42 research outputs found

    Wnt3a upregulates brain-derived insulin by increasing NeuroD1 via Wnt/beta-catenin signaling in the hypothalamus

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    Background: Insulin plays diverse roles in the brain. Although insulin produced by pancreatic ฮฒ-cells that crosses the blood-brain barrier is a major source of brain insulin, recent studies suggest that insulin is also produced locally within the brain. However, the mechanisms underlying the production of brain-derived insulin (BDI) are not yet known. Results: Here, we examined the effect of Wnt3a on BDI production in a hypothalamic cell line and hypothalamic tissue. In N39 hypothalamic cells, Wnt3a treatment significantly increased the expression of the Ins2 gene, which encodes the insulin isoform predominant in the mouse brain, by activating Wnt/ฮฒ-catenin signaling. The concentration of insulin was higher in culture medium of Wnt3a-treated cells than in that of untreated cells. Interestingly, neurogenic differentiation 1 (NeuroD1), a target of Wnt/ฮฒ-catenin signaling and one of transcription factors for insulin, was also induced by Wnt3a treatment in a time- and dose-dependent manner. In addition, the treatment of BIO, a GSK3 inhibitor, also increased the expression of Ins2 and NeuroD1. Knockdown of NeuroD1 by lentiviral shRNAs reduced the basal expression of Ins2 and suppressed Wnt3a-induced Ins2 expression. To confirm the Wnt3a-induced increase in Ins2 expression in vivo, Wnt3a was injected into the hypothalamus of mice. Wnt3a increased the expression of NeuroD1 and Ins2 in the hypothalamus in a manner similar to that observed in vitro. Conclusion: Taken together, these results suggest that BDI production is regulated by the Wnt/ฮฒ-catenin/NeuroD1 pathway in the hypothalamus. Our findings will help to unravel the regulation of BDI production in the hypothalamus.1

    Valosin-containing protein is a key mediator between autophagic cell death and apoptosis in adult hippocampal neural stem cells following insulin withdrawal

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    Background: Programmed cell death (PCD) plays essential roles in the regulation of survival and function of neural stem cells (NSCs). Abnormal regulation of this process is associated with developmental and degenerative neuronal disorders. However, the mechanisms underlying the PCD of NSCs remain largely unknown. Understanding the mechanisms of PCD in NSCs is crucial for exploring therapeutic strategies for the treatment of neurodegenerative diseases. Result: We have previously reported that adult rat hippocampal neural stem (HCN) cells undergo autophagic cell death (ACD) following insulin withdrawal without apoptotic signs despite their normal apoptotic capabilities. It is unknown how interconnection between ACD and apoptosis is mediated in HCN cells. Valosin-containing protein (VCP) is known to be essential for autophagosome maturation in mammalian cells. VCP is abundantly expressed in HCN cells compared to hippocampal tissue and neurons. Pharmacological and genetic inhibition of VCP at basal state in the presence of insulin modestly impaired autophagic flux, consistent with its known role in autophagosome maturation. Of note, VCP inaction in insulin-deprived HCN cells significantly decreased ACD and down-regulated autophagy initiation signals with robust induction of apoptosis. Overall autophagy level was also substantially reduced, suggesting the novel roles of VCP at initial step of autophagy. Conclusion: Taken together, these data demonstrate that VCP may play an essential role in the initiation of autophagy and mediation of crosstalk between ACD and apoptosis in HCN cells when autophagy level is high upon insulin withdrawal. This is the first report on the role of VCP in regulation of NSC cell death. Elucidating the mechanism by which VCP regulates the crosstalk of ACD and apoptosis will contribute to understanding the molecular mechanism of PCD in NSCs. ยฉ 2016 Yeo et al.1

    Significance of albumin to globulin ratio as a predictor of febrile urinary tract infection after ureteroscopic lithotripsy

