Computer-aided Visualization of Colonoscopy

Colonoscopy is the most widely used medical technique to examine the human large intestine (colon) and eliminate precancerous or malignant lesions, i.e., polyps. It uses a high-definition camera to examine the inner surface of the colon. Very often, a portion of the colon surface is not visualized during the procedure. Unsurveyed portions of the colon can harbor polyps that then progress to colorectal cancer. Unfortunately, it is hard for the endoscopist to realize there is unsurveyed surface from the video as it is formed. A system to alert endoscopists to missed surface area could thus more fully protect patients from colorectal cancer following colonoscopy. In this dissertation computer-aided visualization techniques were developed in order to solve this problem:1. A novel Simultaneous Localization and Mapping (SLAM) algorithm called RNNSLAM was proposed to address the difficulties of applying a traditional SLAM system on colonic images. I improved a standard SLAM system with a previously proposed Recurrent Neural Network for Depth and Pose Estimation (RNN-DP). The combination of SLAM’s optimization mechanism and RNN-DP’s prior knowledge achieved state-of-the-art performance on colonoscopy, especially addressing the drift problem in both SLAM and RNN-DP. A fusion module was added to this system to generate a dense 3D surface.2. I conducted exploration research on recognizing colonic places that have been visited based on video frames. This technique called image relocalization or retrieval is needed for helping the endoscopist to fully survey the previously unsurveyed regions. A benchmark testing dataset was created for colon image retrieval. Deep neural networks were successfully trained using Structure from Motion results on colonoscopy and achieved promising results.3. To visualize highly-curved portions of a colon or the whole colon, a generalized cylinder deformation algorithm was proposed to semi-flatten the geometry of the colon model for more succinct and global visualization.Doctor of Philosoph

Original Article Establishing a rapid animal model of osteoporosis with ovariectomy plus low calcium diet in rats

Abstract: The objective of this study was to rapidly develop osteoporotic model animals by combining ovariectomy with a low calcium diet in rats. Thirty, eight-week-old, female, Sprague-Dawley rats were either sham-operated (Sham) or ovariectomized (Ovx) and divided into three groups: Sham, Ovx, and Ovx + low calcium diet. Rats in the Sham and Ovx groups were fed a standard diet containing 1.1% w/w calcium while rats in the Ovx + low calcium diet group were fed a diet containing 0.1% w/w calcium. Serum osteocalcin and bone mineral density (BMD) of the lumbar vertebrae were measured 4 and 8 weeks after surgery. The rats were euthanized 12 weeks after surgery, and the BMD of the right femur and histomorphometry of the femoral neck were assessed at that time. The Ovx + low-calcium diet group had a significantly lower mean BMD of the lumbar vertebra and higher mean serum osteocalcin concentration than the Sham and Ovx groups. Twelve weeks after surgery, rats in the Ovx + low calcium diet group had a significantly lower BMD, smaller Tb.Th and Tb.N, and larger Tb.Sp of the right femoral neck than did rats in the Sham and Ovx groups. These data indicate that a low calcium diet can significantly accelerate bone loss in ovariectomized rats. Combining ovariectomy and a low calcium diet can save considerable time in the creation of osteoporotic model animals

A scheme for determining vehicle routes based on Arc-based service network design

In freight transportation, less-than-truckload carriers often need to assign each vehicle a cyclic route so that drivers can come back home after a certain period of time. However, the Node-Arc model for service network design addresses decisions on each arc and does not determine routes directly, although the vehicle balancing constraint ensures that the number of outgoing vehicles equals the number of incoming vehicles at each node. How to transform the optimized service network into a set of vehicle routes remains an important problem that has not yet been studied. In this paper, we propose a three-phase scheme to address this problem. In the first stage, we present an algorithm based on the depth-first search to find all of the different cyclic routes in a service network design solution. In the second stage, we propose to prune poor cyclic routes using real-life constraints so that a collection of acceptable vehicle routes can be obtained before route assignment. Some of the pruning can also be done in the first stage to speed up the proposed algorithm. In the third stage, we formulate the problem of selecting a set of cyclic routes to cover the entire network as a weighted set covering problem. The resulting model is formulated as an integer program and solved with IBM ILOG CPLEX solver. Experimental results on benchmark instances for service network design indicate the effectiveness of the proposed scheme which gives high-quality solutions in an efficient way

LyricWhiz: Robust Multilingual Zero-shot Lyrics Transcription by Whispering to ChatGPT

