Planktonic foraminifera are extremely well suited to studying evolutionary change in the fossil record due to their abundant deposits and global distribution. Species are typically conservative in their shell morphology, with the same geometric shapes appearing repeatedly through iterative evolution, but the mechanisms behind the architectural limits on foraminiferal shell shape are still not well understood. To determine how these developmental constraints arise, we study morphological change leading up to the origination of the unusually ornate species Globigerinoidesella fistulosa. We measured the size and circularity of more than 900 specimens of G. fistulosa, its ancestor the Trilobatus sacculifer plexus, and intermediate forms from a site in the western equatorial Pacific. Our results show that the origination of G. fistulosa from the T. sacculifer plexus involved a combination of two heterochronic expressions: earlier onset of protuberances (pre-displacement) and a steeper allometric slope (acceleration) as compared with its ancestor. Our work provides a case study of the complex morphological and developmental changes required to produce unusual shell shapes and highlights the importance of developmental deviations in evolutionary origination.

Prenatal glucocorticoid exposure has been negatively associated with infant neurocognitive outcomes. However, questions about developmental timing effects across gestation remain. Participants were 253 mother-child dyads who participated in a prospective cohort study recruited in the first trimester of pregnancy. Diurnal cortisol was measured in maternal saliva samples collected across a single day within each trimester of pregnancy. Children (49.8% female) completed the Bayley Mental Development Scales, Third Edition at 6, 12, and 24 months and completed three observational executive function tasks at 24 months. Structural equation models adjusting for sociodemographic covariates were used to test study hypotheses. There was significant evidence for timing sensitivity. First-trimester diurnal cortisol (area under the curve) was negatively associated with cognitive and language development at 12 months and poorer inhibition at 24 months. Second-trimester cortisol exposure was negatively associated with language scores at 24 months. Third-trimester cortisol positively predicted performance in shifting between task rules (set shifting) at 24 months. Associations were not reliably moderated by child sex. Findings suggest that neurocognitive development is sensitive to prenatal glucocorticoid exposure as early as the first trimester and underscore the importance of assessing developmental timing in research on prenatal exposures for child health outcomes.

Clinical outcomes of repetitive transcranial magnetic stimulation (rTMS) for treatment of treatment-resistant depression (TRD) vary widely and there is no mood rating scale that is standard for assessing rTMS outcome. It remains unclear whether TMS is as efficacious in older adults with late-life depression (LLD) compared to younger adults with major depressive disorder (MDD). This study examined the effect of age on outcomes of rTMS treatment of adults with TRD. Self-report and observer mood ratings were measured weekly in 687 subjects ages 16–100 years undergoing rTMS treatment using the Inventory of Depressive Symptomatology 30-item Self-Report (IDS-SR), Patient Health Questionnaire 9-item (PHQ), Profile of Mood States 30-item, and Hamilton Depression Rating Scale 17-item (HDRS). All rating scales detected significant improvement with treatment; response and remission rates varied by scale but not by age (response/remission ≥ 60: 38%–57%/25%–33%; <60: 32%–49%/18%–25%). Proportional hazards models showed early improvement predicted later improvement across ages, though early improvements in PHQ and HDRS were more predictive of remission in those < 60 years (relative to those ≥ 60) and greater baseline IDS burden was more predictive of non-remission in those ≥ 60 years (relative to those < 60). These results indicate there is no significant effect of age on treatment outcomes in rTMS for TRD, though rating instruments may differ in assessment of symptom burden between younger and older adults during treatment.

The interaction of relativistically intense lasers with opaque targets represents a highly non-linear, multi-dimensional parameter space. This limits the utility of sequential 1D scanning of experimental parameters for the optimization of secondary radiation, although to-date this has been the accepted methodology due to low data acquisition rates. High repetition-rate (HRR) lasers augmented by machine learning present a valuable opportunity for efficient source optimization. Here, an automated, HRR-compatible system produced high-fidelity parameter scans, revealing the influence of laser intensity on target pre-heating and proton generation. A closed-loop Bayesian optimization of maximum proton energy, through control of the laser wavefront and target position, produced proton beams with equivalent maximum energy to manually optimized laser pulses but using only 60% of the laser energy. This demonstration of automated optimization of laser-driven proton beams is a crucial step towards deeper physical insight and the construction of future radiation sources.

