Research on the International Sustainable Practice of the Taiwanese Food and Agricultural Education Law under the Current Global Food Security Challenges.

On 19 April 2022, in order to overcome a succession of recent global food health and agriculture safety (FHAS) incidents, the Taiwanese government instituted and announced 20 comprehensive Articles under the Taiwanese Food and Agricultural Education Law (TFAEL) for regulating the Taiwanese FHAS in order to completely construct the international strategies under the current global food security challenges of the FAHS. As a result, this research study firstly employs the learning community model (LCM) of the learning theory to measure the implementing depth of the international sustainability practice of the 20 Articles of the Taiwanese FHASE from a learning performance analytical perspective. Then, the social learning theory (SLT) of the educational theory is applied to consolidate the individual behaviors of the relative stakeholders, the organizational management of the relative industries and the socialized consent of the multilateral organization performance assessment network (MOPAN) of the Food and Agriculture Organization (FAO) of the United Nations (UN). After completing quantitative and qualitative analyses, the two most valuable conclusions and findings were as follows: (1) Not only Article 12, but also Articles 3, 4 and 5 of the new law can empirically facilitate strategic management for supporting and promoting the Taiwanese FHAS educational policy dialogue at a global, regional and country scale in order to effectively advance the international sustainable compatibility of the TFAEL. (2) Articles 6, 11 and 16 of the new law can completely construct operational management for building normative and standard-setting Taiwanese FHAS educational works in order to efficiently advance the international sustainable compatibility of the TFAEL. Therefore, beyond the analytical results of this research, the international sustainable compatibility of the TFAEL provides a direction for the development of sustainable food systems, food policies, agricultural food markets and food chains in order to overcome the various contemporary global food security challenges.

Left ventricular hypertrophy detection using electrocardiographic signal.

Left ventricular hypertrophy (LVH) indicates subclinical organ damage, associating with the incidence of cardiovascular diseases. From the medical perspective, electrocardiogram (ECG) is a low-cost, non-invasive, and easily reproducible tool that is often used as a preliminary diagnosis for the detection of heart disease. Nowadays, there are many criteria for assessing LVH by ECG. These criteria usually include that voltage combination of RS peaks in multi-lead ECG must be greater than one or more thresholds for diagnosis. We developed a system for detecting LVH using ECG signals by two steps: firstly, the R-peak and S-valley amplitudes of the 12-lead ECG were extracted to automatically obtain a total of 24 features and ECG beats of each case (LVH or non-LVH) were segmented; secondly, a back propagation neural network (BPN) was trained using a dataset with these features. Echocardiography (ECHO) was used as the gold standard for diagnosing LVH. The number of LVH cases (of a Taiwanese population) identified was 173. As each ECG sequence generally included 8 to 13 cycles (heartbeats) due to differences in heart rate, etc., we identified 1466 ECG cycles of LVH patients after beat segmentation. Results showed that our BPN model for detecting LVH reached the testing accuracy, precision, sensitivity, and specificity of 0.961, 0.958, 0.966 and 0.956, respectively. Detection performances of our BPN model, on the whole, outperform 7 methods using ECG criteria and many ECG-based artificial intelligence (AI) models reported previously for detecting LVH.

Intelligent image analysis recognizes important orchid viral diseases.

Phalaenopsis orchids are one of the most important exporting commodities for Taiwan. Most orchids are planted and grown in greenhouses. Early detection of orchid diseases is crucially valuable to orchid farmers during orchid cultivation. At present, orchid viral diseases are generally identified with manual observation and the judgment of the grower's experience. The most commonly used assays for virus identification are nucleic acid amplification and serology. However, it is neither time nor cost efficient. Therefore, this study aimed to create a system for automatically identifying the common viral diseases in orchids using the orchid image. Our methods include the following steps: the image preprocessing by color space transformation and gamma correction, detection of leaves by a U-net model, removal of non-leaf fragment areas by connected component labeling, feature acquisition of leaf texture, and disease identification by the two-stage model with the integration of a random forest model and an inception network (deep learning) model. Thereby, the proposed system achieved the excellent accuracy of 0.9707 and 0.9180 for the image segmentation of orchid leaves and disease identification, respectively. Furthermore, this system outperformed the naked-eye identification for the easily misidentified categories [cymbidium mosaic virus (CymMV) and odontoglossum ringspot virus (ORSV)] with the accuracy of 0.842 using two-stage model and 0.667 by naked-eye identification. This system would benefit the orchid disease recognition for Phalaenopsis cultivation.

Preliminary Research on the Sustainable Determinants of Taiwanese Ecotourism with the International Standards.