We introduce LyricWhiz, a robust, multilingual, and zero-shot automatic lyrics transcription method achieving state-of-the-art performance on various lyrics transcription datasets, even in challenging genres such as rock and metal. Our novel, training-free approach utilizes Whisper, a weakly supervised robust speech recognition model, and GPT-4, today's most performant chat-based large language model. In the proposed method, Whisper functions as the "ear" by transcribing the audio, while GPT-4 serves as the "brain," acting as an annotator with a strong performance for contextualized output selection and correction. Our experiments show that LyricWhiz significantly reduces Word Error Rate compared to existing methods in English and can effectively transcribe lyrics across multiple languages. Furthermore, we use LyricWhiz to create the first publicly available, large-scale, multilingual lyrics transcription dataset with a CC-BY-NC-SA copyright license, based on MTG-Jamendo, and offer a human-annotated subset for noise level estimation and evaluation. We anticipate that our proposed method and dataset will advance the development of multilingual lyrics transcription, a challenging and emerging task.Comment: 9 pages, 2 figures, 5 tables, accepted by ISMIR 202

On the Effectiveness of Speech Self-supervised Learning for Music

Self-supervised learning (SSL) has shown promising results in various speech and natural language processing applications. However, its efficacy in music information retrieval (MIR) still remains largely unexplored. While previous SSL models pre-trained on music recordings may have been mostly closed-sourced, recent speech models such as wav2vec2.0 have shown promise in music modelling. Nevertheless, research exploring the effectiveness of applying speech SSL models to music recordings has been limited. We explore the music adaption of SSL with two distinctive speech-related models, data2vec1.0 and Hubert, and refer to them as music2vec and musicHuBERT, respectively. We train $12$ SSL models with 95M parameters under various pre-training configurations and systematically evaluate the MIR task performances with 13 different MIR tasks. Our findings suggest that training with music data can generally improve performance on MIR tasks, even when models are trained using paradigms designed for speech. However, we identify the limitations of such existing speech-oriented designs, especially in modelling polyphonic information. Based on the experimental results, empirical suggestions are also given for designing future musical SSL strategies and paradigms

MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training

Self-supervised learning (SSL) has recently emerged as a promising paradigm for training generalisable models on large-scale data in the fields of vision, text, and speech. Although SSL has been proven effective in speech and audio, its application to music audio has yet to be thoroughly explored. This is primarily due to the distinctive challenges associated with modelling musical knowledge, particularly its tonal and pitched characteristics of music. To address this research gap, we propose an acoustic Music undERstanding model with large-scale self-supervised Training (MERT), which incorporates teacher models to provide pseudo labels in the masked language modelling (MLM) style acoustic pre-training. In our exploration, we identified a superior combination of teacher models, which outperforms conventional speech and audio approaches in terms of performance. This combination includes an acoustic teacher based on Residual Vector Quantization - Variational AutoEncoder (RVQ-VAE) and a musical teacher based on the Constant-Q Transform (CQT). These teachers effectively guide our student model, a BERT-style transformer encoder, to better model music audio. In addition, we introduce an in-batch noise mixture augmentation to enhance the representation robustness. Furthermore, we explore a wide range of settings to overcome the instability in acoustic language model pre-training, which allows our designed paradigm to scale from 95M to 330M parameters. Experimental results indicate that our model can generalise and perform well on 14 music understanding tasks and attains state-of-the-art (SOTA) overall scores. The code and models are online: https://github.com/yizhilll/MERT

