The conventional wisdom, dating back to 2012, is that the mass distribution of Galactic double neutron stars (DNSs) is well-fit by a Gaussian distribution with a mean of 1.33 M and a width of 0.09 M .... With the recent discovery of new Galactic DNSs and GW170817, the first neutron star merger event to be observed with gravitational waves, it is timely to revisit this model. In order to constrain the mass distribution of DNSs, we perform Bayesian inference using a sample of 17 Galactic DNSs, effectively doubling the sample used in previous studies. We expand the space of models so that the recycled neutron star need not be drawn from the same distribution as the nonrecycled companion. Moreover, we consider different functional forms including uniform, single-Gaussian, and two-Gaussian distributions. While there is insufficient data to draw firm conclusions, we find positive support (a Bayes factor (BF) of 9) for the hypothesis that recycled and nonrecycled neutron stars have distinct mass distributions. The most probable model-preferred with a BF of 29 over the conventional model-is one in which the recycled neutron star mass is distributed according to a two-Gaussian distribution, and the nonrecycled neutron star mass is distributed uniformly. We show that precise component mass measurements of 20 DNSs are required in order to determine with high confidence (a BF of 150) whether recycled and nonrecycled neutron stars come from a common distribution. Approximately 60 DNSs are needed in order to establish the detailed shape of the distributions.
Metabolomics quantitatively measures metabolites in a given biological system and facilitates the understanding of physiological and pathological activities. With the recent advancement of mass ...spectrometry (MS) technology, liquid chromatography-mass spectrometry (LC-MS) with data-independent acquisition (DIA) has been emerged as a powerful technology for untargeted metabolomics due to its capability to acquire all MS2 spectra and high quantitative accuracy. In this trend article, we first introduced the basic principles of several common DIA techniques including MS
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, all ion fragmentation (AIF), SWATH, and MSX. Then, we summarized and compared the data analysis strategies to process DIA-based untargeted metabolomics data, including metabolite identification and quantification. We think the advantages of the DIA technique will enable its broad application in untargeted metabolomics.
Abstract
Gravitational-wave astronomy provides a unique new way to study the expansion history of the universe. In this work, we investigate the impact future gravitational-wave observatories will ...have on cosmology. Third-generation observatories like the Einstein Telescope and Cosmic Explorer will be sensitive to essentially all of the binary black hole coalescence events in the universe. Recent work by Farr et al. points out that features in the stellar-mass black hole population break the mass–redshift degeneracy, facilitating precise determination of the Hubble parameter without electromagnetic counterparts or host galaxy catalogs. Using a hierarchical Bayesian inference model, we show that with one year of observations by the Einstein Telescope, the Hubble constant will be measured to ≲1%. We also show that this method can be used to perform Bayesian model selection between cosmological models. As an illustrative example, we find that a decisive statement can be made comparing the ΛCDM and RHCT cosmological models using two weeks of data from the Einstein Telescope.
Supermassive binary black holes at subparsec orbital separations have yet to be discovered, with the possible exception of blazar OJ 287. In parallel to the global hunt for nanohertz gravitational ...waves from supermassive binaries using pulsar timing arrays, there has been a growing sample of candidates reported from electromagnetic surveys, particularly searches for periodic variations in the optical light curves of quasars. However, the periodicity search is prone to false positives from quasar red noise and quasiperiodic oscillations from the accretion disk of a single supermassive black hole, especially when the data span fewer than a few signal cycles. We present a Bayesian method for the detection of quasar (quasi)periodicity in the presence of red noise. We apply this method to the binary candidate PG 1302−102 and show that (a) there is very strong support (Bayes factor >106) for quasiperiodicity and (b) the data slightly favor a quasiperiodic oscillation over a sinusoidal signal, which we interpret as modest evidence against the binary black hole hypothesis. We also find that the prevalent damped random walk red-noise model is disfavored with more than 99.9% credibility. Finally, we outline future work that may enable the unambiguous identification of supermassive binary black holes.
The metabolome includes not just known but also unknown metabolites; however, metabolite annotation remains the bottleneck in untargeted metabolomics. Ion mobility - mass spectrometry (IM-MS) has ...emerged as a promising technology by providing multi-dimensional characterizations of metabolites. Here, we curate an ion mobility CCS atlas, namely AllCCS, and develop an integrated strategy for metabolite annotation using known or unknown chemical structures. The AllCCS atlas covers vast chemical structures with >5000 experimental CCS records and ~12 million calculated CCS values for >1.6 million small molecules. We demonstrate the high accuracy and wide applicability of AllCCS with medium relative errors of 0.5-2% for a broad spectrum of small molecules. AllCCS combined with in silico MS/MS spectra facilitates multi-dimensional match and substantially improves the accuracy and coverage of both known and unknown metabolite annotation from biological samples. Together, AllCCS is a versatile resource that enables confident metabolite annotation, revealing comprehensive chemical and metabolic insights towards biological processes.
Large-scale metabolite annotation is a challenge in liquid chromatogram-mass spectrometry (LC-MS)-based untargeted metabolomics. Here, we develop a metabolic reaction network (MRN)-based recursive ...algorithm (MetDNA) that expands metabolite annotations without the need for a comprehensive standard spectral library. MetDNA is based on the rationale that seed metabolites and their reaction-paired neighbors tend to share structural similarities resulting in similar MS2 spectra. MetDNA characterizes initial seed metabolites using a small library of MS2 spectra, and utilizes their experimental MS2 spectra as surrogate spectra to annotate their reaction-paired neighbor metabolites, which subsequently serve as the basis for recursive analysis. Using different LC-MS platforms, data acquisition methods, and biological samples, we showcase the utility and versatility of MetDNA and demonstrate that about 2000 metabolites can cumulatively be annotated from one experiment. Our results demonstrate that MetDNA substantially expands metabolite annotation, enabling quantitative assessment of metabolic pathways and facilitating integrative multi-omics analysis.
