Superlattice Engineering of Topology in Massive Dirac Fermions

We show that a superlattice potential can be employed to engineer topology in massive Dirac fermions in systems such as bilayer graphene, moir\'e graphene-boron nitride, and transition-metal dichalcogenide (TMD) monolayers and bilayers. We use symmetry analysis to analyze band inversions to determine the Chern number $\mathscr C$ for the valence band as a function of tunable potential parameters for a class of $C_4$ and $C_3$ symmetric potentials. We present a novel method to engineer Chern number $\mathscr{C}=2$ for the valence band and show that the applied potential at minimum must have a scalar together with a non-scalar periodic part. We discover that certain forms of the superlattice potential, which may be difficult to realize in naturally occurring moir\'e patterns, allow for the possibility of non-trivial topological transitions. These forms may be achievable using an external superlattice potential that can be created using contemporary experimental techniques. Our work paves the way to realize the quantum Spin Hall effect (QSHE), quantum anomalous Hall effect (QAHE), and even exotic non-Abelian anyons in the fractional quantum Hall effect (FQHE)

Electrical probes of the non-Abelian spin liquid in Kitaev materials

Recent thermal-conductivity measurements evidence a magnetic-field-induced non-Abelian spin liquid phase in the Kitaev material $\alpha$-$\mathrm{RuCl}_{3}$. Although the platform is a good Mott insulator, we propose experiments that electrically probe the spin liquid's hallmark chiral Majorana edge state and bulk anyons, including their exotic exchange statistics. We specifically introduce circuits that exploit interfaces between electrically active systems and Kitaev materials to `perfectly' convert electrons from the former into emergent fermions in the latter---thereby enabling variations of transport probes invented for topological superconductors and fractional quantum Hall states. Along the way we resolve puzzles in the literature concerning interacting Majorana fermions, and also develop an anyon-interferometry framework that incorporates nontrivial energy-partitioning effects. Our results illuminate a partial pathway towards topological quantum computation with Kitaev materials.Comment: 35 pages, 17 figure

Electrical Probes of the Non-Abelian Spin Liquid in Kitaev Materials

Recent thermal-conductivity measurements evidence a magnetic-field-induced non-Abelian spin-liquid phase in the Kitaev material α−RuCl₃. Although the platform is a good Mott insulator, we propose experiments that electrically probe the spin liquid’s hallmark chiral Majorana edge state and bulk anyons, including their exotic exchange statistics. We specifically introduce circuits that exploit interfaces between electrically active systems and Kitaev materials to “perfectly” convert electrons from the former into emergent fermions in the latter—thereby enabling variations of transport probes invented for topological superconductors and fractional quantum-Hall states. Along the way, we resolve puzzles in the literature concerning interacting Majorana fermions, and also develop an anyon-interferometry framework that incorporates nontrivial energy-partitioning effects. Our results illuminate a partial pathway toward topological quantum computation with Kitaev materials

Quantum spin Hall edge states and interlayer coupling in twisted-bilayer WTe2_2

The quantum spin Hall (QSH) effect, characterized by topologically protected spin-polarized edge states, was recently demonstrated in monolayers of the transition metal dichalcogenide (TMD) WTe$_2$. However, the robustness of this topological protection remains largely unexplored in van der Waals heterostructures containing one or more layers of a QSH insulator. In this work, we use scanning tunneling microscopy and spectroscopy (STM/STS) to explore the topological nature of twisted bilayer (tBL) WTe$_2$ which is produce from folded monolayers, as well as, tear-and-stack fabrication. At the tBL bilayer edge, we observe the characteristic spectroscopic signature of the QSH edge state that is absent in topologically trivial as-grown bilayer. For small twist angles, a rectangular moir\'e pattern develops, which results in local modifications of the band structure. Using first principles calculations, we quantify the interactions in tBL WTe$_2$ and its topological edge states as function of interlayer distance and conclude that it is possible to tune the topology of WTe$_2$ bilayers via the twist angle as well as interlayer interactions

What you know can influence what you are going to know (especially for older adults)