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    Background We aimed to analyze the effectiveness of albumin to globulin ratio (AGR) in predicting postoperative febrile urinary tract infection (fUTI) after ureteroscopic lithotripsy (URS) and retrograde intrarenal surgery (RIRS). Methods From January 2013 to May 2018, 332 patients underwent URS and RIRS. The rate of postoperative fUTI and risk factors for postoperative fUTI were analyzed using logistic regression. Patients were divided into postoperative fUTI and non-postoperative fUTI (non-fUTI) groups. AGR with other demographic and perioperative data were compared between the two groups to predict the development of fUTI after URS. Results Of the 332 patients, postoperative fUTI occurred in 41 (12.3%). Preoperative pyuria, microscopic hematuria, diabetes mellitus, hypoalbuminemia, and hyperglobulinemia were more prevalent in the fUTI group. Patients in the fUTI group had larger stone size, lower preoperative AGR, longer operation time, and longer preoperative antibiotic coverage period. In a multivariable logistic analysis, preoperative pyuria, AGR, and stone size were independently correlated with postoperative fUTI (p<0.001, p=0.008, and p=0.041, respectively). Receiver operating curve analysis showed that the cutoff value of AGR that could predict a high risk of fUTI after URS was 1.437 (sensitivity, 77.3%; specificity, 76.9%), while the cutoff value of stone size was 8.5 mm (sensitivity, 55.3%; specificity, 44.7%). Conclusion This study demonstrated that preoperative pyuria, AGR, and stone size can serve as prognostic factors for predicting fUTI after URS

    Manipulating magnetism in the topological semimetal EuCd2As2

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    EuCd2As2 is a magnetic semimetal that has the potential of manifesting nontrivial electronic states, depending on its low temperature magnetic ordering. Here, we report the successful synthesis of single crystals of EuCd2As2 that order ferromagnetically or antiferromagnetically depending on the level of band filling, thus allowing for the use of magnetism to tune the topological properties within the same host. We explored their physical properties via magnetization, electrical transport, heat capacity, and angle-resolved photoemission spectroscopy measurements and conclude that EuCd2As2 is an excellent, tunable system for exploring the interplay of magnetic ordering and topology

    ์ •์ค‘์œต๊ธฐ๋กœ ํˆฌ์‚ฌํ•˜๋Š” ์†Œ์„ธํฌ์„ฑ ์‹ ๊ฒฝ๋ถ„๋น„์„ธํฌ์—์„œ ๋ฐœํ˜„๋˜๋Š” ๋‡Œ ์œ ๋ž˜ ์ธ์Š๋ฆฐ์˜ ๋‡Œํ•˜์ˆ˜์ฒด ์ „์—ฝ ๋‚ด ์„ฑ์žฅํ˜ธ๋ฅด๋ชฌ ์ƒ์‚ฐ ์กฐ์ ˆ

