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With the rapid technological change of society with Artificial Intelligence, elementary schools' goal should be to prepare the next generations according to competencies. We propose an AI curriculum to cultivate students' AI literacy to answer the question of ‘why and what to teach’ on AI. The proposed AI curriculum focuses on achieving AI literacy based on three competencies: AI Knowledge, AI Skill, and AI Attitude. We anticipate that the proposed curriculum will equip students with core competencies for the future with AI.

The constraint in sharing the same physical learning environment with students in distance learning poses difficulties to teachers. A significant teacher-student interaction without observing students' academic status is undesirable in the constructivist view on education. To remedy teachers' hardships in estimating students' knowledge state, we propose a Student Knowledge Prediction Framework that models and explains student's knowledge state for teachers. The knowledge state of a student is modeled to predict the future mastery level on a knowledge concept. The proposed framework is integrated into an e-learning application as a measure of automated feedback. We verified the applicability of the assessment framework through an expert survey. We anticipate that the proposed framework will achieve active teacher-student interaction by informing student knowledge state to teachers in distance learning.

Artificial neural networks evolve into deep learning recently and perform well in various fields, such as image and speech recognition and translation. However, there is a problem that it is diffic...

Colorectal cancer (CRC) is the third leading cause of cancer-related deaths in the US. Understanding the mechanisms of CRC progression is essential to improve treatment. Mitochondria is the powerhouse for healthy cells. However, in tumor cells, less energy is produced by the mitochondria and metabolic reprogramming is an early hallmark of cancer. The metabolic differences between normal and cancer cells are being interrogated to uncover new therapeutic approaches. Mitochondria targeting PTEN-induced kinase 1 (PINK1) is a key regulator of mitophagy, the selective elimination of damaged mitochondria by autophagy. Defective mitophagy is increasingly associated with various diseases including CRC. However, a significant gap exists in our understanding of how PINK1-dependent mitophagy participates in the metabolic regulation of CRC. By mining Oncomine, we found that PINK1 expression was downregulated in human CRC tissues compared to normal colons. Moreover, disruption of PINK1 increased colon tumorigenesis in two colitis-associated CRC mouse models, suggesting that PINK1 functions as a tumor suppressor in CRC. PINK1 overexpression in murine colon tumor cells promoted mitophagy, decreased glycolysis and increased mitochondrial respiration potentially via activation of p53 signaling pathways. In contrast, PINK1 deletion decreased apoptosis, increased glycolysis, and reduced mitochondrial respiration and p53 signaling. Interestingly, PINK1 overexpression in vivo increased apoptotic cell death and suppressed colon tumor xenograft growth. Metabolomic analysis revealed that acetyl-CoA was significantly reduced in tumors with PINK1 overexpression, which was partly due to activation of the HIF-1α-pyruvate dehydrogenase (PDH) kinase 1 (PDHK1)-PDHE1α axis. Strikingly, treating mice with acetate increased acetyl-CoA levels and rescued PINK1-suppressed tumor growth. Importantly, PINK1 disruption simultaneously increased xenografted tumor growth and acetyl-CoA production. In conclusion, mitophagy protein PINK1 suppresses colon tumor growth by metabolic reprogramming and reducing acetyl-CoA production.

Deep learning for 3D data has become a popular research theme in many fields. However, most of the research on 3D data is based on voxels, 2D images, and point clouds. At actual industrial sites, face-based geometry data are being used, but their direct application to industrial sites remains limited due to a lack of existing research. In this study, to overcome these limitations, we present a face-based variational autoencoder (FVAE) model that generates 3D geometry data using a variational autoencoder (VAE) model directly from face-based geometric data. Our model improves the existing node and edge-based adjacency matrix and optimizes it for geometric learning by using a face- and edge-based adjacency matrix according to the 3D geometry structure. In the experiment, we achieved the result of generating adjacency matrix information with 72% precision and 69% recall through end-to-end learning of Face-Based 3D Geometry. In addition, we presented various structurization methods for 3D unstructured geometry and compared their performance, and proved the method to effectively perform reconstruction of the learned structured data through experiments.

