Gordon and Betty Moore Foundation

The Global Biodiversity Framework (GBF), signed in 2022 by Parties to the Convention on Biological Diversity, recognized the importance of area-based conservation, and its goals and targets specify the characteristics of protected and conserved areas (PCAs) that disproportionately contribute to biodiversity conservation. To achieve the GBF’s target of conserving a global area of 30% by 2030, this Essay argues for recognizing these characteristics and scaling them up through the conservation of areas that are: extensive (typically larger than 5,000 km2); have interconnected PCAs (either physically or as part of a jurisdictional network, and frequently embedded in larger conservation landscapes); have high ecological integrity; and are effectively managed and equitably governed. These areas are presented as “Nature’s Strongholds,” illustrated by examples from the Congo and Amazon basins. Conserving Nature’s Strongholds offers an approach to scale up initiatives to address global threats to biodiversity.

We present the Aquatic Symbiosis Genomics Project, a global collaboration to generate high quality genome sequences for a wide range of eukaryotes and their microbial symbionts. Launched under the Symbiosis in Aquatic Systems Initiative of the Gordon and Betty Moore Foundation, the ASG Project brings together researchers from across the globe who hope to use these reference genomes to augment and extend their analyses of the dynamics, mechanisms and environmental importance of symbioses. Applying large-scale, high-throughput sequencing and assembly technologies, the ASG collaboration will assemble and annotate the genomes of 500 symbiotic organisms – both the “hosts” and the microbial symbionts with which they associate. These data will be released openly to benefit all who work on symbioses, from conservation geneticists to those interested in the origin of the eukaryotic cell.

We combine nanosecond laser shock compression with in-situ picosecond X-ray diffraction to provide structural data on iron up to 275 GPa. We constrain the extent of hcp-liquid coexistence, the onset of total melt, and the structure within the liquid phase. Our results indicate that iron, under shock compression, melts completely by 258(8) GPa. A coordination number analysis indicates that iron is a simple liquid at these pressure-temperature conditions. We also perform texture analysis between the ambient body-centered-cubic (bcc) α, and the hexagonal-closed-packed (hcp) high-pressure ϵ−phase. We rule out the Rong-Dunlop orientation relationship (OR) between the α and ϵ−phases. However, we cannot distinguish between three other closely related ORs: Burger's, Mao-Bassett-Takahashi, and Potter's OR. The solid-liquid coexistence region is constrained from a melt onset pressure of 225(3) GPa from previously published sound speed measurements and full melt (246.5(1.8)-258(8) GPa) from X-ray diffraction measurements, with an associated maximum latent heat of melting of 623 J/g. This value is lower than recently reported theoretical estimates and suggests that the contribution to the earth's geodynamo energy budget from heat release due to freezing of the inner core is smaller than previously thought. Melt pressures for these nanosecond shock experiments are consistent with gas gun shock experiments that last for microseconds, indicating that the melt transition occurs rapidly.

Quantitative evidence is presented that counter the normative cultural ones as to who does physics and why one does physics, helps to support women and minoritized groups in envisioning a future for themselves in physics.

Type Ia supernovae (SNe Ia) are more precise standardizable candles when measured in the near-infrared (NIR) than in the optical. With this motivation, from 2012 to 2017 we embarked on the RAISIN program with the Hubble Space Telescope (HST) to obtain rest-frame NIR light curves for a cosmologically distant sample of 37 SNe Ia (0.2 ≲ z ≲ 0.6) discovered by Pan-STARRS and the Dark Energy Survey. By comparing higher-z HST data with 42 SNe Ia at z < 0.1 observed in the NIR by the Carnegie Supernova Project, we construct a Hubble diagram from NIR observations (with only time of maximum light and some selection cuts from optical photometry) to pursue a unique avenue to constrain the dark energy equation-of-state parameter, w. We analyze the dependence of the full set of Hubble residuals on the SN Ia host galaxy mass and find Hubble residual steps of size ∼0.06-0.1 mag with 1.5σ−2.5σ significance depending on the method and step location used. Combining our NIR sample with cosmic microwave background constraints, we find 1 + w = −0.17 ± 0.12 (statistical + systematic errors). The largest systematic errors are the redshift-dependent SN selection biases and the properties of the NIR mass step. We also use these data to measure H 0 = 75.9 ± 2.2 km s−1 Mpc−1 from stars with geometric distance calibration in the hosts of eight SNe Ia observed in the NIR versus H 0 = 71.2 ± 3.8 km s−1 Mpc−1 using an inverse distance ladder approach tied to Planck. Using optical data, we find 1 + w = −0.10 ± 0.09, and with optical and NIR data combined, we find 1 + w = −0.06 ± 0.07; these shifts of up to ∼0.11 in w could point to inconsistency in the optical versus NIR SN models. There will be many opportunities to improve this NIR measurement and better understand systematic uncertainties through larger low-z samples, new light-curve models, calibration improvements, and eventually by building high-z samples from the Roman Space Telescope.

