Development of advanced photon calibrator for Kamioka gravitational wave detector (KAGRA).

The Kamioka Gravitational wave detector (KAGRA) cryogenic gravitational-wave observatory has commenced joint observations with the worldwide gravitational wave detector network. Precise calibration of the detector response is essential for accurately estimating parameters of gravitational wave sources. A photon calibrator is a crucial calibration tool used in laser interferometer gravitational-wave observatory, Virgo, and KAGRA, and it was utilized in joint observation 3 with GEO600 in Germany in April 2020. In this paper, KAGRA implemented three key enhancements: a high-power laser, a power stabilization system, and remote beam position control. KAGRA employs a 20 W laser divided into two beams that are injected onto the mirror surface. By utilizing a high-power laser, the response of the detector at kHz frequencies can be calibrated. To independently control the power of each laser beam, an optical follower servo was installed for power stabilization. The optical path of the photon calibrator's beam positions was controlled using pico-motors, allowing for the characterization of the detector's rotation response. Additionally, a telephoto camera and quadrant photodetectors were installed to monitor beam positions, and beam position control was implemented to optimize the mirror response. In this paper, we discuss the statistical errors associated with the measurement of relative power noise. We also address systematic errors related to the power calibration model of the photon calibrator and the simulation of elastic deformation effects using finite element analysis. Ultimately, we have successfully reduced the total systematic error from the photon calibrator to 2.0%.

Constraints on Cosmic Strings Using Data from the Third Advanced LIGO-Virgo Observing Run.

We search for gravitational-wave signals produced by cosmic strings in the Advanced LIGO and Virgo full O3 dataset. Search results are presented for gravitational waves produced by cosmic string loop features such as cusps, kinks, and, for the first time, kink-kink collisions. A template-based search for short-duration transient signals does not yield a detection. We also use the stochastic gravitational-wave background energy density upper limits derived from the O3 data to constrain the cosmic string tension Gµ as a function of the number of kinks, or the number of cusps, for two cosmic string loop distribution models. Additionally, we develop and test a third model that interpolates between these two models. Our results improve upon the previous LIGO-Virgo constraints on Gµ by 1 to 2 orders of magnitude depending on the model that is tested. In particular, for the one-loop distribution model, we set the most competitive constraints to date: Gµâ‰²4×10^{-15}. In the case of cosmic strings formed at the end of inflation in the context of grand unified theories, these results challenge simple inflationary models.

Prospects for observing and localizing gravitational-wave transients with Advanced LIGO, Advanced Virgo and KAGRA.

We present our current best estimate of the plausible observing scenarios for the Advanced LIGO, Advanced Virgo and KAGRA gravitational-wave detectors over the next several years, with the intention of providing information to facilitate planning for multi-messenger astronomy with gravitational waves. We estimate the sensitivity of the network to transient gravitational-wave signals for the third (O3), fourth (O4) and fifth observing (O5) runs, including the planned upgrades of the Advanced LIGO and Advanced Virgo detectors. We study the capability of the network to determine the sky location of the source for gravitational-wave signals from the inspiral of binary systems of compact objects, that is binary neutron star, neutron star-black hole, and binary black hole systems. The ability to localize the sources is given as a sky-area probability, luminosity distance, and comoving volume. The median sky localization area (90% credible region) is expected to be a few hundreds of square degrees for all types of binary systems during O3 with the Advanced LIGO and Virgo (HLV) network. The median sky localization area will improve to a few tens of square degrees during O4 with the Advanced LIGO, Virgo, and KAGRA (HLVK) network. During O3, the median localization volume (90% credible region) is expected to be on the order of 10 5 , 10 6 , 10 7 Mpc 3 for binary neutron star, neutron star-black hole, and binary black hole systems, respectively. The localization volume in O4 is expected to be about a factor two smaller than in O3. We predict a detection count of 1 - 1 + 12 ( 10 - 10 + 52 ) for binary neutron star mergers, of 0 - 0 + 19 ( 1 - 1 + 91 ) for neutron star-black hole mergers, and 17 - 11 + 22 ( 79 - 44 + 89 ) for binary black hole mergers in a one-calendar-year observing run of the HLV network during O3 (HLVK network during O4). We evaluate sensitivity and localization expectations for unmodeled signal searches, including the search for intermediate mass black hole binary mergers.

