Constrained Circular Hidden Markov Models for Recognizing Deformed Shapes

In this paper, we analyse the properties of the standard circular hidden Markov model (HMM) on 2D shape recognition. We point out the limitations of the circular HMMs and further propose to impose the constraint on the relationship between the initial and final states of circular HMMs to improve the performance. We develop two modified Viterbi algorithms to implement our proposal. The proposed algorithms have been tested on the database of the MPEG-7 Core Experiments Shape-1, Part B. The experiments show that both proposed algorithms can achieve better performance than that of the standard circular HMM in terms of accuracy. In particular, the second proposed algorithm, which is faster than elastic matching algorithms, has much potential due to its accuracy and speed

Image Retrieval Using Circular Hidden Markov Models with a Garbage State

Shape-based image and video retrieval is an active research topic in multimedia information retrieval. It is well known that there are significant variations in shapes of the same category extracted from images and videos. In this paper, we propose to use circular hidden Markov models for shape recognition and image retrieval. In our approach, we use a garbage state to explicitly deal with shape mismatch caused by shape deformation and occlusion. We will propose a modi¯ed circular hidden Markov model (HMM)for shape-based image retrieval and then use circular HMMs with a garbage state to further improve the performance. To evaluate the proposed algorithms, we have conducted experiments using the database of the MPEG-7 Core Experiments Shape-1, Part B. The experiments show that our approaches are robust to shape deformations such as shape variations and occlusion. The performance of our approaches is comparable to that of the state-of-the-art shape-based image retrieval systems in terms of accuracy and speed

Machine learning-based approach for efficient prediction of diagnosis, prognosis and lymph node metastasis of papillary thyroid carcinoma using adhesion signature selection

The association between adhesion function and papillary thyroid carcinoma (PTC) is increasingly recognized; however, the precise role of adhesion function in the pathogenesis and prognosis of PTC remains unclear. In this study, we employed the robust rank aggregation algorithm to identify 64 stable adhesion-related differentially expressed genes (ARDGs). Subsequently, using univariate Cox regression analysis, we identified 16 prognostic ARDGs. To construct PTC survival risk scoring models, we employed Lasso Cox and multivariate + stepwise Cox regression methods. Comparative analysis of these models revealed that the Lasso Cox regression model (LPSRSM) displayed superior performance. Further analyses identified age and LPSRSM as independent prognostic factors for PTC. Notably, patients classified as low-risk by LPSRSM exhibited significantly better prognosis, as demonstrated by Kaplan-Meier survival analyses. Additionally, we investigated the potential impact of adhesion feature on energy metabolism and inflammatory responses. Furthermore, leveraging the CMAP database, we screened 10 drugs that may improve prognosis. Finally, using Lasso regression analysis, we identified four genes for a diagnostic model of lymph node metastasis and three genes for a diagnostic model of tumor. These gene models hold promise for prognosis and disease diagnosis in PTC

High-Throughput Field Imaging and Basic Image Analysis in a Wheat Breeding Programme

Visual assessment of colour-based traits plays a key role within field-crop breeding programmes, though the process is subjective and time-consuming. Digital image analysis has previously been investigated as an objective alternative to visual assessment for a limited number of traits, showing suitability and slight improvement to throughput over visual assessment. However, easily adoptable, field-based high-throughput methods are still lacking. The aim of the current study was to produce a high-throughput digital imaging and analysis pipeline for the assessment of colour-based traits within a wheat breeding programme. This was achieved through the steps of (i) a proof-of-concept study demonstrating basic image analysis methods in a greenhouse, (ii) application of these methods to field trials using hand-held imaging, and (iii) developing a field-based high-throughput imaging infrastructure for data collection. The proof of concept study showed a strong correlation (r = 0.95) between visual and digital assessments of wheat physiological yellowing (PY) in a greenhouse environment, with both scores having similar heritability (H2 = 0.85 and 0.76, respectively). Digital assessment of hand-held field images showed strong correlations to visual scores for PY (r = 0.61 and 0.78), senescence (r = 0.74 and 0.75) and Septoria tritici blotch (STB; r = 0.76), with greater heritability of digital scores, excluding STB. Development of the high-throughput imaging infrastructure allowed for images of field plots to be collected at a rate of 7,400 plots per hour. Images of an advanced breeding trial collected with this system were analysed for canopy cover at two time-points, with digital scores correlating strongly to visual scores (r = 0.88 and 0.86) and having similar or greater heritability. This study details how high-throughput digital phenotyping can be applied to colour-based traits within field trials of a wheat breeding programme. It discusses the logistics of implementing such systems with minimal disruption to the programme, provides a detailed methodology for the basic image analysis methods utilized, and has potential for application to other field-crop breeding or research programmes

