Search

Your search keyword '"Suzuki, Atsushi"' showing total 2,464 results
Start Over Author "Suzuki, Atsushi" Publication Year Range Last 10 years
2,464 results on '"Suzuki, Atsushi"'

1. Extrinsic Calibration of Multiple LiDARs for a Mobile Robot based on Floor Plane And Object Segmentation

Author

Niijima, Shun, Suzuki, Atsushi, Tsuzaki, Ryoichi, and Kinoshita, Masaya

Subjects
Computer Science - Robotics
Abstract

Mobile robots equipped with multiple light detection and ranging (LiDARs) and capable of recognizing their surroundings are increasing due to the minitualization and cost reduction of LiDAR. This paper proposes a target-less extrinsic calibration method of multiple LiDARs with non-overlapping field of view (FoV). The proposed method uses accumulated point clouds of floor plane and objects while in motion. It enables accurate calibration with challenging configuration of LiDARs that directed towards the floor plane, caused by biased feature values. Additionally, the method includes a noise removal module that considers the scanning pattern to address bleeding points, which are noises of significant source of error in point cloud alignment using high-density LiDARs. Evaluations through simulation demonstrate that the proposed method achieved higher accuracy extrinsic calibration with two and four LiDARs than conventional methods, regardless type of objects. Furthermore, the experiments using a real mobile robot has shown that our proposed noise removal module can eliminate noise more precisely than conventional methods, and the estimated extrinsic parameters have successfully created consistent 3D maps., Comment: 8pages, 10figures

Published
2024

2. An explicit substructuring method for overlapping domain decomposition based on stochastic calculus

Author

Morón-Vidal, Jorge, Bernal, Francisco, and Suzuki, Atsushi

Subjects
Mathematics - Numerical Analysis, Mathematics - Analysis of PDEs
Abstract

In a recent paper [{\em F. Bernal, J. Mor\'on-Vidal and J.A. Acebr\'on, Comp.$\&$ Math. App. 146:294-308 (2023)}] an hybrid supercomputing algorithm for elliptic equations has been put forward. The idea is that the interfacial nodal solutions solve a linear system, whose coefficients are expectations of functionals of stochastic differential equations confined within patches of about subdomain size. Compared to standard substructuring techniques such as the Schur complement method for the skeleton, the hybrid approach renders an explicit and sparse shrunken matrix -- hence suitable for being substructured again. The ultimate goal is to push strong scalability beyond the state of the art, by leveraging the scope for parallelisation of stochastic calculus. Here, we present a major revamping of that framework, based on the insight of embedding the domain in a cover of overlapping circles (in two dimensions). This allows for efficient Fourier interpolation along the interfaces (now circumferences) and -- crucially -- for the evaluation of most of the interfacial system entries as the solution of small boundary value problems on a circle. This is both extremely efficient (as they can be solved in parallel and by the pseudospectral method) and free of Monte Carlo error. Stochastic numerics are only needed on the relatively few circles intersecting the domain boundary. In sum, the new formulation is significantly faster, simpler and more accurate, while retaining all of the advantageous properties of PDDSparse. Numerical experiments are included for the purpose of illustration.

Published
2023

5. Tight and fast generalization error bound of graph embedding in metric space

Author

Suzuki, Atsushi, Nitanda, Atsushi, Suzuki, Taiji, Wang, Jing, Tian, Feng, and Yamanishi, Kenji

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning
Abstract

Recent studies have experimentally shown that we can achieve in non-Euclidean metric space effective and efficient graph embedding, which aims to obtain the vertices' representations reflecting the graph's structure in the metric space. Specifically, graph embedding in hyperbolic space has experimentally succeeded in embedding graphs with hierarchical-tree structure, e.g., data in natural languages, social networks, and knowledge bases. However, recent theoretical analyses have shown a much higher upper bound on non-Euclidean graph embedding's generalization error than Euclidean one's, where a high generalization error indicates that the incompleteness and noise in the data can significantly damage learning performance. It implies that the existing bound cannot guarantee the success of graph embedding in non-Euclidean metric space in a practical training data size, which can prevent non-Euclidean graph embedding's application in real problems. This paper provides a novel upper bound of graph embedding's generalization error by evaluating the local Rademacher complexity of the model as a function set of the distances of representation couples. Our bound clarifies that the performance of graph embedding in non-Euclidean metric space, including hyperbolic space, is better than the existing upper bounds suggest. Specifically, our new upper bound is polynomial in the metric space's geometric radius $R$ and can be $O(\frac{1}{S})$ at the fastest, where $S$ is the training data size. Our bound is significantly tighter and faster than the existing one, which can be exponential to $R$ and $O(\frac{1}{\sqrt{S}})$ at the fastest. Specific calculations on example cases show that graph embedding in non-Euclidean metric space can outperform that in Euclidean space with much smaller training data than the existing bound has suggested.

