320 research outputs found
Machine learning used to compare the diagnostic accuracy of risk factors, clinical signs and biomarkers and to develop a new prediction model for neonatal early-onset sepsis
Background: Current strategies for risk stratification and prediction of neonatal early-onset sepsis (EOS) are inefficient and lack diagnostic performance. The aim of this study was to use machine learning to analyze the diagnostic accuracy of risk factors (RFs), clinical signs and biomarkers and to develop a prediction model for culture-proven EOS. We hypothesized that the contribution to diagnostic accuracy of biomarkers is higher than of RFs or clinical signs. Study Design: Secondary analysis of the prospective international multicenter NeoPInS study. Neonates born after completed 34 weeks of gestation with antibiotic therapy due to suspected EOS within the first 72 hours of life participated. Primary outcome was defined as predictive performance for culture-proven EOS with variables known at the start of antibiotic therapy. Machine learning was used in form of a random forest classifier. Results: One thousand six hundred eighty-five neonates treated for suspected infection were analyzed. Biomarkers were superior to clinical signs and RFs for prediction of culture-proven EOS. C-reactive protein and white blood cells were most important for the prediction of the culture result. Our full model achieved an area-under-the-receiver-operating-characteristic-curve of 83.41% (±8.8%) and an area-under-the-precision-recall-curve of 28.42% (±11.5%). The predictive performance of the model with RFs alone was comparable with random. Conclusions: Biomarkers have to be considered in algorithms for the management of neonates suspected of EOS. A 2-step approach with a screening tool for all neonates in combination with our model in the preselected population with an increased risk for EOS may have the potential to reduce the start of unnecessary antibiotics
Transnational Governance as Contested Institution-Building: China, Merchants, and Contract Rules in the Cotton Trade
We are in an era of uncertainty over whose rules will govern global economic integration. With the growing market share of Chinese firms and the power of the Chinese state it is unclear if Western firms will continue to dominate transnational governance. Exploring these dynamics through a study of contract rules in the global cotton trade, this article conceptualizes commodity chain governance as a contested process of institution-building. To this end, the global commodity chain/global value chain (GCC/GVC) framework must be revised to better account for the broader institutional context of commodity chain governance, institutional variation across space, and strategic action in the construction of legitimate governance arrangements. I provide a more dynamic model of GCC governance that stresses how strategic action, existing institutions, and dominant discourses intersect as firms and states compete for institutional power within a commodity chain. This advances our understandings of how commodity chain governance emerges and changes over time
Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses
