9,319 research outputs found
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
The Structure and Function of the Retina in Multiple Sclerosis
Background: Multiple sclerosis (MS) is a complex heterogenous autoimmune inflammatory disease with a prolonged and variable time course. The visual system is frequently implicated, either as the presenting symptom, or, with advancement of the disease. This has been documented in the literature with changes in visual acuity (VA) that are accompanied by functional changes in the optic nerve, measured with the visual evoked potential (VEP) and possible retrograde degeneration involving the retinal ganglion cells in the retina, measured with the pattern reversal electroretinogram (PERG). However, inflammatory episodes may be clinical or subclinical in nature and may go unrecognised. Originating from the same embryological origins, the effect of inflammation in MS on the on the retina is less well known. The research hypothesis was that there is a measurable difference in the function of retinal cells in patients with newly diagnosed multiple sclerosis, suggestive of inflammatory retinopathy compared to healthy controls.
The overall aim was to investigate any differences in the electrophysiological function of the visual pathway of patients newly diagnosed with MS compared to healthy controls.
Methods: The visual system is explored with clinical (VA), electrophysiology (VEP and electroretinography (ERG – pattern and flash) and structural (OCT) measures, in patients presenting with symptoms suggestive of MS to a specialist service. This prospective case control study investigates the visual pathway at the earliest stage of the disease to look for differences in structure and function between patients and healthy volunteers that might serve as a biomarker in the future.
Results: There were a number of variables that were significantly different between the two groups, logistic regression analysis found that VA (p 0.038) and VEP P100 peak-time (p 0.014) from the right eye as significant. Dividing the participants by prolongation of the VEP P100 peak-time as defined in clinical practice, found a number of ERG amplitude variables as well as VA that were consistently different between the groups regardless of symptoms.
Conclusion: The study confirms optic nerve involvement in MS with VEP and VA abnormalities consistent with the literature in this cohort. Additionally, VA and some ERG amplitude variables were significantly reduced in participants with MS, when grouped according to VEP P100 peak-time, suggesting inner and outer retinal changes. Further work would be required to confirm these findings. No OCT structural changes were found in any of the analysis that included the macula thickness, ganglion cell layer or retinal nerve fibre layer.
Keywords: multiple sclerosis (MS), visual evoked potential (VEP), pattern electroretinogram (PERG), electroretinogram (ERG), optical coherence tomography (OCT
A systematic literature review on source code similarity measurement and clone detection: techniques, applications, and challenges
Measuring and evaluating source code similarity is a fundamental software
engineering activity that embraces a broad range of applications, including but
not limited to code recommendation, duplicate code, plagiarism, malware, and
smell detection. This paper proposes a systematic literature review and
meta-analysis on code similarity measurement and evaluation techniques to shed
light on the existing approaches and their characteristics in different
applications. We initially found over 10000 articles by querying four digital
libraries and ended up with 136 primary studies in the field. The studies were
classified according to their methodology, programming languages, datasets,
tools, and applications. A deep investigation reveals 80 software tools,
working with eight different techniques on five application domains. Nearly 49%
of the tools work on Java programs and 37% support C and C++, while there is no
support for many programming languages. A noteworthy point was the existence of
12 datasets related to source code similarity measurement and duplicate codes,
of which only eight datasets were publicly accessible. The lack of reliable
datasets, empirical evaluations, hybrid methods, and focuses on multi-paradigm
languages are the main challenges in the field. Emerging applications of code
similarity measurement concentrate on the development phase in addition to the
maintenance.Comment: 49 pages, 10 figures, 6 table
Evaluation Methodologies in Software Protection Research
Man-at-the-end (MATE) attackers have full control over the system on which
the attacked software runs, and try to break the confidentiality or integrity
of assets embedded in the software. Both companies and malware authors want to
prevent such attacks. This has driven an arms race between attackers and
defenders, resulting in a plethora of different protection and analysis
methods. However, it remains difficult to measure the strength of protections
because MATE attackers can reach their goals in many different ways and a
universally accepted evaluation methodology does not exist. This survey
systematically reviews the evaluation methodologies of papers on obfuscation, a
major class of protections against MATE attacks. For 572 papers, we collected
113 aspects of their evaluation methodologies, ranging from sample set types
and sizes, over sample treatment, to performed measurements. We provide
detailed insights into how the academic state of the art evaluates both the
protections and analyses thereon. In summary, there is a clear need for better
evaluation methodologies. We identify nine challenges for software protection
evaluations, which represent threats to the validity, reproducibility, and
interpretation of research results in the context of MATE attacks
Segmentation of Pathology Images: A Deep Learning Strategy with Annotated Data
Cancer has significantly threatened human life and health for many years. In the clinic, histopathology image segmentation is the golden stand for evaluating the prediction of patient prognosis and treatment outcome. Generally, manually labelling tumour regions in hundreds of high-resolution histopathological images is time-consuming and expensive for pathologists. Recently, the advancements in hardware and computer vision have allowed deep-learning-based methods to become mainstream to segment tumours automatically, significantly reducing the workload of pathologists. However, most current methods rely on large-scale labelled histopathological images. Therefore, this research studies label-effective tumour segmentation methods using deep-learning paradigms to relieve the annotation limitations. Chapter 3 proposes an ensemble framework for fully-supervised tumour segmentation. Usually, the performance of an individual-trained network is limited by significant morphological variances in histopathological images. We propose a fully-supervised learning ensemble fusion model that uses both shallow and deep U-Nets, trained with images of different resolutions and subsets of images, for robust predictions of tumour regions. Noise elimination is achieved with Convolutional Conditional Random Fields. Two open datasets are used to evaluate the proposed