-
1دورية أكاديمية
المؤلفون: Bao A. Ding, Shuang Q. Ma, Zong R. Li, Xi L. Li, Stephen R. Madigosky
المصدر: Veterinary World, Vol 11, Iss 8, Pp 1135-1138 (2018)
مصطلحات موضوعية: green grass, intestine, morphology, Qinghai yak, rumen, Animal culture, SF1-1100, Veterinary medicine, SF600-1100
الوصف: Aim: The aim of the current study was to investigate the effects of seasonal changes in grass quality on the ruminal and intestinal morphology of male Qinghai yaks. Materials and Methods: A total of four male yaks with the same age of 4 years old from each season (summer and winter) were randomly selected and slaughtered to determine the effect of different season on intestinal morphology of yak in the Qinghai-Tibetan Plateau. Results: The histological analysis shows that male yak has the longer and wider papillae in rumen in green season. The height of villi in duodenum and jejunum was significantly higher in green season, and the width of villi on duodenum, jejunum, ileum, and rectum was significantly wider in green season. Surface area of villi and crypt depth in duodenum, jejunum, and ileum was significantly larger and deeper in green season. Submucosa thickness of duodenum, jejunum, ileum, and rectum was significantly thicker in green season. The muscular thickness of jejunum, cecum, and rectum was significantly thicker in green season. Conclusion: According to this research, we found that the seasonal changes of ruminal and intestinal morphology of yak showed different length and width papillae, villi, crypt, and submucosa. This fact was confirmed the functional advantages resulting from the ability to successfully adapt to a dry climate and diets, flat, open, and cold grassland may allow yak to overcome both water shortage and energy deficiency in winter.
وصف الملف: electronic resource
العلاقة: http://www.veterinaryworld.org/Vol.11/August-2018/17.pdfTest; https://doaj.org/toc/0972-8988Test; https://doaj.org/toc/2231-0916Test
-
2دورية أكاديمية
المصدر: International Journal of Nanomedicine, Vol Volume 19, Pp 2625-2638 (2024)
مصطلحات موضوعية: psoriasis, topical delivery, mirna-125b, framework nucleic acid, fna, Medicine (General), R5-920
الوصف: Yunfeng Han,1 Long Xi,1,2 Fang Leng,3 Chenjie Xu,3 Ying Zheng1 1State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau, 999078, People’s Republic of China; 2School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, 510006, People’s Republic of China; 3Department of Biomedical Engineering, City University of Hong Kong, Hong Kong SAR, People’s Republic of ChinaCorrespondence: Ying Zheng; Chenjie Xu, Tel +853 88224687 ; +852 34424169, Fax +853 28841358, Email yzheng@umac.mo; chenjie.xu@cityu.edu.hkPurpose: Psoriasis is a chronic and recurrent inflammatory dermatitis characterized by T cell imbalance and abnormal keratinocyte proliferation. MicroRNAs (miRNAs) hold promise as therapeutic agents for this disease; however, their clinical application is hindered by poor stability and limited skin penetration. This study demonstrates the utilization of Framework Nucleic Acid (FNA) for the topical delivery of miRNAs in psoriasis treatment.Methods: By utilizing miRNA-125b as the model drug, FNA-miR-125b was synthesized via self-assembly. The successful synthesis and stability of FNA-miR-125b in bovine fetal serum (FBS) were verified through gel electrophoresis. Subsequently, flow cytometry was employed to investigate the cell internalization on HaCaT cells, while qPCR determined the effects of FNA-miR-125b on cellular functions. Additionally, the skin penetration ability of FNA-miR-125b was assessed. Finally, a topical administration study involving FNA-miR-125b cream on imiquimod (IMQ)-induced psoriasis mice was conducted to evaluate its therapeutic efficacy.Results: The FNA-miR-125b exhibited excellent stability, efficient cellular internalization, and potent inhibition of keratinocyte proliferation. In the psoriasis mouse model, FNA-miR-125b effectively penetrated the skin tissue, resulting in reduced epidermal thickness and PASI score, as well as decreased levels of inflammatory cytokines. Keywords: psoriasis, topical delivery, miRNA-125b, framework nucleic acid, FNA
وصف الملف: electronic resource
-
3دورية أكاديمية
المصدر: Infection and Drug Resistance, Vol Volume 16, Pp 4687-4696 (2023)
مصطلحات موضوعية: fungal infection, species distribution, antifungal drugs, drug susceptibility, drug resistance, Infectious and parasitic diseases, RC109-216
الوصف: Wenying Cai,1,* Qianqian Ruan,2– 4,* Jiahao Li,1 Li Lin,1 Liyan Xi,1,5 Jiufeng Sun,2,3 Sha Lu1 1Department of Dermatology and Venereology, Sun Yat-sen Memorial Hospital of Sun Yat-Sen University, Guangzhou, People’s Republic of China; 2Guangdong Provincial Institute of Public Health, Guangzhou, People’s Republic of China; 3Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, People’s Republic of China; 4School of Public Health, Sun Yat-sen University, Guangzhou, People’s Republic of China; 5Dermatology Hospital, Southern Medical University, Guangzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Sha Lu, Department of Dermatology and Venereology, Sun Yat-sen Memorial Hospital of Sun Yat-Sen University, 107 West Yanjiang Road, Guangzhou, People’s Republic of China, Email lush7@mail.sysu.edu.cn Jiufeng Sun, Guangdong Provincial Center for Disease Control and Prevention, 160 Qunxian Road, Guangzhou, People’s Republic of China, Email sunjiuf@163.comBackground: Deep fungal infection has become an important cause of infection and death in hospitalized patients, and