Laser stimulated dynamic thermal imaging system for tumor detection

Laser stimulated dynamic thermal imaging system for tumor detectionby Hongyu Meng Doctor of Philosophy in Biomedical Engineering Washington University in St. Louis, 2021 Professor Samuel Achilefu, Chair Recent advances in infrared sensor technology have enabled the rapid application of thermal imaging in materials science, security and medicine. Relying on the infrared characteristics of living systems, thermal imaging has been used to generate individual heat maps, detect inflammation and tumor. As an imaging system, thermal imaging has the advantages of portability, real-time, non-invasive, and non-contact. But the low specificity of thermal imaging hinders its wide clinical application. Unfortunately, label-free DTI is less able to fully capture thermal tissue heterogeneity in high resolution due partly to how thermal stimulation is applied. Current DTI methods apply thermal stimulation to a large area of tissue, which obscures the detection of the unique thermal characteristics of a small area in the thermally disturbed area. Super-resolution DTI grating can improve the spatial resolution, but the system setup is complex. For biological samples, the use of exogenous contrast agents can enhance contrast, but contrast agents increase regulatory hurdles in clinical trials. In this work, we have developed a focused dynamic laser stimulation imaging (FDTI) system to overcome these limitations. The system, which has high resolution, high speed and large field of view uses a short wavelength laser to stimulate small tissue area and a thermal camera to acquire data. We captured thermal images and videos, extracted features, and built classifiers to distinguish tumors from normal tissues. Data analysis showed that FDTI method achieved high accuracy (classifier surpassed 90%) with spatial resolution attaining 1 mm, which surpasses conventional thermal imaging and DTI. We next explored the ability of FDTI to detect early-stage tumors by scanning multiple areas that exhibited normal thermal images with conventional thermal imaging. A bioluminescence imaging (BLI) system was then used to locate the tumor, which was co-registered to the FDTI images to determine the position of the laser spot. By extracting features from the collected thermal images and videos and constructing the classifier, the FDTI system achieved an accuracy greater than 80% in detecting early tumors in different mouse tumor models. Subsequently, the FDTI system was optimized to improve its acquisition speed, automation and robustness. First, we analyzed the influencing factors of imaging and proposed new system hardware designs to improve the data acquisition speed. Then, to shorten the acquisition time from the software level, we tested and analyzed the performance of features at different stages during the acquisition process. We also designed and tested registration markers, including registration results of different features, feature robustness under interference, marker detection from the background, and marker performance in motion correction to improve the degree of automation of the system. Furthermore, we tested the performance of thermal imaging applications in other research fields, including brain tumor detection, nerve damage assessment, and whether temperature changes correlate with stroke. These results show that FDTI is a promising technique for enhancing contrast, improving spatial resolution, determining underlying tumor heterogeneity, and detecting tumors at stages when conventional thermal imaging is ineffective. This work lays a strong foundation for diverse applications and clinical translation of FDTI to address unmet needs of current thermal imaging technologies

Ubiquitous nematic Dirac semimetal emerging from interacting quadratic band touching system

Quadratic band touching (QBT) points are widely observed in 2D and 3D materials, including bilayer graphene and Luttinger semimetals, and attract significant attention from theory to experiment. However, even in its simplest form, the 2D checkerboard lattice QBT model, the phase diagram characterized by temperature and interaction strength still remains unknown beyond the weak-coupling regime. Intense debates persist regarding the existence of various interaction-driven insulating states in this system [1-7]. To address these uncertainties, we employ thermal tensor network simulations, specifically exponential tensor renormalization group [8], along with density matrix renormalization group calculations. Our approach enables us to provide a comprehensive finite-temperature phase diagram for this model and shed light on previous ambiguities. Notably, our findings consistently reveal the emergence of a robust bond-nematic Dirac semimetal (BNDS) phase as an intermediate state between the nematic insulating state and other symmetry broken states. This previously overlooked feature is found to be ubiquitous in interacting QBT systems. We also discuss the implications of these results for experimental systems such as bilayer graphene and iridate compounds

Parallel-Chain Monte Carlo Based on Generative Neural Networks

We design generative neural networks that generate Monte Carlo configurations with complete absence of autocorrelation and from which direct measurements of physical observables can be employed, irrespective of the system locating at the classical critical point, fermionic Mott insulator, Dirac semimetal and quantum critical point. We further propose a generic parallel -chain Monte Carlo scheme based on such neural networks, which provides independent samplings and accelerates the Monte Carlo simulations by reducing the thermalization process. We demonstrate the performance of our approach on the two-dimensional Ising and fermion Hubbard models

Assessing Impulsivity in Chinese: Elaborating Validity of BIS Among Male Prisoners

This study was carried out to test the factor structure and psychometric properties of the Barratt Impulsiveness Scale–Version 11 (BIS-11), and its short versions (the eight-item and 15-item BIS) in a sample of 424 Chinese male prisoners (M = 31.26, SD = 7.43, age = 18-52 years). Confirmatory factor analysis (CFAs) indicated that the single-factor model of BIS with eight items (BIS-8) and the three-factor model of BIS with 15 items (BIS-15) fit the data well. In addition, the item response theory (IRT) approach confirmed the construct and items for the BIS-8 with good discrimination, threshold parameters, and test information curve. Correlations with psychopathic traits, antisocial personality disorder, and aggression suggested that the performance of the eight-item BIS was comparable with that of the original 30-item BIS in measuring general impulsivity.This research was supported by grants from the National Natural Science Foundation of China (Grant 31400904) and Guangzhou University’s 2017 training program for top-notch young people (Grant BJ201715)