Very few genetic variants have been associated with depression and neuroticism, likely because of limitations on sample size in previous studies. Subjective well-being, a phenotype that is genetically correlated with both of these traits, has not yet been studied with genome-wide data. We conducted genome-wide association studies of three phenotypes: subjective well-being (n = 298,420), depressive symptoms (n = 161,460), and neuroticism (n = 170,911). We identify 3 variants associated with subjective well-being, 2 variants associated with depressive symptoms, and 11 variants associated with neuroticism, including 2 inversion polymorphisms. The two loci associated with depressive symptoms replicate in an independent depression sample. Joint analyses that exploit the high genetic correlations between the phenotypes (|Ï^| â 0.8) strengthen the overall credibility of the findings and allow us to identify additional variants. Across our phenotypes, loci regulating expression in central nervous system and adrenal or pancreas tissues are strongly enriched for association.</p
Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals
We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of ~3 million individuals and identify 3,952 approximately uncorrelated genome-wide-significant single-nucleotide polymorphisms (SNPs). A genome-wide polygenic predictor, or polygenic index (PGI), explains 12-16% of EA variance and contributes to risk prediction for ten diseases. Direct effects (i.e., controlling for parental PGIs) explain roughly half the PGI's magnitude of association with EA and other phenotypes. The correlation between mate-pair PGIs is far too large to be consistent with phenotypic assortment alone, implying additional assortment on PGI-associated factors. In an additional GWAS of dominance deviations from the additive model, we identify no genome-wide-significant SNPs, and a separate X-chromosome additive GWAS identifies 57
Genome-wide interaction study of a proxy for stress-sensitivity and its prediction of major depressive disorder
Individual response to stress is correlated with neuroticism and is an important predictor of both neuroticism and the onset of major depressive disorder (MDD). Identification of the genetics underpinning individual differences in response to negative events (stress-sensitivity) may improve our understanding of the molecular pathways involved, and its association with stress-related illnesses. We sought to generate a proxy for stress-sensitivity through modelling the interaction between SNP allele and MDD status on neuroticism score in order to identify genetic variants that contribute to the higher neuroticism seen in individuals with a lifetime diagnosis of depression compared to unaffected individuals. Meta-analysis of genome-wide interaction studies (GWIS) in UK Biobank (N = 23,092) and Generation Scotland: Scottish Family Health Study (N = 7,155) identified no genome-wide significance SNP interactions. However, gene-based tests identified a genome-wide significant gene, ZNF366, a negative regulator of glucocorticoid receptor function implicated in alcohol dependence (p = 1.48x10-7; Bonferroni-corrected significance threshold p < 2.79x10-6). Using summary statistics from the stress-sensitivity term of the GWIS, SNP heritability for stress-sensitivity was estimated at 5.0%. In models fitting polygenic risk scores of both MDD and neuroticism derived from independent GWAS, we show that polygenic risk scores derived from the UK Biobank stress-sensitivity GWIS significantly improved the prediction of MDD in Generation Scotland. This study may improve interpretation of larger genome-wide association studies of MDD and other stress-related illnesses, and the understanding of the etiological mechanisms underpinning stress-sensitivity
Search for anisotropic gravitational-wave backgrounds using data from Advanced LIGO and Advanced Virgo's first three observing runs
We report results from searches for anisotropic stochastic gravitational-wave
backgrounds using data from the first three observing runs of the Advanced LIGO
and Advanced Virgo detectors. For the first time, we include Virgo data in our
analysis and run our search with a new efficient pipeline called {\tt PyStoch}
