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NASA’S Origins, Spectral Interpretation, Resource Identification and Security-Regolith Explorer (OSIRIS-REx) spacecraft recently arrived at the near-Earth asteroid (101955) Bennu, a primitive body that represents the objects that may have brought prebiotic molecules and volatiles such as water to Earth1. Bennu is a low-albedo B-type asteroid2 that has been linked to organic-rich hydrated carbonaceous chondrites3. Such meteorites are altered by ejection from their parent body and contaminated by atmospheric entry and terrestrial microbes. Therefore, the primary mission objective is to return a sample of Bennu to Earth that is pristine—that is, not affected by these processes4. The OSIRIS-REx spacecraft carries a sophisticated suite of instruments to characterize Bennu’s global properties, support the selection of a sampling site and document that site at a sub-centimetre scale5,6,7,8,9,10,11. Here we consider early OSIRIS-REx observations of Bennu to understand how the asteroid’s properties compare to pre-encounter expectations and to assess the prospects for sample return. The bulk composition of Bennu appears to be hydrated and volatile-rich, as expected. However, in contrast to pre-encounter modelling of Bennu’s thermal inertia12 and radar polarization ratios13—which indicated a generally smooth surface covered by centimetre-scale particles—resolved imaging reveals an unexpected surficial diversity. The albedo, texture, particle size and roughness are beyond the spacecraft design specifications. On the basis of our pre-encounter knowledge, we developed a sampling strategy to target 50-metre-diameter patches of loose regolith with grain sizes smaller than two centimetres4. We observe only a small number of apparently hazard-free regions, of the order of 5 to 20 metres in extent, the sampling of which poses a substantial challenge to mission success.

Background Already well known is the fact that disasters and emergencies in large scale will continue to grow in numbers, and nurses will have a role in almost all situations. Schools of nursing need to look for innovative ways to enhance these competencies. Method This project involved two activities: (1) to train lower-level nursing students to be active members in the local Medical Reserve Corporation and (2) to run a community-wide simulated event giving nursing students the opportunity to participate in emergency management. Results Participant feedback was gained through a standardized evaluation form and postsurvey questionnaire, as well as a structured debriefing. Students verbalized an appreciation and excitement regarding future community involvement. Conclusion This project offered students the opportunity to connect what they learned in the classroom to a live situation and also strengthened the school of nursing's community partnerships. [J Nurs Educ. 2025;64(X):XXX–XXX.]

Background: Previous studies suggest that high protein intake in infancy leads to a higher body mass index (BMI) in later childhood. We examined the associations of total, animal and vegetable protein intake in early childhood with detailed measures of body composition at the age of 6 years. Methods: This study was performed in 2911 children participating in a population-based cohort study. Protein intake at the age of 1 year was assessed with a validated food-frequency questionnaire and was adjusted for total energy intake. At the children’s age of 6 years, we measured their anthropometrics and body fat (with dual-energy X-ray absorptiometry). We calculated age- and sex-specific s.d. scores for BMI, fat mass index (FMI) and fat-free mass index (FFMI). Results: After adjustment for confounders, a 10 g per day higher total protein intake at 1 year of age was associated with a 0.05 s.d. (95% confidence interval (CI) 0.00, 0.09) higher BMI at age 6. This association was fully driven by a higher FMI (0.06 s.d. (95%CI 0.01, 0.11)) and not FFMI (−0.01 s.d. (95%CI −0.06, 0.05)). The associations of protein intake with FMI at 6 years remained significant after adjustment for BMI at the age of 1 year. Additional analyses showed that the associations of protein intake with FMI were stronger in girls than in boys ( P for interaction=0.03), stronger among children who had catch-up growth in the first year of life ( P for interaction<0.01) and stronger for intake of animal protein (both dairy and non-dairy protein) than protein from vegetable sources. Conclusions: Our results suggest that high protein intake in early childhood is associated with higher body fat mass, but not fat-free mass. Future studies are needed to investigate whether these changes persist into adulthood and to examine the optimal range of protein intake for infants and young children.

The benefits of collaboration between nursing faculty members and staff nurses in the clinical learning environment are well established. Unfortunately, little is known about improving collaboration to benefit student learning. Descriptive methods were used to examine perceptions of faculty and staff nurses’ collaboration in clinical environments. Data was collected from 497 participants via online Qualtrics survey. Participants were divided into three groups: staff nurses, nursing faculty members, and nurses who worked concurrently in academia and practice. Content analysis procedures were used along with NVivo Pro (Version 11, www.lumivero.com). Four themes were identified: expectations, coordination, communication, and courtesy. Results support that academia and practice professionals wish to work together, but often have different perceptions of each other’s roles. Implications for faculty and staff nurses to improve collaboration are discussed.

I teach in and co-direct the undergraduate program in the Department of Human Evolutionary Biology at Harvard University. During the promotion of my recent book on testosterone and sex differences, I appeared on “Fox and Friends,” a Fox News program, and explained that sex is binary and biological. In response, the director of my department’s Diversity, Inclusion and Belonging task force (a graduate student) accused me on Twitter of transphobia and harming undergraduates, and I responded. The tweets went viral, receiving international news coverage. The public attack by the task force director runs contrary to Harvard’s stated academic freedom principles, yet no disciplinary action was taken, nor did any university administrators publicly support my right to express my views in an environment free of harassment. Unfortunately, what happened to me is not unusual, and an increasing number of scholars face restrictions imposed by formal sanctions or the creation of hostile work environments. In this article, I describe what happened to me, discuss why clear talk about the science of sex and gender is increasingly met with hostility on college campuses, why administrators are largely failing in their responsibilities to protect scholars and their rights to express their views, and what we can do to remedy the situation.