Prospects for observing and localizing gravitational-wave transients with Advanced LIGO, Advanced Virgo and KAGRA.

We present possible observing scenarios for the Advanced LIGO, Advanced Virgo and KAGRA gravitational-wave detectors over the next decade, with the intention of providing information to the astronomy community to facilitate planning for multi-messenger astronomy with gravitational waves. We estimate the sensitivity of the network to transient gravitational-wave signals, and study the capability of the network to determine the sky location of the source. We report our findings for gravitational-wave transients, with particular focus on gravitational-wave signals from the inspiral of binary neutron star systems, which are the most promising targets for multi-messenger astronomy. The ability to localize the sources of the detected signals depends on the geographical distribution of the detectors and their relative sensitivity, and [Formula: see text] credible regions can be as large as thousands of square degrees when only two sensitive detectors are operational. Determining the sky position of a significant fraction of detected signals to areas of 5-[Formula: see text] requires at least three detectors of sensitivity within a factor of [Formula: see text] of each other and with a broad frequency bandwidth. When all detectors, including KAGRA and the third LIGO detector in India, reach design sensitivity, a significant fraction of gravitational-wave signals will be localized to a few square degrees by gravitational-wave observations alone.

The role of internal duplication in the evolution of multi-domain proteins.

Many proteins consist of several structural domains. These multi-domain proteins have likely been generated by selective genome growth dynamics during evolution to perform new functions as well as to create structures that fold on a biologically feasible time scale. Domain units frequently evolved through a variety of genetic shuffling mechanisms. Here we examine the protein domain statistics of more than 1000 organisms including eukaryotic, archaeal and bacterial species. The analysis extends earlier findings on asymmetric statistical laws for proteome to a wider variety of species. While proteins are composed of a wide range of domains, displaying a power-law decay, the computation of domain families for each protein reveals an exponential distribution, characterizing a protein universe composed of a thin number of unique families. Structural studies in proteomics have shown that domain repeats, or internal duplicated domains, represent a small but significant fraction of genome. In spite of its importance, this observation has been largely overlooked until recently. We model the evolutionary dynamics of proteome and demonstrate that these distinct distributions are in fact rooted in an internal duplication mechanism. This process generates the contemporary protein structural domain universe, determines its reduced thickness, and tames its growth. These findings have important implications, ranging from protein interaction network modeling to evolutionary studies based on fundamental mechanisms governing genome expansion.

Distribution and enumeration of attractors in probabilistic Boolean networks.

Many mathematical models for gene regulatory networks have been proposed. In this study, the authors study attractors in probabilistic Boolean networks (PBNs). They study the expected number of singleton attractors in a PBN and show that it is (2 - (1/2)(L-1))(n), where n is the number of nodes in a PBN and L is the number of Boolean functions assigned to each node. In the case of L=2, this number is simplified into 1.5(n). It is an interesting result because it is known that the expected number of singleton attractors in a Boolean network (BN) is 1. Then, we present algorithms for identifying singleton and small attractors and perform both theoretical and computational analyses on their average case time complexities. For example, the average case time complexities for identifying singleton attractors of a PBN with L=2 and L=3 are O(1.601(n)) and O(1.763(n)), respectively. The results of computational experiments suggest that these algorithms are much more efficient than the naive algorithm that examines all possible 2(n) states.

Optimal control policy for probabilistic Boolean networks with hard constraints.

It is well known that the control/intervention of some genes in a genetic regulatory network is useful for avoiding undesirable states associated with some diseases like cancer. For this purpose, both optimal finite-horizon control and infinite-horizon control policies have been proposed. Boolean networks (BNs) and its extension probabilistic Boolean networks (PBNs) as useful and effective tools for modelling gene regulatory systems have received much attention in the biophysics community. The control problem for these models has been studied widely. The optimal control problem in a PBN can be formulated as a probabilistic dynamic programming problem. In the previous studies, the optimal control problems did not take into account the hard constraints, i.e. to include an upper bound for the number of controls that can be applied to the captured PBN. This is important as more treatments may bring more side effects and the patients may not bear too many treatments. A formulation for the optimal finite-horizon control problem with hard constraints introduced by the authors. This model is state independent and the objective function is only dependent on the distance between the desirable states and the terminal states. An approximation method is also given to reduce the computational cost in solving the problem. Experimental results are given to demonstrate the efficiency of our proposed formulations and methods.