this has worsened with increasing antifungal drug resistance.Objective: A 3-year retrospective study was conducted to investigate the clinical characteristics, pathogen spectrum, and drug resistance of deep fungal infection in a regional hospital of Guangzhou, China.Methods: Non-duplicate fungi isolates recovered from blood and other sterile body fluids of in-patients of the clinical department were identified using biochemical tests of pure culture with the API20C AUX and CHROMagar medium. Antifungal susceptibilities were determined by Sensititre YeastOne® panel trays.Results: In this study, 525 patients (283 female, 242 male) with deep fungal infection were included, half of them were elderly patients (≥ 60 years) (54.67%, n=286). A total of 605 non-repetitive fungi were finally isolated from sterile samples, of which urine specimens accounted for 66.12% (n=400). Surgery, ICU, and internal medicine were the top three departments that fungi were frequently detected. The mainly isolated fungal species were Candida albicans (43.97%, n=266), Candida glabrata (20.00%, n=121), and Candida tropicalis (17.02%, n=103), which contributed to over 80% of fungal infection. The susceptibility of the Candida spp. to echinocandins, 5-fluorocytosine, and amphotericin B remained above 95%, while C. glabrata and C. tropicalis to itraconazole were about 95%, and the dose-dependent susceptibility of C. glabrata to fluconazole was more than 90%. The echinocandins had no antifungal activity against Trichosporon asahi in vitro (MIC90> 8 μg/mL), but azole drugs were good, especially voriconazole and itraconazole (MIC90 = 0.25 μg/mL).Conclusion: The main causative agents of fungal infection were still the genus of Candida. Echinocandins were the first choice for clinical therapy of Candida infection, followed with 5-fluorocytosine and amphotericin B. Azole antifungal agents should be used with caution in Candida glabrata and Candida tropicalis infections.Keywords: fungal infection, species distribution, antifungal drugs, drug susceptibility, drug resistance
وصف الملف: electronic resource
-
4دورية أكاديمية
المؤلفون: Ullah, N., Kakakhel, M. A., Bai, Y., Xi, L., Khan, I., Kalra, B. S., Kumar, T., Ahmad, H., Shah, M., Guanlan, L., Zhang, C.
المصدر: Brazilian Journal of Biology. January 2023 83
مصطلحات موضوعية: HCV, ELISA, PCR, prevalence, Mardan
الوصف: Hepatitis C virus (HCV) is the serious global public health burden of liver disease. Approximately 170 million people in the world are infected with (HCV). In Pakistan, where the disease has high occurrence rate. The present study envisages an up-to-date prevalence of HCV and genotypic distribution in the general population of Mardan District, Khyber Pakhtunkhwa (KP), Pakistan. The blood samples from 6,538 individuals including 3,263 males and 3,275 females were analyzed for hepatitis C surface antigen by Immuno-chromatographic test (ICT), Enzyme-linked immunosorbent assay (ELISA), and reverse transcription-polymerase chain reaction (PCR). It was found that 396 (12.13%) out of 3263 individuals contained antibodies in their blood against HCV, while among the different age groups, the highest incidences of HCV antibodies were found in the 31-40 age group (11.01%). The ICT positive samples were further screened by nested PCR to determine the existence of active HCV-RNA. It was identified that 7.11% (3263) of the total population (6538) tested was positive, among which the 461 (14.07%) females possessed antibodies in their blood against HCV. Our data showed total HCV infection in the investigated population was 5.78%. Higher percentage of HCV prevalence was detected in males than females in the age group 31-40 and 41-50. To compare the prevalence of HCV genotypes age-wise in male and female genotype 3a was found most prevalent genotype followed by 1a, 2a and 3b, respectively.
وصف الملف: text/html
-
5دورية أكاديمية
المؤلفون: Ullah, N., Khan, I., Kakakhel, M. A., Xi, L., Bai, Y., Kalra, B. S., Guanlan, L., Kumar, T., Shah, M., Zhang, C.
المصدر: Brazilian Journal of Biology. January 2022 82
مصطلحات موضوعية: HBV prevalence, age groups, anti-HBV antibodies, Mardan, Pakistan
الوصف: Hepatitis B virus infection is perilous among the five types of Hepatitis, as it remains clinically asymptomatic. The present study draws up-to-date prevalence of Hepatitis B virus (HBV) in the general population of Mardan, Khyber Pakhtunkhwa Pakistan. The blood samples from 4803 individuals including 2399 male and 2404 females were investigated. All the suspected samples were analyzed for hepatitis B surface antigen using Immuno-chromatographic test (ICT), Enzyme-linked immunosorbent assay (ELISA), and followed by Reverse transcription-polymerase chain reaction (RT-PCR). Results showed that 312 (13.00%) out of 2399 individuals contained antibodies in their blood against HBV, while among the different age groups, the highest incidences of HBV antibodies were found in the age of 21-30 groups (10.73%). Furthermore, the ICT positive samples were screened by nested polymerase chain reaction to detect the existence of active HBV-DNA. It was observed that 169 (7.04%) out of (2399) male of the total population (4803) tested was positive. On the other hand, the female 463 (19.25%) possessed antibodies in their blood against HBV. Accumulatively, our results showed a higher percentage of HBV prevalence in males than females in the age group 21-30 years. The total HCV infected in Mardan general population was recorded at 5.7% comprising both male and female.
