Development of a Mobile Application, “Wild Flowers of Bukhansan National Park (version 1.0)”, for Identification of Plants in Bukhansan National Park

AbstractWe developed the educational purpose mobile application, named “Wild Flowers of Bukhansan National Park (version 1.0)”, aiming for easy identification of wildflowers for students and visitors in the park. When visitors find a flower or part of plant in the park, visitors can search for its name utilizing the pictures and characters provided in their own smartphone mobile devices or tablet PCs. The application provides pictures of wildflowers in the park and character-based searching system based on 12 diagnostic features (e.g., growth form, leaf arrangement, flower symmetry, petal color, petal number, sepal number, etc). We adopted the complete floristic survey of Chung and Lee (1962) and added species that we confirmed their distribution in the park during the development of this application. In summary, number of vascular plants in this park was estimated to be 428 taxa including 100 families, 280 genera, 327 species, 1 subspecies, 50 varieties, and 5 formas. We provided a total of 588 pictures representing 358 taxa and each taxon includes multiple pictures in many cases. Included identification quizzes can be an efficient educational tool as well as fun activity for students and visitors who are learning plant species in Korea. Our next step will include GPS function in the application for indicating visitor's location and for providing previously reported sites of the species that we interested in the map of the park. The future application which includes GPS function will be a valuable tool for the monitoring of rare plants, plant researches related to the climate changes, etc. We currently provide Korean iPhone version only, and English version and both of android versions will be serviced soon

Phase II Study of Low-dose Paclitaxel and Cisplatin as a Second-line Therapy after 5-Fluorouracil/Platinum Chemotherapy in Gastric Cancer

This study was performed to evaluate the efficacy and toxicity of low-dose paclitaxel/cisplatin chemotherapy in patients with metastatic or recurrent gastric cancer that had failed 5-fluorouracil/platinum-based chemotherapy. Thirty-two patients with documented progression on or within 6 months after discontinuing 5-fluorouracil/platinum-based chemotherapy were enrolled. As a second-line treatment, paclitaxel (145 mg/m2) and cisplatin (60 mg/m2) was administered on day 1 every 3 weeks. Among 32 patients enrolled, 8 (25%) responded partially to paclitaxel/cisplatin, 8 (25%) had stable disease, and 14 (44%) had progressive disease. Two patients (6%) were not evaluable. The median time to progression (TTP) and overall survival for all patients were 2.9 months and 9.1 months, respectively. The most common hematologic toxicity was anemia (47%). Grade 3 neutropenia developed in three patients (9%), but no other grade 3/4 hematologic toxicity occurred. The most common non-hematologic toxicities were emesis (31%) and peripheral neuropathy (38%). Three cases (9%) of grade 3/4 emesis and 2 cases (6%) of grade 3 peripheral neuropathy developed. In conclusion, low-dose paclitaxel and cisplatin chemotherapy showed moderate activity with favorable toxicity profiles. However, relatively short TTP of this regimen warrants the development of more effective paclitaxel-based regimens other than combination with cisplatin in these patients as second-line therapies

Controlling human causal inference through in silico task design

Summary: Learning causal relationships is crucial for survival. The human brain’s functional flexibility allows for effective causal inference, underlying various learning processes. While past studies focused on environmental factors influencing causal inference, a fundamental question remains: can these factors be manipulated for strategic causal inference control? This paper presents a task control framework for orchestrating causal learning task design. It utilizes a two-player game setting where a neural network learns to manipulate task variables by interacting with a human causal inference model. Training the task controller to generate experimental designs, we confirm its ability to accommodate complexities of environmental causal structure. Experiments involving 126 human subjects successfully validate the impact of task control on performance and learning efficiency. Additionally, we find that task control policy reflects the intrinsic nature of human causal inference: one-shot learning. This framework holds promising potential for applications paving the way for targeted behavioral outcomes in humans

Identification of Type 2 Diabetes-associated combination of SNPs using Support Vector Machine

<p>Abstract</p> <p>Background</p> <p>Type 2 diabetes mellitus (T2D), a metabolic disorder characterized by insulin resistance and relative insulin deficiency, is a complex disease of major public health importance. Its incidence is rapidly increasing in the developed countries. Complex diseases are caused by interactions between multiple genes and environmental factors. Most association studies aim to identify individual susceptibility single markers using a simple disease model. Recent studies are trying to estimate the effects of multiple genes and multi-locus in genome-wide association. However, estimating the effects of association is very difficult. We aim to assess the rules for classifying diseased and normal subjects by evaluating potential gene-gene interactions in the same or distinct biological pathways.</p> <p>Results</p> <p>We analyzed the importance of gene-gene interactions in T2D susceptibility by investigating 408 single nucleotide polymorphisms (SNPs) in 87 genes involved in major T2D-related pathways in 462 T2D patients and 456 healthy controls from the Korean cohort studies. We evaluated the support vector machine (SVM) method to differentiate between cases and controls using SNP information in a 10-fold cross-validation test. We achieved a 65.3% prediction rate with a combination of 14 SNPs in 12 genes by using the radial basis function (RBF)-kernel SVM. Similarly, we investigated subpopulation data sets of men and women and identified different SNP combinations with the prediction rates of 70.9% and 70.6%, respectively. As the high-throughput technology for genome-wide SNPs improves, it is likely that a much higher prediction rate with biologically more interesting combination of SNPs can be acquired by using this method.</p> <p>Conclusions</p> <p>Support Vector Machine based feature selection method in this research found novel association between combinations of SNPs and T2D in a Korean population.</p