Research on converting 2D raster drawings into 3D vector data has a long history in the field of pattern recognition. Prior to the achievement of machine learning, existing studies were based on heuristics and rules. In recent years, there have been several studies employing deep learning, but a great effort was required to secure a large amount of data for learning. In this study, to overcome these limitations, we used 3DPlanNet Ensemble methods incorporating rule-based heuristic methods to learn with only a small amount of data (30 floor plan images). Experimentally, this method produced a wall accuracy of more than 95% and an object accuracy similar to that of a previous study using a large amount of learning data. In addition, 2D drawings without dimension information were converted into ground truth sizes with an accuracy of 97% or more, and structural data in the form of 3D models in which layers were divided for each object, such as walls, doors, windows, and rooms, were created. Using the 3DPlanNet Ensemble proposed in this study, we generated 110,000 3D vector data with a wall accuracy of 95% or more from 2D raster drawings end to end.

The significant advantage of deep neural networks is that the upper layer can capture the high-level features of data based on the information acquired from the lower layer by stacking layers deeply. Since it is challenging to interpret what knowledge the neural network has learned, various studies for explaining neural networks have emerged to overcome this problem. However, these studies generate the local explanation of a single instance rather than providing a generalized global interpretation of the neural network model itself. To overcome such drawbacks of the previous approaches, we propose the global interpretation method for the deep neural network through features of the model. We first analyzed the relationship between the input and hidden layers to represent the high-level features of the model, then interpreted the decision-making process of neural networks through high-level features. In addition, we applied network pruning techniques to make concise explanations and analyzed the effect of layer complexity on interpretability. We present experiments on the proposed approach using three different datasets and show that our approach could generate global explanations on deep neural network models with high accuracy and fidelity.

The Pirrit Hills are located in the Ellsworth–Whitmore Mountains of West Antarctica. The Pirrit Hills granite exhibits significant negative Eu anomalies (Eu/Eu* = 0.01~0.25) and a REE tetrad effect indicating intensive magmatic differentiation. Whole-rock Rb-Sr and Sm-Nd geochronologic analysis of the Pirrit Hills granite gave respective ages of 172.8 ± 2.4 Ma with initial 87Sr/86Sr = 0.7065 ± 0.0087 Ma and 169 ± 12 Ma with initial 144Nd/143Nd = 0.512207 ± 0.000017. The isotopic ratio data indicate that the Pirrit Hills granite formed by the remelting of Mesoproterozoic mantle-derived crustal materials. Both chondrite-normalized REE patterns and Sr-Nd isotopic data indicate that the Pirrit Hills granite has geochemical features of chondrite-normalized REE patterns indicating that REE tetrad effects and negative Eu anomalies in the highly fractionated granites were produced from magmatic differentiation under the magmatic-hydrothermal transition system.

The Terra Nova Intrusive Complex (TNIC) in northern Victoria Land, Antarctica, results from widespread magmatism during the Early Paleozoic Ross Orogeny. According to field relationships, geochemistry, and geochronology data, the northern part of the TNIC comprises the Browning Intrusive Unit (BIU), which is associated with an arc crustal melting including migmatization of the Wilson Metamorphic Complex, and the later Campbell Intrusive Unit (CIU), which is attributed to the mantle and crustal melting processes. Zircon U-Pb ages suggest Late Neoproterozoic to Early Cambrian protolith with Late Cambrian metamorphism (502 ± 15 Ma) in the WMC, Late Cambrian formation (~500 Ma) of the BIU, and Early Ordovician formation (~480–470 Ma) of the CIU. Sr-Nd isotopic characteristics of the BIU indicate predominant crustal component (εNd(t) = −8.7 to −8.9), whereas those of the CIU reflect both mantle (εNd(t) = 1.8 to 1.6) and crustal (εNd(t) = −4.0 to −7.5) compositions. These results suggest that the northern TNIC magmatism occurring at ~500–470 Ma originated from partial melting of the mantle–mafic crust components and mixing with felsic crust components. By integrating the results with previous studies, the TNIC is considered to be formed by a combination of the mantle and mafic crust melting, crustal assimilation, felsic crust melting, and magma mixing during the Ross Orogeny.