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    insulin, hypothalamic paraventricular nucleus (PVN), median eminence (ME), parvocellular neurosecretory neuron, restraint stress (RS), pituitary, growth hormone (GH)์ทŒ์žฅ์—์„œ ์ƒ์„ฑ๋˜์–ด ํ˜ˆ์•ก์œผ๋กœ ๋ถ„๋น„๋˜๋Š” ์ธ์Š๋ฆฐ์€ ํฌ๋„๋‹น, ๋‹จ๋ฐฑ์งˆ ๋ฐ ์ง€์งˆ๋Œ€์‚ฌ ์กฐ์ ˆ์— ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•˜๋Š” ํŽฉํ‹ฐ๋“œ ํ˜ธ๋ฅด๋ชฌ์ด๋‹ค. ํ˜ˆ์žฅ ์ธ์Š๋ฆฐ ์ˆ˜์น˜์˜ ์ฆ๊ฐ€๋Š” ๊ฐ„์˜ ํฌ๋„๋‹น ์ƒ์„ฑ ๊ฐ์†Œ, ๊ทผ์œก ๋‚ด ํฌ๋„๋‹น ํก์ˆ˜ ํšจ์œจ ํ–ฅ์ƒ, ๊ทผ์œก ๋‚ด ๋‹จ๋ฐฑ์งˆ ๋ถ„ํ•ด ๋Šฅ๋ ฅ ์ €ํ•˜ ๋ฐ ์ง€๋ฐฉ ์กฐ์ง ๋‚ด ์ง€์งˆ ๋ถ„ํ•ด ์–ต์ œ๋ฅผ ์ดˆ๋ž˜ํ•œ๋‹ค. ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ, ๋‡Œ์—์„œ ์ธ์Š๋ฆฐ์€ ์„ธํฌ์ฆ์‹ ๋ฐ ๋ถ„ํ™”, ์‹ ๊ฒฝ๋ณดํ˜ธ, ์‹ ๊ฒฝ์กฐ์ ˆ, ๊ธฐ์–ต ๋ฐ ์ธ์ง€์—๋„ ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค. ํŠนํžˆ, ์‹œ์ƒํ•˜๋ถ€์—์„œ ์ธ์Š๋ฆฐ์€ ํŠน์ • ์‹œ์ƒํ•˜๋ถ€ ์‹ ๊ฒฝ์„ธํฌ๋“ค์˜ ํ™œ์„ฑ ์กฐ์ ˆ์„ ์ด‰๋ฐœ์‹œํ‚ด์œผ๋กœ์จ ์Œ์‹ ์„ญ์ทจ, ์ฒด์ค‘ ์ฆ๊ฐ€ ๋ฐ ์—๋„ˆ์ง€ ์†Œ๋น„๋ฅผ ์Œ์„ฑ์ ์œผ๋กœ ์กฐ์ ˆํ•˜๋Š” ๊ฒƒ์œผ๋กœ๋„ ์ž˜ ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ํฅ๋ฏธ๋กญ๊ฒŒ๋„, ์ธ์Š๋ฆฐ์ด ์ทŒ์žฅ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์‹œ์ƒํ•˜๋ถ€๋ฅผ ํฌํ•จํ•œ ๋‹ค์–‘ํ•œ ๋‡Œ ์˜์—ญ๋“ค์—์„œ๋„ ์†Œ๋Ÿ‰ ํ•ฉ์„ฑ๋  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์—ฌ์ฃผ๋Š” ์—ฌ๋Ÿฌ ์—ฐ๊ตฌ ๋…ผ๋ฌธ๋“ค์ด 1970๋…„๋Œ€๋ถ€ํ„ฐ ์ง€๊ธˆ๊นŒ์ง€ ๊พธ์ค€ํžˆ ๋ฐœํ‘œ๋˜์–ด ์™”๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด๋Ÿฌํ•œ ๋‡Œ ์œ ๋ž˜ ์ธ์Š๋ฆฐ (brain-derived insulin)์ด ์‹œ์ƒํ•˜๋ถ€๋ฅผ ๊ตฌ์„ฑํ•˜๋Š” ์—ฌ๋Ÿฌ ์‹ ๊ฒฝํ•ต๋“ค ์ค‘์—์„œ ์ •ํ™•ํžˆ ์–ด๋Š ์‹ ๊ฒฝํ•ต ๋‚ด์—์„œ ๋งŒ๋“ค์–ด์ง€๋Š” ์ง€์™€, ์—ฌ๊ธฐ์„œ ๊ตญ์†Œ์ ์œผ๋กœ ๋งŒ๋“ค์–ด์ง„ ์‹œ์ƒํ•˜๋ถ€ ์ธ์Š๋ฆฐ์ด ์–ด๋– ํ•œ ์ƒ๋ฆฌ์ ์ธ ๊ธฐ๋Šฅ์„ ๊ฐ€์ง€๋Š”์ง€๋Š” ์—ฌ์ „ํžˆ ์˜๋ฌธ์œผ๋กœ ๋‚จ์•„์žˆ์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์ฅ ์‹œ์ƒํ•˜๋ถ€์—์„œ ์ธ์Š๋ฆฐ์„ ๋ฐœํ˜„ํ•˜๋Š” ์‹ ๊ฒฝ์„ธํฌ์ฒด๊ฐ€ ๋‡Œ์‹ค๊ณํ•ต (PVN) ๋‚ด์— ์œ„์น˜ํ•˜๋ฉฐ, ์ด ์‹ ๊ฒฝ์„ธํฌ์˜ ์ถ•์‚ญ์ด ์ •์ค‘์œต๊ธฐ (ME)๋กœ ํˆฌ์‚ฌ๋จ์„ ํ™•์ธํ•จ์œผ๋กœ์จ, ์ธ์Š๋ฆฐ์„ ๋งŒ๋“ค์–ด๋‚ด๋Š” ์ƒˆ๋กœ์šด ์†Œ์„ธํฌ์„ฑ ์‹ ๊ฒฝ๋ถ„๋น„์„ธํฌ (parvocellular neurosecretory neuron)๋ฅผ ์ •์˜ํ•˜์˜€๋‹ค. ๋‡Œ์‹ค๊ณํ•ต ์ธ์Š๋ฆฐ ์‹ ๊ฒฝ์„ธํฌ (PVN insulin neuron)์˜ ์•ฝ 85 %๊ฐ€ ์ฝ”๋ฅดํ‹ฐ์ฝ”ํŠธ๋กœํ•€ ๋ถ„๋น„ ํ˜ธ๋ฅด๋ชฌ (CRH)์„ ๊ณต๋™ ๋ฐœํ˜„ํ•˜๊ณ , ์ด ์ฝ”๋ฅดํ‹ฐ์ฝ”ํŠธ๋กœํ•€ ๋ถ„๋น„ ํ˜ธ๋ฅด๋ชฌ์„ ์ธ์Š๋ฆฐ๊ณผ ํ•จ๊ป˜ ์ •์ค‘์œต๊ธฐ๋กœ ์šด๋ฐ˜ํ•˜์˜€๋‹ค. ์ฝ”๋ฅดํ‹ฐ์ฝ”ํŠธ๋กœํ•€ ๋ถ„๋น„ ํ˜ธ๋ฅด๋ชฌ ๋ฐœํ˜„๊ณผ ๋Œ€์กฐ์ ์œผ๋กœ, ๋‡Œ์‹ค๊ณํ•ต์—์„œ์˜ ์ธ์Š๋ฆฐ ๋ฐœํ˜„์€ ๊ธ‰์„ฑ ๊ตฌ์† ์ŠคํŠธ๋ ˆ์Šค (acute RS)์— ์˜ํ•ด ์–ต์ œ๋˜์—ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๊ธ‰์„ฑ ๊ตฌ์† ์ŠคํŠธ๋ ˆ์Šค์— ์˜ํ•œ ๋‡Œ์‹ค๊ณํ•ต ๋‚ด ์ธ์Š๋ฆฐ์˜ ๋ฐœํ˜„ ์–ต์ œ๋Š” ๋‡Œํ•˜์ˆ˜์ฒด ์„ฑ์žฅํ˜ธ๋ฅด๋ชฌ (Gh) mRNA ์ˆ˜์ค€๊ณผ ํ˜ˆ์ฒญ ์„ฑ์žฅํ˜ธ๋ฅด๋ชฌ ๋†๋„๋ฅผ ๋ชจ๋‘ ๊ฐ์†Œ์‹œ์ผฐ์œผ๋ฉฐ, ์ด๋Š” ๋‡Œ์‹ค๊ณํ•ต ์ธ์Š๋ฆฐ์˜ ๊ณผ๋ฐœํ˜„์— ์˜ํ•ด ์•ฝํ™”๋˜์—ˆ๋‹ค. ์œ ์‚ฌํ•˜๊ฒŒ, ๋‡Œ์‹ค๊ณํ•ต์— ์ธ์Š๋ฆฐ์˜ ๋ฐœํ˜„์„ ์–ต์ œ์‹œํ‚ค๋Š” ๋ฐ”์ด๋Ÿฌ์Šค๋ฅผ ์ฃผ์ž…ํ•˜์ž ๋‡Œํ•˜์ˆ˜์ฒด ์„ฑ์žฅํ˜ธ๋ฅด๋ชฌ์˜ ์œ ์ „์ž ๋ฐœํ˜„ ๋ฐ ๋ถ„๋น„๊ฐ€ ํ•˜ํ–ฅ ์กฐ์ ˆ ๋˜์—ˆ๋‹ค. ์ด๋ฒˆ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋Š” ์ •์ƒ ์กฐ๊ฑด๊ณผ ์ŠคํŠธ๋ ˆ์Šค ์กฐ๊ฑด ๋ชจ๋‘์—์„œ, ๋‡Œ์‹ค๊ณํ•ต์˜ ์†Œ์„ธํฌ์„ฑ ์‹ ๊ฒฝ๋ถ„๋น„์„ธํฌ์—์„œ ํ•ฉ์„ฑ ๋˜๋Š” ์ธ์Š๋ฆฐ์ด ๋‡Œํ•˜์ˆ˜์ฒด ์„ฑ์žฅํ˜ธ๋ฅด๋ชฌ ์ƒ์‚ฐ ์กฐ์ ˆ์— ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•˜๊ณ  ์žˆ์Œ์„ ์‹œ์‚ฌํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ์ด ๋…ผ๋ฌธ์€ ๋‡Œ ์œ ๋ž˜ ์ธ์Š๋ฆฐ์˜ ์ƒˆ๋กœ์šด ์ƒ๋ฆฌ์ ์ธ ๊ธฐ๋Šฅ์„ ๋ฐํ˜€๋‚ด๋ฉฐ, ์‹œ์ƒํ•˜๋ถ€โ€“๋‡Œํ•˜์ˆ˜์ฒด ์กฐ์ ˆ์—์„œ ์‹œ์ƒํ•˜๋ถ€ ์ธ์Š๋ฆฐ, ํŠนํžˆ ๋‡Œ์‹ค๊ณํ•ต ์ธ์Š๋ฆฐ์˜ ์—ญํ• ์„ ๋” ์ž˜ ์ดํ•ดํ•  ์ˆ˜ ์žˆ๋„๋ก ๋„์™€์ค€๋‹ค. |Insulin, produced by pancreatic ฮฒ-cells and secreted into the bloodstream, is an influential and instrumental anabolic hormone that controls glucose, protein, and lipid metabolism. The increment of plasma insulin levels results in increased inhibition of hepatic glucose production, enhanced efficiency of glucose uptake in the muscle, decreased the capacity of protein degradation in the muscle, and reduced rate of lipolysis in the adipose tissue. Furthermore, in the brain, insulin has effect on cell proliferation and differentiation, neuroprotection, neuromodulation, memory, and cognition. In particular, insulin triggers the modulation of certain hypothalamic neuronal activity, negatively controlling food intake, weight gain, and energy expenditure. Although pancreatic ฮฒ-cells are considered the major source of insulin in the brain, the evidence has mounted that small amounts of insulin can also be synthesized locally in various brain regions such as the choroid plexus, cerebellum, cerebral cortex, striatum, olfactory bulb, hippocampus, and hypothalamus. Recent studies have revealed regulatory mechanisms underlying insulin gene expression in the hypothalamus. However, the precise hypothalamic sub-regions that are involved in insulin production and the physiological roles of locally produced insulin in the hypothalamus still remain elusive. Herein, I show that in the mouse hypothalamus, the perikarya of insulin-positive neurons are located in the paraventricular nucleus (PVN) and their axons project to the external zone of the median eminence (ME)these findings define novel parvocellular neurosecretory PVN insulin neurons. 