Engineered spin phase diagram of two interacting electrons in semiconductor nanowire quantum dots

Spin properties of two interacting electrons in a quantum dot (QD) embedded in a nanowire with controlled aspect ratio and longitudinal magnetic fields are investigated by using a configuration interaction (CI) method and exact diagonalization (ED) techniques. The developed CI theory based on a three-dimensional (3D) parabolic model provides explicit formulations of the Coulomb matrix elements and allows for straightforward and efficient numerical implementation. Our studies reveal fruitful features of spin singlet-triplet transitions of two electrons confined in a nanowire quantum dot (NWQD), as a consequence of the competing effects of geometry-controlled kinetic energy quantization, the various Coulomb interactions, and spin Zeeman energies. The developed theory is further employed to study the spin phase diagram of two quantum-confined electrons in the regime of "cross over" dimensionality, from quasi-two-dimensional (disk-like) QDs to finite one-dimensional (rod-like) QDs.Comment: 9 pages, 6 figure

A Comparative Study for 2D and 3D Computer-aided Diagnosis Methods for Solitary Pulmonary Nodules

Many computer-aided diagnosis (CAD) methods, including 2D and 3D approaches, have been proposed for solitary pulmonary nodules (SPNs). However, the detection and diagnosis of SPNs remain challenging in many clinical circumstances. One goal of this work is to investigate the relative diagnostic accuracy of 2D and 3D methods. An additional goal is to develop a two-stage approach that combines the simplicity of 2D and the accuracy of 3D methods. The experimental results show statistically significant differences between the diagnostic accuracy of 2D and 3D methods. The results also show that with a very minor drop in diagnostic performance the two-stage approach can significantly reduce the number of nodules needed to be processed by the 3D method, streamlining the computational demand

When Crowdsourcing Meets Mobile Sensing: A Social Network Perspective

Mobile sensing is an emerging technology that utilizes agent-participatory data for decision making or state estimation, including multimedia applications. This article investigates the structure of mobile sensing schemes and introduces crowdsourcing methods for mobile sensing. Inspired by social network, one can establish trust among participatory agents to leverage the wisdom of crowds for mobile sensing. A prototype of social network inspired mobile multimedia and sensing application is presented for illustrative purpose. Numerical experiments on real-world datasets show improved performance of mobile sensing via crowdsourcing. Challenges for mobile sensing with respect to Internet layers are discussed.Comment: To appear in Oct. IEEE Communications Magazine, feature topic on "Social Networks Meet Next Generation Mobile Multimedia Internet

Supervised Collective Classification for Crowdsourcing

Crowdsourcing utilizes the wisdom of crowds for collective classification via information (e.g., labels of an item) provided by labelers. Current crowdsourcing algorithms are mainly unsupervised methods that are unaware of the quality of crowdsourced data. In this paper, we propose a supervised collective classification algorithm that aims to identify reliable labelers from the training data (e.g., items with known labels). The reliability (i.e., weighting factor) of each labeler is determined via a saddle point algorithm. The results on several crowdsourced data show that supervised methods can achieve better classification accuracy than unsupervised methods, and our proposed method outperforms other algorithms.Comment: to appear in IEEE Global Communications Conference (GLOBECOM) Workshop on Networking and Collaboration Issues for the Internet of Everythin

GPER-induced signaling is essential for the survival of breast cancer stem cells.

G protein-coupled estrogen receptor-1 (GPER), a member of the G protein-coupled receptor (GPCR) superfamily, mediates estrogen-induced proliferation of normal and malignant breast epithelial cells. However, its role in breast cancer stem cells (BCSCs) remains unclear. Here we showed greater expression of GPER in BCSCs than non-BCSCs of three patient-derived xenografts of ER- /PR+ breast cancers. GPER silencing reduced stemness features of BCSCs as reflected by reduced mammosphere forming capacity in vitro, and tumor growth in vivo with decreased BCSC populations. Comparative phosphoproteomics revealed greater GPER-mediated PKA/BAD signaling in BCSCs. Activation of GPER by its ligands, including tamoxifen (TMX), induced phosphorylation of PKA and BAD-Ser118 to sustain BCSC characteristics. Transfection with a dominant-negative mutant BAD (Ser118Ala) led to reduced cell survival. Taken together, GPER and its downstream signaling play a key role in maintaining the stemness of BCSCs, suggesting that GPER is a potential therapeutic target for eradicating BCSCs

The characterization of the saddle shaped nickel(III) porphyrin radical cation: an explicative NMR model for a ferromagnetically coupled metallo-porphyrin radical

Ni(III)(OETPP˙)(Br)2 is the first Ni(III) porphyrin radical cation with structural and (1)H and (13)C paramagnetic NMR data for porphyrinate systems. Associating EPR and NMR analyses with DFT calculations as a new model is capable of clearly determining the dominant state from two controversial spin distributions in the ring to be the Ni(III) LS coupled with an a1u spin-up radical

Illness representations and self-care behavior of patients with heart failure

Session presented on Sunday, July 27, 2014: Purpose: The purpose of this presentation is to investigate the relationship between illness representations and self-care behaviors of patients with heart failure and to identify important factors related to illness representations and self-care behaviors among these patients. Methods: This study was conducted based on the self-regulation model. Patients with heart failure were recruited from a medical center in northern Taiwan. A descriptive correlational research design was used. Three questionnaires were administered to the study participants, including the illness representations questionnaire-revised (IPQ-R), the heart failure symptoms experience questionnaire, and the self-care behaviors questionnaires. Data were analyzed using independent t-test, Pearson\u27s correlations and hierarchical regression. Results: A total of 100 patients completed this study (mean age = 64.7-12.3). Age, education levels, and cardiac functional class were significant correlates of illness representation experienced by patients with heart failure. Emotional representation and perceived control of the illness were significantly related to self-care behaviors. Hierarchical regression analyses showed perceived personal control of the illness was the most powerful predictor, explaining 27% of the variance of self-care behaviors in patients with heart failure. Conclusion: Patients may show better self-care behaviors if they perceived greater personal control for their diseases. Results of this study suggest that the development of personalized health education or intervention programs is needed to promote illness representations of patients with heart failure