Oxidative stress is an important event under both physiological and pathological conditions. In this study, we demonstrate how to quantify oxidative stress by measuring total reactive oxygen species (ROS) using 2',7'-dichlorodihydrofluorescein diacetate (DCFH-DA) staining in colorectal cancer cell lines as an example. This protocol describes detailed steps including preparation of DCFH-DA solution, incubation of cells with DCFH-DA solution, and measurement of normalized intensity. DCFH-DA staining is a simple and cost-effective way to detect ROS in cells. It can be used to measure ROS generation after chemical treatment or genetic modifications. Therefore, it is useful for determining cellular oxidative stress upon environment stress, providing clues to mechanistic studies.

The incipient charnockite or orthopyroxene-bearing granitic orthogneiss is a relatively common constituent in ancient granulite-facies terranes, and its formation has been often attributed to an influx of CO2-rich fluid or a removal of aqueous melt. Temporal relationship between the orthopyroxene formation and the fluid or melt migration, however, remains debatable. Here we report the first finding of incipient charnockite hosted by garnet-biotite-bearing granitic gneisses in the Sancheong–Hadong anorthosite complex, Yeongnam Massif. Both lithologies preserve a variety of field and microstructural evidence for partial melting and subsequent melt crystallization, as manifested by granitic leucosomes and biotite-rich residua. Based on the phase equilibria modeling and geothermobarometric calculation, peak metamorphic conditions of migmatities were estimated to be 5.4–6.0 kbar and 800–850 °C, which led to the biotite dehydration melting together with the orthopyroxene formation in incipient charnockite. Sensitive high-resolution ion microprobe (SHRIMP) U–Pb analyses of zircon from an incipient charnockite and a host gneiss yielded the weighted mean 207Pb/206Pb ages identical within errors; 1880 ± 5 Ma and 1872 ± 6 Ma for the cores, and 1862 ± 4 Ma and 1861 ± 4 Ma for the rims, respectively. The former is interpreted to represent the time for magmatic crystallization of granitic protoliths, whereas the latter for subsequent anatexis typified by peritectic growth of orthopyroxene. Such a timeline is consistent with two distinct thermal events previously reported from the Sancheong-Hadong anorthosite complex. By contrast, monazite grains from a biotite-bearing granitic gneiss in the charnockite zone yielded the weighted mean 207Pb/206Pb ages of 1866 ± 14 Ma and 1835 ± 11 Ma for the core and rim, respectively. The latter is consistent with 1842 ± 8 Ma, estimated from monazite neoblasts of incipient charnockite. The monazite growth during a post-peak metamorphic stage at ca. 1.84 Ga is attributed to a fluid influx event, apparently concentrated in the charnockite zone. The charnockite formation in the Yeongnam Massif further attests to the longevity (>ca. 40 m.y.) of hot orogenesis that has governed the Paleoproterozoic crustal evolution in eastern North China Craton.

