Plant mitochondrial genomes have complex configurations resulting from the multipartite structures and highly rearranged substoichiometric molecules created by repetitive sequences. To expedite the reliable classification of the diverse radish (Raphanus sativus L.) cytoplasmic types, we have developed consistent molecular markers within their complex mitochondrial genomes. orf138, a gene responsible for Ogura male-sterility, was detected in normal cultivars in the form of low-copy-number substoichiometric molecules. In addition to the dominant orf138-atp8 Ogura mitochondrial DNA (mtDNA) organization, three novel substoichiometric organizations linked to the atp8 gene were identified in this study. PCR amplification profiles of seven atp8- and atp6-linked sequences were divided into three groups. Interestingly, the normal cytoplasm type, which had previously been considered a single group, showed two patterns by PCR amplification. The most prominent difference between the two normal mtDNAs was size variation within four short-repeat sequences linked to the atp6 gene. This variation appeared to be the result of a double crossover, mediated by these homologous, short-repeat sequences. Specific PCR amplification profiles reflecting the stoichiometry of different mtDNA fragments were conserved within cultivars and across generations. Therefore, the specific sequences detected in these profiles were used as molecular markers for the classification of diverse radish germplasm. Using this classification system, a total of 90 radish cultivars, or accessions, were successfully assigned to three different mitotypes.

The emergence of an aging society is inevitable due to the continued increases in life expectancy and decreases in birth rate. These social changes require new smart healthcare services for use in daily life, and covid-19 has also led to a contactless trend necessitating more non-face-to-face health services. Due to the improvements that have been achieved in healthcare technologies, an increasing number of studies have attempted to predict and analyze certain diseases in advance. Research on stroke diseases is actively underway, particularly with the aging population. Stroke, which is fatal to the elderly, is a disease that requires continuous medical observation and monitoring, as its recurrence rate and mortality rate are very high. Most studies examining stroke disease to date have used MRI or CT images for simple classification. This clinical approach (imaging) is expensive and time-consuming while requiring bulky equipment. Recently, there has been increasing interest in using non-invasive measurable EEGs to compensate for these shortcomings. However, the prediction algorithms and processing procedures are both time-consuming because the raw data needs to be separated before the specific attributes can be obtained. Therefore, in this paper, we propose a new methodology that allows for the immediate application of deep learning models on raw EEG data without using the frequency properties of EEG. This proposed deep learning-based stroke disease prediction model was developed and trained with data collected from real-time EEG sensors. We implemented and compared different deep-learning models (LSTM, Bidirectional LSTM, CNN-LSTM, and CNN-Bidirectional LSTM) that are specialized in time series data classification and prediction. The experimental results confirmed that the raw EEG data, when wielded by the CNN-bidirectional LSTM model, can predict stroke with 94.0% accuracy with low FPR (6.0%) and FNR (5.7%), thus showing high confidence in our system. These experimental results demonstrate the feasibility of non-invasive methods that can easily measure brain waves alone to predict and monitor stroke diseases in real time during daily life. These findings are expected to lead to significant improvements for early stroke detection with reduced cost and discomfort compared to other measuring techniques.

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Since stroke disease often causes death or serious disability, active primary prevention and early detection of prognostic symptoms are very important. Stroke diseases can be divided into ischemic stroke and hemorrhagic stroke, and they should be minimized by emergency treatment such as thrombolytic or coagulant administration by type. First, it is essential to detect in real time the precursor symptoms of stroke, which occur differently for each individual, and to provide professional treatment by a medical institution within the proper treatment window. However, prior studies have focused on developing acute treatment or clinical treatment guidelines after the onset of stroke rather than detecting the prognostic symptoms of stroke. In particular, in recent studies, image analysis such as magnetic resonance imaging (MRI) or computed tomography (CT) has mostly been used to detect and predict prognostic symptoms in stroke patients. Not only are these methodologies difficult to diagnose early in real-time, but they also have limitations in terms of a long test time and a high cost of testing. In this paper, we propose a system that can predict and semantically interpret stroke prognostic symptoms based on machine learning using the multimodal bio-signals of electrocardiogram (ECG) and photoplethysmography (PPG) measured in real-time for the elderly. To predict stroke disease in real-time while walking, we designed and implemented a stroke disease prediction system with an ensemble structure that combines CNN and LSTM. The proposed system considers the convenience of wearing the bio-signal sensors for the elderly, and the bio-signals were collected at a sampling rate of 1,000Hz per second from the three electrodes of the ECG and the index finger for PPG while walking. According to the experimental results, C4.5 decision tree showed a prediction accuracy of 91.56% while RandomForest showed a prediction accuracy of 97.51% during walking by the elderly. In addition, the CNN-LSTM model using raw data of ECG and PPG showed satisfactory prediction accuracy of 99.15%. As a result, the real-time prediction of the elderly stroke patients simultaneously showed high prediction accuracy and performance.INDEX TERMS Deep learning, machine learning, electrocardiogram (ECG), photo plethysmography (PPG), multi-modal bio-signal, real-time stroke prediction, stroke disease analysis.

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