Fractional quantum Hall effect at ν=5/2\nu = 5/2: Ground states, non-Abelian quasiholes, and edge modes in a microscopic model

We present a comprehensive numerical study of a microscopic model of the fractional quantum Hall system at filling fraction $\nu = 5/2$, based on the disc geometry. Our model includes Coulomb interaction and a semi-realistic confining potential. We also mix in some three-body interaction in some cases to help elucidate the physics. We obtain a phase diagram, discuss the conditions under which the ground state can be described by the Moore-Read state, and study its competition with neighboring stripe phases. We also study quasihole excitations and edge excitations in the Moore-Read--like state. From the evolution of edge spectrum, we obtain the velocities of the charge and neutral edge modes, which turn out to be very different. This separation of velocities is a source of decoherence for a non-Abelian quasihole/quasiparticle (with charge $\pm e/4$) when propagating at the edge; using numbers obtained from a specific set of parameters we estimate the decoherence length to be around four microns. This sets an upper bound for the separation of the two point contacts in a double point contact interferometer, designed to detect the non-Abelian nature of such quasiparticles. We also find a state that is a potential candidate for the recently proposed anti-Pfaffian state. We find the speculated anti-Pfaffian state is favored in weak confinement (smooth edge) while the Moore-Read Pfaffian state is favored in strong confinement (sharp edge).Comment: 15 pages, 9 figures; Estimate of e/4 quasiparticle/hole coherence length when propagating along the edge modified in response to a recent revision of Ref. 25, and minor changes elsewher

BOOST: A fast approach to detecting gene-gene interactions in genome-wide case-control studies

Gene-gene interactions have long been recognized to be fundamentally important to understand genetic causes of complex disease traits. At present, identifying gene-gene interactions from genome-wide case-control studies is computationally and methodologically challenging. In this paper, we introduce a simple but powerful method, named `BOolean Operation based Screening and Testing'(BOOST). To discover unknown gene-gene interactions that underlie complex diseases, BOOST allows examining all pairwise interactions in genome-wide case-control studies in a remarkably fast manner. We have carried out interaction analyses on seven data sets from the Wellcome Trust Case Control Consortium (WTCCC). Each analysis took less than 60 hours on a standard 3.0 GHz desktop with 4G memory running Windows XP system. The interaction patterns identified from the type 1 diabetes data set display significant difference from those identified from the rheumatoid arthritis data set, while both data sets share a very similar hit region in the WTCCC report. BOOST has also identified many undiscovered interactions between genes in the major histocompatibility complex (MHC) region in the type 1 diabetes data set. In the coming era of large-scale interaction mapping in genome-wide case-control studies, our method can serve as a computationally and statistically useful tool.Comment: Submitte

Rare Variant Association Testing by Adaptive Combination of P-values

With the development of next-generation sequencing technology, there is a great demand for powerful statistical methods to detect rare variants (minor allele frequencies (MAFs)-MidPmethod (Cheung et al., 2012, Genet Epidemiol 36: 675–685) and propose an approach (named ‘adaptive combination of P-values for rare variant association testing’, abbreviated as ‘ADA’) that adaptively combines per-site P-values with the weights based on MAFs. Before combining P-values, we first imposed a truncation threshold upon the per-site P-values, to guard against the noise caused by the inclusion of neutral variants. ThisADA method is shown to outperform popular burden tests and non-burden tests under many scenarios. ADA is recommended for next-generation sequencing data analysis where many neutral variants may be included in a functional region

Latent profiles of resilience and associations with quality of life in head and neck cancer patients undergoing proton and heavy ion therapy

