Conditional random field approach to prediction of protein-protein interactions using domain information

For understanding cellular systems and biological networks, it is important to analyze functions and interactions of proteins and domains. Many methods for predicting protein-protein interactions have been developed. It is known that mutual information between residues at interacting sites can be higher than that at non-interacting sites. It is based on the thought that amino acid residues at interacting sites have coevolved with those at the corresponding residues in the partner proteins. Several studies have shown that such mutual information is useful for identifying contact residues in interacting proteins

Algorithms and Complexity Analyses for Control of Singleton Attractors in Boolean Networks

A Boolean network (BN) is a mathematical model of genetic networks. We propose several algorithms for control of singleton attractors in BN. We theoretically estimate the average-case time complexities of the proposed algorithms, and confirm them by computer experiments. The results suggest the importance of gene ordering. Especially, setting internal nodes ahead yields shorter computational time than setting external nodes ahead in various types of algorithms. We also present a heuristic algorithm which does not look for the optimal solution but for the solution whose computational time is shorter than that of the exact algorithms

Algorithms for Finding Small Attractors in Boolean Networks

A Boolean network is a model used to study the interactions between different genes in genetic regulatory networks. In this paper, we present several algorithms using gene ordering and feedback vertex sets to identify singleton attractors and small attractors in Boolean networks. We analyze the average case time complexities of some of the proposed algorithms. For instance, it is shown that the outdegree-based ordering algorithm for finding singleton attractors works in O(1.19 n)timeforK = 2, which is much faster than the naive O(2 n) time algorithm, where n is the number of genes and K is the maximum indegree. We performed extensive computational experiments on these algorithms, which resulted in good agreement with theoretical results. In contrast, we give a simple and complete proof for showing that finding an attractor with the shortest period is NP-hard

Comparing biological networks via graph compression

<p>Abstract</p> <p>Background</p> <p>Comparison of various kinds of biological data is one of the main problems in bioinformatics and systems biology. Data compression methods have been applied to comparison of large sequence data and protein structure data. Since it is still difficult to compare global structures of large biological networks, it is reasonable to try to apply data compression methods to comparison of biological networks. In existing compression methods, the uniqueness of compression results is not guaranteed because there is some ambiguity in selection of overlapping edges.</p> <p>Results</p> <p>This paper proposes novel efficient methods, CompressEdge and CompressVertices, for comparing large biological networks. In the proposed methods, an original network structure is compressed by iteratively contracting identical edges and sets of connected edges. Then, the similarity of two networks is measured by a compression ratio of the concatenated networks. The proposed methods are applied to comparison of metabolic networks of several organisms, <it>H. sapiens, M. musculus, A. thaliana, D. melanogaster, C. elegans, E. coli, S. cerevisiae,</it> and <it>B. subtilis,</it> and are compared with an existing method. These results suggest that our methods can efficiently measure the similarities between metabolic networks.</p> <p>Conclusions</p> <p>Our proposed algorithms, which compress node-labeled networks, are useful for measuring the similarity of large biological networks.</p

Detection of High-Energy Gamma-Ray Emission from the Globular Cluster 47 Tucanae with Fermi

Gamma-Ray Pulsar Bonanza Most of the pulsars we know about were detected through their radio emission; a few are known to pulse gamma rays but were first detected at other wavelengths (see the Perspective by Halpern ). Using the Fermi Gamma-Ray Space Telescope, Abdo et al. (p. 840 , published online 2 July; see the cover) report the detection of 16 previously unknown pulsars based on their gamma-ray emission alone. Thirteen of these coincide with previously unidentified gamma-ray sources, solving the 30-year-old mystery of their identities. Pulsars are fast-rotating neutron stars. With time they slow down and cease to radiate; however, if they are in a binary system, they can have their spin rates increased by mass transfer from their companion stars, starting a new life as millisecond pulsars. In another study, Abdo et al. (p. 845 ) report the detection of gamma-ray emission from the globular cluster 47 Tucanae, which is coming from an ensemble of millisecond pulsars in the cluster's core. The data imply that there are up to 60 millisecond pulsars in 47 Tucanae, twice as many as predicted by radio observations. In a further companion study, Abdo et al. (p. 848 , published online 2 July) searched Fermi Large Area Telescope data for pulsations from all known millisecond pulsars outside of stellar clusters, finding gamma-ray pulsations for eight of them. Their properties resemble those of other gamma-ray pulsars, suggesting that they share the same basic emission mechanism. Indeed, both sets of pulsars favor emission models in which the gamma rays are produced in the outer magnetosphere of the neutron star

The Japanese Clinical Practice Guidelines for Management of Sepsis and Septic Shock 2020 (J-SSCG 2020)

The Japanese Clinical Practice Guidelines for Management of Sepsis and Septic Shock 2020 (J-SSCG 2020), a Japanese-specific set of clinical practice guidelines for sepsis and septic shock created as revised from J-SSCG 2016 jointly by the Japanese Society of Intensive Care Medicine and the Japanese Association for Acute Medicine, was first released in September 2020 and published in February 2021. An English-language version of these guidelines was created based on the contents of the original Japanese-language version. The purpose of this guideline is to assist medical staff in making appropriate decisions to improve the prognosis of patients undergoing treatment for sepsis and septic shock. We aimed to provide high-quality guidelines that are easy to use and understand for specialists, general clinicians, and multidisciplinary medical professionals. J-SSCG 2016 took up new subjects that were not present in SSCG 2016 (e.g., ICU-acquired weakness [ICU-AW], post-intensive care syndrome [PICS], and body temperature management). The J-SSCG 2020 covered a total of 22 areas with four additional new areas (patient- and family-centered care, sepsis treatment system, neuro-intensive treatment, and stress ulcers). A total of 118 important clinical issues (clinical questions, CQs) were extracted regardless of the presence or absence of evidence. These CQs also include those that have been given particular focus within Japan. This is a large-scale guideline covering multiple fields; thus, in addition to the 25 committee members, we had the participation and support of a total of 226 members who are professionals (physicians, nurses, physiotherapists, clinical engineers, and pharmacists) and medical workers with a history of sepsis or critical illness. The GRADE method was adopted for making recommendations, and the modified Delphi method was used to determine recommendations by voting from all committee members.As a result, 79 GRADE-based recommendations, 5 Good Practice Statements (GPS), 18 expert consensuses, 27 answers to background questions (BQs), and summaries of definitions and diagnosis of sepsis were created as responses to 118 CQs. We also incorporated visual information for each CQ according to