Emergence of scale-free distribution in protein-protein interaction networks based on random selection of interacting domain pairs.

Recent analyses of biological and artificial networks have revealed a common network architecture, called scale-free topology. The origin of the scale-free topology has been explained by using growth and preferential attachment mechanisms. In a cell, proteins are the most important carriers of function, and are composed of domains as elemental units responsible for the physical interaction between protein pairs. Here, we propose a model for protein-protein interaction networks that reveals the emergence of two possible topologies. We show that depending on the number of randomly selected interacting domain pairs, the connectivity distribution follows either a scale-free distribution, even in the absence of the preferential attachment, or a normal distribution. This new approach only requires an evolutionary model of proteins (nodes) but not for the interactions (edges). The edges are added by means of random interaction of domain pairs. As a result, this model offers a new mechanistic explanation for understanding complex networks with a direct biological interpretation because only protein structures and their functions evolved through genetic modifications of amino acid sequences. These findings are supported by numerical simulations as well as experimental data.

Recent progress on the analysis of power-law features in complex cellular networks.

Complex interactions between different kinds of bio-molecules and essential nutrients are responsible for cellular functions. Rapid advances in theoretical modeling and experimental analyses have shown that drastically different biological and non-biological networks share a common architecture. That is, the probability that a selected node in the network has exactly k edges decays as a power-law. This finding has definitely opened an intense research and debate on the origin and implications of this ubiquitous pattern. In this review, we describe the recent progress on the emergence of power-law distributions in cellular networks. We first show the internal characteristics of the observed complex networks uncovered using graph theory. We then briefly review some works that have significantly contributed to the theoretical analysis of cellular networks and systems, from metabolic and protein networks to gene expression profiles. This prevalent topology observed in so many diverse biological systems suggests the existence of generic laws and organizing principles behind the cellular networks.

The role of log-normal dynamics in the evolution of biochemical pathways.

The study of the scale-free topology in non-biological and biological networks and the dynamics that can explain this fascinating property of complex systems have captured the attention of the scientific community in the last years. Here, we analyze the biochemical pathways of three organisms (Methanococcus jannaschii, Escherichia coli, Saccharomyces cerevisiae) which are representatives of the main kingdoms Archaea, Bacteria and Eukaryotes during the course of the biological evolution. We can consider two complementary representations of the biochemical pathways: the enzymes network and the chemical compounds network. In this article, we propose a stochastic model that explains that the scale-free topology with exponent in the vicinity of gamma approximately 3/2 found across these three organisms is governed by the log-normal dynamics in the evolution of the enzymes network. Precisely, the fluctuations of the connectivity degree of enzymes in the biochemical pathways between evolutionary distant organisms follow the same conserved dynamical principle, which in the end is the origin of the stationary scale-free distribution observed among species, from Archaea to Eukaryotes. In particular, the log-normal dynamics guarantees the conservation of the scale-free distribution in evolving networks. Furthermore, the log-normal dynamics also gives a possible explanation for the restricted range of observed exponents gamma in the scale-free networks (i.e., gamma > or = 3/2). Finally, our model is also applied to the chemical compounds network of biochemical pathways and the Internet network.

Flexible construction of hierarchical scale-free networks with general exponent.

Extensive studies have been done to understand the principles behind architectures of real networks. Recently, evidence for hierarchical organization in many real networks has also been reported. Here, we present a hierarchical model that reproduces the main experimental properties observed in real networks: scale-free of degree distribution P (k) [frequency of the nodes that are connected to k other nodes decays as a power law P (k) approximately k(-gamma) ] and power-law scaling of the clustering coefficient C (k) approximately k(-1) . The major points of our model can be summarized as follows. (a) The model generates networks with scale-free distribution for the degree of nodes with general exponent gamma>2 , and arbitrarily close to any specified value, being able to reproduce most of the observed hierarchical scale-free topologies. In contrast, previous models cannot obtain values of gamma>2.58 . (b) Our model has structural flexibility because (i) it can incorporate various types of basic building blocks (e.g., triangles, tetrahedrons, and, in general, fully connected clusters of n nodes) and (ii) it allows a large variety of configurations (i.e., the model can use more than n-1 copies of basic blocks of n nodes). The structural features of our proposed model might lead to a better understanding of architectures of biological and nonbiological networks.