BackgroundPsychological resilience is the most important psychological protection factor for cancer patients in the face of tumors and treatment. However, few studies have performed meaningful latent profile analyses of resilience to identify unobserved subgroups of head and neck cancer patients.PurposeThe purpose of this study was to investigate the characteristics of resilience in head and neck cancer patients using latent profile analysis (LPA) to determine the sociodemographic and disease characteristics of each profile. In particular, we examined the association of different resilience profiles with the quality of life of head and neck cancer patients.MethodsA total of 254 head and neck cancer patients completed a demographic questionnaire, the Resilience Scale Specific to Cancer and the EOTRC QLQ-C3O, used to assess their resilience and quality of life.ResultsLPA identified three distinct profiles based on varying levels of resilience: “low resilience” group (n = 45; 17.72%), “moderate resilience” group (n = 113; 44.49%), and “high resilience” group (n = 96; 37.80%). Gender (χ2 = 6.20; p < 0.01), education level (χ2 = 1,812.59; p < 0.01), treatment regimen (χ2 = 6.32; p < 0.01), tumor stage (χ2 = 3.92; p ≤ 0.05), and initial recurrence (χ2 = 5.13; p < 0.05) were important predictors. High resilience was significantly related to higher quality of life (χ2 = 15.694; p < 0.001).ConclusionsHead and neck cancer patients’ psychological resilience can be categorized as three resilience profiles; those who are female and have a low education level tend to have lower psychological resilience. Low resilience in patients is linked to poor role function and social function, low quality of life, and more severe pain symptoms, highlighting the need to address resilience in patient care for improved wellbeing

Fully Probabilistic Analysis of FRP-to-Concrete Bonded Joints Considering Model Uncertainty

This work presents a full reliability-based analysis framework for fiber-reinforced polymer(FRP)-to-concrete bonded joints considering model uncertainty. Eight frequently used bond strength models for FRP-to-concrete bonded joints were calibrated by defining a model factor. A total of 641 well-documented tests were considered. Four of the eight models had model factors that correlated with input design parameters and the systematic part of the model factor was removed by a regression equation f. By doing this type of characterization, all eight model factors could be comparatively uniform and described by lognormally distributed random variables. The merit of the uniform model uncertainties after calibration for the eight models was established by the reliability analysis. This study improves the predictability of concrete strengthened with fiber composites and provides useful suggestions on their model uncertainties in engineering practice

Diffusion-based Data Augmentation for Nuclei Image Segmentation

Nuclei segmentation is a fundamental but challenging task in the quantitative analysis of histopathology images. Although fully-supervised deep learning-based methods have made significant progress, a large number of labeled images are required to achieve great segmentation performance. Considering that manually labeling all nuclei instances for a dataset is inefficient, obtaining a large-scale human-annotated dataset is time-consuming and labor-intensive. Therefore, augmenting a dataset with only a few labeled images to improve the segmentation performance is of significant research and application value. In this paper, we introduce the first diffusion-based augmentation method for nuclei segmentation. The idea is to synthesize a large number of labeled images to facilitate training the segmentation model. To achieve this, we propose a two-step strategy. In the first step, we train an unconditional diffusion model to synthesize the Nuclei Structure that is defined as the representation of pixel-level semantic and distance transform. Each synthetic nuclei structure will serve as a constraint on histopathology image synthesis and is further post-processed to be an instance map. In the second step, we train a conditioned diffusion model to synthesize histopathology images based on nuclei structures. The synthetic histopathology images paired with synthetic instance maps will be added to the real dataset for training the segmentation model. The experimental results show that by augmenting 10% labeled real dataset with synthetic samples, one can achieve comparable segmentation results with the fully-supervised baseline.Comment: MICCAI 2023, released code: https://github.com/lhaof/Nudif

Multi-stream Cell Segmentation with Low-level Cues for Multi-modality Images

Cell segmentation for multi-modal microscopy images remains a challenge due to the complex textures, patterns, and cell shapes in these images. To tackle the problem, we first develop an automatic cell classification pipeline to label the microscopy images based on their low-level image characteristics, and then train a classification model based on the category labels. Afterward, we train a separate segmentation model for each category using the images in the corresponding category. Besides, we further deploy two types of segmentation models to segment cells with roundish and irregular shapes respectively. Moreover, an efficient and powerful backbone model is utilized to enhance the efficiency of our segmentation model. Evaluated on the Tuning Set of NeurIPS 2022 Cell Segmentation Challenge, our method achieves an F1-score of 0.8795 and the running time for all cases is within the time tolerance.Comment: The second place in NeurIPS 2022 cell segmentation challenge (https://neurips22-cellseg.grand-challenge.org/), released code: https://github.com/lhaof/CellSe