CSGM Designer: a platform for designing cross-species intron-spanning genic markers linked with genome information of legumes.

BackgroundGenetic markers are tools that can facilitate molecular breeding, even in species lacking genomic resources. An important class of genetic markers is those based on orthologous genes, because they can guide hypotheses about conserved gene function, a situation that is well documented for a number of agronomic traits. For under-studied species a key bottleneck in gene-based marker development is the need to develop molecular tools (e.g., oligonucleotide primers) that reliably access genes with orthology to the genomes of well-characterized reference species.ResultsHere we report an efficient platform for the design of cross-species gene-derived markers in legumes. The automated platform, named CSGM Designer (URL: http://tgil.donga.ac.kr/CSGMdesigner), facilitates rapid and systematic design of cross-species genic markers. The underlying database is composed of genome data from five legume species whose genomes are substantially characterized. Use of CSGM is enhanced by graphical displays of query results, which we describe as "circular viewer" and "search-within-results" functions. CSGM provides a virtual PCR representation (eHT-PCR) that predicts the specificity of each primer pair simultaneously in multiple genomes. CSGM Designer output was experimentally validated for the amplification of orthologous genes using 16 genotypes representing 12 crop and model legume species, distributed among the galegoid and phaseoloid clades. Successful cross-species amplification was obtained for 85.3% of PCR primer combinations.ConclusionCSGM Designer spans the divide between well-characterized crop and model legume species and their less well-characterized relatives. The outcome is PCR primers that target highly conserved genes for polymorphism discovery, enabling functional inferences and ultimately facilitating trait-associated molecular breeding

Comparison of Clinical Manifestations between Patients with Ocular Myasthenia Gravis and Generalized Myasthenia Gravis

PURPOSE: To compare the clinical manifestations between patients with ocular myasthenia gravis and those with generalized myasthenia gravis (MG). METHODS: The medical records of 71 patients diagnosed with MG between January 1995 and December 2007 were reviewed. Demographics, sensitivities of diagnostic methods, the presence of systemic autoimmune diseases, ophthalmic complications caused by MG, and treatments were evaluated and compared. RESULTS: Fourteen patients (20%) were diagnosed with ocular MG and 57 patients (80%) with generalized MG. Sensitivities of anti-acetylcholine receptor antibody and repetitive nerve stimulation tests were significantly higher in the generalized MG group (84%, 89%) compared to those in the ocular MG group (50%, 54%) (p = 0.011, p = 0.008). The sensitivity of the neostigmine test was the highest in both groups (98% of generalized MG, 79% of ocular MG), and the difference between the two groups was borderline significant (p = 0.058). The most common symptoms were ptosis and diplopia, and both groups presented with pain, blurred vision, and tearing. Systemic autoimmune disease was more prominent in the generalized MG group (21%) than in the ocular MG group (14%), and steroid therapy was used more frequently in the generalized MG group (82%) than in the ocular MG group (57%). Ophthalmic complications associated with long-term steroid treatment were more profound in the generalized MG (30%) compared to those of the ocular MG (21%). CONCLUSIONS: The generalized MG group was associated with higher sensitivities to diagnostic tests, more systemic steroid use, higher ophthalmic complications caused by systemic autoimmune disease, and long-term steroid treatment compared to those of the ocular MG groupope

Influence of Oryzanol and Ferulic Acid on the Lipid Metabolism and Antioxidative Status in High Fat-Fed Mice

The comparative effects of oryzanol and ferulic acid on the lipid metabolism and antioxidative status of high fat-fed mice were investigated. The mice were given a diet containing 17% fat (HF), supplemented with oryzanol (HF-O) or ferulic acid for 7 weeks. The control mice (NC) were fed with normal diet. The HF mice exhibited increased body weight gain, plasma and hepatic total cholesterol and triglyceride concentrations, and lipid peroxidation rate, and reduced high-density lipoprotein cholesterol level. In general, they also showed lower hepatic antioxidant and higher lipid-regulating enzymes activities relative to that of NC group. Addition of oryzanol or ferulic acid in the diet counteracted these high fat-induced hyperlipidemia and oxidative stress via increased faecal lipid excretion and regulation of antioxidant and lipogenic enzymes activities. This study illustrates that oryzanol and ferulic acid have relatively similar hypolipidemic actions and could be effective in lowering the risk of high fat diet-induced obesity

Occupational Factors Associated with Changes in the Body Mass Index of Korean Male Manual Workers

OBJECTIVES: This study was carried out to analyze and compare the occupational factors that could influence changes in body mass index (BMI) in male manual workers stratified into short-term and long-term work experience groups. METHODS: The subjects were 299 male manual workers (sampled systematically) from 27 workplaces, who had undergone travelling medical examinations at a university hospital between March 28 and May 10, 2013, and had also undergone medical examinations at the same hospital in 2012. Their general and occupational characteristics were investigated through a structured, self-administered questionnaire. The BMI at each point in time was calculated based on the anthropometric results of the medical examinations. Multiple regression analyses were conducted on outcomes of the BMI change and predictors composed of the general and occupational characteristics, with the subjects stratified into groups with 5 years or less (short-term) versus more than 5 years (long-term) of work experience at the present post. RESULTS: In the short-term work experience group, the BMI increases of 3-shift workers and groups reporting disagreement with feeling “insufficient job control” and “lack of reward” at work, two of the subscales of job stress, were significantly higher than those of daytime workers and high-stress groups, respectively. In the long-term work experience group, However, although the BMI increase for 3-shift workers was also significantly higher than that of daytime workers, none of the job stress factors were significantly associated with a BMI increase, whereas the social factors of education and marital status were significant, and some lifestyle factors (such as smoking and regular exercise) were also significant. CONCLUSION: This study showed that, except for 3-shift work, the factors associated with BMI increase could differ depending on the length of job experience. Consequently, different strategies may be needed for workers with short-term versus long-term job experience when designing interventions for preventing their obesity

Machine Learning-Based Human Recognition Scheme Using a Doppler Radar Sensor for In-Vehicle Applications

In this paper, we propose a Doppler spectrum-based passenger detection scheme for a CW (Continuous Wave) radar sensor in vehicle applications. First, we design two new features, referred to as an ‘extended degree of scattering points’ and a ‘different degree of scattering points’ to represent the characteristics of the non-rigid motion of a moving human in a vehicle. We also design one newly defined feature referred to as the ‘presence of vital signs’, which is related to extracting the Doppler frequency of chest movements due to breathing. Additionally, we use a BDT (Binary Decision Tree) for machine learning during the training and test steps with these three extracted features. We used a 2.45 GHz CW radar front-end module with a single receive antenna and a real-time data acquisition module. Moreover, we built a test-bed with a structure similar to that of an actual vehicle interior. With the test-bed, we measured radar signals in various scenarios. We then repeatedly assessed the classification accuracy and classification error rate using the proposed algorithm with the BDT. We found an average classification accuracy rate of 98.6% for a human with or without motion. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.1