To analyze Taiwanese ecotourism with international standards, this research employed the social learning theory (SLT) to identify the determinants of Taiwanese community ecotourism with the international standards. This basic theory of social psychology comprehensively assays the interplays and interconnections among the three analytical perspectives (ecotourism tours, destinations, and accommodations), the four essential issues (management, social, cultural, and environment) of the Global Sustainable Tourism Council, the six assessed dimensions of ecotourism resolution from the WCC, and the twenty-nine assessed indexes of the KES ecotourism evaluations. It was then possible to comprehensively explore the sustainable determinants of Taiwanese ecotourism with the international standards through the FA of quantitative and qualitative analyses in combination with the highest research validity, reliability, representativeness, and accuracy. After analyzing the evaluated measurements, the empirical and valuable conclusions and findings are (1) these analytical perspectives, appraised attitudes, evaluated criteria, and sub-criteria positively advance Taiwanese ecotourism with the international standards (PITEEICIS); (2) the sustainable determinants of Taiwanese ecotourism with the international standards include the Support for Capacity Building of the Local Community (SCBLC), Cooperation with the Local Community (CLC), Supports for the Local Enterprises (SLE), Local Participation and Benefits Sharing Duties (LPBSD), Tourist Management (TM), and Responsible Tourist Behaviors Inducement (RTBI). Importantly, the majority of ecotourism industrialists and experts still focus on the economic benefits, such as supporting the local community and enterprises, rather than tourist behavior inducement to stimulate ecotourism participation in order to promote and advance the Taiwanese ecotourism to the international standards; (3) in order to promote Taiwanese ecotourism to the international conventions, the Taiwanese government and organizations in ecotourism should contribute toward the local welfare and create and design various training programs and courses to enhance local community's awareness and capability of ecotourism development in order to establish a complete system that stimulates the ongoing planning and decision making of local community participation and regularly monitors, records, and reflects their opinions, based on the area's history, culture, and natural attributes, to develop and sell sustainable local products by creating fair trade principles and valuable products.

Construction of diagnosis system and gene regulatory networks based on microarray analysis.

A microarray analysis generally contains expression data of thousands of genes, but most of them are irrelevant to the disease of interest, making analyzing the genes concerning specific diseases complicated. Therefore, filtering out a few essential genes as well as their regulatory networks is critical, and a disease can be easily diagnosed just depending on the expression profiles of a few critical genes. In this study, a target gene screening (TGS) system, which is a microarray-based information system that integrates F-statistics, pattern recognition matching, a two-layer K-means classifier, a Parameter Detection Genetic Algorithm (PDGA), a genetic-based gene selector (GBG selector) and the association rule, was developed to screen out a small subset of genes that can discriminate malignant stages of cancers. During the first stage, F-statistic, pattern recognition matching, and a two-layer K-means classifier were applied in the system to filter out the 20 critical genes most relevant to ovarian cancer from 9600 genes, and the PDGA was used to decide the fittest values of the parameters for these critical genes. Among the 20 critical genes, 15 are associated with cancer progression. In the second stage, we further employed a GBG selector and the association rule to screen out seven target gene sets, each with only four to six genes, and each of which can precisely identify the malignancy stage of ovarian cancer based on their expression profiles. We further deduced the gene regulatory networks of the 20 critical genes by applying the Pearson correlation coefficient to evaluate the correlationship between the expression of each gene at the same stages and at different stages. Correlationships between gene pairs were calculated, and then, three regulatory networks were deduced. Their correlationships were further confirmed by the Ingenuity pathway analysis. The prognostic significances of the genes identified via regulatory networks were examined using online tools, and most represented biomarker candidates. In summary, our proposed system provides a new strategy to identify critical genes or biomarkers, as well as their regulatory networks, from microarray data.

Blood Smear Image Based Malaria Parasite and Infected-Erythrocyte Detection and Segmentation.

In this study, an automatic malaria parasite detector is proposed to perceive the malaria-infected erythrocytes in a blood smear image and to separate parasites from the infected erythrocytes. The detector hence can verify whether a patient is infected with malaria. It could more objectively and efficiently help a doctor in diagnosing malaria. The experimental results show that the proposed method can provide impressive performance in segmenting the malaria-infected erythrocytes and the parasites from a blood smear image taken under a microscope. This paper also presents a weighted Sobel operation to compute the image gradient. The experimental results demonstrates that the weighted Sobel operation can provide more clear-cut and thinner object contours in object segmentation.

Semi-automatic segmentation and classification of Pap smear cells.

