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Academia Sinica

FacilityTaipei, Taiwan
About: Academia Sinica is a facility organization based out in Taipei, Taiwan. It is known for research contribution in the topics: Population & Gene. The organization has 52086 authors who have published 65998 publications receiving 1728114 citations. The organization is also known as: Central Research Academy.


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Journal ArticleDOI
TL;DR: In this paper, the ISORROPIA II thermodynamic equilibrium model and the positive matrix factorization (PMF) model were applied to explore the likely chemical forms of ionic constituents and to apportion sources for PM2.5.
Abstract: . Daily PM2.5 (aerosol particles with an aerodynamic diameter of less than 2.5 μm) samples were collected at an urban site in Chengdu, an inland megacity in southwest China, during four 1-month periods in 2011, with each period in a different season. Samples were subject to chemical analysis for various chemical components ranging from major water-soluble ions, organic carbon (OC), element carbon (EC), trace elements to biomass burning tracers, anhydrosugar levoglucosan (LG), and mannosan (MN). Two models, the ISORROPIA II thermodynamic equilibrium model and the positive matrix factorization (PMF) model, were applied to explore the likely chemical forms of ionic constituents and to apportion sources for PM2.5. Distinctive seasonal patterns of PM2.5 and associated main chemical components were identified and could be explained by varying emission sources and meteorological conditions. PM2.5 showed a typical seasonality of waxing in winter and waning in summer, with an annual mean of 119 μg m−3. Mineral soil concentrations increased in spring, whereas biomass burning species elevated in autumn and winter. Six major source factors were identified to have contributed to PM2.5 using the PMF model. These were secondary inorganic aerosols, coal combustion, biomass burning, iron and steel manufacturing, Mo-related industries, and soil dust, and they contributed 37 ± 18, 20 ± 12, 11 ± 10, 11 ± 9, 11 ± 9, and 10 ± 12%, respectively, to PM2.5 masses on annual average, while exhibiting large seasonal variability. On annual average, the unknown emission sources that were not identified by the PMF model contributed 1 ± 11% to the measured PM2.5 mass. Various chemical tracers were used for validating PMF performance. Antimony (Sb) was suggested to be a suitable tracer of coal combustion in Chengdu. Results of LG and MN helped constrain the biomass burning sources, with wood burning dominating in winter and agricultural waste burning dominating in autumn. Excessive Fe (Ex-Fe), defined as the excessive portion in measured Fe that cannot be sustained by mineral dust, is corroborated to be a straightforward useful tracer of iron and steel manufacturing pollution. In Chengdu, Mo / Ni mass ratios were persistently higher than unity, and considerably distinct from those usually observed in ambient airs. V / Ni ratios averaged only 0.7. Results revealed that heavy oil fuel combustion should not be a vital anthropogenic source, and additional anthropogenic sources for Mo are yet to be identified. Overall, the emission sources identified in Chengdu could be dominated by local sources located in the vicinity of Sichuan, a result different from those found in Beijing and Shanghai, wherein cross-boundary transport is significant in contributing pronounced PM2.5. These results provided implications for PM2.5 control strategies.