وصف الملف: text/html
-
6دورية أكاديمية
المؤلفون: Tang, L., Wilkin, S., Richter, K., Bleasdale, M., Fernandes, R., He, Y., Li, S., Petraglia, M., Scott, A., Teoh, F., Tong, Y., Tsering, T., Tsho, Y., Xi, L., Yang, F., Yuan, H., Chen, Z., Roberts, P., He, W., Spengler, R., Lu, H., Wangdue, S., Boivin, N.
المصدر: Science Advances
الوصف: The extreme environments of the Tibetan Plateau offer considerable challenges to human survival, demanding novel adaptations. While the role of biological and agricultural adaptations in enabling early human colonization of the plateau has been widely discussed, the contribution of pastoralism is less well understood, especially the dairy pastoralism that has historically been central to Tibetan diets. Here, we analyze ancient proteins from the dental calculus (n = 40) of all human individuals with sufficient calculus preservation from the interior plateau. Our paleoproteomic results demonstrate that dairy pastoralism began on the highland plateau by ~3500 years ago. Patterns of milk protein recovery point to the importance of dairy for individuals who lived in agriculturally poor regions above 3700 m above sea level. Our study suggests that dairy was a critical cultural adaptation that supported expansion of early pastoralists into the region’s vast, non-arable highlands, opening the Tibetan Plateau up to widespread, permanent human occupation.
وصف الملف: application/pdf
العلاقة: http://hdl.handle.net/21.11116/0000-000D-084E-4Test; http://hdl.handle.net/21.11116/0000-000D-0850-0Test
-
7دورية أكاديمية
المؤلفون: Zhao, Y., Tang, W., Feng, J., Wan, Tao Ruan, Xi, L.
مصطلحات موضوعية: Point set registration, Supervised learning, Learning-based optimisation
الوصف: Yes
Point set registration has been actively studied in computer vision and graphics. Optimization algorithms are at the core of solving registration problems. Traditional optimization approaches are mainly based on the gradient of objective functions. The derivation of objective functions makes it challenging to find optimal solutions for complex optimization models, especially for those applications where accuracy is critical. Learning-based optimization is a novel approach to address this problem, which learns the gradient direction from datasets. However, many learning-based optimization algorithms learn gradient directions via a single feature extracted from the dataset, which will cause the updating direction to be vulnerable to perturbations around the data, thus falling into a bad stationary point. This paper proposes the General Discriminative Optimization (GDO) method that updates a gradient path automatically through the trade-off among contributions of different features on updating gradients. We illustrate the benefits of GDO with tasks of 3D point set registrations and show that GDO outperforms the state-of-the-art registration methods in terms of accuracy and robustness to perturbations. -
8
-
9دورية أكاديمية
