Category: 7. Science

Reproductive output does not decline with age in captive green turtles

At the population level, cumulative reproductive output increased by 13% of its current value per unit time (θ = 0.13; 95% CI: 0.12–0.14), which approximated an almost linear increase in reproduction from age 17 to 38 years of age (Fig. 1A; SI Appendix, Fig. S1). This reproductive pattern is consistent with other testudines, such as Blanding’s turtle Emydoidea blandingii²³ and painted turtles Chrysemys picta²⁴, which tend not to show age-related declines in clutch size and reproductive frequency (but see ref. ²⁵). Longitudinal data on Mediterranean green turtles suggest a similar pattern, where the cumulative number of clutches increases linearly with time since their first breeding season²⁶. Unfortunately, the animals used in their study were of unknown age, complicating direct comparisons with our results.

During a typical breeding season, female green turtles laid an average of 393 ± 238 eggs, distributed across 4.5 ± 2.0 clutches, though some individuals produced over 1000 eggs per season. Over their lifetime, females averaged a total reproductive output of 2602 eggs, with this distribution highly right-skewed and heavy-tailed. Importantly, new clutches were not clumped over time (Fig. 1A). As a result, the Poisson distribution provided a better fit to the reproductive data over competing models ((Delta {{{rm{SIC}}}}_{{{rm{GP}}}-{{rm{Poisson}}}}=2253.4); (Delta {{{rm{SIC}}}}_{{{rm{NB}}}-{{rm{Poisson}}}}=2.2)). A major factor affecting the temporal spread of reproduction is the duration between breeding seasons. In the wild, it is uncommon for females to reproduce in consecutive years²⁷, but green turtles at the CTC have an average inter-breeding interval of 1.7 years (wild inter-breeding intervals: 2.9 and 3.2 years^28,29). Factors associated with captivity – ad libitum feeding, higher quality resources, and access to suitable mates – may significantly reduce the physiological costs of reproduction but accelerate reproductive schedules. For instance, captive green turtles have a younger age at first reproduction (AFR) but reach maturity at a similar body size (measured via curved carapace length; CCL)³⁰ to wild populations (CTC: CCL = 98.5 cm, AFR = 12.01 years; Australia (wild): CCL = 102.1 cm, AFR = 32 years³¹; Hawaii (wild): CCL = 94.0 cm, AFR = 27.3 years³²). Only 29 of the 118 captive green turtles in Fig. 1 had an AFR less than 10 years old (range: 7–32).

At the population-level, we did not find evidence that a higher allocation to reproduction per year or a younger age at maturity³³ led to an earlier deceleration in cumulative egg production. These findings are broadly consistent with Dynamic Energy Budget (DEB) models for Australian green turtles after the removal of resource limitation³⁴. The main difference between captive and wild green turtles appears to be reproductive frequency, whereby predator-free, resource-rich captive conditions support the upper limit of their reproductive potential. However, such accelerated reproductive rates, in combination with accelerated growth rates³⁰ could have contributed to a lower expected longevity post-maturity³⁵ (see mortality results), which aligns with a well-supported prediction by Williams^10,36. Rapid development is correlated with rapid senescence. On the other hand, the removal of environmental constraints may dampen or decouple trade-offs expected under natural conditions^37,38, such as the correlation between longevity and aging rate³⁹ or the covariance between lifespan, growth, and reproduction^40,41. As such, any comparison with wild green turtle populations is currently speculative.

Observed differences in reproductive trajectories among captive turtles were mostly a result of individual heterogeneity (SI Appendix, Fig. S2). For example, four turtles laying over 10,000 eggs averaged two more clutches per year and returned to breed half a year sooner. While some individuals maintain high reproductive functioning at older ages, this may ultimately be curtailed by a rising probability of catastrophic death rather than a gradual decline in fitness⁴². In such cases, their reproductive lifespan is effectively truncated by mortality. The contribution of older turtles to the next generation could also decrease if developmental problems are more prevalent in elderly animals^23,25. Thus, the actual number of offspring produced may decline with increasing egg production^40,43. Unfortunately, we were unable to relate egg output to offspring production because hatchling data collection and egg incubation conditions were not consistent over the study period. Investigating the link between age-specific changes in egg production and offspring fitness is a critical area for future research.

Beyond 60 years old, average egg production was projected to stop, reaching a lifetime plateau at about (kappa =) 13,545 eggs (Fig. 1A; SI Appendix, Table S1). Yet, it is unknown if captive green turtles will live this long. Other environmental threats that are an indirect result of aging, such as increased susceptibility to disease⁴⁴, may induce mortality in elderly females. Conversely, females reproducing into old age may be a result of the selective disappearance of low-quality individuals, i.e., selective disappearance effects⁴⁵. Mortality selection alters the composition of older age classes by removing frailer individuals from the population. The strength of this mortality filter depends on the overall risk of death⁴⁶. When the risk of death is high, fitter individuals disproportionately represent the oldest age classes. In captivity, mortality filters are generally weak, extending the lifespan of every individual⁴⁰. However, a filter for reproductive lifespan could mirror a mortality filter in which the oldest individuals reproducing represent a specific subpopulation remaining after selective disappearance⁴⁷. As natural selection weakens with age, it creates a window of optimal fertility, which varies in duration across individuals⁴⁸. As a result, infertility in late life may arise from complex hormonal changes⁴⁸, irrespective of increases in longevity.

Females experiencing a single reproductive season (Fig. 2), hereafter termed one-time nesters (OTN), are not an artifact of captivity but are also regularly observed in wild populations⁴⁹. For instance, 35% of 1770 female loggerhead turtles Caretta caretta nesting on the South-Eastern US had only one observed reproductive event⁵⁰. In our study, OTN accounted for 24% of the 156 females. Compared to multi-season nesters (MSN), OTN did not differ in age at first reproduction (∆_MSN-OTN = 0.5 years, t_59.8 = 0.7, p = 0.5). Notably, their single reproductive event incurred no detectable survival cost. OTN experienced a reproductive lifespan 52% shorter than MSN (see survival results below) but laid significantly fewer eggs in their first breeding season (∆_MSN-OTN = 76.9 eggs, t_72.1 = −2.5, p = 0.02) – a difference equivalent to nearly one full clutch. The fate of short-lived OTN may further point to a selective filter favoring an intrinsic physiological resilience needed for sustained reproduction, a capacity that remains crucial even in captive conditions.