Cytologic screening has been widely used for detecting the cervical cancers. In this study, a semiautomatic PC-based cellular image analysis system was developed for segmenting nuclear and cytoplasmic contours and for computing morphometric and textual features to train support vector machine (SVM) classifiers to classify four different types of cells and to discriminate dysplastic from normal cells. A software program incorporating function, including image reviewing and standardized denomination of file names, was also designed to facilitate and standardize the workflow of cell analyses. Two experiments were conducted to verify the classification performance. The cross-validation results of the first experiment showed that average accuracies of 97.16% and 98.83%, respectively, for differentiating four different types of cells and in discriminating dysplastic from normal cells have been achieved using salient features (8 for four-cluster and 7 for two-cluster classifiers) selected with SVM recursive feature addition. In the second experiment, 70% (837) of the cell images were used for training and 30% (361) for testing, achieving an accuracy of 96.12% and 98.61% for four-cluster and two-cluster classifiers, respectively. The proposed system provides a feasible and effective tool in evaluating cytologic specimens.

Image-quality figure evaluator based on contrast-detail phantom in radiography.

BACKGROUND: In radiology, it is significantly important to produce adequate diagnostic information while minimally affecting the patient with the lowest amount of dose. A contrast-detail phantom is generally used to study the quality of image and the amount of radiation dose for digital X-ray imaging systems. To evaluate the quality of a phantom image, radiologists are traditionally required to manually indicate the location of the holes in each square in the phantom image. Then, the image quality figure (IQF) of the image can be evaluated. However, evaluation by the human eye is subjective as well as time-consuming, and it differs from person to person. METHODS: In this paper, an image processing-based IQF evaluator is proposed to automatically measure the quality of a phantom image. Nine phantom images, each consisting of 2382 × 2212 pixels, were used as test images and were provided by Taichung Hospital, Department of Health, Executive Yuan, Taiwan, Republic of China. The IP-IQF evaluator separates the phantom image into squares and then stretches the contrast of each square to the range 0-255. After that, it splits each square into 3 × 3 equal-sized regions, and recognizes the pattern of the square based on the features computed by mean-difference gradient operation and run length enhancer. Furthermore, a genetic algorithm-based parameter values-detecting algorithm is presented to compute the optimal values of the parameters used in the IP-IQF evaluator. RESULTS: The experimental results demonstrate that CoCIQ and the IP-IQF evaluator can efficiently measure the IQF of a phantom image. The IP-IQF evaluator is more effective than a radiologist and CoCIQ in evaluating the IQF of a phantom image. CONCLUSIONS: The proposed IQF evaluator is more sensitive than not only the observation of radiologists but also the computer program CoCIQ. Moreover, a genetic algorithm is provided to compute the most suitable values of the parameters used in the IQF evaluator.

Leukocyte nucleus segmentation and nucleus lobe counting.

BACKGROUND: Leukocytes play an important role in the human immune system. The family of leukocytes is comprised of lymphocytes, monocytes, eosinophils, basophils, and neutrophils. Any infection or acute stress may increase or decrease the number of leukocytes. An increased percentage of neutrophils may be caused by an acute infection, while an increased percentage of lymphocytes can be caused by a chronic bacterial infection. It is important to realize an abnormal variation in the leukocytes. The five types of leukocytes can be distinguished by their cytoplasmic granules, staining properties of the granules, size of cell, the proportion of the nuclear to the cytoplasmic material, and the type of nucleolar lobes. The number of lobes increased when leukemia, chronic nephritis, liver disease, cancer, sepsis, and vitamin B12 or folate deficiency occurred. Clinical neutrophil hypersegmentation has been widely used as an indicator of B12 or folate deficiency.Biomedical technologists can currently recognize abnormal leukocytes using human eyes. However, the quality and efficiency of diagnosis may be compromised due to the limitations of the biomedical technologists' eyesight, strength, and medical knowledge. Therefore, the development of an automatic leukocyte recognition system is feasible and necessary. It is essential to extract the leukocyte region from a blood smear image in order to develop an automatic leukocyte recognition system. The number of lobes increased when leukemia, chronic nephritis, liver disease, cancer, sepsis, and vitamin B12 or folate deficiency occurred. Clinical neutrophil hypersegmentation has been widely used as an indicator of B12 or folate deficiency. RESULTS: The purpose of this paper is to contribute an automatic leukocyte nuclei image segmentation method for such recognition technology. The other goal of this paper is to develop the method of counting the number of lobes in a cell nucleus. The experimental results demonstrated impressive segmentation accuracy. CONCLUSIONS: Insensitive to the variance of images, the LNS (Leukocyte Nuclei Segmentation) method functioned well to isolate the leukocyte nuclei from a blood smear image with much better UR (Under Segmentation Rate), ER (Overall Error Rate), and RDE (Relative Distance Error). The presented LC (Lobe Counting) method is capable of splitting leukocyte nuclei into lobes. The experimental results illuminated that both methods can give expressive performances. In addition, three advanced image processing techniques were proposed as weighted Sobel operator, GDW (Gradient Direction Weight), and GBPD (Genetic-based Parameter Detector).

