Non-Abelian Majorana Doublets in Time-Reversal Invariant Topological Superconductor

The study of non-Abelian Majorana zero modes advances our understanding of the fundamental physics in quantum matter, and pushes the potential applications of such exotic states to topological quantum computation. It has been shown that in two-dimensional (2D) and 1D chiral superconductors, the isolated Majorana fermions obey non-Abelian statistics. However, Majorana modes in a $Z_2$ time-reversal invariant (TRI) topological superconductor come in pairs due to Kramers' theorem. Therefore, braiding operations in TRI superconductors always exchange two pairs of Majoranas. In this work, we show interestingly that, due to the protection of time-reversal symmetry, non-Abelian statistics can be obtained in 1D TRI topological superconductors and may have advantages in applying to topological quantum computation. Furthermore, we unveil an intriguing phenomenon in the Josephson effect, that the periodicity of Josephson currents depends on the fermion parity of the superconducting state. This effect provides direct measurements of the topological qubit states in such 1D TRI superconductors.Comment: Manuscript: 9 pages + 6 Figs + Appendix + Supplementary material. 2nd version for PRX. Discussions are updated according to Referee report

Environmental-relevant bisphenol A exposure promotes ovarian cancer stemness by regulating microRNA biogenesis

Bisphenol A (BPA) is a ubiquitous environmental xenobiotic impacting millions of people worldwide. BPA has long been proposed to promote ovarian carcinogenesis, but the detrimental mechanistic target remains unclear. Cancer stem cells (CSCs) are considered as the trigger of tumour initiation and progression. Here, we show for the first time that nanomolar (environmentally relevant) concentration of BPA can markedly increase the formation and expansion of ovarian CSCs concomitant. This effect is observed in both oestrogen receptor (ER)-positive and ER-defective ovarian cancer cells, suggesting that is independent of the classical ERs. Rather, the signal is mediated through alternative ER G-protein-coupled receptor 30 (GPR30), but not oestrogen-related receptor α and γ. Moreover, we report a novel role of BPA in the regulation of Exportin-5 that led to dysregulation of microRNA biogenesis through miR-21. The use of GPR30 siRNA or antagonist to inhibit GPR30 expression or activity, respectively, resulted in significant inhibition of ovarian CSCs. Similarly, the CSCs phenotype can be reversed by expression of Exportin-5 siRNA. These results identify for the first time non-classical ER and microRNA dysregulation as novel mediators of low, physiological levels of BPA function in CSCs that may underlie its significant tumour-promoting properties in ovarian cancer

Majorana Flat Bands and Uni-directional Majorana Edge States in Gapless Topological Superconductors

In this work, we show that an in-plane magnetic field can drive a fully gapped $p \pm i p$ topological superconductor into a gapless phase which supports symmetry protected Majorana edge states (MESs). Specifically, an in-plane magnetic field can close the bulk gap and create zero energy Majorana flat bands (MFBs) in the excitation spectrum. We show that the MFBs in the gapless regime are protected by symmetry and are associated with MESs. The MFBs acquire finite slopes when s-wave pairing and Rashba spin-oribit coupling terms are added to the Hamiltonian. In this case, novel uni-directional MESs which propagate in the same direction on opposite edges may appear. Uni-directional MESs can also be found in pure s-wave superconductors with spin-orbit coupling. The MFBs and the uni-directional MESs induce nearly quantized zero bias conductance in tunneling experiments even in the presence of a gapless bulk and disorder.Comment: 5 pages, 5 figure

Generic searches for alternative gravitational wave polarizations with networks of interferometric detectors

The detection of gravitational wave signals by Advanced LIGO and Advanced Virgo enables us to probe the polarization content of gravitational waves. In general relativity, only tensor modes are present, while in a variety of alternative theories one can also have vector or scalar modes. Recently test were performed which compared Bayesian evidences for the hypotheses that either purely tensor, purely vector, or purely scalar polarizations were present. Indeed, with only three detectors in a network and allowing for mixtures of tensor polarizations and alternative polarization states, it is not possible to identify precisely which nonstandard polarizations might be in the signal and by what amounts. However, we demonstrate that one can still infer whether, in addition to tensor polarizations, alternative polarizations are present in the first place, irrespective of the detailed polarization content. We develop two methods to do this for sources with electromagnetic counterparts, both based on the so-called null stream. Apart from being able to detect mixtures of tensor and alternative polarizations, these have the added advantage that no waveform models are needed, and signals from any kind of transient source with known sky position can be used. Both formalisms allow us to combine information from multiple sources so as to arrive at increasingly more stringent bounds. For now we apply these on the binary neutron star signal GW170817, showing consistency with the tensor-only hypothesis with p-values of 0.315 and 0.790 for the two methods

