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Abstract Self-splicing group I and II introns are selfish genetic elements that are widely yet patchily distributed across the tree of life. Their selfish behavior comes from super-Mendelian inheritance behaviors, collectively called “homing”, which allow them to rapidly spread within populations to the specific genomic sites they home into. Observations of self-splicing intron evolutionary dynamics have led to the formulation of an intron “lifecycle” model where, once fixed in a population, the introns lose selection for homing and undergo an extensive period of degradation until their eventual loss. Here, we find that self-splicing introns are common in the mitochondrial genomes of Epichloë species, endophytic fungi that live in symbioses with grasses. However, these introns show substantial intron presence–absence polymorphism, with our analyses suggesting that these result from a combination of vertical intron inheritance coupled with multiple invasion and loss events over the course of Epichloë evolution. Surprisingly, we find little evidence for the extensive intron degradation expected under the existing intron lifecycle model. Instead, these introns in Epichloë appear to be lost soon after fixation, suggesting that Epichloë self-splicing introns have a different lifecycle. However, rapid intron loss alone cannot explain our results, indicating that additional factors, such as the evolution of homing suppressors, also contribute to Epichloë self-splicing intron dynamics. This work shows that self-splicing introns have more diverse evolutionary dynamics than previously appreciated. 

The tools and techniques such as imaging and machine learning used in the measurement of many material and microstructural properties are rapidly evolving. In metals, the grain size is routinely measured to estimate the yield strength. This paper describes some of the algorithms used in processing the microstructures to conduct quantitative measurements. The image processing methods provide the possibility to go beyond calculating the ASTM grain size number and calculate the actual surface area of each grain, grain boundary length, and the shape of the grains. The image analysis methods can be very helpful in conducting detailed quantitative analysis with greater accuracy than many labour-intensive manual methods currently in use. The work describes the complexities in applying the imaging methods and approaches in the metallurgical and materials fields. Successful application of such methods can reduce the time and effort required to characterise microstructures and can provide more precise information. 

Population genetics has been successful at identifying the relationships between human groups and their interconnected histories. However, the link between genetic demography inferred at large scales and the individual human behaviours that ultimately generate that demography is not always clear. While anthropological and historical context are routinely presented as adjuncts in population genetic studies to help describe the past, determining how underlying patterns of human sociocultural behaviour impact genetics still remains challenging. Here, we analyse patterns of genetic variation in village-scale samples from two islands in eastern Indonesia, patrilocal Sumba and a matrilocal region of Timor. Adopting a ‘process modelling’ approach, we iteratively explore combinations of structurally different models as a thinking tool. We find interconnected socio-genetic interactions involving sex-biased migration, lineage-focused founder effects, and on Sumba, heritable social dominance. Strikingly, founder ideology, a cultural model derived from anthropological and archaeological studies at larger regional scales, has both its origins and impact at the scale of villages. Process modelling lets us explore these complex interactions, first by circumventing the complexity of formal inference when studying large datasets with many interacting parts, and then by explicitly testing complex anthropological hypotheses about sociocultural behaviour from a more familiar population genetic standpoint. 

Abstract The Gravitational-Wave Transient Catalog (GWTC) is a collection of short-duration (transient) gravitational-wave signals identified by the LIGO–Virgo–KAGRA Collaboration in gravitational-wave data produced by the eponymous detectors. The catalog provides information about the identified candidates, such as the arrival time and amplitude of the signal and properties of the signal’s source as inferred from the observational data. GWTC is the data release of this dataset, and version 4.0 extends the catalog to include observations made during the first part of the fourth LIGO–Virgo–KAGRA observing run up until 2024 January 31. This Letter marks an introduction to a collection of articles related to this version of the catalog, GWTC-4.0. The collection of articles accompanying the catalog provides documentation of the methods used to analyze the data, summaries of the catalog of events, observational measurements drawn from the population, and detailed discussions of selected candidates. 

Abstract We report the observation of gravitational waves from two binary black hole coalescences during the fourth observing run of the LIGO–Virgo–KAGRA detector network, GW241011 and GW241110. The sources of these two signals are characterized by rapid and precisely measured primary spins, nonnegligible spin–orbit misalignment, and unequal mass ratios between their constituent black holes. These properties are characteristic of binaries in which the more massive object was itself formed from a previous binary black hole merger and suggest that the sources of GW241011 and GW241110 may have formed in dense stellar environments in which repeated mergers can take place. As the third-loudest gravitational-wave event published to date, with a median network signal-to-noise ratio of 36.0, GW241011 furthermore yields stringent constraints on the Kerr nature of black holes, the multipolar structure of gravitational-wave generation, and the existence of ultralight bosons within the mass range 10⁻¹³–10⁻¹²eV. 

Abstract On 2023 November 23, the two LIGO observatories both detected GW231123, a gravitational-wave signal consistent with the merger of two black holes with masses $137_{-18}^{+23}{M}_{\odot }$and $101_{-50}^{+22}{M}_{\odot }$(90% credible intervals), at a luminosity distance of 0.7–4.1 Gpc, a redshift of $0.40_{-0.25}^{+0.27}$, and with a network signal-to-noise ratio of ∼20.7. Both black holes exhibit high spins— $0.90_{-0.19}^{+0.10}$and $0.80_{-0.52}^{+0.20}$, respectively. A massive black hole remnant is supported by an independent ringdown analysis. Some properties of GW231123 are subject to large systematic uncertainties, as indicated by differences in the inferred parameters between signal models. The primary black hole lies within or above the theorized mass gap where black holes between 60–130M_⊙should be rare, due to pair-instability mechanisms, while the secondary spans the gap. The observation of GW231123 therefore suggests the formation of black holes from channels beyond standard stellar collapse and that intermediate-mass black holes of mass ∼200M_⊙form through gravitational-wave-driven mergers.