Unified Formalism for calculating Polarization, Magnetization, and more in a Periodic Insulator

In this paper, we propose a unified formalism, using Green's functions, to integrate out the electrons in an insulator under uniform electromagnetic fields. We derive a perturbative formula for the Green's function in the presence of uniform magnetic or electric fields. Applying the formula, we derive the formula for the polarization, the orbital magnetization, and the orbital magneto-polarizability, without assuming time reversal symmetry. Specifically, we realize that the terms linear in the electric field can only be expressed in terms of the Green's functions in one extra dimension. This observation directly leads to the result that the coefficient of the $\theta$ term in any dimensions is given by a Wess-Zumino-Witten-type term, integrated in the extended space, interpolating between the original physical Brillouin zone and a trivial system, with the group element replaced by the Green's function. This generalizes an earlier result for the case of time reversal invariance [see Z. Wang, X.-L. Qi, and S.-C. Zhang, Phys. Rev. Lett. {\bf 105}, 256803 (2010)].Comment: 16 pages, 1 figure. The version accepted by PR

Cancer-related masculinity threat in young adults with testicular cancer: the moderating role of benefit finding.

Background and Objectives: Perceiving benefit from a health-related stressor such as cancer has been associated with better psychological adjustment in various cancer populations; however, it has not been studied in the context of young adulthood or gender-related cancer threat. This study investigated the role of benefit finding in psychological adjustment among young adults with testicular cancer, and whether BF moderates cancer-related masculine threat.Design: This study utilizes a cross-sectional design with a diverse sample of young adult testicular cancer survivors.Methods: Men with a history of testicular cancer (N = 171; M age = 25.2, SD = 3.32) completed questionnaires of benefit finding, cancer-related masculine threat, and indicators of psychological adjustment.Results: Multiple regression analysis revealed that cancer-related masculine threat was associated with worse adjustment across indicators and that benefit finding was related to higher positive affect and lower depressive symptoms. Benefit finding attenuated the potentially adverse effect of cancer-related masculine threat on negative affect and depressive symptoms such that cancer-related masculine threat demonstrated a stronger association with negative affect and depressive symptoms for people with relatively low BF.Conclusions: For young adult men with testicular cancer, finding benefit appears to promote well-being in the face of masculine cancer threat

Threshold Regression for Survival Analysis: Modeling Event Times by a Stochastic Process Reaching a Boundary

Many researchers have investigated first hitting times as models for survival data. First hitting times arise naturally in many types of stochastic processes, ranging from Wiener processes to Markov chains. In a survival context, the state of the underlying process represents the strength of an item or the health of an individual. The item fails or the individual experiences a clinical endpoint when the process reaches an adverse threshold state for the first time. The time scale can be calendar time or some other operational measure of degradation or disease progression. In many applications, the process is latent (i.e., unobservable). Threshold regression refers to first-hitting-time models with regression structures that accommodate covariate data. The parameters of the process, threshold state and time scale may depend on the covariates. This paper reviews aspects of this topic and discusses fruitful avenues for future research.Comment: Published at http://dx.doi.org/10.1214/088342306000000330 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Privacy-preserving model learning on a blockchain network-of-networks.

ObjectiveTo facilitate clinical/genomic/biomedical research, constructing generalizable predictive models using cross-institutional methods while protecting privacy is imperative. However, state-of-the-art methods assume a "flattened" topology, while real-world research networks may consist of "network-of-networks" which can imply practical issues including training on small data for rare diseases/conditions, prioritizing locally trained models, and maintaining models for each level of the hierarchy. In this study, we focus on developing a hierarchical approach to inherit the benefits of the privacy-preserving methods, retain the advantages of adopting blockchain, and address practical concerns on a research network-of-networks.Materials and methodsWe propose a framework to combine level-wise model learning, blockchain-based model dissemination, and a novel hierarchical consensus algorithm for model ensemble. We developed an example implementation HierarchicalChain (hierarchical privacy-preserving modeling on blockchain), evaluated it on 3 healthcare/genomic datasets, as well as compared its predictive correctness, learning iteration, and execution time with a state-of-the-art method designed for flattened network topology.ResultsHierarchicalChain improves the predictive correctness for small training datasets and provides comparable correctness results with the competing method with higher learning iteration and similar per-iteration execution time, inherits the benefits of the privacy-preserving learning and advantages of blockchain technology, and immutable records models for each level.DiscussionHierarchicalChain is independent of the core privacy-preserving learning method, as well as of the underlying blockchain platform. Further studies are warranted for various types of network topology, complex data, and privacy concerns.ConclusionWe demonstrated the potential of utilizing the information from the hierarchical network-of-networks topology to improve prediction

Increased risk for T cell autoreactivity to ß-cell antigens in the mice expressing the Avy obesity-associated gene.

There has been considerable debate as to whether obesity can act as an accelerator of type 1 diabetes (T1D). We assessed this possibility using transgenic mice (MIP-TF mice) whose ß-cells express enhanced green fluorescent protein (EGFP). Infecting these mice with EGFP-expressing murine herpes virus-68 (MHV68-EGFP) caused occasional transient elevation in their blood glucose, peri-insulitis, and Th1 responses to EGFP which did not spread to other ß-cell antigens. We hypothesized that obesity-related systemic inflammation and ß-cell stress could exacerbate the MHV68-EGFP-induced ß-cell autoreactivity. We crossed MIP-TF mice with Avy mice which develop obesity and provide models of metabolic disease alongside early stage T2D. Unlike their MIP-TF littermates, MHV68-EGFP-infected Avy/MIP-TF mice developed moderate intra-insulitis and transient hyperglycemia. MHV68-EGFP infection induced a more pronounced intra-insulitis in older, more obese, Avy/MIP-TF mice. Moreover, in MHV68-EGFP-infected Avy/MIP-TF mice, Th1 reactivity spread from EGFP to other ß-cell antigens. Thus, the spreading of autoreactivity among ß-cell antigens corresponded with the transition from peri-insulitis to intra-insulitis and occurred in obese Avy/MIP-TF mice but not lean MIP-TF mice. These observations are consistent with the notion that obesity-associated systemic inflammation and ß-cell stress lowers the threshold necessary for T cell autoreactivity to spread from EGFP to other ß-cell autoantigens