Cognitive networks (CNs) are one of the key enablers for the Internet of Things (IoT), where CNs will play an important role in the future Internet in several application scenarios, such as ...healthcare, agriculture, environment monitoring, and smart metering. However, the current low packet transmission efficiency of IoT faces a problem of the crowded spectrum for the rapidly increasing popularities of various wireless applications. Hence, the IoT that uses the advantages of cognitive technology, namely the cognitive radio-based IoT (CIoT), is a promising solution for IoT applications. A major challenge in CIoT is the packet transmission efficiency using CNs. Therefore, a new Q-learning-based transmission scheduling mechanism using deep learning for the CIoT is proposed to solve the problem of how to achieve the appropriate strategy to transmit packets of different buffers through multiple channels to maximize the system throughput. A Markov decision process-based model is formulated to describe the state transformation of the system. A relay is used to transmit packets to the sink for the other nodes. To maximize the system utility in different system states, the reinforcement learning method, i.e., the Q learning algorithm, is introduced to help the relay to find the optimal strategy. In addition, the stacked auto-encoders deep learning model is used to establish the mapping between the state and the action to accelerate the solution of the problem. Finally, the experimental results demonstrate that the new action selection method can converge after a certain number of iterations. Compared with other algorithms, the proposed method can better transmit packets with less power consumption and packet loss.
Porous materials, especially metal–organic frameworks, covalent organic frameworks, and supramolecular organic frameworks, are widely used in heterogeneous catalysis, adsorption, and ion exchange. ...Cucurbitnurils (Qns) suitable building units for porous materials because they possess cavities with neutral electrostatic potential, portal carbonyls with negative electrostatic potential, and outer surfaces with positive electrostatic potential, which may result in the formation of Qn‐based supramolecular frameworks (QSFs) assembled through the interaction of guests within Qns, the coordination of Qns with metal ions, and outer‐surface interaction of Qns (OSIQ). This review summarizes the various QSFs assembled via OSIQs. The QSFs can be classified as being assembled by 1) self‐induced OSIQ, 2) anion‐induced OSIQ, and 3) aromatic‐induced OSIQ. The design and construction of QSFs with novel structures and specific functional properties may establish a new research direction in Qn chemistry.
This review summarizes the outer‐surface interactions of cucurbitnurils (OSIQ) in various simple cucurbitnuril‐based supramolecular frameworks (QSFs) and QSFs assembled via self‐induced OSIQ, anion‐induced OSIQs, and aromatic‐induced OSIQs. The design and construction of QSFs with novel structures and specific functional properties establishes a new research direction in cucurbitnuril chemistry.
Abstract
Radio pulsar observations probe the lives of Galactic double neutron star (DNS) systems while gravitational waves enable us to study extragalactic DNS in their final moments. By combining ...measurements from radio and gravitational-wave astronomy, we seek to gain a more complete understanding of DNS from formation to merger. We analyze the recent gravitational-wave binary neutron star mergers GW170817 and GW190425 in the context of other DNS known from radio astronomy. By employing a model for the birth and evolution of DNS, we measure the mass distribution of DNS at birth, at midlife (in the radio), and at death (in gravitational waves). We consider the hypothesis that the high-mass gravitational-wave event GW190425 is part of a subpopulation formed through unstable case BB mass transfer, which quickly merge in ∼10–100 Myr. We find only mild evidence to support this hypothesis and that GW190425 is not a clear outlier from the radio population as previously claimed. If there are fast-merging binaries, we estimate that they constitute 8%–79% of DNS at birth (90% credibility). We estimate the typical delay time between the birth and death of fast-merging binaries to be ≈5–401 Myr (90% credibility). We discuss the implications for radio and gravitational-wave astronomy.
A complete map of the ocean subsurface temperature is essential for monitoring aspects of climate change such as the ocean heat content (OHC) and sea level changes and for understanding the dynamics ...of the ocean/climate variation. However, global observations have not been available in the past, so a mapping strategy is required to fill the data gaps. In this study, an advancedmappingmethod is proposed to reconstruct the historical ocean subsurface (0–700 m) temperature field from 1940 to 2014 by using ensemble optimal interpolation with a dynamic ensemble (EnOI-DE) approach and a multimodel ensemble of phase 5 of the Coupled Model Intercomparison Project (CMIP5) historical and representative concentration pathway 4.5 simulations. The reconstructed field is a combination of two parts: a first guess provided by the ensemble mean of CMIP5 models and an adjustment by minimizing the analysis error with the assistance of error covariance determined by the CMIP5 models. The uncertainty of the field can also be assessed. This new approach was evaluated using a series of tests, including subsample tests by using data from the Argo period, idealized tests by specifying a truth field from the models, and withdrawn-data tests by removing 20% of the observations for validation. In addition, the authors showed that the oceanmean state, long-termtrends, and interannual and decadal variability are all well represented. Furthermore, the most significant benefit of thismethod is to provide an improved estimate of the long-term historical OHC changes since 1940, which have important implications for Earth’s energy budget.
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