the time course of treatment, and we will also distribute this as an app. The J-SSCG 2020 is expected to be widely used as a useful bedside guideline in the field of sepsis treatment both in Japan and overseas involving multiple disciplines.other authors: Satoru Hashimoto,Daisuke Hasegawa,Junji Hatakeyama,Naoki Hara,Naoki Higashibeppu,Nana Furushima,Hirotaka Furusono,Yujiro Matsuishi,Tasuku Matsuyama,Yusuke Minematsu,Ryoichi Miyashita,Yuji Miyatake,Megumi Moriyasu,Toru Yamada,Hiroyuki Yamada,Ryo Yamamoto,Takeshi Yoshida,Yuhei Yoshida,Jumpei Yoshimura,Ryuichi Yotsumoto,Hiroshi Yonekura,Takeshi Wada,Eizo Watanabe,Makoto Aoki,Hideki Asai,Takakuni Abe,Yutaka Igarashi,Naoya Iguchi,Masami Ishikawa,Go Ishimaru,Shutaro Isokawa,Ryuta Itakura,Hisashi Imahase,Haruki Imura,Takashi Irinoda,Kenji Uehara,Noritaka Ushio,Takeshi Umegaki,Yuko Egawa,Yuki Enomoto,Kohei Ota,Yoshifumi Ohchi,Takanori Ohno,Hiroyuki Ohbe,Kazuyuki Oka,Nobunaga Okada,Yohei Okada,Hiromu Okano,Jun Okamoto,Hiroshi Okuda,Takayuki Ogura,Yu Onodera,Yuhta Oyama,Motoshi Kainuma,Eisuke Kako,Masahiro Kashiura,Hiromi Kato,Akihiro Kanaya,Tadashi Kaneko,Keita Kanehata,Ken-ichi Kano,Hiroyuki Kawano,Kazuya Kikutani,Hitoshi Kikuchi,Takahiro Kido,Sho Kimura,Hiroyuki Koami,Daisuke Kobashi,Iwao Saiki,Masahito Sakai,Ayaka Sakamoto,Tetsuya Sato,Yasuhiro Shiga,Manabu Shimoto,Shinya Shimoyama,Tomohisa Shoko,Yoh Sugawara,Atsunori Sugita,Satoshi Suzuki,Yuji Suzuki,Tomohiro Suhara,Kenji Sonota,Shuhei Takauji,Kohei Takashima,Sho Takahashi,Yoko Takahashi,Jun Takeshita,Yuuki Tanaka,Akihito Tampo,Taichiro Tsunoyama,Kenichi Tetsuhara,Kentaro Tokunaga,Yoshihiro Tomioka,Kentaro Tomita,Naoki Tominaga,Mitsunobu Toyosaki,Yukitoshi Toyoda,Hiromichi Naito,Isao Nagata,Tadashi Nagato,Yoshimi Nakamura,Yuki Nakamori,Isao Nahara,Hiromu Naraba,Chihiro Narita,Norihiro Nishioka,Tomoya Nishimura,Kei Nishiyama,Tomohisa Nomura,Taiki Haga,Yoshihiro Hagiwara,Katsuhiko Hashimoto,Takeshi Hatachi,Toshiaki Hamasaki,Takuya Hayashi,Minoru Hayashi,Atsuki Hayamizu,Go Haraguchi,Yohei Hirano,Ryo Fujii,Motoki Fujita,Naoyuki Fujimura,Hiraku Funakoshi,Masahito Horiguchi,Jun Maki,Naohisa Masunaga,Yosuke Matsumura,Takuya Mayumi,Keisuke Minami,Yuya Miyazaki,Kazuyuki Miyamoto,Teppei Murata,Machi Yanai,Takao Yano,Kohei Yamada,Naoki Yamada,Tomonori Yamamoto,Shodai Yoshihiro,Hiroshi Tanaka,Osamu NishidaGuideline

