From Molecular to Cluster Properties: Rotational Spectroscopy of 2-Aminopyridine and of Its Biomimetic Cluster with Water

We report the observation and analysis of the rotational spectrum of a 1:1 cluster between 2-aminopyridine and water (AMW) carried out with supersonic expansion Fourier transform microwave spectroscopy at 4.7–16.5 GHz. Measurements of the 2-aminopyridine monomer (AMP) were also extended up to 333 GHz for the room-temperature rotational spectrum and to resolve hyperfine splitting resulting from the presence of two 14N quadrupolar nuclei. Supersonic expansion measurements for both AMP and AMW were also carried out for two synthesized isotopic species with single deuteration on the phenyl ring. Nuclear quadrupole hyperfine structure has also been resolved for AMW and the derived splitting constants were used as an aid in structural analysis. The structure of the AMW cluster was determined from the three sets of available rotational constants and the hydrogen bonding configuration is compared with those for clusters with water of similarly sized single-ring molecules. Experimental results aided by quantum chemistry computations allow the conclusion that the water molecule is unusually strongly bound by two hydrogen bonds, OH...N and O...HN, to the NCNH atomic chain of AMP with the potential to replace hydrogen bonds to the identical structural segment in cytosine and adenine in CT and AT nucleic acid base pairs.

A Multiwavelength Dynamical State Analysis of ACT-CL J0019.6+0336

In our study, we show a multiwavelength view of ACT-CL J0019.6+0336 (which hosts a radio halo), to investigate the cluster dynamics, morphology, and ICM. We use a combination of XMM-Newton images, Dark Energy Survey (DES) imaging and photometry, SDSS spectroscopic information, and 1.16 GHz MeerKAT data to study the cluster properties. Various X-ray and optical morphology parameters are calculated to investigate the level of disturbance. We find disturbances in two X-ray parameters and the optical density map shows elongated and axisymmetric structures with the main cluster component southeast of the cluster centre and another component northwest of the cluster centre. We also find a BCG offset of ∼950 km/s from the mean velocity of the cluster, and a discrepancy between the SZ mass, X-ray mass, and dynamical mass (MX,500 and MSZ,500 lies >3σ away from Mdyn,500), showing that J0019 is a merging cluster and probably in a post-merging phase.

Selecting Clustering Algorithms for IBD Mapping

Background: Groups of distantly related individuals who share a short segment of their genome identical-by-descent (IBD) can provide insights about rare traits and diseases in massive biobanks via a process called IBD mapping. Clustering algorithms play an important role in finding these groups. We set out to analyze the fitness of commonly used, fast and scalable clustering algorithms for IBD mapping applications. We designed a realistic benchmark for local IBD graphs and utilized it to compare clustering algorithms in terms of statistical power. We also investigated the effectiveness of common clustering metrics as replacements for statistical power. Results: We simulated 3.4 million clusters across 850 experiments with varying cluster counts, false-positive, and false-negative rates. Infomap and Markov Clustering (MCL) community detection methods have high statistical power in most of the graphs, compared to greedy methods such as Louvain and Leiden. We demonstrate that standard clustering metrics, such as modularity, cannot predict statistical power of algorithms in IBD mapping applications, though they can help with simulating realistic benchmarks. We extend our findings to real datasets by analyzing 3 populations in the Population Architecture using Genomics and Epidemiology (PAGE) Study with ~51,000 members and 2 million shared segments on Chromosome 1, resulting in the extraction of ~39 million local IBD clusters across three different populations in PAGE. We used cluster properties derived in PAGE to increase the accuracy of our simulations and comparison. Conclusions: Markov Clustering produces a 30% increase in statistical power compared to the current state-of-art approach, while reducing runtime by 3 orders of magnitude; making it computationally tractable in modern large-scale genetic datasets. We provide an efficient implementation to enable clustering at scale for IBD mapping and poplation-based linkage for various populations and scenarios.

Updated statistics for crater clusters on Mars

<p>An increasing number of newly formed impact craters on Mars have been detected in the last 15 years. These small craters are normally identified via dark spots in lower resolution images that formed during the impact process, presumably through the removal or disturbance of bright surface material [1]. Later higher resolution images revealed single craters or crater clusters, which form when impactors fragment in the atmosphere, within those halos [1,2]. Due to this detection method, most of the new impact sites found are in dusty regions, which imposes an observational bias [3]. Newly formed clusters consist of two to thousands of individual craters and can be tightly clustered or spread out over hundreds of meters [2]. Since the InSight (Interior Exploration using Seismic Investigations, Geodesy and Heat Transport) mission landed on Mars in 2018 [4], the search for newly formed impact craters has become even more important, because identifying impacts in seismic signals could provide further constraints on both the atmospheric and solid-body effects of impact cratering process on Mars, as well as help place further constraints on the properties of the uppermost layer of the crust. As one of InSight’s mission goals is to estimate the current impact rate on Mars, the seismic detection of impacts is also crucial [4].</p> <p>The aim of this new study is to describe the properties of the complete catalog of known newly formed craters on Mars and examine correlations between different crater cluster properties. We investigated 559 crater clusters and 493 single craters detected between 2008 and 2020 using 25 cm/px HiRISE images. The locations and diameters were noted for each single crater, as well as for every individual crater within a cluster down to 1 m diameter. This was done using ArcMap (ArcGIS) software with the three-point method of the CraterTools add-in [5]. We describe the cluster characteristics, such as the number of craters within a cluster, largest crater in a cluster, cluster effective diameter, cluster dispersion, elevation of the impact sites, and the variation in sizes of craters within a cluster.</p> <p>More than half of the new impact sites form as clusters. We did not find any differences between the spatial distribution of single and crater clusters across Mars. The mapped crater clusters from this study consist of 2 to 2334 individual craters. More than half of all clusters (58%) consist of 10 craters or less. Crater clusters containing more than 100 craters are rare. With regard to the sizes of craters within crater clusters, we found that for highly populated clusters, the majority of craters are very small, and clusters with few craters have a tendency for craters that are more equal in size. Clusters having large effective diameters contain more equally sized craters. Our results show the full range of parameter spaces that are possible for cluster properties, which can help validate theoretical atmospheric fragmentation models.</p> <p><strong>References:</strong></p> <p>[1] Malin M. C. et al. (2006) Science, 314, 1573-1577.</p> <p>[2] Daubar I. J. et al. (2019) JGR, 124, 958-969.</p> <p>[3] Daubar I. J. et al. (2013) Icarus, 225, 506-516.</p> <p>[4] Banerdt B. W. et al. (2020) Nature, 13, 183-189.</p> <p>[5] Kneissl T. et al. (2011) Planet. Space Sci., 59, 1243-1254.</p>