Mortality rates in captive green turtles increase exponentially with age

Survival patterns for captive green turtles follow the Gompertz law, which posits that adult mortality rises exponentially – or linearly on the log-scale – with age after maturity⁵¹. This mortality pattern has also been observed in captive³ and wild⁴ testudines. As expected, captive green turtles have a low initial mortality rate (({{{rm{beta }}}}_{0}): 0.01 – 0.04) and a slow aging rate (({{{rm{beta }}}}_{1}): 0.09–0.10), similar to other testudine species³ and orders of magnitude lower than rates typical for birds and mammals (See Fig. 1 in refs. ⁴,⁴⁰). However, aging rates for both groups [OTN and MSN] were strictly positive (Fig. 3) and significantly different from zero (p < 0.001), revealing green turtles can experience actuarial senescence even in protected environments. This may relate to growth slowing to an asymptotic size rather than continuing indefinitely throughout life. Female green turtles at the CTC reach 95% of their maximum mass and 85% of their maximum length at AFR³⁰, limiting any potential benefits from increasing size on fecundity.

Differences in reproductive dynamics between OTN and MSN translated into distinct mortality functions (Figs. 1B and 2B). This distinction was captured by including a covariate for reproductive status (SI Appendix, Table S2), which provided a better fit to the data over assuming additional unobserved heterogeneity (via a Gamma-Makeham frailty model^52,53). Initial mortality rates, which were 25% lower for MSN, shaped these differences. Both life expectancy (KL_D = 1.0) and lifespan inequality (KL_D = 1.0) were significantly different between OTN and MSN (SI Appendix, Fig. S3 and Table S3–4), with OTN having a 52% shorter reproductive lifespan (({e}_{x}^{{OTN}}) = 10.2; ({e}_{x}^{{MSN}}) = 19.6) and greater variance in age at death (({H}_{x}^{{OTN}}) = 0.6; ({H}_{x}^{{MSN}}) = 0.4). We estimated 5% of captive green turtles will survive beyond 23.4 (OTN) and 35.5 (MSN) years old, and 1% will live past 27.6 (OTN) to 39.6 (MSN) years old. Their respective aging rates correspond to a mortality doubling time of 6.7 years for OTN and 7.4 years for MSN. Whether mortality and reproductive estimates for MSN and OTN reflect individual variation in response to captivity remains unclear⁴⁰. However, since individuals in the study primarily reflect the original, diverse genetic founder stock, our results are unlikely a result of inbreeding depression⁵⁴.

The most common age at death (modal age; estimation based on method in ref. ⁵⁵) was 10 years for OTN and 24 years for MSN. This earlier modal death age for OTN, coupled with a significantly greater variance (see ({H}_{x}^{{OTN}}) above) suggests that deaths occur earlier on average but are more dispersed. In contrast, the narrower age range of death for MSN may indicate a more consistent timing of when late-life physiological decline leads to mortality. The earlier modal age for OTN aligns with an earlier onset of actuarial senescence post-maturity, although we could not attribute this to reproductive costs. Contrary to predictions about the timing of peak mortality³⁶, significant increases did not begin shortly after sexual maturity for MSN but occurred approximately 12 years post-maturity (at 24 years of age), indicating a substantially delayed period before peak mortality risks manifested. This phenomenon of deferred actuarial senescence in MSN, where the primary escalation of mortality risk is postponed, has also been reported in endotherms⁵⁶ and ectotherms^3,4.

To explore the interplay between survival and reproduction further, we focused solely on MSN, as these individuals not only actualized the most common reproductive strategy of green turtles but also allowed us to evaluate how reproductive costs impact survival over successive breeding seasons. Recalling that cumulative reproductive output increased linearly through much of early to mid-adulthood and given that their peak mortality (modal age at death) occurs at around 24 years, the later inflection point in their reproductive trajectory (27 years; Fig. 1) suggests that mortality risk begins to accelerate before any observable decline in reproductive output. While we found no evidence that a higher annual reproductive rate or earlier maturity led to an earlier decline in egg production, the mortality pattern in MSN points to a potential trade-off between current reproduction and future survival.

Comparing mortality and reproductive patterns between captive and wild green turtles

Surprisingly, our estimate for reproductive longevity for captive green turtles (19.6 years post-maturity) closely matches those reported for wild Australian green turtle populations (18–19 years)⁵⁷. Differences in longevity between captive and wild populations seem to be related to maturity time. Although green turtles may remain reproductively active for ~19 years, the waiting time to first reproduction is reduced by around 32% in captivity. Wild green turtles spend almost half their lives gathering the necessary resources to reach a minimum body size needed to meet the requirements for long-distance migration and reproduction, which is related to environmental, developmental, and physiological constraints⁵⁸. Once mature, differences in reproductive rates relate to the time required to gather resources for a subsequent breeding season. In captivity, turtles do not need to migrate thousands of kilometers between breeding and foraging grounds, significantly reducing their energetic costs and increasing their reproductive rate. However, in wild leatherback turtles the stress of reproducing at a higher frequency had negative physiological effects⁵⁹, suggesting a trade-off between current and future reproduction that influences lifetime reproductive success⁶⁰.

Limited resources compounded by extensive migrations result in wild green turtles having fewer reproductive seasons compared to those in captivity. If wild populations follow a similar reproductive trajectory as shown in Fig. 1A, we project a 20-year reproductive lifespan with an inter-breeding interval of two and four years would result in a lifetime output of between 1324 and 4080 eggs – a figure close to the estimated lifetime reproductive output in wild Australian green turtles ( ~ 2000 eggs⁶¹). Yet, such projections from captive populations are speculative. Data from known-aged sea turtles would greatly enhance comparative analyses. Despite the hundreds of global projects monitoring sea turtle populations, several operational since the early 1960s, no long-term longitudinal data exist for known-age sea turtles of any species, rendering the estimation of age-specific mortality and reproductive rates particularly challenging⁶². The inability to accurately determine the absolute chronological age of living wild sea turtles remains the single most significant impediment to precisely understanding their life history⁶³. This limitation impedes the employment of typical demographic tools⁶⁴ to understand phenotypic variation and plasticity in wild populations experiencing vastly differing ecological pressures.