Background Necrotizing enterocolitis (NEC) is a common, potentially catastrophic intestinal disease among very low birthweight premature infants. Affecting up to 15% of neonates born weighing less than 1500 g, NEC causes sudden-onset, progressive intestinal inflammation and necrosis, which can lead to significant bowel loss, multi-organ injury, or death. No unifying cause of NEC has been identified, nor is there any reliable biomarker that indicates an individual patient’s risk of the disease. Without a way to predict NEC in advance, the current medical strategy involves close clinical monitoring in an effort to treat babies with NEC as quickly as possible before irrecoverable intestinal damage occurs. In this report, we describe a novel machine learning application for generating dynamic, individualized NEC risk scores based on intestinal microbiota data, which can be determined from sequencing bacterial DNA from otherwise discarded infant stool. A central insight that differentiates our work from past efforts was the recognition that disease prediction from stool microbiota represents a specific subtype of machine learning problem known as multiple instance learning (MIL). Results We used a neural network-based MIL architecture, which we tested on independent datasets from two cohorts encompassing 3595 stool samples from 261 at-risk infants. Our report also introduces a new concept called the “growing bag” analysis, which applies MIL over time, allowing incorporation of past data into each new risk calculation. This approach allowed early, accurate NEC prediction, with a mean sensitivity of 86% and specificity of 90%. True-positive NEC predictions occurred an average of 8 days before disease onset. We also demonstrate that an attention-gated mechanism incorporated into our MIL algorithm permits interpretation of NEC risk, identifying several bacterial taxa that past work has associated with NEC, and potentially pointing the way toward new hypotheses about NEC pathogenesis. Our system is flexible, accepting microbiota data generated from targeted 16S or “shotgun” whole-genome DNA sequencing. It performs well in the setting of common, potentially confounding preterm neonatal clinical events such as perinatal cardiopulmonary depression, antibiotic administration, feeding disruptions, or transitions between breast feeding and formula. Conclusions We have developed and validated a robust MIL-based system for NEC prediction from harmlessly collected premature infant stool. While this system was developed for NEC prediction, our MIL approach may also be applicable to other diseases characterized by changes in the human microbiota.

A growing body of evidence has associated maternal exposure to air pollution with adverse effects on fetal growth; however, the existing literature is inconsistent. We aimed to quantify the association between maternal exposure to particulate air pollution and term birth weight and low birth weight (LBW) across 14 centers from 9 countries, and to explore the influence of site characteristics and exposure assessment methods on between-center heterogeneity in this association. Using a common analytical protocol, International Collaboration on Air Pollution and Pregnancy Outcomes (ICAPPO) centers generated effect estimates for term LBW and continuous birth weight associated with PM(10) and PM(2.5) (particulate matter ≤ 10 and 2.5 µm). We used meta-analysis to combine the estimates of effect across centers (~ 3 million births) and used meta-regression to evaluate the influence of center characteristics and exposure assessment methods on between-center heterogeneity in reported effect estimates. In random-effects meta-analyses, term LBW was positively associated with a 10-μg/m3 increase in PM10 [odds ratio (OR) = 1.03; 95% CI: 1.01, 1.05] and PM(2.5) (OR = 1.10; 95% CI: 1.03, 1.18) exposure during the entire pregnancy, adjusted for maternal socioeconomic status. A 10-μg/m3 increase in PM(10) exposure was also negatively associated with term birth weight as a continuous outcome in the fully adjusted random-effects meta-analyses (-8.9 g; 95% CI: -13.2, -4.6 g). Meta-regressions revealed that centers with higher median PM(2.5) levels and PM(2.5):PM(10) ratios, and centers that used a temporal exposure assessment (compared with spatiotemporal), tended to report stronger associations. Maternal exposure to particulate pollution was associated with LBW at term across study populations. We detected three site characteristics and aspects of exposure assessment methodology that appeared to contribute to the variation in associations reported by centers.

Background: Exposure to air pollution has been associated with higher C-reactive protein (CRP) levels, suggesting an inflammatory response. Not much is known about this association in pregnancy. Objectives: We investigated the associations of air pollution exposure during pregnancy with maternal and fetal CRP levels in a population-based cohort study in the Netherlands. Methods: Particulate matter (PM) with an aerodynamic diameter ≤ 10 μm (PM₁₀) and nitrogen dioxide (NO₂) levels were estimated at the home address using dispersion modeling for different averaging periods preceding the blood sampling (1 week, 2 weeks, 4 weeks, and total pregnancy). High-sensitivity CRP levels were measured in maternal blood samples in early pregnancy (n = 5,067) and in fetal cord blood samples at birth (n = 4,450). Results: Compared with the lowest quartile, higher PM₁₀ exposure levels for the prior 1 and 2 weeks were associated with elevated maternal CRP levels (> 8 μg/L) in the first trimester [fourth PM, 0 quartile for the prior week: odds ratio (OR), 1.32; 95% confidence interval (CI): 1.08, 1.61; third PM₁₀ quartile for the prior 2 weeks: OR, 1.28; 95% CI: 1.06, 1.56]; however, no clear doseresponse relationships were observed. PM₁₀ and NO₂ exposure levels for 1, 2, and 4 weeks preceding delivery were not consistently associated with fetal CRP levels at delivery. Higher long-term PM₁₀ and NO₂ exposure levels (total pregnancy) were associated with elevated fetal CRP levels (> 1 μg/L) at delivery (fourth quartile PM₁₀: OR, 2.18; 95% CI: 1.08, 4.38; fourth quartile NO 2 : OR, 3.42; 95% CI: 1.36, 8.58;p-values for trend < 0.05). Conclusions: Our results suggest that exposure to air pollution during pregnancy may lead to maternal and fetal inflammatory responses.