The study of the charged top-pion decay processes

In the framework of top-color assisted technicolor(TC2) theory, we study the four decay processes of charged top-pion, i.e., $\Pi^{+}_{t}\to t\bar{b}$, $\Pi^{+}_{t}\to c\bar{b}$, $\Pi^{+}_{t}\to W^{+}\gamma$, $\Pi^{+}_{t}\to W^{+}Z^{0}$, the decay branching ratio of these modes are calculated. The results show that the main decay channels of charged top-pion are the tree level modes: $\Pi_t^+ \to t\bar{b}$ and $\Pi_t^+ \to c\bar{b}$. Light $\Pi_t^+$ is easier to be detected than heavy one at future coliders. So, the study provides us some useful information to search for charged top-pion.Comment: 14 pages, 6 figure

Mixture of Latent Variable Models for Remotely Sensed Image Processing

The processing of remotely sensed data is innately an inverse problem where properties of spatial processes are inferred from the observations based on a generative model. Meaningful data inversion relies on well-defined generative models that capture key factors in the relationship between the underlying physical process and the measurements. Unfortunately, as two mainstream data processing techniques, both mixture models and latent variables models (LVM) are inadequate in describing the complex relationship between the spatial process and the remote sensing data. Consequently, mixture models, such as K-Means, Gaussian Mixture Model (GMM), Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA), characterize a class by statistics in the original space, ignoring the fact that a class can be better represented by discriminative signals in the hidden/latent feature space, while LVMs, such as Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Sparse Representation (SR), seek representational signals in the whole image scene that involves multiple spatial processes, neglecting the fact that signal discovery for individual processes is more efficient. Although the combined use of mixture model and LVMs is required for remote sensing data analysis, there is still a lack of systematic exploration on this important topic in remote sensing literature. Driven by the above considerations, this thesis therefore introduces a mixture of LVM (MLVM) framework for combining the mixture models and LVMs, under which three models are developed in order to address different aspects of remote sensing data processing: (1) a mixture of probabilistic SR (MPSR) is proposed for supervised classification of hyperspectral remote sensing imagery, considering that SR is an emerging and powerful technique for feature extraction and data representation; (2) a mixture model of K “Purified” means (K-P-Means) is proposed for addressing the spectral endmember estimation, which is a fundamental issue in remote sensing data analysis; (3) and a clustering-based PCA model is introduced for SAR image denoising. Under a unified optimization scheme, all models are solved via Expectation and Maximization (EM) algorithm, by iteratively estimating the two groups of parameters, i.e., the labels of pixels and the latent variables. Experiments on simulated data and real remote sensing data demonstrate the advantages of the proposed models in the respective applications

CD166 modulates disease progression and osteolytic disease in multiple myeloma

Indiana University-Purdue University Indianapolis (IUPUI)Multiple myeloma (MM) is an incurable malignancy characterized by the proliferation of neoplastic plasma cells in the bone marrow (BM) and by multiple osteolytic lesions throughout the skeleton. We previously reported that CD166 is a functional molecule on normal hematopoietic stem cells (HSC) that plays a critical role in HSC homing and engraftment, suggesting that CD166 is involved in HSC trafficking and lodgment. CD166, a member of the immunoglobulin superfamily capable of mediating homophilic interactions, has been shown to enhance metastasis and invasion in several tumors. However, whether CD166 is involved in MM and plays a role in MM progression has not been addressed. We demonstrated that a fraction of all human MM cell lines tested and MM patients’ BM CD138+ cells express CD166. Additionally, CD166+ cells preferentially home to the BM of NSG mice. Knocking-down (KD) CD166 expression on MM cells with shRNA reduced their homing to the BM. Furthermore, in a long-term xenograft model, NSG mice inoculated with CD166KD cells showed delayed disease progression and prolonged survival compared to mice receiving mock transduced cells. To examine the potential role of CD166 in osteolytic lesions, we first used a novel Ex Vivo Organ Culture Assay (EVOCA) which creates an in vitro 3D system for the interaction of MM cells with the bone microenvironment. EVOCA data from MM cells lines as well as from primary MM patients’ CD138+ BM cells demonstrated that bone osteolytic resorption was significantly reduced when CD166 was absent on MM cells or calvarial cells. We then confirmed our ex vivo findings with intra-tibial inoculation of MM cells in vivo. Mice inoculated with CD166KD cells had significantly less osteolytic lesions. Further analysis demonstrated that CD166 expression on MM cells alters bone remodeling by inhibiting RUNX2 gene expression in osteoblast precursors and increasing RANKL to OPG ratio in osteoclast precursors. We also identified that CD166 is indispensable for osteoclastogenesis via the activation of TRAF6-dependent signaling pathways. These results suggest that CD166 directs MM cell homing to the BM and promotes MM disease progression and osteolytic disease. CD166 may serve as a therapeutic target in the treatment of MM

