Effect of irrigation frequency during the growing season of winter wheat on the water use efficiency of summer maize in a double cropping system

Our aim was to investigate the potential effects of irrigation frequency during the growing season of winter wheat on the water use efficiency (WUE) of summer maize in a double cropping system. To this end, we conducted a field experiment with winter wheat cultivated with 1, 2, and 3 irrigation applications with 120 mm water at the time of stem elongation, heading, or milking. The results showed that later irrigation applications increased soil moisture before sowing (SMBS) of summer maize. Summer maize grain yield was enhanced in both the common and excessively rainy years with increased SMBS; however, irrigation during the later growing season of winter wheat in rainy years could increase deep percolation of summer maize. In common and rainy years, the more the SMBS, the higher was the grain yield of summer maize. The highest WUE for summer maize was obtained when it was grown after winter wheat irrigated with 120 mm water at milking or 60 mm water at each, the stem elongation and heading stages. Considering the combined WUE of winter wheat and summer maize, the authors think that winter wheat should be irrigated at the stem elongation and heading stages to achieve reasonable WUE and grain yield for both crops

An Analysis of the Cause of Privacy Paradox among SNS Users: take Chinese College Students as an Example

It has been proved that the privacy paradox does exist, yet the cause of the phenomenon remains vague. This article tries to analyze the [Inserted: s]cause of privacy paradox phenomenon on SNS (WeChat) among Chinese college students based on Privacy Calculus Theory and the TPB model and introduces two new factors: the credibility of SNS and the cost of protecting privacy. Through a questionnaire and interview survey,[Inserted: a ] our result shows that there is no significant correlation between users’ privacy concerns and the intention of privacy disclosure. While the more users trust the SNS platform, the more possibility they tend to disclose their private information[Inserted: te], and the cost of privacy protection can somehow weaken the relationship between the intention and the actual behavior. Therefore, [Inserted: ship]by increasing SNS\u27s credibility, users tend to disclose more personal information to SNS providers, which may improve the competitiveness of SNSs and contribute to their sustainable development

Estimating CT Image From MRI Data Using Structured Random Forest and Auto-Context Model

Computed tomography (CT) imaging is an essential tool in various clinical diagnoses and radiotherapy treatment planning. Since CT image intensities are directly related to positron emission tomography (PET) attenuation coefficients, they are indispensable for attenuation correction (AC) of the PET images. However, due to the relatively high dose of radiation exposure in CT scan, it is advised to limit the acquisition of CT images. In addition, in the new PET and magnetic resonance (MR) imaging scanner, only MR images are available, which are unfortunately not directly applicable to AC. These issues greatly motivate the development of methods for reliable estimate of CT image from its corresponding MR image of the same subject. In this paper, we propose a learning-based method to tackle this challenging problem. Specifically, we first partition a given MR image into a set of patches. Then, for each patch, we use the structured random forest to directly predict a CT patch as a structured output, where a new ensemble model is also used to ensure the robust prediction. Image features are innovatively crafted to achieve multi-level sensitivity, with spatial information integrated through only rigid-body alignment to help avoiding the error-prone inter-subject deformable registration. Moreover, we use an auto-context model to iteratively refine the prediction. Finally, we combine all of the predicted CT patches to obtain the final prediction for the given MR image. We demonstrate the efficacy of our method on two datasets: human brain and prostate images. Experimental results show that our method can accurately predict CT images in various scenarios, even for the images undergoing large shape variation, and also outperforms two state-of-the-art methods

A Folate Receptor Beta-Specific Human Monoclonal Antibody Recognizes Activated Macrophage of Rheumatoid Patients and Mediates Antibody-Dependent Cell-Mediated Cytotoxicity.