Several large epidemiological and animal studies demonstrate a direct correlation between dietary heme iron intake and/or systemic iron levels and cancer risk in several cancers including colorectal cancer (CRC). However, the precise mechanisms for how heme iron contributes to CRC and how cancer cells respond to heme iron-induced stress are still unclear. Previously we have shown that one of the stress-inducible proteins, Sestrin2 (SESN2), is a novel tumor suppressor in colon by limiting endoplasmic reticulum stress and mammalian target of rapamycin complex 1 (mTORC1) signaling and tumor growth. But the relationship between heme iron and SESN2, especially in the context of colon carcinogenesis, was not investigated previously. Here, we found that hemin dose-dependently increased SESN2 expression in an oxidative stress and nuclear factor (erythroid-derived 2)-like 2 (NFE2L2, NRF2)-dependent manner. Since SESN2 overexpression reduced hemin-induced oxidative stress, SESN2 could be an important target of NRF2 exerting antioxidant function. Indeed, expression of several oxidative stress responsive proteins such as NRF2 and its target genes was reduced by SESN2. Although we formerly reported that SESN2 expression was reduced after p53 mutation in colon tumors, mouse colon tumors, which have intact p53 and NRF2, induced SESN2 expression in response to iron stimulus. Although SESN2 overexpression decreased murine colon tumor cell growth both in vitro and in vivo, it rendered colon cancer cells more resistant to hemin-induced apoptosis and therefore promoted tumor growth during hemin treatment. Taken together, although SESN2 generally suppresses tumorigenesis, it can produce tumor-promoting role in iron-rich environment by suppressing oxidative stress-associated cancer cell death.

This paper presents a real-time watermarking codec that is robust against re-encoding attacks for high-definition videos. The codec uses a segmentation function and texture detector techniques for applying real-time watermarking to human visual systems. Experimental results confirm that the proposed scheme satisfies the requirements of invisibility, real-time processing, and robustness against format conversion and low bit-rate encoding. The proposed algorithm has the advantages of simplicity, flexibility, and low computational burden; thus, it is a suitable candidate for many novel and interesting applications such as video fingerprinting for set-top boxes, Internet protocol television, personal video recorders, and satellite boxes.

요약: ICP-MS를 이용한 화강암질 암석내 희토류원소의 함량 분석시 저어콘이 미치는 영향과 저어콘의 산분해 정도를 미국지질조사소(USGS)의 화강섬록암 표준시료 GSP-2와 일본지질조사소(GSJ) 화강암 표준시료JG-1a를 이용하여 조사하였다. 아울러 화강암에서 분리된 저어콘 광물에 대해 일반적인 산분해법과 압력용기(bomb)을 이용한 산분해법을 적용하여 지르코늄(Zr)과 희토류원소의 함량을 측정하였다. 실험결과에 의하면,저어콘은 일반 산분해의 경우 50% 정도가 산분해된 것으로 나타났고, 압력용기를 이용한 경우에는 약 90%전후가 분해된 것으로 확인되었다. 그리고 GSP-2와 JG-1a의 경우 추천값에 비해 지르코늄의 함량이 50% 정도인 것으로 나타났다. 이는 일반적인 화강암의 산분해의 경우 저어콘의 용해도가 실제로는 50% 정도 밖에되지 않음을 지시한다. 하지만, 화강암내 희토류원소의 함량의 경우, 저어콘의 분해도와 상관없이, 추천값과거의 일치하였다. 이는 저어콘의 불완전분해가 암석시료에서의 희토류원소 분포도를 이용한 암석학적 혹은 지구화학적 해석에 큰 영향을 주지는 않는다는 것을 지시해준다.핵심어: 화강암, 희토류원소, 저어콘, ICP-MSAbstract

En Coree du Sud, l’education numerique est consideree comme une priorite par tous les gouvernements, au niveau central et local, depuis plus de vingt ans, car les competences et les ressources numeriques sont indispensables pour reussir dans une « societe du savoir » et une economie fondee sur la creativite. Apres la mise en place de plans directeurs et d’infrastructures dans chaque etablissement et chaque salle de classe, il s’est agi de favoriser la qualite de l’education, grâce a des structures chargees de developper les ressources numeriques et de former les enseignants a l’integration des TIC dans leurs pratiques pedagogiques. Recemment, l’enseignement de l’informatique est devenu obligatoire dans le secondaire inferieur. Cette politique educative proactive, menee avec la collaboration des grandes multinationales coreennes, repond aux attentes sociales. Des difficultes subsistent cependant, liees notamment au poids excessif des examens d’entree a l’universite, ainsi qu’aux craintes exprimees par certaines familles au sujet de l’impact de ces technologies sur la sante de leurs enfants.