Effect of sarcopenia on survival of patients with cirrhosis: A meta-analysis

The association between sarcopenia and prognosis in patients with cirrhosis remains to be determined. In this study, we aimed to quantify the association between sarcopenia and the risk of mortality in patients with cirrhosis, by sex, underlying liver disease etiology, and severity of hepatic dysfunction.PubMed, Web of Science, EMBASE, and major scientific conference sessions were searched without language restriction through 13 January 2021 with additional manual search of bibliographies of relevant articles. Cohort studies of ?100 patients with cirrhosis and ?12 months of follow-up that evaluated the association between sarcopenia, muscle mass and the risk of mortality were included.22 studies with 6965 patients with cirrhosis were included. The pooled prevalence of sarcopenia in patients with cirrhosis was 37.5% overall (95% CI 32.4%-42.8%), higher in male patients, patients with alcohol associated liver disease (ALD), patients with CTP grade C, and when sarcopenia was defined in patients by lumbar 3- skeletal muscle index (L3-SMI). Sarcopenia was associated with the increased risk of mortality in patients with cirrhosis (adjusted-hazard ratio [aHR] 2.30, 95% CI 2.01-2.63), with similar findings in sensitivity analysis of cirrhosis patients without HCC (aHR 2.35, 95% CI 1.95-2.83) and in subgroup analysis by sex, liver disease etiology, and severity of hepatic dysfunction. The association between quantitative muscle mass index and mortality further supports the poor prognosis for patients with sarcopenia (aHR 0.95, 95% CI 0.93-0.98). There was no significant heterogeneity in all analyses.Sarcopenia was highly and independently associated with higher risk of mortality in patients with cirrhosis.The prevalence of sarcopenia and its association with death in patients with cirrhosis remain unclear. This meta-analysis indicated that sarcopenia affected about one-third of patients with cirrhosis and up to 50% in patients with ALD or Child's class C cirrhosis. Sarcopenia was independently associated with about 2-fold higher risk of mortality in patients with cirrhosis. The mortality rate increased with greater severity or longer period of having sarcopenia. Increasing awareness about the importance of sarcopenia in patients with cirrhosis among stakeholders must be prioritized

Fast Stereo Matching: Coarser to Finer with Selective Updating

A coarse to fine approach for fast stereo matching is presented. In this approach, a dynamic programming (DP) based algorithm at the top of the pyramid is applied to obtain an initial coarse dense disparity map of high quality and to reduce computational cost. In each finer layer, a new dense disparity map is inherited from the coarser layer by interpolation. The new dense disparity map will be updated only in selected areas, instead of the whole map, according to the local matching cost and the depth difference among neighbouring areas. In this way, the proposed approach is able to obtain a smooth dense disparity map and to preserve discontinuity as well. The approach is evaluated using rectified stereo images and good results are achieved in terms of quality and running speed

Enhanced HMM for the Recognition of Sigma70 Promoters in Escherichia coli

In this paper, we propose an enhanced HMM for the recognition of sigma70 promoters in E. coli. HMMs for -10 and -35 boxes have been proposed to model the positional dependency of motifs which is lost in methods based on weight matrices. We also propose to use a set of spacer states sharing the observation densities to achieve the desired spacer duration probability functions. We have conducted two sets of experiments on recognizing promoters and locating DNA binding sites and the proposed method has achieved very promising results in comparison with earlier neural network approaches

Robust Dynamic Tone Feature Extraction Using 2D Oriented Filters

A novel method is proposed to extract dynamic tone features using 2D oriented filters. As 2D oriented filters are capable of capturing pitch orientations, they can make full use of the information from neighbouring frames to extract dynamic tone features without knowing pitch or using pitch tracking algorithms. Experimental results show that the proposed method can produce dynamic tone features of good quality