85% of PVN insulin neurons co-expressed corticotrophin-releasing hormone (CRH) and transported CRH along with insulin to the ME. Contrary to CRH expression, insulin expression in the PVN was inhibited by acute restraint stress (RS). The acute RS-induced inhibition of PVN insulin expression decreased both pituitary growth hormone (Gh) mRNA level and serum GH concentration, which were attenuated by overexpression of PVN insulin. Similarly, knockdown of PVN insulin down-regulated pituitary GH gene expression and secretion. These results suggest that in both normal and stressful conditions, insulin synthesized in parvocellular neurosecretory neurons of the PVN plays an important role in the regulation of pituitary GH production. Thus, this thesis unveils a new physiological function of brain-derived insulin, and helps a better understanding of the role of hypothalamic insulin, especially PVN insulin in hypothalamicโ€“pituitary regulation.YAbstract i List of Contents iii List of Tables vi List of Figures vii List of abbreviations ix โ… . Introduction 1 1. Insulin 1 1.1 Biosynthesis & Processing 1 1.2 Functions 3 1.3 Local synthesis in the brain 3 2. Hypothalamic paraventricular nucleus (PVN) 5 2.1 Anatomy & Functions 5 2.2 Parvocellular neurosecretory neuron 6 3. Hypothalamic median eminence (ME) 6 4. Anterior pituitary 8 5. Hypothalamic-pituitary regulation 8 5.1 Stress response: hypothalamic-pituitary-adrenal (HPA) axis 8 5.2 Growth & Development: hypothalamic-pituitary-somatotropic (HPS) axis 9 6. Aims of the study 10 โ…ก. Materials & Method 11 1. Animals 11 2. Injection (i.p.) of Fluorogold (FG) 11 3. Injection of GFP-expressing lentivirus into the PVN 12 4. Injection (i.c.v.) of colchicine 12 5. Food deprivation 13 6. Restraint stress (RS) 13 7. Immunofluorescence staining 13 8. In situ hybridization 15 9. Electron microscopic immunohistochemistry 16 10. Preparation of lentiviral shRNAs and Ins2-overexpressing lentivirus 18 11. Administration of lentiviral shRNAs and Ins2-overexpressing lentivirus in to the PVN 18 12. Micro-dissection of PVN for RNA extraction 19 13. RNA extraction and gene expression analysis by qRT-PCR 19 14. Serum GH, corticosterone, and insulin analysis 21 15. Immunoblot analysis 21 16. Experimental design 22 17. Statistics 23 18. Study approval 23 โ…ข. Results 24 1. The hippocampus and cerebral cortex express insulin. 24 2. Hypothalamic insulin is synthesized mainly in the PVN neurons. 24 3. C-peptide, a proxy for processed insulin, is located specifically in the ME neurosecretory nerve terminals within the hypothalamus. 32 4. PVN insulin neurons send their nerve terminals to the external zone of the ME. 35 5. A subset of PVN insulin neurons co-expresses CRH or SST. 41 6. PVN neurons expressing both insulin and CRH co-transport these proteins into the ME. 41 7. Acute RS inhibits PVN insulin expression. 45 8. PVN insulin supports pituitary GH production. 48 9. Acute RSโ€“induced suppression of PVN insulin expression attenuates pituitary GH production. 49 โ…ฃ. Discussion 55 โ…ค. Conclusion 61 โ…ฅ. References 63 Abstract in Korean 72DoctordCollectio