Published
2023

6. Biochemical Markers of Bone Fragility in Patients With Diabetes.

Author

Meier, Christian, Eastell, Richard, Pierroz, Dominique D, Lane, Nancy E, Al-Daghri, Nasser, Suzuki, Atsushi, Napoli, Nicola, Mithal, Ambrish, Chakhtoura, Marlene, Fuleihan, Ghada El-Hajj, and Ferrari, Serge

Subjects
adipokine, advanced glycation end product, bone turnover marker, diabetes, sclerostin, Clinical Sciences, Paediatrics and Reproductive Medicine, Endocrinology & Metabolism
Abstract

ContextThe risk of fragility fractures is increased in both type 1 and type 2 diabetes. Numerous biochemical markers reflecting bone and/or glucose metabolism have been evaluated in this context.ObjectiveThis review summarizes current data on biochemical markers in relation to bone fragility and fracture risk in diabetes.MethodsA group of experts from the International Osteoporosis Foundation and European Calcified Tissue Society reviewed the literature focusing on biochemical markers, diabetes, diabetes treatments, and bone in adults.ResultsAlthough bone resorption and bone formation markers are low and poorly predictive of fracture risk in diabetes, osteoporosis drugs seem to change bone turnover markers (BTMs) in diabetics similarly to nondiabetics, with similar reductions in fracture risk. Several other biochemical markers related to bone and glucose metabolism have been correlated with bone mineral density and/or fracture risk in diabetes, including osteocyte-related markers such as sclerostin, glycated hemoglobin A1c (HbA1c) and advanced glycation end products, inflammatory markers, and adipokines, as well as insulin-like growth factor-1 and calciotropic hormones.ConclusionSeveral biochemical markers and hormonal levels related to bone and/or glucose metabolism have been associated with skeletal parameters in diabetes. Currently, only HbA1c levels seem to provide a reliable estimate of fracture risk, while BTMs could be used to monitor the effects of antiosteoporosis therapy.

Published
2023

7. Biochemical Markers of Bone Fragility in Patients with Diabetes. A Narrative Review by the IOF and the ECTS.

Author

Meier, Christian, Eastell, Richard, Pierroz, Dominique D, Lane, Nancy E, Al-Daghri, Nasser, Suzuki, Atsushi, Napoli, Nicola, Mithal, Ambrish, Chakhtoura, Marlene, El-Hajj Fuleihan, Ghada, and Ferrari, Serge

Subjects
adipokine, advanced glycation endproduct, bone turnover marker, diabetes, sclerostin, Prevention, Diabetes, Nutrition, Aging, Osteoporosis, Detection, screening and diagnosis, 4.1 Discovery and preclinical testing of markers and technologies, Metabolic and endocrine, Musculoskeletal, Clinical Sciences, Paediatrics and Reproductive Medicine, Endocrinology & Metabolism
Abstract