We propose a sentiment analyzer for the prediction of document-level sentiments of English micro-blog messages from Twitter. The proposed method is based on lexicon integrated convolutional neural networks with attention (LCA). Its performance was evaluated using the datasets provided by SemEval competition (Task 4). The proposed sentiment analyzer obtained an average F1 of 55.2%, an average recall of 58.9% and an accuracy of 61.4%.

T cells induce immune responses and thereby eliminate infected micro-organisms when peptides from the microbial proteins are bound to HLAs in the host cell surfaces, It is known that the m ore stable the binding of peptide to HLA is, the stronger the T cell response gets to remove more effectively the source of infectio n. Accordingly, if peptides (HLA binder) which can be bound stably to a certain HLA are found, those peptieds are utilized to the development of peptide vaccine to prevent infectious diseases or even to cancer. However, HLA is highly polymorphic so that HLA has a large number of alleles with some frequencies even in one population. Therefore, it is very inefficient to find the peptides stably bound to a number of HLAs by testing random possible peptides for all the various alleles frequent in the population. In orde r to solve this problem, computational methods have recently be en developed to predict peptides which are stably bound to a certain HLA. These methods could markedly decrease the number of candidate peptides to be examined by biological experiments. Accordingly, this paper not only introduces a method of machine learning to predict peptides binding to an HLA, but also suggests a new prediction model so called ‘knowledge-based genetic algorithm’ that has never been tried for HLA binding peptide prediction. Although based on genetic algorithm (GA). it showed more enhanced performance than GA by incorporating expert knowledge in the process of the algorithm. Furthermore, it could extract rules predicting the binding peptide of the HLA alleles common in Koreans. ☞ keyword : Machine learning(기계학습), Genetic algorithm(유전자알고리즘), Knowledge based genetic algorithm (지식기반 유전자알고리즘), Rule generation(규칙생성), Prediction model(예측모델), HLA binding peptide prediction(HLA 결합 펩타이드 예측 ), Korean common HLA allele(한국인 빈발 HLA 대립유전자)

Summary New human leucocyte antigen (HLA) alleles are assigned largely based on their sequence homologies due to lack of information on the serological reactivities. Artificial neural networks (ANNs) have been tested as a possible tool for helping to predict the serology of new alleles in the absence of serological information. However, an ANN analysis per se imparts no information regarding which residues are important in determining serological specificity. To address this issue, we extracted ANN weights of HLA residues. The ANN was trained using 139 HLA-A, 302 HLA-B and 136 HLA-DRB1 alleles, for which serological specificities were assigned in the 2004 Nomenclature Report. When the trained ANN was evaluated using alleles that were contained in the HLA Dictionary 2008 but had not been employed in the training set, the accuracy was 91% (29/32) for HLA-A, 91% (40/44) for HLA-B and 90% (9/10) for HLA-DR. Finally, ANN residue weights were extracted by summing the weights of directly connected ANN nodes. When we assessed the significance of the ANN residue weights by comparing our data with the results of epitope studies conducted by El-Awar and colleagues, we found that the ANN weights tended to be high at the epitopes described by El-Awar et al. Furthermore, the ANN weights extracted in this work could be used to explain ambiguous characteristics of serological specificities to some extent. Our data are thus considered to support the results of the epitope studies conducted by El-Awar and advance our understanding of the ambiguous serological specificities of some alleles.

A low loss radio frequency(RF) micro electro mechanical systems(MEMS) switch driven by a low actuation voltage was designed for the development of a new RF MEMS switch. The RF MEMS switch should be encapsulated. The glass cap and fabricated RF MEMS switch were assembled by the Au/Sn eutectic bonding principle for wafer-level packaging. The through-vias on the glass substrate was made by the glass dry etching and Au electroplating process. The packaged RF MEMS switch had an actuation voltage of 12.5 V, an insertion loss below 0.25 dB, a return loss above 16.6 dB, and an isolation value above 41.4 dB at 6 GHz.