Investigation on the mechanical properties of contracted collagen gels as a scaffold for tissue engineering.

In this article the mechanical properties of contracted collagen gels were investigated thoroughly by means of uniaxial tensile test. Large type I collagen-Dulbecco's Modified Eagle Medium (DMEM) gels (each was 26 ml in volume, 1.67 mg/ml collagen concentration), each populated with about 2.5 x 106 human fibroblasts, were made in 100 mm diameter plastic dishes precoated with albumin for floating the gels in DMEM. Such identically treated gels were divided into three groups for the mechanical measurements at different culture periods (2, 4, and 10 weeks). Rapid contraction occurred within the first 3 days and then the contraction went slowly in the rest period until it reached about 13% of its original size. The stress-strain curve of the contracted collagen gels demonstrated an exponential behavior at low stress region, followed by linear region, a point of yielding, and finally an ultimate stress point at which the maximum stress was reached. The mechanical strength increased in the first few weeks and then decreased as the culture went on. It is obvious that the collagen fibrils formed and were forced to orientate to the tensile direction after the test. The stress relaxation and cyclic creep phenomena were observed. Based on the morphological analysis of transmission electron microscopy (TEM) of the gels, a nonlinear visco-elastic-plastic constitutive formula was proposed, which was able to reproduce the rheological phenomena of the gels. This experiment shows that the human fibroblasts significantly contracted collagen gels so as to achieve certain mechanical strength, which makes it possible to be a scaffold for tissue engineering. However, a further method to reinforce the mechanical strength by several folds must be considered. Meanwhile, the rheological phenomena should be taken into account in the fabrication and application of the structure.

Genetic mapping of the dominant albino locus in rainbow trout (Oncorhynchus mykiss).

Albinism in animals is generally a recessive trait, but in Japan a dominant oculocutaneous albino (OCA) mutant strain has been isolated in rainbow trout (Oncorhyncus mykiss). After confirming that this trait is not due to a tyrosinase gene mutation that causes OCA1 (tyrosinase-negative OCA), we combined the amplified fragment length polymorphism (AFLP) technique with bulked segregant analysis (BSA) to map the gene involved in dominant oculocutaneous albinism. Four AFLP markers tightly linked to the dominant albino locus were identified. One of these markers was codominant and we have it converted into a GGAGT-repeat microsatellite marker, OmyD-AlbnTUF. Using this pentanucleotide-repeat DNA marker, the dominant albino locus has been mapped on linkage group G of a reference linkage map of rainbow trout. The markers identified here will facilitate cloning of the dominant albino gene in rainbow trout and contribute to a better understanding of tyrosinase-negative OCA in animals.

Interaction of metallopyrazoliumylporphyrins with calf thymus DNA.

The interaction of transition metal complexes of cationic porphyrins bearing five membered rings, meso-tetrakis(1,2-dimethylpyrazolium-4-yl)porphyrin (MPzP, M=Mn(III), Ni(II), Cu(II) or Zn(II)), with calf thymus DNA (ctDNA) has been studied. Metalloporphyrins NiPzP and CuPzP are intercalated into the 5'GC3' step of ctDNA. MnPzP is bound edge-on at the 5'TA3' step of the minor groove of ctDNA, while ZnPzP is bound face-on at the 5'TA3' step of the major groove of ctDNA. The binding constants of the metalloporphyrins to ctDNA range from 1.05x10(5) to 2.66x10(6) M(-1) and are comparable to those of other reported cationic porphyrins. The binding process of the metallopyrazoliumylporphyrins to ctDNA is endothermic and entropically driven. These results have revealed that the kind of central metal ions of metalloporphyrins influences the binding characteristics of the porphyrin to DNA.