Alternative approaches have emerged that circumvent the need for aging live animals and allow for the construction of age- and size-specific reaction norms⁶⁵. While undoubtedly valuable, such methods cannot translate into direct, individual-level changes in reproductive output and survival over a full lifespan, nor can they fully capture the dynamic interplay of environmental factors. For instance, if aging rates are environmentally modulated, as observed in other testudines³ rather than an underlying change in juvenile mortality as seen in primates⁶⁶, wild populations of sea turtles may experience mortality rates that vary with local selective pressures, such as foraging ground productivity⁶⁷, predation, disease, temperature regimes, and climate change⁶⁸. These factors are known to strongly influence species-specific vital rates across testudines. In painted turtles, aging rates vary between and within wild and captive populations (captivity: 0.09¹⁹, 0.10³; wild: 0.04⁴, 0.01⁴, 0.10²⁵). Comparable results have been reported in pond sliders Trachemys scripta^3,4, as well as European pond turtles Emys orbicularis⁴. This environmental sensitivity has important implications in understanding selection for lifespan-extending mechanisms in long-lived species, which is expected to be strong. When extrinsic mortality is low and aging-related mortality is high, as is the case in certain birds and mammals^42,69, extending lifespan further would likely come at a high evolutionary cost. Life extending mechanisms may be less costly in terms of evolutionary fitness for testudines when actuarial senescence is not the primary source of mortality⁴², but this needs to be evaluated with empirical data. While direct longitudinal studies of known-aged individuals in the wild remain paramount, at present, research on the paradoxical life history of sea turtles can be most effectively advanced by tracking individuals from their first breeding season, employing breeding age rather than absolute age.

Comparing mortality and reproductive patterns across captive and wild testudines

To situate our findings on captive green turtles within the broader evolutionary landscape, we conducted a comparative analysis across testudines. Captive green turtles exhibit aging rates similar to those of other testudines (Fig. 3), and the relationships among fitness-related traits in testudines (including sea turtles) generally align with established life-history patterns^3,4,19, irrespective of environment. For instance, an earlier age at first reproduction is linked to a shorter lifespan and body mass is positively correlated with life expectancy (SI Appendix, Fig. S4A, B). A notable divergence, however, emerges in their reproductive investment: green turtles produce a disproportionately heavier reproductive mass relative to their aging rate, life expectancy, and AFR (Fig. 4A–C). Despite this difference, the allometric relationship between reproductive mass and body mass in green turtles matches theoretical expectations (Fig. 4D), a pattern that also holds across wild sea turtle species⁷⁰. Extensive migrations between foraging and breeding grounds impose strict physiological constraints, making the large body size of sea turtles an evolutionary prerequisite for their highly migratory, oceanic lifestyle⁷¹. In general, a larger body size offers significant adaptive advantages, including reduced predation risk, a lower mass-specific metabolic rate, and greater starvation tolerance. However, these benefits are balanced by higher resource requirements, a longer maturation period, and increased susceptibility to extinction due to longer generation times.

Across testudines, we found aging rates do not strongly vary along a slow-fast continuum as typically defined by other life-history traits. Notably, the relationship between aging rates and body mass, AFR, and annual fecundity are substantially weaker than in other reptiles⁴ (Fig. 5A–C). Collectively, these traits explained only 8% of the variation in aging rates (Table 1A). While the direction of these relationships is similar to other reptiles (excluding testudines), the effect sizes are trivial, yielding nearly flat lines across taxa with different fecundities (slope: testudines = 0.01, reptiles = 0.11), ages at first reproduction (slope: testudines = −0.01, reptiles = −0.18), and body masses (slope: testudines = 0.00, reptiles = −0.03). Furthermore, while wild populations in our dataset exhibit slightly faster aging rates, this difference was not statistically significant (Table 1A, Fig. 5).

Consistently slow aging rates across testudines, despite substantial differences in body size, environmental conditions, and habitat use, likely stem from a weakening of life-history trade-offs. Shared protective phenotypes (e.g., shells) and physiological adaptations, such as the ability to endure food scarcity while keeping maintenance costs low relative to energy storage⁷², may buffer testudines against selective pressures that typically drive diverse aging trajectories in other groups. The stronger relationships reported by Reinke et al.⁴ appear predominantly driven by high variation in squamates, with crocodilians and tuataras showing aging patterns more aligned with testudines. Similarly, the correlation between mortality and AFR in Shine and Iverson⁷³ arises from methodological distinctions between age-specific mortality and instantaneous adult mortality ((-{mathrm{ln}}({{rm{S}}})), where S is a point estimate for adult survival probability). Our results suggest that, within testudines, the expected covariance among life-history traits with aging rates along a slow-fast continuum is not evident because they collectively occupy the slow extreme of this broader spectrum (see also Fig. 4 in ref. ⁴).

While variation in reproductive mass does not directly align with the slow-fast continuum, it correlates strongly with body mass – a trait that itself is indicative of a species’ position on that continuum (SI Appendix, Fig. S4A, B). Female body mass in testudines explained 81% of the variation in annual reproductive mass (Fig. 4D). Other life-history traits, such as life expectancy, AFR, aging rate, and environment, did not explain the residual variation, and their effect actually became trivial once body mass was prioritized in a sequential decomposition of variance (Type I ANOVA). The dominant role of body size is anticipated, given its fundamental importance to organismal fitness, and it is both heritable⁷⁴ and exhibits plastic responses to changing environments⁷⁵, particularly during ontogeny³⁰. In line with scaling laws⁷⁶, which predict reproductive mass should follow a power function of the form (alpha cdot {M}_{F}^{beta }), we estimate the scaling exponent ((beta)) to be 0.89 (95% CI: 0.77–1.02), suggesting hypoallometric scaling. This result aligns closely with findings across sea turtles⁷⁰ and testudines in general⁷⁷, whereby larger species produce relatively lighter clutches (slope ≈ 0.77 in ref. ⁷⁷). Interestingly, among individuals of the same species, this pattern is reversed (hyperallometric scaling)^70,78.