Design of multiplex PCR primers using heuristic algorithm for sequential deletion applications.

UNLABELLED: The sequential deletion method is commonly applied to locate the functional domain of a protein. Unfortunately, manually designing primers for multiplex polymerase chain reaction (PCR) is a labor-intensive task. In order to speed up the experimental procedure and to improve the efficiency of producing PCR products, this paper proposes a multiplex PCR primers (MPCRPs) designer to design multiple forward primers with a single 3'-UTR reverse primer for extracting various N-terminal truncated mutants to quickly locate the functional domain of a cDNA sequence. Several factors, including melting temperature, primer length, GC content, internal self-complement, cross-dimerization, terminal limitation, and specificity, are used as the criteria for designing primers. This study obtains a near-optimal solution of primer sets that can be placed in as few test tubes as possible for one multiplex PCR experiment. RESULTS: Homo sapiens ribosomal protein L5, Homo sapiens xylosyltransferase I, and Bacteriophage T4 gene product 11 were used as test examples to verify efficacy of the proposed algorithm. In addition, the designed primers of Homo sapiens ribosomal protein L5 cDNA were applied in multiplex PCR experiments. A total of 48 forward primers and one reverse primer were designed and used to duplicate N-terminal truncated mutants of different lengths from the protein. The primers were classified into eight tube groups (i.e., test tubes) held within the same temperature range (53-57 degrees C), and the validity of the PCR products were verified using polyacrylamide gel electrophoresis (PAGE) with the functional domain correctly located. A software implementation of the proposed algorithm useful in assisting the researcher to design primers for multiplex PCR experiments was developed and available upon request.

Edge enhancement nucleus and cytoplast contour detector of cervical smear images.

This paper presents an edge enhancement nucleus and cytoplast contour (EENCC) detector to enable cutting the nucleus and cytoplast from a cervical smear cell image. To clean up noises from an image, this paper proposes a trim-meaning filter that can effectively remove impulse and Gaussian noises but still preserves the sharpness of object boundaries. In addition, a bigroup enhancer is proposed to make a clear-cut separation of the pixels lying in-between two objects. A mean vector difference enhancer is presented to suppress the gradients of noises and also to brighten the gradients of object contours. What is more, a relative-distance-error measure is put forward to evaluate the segmentation error between the extracted and target object contours. The experimental results show that all the aforementioned techniques proposed have performed impressively. Other than for cervical smear images, these proposed techniques can also be utilized in object segmentation of other images.

NTMG (N-terminal Truncated Mutants Generator for cDNA): an automatic multiplex PCR assays design for generating various N-terminal truncated cDNA mutants.

The sequential deletion method is generally used to locate the functional domain of a protein. With this method, in order to find the various N-terminal truncated mutants, researchers have to investigate the ATG-like codons, to design various multiplex polymerase chain reaction (PCR) forward primers and to do several PCR experiments. This web server (N-terminal Truncated Mutants Generator for cDNA) will automatically generate groups of forward PCR primers and the corresponding reverse PCR primers that can be used in a single batch of a multiplex PCR experiment to extract the various N-terminal truncated mutants. This saves much time and money for those who use the sequential deletion method in their research. This server is available at http://oblab.cs.nchu.edu.tw:8080/WebSDL/.

Identification of mouse mslp2 gene from EST databases by repeated searching, comparison, and assembling.

The NPHS2 gene is expressed in podocytes and encodes the integral membrane protein called podocin, which is believed to play an important role in the renal function of glomerular filtration. Mutations in this gene can cause serious renal function disorders. In this study, we used data-mining techniques and bioinformatic tools to search for the mouse ortholog of the NPHS2-related gene. It might be valuable for future studies of renal diseases. We employed repeated cycles of searching, comparison, and assembling to extend the assembled EST sequences. The discovered gene sequence mslp2, an ortholog of the human SLP2 gene, was found to have a total length of 1253 bp with the amino acid coding region located in 32-1093 nt. It was further verified using the RT-PCR and RACE techniques to ensure its biological accuracy and then registered with the GenBank. When ClustalW was used for comparing the mslp2 and human SLP2 genes for similarities, the similarities were as high as 88% for nucleotide and 92% for amino acid sequences. In conclusion, we propose a method for rapid identification of the mouse ortholog gene from the human genome.