Stimuli related to an individual's knowledge/experience are often more memorable than abstract stimuli, particularly for older adults. This has been found when material that is congruent with knowledge is contrasted with material that is incongruent with knowledge, but there is little research on a possible graded effect of congruency. The present study manipulated the degree of congruency of study material with participants’ knowledge. Young and older participants associated two famous names to nonfamous faces, where the similarity between the nonfamous faces and the real famous individuals varied. These associations were incrementally easier to remember as the name-face combinations became more congruent with prior knowledge, demonstrating a graded congruency effect, as opposed to an effect based simply on the presence or absence of associations to prior knowledge. Older adults tended to show greater susceptibility to the effect than young adults, with a significant age difference for extreme stimuli, in line with previous literature showing that schematic support in memory tasks particularly benefits older adults

Steamed broccoli sprouts alleviate DSS-induced inflammation and retain gut microbial biogeography in mice

Inflammatory bowel diseases (IBDs) are devastating conditions of the gastrointestinal tract with limited treatments, and dietary intervention may be effective and affordable for managing symptoms. Glucosinolate compounds are highly concentrated in broccoli sprouts, especially glucoraphanin (GLR), and can be metabolized by certain mammalian gut bacteria into antiinflammatory isothiocyanates, such as sulforaphane. Gut microbiota exhibit biogeographic patterns, but it is unknown if colitis alters these or whether the location of glucoraphanin-metabolizing bacteria affects antiinflammatory benefits. We fed specific pathogen-free C57BL/6 mice either a control diet or a 10% steamed broccoli sprout diet and gave a three-cycle regimen of 2.5% dextran sodium sulfate (DSS) in drinking water over a 34-day experiment to simulate chronic, relapsing ulcerative colitis (UC). We monitored body weight, fecal characteristics, lipocalin, serum cytokines, and bacterial communities from the luminal- and mucosal-associated populations in the jejunum, cecum, and colon. Mice fed the broccoli sprout diet with DSS treatment performed better than mice fed the control diet with DSS, and had significantly more weight gain, lower Disease Activity Index scores, lower plasma lipocalin and proinflammatory cytokines, and higher bacterial richness in all gut locations. Bacterial communities were assorted by gut location but were more homogenous across locations in the control diet + DSS mice. Importantly, our results showed that broccoli sprout feeding abrogated the effects of DSS on gut microbiota, as bacterial richness and biogeography were similar between mice receiving broccoli sprouts with and without DSS. Collectively, these results support the protective effect of steamed broccoli sprouts against dysbiosis and colitis induced by DSS

Combining random forest and 2D correlation analysis to identify serum spectral signatures for neuro-oncology

Fourier transform infrared (FTIR) spectroscopy has long been established as an analytical tech- nique for the measurement of vibrational modes of molecular systems. More recently, FTIR has been used for the analysis of biofluids with the aim of becoming a tool to aid diagnosis. For the clinician, this represents a convenient, fast, non-subjective option for the study of biofluids and the diagnosis of disease states. The patient also benefits from this method, as the procedure for the collection of serum is much less invasive and stressful than traditional biopsy. This is especially true of patients in whom brain cancer is suspected. A brain biopsy carries a degree of morbidity and mortality and on occasion may even be inconclusive. We therefore present a method for the diagnosis of brain cancer from serum samples using FTIR and machine learning techniques. The scope of the study involved 433 patients from whom were collected 9 spectra each in the range 600-4000 cm−1. To begin development of the novel method, various pre-processing steps were investigated and ranked in terms of final accuracy of the diagnosis. Random Forest machine learning was utilised as a classifier to separate patients into cancer or non-cancer categories based upon the intensities of wavenumbers present in their spectra. Generalised 2D correlational analysis was then employed to further augment the machine learning, and also to establish spec- tral features important for the distinction between cancer and non-cancer serum samples. Using these methods, sensitivities of up to 92.8% and specificities of up to 91.5% were possible. Fur- thermore, ratiometrics were also investigated in order to establish any correlations present in the dataset. We show a rapid, computationally light, accurate, statistically robust methodology for the identification of spectral features present in differing disease states. With current advances in IR technology, such as the development of rapid discrete frequency collection, this approach is import to allow future clinical translation and enables IR to achieve its potential