Characterization of the subunit composition and structure of native adult glycine receptors

The strychnine-sensitive pentameric Glycine Receptor (GlyR) mediates fast inhibitory neurotransmission in the mammalian nervous system. Only heteromeric GlyRs mediate synaptic transmission, since they contain the β subunit that permits clustering at the synapse through its interaction with scaffolding proteins. Here we show that α2 and β subunits assemble with an unexpected 4:1 stoichiometry to produce GlyR with native electrophysiological properties. We determined structures in multiple functional states at 3.6 - 3.8 Å resolutions and show α2β GlyR assembly mechanism. Furthermore, we show that one single β subunit in each GlyR gives rise to the characteristic electrophysiological properties of heteromeric GlyR, while more β subunits renders GlyR non-conductive. A single β subunit ensures a univalent GlyR-scaffold linkage, which means the scaffold alone regulates the cluster properties.

Exploring open cluster properties with Gaia and LAMOST

Context. In Gaia DR2, an unprecedented high level of precision has been reached at sub-milliarcsecond for astrometry and millimagnitudes for photometry. Using cluster members identified with the astrometry and photometry in Gaia DR2, we can obtain a reliable determination of cluster properties. However, because of the shortcomings of Gaia spectroscopic observations in dealing with densely crowded cluster regions, the RVs and metallicity values for cluster member stars from Gaia DR2 are still lacking. It is necessary to combine the Gaia data with the data from large spectroscopic surveys, such as LAMOST, APOGEE, GALAH, and Gaia-ESO. Aims. In this study our aim is to improve the cluster properties by combining the LAMOST spectra. In particular, we provide the list of cluster members with spectroscopic parameters as an add-value catalog in LAMOST DR5, which can be used to perform a detailed study for a better understanding of the stellar properties, by using their spectra and fundamental properties from the host cluster. Methods. We cross-matched the spectroscopic catalog in LAMOST DR5 with the identified cluster members in Cantat-Gaudin et al. (2018, A&A, 618, A93). We then used members with spectroscopic parameters to derive statistical properties of open clusters. Results. We obtained a list of 8811 members with spectroscopic parameters and a catalog of 295 cluster properties. The provided cluster properties include astrometric parameters, spectroscopic parameters, derived kinematic and orbital parameters, and isochrone fitting results. In addition, we study the radial and vertical metallicity gradient and age-metallicity relation with the compiled open clusters as tracers, finding slopes of −0.053 ± 0.004 dex kpc−1, −0.252 ± 0.039 dex kpc−1, and 0.022 ± 0.008 dex Gyr−1, respectively. The slopes of the metallicity distribution relation for young clusters (0.1 Gyr < Age < 2 Gyr) and the age-metallicity relation for clusters within 6 Gyr are both consistent with the literature results. In order to fully study the chemical evolution history in the disk, more spectroscopic observations for old and distant open clusters are needed for further investigation.

Use of Multiensemble Track Clustering to Inform Medium-Range Tropical Cyclone Forecasts

Tropical cyclone ensemble track forecasts from 153 initialization times during 2017–18 are clustered using regression mixture models. Clustering is performed on a four-ensemble dataset [ECMWF + GEFS + UKMET + CMC (EGUC)], and a three-ensemble dataset that excludes the CMC (EGU). For both datasets, five-cluster partitions are selected to analyze, and the relationship between cluster properties (size, ensemble composition) and 96–144-h cluster-mean error is evaluated. For both datasets, small clusters produce very large errors, with the least populous cluster producing the largest error in more than 50% of forecasts. The mean of the most populous EGUC cluster outperforms the most accurate (EGU) ensemble mean in only 43% of forecasts; however, when the most populous EGUC cluster from each forecast contains ≥30% of the ensemble population, its average cluster-mean error is significantly reduced compared to when the most populous cluster is smaller. Forecasts with a highly populous EGUC cluster also appear to have smaller EGUC-, EGU-, and ECMWF-mean errors. Cluster-mean errors also vary substantially by the ensembles composing the cluster. The most accurate clusters are EGUC clusters that contain threshold memberships of ECMWF, GEFS, and UKMET, but not CMC. The elevated accuracy of EGUC CMC-excluding clusters indicates the potential utility of including the CMC in clustering, despite its large ensemble-mean errors. Pruning ensembles by removing members that belong to small clusters reduces 96–144-h forecast errors for both EGUC and EGU clustering. For five-cluster partitions, a pruning threshold of 10% affects 49% and 35% of EGUC and EGU ensembles, respectively, improving 69%–74% of the forecasts affected by pruning.