A Dominant Complement Fixation Pathway for Pneumococcal Polysaccharides Initiated by SIGN-R1 Interacting with C1q

The intricate system of serum complement proteins provides resistance to infection. A pivotal step in the complement pathway is the assembly of a C3 convertase, which digests the C3 complement component to form microbial binding C3 fragments recognized by leukocytes. The spleen and C3 provide resistance against blood-borne S. pneumoniae infection. To better understand the mechanisms involved, we studied SIGN-R1, a lectin that captures microbial polysaccharides in spleen. Surprisingly, conditional SIGN-R1 knockout mice developed deficits in C3 catabolism when given S. pneumoniae or its capsular polysaccharide intravenously. There were marked reductions in proteolysis of serum C3, deposition of C3 on organisms within SIGN-R1+ spleen macrophages, and formation of C3 ligands. We found that SIGN-R1 directly bound the complement C1 subcomponent, C1q, and assembled a C3 convertase, but without the traditional requirement for either antibody or factor B. The transmembrane lectin SIGN-R1 therefore contributes to innate resistance by an unusual C3 activation pathway

Association between blood pressure and the risk of chronic kidney disease in treatment-naïve hypertensive patients

Background Although hypertension is a well-known risk factor for chronic kidney disease (CKD), the blood pressure (BP) at which antihypertensive interventions should be initiated remains to be determined. Therefore, we investigated the association between BP and CKD in treatment-naïve individuals. Methods This prospective cohort study considered 7,343 individuals in the Korean Genome and Epidemiology Study who were not taking antihypertensive medications. Subjects were categorized into six groups according to their systolic BP (SBP) and five groups according to their diastolic BP (DBP). The primary outcome was incident CKD, which was defined as an estimated glomerular filtration rate of <60 mL/min/1.73 m2 or the development of proteinuria. The secondary outcome was incident cardiovascular disease (CVD). Results In the time-varying Cox models, the hazard ratios (95% confidence interval [CI]) for CKD were 1.39 (1.10–1.77) with SBP 130–139 mmHg, 1.79 (1.40–2.28) with SBP 140–159 mmHg, and 3.22 (2.35–4.40) with SBP ≥ 160 mmHg, compared with SBP 100–119 mmHg. In addition, the hazard ratios (95% CI) for CKD were 1.88 (1.48–2.37) with DBP 90–99 mmHg and 4.30 (3.20–5.76) with DBP ≥ 100 mmHg, compared with DBP 70–79 mmHg. A significantly increased CVD risk was also observed in subjects with SBP ≥ 130 mmHg or DBP ≥ 90 mmHg. Conclusion Our findings indicate that SBP ≥ 130 mmHg and DBP ≥ 90 mmHg are associated with an increased risk of CKD. Therefore, BP-lowering strategies should be considered starting at those thresholds to prevent CKD development

DUKKU AND BAEKAM SPRINGWATER INHIBIT THE UREASE ACTIVITY OF HELICOBACTER PYLORI

Background: Springwater (SW) contains many kinds of minerals such as sodium, potassium and copper. These metallic ions may influence the activity of metallo-enzymes such as urease via competitive inhibition. In this study, we investigated the effect of SW on the inhibition of Ni-containing urease activity, which is essential for the colonization of Helicobacter pylori (H. pylori) in the human stomach.Materials and Methods: We studied the growth inhibition of H. pylori by SW. We evaluated ammonia production to detect urease activity and performed western blot analysis of UreA and UreB for enzyme production.Results: SW had no significant effect on bacterial growth. Western blot analysis also showed that SW did not affect the translation of UreA and UreB, but it significantly reduced the urease activities of the Jack bean as well as that of H.pylori from 50 to 75%.Conclusion: These results might indicate that the consumption of SW may prevent the colonization of H. pylori andameliorate the toxic effect on gastric mucosa via the inhibition of urease activity

Students' perceptions of peer and self assessment in a higher education online collaborative learning environment