ContextThe risk of fragility fractures is increased in both type 1 and type 2 diabetes. Numerous biochemical markers reflecting bone and/or glucose metabolism have been evaluated in this context. This review summarizes current data on biochemical markers in relation to bone fragility and fracture risk in diabetes.MethodsLiterature review by a group of experts from the International Osteoporosis Foundation (IOF) and European Calcified Tissue Society (ECTS) focusing on biochemical markers, diabetes, diabetes treatments and bone in adults.ResultsAlthough bone resorption and bone formation markers are low and poorly predictive of fracture risk in diabetes, osteoporosis drugs seem to change bone turnover markers in diabetics similarly to non-diabetics, with similar reductions in fracture risk. Several other biochemical markers related to bone and glucose metabolism have been correlated with BMD and/or fracture risk in diabetes, including osteocyte-related markers such as sclerostin, HbA1c and advanced glycation end products (AGEs), inflammatory markers and adipokines, as well as IGF-1 and calciotropic hormones.ConclusionSeveral biochemical markers and hormonal levels related to bone and/or glucose metabolism have been associated with skeletal parameters in diabetes. Currently, only HbA1c levels seem to provide a reliable estimate of fracture risk, while bone turnover markers could be used to monitor the effects of anti-osteoporosis therapy.

Published
2023

8. Method for Determining the Flow Curve of Steel Considering Work Hardening Behavior

Author

Suzuki, Atsushi, Okamura, Kazuo, Kada, Osamu, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Mocellin, Katia, editor, Bouchard, Pierre-Olivier, editor, Bigot, Régis, editor, and Balan, Tudor, editor

Published
2024
Full Text
View/download PDF

10. A Hybrid Factorization Algorithm for Sparse Matrix with Mixed Precision Arithmetic

Author

Suzuki, Atsushi

Subjects
Mathematics - Numerical Analysis, 6F60, 6F05, 6F10, 6F08, 76M10, 35Q81
Abstract

A new hybrid algorithm for LDU-factorization for large sparse matrix combining iterative solver, which can keep the same accuracy as the classical factorization, is proposed. The last Schur complement will be generated by iterative solver for multiple right-hand sides using block GCR method with the factorization in lower precision as a preconditioner, which achieves mixed precision arithmetic, and then the Schur complement will be factorized in higher precision. In this algorithm, essential procedure is decomposition of the matrix into a union of moderate and hard parts, which is realized by LDU-factorization in lower precision with symmetric pivoting and threshold postponing technique., Comment: 12 pages, 4 figures, submitted to proceedings of ECCOMAS Congress 2022

Published
2022

12. Cumulative Stay-time Representation for Electronic Health Records in Medical Event Time Prediction

Author

Katsuki, Takayuki, Miyaguchi, Kohei, Koseki, Akira, Iwamori, Toshiya, Yanagiya, Ryosuke, and Suzuki, Atsushi

Subjects
Computer Science - Machine Learning
Abstract

We address the problem of predicting when a disease will develop, i.e., medical event time (MET), from a patient's electronic health record (EHR). The MET of non-communicable diseases like diabetes is highly correlated to cumulative health conditions, more specifically, how much time the patient spent with specific health conditions in the past. The common time-series representation is indirect in extracting such information from EHR because it focuses on detailed dependencies between values in successive observations, not cumulative information. We propose a novel data representation for EHR called cumulative stay-time representation (CTR), which directly models such cumulative health conditions. We derive a trainable construction of CTR based on neural networks that has the flexibility to fit the target data and scalability to handle high-dimensional EHR. Numerical experiments using synthetic and real-world datasets demonstrate that CTR alone achieves a high prediction performance, and it enhances the performance of existing models when combined with them., Comment: To be published in IJCAI-22

Published
2022
Full Text
View/download PDF

14. Secretory GFP reconstitution labeling of neighboring cells interrogates cell–cell interactions in metastatic niches

Author

Minegishi, Misa, Kuchimaru, Takahiro, Nishikawa, Kaori, Isagawa, Takayuki, Iwano, Satoshi, Iida, Kei, Hara, Hiromasa, Miura, Shizuka, Sato, Marika, Watanabe, Shigeaki, Shiomi, Akifumi, Mabuchi, Yo, Hamana, Hiroshi, Kishi, Hiroyuki, Sato, Tatsuyuki, Sawaki, Daigo, Sato, Shigeru, Hanazono, Yutaka, Suzuki, Atsushi, Kohro, Takahide, Kadonosono, Tetsuya, Shimogori, Tomomi, Miyawaki, Atsushi, Takeda, Norihiko, Shintaku, Hirofumi, Kizaka-Kondoh, Shinae, and Nishimura, Satoshi