Spatial and temporal clonal evolution of intrahepatic cholangiocarcinoma

Background & Aims: Intrahepatic cholangiocarcinoma (ICC) is the second-most lethal primary liver cancer. Little is known about intratumoral heterogeneity (ITH) and its impact on ICC progression. We aim to investigate its ITH in hope of helping develop new therapeutic strategies. Methods: We obtained 69 spatially distinct regions from 6 operable ICCs. Patient-derived primary cancer cells (PDPCs) were established for each region, followed by whole-exome sequencing(WES) and multi-level validation. Results: We observed widespread ITH for both somatic mutations and clonal architecture, shaped by multiple mechanisms, like clonal “illusion”, parallel evolution and chromosome instability. A median of 60.3% mutations were heterogeneous mutations, among which 85% of the driver mutations located on the branches of tumor phylogenetic trees. Many truncal and clonal driver mutations occurred in tumor-suppressor genes, such as TP53, SMARCB1 and PBRM1 that involved in DNA repair and chromatin-remodeling. Genome doubling occurred in most cases (5/6) after the accumulation of truncal mutations and was shared by all intratumoral subregions. In all cases, ongoing chromosomal instability is evident throughout the evolutionary trajectory of ICC. The recurrence of ICC1239 provided evidence to support the polyclonal metastatic seeding in ICC. The change of mutation landscape and internal diversity among subclones during metastasis, such as the loss of chemoresistance mediator, may be used for new treatment strategy. Targeted therapy against truncal alterations, such as IDH1, JAK1, and KRAS mutations and EGFR amplification, could be developed in 5/6 patients. Conclusions: Integrated investigations of spatial ITH and clonal evolution may provide an important molecular foundation for enhanced understanding of tumorigenesis and progression in ICC. Lay summary: We applied multiregional whole exome sequencing to investigate the evolution trajectory of ICC. The results revealed that many fuels, such as parallel evolution and chromosome instability, may participate and promote the branch diversity of ICC. Interestingly, in one patient with primary and recurrent metastatic tumors, we found some clues of polyclonal metastatic seeding, indicating that symbiotic communities of multiple clones existed and were maintained during metastasis. More realistically, some truncal alterations, such as IDH1, JAK1, and KRAS mutations and EGFR amplification, can be promising treatment targets for ICC patients

Supercapacitors (electrochemical capacitors)

International audienceRapid development of the technologies based on electric energy in the last decades have stimulated intensive research on efficient power sources. Electrochemical energy conversion and storage systems are based on Faradaic reactions (charge transfer) and electrostatic attraction of ions at the electrode/electrolyte interface. The latter might be an interesting solution for applications requiring moderate energy density, high power rates, and long cycle life. Electrochemical capacitors (ECs) store the charge in a physical manner, hence, their energy density is moderate. At the same time, the lack of electrochemical reactions ensures very high power and long cycle life compared to batteries. Activated carbons with their versatile properties (like specific surface area, well-developed and suitable porosity, heteroatoms in the graphene matrix) are the most popular materials in EC application. This chapter provides a comprehensive overview of the carbon-based materials recently developed, with special attention devoted to those obtained by biomass carbonization and activation. Electrochemical properties demonstrated by such carbons are discussed in respect to their physicochemical characteristic

Comprehensive molecular characterization of gastric adenocarcinoma

Gastric cancer is a leading cause of cancer deaths, but analysis of its molecular and clinical characteristics has been complicated by histological and aetiological heterogeneity. Here we describe a comprehensive molecular evaluation of 295 primary gastric adenocarcinomas as part of The Cancer Genome Atlas (TCGA) project. We propose a molecular classification dividing gastric cancer into four subtypes: tumours positive for Epstein–Barr virus, which display recurrent PIK3CA mutations, extreme DNA hypermethylation, and amplification of JAK2, CD274 (also known as PD-L1) and PDCD1LG2 (also knownasPD-L2); microsatellite unstable tumours, which show elevated mutation rates, including mutations of genes encoding targetable oncogenic signalling proteins; genomically stable tumours, which are enriched for the diffuse histological variant and mutations of RHOA or fusions involving RHO-family GTPase-activating proteins; and tumours with chromosomal instability, which show marked aneuploidy and focal amplification of receptor tyrosine kinases. Identification of these subtypes provides a roadmap for patient stratification and trials of targeted therapies

Multiplatform Analysis of 12 Cancer Types Reveals Molecular Classification within and across Tissues of Origin

Recent genomic analyses of pathologically-defined tumor types identify “within-a-tissue” disease subtypes. However, the extent to which genomic signatures are shared across tissues is still unclear. We performed an integrative analysis using five genome-wide platforms and one proteomic platform on 3,527 specimens from 12 cancer types, revealing a unified classification into 11 major subtypes. Five subtypes were nearly identical to their tissue-of-origin counterparts, but several distinct cancer types were found to converge into common subtypes. Lung squamous, head & neck, and a subset of bladder cancers coalesced into one subtype typified by TP53 alterations, TP63 amplifications, and high expression of immune and proliferation pathway genes. Of note, bladder cancers split into three pan-cancer subtypes. The multi-platform classification, while correlated with tissue-of-origin, provides independent information for predicting clinical outcomes. All datasets are available for data-mining from a unified resource to support further biological discoveries and insights into novel therapeutic strategies