on data folded over one sidereal day. We use gravitational-wave radiometry
(broadband and narrow band) to produce sky maps of stochastic
gravitational-wave backgrounds and to search for gravitational waves from point
sources. A spherical harmonic decomposition method is employed to look for
gravitational-wave emission from spatially-extended sources. Neither technique
found evidence of gravitational-wave signals. Hence we derive 95\%
confidence-level upper limit sky maps on the gravitational-wave energy flux
from broadband point sources, ranging from and on the
(normalized) gravitational-wave energy density spectrum from extended sources,
ranging from , depending on direction () and spectral index
(). These limits improve upon previous limits by factors of . We also set 95\% confidence level upper limits on the frequency-dependent
strain amplitudes of quasimonochromatic gravitational waves coming from three
interesting targets, Scorpius X-1, SN 1987A and the Galactic Center, with best
upper limits range from a factor of
improvement compared to previous stochastic radiometer searches.Comment: 23 Pages, 9 Figure
Search of the early O3 LIGO data for continuous gravitational waves from the Cassiopeia A and Vela Jr. supernova remnants
partially_open1412sĂŹWe present directed searches for continuous gravitational waves from the neutron stars in the Cassiopeia A (Cas A) and Vela Jr. supernova remnants. We carry out the searches in the LIGO detector data from the first six months of the third Advanced LIGO and Virgo observing run using the weave semicoherent method, which sums matched-filter detection-statistic values over many time segments spanning the observation period. No gravitational wave signal is detected in the search band of 20â976 Hz for assumed source ages greater than 300 years for Cas A and greater than 700 years for Vela Jr. Estimates from simulated continuous wave signals indicate we achieve the most sensitive results to date across the explored parameter space volume, probing to strain magnitudes as low as
âŒ6.3Ă10^â26 for Cas A and âŒ5.6Ă10^â26 for Vela Jr. at frequencies near 166 Hz at 95% efficiency.openAbbott, R.; Abbott, T.âD.; Acernese, F.; Ackley, K.; Adams, C.; Adhikari, N.; Adhikari, R.âX.; Adya, V.âB.; Affeldt, C.; Agarwal, D.; Agathos, M.; Agatsuma, K.; Aggarwal, N.; Aguiar, O.âD.; Aiello, L.; Ain, A.; Ajith, P.; Albanesi, S.; Allocca, A.; Altin, P.âA.; Amato, A.; Anand, C.; Anand, S.; Ananyeva, A.; Anderson, S.âB.; Anderson, W.âG.; Andrade, T.; Andres, N.; AndriÄ, T.; Angelova, S.âV.; Ansoldi, S.; Antelis, J.âM.; Antier, S.; Appert, S.; Arai, K.; Araya, M.âC.; Areeda, J.âS.; ArĂšne, M.; Arnaud, N.; Aronson, S.âM.; Arun, K.âG.; Asali, Y.; Ashton, G.; Assiduo, M.; Aston, S.âM.; Astone, P.; Aubin, F.; Austin, C.; Babak, S.; Badaracco, F.; Bader, M.âK.âM.; Badger, C.; Bae, S.; Baer, A.âM.; Bagnasco, S.; Bai, Y.; Baird, J.; Ball, M.; Ballardin, G.; Ballmer, S.âW.; Balsamo, A.; Baltus, G.; Banagiri, S.; Bankar, D.; Barayoga, J.âC.; Barbieri, C.; Barish, B.âC.; Barker, D.; Barneo, P.; Barone, F.; Barr, B.; Barsotti, L.; Barsuglia, M.; Barta, D.; Bartlett, J.; Barton, M.âA.; Bartos, I.; Bassiri, R.; Basti, A.; Bawaj, M.; Bayley, J.âC.; Baylor, A.âC.; Bazzan, M.; BĂ©csy, B.; Bedakihale, V.âM.; Bejger, M.; Belahcene, I.; Benedetto, V.; Beniwal, D.; Bennett, T.âF.; Bentley, J.âD.; BenYaala, M.; Bergamin, F.; Berger, B.âK.; Bernuzzi, S.; Bersanetti, D.; Bertolini, A.; Betzwieser, J.; Beveridge, D.; Bhandare, R.; Bhardwaj, U.; Bhattacharjee, D.; Bhaumik, S.; Bilenko, I.âA.; Billingsley, G.; Bini, S.; Birney, R.; Birnholtz, O.; Biscans, S.; Bischi, M.; Biscoveanu, S.; Bisht, A.; Biswas, B.; Bitossi, M.; Bizouard, M.-A.; Blackburn, J.âK.; Blair, C.