المؤلفون: Campbell, PJ, Getz, G, Korbel, JO, Stuart, JM, Jennings, JL, Stein, LD, Perry, MD, Nahal-Bose, HK, Ouellette, BFF, Li, CH, Rheinbay, E, Nielsen, GP, Sgroi, DC, Wu, CL, Faquin, WC, Deshpande, V, Boutros, PC, Lazar, AJ, Hoadley, KA, Louis, DN, Dursi, LJ, Yung, CK, Bailey, MH, Saksena, G, Raine, KM, Buchhalter, I, Kleinheinz, K, Schlesner, M, Zhang, J, Wang, W, Wheeler, DA, Ding, L, Simpson, JT, O'Connor, BD, Yakneen, S, Ellrott, K, Miyoshi, N, Butler, AP, Royo, R, Shorser, S, Vazquez, M, Rausch, T, Tiao, G, Waszak, SM, Rodriguez-Martin, B, Shringarpure, S, Wu, DY, Demidov, GM, Delaneau, O, Hayashi, S, Imoto, S, Habermann, N, Segre, A, Garrison, E, Cafferkey, A, Alvarez, EG, Heredia-Genestar, JM, Muyas, F, Drechsel, O, Bruzos, AL, Temes, J, Zamora, J, Baez-Ortega, A, Kim, HL, Mashl, RJ, Ye, K, DiBiase, A, Huang, KL, Letunic, I, McLellan, MD, Newhouse, SJ, Shmaya, T, Kumar, S, Wedge, DC, Wright, MH, Yellapantula, VD, Gerstein, M, Khurana, E, Marques-Bonet, T, Navarro, A, Bustamante, CD, Siebert, R, Nakagawa, H, Easton, DF, Ossowski, S, Tubio, JMC, De La Vega, FM, Estivill, X, Yuen, D, Mihaiescu, GL, Omberg, L, Ferretti, V, Sabarinathan, R, Pich, O, Gonzalez-Perez, A, Weiner, AT, Fittall, MW, Demeulemeester, J, Tarabichi, M, Roberts, ND, Van Loo, P, Cortes-Ciriano, I, Urban, L, Park, P, in Zhu, Pitkaenen, E, Li, Y, Saini, N, Klimczak, LJ, Weischenfeldt, J, Sidiropoulos, N, Alexandrov, LB, Rabionet, R, Escaramis, G, Bosio, M, Holik, AZ, Susak, H, Prasad, A, Erkek, S, Calabrese, C, Raeder, B, Harrington, E, Mayes, S, Turner, D, Juul, S, Roberts, SA, Song, L, Koster, R, Mirabello, L, Hua, X, Tanskanen, TJ, Tojo, M, Chen, J, Aaltonen, LA, Ratsch, G, Schwarz, RF, Butte, AJ, Brazma, A, Chanock, SJ, Chatterjee, N, Stegle, O, Harismendy, O, Bova, GS, Gordenin, DA, Haan, D, Sieverling, L, Feuerbach, L, Chalmers, D, Joly, Y, Knoppers, B, Molnar-Gabor, F, Phillips, M, Thorogood, A, Townend, D, Goldman, M, Fonseca, NA, Xiang, Q, Craft, B, Pineiro-Yanez, E, Munoz, A, Petryszak, R, Fullgrabe, A, Al-Shahrour, F, Keays, M, Haussler, D, Weinstein, J, Huber, W, Valencia, A, Papatheodorou, I, Zhu, J, Fan, Y, Torrents, D, Bieg, M, Chen, K, Chong, Z, Cibulskis, K, Eils, R, Fulton, RS, Gelpi, JL, Gonzalez, S, Gut, IG, Hach, F, Heinold, M, Hu, T, Huang, V, Hutter, B, Jaeger, N, Jung, J, Kumar, Y, Lalansingh, C, Leshchiner, I, Livitz, D, Ma, EZ, Maruvka, YE, Milovanovic, A, Nielsen, MM, Paramasivam, N, Pedersen, JS, Puiggros, M, Sahinalp, SC, Sarrafi, I, Stewart, C, Stobbe, MD, Wala, JA, Wang, J, Wendl, M, Werner, J, Wu, Z, Xue, H, Yamaguchi, TN, Yellapantula, V, Davis-Dusenbery, BN, Grossman, RL, Kim, Y, Heinold, MC, Hinton, J, Jones, DR, Menzies, A, Stebbings, L, Hess, JM, Rosenberg, M, Dunford, AJ, Gupta, M, Imielinski, M, Meyerson, M, Beroukhim, R, Reimand, J, Dhingra, P, Favero, F, Dentro, S, Wintersinger, J, Rudneva, V, Park, JW, Hong, EP, Heo, SG, Kahles, A, jong-Van Lehmann, Soulette, CM, Shiraishi, Y, Liu, F, He, Y, Demircioglu, D, Davidson, NR, Greger, L, Li, S, Liu, D, Stark, SG, Zhang, F, Amin, SB, Bailey, P, Chateigner, A, Frenkel-Morgenstern, M, Hou, Y, Huska, MR, Kilpinen, H, Lamaze, FC, Li, C, Li, X, Liu, X, Marin, MG, Markowski, J, Nandi, T, Ojesina, A, Pan-Hammarstrom, Q, Park, PJ, Pedamallu, CS, Su, H, Tan, P, Teh, B, Xiong, H, Ye, C, Yung, C, Zhang, XQ, Zheng, LT, Zhu, S, Awadalla, P, Creighton, CJ, Wu, K, Yang, HM, Goke, J, Zhang, Z, Brooks, AN, Martincorena, I, Rubio-Perez, C, Juul, M, Schumacher, S, Shapira, O, Tamborero, D, Mularoni, L, Hornshoj, H, Deu-Pons, J, Muinos, F, Bertl, J, Guo, Q, Bazant, W, Barrera, E, Al-Sedairy, ST, Aretz, A, Bell, C, Betancourt, M, Buchholz, C, Calvo, F, Chomienne, C, Dunn, M, Edmonds, S, Green, E, Gupta, S, Hutter, CM, Jegalian, K, Jones, N, Lu, YY, Nakagama, H, Nettekoven, G, Planko, L, Scott, D, Shibata, T, Shimizu, K, Stratton, MR, Yugawa, T, Tortora, G, VijayRaghavan, K, Zenklusen, JC, Knoppers, BM, Aminou, B, Bartolome, J, Boroevich, KA, Boyce, R, Buchanan, A, Byrne, NJ, Chen, Z, Cho, S, Choi, W, Clapham, P, Dow, MT, Eils, J, Farcas, C, Fayzullaev, N, Flicek, P, Heath, AP, Hofmann, O, Hong, JH, Hudson, TJ, Huebschmann, D, Ivkovic, S, Jeon, SH, Jiao, W, Kabbe, R, Kerssemakers, JNA, Kim, H, Kim, J, Koscher, M, Koures, A, Kovacevic, M, Lawerenz, C, Liu, J, Mijalkovic, S, Mijalkovic-Lazic, AM, Miyano, S, Nastic, M, Nicholson, J, Ocana, D, Ohi, K, Ohno-Machado, L, Pihl, TD, Prinz, M, Radovic, P, Short, C, Sofia, HJ, Spring, J, Struck, AJ, Tijanic, N, Vicente, D, Wang, Z, Williams, A, Woo, Y, Wright, AJ, Yang, L, Hamilton, MP, Johnson, TA, Kahraman, A, Kellis, M, Polak, P, Sallari, R, Sinnott-Armstrong, N, von Mering, C, Beltran, S, Gerhard, DS, Gut, M, Trotta, JR, Whalley, JP, Niu, B, Espiritu, SMG, Gao, S, Huang, Y, Lalansingh, CM, Teague, JW, Wendl, MC, Abascal, F, Bader, GD, Bandopadhayay, P, Barenboim, J, Brunak, S, Fita, JC, Chakravarty, D, Chan, CWY, Choi, JK, Diamanti, K, Fink, JL, Frigola, J, Gambacorti-Passerini, C, Garsed, DW, Haradhvala, NJ, Harmanci, AO, Helmy, M, Herrmann, C, Hobolth, A, Hodzic, E, Hong, C, Isaev, K, Izarzugaza, JMG, Johnson, R, Juul, RI, Kim, JK, Komorowski, J, Lanzos, A, Larsson, E, Lee, D, Lin, Z, Liu, EM, Lochovsky, L, Lou, S, Madsen, T, Marchal, K, Fundichely, AM, McGillivray, PD, Meyerson, W, Paczkowska, M, Park, K, Pons, T, Pulido-Tamayo, S, Salazar, IR, Reyna, MA, Rubin, MA, Salichos, L, Sander, C, Schumacher, SE, Shackleton, M, Shen, C, Shrestha, R, Shuai, S, Tsunoda, T, Umer, HM, Uuskula-Reimand, L, Verbeke, LPC, Wadelius, C, Wadi, L, Warrell, J, Wu, G, Yu, J, Zhang, X, Zhang, Y, Zhao, Z, Zou, L, Lawrence, MS, Raphael, BJ, Bailey, PJ, Craft, D, Goldman, MJ, Aburatani, H, Binder, H, Dinh, HQ, Heath, SC, Hoffmann, S, Imbusch, CD, Kretzmer, H, Laird, PW, Martin-Subero, J, Nagae, G, Shen, H, Wang, Q, Weichenhan, D, Zhou, W, Berman, BP, Brors, B, Plass, C, Akdemir, KC, Bowtell, DDL, Burns, KH, Busanovich, J, Chan, K, Dueso-Barroso, A, Edwards, PA, Etemadmoghadam, D, Haber, JE, Jones, DTW, Ju, YS, Kazanov, MD, Koh, Y, Kumar, K, Lee, EA, Lee, JJK, Lynch, AG, Macintyre, G, Markowetz, F, Navarro, FCP, Pearson, J, Rippe, K, Scully, R, Villasante, I, Waddell, N, Yao, X, Yoon, SS, Zhang, CZ, Bergstrom, EN, Boot, A, Covington, K, Fujimoto, A, Huang, MN, Islam, SMA, McPherson, JR, Morganella, S, Mustonen, V, Ng, AWT, Prokopec, SD, Vazquez-Garcia, I, Wu, Y, Yousif, F, Yu, W, Rozen, SG, Rudneva, VA, Shringarpure, SS, Turner, DJ, Xia, T, Atwal, G, Chang, DK, Cooke, SL, Faltas, BM, Haider, S, Kaiser, VB, Karlic, R, Kato, M, Kubler, K, Margolin, A, Martin, S, Nik-Zainal, S, P'ng, C, Semple, CA, Smith, J, Sun, RX, Thai, K, Wright, DW, Yuan, K, Biankin, A, Garraway, L, Grimmond, SM, Adams, DJ, Anur, P, Cao, S, Christie, EL, Cmero, M, Cun, Y, Dawson, KJ, Dentro, SC, Deshwar, AG, Donmez, N, Drews, RM, Gerstung, M, Ha, G, Haase, K, Jerman, L, Ji, Y, Jolly, C, Lee, J, Lee-Six, H, Malikic, S, Mitchell, TJ, Morris, QD, Oesper, L, Peifer, M, Peto, M, Rosebrock, D, Rubanova, Y, Salcedo, A, Sengupta, S, Shi, R, Shin, SJ, Spiro, O, Vembu, S, Wintersinger, JA, Yang, TP, Yu, K, Zhu, H, Spellman, PT, Weinstein, JN, Chen, Y, Fujita, M, Han, L, Hasegawa, T, Komura, M, Li, J, Mizuno, S, Shimizu, E, Wang, Y, Xu, Y, Yamaguchi, R, Yang, F, Yang, Y, Yoon, CJ, Yuan, Y, Liang, H, Alawi, M, Borozan, I, Brewer, DS, Cooper, CS, Desai, N, Grundhoff, A, Iskar, M, Su, X, Zapatka, M, Lichter, P, Alsop, K, Bruxner, TJC, Christ, AN, Cordner, SM, Cowin, PA, Drapkin, R, Fereday, S, George, J, Hamilton, A, Holmes, O, Hung, JA, Kassahn, KS, Kazakoff, SH, Kennedy, CJ, Leonard, CR, Mileshkin, L, Miller, DK, Arnau, GM, Mitchell, C, Newell, F, Nones, K, Patch, AM, Quinn, MC, Taylor, DF, Thorne, H, Traficante, N, Vedururu, R, Waddell, NM, Waring, PM, Wood, S, Xu, Q, DeFazio, A, Anderson, MJ, Antonello, D, Barbour, AP, Bassi, C, Bersani, S, Cataldo, I, Chantrill, LA, Chiew, YE, Chou, A, Cingarlini, S, Cloonan, N, Corbo, V, Davi, MV, Duthie, FR, Gill, AJ, Graham, JS, Harliwong, I, Jamieson, NB, Johns, AL, Kench, JG, Landoni, L, Lawlor, RT, Mafficini, A, Merrett, ND, Miotto, M, Musgrove, EA, Nagrial, AM, Oien, KA, Pajic, M, Pinese, M, Robertson, AJ, Rooman, I, Rusev, BC, Samra, JS, Scardoni, M, Scarlett, CJ, Scarpa, A, Sereni, E, Sikora, KO, Simbolo, M, Taschuk, ML, Toon, CW, Vicentini, C, Wu, J, Zeps, N, Behren, A, Burke, H, Cebon, J, Dagg, RA, De Paoli-Iseppi, R, Dutton-Regester, K, Field, MA, Fitzgerald, A, Hersey, P, Jakrot, V, Johansson, PA, Kakavand, H, Kefford, RF, Lau, LMS, Long, G, Pickett, HA, Pritchard, AL, Pupo, GM, Saw, RPM, Schramm, SJ, Shang, CA, Shang, P, Spillane, AJ, Stretch, JR, Tembe, V, Thompson, JF, Vilain, RE, Wilmott, JS, Yang, JY, Hayward, NK, Mann, GJ, Scolyer, RA, Bartlett, J, Bavi, P, Chadwick, DE, Chan-Seng-Yue, M, Cleary, S, Connor, AA, Czajka, K, Denroche, RE, Dhani, NC, Eagles, J, Gallinger, S, Grant, RC, Hedley, D, Hollingsworth, MA, Jang, GH, Johns, J, Kalimuthu, S, Liang, SB, Lungu, I, Luo, X, Mbabaali, F, McPherson, TA, Miller, JK, Moore, MJ, Notta, F, Pasternack, D, Petersen, GM, Roehrl, MHA, Sam, M, Selander, I, Serra, S, Shahabi, S, Thayer, SP, Timms, LE, Wilson, GW, Wilson, JM, Wouters, BG, McPherson, JD, Beck, TA, Bhandari, V, Collins, CC, Fleshner, NE, Fox, NS, Fraser, M, Heisler, LE, Lalonde, E, Livingstone, J, Meng, A, Sabelnykova, VY, Shiah, YJ, Van Der Kwast, T, Bristow, RG, Ding, S, Fan, D, Li, L, Nie, Y, Xiao, X, Xing, R, Yang, SL, Yu, YY, Zhou, Y, Banks, RE, Bourque, G, Brennan, P, Letourneau, L, Riazalhosseini, Y, Scelo, G, Vasudev, N, Viksna, J, Lathrop, M, Tost, J, Ahn, SM, Aparicio, S, Arnould, L, Aure, MR, Bhosle, SG, Birney, E, Borg, A, Boyault, S, Brinkman, AB, Brock, JE, Broeks, A, Borresen-Dale, AL, Caldas, C, Chin, SF, Davies, H, Desmedt, C, Dirix, L, Dronov, S, Ehinger, A, Eyfjord, JE, Fatima, A, Foekens, JA, Futreal, PA, Garred, O, Giri, DD, Glodzik, D, Grabau, D, Hilmarsdottir, H, Hooijer, GK, Jacquemier, J, Jang, SJ, Jonasson, JG, Jonkers, J, Kim, HY, King, TA, Knappskog, S, Kong, G, Krishnamurthy, S, Lakhani, SR, Langerod, A, Larsimont, D, Lee, HJ, Lee, JY, Lee, MTM, Lingjaerde, OC, MacGrogan, G, Martens, JWM, O'Meara, S, Pauporte, I, Pinder, S, Pivot, X, Provenzano, E, Purdie, CA, Ramakrishna, M, Ramakrishnan, K, Reis, J, Richardson, AL, Ringner, M, Rodriguez, JB, Rodriguez-Gonzalez, FG, Romieu, G, Salgado, R, Sauer, T, Shepherd, R, Sieuwerts, AM, Simpson, PT, Smid, M, Sotiriou, C, Span, PN, Stefansson, OA, Stenhouse, A, Stunnenberg, HG, Sweep, F, Tan, BKT, Thomas, G, Thompson, AM, Tommasi, S, Treilleux, I, Tutt, A, Ueno, NT, Van Laere, S, Van den Eynden, GG, Vermeulen, P, Viari, A, Vincent-Salomon, A, Wong, BH, Yates, L, Zou, X, van Deurzen, CHM, van de Vijver, MJ, van't