Published
2023
Full Text
View/download PDF

24. Generalization Error Bound for Hyperbolic Ordinal Embedding

Author

Suzuki, Atsushi, Nitanda, Atsushi, Wang, Jing, Xu, Linchuan, Cavazza, Marc, and Yamanishi, Kenji

Subjects
Computer Science - Machine Learning
Abstract

Hyperbolic ordinal embedding (HOE) represents entities as points in hyperbolic space so that they agree as well as possible with given constraints in the form of entity i is more similar to entity j than to entity k. It has been experimentally shown that HOE can obtain representations of hierarchical data such as a knowledge base and a citation network effectively, owing to hyperbolic space's exponential growth property. However, its theoretical analysis has been limited to ideal noiseless settings, and its generalization error in compensation for hyperbolic space's exponential representation ability has not been guaranteed. The difficulty is that existing generalization error bound derivations for ordinal embedding based on the Gramian matrix do not work in HOE, since hyperbolic space is not inner-product space. In this paper, through our novel characterization of HOE with decomposed Lorentz Gramian matrices, we provide a generalization error bound of HOE for the first time, which is at most exponential with respect to the embedding space's radius. Our comparison between the bounds of HOE and Euclidean ordinal embedding shows that HOE's generalization error is reasonable as a cost for its exponential representation ability.

Published
2021

32. Association Between Clonal Hematopoiesis and Left Ventricular Reverse Remodeling in Nonischemic Dilated Cardiomyopathy

Author

Inoue, Shunsuke, Ko, Toshiyuki, Shindo, Akito, Nomura, Seitaro, Yamada, Takanobu, Jimba, Takahiro, Dai, Zhehao, Nakao, Harumi, Suzuki, Atsushi, Kashimura, Takeshi, Iwahana, Togo, Goto, Keiko, Matsushima, Shouji, Ishida, Junichi, Amiya, Eisuke, Zhang, Bo, Kubota, Masayuki, Sawami, Kosuke, Heryed, Tuolisi, Yamada, Shintaro, Katoh, Manami, Katagiri, Mikako, Ito, Masamichi, Nayakama, Yukiteru, Fujiu, Katsuhito, Hatano, Masaru, Takeda, Norifumi, Takimoto, Eiki, Akazawa, Hiroshi, Morita, Hiroyuki, Yamaguchi, Junichi, Inomata, Takayuki, Kobayashi, Yoshio, Minamino, Tohru, Tsutsui, Hiroyuki, Kurokawa, Mineo, Aiba, Atsu, Aburatani, Hiroyuki, and Komuro, Issei

