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Ovulation is critical for sexual reproduction and consists of the process of liberating fertilizable oocytes from their somatic follicle capsules, also known as follicle rupture. The mechanical force for oocyte expulsion is largely unknown in many species. Our previous work demonstrated that Drosophila ovulation, as in mammals, requires the proteolytic degradation of the posterior follicle wall and follicle rupture to release the mature oocyte from a layer of somatic follicle cells. Here, we identified actomyosin contraction in somatic follicle cells as the major mechanical force for follicle rupture. Filamentous actin (F-actin) and nonmuscle myosin II (NMII) are highly enriched in the cortex of follicle cells upon stimulation with octopamine (OA), a monoamine critical for Drosophila ovulation. Pharmacological disruption of F-actin polymerization prevented follicle rupture without interfering with the follicle wall breakdown. In addition, we demonstrated that OA induces Rho1 guanosine triphosphate (GTP)ase activation in the follicle cell cortex, which activates Ras homolog (Rho) kinase to promote actomyosin contraction and follicle rupture. All these results led us to conclude that OA signaling induces actomyosin cortex enrichment and contractility, which generates the mechanical force for follicle rupture during Drosophila ovulation. Due to the conserved nature of actomyosin contraction, this work could shed light on the mechanical force required for follicle rupture in other species including humans. 

Abstract Giant exoplanets orbiting close to their host stars are unlikely to have formed in their present configurations¹. These ‘hot Jupiter’ planets are instead thought to have migrated inward from beyond the ice line and several viable migration channels have been proposed, including eccentricity excitation through angular-momentum exchange with a third body followed by tidally driven orbital circularization^2,3. The discovery of the extremely eccentric (e = 0.93) giant exoplanet HD 80606 b (ref. ⁴) provided observational evidence that hot Jupiters may have formed through this high-eccentricity tidal-migration pathway⁵. However, no similar hot-Jupiter progenitors have been found and simulations predict that one factor affecting the efficacy of this mechanism is exoplanet mass, as low-mass planets are more likely to be tidally disrupted during periastron passage^6–8. Here we present spectroscopic and photometric observations of TIC 241249530 b, a high-mass, transiting warm Jupiter with an extreme orbital eccentricity ofe = 0.94. The orbit of TIC 241249530 b is consistent with a history of eccentricity oscillations and a future tidal circularization trajectory. Our analysis of the mass and eccentricity distributions of the transiting-warm-Jupiter population further reveals a correlation between high mass and high eccentricity. 

Brain age (BA), distinct from chronological age (CA), can be estimated from MRIs to evaluate neuroanatomic aging in cognitively normal (CN) individuals. BA, however, is a cross-sectional measure that summarizes cumulative neuroanatomic aging since birth. Thus, it conveys poorly recent or contemporaneous aging trends, which can be better quantified by the (temporal) pace P of brain aging. Many approaches to map P, however, rely on quantifying DNA methylation in whole-blood cells, which the blood–brain barrier separates from neural brain cells. We introduce a three-dimensional convolutional neural network (3D-CNN) to estimate P noninvasively from longitudinal MRI. Our longitudinal model (LM) is trained on MRIs from 2,055 CN adults, validated in 1,304 CN adults, and further applied to an independent cohort of 104 CN adults and 140 patients with Alzheimer’s disease (AD). In its test set, the LM computes P with a mean absolute error (MAE) of 0.16 y (7% mean error). This significantly outperforms the most accurate cross-sectional model, whose MAE of 1.85 y has 83% error. By synergizing the LM with an interpretable CNN saliency approach, we map anatomic variations in regional brain aging rates that differ according to sex, decade of life, and neurocognitive status. LM estimates of P are significantly associated with changes in cognitive functioning across domains. This underscores the LM’s ability to estimate P in a way that captures the relationship between neuroanatomic and neurocognitive aging. This research complements existing strategies for AD risk assessment that estimate individuals’ rates of adverse cognitive change with age. 

The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack neuroanatomic interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent sample. BA estimation errors are notably lower than those of previous studies. At both individual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N  = 351) and Alzheimer’s disease (AD, N  = 359). In individuals with MCI (54% of whom were diagnosed with dementia within 10.9 y from MRI acquisition), BA is significantly better than CA in capturing dementia symptom severity, functional disability, and executive function. Profiles of sex dimorphism and lateralization in brain aging also map onto patterns of neuroanatomic change that reflect cognitive decline. Significant associations between BA and neurocognitive measures suggest that the proposed framework can map, systematically, the relationship between aging-related neuroanatomy changes in CN individuals and in participants with MCI or AD. Early identification of such neuroanatomy changes can help to screen individuals according to their AD risk.