Mississippi State University

About: Mississippi State University is a education organization based out in Starkville, Mississippi, United States. It is known for research contribution in the topics: Population & Catfish. The organization has 14115 authors who have published 28594 publications receiving 700030 citations. The organization is also known as: The Mississippi State University of Agriculture and Applied Science & Mississippi State University of Agriculture and Applied Science.

Proteome and phosphoproteome differential expression under salinity stress in rice (Oryza sativa) roots.

Abstract: Salinity stress is a major abiotic stress that limits agriculture productivity worldwide. Rice is a model plant of monocotyledons, including cereal crops. Studies have suggested a critical role of protein phosphorylation in salt stress response in plants. However, the phosphoproteome in rice, particularly under salinity stress, has not been well studied. Here, we use Pro-Q Diamond Phosphoprotein Stain to study rice phosphoproteome differential expression under salt stress. Seventeen differentially upregulated and 11 differentially downregulated putative phosphoproteins have been identified. Further analyses indicate that 10 of the 17 upregulated proteins are probably upregulated at post-translational level instead of the protein concentration. Meanwhile, we have identified 31 salt stress differentially regulated proteins using SYPRO Ruby stain. While eight of them are known salt stress response proteins, the majority has not been reported in the literature. Our studies have provided valuable new insight into plant response to salinity stress.

Human resource practices, job embeddedness and intention to quit

Abstract: Purpose – The purpose of this paper is to test the whether job embeddedness is a mediator of the relationship between human resource practices and employees’ intention to quit. The study presented here used job embeddedness, a new construct, to investigate its mediation effect on the relationship between employees’ intentions to leave and four areas of human resource practices: compensation, supervisor support, growth opportunity and training.Design/methodology/approach – A questionnaire was given to employees at a state department of corrections asking their attitudes about their job, their place of employment, and the agency as a whole. The results of this questionnaire were analyzed utilizing the four‐step method for mediation analysis.Findings – Job embeddedness fully mediated compensation and growth opportunity, partially mediated supervisor support, and did not mediate training in relation to employees’ intention to quit. Research limitations/implications –A self‐reported, cross‐sectional questionna...

More Diverse Means Better: Multimodal Deep Learning Meets Remote-Sensing Imagery Classification

Abstract: Classification and identification of the materials lying over or beneath the earth’s surface have long been a fundamental but challenging research topic in geoscience and remote sensing (RS), and have garnered a growing concern owing to the recent advancements of deep learning techniques. Although deep networks have been successfully applied in single-modality-dominated classification tasks, yet their performance inevitably meets the bottleneck in complex scenes that need to be finely classified, due to the limitation of information diversity. In this work, we provide a baseline solution to the aforementioned difficulty by developing a general multimodal deep learning (MDL) framework. In particular, we also investigate a special case of multi-modality learning (MML)—cross-modality learning (CML) that exists widely in RS image classification applications. By focusing on “what,” “where,” and “how” to fuse, we show different fusion strategies as well as how to train deep networks and build the network architecture. Specifically, five fusion architectures are introduced and developed, further being unified in our MDL framework. More significantly, our framework is not only limited to pixel-wise classification tasks but also applicable to spatial information modeling with convolutional neural networks (CNNs). To validate the effectiveness and superiority of the MDL framework, extensive experiments related to the settings of MML and CML are conducted on two different multimodal RS data sets. Furthermore, the codes and data sets will be available at https://github.com/danfenghong/IEEE_TGRS_MDL-RS , contributing to the RS community.