The Japanese Clinical Practice Guidelines for Management of Sepsis and Septic Shock 2020 (J-SSCG 2020)

The Japanese Clinical Practice Guidelines for Management of Sepsis and Septic Shock 2020 (J-SSCG 2020), a Japanese-specific set of clinical practice guidelines for sepsis and septic shock created as revised from J-SSCG 2016 jointly by the Japanese Society of Intensive Care Medicine and the Japanese Association for Acute Medicine, was first released in September 2020 and published in February 2021. An English-language version of these guidelines was created based on the contents of the original Japanese-language version. The purpose of this guideline is to assist medical staff in making appropriate decisions to improve the prognosis of patients undergoing treatment for sepsis and septic shock. We aimed to provide high-quality guidelines that are easy to use and understand for specialists, general clinicians, and multidisciplinary medical professionals. J-SSCG 2016 took up new subjects that were not present in SSCG 2016 (e.g., ICU-acquired weakness [ICU-AW], post-intensive care syndrome [PICS], and body temperature management). The J-SSCG 2020 covered a total of 22 areas with four additional new areas (patient- and family-centered care, sepsis treatment system, neuro-intensive treatment, and stress ulcers). A total of 118 important clinical issues (clinical questions, CQs) were extracted regardless of the presence or absence of evidence. These CQs also include those that have been given particular focus within Japan. This is a large-scale guideline covering multiple fields; thus, in addition to the 25 committee members, we had the participation and support of a total of 226 members who are professionals (physicians, nurses, physiotherapists, clinical engineers, and pharmacists) and medical workers with a history of sepsis or critical illness. The GRADE method was adopted for making recommendations, and the modified Delphi method was used to determine recommendations by voting from all committee members.other authors: Yasuhiro Norisue, Satoru Hashimoto, Daisuke Hasegawa, Junji Hatakeyama, Naoki Hara, Naoki Higashibeppu, Nana Furushima, Hirotaka Furusono, Yujiro Matsuishi, Tasuku Matsuyama, Yusuke Minematsu, Ryoichi Miyashita, Yuji Miyatake, Megumi Moriyasu, Toru Yamada, Hiroyuki Yamada, Ryo Yamamoto, Takeshi Yoshida, Yuhei Yoshida, Jumpei Yoshimura, Ryuichi Yotsumoto, Hiroshi Yonekura, Takeshi Wada, Eizo Watanabe, Makoto Aoki, Hideki Asai, Takakuni Abe, Yutaka Igarashi, Naoya Iguchi, Masami Ishikawa, Go Ishimaru, Shutaro Isokawa, Ryuta Itakura, Hisashi Imahase, Haruki Imura, Takashi Irinoda, Kenji Uehara, Noritaka Ushio, Takeshi Umegaki, Yuko Egawa, Yuki Enomoto, Kohei Ota, Yoshifumi Ohchi, Takanori Ohno, Hiroyuki Ohbe, Kazuyuki Oka, Nobunaga Okada, Yohei Okada, Hiromu Okano, Jun Okamoto, Hiroshi Okuda, Takayuki Ogura, Yu Onodera, Yuhta Oyama, Motoshi Kainuma, Eisuke Kako, Masahiro Kashiura, Hiromi Kato, Akihiro Kanaya, Tadashi Kaneko, Keita Kanehata, Ken-ichi Kano, Hiroyuki Kawano, Kazuya Kikutani, Hitoshi Kikuchi, Takahiro Kido, Sho Kimura, Hiroyuki Koami, Daisuke Kobashi, Iwao Saiki, Masahito Sakai, Ayaka Sakamoto, Tetsuya Sato, Yasuhiro Shiga, Manabu Shimoto, Shinya Shimoyama, Tomohisa Shoko, Yoh Sugawara, Atsunori Sugita, Satoshi Suzuki, Yuji Suzuki, Tomohiro Suhara, Kenji Sonota, Shuhei Takauji, Kohei Takashima, Sho Takahashi, Yoko Takahashi, Jun Takeshita, Yuuki Tanaka, Akihito Tampo, Taichiro Tsunoyama, Kenichi Tetsuhara, Kentaro Tokunaga, Yoshihiro Tomioka, Kentaro Tomita, Naoki Tominaga, Mitsunobu Toyosaki, Yukitoshi Toyoda, Hiromichi Naito, Isao Nagata, Tadashi Nagato, Yoshimi Nakamura, Yuki Nakamori, Isao Nahara, Hiromu Naraba, Chihiro Narita, Norihiro Nishioka, Tomoya Nishimura, Kei Nishiyama, Tomohisa Nomura, Taiki Haga, Yoshihiro Hagiwara, Katsuhiko Hashimoto, Takeshi Hatachi, Toshiaki Hamasaki, Takuya Hayashi, Minoru Hayashi, Atsuki Hayamizu, Go Haraguchi, Yohei Hirano, Ryo Fujii, Motoki Fujita, Naoyuki Fujimura, Hiraku Funakoshi, Masahito Horiguchi, Jun Maki, Naohisa Masunaga, Yosuke Matsumura, Takuya Mayumi, Keisuke Minami, Yuya Miyazaki, Kazuyuki Miyamoto, Teppei Murata, Machi Yanai, Takao Yano, Kohei Yamada, Naoki Yamada, Tomonori Yamamoto, Shodai Yoshihiro, Hiroshi Tanaka & Osamu Nishid