âD.; Blair, D.âG.; Blair, R.âM.; Bobba, F.; Bode, N.; Boer, M.; Bogaert, G.; Boldrini, M.; Bonavena, L.âD.; Bondu, F.; Bonilla, E.; Bonnand, R.; Booker, P.; Boom, B.âA.; Bork, R.; Boschi, V.; Bose, N.; Bose, S.; Bossilkov, V.; Boudart, V.; Bouffanais, Y.; Bozzi, A.; Bradaschia, C.; Brady, P.âR.; Bramley, A.; Branch, A.; Branchesi, M.; Brau, J.âE.; Breschi, M.; Briant, T.; Briggs, J.âH.; Brillet, A.; Brinkmann, M.; Brockill, P.; Brooks, A.âF.; Brooks, J.; Brown, D.âD.; Brunett, S.; Bruno, G.; Bruntz, R.; Bryant, J.; Bulik, T.; Bulten, H.âJ.; Buonanno, A.; Buscicchio, R.; Buskulic, D.; Buy, C.; Byer, R.âL.; Cadonati, L.; Cagnoli, G.; Cahillane, C.; Bustillo, J. 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Dal; DallâOsso, S.; DĂĄlya, G.; Dana, A.; DaneshgaranBajastani, L.âM.; DâAngelo, B.; Danilishin, S.; DâAntonio, S.; Danzmann, K.; Darsow-Fromm, C.; Dasgupta, A.; Datrier, L.âE.âH.; Datta, S.; Dattilo, V.; Dave, I.; Davier, M.; Davies, G.âS.; Davis, D.; Davis, M.âC.; Daw, E.âJ.; Dean, R.; DeBra, D.; Deenadayalan, M.; Degallaix, J.; De Laurentis, M.; DelĂ©glise, S.; Del Favero, V.; De Lillo, F.; De Lillo, N.; Del Pozzo, W.; DeMarchi, L.âM.; De Matteis, F.; DâEmilio, V.; Demos, N.; Dent, T.; Depasse, A.; De Pietri, R.; De Rosa, R.; De Rossi, C.; DeSalvo, R.; De Simone, R.; Dhurandhar, S.; DĂaz, M.âC.; Diaz-Ortiz, M.; Didio, N.âA.; Dietrich, T.; Di Fiore, L.; Di Fronzo, C.; Di Giorgio, C.; Di Giovanni, F.; Di Giovanni, M.; Di Girolamo, T.; Di Lieto, A.; Ding, B.; Di Pace, S.; Di Palma, I.; Di Renzo, F.; Divakarla, A.âK.; Dmitriev, A.; Doctor, Z.; DâOnofrio, L.; Donovan, F.; Dooley, K.âL.; Doravari, S.; Dorrington, I.; Drago, M.; Driggers, J.âC.; Drori, Y.; Ducoin, J.-G.; Dupej, P.; Durante, O.; DâUrso, D.; Duverne, P.-A.; Dwyer, S.âE.; Eassa, C.; Easter, P.âJ.; Ebersold, M.; Eckhardt, T.; Eddolls, G.; Edelman, B.; Edo, T.âB.; Edy, O.; Effler, A.; Eichholz, J.; Eikenberry, S.âS.; Eisenmann, M.; Eisenstein, R.âA.; Ejlli, A.; Engelby, E.; Errico, L.; Essick, R.âC.; EstellĂ©s, H.; Estevez, D.; Etienne, Z.; Etzel, T.; Evans, M.; Evans, T.âM.; Ewing, B.âE.; Fafone, V.; Fair, H.; Fairhurst, S.; Farah, A.âM.; Farinon, S.; Farr, B.; Farr, W.âM.; Farrow, N.âW.; Fauchon-Jones, E.âJ.; Favaro, G.; Favata, M.; Fays, M.; Fazio, M.; Feicht, J.; Fejer, M.âM.; Fenyvesi, E.; Ferguson, D.âL.; Fernandez-Galiana, A.; Ferrante, I.; Ferreira, T.âA.; Fidecaro, F.; Figura, P.; Fiori, I.; Fishbach, M.; Fisher, R.âP.; Fittipaldi, R.; Fiumara, V.; Flaminio, R.; Floden, E.; Fong, H.; Font, J.âA.; Fornal, B.; Forsyth, P.âW.âF.; Franke, A.; Frasca, S.; Frasconi, F.; Frederick, C.; Freed, J.âP.; Frei, Z.; Freise, A.; Frey, R.; Fritschel, P.; Frolov, V.âV.; FronzĂ©, G.âG.; Fulda, P.; Fyffe, M.; Gabbard, H.âA.; Gadre, B.âU.; Gair, J.âR.; Gais, J.; Galaudage, S.; Gamba, R.; Ganapathy, D.; Ganguly, A.; Gaonkar, S.âG.; Garaventa, B.; GarcĂa-NĂșñez, C.; GarcĂa-QuirĂłs, C.; Garufi, F.; Gateley, B.; Gaudio, S.; Gayathri, V.; Gemme, G.; Gennai, A.; George, J.; Gerberding, O.; Gergely, L.; Gewecke, P.; Ghonge, S.; Ghosh, Abhirup; Ghosh, Archisman; Ghosh, Shaon; Ghosh, Shrobana; Giacomazzo, B.; Giacoppo, L.; Giaime, J.âA.; Giardina, K.âD.; Gibson, D.âR.; Gier, C.; Giesler, M.; Giri, P.; Gissi, F.; Glanzer, J.; Gleckl, A.âE.; Godwin, P.; Goetz, E.; Goetz, R.; Gohlke, N.; Goncharov, B.; GonzĂĄlez, G.; Gopakumar, A.; Gosselin, M.; Gouaty, R.; Gould, D.âW.; Grace, B.; Grado, A.; Granata, M.; Granata, V.; Grant, A.; Gras, S.; Grassia, P.; Gray, C.; Gray, R.; Greco, G.; Green, A.âC.; Green, R.; Gretarsson, A.âM.; Gretarsson, E.âM.; Griffith, D.; Griffiths, W.; Griggs, H.âL.; Grignani, G.; Grimaldi, A.; Grimm, S.âJ.; Grote, H.; Grunewald, S.; Gruning, P.; Guerra, D.; Guidi, Gianluca; Guimaraes, A.âR.; GuixĂ©, G.; Gulati, H.