Objective: In recent years, several machine learning approaches have been applied to modeling the specificity of the human immunodeficiency virus type 1 (HIV-1) protease cleavage domain. However, the high dimensional domain dataset contains a small number of samples, which could misguide classification modeling and its interpretation. Appropriate feature selection can alleviate the problem by eliminating irrelevant and redundant features, and thus improve prediction performance. Methods: We introduce a new feature subset selection method, FS-MLP, that selects relevant features using multi-layered perceptron (MLP) learning. The method includes MLP learning with a training dataset and then feature subset selection using decompositional approach to analyze the trained MLP. Our method is able to select a subset of relevant features in high dimensional, multi-variate and non-linear domains. Results: Using five artificial datasets that represent four data types, we verified the FS-MLP performance with seven other feature selection methods. Experimental results showed that the FS-MLP is superior at high dimensional, multi-variate and non-linear domains. In experiments with HIV-1 protease cleavage dataset, the FS-MLP selected a set of 14 highly relevant features among 160 original features. On a validation set of 131 test instances, classifiers that used the 14 features showed about 95% accuracy which outperformed other seven methods in terms of accuracy and the number of features. Conclusions: Our experimental results indicate that the FS-MLP is effective in analyzing multi-variate, non-linear and high dimensional datasets such as HIV-1 protease cleavage dataset. The 14 relevant features which were selected by the FS-MLP provide us with useful insights into the HIV-1 cleavage site domain as well. The FS-MLP is a useful method for computational sequence analysis in general.

【For the application to optic devices, wafer level package including spacer with particular thickness according to optical design could be required. In these cases, the uniformity of spacer thickness is important for bonding strength and optical performance. Packaging process has to be performed at low temperature in order to prevent damage to devices fabricated before packaging. And if photosensitive material is used as spacer layer, size and shape of pattern and thickness of spacer can be easily controlled. This paper presents polymer bonding using thick, uniform and patterned spacing layer of SU-8 2100 photoresist for wafer level package. SU-8, negative photoresist, can be coated uniformly by spin coater and it is cured at $95^{\circ}C$ and bonded well near the temperature. It can be bonded to silicon well, patterned with high aspect ratio and easy to form thick layer due to its high viscosity. It is also mechanically strong, chemically resistive and thermally stable. But adhesion of SU-8 to glass is poor, and in the case of forming thick layer, SU-8 layer leans from the perpendicular due to imbalance to gravity. To solve leaning problem, the wafer rotating system was introduced. Imbalance to gravity of thick layer was cancelled out through rotating wafer during curing time. And depositing additional layer of gold onto glass could improve adhesion strength of SU-8 to glass. Conclusively, we established the coating condition for forming patterned SU-8 layer with $400{\mu}m$ of thickness and 3.25 % of uniformity through single coating. Also we improved tensile strength from hundreds kPa to maximum 9.43 MPa through depositing gold layer onto glass substrate.】

U‐Pb zircon ages of tuffs and sandstones of the Daedong Supergroup (Bansong and Nampo groups) in the Korean Peninsula were determined using a sensitive high‐resolution ion microprobe (SHRIMP) in order to constrain their age of sedimentation and to unravel discrete geologic events as recorded in detrital zircons. The ages of four tuffaceous samples from the Bansong Group imply that the Daedong Supergroup formed at ca. 187–172 Ma in association with the Early‐Middle Jurassic orogeny. These data are in marked contrast with paleomagnetic arguments suggesting that the Bansong and Nampo groups are precollisional Early‐Middle Triassic deposits that are correlative with the North and South China blocks, respectively. Detrital zircons of the Daedong Supergroup define seven age components: (1) Early‐Middle Archean (3.64–2.97 Ga), (2) Late Archean–middle Early Proterozoic (2.63–2.33 Ga), (3) late Early Proterozoic (1.98–1.75 Ga), (4) Middle‐Late Proterozoic (1.2–0.6 Ga), (5) Devonian (400–355 Ma), (6) Early...