textThe purpose of the study was to investigate factors that affect students’ perceptions of the use of online peer and self assessment in an online collaborative learning environment, and to explore the impacts of the assessments on the online collaboration of the students. The setting of this study was a university graduate-level online credit course entitled Computer Supported Collaborative Learning (CSCL), in which all course activities were conducted collaboratively through online communications and online peer and self assessment was provided at the end of every group project. Data sources included: face-to-face or online video conferencing interviews with 14 participants; participants’ written reflections; their portfolios; messages that each participant posted to their group online discussion board; and peers’ and self comments on the online peer and self assessment. Data were analyzed using Strauss and Corbin’s (1998) grounded theory approach. Results of the data analysis showed that many factors allowed students to have varied perceptions, attitudes, and feelings in conducting the online peer and self assessment. The factors were grouped into three: learning context, individual differences, and online learning community. Learning context encompassed all parts of the CSCL online course strongly related to the peer and self assessment, including course elements, online assessment system, types of assessment feedback, and graduate school environment. Categories under the factor of individual differences included stringency-leniency in ratings, objectivity of ratings, previous assessment experience, purpose of the assessments, and degree of self-confidence in assessing their own contributions to the group activity. Categories related to the online learning community included group composition, engagement of group members, and sense of community. Additionally, the results revealed the impact of the use of peer and self assessment on the group collaboration in terms of understanding others’ perspectives, reflections on themselves, awareness of the assessments, interpersonal skills for collaboration, accountability, participation, personal criteria for the assessments, level of confidence with the assessments, and group collaboration.Curriculum and Instructio

Comparison between point cloud and mesh models using images from an unmanned aerial vehicle

Structure from motion (SfM) is a well-known algorithm used for the generating of three-dimensional (3D) spatial information using images. The objective of this study is to compare the measurements of objects ascertained from point cloud and mesh models derived from the SfM algorithm. In particular, we analyze a single tree to determine the correlation between the number of acquired images from the UAVs and the object measurement for each model. The results indicate that the number of images does not have a critical impact on surveys and the point cloud is approximately 2% more accurate than mesh models for individual tree measurement. Our results will be useful in terms of selecting the data acquisition method as well as the data itself for measuring objects based on SfM 3D data. (C) 2019 Elsevier Ltd. All rights reserved.OAIID:RECH_ACHV_DSTSH_NO:T201910909RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A075721CITE_RATE:2.791DEPT_NM:조경·지역시스템공학부EMAIL:[email protected]_YN:YN

Prediction of Severe Drought Area Based on Random Forest: Using Satellite Image and Topography Data

The uncertainty of drought forecasting based on past meteorological data is increasing because of climate change. However, agricultural droughts, associated with food resources and determined by soil moisture, must be predicted several months ahead for timely resource allocation. Accordingly, we designed a severe drought area prediction (SDAP) model for short-term drought without meteorological data. The predictions of our proposed SDAP model indicate a forecast of serious drought areas assuming non-rainfall, not a probability prediction of drought occurrence. Furthermore, this prediction provides more practical information to help with rapid water allocation during a real drought. The model structure using remote sensing data consists of two parts. First, the drought function f(x) from the training area by random forest (RF) learned the changes in the pattern of soil moisture index (SMI) from the past drought and the training performance was found to be root mean square error (RMSE) = 0.052, mean absolute error (MAE) = 0.039, R2 = 0.91. Second, derived f(x) predicted the SMI of the study area, which is 20 times larger than the training area, of the same season of another year as RMSE = 0.382, MAE = 0.375, R2 = 0.58. We also obtained the variable importance stemming from RF and discussed its meaning along with the advantages and limitations of the model, training areas selection, and prediction coverage

A Survey of North Korean Studies in South Korea: Current Status and Prescriptions for Establishment as an Independent Discipline

Hanguk jeongchi hakhoebo (한국정치학회보), vol. 45, no. 3 (June 2011). Note: This article was originally published in English

Prediction of Severe Drought Area Based on Random Forest: Using Satellite Image and Topography Data

The uncertainty of drought forecasting based on past meteorological data is increasing because of climate change. However, agricultural droughts, associated with food resources and determined by soil moisture, must be predicted several months ahead for timely resource allocation. Accordingly, we designed a severe drought area prediction (SDAP) model for short-term drought without meteorological data. The predictions of our proposed SDAP model indicate a forecast of serious drought areas assuming non-rainfall, not a probability prediction of drought occurrence. Furthermore, this prediction provides more practical information to help with rapid water allocation during a real drought. The model structure using remote sensing data consists of two parts. First, the drought function f(x) from the training area by random forest (RF) learned the changes in the pattern of soil moisture index (SMI) from the past drought and the training performance was found to be root mean square error (RMSE) = 0.052, mean absolute error (MAE) = 0.039, R-2 = 0.91. Second, derived f(x) predicted the SMI of the study area, which is 20 times larger than the training area, of the same season of another year as RMSE = 0.382, MAE = 0.375, R-2 = 0.58. We also obtained the variable importance stemming from RF and discussed its meaning along with the advantages and limitations of the model, training areas selection, and prediction coverage.OAIID:RECH_ACHV_DSTSH_NO:T201910911RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A075721CITE_RATE:2.069DEPT_NM:조경·지역시스템공학부EMAIL:[email protected]_YN:YY