âK.; Guo, H.-K.; Guo, Y.; Gupta, Anchal; Gupta, Anuradha; Gupta, P.; Gustafson, E.âK.; Gustafson, R.; Guzman, F.; Haegel, L.; Halim, O.; Hall, E.âD.; Hamilton, E.âZ.; Hammond, G.; Haney, M.; Hanks, J.; Hanna, C.; Hannam, M.âD.; Hannuksela, O.; Hansen, H.; Hansen, T.âJ.; Hanson, J.; Harder, T.; Hardwick, T.; Haris, K.; Harms, J.; Harry, G.âM.; Harry, I.âW.; Hartwig, D.; Haskell, B.; Hasskew, R.âK.; Haster, C.-J.; Haughian, K.; Hayes, F.âJ.; Healy, J.; Heidmann, A.; Heidt, A.; Heintze, M.âC.; Heinze, J.; Heinzel, J.; Heitmann, H.; Hellman, F.; Hello, P.; Helmling-Cornell, A.âF.; Hemming, G.; Hendry, M.; Heng, I.âS.; Hennes, E.; Hennig, J.; Hennig, M.âH.; Hernandez, A.âG.; Vivanco, F. Hernandez; Heurs, M.; Hild, S.; Hill, P.; Hines, A.âS.; Hochheim, S.; Hofman, D.; Hohmann, J.âN.; Holcomb, D.âG.; Holland, N.âA.; Hollows, I.âJ.; Holmes, Z.âJ.; Holt, K.; Holz, D.âE.; Hopkins, P.; Hough, J.; Hourihane, S.; Howell, E.âJ.; Hoy, C.âG.; Hoyland, D.; Hreibi, A.; Hsu, Y.; Huang, Y.; HĂŒbner, M.âT.; Huddart, A.âD.; Hughey, B.; Hui, V.; Husa, S.; Huttner, S.âH.; Huxford, R.; Huynh-Dinh, T.; Idzkowski, B.; Iess, A.; Ingram, C.; Isi, M.; Isleif, K.; Iyer, B.âR.; JaberianHamedan, V.; Jacqmin, T.; Jadhav, S.âJ.; Jadhav, S.âP.; James, A.âL.; Jan, A.âZ.; Jani, K.; Janquart, J.; Janssens, K.; Janthalur, N.âN.; Jaranowski, P.; Jariwala, D.; Jaume, R.; Jenkins, A.âC.; Jenner, K.; Jeunon, M.; Jia, W.; Johns, G.âR.; Jones, A.âW.; Jones, D.âI.; Jones, J.âD.; Jones, P.; Jones, R.; Jonker, R.âJ.âG.; Ju, L.; Junker, J.; Juste, V.; Kalaghatgi, C.âV.; Kalogera, V.; Kamai, B.; Kandhasamy, S.; Kang, G.; Kanner, J.âB.; Kao, Y.; Kapadia, S.âJ.; Kapasi, D.âP.; Karat, S.; Karathanasis, C.; Karki, S.; Kashyap, R.; Kasprzack, M.; Kastaun, W.; Katsanevas, S.; Katsavounidis, E.; Katzman, W.; Kaur, T.; Kawabe, K.; KĂ©fĂ©lian, F.; Keitel, D.; Key, J.âS.; Khadka, S.; Khalili, F.âY.; Khan, S.; Khazanov, E.âA.; Khetan, N.; Khursheed, M.; Kijbunchoo, N.; Kim, C.; Kim, J.âC.; Kim, K.; Kim, W.âS.; Kim, Y.-M.; Kimball, C.; Kinley-Hanlon, M.; Kirchhoff, R.; Kissel, J.âS.; Kleybolte, L.; Klimenko, S.; Knee, A.âM.; Knowles, T.âD.; Knyazev, E.; Koch, P.; Koekoek, G.; Koley, S.; Kolitsidou, P.; Kolstein, M.; Komori, K.; Kondrashov, V.; Kontos, A.; Koper, N.; Korobko, M.; Kovalam, M.; Kozak, D.âB.; Kringel, V.; Krishnendu, N.âV.; KrĂłlak, A.; Kuehn, G.; Kuei, F.; Kuijer, P.; Kumar, A.; Kumar, P.; Kumar, Rahul; Kumar, Rakesh; Kuns, K.; Kuwahara, S.; Lagabbe, P.; Laghi, D.; Lalande, E.; Lam, T.âL.; Lamberts, A.; Landry, M.; Lane, B.âB.; Lang, R.âN.; Lange, J.; Lantz, B.; La Rosa, I.; Lartaux-Vollard, A.; Lasky, P.âD.; Laxen, M.; Lazzarini, A.; Lazzaro, C.; Leaci, P.; Leavey, S.; Lecoeuche, Y.âK.; Lee, H.âM.; Lee, H.âW.; Lee, J.; Lee, K.; Lehmann, J.; LemaĂźtre, A.; Leroy, N.; Letendre, N.; Levesque, C.; Levin, Y.; Leviton, J.âN.; Leyde, K.; Li, A.âK.âY.; Li, B.; Li, J.; Li, T.âG.âF.; Li, X.; Linde, F.; Linker, S.âD.; Linley, J.âN.; Littenberg, T.âB.; Liu, J.; Liu, K.; Liu, X.; Llamas, F.; Llorens-Monteagudo, M.; Lo, R.âK.âL.; Lockwood, A.; London, L.âT.; Longo, A.; Lopez, D.; Portilla, M. Lopez; Lorenzini, M.; Loriette, V.; Lormand, M.; Losurdo, G.; Lott, T.âP.; Lough, J.âD.; Lousto, C.âO.; Lovelace, G.; Lucaccioni, J.âF.; LĂŒck, H.; Lumaca, D.; Lundgren, A.âP.; Lynam, J.âE.; Macas, R.; MacInnis, M.; Macleod, D.âM.; MacMillan, I.âA.âO.; Macquet, A.; Hernandez, I. 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The population of merging compact binaries inferred using gravitational waves through GWTC-3