Recently, to meet the demands of multimode/multiband functions for the next generation communication systems, RF MEMS technology is being developed for the reconfigurable transceiver system. In this article, RF MEMS devices such as switches, high-Q inductors, and high-Q resonators are reviewed for their operating principles and device structures, as well as the reliability and commercial issues. Single pole single throw (SPST)-type MEMS switches show characteristics superior to solid-state switches in the aspects of insertion loss, isolation, and linearity. Single pole multithrow (SPMT) switches will enable ultra small-sized cellular phones, but insertion loss and coupling between channels should be improved. For a high-Q inductor, out-of-plane inductors show improved results, and an inductor with a Q-factor of 75 at 1GHz has been fabricated by using the internal stress of a MoCr film. The MEMS inductor improves the performance of a voltage controlled oscillator (VCO), but proper hermetic packaging and standard libraries are also necessary for mass production. The MEMS resonator can operate up to 1.4 GHz and can be used as an oscillator for the timing device as well as component of the filter circuit. The SiTime Company recently has started delivery of a product with performance similar to the quartz oscillator, in which they solved the reliability issues by modifying the annealing and vacuum packaging. Copyright © 2007 Institute of Electrical Engineers of Japan© 2007 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Ages of six volcanic and plutonic rocks on Barton Peninsula, King George Island, were determined using 40Ar/39Ar and K-Ar isotopic systems. The 40Ar/39Ar and K-Ar ages of basaltic andesite and diorite range from 48 My to 74 My and systematically decrease toward the upper stratigraphic section. Two specimens of basaltic andesite which occur in the lowermost sequence of the peninsula, however, apparently define two distinct plateau ages of 52-53 My and 119-120 My. The latter is interpreted to represent the primary cooling age of basaltic andesite, whereas the former is interpreted as the thermally-reset age caused by the intrusion of Tertiary granitic pluton. The isochron ages calculated from the isotope correlation diagram corroborate our interpretation based on the apparent plateau ages. It is therefore likely that volcanism was active during the Early Cretaceous on Barton Peninsula. When the K-Ar ages of previous studies are taken into account with our result, the ages of basaltic andesite in the northern part of the Barton Peninsula are significantly older than those in the southern part. Across the north-west-south-east trending Barton fault bounding the two parts, there are significant differences in geochronologic and geologic aspects.

Rabies is an important zoonosis in the public and veterinary healthy arenas. This article provides information on the situation of current rabies outbreak, analyzes the current national rabies control system, reviews the weaknesses of the national rabies control strategy, and identifies an appropriate solution to manage the current situation. Current rabies outbreak was shown to be present from rural areas to urban regions. Moreover, the situation worldwide demonstrates that each nation struggles to prevent or control rabies. Proper application and execution of the rabies control program require the overcoming of existing weaknesses. Bait vaccines and other complex programs are suggested to prevent rabies transmission or infection. Acceleration of the rabies control strategy also requires supplementation of current policy and of public information. In addition, these prevention strategies should be executed over a mid- to long-term period to control rabies.

The subvolcanic granitic pluton in the Barton and Weaver Peninsulas, King George Island, is mainly composed of granodiorite with small volumes of gabbro, diorite and aplitic dikes. The pluton shows a vertically compositional zonation; the lower part consists of gabbro and diorite, whereas the upper part is mainly granodiorite. Various geochemical signatures of the pluton confirm that all subunits of the pluton were formed by accumulation and fractionation in a calc-alkaline magma at a shallow level of the crust. The positive Sr and Eu anomalies as well as textural features of gabbro strongly support that gabbro was the cumulate derived from accumulation of calcic plagioclase and in situ crystallization of ferromagnesian minerals. Taking into account the geochemical features of gabbro as a cumulate, the primary magma of the pluton is considered to have been intermediate (dioritic or quartz dioritic) in composition.