Although inter-specific hypoallometry might imply reproduction carries a proportionally higher allometric cost for smaller species, we found no detectable direct effect of reproductive mass itself on aging rates (p = 0.87), AFR (p = 0.86), or life expectancy (p = 0.21). Reproductive mass was slightly heavier in captivity (Table 1B and Fig. 4), but this difference was not significant (∆_captive-wild = 0.05; p = 0.77). Although univariate tests detected a strong phylogenetic signal for both reproductive mass and body mass (Pagel’s λ; SI Appendix, Fig. S5 and Table S5), evolutionary relatedness did not explain the residual variance for reproductive mass, nor aging rates in the PGLS models (SI Appendix, Table S6). Instead, life-history trade-offs associated with the magnitude of reproductive investment primarily manifest through adjustments in other life-history components (SI Appendix, Fig. S4), such as age at first reproduction and overall life expectancy (as suggested by correlations with body mass). For instance, turtles and tortoises are constrained by the size of their body cavity, which is affected by their overall body plan. As such, there is a trade-off between the number of eggs and egg size⁷⁹. Egg size is expected to be subject to stronger directional selection than clutch size, as each egg must provision all essential resources required for hatchling survival. Empirical data support this hypothesis, finding that clutch size shows greater phenotypic variance relative to egg size⁸⁰. Patterns of reproductive allocation may largely reflect these allometric and anatomical constraints, especially as both clutch and egg size generally increase with body size across testudines⁸¹, rather than being strongly lineage-specific⁷².

Scientists uncovered universal laws of entanglement in any dimension. The results strengthen links between particle physics, quantum theory, and gravity.

A group of theoretical physicists has shown that quantum entanglement obeys universal principles in every dimension by applying thermal effective theory. Their findings were recently published in the journal Physical Review Letters, where the paper was selected as an Editors’ Suggestion.

“This study is the first example of applying thermal effective theory to quantum information. The results of this study demonstrate the usefulness of this approach, and we hope to further develop this approach to gain a deeper understanding of quantum entanglement structures,” said lead author and Kyushu University Institute for Advanced Study Associate Professor Yuya Kusuki.

Quantum entanglement and Rényi entropy

In classical physics, particles that are far apart act independently. In contrast, quantum physics shows that two particles can remain strongly correlated even at great distances, a phenomenon known as quantum entanglement. This effect is central to quantum technologies such as quantum computing and quantum communication, making its study essential for both theoretical insight and practical applications.

One of the main tools for characterizing entanglement is the Rényi entropy, which measures the complexity of quantum states and how information is distributed. It plays a key role in classifying quantum states, evaluating whether complex quantum systems can be simulated, and is also widely used in theoretical research on the black hole information loss paradox and quantum gravity.

Despite its importance, uncovering the structure of quantum entanglement remains a major challenge for both physics and quantum information science. Most investigations so far have been restricted to (1+1)-dimensional models, meaning one spatial dimension plus time. Extending this work to higher dimensions has proven much more complex.

A team led by Yuya Kusuki at the University of Tokyo’s Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU, WPI), together with Caltech Professor Hirosi Ooguri and researcher Sridip Pal, has now demonstrated that quantum entanglement exhibits universal patterns even in higher dimensions. They achieved this by adapting theoretical methods originally developed in particle physics to the study of quantum information.

Applying the thermal effective theory

The research team focused on the thermal effective theory, which has recently led to major advances in the analysis of higher-dimensional theories in particle physics. This is a theoretical framework designed to extract universal behavior from complex systems, based on the idea that observable quantities can often be characterized by only a small number of parameters.

By introducing this framework into quantum information theory, the team analyzed the behavior of Rényi entropy in higher-dimensional quantum systems. Rényi entropy is characterized by a parameter known as the replica number.

The team demonstrated that, in the regime of small replica number, the behavior of the Rényi entropy is universally governed by only a few parameters, such as the Casimir energy, a key physical quantity within the theory. Furthermore, by leveraging this result, the team clarified the behavior of the entanglement spectrum in the region where its eigenvalues are large.

They also investigated how universal behavior changes depending on the method used to evaluate the Rényi entropy. These findings hold not only in (1+1) dimensions, but in arbitrary spacetime dimensions, marking a significant step forward in the understanding of quantum entanglement structures in higher dimensions.

The next step for the researchers is to further generalize and refine this framework. This work represents the first demonstration that thermal effective theory can be effectively applied to the study of quantum entanglement structures in higher dimensions, and there remains ample room to further develop this approach. By improving the thermal effective theory with quantum information applications in mind, researchers could gain a deeper understanding of quantum entanglement structures in higher-dimensional systems.

On the applied side, the theoretical insights gained from this research may lead to improvements in numerical simulation methods for higher-dimensional quantum systems, propose new principles for classifying quantum many-body states, and contribute to a quantum-information-theoretic understanding of quantum gravity. These developments hold promise for broad and impactful future applications.

Reference: “Universality of Rényi Entropy in Conformal Field Theory” by Yuya Kusuki, Hirosi Ooguri and Sridip Pal, 5 August 2025, Physical Review Letters.
DOI: 10.1103/fsg7-bs7q

This research was supported in part by the U.S. Department of Energy, Office of Science, Office of High Energy Physics, under Award Number DE-SC0011632 and by the Walter Burke Institute for Theoretical Physics at Caltech. Hirosi Ooguri is also supported in part by the Simons Investigator Award (MP-SIP-00005259) and by JSPS Grants-in-Aid for Scientific Research 23K03379. His work was performed in part at the Kavli Institute for the Physics and Mathematics of the Universe at the University of Tokyo, which is supported by the World Premier International Research Center Initiative, MEXT, Japan, at the Kavli Institute for Theoretical Physics (KITP) at the University of California, Santa Barbara, which is supported by grant NSF PHY-2309135, and at the Aspen Center for Physics, which is supported by NSF grant PHY-1607611. Yuya Kusunoki is also supported by the INAMORI Frontier Program at Kyushu University and JSPS KAKENHI Grant Number 23K20046.

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The inside of a star turned on itself before it spectacularly exploded, according to a new study from NASA’s Chandra X-ray Observatory. Today, this shattered star, known as the Cassiopeia A supernova remnant, is one of the best-known, well-studied objects in the sky.