Multi-messenger observations of the transient sky to detect cosmic explosions and counterparts of gravitational wave mergers critically rely on orbiting wide-FoV telescopes to cover the wide range of wavelengths where atmospheric absorption and emission limit the use of ground facilities. Thanks to continuing technological improvements, miniaturised space instruments operating as distributed-aperture constellations are offering new capabilities for the study of high-energy transients to complement ageing existing satellites. In this paper we characterise the performance of the upcoming joint SpIRIT and HERMES-TP/SP constellation for the localisation of high-energy transients through triangulation of signal arrival times. SpIRIT is an Australian technology and science demonstrator satellite designed to operate in a low-Earth Sun-synchronous Polar orbit that will augment the science operations for the equatorial HERMES-TP/SP constellation. In this work we simulate the improvement to the localisation capabilities of the HERMES-TP/SP constellation when SpIRIT is included in an orbital plane nearly perpendicular (inclination = 97.6°) to the HERMES-TP/SP orbits. For the fraction of GRBs detected by three of the HERMES satellites plus SpIRIT, we find that the combined constellation is capable of localising 60% of long GRBs to within ${\sim}30\,\textrm{deg}^{2}$ on the sky, and 60% of short GRBs within ${\sim}1850\,\textrm{deg}^{2}$ ($1\sigma$ confidence regions), though it is beyond the scope of this work to characterise or rule out systematic uncertainty of the same order of magnitude. Based purely on statistical GRB localisation capabilities (i.e., excluding systematic uncertainties and sky coverage), these figures for long GRBs are comparable to those reported by the Fermi Gamma Burst Monitor instrument. These localisation statistics represents a reduction of the uncertainty for the burst localisation region for both long and short GRBs by a factor of ${\sim}5$ compared to the HERMES-TP/SP alone. Further improvements by an additional factor of 2 (or 4) can be achieved by launching an additional 4 (or 6) SpIRIT-like satellites into a Polar orbit, respectively, which would both increase the fraction of sky covered by multiple satellite elements, and also enable localisation of ${\geq} 60\%$ of long GRBs to within a radius of ${\sim}1.5^{\circ}$ (statistical uncertainty) on the sky, clearly demonstrating the value of a distributed all-sky high-energy transient monitor composed of nano-satellites.

A machine learning model was created to predict the electron spectrum generated by a GeV-class laser wakefield accelerator. The model was constructed from variational convolutional neural networks, which mapped the results of secondary laser and plasma diagnostics to the generated electron spectrum. An ensemble of trained networks was used to predict the electron spectrum and to provide an estimation of the uncertainty of that prediction. It is anticipated that this approach will be useful for inferring the electron spectrum prior to undergoing any process that can alter or destroy the beam. In addition, the model provides insight into the scaling of electron beam properties due to stochastic fluctuations in the laser energy and plasma electron density.

Long-duration gamma-ray burst (GRB) afterglow observations offer cutting-edge opportunities to characterise the star formation history of the Universe back to the epoch of reionisation, and to measure the chemical composition of interstellar and intergalactic gas through absorption spectroscopy. The main barrier to progress is the low efficiency in rapidly and confidently identifying which bursts are high redshift ( $z > 5$ ) candidates before they fade, as this requires low-latency follow-up observations at near-infrared wavelengths (or longer) to determine a reliable photometric redshift estimate. Since no current or planned gamma-ray observatories carry near-infrared telescopes on-board, complementary facilities are needed. So far this task has been performed by instruments on the ground, but sky visibility and weather constraints limit the number of GRB targets that can be observed and the speed at which follow-up is possible. In this work we develop a Monte Carlo simulation framework to investigate an alternative approach based on the use of a rapid-response near-infrared nano-satellite, capable of simultaneous imaging in four bands from $0.8$ to $1.7\,\unicode{x03BC}$ m (a mission concept called SkyHopper). Using as reference a sample of 88 afterglows observed with the GROND instrument on the MPG/ESO telescope, we find that such a nano-satellite is capable of detecting in the H-band (1.6 $\unicode{x03BC}$ m) $72.5\% \pm 3.1\%$ of GRBs concurrently observable with the Swift satellite via its UVOT instrument (and $44.1\% \pm 12.3\%$ of high redshift ( $z>5$ ) GRBs) within 60 min of the GRB prompt emission. This corresponds to detecting ${\sim}55$ GRB afterglows per year, of which 1–3 have $z > 5$ . These rates represent a substantial contribution to the field of high-z GRB science, as only 23 $z > 5$ GRBs have been collectively discovered by the entire astronomical community over the last ${\sim}24$ yr. Future discoveries are critically needed to take advantage of next generation follow-up spectroscopic facilities such as 30m-class ground telescopes and the James Webb Space Telescope. Furthermore, a systematic space-based follow-up of afterglows in the near-infrared will offer new insight on the population of dusty (‘dark’) GRBs which are primarily found at cosmic noon ( $z\sim 1-3$ ). Additionally, we find that launching a mini-constellation of 3 near-infrared nano-satellites would increase the detection fraction of afterglows to ${\sim}83\%$ and substantially reduce the latency in the photometric redshift determination.