Published
2024
Full Text
View/download PDF

38. Safety and Efficacy of Renal Denervation in Patients Taking Antihypertensive Medications

Author

Wang, Yale, Jay, Desmond, McLaurin, Brent, Lomboy, Carl, Allaqaband, Suhail, Jan, Fuad, Gummadi, Bharat, Litt, Marc, Garcia, Fidel, Singh, Jasvindar, Brown, Angela, Choi, James, Paul, Ashley, Sharp, Andrew, Coulson, James, Nanjundappa, Aravinda, Thakker, Ganpat, Campbell, James, Honton, Benjamin, Farah, Bruno, Patel, Manesh, Gutierrez, Antonio, Tyson, Crystal, Svetkey, Laura, Fudim, Marat, Pagidipati, Neha, Jones, Schuyler, Vemulapalli, Sreekanth, Devireddy, Chandan, Kulshreshtha, Ambar, Wells, Bryan, Lea, Janice, Batson, Bryan, Wilkins, Robert, Sharif, Faisal, Khair, Abdullahi Mohamed, Pallippattu, Abhishek Wilson, Alhmoudi, Aishah Matar, Gaffney, Brian, Cawley, Christian, Gorry, Colin, Hamed, Hanan, Carron, Jennifer, Birrane, John, Rosseel, Liesbeth, Lunardi, Mattia, Cronin, Michael, McKittrick, Myles, Almagal, Naeif, Khalid, Noman, Shehahd, Qussai, Shahzad, Shirjeel, Fezzi, Simone, Tyulkin, Stanislav, Bel, Xavier Armario, Jolly, Sanjit, Yip, Gordon, Schwalm, Jon-David, Tsang, Michael, Mehta, Shamir, Lurz, Philipp C., Binner, Christian, Obradovic, Danilo, Fahr, Florian, Richter, Ines, Rotta detto Loria, Johannes, Fengler, Karl, Rommel, Karl-Philipp, Cuartas, Mateo Marin, Lerche, Matthias, Hartung, Philipp, Münch, Phillip, Höllriegel, Robert, John, Silke, Otsuji, Satoru, Kusumoto, Hirofumi, Tamaru, Hiroto, Ishibuchi, Kasumi, Hasegawa, Katsuyuki, Sugimoto, Ken, Kakishita, Mikio, Ishii, Rui, Takiuchi, Shin, Yasuda, Shingo, Nakabayashi, Sho, Yamamoto, Wataru, Higashino, Yorihiko, Shimatani, Yuji, Taniguchi, Yusuke, Tsioufis, Konstantinos, Kasiakogias, Alexandros, Konstantinidis, Dimitris, Andrikou, Eirini, Konstantinou, Konstantinos, Dimitriadis, Kyriakos, Kalos, Theodoros, Cohen, Debbie, Rene, Garvey, Cohen, Jordana, Bonanni, Maria, Denker, Matthew, Wickramisinghe, Rasi, Wilensky, Robert, Kobayashi, Tai, Anjan, Venkatesh, Huan, Yonghong, Haught, Walter Herbert, Krasnow, Joshua, Vasquez, Alejandro, Gessler, Carl, Roth, Christopher, Drenning, David, Velasquez, Enrique, Soliman, George, Murphy, James, Jain, Kaushik, Butler, Michael, Kanitkar, Mihir, Laney, Phillip, Hunter, Ross, Cheng, Shi-Chi, Wright, Thomas, Chapman, Neil, Arnold, Ahran, Nowbar, Alexandra, Janmohamed, Azara, Corden, Ben, Cook, Christopher, Rajkumar, Christopher, Keene, Daniel, Thompson, David, Seligman, Henry, Howard, James, Davies, Justin, Sweeney, Mark, Shun-Shin, Matthew, Foley, Michael, Quaife, Nicholas, Al-Lamee, Rasha, Hadjiphilippou, Savvas, Sen, Sayan, Ahmad, Yousif, Kario, Kazuomi, Shimizu, Hayato, Waki, Hirotaka, Narita, Keisuke, Shimpo, Masahisa, Hoshide, Satoshi, Komori, Takahiro, Ogoyama, Yukako, Ogata, Yukiyo, Oba, Yusuke, Bisharat, Mohanned, Lishmanov, Anton, Jeffords, Travis, Ghali, Magdi, Chia, Beth, Onsrud, Rachel, Rough, Randolph, Aoki, Jiro, Tomii, Daijiro, Mori, Fumiko, Kikushima, Hosei, Ninomiya, Kai, Yahagi, Kazuyuki, Tanabe, Kengo, Nakamura, Kosuke, Komiyama, Kota, Nakase, Masaaki, Asami, Masahiko, Setoguchi, Naoto, Tanaka, Tetsu, Horiuchi, Yu, Watanabe, Yusuke, Feldman, Dmitriy, Bergman, Geoffrey, Sherifi, Ines, Kim, Luke, Amin, Nivee, Minutello, Robert, Wong, S. Chiu, McCullough, Stephen, D'Souza, Richard, Kurdi, Hibba, Statton, Sarah, Watkinson, Tony, Bertolet, Barry, Gupta, Amit, Blossom, Benjamin, Bell, Chris, Hill, Douglas, Sierra, Francisco, Johnson, James, Estess, John, Adams, Joseph, McDuffie, Katie, Ferguson, Lee, Little, Nelson, Ballard, Richard