Generic searches for alternative gravitational wave polarizations with networks of interferometric detectors

The detection of gravitational wave signals by Advanced LIGO and Advanced Virgo enables us to probe the polarization content of gravitational waves. In general relativity, only tensor modes are present, while in a variety of alternative theories one can also have vector or scalar modes. Recently test were performed which compared Bayesian evidences for the hypotheses that either purely tensor, purely vector, or purely scalar polarizations were present. Indeed, with only three detectors in a network and allowing for mixtures of tensor polarizations and alternative polarization states, it is not possible to identify precisely which non-standard polarizations might be in the signal and by what amounts. However, we demonstrate that one can still infer whether, in addition to tensor polarizations, alternative polarizations are present in the first place, irrespective of the detailed polarization content. We develop two methods to do this for sources with electromagnetic counterparts, both based on the so-called null stream. Apart from being able to detect mixtures of tensor and alternative polarizations, these have the added advantage that no waveform models are needed, and signals from any kind of transient source with known sky position can be used. Both formalisms allow us to combine information from multiple sources so as to arrive at increasingly more stringent bounds. For now we apply these on the binary neutron star signal GW170817, showing consistency with the tensor-only hypothesis with p-values of 0.315 and 0.790 for the two methods.Comment: 8 pages, 3 figure

Optimized high-throughput microRNA expression profiling provides novel biomarker assessment of clinical prostate and breast cancer biopsies

BACKGROUND: Recent studies indicate that microRNAs (miRNAs) are mechanistically involved in the development of various human malignancies, suggesting that they represent a promising new class of cancer biomarkers. However, previously reported methods for measuring miRNA expression consume large amounts of tissue, prohibiting high-throughput miRNA profiling from typically small clinical samples such as excision or core needle biopsies of breast or prostate cancer. Here we describe a novel combination of linear amplification and labeling of miRNA for highly sensitive expression microarray profiling requiring only picogram quantities of purified microRNA. RESULTS: Comparison of microarray and qRT-PCR measured miRNA levels from two different prostate cancer cell lines showed concordance between the two platforms (Pearson correlation R(2 )= 0.81); and extension of the amplification, labeling and microarray platform was successfully demonstrated using clinical core and excision biopsy samples from breast and prostate cancer patients. Unsupervised clustering analysis of the prostate biopsy microarrays separated advanced and metastatic prostate cancers from pooled normal prostatic samples and from a non-malignant precursor lesion. Unsupervised clustering of the breast cancer microarrays significantly distinguished ErbB2-positive/ER-negative, ErbB2-positive/ER-positive, and ErbB2-negative/ER-positive breast cancer phenotypes (Fisher exact test, p = 0.03); as well, supervised analysis of these microarray profiles identified distinct miRNA subsets distinguishing ErbB2-positive from ErbB2-negative and ER-positive from ER-negative breast cancers, independent of other clinically important parameters (patient age; tumor size, node status and proliferation index). CONCLUSION: In sum, these findings demonstrate that optimized high-throughput microRNA expression profiling offers novel biomarker identification from typically small clinical samples such as breast and prostate cancer biopsies

Using machine learning to infer reasoning provenance from user interaction log data: based on the data/frame theory of sensemaking

The reconstruction of analysts’ reasoning processes (reasoning provenance) during complex sensemaking tasks can support reflection and decision making. One potential approach to such reconstruction is to automatically infer reasoning from low-level user interaction logs. We explore a novel method for doing this using machine learning. Two user studies were conducted in which participants performed similar intelligence analysis tasks. In one study, participants used a standard web browser and word processor; in the other, they used a system called INVISQUE (Interactive Visual Search and Query Environment). Interaction logs were manually coded for cognitive actions based on captured think-aloud protocol and posttask interviews based on Klein, Phillips, Rall, and Pelusos’s data/frame model of sensemaking as a conceptual framework. This analysis was then used to train an interaction frame mapper, which employed multiple machine learning models to learn relationships between the interaction logs and the codings. Our results show that, for one study at least, classification accuracy was significantly better than chance and compared reasonably to a reported manual provenance reconstruction method. We discuss our results in terms of variations in feature sets from the two studies and what this means for the development of the method for provenance capture and the evaluation of sensemaking systems