342 citations

Journal ArticleDOI
Tsuyoshi Tanaka1, Baltazar A. Antonio1, Shoshi Kikuchi1, Takashi Matsumoto1, Yoshiaki Nagamura1, Hisataka Numa1, Hiroaki Sakai1, Jianzhong Wu1, Takeshi Itoh1, Takeshi Itoh2, Takuji Sasaki1, Ryo Aono, Yasuyuki Fujii3, Takuya Habara, Erimi Harada, Masako Kanno, Yoshihiro Kawahara4, Hiroaki Kawashima, Hiromi Kubooka, Akihiro Matsuya, Hajime Nakaoka, Naomi Saichi, Ryoko Sanbonmatsu, Yoshiharu Sato, Yuji Shinso, Mami Suzuki, Jun-ichi Takeda, Motohiko Tanino, Fusano Todokoro, Kaori Yamaguchi, Naoyuki Yamamoto, Chisato Yamasaki, Tadashi Imanishi2, Toshihisa Okido, Masahito Tada, Kazuho Ikeo, Yoshio Tateno, Takashi Gojobori, Yao-Cheng Lin5, Fu Jin Wei5, Yue-Ie C. Hsing5, Qiang Zhao, Bin Han, Melissa Kramer6, Richard W. McCombie6, David Lonsdale7, Claire O'Donovan7, Eleanor J. Whitfield7, Rolf Apweiler7, Kanako O. Koyanagi8, Jitendra P. Khurana9, Saurabh Raghuvanshi9, Nagendra K. Singh10, Akhilesh K. Tyagi9, Georg Haberer, Masaki Fujisawa, Satomi Hosokawa, Yukiyo Ito, Hiroshi Ikawa, Michie Shibata, Mayu Yamamoto, Richard Bruskiewich11, Douglas R. Hoen12, Thomas E. Bureau12, Nobukazu Namiki13, Hajime Ohyanagi13, Yasumichi Sakai13, Satoshi Nobushima13, Katsumi Sakata13, Roberto A. Barrero14, Yutaka Sato15, Alexandre Souvorov16, Brian Smith-White16, Tatiana Tatusova16, Suyoung An17, Gynheung An17, Satoshi Oota, Galina Fuks18, Joachim Messing, Karen R. Christie19, Damien Lieberherr20, Hyeran Kim21, Andrea Zuccolo21, Rod A. Wing, Kan Nobuta22, Pamela J. Green22, Cheng Lu22, Blake C. Meyers22, Cristian Chaparro23, Benoît Piégu23, Olivier Panaud23, Manuel Echeverria23 
TL;DR: The latest version of the RAP-DB contains a variety of annotation data as follows: clone positions, structures and functions of 31 439 genes validated by cDNAs, RNA genes detected by massively parallel signature sequencing (MPSS) technology and sequence similarity, flanking sequences of mutant lines, transposable elements, etc.
Abstract: The Rice Annotation Project Database (RAP-DB) was created to provide the genome sequence assembly of the International Rice Genome Sequencing Project (IRGSP), manually curated annotation of the sequence, and other genomics information that could be useful for comprehensive understanding of the rice biology. Since the last publication of the RAP-DB, the IRGSP genome has been revised and reassembled. In addition, a large number of rice-expressed sequence tags have been released, and functional genomics resources have been produced worldwide. Thus, we have thoroughly updated our genome annotation by manual curation of all the functional descriptions of rice genes. The latest version of the RAP-DB contains a variety of annotation data as follows: clone positions, structures and functions of 31 439 genes validated by cDNAs, RNA genes detected by massively parallel signature sequencing (MPSS) technology and sequence similarity, flanking sequences of mutant lines, transposable elements, etc. Other annotation data such as Gnomon can be displayed along with those of RAP for comparison. We have also developed a new keyword search system to allow the user to access useful information. The RAP-DB is available at: http://rapdb.dna.affrc.go.jp/ and http://rapdb.lab.nig.ac.jp/.

342 citations

Journal ArticleDOI
TL;DR: The jamming probability can be explained quantitatively by treating the arch as the trajectory of a restricted random walker by observing the disk configurations of the arch in the jamming events.
Abstract: Granular systems consist of particles which interact among themselves only by interparticle contacts [1‐3]. In nature, many important phenomena such as avalanche, landslide, soil fluidization, and blood flow can be related to three-dimensional (3D) granular flow. On the other hand, two-dimensional (2D) flow phenomena can be found in the baggage flow on conveyer belts, the transport of cans and bottles in factories, and traffic jam in a city. Although there are many theoretical, experimental, and computer simulation studies in granular systems, our basic understanding of the static and dynamical properties of granular systems is far from clear. For example, in the simple problem of granular flow through a hopper, one finds that the flow is jammed after a few particles are discharged when the opening is smaller than a critical value [4]. However, very little is known about how the transition from flowing to jamming occurs. With the advance in experimental techniques and fast electronic computers, studies in laboratory experiments [5] and computer simulations [6] showed that jamming is due to arch formation at the hopper opening. Nevertheless, there is not even a quantitative description of the arch that leads to jamming. In this Letter, we report our studies on the basic mechanism of the jamming process of granular flow in a 2D hopper. We measured the jamming probability as a function of the hopper opening. Our results show that the jamming probability can be understood quantitatively by a simple geometrical model in which the arch that leads to jamming is treated as the trajectory of a restricted self-avoiding random walker. The effects of friction and the hopper angle on the jamming probability are also discussed. Figure 1 is a schematic diagram of our experimental setup. We fabricated a 2D hopper with an aluminum base plate. The walls (FP and MP) of this hopper are 4 mm thick aluminum plates each having a cut at the opening of the hopper so that both FP and MP make an angle f with the horizontal direction when the hopper is at the upright position. The hopper angle f can be changed by replacing the walls. The movable wall (MP) is attached to a stepping motor (SM) controlled translation stage (B) such that the hopper opening R can be varied continuously using a stepping motor controller (SMC) through a personal computer (PC). In the hopper, we put 200 monodisperse stainless steel disks of 3 mm thick and D 5 mm in diameter. To observe the disk motion in the hopper, its front plate is made of 2 mm thick transparent Plexiglas. Since MP and FP are 4 mm thick, the disks cannot flip over inside the hopper. The disk surfaces are polished to reduce the friction among the disks and that between the disks and the walls. The hopper is mounted on a vertical rotating stage such that the symmetry axis of the hopper is perpendicular to the axis of the rotation. When the hopper is rotated from the upside down to the upright position, the disks in the hopper will fall down toward the opening. Either all of the disks fall out of the hopper or some disks are left in the hopper due to jamming at the hopper opening. The motion of the disks is captured by a CCD video camera and the video images are taken by a frame grabber (FG) to the same PC that controls R. Image processing software is developed to analyze the captured video and to determine if, in each revolution, the flow in the hopper is jammed or not. Figure 2(a) shows an image of a typical jamming event captured in the experiment. For each opening R, we counted the number of jamming events Na and