Abstract Those building predictive models from transcriptomic data are faced with two conflicting perspectives. The first, based on the inherent high dimensionality of biological systems, supposes that complex non-linear models such as neural networks will better match complex biological systems. The second, imagining that complex systems will still be well predicted by simple dividing lines prefers linear models that are easier to interpret. We compare multi-layer neural networks and logistic regression across multiple prediction tasks on GTEx and Recount3 datasets and find evidence in favor of both possibilities. We verified the presence of non-linear signal for transcriptomic prediction tasks by removing the predictive linear signal with Limma, and showed the removal ablated the performance of linear methods but not non-linear ones. However, we also found that the presence of non-linear signal was not necessarily sufficient for neural networks to outperform logistic regression. Our results demonstrate that while multi-layer neural networks may be useful for making predictions from gene expression data, including a linear baseline model is critical because while biological systems are highdimensional, effective dividing lines for predictive models may not be.

The U.S. public health response to coronavirus disease 2019 (COVID-19) has been widely criticized as having downplayed the potential implications COVID-19 could have on one's personal health. Despite the unprecedented threat of COVID-19, many individuals still believed that it was not at all likely that they would become infected. We sought to investigate trends in adults’ perceived susceptibility to COVID-19 over the first year of the pandemic, whether distinct trajectories emerged, and if these trajectories differed by participant socio-demographic characteristics.This was a longitudinal cohort study with 5 time points of data collection (March 13, 2020–March 3, 2021). Subjects included 627 adults living with ≥1 chronic conditions, who completed a baseline interview and at least one follow-up interview. In addition to collecting relevant socio-demographic characteristics, participants’ perceived susceptibility to COVID-19 across time was assessed and classified into distinct trajectories.Nearly two-thirds (62.2%) of participants perceived themselves to be highly susceptible to COVID-19 from the onset of the pandemic (“early responders”) and sustained this over a year, a third (29.0%) eventually perceived themselves to be highly susceptible (“late responders”), and 8.8% maintained a low likelihood of susceptibility throughout the pandemic (“non-responders”). In multivariable analyses, compared to White participants, Latinx participants were significantly more likely to be non-responders and report low likelihood of perceived susceptibility (Risk Ratio [RR]: 3.46; 95% confidence interval: 1.19, 10.1), as were Black participants (RR: 5.49; 95% confidence interval: 2.19, 13.8).A year into the COVID-19 pandemic, 1 out of 11 participants persistently did not think they might be susceptible and potentially infected. Future studies are needed to understand reasons why certain individuals, particularly those of racial/ethnic minorities, did not perceive themselves at risk for infection.

Abstract There is currently a great deal of work being undertaken to collect, analyze, and synthesize available evidence about the effectiveness of conservation strategies. But substantial challenges still remain in enabling practitioners to assess and apply this evidence to their conservation work in an efficient manner. To solve these challenges, there is growing recognition of the need to use situation assessments and theory of change pathways to detail a set of analytical questions and specific assumptions that can be assessed against the evidence base to “make the case” for a proposed strategy and to identify gaps in knowledge. In this study, we first provide updated definitions of some key terms. We then present and provide examples of an approach to enable practitioners to evaluate the evidence base for the critical assumptions that underlie their specific conservation strategies and to wisely use evidence coming from different knowledge systems. This practical approach, which was developed through a series of pilot tests with Parks Canada projects, involves four iterative steps: (1) identify critical questions and assumptions requiring evidence; (2) assemble and assess the specific and generic evidence for each assumption; (3) determine confidence in evidence and its implications; and (4) validate the assessment and iteratively adapt as needed. Ideally, this approach can be integrated into existing decision‐making frameworks and can also facilitate better cooperation between researchers who synthesize evidence and practitioners who use evidence to make conservation both more effective and efficient.

Abstract We present a multiwavelength photometric and spectroscopic analysis of 13 super-Chandrasekhar-mass/2003fg-like Type Ia supernovae (SNe Ia). Nine of these objects were observed by the Carnegie Supernova Project. The 2003fg-like SNe have slowly declining light curves (Δm 15(B) &lt; 1.3 mag), and peak absolute B-band magnitudes of −19 &lt; M B &lt; −21 mag. Many of the 2003fg-like SNe are located in the same part of the luminosity–width relation as normal SNe Ia. In the optical B and V bands, the 2003fg-like SNe look like normal SNe Ia, but at redder wavelengths they diverge. Unlike other luminous SNe Ia, the 2003fg-like SNe generally have only one i-band maximum, which peaks after the epoch of the B-band maximum, while their near-IR (NIR) light-curve rise times can be ≳40 days longer than those of normal SNe Ia. They are also at least 1 mag brighter in the NIR bands than normal SNe Ia, peaking above M H = −19 mag, and generally have negative Hubble residuals, which may be the cause of some systematics in dark-energy experiments. Spectroscopically, the 2003fg-like SNe exhibit peculiarities such as unburnt carbon well past maximum light, a large spread (8000–12,000 km s−1) in Si ii λ6355 velocities at maximum light with no rapid early velocity decline, and no clear H-band break at +10 days. We find that SNe with a larger pseudo-equivalent width of C ii at maximum light have lower Si ii λ6355 velocities and more slowly declining light curves. There are also multiple factors that contribute to the peak luminosity of 2003fg-like SNe. The explosion of a C–O degenerate core inside a carbon-rich envelope is consistent with these observations. Such a configuration may come from the core-degenerate scenario.