Dane, Williams, Roger, Ikeda, Wanda, Calhoun, William, Carroll, William, Singh, Avneet, Hirsh, Benjamin, Meraj, Perwaiz, Jauhar, Rajiv, Golwala, Harsh, Rueda, Jose, Graham, Kellie, Al-Hakim, Ramsey, Rope, Robert, Kandzari, David, Bahrami, Dariush, Reedus, Denise, David, Shukri, Rehman, Iram, Zakaria, Khalid, Choksi, Nishit, Kazziha, Samer, Kumar, Sarwan, Saba, Souheil, Steigerwalt, Susan, Sirajeldin, Yassir, Hyder, Omar, Aronow, Herbert, Connors, Andrea, Shemin, Douglas, Bent, Clare, Levy, Terry, Kodoth, Vivek, Schlaich, Markus, Kiuchi, Marcio, Carnagarin, Revathy, Shetty, Sharad, Torre, Sabino, Levitt, Howard, Chen, Lin, Fan, Sarah, Waxman, Sergio, Porr, Wendy, Mangos, George, Smyth, Brendan, Lane, Cathie, Sader, Mark, Patel, Kirit Kumar, Rao, Anjani, Milford, Brett, Ahluwalia, Guneet, Sennott, Jaqueline, Field, Justin, Jena, Nihar, Loree, Stacy, Graham, John, Al-Hesayen, Abdul, Bagai, Akshay, Shite, Junya, Kozuki, Amane, Suzuki, Atsushi, Masuko, Eri, Soga, Fumitaka, Asada, Hiroyuki, Shibata, Hiroyuki, Nagoshi, Ryoji, Fujiwara, Ryudo, Kyo, Shokan, Miyata, Tomohiro, Kijima, Yoichi, Weil, Joachim, Griesinger, Lena, Hafer, Lukas, Starick, Rene-Alexander, Keil, Sebastian, Agdirlioglu, Tolga, Desch, Udo, Saito, Shigeru, Koyama, Eiji, Yamanaka, Futoshi, Yokoyama, Hiroaki, Tobita, Kazuki, Shishido, Koki, Yamaguchi, Masashi, Murakami, Masato, Moriyama, Noriaki, Takahashi, Saeko, Mizuno, Shingo, Yokota, Shohei, Hayashi, Takahiro, Yamada, Takashi, Yamagishi, Tamiharu, Sugiyama, Yoichi, Mashimo, Yuka, Tamaki, Yusuke, Tanaka, Yutaka, Lee, David, Ma, Adrian, Yeung, Alan, Noel, Thomas, Umana, Ernesto, Katopodis, John, Dixon, William, Walton, Tony, Rischin, Adam, Chee, Adrian, Leaney, Alexandra, Fernando, Himavan, Hopper, Ingrid, Bloom, Jason, Fairley, Jessica, Stehli, Julia, Segan, Julian, Sharma, Meenal, Fitzgerald, Melissa, Papandony, Michelle, Whyler, Naomi, Noaman, Samer, Ali, Shazeea, Jefferson, Brian, Reyes, David, Johnston, Thomas, Böhm, Michael, Höfling, Alexander, Berger, Ann-Kathrin, Cremers, Bodo, Millenaar, Dominic, Mahfoud, Felix, Zivanovic, Ina, Lucic, Jelena, Dederer, Juliane, Lauder, Lucas, Fischer, Patrick, Kulenthiran, Saarraangan, Ewen, Sebastian, Bewarder, Yvonne, Schmieder, Roland, Bosch, Agnes, Schmid, Axel, Ott, Christian, Kannenkeril, Dennis, Akarca, Ertan, Kistner, Iris, Kolwelter, Julie, Bihlmaier, Karl, Striepe, Kristina, Karg, Marina, Günes-Altan, Merve, Anna Friedrich, Stefanie Sofia, Jung, Susanne, Dienemann, Thomas, Ziakas, Antonios, Kouparanis, Antonios, Pagiantza, Areti, Peteinidou, Emmanouela, Didagelos, Matthaios, Psochias, Polykarpos Akis, Shishehbor, Mehdi, Meade, Ashley, Linetsky, Elena, Robinson, Monique, Kotter, John, Ziada, Khaled, Bachinsky, William, Garton, Alex, Stuck, Jason, Papademetriou, Vasilios, Chung, Annise, Li, Ping, Nagpal, Sameer, Remetz, Michael, Shah, Samit, Lynch, Shannon, Kandzari, David E., Townsend, Raymond R., Weber, Michael A., Schmieder, Roland E., Pocock, Stuart, Konstantinidis, Dimitrios, East, Cara, Cohen, Debbie L., Kobayashi, Taisei, Lee, David P., Schlaich, Markus P., Devireddy, Chandan M., Sharp, Andrew S.P., Anderson, Richard, Fahy, Martin, DeBruin, Vanessa, and Brar, Sandeep