Over three hundred years ago, however, it was a giant star on the brink of self-destruction. The new Chandra study reveals that just hours before it exploded, the star’s interior violently rearranged itself. This last-minute shuffling of its stellar belly has profound implications for understanding how massive stars explode and how their remains behave afterwards.

Cassiopeia A (Cas A for short) was one of the first objects the telescope looked at after its launch in 1999, and astronomers have repeatedly returned to observe it.

“It seems like each time we closely look at Chandra data of Cas A, we learn something new and exciting,” said Toshiki Sato of Meiji University in Japan who led the study. “Now we’ve taken that invaluable X-ray data, combined it with powerful computer models, and found something extraordinary.”

As massive stars age, increasingly heavy elements form in their interiors by nuclear reactions, creating onion-like layers of different elements. Their outer layer is mostly made of hydrogen, followed by layers of helium, carbon and progressively heavier elements – extending all the way down to the center of the star. 

Once iron starts forming in the core of the star, the game changes. As soon as the iron core grows beyond a certain mass (about 1.4 times the mass of the Sun), it can no longer support its own weight and collapses. The outer part of the star falls onto the collapsing core, and rebounds as a core-collapse supernova.

The new research with Chandra data reveals a change that happened deep within the star at the very last moments of its life. After more than a million years, Cas A underwent major changes in its final hours before exploding.

“Our research shows that just before the star in Cas A collapsed, part of an inner layer with large amounts of silicon traveled outwards and broke into a neighboring layer with lots of neon,” said co-author Kai Matsunaga of Kyoto University in Japan. “This is a violent event where the barrier between these two layers disappears.”

This upheaval not only caused material rich in silicon to travel outwards; it also forced material rich in neon to travel inwards. The team found clear traces of these outward silicon flows and inward neon flows in the remains of Cas A’s supernova remnant. Small regions rich in silicon but poor in neon are located near regions rich in neon and poor in silicon. 

The survival of these regions not only provides critical evidence for the star’s upheaval, but also shows that complete mixing of the silicon and neon with other elements did not occur immediately before or after the explosion. This lack of mixing is predicted by detailed computer models of massive stars near the ends of their lives.

There are several significant implications for this inner turmoil inside of the doomed star. First, it may directly explain the lopsided rather than symmetrical shape of the Cas A remnant in three dimensions. Second, a lopsided explosion and debris field may have given a powerful kick to the remaining core of the star, now a neutron star, explaining the high observed speed of this object.

Finally, the strong turbulent flows created by the star’s internal changes may have promoted the development of the supernova blast wave, facilitating the star’s explosion.

“Perhaps the most important effect of this change in the star’s structure is that it may have helped trigger the explosion itself,” said co-author Hiroyuki Uchida, also of Kyoto University. “Such final internal activity of a star may change its fate—whether it will shine as a supernova or not.”

These results have been published in the latest issue of The Astrophysical Journal and are available online.

To learn more about Chandra, visit:

https://science.nasa.gov/chandra

Learn more about the Chandra X-ray Observatory and its mission here:

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https://chandra.si.edu

This release features a composite image of Cassiopeia A, a donut-shaped supernova remnant located about 11,000 light-years from Earth. Included in the image is an inset closeup, which highlights a region with relative abundances of silicon and neon.

Over three hundred years ago, Cassiopeia A, or Cas A, was a star on the brink of self-destruction. In composition it resembled an onion with layers rich in different elements such as hydrogen, helium, carbon, silicon, sulfur, calcium, and neon, wrapped around an iron core. When that iron core grew beyond a certain mass, the star could no longer support its own weight. The outer layers fell into the collapsing core, then rebounded as a supernova. This explosion created the donut-like shape shown in the composite image. The shape is somewhat irregular, with the thinner quadrant of the donut to the upper left of the off-center hole.

In the body of the donut, the remains of the star’s elements create a mottled cloud of colors, marbled with red and blue veins. Here, sulfur is represented by yellow, calcium by green, and iron by purple. The red veins are silicon, and the blue veins, which also line the outer edge of the donut-shape, are the highest energy X-rays detected by Chandra and show the explosion’s blast wave.

The inset uses a different color code and highlights a colorful, mottled region at the thinner, upper left quadrant of Cas A. Here, rich pockets of silicon and neon are identified in the red and blue veins, respectively. New evidence from Chandra indicates that in the hours before the star’s collapse, part of a silicon-rich layer traveled outwards, and broke into a neighboring neon-rich layer. This violent breakdown of layers created strong turbulent flows and may have promoted the development of the supernova’s blast wave, facilitating the star’s explosion. Additionally, upheaval in the interior of the star may have produced a lopsided explosion, resulting in the irregular shape, with an off-center hole (and a thinner bite of donut!) at our upper left.

Megan Watzke
Chandra X-ray Center
Cambridge, Mass.
617-496-7998
mwatzke@cfa.harvard.edu

Corinne Beckinger
Marshall Space Flight Center, Huntsville, Alabama
256-544-0034
corinne.m.beckinger@nasa.gov

Bringing samples from Mars back to Earth is one of NASA’s most ambitious goals. The plan goes back to the 1970s, with NASA’s robotic exploration program, when the first spacecraft landed on the surface of the Red Planet. This planetary exploration program gained momentum with the launch of the Mars rover missions, including Spirit, Opportunity, and Curiosity. In 2021, the rover named Perseverance made history by collecting and caching environmental samples from the Jezero Crater, a site where scientists believe they might find evidence of past life. These samples are sealed in titanium tubes and are stored on the surface of Mars, waiting for the opportunity to reach Earth.

NASA’s original Mars Sample Return (MSR) plan involves a complex, multi-mission campaign. It includes launching a Sample Retrieval Lander that would be equipped with a small rocket and a robotic arm that can gather the sample tubes. Then, the samples would be launched into the orbit of Mars, where an ESA orbiter would intercept them and return to Earth. All this would be achieved by 2033. However, this intricate plan comes with some great challenges. One particular challenge made NASA reassess the feasibility of this project — the budget. According to estimates, it’d cost nearly $11 billion, but it would also involve lots of technical problems and delays. In 2024, the space agency acknowledged that the current MSR project is no longer viable. Now, NASA is exploring cheaper and faster alternatives, including collaboration with some commercial partners like SpaceX and Lockheed.