From Biomimicking to Bioinspired Design of Electrocatalysts for CO2 Reduction to C1 Products

The electrochemical reduction of CO2 (CO2RR) is a promising approach to maintain a carbon cycle balance and produce value-added chemicals. However, CO2RR technology is far from mature, since the conventional CO2RR electrocatalysts suffer from low activity (leading to currents 200 mA cm−2, >8000 h, >90 % selectivity). Significant improvements are possible by taking inspiration from nature, considering biological organisms that efficiently catalyze the CO2 to various products. In this minireview, we present recent examples of enzyme-inspired and enzyme-mimicking CO2RR electrocatalysts enabling the production of C1 products with high faradaic efficiency (FE). At present, these designs do not typically follow a methodical approach, but rather focus on isolated features of biological systems. To achieve disruptive change, we advocate a systematic design methodology that leverages fundamental mechanisms associated with desired properties in nature and adapts them to the context of engineering applications

Nature-Inspired Electrocatalysts for CO_{2} Reduction to C_{2+} Products

The electrocatalytic reduction reaction of carbon dioxide (CO2RR) has gained significant attention as a promising approach to mitigate carbon dioxide emissions and generate valuable chemicals and fuels. However, the practical application of CO2RR has been hindered by the lack of efficient and selective electrocatalysts, particularly to produce multi-carbon (C2+) products. Nature serves as an ideal source of inspiration for the development of CO2RR electrocatalysts, as biological organisms can efficiently catalyze the same reaction and possess robust structures that are inherently scaling. In this review, recent advances in the nature-inspired design of electrocatalysts for CO2RR to C2+ products are summarized and categorized based on their inspiration source, including the coordination sphere of metalloenzymes and the cascade reactions within the enzyme, as well as the local environment. The importance of understanding the fundamental mechanisms and the different contexts between nature and technological application in the design process is highlighted, with the aim to improve the nature-inspired design of electrocatalysts for CO2RR to C2+ products

Highly pathogenic avian influenza H5N1 virus could partly be evacuated by pregnant BALB/c mouse during abortion or preterm delivery

The highly pathogenic avian influenza H5N1 virus is one of candidates for future pandemic. Since H5N1 viruses had previously been isolated only from avian species, the outbreak raised questions about the ability of these viruses to cause severe disease and death in humans. Pregnant women are at increased risk for influenza-associated illness and death. However, little is known about whether influenza viruses could transmit to the fetus through the placenta, and the effects of abortion and preterm delivery to maternal influenza infection are not well understood. We found that the H5N1 viruses could vertical transmit to the fetus through the placenta in the BALB/c mouse model, and the viruses could partly be evacuated by the pregnant mice during abortion or preterm delivery. This study may further our understanding about the transmission of this highly pathogenic avian influenza viruses, supply optimized clinical treatment method for pregnant women, and shed some light on better preventing and controlling for future potential outbreak of H5N1 influenza pandemic

Research on Construction Project Management Strategy Based on EPC General Contracting

At present, the rapid development of urban economy in our country and the rise of the construction industry make the contract management model more specialized and standardized. Among them, EPC is a common type of project general contracting mode, which can be divided into design, procurement, construction and other modules according to the requirements and actual conditions of project construction. When carrying out project management of construction projects, the use of EPC general contracting mode can achieve good management results. Therefore, from the perspective of EPC general contracting, this paper discusses the relevant countermeasures of construction project management, aiming to realize the unified and standardizing management of all aspects of engineering construction and improving the level of project management