Knowledge is often represented as a set of rules or a semantic network in intelligent systems. Recently, ontology has been widely used to represent semantic knowledge, because it organizes thesaurus and hierarchal information between concepts in a particular domain. However, it is not easy to collect semantic relationships among concepts. Much time and expense are incurred in ontology construction. Collective intelligence can be a good alternative approach to solve these problems. In this paper, we propose a collective intelligence approach of Games With A Purpose (GWAP) to collect various semantic resources, such as words and word-senses. We detail how to construct the semantic information model or ontology from the collected semantic resources, constructing a system named FunWords. FunWords is a Korean lexical-based semantic resource collection tool. Experiments demonstrated the resources were grouped as common nouns, abstract nouns, adjective and neologism. Finally, we analyzed their characteristics, acquiring the semantic relationships noted above. Common nouns, with structural semantic relationships, such as hypernym and hyponym, are highlighted. Abstract nouns, with descriptive and characteristic semantic relationships, such as synonym and antonym are underlined. Adjectives, with such semantic relationships, as description and status, illustration - for example, color and sound - are expressed more. Last, neologism, with the semantic relationships, such as description and characteristics, are emphasized. Weighting the semantic relationships with these characteristics can help reduce time and cost, because it need not consider unnecessary or slightly related factors. This can improve the expressive power, such as readability, concentrating on the weighted characteristics. Our proposal to collect semantic resources from the collective intelligence approach of GWAP (our FunWords) and to weight their semantic relationship can help construct the semantic information model or ontology would be a more effective and expressive alternative.

This paper proposes a novel of a personalized Computer Assisted Language Learning (CALL) system based on learner’s cognitive abilities related to foreign language proficiency. In this CALL system, a strategy of retrieval learning, a method of learning memory cycle, and a method of repeated learning are applied for effective vocabulary memorization. The system is designed to offer personalized learning based on cognitive abilities related to the human language process. For this, the proposed CALL system has a cognitive diagnosis module which can measure five types of cognitive abilities. The results of this diagnosis are used to create dynamic learning scenarios for personalized learning and to evaluate user performance in the learning. This system is also designed in order to have users be able to create learning word lists and to share them simply with various functions based on open APIs. Additionally, through experiments, it has shown that this system helps students to learn English vocabulary effectively and enhances their foreign language skills.

Cleavage site prediction is an important issue in molecular biology. We present a new method that generates prediction rules for SARS-CoV protease cleavage sites. Our method includes rule extraction from a trained neural network and then enhancing the extracted rules by genetic evolution to improve its quality. Experimental results show that the method could generate new rules for cleavage site prediction, which are more general and accurate than consensus patterns.

In this paper, we demonstrate that the machine learning approach of rule extraction from a trained neural network can be successfully applied to SARS-coronavirus cleavage site analysis. The extracted rules predict cleavage sites better than consensus patterns. Empirical experiments are also shown.

Named entities in the biomedical domain are often written using a Noun Phrase (NP) along with a coordinating conjunction such as ‘and’ and ‘or’. In addition, repeated words among named entity mentions are frequently omitted. It is often difficult to identify named entities. Although various Named Entity Recognition (NER) methods have tried to solve this problem, these methods can only deal with relatively simple elliptical patterns in coordinated NPs. We propose a new NER method for identifying non-elliptical entity mentions with simple or complex ellipses using linguistic rules and an entity mention dictionary.The GENIA and CRAFT corpora were used to evaluate the performance of the proposed system. The GENIA corpus was used to evaluate the performance of the system according to the quality of the dictionary. The GENIA corpus comprises 3434 non-elliptical entity mentions in 1585 coordinated NPs with ellipses. The system achieves 92.11% precision, 95.20% recall, and 93.63% F-score in identification of non-elliptical entity mentions in coordinated NPs. The accuracy of the system in resolving simple and complex ellipses is 94.54% and 91.95%, respectively. The CRAFT corpus was used to evaluate the performance of the system under realistic conditions. The system achieved 78.47% precision, 67.10% recall, and 72.34% F-score in coordinated NPs. The performance evaluations of the system show that it efficiently solves the problem caused by ellipses, and improves NER performance. The algorithm is implemented in PHP and the code can be downloaded from https://code.google.com/p/medtextmining/.