    Optimal Color Lighting for Scanning Images of Flat Panel Display using Simplex Search

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    A system for inspecting flat panel displays (FPDs) acquires scanning images using multiline charge-coupled device (CCD) cameras and industrial machine vision. Optical filters are currently installed in front of these inspection systems to obtain high-quality images. However, the combination of optical filters required is determined manually and by using empirical methods; this is referred to as passive color control. In this study, active color control is proposed for inspecting FPDs. This inspection scheme requires the scanning of images, which is achieved using a mixed color light source and a mixing algorithm. The light source utilizes high-power light emitting diodes (LEDs) of multiple colors and a communication port to dim their level. Mixed light illuminates an active-matrix organic light-emitting diode (AMOLED) panel after passing through a beam expander and after being shaped into a line beam. The image quality is then evaluated using the Tenenbaum gradient after intensity calibration of the scanning images. The dimming levels are determined using the simplex search method which maximizes the image quality. The color of the light was varied after every scan of an AMOLED panel, and the variation was iterated until the image quality approached a local maximization. The number of scans performed was less than 225, while the number of dimming level combinations was 20484. The proposed method can reduce manual tasks in setting-up inspection machines, and hence is useful for the inspection machines in FPD processes

    Generating Selected Color using RGB, Auxiliary Lights, and Simplex Search

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    A mixed light source generates various colors, with the potential to adjust intensities of multiple LEDs, which makes it possible to generate arbitrary colors. Currently, PCs and OSs provide color selection windows that can obtain the RGB or HSL color coordinates of a userโ€™s selection. Mixed light sources are usually composed of LEDs in the primary colors, with LEDs in auxiliary colors such as white and yellow used in a few cases. When using auxiliary color LEDs, the number of LED inputs, the dimming levels, is larger than the number of elements in the color coordinate, which causes an under-determined problem. This study proposed how to determine the dimming levels of LEDs based on the selected color. Commercial LEDs have di_erent optical power values and impure color coordinates, even if they are RGB. Hence, the characteristics of the LEDs were described using a linear model derived from the tri-stimulus values (an XYZ color coordinate model) and dimming levels. Color mixing models were derived for the arbitrary number of auxiliary color LEDs. The under-determined problem was solved using a simplex search method without an inverse matrix operation. The proposed method can be applied to a machine vision system and an RGBW light mixer for semiconductor inspection. The dimming levels, obtained using the proposed method were better than derived using other methods
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