Veer, L, Ammerpohl, O, Aukema, S, Bergmann, AK, Bernhart, SH, Borkhardt, A, Borst, C, Burkhardt, B, Claviez, A, Goebler, ME, Haake, A, Haas, S, Hansmann, M, Hoell, J, Hummel, M, Karsch, D, Klapper, W, Kneba, M, Kreuz, M, Kube, D, Kueppers, R, Lenze, D, Loeffler, M, Lopez, C, Mantovani-Loeffler, L, Moeller, P, Ott, G, Radlwimmer, B, Richter, J, Rohde, M, Rosenstiel, PC, Rosenwald, A, Schilhabel, MB, Schreiber, S, Stadler, PF, Staib, P, Stilgenbauer, S, Sungalee, S, Szczepanowski, M, Toprak, UH, Truemper, LHP, Wagener, R, Zenz, T, Hovestadt, V, von Kalle, C, Kool, M, Korshunov, A, Landgraf, P, Lehrach, H, Northcott, PA, Pfister, SM, Reifenberger, G, Warnatz, HJ, Wolf, S, Yaspo, ML, Assenov, Y, Gerhauser, C, Minner, S, Schlomm, T, Simon, R, Sauter, G, Sueltmann, H, Biswas, NK, Maitra, A, Majumder, PP, Sarin, R, Barbi, S, Bonizzato, G, Cantu, C, Dei Tos, AP, Fassan, M, Grimaldi, S, Luchini, C, Malleo, G, Marchegiani, G, Milella, M, Paiella, S, Pea, A, Pederzoli, P, Ruzzenente, A, Salvia, R, Sperandio, N, Arai, Y, Hama, N, Hiraoka, N, Hosoda, F, Nakamura, H, Ojima, H, Okusaka, T, Totoki, Y, Urushidate, T, Fukayama, M, Ishikawa, S, Katai, H, Katoh, H, Komura, D, Rokutan, H, Saito-Adachi, M, Suzuki, A, Taniguchi, H, Tatsuno, K, Ushiku, T, Yachida, S, Yamamoto, S, Aikata, H, Arihiro, K, Ariizumi, S, Chayama, K, Furuta, M, Gotoh, K, Hayami, S, Hirano, S, Kawakami, Y, Maejima, K, Nakamura, T, Nakano, K, Ohdan, H, Sasaki-Oku, A, Tanaka, H, Ueno, M, Yamamoto, M, Yamaue, H, Choo, SP, Cutcutache, I, Khuntikeo, N, Ong, CK, Pairojkul, C, Popescu, I, Ahn, KS, Aymerich, M, Lopez-Guillermo, A, Lopez-Otin, C, Puente, XS, Campo, E, Amary, F, Baumhoer, D, Behjati, S, Bjerkehagen, B, Myklebost, O, Pillay, N, Tarpey, P, Tirabosco, R, Zaikova, O, Flanagan, AM, Boultwood, J, Bowen, DT, Cazzola, M, Green, AR, Hellstrom-Lindberg, E, Malcovati, L, Nangalia, J, Papaemmanuil, E, Vyas, P, Ang, Y, Barr, H, Beardsmore, D, Eldridge, M, Gossage, J, Grehan, N, Hanna, GB, Hayes, SJ, Hupp, TR, Khoo, D, Lagergren, J, Lovat, LB, MacRae, S, O'Donovan, M, O'Neill, JR, Parsons, SL, Preston, SR, Puig, S, Roques, T, Sanders, G, Sothi, S, Tavare, S, Tucker, O, Turkington, R, Underwood, TJ, Welch, I, Fitzgerald, RC, Berney, DM, De Bono, JS, Cahill, D, Camacho, N, Dennis, NM, Dudderidge, T, Edwards, SE, Fisher, C, Foster, CS, Ghori, M, Gill, P, Gnanapragasam, VJ, Gundem, G, Hamdy, FC, Hawkins, S, Hazell, S, Howat, W, Isaacs, WB, Karaszi, K, Kay, JD, Khoo, V, Kote-Jarai, Z, Kremeyer, B, Kumar, P, Lambert, A, Leongamornlert, DA, Livni, N, Lu, YJ, Luxton, HJ, Marsden, L, Massie, CE, Matthews, L, Mayer, E, McDermott, U, Merson, S, Neal, DE, Ng, A, Nicol, D, Ogden, C, Rowe, EW, Shah, NC, Thomas, S, Thompson, A, Verrill, C, Visakorpi, T, Warren, AY, Whitaker, HC, Zhang, H, van As, N, Eeles, RA, Abeshouse, A, Agrawal, N, Akbani, R, Al Ahmadie, H, Albert, M, Aldape, K, Ally, A, Appelbaum, EL, Armenia, J, Asa, S, Auman, JT, Balasundaram, M, Balu, S, Barnholtz-Sloan, J, Bathe, OF, Baylin, SB, Benz, C, Berchuck, A, Berrios, M, Bigner, D, Birrer, M, Bodenheimer, T, Boice, L, Bootwalla, MS, Bosenberg, M, Bowlby, R, Boyd, J, Broaddus, RR, Brock, M, Brooks, D, Bullman, S, Caesar-Johnson, SJ, Carey, TE, Carlsen, R, Cerfolio, R, Chandan, VS, Chen, HW, Cherniack, AD, Chien, J, Cho, J, Chuah, E, Cibulskis, C, Cope, L, Cordes, MG, Curley, E, Czerniak, B, Danilova, L, Davis, IJ, Defreitas, T, Demchok, JA, Dhalla, N, Dhir, R, Doddapaneni, H, El-Naggar, A, Felau, I, Ferguson, ML, Finocchiaro, G, Fong, KM, Frazer, S, Friedman, W, Fronick, CC, Fulton, LA, Gabriel, SB, Gao, J, Gehlenborg, N, Gershenwald, JE, Ghossein, R, Giama, NH, Gibbs, RA, Gomez, C, Govindan, R, Hayes, DN, Hegde, AM, Heiman, D, Heins, Z, Hepperla, AJ, Holbrook, A, Holt, RA, Hoyle, AP, Hruban, RH, Hu, J, Huang, M, Huntsman, D, Huse, J, Donahue, CAI, Ittmann, M, Jayaseelan, JC, Jefferys, SR, Jones, CD, Jones, SJM, Juhl, H, Kang, KJ, Karlan, B, Kasaian, K, Kebebew, E, Kim, HK, Korchina, V, Kundra, R, Lai, PH, Lander, E, Le, X, Levine, DA, Lewis, L, Ley, T, Li, HI, Lin, P, Linehan, WM, Liu, FF, Lu, Y, Lype, L, Ma, Y, Maglinte, DT, Mardis, ER, Marks, J, Marra, MA, Matthew, TJ, Mayo, M, McCune, K, Meier, SR, Meng, S, Mieczkowski, PA, Mikkelsen, T, Miller, CA, Mills, GB, Moore, RA, Morrison, C, Mose, LE, Moser, CD, Mungall, AJ, Mungall, K, Mutch, D, Muzny, DM, Myers, J, Newton, Y, Noble, MS, O'Donnell, P, O'Neill, BP, Ochoa, A, Parker, JS, Pass, H, Pastore, A, Pennell, NA, Perou, CM, Petrelli, N, Potapova, O, Rader, JS, Ramalingam, S, Rathmell, WK, Reuter, V, Reynolds, SM, Ringel, M, Roach, J, Roberts, LR, Robertson, AG, Sadeghi, S, Saller, C, Sanchez-Vega, F, Schadendorf, D, Schein, JE, Schmidt, HK, Schultz, N, Seethala, R, Senbabaoglu, Y, Shelton, T, Shi, Y, Shih, J, Shmulevich, I, Shriver, C, Signoretti, S, Simons, J, Singer, S, Sipahimalani, P, Skelly, TJ, McCune, KS, Socci, ND, Soloway, MG, Sood, AK, Tam, A, Tan, D, Tarnuzzer, R, Thiessen, N, Thompson, RH, Thorne, LB, Tsao, M, Umbricht, C, Van Den Berg, DJ, Van Meir, EG, Veluvolu, U, Voet, D, Wang, L, Weinberger, P, Weisenberger, DJ, Wigle, D, Wilkerson, MD, Wilson, RK, Winterhoff, B, Wiznerowicz, M, Wong, T, Wong, W, Xi, L, Yau, C
المصدر: Nature. 578(7793):82
مصطلحات موضوعية: Medicin och hälsovetenskap
-
10دورية أكاديمية
مصطلحات موضوعية: 3D point clouds, Monocular endoscopic scenes, Artificial intelligence/deep learning, Augmented reality, Virtual reality, Minimally invasive surgery
الوصف: Yes
Recovering high-quality 3D point clouds from monocular endoscopic images is a challenging task. This paper proposes a novel deep learning-based computational framework for 3D point cloud reconstruction from single monocular endoscopic images. An unsupervised mono-depth learning network is used to generate depth information from monocular images. Given a single mono endoscopic image, the network is capable of depicting a depth map. The depth map is then used to recover a dense 3D point cloud. A generative Endo-AE network based on an auto-encoder is trained to repair defects of the dense point cloud by generating the best representation from the incomplete data. The performance of the proposed framework is evaluated against state-of-the-art learning-based methods. The results are also compared with non-learning based stereo 3D reconstruction algorithms. Our proposed methods outperform both the state-of-the-art learning-based and non-learning based methods for 3D point cloud reconstruction. The Endo-AE model for point cloud completion can generate high-quality, dense 3D endoscopic point clouds from incomplete point clouds with holes. Our framework is able to recover complete 3D point clouds with the missing rate of information up to 60%. Five large medical in-vivo databases of 3D point clouds of real endoscopic scenes have been generated and two synthetic 3D medical datasets are created. We have made these datasets publicly available for researchers free of charge. The proposed computational framework can produce high-quality and dense 3D point clouds from single mono-endoscopy images for augmented reality, virtual reality and other computer-mediated medical applications.
النص الكامل