Read more: How Many Meteors Actually Hit Earth Every Year?

Launching From The Surface Of Mars

One of the main problems scientists encountered during their Mars Sample Return mission planning was how they would lift the samples off the surface of Mars. At the heart of the original MSR plan was the Mars Ascent Vehicle (MAV), a small rocket designed to launch the sealed sample tubes into the orbit of the Red Planet. While launching a rocket from Earth is no easy task, it has become routine. Launching it from the surface of another planet would mean doing it from a long distance, with no margin for error. The MAV would need to safely land on Mars, endure months of the planet’s harsh environment while remaining dormant, and then perform a flawless vertical launch into Mars’ orbit successfully. Designing a rocket that would be light enough to deliver to Mars, and powerful enough to reach the orbit of Mars from the planet’s surface, is a delicate engineering job. There’s also the concern of stability, propulsion reliability, and designing guidance systems for the unpredictable Martian environment.

Another concern for NASA scientists is the Martian atmosphere. While it’s lighter than Earth’s atmosphere, it comes with some specific challenges. It’s still thick enough that any spacecraft would simply burn upon entry into the Martian atmosphere if not protected by a special thermal shielding. On the other hand, it’s also thin enough that no spacecraft can rely on a parachute alone to be slowed down enough to land safely.

Two Ways Forward

NASA is now reevaluating its approach, and two alternative strategies are already outlined and still under review as of 2025.

The first is NASA’s original MSR plan, but significantly simplified. It would rely on the already proven Sky Crane landing system. This is the same technology used to safely deliver the Curiosity and Perseverance rovers to the surface of Mars. A scaled-down lander would be equipped with the new version of MAV. To make the vehicle lighter, the scientists opted for a Radioisotope Power System instead of solar panels. This new system is capable of providing reliable power and heat to the MAV. Instead of launching a new rover, this plan involves relying on Perseverance to deliver the samples directly to the MAV. This reduces the mission complexity while still retaining the key elements of NASA’s original plan.

The second strategy involves looking towards companies such as SpaceX, Blue Origin, and Lockheed. NASA has accepted 11 studies from the scientific community and space industry, each proposing the best way to return Mars samples to Earth. Some of these studies include modular systems, novel propulsion techniques, or entirely new vehicle designs. What’s common to these proposals is that their costs are estimated between $5.5 and $7.5 billion. By the end of 2026, NASA should be able to concentrate on one Mars Sample Return plan, and they’re confident they will reach Earth by 2040.

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For several years, astronomers have been seeing the hallmark of planet formation around stellar protoplanetary disks. A baby star is surrounded by a disk of gas and dust, and to observatories like ALMA, grooves in the disk appear as dark rings. These grooves are believed to be formed by fledgling planets, but no planets have been seen in them. Until now.

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Researchers report revolutionary observations of WISPIT 2, which show – both in infrared light and optical – a planet among the dark rings. WISPIT 2b, as it is called, is estimated to be very young, but a lot more massive than the planets in the Solar System – around five times the mass of Jupiter, orbiting 56 times further away from its star than Earth is from the Sun.

“Dozens of theory papers have been written about these observed disk gaps being caused by protoplanets, but no one’s ever found a definitive one until today,” co-lead author Laird Close, professor of astronomy at the University of Arizona, said in a statement.

“It’s been a point of tension, actually, in the literature and in astronomy in general, that we have these really dark gaps, but we cannot detect the faint exoplanets in them. Many have doubted that protoplanets can make these gaps, but now we know that in fact, they can.”

But that is not the only find in this system. The team also found evidence of a second candidate planet, currently named CC1, which is about nine times the mass of Jupiter but orbiting a lot closer, about 15 times the Earth-Sun distance. In Solar System terms, that’s between Saturn and Uranus.

“Capturing an image of these forming planets has proven extremely challenging, and it gives us a real chance to understand why the many thousands of older exoplanet systems out there look so diverse and so different from our own solar system. I think many of our colleagues who study planet formation will take a close look at this system in the years to come,” co-author Dr Christian Ginski from the University of Galway said in a statement.

This whole system is like looking back into the past of the Solar System. While we do not have planets quite as big around the Sun, we are seeing the birth of a planetary system not too dissimilar.

“Discovering this planet was an amazing experience – we were incredibly lucky. WISPIT 2, a young version of our Sun, is located in a little-studied group of young stars, and we did not expect to find such a spectacular system. This system will likely be a benchmark for years to come,” co-lead author Richelle van Capelleveen, from Leiden University, explained.

The work was possible thanks to the MagAO-X extreme adaptive optics system at the Magellan Telescope in Chile, the Large Binocular Telescope in Arizona, and the Very Large Telescope at the European Southern Observatory in Chile.

The study is published in two papers in The Astrophysical Journal Letters.

Deep in the first moments of the Big Bang, the entire cosmos shook and rumbled. Those quakes still reverberate to the present day. It will take the most sensitive instruments ever imagined to reveal those ripples, but if they are discovered, they will change our understanding of the entire universe.

In 1916, Albert Einstein discovered that his theory of general relativity predicted the existence of gravitational waves — ripples in the fabric of space-time caused by anything with mass that accelerates. But gravity is by far the weakest of the known forces, and gravitational waves are weaker still. So, although the idea of gravitational waves was interesting, Einstein believed they could never be detected.

A gigantic crocodile-like hypercarnivore likely hunted dinosaurs 70 million years ago in what is now Argentina, a new study reveals.

Researchers discovered the fossilized skeleton from the extinct apex predator in southern Patagonia in 2020. It grew up to around 11.5 feet (3.5 meters) long and weighed about 550 pounds (250 kilograms).

Aug. 28, 2025 — In July 2025, the online web platform DesignSafe proudly marked its 10th anniversary as the nation’s premier cyberinfrastructure for engineering and social science research on natural hazards.

DesignSafe’s computational resources, software, and expertise has empowered 10,000 users—scientists, engineers, students, and other researchers with tools for managing, analyzing, and sharing data on an expanding list of natural hazards including earthquakes, hurricanes, inland flooding, tornadoes, landslides, and wildfires.

As a core component of the National Science Foundation (NSF)-funded Natural Hazards Engineering Research Infrastructure (NHERI), DesignSafe’s impact extends far beyond technological innovation, playing a transformative role in advancing research practices and collaboration in the natural hazards field.

