Manual inspection is time-consuming, labor-intensive and prone to human errors, therefore, there is a clearly urgent need to develop more effective and efficient disease grading strategy by using automated approaches. However, the differences between wheat leaves of different levels of stripe rust infection are usually tiny and subtle, and, as a result, ordinary deep learning networks fail to achieve satisfying performance. By formulating this challenge as a fine-grained image classification problem, this study proposes a novel deep learning network C-DenseNet which embeds Convolutional Block Attention Module (CBAM) in the densely connected convolutional network (DenseNet). The performance of C-DenseNet and its variants is demonstrated via a newly collected wheat stripe rust grading dataset (WSRgrading dataset) at Northwest A&F University, Shaanxi Province, China, which contains a total of 5,242 wheat leaf images with 6 levels of stripe rust infection. The dataset was collected by using various mobile devices in the natural field condition. Comparative experiments show that C-DenseNet with a test accuracy of 97.99% outperforms the classical DenseNet (92.53%) and ResNet (73.43%). GradCAM++ network visualization also shows that C-DenseNet is able to pay more attention to the key areas in making the decision. It is concluded that C-DenseNet with an attention mechanism is suitable for wheat stripe rust disease grading in field conditions

Hot Spine Loops and the Nature of a Late-Phase Solar Flare

The fan-spine magnetic topology is believed to be responsible for many curious features in solar explosive events. A spine field line links distinct flux domains, but direct observation of such feature has been rare. Here we report a unique event observed by the Solar Dynamic Observatory where a set of hot coronal loops (over 10 MK) connected to a quasi-circular chromospheric ribbon at one end and a remote brightening at the other. Magnetic field extrapolation suggests these loops are partly tracer of the evolving spine field line. Continuous slipping- and null-point-type reconnections were likely at work, energizing the loop plasma and transferring magnetic flux within and across the fan quasi-separatrix layer. We argue that the initial reconnection is of the "breakout" type, which then transitioned to a more violent flare reconnection with an eruption from the fan dome. Significant magnetic field changes are expected and indeed ensued. This event also features an extreme-ultraviolet (EUV) late phase, i.e. a delayed secondary emission peak in warm EUV lines (about 2-7 MK). We show that this peak comes from the cooling of large post-reconnection loops beside and above the compact fan, a direct product of eruption in such topological settings. The long cooling time of the large arcades contributes to the long delay; additional heating may also be required. Our result demonstrates the critical nature of cross-scale magnetic coupling - topological change in a sub-system may lead to explosions on a much larger scale.Comment: Accepted for publication in ApJ. Animations linked from pd

Assessing the size, stability, and utility of isotropically tumbling bicelle systems for structural biology

AbstractAqueous phospholipid mixtures that form bilayered micelles (bicelles) have gained wide use by molecular biophysicists during the past 20 years for spectroscopic studies of membrane-bound peptides and structural refinement of soluble protein structures. Nonetheless, the utility of bicelle systems may be compromised by considerations of cost, chemical stability, and preservation of the bicelle aggregate organization under a broad range of temperature, concentration, pH, and ionic strength conditions. In the current work, 31P nuclear magnetic resonance (NMR) and atomic force microscopy (AFM) have been used to monitor the size and morphology of isotropically tumbling small bicelles formed by mixtures of 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) or 1,2-di-O-tetradecyl-sn-glycero-3-phosphocholine (DIOMPC) with either 1,2-dihexanoyl-sn-glycero-3-phosphocholine (DHPC) or 1,2-di-O-hexyl-sn-glycero-3-phosphocholine (DIOHPC), testing their tolerance of variations in commonly used experimental conditions. 1H-15N 2D NMR has been used to demonstrate the usefulness of the robust DMPC–DIOHPC system for conformational studies of a fatty acid-binding protein that shuttles small ligands to and from biological membranes

Semi-supervised Text Regression with Conditional Generative Adversarial Networks

Enormous online textual information provides intriguing opportunities for understandings of social and economic semantics. In this paper, we propose a novel text regression model based on a conditional generative adversarial network (GAN), with an attempt to associate textual data and social outcomes in a semi-supervised manner. Besides promising potential of predicting capabilities, our superiorities are twofold: (i) the model works with unbalanced datasets of limited labelled data, which align with real-world scenarios; and (ii) predictions are obtained by an end-to-end framework, without explicitly selecting high-level representations. Finally we point out related datasets for experiments and future research directions

Plant C, N, P Stoichiometry Was Effected with Nitrogen Deposition on the Loess Plateau, China

The mass ratio of C:N:P (Carbon: Nitrogen: Phosphorus) in plant tissue can reflect the utilization efficiency of these basic elements (Piao et al. 2005). Additionally, the N:P ratio in plant tissue can be used to detect nutrient limita-tions as N, P or both are most often the driving force for ecosystem development and change (Koerselman and Meuleman 1996). N deposition has been increasing dramatically and it has great influence on the productivity, stability and nutrient supply conditions in the terrestrial and aquatic ecosystem in recent years (Wang and Yu 2008). Many scholars have focused on the C:N:P stoichiometry of individual plant or the whole plant communities which were based on weighted average of important key species and their element contents, rather than by collecting bio-mass samples of all the plants in the community level. Moreover, less is known of the effects of N deposition on C:N:P stoichiometry characteristics of plant communities, especially the grasslands of the Loess Plateau. An experiment was set to study the effects of N deposition on C, N,P stoichiometry characteristics by simulating N deposition by way of N addition

Long Non Coding RNA MALAT1 Promotes Tumor Growth and Metastasis by Inducing Epithelial-Mesenchymal Transition in Oral Squamous Cell Carcinoma

The prognosis of advanced oral squamous cell carcinoma (OSCC) patients remains dismal, and a better understanding of the underlying mechanisms is critical for identifying effective targets with therapeutic potential to improve the survival of patients with OSCC. This study aims to clarify the clinical and biological significance of metastasis-associated long non-coding RNA, metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) in OSCC. We found that MALAT1 is overexpressed in OSCC tissues compared to normal oral mucosa by real-time PCR. MALAT1 served as a new prognostic factor in OSCC patients. When knockdown by small interfering RNA (siRNA) in OSCC cell lines TSCCA and Tca8113, MALAT1 was shown to be required for maintaining epithelial-mesenchymal transition (EMT) mediated cell migration and invasion. Western blot and immunofluorescence staining showed that MALAT1 knockdown significantly suppressed N-cadherin and Vimentin expression but induced E-cadherin expression in vitro. Meanwhile, both nucleus and cytoplasm levels of β-catenin and NF-κB were attenuated, while elevated MALAT1 level triggered the expression of β-catenin and NF-κB. More importantly, targeting MALAT1 inhibited TSCCA cell-induced xenograft tumor growth in vivo. Therefore, these findings provide mechanistic insight into the role of MALAT1 in regulating OSCC metastasis, suggesting that MALAT1 is an important prognostic factor and therapeutic target for OSCC