method: the ACDC@LungHP challenge at ISBI2019 and the DigestPath challenge at MICCAI2019. With a dice coefficient of 79.7 %, the proposed method takes third place in ACDC@LungHP. In DigestPath 2019, the proposed method achieves a dice coefficient 77.3 %. Well-annotated images are an indispensable part of training fully-supervised segmentation strategies. However, large-scale histopathology images are hardly annotated finely in clinical practice. It is common for labels to be of poor quality or for only a few images to be manually marked by experts. Consequently, fully-supervised methods cannot perform well in these cases. Chapter 4 proposes a self-supervised contrast learning for tumour segmentation. A self-supervised cancer segmentation framework is proposed to reduce label dependency. An innovative contrastive learning scheme is developed to represent tumour features based on unlabelled images. Unlike a normal U-Net, the backbone is a patch-based segmentation network. Additionally, data augmentation and contrastive losses are applied to improve the discriminability of tumour features. A convolutional Conditional Random Field is used to smooth and eliminate noise. Three labelled, and fourteen unlabelled images are collected from a private skin cancer dataset called BSS. Experimental results show that the proposed method achieves better tumour segmentation performance than other popular self-supervised methods. However, by evaluated on the same public dataset as chapter 3, the proposed self-supervised method is hard to handle fine-grained segmentation around tumour boundaries compared to the supervised method we proposed. Chapter 5 proposes a sketch-based weakly-supervised tumour segmentation method. To segment tumour regions precisely with coarse annotations, a sketch-supervised method is proposed, containing a dual CNN-Transformer network and a global normalised class activation map. CNN-Transformer networks simultaneously model global and local tumour features. With the global normalised class activation map, a gradient-based tumour representation can be obtained from the dual network predictions. We invited experts to mark fine and coarse annotations in the private BSS and the public PAIP2019 datasets to facilitate reproducible performance comparisons. Using the BSS dataset, the proposed method achieves 76.686 % IOU and 86.6 % Dice scores, outperforming state-of-the-art methods. Additionally, the proposed method achieves a Dice gain of 8.372 % compared with U-Net on the PAIP2019 dataset. The thesis presents three approaches to segmenting cancers from histology images: fully-supervised, unsupervised, and weakly supervised methods. This research effectively segments tumour regions based on histopathological annotations and well-designed modules. Our studies comprehensively demonstrate label-effective automatic histopathological image segmentation. Experimental results prove that our works achieve state-of-the-art segmentation performances on private and public datasets. In the future, we plan to integrate more tumour feature representation technologies with other medical modalities and apply them to clinical research
Machine Learning Approaches for the Prioritisation of Cardiovascular Disease Genes Following Genome- wide Association Study
Genome-wide association studies (GWAS) have revealed thousands of genetic loci, establishing itself as a valuable method for unravelling the complex biology of many diseases. As GWAS has grown in size and improved in study design to detect effects, identifying real causal signals, disentangling from other highly correlated markers associated by linkage disequilibrium (LD) remains challenging. This has severely limited GWAS findings and brought the method’s value into question. Although thousands of disease susceptibility loci have been reported, causal variants and genes at these loci remain elusive. Post-GWAS analysis aims to dissect the heterogeneity of variant and gene signals. In recent years, machine learning (ML) models have been developed for post-GWAS prioritisation. ML models have ranged from using logistic regression to more complex ensemble models such as random forests and gradient boosting, as well as deep learning models (i.e., neural networks). When combined with functional validation, these methods have shown important translational insights, providing a strong evidence-based approach to direct post-GWAS research. However, ML approaches are in their infancy across biological applications, and as they continue to evolve an evaluation of their robustness for GWAS prioritisation is needed. Here, I investigate the landscape of ML across: selected models, input features, bias risk, and output model performance, with a focus on building a prioritisation framework that is applied to blood pressure GWAS results and tested on re-application to blood lipid traits
Boundary Spanner Corruption in Business Relationships
Boundary spanner corruption—voluntary collaborative behaviour between individuals representing different organisations that violates their organisations’ norms—is a serious problem in business relationships. Drawing on insights from the literatures on general corruption perspectives, the dark side of business relationships and deviance in sales and service organisations, this dissertation identifies boundary spanner corruption as a potential dark side complication inherent in close business relationships It builds research questions from these literature streams and proposes a research structure based upon commonly used methods in corruption research to address this new concept. In the first study, using an exploratory survey of boundary spanner practitioners, the dissertation finds that the nature of boundary spanner corruption is broad and encompasses severe and non-severe types. The survey also finds that these deviance types are prevalent in a widespread of geographies and industries. This prevalence is particularly noticeable for less-severe corruption types, which may be an under-researched phenomenon in general corruption research. The consequences of boundary spanner corruption can be serious for both individuals and organisations. Indeed, even less-severe types can generate long-term negative consequences. A second interview-based study found that multi-level trust factors could also motivate the emergence of boundary spanner corruption. This was integrated into a theoretical model that illustrates how trust at the interpersonal, intraorganisational, and interorganisational levels enables corrupt behaviours by allowing deviance-inducing factors stemming from the task environment or from the individual boundary spanner to manifest in boundary spanner corruption. Interpersonal trust between representatives of different organisations, interorganisational trust between these organisations, and intraorganisational agency trust of management in their representatives foster the development of a boundary-spanning social cocoon—a mechanism that can inculcate deviant norms leading to corrupt behaviour. This conceptualisation and model of boundary spanner corruption highlights intriguing directions for future research to support practitioners engaged in a difficult problem in business relationships
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