“NHERI—and DesignSafe in particular—plays a critical role for us to perform research to improve our understanding of the consequences of natural hazards,” said DesignSafe Principal Investigator Ellen Rathje, a professor and the Janet S. Cockrell Centennial Chair in Engineering at the Cockrell School of Engineering at UT Austin.

The engine behind DesignSafe is the Texas Advanced Computing Center (TACC), enabling researchers to perform advanced computational research, manage massive datasets, and collaborate across institutions—all crucial for advancing natural hazards engineering.

“TACC is a partner that puts the researcher first, and they realize that it’s important to build a bridge to the natural hazards research community,” Rathje said. “We have an amazing partnership. I credit TACC and its leadership for helping DesignSafe have a successful first decade.”

DesignSafe’s Structure

DesignSafe has three core components—the Data Depot, Tools and Applications for cloud-based simulation and analytics, and the Reconnaissance Portal for field research data.

Researchers rely on the Data Depot repository for robust data management, curation, and publication. To date, it hosts more than 1,800 published datasets and over 50 million files, which have been accessed more than 800,000 times in the past decade—demonstrating its critical role in supporting open, reusable, and impactful natural hazards research.

“We see natural hazards data not only being collected but being published and available to the broader research community to accelerate research discoveries,” Rathje said.

On the computational side, DesignSafe provides access to software Tools and Applications at TACC that enables researchers to couple their data with computational simulation, AI, and machine learning to further accelerate discoveries. “These tools can reduce the impact of natural hazards on the built environment and our communities,” Rathje added.

The Reconnaissance (Recon) Portal is a dynamic, interactive map that geospatially organizes worldwide reconnaissance data from natural hazard events, enabling rapid access to data collected by other NHERI components, such as the NHERI RAPID reconnaissance facility and the CONVERGE Extreme Events Reconnaissance (EER) teams. These data document the impact of natural hazards on the built environment, allowing researchers to learn from events to improve resilience of our communities in the future.

Improved Building Safety After Florida Hurricanes

Rathje points to a powerful example of DesignSafe’s impact: field reconnaissance images from the devastating 2017–2018 hurricanes in Florida—including Hurricanes Irma and Michael—were used to identify critical vulnerabilities in building performance, leading to improvements in safety and resilience in future building design.

“These structures were elevated because of the water hazard but doing so enhanced the potential for wind damage,” Rathje said.

Building on the hurricane field observations, NHERI researchers conducted controlled wind tunnel experiments at the Florida International University Wall of Wind facility, capable of generating Category 5 hurricane-force winds. Scaled models of both elevated and non-elevated buildings were tested, and the results confirmed the field observations: elevated structures experienced greater wind-related damage. These experiments provided critical validation of the observed trade-offs between flood protection and wind vulnerability.

“As a direct result of these findings, recent updates to the building codes now include wind loading force coefficients associated with elevated structures such that buildings built in the future will be designed better to withstand elevated wind loads,” Rathje said.

DesignSafe Launches Wildfire Hazard Research

Wildfire is the newest natural hazard incorporated into DesignSafe’s portfolio.

Researchers began publishing data associated with wildfires in DesignSafe after the 2018 Camp Fire in Paradise, California, and since that time the interest in wildfires has continued to grow, culminating in 2025 with reconnaissance efforts capturing critical imagery from the Palisades wildfire of Los Angeles.

“This marked DesignSafe’s first major wildfire research response following the NSF’s directive to expand its focus to include this increasingly devastating hazard,” Rathje said. “Researchers are going into the field to understand the evolution of wildfires as well as the damage to the infrastructure.”

Researcher Erica Fischer of Oregon State University has been actively engaged with DesignSafe and the NHERI RAPID facility to study wildfire impacts on civil infrastructure since the 2018 Camp Fire. An associate professor of Civil and Construction Engineering, Fischer has contributed multiple comprehensive wildfire datasets to DesignSafe, featuring Google Street View imagery, drone images, and high-resolution 3D LIDAR scans.

The wildfire data on DesignSafe are supporting a wide variety of research, including the development of AI-driven machine learning algorithms that use post-disaster drone imagery to rapidly create detailed damage maps for use by emergency managers.

“Numerical simulations and machine learning require significant amounts of data for validation and training. While wide-spread fire impacts on communities have been occurring in the US for over a century, it is only in the last decade that we are systematically collecting post-fire data on civil infrastructure,” Fischer states. “Without this data, across all hazards, we are unable to validate our community impact simulations and are limited in training our machine learning models.”

To explore the application of AI models trained in wildfires to other hazards and vice versa, the team used the years of data stored within DesignSafe. “We dove deep into DesignSafe’s extensive data archives to gather drone image from other NHERI researchers,” Fischer said. “Our goal was to know whether our algorithm could also be adapted to hurricanes, earthquakes, and a broad range of other natural hazards.”

“The most important thing for science is that it be transparent,” she added. “DesignSafe allows sharing of data, numerical simulation models, reports, and milestones in ways that are citable. It allows us full transparency of all the science that we are doing—it is quite powerful.”

Bridging AI and Civil Engineering

DesignSafe is increasingly integrating artificial intelligence (AI) and machine learning (ML) to enhance its capabilities and impact. This includes using AI for tasks like building recognition, damage assessment from images, and predicting wind pressure coefficients. Additionally, DesignSafe provides resources for researchers to utilize AI/ML in their own projects, such as through Jupyter notebooks, interactive data analytics, and access to high performance computing.

Beyond enabling scientists to mine publicly available data for AI-driven scientific discovery, DesignSafe is also pioneering the development of AI-powered chatbots to enhance its interface, making the platform more intuitive and accessible for researchers.

“The AI chatbot will come back with a clear, textual, description of exactly what you are asking of DesignSafe, whether it is ‘What is all the LIDAR data that’s available at a location?’ Or ‘How do I run one million simulations with the data on a high performance computer,’” Rathje said.

A notable example of AI innovation at DesignSafe is the NSF-funded Chishiki-AI project, launched in 2023 and led by AI and civil engineering experts at UT Austin in collaboration with Cornell University. Named after the Japanese word for ‘knowledge,’ Chishiki-AI aims to accelerate the integration of AI into civil engineering, advancing research, education, and practical applications in hazard resilience.