Introduction Folate receptor beta (FRβ) is only detectable in placenta and limited to some hematopoietic cells of myeloid lineage in healthy people. Studies have indicated that FRβ is over-expressed in activated macrophages in autoimmune diseases and some cancer cells. In this study we aimed to develop an FRβ-specific human monoclonal antibody (mAb) that could be used as a therapeutic agent to treat rheumatoid arthritis and other autoimmune diseases, as well as FRβ positive cancers. Methods Functional recombinant FRβ protein was produced in insect cells and used as antigen to isolate a mAb, m909, from a human naïve Fab phage display library. Binding of Fab and IgG1 m909 to FRβ was measured by ELISA, surface plasmon resonance, immune fluorescence staining, and flow cytometry. Antibody-dependent cell-mediated cytotoxicity (ADCC) was evaluated with FRβ positive CHO cells as target cells and isolated peripheral blood monocytes as effector cells in an in vitroassay. Results Fab m909 bound with relatively high affinity (equilibrium dissociation constant 57 nM) to FRβ. The IgG1 m909 showed much higher (femtomolar) avidity as measured by ELISA, and it bound to FRβ positive cells in a dose-dependent manner, but not to parental FRβ negative cells. m909 did not compete with folate for the binding to FRβ on cells. m909 was not only able to select FRβ positive, activated macrophages from synovial fluid cells of arthritis patients as efficiently as folate, but also able to mediate ADCC in FRβ positive cells. Conclusions Unlike folate-drug conjugates, m909 selectively binds to FRβ, does not recognize FRα, and has at least one effector function. m909 alone has potential to eliminate FRβ positive cells. Because m909 does not compete with folate for receptor binding, it can be used with folate-drug conjugates in a combination therapy. m909 can also be a valuable research reagent

Photometric Metallicity Calibration with SDSS and SCUSS and its Application to distant stars in the South Galactic Cap

Based on SDSS g, r and SCUSS (South Galactic Cap of u-band Sky Survey) $u$ photometry, we develop a photometric calibration for estimating the stellar metallicity from $u-g$ and $g-r$ colors by using the SDSS spectra of 32,542 F- and G-type main sequence stars, which cover almost $3700$ deg$^{2}$ in the south Galactic cap. The rms scatter of the photometric metallicity residuals relative to spectrum-based metallicity is $0.14$ dex when $g-r<0.4$, and $0.16$ dex when $g-r>0.4$. Due to the deeper and more accurate magnitude of SCUSS $u$ band, the estimate can be used up to the faint magnitude of $g=21$. This application range of photometric metallicity calibration is wide enough so that it can be used to study metallicity distribution of distant stars. In this study, we select the Sagittarius (Sgr) stream and its neighboring field halo stars in south Galactic cap to study their metallicity distribution. We find that the Sgr stream at the cylindrical Galactocentric coordinate of $R\sim 19$ kpc, $\left| z\right| \sim 14$ kpc exhibits a relative rich metallicity distribution, and the neighboring field halo stars in our studied fields can be modeled by two-Gaussian model, with peaks respectively at [Fe/H]$=-1.9$ and [Fe/H]$=-1.5$.Comment: 8 pages, 7 figures, Accepted for publication in MNRA

Attention-based High-order Feature Interactions to Enhance the Recommender System for Web-based Knowledge-Sharing Servic

Providing personalized online learning services has become a hot research topic. Online knowledge-sharing services represents a popular approach to enable learners to use fragmented spare time. User asks and answers questions in the platform, and the platform also recommends relevant questions to users based on their learning interested and context. However, in the big data era, information overload is a challenge, as both online learners and learning resources are embedded in data rich environment. Offering such web services requires an intelligent recommender system to automatically filter out irrelevant information, mine underling user preference, and distil latent information. Such a recommender system needs to be able to mine complex latent information, distinguish differences between users efficiently. In this study, we refine a recommender system of a prior work for web-based knowledge sharing. The system utilizes attention-based mechanisms and involves high-order feature interactions. Our experimental results show that the system outperforms known benchmarks and has great potential to be used for the web-based learning service