Quantitative trait loci (QTLs) associated with resistance/susceptibility to infectious pancreatic necrosis virus (IPNV) in rainbow trout (Oncorhynchus mykiss).

Infectious pancreatic necrosis (IPN) is a well-known acute viral disease of salmonid species. We have identified quantitative trait loci (QTLs) associated with resistance to this disease in rainbow trout. We searched for linkage among 51 microsatellite markers used to construct a framework linkage map in backcross families of rainbow trout (Oncorhynchus mykiss), produced by crossing IPN-resistant (YN-RT201) and -susceptible (YK-RT101) strains. Two putative QTLs affecting disease resistance were detected on chromosomes A (IPN R S-1) and C (IPN R/S-2), respectively, suggesting that this is a polygenic trait in rainbow trout. These markers have great potential for use in marker-assisted selection (MAS) for IPN resistance and provide the basis for cloning of IPN resistance genes. Clarification of the genetic bases of complex traits has broad implications for fundamental research, but will also be of practical benefit to fish breeding.

Local multiple alignment of numerical sequences: detection of subtle motifs from protein sequences and structures.

This paper presents a new method to find motifs from multiple protein sequences and multiple protein structures. The method consists of two parts: quantification and local multiple alignment. In the former part, protein sequences and protein structures are transformed into sequences of real numbers and real vectors respectively. In the latter part, fixed length regions having similar shapes are located. A Gibbs sampling algorithm for sequences of real numbers/vectors is newly developed for finding common regions. The results of the comparison with a standard Gibbs sampling program show that the method is particularly useful when structural information is available.

Algorithms for identifying Boolean networks and related biological networks based on matrix multiplication and fingerprint function.

Due to the recent progress of the DNA microarray technology, a large number of gene expression profile data are being produced. How to analyze gene expression data is an important topic in computational molecular biology. Several studies have been done using the Boolean network as a model of a genetic network. This paper proposes efficient algorithms for identifying Boolean networks of bounded indegree and related biological networks, where identification of a Boolean network can be formalized as a problem of identifying many Boolean functions simultaneously. For the identification of a Boolean network, an O(mnD+1) time naive algorithm and a simple O (mnD) time algorithm are known, where n denotes the number of nodes, m denotes the number of examples, and D denotes the maximum in degree. This paper presents an improved O(momega-2nD + mnD+omega-3) time Monte-Carlo type randomized algorithm, where omega is the exponent of matrix multiplication (currently, omega < 2.376). The algorithm is obtained by combining fast matrix multiplication with the randomized fingerprint function for string matching. Although the algorithm and its analysis are simple, the result is nontrivial and the technique can be applied to several related problems.

Inferring qualitative relations in genetic networks and metabolic pathways.

MOTIVATION: Inferring genetic network architecture from time series data of gene expression patterns is an important topic in bioinformatics. Although inference algorithms based on the Boolean network were proposed, the Boolean network was not sufficient as a model of a genetic network. RESULTS: First, a Boolean network model with noise is proposed, together with an inference algorithm for it. Next, a qualitative network model is proposed, in which regulation rules are represented as qualitative rules and embedded in the network structure. Algorithms are also presented for inferring qualitative relations from time series data. Then, an algorithm for inferring S-systems (synergistic and saturable systems) from time series data is presented, where S-systems are based on a particular kind of nonlinear differential equation and have been applied to the analysis of various biological systems. Theoretical results are shown for Boolean networks with noises and simple qualitative networks. Computational results are shown for Boolean networks with noises and S-systems, where real data are not used because the proposed models are still conceptual and the quantity and quality of currently available data are not enough for the application of the proposed methods.

Algorithms for inferring qualitative models of biological networks.

Modeling genetic networks and metabolic networks is an important topic in bioinformatics. We propose a qualitative network model which is a combination of the Boolean network and qualitative reasoning, where qualitative reasoning is a kind of reasoning method well-studied in Artificial Intelligence. We also present algorithms for inferring qualitative networks from time series data and an algorithm for inferring S-systems (synergistic and saturable systems) from time series data, where S-systems are based on a particular kind of nonlinear differential equation and have been applied to the analysis of various biological systems.