340 citations

Journal ArticleDOI
23 Dec 2014-eLife
TL;DR: Evidence is presented that a group of intrinsically disordered, serine-rich proteins regulate the dynamics of P granules in C. elegans embryos, and it is concluded that P granule assembly in embryos is regulated by phosphorylation.
Abstract: For a gene to be expressed as a protein, its DNA is first used as a template to make a molecule of RNA, which is then translated to make the protein. In most cells, RNA molecules concentrate into aggregates called RNA granules. These granules contain both RNA and proteins that bind to RNA and are used to transport, store, and regulate the translation and breakdown of RNA molecules. Unlike many other structures within cells, RNA granules are not surrounded by a membrane; and the molecules that hold RNA granules together are not known. P granules are a type of RNA granule that is found in the germ cells (the cells that go on to form eggs and sperm) of a microscopic worm called C. elegans. When a C. elegans embryo is still a single cell, P granules move throughout the cell and the P granules at the front of the cell dissolve, while those at the back condense. As such, when the single-celled embryo divides, the front forms a cell without P granules (that will go on to form the tissues of the worm's body) and the back becomes a P granule-containing germ cell. Two proteins called MBK-2 and PPTR-1 have opposite effects on P granules: MBK-2 causes P granules to dissolve, while PPTR-1 makes them form. MBK-2 is an enzyme that adds phosphate groups onto other proteins, whereas PPTR-1 is part of an enzyme that removes such groups. Wang et al. have now searched for proteins that interact with MBK-2 and PPTR-1 in order to identify the molecules that regulate the assembly of P granules. They found that a group of proteins, known as MEG proteins, are acted upon by both of these proteins. Wang et al. found that MBK-2 adds phosphate groups to MEG proteins, which encourages granules to disassemble, while PPTR-1 removes these groups to promote granule assembly. Wang et al. generated mutant worms that lacked each of the MEG proteins. These mutant worms had fewer and smaller P granules than normal worms. Without MEG proteins, P granules failed to assemble or disassemble normally and the worms were infertile. Using high resolution microscopy, Wang et al. observed that the MEG proteins wrap around the P granules and that one of the MEG proteins—called MEG-3—follows an almost ribbon-like path that surrounds and enters each granule. These observations suggest that the MEG proteins stabilize RNA granules by forming a cage-like scaffold around each granule. How the MEG proteins—which are predicted to lack a fixed or ordered three-dimensional structure and show no similarity to proteins with known functions—assemble into a scaffold will be the focus of future studies.

340 citations

Journal ArticleDOI
01 Apr 1991-Catena
TL;DR: The dust flux of over 25 g/cm2/103yr was reached in the central part of the chinese Loess Plateau during the last glacial maximum as mentioned in this paper.
Abstract: The dust flux of over 25 g/cm2/103yr was reached in the central part of the chinese Loess Plateau during the last glacial maximum This is more than three times higher than during the last interglacial and early glacial time The grain size of the pleniglacial dust reached a peak shortly after 20,000 years ago and testifies to the occurrence of strong winds and frequent dust storms

340 citations


Authors

Showing all 52129 results

NameH-indexPapersCitations
Yi Chen2174342293080
Jing Wang1844046202769
Jie Zhang1784857221720
Hyun-Chul Kim1764076183227
Yang Yang1642704144071
Yuh Nung Jan16246074818
Jongmin Lee1502257134772
Hui-Ming Cheng147880111921
Teruki Kamon1422034115633
Jian Yang1421818111166
I. V. Gorelov1391916103133
S. R. Hou1391845106563
Kaori Maeshima1391850105218
Jiangyong Jia138117391163
Kenneth Bloom1381958110129
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202315
2022111
20212,414
20202,356
20192,330
20182,349