“As AI continues to evolve, we see an urgent need to educate civil engineers and equip cyberinfrastructure professionals to handle the new challenges which AI and civil engineering brings together,” said Krishna Kumar, principal investigator of Chishiki-AI and an assistant professor in the Maseeh Department of Civil, Architectural and Environmental Engineering, as well as serving at the Oden Institute for Computational Engineering and Sciences at UT Austin.

For example, engineers must understand the limitations of AI, such as convolutional neural networks trained to recognize structures in environments with bright lighting or minimal graffiti, which may underperform in more variable, real-world situations. Conversely, cyberinfrastructure professionals need deeper insight into the unique demands of civil engineering, including how to deploy large-scale AI models in post-disaster scenarios where power and connectivity are limited.

“Civil engineering presents unique challenges, not only in the context of natural hazards addressed by DesignSafe, but also in emerging areas like autonomous construction,” Kumar said. “As we push the field forward, we are embracing an AI-accelerated engineering paradigm that redefines how we meet society’s evolving infrastructure needs.”

Looking ahead, Rathje envisions a deeper, more transformative integration of AI with DesignSafe data, unlocking new possibilities for research and innovation.

“We want to make AI and machine learning training more seamless,” she said. “One way we’re working to make it easier is developing models that can automatically apply tags to images upon upload with damage information.”

In addition, AI-enabled pre-curation of data shows strong potential to streamline the initial stages of data organization, helping DesignSafe users jump-start the upload process with greater efficiency and confidence.

Reflecting on DesignSafe’s decade of achievements, the platform has provided researchers with far more than data and tools—it has empowered groundbreaking discoveries, fostered collaboration, and transformed the way natural hazards research is conducted.

DesignSafe is a comprehensive cyberinfrastructure that is part of the NSF-funded Natural Hazard Engineering Research Infrastructure (NHERI) and provides cloud-based tools to manage, analyze, understand, and publish critical data for research to understand the impacts of natural hazards. The capabilities within the DesignSafe infrastructure are available at no-cost to all researchers working in natural hazards. The cyberinfrastructure and software development team is located at the Texas Advanced Computing Center (TACC) at The University of Texas at Austin, with a team of natural hazards researchers from the University of Texas, the Florida Institute of Technology, and Rice University comprising the senior management team.

NHERI is supported by multiple grants from the National Science Foundation, including the DesignSafe Cyberinfrastructure, Award #2022469.

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Source: NSF NHERI DesignSafe

FRANKFURT. When a tree dies, it forms the foundation for new life: In a slow, invisible process, leaves, wood and roots are gradually decomposed – not by wind or weather but by millions and millions of tiny organisms. Fungi thread their way through the dead wood and degrade cell walls. Tiny animals such as insect larvae and mites gnaw through the tissue. And something very important happens in the process: The carbon stored in the plant is released, ultimately placing it at the disposal of plants again for the purpose of photosynthesis. But what exactly is responsible for performing this task in the global carbon cycle? And which molecular tools do the organisms use for it? To answer these questions, the researchers have developed a new bioinformatics-based method, which they have now presented in Molecular Biology and Evolution.

18,000 species in the spotlight

This method, called fDOG (Feature architecture-aware directed ortholog search), makes it possible to search in the genetic material of various organisms for genes that have evolved from the same precursor gene. It is assumed that these genes, known as “orthologs”, encode proteins with similar functions. For the current study, the scientists searched for the genes of plant cell wall-degrading enzymes (PCDs). Unlike previous methods, fDOG not only searches through masses of genomic information but also analyzes the architecture of the proteins found – i.e. their structural composition, which reveals a lot about an enzyme’s function.

“We start with a gene from one species, referred to as the seed, and then trawl through tens of thousands of species in the search for orthologous genes,” explains Ingo Ebersberger, Professor for Applied Bioinformatics at Goethe University Frankfurt. “In the process, we constantly monitor whether the genes we find perhaps differ from the seed in terms of function and structure – for example, through the loss or gain of individual areas relevant for function.”

The research team used this method to search for more than 200 potential PCD candidates in over 18,000 species from all three domains of life – bacteria, archaea and eukaryotes (plants, animals, fungi). The result is a detailed global map – with unprecedented accuracy – of enzymes capable of degrading plant cell walls.

Surprising discoveries among fungi and animals

The researchers devised special visualization methods to analyze the vast amounts of data and detect patterns. This revealed characteristic changes in the enzyme repertoire of the fungi under study, indicating a change in lifestyle of certain fungal species: From a decomposing lifestyle – i.e. the degradation of dead plants – to a parasitic lifestyle in which they infest living animals. Such evolutionary transitions are mirrored in characteristic patterns of enzyme loss.

A special surprise in the animal kingdom was the discovery that some arthropods possess an unexpectedly wide range of plant cell wall-degrading enzymes. These enzymes presumably originated from fungi and bacteria and entered the genome of invertebrates via direct gene transfer between different organisms – i.e. horizontal gene transfer. This suggests that they might be able to degrade plant material independently and are not reliant on the bacteria in their intestines, as was previously assumed. In another case, however, it emerged that the potential PCD genes in the analyzed sequence could be ascribed to microbial contamination – an important sign that such data need to be checked very carefully.

New insights into the global carbon cycle

The study shows how fDOG can be used to systematically map biological capabilities across the entire tree of life – from broad-scale overviews to detailed investigations of individual species. With this method, it is possible both to track evolutionary trajectories and to identify players previously overlooked in the global carbon cycle. Since soils contain large amounts of dead plant material and therefore constitute the largest terrestrial carbon sink, the decomposition of plant material is an important driver of the global carbon cycle. “Our method gives us a fresh view of how metabolic capacities are distributed across the tree of life,” says Ebersberger. “We can now conduct multi-scale analyses and in the process detect both recent evolutionary changes and large patterns.”

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Journal

Molecular Biology and Evolution

Method of Research

Data/statistical analysis

Subject of Research

Animals

Article Title

Feature architecture-aware ortholog search with fDOG reveals the distribution of plant cell wall-degrading enzymes across life

Article Publication Date

9-Jun-2025