Published
2023
Full Text
View/download PDF

43. Meiosis-specific ZFP541 repressor complex promotes developmental progression of meiotic prophase towards completion during mouse spermatogenesis

Author

Horisawa-Takada, Yuki, Kodera, Chisato, Takemoto, Kazumasa, Sakashita, Akihiko, Horisawa, Kenichi, Maeda, Ryo, Shimada, Ryuki, Usuki, Shingo, Fujimura, Sayoko, Tani, Naoki, Matsuura, Kumi, Akiyama, Tomohiko, Suzuki, Atsushi, Niwa, Hitoshi, Tachibana, Makoto, Ohba, Takashi, Katabuchi, Hidetaka, Namekawa, Satoshi H, Araki, Kimi, and Ishiguro, Kei-Ichiro

Subjects
Biochemistry and Cell Biology, Biomedical and Clinical Sciences, Biological Sciences, Genetics, Reproductive Medicine, Stem Cell Research - Nonembryonic - Non-Human, Biotechnology, Human Genome, Stem Cell Research, Contraception/Reproduction, Underpinning research, 1.1 Normal biological development and functioning, Animals, Cell Cycle Proteins, Chromatin Immunoprecipitation Sequencing, Chromosomal Proteins, Non-Histone, Disease Models, Animal, Female, Histone Deacetylase 1, Histone Deacetylase 2, Humans, Infertility, Male, Male, Mice, Mice, Knockout, Nuclear Proteins, Oocytes, Pachytene Stage, Potassium Channels, Voltage-Gated, RNA-Seq, Spermatids, Spermatogenesis, Transcription Factors, Transcription, Genetic
Abstract

During spermatogenesis, meiosis is accompanied by a robust alteration in gene expression and chromatin status. However, it remains elusive how the meiotic transcriptional program is established to ensure completion of meiotic prophase. Here, we identify a protein complex that consists of germ-cell-specific zinc-finger protein ZFP541 and its interactor KCTD19 as the key transcriptional regulators in mouse meiotic prophase progression. Our genetic study shows that ZFP541 and KCTD19 are co-expressed from pachytene onward and play an essential role in the completion of the meiotic prophase program in the testis. Furthermore, our ChIP-seq and transcriptome analyses identify that ZFP541 binds to and suppresses a broad range of genes whose function is associated with biological processes of transcriptional regulation and covalent chromatin modification. The present study demonstrates that a germ-cell specific complex that contains ZFP541 and KCTD19 promotes the progression of meiotic prophase towards completion in male mice, and triggers the reconstruction of the transcriptional network and chromatin organization leading to post-meiotic development.

Published
2021

50. Efficient PageRank Computation via Distributed Algorithms with Web Clustering

Author

Suzuki, Atsushi and Ishii, Hideaki

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

PageRank is a well-known centrality measure for the web used in search engines, representing the importance of each web page. In this paper, we follow the line of recent research on the development of distributed algorithms for computation of PageRank, where each page computes its own PageRank value by interacting with pages connected over hyperlinks. Our approach is novel in that it is based on a reinterpretation of PageRank, which leads us to a set of algorithms with exponential convergence rates. We first employ gossip-type randomization for the page selections in the update iterations. Then, the algorithms are generalized to deterministic ones, allowing simultaneous updates by multiple pages. Finally, based on these algorithms, we propose a clustering-based scheme, in which groups of pages make updates by locally interacting among themselves many times to expedite the convergence. In comparison with other existing techniques, significant advantages can be exhibited in their convergence performance, as demonstrated via numerical examples using real web data, and also in the limited amount of communication required among pages., Comment: 16 pages, 8 figures

Published
2019

Catalog

Books, media, physical & digital resources
See catalog results