We report on the population properties of 76 compact binary mergers detected with gravitational waves below a false alarm rate of 1 per year through GWTC-3. The catalog contains three classes of binary mergers: BBH, BNS, and NSBH mergers. We infer the BNS merger rate to be between 10 and 1700 and the NSBH merger rate to be between 7.8 and 140 , assuming a constant rate density versus comoving volume and taking the union of 90% credible intervals for methods used in this work. Accounting for the BBH merger rate to evolve with redshift, we find the BBH merger rate to be between 17.9 and 44 at a fiducial redshift (z=0.2). We obtain a broad neutron star mass distribution extending from to . We can confidently identify a rapid decrease in merger rate versus component mass between neutron star-like masses and black-hole-like masses, but there is no evidence that the merger rate increases again before 10 . We also find the BBH mass distribution has localized over- and under-densities relative to a power law distribution. While we continue to find the mass distribution of a binary's more massive component strongly decreases as a function of primary mass, we observe no evidence of a strongly suppressed merger rate above . The rate of BBH mergers is observed to increase with redshift at a rate proportional to with for . Observed black hole spins are small, with half of spin magnitudes below . We observe evidence of negative aligned spins in the population, and an increase in spin magnitude for systems with more unequal mass ratio
Population of Merging Compact Binaries Inferred Using Gravitational Waves through GWTC-3
We report on the population properties of compact binary mergers inferred from gravitational-wave observations of these systems during the first three LIGO-Virgo observing runs. The Gravitational-Wave Transient Catalog 3 (GWTC-3) contains signals consistent with three classes of binary mergers: binary black hole, binary neutron star, and neutron star-black hole mergers. We infer the binary neutron star merger rate to be between 10 and 1700 Gpc-3 yr-1 and the neutron star-black hole merger rate to be between 7.8 and 140 Gpc-3 yr-1, assuming a constant rate density in the comoving frame and taking the union of 90% credible intervals for methods used in this work. We infer the binary black hole merger rate, allowing for evolution with redshift, to be between 17.9 and 44 Gpc-3 yr-1 at a fiducial redshift (z=0.2). The rate of binary black hole mergers is observed to increase with redshift at a rate proportional to (1+z)Îș with Îș=2.9-1.8+1.7 for zâČ1. Using both binary neutron star and neutron star-black hole binaries, we obtain a broad, relatively flat neutron star mass distribution extending from 1.2-0.2+0.1 to 2.0-0.3+0.3Mâ. We confidently determine that the merger rate as a function of mass sharply declines after the expected maximum neutron star mass, but cannot yet confirm or rule out the existence of a lower mass gap between neutron stars and black holes. We also find the binary black hole mass distribution has localized over- and underdensities relative to a power-law distribution, with peaks emerging at chirp masses of 8.3-0.5+0.3 and 27.9-1.8+1.9Mâ. While we continue to find that the mass distribution of a binary's more massive component strongly decreases as a function of primary mass, we observe no evidence of a strongly suppressed merger rate above approximately 60Mâ, which would indicate the presence of a upper mass gap. Observed black hole spins are small, with half of spin magnitudes below Ïiâ0.25. While the majority of spins are preferentially aligned with the orbital angular momentum, we infer evidence of antialigned spins among the binary population. We observe an increase in spin magnitude for systems with more unequal-mass ratio. We also observe evidence of misalignment of spins relative to the orbital angular momentum
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