Hematopoietic Stem Cells and Bone Marrow Microenvironment: Current and Emerging Concepts (골수 미세환경에서 조혈줄기세포의 기능조절에 대한 고찰- 현재 및 새로운 개념)
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- Journal of Life Science
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- v.32 no.6
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- pp.468-475
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- 2022
The functional distinction between stem and progenitor cells is well established in several tissues, particularly in the blood. There, hematopoietic stem cells preserve their self-renewal potential and reconstitution ability in the bone marrow niche. Bone marrow represents a unique setting in which to examine how stroma influences tissue function. It was the setting in which the experimental definition of a niche was first provided in mammalian stem cell biology and where clear evidence for non-cell-autonomous oncogenesis was first defined. The relationship between bone and blood is ancient as all animals since the divergence of fish that have bones and blood, make blood in their bones. This long coevolution engendered complex interrelationships, including the first proposed and first experimentally defined niche for stem cells in mammals. Multiple bone marrow stromal cell types serve as regulators of hematopoiesis, and the dysfunction of some causes myelodysplasia and leukemia. However, no comprehensive atlas of stromal subpopulations exists. Therefore, we think these data point to something of importance, such as how the needs and challenges of the organism become translated down to distinct cell types that critically govern specific functions within tissues and do so at the level of a single molecule. We think this will be of broad interest to those focusing on systems biology and the physiology of organisms, particularly those seeking a molecular basis for understanding cell and tissue behavior. We summarized the current and emerging concepts of hematopoietic stem cells and bone marrow niche.
In deterministic and Monte Carlo transport codes, b-delayed emission is included using a group structure where all of the precursors are grouped together in 6 groups or families, but given the increase in computational power, nowadays there is no reason to keep this structure. Furthermore, there have been recent efforts to compile and evaluate all the available b-delayed neutron emission data and to measure new and improved data on individual precursors. In order to be able to perform a transient Monte Carlo simulation, data from individual precursors needs to be implemented in a transport code. This work is the first step towards the development of a tool to explore the effect of individual precursors in a fissile system. In concrete, individual precursor data is included by expanding the capabilities of the open source Monte Carlo code OpenMC. In the modified code - named Time Dependent OpenMC or OpenMC(TD)- time dependency related to β-delayed neutron emission was handled by using forced decay of precursors and combing of the particle population. The data for continuous energy neutron cross-sections was taken from JEFF-3.1.1 library. Regarding the data needed to include the individual precursors, cumulative yields were taken from JEFF-3.1.1 and delayed neutron emission probabilities and delayed neutron spectra were taken from ENDF-B/VIII.0. OpenMC(TD) was tested in a monoenergetic system, an energy dependent unmoderated system where the precursors were taken individually or in a group structure, and in a light-water moderated energy dependent system, using 6-groups, 50 and 40 individual precursors. Neutron flux as a function of time was obtained for each of the systems studied. These results show the potential of OpenMC(TD) as a tool to study the impact of individual precursor data on fissile systems, thus motivating further research to simulate more complex fissile systems.
Background: Mitral valve abnormalities in the pediatric population are rare. Mitral valve replacement or pediatric mitral lesions can cause problems such as a lack of growth potential. There re only limited experiences with mitral valve repair at any institution, so the purpose of his study is to evaluate the outcomes of mitral valve repair n pediatric patients. Material and Method: Sixty-four consecutive children (28 males and 36 females) with a mean age of
Sea cucumbers contain more than 50% protein in their solid content, and they also possess various bioactive substances such as saponins and mucopolysaccharides. This study analyzed the activities of various enzymes derived from Bacillus and lactic acid bacteria and determined to degrade the components of sea cucumbers. Among the analyzed strains, B. subtilis K26 showed the highest activities in protease and xylanase and relatively high activity in cellulase. Accordingly, samples of sea cucumber and water were mixed in equal proportions, sterilized, and then fermented by inoculating them with B. subtilis K26. Following this, a higher amino acid content was observed between 1.5 and 7.5 hr, a lower residual solid content in this time, and a lesser fermentation odor. The saponin content in fermented sea cucumber powder extracted with butanol was measured to be 1.12 mg/g. The chondroitin sulfate content was evaluated to be 5.11 mg/g in raw sea cucumber. The total polyphenol content, flavonoid content, and antioxidant activities were 6.95 mg gallic acid equivalent/g, 3.69 mg quercetin equivalent/g, and 3.69 mg quercetin equivalent/g in raw sea cucumber, respectively. Moreover, the DNA damage protective effect of fermented sea cucumber extract was found to be concentration-dependent, with a very strong effect at very low concentrations. Overall, we suggest that sea cucumber fermented with B. subtilis K26 has a high potential as a food for inhibiting oxidation, enhancing immunity, and improving muscle function in the human body thanks to its high free amino acid content.
Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70