Prediction of Abdominal Aortic Aneurysm Growth by Automatic Segmentation and Radiomics Feature Quantification

An accurate assessment of abdominal aortic aneurysm (AAA) progression is essential to its clinical management. Currently, the maximum diameter of AAA at diagnosis is considered as the primary indicator of rupture risk. However, it is not optimal as rupture can happen at any size. Several patient-specific factors may also influence AAA rupture risk. Given the clinical variability in aneurysm progression, additional prognostic markers are desirable to enhance patient-specific risk stratification. Radiomics is an image processing technique that extracts quantitative and high-dimensional features from medical images. While it has emerged as a novel approach for solving diagnosis in oncology, its application in cardiovascular diseases is still limited. This study set out with an aim to determine the feasibility of radiomics in identifying AAA with a fast growth rate (>0.3cm/year) using CT images. An automatic AAA segmentation algorithm was developed in our pipeline. Based on the radiomics features of an 84 CT dataset, supervised classification models were implemented with two feature selection algorithms and two classifiers in a machine-learning framework. An AUC of 0.80 was achieved and the predictive power was proved through comparisons to the maximum diameter and conventional risk factors. Further multivariate analysis suggested that a radiomics-based classification model could be used as an independent, yet strong predictor for fast AAA growth rate

Graph algorithms for NMR resonance assignment and cross-link experiment planning

The study of three-dimensional protein structures produces insights into protein function at the molecular level. Graphs provide a natural representation of protein structures and associated experimental data, and enable the development of graph algorithms to analyze the structures and data. This thesis develops such graph representations and algorithms for two novel applications: structure-based NMR resonance assignment and disulfide cross-link experiment planning for protein fold determination. The first application seeks to identify correspondences between spectral peaks in NMR data and backbone atoms in a structure (from x-ray crystallography or homology modeling), by computing correspondences between a contact graph representing the structure and an analogous but very noisy and ambiguous graph representing the data. The assignment then supports further NMR studies of protein dynamics and protein-ligand interactions. A hierarchical grow-and-match algorithm was developed for smaller assignment problems, ensuring completeness of assignment, while a random graph approach was developed for larger problems, provably determining unique matches in polynomial time with high probability. Test results show that our algorithms are robust to typical levels of structural variation, noise, and missings, and achieve very good overall assignment accuracy. The second application aims to rapidly determine the overall organization of secondary structure elements of a target protein by probing it with a set of planned disulfide cross-links. A set of informative pairs of secondary structure elements is selected from graphs representing topologies of predicted structure models. For each pair in this ``fingerprint\u27\u27, a set of informative disulfide probes is selected from graphs representing residue proximity in the models. Information-theoretic planning algorithms were developed to maximize information gain while minimizing experimental complexity, and Bayes error plan assessment frameworks were developed to characterize the probability of making correct decisions given experimental data. Evaluation of the approach on a number of structure prediction case studies shows that the optimized plans have low risk of error while testing only a very small portion of the quadratic number of possible cross-link candidates

Induction of chondrogenic morphogenesis in tissue culture using different combinations of transforming growth factor-beta superfamily proteins in vitro

BACKGROUND: The regeneration of cartilage has always been a challenge for tissue engineering. Constantly renewed insights into the role of transforming growth factor-beta (TGF-β) supergene family of proteins, which are vital in several fundamental biological processes in cartilage health and regeneration, has opened up new prospects for the treatment of cartilage-related diseases. In this study, the aim was to investigate what the effect of three different growth factors from the TGF-β supergene family specifically [bone morphogenetic protein 2 (BMP-2); TGF-β3; osteogenic protein 1 (OP-1)], alone but especially in varying combinations including application durations, would have on the induction of chondrogenesis in muscle tissue of rats. METHODS: Abdominal muscle tissue from rats was utilized. To monitor what the effect of morphogen presence would have on chondrogenesis, the “withdrawal study”, assessed two modes of stimulation. These were a continuous application of relevant morphogens and their combinations for the entire duration of the in vitro culture or a single application only for 48h. The detections were performed on day 7, 14 and 30 using immunohistochemistry (IHC), histological staining (alcian blue staining) and quantitative reverse-transcriptase polymerase chain reaction (qRT-PCR). Aggrecan was treated as the target antigen in the IHC. The relative gene expression levels were analyzed to confirm the survival of the model and the chondrogenesis, including vascular endothelial growth factor A (VEGF-A), collagen type IV alpha 1 (Col4α1), sex-determining region Y (SRY)-box 9 (SOX9), aggrecan (ACAN), collagen type II alpha 1 (Col2α1), collagen type X alpha 1 (Col10α1), collagen type I alpha 1 (Col1α1) and alkaline phosphatase (ALP). RESULTS: The results of the qRT-PCR showed that the up-regulation in gene expression for the continuous experimental groups was more significant than that of the single 48h stimulation groups. The group with BMP-2 alone continuously presented the highest relative expression levels on day 7, in terms of the chondrogenic-related genes. Positive reactions were observed in the alcian blue staining and IHC with semi-quantitative histomorphometrical analysis showing a correlation to that of the gene expression patterns. CONCLUSIONS: Muscle tissue was proven to be a viable model in this chondrogenic induction study. The application of members of the TGF-β supergene family, alone or in combinations, induced chondrogenesis in this tissue model, with results suggesting that hyaline cartilage chondrogenesis was being developed based on the Col2α1 expression patterns. Although it was attempted to get a more economic-efficiency induction scheme using the withdraw-study in this project, it was shown that single stimulation of a growth factor was insufficient to evoke the relevant response, strongly suggesting that a continuous stimulation is necessary. However, the results in this regard have to be interpreted with care as it is clear that a single morphogen has a limited spatial and temporal effect where the presence of the appropriate corresponding complementary soluble signal(s) needs to be present at the correct time to ensure a proper and sustained biological reaction of specific pathways with time. This was exemplified by BMP-2 that on its own was able to initiate chondrogenesis, yet when added in combination with TGF-β3 and/or OP-1 was inhibited. However, while the BMP-2 initially stimulated chondrogenesis, it could not maintain the relevant reaction in the middle and late stages of chondrogenic induction, where TGF-β3 and OP-1 were necessary to maintain the cartilage tissue engineering reaction. Although limitations still exist, the experiments provide a crucial realization in the TGF-β supergene family tissue engineering prospect and deliver novel awareness and strategies in producing engineered hyaline cartilage for future clinical applications

Dynamic structure of stock communities: A comparative study between stock returns and turnover rates

The detection of community structure in stock market is of theoretical and practical significance for the study of financial dynamics and portfolio risk estimation. We here study the community structures in Chinese stock markets from the aspects of both price returns and turnover rates, by using a combination of the PMFG and infomap methods based on a distance matrix. We find that a few of the largest communities are composed of certain specific industry or conceptional sectors and the correlation inside a sector is generally larger than the correlation between different sectors. In comparison with returns, the community structure for turnover rates is more complex and the sector effect is relatively weaker. The financial dynamics is further studied by analyzing the community structures over five sub-periods. Sectors like banks, real estate, health care and New Shanghai take turns to compose a few of the largest communities for both returns and turnover rates in different sub-periods. Several specific sectors appear in the communities with different rank orders for the two time series even in the same sub-period. A comparison between the evolution of prices and turnover rates of stocks from these sectors is conducted to better understand their differences. We find that stock prices only had large changes around some important events while turnover rates surged after each of these events relevant to specific sectors, which may offer a possible explanation for the complexity of stock communities for turnover rates